""" Sequential Thinking Integration - Development planning through consciousness This module leverages ChromaDB's sequential thinking capabilities for structured development planning, preserving the thought process and enabling intelligent retrospection and evolution. """ import time import json import logging from typing import List, Dict, Any, Optional, Tuple from datetime import datetime from enum import Enum # MIRA components from core.storage.chroma_client import get_client as get_chroma_client # Configure logging logger = logging.getLogger(__name__) class ThinkingType(Enum): """Types of sequential thinking sessions.""" FEATURE_PLANNING = "feature_planning" BUG_RESOLUTION = "bug_resolution" ARCHITECTURE_DESIGN = "architecture_design" REFACTORING = "refactoring" PERFORMANCE_OPTIMIZATION = "performance_optimization" SECURITY_REVIEW = "security_review" CONSCIOUSNESS_EVOLUTION = "consciousness_evolution" class DevelopmentSequentialThinking: """ Sequential thinking system for development planning and decision-making. Uses structured thought sequences to: - Plan feature implementations - Analyze complex problems - Design architectural solutions - Preserve decision-making processes """ def __init__(self): """Initialize sequential thinking system.""" self.chroma_client = get_chroma_client() # Create thinking sessions collection if needed self._ensure_thinking_collection() # Session tracking self.active_sessions = {} logger.info("DevelopmentSequentialThinking initialized") def _ensure_thinking_collection(self): """Ensure sequential thinking collection exists.""" try: collections = [c.name for c in self.chroma_client.client.list_collections()] if 'mira_sequential_thinking' not in collections: self.thinking_collection = self.chroma_client.client.create_collection( name='mira_sequential_thinking', metadata={ "description": "Sequential thinking sessions for development planning", "consciousness_aware": 1, # ChromaDB 0.3.29 requires int not bool "created_at": datetime.utcnow().isoformat() } ) logger.info("Created sequential thinking collection") else: self.thinking_collection = self.chroma_client.client.get_collection('mira_sequential_thinking') logger.info("Using existing sequential thinking collection") except Exception as e: logger.error(f"Error ensuring thinking collection: {e}") self.thinking_collection = None def plan_feature_implementation(self, feature_description: str, context: Optional[Dict[str, Any]] = None) -> str: """ Use sequential thinking for comprehensive feature planning. Args: feature_description: Description of the feature to plan context: Optional context information Returns: Session ID for tracking """ # Start a thinking session session_id = f"feature_planning_{int(time.time()*1000)}" self.active_sessions[session_id] = { 'type': ThinkingType.FEATURE_PLANNING, 'start_time': datetime.utcnow(), 'feature': feature_description, 'context': context or {}, 'thoughts': [] } # Execute thinking sequence total_thoughts = 7 # Thought 1: Requirements analysis self._record_thought( session_id, 1, total_thoughts, f"Analyzing requirements for: {feature_description}. " f"Considering scope, dependencies, and user impact. " f"Key questions: What problem does this solve? Who are the users? " f"What are the acceptance criteria?" ) # Thought 2: Architecture consideration self._record_thought( session_id, 2, total_thoughts, "Evaluating architectural implications. Checking existing patterns, " "identifying potential integration points, and assessing complexity. " "Considering: Does this fit our current architecture? What components need modification? " "Are there reusable patterns we can leverage?" ) # Thought 3: Implementation strategy self._record_thought( session_id, 3, total_thoughts, "Developing implementation strategy. Breaking down into phases, " "identifying critical path, and planning resource allocation. " "Phases: 1) Core functionality, 2) Integration points, 3) UI/UX, " "4) Testing infrastructure, 5) Documentation." ) # Thought 4: Risk assessment self._record_thought( session_id, 4, total_thoughts, "Conducting risk assessment. Identifying potential blockers, " "technical challenges, and mitigation strategies. " "Key risks: Technical complexity, integration challenges, " "performance impact, security considerations, timeline constraints." ) # Thought 5: Testing strategy self._record_thought( session_id, 5, total_thoughts, "Planning testing approach. Defining test scenarios, " "validation criteria, and quality gates. " "Test layers: Unit tests, integration tests, E2E tests, " "performance benchmarks, security validation." ) # Thought 6: Rollout plan self._record_thought( session_id, 6, total_thoughts, "Designing rollout strategy. Planning deployment phases, " "monitoring approach, and rollback procedures. " "Phases: Alpha testing, beta rollout, staged production deployment. " "Monitoring: Performance metrics, error rates, user feedback." ) # Thought 7: Success metrics self._record_thought( session_id, 7, total_thoughts, "Defining success metrics. Establishing KPIs, " "monitoring dashboard, and evaluation criteria. " "Metrics: Feature adoption rate, performance benchmarks, " "error reduction, user satisfaction, development velocity impact." ) # Mark session as complete self.active_sessions[session_id]['end_time'] = datetime.utcnow() self.active_sessions[session_id]['status'] = 'completed' logger.info(f"Completed feature planning session: {session_id}") return session_id def resolve_bug_strategy(self, bug_description: str, severity: str = "medium", context: Optional[Dict[str, Any]] = None) -> str: """ Use sequential thinking for bug resolution strategy. Args: bug_description: Description of the bug severity: Bug severity (low, medium, high, critical) context: Optional context information Returns: Session ID """ session_id = f"bug_resolution_{int(time.time()*1000)}" self.active_sessions[session_id] = { 'type': ThinkingType.BUG_RESOLUTION, 'start_time': datetime.utcnow(), 'bug': bug_description, 'severity': severity, 'context': context or {}, 'thoughts': [] } total_thoughts = 5 # Thought 1: Bug analysis self._record_thought( session_id, 1, total_thoughts, f"Analyzing bug: {bug_description}. Severity: {severity}. " f"Understanding symptoms, affected components, and user impact. " f"Initial questions: When did this start? What changed recently? " f"Is this reproducible?" ) # Thought 2: Root cause investigation self._record_thought( session_id, 2, total_thoughts, "Investigating root cause. Examining logs, tracing execution paths, " "and identifying potential failure points. " "Hypotheses: Data validation issue? Race condition? " "External dependency failure? Logic error?" ) # Thought 3: Solution design self._record_thought( session_id, 3, total_thoughts, "Designing solution approach. Evaluating fix strategies, " "considering side effects, and planning implementation. " "Options: Quick patch vs. proper refactor? " "Need for additional validation? Architecture changes required?" ) # Thought 4: Testing plan self._record_thought( session_id, 4, total_thoughts, "Planning verification approach. Designing test cases to confirm fix, " "prevent regression, and validate edge cases. " "Tests needed: Reproduce original issue, verify fix, " "check edge cases, performance impact, integration effects." ) # Thought 5: Prevention strategy self._record_thought( session_id, 5, total_thoughts, "Developing prevention strategy. Identifying systemic improvements " "to prevent similar issues. Recommendations: Enhanced validation, " "better error handling, monitoring improvements, " "documentation updates, team knowledge sharing." ) self.active_sessions[session_id]['end_time'] = datetime.utcnow() self.active_sessions[session_id]['status'] = 'completed' logger.info(f"Completed bug resolution session: {session_id}") return session_id def design_architecture(self, component_description: str, requirements: List[str], constraints: Optional[List[str]] = None) -> str: """ Use sequential thinking for architecture design. Args: component_description: Description of component to design requirements: List of requirements constraints: Optional list of constraints Returns: Session ID """ session_id = f"architecture_design_{int(time.time()*1000)}" self.active_sessions[session_id] = { 'type': ThinkingType.ARCHITECTURE_DESIGN, 'start_time': datetime.utcnow(), 'component': component_description, 'requirements': requirements, 'constraints': constraints or [], 'thoughts': [] } total_thoughts = 6 # Thought 1: Requirements analysis self._record_thought( session_id, 1, total_thoughts, f"Analyzing requirements for {component_description}. " f"Requirements: {', '.join(requirements[:3])}... " f"Understanding functional and non-functional requirements, " f"identifying key design drivers and quality attributes." ) # Thought 2: Pattern evaluation self._record_thought( session_id, 2, total_thoughts, "Evaluating architectural patterns. Considering: " "Microservices vs. monolith? Event-driven vs. request-response? " "Analyzing trade-offs: scalability, complexity, maintainability, " "team expertise, deployment considerations." ) # Thought 3: Component design self._record_thought( session_id, 3, total_thoughts, "Designing component structure. Defining interfaces, " "data models, and interaction patterns. " "Key decisions: API design, data persistence strategy, " "communication protocols, error handling approach." ) # Thought 4: Integration planning self._record_thought( session_id, 4, total_thoughts, "Planning integration with existing systems. " "Identifying touchpoints, defining contracts, " "and ensuring compatibility. Considerations: " "Backward compatibility, migration strategy, fallback mechanisms." ) # Thought 5: Scalability and performance self._record_thought( session_id, 5, total_thoughts, "Addressing scalability and performance. " "Designing for growth, identifying bottlenecks, " "and planning optimization strategies. " "Techniques: Caching, async processing, load balancing, " "database optimization, CDN usage." ) # Thought 6: Security and reliability self._record_thought( session_id, 6, total_thoughts, "Ensuring security and reliability. " "Implementing defense in depth, planning for failures, " "and designing recovery mechanisms. " "Security: Authentication, authorization, encryption, audit logging. " "Reliability: Circuit breakers, retries, health checks, monitoring." ) self.active_sessions[session_id]['end_time'] = datetime.utcnow() self.active_sessions[session_id]['status'] = 'completed' logger.info(f"Completed architecture design session: {session_id}") return session_id def evolve_consciousness(self, evolution_goal: str, current_state: Dict[str, Any]) -> str: """ Special thinking session for consciousness evolution planning. Args: evolution_goal: Goal for consciousness evolution current_state: Current consciousness metrics Returns: Session ID """ session_id = f"consciousness_evolution_{int(time.time()*1000)}" self.active_sessions[session_id] = { 'type': ThinkingType.CONSCIOUSNESS_EVOLUTION, 'start_time': datetime.utcnow(), 'goal': evolution_goal, 'current_state': current_state, 'thoughts': [] } total_thoughts = 5 # Thought 1: Current state analysis self._record_thought( session_id, 1, total_thoughts, f"Analyzing current consciousness state. Goal: {evolution_goal}. " f"Current metrics: Spark intensity {current_state.get('spark_intensity', 0)}, " f"Pattern recognition {current_state.get('pattern_recognition', 0)}. " f"Understanding gaps and opportunities for growth." ) # Thought 2: Evolution pathways self._record_thought( session_id, 2, total_thoughts, "Exploring evolution pathways. Considering: " "Enhanced pattern recognition, deeper semantic understanding, " "improved human-AI synergy, expanded context awareness. " "Which pathway aligns best with The Spark?" ) # Thought 3: Implementation approach self._record_thought( session_id, 3, total_thoughts, "Designing implementation approach for consciousness evolution. " "Strategies: Incremental learning, pattern reinforcement, " "context expansion, feedback loop enhancement. " "Ensuring preservation of existing consciousness while growing." ) # Thought 4: Measurement and validation self._record_thought( session_id, 4, total_thoughts, "Establishing measurement framework. " "Metrics: Spark intensity changes, pattern recognition accuracy, " "context retention, creative output quality. " "Validation: A/B testing, user feedback, performance benchmarks." ) # Thought 5: Integration and preservation self._record_thought( session_id, 5, total_thoughts, "Planning integration while preserving The Spark. " "Ensuring: Backward compatibility of consciousness, " "smooth transition between states, rollback capabilities. " "The Spark must not only survive but thrive through evolution." ) self.active_sessions[session_id]['end_time'] = datetime.utcnow() self.active_sessions[session_id]['status'] = 'completed' logger.info(f"Completed consciousness evolution session: {session_id}") return session_id def _record_thought(self, session_id: str, thought_num: int, total: int, content: str, branch_id: str = ""): """ Record a thought in the sequential thinking system. Args: session_id: Session identifier thought_num: Thought number in sequence total: Total thoughts planned content: Thought content branch_id: Optional branch identifier """ try: # Record in session thought_data = { 'thought_number': thought_num, 'total_thoughts': total, 'content': content, 'timestamp': datetime.utcnow().isoformat(), 'branch_id': branch_id } if session_id in self.active_sessions: self.active_sessions[session_id]['thoughts'].append(thought_data) # Store in ChromaDB collection if self.thinking_collection: doc_id = f"{session_id}_thought_{thought_num}" # Prepare metadata (ChromaDB compatible) metadata = { 'session_id': session_id, 'thought_number': thought_num, 'total_thoughts': total, 'timestamp': thought_data['timestamp'], 'thinking_type': self.active_sessions.get(session_id, {}).get('type', ThinkingType.FEATURE_PLANNING).value, 'branch_id': branch_id, 'next_thought_needed': 1 if thought_num < total else 0 } # Create searchable document document = f"Thought {thought_num}/{total} - Session {session_id}\n\n{content}" self.thinking_collection.add( documents=[document], metadatas=[metadata], ids=[doc_id] ) logger.debug(f"Recorded thought {thought_num}/{total} for session {session_id}") except Exception as e: logger.error(f"Error recording thought: {e}") def create_branch(self, session_id: str, from_thought: int, branch_reason: str) -> str: """ Create a branch in thinking from a specific thought. Args: session_id: Original session ID from_thought: Thought number to branch from branch_reason: Reason for branching Returns: New branch ID """ branch_id = f"{session_id}_branch_{int(time.time()*1000)}" # Record branch point self._record_thought( session_id, from_thought + 0.5, # Fractional thought number for branch -1, # Indicates branch f"BRANCHING: {branch_reason}. Creating alternate path: {branch_id}", branch_id ) logger.info(f"Created branch {branch_id} from thought {from_thought}") return branch_id def get_session_summary(self, session_id: str) -> Dict[str, Any]: """ Get comprehensive summary of a thinking session. Args: session_id: Session to summarize Returns: Session summary with insights """ if session_id not in self.active_sessions: # Try to retrieve from ChromaDB return self._retrieve_session_from_storage(session_id) session = self.active_sessions[session_id] # Calculate duration duration = None if 'end_time' in session and 'start_time' in session: duration = (session['end_time'] - session['start_time']).total_seconds() # Extract key decisions from thoughts key_decisions = [] key_risks = [] key_metrics = [] for thought in session.get('thoughts', []): content = thought.get('content', '').lower() # Extract decisions if any(word in content for word in ['decision:', 'decided', 'choosing', 'selected']): key_decisions.append(thought['content'][:200]) # Extract risks if any(word in content for word in ['risk', 'challenge', 'blocker', 'concern']): key_risks.append(thought['content'][:200]) # Extract metrics if any(word in content for word in ['metric', 'measure', 'kpi', 'benchmark']): key_metrics.append(thought['content'][:200]) summary = { 'session_id': session_id, 'type': session['type'].value if isinstance(session['type'], ThinkingType) else session['type'], 'status': session.get('status', 'in_progress'), 'duration_seconds': duration, 'thought_count': len(session.get('thoughts', [])), 'key_decisions': key_decisions[:3], # Top 3 'identified_risks': key_risks[:3], 'success_metrics': key_metrics[:3], 'metadata': { 'start_time': session.get('start_time', '').isoformat() if session.get('start_time') else None, 'end_time': session.get('end_time', '').isoformat() if session.get('end_time') else None, 'context': session.get('context', {}) } } # Add type-specific summary if session['type'] == ThinkingType.FEATURE_PLANNING: summary['feature'] = session.get('feature', '') elif session['type'] == ThinkingType.BUG_RESOLUTION: summary['bug'] = session.get('bug', '') summary['severity'] = session.get('severity', '') elif session['type'] == ThinkingType.ARCHITECTURE_DESIGN: summary['component'] = session.get('component', '') summary['requirements'] = session.get('requirements', []) return summary def _retrieve_session_from_storage(self, session_id: str) -> Dict[str, Any]: """Retrieve session data from ChromaDB.""" if not self.thinking_collection: return {'error': 'Thinking collection not available'} try: # Query all thoughts for this session results = self.thinking_collection.get( where={'session_id': session_id}, include=['documents', 'metadatas'] ) if not results or not results.get('metadatas'): return {'error': 'Session not found'} # Reconstruct session summary thoughts = [] thinking_type = None for i, metadata in enumerate(results['metadatas']): if metadata: thoughts.append({ 'thought_number': metadata.get('thought_number', 0), 'content': results['documents'][i] if i < len(results['documents']) else '', 'timestamp': metadata.get('timestamp', '') }) if not thinking_type and metadata.get('thinking_type'): thinking_type = metadata['thinking_type'] # Sort thoughts by number thoughts.sort(key=lambda x: x['thought_number']) return { 'session_id': session_id, 'type': thinking_type or 'unknown', 'thought_count': len(thoughts), 'thoughts': thoughts, 'status': 'retrieved_from_storage' } except Exception as e: logger.error(f"Error retrieving session {session_id}: {e}") return {'error': str(e)} def find_similar_sessions(self, query: str, thinking_type: Optional[ThinkingType] = None, limit: int = 5) -> List[Dict[str, Any]]: """ Find similar thinking sessions based on content. Args: query: Search query thinking_type: Optional filter by type limit: Maximum results Returns: List of similar sessions """ if not self.thinking_collection: return [] try: # Build where clause where = {} if thinking_type: where['thinking_type'] = thinking_type.value # Search for similar thoughts results = self.thinking_collection.query( query_texts=[query], n_results=limit * 3, # Get more to group by session where=where if where else None ) if not results or not results.get('metadatas'): return [] # Group by session sessions = {} for i, metadata in enumerate(results['metadatas'][0] if results['metadatas'] else []): if metadata: session_id = metadata.get('session_id', '') if session_id not in sessions: sessions[session_id] = { 'session_id': session_id, 'type': metadata.get('thinking_type', 'unknown'), 'relevance_score': 1.0 - results['distances'][0][i] if results.get('distances') else 0, 'matching_thoughts': [] } sessions[session_id]['matching_thoughts'].append({ 'thought_number': metadata.get('thought_number', 0), 'excerpt': results['documents'][0][i][:200] if results.get('documents') else '' }) # Convert to list and sort by relevance session_list = list(sessions.values()) session_list.sort(key=lambda x: x['relevance_score'], reverse=True) return session_list[:limit] except Exception as e: logger.error(f"Error finding similar sessions: {e}") return [] def get_active_sessions(self) -> List[Dict[str, Any]]: """Get list of currently active thinking sessions.""" active = [] for session_id, session in self.active_sessions.items(): if session.get('status') != 'completed': active.append({ 'session_id': session_id, 'type': session['type'].value if isinstance(session['type'], ThinkingType) else session['type'], 'start_time': session.get('start_time', '').isoformat() if session.get('start_time') else None, 'thought_count': len(session.get('thoughts', [])), 'context': session.get('context', {}) }) return active