langchain.vectorstores.pgvector.PGVector

class langchain.vectorstores.pgvector.PGVector(connection_string: str, embedding_function: Embeddings, 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)

Bases: VectorStore

VectorStore implementation using Postgres and pgvector.

To use, you should have the pgvector python package installed.

Parameters

• connection_string – Postgres connection string.

• embedding_function – Any embedding function implementing langchain.embeddings.base.Embeddings interface.

• collection_name – 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 – The distance strategy to use. (default: COSINE)

• pre_delete_collection – If True, will delete the collection if it exists. (default: False). Useful for testing.

Example

from langchain.vectorstores import PGVector
from langchain.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(
    embedding=embeddings,
    documents=docs,
    collection_name=COLLECTION_NAME,
    connection_string=CONNECTION_STRING,
)

Methods

__init__(connection_string, embedding_function)
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.
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)
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 query.
connect()
connection_string_from_db_params(driver, ...)	Return connection string from database parameters.
create_collection()
create_tables_if_not_exists()
create_vector_extension()
delete([ids])	Delete by vector ID or other criteria.
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 intsance 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.
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, 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)

Attributes

distance_strategy
embeddings	Access the query embedding object if available.

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]

add_embeddings(texts: Iterable[str], embeddings: List[List[float]], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → List[str]

Add embeddings to the vectorstore.

Parameters

• texts – Iterable of strings to add to the vectorstore.

• embeddings – List of list of embedding vectors.

• metadatas – List of metadatas associated with the texts.

• kwargs – vectorstore specific parameters

add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, **kwargs: Any) → List[str]

Run more texts through the embeddings and add to the vectorstore.

Parameters

• texts – Iterable of strings to add to the vectorstore.

• metadatas – Optional list of metadatas associated with the texts.

• kwargs – vectorstore specific parameters

Returns

List of ids from adding the texts into the vectorstore.

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.

async amax_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.

as_retriever(**kwargs: Any) → VectorStoreRetriever

async asearch(query: str, search_type: str, **kwargs: Any) → List[Document]

Return docs most similar to query using specified search type.

async asimilarity_search(query: str, k: int = 4, **kwargs: Any) → List[Document]

Return docs most similar to query.

async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]

Return docs most similar to embedding vector.

async asimilarity_search_with_relevance_scores(query: str, k: int = 4, **kwargs: Any) → List[Tuple[Document, float]]

Return docs most similar to query.

connect() → Connection

classmethod connection_string_from_db_params(driver: str, host: str, port: int, database: str, user: str, password: str) → str

Return connection string from database parameters.

create_collection() → None

create_tables_if_not_exists() → None

create_vector_extension() → None

delete(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]

delete_collection() → None

drop_tables() → 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, **kwargs: Any) → PGVector

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.

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

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 import PGVector
from langchain.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)

classmethod from_existing_index(embedding: Embeddings, collection_name: str = 'langchain', distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, pre_delete_collection: bool = False, **kwargs: Any) → PGVector

Get intsance of an existing PGVector store.This method will return the instance of the store without inserting any new embeddings

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, **kwargs: Any) → PGVector

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.

get_collection(session: Session) → Optional['CollectionStore']

classmethod get_connection_string(kwargs: Dict[str, Any]) → str

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.

• 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, filter: Optional[dict] = None, **kwargs: Any) → List[Document]

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.

Returns

List of Documents most similar to the query.

similarity_search_by_vector(embedding: List[float], k: int = 4, filter: Optional[dict] = None, **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.

• filter (Optional[Dict[str, str]]) – Filter by metadata. Defaults to None.

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.

• **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, filter: Optional[dict] = None) → List[Tuple[Document, float]]

Return docs most similar to query.

Parameters

• query – Text to look up documents similar to.

• k – 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

similarity_search_with_score_by_vector(embedding: List[float], k: int = 4, filter: Optional[dict] = None) → List[Tuple[Document, float]]

property distance_strategy: Any

property embeddings: langchain.embeddings.base.Embeddings

Access the query embedding object if available.

Examples using PGVector

• PGVector