Milestone 6: RAG & Contextual Intelligence
Milestone 6: RAG & Contextual Intelligence
Status: 📅 PLANNED
Target: Q3-Q4 2025
Duration: 4 weeks
Total Effort: 32-40 hours
Overview
Deep codebase understanding through vector search and retrieval-augmented generation.
Goals
- ✅ Answer architectural questions with 90% accuracy
- ✅ Provide context from 5-10 files simultaneously
- ✅ Reduce "I don't have enough context" responses by 90%
- ✅ Enable semantic code search
Features
1. Vector Database Integration ⭐
Priority: VERY HIGH
Effort: 10-12 hours
Value: VERY HIGH
Description:
ChromaDB or Qdrant for code embeddings and semantic search.
Features:
- Store code chunks as vector embeddings
- Semantic similarity search
- Multi-file context retrieval
- Incremental updates
Tech Stack:
- ChromaDB or Qdrant (lightweight, open source)
- OpenAI text-embedding-3-small or FastEmbed
Files to Add:
rag/vector_store.pyrag/embeddings.py
2. Codebase Indexing Pipeline
Priority: HIGH
Effort: 8-10 hours
Value: HIGH
Description:
Nightly job to parse codebase, chunk code, embed, and store in vector DB.
Features:
- Parse codebase into functions/classes
- Chunk code intelligently (respect boundaries)
- Generate embeddings
- Store with metadata (file, line, language)
- Incremental updates (only changed files)
Workflow:
# .gitea/workflows/rag-index.yml
on:
schedule:
- cron: "0 2 * * *" # Nightly at 2 AM
Files to Add:
rag/indexer.py.gitea/workflows/rag-index.yml
3. Semantic Code Search
Priority: VERY HIGH
Effort: 6-8 hours
Value: VERY HIGH
Description:
Natural language search: "Where is authentication handled?" → Relevant files.
Features:
- Convert question to embedding
- Search vector DB for similar code
- Return top K results
- Inject into LLM context
Example:
User: @codebot Where is rate limiting implemented?
Bot: Rate limiting is implemented in the following locations:
1. **enterprise/rate_limiter.py** (lines 45-78)
- `RateLimiter` class handles request throttling
- Uses token bucket algorithm
2. **agents/base_agent.py** (lines 120-135)
- `_rate_limit()` method enforces delays
3. **config.yml** (lines 67-72)
- Configuration: requests_per_minute, max_concurrent
Files to Modify:
agents/chat_agent.py- New:
rag/search.py
4. Cross-File Context
Priority: HIGH
Effort: 8-10 hours
Value: HIGH
Description:
Provide context from multiple related files when answering questions.
Features:
- Detect related files (imports, references)
- Retrieve context from dependencies
- Build comprehensive context window
- Avoid context overload (smart truncation)
Files to Add:
rag/context_builder.py
Success Metrics
- Answer architectural questions accurately
- Provide context from 5-10 files
- 90% reduction in "insufficient context" responses
- Semantic search finds relevant code in <1 second
- Indexed 100% of codebase
Implementation Plan
Week 1: Vector Database Setup
- Set up ChromaDB/Qdrant
- Implement embedding generation
- Test vector storage/retrieval
Week 2: Indexing Pipeline
- Build code parser (functions/classes)
- Implement chunking strategy
- Create nightly indexing workflow
Week 3: Semantic Search
- Implement search_codebase with vectors
- Integrate with ChatAgent
- Test with real queries
Week 4: Cross-File Context & Polish
- Build context builder
- Optimize query performance
- Documentation and testing
Infrastructure
Storage:
- Vector DB: ~100MB - 1GB (depends on codebase size)
- Metadata: SQLite or built-in
Compute:
- Embedding generation: CPU (or GPU if available)
- Nightly indexing: ~5-15 minutes
Cost:
- OpenAI embeddings: ~$0.10 per 1M tokens (one-time + incremental)
- Or use FastEmbed (free, local)
Dependencies
Required:
- Milestones 1-5 complete
- Vector database (ChromaDB/Qdrant)
- Embedding model access
Optional:
- GPU for faster embeddings
- Redis for caching
Last Updated: December 28, 2024
Status: 📅 PLANNED
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