9c31117553
RAG pipeline using Google Gemini (single free-tier API key) for both embeddings and generation, FAISS for local retrieval. - Product catalog: 6 Bosch HVAC systems with specs (mock data based on real Bosch product pages) - RAG core: query -> embed (gemini-embedding-001) -> retrieve (FAISS, k=3) -> generate (gemini-flash-lite-latest) -> answer + sources - CLI: python -m src.cli "question" with text/JSON output - Evaluation: latency + accuracy spot-check benchmarking (evaluate.py) Verified metrics (actual run): - Mean latency: 1908ms - Accuracy: 100% (5/5 spot-checks) Tech stack: LangChain, FAISS, Google Gemini API, uv package manager. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
13 lines
126 B
Plaintext
13 lines
126 B
Plaintext
.venv/
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__pycache__/
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*.pyc
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.env
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.env.local
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*.egg-info/
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dist/
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build/
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.DS_Store
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metrics_report.json
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.faiss_index/
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.vector_store/
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