🧠 Implement Chain of Thought (CoT) Reasoning for Enhanced RAG Search Quality

[manavgup]

📋 Summary

Implement Chain of Thought (CoT) reasoning capabilities in the RAG pipeline to significantly improve search quality for complex, multi-step queries through question decomposition, iterative reasoning, and transparent answer synthesis.

🎯 Problem Statement

Current Limitations

The existing single-shot RAG pipeline struggles with:

• Complex multi-part questions ("How does X work and what are its security implications?")
• Causal reasoning ("Why does X cause Y?")
• Comparative analysis ("Compare approach A vs B")
• Sequential procedures ("How to implement X step-by-step?")
• Questions requiring logical deduction across multiple facts

Impact

• Suboptimal search results for complex queries
• Missing logical connections between retrieved information
• No transparency in reasoning process
• Inability to handle questions requiring multi-step problem solving

🚀 Proposed Solution

Add a Chain of Thought reasoning layer that:

1. Decomposes complex questions into manageable sub-questions
2. Iteratively retrieves information for each reasoning step
3. Maintains context across multiple reasoning iterations
4. Synthesizes comprehensive answers with transparent reasoning
5. Remains backward compatible with existing pipeline

🏗️ Architecture Overview

Search Request → SearchService (Route Decision)
                    ↓                ↓
            Standard Pipeline   CoT Pipeline Service
                                      ↓
                            [Decomposer → Reasoner → Synthesizer]
                                      ↓
                              Enhanced Answer with Reasoning Chain

📦 Key Components to Implement

1. New Services

• ChainOfThoughtService - Main orchestration service
• QuestionDecomposer - Breaks complex questions into sub-questions
• IterativeReasoner - Multi-step reasoning with context management
• AnswerSynthesizer - Combines reasoning steps into coherent answer
• ContextManager - Maintains context across iterations

2. Schema Changes

Request Schema

class CoTConfig(BaseModel):
    enabled: bool = False
    strategy: ReasoningStrategy = "decomposition"
    max_reasoning_steps: int = 5
    include_reasoning_chain: bool = True

class CoTSearchInput(BaseModel):
    question: str
    collection_id: UUID
    pipeline_id: UUID
    user_id: UUID
    cot_config: Optional[CoTConfig] = None

Response Schema

class CoTSearchOutput(BaseModel):
    answer: str
    documents: List[DocumentMetadata]
    reasoning_chain: Optional[List[ReasoningStep]]
    sub_questions: Optional[List[str]]
    confidence_score: Optional[float]

3. Database Changes

• New cot_executions table for tracking CoT pipeline runs
• JSONB storage for reasoning chains
• Performance indexes on search_id and strategy

🎯 Implementation Phases

Phase 1: Foundation (Week 1-2)

• Create schema definitions and migrations
• Implement basic CoT service structure
• Add question classifier module
• Create simple decomposer
• Integrate with SearchService

Phase 2: Core Reasoning (Week 3-4)

• Implement iterative reasoner
• Add context management
• Create answer synthesizer
• Implement basic strategies
• Add CoT-specific prompts

Phase 3: Advanced Features (Week 5-6)

• Add Tree of Thought strategy
• Implement self-consistency
• Add parallel decomposition
• Create reasoning validation
• Implement confidence scoring

Phase 4: Optimization (Week 7-8)

• Add caching for reasoning chains
• Optimize retrieval rounds
• Implement adaptive strategies
• Add performance monitoring
• Create fallback mechanisms

Phase 5: Testing & Refinement (Week 9-10)

• Comprehensive testing suite
• Performance benchmarking
• A/B testing framework
• Documentation and examples
• UI integration support

📊 Success Metrics

Quantitative

• 25% increase in evaluation scores for complex queries
• 40% reduction in "incomplete answer" feedback
• 80% coverage of multi-part questions
• <10 seconds average CoT latency
• <3x token usage vs standard pipeline

Qualitative

• Improved user satisfaction with complex answers
• Transparent reasoning chains visible to users
• Logical consistency in multi-step reasoning
• Clear evidence attribution

🔧 Technical Implementation Details

File Structure

backend/rag_solution/
├── reasoning/
│   ├── __init__.py
│   ├── decomposer.py      # Question decomposition logic
│   ├── reasoner.py        # Iterative reasoning engine
│   ├── synthesizer.py     # Answer synthesis
│   └── context_manager.py # Context management
├── services/
│   └── cot_service.py     # Main CoT orchestration
├── schemas/
│   └── cot_schema.py      # CoT-specific schemas
└── prompts/
    └── cot_prompts.py     # Reasoning prompts

API Endpoints

• POST /api/search/cot - Execute CoT-enhanced search
• POST /api/search/analyze-question - Analyze question complexity
• GET /api/search/cot/strategies - List available strategies
• POST /api/search/cot/preview - Preview decomposition

⚡ Performance Considerations

Optimizations

• Adaptive reasoning depth based on complexity
• Context pruning between steps
• Caching of intermediate results
• Parallel sub-question processing
• Progressive response streaming

Resource Management

• Token budget management per request
• Sliding window for context
• Efficient fact compression
• Early termination for sufficient answers

🧪 Testing Strategy

Test Coverage

• Unit tests for each reasoning component
• Integration tests for full CoT pipeline
• Quality tests for reasoning coherence
• Performance benchmarks
• A/B testing framework

Test Files

backend/tests/
├── reasoning/
│   ├── test_decomposer.py
│   ├── test_reasoner.py
│   └── test_synthesizer.py
├── integration/
│   └── test_cot_pipeline.py
└── quality/
    └── test_reasoning_quality.py

📚 Documentation

Full design document available at: claudeDev_Docs/sprintDocs/DESIGN_CoT_RAG_Enhancement.md

The document includes:

• Detailed architecture diagrams
• Complete schema definitions
• Example prompts and configurations
• Risk mitigation strategies
• Monitoring metrics

✅ Acceptance Criteria

• CoT service successfully decomposes complex questions
• Iterative reasoning maintains context across steps
• Answer synthesis produces coherent responses
• Reasoning chains are transparent and traceable
• Performance stays within defined limits
• Backward compatibility maintained
• Comprehensive tests pass
• Documentation complete

🏷️ Labels

• enhancement
• search-quality
• architectural-change

🔗 Related Issues

• 🔍 Create CLI-based Search Quality Testing Framework #131 - CLI-based Search Quality Testing Framework
• 🔍 Enhancement: Comprehensive Code Quality and Linting Infrastructure #130 - Comprehensive Linting Enhancement

📝 Notes

• This is a major enhancement that will significantly improve search quality
• Implementation should be incremental with feature flags
• Each phase delivers working functionality
• Design maintains full backward compatibility