feat: implement RAG evaluation framework with CLI tooling

- 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>

pyproject.toml

@@ -102,7 +102,9 @@ module-root = ""
 
 [dependency-groups]
 dev = [
+    "anthropic>=0.42.0",  # For RAG evaluation with Anthropic LLMs
     "commitizen>=4.8.2",
+    "datasets>=3.3.0",  # For BeIR nfcorpus dataset loading
     "ipython>=9.2.0",
     "playwright>=1.49.1",
     "pytest>=8.3.5",