langchain.vectorstores.azuresearch.AzureSearch

class langchain.vectorstores.azuresearch.AzureSearch(azure_search_endpoint: str, azure_search_key: str, index_name: str, embedding_function: Callable, search_type: str = 'hybrid', semantic_configuration_name: Optional[str] = None, semantic_query_language: str = 'en-us', fields: Optional[List[SearchField]] = None, vector_search: Optional[VectorSearch] = None, semantic_settings: Optional[SemanticSettings] = None, scoring_profiles: Optional[List[ScoringProfile]] = None, default_scoring_profile: Optional[str] = None, **kwargs: Any)

Bases: VectorStore

Azure Cognitive Search vector store.

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.
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)
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 most similar to query.
delete([ids])	Delete by vector ID or other criteria.
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_score(query[, k, filters])	Return docs most similar to query with an 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[, ...])	Return docs most similar to query with an 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.

Attributes

embeddings

Access the query embedding object if available.

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.

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.

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.

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.

async classmethod afrom_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST

Return VectorStore initialized from documents and embeddings.

async classmethod afrom_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, **kwargs: Any) → VST

Return VectorStore initialized from texts and embeddings.

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.

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.

as_retriever(**kwargs: Any) → VectorStoreRetriever

async asearch(query: str, search_type: str, **kwargs: Any) → List[Document]

Return docs most similar to query using specified search type.

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

Return docs most similar to query.

async asimilarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]

Return docs most similar to embedding vector.

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

Return docs most similar to query.

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

Delete by vector ID or other criteria.

Parameters

• ids – List of ids to delete.

• **kwargs – 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_documents(documents: List[Document], embedding: Embeddings, **kwargs: Any) → VST

Return VectorStore initialized from documents and embeddings.

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', **kwargs: Any) → AzureSearch

Return VectorStore initialized from texts and embeddings.

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.

Returns

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

Return type

List[Document]

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

Return docs most similar to query with an hybrid query.

Parameters

• query – Text to look up documents similar to.

• k – Number of Documents to return. Defaults to 4.

Returns

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

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 – Text to look up documents similar to.

• k – Number of Documents to return. Defaults to 4.

• fetch_k – Number of Documents to fetch to pass to MMR algorithm.

• lambda_mult – 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.

Returns

List of Documents selected by maximal marginal relevance.

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 – Embedding to look up documents similar to.

• k – Number of Documents to return. Defaults to 4.

• fetch_k – Number of Documents to fetch to pass to MMR algorithm.

• lambda_mult – 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.

Returns

List of Documents selected by maximal marginal relevance.

search(query: str, search_type: str, **kwargs: Any) → List[Document]

Return docs most similar to query using specified search type.

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.

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, filters: Optional[str] = None) → List[Tuple[Document, float]]

Return docs most similar to query with an hybrid query.

Parameters

• query – Text to look up documents similar to.

• k – Number of Documents to return. Defaults to 4.

Returns

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

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

Return docs most similar to query.

similarity_search_by_vector(embedding: List[float], k: int = 4, **kwargs: Any) → List[Document]

Return docs most similar to embedding vector.

Parameters

• embedding – Embedding to look up documents similar to.

• k – Number of Documents to return. Defaults to 4.

Returns

List of Documents most similar to the query vector.

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 – input text

• k – Number of Documents to return. Defaults to 4.

• **kwargs –

  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)

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

Run similarity search with distance.

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.

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 – Text to look up documents similar to.

• k – Number of Documents to return. Defaults to 4.

Returns

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

property embeddings: Optional[langchain.embeddings.base.Embeddings]

Access the query embedding object if available.

Examples using AzureSearch

• Azure Cognitive Search