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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
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4.1 KiB
phase, plan, subsystem, tags, requires, provides, affects, tech-stack, key-files, key-decisions, patterns-established, duration, completed
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5 min | 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:
- Task 1: Create conversation data structures -
221717d(feat) - 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 validationsrc/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