init: Bosch HVAC Product Knowledge Bot - RAG system with CLI

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>

.env.example

@@ -0,0 +1,5 @@
+# Copy this file to .env and fill in your key
+
+# Google AI Studio API key (free tier) — used for both embeddings and generation
+# Get one at aistudio.google.com
+GOOGLE_API_KEY=your-google-api-key-here