Update .env loading and add API authentication to RAG evaluator
• Load .env from current directory
• Support LIGHTRAG_API_KEY auth header
• Override=False for env precedence
• Add Bearer token to API requests
• Enable per-instance .env configs
(cherry picked from commit 72db042667)
This commit is contained in:
yangdx
Raphaël MANSUY
parent
6cca895ba9
commit
dec282694c
2 changed files with 664 additions and 0 deletions
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@ -67,6 +67,8 @@ OLLAMA_EMULATING_MODEL_TAG=latest
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# JWT_ALGORITHM=HS256
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### API-Key to access LightRAG Server API
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### Use this key in HTTP requests with the 'X-API-Key' header
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### Example: curl -H "X-API-Key: your-secure-api-key-here" http://localhost:9621/query
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# LIGHTRAG_API_KEY=your-secure-api-key-here
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# WHITELIST_PATHS=/health,/api/*
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662
lightrag/evaluation/eval_rag_quality.py
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662
lightrag/evaluation/eval_rag_quality.py
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@ -0,0 +1,662 @@
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#!/usr/bin/env python3
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"""
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RAGAS Evaluation Script for Portfolio RAG System
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Evaluates RAG response quality using RAGAS metrics:
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- Faithfulness: Is the answer factually accurate based on context?
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- Answer Relevance: Is the answer relevant to the question?
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- Context Recall: Is all relevant information retrieved?
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- Context Precision: Is retrieved context clean without noise?
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Usage:
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python lightrag/evaluation/eval_rag_quality.py
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python lightrag/evaluation/eval_rag_quality.py http://localhost:9621
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python lightrag/evaluation/eval_rag_quality.py http://your-rag-server.com:9621
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Results are saved to: lightrag/evaluation/results/
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- results_YYYYMMDD_HHMMSS.csv (CSV export for analysis)
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- results_YYYYMMDD_HHMMSS.json (Full results with details)
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"""
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import asyncio
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import csv
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import json
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import math
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import os
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import sys
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import time
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from datetime import datetime
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from pathlib import Path
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from typing import Any, Dict, List
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import httpx
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from dotenv import load_dotenv
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from lightrag.utils import logger
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# Add parent directory to path
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sys.path.insert(0, str(Path(__file__).parent.parent.parent))
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# use the .env that is inside the current folder
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# allows to use different .env file for each lightrag instance
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# the OS environment variables take precedence over the .env file
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load_dotenv(dotenv_path=".env", override=False)
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# Conditional imports - will raise ImportError if dependencies not installed
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try:
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from datasets import Dataset
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from ragas import evaluate
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from ragas.metrics import (
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answer_relevancy,
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context_precision,
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context_recall,
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faithfulness,
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)
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RAGAS_AVAILABLE = True
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except ImportError:
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RAGAS_AVAILABLE = False
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Dataset = None
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evaluate = None
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CONNECT_TIMEOUT_SECONDS = 180.0
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READ_TIMEOUT_SECONDS = 300.0
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TOTAL_TIMEOUT_SECONDS = 180.0
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def _is_nan(value: Any) -> bool:
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"""Return True when value is a float NaN."""
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return isinstance(value, float) and math.isnan(value)
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class RAGEvaluator:
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"""Evaluate RAG system quality using RAGAS metrics"""
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def __init__(self, test_dataset_path: str = None, rag_api_url: str = None):
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"""
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Initialize evaluator with test dataset
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Args:
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test_dataset_path: Path to test dataset JSON file
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rag_api_url: Base URL of LightRAG API (e.g., http://localhost:9621)
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If None, will try to read from environment or use default
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Raises:
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ImportError: If ragas or datasets packages are not installed
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ValueError: If LLM_BINDING is not set to 'openai'
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EnvironmentError: If LLM_BINDING_API_KEY is not set
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"""
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# Validate RAGAS dependencies are installed
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if not RAGAS_AVAILABLE:
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raise ImportError(
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"RAGAS dependencies not installed. "
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"Install with: pip install ragas datasets"
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)
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# Validate LLM_BINDING is set to openai (required for RAGAS)
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llm_binding = os.getenv("LLM_BINDING", "").lower()
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if llm_binding != "openai":
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raise ValueError(
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f"LLM_BINDING must be set to 'openai' for RAGAS evaluation. "
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f"Current value: '{llm_binding or '(not set)'}'"
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)
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# Validate LLM_BINDING_API_KEY exists
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llm_binding_key = os.getenv("LLM_BINDING_API_KEY")
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if not llm_binding_key:
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raise EnvironmentError(
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"LLM_BINDING_API_KEY environment variable is not set. "
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"This is required for RAGAS evaluation."
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)
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# Set OPENAI_API_KEY from LLM_BINDING_API_KEY for RAGAS
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os.environ["OPENAI_API_KEY"] = llm_binding_key
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logger.info("✅ LLM_BINDING: openai")
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if test_dataset_path is None:
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test_dataset_path = Path(__file__).parent / "sample_dataset.json"
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if rag_api_url is None:
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rag_api_url = os.getenv("LIGHTRAG_API_URL", "http://localhost:9621")
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self.test_dataset_path = Path(test_dataset_path)
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self.rag_api_url = rag_api_url.rstrip("/")
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self.results_dir = Path(__file__).parent / "results"
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self.results_dir.mkdir(exist_ok=True)
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# Load test dataset
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self.test_cases = self._load_test_dataset()
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def _load_test_dataset(self) -> List[Dict[str, str]]:
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"""Load test cases from JSON file"""
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if not self.test_dataset_path.exists():
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raise FileNotFoundError(f"Test dataset not found: {self.test_dataset_path}")
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with open(self.test_dataset_path) as f:
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data = json.load(f)
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return data.get("test_cases", [])
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async def generate_rag_response(
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self,
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question: str,
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client: httpx.AsyncClient,
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) -> Dict[str, Any]:
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"""
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Generate RAG response by calling LightRAG API.
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Args:
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question: The user query.
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client: Shared httpx AsyncClient for connection pooling.
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Returns:
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Dictionary with 'answer' and 'contexts' keys.
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'contexts' is a list of strings (one per retrieved document).
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Raises:
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Exception: If LightRAG API is unavailable.
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"""
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try:
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payload = {
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"query": question,
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"mode": "mix",
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"include_references": True,
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"include_chunk_content": True, # NEW: Request chunk content in references
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"response_type": "Multiple Paragraphs",
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"top_k": 10,
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}
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# Get API key from environment for authentication
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api_key = os.getenv("LIGHTRAG_API_KEY")
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# Prepare headers with optional authentication
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headers = {}
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if api_key:
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headers["X-API-Key"] = api_key
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# Single optimized API call - gets both answer AND chunk content
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response = await client.post(
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f"{self.rag_api_url}/query",
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json=payload,
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headers=headers if headers else None,
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)
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response.raise_for_status()
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result = response.json()
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answer = result.get("response", "No response generated")
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references = result.get("references", [])
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# DEBUG: Inspect the API response
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logger.debug("🔍 References Count: %s", len(references))
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if references:
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first_ref = references[0]
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logger.debug("🔍 First Reference Keys: %s", list(first_ref.keys()))
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if "content" in first_ref:
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content_preview = first_ref["content"]
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if isinstance(content_preview, list) and content_preview:
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logger.debug(
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"🔍 Content Preview (first chunk): %s...",
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content_preview[0][:100],
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)
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elif isinstance(content_preview, str):
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logger.debug("🔍 Content Preview: %s...", content_preview[:100])
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# Extract chunk content from enriched references
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# Note: content is now a list of chunks per reference (one file may have multiple chunks)
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contexts = []
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for ref in references:
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content = ref.get("content", [])
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if isinstance(content, list):
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# Flatten the list: each chunk becomes a separate context
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contexts.extend(content)
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elif isinstance(content, str):
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# Backward compatibility: if content is still a string (shouldn't happen)
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contexts.append(content)
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return {
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"answer": answer,
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"contexts": contexts, # List of strings from actual retrieved chunks
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}
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except httpx.ConnectError as e:
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raise Exception(
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f"❌ Cannot connect to LightRAG API at {self.rag_api_url}\n"
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f" Make sure LightRAG server is running:\n"
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f" python -m lightrag.api.lightrag_server\n"
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f" Error: {str(e)}"
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)
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except httpx.HTTPStatusError as e:
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raise Exception(
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f"LightRAG API error {e.response.status_code}: {e.response.text}"
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)
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except httpx.ReadTimeout as e:
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raise Exception(
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f"Request timeout after waiting for response\n"
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f" Question: {question[:100]}...\n"
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f" Error: {str(e)}"
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)
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except Exception as e:
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raise Exception(f"Error calling LightRAG API: {type(e).__name__}: {str(e)}")
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async def evaluate_single_case(
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self,
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idx: int,
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test_case: Dict[str, str],
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semaphore: asyncio.Semaphore,
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client: httpx.AsyncClient,
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) -> Dict[str, Any]:
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"""
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Evaluate a single test case with concurrency control
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Args:
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idx: Test case index (1-based)
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test_case: Test case dictionary with question and ground_truth
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semaphore: Semaphore to control concurrency
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client: Shared httpx AsyncClient for connection pooling
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Returns:
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Evaluation result dictionary
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"""
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total_cases = len(self.test_cases)
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async with semaphore:
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question = test_case["question"]
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ground_truth = test_case["ground_truth"]
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logger.info("[%s/%s] Evaluating: %s...", idx, total_cases, question[:60])
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# Generate RAG response by calling actual LightRAG API
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rag_response = await self.generate_rag_response(
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question=question, client=client
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)
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# *** CRITICAL FIX: Use actual retrieved contexts, NOT ground_truth ***
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retrieved_contexts = rag_response["contexts"]
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# DEBUG: Print what was actually retrieved
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logger.debug("📝 Retrieved %s contexts", len(retrieved_contexts))
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if retrieved_contexts:
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logger.debug(
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"📄 First context preview: %s...", retrieved_contexts[0][:100]
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)
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else:
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logger.warning("⚠️ No contexts retrieved!")
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# Prepare dataset for RAGAS evaluation with CORRECT contexts
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eval_dataset = Dataset.from_dict(
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{
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"question": [question],
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"answer": [rag_response["answer"]],
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"contexts": [retrieved_contexts],
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"ground_truth": [ground_truth],
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}
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)
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# Run RAGAS evaluation
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try:
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eval_results = evaluate(
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dataset=eval_dataset,
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metrics=[
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faithfulness,
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answer_relevancy,
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context_recall,
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context_precision,
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],
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)
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# Convert to DataFrame (RAGAS v0.3+ API)
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df = eval_results.to_pandas()
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# Extract scores from first row
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scores_row = df.iloc[0]
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# Extract scores (RAGAS v0.3+ uses .to_pandas())
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result = {
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"question": question,
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"answer": rag_response["answer"][:200] + "..."
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if len(rag_response["answer"]) > 200
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else rag_response["answer"],
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"ground_truth": ground_truth[:200] + "..."
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if len(ground_truth) > 200
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else ground_truth,
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"project": test_case.get("project_context", "unknown"),
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"metrics": {
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"faithfulness": float(scores_row.get("faithfulness", 0)),
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"answer_relevance": float(
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scores_row.get("answer_relevancy", 0)
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),
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"context_recall": float(scores_row.get("context_recall", 0)),
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"context_precision": float(
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scores_row.get("context_precision", 0)
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),
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},
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"timestamp": datetime.now().isoformat(),
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}
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# Calculate RAGAS score (average of all metrics)
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metrics = result["metrics"]
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ragas_score = sum(metrics.values()) / len(metrics) if metrics else 0
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result["ragas_score"] = round(ragas_score, 4)
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logger.info("✅ Faithfulness: %.4f", metrics["faithfulness"])
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logger.info("✅ Answer Relevance: %.4f", metrics["answer_relevance"])
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logger.info("✅ Context Recall: %.4f", metrics["context_recall"])
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logger.info("✅ Context Precision: %.4f", metrics["context_precision"])
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logger.info("📊 RAGAS Score: %.4f", result["ragas_score"])
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return result
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except Exception as e:
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logger.exception("❌ Error evaluating: %s", e)
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return {
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"question": question,
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"error": str(e),
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"metrics": {},
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"ragas_score": 0,
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"timestamp": datetime.now().isoformat(),
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}
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async def evaluate_responses(self) -> List[Dict[str, Any]]:
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"""
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Evaluate all test cases in parallel and return metrics
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Returns:
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List of evaluation results with metrics
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"""
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# Get MAX_ASYNC from environment (default to 4 if not set)
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max_async = int(os.getenv("MAX_ASYNC", "4"))
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logger.info("")
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logger.info("%s", "=" * 70)
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logger.info("🚀 Starting RAGAS Evaluation of Portfolio RAG System")
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logger.info("🔧 Parallel evaluations: %s", max_async)
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logger.info("%s", "=" * 70)
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# Create semaphore to limit concurrent evaluations
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semaphore = asyncio.Semaphore(max_async)
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# Create shared HTTP client with connection pooling and proper timeouts
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# Timeout: 3 minutes for connect, 5 minutes for read (LLM can be slow)
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timeout = httpx.Timeout(
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TOTAL_TIMEOUT_SECONDS,
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connect=CONNECT_TIMEOUT_SECONDS,
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read=READ_TIMEOUT_SECONDS,
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)
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limits = httpx.Limits(
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max_connections=max_async * 2, # Allow some buffer
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max_keepalive_connections=max_async,
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)
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async with httpx.AsyncClient(timeout=timeout, limits=limits) as client:
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# Create tasks for all test cases
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tasks = [
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self.evaluate_single_case(idx, test_case, semaphore, client)
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for idx, test_case in enumerate(self.test_cases, 1)
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]
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# Run all evaluations in parallel (limited by semaphore)
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results = await asyncio.gather(*tasks)
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return list(results)
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def _export_to_csv(self, results: List[Dict[str, Any]]) -> Path:
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"""
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Export evaluation results to CSV file
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Args:
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results: List of evaluation results
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Returns:
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Path to the CSV file
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CSV Format:
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- question: The test question
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- project: Project context
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- faithfulness: Faithfulness score (0-1)
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- answer_relevance: Answer relevance score (0-1)
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- context_recall: Context recall score (0-1)
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- context_precision: Context precision score (0-1)
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- ragas_score: Overall RAGAS score (0-1)
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- timestamp: When evaluation was run
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"""
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csv_path = (
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self.results_dir / f"results_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv"
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)
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with open(csv_path, "w", newline="", encoding="utf-8") as f:
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fieldnames = [
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"test_number",
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"question",
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"project",
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"faithfulness",
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"answer_relevance",
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"context_recall",
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"context_precision",
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"ragas_score",
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"status",
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"timestamp",
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]
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writer = csv.DictWriter(f, fieldnames=fieldnames)
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writer.writeheader()
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for idx, result in enumerate(results, 1):
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metrics = result.get("metrics", {})
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writer.writerow(
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{
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"test_number": idx,
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"question": result.get("question", ""),
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"project": result.get("project", "unknown"),
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"faithfulness": f"{metrics.get('faithfulness', 0):.4f}",
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"answer_relevance": f"{metrics.get('answer_relevance', 0):.4f}",
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"context_recall": f"{metrics.get('context_recall', 0):.4f}",
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"context_precision": f"{metrics.get('context_precision', 0):.4f}",
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"ragas_score": f"{result.get('ragas_score', 0):.4f}",
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"status": "success" if metrics else "error",
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"timestamp": result.get("timestamp", ""),
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}
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)
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return csv_path
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def _calculate_benchmark_stats(
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self, results: List[Dict[str, Any]]
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) -> Dict[str, Any]:
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"""
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Calculate benchmark statistics from evaluation results
|
||||
|
||||
Args:
|
||||
results: List of evaluation results
|
||||
|
||||
Returns:
|
||||
Dictionary with benchmark statistics
|
||||
"""
|
||||
# Filter out results with errors
|
||||
valid_results = [r for r in results if r.get("metrics")]
|
||||
total_tests = len(results)
|
||||
successful_tests = len(valid_results)
|
||||
failed_tests = total_tests - successful_tests
|
||||
|
||||
if not valid_results:
|
||||
return {
|
||||
"total_tests": total_tests,
|
||||
"successful_tests": 0,
|
||||
"failed_tests": failed_tests,
|
||||
"success_rate": 0.0,
|
||||
}
|
||||
|
||||
# Calculate averages for each metric (handling NaN values)
|
||||
metrics_sum = {
|
||||
"faithfulness": 0.0,
|
||||
"answer_relevance": 0.0,
|
||||
"context_recall": 0.0,
|
||||
"context_precision": 0.0,
|
||||
"ragas_score": 0.0,
|
||||
}
|
||||
|
||||
for result in valid_results:
|
||||
metrics = result.get("metrics", {})
|
||||
# Skip NaN values when summing
|
||||
faithfulness = metrics.get("faithfulness", 0)
|
||||
if not _is_nan(faithfulness):
|
||||
metrics_sum["faithfulness"] += faithfulness
|
||||
|
||||
answer_relevance = metrics.get("answer_relevance", 0)
|
||||
if not _is_nan(answer_relevance):
|
||||
metrics_sum["answer_relevance"] += answer_relevance
|
||||
|
||||
context_recall = metrics.get("context_recall", 0)
|
||||
if not _is_nan(context_recall):
|
||||
metrics_sum["context_recall"] += context_recall
|
||||
|
||||
context_precision = metrics.get("context_precision", 0)
|
||||
if not _is_nan(context_precision):
|
||||
metrics_sum["context_precision"] += context_precision
|
||||
|
||||
ragas_score = result.get("ragas_score", 0)
|
||||
if not _is_nan(ragas_score):
|
||||
metrics_sum["ragas_score"] += ragas_score
|
||||
|
||||
# Calculate averages
|
||||
n = len(valid_results)
|
||||
avg_metrics = {}
|
||||
for k, v in metrics_sum.items():
|
||||
avg_val = v / n if n > 0 else 0
|
||||
# Handle NaN in average
|
||||
avg_metrics[k] = round(avg_val, 4) if not _is_nan(avg_val) else 0.0
|
||||
|
||||
# Find min and max RAGAS scores (filter out NaN)
|
||||
ragas_scores = []
|
||||
for r in valid_results:
|
||||
score = r.get("ragas_score", 0)
|
||||
if _is_nan(score):
|
||||
continue # Skip NaN values
|
||||
ragas_scores.append(score)
|
||||
|
||||
min_score = min(ragas_scores) if ragas_scores else 0
|
||||
max_score = max(ragas_scores) if ragas_scores else 0
|
||||
|
||||
return {
|
||||
"total_tests": total_tests,
|
||||
"successful_tests": successful_tests,
|
||||
"failed_tests": failed_tests,
|
||||
"success_rate": round(successful_tests / total_tests * 100, 2),
|
||||
"average_metrics": avg_metrics,
|
||||
"min_ragas_score": round(min_score, 4),
|
||||
"max_ragas_score": round(max_score, 4),
|
||||
}
|
||||
|
||||
async def run(self) -> Dict[str, Any]:
|
||||
"""Run complete evaluation pipeline"""
|
||||
|
||||
start_time = time.time()
|
||||
|
||||
# Evaluate responses
|
||||
results = await self.evaluate_responses()
|
||||
|
||||
elapsed_time = time.time() - start_time
|
||||
|
||||
# Calculate benchmark statistics
|
||||
benchmark_stats = self._calculate_benchmark_stats(results)
|
||||
|
||||
# Save results
|
||||
summary = {
|
||||
"timestamp": datetime.now().isoformat(),
|
||||
"total_tests": len(results),
|
||||
"elapsed_time_seconds": round(elapsed_time, 2),
|
||||
"benchmark_stats": benchmark_stats,
|
||||
"results": results,
|
||||
}
|
||||
|
||||
# Save JSON results
|
||||
json_path = (
|
||||
self.results_dir
|
||||
/ f"results_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
|
||||
)
|
||||
with open(json_path, "w") as f:
|
||||
json.dump(summary, f, indent=2)
|
||||
logger.info("✅ JSON results saved to: %s", json_path)
|
||||
|
||||
# Export to CSV
|
||||
csv_path = self._export_to_csv(results)
|
||||
logger.info("✅ CSV results saved to: %s", csv_path)
|
||||
|
||||
# Print summary
|
||||
logger.info("")
|
||||
logger.info("%s", "=" * 70)
|
||||
logger.info("📊 EVALUATION COMPLETE")
|
||||
logger.info("%s", "=" * 70)
|
||||
logger.info("Total Tests: %s", len(results))
|
||||
logger.info("Successful: %s", benchmark_stats["successful_tests"])
|
||||
logger.info("Failed: %s", benchmark_stats["failed_tests"])
|
||||
logger.info("Success Rate: %.2f%%", benchmark_stats["success_rate"])
|
||||
logger.info("Elapsed Time: %.2f seconds", elapsed_time)
|
||||
logger.info("Avg Time/Test: %.2f seconds", elapsed_time / len(results))
|
||||
|
||||
# Print benchmark metrics
|
||||
logger.info("")
|
||||
logger.info("%s", "=" * 70)
|
||||
logger.info("📈 BENCHMARK RESULTS (Average)")
|
||||
logger.info("%s", "=" * 70)
|
||||
avg = benchmark_stats["average_metrics"]
|
||||
logger.info("Average Faithfulness: %.4f", avg["faithfulness"])
|
||||
logger.info("Average Answer Relevance: %.4f", avg["answer_relevance"])
|
||||
logger.info("Average Context Recall: %.4f", avg["context_recall"])
|
||||
logger.info("Average Context Precision: %.4f", avg["context_precision"])
|
||||
logger.info("Average RAGAS Score: %.4f", avg["ragas_score"])
|
||||
logger.info("")
|
||||
logger.info(
|
||||
"Min RAGAS Score: %.4f",
|
||||
benchmark_stats["min_ragas_score"],
|
||||
)
|
||||
logger.info(
|
||||
"Max RAGAS Score: %.4f",
|
||||
benchmark_stats["max_ragas_score"],
|
||||
)
|
||||
|
||||
logger.info("")
|
||||
logger.info("%s", "=" * 70)
|
||||
logger.info("📁 GENERATED FILES")
|
||||
logger.info("%s", "=" * 70)
|
||||
logger.info("Results Dir: %s", self.results_dir.absolute())
|
||||
logger.info(" • CSV: %s", csv_path.name)
|
||||
logger.info(" • JSON: %s", json_path.name)
|
||||
logger.info("%s", "=" * 70)
|
||||
|
||||
return summary
|
||||
|
||||
|
||||
async def main():
|
||||
"""
|
||||
Main entry point for RAGAS evaluation
|
||||
|
||||
Usage:
|
||||
python lightrag/evaluation/eval_rag_quality.py
|
||||
python lightrag/evaluation/eval_rag_quality.py http://localhost:9621
|
||||
python lightrag/evaluation/eval_rag_quality.py http://your-server.com:9621
|
||||
"""
|
||||
try:
|
||||
# Get RAG API URL from command line or environment
|
||||
rag_api_url = None
|
||||
if len(sys.argv) > 1:
|
||||
rag_api_url = sys.argv[1]
|
||||
|
||||
logger.info("")
|
||||
logger.info("%s", "=" * 70)
|
||||
logger.info("🔍 RAGAS Evaluation - Using Real LightRAG API")
|
||||
logger.info("%s", "=" * 70)
|
||||
if rag_api_url:
|
||||
logger.info("📡 RAG API URL: %s", rag_api_url)
|
||||
else:
|
||||
logger.info("📡 RAG API URL: http://localhost:9621 (default)")
|
||||
logger.info("%s", "=" * 70)
|
||||
|
||||
evaluator = RAGEvaluator(rag_api_url=rag_api_url)
|
||||
await evaluator.run()
|
||||
except Exception as e:
|
||||
logger.exception("❌ Error: %s", e)
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
Loading…
Add table
Reference in a new issue