File size: 30,127 Bytes
6a2a4bf |
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 |
{
"cells": [
{
"cell_type": "markdown",
"id": "e5270bcc",
"metadata": {},
"source": [
"# Final project of Agents course: An Agent that response the GAIA benchmark\n",
"\n",
"In this notebook, **we're going to read the GAIA questions from metadata and we'll store in a chroma database to use as retriever**.\n",
"\n",
"This notebook is part of the <a href=\"https://www.hf.co/learn/agents-course\">Hugging Face Agents Course</a>, a free course from beginner to expert, where you learn to build Agents.\n",
"\n",
"## What we'll do\n",
"\n",
"In this notebook, we'll do:\n",
"\n",
"1. Reading json metadata with GAIA questions\n",
"2. Creating chroma vector store\n",
"3. Querying the database and converting to a retriever to search using\n",
"4. Loading the database from directory\n",
"5. Creating supabase vector store\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "1e1d8e79",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import os\n",
"import json\n",
"from dotenv import load_dotenv\n",
"from langchain_huggingface import HuggingFaceEmbeddings\n",
"from langchain_chroma import Chroma\n",
"import random\n",
"\n",
"# from langchain.schema import Document\n",
"from langchain_core.documents import Document\n",
"from uuid import uuid4\n",
"\n",
"from langchain_community.vectorstores import SupabaseVectorStore\n",
"from supabase.client import Client, create_client\n",
"\n",
"load_dotenv()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "bf2a78a5",
"metadata": {},
"outputs": [],
"source": [
"os.chdir(os.path.abspath(\"..\"))"
]
},
{
"cell_type": "markdown",
"id": "c0513724",
"metadata": {},
"source": [
"<div style=\"background-color:lightblue; color:black; font-weight:bold\">\n",
"\n",
"## 1. Reading json metadata\n",
"\n",
"</div>\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "c8f343d6",
"metadata": {},
"outputs": [],
"source": [
"# Load the metadata.jsonl file\n",
"with open(\"data/metadata.jsonl\", \"r\") as jsonl_file:\n",
" json_list = list(jsonl_file)\n",
"\n",
"json_QA = []\n",
"for json_str in json_list:\n",
" json_data = json.loads(json_str)\n",
" json_QA.append(json_data)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "e7191ac5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"==================================================\n",
"Task ID: 56137764-b4e0-45b8-9c52-1866420c3df5\n",
"Question: Which contributor to the version of OpenCV where support was added for the Mask-RCNN model has the same name as a former Chinese head of government when the names are transliterated to the Latin alphabet?\n",
"Level: 2\n",
"Final Answer: Li Peng\n",
"Annotator Metadata: \n",
" ├── Steps: \n",
" │ ├── 1. Use search engine to search for \"OpenCV change log\".\n",
" │ ├── 2. Open the top result from GitHub and search the page for \"Mask-RCNN\".\n",
" │ ├── 3. Observe that support for Mask-RCNN model was added in OpenCV version 4.0.0.\n",
" │ ├── 4. Expand the two lists of contributors for version 4.0.0.\n",
" │ ├── 5. Go to the Wikipedia page for head of government. \n",
" │ ├── 6. Scan through and note that for China, the head of government is the premier.\n",
" │ ├── 7. Go to the Wikipedia page for premier of the People's Republic of China.\n",
" │ ├── 8. Go to the linked page for List of premiers of the People's Republic of China.\n",
" │ ├── 9. Compare the list of OpenCV version 4.0.0 contributors' names and the list of premiers of China to find that Li Peng is present in both lists.\n",
" ├── Number of steps: 9\n",
" ├── How long did this take?: 5 minutes\n",
" ├── Tools:\n",
" │ ├── 1. Web browser\n",
" │ ├── 2. Search engine\n",
" └── Number of tools: 2\n",
"==================================================\n"
]
}
],
"source": [
"# randomly select 3 samples\n",
"# {\"task_id\": \"c61d22de-5f6c-4958-a7f6-5e9707bd3466\", \"Question\": \"A paper about AI regulation that was originally submitted to arXiv.org in June 2022 shows a figure with three axes, where each axis has a label word at both ends. Which of these words is used to describe a type of society in a Physics and Society article submitted to arXiv.org on August 11, 2016?\", \"Level\": 2, \"Final answer\": \"egalitarian\", \"file_name\": \"\", \"Annotator Metadata\": {\"Steps\": \"1. Go to arxiv.org and navigate to the Advanced Search page.\\n2. Enter \\\"AI regulation\\\" in the search box and select \\\"All fields\\\" from the dropdown.\\n3. Enter 2022-06-01 and 2022-07-01 into the date inputs, select \\\"Submission date (original)\\\", and submit the search.\\n4. Go through the search results to find the article that has a figure with three axes and labels on each end of the axes, titled \\\"Fairness in Agreement With European Values: An Interdisciplinary Perspective on AI Regulation\\\".\\n5. Note the six words used as labels: deontological, egalitarian, localized, standardized, utilitarian, and consequential.\\n6. Go back to arxiv.org\\n7. Find \\\"Physics and Society\\\" and go to the page for the \\\"Physics and Society\\\" category.\\n8. Note that the tag for this category is \\\"physics.soc-ph\\\".\\n9. Go to the Advanced Search page.\\n10. Enter \\\"physics.soc-ph\\\" in the search box and select \\\"All fields\\\" from the dropdown.\\n11. Enter 2016-08-11 and 2016-08-12 into the date inputs, select \\\"Submission date (original)\\\", and submit the search.\\n12. Search for instances of the six words in the results to find the paper titled \\\"Phase transition from egalitarian to hierarchical societies driven by competition between cognitive and social constraints\\\", indicating that \\\"egalitarian\\\" is the correct answer.\", \"Number of steps\": \"12\", \"How long did this take?\": \"8 minutes\", \"Tools\": \"1. Web browser\\n2. Image recognition tools (to identify and parse a figure with three axes)\", \"Number of tools\": \"2\"}}\n",
"# random.seed(42)\n",
"random_samples = random.sample(json_QA, 1)\n",
"for sample in random_samples:\n",
" print(\"=\" * 50)\n",
" print(f\"Task ID: {sample['task_id']}\")\n",
" print(f\"Question: {sample['Question']}\")\n",
" print(f\"Level: {sample['Level']}\")\n",
" print(f\"Final Answer: {sample['Final answer']}\")\n",
" print(\"Annotator Metadata: \")\n",
" print(\" ├── Steps: \")\n",
" for step in sample[\"Annotator Metadata\"][\"Steps\"].split(\"\\n\"):\n",
" print(f\" │ ├── {step}\")\n",
" print(f\" ├── Number of steps: {sample['Annotator Metadata']['Number of steps']}\")\n",
" print(\n",
" f\" ├── How long did this take?: {sample['Annotator Metadata']['How long did this take?']}\"\n",
" )\n",
" print(\" ├── Tools:\")\n",
" for tool in sample[\"Annotator Metadata\"][\"Tools\"].split(\"\\n\"):\n",
" print(f\" │ ├── {tool}\")\n",
" print(f\" └── Number of tools: {sample['Annotator Metadata']['Number of tools']}\")\n",
"print(\"=\" * 50)"
]
},
{
"cell_type": "markdown",
"id": "81c30287",
"metadata": {},
"source": [
"<div style=\"background-color:lightblue; color:black; font-weight:bold\">\n",
"\n",
"## 2. Creating chroma vector store\n",
"\n",
"</div>\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "046462f4",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "87ec1aea91cf4cf799c882f649ef7e8c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"modules.json: 0%| | 0.00/349 [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\ee5011\\Anaconda3\\envs\\llm-huggingface-agents-course-env\\Lib\\site-packages\\huggingface_hub\\file_download.py:143: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\\Users\\ee5011\\.cache\\huggingface\\hub\\models--sentence-transformers--all-mpnet-base-v2. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.\n",
"To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development\n",
" warnings.warn(message)\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e0df64b800c14d0eaca2214438eaf4e0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"config_sentence_transformers.json: 0%| | 0.00/116 [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fe6e89ddbb5540b4869935bda0ededc0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"README.md: 0%| | 0.00/10.4k [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6ac82a744f0b4d33a64b21346af272d2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"sentence_bert_config.json: 0%| | 0.00/53.0 [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "877342a7a662471cb133598c298f6304",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"config.json: 0%| | 0.00/571 [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Xet Storage is enabled for this repo, but the 'hf_xet' package is not installed. Falling back to regular HTTP download. For better performance, install the package with: `pip install huggingface_hub[hf_xet]` or `pip install hf_xet`\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "29fdd3411b504b2ca875d7e0007db775",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"model.safetensors: 0%| | 0.00/438M [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "102778342b534cddb8b84d3698371c89",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"tokenizer_config.json: 0%| | 0.00/363 [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "209ab2f278b949e5846abd5b60e42130",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"vocab.txt: 0%| | 0.00/232k [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "aced6d0bf212458a89c05dde7a22411b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"tokenizer.json: 0%| | 0.00/466k [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "105ea3c52b7444f281aa22bddf40d582",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"special_tokens_map.json: 0%| | 0.00/239 [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "abbfcff9883645de86ea66f61931d31a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"config.json: 0%| | 0.00/190 [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Necessary packages:\n",
"# - langchain-chroma>=0.1.2\n",
"# - langchain-huggingface\n",
"# Source:\n",
"# http://python.langchain.com/docs/integrations/vectorstores/chroma/\n",
"\n",
"embeddings = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-mpnet-base-v2\")\n",
"vector_store = Chroma(\n",
" collection_name=\"gaia_dataset\",\n",
" embedding_function=embeddings,\n",
" persist_directory=\"./data/chroma_langchain_db\", # Where to save data locally, remove if not necessary\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "69e1f1b3",
"metadata": {},
"outputs": [],
"source": [
"# wrap the metadata.jsonl's questions and answers into a list of document\n",
"docs = []\n",
"for i, sample in enumerate(json_QA):\n",
" content = (\n",
" f\"Question : {sample['Question']}\\n\\nFinal answer : {sample['Final answer']}\"\n",
" )\n",
" doc = Document(\n",
" page_content=content,\n",
" metadata={\"source\": sample[\"task_id\"]},\n",
" id=i,\n",
" )\n",
" docs.append(doc)\n",
"\n",
"# upload the documents to the vector database\n",
"try:\n",
" uuids = [str(uuid4()) for _ in range(len(docs))]\n",
" vector_store.add_documents(documents=docs, ids=uuids)\n",
"except Exception as exception:\n",
" print(\"Error inserting data into Supabase:\", exception)\n",
"\n",
"# ALTERNATIVE : Save the documents (a list of dict) into a csv file, and manually upload it to Supabase\n",
"# import pandas as pd\n",
"# df = pd.DataFrame(docs)\n",
"# df.to_csv('supabase_docs.csv', index=False)"
]
},
{
"cell_type": "markdown",
"id": "d1701d7c",
"metadata": {},
"source": [
"<div style=\"background-color:lightblue; color:black; font-weight:bold\">\n",
"\n",
"## 3. Querying the database and converting to a retriever to search using\n",
"\n",
"</div>\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0a7763a1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* Question : How many studio albums were published by Mercedes Sosa between 2000 and 2009 (included)? You can use the latest 2022 version of english wikipedia.\n",
"\n",
"Final answer : 3 [{'source': '8e867cd7-cff9-4e6c-867a-ff5ddc2550be'}]\n",
"* Question : It is 1999. Before you party like it is 1999, please assist me in settling a bet.\n",
"\n",
"Fiona Apple and Paula Cole released albums prior to 1999. Of these albums, which didn't receive a letter grade from Robert Christgau? Provide your answer as a comma delimited list of album titles, sorted alphabetically.\n",
"\n",
"Final answer : Harbinger, Tidal [{'source': 'f46b4380-207e-4434-820b-f32ce04ae2a4'}]\n"
]
}
],
"source": [
"# Querying directly from the vector database\n",
"query = \"How many studio albums were published by Mercedes Sosa between 2000 and 2009 (included)? You can use the latest 2022 version of english wikipedia.\"\n",
"\n",
"results = vector_store.similarity_search(\n",
" query=query,\n",
" k=2,\n",
")\n",
"for res in results:\n",
" print(f\"* {res.page_content} [{res.metadata}]\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "05895e6c",
"metadata": {},
"outputs": [],
"source": [
"# Querying using a retriever\n",
"# Conver to a retriever\n",
"retriever = vector_store.as_retriever(\n",
" search_type=\"mmr\", search_kwargs={\"k\": 1, \"fetch_k\": 5}\n",
")\n",
"results = retriever.invoke(input=query)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "31649cce",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Document(id='454c552a-b27f-4eba-ac9a-d37300e515cd', metadata={'source': '8e867cd7-cff9-4e6c-867a-ff5ddc2550be'}, page_content='Question : How many studio albums were published by Mercedes Sosa between 2000 and 2009 (included)? You can use the latest 2022 version of english wikipedia.\\n\\nFinal answer : 3')]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results"
]
},
{
"cell_type": "markdown",
"id": "ccfa3c25",
"metadata": {},
"source": [
"<div style=\"background-color:lightblue; color:black; font-weight:bold\">\n",
"\n",
"## 4. Loading the database from directory\n",
"\n",
"</div>\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4715fbd1",
"metadata": {},
"outputs": [],
"source": [
"# Set the proxy for the HuggingFaceEmbeddings and SUpabase client\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6f734773",
"metadata": {},
"outputs": [],
"source": [
"embeddings = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-mpnet-base-v2\")\n",
"vector_store_loaded = Chroma(\n",
" collection_name=\"gaia_dataset\",\n",
" embedding_function=embeddings,\n",
" persist_directory=\"./data/chroma_langchain_db\", # Where to save data locally, remove if not necessary\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e0fefcdf",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* Question : How many studio albums were published by Mercedes Sosa between 2000 and 2009 (included)? You can use the latest 2022 version of english wikipedia.\n",
"\n",
"Final answer : 3 [{'source': '8e867cd7-cff9-4e6c-867a-ff5ddc2550be'}]\n",
"* Question : It is 1999. Before you party like it is 1999, please assist me in settling a bet.\n",
"\n",
"Fiona Apple and Paula Cole released albums prior to 1999. Of these albums, which didn't receive a letter grade from Robert Christgau? Provide your answer as a comma delimited list of album titles, sorted alphabetically.\n",
"\n",
"Final answer : Harbinger, Tidal [{'source': 'f46b4380-207e-4434-820b-f32ce04ae2a4'}]\n"
]
}
],
"source": [
"query = \"How many studio albums were published by Mercedes Sosa between 2000 and 2009 (included)? You can use the latest 2022 version of english wikipedia.\"\n",
"\n",
"results = vector_store_loaded.similarity_search(\n",
" query=query,\n",
" k=2,\n",
")\n",
"for res in results:\n",
" print(f\"* {res.page_content} [{res.metadata}]\")\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "9a42742c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Document(id='454c552a-b27f-4eba-ac9a-d37300e515cd', metadata={'source': '8e867cd7-cff9-4e6c-867a-ff5ddc2550be'}, page_content='Question : How many studio albums were published by Mercedes Sosa between 2000 and 2009 (included)? You can use the latest 2022 version of english wikipedia.\\n\\nFinal answer : 3')"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results[0]"
]
},
{
"cell_type": "markdown",
"id": "2749a453",
"metadata": {},
"source": [
"<div style=\"background-color:lightblue; color:black; font-weight:bold\">\n",
"\n",
"## 5. Creating supabase vector store\n",
"\n",
"</div>\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9138e761",
"metadata": {},
"outputs": [],
"source": [
"# Set the proxy for the HuggingFaceEmbeddings and SUpabase client\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fb4e2b68",
"metadata": {},
"outputs": [],
"source": [
"supabase_url = os.environ.get(\"SUPABASE_URL\")\n",
"supabase_key = os.environ.get(\"SUPABASE_SERVICE_KEY\")\n",
"supabase: Client = create_client(supabase_url, supabase_key)\n",
"\n",
"embeddings = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-mpnet-base-v2\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c49c6926",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"768\n"
]
}
],
"source": [
"# Review the length of the vector dimensions\n",
"vector = embeddings.embed_query(\"test\")\n",
"print(len(vector))\n"
]
},
{
"cell_type": "markdown",
"id": "74f48c63",
"metadata": {},
"source": [
"<div style=\"color:red;font-weight:bold\">\n",
"IMPORTANT: you need to create before run any code with supabase the table:\n",
"</div>\n",
"\n",
"```sql\n",
"-- enable the vector extension once if not already enabled\n",
"create extension if not exists vector;\n",
"\n",
"-- create your vectorstore table\n",
"create table gaia_dataset (\n",
" id uuid primary key default uuid_generate_v4(),\n",
" content text,\n",
" embedding vector(768),\n",
" metadata jsonb\n",
");\n",
"```\n"
]
},
{
"cell_type": "markdown",
"id": "fb3608d4",
"metadata": {},
"source": [
"- Option 1) with sql statements\n"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "02fc6c68",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of documents to insert: 165\n"
]
}
],
"source": [
"# wrap the metadata.jsonl's questions and answers into a list of document\n",
"# from langchain.schema import Document\n",
"docs = []\n",
"for sample in json_QA:\n",
" content = (\n",
" f\"Question : {sample['Question']}\\n\\nFinal answer : {sample['Final answer']}\"\n",
" )\n",
" doc = {\n",
" \"content\": content,\n",
" \"metadata\": { # meatadata的格式必须时source键，否则会报错\n",
" \"source\": sample[\"task_id\"]\n",
" },\n",
" \"embedding\": embeddings.embed_query(content),\n",
" }\n",
" docs.append(doc)\n",
"\n",
"print(f\"Number of documents to insert: {len(docs)}\")\n",
"# upload the documents to the vector database\n",
"try:\n",
" response = supabase.table(\"gaia_dataset\").insert(docs).execute()\n",
"except Exception as exception:\n",
" print(\"Error inserting data into Supabase:\", exception)\n",
"\n",
"# ALTERNATIVE : Save the documents (a list of dict) into a csv file, and manually upload it to Supabase\n",
"# import pandas as pd\n",
"# df = pd.DataFrame(docs)\n",
"# df.to_csv('supabase_docs.csv', index=False)\n"
]
},
{
"cell_type": "markdown",
"id": "7ff670b5",
"metadata": {},
"source": [
"- Option 2) with class of langchain\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8c20877a",
"metadata": {},
"outputs": [],
"source": [
"# wrap the metadata.jsonl's questions and answers into a list of document\n",
"docs = []\n",
"for i, sample in enumerate(json_QA):\n",
" content = (\n",
" f\"Question : {sample['Question']}\\n\\nFinal answer : {sample['Final answer']}\"\n",
" )\n",
" doc = Document(\n",
" page_content=content,\n",
" metadata={\"source\": sample[\"task_id\"]},\n",
" id=i,\n",
" )\n",
" docs.append(doc)\n",
"\n",
"# Initialize the vector store with Supabase\n",
"vectorstore = SupabaseVectorStore(\n",
" client=supabase, embedding=embeddings, table_name=\"gaia_dataset\"\n",
")\n",
"\n",
"# upload the documents to the vector database\n",
"try:\n",
" vector_store = SupabaseVectorStore.from_documents(\n",
" docs,\n",
" embeddings,\n",
" client=supabase,\n",
" table_name=\"gaia_dataset\",\n",
" query_name=\"match_documents\",\n",
" # chunk_size=500,\n",
" )\n",
"except Exception as exception:\n",
" print(\"Error inserting data into Supabase:\", exception)\n"
]
},
{
"cell_type": "markdown",
"id": "32ea05c4",
"metadata": {},
"source": [
"Let's connect to the table and do some retrieval\n"
]
},
{
"cell_type": "markdown",
"id": "9aaf97c0",
"metadata": {},
"source": [
"<div style=\"color:red;font-weight:bold\">\n",
"IMPORTANT: you also need to create the match_documents function with SQL:\n",
"</div>\n",
"\n",
"```sql\n",
"create or replace function match_documents(query_embedding vector(768), match_count int default 5)\n",
"returns table (\n",
" id uuid,\n",
" content text,\n",
" embedding vector(768),\n",
" metadata jsonb,\n",
" similarity float\n",
")\n",
"language sql\n",
"as $$\n",
" select\n",
" id,\n",
" content,\n",
" embedding,\n",
" metadata,\n",
" 1 - (embedding <=> query_embedding) as similarity\n",
" from gaia_dataset\n",
" order by embedding <=> query_embedding\n",
" limit match_count;\n",
"$$;\n",
"```\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "da2db9cc",
"metadata": {},
"outputs": [],
"source": [
"vector_store = SupabaseVectorStore(\n",
" embedding=embeddings,\n",
" client=supabase,\n",
" table_name=\"gaia_dataset\",\n",
" query_name=\"match_documents\",\n",
")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "eec1f65f",
"metadata": {},
"outputs": [],
"source": [
"query = \"On June 6, 2023, an article by Carolyn Collins Petersen was published in Universe Today. This article mentions a team that produced a paper about their observations, linked at the bottom of the article. Find this paper. Under what NASA award number was the work performed by R. G. Arendt supported by?\"\n",
"matched_docs = vector_store.similarity_search(query)\n"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "1e09d302",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Document(metadata={'source': '840bfca7-4f7b-481a-8794-c560c340185d'}, page_content='Question : On June 6, 2023, an article by Carolyn Collins Petersen was published in Universe Today. This article mentions a team that produced a paper about their observations, linked at the bottom of the article. Find this paper. Under what NASA award number was the work performed by R. G. Arendt supported by?\\n\\nFinal answer : 80GSFC21M0002'),\n",
" Document(metadata={'source': 'a7feb290-76bb-4cb7-8800-7edaf7954f2f'}, page_content='Question : How many High Energy Physics - Lattice articles listed in January 2020 on Arxiv had ps versions available?\\n\\nFinal answer : 31'),\n",
" Document(metadata={'source': '0e9e85b8-52b9-4de4-b402-5f635ab9631f'}, page_content=\"Question : What is the latest chronological year date written in the image on the webpage found when following the first citation reference link on the latest version of Carl Nebel's Wikipedia page as of August 2023?\\n\\nFinal answer : 1927\"),\n",
" Document(metadata={'source': 'bec74516-02fc-48dc-b202-55e78d0e17cf'}, page_content='Question : What is the average number of pre-2020 works on the open researcher and contributor identification pages of the people whose identification is in this file?\\n\\nFinal answer : 26.4')]"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"matched_docs"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "llm-huggingface-agents-course-env",
"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": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|