⏳ docstrings: vectorstores consistency (#9349)

⏳ 
- updated the top-level descriptions to a consistent format;
- changed several `ValueError` to `ImportError` in the import cases;
- changed the format of several internal functions from "name" to
"_name". So, these functions are not shown in the Top-level API
Reference page (with lists of classes/functions)

libs/langchain/langchain/vectorstores/__init__.py

@@ -78,60 +78,60 @@ __all__ = [
     "AlibabaCloudOpenSearchSettings",
     "AnalyticDB",
     "Annoy",
+    "Annoy",
+    "AtlasDB",
     "AtlasDB",
     "AwaDB",
     "AzureSearch",
     "Bagel",
     "Cassandra",
     "Chroma",
+    "Chroma",
+    "Clarifai",
     "Clickhouse",
     "ClickhouseSettings",
     "DashVector",
     "DeepLake",
+    "DeepLake",
     "Dingo",
     "DocArrayHnswSearch",
     "DocArrayInMemorySearch",
-    "ElasticVectorSearch",
     "ElasticKnnSearch",
+    "ElasticVectorSearch",
     "ElasticsearchStore",
     "Epsilla",
     "FAISS",
-    "PGEmbedding",
     "Hologres",
     "LanceDB",
-    "MatchingEngine",
     "Marqo",
+    "MatchingEngine",
     "Meilisearch",
     "Milvus",
-    "Zilliz",
-    "SingleStoreDB",
-    "Chroma",
-    "Clarifai",
-    "OpenSearchVectorSearch",
-    "AtlasDB",
-    "DeepLake",
-    "Annoy",
     "MongoDBAtlasVectorSearch",
     "MyScale",
     "MyScaleSettings",
     "OpenSearchVectorSearch",
+    "OpenSearchVectorSearch",
+    "PGEmbedding",
+    "PGVector",
     "Pinecone",
     "Qdrant",
     "Redis",
     "Rockset",
-    "ScaNN",
     "SKLearnVectorStore",
+    "ScaNN",
+    "SingleStoreDB",
     "SingleStoreDB",
     "StarRocks",
     "SupabaseVectorStore",
     "Tair",
     "Tigris",
     "Typesense",
+    "USearch",
     "Vectara",
     "VectorStore",
     "Weaviate",
     "ZepVectorStore",
     "Zilliz",
-    "PGVector",
+    "Zilliz",
-    "USearch",
 ]

libs/langchain/langchain/vectorstores/_pgvector_data_models.py

@@ -9,6 +9,8 @@ from langchain.vectorstores.pgvector import BaseModel
 
 
 class CollectionStore(BaseModel):
+    """Collection store."""
+
     __tablename__ = "langchain_pg_collection"
 
     name = sqlalchemy.Column(sqlalchemy.String)
@@ -48,6 +50,8 @@ class CollectionStore(BaseModel):
 
 
 class EmbeddingStore(BaseModel):
+    """Embedding store."""
+
     __tablename__ = "langchain_pg_embedding"
 
     collection_id = sqlalchemy.Column(

libs/langchain/langchain/vectorstores/alibabacloud_opensearch.py

@@ -12,7 +12,7 @@ logger = logging.getLogger()
 
 
 class AlibabaCloudOpenSearchSettings:
-    """Alibaba Cloud Opensearch Client Configuration.
+    """`Alibaba Cloud Opensearch` client configuration.
 
     Attribute:
         endpoint (str) : The endpoint of opensearch instance, You can find it
@@ -90,7 +90,7 @@ def create_metadata(fields: Dict[str, Any]) -> Dict[str, Any]:
 
 
 class AlibabaCloudOpenSearch(VectorStore):
-    """Alibaba Cloud OpenSearch Vector Store"""
+    """`Alibaba Cloud OpenSearch` vector store."""
 
     def __init__(
         self,
@@ -102,7 +102,7 @@ class AlibabaCloudOpenSearch(VectorStore):
             from alibabacloud_ha3engine import client, models
             from alibabacloud_tea_util import models as util_models
         except ImportError:
-            raise ValueError(
+            raise ImportError(
                 "Could not import alibaba cloud opensearch python package. "
                 "Please install it with `pip install alibabacloud-ha3engine`."
             )

libs/langchain/langchain/vectorstores/analyticdb.py

@@ -1,4 +1,3 @@
-"""VectorStore wrapper around a Postgres/PGVector database."""
 from __future__ import annotations
 
 import logging
@@ -25,7 +24,7 @@ Base = declarative_base()  # type: Any
 
 
 class AnalyticDB(VectorStore):
-    """VectorStore implementation using AnalyticDB.
+    """`AnalyticDB` (distributed PostgreSQL) vector store.
 
     AnalyticDB is a distributed full postgresql syntax cloud-native database.
     - `connection_string` is a postgres connection string.

libs/langchain/langchain/vectorstores/annoy.py

@@ -1,4 +1,3 @@
-"""Wrapper around Annoy vector database."""
 from __future__ import annotations
 
 import os
@@ -26,7 +25,7 @@ def dependable_annoy_import() -> Any:
     try:
         import annoy
     except ImportError:
-        raise ValueError(
+        raise ImportError(
             "Could not import annoy python package. "
             "Please install it with `pip install --user annoy` "
         )
@@ -34,7 +33,7 @@ def dependable_annoy_import() -> Any:
 
 
 class Annoy(VectorStore):
-    """Wrapper around Annoy vector database.
+    """`Annoy` vector store.
 
     To use, you should have the ``annoy`` python package installed.
 

libs/langchain/langchain/vectorstores/atlas.py

@@ -1,4 +1,3 @@
-"""Wrapper around Atlas by Nomic."""
 from __future__ import annotations
 
 import logging
@@ -15,7 +14,9 @@ logger = logging.getLogger(__name__)
 
 
 class AtlasDB(VectorStore):
-    """Wrapper around Atlas: Nomic's neural database and rhizomatic instrument.
+    """`Atlas` vector store.
+
+     Atlas is the `Nomic's` neural database and `rhizomatic` instrument.
 
     To use, you should have the ``nomic`` python package installed.
 
@@ -61,7 +62,7 @@ class AtlasDB(VectorStore):
             import nomic
             from nomic import AtlasProject
         except ImportError:
-            raise ValueError(
+            raise ImportError(
                 "Could not import nomic python package. "
                 "Please install it with `pip install nomic`."
             )

libs/langchain/langchain/vectorstores/awadb.py

@@ -1,4 +1,3 @@
-"""Wrapper around AwaDB for embedding vectors"""
 from __future__ import annotations
 
 import logging
@@ -20,7 +19,7 @@ DEFAULT_TOPN = 4
 
 
 class AwaDB(VectorStore):
-    """Interface implemented by AwaDB vector stores."""
+    """`AwaDB` vector store."""
 
     _DEFAULT_TABLE_NAME = "langchain_awadb"
 
@@ -50,7 +49,7 @@ class AwaDB(VectorStore):
         try:
             import awadb
         except ImportError:
-            raise ValueError(
+            raise ImportError(
                 "Could not import awadb python package. "
                 "Please install it with `pip install awadb`."
             )

libs/langchain/langchain/vectorstores/azuresearch.py

@@ -1,4 +1,3 @@
-"""Wrapper around Azure Cognitive Search."""
 from __future__ import annotations
 
 import base64
@@ -177,7 +176,7 @@ def _get_search_client(
 
 
 class AzureSearch(VectorStore):
-    """Azure Cognitive Search vector store."""
+    """`Azure Cognitive Search` vector store."""
 
     def __init__(
         self,
@@ -526,7 +525,7 @@ class AzureSearch(VectorStore):
 
 
 class AzureSearchVectorStoreRetriever(BaseRetriever):
-    """Retriever that uses Azure Search to find similar documents."""
+    """Retriever that uses `Azure Cognitive Search`."""
 
     vectorstore: AzureSearch
     """Azure Search instance used to find similar documents."""

libs/langchain/langchain/vectorstores/bageldb.py

@@ -1,4 +1,3 @@
-"""BagelDB integration"""
 from __future__ import annotations
 
 import uuid
@@ -43,7 +42,7 @@ def _results_to_docs_and_scores(results: Any) -> List[Tuple[Document, float]]:
 
 
 class Bagel(VectorStore):
-    """Wrapper around BagelDB.ai vector store.
+    """``BagelDB.ai`` vector store.
 
     To use, you should have the ``betabageldb`` python package installed.
 
@@ -70,7 +69,7 @@ class Bagel(VectorStore):
             import bagel
             import bagel.config
         except ImportError:
-            raise ValueError("Please install bagel `pip install betabageldb`.")
+            raise ImportError("Please install bagel `pip install betabageldb`.")
         if client is not None:
             self._client_settings = client_settings
             self._client = client

libs/langchain/langchain/vectorstores/base.py

@@ -1,5 +1,3 @@
-"""Interface for vector stores."""
-
 from __future__ import annotations
 
 import asyncio
@@ -37,7 +35,7 @@ VST = TypeVar("VST", bound="VectorStore")
 
 
 class VectorStore(ABC):
-    """Interface for vector stores."""
+    """Interface for vector store."""
 
     @abstractmethod
     def add_texts(
@@ -520,7 +518,7 @@ class VectorStore(ABC):
 
 
 class VectorStoreRetriever(BaseRetriever):
-    """Retriever class for VectorStore."""
+    """Base Retriever class for VectorStore."""
 
     vectorstore: VectorStore
     """VectorStore to use for retrieval."""

libs/langchain/langchain/vectorstores/cassandra.py

@@ -1,4 +1,3 @@
-"""Wrapper around Cassandra vector-store capabilities, based on cassIO."""
 from __future__ import annotations
 
 import typing
@@ -19,8 +18,9 @@ CVST = TypeVar("CVST", bound="Cassandra")
 
 
 class Cassandra(VectorStore):
-    """Wrapper around Cassandra embeddings platform.
+    """`Cassandra` vector store.
 
+    It based on the Cassandra vector-store capabilities, based on cassIO.
     There is no notion of a default table name, since each embedding
     function implies its own vector dimension, which is part of the schema.
 

libs/langchain/langchain/vectorstores/chroma.py

@@ -1,4 +1,3 @@
-"""Wrapper around ChromaDB embeddings platform."""
 from __future__ import annotations
 
 import logging
@@ -50,7 +49,7 @@ def _results_to_docs_and_scores(results: Any) -> List[Tuple[Document, float]]:
 
 
 class Chroma(VectorStore):
-    """Wrapper around ChromaDB embeddings platform.
+    """`ChromaDB` vector store.
 
     To use, you should have the ``chromadb`` python package installed.
 
@@ -76,12 +75,12 @@ class Chroma(VectorStore):
         client: Optional[chromadb.Client] = None,
         relevance_score_fn: Optional[Callable[[float], float]] = None,
     ) -> None:
-        """Initialize with Chroma client."""
+        """Initialize with a Chroma client."""
         try:
             import chromadb
             import chromadb.config
         except ImportError:
-            raise ValueError(
+            raise ImportError(
                 "Could not import chromadb python package. "
                 "Please install it with `pip install chromadb`."
             )

libs/langchain/langchain/vectorstores/clarifai.py

@@ -16,7 +16,7 @@ logger = logging.getLogger(__name__)
 
 
 class Clarifai(VectorStore):
-    """Wrapper around Clarifai AI platform's vector store.
+    """`Clarifai AI` vector store.
 
     To use, you should have the ``clarifai`` python package installed.
 
@@ -55,7 +55,7 @@ class Clarifai(VectorStore):
             from clarifai.auth.helper import DEFAULT_BASE, ClarifaiAuthHelper
             from clarifai.client import create_stub
         except ImportError:
-            raise ValueError(
+            raise ImportError(
                 "Could not import clarifai python package. "
                 "Please install it with `pip install clarifai`."
             )

libs/langchain/langchain/vectorstores/clickhouse.py

@@ -1,5 +1,3 @@
-"""Wrapper around open source ClickHouse VectorSearch capability."""
-
 from __future__ import annotations
 
 import json
@@ -33,7 +31,7 @@ def has_mul_sub_str(s: str, *args: Any) -> bool:
 
 
 class ClickhouseSettings(BaseSettings):
-    """ClickHouse Client Configuration
+    """`ClickHouse` client configuration.
 
     Attribute:
         clickhouse_host (str) : An URL to connect to MyScale backend.
@@ -101,7 +99,7 @@ class ClickhouseSettings(BaseSettings):
 
 
 class Clickhouse(VectorStore):
-    """Wrapper around ClickHouse vector database
+    """`ClickHouse VectorSearch` vector store.
 
     You need a `clickhouse-connect` python package, and a valid account
     to connect to ClickHouse.
@@ -130,7 +128,7 @@ class Clickhouse(VectorStore):
         try:
             from clickhouse_connect import get_client
         except ImportError:
-            raise ValueError(
+            raise ImportError(
                 "Could not import clickhouse connect python package. "
                 "Please install it with `pip install clickhouse-connect`."
             )

libs/langchain/langchain/vectorstores/dashvector.py

@@ -1,4 +1,3 @@
-"""Wrapper around DashVector vector database."""
 from __future__ import annotations
 
 import logging
@@ -23,7 +22,7 @@ logger = logging.getLogger(__name__)
 
 
 class DashVector(VectorStore):
-    """Wrapper around DashVector vector database.
+    """`DashVector` vector store.
 
     To use, you should have the ``dashvector`` python package installed.
 

libs/langchain/langchain/vectorstores/deeplake.py

@@ -1,4 +1,3 @@
-"""Wrapper around Activeloop Deep Lake."""
 from __future__ import annotations
 
 import logging
@@ -24,9 +23,9 @@ logger = logging.getLogger(__name__)
 
 
 class DeepLake(VectorStore):
-    """Wrapper around Deep Lake, a data lake for deep learning applications.
+    """`Activeloop Deep Lake` vector store.
 
-    We integrated deeplake's similarity search and filtering for fast prototyping,
+    We integrated deeplake's similarity search and filtering for fast prototyping.
     Now, it supports Tensor Query Language (TQL) for production use cases
     over billion rows.
 
@@ -126,7 +125,7 @@ class DeepLake(VectorStore):
         self.verbose = verbose
 
         if _DEEPLAKE_INSTALLED is False:
-            raise ValueError(
+            raise ImportError(
                 "Could not import deeplake python package. "
                 "Please install it with `pip install deeplake[enterprise]`."
             )
@@ -135,7 +134,7 @@ class DeepLake(VectorStore):
             kwargs.get("runtime") == {"tensor_db": True}
             and version_compare(deeplake.__version__, "3.6.7") == -1
         ):
-            raise ValueError(
+            raise ImportError(
                 "To use tensor_db option you need to update deeplake to `3.6.7`. "
                 f"Currently installed deeplake version is {deeplake.__version__}. "
             )

libs/langchain/langchain/vectorstores/dingo.py

@@ -1,4 +1,3 @@
-"""Wrapper around the Dingo vector database."""
 from __future__ import annotations
 
 import logging
@@ -16,7 +15,7 @@ logger = logging.getLogger(__name__)
 
 
 class Dingo(VectorStore):
-    """Wrapper around Dingo vector database.
+    """`Dingo` vector store.
 
     To use, you should have the ``dingodb`` python package installed.
 

libs/langchain/langchain/vectorstores/docarray/base.py

@@ -33,6 +33,8 @@ def _check_docarray_import() -> None:
 
 
 class DocArrayIndex(VectorStore, ABC):
+    """Base class for `DocArray` based vector stores."""
+
     def __init__(
         self,
         doc_index: "BaseDocIndex",
@@ -67,7 +69,7 @@ class DocArrayIndex(VectorStore, ABC):
         metadatas: Optional[List[dict]] = None,
         **kwargs: Any,
     ) -> List[str]:
-        """Run more texts through the embeddings and add to the vectorstore.
+        """Embed texts and add to the vector store.
 
         Args:
             texts: Iterable of strings to add to the vectorstore.

libs/langchain/langchain/vectorstores/docarray/hnsw.py

@@ -1,4 +1,3 @@
-"""Wrapper around Hnswlib store."""
 from __future__ import annotations
 
 from typing import Any, List, Literal, Optional
@@ -11,7 +10,7 @@ from langchain.vectorstores.docarray.base import (
 
 
 class DocArrayHnswSearch(DocArrayIndex):
-    """Wrapper around HnswLib storage.
+    """`HnswLib` storage using `DocArray` package.
 
     To use it, you should have the ``docarray`` package with version >=0.32.0 installed.
     You can install it with `pip install "langchain[docarray]"`.

libs/langchain/langchain/vectorstores/docarray/in_memory.py

@@ -11,7 +11,7 @@ from langchain.vectorstores.docarray.base import (
 
 
 class DocArrayInMemorySearch(DocArrayIndex):
-    """Wrapper around in-memory storage for exact search.
+    """In-memory `DocArray` storage for exact search.
 
     To use it, you should have the ``docarray`` package with version >=0.32.0 installed.
     You can install it with `pip install "langchain[docarray]"`.

libs/langchain/langchain/vectorstores/elastic_vector_search.py

@@ -53,9 +53,9 @@ def _default_script_query(query_vector: List[float], filter: Optional[dict]) ->
 
 @deprecated("0.0.265", alternative="ElasticsearchStore class.", pending=True)
 class ElasticVectorSearch(VectorStore):
-    """Wrapper around Elasticsearch as a vector database.
+    """[DEPRECATED] `Elasticsearch` vector store.
 
-    To connect to an Elasticsearch instance that does not require
+    To connect to an `Elasticsearch` instance that does not require
     login credentials, pass the Elasticsearch URL and index name along with the
     embedding object to the constructor.
 
@@ -340,11 +340,10 @@ class ElasticVectorSearch(VectorStore):
 
 
 class ElasticKnnSearch(VectorStore):
-    """
+    """[DEPRECATED] `Elasticsearch` with k-nearest neighbor search
-    ElasticKnnSearch is a class for performing k-nearest neighbor
+    (`k-NN`) vector store.
-    (k-NN) searches on text data using Elasticsearch.
 
-    This class is used to create an Elasticsearch index of text data that
+    It creates an Elasticsearch index of text data that
     can be searched using k-NN search. The text data is transformed into
     vector embeddings using a provided embedding model, and these embeddings
     are stored in the Elasticsearch index.

libs/langchain/langchain/vectorstores/elasticsearch.py

@@ -1,5 +1,3 @@
-"""Wrapper around Elasticsearch vector database."""
-
 import logging
 import uuid
 from abc import ABC, abstractmethod
@@ -28,6 +26,8 @@ logger = logging.getLogger(__name__)
 
 
 class BaseRetrievalStrategy(ABC):
+    """Base class for `Elasticsearch` retrieval strategies."""
+
     @abstractmethod
     def query(
         self,
@@ -109,6 +109,8 @@ class BaseRetrievalStrategy(ABC):
 
 
 class ApproxRetrievalStrategy(BaseRetrievalStrategy):
+    """Approximate retrieval strategy using the `HNSW` algorithm."""
+
     def __init__(
         self,
         query_model_id: Optional[str] = None,
@@ -211,6 +213,8 @@ class ApproxRetrievalStrategy(BaseRetrievalStrategy):
 
 
 class ExactRetrievalStrategy(BaseRetrievalStrategy):
+    """Exact retrieval strategy using the `script_score` query."""
+
     def query(
         self,
         query_vector: Union[List[float], None],
@@ -276,6 +280,8 @@ class ExactRetrievalStrategy(BaseRetrievalStrategy):
 
 
 class SparseRetrievalStrategy(BaseRetrievalStrategy):
+    """Sparse retrieval strategy using the `text_expansion` processor."""
+
     def __init__(self, model_id: Optional[str] = None):
         self.model_id = model_id or ".elser_model_1"
 
@@ -355,8 +361,7 @@ class SparseRetrievalStrategy(BaseRetrievalStrategy):
 
 
 class ElasticsearchStore(VectorStore):
-
+    """`Elasticsearch` vector store.
-    """Wrapper around Elasticsearch search database.
 
     Example:
         .. code-block:: python

libs/langchain/langchain/vectorstores/faiss.py

@@ -1,4 +1,3 @@
-"""Wrapper around FAISS vector database."""
 from __future__ import annotations
 
 import operator
@@ -65,7 +64,7 @@ def _len_check_if_sized(x: Any, y: Any, x_name: str, y_name: str) -> None:
 
 
 class FAISS(VectorStore):
-    """Wrapper around FAISS vector database.
+    """`Meta Faiss` vector store.
 
     To use, you must have the ``faiss`` python package installed.
 

libs/langchain/langchain/vectorstores/hologres.py

@@ -1,4 +1,3 @@
-"""VectorStore wrapper around a Hologres database."""
 from __future__ import annotations
 
 import json
@@ -16,7 +15,7 @@ _LANGCHAIN_DEFAULT_TABLE_NAME = "langchain_pg_embedding"
 
 
 class HologresWrapper:
-    """Wrapper around Hologres service."""
+    """`Hologres API` wrapper."""
 
     def __init__(self, connection_string: str, ndims: int, table_name: str) -> None:
         """Initialize the wrapper.
@@ -114,7 +113,7 @@ document text);"""
 
 
 class Hologres(VectorStore):
-    """VectorStore implementation using Hologres.
+    """`Hologres API` vector store.
 
     - `connection_string` is a hologres connection string.
     - `embedding_function` any embedding function implementing

libs/langchain/langchain/vectorstores/lancedb.py

@@ -1,4 +1,3 @@
-"""Wrapper around LanceDB vector database"""
 from __future__ import annotations
 
 import uuid
@@ -10,7 +9,7 @@ from langchain.vectorstores.base import VectorStore
 
 
 class LanceDB(VectorStore):
-    """Wrapper around LanceDB vector database.
+    """`LanceDB` vector store.
 
     To use, you should have ``lancedb`` python package installed.
 
@@ -36,7 +35,7 @@ class LanceDB(VectorStore):
         try:
             import lancedb
         except ImportError:
-            raise ValueError(
+            raise ImportError(
                 "Could not import lancedb python package. "
                 "Please install it with `pip install lancedb`."
             )

libs/langchain/langchain/vectorstores/marqo.py

@@ -1,4 +1,3 @@
-"""Wrapper around weaviate vector database."""
 from __future__ import annotations
 
 import json
@@ -25,7 +24,7 @@ if TYPE_CHECKING:
 
 
 class Marqo(VectorStore):
-    """Wrapper around Marqo database.
+    """`Marqo` vector store.
 
     Marqo indexes have their own models associated with them to generate your
     embeddings. This means that you can selected from a range of different models

libs/langchain/langchain/vectorstores/matching_engine.py

@@ -1,4 +1,3 @@
-"""Vertex Matching Engine implementation of the vector store."""
 from __future__ import annotations
 
 import json
@@ -21,7 +20,7 @@ logger = logging.getLogger()
 
 
 class MatchingEngine(VectorStore):
-    """Vertex Matching Engine implementation of the vector store.
+    """`Google Vertex AI Matching Engine` vector store.
 
     While the embeddings are stored in the Matching Engine, the embedded
     documents will be stored in GCS.

libs/langchain/langchain/vectorstores/meilisearch.py

@@ -1,4 +1,3 @@
-"""Wrapper around Meilisearch vector database."""
 from __future__ import annotations
 
 import uuid
@@ -45,7 +44,7 @@ def _create_client(
 
 
 class Meilisearch(VectorStore):
-    """Initialize wrapper around Meilisearch vector database.
+    """`Meilisearch` vector store.
 
     To use this, you need to have `meilisearch` python package installed,
     and a running Meilisearch instance.

libs/langchain/langchain/vectorstores/milvus.py

@@ -1,4 +1,3 @@
-"""Wrapper around the Milvus vector database."""
 from __future__ import annotations
 
 import logging
@@ -24,19 +23,18 @@ DEFAULT_MILVUS_CONNECTION = {
 
 
 class Milvus(VectorStore):
-    """Initialize wrapper around the milvus vector database.
+    """`Milvus` vector store.
 
-    In order to use this you need to have `pymilvus` installed and a
+    You need to install `pymilvus` and run Milvus.
-    running Milvus
 
     See the following documentation for how to run a Milvus instance:
     https://milvus.io/docs/install_standalone-docker.md
 
     If looking for a hosted Milvus, take a look at this documentation:
     https://zilliz.com/cloud and make use of the Zilliz vectorstore found in
-    this project,
+    this project.
 
-    IF USING L2/IP metric IT IS HIGHLY SUGGESTED TO NORMALIZE YOUR DATA.
+    IF USING L2/IP metric, IT IS HIGHLY SUGGESTED TO NORMALIZE YOUR DATA.
 
     Args:
         embedding_function (Embeddings): Function used to embed the text.

libs/langchain/langchain/vectorstores/mongodb_atlas.py

@@ -32,7 +32,7 @@ DEFAULT_INSERT_BATCH_SIZE = 100
 
 
 class MongoDBAtlasVectorSearch(VectorStore):
-    """Wrapper around MongoDB Atlas Vector Search.
+    """`MongoDB Atlas Vector Search` vector store.
 
     To use, you should have both:
     - the ``pymongo`` python package installed

libs/langchain/langchain/vectorstores/myscale.py

@@ -1,4 +1,3 @@
-"""Wrapper around MyScale vector database."""
 from __future__ import annotations
 
 import json
@@ -32,7 +31,7 @@ def has_mul_sub_str(s: str, *args: Any) -> bool:
 
 
 class MyScaleSettings(BaseSettings):
-    """MyScale Client Configuration
+    """MyScale client configuration.
 
     Attribute:
         myscale_host (str) : An URL to connect to MyScale backend.
@@ -93,13 +92,13 @@ class MyScaleSettings(BaseSettings):
 
 
 class MyScale(VectorStore):
-    """Wrapper around MyScale vector database
+    """`MyScale` vector store.
 
     You need a `clickhouse-connect` python package, and a valid account
     to connect to MyScale.
 
-    MyScale can not only search with simple vector indexes,
+    MyScale can not only search with simple vector indexes.
-    it also supports complex query with multiple conditions,
+    It also supports a complex query with multiple conditions,
     constraints and even sub-queries.
 
     For more information, please visit
@@ -122,7 +121,7 @@ class MyScale(VectorStore):
         try:
             from clickhouse_connect import get_client
         except ImportError:
-            raise ValueError(
+            raise ImportError(
                 "Could not import clickhouse connect python package. "
                 "Please install it with `pip install clickhouse-connect`."
             )

libs/langchain/langchain/vectorstores/opensearch_vector_search.py

@@ -1,4 +1,3 @@
-"""Wrapper around OpenSearch vector database."""
 from __future__ import annotations
 
 import uuid
@@ -26,7 +25,7 @@ def _import_opensearch() -> Any:
     try:
         from opensearchpy import OpenSearch
     except ImportError:
-        raise ValueError(IMPORT_OPENSEARCH_PY_ERROR)
+        raise ImportError(IMPORT_OPENSEARCH_PY_ERROR)
     return OpenSearch
 
 
@@ -35,7 +34,7 @@ def _import_bulk() -> Any:
     try:
         from opensearchpy.helpers import bulk
     except ImportError:
-        raise ValueError(IMPORT_OPENSEARCH_PY_ERROR)
+        raise ImportError(IMPORT_OPENSEARCH_PY_ERROR)
     return bulk
 
 
@@ -44,7 +43,7 @@ def _import_not_found_error() -> Any:
     try:
         from opensearchpy.exceptions import NotFoundError
     except ImportError:
-        raise ValueError(IMPORT_OPENSEARCH_PY_ERROR)
+        raise ImportError(IMPORT_OPENSEARCH_PY_ERROR)
     return NotFoundError
 
 
@@ -54,7 +53,7 @@ def _get_opensearch_client(opensearch_url: str, **kwargs: Any) -> Any:
         opensearch = _import_opensearch()
         client = opensearch(opensearch_url, **kwargs)
     except ValueError as e:
-        raise ValueError(
+        raise ImportError(
             f"OpenSearch client string provided is not in proper format. "
             f"Got error: {e} "
         )
@@ -315,7 +314,7 @@ def _get_kwargs_value(kwargs: Any, key: str, default_value: Any) -> Any:
 
 
 class OpenSearchVectorSearch(VectorStore):
-    """Wrapper around OpenSearch as a vector database.
+    """`Amazon OpenSearch Vector Engine` vector store.
 
     Example:
         .. code-block:: python

libs/langchain/langchain/vectorstores/pgembedding.py

@@ -1,4 +1,3 @@
-"""VectorStore wrapper around a Postgres database."""
 from __future__ import annotations
 
 import logging
@@ -23,11 +22,15 @@ _LANGCHAIN_DEFAULT_COLLECTION_NAME = "langchain"
 
 
 class BaseModel(Base):
+    """Base model for all SQL stores."""
+
     __abstract__ = True
     uuid = sqlalchemy.Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
 
 
 class CollectionStore(BaseModel):
+    """Collection store."""
+
     __tablename__ = "langchain_pg_collection"
 
     name = sqlalchemy.Column(sqlalchemy.String)
@@ -67,6 +70,8 @@ class CollectionStore(BaseModel):
 
 
 class EmbeddingStore(BaseModel):
+    """Embedding store."""
+
     __tablename__ = "langchain_pg_embedding"
 
     collection_id = sqlalchemy.Column(
@@ -87,15 +92,14 @@ class EmbeddingStore(BaseModel):
 
 
 class QueryResult:
-    """QueryResult is a result from a query."""
+    """Result from a query."""
 
     EmbeddingStore: EmbeddingStore
     distance: float
 
 
 class PGEmbedding(VectorStore):
-    """
+    """`Postgres` with the `pg_embedding` extension as a vector store.
-    VectorStore implementation using Postgres and the pg_embedding extension.
 
     pg_embedding uses sequential scan by default. but you can create a HNSW index
     using the create_hnsw_index method.

libs/langchain/langchain/vectorstores/pgvector.py

@@ -1,4 +1,3 @@
-"""VectorStore wrapper around a Postgres/PGVector database."""
 from __future__ import annotations
 
 import enum
@@ -46,12 +45,14 @@ _LANGCHAIN_DEFAULT_COLLECTION_NAME = "langchain"
 
 
 class BaseModel(Base):
+    """Base model for the SQL stores."""
+
     __abstract__ = True
     uuid = sqlalchemy.Column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
 
 
 class PGVector(VectorStore):
-    """VectorStore implementation using Postgres and pgvector.
+    """`Postgres`/`PGVector` vector store.
 
     To use, you should have the ``pgvector`` python package installed.
 

libs/langchain/langchain/vectorstores/pinecone.py

@@ -1,4 +1,3 @@
-"""Wrapper around Pinecone vector database."""
 from __future__ import annotations
 
 import logging
@@ -17,7 +16,7 @@ logger = logging.getLogger(__name__)
 
 
 class Pinecone(VectorStore):
-    """Wrapper around Pinecone vector database.
+    """`Pinecone` vector store.
 
     To use, you should have the ``pinecone-client`` python package installed.
 

libs/langchain/langchain/vectorstores/qdrant.py

@@ -1,4 +1,3 @@
-"""Wrapper around Qdrant vector database."""
 from __future__ import annotations
 
 import asyncio
@@ -40,7 +39,7 @@ if TYPE_CHECKING:
 
 
 class QdrantException(Exception):
-    """Base class for all the Qdrant related exceptions"""
+    """`Qdrant` related exceptions."""
 
 
 def sync_call_fallback(method: Callable) -> Callable:
@@ -68,7 +67,7 @@ def sync_call_fallback(method: Callable) -> Callable:
 
 
 class Qdrant(VectorStore):
-    """Wrapper around Qdrant vector database.
+    """`Qdrant` vector store.
 
     To use you should have the ``qdrant-client`` package installed.
 
@@ -102,7 +101,7 @@ class Qdrant(VectorStore):
         try:
             import qdrant_client
         except ImportError:
-            raise ValueError(
+            raise ImportError(
                 "Could not import qdrant-client python package. "
                 "Please install it with `pip install qdrant-client`."
             )

libs/langchain/langchain/vectorstores/redis.py

@@ -1,5 +1,3 @@
-"""Wrapper around Redis vector database."""
-
 from __future__ import annotations
 
 import json
@@ -96,7 +94,7 @@ def _default_relevance_score(val: float) -> float:
 
 
 class Redis(VectorStore):
-    """Wrapper around Redis vector database.
+    """`Redis` vector store.
 
     To use, you should have the ``redis`` python package installed.
 
@@ -184,7 +182,7 @@ class Redis(VectorStore):
             from redis.commands.search.field import TextField, VectorField
             from redis.commands.search.indexDefinition import IndexDefinition, IndexType
         except ImportError:
-            raise ValueError(
+            raise ImportError(
                 "Could not import redis python package. "
                 "Please install it with `pip install redis`."
             )
@@ -612,7 +610,7 @@ class Redis(VectorStore):
 
 
 class RedisVectorStoreRetriever(VectorStoreRetriever):
-    """Retriever for Redis VectorStore."""
+    """Retriever for `Redis` vector store."""
 
     vectorstore: Redis
     """Redis VectorStore."""

libs/langchain/langchain/vectorstores/rocksetdb.py

@@ -1,4 +1,3 @@
-"""Wrapper around Rockset vector database."""
 from __future__ import annotations
 
 import logging
@@ -13,7 +12,7 @@ logger = logging.getLogger(__name__)
 
 
 class Rockset(VectorStore):
-    """Wrapper arpund Rockset vector database.
+    """`Rockset` vector store.
 
     To use, you should have the `rockset` python package installed. Note that to use
     this, the collection being used must already exist in your Rockset instance.

libs/langchain/langchain/vectorstores/scann.py

@@ -1,4 +1,3 @@
-"""Wrapper around ScaNN vector database."""
 from __future__ import annotations
 
 import operator
@@ -25,7 +24,7 @@ def normalize(x: np.ndarray) -> np.ndarray:
 
 def dependable_scann_import() -> Any:
     """
-    Import scann if available, otherwise raise error.
+    Import `scann` if available, otherwise raise error.
     """
     try:
         import scann
@@ -38,7 +37,7 @@ def dependable_scann_import() -> Any:
 
 
 class ScaNN(VectorStore):
-    """Wrapper around ScaNN vector database.
+    """`ScaNN` vector store.
 
     To use, you should have the ``scann`` python package installed.
 

libs/langchain/langchain/vectorstores/singlestoredb.py

@@ -1,5 +1,3 @@
-"""Wrapper around SingleStore DB."""
-
 from __future__ import annotations
 
 import json
@@ -35,8 +33,7 @@ ORDERING_DIRECTIVE: dict = {
 
 
 class SingleStoreDB(VectorStore):
-    """
+    """`SingleStore DB` vector store.
-    This class serves as a Pythonic interface to the SingleStore DB database.
 
     The prerequisite for using this class is the installation of the ``singlestoredb``
     Python package.

libs/langchain/langchain/vectorstores/sklearn.py

@@ -21,7 +21,7 @@ DEFAULT_FETCH_K = 20  # Number of Documents to initially fetch during MMR search
 
 
 class BaseSerializer(ABC):
-    """Abstract base class for saving and loading data."""
+    """Base class for serializing data."""
 
     def __init__(self, persist_path: str) -> None:
         self.persist_path = persist_path
@@ -57,7 +57,7 @@ class JsonSerializer(BaseSerializer):
 
 
 class BsonSerializer(BaseSerializer):
-    """Serializes data in binary json using the bson python package."""
+    """Serializes data in binary json using the `bson` python package."""
 
     def __init__(self, persist_path: str) -> None:
         super().__init__(persist_path)
@@ -77,7 +77,7 @@ class BsonSerializer(BaseSerializer):
 
 
 class ParquetSerializer(BaseSerializer):
-    """Serializes data in Apache Parquet format using the pyarrow package."""
+    """Serializes data in `Apache Parquet` format using the `pyarrow` package."""
 
     def __init__(self, persist_path: str) -> None:
         super().__init__(persist_path)
@@ -125,8 +125,8 @@ class SKLearnVectorStoreException(RuntimeError):
 
 
 class SKLearnVectorStore(VectorStore):
-    """A simple in-memory vector store based on the scikit-learn library
+    """Simple in-memory vector store based on the `scikit-learn` library
-    NearestNeighbors implementation."""
+    `NearestNeighbors` implementation."""
 
     def __init__(
         self,

libs/langchain/langchain/vectorstores/starrocks.py

@@ -1,5 +1,3 @@
-"""Wrapper around open source StarRocks VectorSearch capability."""
-
 from __future__ import annotations
 
 import json
@@ -69,7 +67,7 @@ def get_named_result(connection: Any, query: str) -> List[dict[str, Any]]:
 
 
 class StarRocksSettings(BaseSettings):
-    """StarRocks Client Configuration
+    """StarRocks client configuration.
 
     Attribute:
         StarRocks_host (str) : An URL to connect to MyScale backend.
@@ -121,7 +119,7 @@ class StarRocksSettings(BaseSettings):
 
 
 class StarRocks(VectorStore):
-    """Wrapper around StarRocks vector database
+    """`StarRocks` vector store.
 
     You need a `pymysql` python package, and a valid account
     to connect to StarRocks.

libs/langchain/langchain/vectorstores/supabase.py

@@ -26,7 +26,9 @@ if TYPE_CHECKING:
 
 
 class SupabaseVectorStore(VectorStore):
-    """VectorStore for a Supabase postgres database. Assumes you have the `pgvector`
+    """`Supabase Postgres` vector store.
+
+    It assumes you have the `pgvector`
     extension installed and a `match_documents` (or similar) function. For more details:
     https://integrations.langchain.com/vectorstores?integration_name=SupabaseVectorStore
 
@@ -92,7 +94,7 @@ class SupabaseVectorStore(VectorStore):
         try:
             import supabase  # noqa: F401
         except ImportError:
-            raise ValueError(
+            raise ImportError(
                 "Could not import supabase python package. "
                 "Please install it with `pip install supabase`."
             )

libs/langchain/langchain/vectorstores/tair.py

@@ -1,4 +1,3 @@
-"""Wrapper around Tair Vector."""
 from __future__ import annotations
 
 import json
@@ -19,7 +18,7 @@ def _uuid_key() -> str:
 
 
 class Tair(VectorStore):
-    """Wrapper around Tair Vector store."""
+    """`Tair` vector store."""
 
     def __init__(
         self,

libs/langchain/langchain/vectorstores/tigris.py

@@ -15,12 +15,14 @@ if TYPE_CHECKING:
 
 
 class Tigris(VectorStore):
+    """`Tigris` vector store."""
+
     def __init__(self, client: TigrisClient, embeddings: Embeddings, index_name: str):
-        """Initialize Tigris vector store"""
+        """Initialize Tigris vector store."""
         try:
             import tigrisdb  # noqa: F401
         except ImportError:
-            raise ValueError(
+            raise ImportError(
                 "Could not import tigrisdb python package. "
                 "Please install it with `pip install tigrisdb`"
             )

libs/langchain/langchain/vectorstores/typesense.py

@@ -1,4 +1,3 @@
-"""Wrapper around Typesense vector search"""
 from __future__ import annotations
 
 import uuid
@@ -15,7 +14,7 @@ if TYPE_CHECKING:
 
 
 class Typesense(VectorStore):
-    """Wrapper around Typesense vector search.
+    """`Typesense` vector store.
 
     To use, you should have the ``typesense`` python package installed.
 
@@ -61,7 +60,7 @@ class Typesense(VectorStore):
         try:
             from typesense import Client
         except ImportError:
-            raise ValueError(
+            raise ImportError(
                 "Could not import typesense python package. "
                 "Please install it with `pip install typesense`."
             )

libs/langchain/langchain/vectorstores/usearch.py

@@ -1,4 +1,3 @@
-"""Wrapper around USearch vector database."""
 from __future__ import annotations
 
 from typing import Any, Dict, Iterable, List, Optional, Tuple
@@ -27,7 +26,8 @@ def dependable_usearch_import() -> Any:
 
 
 class USearch(VectorStore):
-    """Wrapper around USearch vector database.
+    """`USearch` vector store.
+
     To use, you should have the ``usearch`` python package installed.
     """
 

libs/langchain/langchain/vectorstores/vectara.py

@@ -1,4 +1,3 @@
-"""Wrapper around Vectara vector database."""
 from __future__ import annotations
 
 import json
@@ -18,7 +17,7 @@ logger = logging.getLogger(__name__)
 
 
 class Vectara(VectorStore):
-    """Implementation of Vector Store using Vectara.
+    """`Vectara API` vector store.
 
      See (https://vectara.com).
 
@@ -426,7 +425,7 @@ class Vectara(VectorStore):
 
 
 class VectaraRetriever(VectorStoreRetriever):
-    """Retriever class for Vectara."""
+    """Retriever class for `Vectara`."""
 
     vectorstore: Vectara
     """Vectara vectorstore."""

libs/langchain/langchain/vectorstores/weaviate.py

@@ -1,4 +1,3 @@
-"""Wrapper around weaviate vector database."""
 from __future__ import annotations
 
 import datetime
@@ -44,7 +43,7 @@ def _create_weaviate_client(**kwargs: Any) -> Any:
     try:
         import weaviate
     except ImportError:
-        raise ValueError(
+        raise ImportError(
             "Could not import weaviate python  package. "
             "Please install it with `pip install weaviate-client`"
         )
@@ -70,7 +69,7 @@ def _json_serializable(value: Any) -> Any:
 
 
 class Weaviate(VectorStore):
-    """Wrapper around Weaviate vector database.
+    """`Weaviate` vector store.
 
     To use, you should have the ``weaviate-client`` python package installed.
 

libs/langchain/langchain/vectorstores/xata.py

@@ -1,5 +1,3 @@
-"""Wrapper around Xata as a vector database."""
-
 from __future__ import annotations
 
 import time
@@ -12,7 +10,9 @@ from langchain.vectorstores.base import VectorStore
 
 
 class XataVectorStore(VectorStore):
-    """VectorStore for a Xata database. Assumes you have a Xata database
+    """`Xata` vector store.
+
+    It assumes you have a Xata database
     created with the right schema. See the guide at:
     https://integrations.langchain.com/vectorstores?integration_name=XataVectorStore
 
@@ -29,7 +29,7 @@ class XataVectorStore(VectorStore):
         try:
             from xata.client import XataClient  # noqa: F401
         except ImportError:
-            raise ValueError(
+            raise ImportError(
                 "Could not import xata python package. "
                 "Please install it with `pip install xata`."
             )

libs/langchain/langchain/vectorstores/zep.py

@@ -22,8 +22,7 @@ logger = logging.getLogger()
 
 @dataclass
 class CollectionConfig:
-    """
+    """Configuration for a `Zep Collection`.
-    A configuration class for a Zep Collection.
 
     If the collection does not exist, it will be created.
 
@@ -46,9 +45,9 @@ class CollectionConfig:
 
 
 class ZepVectorStore(VectorStore):
-    """
+    """`Zep` vector store.
-    ZepVectorStore is a VectorStore implementation that uses the Zep long-term memory
+
-    store as a backend. It provides methods for adding texts or documents to the store,
+    It provides methods for adding texts or documents to the store,
     searching for similar documents, and deleting documents.
 
     Search scores are calculated using cosine similarity normalized to [0, 1].

libs/langchain/langchain/vectorstores/zilliz.py

@@ -10,9 +10,9 @@ logger = logging.getLogger(__name__)
 
 
 class Zilliz(Milvus):
-    """Initialize wrapper around the Zilliz vector database.
+    """`Zilliz` vector store.
 
-    In order to use this you need to have `pymilvus` installed and a
+    You need to have `pymilvus` installed and a
     running Zilliz database.
 
     See the following documentation for how to run a Zilliz instance: