langchain.vectorstores.docarray.hnsw.DocArrayHnswSearch¶
class langchain.vectorstores.docarray.hnsw.DocArrayHnswSearch(doc_index: BaseDocIndex, embedding: Embeddings)[source]¶
HnswLib storage using DocArray package.
To use it, you should have the docarray package with version >=0.32.0 installed.
You can install it with pip install “langchain[docarray]”.
Initialize a vector store from DocArray’s DocIndex.
Attributes
doc_cls
embeddings
Access the query embedding object if available.
Methods
__init__(doc_index, embedding)
Initialize a vector store from DocArray's DocIndex.
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])
Embed texts and add to the vector store.
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])