Toggle Menu
Prev Up Next

# `langchain_community.vectorstores.azuresearch`.AzureSearch

class langchain_community.vectorstores.azuresearch.AzureSearch(azure_search_endpoint: str, azure_search_key: str, index_name: str, embedding_function: Union[Callable, Embeddings], search_type: str = 'hybrid', semantic_configuration_name: Optional[str] = None, fields: Optional[List[SearchField]] = None, vector_search: Optional[VectorSearch] = None, semantic_configurations: Optional[Union[SemanticConfiguration, List[SemanticConfiguration]]] = None, scoring_profiles: Optional[List[ScoringProfile]] = None, default_scoring_profile: Optional[str] = None, cors_options: Optional[CorsOptions] = None, **kwargs: Any)

  Azure Cognitive Search vector store.

  Attributes

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

  Methods

  `__init__`(azure_search_endpoint, ...[, ...]) |
  `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]) | Add texts data to an existing 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 AzureSearchVectorStoreRetriever 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.
  `from_documents`(documents, embedding, **kwargs) | Return VectorStore initialized from documents and embeddings.
  `from_texts`(texts, embedding[, metadatas, ...]) | Return VectorStore initialized from texts and embeddings.
  `hybrid_search`(query[, k]) | Returns the most similar indexed documents to the query text.
  `hybrid_search_with_relevance_scores`(query[, k]) |
  `hybrid_search_with_score`(query[, k, filters]) | Return docs most similar to query with a hybrid query.
  `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.
  `semantic_hybrid_search`(query[, k]) | Returns the most similar indexed documents to the query text.
  `semantic_hybrid_search_with_score`(query[, k]) | Returns the most similar indexed documents to the query text.
  `semantic_hybrid_search_with_score_and_rerank`(query) | Return docs most similar to query with a hybrid query.
  `similarity_search`(query[, k]) | Return docs most similar to query.
  `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`(*args, **kwargs) | Run similarity search with distance.
  `vector_search`(query[, k]) | Returns the most similar indexed documents to the query text.
  `vector_search_with_score`(query[, k, filters]) | Return docs most similar to query.

  Parameters

    - azure_search_endpoint (str) –

    - azure_search_key (str) –

    - index_name (str) –

    - embedding_function (Union[Callable, Embeddings]) –

    - search_type (str) –

    - semantic_configuration_name (Optional[str]) –

    - fields (Optional[List[SearchField]]) –

    - vector_search (Optional[VectorSearch]) –

    - semantic_configurations (Optional[Union[SemanticConfiguration, List[SemanticConfiguration]]]) –

    - scoring_profiles (Optional[List[ScoringProfile]]) –

    - default_scoring_profile (Optional[str]) –

    - cors_options (Optional[CorsOptions]) –

    - kwargs (Any) –

  __init__(azure_search_endpoint: str, azure_search_key: str, index_name: str, embedding_function: Union[Callable, Embeddings], search_type: str = 'hybrid', semantic_configuration_name: Optional[str] = None, fields: Optional[List[SearchField]] = None, vector_search: Optional[VectorSearch] = None, semantic_configurations: Optional[Union[SemanticConfiguration, List[SemanticConfiguration]]] = None, scoring_profiles: Optional[List[ScoringProfile]] = None, default_scoring_profile: Optional[str] = None, cors_options: Optional[CorsOptions] = None, **kwargs: Any)
    Parameters

      - azure_search_endpoint (str) –

      - azure_search_key (str) –

      - index_name (str) –

      - embedding_function (Union[Callable, Embeddings]) –

      - search_type (str) –

      - semantic_configuration_name (Optional[str]) –

      - fields (Optional[List[SearchField]]) –

      - vector_search (Optional[VectorSearch]) –

      - semantic_configurations (Optional[Union[SemanticConfiguration, List[SemanticConfiguration]]]) –

      - scoring_profiles (Optional[List[ScoringProfile]]) –

      - default_scoring_profile (Optional[str]) –

      - cors_options (Optional[CorsOptions]) –

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

    Add texts data to an existing index.

    Parameters

      - texts (Iterable[str]) –

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

      - 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) → AzureSearchVectorStoreRetriever

    Return AzureSearchVectorStoreRetriever initialized from this VectorStore.

    Parameters

      - search_type (Optional[str]) –

        Defines the type of search that the Retriever should perform. Can be “similarity” (default), “hybrid”, or

        > ”semantic_hybrid”.

      - 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

      AzureSearchVectorStoreRetriever

  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) → bool

    Delete by vector ID.

    Parameters

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

      - kwargs (Any) –

    Returns

      True if deletion is successful, False otherwise.

    Return type

      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, azure_search_endpoint: str = '', azure_search_key: str = '', index_name: str = 'langchain-index', fields: Optional[List[SearchField]] = None, **kwargs: Any) → AzureSearch

    Return VectorStore initialized from texts and embeddings.

    Parameters

      - texts (List[str]) –

      - embedding (Embeddings) –

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

      - azure_search_endpoint (str) –

      - azure_search_key (str) –

      - index_name (str) –

      - fields (Optional[List[SearchField]]) –

      - kwargs (Any) –

    Return type

      AzureSearch

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

    Returns the most similar indexed documents to the query text.

    Parameters

      - query (str) – The query text for which to find similar documents.

      - k (int) – The number of documents to return. Default is 4.

      - kwargs (Any) –

    Returns

      A list of documents that are most similar to the query text.

    Return type

      List[Document]

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

      - query (str) –

      - k (int) –

      - kwargs (Any) –

    Return type

      List[Tuple[Document, float]]

  hybrid_search_with_score(query: str, k: int = 4, filters: Optional[str] = None) → List[Tuple[Document, float]]

    Return docs most similar to query with a hybrid query.

    Parameters

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

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

      - filters (Optional[str]) –

    Returns

      List of Documents most similar to the query and score for each

    Return type

      List[Tuple[Document, float]]

  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]

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

    Returns the most similar indexed documents to the query text.

    Parameters

      - query (str) – The query text for which to find similar documents.

      - k (int) – The number of documents to return. Default is 4.

      - kwargs (Any) –

    Returns

      A list of documents that are most similar to the query text.

    Return type

      List[Document]

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

    Returns the most similar indexed documents to the query text.

    Parameters

      - query (str) – The query text for which to find similar documents.

      - k (int) – The number of documents to return. Default is 4.

      - kwargs (Any) –

    Returns

      A list of documents that are most similar to the query text.

    Return type

      List[Document]

  semantic_hybrid_search_with_score_and_rerank(query: str, k: int = 4, filters: Optional[str] = None) → List[Tuple[Document, float, float]]

    Return docs most similar to query with a hybrid query.

    Parameters

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

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

      - filters (Optional[str]) –

    Returns

      List of Documents most similar to the query and score for each

    Return type

      List[Tuple[Document, float, float]]

  similarity_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]

  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(*args: Any, **kwargs: Any) → List[Tuple[Document, float]]

    Run similarity search with distance.

    Parameters

      - args (Any) –

      - kwargs (Any) –

    Return type

      List[Tuple[Document, float]]

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

    Returns the most similar indexed documents to the query text.

    Parameters

      - query (str) – The query text for which to find similar documents.

      - k (int) – The number of documents to return. Default is 4.

      - kwargs (Any) –

    Returns

      A list of documents that are most similar to the query text.

    Return type

      List[Document]

  vector_search_with_score(query: str, k: int = 4, filters: Optional[str] = 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.

      - filters (Optional[str]) –

    Returns

      List of Documents most similar to the query and score for each

    Return type

      List[Tuple[Document, float]]

# Examples using AzureSearch

- Option 1: use an OpenAI account

- azure_ai_search.md