Tony20100/langchain_full huggingface dataset:
Parameters
query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
fetch_k (int) – Number of Documents to fetch before filtering (if needed) to
pass to MMR algorithm.
lambda_mult (float) – 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.
filter (Optional[Union[Callable, Dict[str, Any]]]) – 
kwargs (Any) – 
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[Document]
max_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Union[Callable, Dict[str, Any]]] = None, **kwargs: Any) → List[Document][source]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
fetch_k (int) – Number of Documents to fetch before filtering to
pass to MMR algorithm.
lambda_mult (float) – 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.
filter (Optional[Union[Callable, Dict[str, Any]]]) – 
kwargs (Any) – 
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[Document]
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[Document]
max_marginal_relevance_search_with_score_by_vector(embedding: List[float], *, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Union[Callable, Dict[str, Any]]] = None) → List[Tuple[Document, float]][source]¶
Return docs and their similarity scores selected using the maximal marginalrelevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
fetch_k (int) – Number of Documents to fetch before filtering to
pass to MMR algorithm.
lambda_mult (float) – 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.
filter (Optional[Union[Callable, Dict[str, Any]]]) – 
Returns
List of Documents and similarity scores selected by maximal marginalrelevance and score for each.
Return type
List[Tuple[Document, float]]
merge_from(target: FAISS) → None[source]¶
Merge another FAISS object with the current one.
Add the target FAISS to the current one.
Parameters
target (FAISS) – FAISS object you wish to merge into the current one
Returns
None.
Return type
None
save_local(folder_path: str, index_name: str = 'index') → None[source]¶
Save FAISS index, docstore, and index_to_docstore_id to disk.
Parameters
Save FAISS index, docstore, and index_to_docstore_id to disk.
Parameters
folder_path (str) – folder path to save index, docstore,
and index_to_docstore_id to.
index_name (str) – for saving with a specific index file name
Return type
None
search(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
Parameters
query (str) – Input text
search_type (str) – Type of search to perform. Can be “similarity”,
“mmr”, or “similarity_score_threshold”.
**kwargs (Any) – Arguments to pass to the search method.
Return type
List[Document]
serialize_to_bytes() → bytes[source]¶
Serialize FAISS index, docstore, and index_to_docstore_id to bytes.
Return type
bytes
similarity_search(query: str, k: int = 4, filter: Optional[Union[Callable, Dict[str, Any]]] = None, fetch_k: int = 20, **kwargs: Any) → List[Document][source]¶
Return docs most similar to query.
Parameters
query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Union[Callable, Dict[str, Any]]]) – (Optional[Dict[str, str]]): Filter by metadata. Defaults to None.
fetch_k (int) – (Optional[int]) Number of Documents to fetch before filtering.
Defaults to 20.
kwargs (Any) – 
Returns
List of Documents most similar to the query.
Return type
List[Document]
Returns
List of Documents most similar to the query.
Return type
List[Document]
similarity_search_by_vector(embedding: List[float], k: int = 4, filter: Optional[Dict[str, Any]] = None, fetch_k: int = 20, **kwargs: Any) → List[Document][source]¶
Return docs most similar to embedding vector.
Parameters
embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata.
Defaults to None. If a callable, it must take as input the
metadata dict of Document and return a bool.
fetch_k (int) – (Optional[int]) Number of Documents to fetch before filtering.
Defaults to 20.
kwargs (Any) – 
Returns
List of Documents most similar to the embedding.
Return type
List[Document]
similarity_search_with_relevance_scores(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.
Parameters
query (str) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
**kwargs (Any) – 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)
Return type
List[Tuple[Document, float]]
Return type
List[Tuple[Document, float]]
similarity_search_with_score(query: str, k: int = 4, filter: Optional[Union[Callable, Dict[str, Any]]] = None, fetch_k: int = 20, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Return docs most similar to query.
Parameters
query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata.
Defaults to None. If a callable, it must take as input the
metadata dict of Document and return a bool.
fetch_k (int) – (Optional[int]) Number of Documents to fetch before filtering.
Defaults to 20.
kwargs (Any) – 
Returns
List of documents most similar to the query text with
L2 distance in float. Lower score represents more similarity.
Return type
List[Tuple[Document, float]]
similarity_search_with_score_by_vector(embedding: List[float], k: int = 4, filter: Optional[Union[Callable, Dict[str, Any]]] = None, fetch_k: int = 20, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Return docs most similar to query.
Parameters
embedding (List[float]) – Embedding vector to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[Union[Callable, Dict[str, Any]]]) – Filter by metadata.
Defaults to None. If a callable, it must take as input the
metadata dict of Document and return a bool.
fetch_k (int) – (Optional[int]) Number of Documents to fetch before filtering.
Defaults to 20.
Defaults to 20.
**kwargs (Any) – kwargs to be passed to similarity search. Can include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
Returns
List of documents most similar to the query text and L2 distance
in float for each. Lower score represents more similarity.
Return type
List[Tuple[Document, float]]
Examples using FAISS¶
# Oracle Cloud Infrastructure Generative AI
Build a Question/Answering system over SQL data
Build an Agent
Caching
Cohere reranker
Cross Encoder Reranker
Document Comparison
Facebook - Meta
Faiss
Faiss (Async)
FlashRank reranker
Fleet AI Context
How to add values to a chain’s state
How to better prompt when doing SQL question-answering
How to combine results from multiple retrievers
How to create and query vector stores
How to deal with large databases when doing SQL question-answering
How to do retrieval with contextual compression
How to handle long text when doing extraction
How to inspect runnables
How to invoke runnables in parallel
How to load PDFs
How to pass through arguments from one step to the next
How to select examples by maximal marginal relevance (MMR)
How to stream runnables
How to use a time-weighted vector store retriever
How to use a vectorstore as a retriever
Jina Reranker
LLMLingua Document Compressor
NVIDIA NIMs 
OpenVINO Reranker
RAGatouille
RankLLM Reranker
UpTrain
VoyageAI Reranker
YouTube audio
langchain_community.vectorstores.kinetica.DistanceStrategy¶
class langchain_community.vectorstores.kinetica.DistanceStrategy(value)[source]¶
Enumerator of the Distance strategies.
EUCLIDEAN = 'l2'¶
COSINE = 'cosine'¶
MAX_INNER_PRODUCT = 'inner'¶
Examples using DistanceStrategy¶
Google BigQuery Vector Search
Kinetica Vectorstore API
Oracle AI Vector Search: Vector Store
SAP HANA Cloud Vector Engine
SemaDB
SingleStoreDB
langchain_community.vectorstores.pgvecto_rs.PGVecto_rs¶
class langchain_community.vectorstores.pgvecto_rs.PGVecto_rs(embedding: Embeddings, dimension: int, db_url: str, collection_name: str, new_table: bool = False)[source]¶
VectorStore backed by pgvecto_rs.
Initialize a PGVecto_rs vectorstore.
Parameters
embedding (Embeddings) – Embeddings to use.
dimension (int) – Dimension of the embeddings.
db_url (str) – Database URL.
collection_name (str) – Name of the collection.
new_table (bool) – Whether to create a new table or connect to an existing one.
true (If) – 
exists (the table will be dropped if) – 
recreated. (then) – 
False. (Defaults to) – 
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(embedding, dimension, db_url, ...)
Initialize a PGVecto_rs vectorstore.
aadd_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
aadd_texts(texts[, metadatas])
Run more texts through the embeddings and add to the vectorstore.
add_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
add_texts(texts[, metadatas])
Run more texts through the embeddings and add to the vectorstore.
adelete([ids])
Delete by vector ID or other criteria.
afrom_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
afrom_texts(texts, embedding[, metadatas])
Return VectorStore initialized from texts and embeddings.
Return VectorStore initialized from texts and embeddings.
amax_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
amax_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance.
as_retriever(**kwargs)
Return VectorStoreRetriever initialized from this VectorStore.
asearch(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
asimilarity_search(query[, k])
Return docs most similar to query.
asimilarity_search_by_vector(embedding[, k])
Return docs most similar to embedding vector.
asimilarity_search_with_relevance_scores(query)
Return docs and relevance scores in the range [0, 1].
asimilarity_search_with_score(*args, **kwargs)
Run similarity search with distance.
delete([ids])
Delete by vector ID or other criteria.
from_collection_name(embedding, db_url, ...)
Create new empty vectorstore with collection_name.
from_documents(documents, embedding[, ...])
Return VectorStore initialized from documents.
from_texts(texts, embedding[, metadatas, ...])
Return VectorStore initialized from texts and optional metadatas.
max_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
max_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance.
search(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
similarity_search(query[, k, distance_func])
Return docs most similar to query.
similarity_search_by_vector(embedding[, k, ...])
Return docs most similar to embedding vector.
Return docs most similar to embedding vector.
similarity_search_with_relevance_scores(query)
Return docs and relevance scores in the range [0, 1].
similarity_search_with_score(query[, k, ...])
Run similarity search with distance.
similarity_search_with_score_by_vector(...)
Return docs most similar to query vector, with its score.
__init__(embedding: Embeddings, dimension: int, db_url: str, collection_name: str, new_table: bool = False) → None[source]¶
Initialize a PGVecto_rs vectorstore.
Parameters
embedding (Embeddings) – Embeddings to use.
dimension (int) – Dimension of the embeddings.
db_url (str) – Database URL.
collection_name (str) – Name of the collection.
new_table (bool) – Whether to create a new table or connect to an existing one.
true (If) – 
exists (the table will be dropped if) – 
recreated. (then) – 
False. (Defaults to) – 
Return type
None
async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
documents (List[Document]) – Documents to add to the vectorstore.
kwargs (Any) – 
Returns
List of IDs of the added texts.
Return type
List[str]
async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str]¶
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
**kwargs (Any) – vectorstore specific parameters.
Returns
List of ids from adding the texts into the vectorstore.
Return type
List[str]
add_documents(documents: List[Document], **kwargs: Any) → List[str][source]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
documents (List[Document]) – List of documents to add to the vectorstore.
kwargs (Any) – 
Returns
List of ids of the added documents.
Return type
List[str]
add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, **kwargs: Any) → List[str][source]¶
Run more texts through the embeddings and add to the vectorstore.
Parameters
texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
kwargs (Any) – vectorstore specific parameters
Returns
List of ids of the added texts.
Return type
List[str]
async adelete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]¶
Delete by vector ID or other criteria.
Parameters
ids (Optional[List[str]]) – List of ids to delete.
**kwargs (Any) – Other keyword arguments that subclasses might use.
Returns
True if deletion is successful,
False otherwise, None if not implemented.
Return type
Optional[bool]
async classmethod afrom_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶
Return VectorStore initialized from documents and embeddings.
Parameters
documents (List[Document]) – List of Documents to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
kwargs (Any) –
Delete by vector ID or other criteria.
Parameters
ids (Optional[List[str]]) – List of ids to delete.
**kwargs (Any) – Other keyword arguments that subclasses might use.
Returns
True if deletion is successful,
False otherwise, None if not implemented.
Return type
Optional[bool]
classmethod from_collection_name(embedding: Embeddings, db_url: str, collection_name: str) → PGVecto_rs[source]¶
Create new empty vectorstore with collection_name.
Or connect to an existing vectorstore in database if exists.
Arguments should be the same as when the vectorstore was created.
Parameters
embedding (Embeddings) – 
db_url (str) – 
collection_name (str) – 
Return type
PGVecto_rs
classmethod from_documents(documents: List[Document], embedding: Embeddings, db_url: str = '', collection_name: str = '543ac0de0b394acfbd5903f2f50e7576', **kwargs: Any) → PGVecto_rs[source]¶
Return VectorStore initialized from documents.
Parameters
documents (List[Document]) – 
embedding (Embeddings) – 
db_url (str) – 
collection_name (str) – 
kwargs (Any) – 
Return type
PGVecto_rs
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, db_url: str = '', collection_name: str = 'ad848132c11f4b85ad38d925f20275c9', **kwargs: Any) → PGVecto_rs[source]¶
Return VectorStore initialized from texts and optional metadatas.
Parameters
texts (List[str]) – 
embedding (Embeddings) – 
metadatas (Optional[List[dict]]) –
embedding (Embeddings) – 
metadatas (Optional[List[dict]]) – 
db_url (str) – 
collection_name (str) – 
kwargs (Any) – 
Return type
PGVecto_rs
max_marginal_relevance_search(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.
Parameters
query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult (float) – 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 (Any) – 
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[Document]
max_marginal_relevance_search_by_vector(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.
Parameters
embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm.
fetch_k (int) – Number of Documents to fetch to pass to MMR algorithm.
lambda_mult (float) – 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 (Any) – 
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[Document]
search(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
Parameters
query (str) – Input text
search_type (str) – Type of search to perform. Can be “similarity”,
“mmr”, or “similarity_score_threshold”.
**kwargs (Any) – Arguments to pass to the search method.
Return type
List[Document]
similarity_search(query: str, k: int = 4, distance_func: Literal['sqrt_euclid', 'neg_dot_prod', 'ned_cos'] = 'sqrt_euclid', **kwargs: Any) → List[Document][source]¶
Return docs most similar to query.
Parameters
query (str) – 
k (int) – 
distance_func (Literal['sqrt_euclid', 'neg_dot_prod', 'ned_cos']) – 
kwargs (Any) – 
Return type
List[Document]
similarity_search_by_vector(embedding: List[float], k: int = 4, distance_func: Literal['sqrt_euclid', 'neg_dot_prod', 'ned_cos'] = 'sqrt_euclid', filter: Optional[Any] = None, **kwargs: Any) → List[Document][source]¶
Return docs most similar to embedding vector.
Parameters
embedding (List[float]) – Embedding to look up documents similar to.
Parameters
embedding (List[float]) – Embedding to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
distance_func (Literal['sqrt_euclid', 'neg_dot_prod', 'ned_cos']) – 
filter (Optional[Any]) – 
kwargs (Any) – 
Returns
List of Documents most similar to the query vector.
Return type
List[Document]
similarity_search_with_relevance_scores(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.
Parameters
query (str) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
**kwargs (Any) – 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)
Return type
List[Tuple[Document, float]]
similarity_search_with_score(query: str, k: int = 4, distance_func: Literal['sqrt_euclid', 'neg_dot_prod', 'ned_cos'] = 'sqrt_euclid', **kwargs: Any) → List[Tuple[Document, float]][source]¶
Run similarity search with distance.
Returns
List of Tuples of (doc, similarity_score)
Parameters
query (str) – 
k (int) – 
distance_func (Literal['sqrt_euclid', 'neg_dot_prod', 'ned_cos']) – 
kwargs (Any) – 
Return type
List[Tuple[Document, float]]
kwargs (Any) – 
Return type
List[Tuple[Document, float]]
similarity_search_with_score_by_vector(query_vector: List[float], k: int = 4, distance_func: Literal['sqrt_euclid', 'neg_dot_prod', 'ned_cos'] = 'sqrt_euclid', filter: Union[None, Dict[str, Any], Any] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Return docs most similar to query vector, with its score.
Parameters
query_vector (List[float]) – 
k (int) – 
distance_func (Literal['sqrt_euclid', 'neg_dot_prod', 'ned_cos']) – 
filter (Union[None, Dict[str, Any], Any]) – 
kwargs (Any) – 
Return type
List[Tuple[Document, float]]
Examples using PGVecto_rs¶
PGVecto.rs
langchain_community.vectorstores.redis.filters.RedisFilterField¶
class langchain_community.vectorstores.redis.filters.RedisFilterField(field: str)[source]¶
Base class for RedisFilterFields.
Attributes
OPERATORS
escaper
Methods
__init__(field)
equals(other)
Parameters
field (str) – 
__init__(field: str)[source]¶
Parameters
field (str) – 
equals(other: RedisFilterField) → bool[source]¶
Parameters
other (RedisFilterField) – 
Return type
bool
langchain_google_vertexai.vectorstores.vectorstores.VectorSearchVectorStore¶
class langchain_google_vertexai.vectorstores.vectorstores.VectorSearchVectorStore(searcher: Searcher, document_storage: DocumentStorage, embbedings: Optional[Embeddings] = None)[source]¶
VertexAI VectorStore that handles the search and indexing using Vector Search
and stores the documents in Google Cloud Storage.
Constructor.
:param searcher: Object in charge of searching and storing the index.
:param document_storage: Object in charge of storing and retrieving documents.
:param embbedings: Object in charge of transforming text to embbeddings.
Attributes
embbedings
Returns the embeddings object.
embeddings
Access the query embedding object if available.
Methods
__init__(searcher, document_storage[, ...])
Constructor.
aadd_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
aadd_texts(texts[, metadatas])
Run more texts through the embeddings and add to the vectorstore.
add_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
add_texts(texts[, metadatas, ...])
Run more texts through the embeddings and add to the vectorstore.
adelete([ids])
Delete by vector ID or other criteria.
afrom_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
afrom_texts(texts, embedding[, metadatas])
Return VectorStore initialized from texts and embeddings.
amax_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
amax_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance.
as_retriever(**kwargs)