Tony20100/langchain_full huggingface dataset:
NOTE: This is not mandatory. Defining it will prevent vectors of
any other size to be added to the embeddings table but, without it,
the embeddings can’t be indexed.
collection_name (str) – The name of the collection to use. (default: langchain)
NOTE: This is not the name of the table, but the name of the collection.
The tables will be created when initializing the store (if not exists)
So, make sure the user has the right permissions to create tables.
distance_strategy (DistanceStrategy) – The distance strategy to use. (default: COSINE)
pre_delete_collection (bool) – If True, will delete the collection if it exists.
(default: False). Useful for testing.
engine_args (Optional[dict[str, Any]]) – SQLAlchemy’s create engine arguments.
use_jsonb (bool) – Use JSONB instead of JSON for metadata. (default: True)
Strongly discouraged from using JSON as it’s not as efficient
for querying.
It’s provided here for backwards compatibility with older versions,
and will be removed in the future.
create_extension (bool) – If True, will create the vector extension if it doesn’t exist.
disabling creation is useful when using ReadOnly Databases.
collection_metadata (Optional[dict]) – 
logger (Optional[logging.Logger]) – 
relevance_score_fn (Optional[Callable[[float], float]]) – 
connection (Optional[sqlalchemy.engine.Connection]) – 
Example
from langchain_community.vectorstores import PGVector
from langchain_community.embeddings.openai import OpenAIEmbeddings
CONNECTION_STRING = "postgresql+psycopg2://hwc@localhost:5432/test3"
COLLECTION_NAME = "state_of_the_union_test"
embeddings = OpenAIEmbeddings()
vectorestore = PGVector.from_documents(
embeddings = OpenAIEmbeddings()
vectorestore = PGVector.from_documents(
    embedding=embeddings,
    documents=docs,
    collection_name=COLLECTION_NAME,
    connection_string=CONNECTION_STRING,
    use_jsonb=True,
)
Notes
Deprecated since version 0.0.31: This class is pending deprecation and may be removed in a future version. You can swap to using the PGVector implementation in langchain_postgres. Please read the guidelines in the doc-string of this class to follow prior to migrating as there are some differences between the implementations. See https://github.com/langchain-ai/langchain-postgres for details aboutthe new implementation. Use from langchain_postgres import PGVector; instead.
Initialize the PGVector store.
Attributes
distance_strategy
embeddings
Access the query embedding object if available.
Methods
__init__(connection_string, embedding_function)
Initialize the PGVector store.
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_embeddings(texts, embeddings[, ...])
Add embeddings to the vectorstore.
add_texts(texts[, metadatas, ids])
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.
connection_string_from_db_params(driver, ...)
Return connection string from database parameters.
create_collection()
create_tables_if_not_exists()
create_vector_extension()
delete([ids, collection_only])
Delete vectors by ids or uuids.
delete_collection()
drop_tables()
from_documents(documents, embedding[, ...])
Return VectorStore initialized from documents and embeddings.
from_embeddings(text_embeddings, embedding)
Construct PGVector wrapper from raw documents and pre- generated embeddings.
from_existing_index(embedding[, ...])
Get instance of an existing PGVector store.This method will return the instance of the store without inserting any new embeddings
from_texts(texts, embedding[, metadatas, ...])
Return VectorStore initialized from texts and embeddings.
get_collection(session)
get_connection_string(kwargs)
max_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
Return docs selected using the maximal marginal relevance.
max_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance
max_marginal_relevance_search_with_score(query)
Return docs selected using the maximal marginal relevance with score.
max_marginal_relevance_search_with_score_by_vector(...)
Return docs selected using the maximal marginal relevance with score
search(query, search_type, **kwargs)
Return docs most similar to query using specified search type.
similarity_search(query[, k, filter])
Run similarity search with PGVector with distance.
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, filter])
Return docs most similar to query.
similarity_search_with_score_by_vector(embedding)
__init__(connection_string: str, embedding_function: Embeddings, embedding_length: Optional[int] = None, collection_name: str = 'langchain', collection_metadata: Optional[dict] = None, distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, pre_delete_collection: bool = False, logger: Optional[Logger] = None, relevance_score_fn: Optional[Callable[[float], float]] = None, *, connection: Optional[Connection] = None, engine_args: Optional[dict[str, Any]] = None, use_jsonb: bool = False, create_extension: bool = True) → None[source]¶
Initialize the PGVector store.
Parameters
connection_string (str) – 
embedding_function (Embeddings) – 
embedding_length (Optional[int]) – 
collection_name (str) – 
collection_metadata (Optional[dict]) – 
distance_strategy (DistanceStrategy) –
collection_metadata (Optional[dict]) – 
distance_strategy (DistanceStrategy) – 
pre_delete_collection (bool) – 
logger (Optional[Logger]) – 
relevance_score_fn (Optional[Callable[[float], float]]) – 
connection (Optional[Connection]) – 
engine_args (Optional[dict[str, Any]]) – 
use_jsonb (bool) – 
create_extension (bool) – 
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]¶
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]
Defaults to 0.5.
kwargs (Any) – 
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[Document]
async amax_marginal_relevance_search_by_vector(embedding: List[float], k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Dict[str, str]] = None, **kwargs: Any) → List[Document][source]¶
Return docs selected using the maximal marginal relevance.
Parameters
embedding (List[float]) – 
k (int) – 
fetch_k (int) – 
lambda_mult (float) – 
filter (Optional[Dict[str, str]]) – 
kwargs (Any) – 
Return type
List[Document]
as_retriever(**kwargs: Any) → VectorStoreRetriever¶
Return VectorStoreRetriever initialized from this VectorStore.
Parameters
search_type (Optional[str]) – Defines the type of search that
the Retriever should perform.
Can be “similarity” (default), “mmr”, or
“similarity_score_threshold”.
search_kwargs (Optional[Dict]) – Keyword arguments to pass to the
search function. Can include things like:
k: Amount of documents to return (Default: 4)
score_threshold: Minimum relevance threshold
for similarity_score_threshold
fetch_k: Amount of documents to pass to MMR algorithm (Default: 20)
lambda_mult: Diversity of results returned by MMR;
1 for minimum diversity and 0 for maximum. (Default: 0.5)
filter: Filter by document metadata
kwargs (Any) – 
Returns
Retriever class for VectorStore.
Return type
VectorStoreRetriever
Examples:
# Retrieve more documents with higher diversity
Return type
VectorStoreRetriever
Examples:
# Retrieve more documents with higher diversity
# Useful if your dataset has many similar documents
docsearch.as_retriever(
    search_type="mmr",
    search_kwargs={'k': 6, 'lambda_mult': 0.25}
)
# Fetch more documents for the MMR algorithm to consider
# But only return the top 5
docsearch.as_retriever(
    search_type="mmr",
    search_kwargs={'k': 5, 'fetch_k': 50}
)
# Only retrieve documents that have a relevance score
# Above a certain threshold
docsearch.as_retriever(
    search_type="similarity_score_threshold",
    search_kwargs={'score_threshold': 0.8}
)
# Only get the single most similar document from the dataset
docsearch.as_retriever(search_kwargs={'k': 1})
# Use a filter to only retrieve documents from a specific paper
docsearch.as_retriever(
    search_kwargs={'filter': {'paper_title':'GPT-4 Technical Report'}}
)
async asearch(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]
async asimilarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document]¶
Return docs most similar to query.
Parameters
query (str) – Input text.
Return docs most similar to query.
Parameters
query (str) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
kwargs (Any) – 
Returns
List of Documents most similar to the query.
Return type
List[Document]
async asimilarity_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.
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]
async asimilarity_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]]
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
Parameters
args (Any) – 
kwargs (Any) – 
Return type
List[Tuple[Document, float]]
classmethod connection_string_from_db_params(driver: str, host: str, port: int, database: str, user: str, password: str) → str[source]¶
Return connection string from database parameters.
Parameters
driver (str) – 
host (str) – 
port (int) – 
database (str) – 
user (str) – 
password (str) – 
Return type
str
create_collection() → None[source]¶
Return type
None
create_tables_if_not_exists() → None[source]¶
Return type
None
create_vector_extension() → None[source]¶
Return type
None
delete(ids: Optional[List[str]] = None, collection_only: bool = False, **kwargs: Any) → None[source]¶
Delete vectors by ids or uuids.
Parameters
ids (Optional[List[str]]) – List of ids to delete.
collection_only (bool) – Only delete ids in the collection.
kwargs (Any) – 
Return type
None
delete_collection() → None[source]¶
Return type
None
drop_tables() → None[source]¶
Return type
None
classmethod from_documents(documents: List[Document], embedding: Embeddings, collection_name: str = 'langchain', distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, ids: Optional[List[str]] = None, pre_delete_collection: bool = False, *, use_jsonb: bool = False, **kwargs: Any) → PGVector[source]¶
Return VectorStore initialized from documents and embeddings.
Postgres connection string is required
“Either pass it as a parameter
or set the PGVECTOR_CONNECTION_STRING environment variable.
Parameters
documents (List[Document]) –
Parameters
documents (List[Document]) – 
embedding (Embeddings) – 
collection_name (str) – 
distance_strategy (DistanceStrategy) – 
ids (Optional[List[str]]) – 
pre_delete_collection (bool) – 
use_jsonb (bool) – 
kwargs (Any) – 
Return type
PGVector
classmethod from_embeddings(text_embeddings: List[Tuple[str, List[float]]], embedding: Embeddings, metadatas: Optional[List[dict]] = None, collection_name: str = 'langchain', distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, ids: Optional[List[str]] = None, pre_delete_collection: bool = False, **kwargs: Any) → PGVector[source]¶
Construct PGVector wrapper from raw documents and pre-
generated embeddings.
Return VectorStore initialized from documents and embeddings.
Postgres connection string is required
“Either pass it as a parameter
or set the PGVECTOR_CONNECTION_STRING environment variable.
Example
from langchain_community.vectorstores import PGVector
from langchain_community.embeddings import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
text_embeddings = embeddings.embed_documents(texts)
text_embedding_pairs = list(zip(texts, text_embeddings))
faiss = PGVector.from_embeddings(text_embedding_pairs, embeddings)
Parameters
text_embeddings (List[Tuple[str, List[float]]]) – 
embedding (Embeddings) – 
metadatas (Optional[List[dict]]) – 
collection_name (str) – 
distance_strategy (DistanceStrategy) – 
ids (Optional[List[str]]) – 
pre_delete_collection (bool) – 
kwargs (Any) – 
Return type
PGVector
kwargs (Any) – 
Return type
PGVector
classmethod from_existing_index(embedding: Embeddings, collection_name: str = 'langchain', distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, pre_delete_collection: bool = False, **kwargs: Any) → PGVector[source]¶
Get instance of an existing PGVector store.This method will
return the instance of the store without inserting any new
embeddings
Parameters
embedding (Embeddings) – 
collection_name (str) – 
distance_strategy (DistanceStrategy) – 
pre_delete_collection (bool) – 
kwargs (Any) – 
Return type
PGVector
classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, collection_name: str = 'langchain', distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, ids: Optional[List[str]] = None, pre_delete_collection: bool = False, *, use_jsonb: bool = False, **kwargs: Any) → PGVector[source]¶
Return VectorStore initialized from texts and embeddings.
Postgres connection string is required
“Either pass it as a parameter
or set the PGVECTOR_CONNECTION_STRING environment variable.
Parameters
texts (List[str]) – 
embedding (Embeddings) – 
metadatas (Optional[List[dict]]) – 
collection_name (str) – 
distance_strategy (DistanceStrategy) – 
ids (Optional[List[str]]) – 
pre_delete_collection (bool) – 
use_jsonb (bool) – 
kwargs (Any) – 
Return type
PGVector
get_collection(session: Session) → Any[source]¶
Parameters
session (Session) – 
Return type
Any
classmethod get_connection_string(kwargs: Dict[str, Any]) → str[source]¶
Parameters
classmethod get_connection_string(kwargs: Dict[str, Any]) → str[source]¶
Parameters
kwargs (Dict[str, Any]) – 
Return type
str
max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[Dict[str, str]] = None, **kwargs: Any) → List[Document][source]¶
Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversityamong 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.
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.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
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[Dict[str, str]] = None, **kwargs: Any) → List[Document][source]¶
Return docs selected using the maximal marginal relevanceto embedding vector.
Maximal marginal relevance optimizes for similarity to query AND diversityamong selected documents.
Parameters
embedding (str) – Text to look up documents similar to.
Parameters
embedding (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.
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.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
kwargs (Any) – 
Returns
List of Documents selected by maximal marginal relevance.
Return type
List[Document]
max_marginal_relevance_search_with_score(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filter: Optional[dict] = None, **kwargs: Any) → List[Tuple[Document, float]][source]¶
Return docs selected using the maximal marginal relevance with score.
Maximal marginal relevance optimizes for similarity to query AND diversityamong 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.
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.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
kwargs (Any) – 
Returns
List of Documents selected by maximal marginalrelevance to the query and score for each.
Return type
Return type
List[Document]
similarity_search(query: str, k: int = 4, filter: Optional[dict] = None, **kwargs: Any) → List[Document][source]¶
Run similarity search with PGVector with distance.
Parameters
query (str) – Query text to search for.
k (int) – Number of results to return. Defaults to 4.
filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.
kwargs (Any) – 
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] = None, **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.
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
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) → 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.
Returns
List of Documents most similar to the query and score for each.
Return type
List[Tuple[Document, float]]
similarity_search_with_score_by_vector(embedding: List[float], k: int = 4, filter: Optional[dict] = None) → List[Tuple[Document, float]][source]¶
Parameters
embedding (List[float]) – 
k (int) – 
filter (Optional[dict]) – 
Return type
List[Tuple[Document, float]]
Examples using PGVector¶
PGVector
PGVector (Postgres)
langchain_community.vectorstores.zep.CollectionConfig¶
class langchain_community.vectorstores.zep.CollectionConfig(name: str, description: Optional[str], metadata: Optional[Dict[str, Any]], embedding_dimensions: int, is_auto_embedded: bool)[source]¶
Configuration for a Zep Collection.
If the collection does not exist, it will be created.
Parameters
name (str) – 
description (Optional[str]) – 
metadata (Optional[Dict[str, Any]]) – 
embedding_dimensions (int) – 
is_auto_embedded (bool) – 
name¶
The name of the collection.
Type
str
description¶
An optional description of the collection.
Type
Optional[str]
metadata¶
Optional metadata for the collection.
Type
Optional[Dict[str, Any]]
embedding_dimensions¶
The number of dimensions for the embeddings in
the collection. This should match the Zep server configuration
if auto-embed is true.
Type
int
is_auto_embedded¶
A flag indicating whether the collection is
automatically embedded by Zep.
Type
bool
Attributes
name
description
metadata
embedding_dimensions
is_auto_embedded
Methods
__init__(name, description, metadata, ...)
__init__(name: str, description: Optional[str], metadata: Optional[Dict[str, Any]], embedding_dimensions: int, is_auto_embedded: bool) → None¶
Parameters
name (str) – 
description (Optional[str]) – 
metadata (Optional[Dict[str, Any]]) – 
embedding_dimensions (int) – 
is_auto_embedded (bool) – 
Return type
None
Examples using CollectionConfig¶
Zep