Files
Mai/.planning/phases/01-model-interface/01-02-SUMMARY.md
Mai Development 2e04873b1a
Some checks failed
Discord Webhook / git (push) Has been cancelled
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
2026-01-27 12:15:57 -05:00

4.1 KiB

phase, plan, subsystem, tags, requires, provides, affects, tech-stack, key-files, key-decisions, patterns-established, duration, completed
phase plan subsystem tags requires provides affects tech-stack key-files key-decisions patterns-established duration completed
01-model-interface 02 database, memory
sqlite
pydantic
context-management
compression
conversation-history
phase plan provides
01-model-interface 01 LM Studio connectivity and resource monitoring foundation
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
01-model-interface-03
02-memory
added patterns
pydantic for data validation
sqlite for storage (planned)
token estimation heuristics
hybrid compression strategy
importance-based message retention
adaptive context windows
created modified
src/models/conversation.py
src/models/context_manager.py
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
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
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:

  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