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Use logger in RAG evaluation and optimize reference content joins

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This commit is contained in:
anouarbm 2025-11-02 18:43:53 +01:00
parent 963ad4c637
commit 0b5e3f9dc4
2 changed files with 99 additions and 90 deletions
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9
lightrag/api/routers/query_routes.py
View file

@ -412,11 +412,8 @@ def create_query_routes(rag, api_key: Optional[str] = None, top_k: int = 60):
ref_id = chunk.get("reference_id", "")
content = chunk.get("content", "")
if ref_id and content:
# If multiple chunks have same reference_id, concatenate
if ref_id in ref_id_to_content:
ref_id_to_content[ref_id] += "\n\n" + content
else:
ref_id_to_content[ref_id] = content
# Collect chunk content; join later to avoid quadratic string concatenation
ref_id_to_content.setdefault(ref_id, []).append(content)
# Add content to references
enriched_references = []
@ -424,7 +421,7 @@ def create_query_routes(rag, api_key: Optional[str] = None, top_k: int = 60):
ref_copy = ref.copy()
ref_id = ref.get("reference_id", "")
if ref_id in ref_id_to_content:
ref_copy["content"] = ref_id_to_content[ref_id]
ref_copy["content"] = "\n\n".join(ref_id_to_content[ref_id])
enriched_references.append(ref_copy)
references = enriched_references

180
lightrag/evaluation/eval_rag_quality.py
View file

@ -21,6 +21,7 @@ Results are saved to: lightrag/evaluation/results/
import asyncio
import csv
import json
import math
import os
import sys
import time
@ -30,6 +31,7 @@ from typing import Any, Dict, List
import httpx
from dotenv import load_dotenv
from lightrag.utils import logger
# Add parent directory to path
sys.path.insert(0, str(Path(__file__).parent.parent.parent))
@ -56,11 +58,21 @@ try:
faithfulness,
)
except ImportError as e:
print(f"❌ RAGAS import error: {e}")
print(" Install with: pip install ragas datasets")
logger.error("❌ RAGAS import error: %s", e)
logger.error(" Install with: pip install ragas datasets")
sys.exit(1)
CONNECT_TIMEOUT_SECONDS = 180.0
READ_TIMEOUT_SECONDS = 300.0
TOTAL_TIMEOUT_SECONDS = 180.0
def _is_nan(value: Any) -> bool:
"""Return True when value is a float NaN."""
return isinstance(value, float) and math.isnan(value)
class RAGEvaluator:
"""Evaluate RAG system quality using RAGAS metrics"""
@ -138,12 +150,14 @@ class RAGEvaluator:
references = result.get("references", [])
# DEBUG: Inspect the API response
print(f" 🔍 References Count: {len(references)}")
logger.debug("🔍 References Count: %s", len(references))
if references:
first_ref = references[0]
print(f" 🔍 First Reference Keys: {list(first_ref.keys())}")
logger.debug("🔍 First Reference Keys: %s", list(first_ref.keys()))
if "content" in first_ref:
print(f" 🔍 Content Preview: {first_ref['content'][:100]}...")
logger.debug(
"🔍 Content Preview: %s...", first_ref["content"][:100]
)
# Extract chunk content from enriched references
contexts = [
@ -194,11 +208,13 @@ class RAGEvaluator:
Returns:
Evaluation result dictionary
"""
total_cases = len(self.test_cases)
async with semaphore:
question = test_case["question"]
ground_truth = test_case["ground_truth"]
print(f"[{idx}/{len(self.test_cases)}] Evaluating: {question[:60]}...")
logger.info("[%s/%s] Evaluating: %s...", idx, total_cases, question[:60])
# Generate RAG response by calling actual LightRAG API
rag_response = await self.generate_rag_response(
@ -209,11 +225,13 @@ class RAGEvaluator:
retrieved_contexts = rag_response["contexts"]
# DEBUG: Print what was actually retrieved
print(f" 📝 Retrieved {len(retrieved_contexts)} contexts")
logger.debug("📝 Retrieved %s contexts", len(retrieved_contexts))
if retrieved_contexts:
print(f" 📄 First context preview: {retrieved_contexts[0][:100]}...")
logger.debug(
"📄 First context preview: %s...", retrieved_contexts[0][:100]
)
else:
print(" ⚠️ WARNING: No contexts retrieved!")
logger.warning("⚠️ No contexts retrieved!")
# Prepare dataset for RAGAS evaluation with CORRECT contexts
eval_dataset = Dataset.from_dict(
@ -271,20 +289,16 @@ class RAGEvaluator:
ragas_score = sum(metrics.values()) / len(metrics) if metrics else 0
result["ragas_score"] = round(ragas_score, 4)
# Print metrics
print(f" ✅ Faithfulness: {metrics['faithfulness']:.4f}")
print(f" ✅ Answer Relevance: {metrics['answer_relevance']:.4f}")
print(f" ✅ Context Recall: {metrics['context_recall']:.4f}")
print(f" ✅ Context Precision: {metrics['context_precision']:.4f}")
print(f" 📊 RAGAS Score: {result['ragas_score']:.4f}\n")
logger.info("✅ Faithfulness: %.4f", metrics["faithfulness"])
logger.info("✅ Answer Relevance: %.4f", metrics["answer_relevance"])
logger.info("✅ Context Recall: %.4f", metrics["context_recall"])
logger.info("✅ Context Precision: %.4f", metrics["context_precision"])
logger.info("📊 RAGAS Score: %.4f", result["ragas_score"])
return result
except Exception as e:
import traceback
print(f" ❌ Error evaluating: {str(e)}")
print(f" 🔍 Full traceback:\n{traceback.format_exc()}\n")
logger.exception("❌ Error evaluating: %s", e)
return {
"question": question,
"error": str(e),
@ -303,17 +317,22 @@ class RAGEvaluator:
# Get MAX_ASYNC from environment (default to 4 if not set)
max_async = int(os.getenv("MAX_ASYNC", "4"))
print("\n" + "=" * 70)
print("🚀 Starting RAGAS Evaluation of Portfolio RAG System")
print(f"🔧 Parallel evaluations: {max_async}")
print("=" * 70 + "\n")
logger.info("")
logger.info("%s", "=" * 70)
logger.info("🚀 Starting RAGAS Evaluation of Portfolio RAG System")
logger.info("🔧 Parallel evaluations: %s", max_async)
logger.info("%s", "=" * 70)
# Create semaphore to limit concurrent evaluations
semaphore = asyncio.Semaphore(max_async)
# 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(180.0, connect=180.0, read=300.0)
timeout = httpx.Timeout(
TOTAL_TIMEOUT_SECONDS,
connect=CONNECT_TIMEOUT_SECONDS,
read=READ_TIMEOUT_SECONDS,
)
limits = httpx.Limits(
max_connections=max_async * 2, # Allow some buffer
max_keepalive_connections=max_async,
@ -418,8 +437,6 @@ class RAGEvaluator:
}
# Calculate averages for each metric (handling NaN values)
import math
metrics_sum = {
"faithfulness": 0.0,
"answer_relevance": 0.0,
@ -432,39 +449,23 @@ class RAGEvaluator:
metrics = result.get("metrics", {})
# Skip NaN values when summing
faithfulness = metrics.get("faithfulness", 0)
if (
not math.isnan(faithfulness)
if isinstance(faithfulness, float)
else True
):
if not _is_nan(faithfulness):
metrics_sum["faithfulness"] += faithfulness
answer_relevance = metrics.get("answer_relevance", 0)
if (
not math.isnan(answer_relevance)
if isinstance(answer_relevance, float)
else True
):
if not _is_nan(answer_relevance):
metrics_sum["answer_relevance"] += answer_relevance
context_recall = metrics.get("context_recall", 0)
if (
not math.isnan(context_recall)
if isinstance(context_recall, float)
else True
):
if not _is_nan(context_recall):
metrics_sum["context_recall"] += context_recall
context_precision = metrics.get("context_precision", 0)
if (
not math.isnan(context_precision)
if isinstance(context_precision, float)
else True
):
if not _is_nan(context_precision):
metrics_sum["context_precision"] += context_precision
ragas_score = result.get("ragas_score", 0)
if not math.isnan(ragas_score) if isinstance(ragas_score, float) else True:
if not _is_nan(ragas_score):
metrics_sum["ragas_score"] += ragas_score
# Calculate averages
@ -473,13 +474,13 @@ class RAGEvaluator:
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 math.isnan(avg_val) else 0.0
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 isinstance(score, float) and math.isnan(score):
if _is_nan(score):
continue # Skip NaN values
ragas_scores.append(score)
@ -525,43 +526,53 @@ class RAGEvaluator:
)
with open(json_path, "w") as f:
json.dump(summary, f, indent=2)
print(f"✅ JSON results saved to: {json_path}")
logger.info("✅ JSON results saved to: %s", json_path)
# Export to CSV
csv_path = self._export_to_csv(results)
print(f"✅ CSV results saved to: {csv_path}")
logger.info("✅ CSV results saved to: %s", csv_path)
# Print summary
print("\n" + "=" * 70)
print("📊 EVALUATION COMPLETE")
print("=" * 70)
print(f"Total Tests: {len(results)}")
print(f"Successful: {benchmark_stats['successful_tests']}")
print(f"Failed: {benchmark_stats['failed_tests']}")
print(f"Success Rate: {benchmark_stats['success_rate']:.2f}%")
print(f"Elapsed Time: {elapsed_time:.2f} seconds")
print(f"Avg Time/Test: {elapsed_time / len(results):.2f} seconds")
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
print("\n" + "=" * 70)
print("📈 BENCHMARK RESULTS (Moyennes)")
print("=" * 70)
logger.info("")
logger.info("%s", "=" * 70)
logger.info("📈 BENCHMARK RESULTS (Moyennes)")
logger.info("%s", "=" * 70)
avg = benchmark_stats["average_metrics"]
print(f"Moyenne Faithfulness: {avg['faithfulness']:.4f}")
print(f"Moyenne Answer Relevance: {avg['answer_relevance']:.4f}")
print(f"Moyenne Context Recall: {avg['context_recall']:.4f}")
print(f"Moyenne Context Precision: {avg['context_precision']:.4f}")
print(f"Moyenne RAGAS Score: {avg['ragas_score']:.4f}")
print(f"\nMin RAGAS Score: {benchmark_stats['min_ragas_score']:.4f}")
print(f"Max RAGAS Score: {benchmark_stats['max_ragas_score']:.4f}")
logger.info("Moyenne Faithfulness: %.4f", avg["faithfulness"])
logger.info("Moyenne Answer Relevance: %.4f", avg["answer_relevance"])
logger.info("Moyenne Context Recall: %.4f", avg["context_recall"])
logger.info("Moyenne Context Precision: %.4f", avg["context_precision"])
logger.info("Moyenne 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"],
)
print("\n" + "=" * 70)
print("📁 GENERATED FILES")
print("=" * 70)
print(f"Results Dir: {self.results_dir.absolute()}")
print(f" • CSV: {csv_path.name}")
print(f" • JSON: {json_path.name}")
print("=" * 70 + "\n")
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
@ -581,19 +592,20 @@ async def main():
if len(sys.argv) > 1:
rag_api_url = sys.argv[1]
print("\n" + "=" * 70)
print("🔍 RAGAS Evaluation - Using Real LightRAG API")
print("=" * 70)
logger.info("")
logger.info("%s", "=" * 70)
logger.info("🔍 RAGAS Evaluation - Using Real LightRAG API")
logger.info("%s", "=" * 70)
if rag_api_url:
print(f"📡 RAG API URL: {rag_api_url}")
logger.info("📡 RAG API URL: %s", rag_api_url)
else:
print("📡 RAG API URL: http://localhost:9621 (default)")
print("=" * 70 + "\n")
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:
print(f"\n❌ Error: {str(e)}\n")
logger.exception("❌ Error: %s", e)
sys.exit(1)