Reciprocal Rank Fusion Retriever

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Node Details

• Name: RRFRetriever

• Type: RRFRetriever

• Category: Retrievers

• Version: 1.0

• Base Classes: RRFRetriever, BaseRetriever

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Input Parameters

1. Vector Store Retriever (required)

   • Type: VectorStoreRetriever

   • Description: The base retriever used for initial document retrieval.

2. Language Model (required)

   • Type: BaseLanguageModel

   • Description: The language model used for generating synthetic queries.

3. Query (optional)

   • Type: string

   • Description: Custom query to retrieve documents. If not specified, the user’s question will be used.

4. Query Count (optional)

   • Type: number

   • Default: 4

   • Description: Number of synthetic queries to generate.

5. Top K (optional)

   • Type: number

   • Description: Number of top results to fetch. Defaults to the TopK of the Base Retriever.

6. Constant (optional)

   • Type: number

   • Default: 60

   • Description: A constant added to the rank, controlling the balance between high-ranked and lower-ranked items.

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Outputs

1. Reciprocal Rank Fusion Retriever

   • Type: RRFRetriever, BaseRetriever

   • Description: The initialized RRF retriever object.

2. Document

   • Type: Document, json

   • Description: Array of document objects containing metadata and pageContent.

3. Text

   • Type: string, json

   • Description: Concatenated string from pageContent of retrieved documents.

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How It Works

1. The node initializes a ReciprocalRankFusion object using the provided language model, base retriever, and configuration parameters.

2. It then wraps this object in a ContextualCompressionRetriever for additional processing.

3. Depending on the selected output, the node can return:

   • The retriever object itself

   • An array of relevant documents

   • A concatenated text of the retrieved documents’ content

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Use Cases

• Improving search relevance in document retrieval systems

• Enhancing question-answering systems with more diverse and relevant context

• Boosting the performance of RAG (Retrieval-Augmented Generation) applications

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Notes

• The node uses escape character handling for text output to ensure proper formatting.

• The synthetic query generation and re-ranking process happens internally within the ReciprocalRankFusion class.