Astra Vector Store

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Node Details

• Name: Astra
• Type: Astra
• Version: 2.0
• Category: Vector Stores

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Base Classes

• Astra
• VectorStoreRetriever
• BaseRetriever

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Parameters

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Input Parameters

1. Document (optional, list)
   • Type: Document
   • Description: List of documents to be stored in the vector database.
2. Embeddings
   • Type: Embeddings
   • Description: The embedding model used to convert documents into vector representations.
3. Namespace
   • Type: string
   • Description: The namespace in Astra DB where the data will be stored.
4. Collection
   • Type: string
   • Description: The collection name in Astra DB where the data will be stored.
5. Vector Dimension (optional)
   • Type: number
   • Default: 1536
   • Description: The dimension of the vector embeddings.
6. Similarity Metric (optional)
   • Type: string
   • Options: cosine, euclidean, dot_product
   • Default: cosine
   • Description: The metric used to calculate similarity between vectors.
7. Top K (optional)
   • Type: number
   • Default: 4
   • Description: Number of top results to fetch during retrieval.
8. MMR Parameters (optional)
   • Additional parameters for Maximal Marginal Relevance search.

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Credential Parameter

• Connect Credential
  • Type: credential
  • Credential Name: AstraDBApi
  • Description: Authentication credentials for connecting to Astra DB.

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Outputs

1. Astra Retriever
   • Type: VectorStoreRetriever
   • Description: A retriever object for querying the Astra vector store.
2. Astra Vector Store
   • Type: AstraDBVectorStore
   • Description: The vector store object for direct interactions with Astra DB.

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Functionality

1. Upsert Operation:
   • Allows inserting or updating documents in the Astra DB vector store.
   • Converts documents to vector embeddings before storage.
2. Initialization:
   • Sets up the connection to Astra DB using provided credentials and parameters.
   • Creates or connects to the specified namespace and collection.
3. Retrieval:
   • Supports similarity search and MMR search on the stored vector embeddings.

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Use Cases

• Semantic search applications
• Content recommendation systems
• Document similarity analysis
• Any AI application requiring efficient storage and retrieval of vector embeddings

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Note