CassandraGraphVectorStore

Add nodes to the graph store.

Add nodes to the graph store.

Retrieve documents from this graph store.

Retrieve documents from this graph store.

Return docs most similar to embedding vector.

Return docs most similar to embedding vector.

Get documents via a metadata search.

Get documents via a metadata search.

Retrieve a single document from the store, given its document ID.

Retrieve a single document from the store, given its document ID.

Retrieve a single node from the store, given its ID.

Retrieve documents from this graph store using MMR-traversal.

This strategy first retrieves the top fetch_k results by similarity to the question. It then selects the top k results based on maximum-marginal relevance using the given lambda_mult.

At each step, it considers the (remaining) documents from fetch_k as well as any documents connected by edges to a selected document retrieved based on similarity (a "root").

Retrieve documents from this graph store using MMR-traversal.

This strategy first retrieves the top fetch_k results by similarity to the question. It then selects the top k results based on maximum-marginal relevance using the given lambda_mult.

At each step, it considers the (remaining) documents from fetch_k as well as any documents connected by edges to a selected document retrieved based on similarity (a "root").

Retrieve documents from this knowledge store.

First, k nodes are retrieved using a vector search for the query string. Then, additional nodes are discovered up to the given depth from those starting nodes.

Retrieve documents from this knowledge store.

First, k nodes are retrieved using a vector search for the query string. Then, additional nodes are discovered up to the given depth from those starting nodes.

Create a CassandraGraphVectorStore from raw texts.

Create a CassandraGraphVectorStore from raw texts.

Create a CassandraGraphVectorStore from a document list.

Create a CassandraGraphVectorStore from a document list.