Spaces:
Sleeping
Sleeping
File size: 3,000 Bytes
b3f9415 |
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 |
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "a9f7a25f",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/kpatelis/projects/Agents_Course_Assignment/.venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
}
],
"source": [
"import os\n",
"import json\n",
"from dotenv import load_dotenv\n",
"from supabase.client import Client, create_client\n",
"from langchain_huggingface import HuggingFaceEmbeddings\n",
"from langchain.schema import Document\n",
"\n",
"load_dotenv()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2c948d46",
"metadata": {},
"outputs": [],
"source": [
"supabase: Client = create_client(\n",
" os.environ.get(\"SUPABASE_URL\"), \n",
" os.environ.get(\"SUPABASE_SERVICE_KEY\"))\n",
"\n",
"embeddings = HuggingFaceEmbeddings(model_name=\"Alibaba-NLP/gte-modernbert-base\")"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "f2c5492b",
"metadata": {},
"outputs": [],
"source": [
"with open('metadata.jsonl', 'r') as jsonl_file:\n",
" json_list = list(jsonl_file)\n",
"\n",
"documents = []\n",
"for json_str in json_list:\n",
" json_data = json.loads(json_str)\n",
" content = f\"Question : {json_data['Question']}\\n\\nFinal answer : {json_data['Final answer']}\"\n",
" embedding = embeddings.embed_query(content)\n",
" document = {\n",
" \"content\" : content,\n",
" \"metadata\" : {\n",
" \"source\" : json_data['task_id']\n",
" },\n",
" \"embedding\" : embedding,\n",
" }\n",
" documents.append(document)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "26ddbafd",
"metadata": {},
"outputs": [],
"source": [
"# pgvector needs to be enabled, to turn to vector database\n",
"# Table needs to be created beforehand in Supabase, with column types\n",
"try:\n",
" response = (\n",
" supabase.table(\"gaia_documents\")\n",
" .insert(documents)\n",
" .execute()\n",
" )\n",
"except Exception as exception:\n",
" print(\"Error inserting data into Supabase:\", exception)"
]
}
],
"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": "3.12.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|