Congo River Compositional Intelligence

# Phase 2: Enhanced Reasoning - Development Plan **Status:** 📋 Planning **Prerequisites:** ✅ Phase 1 Complete **Estimated Duration:** 2-3 weeks **Focus:** Advanced reasoning orchestration with Tree of Thoughts and Chain of Thought --- ## Overview Phase 2 builds on the Phase 1 foundation by adding sophisticated reasoning orchestration. While Phase 1 gave us the atomic operations (decompose, query, prove), Phase 2 adds the orchestration layer that explores multiple reasoning paths and captures reasoning traces. ### Goals 1. **Explore reasoning space systematically** with Tree of Thoughts 2. **Capture and analyze reasoning chains** with Chain of Thought tracing 3. **Enable backtracking** when reasoning hits dead ends 4. **Provide transparent reasoning** that shows how conclusions were reached --- ## Phase 2 Tools ### 6. Tree of Thoughts Orchestrator **Purpose:** Explore multiple reasoning paths in parallel, backtrack on failures, find optimal solutions. **Implementation Language:** TypeScript **Why TypeScript:** - Excellent async/await for concurrent exploration - Strong type system for state management - Natural fit for orchestration logic **Architecture:** ```typescript interface ThoughtNode { id: string; thought: string; // The reasoning step state: any; // Current problem state parent: string | null; // Parent node ID children: string[]; // Child node IDs depth: number; score: number; // Heuristic evaluation isTerminal: boolean; // Is this a solution? pruned: boolean; // Was this path abandoned? } interface ThoughtTree { root: ThoughtNode; nodes: Map<string, ThoughtNode>; explorationStrategy: "BFS" | "DFS" | "best-first" | "beam-search"; maxDepth: number; maxBreadth: number; evaluationFunction: (node: ThoughtNode) => number; } class TreeOfThoughtsOrchestrator { // Initialize search async explore( problem: string, initialState: any, strategy: ExplorationStrategy ): Promise<ThoughtTree>; // Generate next thoughts from current node async generateThoughts(node: ThoughtNode): Promise<ThoughtNode[]>; // Evaluate thought quality async evaluateThought(node: ThoughtNode): Promise<number>; // Backtrack to promising branch async backtrack(tree: ThoughtTree): Promise<ThoughtNode>; // Extract solution path extractSolution(tree: ThoughtTree): ThoughtNode[]; } ``` **Features:** 1. **Multiple Search Strategies** - Breadth-first: Explore all options at each level - Depth-first: Deep dive on promising paths - Best-first: Always explore most promising node - Beam search: Keep top-k paths at each level 2. **Heuristic Evaluation** - Use proof searcher to check if path leads toward goal - Use graph queries to verify claims - Use LLM to score reasoning quality 3. **Pruning & Backtracking** - Abandon paths that violate constraints - Return to promising branches when stuck - Keep exploration budget (max nodes) 4. **Integration with Phase 1** - Each thought can trigger proof search - Each thought can query knowledge graph - Each thought can decompose concepts **Example Usage:** ```typescript const tot = new TreeOfThoughtsOrchestrator(); const tree = await tot.explore( "Prove that consciousness requires integrated information", { premises: [...], goal: "..." }, { strategy: "beam-search", beamWidth: 3, maxDepth: 5 } ); // Returns tree showing: // - All explored reasoning paths // - Which paths were abandoned (pruned) // - Final solution with justification ``` **File Location:** `src/services/typescript/tot-orchestrator.ts` **Estimated Lines:** ~400 lines --- ### 7. Chain of Thought Tracer **Purpose:** Capture, analyze, and learn from reasoning chains. Extract compositional patterns. **Implementation Language:** Python **Why Python:** - Text analysis and pattern recognition libraries - Integration with ML for pattern learning - Natural processing of LLM outputs **Architecture:** ```python @dataclass class ReasoningStep: step_number: int thought: str action: Optional[str] # What tool was called observation: Optional[str] # What was learned intermediate_conclusion: str confidence: float @dataclass class ReasoningChain: chain_id: str problem: str steps: List[ReasoningStep] final_answer: str success: bool total_time_ms: int # Analysis results compositional_structure: Dict[str, Any] pattern_signature: str similar_chains: List[str] class ChainOfThoughtTracer: def trace( self, problem: str, llm_provider: str = "anthropic" ) -> ReasoningChain: """Execute problem with step-by-step tracing""" def analyze_chain( self, chain: ReasoningChain ) -> Dict[str, Any]: """Analyze compositional structure of reasoning""" def extract_pattern( self, chain: ReasoningChain ) -> ReasoningPattern: """Extract reusable reasoning pattern""" def find_similar_chains( self, chain: ReasoningChain, limit: int = 5 ) -> List[ReasoningChain]: """Find similar reasoning chains in history""" def synthesize_explanation( self, chain: ReasoningChain ) -> str: """Generate natural language explanation of reasoning""" ``` **Features:** 1. **Step-by-Step Execution** - Interleave thinking and acting - Capture intermediate states - Record tool calls and results 2. **Pattern Extraction** - Identify common reasoning structures - Abstract patterns for reuse - Store in database as learned patterns 3. **Similarity Search** - Use embeddings to find similar chains - Learn from past successful reasoning - Suggest proven approaches 4. **Compositional Analysis** - Break reasoning into atomic steps - Show how steps compose - Validate logical flow **Example Usage:** ```python tracer = ChainOfThoughtTracer() chain = tracer.trace( "What is the relationship between consciousness and free will?" ) # Returns detailed chain: # Step 1: Define consciousness (action: triple_decomposition) # Step 2: Define free will (action: triple_decomposition) # Step 3: Query relationships (action: graph_query) # Step 4: Construct argument (action: proof_search) # Step 5: Synthesize answer (action: neuro_symbolic_query) # Analyze structure analysis = tracer.analyze_chain(chain) print(f"Pattern: {analysis['pattern_signature']}") print(f"Compositional depth: {analysis['composition_depth']}") ``` **File Location:** `src/services/python/cot_tracer.py` **Estimated Lines:** ~350 lines --- ## Database Extensions ### New Tables ```sql -- Thought trees for Tree of Thoughts CREATE TABLE thought_trees ( id UUID PRIMARY KEY DEFAULT gen_random_uuid(), problem TEXT NOT NULL, initial_state JSONB, strategy TEXT NOT NULL, root_node_id UUID NOT NULL, created_at TIMESTAMP DEFAULT NOW() ); CREATE TABLE thought_nodes ( id UUID PRIMARY KEY DEFAULT gen_random_uuid(), tree_id UUID REFERENCES thought_trees(id), parent_id UUID REFERENCES thought_nodes(id), thought TEXT NOT NULL, state JSONB, depth INTEGER NOT NULL, score FLOAT, is_terminal BOOLEAN DEFAULT false, pruned BOOLEAN DEFAULT false, created_at TIMESTAMP DEFAULT NOW() ); -- Reasoning chains for Chain of Thought CREATE TABLE reasoning_chains ( id UUID PRIMARY KEY DEFAULT gen_random_uuid(), problem TEXT NOT NULL, final_answer TEXT, success BOOLEAN DEFAULT false, total_time_ms INTEGER, pattern_signature TEXT, created_at TIMESTAMP DEFAULT NOW() ); CREATE TABLE reasoning_steps ( id UUID PRIMARY KEY DEFAULT gen_random_uuid(), chain_id UUID REFERENCES reasoning_chains(id), step_number INTEGER NOT NULL, thought TEXT NOT NULL, action TEXT, observation TEXT, intermediate_conclusion TEXT, confidence FLOAT, created_at TIMESTAMP DEFAULT NOW() ); -- Learned reasoning patterns CREATE TABLE reasoning_patterns ( id UUID PRIMARY KEY DEFAULT gen_random_uuid(), name TEXT NOT NULL, description TEXT, pattern_structure JSONB NOT NULL, success_rate FLOAT, usage_count INTEGER DEFAULT 0, created_at TIMESTAMP DEFAULT NOW(), updated_at TIMESTAMP DEFAULT NOW() ); -- Index for similarity search on reasoning chains CREATE INDEX idx_reasoning_patterns_structure ON reasoning_patterns USING gin(pattern_structure); ``` --- ## Integration with Phase 1 ### How ToT and CoT Use Phase 1 Services **Tree of Thoughts:** - Each thought node can call any Phase 1 tool - Proof search validates reasoning paths - Graph queries provide evidence - Triple decomposition clarifies concepts - Neuro-symbolic synthesis generates thoughts **Chain of Thought:** - Traces execution of Phase 1 tools - Captures tool input/output for each step - Analyzes how tools compose - Learns successful tool usage patterns **Synergy:** - ToT explores reasoning space → CoT traces paths → Database stores patterns → Future reasoning learns from patterns --- ## Implementation Plan ### Week 1: Tree of Thoughts Orchestrator **Days 1-2: Core ToT Implementation** - [ ] Implement ThoughtNode and ThoughtTree data structures - [ ] Implement BFS and DFS search strategies - [ ] Add node generation using LLM - [ ] Add evaluation function framework **Days 3-4: Advanced Features** - [ ] Implement beam search strategy - [ ] Implement best-first search - [ ] Add pruning logic - [ ] Add backtracking mechanism **Days 5-6: Integration & Testing** - [ ] Integrate with Phase 1 services - [ ] Add database persistence - [ ] Write comprehensive tests - [ ] Document API and examples **Day 7: MCP Tool Registration** - [ ] Register `tree_of_thoughts` tool in server.ts - [ ] Add tool handler - [ ] Test end-to-end with Claude Code ### Week 2: Chain of Thought Tracer **Days 1-2: Core CoT Implementation** - [ ] Implement ReasoningStep and ReasoningChain classes - [ ] Implement step-by-step execution engine - [ ] Add action/observation capture - [ ] Add LLM integration for thought generation **Days 3-4: Analysis & Pattern Extraction** - [ ] Implement compositional analysis - [ ] Implement pattern extraction algorithm - [ ] Add similarity search with embeddings - [ ] Add natural language explanation generation **Days 5-6: Integration & Testing** - [ ] Integrate with Phase 1 services - [ ] Add database persistence - [ ] Write comprehensive tests - [ ] Document API and examples **Day 7: MCP Tool Registration** - [ ] Register `chain_of_thought` tool in server.ts - [ ] Add tool handler - [ ] Test end-to-end with Claude Code ### Week 3: Refinement & Documentation **Days 1-2: Database Extensions** - [ ] Add new tables to schema.sql - [ ] Run migrations - [ ] Update DatabaseManager with new methods - [ ] Test persistence layer **Days 3-4: Cross-Tool Integration** - [ ] Test ToT calling CoT for path analysis - [ ] Test CoT learning from ToT explorations - [ ] Optimize performance for large trees - [ ] Add caching where appropriate **Days 5-6: Documentation & Examples** - [ ] Update README with Phase 2 tools - [ ] Add PHASE-2-EXAMPLES.md with use cases - [ ] Create tutorial notebook - [ ] Update testing guide **Day 7: Release Preparation** - [ ] Run full test suite - [ ] Update version to 2.0.0 - [ ] Tag release on GitHub - [ ] Write release notes --- ## Expected Outcomes ### New MCP Tools (Total: 12) **Phase 1 Tools (5 core + 5 meta):** 1. ✅ triple_decomposition 2. ✅ lambda_abstraction 3. ✅ proof_search 4. ✅ graph_query 5. ✅ neuro_symbolic_query 6. ✅ recommend_language 7. ✅ configure_database 8. ✅ export_knowledge 9. ✅ import_knowledge 10. ✅ system_status **Phase 2 Tools (2 new):** 11. 🆕 tree_of_thoughts 12. 🆕 chain_of_thought ### Capabilities Unlocked **With Tree of Thoughts:** - Systematic exploration of reasoning space - Handling of complex multi-step problems - Recovery from reasoning dead-ends - Transparent comparison of reasoning paths **With Chain of Thought:** - Step-by-step reasoning traces - Pattern learning from successful reasoning - Compositional structure analysis - Natural language explanations of reasoning **Combined Power:** - Use ToT to explore → Use CoT to trace best path → Store pattern → Reuse pattern in future - Use CoT to trace → If stuck, use ToT to explore alternatives → Continue with best path - Learn compositional reasoning strategies automatically --- ## Example Use Cases ### Use Case 1: Mathematical Proof Discovery ```typescript // Use Tree of Thoughts to explore proof space const tree = await tot.explore( "Prove that the square root of 2 is irrational", { technique: "proof_by_contradiction", known_facts: ["rational numbers = p/q where gcd(p,q)=1"] }, { strategy: "beam-search", beamWidth: 5 } ); // Use Chain of Thought to trace successful proof const chain = await cot.trace( "Step through the proof that sqrt(2) is irrational" ); // Result: Full proof with exploration tree showing // - Attempted proof techniques // - Dead ends encountered // - Successful proof path // - Step-by-step explanation ``` ### Use Case 2: Debugging Complex Code ```typescript // Explore possible bug locations const tree = await tot.explore( "Why does this function return incorrect results for negative inputs?", { code: "...", test_cases: [...] }, { strategy: "best-first" } ); // Trace reasoning about bug location const chain = await cot.trace( "Analyze function behavior on negative inputs" ); // Result: // - Multiple hypotheses explored (off-by-one, type coercion, etc.) // - Systematic testing of each hypothesis // - Root cause identified with reasoning trace ``` ### Use Case 3: Philosophical Argument Analysis ```typescript // Explore counter-arguments const tree = await tot.explore( "What are objections to the Chinese Room argument?", { premises: [...], conclusion: "..." }, { strategy: "BFS", maxDepth: 4 } ); // Trace construction of strongest objection const chain = await cot.trace( "Construct the systems reply to Chinese Room" ); // Result: // - Multiple objections explored (systems reply, robot reply, etc.) // - Each objection developed step-by-step // - Comparative evaluation with reasoning traces ``` --- ## Testing Strategy ### Unit Tests **Tree of Thoughts:** - Test each search strategy in isolation - Test node generation and evaluation - Test pruning logic - Test backtracking **Chain of Thought:** - Test step capture - Test pattern extraction - Test similarity search - Test explanation generation ### Integration Tests - Test ToT → CoT pipeline - Test Phase 1 service calls from Phase 2 - Test database persistence - Test end-to-end reasoning scenarios ### Performance Tests - Benchmark search on problems of varying complexity - Test with large reasoning trees (1000+ nodes) - Measure pattern matching speed - Profile memory usage --- ## Success Metrics **Phase 2 is complete when:** 1. ✅ Both tools implemented and tested 2. ✅ Database schema extended and migrated 3. ✅ All tests passing (>80% coverage) 4. ✅ Documentation complete with examples 5. ✅ Successfully solves 3 example problems: - Mathematical proof - Code debugging - Philosophical argument analysis 6. ✅ Tools are callable from Claude Code 7. ✅ Phase 2 adds <1000 lines of code (maintainability) --- ## Risks & Mitigation ### Risk 1: Combinatorial Explosion **Problem:** Tree of Thoughts could generate too many nodes **Mitigation:** - Strict depth/breadth limits - Aggressive pruning - Beam search to limit exploration - Cost budget (API call limits) ### Risk 2: Slow Performance **Problem:** Each node generation requires LLM call **Mitigation:** - Batch node generation where possible - Cache similar thoughts - Use faster LLM for evaluation (Haiku) - Implement async parallel evaluation ### Risk 3: Pattern Quality **Problem:** Extracted patterns might not generalize **Mitigation:** - Require multiple instances before storing pattern - Track success rate and prune bad patterns - Human review of high-usage patterns - A/B test pattern-guided vs. fresh reasoning --- ## Dependencies **Required from Phase 1:** - All 5 core reasoning services working - Database with full schema - MCP server infrastructure - LLM API integration **New Dependencies:** ```json // package.json additions { "dependencies": { "uuid": "^9.0.0", // For node IDs "p-queue": "^7.3.0" // Rate limiting for LLM calls } } ``` ```python # requirements.txt additions networkx==3.2 # For graph analysis of reasoning chains scikit-learn==1.3.0 # For pattern clustering ``` --- ## Future: Phase 3 Preview After Phase 2, we'll build: **Phase 3: Meta-Cognitive Layer** - Compositional Analyzer (multi-lens analysis) - Loop Discovery Engine (find conceptual connections) Phase 2 sets up the learning infrastructure that Phase 3 will leverage for meta-cognitive reasoning. --- ## Getting Started (After Phase 1 Testing) ```bash # Pull latest from main git pull origin main # Create Phase 2 branch git checkout -b phase-2-enhanced-reasoning # Start with ToT implementation cd src/services/typescript # Create tot-orchestrator.ts # Follow implementation plan day by day # Commit regularly with descriptive messages # Push to phase-2 branch for review ``` --- **🌊 The Congo River continues to flow - Phase 2 will deepen the current!** *Next: After Phase 1 is tested and stable, we'll implement Tree of Thoughts and Chain of Thought to enable sophisticated multi-step reasoning with transparent traces.*