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