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, *, vector_search_dimensions: Optional[int] = None, **kwargs: Any)[source]

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_embeddings(text_embeddings[, ...])

Add embeddings to an existing index.

aadd_texts(texts[, metadatas, keys])

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_embeddings(text_embeddings[, metadatas, ...])

Add embeddings to an existing index.

add_texts(texts[, metadatas, keys])

Add texts data to an existing index.

adelete([ids])

Delete by vector ID.

afrom_documents(documents, embedding, **kwargs)

Return VectorStore initialized from documents and embeddings.

afrom_embeddings(text_embeddings, embedding)

afrom_texts(texts, embedding[, metadatas, ...])

Return VectorStore initialized from texts and embeddings.

ahybrid_max_marginal_relevance_search_with_score(query)

Return docs most similar to query with a hybrid query

ahybrid_search(query[, k])

Returns the most similar indexed documents to the query text.

ahybrid_search_with_relevance_scores(query)

ahybrid_search_with_score(query[, k, filters])

Return docs most similar to query with a hybrid query.

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.

amax_marginal_relevance_search_with_score(query)

Perform a search and return results that are reordered by MMR.

as_retriever(**kwargs)

Return AzureSearchVectorStoreRetriever initialized from this VectorStore.

asearch(query, search_type, **kwargs)

Return docs most similar to query using specified search type.

asemantic_hybrid_search(query[, k])

Returns the most similar indexed documents to the query text.

asemantic_hybrid_search_with_score(query[, ...])

Returns the most similar indexed documents to the query text.

asemantic_hybrid_search_with_score_and_rerank(query)

Return docs most similar to query with a hybrid query.

asimilarity_search(query[, k, search_type])

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].

asimilarity_search_with_score(query, *[, k])

Run similarity search with distance.

avector_search(query[, k, filters])

Returns the most similar indexed documents to the query text.

avector_search_with_score(query[, k, filters])

Return docs most similar to query.

delete([ids])

Delete by vector ID.

from_documents(documents, embedding, **kwargs)

Return VectorStore initialized from documents and embeddings.

from_embeddings(text_embeddings, embedding)

from_texts(texts, embedding[, metadatas, ...])

Return VectorStore initialized from texts and embeddings.

hybrid_max_marginal_relevance_search_with_score(query)

Return docs most similar to query with a hybrid query

hybrid_search(query[, k])

Returns the most similar indexed documents to the query text.

hybrid_search_with_relevance_scores(query[, ...])

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.

max_marginal_relevance_search_with_score(query)

Perform a search and return results that are reordered by MMR.

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, search_type])

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(query, *[, k])

Run similarity search with distance.

vector_search(query[, k, filters])

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]) –

  • vector_search_dimensions (Optional[int]) –

  • 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, *, vector_search_dimensions: Optional[int] = None, **kwargs: Any)[source]
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]) –

  • vector_search_dimensions (Optional[int]) –

  • kwargs (Any) –

async aadd_documents(documents: List[Document], **kwargs: Any) List[str]

Run more documents through the embeddings and add to the vectorstore.

Parameters
  • documents (List[Document]) – Documents to add to the vectorstore.

  • kwargs (Any) –

Returns

List of IDs of the added texts.

Return type

List[str]

async aadd_embeddings(text_embeddings: Iterable[Tuple[str, List[float]]], metadatas: Optional[List[dict]] = None, *, keys: Optional[List[str]] = None) List[str][source]

Add embeddings to an existing index.

Parameters
  • text_embeddings (Iterable[Tuple[str, List[float]]]) –

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

  • keys (Optional[List[str]]) –

Return type

List[str]

async aadd_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, *, keys: Optional[List[str]] = None, **kwargs: Any) List[str][source]

Run more texts through the embeddings and add to the vectorstore.

Parameters
  • texts (Iterable[str]) – Iterable of strings to add to the vectorstore.

  • metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.

  • **kwargs (Any) – vectorstore specific parameters.

  • keys (Optional[List[str]]) –

  • **kwargs

Returns

List of ids from adding the texts into the vectorstore.

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
  • documents (List[Document]) – Documents to add to the vectorstore.

  • kwargs (Any) –

Returns

List of IDs of the added texts.

Return type

List[str]

add_embeddings(text_embeddings: Iterable[Tuple[str, List[float]]], metadatas: Optional[List[dict]] = None, *, keys: Optional[List[str]] = None) List[str][source]

Add embeddings to an existing index.

Parameters
  • text_embeddings (Iterable[Tuple[str, List[float]]]) –

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

  • keys (Optional[List[str]]) –

Return type

List[str]

add_texts(texts: Iterable[str], metadatas: Optional[List[dict]] = None, *, keys: Optional[List[str]] = None, **kwargs: Any) List[str][source]

Add texts data to an existing index.

Parameters
  • texts (Iterable[str]) –

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

  • keys (Optional[List[str]]) –

  • kwargs (Any) –

Return type

List[str]

async adelete(ids: Optional[List[str]] = None, **kwargs: Any) bool[source]

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

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

Return VectorStore initialized from documents and embeddings.

Parameters
  • documents (List[Document]) – List of Documents to add to the vectorstore.

  • embedding (Embeddings) – Embedding function to use.

  • kwargs (Any) –

Return type

VST

async classmethod afrom_embeddings(text_embeddings: Iterable[Tuple[str, List[float]]], 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[source]
Parameters
  • text_embeddings (Iterable[Tuple[str, List[float]]]) –

  • 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

async classmethod afrom_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[source]

Return VectorStore initialized from texts and embeddings.

Parameters
  • texts (List[str]) – Texts to add to the vectorstore.

  • metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.

  • embedding (Embeddings) – Embedding function to use.

  • azure_search_endpoint (str) –

  • azure_search_key (str) –

  • index_name (str) –

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

  • kwargs (Any) –

Return type

AzureSearch

async ahybrid_max_marginal_relevance_search_with_score(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, *, filters: Optional[str] = None, **kwargs: Any) List[Tuple[Document, float]][source]
Return docs most similar to query with a hybrid query

and reorder results by MMR.

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

  • k (int, optional) – Number of Documents to return. Defaults to 4.

  • fetch_k (int, optional) – Total results to select k from. Defaults to 20.

  • 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 (str, optional) – Filtering expression. Defaults to None.

  • kwargs (Any) –

Returns

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

Return type

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]

async ahybrid_search_with_relevance_scores(query: str, k: int = 4, *, score_threshold: Optional[float] = None, **kwargs: Any) List[Tuple[Document, float]][source]
Parameters
  • query (str) –

  • k (int) –

  • score_threshold (Optional[float]) –

  • kwargs (Any) –

Return type

List[Tuple[Document, float]]

async ahybrid_search_with_score(query: str, k: int = 4, filters: Optional[str] = None, **kwargs: Any) List[Tuple[Document, float]][source]

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]) –

  • kwargs (Any) –

Returns

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

Return type

List[Tuple[Document, float]]

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.

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]

async amax_marginal_relevance_search_with_score(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, *, filters: Optional[str] = None, **kwargs: Any) List[Tuple[Document, float]][source]

Perform a search and return results that are reordered by MMR.

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

  • k (int, optional) – How many results to give. Defaults to 4.

  • fetch_k (int, optional) – Total results to select k from. Defaults to 20.

  • 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 (str, optional) – Filtering expression. Defaults to None.

  • kwargs (Any) –

Returns

List of Documents most similar

to the query and score for each

Return type

List[Tuple[Document, float]]

as_retriever(**kwargs: Any) AzureSearchVectorStoreRetriever[source]

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:

    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) – Input text.

  • search_type (str) – Type of search to perform. Can be “similarity”, “mmr”, or “similarity_score_threshold”.

  • **kwargs (Any) – Arguments to pass to the search method.

Return type

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.

  • filters – Filtering expression.

  • kwargs (Any) –

Returns

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

Return type

List[Document]

async asemantic_hybrid_search_with_score(query: str, k: int = 4, score_type: Literal['score', 'reranker_score'] = 'score', *, score_threshold: Optional[float] = None, **kwargs: Any) List[Tuple[Document, float]][source]

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”.

  • filters – Filtering expression.

  • score_threshold (Optional[float]) –

  • kwargs (Any) –

Returns

A list of documents and their

corresponding scores.

Return type

List[Tuple[Document, float]]

async asemantic_hybrid_search_with_score_and_rerank(query: str, k: int = 4, *, filters: Optional[str] = None, **kwargs: Any) List[Tuple[Document, float, float]][source]

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]) – Filtering expression.

  • kwargs (Any) –

Returns

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

Return type

List[Tuple[Document, float, float]]

Return docs most similar to query.

Parameters
  • query (str) – Input text.

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

  • search_type (Optional[str]) –

  • kwargs (Any) –

Returns

List of Documents most similar to the query.

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]) – 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]

async asimilarity_search_with_relevance_scores(query: str, k: int = 4, *, score_threshold: Optional[float] = None, **kwargs: Any) List[Tuple[Document, float]][source]

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

  • score_threshold (Optional[float]) –

  • **kwargs

Returns

List of Tuples of (doc, similarity_score)

Return type

List[Tuple[Document, float]]

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

Run similarity search with distance.

Parameters
  • query (str) –

  • k (int) –

  • kwargs (Any) –

Return type

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.

  • filters (Optional[str]) –

  • kwargs (Any) –

Returns

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

Return type

List[Document]

async avector_search_with_score(query: str, k: int = 4, filters: Optional[str] = None, **kwargs: Any) List[Tuple[Document, float]][source]

Return docs most similar to query.

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

  • k (int, optional) – Number of Documents to return. Defaults to 4.

  • filters (str, optional) – Filtering expression. Defaults to None.

  • kwargs (Any) –

Returns

List of Documents most similar

to the query and score for each

Return type

List[Tuple[Document, float]]

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

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]) – List of Documents to add to the vectorstore.

  • embedding (Embeddings) – Embedding function to use.

  • kwargs (Any) –

Return type

VST

classmethod from_embeddings(text_embeddings: Iterable[Tuple[str, List[float]]], 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[source]
Parameters
  • text_embeddings (Iterable[Tuple[str, List[float]]]) –

  • 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

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[source]

Return VectorStore initialized from texts and embeddings.

Parameters
  • texts (List[str]) – Texts to add to the vectorstore.

  • metadatas (Optional[List[dict]]) – Optional list of metadatas associated with the texts.

  • embedding (Embeddings) – Embedding function to use.

  • azure_search_endpoint (str) –

  • azure_search_key (str) –

  • index_name (str) –

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

  • kwargs (Any) –

Return type

AzureSearch

hybrid_max_marginal_relevance_search_with_score(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, *, filters: Optional[str] = None, **kwargs: Any) List[Tuple[Document, float]][source]
Return docs most similar to query with a hybrid query

and reorder results by MMR.

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

  • k (int, optional) – Number of Documents to return. Defaults to 4.

  • fetch_k (int, optional) – Total results to select k from. Defaults to 20.

  • 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 (str, optional) – Filtering expression. Defaults to None.

  • kwargs (Any) –

Returns

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

Return type

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]

hybrid_search_with_relevance_scores(query: str, k: int = 4, *, score_threshold: Optional[float] = None, **kwargs: Any) List[Tuple[Document, float]][source]
Parameters
  • query (str) –

  • k (int) –

  • score_threshold (Optional[float]) –

  • kwargs (Any) –

Return type

List[Tuple[Document, float]]

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

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]) –

  • kwargs (Any) –

Returns

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

Return type

List[Tuple[Document, float]]

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]

max_marginal_relevance_search_with_score(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, *, filters: Optional[str] = None, **kwargs: Any) List[Tuple[Document, float]][source]

Perform a search and return results that are reordered by MMR.

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

  • k (int, optional) – How many results to give. Defaults to 4.

  • fetch_k (int, optional) – Total results to select k from. Defaults to 20.

  • 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 (str, optional) – Filtering expression. Defaults to None.

  • kwargs (Any) –

Returns

List of Documents most similar

to the query and score for each

Return type

List[Tuple[Document, float]]

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

Return docs most similar to query using specified search type.

Parameters
  • query (str) – Input text

  • search_type (str) – Type of search to perform. Can be “similarity”, “mmr”, or “similarity_score_threshold”.

  • **kwargs (Any) – Arguments to pass to the search method.

Return type

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.

  • filters – Filtering expression.

  • 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', *, score_threshold: Optional[float] = None, **kwargs: Any) List[Tuple[Document, float]][source]

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”.

  • filters – Filtering expression.

  • score_threshold (Optional[float]) –

  • 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, **kwargs: Any) List[Tuple[Document, float, float]][source]

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]) – Filtering expression.

  • kwargs (Any) –

Returns

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

Return type

List[Tuple[Document, float, float]]

Return docs most similar to query.

Parameters
  • query (str) – Input text.

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

  • search_type (Optional[str]) –

  • kwargs (Any) –

Returns

List of Documents most similar to the query.

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, *, score_threshold: Optional[float] = None, **kwargs: Any) List[Tuple[Document, float]][source]

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

  • score_threshold (Optional[float]) –

  • **kwargs

Returns

List of Tuples of (doc, similarity_score)

Return type

List[Tuple[Document, float]]

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

Run similarity search with distance.

Parameters
  • query (str) –

  • k (int) –

  • kwargs (Any) –

Return type

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.

  • filters (Optional[str]) –

  • 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, **kwargs: Any) List[Tuple[Document, float]][source]

Return docs most similar to query.

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

  • k (int, optional) – Number of Documents to return. Defaults to 4.

  • filters (str, optional) – Filtering expression. Defaults to None.

  • kwargs (Any) –

Returns

List of Documents most similar

to the query and score for each

Return type

List[Tuple[Document, float]]

Examples using AzureSearch