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Node Details

  • Name: vectorStoreRetriever
  • Type: VectorStoreRetriever
  • Category: Retrievers
  • Version: 1.0

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Input Parameters

  1. Vector Store
    • Label: Vector Store
    • Name: vectorStore
    • Type: VectorStore
    • Description: The vector store to be used as the basis for the retriever.
  2. Retriever Name
    • Label: Retriever Name
    • Name: name
    • Type: string
    • Placeholder: “netflix movies”
    • Description: A unique identifier for the retriever.
  3. Retriever Description
    • Label: Retriever Description
    • Name: description
    • Type: string
    • Rows: 3
    • Description: A brief explanation of when to use this specific vector store retriever.
    • Placeholder: “Good for answering questions about netflix movies”

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Output

The node initializes and returns a VectorStoreRetriever object, which encapsulates:
  • The provided vector store
  • The specified name
  • The given description

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Usage

This node is typically used in workflows where:
  1. You have pre-processed data stored in a vector format.
  2. You need to retrieve this data efficiently based on similarity searches.
  3. You want to integrate this retrieval mechanism into a larger question-answering or information retrieval system.

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Integration

The Vector Store Retriever is designed to work seamlessly with other components in a langchain-based system, particularly with MultiRetrievalQAChain for complex question-answering tasks that require querying multiple data sources.

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Implementation Notes

  • The node uses the VectorStore class from ‘@langchain/core/vectorstores’.
  • It implements the INode interface, ensuring compatibility with the broader node-based architecture.
  • The init method is responsible for creating and returning the VectorStoreRetriever object based on the provided inputs.

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Best Practices

  • Provide clear and descriptive names for your retrievers to easily identify them in complex workflows.
  • Use the description field to specify the domain or type of questions this retriever is best suited for.
  • Ensure that the vector store provided is properly initialized and contains relevant, high-quality data for optimal retrieval performance.