Felipe Cardoso
96e6400bd8
feat(context): enhance performance, caching, and settings management
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- Replace hard-coded limits with configurable settings (e.g., cache memory size, truncation strategy, relevance settings).
- Optimize parallel execution in token counting, scoring, and reranking for source diversity.
- Improve caching logic:
- Add per-context locks for safe parallel scoring.
- Reuse precomputed fingerprints for cache efficiency.
- Make truncation, scoring, and ranker behaviors fully configurable via settings.
- Add support for middle truncation, context hash-based hashing, and dynamic token limiting.
- Refactor methods for scalability and better error handling.
Tests: Updated all affected components with additional test cases.
2026-01-04 12:37:58 +01:00
Felipe Cardoso
6c7b72f130
chore(context): apply linter fixes and sort imports ( #86 )
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Phase 8 of Context Management Engine - Final Cleanup:
- Sort __all__ exports alphabetically
- Sort imports per isort conventions
- Fix minor linting issues
Final test results:
- 311 context management tests passing
- 2507 total backend tests passing
- 85% code coverage
Context Management Engine is complete with all 8 phases:
1. Foundation: Types, Config, Exceptions
2. Token Budget Management
3. Context Scoring & Ranking
4. Context Assembly Pipeline
5. Model Adapters (Claude, OpenAI)
6. Caching Layer (Redis + in-memory)
7. Main Engine & Integration
8. Testing & Documentation
🤖 Generated with [Claude Code](https://claude.com/claude-code )
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com >
2026-01-04 02:46:56 +01:00
Felipe Cardoso
0d2005ddcb
feat(context): implement context scoring and ranking (Phase 3)
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Add comprehensive scoring system with three strategies:
- RelevanceScorer: Semantic similarity with keyword fallback
- RecencyScorer: Exponential decay with type-specific half-lives
- PriorityScorer: Priority-based scoring with type bonuses
Implement CompositeScorer combining all strategies with configurable
weights (default: 50% relevance, 30% recency, 20% priority).
Add ContextRanker for budget-aware context selection with:
- Greedy selection algorithm respecting token budgets
- CRITICAL priority contexts always included
- Diversity reranking to prevent source dominance
- Comprehensive selection statistics
68 tests covering all scoring and ranking functionality.
Part of #61 - Context Management Engine
🤖 Generated with [Claude Code](https://claude.com/claude-code )
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com >
2026-01-04 02:24:06 +01:00
Felipe Cardoso
22ecb5e989
feat(context): Phase 1 - Foundation types, config and exceptions ( #79 )
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Implements the foundation for Context Management Engine:
Types (backend/app/services/context/types/):
- BaseContext: Abstract base with ID, content, priority, scoring
- SystemContext: System prompts, personas, instructions
- KnowledgeContext: RAG results from Knowledge Base MCP
- ConversationContext: Chat history with role support
- TaskContext: Task/issue context with acceptance criteria
- ToolContext: Tool definitions and execution results
- AssembledContext: Final assembled context result
Configuration (config.py):
- Token budget allocation (system 5%, task 10%, knowledge 40%, etc.)
- Scoring weights (relevance 50%, recency 30%, priority 20%)
- Cache settings (TTL, prefix)
- Performance settings (max assembly time, parallel scoring)
- Environment variable overrides with CTX_ prefix
Exceptions (exceptions.py):
- ContextError: Base exception
- BudgetExceededError: Token budget violations
- TokenCountError: Token counting failures
- CompressionError: Compression failures
- AssemblyTimeoutError: Assembly timeout
- ScoringError, FormattingError, CacheError
- ContextNotFoundError, InvalidContextError
All 86 tests pass.
🤖 Generated with [Claude Code](https://claude.com/claude-code )
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com >
2026-01-04 02:07:39 +01:00
