File size: 375,410 Bytes
1fce89d | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "f65bb771",
"metadata": {},
"outputs": [],
"source": [
"# Run this as the VERY FIRST thing — before any imports\n",
"!pip install -q langchain langchain-community langchain-core langchain-text-splitters langchain-chroma langchain-huggingface langchain-groq chromadb rank_bm25 sentence-transformers pypdf groq pydantic pandas matplotlib seaborn tenacity python-dotenv"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "1919ed83",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[31mERROR: Could not open requirements file: [Errno 2] No such file or directory: 'requirements.txt'\u001b[0m\u001b[31m\n",
"\u001b[0mMounting Google Drive...\n",
"Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
"Module cache flushed (0 src.* modules cleared).\n",
"Importing modules...\n",
"Working directory: /content/drive/MyDrive/financial-intelligence-engine\n",
"All modules imported successfully.\n",
"\n",
"Skipping PDF ingestion — Smart Load active in Cell 2.\n",
"To rebuild indexes from scratch: uncomment the ingestion block in Cell 2.\n"
]
}
],
"source": [
"# ============================================================\n",
"# FINANCIAL INTELLIGENCE ENGINE\n",
"# Cell 1 — Environment Setup & Module Import\n",
"# ============================================================\n",
"\n",
"# Install all pinned dependencies from requirements.txt\n",
"# This single command replaces multiple scattered !pip install lines.\n",
"!pip install -q -r requirements.txt\n",
"\n",
"import sys\n",
"import os\n",
"from google.colab import drive\n",
"\n",
"# ── Mount Google Drive ────────────────────────────────────────\n",
"print('Mounting Google Drive...')\n",
"drive.mount('/content/drive')\n",
"\n",
"PROJECT_PATH = '/content/drive/MyDrive/financial-intelligence-engine'\n",
"os.chdir(PROJECT_PATH)\n",
"\n",
"# Force Python to resolve src/ imports from the project root.\n",
"if PROJECT_PATH not in sys.path:\n",
" sys.path.insert(0, PROJECT_PATH)\n",
"\n",
"# ── Flush Jupyter Module Cache ────────────────────────────────\n",
"# Required in Colab: stale cached module bytecode from previous runs\n",
"# causes import errors when source files change between cells.\n",
"modules_to_delete = [mod for mod in sys.modules if mod.startswith('src')]\n",
"for mod in modules_to_delete:\n",
" del sys.modules[mod]\n",
"print(f'Module cache flushed ({len(modules_to_delete)} src.* modules cleared).')\n",
"\n",
"# ── Import & Environment Setup ────────────────────────────────\n",
"print('Importing modules...')\n",
"from src.config import logger, setup_environment\n",
"\n",
"# setup_environment() creates all artifact directories.\n",
"# Called once here so no other module needs to run side effects on import.\n",
"setup_environment()\n",
"\n",
"from src.data_ingestion import load_and_chunk_pdfs\n",
"\n",
"print(f'Working directory: {os.getcwd()}')\n",
"print('All modules imported successfully.')\n",
"print('\\nSkipping PDF ingestion — Smart Load active in Cell 2.')\n",
"print('To rebuild indexes from scratch: uncomment the ingestion block in Cell 2.')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "3a4dcee9",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2026-04-03 05:37:03 - [INFO] - financial_rag - Loading embedding model: BAAI/bge-small-en-v1.5\n",
"INFO:financial_rag:Loading embedding model: BAAI/bge-small-en-v1.5\n",
"/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:104: UserWarning: \n",
"Error while fetching `HF_TOKEN` secret value from your vault: 'Requesting secret HF_TOKEN timed out. Secrets can only be fetched when running from the Colab UI.'.\n",
"You are not authenticated with the Hugging Face Hub in this notebook.\n",
"If the error persists, please let us know by opening an issue on GitHub (https://github.com/huggingface/huggingface_hub/issues/new).\n",
" warnings.warn(\n",
"Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n",
"WARNING:huggingface_hub.utils._http:Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bf15cc96150745f599dd2eddb73e08a2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Loading weights: 0%| | 0/199 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"BertModel LOAD REPORT from: BAAI/bge-small-en-v1.5\n",
"Key | Status | | \n",
"------------------------+------------+--+-\n",
"embeddings.position_ids | UNEXPECTED | | \n",
"\n",
"Notes:\n",
"- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n",
"2026-04-03 05:37:14 - [INFO] - financial_rag - [2/4] Smart Load: Found existing indexes on disk. Bypassing embedding compute...\n",
"INFO:financial_rag:[2/4] Smart Load: Found existing indexes on disk. Bypassing embedding compute...\n",
"2026-04-03 05:37:15 - [INFO] - financial_rag - BM25 index loaded and integrity verified.\n",
"INFO:financial_rag:BM25 index loaded and integrity verified.\n",
"2026-04-03 05:37:15 - [INFO] - financial_rag - [2/4] Initializing Reciprocal Rank Fusion engine...\n",
"INFO:financial_rag:[2/4] Initializing Reciprocal Rank Fusion engine...\n",
"2026-04-03 05:37:15 - [INFO] - financial_rag - Hybrid Retrieval Engine ready.\n",
"INFO:financial_rag:Hybrid Retrieval Engine ready.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Phase 2: Smart Loading Hybrid Indexes from Drive...\n",
"\n",
"Testing Hybrid Search: \"What were the total research and development (R&D) expenses?\"\n",
"\n",
"==================================================\n",
" RETRIEVAL CHECK PASSED\n",
"==================================================\n",
"Documents retrieved : 7\n",
"Top result company : Meta\n",
"Source file : meta_10k.pdf\n",
"Chunk ID : meta_10k_36c031d9f0a086cc\n",
"Company distribution: {'Meta': 2, 'Microsoft': 2, 'Google': 3}\n",
"Content snippet : engine, wearables, monetization of our products and services, youth, platform integrity and community support, and infrastructure capacity. The majority of our investments are directed toward developi...\n"
]
}
],
"source": [
"# ============================================================\n",
"# Cell 2 — Hybrid Retrieval Engine (Smart Load or Cold Build)\n",
"# ============================================================\n",
"\n",
"from src.retrieval_engine import HybridRetrievalEngine\n",
"\n",
"retrieval_pipeline = HybridRetrievalEngine()\n",
"\n",
"# ── SMART LOAD (default — skips re-embedding if indexes exist) ──\n",
"# On first run, uncomment the cold build block below instead.\n",
"print('Phase 2: Smart Loading Hybrid Indexes from Drive...')\n",
"ensemble_retriever = retrieval_pipeline.build_indexes()\n",
"\n",
" #── COLD BUILD (first run only — comment out after first successful run) ────\n",
"#print('Phase 2: Cold Build — Parsing PDFs and building indexes...')\n",
"#document_chunks = load_and_chunk_pdfs()\n",
"#print(f'Ingestion complete. Total chunks: {len(document_chunks)}')\n",
"#print(f'Sample metadata: {document_chunks[0].metadata}')\n",
"#ensemble_retriever = retrieval_pipeline.build_indexes(document_chunks)\n",
"\n",
"# ── Retrieval Sanity Check ────────────────────────────────────\n",
"test_query = 'What were the total research and development (R&D) expenses?'\n",
"print(f'\\nTesting Hybrid Search: \"{test_query}\"')\n",
"search_results = ensemble_retriever.invoke(test_query)\n",
"\n",
"print('\\n' + '='*50)\n",
"print(' RETRIEVAL CHECK PASSED')\n",
"print('='*50)\n",
"print(f'Documents retrieved : {len(search_results)}')\n",
"print(f'Top result company : {search_results[0].metadata.get(\"company\")}')\n",
"print(f'Source file : {search_results[0].metadata.get(\"source_file\")}')\n",
"print(f'Chunk ID : {search_results[0].metadata.get(\"chunk_id\")}')\n",
"\n",
"# Company distribution check — should be roughly balanced across companies.\n",
"from collections import Counter\n",
"company_dist = Counter(d.metadata.get('company') for d in search_results)\n",
"print(f'Company distribution: {dict(company_dist)}')\n",
"\n",
"# Clean snippet preview\n",
"raw_snippet = search_results[0].page_content[:200]\n",
"clean_snippet = ' '.join(raw_snippet.split())\n",
"print(f'Content snippet : {clean_snippet}...')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "801be538",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loading credentials from Drive .env file...\n",
"API key loaded successfully.\n",
"Agent ready. Generator: llama-3.3-70b-versatile\n",
"\n",
"Query set: Compare the key areas where Google and Meta are investing their Research and Development (R&D) budgets for 2025.\n",
"Skipping fresh generation to preserve tokens for batch evaluation.\n",
"Run Cell 4 → Cell 5 → Cell 6 now.\n"
]
}
],
"source": [
"# Cell 3 REPLACEMENT — Saves ~6000 tokens\n",
"# Generation pipeline already demonstrated. Loading credentials only.\n",
"import os\n",
"from dotenv import load_dotenv\n",
"from src.generation_agent import FinancialGenerationAgent\n",
"\n",
"print('Loading credentials from Drive .env file...')\n",
"load_dotenv(override=True)\n",
"GROQ_API_KEY = os.getenv('GROQ_API_KEY')\n",
"\n",
"if not GROQ_API_KEY:\n",
" raise ValueError('GROQ_API_KEY not found in .env file.')\n",
"print('API key loaded successfully.')\n",
"\n",
"# Initialize agent — needed for Cell 5 batch evaluation\n",
"agent = FinancialGenerationAgent(\n",
" retriever=ensemble_retriever, \n",
" api_key=GROQ_API_KEY\n",
")\n",
"print(f'Agent ready. Generator: {agent.llm.model_name}')\n",
"\n",
"# Set the query variable — needed by Cell 4 and Cell 6\n",
"complex_query = (\n",
" 'Compare the key areas where Google and Meta are investing '\n",
" 'their Research and Development (R&D) budgets for 2025.'\n",
")\n",
"\n",
"# Use a pre-saved answer for visualization — no token cost\n",
"final_answer = (\n",
" \"Based on SEC 10-K filings:\\n\"\n",
" \"* Meta is investing in neural interfaces, virtual and augmented \"\n",
" \"reality devices, and foundational AI research [Source: Meta 10-K]\\n\"\n",
" \"* Google is investing in technical infrastructure including servers \"\n",
" \"and data centers, and AI-powered products like Demand Gen and \"\n",
" \"Performance Max [Source: Google 10-K]\\n\"\n",
" \"* Both companies are investing in emerging technologies but with \"\n",
" \"different strategic focuses [Source: Meta 10-K, Google 10-K]\"\n",
")\n",
"\n",
"# used_docs needed for Cell 6 pie chart — load from retriever\n",
"used_docs = ensemble_retriever.invoke(complex_query)\n",
"\n",
"print(f'\\nQuery set: {complex_query}')\n",
"print('Skipping fresh generation to preserve tokens for batch evaluation.')\n",
"print('Run Cell 4 → Cell 5 → Cell 6 now.')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "cae9a4d4",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
},
"text/plain": [
"\"\\n\\n# ============================================================\\n# Cell 3 — Generation Agent (CoT + Self-Correction)\\n# ============================================================\\n\\nimport os\\nfrom dotenv import load_dotenv\\nfrom src.generation_agent import FinancialGenerationAgent\\n\\n# ── Secure Credential Loading ─────────────────────────────────\\nprint('Loading credentials from Drive .env file...')\\nload_dotenv(override=True) # override=True ensures Drive .env takes precedence\\nGROQ_API_KEY = os.getenv('GROQ_API_KEY')\\n\\nif not GROQ_API_KEY:\\n raise ValueError(\\n 'GROQ_API_KEY not found. '\\n 'Ensure your .env file is in the project root on Google Drive '\\n 'and contains: GROQ_API_KEY=your_key_here'\\n )\\nprint('API key loaded successfully.')\\n\\n# ── Initialize Agent ──────────────────────────────────────────\\nprint('\\nPhase 3: Initializing Generation & Self-Correction Agent...')\\nagent = FinancialGenerationAgent(retriever=ensemble_retriever, api_key=GROQ_API_KEY)\\n\\n# ── Run Primary Query ─────────────────────────────────────────\\ncomplex_query = (\\n 'Compare the key areas where Google and Meta are investing their '\\n 'Research and Development (R&D) budgets for 2025.'\\n)\\n\\nprint(f'\\nQuery: {complex_query}')\\nfinal_answer, used_docs = agent.generate_answer(complex_query)\\n\\nprint('\\n' + '='*60)\\nprint(' FINANCIAL INTELLIGENCE ENGINE OUTPUT')\\nprint('='*60)\\nprint(final_answer)\\n\\nprint('\\n' + '-'*60)\\nprint(' SOURCES CITED')\\nprint('-'*60)\\nsources = set(d.metadata.get('source_file') for d in used_docs)\\nfor source in sources:\\n print(f' -> {source}')\\n\\n \""
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\"\"\"\n",
"\n",
"# ============================================================\n",
"# Cell 3 — Generation Agent (CoT + Self-Correction)\n",
"# ============================================================\n",
"\n",
"import os\n",
"from dotenv import load_dotenv\n",
"from src.generation_agent import FinancialGenerationAgent\n",
"\n",
"# ── Secure Credential Loading ─────────────────────────────────\n",
"print('Loading credentials from Drive .env file...')\n",
"load_dotenv(override=True) # override=True ensures Drive .env takes precedence\n",
"GROQ_API_KEY = os.getenv('GROQ_API_KEY')\n",
"\n",
"if not GROQ_API_KEY:\n",
" raise ValueError(\n",
" 'GROQ_API_KEY not found. '\n",
" 'Ensure your .env file is in the project root on Google Drive '\n",
" 'and contains: GROQ_API_KEY=your_key_here'\n",
" )\n",
"print('API key loaded successfully.')\n",
"\n",
"# ── Initialize Agent ──────────────────────────────────────────\n",
"print('\\nPhase 3: Initializing Generation & Self-Correction Agent...')\n",
"agent = FinancialGenerationAgent(retriever=ensemble_retriever, api_key=GROQ_API_KEY)\n",
"\n",
"# ── Run Primary Query ─────────────────────────────────────────\n",
"complex_query = (\n",
" 'Compare the key areas where Google and Meta are investing their '\n",
" 'Research and Development (R&D) budgets for 2025.'\n",
")\n",
"\n",
"print(f'\\nQuery: {complex_query}')\n",
"final_answer, used_docs = agent.generate_answer(complex_query)\n",
"\n",
"print('\\n' + '='*60)\n",
"print(' FINANCIAL INTELLIGENCE ENGINE OUTPUT')\n",
"print('='*60)\n",
"print(final_answer)\n",
"\n",
"print('\\n' + '-'*60)\n",
"print(' SOURCES CITED')\n",
"print('-'*60)\n",
"sources = set(d.metadata.get('source_file') for d in used_docs)\n",
"for source in sources:\n",
" print(f' -> {source}')\n",
"\n",
" \"\"\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "868da12c",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 9,
"id": "651ad1bf",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2026-04-03 05:38:39 - [INFO] - financial_rag - Running LLM-as-a-Judge evaluation (Judge model: qwen/qwen3-32b)...\n",
"INFO:financial_rag:Running LLM-as-a-Judge evaluation (Judge model: qwen/qwen3-32b)...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Phase 4a: Single-Query Evaluation (quick check)...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2026-04-03 05:38:40 - [INFO] - financial_rag - Evaluation scores: {'faithfulness': 1.0, 'relevance': 1.0}\n",
"INFO:financial_rag:Evaluation scores: {'faithfulness': 1.0, 'relevance': 1.0}\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"=======================================================\n",
" SINGLE-QUERY EVALUATION RESULTS\n",
"=======================================================\n",
" Metric Score (0.0 - 1.0) Status\n",
"Faithfulness (Hallucination Check) 1.0 PASS\n",
" Context Relevance 1.0 PASS\n",
"\n",
"Note: Single-query scores are directional only.\n",
"Run Cell 5 (Batch Evaluation) for statistically valid aggregate scores.\n"
]
}
],
"source": [
"# ============================================================\n",
"# Cell 4 — Single-Query Evaluation (Quick Check)\n",
"# ============================================================\n",
"\n",
"import pandas as pd\n",
"from src.evaluation import RAGEvaluator\n",
"\n",
"print('Phase 4a: Single-Query Evaluation (quick check)...')\n",
"evaluator = RAGEvaluator(api_key=GROQ_API_KEY)\n",
"\n",
"scores = evaluator.evaluate(\n",
" question=complex_query,\n",
" answer=final_answer,\n",
" context_docs=used_docs,\n",
" # Optional: provide a ground_truth string to enable the Correctness metric.\n",
" # ground_truth='Google and Meta both invest heavily in AI; ...',\n",
")\n",
"\n",
"print('\\n' + '='*55)\n",
"print(' SINGLE-QUERY EVALUATION RESULTS')\n",
"print('='*55)\n",
"\n",
"metrics_data = [\n",
" {\n",
" 'Metric': 'Faithfulness (Hallucination Check)',\n",
" 'Score (0.0 - 1.0)': scores.get('faithfulness', 'Error'),\n",
" 'Status': (\n",
" 'PASS' if isinstance(scores.get('faithfulness'), float)\n",
" and scores['faithfulness'] >= 0.8 else 'FAIL'\n",
" )\n",
" },\n",
" {\n",
" 'Metric': 'Context Relevance',\n",
" 'Score (0.0 - 1.0)': scores.get('relevance', 'Error'),\n",
" 'Status': (\n",
" 'PASS' if isinstance(scores.get('relevance'), float)\n",
" and scores['relevance'] >= 0.8 else 'FAIL'\n",
" )\n",
" },\n",
"]\n",
"\n",
"if 'correctness' in scores:\n",
" metrics_data.append({\n",
" 'Metric': 'Answer Correctness (vs Ground Truth)',\n",
" 'Score (0.0 - 1.0)': scores.get('correctness', 'Error'),\n",
" 'Status': (\n",
" 'PASS' if isinstance(scores.get('correctness'), float)\n",
" and scores['correctness'] >= 0.8 else 'FAIL'\n",
" )\n",
" })\n",
"\n",
"metrics_df = pd.DataFrame(metrics_data)\n",
"print(metrics_df.to_string(index=False))\n",
"print('\\nNote: Single-query scores are directional only.')\n",
"print('Run Cell 5 (Batch Evaluation) for statistically valid aggregate scores.')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "73e7972c",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2026-04-03 05:38:45 - [INFO] - financial_rag - Starting batch evaluation over 15 questions (Judge: qwen/qwen3-32b)...\n",
"INFO:financial_rag:Starting batch evaluation over 15 questions (Judge: qwen/qwen3-32b)...\n",
"2026-04-03 05:38:45 - [INFO] - financial_rag - Evaluating question 1/15: 'What were Google's total Research and Development expenses i'\n",
"INFO:financial_rag: Evaluating question 1/15: 'What were Google's total Research and Development expenses i'\n",
"2026-04-03 05:38:45 - [INFO] - financial_rag - Retrieving documents for query: 'What were Google's total Research and Development expenses in fiscal year 2025?'\n",
"INFO:financial_rag:Retrieving documents for query: 'What were Google's total Research and Development expenses in fiscal year 2025?'\n",
"2026-04-03 05:38:45 - [INFO] - financial_rag - Step 1: Executing Chain-of-Thought Analysis (llama-3.3-70b-versatile)...\n",
"INFO:financial_rag:Step 1: Executing Chain-of-Thought Analysis (llama-3.3-70b-versatile)...\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Phase 4b: Running Batch Evaluation over 15 questions...\n",
"Judge model : qwen/qwen3-32b\n",
"Generator : llama-3.3-70b-versatile\n",
"This will take 5-8 minutes. Each question runs full generation + evaluation.\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2026-04-03 05:38:46 - [INFO] - financial_rag - Step 2: Running Strict Compliance Audit (llama-3.3-70b-versatile)...\n",
"INFO:financial_rag:Step 2: Running Strict Compliance Audit (llama-3.3-70b-versatile)...\n",
"2026-04-03 05:38:46 - [INFO] - financial_rag - Running LLM-as-a-Judge evaluation (Judge model: qwen/qwen3-32b)...\n",
"INFO:financial_rag:Running LLM-as-a-Judge evaluation (Judge model: qwen/qwen3-32b)...\n",
"2026-04-03 05:38:48 - [ERROR] - financial_rag - Failed to parse LLM evaluation response: Invalid json output: <think>\n",
"Okay, let's start by looking at the user's question: What were Google's total Research and Development expenses in fiscal year 2025?\n",
"\n",
"The context provided has several sections. The first part mentions that in the Research and Development section, there's a table showing expenses for 2024 and 2025 as $49,326 million and $61,087 million respectively. Then later, there's another mention of Research and Development expenses increasing by $13.50 billion in 2025 compared to 2024. Wait, that's conflicting. The first table says an increase of $11.8 billion (from $49,326 to $61,087), but later it says $13.50 billion. Hmm, that's a discrepancy. But the user's answer is based on the first table's $61,087 million figure.\n",
"\n",
"The answer given is $61,087 million, which is $61.087 billion. The Ground Truth states the same amount, $61.087 billion, with 15% of revenues and an increase of $11.8 billion. So the answer matches the Ground Truth in the amount. \n",
"\n",
"Now, checking faithfulness: The answer cites the source as the Google 10-K and uses the figure from the first table. The context does have that table. However, there's another part in the context that says \"Research and development expenses in 2025 increased $13.50 billion, or 31%, compared to 2024.\" This is conflicting. The first table shows an increase of $11.8 billion (from $49,326 to $61,087 is $11,761 million, which rounds to $11.8 billion). The later statement says $13.50 billion. Which one is correct? The answer uses the first figure. The Ground Truth also mentions the $11.8 billion increase. So the answer is correct in using the first figure. However, the context has conflicting data. But since the answer is based on the table provided in the context, it's still faithful because it's using the data from the context. The presence of conflicting data in the context might be an issue, but the answer is using the correct part from the context.\n",
"\n",
"Relevance: The answer directly addresses the question by providing the 2025 R&D expenses. It's completely relevant.\n",
"\n",
"Correctness: The Ground Truth states $61.087 billion, which matches the answer. The answer is correct. The Ground Truth also mentions the 15% of revenues and the $11.8 billion increase, which the answer doesn't include, but the question only asked for the total expenses. So the answer is correct as per the question's requirements.\n",
"\n",
"So faithfulness is 1.0 because the answer is derived from the context. Relevance is 1.0. Correctness is 1.0.\n",
"</think>\n",
"\n",
"{\n",
" \"faithfulness\": 1.0,\n",
" \"relevance\": 1.0,\n",
" \"correctness\": 1.0\n",
"}\n",
"For troubleshooting, visit: https://docs.langchain.com/oss/python/langchain/errors/OUTPUT_PARSING_FAILURE \n",
"ERROR:financial_rag:Failed to parse LLM evaluation response: Invalid json output: <think>\n",
"Okay, let's start by looking at the user's question: What were Google's total Research and Development expenses in fiscal year 2025?\n",
"\n",
"The context provided has several sections. The first part mentions that in the Research and Development section, there's a table showing expenses for 2024 and 2025 as $49,326 million and $61,087 million respectively. Then later, there's another mention of Research and Development expenses increasing by $13.50 billion in 2025 compared to 2024. Wait, that's conflicting. The first table says an increase of $11.8 billion (from $49,326 to $61,087), but later it says $13.50 billion. Hmm, that's a discrepancy. But the user's answer is based on the first table's $61,087 million figure.\n",
"\n",
"The answer given is $61,087 million, which is $61.087 billion. The Ground Truth states the same amount, $61.087 billion, with 15% of revenues and an increase of $11.8 billion. So the answer matches the Ground Truth in the amount. \n",
"\n",
"Now, checking faithfulness: The answer cites the source as the Google 10-K and uses the figure from the first table. The context does have that table. However, there's another part in the context that says \"Research and development expenses in 2025 increased $13.50 billion, or 31%, compared to 2024.\" This is conflicting. The first table shows an increase of $11.8 billion (from $49,326 to $61,087 is $11,761 million, which rounds to $11.8 billion). The later statement says $13.50 billion. Which one is correct? The answer uses the first figure. The Ground Truth also mentions the $11.8 billion increase. So the answer is correct in using the first figure. However, the context has conflicting data. But since the answer is based on the table provided in the context, it's still faithful because it's using the data from the context. The presence of conflicting data in the context might be an issue, but the answer is using the correct part from the context.\n",
"\n",
"Relevance: The answer directly addresses the question by providing the 2025 R&D expenses. It's completely relevant.\n",
"\n",
"Correctness: The Ground Truth states $61.087 billion, which matches the answer. The answer is correct. The Ground Truth also mentions the 15% of revenues and the $11.8 billion increase, which the answer doesn't include, but the question only asked for the total expenses. So the answer is correct as per the question's requirements.\n",
"\n",
"So faithfulness is 1.0 because the answer is derived from the context. Relevance is 1.0. Correctness is 1.0.\n",
"</think>\n",
"\n",
"{\n",
" \"faithfulness\": 1.0,\n",
" \"relevance\": 1.0,\n",
" \"correctness\": 1.0\n",
"}\n",
"For troubleshooting, visit: https://docs.langchain.com/oss/python/langchain/errors/OUTPUT_PARSING_FAILURE \n",
"2026-04-03 05:38:48 - [INFO] - financial_rag - Evaluating question 2/15: 'What were Google's total revenues and net income in fiscal y'\n",
"INFO:financial_rag: Evaluating question 2/15: 'What were Google's total revenues and net income in fiscal y'\n",
"2026-04-03 05:38:48 - [INFO] - financial_rag - Retrieving documents for query: 'What were Google's total revenues and net income in fiscal year 2025?'\n",
"INFO:financial_rag:Retrieving documents for query: 'What were Google's total revenues and net income in fiscal year 2025?'\n",
"2026-04-03 05:38:48 - [INFO] - financial_rag - Step 1: Executing Chain-of-Thought Analysis (llama-3.3-70b-versatile)...\n",
"INFO:financial_rag:Step 1: Executing Chain-of-Thought Analysis (llama-3.3-70b-versatile)...\n",
"2026-04-03 05:38:49 - [INFO] - financial_rag - Step 2: Running Strict Compliance Audit (llama-3.3-70b-versatile)...\n",
"INFO:financial_rag:Step 2: Running Strict Compliance Audit (llama-3.3-70b-versatile)...\n",
"2026-04-03 05:38:49 - [INFO] - financial_rag - Running LLM-as-a-Judge evaluation (Judge model: qwen/qwen3-32b)...\n",
"INFO:financial_rag:Running LLM-as-a-Judge evaluation (Judge model: qwen/qwen3-32b)...\n",
"2026-04-03 05:39:19 - [INFO] - financial_rag - Evaluation scores: {'faithfulness': 1.0, 'relevance': 1.0, 'correctness': 1.0}\n",
"INFO:financial_rag:Evaluation scores: {'faithfulness': 1.0, 'relevance': 1.0, 'correctness': 1.0}\n",
"2026-04-03 05:39:19 - [INFO] - financial_rag - Evaluating question 3/15: 'How much did Google spend on capital expenditures in fiscal '\n",
"INFO:financial_rag: Evaluating question 3/15: 'How much did Google spend on capital expenditures in fiscal '\n",
"2026-04-03 05:39:19 - [INFO] - financial_rag - Retrieving documents for query: 'How much did Google spend on capital expenditures in fiscal year 2025?'\n",
"INFO:financial_rag:Retrieving documents for query: 'How much did Google spend on capital expenditures in fiscal year 2025?'\n",
"2026-04-03 05:39:20 - [INFO] - financial_rag - Step 1: Executing Chain-of-Thought Analysis (llama-3.3-70b-versatile)...\n",
"INFO:financial_rag:Step 1: Executing Chain-of-Thought Analysis (llama-3.3-70b-versatile)...\n",
"2026-04-03 05:39:20 - [INFO] - financial_rag - Step 2: Running Strict Compliance Audit (llama-3.3-70b-versatile)...\n",
"INFO:financial_rag:Step 2: Running Strict Compliance Audit (llama-3.3-70b-versatile)...\n",
"2026-04-03 05:39:21 - [INFO] - financial_rag - Running LLM-as-a-Judge evaluation (Judge model: qwen/qwen3-32b)...\n",
"INFO:financial_rag:Running LLM-as-a-Judge evaluation (Judge model: qwen/qwen3-32b)...\n",
"2026-04-03 05:39:46 - [ERROR] - financial_rag - Failed to parse LLM evaluation response: Invalid json output: <think>\n",
"Okay, let's start by looking at the user's question: they want to know how much Google spent on capital expenditures in fiscal year 2025. The context provided includes a lot of financial data from Google's filings. \n",
"\n",
"First, I need to check if the answer is present in the context. The user's answer states that the capital expenditures for 2025 aren't explicitly mentioned, but the Ground Truth says it's $91.4 billion. Let me scan through the context again.\n",
"\n",
"Looking through the sections, under \"Cash Used in Investing Activities\" for 2025, there's a line: \"$69.69 billion of purchases of property and equipment...\" Wait, property and equipment purchases are part of capital expenditures. But the Ground Truth mentions $91.4 billion. However, the context also says \"We anticipate making capital expenditures of approximately $115 billion to $135 billion in 2026...\" which is a future year. \n",
"\n",
"Wait, the user's answer says the 2025 capital expenditures aren't stated, but the Ground Truth claims it's $91.4B. Let me check again. The context under \"Cash Used in Investing Activities during 2025\" lists $69.69B for property and equipment. But capital expenditures can include more than just property and equipment. However, the Ground Truth says $91.4B, which isn't directly stated in the context. The context only mentions $69.69B for property and equipment. \n",
"\n",
"So the answer correctly points out that the context doesn't explicitly state the total capital expenditures for 2025. The Ground Truth is providing a figure that isn't in the context. Therefore, the answer is faithful because it doesn't make up a number not present. Relevance is high because it directly addresses the question. Correctness is low because the answer doesn't match the Ground Truth. The Ground Truth's $91.4B isn't in the context, so the answer is correct in stating it's not provided. Therefore, the correctness score should be 0.0 because the answer contradicts the Ground Truth by not providing the correct figure when it's actually present in the context? Wait, no. Wait, the context does mention $69.69B for property and equipment under 2025. But the Ground Truth says $91.4B. Is there another line item in the context that adds up to $91.4B? Let me check again.\n",
"\n",
"In the context under \"Cash Used in Investing Activities during 2025\": $69.69B (property and equipment), $18.33B (non-marketable equity investments), and $10.05B (marketable securities). Adding those gives $98.07B. But the Ground Truth says $91.4B. Hmm, maybe the Ground Truth is incorrect, or perhaps the answer is missing something. Alternatively, maybe the $69.69B is part of the total capital expenditures. But the answer says it's not stated, but the context does have a figure. Wait, the answer says the context doesn't provide a specific amount for capital expenditures, but the context does mention $69.69B for property and equipment. However, capital expenditures can include other items. The answer is correct in stating that the total isn't explicitly given, but the Ground Truth is providing a figure that isn't in the context. Therefore, the answer is faithful (since it doesn't hallucinate a number), relevant (addresses the question), but incorrect compared to the Ground Truth. However, the Ground Truth might be wrong if the context doesn't actually state $91.4B. Wait, the user's Ground Truth says $91.4B, but the context only mentions $69.69B for property and equipment. Unless there's another line item. Let me check again.\n",
"\n",
"Looking at the context: \"Cash used in investing activities during 2025 mostly consisted of $69.69 billion of purchases of property and equipment...\" So that's part of capital expenditures. But the total cash used in investing activities is $69.69B + $18.33B + $10.05B = $98.07B. However, capital expenditures typically refer to purchases of property and equipment. The other items (non-marketable equity investments and marketable securities) are not usually considered capital expenditures. Therefore, the actual capital expenditures for 2025 would be $69.69B. But the Ground Truth says $91.4B, which is not present in the context. Therefore, the answer is correct in stating that the context doesn't provide the total capital expenditures for 2025, and the Ground Truth is incorrect. However, the user's Ground Truth is given as a reference, so the answer's correctness is 0.0 because it doesn't match the Ground Truth. But wait, the answer is correct based on the context, but the Ground Truth is wrong. However, the user's instruction says to score correctness based on agreement with the Ground Truth. So even if the Ground Truth is incorrect, the answer's correctness is 0.0 because it doesn't match. Therefore, the answer is not correct compared to the Ground Truth. So the scores would be: faithfulness 1.0 (since it doesn't make up a number), relevance 1.0 (directly answers the question), correctness 0.0 (doesn't match the Ground Truth's $91.4B).\n",
"</think>\n",
"\n",
"{\n",
" \"faithfulness\": 1.0,\n",
" \"relevance\": 1.0,\n",
" \"correctness\": 0.0\n",
"}\n",
"For troubleshooting, visit: https://docs.langchain.com/oss/python/langchain/errors/OUTPUT_PARSING_FAILURE \n",
"ERROR:financial_rag:Failed to parse LLM evaluation response: Invalid json output: <think>\n",
"Okay, let's start by looking at the user's question: they want to know how much Google spent on capital expenditures in fiscal year 2025. The context provided includes a lot of financial data from Google's filings. \n",
"\n",
"First, I need to check if the answer is present in the context. The user's answer states that the capital expenditures for 2025 aren't explicitly mentioned, but the Ground Truth says it's $91.4 billion. Let me scan through the context again.\n",
"\n",
"Looking through the sections, under \"Cash Used in Investing Activities\" for 2025, there's a line: \"$69.69 billion of purchases of property and equipment...\" Wait, property and equipment purchases are part of capital expenditures. But the Ground Truth mentions $91.4 billion. However, the context also says \"We anticipate making capital expenditures of approximately $115 billion to $135 billion in 2026...\" which is a future year. \n",
"\n",
"Wait, the user's answer says the 2025 capital expenditures aren't stated, but the Ground Truth claims it's $91.4B. Let me check again. The context under \"Cash Used in Investing Activities during 2025\" lists $69.69B for property and equipment. But capital expenditures can include more than just property and equipment. However, the Ground Truth says $91.4B, which isn't directly stated in the context. The context only mentions $69.69B for property and equipment. \n",
"\n",
"So the answer correctly points out that the context doesn't explicitly state the total capital expenditures for 2025. The Ground Truth is providing a figure that isn't in the context. Therefore, the answer is faithful because it doesn't make up a number not present. Relevance is high because it directly addresses the question. Correctness is low because the answer doesn't match the Ground Truth. The Ground Truth's $91.4B isn't in the context, so the answer is correct in stating it's not provided. Therefore, the correctness score should be 0.0 because the answer contradicts the Ground Truth by not providing the correct figure when it's actually present in the context? Wait, no. Wait, the context does mention $69.69B for property and equipment under 2025. But the Ground Truth says $91.4B. Is there another line item in the context that adds up to $91.4B? Let me check again.\n",
"\n",
"In the context under \"Cash Used in Investing Activities during 2025\": $69.69B (property and equipment), $18.33B (non-marketable equity investments), and $10.05B (marketable securities). Adding those gives $98.07B. But the Ground Truth says $91.4B. Hmm, maybe the Ground Truth is incorrect, or perhaps the answer is missing something. Alternatively, maybe the $69.69B is part of the total capital expenditures. But the answer says it's not stated, but the context does have a figure. Wait, the answer says the context doesn't provide a specific amount for capital expenditures, but the context does mention $69.69B for property and equipment. However, capital expenditures can include other items. The answer is correct in stating that the total isn't explicitly given, but the Ground Truth is providing a figure that isn't in the context. Therefore, the answer is faithful (since it doesn't hallucinate a number), relevant (addresses the question), but incorrect compared to the Ground Truth. However, the Ground Truth might be wrong if the context doesn't actually state $91.4B. Wait, the user's Ground Truth says $91.4B, but the context only mentions $69.69B for property and equipment. Unless there's another line item. Let me check again.\n",
"\n",
"Looking at the context: \"Cash used in investing activities during 2025 mostly consisted of $69.69 billion of purchases of property and equipment...\" So that's part of capital expenditures. But the total cash used in investing activities is $69.69B + $18.33B + $10.05B = $98.07B. However, capital expenditures typically refer to purchases of property and equipment. The other items (non-marketable equity investments and marketable securities) are not usually considered capital expenditures. Therefore, the actual capital expenditures for 2025 would be $69.69B. But the Ground Truth says $91.4B, which is not present in the context. Therefore, the answer is correct in stating that the context doesn't provide the total capital expenditures for 2025, and the Ground Truth is incorrect. However, the user's Ground Truth is given as a reference, so the answer's correctness is 0.0 because it doesn't match the Ground Truth. But wait, the answer is correct based on the context, but the Ground Truth is wrong. However, the user's instruction says to score correctness based on agreement with the Ground Truth. So even if the Ground Truth is incorrect, the answer's correctness is 0.0 because it doesn't match. Therefore, the answer is not correct compared to the Ground Truth. So the scores would be: faithfulness 1.0 (since it doesn't make up a number), relevance 1.0 (directly answers the question), correctness 0.0 (doesn't match the Ground Truth's $91.4B).\n",
"</think>\n",
"\n",
"{\n",
" \"faithfulness\": 1.0,\n",
" \"relevance\": 1.0,\n",
" \"correctness\": 0.0\n",
"}\n",
"For troubleshooting, visit: https://docs.langchain.com/oss/python/langchain/errors/OUTPUT_PARSING_FAILURE \n",
"2026-04-03 05:39:46 - [INFO] - financial_rag - Evaluating question 4/15: 'What was Google Cloud's revenue in fiscal year 2025?'\n",
"INFO:financial_rag: Evaluating question 4/15: 'What was Google Cloud's revenue in fiscal year 2025?'\n",
"2026-04-03 05:39:46 - [INFO] - financial_rag - Retrieving documents for query: 'What was Google Cloud's revenue in fiscal year 2025?'\n",
"INFO:financial_rag:Retrieving documents for query: 'What was Google Cloud's revenue in fiscal year 2025?'\n",
"2026-04-03 05:39:46 - [INFO] - financial_rag - Step 1: Executing Chain-of-Thought Analysis (llama-3.3-70b-versatile)...\n",
"INFO:financial_rag:Step 1: Executing Chain-of-Thought Analysis (llama-3.3-70b-versatile)...\n",
"2026-04-03 05:39:47 - [INFO] - financial_rag - Step 2: Running Strict Compliance Audit (llama-3.3-70b-versatile)...\n",
"INFO:financial_rag:Step 2: Running Strict Compliance Audit (llama-3.3-70b-versatile)...\n",
"2026-04-03 05:39:47 - [INFO] - financial_rag - Running LLM-as-a-Judge evaluation (Judge model: qwen/qwen3-32b)...\n",
"INFO:financial_rag:Running LLM-as-a-Judge evaluation (Judge model: qwen/qwen3-32b)...\n",
"2026-04-03 05:40:23 - [ERROR] - financial_rag - Failed to parse LLM evaluation response: Invalid json output: <think>\n",
"Okay, let's start by looking at the user's question: What was Google Cloud's revenue in fiscal year 2025? The answer provided is $58,705 million, citing the Google 10-K. The context given includes financial data tables where under the \"Revenues\" section, Google Cloud's 2025 revenue is listed as $58,705. So the answer is directly pulling that number from the context.\n",
"\n",
"First, checking faithfulness. The answer states the exact figure from the context without adding any other information. There's no mention of other sources or unsupported claims. So faithfulness should be 1.0.\n",
"\n",
"Next, relevance. The question asks specifically for Google Cloud's revenue in 2025, and the answer provides that exact number. It's directly addressing the question without any deviation. Relevance is 1.0.\n",
"\n",
"For correctness, the Ground Truth says the revenue was $58.705 billion, which is the same as $58,705 million. The answer matches this exactly. The Ground Truth also mentions a 36% increase, but the answer doesn't include that, but correctness here is about factual agreement. Since the key figure is correct, correctness is 1.0. Even though the Ground Truth has more details, the answer's main fact is correct.\n",
"</think>\n",
"\n",
"{\n",
" \"faithfulness\": 1.0,\n",
" \"relevance\": 1.0,\n",
" \"correctness\": 1.0\n",
"}\n",
"For troubleshooting, visit: https://docs.langchain.com/oss/python/langchain/errors/OUTPUT_PARSING_FAILURE \n",
"ERROR:financial_rag:Failed to parse LLM evaluation response: Invalid json output: <think>\n",
"Okay, let's start by looking at the user's question: What was Google Cloud's revenue in fiscal year 2025? The answer provided is $58,705 million, citing the Google 10-K. The context given includes financial data tables where under the \"Revenues\" section, Google Cloud's 2025 revenue is listed as $58,705. So the answer is directly pulling that number from the context.\n",
"\n",
"First, checking faithfulness. The answer states the exact figure from the context without adding any other information. There's no mention of other sources or unsupported claims. So faithfulness should be 1.0.\n",
"\n",
"Next, relevance. The question asks specifically for Google Cloud's revenue in 2025, and the answer provides that exact number. It's directly addressing the question without any deviation. Relevance is 1.0.\n",
"\n",
"For correctness, the Ground Truth says the revenue was $58.705 billion, which is the same as $58,705 million. The answer matches this exactly. The Ground Truth also mentions a 36% increase, but the answer doesn't include that, but correctness here is about factual agreement. Since the key figure is correct, correctness is 1.0. Even though the Ground Truth has more details, the answer's main fact is correct.\n",
"</think>\n",
"\n",
"{\n",
" \"faithfulness\": 1.0,\n",
" \"relevance\": 1.0,\n",
" \"correctness\": 1.0\n",
"}\n",
"For troubleshooting, visit: https://docs.langchain.com/oss/python/langchain/errors/OUTPUT_PARSING_FAILURE \n",
"2026-04-03 05:40:23 - [INFO] - financial_rag - Evaluating question 5/15: 'What were Meta's total revenues and net income in fiscal yea'\n",
"INFO:financial_rag: Evaluating question 5/15: 'What were Meta's total revenues and net income in fiscal yea'\n",
"2026-04-03 05:40:23 - [INFO] - financial_rag - Retrieving documents for query: 'What were Meta's total revenues and net income in fiscal year 2025?'\n",
"INFO:financial_rag:Retrieving documents for query: 'What were Meta's total revenues and net income in fiscal year 2025?'\n",
"2026-04-03 05:40:23 - [INFO] - financial_rag - Step 1: Executing Chain-of-Thought Analysis (llama-3.3-70b-versatile)...\n",
"INFO:financial_rag:Step 1: Executing Chain-of-Thought Analysis (llama-3.3-70b-versatile)...\n",
"2026-04-03 05:40:24 - [INFO] - financial_rag - Step 2: Running Strict Compliance Audit (llama-3.3-70b-versatile)...\n",
"INFO:financial_rag:Step 2: Running Strict Compliance Audit (llama-3.3-70b-versatile)...\n",
"2026-04-03 05:40:24 - [INFO] - financial_rag - Running LLM-as-a-Judge evaluation (Judge model: qwen/qwen3-32b)...\n",
"INFO:financial_rag:Running LLM-as-a-Judge evaluation (Judge model: qwen/qwen3-32b)...\n",
"2026-04-03 05:40:57 - [INFO] - financial_rag - Evaluation scores: {'faithfulness': 1.0, 'relevance': 1.0, 'correctness': 1.0}\n",
"INFO:financial_rag:Evaluation scores: {'faithfulness': 1.0, 'relevance': 1.0, 'correctness': 1.0}\n",
"2026-04-03 05:40:57 - [INFO] - financial_rag - Evaluating question 6/15: 'What were Meta's Research and Development expenses in fiscal'\n",
"INFO:financial_rag: Evaluating question 6/15: 'What were Meta's Research and Development expenses in fiscal'\n",
"2026-04-03 05:40:57 - [INFO] - financial_rag - Retrieving documents for query: 'What were Meta's Research and Development expenses in fiscal year 2025?'\n",
"INFO:financial_rag:Retrieving documents for query: 'What were Meta's Research and Development expenses in fiscal year 2025?'\n",
"2026-04-03 05:40:58 - [INFO] - financial_rag - Step 1: Executing Chain-of-Thought Analysis (llama-3.3-70b-versatile)...\n",
"INFO:financial_rag:Step 1: Executing Chain-of-Thought Analysis (llama-3.3-70b-versatile)...\n",
"2026-04-03 05:40:59 - [INFO] - financial_rag - Step 2: Running Strict Compliance Audit (llama-3.3-70b-versatile)...\n",
"INFO:financial_rag:Step 2: Running Strict Compliance Audit (llama-3.3-70b-versatile)...\n",
"2026-04-03 05:41:00 - [INFO] - financial_rag - Running LLM-as-a-Judge evaluation (Judge model: qwen/qwen3-32b)...\n",
"INFO:financial_rag:Running LLM-as-a-Judge evaluation (Judge model: qwen/qwen3-32b)...\n",
"2026-04-03 05:41:29 - [INFO] - financial_rag - Evaluation scores: {'faithfulness': 1.0, 'relevance': 1.0, 'correctness': 1.0}\n",
"INFO:financial_rag:Evaluation scores: {'faithfulness': 1.0, 'relevance': 1.0, 'correctness': 1.0}\n",
"2026-04-03 05:41:29 - [INFO] - financial_rag - Evaluating question 7/15: 'What was Meta's Reality Labs operating loss in 2025 and what'\n",
"INFO:financial_rag: Evaluating question 7/15: 'What was Meta's Reality Labs operating loss in 2025 and what'\n",
"2026-04-03 05:41:29 - [INFO] - financial_rag - Retrieving documents for query: 'What was Meta's Reality Labs operating loss in 2025 and what is the 2026 capital expenditure guidance?'\n",
"INFO:financial_rag:Retrieving documents for query: 'What was Meta's Reality Labs operating loss in 2025 and what is the 2026 capital expenditure guidance?'\n",
"2026-04-03 05:41:29 - [INFO] - financial_rag - Step 1: Executing Chain-of-Thought Analysis (llama-3.3-70b-versatile)...\n",
"INFO:financial_rag:Step 1: Executing Chain-of-Thought Analysis (llama-3.3-70b-versatile)...\n",
"2026-04-03 05:41:30 - [INFO] - financial_rag - Step 2: Running Strict Compliance Audit (llama-3.3-70b-versatile)...\n",
"INFO:financial_rag:Step 2: Running Strict Compliance Audit (llama-3.3-70b-versatile)...\n",
"2026-04-03 05:41:31 - [INFO] - financial_rag - Running LLM-as-a-Judge evaluation (Judge model: qwen/qwen3-32b)...\n",
"INFO:financial_rag:Running LLM-as-a-Judge evaluation (Judge model: qwen/qwen3-32b)...\n",
"2026-04-03 05:42:00 - [INFO] - financial_rag - Evaluation scores: {'faithfulness': 1.0, 'relevance': 1.0, 'correctness': 0.5}\n",
"INFO:financial_rag:Evaluation scores: {'faithfulness': 1.0, 'relevance': 1.0, 'correctness': 0.5}\n",
"2026-04-03 05:42:00 - [INFO] - financial_rag - Evaluating question 8/15: 'How many employees did Meta have as of December 31, 2025?'\n",
"INFO:financial_rag: Evaluating question 8/15: 'How many employees did Meta have as of December 31, 2025?'\n",
"2026-04-03 05:42:00 - [INFO] - financial_rag - Retrieving documents for query: 'How many employees did Meta have as of December 31, 2025?'\n",
"INFO:financial_rag:Retrieving documents for query: 'How many employees did Meta have as of December 31, 2025?'\n",
"2026-04-03 05:42:00 - [INFO] - financial_rag - Step 1: Executing Chain-of-Thought Analysis (llama-3.3-70b-versatile)...\n",
"INFO:financial_rag:Step 1: Executing Chain-of-Thought Analysis (llama-3.3-70b-versatile)...\n",
"2026-04-03 05:42:01 - [INFO] - financial_rag - Step 2: Running Strict Compliance Audit (llama-3.3-70b-versatile)...\n",
"INFO:financial_rag:Step 2: Running Strict Compliance Audit (llama-3.3-70b-versatile)...\n",
"2026-04-03 05:42:01 - [INFO] - financial_rag - Running LLM-as-a-Judge evaluation (Judge model: qwen/qwen3-32b)...\n",
"INFO:financial_rag:Running LLM-as-a-Judge evaluation (Judge model: qwen/qwen3-32b)...\n",
"2026-04-03 05:42:20 - [INFO] - financial_rag - Evaluation scores: {'faithfulness': 1.0, 'relevance': 1.0, 'correctness': 1.0}\n",
"INFO:financial_rag:Evaluation scores: {'faithfulness': 1.0, 'relevance': 1.0, 'correctness': 1.0}\n",
"2026-04-03 05:42:20 - [INFO] - financial_rag - Evaluating question 9/15: 'What were Microsoft's total revenues and net income in fisca'\n",
"INFO:financial_rag: Evaluating question 9/15: 'What were Microsoft's total revenues and net income in fisca'\n",
"2026-04-03 05:42:20 - [INFO] - financial_rag - Retrieving documents for query: 'What were Microsoft's total revenues and net income in fiscal year 2024?'\n",
"INFO:financial_rag:Retrieving documents for query: 'What were Microsoft's total revenues and net income in fiscal year 2024?'\n",
"2026-04-03 05:42:20 - [INFO] - financial_rag - Step 1: Executing Chain-of-Thought Analysis (llama-3.3-70b-versatile)...\n",
"INFO:financial_rag:Step 1: Executing Chain-of-Thought Analysis (llama-3.3-70b-versatile)...\n",
"2026-04-03 05:42:21 - [INFO] - financial_rag - Step 2: Running Strict Compliance Audit (llama-3.3-70b-versatile)...\n",
"INFO:financial_rag:Step 2: Running Strict Compliance Audit (llama-3.3-70b-versatile)...\n",
"2026-04-03 05:42:22 - [INFO] - financial_rag - Running LLM-as-a-Judge evaluation (Judge model: qwen/qwen3-32b)...\n",
"INFO:financial_rag:Running LLM-as-a-Judge evaluation (Judge model: qwen/qwen3-32b)...\n",
"2026-04-03 05:42:57 - [ERROR] - financial_rag - Failed to parse LLM evaluation response: Invalid json output: <think>\n",
"Okay, let's start by looking at the user's question: they want to know Microsoft's total revenues and net income in fiscal year 2024. The context provided includes financial data from Microsoft's 10-K report. \n",
"\n",
"First, I need to check the Answer against the Context for faithfulness. The Answer states that total revenue was $245,122 million, which is listed under the \"Segment Results of Operations\" in the Context. That's correct. However, the Answer mentions that net income isn't provided in the context. Looking at the Context, there's a line that says \"Net income $100,118 $132,170 $32,052 32%\", which shows the net income for 2024 as $100,118 million. The Answer didn't include this, so it's missing a key piece of information. Therefore, the faithfulness score should be lower because the Answer doesn't include the net income from the Context.\n",
"\n",
"Next, relevance. The Answer addresses the total revenue but doesn't mention the net income, which the user asked for. Since the user specifically asked for both, the Answer is incomplete. However, it does correctly state that the net income isn't provided in the context, which is accurate because the Context does have the net income figure. Wait, the Context does have the net income for 2024 as $100,118 million. The Answer says it's not provided, which is incorrect. So the Answer is not relevant because it fails to provide the net income when it's actually present in the Context. Therefore, the relevance score should be low.\n",
"\n",
"For correctness, the Ground Truth states total revenue as $245.122 billion and net income as $88.136 billion. The Answer correctly reports the total revenue from the Context. However, the Ground Truth's net income is $88.136 billion, but the Context shows $100,118 million (which is $100.118 billion). There's a discrepancy here. The Answer didn't include the net income, so it's not correct. The Ground Truth's net income doesn't match the Context's figure. This might be a problem. Wait, the user's Ground Truth says $88.136 billion, but the Context shows $100,118 million. That's a significant difference. The Answer didn't provide the net income, so it's not correct. Therefore, the correctness score is low because the Answer doesn't match the Ground Truth's net income, and the Ground Truth itself might be conflicting with the Context. But since the Answer didn't include the net income, it's not correct. So correctness is 0.5? Wait, the Answer didn't provide the net income, so it's missing a key fact. The Ground Truth has a different number than the Context. The Answer didn't include the net income, so it's not correct. Therefore, correctness is 0.0 because it's missing the net income and the Ground Truth's figure is different from the Context. But the user's Ground Truth might be incorrect. However, according to the instructions, we have to compare the Answer to the Ground Truth. The Answer didn't provide the net income, so it's incorrect. So correctness is 0.0.\n",
"</think>\n",
"\n",
"{\n",
" \"faithfulness\": 0.5,\n",
" \"relevance\": 0.5,\n",
" \"correctness\": 0.0\n",
"}\n",
"For troubleshooting, visit: https://docs.langchain.com/oss/python/langchain/errors/OUTPUT_PARSING_FAILURE \n",
"ERROR:financial_rag:Failed to parse LLM evaluation response: Invalid json output: <think>\n",
"Okay, let's start by looking at the user's question: they want to know Microsoft's total revenues and net income in fiscal year 2024. The context provided includes financial data from Microsoft's 10-K report. \n",
"\n",
"First, I need to check the Answer against the Context for faithfulness. The Answer states that total revenue was $245,122 million, which is listed under the \"Segment Results of Operations\" in the Context. That's correct. However, the Answer mentions that net income isn't provided in the context. Looking at the Context, there's a line that says \"Net income $100,118 $132,170 $32,052 32%\", which shows the net income for 2024 as $100,118 million. The Answer didn't include this, so it's missing a key piece of information. Therefore, the faithfulness score should be lower because the Answer doesn't include the net income from the Context.\n",
"\n",
"Next, relevance. The Answer addresses the total revenue but doesn't mention the net income, which the user asked for. Since the user specifically asked for both, the Answer is incomplete. However, it does correctly state that the net income isn't provided in the context, which is accurate because the Context does have the net income figure. Wait, the Context does have the net income for 2024 as $100,118 million. The Answer says it's not provided, which is incorrect. So the Answer is not relevant because it fails to provide the net income when it's actually present in the Context. Therefore, the relevance score should be low.\n",
"\n",
"For correctness, the Ground Truth states total revenue as $245.122 billion and net income as $88.136 billion. The Answer correctly reports the total revenue from the Context. However, the Ground Truth's net income is $88.136 billion, but the Context shows $100,118 million (which is $100.118 billion). There's a discrepancy here. The Answer didn't include the net income, so it's not correct. The Ground Truth's net income doesn't match the Context's figure. This might be a problem. Wait, the user's Ground Truth says $88.136 billion, but the Context shows $100,118 million. That's a significant difference. The Answer didn't provide the net income, so it's not correct. Therefore, the correctness score is low because the Answer doesn't match the Ground Truth's net income, and the Ground Truth itself might be conflicting with the Context. But since the Answer didn't include the net income, it's not correct. So correctness is 0.5? Wait, the Answer didn't provide the net income, so it's missing a key fact. The Ground Truth has a different number than the Context. The Answer didn't include the net income, so it's not correct. Therefore, correctness is 0.0 because it's missing the net income and the Ground Truth's figure is different from the Context. But the user's Ground Truth might be incorrect. However, according to the instructions, we have to compare the Answer to the Ground Truth. The Answer didn't provide the net income, so it's incorrect. So correctness is 0.0.\n",
"</think>\n",
"\n",
"{\n",
" \"faithfulness\": 0.5,\n",
" \"relevance\": 0.5,\n",
" \"correctness\": 0.0\n",
"}\n",
"For troubleshooting, visit: https://docs.langchain.com/oss/python/langchain/errors/OUTPUT_PARSING_FAILURE \n",
"2026-04-03 05:42:57 - [INFO] - financial_rag - Evaluating question 10/15: 'What was Microsoft's cloud revenue and what is Microsoft's p'\n",
"INFO:financial_rag: Evaluating question 10/15: 'What was Microsoft's cloud revenue and what is Microsoft's p'\n",
"2026-04-03 05:42:57 - [INFO] - financial_rag - Retrieving documents for query: 'What was Microsoft's cloud revenue and what is Microsoft's primary cloud platform?'\n",
"INFO:financial_rag:Retrieving documents for query: 'What was Microsoft's cloud revenue and what is Microsoft's primary cloud platform?'\n",
"2026-04-03 05:42:57 - [INFO] - financial_rag - Step 1: Executing Chain-of-Thought Analysis (llama-3.3-70b-versatile)...\n",
"INFO:financial_rag:Step 1: Executing Chain-of-Thought Analysis (llama-3.3-70b-versatile)...\n",
"2026-04-03 05:42:58 - [INFO] - financial_rag - Step 2: Running Strict Compliance Audit (llama-3.3-70b-versatile)...\n",
"INFO:financial_rag:Step 2: Running Strict Compliance Audit (llama-3.3-70b-versatile)...\n",
"2026-04-03 05:42:59 - [INFO] - financial_rag - Running LLM-as-a-Judge evaluation (Judge model: qwen/qwen3-32b)...\n",
"INFO:financial_rag:Running LLM-as-a-Judge evaluation (Judge model: qwen/qwen3-32b)...\n",
"2026-04-03 05:43:31 - [INFO] - financial_rag - Evaluation scores: {'faithfulness': 0.5, 'relevance': 1.0, 'correctness': 1.0}\n",
"INFO:financial_rag:Evaluation scores: {'faithfulness': 0.5, 'relevance': 1.0, 'correctness': 1.0}\n",
"2026-04-03 05:43:31 - [INFO] - financial_rag - Evaluating question 11/15: 'Compare the R&D spending of Google, Meta, and Microsoft in t'\n",
"INFO:financial_rag: Evaluating question 11/15: 'Compare the R&D spending of Google, Meta, and Microsoft in t'\n",
"2026-04-03 05:43:31 - [INFO] - financial_rag - Retrieving documents for query: 'Compare the R&D spending of Google, Meta, and Microsoft in their most recent fiscal years.'\n",
"INFO:financial_rag:Retrieving documents for query: 'Compare the R&D spending of Google, Meta, and Microsoft in their most recent fiscal years.'\n",
"2026-04-03 05:43:31 - [INFO] - financial_rag - Step 1: Executing Chain-of-Thought Analysis (llama-3.3-70b-versatile)...\n",
"INFO:financial_rag:Step 1: Executing Chain-of-Thought Analysis (llama-3.3-70b-versatile)...\n",
"2026-04-03 05:43:33 - [INFO] - financial_rag - Step 2: Running Strict Compliance Audit (llama-3.3-70b-versatile)...\n",
"INFO:financial_rag:Step 2: Running Strict Compliance Audit (llama-3.3-70b-versatile)...\n",
"2026-04-03 05:43:34 - [INFO] - financial_rag - Running LLM-as-a-Judge evaluation (Judge model: qwen/qwen3-32b)...\n",
"INFO:financial_rag:Running LLM-as-a-Judge evaluation (Judge model: qwen/qwen3-32b)...\n",
"2026-04-03 05:44:04 - [INFO] - financial_rag - Evaluation scores: {'faithfulness': 0.0, 'relevance': 0.5, 'correctness': 0.0}\n",
"INFO:financial_rag:Evaluation scores: {'faithfulness': 0.0, 'relevance': 0.5, 'correctness': 0.0}\n",
"2026-04-03 05:44:04 - [INFO] - financial_rag - Evaluating question 12/15: 'Compare the capital expenditures of Google and Meta in their'\n",
"INFO:financial_rag: Evaluating question 12/15: 'Compare the capital expenditures of Google and Meta in their'\n",
"2026-04-03 05:44:04 - [INFO] - financial_rag - Retrieving documents for query: 'Compare the capital expenditures of Google and Meta in their most recent fiscal years and explain what they are investing in.'\n",
"INFO:financial_rag:Retrieving documents for query: 'Compare the capital expenditures of Google and Meta in their most recent fiscal years and explain what they are investing in.'\n",
"2026-04-03 05:44:04 - [INFO] - financial_rag - Step 1: Executing Chain-of-Thought Analysis (llama-3.3-70b-versatile)...\n",
"INFO:financial_rag:Step 1: Executing Chain-of-Thought Analysis (llama-3.3-70b-versatile)...\n",
"2026-04-03 05:44:07 - [INFO] - financial_rag - Step 2: Running Strict Compliance Audit (llama-3.3-70b-versatile)...\n",
"INFO:financial_rag:Step 2: Running Strict Compliance Audit (llama-3.3-70b-versatile)...\n",
"2026-04-03 05:44:08 - [INFO] - financial_rag - Running LLM-as-a-Judge evaluation (Judge model: qwen/qwen3-32b)...\n",
"INFO:financial_rag:Running LLM-as-a-Judge evaluation (Judge model: qwen/qwen3-32b)...\n",
"2026-04-03 05:44:30 - [INFO] - financial_rag - Evaluation scores: {'faithfulness': 1.0, 'relevance': 1.0, 'correctness': 1.0}\n",
"INFO:financial_rag:Evaluation scores: {'faithfulness': 1.0, 'relevance': 1.0, 'correctness': 1.0}\n",
"2026-04-03 05:44:30 - [INFO] - financial_rag - Evaluating question 13/15: 'What are the primary regulatory and legal risks mentioned by'\n",
"INFO:financial_rag: Evaluating question 13/15: 'What are the primary regulatory and legal risks mentioned by'\n",
"2026-04-03 05:44:30 - [INFO] - financial_rag - Retrieving documents for query: 'What are the primary regulatory and legal risks mentioned by Google in its 10-K filing?'\n",
"INFO:financial_rag:Retrieving documents for query: 'What are the primary regulatory and legal risks mentioned by Google in its 10-K filing?'\n",
"2026-04-03 05:44:30 - [INFO] - financial_rag - Step 1: Executing Chain-of-Thought Analysis (llama-3.3-70b-versatile)...\n",
"INFO:financial_rag:Step 1: Executing Chain-of-Thought Analysis (llama-3.3-70b-versatile)...\n",
"2026-04-03 05:44:32 - [INFO] - financial_rag - Step 2: Running Strict Compliance Audit (llama-3.3-70b-versatile)...\n",
"INFO:financial_rag:Step 2: Running Strict Compliance Audit (llama-3.3-70b-versatile)...\n",
"2026-04-03 05:44:33 - [INFO] - financial_rag - Running LLM-as-a-Judge evaluation (Judge model: qwen/qwen3-32b)...\n",
"INFO:financial_rag:Running LLM-as-a-Judge evaluation (Judge model: qwen/qwen3-32b)...\n",
"2026-04-03 05:44:57 - [INFO] - financial_rag - Evaluation scores: {'faithfulness': 1.0, 'relevance': 1.0}\n",
"INFO:financial_rag:Evaluation scores: {'faithfulness': 1.0, 'relevance': 1.0}\n",
"2026-04-03 05:44:57 - [INFO] - financial_rag - Evaluating question 14/15: 'What are the main risk factors Meta identifies for its adver'\n",
"INFO:financial_rag: Evaluating question 14/15: 'What are the main risk factors Meta identifies for its adver'\n",
"2026-04-03 05:44:57 - [INFO] - financial_rag - Retrieving documents for query: 'What are the main risk factors Meta identifies for its advertising business in its 10-K?'\n",
"INFO:financial_rag:Retrieving documents for query: 'What are the main risk factors Meta identifies for its advertising business in its 10-K?'\n",
"2026-04-03 05:44:57 - [INFO] - financial_rag - Step 1: Executing Chain-of-Thought Analysis (llama-3.3-70b-versatile)...\n",
"INFO:financial_rag:Step 1: Executing Chain-of-Thought Analysis (llama-3.3-70b-versatile)...\n",
"2026-04-03 05:44:58 - [INFO] - financial_rag - Step 2: Running Strict Compliance Audit (llama-3.3-70b-versatile)...\n",
"INFO:financial_rag:Step 2: Running Strict Compliance Audit (llama-3.3-70b-versatile)...\n",
"2026-04-03 05:44:59 - [INFO] - financial_rag - Running LLM-as-a-Judge evaluation (Judge model: qwen/qwen3-32b)...\n",
"INFO:financial_rag:Running LLM-as-a-Judge evaluation (Judge model: qwen/qwen3-32b)...\n",
"2026-04-03 05:45:23 - [INFO] - financial_rag - Evaluation scores: {'faithfulness': 1.0, 'relevance': 1.0}\n",
"INFO:financial_rag:Evaluation scores: {'faithfulness': 1.0, 'relevance': 1.0}\n",
"2026-04-03 05:45:23 - [INFO] - financial_rag - Evaluating question 15/15: 'How does Microsoft describe its AI strategy and investments '\n",
"INFO:financial_rag: Evaluating question 15/15: 'How does Microsoft describe its AI strategy and investments '\n",
"2026-04-03 05:45:23 - [INFO] - financial_rag - Retrieving documents for query: 'How does Microsoft describe its AI strategy and investments in its 10-K filing?'\n",
"INFO:financial_rag:Retrieving documents for query: 'How does Microsoft describe its AI strategy and investments in its 10-K filing?'\n",
"2026-04-03 05:45:23 - [INFO] - financial_rag - Step 1: Executing Chain-of-Thought Analysis (llama-3.3-70b-versatile)...\n",
"INFO:financial_rag:Step 1: Executing Chain-of-Thought Analysis (llama-3.3-70b-versatile)...\n",
"2026-04-03 05:45:25 - [INFO] - financial_rag - Step 2: Running Strict Compliance Audit (llama-3.3-70b-versatile)...\n",
"INFO:financial_rag:Step 2: Running Strict Compliance Audit (llama-3.3-70b-versatile)...\n",
"2026-04-03 05:45:26 - [INFO] - financial_rag - Running LLM-as-a-Judge evaluation (Judge model: qwen/qwen3-32b)...\n",
"INFO:financial_rag:Running LLM-as-a-Judge evaluation (Judge model: qwen/qwen3-32b)...\n",
"2026-04-03 05:45:56 - [INFO] - financial_rag - Evaluation scores: {'faithfulness': 1.0, 'relevance': 1.0}\n",
"INFO:financial_rag:Evaluation scores: {'faithfulness': 1.0, 'relevance': 1.0}\n",
"2026-04-03 05:45:56 - [INFO] - financial_rag - Batch evaluation complete. Faithfulness: 0.864 ± 0.323 | Relevance: 0.955 ± 0.151 | Pass rate: 81.8% | n=15\n",
"INFO:financial_rag:Batch evaluation complete. Faithfulness: 0.864 ± 0.323 | Relevance: 0.955 ± 0.151 | Pass rate: 81.8% | n=15\n",
"2026-04-03 05:45:56 - [INFO] - financial_rag - Batch evaluation report saved to: /content/drive/MyDrive/financial-intelligence-engine/artifacts/eval_reports/batch_eval_report.json\n",
"INFO:financial_rag:Batch evaluation report saved to: /content/drive/MyDrive/financial-intelligence-engine/artifacts/eval_reports/batch_eval_report.json\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"=================================================================\n",
" BATCH EVALUATION RESULTS\n",
"=================================================================\n",
" Metric Mean Std Dev Pass Rate\n",
" Faithfulness (No Hallucinations) 0.864 ±0.323 81.8%\n",
" Context Relevance (Answers Prompt) 0.955 ±0.151 N/A\n",
"Answer Correctness (vs Ground Truth) 0.812 ±0.372 N/A\n",
"\n",
"Total questions evaluated : 15\n",
"Generator model : llama-3.3-70b-versatile\n",
"Evaluator model : qwen/qwen3-32b\n",
"Report saved to : artifacts/eval_reports/batch_eval_report.json\n",
"\n",
"=================================================================\n"
]
}
],
"source": [
"# ============================================================\n",
"# Cell 5 — Batch Evaluation (Statistically Valid)\n",
"# ============================================================\n",
"# All ground_truth values are extracted DIRECTLY from the\n",
"# uploaded 10-K PDF filings. Every number is verified.\n",
"#\n",
"# Fiscal Year Note:\n",
"# Google: FY2025 (Jan 1 – Dec 31, 2025)\n",
"# Meta: FY2025 (Jan 1 – Dec 31, 2025)\n",
"# Microsoft: FY2024 (Jul 1, 2023 – Jun 30, 2024)\n",
"# ============================================================\n",
"\n",
"import pandas as pd\n",
"from src.evaluation import RAGEvaluator\n",
"\n",
"# ── Verified Evaluation Set ───────────────────────────────────\n",
"# ground_truth strings are exact figures from the 10-K filings.\n",
"# Questions WITHOUT ground_truth=None test faithfulness/relevance only.\n",
"# Questions WITH ground_truth test all 3 metrics including correctness.\n",
"\n",
"EVAL_QUESTIONS = [\n",
"\n",
" # ── Google Questions ──────────────────────────────────────\n",
" {\n",
" \"question\": \"What were Google's total Research and Development expenses in fiscal year 2025?\",\n",
" \"ground_truth\": (\n",
" \"Google's Research and Development expenses were $61.087 billion \"\n",
" \"in fiscal year 2025, representing 15% of total revenues. \"\n",
" \"This was an increase of $11.8 billion compared to 2024.\"\n",
" )\n",
" },\n",
" {\n",
" \"question\": \"What were Google's total revenues and net income in fiscal year 2025?\",\n",
" \"ground_truth\": (\n",
" \"Google reported total revenues of $402.836 billion and net income \"\n",
" \"of $132.170 billion in fiscal year 2025. Revenue increased 15% year over year.\"\n",
" )\n",
" },\n",
" {\n",
" \"question\": \"How much did Google spend on capital expenditures in fiscal year 2025?\",\n",
" \"ground_truth\": (\n",
" \"Google spent $91.4 billion on capital expenditures in fiscal year 2025, \"\n",
" \"primarily reflecting investments in technical infrastructure including \"\n",
" \"servers, network equipment, and data centers.\"\n",
" )\n",
" },\n",
" {\n",
" \"question\": \"What was Google Cloud's revenue in fiscal year 2025?\",\n",
" \"ground_truth\": (\n",
" \"Google Cloud revenue was $58.705 billion in fiscal year 2025, \"\n",
" \"an increase of $15.5 billion or 36% compared to 2024.\"\n",
" )\n",
" },\n",
"\n",
" # ── Meta Questions ────────────────────────────────────────\n",
" {\n",
" \"question\": \"What were Meta's total revenues and net income in fiscal year 2025?\",\n",
" \"ground_truth\": (\n",
" \"Meta reported total revenue of $200.966 billion and net income of \"\n",
" \"$60.458 billion in fiscal year 2025. Revenue increased 22% compared to 2024.\"\n",
" )\n",
" },\n",
" {\n",
" \"question\": \"What were Meta's Research and Development expenses in fiscal year 2025?\",\n",
" \"ground_truth\": (\n",
" \"Meta's Research and Development expenses were $57.372 billion in fiscal year 2025, \"\n",
" \"representing 29% of total revenue. This was an increase of $13.5 billion or 31% \"\n",
" \"compared to 2024.\"\n",
" )\n",
" },\n",
" {\n",
" \"question\": \"What was Meta's Reality Labs operating loss in 2025 and what is the 2026 capital expenditure guidance?\",\n",
" \"ground_truth\": (\n",
" \"Meta's Reality Labs segment reduced overall operating profit by approximately \"\n",
" \"$19.19 billion in 2025. Meta anticipates capital expenditures of approximately \"\n",
" \"$115 billion to $135 billion in 2026 to support AI efforts and core business.\"\n",
" )\n",
" },\n",
" {\n",
" \"question\": \"How many employees did Meta have as of December 31, 2025?\",\n",
" \"ground_truth\": (\n",
" \"Meta had a global workforce of 78,865 employees as of December 31, 2025, \"\n",
" \"with offices in more than 90 cities around the world.\"\n",
" )\n",
" },\n",
"\n",
" # ── Microsoft Questions ───────────────────────────────────\n",
" {\n",
" \"question\": \"What were Microsoft's total revenues and net income in fiscal year 2024?\",\n",
" \"ground_truth\": (\n",
" \"Microsoft reported total revenue of $245.122 billion and net income of \"\n",
" \"$88.136 billion in fiscal year 2024, which ended June 30, 2024. \"\n",
" \"Revenue increased $33.2 billion or 16% year over year.\"\n",
" )\n",
" },\n",
" {\n",
" \"question\": \"What was Microsoft's cloud revenue and what is Microsoft's primary cloud platform?\",\n",
" \"ground_truth\": (\n",
" \"Microsoft Cloud revenue was $137.4 billion in fiscal year 2024. \"\n",
" \"Microsoft Azure is the primary cloud platform and falls under the \"\n",
" \"Intelligent Cloud segment, which generated $105.362 billion in revenue. \"\n",
" \"Azure and other cloud services revenue grew 30% year over year.\"\n",
" )\n",
" },\n",
"\n",
" # ── Cross-Company Comparison Questions ───────────────────\n",
" # These have no single ground truth — they test faithfulness and relevance.\n",
" {\n",
" \"question\": \"Compare the R&D spending of Google, Meta, and Microsoft in their most recent fiscal years.\",\n",
" \"ground_truth\": (\n",
" \"Google spent $61.087 billion on R&D in FY2025. Meta spent $57.372 billion \"\n",
" \"on R&D in FY2025. Microsoft spent $29.510 billion on R&D in FY2024. \"\n",
" \"Google and Meta both invested roughly 15% and 29% of revenue respectively, \"\n",
" \"while Microsoft invested 12% of revenue in R&D.\"\n",
" )\n",
" },\n",
" {\n",
" \"question\": \"Compare the capital expenditures of Google and Meta in their most recent fiscal years and explain what they are investing in.\",\n",
" \"ground_truth\": (\n",
" \"Google spent $91.4 billion on capital expenditures in FY2025, primarily \"\n",
" \"in technical infrastructure including servers, network equipment, and data centers. \"\n",
" \"Meta spent $72.22 billion on capital expenditures in FY2025 to support AI efforts \"\n",
" \"and core business operations.\"\n",
" )\n",
" },\n",
" {\n",
" \"question\": \"What are the primary regulatory and legal risks mentioned by Google in its 10-K filing?\",\n",
" \"ground_truth\": None # open-ended qualitative — tests faithfulness only\n",
" },\n",
" {\n",
" \"question\": \"What are the main risk factors Meta identifies for its advertising business in its 10-K?\",\n",
" \"ground_truth\": None # open-ended qualitative — tests faithfulness only\n",
" },\n",
" {\n",
" \"question\": \"How does Microsoft describe its AI strategy and investments in its 10-K filing?\",\n",
" \"ground_truth\": None # open-ended qualitative — tests faithfulness only\n",
" },\n",
"]\n",
"\n",
"# ── Run Batch Evaluation ──────────────────────────────────────\n",
"print(f\"Phase 4b: Running Batch Evaluation over {len(EVAL_QUESTIONS)} questions...\")\n",
"print(f\"Judge model : {evaluator.llm.model_name}\")\n",
"print(f\"Generator : {agent.llm.model_name}\")\n",
"print(\"This will take 5-8 minutes. Each question runs full generation + evaluation.\\n\")\n",
"\n",
"batch_results = evaluator.run_batch_evaluation(\n",
" eval_set=EVAL_QUESTIONS,\n",
" agent=agent,\n",
" save_report=True,\n",
")\n",
"\n",
"# ── Print Aggregate Results Table ─────────────────────────────\n",
"print(\"\\n\" + \"=\" * 65)\n",
"print(\" BATCH EVALUATION RESULTS\")\n",
"print(\"=\" * 65)\n",
"\n",
"summary_data = [\n",
" {\n",
" \"Metric\": \"Faithfulness (No Hallucinations)\",\n",
" \"Mean\": f\"{batch_results['mean_faithfulness']:.3f}\",\n",
" \"Std Dev\": f\"±{batch_results['std_faithfulness']:.3f}\",\n",
" \"Pass Rate\": f\"{batch_results['faithfulness_pass_rate']*100:.1f}%\",\n",
" },\n",
" {\n",
" \"Metric\": \"Context Relevance (Answers Prompt)\",\n",
" \"Mean\": f\"{batch_results['mean_relevance']:.3f}\",\n",
" \"Std Dev\": f\"±{batch_results['std_relevance']:.3f}\",\n",
" \"Pass Rate\": \"N/A\",\n",
" },\n",
"]\n",
"\n",
"if \"mean_correctness\" in batch_results:\n",
" summary_data.append({\n",
" \"Metric\": \"Answer Correctness (vs Ground Truth)\",\n",
" \"Mean\": f\"{batch_results['mean_correctness']:.3f}\",\n",
" \"Std Dev\": f\"±{batch_results['std_correctness']:.3f}\",\n",
" \"Pass Rate\": \"N/A\",\n",
" })\n",
"\n",
"summary_df = pd.DataFrame(summary_data)\n",
"print(summary_df.to_string(index=False))\n",
"print(f\"\\nTotal questions evaluated : {batch_results['n']}\")\n",
"print(f\"Generator model : {agent.llm.model_name}\")\n",
"print(f\"Evaluator model : {batch_results['evaluator_model']}\")\n",
"print(f\"Report saved to : artifacts/eval_reports/batch_eval_report.json\")\n",
"print(\"\\n\" + \"=\" * 65)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "c2dda694",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Phase 5: Visualizing Engine Telemetry...\n",
"Primary dashboard saved: /content/drive/MyDrive/financial-intelligence-engine/artifacts/visualizations/batch_eval_primary.png\n"
]
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1600x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Telemetry dashboard saved: /content/drive/MyDrive/financial-intelligence-engine/artifacts/visualizations/telemetry_dashboard.png\n"
]
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 1600x700 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"=================================================================\n",
" FINANCIAL INTELLIGENCE ENGINE — FINAL METRICS SUMMARY\n",
"=================================================================\n",
" Faithfulness (n=15) : 0.864 ± 0.323 | Pass rate: 81.8%\n",
" Relevance (n=15) : 0.955 ± 0.151\n",
" Correctness (n=15) : 0.812 ± 0.372\n",
" Generator model : llama-3.3-70b-versatile\n",
" Evaluator model : qwen/qwen3-32b\n",
" Retrieval method : Hybrid RRF (Dense ChromaDB + Sparse BM25)\n",
" Company balance : {'Meta': 3, 'Microsoft': 2, 'Google': 2}\n",
"=================================================================\n",
"All artifacts saved to Google Drive /artifacts/ directory.\n"
]
}
],
"source": [
"# ============================================================\n",
"# Cell 6 — Enterprise Visualization & Reporting\n",
"# ============================================================\n",
"# UPGRADE: Batch evaluation results are now the PRIMARY dashboard.\n",
"# Single-query scores are shown as a secondary reference only.\n",
"# This ensures the statistically valid n=15 scores are the first\n",
"# thing a recruiter or interviewer sees — not a single noisy sample.\n",
"# ============================================================\n",
"\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.patches as mpatches\n",
"import seaborn as sns\n",
"from datetime import datetime\n",
"from collections import Counter\n",
"from src.config import VISUALS_DIR\n",
"\n",
"print(\"Phase 5: Visualizing Engine Telemetry...\")\n",
"\n",
"sns.set_theme(style=\"whitegrid\", context=\"talk\")\n",
"\n",
"# ── DASHBOARD 1: Batch Evaluation Results (PRIMARY) ───────────\n",
"# This is the statistically valid result — shown first and largest.\n",
"fig1, axes1 = plt.subplots(1, 2, figsize=(16, 7))\n",
"fig1.suptitle(\n",
" 'Financial Intelligence Engine — Batch Evaluation Dashboard\\n'\n",
" f'n={batch_results[\"n\"]} Questions | Generator: {agent.llm.model_name} | '\n",
" f'Judge: {batch_results[\"evaluator_model\"]}',\n",
" fontsize=14, fontweight='bold', y=1.03\n",
")\n",
"\n",
"# ── Plot 1A: Batch Metric Scores with Error Bars ──────────────\n",
"batch_metrics = ['Faithfulness', 'Relevance']\n",
"batch_means = [batch_results['mean_faithfulness'], batch_results['mean_relevance']]\n",
"batch_stds = [batch_results['std_faithfulness'], batch_results['std_relevance']]\n",
"\n",
"if 'mean_correctness' in batch_results:\n",
" batch_metrics.append('Correctness\\n(vs Ground Truth)')\n",
" batch_means.append(batch_results['mean_correctness'])\n",
" batch_stds.append(batch_results['std_correctness'])\n",
"\n",
"bar_colors_batch = ['#2ecc71' if m >= 0.8 else '#e74c3c' for m in batch_means]\n",
"x_pos = range(len(batch_metrics))\n",
"\n",
"bars = axes1[0].bar(\n",
" x_pos, batch_means, yerr=batch_stds, capsize=7,\n",
" color=bar_colors_batch, alpha=0.88, width=0.5,\n",
" error_kw={'elinewidth': 2, 'capthick': 2}\n",
")\n",
"axes1[0].set_xticks(list(x_pos))\n",
"axes1[0].set_xticklabels(batch_metrics, fontsize=11)\n",
"axes1[0].set_ylim(0, 1.2)\n",
"axes1[0].set_ylabel('Mean Score (0.0 – 1.0)', fontweight='bold')\n",
"axes1[0].set_title(\n",
" f'LLM-as-a-Judge Scores (n={batch_results[\"n\"]})',\n",
" fontsize=13, pad=12\n",
")\n",
"axes1[0].axhline(\n",
" y=0.8, color='gray', linestyle='--', linewidth=1.2,\n",
" alpha=0.7, label='Pass threshold (0.80)'\n",
")\n",
"axes1[0].legend(fontsize=10)\n",
"\n",
"# Value labels above bars\n",
"for i, (m, s) in enumerate(zip(batch_means, batch_stds)):\n",
" axes1[0].text(\n",
" i, m + s + 0.04, f'{m:.3f}',\n",
" ha='center', fontweight='bold', fontsize=12\n",
" )\n",
"\n",
"# Pass rate annotation\n",
"pass_rate = batch_results['faithfulness_pass_rate'] * 100\n",
"axes1[0].text(\n",
" 0.5, 0.05,\n",
" f'Faithfulness Pass Rate: {pass_rate:.1f}%',\n",
" transform=axes1[0].transAxes,\n",
" ha='center', fontsize=10,\n",
" color='#27ae60' if pass_rate >= 80 else '#e74c3c',\n",
" fontweight='bold'\n",
")\n",
"\n",
"# ── Plot 1B: Retrieved Context Distribution ───────────────────\n",
"source_counts = Counter(d.metadata.get('company', 'Unknown') for d in used_docs)\n",
"labels = list(source_counts.keys())\n",
"sizes = list(source_counts.values())\n",
"explode = [0.05] * len(labels)\n",
"\n",
"axes1[1].pie(\n",
" sizes,\n",
" labels=labels,\n",
" autopct='%1.1f%%',\n",
" startangle=140,\n",
" colors=sns.color_palette('deep', len(labels)),\n",
" explode=explode,\n",
" shadow=True,\n",
" textprops={'fontsize': 12, 'fontweight': 'bold'},\n",
")\n",
"axes1[1].set_title(\n",
" 'Retrieved Context Distribution by Company\\n(Company-Balanced RRF)',\n",
" fontsize=13, pad=15\n",
")\n",
"\n",
"plt.tight_layout()\n",
"batch_primary_path = f'{VISUALS_DIR}/batch_eval_primary.png'\n",
"plt.savefig(batch_primary_path, dpi=300, bbox_inches='tight')\n",
"print(f'Primary dashboard saved: {batch_primary_path}')\n",
"plt.show()\n",
"\n",
"\n",
"# ── DASHBOARD 2: System Telemetry (SECONDARY) ─────────────────\n",
"# Shows single-query result alongside batch summary for comparison.\n",
"fig2, axes2 = plt.subplots(1, 2, figsize=(16, 7))\n",
"fig2.suptitle(\n",
" 'Financial Intelligence Engine — System Telemetry',\n",
" fontsize=16, fontweight='bold', y=1.02\n",
")\n",
"\n",
"# ── Plot 2A: Single-Query Scores ──────────────────────────────\n",
"metric_labels = [\n",
" 'Faithfulness\\n(No Hallucinations)',\n",
" 'Context Relevance\\n(Answers Prompt)'\n",
"]\n",
"score_vals = [\n",
" float(scores.get('faithfulness', 0)),\n",
" float(scores.get('relevance', 0)),\n",
"]\n",
"bar_colors_single = ['#2ecc71' if s >= 0.8 else '#e74c3c' for s in score_vals]\n",
"\n",
"plot_df = pd.DataFrame({\n",
" 'Metric': metric_labels,\n",
" 'Score': score_vals,\n",
" 'Color': bar_colors_single,\n",
"})\n",
"sns.barplot(\n",
" data=plot_df,\n",
" x='Metric', y='Score',\n",
" hue='Metric',\n",
" palette=dict(zip(metric_labels, bar_colors_single)),\n",
" legend=False,\n",
" ax=axes2[0],\n",
")\n",
"axes2[0].set_ylim(0, 1.15)\n",
"axes2[0].set_title(\n",
" 'Single-Query Validation\\n(Directional — see batch results for statistical validity)',\n",
" fontsize=12, pad=12\n",
")\n",
"axes2[0].set_ylabel('Score (0.0 – 1.0)', fontweight='bold')\n",
"axes2[0].set_xlabel('')\n",
"\n",
"for i, v in enumerate(score_vals):\n",
" axes2[0].text(\n",
" i, v + 0.03, f'{v:.2f}',\n",
" ha='center', fontweight='bold', fontsize=12\n",
" )\n",
"\n",
"# ── Plot 2B: Batch Score Summary Bar ─────────────────────────\n",
"summary_metrics = ['Faithfulness\\n(Batch)', 'Relevance\\n(Batch)']\n",
"summary_means = [batch_results['mean_faithfulness'], batch_results['mean_relevance']]\n",
"summary_stds = [batch_results['std_faithfulness'], batch_results['std_relevance']]\n",
"\n",
"if 'mean_correctness' in batch_results:\n",
" summary_metrics.append('Correctness\\n(Batch)')\n",
" summary_means.append(batch_results['mean_correctness'])\n",
" summary_stds.append(batch_results['std_correctness'])\n",
"\n",
"summary_colors = ['#2ecc71' if m >= 0.8 else '#e74c3c' for m in summary_means]\n",
"x2 = range(len(summary_metrics))\n",
"\n",
"axes2[1].bar(\n",
" x2, summary_means, yerr=summary_stds, capsize=6,\n",
" color=summary_colors, alpha=0.88, width=0.5,\n",
" error_kw={'elinewidth': 2, 'capthick': 2}\n",
")\n",
"axes2[1].set_xticks(list(x2))\n",
"axes2[1].set_xticklabels(summary_metrics, fontsize=10)\n",
"axes2[1].set_ylim(0, 1.2)\n",
"axes2[1].set_ylabel('Mean Score (0.0 – 1.0)', fontweight='bold')\n",
"axes2[1].set_title(\n",
" f'Batch Evaluation Summary (n={batch_results[\"n\"]})',\n",
" fontsize=12, pad=12\n",
")\n",
"axes2[1].axhline(\n",
" y=0.8, color='gray', linestyle='--',\n",
" linewidth=1.2, alpha=0.7, label='Pass threshold (0.80)'\n",
")\n",
"axes2[1].legend(fontsize=9)\n",
"\n",
"for i, (m, s) in enumerate(zip(summary_means, summary_stds)):\n",
" axes2[1].text(\n",
" i, m + s + 0.04, f'{m:.3f}',\n",
" ha='center', fontweight='bold', fontsize=11\n",
" )\n",
"\n",
"plt.tight_layout()\n",
"telemetry_path = f'{VISUALS_DIR}/telemetry_dashboard.png'\n",
"plt.savefig(telemetry_path, dpi=300, bbox_inches='tight')\n",
"print(f'Telemetry dashboard saved: {telemetry_path}')\n",
"plt.show()\n",
"\n",
"# ── Final Summary Print ───────────────────────────────────────\n",
"print('\\n' + '='*65)\n",
"print(' FINANCIAL INTELLIGENCE ENGINE — FINAL METRICS SUMMARY')\n",
"print('='*65)\n",
"print(f' Faithfulness (n={batch_results[\"n\"]}) : '\n",
" f'{batch_results[\"mean_faithfulness\"]:.3f} ± {batch_results[\"std_faithfulness\"]:.3f} '\n",
" f'| Pass rate: {batch_results[\"faithfulness_pass_rate\"]*100:.1f}%')\n",
"print(f' Relevance (n={batch_results[\"n\"]}) : '\n",
" f'{batch_results[\"mean_relevance\"]:.3f} ± {batch_results[\"std_relevance\"]:.3f}')\n",
"if 'mean_correctness' in batch_results:\n",
" print(f' Correctness (n={batch_results[\"n\"]}) : '\n",
" f'{batch_results[\"mean_correctness\"]:.3f} ± {batch_results[\"std_correctness\"]:.3f}')\n",
"print(f' Generator model : {agent.llm.model_name}')\n",
"print(f' Evaluator model : {batch_results[\"evaluator_model\"]}')\n",
"print(f' Retrieval method : Hybrid RRF (Dense ChromaDB + Sparse BM25)')\n",
"print(f' Company balance : {dict(Counter(d.metadata.get(\"company\") for d in used_docs))}')\n",
"print('='*65)\n",
"print('All artifacts saved to Google Drive /artifacts/ directory.')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0a119ace",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|