AzureDocumentDBVectorSearch

Returns the embeddings.

Returns the index name.

Creates an Instance of AzureDocumentDBVectorSearch from a Connection String.

Verifies if the specified index name during instance construction exists on the collection.

Deletes the index specified during instance construction if it exists.

Creates an index using the index name specified at instance construction.

Setting the numLists parameter correctly is important for achieving good accuracy and performance. Since the vector store uses IVF as the indexing strategy, you should create the index only after you have loaded a large enough sample documents to ensure that the centroids for the respective buckets are faily distributed.

We recommend that numLists is set to documentCount/1000 for up to 1 million documents and to sqrt(documentCount) for more than 1 million documents. As the number of items in your database grows, you should tune numLists to be larger in order to achieve good latency performance for vector search.

If you're experimenting with a new scenario or creating a
small demo, you can start with numLists
set to 1 to perform a brute-force search across all vectors.
This should provide you with the most
accurate results from the vector search, however be aware that
the search speed and latency will be slow.
After your initial setup, you should go ahead and tune
the numLists parameter using the above guidance.

Creates a filter index.

Used to Load Documents into the collection.

Creates Azure DocumentDB vector store with MongoDB compatibility using the texts provided.

Removes the documents with the list of documentIds provided from the collection.

Removes a Specific Document by Id.

Returns a list of similar documents with their scores.

Returns a list of similar documents.

Retrieves the docs with similarity scores.

Retrieves the similar docs.

Returns the collection.