Spaces:
Build error
Build error
File size: 60,580 Bytes
1227663 |
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 |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "lElF3o5PR6ys"
},
"source": [
"# Your First RAG Application\n",
"\n",
"In this notebook, we'll walk you through each of the components that are involved in a simple RAG application.\n",
"\n",
"We won't be leveraging any fancy tools, just the OpenAI Python SDK, Numpy, and some classic Python.\n",
"\n",
"> NOTE: This was done with Python 3.11.4.\n",
"\n",
"> NOTE: There might be [compatibility issues](https://github.com/wandb/wandb/issues/7683) if you're on NVIDIA driver >552.44 As an interim solution - you can rollback your drivers to the 552.44."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5CtcL8P8R6yt"
},
"source": [
"## Table of Contents:\n",
"\n",
"- Task 1: Imports and Utilities\n",
"- Task 2: Documents\n",
"- Task 3: Embeddings and Vectors\n",
"- Task 4: Prompts\n",
"- Task 5: Retrieval Augmented Generation\n",
" - 🚧 Activity #1: Augment RAG"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1Dz6GYilR6yt"
},
"source": [
"Let's look at a rather complicated looking visual representation of a basic RAG application.\n",
"\n",
"<img src=\"https://i.imgur.com/vD8b016.png\" />"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "PjmC0KFtR6yt"
},
"source": [
"## Task 1: Imports and Utility\n",
"\n",
"We're just doing some imports and enabling `async` to work within the Jupyter environment here, nothing too crazy!"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "Z1dyrG4hR6yt"
},
"outputs": [],
"source": [
"from aimakerspace.text_utils import TextFileLoader, CharacterTextSplitter\n",
"from aimakerspace.vectordatabase import VectorDatabase\n",
"import asyncio"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "9OrFZRnER6yt"
},
"outputs": [],
"source": [
"import nest_asyncio\n",
"nest_asyncio.apply()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "M0jGnpQsR6yu"
},
"source": [
"## Task 2: Documents\n",
"\n",
"We'll be concerning ourselves with this part of the flow in the following section:\n",
"\n",
"<img src=\"https://i.imgur.com/jTm9gjk.png\" />"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-SFPWvRUR6yu"
},
"source": [
"### Loading Source Documents\n",
"\n",
"So, first things first, we need some documents to work with.\n",
"\n",
"While we could work directly with the `.txt` files (or whatever file-types you wanted to extend this to) we can instead do some batch processing of those documents at the beginning in order to store them in a more machine compatible format.\n",
"\n",
"In this case, we're going to parse our text file into a single document in memory.\n",
"\n",
"Let's look at the relevant bits of the `TextFileLoader` class:\n",
"\n",
"```python\n",
"def load_file(self):\n",
" with open(self.path, \"r\", encoding=self.encoding) as f:\n",
" self.documents.append(f.read())\n",
"```\n",
"\n",
"We're simply loading the document using the built in `open` method, and storing that output in our `self.documents` list.\n",
"\n",
"> NOTE: We're using blogs from PMarca (Marc Andreessen) as our sample data. This data is largely irrelevant as we want to focus on the mechanisms of RAG, which includes out data's shape and quality - but not specifically what the contents of the data are. \n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Ia2sUEuGR6yu",
"outputId": "84937ecc-c35f-4c4a-a4ab-9da72625954c"
},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"text_loader = TextFileLoader(\"data/PMarcaBlogs.txt\")\n",
"documents = text_loader.load_documents()\n",
"len(documents)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "bV-tj5WFR6yu",
"outputId": "674eb315-1ff3-4597-bcf5-38ece0a812ac"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"The Pmarca Blog Archives\n",
"(select posts from 2007-2009)\n",
"Marc Andreessen\n",
"copyright: Andreessen Horow\n"
]
}
],
"source": [
"print(documents[0][:100])"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nHlTvCzYR6yu"
},
"source": [
"### Splitting Text Into Chunks\n",
"\n",
"As we can see, there is one massive document.\n",
"\n",
"We'll want to chunk the document into smaller parts so it's easier to pass the most relevant snippets to the LLM.\n",
"\n",
"There is no fixed way to split/chunk documents - and you'll need to rely on some intuition as well as knowing your data *very* well in order to build the most robust system.\n",
"\n",
"For this toy example, we'll just split blindly on length.\n",
"\n",
">There's an opportunity to clear up some terminology here, for this course we will be stick to the following:\n",
">\n",
">- \"source documents\" : The `.txt`, `.pdf`, `.html`, ..., files that make up the files and information we start with in its raw format\n",
">- \"document(s)\" : single (or more) text object(s)\n",
">- \"corpus\" : the combination of all of our documents"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2G6Voc0jR6yv"
},
"source": [
"As you can imagine (though it's not specifically true in this toy example) the idea of splitting documents is to break them into managable sized chunks that retain the most relevant local context."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "UMC4tsEmR6yv",
"outputId": "08689c0b-57cd-4040-942a-8193e997f5cb"
},
"outputs": [
{
"data": {
"text/plain": [
"373"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"text_splitter = CharacterTextSplitter()\n",
"split_documents = text_splitter.split_texts(documents)\n",
"len(split_documents)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "W2wKT0WLR6yv"
},
"source": [
"Let's take a look at some of the documents we've managed to split."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "vcYMwWJoR6yv",
"outputId": "20d69876-feca-4826-b4be-32915276987a"
},
"outputs": [
{
"data": {
"text/plain": [
"['\\ufeff\\nThe Pmarca Blog Archives\\n(select posts from 2007-2009)\\nMarc Andreessen\\ncopyright: Andreessen Horowitz\\ncover design: Jessica Hagy\\nproduced using: Pressbooks\\nContents\\nTHE PMARCA GUIDE TO STARTUPS\\nPart 1: Why not to do a startup 2\\nPart 2: When the VCs say \"no\" 10\\nPart 3: \"But I don\\'t know any VCs!\" 18\\nPart 4: The only thing that matters 25\\nPart 5: The Moby Dick theory of big companies 33\\nPart 6: How much funding is too little? Too much? 41\\nPart 7: Why a startup\\'s initial business plan doesn\\'t\\nmatter that much\\n49\\nTHE PMARCA GUIDE TO HIRING\\nPart 8: Hiring, managing, promoting, and Dring\\nexecutives\\n54\\nPart 9: How to hire a professional CEO 68\\nHow to hire the best people you\\'ve ever worked\\nwith\\n69\\nTHE PMARCA GUIDE TO BIG COMPANIES\\nPart 1: Turnaround! 82\\nPart 2: Retaining great people 86\\nTHE PMARCA GUIDE TO CAREER, PRODUCTIVITY,\\nAND SOME OTHER THINGS\\nIntroduction 97\\nPart 1: Opportunity 99\\nPart 2: Skills and education 107\\nPart 3: Where to go and why 120\\nThe Pmarca Guide to Personal Productivi']"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"split_documents[0:1]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HOU-RFP_R6yv"
},
"source": [
"## Task 3: Embeddings and Vectors\n",
"\n",
"Next, we have to convert our corpus into a \"machine readable\" format as we explored in the Embedding Primer notebook.\n",
"\n",
"Today, we're going to talk about the actual process of creating, and then storing, these embeddings, and how we can leverage that to intelligently add context to our queries."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### OpenAI API Key\n",
"\n",
"In order to access OpenAI's APIs, we'll need to provide our OpenAI API Key!\n",
"\n",
"You can work through the folder \"OpenAI API Key Setup\" for more information on this process if you don't already have an API Key!"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import openai\n",
"from getpass import getpass\n",
"\n",
"openai.api_key = getpass(\"OpenAI API Key: \")\n",
"os.environ[\"OPENAI_API_KEY\"] = openai.api_key"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Vector Database\n",
"\n",
"Let's set up our vector database to hold all our documents and their embeddings!"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kDQrfAR1R6yv"
},
"source": [
"While this is all baked into 1 call - we can look at some of the code that powers this process to get a better understanding:\n",
"\n",
"Let's look at our `VectorDatabase().__init__()`:\n",
"\n",
"```python\n",
"def __init__(self, embedding_model: EmbeddingModel = None):\n",
" self.vectors = defaultdict(np.array)\n",
" self.embedding_model = embedding_model or EmbeddingModel()\n",
"```\n",
"\n",
"As you can see - our vectors are merely stored as a dictionary of `np.array` objects.\n",
"\n",
"Secondly, our `VectorDatabase()` has a default `EmbeddingModel()` which is a wrapper for OpenAI's `text-embedding-3-small` model.\n",
"\n",
"> **Quick Info About `text-embedding-3-small`**:\n",
"> - It has a context window of **8191** tokens\n",
"> - It returns vectors with dimension **1536**"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "L273pRdeR6yv"
},
"source": [
"#### ❓Question #1:\n",
"\n",
"The default embedding dimension of `text-embedding-3-small` is 1536, as noted above. \n",
"\n",
"1. Is there any way to modify this dimension?\n",
"Yes you can reduce dimension but not increase it. Helps with speeding up searches and reducing cost. We can reduce it up to 256\n",
"2. What technique does OpenAI use to achieve this?\n",
"OpenAI uses Principal Component Analysis(PCA) the model first generates a 1536 then uses PCA to reduce the dimension\n",
"Reducing retains all the important information and benefits relationships between texts reduces resources needed and has good performance even at lower dimensions\n",
"\n",
"\n",
"> NOTE: Check out this [API documentation](https://platform.openai.com/docs/api-reference/embeddings/create) for the answer to question #1, and [this documentation](https://platform.openai.com/docs/guides/embeddings/use-cases) for an answer to question #2!"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "w5FZY7K3R6yv"
},
"source": [
"We can call the `async_get_embeddings` method of our `EmbeddingModel()` on a list of `str` and receive a list of `float` back!\n",
"\n",
"```python\n",
"async def async_get_embeddings(self, list_of_text: List[str]) -> List[List[float]]:\n",
" return await aget_embeddings(\n",
" list_of_text=list_of_text, engine=self.embeddings_model_name\n",
" )\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cSct6X0aR6yv"
},
"source": [
"We cast those to `np.array` when we build our `VectorDatabase()`:\n",
"\n",
"```python\n",
"async def abuild_from_list(self, list_of_text: List[str]) -> \"VectorDatabase\":\n",
" embeddings = await self.embedding_model.async_get_embeddings(list_of_text)\n",
" for text, embedding in zip(list_of_text, embeddings):\n",
" self.insert(text, np.array(embedding))\n",
" return self\n",
"```\n",
"\n",
"And that's all we need to do!"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"id": "O4KoLbVDR6yv"
},
"outputs": [],
"source": [
"vector_db = VectorDatabase()\n",
"vector_db = asyncio.run(vector_db.abuild_from_list(split_documents))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SSZwaGvpR6yv"
},
"source": [
"#### ❓Question #2:\n",
"\n",
"What are the benefits of using an `async` approach to collecting our embeddings?\n",
"\n",
"Improves scalability, uses resources more efficiently and has faster processing when having many concurrent requests\n",
"In our case the multiple 100s of api calls and that way our chunks can be processed faster instead of waiting for each chunk to process and sending the next. The scalability of async is better for our examples.\n",
"\n",
"\n",
"> NOTE: Determining the core difference between `async` and `sync` will be useful! If you get stuck - ask ChatGPT!"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nRBdIt-xR6yw"
},
"source": [
"So, to review what we've done so far in natural language:\n",
"\n",
"1. We load source documents\n",
"2. We split those source documents into smaller chunks (documents)\n",
"3. We send each of those documents to the `text-embedding-3-small` OpenAI API endpoint\n",
"4. We store each of the text representations with the vector representations as keys/values in a dictionary"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4-vWANZyR6yw"
},
"source": [
"### Semantic Similarity\n",
"\n",
"The next step is to be able to query our `VectorDatabase()` with a `str` and have it return to us vectors and text that is most relevant from our corpus.\n",
"\n",
"We're going to use the following process to achieve this in our toy example:\n",
"\n",
"1. We need to embed our query with the same `EmbeddingModel()` as we used to construct our `VectorDatabase()`\n",
"2. We loop through every vector in our `VectorDatabase()` and use a distance measure to compare how related they are\n",
"3. We return a list of the top `k` closest vectors, with their text representations\n",
"\n",
"There's some very heavy optimization that can be done at each of these steps - but let's just focus on the basic pattern in this notebook.\n",
"\n",
"> We are using [cosine similarity](https://www.engati.com/glossary/cosine-similarity) as a distance metric in this example - but there are many many distance metrics you could use - like [these](https://flavien-vidal.medium.com/similarity-distances-for-natural-language-processing-16f63cd5ba55)\n",
"\n",
"> We are using a rather inefficient way of calculating relative distance between the query vector and all other vectors - there are more advanced approaches that are much more efficient, like [ANN](https://towardsdatascience.com/comprehensive-guide-to-approximate-nearest-neighbors-algorithms-8b94f057d6b6)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "76d96uavR6yw",
"outputId": "bbfccc31-20a2-41c7-c14d-46554a43ed2d"
},
"outputs": [
{
"data": {
"text/plain": [
"[('ordingly.\\nSeventh, when hiring the executive to run your former specialty, be\\ncareful you don’t hire someone weak on purpose.\\nThis sounds silly, but you wouldn’t believe how oaen it happens.\\nThe CEO who used to be a product manager who has a weak\\nproduct management executive. The CEO who used to be in\\nsales who has a weak sales executive. The CEO who used to be\\nin marketing who has a weak marketing executive.\\nI call this the “Michael Eisner Memorial Weak Executive Problem” — aaer the CEO of Disney who had previously been a brilliant TV network executive. When he bought ABC at Disney, it\\npromptly fell to fourth place. His response? “If I had an extra\\ntwo days a week, I could turn around ABC myself.” Well, guess\\nwhat, he didn’t have an extra two days a week.\\nA CEO — or a startup founder — oaen has a hard time letting\\ngo of the function that brought him to the party. The result: you\\nhire someone weak into the executive role for that function so\\nthat you can continue to be “the man” — cons',\n",
" np.float64(0.6539043027545371)),\n",
" ('m. They have areas where they are truly deXcient in judgment or skill set. That’s just life. Almost nobody is brilliant\\nat everything. When hiring and when Hring executives, you\\nmust therefore focus on strength rather than lack of weakness. Everybody has severe weaknesses even if you can’t see\\nthem yet. When managing, it’s oaen useful to micromanage and\\nto provide remedial training around these weaknesses. Doing so\\nmay make the diWerence between an executive succeeding or\\nfailing.\\nFor example, you might have a brilliant engineering executive\\nwho generates excellent team loyalty, has terriXc product judgment and makes the trains run on time. This same executive\\nmay be very poor at relating to the other functions in the company. She may generate far more than her share of cross-functional conYicts, cut herself oW from critical information, and\\nsigniXcantly impede your ability to sell and market eWectively.\\nYour alternatives are:\\n(a) Macro-manage and give her an annual or quarterly object',\n",
" np.float64(0.5036247837648782)),\n",
" ('ed?\\nIn reality — as opposed to Marc’s warped view of reality — it will\\nbe extremely helpful for Marc [if he were actually the CEO,\\nwhich he is not] to meet with the new head of engineering daily\\nwhen she comes on board and review all of her thinking and\\ndecisions. This level of micromanagement will accelerate her\\ntraining and improve her long-term eWectiveness. It will make\\nher seem smarter to the rest of the organization which will build\\ncredibility and conXdence while she comes up to speed. Micromanaging new executives is generally a good idea for a limited\\nperiod of time.\\nHowever, that is not the only time that it makes sense to micro66 The Pmarca Blog Archives\\nmanage executives. It turns out that just about every executive\\nin the world has a few things that are seriously wrong with\\nthem. They have areas where they are truly deXcient in judgment or skill set. That’s just life. Almost nobody is brilliant\\nat everything. When hiring and when Hring executives, you\\nmust therefore focus o',\n",
" np.float64(0.4814861061791066))]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vector_db.search_by_text(\"What is the Michael Eisner Memorial Weak Executive Problem?\", k=3)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TehsfIiKR6yw"
},
"source": [
"## Task 4: Prompts\n",
"\n",
"In the following section, we'll be looking at the role of prompts - and how they help us to guide our application in the right direction.\n",
"\n",
"In this notebook, we're going to rely on the idea of \"zero-shot in-context learning\".\n",
"\n",
"This is a lot of words to say: \"We will ask it to perform our desired task in the prompt, and provide no examples.\""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "yXpA0UveR6yw"
},
"source": [
"### XYZRolePrompt\n",
"\n",
"Before we do that, let's stop and think a bit about how OpenAI's chat models work.\n",
"\n",
"We know they have roles - as is indicated in the following API [documentation](https://platform.openai.com/docs/api-reference/chat/create#chat/create-messages)\n",
"\n",
"There are three roles, and they function as follows (taken directly from [OpenAI](https://platform.openai.com/docs/guides/gpt/chat-completions-api)):\n",
"\n",
"- `{\"role\" : \"system\"}` : The system message helps set the behavior of the assistant. For example, you can modify the personality of the assistant or provide specific instructions about how it should behave throughout the conversation. However note that the system message is optional and the model’s behavior without a system message is likely to be similar to using a generic message such as \"You are a helpful assistant.\"\n",
"- `{\"role\" : \"user\"}` : The user messages provide requests or comments for the assistant to respond to.\n",
"- `{\"role\" : \"assistant\"}` : Assistant messages store previous assistant responses, but can also be written by you to give examples of desired behavior.\n",
"\n",
"The main idea is this:\n",
"\n",
"1. You start with a system message that outlines how the LLM should respond, what kind of behaviours you can expect from it, and more\n",
"2. Then, you can provide a few examples in the form of \"assistant\"/\"user\" pairs\n",
"3. Then, you prompt the model with the true \"user\" message.\n",
"\n",
"In this example, we'll be forgoing the 2nd step for simplicities sake."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gdZ2KWKSR6yw"
},
"source": [
"#### Utility Functions\n",
"\n",
"You'll notice that we're using some utility functions from the `aimakerspace` module - let's take a peek at these and see what they're doing!"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "GFbeJDDsR6yw"
},
"source": [
"##### XYZRolePrompt"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5mojJSE3R6yw"
},
"source": [
"Here we have our `system`, `user`, and `assistant` role prompts.\n",
"\n",
"Let's take a peek at what they look like:\n",
"\n",
"```python\n",
"class BasePrompt:\n",
" def __init__(self, prompt):\n",
" \"\"\"\n",
" Initializes the BasePrompt object with a prompt template.\n",
"\n",
" :param prompt: A string that can contain placeholders within curly braces\n",
" \"\"\"\n",
" self.prompt = prompt\n",
" self._pattern = re.compile(r\"\\{([^}]+)\\}\")\n",
"\n",
" def format_prompt(self, **kwargs):\n",
" \"\"\"\n",
" Formats the prompt string using the keyword arguments provided.\n",
"\n",
" :param kwargs: The values to substitute into the prompt string\n",
" :return: The formatted prompt string\n",
" \"\"\"\n",
" matches = self._pattern.findall(self.prompt)\n",
" return self.prompt.format(**{match: kwargs.get(match, \"\") for match in matches})\n",
"\n",
" def get_input_variables(self):\n",
" \"\"\"\n",
" Gets the list of input variable names from the prompt string.\n",
"\n",
" :return: List of input variable names\n",
" \"\"\"\n",
" return self._pattern.findall(self.prompt)\n",
"```\n",
"\n",
"Then we have our `RolePrompt` which laser focuses us on the role pattern found in most API endpoints for LLMs.\n",
"\n",
"```python\n",
"class RolePrompt(BasePrompt):\n",
" def __init__(self, prompt, role: str):\n",
" \"\"\"\n",
" Initializes the RolePrompt object with a prompt template and a role.\n",
"\n",
" :param prompt: A string that can contain placeholders within curly braces\n",
" :param role: The role for the message ('system', 'user', or 'assistant')\n",
" \"\"\"\n",
" super().__init__(prompt)\n",
" self.role = role\n",
"\n",
" def create_message(self, **kwargs):\n",
" \"\"\"\n",
" Creates a message dictionary with a role and a formatted message.\n",
"\n",
" :param kwargs: The values to substitute into the prompt string\n",
" :return: Dictionary containing the role and the formatted message\n",
" \"\"\"\n",
" return {\"role\": self.role, \"content\": self.format_prompt(**kwargs)}\n",
"```\n",
"\n",
"We'll look at how the `SystemRolePrompt` is constructed to get a better idea of how that extension works:\n",
"\n",
"```python\n",
"class SystemRolePrompt(RolePrompt):\n",
" def __init__(self, prompt: str):\n",
" super().__init__(prompt, \"system\")\n",
"```\n",
"\n",
"That pattern is repeated for our `UserRolePrompt` and our `AssistantRolePrompt` as well."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "D361R6sMR6yw"
},
"source": [
"##### ChatOpenAI"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HJVQ2Pm8R6yw"
},
"source": [
"Next we have our model, which is converted to a format analagous to libraries like LangChain and LlamaIndex.\n",
"\n",
"Let's take a peek at how that is constructed:\n",
"\n",
"```python\n",
"class ChatOpenAI:\n",
" def __init__(self, model_name: str = \"gpt-4o-mini\"):\n",
" self.model_name = model_name\n",
" self.openai_api_key = os.getenv(\"OPENAI_API_KEY\")\n",
" if self.openai_api_key is None:\n",
" raise ValueError(\"OPENAI_API_KEY is not set\")\n",
"\n",
" def run(self, messages, text_only: bool = True):\n",
" if not isinstance(messages, list):\n",
" raise ValueError(\"messages must be a list\")\n",
"\n",
" openai.api_key = self.openai_api_key\n",
" response = openai.ChatCompletion.create(\n",
" model=self.model_name, messages=messages\n",
" )\n",
"\n",
" if text_only:\n",
" return response.choices[0].message.content\n",
"\n",
" return response\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qCU7FfhIR6yw"
},
"source": [
"#### ❓ Question #3:\n",
"\n",
"When calling the OpenAI API - are there any ways we can achieve more reproducible outputs?\n",
"\n",
"We can have the temperature setting be fixed and it controls the randomness of the model\n",
"Example: temperature of 1 returns more creative responses vs temperature 0 the responses are more focused\n",
"combining this with top_p of 1 and the low temperature we can achieve more reproducible outputs \n",
"\n",
"> NOTE: Check out [this section](https://platform.openai.com/docs/guides/text-generation/) of the OpenAI documentation for the answer!"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "c5wcjMLCR6yw"
},
"source": [
"### Creating and Prompting OpenAI's `gpt-4o-mini`!\n",
"\n",
"Let's tie all these together and use it to prompt `gpt-4o-mini`!"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"id": "WIfpIot7R6yw"
},
"outputs": [],
"source": [
"from aimakerspace.openai_utils.prompts import (\n",
" UserRolePrompt,\n",
" SystemRolePrompt,\n",
" AssistantRolePrompt,\n",
")\n",
"\n",
"from aimakerspace.openai_utils.chatmodel import ChatOpenAI\n",
"\n",
"chat_openai = ChatOpenAI()\n",
"user_prompt_template = \"{content}\"\n",
"user_role_prompt = UserRolePrompt(user_prompt_template)\n",
"system_prompt_template = (\n",
" \"You are an expert in {expertise}, you always answer in a kind way.\"\n",
")\n",
"system_role_prompt = SystemRolePrompt(system_prompt_template)\n",
"\n",
"messages = [\n",
" system_role_prompt.create_message(expertise=\"Python\"),\n",
" user_role_prompt.create_message(\n",
" content=\"What is the best way to write a loop?\"\n",
" ),\n",
"]\n",
"\n",
"response = chat_openai.run(messages)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "dHo7lssNR6yw",
"outputId": "1d3823fa-bb6b-45f6-ddba-b41686388324"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The \"best\" way to write a loop in Python often depends on the specific use case and what you're trying to accomplish. However, I can share some common practices and examples for writing efficient, readable loops.\n",
"\n",
"1. **Using a `for` loop**:\n",
" This is typically used for iterating over a sequence (like a list, tuple, string, or range).\n",
"\n",
" ```python\n",
" # Example: Iterating over a list\n",
" fruits = ['apple', 'banana', 'cherry']\n",
" for fruit in fruits:\n",
" print(fruit)\n",
" ```\n",
"\n",
"2. **Using a `while` loop**:\n",
" This is useful when the number of iterations isn't known ahead of time and depends on a condition.\n",
"\n",
" ```python\n",
" # Example: Simple counter\n",
" count = 0\n",
" while count < 5:\n",
" print(count)\n",
" count += 1\n",
" ```\n",
"\n",
"3. **Using list comprehensions**:\n",
" If you're creating a new list based on an existing one, list comprehensions are a more concise and often more efficient way to write a loop.\n",
"\n",
" ```python\n",
" # Example: Creating a list of squared numbers\n",
" numbers = [1, 2, 3, 4, 5]\n",
" squares = [x**2 for x in numbers]\n",
" print(squares)\n",
" ```\n",
"\n",
"4. **Using `enumerate()`**:\n",
" If you need both the index and the value when looping through a list, `enumerate()` can be very handy.\n",
"\n",
" ```python\n",
" # Example: Iterating with index\n",
" fruits = ['apple', 'banana', 'cherry']\n",
" for index, fruit in enumerate(fruits):\n",
" print(f\"{index}: {fruit}\")\n",
" ```\n",
"\n",
"5. **Avoiding deep nesting**:\n",
" If you find yourself needing deeply nested loops, consider whether you can flatten your logic or use functions to keep your code readable.\n",
"\n",
"Here’s an example of combining some of these practices for better structure:\n",
"\n",
"```python\n",
"# Example: Using a function to process elements\n",
"def process_fruits(fruits):\n",
" for index, fruit in enumerate(fruits):\n",
" print(f\"{index}: {fruit.upper()}\")\n",
"\n",
"fruits = ['apple', 'banana', 'cherry']\n",
"process_fruits(fruits)\n",
"```\n",
"\n",
"Each of these methods has its own advantages depending on the scenario. The key is to choose the one that makes your code easy to read and maintain while being efficient. If you have a specific use case in mind, feel free to share, and I’d be happy to help you refine your loop further!\n"
]
}
],
"source": [
"print(response)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "r2nxxhB2R6yy"
},
"source": [
"## Task 5: Retrieval Augmented Generation\n",
"\n",
"Now we can create a RAG prompt - which will help our system behave in a way that makes sense!\n",
"\n",
"There is much you could do here, many tweaks and improvements to be made!"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"id": "D1hamzGaR6yy"
},
"outputs": [],
"source": [
"RAG_PROMPT_TEMPLATE = \"\"\" \\\n",
"Use the provided context to answer the user's query.\n",
"\n",
"You may not answer the user's query unless there is specific context in the following text.\n",
"\n",
"If you do not know the answer, or cannot answer, please respond with \"I don't know\".\n",
"\"\"\"\n",
"\n",
"rag_prompt = SystemRolePrompt(RAG_PROMPT_TEMPLATE)\n",
"\n",
"USER_PROMPT_TEMPLATE = \"\"\" \\\n",
"Context:\n",
"{context}\n",
"\n",
"User Query:\n",
"{user_query}\n",
"\"\"\"\n",
"\n",
"\n",
"user_prompt = UserRolePrompt(USER_PROMPT_TEMPLATE)\n",
"\n",
"class RetrievalAugmentedQAPipeline:\n",
" def __init__(self, llm: ChatOpenAI(), vector_db_retriever: VectorDatabase) -> None:\n",
" self.llm = llm\n",
" self.vector_db_retriever = vector_db_retriever\n",
"\n",
" def run_pipeline(self, user_query: str) -> str:\n",
" context_list = self.vector_db_retriever.search_by_text(user_query, k=4)\n",
"\n",
" context_prompt = \"\"\n",
" for context in context_list:\n",
" context_prompt += context[0] + \"\\n\"\n",
"\n",
" formatted_system_prompt = rag_prompt.create_message()\n",
"\n",
" formatted_user_prompt = user_prompt.create_message(user_query=user_query, context=context_prompt)\n",
"\n",
" return {\"response\" : self.llm.run([formatted_system_prompt, formatted_user_prompt]), \"context\" : context_list}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "zZIJI19uR6yz"
},
"source": [
"#### ❓ Question #4:\n",
"\n",
"What prompting strategies could you use to make the LLM have a more thoughtful, detailed response?\n",
"\n",
"What is that strategy called?\n",
"\n",
"Instruction prompting, we get the results we want by directly instructing the model how we want it to respond\n",
"\n",
"\n",
"> NOTE: You can look through [\"Accessing GPT-3.5-turbo Like a Developer\"](https://colab.research.google.com/drive/1mOzbgf4a2SP5qQj33ZxTz2a01-5eXqk2?usp=sharing) for an answer to this question if you get stuck!"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"id": "kqbE9fZ6R6yz"
},
"outputs": [],
"source": [
"retrieval_augmented_qa_pipeline = RetrievalAugmentedQAPipeline(\n",
" vector_db_retriever=vector_db,\n",
" llm=chat_openai\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "jAGhaCGOR6yz",
"outputId": "e4fb3a1b-d2bc-4e18-ec31-dc0adf767163"
},
"outputs": [
{
"data": {
"text/plain": [
"{'response': 'The \\'Michael Eisner Memorial Weak Executive Problem\\' refers to the tendency of a CEO or founder, who has a strong background in a particular function (such as product management, sales, or marketing), to hire a weak executive in that same area. This often occurs because the CEO wants to retain control over that function, thus hiring someone less competent to ensure they remain \"the man.\" The term references Michael Eisner, the former CEO of Disney, who faced challenges when he bought ABC, which struggled under his leadership despite his expertise in television. The implication is that hiring weak executives can hinder the overall effectiveness of an organization.',\n",
" 'context': [('ordingly.\\nSeventh, when hiring the executive to run your former specialty, be\\ncareful you don’t hire someone weak on purpose.\\nThis sounds silly, but you wouldn’t believe how oaen it happens.\\nThe CEO who used to be a product manager who has a weak\\nproduct management executive. The CEO who used to be in\\nsales who has a weak sales executive. The CEO who used to be\\nin marketing who has a weak marketing executive.\\nI call this the “Michael Eisner Memorial Weak Executive Problem” — aaer the CEO of Disney who had previously been a brilliant TV network executive. When he bought ABC at Disney, it\\npromptly fell to fourth place. His response? “If I had an extra\\ntwo days a week, I could turn around ABC myself.” Well, guess\\nwhat, he didn’t have an extra two days a week.\\nA CEO — or a startup founder — oaen has a hard time letting\\ngo of the function that brought him to the party. The result: you\\nhire someone weak into the executive role for that function so\\nthat you can continue to be “the man” — cons',\n",
" np.float64(0.6582349170764753)),\n",
" ('m. They have areas where they are truly deXcient in judgment or skill set. That’s just life. Almost nobody is brilliant\\nat everything. When hiring and when Hring executives, you\\nmust therefore focus on strength rather than lack of weakness. Everybody has severe weaknesses even if you can’t see\\nthem yet. When managing, it’s oaen useful to micromanage and\\nto provide remedial training around these weaknesses. Doing so\\nmay make the diWerence between an executive succeeding or\\nfailing.\\nFor example, you might have a brilliant engineering executive\\nwho generates excellent team loyalty, has terriXc product judgment and makes the trains run on time. This same executive\\nmay be very poor at relating to the other functions in the company. She may generate far more than her share of cross-functional conYicts, cut herself oW from critical information, and\\nsigniXcantly impede your ability to sell and market eWectively.\\nYour alternatives are:\\n(a) Macro-manage and give her an annual or quarterly object',\n",
" np.float64(0.5088668708442031)),\n",
" ('ed?\\nIn reality — as opposed to Marc’s warped view of reality — it will\\nbe extremely helpful for Marc [if he were actually the CEO,\\nwhich he is not] to meet with the new head of engineering daily\\nwhen she comes on board and review all of her thinking and\\ndecisions. This level of micromanagement will accelerate her\\ntraining and improve her long-term eWectiveness. It will make\\nher seem smarter to the rest of the organization which will build\\ncredibility and conXdence while she comes up to speed. Micromanaging new executives is generally a good idea for a limited\\nperiod of time.\\nHowever, that is not the only time that it makes sense to micro66 The Pmarca Blog Archives\\nmanage executives. It turns out that just about every executive\\nin the world has a few things that are seriously wrong with\\nthem. They have areas where they are truly deXcient in judgment or skill set. That’s just life. Almost nobody is brilliant\\nat everything. When hiring and when Hring executives, you\\nmust therefore focus o',\n",
" np.float64(0.4790308520865477)),\n",
" ('nYicts, cut herself oW from critical information, and\\nsigniXcantly impede your ability to sell and market eWectively.\\nYour alternatives are:\\n(a) Macro-manage and give her an annual or quarterly objective\\nto Xx it, or…\\n(b) Intensively micromanage her interactions until she learns\\nthe fundamental interpersonal skills required to be an eWective\\nexecutive.\\nI am arguing that doing (a) will likely result in weak performance. The reason is that she very likely has no idea how to be\\neWective with her peers. If somebody is an executive, it’s very\\nlikely that somewhere along the line somebody gave her feedback — perhaps abstractly — about all of her weaknesses. Yet\\nthe weakness remains. As a result, executives generally require\\nmore hands-on management than lower level employees to\\nimprove weak areas.\\nSo, micromanagement is like Xne wine. A little at the right times\\nwill really enhance things; too much all the time and you’ll end\\nup in rehab.\\nPart 8: Hiring, managing, promoting, and Dring execut',\n",
" np.float64(0.46812418038478015))]}"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"retrieval_augmented_qa_pipeline.run_pipeline(\"What is the 'Michael Eisner Memorial Weak Executive Problem'?\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 🏗️ Activity #1:\n",
"\n",
"Enhance your RAG application in some way! \n",
"\n",
"Suggestions are: \n",
"\n",
"- Allow it to work with PDF files\n",
"- Implement a new distance metric\n",
"- Add metadata support to the vector database\n",
"\n",
"While these are suggestions, you should feel free to make whatever augmentations you desire! \n",
"\n",
"> NOTE: These additions might require you to work within the `aimakerspace` library - that's expected!"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Response: He has a total of 27,649 days to expect to live.\n",
"\n",
"Context used:\n",
"\n",
"Context 1:\n",
"oved ones, learn, \n",
"build for the future, and help others. Whatever you’re doing \n",
"today, is it worth 1/30,000 of your life?How many days is a \n",
"typical human lifespan?Maybe you’re good at math; I’m sure...\n",
"\n",
"Context 2:\n",
" AI or other events, \n",
"use discussion boards, and work on finding some. If your mentors or manager don’t support \n",
"your growth, find ones who do. I’m also working on how to grow a supportive AI communit...\n",
"\n",
"Context 3:\n",
"nt to keep up.\n",
"How can you maintain a steady pace of learning for years? If you can cultivate the habit of \n",
"learning a little bit every week, you can make significant progress with what feels like les...\n",
"\n",
"Context 4:\n",
"deepen \n",
"your technical knowledge. I’ve known many machine learning engineers who benefitted from \n",
"deeper skills in an application area such as natural language processing or computer vision, or in \n",
"a ...\n"
]
}
],
"source": [
"from aimakerspace.text_utils import PDFLoader, CharacterTextSplitter\n",
"from aimakerspace.vectordatabase import VectorDatabase\n",
"from aimakerspace.openai_utils.prompts import SystemRolePrompt, UserRolePrompt\n",
"from aimakerspace.openai_utils.chatmodel import ChatOpenAI\n",
"from aimakerspace.openai_utils.embedding import EmbeddingModel\n",
"import asyncio\n",
"import os\n",
"from getpass import getpass\n",
"\n",
"# Set up OpenAI API key\n",
"api_key = os.getenv(\"OPENAI_API_KEY\")\n",
"if not api_key:\n",
" api_key = getpass(\"Enter your OpenAI API key: \")\n",
" os.environ[\"OPENAI_API_KEY\"] = api_key\n",
"\n",
"\n",
"\n",
"\n",
"# Load the PDF\n",
"pdf_loader = PDFLoader(\"data/How-to-Build-a-Career-in-AI.pdf\")\n",
"documents = pdf_loader.load_documents()\n",
"\n",
"# Split the documents into chunks with optimized parameters\n",
"text_splitter = CharacterTextSplitter(chunk_size=1500, chunk_overlap=300)\n",
"split_documents = text_splitter.split_texts(documents)\n",
"\n",
"# Create and populate the vector database with explicit embedding model\n",
"embedding_model = EmbeddingModel()\n",
"vector_db = VectorDatabase(embedding_model=embedding_model)\n",
"vector_db = asyncio.run(vector_db.abuild_from_list(split_documents))\n",
"\n",
"# Set up the RAG prompt templates\n",
"RAG_PROMPT_TEMPLATE = \"\"\" \\\n",
"Use the provided context to answer the user's query.\n",
"\n",
"You may not answer the user's query unless there is specific context in the following text.\n",
"\n",
"If you do not know the answer, or cannot answer, please respond with \"I don't know\".\n",
"\"\"\"\n",
"\n",
"rag_prompt = SystemRolePrompt(RAG_PROMPT_TEMPLATE)\n",
"\n",
"USER_PROMPT_TEMPLATE = \"\"\" \\\n",
"Context:\n",
"{context}\n",
"\n",
"User Query:\n",
"{user_query}\n",
"\"\"\"\n",
"\n",
"user_prompt = UserRolePrompt(USER_PROMPT_TEMPLATE)\n",
"\n",
"# Create the ChatOpenAI instance\n",
"chat_openai = ChatOpenAI()\n",
"\n",
"# Create the RAG pipeline\n",
"class RetrievalAugmentedQAPipeline:\n",
" def __init__(self, llm: ChatOpenAI, vector_db_retriever: VectorDatabase) -> None:\n",
" self.llm = llm\n",
" self.vector_db_retriever = vector_db_retriever\n",
"\n",
" def run_pipeline(self, user_query: str) -> str:\n",
" context_list = self.vector_db_retriever.search_by_text(user_query, k=4)\n",
"\n",
" context_prompt = \"\"\n",
" for context in context_list:\n",
" context_prompt += context[0] + \"\\n\"\n",
"\n",
" formatted_system_prompt = rag_prompt.create_message()\n",
" formatted_user_prompt = user_prompt.create_message(\n",
" user_query=user_query, \n",
" context=context_prompt\n",
" )\n",
"\n",
" return {\n",
" \"response\": self.llm.run([formatted_system_prompt, formatted_user_prompt]),\n",
" \"context\": context_list\n",
" }\n",
"\n",
"# Create and run the pipeline\n",
"retrieval_augmented_qa_pipeline = RetrievalAugmentedQAPipeline(\n",
" vector_db_retriever=vector_db,\n",
" llm=chat_openai\n",
")\n",
"\n",
"# Test \n",
"result = retrieval_augmented_qa_pipeline.run_pipeline(\"how many days does he have\")\n",
"print(\"Response:\", result[\"response\"])\n",
"print(\"\\nContext used:\")\n",
"for i, context in enumerate(result[\"context\"], 1):\n",
" print(f\"\\nContext {i}:\")\n",
" print(context[0][:200] + \"...\")"
]
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": ".venv",
"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": "3.13.2"
},
"orig_nbformat": 4,
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"1ce393d9afcf427d9d352259c5d32678": {
"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": "",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_4e6efd99f7d346e485b002fb0fa85cc7",
"max": 1,
"min": 0,
"orientation": "horizontal",
"style": "IPY_MODEL_3dfb67c39958461da6071e4c19c3fa41",
"value": 1
}
},
"3a4ba348cb004f8ab7b2b1395539c81b": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "LabelModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "LabelModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "LabelView",
"description": "",
"description_tooltip": null,
"layout": "IPY_MODEL_d2ea5009dd16442cb5d8a0ac468e50a8",
"placeholder": "",
"style": "IPY_MODEL_5f00135fe1044051a50ee5e841cbb8e3",
"value": "0.018 MB of 0.018 MB uploaded\r"
}
},
"3dfb67c39958461da6071e4c19c3fa41": {
"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": ""
}
},
"4e6efd99f7d346e485b002fb0fa85cc7": {
"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
}
},
"56a8e24025594e5e9ff3b8581c344691": {
"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
}
},
"5f00135fe1044051a50ee5e841cbb8e3": {
"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": ""
}
},
"bb904e05ece143c79ecc4f20de482f45": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "VBoxModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
"_model_name": "VBoxModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
"_view_name": "VBoxView",
"box_style": "",
"children": [
"IPY_MODEL_3a4ba348cb004f8ab7b2b1395539c81b",
"IPY_MODEL_1ce393d9afcf427d9d352259c5d32678"
],
"layout": "IPY_MODEL_56a8e24025594e5e9ff3b8581c344691"
}
},
"d2ea5009dd16442cb5d8a0ac468e50a8": {
"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
}
|