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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "a600d7fc",
"metadata": {},
"outputs": [],
"source": [
"import json \n",
"with open('metadata.jsonl', 'r') as f: \n",
" json_list = list(f)\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": 2,
"id": "fa5d8eb8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"==================================================\n",
"Task ID: 853c8244-429e-46ca-89f2-addf40dfb2bd\n",
"Question: In the 2015 Metropolitan Museum of Art exhibition titled after the Chinese zodiac animal of 2015, how many of the \"twelve animals of the Chinese zodiac\" have a hand visible?\n",
"Level: 2\n",
"Final Answer: 11\n",
"Annotator Metadata: \n",
" ├── Steps: \n",
" │ ├── 1. Search \"2015 Chinese zodiac animal\" on Google search.\n",
" │ ├── 2. Note the animal (ram).\n",
" │ ├── 3. Search \"Metropolitan Museum of Art\" on Google search.\n",
" │ ├── 4. Open the Metropolitan Museum of Art website.\n",
" │ ├── 5. Click \"Exhibitions\" under \"Exhibitions and Events\" \n",
" │ ├── 6. Click \"Past\".\n",
" │ ├── 7. Set the year to 2015.\n",
" │ ├── 8. Scroll to find the exhibit mentioning rams and click \"Celebration of the Year of the Ram\".\n",
" │ ├── 9. Click \"View All Objects\".\n",
" │ ├── 10. Click \"Twelve animals of the Chinese zodiac\" to open the image.\n",
" │ ├── 11. Count how many have a visible hand.\n",
" ├── Number of steps: 11\n",
" ├── How long did this take?: 10 minutes\n",
" ├── Tools:\n",
" │ ├── 1. Web browser\n",
" │ ├── 2. Search engine\n",
" │ ├── 3. Image recognition tools\n",
" └── Number of tools: 3\n",
"==================================================\n"
]
}
],
"source": [
"import random\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(f\"Annotator Metadata: \")\n",
" print(f\" ├── 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(f\" ├── How long did this take?: {sample['Annotator Metadata']['How long did this take?']}\")\n",
" print(f\" ├── 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": "code",
"execution_count": 3,
"id": "05076516",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\franc\\repos\\gaia-agent\\gaia-agent\\.venv\\Lib\\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",
"c:\\Users\\franc\\repos\\gaia-agent\\gaia-agent\\.venv\\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\\franc\\.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",
"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"
]
}
],
"source": [
"import os\n",
"from dotenv import load_dotenv\n",
"from langchain_huggingface import HuggingFaceEmbeddings\n",
"from langchain_community.vectorstores import SupabaseVectorStore\n",
"from supabase.client import Client, create_client\n",
"\n",
"\n",
"load_dotenv()\n",
"embeddings = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-mpnet-base-v2\") # dim=768\n",
"\n",
"supabase_url = os.environ.get(\"SUPABASE_URL\")\n",
"supabase_key = os.environ.get(\"SUPABASE_SERVICE_ROLE_KEY\")\n",
"supabase: Client = create_client(supabase_url, supabase_key)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "aa1402e3",
"metadata": {},
"outputs": [],
"source": [
"from langchain.schema import Document\n",
"docs = []\n",
"cnt = 0 \n",
"for sample in json_QA:\n",
" content = f\"Question : {sample['Question']}\\n\\nFinal answer : {sample['Final answer']}\"\n",
" doc = {\n",
" \"id\" : cnt,\n",
" \"content\" : content,\n",
" \"metadata\" : {\n",
" \"source\" : sample['task_id']\n",
" },\n",
" \"embedding\" : embeddings.embed_query(content),\n",
" }\n",
" docs.append(doc)\n",
" cnt += 1\n",
"\n",
"# upload the documents to the vector database\n",
"try:\n",
" response = (\n",
" supabase.table(\"documents\")\n",
" .insert(docs)\n",
" .execute()\n",
" )\n",
"except Exception as exception:\n",
" print(\"Error inserting data into Supabase:\", exception)\n",
"\n",
"# # 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": "code",
"execution_count": 7,
"id": "9aa7eb5e",
"metadata": {},
"outputs": [],
"source": [
"# add items to vector database\n",
"vector_store = SupabaseVectorStore(\n",
" client=supabase,\n",
" embedding= embeddings,\n",
" table_name=\"documents\",\n",
" query_name=\"match_documents_langchain\",\n",
")\n",
"retriever = vector_store.as_retriever()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "9eecafd1",
"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, k=2)\n",
"docs = retriever.invoke(query)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "ff917840",
"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')"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"docs[0]"
]
},
{
"cell_type": "code",
"execution_count": 44,
"id": "01c8f337",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"List of tools used in all samples:\n",
"Total number of tools used: 83\n",
" ├── web browser: 107\n",
" ├── image recognition tools (to identify and parse a figure with three axes): 1\n",
" ├── search engine: 101\n",
" ├── calculator: 34\n",
" ├── unlambda compiler (optional): 1\n",
" ├── a web browser.: 2\n",
" ├── a search engine.: 2\n",
" ├── a calculator.: 1\n",
" ├── microsoft excel: 5\n",
" ├── google search: 1\n",
" ├── ne: 9\n",
" ├── pdf access: 7\n",
" ├── file handling: 2\n",
" ├── python: 3\n",
" ├── image recognition tools: 12\n",
" ├── jsonld file access: 1\n",
" ├── video parsing: 1\n",
" ├── python compiler: 1\n",
" ├── video recognition tools: 3\n",
" ├── pdf viewer: 7\n",
" ├── microsoft excel / google sheets: 3\n",
" ├── word document access: 1\n",
" ├── tool to extract text from images: 1\n",
" ├── a word reversal tool / script: 1\n",
" ├── counter: 1\n",
" ├── excel: 3\n",
" ├── image recognition: 5\n",
" ├── color recognition: 3\n",
" ├── excel file access: 3\n",
" ├── xml file access: 1\n",
" ├── access to the internet archive, web.archive.org: 1\n",
" ├── text processing/diff tool: 1\n",
" ├── gif parsing tools: 1\n",
" ├── a web browser: 7\n",
" ├── a search engine: 7\n",
" ├── a speech-to-text tool: 2\n",
" ├── code/data analysis tools: 1\n",
" ├── audio capability: 2\n",
" ├── pdf reader: 1\n",
" ├── markdown: 1\n",
" ├── a calculator: 5\n",
" ├── access to wikipedia: 3\n",
" ├── image recognition/ocr: 3\n",
" ├── google translate access: 1\n",
" ├── ocr: 4\n",
" ├── bass note data: 1\n",
" ├── text editor: 1\n",
" ├── xlsx file access: 1\n",
" ├── powerpoint viewer: 1\n",
" ├── csv file access: 1\n",
" ├── calculator (or use excel): 1\n",
" ├── computer algebra system: 1\n",
" ├── video processing software: 1\n",
" ├── audio processing software: 1\n",
" ├── computer vision: 1\n",
" ├── google maps: 1\n",
" ├── access to excel files: 1\n",
" ├── calculator (or ability to count): 1\n",
" ├── a file interface: 3\n",
" ├── a python ide: 1\n",
" ├── spreadsheet editor: 1\n",
" ├── tools required: 1\n",
" ├── b browser: 1\n",
" ├── image recognition and processing tools: 1\n",
" ├── computer vision or ocr: 1\n",
" ├── c++ compiler: 1\n",
" ├── access to google maps: 1\n",
" ├── youtube player: 1\n",
" ├── natural language processor: 1\n",
" ├── graph interaction tools: 1\n",
" ├── bablyonian cuniform -> arabic legend: 1\n",
" ├── access to youtube: 1\n",
" ├── image search tools: 1\n",
" ├── calculator or counting function: 1\n",
" ├── a speech-to-text audio processing tool: 1\n",
" ├── access to academic journal websites: 1\n",
" ├── pdf reader/extracter: 1\n",
" ├── rubik's cube model: 1\n",
" ├── wikipedia: 1\n",
" ├── video capability: 1\n",
" ├── image processing tools: 1\n",
" ├── age recognition software: 1\n",
" ├── youtube: 1\n"
]
}
],
"source": [
"# list of the tools used in all the samples\n",
"from collections import Counter, OrderedDict\n",
"\n",
"tools = []\n",
"for sample in json_QA:\n",
" for tool in sample['Annotator Metadata']['Tools'].split('\\n'):\n",
" tool = tool[2:].strip().lower()\n",
" if tool.startswith(\"(\"):\n",
" tool = tool[11:].strip()\n",
" tools.append(tool)\n",
"tools_counter = OrderedDict(Counter(tools))\n",
"print(\"List of tools used in all samples:\")\n",
"print(\"Total number of tools used:\", len(tools_counter))\n",
"for tool, count in tools_counter.items():\n",
" print(f\" ├── {tool}: {count}\")"
]
}
],
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"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",
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