DataStax Solutions

DataStax vector database components provide integration with Astra DB, Cassandra, and HCD (Hyper-Converged Database) for scalable vector storage and retrieval.

Astra DB Vector Store

This component implements a Vector Store using Astra DB with search capabilities.

For more information, see the DataStax documentation.

Use a vector store component in a flow

This example uses the Astra DB vector store component. Your vector store component's parameters and authentication may be different, but the document ingestion workflow is the same. A document is loaded from a local machine and chunked. The Astra DB vector store generates embeddings with the connected model component, and stores them in the connected Astra DB database.

This vector data can then be retrieved for workloads like Retrieval Augmented Generation.

Inputs

Name	Display Name	Info
token	Astra DB Application Token	The authentication token for accessing Astra DB.
environment	Environment	The environment for the Astra DB API Endpoint. For example, dev or prod.
database_name	Database	The database name for the Astra DB instance.
api_endpoint	Astra DB API Endpoint	The API endpoint for the Astra DB instance. This supersedes the database selection.
collection_name	Collection	The name of the collection within Astra DB where the vectors are stored.
keyspace	Keyspace	An optional keyspace within Astra DB to use for the collection.
embedding_choice	Embedding Model or Astra Vectorize	Choose an embedding model or use Astra vectorize.
embedding_model	Embedding Model	Specify the embedding model. Not required for Astra vectorize collections.
number_of_results	Number of Search Results	The number of search results to return (default: 4).
search_type	Search Type	The search type to use. The options are Similarity, Similarity with score threshold, and MMR (Max Marginal Relevance).
search_score_threshold	Search Score Threshold	The minimum similarity score threshold for search results when using the Similarity with score threshold option.
advanced_search_filter	Search Metadata Filter	An optional dictionary of filters to apply to the search query.
autodetect_collection	Autodetect Collection	A boolean flag to determine whether to autodetect the collection.
content_field	Content Field	A field to use as the text content field for the vector store.
deletion_field	Deletion Based On Field	When provided, documents in the target collection with metadata field values matching the input metadata field value are deleted before new data is loaded.
ignore_invalid_documents	Ignore Invalid Documents	A boolean flag to determine whether to ignore invalid documents at runtime.
astradb_vectorstore_kwargs	AstraDBVectorStore Parameters	An optional dictionary of additional parameters for the AstraDBVectorStore.

Outputs

Name	Display Name	Info
vector_store	Vector Store	Astra DB vector store instance configured with the specified parameters.
search_results	Search Results	The results of the similarity search as a list of Data objects.

Generate embeddings

The Astra DB Vector Store component offers two methods for generating embeddings.

1. Embedding Model: Use your own embedding model by connecting an Embeddings component.

2. Astra Vectorize: Use Astra DB's built-in embedding generation service. When creating a new collection, choose the embeddings provider and models, including NVIDIA's NV-Embed-QA model hosted by Datastax.

Hybrid search

The Astra DB component includes hybrid search, which is enabled by default.

The component fields related to hybrid search are Search Query, Lexical Terms, and Reranker.

• Search Query finds results by vector similarity.

• Lexical Terms is a comma-separated string of keywords, like features, data, attributes, characteristics.

• Reranker is the re-ranker model used in the hybrid search. The re-ranker model is nvidia/llama-3.2-nv.reranker.

Hybrid search performs a vector similarity search and a lexical search, compares the results of both searches, and then returns the most relevant results overall.

Cassandra

This component creates a Cassandra Vector Store with search capabilities. For more information, see the Cassandra documentation.

Inputs

Name	Type	Description
database_ref	String	Contact points for the database or AstraDB database ID
username	String	Username for the database (leave empty for AstraDB)
token	SecretString	User password for the database or AstraDB token
keyspace	String	Table Keyspace or AstraDB namespace
table_name	String	Name of the table or AstraDB collection
ttl_seconds	Integer	Time-to-live for added texts
batch_size	Integer	Number of data to process in a single batch
setup_mode	String	Configuration mode for setting up the Cassandra table
cluster_kwargs	Dict	Additional keyword arguments for the Cassandra cluster
search_query	String	Query for similarity search
ingest_data	Data	Data to be ingested into the vector store
embedding	Embeddings	Embedding function to use
number_of_results	Integer	Number of results to return in search
search_type	String	Type of search to perform
search_score_threshold	Float	Minimum similarity score for search results
search_filter	Dict	Metadata filters for search query
body_search	String	Document textual search terms
enable_body_search	Boolean	Flag to enable body search

Outputs

Name	Type	Description
vector_store	Cassandra	A Cassandra vector store instance configured with the specified parameters.
search_results	List[Data]	The results of the similarity search as a list of Data objects.

Usage Notes

• Enterprise Scale: DataStax solutions provide enterprise-grade scalability and reliability

• Cloud Native: Astra DB is a fully managed cloud service

• Hybrid Deployment: Cassandra can be deployed on-premises or in hybrid environments

• Advanced Features: Support for hybrid search, graph capabilities, and multi-modal data