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# `langchain_community.vectorstores.databricks_vector_search`.DatabricksVectorSearch

class langchain_community.vectorstores.databricks_vector_search.DatabricksVectorSearch(index: VectorSearchIndex, *, embedding: Optional[Embeddings] = None, text_column: Optional[str] = None, columns: Optional[List[str]] = None)

  Databricks Vector Search vector store.

  To use, you should have the `databricks-vectorsearch` python package installed.

  Example

      from langchain_community.vectorstores import DatabricksVectorSearch
      from databricks.vector_search.client import VectorSearchClient

      vs_client = VectorSearchClient()
      vs_index = vs_client.get_index(
        endpoint_name="vs_endpoint",
        index_name="ml.llm.index"
      )
      vectorstore = DatabricksVectorSearch(vs_index)

  Parameters

    - index (VectorSearchIndex) – A Databricks Vector Search index object.

    - embedding (Optional[Embeddings]) – The embedding model. Required for direct-access index or delta-sync index with self-managed embeddings.

    - text_column (Optional[str]) – The name of the text column to use for the embeddings. Required for direct-access index or delta-sync index with self-managed embeddings. Make sure the text column specified is in the index.

    - columns (Optional[List[str]]) – The list of column names to get when doing the search. Defaults to `[primary_key, text_column]`.

  Delta-sync index with Databricks-managed embeddings manages the ingestion, deletion, and embedding for you. Manually ingestion/deletion of the documents/texts is not supported for delta-sync index.

  If you want to use a delta-sync index with self-managed embeddings, you need to provide the embedding model and text column name to use for the embeddings.

  Example

      from langchain_community.vectorstores import DatabricksVectorSearch
      from databricks.vector_search.client import VectorSearchClient
      from langchain_community.embeddings.openai import OpenAIEmbeddings

      vs_client = VectorSearchClient()
      vs_index = vs_client.get_index(
        endpoint_name="vs_endpoint",
        index_name="ml.llm.index"
      )
      vectorstore = DatabricksVectorSearch(
        index=vs_index,
        embedding=OpenAIEmbeddings(),
        text_column="document_content"
      )

  If you want to manage the documents ingestion/deletion yourself, you can use a direct-access index.

  Example

      from langchain_community.vectorstores import DatabricksVectorSearch
      from databricks.vector_search.client import VectorSearchClient
      from langchain_community.embeddings.openai import OpenAIEmbeddings

      vs_client = VectorSearchClient()
      vs_index = vs_client.get_index(
        endpoint_name="vs_endpoint",
        index_name="ml.llm.index"
      )
      vectorstore = DatabricksVectorSearch(
        index=vs_index,
        embedding=OpenAIEmbeddings(),
        text_column="document_content"
      )
      vectorstore.add_texts(
        texts=["text1", "text2"]
      )

  For more information on Databricks Vector Search, see `Databricks Vector Search documentation: https://docs.databricks.com/en/generative-ai/vector-search.html.

  Attributes

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

  Methods

  `__init__`(index, *[, embedding, text_column, ...]) |
  `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, ids]) | Add texts to the index.
  `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 documents from the index.
  `from_documents`(documents, embedding, **kwargs) | Return VectorStore initialized from documents and embeddings.
  `from_texts`(texts, embedding[, metadatas]) | Return VectorStore initialized from texts and embeddings.
  `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, filters]) | Return docs most similar to query.
  `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 embedding vector, along with scores.
  `similarity_search_with_relevance_scores`(query) | Return docs and relevance scores in the range [0, 1].
  `similarity_search_with_score`(query[, k, filters]) | Return docs most similar to query, along with scores.

  __init__(index: VectorSearchIndex, *, embedding: Optional[Embeddings] = None, text_column: Optional[str] = None, columns: Optional[List[str]] = None)
    Parameters

      - index (VectorSearchIndex) –

      - embedding (Optional[Embeddings]) –

      - text_column (Optional[str]) –

      - columns (Optional[List[str]]) –

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

    Add texts to the index.

    Only support direct-access index.

    Parameters

      - texts (Iterable[str]) – List of texts to add.

      - metadatas (Optional[List[dict]]) – List of metadata for each text. Defaults to None.

      - ids (Optional[List[Any]]) – List of ids for each text. Defaults to None. If not provided, a random uuid will be generated for each text.

      - kwargs (Any) –

    Returns

      List of ids from adding the texts into the index.

    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[Any]] = None, **kwargs: Any) → Optional[bool]

    Delete documents from the index.

    Only support direct-access index.

    Parameters

      - ids (Optional[List[Any]]) – List of ids of documents to delete.

      - kwargs (Any) –

    Returns

      True if successful.

    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: 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

  max_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, filters: Optional[Any] = 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.

      - 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.

      - filters (Optional[Any]) – Filters to apply to the query. 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, filters: Optional[Any] = 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

      - 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.

      - filters (Optional[Any]) – Filters to apply to the query. Defaults to None.

      - 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, filters: Optional[Any] = None, **kwargs: Any) → List[Document]

    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.

      - filters (Optional[Any]) – Filters to apply to the query. Defaults to None.

      - kwargs (Any) –

    Returns

      List of Documents most similar to the embedding.

    Return type

      List[Document]

  similarity_search_by_vector(embedding: List[float], k: int = 4, filters: Optional[Any] = 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.

      - filters (Optional[Any]) – Filters to apply to the query. Defaults to None.

      - kwargs (Any) –

    Returns

      List of Documents most similar to the embedding.

    Return type

      List[Document]

  similarity_search_by_vector_with_score(embedding: List[float], k: int = 4, filters: Optional[Any] = None, **kwargs: Any) → List[Tuple[Document, float]]

    Return docs most similar to embedding vector, along with scores.

    Parameters

      - embedding (List[float]) – Embedding to look up documents similar to.

      - k (int) – Number of Documents to return. Defaults to 4.

      - filters (Optional[Any]) – Filters to apply to the query. Defaults to None.

      - kwargs (Any) –

    Returns

      List of Documents most similar to the embedding and score for each.

    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, filters: Optional[Any] = None, **kwargs: Any) → List[Tuple[Document, float]]

    Return docs most similar to query, along with scores.

    Parameters

      - query (str) – Text to look up documents similar to.

      - k (int) – Number of Documents to return. Defaults to 4.

      - filters (Optional[Any]) – Filters to apply to the query. Defaults to None.

      - kwargs (Any) –

    Returns

      List of Documents most similar to the embedding and score for each.

    Return type

      List[Tuple[Document, float]]

# Examples using DatabricksVectorSearch

- This example only specifies a relevant query

- databricks_vector_search.md