Creates an Instance of AzureCosmosDBVectorSearch from a Connection String
from_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
from_texts(texts, embedding[, metadatas, ...])
Return VectorStore initialized from texts and embeddings.
get_index_name()
Returns the index name
index_exists()
Verifies if the specified index name during instance
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])
Return docs most similar to query.
similarity_search_by_vector(embedding[, k])
Return docs most similar to embedding vector.
similarity_search_with_relevance_scores(query)
Return docs and relevance scores in the range [0, 1].
similarity_search_with_score(query[, k])
Run similarity search with distance.
__init__(collection: Collection[CosmosDBDocumentType], embedding: Embeddings, *, index_name: str = 'vectorSearchIndex', text_key: str = 'textContent', embedding_key: str = 'vectorContent')[source]¶
Constructor for AzureCosmosDBVectorSearch
Parameters
collection – MongoDB collection to add the texts to.
embedding – Text embedding model to use.
index_name – Name of the Atlas Search index.
text_key – MongoDB field that will contain the text
for each document.
embedding_key – MongoDB field that will contain the embedding
for each document.
async aadd_documents(documents: List[Document], **kwargs: Any) → List[str]¶