From 4f5b69606aacd0652abbf572c5fd090c9bcad3ed Mon Sep 17 00:00:00 2001
From: stx <31013941@qq.com>
Date: Mon, 21 Jul 2025 14:18:12 +0800
Subject: [PATCH 01/34] feat: one-click deployment with docker

---
 .env.example       | 29 +++++++++++++++++++++
 Dockerfile         | 35 ++++++++++++++++++++++++++
 docker-compose.yml | 63 ++++++++++++++++++++++++++++++++++++++++++++++
 3 files changed, 127 insertions(+)
 create mode 100644 .env.example
 create mode 100644 Dockerfile
 create mode 100644 docker-compose.yml

diff --git a/.env.example b/.env.example
new file mode 100644
index 000000000..33f7ae853
--- /dev/null
+++ b/.env.example
@@ -0,0 +1,29 @@
+# MemOS Environment Variables Configuration
+
+# Path to memory storage (e.g. /tmp/data_test)
+MOS_CUBE_PATH=
+
+# OpenAI Configuration
+OPENAI_API_KEY=            # Your OpenAI API key
+OPENAI_API_BASE=           # OpenAI API base URL (default: https://api.openai.com/v1)
+
+# MemOS Feature Toggles
+MOS_ENABLE_DEFAULT_CUBE_CONFIG=   # Enable default cube config (true/false)
+MOS_ENABLE_SCHEDULER=             # Enable background scheduler (true/false)
+
+# Neo4j Configuration
+NEO4J_URI=               # Neo4j connection URI (e.g. bolt://localhost:7687)
+NEO4J_USER=              # Neo4j username
+NEO4J_PASSWORD=          # Neo4j password
+MOS_NEO4J_SHARED_DB=     # Shared Neo4j database name (if using multi-db)
+
+# MemOS User Configuration
+MOS_USER_ID=             # Unique user ID
+MOS_SESSION_ID=          # Session ID for current chat
+MOS_MAX_TURNS_WINDOW=    # Max number of turns to keep in memory
+
+# Ollama Configuration (for local embedding models)
+OLLAMA_API_BASE=         # Ollama API base URL (e.g. http://localhost:11434)
+
+# Embedding Configuration
+MOS_EMBEDDER_BACKEND=    # Embedding backend: openai, ollama, etc.
diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 000000000..1e55e2511
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,35 @@
+FROM python:3.11-slim
+
+# Install necessary tools
+RUN apt-get update && apt-get install -y curl && rm -rf /var/lib/apt/lists/*
+
+# Install Poetry
+RUN curl -sSL https://install.python-poetry.org | python3 - && \
+    ln -s /root/.local/bin/poetry /usr/local/bin/poetry
+
+# Disable Poetry virtual environments
+RUN poetry config virtualenvs.create false
+
+# Set Hugging Face mirror
+ENV HF_ENDPOINT=https://hf-mirror.com
+
+# Set working directory
+WORKDIR /app
+
+# Copy dependency files
+COPY pyproject.toml poetry.lock* Makefile ./
+
+# Install dependencies
+RUN poetry install --no-root --with dev --with test
+
+# Copy source code
+COPY . .
+
+# Set PYTHONPATH
+ENV PYTHONPATH=/app/src
+
+# Expose port
+EXPOSE 8000
+
+# Start API service
+CMD ["poetry", "run", "uvicorn", "memos.api.product_api:app", "--host", "0.0.0.0", "--port", "8000"]
diff --git a/docker-compose.yml b/docker-compose.yml
new file mode 100644
index 000000000..d52f1d3b4
--- /dev/null
+++ b/docker-compose.yml
@@ -0,0 +1,63 @@
+name: memos-dev
+
+services:
+  memos:
+    build:
+      context: .
+      dockerfile: Dockerfile
+    ports:
+      - "8000:8000"
+    env_file:
+      - .env
+    depends_on:
+      - neo4j
+      - qdrant
+    environment:
+      - PYTHONPATH=/app/src
+    volumes:
+      - .:/app
+    networks:
+      - memos_network
+
+  neo4j:
+    image: neo4j:5.26.4
+    ports:
+      - "7474:7474"   # HTTP
+      - "7687:7687"   # Bolt
+    healthcheck:
+      test: wget http://localhost:7687 || exit 1
+      interval: 1s
+      timeout: 10s
+      retries: 20
+      start_period: 3s
+    environment:
+      NEO4J_ACCEPT_LICENSE_AGREEMENT: "yes"
+      NEO4J_AUTH: "neo4j/12345678"
+    volumes:
+      - neo4j_data:/data
+      - neo4j_logs:/logs
+    networks:
+      - memos_network
+
+  qdrant:
+    image: qdrant/qdrant:v1.15.0
+    container_name: qdrant-server
+    ports:
+      - "6333:6333"  # REST API
+      - "6334:6334"  # gRPC API
+    volumes:
+      - ./qdrant_data:/qdrant/storage
+    environment:
+      QDRANT__SERVICE__GRPC_PORT: 6334
+      QDRANT__SERVICE__HTTP_PORT: 6333
+    restart: unless-stopped
+    networks:
+      - memos_network
+
+volumes:
+  neo4j_data:
+  neo4j_logs:
+
+networks:
+  memos_network:
+    driver: bridge

From faeea51c13b83f7aaada37475e64682d4789cb74 Mon Sep 17 00:00:00 2001
From: stx <31013941@qq.com>
Date: Mon, 21 Jul 2025 14:22:45 +0800
Subject: [PATCH 02/34] feat: one-click deployment with docker

---
 Dockerfile | 1 +
 1 file changed, 1 insertion(+)

diff --git a/Dockerfile b/Dockerfile
index 1e55e2511..5bb1c85ea 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -31,5 +31,6 @@ ENV PYTHONPATH=/app/src
 # Expose port
 EXPOSE 8000
 
+
 # Start API service
 CMD ["poetry", "run", "uvicorn", "memos.api.product_api:app", "--host", "0.0.0.0", "--port", "8000"]

From a1158c9016c73b0a6acf659543b45a4fcde83e8f Mon Sep 17 00:00:00 2001
From: stx <31013941@qq.com>
Date: Mon, 21 Jul 2025 19:51:39 +0800
Subject: [PATCH 03/34] feat: one-click deployment with docker

---
 Dockerfile              |  37 +++------
 docker-compose.yml      |   2 +
 src/memos/api/config.py | 171 +++++++++++++++++++++++++++++-----------
 3 files changed, 141 insertions(+), 69 deletions(-)

diff --git a/Dockerfile b/Dockerfile
index 5bb1c85ea..1957d17a7 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,36 +1,25 @@
 FROM python:3.11-slim
 
-# Install necessary tools
-RUN apt-get update && apt-get install -y curl && rm -rf /var/lib/apt/lists/*
+RUN apt-get update && apt-get install -y \
+    gcc \
+    g++ \
+    build-essential \
+    libffi-dev \
+    python3-dev \
+    curl \
+    && rm -rf /var/lib/apt/lists/*
 
-# Install Poetry
-RUN curl -sSL https://install.python-poetry.org | python3 - && \
-    ln -s /root/.local/bin/poetry /usr/local/bin/poetry
-
-# Disable Poetry virtual environments
-RUN poetry config virtualenvs.create false
+WORKDIR /app
 
-# Set Hugging Face mirror
 ENV HF_ENDPOINT=https://hf-mirror.com
 
-# Set working directory
-WORKDIR /app
-
-# Copy dependency files
-COPY pyproject.toml poetry.lock* Makefile ./
+COPY requirements.txt .
+RUN pip install --upgrade pip && pip install --no-cache-dir -r requirements.txt
 
-# Install dependencies
-RUN poetry install --no-root --with dev --with test
+RUN pip install chonkie
 
-# Copy source code
 COPY . .
-
-# Set PYTHONPATH
 ENV PYTHONPATH=/app/src
 
-# Expose port
 EXPOSE 8000
-
-
-# Start API service
-CMD ["poetry", "run", "uvicorn", "memos.api.product_api:app", "--host", "0.0.0.0", "--port", "8000"]
+CMD ["uvicorn", "memos.api.product_api:app", "--host", "0.0.0.0", "--port", "8000", "--reload"]
diff --git a/docker-compose.yml b/docker-compose.yml
index d52f1d3b4..147ae1504 100644
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -2,6 +2,7 @@ name: memos-dev
 
 services:
   memos:
+    container_name: memos-api-server
     build:
       context: .
       dockerfile: Dockerfile
@@ -21,6 +22,7 @@ services:
 
   neo4j:
     image: neo4j:5.26.4
+    container_name: neo4j-server
     ports:
       - "7474:7474"   # HTTP
       - "7687:7687"   # Bolt
diff --git a/src/memos/api/config.py b/src/memos/api/config.py
index feb267ccb..75bec5569 100644
--- a/src/memos/api/config.py
+++ b/src/memos/api/config.py
@@ -115,6 +115,32 @@ def get_embedder_config() -> dict[str, Any]:
                 },
             }
 
+    @staticmethod
+    def get_neo4j_community_config(user_id: str | None = None) -> dict[str, Any]:
+        """Get Neo4j community configuration."""
+        return {
+            "uri": os.getenv("NEO4J_URI", "bolt://localhost:7687"),
+            "user": os.getenv("NEO4J_USER", "neo4j"),
+            "db_name": os.getenv("NEO4J_DB_NAME", "shared-tree-textual-memory"),
+            "password": os.getenv("NEO4J_PASSWORD", "12345678"),
+            "user_name": f"memos{user_id.replace('-', '')}",
+            "auto_create": True,
+            "use_multi_db": False,
+            "embedding_dimension": 3072,
+            "vec_config": {
+                # Pass nested config to initialize external vector DB
+                # If you use qdrant, please use Server instead of local mode.
+                "backend": "qdrant",
+                "config": {
+                    "collection_name": "neo4j_vec_db",
+                    "vector_dimension": 3072,
+                    "distance_metric": "cosine",
+                    "host": "localhost",
+                    "port": 6333,
+                },
+            },
+        }
+
     @staticmethod
     def get_neo4j_config(user_id: str | None = None) -> dict[str, Any]:
         """Get Neo4j configuration."""
@@ -274,7 +300,7 @@ def get_start_default_config() -> dict[str, Any]:
     def create_user_config(user_name: str, user_id: str) -> tuple[MOSConfig, GeneralMemCube]:
         """Create configuration for a specific user."""
         openai_config = APIConfig.get_openai_config()
-        neo4j_config = APIConfig.get_neo4j_config(user_id)
+
         qwen_config = APIConfig.qwen_config()
         vllm_config = APIConfig.vllm_config()
         backend = os.getenv("MOS_CHAT_MODEL_PROVIDER", "openai")
@@ -325,29 +351,56 @@ def create_user_config(user_name: str, user_id: str) -> tuple[MOSConfig, General
 
         default_config = MOSConfig(**config_dict)
 
-        # Create MemCube config
-        default_cube_config = GeneralMemCubeConfig.model_validate(
-            {
-                "user_id": user_id,
-                "cube_id": f"{user_name}_default_cube",
-                "text_mem": {
-                    "backend": "tree_text",
-                    "config": {
-                        "extractor_llm": {"backend": "openai", "config": openai_config},
-                        "dispatcher_llm": {"backend": "openai", "config": openai_config},
-                        "graph_db": {
-                            "backend": "neo4j",
-                            "config": neo4j_config,
+        if os.getenv("NEO4J_BACKEND", "neo4j_community").lower() == "neo4j_community":
+            neo4j_community_config = APIConfig.get_neo4j_community_config(user_id)
+            # Create MemCube config
+            default_cube_config = GeneralMemCubeConfig.model_validate(
+                {
+                    "user_id": user_id,
+                    "cube_id": f"{user_name}_default_cube",
+                    "text_mem": {
+                        "backend": "tree_text",
+                        "config": {
+                            "extractor_llm": {"backend": "openai", "config": openai_config},
+                            "dispatcher_llm": {"backend": "openai", "config": openai_config},
+                            "graph_db": {
+                                "backend": "neo4j-community",
+                                "config": neo4j_community_config,
+                            },
+                            "embedder": APIConfig.get_embedder_config(),
                         },
-                        "embedder": APIConfig.get_embedder_config(),
                     },
-                },
-                "act_mem": {}
-                if os.getenv("ENABLE_ACTIVATION_MEMORY", "false").lower() == "false"
-                else APIConfig.get_activation_vllm_config(),
-                "para_mem": {},
-            }
-        )
+                    "act_mem": {}
+                    if os.getenv("ENABLE_ACTIVATION_MEMORY", "false").lower() == "false"
+                    else APIConfig.get_activation_vllm_config(),
+                    "para_mem": {},
+                }
+            )
+        else:
+            neo4j_config = APIConfig.get_neo4j_config(user_id)
+            # Create MemCube config
+            default_cube_config = GeneralMemCubeConfig.model_validate(
+                {
+                    "user_id": user_id,
+                    "cube_id": f"{user_name}_default_cube",
+                    "text_mem": {
+                        "backend": "tree_text",
+                        "config": {
+                            "extractor_llm": {"backend": "openai", "config": openai_config},
+                            "dispatcher_llm": {"backend": "openai", "config": openai_config},
+                            "graph_db": {
+                                "backend": "neo4j",
+                                "config": neo4j_config,
+                            },
+                            "embedder": APIConfig.get_embedder_config(),
+                        },
+                    },
+                    "act_mem": {}
+                    if os.getenv("ENABLE_ACTIVATION_MEMORY", "false").lower() == "false"
+                    else APIConfig.get_activation_vllm_config(),
+                    "para_mem": {},
+                }
+            )
 
         default_mem_cube = GeneralMemCube(default_cube_config)
         return default_config, default_mem_cube
@@ -363,28 +416,56 @@ def get_default_cube_config() -> GeneralMemCubeConfig | None:
             return None
 
         openai_config = APIConfig.get_openai_config()
-        neo4j_config = APIConfig.get_neo4j_config(user_id="default")
-
-        return GeneralMemCubeConfig.model_validate(
-            {
-                "user_id": "default",
-                "cube_id": "default_cube",
-                "text_mem": {
-                    "backend": "tree_text",
-                    "config": {
-                        "extractor_llm": {"backend": "openai", "config": openai_config},
-                        "dispatcher_llm": {"backend": "openai", "config": openai_config},
-                        "graph_db": {
-                            "backend": "neo4j",
-                            "config": neo4j_config,
+
+        if os.getenv("NEO4J_BACKEND", "neo4j_community").lower() == "neo4j_community":
+            neo4j_community_config = APIConfig.get_neo4j_community_config(user_id="default")
+            return GeneralMemCubeConfig.model_validate(
+                {
+                    "user_id": "default",
+                    "cube_id": "default_cube",
+                    "text_mem": {
+                        "backend": "tree_text",
+                        "config": {
+                            "extractor_llm": {"backend": "openai", "config": openai_config},
+                            "dispatcher_llm": {"backend": "openai", "config": openai_config},
+                            "graph_db": {
+                                "backend": "neo4j-community",
+                                "config": neo4j_community_config,
+                            },
+                            "embedder": APIConfig.get_embedder_config(),
+                            "reorganize": os.getenv("MOS_ENABLE_REORGANIZE", "false").lower()
+                            == "true",
                         },
-                        "embedder": APIConfig.get_embedder_config(),
-                        "reorganize": os.getenv("MOS_ENABLE_REORGANIZE", "false").lower() == "true",
                     },
-                },
-                "act_mem": {}
-                if os.getenv("ENABLE_ACTIVATION_MEMORY", "false").lower() == "false"
-                else APIConfig.get_activation_vllm_config(),
-                "para_mem": {},
-            }
-        )
+                    "act_mem": {}
+                    if os.getenv("ENABLE_ACTIVATION_MEMORY", "false").lower() == "false"
+                    else APIConfig.get_activation_vllm_config(),
+                    "para_mem": {},
+                }
+            )
+        else:
+            neo4j_config = APIConfig.get_neo4j_config(user_id="default")
+            return GeneralMemCubeConfig.model_validate(
+                {
+                    "user_id": "default",
+                    "cube_id": "default_cube",
+                    "text_mem": {
+                        "backend": "tree_text",
+                        "config": {
+                            "extractor_llm": {"backend": "openai", "config": openai_config},
+                            "dispatcher_llm": {"backend": "openai", "config": openai_config},
+                            "graph_db": {
+                                "backend": "neo4j",
+                                "config": neo4j_config,
+                            },
+                            "embedder": APIConfig.get_embedder_config(),
+                            "reorganize": os.getenv("MOS_ENABLE_REORGANIZE", "false").lower()
+                            == "true",
+                        },
+                    },
+                    "act_mem": {}
+                    if os.getenv("ENABLE_ACTIVATION_MEMORY", "false").lower() == "false"
+                    else APIConfig.get_activation_vllm_config(),
+                    "para_mem": {},
+                }
+            )

From 3964580868489d64f98974d84f3698c88109a0c2 Mon Sep 17 00:00:00 2001
From: stx <31013941@qq.com>
Date: Mon, 21 Jul 2025 20:54:43 +0800
Subject: [PATCH 04/34] feat: one-click deployment with docker

---
 Dockerfile | 32 ++++++++++++++++++++++++--------
 1 file changed, 24 insertions(+), 8 deletions(-)

diff --git a/Dockerfile b/Dockerfile
index 1957d17a7..66ef08aac 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,25 +1,41 @@
 FROM python:3.11-slim
 
+# Install necessary tools
 RUN apt-get update && apt-get install -y \
+    curl \
     gcc \
-    g++ \
     build-essential \
-    libffi-dev \
     python3-dev \
-    curl \
+    libffi-dev \
     && rm -rf /var/lib/apt/lists/*
 
-WORKDIR /app
+# Install Poetry
+RUN curl -sSL https://install.python-poetry.org | python3 - && \
+    ln -s /root/.local/bin/poetry /usr/local/bin/poetry
 
+# Disable Poetry virtual environments
+RUN poetry config virtualenvs.create false
+
+# Set Hugging Face mirror
 ENV HF_ENDPOINT=https://hf-mirror.com
 
-COPY requirements.txt .
-RUN pip install --upgrade pip && pip install --no-cache-dir -r requirements.txt
+# Set working directory
+WORKDIR /app
+
+# Copy dependency files
+COPY pyproject.toml poetry.lock* Makefile ./
 
-RUN pip install chonkie
+# Install dependencies
+RUN poetry install --extras tree-mem --extras mem-reader --with dev --with test
 
+# Copy source code
 COPY . .
+
+# Set PYTHONPATH
 ENV PYTHONPATH=/app/src
 
+# Expose port
 EXPOSE 8000
-CMD ["uvicorn", "memos.api.product_api:app", "--host", "0.0.0.0", "--port", "8000", "--reload"]
+
+# Start API service
+CMD ["poetry", "run", "uvicorn", "memos.api.product_api:app", "--host", "0.0.0.0", "--port", "8000"]

From 879a0e889e3354953dad030c1b3a6068de478f07 Mon Sep 17 00:00:00 2001
From: stx <31013941@qq.com>
Date: Mon, 21 Jul 2025 21:04:58 +0800
Subject: [PATCH 05/34] feat: one-click deployment with docker

---
 Dockerfile | 1 -
 1 file changed, 1 deletion(-)

diff --git a/Dockerfile b/Dockerfile
index 66ef08aac..bc16fa965 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,5 +1,4 @@
 FROM python:3.11-slim
-
 # Install necessary tools
 RUN apt-get update && apt-get install -y \
     curl \

From 4a968f7ba11de1be105c52ba0c6c1cf9639336f3 Mon Sep 17 00:00:00 2001
From: stx <31013941@qq.com>
Date: Mon, 21 Jul 2025 21:42:55 +0800
Subject: [PATCH 06/34] feat: one-click deployment with docker

---
 Dockerfile       | 33 +++++------------
 requirements.txt | 92 ++++++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 101 insertions(+), 24 deletions(-)
 create mode 100644 requirements.txt

diff --git a/Dockerfile b/Dockerfile
index bc16fa965..1957d17a7 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,40 +1,25 @@
 FROM python:3.11-slim
-# Install necessary tools
+
 RUN apt-get update && apt-get install -y \
-    curl \
     gcc \
+    g++ \
     build-essential \
-    python3-dev \
     libffi-dev \
+    python3-dev \
+    curl \
     && rm -rf /var/lib/apt/lists/*
 
-# Install Poetry
-RUN curl -sSL https://install.python-poetry.org | python3 - && \
-    ln -s /root/.local/bin/poetry /usr/local/bin/poetry
-
-# Disable Poetry virtual environments
-RUN poetry config virtualenvs.create false
+WORKDIR /app
 
-# Set Hugging Face mirror
 ENV HF_ENDPOINT=https://hf-mirror.com
 
-# Set working directory
-WORKDIR /app
-
-# Copy dependency files
-COPY pyproject.toml poetry.lock* Makefile ./
+COPY requirements.txt .
+RUN pip install --upgrade pip && pip install --no-cache-dir -r requirements.txt
 
-# Install dependencies
-RUN poetry install --extras tree-mem --extras mem-reader --with dev --with test
+RUN pip install chonkie
 
-# Copy source code
 COPY . .
-
-# Set PYTHONPATH
 ENV PYTHONPATH=/app/src
 
-# Expose port
 EXPOSE 8000
-
-# Start API service
-CMD ["poetry", "run", "uvicorn", "memos.api.product_api:app", "--host", "0.0.0.0", "--port", "8000"]
+CMD ["uvicorn", "memos.api.product_api:app", "--host", "0.0.0.0", "--port", "8000", "--reload"]
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 000000000..596e94b47
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,92 @@
+annotated-types==0.7.0 ; python_version >= "3.10" and python_version < "4.0"
+anyio==4.9.0 ; python_version >= "3.10" and python_version < "4.0"
+attrs==25.3.0 ; python_version >= "3.10" and python_version < "4.0"
+authlib==1.6.0 ; python_version >= "3.10" and python_version < "4.0"
+certifi==2025.7.14 ; python_version >= "3.10" and python_version < "4.0"
+cffi==1.17.1 ; python_version >= "3.10" and python_version < "4.0" and platform_python_implementation != "PyPy"
+charset-normalizer==3.4.2 ; python_version >= "3.10" and python_version < "4.0"
+click==8.2.1 ; python_version >= "3.10" and python_version < "4.0"
+colorama==0.4.6 ; python_version >= "3.10" and python_version < "4.0" and (platform_system == "Windows" or sys_platform == "win32")
+cryptography==45.0.5 ; python_version >= "3.10" and python_version < "4.0"
+cyclopts==3.22.2 ; python_version >= "3.10" and python_version < "4.0"
+distro==1.9.0 ; python_version >= "3.10" and python_version < "4.0"
+dnspython==2.7.0 ; python_version >= "3.10" and python_version < "4.0"
+docstring-parser==0.16 ; python_version >= "3.10" and python_version < "4.0"
+docutils==0.21.2 ; python_version >= "3.10" and python_version < "4.0"
+email-validator==2.2.0 ; python_version >= "3.10" and python_version < "4.0"
+exceptiongroup==1.3.0 ; python_version >= "3.10" and python_version < "4.0"
+fastapi-cli==0.0.8 ; python_version >= "3.10" and python_version < "4.0"
+fastapi-cloud-cli==0.1.4 ; python_version >= "3.10" and python_version < "4.0"
+fastapi==0.115.14 ; python_version >= "3.10" and python_version < "4.0"
+fastmcp==2.10.5 ; python_version >= "3.10" and python_version < "4.0"
+filelock==3.18.0 ; python_version >= "3.10" and python_version < "4.0"
+fsspec==2025.7.0 ; python_version >= "3.10" and python_version < "4.0"
+greenlet==3.2.3 ; python_version >= "3.10" and python_version < "3.14" and (platform_machine == "aarch64" or platform_machine == "ppc64le" or platform_machine == "x86_64" or platform_machine == "amd64" or platform_machine == "AMD64" or platform_machine == "win32" or platform_machine == "WIN32")
+h11==0.16.0 ; python_version >= "3.10" and python_version < "4.0"
+hf-xet==1.1.5 ; python_version >= "3.10" and python_version < "4.0" and (platform_machine == "x86_64" or platform_machine == "amd64" or platform_machine == "arm64" or platform_machine == "aarch64")
+httpcore==1.0.9 ; python_version >= "3.10" and python_version < "4.0"
+httptools==0.6.4 ; python_version >= "3.10" and python_version < "4.0"
+httpx-sse==0.4.1 ; python_version >= "3.10" and python_version < "4.0"
+httpx==0.28.1 ; python_version >= "3.10" and python_version < "4.0"
+huggingface-hub==0.33.4 ; python_version >= "3.10" and python_version < "4.0"
+idna==3.10 ; python_version >= "3.10" and python_version < "4.0"
+itsdangerous==2.2.0 ; python_version >= "3.10" and python_version < "4.0"
+jinja2==3.1.6 ; python_version >= "3.10" and python_version < "4.0"
+jiter==0.10.0 ; python_version >= "3.10" and python_version < "4.0"
+joblib==1.5.1 ; python_version >= "3.10" and python_version < "4.0"
+jsonschema-specifications==2025.4.1 ; python_version >= "3.10" and python_version < "4.0"
+jsonschema==4.24.1 ; python_version >= "3.10" and python_version < "4.0"
+markdown-it-py==3.0.0 ; python_version >= "3.10" and python_version < "4.0"
+markupsafe==3.0.2 ; python_version >= "3.10" and python_version < "4.0"
+mcp==1.12.0 ; python_version >= "3.10" and python_version < "4.0"
+mdurl==0.1.2 ; python_version >= "3.10" and python_version < "4.0"
+numpy==2.2.6 ; python_version == "3.10"
+numpy==2.3.1 ; python_version >= "3.11" and python_version < "4.0"
+ollama==0.4.9 ; python_version >= "3.10" and python_version < "4.0"
+openai==1.97.0 ; python_version >= "3.10" and python_version < "4.0"
+openapi-pydantic==0.5.1 ; python_version >= "3.10" and python_version < "4.0"
+orjson==3.11.0 ; python_version >= "3.10" and python_version < "4.0"
+packaging==25.0 ; python_version >= "3.10" and python_version < "4.0"
+pycparser==2.22 ; python_version >= "3.10" and python_version < "4.0" and platform_python_implementation != "PyPy"
+pydantic-core==2.33.2 ; python_version >= "3.10" and python_version < "4.0"
+pydantic-extra-types==2.10.5 ; python_version >= "3.10" and python_version < "4.0"
+pydantic-settings==2.10.1 ; python_version >= "3.10" and python_version < "4.0"
+pydantic==2.11.7 ; python_version >= "3.10" and python_version < "4.0"
+pygments==2.19.2 ; python_version >= "3.10" and python_version < "4.0"
+pyperclip==1.9.0 ; python_version >= "3.10" and python_version < "4.0"
+python-dotenv==1.1.1 ; python_version >= "3.10" and python_version < "4.0"
+python-multipart==0.0.20 ; python_version >= "3.10" and python_version < "4.0"
+pywin32==311 ; python_version >= "3.10" and python_version < "4.0" and (platform_system == "Windows" or sys_platform == "win32")
+pyyaml==6.0.2 ; python_version >= "3.10" and python_version < "4.0"
+referencing==0.36.2 ; python_version >= "3.10" and python_version < "4.0"
+regex==2024.11.6 ; python_version >= "3.10" and python_version < "4.0"
+requests==2.32.4 ; python_version >= "3.10" and python_version < "4.0"
+rich-rst==1.3.1 ; python_version >= "3.10" and python_version < "4.0"
+rich-toolkit==0.14.8 ; python_version >= "3.10" and python_version < "4.0"
+rich==14.0.0 ; python_version >= "3.10" and python_version < "4.0"
+rignore==0.6.2 ; python_version >= "3.10" and python_version < "4.0"
+rpds-py==0.26.0 ; python_version >= "3.10" and python_version < "4.0"
+safetensors==0.5.3 ; python_version >= "3.10" and python_version < "4.0"
+scikit-learn==1.7.0 ; python_version >= "3.10" and python_version < "4.0"
+scipy==1.15.3 ; python_version == "3.10"
+scipy==1.16.0 ; python_version >= "3.11" and python_version < "4.0"
+sentry-sdk==2.33.0 ; python_version >= "3.10" and python_version < "4.0"
+shellingham==1.5.4 ; python_version >= "3.10" and python_version < "4.0"
+sniffio==1.3.1 ; python_version >= "3.10" and python_version < "4.0"
+sqlalchemy==2.0.41 ; python_version >= "3.10" and python_version < "4.0"
+sse-starlette==2.4.1 ; python_version >= "3.10" and python_version < "4.0"
+starlette==0.46.2 ; python_version >= "3.10" and python_version < "4.0"
+tenacity==9.1.2 ; python_version >= "3.10" and python_version < "4.0"
+threadpoolctl==3.6.0 ; python_version >= "3.10" and python_version < "4.0"
+tokenizers==0.21.2 ; python_version >= "3.10" and python_version < "4.0"
+tqdm==4.67.1 ; python_version >= "3.10" and python_version < "4.0"
+transformers==4.53.2 ; python_version >= "3.10" and python_version < "4.0"
+typer==0.16.0 ; python_version >= "3.10" and python_version < "4.0"
+typing-extensions==4.14.1 ; python_version >= "3.10" and python_version < "4.0"
+typing-inspection==0.4.1 ; python_version >= "3.10" and python_version < "4.0"
+ujson==5.10.0 ; python_version >= "3.10" and python_version < "4.0"
+urllib3==2.5.0 ; python_version >= "3.10" and python_version < "4.0"
+uvicorn==0.35.0 ; python_version >= "3.10" and python_version < "4.0"
+uvloop==0.21.0 ; python_version >= "3.10" and python_version < "4.0" and platform_python_implementation != "PyPy" and sys_platform != "win32" and sys_platform != "cygwin"
+watchfiles==1.1.0 ; python_version >= "3.10" and python_version < "4.0"
+websockets==15.0.1 ; python_version >= "3.10" and python_version < "4.0"

From 4fd35738b1ea6c52bf3259b679e9d3ef744e2641 Mon Sep 17 00:00:00 2001
From: stx <31013941@qq.com>
Date: Mon, 21 Jul 2025 22:35:55 +0800
Subject: [PATCH 07/34] feat: one-click deployment with docker

---
 .env.example => docker/.env.example           |  0
 Dockerfile => docker/Dockerfile               |  2 +-
 .../docker-compose.yml                        |  4 +-
 requirements.txt                              | 92 -------------------
 4 files changed, 3 insertions(+), 95 deletions(-)
 rename .env.example => docker/.env.example (100%)
 rename Dockerfile => docker/Dockerfile (97%)
 rename docker-compose.yml => docker/docker-compose.yml (97%)
 delete mode 100644 requirements.txt

diff --git a/.env.example b/docker/.env.example
similarity index 100%
rename from .env.example
rename to docker/.env.example
diff --git a/Dockerfile b/docker/Dockerfile
similarity index 97%
rename from Dockerfile
rename to docker/Dockerfile
index 1957d17a7..ea158b111 100644
--- a/Dockerfile
+++ b/docker/Dockerfile
@@ -18,7 +18,7 @@ RUN pip install --upgrade pip && pip install --no-cache-dir -r requirements.txt
 
 RUN pip install chonkie
 
-COPY . .
+COPY ../. .
 ENV PYTHONPATH=/app/src
 
 EXPOSE 8000
diff --git a/docker-compose.yml b/docker/docker-compose.yml
similarity index 97%
rename from docker-compose.yml
rename to docker/docker-compose.yml
index 147ae1504..64d383a25 100644
--- a/docker-compose.yml
+++ b/docker/docker-compose.yml
@@ -4,12 +4,12 @@ services:
   memos:
     container_name: memos-api-server
     build:
-      context: .
+      context: ..
       dockerfile: Dockerfile
     ports:
       - "8000:8000"
     env_file:
-      - .env
+      - ../.env
     depends_on:
       - neo4j
       - qdrant
diff --git a/requirements.txt b/requirements.txt
deleted file mode 100644
index 596e94b47..000000000
--- a/requirements.txt
+++ /dev/null
@@ -1,92 +0,0 @@
-annotated-types==0.7.0 ; python_version >= "3.10" and python_version < "4.0"
-anyio==4.9.0 ; python_version >= "3.10" and python_version < "4.0"
-attrs==25.3.0 ; python_version >= "3.10" and python_version < "4.0"
-authlib==1.6.0 ; python_version >= "3.10" and python_version < "4.0"
-certifi==2025.7.14 ; python_version >= "3.10" and python_version < "4.0"
-cffi==1.17.1 ; python_version >= "3.10" and python_version < "4.0" and platform_python_implementation != "PyPy"
-charset-normalizer==3.4.2 ; python_version >= "3.10" and python_version < "4.0"
-click==8.2.1 ; python_version >= "3.10" and python_version < "4.0"
-colorama==0.4.6 ; python_version >= "3.10" and python_version < "4.0" and (platform_system == "Windows" or sys_platform == "win32")
-cryptography==45.0.5 ; python_version >= "3.10" and python_version < "4.0"
-cyclopts==3.22.2 ; python_version >= "3.10" and python_version < "4.0"
-distro==1.9.0 ; python_version >= "3.10" and python_version < "4.0"
-dnspython==2.7.0 ; python_version >= "3.10" and python_version < "4.0"
-docstring-parser==0.16 ; python_version >= "3.10" and python_version < "4.0"
-docutils==0.21.2 ; python_version >= "3.10" and python_version < "4.0"
-email-validator==2.2.0 ; python_version >= "3.10" and python_version < "4.0"
-exceptiongroup==1.3.0 ; python_version >= "3.10" and python_version < "4.0"
-fastapi-cli==0.0.8 ; python_version >= "3.10" and python_version < "4.0"
-fastapi-cloud-cli==0.1.4 ; python_version >= "3.10" and python_version < "4.0"
-fastapi==0.115.14 ; python_version >= "3.10" and python_version < "4.0"
-fastmcp==2.10.5 ; python_version >= "3.10" and python_version < "4.0"
-filelock==3.18.0 ; python_version >= "3.10" and python_version < "4.0"
-fsspec==2025.7.0 ; python_version >= "3.10" and python_version < "4.0"
-greenlet==3.2.3 ; python_version >= "3.10" and python_version < "3.14" and (platform_machine == "aarch64" or platform_machine == "ppc64le" or platform_machine == "x86_64" or platform_machine == "amd64" or platform_machine == "AMD64" or platform_machine == "win32" or platform_machine == "WIN32")
-h11==0.16.0 ; python_version >= "3.10" and python_version < "4.0"
-hf-xet==1.1.5 ; python_version >= "3.10" and python_version < "4.0" and (platform_machine == "x86_64" or platform_machine == "amd64" or platform_machine == "arm64" or platform_machine == "aarch64")
-httpcore==1.0.9 ; python_version >= "3.10" and python_version < "4.0"
-httptools==0.6.4 ; python_version >= "3.10" and python_version < "4.0"
-httpx-sse==0.4.1 ; python_version >= "3.10" and python_version < "4.0"
-httpx==0.28.1 ; python_version >= "3.10" and python_version < "4.0"
-huggingface-hub==0.33.4 ; python_version >= "3.10" and python_version < "4.0"
-idna==3.10 ; python_version >= "3.10" and python_version < "4.0"
-itsdangerous==2.2.0 ; python_version >= "3.10" and python_version < "4.0"
-jinja2==3.1.6 ; python_version >= "3.10" and python_version < "4.0"
-jiter==0.10.0 ; python_version >= "3.10" and python_version < "4.0"
-joblib==1.5.1 ; python_version >= "3.10" and python_version < "4.0"
-jsonschema-specifications==2025.4.1 ; python_version >= "3.10" and python_version < "4.0"
-jsonschema==4.24.1 ; python_version >= "3.10" and python_version < "4.0"
-markdown-it-py==3.0.0 ; python_version >= "3.10" and python_version < "4.0"
-markupsafe==3.0.2 ; python_version >= "3.10" and python_version < "4.0"
-mcp==1.12.0 ; python_version >= "3.10" and python_version < "4.0"
-mdurl==0.1.2 ; python_version >= "3.10" and python_version < "4.0"
-numpy==2.2.6 ; python_version == "3.10"
-numpy==2.3.1 ; python_version >= "3.11" and python_version < "4.0"
-ollama==0.4.9 ; python_version >= "3.10" and python_version < "4.0"
-openai==1.97.0 ; python_version >= "3.10" and python_version < "4.0"
-openapi-pydantic==0.5.1 ; python_version >= "3.10" and python_version < "4.0"
-orjson==3.11.0 ; python_version >= "3.10" and python_version < "4.0"
-packaging==25.0 ; python_version >= "3.10" and python_version < "4.0"
-pycparser==2.22 ; python_version >= "3.10" and python_version < "4.0" and platform_python_implementation != "PyPy"
-pydantic-core==2.33.2 ; python_version >= "3.10" and python_version < "4.0"
-pydantic-extra-types==2.10.5 ; python_version >= "3.10" and python_version < "4.0"
-pydantic-settings==2.10.1 ; python_version >= "3.10" and python_version < "4.0"
-pydantic==2.11.7 ; python_version >= "3.10" and python_version < "4.0"
-pygments==2.19.2 ; python_version >= "3.10" and python_version < "4.0"
-pyperclip==1.9.0 ; python_version >= "3.10" and python_version < "4.0"
-python-dotenv==1.1.1 ; python_version >= "3.10" and python_version < "4.0"
-python-multipart==0.0.20 ; python_version >= "3.10" and python_version < "4.0"
-pywin32==311 ; python_version >= "3.10" and python_version < "4.0" and (platform_system == "Windows" or sys_platform == "win32")
-pyyaml==6.0.2 ; python_version >= "3.10" and python_version < "4.0"
-referencing==0.36.2 ; python_version >= "3.10" and python_version < "4.0"
-regex==2024.11.6 ; python_version >= "3.10" and python_version < "4.0"
-requests==2.32.4 ; python_version >= "3.10" and python_version < "4.0"
-rich-rst==1.3.1 ; python_version >= "3.10" and python_version < "4.0"
-rich-toolkit==0.14.8 ; python_version >= "3.10" and python_version < "4.0"
-rich==14.0.0 ; python_version >= "3.10" and python_version < "4.0"
-rignore==0.6.2 ; python_version >= "3.10" and python_version < "4.0"
-rpds-py==0.26.0 ; python_version >= "3.10" and python_version < "4.0"
-safetensors==0.5.3 ; python_version >= "3.10" and python_version < "4.0"
-scikit-learn==1.7.0 ; python_version >= "3.10" and python_version < "4.0"
-scipy==1.15.3 ; python_version == "3.10"
-scipy==1.16.0 ; python_version >= "3.11" and python_version < "4.0"
-sentry-sdk==2.33.0 ; python_version >= "3.10" and python_version < "4.0"
-shellingham==1.5.4 ; python_version >= "3.10" and python_version < "4.0"
-sniffio==1.3.1 ; python_version >= "3.10" and python_version < "4.0"
-sqlalchemy==2.0.41 ; python_version >= "3.10" and python_version < "4.0"
-sse-starlette==2.4.1 ; python_version >= "3.10" and python_version < "4.0"
-starlette==0.46.2 ; python_version >= "3.10" and python_version < "4.0"
-tenacity==9.1.2 ; python_version >= "3.10" and python_version < "4.0"
-threadpoolctl==3.6.0 ; python_version >= "3.10" and python_version < "4.0"
-tokenizers==0.21.2 ; python_version >= "3.10" and python_version < "4.0"
-tqdm==4.67.1 ; python_version >= "3.10" and python_version < "4.0"
-transformers==4.53.2 ; python_version >= "3.10" and python_version < "4.0"
-typer==0.16.0 ; python_version >= "3.10" and python_version < "4.0"
-typing-extensions==4.14.1 ; python_version >= "3.10" and python_version < "4.0"
-typing-inspection==0.4.1 ; python_version >= "3.10" and python_version < "4.0"
-ujson==5.10.0 ; python_version >= "3.10" and python_version < "4.0"
-urllib3==2.5.0 ; python_version >= "3.10" and python_version < "4.0"
-uvicorn==0.35.0 ; python_version >= "3.10" and python_version < "4.0"
-uvloop==0.21.0 ; python_version >= "3.10" and python_version < "4.0" and platform_python_implementation != "PyPy" and sys_platform != "win32" and sys_platform != "cygwin"
-watchfiles==1.1.0 ; python_version >= "3.10" and python_version < "4.0"
-websockets==15.0.1 ; python_version >= "3.10" and python_version < "4.0"

From 872312ff739a64fed793cb3c70aa85d150130bd0 Mon Sep 17 00:00:00 2001
From: stx <31013941@qq.com>
Date: Mon, 21 Jul 2025 22:38:48 +0800
Subject: [PATCH 08/34] feat: one-click deployment with docker

---
 docker/requirements.txt | 92 +++++++++++++++++++++++++++++++++++++++++
 1 file changed, 92 insertions(+)
 create mode 100644 docker/requirements.txt

diff --git a/docker/requirements.txt b/docker/requirements.txt
new file mode 100644
index 000000000..596e94b47
--- /dev/null
+++ b/docker/requirements.txt
@@ -0,0 +1,92 @@
+annotated-types==0.7.0 ; python_version >= "3.10" and python_version < "4.0"
+anyio==4.9.0 ; python_version >= "3.10" and python_version < "4.0"
+attrs==25.3.0 ; python_version >= "3.10" and python_version < "4.0"
+authlib==1.6.0 ; python_version >= "3.10" and python_version < "4.0"
+certifi==2025.7.14 ; python_version >= "3.10" and python_version < "4.0"
+cffi==1.17.1 ; python_version >= "3.10" and python_version < "4.0" and platform_python_implementation != "PyPy"
+charset-normalizer==3.4.2 ; python_version >= "3.10" and python_version < "4.0"
+click==8.2.1 ; python_version >= "3.10" and python_version < "4.0"
+colorama==0.4.6 ; python_version >= "3.10" and python_version < "4.0" and (platform_system == "Windows" or sys_platform == "win32")
+cryptography==45.0.5 ; python_version >= "3.10" and python_version < "4.0"
+cyclopts==3.22.2 ; python_version >= "3.10" and python_version < "4.0"
+distro==1.9.0 ; python_version >= "3.10" and python_version < "4.0"
+dnspython==2.7.0 ; python_version >= "3.10" and python_version < "4.0"
+docstring-parser==0.16 ; python_version >= "3.10" and python_version < "4.0"
+docutils==0.21.2 ; python_version >= "3.10" and python_version < "4.0"
+email-validator==2.2.0 ; python_version >= "3.10" and python_version < "4.0"
+exceptiongroup==1.3.0 ; python_version >= "3.10" and python_version < "4.0"
+fastapi-cli==0.0.8 ; python_version >= "3.10" and python_version < "4.0"
+fastapi-cloud-cli==0.1.4 ; python_version >= "3.10" and python_version < "4.0"
+fastapi==0.115.14 ; python_version >= "3.10" and python_version < "4.0"
+fastmcp==2.10.5 ; python_version >= "3.10" and python_version < "4.0"
+filelock==3.18.0 ; python_version >= "3.10" and python_version < "4.0"
+fsspec==2025.7.0 ; python_version >= "3.10" and python_version < "4.0"
+greenlet==3.2.3 ; python_version >= "3.10" and python_version < "3.14" and (platform_machine == "aarch64" or platform_machine == "ppc64le" or platform_machine == "x86_64" or platform_machine == "amd64" or platform_machine == "AMD64" or platform_machine == "win32" or platform_machine == "WIN32")
+h11==0.16.0 ; python_version >= "3.10" and python_version < "4.0"
+hf-xet==1.1.5 ; python_version >= "3.10" and python_version < "4.0" and (platform_machine == "x86_64" or platform_machine == "amd64" or platform_machine == "arm64" or platform_machine == "aarch64")
+httpcore==1.0.9 ; python_version >= "3.10" and python_version < "4.0"
+httptools==0.6.4 ; python_version >= "3.10" and python_version < "4.0"
+httpx-sse==0.4.1 ; python_version >= "3.10" and python_version < "4.0"
+httpx==0.28.1 ; python_version >= "3.10" and python_version < "4.0"
+huggingface-hub==0.33.4 ; python_version >= "3.10" and python_version < "4.0"
+idna==3.10 ; python_version >= "3.10" and python_version < "4.0"
+itsdangerous==2.2.0 ; python_version >= "3.10" and python_version < "4.0"
+jinja2==3.1.6 ; python_version >= "3.10" and python_version < "4.0"
+jiter==0.10.0 ; python_version >= "3.10" and python_version < "4.0"
+joblib==1.5.1 ; python_version >= "3.10" and python_version < "4.0"
+jsonschema-specifications==2025.4.1 ; python_version >= "3.10" and python_version < "4.0"
+jsonschema==4.24.1 ; python_version >= "3.10" and python_version < "4.0"
+markdown-it-py==3.0.0 ; python_version >= "3.10" and python_version < "4.0"
+markupsafe==3.0.2 ; python_version >= "3.10" and python_version < "4.0"
+mcp==1.12.0 ; python_version >= "3.10" and python_version < "4.0"
+mdurl==0.1.2 ; python_version >= "3.10" and python_version < "4.0"
+numpy==2.2.6 ; python_version == "3.10"
+numpy==2.3.1 ; python_version >= "3.11" and python_version < "4.0"
+ollama==0.4.9 ; python_version >= "3.10" and python_version < "4.0"
+openai==1.97.0 ; python_version >= "3.10" and python_version < "4.0"
+openapi-pydantic==0.5.1 ; python_version >= "3.10" and python_version < "4.0"
+orjson==3.11.0 ; python_version >= "3.10" and python_version < "4.0"
+packaging==25.0 ; python_version >= "3.10" and python_version < "4.0"
+pycparser==2.22 ; python_version >= "3.10" and python_version < "4.0" and platform_python_implementation != "PyPy"
+pydantic-core==2.33.2 ; python_version >= "3.10" and python_version < "4.0"
+pydantic-extra-types==2.10.5 ; python_version >= "3.10" and python_version < "4.0"
+pydantic-settings==2.10.1 ; python_version >= "3.10" and python_version < "4.0"
+pydantic==2.11.7 ; python_version >= "3.10" and python_version < "4.0"
+pygments==2.19.2 ; python_version >= "3.10" and python_version < "4.0"
+pyperclip==1.9.0 ; python_version >= "3.10" and python_version < "4.0"
+python-dotenv==1.1.1 ; python_version >= "3.10" and python_version < "4.0"
+python-multipart==0.0.20 ; python_version >= "3.10" and python_version < "4.0"
+pywin32==311 ; python_version >= "3.10" and python_version < "4.0" and (platform_system == "Windows" or sys_platform == "win32")
+pyyaml==6.0.2 ; python_version >= "3.10" and python_version < "4.0"
+referencing==0.36.2 ; python_version >= "3.10" and python_version < "4.0"
+regex==2024.11.6 ; python_version >= "3.10" and python_version < "4.0"
+requests==2.32.4 ; python_version >= "3.10" and python_version < "4.0"
+rich-rst==1.3.1 ; python_version >= "3.10" and python_version < "4.0"
+rich-toolkit==0.14.8 ; python_version >= "3.10" and python_version < "4.0"
+rich==14.0.0 ; python_version >= "3.10" and python_version < "4.0"
+rignore==0.6.2 ; python_version >= "3.10" and python_version < "4.0"
+rpds-py==0.26.0 ; python_version >= "3.10" and python_version < "4.0"
+safetensors==0.5.3 ; python_version >= "3.10" and python_version < "4.0"
+scikit-learn==1.7.0 ; python_version >= "3.10" and python_version < "4.0"
+scipy==1.15.3 ; python_version == "3.10"
+scipy==1.16.0 ; python_version >= "3.11" and python_version < "4.0"
+sentry-sdk==2.33.0 ; python_version >= "3.10" and python_version < "4.0"
+shellingham==1.5.4 ; python_version >= "3.10" and python_version < "4.0"
+sniffio==1.3.1 ; python_version >= "3.10" and python_version < "4.0"
+sqlalchemy==2.0.41 ; python_version >= "3.10" and python_version < "4.0"
+sse-starlette==2.4.1 ; python_version >= "3.10" and python_version < "4.0"
+starlette==0.46.2 ; python_version >= "3.10" and python_version < "4.0"
+tenacity==9.1.2 ; python_version >= "3.10" and python_version < "4.0"
+threadpoolctl==3.6.0 ; python_version >= "3.10" and python_version < "4.0"
+tokenizers==0.21.2 ; python_version >= "3.10" and python_version < "4.0"
+tqdm==4.67.1 ; python_version >= "3.10" and python_version < "4.0"
+transformers==4.53.2 ; python_version >= "3.10" and python_version < "4.0"
+typer==0.16.0 ; python_version >= "3.10" and python_version < "4.0"
+typing-extensions==4.14.1 ; python_version >= "3.10" and python_version < "4.0"
+typing-inspection==0.4.1 ; python_version >= "3.10" and python_version < "4.0"
+ujson==5.10.0 ; python_version >= "3.10" and python_version < "4.0"
+urllib3==2.5.0 ; python_version >= "3.10" and python_version < "4.0"
+uvicorn==0.35.0 ; python_version >= "3.10" and python_version < "4.0"
+uvloop==0.21.0 ; python_version >= "3.10" and python_version < "4.0" and platform_python_implementation != "PyPy" and sys_platform != "win32" and sys_platform != "cygwin"
+watchfiles==1.1.0 ; python_version >= "3.10" and python_version < "4.0"
+websockets==15.0.1 ; python_version >= "3.10" and python_version < "4.0"

From b89d091205f0bb7e7773950a09321f9918b7f794 Mon Sep 17 00:00:00 2001
From: stx <31013941@qq.com>
Date: Tue, 22 Jul 2025 10:39:41 +0800
Subject: [PATCH 09/34] feat: one-click deployment with docker

---
 examples/data/config/tree_config.json   | 11 +++++++----
 examples/mem_os/simple_openapi_memos.py | 22 +++++++++++++++-------
 src/memos/configs/graph_db.py           |  2 +-
 3 files changed, 23 insertions(+), 12 deletions(-)

diff --git a/examples/data/config/tree_config.json b/examples/data/config/tree_config.json
index bcb07b3a4..0a1faaafb 100644
--- a/examples/data/config/tree_config.json
+++ b/examples/data/config/tree_config.json
@@ -18,9 +18,12 @@
     }
   },
   "embedder": {
-    "backend": "ollama",
+    "backend": "universal_api",
     "config": {
-      "model_name_or_path": "nomic-embed-text:latest"
+      "provider": "openai",
+      "api_key": "sk-jz4BGHJftedu1UYMPTt3r2h9s1DuQFU2Co74YZa1aMUUNokK",
+      "model_name_or_path": "text-embedding-3-large",
+      "base_url": "http://123.129.219.111:3000/v1"
     }
   },
   "graph_db": {
@@ -31,8 +34,8 @@
       "password": "12345678",
       "db_name": "user08alice",
       "auto_create": true,
-      "embedding_dimension": 768
+      "embedding_dimension": 3072
     }
   },
-  "reorganize": false
+  "reorganize": true
 }
diff --git a/examples/mem_os/simple_openapi_memos.py b/examples/mem_os/simple_openapi_memos.py
index a9faa3dc8..101ae1ecb 100644
--- a/examples/mem_os/simple_openapi_memos.py
+++ b/examples/mem_os/simple_openapi_memos.py
@@ -50,9 +50,12 @@
                 "config": openapi_config,
             },
             "embedder": {
-                "backend": "ollama",
+                "backend": "universal_api",
                 "config": {
-                    "model_name_or_path": "nomic-embed-text:latest",
+                    "provider": "openai",
+                    "api_key": "sk-jz4BGHJftedu1UYMPTt3r2h9s1DuQFU2Co74YZa1aMUUNokK",
+                    "model_name_or_path": "text-embedding-3-large",
+                    "base_url": "http://123.129.219.111:3000/v1",
                 },
             },
             "chunker": {
@@ -105,17 +108,21 @@
                     "config": {
                         "uri": neo4j_uri,
                         "user": "neo4j",
-                        "password": "12345678",
+                        "password": "iaarlichunyu",
                         "db_name": f"db{user_name.replace('-', '')}",
                         "auto_create": True,
                     },
                 },
                 "embedder": {
-                    "backend": "ollama",
+                    "backend": "universal_api",
                     "config": {
-                        "model_name_or_path": "nomic-embed-text:latest",
+                        "provider": "openai",
+                        "api_key": "sk-jz4BGHJftedu1UYMPTt3r2h9s1DuQFU2Co74YZa1aMUUNokK",
+                        "model_name_or_path": "text-embedding-3-large",
+                        "base_url": "http://123.129.219.111:3000/v1",
                     },
                 },
+                "reorganize": True,
             },
         },
         "act_mem": {},
@@ -259,11 +266,12 @@ def filter_memory_data(memories_data):
 # 7. Add document
 print(f"🚀 [{datetime.now().strftime('%H:%M:%S')}] Starting to add document...")
 start_time = time.time()
-## 7.1 add pdf for ./tmp/data if use doc mem mos.add(doc_path="./tmp/data/")
-start_time = time.time()
 
+## 7.1 add pdf for ./tmp/data if use doc mem mos.add(doc_path="./tmp/data/")
+mos.add(doc_path="examples/data/docs/test/")
 get_all_results = mos.get_all()
 filtered_results = filter_memory_data(get_all_results)
+
 print(f"Get all results after add doc: {filtered_results}")
 
 print(
diff --git a/src/memos/configs/graph_db.py b/src/memos/configs/graph_db.py
index cca93fede..1626da42f 100644
--- a/src/memos/configs/graph_db.py
+++ b/src/memos/configs/graph_db.py
@@ -68,7 +68,7 @@ class Neo4jGraphDBConfig(BaseGraphDBConfig):
         ),
     )
 
-    embedding_dimension: int = Field(default=768, description="Dimension of vector embedding")
+    embedding_dimension: int = Field(default=3072, description="Dimension of vector embedding")
 
     @model_validator(mode="after")
     def validate_config(self):

From 2aa5db8e1b2975420e4e7c97d6d87226de906bc9 Mon Sep 17 00:00:00 2001
From: stx <31013941@qq.com>
Date: Tue, 22 Jul 2025 11:39:03 +0800
Subject: [PATCH 10/34] feat: one-click deployment with docker

---
 docker/.env.example => .env.example   |  0
 {docker => server}/Dockerfile         |  7 +++--
 {docker => server}/docker-compose.yml |  6 ++--
 {docker => server}/requirements.txt   | 45 +++++++++++++++++++++++++++
 4 files changed, 53 insertions(+), 5 deletions(-)
 rename docker/.env.example => .env.example (100%)
 rename {docker => server}/Dockerfile (81%)
 rename {docker => server}/docker-compose.yml (90%)
 rename {docker => server}/requirements.txt (69%)

diff --git a/docker/.env.example b/.env.example
similarity index 100%
rename from docker/.env.example
rename to .env.example
diff --git a/docker/Dockerfile b/server/Dockerfile
similarity index 81%
rename from docker/Dockerfile
rename to server/Dockerfile
index ea158b111..985be3f13 100644
--- a/docker/Dockerfile
+++ b/server/Dockerfile
@@ -13,12 +13,13 @@ WORKDIR /app
 
 ENV HF_ENDPOINT=https://hf-mirror.com
 
-COPY requirements.txt .
+COPY server/requirements.txt .
 RUN pip install --upgrade pip && pip install --no-cache-dir -r requirements.txt
 
-RUN pip install chonkie
+# 复制 server 和 src 代码
+COPY server/ ./server/
+COPY src/ ./src/
 
-COPY ../. .
 ENV PYTHONPATH=/app/src
 
 EXPOSE 8000
diff --git a/docker/docker-compose.yml b/server/docker-compose.yml
similarity index 90%
rename from docker/docker-compose.yml
rename to server/docker-compose.yml
index 64d383a25..ceca26e91 100644
--- a/docker/docker-compose.yml
+++ b/server/docker-compose.yml
@@ -5,7 +5,7 @@ services:
     container_name: memos-api-server
     build:
       context: ..
-      dockerfile: Dockerfile
+      dockerfile: server/Dockerfile
     ports:
       - "8000:8000"
     env_file:
@@ -15,8 +15,10 @@ services:
       - qdrant
     environment:
       - PYTHONPATH=/app/src
+      - HF_ENDPOINT=https://hf-mirror.com
     volumes:
-      - .:/app
+      - ../src:/app/src
+      - .:/app/server
     networks:
       - memos_network
 
diff --git a/docker/requirements.txt b/server/requirements.txt
similarity index 69%
rename from docker/requirements.txt
rename to server/requirements.txt
index 596e94b47..211ec3cae 100644
--- a/docker/requirements.txt
+++ b/server/requirements.txt
@@ -2,24 +2,33 @@ annotated-types==0.7.0 ; python_version >= "3.10" and python_version < "4.0"
 anyio==4.9.0 ; python_version >= "3.10" and python_version < "4.0"
 attrs==25.3.0 ; python_version >= "3.10" and python_version < "4.0"
 authlib==1.6.0 ; python_version >= "3.10" and python_version < "4.0"
+beautifulsoup4==4.13.4 ; python_version >= "3.10" and python_version < "4.0"
 certifi==2025.7.14 ; python_version >= "3.10" and python_version < "4.0"
 cffi==1.17.1 ; python_version >= "3.10" and python_version < "4.0" and platform_python_implementation != "PyPy"
+cfgv==3.4.0 ; python_version >= "3.10" and python_version < "4.0"
 charset-normalizer==3.4.2 ; python_version >= "3.10" and python_version < "4.0"
+chonkie==1.1.1 ; python_version >= "3.10" and python_version < "4.0"
 click==8.2.1 ; python_version >= "3.10" and python_version < "4.0"
+cobble==0.1.4 ; python_version >= "3.10" and python_version < "4.0"
 colorama==0.4.6 ; python_version >= "3.10" and python_version < "4.0" and (platform_system == "Windows" or sys_platform == "win32")
+coloredlogs==15.0.1 ; python_version >= "3.10" and python_version < "4.0"
 cryptography==45.0.5 ; python_version >= "3.10" and python_version < "4.0"
 cyclopts==3.22.2 ; python_version >= "3.10" and python_version < "4.0"
+defusedxml==0.7.1 ; python_version >= "3.10" and python_version < "4.0"
+distlib==0.4.0 ; python_version >= "3.10" and python_version < "4.0"
 distro==1.9.0 ; python_version >= "3.10" and python_version < "4.0"
 dnspython==2.7.0 ; python_version >= "3.10" and python_version < "4.0"
 docstring-parser==0.16 ; python_version >= "3.10" and python_version < "4.0"
 docutils==0.21.2 ; python_version >= "3.10" and python_version < "4.0"
 email-validator==2.2.0 ; python_version >= "3.10" and python_version < "4.0"
+et-xmlfile==2.0.0 ; python_version >= "3.10" and python_version < "4.0"
 exceptiongroup==1.3.0 ; python_version >= "3.10" and python_version < "4.0"
 fastapi-cli==0.0.8 ; python_version >= "3.10" and python_version < "4.0"
 fastapi-cloud-cli==0.1.4 ; python_version >= "3.10" and python_version < "4.0"
 fastapi==0.115.14 ; python_version >= "3.10" and python_version < "4.0"
 fastmcp==2.10.5 ; python_version >= "3.10" and python_version < "4.0"
 filelock==3.18.0 ; python_version >= "3.10" and python_version < "4.0"
+flatbuffers==25.2.10 ; python_version >= "3.10" and python_version < "4.0"
 fsspec==2025.7.0 ; python_version >= "3.10" and python_version < "4.0"
 greenlet==3.2.3 ; python_version >= "3.10" and python_version < "3.14" and (platform_machine == "aarch64" or platform_machine == "ppc64le" or platform_machine == "x86_64" or platform_machine == "amd64" or platform_machine == "AMD64" or platform_machine == "win32" or platform_machine == "WIN32")
 h11==0.16.0 ; python_version >= "3.10" and python_version < "4.0"
@@ -29,24 +38,44 @@ httptools==0.6.4 ; python_version >= "3.10" and python_version < "4.0"
 httpx-sse==0.4.1 ; python_version >= "3.10" and python_version < "4.0"
 httpx==0.28.1 ; python_version >= "3.10" and python_version < "4.0"
 huggingface-hub==0.33.4 ; python_version >= "3.10" and python_version < "4.0"
+humanfriendly==10.0 ; python_version >= "3.10" and python_version < "4.0"
+identify==2.6.12 ; python_version >= "3.10" and python_version < "4.0"
 idna==3.10 ; python_version >= "3.10" and python_version < "4.0"
+iniconfig==2.1.0 ; python_version >= "3.10" and python_version < "4.0"
 itsdangerous==2.2.0 ; python_version >= "3.10" and python_version < "4.0"
 jinja2==3.1.6 ; python_version >= "3.10" and python_version < "4.0"
 jiter==0.10.0 ; python_version >= "3.10" and python_version < "4.0"
 joblib==1.5.1 ; python_version >= "3.10" and python_version < "4.0"
 jsonschema-specifications==2025.4.1 ; python_version >= "3.10" and python_version < "4.0"
 jsonschema==4.24.1 ; python_version >= "3.10" and python_version < "4.0"
+lxml==6.0.0 ; python_version >= "3.10" and python_version < "4.0"
+magika==0.6.2 ; python_version >= "3.10" and python_version < "4.0"
+mammoth==1.9.1 ; python_version >= "3.10" and python_version < "4.0"
 markdown-it-py==3.0.0 ; python_version >= "3.10" and python_version < "4.0"
+markdownify==1.1.0 ; python_version >= "3.10" and python_version < "4.0"
+markitdown==0.1.2 ; python_version >= "3.10" and python_version < "4.0"
 markupsafe==3.0.2 ; python_version >= "3.10" and python_version < "4.0"
 mcp==1.12.0 ; python_version >= "3.10" and python_version < "4.0"
 mdurl==0.1.2 ; python_version >= "3.10" and python_version < "4.0"
+mpmath==1.3.0 ; python_version >= "3.10" and python_version < "4.0"
+neo4j==5.28.1 ; python_version >= "3.10" and python_version < "4.0"
+nodeenv==1.9.1 ; python_version >= "3.10" and python_version < "4.0"
 numpy==2.2.6 ; python_version == "3.10"
 numpy==2.3.1 ; python_version >= "3.11" and python_version < "4.0"
 ollama==0.4.9 ; python_version >= "3.10" and python_version < "4.0"
+onnxruntime==1.22.1 ; python_version >= "3.10" and python_version < "4.0"
 openai==1.97.0 ; python_version >= "3.10" and python_version < "4.0"
 openapi-pydantic==0.5.1 ; python_version >= "3.10" and python_version < "4.0"
+openpyxl==3.1.5 ; python_version >= "3.10" and python_version < "4.0"
 orjson==3.11.0 ; python_version >= "3.10" and python_version < "4.0"
 packaging==25.0 ; python_version >= "3.10" and python_version < "4.0"
+pandas==2.3.1 ; python_version >= "3.10" and python_version < "4.0"
+pdfminer-six==20250506 ; python_version >= "3.10" and python_version < "4.0"
+pillow==11.3.0 ; python_version >= "3.10" and python_version < "4.0"
+platformdirs==4.3.8 ; python_version >= "3.10" and python_version < "4.0"
+pluggy==1.6.0 ; python_version >= "3.10" and python_version < "4.0"
+pre-commit==4.2.0 ; python_version >= "3.10" and python_version < "4.0"
+protobuf==6.31.1 ; python_version >= "3.10" and python_version < "4.0"
 pycparser==2.22 ; python_version >= "3.10" and python_version < "4.0" and platform_python_implementation != "PyPy"
 pydantic-core==2.33.2 ; python_version >= "3.10" and python_version < "4.0"
 pydantic-extra-types==2.10.5 ; python_version >= "3.10" and python_version < "4.0"
@@ -54,8 +83,14 @@ pydantic-settings==2.10.1 ; python_version >= "3.10" and python_version < "4.0"
 pydantic==2.11.7 ; python_version >= "3.10" and python_version < "4.0"
 pygments==2.19.2 ; python_version >= "3.10" and python_version < "4.0"
 pyperclip==1.9.0 ; python_version >= "3.10" and python_version < "4.0"
+pyreadline3==3.5.4 ; python_version >= "3.10" and python_version < "4.0" and sys_platform == "win32"
+pytest-asyncio==0.23.8 ; python_version >= "3.10" and python_version < "4.0"
+pytest==8.4.1 ; python_version >= "3.10" and python_version < "4.0"
+python-dateutil==2.9.0.post0 ; python_version >= "3.10" and python_version < "4.0"
 python-dotenv==1.1.1 ; python_version >= "3.10" and python_version < "4.0"
 python-multipart==0.0.20 ; python_version >= "3.10" and python_version < "4.0"
+python-pptx==1.0.2 ; python_version >= "3.10" and python_version < "4.0"
+pytz==2025.2 ; python_version >= "3.10" and python_version < "4.0"
 pywin32==311 ; python_version >= "3.10" and python_version < "4.0" and (platform_system == "Windows" or sys_platform == "win32")
 pyyaml==6.0.2 ; python_version >= "3.10" and python_version < "4.0"
 referencing==0.36.2 ; python_version >= "3.10" and python_version < "4.0"
@@ -66,27 +101,37 @@ rich-toolkit==0.14.8 ; python_version >= "3.10" and python_version < "4.0"
 rich==14.0.0 ; python_version >= "3.10" and python_version < "4.0"
 rignore==0.6.2 ; python_version >= "3.10" and python_version < "4.0"
 rpds-py==0.26.0 ; python_version >= "3.10" and python_version < "4.0"
+ruff==0.11.13 ; python_version >= "3.10" and python_version < "4.0"
 safetensors==0.5.3 ; python_version >= "3.10" and python_version < "4.0"
+schedule==1.2.2 ; python_version >= "3.10" and python_version < "4.0"
 scikit-learn==1.7.0 ; python_version >= "3.10" and python_version < "4.0"
 scipy==1.15.3 ; python_version == "3.10"
 scipy==1.16.0 ; python_version >= "3.11" and python_version < "4.0"
 sentry-sdk==2.33.0 ; python_version >= "3.10" and python_version < "4.0"
 shellingham==1.5.4 ; python_version >= "3.10" and python_version < "4.0"
+six==1.17.0 ; python_version >= "3.10" and python_version < "4.0"
 sniffio==1.3.1 ; python_version >= "3.10" and python_version < "4.0"
+soupsieve==2.7 ; python_version >= "3.10" and python_version < "4.0"
 sqlalchemy==2.0.41 ; python_version >= "3.10" and python_version < "4.0"
 sse-starlette==2.4.1 ; python_version >= "3.10" and python_version < "4.0"
 starlette==0.46.2 ; python_version >= "3.10" and python_version < "4.0"
+sympy==1.14.0 ; python_version >= "3.10" and python_version < "4.0"
 tenacity==9.1.2 ; python_version >= "3.10" and python_version < "4.0"
 threadpoolctl==3.6.0 ; python_version >= "3.10" and python_version < "4.0"
 tokenizers==0.21.2 ; python_version >= "3.10" and python_version < "4.0"
+tomli==2.2.1 ; python_version == "3.10"
 tqdm==4.67.1 ; python_version >= "3.10" and python_version < "4.0"
 transformers==4.53.2 ; python_version >= "3.10" and python_version < "4.0"
 typer==0.16.0 ; python_version >= "3.10" and python_version < "4.0"
 typing-extensions==4.14.1 ; python_version >= "3.10" and python_version < "4.0"
 typing-inspection==0.4.1 ; python_version >= "3.10" and python_version < "4.0"
+tzdata==2025.2 ; python_version >= "3.10" and python_version < "4.0"
 ujson==5.10.0 ; python_version >= "3.10" and python_version < "4.0"
 urllib3==2.5.0 ; python_version >= "3.10" and python_version < "4.0"
 uvicorn==0.35.0 ; python_version >= "3.10" and python_version < "4.0"
 uvloop==0.21.0 ; python_version >= "3.10" and python_version < "4.0" and platform_python_implementation != "PyPy" and sys_platform != "win32" and sys_platform != "cygwin"
+virtualenv==20.31.2 ; python_version >= "3.10" and python_version < "4.0"
 watchfiles==1.1.0 ; python_version >= "3.10" and python_version < "4.0"
 websockets==15.0.1 ; python_version >= "3.10" and python_version < "4.0"
+xlrd==2.0.2 ; python_version >= "3.10" and python_version < "4.0"
+xlsxwriter==3.2.5 ; python_version >= "3.10" and python_version < "4.0"

From 19bdbfb387c1dbf170cdcfdfe1686eff0a274c1e Mon Sep 17 00:00:00 2001
From: stx <31013941@qq.com>
Date: Tue, 22 Jul 2025 14:34:26 +0800
Subject: [PATCH 11/34] feat: docker settings modify

---
 server/Dockerfile | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/server/Dockerfile b/server/Dockerfile
index 985be3f13..d0e1ec290 100644
--- a/server/Dockerfile
+++ b/server/Dockerfile
@@ -1,5 +1,7 @@
+# Base image
 FROM python:3.11-slim
 
+# Install dependencies
 RUN apt-get update && apt-get install -y \
     gcc \
     g++ \
@@ -9,18 +11,25 @@ RUN apt-get update && apt-get install -y \
     curl \
     && rm -rf /var/lib/apt/lists/*
 
+# Set working directory
 WORKDIR /app
 
+# Set Hugging Face mirror
 ENV HF_ENDPOINT=https://hf-mirror.com
 
+# Install Python packages
 COPY server/requirements.txt .
 RUN pip install --upgrade pip && pip install --no-cache-dir -r requirements.txt
 
-# 复制 server 和 src 代码
+# Copy application code
 COPY server/ ./server/
 COPY src/ ./src/
 
+# Set Python import path
 ENV PYTHONPATH=/app/src
 
+# Expose port
 EXPOSE 8000
+
+# Start the server
 CMD ["uvicorn", "memos.api.product_api:app", "--host", "0.0.0.0", "--port", "8000", "--reload"]

From 1558921af654894498b344deeb37408f10fe41b4 Mon Sep 17 00:00:00 2001
From: Tianxing Shi <92901816+tianxing02@users.noreply.github.com>
Date: Wed, 23 Jul 2025 15:28:38 +0800
Subject: [PATCH 12/34] Update tree_config.json

---
 examples/data/config/tree_config.json | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/data/config/tree_config.json b/examples/data/config/tree_config.json
index 0a1faaafb..2fbcce164 100644
--- a/examples/data/config/tree_config.json
+++ b/examples/data/config/tree_config.json
@@ -21,9 +21,9 @@
     "backend": "universal_api",
     "config": {
       "provider": "openai",
-      "api_key": "sk-jz4BGHJftedu1UYMPTt3r2h9s1DuQFU2Co74YZa1aMUUNokK",
+      "api_key": "sk-xxxxxxxxx",
       "model_name_or_path": "text-embedding-3-large",
-      "base_url": "http://123.129.219.111:3000/v1"
+      "base_url": "http://xxx.xxx.xxx.xxx:xxxx/v1"
     }
   },
   "graph_db": {

From ed204685017bb949ce23c27b44f9f244b6c02678 Mon Sep 17 00:00:00 2001
From: Tianxing Shi <92901816+tianxing02@users.noreply.github.com>
Date: Wed, 23 Jul 2025 15:29:34 +0800
Subject: [PATCH 13/34] Update simple_openapi_memos.py

---
 examples/mem_os/simple_openapi_memos.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/examples/mem_os/simple_openapi_memos.py b/examples/mem_os/simple_openapi_memos.py
index 101ae1ecb..3477106fb 100644
--- a/examples/mem_os/simple_openapi_memos.py
+++ b/examples/mem_os/simple_openapi_memos.py
@@ -53,9 +53,9 @@
                 "backend": "universal_api",
                 "config": {
                     "provider": "openai",
-                    "api_key": "sk-jz4BGHJftedu1UYMPTt3r2h9s1DuQFU2Co74YZa1aMUUNokK",
+                    "api_key": "sk-xxxxxxxxx",
                     "model_name_or_path": "text-embedding-3-large",
-                    "base_url": "http://123.129.219.111:3000/v1",
+                    "base_url": "http://xxx.xxx.xxx.xxx:xxxx/v1"
                 },
             },
             "chunker": {
@@ -116,10 +116,10 @@
                 "embedder": {
                     "backend": "universal_api",
                     "config": {
-                        "provider": "openai",
-                        "api_key": "sk-jz4BGHJftedu1UYMPTt3r2h9s1DuQFU2Co74YZa1aMUUNokK",
-                        "model_name_or_path": "text-embedding-3-large",
-                        "base_url": "http://123.129.219.111:3000/v1",
+                         "provider": "openai",
+                          "api_key": "sk-xxxxxxxxx",
+                          "model_name_or_path": "text-embedding-3-large",
+                          "base_url": "http://xxx.xxx.xxx.xxx:xxxx/v1"
                     },
                 },
                 "reorganize": True,

From b13d633b5c5b9fac827fc1cd0fef0c6124014b54 Mon Sep 17 00:00:00 2001
From: stx <31013941@qq.com>
Date: Wed, 23 Jul 2025 15:42:32 +0800
Subject: [PATCH 14/34] feat: docker settings modify

---
 {server => docker}/Dockerfile         |   6 +-
 {server => docker}/docker-compose.yml |  10 +-
 server/requirements.txt               | 137 --------------------------
 3 files changed, 8 insertions(+), 145 deletions(-)
 rename {server => docker}/Dockerfile (89%)
 rename {server => docker}/docker-compose.yml (88%)
 delete mode 100644 server/requirements.txt

diff --git a/server/Dockerfile b/docker/Dockerfile
similarity index 89%
rename from server/Dockerfile
rename to docker/Dockerfile
index d0e1ec290..29636881c 100644
--- a/server/Dockerfile
+++ b/docker/Dockerfile
@@ -18,11 +18,11 @@ WORKDIR /app
 ENV HF_ENDPOINT=https://hf-mirror.com
 
 # Install Python packages
-COPY server/requirements.txt .
+COPY docker/requirements.txt .
 RUN pip install --upgrade pip && pip install --no-cache-dir -r requirements.txt
 
 # Copy application code
-COPY server/ ./server/
+COPY docker/ ./docker/
 COPY src/ ./src/
 
 # Set Python import path
@@ -31,5 +31,5 @@ ENV PYTHONPATH=/app/src
 # Expose port
 EXPOSE 8000
 
-# Start the server
+# Start the docker
 CMD ["uvicorn", "memos.api.product_api:app", "--host", "0.0.0.0", "--port", "8000", "--reload"]
diff --git a/server/docker-compose.yml b/docker/docker-compose.yml
similarity index 88%
rename from server/docker-compose.yml
rename to docker/docker-compose.yml
index ceca26e91..d8998b6f7 100644
--- a/server/docker-compose.yml
+++ b/docker/docker-compose.yml
@@ -2,10 +2,10 @@ name: memos-dev
 
 services:
   memos:
-    container_name: memos-api-server
+    container_name: memos-api-docker
     build:
       context: ..
-      dockerfile: server/Dockerfile
+      dockerfile: docker/Dockerfile
     ports:
       - "8000:8000"
     env_file:
@@ -18,13 +18,13 @@ services:
       - HF_ENDPOINT=https://hf-mirror.com
     volumes:
       - ../src:/app/src
-      - .:/app/server
+      - .:/app/docker
     networks:
       - memos_network
 
   neo4j:
     image: neo4j:5.26.4
-    container_name: neo4j-server
+    container_name: neo4j-docker
     ports:
       - "7474:7474"   # HTTP
       - "7687:7687"   # Bolt
@@ -45,7 +45,7 @@ services:
 
   qdrant:
     image: qdrant/qdrant:v1.15.0
-    container_name: qdrant-server
+    container_name: qdrant-docker
     ports:
       - "6333:6333"  # REST API
       - "6334:6334"  # gRPC API
diff --git a/server/requirements.txt b/server/requirements.txt
deleted file mode 100644
index 211ec3cae..000000000
--- a/server/requirements.txt
+++ /dev/null
@@ -1,137 +0,0 @@
-annotated-types==0.7.0 ; python_version >= "3.10" and python_version < "4.0"
-anyio==4.9.0 ; python_version >= "3.10" and python_version < "4.0"
-attrs==25.3.0 ; python_version >= "3.10" and python_version < "4.0"
-authlib==1.6.0 ; python_version >= "3.10" and python_version < "4.0"
-beautifulsoup4==4.13.4 ; python_version >= "3.10" and python_version < "4.0"
-certifi==2025.7.14 ; python_version >= "3.10" and python_version < "4.0"
-cffi==1.17.1 ; python_version >= "3.10" and python_version < "4.0" and platform_python_implementation != "PyPy"
-cfgv==3.4.0 ; python_version >= "3.10" and python_version < "4.0"
-charset-normalizer==3.4.2 ; python_version >= "3.10" and python_version < "4.0"
-chonkie==1.1.1 ; python_version >= "3.10" and python_version < "4.0"
-click==8.2.1 ; python_version >= "3.10" and python_version < "4.0"
-cobble==0.1.4 ; python_version >= "3.10" and python_version < "4.0"
-colorama==0.4.6 ; python_version >= "3.10" and python_version < "4.0" and (platform_system == "Windows" or sys_platform == "win32")
-coloredlogs==15.0.1 ; python_version >= "3.10" and python_version < "4.0"
-cryptography==45.0.5 ; python_version >= "3.10" and python_version < "4.0"
-cyclopts==3.22.2 ; python_version >= "3.10" and python_version < "4.0"
-defusedxml==0.7.1 ; python_version >= "3.10" and python_version < "4.0"
-distlib==0.4.0 ; python_version >= "3.10" and python_version < "4.0"
-distro==1.9.0 ; python_version >= "3.10" and python_version < "4.0"
-dnspython==2.7.0 ; python_version >= "3.10" and python_version < "4.0"
-docstring-parser==0.16 ; python_version >= "3.10" and python_version < "4.0"
-docutils==0.21.2 ; python_version >= "3.10" and python_version < "4.0"
-email-validator==2.2.0 ; python_version >= "3.10" and python_version < "4.0"
-et-xmlfile==2.0.0 ; python_version >= "3.10" and python_version < "4.0"
-exceptiongroup==1.3.0 ; python_version >= "3.10" and python_version < "4.0"
-fastapi-cli==0.0.8 ; python_version >= "3.10" and python_version < "4.0"
-fastapi-cloud-cli==0.1.4 ; python_version >= "3.10" and python_version < "4.0"
-fastapi==0.115.14 ; python_version >= "3.10" and python_version < "4.0"
-fastmcp==2.10.5 ; python_version >= "3.10" and python_version < "4.0"
-filelock==3.18.0 ; python_version >= "3.10" and python_version < "4.0"
-flatbuffers==25.2.10 ; python_version >= "3.10" and python_version < "4.0"
-fsspec==2025.7.0 ; python_version >= "3.10" and python_version < "4.0"
-greenlet==3.2.3 ; python_version >= "3.10" and python_version < "3.14" and (platform_machine == "aarch64" or platform_machine == "ppc64le" or platform_machine == "x86_64" or platform_machine == "amd64" or platform_machine == "AMD64" or platform_machine == "win32" or platform_machine == "WIN32")
-h11==0.16.0 ; python_version >= "3.10" and python_version < "4.0"
-hf-xet==1.1.5 ; python_version >= "3.10" and python_version < "4.0" and (platform_machine == "x86_64" or platform_machine == "amd64" or platform_machine == "arm64" or platform_machine == "aarch64")
-httpcore==1.0.9 ; python_version >= "3.10" and python_version < "4.0"
-httptools==0.6.4 ; python_version >= "3.10" and python_version < "4.0"
-httpx-sse==0.4.1 ; python_version >= "3.10" and python_version < "4.0"
-httpx==0.28.1 ; python_version >= "3.10" and python_version < "4.0"
-huggingface-hub==0.33.4 ; python_version >= "3.10" and python_version < "4.0"
-humanfriendly==10.0 ; python_version >= "3.10" and python_version < "4.0"
-identify==2.6.12 ; python_version >= "3.10" and python_version < "4.0"
-idna==3.10 ; python_version >= "3.10" and python_version < "4.0"
-iniconfig==2.1.0 ; python_version >= "3.10" and python_version < "4.0"
-itsdangerous==2.2.0 ; python_version >= "3.10" and python_version < "4.0"
-jinja2==3.1.6 ; python_version >= "3.10" and python_version < "4.0"
-jiter==0.10.0 ; python_version >= "3.10" and python_version < "4.0"
-joblib==1.5.1 ; python_version >= "3.10" and python_version < "4.0"
-jsonschema-specifications==2025.4.1 ; python_version >= "3.10" and python_version < "4.0"
-jsonschema==4.24.1 ; python_version >= "3.10" and python_version < "4.0"
-lxml==6.0.0 ; python_version >= "3.10" and python_version < "4.0"
-magika==0.6.2 ; python_version >= "3.10" and python_version < "4.0"
-mammoth==1.9.1 ; python_version >= "3.10" and python_version < "4.0"
-markdown-it-py==3.0.0 ; python_version >= "3.10" and python_version < "4.0"
-markdownify==1.1.0 ; python_version >= "3.10" and python_version < "4.0"
-markitdown==0.1.2 ; python_version >= "3.10" and python_version < "4.0"
-markupsafe==3.0.2 ; python_version >= "3.10" and python_version < "4.0"
-mcp==1.12.0 ; python_version >= "3.10" and python_version < "4.0"
-mdurl==0.1.2 ; python_version >= "3.10" and python_version < "4.0"
-mpmath==1.3.0 ; python_version >= "3.10" and python_version < "4.0"
-neo4j==5.28.1 ; python_version >= "3.10" and python_version < "4.0"
-nodeenv==1.9.1 ; python_version >= "3.10" and python_version < "4.0"
-numpy==2.2.6 ; python_version == "3.10"
-numpy==2.3.1 ; python_version >= "3.11" and python_version < "4.0"
-ollama==0.4.9 ; python_version >= "3.10" and python_version < "4.0"
-onnxruntime==1.22.1 ; python_version >= "3.10" and python_version < "4.0"
-openai==1.97.0 ; python_version >= "3.10" and python_version < "4.0"
-openapi-pydantic==0.5.1 ; python_version >= "3.10" and python_version < "4.0"
-openpyxl==3.1.5 ; python_version >= "3.10" and python_version < "4.0"
-orjson==3.11.0 ; python_version >= "3.10" and python_version < "4.0"
-packaging==25.0 ; python_version >= "3.10" and python_version < "4.0"
-pandas==2.3.1 ; python_version >= "3.10" and python_version < "4.0"
-pdfminer-six==20250506 ; python_version >= "3.10" and python_version < "4.0"
-pillow==11.3.0 ; python_version >= "3.10" and python_version < "4.0"
-platformdirs==4.3.8 ; python_version >= "3.10" and python_version < "4.0"
-pluggy==1.6.0 ; python_version >= "3.10" and python_version < "4.0"
-pre-commit==4.2.0 ; python_version >= "3.10" and python_version < "4.0"
-protobuf==6.31.1 ; python_version >= "3.10" and python_version < "4.0"
-pycparser==2.22 ; python_version >= "3.10" and python_version < "4.0" and platform_python_implementation != "PyPy"
-pydantic-core==2.33.2 ; python_version >= "3.10" and python_version < "4.0"
-pydantic-extra-types==2.10.5 ; python_version >= "3.10" and python_version < "4.0"
-pydantic-settings==2.10.1 ; python_version >= "3.10" and python_version < "4.0"
-pydantic==2.11.7 ; python_version >= "3.10" and python_version < "4.0"
-pygments==2.19.2 ; python_version >= "3.10" and python_version < "4.0"
-pyperclip==1.9.0 ; python_version >= "3.10" and python_version < "4.0"
-pyreadline3==3.5.4 ; python_version >= "3.10" and python_version < "4.0" and sys_platform == "win32"
-pytest-asyncio==0.23.8 ; python_version >= "3.10" and python_version < "4.0"
-pytest==8.4.1 ; python_version >= "3.10" and python_version < "4.0"
-python-dateutil==2.9.0.post0 ; python_version >= "3.10" and python_version < "4.0"
-python-dotenv==1.1.1 ; python_version >= "3.10" and python_version < "4.0"
-python-multipart==0.0.20 ; python_version >= "3.10" and python_version < "4.0"
-python-pptx==1.0.2 ; python_version >= "3.10" and python_version < "4.0"
-pytz==2025.2 ; python_version >= "3.10" and python_version < "4.0"
-pywin32==311 ; python_version >= "3.10" and python_version < "4.0" and (platform_system == "Windows" or sys_platform == "win32")
-pyyaml==6.0.2 ; python_version >= "3.10" and python_version < "4.0"
-referencing==0.36.2 ; python_version >= "3.10" and python_version < "4.0"
-regex==2024.11.6 ; python_version >= "3.10" and python_version < "4.0"
-requests==2.32.4 ; python_version >= "3.10" and python_version < "4.0"
-rich-rst==1.3.1 ; python_version >= "3.10" and python_version < "4.0"
-rich-toolkit==0.14.8 ; python_version >= "3.10" and python_version < "4.0"
-rich==14.0.0 ; python_version >= "3.10" and python_version < "4.0"
-rignore==0.6.2 ; python_version >= "3.10" and python_version < "4.0"
-rpds-py==0.26.0 ; python_version >= "3.10" and python_version < "4.0"
-ruff==0.11.13 ; python_version >= "3.10" and python_version < "4.0"
-safetensors==0.5.3 ; python_version >= "3.10" and python_version < "4.0"
-schedule==1.2.2 ; python_version >= "3.10" and python_version < "4.0"
-scikit-learn==1.7.0 ; python_version >= "3.10" and python_version < "4.0"
-scipy==1.15.3 ; python_version == "3.10"
-scipy==1.16.0 ; python_version >= "3.11" and python_version < "4.0"
-sentry-sdk==2.33.0 ; python_version >= "3.10" and python_version < "4.0"
-shellingham==1.5.4 ; python_version >= "3.10" and python_version < "4.0"
-six==1.17.0 ; python_version >= "3.10" and python_version < "4.0"
-sniffio==1.3.1 ; python_version >= "3.10" and python_version < "4.0"
-soupsieve==2.7 ; python_version >= "3.10" and python_version < "4.0"
-sqlalchemy==2.0.41 ; python_version >= "3.10" and python_version < "4.0"
-sse-starlette==2.4.1 ; python_version >= "3.10" and python_version < "4.0"
-starlette==0.46.2 ; python_version >= "3.10" and python_version < "4.0"
-sympy==1.14.0 ; python_version >= "3.10" and python_version < "4.0"
-tenacity==9.1.2 ; python_version >= "3.10" and python_version < "4.0"
-threadpoolctl==3.6.0 ; python_version >= "3.10" and python_version < "4.0"
-tokenizers==0.21.2 ; python_version >= "3.10" and python_version < "4.0"
-tomli==2.2.1 ; python_version == "3.10"
-tqdm==4.67.1 ; python_version >= "3.10" and python_version < "4.0"
-transformers==4.53.2 ; python_version >= "3.10" and python_version < "4.0"
-typer==0.16.0 ; python_version >= "3.10" and python_version < "4.0"
-typing-extensions==4.14.1 ; python_version >= "3.10" and python_version < "4.0"
-typing-inspection==0.4.1 ; python_version >= "3.10" and python_version < "4.0"
-tzdata==2025.2 ; python_version >= "3.10" and python_version < "4.0"
-ujson==5.10.0 ; python_version >= "3.10" and python_version < "4.0"
-urllib3==2.5.0 ; python_version >= "3.10" and python_version < "4.0"
-uvicorn==0.35.0 ; python_version >= "3.10" and python_version < "4.0"
-uvloop==0.21.0 ; python_version >= "3.10" and python_version < "4.0" and platform_python_implementation != "PyPy" and sys_platform != "win32" and sys_platform != "cygwin"
-virtualenv==20.31.2 ; python_version >= "3.10" and python_version < "4.0"
-watchfiles==1.1.0 ; python_version >= "3.10" and python_version < "4.0"
-websockets==15.0.1 ; python_version >= "3.10" and python_version < "4.0"
-xlrd==2.0.2 ; python_version >= "3.10" and python_version < "4.0"
-xlsxwriter==3.2.5 ; python_version >= "3.10" and python_version < "4.0"

From 70c475a6623d5b2a69acb281e195ffda99d5a7f8 Mon Sep 17 00:00:00 2001
From: stx <31013941@qq.com>
Date: Tue, 12 Aug 2025 20:11:58 +0800
Subject: [PATCH 15/34] =?UTF-8?q?feat=EF=BC=9Aadd=20hotpotQA=20evaluation?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 evaluation/scripts/hotpot/hotpot_eval.py      | 259 ++++++++++++++++++
 .../scripts/hotpot/hotpot_evaluate_v1.py      | 151 ++++++++++
 2 files changed, 410 insertions(+)
 create mode 100644 evaluation/scripts/hotpot/hotpot_eval.py
 create mode 100644 evaluation/scripts/hotpot/hotpot_evaluate_v1.py

diff --git a/evaluation/scripts/hotpot/hotpot_eval.py b/evaluation/scripts/hotpot/hotpot_eval.py
new file mode 100644
index 000000000..2c690f060
--- /dev/null
+++ b/evaluation/scripts/hotpot/hotpot_eval.py
@@ -0,0 +1,259 @@
+import json
+import os
+import time
+import uuid
+
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from datetime import datetime
+
+from dotenv import load_dotenv
+from tqdm import tqdm
+
+from memos.configs.mem_cube import GeneralMemCubeConfig
+from memos.configs.mem_os import MOSConfig
+from memos.mem_cube.general import GeneralMemCube
+from memos.mem_os.main import MOS
+
+
+load_dotenv()
+
+# 1. Create MOS Config and set openai config
+print(f"🚀 [{datetime.now().strftime('%H:%M:%S')}] Starting to create MOS configuration...")
+start_time = time.time()
+
+user_name = str(uuid.uuid4())
+print(user_name)
+
+# 1.1 Set openai config
+openapi_config = {
+    "model_name_or_path": "gpt-4o-mini",
+    "temperature": 0.8,
+    "max_tokens": 1024,
+    "top_p": 0.9,
+    "top_k": 50,
+    "remove_think_prefix": True,
+    "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
+    "api_base": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
+}
+# 1.2 Set neo4j config
+neo4j_uri = os.getenv("NEO4J_URI", "bolt://localhost:7687")
+
+# 1.3  Create MOS Config
+config = {
+    "user_id": user_name,
+    "chat_model": {
+        "backend": "openai",
+        "config": openapi_config,
+    },
+    "mem_reader": {
+        "backend": "simple_struct",
+        "config": {
+            "llm": {
+                "backend": "openai",
+                "config": openapi_config,
+            },
+            "embedder": {
+                "backend": "universal_api",
+                "config": {
+                    "provider": "openai",
+                    "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
+                    "model_name_or_path": "text-embedding-3-large",
+                    "base_url": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
+                },
+            },
+            "chunker": {
+                "backend": "sentence",
+                "config": {
+                    "tokenizer_or_token_counter": "gpt2",
+                    "chunk_size": 512,
+                    "chunk_overlap": 128,
+                    "min_sentences_per_chunk": 1,
+                },
+            },
+        },
+    },
+    "max_turns_window": 20,
+    "top_k": 5,
+    "enable_textual_memory": True,
+    "enable_activation_memory": False,
+    "enable_parametric_memory": False,
+}
+
+mos_config = MOSConfig(**config)
+# you can set PRO_MODE to True to enable CoT enhancement mos_config.PRO_MODE = True
+mos = MOS(mos_config)
+
+
+# Filter out embedding fields, keeping only necessary fields
+def filter_memory_data(memories_data):
+    filtered_data = {}
+    for key, value in memories_data.items():
+        if key == "text_mem":
+            filtered_data[key] = []
+            for mem_group in value:
+                # Check if it's the new data structure (list of TextualMemoryItem objects)
+                if "memories" in mem_group and isinstance(mem_group["memories"], list):
+                    # New data structure: directly a list of TextualMemoryItem objects
+                    filtered_memories = []
+                    for memory_item in mem_group["memories"]:
+                        # Create filtered dictionary
+                        filtered_item = {
+                            "id": memory_item.id,
+                            "memory": memory_item.memory,
+                            "metadata": {},
+                        }
+                        # Filter metadata, excluding embedding
+                        if hasattr(memory_item, "metadata") and memory_item.metadata:
+                            for attr_name in dir(memory_item.metadata):
+                                if not attr_name.startswith("_") and attr_name != "embedding":
+                                    attr_value = getattr(memory_item.metadata, attr_name)
+                                    if not callable(attr_value):
+                                        filtered_item["metadata"][attr_name] = attr_value
+                        filtered_memories.append(filtered_item)
+
+                    filtered_group = {
+                        "cube_id": mem_group.get("cube_id", ""),
+                        "memories": filtered_memories,
+                    }
+                    filtered_data[key].append(filtered_group)
+                else:
+                    # Old data structure: dictionary with nodes and edges
+                    filtered_group = {
+                        "memories": {"nodes": [], "edges": mem_group["memories"].get("edges", [])}
+                    }
+                    for node in mem_group["memories"].get("nodes", []):
+                        filtered_node = {
+                            "id": node.get("id"),
+                            "memory": node.get("memory"),
+                            "metadata": {
+                                k: v
+                                for k, v in node.get("metadata", {}).items()
+                                if k != "embedding"
+                            },
+                        }
+                        filtered_group["memories"]["nodes"].append(filtered_node)
+                    filtered_data[key].append(filtered_group)
+        else:
+            filtered_data[key] = value
+    return filtered_data
+
+
+print(
+    f"✅ [{datetime.now().strftime('%H:%M:%S')}] MOS configuration created successfully, time elapsed: {time.time() - start_time:.2f}s\n"
+)
+
+# 2. Initialize memory cube
+print(f"🚀 [{datetime.now().strftime('%H:%M:%S')}] Starting to initialize MemCube configuration...")
+start_time = time.time()
+
+config = GeneralMemCubeConfig.model_validate(
+    {
+        "user_id": user_name,
+        "cube_id": f"{user_name}",
+        "text_mem": {
+            "backend": "tree_text",
+            "config": {
+                "extractor_llm": {
+                    "backend": "openai",
+                    "config": openapi_config,
+                },
+                "dispatcher_llm": {
+                    "backend": "openai",
+                    "config": openapi_config,
+                },
+                "graph_db": {
+                    "backend": "neo4j",
+                    "config": {
+                        "uri": neo4j_uri,
+                        "user": "neo4j",
+                        "password": "iaarlichunyu",
+                        "db_name": "hotpot-evaluation",
+                        "auto_create": True,
+                    },
+                },
+                "embedder": {
+                    "backend": "universal_api",
+                    "config": {
+                        "provider": "openai",
+                        "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
+                        "model_name_or_path": "text-embedding-3-large",
+                        "base_url": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
+                    },
+                },
+                "reorganize": True,
+            },
+        },
+        "act_mem": {},
+        "para_mem": {},
+    },
+)
+
+print(
+    f"✅ [{datetime.now().strftime('%H:%M:%S')}] MemCube configuration initialization completed, time elapsed: {time.time() - start_time:.2f}s\n"
+)
+
+# 3. Initialize the MemCube with the configuration
+print(f"🚀 [{datetime.now().strftime('%H:%M:%S')}] Starting to create MemCube instance...")
+start_time = time.time()
+
+mem_cube = GeneralMemCube(config)
+try:
+    mem_cube.dump(f"/tmp/{user_name}/")
+    print(
+        f"✅ [{datetime.now().strftime('%H:%M:%S')}] MemCube created and saved successfully, time elapsed: {time.time() - start_time:.2f}s\n"
+    )
+except Exception as e:
+    print(
+        f"❌ [{datetime.now().strftime('%H:%M:%S')}] MemCube save failed: {e}, time elapsed: {time.time() - start_time:.2f}s\n"
+    )
+
+# 4. Register the MemCube
+print(f"🚀 [{datetime.now().strftime('%H:%M:%S')}] Starting to register MemCube...")
+start_time = time.time()
+
+mos.register_mem_cube(f"/tmp/{user_name}", mem_cube_id=user_name)
+
+print(
+    f"✅ [{datetime.now().strftime('%H:%M:%S')}] MemCube registration completed, time elapsed: {time.time() - start_time:.2f}s\n"
+)
+
+with open("evaluation/data/hotpot/hotpot_dev_distractor_v1.json") as f:
+    data = json.load(f)
+
+
+def build_context_text(context_list):
+    parts = []
+    for title, sentences in context_list:
+        text = " ".join(s.strip() for s in sentences if s.strip())
+        parts.append(f"{title}: {text}")
+    return "\n".join(parts)
+
+
+def build_and_ask(item):
+    qid = item["_id"]
+    question = item["question"]
+
+    for title, sentences in item["context"]:
+        text = " ".join(s.strip() for s in sentences if s.strip())
+        memory_content = f"{title}: {text}"
+        mos.add(memory_content=memory_content)
+
+    answer = mos.chat(question).strip()
+    return qid, answer
+
+
+pred_answers = {}
+
+with ThreadPoolExecutor(max_workers=5) as executor:
+    futures = {executor.submit(build_and_ask, item): item for item in data}
+    for future in tqdm(as_completed(futures), total=len(futures)):
+        try:
+            qid, answer = future.result()
+            pred_answers[qid] = answer
+        except Exception as e:
+            print(f"Error: {e}")
+
+predictions = {"answer": pred_answers, "sp": []}
+
+with open("evaluation/data/hotpot/output/dev_distractor_pred.json", "w") as f:
+    json.dump(predictions, f, ensure_ascii=False, indent=2)
diff --git a/evaluation/scripts/hotpot/hotpot_evaluate_v1.py b/evaluation/scripts/hotpot/hotpot_evaluate_v1.py
new file mode 100644
index 000000000..d4d6e71e1
--- /dev/null
+++ b/evaluation/scripts/hotpot/hotpot_evaluate_v1.py
@@ -0,0 +1,151 @@
+import re
+import string
+import sys
+
+from collections import Counter
+
+import ujson as json
+
+
+def normalize_answer(s):
+    def remove_articles(text):
+        return re.sub(r"\b(a|an|the)\b", " ", text)
+
+    def white_space_fix(text):
+        return " ".join(text.split())
+
+    def remove_punc(text):
+        exclude = set(string.punctuation)
+        return "".join(ch for ch in text if ch not in exclude)
+
+    def lower(text):
+        return text.lower()
+
+    return white_space_fix(remove_articles(remove_punc(lower(s))))
+
+
+def f1_score(prediction, ground_truth):
+    normalized_prediction = normalize_answer(prediction)
+    normalized_ground_truth = normalize_answer(ground_truth)
+
+    zero_metric = (0, 0, 0)
+
+    if (
+        normalized_prediction in ["yes", "no", "noanswer"]
+        and normalized_prediction != normalized_ground_truth
+    ):
+        return zero_metric
+    if (
+        normalized_ground_truth in ["yes", "no", "noanswer"]
+        and normalized_prediction != normalized_ground_truth
+    ):
+        return zero_metric
+
+    prediction_tokens = normalized_prediction.split()
+    ground_truth_tokens = normalized_ground_truth.split()
+    common = Counter(prediction_tokens) & Counter(ground_truth_tokens)
+    num_same = sum(common.values())
+    if num_same == 0:
+        return zero_metric
+    precision = 1.0 * num_same / len(prediction_tokens)
+    recall = 1.0 * num_same / len(ground_truth_tokens)
+    f1 = (2 * precision * recall) / (precision + recall)
+    return f1, precision, recall
+
+
+def exact_match_score(prediction, ground_truth):
+    return normalize_answer(prediction) == normalize_answer(ground_truth)
+
+
+def update_answer(metrics, prediction, gold):
+    em = exact_match_score(prediction, gold)
+    f1, prec, recall = f1_score(prediction, gold)
+    metrics["em"] += float(em)
+    metrics["f1"] += f1
+    metrics["prec"] += prec
+    metrics["recall"] += recall
+    return em, prec, recall
+
+
+def update_sp(metrics, prediction, gold):
+    cur_sp_pred = set(map(tuple, prediction))
+    gold_sp_pred = set(map(tuple, gold))
+    tp, fp, fn = 0, 0, 0
+    for e in cur_sp_pred:
+        if e in gold_sp_pred:
+            tp += 1
+        else:
+            fp += 1
+    for e in gold_sp_pred:
+        if e not in cur_sp_pred:
+            fn += 1
+    prec = 1.0 * tp / (tp + fp) if tp + fp > 0 else 0.0
+    recall = 1.0 * tp / (tp + fn) if tp + fn > 0 else 0.0
+    f1 = 2 * prec * recall / (prec + recall) if prec + recall > 0 else 0.0
+    em = 1.0 if fp + fn == 0 else 0.0
+    metrics["sp_em"] += em
+    metrics["sp_f1"] += f1
+    metrics["sp_prec"] += prec
+    metrics["sp_recall"] += recall
+    return em, prec, recall
+
+
+def eval(prediction_file, gold_file):
+    with open(prediction_file) as f:
+        prediction = json.load(f)
+    with open(gold_file) as f:
+        gold = json.load(f)
+
+    metrics = {
+        "em": 0,
+        "f1": 0,
+        "prec": 0,
+        "recall": 0,
+        "sp_em": 0,
+        "sp_f1": 0,
+        "sp_prec": 0,
+        "sp_recall": 0,
+        "joint_em": 0,
+        "joint_f1": 0,
+        "joint_prec": 0,
+        "joint_recall": 0,
+    }
+    for dp in gold:
+        cur_id = dp["_id"]
+        can_eval_joint = True
+        if cur_id not in prediction["answer"]:
+            print(f"missing answer {cur_id}")
+            can_eval_joint = False
+        else:
+            em, prec, recall = update_answer(metrics, prediction["answer"][cur_id], dp["answer"])
+        if cur_id not in prediction["sp"]:
+            print(f"missing sp fact {cur_id}")
+            can_eval_joint = False
+        else:
+            sp_em, sp_prec, sp_recall = update_sp(
+                metrics, prediction["sp"][cur_id], dp["supporting_facts"]
+            )
+
+        if can_eval_joint:
+            joint_prec = prec * sp_prec
+            joint_recall = recall * sp_recall
+            if joint_prec + joint_recall > 0:
+                joint_f1 = 2 * joint_prec * joint_recall / (joint_prec + joint_recall)
+            else:
+                joint_f1 = 0.0
+            joint_em = em * sp_em
+
+            metrics["joint_em"] += joint_em
+            metrics["joint_f1"] += joint_f1
+            metrics["joint_prec"] += joint_prec
+            metrics["joint_recall"] += joint_recall
+
+    n = len(gold)
+    for k in metrics:
+        metrics[k] /= n
+
+    print(metrics)
+
+
+if __name__ == "__main__":
+    eval(sys.argv[1], sys.argv[2])

From 7e23cabc093fb32628cec02b4d888e5c5e8650bf Mon Sep 17 00:00:00 2001
From: stx <31013941@qq.com>
Date: Fri, 15 Aug 2025 11:12:55 +0800
Subject: [PATCH 16/34] =?UTF-8?q?feat=EF=BC=9Aadd=20MMLongbench-doc=20eval?=
 =?UTF-8?q?uation?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../scripts/mmlongbench/asset/top_figure.png  | Bin 0 -> 1983216 bytes
 .../scripts/mmlongbench/eval/__init__.py      |   0
 .../scripts/mmlongbench/eval/eval_score.py    | 248 ++++++++++++++++++
 .../mmlongbench/eval/extract_answer.py        |  30 +++
 .../eval/prompt_for_answer_extraction.md      |  35 +++
 .../scripts/mmlongbench/models/__init__.py    |   0
 .../mmlongbench/models/internlm_xc2_4khd.py   | 128 +++++++++
 .../mmlongbench/models/internvl_chat.py       | 137 ++++++++++
 .../mmlongbench/models/minicpm_llama3.py      |  56 ++++
 evaluation/scripts/mmlongbench/run_api.py     | 244 +++++++++++++++++
 10 files changed, 878 insertions(+)
 create mode 100644 evaluation/scripts/mmlongbench/asset/top_figure.png
 create mode 100644 evaluation/scripts/mmlongbench/eval/__init__.py
 create mode 100644 evaluation/scripts/mmlongbench/eval/eval_score.py
 create mode 100644 evaluation/scripts/mmlongbench/eval/extract_answer.py
 create mode 100644 evaluation/scripts/mmlongbench/eval/prompt_for_answer_extraction.md
 create mode 100644 evaluation/scripts/mmlongbench/models/__init__.py
 create mode 100644 evaluation/scripts/mmlongbench/models/internlm_xc2_4khd.py
 create mode 100644 evaluation/scripts/mmlongbench/models/internvl_chat.py
 create mode 100644 evaluation/scripts/mmlongbench/models/minicpm_llama3.py
 create mode 100644 evaluation/scripts/mmlongbench/run_api.py

diff --git a/evaluation/scripts/mmlongbench/asset/top_figure.png b/evaluation/scripts/mmlongbench/asset/top_figure.png
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HcmV?d00001

diff --git a/evaluation/scripts/mmlongbench/eval/__init__.py b/evaluation/scripts/mmlongbench/eval/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/evaluation/scripts/mmlongbench/eval/eval_score.py b/evaluation/scripts/mmlongbench/eval/eval_score.py
new file mode 100644
index 000000000..d1136bd40
--- /dev/null
+++ b/evaluation/scripts/mmlongbench/eval/eval_score.py
@@ -0,0 +1,248 @@
+import re
+
+from collections import defaultdict
+from math import isclose
+
+
+def levenshtein_distance(s1, s2):
+    if len(s1) > len(s2):
+        s1, s2 = s2, s1
+
+    distances = range(len(s1) + 1)
+    for i2, c2 in enumerate(s2):
+        distances_ = [i2 + 1]
+        for i1, c1 in enumerate(s1):
+            if c1 == c2:
+                distances_.append(distances[i1])
+            else:
+                distances_.append(1 + min((distances[i1], distances[i1 + 1], distances_[-1])))
+        distances = distances_
+    return distances[-1]
+
+
+def anls_compute(groundtruth, prediction, threshold=0.5):
+    dist = levenshtein_distance(groundtruth, prediction)
+    length = max(len(groundtruth.upper()), len(prediction.upper()))
+    value = 0.0 if length == 0 else float(dist) / float(length)
+    anls = 1.0 - value
+    if anls <= threshold:
+        anls = 0.0
+    return anls
+
+
+def is_float_equal(
+    reference, prediction, include_percentage: bool = False, is_close: float = False
+) -> bool:
+    def get_precision(gt_ans: float) -> int:
+        precision = 3
+        if "." in str(gt_ans):
+            precision = len(str(gt_ans).split(".")[-1])
+        return precision
+
+    reference = float(str(reference).strip().rstrip("%").strip())
+    try:
+        prediction = float(str(prediction).strip().rstrip("%").strip())
+    except Exception:
+        return False
+
+    gt_result = [reference / 100, reference, reference * 100] if include_percentage else [reference]
+    for item in gt_result:
+        try:
+            if is_close and isclose(item, prediction, rel_tol=0.01):
+                return True
+            precision = max(min(get_precision(prediction), get_precision(item)), 2)
+            if round(prediction, precision) == round(item, precision):
+                return True
+        except Exception:
+            continue
+    return False
+
+
+def get_clean_string(s):
+    s = str(s).lower().strip()
+
+    for suffix in ["mile", "miles", "million"]:
+        if s.endswith(suffix):
+            s = s[: -len(suffix)].strip()
+
+    s = re.sub(r"\s*\([^)]*\)", "", s).strip()
+    s = re.sub(r"^['\"]|['\"]$", "", s).strip()
+    s = s.lstrip("$").rstrip("%").strip()
+
+    return s
+
+
+def is_exact_match(s):
+    flag = False
+    # Website
+    if "https://" in s:
+        flag = True
+    # code file
+    if s.endswith((".py", ".ipynb")) or s.startswith("page"):
+        flag = True
+    # telephone number
+    if re.fullmatch(r"\b\d+(-\d+|\s\d+)?\b", s):
+        flag = True
+    # time
+    if "a.m." in s or "p.m." in s:
+        flag = True
+    # YYYY-MM-DD
+    if re.fullmatch(r"\b\d{4}[-\s]\d{2}[-\s]\d{2}\b", s):
+        flag = True
+    # YYYY-MM
+    if re.fullmatch(r"\b\d{4}[-\s]\d{2}\b", s):
+        flag = True
+    # Email address
+    if re.fullmatch(r"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}", s):
+        flag = True
+    return flag
+
+
+def isfloat(num):
+    try:
+        float(num)
+        return True
+    except ValueError:
+        return False
+
+
+def eval_score(gt, pred, answer_type):
+    if answer_type == "Int":
+        try:
+            gt, pred = int(gt), int(float(pred))
+        except Exception:
+            pred = ""
+        score = gt == pred
+    elif answer_type == "Float":
+        try:
+            gt = float(get_clean_string(str(gt)))
+            pred = float(get_clean_string(str(pred)))
+        except Exception:
+            pred = ""
+        score = is_float_equal(gt, pred, include_percentage=True, is_close=True)
+    elif answer_type in ["Str", "None"]:
+        gt = get_clean_string(gt)
+        pred = get_clean_string(pred)
+        score = gt == pred if is_exact_match(gt) else anls_compute(gt, pred)
+    else:
+        if isinstance(gt, str) and gt.startswith("["):
+            gt = eval(gt)
+        if not isinstance(gt, list):
+            gt = [gt]
+        if isinstance(pred, str) and pred.startswith("["):
+            pred = eval(pred)
+        if not isinstance(pred, list):
+            pred = [pred]
+        print(len(gt), len(pred))
+        if len(gt) != len(pred):
+            score = 0.0
+        else:
+            gt = sorted([get_clean_string(a) for a in gt])
+            pred = sorted([get_clean_string(a) for a in pred])
+            print(gt, pred)
+            if isfloat(gt[0]) or is_exact_match(gt[0]):
+                score = "-".join(gt) == "-".join(pred)
+            else:
+                score = min(
+                    [anls_compute(gt_v, pred_v) for gt_v, pred_v in zip(gt, pred, strict=False)]
+                )
+
+    return float(score)
+
+
+def eval_acc_and_f1(samples):
+    evaluated_samples = [sample for sample in samples if "score" in sample]
+    if not evaluated_samples:
+        return 0.0, 0.0
+
+    acc = sum([sample["score"] for sample in evaluated_samples]) / len(evaluated_samples)
+    try:
+        recall = sum(
+            [
+                sample["score"]
+                for sample in evaluated_samples
+                if sample["answer"] != "Not answerable"
+            ]
+        ) / len([sample for sample in evaluated_samples if sample["answer"] != "Not answerable"])
+        precision = sum(
+            [
+                sample["score"]
+                for sample in evaluated_samples
+                if sample["answer"] != "Not answerable"
+            ]
+        ) / len([sample for sample in evaluated_samples if sample["pred"] != "Not answerable"])
+        f1 = 2 * recall * precision / (recall + precision) if (recall + precision) > 0.0 else 0.0
+    except Exception:
+        f1 = 0.0
+
+    return acc, f1
+
+
+def show_results(samples, show_path=None):
+    for sample in samples:
+        sample["evidence_pages"] = eval(sample["evidence_pages"])
+        sample["evidence_sources"] = eval(sample["evidence_sources"])
+
+    with open(show_path, "w") as f:
+        acc, f1 = eval_acc_and_f1(samples)
+        f.write(f"Overall Acc: {acc} | Question Number: {len(samples)}\n")
+        f.write(f"Overall F1-score: {f1} | Question Number: {len(samples)}\n")
+        f.write("-----------------------\n")
+
+        #####################
+        acc_single_page, _ = eval_acc_and_f1(
+            [sample for sample in samples if len(sample["evidence_pages"]) == 1]
+        )
+        acc_multi_page, _ = eval_acc_and_f1(
+            [
+                sample
+                for sample in samples
+                if len(sample["evidence_pages"]) != 1 and sample["answer"] != "Not answerable"
+            ]
+        )
+        acc_neg, _ = eval_acc_and_f1(
+            [sample for sample in samples if sample["answer"] == "Not answerable"]
+        )
+
+        f.write(
+            "Single-page | Accuracy: {} | Question Number: {}\n".format(
+                acc_single_page,
+                len([sample for sample in samples if len(sample["evidence_pages"]) == 1]),
+            )
+        )
+        f.write(
+            "Cross-page | Accuracy: {} | Question Number: {}\n".format(
+                acc_multi_page,
+                len(
+                    [
+                        sample
+                        for sample in samples
+                        if len(sample["evidence_pages"]) != 1
+                        and sample["answer"] != "Not answerable"
+                    ]
+                ),
+            )
+        )
+        f.write(
+            "Unanswerable | Accuracy: {} | Question Number: {}\n".format(
+                acc_neg, len([sample for sample in samples if sample["answer"] == "Not answerable"])
+            )
+        )
+        f.write("-----------------------\n")
+
+        #####################
+        source_sample_dict, document_type_dict = defaultdict(list), defaultdict(list)
+        for sample in samples:
+            for answer_source in sample["evidence_sources"]:
+                source_sample_dict[answer_source].append(sample)
+            document_type_dict[sample["doc_type"]].append(sample)
+        for type, sub_samples in source_sample_dict.items():
+            f.write(
+                f"Evidence Sources: {type} | Accuracy: {eval_acc_and_f1(sub_samples)[0]} | Question Number: {len(sub_samples)}\n"
+            )
+
+        f.write("-----------------------\n")
+        for type, sub_samples in document_type_dict.items():
+            f.write(
+                f"Document Type: {type} | Accuracy: {eval_acc_and_f1(sub_samples)[0]} | Question Number: {len(sub_samples)}\n"
+            )
diff --git a/evaluation/scripts/mmlongbench/eval/extract_answer.py b/evaluation/scripts/mmlongbench/eval/extract_answer.py
new file mode 100644
index 000000000..65b56ccb2
--- /dev/null
+++ b/evaluation/scripts/mmlongbench/eval/extract_answer.py
@@ -0,0 +1,30 @@
+import os
+
+import openai
+
+
+client = openai.Client(
+    api_key=os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
+    base_url=os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
+)
+
+
+def extract_answer(question, output, prompt, model_name="gpt-4o"):
+    response = client.chat.completions.create(
+        model=model_name,
+        messages=[
+            {
+                "role": "user",
+                "content": prompt,
+            },
+            {"role": "assistant", "content": f"\n\nQuestion:{question}\nAnalysis:{output}\n"},
+        ],
+        temperature=0.0,
+        max_tokens=256,
+        top_p=1,
+        frequency_penalty=0,
+        presence_penalty=0,
+    )
+    response = response.choices[0].message.content
+
+    return response
diff --git a/evaluation/scripts/mmlongbench/eval/prompt_for_answer_extraction.md b/evaluation/scripts/mmlongbench/eval/prompt_for_answer_extraction.md
new file mode 100644
index 000000000..a309c0935
--- /dev/null
+++ b/evaluation/scripts/mmlongbench/eval/prompt_for_answer_extraction.md
@@ -0,0 +1,35 @@
+Given the question and analysis, you are tasked to extract answers with required formats from the free-form analysis.
+- Your extracted answers should be one of the following formats: (1) Integer, (2) Float, (3) String and (4) List. If you find the analysis the question can not be answered from the given documents, type "Not answerable". Exception: If the analysis only tells you that it can not read/understand the images or documents, type "Fail to answer".
+- Please make your response as concise as possible. Also note that your response should be formatted as below:
+```
+Extracted answer: [answer]
+Answer format: [answer format]
+```
+
+Please read the following example, then extract the answer from the model response and type it at the end of the prompt.
+
+---
+Question: List the primary questions asked about the services in this report.
+Analysis:  The primary questions asked about the services in the report for The Limes Residential Home are:\n\n1. Is the service safe?\n2. Is the service effective?\n3. Is the service caring?\n4. Is the service responsive?\n5. Is the service well-led?
+Extracted answer: ['Is the servife safe?', 'Is the service effective', 'Is the serve caring?', 'Is the service responsive?', 'Is the service well-led?']
+Answer format: List
+
+---
+Question: How many regulations of the HSCA 2008 are breached in all according to this report?
+Analysis: According to the report, the provider breached 10 Health and Social Care Act 2008 (Regulated Activities) Regulations in total. Here are the specifics:\n\n1. Regulation 13: Safeguarding service users from abuse and improper treatment\n2. Regulation 12: Safe care and treatment\n3. Regulation 18: Staffing\n4. Regulation 11: Need for consent\n5. Regulation 10: Dignity and respect\n6. Regulation 9: Person-centred care\n7. Regulation 17: Good governance\n8. Regulation 18 (CQC Registration Regulations 2009): Notification of other incidents\n9. Regulation 18: Failure to maintain an accurate and up-to-date care plan\n10. Regulation 11: Failure to implement the Mental Capacity Act 2005 code of practice effectively\n\nThese breaches involve issues concerning staffing, safeguarding, medicines management, dignity and respect, consent, care planning, governance, and failure to notify the CQC of incidents.
+Extracted answer: 10
+Answer format: Integer
+
+---
+Question: According to the survey that is the percentage of Chinese who are paying more or about the same attention to politics after Trump's election?
+Analysis: The survey provided does not specify the percentage of Chinese individuals specifically who are paying more or about the same attention to politics after Trump's election. The report focuses primarily on American demographics and does not include specific details about the Chinese population in relation to this question. If you need information about a different demographic or a summary of the findings from the American demographic, I can certainly help with that!
+Extracted answer: Not answerable
+Answer format: String
+
+---
+Question: How many quotations from male respondent over 50 years old are included in this report?
+Analysis: The image you've provided appears to be a screenshot of a document with multiple charts. However, the text is too small and blurry to read accurately. If you can provide a clearer image or more context, I might be able to help you with your question.
+Extracted answer: Fail to answer
+Answer format: String
+
+---
diff --git a/evaluation/scripts/mmlongbench/models/__init__.py b/evaluation/scripts/mmlongbench/models/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/evaluation/scripts/mmlongbench/models/internlm_xc2_4khd.py b/evaluation/scripts/mmlongbench/models/internlm_xc2_4khd.py
new file mode 100644
index 000000000..ae62eec9a
--- /dev/null
+++ b/evaluation/scripts/mmlongbench/models/internlm_xc2_4khd.py
@@ -0,0 +1,128 @@
+import torch
+import torch.nn.functional as func
+
+from transformers import AutoModel, AutoTokenizer
+
+
+torch.set_grad_enabled(False)
+
+
+try:
+    from transformers.generation.streamers import BaseStreamer
+except Exception:
+    BaseStreamer = None
+
+
+def chat(
+    model,
+    tokenizer,
+    query: str,
+    image: None,
+    hd_num: int = 25,
+    history: list[tuple[str, str]] | None = None,
+    streamer: BaseStreamer | None = None,
+    max_new_tokens: int = 1024,
+    temperature: float = 1.0,
+    top_p: float = 0.8,
+    repetition_penalty: float = 1.005,
+    meta_instruction: str = "You are an AI assistant whose name is InternLM-XComposer (浦语·灵笔).\n"
+    "- InternLM-XComposer (浦语·灵笔) is a multi-modality conversational language model that is developed by Shanghai AI Laboratory (上海人工智能实验室). It is designed to be helpful, honest, and harmless.\n"
+    "- InternLM-XComposer (浦语·灵笔) can understand and communicate fluently in the language chosen by the user such as English and 中文.\n"
+    "- InternLM-XComposer (浦语·灵笔) is capable of comprehending and articulating responses effectively based on the provided image.",
+    **kwargs,
+):
+    if history is None:
+        history = []
+    if image is None:
+        inputs = model.build_inputs(tokenizer, query, history, meta_instruction)
+        im_mask = torch.zeros(inputs["input_ids"].shape[:2]).cuda().bool()
+    else:
+        if isinstance(image, str):
+            with torch.cuda.amp.autocast():
+                image = model.encode_img(image, hd_num=hd_num)
+            inputs, im_mask = model.interleav_wrap_chat(
+                tokenizer, query, image, history, meta_instruction
+            )
+        if isinstance(image, str):
+            image_list = []
+            with torch.cuda.amp.autocast():
+                for image_path in image:
+                    tmp = model.encode_img(image_path, hd_num=hd_num)
+                    image_list.append(tmp)
+            if len(image_list) > 1 and image_list[-1].shape[1] != image_list[-2].shape[1]:
+                image_list[-1] = func.interpolate(
+                    image_list[-1].unsqueeze(1), size=image_list[-2].shape[1:], mode="bilinear"
+                ).squeeze(1)
+            image = torch.cat(image_list, dim=0)
+            with torch.cuda.amp.autocast():
+                inputs, im_mask = model.interleav_wrap_chat(
+                    tokenizer, query, image, history, meta_instruction
+                )
+        else:
+            raise NotImplementedError
+    inputs = {k: v.to(model.device) for k, v in inputs.items() if torch.is_tensor(v)}
+    # also add end-of-assistant token in eos token id to avoid unnecessary generation
+    eos_token_id = [
+        tokenizer.eos_token_id,
+        tokenizer.convert_tokens_to_ids(["[UNUSED_TOKEN_145]"])[0],
+    ]
+    with torch.cuda.amp.autocast():
+        outputs = model.generate(
+            **inputs,
+            streamer=streamer,
+            max_new_tokens=max_new_tokens,
+            do_sample=temperature != 0.0,
+            temperature=temperature,
+            top_p=top_p,
+            eos_token_id=eos_token_id,
+            repetition_penalty=repetition_penalty,
+            im_mask=im_mask,
+            **kwargs,
+        )
+    if image is None:
+        outputs = outputs[0].cpu().tolist()[len(inputs["input_ids"][0]) :]
+    else:
+        outputs = outputs[0].cpu().tolist()
+
+    response = tokenizer.decode(outputs, skip_special_tokens=True)
+    response = response.partition("[UNUSED_TOKEN_145]")[0]
+
+    history = [*history, (query, response)]
+    return response, history
+
+
+def init_model(cache_path):
+    model_path = (
+        cache_path
+        if (cache_path is not None and cache_path != "None")
+        else "internlm/internlm-xcomposer2-4khd-7b"
+    )
+    model = AutoModel.from_pretrained(
+        model_path,
+        torch_dtype=torch.bfloat16,
+        low_cpu_mem_usage=True,
+        trust_remote_code=True,
+        device_map="auto",
+    ).eval()
+
+    tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+    model.tokenizer = tokenizer
+    return model
+
+
+def get_response_concat(model, question, image_path_list, max_new_tokens=1024, temperature=1.0):
+    query = "<ImageHere> " * len(image_path_list) + question
+    try:
+        response, _ = chat(
+            model,
+            model.tokenizer,
+            query=query,
+            image=image_path_list,
+            max_new_tokens=max_new_tokens,
+            hd_num=16,
+            temperature=temperature,
+        )
+    except Exception as e:
+        print(e)
+        response = "Failed"
+    return response
diff --git a/evaluation/scripts/mmlongbench/models/internvl_chat.py b/evaluation/scripts/mmlongbench/models/internvl_chat.py
new file mode 100644
index 000000000..793f1d35a
--- /dev/null
+++ b/evaluation/scripts/mmlongbench/models/internvl_chat.py
@@ -0,0 +1,137 @@
+import torch
+import torchvision.transforms as tf
+
+from PIL import Image
+from torchvision.transforms.functional import InterpolationMode
+from transformers import AutoModel, AutoTokenizer
+
+
+IMAGENET_MEAN = (0.485, 0.456, 0.406)
+IMAGENET_STD = (0.229, 0.224, 0.225)
+
+
+def build_transform(input_size, mean=IMAGENET_MEAN, std=IMAGENET_STD):
+    return tf.Compose(
+        [
+            tf.Lambda(lambda img: img.convert("RGB") if img.mode != "RGB" else img),
+            tf.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
+            tf.ToTensor(),
+            tf.Normalize(mean=mean, std=std),
+        ]
+    )
+
+
+def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
+    best_ratio_diff = float("inf")
+    best_ratio = (1, 1)
+    area = width * height
+    for ratio in target_ratios:
+        target_aspect_ratio = ratio[0] / ratio[1]
+        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
+        if ratio_diff < best_ratio_diff:
+            best_ratio_diff = ratio_diff
+            best_ratio = ratio
+        elif ratio_diff == best_ratio_diff:
+            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
+                best_ratio = ratio
+    return best_ratio
+
+
+def dynamic_preprocess(image, min_num=1, max_num=6, image_size=448, use_thumbnail=False):
+    orig_width, orig_height = image.size
+    aspect_ratio = orig_width / orig_height
+
+    # calculate the existing image aspect ratio
+    target_ratios = {
+        (i, j)
+        for n in range(min_num, max_num + 1)
+        for i in range(1, n + 1)
+        for j in range(1, n + 1)
+        if min_num <= i * j <= max_num
+    }
+    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
+
+    # find the closest aspect ratio to the target
+    target_aspect_ratio = find_closest_aspect_ratio(
+        aspect_ratio, target_ratios, orig_width, orig_height, image_size
+    )
+
+    # calculate the target width and height
+    target_width = image_size * target_aspect_ratio[0]
+    target_height = image_size * target_aspect_ratio[1]
+    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
+
+    # resize the image
+    resized_img = image.resize((target_width, target_height))
+    processed_images = []
+    for i in range(blocks):
+        box = (
+            (i % (target_width // image_size)) * image_size,
+            (i // (target_width // image_size)) * image_size,
+            ((i % (target_width // image_size)) + 1) * image_size,
+            ((i // (target_width // image_size)) + 1) * image_size,
+        )
+        # split the image
+        split_img = resized_img.crop(box)
+        processed_images.append(split_img)
+    assert len(processed_images) == blocks
+    if use_thumbnail and len(processed_images) != 1:
+        thumbnail_img = image.resize((image_size, image_size))
+        processed_images.append(thumbnail_img)
+    return processed_images
+
+
+def load_image(image_file, input_size=448, max_num=6):
+    image = Image.open(image_file).convert("RGB")
+    transform = build_transform(input_size=input_size)
+    images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
+    pixel_values = [transform(image) for image in images]
+    pixel_values = torch.stack(pixel_values)
+    return pixel_values
+
+
+def init_model(cache_path):
+    import os
+
+    os.environ["CUDA_LAUNCH_BLOCKING"] = "1"
+
+    model_path = (
+        cache_path
+        if (cache_path is not None and cache_path != "None")
+        else "OpenGVLab/InternVL-Chat-V1-5"
+    )
+    model = AutoModel.from_pretrained(
+        model_path,
+        torch_dtype=torch.bfloat16,
+        low_cpu_mem_usage=True,
+        trust_remote_code=True,
+        device_map="auto",
+    ).eval()
+    tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+    model.tokenizer = tokenizer
+    return model
+
+
+def get_response_concat(
+    model, question, image_path_list, max_new_tokens=1024, temperature=1.0, max_num=6
+):
+    generation_config = {
+        "num_beams": 1,
+        "max_new_tokens": max_new_tokens,
+        "do_sample": temperature != 0.0,
+        "temperature": temperature,
+    }
+    pixel_values_list = [
+        load_image(image_path, max_num=max_num).to(torch.bfloat16).cuda()
+        for image_path in image_path_list
+    ]
+    pixel_values = torch.cat(pixel_values_list, dim=0)
+    response, _ = model.chat(
+        model.tokenizer,
+        pixel_values,
+        question,
+        generation_config,
+        history=None,
+        return_history=True,
+    )
+    return response
diff --git a/evaluation/scripts/mmlongbench/models/minicpm_llama3.py b/evaluation/scripts/mmlongbench/models/minicpm_llama3.py
new file mode 100644
index 000000000..7f6d4b743
--- /dev/null
+++ b/evaluation/scripts/mmlongbench/models/minicpm_llama3.py
@@ -0,0 +1,56 @@
+import torch
+
+from PIL import Image
+from transformers import AutoModel, AutoTokenizer
+
+
+def init_model(cache_path):
+    model_path = (
+        cache_path
+        if (cache_path is not None and cache_path != "None")
+        else "openbmb/MiniCPM-Llama3-V-2_5"
+    )
+    model = AutoModel.from_pretrained(
+        model_path,
+        torch_dtype=torch.bfloat16,
+        low_cpu_mem_usage=True,
+        trust_remote_code=True,
+        device_map="auto",
+    ).eval()
+
+    tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+    model.tokenizer = tokenizer
+    return model
+
+
+def get_response_concat(model, question, image_path_list, max_new_tokens=1024, temperature=1.0):
+    msgs = []
+    system_prompt = "Answer in detail."
+    if system_prompt:
+        msgs.append({"type": "text", "value": system_prompt})
+    if isinstance(image_path_list, list):
+        msgs.extend([{"type": "image", "value": p} for p in image_path_list])
+    else:
+        msgs = [{"type": "image", "value": image_path_list}]
+    msgs.append({"type": "text", "value": question})
+
+    content = []
+    for x in msgs:
+        if x["type"] == "text":
+            content.append(x["value"])
+        elif x["type"] == "image":
+            image = Image.open(x["value"]).convert("RGB")
+            content.append(image)
+    msgs = [{"role": "user", "content": content}]
+
+    with torch.cuda.amp.autocast():
+        res = model.chat(
+            msgs=msgs,
+            context=None,
+            image=None,
+            max_new_tokens=max_new_tokens,
+            temperature=temperature,
+            do_sample=temperature != 0.0,
+            tokenizer=model.tokenizer,
+        )
+    return res
diff --git a/evaluation/scripts/mmlongbench/run_api.py b/evaluation/scripts/mmlongbench/run_api.py
new file mode 100644
index 000000000..26bdc7f78
--- /dev/null
+++ b/evaluation/scripts/mmlongbench/run_api.py
@@ -0,0 +1,244 @@
+import argparse
+import json
+import os
+import re
+import time
+import uuid
+
+from datetime import datetime
+
+from dotenv import load_dotenv
+from eval.eval_score import eval_acc_and_f1, eval_score, show_results
+from eval.extract_answer import extract_answer
+from tqdm import tqdm
+
+from memos.configs.mem_cube import GeneralMemCubeConfig
+from memos.configs.mem_os import MOSConfig
+from memos.mem_cube.general import GeneralMemCube
+from memos.mem_os.main import MOS
+
+
+load_dotenv()
+
+# 1. Create MOS Config and set openai config
+print(f"🚀 [{datetime.now().strftime('%H:%M:%S')}] Starting to create MOS configuration...")
+start_time = time.time()
+
+user_name = str(uuid.uuid4())
+print(user_name)
+
+# 1.1 Set openai config
+openapi_config = {
+    "model_name_or_path": "gpt-4o-mini",
+    "temperature": 0.8,
+    "max_tokens": 1024,
+    "top_p": 0.9,
+    "top_k": 50,
+    "remove_think_prefix": True,
+    "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
+    "api_base": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
+}
+# 1.2 Set neo4j config
+neo4j_uri = os.getenv("NEO4J_URI", "bolt://47.117.41.207:7687")
+
+# 1.3  Create MOS Config
+config = {
+    "user_id": user_name,
+    "chat_model": {
+        "backend": "openai",
+        "config": openapi_config,
+    },
+    "mem_reader": {
+        "backend": "simple_struct",
+        "config": {
+            "llm": {
+                "backend": "openai",
+                "config": openapi_config,
+            },
+            "embedder": {
+                "backend": "universal_api",
+                "config": {
+                    "provider": "openai",
+                    "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
+                    "model_name_or_path": "text-embedding-3-large",
+                    "base_url": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
+                },
+            },
+            "chunker": {
+                "backend": "sentence",
+                "config": {
+                    "tokenizer_or_token_counter": "gpt2",
+                    "chunk_size": 512,
+                    "chunk_overlap": 128,
+                    "min_sentences_per_chunk": 1,
+                },
+            },
+        },
+    },
+    "max_turns_window": 20,
+    "top_k": 5,
+    "enable_textual_memory": True,
+    "enable_activation_memory": False,
+    "enable_parametric_memory": False,
+}
+
+mos_config = MOSConfig(**config)
+# you can set PRO_MODE to True to enable CoT enhancement mos_config.PRO_MODE = True
+mos = MOS(mos_config)
+
+print(
+    f"✅ [{datetime.now().strftime('%H:%M:%S')}] MOS configuration created successfully, time elapsed: {time.time() - start_time:.2f}s\n"
+)
+
+# 2. Initialize memory cube
+print(f"🚀 [{datetime.now().strftime('%H:%M:%S')}] Starting to initialize MemCube configuration...")
+start_time = time.time()
+
+config = GeneralMemCubeConfig.model_validate(
+    {
+        "user_id": user_name,
+        "cube_id": f"{user_name}",
+        "text_mem": {
+            "backend": "tree_text",
+            "config": {
+                "extractor_llm": {
+                    "backend": "openai",
+                    "config": openapi_config,
+                },
+                "dispatcher_llm": {
+                    "backend": "openai",
+                    "config": openapi_config,
+                },
+                "graph_db": {
+                    "backend": "neo4j",
+                    "config": {
+                        "uri": neo4j_uri,
+                        "user": "neo4j",
+                        "password": "iaarlichunyu",
+                        "db_name": "mm-long-bench-doc-eval",
+                        "auto_create": True,
+                    },
+                },
+                "embedder": {
+                    "backend": "universal_api",
+                    "config": {
+                        "provider": "openai",
+                        "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
+                        "model_name_or_path": "text-embedding-3-large",
+                        "base_url": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
+                    },
+                },
+                "reorganize": True,
+            },
+        },
+        "act_mem": {},
+        "para_mem": {},
+    },
+)
+
+print(
+    f"✅ [{datetime.now().strftime('%H:%M:%S')}] MemCube configuration initialization completed, time elapsed: {time.time() - start_time:.2f}s\n"
+)
+
+# 3. Initialize the MemCube with the configuration
+print(f"🚀 [{datetime.now().strftime('%H:%M:%S')}] Starting to create MemCube instance...")
+start_time = time.time()
+
+mem_cube = GeneralMemCube(config)
+try:
+    mem_cube.dump(f"/tmp/{user_name}/")
+    print(
+        f"✅ [{datetime.now().strftime('%H:%M:%S')}] MemCube created and saved successfully, time elapsed: {time.time() - start_time:.2f}s\n"
+    )
+except Exception as e:
+    print(
+        f"❌ [{datetime.now().strftime('%H:%M:%S')}] MemCube save failed: {e}, time elapsed: {time.time() - start_time:.2f}s\n"
+    )
+
+# 4. Register the MemCube
+print(f"🚀 [{datetime.now().strftime('%H:%M:%S')}] Starting to register MemCube...")
+start_time = time.time()
+
+mos.register_mem_cube(f"/tmp/{user_name}", mem_cube_id=user_name)
+
+print(
+    f"✅ [{datetime.now().strftime('%H:%M:%S')}] MemCube registration completed, time elapsed: {time.time() - start_time:.2f}s\n"
+)
+
+mos.add(doc_path="evaluation/data/mmlongbench/documents/")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--input_path", type=str, default="evaluation/data/mmlongbench/samples.json"
+    )
+    parser.add_argument(
+        "--document_path", type=str, default="evaluation/data/mmlongbench/documents"
+    )
+    parser.add_argument("--model_name", type=str, default="gpt-4o")
+    parser.add_argument("--max_pages", type=int, default=120)
+    parser.add_argument("--resolution", type=int, default=144)
+    parser.add_argument("--max_try", type=int, default=10)
+    parser.add_argument("--max_tokens", type=int, default=1024)
+    parser.add_argument("--temperature", type=float, default=0.0)
+    parser.add_argument(
+        "--extractor_prompt_path",
+        type=str,
+        default="evaluation/scripts/mmlongbench/eval/prompt_for_answer_extraction.md",
+    )
+    args = parser.parse_args()
+
+    args.output_path = "evaluation/data/mmlongbench/res.json"
+
+    with open(args.extractor_prompt_path) as f:
+        prompt = f.read()
+    if os.path.exists(args.output_path):
+        with open(args.output_path) as f:
+            samples = json.load(f)
+    else:
+        with open(args.input_path) as f:
+            samples = json.load(f)
+
+    for sample in tqdm(samples):
+        if sample["evidence_sources"] != "['Pure-text (Plain-text)']":
+            continue
+
+        messages = sample["question"]
+
+        try_cnt = 0
+        is_success = False
+        while True:
+            try:
+                mos.clear_messages()
+                response = mos.chat(messages)
+                is_success = True
+            except Exception:
+                try_cnt += 1
+                response = "Failed"
+            if is_success or try_cnt > args.max_try:
+                break
+
+        sample["response"] = response
+        extracted_res = extract_answer(sample["question"], response, prompt)
+        sample["extracted_res"] = extracted_res
+        print("llm res:", extracted_res)
+        pred_ans = extracted_res.split("Answer format:")[0].split("Extracted answer:")[1].strip()
+        score = eval_score(sample["answer"], pred_ans, sample["answer_format"])
+        sample["pred"] = pred_ans
+        sample["score"] = score
+
+        acc, f1 = eval_acc_and_f1(samples)
+        print("--------------------------------------")
+        print("Question: {}".format(sample["question"]))
+        print("Response: {}".format(sample["response"]))
+        print(
+            "Gt: {}\tPred: {}\tScore: {}".format(sample["answer"], sample["pred"], sample["score"])
+        )
+        print(f"Avg acc: {acc}")
+        print(f"Avg f1: {f1}")
+
+        with open(args.output_path, "w") as f:
+            json.dump(samples, f)
+
+    show_results(samples, show_path=re.sub("\.json$", ".txt", args.output_path))

From 03329c428a50a08c5e03e1d3c308406f8e6ec48b Mon Sep 17 00:00:00 2001
From: stx <31013941@qq.com>
Date: Tue, 19 Aug 2025 20:59:58 +0800
Subject: [PATCH 17/34] =?UTF-8?q?feat=EF=BC=9Aadd=20MMLongbench-doc=20eval?=
 =?UTF-8?q?uation?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 evaluation/scripts/mmlongbench/run_api.py | 326 +++++++++++-----------
 1 file changed, 161 insertions(+), 165 deletions(-)

diff --git a/evaluation/scripts/mmlongbench/run_api.py b/evaluation/scripts/mmlongbench/run_api.py
index 26bdc7f78..92bb8a51c 100644
--- a/evaluation/scripts/mmlongbench/run_api.py
+++ b/evaluation/scripts/mmlongbench/run_api.py
@@ -2,11 +2,8 @@
 import json
 import os
 import re
-import time
 import uuid
 
-from datetime import datetime
-
 from dotenv import load_dotenv
 from eval.eval_score import eval_acc_and_f1, eval_score, show_results
 from eval.extract_answer import extract_answer
@@ -19,15 +16,6 @@
 
 
 load_dotenv()
-
-# 1. Create MOS Config and set openai config
-print(f"🚀 [{datetime.now().strftime('%H:%M:%S')}] Starting to create MOS configuration...")
-start_time = time.time()
-
-user_name = str(uuid.uuid4())
-print(user_name)
-
-# 1.1 Set openai config
 openapi_config = {
     "model_name_or_path": "gpt-4o-mini",
     "temperature": 0.8,
@@ -38,87 +26,26 @@
     "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
     "api_base": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
 }
-# 1.2 Set neo4j config
 neo4j_uri = os.getenv("NEO4J_URI", "bolt://47.117.41.207:7687")
 
-# 1.3  Create MOS Config
-config = {
-    "user_id": user_name,
-    "chat_model": {
-        "backend": "openai",
-        "config": openapi_config,
-    },
-    "mem_reader": {
-        "backend": "simple_struct",
-        "config": {
-            "llm": {
-                "backend": "openai",
-                "config": openapi_config,
-            },
-            "embedder": {
-                "backend": "universal_api",
-                "config": {
-                    "provider": "openai",
-                    "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
-                    "model_name_or_path": "text-embedding-3-large",
-                    "base_url": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
-                },
-            },
-            "chunker": {
-                "backend": "sentence",
-                "config": {
-                    "tokenizer_or_token_counter": "gpt2",
-                    "chunk_size": 512,
-                    "chunk_overlap": 128,
-                    "min_sentences_per_chunk": 1,
-                },
-            },
-        },
-    },
-    "max_turns_window": 20,
-    "top_k": 5,
-    "enable_textual_memory": True,
-    "enable_activation_memory": False,
-    "enable_parametric_memory": False,
-}
-
-mos_config = MOSConfig(**config)
-# you can set PRO_MODE to True to enable CoT enhancement mos_config.PRO_MODE = True
-mos = MOS(mos_config)
-
-print(
-    f"✅ [{datetime.now().strftime('%H:%M:%S')}] MOS configuration created successfully, time elapsed: {time.time() - start_time:.2f}s\n"
-)
 
-# 2. Initialize memory cube
-print(f"🚀 [{datetime.now().strftime('%H:%M:%S')}] Starting to initialize MemCube configuration...")
-start_time = time.time()
-
-config = GeneralMemCubeConfig.model_validate(
-    {
+def process_document(doc_path):
+    """Process a single document by creating MOS and MemCube with unique user ID"""
+    # Create MOS Config with unique user_id
+    user_name = str(uuid.uuid4())
+    config = {
         "user_id": user_name,
-        "cube_id": f"{user_name}",
-        "text_mem": {
-            "backend": "tree_text",
+        "chat_model": {
+            "backend": "openai",
+            "config": openapi_config,
+        },
+        "mem_reader": {
+            "backend": "simple_struct",
             "config": {
-                "extractor_llm": {
-                    "backend": "openai",
-                    "config": openapi_config,
-                },
-                "dispatcher_llm": {
+                "llm": {
                     "backend": "openai",
                     "config": openapi_config,
                 },
-                "graph_db": {
-                    "backend": "neo4j",
-                    "config": {
-                        "uri": neo4j_uri,
-                        "user": "neo4j",
-                        "password": "iaarlichunyu",
-                        "db_name": "mm-long-bench-doc-eval",
-                        "auto_create": True,
-                    },
-                },
                 "embedder": {
                     "backend": "universal_api",
                     "config": {
@@ -128,44 +55,79 @@
                         "base_url": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
                     },
                 },
-                "reorganize": True,
+                "chunker": {
+                    "backend": "sentence",
+                    "config": {
+                        "tokenizer_or_token_counter": "gpt2",
+                        "chunk_size": 512,
+                        "chunk_overlap": 128,
+                        "min_sentences_per_chunk": 1,
+                    },
+                },
             },
         },
-        "act_mem": {},
-        "para_mem": {},
-    },
-)
-
-print(
-    f"✅ [{datetime.now().strftime('%H:%M:%S')}] MemCube configuration initialization completed, time elapsed: {time.time() - start_time:.2f}s\n"
-)
-
-# 3. Initialize the MemCube with the configuration
-print(f"🚀 [{datetime.now().strftime('%H:%M:%S')}] Starting to create MemCube instance...")
-start_time = time.time()
-
-mem_cube = GeneralMemCube(config)
-try:
-    mem_cube.dump(f"/tmp/{user_name}/")
-    print(
-        f"✅ [{datetime.now().strftime('%H:%M:%S')}] MemCube created and saved successfully, time elapsed: {time.time() - start_time:.2f}s\n"
-    )
-except Exception as e:
-    print(
-        f"❌ [{datetime.now().strftime('%H:%M:%S')}] MemCube save failed: {e}, time elapsed: {time.time() - start_time:.2f}s\n"
+        "max_turns_window": 20,
+        "top_k": 5,
+        "enable_textual_memory": True,
+        "enable_activation_memory": False,
+        "enable_parametric_memory": False,
+    }
+    mos_config = MOSConfig(**config)
+    mos = MOS(mos_config)
+
+    # Create MemCube configuration with fixed database name
+    mem_cube_config = GeneralMemCubeConfig.model_validate(
+        {
+            "user_id": user_name,
+            "cube_id": user_name,
+            "text_mem": {
+                "backend": "tree_text",
+                "config": {
+                    "extractor_llm": {
+                        "backend": "openai",
+                        "config": openapi_config,
+                    },
+                    "dispatcher_llm": {
+                        "backend": "openai",
+                        "config": openapi_config,
+                    },
+                    "graph_db": {
+                        "backend": "neo4j",
+                        "config": {
+                            "uri": neo4j_uri,
+                            "user": "neo4j",
+                            "password": "iaarlichunyu",
+                            "db_name": "mm-long-bench-shared",
+                            "auto_create": True,
+                        },
+                    },
+                    "embedder": {
+                        "backend": "universal_api",
+                        "config": {
+                            "provider": "openai",
+                            "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
+                            "model_name_or_path": "text-embedding-3-large",
+                            "base_url": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
+                        },
+                    },
+                    "reorganize": True,
+                },
+            },
+            "act_mem": {},
+            "para_mem": {},
+        },
     )
+    mem_cube = GeneralMemCube(mem_cube_config)
 
-# 4. Register the MemCube
-print(f"🚀 [{datetime.now().strftime('%H:%M:%S')}] Starting to register MemCube...")
-start_time = time.time()
-
-mos.register_mem_cube(f"/tmp/{user_name}", mem_cube_id=user_name)
+    # Save and register MemCube
+    temp_dir = f"/tmp/{user_name}"
+    os.makedirs(temp_dir, exist_ok=True)
+    mem_cube.dump(temp_dir)
+    mos.register_mem_cube(temp_dir, mem_cube_id=user_name)
 
-print(
-    f"✅ [{datetime.now().strftime('%H:%M:%S')}] MemCube registration completed, time elapsed: {time.time() - start_time:.2f}s\n"
-)
-
-mos.add(doc_path="evaluation/data/mmlongbench/documents/")
+    # Add the current document
+    mos.add(doc_path=[doc_path])
+    return mos
 
 
 if __name__ == "__main__":
@@ -188,57 +150,91 @@
         default="evaluation/scripts/mmlongbench/eval/prompt_for_answer_extraction.md",
     )
     args = parser.parse_args()
-
     args.output_path = "evaluation/data/mmlongbench/res.json"
 
     with open(args.extractor_prompt_path) as f:
         prompt = f.read()
-    if os.path.exists(args.output_path):
-        with open(args.output_path) as f:
-            samples = json.load(f)
-    else:
-        with open(args.input_path) as f:
-            samples = json.load(f)
-
-    for sample in tqdm(samples):
-        if sample["evidence_sources"] != "['Pure-text (Plain-text)']":
-            continue
-
-        messages = sample["question"]
-
-        try_cnt = 0
-        is_success = False
-        while True:
-            try:
-                mos.clear_messages()
-                response = mos.chat(messages)
-                is_success = True
-            except Exception:
-                try_cnt += 1
-                response = "Failed"
-            if is_success or try_cnt > args.max_try:
-                break
-
-        sample["response"] = response
-        extracted_res = extract_answer(sample["question"], response, prompt)
-        sample["extracted_res"] = extracted_res
-        print("llm res:", extracted_res)
-        pred_ans = extracted_res.split("Answer format:")[0].split("Extracted answer:")[1].strip()
-        score = eval_score(sample["answer"], pred_ans, sample["answer_format"])
-        sample["pred"] = pred_ans
-        sample["score"] = score
-
-        acc, f1 = eval_acc_and_f1(samples)
-        print("--------------------------------------")
-        print("Question: {}".format(sample["question"]))
-        print("Response: {}".format(sample["response"]))
-        print(
-            "Gt: {}\tPred: {}\tScore: {}".format(sample["answer"], sample["pred"], sample["score"])
-        )
-        print(f"Avg acc: {acc}")
-        print(f"Avg f1: {f1}")
 
+    # Load input samples
+    with open(args.input_path) as f:
+        samples = json.load(f)
+
+    # Get list of document files to process
+    doc_files = [
+        f
+        for f in os.listdir(args.document_path)
+        if os.path.isfile(os.path.join(args.document_path, f))
+    ]
+
+    # Process each document separately
+    res_samples = []
+    for doc_file in tqdm(doc_files, desc="Processing documents"):
+        print(f"Processing {doc_file}")
+        doc_path = os.path.join(args.document_path, doc_file)
+
+        # Process current document and create MOS
+        mos = process_document(doc_path)
+
+        # Filter samples for this document
+        doc_id = os.path.basename(doc_path)
+        doc_samples = [s for s in samples if s["doc_id"] == doc_id]
+
+        # Process samples for this document
+        for sample in doc_samples:
+            if sample["evidence_sources"] != "['Pure-text (Plain-text)']":
+                continue
+
+            try_cnt = 0
+            while try_cnt <= args.max_try:
+                try:
+                    mos.clear_messages()
+                    response = mos.chat(sample["question"])
+                    break
+                except Exception as e:
+                    try_cnt += 1
+                    response = f"Error: {e!s}"
+                    if try_cnt > args.max_try:
+                        break
+
+            sample["response"] = response
+            # Extract and evaluate response
+            if "Error:" not in response:
+                extracted_res = extract_answer(sample["question"], response, prompt)
+                sample["extracted_res"] = extracted_res
+                try:
+                    pred_ans = (
+                        extracted_res.split("Answer format:")[0]
+                        .split("Extracted answer:")[1]
+                        .strip()
+                    )
+                    score = eval_score(sample["answer"], pred_ans, sample["answer_format"])
+                except Exception:
+                    pred_ans = ""
+                    score = 0
+                sample["pred"] = pred_ans
+                sample["score"] = score
+            else:
+                sample["pred"] = ""
+                sample["score"] = 0
+
+            res_samples.append(sample)
+            print("--------------------------------------")
+            print("Question: {}".format(sample["question"]))
+            print("Response: {}".format(sample["response"]))
+            print(
+                "Gt: {}\tPred: {}\tScore: {}".format(
+                    sample["answer"], sample["pred"], sample["score"]
+                )
+            )
+
+        acc, f1 = eval_acc_and_f1(res_samples)
+        print(f"\nMetrics - Accuracy: {acc:.4f}, F1 Score: {f1:.4f}")
+
+        # Save progress after each sample
         with open(args.output_path, "w") as f:
-            json.dump(samples, f)
+            json.dump(res_samples, f)
 
-    show_results(samples, show_path=re.sub("\.json$", ".txt", args.output_path))
+    # Calculate and display final results
+    acc, f1 = eval_acc_and_f1(res_samples)
+    print(f"\nFinal Metrics - Accuracy: {acc:.4f}, F1 Score: {f1:.4f}")
+    show_results(res_samples, show_path=re.sub("\.json$", ".txt", args.output_path))

From 1331c697147ee8771b8e173ba5caa77d54c27de8 Mon Sep 17 00:00:00 2001
From: stx <31013941@qq.com>
Date: Tue, 19 Aug 2025 21:03:02 +0800
Subject: [PATCH 18/34] =?UTF-8?q?feat=EF=BC=9Aadd=20MMLongbench-doc=20eval?=
 =?UTF-8?q?uation?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../scripts/mmlongbench/asset/top_figure.png  | Bin 1983216 -> 0 bytes
 .../mmlongbench/eval/extract_answer.py        |   3 +++
 2 files changed, 3 insertions(+)
 delete mode 100644 evaluation/scripts/mmlongbench/asset/top_figure.png

diff --git a/evaluation/scripts/mmlongbench/asset/top_figure.png b/evaluation/scripts/mmlongbench/asset/top_figure.png
deleted file mode 100644
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diff --git a/evaluation/scripts/mmlongbench/eval/extract_answer.py b/evaluation/scripts/mmlongbench/eval/extract_answer.py
index 65b56ccb2..b7f7e6863 100644
--- a/evaluation/scripts/mmlongbench/eval/extract_answer.py
+++ b/evaluation/scripts/mmlongbench/eval/extract_answer.py
@@ -2,7 +2,10 @@
 
 import openai
 
+from dotenv import load_dotenv
 
+
+load_dotenv()
 client = openai.Client(
     api_key=os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
     base_url=os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),

From 2e6e692dbc2941c589636d8428e39cf6c4bfc838 Mon Sep 17 00:00:00 2001
From: stx <31013941@qq.com>
Date: Thu, 21 Aug 2025 11:21:12 +0800
Subject: [PATCH 19/34] =?UTF-8?q?feat=EF=BC=9Aadd=20MMLongbench-doc=20eval?=
 =?UTF-8?q?uation?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../scripts/mmlongbench/eval/eval_score.py    |   2 -
 evaluation/scripts/mmlongbench/run_api.py     | 784 ++++++++++++++----
 2 files changed, 619 insertions(+), 167 deletions(-)

diff --git a/evaluation/scripts/mmlongbench/eval/eval_score.py b/evaluation/scripts/mmlongbench/eval/eval_score.py
index d1136bd40..02ef6eb53 100644
--- a/evaluation/scripts/mmlongbench/eval/eval_score.py
+++ b/evaluation/scripts/mmlongbench/eval/eval_score.py
@@ -189,7 +189,6 @@ def show_results(samples, show_path=None):
         f.write(f"Overall F1-score: {f1} | Question Number: {len(samples)}\n")
         f.write("-----------------------\n")
 
-        #####################
         acc_single_page, _ = eval_acc_and_f1(
             [sample for sample in samples if len(sample["evidence_pages"]) == 1]
         )
@@ -230,7 +229,6 @@ def show_results(samples, show_path=None):
         )
         f.write("-----------------------\n")
 
-        #####################
         source_sample_dict, document_type_dict = defaultdict(list), defaultdict(list)
         for sample in samples:
             for answer_source in sample["evidence_sources"]:
diff --git a/evaluation/scripts/mmlongbench/run_api.py b/evaluation/scripts/mmlongbench/run_api.py
index 92bb8a51c..a7c91f0c0 100644
--- a/evaluation/scripts/mmlongbench/run_api.py
+++ b/evaluation/scripts/mmlongbench/run_api.py
@@ -2,8 +2,14 @@
 import json
 import os
 import re
+import signal
+import sys
+import threading
+import time
 import uuid
 
+from concurrent.futures import ThreadPoolExecutor, as_completed
+
 from dotenv import load_dotenv
 from eval.eval_score import eval_acc_and_f1, eval_score, show_results
 from eval.extract_answer import extract_answer
@@ -27,80 +33,207 @@
     "api_base": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
 }
 neo4j_uri = os.getenv("NEO4J_URI", "bolt://47.117.41.207:7687")
+db_name = "mm-long-bench-single-"
 
 
-def process_document(doc_path):
-    """Process a single document by creating MOS and MemCube with unique user ID"""
-    # Create MOS Config with unique user_id
-    user_name = str(uuid.uuid4())
-    config = {
-        "user_id": user_name,
-        "chat_model": {
-            "backend": "openai",
-            "config": openapi_config,
-        },
-        "mem_reader": {
-            "backend": "simple_struct",
-            "config": {
-                "llm": {
-                    "backend": "openai",
-                    "config": openapi_config,
-                },
-                "embedder": {
-                    "backend": "universal_api",
-                    "config": {
-                        "provider": "openai",
-                        "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
-                        "model_name_or_path": "text-embedding-3-large",
-                        "base_url": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
+# Global state tracking
+class ProcessingState:
+    def __init__(self):
+        self.lock = threading.Lock()
+        self.completed_docs = set()
+        self.completed_samples = set()
+        self.skipped = 0
+        self.doc_user_map = {}  # Map document name to user_name
+        self.doc_temp_dir = {}  # Map document name to temp directory
+
+    def mark_doc_completed(self, doc_id):
+        with self.lock:
+            self.completed_docs.add(doc_id)
+
+    def is_doc_completed(self, doc_id):
+        with self.lock:
+            return doc_id in self.completed_docs
+
+    def mark_sample_completed(self, doc_id, sample_id):
+        with self.lock:
+            self.completed_samples.add((doc_id, sample_id))
+
+    def is_sample_completed(self, doc_id, sample_id):
+        with self.lock:
+            return (doc_id, sample_id) in self.completed_samples
+
+    def increment_skipped(self):
+        with self.lock:
+            self.skipped += 1
+
+    def get_skipped(self):
+        with self.lock:
+            return self.skipped
+
+    def add_doc_user_mapping(self, doc_name, user_name, temp_dir):
+        with self.lock:
+            self.doc_user_map[doc_name] = user_name
+            self.doc_temp_dir[doc_name] = temp_dir
+
+    def get_user_name_for_doc(self, doc_name):
+        with self.lock:
+            return self.doc_user_map.get(doc_name)
+
+    def get_temp_dir_for_doc(self, doc_name):
+        with self.lock:
+            return self.doc_temp_dir.get(doc_name)
+
+    def to_dict(self):
+        """Convert state to dictionary for JSON serialization"""
+        with self.lock:
+            return {
+                "completed_docs": list(self.completed_docs),
+                "completed_samples": [{"doc": d, "sample": s} for d, s in self.completed_samples],
+                "skipped": self.skipped,
+                "doc_user_map": self.doc_user_map,
+                "doc_temp_dir": self.doc_temp_dir,
+            }
+
+    def from_dict(self, data):
+        """Load state from dictionary"""
+        with self.lock:
+            self.completed_docs = set(data.get("completed_docs", []))
+            self.completed_samples = set()
+            for item in data.get("completed_samples", []):
+                self.completed_samples.add((item["doc"], item["sample"]))
+            self.skipped = data.get("skipped", 0)
+            self.doc_user_map = data.get("doc_user_map", {})
+            self.doc_temp_dir = data.get("doc_temp_dir", {})
+
+
+# Initialize global state
+state = ProcessingState()
+
+
+# Graceful shutdown handler
+def graceful_shutdown(signum, frame):
+    """Handle interrupt signals and exit gracefully"""
+    print("\n🛑 Received interrupt signal. Saving progress and exiting...")
+    save_test_results(args.test_results_path, test_results)
+    save_doc_processing_state(args.doc_state_path, state.to_dict())
+    sys.exit(1)
+
+
+# Register signal handlers
+signal.signal(signal.SIGINT, graceful_shutdown)
+signal.signal(signal.SIGTERM, graceful_shutdown)
+
+
+def import_document(doc_path):
+    """Import a single document and return user_name and temp_dir"""
+    try:
+        user_name = str(uuid.uuid4())
+        config = {
+            "user_id": user_name,
+            "chat_model": {
+                "backend": "openai",
+                "config": openapi_config,
+            },
+            "mem_reader": {
+                "backend": "simple_struct",
+                "config": {
+                    "llm": {"backend": "openai", "config": openapi_config},
+                    "embedder": {
+                        "backend": "universal_api",
+                        "config": {
+                            "provider": "openai",
+                            "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
+                            "model_name_or_path": "text-embedding-3-large",
+                            "base_url": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
+                        },
+                    },
+                    "chunker": {
+                        "backend": "sentence",
+                        "config": {
+                            "tokenizer_or_token_counter": "gpt2",
+                            "chunk_size": 512,
+                            "chunk_overlap": 128,
+                            "min_sentences_per_chunk": 1,
+                        },
                     },
                 },
-                "chunker": {
-                    "backend": "sentence",
+            },
+            "max_turns_window": 20,
+            "top_k": 5,
+            "enable_textual_memory": True,
+            "enable_activation_memory": False,
+            "enable_parametric_memory": False,
+        }
+        mos_config = MOSConfig(**config)
+        mos = MOS(mos_config)
+
+        mem_cube_config = GeneralMemCubeConfig.model_validate(
+            {
+                "user_id": user_name,
+                "cube_id": user_name,
+                "text_mem": {
+                    "backend": "tree_text",
                     "config": {
-                        "tokenizer_or_token_counter": "gpt2",
-                        "chunk_size": 512,
-                        "chunk_overlap": 128,
-                        "min_sentences_per_chunk": 1,
+                        "extractor_llm": {"backend": "openai", "config": openapi_config},
+                        "dispatcher_llm": {"backend": "openai", "config": openapi_config},
+                        "graph_db": {
+                            "backend": "neo4j",
+                            "config": {
+                                "uri": neo4j_uri,
+                                "user": "neo4j",
+                                "password": "iaarlichunyu",
+                                "db_name": db_name + user_name[:8],
+                                "auto_create": True,
+                            },
+                        },
+                        "embedder": {
+                            "backend": "universal_api",
+                            "config": {
+                                "provider": "openai",
+                                "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
+                                "model_name_or_path": "text-embedding-3-large",
+                                "base_url": os.getenv(
+                                    "OPENAI_API_BASE", "https://api.openai.com/v1"
+                                ),
+                            },
+                        },
+                        "reorganize": True,
                     },
                 },
-            },
-        },
-        "max_turns_window": 20,
-        "top_k": 5,
-        "enable_textual_memory": True,
-        "enable_activation_memory": False,
-        "enable_parametric_memory": False,
-    }
-    mos_config = MOSConfig(**config)
-    mos = MOS(mos_config)
-
-    # Create MemCube configuration with fixed database name
-    mem_cube_config = GeneralMemCubeConfig.model_validate(
-        {
+                "act_mem": {},
+                "para_mem": {},
+            }
+        )
+        mem_cube = GeneralMemCube(mem_cube_config)
+        temp_dir = f"/tmp/{user_name}"
+        os.makedirs(temp_dir, exist_ok=True)
+        mem_cube.dump(temp_dir)
+        mos.register_mem_cube(temp_dir, mem_cube_id=user_name)
+        mos.add(doc_path=[doc_path])
+
+        # Save mapping
+        doc_name = os.path.basename(doc_path)
+        state.add_doc_user_mapping(doc_name, user_name, temp_dir)
+
+        return user_name, temp_dir
+    except Exception as e:
+        print(f"❌ Failed to import document {os.path.basename(doc_path)}: {e!s}")
+        return None, None
+
+
+def create_mos_for_doc(user_name, temp_dir):
+    """Create MOS instance for a document using saved user_name and temp_dir"""
+    try:
+        config = {
             "user_id": user_name,
-            "cube_id": user_name,
-            "text_mem": {
-                "backend": "tree_text",
+            "chat_model": {
+                "backend": "openai",
+                "config": openapi_config,
+            },
+            "mem_reader": {
+                "backend": "simple_struct",
                 "config": {
-                    "extractor_llm": {
-                        "backend": "openai",
-                        "config": openapi_config,
-                    },
-                    "dispatcher_llm": {
-                        "backend": "openai",
-                        "config": openapi_config,
-                    },
-                    "graph_db": {
-                        "backend": "neo4j",
-                        "config": {
-                            "uri": neo4j_uri,
-                            "user": "neo4j",
-                            "password": "iaarlichunyu",
-                            "db_name": "mm-long-bench-shared",
-                            "auto_create": True,
-                        },
-                    },
+                    "llm": {"backend": "openai", "config": openapi_config},
                     "embedder": {
                         "backend": "universal_api",
                         "config": {
@@ -110,24 +243,399 @@ def process_document(doc_path):
                             "base_url": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
                         },
                     },
-                    "reorganize": True,
+                    "chunker": {
+                        "backend": "sentence",
+                        "config": {
+                            "tokenizer_or_token_counter": "gpt2",
+                            "chunk_size": 512,
+                            "chunk_overlap": 128,
+                            "min_sentences_per_chunk": 1,
+                        },
+                    },
                 },
             },
-            "act_mem": {},
-            "para_mem": {},
-        },
-    )
-    mem_cube = GeneralMemCube(mem_cube_config)
+            "max_turns_window": 20,
+            "top_k": 5,
+            "enable_textual_memory": True,
+            "enable_activation_memory": False,
+            "enable_parametric_memory": False,
+        }
+        mos_config = MOSConfig(**config)
+        mos = MOS(mos_config)
+        mos.register_mem_cube(temp_dir, mem_cube_id=user_name)
+        return mos
+    except Exception as e:
+        print(f"❌ Failed to create MOS for user {user_name}: {e!s}")
+        return None
+
+
+# Improved answer extraction with better error handling
+def extract_answer_safe(question, response, prompt):
+    """Robust answer extraction with fallback handling"""
+    try:
+        return extract_answer(question, response, prompt)
+    except Exception as e:
+        print(f"⚠️ Answer extraction failed: {e!s}")
+        # Fallback: return the entire response if extraction fails
+        return response
+
+
+def process_sample(sample, mos, prompt, max_try, doc_file):
+    """Process a single sample with retry and evaluation"""
+    if sample["evidence_sources"] != "['Pure-text (Plain-text)']":
+        state.increment_skipped()
+        return None
+
+    doc_id = os.path.basename(doc_file)
+    sample_id = sample.get("question_id", hash(sample["question"]))
+
+    # Skip already completed samples
+    if state.is_sample_completed(doc_id, sample_id):
+        state.increment_skipped()
+        return None
+
+    try_cnt = 0
+    response = ""
+    while try_cnt <= max_try:
+        try:
+            mos.clear_messages()
+            response = mos.chat(sample["question"])
+            break
+        except Exception as e:
+            try_cnt += 1
+            response = f"Error: {e!s}"
+            if try_cnt > max_try:
+                break
+            # Exponential backoff
+            sleep_time = min(2**try_cnt, 30)  # Cap at 30 seconds
+            print(f"⚠️ Retrying sample {sample_id} (attempt {try_cnt}/{max_try}) in {sleep_time}s")
+            time.sleep(sleep_time)
+
+    sample["response"] = response
+    sample["doc_file"] = doc_file  # Track which doc it came from
+
+    if "Error:" not in response:
+        extracted_res = extract_answer_safe(sample["question"], response, prompt)
+        sample["extracted_res"] = extracted_res
+        try:
+            # Handle different extraction formats
+            if "Extracted answer:" in extracted_res:
+                pred_ans = (
+                    extracted_res.split("Answer format:")[0].split("Extracted answer:")[1].strip()
+                )
+            else:
+                pred_ans = extracted_res.strip()
+
+            score = eval_score(sample["answer"], pred_ans, sample["answer_format"])
+            sample["pred"] = pred_ans
+            sample["score"] = score
+        except Exception as e:
+            print(f"⚠️ Evaluation failed for sample {sample_id}: {e!s}")
+            sample["pred"] = ""
+            sample["score"] = 0
+    else:
+        sample["pred"] = ""
+        sample["score"] = 0
+
+    # Print detailed results
+    print("\n" + "-" * 80)
+    print(f"📄 Document: {doc_file}")
+    print(f"📌 Sample ID: {sample_id}")
+    print(f"❓ Question: {sample['question']}")
+    print(f"💬 Response: {response[:500]}{'...' if len(response) > 500 else ''}")
+    print(f"🎯 Ground Truth: {sample['answer']}")
+    print(f"📦 Extracted: {sample.get('pred', '')}")
+    print(f"⭐ Score: {sample.get('score', 0)}")
+    print("-" * 80 + "\n")
+
+    # Mark sample as completed
+    state.mark_sample_completed(doc_id, sample_id)
+    return sample
+
+
+def import_document_task(doc_file, args):
+    """Task for importing a single document"""
+    doc_path = os.path.join(args.document_path, doc_file)
+    user_name, temp_dir = import_document(doc_path)
+    return doc_file, user_name, temp_dir
+
+
+def import_all_documents(args):
+    """Import all documents concurrently and save user_name mappings"""
+    print("\n" + "=" * 80)
+    print("🚀 Starting document import phase")
+    print("=" * 80)
+
+    # Find all document files
+    doc_files = [
+        f
+        for f in os.listdir(args.document_path)
+        if os.path.isfile(os.path.join(args.document_path, f))
+    ]
 
-    # Save and register MemCube
-    temp_dir = f"/tmp/{user_name}"
-    os.makedirs(temp_dir, exist_ok=True)
-    mem_cube.dump(temp_dir)
-    mos.register_mem_cube(temp_dir, mem_cube_id=user_name)
+    # Load existing mappings if available
+    if os.path.exists(args.doc_state_path):
+        state_data = load_doc_processing_state(args.doc_state_path)
+        state.from_dict(state_data)
+        print(
+            f"🔍 Loaded existing document processing state for {len(state.doc_user_map)} documents"
+        )
 
-    # Add the current document
-    mos.add(doc_path=[doc_path])
-    return mos
+    # Import new documents
+    new_docs = [doc for doc in doc_files[:20] if doc not in state.doc_user_map]
+
+    if not new_docs:
+        print("✅ All documents already imported")
+        return
+
+    print(f"📂 Importing {len(new_docs)} new documents")
+
+    # Use ThreadPoolExecutor for concurrent imports
+    with ThreadPoolExecutor(max_workers=8) as executor:
+        futures = {
+            executor.submit(import_document_task, doc_file, args): doc_file for doc_file in new_docs
+        }
+
+        for future in tqdm(as_completed(futures), total=len(futures), desc="Importing documents"):
+            doc_file = futures[future]
+            print(f"📂 Importing document：{doc_file}")
+            try:
+                doc_file, user_name, temp_dir = future.result()
+                if user_name and temp_dir:
+                    print(f"✅ Imported {doc_file} with user {user_name}")
+                    # Save state after each document
+                    save_doc_processing_state(args.doc_state_path, state.to_dict())
+                else:
+                    print(f"❌ Failed to import {doc_file}")
+            except Exception as e:
+                print(f"❌ Error importing {doc_file}: {e!s}")
+
+    print("✅ Document import completed")
+
+
+def process_document_with_samples(doc_file, args, samples, prompt):
+    """Process one document and all its samples concurrently"""
+    # Skip already completed documents
+    if state.is_doc_completed(doc_file):
+        print(f"⏭️ Skipping already processed document: {doc_file}")
+        return []
+
+    print(f"🔄 Processing document: {doc_file}")
+
+    # Get user_name and temp_dir for this document
+    user_name = state.get_user_name_for_doc(doc_file)
+    temp_dir = state.get_temp_dir_for_doc(doc_file)
+    if not user_name or not temp_dir:
+        print(f"❌ No user mapping found for {doc_file}. Skipping.")
+        return []
+
+    # Create MOS instance for this document
+    mos = create_mos_for_doc(user_name, temp_dir)
+    if not mos:
+        print(f"❌ Failed to create MOS for {doc_file}. Skipping.")
+        return []
+
+    doc_id = os.path.basename(doc_file)
+    doc_samples = [s for s in samples if s.get("doc_id") == doc_id]
+
+    results = []
+    # Process samples concurrently
+    with ThreadPoolExecutor(max_workers=args.max_concurrent_samples) as executor:
+        futures = [
+            executor.submit(process_sample, sample, mos, prompt, args.max_try, doc_file)
+            for sample in doc_samples
+            if not state.is_sample_completed(
+                doc_file, sample.get("question_id", hash(sample["question"]))
+            )
+        ]
+
+        for future in tqdm(
+            as_completed(futures), total=len(futures), desc=f"Processing samples in {doc_file}"
+        ):
+            try:
+                result = future.result()
+                if result:
+                    results.append(result)
+            except Exception as e:
+                print(f"⚠️ Error processing sample: {e!s}")
+
+    # Mark document as completed
+    state.mark_doc_completed(doc_file)
+    return results
+
+
+def load_test_results(output_path):
+    """Load existing test results from output file"""
+    test_results = []
+    if os.path.exists(output_path):
+        print(f"🔍 Found existing test results file: {output_path}")
+        try:
+            with open(output_path) as f:
+                test_results = json.load(f)
+
+            # Rebuild progress state from loaded samples
+            for sample in test_results:
+                if "doc_file" in sample:
+                    doc_file = sample["doc_file"]
+                    sample_id = sample.get("question_id", hash(sample["question"]))
+                    state.mark_doc_completed(doc_file)
+                    state.mark_sample_completed(doc_file, sample_id)
+
+            processed_docs = len({s["doc_file"] for s in test_results if "doc_file" in s})
+            print(f"📊 Loaded existing test results: {len(test_results)} samples")
+            print(f"📂 {processed_docs} documents already processed")
+        except Exception as e:
+            print(f"⚠️ Failed to load test results file: {e!s}")
+            # Clear the broken file
+            os.remove(output_path)
+            print("🚮 Removed corrupted test results file")
+    return test_results
+
+
+def save_test_results(output_path, test_results):
+    """Save current test results to output file"""
+    print(f"💾 Auto-saving test results to {output_path}")
+    try:
+        # Ensure directory exists
+        os.makedirs(os.path.dirname(output_path), exist_ok=True)
+
+        # Save with pretty formatting
+        with open(output_path, "w") as f:
+            json.dump(test_results, f, indent=2)
+        return True
+    except Exception as e:
+        print(f"❌ Failed to save test results: {e!s}")
+        return False
+
+
+def save_doc_processing_state(map_path, state_data):
+    """Save document processing state to JSON file"""
+    try:
+        # Ensure directory exists
+        os.makedirs(os.path.dirname(map_path), exist_ok=True)
+
+        # Save as JSON
+        with open(map_path, "w") as f:
+            json.dump(state_data, f, indent=2)
+        return True
+    except Exception as e:
+        print(f"❌ Failed to save document processing state: {e!s}")
+        return False
+
+
+def load_doc_processing_state(map_path):
+    """Load document processing state from JSON file"""
+    if os.path.exists(map_path):
+        try:
+            with open(map_path) as f:
+                return json.load(f)
+        except Exception as e:
+            print(f"⚠️ Failed to load document processing state: {e!s}")
+            return {}
+    return {}
+
+
+def main(args):
+    global test_results
+    # Load existing test results
+    test_results = load_test_results(args.test_results_path)
+
+    # Load document processing state
+    if os.path.exists(args.doc_state_path):
+        state_data = load_doc_processing_state(args.doc_state_path)
+        state.from_dict(state_data)
+        print(f"🔍 Loaded document processing state for {len(state.doc_user_map)} documents")
+
+    # Import all documents first
+    import_all_documents(args)
+
+    with open(args.extractor_prompt_path) as f:
+        prompt = f.read()
+
+    # Load all input samples (original data)
+    with open(args.input_path) as f:
+        original_samples = json.load(f)
+
+    # Find all document files
+    doc_files = [
+        f
+        for f in os.listdir(args.document_path)
+        if os.path.isfile(os.path.join(args.document_path, f))
+    ]
+
+    print("\n" + "=" * 80)
+    print(f"🚀 Starting sample processing with {len(doc_files)} documents")
+    print(f"📂 {len(original_samples)} total samples in dataset")
+    print(f"⏭️ {state.get_skipped()} samples skipped due to filtering")
+    print("=" * 80 + "\n")
+
+    # Process documents concurrently
+    completed = 0
+    with ThreadPoolExecutor(max_workers=min(args.max_concurrent_docs, len(doc_files))) as executor:
+        future_to_doc = {
+            executor.submit(
+                process_document_with_samples, doc_file, args, original_samples, prompt
+            ): doc_file
+            for doc_file in doc_files
+        }
+
+        for future in tqdm(
+            as_completed(future_to_doc), total=len(future_to_doc), desc="Processing documents"
+        ):
+            doc_file = future_to_doc[future]
+            try:
+                doc_results = future.result()
+                if doc_results:
+                    test_results.extend(doc_results)
+                    completed += 1
+
+                    # Save test results after each document
+                    save_test_results(args.test_results_path, test_results)
+
+                    # Save document processing state
+                    save_doc_processing_state(args.doc_state_path, state.to_dict())
+
+                    # Calculate metrics
+                    processed_samples = [s for s in test_results if "score" in s]
+                    if processed_samples:
+                        acc, f1 = eval_acc_and_f1(processed_samples)
+                        print("\n" + "=" * 80)
+                        print(f"📈 Cumulative Metrics After Document: {doc_file}")
+                        print(f"✅ Processed: {completed}/{len(doc_files)} documents")
+                        print(
+                            f"🧪 Processed: {len(processed_samples)}/{len(original_samples)} samples"
+                        )
+                        print(f"🎯 Accuracy: {acc:.4f}")
+                        print(f"📊 F1 Score: {f1:.4f}")
+                        print("=" * 80 + "\n")
+            except Exception as e:
+                print(f"⛔ Unhandled error processing {doc_file}: {e!s}")
+                # Try to save current progress on error
+                save_test_results(args.test_results_path, test_results)
+                save_doc_processing_state(args.doc_state_path, state.to_dict())
+
+    # Final evaluation
+    if test_results:
+        print("\n" + "=" * 80)
+        print("🏆 FINAL RESULTS")
+        print("=" * 80)
+
+        processed_samples = [s for s in test_results if "score" in s]
+        acc, f1 = eval_acc_and_f1(processed_samples)
+
+        print(f"✅ Completed: {completed}/{len(doc_files)} documents")
+        print(f"🧪 Completed: {len(processed_samples)}/{len(original_samples)} samples")
+        print(f"⏭️ Skipped: {state.get_skipped()} samples (filtered)")
+        print(f"🎯 Final Accuracy: {acc:.4f}")
+        print(f"📊 Final F1 Score: {f1:.4f}")
+        print("=" * 80)
+
+        # Generate detailed report
+        report_path = re.sub("\.json$", "_report.txt", args.test_results_path)
+        show_results(test_results, show_path=report_path)
+        print(f"📝 Detailed report saved to: {report_path}")
+    else:
+        print("⚠️ No results to report. Processing may have failed.")
 
 
 if __name__ == "__main__":
@@ -149,92 +657,38 @@ def process_document(doc_path):
         type=str,
         default="evaluation/scripts/mmlongbench/eval/prompt_for_answer_extraction.md",
     )
+    parser.add_argument(
+        "--doc_state_path",
+        type=str,
+        default="evaluation/data/mmlongbench/doc_processing_state.json",
+    )
+    parser.add_argument(
+        "--test_results_path",
+        type=str,
+        default="evaluation/data/mmlongbench/test_results.json",
+    )
+    parser.add_argument(
+        "--max_concurrent_docs",
+        type=int,
+        default=5,
+        help="Maximum number of documents to process concurrently",
+    )
+    parser.add_argument(
+        "--max_concurrent_samples",
+        type=int,
+        default=4,
+        help="Maximum number of samples to process concurrently per document",
+    )
     args = parser.parse_args()
-    args.output_path = "evaluation/data/mmlongbench/res.json"
-
-    with open(args.extractor_prompt_path) as f:
-        prompt = f.read()
-
-    # Load input samples
-    with open(args.input_path) as f:
-        samples = json.load(f)
-
-    # Get list of document files to process
-    doc_files = [
-        f
-        for f in os.listdir(args.document_path)
-        if os.path.isfile(os.path.join(args.document_path, f))
-    ]
-
-    # Process each document separately
-    res_samples = []
-    for doc_file in tqdm(doc_files, desc="Processing documents"):
-        print(f"Processing {doc_file}")
-        doc_path = os.path.join(args.document_path, doc_file)
-
-        # Process current document and create MOS
-        mos = process_document(doc_path)
-
-        # Filter samples for this document
-        doc_id = os.path.basename(doc_path)
-        doc_samples = [s for s in samples if s["doc_id"] == doc_id]
-
-        # Process samples for this document
-        for sample in doc_samples:
-            if sample["evidence_sources"] != "['Pure-text (Plain-text)']":
-                continue
-
-            try_cnt = 0
-            while try_cnt <= args.max_try:
-                try:
-                    mos.clear_messages()
-                    response = mos.chat(sample["question"])
-                    break
-                except Exception as e:
-                    try_cnt += 1
-                    response = f"Error: {e!s}"
-                    if try_cnt > args.max_try:
-                        break
-
-            sample["response"] = response
-            # Extract and evaluate response
-            if "Error:" not in response:
-                extracted_res = extract_answer(sample["question"], response, prompt)
-                sample["extracted_res"] = extracted_res
-                try:
-                    pred_ans = (
-                        extracted_res.split("Answer format:")[0]
-                        .split("Extracted answer:")[1]
-                        .strip()
-                    )
-                    score = eval_score(sample["answer"], pred_ans, sample["answer_format"])
-                except Exception:
-                    pred_ans = ""
-                    score = 0
-                sample["pred"] = pred_ans
-                sample["score"] = score
-            else:
-                sample["pred"] = ""
-                sample["score"] = 0
-
-            res_samples.append(sample)
-            print("--------------------------------------")
-            print("Question: {}".format(sample["question"]))
-            print("Response: {}".format(sample["response"]))
-            print(
-                "Gt: {}\tPred: {}\tScore: {}".format(
-                    sample["answer"], sample["pred"], sample["score"]
-                )
-            )
-
-        acc, f1 = eval_acc_and_f1(res_samples)
-        print(f"\nMetrics - Accuracy: {acc:.4f}, F1 Score: {f1:.4f}")
 
-        # Save progress after each sample
-        with open(args.output_path, "w") as f:
-            json.dump(res_samples, f)
+    # Initialize test results list
+    test_results = []
 
-    # Calculate and display final results
-    acc, f1 = eval_acc_and_f1(res_samples)
-    print(f"\nFinal Metrics - Accuracy: {acc:.4f}, F1 Score: {f1:.4f}")
-    show_results(res_samples, show_path=re.sub("\.json$", ".txt", args.output_path))
+    try:
+        main(args)
+    except Exception as e:
+        print(f"⛔ Critical error: {e!s}")
+        print("💾 Attempting to save progress...")
+        save_test_results(args.test_results_path, test_results)
+        save_doc_processing_state(args.doc_state_path, state.to_dict())
+        sys.exit(1)

From 90464d6e82f1e189a0f3f2d59d1c3352305eb2be Mon Sep 17 00:00:00 2001
From: stx <31013941@qq.com>
Date: Thu, 28 Aug 2025 10:49:52 +0800
Subject: [PATCH 20/34] =?UTF-8?q?feat=EF=BC=9Aadd=20MMLongbench-doc=20eval?=
 =?UTF-8?q?uation?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 evaluation/scripts/mmlongbench/run_api.py | 305 ++++++++--------------
 1 file changed, 106 insertions(+), 199 deletions(-)

diff --git a/evaluation/scripts/mmlongbench/run_api.py b/evaluation/scripts/mmlongbench/run_api.py
index a7c91f0c0..4aa6e60d7 100644
--- a/evaluation/scripts/mmlongbench/run_api.py
+++ b/evaluation/scripts/mmlongbench/run_api.py
@@ -4,12 +4,9 @@
 import re
 import signal
 import sys
-import threading
 import time
 import uuid
 
-from concurrent.futures import ThreadPoolExecutor, as_completed
-
 from dotenv import load_dotenv
 from eval.eval_score import eval_acc_and_f1, eval_score, show_results
 from eval.extract_answer import extract_answer
@@ -35,79 +32,12 @@
 neo4j_uri = os.getenv("NEO4J_URI", "bolt://47.117.41.207:7687")
 db_name = "mm-long-bench-single-"
 
-
 # Global state tracking
-class ProcessingState:
-    def __init__(self):
-        self.lock = threading.Lock()
-        self.completed_docs = set()
-        self.completed_samples = set()
-        self.skipped = 0
-        self.doc_user_map = {}  # Map document name to user_name
-        self.doc_temp_dir = {}  # Map document name to temp directory
-
-    def mark_doc_completed(self, doc_id):
-        with self.lock:
-            self.completed_docs.add(doc_id)
-
-    def is_doc_completed(self, doc_id):
-        with self.lock:
-            return doc_id in self.completed_docs
-
-    def mark_sample_completed(self, doc_id, sample_id):
-        with self.lock:
-            self.completed_samples.add((doc_id, sample_id))
-
-    def is_sample_completed(self, doc_id, sample_id):
-        with self.lock:
-            return (doc_id, sample_id) in self.completed_samples
-
-    def increment_skipped(self):
-        with self.lock:
-            self.skipped += 1
-
-    def get_skipped(self):
-        with self.lock:
-            return self.skipped
-
-    def add_doc_user_mapping(self, doc_name, user_name, temp_dir):
-        with self.lock:
-            self.doc_user_map[doc_name] = user_name
-            self.doc_temp_dir[doc_name] = temp_dir
-
-    def get_user_name_for_doc(self, doc_name):
-        with self.lock:
-            return self.doc_user_map.get(doc_name)
-
-    def get_temp_dir_for_doc(self, doc_name):
-        with self.lock:
-            return self.doc_temp_dir.get(doc_name)
-
-    def to_dict(self):
-        """Convert state to dictionary for JSON serialization"""
-        with self.lock:
-            return {
-                "completed_docs": list(self.completed_docs),
-                "completed_samples": [{"doc": d, "sample": s} for d, s in self.completed_samples],
-                "skipped": self.skipped,
-                "doc_user_map": self.doc_user_map,
-                "doc_temp_dir": self.doc_temp_dir,
-            }
-
-    def from_dict(self, data):
-        """Load state from dictionary"""
-        with self.lock:
-            self.completed_docs = set(data.get("completed_docs", []))
-            self.completed_samples = set()
-            for item in data.get("completed_samples", []):
-                self.completed_samples.add((item["doc"], item["sample"]))
-            self.skipped = data.get("skipped", 0)
-            self.doc_user_map = data.get("doc_user_map", {})
-            self.doc_temp_dir = data.get("doc_temp_dir", {})
-
-
-# Initialize global state
-state = ProcessingState()
+completed_docs = set()
+completed_samples = set()
+skipped = 0
+doc_user_map = {}  # Map document name to user_name
+doc_temp_dir = {}  # Map document name to temp directory
 
 
 # Graceful shutdown handler
@@ -115,7 +45,7 @@ def graceful_shutdown(signum, frame):
     """Handle interrupt signals and exit gracefully"""
     print("\n🛑 Received interrupt signal. Saving progress and exiting...")
     save_test_results(args.test_results_path, test_results)
-    save_doc_processing_state(args.doc_state_path, state.to_dict())
+    save_doc_processing_state(args.doc_state_path, get_state_dict())
     sys.exit(1)
 
 
@@ -124,6 +54,29 @@ def graceful_shutdown(signum, frame):
 signal.signal(signal.SIGTERM, graceful_shutdown)
 
 
+def get_state_dict():
+    """Convert state to dictionary for JSON serialization"""
+    return {
+        "completed_docs": list(completed_docs),
+        "completed_samples": [{"doc": d, "sample": s} for d, s in completed_samples],
+        "skipped": skipped,
+        "doc_user_map": doc_user_map,
+        "doc_temp_dir": doc_temp_dir,
+    }
+
+
+def load_state_dict(data):
+    """Load state from dictionary"""
+    global completed_docs, completed_samples, skipped, doc_user_map, doc_temp_dir
+    completed_docs = set(data.get("completed_docs", []))
+    completed_samples = set()
+    for item in data.get("completed_samples", []):
+        completed_samples.add((item["doc"], item["sample"]))
+    skipped = data.get("skipped", 0)
+    doc_user_map = data.get("doc_user_map", {})
+    doc_temp_dir = data.get("doc_temp_dir", {})
+
+
 def import_document(doc_path):
     """Import a single document and return user_name and temp_dir"""
     try:
@@ -213,7 +166,8 @@ def import_document(doc_path):
 
         # Save mapping
         doc_name = os.path.basename(doc_path)
-        state.add_doc_user_mapping(doc_name, user_name, temp_dir)
+        doc_user_map[doc_name] = user_name
+        doc_temp_dir[doc_name] = temp_dir
 
         return user_name, temp_dir
     except Exception as e:
@@ -282,16 +236,17 @@ def extract_answer_safe(question, response, prompt):
 
 def process_sample(sample, mos, prompt, max_try, doc_file):
     """Process a single sample with retry and evaluation"""
+    global skipped
     if sample["evidence_sources"] != "['Pure-text (Plain-text)']":
-        state.increment_skipped()
+        skipped += 1
         return None
 
     doc_id = os.path.basename(doc_file)
     sample_id = sample.get("question_id", hash(sample["question"]))
 
     # Skip already completed samples
-    if state.is_sample_completed(doc_id, sample_id):
-        state.increment_skipped()
+    if (doc_id, sample_id) in completed_samples:
+        skipped += 1
         return None
 
     try_cnt = 0
@@ -349,19 +304,12 @@ def process_sample(sample, mos, prompt, max_try, doc_file):
     print("-" * 80 + "\n")
 
     # Mark sample as completed
-    state.mark_sample_completed(doc_id, sample_id)
+    completed_samples.add((doc_id, sample_id))
     return sample
 
 
-def import_document_task(doc_file, args):
-    """Task for importing a single document"""
-    doc_path = os.path.join(args.document_path, doc_file)
-    user_name, temp_dir = import_document(doc_path)
-    return doc_file, user_name, temp_dir
-
-
 def import_all_documents(args):
-    """Import all documents concurrently and save user_name mappings"""
+    """Import all documents sequentially"""
     print("\n" + "=" * 80)
     print("🚀 Starting document import phase")
     print("=" * 80)
@@ -376,13 +324,11 @@ def import_all_documents(args):
     # Load existing mappings if available
     if os.path.exists(args.doc_state_path):
         state_data = load_doc_processing_state(args.doc_state_path)
-        state.from_dict(state_data)
-        print(
-            f"🔍 Loaded existing document processing state for {len(state.doc_user_map)} documents"
-        )
+        load_state_dict(state_data)
+        print(f"🔍 Loaded existing document processing state for {len(doc_user_map)} documents")
 
     # Import new documents
-    new_docs = [doc for doc in doc_files[:20] if doc not in state.doc_user_map]
+    new_docs = [doc for doc in doc_files if doc not in doc_user_map]
 
     if not new_docs:
         print("✅ All documents already imported")
@@ -390,41 +336,33 @@ def import_all_documents(args):
 
     print(f"📂 Importing {len(new_docs)} new documents")
 
-    # Use ThreadPoolExecutor for concurrent imports
-    with ThreadPoolExecutor(max_workers=8) as executor:
-        futures = {
-            executor.submit(import_document_task, doc_file, args): doc_file for doc_file in new_docs
-        }
-
-        for future in tqdm(as_completed(futures), total=len(futures), desc="Importing documents"):
-            doc_file = futures[future]
-            print(f"📂 Importing document：{doc_file}")
-            try:
-                doc_file, user_name, temp_dir = future.result()
-                if user_name and temp_dir:
-                    print(f"✅ Imported {doc_file} with user {user_name}")
-                    # Save state after each document
-                    save_doc_processing_state(args.doc_state_path, state.to_dict())
-                else:
-                    print(f"❌ Failed to import {doc_file}")
-            except Exception as e:
-                print(f"❌ Error importing {doc_file}: {e!s}")
+    for doc_file in tqdm(new_docs, desc="Importing documents"):
+        print(f"📂 Importing document：{doc_file}")
+        doc_path = os.path.join(args.document_path, doc_file)
+        user_name, temp_dir = import_document(doc_path)
+        if user_name and temp_dir:
+            print(f"✅ Imported {doc_file} with user {user_name}")
+            # Save state after each document
+            save_doc_processing_state(args.doc_state_path, get_state_dict())
+        else:
+            print(f"❌ Failed to import {doc_file}")
 
     print("✅ Document import completed")
 
 
 def process_document_with_samples(doc_file, args, samples, prompt):
-    """Process one document and all its samples concurrently"""
+    """Process one document and all its samples sequentially"""
+    global skipped
     # Skip already completed documents
-    if state.is_doc_completed(doc_file):
+    if doc_file in completed_docs:
         print(f"⏭️ Skipping already processed document: {doc_file}")
         return []
 
     print(f"🔄 Processing document: {doc_file}")
 
     # Get user_name and temp_dir for this document
-    user_name = state.get_user_name_for_doc(doc_file)
-    temp_dir = state.get_temp_dir_for_doc(doc_file)
+    user_name = doc_user_map.get(doc_file)
+    temp_dir = doc_temp_dir.get(doc_file)
     if not user_name or not temp_dir:
         print(f"❌ No user mapping found for {doc_file}. Skipping.")
         return []
@@ -439,28 +377,21 @@ def process_document_with_samples(doc_file, args, samples, prompt):
     doc_samples = [s for s in samples if s.get("doc_id") == doc_id]
 
     results = []
-    # Process samples concurrently
-    with ThreadPoolExecutor(max_workers=args.max_concurrent_samples) as executor:
-        futures = [
-            executor.submit(process_sample, sample, mos, prompt, args.max_try, doc_file)
-            for sample in doc_samples
-            if not state.is_sample_completed(
-                doc_file, sample.get("question_id", hash(sample["question"]))
-            )
-        ]
-
-        for future in tqdm(
-            as_completed(futures), total=len(futures), desc=f"Processing samples in {doc_file}"
-        ):
-            try:
-                result = future.result()
-                if result:
-                    results.append(result)
-            except Exception as e:
-                print(f"⚠️ Error processing sample: {e!s}")
+    for sample in tqdm(doc_samples, desc=f"Processing samples in {doc_file}"):
+        sample_id = sample.get("question_id", hash(sample["question"]))
+        if (doc_file, sample_id) in completed_samples:
+            skipped += 1
+            continue
+
+        try:
+            result = process_sample(sample, mos, prompt, args.max_try, doc_file)
+            if result:
+                results.append(result)
+        except Exception as e:
+            print(f"⚠️ Error processing sample: {e!s}")
 
     # Mark document as completed
-    state.mark_doc_completed(doc_file)
+    completed_docs.add(doc_file)
     return results
 
 
@@ -478,8 +409,8 @@ def load_test_results(output_path):
                 if "doc_file" in sample:
                     doc_file = sample["doc_file"]
                     sample_id = sample.get("question_id", hash(sample["question"]))
-                    state.mark_doc_completed(doc_file)
-                    state.mark_sample_completed(doc_file, sample_id)
+                    completed_docs.add(doc_file)
+                    completed_samples.add((doc_file, sample_id))
 
             processed_docs = len({s["doc_file"] for s in test_results if "doc_file" in s})
             print(f"📊 Loaded existing test results: {len(test_results)} samples")
@@ -537,14 +468,12 @@ def load_doc_processing_state(map_path):
 
 def main(args):
     global test_results
-    # Load existing test results
-    test_results = load_test_results(args.test_results_path)
 
     # Load document processing state
     if os.path.exists(args.doc_state_path):
         state_data = load_doc_processing_state(args.doc_state_path)
-        state.from_dict(state_data)
-        print(f"🔍 Loaded document processing state for {len(state.doc_user_map)} documents")
+        load_state_dict(state_data)
+        print(f"🔍 Loaded document processing state for {len(doc_user_map)} documents")
 
     # Import all documents first
     import_all_documents(args)
@@ -566,53 +495,43 @@ def main(args):
     print("\n" + "=" * 80)
     print(f"🚀 Starting sample processing with {len(doc_files)} documents")
     print(f"📂 {len(original_samples)} total samples in dataset")
-    print(f"⏭️ {state.get_skipped()} samples skipped due to filtering")
+    print(f"⏭️ {skipped} samples skipped due to filtering")
     print("=" * 80 + "\n")
 
-    # Process documents concurrently
+    # Load existing test results
+    test_results = load_test_results(args.test_results_path)
+
+    # Process documents sequentially
     completed = 0
-    with ThreadPoolExecutor(max_workers=min(args.max_concurrent_docs, len(doc_files))) as executor:
-        future_to_doc = {
-            executor.submit(
-                process_document_with_samples, doc_file, args, original_samples, prompt
-            ): doc_file
-            for doc_file in doc_files
-        }
+    for doc_file in tqdm(doc_files, desc="Processing documents"):
+        try:
+            doc_results = process_document_with_samples(doc_file, args, original_samples, prompt)
+            if doc_results:
+                test_results.extend(doc_results)
+                completed += 1
 
-        for future in tqdm(
-            as_completed(future_to_doc), total=len(future_to_doc), desc="Processing documents"
-        ):
-            doc_file = future_to_doc[future]
-            try:
-                doc_results = future.result()
-                if doc_results:
-                    test_results.extend(doc_results)
-                    completed += 1
-
-                    # Save test results after each document
-                    save_test_results(args.test_results_path, test_results)
-
-                    # Save document processing state
-                    save_doc_processing_state(args.doc_state_path, state.to_dict())
-
-                    # Calculate metrics
-                    processed_samples = [s for s in test_results if "score" in s]
-                    if processed_samples:
-                        acc, f1 = eval_acc_and_f1(processed_samples)
-                        print("\n" + "=" * 80)
-                        print(f"📈 Cumulative Metrics After Document: {doc_file}")
-                        print(f"✅ Processed: {completed}/{len(doc_files)} documents")
-                        print(
-                            f"🧪 Processed: {len(processed_samples)}/{len(original_samples)} samples"
-                        )
-                        print(f"🎯 Accuracy: {acc:.4f}")
-                        print(f"📊 F1 Score: {f1:.4f}")
-                        print("=" * 80 + "\n")
-            except Exception as e:
-                print(f"⛔ Unhandled error processing {doc_file}: {e!s}")
-                # Try to save current progress on error
+                # Save test results after each document
                 save_test_results(args.test_results_path, test_results)
-                save_doc_processing_state(args.doc_state_path, state.to_dict())
+
+                # Save document processing state
+                save_doc_processing_state(args.doc_state_path, get_state_dict())
+
+                # Calculate metrics
+                processed_samples = [s for s in test_results if "score" in s]
+                if processed_samples:
+                    acc, f1 = eval_acc_and_f1(processed_samples)
+                    print("\n" + "=" * 80)
+                    print(f"📈 Cumulative Metrics After Document: {doc_file}")
+                    print(f"✅ Processed: {completed}/{len(doc_files)} documents")
+                    print(f"🧪 Processed: {len(processed_samples)}/{len(original_samples)} samples")
+                    print(f"🎯 Accuracy: {acc:.4f}")
+                    print(f"📊 F1 Score: {f1:.4f}")
+                    print("=" * 80 + "\n")
+        except Exception as e:
+            print(f"⛔ Unhandled error processing {doc_file}: {e!s}")
+            # Try to save current progress on error
+            save_test_results(args.test_results_path, test_results)
+            save_doc_processing_state(args.doc_state_path, get_state_dict())
 
     # Final evaluation
     if test_results:
@@ -625,7 +544,7 @@ def main(args):
 
         print(f"✅ Completed: {completed}/{len(doc_files)} documents")
         print(f"🧪 Completed: {len(processed_samples)}/{len(original_samples)} samples")
-        print(f"⏭️ Skipped: {state.get_skipped()} samples (filtered)")
+        print(f"⏭️ Skipped: {skipped} samples (filtered)")
         print(f"🎯 Final Accuracy: {acc:.4f}")
         print(f"📊 Final F1 Score: {f1:.4f}")
         print("=" * 80)
@@ -667,18 +586,6 @@ def main(args):
         type=str,
         default="evaluation/data/mmlongbench/test_results.json",
     )
-    parser.add_argument(
-        "--max_concurrent_docs",
-        type=int,
-        default=5,
-        help="Maximum number of documents to process concurrently",
-    )
-    parser.add_argument(
-        "--max_concurrent_samples",
-        type=int,
-        default=4,
-        help="Maximum number of samples to process concurrently per document",
-    )
     args = parser.parse_args()
 
     # Initialize test results list
@@ -690,5 +597,5 @@ def main(args):
         print(f"⛔ Critical error: {e!s}")
         print("💾 Attempting to save progress...")
         save_test_results(args.test_results_path, test_results)
-        save_doc_processing_state(args.doc_state_path, state.to_dict())
+        save_doc_processing_state(args.doc_state_path, get_state_dict())
         sys.exit(1)

From c398fea4945bfcaa510b4129ac175559eb2cd0c9 Mon Sep 17 00:00:00 2001
From: stx <31013941@qq.com>
Date: Fri, 29 Aug 2025 14:58:43 +0800
Subject: [PATCH 21/34] =?UTF-8?q?feat=EF=BC=9Aadd=20MMLongbench-doc=20eval?=
 =?UTF-8?q?uation?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 evaluation/scripts/mmlongbench/run_api.py | 104 ++++++++++++++--------
 1 file changed, 69 insertions(+), 35 deletions(-)

diff --git a/evaluation/scripts/mmlongbench/run_api.py b/evaluation/scripts/mmlongbench/run_api.py
index 4aa6e60d7..1d554e839 100644
--- a/evaluation/scripts/mmlongbench/run_api.py
+++ b/evaluation/scripts/mmlongbench/run_api.py
@@ -4,9 +4,12 @@
 import re
 import signal
 import sys
+import threading
 import time
 import uuid
 
+from concurrent.futures import ThreadPoolExecutor, as_completed
+
 from dotenv import load_dotenv
 from eval.eval_score import eval_acc_and_f1, eval_score, show_results
 from eval.extract_answer import extract_answer
@@ -38,6 +41,8 @@
 skipped = 0
 doc_user_map = {}  # Map document name to user_name
 doc_temp_dir = {}  # Map document name to temp directory
+results_lock = threading.Lock()
+completed_counter = 0
 
 
 # Graceful shutdown handler
@@ -257,6 +262,7 @@ def process_sample(sample, mos, prompt, max_try, doc_file):
             response = mos.chat(sample["question"])
             break
         except Exception as e:
+            print(e)
             try_cnt += 1
             response = f"Error: {e!s}"
             if try_cnt > max_try:
@@ -309,7 +315,7 @@ def process_sample(sample, mos, prompt, max_try, doc_file):
 
 
 def import_all_documents(args):
-    """Import all documents sequentially"""
+    """Import all documents concurrently"""
     print("\n" + "=" * 80)
     print("🚀 Starting document import phase")
     print("=" * 80)
@@ -336,17 +342,24 @@ def import_all_documents(args):
 
     print(f"📂 Importing {len(new_docs)} new documents")
 
-    for doc_file in tqdm(new_docs, desc="Importing documents"):
-        print(f"📂 Importing document：{doc_file}")
+    def import_doc(doc_file):
+        """Import a single document and save state"""
         doc_path = os.path.join(args.document_path, doc_file)
         user_name, temp_dir = import_document(doc_path)
         if user_name and temp_dir:
             print(f"✅ Imported {doc_file} with user {user_name}")
-            # Save state after each document
+            # Save state after each document (safely in a thread-safe way)
             save_doc_processing_state(args.doc_state_path, get_state_dict())
         else:
             print(f"❌ Failed to import {doc_file}")
 
+    # Use ThreadPoolExecutor to process documents concurrently
+    with ThreadPoolExecutor(max_workers=8) as executor:
+        futures = {executor.submit(import_doc, doc): doc for doc in new_docs}
+
+        for future in tqdm(as_completed(futures), desc="Importing documents", total=len(new_docs)):
+            future.result()  # Wait for each document to finish and handle exceptions if needed
+
     print("✅ Document import completed")
 
 
@@ -466,6 +479,43 @@ def load_doc_processing_state(map_path):
     return {}
 
 
+def process_document_wrapper(doc_file, args, original_samples, prompt):
+    try:
+        doc_results = process_document_with_samples(doc_file, args, original_samples, prompt)
+        return {"success": True, "file": doc_file, "results": doc_results}
+    except Exception as e:
+        return {"success": False, "file": doc_file, "error": e}
+
+
+def update_shared_state(doc_data, args, original_samples, file_nums):
+    global completed_counter, test_results
+
+    with results_lock:
+        if doc_data["success"]:
+            if doc_data["results"]:
+                test_results.extend(doc_data["results"])
+
+            completed_counter += 1
+
+            save_test_results(args.test_results_path, test_results)
+            save_doc_processing_state(args.doc_state_path, get_state_dict())
+
+            processed_samples = [s for s in test_results if "score" in s]
+            if processed_samples:
+                acc, f1 = eval_acc_and_f1(processed_samples)
+                print("\n" + "=" * 80)
+                print(f"📈 Cumulative Metrics After Document: {doc_data['file']}")
+                print(f"✅ Processed: {completed_counter}/{file_nums} documents")
+                print(f"🧪 Processed: {len(processed_samples)}/{len(original_samples)} samples")
+                print(f"🎯 Accuracy: {acc:.4f}")
+                print(f"📊 F1 Score: {f1:.4f}")
+                print("=" * 80 + "\n")
+        else:
+            print(f"⛔ Unhandled error processing {doc_data['file']}: {doc_data['error']!s}")
+            save_test_results(args.test_results_path, test_results)
+            save_doc_processing_state(args.doc_state_path, get_state_dict())
+
+
 def main(args):
     global test_results
 
@@ -502,36 +552,20 @@ def main(args):
     test_results = load_test_results(args.test_results_path)
 
     # Process documents sequentially
-    completed = 0
-    for doc_file in tqdm(doc_files, desc="Processing documents"):
-        try:
-            doc_results = process_document_with_samples(doc_file, args, original_samples, prompt)
-            if doc_results:
-                test_results.extend(doc_results)
-                completed += 1
-
-                # Save test results after each document
-                save_test_results(args.test_results_path, test_results)
-
-                # Save document processing state
-                save_doc_processing_state(args.doc_state_path, get_state_dict())
-
-                # Calculate metrics
-                processed_samples = [s for s in test_results if "score" in s]
-                if processed_samples:
-                    acc, f1 = eval_acc_and_f1(processed_samples)
-                    print("\n" + "=" * 80)
-                    print(f"📈 Cumulative Metrics After Document: {doc_file}")
-                    print(f"✅ Processed: {completed}/{len(doc_files)} documents")
-                    print(f"🧪 Processed: {len(processed_samples)}/{len(original_samples)} samples")
-                    print(f"🎯 Accuracy: {acc:.4f}")
-                    print(f"📊 F1 Score: {f1:.4f}")
-                    print("=" * 80 + "\n")
-        except Exception as e:
-            print(f"⛔ Unhandled error processing {doc_file}: {e!s}")
-            # Try to save current progress on error
-            save_test_results(args.test_results_path, test_results)
-            save_doc_processing_state(args.doc_state_path, get_state_dict())
+    with ThreadPoolExecutor(max_workers=8) as executor:
+        future_to_file = {
+            executor.submit(
+                process_document_wrapper, doc_file, args, original_samples, prompt
+            ): doc_file
+            for doc_file in doc_files
+        }
+
+        progress = tqdm(total=len(doc_files), desc="Processing documents", position=0, leave=True)
+
+        for future in as_completed(future_to_file):
+            doc_data = future.result()
+            update_shared_state(doc_data, args, original_samples, len(doc_files))
+            progress.update(1)
 
     # Final evaluation
     if test_results:
@@ -542,7 +576,7 @@ def main(args):
         processed_samples = [s for s in test_results if "score" in s]
         acc, f1 = eval_acc_and_f1(processed_samples)
 
-        print(f"✅ Completed: {completed}/{len(doc_files)} documents")
+        print(f"✅ Completed: {completed_counter}/{len(doc_files)} documents")
         print(f"🧪 Completed: {len(processed_samples)}/{len(original_samples)} samples")
         print(f"⏭️ Skipped: {skipped} samples (filtered)")
         print(f"🎯 Final Accuracy: {acc:.4f}")

From 485b3e8d9fce4ed677a8132b50491b01463b05e3 Mon Sep 17 00:00:00 2001
From: stx <31013941@qq.com>
Date: Fri, 29 Aug 2025 15:07:57 +0800
Subject: [PATCH 22/34] =?UTF-8?q?feat=EF=BC=9Aadd=20MMLongbench-doc=20eval?=
 =?UTF-8?q?uation?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 src/memos/mem_os/core.py                      | 44 ++++++++++++++++++-
 src/memos/mem_reader/simple_struct.py         | 16 ++++---
 .../tree_text_memory/retrieve/reranker.py     | 12 ++++-
 3 files changed, 63 insertions(+), 9 deletions(-)

diff --git a/src/memos/mem_os/core.py b/src/memos/mem_os/core.py
index e188e0b67..679a50051 100644
--- a/src/memos/mem_os/core.py
+++ b/src/memos/mem_os/core.py
@@ -267,6 +267,32 @@ def chat(self, query: str, user_id: str | None = None, base_prompt: str | None =
 
         chat_history = self.chat_history_manager[target_user_id]
 
+        # Helper function to check if memories are sufficient
+        def check_memory_sufficiency(memories: list, query_str: str) -> bool:
+            """Use model to determine if memories are sufficient to answer the query"""
+            # Construct judgment prompt
+            judgment_prompt = (
+                "Strictly based on the provided memory fragments, determine if you can answer the user's question. "
+                "Respond only with 'yes' or 'no'\n\n"
+                "### Memory Fragments:\n"
+                f"{self._str_memories(memories)}\n\n"
+                "### User Question:\n"
+                f"{query_str}"
+            )
+
+            # Call model for judgment
+            judgment_response = self.chat_llm.generate(
+                [
+                    {"role": "system", "content": "You are a memory sufficiency evaluator"},
+                    {"role": "user", "content": judgment_prompt},
+                ]
+            )
+
+            # Parse model response
+            judgment = judgment_response.strip().lower()
+            logger.info(f"🧠 [Memory Judgment] Result: '{judgment}'")
+            return "yes" in judgment
+
         if self.config.enable_textual_memory and self.mem_cubes:
             memories_all = []
             for mem_cube_id, mem_cube in self.mem_cubes.items():
@@ -298,6 +324,19 @@ def chat(self, query: str, user_id: str | None = None, base_prompt: str | None =
                 )
                 memories_all.extend(memories)
             logger.info(f"🧠 [Memory] Searched memories:\n{self._str_memories(memories_all)}\n")
+
+            # Check memory sufficiency if we have memories
+            memory_sufficient = False
+            if memories_all:
+                memory_sufficient = check_memory_sufficiency(memories_all, query)
+
+            # Return early if memories are insufficient
+            if not memory_sufficient and memories_all:
+                logger.warning("🧠 [Memory] Insufficient information to answer")
+                for memory_item in memories_all:
+                    if hasattr(memory_item.metadata, "sources") and memory_item.metadata.sources:
+                        memory_item.memory = str(memory_item.metadata.sources)
+
             system_prompt = self._build_system_prompt(memories_all, base_prompt=base_prompt)
         else:
             system_prompt = self._build_system_prompt(base_prompt=base_prompt)
@@ -617,7 +656,7 @@ def add(
         self,
         messages: MessageList | None = None,
         memory_content: str | None = None,
-        doc_path: str | None = None,
+        doc_path: list[str] | None = None,
         mem_cube_id: str | None = None,
         user_id: str | None = None,
     ) -> None:
@@ -745,7 +784,7 @@ def add(
             and self.config.enable_textual_memory
             and self.mem_cubes[mem_cube_id].text_mem
         ):
-            documents = self._get_all_documents(doc_path)
+            documents = doc_path
             doc_memories = self.mem_reader.get_memory(
                 documents,
                 type="doc",
@@ -755,6 +794,7 @@ def add(
             mem_ids = []
             for mem in doc_memories:
                 mem_id_list: list[str] = self.mem_cubes[mem_cube_id].text_mem.add(mem)
+                self.mem_cubes[mem_cube_id].text_mem.memory_manager.wait_reorganizer()
                 mem_ids.extend(mem_id_list)
 
             # submit messages for scheduler
diff --git a/src/memos/mem_reader/simple_struct.py b/src/memos/mem_reader/simple_struct.py
index 03d440f70..3f9428760 100644
--- a/src/memos/mem_reader/simple_struct.py
+++ b/src/memos/mem_reader/simple_struct.py
@@ -1,6 +1,7 @@
 import concurrent.futures
 import copy
 import json
+import os
 import re
 
 from abc import ABC
@@ -19,11 +20,12 @@
     SIMPLE_STRUCT_DOC_READER_PROMPT,
     SIMPLE_STRUCT_DOC_READER_PROMPT_ZH,
     SIMPLE_STRUCT_MEM_READER_EXAMPLE,
-    SIMPLE_STRUCT_MEM_READER_PROMPT_ZH,
-    SIMPLE_STRUCT_MEM_READER_PROMPT,
     SIMPLE_STRUCT_MEM_READER_EXAMPLE_ZH,
+    SIMPLE_STRUCT_MEM_READER_PROMPT,
+    SIMPLE_STRUCT_MEM_READER_PROMPT_ZH,
 )
 
+
 logger = log.get_logger(__name__)
 PROMPT_DICT = {
     "chat": {
@@ -207,12 +209,14 @@ def get_scene_data_info(self, scene_data: list, type: str) -> list[str]:
         elif type == "doc":
             for item in scene_data:
                 try:
-                    if not isinstance(item, str):
+                    if os.path.exists(item):
                         parsed_text = parser.parse(item)
-                        results.append({"file": "pure_text", "text": parsed_text})
+                        results.append(
+                            {"file": "pure_text", "file_name": item, "text": parsed_text}
+                        )
                     else:
                         parsed_text = item
-                        results.append({"file": item, "text": parsed_text})
+                        results.append({"file": item, "file_name": item, "text": parsed_text})
                 except Exception as e:
                     print(f"Error parsing file {item}: {e!s}")
 
@@ -251,7 +255,7 @@ def _process_doc_data(self, scene_data_info, info):
                         key=chunk_res["key"],
                         embedding=self.embedder.embed([chunk_res["value"]])[0],
                         usage=[],
-                        sources=[f"{scene_data_info['file']}_{i}"],
+                        sources=[str({scene_data_info["file_name"]: chunks[i].text})],
                         background="",
                         confidence=0.99,
                         type="fact",
diff --git a/src/memos/memories/textual/tree_text_memory/retrieve/reranker.py b/src/memos/memories/textual/tree_text_memory/retrieve/reranker.py
index 861343e20..479622f33 100644
--- a/src/memos/memories/textual/tree_text_memory/retrieve/reranker.py
+++ b/src/memos/memories/textual/tree_text_memory/retrieve/reranker.py
@@ -1,3 +1,5 @@
+import copy
+
 import numpy as np
 
 from memos.embedders.factory import OllamaEmbedder
@@ -74,7 +76,15 @@ def rerank(
             list(tuple): Ranked list of memory items with similarity score.
         """
         # Step 1: Filter out items without embeddings
-        items_with_embeddings = [item for item in graph_results if item.metadata.embedding]
+        items_with_embeddings = []
+        for item in graph_results:
+            if item.metadata.embedding:
+                items_with_embeddings.append(copy.deepcopy(item))
+                detail = str(item.metadata.sources)
+                item.memory = detail
+                item.metadata.embedding = self.embedder.embed([detail])[0]
+                items_with_embeddings.append(copy.deepcopy(item))
+
         embeddings = [item.metadata.embedding for item in items_with_embeddings]
 
         if not embeddings:

From 313bbc14f63307b910309d778f43371e1dba540d Mon Sep 17 00:00:00 2001
From: stx <31013941@qq.com>
Date: Thu, 23 Oct 2025 16:11:52 +0800
Subject: [PATCH 23/34] =?UTF-8?q?feat=EF=BC=9Aupdate=2010.23?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 evaluation/scripts/mmlongbench/check_db.py    |  61 +++
 evaluation/scripts/mmlongbench/drop_db.py     |  48 +++
 .../scripts/mmlongbench/eval/eval_score2.py   | 279 +++++++++++++
 evaluation/scripts/mmlongbench/eval_docs.py   | 207 ++++++++++
 evaluation/scripts/mmlongbench/eval_docs2.py  | 217 ++++++++++
 evaluation/scripts/mmlongbench/import_docs.py |  72 ++++
 evaluation/scripts/mmlongbench/run_api.py     |  84 +++-
 examples/basic_modules/reranker.py            | 172 ++++++++
 examples/core_memories/tree_textual_memory.py | 224 +---------
 .../config/simple_struct_reader_config.json   |  19 +-
 .../config/tree_config_shared_database.json   |  53 ++-
 examples/mem_os/simple_openapi_memos.py       |  19 +-
 src/memos/chunkers/sentence_chunker.py        |  13 +-
 src/memos/configs/embedder.py                 |   7 +-
 src/memos/configs/mem_os.py                   |   2 +-
 src/memos/configs/reranker.py                 |  18 +
 src/memos/graph_dbs/nebular.py                |   3 +-
 src/memos/graph_dbs/neo4j.py                  |   9 +-
 src/memos/llms/openai.py                      |   3 -
 src/memos/mem_os/core.py                      |  44 +-
 src/memos/mem_reader/simple_struct.py         |  21 +-
 .../tree_text_memory/organize/handler.py      |  10 +-
 .../tree_text_memory/organize/reorganizer.py  |   2 +-
 .../tree_text_memory/retrieve/reranker.py     |  12 +-
 .../tree_text_memory/retrieve/searcher.py     |  61 ++-
 src/memos/memos_tools/notification_utils.py   |  46 +++
 src/memos/memos_tools/singleton.py            | 174 ++++++++
 src/memos/memos_tools/thread_safe_dict.py     |  22 +
 .../memos_tools/thread_safe_dict_segment.py   | 382 ++++++++++++++++++
 src/memos/parsers/markitdown.py               | 232 ++++++++++-
 src/memos/reranker/__init__.py                |   4 +
 src/memos/reranker/base.py                    |  24 ++
 src/memos/reranker/concat.py                  |  59 +++
 src/memos/reranker/cosine_local.py            |  96 +++++
 src/memos/reranker/factory.py                 |  48 +++
 src/memos/reranker/http_bge.py                | 312 ++++++++++++++
 src/memos/reranker/noop.py                    |  16 +
 37 files changed, 2708 insertions(+), 367 deletions(-)
 create mode 100644 evaluation/scripts/mmlongbench/check_db.py
 create mode 100644 evaluation/scripts/mmlongbench/drop_db.py
 create mode 100644 evaluation/scripts/mmlongbench/eval/eval_score2.py
 create mode 100644 evaluation/scripts/mmlongbench/eval_docs.py
 create mode 100644 evaluation/scripts/mmlongbench/eval_docs2.py
 create mode 100644 evaluation/scripts/mmlongbench/import_docs.py
 create mode 100644 examples/basic_modules/reranker.py
 create mode 100644 src/memos/configs/reranker.py
 create mode 100644 src/memos/memos_tools/singleton.py
 create mode 100644 src/memos/memos_tools/thread_safe_dict_segment.py
 create mode 100644 src/memos/reranker/__init__.py
 create mode 100644 src/memos/reranker/base.py
 create mode 100644 src/memos/reranker/concat.py
 create mode 100644 src/memos/reranker/cosine_local.py
 create mode 100644 src/memos/reranker/factory.py
 create mode 100644 src/memos/reranker/http_bge.py
 create mode 100644 src/memos/reranker/noop.py

diff --git a/evaluation/scripts/mmlongbench/check_db.py b/evaluation/scripts/mmlongbench/check_db.py
new file mode 100644
index 000000000..8127be792
--- /dev/null
+++ b/evaluation/scripts/mmlongbench/check_db.py
@@ -0,0 +1,61 @@
+import os
+
+from dotenv import load_dotenv
+from neo4j import GraphDatabase
+from neo4j.exceptions import ServiceUnavailable
+
+
+load_dotenv()
+
+NEO4J_URI = os.getenv("NEO4J_URI", "bolt://localhost:7687")
+NEO4J_USER = "neo4j"
+NEO4J_PASSWORD = "iaarlichunyu"
+
+
+def create_driver():
+    return GraphDatabase.driver(NEO4J_URI, auth=(NEO4J_USER, NEO4J_PASSWORD))
+
+
+def get_databases(driver):
+    query = "SHOW DATABASES"
+    with driver.session() as session:
+        result = session.run(query)
+        databases = [record["name"] for record in result]
+    return databases
+
+
+def is_database_empty(driver, db_name):
+    query = "MATCH (n) RETURN count(n) AS cnt"
+    with driver.session(database=db_name) as session:
+        result = session.run(query).single()
+        node_count = result["cnt"]
+    return node_count == 0
+
+
+def count_empty_bench_databases(driver):
+    databases = get_databases(driver)
+    empty_count = 0
+    empty_dbs = []
+    for db in databases:
+        if db.startswith("mm-long-bench-single-"):
+            try:
+                if is_database_empty(driver, db):
+                    empty_count += 1
+                    empty_dbs.append(db)
+            except Exception as e:
+                print(f"⚠️ Error checking database {db}: {e}")
+    print(f"共有 {empty_count} 个空的 mm-long-bench-single-* 数据库")
+    if empty_dbs:
+        print("空数据库列表:")
+        for db in empty_dbs:
+            print(f"  - {db}")
+
+
+if __name__ == "__main__":
+    driver = create_driver()
+    try:
+        count_empty_bench_databases(driver)
+    except ServiceUnavailable as e:
+        print(f"Error: {e}")
+    finally:
+        driver.close()
diff --git a/evaluation/scripts/mmlongbench/drop_db.py b/evaluation/scripts/mmlongbench/drop_db.py
new file mode 100644
index 000000000..db73c3ab2
--- /dev/null
+++ b/evaluation/scripts/mmlongbench/drop_db.py
@@ -0,0 +1,48 @@
+import os
+
+from dotenv import load_dotenv
+from neo4j import GraphDatabase
+from neo4j.exceptions import ServiceUnavailable
+
+
+load_dotenv()
+
+NEO4J_URI = os.getenv("NEO4J_URI", "bolt://localhost:7687")
+NEO4J_USER = "neo4j"
+NEO4J_PASSWORD = "iaarlichunyu"
+
+
+def create_driver():
+    return GraphDatabase.driver(NEO4J_URI, auth=(NEO4J_USER, NEO4J_PASSWORD))
+
+
+def get_databases(driver):
+    query = "SHOW DATABASES"
+    with driver.session() as session:
+        result = session.run(query)
+        databases = [record["name"] for record in result]
+    return databases
+
+
+def drop_database(driver, db_name):
+    query = f"DROP DATABASE `{db_name}`"
+    with driver.session() as session:
+        session.run(query)
+        print(f"Database '{db_name}' has been deleted.")
+
+
+def delete_bench_databases(driver):
+    databases = get_databases(driver)
+    for db in databases:
+        if db.startswith("mm-long-bench-single-"):
+            drop_database(driver, db)
+
+
+if __name__ == "__main__":
+    driver = create_driver()
+    try:
+        delete_bench_databases(driver)
+    except ServiceUnavailable as e:
+        print(f"Error: {e}")
+    finally:
+        driver.close()
diff --git a/evaluation/scripts/mmlongbench/eval/eval_score2.py b/evaluation/scripts/mmlongbench/eval/eval_score2.py
new file mode 100644
index 000000000..f5764ce39
--- /dev/null
+++ b/evaluation/scripts/mmlongbench/eval/eval_score2.py
@@ -0,0 +1,279 @@
+import os
+import re
+import traceback
+
+from collections import defaultdict
+from math import isclose
+
+from memos.configs.mem_os import MOSConfig
+from memos.llms.factory import LLMFactory
+
+
+openapi_config = {
+    "model_name_or_path": "gpt-5-nano",
+    "top_k": 50,
+    "remove_think_prefix": True,
+    "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
+    "api_base": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
+}
+config = {
+    "user_id": "user_name",
+    "chat_model": {
+        "backend": "openai",
+        "config": openapi_config,
+    },
+    "mem_reader": {
+        "backend": "simple_struct",
+        "config": {
+            "llm": {"backend": "openai", "config": openapi_config},
+            "embedder": {
+                "backend": "universal_api",
+                "config": {
+                    "provider": "openai",
+                    "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
+                    "model_name_or_path": "text-embedding-3-large",
+                    "base_url": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
+                },
+            },
+            "chunker": {
+                "backend": "sentence",
+                "config": {
+                    "tokenizer_or_token_counter": "gpt2",
+                    "chunk_size": 512,
+                    "chunk_overlap": 128,
+                    "min_sentences_per_chunk": 1,
+                },
+            },
+        },
+    },
+    "max_turns_window": 20,
+    "top_k": 5,
+    "enable_textual_memory": True,
+    "enable_activation_memory": False,
+    "enable_parametric_memory": False,
+}
+mos_config = MOSConfig(**config)
+chat_llm = LLMFactory.from_config(mos_config.chat_model)
+
+
+def is_float_equal(
+    reference, prediction, include_percentage: bool = False, is_close: float = False
+) -> bool:
+    def get_precision(gt_ans: float) -> int:
+        precision = 3
+        if "." in str(gt_ans):
+            precision = len(str(gt_ans).split(".")[-1])
+        return precision
+
+    reference = float(str(reference).strip().rstrip("%").strip())
+    try:
+        prediction = float(str(prediction).strip().rstrip("%").strip())
+    except Exception:
+        return False
+
+    gt_result = [reference / 100, reference, reference * 100] if include_percentage else [reference]
+    for item in gt_result:
+        try:
+            if is_close and isclose(item, prediction, rel_tol=0.01):
+                return True
+            precision = max(min(get_precision(prediction), get_precision(item)), 2)
+            if round(prediction, precision) == round(item, precision):
+                return True
+        except Exception:
+            continue
+    return False
+
+
+def get_clean_string(s):
+    s = str(s).lower().strip()
+
+    for suffix in ["mile", "miles", "million"]:
+        if s.endswith(suffix):
+            s = s[: -len(suffix)].strip()
+
+    s = re.sub(r"\s*\([^)]*\)", "", s).strip()
+    s = re.sub(r"^['\"]|['\"]$", "", s).strip()
+    s = s.lstrip("$").rstrip("%").strip()
+
+    return s
+
+
+def is_exact_match(s):
+    flag = False
+    # Website
+    if "https://" in s:
+        flag = True
+    # code file
+    if s.endswith((".py", ".ipynb")) or s.startswith("page"):
+        flag = True
+    # telephone number
+    if re.fullmatch(r"\b\d+(-\d+|\s\d+)?\b", s):
+        flag = True
+    # time
+    if "a.m." in s or "p.m." in s:
+        flag = True
+    # YYYY-MM-DD
+    if re.fullmatch(r"\b\d{4}[-\s]\d{2}[-\s]\d{2}\b", s):
+        flag = True
+    # YYYY-MM
+    if re.fullmatch(r"\b\d{4}[-\s]\d{2}\b", s):
+        flag = True
+    # Email address
+    if re.fullmatch(r"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}", s):
+        flag = True
+    return flag
+
+
+def isfloat(num):
+    try:
+        float(num)
+        return True
+    except ValueError:
+        return False
+
+
+def eval_score(question, gt, pred):
+    prompt = """
+        你是一个评委，根据问题和标准答案对学生的答案进行打分。打分规则如下：
+
+        完全不对（0分）：
+        学生答案与问题无关，未展示出任何相关概念或知识。
+        对了一部分（0.5分）：
+        学生答案提供了一些相关信息，但未能直接回答问题。
+        答案中包含部分正确内容，但缺乏关键信息，导致整体理解不清。
+        基本正确（0.7分）：
+        学生答案提供了大部分关键信息，不过依然距离标准答案有一定缺失。
+        答案中包含部分关键内容，但缺乏部分信息，导致不够完整。
+        完全正确（1分）：
+        学生答案准确地回答了问题，涵盖所有关键信息。
+        表达清晰，逻辑合理，直接且有效地回应了问题。
+
+        问题：{}
+
+        标准答案：{}
+
+        学生答案：{}
+        """
+
+    max_try = 20
+    try_i = 0
+    while try_i < max_try:
+        try:
+            llm_input_prompt_score = (
+                prompt.format(question, gt, pred)
+                + """请返回给我一个json：
+            {
+                "分数": 1,
+                "理由": "xxxx"
+            }"""
+            )
+            score = chat_llm.generate(
+                [
+                    {"role": "user", "content": llm_input_prompt_score},
+                ]
+            )
+
+            print(f"score: {score}")
+            score_real = eval(score.replace("json", "").replace("\n", "").replace("```", ""))
+            return float(score_real["分数"])
+        except Exception:
+            traceback.print_exc()
+            print(f"trying num {try_i}")
+            try_i += 1
+    return -1
+
+
+def eval_acc_and_f1(samples):
+    evaluated_samples = [sample for sample in samples if "score" in sample]
+    if not evaluated_samples:
+        return 0.0, 0.0
+
+    acc = sum([sample["score"] for sample in evaluated_samples]) / len(evaluated_samples)
+    try:
+        recall = sum(
+            [
+                sample["score"]
+                for sample in evaluated_samples
+                if sample["answer"] != "Not answerable"
+            ]
+        ) / len([sample for sample in evaluated_samples if sample["answer"] != "Not answerable"])
+        precision = sum(
+            [
+                sample["score"]
+                for sample in evaluated_samples
+                if sample["answer"] != "Not answerable"
+            ]
+        ) / len([sample for sample in evaluated_samples if sample["pred"] != "Not answerable"])
+        f1 = 2 * recall * precision / (recall + precision) if (recall + precision) > 0.0 else 0.0
+    except Exception:
+        f1 = 0.0
+
+    return acc, f1
+
+
+def show_results(samples, show_path=None):
+    for sample in samples:
+        sample["evidence_pages"] = eval(sample["evidence_pages"])
+        sample["evidence_sources"] = eval(sample["evidence_sources"])
+
+    with open(show_path, "w") as f:
+        acc, f1 = eval_acc_and_f1(samples)
+        f.write(f"Overall Acc: {acc} | Question Number: {len(samples)}\n")
+        f.write(f"Overall F1-score: {f1} | Question Number: {len(samples)}\n")
+        f.write("-----------------------\n")
+
+        acc_single_page, _ = eval_acc_and_f1(
+            [sample for sample in samples if len(sample["evidence_pages"]) == 1]
+        )
+        acc_multi_page, _ = eval_acc_and_f1(
+            [
+                sample
+                for sample in samples
+                if len(sample["evidence_pages"]) != 1 and sample["answer"] != "Not answerable"
+            ]
+        )
+        acc_neg, _ = eval_acc_and_f1(
+            [sample for sample in samples if sample["answer"] == "Not answerable"]
+        )
+
+        f.write(
+            "Single-page | Accuracy: {} | Question Number: {}\n".format(
+                acc_single_page,
+                len([sample for sample in samples if len(sample["evidence_pages"]) == 1]),
+            )
+        )
+        f.write(
+            "Cross-page | Accuracy: {} | Question Number: {}\n".format(
+                acc_multi_page,
+                len(
+                    [
+                        sample
+                        for sample in samples
+                        if len(sample["evidence_pages"]) != 1
+                        and sample["answer"] != "Not answerable"
+                    ]
+                ),
+            )
+        )
+        f.write(
+            "Unanswerable | Accuracy: {} | Question Number: {}\n".format(
+                acc_neg, len([sample for sample in samples if sample["answer"] == "Not answerable"])
+            )
+        )
+        f.write("-----------------------\n")
+
+        source_sample_dict, document_type_dict = defaultdict(list), defaultdict(list)
+        for sample in samples:
+            for answer_source in sample["evidence_sources"]:
+                source_sample_dict[answer_source].append(sample)
+            document_type_dict[sample["doc_type"]].append(sample)
+        for type, sub_samples in source_sample_dict.items():
+            f.write(
+                f"Evidence Sources: {type} | Accuracy: {eval_acc_and_f1(sub_samples)[0]} | Question Number: {len(sub_samples)}\n"
+            )
+
+        f.write("-----------------------\n")
+        for type, sub_samples in document_type_dict.items():
+            f.write(
+                f"Document Type: {type} | Accuracy: {eval_acc_and_f1(sub_samples)[0]} | Question Number: {len(sub_samples)}\n"
+            )
diff --git a/evaluation/scripts/mmlongbench/eval_docs.py b/evaluation/scripts/mmlongbench/eval_docs.py
new file mode 100644
index 000000000..de0d02a69
--- /dev/null
+++ b/evaluation/scripts/mmlongbench/eval_docs.py
@@ -0,0 +1,207 @@
+import json
+import os
+import re
+import traceback
+
+from concurrent.futures import ThreadPoolExecutor, as_completed
+
+from dotenv import load_dotenv
+from eval.eval_score import eval_acc_and_f1, eval_score, show_results
+from eval.extract_answer import extract_answer
+from tqdm import tqdm
+
+from memos.configs.mem_cube import GeneralMemCubeConfig
+from memos.configs.mem_os import MOSConfig
+from memos.mem_cube.general import GeneralMemCube
+from memos.mem_os.main import MOS
+
+
+load_dotenv()
+
+openapi_config = {
+    "model_name_or_path": "gpt-5-nano",
+    "top_k": 50,
+    "remove_think_prefix": True,
+    "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
+    "api_base": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
+}
+neo4j_uri = os.getenv("NEO4J_URI", "bolt://47.117.41.207:7687")
+space_name = "nebula-longbench-stx-5"
+
+doc_paths = [
+    f
+    for f in os.listdir("evaluation/data/mmlongbench/documents")
+    if os.path.isfile(os.path.join("evaluation/data/mmlongbench/documents", f))
+]
+
+with open("evaluation/data/mmlongbench/samples.json") as f:
+    samples = json.load(f)
+
+
+def process_doc(doc_file):
+    user_name = "user_" + doc_file
+    config = {
+        "user_id": user_name,
+        "chat_model": {
+            "backend": "openai",
+            "config": openapi_config,
+        },
+        "mem_reader": {
+            "backend": "simple_struct",
+            "config": {
+                "llm": {"backend": "openai", "config": openapi_config},
+                "embedder": {
+                    "backend": "universal_api",
+                    "config": {
+                        "provider": "openai",
+                        "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
+                        "model_name_or_path": "text-embedding-3-large",
+                        "base_url": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
+                    },
+                },
+                "chunker": {
+                    "backend": "sentence",
+                    "config": {
+                        "tokenizer_or_token_counter": "gpt2",
+                        "chunk_size": 512,
+                        "chunk_overlap": 128,
+                        "min_sentences_per_chunk": 1,
+                    },
+                },
+            },
+        },
+        "max_turns_window": 20,
+        "top_k": 5,
+        "enable_textual_memory": True,
+        "enable_activation_memory": False,
+        "enable_parametric_memory": False,
+    }
+    mos_config = MOSConfig(**config)
+    mos = MOS(mos_config)
+
+    mem_cube_config = GeneralMemCubeConfig.model_validate(
+        {
+            "user_id": user_name,
+            "cube_id": user_name,
+            "text_mem": {
+                "backend": "tree_text",
+                "config": {
+                    "extractor_llm": {"backend": "openai", "config": openapi_config},
+                    "dispatcher_llm": {"backend": "openai", "config": openapi_config},
+                    "graph_db": {
+                        "backend": "nebular",
+                        "config": {
+                            "uri": json.loads(os.getenv("NEBULAR_HOSTS", "localhost")),
+                            "user": os.getenv("NEBULAR_USER", "root"),
+                            "password": os.getenv("NEBULAR_PASSWORD", "xxxxxx"),
+                            "space": space_name,
+                            "user_name": user_name,
+                            "use_multi_db": False,
+                            "auto_create": True,
+                            "embedding_dimension": 3072,
+                        },
+                    },
+                    "embedder": {
+                        "backend": "universal_api",
+                        "config": {
+                            "provider": "openai",
+                            "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
+                            "model_name_or_path": "text-embedding-3-large",
+                            "base_url": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
+                        },
+                    },
+                    "reorganize": False,
+                },
+            },
+            "act_mem": {},
+            "para_mem": {},
+        }
+    )
+    mem_cube = GeneralMemCube(mem_cube_config)
+
+    temp_dir = "tmp/" + doc_file
+    if not os.path.exists(temp_dir) or not os.listdir(temp_dir):
+        mem_cube.dump(temp_dir)
+
+    mos.register_mem_cube(temp_dir, mem_cube_id=user_name)
+
+    with open("evaluation/scripts/mmlongbench/eval/prompt_for_answer_extraction.md") as f:
+        prompt = f.read()
+
+    samples_res = []
+    doc_samples = [
+        s
+        for s in samples
+        if s.get("doc_id") == doc_file and s.get("evidence_sources") == "['Pure-text (Plain-text)']"
+    ]
+    if len(doc_samples) == 0:
+        return []
+
+    for sample in tqdm(doc_samples, desc=f"Processing {doc_file}"):
+        messages = sample["question"]
+        try_cnt, is_success = 0, False
+
+        while True:
+            try:
+                mos.clear_messages()
+                response = mos.chat(messages, user_name)
+                is_success = True
+            except Exception as e:
+                print(f"[{doc_file}] Error:", e)
+                traceback.print_exc()
+                try_cnt += 1
+                response = "Failed"
+            if is_success or try_cnt > 5:
+                break
+
+        sample["response"] = response
+        extracted_res = extract_answer(sample["question"], response, prompt)
+        sample["extracted_res"] = extracted_res
+
+        pred_ans = extracted_res.split("Answer format:")[0].split("Extracted answer:")[1].strip()
+        score = eval_score(sample["answer"], pred_ans, sample["answer_format"])
+
+        sample["pred"] = pred_ans
+        sample["score"] = score
+        samples_res.append(sample)
+
+        print("--------------------------------------")
+        print(f"Question: {sample['question']}")
+        print(f"Response: {sample['response']}")
+        print(f"Ground true: {sample['answer']}\tPred: {sample['pred']}\tScore: {sample['score']}")
+
+    return samples_res
+
+
+if __name__ == "__main__":
+    results = []
+
+    with ThreadPoolExecutor(max_workers=1) as executor:
+        future_to_doc = {executor.submit(process_doc, doc_file): doc_file for doc_file in doc_paths}
+
+        for future in as_completed(future_to_doc):
+            doc_file = future_to_doc[future]
+            try:
+                res = future.result()
+                results.extend(res)
+
+                if len(res) > 0:
+                    acc, f1 = eval_acc_and_f1(results)
+                    print()
+                    print(f"Avg acc: {acc}")
+                    print(f"Avg f1: {f1}")
+
+                with open("evaluation/data/mmlongbench/test_results.json", "w") as f:
+                    json.dump(results, f)
+            except Exception as e:
+                print(f"[{doc_file}] failed with {e}")
+
+    acc, f1 = eval_acc_and_f1(results)
+    print("--------------------------------------")
+    print(f"Final avg acc: {acc}")
+    print(f"Final avg f1: {f1}")
+
+    show_results(
+        results,
+        show_path=re.sub(r"\.json$", ".txt", "evaluation/data/mmlongbench/test_results_report.txt"),
+    )
diff --git a/evaluation/scripts/mmlongbench/eval_docs2.py b/evaluation/scripts/mmlongbench/eval_docs2.py
new file mode 100644
index 000000000..03a89841c
--- /dev/null
+++ b/evaluation/scripts/mmlongbench/eval_docs2.py
@@ -0,0 +1,217 @@
+import json
+import os
+import re
+import traceback
+
+from concurrent.futures import ThreadPoolExecutor, as_completed
+
+from dotenv import load_dotenv
+from eval.eval_score2 import eval_acc_and_f1, eval_score, show_results
+from eval.extract_answer import extract_answer
+
+from memos.configs.mem_cube import GeneralMemCubeConfig
+from memos.configs.mem_os import MOSConfig
+from memos.mem_cube.general import GeneralMemCube
+from memos.mem_os.main import MOS
+
+
+load_dotenv()
+openapi_config = {
+    "model_name_or_path": "gpt-5-nano",
+    "top_k": 50,
+    "remove_think_prefix": True,
+    "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
+    "api_base": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
+}
+neo4j_uri = os.getenv("NEO4J_URI", "bolt://47.117.41.207:7687")
+space_name = "nebula-longbench-stx-new"
+
+doc_paths = [
+    f
+    for f in os.listdir("evaluation/data/mmlongbench/documents_xcy")
+    if os.path.isfile(os.path.join("evaluation/data/mmlongbench/documents_xcy", f))
+]
+
+with open("evaluation/data/mmlongbench/all_samples_with_gt.json") as f:
+    samples = json.load(f)
+
+
+def process_doc(doc_file):
+    user_name = "user_" + doc_file
+    config = {
+        "user_id": user_name,
+        "chat_model": {
+            "backend": "openai",
+            "config": openapi_config,
+        },
+        "mem_reader": {
+            "backend": "simple_struct",
+            "config": {
+                "llm": {"backend": "openai", "config": openapi_config},
+                "embedder": {
+                    "backend": "universal_api",
+                    "config": {
+                        "provider": "openai",
+                        "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
+                        "model_name_or_path": "text-embedding-3-large",
+                        "base_url": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
+                    },
+                },
+                "chunker": {
+                    "backend": "sentence",
+                    "config": {
+                        "tokenizer_or_token_counter": "gpt2",
+                        "chunk_size": 512,
+                        "chunk_overlap": 128,
+                        "min_sentences_per_chunk": 1,
+                    },
+                },
+            },
+        },
+        "max_turns_window": 20,
+        "top_k": 5,
+        "enable_textual_memory": True,
+        "enable_activation_memory": False,
+        "enable_parametric_memory": False,
+    }
+    mos_config = MOSConfig(**config)
+    mos = MOS(mos_config)
+
+    mem_cube_config = GeneralMemCubeConfig.model_validate(
+        {
+            "user_id": user_name,
+            "cube_id": user_name,
+            "text_mem": {
+                "backend": "tree_text",
+                "config": {
+                    "extractor_llm": {"backend": "openai", "config": openapi_config},
+                    "dispatcher_llm": {"backend": "openai", "config": openapi_config},
+                    "graph_db": {
+                        "backend": "nebular",
+                        "config": {
+                            "uri": json.loads(os.getenv("NEBULAR_HOSTS", "localhost")),
+                            "user": os.getenv("NEBULAR_USER", "root"),
+                            "password": os.getenv("NEBULAR_PASSWORD", "xxxxxx"),
+                            "space": space_name,
+                            "user_name": user_name,
+                            "use_multi_db": False,
+                            "auto_create": True,
+                            "embedding_dimension": 3072,
+                        },
+                    },
+                    "embedder": {
+                        "backend": "universal_api",
+                        "config": {
+                            "provider": "openai",
+                            "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
+                            "model_name_or_path": "text-embedding-3-large",
+                            "base_url": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
+                        },
+                    },
+                    "reorganize": False,
+                },
+            },
+            "act_mem": {},
+            "para_mem": {},
+        }
+    )
+    mem_cube = GeneralMemCube(mem_cube_config)
+
+    temp_dir = "tmp/" + doc_file
+    if not os.path.exists(temp_dir) or not os.listdir(temp_dir):
+        mem_cube.dump(temp_dir)
+
+    mos.register_mem_cube(temp_dir, mem_cube_id=user_name)
+
+    with open("evaluation/scripts/mmlongbench/eval/prompt_for_answer_extraction.md") as f:
+        prompt = f.read()
+
+    samples_res = []
+    doc_samples = [s for s in samples if s.get("doc_id") == doc_file]
+
+    if len(doc_samples) == 0:
+        return []
+
+    sample = doc_samples[0]
+    question_list = sample["question"]
+    answer_list = sample["answer"]
+
+    for idx, question in enumerate(question_list):
+        gt = answer_list.get(str(idx))
+
+        try_cnt, is_success = 0, False
+        while True:
+            try:
+                mos.clear_messages()
+                response = mos.chat(question, user_name)
+                is_success = True
+            except Exception as e:
+                print(f"[{doc_file}] Error:", e)
+                traceback.print_exc()
+                try_cnt += 1
+                response = "Failed"
+            if is_success or try_cnt > 5:
+                break
+
+        sample_item = dict(sample)
+        sample_item["question"] = question
+        sample_item["answer"] = gt
+        sample_item["response"] = response
+
+        extracted_res = extract_answer(sample_item["question"], response, prompt)
+        sample_item["extracted_res"] = extracted_res
+
+        print("--------------------------------------")
+        pred_ans = extracted_res.split("Answer format:")[0].split("Extracted answer:")[1].strip()
+        score = eval_score(question, gt, response)
+
+        sample_item["pred"] = pred_ans
+        sample_item["score"] = score
+        samples_res.append(sample_item)
+
+        print(f"Question: {question}")
+        print(f"Response: {sample_item['response']}")
+        print(f"Ground true: {gt}\tPred: {sample_item['pred']}\tScore: {sample_item['score']}")
+
+    print("samples_res length: ", len(samples_res))
+    return samples_res
+
+
+if __name__ == "__main__":
+    results = []
+
+    with ThreadPoolExecutor(max_workers=4) as executor:
+        future_to_doc = {executor.submit(process_doc, doc_file): doc_file for doc_file in doc_paths}
+
+        for future in as_completed(future_to_doc):
+            doc_file = future_to_doc[future]
+            try:
+                res = future.result()
+                results.extend(res)
+
+                if len(res) > 0:
+                    acc, f1 = eval_acc_and_f1(results)
+                    print()
+                    print(f"Avg acc: {acc}")
+                    print(f"Avg f1: {f1}")
+
+                with open(
+                    "evaluation/data/mmlongbench/test_results2.json", "w", encoding="utf-8"
+                ) as f:
+                    json.dump(results, f, ensure_ascii=False, indent=2)
+
+            except Exception as e:
+                print(f"[{doc_file}] failed with {e}")
+                traceback.print_exc()
+
+    acc, f1 = eval_acc_and_f1(results)
+    print("--------------------------------------")
+    print(f"Final avg acc: {acc}")
+    print(f"Final avg f1: {f1}")
+
+    show_results(
+        results,
+        show_path=re.sub(
+            r"\.json$", ".txt", "evaluation/data/mmlongbench/test_results_report2.txt"
+        ),
+    )
diff --git a/evaluation/scripts/mmlongbench/import_docs.py b/evaluation/scripts/mmlongbench/import_docs.py
new file mode 100644
index 000000000..4bff1f333
--- /dev/null
+++ b/evaluation/scripts/mmlongbench/import_docs.py
@@ -0,0 +1,72 @@
+import asyncio
+import os
+import uuid
+
+from memos import log
+from memos.configs.mem_reader import SimpleStructMemReaderConfig
+from memos.configs.memory import TreeTextMemoryConfig
+from memos.mem_reader.simple_struct import SimpleStructMemReader
+from memos.memories.textual.tree import TreeTextMemory
+
+
+logger = log.get_logger(__name__)
+
+# Create a memory reader instance
+reader_config = SimpleStructMemReaderConfig.from_json_file(
+    "examples/data/config/simple_struct_reader_config.json"
+)
+reader = SimpleStructMemReader(reader_config)
+
+# Processing Documents
+doc_paths = [
+    "evaluation/data/mmlongbench/documents_xcy/" + f
+    for f in os.listdir("evaluation/data/mmlongbench/documents_xcy")
+    if os.path.isfile(os.path.join("evaluation/data/mmlongbench/documents_xcy", f))
+]
+
+
+async def process_doc(doc_path):
+    print(f"🔄 Processing document: {doc_path}")
+    session_id = str(uuid.uuid4())
+    tree_config = TreeTextMemoryConfig.from_json_file(
+        "examples/data/config/tree_config_shared_database.json"
+    )
+    doc_file = doc_path.split("/")[-1]
+
+    user_id = "user_" + doc_file
+    tree_config.graph_db.config.user_name = user_id
+    temp_dir = "tmp/" + doc_file
+
+    my_tree_textual_memory = TreeTextMemory(tree_config)
+    doc_memory = await reader.get_memory(
+        [doc_path], "doc", info={"user_id": user_id, "session_id": session_id}
+    )
+
+    count = 0
+    for m_list in doc_memory:
+        my_tree_textual_memory.add(m_list)
+    print("total memories: ", count)
+
+    my_tree_textual_memory.dump(temp_dir)
+    return doc_path
+
+
+async def main():
+    batch_size = 1
+
+    for i in range(0, len(doc_paths), batch_size):
+        batch = doc_paths[i : i + batch_size]
+        print(f"🚀 Starting batch {i // batch_size + 1} with {len(batch)} docs")
+
+        tasks = [process_doc(p) for p in batch]
+        results = await asyncio.gather(*tasks, return_exceptions=True)
+
+        for p, result in zip(batch, results, strict=False):
+            if isinstance(result, Exception):
+                print(f"❌ Error processing {p}: {result}")
+            else:
+                print(f"✅ Finished {result}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/evaluation/scripts/mmlongbench/run_api.py b/evaluation/scripts/mmlongbench/run_api.py
index 1d554e839..855daf238 100644
--- a/evaluation/scripts/mmlongbench/run_api.py
+++ b/evaluation/scripts/mmlongbench/run_api.py
@@ -17,8 +17,12 @@
 
 from memos.configs.mem_cube import GeneralMemCubeConfig
 from memos.configs.mem_os import MOSConfig
+from memos.configs.mem_reader import SimpleStructMemReaderConfig
+from memos.configs.memory import TreeTextMemoryConfig
 from memos.mem_cube.general import GeneralMemCube
 from memos.mem_os.main import MOS
+from memos.mem_reader.simple_struct import SimpleStructMemReader
+from memos.memories.textual.tree import TreeTextMemory
 
 
 load_dotenv()
@@ -33,6 +37,7 @@
     "api_base": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
 }
 neo4j_uri = os.getenv("NEO4J_URI", "bolt://47.117.41.207:7687")
+space_name = "nebula-longbench-stx"
 db_name = "mm-long-bench-single-"
 
 # Global state tracking
@@ -135,13 +140,16 @@ def import_document(doc_path):
                         "extractor_llm": {"backend": "openai", "config": openapi_config},
                         "dispatcher_llm": {"backend": "openai", "config": openapi_config},
                         "graph_db": {
-                            "backend": "neo4j",
+                            "backend": "nebular",
                             "config": {
-                                "uri": neo4j_uri,
-                                "user": "neo4j",
-                                "password": "iaarlichunyu",
-                                "db_name": db_name + user_name[:8],
+                                "uri": json.loads(os.getenv("NEBULAR_HOSTS", "localhost")),
+                                "user": os.getenv("NEBULAR_USER", "root"),
+                                "password": os.getenv("NEBULAR_PASSWORD", "xxxxxx"),
+                                "space": space_name,  # 可以新建
+                                "user_name": user_name,  # cube_name
+                                "use_multi_db": False,  # 建议不要改
                                 "auto_create": True,
+                                "embedding_dimension": 3072,
                             },
                         },
                         "embedder": {
@@ -155,7 +163,7 @@ def import_document(doc_path):
                                 ),
                             },
                         },
-                        "reorganize": True,
+                        "reorganize": False,
                     },
                 },
                 "act_mem": {},
@@ -221,6 +229,49 @@ def create_mos_for_doc(user_name, temp_dir):
         }
         mos_config = MOSConfig(**config)
         mos = MOS(mos_config)
+
+        mem_cube_config = GeneralMemCubeConfig.model_validate(
+            {
+                "user_id": user_name,
+                "cube_id": user_name,
+                "text_mem": {
+                    "backend": "tree_text",
+                    "config": {
+                        "extractor_llm": {"backend": "openai", "config": openapi_config},
+                        "dispatcher_llm": {"backend": "openai", "config": openapi_config},
+                        "graph_db": {
+                            "backend": "neo4j",
+                            "config": {
+                                "uri": neo4j_uri,
+                                "user": "neo4j",
+                                "password": "iaarlichunyu",
+                                "db_name": space_name,
+                                "auto_create": True,
+                            },
+                        },
+                        "embedder": {
+                            "backend": "universal_api",
+                            "config": {
+                                "provider": "openai",
+                                "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
+                                "model_name_or_path": "text-embedding-3-large",
+                                "base_url": os.getenv(
+                                    "OPENAI_API_BASE", "https://api.openai.com/v1"
+                                ),
+                            },
+                        },
+                        "reorganize": True,
+                    },
+                },
+                "act_mem": {},
+                "para_mem": {},
+            }
+        )
+        mem_cube = GeneralMemCube(mem_cube_config)
+
+        if not os.path.exists(temp_dir) or not os.listdir(temp_dir):
+            os.makedirs(temp_dir, exist_ok=True)
+            mem_cube.dump(temp_dir)
         mos.register_mem_cube(temp_dir, mem_cube_id=user_name)
         return mos
     except Exception as e:
@@ -334,7 +385,7 @@ def import_all_documents(args):
         print(f"🔍 Loaded existing document processing state for {len(doc_user_map)} documents")
 
     # Import new documents
-    new_docs = [doc for doc in doc_files if doc not in doc_user_map]
+    new_docs = [doc for doc in doc_files[:20] if doc not in doc_user_map]
 
     if not new_docs:
         print("✅ All documents already imported")
@@ -342,6 +393,20 @@ def import_all_documents(args):
 
     print(f"📂 Importing {len(new_docs)} new documents")
 
+    # Create a memory reader instance
+    reader_config = SimpleStructMemReaderConfig.from_json_file(
+        "examples/data/config/simple_struct_reader_config.json"
+    )
+    reader = SimpleStructMemReader(reader_config)
+    tree_config = TreeTextMemoryConfig.from_json_file(
+        "examples/data/config/tree_config_shared_database.json"
+    )
+    my_tree_textual_memory = TreeTextMemory(tree_config)
+    doc_memory = reader.get_memory(new_docs, "doc", info={"user_id": "1111", "session_id": "2222"})
+    for m_list in doc_memory:
+        my_tree_textual_memory.add(m_list)
+        my_tree_textual_memory.memory_manager.wait_reorganizer()
+
     def import_doc(doc_file):
         """Import a single document and save state"""
         doc_path = os.path.join(args.document_path, doc_file)
@@ -374,8 +439,8 @@ def process_document_with_samples(doc_file, args, samples, prompt):
     print(f"🔄 Processing document: {doc_file}")
 
     # Get user_name and temp_dir for this document
-    user_name = doc_user_map.get(doc_file)
-    temp_dir = doc_temp_dir.get(doc_file)
+    user_name = "user_" + doc_file
+    temp_dir = "tmp/" + doc_file
     if not user_name or not temp_dir:
         print(f"❌ No user mapping found for {doc_file}. Skipping.")
         return []
@@ -526,7 +591,6 @@ def main(args):
         print(f"🔍 Loaded document processing state for {len(doc_user_map)} documents")
 
     # Import all documents first
-    import_all_documents(args)
 
     with open(args.extractor_prompt_path) as f:
         prompt = f.read()
diff --git a/examples/basic_modules/reranker.py b/examples/basic_modules/reranker.py
new file mode 100644
index 000000000..47bf1405c
--- /dev/null
+++ b/examples/basic_modules/reranker.py
@@ -0,0 +1,172 @@
+import os
+import uuid
+
+from dotenv import load_dotenv
+
+from memos import log
+from memos.configs.embedder import EmbedderConfigFactory
+from memos.configs.reranker import RerankerConfigFactory
+from memos.embedders.factory import EmbedderFactory
+from memos.memories.textual.item import TextualMemoryItem, TreeNodeTextualMemoryMetadata
+from memos.reranker.factory import RerankerFactory
+
+
+load_dotenv()
+logger = log.get_logger(__name__)
+
+
+def make_item(text: str) -> TextualMemoryItem:
+    """Build a minimal TextualMemoryItem; embedding will be populated later."""
+    return TextualMemoryItem(
+        id=str(uuid.uuid4()),
+        memory=text,
+        metadata=TreeNodeTextualMemoryMetadata(
+            user_id=None,
+            session_id=None,
+            status="activated",
+            type="fact",
+            memory_time="2024-01-01",
+            source="conversation",
+            confidence=100.0,
+            tags=[],
+            visibility="public",
+            updated_at="2025-01-01T00:00:00",
+            memory_type="LongTermMemory",
+            key="demo_key",
+            sources=["demo://example"],
+            embedding=[],
+            background="demo background...",
+        ),
+    )
+
+
+def show_ranked(title: str, ranked: list[tuple[TextualMemoryItem, float]], top_n: int = 5) -> None:
+    print(f"\n=== {title} ===")
+    for i, (item, score) in enumerate(ranked[:top_n], start=1):
+        preview = (item.memory[:80] + "...") if len(item.memory) > 80 else item.memory
+        print(f"[#{i}] score={score:.6f} | {preview}")
+
+
+def main():
+    # -------------------------------
+    # 1) Build the embedder (real vectors)
+    # -------------------------------
+    embedder_cfg = EmbedderConfigFactory.model_validate(
+        {
+            "backend": "universal_api",
+            "config": {
+                "provider": "openai",  # or "azure"
+                "api_key": os.getenv("OPENAI_API_KEY"),
+                "model_name_or_path": "text-embedding-3-large",
+                "base_url": os.getenv("OPENAI_API_BASE"),  # optional
+            },
+        }
+    )
+    embedder = EmbedderFactory.from_config(embedder_cfg)
+
+    # -------------------------------
+    # 2) Prepare query + documents
+    # -------------------------------
+    query = "What is the capital of France?"
+    items = [
+        make_item("Paris is the capital of France."),
+        make_item("Berlin is the capital of Germany."),
+        make_item("The capital of Brazil is Brasilia."),
+        make_item("Apples and bananas are common fruits."),
+        make_item("The Eiffel Tower is a famous landmark in Paris."),
+    ]
+
+    # -------------------------------
+    # 3) Embed query + docs with real embeddings
+    # -------------------------------
+    texts_to_embed = [query] + [it.memory for it in items]
+    vectors = embedder.embed(texts_to_embed)  # real vectors from your provider/model
+    query_embedding = vectors[0]
+    doc_embeddings = vectors[1:]
+
+    # attach real embeddings back to items
+    for it, emb in zip(items, doc_embeddings, strict=False):
+        it.metadata.embedding = emb
+
+    items[0].metadata.user_id = "u_123"
+    items[0].metadata.session_id = "s_abc"
+    items[0].metadata.tags = [*items[0].metadata.tags, "paris"]
+
+    items[1].metadata.user_id = "u_124"
+    items[1].metadata.session_id = "s_xyz"
+    items[1].metadata.tags = [*items[1].metadata.tags, "germany"]
+    items[2].metadata.user_id = "u_125"
+    items[2].metadata.session_id = "s_ss3"
+    items[3].metadata.user_id = "u_126"
+    items[3].metadata.session_id = "s_ss4"
+    items[4].metadata.user_id = "u_127"
+    items[4].metadata.session_id = "s_ss5"
+
+    # -------------------------------
+    # 4) Rerank with cosine_local (uses your real embeddings)
+    # -------------------------------
+    cosine_cfg = RerankerConfigFactory.model_validate(
+        {
+            "backend": "cosine_local",
+            "config": {
+                # structural boosts (optional): uses metadata.background
+                "level_weights": {"topic": 1.0, "concept": 1.0, "fact": 1.0},
+                "level_field": "background",
+            },
+        }
+    )
+    cosine_reranker = RerankerFactory.from_config(cosine_cfg)
+
+    ranked_cosine = cosine_reranker.rerank(
+        query=query,
+        graph_results=items,
+        top_k=10,
+        query_embedding=query_embedding,  # required by cosine_local
+    )
+    show_ranked("CosineLocal Reranker (with real embeddings)", ranked_cosine, top_n=5)
+
+    # -------------------------------
+    # 5) (Optional) Rerank with HTTP BGE (OpenAI-style /query+documents)
+    #     Requires the service URL; no need for embeddings here
+    # -------------------------------
+    bge_url = os.getenv("BGE_RERANKER_URL")  # e.g., "http://xxx.x.xxxxx.xxx:xxxx/v1/rerank"
+    if bge_url:
+        http_cfg = RerankerConfigFactory.model_validate(
+            {
+                "backend": "http_bge",
+                "config": {
+                    "url": bge_url,
+                    "model": os.getenv("BGE_RERANKER_MODEL", "bge-reranker-v2-m3"),
+                    "timeout": int(os.getenv("BGE_RERANKER_TIMEOUT", "10")),
+                    "boost_weights": {"user_id": 0.5, "tags": 0.2},
+                },
+            }
+        )
+        http_reranker = RerankerFactory.from_config(http_cfg)
+
+        ranked_http = http_reranker.rerank(
+            query=query,
+            graph_results=items,  # uses item.memory internally as documents
+            top_k=10,
+        )
+        show_ranked("HTTP BGE Reranker (OpenAI-style API)", ranked_http, top_n=5)
+
+        # --- NEW: search_filter with rerank ---
+        # hit rule:
+        # - user_id == "u_123" → score * (1 + 0.5) = 1.5
+        # - tags including "paris" → score * (1 + 0.2) = 1.2
+        # - project_id(not exist) → warning unrelated with score
+        search_filter = {"session_id": "germany", "tags": "germany", "project_id": "demo-p1"}
+        ranked_http_boosted = http_reranker.rerank(
+            query=query,
+            graph_results=items,
+            top_k=10,
+            search_filter=search_filter,
+        )
+        show_ranked("HTTP BGE Reranker (with search_filter boosts)", ranked_http_boosted, top_n=5)
+    else:
+        print("\n[Info] Skipped HTTP BGE scenario because BGE_RERANKER_URL is not set.")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/core_memories/tree_textual_memory.py b/examples/core_memories/tree_textual_memory.py
index d2e197e5b..f27c69b6f 100644
--- a/examples/core_memories/tree_textual_memory.py
+++ b/examples/core_memories/tree_textual_memory.py
@@ -1,5 +1,3 @@
-import time
-
 from memos import log
 from memos.configs.mem_reader import SimpleStructMemReaderConfig
 from memos.configs.memory import TreeTextMemoryConfig
@@ -8,8 +6,6 @@
 
 
 logger = log.get_logger(__name__)
-
-
 tree_config = TreeTextMemoryConfig.from_json_file(
     "examples/data/config/tree_config_shared_database.json"
 )
@@ -22,232 +18,16 @@
 )
 reader = SimpleStructMemReader(reader_config)
 
-scene_data = [
-    [
-        {"role": "user", "chat_time": "3 May 2025", "content": "I’m feeling a bit down today."},
-        {
-            "role": "assistant",
-            "chat_time": "3 May 2025",
-            "content": "I’m sorry to hear that. Do you want to talk about what’s been going on?",
-        },
-        {
-            "role": "user",
-            "chat_time": "3 May 2025",
-            "content": "It’s just been a tough couple of days, you know? Everything feels a bit overwhelming, and I just can’t seem to shake it off.",
-        },
-        {
-            "role": "assistant",
-            "chat_time": "3 May 2025",
-            "content": "It sounds like you're going through a lot right now. Sometimes it helps to talk things out. Is there something specific that's been weighing on you, or is it more of a general feeling?",
-        },
-        {
-            "role": "user",
-            "chat_time": "3 May 2025",
-            "content": "It’s a mix, I guess. Work’s been really stressful, and on top of that, I’ve been feeling kinda disconnected from the people around me.",
-        },
-        {
-            "role": "assistant",
-            "chat_time": "3 May 2025",
-            "content": "That can be really draining, especially when you’re feeling isolated on top of the stress. Do you think there’s something from your past that’s contributing to how you’re feeling now? Sometimes our emotions are tied to older experiences.",
-        },
-        {
-            "role": "user",
-            "chat_time": "3 May 2025",
-            "content": "Yeah, now that you mention it… I’ve been thinking a lot about my childhood lately. I didn’t have the easiest upbringing. My parents were always busy, and I often felt like I had to figure things out on my own. I remember one winter, I got really sick with a high fever. My parents were both working, and I didn’t want to bother them. I just lay in bed, crying by myself, not knowing what to do.",
-        },
-        {
-            "role": "assistant",
-            "chat_time": "3 May 2025",
-            "content": "That must have been very lonely. Growing up without that support can really make you feel like you have to handle everything on your own. How do you think that experience has affected you now?",
-        },
-        {
-            "role": "user",
-            "chat_time": "3 May 2025",
-            "content": "Yeah, it was really lonely. I remember spending a lot of time doing things on my own, like drawing, because it was my only way to escape. I used to draw animals, and I created my own little world on paper. It was peaceful in a way, just me and my imagination, with no outside distractions.",
-        },
-        {
-            "role": "assistant",
-            "chat_time": "3 May 2025",
-            "content": "Drawing sounds like a beautiful way to express yourself and find some peace in that solitude. It must have been a very quiet and private world for you. Do you think that sense of loneliness from back then has shaped how you connect with others today?",
-        },
-        {
-            "role": "user",
-            "chat_time": "3 May 2025",
-            "content": "I think it has. I’ve always found it hard to let others in. I feel like I have to handle everything on my own. If I rely on someone else, I feel vulnerable, and I start to feel that loneliness again.",
-        },
-        {
-            "role": "assistant",
-            "chat_time": "3 May 2025",
-            "content": "It sounds like this was a protective mechanism that helped you cope with your feelings of loneliness back then. But as you’ve grown, it seems like this pattern might not be serving you as well anymore. Have you thought about trying to break this cycle, or finding new ways to connect with others?",
-        },
-        {
-            "role": "user",
-            "chat_time": "3 May 2025",
-            "content": "I’ve thought about it, though it’s hard. But I also know I can’t keep going like this forever. Maybe it’s time to seek some help.",
-        },
-        {
-            "role": "assistant",
-            "chat_time": "3 May 2025",
-            "content": "It’s a big step, but a positive one. Exploring your emotions and past experiences could really help you understand how they’ve shaped your present. Therapy could be a great place to start, as it would help you connect with both yourself and others. Would you be open to trying it?",
-        },
-        {
-            "role": "user",
-            "chat_time": "3 May 2025",
-            "content": "I’m open to trying. Thank you for encouraging me. It feels like it might be a good starting point for me.",
-        },
-        {
-            "role": "assistant",
-            "chat_time": "3 May 2025",
-            "content": "I’m really glad to hear that. Taking that first step is always difficult, but you’ve made an important decision. If you need help finding the right therapist, or just want to talk, I’m here for you.",
-        },
-    ],
-    [
-        {
-            "role": "user",
-            "chat_time": "3 May 2025",
-            "content": "This week I’ve been feeling a bit off, especially when playing tennis. My body just doesn’t feel right.",
-        },
-        {
-            "role": "assistant",
-            "chat_time": "3 May 2025",
-            "content": "It sounds like you've been having some physical discomfort lately. Where specifically are you feeling unwell? Is there something particular about tennis that's been difficult?",
-        },
-        {
-            "role": "user",
-            "chat_time": "3 May 2025",
-            "content": "I just feel a bit fatigued, and every time I play tennis, my movements feel off. I used to play pretty well, but lately, I’ve been feeling weak and my movements aren’t as coordinated as they used to be.",
-        },
-        {
-            "role": "assistant",
-            "chat_time": "3 May 2025",
-            "content": "Physical discomfort can definitely affect performance, especially in a sport like tennis that requires quick reactions and coordination. Have you noticed anything specific that might be causing these changes? Could it be overtraining, or is there another physical issue making you feel off?",
-        },
-        {
-            "role": "user",
-            "chat_time": "3 May 2025",
-            "content": "I think it might be due to stress and lack of sleep recently. I just feel drained all the time. Plus, I’ve been frustrated with my tennis performance, which makes me feel even worse, like I’m stuck in a vicious cycle.",
-        },
-        {
-            "role": "assistant",
-            "chat_time": "3 May 2025",
-            "content": "Stress and poor sleep quality can definitely affect your physical performance, especially in sports. When you’re also feeling down about your performance, it’s easy to fall into a negative cycle. Have you tried anything to relieve some of the stress, or to improve your sleep?",
-        },
-        {
-            "role": "user",
-            "chat_time": "3 May 2025",
-            "content": "I’ve tried some relaxation techniques, like deep breathing and simple meditation, but it doesn’t seem to help much. I still can’t focus during tennis, and I feel like my mind is distracted by other things.",
-        },
-        {
-            "role": "assistant",
-            "chat_time": "3 May 2025",
-            "content": "Relaxation techniques like deep breathing and meditation can help reduce stress, but sometimes we need a more structured approach to improve both our physical and mental state. For example, you might want to focus on specific aspects of your tennis training, or adjust your rest and recovery time. Have you thought about setting smaller goals for yourself? Like focusing on a specific tennis move each day, rather than expecting perfection right away? That might help you gradually regain confidence.",
-        },
-        {
-            "role": "user",
-            "chat_time": "3 May 2025",
-            "content": "That’s a good idea. I think I’ve been expecting too much from myself, which just makes me more anxious. Maybe I can start with small practice sessions and gradually find my rhythm again.",
-        },
-        {
-            "role": "assistant",
-            "chat_time": "3 May 2025",
-            "content": "Exactly! Setting small and clear goals will help reduce pressure and give you a sense of accomplishment. And don’t forget that rest is just as important—taking time to recover will help you feel better physically and mentally. Both physical and mental recovery are key to improving your performance and health.",
-        },
-        {
-            "role": "user",
-            "chat_time": "3 May 2025",
-            "content": "Thanks, I feel like I need to rethink a few things. Maybe I really need to be kinder to myself and take things step by step.",
-        },
-        {
-            "role": "assistant",
-            "chat_time": "3 May 2025",
-            "content": "You’re on the right track. Rethinking things and giving yourself space and patience is so important. You’ve already taken the first step, now just keep moving forward, one step at a time. If you need anything, I’m always here to help.",
-        },
-    ],
-]
-
-# Acquiring memories
-memory = reader.get_memory(scene_data, type="chat", info={"user_id": "1234", "session_id": "2222"})
-
-for m_list in memory:
-    added_ids = my_tree_textual_memory.add(m_list)
-    for i, id in enumerate(added_ids):
-        print(f"{i}'th added result is:" + my_tree_textual_memory.get(id).memory)
-    my_tree_textual_memory.memory_manager.wait_reorganizer()
-
-time.sleep(60)
-
-init_time = time.time()
-results = my_tree_textual_memory.search(
-    "Talk about the user's childhood story?",
-    top_k=10,
-    info={
-        "query": "Talk about the user's childhood story?",
-        "user_id": "111",
-        "session_id": "2234",
-        "chat_history": [{"role": "user", "content": "xxxxx"}],
-    },
-)
-for i, r in enumerate(results):
-    r = r.to_dict()
-    print(f"{i}'th similar result is: " + str(r["memory"]))
-print(f"Successfully search {len(results)} memories in {round(time.time() - init_time)}s")
-
-# try this when use 'fine' mode (Note that you should pass the internet Config, refer to examples/core_memories/textual_internet_memoy.py)
-init_time = time.time()
-results_fine_search = my_tree_textual_memory.search(
-    "Recent news in the first city you've mentioned.",
-    top_k=10,
-    mode="fine",
-    info={
-        "query": "Recent news in NewYork",
-        "user_id": "111",
-        "session_id": "2234",
-        "chat_history": [
-            {"role": "user", "content": "I want to know three beautiful cities"},
-            {"role": "assistant", "content": "New York, London, and Shanghai"},
-        ],
-    },
-)
-
-for i, r in enumerate(results_fine_search):
-    r = r.to_dict()
-    print(f"{i}'th similar result is: " + str(r["memory"]))
-print(
-    f"Successfully search {len(results_fine_search)} memories in {round(time.time() - init_time)}s"
-)
-
-# find related nodes
-related_nodes = my_tree_textual_memory.get_relevant_subgraph("Painting")
-
-# get current memory_size
-print(f"Current Memory Size is {my_tree_textual_memory.get_current_memory_size()}")
-
-logger.info("Start doc search example...")
 # Processing Documents
 doc_paths = [
-    "./text1.txt",
-    "./text2.txt",
+    "evaluation/data/mmlongbench/documents/0b85477387a9d0cc33fca0f4becaa0e5.pdf",
+    "evaluation/data/mmlongbench/documents/0e94b4197b10096b1f4c699701570fbf.pdf",
 ]
 # Acquiring memories from documents
 doc_memory = reader.get_memory(doc_paths, "doc", info={"user_id": "1111", "session_id": "2222"})
 
 for m_list in doc_memory:
     added_ids = my_tree_textual_memory.add(m_list)
-    my_tree_textual_memory.memory_manager.wait_reorganizer()
-
-results = my_tree_textual_memory.search(
-    "Tell me about what memos consist of?",
-    top_k=30,
-    info={"query": "Tell me about what memos consist of?", "user_id": "111", "session": "2234"},
-)
-for i, r in enumerate(results):
-    r = r.to_dict()
-    print(f"{i}'th similar result is: " + str(r["memory"]))
-print(f"Successfully search {len(results)} memories")
-
-# close the synchronous thread in memory manager
-my_tree_textual_memory.memory_manager.close()
 
 # my_tree_textual_memory.dump
 my_tree_textual_memory.dump("tmp/my_tree_textual_memory")
-my_tree_textual_memory.drop()
diff --git a/examples/data/config/simple_struct_reader_config.json b/examples/data/config/simple_struct_reader_config.json
index 8a27de457..b0cc139c2 100644
--- a/examples/data/config/simple_struct_reader_config.json
+++ b/examples/data/config/simple_struct_reader_config.json
@@ -1,17 +1,24 @@
 {
   "llm": {
-    "backend": "ollama",
+    "backend": "openai",
     "config": {
-      "model_name_or_path": "qwen3:0.6b",
-      "temperature": 0.0,
+      "model_name_or_path": "gpt-4o-mini",
+      "temperature": 0.8,
+      "max_tokens": 1024,
+      "top_p": 0.9,
+      "top_k": 50,
       "remove_think_prefix": true,
-      "max_tokens": 8192
+      "api_key": "sk-5hTdYsmX7ErNsLQKJ8jUTahHzyDjQ3H5mpgAeTfUfeRyIUYV",
+      "api_base": "http://123.129.219.111:3000/v1"
     }
   },
   "embedder": {
-    "backend": "ollama",
+    "backend": "universal_api",
     "config": {
-      "model_name_or_path": "nomic-embed-text:latest"
+      "provider": "openai",
+      "api_key": "sk-5hTdYsmX7ErNsLQKJ8jUTahHzyDjQ3H5mpgAeTfUfeRyIUYV",
+      "model_name_or_path": "text-embedding-3-large",
+      "base_url": "http://123.129.219.111:3000/v1"
     }
   },
   "chunker": {
diff --git a/examples/data/config/tree_config_shared_database.json b/examples/data/config/tree_config_shared_database.json
index 914f3c725..d8dc40cf6 100644
--- a/examples/data/config/tree_config_shared_database.json
+++ b/examples/data/config/tree_config_shared_database.json
@@ -1,40 +1,55 @@
 {
   "extractor_llm": {
-    "backend": "ollama",
+    "backend": "openai",
     "config": {
-      "model_name_or_path": "qwen3:0.6b",
-      "temperature": 0.0,
+      "model_name_or_path": "gpt-4o-mini",
+      "temperature": 0.8,
+      "max_tokens": 1024,
+      "top_p": 0.9,
+      "top_k": 50,
       "remove_think_prefix": true,
-      "max_tokens": 8192
+      "api_key": "sk-5hTdYsmX7ErNsLQKJ8jUTahHzyDjQ3H5mpgAeTfUfeRyIUYV",
+      "api_base": "http://123.129.219.111:3000/v1"
     }
   },
   "dispatcher_llm": {
-    "backend": "ollama",
+    "backend": "openai",
     "config": {
-      "model_name_or_path": "qwen3:0.6b",
-      "temperature": 0.0,
+      "model_name_or_path": "gpt-4o-mini",
+      "temperature": 0.8,
+      "max_tokens": 1024,
+      "top_p": 0.9,
+      "top_k": 50,
       "remove_think_prefix": true,
-      "max_tokens": 8192
+      "api_key": "sk-5hTdYsmX7ErNsLQKJ8jUTahHzyDjQ3H5mpgAeTfUfeRyIUYV",
+      "api_base": "http://123.129.219.111:3000/v1"
     }
   },
   "embedder": {
-    "backend": "ollama",
+    "backend": "universal_api",
     "config": {
-      "model_name_or_path": "nomic-embed-text:latest"
+      "provider": "openai",
+      "api_key": "sk-5hTdYsmX7ErNsLQKJ8jUTahHzyDjQ3H5mpgAeTfUfeRyIUYV",
+      "model_name_or_path": "text-embedding-3-large",
+      "base_url": "http://123.129.219.111:3000/v1"
     }
   },
   "graph_db": {
-    "backend": "neo4j",
+    "backend": "nebular",
     "config": {
-      "uri": "bolt://localhost:7687",
-      "user": "neo4j",
-      "password": "12345678",
-      "db_name": "shared-tree-textual-memory",
-      "user_name": "alice",
-      "auto_create": true,
+      "uri": [
+        "106.14.142.60:9669",
+        "120.55.160.164:9669",
+        "106.15.38.5:9669"
+      ],
+      "user": "root",
+      "password": "NebulaMemOS0724",
+      "space": "nebula-longbench-stx-new",
+      "user_name": "user_name",
       "use_multi_db": false,
-      "embedding_dimension": 768
+      "auto_create": true,
+      "embedding_dimension": 3072
     }
   },
-  "reorganize": true
+  "reorganize": false
 }
diff --git a/examples/mem_os/simple_openapi_memos.py b/examples/mem_os/simple_openapi_memos.py
index 3477106fb..cf26c17f9 100644
--- a/examples/mem_os/simple_openapi_memos.py
+++ b/examples/mem_os/simple_openapi_memos.py
@@ -14,6 +14,7 @@
 
 load_dotenv()
 
+db_name = "stx-pdf-doc-test"
 # 1. Create MOS Config and set openai config
 print(f"🚀 [{datetime.now().strftime('%H:%M:%S')}] Starting to create MOS configuration...")
 start_time = time.time()
@@ -53,9 +54,9 @@
                 "backend": "universal_api",
                 "config": {
                     "provider": "openai",
-                    "api_key": "sk-xxxxxxxxx",
+                    "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
                     "model_name_or_path": "text-embedding-3-large",
-                    "base_url": "http://xxx.xxx.xxx.xxx:xxxx/v1"
+                    "base_url": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
                 },
             },
             "chunker": {
@@ -109,17 +110,17 @@
                         "uri": neo4j_uri,
                         "user": "neo4j",
                         "password": "iaarlichunyu",
-                        "db_name": f"db{user_name.replace('-', '')}",
+                        "db_name": db_name,
                         "auto_create": True,
                     },
                 },
                 "embedder": {
                     "backend": "universal_api",
                     "config": {
-                         "provider": "openai",
-                          "api_key": "sk-xxxxxxxxx",
-                          "model_name_or_path": "text-embedding-3-large",
-                          "base_url": "http://xxx.xxx.xxx.xxx:xxxx/v1"
+                        "provider": "openai",
+                        "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
+                        "model_name_or_path": "text-embedding-3-large",
+                        "base_url": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
                     },
                 },
                 "reorganize": True,
@@ -267,8 +268,8 @@ def filter_memory_data(memories_data):
 print(f"🚀 [{datetime.now().strftime('%H:%M:%S')}] Starting to add document...")
 start_time = time.time()
 
-## 7.1 add pdf for ./tmp/data if use doc mem mos.add(doc_path="./tmp/data/")
-mos.add(doc_path="examples/data/docs/test/")
+# 7.1 add pdf for ./tmp/data if use doc mem mos.add(doc_path="./tmp/data/")
+mos.add(doc_path=["examples/data/docs/test/0b85477387a9d0cc33fca0f4becaa0e5.pdf"])
 get_all_results = mos.get_all()
 filtered_results = filter_memory_data(get_all_results)
 
diff --git a/src/memos/chunkers/sentence_chunker.py b/src/memos/chunkers/sentence_chunker.py
index 4de0cf32b..485d45f89 100644
--- a/src/memos/chunkers/sentence_chunker.py
+++ b/src/memos/chunkers/sentence_chunker.py
@@ -1,3 +1,5 @@
+from langchain_text_splitters import Language, RecursiveCharacterTextSplitter
+
 from memos.configs.chunker import SentenceChunkerConfig
 from memos.dependency import require_python_package
 from memos.log import get_logger
@@ -17,24 +19,21 @@ class SentenceChunker(BaseChunker):
         install_link="https://docs.chonkie.ai/python-sdk/getting-started/installation",
     )
     def __init__(self, config: SentenceChunkerConfig):
-        from chonkie import SentenceChunker as ChonkieSentenceChunker
-
         self.config = config
-        self.chunker = ChonkieSentenceChunker(
-            tokenizer_or_token_counter=config.tokenizer_or_token_counter,
+        self.chunker = RecursiveCharacterTextSplitter.from_language(
+            language=Language.PYTHON,
             chunk_size=config.chunk_size,
             chunk_overlap=config.chunk_overlap,
-            min_sentences_per_chunk=config.min_sentences_per_chunk,
         )
         logger.info(f"Initialized SentenceChunker with config: {config}")
 
     def chunk(self, text: str) -> list[Chunk]:
         """Chunk the given text into smaller chunks based on sentences."""
-        chonkie_chunks = self.chunker.chunk(text)
+        chonkie_chunks = self.chunker.split_text(text)
 
         chunks = []
         for c in chonkie_chunks:
-            chunk = Chunk(text=c.text, token_count=c.token_count, sentences=c.sentences)
+            chunk = Chunk(text=c, token_count=-1, sentences=[])
             chunks.append(chunk)
 
         logger.debug(f"Generated {len(chunks)} chunks from input text")
diff --git a/src/memos/configs/embedder.py b/src/memos/configs/embedder.py
index 70095a194..6229680b0 100644
--- a/src/memos/configs/embedder.py
+++ b/src/memos/configs/embedder.py
@@ -45,10 +45,11 @@ class UniversalAPIEmbedderConfig(BaseEmbedderConfig):
     OpenAI, etc.
     """
 
-    provider: str = Field(..., description="Provider name, e.g., 'openai'")
-    api_key: str = Field(..., description="API key for the embedding provider")
+    provider: str = Field(default="openai", description="Provider name, e.g., 'openai'")
+    api_key: str = Field(default="EMPTY", description="API key for the embedding provider")
     base_url: str | None = Field(
-        default=None, description="Optional base URL for custom or proxied endpoint"
+        default="http://111.31.225.48:8078/v1",
+        description="Optional base URL for custom or proxied endpoint",
     )
 
 
diff --git a/src/memos/configs/mem_os.py b/src/memos/configs/mem_os.py
index 0645fce44..7da2f791d 100644
--- a/src/memos/configs/mem_os.py
+++ b/src/memos/configs/mem_os.py
@@ -43,7 +43,7 @@ class MOSConfig(BaseConfig):
         description="Maximum number of turns to keep in the conversation history",
     )
     top_k: int = Field(
-        default=5,
+        default=50,
         description="Maximum number of memories to retrieve for each query",
     )
     enable_textual_memory: bool = Field(
diff --git a/src/memos/configs/reranker.py b/src/memos/configs/reranker.py
new file mode 100644
index 000000000..b4c243b75
--- /dev/null
+++ b/src/memos/configs/reranker.py
@@ -0,0 +1,18 @@
+# memos/configs/reranker.py
+from __future__ import annotations
+
+from typing import Any
+
+from pydantic import BaseModel, Field
+
+
+class RerankerConfigFactory(BaseModel):
+    """
+    {
+      "backend": "http_bge" | "cosine_local" | "noop",
+      "config": { ... backend-specific ... }
+    }
+    """
+
+    backend: str = Field(..., description="Reranker backend id")
+    config: dict[str, Any] = Field(default_factory=dict, description="Backend-specific options")
diff --git a/src/memos/graph_dbs/nebular.py b/src/memos/graph_dbs/nebular.py
index 17cadafeb..26b70ea2e 100644
--- a/src/memos/graph_dbs/nebular.py
+++ b/src/memos/graph_dbs/nebular.py
@@ -621,6 +621,7 @@ def get_nodes(self, ids: list[str], include_embedding: bool = False) -> list[dic
                     props = {k: v.value for k, v in row.items()}
                 nodes.append(self._parse_node(props))
         except Exception as e:
+            print("============> query:", query)
             logger.error(
                 f"[get_nodes] Failed to retrieve nodes {ids}: {e}, trace: {traceback.format_exc()}"
             )
@@ -938,7 +939,7 @@ def get_by_metadata(self, filters: list[dict[str, Any]]) -> list[str]:
 
         def _escape_value(value):
             if isinstance(value, str):
-                return f'"{value}"'
+                return f'"{value.replace('"', '\\"')}"'
             elif isinstance(value, list):
                 return "[" + ", ".join(_escape_value(v) for v in value) + "]"
             else:
diff --git a/src/memos/graph_dbs/neo4j.py b/src/memos/graph_dbs/neo4j.py
index 86e1b5d75..8a20b4602 100644
--- a/src/memos/graph_dbs/neo4j.py
+++ b/src/memos/graph_dbs/neo4j.py
@@ -69,7 +69,14 @@ def __init__(self, config: Neo4jGraphDBConfig):
         from neo4j import GraphDatabase
 
         self.config = config
-        self.driver = GraphDatabase.driver(config.uri, auth=(config.user, config.password))
+        self.driver = GraphDatabase.driver(
+            config.uri,
+            auth=(config.user, config.password),
+            connection_timeout=600,
+            max_connection_lifetime=3600,
+            max_connection_pool_size=50,
+            max_transaction_retry_time=60,
+        )
         self.db_name = config.db_name
         self.user_name = config.user_name
 
diff --git a/src/memos/llms/openai.py b/src/memos/llms/openai.py
index 148f6a2ca..66e85ae27 100644
--- a/src/memos/llms/openai.py
+++ b/src/memos/llms/openai.py
@@ -25,9 +25,6 @@ def generate(self, messages: MessageList) -> str:
             model=self.config.model_name_or_path,
             messages=messages,
             extra_body=self.config.extra_body,
-            temperature=self.config.temperature,
-            max_tokens=self.config.max_tokens,
-            top_p=self.config.top_p,
         )
         logger.info(f"Response from OpenAI: {response.model_dump_json()}")
         response_content = response.choices[0].message.content
diff --git a/src/memos/mem_os/core.py b/src/memos/mem_os/core.py
index 679a50051..03350346f 100644
--- a/src/memos/mem_os/core.py
+++ b/src/memos/mem_os/core.py
@@ -267,32 +267,6 @@ def chat(self, query: str, user_id: str | None = None, base_prompt: str | None =
 
         chat_history = self.chat_history_manager[target_user_id]
 
-        # Helper function to check if memories are sufficient
-        def check_memory_sufficiency(memories: list, query_str: str) -> bool:
-            """Use model to determine if memories are sufficient to answer the query"""
-            # Construct judgment prompt
-            judgment_prompt = (
-                "Strictly based on the provided memory fragments, determine if you can answer the user's question. "
-                "Respond only with 'yes' or 'no'\n\n"
-                "### Memory Fragments:\n"
-                f"{self._str_memories(memories)}\n\n"
-                "### User Question:\n"
-                f"{query_str}"
-            )
-
-            # Call model for judgment
-            judgment_response = self.chat_llm.generate(
-                [
-                    {"role": "system", "content": "You are a memory sufficiency evaluator"},
-                    {"role": "user", "content": judgment_prompt},
-                ]
-            )
-
-            # Parse model response
-            judgment = judgment_response.strip().lower()
-            logger.info(f"🧠 [Memory Judgment] Result: '{judgment}'")
-            return "yes" in judgment
-
         if self.config.enable_textual_memory and self.mem_cubes:
             memories_all = []
             for mem_cube_id, mem_cube in self.mem_cubes.items():
@@ -315,7 +289,7 @@ def check_memory_sufficiency(memories: list, query_str: str) -> bool:
 
                 memories = mem_cube.text_mem.search(
                     query,
-                    top_k=self.config.top_k,
+                    top_k=100,
                     info={
                         "user_id": target_user_id,
                         "session_id": self.session_id,
@@ -323,23 +297,13 @@ def check_memory_sufficiency(memories: list, query_str: str) -> bool:
                     },
                 )
                 memories_all.extend(memories)
-            logger.info(f"🧠 [Memory] Searched memories:\n{self._str_memories(memories_all)}\n")
-
-            # Check memory sufficiency if we have memories
-            memory_sufficient = False
-            if memories_all:
-                memory_sufficient = check_memory_sufficiency(memories_all, query)
 
-            # Return early if memories are insufficient
-            if not memory_sufficient and memories_all:
-                logger.warning("🧠 [Memory] Insufficient information to answer")
-                for memory_item in memories_all:
-                    if hasattr(memory_item.metadata, "sources") and memory_item.metadata.sources:
-                        memory_item.memory = str(memory_item.metadata.sources)
+            logger.info(f"🧠 [Memory] Searched memories:\n{self._str_memories(memories_all)}\n")
 
             system_prompt = self._build_system_prompt(memories_all, base_prompt=base_prompt)
         else:
             system_prompt = self._build_system_prompt(base_prompt=base_prompt)
+
         current_messages = [
             {"role": "system", "content": system_prompt},
             *chat_history.chat_history,
@@ -373,6 +337,7 @@ def check_memory_sufficiency(memories: list, query_str: str) -> bool:
         # submit message to scheduler
         for accessible_mem_cube in accessible_cubes:
             mem_cube_id = accessible_mem_cube.cube_id
+            mem_cube_id = mem_cube_id.replace("cube_", "")
             mem_cube = self.mem_cubes[mem_cube_id]
             if self.enable_mem_scheduler and self.mem_scheduler is not None:
                 message_item = ScheduleMessageItem(
@@ -794,7 +759,6 @@ def add(
             mem_ids = []
             for mem in doc_memories:
                 mem_id_list: list[str] = self.mem_cubes[mem_cube_id].text_mem.add(mem)
-                self.mem_cubes[mem_cube_id].text_mem.memory_manager.wait_reorganizer()
                 mem_ids.extend(mem_id_list)
 
             # submit messages for scheduler
diff --git a/src/memos/mem_reader/simple_struct.py b/src/memos/mem_reader/simple_struct.py
index 3f9428760..ec4385421 100644
--- a/src/memos/mem_reader/simple_struct.py
+++ b/src/memos/mem_reader/simple_struct.py
@@ -107,7 +107,7 @@ def _process_chat_data(self, scene_data_info, info):
 
         return chat_read_nodes
 
-    def get_memory(
+    async def get_memory(
         self, scene_data: list, type: str, info: dict[str, Any]
     ) -> list[list[TextualMemoryItem]]:
         """
@@ -145,7 +145,7 @@ def get_memory(
         if not all(isinstance(info[field], str) for field in required_fields):
             raise ValueError("user_id and session_id must be strings")
 
-        list_scene_data_info = self.get_scene_data_info(scene_data, type)
+        list_scene_data_info = await self.get_scene_data_info(scene_data, type)
 
         memory_list = []
 
@@ -168,7 +168,7 @@ def get_memory(
 
         return memory_list
 
-    def get_scene_data_info(self, scene_data: list, type: str) -> list[str]:
+    async def get_scene_data_info(self, scene_data: list, type: str) -> list[str]:
         """
         Get raw information from scene_data.
         If scene_data contains dictionaries, convert them to strings.
@@ -210,7 +210,7 @@ def get_scene_data_info(self, scene_data: list, type: str) -> list[str]:
             for item in scene_data:
                 try:
                     if os.path.exists(item):
-                        parsed_text = parser.parse(item)
+                        parsed_text = await parser.parse(item)
                         results.append(
                             {"file": "pure_text", "file_name": item, "text": parsed_text}
                         )
@@ -223,7 +223,14 @@ def get_scene_data_info(self, scene_data: list, type: str) -> list[str]:
         return results
 
     def _process_doc_data(self, scene_data_info, info):
-        chunks = self.chunker.chunk(scene_data_info["text"])
+        full_text = scene_data_info["text"]
+        for ch in ["\\", "\n", "\r"]:
+            full_text = full_text.replace(ch, " ")
+        full_text = re.sub(r"\$\d+\\\%\$", "", full_text)
+        full_text = full_text.replace('"', '\\"')
+        full_text = full_text.strip()
+
+        chunks = self.chunker.chunk(full_text)
         messages = []
         for chunk in chunks:
             lang = detect_lang(chunk.text)
@@ -242,6 +249,8 @@ def _process_doc_data(self, scene_data_info, info):
 
         processed_chunks = [self.parse_json_result(r) for r in processed_chunks]
         doc_nodes = []
+
+        file_name = os.path.basename(scene_data_info["file_name"])
         for i, chunk_res in enumerate(processed_chunks):
             if chunk_res:
                 node_i = TextualMemoryItem(
@@ -255,7 +264,7 @@ def _process_doc_data(self, scene_data_info, info):
                         key=chunk_res["key"],
                         embedding=self.embedder.embed([chunk_res["value"]])[0],
                         usage=[],
-                        sources=[str({scene_data_info["file_name"]: chunks[i].text})],
+                        sources=[f"{file_name}####{chunks[i].text}"],
                         background="",
                         confidence=0.99,
                         type="fact",
diff --git a/src/memos/memories/textual/tree_text_memory/organize/handler.py b/src/memos/memories/textual/tree_text_memory/organize/handler.py
index a1121fcd2..4b0a9806e 100644
--- a/src/memos/memories/textual/tree_text_memory/organize/handler.py
+++ b/src/memos/memories/textual/tree_text_memory/organize/handler.py
@@ -1,5 +1,6 @@
 import json
 import re
+
 from datetime import datetime
 
 from dateutil import parser
@@ -14,6 +15,7 @@
     MEMORY_RELATION_RESOLVER_PROMPT,
 )
 
+
 logger = get_logger(__name__)
 
 
@@ -50,14 +52,10 @@ def detect(self, memory, top_k: int = 5, scope=None):
             ]
             result = self.llm.generate(prompt).strip()
             if result == "contradictory":
-                logger.warning(
-                    f'detected "{memory.memory}" <==CONFLICT==> "{embedding_candidate.memory}"'
-                )
+                logger.warning("detected <==CONFLICT==>")
                 detected_relationships.append([memory, embedding_candidate, "contradictory"])
             elif result == "redundant":
-                logger.warning(
-                    f'detected "{memory.memory}" <==REDUNDANT==> "{embedding_candidate.memory}"'
-                )
+                logger.warning("detected <==REDUNDANT==>")
                 detected_relationships.append([memory, embedding_candidate, "redundant"])
             elif result == "independent":
                 pass
diff --git a/src/memos/memories/textual/tree_text_memory/organize/reorganizer.py b/src/memos/memories/textual/tree_text_memory/organize/reorganizer.py
index 586deaabe..a3f06b27d 100644
--- a/src/memos/memories/textual/tree_text_memory/organize/reorganizer.py
+++ b/src/memos/memories/textual/tree_text_memory/organize/reorganizer.py
@@ -350,7 +350,7 @@ def _local_subcluster(
 
         joined_scene = "\n".join(scene_lines)
         if len(joined_scene) > max_length:
-            logger.warning(f"Sub-cluster too long: {joined_scene}")
+            logger.warning(f"Sub-cluster too long: {joined_scene[:100]}")
         prompt = LOCAL_SUBCLUSTER_PROMPT.replace("{joined_scene}", joined_scene[:max_length])
 
         messages = [{"role": "user", "content": prompt}]
diff --git a/src/memos/memories/textual/tree_text_memory/retrieve/reranker.py b/src/memos/memories/textual/tree_text_memory/retrieve/reranker.py
index 479622f33..861343e20 100644
--- a/src/memos/memories/textual/tree_text_memory/retrieve/reranker.py
+++ b/src/memos/memories/textual/tree_text_memory/retrieve/reranker.py
@@ -1,5 +1,3 @@
-import copy
-
 import numpy as np
 
 from memos.embedders.factory import OllamaEmbedder
@@ -76,15 +74,7 @@ def rerank(
             list(tuple): Ranked list of memory items with similarity score.
         """
         # Step 1: Filter out items without embeddings
-        items_with_embeddings = []
-        for item in graph_results:
-            if item.metadata.embedding:
-                items_with_embeddings.append(copy.deepcopy(item))
-                detail = str(item.metadata.sources)
-                item.memory = detail
-                item.metadata.embedding = self.embedder.embed([detail])[0]
-                items_with_embeddings.append(copy.deepcopy(item))
-
+        items_with_embeddings = [item for item in graph_results if item.metadata.embedding]
         embeddings = [item.metadata.embedding for item in items_with_embeddings]
 
         if not embeddings:
diff --git a/src/memos/memories/textual/tree_text_memory/retrieve/searcher.py b/src/memos/memories/textual/tree_text_memory/retrieve/searcher.py
index a225d2287..4f4d9c0f0 100644
--- a/src/memos/memories/textual/tree_text_memory/retrieve/searcher.py
+++ b/src/memos/memories/textual/tree_text_memory/retrieve/searcher.py
@@ -1,19 +1,23 @@
 import concurrent.futures
+import copy
 import json
+import os
+import traceback
 
 from datetime import datetime
 
-from memos.embedders.factory import OllamaEmbedder
-from memos.graph_dbs.factory import Neo4jGraphDB
+from memos.configs.reranker import RerankerConfigFactory
+from memos.embedders.factory import UniversalAPIEmbedder
+from memos.graph_dbs.factory import NebulaGraphDB
 from memos.llms.factory import AzureLLM, OllamaLLM, OpenAILLM
 from memos.log import get_logger
 from memos.memories.textual.item import SearchedTreeNodeTextualMemoryMetadata, TextualMemoryItem
+from memos.reranker.factory import RerankerFactory
 from memos.utils import timed
 
 from .internet_retriever_factory import InternetRetrieverFactory
 from .reasoner import MemoryReasoner
 from .recall import GraphMemoryRetriever
-from .reranker import MemoryReranker
 from .task_goal_parser import TaskGoalParser
 
 
@@ -24,16 +28,30 @@ class Searcher:
     def __init__(
         self,
         dispatcher_llm: OpenAILLM | OllamaLLM | AzureLLM,
-        graph_store: Neo4jGraphDB,
-        embedder: OllamaEmbedder,
+        graph_store: NebulaGraphDB,
+        embedder: UniversalAPIEmbedder,
         internet_retriever: InternetRetrieverFactory | None = None,
     ):
+        bge_url = os.getenv("BGE_RERANKER_URL")  # e.g., "http://xxx.x.xxxxx.xxx:xxxx/v1/rerank"
+        if bge_url:
+            http_cfg = RerankerConfigFactory.model_validate(
+                {
+                    "backend": "http_bge",
+                    "config": {
+                        "url": bge_url,
+                        "model": os.getenv("BGE_RERANKER_MODEL", "bge-reranker-v2-m3"),
+                        "timeout": int(os.getenv("BGE_RERANKER_TIMEOUT", "10")),
+                        "boost_weights": {"user_id": 0.5, "tags": 0.2},
+                    },
+                }
+            )
+            reranker = RerankerFactory.from_config(http_cfg)
+            self.reranker = reranker
+
         self.graph_store = graph_store
         self.embedder = embedder
-
         self.task_goal_parser = TaskGoalParser(dispatcher_llm)
         self.graph_retriever = GraphMemoryRetriever(self.graph_store, self.embedder)
-        self.reranker = MemoryReranker(dispatcher_llm, self.embedder)
         self.reasoner = MemoryReasoner(dispatcher_llm)
 
         # Create internet retriever from config if provided
@@ -177,10 +195,25 @@ def _retrieve_from_working_memory(
         items = self.graph_retriever.retrieve(
             query=query, parsed_goal=parsed_goal, top_k=top_k, memory_scope="WorkingMemory"
         )
+
+        items_with_detail = []
+        for item in items:
+            if item.metadata.embedding:
+                items_with_detail.append(copy.deepcopy(item))
+                detail = str(item.metadata.sources)
+                item.memory = detail
+                try:
+                    item.metadata.embedding = self.embedder.embed([detail])[0]
+                except Exception as e:
+                    print("Error:", e)
+                    traceback.print_exc()
+                    print("=====> detail:", detail)
+                items_with_detail.append(copy.deepcopy(item))
+
         return self.reranker.rerank(
             query=query,
             query_embedding=query_embedding[0],
-            graph_results=items,
+            graph_results=items_with_detail,
             top_k=top_k,
             parsed_goal=parsed_goal,
         )
@@ -208,10 +241,20 @@ def _retrieve_from_long_term_and_user(
                 top_k=top_k * 2,
                 memory_scope="UserMemory",
             )
+
+        items_with_detail = []
+        for item in results:
+            if item.metadata.embedding:
+                items_with_detail.append(copy.deepcopy(item))
+                detail = str(item.metadata.sources)
+                item.memory = detail
+                item.metadata.embedding = self.embedder.embed([detail])[0]
+                items_with_detail.append(copy.deepcopy(item))
+
         return self.reranker.rerank(
             query=query,
             query_embedding=query_embedding[0],
-            graph_results=results,
+            graph_results=items_with_detail,
             top_k=top_k * 2,
             parsed_goal=parsed_goal,
         )
diff --git a/src/memos/memos_tools/notification_utils.py b/src/memos/memos_tools/notification_utils.py
index 390a9a556..af104e08e 100644
--- a/src/memos/memos_tools/notification_utils.py
+++ b/src/memos/memos_tools/notification_utils.py
@@ -2,6 +2,7 @@
 Notification utilities for MemOS product.
 """
 
+import asyncio
 import logging
 
 from collections.abc import Callable
@@ -51,6 +52,51 @@ def send_online_bot_notification(
         logger.warning(f"Failed to send online bot notification: {e}")
 
 
+async def send_online_bot_notification_async(
+    online_bot: Callable | None,
+    header_name: str,
+    sub_title_name: str,
+    title_color: str,
+    other_data1: dict[str, Any],
+    other_data2: dict[str, Any],
+    emoji: dict[str, str],
+) -> None:
+    """
+    Send notification via online_bot asynchronously if available.
+
+    Args:
+        online_bot: The online_bot function or None
+        header_name: Header name for the report
+        sub_title_name: Subtitle for the report
+        title_color: Title color
+        other_data1: First data dict
+        other_data2: Second data dict
+        emoji: Emoji configuration dict
+    """
+    if online_bot is None:
+        return
+
+    try:
+        # Run the potentially blocking notification in a thread pool
+        loop = asyncio.get_event_loop()
+        await loop.run_in_executor(
+            None,
+            lambda: online_bot(
+                header_name=header_name,
+                sub_title_name=sub_title_name,
+                title_color=title_color,
+                other_data1=other_data1,
+                other_data2=other_data2,
+                emoji=emoji,
+            ),
+        )
+
+        logger.info(f"Online bot notification sent successfully (async): {header_name}")
+
+    except Exception as e:
+        logger.warning(f"Failed to send online bot notification (async): {e}")
+
+
 def send_error_bot_notification(
     error_bot: Callable | None,
     err: str,
diff --git a/src/memos/memos_tools/singleton.py b/src/memos/memos_tools/singleton.py
new file mode 100644
index 000000000..c3171edb0
--- /dev/null
+++ b/src/memos/memos_tools/singleton.py
@@ -0,0 +1,174 @@
+"""
+Singleton decorator module for caching factory instances to avoid excessive memory usage
+from repeated initialization.
+"""
+
+import hashlib
+import json
+
+from collections.abc import Callable
+from functools import wraps
+from typing import Any, TypeVar
+from weakref import WeakValueDictionary
+
+
+T = TypeVar("T")
+
+
+class FactorySingleton:
+    """Factory singleton manager that caches instances based on configuration parameters"""
+
+    def __init__(self):
+        # Use weak reference dictionary for automatic cleanup when instances are no longer referenced
+        self._instances: dict[str, WeakValueDictionary] = {}
+
+    def _generate_cache_key(self, config: Any, *args, **kwargs) -> str:
+        """Generate cache key based on configuration only (ignoring other parameters)"""
+
+        # Handle configuration objects - only use the config parameter
+        if hasattr(config, "model_dump"):  # Pydantic model
+            config_data = config.model_dump()
+        elif hasattr(config, "dict"):  # Legacy Pydantic model
+            config_data = config.dict()
+        elif isinstance(config, dict):
+            config_data = config
+        else:
+            # For other types, try to convert to string
+            config_data = str(config)
+
+        # Filter out time-related fields that shouldn't affect caching
+        filtered_config = self._filter_temporal_fields(config_data)
+
+        # Generate hash key based only on config
+        try:
+            cache_str = json.dumps(filtered_config, sort_keys=True, ensure_ascii=False, default=str)
+        except (TypeError, ValueError):
+            # If JSON serialization fails, convert the entire config to string
+            cache_str = str(filtered_config)
+
+        return hashlib.md5(cache_str.encode("utf-8")).hexdigest()
+
+    def _filter_temporal_fields(self, config_data: Any) -> Any:
+        """Filter out temporal fields that shouldn't affect instance caching"""
+        if isinstance(config_data, dict):
+            filtered = {}
+            for key, value in config_data.items():
+                # Skip common temporal field names
+                if key.lower() in {
+                    "created_at",
+                    "updated_at",
+                    "timestamp",
+                    "time",
+                    "date",
+                    "created_time",
+                    "updated_time",
+                    "last_modified",
+                    "modified_at",
+                    "start_time",
+                    "end_time",
+                    "execution_time",
+                    "run_time",
+                }:
+                    continue
+                # Recursively filter nested dictionaries
+                filtered[key] = self._filter_temporal_fields(value)
+            return filtered
+        elif isinstance(config_data, list):
+            # Recursively filter lists
+            return [self._filter_temporal_fields(item) for item in config_data]
+        else:
+            # For primitive types, return as-is
+            return config_data
+
+    def get_or_create(self, factory_class: type, cache_key: str, creator_func: Callable) -> Any:
+        """Get or create instance"""
+        class_name = factory_class.__name__
+
+        if class_name not in self._instances:
+            self._instances[class_name] = WeakValueDictionary()
+
+        class_cache = self._instances[class_name]
+
+        if cache_key in class_cache:
+            return class_cache[cache_key]
+
+        # Create new instance
+        instance = creator_func()
+        class_cache[cache_key] = instance
+        return instance
+
+    def clear_cache(self, factory_class: type | None = None):
+        """Clear cache"""
+        if factory_class:
+            class_name = factory_class.__name__
+            if class_name in self._instances:
+                self._instances[class_name].clear()
+        else:
+            for cache in self._instances.values():
+                cache.clear()
+
+
+# Global singleton manager
+_factory_singleton = FactorySingleton()
+
+
+def singleton_factory(factory_class: type | str | None = None):
+    """
+    Factory singleton decorator
+
+    Usage:
+    @singleton_factory()
+    def from_config(cls, config):
+        return SomeClass(config)
+
+    Or specify factory class:
+    @singleton_factory(EmbedderFactory)
+    def from_config(cls, config):
+        return SomeClass(config)
+    """
+
+    def decorator(func: Callable[..., T]) -> Callable[..., T]:
+        @wraps(func)
+        def wrapper(*args, **kwargs) -> T:
+            # Determine factory class and config parameter
+            target_factory_class = factory_class
+            config = None
+
+            # Simple logic: check if first parameter is a class or config
+            if args:
+                if hasattr(args[0], "__name__") and hasattr(args[0], "__module__"):
+                    # First parameter is a class (cls), so this is a @classmethod
+                    if target_factory_class is None:
+                        target_factory_class = args[0]
+                    config = args[1] if len(args) > 1 else None
+                else:
+                    # First parameter is config, so this is a @staticmethod
+                    if target_factory_class is None:
+                        raise ValueError(
+                            "Factory class must be explicitly specified for static methods"
+                        )
+                    if isinstance(target_factory_class, str):
+                        # Convert string to a mock class for caching purposes
+                        class MockFactoryClass:
+                            __name__ = target_factory_class
+
+                        target_factory_class = MockFactoryClass
+                    config = args[0]
+
+            if config is None:
+                # If no configuration parameter, call original function directly
+                return func(*args, **kwargs)
+
+            # Generate cache key based only on config
+            cache_key = _factory_singleton._generate_cache_key(config)
+
+            # Function to create instance
+            def creator():
+                return func(*args, **kwargs)
+
+            # Get or create instance
+            return _factory_singleton.get_or_create(target_factory_class, cache_key, creator)
+
+        return wrapper
+
+    return decorator
diff --git a/src/memos/memos_tools/thread_safe_dict.py b/src/memos/memos_tools/thread_safe_dict.py
index dc7f98f43..1e9fb6a1d 100644
--- a/src/memos/memos_tools/thread_safe_dict.py
+++ b/src/memos/memos_tools/thread_safe_dict.py
@@ -7,10 +7,15 @@
 from collections.abc import ItemsView, Iterator, KeysView, ValuesView
 from typing import Generic, TypeVar
 
+from memos.log import get_logger
+from memos.utils import timed
+
 
 K = TypeVar("K")
 V = TypeVar("V")
 
+logger = get_logger(__name__)
+
 
 class ReadWriteLock:
     """A simple read-write lock implementation. use for product-server scenario"""
@@ -19,6 +24,7 @@ def __init__(self):
         self._read_ready = threading.Condition(threading.RLock())
         self._readers = 0
 
+    @timed
     def acquire_read(self):
         """Acquire a read lock. Multiple readers can hold the lock simultaneously."""
         self._read_ready.acquire()
@@ -37,6 +43,7 @@ def release_read(self):
         finally:
             self._read_ready.release()
 
+    @timed
     def acquire_write(self):
         """Acquire a write lock. Only one writer can hold the lock."""
         self._read_ready.acquire()
@@ -67,6 +74,7 @@ def __init__(self, initial_dict: dict[K, V] | None = None):
         self._dict: dict[K, V] = initial_dict.copy() if initial_dict else {}
         self._lock = ReadWriteLock()
 
+    @timed
     def __getitem__(self, key: K) -> V:
         """Get item by key."""
         self._lock.acquire_read()
@@ -75,6 +83,7 @@ def __getitem__(self, key: K) -> V:
         finally:
             self._lock.release_read()
 
+    @timed
     def __setitem__(self, key: K, value: V) -> None:
         """Set item by key."""
         self._lock.acquire_write()
@@ -83,6 +92,7 @@ def __setitem__(self, key: K, value: V) -> None:
         finally:
             self._lock.release_write()
 
+    @timed
     def __delitem__(self, key: K) -> None:
         """Delete item by key."""
         self._lock.acquire_write()
@@ -91,6 +101,7 @@ def __delitem__(self, key: K) -> None:
         finally:
             self._lock.release_write()
 
+    @timed
     def __contains__(self, key: K) -> bool:
         """Check if key exists in dictionary."""
         self._lock.acquire_read()
@@ -99,6 +110,7 @@ def __contains__(self, key: K) -> bool:
         finally:
             self._lock.release_read()
 
+    @timed
     def __len__(self) -> int:
         """Get length of dictionary."""
         self._lock.acquire_read()
@@ -115,6 +127,7 @@ def __bool__(self) -> bool:
         finally:
             self._lock.release_read()
 
+    @timed
     def __iter__(self) -> Iterator[K]:
         """Iterate over keys. Returns a snapshot to avoid iteration issues."""
         self._lock.acquire_read()
@@ -124,6 +137,7 @@ def __iter__(self) -> Iterator[K]:
         finally:
             self._lock.release_read()
 
+    @timed
     def get(self, key: K, default: V | None = None) -> V:
         """Get item by key with optional default."""
         self._lock.acquire_read()
@@ -132,6 +146,7 @@ def get(self, key: K, default: V | None = None) -> V:
         finally:
             self._lock.release_read()
 
+    @timed
     def pop(self, key: K, *args) -> V:
         """Pop item by key."""
         self._lock.acquire_write()
@@ -140,6 +155,7 @@ def pop(self, key: K, *args) -> V:
         finally:
             self._lock.release_write()
 
+    @timed
     def update(self, *args, **kwargs) -> None:
         """Update dictionary."""
         self._lock.acquire_write()
@@ -148,6 +164,7 @@ def update(self, *args, **kwargs) -> None:
         finally:
             self._lock.release_write()
 
+    @timed
     def clear(self) -> None:
         """Clear all items."""
         self._lock.acquire_write()
@@ -156,6 +173,7 @@ def clear(self) -> None:
         finally:
             self._lock.release_write()
 
+    @timed
     def keys(self) -> KeysView[K]:
         """Get dictionary keys view (snapshot)."""
         self._lock.acquire_read()
@@ -164,6 +182,7 @@ def keys(self) -> KeysView[K]:
         finally:
             self._lock.release_read()
 
+    @timed
     def values(self) -> ValuesView[V]:
         """Get dictionary values view (snapshot)."""
         self._lock.acquire_read()
@@ -172,6 +191,7 @@ def values(self) -> ValuesView[V]:
         finally:
             self._lock.release_read()
 
+    @timed
     def items(self) -> ItemsView[K, V]:
         """Get dictionary items view (snapshot)."""
         self._lock.acquire_read()
@@ -180,6 +200,7 @@ def items(self) -> ItemsView[K, V]:
         finally:
             self._lock.release_read()
 
+    @timed
     def copy(self) -> dict[K, V]:
         """Create a copy of the dictionary."""
         self._lock.acquire_read()
@@ -188,6 +209,7 @@ def copy(self) -> dict[K, V]:
         finally:
             self._lock.release_read()
 
+    @timed
     def setdefault(self, key: K, default: V | None = None) -> V:
         """Set default value for key if not exists."""
         self._lock.acquire_write()
diff --git a/src/memos/memos_tools/thread_safe_dict_segment.py b/src/memos/memos_tools/thread_safe_dict_segment.py
new file mode 100644
index 000000000..c1c10e3e1
--- /dev/null
+++ b/src/memos/memos_tools/thread_safe_dict_segment.py
@@ -0,0 +1,382 @@
+import threading
+import time
+
+from collections.abc import Iterator
+from contextlib import contextmanager
+from typing import Any, Generic, TypeVar
+
+
+K = TypeVar("K")
+V = TypeVar("V")
+
+
+class FastReadWriteLock:
+    """Read-write lock optimized for FastAPI scenarios:
+    reader priority with writer starvation prevention"""
+
+    def __init__(self):
+        self._readers = 0
+        self._writers = 0
+        self._waiting_writers = 0
+        self._lock = threading.RLock()
+        self._read_ready = threading.Condition(self._lock)
+        self._write_ready = threading.Condition(self._lock)
+        # Writer starvation detection
+        self._last_write_time = 0
+        self._write_starvation_threshold = 0.1  # 100ms
+
+    def acquire_read(self) -> bool:
+        """Fast read lock acquisition"""
+        with self._lock:
+            # Check if writers are starving
+            current_time = time.time()
+            write_starving = (
+                self._waiting_writers > 0
+                and current_time - self._last_write_time > self._write_starvation_threshold
+            )
+
+            # If no writers are active and no starvation, allow readers to continue
+            if self._writers == 0 and not write_starving:
+                self._readers += 1
+                return True
+
+            # Otherwise wait
+            while self._writers > 0 or write_starving:
+                self._read_ready.wait()
+                current_time = time.time()
+                write_starving = (
+                    self._waiting_writers > 0
+                    and current_time - self._last_write_time > self._write_starvation_threshold
+                )
+
+            self._readers += 1
+            return True
+
+    def release_read(self):
+        """Release read lock"""
+        with self._lock:
+            self._readers -= 1
+            if self._readers == 0:
+                self._write_ready.notify()
+
+    def acquire_write(self) -> bool:
+        """Write lock acquisition"""
+        with self._lock:
+            self._waiting_writers += 1
+            try:
+                while self._readers > 0 or self._writers > 0:
+                    self._write_ready.wait()
+
+                self._writers = 1
+                self._waiting_writers -= 1
+                self._last_write_time = time.time()
+                return True
+            except:
+                self._waiting_writers -= 1
+                raise
+
+    def release_write(self):
+        """Release write lock"""
+        with self._lock:
+            self._writers = 0
+            # Prioritize notifying readers (reader priority strategy)
+            self._read_ready.notify_all()
+            self._write_ready.notify()
+
+
+class SegmentedLock:
+    """Segmented lock, segments based on key hash"""
+
+    def __init__(self, segment_count: int = 64):
+        self.segment_count = segment_count
+        self.locks = [FastReadWriteLock() for _ in range(segment_count)]
+
+    def get_lock(self, key: K) -> FastReadWriteLock:
+        """Get the corresponding lock based on key"""
+        segment = hash(key) % self.segment_count
+        return self.locks[segment]
+
+    @contextmanager
+    def read_lock(self, key: K):
+        """Read lock context manager"""
+        lock = self.get_lock(key)
+        lock.acquire_read()
+        try:
+            yield
+        finally:
+            lock.release_read()
+
+    @contextmanager
+    def write_lock(self, key: K):
+        """Write lock context manager"""
+        lock = self.get_lock(key)
+        lock.acquire_write()
+        try:
+            yield
+        finally:
+            lock.release_write()
+
+
+class OptimizedThreadSafeDict(Generic[K, V]):
+    """
+    Thread-safe dictionary optimized for FastAPI scenarios:
+    - Segmented locks to reduce contention
+    - Reader priority with writer starvation prevention
+    - Support for large object storage
+    - Strong consistency guarantee
+    """
+
+    def __init__(
+        self, initial_dict: dict[K, V] | None = None, segment_count: int = 128
+    ):  # More segments for high concurrency
+        self._segments: list[dict[K, V]] = [{} for _ in range(segment_count)]
+        self._segment_count = segment_count
+        self._segmented_lock = SegmentedLock(segment_count)
+
+        # Initialize data
+        if initial_dict:
+            for k, v in initial_dict.items():
+                segment_idx = self._get_segment(k)
+                self._segments[segment_idx][k] = v
+
+    def _get_segment(self, key: K) -> int:
+        """Calculate the segment corresponding to the key"""
+        return hash(key) % self._segment_count
+
+    def __getitem__(self, key: K) -> V:
+        """Get element"""
+        segment_idx = self._get_segment(key)
+        with self._segmented_lock.read_lock(key):
+            return self._segments[segment_idx][key]
+
+    def __setitem__(self, key: K, value: V) -> None:
+        """Set element - key optimization point"""
+        segment_idx = self._get_segment(key)
+        with self._segmented_lock.write_lock(key):
+            self._segments[segment_idx][key] = value
+
+    def __delitem__(self, key: K) -> None:
+        """Delete element"""
+        segment_idx = self._get_segment(key)
+        with self._segmented_lock.write_lock(key):
+            del self._segments[segment_idx][key]
+
+    def __contains__(self, key: K) -> bool:
+        """Check if key is contained"""
+        segment_idx = self._get_segment(key)
+        with self._segmented_lock.read_lock(key):
+            return key in self._segments[segment_idx]
+
+    def get(self, key: K, default: V | None = None) -> V | None:
+        """Safely get element"""
+        segment_idx = self._get_segment(key)
+        with self._segmented_lock.read_lock(key):
+            return self._segments[segment_idx].get(key, default)
+
+    def pop(self, key: K, *args) -> V:
+        """Pop element"""
+        segment_idx = self._get_segment(key)
+        with self._segmented_lock.write_lock(key):
+            return self._segments[segment_idx].pop(key, *args)
+
+    def setdefault(self, key: K, default: V | None = None) -> V:
+        """Set default value"""
+        segment_idx = self._get_segment(key)
+        with self._segmented_lock.write_lock(key):
+            return self._segments[segment_idx].setdefault(key, default)
+
+    def update(self, other=None, **kwargs) -> None:
+        """Batch update - optimized batch operation"""
+        items = (other.items() if hasattr(other, "items") else other) if other is not None else []
+
+        # Group update items by segment
+        segment_updates: dict[int, list[tuple[K, V]]] = {}
+
+        for k, v in items:
+            segment_idx = self._get_segment(k)
+            if segment_idx not in segment_updates:
+                segment_updates[segment_idx] = []
+            segment_updates[segment_idx].append((k, v))
+
+        for k, v in kwargs.items():
+            segment_idx = self._get_segment(k)
+            if segment_idx not in segment_updates:
+                segment_updates[segment_idx] = []
+            segment_updates[segment_idx].append((k, v))
+
+        # Update segment by segment to reduce lock holding time
+        for segment_idx, updates in segment_updates.items():
+            # Use the first key to get the lock (all keys in the same segment map to the same lock)
+            first_key = updates[0][0]
+            with self._segmented_lock.write_lock(first_key):
+                for k, v in updates:
+                    self._segments[segment_idx][k] = v
+
+    def clear(self) -> None:
+        """Clear all elements - need to acquire all locks"""
+        # Acquire all locks in order to avoid deadlock
+        acquired_locks = []
+        try:
+            for i in range(self._segment_count):
+                lock = self._segmented_lock.locks[i]
+                lock.acquire_write()
+                acquired_locks.append(lock)
+
+            # Clear all segments
+            for segment in self._segments:
+                segment.clear()
+
+        finally:
+            # Release locks in reverse order
+            for lock in reversed(acquired_locks):
+                lock.release_write()
+
+    def __len__(self) -> int:
+        """Get total length - snapshot read"""
+        total = 0
+        acquired_locks = []
+        try:
+            # Acquire all read locks
+            for i in range(self._segment_count):
+                lock = self._segmented_lock.locks[i]
+                lock.acquire_read()
+                acquired_locks.append(lock)
+
+            # Calculate total length
+            for segment in self._segments:
+                total += len(segment)
+
+            return total
+
+        finally:
+            # Release all read locks
+            for lock in reversed(acquired_locks):
+                lock.release_read()
+
+    def __bool__(self) -> bool:
+        """Check if empty"""
+        return len(self) > 0
+
+    def keys(self) -> list[K]:
+        """Get snapshot of all keys"""
+        all_keys = []
+        acquired_locks = []
+
+        try:
+            # Acquire all read locks
+            for i in range(self._segment_count):
+                lock = self._segmented_lock.locks[i]
+                lock.acquire_read()
+                acquired_locks.append(lock)
+
+            # Collect all keys
+            for segment in self._segments:
+                all_keys.extend(segment.keys())
+
+            return all_keys
+
+        finally:
+            for lock in reversed(acquired_locks):
+                lock.release_read()
+
+    def values(self) -> list[V]:
+        """Get snapshot of all values"""
+        all_values = []
+        acquired_locks = []
+
+        try:
+            for i in range(self._segment_count):
+                lock = self._segmented_lock.locks[i]
+                lock.acquire_read()
+                acquired_locks.append(lock)
+
+            for segment in self._segments:
+                all_values.extend(segment.values())
+
+            return all_values
+
+        finally:
+            for lock in reversed(acquired_locks):
+                lock.release_read()
+
+    def items(self) -> list[tuple[K, V]]:
+        """Get snapshot of all items"""
+        all_items = []
+        acquired_locks = []
+
+        try:
+            for i in range(self._segment_count):
+                lock = self._segmented_lock.locks[i]
+                lock.acquire_read()
+                acquired_locks.append(lock)
+
+            for segment in self._segments:
+                all_items.extend(segment.items())
+
+            return all_items
+
+        finally:
+            for lock in reversed(acquired_locks):
+                lock.release_read()
+
+    def copy(self) -> dict[K, V]:
+        """Create dictionary copy"""
+        result = {}
+        acquired_locks = []
+
+        try:
+            for i in range(self._segment_count):
+                lock = self._segmented_lock.locks[i]
+                lock.acquire_read()
+                acquired_locks.append(lock)
+
+            for segment in self._segments:
+                result.update(segment)
+
+            return result
+
+        finally:
+            for lock in reversed(acquired_locks):
+                lock.release_read()
+
+    def __iter__(self) -> Iterator[K]:
+        """Iterator - returns snapshot"""
+        return iter(self.keys())
+
+    def __repr__(self) -> str:
+        """String representation"""
+        return f"OptimizedThreadSafeDict({dict(self.items())})"
+
+    def stats(self) -> dict[str, Any]:
+        """Get statistics"""
+        segment_sizes = []
+        total_items = 0
+
+        acquired_locks = []
+        try:
+            for i in range(self._segment_count):
+                lock = self._segmented_lock.locks[i]
+                lock.acquire_read()
+                acquired_locks.append(lock)
+
+            for segment in self._segments:
+                size = len(segment)
+                segment_sizes.append(size)
+                total_items += size
+
+            avg_size = total_items / self._segment_count if self._segment_count > 0 else 0
+            max_size = max(segment_sizes) if segment_sizes else 0
+            min_size = min(segment_sizes) if segment_sizes else 0
+
+            return {
+                "total_items": total_items,
+                "segment_count": self._segment_count,
+                "avg_segment_size": avg_size,
+                "max_segment_size": max_size,
+                "min_segment_size": min_size,
+                "load_balance_ratio": min_size / max_size if max_size > 0 else 1.0,
+            }
+
+        finally:
+            for lock in reversed(acquired_locks):
+                lock.release_read()
diff --git a/src/memos/parsers/markitdown.py b/src/memos/parsers/markitdown.py
index 02e75355f..c9827bd7d 100644
--- a/src/memos/parsers/markitdown.py
+++ b/src/memos/parsers/markitdown.py
@@ -1,11 +1,190 @@
+import asyncio
+import shlex
+import time
+
+from pathlib import Path
+
+import httpx
+
 from memos.configs.parser import MarkItDownParserConfig
-from memos.dependency import require_python_package
 from memos.log import get_logger
 from memos.parsers.base import BaseParser
 
 
 logger = get_logger(__name__)
 
+BASE_URL = "http://106.75.235.231:8001"
+LOCAL_TOKEN = "local_only_a8f3d2c1b5e7f9a6c4d8e2b1f7c3a9e5d2f8b4c6a1e9d7f3c5b8a2e4f6d9c1a3"
+HEADERS = {"Authorization": f"Bearer {LOCAL_TOKEN}"}
+
+
+async def check_service_health(client: httpx.AsyncClient):
+    try:
+        response = await client.get(f"{BASE_URL}/health", timeout=5)
+        response.raise_for_status()
+        print("✅ API服务运行正常")
+        return True
+    except (httpx.ConnectError, httpx.HTTPStatusError) as e:
+        print(f"❌ API服务异常: {e}")
+        return False
+
+
+async def upload_ppt_file(client: httpx.AsyncClient, file_path: Path) -> str:
+    print(f"\n📤 上传PPT文件: {file_path}")
+    print(f"   文件大小: {file_path.stat().st_size / 1024 / 1024:.1f} MB")
+
+    external_file_id = f"ppt-test-{int(time.time())}"
+
+    print("🔄 使用直接文件上传接口...")
+
+    with open(file_path, "rb") as f:
+        files = {"file": (file_path.name, f, "application/vnd.ms-powerpoint")}
+        data = {
+            "fileId": external_file_id,
+            "force_ocr": "false",
+            "ocr_all_images": "true",
+        }
+
+        print("📡 发送上传请求到 /api/file/upload...")
+        response = await client.post(
+            f"{BASE_URL}/api/file/upload", files=files, data=data, headers=HEADERS
+        )
+
+    response.raise_for_status()
+
+    result = response.json()
+    print(f"📄 上传响应: {result}")
+
+    if result["code"] != 200:
+        raise Exception(f"API返回错误: {result.get('message', '未知错误')}")
+
+    generated_ids = result["data"]["generated_ids"]
+    if len(generated_ids) == 0:
+        raise Exception(f"服务器未生成文件ID，响应: {result}")
+
+    file_id = generated_ids[0]
+    print(f"🆔 获得内部file_id: {file_id}")
+    return file_id
+
+
+async def poll_status(client: httpx.AsyncClient, file_id: str):
+    print(f"\n⏳ 监控处理状态: {file_id}")
+    print("   注意: PPT文件处理需要更长时间（LibreOffice转换 + AI处理）")
+
+    max_retries = 720
+    poll_interval = 5
+
+    for i in range(max_retries):
+        response = await client.get(f"{BASE_URL}/api/v6/status/{file_id}", headers=HEADERS)
+        response.raise_for_status()
+
+        status_data = response.json()
+        status = status_data.get("status")
+        print(f"  - 尝试 {i + 1}/{max_retries}: 状态 '{status}'")
+
+        if status == "completed":
+            print("✅ 处理完成！")
+            return
+        elif status == "failed":
+            error_msg = status_data.get("error_message", "未知错误")
+            raise Exception(f"处理失败: {error_msg}")
+
+        await asyncio.sleep(poll_interval)
+
+    raise Exception("状态轮询超时")
+
+
+async def download_and_verify_ppt(client: httpx.AsyncClient, file_id: str, temp_dir: Path):
+    """下载并验证PPT处理结果"""
+    print(f"\n📥 下载处理结果: {file_id}")
+
+    temp_dir.mkdir(exist_ok=True, parents=True)
+
+    archive_path = temp_dir / f"{file_id}.tar.zstd"
+    curl_cmd = [
+        "curl",
+        "-o",
+        str(archive_path),
+        "-H",
+        f"Authorization: Bearer {LOCAL_TOKEN}",
+        f"{BASE_URL}/api/v6/download/{file_id}",
+    ]
+
+    proc = await asyncio.create_subprocess_exec(*curl_cmd)
+    await proc.wait()
+
+    if proc.returncode != 0:
+        raise Exception(f"curl下载失败，返回码: {proc.returncode}")
+
+    file_size = archive_path.stat().st_size
+    print(f"📦 压缩包已保存: {archive_path} ({file_size} 字节)")
+
+    extract_dir = temp_dir / "extracted"
+    extract_dir.mkdir()
+
+    print("🔄 解压缩文件 (macOS兼容方式)...")
+    decompress_cmd = (
+        f"zstd -d < {shlex.quote(str(archive_path))} | tar -xf - -C {shlex.quote(str(extract_dir))}"
+    )
+
+    proc = await asyncio.create_subprocess_shell(
+        decompress_cmd, stdout=asyncio.subprocess.DEVNULL, stderr=asyncio.subprocess.PIPE
+    )
+    stdout, stderr = await proc.communicate()
+
+    if proc.returncode != 0:
+        stderr_text = stderr.decode() if stderr else "未知错误"
+        print(f"⚠️ 管道方式失败，尝试分步解压: {stderr_text}")
+
+        temp_tar = temp_dir / f"{file_id}.tar"
+
+        zstd_cmd = ["zstd", "-d", str(archive_path), "-o", str(temp_tar)]
+        proc = await asyncio.create_subprocess_exec(*zstd_cmd)
+        await proc.wait()
+
+        if proc.returncode != 0:
+            raise Exception(f"zstd解压失败，返回码: {proc.returncode}")
+
+        tar_cmd = ["tar", "-xf", str(temp_tar), "-C", str(extract_dir)]
+        proc = await asyncio.create_subprocess_exec(*tar_cmd)
+        await proc.wait()
+
+        if proc.returncode != 0:
+            raise Exception(f"tar解压失败，返回码: {proc.returncode}")
+
+        temp_tar.unlink(missing_ok=True)
+
+    print(f"📂 文件已解压到: {extract_dir}")
+
+    result_content_dir = extract_dir / file_id
+
+    md_files = list(result_content_dir.glob("**/*.md"))
+    if not md_files:
+        raise Exception("未找到markdown结果文件")
+
+    result_file = md_files[0]
+    print(f"✔️ 找到结果文件: {result_file}")
+
+    content = result_file.read_text(encoding="utf-8")
+    print(f"📄 提取的内容长度: {len(content)} 字符")
+
+    lines = content.split("\n")
+
+    text_content = ""
+    for line in lines:
+        if line.strip():
+            text_content += line
+
+    print("👍 PPT文件处理验证完成！")
+    return text_content
+
+
+async def cleanup_server_file(client: httpx.AsyncClient, file_id: str):
+    print(f"\n🧹 清理服务器文件: {file_id}")
+    response = await client.delete(f"{BASE_URL}/api/v6/delete/{file_id}", headers=HEADERS)
+    response.raise_for_status()
+    print("✅ 服务器清理完成")
+
 
 class MarkItDownParser(BaseParser):
     """MarkItDown Parser class."""
@@ -13,16 +192,45 @@ class MarkItDownParser(BaseParser):
     def __init__(self, config: MarkItDownParserConfig):
         self.config = config
 
-    @require_python_package(
-        import_name="markitdown",
-        install_command="pip install markitdown[all]",
-        install_link="https://github.com/microsoft/markitdown",
-    )
-    def parse(self, file_path: str) -> str:
-        from markitdown import MarkItDown
+    async def parse(self, file_path: str) -> str:
+        ppt_file = Path(file_path)
+
+        if not ppt_file.exists():
+            print(f"❌ PPT文件不存在: {ppt_file}")
+            return
+
+        temp_dir = Path(f"./ppt_test_result/ppt_test_results_{int(time.time())}")
+        temp_dir.mkdir(exist_ok=True)
+
+        try:
+            async with httpx.AsyncClient(timeout=30.0) as client:
+                # 0. 健康检查
+                if not await check_service_health(client):
+                    print("中止测试")
+                    return
+
+                file_id = await upload_ppt_file(client, ppt_file)
+
+                await poll_status(client, file_id)
+
+                text_content = await download_and_verify_ppt(client, file_id, temp_dir)
+
+        except Exception as e:
+            print(f"\n❌ 测试失败: {e}")
+            print(f"🔍 文件保留在代码目录用于检查: ./{temp_dir.name}/")
+            return
+
+        try:
+            if file_id:
+                async with httpx.AsyncClient() as client:
+                    await cleanup_server_file(client, file_id)
+
+            print(f"📂 下载的文件保留在代码目录: ./{temp_dir.name}/")
+            print(f"   - 压缩包: ./{temp_dir.name}/file_*.tar.zstd")
+            print(f"   - 解压内容: ./{temp_dir.name}/extracted/")
+            print("💡 您可以在代码目录下直接检查处理结果和文件质量")
 
-        """Parse the file at the given path and return its content as a MarkDown string."""
-        md = MarkItDown(enable_plugins=False)
-        result = md.convert(file_path)
+        except Exception as e:
+            print(f"⚠️ 服务器清理失败: {e}")
 
-        return result.text_content
+        return text_content
diff --git a/src/memos/reranker/__init__.py b/src/memos/reranker/__init__.py
new file mode 100644
index 000000000..3499fccda
--- /dev/null
+++ b/src/memos/reranker/__init__.py
@@ -0,0 +1,4 @@
+from .factory import RerankerFactory
+
+
+__all__ = ["RerankerFactory"]
diff --git a/src/memos/reranker/base.py b/src/memos/reranker/base.py
new file mode 100644
index 000000000..77a24c164
--- /dev/null
+++ b/src/memos/reranker/base.py
@@ -0,0 +1,24 @@
+# memos/reranker/base.py
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from typing import TYPE_CHECKING
+
+
+if TYPE_CHECKING:
+    from memos.memories.textual.item import TextualMemoryItem
+
+
+class BaseReranker(ABC):
+    """Abstract interface for memory rerankers."""
+
+    @abstractmethod
+    def rerank(
+        self,
+        query: str,
+        graph_results: list,
+        top_k: int,
+        **kwargs,
+    ) -> list[tuple[TextualMemoryItem, float]]:
+        """Return top_k (item, score) sorted by score desc."""
+        raise NotImplementedError
diff --git a/src/memos/reranker/concat.py b/src/memos/reranker/concat.py
new file mode 100644
index 000000000..5ad339529
--- /dev/null
+++ b/src/memos/reranker/concat.py
@@ -0,0 +1,59 @@
+import re
+
+from typing import Any
+
+
+_TAG1 = re.compile(r"^\s*\[[^\]]*\]\s*")
+
+
+def process_source(
+    items: list[tuple[Any, str | dict[str, Any] | list[Any]]] | None = None, recent_num: int = 3
+) -> str:
+    """
+    Args:
+        items: List of tuples where each tuple contains (memory, source).
+               source can be str, Dict, or List.
+        recent_num: Number of recent items to concatenate.
+    Returns:
+        str: Concatenated source.
+    """
+    if items is None:
+        items = []
+    concat_data = []
+    memory = None
+    for item in items:
+        memory, source = item
+        for content in source:
+            if isinstance(content, str):
+                if "assistant:" in content:
+                    continue
+                concat_data.append(content)
+    if memory is not None:
+        concat_data = [memory, *concat_data]
+    return "\n".join(concat_data)
+
+
+def concat_original_source(
+    graph_results: list,
+    merge_field: list[str] | None = None,
+) -> list[str]:
+    """
+    Merge memory items with original dialogue.
+    Args:
+        graph_results (list[TextualMemoryItem]): List of memory items with embeddings.
+        merge_field (List[str]): List of fields to merge.
+    Returns:
+        list[str]: List of memory and concat orginal memory.
+    """
+    if merge_field is None:
+        merge_field = ["sources"]
+    documents = []
+    for item in graph_results:
+        memory = _TAG1.sub("", m) if isinstance((m := getattr(item, "memory", None)), str) else m
+        sources = []
+        for field in merge_field:
+            source = getattr(item.metadata, field, "")
+            sources.append((memory, source))
+        concat_string = process_source(sources)
+        documents.append(concat_string)
+    return documents
diff --git a/src/memos/reranker/cosine_local.py b/src/memos/reranker/cosine_local.py
new file mode 100644
index 000000000..000b64cf4
--- /dev/null
+++ b/src/memos/reranker/cosine_local.py
@@ -0,0 +1,96 @@
+# memos/reranker/cosine_local.py
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from .base import BaseReranker
+
+
+if TYPE_CHECKING:
+    from memos.memories.textual.item import TextualMemoryItem
+
+try:
+    import numpy as _np
+
+    _HAS_NUMPY = True
+except Exception:
+    _HAS_NUMPY = False
+
+
+def _cosine_one_to_many(q: list[float], m: list[list[float]]) -> list[float]:
+    """
+    Compute cosine similarities between a single vector q and a matrix m (rows are candidates).
+    """
+    if not _HAS_NUMPY:
+
+        def dot(a, b):  # lowercase per N806
+            return sum(x * y for x, y in zip(a, b, strict=False))
+
+        def norm(a):  # lowercase per N806
+            return sum(x * x for x in a) ** 0.5
+
+        qn = norm(q) or 1e-10
+        sims = []
+        for v in m:
+            vn = norm(v) or 1e-10
+            sims.append(dot(q, v) / (qn * vn))
+        return sims
+
+    qv = _np.asarray(q, dtype=float)  # lowercase
+    mv = _np.asarray(m, dtype=float)  # lowercase
+    qn = _np.linalg.norm(qv) or 1e-10
+    mn = _np.linalg.norm(mv, axis=1)  # lowercase
+    dots = mv @ qv
+    return (dots / (mn * qn + 1e-10)).tolist()
+
+
+class CosineLocalReranker(BaseReranker):
+    def __init__(
+        self,
+        level_weights: dict[str, float] | None = None,
+        level_field: str = "background",
+        **kwargs,
+    ):
+        self.level_weights = level_weights or {"topic": 1.0, "concept": 1.0, "fact": 1.0}
+        self.level_field = level_field
+
+    def rerank(
+        self,
+        query: str,
+        graph_results: list,
+        top_k: int,
+        **kwargs,
+    ) -> list[tuple[TextualMemoryItem, float]]:
+        if not graph_results:
+            return []
+
+        query_embedding: list[float] | None = kwargs.get("query_embedding")
+        if not query_embedding:
+            return [(item, 0.0) for item in graph_results[:top_k]]
+
+        items_with_emb = [
+            it
+            for it in graph_results
+            if getattr(it, "metadata", None) and getattr(it.metadata, "embedding", None)
+        ]
+        if not items_with_emb:
+            return [(item, 0.5) for item in graph_results[:top_k]]
+
+        cand_vecs = [it.metadata.embedding for it in items_with_emb]
+        sims = _cosine_one_to_many(query_embedding, cand_vecs)
+
+        def get_weight(it: TextualMemoryItem) -> float:
+            level = getattr(it.metadata, self.level_field, None)
+            return self.level_weights.get(level, 1.0)
+
+        weighted = [sim * get_weight(it) for sim, it in zip(sims, items_with_emb, strict=False)]
+        scored_pairs = list(zip(items_with_emb, weighted, strict=False))
+        scored_pairs.sort(key=lambda x: x[1], reverse=True)
+
+        top_items = scored_pairs[:top_k]
+        if len(top_items) < top_k:
+            chosen = {it.id for it, _ in top_items}
+            remain = [(it, -1.0) for it in graph_results if it.id not in chosen]
+            top_items.extend(remain[: top_k - len(top_items)])
+
+        return top_items
diff --git a/src/memos/reranker/factory.py b/src/memos/reranker/factory.py
new file mode 100644
index 000000000..134e29eb9
--- /dev/null
+++ b/src/memos/reranker/factory.py
@@ -0,0 +1,48 @@
+# memos/reranker/factory.py
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any
+
+# Import singleton decorator
+from memos.memos_tools.singleton import singleton_factory
+
+from .cosine_local import CosineLocalReranker
+from .http_bge import HTTPBGEReranker
+from .noop import NoopReranker
+
+
+if TYPE_CHECKING:
+    from memos.configs.reranker import RerankerConfigFactory
+
+    from .base import BaseReranker
+
+
+class RerankerFactory:
+    @staticmethod
+    @singleton_factory("RerankerFactory")
+    def from_config(cfg: RerankerConfigFactory | None) -> BaseReranker | None:
+        if not cfg:
+            return None
+
+        backend = (cfg.backend or "").lower()
+        c: dict[str, Any] = cfg.config or {}
+
+        if backend in {"http_bge", "bge"}:
+            return HTTPBGEReranker(
+                reranker_url=c.get("url") or c.get("endpoint") or c.get("reranker_url"),
+                model=c.get("model", "bge-reranker-v2-m3"),
+                timeout=int(c.get("timeout", 10)),
+                headers_extra=c.get("headers_extra"),
+                rerank_source=c.get("rerank_source"),
+            )
+
+        if backend in {"cosine_local", "cosine"}:
+            return CosineLocalReranker(
+                level_weights=c.get("level_weights"),
+                level_field=c.get("level_field", "background"),
+            )
+
+        if backend in {"noop", "none", "disabled"}:
+            return NoopReranker()
+
+        raise ValueError(f"Unknown reranker backend: {cfg.backend}")
diff --git a/src/memos/reranker/http_bge.py b/src/memos/reranker/http_bge.py
new file mode 100644
index 000000000..f0f5d17a0
--- /dev/null
+++ b/src/memos/reranker/http_bge.py
@@ -0,0 +1,312 @@
+# memos/reranker/http_bge.py
+from __future__ import annotations
+
+import re
+
+from collections.abc import Iterable
+from typing import TYPE_CHECKING, Any
+
+import requests
+
+from memos.log import get_logger
+
+from .base import BaseReranker
+from .concat import concat_original_source
+
+
+logger = get_logger(__name__)
+
+
+if TYPE_CHECKING:
+    from memos.memories.textual.item import TextualMemoryItem
+
+# Strip a leading "[...]" tag (e.g., "[2025-09-01] ..." or "[meta] ...")
+# before sending text to the reranker. This keeps inputs clean and
+# avoids misleading the model with bracketed prefixes.
+_TAG1 = re.compile(r"^\s*\[[^\]]*\]\s*")
+DEFAULT_BOOST_WEIGHTS = {"user_id": 0.5, "tags": 0.2, "session_id": 0.3}
+
+
+def _value_matches(item_value: Any, wanted: Any) -> bool:
+    """
+    Generic matching:
+    - if item_value is list/tuple/set: check membership (any match if wanted is iterable)
+    - else: equality (any match if wanted is iterable)
+    """
+
+    def _iterable(x):
+        # exclude strings from "iterable"
+        return isinstance(x, Iterable) and not isinstance(x, str | bytes)
+
+    if _iterable(item_value):
+        if _iterable(wanted):
+            return any(w in item_value for w in wanted)
+        return wanted in item_value
+    else:
+        if _iterable(wanted):
+            return any(item_value == w for w in wanted)
+        return item_value == wanted
+
+
+class HTTPBGEReranker(BaseReranker):
+    """
+    HTTP-based BGE reranker.
+
+    This class sends (query, documents[]) to a remote HTTP endpoint that
+    performs cross-encoder-style re-ranking (e.g., BGE reranker) and returns
+    relevance scores. It then maps those scores back onto the original
+    TextualMemoryItem list and returns (item, score) pairs sorted by score.
+
+    Notes
+    -----
+    - The endpoint is expected to accept JSON:
+        {
+          "model": "<model-name>",
+          "query": "<query text>",
+          "documents": ["doc1", "doc2", ...]
+        }
+    - Two response shapes are supported:
+        1) {"results": [{"index": <int>, "relevance_score": <float>}, ...]}
+           where "index" refers to the *position in the documents array*.
+        2) {"data": [{"score": <float>}, ...]} (aligned by list order)
+    - If the service fails or responds unexpectedly, this falls back to
+      returning the original items with 0.0 scores (best-effort).
+    """
+
+    def __init__(
+        self,
+        reranker_url: str,
+        token: str = "",
+        model: str = "bge-reranker-v2-m3",
+        timeout: int = 10,
+        headers_extra: dict | None = None,
+        rerank_source: list[str] | None = None,
+        boost_weights: dict[str, float] | None = None,
+        boost_default: float = 0.0,
+        warn_unknown_filter_keys: bool = True,
+        **kwargs,
+    ):
+        """
+        Parameters
+        ----------
+        reranker_url : str
+            HTTP endpoint for the reranker service.
+        token : str, optional
+            Bearer token for auth. If non-empty, added to the Authorization header.
+        model : str, optional
+            Model identifier understood by the server.
+        timeout : int, optional
+            Request timeout (seconds).
+        headers_extra : dict | None, optional
+            Additional headers to merge into the request headers.
+        """
+        if not reranker_url:
+            raise ValueError("reranker_url must not be empty")
+        self.reranker_url = reranker_url
+        self.token = token or ""
+        self.model = model
+        self.timeout = timeout
+        self.headers_extra = headers_extra or {}
+        self.concat_source = rerank_source
+
+        self.boost_weights = (
+            DEFAULT_BOOST_WEIGHTS.copy()
+            if boost_weights is None
+            else {k: float(v) for k, v in boost_weights.items()}
+        )
+        self.boost_default = float(boost_default)
+        self.warn_unknown_filter_keys = bool(warn_unknown_filter_keys)
+        self._warned_missing_keys: set[str] = set()
+
+    def rerank(
+        self,
+        query: str,
+        graph_results: list[TextualMemoryItem],
+        top_k: int,
+        search_filter: dict | None = None,
+        **kwargs,
+    ) -> list[tuple[TextualMemoryItem, float]]:
+        """
+        Rank candidate memories by relevance to the query.
+
+        Parameters
+        ----------
+        query : str
+            The search query.
+        graph_results : list[TextualMemoryItem]
+            Candidate items to re-rank. Each item is expected to have a
+            `.memory` str field; non-strings are ignored.
+        top_k : int
+            Return at most this many items.
+        search_filter : dict | None
+            Currently unused. Present to keep signature compatible.
+
+        Returns
+        -------
+        list[tuple[TextualMemoryItem, float]]
+            Re-ranked items with scores, sorted descending by score.
+        """
+        if not graph_results:
+            return []
+
+        # Build a mapping from "payload docs index" -> "original graph_results index"
+        # Only include items that have a non-empty string memory. This ensures that
+        # any index returned by the server can be mapped back correctly.
+        if self.concat_source:
+            documents = concat_original_source(graph_results, self.concat_source)
+        else:
+            documents = [
+                (_TAG1.sub("", m) if isinstance((m := getattr(item, "memory", None)), str) else m)
+                for item in graph_results
+            ]
+            documents = [d for d in documents if isinstance(d, str) and d]
+
+        logger.info(f"[HTTPBGERerankerSample] query: {query} , documents: {documents[:5]}...")
+
+        if not documents:
+            return []
+
+        headers = {"Content-Type": "application/json", **self.headers_extra}
+        payload = {"model": self.model, "query": query, "documents": documents}
+
+        try:
+            # Make the HTTP request to the reranker service
+            resp = requests.post(
+                self.reranker_url, headers=headers, json=payload, timeout=self.timeout
+            )
+            resp.raise_for_status()
+            data = resp.json()
+
+            scored_items: list[tuple[TextualMemoryItem, float]] = []
+
+            if "results" in data:
+                # Format:
+                # dict("results": [{"index": int, "relevance_score": float},
+                # ...])
+                rows = data.get("results", [])
+                for r in rows:
+                    idx = r.get("index")
+                    # The returned index refers to 'documents' (i.e., our 'pairs' order),
+                    # so we must map it back to the original graph_results index.
+                    if isinstance(idx, int) and 0 <= idx < len(graph_results):
+                        raw_score = float(r.get("relevance_score", r.get("score", 0.0)))
+                        item = graph_results[idx]
+                        # generic boost
+                        score = self._apply_boost_generic(item, raw_score, search_filter)
+                        scored_items.append((item, score))
+
+                scored_items.sort(key=lambda x: x[1], reverse=True)
+                return scored_items[: min(top_k, len(scored_items))]
+
+            elif "data" in data:
+                # Format: {"data": [{"score": float}, ...]} aligned by list order
+                rows = data.get("data", [])
+                # Build a list of scores aligned with our 'documents' (pairs)
+                score_list = [float(r.get("score", 0.0)) for r in rows]
+
+                if len(score_list) < len(graph_results):
+                    score_list += [0.0] * (len(graph_results) - len(score_list))
+                elif len(score_list) > len(graph_results):
+                    score_list = score_list[: len(graph_results)]
+
+                scored_items = []
+                for item, raw_score in zip(graph_results, score_list, strict=False):
+                    score = self._apply_boost_generic(item, raw_score, search_filter)
+                    scored_items.append((item, score))
+
+                scored_items.sort(key=lambda x: x[1], reverse=True)
+                return scored_items[: min(top_k, len(scored_items))]
+
+            else:
+                # Unexpected response schema: return a 0.0-scored fallback of the first top_k valid docs
+                # Note: we use 'pairs' to keep alignment with valid (string) docs.
+                return [(item, 0.0) for item in graph_results[:top_k]]
+
+        except Exception as e:
+            # Network error, timeout, JSON decode error, etc.
+            # Degrade gracefully by returning first top_k valid docs with 0.0 score.
+            logger.error(f"[HTTPBGEReranker] request failed: {e}")
+            return [(item, 0.0) for item in graph_results[:top_k]]
+
+    def _get_attr_or_key(self, obj: Any, key: str) -> Any:
+        """
+        Resolve `key` on `obj` with one-level fallback into `obj.metadata`.
+
+        Priority:
+          1) obj.<key>
+          2) obj[key]
+          3) obj.metadata.<key>
+          4) obj.metadata[key]
+        """
+        if obj is None:
+            return None
+
+        # support input like "metadata.user_id"
+        if "." in key:
+            head, tail = key.split(".", 1)
+            base = self._get_attr_or_key(obj, head)
+            return self._get_attr_or_key(base, tail)
+
+        def _resolve(o: Any, k: str):
+            if o is None:
+                return None
+            v = getattr(o, k, None)
+            if v is not None:
+                return v
+            if hasattr(o, "get"):
+                try:
+                    return o.get(k)
+                except Exception:
+                    return None
+            return None
+
+        # 1) find in obj
+        v = _resolve(obj, key)
+        if v is not None:
+            return v
+
+        # 2) find in obj.metadata
+        meta = _resolve(obj, "metadata")
+        if meta is not None:
+            return _resolve(meta, key)
+
+        return None
+
+    def _apply_boost_generic(
+        self,
+        item: TextualMemoryItem,
+        base_score: float,
+        search_filter: dict | None,
+    ) -> float:
+        """
+        Multiply base_score by (1 + weight) for each matching key in search_filter.
+        - key resolution: self._get_attr_or_key(item, key)
+        - weight = boost_weights.get(key, self.boost_default)
+        - unknown key -> one-time warning
+        """
+        if not search_filter:
+            return base_score
+
+        score = float(base_score)
+
+        for key, wanted in search_filter.items():
+            # _get_attr_or_key automatically find key in item and
+            # item.metadata ("metadata.user_id" supported)
+            resolved = self._get_attr_or_key(item, key)
+
+            if resolved is None:
+                if self.warn_unknown_filter_keys and key not in self._warned_missing_keys:
+                    logger.warning(
+                        "[HTTPBGEReranker] search_filter key '%s' not found on TextualMemoryItem or metadata",
+                        key,
+                    )
+                    self._warned_missing_keys.add(key)
+                continue
+
+            if _value_matches(resolved, wanted):
+                w = float(self.boost_weights.get(key, self.boost_default))
+                if w != 0.0:
+                    score *= 1.0 + w
+                    score = min(max(0.0, score), 1.0)
+
+        return score
diff --git a/src/memos/reranker/noop.py b/src/memos/reranker/noop.py
new file mode 100644
index 000000000..7a9c02f60
--- /dev/null
+++ b/src/memos/reranker/noop.py
@@ -0,0 +1,16 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from .base import BaseReranker
+
+
+if TYPE_CHECKING:
+    from memos.memories.textual.item import TextualMemoryItem
+
+
+class NoopReranker(BaseReranker):
+    def rerank(
+        self, query: str, graph_results: list, top_k: int, **kwargs
+    ) -> list[tuple[TextualMemoryItem, float]]:
+        return [(item, 0.0) for item in graph_results[:top_k]]

From b589acd5061bb7be14cbbda47f168780f7aa2c8c Mon Sep 17 00:00:00 2001
From: stx <31013941@qq.com>
Date: Wed, 12 Nov 2025 15:30:02 +0800
Subject: [PATCH 24/34] =?UTF-8?q?feat=EF=BC=9Aupdate=2011.12?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .vscode/settings.json                         |   3 +-
 evaluation/scripts/mmlongbench/eval_docs2.py  |  21 +--
 evaluation/scripts/mmlongbench/import_docs.py |   8 +-
 .../config/tree_config_shared_database.json   |  19 +-
 src/memos/graph_dbs/nebular.py                |   2 +-
 src/memos/graph_dbs/neo4j.py                  |  78 ++++++--
 src/memos/mem_os/core.py                      | 171 +++++++++++++++++-
 src/memos/parsers/markitdown.py               |  17 +-
 src/memos/templates/mos_prompts.py            |  16 --
 9 files changed, 267 insertions(+), 68 deletions(-)

diff --git a/.vscode/settings.json b/.vscode/settings.json
index 815846edc..bf31e7a98 100644
--- a/.vscode/settings.json
+++ b/.vscode/settings.json
@@ -5,5 +5,6 @@
   ],
   "python.testing.unittestEnabled": false,
   "python.testing.pytestEnabled": true,
-  "python.analysis.typeCheckingMode": "off"
+  "python.analysis.typeCheckingMode": "off",
+  "cursorpyright.analysis.typeCheckingMode": "off"
 }
diff --git a/evaluation/scripts/mmlongbench/eval_docs2.py b/evaluation/scripts/mmlongbench/eval_docs2.py
index 03a89841c..33438f9d4 100644
--- a/evaluation/scripts/mmlongbench/eval_docs2.py
+++ b/evaluation/scripts/mmlongbench/eval_docs2.py
@@ -17,7 +17,7 @@
 
 load_dotenv()
 openapi_config = {
-    "model_name_or_path": "gpt-5-nano",
+    "model_name_or_path": "gpt-4o",
     "top_k": 50,
     "remove_think_prefix": True,
     "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
@@ -87,16 +87,13 @@ def process_doc(doc_file):
                     "extractor_llm": {"backend": "openai", "config": openapi_config},
                     "dispatcher_llm": {"backend": "openai", "config": openapi_config},
                     "graph_db": {
-                        "backend": "nebular",
+                        "backend": "neo4j",
                         "config": {
-                            "uri": json.loads(os.getenv("NEBULAR_HOSTS", "localhost")),
-                            "user": os.getenv("NEBULAR_USER", "root"),
-                            "password": os.getenv("NEBULAR_PASSWORD", "xxxxxx"),
-                            "space": space_name,
-                            "user_name": user_name,
-                            "use_multi_db": False,
+                            "uri": "bolt://47.117.45.189:7687",
+                            "user": "neo4j",
+                            "password": "iaarlichunyu",
+                            "db_name": "stx-mmlongbench-001",
                             "auto_create": True,
-                            "embedding_dimension": 3072,
                         },
                     },
                     "embedder": {
@@ -117,8 +114,10 @@ def process_doc(doc_file):
     )
     mem_cube = GeneralMemCube(mem_cube_config)
 
-    temp_dir = "tmp/" + doc_file
-    if not os.path.exists(temp_dir) or not os.listdir(temp_dir):
+    temp_dir = os.path.join("tmp", doc_file)
+    # Only dump when the directory does not exist or is empty
+    # Avoid calling dump on a non-empty directory to prevent MemCubeError
+    if (not os.path.exists(temp_dir)) or (not os.listdir(temp_dir)):
         mem_cube.dump(temp_dir)
 
     mos.register_mem_cube(temp_dir, mem_cube_id=user_name)
diff --git a/evaluation/scripts/mmlongbench/import_docs.py b/evaluation/scripts/mmlongbench/import_docs.py
index 4bff1f333..bc7c966d0 100644
--- a/evaluation/scripts/mmlongbench/import_docs.py
+++ b/evaluation/scripts/mmlongbench/import_docs.py
@@ -1,5 +1,6 @@
 import asyncio
 import os
+import traceback
 import uuid
 
 from memos import log
@@ -32,6 +33,8 @@ async def process_doc(doc_path):
         "examples/data/config/tree_config_shared_database.json"
     )
     doc_file = doc_path.split("/")[-1]
+    if doc_file != "38331-h80.pdf":
+        return doc_path
 
     user_id = "user_" + doc_file
     tree_config.graph_db.config.user_name = user_id
@@ -44,6 +47,7 @@ async def process_doc(doc_path):
 
     count = 0
     for m_list in doc_memory:
+        count += len(m_list)
         my_tree_textual_memory.add(m_list)
     print("total memories: ", count)
 
@@ -52,7 +56,7 @@ async def process_doc(doc_path):
 
 
 async def main():
-    batch_size = 1
+    batch_size = 4
 
     for i in range(0, len(doc_paths), batch_size):
         batch = doc_paths[i : i + batch_size]
@@ -64,6 +68,8 @@ async def main():
         for p, result in zip(batch, results, strict=False):
             if isinstance(result, Exception):
                 print(f"❌ Error processing {p}: {result}")
+                tb_text = "".join(traceback.TracebackException.from_exception(result).format())
+                print(tb_text)
             else:
                 print(f"✅ Finished {result}")
 
diff --git a/examples/data/config/tree_config_shared_database.json b/examples/data/config/tree_config_shared_database.json
index d8dc40cf6..2878ed561 100644
--- a/examples/data/config/tree_config_shared_database.json
+++ b/examples/data/config/tree_config_shared_database.json
@@ -35,20 +35,13 @@
     }
   },
   "graph_db": {
-    "backend": "nebular",
+    "backend": "neo4j",
     "config": {
-      "uri": [
-        "106.14.142.60:9669",
-        "120.55.160.164:9669",
-        "106.15.38.5:9669"
-      ],
-      "user": "root",
-      "password": "NebulaMemOS0724",
-      "space": "nebula-longbench-stx-new",
-      "user_name": "user_name",
-      "use_multi_db": false,
-      "auto_create": true,
-      "embedding_dimension": 3072
+      "uri": "bolt://47.117.45.189:7687",
+      "user": "neo4j",
+      "password": "iaarlichunyu",
+      "db_name": "stx-mmlongbench-001",
+      "auto_create": true
     }
   },
   "reorganize": false
diff --git a/src/memos/graph_dbs/nebular.py b/src/memos/graph_dbs/nebular.py
index 26b70ea2e..f01a165da 100644
--- a/src/memos/graph_dbs/nebular.py
+++ b/src/memos/graph_dbs/nebular.py
@@ -1389,7 +1389,7 @@ def _ensure_database_exists(self):
             """
             self.execute_query(create_tag, auto_set_db=False)
         else:
-            describe_query = f"DESCRIBE NODE TYPE Memory OF {graph_type_name};"
+            describe_query = f"DESCRIBE NODE TYPE Memory OF {graph_type_name}"
             desc_result = self.execute_query(describe_query, auto_set_db=False)
 
             memory_fields = []
diff --git a/src/memos/graph_dbs/neo4j.py b/src/memos/graph_dbs/neo4j.py
index 8a20b4602..0c35f205a 100644
--- a/src/memos/graph_dbs/neo4j.py
+++ b/src/memos/graph_dbs/neo4j.py
@@ -87,6 +87,50 @@ def __init__(self, config: Neo4jGraphDBConfig):
         # Create only if not exists
         self.create_index(dimensions=config.embedding_dimension)
 
+    def _run_with_retry(
+        self,
+        query: str,
+        params: dict | None = None,
+        *,
+        max_retries: int = 3,
+        base_sleep: float = 1.0,
+    ):
+        """Execute a Cypher query with retry on transient connection errors.
+
+        Retries on neo4j ServiceUnavailable/TransientError and common network timeouts.
+
+        Args:
+            query: Cypher query string
+            params: parameters dictionary
+            max_retries: maximum number of attempts
+            base_sleep: base sleep for exponential backoff
+
+        Returns:
+            neo4j.Result
+
+        Raises:
+            Last exception encountered after exhausting retries.
+        """
+
+        attempt = 0
+        last_exc = None
+        while attempt < max_retries:
+            try:
+                with self.driver.session(database=self.db_name) as session:
+                    return session.run(query, parameters=(params or {}))
+            except Exception as e:
+                last_exc = e
+                attempt += 1
+                logger.warning(
+                    f"Neo4j query failed (attempt {attempt}/{max_retries}): {e}. Retrying..."
+                )
+                time.sleep(base_sleep * (2 ** (attempt - 1)))
+
+        logger.error(
+            f"Neo4j query exhausted retries ({max_retries}). Raising last error: {last_exc}"
+        )
+        raise last_exc
+
     def create_index(
         self,
         label: str = "Memory",
@@ -160,8 +204,7 @@ def remove_oldest_memory(self, memory_type: str, keep_latest: int) -> None:
             SKIP {keep_latest}
             DETACH DELETE n
         """
-        with self.driver.session(database=self.db_name) as session:
-            session.run(query)
+        self._run_with_retry(query)
 
     def add_node(self, id: str, memory: str, metadata: dict[str, Any]) -> None:
         if not self.config.use_multi_db and self.config.user_name:
@@ -181,15 +224,16 @@ def add_node(self, id: str, memory: str, metadata: dict[str, Any]) -> None:
                 n.updated_at = datetime($updated_at),
                 n += $metadata
         """
-        with self.driver.session(database=self.db_name) as session:
-            session.run(
-                query,
-                id=id,
-                memory=memory,
-                created_at=created_at,
-                updated_at=updated_at,
-                metadata=metadata,
-            )
+        self._run_with_retry(
+            query,
+            {
+                "id": id,
+                "memory": memory,
+                "created_at": created_at,
+                "updated_at": updated_at,
+                "metadata": metadata,
+            },
+        )
 
     def update_node(self, id: str, fields: dict[str, Any]) -> None:
         """
@@ -217,8 +261,7 @@ def update_node(self, id: str, fields: dict[str, Any]) -> None:
 
         query += f"\nSET {set_clause_str}"
 
-        with self.driver.session(database=self.db_name) as session:
-            session.run(query, **params)
+        self._run_with_retry(query, params)
 
     def delete_node(self, id: str) -> None:
         """
@@ -235,8 +278,7 @@ def delete_node(self, id: str) -> None:
 
         query += " DETACH DELETE n"
 
-        with self.driver.session(database=self.db_name) as session:
-            session.run(query, **params)
+        self._run_with_retry(query, params)
 
     # Edge (Relationship) Management
     def add_edge(self, source_id: str, target_id: str, type: str) -> None:
@@ -260,8 +302,7 @@ def add_edge(self, source_id: str, target_id: str, type: str) -> None:
 
         query += f"\nMERGE (a)-[:{type}]->(b)"
 
-        with self.driver.session(database=self.db_name) as session:
-            session.run(query, params)
+        self._run_with_retry(query, params)
 
     def delete_edge(self, source_id: str, target_id: str, type: str) -> None:
         """
@@ -284,8 +325,7 @@ def delete_edge(self, source_id: str, target_id: str, type: str) -> None:
 
         query += "\nDELETE r"
 
-        with self.driver.session(database=self.db_name) as session:
-            session.run(query, params)
+        self._run_with_retry(query, params)
 
     def edge_exists(
         self, source_id: str, target_id: str, type: str = "ANY", direction: str = "OUTGOING"
diff --git a/src/memos/mem_os/core.py b/src/memos/mem_os/core.py
index 03350346f..01cb0323c 100644
--- a/src/memos/mem_os/core.py
+++ b/src/memos/mem_os/core.py
@@ -1,8 +1,12 @@
+import base64
 import json
+import mimetypes
 import os
+import re
 import time
 
 from datetime import datetime
+from functools import lru_cache
 from pathlib import Path
 from threading import Lock
 from typing import Any, Literal
@@ -31,6 +35,166 @@
 
 logger = get_logger(__name__)
 
+# -----------------------
+# Helper functions for image handling in chat
+# -----------------------
+
+
+def _encode_image_to_data_url(image_path: str) -> str | None:
+    """Encode local image file to base64 data URL for OpenAI-compatible image messages.
+
+    Returns a data URL like: data:image/jpeg;base64,<...>
+    """
+    try:
+        mime, _ = mimetypes.guess_type(image_path)
+        if not mime:
+            # default to jpeg
+            mime = "image/jpeg"
+        with open(image_path, "rb") as f:
+            b64 = base64.b64encode(f.read()).decode("ascii")
+        return f"data:{mime};base64,{b64}"
+    except Exception as e:
+        logger.warning(f"Failed to encode image '{image_path}' to data URL: {e}")
+        return None
+
+
+@lru_cache(maxsize=1)
+def _build_images_index() -> dict[str, str]:
+    """Scan `./ppt_test_result` recursively and index images by filename.
+
+    New structure example:
+    ./ppt_test_result/<pdf-name>/extracted/file_*/<pdf-name>/auto/images/*.{png,jpg,jpeg,webp,gif}
+
+    Also compatible with previous layouts. Returns mapping:
+    basename (e.g. img_123.jpg) -> absolute path
+    """
+    base_dir = Path("./ppt_test_result")
+    index: dict[str, str] = {}
+    if not base_dir.exists():
+        return index
+
+    # Recursively find any `auto/images` directories under ppt_test_result
+    for images_dir in base_dir.rglob("auto/images"):
+        if images_dir.is_dir():
+            for img_file in images_dir.iterdir():
+                if img_file.is_file():
+                    index[img_file.name] = str(img_file.resolve())
+    logger.info(f"Image index built with {len(index)} entries")
+    return index
+
+
+def get_images(sources: list[str]) -> list[str]:
+    """Extract image absolute paths from metadata sources.
+
+    Supports patterns like: ![](images/<hash>.jpg) or any 'images/...jpg' substring.
+    Falls back to scanning the ppt_test_result index to resolve basenames.
+    """
+    if not sources:
+        return []
+
+    # Ensure index exists
+    index = _build_images_index()
+    found: list[str] = []
+
+    md_img_pattern = re.compile(r"!\[[^\]]*\]\(([^\)]+)\)")
+    images_substr_pattern = re.compile(r"images/[^\s)]+\.(?:png|jpg|jpeg|webp)", re.IGNORECASE)
+
+    for src in sources:
+        if not src:
+            continue
+        # 1) markdown image syntax
+        for m in md_img_pattern.findall(src):
+            candidate = m.strip()
+            # if it's a relative like 'images/xxx.jpg', resolve via index
+            basename = os.path.basename(candidate)
+            if basename in index:
+                found.append(index[basename])
+            else:
+                # try direct path (absolute or relative)
+                p = Path(candidate)
+                if not p.is_absolute():
+                    p = Path.cwd() / p
+                if p.exists():
+                    found.append(str(p.resolve()))
+
+        # 2) any 'images/xxx.jpg' substring
+        for m in images_substr_pattern.findall(src):
+            candidate = m.strip()
+            basename = os.path.basename(candidate)
+            if basename in index:
+                found.append(index[basename])
+            else:
+                p = Path(candidate)
+                if not p.is_absolute():
+                    p = Path.cwd() / p
+                if p.exists():
+                    found.append(str(p.resolve()))
+
+    # Deduplicate preserving order
+    dedup: list[str] = []
+    seen = set()
+    for path in found:
+        if path not in seen:
+            dedup.append(path)
+            seen.add(path)
+    return dedup
+
+
+def add_images_context(current_messages: list[dict[str, Any]], images: list[str]) -> None:
+    """Append images in OpenAI-compatible multi-part format and ensure message structure.
+
+    - Deduplicates image paths.
+    - Ensures a system message exists with a concise CN vision instruction.
+    - Ensures the last user message has multi-part content: [text, image_url...].
+    - Uses base64 data URLs. Limits to 6 images.
+    - In-place modification of `current_messages`.
+    """
+    if not images:
+        return
+
+    # Deduplicate images while preserving order
+    unique_images: list[str] = []
+    seen_paths: set[str] = set()
+    for p in images:
+        if p not in seen_paths:
+            unique_images.append(p)
+            seen_paths.add(p)
+
+    # Locate or create the last user message
+    user_idx = None
+    for i in range(len(current_messages) - 1, -1, -1):
+        if current_messages[i].get("role") == "user":
+            user_idx = i
+            break
+
+    user_msg = current_messages[user_idx]
+    orig_content = user_msg.get("content", "")
+
+    # Normalize user content to multi-part format using original query as text (no fallback)
+    content_parts: list[dict[str, Any]]
+    if isinstance(orig_content, str):
+        content_parts = [{"type": "text", "text": orig_content}]
+    elif isinstance(orig_content, list):
+        content_parts = orig_content
+    else:
+        content_parts = [{"type": "text", "text": str(orig_content)}]
+
+    # 5) Append up to 3 images as data URLs
+    limit = 3
+    count = 0
+    for img_path in unique_images:
+        if count >= limit:
+            break
+        data_url = _encode_image_to_data_url(img_path)
+        if data_url:
+            content_parts.append({"type": "image_url", "image_url": {"url": data_url}})
+            count += 1
+
+    user_msg["content"] = content_parts
+    current_messages[user_idx] = user_msg
+    logger.info(f"Attached {count} images to user message (deduplicated from {len(images)})")
+    return current_messages
+
 
 class MOSCore:
     """
@@ -269,12 +433,12 @@ def chat(self, query: str, user_id: str | None = None, base_prompt: str | None =
 
         if self.config.enable_textual_memory and self.mem_cubes:
             memories_all = []
+            images_all = []
             for mem_cube_id, mem_cube in self.mem_cubes.items():
                 if mem_cube_id not in user_cube_ids:
                     continue
                 if not mem_cube.text_mem:
                     continue
-
                 # submit message to scheduler
                 if self.enable_mem_scheduler and self.mem_scheduler is not None:
                     message_item = ScheduleMessageItem(
@@ -297,6 +461,10 @@ def chat(self, query: str, user_id: str | None = None, base_prompt: str | None =
                     },
                 )
                 memories_all.extend(memories)
+                for memory in memories:
+                    images_list = get_images(memory.metadata.sources)
+                    if len(images_list):
+                        images_all.extend(images_list)
 
             logger.info(f"🧠 [Memory] Searched memories:\n{self._str_memories(memories_all)}\n")
 
@@ -309,6 +477,7 @@ def chat(self, query: str, user_id: str | None = None, base_prompt: str | None =
             *chat_history.chat_history,
             {"role": "user", "content": query},
         ]
+        current_messages = add_images_context(current_messages, images_all)
         past_key_values = None
 
         if self.config.enable_activation_memory:
diff --git a/src/memos/parsers/markitdown.py b/src/memos/parsers/markitdown.py
index c9827bd7d..822020bd4 100644
--- a/src/memos/parsers/markitdown.py
+++ b/src/memos/parsers/markitdown.py
@@ -1,6 +1,7 @@
 import asyncio
 import shlex
 import time
+import traceback
 
 from pathlib import Path
 
@@ -197,17 +198,19 @@ async def parse(self, file_path: str) -> str:
 
         if not ppt_file.exists():
             print(f"❌ PPT文件不存在: {ppt_file}")
-            return
-
-        temp_dir = Path(f"./ppt_test_result/ppt_test_results_{int(time.time())}")
+            return ""
+        file_stem = ppt_file.stem
+        temp_dir = Path("./ppt_test_result/" + file_stem)
         temp_dir.mkdir(exist_ok=True)
+        file_id: str | None = None
+        text_content: str = ""
 
         try:
             async with httpx.AsyncClient(timeout=30.0) as client:
                 # 0. 健康检查
                 if not await check_service_health(client):
                     print("中止测试")
-                    return
+                    return ""
 
                 file_id = await upload_ppt_file(client, ppt_file)
 
@@ -217,8 +220,10 @@ async def parse(self, file_path: str) -> str:
 
         except Exception as e:
             print(f"\n❌ 测试失败: {e}")
+            tb_text = "".join(traceback.TracebackException.from_exception(e).format())
+            print(tb_text)
             print(f"🔍 文件保留在代码目录用于检查: ./{temp_dir.name}/")
-            return
+            return ""
 
         try:
             if file_id:
@@ -232,5 +237,7 @@ async def parse(self, file_path: str) -> str:
 
         except Exception as e:
             print(f"⚠️ 服务器清理失败: {e}")
+            tb_text = "".join(traceback.TracebackException.from_exception(e).format())
+            print(tb_text)
 
         return text_content
diff --git a/src/memos/templates/mos_prompts.py b/src/memos/templates/mos_prompts.py
index ac0e271e9..e2a0f096d 100644
--- a/src/memos/templates/mos_prompts.py
+++ b/src/memos/templates/mos_prompts.py
@@ -30,22 +30,6 @@
 ✅ Chain of Thought (CoT) enhancement is now enabled by default
 ✅ Complex queries will be automatically decomposed and enhanced
 
-🌐 To enable Internet search capabilities:
-   1. Go to your cube's textual memory configuration
-   2. Set the backend to 'google' in the internet_retriever section
-   3. Configure the following parameters:
-      - api_key: Your Google Search API key
-      - cse_id: Your Custom Search Engine ID
-      - num_results: Number of search results (default: 5)
-
-📝 Example configuration at cube config for tree_text_memory :
-   internet_retriever:
-     backend: 'google'
-     config:
-       api_key: 'your_google_api_key_here'
-       cse_id: 'your_custom_search_engine_id'
-       num_results: 5
-details: https://github.com/memos-ai/memos/blob/main/examples/core_memories/tree_textual_w_internet_memoy.py
 ============================================================
 """
 

From b0500f149faf5f352dd08af7a3e3cb657d8f2c7c Mon Sep 17 00:00:00 2001
From: stx <31013941@qq.com>
Date: Wed, 12 Nov 2025 15:31:53 +0800
Subject: [PATCH 25/34] =?UTF-8?q?feat=EF=BC=9Aupdate=2011.12?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 evaluation/scripts/mmlongbench/eval_docs2.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/evaluation/scripts/mmlongbench/eval_docs2.py b/evaluation/scripts/mmlongbench/eval_docs2.py
index 33438f9d4..6f8895063 100644
--- a/evaluation/scripts/mmlongbench/eval_docs2.py
+++ b/evaluation/scripts/mmlongbench/eval_docs2.py
@@ -115,6 +115,7 @@ def process_doc(doc_file):
     mem_cube = GeneralMemCube(mem_cube_config)
 
     temp_dir = os.path.join("tmp", doc_file)
+
     # Only dump when the directory does not exist or is empty
     # Avoid calling dump on a non-empty directory to prevent MemCubeError
     if (not os.path.exists(temp_dir)) or (not os.listdir(temp_dir)):

From 43f52a548db59d1a14f6ab310849175c0dec849b Mon Sep 17 00:00:00 2001
From: stx <31013941@qq.com>
Date: Wed, 12 Nov 2025 17:02:47 +0800
Subject: [PATCH 26/34] =?UTF-8?q?feat=EF=BC=9Aupdate=2011.12?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .gitignore               | 4 +++-
 src/memos/mem_os/core.py | 9 ++++++---
 2 files changed, 9 insertions(+), 4 deletions(-)

diff --git a/.gitignore b/.gitignore
index d3d9957f0..4d531866b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -19,7 +19,9 @@ evaluation/scripts/personamem
 
 # benchmarks
 benchmarks/
-   
+
+ppt_test_result/
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
diff --git a/src/memos/mem_os/core.py b/src/memos/mem_os/core.py
index 01cb0323c..b5be4162f 100644
--- a/src/memos/mem_os/core.py
+++ b/src/memos/mem_os/core.py
@@ -140,7 +140,9 @@ def get_images(sources: list[str]) -> list[str]:
     return dedup
 
 
-def add_images_context(current_messages: list[dict[str, Any]], images: list[str]) -> None:
+def add_images_context(
+    current_messages: list[dict[str, Any]], images: list[str]
+) -> list[dict[str, Any]]:
     """Append images in OpenAI-compatible multi-part format and ensure message structure.
 
     - Deduplicates image paths.
@@ -150,7 +152,7 @@ def add_images_context(current_messages: list[dict[str, Any]], images: list[str]
     - In-place modification of `current_messages`.
     """
     if not images:
-        return
+        return current_messages
 
     # Deduplicate images while preserving order
     unique_images: list[str] = []
@@ -180,7 +182,7 @@ def add_images_context(current_messages: list[dict[str, Any]], images: list[str]
         content_parts = [{"type": "text", "text": str(orig_content)}]
 
     # 5) Append up to 3 images as data URLs
-    limit = 3
+    limit = 6
     count = 0
     for img_path in unique_images:
         if count >= limit:
@@ -423,6 +425,7 @@ def chat(self, query: str, user_id: str | None = None, base_prompt: str | None =
         Returns:
             str: The response from the MOS.
         """
+        global images_all
         target_user_id = user_id if user_id is not None else self.user_id
         accessible_cubes = self.user_manager.get_user_cubes(target_user_id)
         user_cube_ids = [cube.cube_id for cube in accessible_cubes]

From 260834687c8d5a74b7bd9d763b2aaab62168cf37 Mon Sep 17 00:00:00 2001
From: stx <31013941@qq.com>
Date: Mon, 17 Nov 2025 16:40:17 +0800
Subject: [PATCH 27/34] =?UTF-8?q?feat=EF=BC=9Aupdate=2011.17?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 evaluation/scripts/mmlongbench/eval_docs2.py  |   1 +
 evaluation/scripts/mmlongbench/import_docs.py |  28 +-
 .../scripts/mmlongbench/multimodal_test.py    | 186 ++++++++
 .../config/tree_config_shared_database.json   |   2 +-
 src/memos/api/config.py                       |   1 +
 src/memos/graph_dbs/neo4j.py                  | 425 +++++++++++-------
 src/memos/mem_os/core.py                      |  10 +-
 src/memos/mem_reader/simple_struct.py         |   1 -
 src/memos/memories/textual/tree.py            |   1 -
 .../tree_text_memory/retrieve/searcher.py     |  48 +-
 src/memos/reranker/factory.py                 |   2 +-
 src/memos/reranker/http_bge.py                |   2 +-
 12 files changed, 518 insertions(+), 189 deletions(-)
 create mode 100644 evaluation/scripts/mmlongbench/multimodal_test.py

diff --git a/evaluation/scripts/mmlongbench/eval_docs2.py b/evaluation/scripts/mmlongbench/eval_docs2.py
index 6f8895063..ff9f3e8f5 100644
--- a/evaluation/scripts/mmlongbench/eval_docs2.py
+++ b/evaluation/scripts/mmlongbench/eval_docs2.py
@@ -38,6 +38,7 @@
 
 def process_doc(doc_file):
     user_name = "user_" + doc_file
+    print(user_name)
     config = {
         "user_id": user_name,
         "chat_model": {
diff --git a/evaluation/scripts/mmlongbench/import_docs.py b/evaluation/scripts/mmlongbench/import_docs.py
index bc7c966d0..f3610cf26 100644
--- a/evaluation/scripts/mmlongbench/import_docs.py
+++ b/evaluation/scripts/mmlongbench/import_docs.py
@@ -18,6 +18,10 @@
 )
 reader = SimpleStructMemReader(reader_config)
 
+tree_config = TreeTextMemoryConfig.from_json_file(
+    "examples/data/config/tree_config_shared_database.json"
+)
+tree_config.graph_db.config.db_name = "stx-mmlongbench-002"
 # Processing Documents
 doc_paths = [
     "evaluation/data/mmlongbench/documents_xcy/" + f
@@ -28,21 +32,23 @@
 
 async def process_doc(doc_path):
     print(f"🔄 Processing document: {doc_path}")
-    session_id = str(uuid.uuid4())
-    tree_config = TreeTextMemoryConfig.from_json_file(
-        "examples/data/config/tree_config_shared_database.json"
-    )
-    doc_file = doc_path.split("/")[-1]
-    if doc_file != "38331-h80.pdf":
-        return doc_path
+    doc_file = doc_path.split("/")[-1].rsplit(".", 1)[0]
+
+    # Generate random user id: 'user_' + random short hex
+    user_id = "user_" + uuid.uuid4().hex[:8]
+    # Persist mapping between user_id and doc_path
+    try:
+        os.makedirs("evaluation/data/mmlongbench", exist_ok=True)
+        with open("evaluation/data/mmlongbench/user_doc_map.csv", "a", encoding="utf-8") as f:
+            f.write(f"{user_id},{doc_path}\n")
+    except Exception as e:
+        logger.error(f"Failed to write user-doc mapping: {e}")
 
-    user_id = "user_" + doc_file
     tree_config.graph_db.config.user_name = user_id
     temp_dir = "tmp/" + doc_file
-
     my_tree_textual_memory = TreeTextMemory(tree_config)
     doc_memory = await reader.get_memory(
-        [doc_path], "doc", info={"user_id": user_id, "session_id": session_id}
+        [doc_path], "doc", info={"user_id": user_id, "session_id": "session_" + str(uuid.uuid4())}
     )
 
     count = 0
@@ -56,7 +62,7 @@ async def process_doc(doc_path):
 
 
 async def main():
-    batch_size = 4
+    batch_size = 1
 
     for i in range(0, len(doc_paths), batch_size):
         batch = doc_paths[i : i + batch_size]
diff --git a/evaluation/scripts/mmlongbench/multimodal_test.py b/evaluation/scripts/mmlongbench/multimodal_test.py
new file mode 100644
index 000000000..b8134be8a
--- /dev/null
+++ b/evaluation/scripts/mmlongbench/multimodal_test.py
@@ -0,0 +1,186 @@
+import os
+import shutil
+
+from dotenv import load_dotenv
+
+from memos.configs.mem_cube import GeneralMemCubeConfig
+from memos.configs.mem_os import MOSConfig
+from memos.mem_cube.general import GeneralMemCube
+from memos.mem_os.main import MOS
+
+
+load_dotenv()
+
+db_name = "stx-mmlongbench-002"
+user_id = "user_dc812220"
+
+# 1.1 Set openai config
+openapi_config = {
+    "model_name_or_path": "gpt-4o",
+    "top_k": 50,
+    "remove_think_prefix": True,
+    "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
+    "api_base": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
+}
+# 1.2 Set neo4j config
+neo4j_uri = os.getenv("NEO4J_URI", "bolt://localhost:7687")
+
+# 1.3  Create MOS Config
+config = {
+    "user_id": user_id,
+    "chat_model": {
+        "backend": "openai",
+        "config": openapi_config,
+    },
+    "mem_reader": {
+        "backend": "simple_struct",
+        "config": {
+            "llm": {
+                "backend": "openai",
+                "config": openapi_config,
+            },
+            "embedder": {
+                "backend": "universal_api",
+                "config": {
+                    "provider": "openai",
+                    "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
+                    "model_name_or_path": "text-embedding-3-large",
+                    "base_url": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
+                },
+            },
+            "chunker": {
+                "backend": "sentence",
+                "config": {
+                    "tokenizer_or_token_counter": "gpt2",
+                    "chunk_size": 512,
+                    "chunk_overlap": 128,
+                    "min_sentences_per_chunk": 1,
+                },
+            },
+        },
+    },
+    "max_turns_window": 20,
+    "top_k": 5,
+    "enable_textual_memory": True,
+    "enable_activation_memory": False,
+    "enable_parametric_memory": False,
+}
+
+mos_config = MOSConfig(**config)
+# you can set PRO_MODE to True to enable CoT enhancement mos_config.PRO_MODE = True
+mos = MOS(mos_config)
+
+config = GeneralMemCubeConfig.model_validate(
+    {
+        "user_id": user_id,
+        "cube_id": f"{user_id}",
+        "text_mem": {
+            "backend": "tree_text",
+            "config": {
+                "extractor_llm": {
+                    "backend": "openai",
+                    "config": openapi_config,
+                },
+                "dispatcher_llm": {
+                    "backend": "openai",
+                    "config": openapi_config,
+                },
+                "graph_db": {
+                    "backend": "neo4j",
+                    "config": {
+                        "uri": neo4j_uri,
+                        "user": "neo4j",
+                        "password": "iaarlichunyu",
+                        "db_name": db_name,
+                        "user_name": user_id,
+                        "use_multi_db": False,
+                        "auto_create": True,
+                        "embedding_dimension": 3072,
+                    },
+                },
+                "embedder": {
+                    "backend": "universal_api",
+                    "config": {
+                        "provider": "openai",
+                        "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
+                        "model_name_or_path": "text-embedding-3-large",
+                        "base_url": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
+                    },
+                },
+                "reorganize": False,
+            },
+        },
+        "act_mem": {},
+        "para_mem": {},
+    },
+)
+
+
+# Filter out embedding fields, keeping only necessary fields
+def filter_memory_data(memories_data):
+    filtered_data = {}
+    for key, value in memories_data.items():
+        if key == "text_mem":
+            filtered_data[key] = []
+            for mem_group in value:
+                # Check if it's the new data structure (list of TextualMemoryItem objects)
+                if "memories" in mem_group and isinstance(mem_group["memories"], list):
+                    # New data structure: directly a list of TextualMemoryItem objects
+                    filtered_memories = []
+                    for memory_item in mem_group["memories"]:
+                        # Create filtered dictionary
+                        filtered_item = {
+                            "id": memory_item.id,
+                            "memory": memory_item.memory,
+                            "metadata": {},
+                        }
+                        # Filter metadata, excluding embedding
+                        if hasattr(memory_item, "metadata") and memory_item.metadata:
+                            for attr_name in dir(memory_item.metadata):
+                                if not attr_name.startswith("_") and attr_name != "embedding":
+                                    attr_value = getattr(memory_item.metadata, attr_name)
+                                    if not callable(attr_value):
+                                        filtered_item["metadata"][attr_name] = attr_value
+                        filtered_memories.append(filtered_item)
+
+                    filtered_group = {
+                        "cube_id": mem_group.get("cube_id", ""),
+                        "memories": filtered_memories,
+                    }
+                    filtered_data[key].append(filtered_group)
+                else:
+                    # Old data structure: dictionary with nodes and edges
+                    filtered_group = {
+                        "memories": {"nodes": [], "edges": mem_group["memories"].get("edges", [])}
+                    }
+                    for node in mem_group["memories"].get("nodes", []):
+                        filtered_node = {
+                            "id": node.get("id"),
+                            "memory": node.get("memory"),
+                            "metadata": {
+                                k: v
+                                for k, v in node.get("metadata", {}).items()
+                                if k != "embedding"
+                            },
+                        }
+                        filtered_group["memories"]["nodes"].append(filtered_node)
+                    filtered_data[key].append(filtered_group)
+        else:
+            filtered_data[key] = value
+    return filtered_data
+
+
+mem_cube = GeneralMemCube(config)
+
+temp_dir = f"/tmp/{user_id}"
+if os.path.exists(temp_dir):
+    shutil.rmtree(temp_dir)
+mem_cube.dump(temp_dir)
+mos.register_mem_cube(temp_dir, mem_cube_id=user_id)
+
+
+print("start answering...")
+user_query = "图8美股变化的影响是什么"
+print(f"👤 User query: {user_query}")
+response = mos.chat(user_query)
+print(f"🤖 Response: {response}")
diff --git a/examples/data/config/tree_config_shared_database.json b/examples/data/config/tree_config_shared_database.json
index 2878ed561..cb4dffb28 100644
--- a/examples/data/config/tree_config_shared_database.json
+++ b/examples/data/config/tree_config_shared_database.json
@@ -40,7 +40,7 @@
       "uri": "bolt://47.117.45.189:7687",
       "user": "neo4j",
       "password": "iaarlichunyu",
-      "db_name": "stx-mmlongbench-001",
+      "db_name": "",
       "auto_create": true
     }
   },
diff --git a/src/memos/api/config.py b/src/memos/api/config.py
index 83eee738b..6f66599e5 100644
--- a/src/memos/api/config.py
+++ b/src/memos/api/config.py
@@ -186,6 +186,7 @@ def get_neo4j_community_config(user_id: str | None = None) -> dict[str, Any]:
     @staticmethod
     def get_neo4j_config(user_id: str | None = None) -> dict[str, Any]:
         """Get Neo4j configuration."""
+        print("MOS_NEO4J_SHARED_DB: ", os.getenv("MOS_NEO4J_SHARED_DB", "false"))
         if os.getenv("MOS_NEO4J_SHARED_DB", "false").lower() == "true":
             return APIConfig.get_neo4j_shared_config(user_id)
         else:
diff --git a/src/memos/graph_dbs/neo4j.py b/src/memos/graph_dbs/neo4j.py
index 0c35f205a..9140fe532 100644
--- a/src/memos/graph_dbs/neo4j.py
+++ b/src/memos/graph_dbs/neo4j.py
@@ -1,3 +1,5 @@
+import json
+import os
 import time
 
 from datetime import datetime
@@ -37,6 +39,10 @@ def _prepare_node_metadata(metadata: dict[str, Any]) -> dict[str, Any]:
     if embedding and isinstance(embedding, list):
         metadata["embedding"] = [float(x) for x in embedding]
 
+    # serialization
+    if metadata["sources"]:
+        for idx in range(len(metadata["sources"])):
+            metadata["sources"][idx] = json.dumps(metadata["sources"][idx])
     return metadata
 
 
@@ -69,14 +75,7 @@ def __init__(self, config: Neo4jGraphDBConfig):
         from neo4j import GraphDatabase
 
         self.config = config
-        self.driver = GraphDatabase.driver(
-            config.uri,
-            auth=(config.user, config.password),
-            connection_timeout=600,
-            max_connection_lifetime=3600,
-            max_connection_pool_size=50,
-            max_transaction_retry_time=60,
-        )
+        self.driver = GraphDatabase.driver(config.uri, auth=(config.user, config.password))
         self.db_name = config.db_name
         self.user_name = config.user_name
 
@@ -87,50 +86,6 @@ def __init__(self, config: Neo4jGraphDBConfig):
         # Create only if not exists
         self.create_index(dimensions=config.embedding_dimension)
 
-    def _run_with_retry(
-        self,
-        query: str,
-        params: dict | None = None,
-        *,
-        max_retries: int = 3,
-        base_sleep: float = 1.0,
-    ):
-        """Execute a Cypher query with retry on transient connection errors.
-
-        Retries on neo4j ServiceUnavailable/TransientError and common network timeouts.
-
-        Args:
-            query: Cypher query string
-            params: parameters dictionary
-            max_retries: maximum number of attempts
-            base_sleep: base sleep for exponential backoff
-
-        Returns:
-            neo4j.Result
-
-        Raises:
-            Last exception encountered after exhausting retries.
-        """
-
-        attempt = 0
-        last_exc = None
-        while attempt < max_retries:
-            try:
-                with self.driver.session(database=self.db_name) as session:
-                    return session.run(query, parameters=(params or {}))
-            except Exception as e:
-                last_exc = e
-                attempt += 1
-                logger.warning(
-                    f"Neo4j query failed (attempt {attempt}/{max_retries}): {e}. Retrying..."
-                )
-                time.sleep(base_sleep * (2 ** (attempt - 1)))
-
-        logger.error(
-            f"Neo4j query exhausted retries ({max_retries}). Raising last error: {last_exc}"
-        )
-        raise last_exc
-
     def create_index(
         self,
         label: str = "Memory",
@@ -147,31 +102,62 @@ def create_index(
         # Create indexes
         self._create_basic_property_indexes()
 
-    def get_memory_count(self, memory_type: str) -> int:
+    def _run_with_retry(self, op, op_name: str):
+        """Run an operation with retry for transient/timeout errors.
+
+        Controlled via env vars:
+        - NEO4J_RETRY_MAX_ATTEMPTS (default: 3)
+        - NEO4J_RETRY_BACKOFF_SECONDS (default: 2, exponential backoff)
+        """
+        attempts = int(os.getenv("NEO4J_RETRY_MAX_ATTEMPTS", "3"))
+        base_backoff = float(os.getenv("NEO4J_RETRY_BACKOFF_SECONDS", "2"))
+        try:
+            from neo4j.exceptions import Neo4jError, ServiceUnavailable, TransientError
+        except Exception:
+            # If neo4j isn't available for some reason, run once and let it raise
+            return op()
+
+        for i in range(attempts):
+            try:
+                return op()
+            except (ServiceUnavailable, TransientError, Neo4jError, TimeoutError, OSError) as e:
+                if i == attempts - 1:
+                    logger.error(f"Neo4j operation {op_name} failed after {attempts} attempts: {e}")
+                    raise
+                sleep_s = base_backoff * (2**i)
+                logger.warning(
+                    f"Neo4j operation {op_name} failed (attempt {i + 1}/{attempts}): {e}. "
+                    f"Retrying in {sleep_s:.1f}s"
+                )
+                time.sleep(sleep_s)
+
+    def get_memory_count(self, memory_type: str, user_name: str | None = None) -> int:
+        user_name = user_name if user_name else self.config.user_name
         query = """
         MATCH (n:Memory)
         WHERE n.memory_type = $memory_type
         """
-        if not self.config.use_multi_db and self.config.user_name:
-            query += "\nAND n.user_name = $user_name"
+        if not self.config.use_multi_db and (self.config.user_name or user_name):
+            query += "\nAND n.user_id = $user_name"
         query += "\nRETURN COUNT(n) AS count"
         with self.driver.session(database=self.db_name) as session:
             result = session.run(
                 query,
                 {
                     "memory_type": memory_type,
-                    "user_name": self.config.user_name if self.config.user_name else None,
+                    "user_name": user_name,
                 },
             )
             return result.single()["count"]
 
-    def node_not_exist(self, scope: str) -> int:
+    def node_not_exist(self, scope: str, user_name: str | None = None) -> int:
+        user_name = user_name if user_name else self.config.user_name
         query = """
         MATCH (n:Memory)
         WHERE n.memory_type = $scope
         """
-        if not self.config.use_multi_db and self.config.user_name:
-            query += "\nAND n.user_name = $user_name"
+        if not self.config.use_multi_db and (self.config.user_name or user_name):
+            query += "\nAND n.user_id = $user_name"
         query += "\nRETURN n LIMIT 1"
 
         with self.driver.session(database=self.db_name) as session:
@@ -179,36 +165,44 @@ def node_not_exist(self, scope: str) -> int:
                 query,
                 {
                     "scope": scope,
-                    "user_name": self.config.user_name if self.config.user_name else None,
+                    "user_name": user_name,
                 },
             )
             return result.single() is None
 
-    def remove_oldest_memory(self, memory_type: str, keep_latest: int) -> None:
+    def remove_oldest_memory(
+        self, memory_type: str, keep_latest: int, user_name: str | None = None
+    ) -> None:
         """
         Remove all WorkingMemory nodes except the latest `keep_latest` entries.
 
         Args:
             memory_type (str): Memory type (e.g., 'WorkingMemory', 'LongTermMemory').
             keep_latest (int): Number of latest WorkingMemory entries to keep.
+            user_name(str): optional user_name.
         """
+        user_name = user_name if user_name else self.config.user_name
         query = f"""
         MATCH (n:Memory)
         WHERE n.memory_type = '{memory_type}'
         """
-        if not self.config.use_multi_db and self.config.user_name:
-            query += f"\nAND n.user_name = '{self.config.user_name}'"
-
+        if not self.config.use_multi_db and (self.config.user_name or user_name):
+            query += f"\nAND n.user_id = '{user_name}'"
+        keep_latest = int(keep_latest)
         query += f"""
             WITH n ORDER BY n.updated_at DESC
             SKIP {keep_latest}
             DETACH DELETE n
         """
-        self._run_with_retry(query)
+        with self.driver.session(database=self.db_name) as session:
+            session.run(query)
 
-    def add_node(self, id: str, memory: str, metadata: dict[str, Any]) -> None:
-        if not self.config.use_multi_db and self.config.user_name:
-            metadata["user_name"] = self.config.user_name
+    def add_node(
+        self, id: str, memory: str, metadata: dict[str, Any], user_name: str | None = None
+    ) -> None:
+        user_name = user_name if user_name else self.config.user_name
+        if not self.config.use_multi_db and (self.config.user_name or user_name):
+            metadata["user_name"] = user_name
 
         # Safely process metadata
         metadata = _prepare_node_metadata(metadata)
@@ -224,21 +218,37 @@ def add_node(self, id: str, memory: str, metadata: dict[str, Any]) -> None:
                 n.updated_at = datetime($updated_at),
                 n += $metadata
         """
-        self._run_with_retry(
-            query,
-            {
-                "id": id,
-                "memory": memory,
-                "created_at": created_at,
-                "updated_at": updated_at,
-                "metadata": metadata,
-            },
-        )
+        # serialization
+        if metadata["sources"]:
+            for idx in range(len(metadata["sources"])):
+                metadata["sources"][idx] = json.dumps(metadata["sources"][idx])
+
+        def _op():
+            with self.driver.session(database=self.db_name) as session:
+                result = session.run(
+                    query,
+                    id=id,
+                    memory=memory,
+                    created_at=created_at,
+                    updated_at=updated_at,
+                    metadata=metadata,
+                )
+                summary = result.consume()
+                created = summary.counters.nodes_created
+                print(
+                    "add_node id=%s action=%s",
+                    id,
+                    "created" if created else "matched",
+                )
+                return created
+
+        self._run_with_retry(_op, "add_node")
 
-    def update_node(self, id: str, fields: dict[str, Any]) -> None:
+    def update_node(self, id: str, fields: dict[str, Any], user_name: str | None = None) -> None:
         """
         Update node fields in Neo4j, auto-converting `created_at` and `updated_at` to datetime type if present.
         """
+        user_name = user_name if user_name else self.config.user_name
         fields = fields.copy()  # Avoid mutating external dict
         set_clauses = []
         params = {"id": id, "fields": fields}
@@ -255,33 +265,44 @@ def update_node(self, id: str, fields: dict[str, Any]) -> None:
         query = """
         MATCH (n:Memory {id: $id})
         """
-        if not self.config.use_multi_db and self.config.user_name:
-            query += "\nWHERE n.user_name = $user_name"
-            params["user_name"] = self.config.user_name
+        if not self.config.use_multi_db and (self.config.user_name or user_name):
+            query += "\nWHERE n.user_id = $user_name"
+            params["user_name"] = user_name
 
         query += f"\nSET {set_clause_str}"
 
-        self._run_with_retry(query, params)
+        def _op():
+            with self.driver.session(database=self.db_name) as session:
+                session.run(query, **params)
+
+        self._run_with_retry(_op, "update_node")
 
-    def delete_node(self, id: str) -> None:
+    def delete_node(self, id: str, user_name: str | None = None) -> None:
         """
         Delete a node from the graph.
         Args:
             id: Node identifier to delete.
         """
+        user_name = user_name if user_name else self.config.user_name
         query = "MATCH (n:Memory {id: $id})"
 
         params = {"id": id}
-        if not self.config.use_multi_db and self.config.user_name:
-            query += " WHERE n.user_name = $user_name"
-            params["user_name"] = self.config.user_name
+        if not self.config.use_multi_db and (self.config.user_name or user_name):
+            query += " WHERE n.user_id = $user_name"
+            params["user_name"] = user_name
 
         query += " DETACH DELETE n"
 
-        self._run_with_retry(query, params)
+        def _op():
+            with self.driver.session(database=self.db_name) as session:
+                session.run(query, **params)
+
+        self._run_with_retry(_op, "delete_node")
 
     # Edge (Relationship) Management
-    def add_edge(self, source_id: str, target_id: str, type: str) -> None:
+    def add_edge(
+        self, source_id: str, target_id: str, type: str, user_name: str | None = None
+    ) -> None:
         """
         Create an edge from source node to target node.
         Args:
@@ -289,22 +310,29 @@ def add_edge(self, source_id: str, target_id: str, type: str) -> None:
             target_id: ID of the target node.
             type: Relationship type (e.g., 'RELATE_TO', 'PARENT').
         """
+        user_name = user_name if user_name else self.config.user_name
         query = """
                 MATCH (a:Memory {id: $source_id})
                 MATCH (b:Memory {id: $target_id})
             """
         params = {"source_id": source_id, "target_id": target_id}
-        if not self.config.use_multi_db and self.config.user_name:
+        if not self.config.use_multi_db and (self.config.user_name or user_name):
             query += """
                     WHERE a.user_name = $user_name AND b.user_name = $user_name
                 """
-            params["user_name"] = self.config.user_name
+            params["user_name"] = user_name
 
         query += f"\nMERGE (a)-[:{type}]->(b)"
 
-        self._run_with_retry(query, params)
+        def _op():
+            with self.driver.session(database=self.db_name) as session:
+                session.run(query, params)
+
+        self._run_with_retry(_op, "add_edge")
 
-    def delete_edge(self, source_id: str, target_id: str, type: str) -> None:
+    def delete_edge(
+        self, source_id: str, target_id: str, type: str, user_name: str | None = None
+    ) -> None:
         """
         Delete a specific edge between two nodes.
         Args:
@@ -312,6 +340,7 @@ def delete_edge(self, source_id: str, target_id: str, type: str) -> None:
             target_id: ID of the target node.
             type: Relationship type to remove.
         """
+        user_name = user_name if user_name else self.config.user_name
         query = f"""
             MATCH (a:Memory {{id: $source}})
             -[r:{type}]->
@@ -319,16 +348,25 @@ def delete_edge(self, source_id: str, target_id: str, type: str) -> None:
         """
         params = {"source": source_id, "target": target_id}
 
-        if not self.config.use_multi_db and self.config.user_name:
+        if not self.config.use_multi_db and (self.config.user_name or user_name):
             query += "\nWHERE a.user_name = $user_name AND b.user_name = $user_name"
-            params["user_name"] = self.config.user_name
+            params["user_name"] = user_name
 
         query += "\nDELETE r"
 
-        self._run_with_retry(query, params)
+        def _op():
+            with self.driver.session(database=self.db_name) as session:
+                session.run(query, params)
+
+        self._run_with_retry(_op, "delete_edge")
 
     def edge_exists(
-        self, source_id: str, target_id: str, type: str = "ANY", direction: str = "OUTGOING"
+        self,
+        source_id: str,
+        target_id: str,
+        type: str = "ANY",
+        direction: str = "OUTGOING",
+        user_name: str | None = None,
     ) -> bool:
         """
         Check if an edge exists between two nodes.
@@ -341,6 +379,7 @@ def edge_exists(
         Returns:
             True if the edge exists, otherwise False.
         """
+        user_name = user_name if user_name else self.config.user_name
         # Prepare the relationship pattern
         rel = "r" if type == "ANY" else f"r:{type}"
 
@@ -358,9 +397,9 @@ def edge_exists(
         query = f"MATCH {pattern}"
         params = {"source": source_id, "target": target_id}
 
-        if not self.config.use_multi_db and self.config.user_name:
+        if not self.config.use_multi_db and (self.config.user_name or user_name):
             query += "\nWHERE a.user_name = $user_name AND b.user_name = $user_name"
-            params["user_name"] = self.config.user_name
+            params["user_name"] = user_name
 
         query += "\nRETURN r"
 
@@ -370,21 +409,20 @@ def edge_exists(
             return result.single() is not None
 
     # Graph Query & Reasoning
-    def get_node(self, id: str, include_embedding: bool = True) -> dict[str, Any] | None:
+    def get_node(self, id: str, **kwargs) -> dict[str, Any] | None:
         """
         Retrieve the metadata and memory of a node.
         Args:
             id: Node identifier.
-            include_embedding (bool): Whether to include the large embedding field.
         Returns:
             Dictionary of node fields, or None if not found.
         """
-
+        user_name = kwargs.get("user_name") if kwargs.get("user_name") else self.config.user_name
         where_user = ""
         params = {"id": id}
-        if not self.config.use_multi_db and self.config.user_name:
-            where_user = " AND n.user_name = $user_name"
-            params["user_name"] = self.config.user_name
+        if not self.config.use_multi_db and (self.config.user_name or user_name):
+            where_user = " AND n.user_id = $user_name"
+            params["user_name"] = user_name
 
         query = f"MATCH (n:Memory) WHERE n.id = $id {where_user} RETURN n"
 
@@ -392,12 +430,11 @@ def get_node(self, id: str, include_embedding: bool = True) -> dict[str, Any] |
             record = session.run(query, params).single()
             return self._parse_node(dict(record["n"])) if record else None
 
-    def get_nodes(self, ids: list[str], include_embedding: bool = True) -> list[dict[str, Any]]:
+    def get_nodes(self, ids: list[str], **kwargs) -> list[dict[str, Any]]:
         """
         Retrieve the metadata and memory of a list of nodes.
         Args:
             ids: List of Node identifier.
-            include_embedding (bool): Whether to include the large embedding field.
         Returns:
         list[dict]: Parsed node records containing 'id', 'memory', and 'metadata'.
 
@@ -408,13 +445,16 @@ def get_nodes(self, ids: list[str], include_embedding: bool = True) -> list[dict
 
         if not ids:
             return []
-
+        user_name = kwargs.get("user_name") if kwargs.get("user_name") else self.config.user_name
         where_user = ""
         params = {"ids": ids}
 
-        if not self.config.use_multi_db and self.config.user_name:
-            where_user = " AND n.user_name = $user_name"
-            params["user_name"] = self.config.user_name
+        if not self.config.use_multi_db and (self.config.user_name or user_name):
+            where_user = " AND n.user_id = $user_name"
+            if kwargs.get("cube_name"):
+                params["user_name"] = kwargs["cube_name"]
+            else:
+                params["user_name"] = user_name
 
         query = f"MATCH (n:Memory) WHERE n.id IN $ids{where_user} RETURN n"
 
@@ -422,7 +462,9 @@ def get_nodes(self, ids: list[str], include_embedding: bool = True) -> list[dict
             results = session.run(query, params)
             return [self._parse_node(dict(record["n"])) for record in results]
 
-    def get_edges(self, id: str, type: str = "ANY", direction: str = "ANY") -> list[dict[str, str]]:
+    def get_edges(
+        self, id: str, type: str = "ANY", direction: str = "ANY", user_name: str | None = None
+    ) -> list[dict[str, str]]:
         """
         Get edges connected to a node, with optional type and direction filter.
 
@@ -438,6 +480,7 @@ def get_edges(self, id: str, type: str = "ANY", direction: str = "ANY") -> list[
               ...
             ]
         """
+        user_name = user_name if user_name else self.config.user_name
         # Build relationship type filter
         rel_type = "" if type == "ANY" else f":{type}"
 
@@ -456,9 +499,9 @@ def get_edges(self, id: str, type: str = "ANY", direction: str = "ANY") -> list[
 
         params = {"id": id}
 
-        if not self.config.use_multi_db and self.config.user_name:
+        if not self.config.use_multi_db and (self.config.user_name or user_name):
             where_clause += " AND a.user_name = $user_name AND b.user_name = $user_name"
-            params["user_name"] = self.config.user_name
+            params["user_name"] = user_name
 
         query = f"""
                 MATCH {pattern}
@@ -476,7 +519,11 @@ def get_edges(self, id: str, type: str = "ANY", direction: str = "ANY") -> list[
             return edges
 
     def get_neighbors(
-        self, id: str, type: str, direction: Literal["in", "out", "both"] = "out"
+        self,
+        id: str,
+        type: str,
+        direction: Literal["in", "out", "both"] = "out",
+        user_name: str | None = None,
     ) -> list[str]:
         """
         Get connected node IDs in a specific direction and relationship type.
@@ -495,6 +542,7 @@ def get_neighbors_by_tag(
         exclude_ids: list[str],
         top_k: int = 5,
         min_overlap: int = 1,
+        user_name: str | None = None,
     ) -> list[dict[str, Any]]:
         """
         Find top-K neighbor nodes with maximum tag overlap.
@@ -508,6 +556,7 @@ def get_neighbors_by_tag(
         Returns:
             List of dicts with node details and overlap count.
         """
+        user_name = user_name if user_name else self.config.user_name
         where_user = ""
         params = {
             "tags": tags,
@@ -516,9 +565,9 @@ def get_neighbors_by_tag(
             "top_k": top_k,
         }
 
-        if not self.config.use_multi_db and self.config.user_name:
-            where_user = "AND n.user_name = $user_name"
-            params["user_name"] = self.config.user_name
+        if not self.config.use_multi_db and (self.config.user_name or user_name):
+            where_user = "AND n.user_id = $user_name"
+            params["user_name"] = user_name
 
         query = f"""
                 MATCH (n:Memory)
@@ -538,13 +587,16 @@ def get_neighbors_by_tag(
             result = session.run(query, params)
             return [self._parse_node(dict(record["n"])) for record in result]
 
-    def get_children_with_embeddings(self, id: str) -> list[dict[str, Any]]:
+    def get_children_with_embeddings(
+        self, id: str, user_name: str | None = None
+    ) -> list[dict[str, Any]]:
+        user_name = user_name if user_name else self.config.user_name
         where_user = ""
         params = {"id": id}
 
-        if not self.config.use_multi_db and self.config.user_name:
+        if not self.config.use_multi_db and (self.config.user_name or user_name):
             where_user = "AND p.user_name = $user_name AND c.user_name = $user_name"
-            params["user_name"] = self.config.user_name
+            params["user_name"] = user_name
 
         query = f"""
                 MATCH (p:Memory)-[:PARENT]->(c:Memory)
@@ -558,7 +610,9 @@ def get_children_with_embeddings(self, id: str) -> list[dict[str, Any]]:
                 {"id": r["id"], "embedding": r["embedding"], "memory": r["memory"]} for r in result
             ]
 
-    def get_path(self, source_id: str, target_id: str, max_depth: int = 3) -> list[str]:
+    def get_path(
+        self, source_id: str, target_id: str, max_depth: int = 3, user_name: str | None = None
+    ) -> list[str]:
         """
         Get the path of nodes from source to target within a limited depth.
         Args:
@@ -571,7 +625,11 @@ def get_path(self, source_id: str, target_id: str, max_depth: int = 3) -> list[s
         raise NotImplementedError
 
     def get_subgraph(
-        self, center_id: str, depth: int = 2, center_status: str = "activated"
+        self,
+        center_id: str,
+        depth: int = 2,
+        center_status: str = "activated",
+        user_name: str | None = None,
     ) -> dict[str, Any]:
         """
         Retrieve a local subgraph centered at a given node.
@@ -586,15 +644,16 @@ def get_subgraph(
                 "edges": [...]
             }
         """
+        user_name = user_name if user_name else self.config.user_name
         with self.driver.session(database=self.db_name) as session:
             params = {"center_id": center_id}
             center_user_clause = ""
             neighbor_user_clause = ""
 
-            if not self.config.use_multi_db and self.config.user_name:
+            if not self.config.use_multi_db and (self.config.user_name or user_name):
                 center_user_clause = " AND center.user_name = $user_name"
                 neighbor_user_clause = " WHERE neighbor.user_name = $user_name"
-                params["user_name"] = self.config.user_name
+                params["user_name"] = user_name
             status_clause = f" AND center.status = '{center_status}'" if center_status else ""
 
             query = f"""
@@ -652,6 +711,9 @@ def search_by_embedding(
         scope: str | None = None,
         status: str | None = None,
         threshold: float | None = None,
+        search_filter: dict | None = None,
+        user_name: str | None = None,
+        **kwargs,
     ) -> list[dict]:
         """
         Retrieve node IDs based on vector similarity.
@@ -660,9 +722,11 @@ def search_by_embedding(
             vector (list[float]): The embedding vector representing query semantics.
             top_k (int): Number of top similar nodes to retrieve.
             scope (str, optional): Memory type filter (e.g., 'WorkingMemory', 'LongTermMemory').
-            status (str, optional): Node status filter (e.g., 'active', 'archived').
+            status (str, optional): Node status filter (e.g., 'activated', 'archived').
                             If provided, restricts results to nodes with matching status.
             threshold (float, optional): Minimum similarity score threshold (0 ~ 1).
+            search_filter (dict, optional): Additional metadata filters for search results.
+                            Keys should match node properties, values are the expected values.
 
         Returns:
             list[dict]: A list of dicts with 'id' and 'score', ordered by similarity.
@@ -672,18 +736,26 @@ def search_by_embedding(
             - If scope is provided, it restricts results to nodes with matching memory_type.
             - If 'status' is provided, only nodes with the matching status will be returned.
             - If threshold is provided, only results with score >= threshold will be returned.
+            - If search_filter is provided, additional WHERE clauses will be added for metadata filtering.
             - Typical use case: restrict to 'status = activated' to avoid
             matching archived or merged nodes.
         """
+        user_name = user_name if user_name else self.config.user_name
         # Build WHERE clause dynamically
         where_clauses = []
         if scope:
             where_clauses.append("node.memory_type = $scope")
         if status:
             where_clauses.append("node.status = $status")
-        if not self.config.use_multi_db and self.config.user_name:
+        if not self.config.use_multi_db and (self.config.user_name or user_name):
             where_clauses.append("node.user_name = $user_name")
 
+        # Add search_filter conditions
+        if search_filter:
+            for key, _ in search_filter.items():
+                param_name = f"filter_{key}"
+                where_clauses.append(f"node.{key} = ${param_name}")
+
         where_clause = ""
         if where_clauses:
             where_clause = "WHERE " + " AND ".join(where_clauses)
@@ -695,13 +767,23 @@ def search_by_embedding(
             RETURN node.id AS id, score
         """
 
-        parameters = {"embedding": vector, "k": top_k, "scope": scope}
+        parameters = {"embedding": vector, "k": top_k}
+
         if scope:
             parameters["scope"] = scope
         if status:
             parameters["status"] = status
-        if not self.config.use_multi_db and self.config.user_name:
-            parameters["user_name"] = self.config.user_name
+        if not self.config.use_multi_db and (self.config.user_name or user_name):
+            if kwargs.get("cube_name"):
+                parameters["user_name"] = kwargs["cube_name"]
+            else:
+                parameters["user_name"] = user_name
+
+        # Add search_filter parameters
+        if search_filter:
+            for key, value in search_filter.items():
+                param_name = f"filter_{key}"
+                parameters[param_name] = value
 
         with self.driver.session(database=self.db_name) as session:
             result = session.run(query, parameters)
@@ -713,7 +795,9 @@ def search_by_embedding(
 
         return records
 
-    def get_by_metadata(self, filters: list[dict[str, Any]]) -> list[str]:
+    def get_by_metadata(
+        self, filters: list[dict[str, Any]], user_name: str | None = None
+    ) -> list[str]:
         """
         TODO:
         1. ADD logic: "AND" vs "OR"(support logic combination);
@@ -738,6 +822,7 @@ def get_by_metadata(self, filters: list[dict[str, Any]]) -> list[str]:
             - Supports structured querying such as tag/category/importance/time filtering.
             - Can be used for faceted recall or prefiltering before embedding rerank.
         """
+        user_name = user_name if user_name else self.config.user_name
         where_clauses = []
         params = {}
 
@@ -769,13 +854,14 @@ def get_by_metadata(self, filters: list[dict[str, Any]]) -> list[str]:
             else:
                 raise ValueError(f"Unsupported operator: {op}")
 
-        if not self.config.use_multi_db and self.config.user_name:
-            where_clauses.append("n.user_name = $user_name")
-            params["user_name"] = self.config.user_name
+        if not self.config.use_multi_db and (self.config.user_name or user_name):
+            where_clauses.append("n.user_id = $user_name")
+            params["user_name"] = user_name
 
         where_str = " AND ".join(where_clauses)
         query = f"MATCH (n:Memory) WHERE {where_str} RETURN n.id AS id"
 
+        print(query, params)
         with self.driver.session(database=self.db_name) as session:
             result = session.run(query, params)
             return [record["id"] for record in result]
@@ -785,6 +871,7 @@ def get_grouped_counts(
         group_fields: list[str],
         where_clause: str = "",
         params: dict[str, Any] | None = None,
+        user_name: str | None = None,
     ) -> list[dict[str, Any]]:
         """
         Count nodes grouped by any fields.
@@ -798,14 +885,15 @@ def get_grouped_counts(
         Returns:
             list[dict]: e.g., [{ 'memory_type': 'WorkingMemory', 'status': 'active', 'count': 10 }, ...]
         """
+        user_name = user_name if user_name else self.config.user_name
         if not group_fields:
             raise ValueError("group_fields cannot be empty")
 
         final_params = params.copy() if params else {}
 
-        if not self.config.use_multi_db and self.config.user_name:
-            user_clause = "n.user_name = $user_name"
-            final_params["user_name"] = self.config.user_name
+        if not self.config.use_multi_db and (self.config.user_name or user_name):
+            user_clause = "n.user_id = $user_name"
+            final_params["user_name"] = user_name
             if where_clause:
                 where_clause = where_clause.strip()
                 if where_clause.upper().startswith("WHERE"):
@@ -859,14 +947,15 @@ def merge_nodes(self, id1: str, id2: str) -> str:
         raise NotImplementedError
 
     # Utilities
-    def clear(self) -> None:
+    def clear(self, user_name: str | None = None) -> None:
         """
         Clear the entire graph if the target database exists.
         """
+        user_name = user_name if user_name else self.config.user_name
         try:
-            if not self.config.use_multi_db and self.config.user_name:
-                query = "MATCH (n:Memory) WHERE n.user_name = $user_name DETACH DELETE n"
-                params = {"user_name": self.config.user_name}
+            if not self.config.use_multi_db and (self.config.user_name or user_name):
+                query = "MATCH (n:Memory) WHERE n.user_id = $user_name DETACH DELETE n"
+                params = {"user_name": user_name}
             else:
                 query = "MATCH (n) DETACH DELETE n"
                 params = {}
@@ -880,7 +969,7 @@ def clear(self) -> None:
             logger.error(f"[ERROR] Failed to clear database '{self.db_name}': {e}")
             raise
 
-    def export_graph(self, include_embedding: bool = True) -> dict[str, Any]:
+    def export_graph(self, **kwargs) -> dict[str, Any]:
         """
         Export all graph nodes and edges in a structured form.
 
@@ -890,16 +979,17 @@ def export_graph(self, include_embedding: bool = True) -> dict[str, Any]:
                 "edges": [ { "source": ..., "target": ..., "type": ... }, ... ]
             }
         """
+        user_name = kwargs.get("user_name") if kwargs.get("user_name") else self.config.user_name
         with self.driver.session(database=self.db_name) as session:
             # Export nodes
             node_query = "MATCH (n:Memory)"
             edge_query = "MATCH (a:Memory)-[r]->(b:Memory)"
             params = {}
 
-            if not self.config.use_multi_db and self.config.user_name:
-                node_query += " WHERE n.user_name = $user_name"
+            if not self.config.use_multi_db and (self.config.user_name or user_name):
+                node_query += " WHERE n.user_id = $user_name"
                 edge_query += " WHERE a.user_name = $user_name AND b.user_name = $user_name"
-                params["user_name"] = self.config.user_name
+                params["user_name"] = user_name
 
             node_result = session.run(f"{node_query} RETURN n", params)
             nodes = [self._parse_node(dict(record["n"])) for record in node_result]
@@ -915,19 +1005,20 @@ def export_graph(self, include_embedding: bool = True) -> dict[str, Any]:
 
             return {"nodes": nodes, "edges": edges}
 
-    def import_graph(self, data: dict[str, Any]) -> None:
+    def import_graph(self, data: dict[str, Any], user_name: str | None = None) -> None:
         """
         Import the entire graph from a serialized dictionary.
 
         Args:
             data: A dictionary containing all nodes and edges to be loaded.
         """
+        user_name = user_name if user_name else self.config.user_name
         with self.driver.session(database=self.db_name) as session:
             for node in data.get("nodes", []):
                 id, memory, metadata = _compose_node(node)
 
-                if not self.config.use_multi_db and self.config.user_name:
-                    metadata["user_name"] = self.config.user_name
+                if not self.config.use_multi_db and (self.config.user_name or user_name):
+                    metadata["user_name"] = user_name
 
                 metadata = _prepare_node_metadata(metadata)
 
@@ -961,27 +1052,28 @@ def import_graph(self, data: dict[str, Any]) -> None:
                     target_id=edge["target"],
                 )
 
-    def get_all_memory_items(self, scope: str, include_embedding: bool = True) -> list[dict]:
+    def get_all_memory_items(self, scope: str, **kwargs) -> list[dict]:
         """
         Retrieve all memory items of a specific memory_type.
 
         Args:
             scope (str): Must be one of 'WorkingMemory', 'LongTermMemory', or 'UserMemory'.
-            include_embedding (bool): Whether to include the large embedding field.
         Returns:
 
         Returns:
             list[dict]: Full list of memory items under this scope.
         """
+        user_name = kwargs.get("user_name") if kwargs.get("user_name") else self.config.user_name
         if scope not in {"WorkingMemory", "LongTermMemory", "UserMemory", "OuterMemory"}:
             raise ValueError(f"Unsupported memory type scope: {scope}")
 
         where_clause = "WHERE n.memory_type = $scope"
         params = {"scope": scope}
 
-        if not self.config.use_multi_db and self.config.user_name:
-            where_clause += " AND n.user_name = $user_name"
-            params["user_name"] = self.config.user_name
+        print("user_name: ", user_name)
+        if not self.config.use_multi_db and (self.config.user_name or user_name):
+            where_clause += " AND n.user_id = $user_name"
+            params["user_name"] = user_name
 
         query = f"""
             MATCH (n:Memory)
@@ -989,19 +1081,18 @@ def get_all_memory_items(self, scope: str, include_embedding: bool = True) -> li
             RETURN n
             """
 
+        print(query, params)
         with self.driver.session(database=self.db_name) as session:
             results = session.run(query, params)
             return [self._parse_node(dict(record["n"])) for record in results]
 
-    def get_structure_optimization_candidates(
-        self, scope: str, include_embedding: bool = True
-    ) -> list[dict]:
+    def get_structure_optimization_candidates(self, scope: str, **kwargs) -> list[dict]:
         """
         Find nodes that are likely candidates for structure optimization:
         - Isolated nodes, nodes with empty background, or nodes with exactly one child.
         - Plus: the child of any parent node that has exactly one child.
         """
-
+        user_name = kwargs.get("user_name") if kwargs.get("user_name") else self.config.user_name
         where_clause = """
                 WHERE n.memory_type = $scope
                   AND n.status = 'activated'
@@ -1009,9 +1100,9 @@ def get_structure_optimization_candidates(
             """
         params = {"scope": scope}
 
-        if not self.config.use_multi_db and self.config.user_name:
-            where_clause += " AND n.user_name = $user_name"
-            params["user_name"] = self.config.user_name
+        if not self.config.use_multi_db and (self.config.user_name or user_name):
+            where_clause += " AND n.user_id = $user_name"
+            params["user_name"] = user_name
 
         query = f"""
             MATCH (n:Memory)
@@ -1036,7 +1127,7 @@ def drop_database(self) -> None:
 
             with self.driver.session(database=self.system_db_name) as session:
                 session.run(f"DROP DATABASE {self.db_name} IF EXISTS")
-                print(f"Database '{self.db_name}' has been dropped.")
+                logger.info(f"Database '{self.db_name}' has been dropped.")
         else:
             raise ValueError(
                 f"Refusing to drop protected database: {self.db_name} in "
@@ -1128,7 +1219,7 @@ def _create_basic_property_indexes(self) -> None:
                     session.run(
                         """
                         CREATE INDEX memory_user_name_index IF NOT EXISTS
-                        FOR (n:Memory) ON (n.user_name)
+                        FOR (n:Memory) ON (n.user_id)
                         """
                     )
                 logger.debug("Index 'memory_user_name_index' ensured.")
@@ -1156,4 +1247,14 @@ def _parse_node(self, node_data: dict[str, Any]) -> dict[str, Any]:
                 node[time_field] = node[time_field].isoformat()
         node.pop("user_name", None)
 
+        # serialization
+        if node["sources"]:
+            for idx in range(len(node["sources"])):
+                if not (
+                    isinstance(node["sources"][idx], str)
+                    and node["sources"][idx][0] == "{"
+                    and node["sources"][idx][0] == "}"
+                ):
+                    break
+                node["sources"][idx] = json.loads(node["sources"][idx])
         return {"id": node.pop("id"), "memory": node.pop("memory", ""), "metadata": node}
diff --git a/src/memos/mem_os/core.py b/src/memos/mem_os/core.py
index b5be4162f..6734a37bc 100644
--- a/src/memos/mem_os/core.py
+++ b/src/memos/mem_os/core.py
@@ -184,6 +184,10 @@ def add_images_context(
     # 5) Append up to 3 images as data URLs
     limit = 6
     count = 0
+    print("=========> images path:")
+    for image_path in unique_images:
+        print(image_path)
+
     for img_path in unique_images:
         if count >= limit:
             break
@@ -470,7 +474,11 @@ def chat(self, query: str, user_id: str | None = None, base_prompt: str | None =
                         images_all.extend(images_list)
 
             logger.info(f"🧠 [Memory] Searched memories:\n{self._str_memories(memories_all)}\n")
-
+            print("===========> memories:\n")
+            for memory in memories_all:
+                print("memory relativity: ", memory.metadata.relativity)
+                print("memory user id: ", memory.metadata.user_id)
+                print(memory.memory)
             system_prompt = self._build_system_prompt(memories_all, base_prompt=base_prompt)
         else:
             system_prompt = self._build_system_prompt(base_prompt=base_prompt)
diff --git a/src/memos/mem_reader/simple_struct.py b/src/memos/mem_reader/simple_struct.py
index ec4385421..2612d7532 100644
--- a/src/memos/mem_reader/simple_struct.py
+++ b/src/memos/mem_reader/simple_struct.py
@@ -226,7 +226,6 @@ def _process_doc_data(self, scene_data_info, info):
         full_text = scene_data_info["text"]
         for ch in ["\\", "\n", "\r"]:
             full_text = full_text.replace(ch, " ")
-        full_text = re.sub(r"\$\d+\\\%\$", "", full_text)
         full_text = full_text.replace('"', '\\"')
         full_text = full_text.strip()
 
diff --git a/src/memos/memories/textual/tree.py b/src/memos/memories/textual/tree.py
index 601597b19..17b910856 100644
--- a/src/memos/memories/textual/tree.py
+++ b/src/memos/memories/textual/tree.py
@@ -44,7 +44,6 @@ def __init__(self, config: TreeTextMemoryConfig):
         self.memory_manager: MemoryManager = MemoryManager(
             self.graph_store, self.embedder, self.extractor_llm, is_reorganize=self.is_reorganize
         )
-
         # Create internet retriever if configured
         self.internet_retriever = None
         if config.internet_retriever is not None:
diff --git a/src/memos/memories/textual/tree_text_memory/retrieve/searcher.py b/src/memos/memories/textual/tree_text_memory/retrieve/searcher.py
index 4f4d9c0f0..c70d37e8d 100644
--- a/src/memos/memories/textual/tree_text_memory/retrieve/searcher.py
+++ b/src/memos/memories/textual/tree_text_memory/retrieve/searcher.py
@@ -2,13 +2,12 @@
 import copy
 import json
 import os
-import traceback
 
 from datetime import datetime
 
 from memos.configs.reranker import RerankerConfigFactory
 from memos.embedders.factory import UniversalAPIEmbedder
-from memos.graph_dbs.factory import NebulaGraphDB
+from memos.graph_dbs.neo4j import Neo4jGraphDB
 from memos.llms.factory import AzureLLM, OllamaLLM, OpenAILLM
 from memos.log import get_logger
 from memos.memories.textual.item import SearchedTreeNodeTextualMemoryMetadata, TextualMemoryItem
@@ -28,7 +27,7 @@ class Searcher:
     def __init__(
         self,
         dispatcher_llm: OpenAILLM | OllamaLLM | AzureLLM,
-        graph_store: NebulaGraphDB,
+        graph_store: Neo4jGraphDB,
         embedder: UniversalAPIEmbedder,
         internet_retriever: InternetRetrieverFactory | None = None,
     ):
@@ -40,7 +39,8 @@ def __init__(
                     "config": {
                         "url": bge_url,
                         "model": os.getenv("BGE_RERANKER_MODEL", "bge-reranker-v2-m3"),
-                        "timeout": int(os.getenv("BGE_RERANKER_TIMEOUT", "10")),
+                        # Increase default timeout from 10s to 60s; env can override via BGE_RERANKER_TIMEOUT
+                        "timeout": int(os.getenv("BGE_RERANKER_TIMEOUT", "60")),
                         "boost_weights": {"user_id": 0.5, "tags": 0.2},
                     },
                 }
@@ -196,6 +196,7 @@ def _retrieve_from_working_memory(
             query=query, parsed_goal=parsed_goal, top_k=top_k, memory_scope="WorkingMemory"
         )
 
+        print("items: ", len(items))
         items_with_detail = []
         for item in items:
             if item.metadata.embedding:
@@ -204,11 +205,23 @@ def _retrieve_from_working_memory(
                 item.memory = detail
                 try:
                     item.metadata.embedding = self.embedder.embed([detail])[0]
+                    # only append the detailed variant if embedding succeeds
+                    items_with_detail.append(copy.deepcopy(item))
                 except Exception as e:
-                    print("Error:", e)
-                    traceback.print_exc()
-                    print("=====> detail:", detail)
-                items_with_detail.append(copy.deepcopy(item))
+                    # Only skip when token/context exceeds model limits; otherwise log error
+                    err_msg = str(e)
+                    if "maximum context length" in err_msg or (
+                        "requested" in err_msg and "tokens" in err_msg
+                    ):
+                        logger.warning(
+                            f"[PATH-A] Skip embedding due to token limit for memory_id={item.id}"
+                        )
+                    else:
+                        logger.error(
+                            f"[PATH-A] Embedding failed for memory_id={item.id}: {err_msg}",
+                            exc_info=True,
+                        )
+                    # Do not append the detailed variant when embedding fails
 
         return self.reranker.rerank(
             query=query,
@@ -242,14 +255,29 @@ def _retrieve_from_long_term_and_user(
                 memory_scope="UserMemory",
             )
 
+        print("_retrieve_from_long_term_and_user: ", len(results))
         items_with_detail = []
         for item in results:
             if item.metadata.embedding:
                 items_with_detail.append(copy.deepcopy(item))
                 detail = str(item.metadata.sources)
                 item.memory = detail
-                item.metadata.embedding = self.embedder.embed([detail])[0]
-                items_with_detail.append(copy.deepcopy(item))
+                try:
+                    item.metadata.embedding = self.embedder.embed([detail])[0]
+                    items_with_detail.append(copy.deepcopy(item))
+                except Exception as e:
+                    err_msg = str(e)
+                    if "maximum context length" in err_msg or (
+                        "requested" in err_msg and "tokens" in err_msg
+                    ):
+                        logger.warning(
+                            f"[PATH-B] Skip embedding due to token limit for memory_id={item.id}"
+                        )
+                    else:
+                        logger.error(
+                            f"[PATH-B] Embedding failed for memory_id={item.id}: {err_msg}",
+                            exc_info=True,
+                        )
 
         return self.reranker.rerank(
             query=query,
diff --git a/src/memos/reranker/factory.py b/src/memos/reranker/factory.py
index 134e29eb9..e5645107b 100644
--- a/src/memos/reranker/factory.py
+++ b/src/memos/reranker/factory.py
@@ -31,7 +31,7 @@ def from_config(cfg: RerankerConfigFactory | None) -> BaseReranker | None:
             return HTTPBGEReranker(
                 reranker_url=c.get("url") or c.get("endpoint") or c.get("reranker_url"),
                 model=c.get("model", "bge-reranker-v2-m3"),
-                timeout=int(c.get("timeout", 10)),
+                timeout=int(c.get("timeout", 60)),
                 headers_extra=c.get("headers_extra"),
                 rerank_source=c.get("rerank_source"),
             )
diff --git a/src/memos/reranker/http_bge.py b/src/memos/reranker/http_bge.py
index f0f5d17a0..aced03c9a 100644
--- a/src/memos/reranker/http_bge.py
+++ b/src/memos/reranker/http_bge.py
@@ -78,7 +78,7 @@ def __init__(
         reranker_url: str,
         token: str = "",
         model: str = "bge-reranker-v2-m3",
-        timeout: int = 10,
+        timeout: int = 60,
         headers_extra: dict | None = None,
         rerank_source: list[str] | None = None,
         boost_weights: dict[str, float] | None = None,

From 5d2e3339c393d5cd41f13d6c906ed7f0d92842fa Mon Sep 17 00:00:00 2001
From: stx <31013941@qq.com>
Date: Mon, 17 Nov 2025 16:59:01 +0800
Subject: [PATCH 28/34] =?UTF-8?q?feat=EF=BC=9Aupdate=2011.17?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 evaluation/scripts/mmlongbench/multimodal_test.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/evaluation/scripts/mmlongbench/multimodal_test.py b/evaluation/scripts/mmlongbench/multimodal_test.py
index b8134be8a..929215229 100644
--- a/evaluation/scripts/mmlongbench/multimodal_test.py
+++ b/evaluation/scripts/mmlongbench/multimodal_test.py
@@ -67,7 +67,6 @@
 }
 
 mos_config = MOSConfig(**config)
-# you can set PRO_MODE to True to enable CoT enhancement mos_config.PRO_MODE = True
 mos = MOS(mos_config)
 
 config = GeneralMemCubeConfig.model_validate(

From 4f62e35b0b05a78432dd13c40dc21c91c81005df Mon Sep 17 00:00:00 2001
From: stx <31013941@qq.com>
Date: Tue, 25 Nov 2025 15:48:41 +0800
Subject: [PATCH 29/34] =?UTF-8?q?feat=EF=BC=9Aupdate=2011.25?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .gitignore                                    |   1 +
 evaluation/scripts/hotpot/hotpot_eval.py      |  43 +-
 evaluation/scripts/mmlongbench/check_db.py    |  61 --
 evaluation/scripts/mmlongbench/drop_db.py     |  48 --
 evaluation/scripts/mmlongbench/eval_docs.py   |  98 ++-
 evaluation/scripts/mmlongbench/import_docs.py |  30 +-
 evaluation/scripts/mmlongbench/run_api.py     | 699 ------------------
 evaluation/scripts/xinyu/eval/__init__.py     |   0
 .../eval/eval_score_llm.py}                   |   0
 .../eval_docs2.py => xinyu/eval_docs.py}      |  48 +-
 evaluation/scripts/xinyu/import_docs.py       |  85 +++
 src/memos/graph_dbs/neo4j.py                  |  13 +-
 .../tree_text_memory/organize/manager.py      |  10 -
 .../tree_text_memory/retrieve/searcher.py     |   2 -
 14 files changed, 216 insertions(+), 922 deletions(-)
 delete mode 100644 evaluation/scripts/mmlongbench/check_db.py
 delete mode 100644 evaluation/scripts/mmlongbench/drop_db.py
 delete mode 100644 evaluation/scripts/mmlongbench/run_api.py
 create mode 100644 evaluation/scripts/xinyu/eval/__init__.py
 rename evaluation/scripts/{mmlongbench/eval/eval_score2.py => xinyu/eval/eval_score_llm.py} (100%)
 rename evaluation/scripts/{mmlongbench/eval_docs2.py => xinyu/eval_docs.py} (82%)
 create mode 100644 evaluation/scripts/xinyu/import_docs.py

diff --git a/.gitignore b/.gitignore
index 4d531866b..ce74c7cac 100644
--- a/.gitignore
+++ b/.gitignore
@@ -21,6 +21,7 @@ evaluation/scripts/personamem
 benchmarks/
 
 ppt_test_result/
+ppt_test_result_xinyu/
 
 # Byte-compiled / optimized / DLL files
 __pycache__/
diff --git a/evaluation/scripts/hotpot/hotpot_eval.py b/evaluation/scripts/hotpot/hotpot_eval.py
index 2c690f060..05ff52349 100644
--- a/evaluation/scripts/hotpot/hotpot_eval.py
+++ b/evaluation/scripts/hotpot/hotpot_eval.py
@@ -1,10 +1,8 @@
 import json
 import os
-import time
 import uuid
 
 from concurrent.futures import ThreadPoolExecutor, as_completed
-from datetime import datetime
 
 from dotenv import load_dotenv
 from tqdm import tqdm
@@ -17,12 +15,10 @@
 
 load_dotenv()
 
-# 1. Create MOS Config and set openai config
-print(f"🚀 [{datetime.now().strftime('%H:%M:%S')}] Starting to create MOS configuration...")
-start_time = time.time()
+db_name = "stx-hotpot-001"
+
 
 user_name = str(uuid.uuid4())
-print(user_name)
 
 # 1.1 Set openai config
 openapi_config = {
@@ -138,14 +134,6 @@ def filter_memory_data(memories_data):
     return filtered_data
 
 
-print(
-    f"✅ [{datetime.now().strftime('%H:%M:%S')}] MOS configuration created successfully, time elapsed: {time.time() - start_time:.2f}s\n"
-)
-
-# 2. Initialize memory cube
-print(f"🚀 [{datetime.now().strftime('%H:%M:%S')}] Starting to initialize MemCube configuration...")
-start_time = time.time()
-
 config = GeneralMemCubeConfig.model_validate(
     {
         "user_id": user_name,
@@ -167,7 +155,7 @@ def filter_memory_data(memories_data):
                         "uri": neo4j_uri,
                         "user": "neo4j",
                         "password": "iaarlichunyu",
-                        "db_name": "hotpot-evaluation",
+                        "db_name": db_name,
                         "auto_create": True,
                     },
                 },
@@ -188,34 +176,11 @@ def filter_memory_data(memories_data):
     },
 )
 
-print(
-    f"✅ [{datetime.now().strftime('%H:%M:%S')}] MemCube configuration initialization completed, time elapsed: {time.time() - start_time:.2f}s\n"
-)
-
-# 3. Initialize the MemCube with the configuration
-print(f"🚀 [{datetime.now().strftime('%H:%M:%S')}] Starting to create MemCube instance...")
-start_time = time.time()
-
 mem_cube = GeneralMemCube(config)
-try:
-    mem_cube.dump(f"/tmp/{user_name}/")
-    print(
-        f"✅ [{datetime.now().strftime('%H:%M:%S')}] MemCube created and saved successfully, time elapsed: {time.time() - start_time:.2f}s\n"
-    )
-except Exception as e:
-    print(
-        f"❌ [{datetime.now().strftime('%H:%M:%S')}] MemCube save failed: {e}, time elapsed: {time.time() - start_time:.2f}s\n"
-    )
-
-# 4. Register the MemCube
-print(f"🚀 [{datetime.now().strftime('%H:%M:%S')}] Starting to register MemCube...")
-start_time = time.time()
+
 
 mos.register_mem_cube(f"/tmp/{user_name}", mem_cube_id=user_name)
 
-print(
-    f"✅ [{datetime.now().strftime('%H:%M:%S')}] MemCube registration completed, time elapsed: {time.time() - start_time:.2f}s\n"
-)
 
 with open("evaluation/data/hotpot/hotpot_dev_distractor_v1.json") as f:
     data = json.load(f)
diff --git a/evaluation/scripts/mmlongbench/check_db.py b/evaluation/scripts/mmlongbench/check_db.py
deleted file mode 100644
index 8127be792..000000000
--- a/evaluation/scripts/mmlongbench/check_db.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import os
-
-from dotenv import load_dotenv
-from neo4j import GraphDatabase
-from neo4j.exceptions import ServiceUnavailable
-
-
-load_dotenv()
-
-NEO4J_URI = os.getenv("NEO4J_URI", "bolt://localhost:7687")
-NEO4J_USER = "neo4j"
-NEO4J_PASSWORD = "iaarlichunyu"
-
-
-def create_driver():
-    return GraphDatabase.driver(NEO4J_URI, auth=(NEO4J_USER, NEO4J_PASSWORD))
-
-
-def get_databases(driver):
-    query = "SHOW DATABASES"
-    with driver.session() as session:
-        result = session.run(query)
-        databases = [record["name"] for record in result]
-    return databases
-
-
-def is_database_empty(driver, db_name):
-    query = "MATCH (n) RETURN count(n) AS cnt"
-    with driver.session(database=db_name) as session:
-        result = session.run(query).single()
-        node_count = result["cnt"]
-    return node_count == 0
-
-
-def count_empty_bench_databases(driver):
-    databases = get_databases(driver)
-    empty_count = 0
-    empty_dbs = []
-    for db in databases:
-        if db.startswith("mm-long-bench-single-"):
-            try:
-                if is_database_empty(driver, db):
-                    empty_count += 1
-                    empty_dbs.append(db)
-            except Exception as e:
-                print(f"⚠️ Error checking database {db}: {e}")
-    print(f"共有 {empty_count} 个空的 mm-long-bench-single-* 数据库")
-    if empty_dbs:
-        print("空数据库列表:")
-        for db in empty_dbs:
-            print(f"  - {db}")
-
-
-if __name__ == "__main__":
-    driver = create_driver()
-    try:
-        count_empty_bench_databases(driver)
-    except ServiceUnavailable as e:
-        print(f"Error: {e}")
-    finally:
-        driver.close()
diff --git a/evaluation/scripts/mmlongbench/drop_db.py b/evaluation/scripts/mmlongbench/drop_db.py
deleted file mode 100644
index db73c3ab2..000000000
--- a/evaluation/scripts/mmlongbench/drop_db.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import os
-
-from dotenv import load_dotenv
-from neo4j import GraphDatabase
-from neo4j.exceptions import ServiceUnavailable
-
-
-load_dotenv()
-
-NEO4J_URI = os.getenv("NEO4J_URI", "bolt://localhost:7687")
-NEO4J_USER = "neo4j"
-NEO4J_PASSWORD = "iaarlichunyu"
-
-
-def create_driver():
-    return GraphDatabase.driver(NEO4J_URI, auth=(NEO4J_USER, NEO4J_PASSWORD))
-
-
-def get_databases(driver):
-    query = "SHOW DATABASES"
-    with driver.session() as session:
-        result = session.run(query)
-        databases = [record["name"] for record in result]
-    return databases
-
-
-def drop_database(driver, db_name):
-    query = f"DROP DATABASE `{db_name}`"
-    with driver.session() as session:
-        session.run(query)
-        print(f"Database '{db_name}' has been deleted.")
-
-
-def delete_bench_databases(driver):
-    databases = get_databases(driver)
-    for db in databases:
-        if db.startswith("mm-long-bench-single-"):
-            drop_database(driver, db)
-
-
-if __name__ == "__main__":
-    driver = create_driver()
-    try:
-        delete_bench_databases(driver)
-    except ServiceUnavailable as e:
-        print(f"Error: {e}")
-    finally:
-        driver.close()
diff --git a/evaluation/scripts/mmlongbench/eval_docs.py b/evaluation/scripts/mmlongbench/eval_docs.py
index de0d02a69..510a0b1ed 100644
--- a/evaluation/scripts/mmlongbench/eval_docs.py
+++ b/evaluation/scripts/mmlongbench/eval_docs.py
@@ -1,3 +1,4 @@
+import csv
 import json
 import os
 import re
@@ -17,16 +18,15 @@
 
 
 load_dotenv()
-
 openapi_config = {
-    "model_name_or_path": "gpt-5-nano",
+    "model_name_or_path": "gpt-4o",
     "top_k": 50,
     "remove_think_prefix": True,
     "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
     "api_base": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
 }
 neo4j_uri = os.getenv("NEO4J_URI", "bolt://47.117.41.207:7687")
-space_name = "nebula-longbench-stx-5"
+db_name = "stx-mmlongbench-004"
 
 doc_paths = [
     f
@@ -37,9 +37,50 @@
 with open("evaluation/data/mmlongbench/samples.json") as f:
     samples = json.load(f)
 
+RESULTS_PATH = "evaluation/data/mmlongbench/test_results.json"
+completed_pairs: set[tuple[str, str]] = set()
+
+
+def _load_existing_results():
+    global completed_pairs
+    if os.path.exists(RESULTS_PATH):
+        try:
+            with open(RESULTS_PATH, encoding="utf-8") as f:
+                existing = json.load(f)
+            for r in existing:
+                did = r.get("doc_id")
+                q = r.get("question")
+                if did and q:
+                    completed_pairs.add((did, q))
+            return existing
+        except Exception:
+            return []
+    return []
+
+
+def _doc_has_pending(doc_file: str) -> bool:
+    for s in samples:
+        if s.get("doc_id") == doc_file and (doc_file, s.get("question")) not in completed_pairs:
+            return True
+    return False
+
+
+def get_user_name(doc_file):
+    csv_path = "evaluation/data/mmlongbench/user_doc_map.csv"
+    if os.path.exists(csv_path):
+        with open(csv_path, newline="", encoding="utf-8") as f:
+            reader = csv.reader(f)
+            for row in reader:
+                uid, path = row[0], row[1]
+                base = os.path.basename(path)
+                if base == doc_file or os.path.splitext(base)[0] == os.path.splitext(doc_file)[0]:
+                    return uid
+    return ""
+
 
 def process_doc(doc_file):
-    user_name = "user_" + doc_file
+    user_name = get_user_name(doc_file)
+    print(user_name, doc_file)
     config = {
         "user_id": user_name,
         "chat_model": {
@@ -89,12 +130,12 @@ def process_doc(doc_file):
                     "extractor_llm": {"backend": "openai", "config": openapi_config},
                     "dispatcher_llm": {"backend": "openai", "config": openapi_config},
                     "graph_db": {
-                        "backend": "nebular",
+                        "backend": "neo4j",
                         "config": {
-                            "uri": json.loads(os.getenv("NEBULAR_HOSTS", "localhost")),
-                            "user": os.getenv("NEBULAR_USER", "root"),
-                            "password": os.getenv("NEBULAR_PASSWORD", "xxxxxx"),
-                            "space": space_name,
+                            "uri": neo4j_uri,
+                            "user": "neo4j",
+                            "password": "iaarlichunyu",
+                            "db_name": db_name,
                             "user_name": user_name,
                             "use_multi_db": False,
                             "auto_create": True,
@@ -129,15 +170,13 @@ def process_doc(doc_file):
         prompt = f.read()
 
     samples_res = []
-    doc_samples = [
-        s
-        for s in samples
-        if s.get("doc_id") == doc_file and s.get("evidence_sources") == "['Pure-text (Plain-text)']"
-    ]
+    doc_samples = [s for s in samples if s.get("doc_id") == doc_file]
     if len(doc_samples) == 0:
         return []
 
     for sample in tqdm(doc_samples, desc=f"Processing {doc_file}"):
+        if (doc_file, sample.get("question")) in completed_pairs:
+            continue
         messages = sample["question"]
         try_cnt, is_success = 0, False
 
@@ -174,10 +213,29 @@ def process_doc(doc_file):
 
 
 if __name__ == "__main__":
-    results = []
-
-    with ThreadPoolExecutor(max_workers=1) as executor:
-        future_to_doc = {executor.submit(process_doc, doc_file): doc_file for doc_file in doc_paths}
+    results = _load_existing_results()
+    total_samples = len(samples)
+    processed_samples = len(completed_pairs)
+    pending_samples = total_samples - processed_samples
+    sample_doc_ids = [s.get("doc_id") for s in samples if s.get("doc_id")]
+    all_docs_in_samples = set(sample_doc_ids)
+    processed_docs = {d for d, _ in completed_pairs}
+    with ThreadPoolExecutor(max_workers=4) as executor:
+        pending_docs = [d for d in doc_paths if _doc_has_pending(d)]
+        print("\n" + "=" * 80)
+        print("📊 评测进度统计")
+        print("=" * 80)
+        print(f"✅ 已加载历史结果: {len(results)} 条")
+        print(f"📂 数据集总样本: {total_samples}")
+        print(f"🧪 已完成样本: {processed_samples}")
+        print(f"⏳ 待处理样本: {pending_samples}")
+        print(f"📄 数据集中总文档: {len(all_docs_in_samples)}")
+        print(f"✔️ 已完成文档: {len(processed_docs)}")
+        print(f"➡️ 待处理文档(本次将运行): {len(pending_docs)}")
+        print("=" * 80 + "\n")
+        future_to_doc = {
+            executor.submit(process_doc, doc_file): doc_file for doc_file in pending_docs
+        }
 
         for future in as_completed(future_to_doc):
             doc_file = future_to_doc[future]
@@ -191,8 +249,8 @@ def process_doc(doc_file):
                     print(f"Avg acc: {acc}")
                     print(f"Avg f1: {f1}")
 
-                with open("evaluation/data/mmlongbench/test_results.json", "w") as f:
-                    json.dump(results, f)
+                with open(RESULTS_PATH, "w", encoding="utf-8") as f:
+                    json.dump(results, f, ensure_ascii=False, indent=2)
             except Exception as e:
                 print(f"[{doc_file}] failed with {e}")
 
diff --git a/evaluation/scripts/mmlongbench/import_docs.py b/evaluation/scripts/mmlongbench/import_docs.py
index f3610cf26..540c8f960 100644
--- a/evaluation/scripts/mmlongbench/import_docs.py
+++ b/evaluation/scripts/mmlongbench/import_docs.py
@@ -11,7 +11,7 @@
 
 
 logger = log.get_logger(__name__)
-
+db_name = "stx-mmlongbench-004"
 # Create a memory reader instance
 reader_config = SimpleStructMemReaderConfig.from_json_file(
     "examples/data/config/simple_struct_reader_config.json"
@@ -21,19 +21,26 @@
 tree_config = TreeTextMemoryConfig.from_json_file(
     "examples/data/config/tree_config_shared_database.json"
 )
-tree_config.graph_db.config.db_name = "stx-mmlongbench-002"
+tree_config.graph_db.config.db_name = db_name
 # Processing Documents
-doc_paths = [
-    "evaluation/data/mmlongbench/documents_xcy/" + f
-    for f in os.listdir("evaluation/data/mmlongbench/documents_xcy")
-    if os.path.isfile(os.path.join("evaluation/data/mmlongbench/documents_xcy", f))
-]
+existing_names = {
+    d for d in os.listdir("ppt_test_result") if os.path.isdir(os.path.join("ppt_test_result", d))
+}
+doc_paths = []
+for f in os.listdir("evaluation/data/mmlongbench/documents"):
+    fp = os.path.join("evaluation/data/mmlongbench/documents", f)
+    if os.path.isfile(fp):
+        name = os.path.splitext(f)[0]
+        if name in existing_names:
+            continue
+        doc_paths.append(fp)
+
+print("existing_names length:", len(existing_names))
+print("doc_paths length:", len(doc_paths))
 
 
 async def process_doc(doc_path):
     print(f"🔄 Processing document: {doc_path}")
-    doc_file = doc_path.split("/")[-1].rsplit(".", 1)[0]
-
     # Generate random user id: 'user_' + random short hex
     user_id = "user_" + uuid.uuid4().hex[:8]
     # Persist mapping between user_id and doc_path
@@ -45,7 +52,6 @@ async def process_doc(doc_path):
         logger.error(f"Failed to write user-doc mapping: {e}")
 
     tree_config.graph_db.config.user_name = user_id
-    temp_dir = "tmp/" + doc_file
     my_tree_textual_memory = TreeTextMemory(tree_config)
     doc_memory = await reader.get_memory(
         [doc_path], "doc", info={"user_id": user_id, "session_id": "session_" + str(uuid.uuid4())}
@@ -57,13 +63,11 @@ async def process_doc(doc_path):
         my_tree_textual_memory.add(m_list)
     print("total memories: ", count)
 
-    my_tree_textual_memory.dump(temp_dir)
     return doc_path
 
 
 async def main():
-    batch_size = 1
-
+    batch_size = 4
     for i in range(0, len(doc_paths), batch_size):
         batch = doc_paths[i : i + batch_size]
         print(f"🚀 Starting batch {i // batch_size + 1} with {len(batch)} docs")
diff --git a/evaluation/scripts/mmlongbench/run_api.py b/evaluation/scripts/mmlongbench/run_api.py
deleted file mode 100644
index 855daf238..000000000
--- a/evaluation/scripts/mmlongbench/run_api.py
+++ /dev/null
@@ -1,699 +0,0 @@
-import argparse
-import json
-import os
-import re
-import signal
-import sys
-import threading
-import time
-import uuid
-
-from concurrent.futures import ThreadPoolExecutor, as_completed
-
-from dotenv import load_dotenv
-from eval.eval_score import eval_acc_and_f1, eval_score, show_results
-from eval.extract_answer import extract_answer
-from tqdm import tqdm
-
-from memos.configs.mem_cube import GeneralMemCubeConfig
-from memos.configs.mem_os import MOSConfig
-from memos.configs.mem_reader import SimpleStructMemReaderConfig
-from memos.configs.memory import TreeTextMemoryConfig
-from memos.mem_cube.general import GeneralMemCube
-from memos.mem_os.main import MOS
-from memos.mem_reader.simple_struct import SimpleStructMemReader
-from memos.memories.textual.tree import TreeTextMemory
-
-
-load_dotenv()
-openapi_config = {
-    "model_name_or_path": "gpt-4o-mini",
-    "temperature": 0.8,
-    "max_tokens": 1024,
-    "top_p": 0.9,
-    "top_k": 50,
-    "remove_think_prefix": True,
-    "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
-    "api_base": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
-}
-neo4j_uri = os.getenv("NEO4J_URI", "bolt://47.117.41.207:7687")
-space_name = "nebula-longbench-stx"
-db_name = "mm-long-bench-single-"
-
-# Global state tracking
-completed_docs = set()
-completed_samples = set()
-skipped = 0
-doc_user_map = {}  # Map document name to user_name
-doc_temp_dir = {}  # Map document name to temp directory
-results_lock = threading.Lock()
-completed_counter = 0
-
-
-# Graceful shutdown handler
-def graceful_shutdown(signum, frame):
-    """Handle interrupt signals and exit gracefully"""
-    print("\n🛑 Received interrupt signal. Saving progress and exiting...")
-    save_test_results(args.test_results_path, test_results)
-    save_doc_processing_state(args.doc_state_path, get_state_dict())
-    sys.exit(1)
-
-
-# Register signal handlers
-signal.signal(signal.SIGINT, graceful_shutdown)
-signal.signal(signal.SIGTERM, graceful_shutdown)
-
-
-def get_state_dict():
-    """Convert state to dictionary for JSON serialization"""
-    return {
-        "completed_docs": list(completed_docs),
-        "completed_samples": [{"doc": d, "sample": s} for d, s in completed_samples],
-        "skipped": skipped,
-        "doc_user_map": doc_user_map,
-        "doc_temp_dir": doc_temp_dir,
-    }
-
-
-def load_state_dict(data):
-    """Load state from dictionary"""
-    global completed_docs, completed_samples, skipped, doc_user_map, doc_temp_dir
-    completed_docs = set(data.get("completed_docs", []))
-    completed_samples = set()
-    for item in data.get("completed_samples", []):
-        completed_samples.add((item["doc"], item["sample"]))
-    skipped = data.get("skipped", 0)
-    doc_user_map = data.get("doc_user_map", {})
-    doc_temp_dir = data.get("doc_temp_dir", {})
-
-
-def import_document(doc_path):
-    """Import a single document and return user_name and temp_dir"""
-    try:
-        user_name = str(uuid.uuid4())
-        config = {
-            "user_id": user_name,
-            "chat_model": {
-                "backend": "openai",
-                "config": openapi_config,
-            },
-            "mem_reader": {
-                "backend": "simple_struct",
-                "config": {
-                    "llm": {"backend": "openai", "config": openapi_config},
-                    "embedder": {
-                        "backend": "universal_api",
-                        "config": {
-                            "provider": "openai",
-                            "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
-                            "model_name_or_path": "text-embedding-3-large",
-                            "base_url": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
-                        },
-                    },
-                    "chunker": {
-                        "backend": "sentence",
-                        "config": {
-                            "tokenizer_or_token_counter": "gpt2",
-                            "chunk_size": 512,
-                            "chunk_overlap": 128,
-                            "min_sentences_per_chunk": 1,
-                        },
-                    },
-                },
-            },
-            "max_turns_window": 20,
-            "top_k": 5,
-            "enable_textual_memory": True,
-            "enable_activation_memory": False,
-            "enable_parametric_memory": False,
-        }
-        mos_config = MOSConfig(**config)
-        mos = MOS(mos_config)
-
-        mem_cube_config = GeneralMemCubeConfig.model_validate(
-            {
-                "user_id": user_name,
-                "cube_id": user_name,
-                "text_mem": {
-                    "backend": "tree_text",
-                    "config": {
-                        "extractor_llm": {"backend": "openai", "config": openapi_config},
-                        "dispatcher_llm": {"backend": "openai", "config": openapi_config},
-                        "graph_db": {
-                            "backend": "nebular",
-                            "config": {
-                                "uri": json.loads(os.getenv("NEBULAR_HOSTS", "localhost")),
-                                "user": os.getenv("NEBULAR_USER", "root"),
-                                "password": os.getenv("NEBULAR_PASSWORD", "xxxxxx"),
-                                "space": space_name,  # 可以新建
-                                "user_name": user_name,  # cube_name
-                                "use_multi_db": False,  # 建议不要改
-                                "auto_create": True,
-                                "embedding_dimension": 3072,
-                            },
-                        },
-                        "embedder": {
-                            "backend": "universal_api",
-                            "config": {
-                                "provider": "openai",
-                                "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
-                                "model_name_or_path": "text-embedding-3-large",
-                                "base_url": os.getenv(
-                                    "OPENAI_API_BASE", "https://api.openai.com/v1"
-                                ),
-                            },
-                        },
-                        "reorganize": False,
-                    },
-                },
-                "act_mem": {},
-                "para_mem": {},
-            }
-        )
-        mem_cube = GeneralMemCube(mem_cube_config)
-        temp_dir = f"/tmp/{user_name}"
-        os.makedirs(temp_dir, exist_ok=True)
-        mem_cube.dump(temp_dir)
-        mos.register_mem_cube(temp_dir, mem_cube_id=user_name)
-        mos.add(doc_path=[doc_path])
-
-        # Save mapping
-        doc_name = os.path.basename(doc_path)
-        doc_user_map[doc_name] = user_name
-        doc_temp_dir[doc_name] = temp_dir
-
-        return user_name, temp_dir
-    except Exception as e:
-        print(f"❌ Failed to import document {os.path.basename(doc_path)}: {e!s}")
-        return None, None
-
-
-def create_mos_for_doc(user_name, temp_dir):
-    """Create MOS instance for a document using saved user_name and temp_dir"""
-    try:
-        config = {
-            "user_id": user_name,
-            "chat_model": {
-                "backend": "openai",
-                "config": openapi_config,
-            },
-            "mem_reader": {
-                "backend": "simple_struct",
-                "config": {
-                    "llm": {"backend": "openai", "config": openapi_config},
-                    "embedder": {
-                        "backend": "universal_api",
-                        "config": {
-                            "provider": "openai",
-                            "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
-                            "model_name_or_path": "text-embedding-3-large",
-                            "base_url": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
-                        },
-                    },
-                    "chunker": {
-                        "backend": "sentence",
-                        "config": {
-                            "tokenizer_or_token_counter": "gpt2",
-                            "chunk_size": 512,
-                            "chunk_overlap": 128,
-                            "min_sentences_per_chunk": 1,
-                        },
-                    },
-                },
-            },
-            "max_turns_window": 20,
-            "top_k": 5,
-            "enable_textual_memory": True,
-            "enable_activation_memory": False,
-            "enable_parametric_memory": False,
-        }
-        mos_config = MOSConfig(**config)
-        mos = MOS(mos_config)
-
-        mem_cube_config = GeneralMemCubeConfig.model_validate(
-            {
-                "user_id": user_name,
-                "cube_id": user_name,
-                "text_mem": {
-                    "backend": "tree_text",
-                    "config": {
-                        "extractor_llm": {"backend": "openai", "config": openapi_config},
-                        "dispatcher_llm": {"backend": "openai", "config": openapi_config},
-                        "graph_db": {
-                            "backend": "neo4j",
-                            "config": {
-                                "uri": neo4j_uri,
-                                "user": "neo4j",
-                                "password": "iaarlichunyu",
-                                "db_name": space_name,
-                                "auto_create": True,
-                            },
-                        },
-                        "embedder": {
-                            "backend": "universal_api",
-                            "config": {
-                                "provider": "openai",
-                                "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
-                                "model_name_or_path": "text-embedding-3-large",
-                                "base_url": os.getenv(
-                                    "OPENAI_API_BASE", "https://api.openai.com/v1"
-                                ),
-                            },
-                        },
-                        "reorganize": True,
-                    },
-                },
-                "act_mem": {},
-                "para_mem": {},
-            }
-        )
-        mem_cube = GeneralMemCube(mem_cube_config)
-
-        if not os.path.exists(temp_dir) or not os.listdir(temp_dir):
-            os.makedirs(temp_dir, exist_ok=True)
-            mem_cube.dump(temp_dir)
-        mos.register_mem_cube(temp_dir, mem_cube_id=user_name)
-        return mos
-    except Exception as e:
-        print(f"❌ Failed to create MOS for user {user_name}: {e!s}")
-        return None
-
-
-# Improved answer extraction with better error handling
-def extract_answer_safe(question, response, prompt):
-    """Robust answer extraction with fallback handling"""
-    try:
-        return extract_answer(question, response, prompt)
-    except Exception as e:
-        print(f"⚠️ Answer extraction failed: {e!s}")
-        # Fallback: return the entire response if extraction fails
-        return response
-
-
-def process_sample(sample, mos, prompt, max_try, doc_file):
-    """Process a single sample with retry and evaluation"""
-    global skipped
-    if sample["evidence_sources"] != "['Pure-text (Plain-text)']":
-        skipped += 1
-        return None
-
-    doc_id = os.path.basename(doc_file)
-    sample_id = sample.get("question_id", hash(sample["question"]))
-
-    # Skip already completed samples
-    if (doc_id, sample_id) in completed_samples:
-        skipped += 1
-        return None
-
-    try_cnt = 0
-    response = ""
-    while try_cnt <= max_try:
-        try:
-            mos.clear_messages()
-            response = mos.chat(sample["question"])
-            break
-        except Exception as e:
-            print(e)
-            try_cnt += 1
-            response = f"Error: {e!s}"
-            if try_cnt > max_try:
-                break
-            # Exponential backoff
-            sleep_time = min(2**try_cnt, 30)  # Cap at 30 seconds
-            print(f"⚠️ Retrying sample {sample_id} (attempt {try_cnt}/{max_try}) in {sleep_time}s")
-            time.sleep(sleep_time)
-
-    sample["response"] = response
-    sample["doc_file"] = doc_file  # Track which doc it came from
-
-    if "Error:" not in response:
-        extracted_res = extract_answer_safe(sample["question"], response, prompt)
-        sample["extracted_res"] = extracted_res
-        try:
-            # Handle different extraction formats
-            if "Extracted answer:" in extracted_res:
-                pred_ans = (
-                    extracted_res.split("Answer format:")[0].split("Extracted answer:")[1].strip()
-                )
-            else:
-                pred_ans = extracted_res.strip()
-
-            score = eval_score(sample["answer"], pred_ans, sample["answer_format"])
-            sample["pred"] = pred_ans
-            sample["score"] = score
-        except Exception as e:
-            print(f"⚠️ Evaluation failed for sample {sample_id}: {e!s}")
-            sample["pred"] = ""
-            sample["score"] = 0
-    else:
-        sample["pred"] = ""
-        sample["score"] = 0
-
-    # Print detailed results
-    print("\n" + "-" * 80)
-    print(f"📄 Document: {doc_file}")
-    print(f"📌 Sample ID: {sample_id}")
-    print(f"❓ Question: {sample['question']}")
-    print(f"💬 Response: {response[:500]}{'...' if len(response) > 500 else ''}")
-    print(f"🎯 Ground Truth: {sample['answer']}")
-    print(f"📦 Extracted: {sample.get('pred', '')}")
-    print(f"⭐ Score: {sample.get('score', 0)}")
-    print("-" * 80 + "\n")
-
-    # Mark sample as completed
-    completed_samples.add((doc_id, sample_id))
-    return sample
-
-
-def import_all_documents(args):
-    """Import all documents concurrently"""
-    print("\n" + "=" * 80)
-    print("🚀 Starting document import phase")
-    print("=" * 80)
-
-    # Find all document files
-    doc_files = [
-        f
-        for f in os.listdir(args.document_path)
-        if os.path.isfile(os.path.join(args.document_path, f))
-    ]
-
-    # Load existing mappings if available
-    if os.path.exists(args.doc_state_path):
-        state_data = load_doc_processing_state(args.doc_state_path)
-        load_state_dict(state_data)
-        print(f"🔍 Loaded existing document processing state for {len(doc_user_map)} documents")
-
-    # Import new documents
-    new_docs = [doc for doc in doc_files[:20] if doc not in doc_user_map]
-
-    if not new_docs:
-        print("✅ All documents already imported")
-        return
-
-    print(f"📂 Importing {len(new_docs)} new documents")
-
-    # Create a memory reader instance
-    reader_config = SimpleStructMemReaderConfig.from_json_file(
-        "examples/data/config/simple_struct_reader_config.json"
-    )
-    reader = SimpleStructMemReader(reader_config)
-    tree_config = TreeTextMemoryConfig.from_json_file(
-        "examples/data/config/tree_config_shared_database.json"
-    )
-    my_tree_textual_memory = TreeTextMemory(tree_config)
-    doc_memory = reader.get_memory(new_docs, "doc", info={"user_id": "1111", "session_id": "2222"})
-    for m_list in doc_memory:
-        my_tree_textual_memory.add(m_list)
-        my_tree_textual_memory.memory_manager.wait_reorganizer()
-
-    def import_doc(doc_file):
-        """Import a single document and save state"""
-        doc_path = os.path.join(args.document_path, doc_file)
-        user_name, temp_dir = import_document(doc_path)
-        if user_name and temp_dir:
-            print(f"✅ Imported {doc_file} with user {user_name}")
-            # Save state after each document (safely in a thread-safe way)
-            save_doc_processing_state(args.doc_state_path, get_state_dict())
-        else:
-            print(f"❌ Failed to import {doc_file}")
-
-    # Use ThreadPoolExecutor to process documents concurrently
-    with ThreadPoolExecutor(max_workers=8) as executor:
-        futures = {executor.submit(import_doc, doc): doc for doc in new_docs}
-
-        for future in tqdm(as_completed(futures), desc="Importing documents", total=len(new_docs)):
-            future.result()  # Wait for each document to finish and handle exceptions if needed
-
-    print("✅ Document import completed")
-
-
-def process_document_with_samples(doc_file, args, samples, prompt):
-    """Process one document and all its samples sequentially"""
-    global skipped
-    # Skip already completed documents
-    if doc_file in completed_docs:
-        print(f"⏭️ Skipping already processed document: {doc_file}")
-        return []
-
-    print(f"🔄 Processing document: {doc_file}")
-
-    # Get user_name and temp_dir for this document
-    user_name = "user_" + doc_file
-    temp_dir = "tmp/" + doc_file
-    if not user_name or not temp_dir:
-        print(f"❌ No user mapping found for {doc_file}. Skipping.")
-        return []
-
-    # Create MOS instance for this document
-    mos = create_mos_for_doc(user_name, temp_dir)
-    if not mos:
-        print(f"❌ Failed to create MOS for {doc_file}. Skipping.")
-        return []
-
-    doc_id = os.path.basename(doc_file)
-    doc_samples = [s for s in samples if s.get("doc_id") == doc_id]
-
-    results = []
-    for sample in tqdm(doc_samples, desc=f"Processing samples in {doc_file}"):
-        sample_id = sample.get("question_id", hash(sample["question"]))
-        if (doc_file, sample_id) in completed_samples:
-            skipped += 1
-            continue
-
-        try:
-            result = process_sample(sample, mos, prompt, args.max_try, doc_file)
-            if result:
-                results.append(result)
-        except Exception as e:
-            print(f"⚠️ Error processing sample: {e!s}")
-
-    # Mark document as completed
-    completed_docs.add(doc_file)
-    return results
-
-
-def load_test_results(output_path):
-    """Load existing test results from output file"""
-    test_results = []
-    if os.path.exists(output_path):
-        print(f"🔍 Found existing test results file: {output_path}")
-        try:
-            with open(output_path) as f:
-                test_results = json.load(f)
-
-            # Rebuild progress state from loaded samples
-            for sample in test_results:
-                if "doc_file" in sample:
-                    doc_file = sample["doc_file"]
-                    sample_id = sample.get("question_id", hash(sample["question"]))
-                    completed_docs.add(doc_file)
-                    completed_samples.add((doc_file, sample_id))
-
-            processed_docs = len({s["doc_file"] for s in test_results if "doc_file" in s})
-            print(f"📊 Loaded existing test results: {len(test_results)} samples")
-            print(f"📂 {processed_docs} documents already processed")
-        except Exception as e:
-            print(f"⚠️ Failed to load test results file: {e!s}")
-            # Clear the broken file
-            os.remove(output_path)
-            print("🚮 Removed corrupted test results file")
-    return test_results
-
-
-def save_test_results(output_path, test_results):
-    """Save current test results to output file"""
-    print(f"💾 Auto-saving test results to {output_path}")
-    try:
-        # Ensure directory exists
-        os.makedirs(os.path.dirname(output_path), exist_ok=True)
-
-        # Save with pretty formatting
-        with open(output_path, "w") as f:
-            json.dump(test_results, f, indent=2)
-        return True
-    except Exception as e:
-        print(f"❌ Failed to save test results: {e!s}")
-        return False
-
-
-def save_doc_processing_state(map_path, state_data):
-    """Save document processing state to JSON file"""
-    try:
-        # Ensure directory exists
-        os.makedirs(os.path.dirname(map_path), exist_ok=True)
-
-        # Save as JSON
-        with open(map_path, "w") as f:
-            json.dump(state_data, f, indent=2)
-        return True
-    except Exception as e:
-        print(f"❌ Failed to save document processing state: {e!s}")
-        return False
-
-
-def load_doc_processing_state(map_path):
-    """Load document processing state from JSON file"""
-    if os.path.exists(map_path):
-        try:
-            with open(map_path) as f:
-                return json.load(f)
-        except Exception as e:
-            print(f"⚠️ Failed to load document processing state: {e!s}")
-            return {}
-    return {}
-
-
-def process_document_wrapper(doc_file, args, original_samples, prompt):
-    try:
-        doc_results = process_document_with_samples(doc_file, args, original_samples, prompt)
-        return {"success": True, "file": doc_file, "results": doc_results}
-    except Exception as e:
-        return {"success": False, "file": doc_file, "error": e}
-
-
-def update_shared_state(doc_data, args, original_samples, file_nums):
-    global completed_counter, test_results
-
-    with results_lock:
-        if doc_data["success"]:
-            if doc_data["results"]:
-                test_results.extend(doc_data["results"])
-
-            completed_counter += 1
-
-            save_test_results(args.test_results_path, test_results)
-            save_doc_processing_state(args.doc_state_path, get_state_dict())
-
-            processed_samples = [s for s in test_results if "score" in s]
-            if processed_samples:
-                acc, f1 = eval_acc_and_f1(processed_samples)
-                print("\n" + "=" * 80)
-                print(f"📈 Cumulative Metrics After Document: {doc_data['file']}")
-                print(f"✅ Processed: {completed_counter}/{file_nums} documents")
-                print(f"🧪 Processed: {len(processed_samples)}/{len(original_samples)} samples")
-                print(f"🎯 Accuracy: {acc:.4f}")
-                print(f"📊 F1 Score: {f1:.4f}")
-                print("=" * 80 + "\n")
-        else:
-            print(f"⛔ Unhandled error processing {doc_data['file']}: {doc_data['error']!s}")
-            save_test_results(args.test_results_path, test_results)
-            save_doc_processing_state(args.doc_state_path, get_state_dict())
-
-
-def main(args):
-    global test_results
-
-    # Load document processing state
-    if os.path.exists(args.doc_state_path):
-        state_data = load_doc_processing_state(args.doc_state_path)
-        load_state_dict(state_data)
-        print(f"🔍 Loaded document processing state for {len(doc_user_map)} documents")
-
-    # Import all documents first
-
-    with open(args.extractor_prompt_path) as f:
-        prompt = f.read()
-
-    # Load all input samples (original data)
-    with open(args.input_path) as f:
-        original_samples = json.load(f)
-
-    # Find all document files
-    doc_files = [
-        f
-        for f in os.listdir(args.document_path)
-        if os.path.isfile(os.path.join(args.document_path, f))
-    ]
-
-    print("\n" + "=" * 80)
-    print(f"🚀 Starting sample processing with {len(doc_files)} documents")
-    print(f"📂 {len(original_samples)} total samples in dataset")
-    print(f"⏭️ {skipped} samples skipped due to filtering")
-    print("=" * 80 + "\n")
-
-    # Load existing test results
-    test_results = load_test_results(args.test_results_path)
-
-    # Process documents sequentially
-    with ThreadPoolExecutor(max_workers=8) as executor:
-        future_to_file = {
-            executor.submit(
-                process_document_wrapper, doc_file, args, original_samples, prompt
-            ): doc_file
-            for doc_file in doc_files
-        }
-
-        progress = tqdm(total=len(doc_files), desc="Processing documents", position=0, leave=True)
-
-        for future in as_completed(future_to_file):
-            doc_data = future.result()
-            update_shared_state(doc_data, args, original_samples, len(doc_files))
-            progress.update(1)
-
-    # Final evaluation
-    if test_results:
-        print("\n" + "=" * 80)
-        print("🏆 FINAL RESULTS")
-        print("=" * 80)
-
-        processed_samples = [s for s in test_results if "score" in s]
-        acc, f1 = eval_acc_and_f1(processed_samples)
-
-        print(f"✅ Completed: {completed_counter}/{len(doc_files)} documents")
-        print(f"🧪 Completed: {len(processed_samples)}/{len(original_samples)} samples")
-        print(f"⏭️ Skipped: {skipped} samples (filtered)")
-        print(f"🎯 Final Accuracy: {acc:.4f}")
-        print(f"📊 Final F1 Score: {f1:.4f}")
-        print("=" * 80)
-
-        # Generate detailed report
-        report_path = re.sub("\.json$", "_report.txt", args.test_results_path)
-        show_results(test_results, show_path=report_path)
-        print(f"📝 Detailed report saved to: {report_path}")
-    else:
-        print("⚠️ No results to report. Processing may have failed.")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--input_path", type=str, default="evaluation/data/mmlongbench/samples.json"
-    )
-    parser.add_argument(
-        "--document_path", type=str, default="evaluation/data/mmlongbench/documents"
-    )
-    parser.add_argument("--model_name", type=str, default="gpt-4o")
-    parser.add_argument("--max_pages", type=int, default=120)
-    parser.add_argument("--resolution", type=int, default=144)
-    parser.add_argument("--max_try", type=int, default=10)
-    parser.add_argument("--max_tokens", type=int, default=1024)
-    parser.add_argument("--temperature", type=float, default=0.0)
-    parser.add_argument(
-        "--extractor_prompt_path",
-        type=str,
-        default="evaluation/scripts/mmlongbench/eval/prompt_for_answer_extraction.md",
-    )
-    parser.add_argument(
-        "--doc_state_path",
-        type=str,
-        default="evaluation/data/mmlongbench/doc_processing_state.json",
-    )
-    parser.add_argument(
-        "--test_results_path",
-        type=str,
-        default="evaluation/data/mmlongbench/test_results.json",
-    )
-    args = parser.parse_args()
-
-    # Initialize test results list
-    test_results = []
-
-    try:
-        main(args)
-    except Exception as e:
-        print(f"⛔ Critical error: {e!s}")
-        print("💾 Attempting to save progress...")
-        save_test_results(args.test_results_path, test_results)
-        save_doc_processing_state(args.doc_state_path, get_state_dict())
-        sys.exit(1)
diff --git a/evaluation/scripts/xinyu/eval/__init__.py b/evaluation/scripts/xinyu/eval/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/evaluation/scripts/mmlongbench/eval/eval_score2.py b/evaluation/scripts/xinyu/eval/eval_score_llm.py
similarity index 100%
rename from evaluation/scripts/mmlongbench/eval/eval_score2.py
rename to evaluation/scripts/xinyu/eval/eval_score_llm.py
diff --git a/evaluation/scripts/mmlongbench/eval_docs2.py b/evaluation/scripts/xinyu/eval_docs.py
similarity index 82%
rename from evaluation/scripts/mmlongbench/eval_docs2.py
rename to evaluation/scripts/xinyu/eval_docs.py
index ff9f3e8f5..03a333201 100644
--- a/evaluation/scripts/mmlongbench/eval_docs2.py
+++ b/evaluation/scripts/xinyu/eval_docs.py
@@ -1,3 +1,4 @@
+import csv
 import json
 import os
 import re
@@ -6,9 +7,9 @@
 from concurrent.futures import ThreadPoolExecutor, as_completed
 
 from dotenv import load_dotenv
-from eval.eval_score2 import eval_acc_and_f1, eval_score, show_results
-from eval.extract_answer import extract_answer
 
+from evaluation.scripts.mmlongbench.eval.extract_answer import extract_answer
+from evaluation.scripts.xinyu.eval.eval_score_llm import eval_acc_and_f1, eval_score, show_results
 from memos.configs.mem_cube import GeneralMemCubeConfig
 from memos.configs.mem_os import MOSConfig
 from memos.mem_cube.general import GeneralMemCube
@@ -24,21 +25,33 @@
     "api_base": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
 }
 neo4j_uri = os.getenv("NEO4J_URI", "bolt://47.117.41.207:7687")
-space_name = "nebula-longbench-stx-new"
-
+db_name = "stx-mmlongbench-003"
 doc_paths = [
     f
-    for f in os.listdir("evaluation/data/mmlongbench/documents_xcy")
-    if os.path.isfile(os.path.join("evaluation/data/mmlongbench/documents_xcy", f))
+    for f in os.listdir("evaluation/data/xinyu/documents")
+    if os.path.isfile(os.path.join("evaluation/data/xinyu/documents", f))
 ]
 
-with open("evaluation/data/mmlongbench/all_samples_with_gt.json") as f:
+with open("evaluation/data/xinyu/all_samples_with_gt.json") as f:
     samples = json.load(f)
 
 
+def get_user_name(doc_file):
+    csv_path = "evaluation/data/xinyu/user_doc_map.csv"
+    if os.path.exists(csv_path):
+        with open(csv_path, newline="", encoding="utf-8") as f:
+            reader = csv.reader(f)
+            for row in reader:
+                uid, path = row[0], row[1]
+                base = os.path.basename(path)
+                if base == doc_file or os.path.splitext(base)[0] == os.path.splitext(doc_file)[0]:
+                    return uid
+    return ""
+
+
 def process_doc(doc_file):
-    user_name = "user_" + doc_file
-    print(user_name)
+    user_name = get_user_name(doc_file)
+    print(user_name, doc_file)
     config = {
         "user_id": user_name,
         "chat_model": {
@@ -90,11 +103,14 @@ def process_doc(doc_file):
                     "graph_db": {
                         "backend": "neo4j",
                         "config": {
-                            "uri": "bolt://47.117.45.189:7687",
+                            "uri": neo4j_uri,
                             "user": "neo4j",
                             "password": "iaarlichunyu",
-                            "db_name": "stx-mmlongbench-001",
+                            "db_name": db_name,
+                            "user_name": user_name,
+                            "use_multi_db": False,
                             "auto_create": True,
+                            "embedding_dimension": 3072,
                         },
                     },
                     "embedder": {
@@ -117,8 +133,6 @@ def process_doc(doc_file):
 
     temp_dir = os.path.join("tmp", doc_file)
 
-    # Only dump when the directory does not exist or is empty
-    # Avoid calling dump on a non-empty directory to prevent MemCubeError
     if (not os.path.exists(temp_dir)) or (not os.listdir(temp_dir)):
         mem_cube.dump(temp_dir)
 
@@ -196,9 +210,7 @@ def process_doc(doc_file):
                     print(f"Avg acc: {acc}")
                     print(f"Avg f1: {f1}")
 
-                with open(
-                    "evaluation/data/mmlongbench/test_results2.json", "w", encoding="utf-8"
-                ) as f:
+                with open("evaluation/data/xinyu/test_results.json", "w", encoding="utf-8") as f:
                     json.dump(results, f, ensure_ascii=False, indent=2)
 
             except Exception as e:
@@ -212,7 +224,5 @@ def process_doc(doc_file):
 
     show_results(
         results,
-        show_path=re.sub(
-            r"\.json$", ".txt", "evaluation/data/mmlongbench/test_results_report2.txt"
-        ),
+        show_path=re.sub(r"\.json$", ".txt", "evaluation/data/xinyu/test_results_report.txt"),
     )
diff --git a/evaluation/scripts/xinyu/import_docs.py b/evaluation/scripts/xinyu/import_docs.py
new file mode 100644
index 000000000..f9d2619a4
--- /dev/null
+++ b/evaluation/scripts/xinyu/import_docs.py
@@ -0,0 +1,85 @@
+import asyncio
+import os
+import traceback
+import uuid
+
+from memos import log
+from memos.configs.mem_reader import SimpleStructMemReaderConfig
+from memos.configs.memory import TreeTextMemoryConfig
+from memos.mem_reader.simple_struct import SimpleStructMemReader
+from memos.memories.textual.tree import TreeTextMemory
+
+
+logger = log.get_logger(__name__)
+db_name = "stx-mmlongbench-003"
+# Create a memory reader instance
+reader_config = SimpleStructMemReaderConfig.from_json_file(
+    "examples/data/config/simple_struct_reader_config.json"
+)
+reader = SimpleStructMemReader(reader_config)
+
+tree_config = TreeTextMemoryConfig.from_json_file(
+    "examples/data/config/tree_config_shared_database.json"
+)
+tree_config.graph_db.config.db_name = db_name
+# Processing Documents
+existing_names = {
+    d for d in os.listdir("ppt_test_result") if os.path.isdir(os.path.join("ppt_test_result", d))
+}
+doc_paths = []
+for f in os.listdir("evaluation/data/xinyu/documents"):
+    fp = os.path.join("evaluation/data/xinyu/documents", f)
+    if os.path.isfile(fp):
+        name = os.path.splitext(f)[0]
+        if name in existing_names:
+            continue
+        doc_paths.append(fp)
+
+
+async def process_doc(doc_path):
+    print(f"🔄 Processing document: {doc_path}")
+    doc_file = doc_path.split("/")[-1].rsplit(".", 1)[0]
+
+    # Generate random user id: 'user_' + random short hex
+    user_id = "user_" + uuid.uuid4().hex[:8]
+    # Persist mapping between user_id and doc_path
+    with open("evaluation/data/xinyu/user_doc_map.csv", "a", encoding="utf-8") as f:
+        f.write(f"{user_id},{doc_path}\n")
+
+    tree_config.graph_db.config.user_name = user_id
+    temp_dir = "tmp/" + doc_file
+    my_tree_textual_memory = TreeTextMemory(tree_config)
+    doc_memory = await reader.get_memory(
+        [doc_path], "doc", info={"user_id": user_id, "session_id": "session_" + str(uuid.uuid4())}
+    )
+
+    count = 0
+    for m_list in doc_memory:
+        count += len(m_list)
+        my_tree_textual_memory.add(m_list)
+    print("total memories: ", count)
+
+    my_tree_textual_memory.dump(temp_dir)
+    return doc_path
+
+
+async def main():
+    batch_size = 2
+    for i in range(0, len(doc_paths), batch_size):
+        batch = doc_paths[i : i + batch_size]
+        print(f"🚀 Starting batch {i // batch_size + 1} with {len(batch)} docs")
+
+        tasks = [process_doc(p) for p in batch]
+        results = await asyncio.gather(*tasks, return_exceptions=True)
+
+        for p, result in zip(batch, results, strict=False):
+            if isinstance(result, Exception):
+                print(f"❌ Error processing {p}: {result}")
+                tb_text = "".join(traceback.TracebackException.from_exception(result).format())
+                print(tb_text)
+            else:
+                print(f"✅ Finished {result}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/src/memos/graph_dbs/neo4j.py b/src/memos/graph_dbs/neo4j.py
index 9140fe532..96e4f1c25 100644
--- a/src/memos/graph_dbs/neo4j.py
+++ b/src/memos/graph_dbs/neo4j.py
@@ -1,5 +1,4 @@
 import json
-import os
 import time
 
 from datetime import datetime
@@ -109,8 +108,8 @@ def _run_with_retry(self, op, op_name: str):
         - NEO4J_RETRY_MAX_ATTEMPTS (default: 3)
         - NEO4J_RETRY_BACKOFF_SECONDS (default: 2, exponential backoff)
         """
-        attempts = int(os.getenv("NEO4J_RETRY_MAX_ATTEMPTS", "3"))
-        base_backoff = float(os.getenv("NEO4J_RETRY_BACKOFF_SECONDS", "2"))
+        attempts = 8
+        base_backoff = 2
         try:
             from neo4j.exceptions import Neo4jError, ServiceUnavailable, TransientError
         except Exception:
@@ -235,11 +234,6 @@ def _op():
                 )
                 summary = result.consume()
                 created = summary.counters.nodes_created
-                print(
-                    "add_node id=%s action=%s",
-                    id,
-                    "created" if created else "matched",
-                )
                 return created
 
         self._run_with_retry(_op, "add_node")
@@ -861,7 +855,6 @@ def get_by_metadata(
         where_str = " AND ".join(where_clauses)
         query = f"MATCH (n:Memory) WHERE {where_str} RETURN n.id AS id"
 
-        print(query, params)
         with self.driver.session(database=self.db_name) as session:
             result = session.run(query, params)
             return [record["id"] for record in result]
@@ -1070,7 +1063,6 @@ def get_all_memory_items(self, scope: str, **kwargs) -> list[dict]:
         where_clause = "WHERE n.memory_type = $scope"
         params = {"scope": scope}
 
-        print("user_name: ", user_name)
         if not self.config.use_multi_db and (self.config.user_name or user_name):
             where_clause += " AND n.user_id = $user_name"
             params["user_name"] = user_name
@@ -1081,7 +1073,6 @@ def get_all_memory_items(self, scope: str, **kwargs) -> list[dict]:
             RETURN n
             """
 
-        print(query, params)
         with self.driver.session(database=self.db_name) as session:
             results = session.run(query, params)
             return [self._parse_node(dict(record["n"])) for record in results]
diff --git a/src/memos/memories/textual/tree_text_memory/organize/manager.py b/src/memos/memories/textual/tree_text_memory/organize/manager.py
index 6b0a6a55e..98174a2db 100644
--- a/src/memos/memories/textual/tree_text_memory/organize/manager.py
+++ b/src/memos/memories/textual/tree_text_memory/organize/manager.py
@@ -64,16 +64,6 @@ def add(self, memories: list[TextualMemoryItem]) -> list[str]:
                 except Exception as e:
                     logger.exception("Memory processing error: ", exc_info=e)
 
-        self.graph_store.remove_oldest_memory(
-            memory_type="WorkingMemory", keep_latest=self.memory_size["WorkingMemory"]
-        )
-        self.graph_store.remove_oldest_memory(
-            memory_type="LongTermMemory", keep_latest=self.memory_size["LongTermMemory"]
-        )
-        self.graph_store.remove_oldest_memory(
-            memory_type="UserMemory", keep_latest=self.memory_size["UserMemory"]
-        )
-
         self._refresh_memory_size()
         return added_ids
 
diff --git a/src/memos/memories/textual/tree_text_memory/retrieve/searcher.py b/src/memos/memories/textual/tree_text_memory/retrieve/searcher.py
index c70d37e8d..3049431a5 100644
--- a/src/memos/memories/textual/tree_text_memory/retrieve/searcher.py
+++ b/src/memos/memories/textual/tree_text_memory/retrieve/searcher.py
@@ -196,7 +196,6 @@ def _retrieve_from_working_memory(
             query=query, parsed_goal=parsed_goal, top_k=top_k, memory_scope="WorkingMemory"
         )
 
-        print("items: ", len(items))
         items_with_detail = []
         for item in items:
             if item.metadata.embedding:
@@ -255,7 +254,6 @@ def _retrieve_from_long_term_and_user(
                 memory_scope="UserMemory",
             )
 
-        print("_retrieve_from_long_term_and_user: ", len(results))
         items_with_detail = []
         for item in results:
             if item.metadata.embedding:

From dd34693fea55ba0279f39256be3a7e4238e8d574 Mon Sep 17 00:00:00 2001
From: stx <31013941@qq.com>
Date: Tue, 25 Nov 2025 16:10:54 +0800
Subject: [PATCH 30/34] feat: add MMLongbench-Doc, HotpotQA, xinyu evaluation

---
 evaluation/scripts/hotpot/hotpot_eval.py      | 224 ++++++++++++++
 .../scripts/hotpot/hotpot_evaluate_v1.py      | 151 ++++++++++
 .../scripts/mmlongbench/eval/__init__.py      |   0
 .../scripts/mmlongbench/eval/eval_score.py    | 246 +++++++++++++++
 .../mmlongbench/eval/extract_answer.py        |  33 +++
 .../eval/prompt_for_answer_extraction.md      |  35 +++
 evaluation/scripts/mmlongbench/eval_docs.py   | 265 +++++++++++++++++
 evaluation/scripts/mmlongbench/import_docs.py |  88 ++++++
 .../scripts/mmlongbench/models/__init__.py    |   0
 .../mmlongbench/models/minicpm_llama3.py      |  56 ++++
 .../scripts/mmlongbench/multimodal_test.py    | 185 ++++++++++++
 evaluation/scripts/xinyu/eval/__init__.py     |   0
 .../scripts/xinyu/eval/eval_score_llm.py      | 279 ++++++++++++++++++
 evaluation/scripts/xinyu/eval_docs.py         | 228 ++++++++++++++
 evaluation/scripts/xinyu/import_docs.py       |  85 ++++++
 15 files changed, 1875 insertions(+)
 create mode 100644 evaluation/scripts/hotpot/hotpot_eval.py
 create mode 100644 evaluation/scripts/hotpot/hotpot_evaluate_v1.py
 create mode 100644 evaluation/scripts/mmlongbench/eval/__init__.py
 create mode 100644 evaluation/scripts/mmlongbench/eval/eval_score.py
 create mode 100644 evaluation/scripts/mmlongbench/eval/extract_answer.py
 create mode 100644 evaluation/scripts/mmlongbench/eval/prompt_for_answer_extraction.md
 create mode 100644 evaluation/scripts/mmlongbench/eval_docs.py
 create mode 100644 evaluation/scripts/mmlongbench/import_docs.py
 create mode 100644 evaluation/scripts/mmlongbench/models/__init__.py
 create mode 100644 evaluation/scripts/mmlongbench/models/minicpm_llama3.py
 create mode 100644 evaluation/scripts/mmlongbench/multimodal_test.py
 create mode 100644 evaluation/scripts/xinyu/eval/__init__.py
 create mode 100644 evaluation/scripts/xinyu/eval/eval_score_llm.py
 create mode 100644 evaluation/scripts/xinyu/eval_docs.py
 create mode 100644 evaluation/scripts/xinyu/import_docs.py

diff --git a/evaluation/scripts/hotpot/hotpot_eval.py b/evaluation/scripts/hotpot/hotpot_eval.py
new file mode 100644
index 000000000..05ff52349
--- /dev/null
+++ b/evaluation/scripts/hotpot/hotpot_eval.py
@@ -0,0 +1,224 @@
+import json
+import os
+import uuid
+
+from concurrent.futures import ThreadPoolExecutor, as_completed
+
+from dotenv import load_dotenv
+from tqdm import tqdm
+
+from memos.configs.mem_cube import GeneralMemCubeConfig
+from memos.configs.mem_os import MOSConfig
+from memos.mem_cube.general import GeneralMemCube
+from memos.mem_os.main import MOS
+
+
+load_dotenv()
+
+db_name = "stx-hotpot-001"
+
+
+user_name = str(uuid.uuid4())
+
+# 1.1 Set openai config
+openapi_config = {
+    "model_name_or_path": "gpt-4o-mini",
+    "temperature": 0.8,
+    "max_tokens": 1024,
+    "top_p": 0.9,
+    "top_k": 50,
+    "remove_think_prefix": True,
+    "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
+    "api_base": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
+}
+# 1.2 Set neo4j config
+neo4j_uri = os.getenv("NEO4J_URI", "bolt://localhost:7687")
+
+# 1.3  Create MOS Config
+config = {
+    "user_id": user_name,
+    "chat_model": {
+        "backend": "openai",
+        "config": openapi_config,
+    },
+    "mem_reader": {
+        "backend": "simple_struct",
+        "config": {
+            "llm": {
+                "backend": "openai",
+                "config": openapi_config,
+            },
+            "embedder": {
+                "backend": "universal_api",
+                "config": {
+                    "provider": "openai",
+                    "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
+                    "model_name_or_path": "text-embedding-3-large",
+                    "base_url": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
+                },
+            },
+            "chunker": {
+                "backend": "sentence",
+                "config": {
+                    "tokenizer_or_token_counter": "gpt2",
+                    "chunk_size": 512,
+                    "chunk_overlap": 128,
+                    "min_sentences_per_chunk": 1,
+                },
+            },
+        },
+    },
+    "max_turns_window": 20,
+    "top_k": 5,
+    "enable_textual_memory": True,
+    "enable_activation_memory": False,
+    "enable_parametric_memory": False,
+}
+
+mos_config = MOSConfig(**config)
+# you can set PRO_MODE to True to enable CoT enhancement mos_config.PRO_MODE = True
+mos = MOS(mos_config)
+
+
+# Filter out embedding fields, keeping only necessary fields
+def filter_memory_data(memories_data):
+    filtered_data = {}
+    for key, value in memories_data.items():
+        if key == "text_mem":
+            filtered_data[key] = []
+            for mem_group in value:
+                # Check if it's the new data structure (list of TextualMemoryItem objects)
+                if "memories" in mem_group and isinstance(mem_group["memories"], list):
+                    # New data structure: directly a list of TextualMemoryItem objects
+                    filtered_memories = []
+                    for memory_item in mem_group["memories"]:
+                        # Create filtered dictionary
+                        filtered_item = {
+                            "id": memory_item.id,
+                            "memory": memory_item.memory,
+                            "metadata": {},
+                        }
+                        # Filter metadata, excluding embedding
+                        if hasattr(memory_item, "metadata") and memory_item.metadata:
+                            for attr_name in dir(memory_item.metadata):
+                                if not attr_name.startswith("_") and attr_name != "embedding":
+                                    attr_value = getattr(memory_item.metadata, attr_name)
+                                    if not callable(attr_value):
+                                        filtered_item["metadata"][attr_name] = attr_value
+                        filtered_memories.append(filtered_item)
+
+                    filtered_group = {
+                        "cube_id": mem_group.get("cube_id", ""),
+                        "memories": filtered_memories,
+                    }
+                    filtered_data[key].append(filtered_group)
+                else:
+                    # Old data structure: dictionary with nodes and edges
+                    filtered_group = {
+                        "memories": {"nodes": [], "edges": mem_group["memories"].get("edges", [])}
+                    }
+                    for node in mem_group["memories"].get("nodes", []):
+                        filtered_node = {
+                            "id": node.get("id"),
+                            "memory": node.get("memory"),
+                            "metadata": {
+                                k: v
+                                for k, v in node.get("metadata", {}).items()
+                                if k != "embedding"
+                            },
+                        }
+                        filtered_group["memories"]["nodes"].append(filtered_node)
+                    filtered_data[key].append(filtered_group)
+        else:
+            filtered_data[key] = value
+    return filtered_data
+
+
+config = GeneralMemCubeConfig.model_validate(
+    {
+        "user_id": user_name,
+        "cube_id": f"{user_name}",
+        "text_mem": {
+            "backend": "tree_text",
+            "config": {
+                "extractor_llm": {
+                    "backend": "openai",
+                    "config": openapi_config,
+                },
+                "dispatcher_llm": {
+                    "backend": "openai",
+                    "config": openapi_config,
+                },
+                "graph_db": {
+                    "backend": "neo4j",
+                    "config": {
+                        "uri": neo4j_uri,
+                        "user": "neo4j",
+                        "password": "iaarlichunyu",
+                        "db_name": db_name,
+                        "auto_create": True,
+                    },
+                },
+                "embedder": {
+                    "backend": "universal_api",
+                    "config": {
+                        "provider": "openai",
+                        "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
+                        "model_name_or_path": "text-embedding-3-large",
+                        "base_url": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
+                    },
+                },
+                "reorganize": True,
+            },
+        },
+        "act_mem": {},
+        "para_mem": {},
+    },
+)
+
+mem_cube = GeneralMemCube(config)
+
+
+mos.register_mem_cube(f"/tmp/{user_name}", mem_cube_id=user_name)
+
+
+with open("evaluation/data/hotpot/hotpot_dev_distractor_v1.json") as f:
+    data = json.load(f)
+
+
+def build_context_text(context_list):
+    parts = []
+    for title, sentences in context_list:
+        text = " ".join(s.strip() for s in sentences if s.strip())
+        parts.append(f"{title}: {text}")
+    return "\n".join(parts)
+
+
+def build_and_ask(item):
+    qid = item["_id"]
+    question = item["question"]
+
+    for title, sentences in item["context"]:
+        text = " ".join(s.strip() for s in sentences if s.strip())
+        memory_content = f"{title}: {text}"
+        mos.add(memory_content=memory_content)
+
+    answer = mos.chat(question).strip()
+    return qid, answer
+
+
+pred_answers = {}
+
+with ThreadPoolExecutor(max_workers=5) as executor:
+    futures = {executor.submit(build_and_ask, item): item for item in data}
+    for future in tqdm(as_completed(futures), total=len(futures)):
+        try:
+            qid, answer = future.result()
+            pred_answers[qid] = answer
+        except Exception as e:
+            print(f"Error: {e}")
+
+predictions = {"answer": pred_answers, "sp": []}
+
+with open("evaluation/data/hotpot/output/dev_distractor_pred.json", "w") as f:
+    json.dump(predictions, f, ensure_ascii=False, indent=2)
diff --git a/evaluation/scripts/hotpot/hotpot_evaluate_v1.py b/evaluation/scripts/hotpot/hotpot_evaluate_v1.py
new file mode 100644
index 000000000..d4d6e71e1
--- /dev/null
+++ b/evaluation/scripts/hotpot/hotpot_evaluate_v1.py
@@ -0,0 +1,151 @@
+import re
+import string
+import sys
+
+from collections import Counter
+
+import ujson as json
+
+
+def normalize_answer(s):
+    def remove_articles(text):
+        return re.sub(r"\b(a|an|the)\b", " ", text)
+
+    def white_space_fix(text):
+        return " ".join(text.split())
+
+    def remove_punc(text):
+        exclude = set(string.punctuation)
+        return "".join(ch for ch in text if ch not in exclude)
+
+    def lower(text):
+        return text.lower()
+
+    return white_space_fix(remove_articles(remove_punc(lower(s))))
+
+
+def f1_score(prediction, ground_truth):
+    normalized_prediction = normalize_answer(prediction)
+    normalized_ground_truth = normalize_answer(ground_truth)
+
+    zero_metric = (0, 0, 0)
+
+    if (
+        normalized_prediction in ["yes", "no", "noanswer"]
+        and normalized_prediction != normalized_ground_truth
+    ):
+        return zero_metric
+    if (
+        normalized_ground_truth in ["yes", "no", "noanswer"]
+        and normalized_prediction != normalized_ground_truth
+    ):
+        return zero_metric
+
+    prediction_tokens = normalized_prediction.split()
+    ground_truth_tokens = normalized_ground_truth.split()
+    common = Counter(prediction_tokens) & Counter(ground_truth_tokens)
+    num_same = sum(common.values())
+    if num_same == 0:
+        return zero_metric
+    precision = 1.0 * num_same / len(prediction_tokens)
+    recall = 1.0 * num_same / len(ground_truth_tokens)
+    f1 = (2 * precision * recall) / (precision + recall)
+    return f1, precision, recall
+
+
+def exact_match_score(prediction, ground_truth):
+    return normalize_answer(prediction) == normalize_answer(ground_truth)
+
+
+def update_answer(metrics, prediction, gold):
+    em = exact_match_score(prediction, gold)
+    f1, prec, recall = f1_score(prediction, gold)
+    metrics["em"] += float(em)
+    metrics["f1"] += f1
+    metrics["prec"] += prec
+    metrics["recall"] += recall
+    return em, prec, recall
+
+
+def update_sp(metrics, prediction, gold):
+    cur_sp_pred = set(map(tuple, prediction))
+    gold_sp_pred = set(map(tuple, gold))
+    tp, fp, fn = 0, 0, 0
+    for e in cur_sp_pred:
+        if e in gold_sp_pred:
+            tp += 1
+        else:
+            fp += 1
+    for e in gold_sp_pred:
+        if e not in cur_sp_pred:
+            fn += 1
+    prec = 1.0 * tp / (tp + fp) if tp + fp > 0 else 0.0
+    recall = 1.0 * tp / (tp + fn) if tp + fn > 0 else 0.0
+    f1 = 2 * prec * recall / (prec + recall) if prec + recall > 0 else 0.0
+    em = 1.0 if fp + fn == 0 else 0.0
+    metrics["sp_em"] += em
+    metrics["sp_f1"] += f1
+    metrics["sp_prec"] += prec
+    metrics["sp_recall"] += recall
+    return em, prec, recall
+
+
+def eval(prediction_file, gold_file):
+    with open(prediction_file) as f:
+        prediction = json.load(f)
+    with open(gold_file) as f:
+        gold = json.load(f)
+
+    metrics = {
+        "em": 0,
+        "f1": 0,
+        "prec": 0,
+        "recall": 0,
+        "sp_em": 0,
+        "sp_f1": 0,
+        "sp_prec": 0,
+        "sp_recall": 0,
+        "joint_em": 0,
+        "joint_f1": 0,
+        "joint_prec": 0,
+        "joint_recall": 0,
+    }
+    for dp in gold:
+        cur_id = dp["_id"]
+        can_eval_joint = True
+        if cur_id not in prediction["answer"]:
+            print(f"missing answer {cur_id}")
+            can_eval_joint = False
+        else:
+            em, prec, recall = update_answer(metrics, prediction["answer"][cur_id], dp["answer"])
+        if cur_id not in prediction["sp"]:
+            print(f"missing sp fact {cur_id}")
+            can_eval_joint = False
+        else:
+            sp_em, sp_prec, sp_recall = update_sp(
+                metrics, prediction["sp"][cur_id], dp["supporting_facts"]
+            )
+
+        if can_eval_joint:
+            joint_prec = prec * sp_prec
+            joint_recall = recall * sp_recall
+            if joint_prec + joint_recall > 0:
+                joint_f1 = 2 * joint_prec * joint_recall / (joint_prec + joint_recall)
+            else:
+                joint_f1 = 0.0
+            joint_em = em * sp_em
+
+            metrics["joint_em"] += joint_em
+            metrics["joint_f1"] += joint_f1
+            metrics["joint_prec"] += joint_prec
+            metrics["joint_recall"] += joint_recall
+
+    n = len(gold)
+    for k in metrics:
+        metrics[k] /= n
+
+    print(metrics)
+
+
+if __name__ == "__main__":
+    eval(sys.argv[1], sys.argv[2])
diff --git a/evaluation/scripts/mmlongbench/eval/__init__.py b/evaluation/scripts/mmlongbench/eval/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/evaluation/scripts/mmlongbench/eval/eval_score.py b/evaluation/scripts/mmlongbench/eval/eval_score.py
new file mode 100644
index 000000000..02ef6eb53
--- /dev/null
+++ b/evaluation/scripts/mmlongbench/eval/eval_score.py
@@ -0,0 +1,246 @@
+import re
+
+from collections import defaultdict
+from math import isclose
+
+
+def levenshtein_distance(s1, s2):
+    if len(s1) > len(s2):
+        s1, s2 = s2, s1
+
+    distances = range(len(s1) + 1)
+    for i2, c2 in enumerate(s2):
+        distances_ = [i2 + 1]
+        for i1, c1 in enumerate(s1):
+            if c1 == c2:
+                distances_.append(distances[i1])
+            else:
+                distances_.append(1 + min((distances[i1], distances[i1 + 1], distances_[-1])))
+        distances = distances_
+    return distances[-1]
+
+
+def anls_compute(groundtruth, prediction, threshold=0.5):
+    dist = levenshtein_distance(groundtruth, prediction)
+    length = max(len(groundtruth.upper()), len(prediction.upper()))
+    value = 0.0 if length == 0 else float(dist) / float(length)
+    anls = 1.0 - value
+    if anls <= threshold:
+        anls = 0.0
+    return anls
+
+
+def is_float_equal(
+    reference, prediction, include_percentage: bool = False, is_close: float = False
+) -> bool:
+    def get_precision(gt_ans: float) -> int:
+        precision = 3
+        if "." in str(gt_ans):
+            precision = len(str(gt_ans).split(".")[-1])
+        return precision
+
+    reference = float(str(reference).strip().rstrip("%").strip())
+    try:
+        prediction = float(str(prediction).strip().rstrip("%").strip())
+    except Exception:
+        return False
+
+    gt_result = [reference / 100, reference, reference * 100] if include_percentage else [reference]
+    for item in gt_result:
+        try:
+            if is_close and isclose(item, prediction, rel_tol=0.01):
+                return True
+            precision = max(min(get_precision(prediction), get_precision(item)), 2)
+            if round(prediction, precision) == round(item, precision):
+                return True
+        except Exception:
+            continue
+    return False
+
+
+def get_clean_string(s):
+    s = str(s).lower().strip()
+
+    for suffix in ["mile", "miles", "million"]:
+        if s.endswith(suffix):
+            s = s[: -len(suffix)].strip()
+
+    s = re.sub(r"\s*\([^)]*\)", "", s).strip()
+    s = re.sub(r"^['\"]|['\"]$", "", s).strip()
+    s = s.lstrip("$").rstrip("%").strip()
+
+    return s
+
+
+def is_exact_match(s):
+    flag = False
+    # Website
+    if "https://" in s:
+        flag = True
+    # code file
+    if s.endswith((".py", ".ipynb")) or s.startswith("page"):
+        flag = True
+    # telephone number
+    if re.fullmatch(r"\b\d+(-\d+|\s\d+)?\b", s):
+        flag = True
+    # time
+    if "a.m." in s or "p.m." in s:
+        flag = True
+    # YYYY-MM-DD
+    if re.fullmatch(r"\b\d{4}[-\s]\d{2}[-\s]\d{2}\b", s):
+        flag = True
+    # YYYY-MM
+    if re.fullmatch(r"\b\d{4}[-\s]\d{2}\b", s):
+        flag = True
+    # Email address
+    if re.fullmatch(r"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}", s):
+        flag = True
+    return flag
+
+
+def isfloat(num):
+    try:
+        float(num)
+        return True
+    except ValueError:
+        return False
+
+
+def eval_score(gt, pred, answer_type):
+    if answer_type == "Int":
+        try:
+            gt, pred = int(gt), int(float(pred))
+        except Exception:
+            pred = ""
+        score = gt == pred
+    elif answer_type == "Float":
+        try:
+            gt = float(get_clean_string(str(gt)))
+            pred = float(get_clean_string(str(pred)))
+        except Exception:
+            pred = ""
+        score = is_float_equal(gt, pred, include_percentage=True, is_close=True)
+    elif answer_type in ["Str", "None"]:
+        gt = get_clean_string(gt)
+        pred = get_clean_string(pred)
+        score = gt == pred if is_exact_match(gt) else anls_compute(gt, pred)
+    else:
+        if isinstance(gt, str) and gt.startswith("["):
+            gt = eval(gt)
+        if not isinstance(gt, list):
+            gt = [gt]
+        if isinstance(pred, str) and pred.startswith("["):
+            pred = eval(pred)
+        if not isinstance(pred, list):
+            pred = [pred]
+        print(len(gt), len(pred))
+        if len(gt) != len(pred):
+            score = 0.0
+        else:
+            gt = sorted([get_clean_string(a) for a in gt])
+            pred = sorted([get_clean_string(a) for a in pred])
+            print(gt, pred)
+            if isfloat(gt[0]) or is_exact_match(gt[0]):
+                score = "-".join(gt) == "-".join(pred)
+            else:
+                score = min(
+                    [anls_compute(gt_v, pred_v) for gt_v, pred_v in zip(gt, pred, strict=False)]
+                )
+
+    return float(score)
+
+
+def eval_acc_and_f1(samples):
+    evaluated_samples = [sample for sample in samples if "score" in sample]
+    if not evaluated_samples:
+        return 0.0, 0.0
+
+    acc = sum([sample["score"] for sample in evaluated_samples]) / len(evaluated_samples)
+    try:
+        recall = sum(
+            [
+                sample["score"]
+                for sample in evaluated_samples
+                if sample["answer"] != "Not answerable"
+            ]
+        ) / len([sample for sample in evaluated_samples if sample["answer"] != "Not answerable"])
+        precision = sum(
+            [
+                sample["score"]
+                for sample in evaluated_samples
+                if sample["answer"] != "Not answerable"
+            ]
+        ) / len([sample for sample in evaluated_samples if sample["pred"] != "Not answerable"])
+        f1 = 2 * recall * precision / (recall + precision) if (recall + precision) > 0.0 else 0.0
+    except Exception:
+        f1 = 0.0
+
+    return acc, f1
+
+
+def show_results(samples, show_path=None):
+    for sample in samples:
+        sample["evidence_pages"] = eval(sample["evidence_pages"])
+        sample["evidence_sources"] = eval(sample["evidence_sources"])
+
+    with open(show_path, "w") as f:
+        acc, f1 = eval_acc_and_f1(samples)
+        f.write(f"Overall Acc: {acc} | Question Number: {len(samples)}\n")
+        f.write(f"Overall F1-score: {f1} | Question Number: {len(samples)}\n")
+        f.write("-----------------------\n")
+
+        acc_single_page, _ = eval_acc_and_f1(
+            [sample for sample in samples if len(sample["evidence_pages"]) == 1]
+        )
+        acc_multi_page, _ = eval_acc_and_f1(
+            [
+                sample
+                for sample in samples
+                if len(sample["evidence_pages"]) != 1 and sample["answer"] != "Not answerable"
+            ]
+        )
+        acc_neg, _ = eval_acc_and_f1(
+            [sample for sample in samples if sample["answer"] == "Not answerable"]
+        )
+
+        f.write(
+            "Single-page | Accuracy: {} | Question Number: {}\n".format(
+                acc_single_page,
+                len([sample for sample in samples if len(sample["evidence_pages"]) == 1]),
+            )
+        )
+        f.write(
+            "Cross-page | Accuracy: {} | Question Number: {}\n".format(
+                acc_multi_page,
+                len(
+                    [
+                        sample
+                        for sample in samples
+                        if len(sample["evidence_pages"]) != 1
+                        and sample["answer"] != "Not answerable"
+                    ]
+                ),
+            )
+        )
+        f.write(
+            "Unanswerable | Accuracy: {} | Question Number: {}\n".format(
+                acc_neg, len([sample for sample in samples if sample["answer"] == "Not answerable"])
+            )
+        )
+        f.write("-----------------------\n")
+
+        source_sample_dict, document_type_dict = defaultdict(list), defaultdict(list)
+        for sample in samples:
+            for answer_source in sample["evidence_sources"]:
+                source_sample_dict[answer_source].append(sample)
+            document_type_dict[sample["doc_type"]].append(sample)
+        for type, sub_samples in source_sample_dict.items():
+            f.write(
+                f"Evidence Sources: {type} | Accuracy: {eval_acc_and_f1(sub_samples)[0]} | Question Number: {len(sub_samples)}\n"
+            )
+
+        f.write("-----------------------\n")
+        for type, sub_samples in document_type_dict.items():
+            f.write(
+                f"Document Type: {type} | Accuracy: {eval_acc_and_f1(sub_samples)[0]} | Question Number: {len(sub_samples)}\n"
+            )
diff --git a/evaluation/scripts/mmlongbench/eval/extract_answer.py b/evaluation/scripts/mmlongbench/eval/extract_answer.py
new file mode 100644
index 000000000..b7f7e6863
--- /dev/null
+++ b/evaluation/scripts/mmlongbench/eval/extract_answer.py
@@ -0,0 +1,33 @@
+import os
+
+import openai
+
+from dotenv import load_dotenv
+
+
+load_dotenv()
+client = openai.Client(
+    api_key=os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
+    base_url=os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
+)
+
+
+def extract_answer(question, output, prompt, model_name="gpt-4o"):
+    response = client.chat.completions.create(
+        model=model_name,
+        messages=[
+            {
+                "role": "user",
+                "content": prompt,
+            },
+            {"role": "assistant", "content": f"\n\nQuestion:{question}\nAnalysis:{output}\n"},
+        ],
+        temperature=0.0,
+        max_tokens=256,
+        top_p=1,
+        frequency_penalty=0,
+        presence_penalty=0,
+    )
+    response = response.choices[0].message.content
+
+    return response
diff --git a/evaluation/scripts/mmlongbench/eval/prompt_for_answer_extraction.md b/evaluation/scripts/mmlongbench/eval/prompt_for_answer_extraction.md
new file mode 100644
index 000000000..a309c0935
--- /dev/null
+++ b/evaluation/scripts/mmlongbench/eval/prompt_for_answer_extraction.md
@@ -0,0 +1,35 @@
+Given the question and analysis, you are tasked to extract answers with required formats from the free-form analysis.
+- Your extracted answers should be one of the following formats: (1) Integer, (2) Float, (3) String and (4) List. If you find the analysis the question can not be answered from the given documents, type "Not answerable". Exception: If the analysis only tells you that it can not read/understand the images or documents, type "Fail to answer".
+- Please make your response as concise as possible. Also note that your response should be formatted as below:
+```
+Extracted answer: [answer]
+Answer format: [answer format]
+```
+
+Please read the following example, then extract the answer from the model response and type it at the end of the prompt.
+
+---
+Question: List the primary questions asked about the services in this report.
+Analysis:  The primary questions asked about the services in the report for The Limes Residential Home are:\n\n1. Is the service safe?\n2. Is the service effective?\n3. Is the service caring?\n4. Is the service responsive?\n5. Is the service well-led?
+Extracted answer: ['Is the servife safe?', 'Is the service effective', 'Is the serve caring?', 'Is the service responsive?', 'Is the service well-led?']
+Answer format: List
+
+---
+Question: How many regulations of the HSCA 2008 are breached in all according to this report?
+Analysis: According to the report, the provider breached 10 Health and Social Care Act 2008 (Regulated Activities) Regulations in total. Here are the specifics:\n\n1. Regulation 13: Safeguarding service users from abuse and improper treatment\n2. Regulation 12: Safe care and treatment\n3. Regulation 18: Staffing\n4. Regulation 11: Need for consent\n5. Regulation 10: Dignity and respect\n6. Regulation 9: Person-centred care\n7. Regulation 17: Good governance\n8. Regulation 18 (CQC Registration Regulations 2009): Notification of other incidents\n9. Regulation 18: Failure to maintain an accurate and up-to-date care plan\n10. Regulation 11: Failure to implement the Mental Capacity Act 2005 code of practice effectively\n\nThese breaches involve issues concerning staffing, safeguarding, medicines management, dignity and respect, consent, care planning, governance, and failure to notify the CQC of incidents.
+Extracted answer: 10
+Answer format: Integer
+
+---
+Question: According to the survey that is the percentage of Chinese who are paying more or about the same attention to politics after Trump's election?
+Analysis: The survey provided does not specify the percentage of Chinese individuals specifically who are paying more or about the same attention to politics after Trump's election. The report focuses primarily on American demographics and does not include specific details about the Chinese population in relation to this question. If you need information about a different demographic or a summary of the findings from the American demographic, I can certainly help with that!
+Extracted answer: Not answerable
+Answer format: String
+
+---
+Question: How many quotations from male respondent over 50 years old are included in this report?
+Analysis: The image you've provided appears to be a screenshot of a document with multiple charts. However, the text is too small and blurry to read accurately. If you can provide a clearer image or more context, I might be able to help you with your question.
+Extracted answer: Fail to answer
+Answer format: String
+
+---
diff --git a/evaluation/scripts/mmlongbench/eval_docs.py b/evaluation/scripts/mmlongbench/eval_docs.py
new file mode 100644
index 000000000..510a0b1ed
--- /dev/null
+++ b/evaluation/scripts/mmlongbench/eval_docs.py
@@ -0,0 +1,265 @@
+import csv
+import json
+import os
+import re
+import traceback
+
+from concurrent.futures import ThreadPoolExecutor, as_completed
+
+from dotenv import load_dotenv
+from eval.eval_score import eval_acc_and_f1, eval_score, show_results
+from eval.extract_answer import extract_answer
+from tqdm import tqdm
+
+from memos.configs.mem_cube import GeneralMemCubeConfig
+from memos.configs.mem_os import MOSConfig
+from memos.mem_cube.general import GeneralMemCube
+from memos.mem_os.main import MOS
+
+
+load_dotenv()
+openapi_config = {
+    "model_name_or_path": "gpt-4o",
+    "top_k": 50,
+    "remove_think_prefix": True,
+    "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
+    "api_base": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
+}
+neo4j_uri = os.getenv("NEO4J_URI", "bolt://47.117.41.207:7687")
+db_name = "stx-mmlongbench-004"
+
+doc_paths = [
+    f
+    for f in os.listdir("evaluation/data/mmlongbench/documents")
+    if os.path.isfile(os.path.join("evaluation/data/mmlongbench/documents", f))
+]
+
+with open("evaluation/data/mmlongbench/samples.json") as f:
+    samples = json.load(f)
+
+RESULTS_PATH = "evaluation/data/mmlongbench/test_results.json"
+completed_pairs: set[tuple[str, str]] = set()
+
+
+def _load_existing_results():
+    global completed_pairs
+    if os.path.exists(RESULTS_PATH):
+        try:
+            with open(RESULTS_PATH, encoding="utf-8") as f:
+                existing = json.load(f)
+            for r in existing:
+                did = r.get("doc_id")
+                q = r.get("question")
+                if did and q:
+                    completed_pairs.add((did, q))
+            return existing
+        except Exception:
+            return []
+    return []
+
+
+def _doc_has_pending(doc_file: str) -> bool:
+    for s in samples:
+        if s.get("doc_id") == doc_file and (doc_file, s.get("question")) not in completed_pairs:
+            return True
+    return False
+
+
+def get_user_name(doc_file):
+    csv_path = "evaluation/data/mmlongbench/user_doc_map.csv"
+    if os.path.exists(csv_path):
+        with open(csv_path, newline="", encoding="utf-8") as f:
+            reader = csv.reader(f)
+            for row in reader:
+                uid, path = row[0], row[1]
+                base = os.path.basename(path)
+                if base == doc_file or os.path.splitext(base)[0] == os.path.splitext(doc_file)[0]:
+                    return uid
+    return ""
+
+
+def process_doc(doc_file):
+    user_name = get_user_name(doc_file)
+    print(user_name, doc_file)
+    config = {
+        "user_id": user_name,
+        "chat_model": {
+            "backend": "openai",
+            "config": openapi_config,
+        },
+        "mem_reader": {
+            "backend": "simple_struct",
+            "config": {
+                "llm": {"backend": "openai", "config": openapi_config},
+                "embedder": {
+                    "backend": "universal_api",
+                    "config": {
+                        "provider": "openai",
+                        "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
+                        "model_name_or_path": "text-embedding-3-large",
+                        "base_url": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
+                    },
+                },
+                "chunker": {
+                    "backend": "sentence",
+                    "config": {
+                        "tokenizer_or_token_counter": "gpt2",
+                        "chunk_size": 512,
+                        "chunk_overlap": 128,
+                        "min_sentences_per_chunk": 1,
+                    },
+                },
+            },
+        },
+        "max_turns_window": 20,
+        "top_k": 5,
+        "enable_textual_memory": True,
+        "enable_activation_memory": False,
+        "enable_parametric_memory": False,
+    }
+    mos_config = MOSConfig(**config)
+    mos = MOS(mos_config)
+
+    mem_cube_config = GeneralMemCubeConfig.model_validate(
+        {
+            "user_id": user_name,
+            "cube_id": user_name,
+            "text_mem": {
+                "backend": "tree_text",
+                "config": {
+                    "extractor_llm": {"backend": "openai", "config": openapi_config},
+                    "dispatcher_llm": {"backend": "openai", "config": openapi_config},
+                    "graph_db": {
+                        "backend": "neo4j",
+                        "config": {
+                            "uri": neo4j_uri,
+                            "user": "neo4j",
+                            "password": "iaarlichunyu",
+                            "db_name": db_name,
+                            "user_name": user_name,
+                            "use_multi_db": False,
+                            "auto_create": True,
+                            "embedding_dimension": 3072,
+                        },
+                    },
+                    "embedder": {
+                        "backend": "universal_api",
+                        "config": {
+                            "provider": "openai",
+                            "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
+                            "model_name_or_path": "text-embedding-3-large",
+                            "base_url": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
+                        },
+                    },
+                    "reorganize": False,
+                },
+            },
+            "act_mem": {},
+            "para_mem": {},
+        }
+    )
+    mem_cube = GeneralMemCube(mem_cube_config)
+
+    temp_dir = "tmp/" + doc_file
+    if not os.path.exists(temp_dir) or not os.listdir(temp_dir):
+        mem_cube.dump(temp_dir)
+
+    mos.register_mem_cube(temp_dir, mem_cube_id=user_name)
+
+    with open("evaluation/scripts/mmlongbench/eval/prompt_for_answer_extraction.md") as f:
+        prompt = f.read()
+
+    samples_res = []
+    doc_samples = [s for s in samples if s.get("doc_id") == doc_file]
+    if len(doc_samples) == 0:
+        return []
+
+    for sample in tqdm(doc_samples, desc=f"Processing {doc_file}"):
+        if (doc_file, sample.get("question")) in completed_pairs:
+            continue
+        messages = sample["question"]
+        try_cnt, is_success = 0, False
+
+        while True:
+            try:
+                mos.clear_messages()
+                response = mos.chat(messages, user_name)
+                is_success = True
+            except Exception as e:
+                print(f"[{doc_file}] Error:", e)
+                traceback.print_exc()
+                try_cnt += 1
+                response = "Failed"
+            if is_success or try_cnt > 5:
+                break
+
+        sample["response"] = response
+        extracted_res = extract_answer(sample["question"], response, prompt)
+        sample["extracted_res"] = extracted_res
+
+        pred_ans = extracted_res.split("Answer format:")[0].split("Extracted answer:")[1].strip()
+        score = eval_score(sample["answer"], pred_ans, sample["answer_format"])
+
+        sample["pred"] = pred_ans
+        sample["score"] = score
+        samples_res.append(sample)
+
+        print("--------------------------------------")
+        print(f"Question: {sample['question']}")
+        print(f"Response: {sample['response']}")
+        print(f"Ground true: {sample['answer']}\tPred: {sample['pred']}\tScore: {sample['score']}")
+
+    return samples_res
+
+
+if __name__ == "__main__":
+    results = _load_existing_results()
+    total_samples = len(samples)
+    processed_samples = len(completed_pairs)
+    pending_samples = total_samples - processed_samples
+    sample_doc_ids = [s.get("doc_id") for s in samples if s.get("doc_id")]
+    all_docs_in_samples = set(sample_doc_ids)
+    processed_docs = {d for d, _ in completed_pairs}
+    with ThreadPoolExecutor(max_workers=4) as executor:
+        pending_docs = [d for d in doc_paths if _doc_has_pending(d)]
+        print("\n" + "=" * 80)
+        print("📊 评测进度统计")
+        print("=" * 80)
+        print(f"✅ 已加载历史结果: {len(results)} 条")
+        print(f"📂 数据集总样本: {total_samples}")
+        print(f"🧪 已完成样本: {processed_samples}")
+        print(f"⏳ 待处理样本: {pending_samples}")
+        print(f"📄 数据集中总文档: {len(all_docs_in_samples)}")
+        print(f"✔️ 已完成文档: {len(processed_docs)}")
+        print(f"➡️ 待处理文档(本次将运行): {len(pending_docs)}")
+        print("=" * 80 + "\n")
+        future_to_doc = {
+            executor.submit(process_doc, doc_file): doc_file for doc_file in pending_docs
+        }
+
+        for future in as_completed(future_to_doc):
+            doc_file = future_to_doc[future]
+            try:
+                res = future.result()
+                results.extend(res)
+
+                if len(res) > 0:
+                    acc, f1 = eval_acc_and_f1(results)
+                    print()
+                    print(f"Avg acc: {acc}")
+                    print(f"Avg f1: {f1}")
+
+                with open(RESULTS_PATH, "w", encoding="utf-8") as f:
+                    json.dump(results, f, ensure_ascii=False, indent=2)
+            except Exception as e:
+                print(f"[{doc_file}] failed with {e}")
+
+    acc, f1 = eval_acc_and_f1(results)
+    print("--------------------------------------")
+    print(f"Final avg acc: {acc}")
+    print(f"Final avg f1: {f1}")
+
+    show_results(
+        results,
+        show_path=re.sub(r"\.json$", ".txt", "evaluation/data/mmlongbench/test_results_report.txt"),
+    )
diff --git a/evaluation/scripts/mmlongbench/import_docs.py b/evaluation/scripts/mmlongbench/import_docs.py
new file mode 100644
index 000000000..540c8f960
--- /dev/null
+++ b/evaluation/scripts/mmlongbench/import_docs.py
@@ -0,0 +1,88 @@
+import asyncio
+import os
+import traceback
+import uuid
+
+from memos import log
+from memos.configs.mem_reader import SimpleStructMemReaderConfig
+from memos.configs.memory import TreeTextMemoryConfig
+from memos.mem_reader.simple_struct import SimpleStructMemReader
+from memos.memories.textual.tree import TreeTextMemory
+
+
+logger = log.get_logger(__name__)
+db_name = "stx-mmlongbench-004"
+# Create a memory reader instance
+reader_config = SimpleStructMemReaderConfig.from_json_file(
+    "examples/data/config/simple_struct_reader_config.json"
+)
+reader = SimpleStructMemReader(reader_config)
+
+tree_config = TreeTextMemoryConfig.from_json_file(
+    "examples/data/config/tree_config_shared_database.json"
+)
+tree_config.graph_db.config.db_name = db_name
+# Processing Documents
+existing_names = {
+    d for d in os.listdir("ppt_test_result") if os.path.isdir(os.path.join("ppt_test_result", d))
+}
+doc_paths = []
+for f in os.listdir("evaluation/data/mmlongbench/documents"):
+    fp = os.path.join("evaluation/data/mmlongbench/documents", f)
+    if os.path.isfile(fp):
+        name = os.path.splitext(f)[0]
+        if name in existing_names:
+            continue
+        doc_paths.append(fp)
+
+print("existing_names length:", len(existing_names))
+print("doc_paths length:", len(doc_paths))
+
+
+async def process_doc(doc_path):
+    print(f"🔄 Processing document: {doc_path}")
+    # Generate random user id: 'user_' + random short hex
+    user_id = "user_" + uuid.uuid4().hex[:8]
+    # Persist mapping between user_id and doc_path
+    try:
+        os.makedirs("evaluation/data/mmlongbench", exist_ok=True)
+        with open("evaluation/data/mmlongbench/user_doc_map.csv", "a", encoding="utf-8") as f:
+            f.write(f"{user_id},{doc_path}\n")
+    except Exception as e:
+        logger.error(f"Failed to write user-doc mapping: {e}")
+
+    tree_config.graph_db.config.user_name = user_id
+    my_tree_textual_memory = TreeTextMemory(tree_config)
+    doc_memory = await reader.get_memory(
+        [doc_path], "doc", info={"user_id": user_id, "session_id": "session_" + str(uuid.uuid4())}
+    )
+
+    count = 0
+    for m_list in doc_memory:
+        count += len(m_list)
+        my_tree_textual_memory.add(m_list)
+    print("total memories: ", count)
+
+    return doc_path
+
+
+async def main():
+    batch_size = 4
+    for i in range(0, len(doc_paths), batch_size):
+        batch = doc_paths[i : i + batch_size]
+        print(f"🚀 Starting batch {i // batch_size + 1} with {len(batch)} docs")
+
+        tasks = [process_doc(p) for p in batch]
+        results = await asyncio.gather(*tasks, return_exceptions=True)
+
+        for p, result in zip(batch, results, strict=False):
+            if isinstance(result, Exception):
+                print(f"❌ Error processing {p}: {result}")
+                tb_text = "".join(traceback.TracebackException.from_exception(result).format())
+                print(tb_text)
+            else:
+                print(f"✅ Finished {result}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/evaluation/scripts/mmlongbench/models/__init__.py b/evaluation/scripts/mmlongbench/models/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/evaluation/scripts/mmlongbench/models/minicpm_llama3.py b/evaluation/scripts/mmlongbench/models/minicpm_llama3.py
new file mode 100644
index 000000000..7f6d4b743
--- /dev/null
+++ b/evaluation/scripts/mmlongbench/models/minicpm_llama3.py
@@ -0,0 +1,56 @@
+import torch
+
+from PIL import Image
+from transformers import AutoModel, AutoTokenizer
+
+
+def init_model(cache_path):
+    model_path = (
+        cache_path
+        if (cache_path is not None and cache_path != "None")
+        else "openbmb/MiniCPM-Llama3-V-2_5"
+    )
+    model = AutoModel.from_pretrained(
+        model_path,
+        torch_dtype=torch.bfloat16,
+        low_cpu_mem_usage=True,
+        trust_remote_code=True,
+        device_map="auto",
+    ).eval()
+
+    tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+    model.tokenizer = tokenizer
+    return model
+
+
+def get_response_concat(model, question, image_path_list, max_new_tokens=1024, temperature=1.0):
+    msgs = []
+    system_prompt = "Answer in detail."
+    if system_prompt:
+        msgs.append({"type": "text", "value": system_prompt})
+    if isinstance(image_path_list, list):
+        msgs.extend([{"type": "image", "value": p} for p in image_path_list])
+    else:
+        msgs = [{"type": "image", "value": image_path_list}]
+    msgs.append({"type": "text", "value": question})
+
+    content = []
+    for x in msgs:
+        if x["type"] == "text":
+            content.append(x["value"])
+        elif x["type"] == "image":
+            image = Image.open(x["value"]).convert("RGB")
+            content.append(image)
+    msgs = [{"role": "user", "content": content}]
+
+    with torch.cuda.amp.autocast():
+        res = model.chat(
+            msgs=msgs,
+            context=None,
+            image=None,
+            max_new_tokens=max_new_tokens,
+            temperature=temperature,
+            do_sample=temperature != 0.0,
+            tokenizer=model.tokenizer,
+        )
+    return res
diff --git a/evaluation/scripts/mmlongbench/multimodal_test.py b/evaluation/scripts/mmlongbench/multimodal_test.py
new file mode 100644
index 000000000..929215229
--- /dev/null
+++ b/evaluation/scripts/mmlongbench/multimodal_test.py
@@ -0,0 +1,185 @@
+import os
+import shutil
+
+from dotenv import load_dotenv
+
+from memos.configs.mem_cube import GeneralMemCubeConfig
+from memos.configs.mem_os import MOSConfig
+from memos.mem_cube.general import GeneralMemCube
+from memos.mem_os.main import MOS
+
+
+load_dotenv()
+
+db_name = "stx-mmlongbench-002"
+user_id = "user_dc812220"
+
+# 1.1 Set openai config
+openapi_config = {
+    "model_name_or_path": "gpt-4o",
+    "top_k": 50,
+    "remove_think_prefix": True,
+    "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
+    "api_base": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
+}
+# 1.2 Set neo4j config
+neo4j_uri = os.getenv("NEO4J_URI", "bolt://localhost:7687")
+
+# 1.3  Create MOS Config
+config = {
+    "user_id": user_id,
+    "chat_model": {
+        "backend": "openai",
+        "config": openapi_config,
+    },
+    "mem_reader": {
+        "backend": "simple_struct",
+        "config": {
+            "llm": {
+                "backend": "openai",
+                "config": openapi_config,
+            },
+            "embedder": {
+                "backend": "universal_api",
+                "config": {
+                    "provider": "openai",
+                    "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
+                    "model_name_or_path": "text-embedding-3-large",
+                    "base_url": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
+                },
+            },
+            "chunker": {
+                "backend": "sentence",
+                "config": {
+                    "tokenizer_or_token_counter": "gpt2",
+                    "chunk_size": 512,
+                    "chunk_overlap": 128,
+                    "min_sentences_per_chunk": 1,
+                },
+            },
+        },
+    },
+    "max_turns_window": 20,
+    "top_k": 5,
+    "enable_textual_memory": True,
+    "enable_activation_memory": False,
+    "enable_parametric_memory": False,
+}
+
+mos_config = MOSConfig(**config)
+mos = MOS(mos_config)
+
+config = GeneralMemCubeConfig.model_validate(
+    {
+        "user_id": user_id,
+        "cube_id": f"{user_id}",
+        "text_mem": {
+            "backend": "tree_text",
+            "config": {
+                "extractor_llm": {
+                    "backend": "openai",
+                    "config": openapi_config,
+                },
+                "dispatcher_llm": {
+                    "backend": "openai",
+                    "config": openapi_config,
+                },
+                "graph_db": {
+                    "backend": "neo4j",
+                    "config": {
+                        "uri": neo4j_uri,
+                        "user": "neo4j",
+                        "password": "iaarlichunyu",
+                        "db_name": db_name,
+                        "user_name": user_id,
+                        "use_multi_db": False,
+                        "auto_create": True,
+                        "embedding_dimension": 3072,
+                    },
+                },
+                "embedder": {
+                    "backend": "universal_api",
+                    "config": {
+                        "provider": "openai",
+                        "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
+                        "model_name_or_path": "text-embedding-3-large",
+                        "base_url": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
+                    },
+                },
+                "reorganize": False,
+            },
+        },
+        "act_mem": {},
+        "para_mem": {},
+    },
+)
+
+
+# Filter out embedding fields, keeping only necessary fields
+def filter_memory_data(memories_data):
+    filtered_data = {}
+    for key, value in memories_data.items():
+        if key == "text_mem":
+            filtered_data[key] = []
+            for mem_group in value:
+                # Check if it's the new data structure (list of TextualMemoryItem objects)
+                if "memories" in mem_group and isinstance(mem_group["memories"], list):
+                    # New data structure: directly a list of TextualMemoryItem objects
+                    filtered_memories = []
+                    for memory_item in mem_group["memories"]:
+                        # Create filtered dictionary
+                        filtered_item = {
+                            "id": memory_item.id,
+                            "memory": memory_item.memory,
+                            "metadata": {},
+                        }
+                        # Filter metadata, excluding embedding
+                        if hasattr(memory_item, "metadata") and memory_item.metadata:
+                            for attr_name in dir(memory_item.metadata):
+                                if not attr_name.startswith("_") and attr_name != "embedding":
+                                    attr_value = getattr(memory_item.metadata, attr_name)
+                                    if not callable(attr_value):
+                                        filtered_item["metadata"][attr_name] = attr_value
+                        filtered_memories.append(filtered_item)
+
+                    filtered_group = {
+                        "cube_id": mem_group.get("cube_id", ""),
+                        "memories": filtered_memories,
+                    }
+                    filtered_data[key].append(filtered_group)
+                else:
+                    # Old data structure: dictionary with nodes and edges
+                    filtered_group = {
+                        "memories": {"nodes": [], "edges": mem_group["memories"].get("edges", [])}
+                    }
+                    for node in mem_group["memories"].get("nodes", []):
+                        filtered_node = {
+                            "id": node.get("id"),
+                            "memory": node.get("memory"),
+                            "metadata": {
+                                k: v
+                                for k, v in node.get("metadata", {}).items()
+                                if k != "embedding"
+                            },
+                        }
+                        filtered_group["memories"]["nodes"].append(filtered_node)
+                    filtered_data[key].append(filtered_group)
+        else:
+            filtered_data[key] = value
+    return filtered_data
+
+
+mem_cube = GeneralMemCube(config)
+
+temp_dir = f"/tmp/{user_id}"
+if os.path.exists(temp_dir):
+    shutil.rmtree(temp_dir)
+mem_cube.dump(temp_dir)
+mos.register_mem_cube(temp_dir, mem_cube_id=user_id)
+
+
+print("start answering...")
+user_query = "图8美股变化的影响是什么"
+print(f"👤 User query: {user_query}")
+response = mos.chat(user_query)
+print(f"🤖 Response: {response}")
diff --git a/evaluation/scripts/xinyu/eval/__init__.py b/evaluation/scripts/xinyu/eval/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/evaluation/scripts/xinyu/eval/eval_score_llm.py b/evaluation/scripts/xinyu/eval/eval_score_llm.py
new file mode 100644
index 000000000..f5764ce39
--- /dev/null
+++ b/evaluation/scripts/xinyu/eval/eval_score_llm.py
@@ -0,0 +1,279 @@
+import os
+import re
+import traceback
+
+from collections import defaultdict
+from math import isclose
+
+from memos.configs.mem_os import MOSConfig
+from memos.llms.factory import LLMFactory
+
+
+openapi_config = {
+    "model_name_or_path": "gpt-5-nano",
+    "top_k": 50,
+    "remove_think_prefix": True,
+    "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
+    "api_base": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
+}
+config = {
+    "user_id": "user_name",
+    "chat_model": {
+        "backend": "openai",
+        "config": openapi_config,
+    },
+    "mem_reader": {
+        "backend": "simple_struct",
+        "config": {
+            "llm": {"backend": "openai", "config": openapi_config},
+            "embedder": {
+                "backend": "universal_api",
+                "config": {
+                    "provider": "openai",
+                    "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
+                    "model_name_or_path": "text-embedding-3-large",
+                    "base_url": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
+                },
+            },
+            "chunker": {
+                "backend": "sentence",
+                "config": {
+                    "tokenizer_or_token_counter": "gpt2",
+                    "chunk_size": 512,
+                    "chunk_overlap": 128,
+                    "min_sentences_per_chunk": 1,
+                },
+            },
+        },
+    },
+    "max_turns_window": 20,
+    "top_k": 5,
+    "enable_textual_memory": True,
+    "enable_activation_memory": False,
+    "enable_parametric_memory": False,
+}
+mos_config = MOSConfig(**config)
+chat_llm = LLMFactory.from_config(mos_config.chat_model)
+
+
+def is_float_equal(
+    reference, prediction, include_percentage: bool = False, is_close: float = False
+) -> bool:
+    def get_precision(gt_ans: float) -> int:
+        precision = 3
+        if "." in str(gt_ans):
+            precision = len(str(gt_ans).split(".")[-1])
+        return precision
+
+    reference = float(str(reference).strip().rstrip("%").strip())
+    try:
+        prediction = float(str(prediction).strip().rstrip("%").strip())
+    except Exception:
+        return False
+
+    gt_result = [reference / 100, reference, reference * 100] if include_percentage else [reference]
+    for item in gt_result:
+        try:
+            if is_close and isclose(item, prediction, rel_tol=0.01):
+                return True
+            precision = max(min(get_precision(prediction), get_precision(item)), 2)
+            if round(prediction, precision) == round(item, precision):
+                return True
+        except Exception:
+            continue
+    return False
+
+
+def get_clean_string(s):
+    s = str(s).lower().strip()
+
+    for suffix in ["mile", "miles", "million"]:
+        if s.endswith(suffix):
+            s = s[: -len(suffix)].strip()
+
+    s = re.sub(r"\s*\([^)]*\)", "", s).strip()
+    s = re.sub(r"^['\"]|['\"]$", "", s).strip()
+    s = s.lstrip("$").rstrip("%").strip()
+
+    return s
+
+
+def is_exact_match(s):
+    flag = False
+    # Website
+    if "https://" in s:
+        flag = True
+    # code file
+    if s.endswith((".py", ".ipynb")) or s.startswith("page"):
+        flag = True
+    # telephone number
+    if re.fullmatch(r"\b\d+(-\d+|\s\d+)?\b", s):
+        flag = True
+    # time
+    if "a.m." in s or "p.m." in s:
+        flag = True
+    # YYYY-MM-DD
+    if re.fullmatch(r"\b\d{4}[-\s]\d{2}[-\s]\d{2}\b", s):
+        flag = True
+    # YYYY-MM
+    if re.fullmatch(r"\b\d{4}[-\s]\d{2}\b", s):
+        flag = True
+    # Email address
+    if re.fullmatch(r"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}", s):
+        flag = True
+    return flag
+
+
+def isfloat(num):
+    try:
+        float(num)
+        return True
+    except ValueError:
+        return False
+
+
+def eval_score(question, gt, pred):
+    prompt = """
+        你是一个评委，根据问题和标准答案对学生的答案进行打分。打分规则如下：
+
+        完全不对（0分）：
+        学生答案与问题无关，未展示出任何相关概念或知识。
+        对了一部分（0.5分）：
+        学生答案提供了一些相关信息，但未能直接回答问题。
+        答案中包含部分正确内容，但缺乏关键信息，导致整体理解不清。
+        基本正确（0.7分）：
+        学生答案提供了大部分关键信息，不过依然距离标准答案有一定缺失。
+        答案中包含部分关键内容，但缺乏部分信息，导致不够完整。
+        完全正确（1分）：
+        学生答案准确地回答了问题，涵盖所有关键信息。
+        表达清晰，逻辑合理，直接且有效地回应了问题。
+
+        问题：{}
+
+        标准答案：{}
+
+        学生答案：{}
+        """
+
+    max_try = 20
+    try_i = 0
+    while try_i < max_try:
+        try:
+            llm_input_prompt_score = (
+                prompt.format(question, gt, pred)
+                + """请返回给我一个json：
+            {
+                "分数": 1,
+                "理由": "xxxx"
+            }"""
+            )
+            score = chat_llm.generate(
+                [
+                    {"role": "user", "content": llm_input_prompt_score},
+                ]
+            )
+
+            print(f"score: {score}")
+            score_real = eval(score.replace("json", "").replace("\n", "").replace("```", ""))
+            return float(score_real["分数"])
+        except Exception:
+            traceback.print_exc()
+            print(f"trying num {try_i}")
+            try_i += 1
+    return -1
+
+
+def eval_acc_and_f1(samples):
+    evaluated_samples = [sample for sample in samples if "score" in sample]
+    if not evaluated_samples:
+        return 0.0, 0.0
+
+    acc = sum([sample["score"] for sample in evaluated_samples]) / len(evaluated_samples)
+    try:
+        recall = sum(
+            [
+                sample["score"]
+                for sample in evaluated_samples
+                if sample["answer"] != "Not answerable"
+            ]
+        ) / len([sample for sample in evaluated_samples if sample["answer"] != "Not answerable"])
+        precision = sum(
+            [
+                sample["score"]
+                for sample in evaluated_samples
+                if sample["answer"] != "Not answerable"
+            ]
+        ) / len([sample for sample in evaluated_samples if sample["pred"] != "Not answerable"])
+        f1 = 2 * recall * precision / (recall + precision) if (recall + precision) > 0.0 else 0.0
+    except Exception:
+        f1 = 0.0
+
+    return acc, f1
+
+
+def show_results(samples, show_path=None):
+    for sample in samples:
+        sample["evidence_pages"] = eval(sample["evidence_pages"])
+        sample["evidence_sources"] = eval(sample["evidence_sources"])
+
+    with open(show_path, "w") as f:
+        acc, f1 = eval_acc_and_f1(samples)
+        f.write(f"Overall Acc: {acc} | Question Number: {len(samples)}\n")
+        f.write(f"Overall F1-score: {f1} | Question Number: {len(samples)}\n")
+        f.write("-----------------------\n")
+
+        acc_single_page, _ = eval_acc_and_f1(
+            [sample for sample in samples if len(sample["evidence_pages"]) == 1]
+        )
+        acc_multi_page, _ = eval_acc_and_f1(
+            [
+                sample
+                for sample in samples
+                if len(sample["evidence_pages"]) != 1 and sample["answer"] != "Not answerable"
+            ]
+        )
+        acc_neg, _ = eval_acc_and_f1(
+            [sample for sample in samples if sample["answer"] == "Not answerable"]
+        )
+
+        f.write(
+            "Single-page | Accuracy: {} | Question Number: {}\n".format(
+                acc_single_page,
+                len([sample for sample in samples if len(sample["evidence_pages"]) == 1]),
+            )
+        )
+        f.write(
+            "Cross-page | Accuracy: {} | Question Number: {}\n".format(
+                acc_multi_page,
+                len(
+                    [
+                        sample
+                        for sample in samples
+                        if len(sample["evidence_pages"]) != 1
+                        and sample["answer"] != "Not answerable"
+                    ]
+                ),
+            )
+        )
+        f.write(
+            "Unanswerable | Accuracy: {} | Question Number: {}\n".format(
+                acc_neg, len([sample for sample in samples if sample["answer"] == "Not answerable"])
+            )
+        )
+        f.write("-----------------------\n")
+
+        source_sample_dict, document_type_dict = defaultdict(list), defaultdict(list)
+        for sample in samples:
+            for answer_source in sample["evidence_sources"]:
+                source_sample_dict[answer_source].append(sample)
+            document_type_dict[sample["doc_type"]].append(sample)
+        for type, sub_samples in source_sample_dict.items():
+            f.write(
+                f"Evidence Sources: {type} | Accuracy: {eval_acc_and_f1(sub_samples)[0]} | Question Number: {len(sub_samples)}\n"
+            )
+
+        f.write("-----------------------\n")
+        for type, sub_samples in document_type_dict.items():
+            f.write(
+                f"Document Type: {type} | Accuracy: {eval_acc_and_f1(sub_samples)[0]} | Question Number: {len(sub_samples)}\n"
+            )
diff --git a/evaluation/scripts/xinyu/eval_docs.py b/evaluation/scripts/xinyu/eval_docs.py
new file mode 100644
index 000000000..03a333201
--- /dev/null
+++ b/evaluation/scripts/xinyu/eval_docs.py
@@ -0,0 +1,228 @@
+import csv
+import json
+import os
+import re
+import traceback
+
+from concurrent.futures import ThreadPoolExecutor, as_completed
+
+from dotenv import load_dotenv
+
+from evaluation.scripts.mmlongbench.eval.extract_answer import extract_answer
+from evaluation.scripts.xinyu.eval.eval_score_llm import eval_acc_and_f1, eval_score, show_results
+from memos.configs.mem_cube import GeneralMemCubeConfig
+from memos.configs.mem_os import MOSConfig
+from memos.mem_cube.general import GeneralMemCube
+from memos.mem_os.main import MOS
+
+
+load_dotenv()
+openapi_config = {
+    "model_name_or_path": "gpt-4o",
+    "top_k": 50,
+    "remove_think_prefix": True,
+    "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
+    "api_base": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
+}
+neo4j_uri = os.getenv("NEO4J_URI", "bolt://47.117.41.207:7687")
+db_name = "stx-mmlongbench-003"
+doc_paths = [
+    f
+    for f in os.listdir("evaluation/data/xinyu/documents")
+    if os.path.isfile(os.path.join("evaluation/data/xinyu/documents", f))
+]
+
+with open("evaluation/data/xinyu/all_samples_with_gt.json") as f:
+    samples = json.load(f)
+
+
+def get_user_name(doc_file):
+    csv_path = "evaluation/data/xinyu/user_doc_map.csv"
+    if os.path.exists(csv_path):
+        with open(csv_path, newline="", encoding="utf-8") as f:
+            reader = csv.reader(f)
+            for row in reader:
+                uid, path = row[0], row[1]
+                base = os.path.basename(path)
+                if base == doc_file or os.path.splitext(base)[0] == os.path.splitext(doc_file)[0]:
+                    return uid
+    return ""
+
+
+def process_doc(doc_file):
+    user_name = get_user_name(doc_file)
+    print(user_name, doc_file)
+    config = {
+        "user_id": user_name,
+        "chat_model": {
+            "backend": "openai",
+            "config": openapi_config,
+        },
+        "mem_reader": {
+            "backend": "simple_struct",
+            "config": {
+                "llm": {"backend": "openai", "config": openapi_config},
+                "embedder": {
+                    "backend": "universal_api",
+                    "config": {
+                        "provider": "openai",
+                        "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
+                        "model_name_or_path": "text-embedding-3-large",
+                        "base_url": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
+                    },
+                },
+                "chunker": {
+                    "backend": "sentence",
+                    "config": {
+                        "tokenizer_or_token_counter": "gpt2",
+                        "chunk_size": 512,
+                        "chunk_overlap": 128,
+                        "min_sentences_per_chunk": 1,
+                    },
+                },
+            },
+        },
+        "max_turns_window": 20,
+        "top_k": 5,
+        "enable_textual_memory": True,
+        "enable_activation_memory": False,
+        "enable_parametric_memory": False,
+    }
+    mos_config = MOSConfig(**config)
+    mos = MOS(mos_config)
+
+    mem_cube_config = GeneralMemCubeConfig.model_validate(
+        {
+            "user_id": user_name,
+            "cube_id": user_name,
+            "text_mem": {
+                "backend": "tree_text",
+                "config": {
+                    "extractor_llm": {"backend": "openai", "config": openapi_config},
+                    "dispatcher_llm": {"backend": "openai", "config": openapi_config},
+                    "graph_db": {
+                        "backend": "neo4j",
+                        "config": {
+                            "uri": neo4j_uri,
+                            "user": "neo4j",
+                            "password": "iaarlichunyu",
+                            "db_name": db_name,
+                            "user_name": user_name,
+                            "use_multi_db": False,
+                            "auto_create": True,
+                            "embedding_dimension": 3072,
+                        },
+                    },
+                    "embedder": {
+                        "backend": "universal_api",
+                        "config": {
+                            "provider": "openai",
+                            "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
+                            "model_name_or_path": "text-embedding-3-large",
+                            "base_url": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
+                        },
+                    },
+                    "reorganize": False,
+                },
+            },
+            "act_mem": {},
+            "para_mem": {},
+        }
+    )
+    mem_cube = GeneralMemCube(mem_cube_config)
+
+    temp_dir = os.path.join("tmp", doc_file)
+
+    if (not os.path.exists(temp_dir)) or (not os.listdir(temp_dir)):
+        mem_cube.dump(temp_dir)
+
+    mos.register_mem_cube(temp_dir, mem_cube_id=user_name)
+
+    with open("evaluation/scripts/mmlongbench/eval/prompt_for_answer_extraction.md") as f:
+        prompt = f.read()
+
+    samples_res = []
+    doc_samples = [s for s in samples if s.get("doc_id") == doc_file]
+
+    if len(doc_samples) == 0:
+        return []
+
+    sample = doc_samples[0]
+    question_list = sample["question"]
+    answer_list = sample["answer"]
+
+    for idx, question in enumerate(question_list):
+        gt = answer_list.get(str(idx))
+
+        try_cnt, is_success = 0, False
+        while True:
+            try:
+                mos.clear_messages()
+                response = mos.chat(question, user_name)
+                is_success = True
+            except Exception as e:
+                print(f"[{doc_file}] Error:", e)
+                traceback.print_exc()
+                try_cnt += 1
+                response = "Failed"
+            if is_success or try_cnt > 5:
+                break
+
+        sample_item = dict(sample)
+        sample_item["question"] = question
+        sample_item["answer"] = gt
+        sample_item["response"] = response
+
+        extracted_res = extract_answer(sample_item["question"], response, prompt)
+        sample_item["extracted_res"] = extracted_res
+
+        print("--------------------------------------")
+        pred_ans = extracted_res.split("Answer format:")[0].split("Extracted answer:")[1].strip()
+        score = eval_score(question, gt, response)
+
+        sample_item["pred"] = pred_ans
+        sample_item["score"] = score
+        samples_res.append(sample_item)
+
+        print(f"Question: {question}")
+        print(f"Response: {sample_item['response']}")
+        print(f"Ground true: {gt}\tPred: {sample_item['pred']}\tScore: {sample_item['score']}")
+
+    print("samples_res length: ", len(samples_res))
+    return samples_res
+
+
+if __name__ == "__main__":
+    results = []
+
+    with ThreadPoolExecutor(max_workers=4) as executor:
+        future_to_doc = {executor.submit(process_doc, doc_file): doc_file for doc_file in doc_paths}
+
+        for future in as_completed(future_to_doc):
+            doc_file = future_to_doc[future]
+            try:
+                res = future.result()
+                results.extend(res)
+
+                if len(res) > 0:
+                    acc, f1 = eval_acc_and_f1(results)
+                    print()
+                    print(f"Avg acc: {acc}")
+                    print(f"Avg f1: {f1}")
+
+                with open("evaluation/data/xinyu/test_results.json", "w", encoding="utf-8") as f:
+                    json.dump(results, f, ensure_ascii=False, indent=2)
+
+            except Exception as e:
+                print(f"[{doc_file}] failed with {e}")
+                traceback.print_exc()
+
+    acc, f1 = eval_acc_and_f1(results)
+    print("--------------------------------------")
+    print(f"Final avg acc: {acc}")
+    print(f"Final avg f1: {f1}")
+
+    show_results(
+        results,
+        show_path=re.sub(r"\.json$", ".txt", "evaluation/data/xinyu/test_results_report.txt"),
+    )
diff --git a/evaluation/scripts/xinyu/import_docs.py b/evaluation/scripts/xinyu/import_docs.py
new file mode 100644
index 000000000..f9d2619a4
--- /dev/null
+++ b/evaluation/scripts/xinyu/import_docs.py
@@ -0,0 +1,85 @@
+import asyncio
+import os
+import traceback
+import uuid
+
+from memos import log
+from memos.configs.mem_reader import SimpleStructMemReaderConfig
+from memos.configs.memory import TreeTextMemoryConfig
+from memos.mem_reader.simple_struct import SimpleStructMemReader
+from memos.memories.textual.tree import TreeTextMemory
+
+
+logger = log.get_logger(__name__)
+db_name = "stx-mmlongbench-003"
+# Create a memory reader instance
+reader_config = SimpleStructMemReaderConfig.from_json_file(
+    "examples/data/config/simple_struct_reader_config.json"
+)
+reader = SimpleStructMemReader(reader_config)
+
+tree_config = TreeTextMemoryConfig.from_json_file(
+    "examples/data/config/tree_config_shared_database.json"
+)
+tree_config.graph_db.config.db_name = db_name
+# Processing Documents
+existing_names = {
+    d for d in os.listdir("ppt_test_result") if os.path.isdir(os.path.join("ppt_test_result", d))
+}
+doc_paths = []
+for f in os.listdir("evaluation/data/xinyu/documents"):
+    fp = os.path.join("evaluation/data/xinyu/documents", f)
+    if os.path.isfile(fp):
+        name = os.path.splitext(f)[0]
+        if name in existing_names:
+            continue
+        doc_paths.append(fp)
+
+
+async def process_doc(doc_path):
+    print(f"🔄 Processing document: {doc_path}")
+    doc_file = doc_path.split("/")[-1].rsplit(".", 1)[0]
+
+    # Generate random user id: 'user_' + random short hex
+    user_id = "user_" + uuid.uuid4().hex[:8]
+    # Persist mapping between user_id and doc_path
+    with open("evaluation/data/xinyu/user_doc_map.csv", "a", encoding="utf-8") as f:
+        f.write(f"{user_id},{doc_path}\n")
+
+    tree_config.graph_db.config.user_name = user_id
+    temp_dir = "tmp/" + doc_file
+    my_tree_textual_memory = TreeTextMemory(tree_config)
+    doc_memory = await reader.get_memory(
+        [doc_path], "doc", info={"user_id": user_id, "session_id": "session_" + str(uuid.uuid4())}
+    )
+
+    count = 0
+    for m_list in doc_memory:
+        count += len(m_list)
+        my_tree_textual_memory.add(m_list)
+    print("total memories: ", count)
+
+    my_tree_textual_memory.dump(temp_dir)
+    return doc_path
+
+
+async def main():
+    batch_size = 2
+    for i in range(0, len(doc_paths), batch_size):
+        batch = doc_paths[i : i + batch_size]
+        print(f"🚀 Starting batch {i // batch_size + 1} with {len(batch)} docs")
+
+        tasks = [process_doc(p) for p in batch]
+        results = await asyncio.gather(*tasks, return_exceptions=True)
+
+        for p, result in zip(batch, results, strict=False):
+            if isinstance(result, Exception):
+                print(f"❌ Error processing {p}: {result}")
+                tb_text = "".join(traceback.TracebackException.from_exception(result).format())
+                print(tb_text)
+            else:
+                print(f"✅ Finished {result}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

From d9d2f8cfec75a4ff0b813ebd7e56adbaebad0877 Mon Sep 17 00:00:00 2001
From: stx <31013941@qq.com>
Date: Thu, 27 Nov 2025 10:31:06 +0800
Subject: [PATCH 31/34] =?UTF-8?q?feat=EF=BC=9Aupdate=2011.27?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 evaluation/scripts/hotpot/hotpot_eval.py      | 205 +++++++++++-------
 evaluation/scripts/longbenchV2/import_data.py |  20 ++
 .../scripts/xinyu/eval/eval_score_llm.py      |  22 +-
 .../scripts/xinyu/eval/extract_answer.py      |  33 +++
 evaluation/scripts/xinyu/eval_docs.py         |  77 ++++++-
 evaluation/scripts/xinyu/import_docs.py       |   2 +
 6 files changed, 272 insertions(+), 87 deletions(-)
 create mode 100644 evaluation/scripts/longbenchV2/import_data.py
 create mode 100644 evaluation/scripts/xinyu/eval/extract_answer.py

diff --git a/evaluation/scripts/hotpot/hotpot_eval.py b/evaluation/scripts/hotpot/hotpot_eval.py
index 05ff52349..8be096e94 100644
--- a/evaluation/scripts/hotpot/hotpot_eval.py
+++ b/evaluation/scripts/hotpot/hotpot_eval.py
@@ -1,9 +1,11 @@
 import json
 import os
-import uuid
 
 from concurrent.futures import ThreadPoolExecutor, as_completed
 
+import datasets as ds_mod
+
+from datasets import load_dataset
 from dotenv import load_dotenv
 from tqdm import tqdm
 
@@ -17,26 +19,16 @@
 
 db_name = "stx-hotpot-001"
 
-
-user_name = str(uuid.uuid4())
-
-# 1.1 Set openai config
 openapi_config = {
-    "model_name_or_path": "gpt-4o-mini",
-    "temperature": 0.8,
-    "max_tokens": 1024,
-    "top_p": 0.9,
+    "model_name_or_path": "gpt-4o",
     "top_k": 50,
     "remove_think_prefix": True,
     "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
     "api_base": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
 }
-# 1.2 Set neo4j config
 neo4j_uri = os.getenv("NEO4J_URI", "bolt://localhost:7687")
 
-# 1.3  Create MOS Config
-config = {
-    "user_id": user_name,
+base_config = {
     "chat_model": {
         "backend": "openai",
         "config": openapi_config,
@@ -44,10 +36,7 @@
     "mem_reader": {
         "backend": "simple_struct",
         "config": {
-            "llm": {
-                "backend": "openai",
-                "config": openapi_config,
-            },
+            "llm": {"backend": "openai", "config": openapi_config},
             "embedder": {
                 "backend": "universal_api",
                 "config": {
@@ -75,12 +64,7 @@
     "enable_parametric_memory": False,
 }
 
-mos_config = MOSConfig(**config)
-# you can set PRO_MODE to True to enable CoT enhancement mos_config.PRO_MODE = True
-mos = MOS(mos_config)
-
 
-# Filter out embedding fields, keeping only necessary fields
 def filter_memory_data(memories_data):
     filtered_data = {}
     for key, value in memories_data.items():
@@ -134,56 +118,71 @@ def filter_memory_data(memories_data):
     return filtered_data
 
 
-config = GeneralMemCubeConfig.model_validate(
-    {
-        "user_id": user_name,
-        "cube_id": f"{user_name}",
-        "text_mem": {
-            "backend": "tree_text",
-            "config": {
-                "extractor_llm": {
-                    "backend": "openai",
-                    "config": openapi_config,
-                },
-                "dispatcher_llm": {
-                    "backend": "openai",
-                    "config": openapi_config,
-                },
-                "graph_db": {
-                    "backend": "neo4j",
-                    "config": {
-                        "uri": neo4j_uri,
-                        "user": "neo4j",
-                        "password": "iaarlichunyu",
-                        "db_name": db_name,
-                        "auto_create": True,
+def init_mos_and_cube(user_name: str) -> MOS:
+    cfg = dict(base_config)
+    cfg["user_id"] = user_name
+    mos_config = MOSConfig(**cfg)
+    mos = MOS(mos_config)
+    cube_conf = GeneralMemCubeConfig.model_validate(
+        {
+            "user_id": user_name,
+            "cube_id": f"{user_name}",
+            "text_mem": {
+                "backend": "tree_text",
+                "config": {
+                    "extractor_llm": {"backend": "openai", "config": openapi_config},
+                    "dispatcher_llm": {"backend": "openai", "config": openapi_config},
+                    "graph_db": {
+                        "backend": "neo4j",
+                        "config": {
+                            "uri": neo4j_uri,
+                            "user": "neo4j",
+                            "password": "iaarlichunyu",
+                            "db_name": db_name,
+                            "auto_create": True,
+                        },
                     },
-                },
-                "embedder": {
-                    "backend": "universal_api",
-                    "config": {
-                        "provider": "openai",
-                        "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
-                        "model_name_or_path": "text-embedding-3-large",
-                        "base_url": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
+                    "embedder": {
+                        "backend": "universal_api",
+                        "config": {
+                            "provider": "openai",
+                            "api_key": os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
+                            "model_name_or_path": "text-embedding-3-large",
+                            "base_url": os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
+                        },
                     },
+                    "reorganize": True,
                 },
-                "reorganize": True,
             },
-        },
-        "act_mem": {},
-        "para_mem": {},
-    },
-)
+            "act_mem": {},
+            "para_mem": {},
+        }
+    )
+    mem_cube = GeneralMemCube(cube_conf)
+    temp_dir = "tmp/" + user_name
+    if not os.path.exists(temp_dir) or not os.listdir(temp_dir):
+        mem_cube.dump(temp_dir)
+    mos.register_mem_cube(temp_dir, mem_cube_id=user_name)
+    return mos
+
 
-mem_cube = GeneralMemCube(config)
+data = load_dataset("hotpotqa/hotpot_qa", "distractor")
 
 
-mos.register_mem_cube(f"/tmp/{user_name}", mem_cube_id=user_name)
+def get_dataset_items(ds):
+    dataset_cls = getattr(ds_mod, "Dataset", None) if ds_mod else None
+    dataset_dict_cls = getattr(ds_mod, "DatasetDict", None) if ds_mod else None
+    if dataset_dict_cls and isinstance(ds, dataset_dict_cls):
+        split = ds.get("validation") or ds.get("train") or next(iter(ds.values()))
+        return [split[i] for i in range(len(split))]
+    if dataset_cls and isinstance(ds, dataset_cls):
+        return [ds[i] for i in range(len(ds))]
+    if isinstance(ds, list):
+        return ds
+    return []
 
 
-with open("evaluation/data/hotpot/hotpot_dev_distractor_v1.json") as f:
-    data = json.load(f)
+items = get_dataset_items(data)
 
 
 def build_context_text(context_list):
@@ -195,30 +194,76 @@ def build_context_text(context_list):
 
 
 def build_and_ask(item):
-    qid = item["_id"]
+    qid = item.get("_id") or item.get("id")
     question = item["question"]
-
-    for title, sentences in item["context"]:
-        text = " ".join(s.strip() for s in sentences if s.strip())
-        memory_content = f"{title}: {text}"
-        mos.add(memory_content=memory_content)
-
-    answer = mos.chat(question).strip()
+    mos = init_mos_and_cube(qid)
+    for entry in item["context"]:
+        if isinstance(entry, list) and len(entry) >= 2:
+            title, sentences = entry[0], entry[1]
+        elif isinstance(entry, dict):
+            title = entry.get("title", "")
+            sentences = entry.get("sentences", [])
+        else:
+            continue
+        text = " ".join(s.strip() for s in sentences if isinstance(s, str) and s.strip())
+        if title or text:
+            mos.add(memory_content=f"{title}: {text}")
+
+    answer = mos.chat(question, qid).strip()
+    print("question:", question)
+    print("answer:", answer)
     return qid, answer
 
 
 pred_answers = {}
-
-with ThreadPoolExecutor(max_workers=5) as executor:
-    futures = {executor.submit(build_and_ask, item): item for item in data}
+output_dir = "evaluation/data/hotpot/output"
+os.makedirs(output_dir, exist_ok=True)
+pred_path = os.path.join(output_dir, "dev_distractor_pred.json")
+gold_path = os.path.join(output_dir, "dev_distractor_gold.json")
+
+
+def write_gold(items_list):
+    try:
+        with open(gold_path, "w", encoding="utf-8") as f:
+            json.dump(items_list, f, ensure_ascii=False)
+    except Exception:
+        pass
+
+
+def run_eval():
+    try:
+        import importlib.util
+
+        spec = importlib.util.spec_from_file_location(
+            "hotpot_eval_v1", "evaluation/scripts/hotpot/hotpot_evaluate_v1.py"
+        )
+        m = importlib.util.module_from_spec(spec)
+        spec.loader.exec_module(m)
+        print("评估分数:")
+        m.eval(pred_path, gold_path)
+    except Exception as e:
+        print("评估失败:", e)
+
+
+write_gold(items)
+processed = 0
+interval = 200
+print("开始评估，总样本:", len(items))
+with ThreadPoolExecutor(max_workers=4) as executor:
+    futures = {executor.submit(build_and_ask, item): idx for idx, item in enumerate(items)}
     for future in tqdm(as_completed(futures), total=len(futures)):
         try:
             qid, answer = future.result()
             pred_answers[qid] = answer
+            processed += 1
+            if processed % 50 == 0:
+                print("已完成:", processed, "剩余:", len(items) - processed)
+            with open(pred_path, "w", encoding="utf-8") as f:
+                json.dump({"answer": pred_answers, "sp": {}}, f, ensure_ascii=False)
+            if processed % interval == 0:
+                print("阶段评估，当前进度:", processed)
+                run_eval()
         except Exception as e:
-            print(f"Error: {e}")
-
-predictions = {"answer": pred_answers, "sp": []}
-
-with open("evaluation/data/hotpot/output/dev_distractor_pred.json", "w") as f:
-    json.dump(predictions, f, ensure_ascii=False, indent=2)
+            print("Error:", e)
+print("最终评估:")
+run_eval()
diff --git a/evaluation/scripts/longbenchV2/import_data.py b/evaluation/scripts/longbenchV2/import_data.py
new file mode 100644
index 000000000..ff17cd275
--- /dev/null
+++ b/evaluation/scripts/longbenchV2/import_data.py
@@ -0,0 +1,20 @@
+from datasets import load_dataset
+
+
+dataset = load_dataset("zai-org/LongBench-v2", split="train")
+print(dataset)
+
+
+def truncate(value, max_len=200):
+    if isinstance(value, str) and len(value) > max_len:
+        return value[:max_len] + "... [TRUNCATED]"
+    return value
+
+
+for i in range(10):
+    sample = dataset[i]
+    print(f"========== Sample {i} ==========")
+    for key, value in sample.items():
+        print(f"{key}: {truncate(value)}")
+
+    print("\n")
diff --git a/evaluation/scripts/xinyu/eval/eval_score_llm.py b/evaluation/scripts/xinyu/eval/eval_score_llm.py
index f5764ce39..622c1e8d4 100644
--- a/evaluation/scripts/xinyu/eval/eval_score_llm.py
+++ b/evaluation/scripts/xinyu/eval/eval_score_llm.py
@@ -213,8 +213,26 @@ def eval_acc_and_f1(samples):
 
 def show_results(samples, show_path=None):
     for sample in samples:
-        sample["evidence_pages"] = eval(sample["evidence_pages"])
-        sample["evidence_sources"] = eval(sample["evidence_sources"])
+        ep = sample.get("evidence_pages")
+        es = sample.get("evidence_sources")
+        if isinstance(ep, str):
+            try:
+                sample["evidence_pages"] = eval(ep)
+            except Exception:
+                sample["evidence_pages"] = []
+        elif isinstance(ep, list):
+            sample["evidence_pages"] = ep
+        else:
+            sample["evidence_pages"] = []
+        if isinstance(es, str):
+            try:
+                sample["evidence_sources"] = eval(es)
+            except Exception:
+                sample["evidence_sources"] = []
+        elif isinstance(es, list):
+            sample["evidence_sources"] = es
+        else:
+            sample["evidence_sources"] = []
 
     with open(show_path, "w") as f:
         acc, f1 = eval_acc_and_f1(samples)
diff --git a/evaluation/scripts/xinyu/eval/extract_answer.py b/evaluation/scripts/xinyu/eval/extract_answer.py
new file mode 100644
index 000000000..b7f7e6863
--- /dev/null
+++ b/evaluation/scripts/xinyu/eval/extract_answer.py
@@ -0,0 +1,33 @@
+import os
+
+import openai
+
+from dotenv import load_dotenv
+
+
+load_dotenv()
+client = openai.Client(
+    api_key=os.getenv("OPENAI_API_KEY", "sk-xxxxx"),
+    base_url=os.getenv("OPENAI_API_BASE", "https://api.openai.com/v1"),
+)
+
+
+def extract_answer(question, output, prompt, model_name="gpt-4o"):
+    response = client.chat.completions.create(
+        model=model_name,
+        messages=[
+            {
+                "role": "user",
+                "content": prompt,
+            },
+            {"role": "assistant", "content": f"\n\nQuestion:{question}\nAnalysis:{output}\n"},
+        ],
+        temperature=0.0,
+        max_tokens=256,
+        top_p=1,
+        frequency_penalty=0,
+        presence_penalty=0,
+    )
+    response = response.choices[0].message.content
+
+    return response
diff --git a/evaluation/scripts/xinyu/eval_docs.py b/evaluation/scripts/xinyu/eval_docs.py
index 03a333201..0e113eaf9 100644
--- a/evaluation/scripts/xinyu/eval_docs.py
+++ b/evaluation/scripts/xinyu/eval_docs.py
@@ -7,9 +7,9 @@
 from concurrent.futures import ThreadPoolExecutor, as_completed
 
 from dotenv import load_dotenv
+from eval.eval_score_llm import eval_acc_and_f1, eval_score, show_results
+from eval.extract_answer import extract_answer
 
-from evaluation.scripts.mmlongbench.eval.extract_answer import extract_answer
-from evaluation.scripts.xinyu.eval.eval_score_llm import eval_acc_and_f1, eval_score, show_results
 from memos.configs.mem_cube import GeneralMemCubeConfig
 from memos.configs.mem_os import MOSConfig
 from memos.mem_cube.general import GeneralMemCube
@@ -35,6 +35,36 @@
 with open("evaluation/data/xinyu/all_samples_with_gt.json") as f:
     samples = json.load(f)
 
+RESULTS_PATH = "evaluation/data/xinyu/test_results.json"
+completed_pairs: set[tuple[str, str]] = set()
+
+
+def _load_existing_results():
+    global completed_pairs
+    if os.path.exists(RESULTS_PATH):
+        try:
+            with open(RESULTS_PATH, encoding="utf-8") as f:
+                existing = json.load(f)
+            for r in existing:
+                did = r.get("doc_id")
+                q = r.get("question")
+                if did and q:
+                    completed_pairs.add((did, q))
+            return existing
+        except Exception:
+            return []
+    return []
+
+
+def _doc_has_pending(doc_file: str) -> bool:
+    for s in samples:
+        if s.get("doc_id") == doc_file:
+            qs = s.get("question")
+            if isinstance(qs, dict):
+                return any((doc_file, q) not in completed_pairs for _, q in qs.items())
+            return any((doc_file, q) not in completed_pairs for q in qs)
+    return False
+
 
 def get_user_name(doc_file):
     csv_path = "evaluation/data/xinyu/user_doc_map.csv"
@@ -151,7 +181,13 @@ def process_doc(doc_file):
     question_list = sample["question"]
     answer_list = sample["answer"]
 
+    if isinstance(question_list, dict):
+        ordered = sorted(question_list.items(), key=lambda kv: int(kv[0]))
+        question_list = [v for _, v in ordered]
+
     for idx, question in enumerate(question_list):
+        if (doc_file, question) in completed_pairs:
+            continue
         gt = answer_list.get(str(idx))
 
         try_cnt, is_success = 0, False
@@ -193,10 +229,41 @@ def process_doc(doc_file):
 
 
 if __name__ == "__main__":
-    results = []
+    results = _load_existing_results()
 
     with ThreadPoolExecutor(max_workers=4) as executor:
-        future_to_doc = {executor.submit(process_doc, doc_file): doc_file for doc_file in doc_paths}
+        pending_docs = [d for d in doc_paths if _doc_has_pending(d)]
+        total_samples = 0
+        for s in samples:
+            qs = s.get("question", [])
+            total_samples += len(qs) if not isinstance(qs, dict) else len(list(qs.values()))
+        processed_samples = len(completed_pairs)
+        pending_samples = max(0, total_samples - processed_samples)
+        sample_doc_ids = []
+        for s in samples:
+            did = s.get("doc_id")
+            if not did:
+                continue
+            if isinstance(did, list):
+                sample_doc_ids.extend([os.path.basename(x) for x in did if isinstance(x, str)])
+            elif isinstance(did, str):
+                sample_doc_ids.append(os.path.basename(did))
+        all_docs_in_samples = set(sample_doc_ids)
+        processed_docs = len(all_docs_in_samples) - len(pending_docs)
+        print("\n" + "=" * 80)
+        print("📊 评测进度统计")
+        print("=" * 80)
+        print(f"✅ 已加载历史结果: {len(results)} 条")
+        print(f"📂 数据集总样本: {total_samples}")
+        print(f"🧪 已完成样本: {processed_samples}")
+        print(f"⏳ 待处理样本: {pending_samples}")
+        print(f"📄 数据集中总文档: {len(all_docs_in_samples)}")
+        print(f"✔️ 已完成文档: {processed_docs}")
+        print(f"➡️ 待处理文档(本次将运行): {len(pending_docs)}")
+        print("=" * 80 + "\n")
+        future_to_doc = {
+            executor.submit(process_doc, doc_file): doc_file for doc_file in pending_docs
+        }
 
         for future in as_completed(future_to_doc):
             doc_file = future_to_doc[future]
@@ -210,7 +277,7 @@ def process_doc(doc_file):
                     print(f"Avg acc: {acc}")
                     print(f"Avg f1: {f1}")
 
-                with open("evaluation/data/xinyu/test_results.json", "w", encoding="utf-8") as f:
+                with open(RESULTS_PATH, "w", encoding="utf-8") as f:
                     json.dump(results, f, ensure_ascii=False, indent=2)
 
             except Exception as e:
diff --git a/evaluation/scripts/xinyu/import_docs.py b/evaluation/scripts/xinyu/import_docs.py
index f9d2619a4..6fe2f4e34 100644
--- a/evaluation/scripts/xinyu/import_docs.py
+++ b/evaluation/scripts/xinyu/import_docs.py
@@ -34,6 +34,8 @@
         if name in existing_names:
             continue
         doc_paths.append(fp)
+print(f"existing_names length: {len(existing_names)}")
+print(f"doc_paths length: {len(doc_paths)}")
 
 
 async def process_doc(doc_path):

From 8207cc9b300c40be63386b6fb6da9b01be4f7a3a Mon Sep 17 00:00:00 2001
From: stx <31013941@qq.com>
Date: Thu, 27 Nov 2025 10:35:28 +0800
Subject: [PATCH 32/34] =?UTF-8?q?feat=EF=BC=9Aupdate=2011.27?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 evaluation/scripts/hotpot/hotpot_eval.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/evaluation/scripts/hotpot/hotpot_eval.py b/evaluation/scripts/hotpot/hotpot_eval.py
index 8be096e94..35393a420 100644
--- a/evaluation/scripts/hotpot/hotpot_eval.py
+++ b/evaluation/scripts/hotpot/hotpot_eval.py
@@ -71,7 +71,6 @@ def filter_memory_data(memories_data):
         if key == "text_mem":
             filtered_data[key] = []
             for mem_group in value:
-                # Check if it's the new data structure (list of TextualMemoryItem objects)
                 if "memories" in mem_group and isinstance(mem_group["memories"], list):
                     # New data structure: directly a list of TextualMemoryItem objects
                     filtered_memories = []

From 74654a6229d3f4eb10e50e48772c44c0af075ab0 Mon Sep 17 00:00:00 2001
From: stx <31013941@qq.com>
Date: Thu, 27 Nov 2025 10:40:36 +0800
Subject: [PATCH 33/34] =?UTF-8?q?feat=EF=BC=9Aupdate=2011.27?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 evaluation/scripts/hotpot/hotpot_eval.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/evaluation/scripts/hotpot/hotpot_eval.py b/evaluation/scripts/hotpot/hotpot_eval.py
index 35393a420..8be096e94 100644
--- a/evaluation/scripts/hotpot/hotpot_eval.py
+++ b/evaluation/scripts/hotpot/hotpot_eval.py
@@ -71,6 +71,7 @@ def filter_memory_data(memories_data):
         if key == "text_mem":
             filtered_data[key] = []
             for mem_group in value:
+                # Check if it's the new data structure (list of TextualMemoryItem objects)
                 if "memories" in mem_group and isinstance(mem_group["memories"], list):
                     # New data structure: directly a list of TextualMemoryItem objects
                     filtered_memories = []

From 9cae814bbb460939e1db5f23649e614fc01f5819 Mon Sep 17 00:00:00 2001
From: stx <31013941@qq.com>
Date: Thu, 27 Nov 2025 10:46:31 +0800
Subject: [PATCH 34/34] =?UTF-8?q?feat=EF=BC=9Aupdate=2011.27?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .gitignore               |  1 +
 src/memos/mem_os/core.py | 10 +---------
 2 files changed, 2 insertions(+), 9 deletions(-)

diff --git a/.gitignore b/.gitignore
index ce74c7cac..6c98f0b7a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -22,6 +22,7 @@ benchmarks/
 
 ppt_test_result/
 ppt_test_result_xinyu/
+ppt_test_result_longbench/
 
 # Byte-compiled / optimized / DLL files
 __pycache__/
diff --git a/src/memos/mem_os/core.py b/src/memos/mem_os/core.py
index 6734a37bc..9483b79ef 100644
--- a/src/memos/mem_os/core.py
+++ b/src/memos/mem_os/core.py
@@ -184,9 +184,6 @@ def add_images_context(
     # 5) Append up to 3 images as data URLs
     limit = 6
     count = 0
-    print("=========> images path:")
-    for image_path in unique_images:
-        print(image_path)
 
     for img_path in unique_images:
         if count >= limit:
@@ -474,11 +471,6 @@ def chat(self, query: str, user_id: str | None = None, base_prompt: str | None =
                         images_all.extend(images_list)
 
             logger.info(f"🧠 [Memory] Searched memories:\n{self._str_memories(memories_all)}\n")
-            print("===========> memories:\n")
-            for memory in memories_all:
-                print("memory relativity: ", memory.metadata.relativity)
-                print("memory user id: ", memory.metadata.user_id)
-                print(memory.memory)
             system_prompt = self._build_system_prompt(memories_all, base_prompt=base_prompt)
         else:
             system_prompt = self._build_system_prompt(base_prompt=base_prompt)
@@ -901,7 +893,7 @@ def add(
                     type="chat",
                     info={"user_id": target_user_id, "session_id": self.session_id},
                 )
-
+                print("memories lenght:", len(memories))
                 mem_ids = []
                 for mem in memories:
                     mem_id_list: list[str] = self.mem_cubes[mem_cube_id].text_mem.add(mem)