docs(01-02): complete conversation context management plan

Tasks completed: 2/2
- Created conversation data structures with Pydantic validation
- Implemented intelligent context manager with hybrid compression

SUMMARY: .planning/phases/01-model-interface/01-02-SUMMARY.md
STATE: Updated to reflect Plan 2 completion
ROADMAP: Updated Plan 2 as complete

.planning/ROADMAP.md

@@ -16,8 +16,8 @@ Mai's development is organized into three major milestones, each delivering dist
 - Manage model context efficiently (conversation history, system prompt, token budget)
 
 **Plans:** 3 plans in 2 waves
-- [ ] 01-01-PLAN.md — LM Studio connectivity and resource monitoring foundation
+- [x] 01-01-PLAN.md — LM Studio connectivity and resource monitoring foundation
-- [ ] 01-02-PLAN.md — Conversation context management and memory system  
+- [x] 01-02-PLAN.md — Conversation context management and memory system  
 - [ ] 01-03-PLAN.md — Intelligent model switching integration
 
 ### Phase 2: Safety & Sandboxing

.planning/STATE.md

@@ -1,7 +1,7 @@
 # Project State & Progress
 
 **Last Updated:** 2026-01-27
-**Current Status:** Phase 1, Plan 1 complete - foundation infrastructure ready
+**Current Status:** Phase 1, Plan 2 complete - conversation context management implemented
 
 ---
 
@@ -11,9 +11,9 @@
 |--------|-------|
 | **Milestone** | v1.0 Core (Phases 1-5) |
 | **Current Phase** | 01: Model Interface & Switching |
-| **Current Plan** | 01 of 3 (in phase) |
+| **Current Plan** | 02 of 3 (in phase) |
-| **Overall Progress** | 1/15 phases complete |
+| **Overall Progress** | 2/15 phases complete |
-| **Progress Bar** | ███░░░░░░░░░░░ 7% |
+| **Progress Bar** | ████░░░░░░░░ 13% |
 | **Model Profile** | Budget (haiku priority) |
 
 ---
@@ -41,18 +41,19 @@
 - **2026-01-27**: Gathered Phase 1 context and created detailed execution plan (01-01-PLAN.md)
 - **2026-01-27**: Configured GSD workflow with MCP tools (Hugging Face, WebSearch)
 - **2026-01-27**: **EXECUTED** Phase 1, Plan 1 - Created LM Studio connectivity and resource monitoring foundation
+- **2026-01-27**: **EXECUTED** Phase 1, Plan 2 - Implemented conversation context management and memory system
 
 ---
 
 ## What's Next
 
-**Phase 1, Plan 2: Conversation context management and memory system**
+**Phase 1, Plan 3: Intelligent model switching integration**
-- Implement conversation history storage (SQLite or lightweight DB)
+- Implement model selection based on system resources
-- Create context window management with token tracking
+- Create seamless switching between LM Studio models
-- Implement hybrid compression strategy (summarize old, keep recent)
+- Add context preservation during model changes
-- Add memory retrieval and pattern recognition
+- Implement failure recovery and fallback mechanisms
 
-Status: Ready to execute 01-02-PLAN.md when available.
+Status: Ready to execute 01-03-PLAN.md when available.
 
 ---
 

.planning/phases/01-model-interface/01-02-SUMMARY.md

@@ -0,0 +1,116 @@
+---
+phase: 01-model-interface
+plan: 02
+subsystem: database, memory
+tags: [sqlite, pydantic, context-management, compression, conversation-history]
+
+# Dependency graph
+requires:
+  - phase: 01-model-interface
+    plan: 01
+    provides: "LM Studio connectivity and resource monitoring foundation"
+provides:
+  - Conversation data structures with validation and serialization
+  - Intelligent context management with hybrid compression strategy
+  - Token budgeting and window management for different model sizes
+  - Message importance scoring and selective retention
+  - Conversation persistence and session management
+affects: [01-model-interface-03, 02-memory]
+
+# Tech tracking
+tech-stack:
+  added: [pydantic for data validation, sqlite for storage (planned), token estimation heuristics]
+  patterns: [hybrid compression strategy, importance-based message retention, adaptive context windows]
+
+key-files:
+  created: [src/models/conversation.py, src/models/context_manager.py]
+  modified: []
+
+key-decisions:
+  - "Used Pydantic models for type safety and validation instead of dataclasses"
+  - "Implemented hybrid compression: summarize very old, keep some middle, preserve all recent"
+  - "Fixed 70% compression threshold from CONTEXT.md for consistent behavior"
+  - "Added message importance scoring based on role, content, and recency"
+  - "Implemented adaptive context sizing for different model capabilities"
+
+patterns-established:
+  - "Pattern 1: Message importance scoring for compression decisions"
+  - "Pattern 2: Hybrid compression preserving user instructions and system messages"
+  - "Pattern 3: Token budget management with safety margins"
+  - "Pattern 4: Context window adaptation to different model sizes"
+
+# Metrics
+duration: 5 min
+completed: 2026-01-27
+---
+
+# Phase 1 Plan 2: Conversation Context Management Summary
+
+**Implemented conversation history storage with intelligent compression and token budget management**
+
+## Performance
+
+- **Duration:** 5 min
+- **Started:** 2026-01-27T17:05:37Z
+- **Completed:** 2026-01-27T17:10:46Z
+- **Tasks:** 2
+- **Files modified:** 2
+
+## Accomplishments
+- Created comprehensive conversation data models with Pydantic validation
+- Implemented intelligent context manager with hybrid compression at 70% threshold
+- Added message importance scoring based on role, content type, and recency
+- Built token estimation and budget management system
+- Established adaptive context windows for different model sizes
+
+## Task Commits
+
+Each task was committed atomically:
+
+1. **Task 1: Create conversation data structures** - `221717d` (feat)
+2. **Task 2: Implement context manager with compression** - `ef2eba2` (feat)
+
+**Plan metadata:** N/A (docs only)
+
+## Files Created/Modified
+- `src/models/conversation.py` - Data models for messages, conversations, and context windows with validation
+- `src/models/context_manager.py` - Context management with intelligent compression and token budgeting
+
+## Decisions Made
+
+- Used Pydantic models over dataclasses for automatic validation and serialization
+- Implemented rule-based compression strategy instead of LLM-based for v1 simplicity
+- Fixed compression threshold at 70% per CONTEXT.md requirements
+- Added message importance scoring for selective retention during compression
+- Created adaptive context windows to support different model sizes
+
+## Deviations from Plan
+
+None - plan executed exactly as written.
+
+## Issues Encountered
+
+None
+
+## User Setup Required
+
+None - no external service configuration required.
+
+## Next Phase Readiness
+
+Conversation management foundation is ready:
+- Message storage and retrieval working correctly
+- Context compression triggers at 70% threshold preserving important information
+- System supports adaptive context windows for different models
+- Ready for integration with model switching logic in next plan
+
+All verification tests passed:
+- ✓ Messages can be added and retrieved correctly
+- ✓ Context compression triggers at correct thresholds  
+- ✓ Important messages are preserved during compression
+- ✓ Token estimation works reasonably well
+- ✓ Context adapts to different model window sizes
+
+---
+*Phase: 01-model-interface*
+*Completed: 2026-01-27*