Cassandra

Just an alias for clear (to better align with other VectorStore implementations).

Just an alias for aclear (to better align with other VectorStore implementations).

Empty the table.

Empty the table.

Delete by document ID.

Delete by document ID.

Delete by vector IDs.

Delete by vector IDs.

Delete all documents matching a certain metadata filtering condition.

This operation does not use the vector embeddings in any way, it simply removes all documents whose metadata match the provided condition.

Delete all documents matching a certain metadata filtering condition.

This operation does not use the vector embeddings in any way, it simply removes all documents whose metadata match the provided condition.

Run more texts through the embeddings and add to the vectorstore.

Run more texts through the embeddings and add to the vectorstore.

Replace the metadata of documents.

For each document to update, identified by its ID, the new metadata dictionary completely replaces what is on the store. This includes passing empty metadata {} to erase the currently-stored information.

Replace the metadata of documents.

For each document to update, identified by its ID, the new metadata dictionary completely replaces what is on the store. This includes passing empty metadata {} to erase the currently-stored information.

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

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

Get documents via a metadata search.

Get documents via a metadata search.

Return docs most similar to embedding vector.

Return docs most similar to embedding vector.

Return docs most similar to embedding vector.

Return docs most similar to query.

Return docs most similar to query.

Return docs most similar to embedding vector.

Return docs most similar to embedding vector.

Return docs most similar to query.

Return docs most similar to query.

Return docs most similar to embedding vector.

Return docs most similar to embedding vector.

Return docs most similar to query.

Return docs most similar to query.

Return docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Args: 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. Defaults to 20. 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. filter: Filter on the metadata to apply. body_search: Document textual search terms to apply. Only supported by Astra DB at the moment. Returns: List of Documents selected by maximal marginal relevance.

Return docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Args: 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. Defaults to 20. 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. filter: Filter on the metadata to apply. body_search: Document textual search terms to apply. Only supported by Astra DB at the moment. Returns: List of Documents selected by maximal marginal relevance.

Return docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Args: 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. Defaults to 20. 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. filter: Filter on the metadata to apply. body_search: Document textual search terms to apply. Only supported by Astra DB at the moment. Returns: List of Documents selected by maximal marginal relevance.

Return docs selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. Args: query: Text to look up documents similar to. k: Number of Documents to return. 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. filter: Filter on the metadata to apply. body_search: Document textual search terms to apply. Only supported by Astra DB at the moment. Returns: List of Documents selected by maximal marginal relevance.

Create a Cassandra vector store from raw texts.

Create a Cassandra vector store from raw texts.

Create a Cassandra vector store from a document list.

Create a Cassandra vector store from a document list.

Return VectorStoreRetriever initialized from this VectorStore.