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community[minor]: Neo4j Fixed similarity docs (#23913)

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**Description:** There was missing some documentation regarding the
`filter` and `params` attributes in similarity search methods.

---------

Co-authored-by: rpereira <rafael.pereira@criticalsoftware.com>
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Rafael Pereira 2024-07-11 15:16:48 +01:00 committed by GitHub
parent 10d8c3cbfa
commit 092e9ee0e6
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1 changed files with 22 additions and 1 deletions
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23
libs/community/langchain_community/vectorstores/neo4j_vector.py
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@ -909,6 +909,11 @@ class Neo4jVector(VectorStore):
Args:
query (str): Query text to search for.
k (int): Number of results to return. Defaults to 4.
params (Dict[str, Any]): The search params for the index type.
Defaults to empty dict.
filter (Optional[Dict[str, Any]]): Dictionary of argument(s) to
filter on metadata.
Defaults to None.
Returns:
List of Documents most similar to the query.
@ -936,6 +941,11 @@ class Neo4jVector(VectorStore):
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
params (Dict[str, Any]): The search params for the index type.
Defaults to empty dict.
filter (Optional[Dict[str, Any]]): Dictionary of argument(s) to
filter on metadata.
Defaults to None.
Returns:
List of Documents most similar to the query and score for each
@ -972,6 +982,11 @@ class Neo4jVector(VectorStore):
Args:
embedding (List[float]): The embedding vector to compare against.
k (int, optional): The number of top similar documents to retrieve.
filter (Optional[Dict[str, Any]]): Dictionary of argument(s) to
filter on metadata.
Defaults to None.
params (Dict[str, Any]): The search params for the index type.
Defaults to empty dict.
Returns:
List[Tuple[Document, float]]: A list of tuples, each containing
@ -1077,6 +1092,7 @@ class Neo4jVector(VectorStore):
embedding: List[float],
k: int = 4,
filter: Optional[Dict[str, Any]] = None,
params: Dict[str, Any] = {},
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to embedding vector.
@ -1084,12 +1100,17 @@ class Neo4jVector(VectorStore):
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, Any]]): Dictionary of argument(s) to
filter on metadata.
Defaults to None.
params (Dict[str, Any]): The search params for the index type.
Defaults to empty dict.
Returns:
List of Documents most similar to the query vector.
"""
docs_and_scores = self.similarity_search_with_score_by_vector(
embedding=embedding, k=k, filter=filter, **kwargs
embedding=embedding, k=k, filter=filter, params=params, **kwargs
)
return [doc for doc, _ in docs_and_scores]