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# `langchain_google_genai.google_vector_store`.GoogleVectorStore

class langchain_google_genai.google_vector_store.GoogleVectorStore(*, corpus_id: str, document_id: Optional[str] = None, **kwargs: Any)

  Google GenerativeAI Vector Store.

  Currently, it computes the embedding vectors on the server side.

  Example: Add texts to an existing corpus.

  > store = GoogleVectorStore(corpus_id=”123”) store.add_documents(documents, document_id=”456”)

  Example: Create a new corpus.

  > store = GoogleVectorStore.create_corpus(

  >   corpus_id=”123”, display_name=”My Google corpus”)

  Example: Query the corpus for relevant passages.

  > store.as_retriever() .get_relevant_documents(“Who caught the gingerbread man?”)

  Example: Ask the corpus for grounded responses!

  > aqa = store.as_aqa() response = aqa.invoke(“Who caught the gingerbread man?”) print(response.answer) print(response.attributed_passages) print(response.answerability_probability)

  You can also operate at Google’s Document level.

  Example: Add texts to an existing Google Vector Store Document.

  > doc_store = GoogleVectorStore(corpus_id=”123”, document_id=”456”) doc_store.add_documents(documents)

  Example: Create a new Google Vector Store Document.

  > doc_store = GoogleVectorStore.create_document(

  >   corpus_id=”123”, document_id=”456”, display_name=”My Google document”)

  Example: Query the Google document.

  > doc_store.as_retriever() .get_relevant_documents(“Who caught the gingerbread man?”)

  For more details, see the class’s methods.

  Returns an existing Google Semantic Retriever corpus or document.

  If just the corpus ID is provided, the vector store operates over all documents within that corpus.

  If the document ID is provided, the vector store operates over just that document.

  Raises

    DoesNotExistsException if the IDs do not match to anything on Google – server. In this case, consider using create_corpus or create_document to create one.

  Parameters

    - corpus_id (str) –

    - document_id (Optional[str]) –

    - kwargs (Any) –

  Attributes

  `corpus_id` | Returns the corpus ID managed by this vector store.
  `document_id` | Returns the document ID managed by this vector store.
  `embeddings` | Access the query embedding object if available.
  `name` | Returns the name of the Google entity.

  Methods

  `__init__`(*, corpus_id[, document_id]) | Returns an existing Google Semantic Retriever corpus or document.
  `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, document_id]) | Add texts to the vector 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_aqa`(**kwargs) | Construct a Google Generative AI AQA engine.
  `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.
  `create_corpus`([corpus_id, display_name]) | Create a Google Semantic Retriever corpus.
  `create_document`(corpus_id[, document_id, ...]) | Create a Google Semantic Retriever document.
  `delete`([ids]) | Delete chunnks.
  `from_documents`(documents, embedding, **kwargs) | Return VectorStore initialized from documents and embeddings.
  `from_texts`(texts[, embedding, metadatas, ...]) | Returns a vector store of an existing document with the specified text.
  `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]) | Search the vector store for relevant texts.
  `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]) | Run similarity search with distance.

  __init__(*, corpus_id: str, document_id: Optional[str] = None, **kwargs: Any)

    Returns an existing Google Semantic Retriever corpus or document.

    If just the corpus ID is provided, the vector store operates over all documents within that corpus.

    If the document ID is provided, the vector store operates over just that document.

    Raises

      DoesNotExistsException if the IDs do not match to anything on Google – server. In this case, consider using create_corpus or create_document to create one.

    Parameters

      - corpus_id (str) –

      - document_id (Optional[str]) –

      - kwargs (Any) –

  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_texts(texts: Iterable[str], metadatas: Optional[List[Dict[str, Any]]] = None, *, document_id: Optional[str] = None, **kwargs: Any) → List[str]

    Add texts to the vector store.

    If the vector store points to a corpus (instead of a document), you must also provide a document_id.

    Returns

      Chunk’s names created on Google servers.

    Parameters

      - texts (Iterable[str]) –

      - metadatas (Optional[List[Dict[str, Any]]]) –

      - document_id (Optional[str]) –

      - kwargs (Any) –

    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, **kwargs: Any) → List[Document]

    Return docs selected using the maximal marginal relevance.

    Parameters

      - embedding (List[float]) –

      - k (int) –

      - fetch_k (int) –

      - lambda_mult (float) –

      - kwargs (Any) –

    Return type

      List[Document]

  as_aqa(**kwargs: Any) → Runnable[str, AqaOutput]

    Construct a Google Generative AI AQA engine.

    All arguments are optional.

    Parameters

      - answer_style – See google.ai.generativelanguage.GenerateAnswerRequest.AnswerStyle.

      - safety_settings – See google.ai.generativelanguage.SafetySetting.

      - temperature – 0.0 to 1.0.

      - kwargs (Any) –

    Return type

      Runnable[str, AqaOutput]

  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 create_corpus(corpus_id: Optional[str] = None, display_name: Optional[str] = None) → GoogleVectorStore

    Create a Google Semantic Retriever corpus.

    Parameters

      - corpus_id (Optional[str]) – The ID to use to create the new corpus. If not provided, Google server will provide one.

      - display_name (Optional[str]) – The title of the new corpus. If not provided, Google server will provide one.

    Returns

      An instance of vector store that points to the newly created corpus.

    Return type

      GoogleVectorStore

  classmethod create_document(corpus_id: str, document_id: Optional[str] = None, display_name: Optional[str] = None, metadata: Optional[Dict[str, Any]] = None) → GoogleVectorStore

    Create a Google Semantic Retriever document.

    Parameters

      - corpus_id (str) – ID of an existing corpus.

      - document_id (Optional[str]) – The ID to use to create the new Google Semantic Retriever document. If not provided, Google server will provide one.

      - display_name (Optional[str]) – The title of the new document. If not provided, Google server will provide one.

      - metadata (Optional[Dict[str, Any]]) –

    Returns

      An instance of vector store that points to the newly created document.

    Return type

      GoogleVectorStore

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

    Delete chunnks.

    Note that the “ids” are not corpus ID or document ID. Rather, these are the entity names returned by add_texts.

    Returns

      True if successful. Otherwise, you should get an exception anyway.

    Parameters

      - ids (Optional[List[str]]) –

      - kwargs (Any) –

    Return type

      Optional[bool]

  classmethod from_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

  classmethod from_texts(texts: List[str], embedding: Optional[Embeddings] = None, metadatas: Optional[List[dict[str, Any]]] = None, *, corpus_id: Optional[str] = None, document_id: Optional[str] = None, **kwargs: Any) → GoogleVectorStore

    Returns a vector store of an existing document with the specified text.

    Parameters

      - corpus_id (Optional[str]) – REQUIRED. Must be an existing corpus.

      - document_id (Optional[str]) – REQUIRED. Must be an existing document.

      - texts (List[str]) – Texts to be loaded into the vector store.

      - embedding (Optional[Embeddings]) –

      - metadatas (Optional[List[dict[str, Any]]]) –

      - kwargs (Any) –

    Returns

      A vector store pointing to the specified Google Semantic Retriever Document.

    Raises

      DoesNotExistsException if the IDs do not match to anything at – Google server.

    Return type

      GoogleVectorStore

  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 (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]

  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]

  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[str, Any]] = None, **kwargs: Any) → List[Document]

    Search the vector store for relevant texts.

    Parameters

      - query (str) –

      - k (int) –

      - filter (Optional[Dict[str, Any]]) –

      - kwargs (Any) –

    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.

      - 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, filter: Optional[Dict[str, Any]] = None, **kwargs: Any) → List[Tuple[Document, float]]

    Run similarity search with distance.

    Parameters

      - query (str) –

      - k (int) –

      - filter (Optional[Dict[str, Any]]) –

      - kwargs (Any) –

    Return type

      List[Tuple[Document, float]]