e5abe9dd3d
• Move rag_semaphore to wrap full function • Increase RAG concurrency to 2x eval limit • Prevent memory buildup from slow evals • Keep eval_semaphore for RAGAS control
942 lines
35 KiB
Python
942 lines
35 KiB
Python
#!/usr/bin/env python3
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"""
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RAGAS Evaluation Script for LightRAG 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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# Use defaults (sample_dataset.json, http://localhost:9621)
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python lightrag/evaluation/eval_rag_quality.py
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# Specify custom dataset
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python lightrag/evaluation/eval_rag_quality.py --dataset my_test.json
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python lightrag/evaluation/eval_rag_quality.py -d my_test.json
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# Specify custom RAG endpoint
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python lightrag/evaluation/eval_rag_quality.py --ragendpoint http://my-server.com:9621
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python lightrag/evaluation/eval_rag_quality.py -r http://my-server.com:9621
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# Specify both
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python lightrag/evaluation/eval_rag_quality.py -d my_test.json -r http://localhost:9621
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# Get help
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python lightrag/evaluation/eval_rag_quality.py --help
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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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Technical Notes:
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- Uses stable RAGAS API (LangchainLLMWrapper) for maximum compatibility
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- Supports custom OpenAI-compatible endpoints via EVAL_LLM_BINDING_HOST
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- Enables bypass_n mode for endpoints that don't support 'n' parameter
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- Deprecation warnings are suppressed for cleaner output
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"""
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import argparse
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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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import warnings
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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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# Suppress LangchainLLMWrapper deprecation warning
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# We use LangchainLLMWrapper for stability and compatibility with all RAGAS versions
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warnings.filterwarnings(
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"ignore",
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message=".*LangchainLLMWrapper is deprecated.*",
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category=DeprecationWarning,
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)
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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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AnswerRelevancy,
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ContextPrecision,
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ContextRecall,
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Faithfulness,
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)
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from ragas.llms import LangchainLLMWrapper
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from langchain_openai import ChatOpenAI, OpenAIEmbeddings
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from tqdm import tqdm
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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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LangchainLLMWrapper = 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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Environment Variables:
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EVAL_LLM_MODEL: LLM model for evaluation (default: gpt-4o-mini)
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EVAL_EMBEDDING_MODEL: Embedding model for evaluation (default: text-embedding-3-small)
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EVAL_LLM_BINDING_API_KEY: API key for evaluation models (fallback to OPENAI_API_KEY)
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EVAL_LLM_BINDING_HOST: Custom endpoint URL for evaluation models (optional)
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Raises:
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ImportError: If ragas or datasets packages are not installed
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EnvironmentError: If EVAL_LLM_BINDING_API_KEY and OPENAI_API_KEY are both 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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# Configure evaluation models (for RAGAS scoring)
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eval_api_key = os.getenv("EVAL_LLM_BINDING_API_KEY") or os.getenv(
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"OPENAI_API_KEY"
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)
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if not eval_api_key:
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raise EnvironmentError(
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"EVAL_LLM_BINDING_API_KEY or OPENAI_API_KEY is required for evaluation. "
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"Set EVAL_LLM_BINDING_API_KEY to use a custom API key, "
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"or ensure OPENAI_API_KEY is set."
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)
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eval_model = os.getenv("EVAL_LLM_MODEL", "gpt-4o-mini")
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eval_embedding_model = os.getenv(
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"EVAL_EMBEDDING_MODEL", "text-embedding-3-large"
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)
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eval_base_url = os.getenv("EVAL_LLM_BINDING_HOST")
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# Create LLM and Embeddings instances for RAGAS
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llm_kwargs = {
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"model": eval_model,
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"api_key": eval_api_key,
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"max_retries": int(os.getenv("EVAL_LLM_MAX_RETRIES", "5")),
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"request_timeout": int(os.getenv("EVAL_LLM_TIMEOUT", "180")),
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}
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embedding_kwargs = {"model": eval_embedding_model, "api_key": eval_api_key}
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if eval_base_url:
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llm_kwargs["base_url"] = eval_base_url
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embedding_kwargs["base_url"] = eval_base_url
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# Create base LangChain LLM
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base_llm = ChatOpenAI(**llm_kwargs)
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self.eval_embeddings = OpenAIEmbeddings(**embedding_kwargs)
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# Wrap LLM with LangchainLLMWrapper and enable bypass_n mode for custom endpoints
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# This ensures compatibility with endpoints that don't support the 'n' parameter
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# by generating multiple outputs through repeated prompts instead of using 'n' parameter
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try:
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self.eval_llm = LangchainLLMWrapper(
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langchain_llm=base_llm,
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bypass_n=True, # Enable bypass_n to avoid passing 'n' to OpenAI API
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)
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logger.debug("Successfully configured bypass_n mode for LLM wrapper")
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except Exception as e:
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logger.warning(
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"Could not configure LangchainLLMWrapper with bypass_n: %s. "
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"Using base LLM directly, which may cause warnings with custom endpoints.",
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e,
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)
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self.eval_llm = base_llm
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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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# Store configuration values for display
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self.eval_model = eval_model
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self.eval_embedding_model = eval_embedding_model
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self.eval_base_url = eval_base_url
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self.eval_max_retries = llm_kwargs["max_retries"]
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self.eval_timeout = llm_kwargs["request_timeout"]
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# Display configuration
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self._display_configuration()
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def _display_configuration(self):
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"""Display all evaluation configuration settings"""
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logger.info("Evaluation Models:")
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logger.info(" • LLM Model: %s", self.eval_model)
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logger.info(" • Embedding Model: %s", self.eval_embedding_model)
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if self.eval_base_url:
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logger.info(" • Custom Endpoint: %s", self.eval_base_url)
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logger.info(
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" • Bypass N-Parameter: Enabled (use LangchainLLMWrapperfor compatibility)"
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)
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else:
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logger.info(" • Endpoint: OpenAI Official API")
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logger.info("Concurrency & Rate Limiting:")
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query_top_k = int(os.getenv("EVAL_QUERY_TOP_K", "10"))
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logger.info(" • Query Top-K: %s Entities/Relations", query_top_k)
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logger.info(" • LLM Max Retries: %s", self.eval_max_retries)
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logger.info(" • LLM Timeout: %s seconds", self.eval_timeout)
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logger.info("Test Configuration:")
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logger.info(" • Total Test Cases: %s", len(self.test_cases))
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logger.info(" • Test Dataset: %s", self.test_dataset_path.name)
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logger.info(" • LightRAG API: %s", self.rag_api_url)
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logger.info(" • Results Directory: %s", self.results_dir.name)
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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": int(os.getenv("EVAL_QUERY_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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rag_semaphore: asyncio.Semaphore,
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eval_semaphore: asyncio.Semaphore,
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client: httpx.AsyncClient,
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progress_counter: Dict[str, int],
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) -> Dict[str, Any]:
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"""
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Evaluate a single test case with two-stage pipeline 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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rag_semaphore: Semaphore to control overall concurrency (covers entire function)
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eval_semaphore: Semaphore to control RAGAS evaluation concurrency (Stage 2)
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client: Shared httpx AsyncClient for connection pooling
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progress_counter: Shared dictionary for progress tracking
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Returns:
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Evaluation result dictionary
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"""
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# rag_semaphore controls the entire evaluation process to prevent
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# all RAG responses from being generated at once when eval is slow
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async with rag_semaphore:
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question = test_case["question"]
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ground_truth = test_case["ground_truth"]
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# Stage 1: Generate RAG response
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try:
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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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except Exception as e:
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logger.error("Error generating response for test %s: %s", idx, str(e))
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progress_counter["completed"] += 1
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return {
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"test_number": idx,
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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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# *** CRITICAL FIX: Use actual retrieved contexts, NOT ground_truth ***
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retrieved_contexts = rag_response["contexts"]
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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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# Stage 2: Run RAGAS evaluation (controlled by eval_semaphore)
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# IMPORTANT: Create fresh metric instances for each evaluation to avoid
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# concurrent state conflicts when multiple tasks run in parallel
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async with eval_semaphore:
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pbar = None
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try:
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# Create standard tqdm progress bar for RAGAS evaluation
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pbar = tqdm(total=4, desc=f"Eval-{idx}", leave=True)
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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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AnswerRelevancy(),
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ContextRecall(),
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ContextPrecision(),
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],
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llm=self.eval_llm,
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embeddings=self.eval_embeddings,
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_pbar=pbar,
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)
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pbar.close()
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pbar = None
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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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"test_number": idx,
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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", "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(
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scores_row.get("context_recall", 0)
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),
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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, excluding NaN values)
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metrics = result["metrics"]
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valid_metrics = [v for v in metrics.values() if not _is_nan(v)]
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ragas_score = (
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sum(valid_metrics) / len(valid_metrics) if valid_metrics else 0
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)
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result["ragas_score"] = round(ragas_score, 4)
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# Update progress counter
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progress_counter["completed"] += 1
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return result
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except Exception as e:
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logger.error("Error evaluating test %s: %s", idx, str(e))
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progress_counter["completed"] += 1
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return {
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"test_number": idx,
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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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finally:
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# Force close progress bar to ensure completion
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if pbar is not None:
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pbar.close()
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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 with two-stage pipeline and return metrics
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Returns:
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List of evaluation results with metrics
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"""
|
|
# Get evaluation concurrency from environment (default to 2 for parallel evaluation)
|
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max_async = int(os.getenv("EVAL_MAX_CONCURRENT", "2"))
|
|
|
|
logger.info("%s", "=" * 70)
|
|
logger.info("🚀 Starting RAGAS Evaluation of LightRAG System")
|
|
logger.info("🔧 Two-Stage Pipeline Configuration:")
|
|
logger.info(" • RAGAS Evaluation (Stage 2): %s concurrent", max_async)
|
|
logger.info("%s", "=" * 70)
|
|
|
|
# Create two-stage pipeline semaphores
|
|
# Stage 1: RAG generation - allow x2 concurrency to keep evaluation fed
|
|
rag_semaphore = asyncio.Semaphore(max_async * 2)
|
|
# Stage 2: RAGAS evaluation - primary bottleneck
|
|
eval_semaphore = asyncio.Semaphore(max_async)
|
|
|
|
# Create progress counter (shared across all tasks)
|
|
progress_counter = {"completed": 0}
|
|
|
|
# Create shared HTTP client with connection pooling and proper timeouts
|
|
# Timeout: 3 minutes for connect, 5 minutes for read (LLM can be slow)
|
|
timeout = httpx.Timeout(
|
|
TOTAL_TIMEOUT_SECONDS,
|
|
connect=CONNECT_TIMEOUT_SECONDS,
|
|
read=READ_TIMEOUT_SECONDS,
|
|
)
|
|
limits = httpx.Limits(
|
|
max_connections=(max_async + 1) * 2, # Allow buffer for RAG stage
|
|
max_keepalive_connections=max_async + 1,
|
|
)
|
|
|
|
async with httpx.AsyncClient(timeout=timeout, limits=limits) as client:
|
|
# Create tasks for all test cases
|
|
tasks = [
|
|
self.evaluate_single_case(
|
|
idx,
|
|
test_case,
|
|
rag_semaphore,
|
|
eval_semaphore,
|
|
client,
|
|
progress_counter,
|
|
)
|
|
for idx, test_case in enumerate(self.test_cases, 1)
|
|
]
|
|
|
|
# Run all evaluations in parallel (limited by two-stage semaphores)
|
|
results = await asyncio.gather(*tasks)
|
|
|
|
return list(results)
|
|
|
|
def _export_to_csv(self, results: List[Dict[str, Any]]) -> Path:
|
|
"""
|
|
Export evaluation results to CSV file
|
|
|
|
Args:
|
|
results: List of evaluation results
|
|
|
|
Returns:
|
|
Path to the CSV file
|
|
|
|
CSV Format:
|
|
- question: The test question
|
|
- project: Project context
|
|
- faithfulness: Faithfulness score (0-1)
|
|
- answer_relevance: Answer relevance score (0-1)
|
|
- context_recall: Context recall score (0-1)
|
|
- context_precision: Context precision score (0-1)
|
|
- ragas_score: Overall RAGAS score (0-1)
|
|
- timestamp: When evaluation was run
|
|
"""
|
|
csv_path = (
|
|
self.results_dir / f"results_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv"
|
|
)
|
|
|
|
with open(csv_path, "w", newline="", encoding="utf-8") as f:
|
|
fieldnames = [
|
|
"test_number",
|
|
"question",
|
|
"project",
|
|
"faithfulness",
|
|
"answer_relevance",
|
|
"context_recall",
|
|
"context_precision",
|
|
"ragas_score",
|
|
"status",
|
|
"timestamp",
|
|
]
|
|
|
|
writer = csv.DictWriter(f, fieldnames=fieldnames)
|
|
writer.writeheader()
|
|
|
|
for idx, result in enumerate(results, 1):
|
|
metrics = result.get("metrics", {})
|
|
writer.writerow(
|
|
{
|
|
"test_number": idx,
|
|
"question": result.get("question", ""),
|
|
"project": result.get("project", "unknown"),
|
|
"faithfulness": f"{metrics.get('faithfulness', 0):.4f}",
|
|
"answer_relevance": f"{metrics.get('answer_relevance', 0):.4f}",
|
|
"context_recall": f"{metrics.get('context_recall', 0):.4f}",
|
|
"context_precision": f"{metrics.get('context_precision', 0):.4f}",
|
|
"ragas_score": f"{result.get('ragas_score', 0):.4f}",
|
|
"status": "success" if metrics else "error",
|
|
"timestamp": result.get("timestamp", ""),
|
|
}
|
|
)
|
|
|
|
return csv_path
|
|
|
|
def _format_metric(self, value: float, width: int = 6) -> str:
|
|
"""
|
|
Format a metric value for display, handling NaN gracefully
|
|
|
|
Args:
|
|
value: The metric value to format
|
|
width: The width of the formatted string
|
|
|
|
Returns:
|
|
Formatted string (e.g., "0.8523" or " N/A ")
|
|
"""
|
|
if _is_nan(value):
|
|
return "N/A".center(width)
|
|
return f"{value:.4f}".rjust(width)
|
|
|
|
def _display_results_table(self, results: List[Dict[str, Any]]):
|
|
"""
|
|
Display evaluation results in a formatted table
|
|
|
|
Args:
|
|
results: List of evaluation results
|
|
"""
|
|
logger.info("%s", "=" * 115)
|
|
logger.info("📊 EVALUATION RESULTS SUMMARY")
|
|
logger.info("%s", "=" * 115)
|
|
|
|
# Table header
|
|
logger.info(
|
|
"%-4s | %-50s | %6s | %7s | %6s | %7s | %6s | %6s",
|
|
"#",
|
|
"Question",
|
|
"Faith",
|
|
"AnswRel",
|
|
"CtxRec",
|
|
"CtxPrec",
|
|
"RAGAS",
|
|
"Status",
|
|
)
|
|
logger.info("%s", "-" * 115)
|
|
|
|
# Table rows
|
|
for result in results:
|
|
test_num = result.get("test_number", 0)
|
|
question = result.get("question", "")
|
|
# Truncate question to 50 chars
|
|
question_display = (
|
|
(question[:47] + "...") if len(question) > 50 else question
|
|
)
|
|
|
|
metrics = result.get("metrics", {})
|
|
if metrics:
|
|
# Success case - format each metric, handling NaN values
|
|
faith = metrics.get("faithfulness", 0)
|
|
ans_rel = metrics.get("answer_relevance", 0)
|
|
ctx_rec = metrics.get("context_recall", 0)
|
|
ctx_prec = metrics.get("context_precision", 0)
|
|
ragas = result.get("ragas_score", 0)
|
|
status = "✓"
|
|
|
|
logger.info(
|
|
"%-4d | %-50s | %s | %s | %s | %s | %s | %6s",
|
|
test_num,
|
|
question_display,
|
|
self._format_metric(faith, 6),
|
|
self._format_metric(ans_rel, 7),
|
|
self._format_metric(ctx_rec, 6),
|
|
self._format_metric(ctx_prec, 7),
|
|
self._format_metric(ragas, 6),
|
|
status,
|
|
)
|
|
else:
|
|
# Error case
|
|
error = result.get("error", "Unknown error")
|
|
error_display = (error[:20] + "...") if len(error) > 23 else error
|
|
logger.info(
|
|
"%-4d | %-50s | %6s | %7s | %6s | %7s | %6s | ✗ %s",
|
|
test_num,
|
|
question_display,
|
|
"N/A",
|
|
"N/A",
|
|
"N/A",
|
|
"N/A",
|
|
"N/A",
|
|
error_display,
|
|
)
|
|
|
|
logger.info("%s", "=" * 115)
|
|
|
|
def _calculate_benchmark_stats(
|
|
self, results: List[Dict[str, Any]]
|
|
) -> Dict[str, Any]:
|
|
"""
|
|
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 correctly)
|
|
# Track both sum and count for each metric to handle NaN values properly
|
|
metrics_data = {
|
|
"faithfulness": {"sum": 0.0, "count": 0},
|
|
"answer_relevance": {"sum": 0.0, "count": 0},
|
|
"context_recall": {"sum": 0.0, "count": 0},
|
|
"context_precision": {"sum": 0.0, "count": 0},
|
|
"ragas_score": {"sum": 0.0, "count": 0},
|
|
}
|
|
|
|
for result in valid_results:
|
|
metrics = result.get("metrics", {})
|
|
|
|
# For each metric, sum non-NaN values and count them
|
|
faithfulness = metrics.get("faithfulness", 0)
|
|
if not _is_nan(faithfulness):
|
|
metrics_data["faithfulness"]["sum"] += faithfulness
|
|
metrics_data["faithfulness"]["count"] += 1
|
|
|
|
answer_relevance = metrics.get("answer_relevance", 0)
|
|
if not _is_nan(answer_relevance):
|
|
metrics_data["answer_relevance"]["sum"] += answer_relevance
|
|
metrics_data["answer_relevance"]["count"] += 1
|
|
|
|
context_recall = metrics.get("context_recall", 0)
|
|
if not _is_nan(context_recall):
|
|
metrics_data["context_recall"]["sum"] += context_recall
|
|
metrics_data["context_recall"]["count"] += 1
|
|
|
|
context_precision = metrics.get("context_precision", 0)
|
|
if not _is_nan(context_precision):
|
|
metrics_data["context_precision"]["sum"] += context_precision
|
|
metrics_data["context_precision"]["count"] += 1
|
|
|
|
ragas_score = result.get("ragas_score", 0)
|
|
if not _is_nan(ragas_score):
|
|
metrics_data["ragas_score"]["sum"] += ragas_score
|
|
metrics_data["ragas_score"]["count"] += 1
|
|
|
|
# Calculate averages using actual counts for each metric
|
|
avg_metrics = {}
|
|
for metric_name, data in metrics_data.items():
|
|
if data["count"] > 0:
|
|
avg_val = data["sum"] / data["count"]
|
|
avg_metrics[metric_name] = (
|
|
round(avg_val, 4) if not _is_nan(avg_val) else 0.0
|
|
)
|
|
else:
|
|
avg_metrics[metric_name] = 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)
|
|
|
|
# Display results table
|
|
self._display_results_table(results)
|
|
|
|
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
|
|
|
|
Command-line arguments:
|
|
--dataset, -d: Path to test dataset JSON file (default: sample_dataset.json)
|
|
--ragendpoint, -r: LightRAG API endpoint URL (default: http://localhost:9621 or $LIGHTRAG_API_URL)
|
|
|
|
Usage:
|
|
python lightrag/evaluation/eval_rag_quality.py
|
|
python lightrag/evaluation/eval_rag_quality.py --dataset my_test.json
|
|
python lightrag/evaluation/eval_rag_quality.py -d my_test.json -r http://localhost:9621
|
|
"""
|
|
try:
|
|
# Parse command-line arguments
|
|
parser = argparse.ArgumentParser(
|
|
description="RAGAS Evaluation Script for LightRAG System",
|
|
formatter_class=argparse.RawDescriptionHelpFormatter,
|
|
epilog="""
|
|
Examples:
|
|
# Use defaults
|
|
python lightrag/evaluation/eval_rag_quality.py
|
|
|
|
# Specify custom dataset
|
|
python lightrag/evaluation/eval_rag_quality.py --dataset my_test.json
|
|
|
|
# Specify custom RAG endpoint
|
|
python lightrag/evaluation/eval_rag_quality.py --ragendpoint http://my-server.com:9621
|
|
|
|
# Specify both
|
|
python lightrag/evaluation/eval_rag_quality.py -d my_test.json -r http://localhost:9621
|
|
""",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--dataset",
|
|
"-d",
|
|
type=str,
|
|
default=None,
|
|
help="Path to test dataset JSON file (default: sample_dataset.json in evaluation directory)",
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--ragendpoint",
|
|
"-r",
|
|
type=str,
|
|
default=None,
|
|
help="LightRAG API endpoint URL (default: http://localhost:9621 or $LIGHTRAG_API_URL environment variable)",
|
|
)
|
|
|
|
args = parser.parse_args()
|
|
|
|
logger.info("%s", "=" * 70)
|
|
logger.info("🔍 RAGAS Evaluation - Using Real LightRAG API")
|
|
logger.info("%s", "=" * 70)
|
|
|
|
evaluator = RAGEvaluator(
|
|
test_dataset_path=args.dataset, rag_api_url=args.ragendpoint
|
|
)
|
|
await evaluator.run()
|
|
except Exception as e:
|
|
logger.exception("❌ Error: %s", e)
|
|
sys.exit(1)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
asyncio.run(main())
|