Harrison/move vectorstore base (#11030)

docs/extras/use_cases/more/agents/autonomous_agents/baby_agi.ipynb

@@ -36,7 +36,7 @@
     "from langchain.chains import LLMChain\nfrom langchain.llms import OpenAI\nfrom langchain.prompts import PromptTemplate\n",
     "from langchain.embeddings import OpenAIEmbeddings\n",
     "from langchain.llms import BaseLLM\n",
-    "from langchain.vectorstores.base import VectorStore\n",
+    "from langchain.schema.vectorstore import VectorStore\n",
     "from pydantic import BaseModel, Field\n",
     "from langchain.chains.base import Chain\n",
     "from langchain_experimental.autonomous_agents import BabyAGI"

docs/extras/use_cases/more/agents/autonomous_agents/baby_agi_with_agent.ipynb

@@ -32,7 +32,7 @@
     "from langchain.chains import LLMChain\nfrom langchain.llms import OpenAI\nfrom langchain.prompts import PromptTemplate\n",
     "from langchain.embeddings import OpenAIEmbeddings\n",
     "from langchain.llms import BaseLLM\n",
-    "from langchain.vectorstores.base import VectorStore\n",
+    "from langchain.schema.vectorstore import VectorStore\n",
     "from pydantic import BaseModel, Field\n",
     "from langchain.chains.base import Chain\n",
     "from langchain_experimental.autonomous_agents import BabyAGI"

libs/experimental/langchain_experimental/autonomous_agents/autogpt/agent.py

@@ -10,9 +10,9 @@ from langchain.schema import (
     Document,
 )
 from langchain.schema.messages import AIMessage, HumanMessage, SystemMessage
+from langchain.schema.vectorstore import VectorStoreRetriever
 from langchain.tools.base import BaseTool
 from langchain.tools.human.tool import HumanInputRun
-from langchain.vectorstores.base import VectorStoreRetriever
 
 from langchain_experimental.autonomous_agents.autogpt.output_parser import (
     AutoGPTOutputParser,

libs/experimental/langchain_experimental/autonomous_agents/autogpt/memory.py

@@ -1,7 +1,7 @@
 from typing import Any, Dict, List
 
 from langchain.memory.chat_memory import BaseChatMemory, get_prompt_input_key
-from langchain.vectorstores.base import VectorStoreRetriever
+from langchain.schema.vectorstore import VectorStoreRetriever
 
 from langchain_experimental.pydantic_v1 import Field
 

libs/experimental/langchain_experimental/autonomous_agents/autogpt/prompt.py

@@ -5,8 +5,8 @@ from langchain.prompts.chat import (
     BaseChatPromptTemplate,
 )
 from langchain.schema.messages import BaseMessage, HumanMessage, SystemMessage
+from langchain.schema.vectorstore import VectorStoreRetriever
 from langchain.tools.base import BaseTool
-from langchain.vectorstores.base import VectorStoreRetriever
 
 from langchain_experimental.autonomous_agents.autogpt.prompt_generator import get_prompt
 from langchain_experimental.pydantic_v1 import BaseModel

libs/experimental/langchain_experimental/autonomous_agents/baby_agi/baby_agi.py

@@ -5,7 +5,7 @@ from typing import Any, Dict, List, Optional
 from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.chains.base import Chain
 from langchain.schema.language_model import BaseLanguageModel
-from langchain.vectorstores.base import VectorStore
+from langchain.schema.vectorstore import VectorStore
 
 from langchain_experimental.autonomous_agents.baby_agi.task_creation import (
     TaskCreationChain,

libs/langchain/langchain/agents/agent_toolkits/vectorstore/toolkit.py

@@ -5,12 +5,12 @@ from langchain.agents.agent_toolkits.base import BaseToolkit
 from langchain.llms.openai import OpenAI
 from langchain.pydantic_v1 import BaseModel, Field
 from langchain.schema.language_model import BaseLanguageModel
+from langchain.schema.vectorstore import VectorStore
 from langchain.tools import BaseTool
 from langchain.tools.vectorstore.tool import (
     VectorStoreQATool,
     VectorStoreQAWithSourcesTool,
 )
-from langchain.vectorstores.base import VectorStore
 
 
 class VectorStoreInfo(BaseModel):

libs/langchain/langchain/chains/conversational_retrieval/base.py

@@ -22,7 +22,7 @@ from langchain.pydantic_v1 import Extra, Field, root_validator
 from langchain.schema import BasePromptTemplate, BaseRetriever, Document
 from langchain.schema.language_model import BaseLanguageModel
 from langchain.schema.messages import BaseMessage
-from langchain.vectorstores.base import VectorStore
+from langchain.schema.vectorstore import VectorStore
 
 # Depending on the memory type and configuration, the chat history format may differ.
 # This needs to be consolidated.

libs/langchain/langchain/chains/qa_with_sources/vector_db.py

@@ -11,7 +11,7 @@ from langchain.chains.combine_documents.stuff import StuffDocumentsChain
 from langchain.chains.qa_with_sources.base import BaseQAWithSourcesChain
 from langchain.docstore.document import Document
 from langchain.pydantic_v1 import Field, root_validator
-from langchain.vectorstores.base import VectorStore
+from langchain.schema.vectorstore import VectorStore
 
 
 class VectorDBQAWithSourcesChain(BaseQAWithSourcesChain):

libs/langchain/langchain/chains/retrieval_qa/base.py

@@ -21,7 +21,7 @@ from langchain.prompts import PromptTemplate
 from langchain.pydantic_v1 import Extra, Field, root_validator
 from langchain.schema import BaseRetriever, Document
 from langchain.schema.language_model import BaseLanguageModel
-from langchain.vectorstores.base import VectorStore
+from langchain.schema.vectorstore import VectorStore
 
 
 class BaseRetrievalQA(Chain):
@@ -198,7 +198,7 @@ class RetrievalQA(BaseRetrievalQA):
             from langchain.llms import OpenAI
             from langchain.chains import RetrievalQA
             from langchain.faiss import FAISS
-            from langchain.vectorstores.base import VectorStoreRetriever
+            from langchain.schema.vectorstore import VectorStoreRetriever
             retriever = VectorStoreRetriever(vectorstore=FAISS(...))
             retrievalQA = RetrievalQA.from_llm(llm=OpenAI(), retriever=retriever)
 

libs/langchain/langchain/chains/router/embedding_router.py

@@ -7,7 +7,7 @@ from langchain.chains.router.base import RouterChain
 from langchain.docstore.document import Document
 from langchain.pydantic_v1 import Extra
 from langchain.schema.embeddings import Embeddings
-from langchain.vectorstores.base import VectorStore
+from langchain.schema.vectorstore import VectorStore
 
 
 class EmbeddingRouterChain(RouterChain):

libs/langchain/langchain/indexes/_api.py

@@ -25,7 +25,7 @@ from langchain.document_loaders.base import BaseLoader
 from langchain.indexes.base import NAMESPACE_UUID, RecordManager
 from langchain.pydantic_v1 import root_validator
 from langchain.schema import Document
-from langchain.vectorstores.base import VectorStore
+from langchain.schema.vectorstore import VectorStore
 
 T = TypeVar("T")
 

libs/langchain/langchain/indexes/vectorstore.py

@@ -9,8 +9,8 @@ from langchain.pydantic_v1 import BaseModel, Extra, Field
 from langchain.schema import Document
 from langchain.schema.embeddings import Embeddings
 from langchain.schema.language_model import BaseLanguageModel
+from langchain.schema.vectorstore import VectorStore
 from langchain.text_splitter import RecursiveCharacterTextSplitter, TextSplitter
-from langchain.vectorstores.base import VectorStore
 from langchain.vectorstores.chroma import Chroma
 
 

libs/langchain/langchain/memory/vectorstore.py

@@ -6,7 +6,7 @@ from langchain.memory.chat_memory import BaseMemory
 from langchain.memory.utils import get_prompt_input_key
 from langchain.pydantic_v1 import Field
 from langchain.schema import Document
-from langchain.vectorstores.base import VectorStoreRetriever
+from langchain.schema.vectorstore import VectorStoreRetriever
 
 
 class VectorStoreRetrieverMemory(BaseMemory):

libs/langchain/langchain/prompts/example_selector/semantic_similarity.py

@@ -6,7 +6,7 @@ from typing import Any, Dict, List, Optional, Type
 from langchain.prompts.example_selector.base import BaseExampleSelector
 from langchain.pydantic_v1 import BaseModel, Extra
 from langchain.schema.embeddings import Embeddings
-from langchain.vectorstores.base import VectorStore
+from langchain.schema.vectorstore import VectorStore
 
 
 def sorted_values(values: Dict[str, str]) -> List[Any]:

libs/langchain/langchain/retrievers/time_weighted_retriever.py

@@ -5,7 +5,7 @@ from typing import Any, Dict, List, Optional, Tuple
 from langchain.callbacks.manager import CallbackManagerForRetrieverRun
 from langchain.pydantic_v1 import Field
 from langchain.schema import BaseRetriever, Document
-from langchain.vectorstores.base import VectorStore
+from langchain.schema.vectorstore import VectorStore
 
 
 def _get_hours_passed(time: datetime.datetime, ref_time: datetime.datetime) -> float:

libs/langchain/langchain/retrievers/web_research.py

@@ -16,9 +16,9 @@ from langchain.output_parsers.pydantic import PydanticOutputParser
 from langchain.prompts import BasePromptTemplate, PromptTemplate
 from langchain.pydantic_v1 import BaseModel, Field
 from langchain.schema import BaseRetriever, Document
+from langchain.schema.vectorstore import VectorStore
 from langchain.text_splitter import RecursiveCharacterTextSplitter, TextSplitter
 from langchain.utilities import GoogleSearchAPIWrapper
-from langchain.vectorstores.base import VectorStore
 
 logger = logging.getLogger(__name__)
 

libs/langchain/langchain/schema/vectorstore.py

@@ -0,0 +1,611 @@
+from __future__ import annotations
+
+import asyncio
+import logging
+import math
+import warnings
+from abc import ABC, abstractmethod
+from functools import partial
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    ClassVar,
+    Collection,
+    Dict,
+    Iterable,
+    List,
+    Optional,
+    Tuple,
+    Type,
+    TypeVar,
+)
+
+from langchain.pydantic_v1 import Field, root_validator
+from langchain.schema import BaseRetriever
+from langchain.schema.document import Document
+from langchain.schema.embeddings import Embeddings
+
+if TYPE_CHECKING:
+    from langchain.callbacks.manager import (
+        AsyncCallbackManagerForRetrieverRun,
+        CallbackManagerForRetrieverRun,
+    )
+
+logger = logging.getLogger(__name__)
+
+VST = TypeVar("VST", bound="VectorStore")
+
+
+class VectorStore(ABC):
+    """Interface for vector store."""
+
+    @abstractmethod
+    def add_texts(
+        self,
+        texts: Iterable[str],
+        metadatas: Optional[List[dict]] = None,
+        **kwargs: Any,
+    ) -> List[str]:
+        """Run more texts through the embeddings and add to the vectorstore.
+
+        Args:
+            texts: Iterable of strings to add to the vectorstore.
+            metadatas: Optional list of metadatas associated with the texts.
+            kwargs: vectorstore specific parameters
+
+        Returns:
+            List of ids from adding the texts into the vectorstore.
+        """
+
+    @property
+    def embeddings(self) -> Optional[Embeddings]:
+        """Access the query embedding object if available."""
+        logger.debug(
+            f"{Embeddings.__name__} is not implemented for {self.__class__.__name__}"
+        )
+        return None
+
+    def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> Optional[bool]:
+        """Delete by vector ID or other criteria.
+
+        Args:
+            ids: List of ids to delete.
+            **kwargs: Other keyword arguments that subclasses might use.
+
+        Returns:
+            Optional[bool]: True if deletion is successful,
+            False otherwise, None if not implemented.
+        """
+
+        raise NotImplementedError("delete method must be implemented by subclass.")
+
+    async def aadd_texts(
+        self,
+        texts: Iterable[str],
+        metadatas: Optional[List[dict]] = None,
+        **kwargs: Any,
+    ) -> List[str]:
+        """Run more texts through the embeddings and add to the vectorstore."""
+        raise NotImplementedError
+
+    def add_documents(self, documents: List[Document], **kwargs: Any) -> List[str]:
+        """Run more documents through the embeddings and add to the vectorstore.
+
+        Args:
+            documents (List[Document]: Documents to add to the vectorstore.
+
+        Returns:
+            List[str]: List of IDs of the added texts.
+        """
+        # TODO: Handle the case where the user doesn't provide ids on the Collection
+        texts = [doc.page_content for doc in documents]
+        metadatas = [doc.metadata for doc in documents]
+        return self.add_texts(texts, metadatas, **kwargs)
+
+    async def aadd_documents(
+        self, documents: List[Document], **kwargs: Any
+    ) -> List[str]:
+        """Run more documents through the embeddings and add to the vectorstore.
+
+        Args:
+            documents (List[Document]: Documents to add to the vectorstore.
+
+        Returns:
+            List[str]: List of IDs of the added texts.
+        """
+        texts = [doc.page_content for doc in documents]
+        metadatas = [doc.metadata for doc in documents]
+        return await self.aadd_texts(texts, metadatas, **kwargs)
+
+    def search(self, query: str, search_type: str, **kwargs: Any) -> List[Document]:
+        """Return docs most similar to query using specified search type."""
+        if search_type == "similarity":
+            return self.similarity_search(query, **kwargs)
+        elif search_type == "mmr":
+            return self.max_marginal_relevance_search(query, **kwargs)
+        else:
+            raise ValueError(
+                f"search_type of {search_type} not allowed. Expected "
+                "search_type to be 'similarity' or 'mmr'."
+            )
+
+    async def asearch(
+        self, query: str, search_type: str, **kwargs: Any
+    ) -> List[Document]:
+        """Return docs most similar to query using specified search type."""
+        if search_type == "similarity":
+            return await self.asimilarity_search(query, **kwargs)
+        elif search_type == "mmr":
+            return await self.amax_marginal_relevance_search(query, **kwargs)
+        else:
+            raise ValueError(
+                f"search_type of {search_type} not allowed. Expected "
+                "search_type to be 'similarity' or 'mmr'."
+            )
+
+    @abstractmethod
+    def similarity_search(
+        self, query: str, k: int = 4, **kwargs: Any
+    ) -> List[Document]:
+        """Return docs most similar to query."""
+
+    @staticmethod
+    def _euclidean_relevance_score_fn(distance: float) -> float:
+        """Return a similarity score on a scale [0, 1]."""
+        # The 'correct' relevance function
+        # may differ depending on a few things, including:
+        # - the distance / similarity metric used by the VectorStore
+        # - the scale of your embeddings (OpenAI's are unit normed. Many
+        #  others are not!)
+        # - embedding dimensionality
+        # - etc.
+        # This function converts the euclidean norm of normalized embeddings
+        # (0 is most similar, sqrt(2) most dissimilar)
+        # to a similarity function (0 to 1)
+        return 1.0 - distance / math.sqrt(2)
+
+    @staticmethod
+    def _cosine_relevance_score_fn(distance: float) -> float:
+        """Normalize the distance to a score on a scale [0, 1]."""
+
+        return 1.0 - distance
+
+    @staticmethod
+    def _max_inner_product_relevance_score_fn(distance: float) -> float:
+        """Normalize the distance to a score on a scale [0, 1]."""
+        if distance > 0:
+            return 1.0 - distance
+
+        return -1.0 * distance
+
+    def _select_relevance_score_fn(self) -> Callable[[float], float]:
+        """
+        The 'correct' relevance function
+        may differ depending on a few things, including:
+        - the distance / similarity metric used by the VectorStore
+        - the scale of your embeddings (OpenAI's are unit normed. Many others are not!)
+        - embedding dimensionality
+        - etc.
+
+        Vectorstores should define their own selection based method of relevance.
+        """
+        raise NotImplementedError
+
+    def similarity_search_with_score(
+        self, *args: Any, **kwargs: Any
+    ) -> List[Tuple[Document, float]]:
+        """Run similarity search with distance."""
+        raise NotImplementedError
+
+    def _similarity_search_with_relevance_scores(
+        self,
+        query: str,
+        k: int = 4,
+        **kwargs: Any,
+    ) -> List[Tuple[Document, float]]:
+        """
+        Default similarity search with relevance scores. Modify if necessary
+        in subclass.
+        Return docs and relevance scores in the range [0, 1].
+
+        0 is dissimilar, 1 is most similar.
+
+        Args:
+            query: input text
+            k: Number of Documents to return. Defaults to 4.
+            **kwargs: kwargs to be passed to similarity search. Should include:
+                score_threshold: Optional, a floating point value between 0 to 1 to
+                    filter the resulting set of retrieved docs
+
+        Returns:
+            List of Tuples of (doc, similarity_score)
+        """
+        relevance_score_fn = self._select_relevance_score_fn()
+        docs_and_scores = self.similarity_search_with_score(query, k, **kwargs)
+        return [(doc, relevance_score_fn(score)) for doc, score in docs_and_scores]
+
+    def similarity_search_with_relevance_scores(
+        self,
+        query: str,
+        k: int = 4,
+        **kwargs: Any,
+    ) -> List[Tuple[Document, float]]:
+        """Return docs and relevance scores in the range [0, 1].
+
+        0 is dissimilar, 1 is most similar.
+
+        Args:
+            query: input text
+            k: Number of Documents to return. Defaults to 4.
+            **kwargs: kwargs to be passed to similarity search. Should include:
+                score_threshold: Optional, a floating point value between 0 to 1 to
+                    filter the resulting set of retrieved docs
+
+        Returns:
+            List of Tuples of (doc, similarity_score)
+        """
+        score_threshold = kwargs.pop("score_threshold", None)
+
+        docs_and_similarities = self._similarity_search_with_relevance_scores(
+            query, k=k, **kwargs
+        )
+        if any(
+            similarity < 0.0 or similarity > 1.0
+            for _, similarity in docs_and_similarities
+        ):
+            warnings.warn(
+                "Relevance scores must be between"
+                f" 0 and 1, got {docs_and_similarities}"
+            )
+
+        if score_threshold is not None:
+            docs_and_similarities = [
+                (doc, similarity)
+                for doc, similarity in docs_and_similarities
+                if similarity >= score_threshold
+            ]
+            if len(docs_and_similarities) == 0:
+                warnings.warn(
+                    "No relevant docs were retrieved using the relevance score"
+                    f" threshold {score_threshold}"
+                )
+        return docs_and_similarities
+
+    async def asimilarity_search_with_relevance_scores(
+        self, query: str, k: int = 4, **kwargs: Any
+    ) -> List[Tuple[Document, float]]:
+        """Return docs most similar to query."""
+
+        # This is a temporary workaround to make the similarity search
+        # asynchronous. The proper solution is to make the similarity search
+        # asynchronous in the vector store implementations.
+        func = partial(
+            self.similarity_search_with_relevance_scores, query, k=k, **kwargs
+        )
+        return await asyncio.get_event_loop().run_in_executor(None, func)
+
+    async def asimilarity_search(
+        self, query: str, k: int = 4, **kwargs: Any
+    ) -> List[Document]:
+        """Return docs most similar to query."""
+
+        # This is a temporary workaround to make the similarity search
+        # asynchronous. The proper solution is to make the similarity search
+        # asynchronous in the vector store implementations.
+        func = partial(self.similarity_search, query, k=k, **kwargs)
+        return await asyncio.get_event_loop().run_in_executor(None, func)
+
+    def similarity_search_by_vector(
+        self, embedding: List[float], k: int = 4, **kwargs: Any
+    ) -> List[Document]:
+        """Return docs most similar to embedding vector.
+
+        Args:
+            embedding: Embedding to look up documents similar to.
+            k: Number of Documents to return. Defaults to 4.
+
+        Returns:
+            List of Documents most similar to the query vector.
+        """
+        raise NotImplementedError
+
+    async def asimilarity_search_by_vector(
+        self, embedding: List[float], k: int = 4, **kwargs: Any
+    ) -> List[Document]:
+        """Return docs most similar to embedding vector."""
+
+        # This is a temporary workaround to make the similarity search
+        # asynchronous. The proper solution is to make the similarity search
+        # asynchronous in the vector store implementations.
+        func = partial(self.similarity_search_by_vector, embedding, k=k, **kwargs)
+        return await asyncio.get_event_loop().run_in_executor(None, func)
+
+    def max_marginal_relevance_search(
+        self,
+        query: str,
+        k: int = 4,
+        fetch_k: int = 20,
+        lambda_mult: float = 0.5,
+        **kwargs: Any,
+    ) -> List[Document]:
+        """Return docs selected using the maximal marginal relevance.
+
+        Maximal marginal relevance optimizes for similarity to query AND diversity
+        among selected documents.
+
+        Args:
+            query: Text to look up documents similar to.
+            k: Number of Documents to return. Defaults to 4.
+            fetch_k: Number of Documents to fetch to pass to MMR algorithm.
+            lambda_mult: Number between 0 and 1 that determines the degree
+                        of diversity among the results with 0 corresponding
+                        to maximum diversity and 1 to minimum diversity.
+                        Defaults to 0.5.
+        Returns:
+            List of Documents selected by maximal marginal relevance.
+        """
+        raise NotImplementedError
+
+    async def amax_marginal_relevance_search(
+        self,
+        query: str,
+        k: int = 4,
+        fetch_k: int = 20,
+        lambda_mult: float = 0.5,
+        **kwargs: Any,
+    ) -> List[Document]:
+        """Return docs selected using the maximal marginal relevance."""
+
+        # This is a temporary workaround to make the similarity search
+        # asynchronous. The proper solution is to make the similarity search
+        # asynchronous in the vector store implementations.
+        func = partial(
+            self.max_marginal_relevance_search,
+            query,
+            k=k,
+            fetch_k=fetch_k,
+            lambda_mult=lambda_mult,
+            **kwargs,
+        )
+        return await asyncio.get_event_loop().run_in_executor(None, func)
+
+    def max_marginal_relevance_search_by_vector(
+        self,
+        embedding: List[float],
+        k: int = 4,
+        fetch_k: int = 20,
+        lambda_mult: float = 0.5,
+        **kwargs: Any,
+    ) -> List[Document]:
+        """Return docs selected using the maximal marginal relevance.
+
+        Maximal marginal relevance optimizes for similarity to query AND diversity
+        among selected documents.
+
+        Args:
+            embedding: Embedding to look up documents similar to.
+            k: Number of Documents to return. Defaults to 4.
+            fetch_k: Number of Documents to fetch to pass to MMR algorithm.
+            lambda_mult: Number between 0 and 1 that determines the degree
+                        of diversity among the results with 0 corresponding
+                        to maximum diversity and 1 to minimum diversity.
+                        Defaults to 0.5.
+        Returns:
+            List of Documents selected by maximal marginal relevance.
+        """
+        raise NotImplementedError
+
+    async def amax_marginal_relevance_search_by_vector(
+        self,
+        embedding: List[float],
+        k: int = 4,
+        fetch_k: int = 20,
+        lambda_mult: float = 0.5,
+        **kwargs: Any,
+    ) -> List[Document]:
+        """Return docs selected using the maximal marginal relevance."""
+        raise NotImplementedError
+
+    @classmethod
+    def from_documents(
+        cls: Type[VST],
+        documents: List[Document],
+        embedding: Embeddings,
+        **kwargs: Any,
+    ) -> VST:
+        """Return VectorStore initialized from documents and embeddings."""
+        texts = [d.page_content for d in documents]
+        metadatas = [d.metadata for d in documents]
+        return cls.from_texts(texts, embedding, metadatas=metadatas, **kwargs)
+
+    @classmethod
+    async def afrom_documents(
+        cls: Type[VST],
+        documents: List[Document],
+        embedding: Embeddings,
+        **kwargs: Any,
+    ) -> VST:
+        """Return VectorStore initialized from documents and embeddings."""
+        texts = [d.page_content for d in documents]
+        metadatas = [d.metadata for d in documents]
+        return await cls.afrom_texts(texts, embedding, metadatas=metadatas, **kwargs)
+
+    @classmethod
+    @abstractmethod
+    def from_texts(
+        cls: Type[VST],
+        texts: List[str],
+        embedding: Embeddings,
+        metadatas: Optional[List[dict]] = None,
+        **kwargs: Any,
+    ) -> VST:
+        """Return VectorStore initialized from texts and embeddings."""
+
+    @classmethod
+    async def afrom_texts(
+        cls: Type[VST],
+        texts: List[str],
+        embedding: Embeddings,
+        metadatas: Optional[List[dict]] = None,
+        **kwargs: Any,
+    ) -> VST:
+        """Return VectorStore initialized from texts and embeddings."""
+        raise NotImplementedError
+
+    def _get_retriever_tags(self) -> List[str]:
+        """Get tags for retriever."""
+        tags = [self.__class__.__name__]
+        if self.embeddings:
+            tags.append(self.embeddings.__class__.__name__)
+        return tags
+
+    def as_retriever(self, **kwargs: Any) -> VectorStoreRetriever:
+        """Return VectorStoreRetriever initialized from this VectorStore.
+
+        Args:
+            search_type (Optional[str]): Defines the type of search that
+                the Retriever should perform.
+                Can be "similarity" (default), "mmr", or
+                "similarity_score_threshold".
+            search_kwargs (Optional[Dict]): Keyword arguments to pass to the
+                search function. Can include things like:
+                    k: Amount of documents to return (Default: 4)
+                    score_threshold: Minimum relevance threshold
+                        for similarity_score_threshold
+                    fetch_k: Amount of documents to pass to MMR algorithm (Default: 20)
+                    lambda_mult: Diversity of results returned by MMR;
+                        1 for minimum diversity and 0 for maximum. (Default: 0.5)
+                    filter: Filter by document metadata
+
+        Returns:
+            VectorStoreRetriever: Retriever class for VectorStore.
+
+        Examples:
+
+        .. code-block:: python
+
+            # Retrieve more documents with higher diversity
+            # Useful if your dataset has many similar documents
+            docsearch.as_retriever(
+                search_type="mmr",
+                search_kwargs={'k': 6, 'lambda_mult': 0.25}
+            )
+
+            # Fetch more documents for the MMR algorithm to consider
+            # But only return the top 5
+            docsearch.as_retriever(
+                search_type="mmr",
+                search_kwargs={'k': 5, 'fetch_k': 50}
+            )
+
+            # Only retrieve documents that have a relevance score
+            # Above a certain threshold
+            docsearch.as_retriever(
+                search_type="similarity_score_threshold",
+                search_kwargs={'score_threshold': 0.8}
+            )
+
+            # Only get the single most similar document from the dataset
+            docsearch.as_retriever(search_kwargs={'k': 1})
+
+            # Use a filter to only retrieve documents from a specific paper
+            docsearch.as_retriever(
+                search_kwargs={'filter': {'paper_title':'GPT-4 Technical Report'}}
+            )
+        """
+        tags = kwargs.pop("tags", None) or []
+        tags.extend(self._get_retriever_tags())
+
+        return VectorStoreRetriever(vectorstore=self, **kwargs, tags=tags)
+
+
+class VectorStoreRetriever(BaseRetriever):
+    """Base Retriever class for VectorStore."""
+
+    vectorstore: VectorStore
+    """VectorStore to use for retrieval."""
+    search_type: str = "similarity"
+    """Type of search to perform. Defaults to "similarity"."""
+    search_kwargs: dict = Field(default_factory=dict)
+    """Keyword arguments to pass to the search function."""
+    allowed_search_types: ClassVar[Collection[str]] = (
+        "similarity",
+        "similarity_score_threshold",
+        "mmr",
+    )
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        arbitrary_types_allowed = True
+
+    @root_validator()
+    def validate_search_type(cls, values: Dict) -> Dict:
+        """Validate search type."""
+        search_type = values["search_type"]
+        if search_type not in cls.allowed_search_types:
+            raise ValueError(
+                f"search_type of {search_type} not allowed. Valid values are: "
+                f"{cls.allowed_search_types}"
+            )
+        if search_type == "similarity_score_threshold":
+            score_threshold = values["search_kwargs"].get("score_threshold")
+            if (score_threshold is None) or (not isinstance(score_threshold, float)):
+                raise ValueError(
+                    "`score_threshold` is not specified with a float value(0~1) "
+                    "in `search_kwargs`."
+                )
+        return values
+
+    def _get_relevant_documents(
+        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
+    ) -> List[Document]:
+        if self.search_type == "similarity":
+            docs = self.vectorstore.similarity_search(query, **self.search_kwargs)
+        elif self.search_type == "similarity_score_threshold":
+            docs_and_similarities = (
+                self.vectorstore.similarity_search_with_relevance_scores(
+                    query, **self.search_kwargs
+                )
+            )
+            docs = [doc for doc, _ in docs_and_similarities]
+        elif self.search_type == "mmr":
+            docs = self.vectorstore.max_marginal_relevance_search(
+                query, **self.search_kwargs
+            )
+        else:
+            raise ValueError(f"search_type of {self.search_type} not allowed.")
+        return docs
+
+    async def _aget_relevant_documents(
+        self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
+    ) -> List[Document]:
+        if self.search_type == "similarity":
+            docs = await self.vectorstore.asimilarity_search(
+                query, **self.search_kwargs
+            )
+        elif self.search_type == "similarity_score_threshold":
+            docs_and_similarities = (
+                await self.vectorstore.asimilarity_search_with_relevance_scores(
+                    query, **self.search_kwargs
+                )
+            )
+            docs = [doc for doc, _ in docs_and_similarities]
+        elif self.search_type == "mmr":
+            docs = await self.vectorstore.amax_marginal_relevance_search(
+                query, **self.search_kwargs
+            )
+        else:
+            raise ValueError(f"search_type of {self.search_type} not allowed.")
+        return docs
+
+    def add_documents(self, documents: List[Document], **kwargs: Any) -> List[str]:
+        """Add documents to vectorstore."""
+        return self.vectorstore.add_documents(documents, **kwargs)
+
+    async def aadd_documents(
+        self, documents: List[Document], **kwargs: Any
+    ) -> List[str]:
+        """Add documents to vectorstore."""
+        return await self.vectorstore.aadd_documents(documents, **kwargs)

libs/langchain/langchain/tools/vectorstore/tool.py

@@ -8,8 +8,8 @@ from langchain.chains import RetrievalQA, RetrievalQAWithSourcesChain
 from langchain.llms.openai import OpenAI
 from langchain.pydantic_v1 import BaseModel, Field
 from langchain.schema.language_model import BaseLanguageModel
+from langchain.schema.vectorstore import VectorStore
 from langchain.tools.base import BaseTool
-from langchain.vectorstores.base import VectorStore
 
 
 class BaseVectorStoreTool(BaseModel):

libs/langchain/langchain/vectorstores/__init__.py

@@ -18,6 +18,7 @@ and retrieve the data that are 'most similar' to the embedded query.
 
     Embeddings, Document
 """  # noqa: E501
+from langchain.schema.vectorstore import VectorStore
 from langchain.vectorstores.alibabacloud_opensearch import (
     AlibabaCloudOpenSearch,
     AlibabaCloudOpenSearchSettings,
@@ -28,7 +29,6 @@ from langchain.vectorstores.atlas import AtlasDB
 from langchain.vectorstores.awadb import AwaDB
 from langchain.vectorstores.azuresearch import AzureSearch
 from langchain.vectorstores.bageldb import Bagel
-from langchain.vectorstores.base import VectorStore
 from langchain.vectorstores.cassandra import Cassandra
 from langchain.vectorstores.chroma import Chroma
 from langchain.vectorstores.clarifai import Clarifai

libs/langchain/langchain/vectorstores/alibabacloud_opensearch.py

@@ -6,7 +6,7 @@ from typing import Any, Dict, Iterable, List, Optional, Tuple
 
 from langchain.schema import Document
 from langchain.schema.embeddings import Embeddings
-from langchain.vectorstores.base import VectorStore
+from langchain.schema.vectorstore import VectorStore
 
 logger = logging.getLogger()
 

libs/langchain/langchain/vectorstores/analyticdb.py

@@ -14,8 +14,8 @@ except ImportError:
 
 from langchain.docstore.document import Document
 from langchain.schema.embeddings import Embeddings
+from langchain.schema.vectorstore import VectorStore
 from langchain.utils import get_from_dict_or_env
-from langchain.vectorstores.base import VectorStore
 
 _LANGCHAIN_DEFAULT_EMBEDDING_DIM = 1536
 _LANGCHAIN_DEFAULT_COLLECTION_NAME = "langchain_document"

libs/langchain/langchain/vectorstores/annoy.py

@@ -13,7 +13,7 @@ from langchain.docstore.base import Docstore
 from langchain.docstore.document import Document
 from langchain.docstore.in_memory import InMemoryDocstore
 from langchain.schema.embeddings import Embeddings
-from langchain.vectorstores.base import VectorStore
+from langchain.schema.vectorstore import VectorStore
 from langchain.vectorstores.utils import maximal_marginal_relevance
 
 INDEX_METRICS = frozenset(["angular", "euclidean", "manhattan", "hamming", "dot"])

libs/langchain/langchain/vectorstores/atlas.py

@@ -8,7 +8,7 @@ import numpy as np
 
 from langchain.docstore.document import Document
 from langchain.schema.embeddings import Embeddings
-from langchain.vectorstores.base import VectorStore
+from langchain.schema.vectorstore import VectorStore
 
 logger = logging.getLogger(__name__)
 

libs/langchain/langchain/vectorstores/awadb.py

@@ -8,7 +8,7 @@ import numpy as np
 
 from langchain.docstore.document import Document
 from langchain.schema.embeddings import Embeddings
-from langchain.vectorstores.base import VectorStore
+from langchain.schema.vectorstore import VectorStore
 from langchain.vectorstores.utils import maximal_marginal_relevance
 
 if TYPE_CHECKING:

libs/langchain/langchain/vectorstores/azuresearch.py

@@ -26,8 +26,8 @@ from langchain.docstore.document import Document
 from langchain.pydantic_v1 import root_validator
 from langchain.schema import BaseRetriever
 from langchain.schema.embeddings import Embeddings
+from langchain.schema.vectorstore import VectorStore
 from langchain.utils import get_from_env
-from langchain.vectorstores.base import VectorStore
 
 logger = logging.getLogger()
 

libs/langchain/langchain/vectorstores/bageldb.py

@@ -20,8 +20,8 @@ if TYPE_CHECKING:
 
 from langchain.docstore.document import Document
 from langchain.schema.embeddings import Embeddings
+from langchain.schema.vectorstore import VectorStore
 from langchain.utils import xor_args
-from langchain.vectorstores.base import VectorStore
 
 DEFAULT_K = 5
 

libs/langchain/langchain/vectorstores/base.py

@@ -1,608 +1,3 @@
-from __future__ import annotations
+from langchain.schema.vectorstore import VectorStore, VectorStoreRetriever
 
-import asyncio
-import logging
-import math
-import warnings
-from abc import ABC, abstractmethod
-from functools import partial
-from typing import (
-    Any,
-    Callable,
-    ClassVar,
-    Collection,
-    Dict,
-    Iterable,
-    List,
-    Optional,
-    Tuple,
-    Type,
-    TypeVar,
-)
-
-from langchain.callbacks.manager import (
-    AsyncCallbackManagerForRetrieverRun,
-    CallbackManagerForRetrieverRun,
-)
-from langchain.docstore.document import Document
-from langchain.pydantic_v1 import Field, root_validator
-from langchain.schema import BaseRetriever
-from langchain.schema.embeddings import Embeddings
-
-logger = logging.getLogger(__name__)
-
-VST = TypeVar("VST", bound="VectorStore")
-
-
-class VectorStore(ABC):
-    """Interface for vector store."""
-
-    @abstractmethod
-    def add_texts(
-        self,
-        texts: Iterable[str],
-        metadatas: Optional[List[dict]] = None,
-        **kwargs: Any,
-    ) -> List[str]:
-        """Run more texts through the embeddings and add to the vectorstore.
-
-        Args:
-            texts: Iterable of strings to add to the vectorstore.
-            metadatas: Optional list of metadatas associated with the texts.
-            kwargs: vectorstore specific parameters
-
-        Returns:
-            List of ids from adding the texts into the vectorstore.
-        """
-
-    @property
-    def embeddings(self) -> Optional[Embeddings]:
-        """Access the query embedding object if available."""
-        logger.debug(
-            f"{Embeddings.__name__} is not implemented for {self.__class__.__name__}"
-        )
-        return None
-
-    def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> Optional[bool]:
-        """Delete by vector ID or other criteria.
-
-        Args:
-            ids: List of ids to delete.
-            **kwargs: Other keyword arguments that subclasses might use.
-
-        Returns:
-            Optional[bool]: True if deletion is successful,
-            False otherwise, None if not implemented.
-        """
-
-        raise NotImplementedError("delete method must be implemented by subclass.")
-
-    async def aadd_texts(
-        self,
-        texts: Iterable[str],
-        metadatas: Optional[List[dict]] = None,
-        **kwargs: Any,
-    ) -> List[str]:
-        """Run more texts through the embeddings and add to the vectorstore."""
-        raise NotImplementedError
-
-    def add_documents(self, documents: List[Document], **kwargs: Any) -> List[str]:
-        """Run more documents through the embeddings and add to the vectorstore.
-
-        Args:
-            documents (List[Document]: Documents to add to the vectorstore.
-
-        Returns:
-            List[str]: List of IDs of the added texts.
-        """
-        # TODO: Handle the case where the user doesn't provide ids on the Collection
-        texts = [doc.page_content for doc in documents]
-        metadatas = [doc.metadata for doc in documents]
-        return self.add_texts(texts, metadatas, **kwargs)
-
-    async def aadd_documents(
-        self, documents: List[Document], **kwargs: Any
-    ) -> List[str]:
-        """Run more documents through the embeddings and add to the vectorstore.
-
-        Args:
-            documents (List[Document]: Documents to add to the vectorstore.
-
-        Returns:
-            List[str]: List of IDs of the added texts.
-        """
-        texts = [doc.page_content for doc in documents]
-        metadatas = [doc.metadata for doc in documents]
-        return await self.aadd_texts(texts, metadatas, **kwargs)
-
-    def search(self, query: str, search_type: str, **kwargs: Any) -> List[Document]:
-        """Return docs most similar to query using specified search type."""
-        if search_type == "similarity":
-            return self.similarity_search(query, **kwargs)
-        elif search_type == "mmr":
-            return self.max_marginal_relevance_search(query, **kwargs)
-        else:
-            raise ValueError(
-                f"search_type of {search_type} not allowed. Expected "
-                "search_type to be 'similarity' or 'mmr'."
-            )
-
-    async def asearch(
-        self, query: str, search_type: str, **kwargs: Any
-    ) -> List[Document]:
-        """Return docs most similar to query using specified search type."""
-        if search_type == "similarity":
-            return await self.asimilarity_search(query, **kwargs)
-        elif search_type == "mmr":
-            return await self.amax_marginal_relevance_search(query, **kwargs)
-        else:
-            raise ValueError(
-                f"search_type of {search_type} not allowed. Expected "
-                "search_type to be 'similarity' or 'mmr'."
-            )
-
-    @abstractmethod
-    def similarity_search(
-        self, query: str, k: int = 4, **kwargs: Any
-    ) -> List[Document]:
-        """Return docs most similar to query."""
-
-    @staticmethod
-    def _euclidean_relevance_score_fn(distance: float) -> float:
-        """Return a similarity score on a scale [0, 1]."""
-        # The 'correct' relevance function
-        # may differ depending on a few things, including:
-        # - the distance / similarity metric used by the VectorStore
-        # - the scale of your embeddings (OpenAI's are unit normed. Many
-        #  others are not!)
-        # - embedding dimensionality
-        # - etc.
-        # This function converts the euclidean norm of normalized embeddings
-        # (0 is most similar, sqrt(2) most dissimilar)
-        # to a similarity function (0 to 1)
-        return 1.0 - distance / math.sqrt(2)
-
-    @staticmethod
-    def _cosine_relevance_score_fn(distance: float) -> float:
-        """Normalize the distance to a score on a scale [0, 1]."""
-
-        return 1.0 - distance
-
-    @staticmethod
-    def _max_inner_product_relevance_score_fn(distance: float) -> float:
-        """Normalize the distance to a score on a scale [0, 1]."""
-        if distance > 0:
-            return 1.0 - distance
-
-        return -1.0 * distance
-
-    def _select_relevance_score_fn(self) -> Callable[[float], float]:
-        """
-        The 'correct' relevance function
-        may differ depending on a few things, including:
-        - the distance / similarity metric used by the VectorStore
-        - the scale of your embeddings (OpenAI's are unit normed. Many others are not!)
-        - embedding dimensionality
-        - etc.
-
-        Vectorstores should define their own selection based method of relevance.
-        """
-        raise NotImplementedError
-
-    def similarity_search_with_score(
-        self, *args: Any, **kwargs: Any
-    ) -> List[Tuple[Document, float]]:
-        """Run similarity search with distance."""
-        raise NotImplementedError
-
-    def _similarity_search_with_relevance_scores(
-        self,
-        query: str,
-        k: int = 4,
-        **kwargs: Any,
-    ) -> List[Tuple[Document, float]]:
-        """
-        Default similarity search with relevance scores. Modify if necessary
-        in subclass.
-        Return docs and relevance scores in the range [0, 1].
-
-        0 is dissimilar, 1 is most similar.
-
-        Args:
-            query: input text
-            k: Number of Documents to return. Defaults to 4.
-            **kwargs: kwargs to be passed to similarity search. Should include:
-                score_threshold: Optional, a floating point value between 0 to 1 to
-                    filter the resulting set of retrieved docs
-
-        Returns:
-            List of Tuples of (doc, similarity_score)
-        """
-        relevance_score_fn = self._select_relevance_score_fn()
-        docs_and_scores = self.similarity_search_with_score(query, k, **kwargs)
-        return [(doc, relevance_score_fn(score)) for doc, score in docs_and_scores]
-
-    def similarity_search_with_relevance_scores(
-        self,
-        query: str,
-        k: int = 4,
-        **kwargs: Any,
-    ) -> List[Tuple[Document, float]]:
-        """Return docs and relevance scores in the range [0, 1].
-
-        0 is dissimilar, 1 is most similar.
-
-        Args:
-            query: input text
-            k: Number of Documents to return. Defaults to 4.
-            **kwargs: kwargs to be passed to similarity search. Should include:
-                score_threshold: Optional, a floating point value between 0 to 1 to
-                    filter the resulting set of retrieved docs
-
-        Returns:
-            List of Tuples of (doc, similarity_score)
-        """
-        score_threshold = kwargs.pop("score_threshold", None)
-
-        docs_and_similarities = self._similarity_search_with_relevance_scores(
-            query, k=k, **kwargs
-        )
-        if any(
-            similarity < 0.0 or similarity > 1.0
-            for _, similarity in docs_and_similarities
-        ):
-            warnings.warn(
-                "Relevance scores must be between"
-                f" 0 and 1, got {docs_and_similarities}"
-            )
-
-        if score_threshold is not None:
-            docs_and_similarities = [
-                (doc, similarity)
-                for doc, similarity in docs_and_similarities
-                if similarity >= score_threshold
-            ]
-            if len(docs_and_similarities) == 0:
-                warnings.warn(
-                    "No relevant docs were retrieved using the relevance score"
-                    f" threshold {score_threshold}"
-                )
-        return docs_and_similarities
-
-    async def asimilarity_search_with_relevance_scores(
-        self, query: str, k: int = 4, **kwargs: Any
-    ) -> List[Tuple[Document, float]]:
-        """Return docs most similar to query."""
-
-        # This is a temporary workaround to make the similarity search
-        # asynchronous. The proper solution is to make the similarity search
-        # asynchronous in the vector store implementations.
-        func = partial(
-            self.similarity_search_with_relevance_scores, query, k=k, **kwargs
-        )
-        return await asyncio.get_event_loop().run_in_executor(None, func)
-
-    async def asimilarity_search(
-        self, query: str, k: int = 4, **kwargs: Any
-    ) -> List[Document]:
-        """Return docs most similar to query."""
-
-        # This is a temporary workaround to make the similarity search
-        # asynchronous. The proper solution is to make the similarity search
-        # asynchronous in the vector store implementations.
-        func = partial(self.similarity_search, query, k=k, **kwargs)
-        return await asyncio.get_event_loop().run_in_executor(None, func)
-
-    def similarity_search_by_vector(
-        self, embedding: List[float], k: int = 4, **kwargs: Any
-    ) -> List[Document]:
-        """Return docs most similar to embedding vector.
-
-        Args:
-            embedding: Embedding to look up documents similar to.
-            k: Number of Documents to return. Defaults to 4.
-
-        Returns:
-            List of Documents most similar to the query vector.
-        """
-        raise NotImplementedError
-
-    async def asimilarity_search_by_vector(
-        self, embedding: List[float], k: int = 4, **kwargs: Any
-    ) -> List[Document]:
-        """Return docs most similar to embedding vector."""
-
-        # This is a temporary workaround to make the similarity search
-        # asynchronous. The proper solution is to make the similarity search
-        # asynchronous in the vector store implementations.
-        func = partial(self.similarity_search_by_vector, embedding, k=k, **kwargs)
-        return await asyncio.get_event_loop().run_in_executor(None, func)
-
-    def max_marginal_relevance_search(
-        self,
-        query: str,
-        k: int = 4,
-        fetch_k: int = 20,
-        lambda_mult: float = 0.5,
-        **kwargs: Any,
-    ) -> List[Document]:
-        """Return docs selected using the maximal marginal relevance.
-
-        Maximal marginal relevance optimizes for similarity to query AND diversity
-        among selected documents.
-
-        Args:
-            query: Text to look up documents similar to.
-            k: Number of Documents to return. Defaults to 4.
-            fetch_k: Number of Documents to fetch to pass to MMR algorithm.
-            lambda_mult: Number between 0 and 1 that determines the degree
-                        of diversity among the results with 0 corresponding
-                        to maximum diversity and 1 to minimum diversity.
-                        Defaults to 0.5.
-        Returns:
-            List of Documents selected by maximal marginal relevance.
-        """
-        raise NotImplementedError
-
-    async def amax_marginal_relevance_search(
-        self,
-        query: str,
-        k: int = 4,
-        fetch_k: int = 20,
-        lambda_mult: float = 0.5,
-        **kwargs: Any,
-    ) -> List[Document]:
-        """Return docs selected using the maximal marginal relevance."""
-
-        # This is a temporary workaround to make the similarity search
-        # asynchronous. The proper solution is to make the similarity search
-        # asynchronous in the vector store implementations.
-        func = partial(
-            self.max_marginal_relevance_search,
-            query,
-            k=k,
-            fetch_k=fetch_k,
-            lambda_mult=lambda_mult,
-            **kwargs,
-        )
-        return await asyncio.get_event_loop().run_in_executor(None, func)
-
-    def max_marginal_relevance_search_by_vector(
-        self,
-        embedding: List[float],
-        k: int = 4,
-        fetch_k: int = 20,
-        lambda_mult: float = 0.5,
-        **kwargs: Any,
-    ) -> List[Document]:
-        """Return docs selected using the maximal marginal relevance.
-
-        Maximal marginal relevance optimizes for similarity to query AND diversity
-        among selected documents.
-
-        Args:
-            embedding: Embedding to look up documents similar to.
-            k: Number of Documents to return. Defaults to 4.
-            fetch_k: Number of Documents to fetch to pass to MMR algorithm.
-            lambda_mult: Number between 0 and 1 that determines the degree
-                        of diversity among the results with 0 corresponding
-                        to maximum diversity and 1 to minimum diversity.
-                        Defaults to 0.5.
-        Returns:
-            List of Documents selected by maximal marginal relevance.
-        """
-        raise NotImplementedError
-
-    async def amax_marginal_relevance_search_by_vector(
-        self,
-        embedding: List[float],
-        k: int = 4,
-        fetch_k: int = 20,
-        lambda_mult: float = 0.5,
-        **kwargs: Any,
-    ) -> List[Document]:
-        """Return docs selected using the maximal marginal relevance."""
-        raise NotImplementedError
-
-    @classmethod
-    def from_documents(
-        cls: Type[VST],
-        documents: List[Document],
-        embedding: Embeddings,
-        **kwargs: Any,
-    ) -> VST:
-        """Return VectorStore initialized from documents and embeddings."""
-        texts = [d.page_content for d in documents]
-        metadatas = [d.metadata for d in documents]
-        return cls.from_texts(texts, embedding, metadatas=metadatas, **kwargs)
-
-    @classmethod
-    async def afrom_documents(
-        cls: Type[VST],
-        documents: List[Document],
-        embedding: Embeddings,
-        **kwargs: Any,
-    ) -> VST:
-        """Return VectorStore initialized from documents and embeddings."""
-        texts = [d.page_content for d in documents]
-        metadatas = [d.metadata for d in documents]
-        return await cls.afrom_texts(texts, embedding, metadatas=metadatas, **kwargs)
-
-    @classmethod
-    @abstractmethod
-    def from_texts(
-        cls: Type[VST],
-        texts: List[str],
-        embedding: Embeddings,
-        metadatas: Optional[List[dict]] = None,
-        **kwargs: Any,
-    ) -> VST:
-        """Return VectorStore initialized from texts and embeddings."""
-
-    @classmethod
-    async def afrom_texts(
-        cls: Type[VST],
-        texts: List[str],
-        embedding: Embeddings,
-        metadatas: Optional[List[dict]] = None,
-        **kwargs: Any,
-    ) -> VST:
-        """Return VectorStore initialized from texts and embeddings."""
-        raise NotImplementedError
-
-    def _get_retriever_tags(self) -> List[str]:
-        """Get tags for retriever."""
-        tags = [self.__class__.__name__]
-        if self.embeddings:
-            tags.append(self.embeddings.__class__.__name__)
-        return tags
-
-    def as_retriever(self, **kwargs: Any) -> VectorStoreRetriever:
-        """Return VectorStoreRetriever initialized from this VectorStore.
-
-        Args:
-            search_type (Optional[str]): Defines the type of search that
-                the Retriever should perform.
-                Can be "similarity" (default), "mmr", or
-                "similarity_score_threshold".
-            search_kwargs (Optional[Dict]): Keyword arguments to pass to the
-                search function. Can include things like:
-                    k: Amount of documents to return (Default: 4)
-                    score_threshold: Minimum relevance threshold
-                        for similarity_score_threshold
-                    fetch_k: Amount of documents to pass to MMR algorithm (Default: 20)
-                    lambda_mult: Diversity of results returned by MMR;
-                        1 for minimum diversity and 0 for maximum. (Default: 0.5)
-                    filter: Filter by document metadata
-
-        Returns:
-            VectorStoreRetriever: Retriever class for VectorStore.
-
-        Examples:
-
-        .. code-block:: python
-
-            # Retrieve more documents with higher diversity
-            # Useful if your dataset has many similar documents
-            docsearch.as_retriever(
-                search_type="mmr",
-                search_kwargs={'k': 6, 'lambda_mult': 0.25}
-            )
-
-            # Fetch more documents for the MMR algorithm to consider
-            # But only return the top 5
-            docsearch.as_retriever(
-                search_type="mmr",
-                search_kwargs={'k': 5, 'fetch_k': 50}
-            )
-
-            # Only retrieve documents that have a relevance score
-            # Above a certain threshold
-            docsearch.as_retriever(
-                search_type="similarity_score_threshold",
-                search_kwargs={'score_threshold': 0.8}
-            )
-
-            # Only get the single most similar document from the dataset
-            docsearch.as_retriever(search_kwargs={'k': 1})
-
-            # Use a filter to only retrieve documents from a specific paper
-            docsearch.as_retriever(
-                search_kwargs={'filter': {'paper_title':'GPT-4 Technical Report'}}
-            )
-        """
-        tags = kwargs.pop("tags", None) or []
-        tags.extend(self._get_retriever_tags())
-
-        return VectorStoreRetriever(vectorstore=self, **kwargs, tags=tags)
-
-
-class VectorStoreRetriever(BaseRetriever):
-    """Base Retriever class for VectorStore."""
-
-    vectorstore: VectorStore
-    """VectorStore to use for retrieval."""
-    search_type: str = "similarity"
-    """Type of search to perform. Defaults to "similarity"."""
-    search_kwargs: dict = Field(default_factory=dict)
-    """Keyword arguments to pass to the search function."""
-    allowed_search_types: ClassVar[Collection[str]] = (
-        "similarity",
-        "similarity_score_threshold",
-        "mmr",
-    )
-
-    class Config:
-        """Configuration for this pydantic object."""
-
-        arbitrary_types_allowed = True
-
-    @root_validator()
-    def validate_search_type(cls, values: Dict) -> Dict:
-        """Validate search type."""
-        search_type = values["search_type"]
-        if search_type not in cls.allowed_search_types:
-            raise ValueError(
-                f"search_type of {search_type} not allowed. Valid values are: "
-                f"{cls.allowed_search_types}"
-            )
-        if search_type == "similarity_score_threshold":
-            score_threshold = values["search_kwargs"].get("score_threshold")
-            if (score_threshold is None) or (not isinstance(score_threshold, float)):
-                raise ValueError(
-                    "`score_threshold` is not specified with a float value(0~1) "
-                    "in `search_kwargs`."
-                )
-        return values
-
-    def _get_relevant_documents(
-        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
-    ) -> List[Document]:
-        if self.search_type == "similarity":
-            docs = self.vectorstore.similarity_search(query, **self.search_kwargs)
-        elif self.search_type == "similarity_score_threshold":
-            docs_and_similarities = (
-                self.vectorstore.similarity_search_with_relevance_scores(
-                    query, **self.search_kwargs
-                )
-            )
-            docs = [doc for doc, _ in docs_and_similarities]
-        elif self.search_type == "mmr":
-            docs = self.vectorstore.max_marginal_relevance_search(
-                query, **self.search_kwargs
-            )
-        else:
-            raise ValueError(f"search_type of {self.search_type} not allowed.")
-        return docs
-
-    async def _aget_relevant_documents(
-        self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
-    ) -> List[Document]:
-        if self.search_type == "similarity":
-            docs = await self.vectorstore.asimilarity_search(
-                query, **self.search_kwargs
-            )
-        elif self.search_type == "similarity_score_threshold":
-            docs_and_similarities = (
-                await self.vectorstore.asimilarity_search_with_relevance_scores(
-                    query, **self.search_kwargs
-                )
-            )
-            docs = [doc for doc, _ in docs_and_similarities]
-        elif self.search_type == "mmr":
-            docs = await self.vectorstore.amax_marginal_relevance_search(
-                query, **self.search_kwargs
-            )
-        else:
-            raise ValueError(f"search_type of {self.search_type} not allowed.")
-        return docs
-
-    def add_documents(self, documents: List[Document], **kwargs: Any) -> List[str]:
-        """Add documents to vectorstore."""
-        return self.vectorstore.add_documents(documents, **kwargs)
-
-    async def aadd_documents(
-        self, documents: List[Document], **kwargs: Any
-    ) -> List[str]:
-        """Add documents to vectorstore."""
-        return await self.vectorstore.aadd_documents(documents, **kwargs)
+__all__ = ["VectorStore", "VectorStoreRetriever"]

libs/langchain/langchain/vectorstores/cassandra.py

@@ -22,7 +22,7 @@ if typing.TYPE_CHECKING:
 
 from langchain.docstore.document import Document
 from langchain.schema.embeddings import Embeddings
-from langchain.vectorstores.base import VectorStore
+from langchain.schema.vectorstore import VectorStore
 from langchain.vectorstores.utils import maximal_marginal_relevance
 
 CVST = TypeVar("CVST", bound="Cassandra")

libs/langchain/langchain/vectorstores/chroma.py

@@ -18,8 +18,8 @@ import numpy as np
 
 from langchain.docstore.document import Document
 from langchain.schema.embeddings import Embeddings
+from langchain.schema.vectorstore import VectorStore
 from langchain.utils import xor_args
-from langchain.vectorstores.base import VectorStore
 from langchain.vectorstores.utils import maximal_marginal_relevance
 
 if TYPE_CHECKING:

libs/langchain/langchain/vectorstores/clarifai.py

@@ -10,7 +10,7 @@ import requests
 
 from langchain.docstore.document import Document
 from langchain.schema.embeddings import Embeddings
-from langchain.vectorstores.base import VectorStore
+from langchain.schema.vectorstore import VectorStore
 
 logger = logging.getLogger(__name__)
 

libs/langchain/langchain/vectorstores/clickhouse.py

@@ -9,7 +9,7 @@ from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
 from langchain.docstore.document import Document
 from langchain.pydantic_v1 import BaseSettings
 from langchain.schema.embeddings import Embeddings
-from langchain.vectorstores.base import VectorStore
+from langchain.schema.vectorstore import VectorStore
 
 logger = logging.getLogger()
 

libs/langchain/langchain/vectorstores/dashvector.py

@@ -14,8 +14,8 @@ import numpy as np
 
 from langchain.docstore.document import Document
 from langchain.schema.embeddings import Embeddings
+from langchain.schema.vectorstore import VectorStore
 from langchain.utils import get_from_env
-from langchain.vectorstores.base import VectorStore
 from langchain.vectorstores.utils import maximal_marginal_relevance
 
 logger = logging.getLogger(__name__)

libs/langchain/langchain/vectorstores/deeplake.py

@@ -16,7 +16,7 @@ except ImportError:
 
 from langchain.docstore.document import Document
 from langchain.schema.embeddings import Embeddings
-from langchain.vectorstores.base import VectorStore
+from langchain.schema.vectorstore import VectorStore
 from langchain.vectorstores.utils import maximal_marginal_relevance
 
 logger = logging.getLogger(__name__)

libs/langchain/langchain/vectorstores/dingo.py

@@ -8,7 +8,7 @@ import numpy as np
 
 from langchain.docstore.document import Document
 from langchain.schema.embeddings import Embeddings
-from langchain.vectorstores.base import VectorStore
+from langchain.schema.vectorstore import VectorStore
 from langchain.vectorstores.utils import maximal_marginal_relevance
 
 logger = logging.getLogger(__name__)

libs/langchain/langchain/vectorstores/elastic_vector_search.py

@@ -18,8 +18,8 @@ from typing import (
 from langchain._api import deprecated
 from langchain.docstore.document import Document
 from langchain.schema.embeddings import Embeddings
+from langchain.schema.vectorstore import VectorStore
 from langchain.utils import get_from_dict_or_env
-from langchain.vectorstores.base import VectorStore
 
 if TYPE_CHECKING:
     from elasticsearch import Elasticsearch

libs/langchain/langchain/vectorstores/elasticsearch.py

@@ -16,7 +16,7 @@ from typing import (
 
 from langchain.docstore.document import Document
 from langchain.schema.embeddings import Embeddings
-from langchain.vectorstores.base import VectorStore
+from langchain.schema.vectorstore import VectorStore
 from langchain.vectorstores.utils import DistanceStrategy
 
 if TYPE_CHECKING:

libs/langchain/langchain/vectorstores/epsilla.py

@@ -7,7 +7,7 @@ from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Type
 
 from langchain.docstore.document import Document
 from langchain.schema.embeddings import Embeddings
-from langchain.vectorstores.base import VectorStore
+from langchain.schema.vectorstore import VectorStore
 
 if TYPE_CHECKING:
     from pyepsilla import vectordb

libs/langchain/langchain/vectorstores/faiss.py

@@ -23,7 +23,7 @@ from langchain.docstore.base import AddableMixin, Docstore
 from langchain.docstore.document import Document
 from langchain.docstore.in_memory import InMemoryDocstore
 from langchain.schema.embeddings import Embeddings
-from langchain.vectorstores.base import VectorStore
+from langchain.schema.vectorstore import VectorStore
 from langchain.vectorstores.utils import DistanceStrategy, maximal_marginal_relevance
 
 

libs/langchain/langchain/vectorstores/hologres.py

@@ -7,8 +7,8 @@ from typing import Any, Dict, Iterable, List, Optional, Tuple, Type
 
 from langchain.docstore.document import Document
 from langchain.schema.embeddings import Embeddings
+from langchain.schema.vectorstore import VectorStore
 from langchain.utils import get_from_dict_or_env
-from langchain.vectorstores.base import VectorStore
 
 ADA_TOKEN_COUNT = 1536
 _LANGCHAIN_DEFAULT_TABLE_NAME = "langchain_pg_embedding"

libs/langchain/langchain/vectorstores/lancedb.py

@@ -5,7 +5,7 @@ from typing import Any, Iterable, List, Optional
 
 from langchain.docstore.document import Document
 from langchain.schema.embeddings import Embeddings
-from langchain.vectorstores.base import VectorStore
+from langchain.schema.vectorstore import VectorStore
 
 
 class LanceDB(VectorStore):

libs/langchain/langchain/vectorstores/llm_rails.py

@@ -13,7 +13,7 @@ import requests
 from langchain.pydantic_v1 import Field
 from langchain.schema import Document
 from langchain.schema.embeddings import Embeddings
-from langchain.vectorstores.base import VectorStore, VectorStoreRetriever
+from langchain.schema.vectorstore import VectorStore, VectorStoreRetriever
 
 
 class ModelChoices(str, Enum):

libs/langchain/langchain/vectorstores/marqo.py

@@ -17,7 +17,7 @@ from typing import (
 
 from langchain.docstore.document import Document
 from langchain.schema.embeddings import Embeddings
-from langchain.vectorstores.base import VectorStore
+from langchain.schema.vectorstore import VectorStore
 
 if TYPE_CHECKING:
     import marqo

libs/langchain/langchain/vectorstores/matching_engine.py

@@ -9,7 +9,7 @@ from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Type
 from langchain.docstore.document import Document
 from langchain.embeddings import TensorflowHubEmbeddings
 from langchain.schema.embeddings import Embeddings
-from langchain.vectorstores.base import VectorStore
+from langchain.schema.vectorstore import VectorStore
 
 if TYPE_CHECKING:
     from google.cloud import storage

libs/langchain/langchain/vectorstores/meilisearch.py

@@ -5,8 +5,8 @@ from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple, Ty
 
 from langchain.docstore.document import Document
 from langchain.schema.embeddings import Embeddings
+from langchain.schema.vectorstore import VectorStore
 from langchain.utils import get_from_env
-from langchain.vectorstores.base import VectorStore
 
 if TYPE_CHECKING:
     from meilisearch import Client

libs/langchain/langchain/vectorstores/milvus.py

@@ -8,7 +8,7 @@ import numpy as np
 
 from langchain.docstore.document import Document
 from langchain.schema.embeddings import Embeddings
-from langchain.vectorstores.base import VectorStore
+from langchain.schema.vectorstore import VectorStore
 from langchain.vectorstores.utils import maximal_marginal_relevance
 
 logger = logging.getLogger(__name__)

libs/langchain/langchain/vectorstores/mongodb_atlas.py

@@ -18,7 +18,7 @@ import numpy as np
 
 from langchain.docstore.document import Document
 from langchain.schema.embeddings import Embeddings
-from langchain.vectorstores.base import VectorStore
+from langchain.schema.vectorstore import VectorStore
 from langchain.vectorstores.utils import maximal_marginal_relevance
 
 if TYPE_CHECKING:

libs/langchain/langchain/vectorstores/myscale.py

@@ -9,7 +9,7 @@ from typing import Any, Dict, Iterable, List, Optional, Tuple
 from langchain.docstore.document import Document
 from langchain.pydantic_v1 import BaseSettings
 from langchain.schema.embeddings import Embeddings
-from langchain.vectorstores.base import VectorStore
+from langchain.schema.vectorstore import VectorStore
 
 logger = logging.getLogger()
 

libs/langchain/langchain/vectorstores/neo4j_vector.py

@@ -16,8 +16,8 @@ from typing import (
 
 from langchain.docstore.document import Document
 from langchain.schema.embeddings import Embeddings
+from langchain.schema.vectorstore import VectorStore
 from langchain.utils import get_from_env
-from langchain.vectorstores.base import VectorStore
 from langchain.vectorstores.utils import DistanceStrategy
 
 DEFAULT_DISTANCE_STRATEGY = DistanceStrategy.COSINE

libs/langchain/langchain/vectorstores/nucliadb.py

@@ -3,7 +3,7 @@ from typing import Any, Dict, Iterable, List, Optional, Type
 
 from langchain.schema.document import Document
 from langchain.schema.embeddings import Embeddings
-from langchain.vectorstores.base import VST, VectorStore
+from langchain.schema.vectorstore import VST, VectorStore
 
 FIELD_TYPES = {
     "f": "files",

libs/langchain/langchain/vectorstores/opensearch_vector_search.py

@@ -8,8 +8,8 @@ import numpy as np
 
 from langchain.schema import Document
 from langchain.schema.embeddings import Embeddings
+from langchain.schema.vectorstore import VectorStore
 from langchain.utils import get_from_dict_or_env
-from langchain.vectorstores.base import VectorStore
 from langchain.vectorstores.utils import maximal_marginal_relevance
 
 IMPORT_OPENSEARCH_PY_ERROR = (

libs/langchain/langchain/vectorstores/pgembedding.py

@@ -11,8 +11,8 @@ from sqlalchemy.orm import Session, declarative_base, relationship
 
 from langchain.docstore.document import Document
 from langchain.schema.embeddings import Embeddings
+from langchain.schema.vectorstore import VectorStore
 from langchain.utils import get_from_dict_or_env
-from langchain.vectorstores.base import VectorStore
 
 Base = declarative_base()  # type: Any
 

libs/langchain/langchain/vectorstores/pgvector.py

@@ -27,8 +27,8 @@ from sqlalchemy.orm import Session, declarative_base
 
 from langchain.docstore.document import Document
 from langchain.schema.embeddings import Embeddings
+from langchain.schema.vectorstore import VectorStore
 from langchain.utils import get_from_dict_or_env
-from langchain.vectorstores.base import VectorStore
 from langchain.vectorstores.utils import maximal_marginal_relevance
 
 if TYPE_CHECKING:

libs/langchain/langchain/vectorstores/pinecone.py

@@ -9,8 +9,8 @@ import numpy as np
 
 from langchain.docstore.document import Document
 from langchain.schema.embeddings import Embeddings
+from langchain.schema.vectorstore import VectorStore
 from langchain.utils.iter import batch_iterate
-from langchain.vectorstores.base import VectorStore
 from langchain.vectorstores.utils import DistanceStrategy, maximal_marginal_relevance
 
 if TYPE_CHECKING:

libs/langchain/langchain/vectorstores/redis/base.py

@@ -30,6 +30,7 @@ from langchain.callbacks.manager import (
 )
 from langchain.docstore.document import Document
 from langchain.schema.embeddings import Embeddings
+from langchain.schema.vectorstore import VectorStore, VectorStoreRetriever
 from langchain.utilities.redis import (
     _array_to_buffer,
     _buffer_to_array,
@@ -37,7 +38,6 @@ from langchain.utilities.redis import (
     get_client,
 )
 from langchain.utils import get_from_dict_or_env
-from langchain.vectorstores.base import VectorStore, VectorStoreRetriever
 from langchain.vectorstores.redis.constants import (
     REDIS_REQUIRED_MODULES,
     REDIS_TAG_SEPARATOR,

libs/langchain/langchain/vectorstores/rocksetdb.py

@@ -6,7 +6,7 @@ from typing import Any, Iterable, List, Optional, Tuple
 
 from langchain.docstore.document import Document
 from langchain.schema.embeddings import Embeddings
-from langchain.vectorstores.base import VectorStore
+from langchain.schema.vectorstore import VectorStore
 
 logger = logging.getLogger(__name__)
 

libs/langchain/langchain/vectorstores/scann.py

@@ -12,7 +12,7 @@ from langchain.docstore.base import AddableMixin, Docstore
 from langchain.docstore.document import Document
 from langchain.docstore.in_memory import InMemoryDocstore
 from langchain.schema.embeddings import Embeddings
-from langchain.vectorstores.base import VectorStore
+from langchain.schema.vectorstore import VectorStore
 from langchain.vectorstores.utils import DistanceStrategy
 
 

libs/langchain/langchain/vectorstores/singlestoredb.py

@@ -21,7 +21,7 @@ from langchain.callbacks.manager import (
 )
 from langchain.docstore.document import Document
 from langchain.schema.embeddings import Embeddings
-from langchain.vectorstores.base import VectorStore, VectorStoreRetriever
+from langchain.schema.vectorstore import VectorStore, VectorStoreRetriever
 from langchain.vectorstores.utils import DistanceStrategy
 
 DEFAULT_DISTANCE_STRATEGY = DistanceStrategy.DOT_PRODUCT

libs/langchain/langchain/vectorstores/sklearn.py

@@ -12,8 +12,8 @@ from uuid import uuid4
 
 from langchain.docstore.document import Document
 from langchain.schema.embeddings import Embeddings
+from langchain.schema.vectorstore import VectorStore
 from langchain.utils import guard_import
-from langchain.vectorstores.base import VectorStore
 from langchain.vectorstores.utils import maximal_marginal_relevance
 
 DEFAULT_K = 4  # Number of Documents to return.

libs/langchain/langchain/vectorstores/sqlitevss.py

@@ -15,7 +15,7 @@ from typing import (
 
 from langchain.docstore.document import Document
 from langchain.schema.embeddings import Embeddings
-from langchain.vectorstores.base import VectorStore
+from langchain.schema.vectorstore import VectorStore
 
 if TYPE_CHECKING:
     import sqlite3

libs/langchain/langchain/vectorstores/starrocks.py

@@ -9,7 +9,7 @@ from typing import Any, Dict, Iterable, List, Optional, Tuple
 from langchain.docstore.document import Document
 from langchain.pydantic_v1 import BaseSettings
 from langchain.schema.embeddings import Embeddings
-from langchain.vectorstores.base import VectorStore
+from langchain.schema.vectorstore import VectorStore
 
 logger = logging.getLogger()
 DEBUG = False

libs/langchain/langchain/vectorstores/supabase.py

@@ -18,7 +18,7 @@ import numpy as np
 
 from langchain.docstore.document import Document
 from langchain.schema.embeddings import Embeddings
-from langchain.vectorstores.base import VectorStore
+from langchain.schema.vectorstore import VectorStore
 from langchain.vectorstores.utils import maximal_marginal_relevance
 
 if TYPE_CHECKING:

libs/langchain/langchain/vectorstores/tair.py

@@ -7,8 +7,8 @@ from typing import Any, Iterable, List, Optional, Type
 
 from langchain.docstore.document import Document
 from langchain.schema.embeddings import Embeddings
+from langchain.schema.vectorstore import VectorStore
 from langchain.utils import get_from_dict_or_env
-from langchain.vectorstores.base import VectorStore
 
 logger = logging.getLogger(__name__)
 

libs/langchain/langchain/vectorstores/tencentvectordb.py

@@ -10,8 +10,8 @@ import numpy as np
 
 from langchain.docstore.document import Document
 from langchain.schema.embeddings import Embeddings
+from langchain.schema.vectorstore import VectorStore
 from langchain.utils import guard_import
-from langchain.vectorstores.base import VectorStore
 from langchain.vectorstores.utils import maximal_marginal_relevance
 
 logger = logging.getLogger(__name__)

libs/langchain/langchain/vectorstores/timescalevector.py

@@ -20,8 +20,8 @@ from typing import (
 
 from langchain.docstore.document import Document
 from langchain.embeddings.base import Embeddings
+from langchain.schema.vectorstore import VectorStore
 from langchain.utils import get_from_dict_or_env
-from langchain.vectorstores.base import VectorStore
 from langchain.vectorstores.utils import DistanceStrategy
 
 if TYPE_CHECKING:

libs/langchain/langchain/vectorstores/typesense.py

@@ -5,8 +5,8 @@ from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Tuple, Union
 
 from langchain.docstore.document import Document
 from langchain.schema.embeddings import Embeddings
+from langchain.schema.vectorstore import VectorStore
 from langchain.utils import get_from_env
-from langchain.vectorstores.base import VectorStore
 
 if TYPE_CHECKING:
     from typesense.client import Client

libs/langchain/langchain/vectorstores/usearch.py

@@ -8,7 +8,7 @@ from langchain.docstore.base import AddableMixin, Docstore
 from langchain.docstore.document import Document
 from langchain.docstore.in_memory import InMemoryDocstore
 from langchain.schema.embeddings import Embeddings
-from langchain.vectorstores.base import VectorStore
+from langchain.schema.vectorstore import VectorStore
 
 
 def dependable_usearch_import() -> Any:

libs/langchain/langchain/vectorstores/vald.py

@@ -7,7 +7,7 @@ import numpy as np
 
 from langchain.docstore.document import Document
 from langchain.schema.embeddings import Embeddings
-from langchain.vectorstores.base import VectorStore
+from langchain.schema.vectorstore import VectorStore
 from langchain.vectorstores.utils import maximal_marginal_relevance
 
 

libs/langchain/langchain/vectorstores/vearch.py

@@ -9,7 +9,7 @@ import numpy as np
 
 from langchain.docstore.document import Document
 from langchain.schema.embeddings import Embeddings
-from langchain.vectorstores.base import VectorStore
+from langchain.schema.vectorstore import VectorStore
 
 if TYPE_CHECKING:
     import vearch

libs/langchain/langchain/vectorstores/vectara.py

@@ -11,7 +11,7 @@ import requests
 from langchain.pydantic_v1 import Field
 from langchain.schema import Document
 from langchain.schema.embeddings import Embeddings
-from langchain.vectorstores.base import VectorStore, VectorStoreRetriever
+from langchain.schema.vectorstore import VectorStore, VectorStoreRetriever
 
 logger = logging.getLogger(__name__)
 

libs/langchain/langchain/vectorstores/weaviate.py

@@ -18,7 +18,7 @@ import numpy as np
 
 from langchain.docstore.document import Document
 from langchain.schema.embeddings import Embeddings
-from langchain.vectorstores.base import VectorStore
+from langchain.schema.vectorstore import VectorStore
 from langchain.vectorstores.utils import maximal_marginal_relevance
 
 if TYPE_CHECKING:

libs/langchain/langchain/vectorstores/xata.py

@@ -6,7 +6,7 @@ from typing import Any, Dict, Iterable, List, Optional, Tuple, Type
 
 from langchain.docstore.document import Document
 from langchain.schema.embeddings import Embeddings
-from langchain.vectorstores.base import VectorStore
+from langchain.schema.vectorstore import VectorStore
 
 
 class XataVectorStore(VectorStore):

libs/langchain/langchain/vectorstores/zep.py

@@ -9,7 +9,7 @@ import numpy as np
 
 from langchain.docstore.document import Document
 from langchain.schema.embeddings import Embeddings
-from langchain.vectorstores.base import VectorStore
+from langchain.schema.vectorstore import VectorStore
 from langchain.vectorstores.utils import maximal_marginal_relevance
 
 if TYPE_CHECKING:

libs/langchain/tests/unit_tests/indexes/test_indexing.py

@@ -9,7 +9,7 @@ from langchain.indexes import index
 from langchain.indexes._sql_record_manager import SQLRecordManager
 from langchain.schema import Document
 from langchain.schema.embeddings import Embeddings
-from langchain.vectorstores.base import VST, VectorStore
+from langchain.schema.vectorstore import VST, VectorStore
 
 
 class ToyLoader(BaseLoader):

libs/langchain/tests/unit_tests/retrievers/test_time_weighted_retriever.py

@@ -11,7 +11,7 @@ from langchain.retrievers.time_weighted_retriever import (
 )
 from langchain.schema import Document
 from langchain.schema.embeddings import Embeddings
-from langchain.vectorstores.base import VectorStore
+from langchain.schema.vectorstore import VectorStore
 
 
 def _get_example_memories(k: int = 4) -> List[Document]: