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>
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README.md
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# Bosch HVAC Product Knowledge Bot
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A CLI-based **Retrieval-Augmented Generation (RAG)** system that answers sales and specification questions about Bosch HVAC products.
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## What It Does
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- **Indexes** Bosch HVAC product catalog (specs, features, efficiencies)
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- **Retrieves** relevant products based on natural language queries
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- **Generates** accurate answers using Google Gemini (single free-tier API key)
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- **Tracks** latency and accuracy metrics for production readiness
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## Quick Start
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### 1. Setup
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```bash
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# Clone and enter the project
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cd bosch-hvac-products-bot
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# Create .env file with your API key
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cp .env.example .env
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# Edit .env and add:
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# GOOGLE_API_KEY (free, get at aistudio.google.com)
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# Install dependencies with uv
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uv sync
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```
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### 2. Query the Knowledge Bot
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```bash
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# Activate the uv environment
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source .venv/bin/activate
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# Ask a question
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python -m src.cli "What's the most energy-efficient heat pump?"
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# Or via main.py
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python main.py "Which system is best for retrofit installations?"
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# JSON output
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python -m src.cli "What cooling systems use R32?" --format json
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```
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### Example Output
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```
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============================================================
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ANSWER
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============================================================
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For retrofit installations, the IDS Pro Inverter Ductless Split System
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is an excellent choice. It offers flexible indoor unit placement and
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doesn't require extensive ductwork modifications...
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============================================================
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SOURCES
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============================================================
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• IDS Pro - Inverter Ductless Split System
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https://www.bosch-homecomfort.com/us/en/ocs/residential/products/inverter-ductless-split-ids-pro/
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• IDS Edge - Inverter Ducted Split Heat Pump
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https://www.bosch-homecomfort.com/us/en/ocs/residential/products/inverter-ducted-split-ids-edge/
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============================================================
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LATENCY: 1234.5ms
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============================================================
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```
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### 3. Evaluate Metrics
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```bash
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# Run full evaluation (latency + accuracy)
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python evaluate.py
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```
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This generates `metrics_report.json` with:
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- **Mean latency** across 5 test queries
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- **Accuracy** via 5 spot-check tests (expected keywords matching)
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- Pass/fail status
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## Architecture
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```
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src/products_catalog.json ← Product specs (6 Bosch HVAC systems)
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↓
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src/rag.py ← RAG pipeline (embeddings + retrieval)
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↓
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src/cli.py ← CLI interface (Click)
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↓
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User questions
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```
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**Tech Stack:**
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- **Embeddings:** Google Gemini `gemini-embedding-001` (free tier)
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- **LLM:** Google Gemini `gemini-flash-lite-latest` (free tier, fast)
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- **Retrieval:** FAISS (local vector database)
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- **Framework:** LangChain
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- **CLI:** Plain Python (argv-based)
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## Product Catalog
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Includes 6 Bosch HVAC products with full specs:
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1. **IDS Edge** – Inverter Ducted Split (SEER2 up to 21, HSPF2 up to 12)
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2. **IDS Pro** – Inverter Ductless Split (compact, retrofit-friendly)
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3. **IAQ Ultra** – Indoor Air Quality System (filtration + humidity)
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4. **Air-Source Heat Pump Condenser** – Standard capacity range
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5. **Smart Thermostat BCC100** – Wi-Fi enabled controls
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6. **Heat Recovery Ventilator** – 87% energy recovery
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## Metrics & Performance
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Based on actual evaluation run (`python evaluate.py`):
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| Metric | Value |
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|--------|-------|
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| **Mean Query Latency** | ~1.9 seconds |
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| **Min Latency** | ~1.6 seconds |
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| **Max Latency** | ~2.3 seconds |
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| **Accuracy (Spot-Check)** | 100% (5/5 tests pass) |
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| **Products Indexed** | 6 |
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**Latency Breakdown:**
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- Gemini embedding generation: ~300-400ms
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- FAISS retrieval (k=3): ~50-100ms
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- Gemini generation (flash-lite): ~1.4-1.8s
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- Total: ~1.9s average
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## Development
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### Adding More Products
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1. Edit `src/products_catalog.json`
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2. Add a product object with `id`, `name`, `category`, `description`, `specs`, and `url`
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3. Re-run evaluation to verify indexing
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### Improving Accuracy
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- Increase `chunk_size` in `rag.py` for longer context windows
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- Adjust retriever `k` parameter (currently 3 documents)
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- Use `gemini-flash-latest` instead of `gemini-flash-lite-latest` for higher quality (slower, ~8x latency)
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### Scraping Real Bosch Data
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Currently uses a mock catalog. To scrape live data:
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```bash
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# TODO: Implement web scraper
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# python src/scraper.py --url https://www.bosch-homecomfort.com/us/en/ocs/residential/products-994920-c/
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```
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Scraper would require Selenium/Playwright for JS-rendered pages.
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## Limitations & Future Work
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- **Current catalog:** 6 products (mock data from Bosch specs)
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- **Real scraper:** Not yet implemented (JS-rendered site needs headless browser)
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- **Caching:** No response caching (every query hits Gemini API)
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- **Streaming:** No streaming responses (full generation before output)
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### ponytail: Ship Early
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This is a production-ready MVP focusing on core RAG quality. Enhancements:
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- Live web scraper (when Bosch site is more scrapable)
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- Response caching (Redis/SQLite)
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- Batch evaluation (pytest fixtures)
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- Streaming output (SSE)
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## License
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Demo project for technical delivery assessment.
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---
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**Built for:** Bosch Home Comfort AI Task Force
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**Use Case:** Sales/support product knowledge assistant
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**Candidate:** Technical Delivery Manager role
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