"""**Vector store** stores embedded data and performs vector search.
One of the most common ways to store and search over unstructured data is to
embed it and store the resulting embedding vectors, and then query the store
and retrieve the data that are 'most similar' to the embedded query.
**Class hierarchy:**
.. code-block::
VectorStore --> <name> # Examples: Annoy, FAISS, Milvus
BaseRetriever --> VectorStoreRetriever --> <name>Retriever # Example: VespaRetriever
**Main helpers:**
.. code-block::
Embeddings, Document
""" # noqa: E501
from __future__ import annotations
import logging
import math
import warnings
from abc import ABC, abstractmethod
from collections.abc import Collection, Iterable, Iterator, Sequence
from itertools import cycle
from typing import (
TYPE_CHECKING,
Any,
Callable,
ClassVar,
Optional,
TypeVar,
)
from pydantic import ConfigDict, Field, model_validator
from langchain_core.embeddings import Embeddings
from langchain_core.retrievers import BaseRetriever, LangSmithRetrieverParams
from langchain_core.runnables.config import run_in_executor
if TYPE_CHECKING:
from langchain_core.callbacks.manager import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
)
from langchain_core.documents import Document
logger = logging.getLogger(__name__)
VST = TypeVar("VST", bound="VectorStore")
[docs]
class VectorStore(ABC):
"""Interface for vector store."""
[docs]
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[list[dict]] = None,
*,
ids: Optional[list[str]] = 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.
ids: Optional list of IDs associated with the texts.
**kwargs: vectorstore specific parameters.
One of the kwargs should be `ids` which is a list of ids
associated with the texts.
Returns:
List of ids from adding the texts into the vectorstore.
Raises:
ValueError: If the number of metadatas does not match the number of texts.
ValueError: If the number of ids does not match the number of texts.
"""
if type(self).add_documents != VectorStore.add_documents:
# Import document in local scope to avoid circular imports
from langchain_core.documents import Document
# This condition is triggered if the subclass has provided
# an implementation of the upsert method.
# The existing add_texts
texts_: Sequence[str] = (
texts if isinstance(texts, (list, tuple)) else list(texts)
)
if metadatas and len(metadatas) != len(texts_):
msg = (
"The number of metadatas must match the number of texts."
f"Got {len(metadatas)} metadatas and {len(texts_)} texts."
)
raise ValueError(msg)
metadatas_ = iter(metadatas) if metadatas else cycle([{}])
ids_: Iterator[Optional[str]] = iter(ids) if ids else cycle([None])
docs = [
Document(id=id_, page_content=text, metadata=metadata_)
for text, metadata_, id_ in zip(texts, metadatas_, ids_)
]
if ids is not None:
# For backward compatibility
kwargs["ids"] = ids
return self.add_documents(docs, **kwargs)
msg = f"`add_texts` has not been implemented for {self.__class__.__name__} "
raise NotImplementedError(msg)
@property
def embeddings(self) -> Optional[Embeddings]:
"""Access the query embedding object if available."""
logger.debug(
f"The embeddings property has not been "
f"implemented for {self.__class__.__name__}"
)
return None
[docs]
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. If None, delete all. Default is None.
**kwargs: Other keyword arguments that subclasses might use.
Returns:
Optional[bool]: True if deletion is successful,
False otherwise, None if not implemented.
"""
msg = "delete method must be implemented by subclass."
raise NotImplementedError(msg)
[docs]
def get_by_ids(self, ids: Sequence[str], /) -> list[Document]:
"""Get documents by their IDs.
The returned documents are expected to have the ID field set to the ID of the
document in the vector store.
Fewer documents may be returned than requested if some IDs are not found or
if there are duplicated IDs.
Users should not assume that the order of the returned documents matches
the order of the input IDs. Instead, users should rely on the ID field of the
returned documents.
This method should **NOT** raise exceptions if no documents are found for
some IDs.
Args:
ids: List of ids to retrieve.
Returns:
List of Documents.
.. versionadded:: 0.2.11
"""
msg = f"{self.__class__.__name__} does not yet support get_by_ids."
raise NotImplementedError(msg)
# Implementations should override this method to provide an async native version.
[docs]
async def aget_by_ids(self, ids: Sequence[str], /) -> list[Document]:
"""Async get documents by their IDs.
The returned documents are expected to have the ID field set to the ID of the
document in the vector store.
Fewer documents may be returned than requested if some IDs are not found or
if there are duplicated IDs.
Users should not assume that the order of the returned documents matches
the order of the input IDs. Instead, users should rely on the ID field of the
returned documents.
This method should **NOT** raise exceptions if no documents are found for
some IDs.
Args:
ids: List of ids to retrieve.
Returns:
List of Documents.
.. versionadded:: 0.2.11
"""
return await run_in_executor(None, self.get_by_ids, ids)
[docs]
async def adelete(
self, ids: Optional[list[str]] = None, **kwargs: Any
) -> Optional[bool]:
"""Async delete by vector ID or other criteria.
Args:
ids: List of ids to delete. If None, delete all. Default is None.
**kwargs: Other keyword arguments that subclasses might use.
Returns:
Optional[bool]: True if deletion is successful,
False otherwise, None if not implemented.
"""
return await run_in_executor(None, self.delete, ids, **kwargs)
[docs]
async def aadd_texts(
self,
texts: Iterable[str],
metadatas: Optional[list[dict]] = None,
*,
ids: Optional[list[str]] = None,
**kwargs: Any,
) -> list[str]:
"""Async 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.
Default is None.
ids: Optional list
**kwargs: vectorstore specific parameters.
Returns:
List of ids from adding the texts into the vectorstore.
Raises:
ValueError: If the number of metadatas does not match the number of texts.
ValueError: If the number of ids does not match the number of texts.
"""
if ids is not None:
# For backward compatibility
kwargs["ids"] = ids
if type(self).aadd_documents != VectorStore.aadd_documents:
# Import document in local scope to avoid circular imports
from langchain_core.documents import Document
# This condition is triggered if the subclass has provided
# an implementation of the upsert method.
# The existing add_texts
texts_: Sequence[str] = (
texts if isinstance(texts, (list, tuple)) else list(texts)
)
if metadatas and len(metadatas) != len(texts_):
msg = (
"The number of metadatas must match the number of texts."
f"Got {len(metadatas)} metadatas and {len(texts_)} texts."
)
raise ValueError(msg)
metadatas_ = iter(metadatas) if metadatas else cycle([{}])
ids_: Iterator[Optional[str]] = iter(ids) if ids else cycle([None])
docs = [
Document(id=id_, page_content=text, metadata=metadata_)
for text, metadata_, id_ in zip(texts, metadatas_, ids_)
]
return await self.aadd_documents(docs, **kwargs)
return await run_in_executor(None, self.add_texts, texts, metadatas, **kwargs)
[docs]
def add_documents(self, documents: list[Document], **kwargs: Any) -> list[str]:
"""Add or update documents in the vectorstore.
Args:
documents: Documents to add to the vectorstore.
kwargs: Additional keyword arguments.
if kwargs contains ids and documents contain ids,
the ids in the kwargs will receive precedence.
Returns:
List of IDs of the added texts.
Raises:
ValueError: If the number of ids does not match the number of documents.
"""
if type(self).add_texts != VectorStore.add_texts:
if "ids" not in kwargs:
ids = [doc.id for doc in documents]
# If there's at least one valid ID, we'll assume that IDs
# should be used.
if any(ids):
kwargs["ids"] = ids
texts = [doc.page_content for doc in documents]
metadatas = [doc.metadata for doc in documents]
return self.add_texts(texts, metadatas, **kwargs)
msg = (
f"`add_documents` and `add_texts` has not been implemented "
f"for {self.__class__.__name__} "
)
raise NotImplementedError(msg)
[docs]
async def aadd_documents(
self, documents: list[Document], **kwargs: Any
) -> list[str]:
"""Async run more documents through the embeddings and add to
the vectorstore.
Args:
documents: Documents to add to the vectorstore.
kwargs: Additional keyword arguments.
Returns:
List of IDs of the added texts.
Raises:
ValueError: If the number of IDs does not match the number of documents.
"""
# If the async method has been overridden, we'll use that.
if type(self).aadd_texts != VectorStore.aadd_texts:
if "ids" not in kwargs:
ids = [doc.id for doc in documents]
# If there's at least one valid ID, we'll assume that IDs
# should be used.
if any(ids):
kwargs["ids"] = ids
texts = [doc.page_content for doc in documents]
metadatas = [doc.metadata for doc in documents]
return await self.aadd_texts(texts, metadatas, **kwargs)
return await run_in_executor(None, self.add_documents, documents, **kwargs)
[docs]
def search(self, query: str, search_type: str, **kwargs: Any) -> list[Document]:
"""Return docs most similar to query using a specified search type.
Args:
query: Input text
search_type: Type of search to perform. Can be "similarity",
"mmr", or "similarity_score_threshold".
**kwargs: Arguments to pass to the search method.
Returns:
List of Documents most similar to the query.
Raises:
ValueError: If search_type is not one of "similarity",
"mmr", or "similarity_score_threshold".
"""
if search_type == "similarity":
return self.similarity_search(query, **kwargs)
elif search_type == "similarity_score_threshold":
docs_and_similarities = self.similarity_search_with_relevance_scores(
query, **kwargs
)
return [doc for doc, _ in docs_and_similarities]
elif search_type == "mmr":
return self.max_marginal_relevance_search(query, **kwargs)
else:
msg = (
f"search_type of {search_type} not allowed. Expected "
"search_type to be 'similarity', 'similarity_score_threshold'"
" or 'mmr'."
)
raise ValueError(msg)
[docs]
async def asearch(
self, query: str, search_type: str, **kwargs: Any
) -> list[Document]:
"""Async return docs most similar to query using a specified search type.
Args:
query: Input text.
search_type: Type of search to perform. Can be "similarity",
"mmr", or "similarity_score_threshold".
**kwargs: Arguments to pass to the search method.
Returns:
List of Documents most similar to the query.
Raises:
ValueError: If search_type is not one of "similarity",
"mmr", or "similarity_score_threshold".
"""
if search_type == "similarity":
return await self.asimilarity_search(query, **kwargs)
elif search_type == "similarity_score_threshold":
docs_and_similarities = await self.asimilarity_search_with_relevance_scores(
query, **kwargs
)
return [doc for doc, _ in docs_and_similarities]
elif search_type == "mmr":
return await self.amax_marginal_relevance_search(query, **kwargs)
else:
msg = (
f"search_type of {search_type} not allowed. Expected "
"search_type to be 'similarity', 'similarity_score_threshold' or 'mmr'."
)
raise ValueError(msg)
[docs]
@abstractmethod
def similarity_search(
self, query: str, k: int = 4, **kwargs: Any
) -> list[Document]:
"""Return docs most similar to query.
Args:
query: Input text.
k: Number of Documents to return. Defaults to 4.
**kwargs: Arguments to pass to the search method.
Returns:
List of Documents most similar to the 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
[docs]
def similarity_search_with_score(
self, *args: Any, **kwargs: Any
) -> list[tuple[Document, float]]:
"""Run similarity search with distance.
Args:
*args: Arguments to pass to the search method.
**kwargs: Arguments to pass to the search method.
Returns:
List of Tuples of (doc, similarity_score).
"""
raise NotImplementedError
[docs]
async def asimilarity_search_with_score(
self, *args: Any, **kwargs: Any
) -> list[tuple[Document, float]]:
"""Async run similarity search with distance.
Args:
*args: Arguments to pass to the search method.
**kwargs: Arguments to pass to the search method.
Returns:
List of Tuples of (doc, similarity_score).
"""
# 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.
return await run_in_executor(
None, self.similarity_search_with_score, *args, **kwargs
)
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]
async def _asimilarity_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 = await self.asimilarity_search_with_score(query, k, **kwargs)
return [(doc, relevance_score_fn(score)) for doc, score in docs_and_scores]
[docs]
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}",
stacklevel=2,
)
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:
logger.warning(
"No relevant docs were retrieved using the relevance score"
f" threshold {score_threshold}"
)
return docs_and_similarities
[docs]
async def asimilarity_search_with_relevance_scores(
self,
query: str,
k: int = 4,
**kwargs: Any,
) -> list[tuple[Document, float]]:
"""Async 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 = await self._asimilarity_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}",
stacklevel=2,
)
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:
logger.warning(
"No relevant docs were retrieved using the relevance score"
f" threshold {score_threshold}"
)
return docs_and_similarities
[docs]
async def asimilarity_search(
self, query: str, k: int = 4, **kwargs: Any
) -> list[Document]:
"""Async return docs most similar to query.
Args:
query: Input text.
k: Number of Documents to return. Defaults to 4.
**kwargs: Arguments to pass to the search method.
Returns:
List of Documents most similar to the 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.
return await run_in_executor(None, self.similarity_search, query, k=k, **kwargs)
[docs]
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.
**kwargs: Arguments to pass to the search method.
Returns:
List of Documents most similar to the query vector.
"""
raise NotImplementedError
[docs]
async def asimilarity_search_by_vector(
self, embedding: list[float], k: int = 4, **kwargs: Any
) -> list[Document]:
"""Async 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.
**kwargs: Arguments to pass to the search method.
Returns:
List of Documents most similar to the query 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.
return await run_in_executor(
None, self.similarity_search_by_vector, embedding, k=k, **kwargs
)
[docs]
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.
Default is 20.
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.
**kwargs: Arguments to pass to the search method.
Returns:
List of Documents selected by maximal marginal relevance.
"""
raise NotImplementedError
[docs]
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]:
"""Async 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.
Default is 20.
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.
"""
# 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.
return await run_in_executor(
None,
self.max_marginal_relevance_search,
query,
k=k,
fetch_k=fetch_k,
lambda_mult=lambda_mult,
**kwargs,
)
[docs]
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.
Default is 20.
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.
**kwargs: Arguments to pass to the search method.
Returns:
List of Documents selected by maximal marginal relevance.
"""
raise NotImplementedError
[docs]
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]:
"""Async 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.
Default is 20.
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.
**kwargs: Arguments to pass to the search method.
Returns:
List of Documents selected by maximal marginal relevance.
"""
return await run_in_executor(
None,
self.max_marginal_relevance_search_by_vector,
embedding,
k=k,
fetch_k=fetch_k,
lambda_mult=lambda_mult,
**kwargs,
)
[docs]
@classmethod
def from_documents(
cls: type[VST],
documents: list[Document],
embedding: Embeddings,
**kwargs: Any,
) -> VST:
"""Return VectorStore initialized from documents and embeddings.
Args:
documents: List of Documents to add to the vectorstore.
embedding: Embedding function to use.
kwargs: Additional keyword arguments.
Returns:
VectorStore: VectorStore initialized from documents and embeddings.
"""
texts = [d.page_content for d in documents]
metadatas = [d.metadata for d in documents]
if "ids" not in kwargs:
ids = [doc.id for doc in documents]
# If there's at least one valid ID, we'll assume that IDs
# should be used.
if any(ids):
kwargs["ids"] = ids
return cls.from_texts(texts, embedding, metadatas=metadatas, **kwargs)
[docs]
@classmethod
async def afrom_documents(
cls: type[VST],
documents: list[Document],
embedding: Embeddings,
**kwargs: Any,
) -> VST:
"""Async return VectorStore initialized from documents and embeddings.
Args:
documents: List of Documents to add to the vectorstore.
embedding: Embedding function to use.
kwargs: Additional keyword arguments.
Returns:
VectorStore: VectorStore initialized from documents and embeddings.
"""
texts = [d.page_content for d in documents]
metadatas = [d.metadata for d in documents]
if "ids" not in kwargs:
ids = [doc.id for doc in documents]
# If there's at least one valid ID, we'll assume that IDs
# should be used.
if any(ids):
kwargs["ids"] = ids
return await cls.afrom_texts(texts, embedding, metadatas=metadatas, **kwargs)
[docs]
@classmethod
@abstractmethod
def from_texts(
cls: type[VST],
texts: list[str],
embedding: Embeddings,
metadatas: Optional[list[dict]] = None,
*,
ids: Optional[list[str]] = None,
**kwargs: Any,
) -> VST:
"""Return VectorStore initialized from texts and embeddings.
Args:
texts: Texts to add to the vectorstore.
embedding: Embedding function to use.
metadatas: Optional list of metadatas associated with the texts.
Default is None.
ids: Optional list of IDs associated with the texts.
kwargs: Additional keyword arguments.
Returns:
VectorStore: VectorStore initialized from texts and embeddings.
"""
[docs]
@classmethod
async def afrom_texts(
cls: type[VST],
texts: list[str],
embedding: Embeddings,
metadatas: Optional[list[dict]] = None,
*,
ids: Optional[list[str]] = None,
**kwargs: Any,
) -> VST:
"""Async return VectorStore initialized from texts and embeddings.
Args:
texts: Texts to add to the vectorstore.
embedding: Embedding function to use.
metadatas: Optional list of metadatas associated with the texts.
Default is None.
ids: Optional list of IDs associated with the texts.
kwargs: Additional keyword arguments.
Returns:
VectorStore: VectorStore initialized from texts and embeddings.
"""
if ids is not None:
kwargs["ids"] = ids
return await run_in_executor(
None, cls.from_texts, texts, embedding, metadatas, **kwargs
)
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
[docs]
def as_retriever(self, **kwargs: Any) -> VectorStoreRetriever:
"""Return VectorStoreRetriever initialized from this VectorStore.
Args:
**kwargs: Keyword arguments to pass to the search function.
Can include:
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 [] + self._get_retriever_tags()
return VectorStoreRetriever(vectorstore=self, tags=tags, **kwargs)
[docs]
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",
)
model_config = ConfigDict(
arbitrary_types_allowed=True,
)
@model_validator(mode="before")
@classmethod
def validate_search_type(cls, values: dict) -> Any:
"""Validate search type.
Args:
values: Values to validate.
Returns:
Values: Validated values.
Raises:
ValueError: If search_type is not one of the allowed search types.
ValueError: If score_threshold is not specified with a float value(0~1)
"""
search_type = values.get("search_type", "similarity")
if search_type not in cls.allowed_search_types:
msg = (
f"search_type of {search_type} not allowed. Valid values are: "
f"{cls.allowed_search_types}"
)
raise ValueError(msg)
if search_type == "similarity_score_threshold":
score_threshold = values.get("search_kwargs", {}).get("score_threshold")
if (score_threshold is None) or (not isinstance(score_threshold, float)):
msg = (
"`score_threshold` is not specified with a float value(0~1) "
"in `search_kwargs`."
)
raise ValueError(msg)
return values
def _get_ls_params(self, **kwargs: Any) -> LangSmithRetrieverParams:
"""Get standard params for tracing."""
_kwargs = self.search_kwargs | kwargs
ls_params = super()._get_ls_params(**_kwargs)
ls_params["ls_vector_store_provider"] = self.vectorstore.__class__.__name__
if self.vectorstore.embeddings:
ls_params["ls_embedding_provider"] = (
self.vectorstore.embeddings.__class__.__name__
)
elif hasattr(self.vectorstore, "embedding") and isinstance(
self.vectorstore.embedding, Embeddings
):
ls_params["ls_embedding_provider"] = (
self.vectorstore.embedding.__class__.__name__
)
return ls_params
def _get_relevant_documents(
self, query: str, *, run_manager: CallbackManagerForRetrieverRun, **kwargs: Any
) -> list[Document]:
_kwargs = self.search_kwargs | kwargs
if self.search_type == "similarity":
docs = self.vectorstore.similarity_search(query, **_kwargs)
elif self.search_type == "similarity_score_threshold":
docs_and_similarities = (
self.vectorstore.similarity_search_with_relevance_scores(
query, **_kwargs
)
)
docs = [doc for doc, _ in docs_and_similarities]
elif self.search_type == "mmr":
docs = self.vectorstore.max_marginal_relevance_search(query, **_kwargs)
else:
msg = f"search_type of {self.search_type} not allowed."
raise ValueError(msg)
return docs
async def _aget_relevant_documents(
self,
query: str,
*,
run_manager: AsyncCallbackManagerForRetrieverRun,
**kwargs: Any,
) -> list[Document]:
_kwargs = self.search_kwargs | kwargs
if self.search_type == "similarity":
docs = await self.vectorstore.asimilarity_search(query, **_kwargs)
elif self.search_type == "similarity_score_threshold":
docs_and_similarities = (
await self.vectorstore.asimilarity_search_with_relevance_scores(
query, **_kwargs
)
)
docs = [doc for doc, _ in docs_and_similarities]
elif self.search_type == "mmr":
docs = await self.vectorstore.amax_marginal_relevance_search(
query, **_kwargs
)
else:
msg = f"search_type of {self.search_type} not allowed."
raise ValueError(msg)
return docs
[docs]
def add_documents(self, documents: list[Document], **kwargs: Any) -> list[str]:
"""Add documents to the vectorstore.
Args:
documents: Documents to add to the vectorstore.
**kwargs: Other keyword arguments that subclasses might use.
Returns:
List of IDs of the added texts.
"""
return self.vectorstore.add_documents(documents, **kwargs)
[docs]
async def aadd_documents(
self, documents: list[Document], **kwargs: Any
) -> list[str]:
"""Async add documents to the vectorstore.
Args:
documents: Documents to add to the vectorstore.
**kwargs: Other keyword arguments that subclasses might use.
Returns:
List of IDs of the added texts.
"""
return await self.vectorstore.aadd_documents(documents, **kwargs)
