Add InMemory and HnswLib vector stores

langchain/vectorstores/__init__.py

@@ -7,6 +7,8 @@
 from langchain.vectorstores.deeplake import DeepLake
 from langchain.vectorstores.elastic_vector_search import ElasticVectorSearch
 from langchain.vectorstores.faiss import FAISS
+from langchain.vectorstores.hnsw_lib import HnswLib
+from langchain.vectorstores.in_memory import InMemory
 from langchain.vectorstores.milvus import Milvus
 from langchain.vectorstores.myscale import MyScale, MyScaleSettings
 from langchain.vectorstores.opensearch_vector_search import OpenSearchVectorSearch
@@ -34,4 +36,6 @@
     "MyScaleSettings",
     "SupabaseVectorStore",
     "AnalyticDB",
+    "HnswLib",
+    "InMemory",
 ]

langchain/vectorstores/hnsw_lib.py

@@ -0,0 +1,141 @@
+"""Wrapper around HnswLib store."""
+from __future__ import annotations
+
+from typing import List, Optional, Type
+
+from langchain.embeddings.base import Embeddings
+from langchain.vectorstores.base import VST
+from langchain.vectorstores.vector_store_from_doc_index import (
+    VecStoreFromDocIndex,
+    _check_docarray_import,
+)
+
+
+class HnswLib(VecStoreFromDocIndex):
+    """Wrapper around HnswLib storage.
+
+    To use it, you should have the ``docarray[hnswlib]`` package with version >=0.31.0 installed.
+    You can install it with `pip install "langchain[hnswlib]"`.
+    """
+
+    def __init__(
+        self,
+        embedding: Embeddings,
+        work_dir: str,
+        n_dim: int,
+        dist_metric: str = "cosine",
+        max_elements: int = 1024,
+        index: bool = True,
+        ef_construction: int = 200,
+        ef: int = 10,
+        M: int = 16,
+        allow_replace_deleted: bool = True,
+        num_threads: int = 1,
+    ) -> None:
+        """Initialize HnswLib store.
+
+        Args:
+            embedding (Embeddings): Embedding function.
+            work_dir (str): path to the location where all the data will be stored.
+            n_dim (int): dimension of an embedding.
+            dist_metric (str): Distance metric for HnswLib can be one of: "cosine",
+                "ip", and "l2". Defaults to "cosine".
+            max_elements (int): Maximum number of vectors that can be stored.
+                Defaults to 1024.
+            index (bool): Whether an index should be built for this field.
+                Defaults to True.
+            ef_construction (int): defines a construction time/accuracy trade-off.
+                Defaults to 200.
+            ef (int): parameter controlling query time/accuracy trade-off.
+                Defaults to 10.
+            M (int): parameter that defines the maximum number of outgoing
+                connections in the graph. Defaults to 16.
+            allow_replace_deleted (bool): Enables replacing of deleted elements
+                with new added ones. Defaults to True.
+            num_threads (int): Sets the number of cpu threads to use. Defaults to 1.
+        """
+        _check_docarray_import()
+        from docarray.index import HnswDocumentIndex
+
+        try:
+            import google.protobuf
+        except ImportError:
+            raise ImportError(
+                "Could not import all required packages. "
+                "Please install it with `pip install \"langchain[hnswlib]\"`."
+            )
+
+        doc_cls = self._get_doc_cls(
+            {
+                "dim": n_dim,
+                "space": dist_metric,
+                "max_elements": max_elements,
+                "index": index,
+                "ef_construction": ef_construction,
+                "ef": ef,
+                "M": M,
+                "allow_replace_deleted": allow_replace_deleted,
+                "num_threads": num_threads,
+            }
+        )
+        doc_index = HnswDocumentIndex[doc_cls](work_dir=work_dir)
+        super().__init__(doc_index, embedding)
+
+    @classmethod
+    def from_texts(
+        cls: Type[VST],
+        texts: List[str],
+        embedding: Embeddings,
+        metadatas: Optional[List[dict]] = None,
+        work_dir: str = None,
+        n_dim: int = None,
+        dist_metric: str = "cosine",
+        max_elements: int = 1024,
+        index: bool = True,
+        ef_construction: int = 200,
+        ef: int = 10,
+        M: int = 16,
+        allow_replace_deleted: bool = True,
+        num_threads: int = 1,
+    ) -> HnswLib:
+        """Create an HnswLib store and insert data.
+
+        Args:
+            texts (List[str]): Text data.
+            embedding (Embeddings): Embedding function.
+            metadatas (Optional[List[dict]]): Metadata for each text if it exists.
+                Defaults to None.
+            work_dir (str): path to the location where all the data will be stored.
+            n_dim (int): dimension of an embedding.
+            dist_metric (str): Distance metric for HnswLib can be one of: "cosine",
+                "ip", and "l2". Defaults to "cosine".
+            max_elements (int): Maximum number of vectors that can be stored.
+                Defaults to 1024.
+            index (bool): Whether an index should be built for this field.
+                Defaults to True.
+            ef_construction (int): defines a construction time/accuracy trade-off.
+                Defaults to 200.
+            ef (int): parameter controlling query time/accuracy trade-off.
+                Defaults to 10.
+            M (int): parameter that defines the maximum number of outgoing
+                connections in the graph. Defaults to 16.
+            allow_replace_deleted (bool): Enables replacing of deleted elements
+                with new added ones. Defaults to True.
+            num_threads (int): Sets the number of cpu threads to use. Defaults to 1.
+
+        Returns:
+            HnswLib Vector Store
+        """
+        if work_dir is None:
+            raise ValueError("`work_dir` parameter hs not been set.")
+        if n_dim is None:
+            raise ValueError("`n_dim` parameter has not been set.")
+
+        store = cls(
+            work_dir=work_dir,
+            n_dim=n_dim,
+            embedding=embedding,
+            dist_metric=dist_metric,
+        )
+        store.add_texts(texts=texts, metadatas=metadatas)
+        return store

langchain/vectorstores/in_memory.py

@@ -0,0 +1,68 @@
+"""Wrapper around in-memory storage."""
+from __future__ import annotations
+
+from typing import List, Optional, Type
+
+from langchain.embeddings.base import Embeddings
+from langchain.vectorstores.base import VST
+from langchain.vectorstores.vector_store_from_doc_index import (
+    VecStoreFromDocIndex,
+    _check_docarray_import,
+)
+
+
+class InMemory(VecStoreFromDocIndex):
+    """Wrapper around in-memory storage.
+
+    To use it, you should have the ``docarray`` package with version >=0.31.0 installed.
+    You can install it with `pip install "langchain[in_memory_store]"`.
+    """
+
+    def __init__(
+        self,
+        embedding: Embeddings,
+        metric: str = "cosine_sim",
+    ) -> None:
+        """Initialize in-memory store.
+
+        Args:
+            embedding (Embeddings): Embedding function.
+            metric (str): metric for exact nearest-neighbor search.
+                Can be one of: "cosine_sim", "euclidean_dist" and "sqeuclidean_dist".
+                Defaults to "cosine_sim".
+        """
+        _check_docarray_import()
+        from docarray.index import InMemoryExactNNIndex
+
+        doc_cls = self._get_doc_cls({"space": metric})
+        doc_index = InMemoryExactNNIndex[doc_cls]()
+        super().__init__(doc_index, embedding)
+
+    @classmethod
+    def from_texts(
+        cls: Type[VST],
+        texts: List[str],
+        embedding: Embeddings,
+        metadatas: Optional[List[dict]] = None,
+        metric: str = "cosine_sim",
+    ) -> InMemory:
+        """Create an in-memory store and insert data.
+
+        Args:
+            texts (List[str]): Text data.
+            embedding (Embeddings): Embedding function.
+            metadatas (Optional[List[dict]]): Metadata for each text if it exists.
+                Defaults to None.
+            metric (str): metric for exact nearest-neighbor search.
+                Can be one of: "cosine_sim", "euclidean_dist" and "sqeuclidean_dist".
+                Defaults to "cosine_sim".
+
+        Returns:
+            InMemory Vector Store
+        """
+        store = cls(
+            embedding=embedding,
+            metric=metric,
+        )
+        store.add_texts(texts=texts, metadatas=metadatas)
+        return store