Spaces:
Sleeping
Sleeping
File size: 81,208 Bytes
664822c 08b8fbf 0bfedbd 08b8fbf 664822c 08b8fbf 0bfedbd 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 0bfedbd 08b8fbf 0bfedbd 08b8fbf 0bfedbd 08b8fbf 0bfedbd 08b8fbf 0bfedbd 08b8fbf 0bfedbd 08b8fbf 0bfedbd 08b8fbf 0bfedbd 08b8fbf 0bfedbd 08b8fbf 664822c 08b8fbf 0bfedbd 08b8fbf 664822c 08b8fbf 0bfedbd 08b8fbf 0bfedbd 08b8fbf 0bfedbd 08b8fbf 0bfedbd 08b8fbf 0bfedbd 08b8fbf 0bfedbd 08b8fbf 0bfedbd 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 0bfedbd 08b8fbf 0bfedbd 08b8fbf 664822c 08b8fbf 0bfedbd 08b8fbf 0bfedbd 08b8fbf 0bfedbd 08b8fbf 664822c 08b8fbf 0bfedbd 08b8fbf 0bfedbd 08b8fbf 0bfedbd 08b8fbf 0bfedbd 08b8fbf 0bfedbd 08b8fbf 664822c 08b8fbf 0bfedbd 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 0bfedbd 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 0bfedbd 08b8fbf 664822c 08b8fbf 0bfedbd 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 0bfedbd 08b8fbf 664822c 08b8fbf 664822c 08b8fbf a4ba306 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 0bfedbd 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 0bfedbd 08b8fbf 664822c 08b8fbf 0bfedbd 08b8fbf 664822c 08b8fbf 0bfedbd 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 0bfedbd 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 664822c 0bfedbd 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 0bfedbd 664822c 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 664822c 08b8fbf 664822c 08b8fbf |
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 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "view-in-github"
},
"source": [
"<a href=\"https://colab.research.google.com/github/towardsai/ai-tutor-rag-system/blob/main/notebooks/14-Adding_Chat.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-zE1h0uQV7uT"
},
"source": [
"# Install Packages and Setup Variables\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "QPJzr-I9XQ7l",
"outputId": "19864102-680b-446b-fb38-7fad066cee09"
},
"outputs": [],
"source": [
"!pip install -q llama-index==0.10.57 openai==1.37.0 llama-index-finetuning llama-index-embeddings-huggingface llama-index-embeddings-cohere llama-index-readers-web cohere==5.6.2 tiktoken==0.7.0 chromadb==0.5.5 html2text sentence_transformers pydantic llama-index-vector-stores-chroma==0.1.10 kaleido==0.2.1 llama-index-llms-gemini==0.1.11"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "riuXwpSPcvWC"
},
"outputs": [],
"source": [
"import os\n",
"\n",
"# Set the following API Keys in the Python environment. Will be used later.\n",
"os.environ[\"OPENAI_API_KEY\"] = \"<YOUR_OPENAI_KEY>\"\n",
"os.environ[\"GOOGLE_API_KEY\"] = \"<YOUR_API_KEY>\""
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "jIEeZzqLbz0J"
},
"outputs": [],
"source": [
"# Allows running asyncio in environments with an existing event loop, like Jupyter notebooks.\n",
"\n",
"import nest_asyncio\n",
"\n",
"nest_asyncio.apply()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Bkgi2OrYzF7q"
},
"source": [
"# Load a Model\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "9oGT6crooSSj"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/louis/Documents/GitHub/ai-tutor-rag-system/.conda/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
}
],
"source": [
"from llama_index.llms.gemini import Gemini\n",
"\n",
"llm = Gemini(model=\"models/gemini-1.5-flash\", temperature=1, max_tokens=512)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0BwVuJXlzHVL"
},
"source": [
"# Create a VectoreStore\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "SQP87lHczHKc"
},
"outputs": [],
"source": [
"import chromadb\n",
"\n",
"# create client and a new collection\n",
"# chromadb.EphemeralClient saves data in-memory.\n",
"chroma_client = chromadb.PersistentClient(path=\"./mini-llama-articles\")\n",
"chroma_collection = chroma_client.create_collection(\"mini-llama-articles\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"id": "zAaGcYMJzHAN"
},
"outputs": [],
"source": [
"from llama_index.vector_stores.chroma import ChromaVectorStore\n",
"\n",
"# Define a storage context object using the created vector database.\n",
"vector_store = ChromaVectorStore(chroma_collection=chroma_collection)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "I9JbAzFcjkpn"
},
"source": [
"# Load the Dataset (CSV)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ceveDuYdWCYk"
},
"source": [
"## Download\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "eZwf6pv7WFmD"
},
"source": [
"The dataset includes several articles from the TowardsAI blog, which provide an in-depth explanation of the LLaMA2 model. Read the dataset as a long string.\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "wl_pbPvMlv1h",
"outputId": "5418de57-b95b-4b90-b7d0-a801ea3c73f7"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" % Total % Received % Xferd Average Speed Time Time Time Current\n",
" Dload Upload Total Spent Left Speed\n",
"100 169k 100 169k 0 0 784k 0 --:--:-- --:--:-- --:--:-- 785k\n"
]
}
],
"source": [
"!curl -o ./mini-llama-articles.csv https://raw.githubusercontent.com/AlaFalaki/tutorial_notebooks/main/data/mini-llama-articles.csv"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "VWBLtDbUWJfA"
},
"source": [
"## Read File\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "0Q9sxuW0g3Gd",
"outputId": "801f2ba8-b498-4923-c1cc-c17d3208850c"
},
"outputs": [
{
"data": {
"text/plain": [
"14"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import csv\n",
"\n",
"rows = []\n",
"\n",
"# Load the file as a JSON\n",
"with open(\"./mini-llama-articles.csv\", mode=\"r\", encoding=\"utf-8\") as file:\n",
" csv_reader = csv.reader(file)\n",
"\n",
" for idx, row in enumerate(csv_reader):\n",
" if idx == 0:\n",
" continue\n",
" # Skip header row\n",
" rows.append(row)\n",
"\n",
"# The number of characters in the dataset.\n",
"len(rows)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "S17g2RYOjmf2"
},
"source": [
"# Convert to Document obj\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"id": "YizvmXPejkJE"
},
"outputs": [],
"source": [
"from llama_index.core import Document\n",
"\n",
"# Convert the chunks to Document objects so the LlamaIndex framework can process them.\n",
"documents = [\n",
" Document(\n",
" text=row[1], metadata={\"title\": row[0], \"url\": row[2], \"source_name\": row[3]}\n",
" )\n",
" for row in rows\n",
"]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qjuLbmFuWsyl"
},
"source": [
"# Transforming\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"id": "9z3t70DGWsjO"
},
"outputs": [],
"source": [
"from llama_index.core.text_splitter import TokenTextSplitter\n",
"\n",
"# Define the splitter object that split the text into segments with 512 tokens,\n",
"# with a 128 overlap between the segments.\n",
"text_splitter = TokenTextSplitter(separator=\" \", chunk_size=512, chunk_overlap=128)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 331,
"referenced_widgets": [
"3fbabd8a8660461ba5e7bc08ef39139a",
"df2365556ae242a2ab1a119f9a31a561",
"5f4b9d32df8f446e858e4c289dc282f9",
"5b588f83a15d42d9aca888e06bbd95ff",
"ad073bca655540809e39f26538d2ec0d",
"13b9c5395bca4c3ba21265240cb936cf",
"47a4586384274577a726c57605e7f8d9",
"96a3bdece738481db57e811ccb74a974",
"5c7973afd79349ed997a69120d0629b2",
"af9b6ae927dd4764b9692507791bc67e",
"134210510d49476e959dd7d032bbdbdc",
"5f9bb065c2b74d2e8ded32e1306a7807",
"73a06bc546a64f7f99a9e4a135319dcd",
"ce48deaf4d8c49cdae92bfdbb3a78df0",
"4a172e8c6aa44e41a42fc1d9cf714fd0",
"0245f2604e4d49c8bd0210302746c47b",
"e956dfab55084a9cbe33c8e331b511e7",
"cb394578badd43a89850873ad2526542",
"193aef33d9184055bb9223f56d456de6",
"abfc9aa911ce4a5ea81c7c451f08295f",
"e7937a1bc68441a080374911a6563376",
"e532ed7bfef34f67b5fcacd9534eb789"
]
},
"id": "P9LDJ7o-Wsc-",
"outputId": "01070c1f-dffa-4ab7-ad71-b07b76b12e03"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Parsing nodes: 0%| | 0/14 [00:00<?, ?it/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Parsing nodes: 100%|██████████| 14/14 [00:00<00:00, 28.48it/s]\n",
"100%|██████████| 108/108 [00:59<00:00, 1.82it/s]\n",
"100%|██████████| 108/108 [01:46<00:00, 1.02it/s]\n",
"100%|██████████| 108/108 [00:28<00:00, 3.75it/s]\n",
"Generating embeddings: 100%|██████████| 108/108 [00:01<00:00, 59.76it/s]\n"
]
}
],
"source": [
"from llama_index.core.extractors import (\n",
" SummaryExtractor,\n",
" QuestionsAnsweredExtractor,\n",
" KeywordExtractor,\n",
")\n",
"from llama_index.embeddings.openai import OpenAIEmbedding\n",
"from llama_index.core.ingestion import IngestionPipeline\n",
"\n",
"# Create the pipeline to apply the transformation on each chunk,\n",
"# and store the transformed text in the chroma vector store.\n",
"pipeline = IngestionPipeline(\n",
" transformations=[\n",
" text_splitter,\n",
" QuestionsAnsweredExtractor(questions=3, llm=llm),\n",
" SummaryExtractor(summaries=[\"prev\", \"self\"], llm=llm),\n",
" KeywordExtractor(keywords=10, llm=llm),\n",
" OpenAIEmbedding(),\n",
" ],\n",
" vector_store=vector_store,\n",
")\n",
"\n",
"nodes = pipeline.run(documents=documents, show_progress=True)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "mPGa85hM2P3P",
"outputId": "c106c463-2459-4b11-bbae-5bd5e2246011"
},
"outputs": [
{
"data": {
"text/plain": [
"108"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(nodes)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"id": "23x20bL3_jRb"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"updating: mini-llama-articles/ (stored 0%)\n",
"updating: mini-llama-articles/chroma.sqlite3 (deflated 65%)\n",
" adding: mini-llama-articles/1a47984b-079a-4e72-809a-387c43e980b6/ (stored 0%)\n",
" adding: mini-llama-articles/1a47984b-079a-4e72-809a-387c43e980b6/data_level0.bin (deflated 100%)\n",
" adding: mini-llama-articles/1a47984b-079a-4e72-809a-387c43e980b6/length.bin (deflated 63%)\n",
" adding: mini-llama-articles/1a47984b-079a-4e72-809a-387c43e980b6/link_lists.bin (stored 0%)\n",
" adding: mini-llama-articles/1a47984b-079a-4e72-809a-387c43e980b6/header.bin (deflated 61%)\n"
]
}
],
"source": [
"# Compress the vector store directory to a zip file to be able to download and use later.\n",
"!zip -r vectorstore.zip mini-llama-articles"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "OWaT6rL7ksp8"
},
"source": [
"# Load Indexes\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BLkmv3Yxp9mu"
},
"source": [
"If you have already uploaded the zip file for the vector store checkpoint, please uncomment the code in the following cell block to extract its contents. After doing so, you will be able to load the dataset from local storage.\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "SodY2Xpf_kxg",
"outputId": "a6f7ae4a-447c-4222-e400-0fe55e7e26d9"
},
"outputs": [],
"source": [
"# !unzip vectorstore.zip"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"id": "mXi56KTXk2sp"
},
"outputs": [],
"source": [
"import chromadb\n",
"from llama_index.vector_stores.chroma import ChromaVectorStore\n",
"\n",
"# Load the vector store from the local storage.\n",
"db = chromadb.PersistentClient(path=\"./mini-llama-articles\")\n",
"chroma_collection = db.get_or_create_collection(\"mini-llama-articles\")\n",
"vector_store = ChromaVectorStore(chroma_collection=chroma_collection)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"id": "jKXURvLtkuTS"
},
"outputs": [],
"source": [
"from llama_index.core import VectorStoreIndex\n",
"\n",
"# Create the index based on the vector store.\n",
"vector_index = VectorStoreIndex.from_vector_store(vector_store)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "q0m5rl195bcz"
},
"source": [
"# Disply result\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"id": "4JpaHEmF5dSS"
},
"outputs": [],
"source": [
"# A simple function to show the response and the sources.\n",
"def display_res(response):\n",
" print(\"Response:\\n\\t\", response.response.replace(\"\\n\", \"\"))\n",
"\n",
" print(\"Sources:\")\n",
" if response.source_nodes:\n",
" for src in response.source_nodes:\n",
" print(\"\\tNode ID\\t\", src.node_id)\n",
" print(\"\\tText\\t\", src.text)\n",
" print(\"\\tScore\\t\", src.score)\n",
" print(\"\\t\" + \"-_\" * 20)\n",
" else:\n",
" print(\"\\tNo sources used!\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hbStjvUJ1cft"
},
"source": [
"# Chat Engine\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"id": "kwWlDpoR1cRI"
},
"outputs": [],
"source": [
"# define the chat_engine by using the index\n",
"chat_engine = vector_index.as_chat_engine(llm=llm) # chat_mode=\"best\""
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ER3Lb-oN46lJ",
"outputId": "8b34da39-622f-43f2-cb45-01a1ff37efd7"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Response:\n",
"\t The LLaMA2 model has four different model sizes with varying parameters: 7 billion, 13 billion, 34 billion, and 70 billion parameters.\n",
"Sources:\n",
"\tNode ID\t c3239b40-e206-4a80-b020-eea87cf471cc\n",
"\tText\t I. Llama 2: Revolutionizing Commercial Use Unlike its predecessor Llama 1, which was limited to research use, Llama 2 represents a major advancement as an open-source commercial model. Businesses can now integrate Llama 2 into products to create AI-powered applications. Availability on Azure and AWS facilitates fine-tuning and adoption. However, restrictions apply to prevent exploitation. Companies with over 700 million active daily users cannot use Llama 2. Additionally, its output cannot be used to improve other language models. II. Llama 2 Model Flavors Llama 2 is available in four different model sizes: 7 billion, 13 billion, 34 billion, and 70 billion parameters. While 7B, 13B, and 70B have already been released, the 34B model is still awaited. The pretrained variant, trained on a whopping 2 trillion tokens, boasts a context window of 4096 tokens, twice the size of its predecessor Llama 1. Meta also released a Llama 2 fine-tuned model for chat applications that was trained on over 1 million human annotations. Such extensive training comes at a cost, with the 70B model taking a staggering 1720320 GPU hours to train. The context window's length determines the amount of content the model can process at once, making Llama 2 a powerful language model in terms of scale and efficiency. III. Safety Considerations: A Top Priority for Meta Meta's commitment to safety and alignment shines through in Llama 2's design. The model demonstrates exceptionally low AI safety violation percentages, surpassing even ChatGPT in safety benchmarks. Finding the right balance between helpfulness and safety when optimizing a model poses significant challenges. While a highly helpful model may be capable of answering any question, including sensitive ones like \"How do I build a bomb?\", it also raises concerns about potential misuse. Thus, striking the perfect equilibrium between providing useful information and ensuring safety is paramount. However, prioritizing safety to an extreme extent can lead to a model that struggles to effectively address a diverse range of questions. This limitation could hinder the model's practical applicability and user experience. Thus, achieving\n",
"\tScore\t 0.7031083612095066\n",
"\t-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_\n",
"\tNode ID\t bc123b3d-b031-4c09-9400-d60ba9a161d6\n",
"\tText\t The model demonstrates exceptionally low AI safety violation percentages, surpassing even ChatGPT in safety benchmarks. Finding the right balance between helpfulness and safety when optimizing a model poses significant challenges. While a highly helpful model may be capable of answering any question, including sensitive ones like \"How do I build a bomb?\", it also raises concerns about potential misuse. Thus, striking the perfect equilibrium between providing useful information and ensuring safety is paramount. However, prioritizing safety to an extreme extent can lead to a model that struggles to effectively address a diverse range of questions. This limitation could hinder the model's practical applicability and user experience. Thus, achieving an optimum balance that allows the model to be both helpful and safe is of utmost importance. To strike the right balance between helpfulness and safety, Meta employed two reward models - one for helpfulness and another for safety - to optimize the model's responses. The 34B parameter model has reported higher safety violations than other variants, possibly contributing to the delay in its release. IV. Helpfulness Comparison: Llama 2 Outperforms Competitors Llama 2 emerges as a strong contender in the open-source language model arena, outperforming its competitors in most categories. The 70B parameter model outperforms all other open-source models, while the 7B and 34B models outshine Falcon in all categories and MPT in all categories except coding. Despite being smaller, Llam a2's performance rivals that of Chat GPT 3.5, a significantly larger closed-source model. While GPT 4 and PalM-2-L, with their larger size, outperform Llama 2, this is expected due to their capacity for handling complex language tasks. Llama 2's impressive ability to compete with larger models highlights its efficiency and potential in the market. However, Llama 2 does face challenges in coding and math problems, where models like Chat GPT 4 excel, given their significantly larger size. Chat GPT 4 performed significantly better than Llama 2 for coding (HumanEval benchmark)and math problem tasks (GSM8k benchmark). Open-source AI technologies, like Llama 2, continue to advance, offering\n",
"\tScore\t 0.7004323686791223\n",
"\t-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_\n"
]
}
],
"source": [
"# First Question:\n",
"response = chat_engine.chat(\n",
" \"Use the tool to answer, How many parameters LLaMA2 model has?\"\n",
")\n",
"display_res(response)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "3RRmiJEQ5R1Q",
"outputId": "15efcc9b-583f-4efe-8e36-fa8b5160da16"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Response:\n",
"\t Why did the scarecrow win an award? Because he was outstanding in his field!\n",
"Sources:\n",
"\tNode ID\t 8685e48d-1fdb-4f55-8f62-6f2ea4cfaf5d\n",
"\tText\t with their larger size, outperform Llama 2, this is expected due to their capacity for handling complex language tasks. Llama 2's impressive ability to compete with larger models highlights its efficiency and potential in the market. However, Llama 2 does face challenges in coding and math problems, where models like Chat GPT 4 excel, given their significantly larger size. Chat GPT 4 performed significantly better than Llama 2 for coding (HumanEval benchmark)and math problem tasks (GSM8k benchmark). Open-source AI technologies, like Llama 2, continue to advance, offering strong competition to closed-source models. V. Ghost Attention: Enhancing Conversational Continuity One unique feature in Llama 2 is Ghost Attention, which ensures continuity in conversations. This means that even after multiple interactions, the model remembers its initial instructions, ensuring more coherent and consistent responses throughout the conversation. This feature significantly enhances the user experience and makes Llama 2 a more reliable language model for interactive applications. In the example below, on the left, it forgets to use an emoji after a few conversations. On the right, with Ghost Attention, even after having many conversations, it will remember the context and continue to use emojis in its response. VI. Temporal Capability: A Leap in Information Organization Meta reported a groundbreaking temporal capability, where the model organizes information based on time relevance. Each question posed to the model is associated with a date, and it responds accordingly by considering the event date before which the question becomes irrelevant. For example, if you ask the question, \"How long ago did Barack Obama become president?\", its only relevant after 2008. This temporal awareness allows Llama 2 to deliver more contextually accurate responses, enriching the user experience further. VII. Open Questions and Future Outlook Meta's open-sourcing of Llama 2 represents a seismic shift, now offering developers and researchers commercial access to a leading language model. With Llama 2 outperforming MosaicML's current MPT models, all eyes are on how Databricks will respond. Can MosaicML's next MPT iteration beat Llama 2? Is it worthwhile to compete\n",
"\tScore\t 0.5624851990178006\n",
"\t-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_\n",
"\tNode ID\t e03eb322-1360-4c76-b461-236ec8312de1\n",
"\tText\t Introduce GPT4All GPT4All is a large language model (LLM) chatbot developed by Nomic AI, the world's first information cartography company. It was fine-tuned from LLaMA 7B model, the leaked large language model from Meta (aka Facebook). GPT4All is trained on a massive dataset of text and code, and it can generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way. GPT4All is available to the public on GitHub. LLaMA is available for commercial use under the GPL-3.0 license - while the LLaMA code is available for commercial use, the WEIGHTS are not. This effectively puts it in the same license class as GPT4All. Nomic is working on a GPT-J-based version of GPT4All with an open commercial license. GPT4All is not going to have a subscription fee ever. GPT4All is Free4All. Although GPT4All is still in its early stages, it has already left a notable mark on the AI landscape. Its popularity and capabilities are expected to expand further in the future. How to Run GPT4All Locally GPT4All Readme provides some details about its usage. Here will briefly demonstrate to run GPT4All locally on M1 CPU Mac. Download gpt4all-lora-quantized.bin from the-eye.Clone this repository, navigate to chat, and place the downloaded file there. Simply run the following command for M1 Mac: Now, it's ready to run locally. Please see a few snapshots below: Similar to ChatGPT, GPT4All has the ability to comprehend Chinese, a feature that Bard lacks. If you want to interact with GPT4All programmatically, you can install the nomic client as follows. Install the nomic client using pip install nomic.Use the following Python script to interact with GPT4All: Chat4All Demystified GPT4All aims to provide a cost-effective and fine-tuned model for high-quality LLM results. The GPT4All model was fine-tuned using an instance of LLaMA\n",
"\tScore\t 0.5615408071202241\n",
"\t-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_\n"
]
}
],
"source": [
"# Second Question:\n",
"response = chat_engine.chat(\"Tell me a joke?\")\n",
"display_res(response)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "8eOzp5Xc5Vbj",
"outputId": "13bc6714-dd89-45b3-a86b-759806245241"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Response:\n",
"\t The first question you asked was \"How many parameters LLaMA2 model has?\"\n",
"Sources:\n",
"\tNo sources used!\n"
]
}
],
"source": [
"# Third Question: (check if it can recall previous interactions)\n",
"response = chat_engine.chat(\"What was the first question I asked?\")\n",
"display_res(response)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"id": "7jfiLpru5VZT"
},
"outputs": [],
"source": [
"# Reset the session to clear the memory\n",
"chat_engine.reset()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Jt0q8RW25VXN",
"outputId": "0e2d0d4e-c0ff-48bf-8df3-478fcdc66abd"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Response:\n",
"\t The first question you asked was \"How can a Q&A bot be built over private documents using OpenAI and LangChain?\"\n",
"Sources:\n",
"\tNode ID\t baa8a99c-f38b-4818-b854-5741598c0776\n",
"\tText\t Private data to be used The example provided can be used with any dataset. I am using a data set that has Analyst recommendations from various stocks. For the purpose of demonstration, I have gathered publicly available analyst recommendations to showcase its capabilities. You can replace this with your own information to try this. Below is a partial extract of the information commonly found in these documents. If you wish to try it yourself, you can download analyst recommendations for your preferred stocks from online sources or access them through subscription platforms like Barron's. Although the example provided focuses on analyst recommendations, the underlying structure can be utilized to query various other types of documents in any industry as well. I have assembled such data for a few stocks for demonstration purposes. This includes Google, Microsoft, Meta, and Tesla. To facilitate easy access and updating of analysts' recommendations, all the recommendations can be organized into a designated folder. Each stock corresponds to a separate file within this folder. For example, if there are recommendations for 20 stocks, there will be 20 individual files. This organization enables convenient updating of information for each stock as new recommendations arrive, streamlining the process of managing and maintaining the most up-to-date data for each stock. Questions this Q&A bot application can answer The data we have for this application is stock market analyst recommendations for many stocks. Let's say you are looking for insight about Microsoft stock. You can ask any of the following questions as an example: What is the median target price for Microsoft (MSFT)?What is the highest price estimate for Microsoft (MSFT)?What is the lowest price estimate for Microsoft (MSFT)?How much percentage increase is expected in the stock price of Microsoft (MSFT)?How many analysts provided price forecasts for Microsoft (MSFT)?What is the current consensus among investment analysts regarding Microsoft (MSFT)?Has the consensus rating for Microsoft (MSFT) changed recently?When was the consensus rating last updated for Microsoft (MSFT)?Is the current recommendation for Microsoft (MSFT) to buy, sell, or hold the stock?Are there any recent analyst reports available for Microsoft (MSFT)? These questions cover various aspects of the stock analysis, including price forecasts, analyst recommendations, and recent changes in ratings. The\n",
"\tScore\t 0.5990934490336279\n",
"\t-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_\n",
"\tNode ID\t d03de2fa-70aa-4b32-8760-3af0dd0ebb24\n",
"\tText\t you want to specify exact documents, you can do it the following way. To load the files you want to ingest, you can specify the path to each file individually. The loaded files can then be saved into a list. This list serves as the input that is sent to the vector database to store the data. The alternative approach is a more versatile method in which we can load all pertinent documents from a designated folder and store the file locations in a list for subsequent processing. This approach offers flexibility and allows for the efficient handling of multiple documents by capturing their locations in a centralized list, enabling seamless data retrieval and analysis. Load the documents into the vector store. When dealing with a vast number of documents, it becomes inefficient to send all documents (analyst recommendations) to your large language model (LLM) when seeking answers to specific questions. For instance, if your question pertains to MSFT, it would be more cost-effective to only send document extracts that reference MSFT to your LLM for answering the question. This approach helps optimize resource utilization. To achieve this, all documents are split into chunks and stored in a vector database in a numeric format (embeddings). When a new question is posed, the system queries the vector database for relevant text chunks related to this question, which is then shared with the LLM to generate an appropriate response. Within the LangChain framework, the VectorstoreIndexCreator class serves as a utility for creating a vector store index. This index stores vector representations of the documents (in chromadb), enabling various text operations, such as finding similar documents based on a specific question. When a user asks a question, a similarity search is performed in the vector store to get document chunks relevant to the question. The question, along with the chunks are sent to OpenAI to get the response back. Now we are ready to query these documents. Setting up the web application The application is presented in the browser using Streamlit, providing a user-friendly interface. Within the application, a text box is available for users to enter their questions. Upon submitting the question by pressing enter, the application processes the input and generates a corresponding response. This response is then displayed below the text box, allowing users to conveniently view the relevant\n",
"\tScore\t 0.5904441993661576\n",
"\t-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_\n"
]
}
],
"source": [
"# Fourth Question: (don't recall the previous interactions.)\n",
"response = chat_engine.chat(\"What was the first question I asked?\")\n",
"display_res(response)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0Egsib7yPJGR"
},
"source": [
"# Streaming\n"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "zanJeMbaPJcq",
"outputId": "de7f0905-c1b1-49ac-fb66-d1578da35cad"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Here is a paragraph about the LLaMA2 model's capabilities:\n",
"\n",
"\"The Llama 2 model showcases impressive capabilities in the realm of open-source language models. It introduces innovative features like Ghost Attention, which enhances conversational continuity by ensuring consistent responses throughout interactions. Additionally, Llama 2 boasts a groundbreaking temporal capability that organizes information based on time relevance, leading to more contextually accurate responses. Despite facing challenges in coding and math problems compared to larger models like Chat GPT 4, Llama 2 demonstrates efficiency and potential in the market, competing well with both open-source and closed-source models. Its ability to balance helpfulness and safety in optimizing responses further solidifies its position as a reliable and advanced language model for commercial use.\""
]
}
],
"source": [
"# Stream the words as soon as they are available instead of waiting for the model to finish generation.\n",
"streaming_response = chat_engine.stream_chat(\n",
" \"Write a paragraph about the LLaMA2 model's capabilities.\"\n",
")\n",
"for token in streaming_response.response_gen:\n",
" print(token, end=\"\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "DuRgOJ2AHMJh"
},
"source": [
"## Condense Question\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Yb2Lt41jq145"
},
"source": [
"Enhance the input prompt by looking at the previous chat history along with the present question. The refined prompt can then be used to fetch the nodes.\n"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"id": "v0gmM5LGIaRl"
},
"outputs": [],
"source": [
"# Define GPT-4 model that will be used by the chat_engine to improve the query.\n",
"gpt4 = OpenAI(temperature=0.9, model=\"gpt-4o\")"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"id": "EDWsaBTBIhK7"
},
"outputs": [],
"source": [
"chat_engine = vector_index.as_chat_engine(\n",
" chat_mode=\"condense_question\", llm=gpt4, verbose=True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "h4c--hJ75VU2",
"outputId": "e80fd9bf-e6d5-4532-8771-8cbf781e782e"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Querying with: Using the tool at your disposal, can you please determine which company released the LLaMA2 model and explain what specific functionality or purpose this model is known for?\n",
"Response:\n",
"\t The LLaMA2 model was released by Meta. The model is known for its temporal awareness feature which enhances the accuracy of its responses by delivering more contextually accurate responses based on time relevance. For example, for the question, \"How long ago did Barack Obama become president?\", it only considers information relevant after 2008. Meta's open-sourcing of LLaMA2 provides developers and researchers with commercial access to the advanced language model, which represents a significant shift in the AI industry.\n",
"Sources:\n",
"\tNode ID\t 7adec56f-6714-4376-8ebf-180b694c4d59\n",
"\tText\t LLaMA: Meta's new AI tool According to the official release, LLaMA is a foundational language model developed to assist 'researchers and academics' in their work (as opposed to the average web user) to understand and study these NLP models. Leveraging AI in such a way could give researchers an edge in terms of time spent. You may not know this, but this would be Meta's third LLM after Blender Bot 3 and Galactica. However, the two LLMs were shut down soon, and Meta stopped their further development, as it produced erroneous results. Before moving further, it is important to emphasize that LLaMA is NOT a chatbot like ChatGPT. As I mentioned before, it is a 'research tool' for researchers. We can expect the initial versions of LLaMA to be a bit more technical and indirect to use as opposed to the case with ChatGPT, which was very direct, interactive, and a lot easy to use. \"Smaller, more performant models such as LLaMA enable ... research community who don't have access to large amounts of infrastructure to study these models.. further democratizing access in this important, fast-changing field,\" said Meta in its official blog. Meta's effort of \"democratizing\" access to the public could shed light on one of the critical issues of Generative AI - toxicity and bias. ChatGPT and other LLMs (obviously, I am referring to Bing) have a track record of responding in a way that is toxic and, well... evil. The Verge and major critics have covered it in much detail. Oh and the community did get the access, but not in the way Meta anticipated. On March 3rd, a downloadable torrent of the LLaMA system was posted on 4chan. 4chan is an anonymous online forum known for its controversial content and diverse range of discussions, which has nearly 222 million unique monthly visitors. LLaMA is currently not in use on any of Meta's products. But Meta has plans to make it available to researchers before they can use them in their own products. It's worth mentioning that Meta did not release\n",
"\tScore\t 0.696738130166742\n",
"\t-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_\n",
"\tNode ID\t d19bbfc9-9ff8-4c21-ba5a-ef78e5db2d87\n",
"\tText\t the question, \"How long ago did Barack Obama become president?\", its only relevant after 2008. This temporal awareness allows Llama 2 to deliver more contextually accurate responses, enriching the user experience further. VII. Open Questions and Future Outlook Meta's open-sourcing of Llama 2 represents a seismic shift, now offering developers and researchers commercial access to a leading language model. With Llama 2 outperforming MosaicML's current MPT models, all eyes are on how Databricks will respond. Can MosaicML's next MPT iteration beat Llama 2? Is it worthwhile to compete with Llama 2 or join hands with the open-source community to make the open-source models better? Meanwhile, Microsoft's move to host Llama 2 on Azure despite having significant investment in ChatGPT raises interesting questions. Will users prefer the capabilities and transparency of an open-source model like Llama 2 over closed, proprietary options? The stakes are high, as Meta's bold democratization play stands to reshape preferences and partnerships in the AI space. One thing is certain - the era of open language model competition has begun. VIII. Conclusion With the launch of Llama 2, Meta has achieved a landmark breakthrough in open-source language models, unleashing new potential through its commercial accessibility. Llama 2's formidable capabilities in natural language processing, along with robust safety protocols and temporal reasoning, set new benchmarks for the field. While select limitations around math and coding exist presently, Llama 2's strengths far outweigh its weaknesses. As Meta continues honing Llama technology, this latest innovation promises to be truly transformative. By open-sourcing such an advanced model, Meta is propelling democratization and proliferation of AI across industries. From healthcare to education and beyond, Llama 2 stands to shape the landscape by putting groundbreaking language modeling into the hands of all developers and researchers. The possibilities unlocked by this open-source approach signal a shift towards a more collaborative, creative AI future.\n",
"\tScore\t 0.692383316770113\n",
"\t-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_\n"
]
}
],
"source": [
"response = chat_engine.chat(\n",
" \"Use the tool to answer, which company released LLaMA2 model? What is the model useful for?\"\n",
")\n",
"display_res(response)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ysL9ONePOsGB"
},
"source": [
"## REACT\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "KiEFmxAtrmF-"
},
"source": [
"ReAct is an agent-based chat mode that uses a loop to decide on querying a data engine during interactions, offering flexibility but relying on the Large Language Model's quality for effective responses, requiring careful management to avoid inaccurate answers.\n"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"id": "-M1jWoKXOs2t"
},
"outputs": [],
"source": [
"chat_engine = vector_index.as_chat_engine(chat_mode=\"react\", verbose=True, llm=llm)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "UZkEW1SSOs0H",
"outputId": "4869c5fc-e0e1-44c6-e7f0-87db92bb2eb6"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Added user message to memory: Which company released LLaMA2 model? What is the model useful for?\n",
"=== Calling Function ===\n",
"Calling function: query_engine_tool with args: {\"input\": \"Which company released LLaMA2 model?\"}\n",
"Got output: Meta released the LLaMA2 model.\n",
"========================\n",
"\n",
"=== Calling Function ===\n",
"Calling function: query_engine_tool with args: {\"input\": \"What is the LLaMA2 model useful for?\"}\n",
"Got output: The Llama 2 model is useful for businesses to integrate into products to create AI-powered applications.\n",
"========================\n",
"\n"
]
}
],
"source": [
"response = chat_engine.chat(\n",
" \"Which company released LLaMA2 model? What is the model useful for?\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "eW5P1lD4Osxf",
"outputId": "b128bc94-081b-49aa-c549-7d7d7be90b63"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Response:\n",
"\t The LLaMA2 model was released by Meta. It is useful for businesses to integrate into products to create AI-powered applications.\n",
"Sources:\n",
"\tNode ID\t 7adec56f-6714-4376-8ebf-180b694c4d59\n",
"\tText\t LLaMA: Meta's new AI tool According to the official release, LLaMA is a foundational language model developed to assist 'researchers and academics' in their work (as opposed to the average web user) to understand and study these NLP models. Leveraging AI in such a way could give researchers an edge in terms of time spent. You may not know this, but this would be Meta's third LLM after Blender Bot 3 and Galactica. However, the two LLMs were shut down soon, and Meta stopped their further development, as it produced erroneous results. Before moving further, it is important to emphasize that LLaMA is NOT a chatbot like ChatGPT. As I mentioned before, it is a 'research tool' for researchers. We can expect the initial versions of LLaMA to be a bit more technical and indirect to use as opposed to the case with ChatGPT, which was very direct, interactive, and a lot easy to use. \"Smaller, more performant models such as LLaMA enable ... research community who don't have access to large amounts of infrastructure to study these models.. further democratizing access in this important, fast-changing field,\" said Meta in its official blog. Meta's effort of \"democratizing\" access to the public could shed light on one of the critical issues of Generative AI - toxicity and bias. ChatGPT and other LLMs (obviously, I am referring to Bing) have a track record of responding in a way that is toxic and, well... evil. The Verge and major critics have covered it in much detail. Oh and the community did get the access, but not in the way Meta anticipated. On March 3rd, a downloadable torrent of the LLaMA system was posted on 4chan. 4chan is an anonymous online forum known for its controversial content and diverse range of discussions, which has nearly 222 million unique monthly visitors. LLaMA is currently not in use on any of Meta's products. But Meta has plans to make it available to researchers before they can use them in their own products. It's worth mentioning that Meta did not release\n",
"\tScore\t 0.6701682333186606\n",
"\t-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_\n",
"\tNode ID\t d19bbfc9-9ff8-4c21-ba5a-ef78e5db2d87\n",
"\tText\t the question, \"How long ago did Barack Obama become president?\", its only relevant after 2008. This temporal awareness allows Llama 2 to deliver more contextually accurate responses, enriching the user experience further. VII. Open Questions and Future Outlook Meta's open-sourcing of Llama 2 represents a seismic shift, now offering developers and researchers commercial access to a leading language model. With Llama 2 outperforming MosaicML's current MPT models, all eyes are on how Databricks will respond. Can MosaicML's next MPT iteration beat Llama 2? Is it worthwhile to compete with Llama 2 or join hands with the open-source community to make the open-source models better? Meanwhile, Microsoft's move to host Llama 2 on Azure despite having significant investment in ChatGPT raises interesting questions. Will users prefer the capabilities and transparency of an open-source model like Llama 2 over closed, proprietary options? The stakes are high, as Meta's bold democratization play stands to reshape preferences and partnerships in the AI space. One thing is certain - the era of open language model competition has begun. VIII. Conclusion With the launch of Llama 2, Meta has achieved a landmark breakthrough in open-source language models, unleashing new potential through its commercial accessibility. Llama 2's formidable capabilities in natural language processing, along with robust safety protocols and temporal reasoning, set new benchmarks for the field. While select limitations around math and coding exist presently, Llama 2's strengths far outweigh its weaknesses. As Meta continues honing Llama technology, this latest innovation promises to be truly transformative. By open-sourcing such an advanced model, Meta is propelling democratization and proliferation of AI across industries. From healthcare to education and beyond, Llama 2 stands to shape the landscape by putting groundbreaking language modeling into the hands of all developers and researchers. The possibilities unlocked by this open-source approach signal a shift towards a more collaborative, creative AI future.\n",
"\tScore\t 0.6696485090138802\n",
"\t-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_\n",
"\tNode ID\t bc123b3d-b031-4c09-9400-d60ba9a161d6\n",
"\tText\t The model demonstrates exceptionally low AI safety violation percentages, surpassing even ChatGPT in safety benchmarks. Finding the right balance between helpfulness and safety when optimizing a model poses significant challenges. While a highly helpful model may be capable of answering any question, including sensitive ones like \"How do I build a bomb?\", it also raises concerns about potential misuse. Thus, striking the perfect equilibrium between providing useful information and ensuring safety is paramount. However, prioritizing safety to an extreme extent can lead to a model that struggles to effectively address a diverse range of questions. This limitation could hinder the model's practical applicability and user experience. Thus, achieving an optimum balance that allows the model to be both helpful and safe is of utmost importance. To strike the right balance between helpfulness and safety, Meta employed two reward models - one for helpfulness and another for safety - to optimize the model's responses. The 34B parameter model has reported higher safety violations than other variants, possibly contributing to the delay in its release. IV. Helpfulness Comparison: Llama 2 Outperforms Competitors Llama 2 emerges as a strong contender in the open-source language model arena, outperforming its competitors in most categories. The 70B parameter model outperforms all other open-source models, while the 7B and 34B models outshine Falcon in all categories and MPT in all categories except coding. Despite being smaller, Llam a2's performance rivals that of Chat GPT 3.5, a significantly larger closed-source model. While GPT 4 and PalM-2-L, with their larger size, outperform Llama 2, this is expected due to their capacity for handling complex language tasks. Llama 2's impressive ability to compete with larger models highlights its efficiency and potential in the market. However, Llama 2 does face challenges in coding and math problems, where models like Chat GPT 4 excel, given their significantly larger size. Chat GPT 4 performed significantly better than Llama 2 for coding (HumanEval benchmark)and math problem tasks (GSM8k benchmark). Open-source AI technologies, like Llama 2, continue to advance, offering\n",
"\tScore\t 0.7141285410107295\n",
"\t-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_\n",
"\tNode ID\t c3239b40-e206-4a80-b020-eea87cf471cc\n",
"\tText\t I. Llama 2: Revolutionizing Commercial Use Unlike its predecessor Llama 1, which was limited to research use, Llama 2 represents a major advancement as an open-source commercial model. Businesses can now integrate Llama 2 into products to create AI-powered applications. Availability on Azure and AWS facilitates fine-tuning and adoption. However, restrictions apply to prevent exploitation. Companies with over 700 million active daily users cannot use Llama 2. Additionally, its output cannot be used to improve other language models. II. Llama 2 Model Flavors Llama 2 is available in four different model sizes: 7 billion, 13 billion, 34 billion, and 70 billion parameters. While 7B, 13B, and 70B have already been released, the 34B model is still awaited. The pretrained variant, trained on a whopping 2 trillion tokens, boasts a context window of 4096 tokens, twice the size of its predecessor Llama 1. Meta also released a Llama 2 fine-tuned model for chat applications that was trained on over 1 million human annotations. Such extensive training comes at a cost, with the 70B model taking a staggering 1720320 GPU hours to train. The context window's length determines the amount of content the model can process at once, making Llama 2 a powerful language model in terms of scale and efficiency. III. Safety Considerations: A Top Priority for Meta Meta's commitment to safety and alignment shines through in Llama 2's design. The model demonstrates exceptionally low AI safety violation percentages, surpassing even ChatGPT in safety benchmarks. Finding the right balance between helpfulness and safety when optimizing a model poses significant challenges. While a highly helpful model may be capable of answering any question, including sensitive ones like \"How do I build a bomb?\", it also raises concerns about potential misuse. Thus, striking the perfect equilibrium between providing useful information and ensuring safety is paramount. However, prioritizing safety to an extreme extent can lead to a model that struggles to effectively address a diverse range of questions. This limitation could hinder the model's practical applicability and user experience. Thus, achieving\n",
"\tScore\t 0.7116485926146265\n",
"\t-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_-_\n"
]
}
],
"source": [
"display_res(response)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "zf6r2AmFOsca"
},
"outputs": [],
"source": []
}
],
"metadata": {
"colab": {
"authorship_tag": "ABX9TyNlfE1aMk+m6avCgDavT2ZF",
"include_colab_link": true,
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"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": "3.12.4"
},
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"0245f2604e4d49c8bd0210302746c47b": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"134210510d49476e959dd7d032bbdbdc": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"13b9c5395bca4c3ba21265240cb936cf": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"193aef33d9184055bb9223f56d456de6": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"3fbabd8a8660461ba5e7bc08ef39139a": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HBoxModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HBoxModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HBoxView",
"box_style": "",
"children": [
"IPY_MODEL_df2365556ae242a2ab1a119f9a31a561",
"IPY_MODEL_5f4b9d32df8f446e858e4c289dc282f9",
"IPY_MODEL_5b588f83a15d42d9aca888e06bbd95ff"
],
"layout": "IPY_MODEL_ad073bca655540809e39f26538d2ec0d"
}
},
"47a4586384274577a726c57605e7f8d9": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"4a172e8c6aa44e41a42fc1d9cf714fd0": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HTMLModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_e7937a1bc68441a080374911a6563376",
"placeholder": "",
"style": "IPY_MODEL_e532ed7bfef34f67b5fcacd9534eb789",
"value": " 108/108 [00:03<00:00, 33.70it/s]"
}
},
"5b588f83a15d42d9aca888e06bbd95ff": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HTMLModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_af9b6ae927dd4764b9692507791bc67e",
"placeholder": "",
"style": "IPY_MODEL_134210510d49476e959dd7d032bbdbdc",
"value": " 14/14 [00:00<00:00, 21.41it/s]"
}
},
"5c7973afd79349ed997a69120d0629b2": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "ProgressStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "ProgressStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"bar_color": null,
"description_width": ""
}
},
"5f4b9d32df8f446e858e4c289dc282f9": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "FloatProgressModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "FloatProgressModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "ProgressView",
"bar_style": "success",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_96a3bdece738481db57e811ccb74a974",
"max": 14,
"min": 0,
"orientation": "horizontal",
"style": "IPY_MODEL_5c7973afd79349ed997a69120d0629b2",
"value": 14
}
},
"5f9bb065c2b74d2e8ded32e1306a7807": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HBoxModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HBoxModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HBoxView",
"box_style": "",
"children": [
"IPY_MODEL_73a06bc546a64f7f99a9e4a135319dcd",
"IPY_MODEL_ce48deaf4d8c49cdae92bfdbb3a78df0",
"IPY_MODEL_4a172e8c6aa44e41a42fc1d9cf714fd0"
],
"layout": "IPY_MODEL_0245f2604e4d49c8bd0210302746c47b"
}
},
"73a06bc546a64f7f99a9e4a135319dcd": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HTMLModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_e956dfab55084a9cbe33c8e331b511e7",
"placeholder": "",
"style": "IPY_MODEL_cb394578badd43a89850873ad2526542",
"value": "Generating embeddings: 100%"
}
},
"96a3bdece738481db57e811ccb74a974": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"abfc9aa911ce4a5ea81c7c451f08295f": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "ProgressStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "ProgressStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"bar_color": null,
"description_width": ""
}
},
"ad073bca655540809e39f26538d2ec0d": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"af9b6ae927dd4764b9692507791bc67e": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"cb394578badd43a89850873ad2526542": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"ce48deaf4d8c49cdae92bfdbb3a78df0": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "FloatProgressModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "FloatProgressModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "ProgressView",
"bar_style": "success",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_193aef33d9184055bb9223f56d456de6",
"max": 108,
"min": 0,
"orientation": "horizontal",
"style": "IPY_MODEL_abfc9aa911ce4a5ea81c7c451f08295f",
"value": 108
}
},
"df2365556ae242a2ab1a119f9a31a561": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HTMLModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_13b9c5395bca4c3ba21265240cb936cf",
"placeholder": "",
"style": "IPY_MODEL_47a4586384274577a726c57605e7f8d9",
"value": "Parsing nodes: 100%"
}
},
"e532ed7bfef34f67b5fcacd9534eb789": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
"description_width": ""
}
},
"e7937a1bc68441a080374911a6563376": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
},
"e956dfab55084a9cbe33c8e331b511e7": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {
"_model_module": "@jupyter-widgets/base",
"_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
"align_self": null,
"border": null,
"bottom": null,
"display": null,
"flex": null,
"flex_flow": null,
"grid_area": null,
"grid_auto_columns": null,
"grid_auto_flow": null,
"grid_auto_rows": null,
"grid_column": null,
"grid_gap": null,
"grid_row": null,
"grid_template_areas": null,
"grid_template_columns": null,
"grid_template_rows": null,
"height": null,
"justify_content": null,
"justify_items": null,
"left": null,
"margin": null,
"max_height": null,
"max_width": null,
"min_height": null,
"min_width": null,
"object_fit": null,
"object_position": null,
"order": null,
"overflow": null,
"overflow_x": null,
"overflow_y": null,
"padding": null,
"right": null,
"top": null,
"visibility": null,
"width": null
}
}
}
}
},
"nbformat": 4,
"nbformat_minor": 0
}
|