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>
17 lines
411 B
TOML
17 lines
411 B
TOML
[project]
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name = "bosch-hvac-products-bot"
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version = "0.1.0"
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description = "Add your description here"
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readme = "README.md"
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requires-python = ">=3.11"
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dependencies = [
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"beautifulsoup4>=4.15.0",
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"faiss-cpu>=1.15.0",
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"langchain>=1.4.0",
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"langchain-community>=0.4.2",
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"langchain-google-genai>=4.4.0",
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"langchain-text-splitters>=1.1.2",
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"python-dotenv>=1.2.3",
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"requests>=2.34.2",
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]
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