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)	Async run more documents through the embeddings and add to the vectorstore.
`aadd_texts`(texts[, metadatas])	Async run more texts through the embeddings and add to the vectorstore.
`add_documents`(documents, **kwargs)	Add or update documents in 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])	Async delete by vector ID or other criteria.
`afrom_documents`(documents, embedding, **kwargs)	Async return VectorStore initialized from documents and embeddings.
`afrom_texts`(texts, embedding[, metadatas])	Async return VectorStore initialized from texts and embeddings.
`aget_by_ids`(ids, /)	Async get documents by their IDs.
`amax_marginal_relevance_search`(query[, k, ...])	Async return docs selected using the maximal marginal relevance.
`amax_marginal_relevance_search_by_vector`(...)	Async return docs selected using the maximal marginal relevance.
`as_retriever`(**kwargs)	Return VectorStoreRetriever initialized from this VectorStore.
`asearch`(query, search_type, **kwargs)	Async return docs most similar to query using a specified search type.
`asimilarity_search`(query[, k])	Async return docs most similar to query.
`asimilarity_search_by_vector`(embedding[, k])	Async return docs most similar to embedding vector.
`asimilarity_search_with_relevance_scores`(query)	Async return docs and relevance scores in the range [0, 1].
`asimilarity_search_with_score`(args, *kwargs)	Async run similarity search with distance.
`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.
`get_by_ids`(ids, /)	Get documents by their IDs.
`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 a 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:

embedding (Embeddings) –
config (AlibabaCloudOpenSearchSettings) –
kwargs (Any) –

__init__(embedding: Embeddings, config: AlibabaCloudOpenSearchSettings, **kwargs: Any) → None[source]#

Parameters:

embedding (Embeddings) –
config (AlibabaCloudOpenSearchSettings) –
kwargs (Any) –

Return type:

None

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

Async run more documents through the embeddings and add to the vectorstore.

Parameters:

documents (List[Document]) – Documents to add to the vectorstore.
kwargs (Any) – Additional keyword arguments.

Returns:

List of IDs of the added texts.

Raises:

ValueError – If the number of IDs does not match the number of documents.

Return type:

List[str]

async aadd_texts(texts: Iterable[str], metadatas: List[dict] | None = None, **kwargs: Any) → List[str]#

Async run more texts through the embeddings and add to the vectorstore.

Parameters:

texts (Iterable[str]) – Iterable of strings to add to the vectorstore.
metadatas (List[dict] | None) – Optional list of metadatas associated with the texts. Default is None.
**kwargs (Any) – vectorstore specific parameters.

Returns:

List of ids from adding the texts into the vectorstore.

Raises:

ValueError – If the number of metadatas does not match the number of texts.
ValueError – If the number of ids does not match the number of texts.

Return type:

List[str]

add_documents(documents: List[Document], **kwargs: Any) → List[str]#

Add or update documents in the vectorstore.

Parameters:

documents (List[Document]) – Documents to add to the vectorstore.
kwargs (Any) – Additional keyword arguments. if kwargs contains ids and documents contain ids, the ids in the kwargs will receive precedence.

Returns:

List of IDs of the added texts.

Raises:

ValueError – If the number of ids does not match the number of documents.

Return type:

List[str]

add_texts(texts: Iterable[str], metadatas: List[dict] | None = 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 (List[dict] | None) – Metadata information.
texts (Iterable[str]) –
kwargs (Any) –

Returns:

List of document IDs.

Return type:

id_list

async adelete(ids: List[str] | None = None, **kwargs: Any) → bool | None#

Async delete by vector ID or other criteria.

Parameters:

ids (List[str] | None) – List of ids to delete. If None, delete all. Default is None.
**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#

Async 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) – Additional keyword arguments.

Returns:

VectorStore initialized from documents and embeddings.

Return type:

VectorStore

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

Async return VectorStore initialized from texts and embeddings.

Parameters:

texts (List[str]) – Texts to add to the vectorstore.
embedding (Embeddings) – Embedding function to use.
metadatas (List[dict] | None) – Optional list of metadatas associated with the texts. Default is None.
kwargs (Any) – Additional keyword arguments.

Returns:

VectorStore initialized from texts and embeddings.

Return type:

VectorStore

async aget_by_ids(ids: Sequence[str], /) → List[Document]#

Async get documents by their IDs.

The returned documents are expected to have the ID field set to the ID of the document in the vector store.

Fewer documents may be returned than requested if some IDs are not found or if there are duplicated IDs.

Users should not assume that the order of the returned documents matches the order of the input IDs. Instead, users should rely on the ID field of the returned documents.

This method should NOT raise exceptions if no documents are found for some IDs.

Parameters:: ids (Sequence[str]) – List of ids to retrieve.
Returns:: List of Documents.
Return type:: List[Document]

New in version 0.2.11.

async amax_marginal_relevance_search(query: str, k: int = 4, fetch_k: int = 20, lambda_mult: float = 0.5, **kwargs: Any) → List[Document]#

Async 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. Default is 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.
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]#

Async 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. Default is 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.
**kwargs (Any) – Arguments to pass to the search method.

Returns:

List of Documents selected by maximal marginal relevance.

Return type:

List[Document]

as_retriever(**kwargs: Any) → VectorStoreRetriever#

Return VectorStoreRetriever initialized from this VectorStore.

Parameters:

**kwargs (Any) –

Keyword arguments to pass to the search function. Can include: 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]#

Async return docs most similar to query using a 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.

Returns:

List of Documents most similar to the query.

Raises:

ValueError – If search_type is not one of “similarity”, “mmr”, or “similarity_score_threshold”.

Return type:

List[Document]

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

Async return docs most similar to query.

Parameters:

query (str) – Input text.
k (int) – Number of Documents to return. Defaults to 4.
**kwargs (Any) – Arguments to pass to the search method.

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]#

Async 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) – Arguments to pass to the search method.

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, **kwargs: Any) → List[Tuple[Document, float]]#

Async 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]]

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

Async run similarity search with distance.

Parameters:

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

Returns:

List of Tuples of (doc, similarity_score).

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: List[str] | None = None, **kwargs: Any) → bool | None#

Delete by vector ID or other criteria.

Parameters:

ids (List[str] | None) – List of ids to delete. If None, delete all. Default is None.
**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: AlibabaCloudOpenSearchSettings | None = 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 (AlibabaCloudOpenSearchSettings | None) – 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: List[dict] | None = None, config: AlibabaCloudOpenSearchSettings | None = 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 (AlibabaCloudOpenSearchSettings | None) – Alibaba OpenSearch instance configuration.
metadatas (List[dict] | None) – Metadata information.
kwargs (Any) –

Returns:

Alibaba cloud opensearch vector store instance.

Return type:

AlibabaCloudOpenSearch

get_by_ids(ids: Sequence[str], /) → List[Document]#

Get documents by their IDs.

The returned documents are expected to have the ID field set to the ID of the document in the vector store.

Fewer documents may be returned than requested if some IDs are not found or if there are duplicated IDs.

Users should not assume that the order of the returned documents matches the order of the input IDs. Instead, users should rely on the ID field of the returned documents.

This method should NOT raise exceptions if no documents are found for some IDs.

Parameters:: ids (Sequence[str]) – List of ids to retrieve.
Returns:: List of Documents.
Return type:: List[Document]

New in version 0.2.11.

inner_embedding_query(embedding: List[float], search_filter: Dict[str, Any] | None = None, k: int = 4) → Dict[str, Any][source]#

Parameters:

embedding (List[float]) –
search_filter (Dict[str, Any] | None) –
k (int) –

Return type:

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

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

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 a 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.

Returns:

List of Documents most similar to the query.

Raises:

ValueError – If search_type is not one of “similarity”, “mmr”, or “similarity_score_threshold”.

Return type:

List[Document]

similarity_search(query: str, k: int = 4, search_filter: Dict[str, Any] | None = None, **kwargs: Any) → List[Document][source]#

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 (Dict[str, Any] | None) –
kwargs (Any) –

similarity_search_by_vector(embedding: List[float], k: int = 4, search_filter: dict | None = 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 (dict | None) –
kwargs (Any) –

similarity_search_with_relevance_scores(query: str, k: int = 4, search_filter: dict | None = 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 (dict | None) –
kwargs (Any) –

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

Run similarity search with distance.

Parameters:

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

Returns:

List of Tuples of (doc, similarity_score).

Return type:

List[Tuple[Document, float]]

Examples using AlibabaCloudOpenSearch