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# `langchain_google_vertexai.vectorstores.vectorstores`.VectorSearchVectorStore

class langchain_google_vertexai.vectorstores.vectorstores.VectorSearchVectorStore(searcher: Searcher, document_storage: DocumentStorage, embbedings: Optional[Embeddings] = None)

  VertexAI VectorStore that handles the search and indexing using Vector Search and stores the documents in Google Cloud Storage.

  Constructor. :param searcher: Object in charge of searching and storing the index. :param document_storage: Object in charge of storing and retrieving documents. :param embbedings: Object in charge of transforming text to embbeddings.

  Attributes

  `embbedings` | Returns the embeddings object.
  `embeddings` | Access the query embedding object if available.

  Methods

  `__init__`(searcher, document_storage[, ...]) | Constructor.
  `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], asynchronously.
  `asimilarity_search_with_score`(*args, **kwargs) | Run similarity search with distance asynchronously.
  `delete`([ids]) | Delete by vector ID or other criteria.
  `from_components`(project_id, region, ...[, ...]) | Takes the object creation out of the constructor. :param project_id: The GCP project id. :param region: The default location making the API calls. It must have :param the same location as the GCS bucket and must be regional.: :param gcs_bucket_name: The location where the vectors will be stored in :param order for the index to be created.: :param index_id: The id of the created index. :param endpoint_id: The id of the created endpoint. :param credentials_path: (Optional) The path of the Google credentials on :param the local file system.: :param embedding: The `Embeddings` that will be used for :param embedding the texts.: :param stream_update: Whether to update with streaming or batching. VectorSearch index must be compatible with stream/batch updates. :param kwargs: Additional keyword arguments to pass to VertexAIVectorSearch.__init__().
  `from_documents`(documents, embedding, **kwargs) | Return VectorStore initialized from documents and embeddings.
  `from_texts`(texts, embedding[, metadatas]) | Use from components instead.
  `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, ...]) | Return docs most similar to query. :param query: The string that will be used to search for similar documents. :param k: The amount of neighbors that will be retrieved. :param filter: Optional. A list of Namespaces for filtering the matching results. For example: [Namespace("color", ["red"], []), Namespace("shape", [], ["squared"])] will match datapoints that satisfy "red color" but not include datapoints with "squared shape". Please refer to https://cloud.google.com/vertex-ai/docs/matching-engine/filtering#json for more detail. :param numeric_filter: Optional. A list of NumericNamespaces for filterning the matching results. Please refer to https://cloud.google.com/vertex-ai/docs/matching-engine/filtering#json for more detail.
  `similarity_search_by_vector`(embedding[, k]) | Return docs most similar to embedding vector.
  `similarity_search_by_vector_with_score`(embedding) | Return docs most similar to the embedding and their cosine distance. :param embedding: Embedding to look up documents similar to. :param k: Number of Documents to return. Defaults to 4. :param filter: Optional. A list of Namespaces for filtering the matching results. For example: [Namespace("color", ["red"], []), Namespace("shape", [], ["squared"])] will match datapoints that satisfy "red color" but not include datapoints with "squared shape". Please refer to https://cloud.google.com/vertex-ai/docs/matching-engine/filtering#json for more detail. :param numeric_filter: Optional. A list of NumericNamespaces for filterning the matching results. Please refer to https://cloud.google.com/vertex-ai/docs/matching-engine/filtering#json for more detail.
  `similarity_search_with_relevance_scores`(query) | Return docs and relevance scores in the range [0, 1].
  `similarity_search_with_score`(query[, k, ...]) | Return docs most similar to query and their cosine distance from the query. :param query: String query look up documents similar to. :param k: Number of Documents to return. Defaults to 4. :param filter: Optional. A list of Namespaces for filtering the matching results. For example: [Namespace("color", ["red"], []), Namespace("shape", [], ["squared"])] will match datapoints that satisfy "red color" but not include datapoints with "squared shape". Please refer to https://cloud.google.com/vertex-ai/docs/matching-engine/filtering#json for more detail. :param numeric_filter: Optional. A list of NumericNamespaces for filterning the matching results. Please refer to https://cloud.google.com/vertex-ai/docs/matching-engine/filtering#json for more detail.

  Parameters

    - searcher (Searcher) –

    - document_storage (DocumentStorage) –

    - embbedings (Optional[Embeddings]) –

  __init__(searcher: Searcher, document_storage: DocumentStorage, embbedings: Optional[Embeddings] = None) → None

    Constructor. :param searcher: Object in charge of searching and storing the index. :param document_storage: Object in charge of storing and retrieving documents. :param embbedings: Object in charge of transforming text to embbeddings.

    Parameters

      - searcher (Searcher) –

      - document_storage (DocumentStorage) –

      - embbedings (Optional[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

      - (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]] = None, is_complete_overwrite: bool = False, **kwargs: Any) → List[str]

    Run more texts through the embeddings and add to the vectorstore. :param texts: Iterable of strings to add to the vectorstore. :param metadatas: Optional list of metadatas associated with the texts. :param kwargs: vectorstore specific parameters.

    Returns

      List of ids from adding the texts into the vectorstore.

    Parameters

      - texts (Iterable[str]) –

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

      - is_complete_overwrite (bool) –

      - 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_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]]

  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_components(project_id: str, region: str, gcs_bucket_name: str, index_id: str, endpoint_id: str, credentials_path: Optional[str] = None, embedding: Optional[Embeddings] = None, stream_update: bool = False, **kwargs: Any) → VectorSearchVectorStore

    Takes the object creation out of the constructor. :param project_id: The GCP project id. :param region: The default location making the API calls. It must have :param the same location as the GCS bucket and must be regional.: :param gcs_bucket_name: The location where the vectors will be stored in :param order for the index to be created.: :param index_id: The id of the created index. :param endpoint_id: The id of the created endpoint. :param credentials_path: (Optional) The path of the Google credentials on :param the local file system.: :param embedding: The `Embeddings` that will be used for :param embedding the texts.: :param stream_update: Whether to update with streaming or batching. VectorSearch

    > index must be compatible with stream/batch updates.

    Parameters

      - kwargs (Any) – Additional keyword arguments to pass to VertexAIVectorSearch.__init__().

      - project_id (str) –

      - region (str) –

      - gcs_bucket_name (str) –

      - index_id (str) –

      - endpoint_id (str) –

      - credentials_path (Optional[str]) –

      - embedding (Optional[Embeddings]) –

      - stream_update (bool) –

    Returns

      A configured VertexAIVectorSearch with the texts added to the index.

    Return type

      VectorSearchVectorStore

  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: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → _BaseVertexAIVectorStore

    Use from components instead.

    Parameters

      - texts (List[str]) –

      - embedding (Embeddings) –

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

      - kwargs (Any) –

    Return type

      _BaseVertexAIVectorStore

  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[List[Namespace]] = None, numeric_filter: Optional[List[NumericNamespace]] = None, **kwargs: Any) → List[Document]

    Return docs most similar to query. :param query: The string that will be used to search for similar documents. :param k: The amount of neighbors that will be retrieved. :param filter: Optional. A list of Namespaces for filtering the matching results.

    > For example: [Namespace(“color”, [“red”], []), Namespace(“shape”, [], [“squared”])] will match datapoints that satisfy “red color” but not include datapoints with “squared shape”. Please refer to https://cloud.google.com/vertex-ai/docs/matching-engine/filtering#json

    > > for more detail.

    Parameters

      - numeric_filter (Optional[List[NumericNamespace]]) – Optional. A list of NumericNamespaces for filterning the matching results. Please refer to https://cloud.google.com/vertex-ai/docs/matching-engine/filtering#json for more detail.

      - query (str) –

      - k (int) –

      - filter (Optional[List[Namespace]]) –

      - kwargs (Any) –

    Returns

      A list of k matching documents.

    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_by_vector_with_score(embedding: List[float], k: int = 4, filter: Optional[List[Namespace]] = None, numeric_filter: Optional[List[NumericNamespace]] = None) → List[Tuple[Document, float]]

    Return docs most similar to the embedding and their cosine distance. :param embedding: Embedding to look up documents similar to. :param k: Number of Documents to return. Defaults to 4. :param filter: Optional. A list of Namespaces for filtering

    > the matching results. For example: [Namespace(“color”, [“red”], []), Namespace(“shape”, [], [“squared”])] will match datapoints that satisfy “red color” but not include datapoints with “squared shape”. Please refer to https://cloud.google.com/vertex-ai/docs/matching-engine/filtering#json for more detail.

    Parameters

      - numeric_filter (Optional[List[NumericNamespace]]) – Optional. A list of NumericNamespaces for filterning the matching results. Please refer to https://cloud.google.com/vertex-ai/docs/matching-engine/filtering#json for more detail.

      - embedding (List[float]) –

      - k (int) –

      - filter (Optional[List[Namespace]]) –

    Returns

      List of documents most similar to the query text and cosine distance in float for each. Lower score represents more similarity.

    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[List[Namespace]] = None, numeric_filter: Optional[List[NumericNamespace]] = None) → List[Tuple[Document, float]]

    Return docs most similar to query and their cosine distance from the query. :param query: String query look up documents similar to. :param k: Number of Documents to return. Defaults to 4. :param filter: Optional. A list of Namespaces for filtering

    > the matching results. For example: [Namespace(“color”, [“red”], []), Namespace(“shape”, [], [“squared”])] will match datapoints that satisfy “red color” but not include datapoints with “squared shape”. Please refer to https://cloud.google.com/vertex-ai/docs/matching-engine/filtering#json for more detail.

    Parameters

      - numeric_filter (Optional[List[NumericNamespace]]) – Optional. A list of NumericNamespaces for filterning the matching results. Please refer to https://cloud.google.com/vertex-ai/docs/matching-engine/filtering#json for more detail.

      - query (str) –

      - k (int) –

      - filter (Optional[List[Namespace]]) –

    Returns

      List of documents most similar to the query text and cosine distance in float for each. Lower score represents more similarity.

    Return type

      List[Tuple[Document, float]]

# Examples using VectorSearchVectorStore

- Google

- TODO : Set values as per your requirements