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core[patch]: Improve VectorStore API doc (#22547)

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pull/22403/head^2
Christophe Bornet 1 month ago committed by GitHub
parent 89128b7a49
commit c34ad8c163
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1 changed files with 132 additions and 26 deletions
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158
libs/core/langchain_core/vectorstores.py
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@ -71,7 +71,7 @@ class VectorStore(ABC):
Args:
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
kwargs: vectorstore specific parameters
**kwargs: vectorstore specific parameters.
Returns:
List of ids from adding the texts into the vectorstore.
@ -120,17 +120,26 @@ class VectorStore(ABC):
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore."""
"""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.
"""
return await run_in_executor(None, self.add_texts, texts, metadatas, **kwargs)
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.
documents: Documents to add to the vectorstore.
Returns:
List[str]: List of IDs of the added texts.
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]
@ -143,17 +152,24 @@ class VectorStore(ABC):
"""Run more documents through the embeddings and add to the vectorstore.
Args:
documents (List[Document]: Documents to add to the vectorstore.
documents: Documents to add to the vectorstore.
Returns:
List[str]: List of IDs of the added texts.
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."""
"""Return docs most similar to query using 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.
"""
if search_type == "similarity":
return self.similarity_search(query, **kwargs)
elif search_type == "similarity_score_threshold":
@ -172,7 +188,14 @@ class VectorStore(ABC):
async def asearch(
self, query: str, search_type: str, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to query using specified search type."""
"""Return docs most similar to query using 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.
"""
if search_type == "similarity":
return await self.asimilarity_search(query, **kwargs)
elif search_type == "similarity_score_threshold":
@ -192,7 +215,15 @@ class VectorStore(ABC):
def similarity_search(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to query."""
"""Return docs most similar to query.
Args:
query: Input text.
k: Number of Documents to return. Defaults to 4.
Returns:
List of Documents most similar to the query.
"""
@staticmethod
def _euclidean_relevance_score_fn(distance: float) -> float:
@ -239,13 +270,21 @@ class VectorStore(ABC):
def similarity_search_with_score(
self, *args: Any, **kwargs: Any
) -> List[Tuple[Document, float]]:
"""Run similarity search with distance."""
"""Run similarity search with distance.
Returns:
List of Tuples of (doc, similarity_score)
"""
raise NotImplementedError
async def asimilarity_search_with_score(
self, *args: Any, **kwargs: Any
) -> List[Tuple[Document, float]]:
"""Run similarity search with distance asynchronously."""
"""Run similarity search with distance.
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
@ -268,7 +307,7 @@ class VectorStore(ABC):
0 is dissimilar, 1 is most similar.
Args:
query: input text
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
@ -288,14 +327,14 @@ class VectorStore(ABC):
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""
Default async similarity search with relevance scores. Modify if necessary
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
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
@ -319,7 +358,7 @@ class VectorStore(ABC):
0 is dissimilar, 1 is most similar.
Args:
query: input text
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
@ -361,12 +400,12 @@ class VectorStore(ABC):
k: int = 4,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs and relevance scores in the range [0, 1], asynchronously.
"""Return docs and relevance scores in the range [0, 1].
0 is dissimilar, 1 is most similar.
Args:
query: input text
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
@ -405,7 +444,15 @@ class VectorStore(ABC):
async def asimilarity_search(
self, query: str, k: int = 4, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to query."""
"""Return docs most similar to query.
Args:
query: Input text.
k: Number of Documents to return. Defaults to 4.
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
@ -429,7 +476,15 @@ class VectorStore(ABC):
async def asimilarity_search_by_vector(
self, embedding: List[float], k: int = 4, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to embedding vector."""
"""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.
"""
# This is a temporary workaround to make the similarity search
# asynchronous. The proper solution is to make the similarity search
@ -536,7 +591,22 @@ class VectorStore(ABC):
lambda_mult: float = 0.5,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance."""
"""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.
"""
return await run_in_executor(
None,
self.max_marginal_relevance_search_by_vector,
@ -554,7 +624,12 @@ class VectorStore(ABC):
embedding: Embeddings,
**kwargs: Any,
) -> VST:
"""Return VectorStore initialized from documents and embeddings."""
"""Return VectorStore initialized from documents and embeddings.
Args:
documents: List of Documents to add to the vectorstore.
embedding: Embedding function to use.
"""
texts = [d.page_content for d in documents]
metadatas = [d.metadata for d in documents]
return cls.from_texts(texts, embedding, metadatas=metadatas, **kwargs)
@ -566,7 +641,12 @@ class VectorStore(ABC):
embedding: Embeddings,
**kwargs: Any,
) -> VST:
"""Return VectorStore initialized from documents and embeddings."""
"""Return VectorStore initialized from documents and embeddings.
Args:
documents: List of Documents to add to the vectorstore.
embedding: Embedding function to use.
"""
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)
@ -580,7 +660,13 @@ class VectorStore(ABC):
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> VST:
"""Return VectorStore initialized from texts and embeddings."""
"""Return VectorStore initialized from texts and embeddings.
Args:
texts: Texts to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
embedding: Embedding function to use.
"""
@classmethod
async def afrom_texts(
@ -590,7 +676,13 @@ class VectorStore(ABC):
metadatas: Optional[List[dict]] = None,
**kwargs: Any,
) -> VST:
"""Return VectorStore initialized from texts and embeddings."""
"""Return VectorStore initialized from texts and embeddings.
Args:
texts: Texts to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
embedding: Embedding function to use.
"""
return await run_in_executor(
None, cls.from_texts, texts, embedding, metadatas, **kwargs
)
@ -741,11 +833,25 @@ class VectorStoreRetriever(BaseRetriever):
return docs
def add_documents(self, documents: List[Document], **kwargs: Any) -> List[str]:
"""Add documents to vectorstore."""
"""Add documents to the vectorstore.
Args:
documents: Documents to add to the vectorstore.
Returns:
List of IDs of the added texts.
"""
return self.vectorstore.add_documents(documents, **kwargs)
async def aadd_documents(
self, documents: List[Document], **kwargs: Any
) -> List[str]:
"""Add documents to vectorstore."""
"""Add documents to the vectorstore.
Args:
documents: Documents to add to the vectorstore.
Returns:
List of IDs of the added texts.
"""
return await self.vectorstore.aadd_documents(documents, **kwargs)

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