viruPVS/langchain-docs huggingface dataset:
Return VectorStore initialized from documents and embeddings.
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, typesense_client: Optional[Client] = None, typesense_client_params: Optional[dict] = None, typesense_collection_name: Optional[str] = None, text_key: str = 'text', **kwargs: Any) → Typesense[source]¶
Construct Typesense wrapper from raw text.
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 – 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.
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 – Embedding to look up documents similar to.
k – Number of Documents to return. Defaults to 4.
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.
search(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
similarity_search(query: str, k: int = 10, filter: Optional[str] = '', **kwargs: Any) → List[Document][source]¶
Return typesense documents most similar to query.
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 10.
Minimum 10 results would be returned.
filter – typesense filter_by expression to filter documents on
Returns
List of Documents most similar to the query and score for each
similarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to embedding vector.
Parameters
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.
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 – input text
k – Number of Documents to return. Defaults to 4.
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)
similarity_search_with_score(query: str, k: int = 10, filter: Optional[str] = '') → List[Tuple[Document, float]][source]¶
Return typesense documents most similar to query, along with scores.
Parameters
query – Text to look up documents similar to.
k – Number of Documents to return. Defaults to 10.
Minimum 10 results would be returned.
filter – typesense filter_by expression to filter documents on
Returns
List of Documents most similar to the query and score for each
Examples using Typesense¶
Typesense
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)
__init__(field: str)[source]¶
equals(other: RedisFilterField) → bool[source]¶
langchain_community.vectorstores.faiss.dependable_faiss_import¶
langchain_community.vectorstores.faiss.dependable_faiss_import(no_avx2: Optional[bool] = None) → Any[source]¶
Import faiss if available, otherwise raise error.
If FAISS_NO_AVX2 environment variable is set, it will be considered
to load FAISS with no AVX2 optimization.
Parameters
no_avx2 – Load FAISS strictly with no AVX2 optimization
so that the vectorstore is portable and compatible with other devices.
langchain_community.vectorstores.sklearn.JsonSerializer¶
class langchain_community.vectorstores.sklearn.JsonSerializer(persist_path: str)[source]¶
Serializes data in json using the json package from python standard library.
Methods
__init__(persist_path)
extension()
The file extension suggested by this serializer (without dot).
load()
Loads the data from the persist_path
save(data)
Saves the data to the persist_path
__init__(persist_path: str) → None¶
classmethod extension() → str[source]¶
The file extension suggested by this serializer (without dot).
load() → Any[source]¶
Loads the data from the persist_path
save(data: Any) → None[source]¶
Saves the data to the persist_path
langchain_community.vectorstores.pgvector.BaseModel¶
class langchain_community.vectorstores.pgvector.BaseModel(**kwargs: Any)[source]¶
Base model for the SQL stores.
A simple constructor that allows initialization from kwargs.
Sets attributes on the constructed instance using the names and
values in kwargs.
Only keys that are present as
attributes of the instance’s class are allowed. These could be,
for example, any mapped columns or relationships.
Attributes
metadata
registry
uuid
Methods
__init__(**kwargs)
A simple constructor that allows initialization from kwargs.
__init__(**kwargs: Any) → None¶
A simple constructor that allows initialization from kwargs.
Sets attributes on the constructed instance using the names and
values in kwargs.
Only keys that are present as
attributes of the instance’s class are allowed. These could be,
for example, any mapped columns or relationships.
langchain_community.vectorstores.pgembedding.BaseModel¶
class langchain_community.vectorstores.pgembedding.BaseModel(**kwargs: Any)[source]¶
Base model for all SQL stores.
A simple constructor that allows initialization from kwargs.
Sets attributes on the constructed instance using the names and
values in kwargs.
Only keys that are present as
attributes of the instance’s class are allowed. These could be,
for example, any mapped columns or relationships.
Attributes
metadata
registry
uuid
Methods
__init__(**kwargs)
A simple constructor that allows initialization from kwargs.
__init__(**kwargs: Any) → None¶
A simple constructor that allows initialization from kwargs.
Sets attributes on the constructed instance using the names and
values in kwargs.
Only keys that are present as
attributes of the instance’s class are allowed. These could be,
for example, any mapped columns or relationships.
langchain_community.vectorstores.utils.DistanceStrategy¶
class langchain_community.vectorstores.utils.DistanceStrategy(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]¶
Enumerator of the Distance strategies for calculating distances
between vectors.
EUCLIDEAN_DISTANCE = 'EUCLIDEAN_DISTANCE'¶
MAX_INNER_PRODUCT = 'MAX_INNER_PRODUCT'¶
DOT_PRODUCT = 'DOT_PRODUCT'¶
JACCARD = 'JACCARD'¶
COSINE = 'COSINE'¶
langchain_community.vectorstores.redis.filters.RedisNum¶
class langchain_community.vectorstores.redis.filters.RedisNum(field: str)[source]¶
A RedisFilterField representing a numeric field in a Redis index.
Attributes
OPERATORS
OPERATOR_MAP
SUPPORTED_VAL_TYPES
escaper
Methods
__init__(field)
equals(other)
__init__(field: str)¶
equals(other: RedisFilterField) → bool¶
Examples using RedisNum¶
Redis
langchain_community.vectorstores.momento_vector_index.MomentoVectorIndex¶
class langchain_community.vectorstores.momento_vector_index.MomentoVectorIndex(embedding: Embeddings, client: PreviewVectorIndexClient, index_name: str = 'default', distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, text_field: str = 'text', ensure_index_exists: bool = True, **kwargs: Any)[source]¶
Momento Vector Index (MVI) vector store.
Momento Vector Index is a serverless vector index that can be used to store and
search vectors. To use you should have the momento python package installed.
Example
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_community.vectorstores import MomentoVectorIndex
from momento import (
    CredentialProvider,
    PreviewVectorIndexClient,
    VectorIndexConfigurations,
)
vectorstore = MomentoVectorIndex(
    embedding=OpenAIEmbeddings(),
    client=PreviewVectorIndexClient(
        VectorIndexConfigurations.Default.latest(),
        credential_provider=CredentialProvider.from_environment_variable(
            "MOMENTO_API_KEY"
        ),
    ),
    index_name="my-index",
)
Initialize a Vector Store backed by Momento Vector Index.
Parameters
embedding (Embeddings) – The embedding function to use.
configuration (VectorIndexConfiguration) – The configuration to initialize
the Vector Index with.
credential_provider (CredentialProvider) – The credential provider to
authenticate the Vector Index with.
index_name (str, optional) – The name of the index to store the documents in.
Defaults to “default”.
distance_strategy (DistanceStrategy, optional) – The distance strategy to
use. If you select DistanceStrategy.EUCLIDEAN_DISTANCE, Momento uses
the squared Euclidean distance. Defaults to DistanceStrategy.COSINE.
the squared Euclidean distance. Defaults to DistanceStrategy.COSINE.
text_field (str, optional) – The name of the metadata field to store the
original text in. Defaults to “text”.
ensure_index_exists (bool, optional) – Whether to ensure that the index
exists before adding documents to it. Defaults to True.
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(embedding, client[, index_name, ...])
Initialize a Vector Store backed by Momento Vector Index.
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)
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(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], asynchronously.
asimilarity_search_with_score(*args, **kwargs)
Run similarity search with distance asynchronously.
delete([ids])
Delete by vector ID.
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding[, metadatas])
Return the Vector Store initialized from texts and embeddings.
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])
Search for similar documents to the query string.
similarity_search_by_vector(embedding[, k])
Search for similar documents to the query vector.
similarity_search_with_relevance_scores(query)
Return docs and relevance scores in the range [0, 1].
similarity_search_with_score(query[, k])
Search for similar documents to the query string.
similarity_search_with_score_by_vector(embedding)
Search for similar documents to the query vector.
__init__(embedding: Embeddings, client: PreviewVectorIndexClient, index_name: str = 'default', distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, text_field: str = 'text', ensure_index_exists: bool = True, **kwargs: Any)[source]¶
Initialize a Vector Store backed by Momento Vector Index.
Initialize a Vector Store backed by Momento Vector Index.
Parameters
embedding (Embeddings) – The embedding function to use.
configuration (VectorIndexConfiguration) – The configuration to initialize
the Vector Index with.
credential_provider (CredentialProvider) – The credential provider to
authenticate the Vector Index with.
index_name (str, optional) – The name of the index to store the documents in.
Defaults to “default”.
distance_strategy (DistanceStrategy, optional) – The distance strategy to
use. If you select DistanceStrategy.EUCLIDEAN_DISTANCE, Momento uses
the squared Euclidean distance. Defaults to DistanceStrategy.COSINE.
text_field (str, optional) – The name of the metadata field to store the
original text in. Defaults to “text”.
ensure_index_exists (bool, optional) – Whether to ensure that the index
exists before adding documents to it. Defaults to True.
async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
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.
add_documents(documents: List[Document], **kwargs: Any) → List[str]¶
Run more documents through the embeddings and add to the vectorstore.
Parameters
(List[Document] (documents) – Documents to add to the vectorstore.
Returns
List of IDs of the added texts.
Return type
List[str]
Returns
List of IDs of the added texts.
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) – Other optional parameters. Specifically:
ids (-) – List of ids to use for the texts.
Defaults to None, in which case uuids are generated.
Returns
List of ids from adding the texts into the vectorstore.
Return type
List[str]
async adelete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool]¶
Delete by vector ID or other criteria.
Parameters
ids – List of ids to delete.
**kwargs – 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.
async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → VST¶
Return VectorStore initialized from texts and embeddings.
async amax_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.
Returns
List of Tuples of (doc, similarity_score)
async asimilarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]¶
Run similarity search with distance asynchronously.
delete(ids: Optional[List[str]] = None, **kwargs: Any) → Optional[bool][source]¶
Delete by vector ID.
Parameters
ids (List[str]) – List of ids to delete.
kwargs (Any) – Other optional parameters (unused)
Returns
True if deletion is successful,
False otherwise, None if not implemented.
Return type
Optional[bool]
classmethod from_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST¶
Return VectorStore initialized from documents and embeddings.
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → VST[source]¶
Return the Vector Store initialized from texts and embeddings.
Parameters
cls (Type[VST]) – The Vector Store class to use to initialize
the Vector Store.
texts (List[str]) – The texts to initialize the Vector Store with.
embedding (Embeddings) – The embedding function to use.
metadatas (Optional[List[dict]], optional) – The metadata associated with
the texts. Defaults to None.
kwargs (Any) – Vector Store specific parameters. The following are forwarded
to the Vector Store constructor and required:
index_name (-) – The name of the index to store the documents
in. Defaults to “default”.
text_field (-) – The name of the metadata field to store the
original text in. Defaults to “text”.
distance_strategy (-) – The distance strategy to
use. Defaults to DistanceStrategy.COSINE. If you select
use. Defaults to DistanceStrategy.COSINE. If you select
DistanceStrategy.EUCLIDEAN_DISTANCE, Momento uses the squared
Euclidean distance.
ensure_index_exists (-) – Whether to ensure that the index
exists before adding documents to it. Defaults to True.
key (Additionally you can either pass in a client or an API) – 
client (-) – The Momento Vector Index client to use.
api_key (-) – The configuration to use to initialize
the Vector Index with. Defaults to None. If None, the configuration
is initialized from the environment variable MOMENTO_API_KEY.
Returns
Momento Vector Index vector store initialized from texts andembeddings.
Return type
VST
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **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
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.
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][source]¶
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.
Parameters
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.
search(query: str, search_type: str, **kwargs: Any) → List[Document]¶
Return docs most similar to query using specified search type.
similarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document][source]¶
Search for similar documents to the query string.
Parameters
query (str) – The query string to search for.
k (int, optional) – The number of results to return. Defaults to 4.
Returns
A list of documents that are similar to the query.
Return type
List[Document]
similarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document][source]¶
Search for similar documents to the query vector.
Parameters
embedding (List[float]) – The query vector to search for.
k (int, optional) – The number of results to return. Defaults to 4.
Returns
A list of documents that are similar to the query.
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 – 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)
similarity_search_with_score(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Search for similar documents to the query string.
Parameters
query (str) – The query string to search for.
k (int, optional) – The number of results to return. Defaults to 4.
kwargs (Any) – Vector Store specific search parameters. The following are
forwarded to the Momento Vector Index:
top_k (-) – The number of results to return.
Returns
A list of tuples of the form(Document, score).
Return type
List[Tuple[Document, float]]
similarity_search_with_score_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Search for similar documents to the query vector.
Parameters
embedding (List[float]) – The query vector to search for.
k (int, optional) – The number of results to return. Defaults to 4.
kwargs (Any) – Vector Store specific search parameters. The following are
forwarded to the Momento Vector Index:
top_k (-) – The number of results to return.
Returns
A list of tuples of the form(Document, score).
Return type
List[Tuple[Document, float]]
langchain_community.vectorstores.vearch.Vearch¶
class langchain_community.vectorstores.vearch.Vearch(embedding_function: Embeddings, path_or_url: Optional[str] = None, table_name: str = 'langchain_vearch', db_name: str = 'cluster_client_db', flag: int = 1, **kwargs: Any)[source]¶
Initialize vearch vector store
flag 1 for cluster,0 for standalone
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(embedding_function[, path_or_url, ...])
Initialize vearch vector store flag 1 for cluster,0 for standalone
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])
returns
List of ids from adding the texts into 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)
Return VectorStoreRetriever initialized from this VectorStore.
asearch(query, search_type, **kwargs)
Return docs most similar to query using specified search type.