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>
This commit is contained in:
Demo User
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.env.example
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.env.example
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# Copy this file to .env and fill in your key
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# Google AI Studio API key (free tier) — used for both embeddings and generation
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# Get one at aistudio.google.com
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GOOGLE_API_KEY=your-google-api-key-here
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.gitignore
vendored
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.gitignore
vendored
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.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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.python-version
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.python-version
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3.11
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README.md
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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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145
evaluate.py
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evaluate.py
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import json
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import os
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import time
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from typing import Optional
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from pathlib import Path
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from dotenv import load_dotenv
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from src.rag import BoschProductRAG
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load_dotenv()
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class Evaluator:
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"""Evaluate RAG performance: latency and accuracy."""
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def __init__(self, catalog_path: str):
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self.catalog_path = catalog_path
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self.rag = BoschProductRAG()
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self.rag.build_index(self.rag.load_products(catalog_path))
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self.results = []
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def run_latency_tests(self, num_queries: int = 5) -> dict:
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"""Measure end-to-end query latency."""
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test_queries = [
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"What's the most energy-efficient inverter system?",
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"Which heat pump can handle the widest operating range?",
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"What cooling systems use R32 refrigerant?",
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"Can you recommend an IAQ system for home filtration?",
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"What smart thermostat models are available?",
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]
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latencies = []
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for i, query in enumerate(test_queries[:num_queries]):
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result = self.rag.query(query)
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latencies.append(result["latency_ms"])
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print(f" Query {i+1}: {result['latency_ms']:.1f}ms")
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return {
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"mean_latency_ms": sum(latencies) / len(latencies),
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"min_latency_ms": min(latencies),
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"max_latency_ms": max(latencies),
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"num_queries": len(latencies),
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"latencies": latencies,
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}
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def run_accuracy_tests(self) -> dict:
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"""Manual spot-check: test queries with expected answer patterns."""
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test_cases = [
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{
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"query": "What's the most energy-efficient heat pump system?",
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"expected_keywords": ["IDS", "SEER2", "efficiency"],
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"description": "Efficiency question should mention high SEER2 models"
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},
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{
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"query": "Which system is best for a retrofit installation?",
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"expected_keywords": ["IDS Pro", "ductless", "retrofit"],
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"description": "Retrofit question should mention ductless options"
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},
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{
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"query": "What's the noise level of your heat pumps?",
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"expected_keywords": ["dB", "noise", "quiet"],
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"description": "Noise question should reference dB ratings"
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},
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{
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"query": "Do you offer smart controls for thermostats?",
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"expected_keywords": ["thermostat", "smart", "control"],
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"description": "Smart controls question should mention thermostat models"
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},
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{
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"query": "What air quality systems do you have?",
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"expected_keywords": ["IAQ", "filtration", "air"],
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"description": "Air quality question should mention IAQ system"
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},
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]
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correct = 0
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for i, test_case in enumerate(test_cases):
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result = self.rag.query(test_case["query"])
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answer = result["answer"].lower()
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# Check if any expected keyword appears in the answer
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found = any(kw.lower() in answer for kw in test_case["expected_keywords"])
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status = "✓" if found else "✗"
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print(f" {status} Test {i+1}: {test_case['description']}")
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if not found:
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print(f" Expected keywords: {test_case['expected_keywords']}")
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if found:
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correct += 1
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accuracy = (correct / len(test_cases)) * 100 if test_cases else 0
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return {
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"accuracy_percent": accuracy,
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"passed": correct,
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"total": len(test_cases),
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"test_cases": test_cases,
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}
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def generate_report(self, output_path: str = "metrics_report.json") -> None:
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"""Run all tests and generate a report."""
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print("\n" + "=" * 60)
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print("RUNNING LATENCY TESTS")
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print("=" * 60)
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latency_results = self.run_latency_tests(num_queries=5)
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print("\n" + "=" * 60)
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print("RUNNING ACCURACY TESTS")
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print("=" * 60)
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accuracy_results = self.run_accuracy_tests()
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report = {
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"timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
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"latency": latency_results,
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"accuracy": accuracy_results,
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"summary": {
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"mean_latency_ms": latency_results["mean_latency_ms"],
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"accuracy_percent": accuracy_results["accuracy_percent"],
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"status": "PASS" if accuracy_results["accuracy_percent"] >= 80 else "NEEDS REVIEW"
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}
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}
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with open(output_path, 'w') as f:
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json.dump(report, f, indent=2)
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print("\n" + "=" * 60)
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print("EVALUATION REPORT")
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print("=" * 60)
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print(f"Mean Latency: {report['summary']['mean_latency_ms']:.1f}ms")
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print(f"Accuracy: {report['summary']['accuracy_percent']:.1f}%")
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print(f"Status: {report['summary']['status']}")
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print(f"Report saved to: {output_path}")
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print("=" * 60 + "\n")
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if __name__ == "__main__":
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import sys
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catalog_path = os.path.join(
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os.path.dirname(__file__), "src", "products_catalog.json"
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)
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evaluator = Evaluator(catalog_path)
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evaluator.generate_report()
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main.py
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main.py
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def main():
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from src.cli import query_knowledge_bot
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query_knowledge_bot()
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if __name__ == "__main__":
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main()
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pyproject.toml
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pyproject.toml
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[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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0
src/__init__.py
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0
src/__init__.py
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63
src/cli.py
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src/cli.py
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import os
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import json
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import sys
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from pathlib import Path
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from dotenv import load_dotenv
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from src.rag import BoschProductRAG
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load_dotenv()
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def query_knowledge_bot(question: str = None, catalog: str = None, format: str = "text"):
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"""Query the Bosch HVAC Product Knowledge Bot."""
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if not question:
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if len(sys.argv) > 1:
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question = sys.argv[1]
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else:
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print("Usage: python -m src.cli 'Your question here'")
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sys.exit(1)
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if not catalog:
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catalog = os.path.join(
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os.path.dirname(__file__), "products_catalog.json"
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)
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try:
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print("Initializing RAG system...")
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rag = BoschProductRAG()
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documents = rag.load_products(catalog)
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rag.build_index(documents)
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print("Querying knowledge base...\n")
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result = rag.query(question)
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if format == "json":
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print(json.dumps(result, indent=2))
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else:
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print("=" * 60)
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print("ANSWER")
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print("=" * 60)
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print(result["answer"])
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print("\n" + "=" * 60)
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print("SOURCES")
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print("=" * 60)
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for source in result["sources"]:
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print(f"• {source['name']}")
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if source["url"]:
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print(f" {source['url']}")
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print("\n" + "=" * 60)
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print(f"LATENCY: {result['latency_ms']:.1f}ms")
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print("=" * 60 + "\n")
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except Exception as e:
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print(f"Error: {e}", file=sys.stderr)
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import traceback
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traceback.print_exc()
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sys.exit(1)
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if __name__ == "__main__":
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query_knowledge_bot()
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142
src/products_catalog.json
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src/products_catalog.json
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{
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"products": [
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{
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"id": "ids-edge",
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"name": "IDS Edge - Inverter Ducted Split Heat Pump",
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"category": "Heating and Cooling Systems",
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"description": "High-efficiency inverter ducted split heat pump system with advanced climate control. Ideal for residential and small commercial applications.",
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"specs": {
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"type": "Inverter Ducted Split",
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"capacity": "1.5 to 5.0 kW",
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"cooling_rating": "4.5 to 14 kW",
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"heating_rating": "5.0 to 16 kW",
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"efficiency_cooling": "SEER2 up to 21",
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"efficiency_heating": "HSPF2 up to 12",
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"refrigerant": "R32",
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"noise_level": "22-28 dB(A)",
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"features": [
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"Smart temperature control",
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"Whisper-quiet operation",
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"Energy-efficient inverter compressor",
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"Wide operating range",
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"Eco-friendly refrigerant"
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]
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},
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"url": "https://www.bosch-homecomfort.com/us/en/ocs/residential/products/inverter-ducted-split-ids-edge/"
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},
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{
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"id": "ids-pro",
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"name": "IDS Pro - Inverter Ductless Split System",
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"category": "Heating and Cooling Systems",
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"description": "Professional-grade inverter ductless split system for flexible zone control. Perfect for retrofit applications and multi-zone installations.",
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"specs": {
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"type": "Inverter Ductless Split",
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"capacity": "1.0 to 4.0 kW",
|
||||
"cooling_rating": "3.5 to 12 kW",
|
||||
"heating_rating": "4.0 to 14 kW",
|
||||
"efficiency_cooling": "SEER2 up to 20",
|
||||
"efficiency_heating": "HSPF2 up to 11",
|
||||
"refrigerant": "R32",
|
||||
"noise_level": "19-26 dB(A)",
|
||||
"features": [
|
||||
"Indoor/outdoor split design",
|
||||
"Multiple indoor unit options",
|
||||
"Precise temperature control",
|
||||
"Low operating cost",
|
||||
"Compact outdoor unit"
|
||||
]
|
||||
},
|
||||
"url": "https://www.bosch-homecomfort.com/us/en/ocs/residential/products/inverter-ductless-split-ids-pro/"
|
||||
},
|
||||
{
|
||||
"id": "iaq-ultra",
|
||||
"name": "IAQ Ultra - Indoor Air Quality System",
|
||||
"category": "Indoor Air Quality",
|
||||
"description": "Advanced indoor air quality system combining filtration, humidification, and dehumidification for optimal climate and air purity.",
|
||||
"specs": {
|
||||
"type": "Multi-function IAQ",
|
||||
"filtration": "MERV 13 with activated carbon",
|
||||
"humidity_range": "30-60% RH",
|
||||
"coverage": "Up to 3000 sq ft",
|
||||
"features": [
|
||||
"High-efficiency filtration",
|
||||
"Humidity balancing",
|
||||
"Odor reduction",
|
||||
"Allergen removal",
|
||||
"Smart sensors"
|
||||
]
|
||||
},
|
||||
"url": "https://www.bosch-homecomfort.com/us/en/ocs/residential/products/indoor-air-quality-iaq-ultra/"
|
||||
},
|
||||
{
|
||||
"id": "heatpump-condenser",
|
||||
"name": "Bosch Air-Source Heat Pump Condenser",
|
||||
"category": "Heating and Cooling Systems",
|
||||
"description": "Efficient air-source heat pump condenser unit for residential heating and cooling. Works with existing ductwork.",
|
||||
"specs": {
|
||||
"type": "Air-Source Heat Pump",
|
||||
"capacity": "2.0 to 6.0 kW",
|
||||
"cooling_rating": "7 to 18 kW",
|
||||
"heating_rating": "8 to 21 kW",
|
||||
"efficiency_cooling": "SEER2 up to 16",
|
||||
"efficiency_heating": "HSPF2 up to 9",
|
||||
"compressor": "Scroll compressor with magnetic bearings",
|
||||
"refrigerant": "R410A or R32 options",
|
||||
"noise_level": "72-78 dB(A)",
|
||||
"installation": "Outdoor unit only, compatible with existing indoor systems",
|
||||
"features": [
|
||||
"Compatible with most furnaces",
|
||||
"Low-vibration mounting",
|
||||
"Durable galvanized steel cabinet",
|
||||
"Wide operating temperature range",
|
||||
"Smart controls ready"
|
||||
]
|
||||
},
|
||||
"url": "https://www.bosch-homecomfort.com/us/en/ocs/residential/products/heat-pump-condenser/"
|
||||
},
|
||||
{
|
||||
"id": "smart-thermostat",
|
||||
"name": "Bosch Smart Thermostat BCC100",
|
||||
"category": "Controls and Accessories",
|
||||
"description": "Intelligent Wi-Fi enabled thermostat for precise climate control and energy monitoring. Compatible with most HVAC systems.",
|
||||
"specs": {
|
||||
"type": "Smart Thermostat",
|
||||
"connectivity": "Wi-Fi 802.11 b/g/n",
|
||||
"display": "3.5-inch color touchscreen",
|
||||
"compatibility": "Heat pump, furnace, AC, and mixed systems",
|
||||
"learning": "Smart scheduling and occupancy detection",
|
||||
"energy_reporting": "Real-time usage tracking",
|
||||
"features": [
|
||||
"Mobile app control",
|
||||
"Geofencing",
|
||||
"Voice assistant integration",
|
||||
"Energy reports",
|
||||
"Backup battery"
|
||||
]
|
||||
},
|
||||
"url": "https://www.bosch-homecomfort.com/us/en/ocs/residential/products/smart-thermostat-bcc100/"
|
||||
},
|
||||
{
|
||||
"id": "ventilation-hru",
|
||||
"name": "Bosch Heat Recovery Ventilator (HRV)",
|
||||
"category": "Ventilation Systems",
|
||||
"description": "Energy recovery ventilator providing fresh air while retaining heating/cooling energy. Essential for modern tight homes.",
|
||||
"specs": {
|
||||
"type": "Heat Recovery Ventilator",
|
||||
"air_flow": "50-150 CFM adjustable",
|
||||
"energy_recovery": "Up to 87% efficiency",
|
||||
"noise_level": "33-41 dB(A)",
|
||||
"filter": "MERV 8 replaceable",
|
||||
"installation": "In-wall or ceiling mounted",
|
||||
"features": [
|
||||
"Silent operation",
|
||||
"Humidity control",
|
||||
"Low maintenance",
|
||||
"Frost protection",
|
||||
"Smart controls compatible"
|
||||
]
|
||||
},
|
||||
"url": "https://www.bosch-homecomfort.com/us/en/ocs/residential/products/heat-recovery-ventilator/"
|
||||
}
|
||||
]
|
||||
}
|
||||
205
src/rag.py
Normal file
205
src/rag.py
Normal file
@@ -0,0 +1,205 @@
|
||||
import json
|
||||
import os
|
||||
import time
|
||||
from typing import Optional
|
||||
|
||||
from langchain_text_splitters import RecursiveCharacterTextSplitter
|
||||
from langchain_community.vectorstores import FAISS
|
||||
from langchain_google_genai import GoogleGenerativeAIEmbeddings, ChatGoogleGenerativeAI
|
||||
from langchain_core.documents import Document
|
||||
from langchain_core.prompts import PromptTemplate
|
||||
from langchain_core.runnables import RunnablePassthrough
|
||||
|
||||
|
||||
class BoschProductRAG:
|
||||
"""RAG system for Bosch HVAC product knowledge base.
|
||||
|
||||
Generation + Embeddings: Google Gemini (single free-tier API key).
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
google_api_key: Optional[str] = None,
|
||||
llm_model: str = "gemini-flash-lite-latest",
|
||||
):
|
||||
"""Initialize RAG with a Google API key (used for both embeddings and generation)."""
|
||||
google_key = google_api_key or os.getenv("GOOGLE_API_KEY")
|
||||
|
||||
if not google_key:
|
||||
raise ValueError(
|
||||
"GOOGLE_API_KEY not set. Please set it in .env or pass it to __init__ "
|
||||
"(free key at aistudio.google.com)"
|
||||
)
|
||||
|
||||
self.embeddings = GoogleGenerativeAIEmbeddings(
|
||||
model="models/gemini-embedding-001", google_api_key=google_key
|
||||
)
|
||||
self.llm = ChatGoogleGenerativeAI(
|
||||
model=llm_model, google_api_key=google_key, temperature=0.7
|
||||
)
|
||||
self.vector_store = None
|
||||
self.retriever = None
|
||||
self.chain = None
|
||||
|
||||
def load_products(self, catalog_path: str) -> list[Document]:
|
||||
"""Load product catalog and convert to LangChain Documents."""
|
||||
with open(catalog_path, 'r') as f:
|
||||
data = json.load(f)
|
||||
|
||||
documents = []
|
||||
for product in data["products"]:
|
||||
# Create a document per product with all details
|
||||
text = self._product_to_text(product)
|
||||
doc = Document(
|
||||
page_content=text,
|
||||
metadata={
|
||||
"product_id": product["id"],
|
||||
"product_name": product["name"],
|
||||
"category": product["category"],
|
||||
"url": product.get("url", ""),
|
||||
}
|
||||
)
|
||||
documents.append(doc)
|
||||
|
||||
return documents
|
||||
|
||||
@staticmethod
|
||||
def _product_to_text(product: dict) -> str:
|
||||
"""Convert product dict to readable text for embedding."""
|
||||
lines = [
|
||||
f"Product: {product['name']}",
|
||||
f"Category: {product['category']}",
|
||||
f"Description: {product['description']}",
|
||||
"Specifications:",
|
||||
]
|
||||
|
||||
for key, value in product["specs"].items():
|
||||
if isinstance(value, list):
|
||||
lines.append(f" {key}: {', '.join(value)}")
|
||||
else:
|
||||
lines.append(f" {key}: {value}")
|
||||
|
||||
return "\n".join(lines)
|
||||
|
||||
def build_index(self, documents: list[Document]) -> None:
|
||||
"""Build FAISS vector index from documents."""
|
||||
if not documents:
|
||||
raise ValueError("No documents to index")
|
||||
|
||||
# Split into smaller chunks for better retrieval
|
||||
splitter = RecursiveCharacterTextSplitter(
|
||||
chunk_size=300, chunk_overlap=50
|
||||
)
|
||||
splits = splitter.split_documents(documents)
|
||||
|
||||
print(f"Building index from {len(splits)} chunks...")
|
||||
self.vector_store = FAISS.from_documents(splits, self.embeddings)
|
||||
self.retriever = self.vector_store.as_retriever(k=3)
|
||||
|
||||
# Create RAG chain
|
||||
template = """You are a helpful Bosch HVAC product specialist. Answer questions about Bosch products based on the provided context.
|
||||
|
||||
Context:
|
||||
{context}
|
||||
|
||||
Question: {question}
|
||||
|
||||
Answer concisely with product names and key specs. Mention which products are most relevant."""
|
||||
|
||||
prompt = PromptTemplate(template=template, input_variables=["context", "question"])
|
||||
|
||||
def format_docs(docs):
|
||||
return "\n\n".join(doc.page_content for doc in docs)
|
||||
|
||||
self.chain = (
|
||||
{"context": self.retriever | format_docs, "question": RunnablePassthrough()}
|
||||
| prompt
|
||||
| self.llm
|
||||
)
|
||||
|
||||
def query(self, question: str) -> dict:
|
||||
"""Query the RAG system and return answer with sources and timing."""
|
||||
if not self.chain or not self.retriever:
|
||||
raise RuntimeError("RAG index not built. Call build_index() first.")
|
||||
|
||||
start = time.time()
|
||||
|
||||
# Get retrieved documents for sources
|
||||
retrieved_docs = self.retriever.invoke(question)
|
||||
|
||||
# Get LLM response
|
||||
response = self.chain.invoke(question)
|
||||
|
||||
latency = time.time() - start
|
||||
|
||||
# Extract source products from retrieved documents
|
||||
source_products = []
|
||||
seen = set()
|
||||
for doc in retrieved_docs:
|
||||
product_name = doc.metadata.get("product_name", "Unknown")
|
||||
if product_name not in seen:
|
||||
source_products.append({
|
||||
"name": product_name,
|
||||
"url": doc.metadata.get("url", ""),
|
||||
})
|
||||
seen.add(product_name)
|
||||
|
||||
return {
|
||||
"answer": self._extract_text(response),
|
||||
"sources": source_products,
|
||||
"latency_ms": latency * 1000,
|
||||
}
|
||||
|
||||
@staticmethod
|
||||
def _extract_text(response) -> str:
|
||||
"""Extract plain text from a LangChain message, handling both string
|
||||
and list-of-content-block formats (Gemini returns structured parts)."""
|
||||
content = response.content if hasattr(response, "content") else response
|
||||
if isinstance(content, str):
|
||||
return content
|
||||
if isinstance(content, list):
|
||||
parts = [
|
||||
block.get("text", "")
|
||||
for block in content
|
||||
if isinstance(block, dict) and block.get("type") == "text"
|
||||
]
|
||||
return "\n".join(p for p in parts if p)
|
||||
return str(content)
|
||||
|
||||
|
||||
def create_rag_demo():
|
||||
"""Create and test a RAG instance (for development)."""
|
||||
import sys
|
||||
|
||||
catalog_path = os.path.join(os.path.dirname(__file__), "products_catalog.json")
|
||||
|
||||
print("Initializing RAG system...")
|
||||
rag = BoschProductRAG()
|
||||
|
||||
print("Loading product catalog...")
|
||||
documents = rag.load_products(catalog_path)
|
||||
|
||||
print(f"Loaded {len(documents)} products")
|
||||
print("Building vector index...")
|
||||
rag.build_index(documents)
|
||||
|
||||
print("\n--- RAG System Ready ---\n")
|
||||
|
||||
# Test queries
|
||||
test_queries = [
|
||||
"What's the most energy-efficient heat pump you offer?",
|
||||
"Can you recommend a system for a retrofit installation?",
|
||||
"What cooling systems have R32 refrigerant?",
|
||||
]
|
||||
|
||||
for query in test_queries:
|
||||
print(f"Q: {query}")
|
||||
result = rag.query(query)
|
||||
print(f"A: {result['answer']}")
|
||||
print(f"Latency: {result['latency_ms']:.1f}ms")
|
||||
print(f"Sources: {', '.join([s['name'] for s in result['sources']])}")
|
||||
print()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
create_rag_demo()
|
||||
Reference in New Issue
Block a user