Apple 1ed1181105 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

2025-11-16 05:12:19 -08:00

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