diff --git a/.github/workflows/docker.yaml b/.github/workflows/docker.yaml index 7231e08..1c5a2ee 100644 --- a/.github/workflows/docker.yaml +++ b/.github/workflows/docker.yaml @@ -8,8 +8,9 @@ on: - "v*" paths-ignore: - "**/*.md" - - ".github/workflows/ruff.yaml" - - ".github/workflows/track.yml" + - ".github/*" + - "LICENSE" + - ".gitignore" env: REGISTRY: ghcr.io @@ -26,6 +27,9 @@ jobs: - name: Checkout repository uses: actions/checkout@v6 + - name: Set up QEMU + uses: docker/setup-qemu-action@v3 + - name: Set up Docker Buildx uses: docker/setup-buildx-action@v3 @@ -46,6 +50,7 @@ jobs: type=semver,pattern={{version}} type=semver,pattern={{major}}.{{minor}} type=semver,pattern={{major}} + type=raw,value={{date 'YYYYMMDD'}}-{{sha}} type=raw,value=latest,enable={{is_default_branch}} - name: Build and push Docker image diff --git a/.github/workflows/ruff.yaml b/.github/workflows/ruff.yaml index 6b9e536..5e13127 100644 --- a/.github/workflows/ruff.yaml +++ b/.github/workflows/ruff.yaml @@ -19,12 +19,12 @@ jobs: - name: Set up Python uses: actions/setup-python@v6 with: - python-version: "3.12" + python-version: "3.13" - name: Install Ruff run: | python -m pip install --upgrade pip - pip install "ruff>=0.11.7" + pip install ruff - name: Run Ruff run: ruff check . diff --git a/Dockerfile b/Dockerfile index 938bc2f..62ce9d1 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,16 +1,27 @@ -FROM ghcr.io/astral-sh/uv:python3.12-bookworm-slim +FROM ghcr.io/astral-sh/uv:python3.13-trixie-slim -LABEL org.opencontainers.image.description="Web-based Gemini models wrapped into an OpenAI-compatible API." +LABEL org.opencontainers.image.title="Gemini-FastAPI" \ + org.opencontainers.image.description="Web-based Gemini models wrapped into an OpenAI-compatible API." WORKDIR /app -# Install dependencies +RUN apt-get update && apt-get install -y --no-install-recommends \ + tini \ + && rm -rf /var/lib/apt/lists/* + +ENV UV_COMPILE_BYTECODE=1 \ + PYTHONUNBUFFERED=1 \ + PYTHONDONTWRITEBYTECODE=1 + COPY pyproject.toml uv.lock ./ -RUN uv sync --no-cache --no-dev +RUN uv sync --no-cache --frozen --no-install-project --no-dev COPY app/ app/ COPY config/ config/ COPY run.py . -# Command to run the application +EXPOSE 8000 + +ENTRYPOINT ["/usr/bin/tini", "--"] + CMD ["uv", "run", "--no-dev", "run.py"] diff --git a/README.md b/README.md index 330e9c8..6b6f485 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # Gemini-FastAPI -[![Python 3.12](https://img.shields.io/badge/python-3.12+-blue.svg)](https://www.python.org/downloads/) +[![Python 3.13](https://img.shields.io/badge/python-3.13+-blue.svg)](https://www.python.org/downloads/) [![FastAPI](https://img.shields.io/badge/FastAPI-0.115+-green.svg)](https://fastapi.tiangolo.com/) [![License](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE) @@ -24,7 +24,7 @@ Web-based Gemini models wrapped into an OpenAI-compatible API. Powered by [Hanao ### Prerequisites -- Python 3.12 +- Python 3.13 - Google account with Gemini access on web - `secure_1psid` and `secure_1psidts` cookies from Gemini web interface @@ -74,6 +74,30 @@ python run.py The server will start on `http://localhost:8000` by default. +## API Endpoints + +The server provides several endpoints, including OpenAI-compatible ones. + +### OpenAI-Compatible Endpoints + +These endpoints are designed to be compatible with OpenAI's API structure, allowing you to use Gemini as a drop-in replacement. + +- **`GET /v1/models`**: Lists all supported Gemini models. +- **`POST /v1/chat/completions`**: Unified chat interface. + - **Streaming**: Set `stream: true` to receive real-time delta chunks. + - **Multi-modal**: Supports text, images, and file uploads. + - **Tool Calling**: Supports function calling via the `tools` parameter. + - **Structured Output**: Supports `response_format` for JSON schema enforcement. + +### Advanced Endpoints + +- **`POST /v1/responses`**: An alternative endpoint for complex interaction patterns, supporting rich output items including generated images and tool calls. + +### Utility Endpoints + +- **`GET /health`**: Health check endpoint. Returns the status of the server, configured Gemini clients, and conversation storage. +- **`GET /images/{filename}`**: Internal endpoint to serve generated images. Requires a valid token (automatically included in image URLs returned by the API). + ## Docker Deployment ### Run with Options diff --git a/README.zh.md b/README.zh.md index 2f9e1b5..d012d32 100644 --- a/README.zh.md +++ b/README.zh.md @@ -1,6 +1,6 @@ # Gemini-FastAPI -[![Python 3.12](https://img.shields.io/badge/python-3.12+-blue.svg)](https://www.python.org/downloads/) +[![Python 3.13](https://img.shields.io/badge/python-3.13+-blue.svg)](https://www.python.org/downloads/) [![FastAPI](https://img.shields.io/badge/FastAPI-0.115+-green.svg)](https://fastapi.tiangolo.com/) [![License](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE) @@ -24,7 +24,7 @@ ### 前置条件 -- Python 3.12 +- Python 3.13 - 拥有网页版 Gemini 访问权限的 Google 账号 - 从 Gemini 网页获取的 `secure_1psid` 和 `secure_1psidts` Cookie @@ -74,6 +74,30 @@ python run.py 服务默认启动在 `http://localhost:8000`。 +## API 接口 + +本服务器提供了一系列接口，重点支持 OpenAI 兼容协议。 + +### OpenAI 兼容接口 + +这些接口遵循 OpenAI 的 API 规范，允许你将 Gemini 作为 **Drop-in 替代方案** 直接接入现有的 AI 应用。 + +- **`GET /v1/models`**: 列出所有可用的 Gemini 模型。 +- **`POST /v1/chat/completions`**: 统一聊天对话接口。 + - **流式传输**: 设置 `stream: true` 即可实时接收增量响应 (Stream Delta)。 + - **多模态支持**: 支持在消息中包含文本、图片以及文件上传。 + - **工具调用**: 支持通过 `tools` 参数进行函数调用 (Function Calling)。 + - **结构化输出**: 支持 `response_format`，可严格遵循 JSON Schema。 + +### 高级接口 + +- **`POST /v1/responses`**: 用于复杂交互模式的专用接口，支持分步输出、生成图片及工具调用等更丰富的响应项。 + +### 辅助与系统接口 + +- **`GET /health`**: 健康检查接口。返回服务器运行状态、已配置的 Gemini 客户端健康度以及对话存储统计信息。 +- **`GET /images/{filename}`**: 用于访问生成的图片的内部接口。需携带有效 Token（API 返回的图片 URL 中已自动包含该 Token）。 + ## Docker 部署 ### 直接运行 diff --git a/app/main.py b/app/main.py index f4e6711..20d15b0 100644 --- a/app/main.py +++ b/app/main.py @@ -2,7 +2,6 @@ from contextlib import asynccontextmanager from fastapi import FastAPI -from fastapi.responses import ORJSONResponse from loguru import logger from .server.chat import router as chat_router @@ -43,7 +42,7 @@ async def _run_retention_cleanup(stop_event: asyncio.Event) -> None: stop_event.wait(), timeout=RETENTION_CLEANUP_INTERVAL_SECONDS, ) - except asyncio.TimeoutError: + except TimeoutError: continue logger.info("LMDB retention cleanup task stopped.") @@ -93,7 +92,6 @@ def create_app() -> FastAPI: description="OpenAI-compatible API for Gemini Web", version="1.0.0", lifespan=lifespan, - default_response_class=ORJSONResponse, ) add_cors_middleware(app) diff --git a/app/models/__init__.py b/app/models/__init__.py index c6a3640..1378f1f 100644 --- a/app/models/__init__.py +++ b/app/models/__init__.py @@ -1 +1,65 @@ -from .models import * # noqa: F403 +from .models import ( + ChatCompletionRequest, + ChatCompletionResponse, + Choice, + ContentItem, + ConversationInStore, + FunctionCall, + HealthCheckResponse, + Message, + ModelData, + ModelListResponse, + ResponseCreateRequest, + ResponseCreateResponse, + ResponseImageGenerationCall, + ResponseImageTool, + ResponseInputContent, + ResponseInputItem, + ResponseOutputContent, + ResponseOutputMessage, + ResponseReasoning, + ResponseReasoningContentPart, + ResponseSummaryPart, + ResponseToolCall, + ResponseToolChoice, + ResponseUsage, + Tool, + ToolCall, + ToolChoiceFunction, + ToolChoiceFunctionDetail, + ToolFunctionDefinition, + Usage, +) + +__all__ = [ + "ChatCompletionRequest", + "ChatCompletionResponse", + "Choice", + "ContentItem", + "ConversationInStore", + "FunctionCall", + "HealthCheckResponse", + "Message", + "ModelData", + "ModelListResponse", + "ResponseCreateRequest", + "ResponseCreateResponse", + "ResponseImageGenerationCall", + "ResponseImageTool", + "ResponseInputContent", + "ResponseInputItem", + "ResponseOutputContent", + "ResponseOutputMessage", + "ResponseReasoning", + "ResponseReasoningContentPart", + "ResponseSummaryPart", + "ResponseToolCall", + "ResponseToolChoice", + "ResponseUsage", + "Tool", + "ToolCall", + "ToolChoiceFunction", + "ToolChoiceFunctionDetail", + "ToolFunctionDefinition", + "Usage", +] diff --git a/app/models/models.py b/app/models/models.py index 64ceaa9..8dbe86c 100644 --- a/app/models/models.py +++ b/app/models/models.py @@ -1,7 +1,7 @@ from __future__ import annotations from datetime import datetime -from typing import Any, Dict, List, Literal, Optional, Union +from typing import Any, Literal from pydantic import BaseModel, Field, model_validator @@ -10,28 +10,28 @@ class ContentItem(BaseModel): """Individual content item (text, image, or file) within a message.""" type: Literal["text", "image_url", "file", "input_audio"] - text: Optional[str] = None - image_url: Optional[Dict[str, str]] = None - input_audio: Optional[Dict[str, Any]] = None - file: Optional[Dict[str, str]] = None - annotations: List[Dict[str, Any]] = Field(default_factory=list) + text: str | None = Field(default=None) + image_url: dict[str, Any] | None = Field(default=None) + input_audio: dict[str, Any] | None = Field(default=None) + file: dict[str, Any] | None = Field(default=None) + annotations: list[dict[str, Any]] = Field(default_factory=list) class Message(BaseModel): """Message model""" role: str - content: Union[str, List[ContentItem], None] = None - name: Optional[str] = None - tool_calls: Optional[List["ToolCall"]] = None - tool_call_id: Optional[str] = None - refusal: Optional[str] = None - reasoning_content: Optional[str] = None - audio: Optional[Dict[str, Any]] = None - annotations: List[Dict[str, Any]] = Field(default_factory=list) + content: str | list[ContentItem] | None = Field(default=None) + name: str | None = Field(default=None) + tool_calls: list[ToolCall] | None = Field(default=None) + tool_call_id: str | None = Field(default=None) + refusal: str | None = Field(default=None) + reasoning_content: str | None = Field(default=None) + audio: dict[str, Any] | None = Field(default=None) + annotations: list[dict[str, Any]] = Field(default_factory=list) @model_validator(mode="after") - def normalize_role(self) -> "Message": + def normalize_role(self) -> Message: """Normalize 'developer' role to 'system' for Gemini compatibility.""" if self.role == "developer": self.role = "system" @@ -44,7 +44,7 @@ class Choice(BaseModel): index: int message: Message finish_reason: str - logprobs: Optional[Dict[str, Any]] = None + logprobs: dict[str, Any] | None = Field(default=None) class FunctionCall(BaseModel): @@ -66,8 +66,8 @@ class ToolFunctionDefinition(BaseModel): """Function definition for tool.""" name: str - description: Optional[str] = None - parameters: Optional[Dict[str, Any]] = None + description: str | None = Field(default=None) + parameters: dict[str, Any] | None = Field(default=None) class Tool(BaseModel): @@ -96,8 +96,8 @@ class Usage(BaseModel): prompt_tokens: int completion_tokens: int total_tokens: int - prompt_tokens_details: Optional[Dict[str, int]] = None - completion_tokens_details: Optional[Dict[str, int]] = None + prompt_tokens_details: dict[str, int] | None = Field(default=None) + completion_tokens_details: dict[str, int] | None = Field(default=None) class ModelData(BaseModel): @@ -113,17 +113,17 @@ class ChatCompletionRequest(BaseModel): """Chat completion request model""" model: str - messages: List[Message] - stream: Optional[bool] = False - user: Optional[str] = None - temperature: Optional[float] = 0.7 - top_p: Optional[float] = 1.0 - max_tokens: Optional[int] = None - tools: Optional[List["Tool"]] = None - tool_choice: Optional[ - Union[Literal["none"], Literal["auto"], Literal["required"], "ToolChoiceFunction"] - ] = None - response_format: Optional[Dict[str, Any]] = None + messages: list[Message] + stream: bool | None = Field(default=False) + user: str | None = Field(default=None) + temperature: float | None = Field(default=0.7) + top_p: float | None = Field(default=1.0) + max_tokens: int | None = Field(default=None) + tools: list[Tool] | None = Field(default=None) + tool_choice: ( + Literal["none"] | Literal["auto"] | Literal["required"] | ToolChoiceFunction | None + ) = Field(default=None) + response_format: dict[str, Any] | None = Field(default=None) class ChatCompletionResponse(BaseModel): @@ -133,7 +133,7 @@ class ChatCompletionResponse(BaseModel): object: str = "chat.completion" created: int model: str - choices: List[Choice] + choices: list[Choice] usage: Usage @@ -141,23 +141,23 @@ class ModelListResponse(BaseModel): """Model list model""" object: str = "list" - data: List[ModelData] + data: list[ModelData] class HealthCheckResponse(BaseModel): """Health check response model""" ok: bool - storage: Optional[Dict[str, str | int]] = None - clients: Optional[Dict[str, bool]] = None - error: Optional[str] = None + storage: dict[str, Any] | None = Field(default=None) + clients: dict[str, bool] | None = Field(default=None) + error: str | None = Field(default=None) class ConversationInStore(BaseModel): """Conversation model for storing in the database.""" - created_at: Optional[datetime] = Field(default=None) - updated_at: Optional[datetime] = Field(default=None) + created_at: datetime | None = Field(default=None) + updated_at: datetime | None = Field(default=None) # Gemini Web API does not support changing models once a conversation is created. model: str = Field(..., description="Model used for the conversation") @@ -171,63 +171,55 @@ class ConversationInStore(BaseModel): class ResponseInputContent(BaseModel): """Content item for Responses API input.""" - type: Literal["input_text", "input_image", "input_file"] - text: Optional[str] = None - image_url: Optional[str] = None - detail: Optional[Literal["auto", "low", "high"]] = None - file_url: Optional[str] = None - file_data: Optional[str] = None - filename: Optional[str] = None - annotations: List[Dict[str, Any]] = Field(default_factory=list) - - @model_validator(mode="before") - @classmethod - def normalize_output_text(cls, data: Any) -> Any: - """Allow output_text (from previous turns) to be treated as input_text.""" - if isinstance(data, dict) and data.get("type") == "output_text": - data["type"] = "input_text" - return data + type: Literal["input_text", "output_text", "reasoning_text", "input_image", "input_file"] + text: str | None = Field(default=None) + image_url: str | None = Field(default=None) + detail: Literal["auto", "low", "high"] | None = Field(default=None) + file_url: str | None = Field(default=None) + file_data: str | None = Field(default=None) + filename: str | None = Field(default=None) + annotations: list[dict[str, Any]] = Field(default_factory=list) class ResponseInputItem(BaseModel): """Single input item for Responses API.""" - type: Optional[Literal["message"]] = "message" + type: Literal["message"] | None = Field(default="message") role: Literal["user", "assistant", "system", "developer"] - content: Union[str, List[ResponseInputContent]] + content: str | list[ResponseInputContent] class ResponseToolChoice(BaseModel): """Tool choice enforcing a specific tool in Responses API.""" type: Literal["function", "image_generation"] - function: Optional[ToolChoiceFunctionDetail] = None + function: ToolChoiceFunctionDetail | None = Field(default=None) class ResponseImageTool(BaseModel): """Image generation tool specification for Responses API.""" type: Literal["image_generation"] - model: Optional[str] = None - output_format: Optional[str] = None + model: str | None = Field(default=None) + output_format: str | None = Field(default=None) class ResponseCreateRequest(BaseModel): """Responses API request payload.""" model: str - input: Union[str, List[ResponseInputItem]] - instructions: Optional[Union[str, List[ResponseInputItem]]] = None - temperature: Optional[float] = 0.7 - top_p: Optional[float] = 1.0 - max_output_tokens: Optional[int] = None - stream: Optional[bool] = False - tool_choice: Optional[Union[str, ResponseToolChoice]] = None - tools: Optional[List[Union[Tool, ResponseImageTool]]] = None - store: Optional[bool] = None - user: Optional[str] = None - response_format: Optional[Dict[str, Any]] = None - metadata: Optional[Dict[str, Any]] = None + input: str | list[ResponseInputItem] + instructions: str | list[ResponseInputItem] | None = Field(default=None) + temperature: float | None = Field(default=0.7) + top_p: float | None = Field(default=1.0) + max_output_tokens: int | None = Field(default=None) + stream: bool | None = Field(default=False) + tool_choice: str | ResponseToolChoice | None = Field(default=None) + tools: list[Tool | ResponseImageTool] | None = Field(default=None) + store: bool | None = Field(default=None) + user: str | None = Field(default=None) + response_format: dict[str, Any] | None = Field(default=None) + metadata: dict[str, Any] | None = Field(default=None) class ResponseUsage(BaseModel): @@ -236,14 +228,16 @@ class ResponseUsage(BaseModel): input_tokens: int output_tokens: int total_tokens: int + input_tokens_details: dict[str, int] | None = Field(default=None) + output_tokens_details: dict[str, int] | None = Field(default=None) class ResponseOutputContent(BaseModel): """Content item for Responses API output.""" type: Literal["output_text"] - text: Optional[str] = "" - annotations: List[Dict[str, Any]] = Field(default_factory=list) + text: str | None = Field(default="") + annotations: list[dict[str, Any]] = Field(default_factory=list) class ResponseOutputMessage(BaseModel): @@ -252,27 +246,53 @@ class ResponseOutputMessage(BaseModel): id: str type: Literal["message"] role: Literal["assistant"] - content: List[ResponseOutputContent] + content: list[ResponseOutputContent] + + +class ResponseSummaryPart(BaseModel): + """Summary part for reasoning.""" + + type: Literal["summary_text"] = Field(default="summary_text") + text: str + + +class ResponseReasoningContentPart(BaseModel): + """Content part for reasoning.""" + + type: Literal["reasoning_text"] = Field(default="reasoning_text") + text: str + + +class ResponseReasoning(BaseModel): + """Reasoning item returned by Responses API.""" + + id: str + type: Literal["reasoning"] = Field(default="reasoning") + status: Literal["in_progress", "completed", "incomplete"] = Field(default="completed") + summary: list[ResponseSummaryPart] | None = Field(default=None) + content: list[ResponseReasoningContentPart] | None = Field(default=None) class ResponseImageGenerationCall(BaseModel): """Image generation call record emitted in Responses API.""" id: str - type: Literal["image_generation_call"] = "image_generation_call" - status: Literal["completed", "in_progress", "generating", "failed"] = "completed" - result: Optional[str] = None - output_format: Optional[str] = None - size: Optional[str] = None - revised_prompt: Optional[str] = None + type: Literal["image_generation_call"] = Field(default="image_generation_call") + status: Literal["completed", "in_progress", "generating", "failed"] = Field(default="completed") + result: str | None = Field(default=None) + output_format: str | None = Field(default=None) + size: str | None = Field(default=None) + revised_prompt: str | None = Field(default=None) class ResponseToolCall(BaseModel): """Tool call record emitted in Responses API.""" id: str - type: Literal["tool_call"] = "tool_call" - status: Literal["in_progress", "completed", "failed", "requires_action"] = "completed" + type: Literal["tool_call"] = Field(default="tool_call") + status: Literal["in_progress", "completed", "failed", "requires_action"] = Field( + default="completed" + ) function: FunctionCall @@ -280,10 +300,12 @@ class ResponseCreateResponse(BaseModel): """Responses API response payload.""" id: str - object: Literal["response"] = "response" + object: Literal["response"] = Field(default="response") created_at: int model: str - output: List[Union[ResponseOutputMessage, ResponseImageGenerationCall, ResponseToolCall]] + output: list[ + ResponseReasoning | ResponseOutputMessage | ResponseImageGenerationCall | ResponseToolCall + ] status: Literal[ "in_progress", "completed", @@ -291,13 +313,13 @@ class ResponseCreateResponse(BaseModel): "incomplete", "cancelled", "requires_action", - ] = "completed" - tool_choice: Optional[Union[str, ResponseToolChoice]] = None - tools: Optional[List[Union[Tool, ResponseImageTool]]] = None + ] = Field(default="completed") + tool_choice: str | ResponseToolChoice | None = Field(default=None) + tools: list[Tool | ResponseImageTool] | None = Field(default=None) usage: ResponseUsage - error: Optional[Dict[str, Any]] = None - metadata: Optional[Dict[str, Any]] = None - input: Optional[Union[str, List[ResponseInputItem]]] = None + error: dict[str, Any] | None = Field(default=None) + metadata: dict[str, Any] | None = Field(default=None) + input: str | list[ResponseInputItem] | None = Field(default=None) # Rebuild models with forward references diff --git a/app/server/chat.py b/app/server/chat.py index 934091b..089db7c 100644 --- a/app/server/chat.py +++ b/app/server/chat.py @@ -3,10 +3,11 @@ import io import reprlib import uuid +from collections.abc import AsyncGenerator from dataclasses import dataclass -from datetime import datetime, timezone +from datetime import UTC, datetime from pathlib import Path -from typing import Any, AsyncGenerator +from typing import Any import orjson from fastapi import APIRouter, Depends, HTTPException, Request, status @@ -17,7 +18,7 @@ from gemini_webapi.types.image import GeneratedImage, Image from loguru import logger -from ..models import ( +from app.models import ( ChatCompletionRequest, ContentItem, ConversationInStore, @@ -32,17 +33,25 @@ ResponseInputItem, ResponseOutputContent, ResponseOutputMessage, + ResponseReasoning, + ResponseReasoningContentPart, ResponseToolCall, ResponseToolChoice, ResponseUsage, Tool, ToolChoiceFunction, ) -from ..services import GeminiClientPool, GeminiClientWrapper, LMDBConversationStore -from ..utils import g_config -from ..utils.helper import ( - TOOL_HINT_LINE_END, - TOOL_HINT_LINE_START, +from app.server.middleware import ( + get_image_store_dir, + get_image_token, + get_temp_dir, + verify_api_key, +) +from app.services import GeminiClientPool, GeminiClientWrapper, LMDBConversationStore +from app.utils import g_config +from app.utils.helper import ( + STREAM_MASTER_RE, + STREAM_TAIL_RE, TOOL_HINT_STRIPPED, TOOL_WRAP_HINT, detect_image_extension, @@ -53,7 +62,6 @@ strip_system_hints, text_from_message, ) -from .middleware import get_image_store_dir, get_image_token, get_temp_dir, verify_api_key MAX_CHARS_PER_REQUEST = int(g_config.gemini.max_chars_per_request * 0.9) METADATA_TTL_MINUTES = 15 @@ -98,11 +106,7 @@ async def _image_to_base64( if not suffix: detected_ext = detect_image_extension(data) - if detected_ext: - suffix = detected_ext - else: - # Fallback if detection fails - suffix = ".png" if isinstance(image, GeneratedImage) else ".jpg" + suffix = detected_ext or (".png" if isinstance(image, GeneratedImage) else ".jpg") random_name = f"img_{uuid.uuid4().hex}{suffix}" new_path = temp_dir / random_name @@ -118,8 +122,9 @@ def _calculate_usage( messages: list[Message], assistant_text: str | None, tool_calls: list[Any] | None, -) -> tuple[int, int, int]: - """Calculate prompt, completion and total tokens consistently.""" + thoughts: str | None = None, +) -> tuple[int, int, int, int]: + """Calculate prompt, completion, total and reasoning tokens consistently.""" prompt_tokens = sum(estimate_tokens(text_from_message(msg)) for msg in messages) tool_args_text = "" if tool_calls: @@ -136,7 +141,15 @@ def _calculate_usage( ) completion_tokens = estimate_tokens(completion_basis) - return prompt_tokens, completion_tokens, prompt_tokens + completion_tokens + reasoning_tokens = estimate_tokens(thoughts) if thoughts else 0 + total_completion_tokens = completion_tokens + reasoning_tokens + + return ( + prompt_tokens, + total_completion_tokens, + prompt_tokens + total_completion_tokens, + reasoning_tokens, + ) def _create_responses_standard_payload( @@ -149,34 +162,50 @@ def _create_responses_standard_payload( usage: ResponseUsage, request: ResponseCreateRequest, normalized_input: Any, + full_thoughts: str | None = None, ) -> ResponseCreateResponse: """Unified factory for building ResponseCreateResponse objects.""" message_id = f"msg_{uuid.uuid4().hex}" - tool_call_items: list[ResponseToolCall] = [] - if detected_tool_calls: - tool_call_items = [ - ResponseToolCall( - id=call.id if hasattr(call, "id") else call["id"], + reason_id = f"reason_{uuid.uuid4().hex}" + + output_items: list[Any] = [] + if full_thoughts: + output_items.append( + ResponseReasoning( + id=reason_id, status="completed", - function=call.function if hasattr(call, "function") else call["function"], + content=[ResponseReasoningContentPart(text=full_thoughts)], ) - for call in detected_tool_calls - ] + ) + + output_items.append( + ResponseOutputMessage( + id=message_id, + type="message", + role="assistant", + content=response_contents, + ) + ) + + if detected_tool_calls: + output_items.extend( + [ + ResponseToolCall( + id=call.id if hasattr(call, "id") else call["id"], + status="completed", + function=call.function if hasattr(call, "function") else call["function"], + ) + for call in detected_tool_calls + ] + ) + + output_items.extend(image_call_items) return ResponseCreateResponse( id=response_id, created_at=created_time, model=model_name, - output=[ - ResponseOutputMessage( - id=message_id, - type="message", - role="assistant", - content=response_contents, - ), - *tool_call_items, - *image_call_items, - ], + output=output_items, status="completed", usage=usage, input=normalized_input or None, @@ -194,6 +223,7 @@ def _create_chat_completion_standard_payload( tool_calls_payload: list[dict] | None, finish_reason: str, usage: dict, + reasoning_content: str | None = None, ) -> dict: """Unified factory for building Chat Completion response dictionaries.""" return { @@ -208,6 +238,7 @@ def _create_chat_completion_standard_payload( "role": "assistant", "content": visible_output or None, "tool_calls": tool_calls_payload or None, + "reasoning_content": reasoning_content or None, }, "finish_reason": finish_reason, } @@ -217,40 +248,41 @@ def _create_chat_completion_standard_payload( def _process_llm_output( - raw_output_with_think: str, - raw_output_clean: str, + thoughts: str | None, + raw_text: str, structured_requirement: StructuredOutputRequirement | None, -) -> tuple[str, str, list[Any]]: +) -> tuple[str | None, str, str, list[Any]]: """ Post-process Gemini output to extract tool calls and prepare clean text for display and storage. - Returns: (visible_text, storage_output, tool_calls) + Returns: (thoughts, visible_text, storage_output, tool_calls) """ - visible_with_think, tool_calls = extract_tool_calls(raw_output_with_think) + if thoughts: + thoughts = thoughts.strip() + + visible_output, tool_calls = extract_tool_calls(raw_text) if tool_calls: logger.debug(f"Detected {len(tool_calls)} tool call(s) in model output.") - visible_output = visible_with_think.strip() + visible_output = visible_output.strip() - storage_output = remove_tool_call_blocks(raw_output_clean) + storage_output = remove_tool_call_blocks(raw_text) storage_output = storage_output.strip() - if structured_requirement: - cleaned_for_json = LMDBConversationStore.remove_think_tags(visible_output) - if cleaned_for_json: - try: - structured_payload = orjson.loads(cleaned_for_json) - canonical_output = orjson.dumps(structured_payload).decode("utf-8") - visible_output = canonical_output - storage_output = canonical_output - logger.debug( - f"Structured response fulfilled (schema={structured_requirement.schema_name})." - ) - except orjson.JSONDecodeError: - logger.warning( - f"Failed to decode JSON for structured response (schema={structured_requirement.schema_name})." - ) + if structured_requirement and visible_output: + try: + structured_payload = orjson.loads(visible_output) + canonical_output = orjson.dumps(structured_payload).decode("utf-8") + visible_output = canonical_output + storage_output = canonical_output + logger.debug( + f"Structured response fulfilled (schema={structured_requirement.schema_name})." + ) + except orjson.JSONDecodeError: + logger.warning( + f"Failed to decode JSON for structured response (schema={structured_requirement.schema_name})." + ) - return visible_output, storage_output, tool_calls + return thoughts, visible_output, storage_output, tool_calls def _persist_conversation( @@ -261,6 +293,7 @@ def _persist_conversation( messages: list[Message], storage_output: str | None, tool_calls: list[Any] | None, + thoughts: str | None = None, ) -> str | None: """Unified logic to save conversation history to LMDB.""" try: @@ -268,6 +301,7 @@ def _persist_conversation( role="assistant", content=storage_output or None, tool_calls=tool_calls or None, + reasoning_content=thoughts or None, ) full_history = [*messages, current_assistant_message] cleaned_history = db.sanitize_messages(full_history) @@ -513,14 +547,22 @@ def _response_items_to_messages( messages.append(Message(role=role, content=content)) else: converted: list[ContentItem] = [] + reasoning_parts: list[str] = [] for part in content: - if part.type == "input_text": + if part.type in ("input_text", "output_text"): text_value = part.text or "" normalized_contents.append( - ResponseInputContent(type="input_text", text=text_value) + ResponseInputContent(type=part.type, text=text_value) ) if text_value: converted.append(ContentItem(type="text", text=text_value)) + elif part.type == "reasoning_text": + text_value = part.text or "" + normalized_contents.append( + ResponseInputContent(type="reasoning_text", text=text_value) + ) + if text_value: + reasoning_parts.append(text_value) elif part.type == "input_image": image_url = part.image_url if image_url: @@ -581,11 +623,16 @@ def _instructions_to_messages( instruction_messages.append(Message(role=role, content=content)) else: converted: list[ContentItem] = [] + reasoning_parts: list[str] = [] for part in content: - if part.type == "input_text": + if part.type in ("input_text", "output_text"): text_value = part.text or "" if text_value: converted.append(ContentItem(type="text", text=text_value)) + elif part.type == "reasoning_text": + text_value = part.text or "" + if text_value: + reasoning_parts.append(text_value) elif part.type == "input_image": image_url = part.image_url if image_url: @@ -607,7 +654,13 @@ def _instructions_to_messages( file_info["url"] = part.file_url if file_info: converted.append(ContentItem(type="file", file=file_info)) - instruction_messages.append(Message(role=role, content=converted or None)) + instruction_messages.append( + Message( + role=role, + content=converted or None, + reasoning_content="\n".join(reasoning_parts) if reasoning_parts else None, + ) + ) return instruction_messages @@ -628,7 +681,7 @@ def _get_model_by_name(name: str) -> Model: def _get_available_models() -> list[ModelData]: """Return a list of available models based on configuration strategy.""" - now = int(datetime.now(tz=timezone.utc).timestamp()) + now = int(datetime.now(tz=UTC).timestamp()) strategy = g_config.gemini.model_strategy models_data = [] @@ -712,7 +765,7 @@ async def _send_with_split( text: str, files: list[Path | str | io.BytesIO] | None = None, stream: bool = False, -) -> AsyncGenerator[ModelOutput, None] | ModelOutput: +) -> AsyncGenerator[ModelOutput] | ModelOutput: """Send text to Gemini, splitting or converting to attachment if too long.""" if len(text) <= MAX_CHARS_PER_REQUEST: try: @@ -749,277 +802,86 @@ async def _send_with_split( class StreamingOutputFilter: """ Filter to suppress technical protocol markers, tool calls, and system hints from the stream. - Uses a state machine to handle fragmentation where markers are split across multiple chunks. + Uses a stack-based state machine to handle nested fragmented markers. """ def __init__(self): self.buffer = "" - self.state = "NORMAL" + self.stack = ["NORMAL"] self.current_role = "" - self.block_buffer = "" - - self.STATE_MARKERS = { - "TOOL": { - "starts": ["[ToolCalls]", "\\[ToolCalls\\]"], - "ends": ["[/ToolCalls]", "\\[\\/ToolCalls\\]"], - }, - "ORPHAN": { - "starts": ["[Call:", "\\[Call\\:"], - "ends": ["[/Call]", "\\[\\/Call\\]"], - }, - "RESP": { - "starts": ["[ToolResults]", "\\[ToolResults\\]"], - "ends": ["[/ToolResults]", "\\[\\/ToolResults\\]"], - }, - "ARG": { - "starts": ["[CallParameter:", "\\[CallParameter\\:"], - "ends": ["[/CallParameter]", "\\[\\/CallParameter\\]"], - }, - "RESULT": { - "starts": ["[ToolResult]", "\\[ToolResult\\]"], - "ends": ["[/ToolResult]", "\\[\\/ToolResult\\]"], - }, - "ITEM": { - "starts": ["[Result:", "\\[Result\\:"], - "ends": ["[/Result]", "\\[\\/Result\\]"], - }, - "TAG": { - "starts": ["<|im_start|>", "\\<\\|im\\_start\\|\\>"], - "ends": ["<|im_end|>", "\\<\\|im\\_end\\|\\>"], - }, - } - hint_start = f"\n{TOOL_HINT_LINE_START}" if TOOL_HINT_LINE_START else "" - if hint_start: - self.STATE_MARKERS["HINT"] = { - "starts": [hint_start], - "ends": [TOOL_HINT_LINE_END], - } + @property + def state(self): + return self.stack[-1] - self.ORPHAN_ENDS = [ - "<|im_end|>", - "\\<\\|im\\_end\\|\\>", - "[/Call]", - "\\[\\/Call\\]", - "[/ToolCalls]", - "\\[\\/ToolCalls\\]", - "[/CallParameter]", - "\\[\\/CallParameter\\]", - "[/ToolResult]", - "\\[\\/ToolResult\\]", - "[/ToolResults]", - "\\[\\/ToolResults\\]", - "[/Result]", - "\\[\\/Result\\]", - ] - - self.WATCH_MARKERS = [] - for cfg in self.STATE_MARKERS.values(): - self.WATCH_MARKERS.extend(cfg["starts"]) - self.WATCH_MARKERS.extend(cfg.get("ends", [])) - self.WATCH_MARKERS.extend(self.ORPHAN_ENDS) + def _is_outputting(self) -> bool: + """Determines if the current state allows yielding text to the stream.""" + return self.state == "NORMAL" or (self.state == "IN_BLOCK" and self.current_role != "tool") def process(self, chunk: str) -> str: self.buffer += chunk output = [] while self.buffer: - buf_low = self.buffer.lower() - if self.state == "NORMAL": - indices = [] - for m_type, cfg in self.STATE_MARKERS.items(): - for p in cfg["starts"]: - idx = buf_low.find(p.lower()) - if idx != -1: - indices.append((idx, m_type, len(p))) - - for p in self.ORPHAN_ENDS: - idx = buf_low.find(p.lower()) - if idx != -1: - indices.append((idx, "SKIP", len(p))) - - if not indices: - keep_len = 0 - for marker in self.WATCH_MARKERS: - m_low = marker.lower() - for i in range(len(m_low) - 1, 0, -1): - if buf_low.endswith(m_low[:i]): - keep_len = max(keep_len, i) - break - yield_len = len(self.buffer) - keep_len - if yield_len > 0: - output.append(self.buffer[:yield_len]) - self.buffer = self.buffer[yield_len:] - break - - indices.sort() - idx, m_type, m_len = indices[0] - output.append(self.buffer[:idx]) - self.buffer = self.buffer[idx:] - - if m_type == "SKIP": - self.buffer = self.buffer[m_len:] + if self.state == "IN_TAG_HEADER": + nl_idx = self.buffer.find("\n") + if nl_idx != -1: + self.current_role = self.buffer[:nl_idx].strip().lower() + self.buffer = self.buffer[nl_idx + 1 :] + self.stack[-1] = "IN_BLOCK" continue - - self.state = f"IN_{m_type}" - if m_type in ("TOOL", "ORPHAN"): - self.block_buffer = "" - - self.buffer = self.buffer[m_len:] - - elif self.state == "IN_HINT": - cfg = self.STATE_MARKERS["HINT"] - found_idx, found_len = -1, 0 - for p in cfg["ends"]: - idx = buf_low.find(p.lower()) - if idx != -1 and (found_idx == -1 or idx < found_idx): - found_idx, found_len = idx, len(p) - - if found_idx != -1: - self.buffer = self.buffer[found_idx + found_len :] - self.state = "NORMAL" else: - max_end_len = max(len(p) for p in cfg["ends"]) - if len(self.buffer) > max_end_len: - self.buffer = self.buffer[-max_end_len:] break - elif self.state == "IN_ARG": - cfg = self.STATE_MARKERS["ARG"] - found_idx, found_len = -1, 0 - for p in cfg["ends"]: - idx = buf_low.find(p.lower()) - if idx != -1 and (found_idx == -1 or idx < found_idx): - found_idx, found_len = idx, len(p) - - if found_idx != -1: - self.buffer = self.buffer[found_idx + found_len :] - self.state = "NORMAL" - else: - max_end_len = max(len(p) for p in cfg["ends"]) - if len(self.buffer) > max_end_len: - self.buffer = self.buffer[-max_end_len:] - break + match = STREAM_MASTER_RE.search(self.buffer) + if not match: + tail_match = STREAM_TAIL_RE.search(self.buffer) + keep_len = len(tail_match.group(0)) if tail_match else 0 + yield_len = len(self.buffer) - keep_len + if yield_len > 0: + if self._is_outputting(): + output.append(self.buffer[:yield_len]) + self.buffer = self.buffer[yield_len:] + break - elif self.state == "IN_RESULT": - cfg = self.STATE_MARKERS["RESULT"] - found_idx, found_len = -1, 0 - for p in cfg["ends"]: - idx = buf_low.find(p.lower()) - if idx != -1 and (found_idx == -1 or idx < found_idx): - found_idx, found_len = idx, len(p) - - if found_idx != -1: - self.buffer = self.buffer[found_idx + found_len :] - self.state = "NORMAL" - else: - max_end_len = max(len(p) for p in cfg["ends"]) - if len(self.buffer) > max_end_len: - self.buffer = self.buffer[-max_end_len:] - break + start, end = match.span() + matched_group = match.lastgroup + pre_text = self.buffer[:start] - elif self.state == "IN_RESP": - cfg = self.STATE_MARKERS["RESP"] - found_idx, found_len = -1, 0 - for p in cfg["ends"]: - idx = buf_low.find(p.lower()) - if idx != -1 and (found_idx == -1 or idx < found_idx): - found_idx, found_len = idx, len(p) - - if found_idx != -1: - self.buffer = self.buffer[found_idx + found_len :] - self.state = "NORMAL" - else: - break + if self._is_outputting(): + output.append(pre_text) - elif self.state == "IN_TOOL": - cfg = self.STATE_MARKERS["TOOL"] - found_idx, found_len = -1, 0 - for p in cfg["ends"]: - idx = buf_low.find(p.lower()) - if idx != -1 and (found_idx == -1 or idx < found_idx): - found_idx, found_len = idx, len(p) - - if found_idx != -1: - self.block_buffer += self.buffer[:found_idx] - self.buffer = self.buffer[found_idx + found_len :] - self.state = "NORMAL" + if matched_group.endswith("_START"): + m_type = matched_group.split("_")[0] + if m_type == "TAG": + self.stack.append("IN_TAG_HEADER") else: - max_end_len = max(len(p) for p in cfg["ends"]) - if len(self.buffer) > max_end_len: - self.block_buffer += self.buffer[:-max_end_len] - self.buffer = self.buffer[-max_end_len:] - break - - elif self.state == "IN_ORPHAN": - cfg = self.STATE_MARKERS["ORPHAN"] - found_idx, found_len = -1, 0 - for p in cfg["ends"]: - idx = buf_low.find(p.lower()) - if idx != -1 and (found_idx == -1 or idx < found_idx): - found_idx, found_len = idx, len(p) - - if found_idx != -1: - self.block_buffer += self.buffer[:found_idx] - self.buffer = self.buffer[found_idx + found_len :] - self.state = "NORMAL" + self.stack.append(f"IN_{m_type}") + elif matched_group in ("PROTOCOL_EXIT", "TAG_EXIT", "HINT_EXIT"): + if len(self.stack) > 1: + self.stack.pop() else: - max_end_len = max(len(p) for p in cfg["ends"]) - if len(self.buffer) > max_end_len: - self.block_buffer += self.buffer[:-max_end_len] - self.buffer = self.buffer[-max_end_len:] - break + self.stack = ["NORMAL"] - elif self.state == "IN_TAG": - nl_idx = self.buffer.find("\n") - if nl_idx != -1: - self.current_role = self.buffer[:nl_idx].strip().lower() - self.buffer = self.buffer[nl_idx + 1 :] - self.state = "IN_BLOCK" - else: - break - - elif self.state == "IN_BLOCK": - cfg = self.STATE_MARKERS["TAG"] - found_idx, found_len = -1, 0 - for p in cfg["ends"]: - idx = buf_low.find(p.lower()) - if idx != -1 and (found_idx == -1 or idx < found_idx): - found_idx, found_len = idx, len(p) - - if found_idx != -1: - content = self.buffer[:found_idx] - if self.current_role != "tool": - output.append(content) - self.buffer = self.buffer[found_idx + found_len :] - self.state = "NORMAL" + if self.state == "NORMAL": self.current_role = "" - else: - max_end_len = max(len(p) for p in cfg["ends"]) - if self.current_role != "tool": - if len(self.buffer) > max_end_len: - output.append(self.buffer[:-max_end_len]) - self.buffer = self.buffer[-max_end_len:] - break - else: - if len(self.buffer) > max_end_len: - self.buffer = self.buffer[-max_end_len:] - break + + self.buffer = self.buffer[end:] return "".join(output) def flush(self) -> str: """Release remaining buffer content and perform final cleanup at stream end.""" res = "" - if self.state in ("IN_TOOL", "IN_ORPHAN", "IN_RESP", "IN_HINT", "IN_ARG", "IN_RESULT"): - res = "" - elif self.state == "IN_BLOCK" and self.current_role != "tool": - res = self.buffer - elif self.state == "NORMAL": + if self._is_outputting(): res = self.buffer + tail_match = STREAM_TAIL_RE.search(res) + if tail_match: + res = res[: -len(tail_match.group(0))] self.buffer = "" - self.state = "NORMAL" + self.stack = ["NORMAL"] + self.current_role = "" return strip_system_hints(res) @@ -1027,7 +889,7 @@ def flush(self) -> str: def _create_real_streaming_response( - generator: AsyncGenerator[ModelOutput, None], + generator: AsyncGenerator[ModelOutput], completion_id: str, created_time: int, model_name: str, @@ -1047,7 +909,6 @@ def _create_real_streaming_response( async def generate_stream(): full_thoughts, full_text = "", "" has_started = False - last_chunk_was_thought = False all_outputs: list[ModelOutput] = [] suppressor = StreamingOutputFilter() try: @@ -1067,8 +928,6 @@ async def generate_stream(): has_started = True if t_delta := chunk.thoughts_delta: - if not last_chunk_was_thought and not full_thoughts: - yield f"data: {orjson.dumps({'id': completion_id, 'object': 'chat.completion.chunk', 'created': created_time, 'model': model_name, 'choices': [{'index': 0, 'delta': {'content': ''}, 'finish_reason': None}]}).decode('utf-8')}\n\n" full_thoughts += t_delta data = { "id": completion_id, @@ -1076,16 +935,16 @@ async def generate_stream(): "created": created_time, "model": model_name, "choices": [ - {"index": 0, "delta": {"content": t_delta}, "finish_reason": None} + { + "index": 0, + "delta": {"reasoning_content": t_delta}, + "finish_reason": None, + } ], } yield f"data: {orjson.dumps(data).decode('utf-8')}\n\n" - last_chunk_was_thought = True if text_delta := chunk.text_delta: - if last_chunk_was_thought: - yield f"data: {orjson.dumps({'id': completion_id, 'object': 'chat.completion.chunk', 'created': created_time, 'model': model_name, 'choices': [{'index': 0, 'delta': {'content': '\n'}, 'finish_reason': None}]}).decode('utf-8')}\n\n" - last_chunk_was_thought = False full_text += text_delta if visible_delta := suppressor.process(text_delta): data = { @@ -1114,9 +973,6 @@ async def generate_stream(): if final_chunk.thoughts: full_thoughts = final_chunk.thoughts - if last_chunk_was_thought: - yield f"data: {orjson.dumps({'id': completion_id, 'object': 'chat.completion.chunk', 'created': created_time, 'model': model_name, 'choices': [{'index': 0, 'delta': {'content': '\n'}, 'finish_reason': None}]}).decode('utf-8')}\n\n" - if remaining_text := suppressor.flush(): data = { "id": completion_id, @@ -1129,10 +985,8 @@ async def generate_stream(): } yield f"data: {orjson.dumps(data).decode('utf-8')}\n\n" - raw_output_with_think = f"{full_thoughts}\n" if full_thoughts else "" - raw_output_with_think += full_text - assistant_text, storage_output, tool_calls = _process_llm_output( - raw_output_with_think, full_text, structured_requirement + _thoughts, assistant_text, storage_output, tool_calls = _process_llm_output( + full_thoughts, full_text, structured_requirement ) images = [] @@ -1193,8 +1047,15 @@ async def generate_stream(): } yield f"data: {orjson.dumps(data).decode('utf-8')}\n\n" - p_tok, c_tok, t_tok = _calculate_usage(messages, assistant_text, tool_calls) - usage = {"prompt_tokens": p_tok, "completion_tokens": c_tok, "total_tokens": t_tok} + p_tok, c_tok, t_tok, r_tok = _calculate_usage( + messages, assistant_text, tool_calls, full_thoughts + ) + usage = { + "prompt_tokens": p_tok, + "completion_tokens": c_tok, + "total_tokens": t_tok, + "completion_tokens_details": {"reasoning_tokens": r_tok}, + } data = { "id": completion_id, "object": "chat.completion.chunk", @@ -1210,9 +1071,10 @@ async def generate_stream(): model.model_name, client_wrapper.id, session.metadata, - messages, # This should be the prepared messages + messages, storage_output, tool_calls, + full_thoughts, ) yield f"data: {orjson.dumps(data).decode('utf-8')}\n\n" yield "data: [DONE]\n\n" @@ -1221,7 +1083,7 @@ async def generate_stream(): def _create_responses_real_streaming_response( - generator: AsyncGenerator[ModelOutput, None], + generator: AsyncGenerator[ModelOutput], response_id: str, created_time: int, model_name: str, @@ -1248,11 +1110,15 @@ def _create_responses_real_streaming_response( async def generate_stream(): yield f"data: {orjson.dumps({**base_event, 'type': 'response.created', 'response': {'id': response_id, 'object': 'response', 'created_at': created_time, 'model': model_name, 'status': 'in_progress', 'metadata': request.metadata, 'input': None, 'tools': request.tools, 'tool_choice': request.tool_choice}}).decode('utf-8')}\n\n" - message_id = f"msg_{uuid.uuid4().hex}" - yield f"data: {orjson.dumps({**base_event, 'type': 'response.output_item.added', 'output_index': 0, 'item': {'id': message_id, 'type': 'message', 'role': 'assistant', 'content': []}}).decode('utf-8')}\n\n" full_thoughts, full_text = "", "" + thought_item_id = f"reason_{uuid.uuid4().hex}" + message_item_id = f"msg_{uuid.uuid4().hex}" + thought_item_added = False + message_item_added = False last_chunk_was_thought = False + current_idx = 0 + all_outputs: list[ModelOutput] = [] suppressor = StreamingOutputFilter() @@ -1260,18 +1126,31 @@ async def generate_stream(): async for chunk in generator: all_outputs.append(chunk) if t_delta := chunk.thoughts_delta: - if not last_chunk_was_thought and not full_thoughts: - yield f"data: {orjson.dumps({**base_event, 'type': 'response.output_text.delta', 'output_index': 0, 'delta': ''}).decode('utf-8')}\n\n" + if not thought_item_added: + yield f"data: {orjson.dumps({**base_event, 'type': 'response.output_item.added', 'output_index': current_idx, 'item': {'id': thought_item_id, 'type': 'reasoning', 'status': 'in_progress', 'content': []}}).decode('utf-8')}\n\n" + yield f"data: {orjson.dumps({**base_event, 'type': 'response.content_part.added', 'output_index': current_idx, 'part_index': 0, 'part': {'type': 'reasoning_text', 'text': ''}}).decode('utf-8')}\n\n" + thought_item_added = True + full_thoughts += t_delta - yield f"data: {orjson.dumps({**base_event, 'type': 'response.output_text.delta', 'output_index': 0, 'delta': t_delta}).decode('utf-8')}\n\n" + yield f"data: {orjson.dumps({**base_event, 'type': 'response.output_text.delta', 'output_index': current_idx, 'part_index': 0, 'delta': t_delta}).decode('utf-8')}\n\n" last_chunk_was_thought = True + if text_delta := chunk.text_delta: if last_chunk_was_thought: - yield f"data: {orjson.dumps({**base_event, 'type': 'response.output_text.delta', 'output_index': 0, 'delta': '\n'}).decode('utf-8')}\n\n" + yield f"data: {orjson.dumps({**base_event, 'type': 'response.output_text.done', 'output_index': current_idx, 'part_index': 0}).decode('utf-8')}\n\n" + yield f"data: {orjson.dumps({**base_event, 'type': 'response.content_part.done', 'output_index': current_idx, 'part_index': 0}).decode('utf-8')}\n\n" + yield f"data: {orjson.dumps({**base_event, 'type': 'response.output_item.done', 'output_index': current_idx, 'item': {'id': thought_item_id, 'type': 'reasoning', 'status': 'completed', 'content': [{'type': 'reasoning_text', 'text': full_thoughts}]}}).decode('utf-8')}\n\n" + current_idx += 1 last_chunk_was_thought = False + + if not message_item_added: + yield f"data: {orjson.dumps({**base_event, 'type': 'response.output_item.added', 'output_index': current_idx, 'item': {'id': message_item_id, 'type': 'message', 'role': 'assistant', 'content': []}}).decode('utf-8')}\n\n" + yield f"data: {orjson.dumps({**base_event, 'type': 'response.content_part.added', 'output_index': current_idx, 'part_index': 0, 'part': {'type': 'output_text', 'text': ''}}).decode('utf-8')}\n\n" + message_item_added = True + full_text += text_delta if visible_delta := suppressor.process(text_delta): - yield f"data: {orjson.dumps({**base_event, 'type': 'response.output_text.delta', 'output_index': 0, 'delta': visible_delta}).decode('utf-8')}\n\n" + yield f"data: {orjson.dumps({**base_event, 'type': 'response.output_text.delta', 'output_index': current_idx, 'part_index': 0, 'delta': visible_delta}).decode('utf-8')}\n\n" except Exception as e: logger.exception(f"Error during Responses API streaming: {e}") yield f"data: {orjson.dumps({**base_event, 'type': 'error', 'error': {'message': 'Streaming error.'}}).decode('utf-8')}\n\n" @@ -1285,17 +1164,33 @@ async def generate_stream(): full_thoughts = final_chunk.thoughts if last_chunk_was_thought: - yield f"data: {orjson.dumps({**base_event, 'type': 'response.output_text.delta', 'output_index': 0, 'delta': '\n'}).decode('utf-8')}\n\n" - if remaining_text := suppressor.flush(): - yield f"data: {orjson.dumps({**base_event, 'type': 'response.output_text.delta', 'output_index': 0, 'delta': remaining_text}).decode('utf-8')}\n\n" - yield f"data: {orjson.dumps({**base_event, 'type': 'response.output_text.done', 'output_index': 0}).decode('utf-8')}\n\n" + yield f"data: {orjson.dumps({**base_event, 'type': 'response.content_part.done', 'output_index': current_idx, 'part_index': 0}).decode('utf-8')}\n\n" + yield f"data: {orjson.dumps({**base_event, 'type': 'response.output_item.done', 'output_index': current_idx, 'item': {'id': thought_item_id, 'type': 'reasoning', 'status': 'completed', 'content': [{'type': 'reasoning_text', 'text': full_thoughts}]}}).decode('utf-8')}\n\n" + current_idx += 1 - raw_output_with_think = f"{full_thoughts}\n" if full_thoughts else "" - raw_output_with_think += full_text - assistant_text, storage_output, detected_tool_calls = _process_llm_output( - raw_output_with_think, full_text, structured_requirement + remaining_from_suppressor = suppressor.flush() + if remaining_from_suppressor: + if not message_item_added: + yield f"data: {orjson.dumps({**base_event, 'type': 'response.output_item.added', 'output_index': current_idx, 'item': {'id': message_item_id, 'type': 'message', 'role': 'assistant', 'content': []}}).decode('utf-8')}\n\n" + yield f"data: {orjson.dumps({**base_event, 'type': 'response.content_part.added', 'output_index': current_idx, 'part_index': 0, 'part': {'type': 'output_text', 'text': ''}}).decode('utf-8')}\n\n" + message_item_added = True + yield f"data: {orjson.dumps({**base_event, 'type': 'response.output_text.delta', 'output_index': current_idx, 'part_index': 0, 'delta': remaining_from_suppressor}).decode('utf-8')}\n\n" + + # IMPORTANT: Process output now to get the final assistant_text + _thoughts, assistant_text, storage_output, detected_tool_calls = _process_llm_output( + full_thoughts, full_text, structured_requirement ) + response_contents: list[ResponseOutputContent] = [] + if message_item_added: + yield f"data: {orjson.dumps({**base_event, 'type': 'response.output_text.done', 'output_index': current_idx, 'part_index': 0}).decode('utf-8')}\n\n" + yield f"data: {orjson.dumps({**base_event, 'type': 'response.content_part.done', 'output_index': current_idx, 'part_index': 0}).decode('utf-8')}\n\n" + + msg_content = [{"type": "output_text", "text": assistant_text}] + yield f"data: {orjson.dumps({**base_event, 'type': 'response.output_item.done', 'output_index': current_idx, 'item': {'id': message_item_id, 'type': 'message', 'role': 'assistant', 'content': msg_content}}).decode('utf-8')}\n\n" + response_contents.append(ResponseOutputContent(type="output_text", text=assistant_text)) + current_idx += 1 + images = [] seen_urls = set() for out in all_outputs: @@ -1305,7 +1200,7 @@ async def generate_stream(): images.append(img) seen_urls.add(img.url) - response_contents, image_call_items = [], [] + image_call_items: list[ResponseImageGenerationCall] = [] seen_hashes = set() for image in images: try: @@ -1321,25 +1216,20 @@ async def generate_stream(): img_id = fname img_format = "png" if isinstance(image, GeneratedImage) else "jpeg" - image_url = f"![{fname}]({base_url}images/{fname}?token={get_image_token(fname)})" - image_call_items.append( - ResponseImageGenerationCall( - id=img_id, - result=b64, - output_format=img_format, - size=f"{w}x{h}" if w and h else None, - ) + img_item = ResponseImageGenerationCall( + id=img_id, + result=b64, + output_format=img_format, + size=f"{w}x{h}" if w and h else None, ) - response_contents.append(ResponseOutputContent(type="output_text", text=image_url)) + image_call_items.append(img_item) + + yield f"data: {orjson.dumps({**base_event, 'type': 'response.output_item.added', 'output_index': current_idx, 'item': img_item.model_dump(mode='json')}).decode('utf-8')}\n\n" + yield f"data: {orjson.dumps({**base_event, 'type': 'response.output_item.done', 'output_index': current_idx, 'item': img_item.model_dump(mode='json')}).decode('utf-8')}\n\n" + current_idx += 1 except Exception as exc: logger.warning(f"Failed to process image in stream: {exc}") - if assistant_text: - response_contents.append(ResponseOutputContent(type="output_text", text=assistant_text)) - if not response_contents: - response_contents.append(ResponseOutputContent(type="output_text", text="")) - - # Aggregate images for storage image_markdown = "" for img_call in image_call_items: fname = f"{img_call.id}.{img_call.output_format}" @@ -1349,21 +1239,28 @@ async def generate_stream(): if image_markdown: storage_output += image_markdown - yield f"data: {orjson.dumps({**base_event, 'type': 'response.output_item.done', 'output_index': 0, 'item': {'id': message_id, 'type': 'message', 'role': 'assistant', 'content': [c.model_dump(mode='json') for c in response_contents]}}).decode('utf-8')}\n\n" - - current_idx = 1 for call in detected_tool_calls: tc_item = ResponseToolCall(id=call.id, status="completed", function=call.function) yield f"data: {orjson.dumps({**base_event, 'type': 'response.output_item.added', 'output_index': current_idx, 'item': tc_item.model_dump(mode='json')}).decode('utf-8')}\n\n" yield f"data: {orjson.dumps({**base_event, 'type': 'response.output_item.done', 'output_index': current_idx, 'item': tc_item.model_dump(mode='json')}).decode('utf-8')}\n\n" current_idx += 1 - for img_call in image_call_items: - yield f"data: {orjson.dumps({**base_event, 'type': 'response.output_item.added', 'output_index': current_idx, 'item': img_call.model_dump(mode='json')}).decode('utf-8')}\n\n" - yield f"data: {orjson.dumps({**base_event, 'type': 'response.output_item.done', 'output_index': current_idx, 'item': img_call.model_dump(mode='json')}).decode('utf-8')}\n\n" - current_idx += 1 - p_tok, c_tok, t_tok = _calculate_usage(messages, assistant_text, detected_tool_calls) - usage = ResponseUsage(input_tokens=p_tok, output_tokens=c_tok, total_tokens=t_tok) + p_tok, c_tok, t_tok, r_tok = _calculate_usage( + messages, assistant_text, detected_tool_calls, full_thoughts + ) + usage = ResponseUsage( + input_tokens=p_tok, + output_tokens=c_tok, + total_tokens=t_tok, + output_tokens_details={"reasoning_tokens": r_tok}, + ) + + # Ensure we have at least one content item if none was created + if not response_contents: + response_contents.append( + ResponseOutputContent(type="output_text", text=assistant_text or "") + ) + payload = _create_responses_standard_payload( response_id, created_time, @@ -1374,6 +1271,7 @@ async def generate_stream(): usage, request, None, + full_thoughts, ) _persist_conversation( db, @@ -1383,8 +1281,10 @@ async def generate_stream(): messages, storage_output, detected_tool_calls, + full_thoughts, ) yield f"data: {orjson.dumps({**base_event, 'type': 'response.completed', 'response': payload.model_dump(mode='json')}).decode('utf-8')}\n\n" + yield f"data: {orjson.dumps({**base_event, 'type': 'response.done'})}\n\n" yield "data: [DONE]\n\n" return StreamingResponse(generate_stream(), media_type="text/event-stream") @@ -1455,10 +1355,12 @@ async def create_chat_completion( m_input, files = await GeminiClientWrapper.process_conversation(msgs, tmp_dir) except Exception as e: logger.exception("Error in preparing conversation") - raise HTTPException(status_code=status.HTTP_503_SERVICE_UNAVAILABLE, detail=str(e)) + raise HTTPException( + status_code=status.HTTP_503_SERVICE_UNAVAILABLE, detail=str(e) + ) from e completion_id = f"chatcmpl-{uuid.uuid4()}" - created_time = int(datetime.now(tz=timezone.utc).timestamp()) + created_time = int(datetime.now(tz=UTC).timestamp()) try: assert session and client @@ -1470,7 +1372,7 @@ async def create_chat_completion( ) except Exception as e: logger.exception("Gemini API error") - raise HTTPException(status_code=status.HTTP_502_BAD_GATEWAY, detail=str(e)) + raise HTTPException(status_code=status.HTTP_502_BAD_GATEWAY, detail=str(e)) from e if request.stream: return _create_real_streaming_response( @@ -1488,7 +1390,7 @@ async def create_chat_completion( ) try: - raw_with_t = GeminiClientWrapper.extract_output(resp_or_stream, include_thoughts=True) + thoughts = resp_or_stream.thoughts raw_clean = GeminiClientWrapper.extract_output(resp_or_stream, include_thoughts=False) except Exception as exc: logger.exception("Gemini output parsing failed.") @@ -1496,8 +1398,8 @@ async def create_chat_completion( status_code=status.HTTP_502_BAD_GATEWAY, detail="Malformed response." ) from exc - visible_output, storage_output, tool_calls = _process_llm_output( - raw_with_t, raw_clean, structured_requirement + thoughts, visible_output, storage_output, tool_calls = _process_llm_output( + thoughts, raw_clean, structured_requirement ) # Process images for OpenAI non-streaming flow @@ -1525,8 +1427,15 @@ async def create_chat_completion( if tool_calls_payload: logger.debug(f"Detected tool calls: {reprlib.repr(tool_calls_payload)}") - p_tok, c_tok, t_tok = _calculate_usage(request.messages, visible_output, tool_calls) - usage = {"prompt_tokens": p_tok, "completion_tokens": c_tok, "total_tokens": t_tok} + p_tok, c_tok, t_tok, r_tok = _calculate_usage( + request.messages, visible_output, tool_calls, thoughts + ) + usage = { + "prompt_tokens": p_tok, + "completion_tokens": c_tok, + "total_tokens": t_tok, + "completion_tokens_details": {"reasoning_tokens": r_tok}, + } payload = _create_chat_completion_standard_payload( completion_id, created_time, @@ -1535,6 +1444,7 @@ async def create_chat_completion( tool_calls_payload, "tool_calls" if tool_calls else "stop", usage, + thoughts, ) _persist_conversation( db, @@ -1544,6 +1454,7 @@ async def create_chat_completion( msgs, # Use prepared messages 'msgs' storage_output, tool_calls, + thoughts, ) return payload @@ -1620,10 +1531,12 @@ async def create_response( m_input, files = await GeminiClientWrapper.process_conversation(messages, tmp_dir) except Exception as e: logger.exception("Error in preparing conversation") - raise HTTPException(status_code=status.HTTP_503_SERVICE_UNAVAILABLE, detail=str(e)) + raise HTTPException( + status_code=status.HTTP_503_SERVICE_UNAVAILABLE, detail=str(e) + ) from e response_id = f"resp_{uuid.uuid4().hex}" - created_time = int(datetime.now(tz=timezone.utc).timestamp()) + created_time = int(datetime.now(tz=UTC).timestamp()) try: assert session and client @@ -1635,7 +1548,7 @@ async def create_response( ) except Exception as e: logger.exception("Gemini API error") - raise HTTPException(status_code=status.HTTP_502_BAD_GATEWAY, detail=str(e)) + raise HTTPException(status_code=status.HTTP_502_BAD_GATEWAY, detail=str(e)) from e if request.stream: return _create_responses_real_streaming_response( @@ -1655,15 +1568,17 @@ async def create_response( ) try: - raw_t = GeminiClientWrapper.extract_output(resp_or_stream, include_thoughts=True) - raw_c = GeminiClientWrapper.extract_output(resp_or_stream, include_thoughts=False) + thoughts = resp_or_stream.thoughts + raw_clean = GeminiClientWrapper.extract_output(resp_or_stream, include_thoughts=False) except Exception as exc: logger.exception("Gemini parsing failed") raise HTTPException( status_code=status.HTTP_502_BAD_GATEWAY, detail="Malformed response." ) from exc - assistant_text, storage_output, tool_calls = _process_llm_output(raw_t, raw_c, struct_req) + thoughts, assistant_text, storage_output, tool_calls = _process_llm_output( + thoughts, raw_clean, struct_req + ) images = resp_or_stream.images or [] if ( request.tool_choice is not None and request.tool_choice.type == "image_generation" @@ -1718,8 +1633,13 @@ async def create_response( if image_markdown: storage_output += image_markdown - p_tok, c_tok, t_tok = _calculate_usage(messages, assistant_text, tool_calls) - usage = ResponseUsage(input_tokens=p_tok, output_tokens=c_tok, total_tokens=t_tok) + p_tok, c_tok, t_tok, r_tok = _calculate_usage(messages, assistant_text, tool_calls, thoughts) + usage = ResponseUsage( + input_tokens=p_tok, + output_tokens=c_tok, + total_tokens=t_tok, + output_tokens_details={"reasoning_tokens": r_tok}, + ) payload = _create_responses_standard_payload( response_id, created_time, @@ -1730,8 +1650,16 @@ async def create_response( usage, request, norm_input, + thoughts, ) _persist_conversation( - db, model.model_name, client.id, session.metadata, messages, storage_output, tool_calls + db, + model.model_name, + client.id, + session.metadata, + messages, + storage_output, + tool_calls, + thoughts, ) return payload diff --git a/app/server/health.py b/app/server/health.py index f521db1..444c938 100644 --- a/app/server/health.py +++ b/app/server/health.py @@ -1,8 +1,8 @@ from fastapi import APIRouter from loguru import logger -from ..models import HealthCheckResponse -from ..services import GeminiClientPool, LMDBConversationStore +from app.models import HealthCheckResponse +from app.services import GeminiClientPool, LMDBConversationStore router = APIRouter() diff --git a/app/server/images.py b/app/server/images.py index fe078f7..e1c161c 100644 --- a/app/server/images.py +++ b/app/server/images.py @@ -1,7 +1,7 @@ from fastapi import APIRouter, HTTPException, Query from fastapi.responses import FileResponse -from ..server.middleware import get_image_store_dir, verify_image_token +from app.server.middleware import get_image_store_dir, verify_image_token router = APIRouter() diff --git a/app/server/middleware.py b/app/server/middleware.py index 630e1f5..b5bc55b 100644 --- a/app/server/middleware.py +++ b/app/server/middleware.py @@ -6,11 +6,11 @@ from fastapi import Depends, FastAPI, HTTPException, Request, status from fastapi.middleware.cors import CORSMiddleware -from fastapi.responses import ORJSONResponse +from fastapi.responses import JSONResponse from fastapi.security import HTTPAuthorizationCredentials, HTTPBearer from loguru import logger -from ..utils import g_config +from app.utils import g_config # Persistent directory for storing generated images IMAGE_STORE_DIR = Path(tempfile.gettempdir()) / "ai_generated_images" @@ -70,12 +70,12 @@ def cleanup_expired_images(retention_days: int) -> int: def global_exception_handler(request: Request, exc: Exception): if isinstance(exc, HTTPException): - return ORJSONResponse( + return JSONResponse( status_code=exc.status_code, content={"error": {"message": exc.detail}}, ) - return ORJSONResponse( + return JSONResponse( status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, content={"error": {"message": str(exc)}}, ) diff --git a/app/services/client.py b/app/services/client.py index 70dfce9..b8f976b 100644 --- a/app/services/client.py +++ b/app/services/client.py @@ -5,9 +5,9 @@ from gemini_webapi import GeminiClient, ModelOutput from loguru import logger -from ..models import Message -from ..utils import g_config -from ..utils.helper import ( +from app.models import Message +from app.utils import g_config +from app.utils.helper import ( add_tag, normalize_llm_text, save_file_to_tempfile, @@ -33,7 +33,7 @@ async def init( timeout: float = cast(float, _UNSET), watchdog_timeout: float = cast(float, _UNSET), auto_close: bool = False, - close_delay: float = 300, + close_delay: float = cast(float, _UNSET), auto_refresh: bool = cast(bool, _UNSET), refresh_interval: float = cast(float, _UNSET), verbose: bool = cast(bool, _UNSET), @@ -44,6 +44,7 @@ async def init( config = g_config.gemini timeout = cast(float, _resolve(timeout, config.timeout)) watchdog_timeout = cast(float, _resolve(watchdog_timeout, config.watchdog_timeout)) + close_delay = timeout auto_refresh = cast(bool, _resolve(auto_refresh, config.auto_refresh)) refresh_interval = cast(float, _resolve(refresh_interval, config.refresh_interval)) verbose = cast(bool, _resolve(verbose, config.verbose)) @@ -146,9 +147,8 @@ async def process_message( model_input = "\n".join(fragment for fragment in text_fragments if fragment is not None) - if model_input or message.role == "tool": - if tagged: - model_input = add_tag(message.role, model_input) + if (model_input or message.role == "tool") and tagged: + model_input = add_tag(message.role, model_input) return model_input, files diff --git a/app/services/lmdb.py b/app/services/lmdb.py index abf8859..07f0a23 100644 --- a/app/services/lmdb.py +++ b/app/services/lmdb.py @@ -1,25 +1,24 @@ import hashlib -import re import string from contextlib import contextmanager from datetime import datetime, timedelta from pathlib import Path -from typing import Any, Dict, List, Optional +from typing import Any import lmdb import orjson from loguru import logger -from ..models import ContentItem, ConversationInStore, Message -from ..utils import g_config -from ..utils.helper import ( +from app.models import ContentItem, ConversationInStore, Message +from app.utils import g_config +from app.utils.helper import ( extract_tool_calls, normalize_llm_text, remove_tool_call_blocks, strip_system_hints, unescape_text, ) -from ..utils.singleton import Singleton +from app.utils.singleton import Singleton _VOLATILE_TRANS_TABLE = str.maketrans("", "", string.whitespace + string.punctuation) @@ -42,7 +41,6 @@ def _normalize_text(text: str | None, fuzzy: bool = False) -> str | None: text = normalize_llm_text(text) text = unescape_text(text) - text = LMDBConversationStore.remove_think_tags(text) text = remove_tool_call_blocks(text) if fuzzy: @@ -60,6 +58,9 @@ def _hash_message(message: Message, fuzzy: bool = False) -> str: "role": message.role, "name": message.name or None, "tool_call_id": message.tool_call_id or None, + "reasoning_content": _normalize_text(message.reasoning_content) + if message.reasoning_content + else None, } content = message.content @@ -125,7 +126,7 @@ def _hash_message(message: Message, fuzzy: bool = False) -> str: def _hash_conversation( - client_id: str, model: str, messages: List[Message], fuzzy: bool = False + client_id: str, model: str, messages: list[Message], fuzzy: bool = False ) -> str: """Generate a hash for a list of messages and model name, tied to a specific client_id.""" combined_hash = hashlib.sha256() @@ -145,9 +146,9 @@ class LMDBConversationStore(metaclass=Singleton): def __init__( self, - db_path: Optional[str] = None, - max_db_size: Optional[int] = None, - retention_days: Optional[int] = None, + db_path: str | None = None, + max_db_size: int | None = None, + retention_days: int | None = None, ): """ Initialize LMDB store. @@ -219,7 +220,7 @@ def _get_transaction(self, write: bool = False): raise @staticmethod - def _decode_index_value(data: bytes) -> List[str]: + def _decode_index_value(data: bytes) -> list[str]: """Decode index value, handling both legacy single-string and new list-of-strings formats.""" if not data: return [] @@ -238,7 +239,7 @@ def _decode_index_value(data: bytes) -> List[str]: @staticmethod def _update_index(txn: lmdb.Transaction, prefix: str, hash_val: str, storage_key: str): """Add a storage key to the index for a given hash, avoiding duplicates.""" - idx_key = f"{prefix}{hash_val}".encode("utf-8") + idx_key = f"{prefix}{hash_val}".encode() existing = txn.get(idx_key) keys = LMDBConversationStore._decode_index_value(existing) if existing else [] if storage_key not in keys: @@ -248,7 +249,7 @@ def _update_index(txn: lmdb.Transaction, prefix: str, hash_val: str, storage_key @staticmethod def _remove_from_index(txn: lmdb.Transaction, prefix: str, hash_val: str, storage_key: str): """Remove a specific storage key from the index for a given hash.""" - idx_key = f"{prefix}{hash_val}".encode("utf-8") + idx_key = f"{prefix}{hash_val}".encode() existing = txn.get(idx_key) if not existing: return @@ -263,7 +264,7 @@ def _remove_from_index(txn: lmdb.Transaction, prefix: str, hash_val: str, storag def store( self, conv: ConversationInStore, - custom_key: Optional[str] = None, + custom_key: str | None = None, ) -> str: """ Store a conversation model in LMDB. @@ -312,7 +313,7 @@ def store( ) raise - def get(self, key: str) -> Optional[ConversationInStore]: + def get(self, key: str) -> ConversationInStore | None: """ Retrieve conversation data by key. @@ -340,7 +341,7 @@ def get(self, key: str) -> Optional[ConversationInStore]: logger.error(f"Unexpected error retrieving messages with key {key[:12]}: {e}") return None - def find(self, model: str, messages: List[Message]) -> Optional[ConversationInStore]: + def find(self, model: str, messages: list[Message]) -> ConversationInStore | None: """ Search conversation data by message list. Tries raw matching, then sanitized matching, and finally fuzzy matching. @@ -360,12 +361,13 @@ def find(self, model: str, messages: List[Message]) -> Optional[ConversationInSt return conv cleaned_messages = self.sanitize_messages(messages) - if cleaned_messages != messages: - if conv := self._find_by_message_list(model, cleaned_messages): - logger.debug( - f"Session found for '{model}' with {len(cleaned_messages)} cleaned messages." - ) - return conv + if cleaned_messages != messages and ( + conv := self._find_by_message_list(model, cleaned_messages) + ): + logger.debug( + f"Session found for '{model}' with {len(cleaned_messages)} cleaned messages." + ) + return conv if conv := self._find_by_message_list(model, messages, fuzzy=True): logger.debug( @@ -379,9 +381,9 @@ def find(self, model: str, messages: List[Message]) -> Optional[ConversationInSt def _find_by_message_list( self, model: str, - messages: List[Message], + messages: list[Message], fuzzy: bool = False, - ) -> Optional[ConversationInStore]: + ) -> ConversationInStore | None: """ Internal find implementation based on a message list. @@ -440,7 +442,7 @@ def exists(self, key: str) -> bool: logger.error(f"Failed to check existence of key {key}: {e}") return False - def delete(self, key: str) -> Optional[ConversationInStore]: + def delete(self, key: str) -> ConversationInStore | None: """Delete conversation model by key.""" try: with self._get_transaction(write=True) as txn: @@ -466,7 +468,7 @@ def delete(self, key: str) -> Optional[ConversationInStore]: logger.error(f"Failed to delete messages with key {key[:12]}: {e}") return None - def keys(self, prefix: str = "", limit: Optional[int] = None) -> List[str]: + def keys(self, prefix: str = "", limit: int | None = None) -> list[str]: """List all keys in the store, optionally filtered by prefix.""" keys = [] try: @@ -492,7 +494,7 @@ def keys(self, prefix: str = "", limit: Optional[int] = None) -> List[str]: logger.error(f"Failed to list keys: {e}") return keys - def cleanup_expired(self, retention_days: Optional[int] = None) -> int: + def cleanup_expired(self, retention_days: int | None = None) -> int: """Delete conversations older than the given retention period.""" retention_value = ( self.retention_days if retention_days is None else max(0, int(retention_days)) @@ -561,7 +563,7 @@ def cleanup_expired(self, retention_days: Optional[int] = None) -> int: return removed - def stats(self) -> Dict[str, Any]: + def stats(self) -> dict[str, Any]: """Get database statistics.""" if not self._env: logger.error("LMDB environment not initialized") @@ -583,21 +585,23 @@ def __del__(self): """Cleanup on destruction.""" self.close() - @staticmethod - def remove_think_tags(text: str) -> str: - """Remove all ... tags and strip whitespace.""" - if not text: - return text - cleaned_content = re.sub(r".*?", "", text, flags=re.DOTALL) - return cleaned_content.strip() - @staticmethod def sanitize_messages(messages: list[Message]) -> list[Message]: """Clean all messages of internal markers, hints and normalize tool calls.""" cleaned_messages = [] for msg in messages: + update_data = {} + content_changed = False + + # Normalize reasoning_content + if msg.reasoning_content: + norm_reasoning = _normalize_text(msg.reasoning_content) + if norm_reasoning != msg.reasoning_content: + update_data["reasoning_content"] = norm_reasoning + content_changed = True + if isinstance(msg.content, str): - text = LMDBConversationStore.remove_think_tags(msg.content) + text = msg.content tool_calls = msg.tool_calls if msg.role == "assistant" and not tool_calls: @@ -607,48 +611,41 @@ def sanitize_messages(messages: list[Message]) -> list[Message]: normalized_content = text.strip() or None - if normalized_content != msg.content or tool_calls != msg.tool_calls: - cleaned_msg = msg.model_copy( - update={ - "content": normalized_content, - "tool_calls": tool_calls or None, - } - ) - cleaned_messages.append(cleaned_msg) - else: - cleaned_messages.append(msg) + if normalized_content != msg.content: + update_data["content"] = normalized_content + content_changed = True + if tool_calls != msg.tool_calls: + update_data["tool_calls"] = tool_calls or None + content_changed = True + elif isinstance(msg.content, list): new_content = [] all_extracted_calls = list(msg.tool_calls or []) - changed = False + list_changed = False for item in msg.content: if isinstance(item, ContentItem) and item.type == "text" and item.text: - text = LMDBConversationStore.remove_think_tags(item.text) + text = item.text if msg.role == "assistant" and not msg.tool_calls: text, extracted = extract_tool_calls(text) if extracted: all_extracted_calls.extend(extracted) - changed = True + list_changed = True else: text = strip_system_hints(text) if text != item.text: - changed = True + list_changed = True item = item.model_copy(update={"text": text.strip() or None}) new_content.append(item) - if changed: - cleaned_messages.append( - msg.model_copy( - update={ - "content": new_content, - "tool_calls": all_extracted_calls or None, - } - ) - ) - else: - cleaned_messages.append(msg) + if list_changed: + update_data["content"] = new_content + update_data["tool_calls"] = all_extracted_calls or None + content_changed = True + + if content_changed: + cleaned_messages.append(msg.model_copy(update=update_data)) else: cleaned_messages.append(msg) return cleaned_messages diff --git a/app/services/pool.py b/app/services/pool.py index decc21a..3c26e3d 100644 --- a/app/services/pool.py +++ b/app/services/pool.py @@ -1,11 +1,13 @@ import asyncio +import inspect from collections import deque -from typing import Dict, List, Optional +from gemini_webapi import GeminiClient from loguru import logger -from ..utils import g_config -from ..utils.singleton import Singleton +from app.utils import g_config +from app.utils.singleton import Singleton + from .client import GeminiClientWrapper @@ -13,21 +15,32 @@ class GeminiClientPool(metaclass=Singleton): """Pool of GeminiClient instances identified by unique ids.""" def __init__(self) -> None: - self._clients: List[GeminiClientWrapper] = [] - self._id_map: Dict[str, GeminiClientWrapper] = {} + self._clients: list[GeminiClientWrapper] = [] + self._id_map: dict[str, GeminiClientWrapper] = {} self._round_robin: deque[GeminiClientWrapper] = deque() - self._restart_locks: Dict[str, asyncio.Lock] = {} + self._restart_locks: dict[str, asyncio.Lock] = {} if len(g_config.gemini.clients) == 0: raise ValueError("No Gemini clients configured") for c in g_config.gemini.clients: - client = GeminiClientWrapper( - client_id=c.id, - secure_1psid=c.secure_1psid, - secure_1psidts=c.secure_1psidts, - proxy=c.proxy, - ) + kwargs = { + "client_id": c.id, + "secure_1psid": c.secure_1psid, + "secure_1psidts": c.secure_1psidts, + "proxy": c.proxy, + } + if c.cookies: + sig = inspect.signature(GeminiClient.__init__) + if "cookies" in sig.parameters: + kwargs["cookies"] = c.cookies + else: + logger.debug( + f"Ignoring 'cookies' in config for client {c.id} because " + "the current version of gemini_webapi doesn't support it." + ) + + client = GeminiClientWrapper(**kwargs) self._clients.append(client) self._id_map[c.id] = client self._round_robin.append(client) @@ -55,7 +68,7 @@ async def init(self) -> None: if success_count == 0: raise RuntimeError("Failed to initialize any Gemini clients") - async def acquire(self, client_id: Optional[str] = None) -> GeminiClientWrapper: + async def acquire(self, client_id: str | None = None) -> GeminiClientWrapper: """Return a healthy client by id or using round-robin.""" if not self._round_robin: raise RuntimeError("No Gemini clients configured") @@ -106,10 +119,10 @@ async def _ensure_client_ready(self, client: GeminiClientWrapper) -> bool: return False @property - def clients(self) -> List[GeminiClientWrapper]: + def clients(self) -> list[GeminiClientWrapper]: """Return managed clients.""" return self._clients - def status(self) -> Dict[str, bool]: + def status(self) -> dict[str, bool]: """Return running status for each client.""" return {client.id: client.running() for client in self._clients} diff --git a/app/utils/config.py b/app/utils/config.py index 4c1709f..7371623 100644 --- a/app/utils/config.py +++ b/app/utils/config.py @@ -1,7 +1,7 @@ import ast import os import sys -from typing import Any, Literal, Optional +from typing import Any, Literal import orjson from loguru import logger @@ -28,7 +28,7 @@ class ServerConfig(BaseModel): host: str = Field(default="0.0.0.0", description="Server host address") port: int = Field(default=8000, ge=1, le=65535, description="Server port number") - api_key: Optional[str] = Field( + api_key: str | None = Field( default=None, description="API key for authentication, if set, will enable API key validation", ) @@ -41,22 +41,35 @@ class GeminiClientSettings(BaseModel): id: str = Field(..., description="Unique identifier for the client") secure_1psid: str = Field(..., description="Gemini Secure 1PSID") secure_1psidts: str = Field(..., description="Gemini Secure 1PSIDTS") - proxy: Optional[str] = Field(default=None, description="Proxy URL for this Gemini client") + proxy: str | None = Field(default=None, description="Proxy URL for this Gemini client") + cookies: dict[str, str] | None = Field( + default=None, description="Optional custom cookies for this Gemini client" + ) @field_validator("proxy", mode="before") @classmethod - def _blank_proxy_to_none(cls, value: Optional[str]) -> Optional[str]: + def _blank_proxy_to_none(cls, value: str | None) -> str | None: if value is None: return None stripped = value.strip() return stripped or None + @field_validator("cookies", mode="before") + @classmethod + def _parse_cookies(cls, v: Any) -> Any: + if isinstance(v, str) and v.strip().startswith("{"): + try: + return orjson.loads(v) + except orjson.JSONDecodeError: + pass + return v + class GeminiModelConfig(BaseModel): """Configuration for a custom Gemini model.""" - model_name: Optional[str] = Field(default=None, description="Name of the model") - model_header: Optional[dict[str, Optional[str]]] = Field( + model_name: str | None = Field(default=None, description="Name of the model") + model_header: dict[str, str | None] | None = Field( default=None, description="Header for the model" ) @@ -67,7 +80,6 @@ def _parse_json_string(cls, v: Any) -> Any: try: return orjson.loads(v) except orjson.JSONDecodeError: - # Return the original value to let Pydantic handle the error or type mismatch return v return v @@ -83,13 +95,11 @@ class GeminiConfig(BaseModel): default="append", description="Strategy for loading models: 'append' merges custom with default, 'overwrite' uses only custom", ) - timeout: int = Field(default=300, ge=30, description="Init timeout in seconds") - watchdog_timeout: int = Field( - default=60, ge=10, le=75, description="Watchdog timeout in seconds (Not more than 75s)" - ) + timeout: int = Field(default=600, ge=30, description="Init timeout in seconds") + watchdog_timeout: int = Field(default=300, ge=30, description="Watchdog timeout in seconds") auto_refresh: bool = Field(True, description="Enable auto-refresh for Gemini cookies") refresh_interval: int = Field( - default=540, + default=600, ge=60, description="Interval in seconds to refresh Gemini cookies (Not less than 60s)", ) diff --git a/app/utils/helper.py b/app/utils/helper.py index 64df4f7..187f310 100644 --- a/app/utils/helper.py +++ b/app/utils/helper.py @@ -10,11 +10,11 @@ from pathlib import Path from urllib.parse import urlparse -import httpx import orjson +from curl_cffi.requests import AsyncSession from loguru import logger -from ..models import FunctionCall, Message, ToolCall +from app.models import FunctionCall, Message, ToolCall VALID_TAG_ROLES = {"user", "assistant", "system", "tool"} TOOL_WRAP_HINT = ( @@ -36,41 +36,88 @@ "CRITICAL: Do NOT mix natural language with protocol tags. Either respond naturally OR provide the protocol block alone. There is no middle ground.\n" ) TOOL_BLOCK_RE = re.compile( - r"(?:\[ToolCalls]|\\\[ToolCalls\\])\s*(.*?)\s*(?:\[/ToolCalls]|\\\[\\/ToolCalls\\])", + r"\\?\[\s*ToolCalls\s*\\?]\s*(.*?)\s*\\?\[\s*\\?/\s*ToolCalls\s*\\?]", re.DOTALL | re.IGNORECASE, ) TOOL_CALL_RE = re.compile( - r"(?:\[Call:|\\\[Call\\:)(?P(?:[^]\\]|\\.)+)(?:]|\\])\s*(?P.*?)\s*(?:\[/Call]|\\\[\\/Call\\])", + r"\\?\[\s*Call\s*\\?:\s*(?P(?:[^]\\]|\\.)+)\s*\\?]\s*(?P.*?)\s*\\?\[\s*\\?/\s*Call\s*\\?]", re.DOTALL | re.IGNORECASE, ) RESPONSE_BLOCK_RE = re.compile( - r"(?:\[ToolResults]|\\\[ToolResults\\])\s*(.*?)\s*(?:\[/ToolResults]|\\\[\\/ToolResults\\])", + r"\\?\[\s*ToolResults\s*\\?]\s*(.*?)\s*\\?\[\s*\\?/\s*ToolResults\s*\\?]", re.DOTALL | re.IGNORECASE, ) RESPONSE_ITEM_RE = re.compile( - r"(?:\[Result:|\\\[Result\\:)(?P(?:[^]\\]|\\.)+)(?:]|\\])\s*(?P.*?)\s*(?:\[/Result]|\\\[\\/Result\\])", + r"\\?\[\s*Result\s*\\?:\s*(?P(?:[^]\\]|\\.)+)\s*\\?]\s*(?P.*?)\s*\\?\[\s*\\?/\s*Result\s*\\?]", re.DOTALL | re.IGNORECASE, ) TAGGED_ARG_RE = re.compile( - r"(?:\[CallParameter:|\\\[CallParameter\\:)(?P(?:[^]\\]|\\.)+)(?:]|\\])\s*(?P.*?)\s*(?:\[/CallParameter]|\\\[\\/CallParameter\\])", + r"\\?\[\s*CallParameter\s*\\?:\s*(?P(?:[^]\\]|\\.)+)\s*\\?]\s*(?P.*?)\s*\\?\[\s*\\?/\s*CallParameter\s*\\?]", re.DOTALL | re.IGNORECASE, ) TAGGED_RESULT_RE = re.compile( - r"(?:\[ToolResult]|\\\[ToolResult\\])\s*(.*?)\s*(?:\[/ToolResult]|\\\[\\/ToolResult\\])", + r"\\?\[\s*ToolResult\s*\\?]\s*(.*?)\s*\\?\[\s*\\?/\s*ToolResult\s*\\?]", re.DOTALL | re.IGNORECASE, ) CONTROL_TOKEN_RE = re.compile( - r"<\|im_(?:start|end)\|>|\\<\\\|im\\_(?:start|end)\\\|\\>", re.IGNORECASE + r"\\?\s*<\s*\\?\|\s*im\s*\\?_(?:start|end)\s*\\?\|\s*>\s*", re.IGNORECASE ) CHATML_START_RE = re.compile( - r"(?:<\|im_start\|>|\\<\\\|im\\_start\\\|\\>)\s*(\w+)\s*\n?", re.IGNORECASE + r"\\?\s*<\s*\\?\|\s*im\s*\\?_start\s*\\?\|\s*>\s*(\w+)\s*\n?", re.IGNORECASE ) -CHATML_END_RE = re.compile(r"<\|im_end\|>|\\<\\\|im\\_end\\\|\\>", re.IGNORECASE) +CHATML_END_RE = re.compile(r"\\?\s*<\s*\\?\|\s*im\s*\\?_end\s*\\?\|\s*>\s*", re.IGNORECASE) COMMONMARK_UNESCAPE_RE = re.compile(r"\\([!\"#$%&'()*+,\-./:;<=>?@\[\\\]^_`{|}~])") +PARAM_FENCE_RE = re.compile(r"^(?P`{3,})") TOOL_HINT_STRIPPED = TOOL_WRAP_HINT.strip() _hint_lines = [line.strip() for line in TOOL_WRAP_HINT.split("\n") if line.strip()] TOOL_HINT_LINE_START = _hint_lines[0] if _hint_lines else "" TOOL_HINT_LINE_END = _hint_lines[-1] if _hint_lines else "" +TOOL_HINT_START_ESC = re.escape(TOOL_HINT_LINE_START) if TOOL_HINT_LINE_START else "" +TOOL_HINT_END_ESC = re.escape(TOOL_HINT_LINE_END) if TOOL_HINT_LINE_END else "" + +HINT_FULL_RE = ( + re.compile(rf"\n?{TOOL_HINT_START_ESC}:?.*?{TOOL_HINT_END_ESC}\n?", re.DOTALL | re.IGNORECASE) + if TOOL_HINT_START_ESC and TOOL_HINT_END_ESC + else None +) +HINT_START_RE = ( + re.compile(rf"\n?{TOOL_HINT_START_ESC}:?\s*", re.IGNORECASE) if TOOL_HINT_START_ESC else None +) +HINT_END_RE = ( + re.compile(rf"\s*{TOOL_HINT_END_ESC}\n?", re.IGNORECASE) if TOOL_HINT_END_ESC else None +) + +# --- Streaming Specific Patterns --- +_START_PATTERNS = { + "TOOL": r"\\?\[\s*ToolCalls\s*\\?\]", + "ORPHAN": r"\\?\[\s*Call\s*\\?:\s*(?:[^\]\\]|\\.)+\s*\\?\]", + "RESP": r"\\?\[\s*ToolResults\s*\\?\]", + "ARG": r"\\?\[\s*CallParameter\s*\\?:\s*(?:[^\]\\]|\\.)+\s*\\?\]", + "RESULT": r"\\?\[\s*ToolResult\s*\\?\]", + "ITEM": r"\\?\[\s*Result\s*\\?:\s*(?:[^\]\\]|\\.)+\s*\\?\]", + "TAG": r"\\?\s*<\s*\\?\|\s*im\s*\\?_start\s*\\?\|\s*>", +} + +_PROTOCOL_ENDS = ( + r"\\?\[\s*\\?/\s*(?:ToolCalls|Call|ToolResults|CallParameter|ToolResult|Result)\s*\\?\]" +) +_TAG_END = r"\\?\s*<\s*\\?\|\s*im\s*\\?_end\s*\\?\|\s*>" + +if TOOL_HINT_START_ESC and TOOL_HINT_END_ESC: + _START_PATTERNS["HINT"] = rf"\n?{TOOL_HINT_START_ESC}:?\s*" + +_master_parts = [f"(?P<{name}_START>{pattern})" for name, pattern in _START_PATTERNS.items()] +_master_parts.append(f"(?P{_PROTOCOL_ENDS})") +_master_parts.append(f"(?P{_TAG_END})") + +if TOOL_HINT_START_ESC and TOOL_HINT_END_ESC: + _master_parts.append(f"(?P{TOOL_HINT_END_ESC}\n?)") + +STREAM_MASTER_RE = re.compile("|".join(_master_parts), re.IGNORECASE) +STREAM_TAIL_RE = re.compile( + r"(?:\\|\\?\[[TCRP/]?\s*[^]]*|\\?\s*<\s*\\?\|?\s*i?\s*m?\s*\\?_?(?:s?t?a?r?t?|e?n?d?)\s*\\?\|?\s*>?|)$", + re.IGNORECASE, +) def add_tag(role: str, content: str, unclose: bool = False) -> str: @@ -113,16 +160,16 @@ def _strip_param_fences(s: str) -> str: if not s: return "" - match = re.match(r"^(?P`{3,})", s) + match = PARAM_FENCE_RE.match(s) if not match or not s.endswith(match.group("fence")): return s + fence = match.group("fence") lines = s.splitlines() - if len(lines) >= 2: + if len(lines) >= 3 and lines[-1].strip() == fence: return "\n".join(lines[1:-1]) - n = len(match.group("fence")) - return s[n:-n].strip() + return s[len(fence) : -len(fence)].strip() def estimate_tokens(text: str | None) -> int: @@ -154,7 +201,7 @@ async def save_url_to_tempfile(url: str, tempdir: Path | None = None) -> Path: data = base64.b64decode(url.split(",")[1]) suffix = mimetypes.guess_extension(mime_type) or f".{mime_type.split('/')[1]}" else: - async with httpx.AsyncClient(follow_redirects=True) as client: + async with AsyncSession(impersonate="chrome", allow_redirects=True) as client: resp = await client.get(url) resp.raise_for_status() data = resp.content @@ -212,14 +259,12 @@ def strip_system_hints(text: str) -> str: cleaned = t_unescaped.replace(TOOL_WRAP_HINT, "").replace(TOOL_HINT_STRIPPED, "") - if TOOL_HINT_LINE_START and TOOL_HINT_LINE_END: - pattern = rf"\n?{re.escape(TOOL_HINT_LINE_START)}.*?{re.escape(TOOL_HINT_LINE_END)}\.?\n?" - cleaned = re.sub(pattern, "", cleaned, flags=re.DOTALL) - - if TOOL_HINT_LINE_START: - cleaned = re.sub(rf"\n?{re.escape(TOOL_HINT_LINE_START)}:?\s*", "", cleaned) - if TOOL_HINT_LINE_END: - cleaned = re.sub(rf"\s*{re.escape(TOOL_HINT_LINE_END)}\.?\n?", "", cleaned) + if HINT_FULL_RE: + cleaned = HINT_FULL_RE.sub("", cleaned) + if HINT_START_RE: + cleaned = HINT_START_RE.sub("", cleaned) + if HINT_END_RE: + cleaned = HINT_END_RE.sub("", cleaned) cleaned = strip_tagged_blocks(cleaned) cleaned = CONTROL_TOKEN_RE.sub("", cleaned) @@ -272,7 +317,7 @@ def _create_tool_call(name: str, raw_args: str) -> None: arguments = "{}" index = len(tool_calls) - seed = f"{name}:{arguments}:{index}".encode("utf-8") + seed = f"{name}:{arguments}:{index}".encode() call_id = f"call_{hashlib.sha256(seed).hexdigest()[:24]}" tool_calls.append( diff --git a/app/utils/singleton.py b/app/utils/singleton.py index 489e87e..2a258af 100644 --- a/app/utils/singleton.py +++ b/app/utils/singleton.py @@ -1,10 +1,10 @@ -from typing import ClassVar, Dict +from typing import ClassVar class Singleton(type): - _instances: ClassVar[Dict[type, object]] = {} + _instances: ClassVar[dict[type, object]] = {} def __call__(cls, *args, **kwargs): if cls not in cls._instances: - cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs) + cls._instances[cls] = super().__call__(*args, **kwargs) return cls._instances[cls] diff --git a/config/config.yaml b/config/config.yaml index 3d5e6f4..f38ef86 100644 --- a/config/config.yaml +++ b/config/config.yaml @@ -22,10 +22,10 @@ gemini: secure_1psid: "YOUR_SECURE_1PSID_HERE" secure_1psidts: "YOUR_SECURE_1PSIDTS_HERE" proxy: null # Optional proxy URL (null/empty means direct connection) - timeout: 300 # Init timeout in seconds (Not less than 30s) - watchdog_timeout: 60 # Watchdog timeout in seconds (Not more than 75s) + timeout: 600 # Init timeout in seconds (Not less than 30s) + watchdog_timeout: 300 # Watchdog timeout in seconds (Not less than 30s) auto_refresh: true # Auto-refresh session cookies - refresh_interval: 540 # Refresh interval in seconds (Not less than 60s) + refresh_interval: 600 # Refresh interval in seconds (Not less than 60s) verbose: false # Enable verbose logging for Gemini requests max_chars_per_request: 1000000 # Maximum characters Gemini Web accepts per request. Non-pro users might have a lower limit model_strategy: "append" # Strategy: 'append' (default + custom) or 'overwrite' (custom only) diff --git a/pyproject.toml b/pyproject.toml index 0cae786..8638b03 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,33 +3,62 @@ name = "gemini-fastapi" version = "1.0.0" description = "FastAPI Server built on Gemini Web API" readme = "README.md" -requires-python = "==3.12.*" +requires-python = "==3.13.*" dependencies = [ - "fastapi>=0.129.0", + "curl-cffi>=0.14.0", + "fastapi>=0.134.0", "gemini-webapi>=1.19.2", + "httptools>=0.7.1", "lmdb>=1.7.5", "loguru>=0.7.3", "orjson>=3.11.7", - "pydantic-settings[yaml]>=2.12.0", - "uvicorn>=0.40.0", + "pydantic-settings[yaml]>=2.13.1", + "uvicorn>=0.41.0", "uvloop>=0.22.1; sys_platform != 'win32'", ] +[project.urls] +Repository = "https://github.com/Nativu5/Gemini-FastAPI" + [project.optional-dependencies] dev = [ - "ruff>=0.15.0", + "pytest>=9.0.2", + "ruff>=0.15.4", +] + +[dependency-groups] +dev = [ + 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