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.
Node Details
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Name: vectorStoreRetriever
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Type: VectorStoreRetriever
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Category: Retrievers
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Version: 1.0
Input Parameters
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Vector Store
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Label: Vector Store
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Name: vectorStore
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Type: VectorStore
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Description: The vector store to be used as the basis for the retriever.
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Retriever Name
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Label: Retriever Name
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Name: name
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Type: string
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Placeholder: “netflix movies”
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Description: A unique identifier for the retriever.
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Retriever Description
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Label: Retriever Description
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Name: description
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Type: string
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Rows: 3
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Description: A brief explanation of when to use this specific vector store retriever.
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Placeholder: “Good for answering questions about netflix movies”
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Output
The node initializes and returns a VectorStoreRetriever object, which encapsulates:
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The provided vector store
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The specified name
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The given description
Usage
This node is typically used in workflows where:
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You have pre-processed data stored in a vector format.
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You need to retrieve this data efficiently based on similarity searches.
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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
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The node uses the VectorStore class from ‘@langchain/core/vectorstores’.
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It implements the INode interface, ensuring compatibility with the broader node-based architecture.
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The init method is responsible for creating and returning the VectorStoreRetriever object based on the provided inputs.
Best Practices
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Provide clear and descriptive names for your retrievers to easily identify them in complex workflows.
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Use the description field to specify the domain or type of questions this retriever is best suited for.
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Ensure that the vector store provided is properly initialized and contains relevant, high-quality data for optimal retrieval performance.
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