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# `langchain_postgres.vectorstores`.PGVector

class langchain_postgres.vectorstores.PGVector(embeddings: Embeddings, *, connection: Optional[Union[Engine, str]] = None, 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, engine_args: Optional[dict[str, Any]] = None, use_jsonb: bool = True, create_extension: bool = True)

  Vectorstore implementation using Postgres as the backend.

  Currently, there is no mechanism for supporting data migration.

  So breaking changes in the vectorstore schema will require the user to recreate the tables and re-add the documents.

  If this is a concern, please use a different vectorstore. If not, this implementation should be fine for your use case.

  To use this vectorstore you need to have the vector extension installed. The vector extension is a Postgres extension that provides vector similarity search capabilities.

  ```sh docker run –name pgvector-container -e POSTGRES_PASSWORD=…

  > -d pgvector/pgvector:pg16

  ```

  Example

      from langchain_postgres.vectorstores import PGVector
      from langchain_openai.embeddings import OpenAIEmbeddings

      connection_string = "postgresql+psycopg://..."
      collection_name = "state_of_the_union_test"
      embeddings = OpenAIEmbeddings()
      vectorstore = PGVector.from_documents(
          embedding=embeddings,
          documents=docs,
          connection=connection_string,
          collection_name=collection_name,
          use_jsonb=True,
      )

  This code has been ported over from langchain_community with minimal changes to allow users to easily transition from langchain_community to langchain_postgres.

  Some changes had to be made to address issues with the community implementation: * langchain_postgres now works with psycopg3. Please update your

  > connection strings from postgresql+psycopg2://… to postgresql+psycopg://langchain:langchain@… (yes, the driver name is psycopg not psycopg3)

  - The schema of the embedding store and collection have been changed to make add_documents work correctly with user specified ids, specifically when overwriting existing documents. You will need to recreate the tables if you are using an existing database.

  - A Connection object has to be provided explicitly. Connections will not be picked up automatically based on env variables.

  Initialize the PGVector store.

  Parameters

    - connection (Optional[Connection]) – Postgres connection string.

    - embeddings (Embeddings) – Any embedding function implementing langchain.embeddings.base.Embeddings interface.

    - embedding_length (Optional[int]) – The length of the embedding vector. (default: None) 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]]) –

  Attributes

  `distance_strategy` |
  `embeddings` | Access the query embedding object if available.

  Methods

  `__init__`(embeddings, *[, connection, ...]) | 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.
  `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], asynchronously.
  `asimilarity_search_with_score`(*args, **kwargs) | Run similarity search with distance asynchronously.
  `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 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 documents 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
  `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__(embeddings: Embeddings, *, connection: Optional[Union[Engine, str]] = None, 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, engine_args: Optional[dict[str, Any]] = None, use_jsonb: bool = True, create_extension: bool = True) → None

    Initialize the PGVector store.

    Parameters

      - connection (Optional[Union[Engine, str]]) – Postgres connection string.

      - embeddings (Embeddings) – Any embedding function implementing langchain.embeddings.base.Embeddings interface.

      - embedding_length (Optional[int]) – The length of the embedding vector. (default: None) 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[Logger]) –

      - relevance_score_fn (Optional[Callable[[float], float]]) –

    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

      - (List[Document] (documents) – Documents to add to the vectorstore.

      - documents (List[Document]) –

      - 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]) –

      - metadatas (Optional[List[dict]]) –

      - kwargs (Any) –

    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

      - (List[Document] (documents) – Documents to add to the vectorstore.

      - documents (List[Document]) –

      - kwargs (Any) –

    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[str]) – Iterable of strings to add to the vectorstore.

      - embeddings (List[List[float]]) – List of list of embedding vectors.

      - metadatas (Optional[List[dict]]) – List of metadatas associated with the texts.

      - kwargs (Any) – vectorstore specific parameters

      - ids (Optional[List[str]]) –

    Return type

      List[str]

  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[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

      - ids (Optional[List[str]]) –

    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 (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]) –

      - embedding (Embeddings) –

      - 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]) –

      - embedding (Embeddings) –

      - metadatas (Optional[List[dict]]) –

      - 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

      - 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]

  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]

    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
        # 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) –

      - search_type (str) –

      - kwargs (Any) –

    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) –

      - k (int) –

      - kwargs (Any) –

    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]) –

      - k (int) –

      - kwargs (Any) –

    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], asynchronously.

    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 asynchronously.

    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

    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
    Return type

      None

  create_tables_if_not_exists() → None
    Return type

      None

  create_vector_extension() → None
    Return type

      None

  delete(ids: Optional[List[str]] = None, collection_only: bool = False, **kwargs: Any) → None

    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
    Return type

      None

  drop_tables() → None
    Return type

      None

  classmethod from_documents(documents: List[Document], embedding: Embeddings, *, connection: Optional[Union[Engine, str]] = None, collection_name: str = 'langchain', distance_strategy: DistanceStrategy = DistanceStrategy.COSINE, ids: Optional[List[str]] = None, pre_delete_collection: bool = False, use_jsonb: bool = True, **kwargs: Any) → PGVector

    Return VectorStore initialized from documents and embeddings.

    Parameters

      - documents (List[Document]) –

      - embedding (Embeddings) –

      - connection (Optional[Union[Engine, str]]) –

      - 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

    Construct PGVector wrapper from raw documents and embeddings.

    Parameters

      - text_embeddings (List[Tuple[str, List[float]]]) – List of tuples of text and embeddings.

      - embedding (Embeddings) – Embeddings object.

      - metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.

      - collection_name (str) – Name of the collection.

      - distance_strategy (DistanceStrategy) – Distance strategy to use.

      - ids (Optional[List[str]]) – Optional list of ids for the documents.

      - pre_delete_collection (bool) – If True, will delete the collection if it exists. Attention: This will delete all the documents in the existing collection.

      - kwargs (Any) – Additional arguments.

    Returns

      PGVector instance.

    Return type

      PGVector

    Example

        from langchain_postgres.vectorstores import PGVector
        from langchain_openai.embeddings import OpenAIEmbeddings

        embeddings = OpenAIEmbeddings()
        text_embeddings = embeddings.embed_documents(texts)
        text_embedding_pairs = list(zip(texts, text_embeddings))
        vectorstore = 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, connection: Optional[Union[Engine, str]] = None, **kwargs: Any) → PGVector

    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) –

      - connection (Optional[Union[Engine, str]]) –

      - 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 = True, **kwargs: Any) → PGVector

    Return VectorStore initialized from documents and embeddings.

    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
    Parameters

      session (Session) –

    Return type

      Any

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

    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. 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]
    Return docs selected using the maximal marginal relevance

      to embedding vector.

    Maximal marginal relevance optimizes for similarity to query AND diversity

      among selected documents.

    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]]

    Return docs selected using the maximal marginal relevance with score.

    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. 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 to the query and score for each.

    Return type

      List[Tuple[Document, float]]

  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[Dict[str, str]] = None, **kwargs: Any) → List[Tuple[Document, float]]
    Return docs selected using the maximal marginal relevance with score

      to embedding vector.

    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. 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 to the query and score for each.

    Return type

      List[Tuple[Document, float]]

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

    Return docs most similar to query using specified search type.

    Parameters

      - query (str) –

      - search_type (str) –

      - kwargs (Any) –

    Return type

      List[Document]

  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.

      - 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]

    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

      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]]

    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]]
    Parameters

      - embedding (List[float]) –

      - k (int) –

      - filter (Optional[dict]) –

    Return type

      List[Tuple[Document, float]]

# Examples using PGVector

- PGVector

- See docker command above to launch a postgres instance with pgvector enabled.

- This example only specifies a relevant query