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db765e0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 | from pathlib import Path
# Remove BOM from Python files
for path in Path("app").rglob("*.py"):
text = path.read_text(encoding="utf-8-sig")
text = text.replace("\ufeff", "")
path.write_text(text, encoding="utf-8")
print("BOM cleanup completed.")
# =====================================================
# 1. Create batch evaluator
# =====================================================
Path("app/evaluation/graphrag_batch_evaluator.py").write_text(r'''
from typing import Dict, Any, List, Optional
from datetime import datetime, timezone
from app.evaluation.graph_fusion_evaluator import compare_graph_fusion_retrieval
DEFAULT_GRAPHRAG_TEST_QUERIES = [
"What is RAG?",
"Why does RAG exist?",
"What are the main components of a RAG system?",
"What is vectorless RAG?",
"Why can vector search fail?",
"How does BM25 help in retrieval?",
"How does RAG reduce hallucination?",
"What is the role of citations in RAG?"
]
def parse_custom_queries(custom_queries: Optional[str]) -> List[str]:
if not custom_queries:
return []
# User can pass queries separated by ||
# Example: What is RAG?||Why does RAG exist?
queries = [
item.strip()
for item in custom_queries.split("||")
if item.strip()
]
return queries
def safe_number(value, default=0.0) -> float:
try:
return float(value)
except Exception:
return default
def summarize_batch_results(results: List[Dict[str, Any]]) -> Dict[str, Any]:
total = len(results)
if total == 0:
return {
"total_questions": 0,
"fusion_improved_count": 0,
"fusion_same_count": 0,
"fusion_worse_count": 0,
"average_normal_quality": 0.0,
"average_graph_quality": 0.0,
"average_fused_quality": 0.0,
"average_fusion_delta": 0.0,
"total_graph_added_chunks": 0,
"total_graph_supported_chunks": 0,
"final_verdict": "no_questions_evaluated"
}
normal_scores = []
graph_scores = []
fused_scores = []
deltas = []
fusion_improved_count = 0
fusion_same_count = 0
fusion_worse_count = 0
total_graph_added_chunks = 0
total_graph_supported_chunks = 0
for result in results:
comparison = result.get("comparison", {})
fusion_stats = result.get("fusion_stats", {})
normal_score = safe_number(comparison.get("normal_average_quality"))
graph_score = safe_number(comparison.get("graph_average_quality"))
fused_score = safe_number(comparison.get("fused_average_quality"))
delta = safe_number(comparison.get("fusion_quality_delta"))
normal_scores.append(normal_score)
graph_scores.append(graph_score)
fused_scores.append(fused_score)
deltas.append(delta)
if delta > 0:
fusion_improved_count += 1
elif delta == 0:
fusion_same_count += 1
else:
fusion_worse_count += 1
total_graph_added_chunks += int(fusion_stats.get("graph_added_count") or 0)
total_graph_supported_chunks += int(fusion_stats.get("graph_supported_count") or 0)
average_normal = round(sum(normal_scores) / total, 4)
average_graph = round(sum(graph_scores) / total, 4)
average_fused = round(sum(fused_scores) / total, 4)
average_delta = round(sum(deltas) / total, 4)
if fusion_improved_count > fusion_worse_count and average_delta > 0:
final_verdict = "graph_fusion_helped_overall"
elif fusion_worse_count > fusion_improved_count and average_delta < 0:
final_verdict = "graph_fusion_added_noise_overall"
else:
final_verdict = "graph_fusion_mixed_or_neutral"
return {
"total_questions": total,
"fusion_improved_count": fusion_improved_count,
"fusion_same_count": fusion_same_count,
"fusion_worse_count": fusion_worse_count,
"average_normal_quality": average_normal,
"average_graph_quality": average_graph,
"average_fused_quality": average_fused,
"average_fusion_delta": average_delta,
"total_graph_added_chunks": total_graph_added_chunks,
"total_graph_supported_chunks": total_graph_supported_chunks,
"final_verdict": final_verdict
}
def build_compact_question_result(
query: str,
full_result: Dict[str, Any]
) -> Dict[str, Any]:
comparison = full_result.get("comparison", {})
fusion_stats = full_result.get("fusion_stats", {})
normal_results = (
full_result
.get("normal_retrieval", {})
.get("results", [])
)
fused_results = (
full_result
.get("fused_retrieval", {})
.get("results", [])
)
return {
"query": query,
"comparison": comparison,
"fusion_stats": fusion_stats,
"top_normal_chunks": [
{
"rank": item.get("rank"),
"chunk_id": item.get("chunk_id"),
"page_number": item.get("page_number"),
"quality_score": item.get("quality_score"),
"penalties": item.get("penalties"),
"preview": item.get("content_preview")
}
for item in normal_results[:3]
],
"top_fused_chunks": [
{
"rank": item.get("rank"),
"chunk_id": item.get("chunk_id"),
"page_number": item.get("page_number"),
"retrieval_source": item.get("retrieval_source"),
"graph_supported": item.get("graph_supported"),
"quality_score": item.get("quality_score"),
"penalties": item.get("penalties"),
"preview": item.get("content_preview")
}
for item in fused_results[:3]
]
}
def run_graphrag_batch_evaluation(
document_id: str,
custom_queries: Optional[str] = None,
top_k: int = 5,
retrieval_mode: str = "hybrid",
use_reranker: bool = True,
graph_entity_limit: int = 8,
graph_retrieval_top_k: int = 5,
compact: bool = True
) -> Dict[str, Any]:
queries = parse_custom_queries(custom_queries)
if not queries:
queries = DEFAULT_GRAPHRAG_TEST_QUERIES
detailed_results = []
compact_results = []
failed_questions = []
for query in queries:
try:
result = compare_graph_fusion_retrieval(
document_id=document_id,
query=query,
top_k=top_k,
retrieval_mode=retrieval_mode,
use_reranker=use_reranker,
graph_entity_limit=graph_entity_limit,
graph_retrieval_top_k=graph_retrieval_top_k
)
detailed_results.append(result)
compact_results.append(
build_compact_question_result(
query=query,
full_result=result
)
)
except Exception as error:
failed_questions.append(
{
"query": query,
"error": str(error)
}
)
summary = summarize_batch_results(detailed_results)
response = {
"status": "success",
"document_id": document_id,
"created_at": datetime.now(timezone.utc).isoformat(),
"evaluation_type": "graphrag_batch_fusion_evaluation",
"settings": {
"top_k": top_k,
"retrieval_mode": retrieval_mode,
"use_reranker": use_reranker,
"graph_entity_limit": graph_entity_limit,
"graph_retrieval_top_k": graph_retrieval_top_k,
"custom_queries_used": bool(custom_queries)
},
"summary": summary,
"failed_questions": failed_questions,
"questions": compact_results if compact else detailed_results,
"notes": [
"This is a heuristic debug report, not a final academic benchmark.",
"The report helps inspect whether graph fusion improves retrieval quality across multiple questions.",
"For formal metrics, create a labeled benchmark with ground-truth relevant chunks."
]
}
return response
''', encoding="utf-8")
# =====================================================
# 2. Patch main.py
# =====================================================
main_path = Path("app/main.py")
text = main_path.read_text(encoding="utf-8-sig")
text = text.replace("\ufeff", "")
if "from app.evaluation.graphrag_batch_evaluator import run_graphrag_batch_evaluation" not in text:
text = "from app.evaluation.graphrag_batch_evaluator import run_graphrag_batch_evaluation\n" + text
old_phases = [
"Phase 19 - GraphRAG Retrieval Fusion Evaluation",
"Phase 18 - Graph Quality Cleanup",
"Phase 17 - Graph Vector Retrieval Fusion"
]
for old in old_phases:
text = text.replace(old, "Phase 20 - GraphRAG Batch Evaluation Report")
if "# GraphRAG batch evaluation endpoint" not in text:
text += '''
# GraphRAG batch evaluation endpoint
@app.get("/documents/{document_id}/evaluation/graph-fusion/batch")
def evaluate_graph_fusion_batch_for_document(
document_id: str,
custom_queries: Optional[str] = None,
top_k: int = Query(5, ge=1, le=20),
retrieval_mode: str = Query("hybrid"),
use_reranker: bool = True,
graph_entity_limit: int = Query(8, ge=1, le=30),
graph_retrieval_top_k: int = Query(5, ge=1, le=20),
compact: bool = True
):
return run_graphrag_batch_evaluation(
document_id=document_id,
custom_queries=custom_queries,
top_k=top_k,
retrieval_mode=retrieval_mode,
use_reranker=use_reranker,
graph_entity_limit=graph_entity_limit,
graph_retrieval_top_k=graph_retrieval_top_k,
compact=compact
)
'''
main_path.write_text(text, encoding="utf-8")
print("Phase 20 GraphRAG batch evaluation report added.")
|