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/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*