langchain_community.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearch¶

class langchain_community.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearch(embedding: Embeddings, config: AlibabaCloudOpenSearchSettings, **kwargs: Any)[source]¶

Alibaba Cloud OpenSearch vector store.

Attributes

embeddings

Access the query embedding object if available.

Methods

__init__(embedding, config, **kwargs)

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])

Insert documents into the instance.. :param texts: The text segments to be inserted into the vector storage, should not be empty. :param metadatas: Metadata information.

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.

create_inverse_metadata(fields)

Create metadata from fields.

create_results(json_result)

Assemble documents.

create_results_with_score(json_result)

Parsing the returned results with scores.

delete([ids])

Delete by vector ID or other criteria.

delete_documents_with_document_id(id_list)

Delete documents based on their IDs.

delete_documents_with_texts(texts)

Delete documents based on their page content.

from_documents(documents, embedding[, config])

Create alibaba cloud opensearch vector store instance.

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

Create alibaba cloud opensearch vector store instance.

inner_embedding_query(embedding[, ...])

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, search_filter])

Perform similarity retrieval based on text.

similarity_search_by_vector(embedding[, k, ...])

Perform retrieval directly using vectors.

similarity_search_with_relevance_scores(query)

Perform similarity retrieval based on text with scores.

similarity_search_with_score(*args, **kwargs)

Run similarity search with distance.

Parameters
__init__(embedding: Embeddings, config: AlibabaCloudOpenSearchSettings, **kwargs: Any) None[source]¶
Parameters
Return type

None

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][source]¶

Insert documents into the instance.. :param texts: The text segments to be inserted into the vector storage,

should not be empty.

Parameters
  • metadatas (Optional[List[dict]]) – Metadata information.

  • texts (Iterable[str]) –

  • kwargs (Any) –

Returns

List of document IDs.

Return type

id_list

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

Return docs selected using the maximal marginal relevance.

Parameters
  • query (str) –

  • k (int) –

  • fetch_k (int) –

  • lambda_mult (float) –

  • kwargs (Any) –

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]

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]]

create_inverse_metadata(fields: Dict[str, Any]) Dict[str, Any][source]¶

Create metadata from fields.

Parameters

fields (Dict[str, Any]) – The fields of the document. The fields must be a dict.

Returns

The metadata of the document. The metadata must be a dict.

Return type

metadata

create_results(json_result: Dict[str, Any]) List[Document][source]¶

Assemble documents.

Parameters

json_result (Dict[str, Any]) –

Return type

List[Document]

create_results_with_score(json_result: Dict[str, Any]) List[Tuple[Document, float]][source]¶

Parsing the returned results with scores. :param json_result: Results from OpenSearch query.

Returns

Results with scores.

Return type

query_result_list

Parameters

json_result (Dict[str, Any]) –

delete(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]

delete_documents_with_document_id(id_list: List[str]) bool[source]¶

Delete documents based on their IDs.

Parameters

id_list (List[str]) – List of document IDs.

Returns

Whether the deletion was successful or not.

Return type

bool

delete_documents_with_texts(texts: List[str]) bool[source]¶

Delete documents based on their page content.

Parameters

texts (List[str]) – List of document page content.

Returns

Whether the deletion was successful or not.

Return type

bool

classmethod from_documents(documents: List[Document], embedding: Embeddings, config: Optional[AlibabaCloudOpenSearchSettings] = None, **kwargs: Any) AlibabaCloudOpenSearch[source]¶

Create alibaba cloud opensearch vector store instance.

Parameters
  • documents (List[Document]) – Documents to be inserted into the vector storage, should not be empty.

  • embedding (Embeddings) – Embedding function, Embedding function.

  • config (Optional[AlibabaCloudOpenSearchSettings]) – Alibaba OpenSearch instance configuration.

  • ids – Specify the ID for the inserted document. If left empty, the ID will be automatically generated based on the text content.

  • kwargs (Any) –

Returns

Alibaba cloud opensearch vector store instance.

Return type

AlibabaCloudOpenSearch

classmethod from_texts(texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, config: Optional[AlibabaCloudOpenSearchSettings] = None, **kwargs: Any) AlibabaCloudOpenSearch[source]¶

Create alibaba cloud opensearch vector store instance.

Parameters
  • texts (List[str]) – The text segments to be inserted into the vector storage, should not be empty.

  • embedding (Embeddings) – Embedding function, Embedding function.

  • config (Optional[AlibabaCloudOpenSearchSettings]) – Alibaba OpenSearch instance configuration.

  • metadatas (Optional[List[dict]]) – Metadata information.

  • kwargs (Any) –

Returns

Alibaba cloud opensearch vector store instance.

Return type

AlibabaCloudOpenSearch

inner_embedding_query(embedding: List[float], search_filter: Optional[Dict[str, Any]] = None, k: int = 4) Dict[str, Any][source]¶
Parameters
  • embedding (List[float]) –

  • search_filter (Optional[Dict[str, Any]]) –

  • k (int) –

Return type

Dict[str, Any]

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]

Perform similarity retrieval based on text. :param query: Vectorize text for retrieval.,should not be empty. :param k: top n. :param search_filter: Additional filtering conditions.

Returns

List of documents.

Return type

document_list

Parameters
  • query (str) –

  • k (int) –

  • search_filter (Optional[Dict[str, Any]]) –

  • kwargs (Any) –

similarity_search_by_vector(embedding: List[float], k: int = 4, search_filter: Optional[dict] = None, **kwargs: Any) List[Document][source]¶

Perform retrieval directly using vectors. :param embedding: vectors. :param k: top n. :param search_filter: Additional filtering conditions.

Returns

List of documents.

Return type

document_list

Parameters
  • embedding (List[float]) –

  • k (int) –

  • search_filter (Optional[dict]) –

  • kwargs (Any) –

similarity_search_with_relevance_scores(query: str, k: int = 4, search_filter: Optional[dict] = None, **kwargs: Any) List[Tuple[Document, float]][source]¶

Perform similarity retrieval based on text with scores. :param query: Vectorize text for retrieval.,should not be empty. :param k: top n. :param search_filter: Additional filtering conditions.

Returns

List of documents.

Return type

document_list

Parameters
  • query (str) –

  • k (int) –

  • search_filter (Optional[dict]) –

  • kwargs (Any) –

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]]

Examples using AlibabaCloudOpenSearch¶