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feat(rag): implement Tier 1 in-memory RAG for interactive follow-ups
- Add embed() to AIClient using Ollama nomic-embed-text via /v1/embeddings
- Add DEFAULT_EMBED_MODEL and embed_model field to AIConfig
- New rag_retriever.py: chunk_report(), EmbeddedChunk, retrieve() (pure-Python cosine)
- prompt_builder: add build_message_with_chunks() for RAG-aware follow-up prompts
- cli: add --no-rag flag, embed report chunks after collection, retrieve top-5 per question
- Graceful fallback to full-context if embedding model unavailable
- 16 new tests in test_rag_retriever.py (67 total, all passing)
- Add chromadb>=0.5 as optional [rag] dep in pyproject.toml
- README: add step 3 (pull nomic-embed-text), update Suggested Tooling table
2026-05-04 18:36:12 +02:00
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