The Astra Vector Store node integrates DataStax Astra DB, a serverless vector database, into your AI workflow. It allows for upsert operations of embedded data and performs similarity or MMR (Maximal Marginal Relevance) search upon query.
Name: Astra
Type: Astra
Version: 2.0
Category: Vector Stores
Astra
VectorStoreRetriever
BaseRetriever
Document (optional, list)
Type: Document
Description: List of documents to be stored in the vector database.
Embeddings
Type: Embeddings
Description: The embedding model used to convert documents into vector representations.
Namespace
Type: string
Description: The namespace in Astra DB where the data will be stored.
Collection
Type: string
Description: The collection name in Astra DB where the data will be stored.
Vector Dimension (optional)
Type: number
Default: 1536
Description: The dimension of the vector embeddings.
Similarity Metric (optional)
Type: string
Options: cosine, euclidean, dot_product
Default: cosine
Description: The metric used to calculate similarity between vectors.
Top K (optional)
Type: number
Default: 4
Description: Number of top results to fetch during retrieval.
MMR Parameters (optional)
Connect Credential
Type: credential
Credential Name: AstraDBApi
Description: Authentication credentials for connecting to Astra DB.
Astra Retriever
Type: VectorStoreRetriever
Description: A retriever object for querying the Astra vector store.
Astra Vector Store
Type: AstraDBVectorStore
Description: The vector store object for direct interactions with Astra DB.
Upsert Operation:
Allows inserting or updating documents in the Astra DB vector store.
Converts documents to vector embeddings before storage.
Initialization:
Sets up the connection to Astra DB using provided credentials and parameters.
Creates or connects to the specified namespace and collection.
Retrieval:
Semantic search applications
Content recommendation systems
Document similarity analysis
Any AI application requiring efficient storage and retrieval of vector embeddings
This node requires proper setup of Astra DB and valid credentials for successful operation. Ensure that the vector dimensions and similarity metrics are consistent with your embedding model and use case requirements.