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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Demo User
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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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