feat: add RAG quality metrics, optimized prompts, and evaluation tools
Optimized Prompts:
- Create utils/rag_prompt_builder.py with citation-optimized prompts
- Specialized for DAO tokenomics and technical documentation
- Proper citation format [1], [2] with doc_id, page, section
- Memory context integration (facts, events, summaries)
- Token count estimation
RAG Service Metrics:
- Add comprehensive logging in query_pipeline.py
- Log: question, doc_ids, scores, retrieval method, timing
- Track: retrieval_time, total_query_time, documents_found, citations_count
- Add metrics in ingest_pipeline.py: pages_processed, blocks_processed, pipeline_time
Router Improvements:
- Use optimized prompt builder in _handle_rag_query()
- Add graceful fallback: if RAG unavailable, use Memory only
- Log prompt token count, RAG usage, Memory usage
- Return detailed metadata (rag_used, memory_used, citations_count, metrics)
Evaluation Tools:
- Create tests/rag_eval.py for systematic quality testing
- Test fixed questions with expected doc_ids
- Save results to JSON and CSV
- Compare RAG Service vs Router results
- Track: citations, expected docs found, query times
Documentation:
- Create docs/RAG_METRICS_PLAN.md
- Plan for Prometheus metrics collection
- Grafana dashboard panels and alerts
- Implementation guide for metrics
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