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[, ...])	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, score_type: Literal['score', 'reranker_score'] = 'score', **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.

• score_type (Literal['score', 'reranker_score']) – Must either be “score” or “reranker_score”. Defaulted to “score”.

• kwargs (Any) –

Returns

A list of documents and their

corresponding scores.

Return type

List[Tuple[Document, float]]

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