---
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*