Tony20100/langchain_full huggingface dataset:
namespace (Optional[str]) – 
Return type
List[Tuple[Document, float]]
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, filter: Optional[dict] = None, namespace: Optional[str] = None) → List[Tuple[Document, float]][source]¶
Return pinecone documents most similar to query, along with scores.
Parameters
query (str) – Text to look up documents similar to.
k (int) – Number of Documents to return. Defaults to 4.
filter (Optional[dict]) – Dictionary of argument(s) to filter on metadata
namespace (Optional[str]) – Namespace to search in. Default will search in ‘’ namespace.
Returns
List of Documents most similar to the query and score for each
Return type
List[Tuple[Document, float]]
langchain_community.vectorstores.redis.filters.RedisNum¶
class langchain_community.vectorstores.redis.filters.RedisNum(field: str)[source]¶
RedisFilterField representing a numeric field in a Redis index.
Attributes
OPERATORS
OPERATOR_MAP
SUPPORTED_VAL_TYPES
escaper
Methods
__init__(field)
equals(other)
Parameters
field (str) – 
__init__(field: str)¶
Parameters
field (str) – 
equals(other: RedisFilterField) → bool¶
Parameters
other (RedisFilterField) – 
Return type
bool
Examples using RedisNum¶
Redis
langchain_community.vectorstores.clickhouse.has_mul_sub_str¶
langchain_community.vectorstores.clickhouse.has_mul_sub_str(s: str, *args: Any) → bool[source]¶
Check if a string contains multiple substrings.
:param s: string to check.
:param *args: substrings to check.
Returns
True if all substrings are in the string, False otherwise.
Parameters
s (str) – 
args (Any) – 
Return type
bool
langchain_community.vectorstores.mongodb_atlas.MongoDBAtlasVectorSearch¶
class langchain_community.vectorstores.mongodb_atlas.MongoDBAtlasVectorSearch(collection: Collection[MongoDBDocumentType], embedding: Embeddings, *, index_name: str = 'default', text_key: str = 'text', embedding_key: str = 'embedding', relevance_score_fn: str = 'cosine')[source]¶
[Deprecated] MongoDB Atlas Vector Search vector store.
To use, you should have both:
- the pymongo python package installed
- a connection string associated with a MongoDB Atlas Cluster having deployed an
Atlas Search index
Example
from langchain_community.vectorstores import MongoDBAtlasVectorSearch
from langchain_community.embeddings.openai import OpenAIEmbeddings
from pymongo import MongoClient
mongo_client = MongoClient("<YOUR-CONNECTION-STRING>")
collection = mongo_client["<db_name>"]["<collection_name>"]
embeddings = OpenAIEmbeddings()
vectorstore = MongoDBAtlasVectorSearch(collection, embeddings)
Notes
Deprecated since version 0.0.25.
Parameters
collection (Collection[MongoDBDocumentType]) – MongoDB collection to add the texts to.
embedding (Embeddings) – Text embedding model to use.
text_key (str) – MongoDB field that will contain the text for each
document.
embedding_key (str) – MongoDB field that will contain the embedding for
each document.
index_name (str) – Name of the Atlas Search index.
relevance_score_fn (str) – The similarity score used for the index.
supported (Currently) – Euclidean, cosine, and dot product.
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(collection, embedding, *[, ...])
param collection
MongoDB collection to add the texts to.
aadd_documents(documents, **kwargs)
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_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_connection_string(connection_string, ...)
Construct a MongoDB Atlas Vector Search vector store from a MongoDB connection URI.
from_documents(documents, embedding, **kwargs)
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding[, metadatas, ...])
Construct a MongoDB Atlas Vector Search vector store from raw documents.
max_marginal_relevance_search(query[, k, ...])
Return documents 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, pre_filter, ...])
Return MongoDB documents most similar to the given query.
similarity_search_by_vector(embedding[, k])
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, ...])
Return MongoDB documents most similar to the given query and their scores.
__init__(collection: Collection[MongoDBDocumentType], embedding: Embeddings, *, index_name: str = 'default', text_key: str = 'text', embedding_key: str = 'embedding', relevance_score_fn: str = 'cosine')[source]¶
Parameters
collection (Collection[MongoDBDocumentType]) – MongoDB collection to add the texts to.
embedding (Embeddings) – Text embedding model to use.
text_key (str) – MongoDB field that will contain the text for each
document.
embedding_key (str) – MongoDB field that will contain the embedding for
each document.
index_name (str) – Name of the Atlas Search index.
relevance_score_fn (str) – The similarity score used for the index.
supported (Currently) – Euclidean, cosine, and dot product.
supported (Currently) – Euclidean, cosine, and dot product.
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]¶
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]
add_texts(texts: Iterable[str], metadatas: Optional[List[Dict[str, Any]]] = None, **kwargs: Any) → List[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[str, Any]]]) – Optional list of metadatas associated with the texts.
kwargs (Any) – 
Returns
kwargs (Any) – 
Returns
List of ids from adding the texts into the vectorstore.
Return type
List
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) – 
Return type
VST
async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → VST¶
Return VectorStore initialized from texts and embeddings.
Parameters
texts (List[str]) – Texts to add to the vectorstore.
metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.
embedding (Embeddings) – Embedding function to use.
kwargs (Any) – 
Return type
VST
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.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Parameters
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]]
async asimilarity_search_with_score(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]¶
Run similarity search with distance.
Returns
List of Tuples of (doc, similarity_score)
Parameters
args (Any) – 
kwargs (Any) – 
Return type
List[Tuple[Document, float]]
delete(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]
classmethod from_connection_string(connection_string: str, namespace: str, embedding: Embeddings, **kwargs: Any) → MongoDBAtlasVectorSearch[source]¶
Construct a MongoDB Atlas Vector Search vector store
from a MongoDB connection URI.
Parameters
connection_string (str) – A valid MongoDB connection URI.
namespace (str) – A valid MongoDB namespace (database and collection).
embedding (Embeddings) – The text embedding model to use for the vector store.
kwargs (Any) – 
Returns
A new MongoDBAtlasVectorSearch instance.
Return type
MongoDBAtlasVectorSearch
Returns
A new MongoDBAtlasVectorSearch instance.
Return type
MongoDBAtlasVectorSearch
classmethod from_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) – 
Return type
VST
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[Dict]] = None, collection: Optional[Collection[MongoDBDocumentType]] = None, **kwargs: Any) → MongoDBAtlasVectorSearch[source]¶
Construct a MongoDB Atlas Vector Search vector store from raw documents.
This is a user-friendly interface that:
Embeds documents.
Adds the documents to a provided MongoDB Atlas Vector Search index(Lucene)
This is intended to be a quick way to get started.
Example
Parameters
texts (List[str]) – 
embedding (Embeddings) – 
metadatas (Optional[List[Dict]]) – 
collection (Optional[Collection[MongoDBDocumentType]]) – 
kwargs (Any) – 
Return type
MongoDBAtlasVectorSearch
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, pre_filter: Optional[Dict] = None, post_filter_pipeline: Optional[List[Dict]] = None, **kwargs: Any) → List[Document][source]¶
Return documents 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.
Parameters
query (str) – Text to look up documents similar to.
k (int) – (Optional) number of documents to return. Defaults to 4.
fetch_k (int) – (Optional) number of documents to fetch before passing to MMR
algorithm. Defaults to 20.
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.
pre_filter (Optional[Dict]) – (Optional) dictionary of argument(s) to prefilter on document
fields.
post_filter_pipeline (Optional[List[Dict]]) – (Optional) pipeline of MongoDB aggregation stages
following the vectorSearch stage.
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.
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]
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, pre_filter: Optional[Dict] = None, post_filter_pipeline: Optional[List[Dict]] = None, **kwargs: Any) → List[Document][source]¶
Return MongoDB documents most similar to the given query.
Uses the vectorSearch operator available in MongoDB Atlas Search.
For more: https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-stage/
Parameters
query (str) – Text to look up documents similar to.
k (int) – (Optional) number of documents to return. Defaults to 4.
pre_filter (Optional[Dict]) – (Optional) dictionary of argument(s) to prefilter document
fields on.
post_filter_pipeline (Optional[List[Dict]]) – (Optional) Pipeline of MongoDB aggregation stages
following the vectorSearch stage.
kwargs (Any) – 
Returns
List of documents most similar to the query and their scores.
Return type
List[Document]
similarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]¶
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.
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, pre_filter: Optional[Dict] = None, post_filter_pipeline: Optional[List[Dict]] = None) → List[Tuple[Document, float]][source]¶
Return MongoDB documents most similar to the given query and their scores.
Uses the vectorSearch operator available in MongoDB Atlas Search.
For more: https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-stage/
Parameters
query (str) – Text to look up documents similar to.
k (int) – (Optional) number of documents to return. Defaults to 4.
pre_filter (Optional[Dict]) – (Optional) dictionary of argument(s) to prefilter document
fields on.
post_filter_pipeline (Optional[List[Dict]]) – (Optional) Pipeline of MongoDB aggregation stages
langchain_community.vectorstores.vectara.MMRConfig¶
class langchain_community.vectorstores.vectara.MMRConfig(is_enabled: bool = False, mmr_k: int = 50, diversity_bias: float = 0.3)[source]¶
Configuration for Maximal Marginal Relevance (MMR) search.This will soon be deprated in favor of RerankConfig.
is_enabled: True if MMR is enabled, False otherwise
mmr_k: number of results to fetch for MMR, defaults to 50
diversity_bias: number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to minimum diversity and 1 to maximum diversity.
Defaults to 0.3.
Note: diversity_bias is equivalent 1-lambda_mult
where lambda_mult is the value often used in max_marginal_relevance_search()
We chose to use that since we believe it’s more intuitive to the user.
Attributes
diversity_bias
is_enabled
mmr_k
Methods
__init__([is_enabled, mmr_k, diversity_bias])
Parameters
is_enabled (bool) – 
mmr_k (int) – 
diversity_bias (float) – 
__init__(is_enabled: bool = False, mmr_k: int = 50, diversity_bias: float = 0.3) → None¶
Parameters
is_enabled (bool) – 
mmr_k (int) – 
diversity_bias (float) – 
Return type
None
langchain_community.vectorstores.lantern.DistanceStrategy¶
class langchain_community.vectorstores.lantern.DistanceStrategy(value)[source]¶
Enumerator of the Distance strategies.
EUCLIDEAN = 'l2sq'¶
COSINE = 'cosine'¶
HAMMING = 'hamming'¶
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.neo4j_vector.combine_queries¶
langchain_community.vectorstores.neo4j_vector.combine_queries(input_queries: List[Tuple[str, Dict[str, Any]]], operator: str) → Tuple[str, Dict[str, Any]][source]¶
Combine multiple queries with an operator.
Parameters
input_queries (List[Tuple[str, Dict[str, Any]]]) – 
operator (str) – 
Return type
Tuple[str, Dict[str, Any]]
langchain_community.vectorstores.inmemory.InMemoryVectorStore¶
class langchain_community.vectorstores.inmemory.InMemoryVectorStore(embedding: Embeddings)[source]¶
In-memory implementation of VectorStore using a dictionary.
Uses numpy to compute cosine similarity for search.
Parameters
embedding (Embeddings) – embedding function to use.
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(embedding)
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, ids])
Add texts to the store.
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_by_vector(embedding[, k])
Return docs most similar to embedding vector.
asimilarity_search_with_relevance_scores(query)
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(query[, k])
Run similarity search with distance.
delete([ids])
Delete by vector ID or other criteria.
dump(path)
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding[, metadatas])
Return VectorStore initialized from texts and embeddings.
load(path, embedding, **kwargs)
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])
Return docs most similar to query.
similarity_search_by_vector(embedding[, k])
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(embedding)
__init__(embedding: Embeddings) → None[source]¶
Parameters
embedding (Embeddings) – 
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