langchain_community.vectorstores.alibabacloud_opensearch.AlibabaCloudOpenSearch

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

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.

__init__(embedding: Embeddings, config: AlibabaCloudOpenSearchSettings, **kwargs: Any) → 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.

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]

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

should not be empty.

Parameters

metadatas – Metadata information.

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 – 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]

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

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

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.

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 and relevance scores in the range [0, 1], asynchronously.

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)

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

Run similarity search with distance asynchronously.

create_inverse_metadata(fields: Dict[str, Any]) → Dict[str, Any]

Create metadata from fields.

Parameters

fields – 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]

Assemble documents.

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

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

Returns

Results with scores.

Return type

query_result_list

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]

delete_documents_with_document_id(id_list: List[str]) → bool

Delete documents based on their IDs.

Parameters

id_list – List of document IDs.

Returns

Whether the deletion was successful or not.

delete_documents_with_texts(texts: List[str]) → bool

Delete documents based on their page content.

Parameters

texts – List of document page content.

Returns

Whether the deletion was successful or not.

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

Create alibaba cloud opensearch vector store instance.

Parameters

• documents – Documents to be inserted into the vector storage, should not be empty.

• embedding – Embedding function, Embedding function.

• config – 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.

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

Create alibaba cloud opensearch vector store instance.

Parameters

• texts – The text segments to be inserted into the vector storage, should not be empty.

• embedding – Embedding function, Embedding function.

• config – Alibaba OpenSearch instance configuration.

• metadatas – Metadata information.

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]

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.

similarity_search(query: str, k: int = 4, search_filter: Optional[Dict[str, Any]] = None, **kwargs: Any) → 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

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

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

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

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

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

Run similarity search with distance.

Examples using AlibabaCloudOpenSearch

• Alibaba Cloud OpenSearch