A Guide to RAG query techniques

Welcome to our comprehensive overview of query translation techniques in Retriever-Augmented Generation (RAG) pipelines. In this series, we've explored four innovative methods revolutionizing user query processing. These are Multi-Query Translation, RAG Fusion, Decomposition, and Step-Back Prompting. We'll demonstrate how these techniques can be implemented in ReByte, complete with agent and app examples.

1. Multi-Query Translation

Multi-Query Translation diversifies a user's query by rephrasing it into various forms. In ReByte, this is achieved using the "LLM-chat" action to generate multiple queries from a single user question.

Here's the prompt for the LLM.

And the LLM generates three sub-queries.

This technique enhances the likelihood of retrieving relevant information, as each query version might align differently with the documents in the database.

For implementation, we use "Map-Reduce" and "Knowledge Search" actions to retrieve information for each query, followed by another "LLM-chat" action to summarize the results.

Agent Demo for Multi-Query

App Demo for Multi-Query

2. RAG Fusion

RAG Fusion, an extension of Multi-Query Translation, includes a crucial reciprocal rank fusion step. This method consolidates results from multiple queries into a single, optimized list, making it ideal for comprehensive information retrieval.

Here's the prompt for the LLM.

And the LLM generates three sub-queries.

RAG Fusion is demonstrated in ReByte through a similar process of generating multiple queries and retrieving documents.

Agent Demo for RAG Fusion

App Demo for RAG Fusion

3. Decomposition

Decomposition addresses complex queries by breaking them into smaller sub-questions, each solved independently. This approach, demonstrated in ReByte, simplifies the retrieval process and allows for detailed responses.

Here's the prompt for the LLM.

And the LLM generates three sub-queries.

Here, the "LLM-chat" action is used to generate sub-queries, which are then rocessed to form a comprehensive answer.

Agent Demo for Decomposition

App Demo for Decomposition

4. Step-Back Prompting

Step-Back Prompting abstracts a specific query into a more general one, broadening the scope of information retrieval. In ReByte, this method generates high-level questions from specific queries, facilitating the retrieval of a wider range of related information.

Here's the prompt for the LLM.

And the LLM generates three more general query.

This technique is particularly effective in contexts where background information is as crucial as the query's specific details.

Agent Demo for Step-Back Prompting

App Demo for Step-Back Prompting

Conclusion

These query translation techniques form a robust toolkit in RAG systems, ensuring accurate, relevant, and comprehensive information retrieval. Multi-Query Translation and RAG Fusion expand search scope, Decomposition simplifies complex queries, and Step-Back Prompting elevates queries to a more abstract level. Stay tuned for further insights and advancements in query translation and RAG pipelines.

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