description: Store vector store as retriever to be later queried by MultiRetrievalQAChain.

Vector Store Retriever

The Vector Store Retriever node is a component used in retrieval-based systems, particularly for storing and querying vector representations of data. It's designed to be used with MultiRetrievalQAChain, making it useful for question-answering tasks that require retrieval from multiple sources.

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"

Output

The node initializes and returns a VectorStoreRetriever object, which encapsulates:

• The provided vector store

• The specified name

• The given description

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.

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.

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.

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.

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