Chris Coutinho
c272ddd82d
- Add ADR-013 documenting RAG evaluation architecture - Implement two-part evaluation: Context Recall (retrieval) + Answer Correctness (generation) - Create Click CLI for ground truth generation and corpus upload - Add pytest fixtures and tests for retrieval/generation quality - Use BeIR/nfcorpus dataset with 5 selected test queries - Support Ollama and Anthropic LLM providers - Generate synthetic ground truth answers offline - Add comprehensive documentation in tests/rag_evaluation/README.md The framework separates one-time setup (generate/upload) from test execution, making tests much faster (~6-12 min vs ~15-25 min per run). Tests are manual only (not in CI) and require external LLM access. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
2 lines
73 B
Python
2 lines
73 B
Python
"""RAG evaluation tests for the Nextcloud MCP semantic search system."""
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