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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "0",
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"\n",
"def load_data(file_path):\n",
" \"\"\"Loads data from a JSONL file.\"\"\"\n",
" data = []\n",
" with open(file_path, 'r', encoding='utf-8') as f:\n",
" for line in f:\n",
" data.append(json.loads(line))\n",
" return data\n",
"\n",
"def search_and_print_task(task_id, data):\n",
" \"\"\"\n",
" Searches for a task by its ID and prints it in a formatted way.\n",
" \"\"\"\n",
" found_task = None\n",
" for sample in data:\n",
" if sample['task_id'] == task_id:\n",
" found_task = sample\n",
" break\n",
"\n",
" if not found_task:\n",
" print(f\"Task with ID '{task_id}' not found.\")\n",
" return\n",
"\n",
" print(\"=\" * 50)\n",
" print(f\"Task ID: {found_task.get('task_id', 'N/A')}\")\n",
" print(f\"Question: {found_task.get('Question', 'N/A')}\")\n",
" print(f\"Level: {found_task.get('Level', 'N/A')}\")\n",
" print(f\"Final Answer: {found_task.get('Final answer', 'N/A')}\")\n",
"\n",
" metadata = found_task.get('Annotator Metadata', {})\n",
" if metadata:\n",
" print(f\"Annotator Metadata: \")\n",
" \n",
" steps = metadata.get('Steps')\n",
" if steps:\n",
" print(f\" ├── Steps: \")\n",
" for step in steps.split('\\\\n'):\n",
" print(f\" │ ├── {step}\")\n",
" \n",
" num_steps = metadata.get('Number of steps')\n",
" if num_steps is not None:\n",
" print(f\" ├── Number of steps: {num_steps}\")\n",
" \n",
" duration = metadata.get('How long did this take?')\n",
" if duration:\n",
" print(f\" ├── How long did this take?: {duration}\")\n",
" \n",
" tools = metadata.get('Tools')\n",
" if tools:\n",
" print(f\" ├── Tools:\")\n",
" for tool in tools.split('\\\\n'):\n",
" print(f\" │ ├── {tool}\")\n",
"\n",
" num_tools = metadata.get('Number of tools')\n",
" if num_tools is not None:\n",
" print(f\" └── Number of tools: {num_tools}\")\n",
" \n",
" print(\"=\" * 50)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1",
"metadata": {},
"outputs": [],
"source": [
"# 1. Load the data\n",
"json_QA = load_data('metadata.jsonl')\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2",
"metadata": {},
"outputs": [],
"source": [
"\n",
"# 2. Choose a task_id to search for. \n",
"# I'll use the first one from the file as an example.\n",
"example_task_id = \"8e867cd7-cff9-4e6c-867a-ff5ddc2550be\"\n",
"\n",
"# 3. Call the function with the task_id\n",
"search_and_print_task(example_task_id, json_QA)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3",
"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": null,
"id": "4",
"metadata": {},
"outputs": [],
"source": [
"# import specific question"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5",
"metadata": {},
"outputs": [],
"source": [
"import random\n",
"random_samples = random.sample(json_QA, 2)\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": null,
"id": "6",
"metadata": {},
"outputs": [],
"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": null,
"id": "7",
"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(\"documents2\")\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": null,
"id": "8",
"metadata": {},
"outputs": [],
"source": [
"# add items to vector database\n",
"vector_store = SupabaseVectorStore(\n",
" client=supabase,\n",
" embedding= embeddings,\n",
" table_name=\"documents2\",\n",
" query_name=\"match_documents_2\",\n",
")\n",
"retriever = vector_store.as_retriever()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9",
"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": null,
"id": "10",
"metadata": {},
"outputs": [],
"source": [
"docs[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "11",
"metadata": {},
"outputs": [],
"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}\")"
]
}
],
"metadata": {
"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": "3.12.2"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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