Multi-step retrieval process for complex queries requiring layered information access
Core Idea: Recursive RAG extends traditional RAG by performing multiple rounds of retrieval based on intermediate findings, allowing the system to progressively gather information needed to answer complex, multi-part questions.
Key Elements
Core Mechanism
- Iterative Retrieval Process:
- Initial query generates preliminary response and identifies knowledge gaps
- System formulates follow-up queries based on these gaps
- New retrievals add context for subsequent generation steps
- Process continues until sufficient information is gathered
- Query Decomposition:
- Breaking complex questions into simpler sub-questions
- Tracking dependencies between sub-questions
- Maintaining context across retrieval iterations
Implementation Approaches
- Fixed-Depth Recursion:
- Predetermined number of retrieval rounds
- Simpler to implement but less adaptive
- Dynamic Recursion:
- Model determines when sufficient information is gathered
- Continues retrieval until confidence threshold is reached
- Self-terminating based on answer completeness
- Tree-Based Recursion:
- Creates branching paths of inquiry
- Pursues multiple information threads in parallel
- Combines findings from different branches
Practical Considerations
- Stopping Criteria:
- Maximum recursion depth to prevent infinite loops
- Confidence threshold for answer sufficiency
- Information novelty detection to avoid redundant retrievals
- Context Management:
- Accumulating relevant information across iterations
- Pruning irrelevant or redundant content
- Preserving question context throughout the process
- Computational Efficiency:
- Increased latency due to multiple retrieval rounds
- Higher API or computational costs
- Caching strategies for intermediate results
Use Cases
- Multi-hop Questions: "What was the economic impact of the policy proposed by the winner of the 2008 Nobel Prize in Economics?"
- Comparative Analysis: "How do the approaches to monetary policy differ between the Fed chairs appointed in 2006 and 2018?"
- Causal Reasoning: "What factors led to the decline of the technology described in the 1995 paper by Smith et al.?"
Connections
- Related Concepts: RAG Systems (parent technique), ReAct (similar multi-step approach), Query Decomposition (essential component)
- Broader Context: Multi-hop Reasoning (theoretical foundation), Complex Question Answering (application area)
- Applications: Research Assistants (implementation scenario), Knowledge Synthesis (practical outcome)
- Components: Document Processing Pipeline (underlying infrastructure), Vector Search (retrieval mechanism)
References
- Asai et al. "Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection" (2023)
- Khattab et al. "Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP" (2022)
#rag #recursive-retrieval #complex-qa #multi-hop-reasoning #information-retrieval
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