{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "!pip install git+https://github.com/monarch-initiative/curate-gpt.git\n",
    "!pip install huggingface_hub pyyaml pandas pyarrow"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "6ccb0b14fb5a11a1"
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [],
   "source": [
    "# Import necessary libraries\n",
    "from huggingface_hub import HfApi, create_repo\n",
    "import yaml"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-02T11:22:16.789896Z",
     "start_time": "2024-08-02T11:22:16.378435Z"
    }
   },
   "id": "105b0e6972a9e087"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "!wget https://data.monarchinitiative.org/monarch-kg/latest/monarch-kg.tar.gz\n",
    "!tar -xvzf monarch-kg.tar.gz"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "fb9336dad1877366"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "!curategpt index -p stagedb -c monarch_kg -m openai: monarch-kg_nodes.tsv"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "f47fce4b73e51127"
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "About to write to monarch_text_embeddings.parquet\n",
      "Embeddings have been successfully exported to monarch_text_embeddings.parquet\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "from curate_gpt import ChromaDBAdapter\n",
    "\n",
    "def fetch_embeddings_from_chromadb(path, collection):\n",
    "    # Initialize the database adapter\n",
    "    db = ChromaDBAdapter(path)\n",
    "    \n",
    "    # Fetch embeddings from the specified collection using get\n",
    "    collection_obj = db.client.get_collection(name=collection)\n",
    "    # results = collection_obj.peek(include=[\"embeddings\"])\n",
    "    results = collection_obj.get(include=[\"embeddings\"])\n",
    "    \n",
    "    return results['embeddings']\n",
    "\n",
    "def export_embeddings_to_parquet(path, collection, output_file):\n",
    "    try:\n",
    "        # Fetch embeddings\n",
    "        embeddings = fetch_embeddings_from_chromadb(path, collection)\n",
    "        \n",
    "        # Convert embeddings to DataFrame\n",
    "        df_embeddings = pd.DataFrame(embeddings)\n",
    "        \n",
    "        # Debugging statement: confirm path before writing\n",
    "        print(f\"About to write to {output_file}\")\n",
    "        \n",
    "        # Export DataFrame to Parquet file\n",
    "        df_embeddings.to_parquet(output_file, engine='pyarrow')\n",
    "        \n",
    "        # Confirm file creation\n",
    "        if os.path.exists(output_file):\n",
    "            print(f\"Embeddings have been successfully exported to {output_file}\")\n",
    "        else:\n",
    "            print(f\"Failed to write file to {output_file}\")\n",
    "    except Exception as e:\n",
    "        print(f\"An error occurred: {e}\")\n",
    "\n",
    "# Example usage\n",
    "path_to_chromadb = '../../stagedb'\n",
    "collection_name = 'monarch_kg'\n",
    "output_parquet_file = 'monarch_text_embeddings.parquet'\n",
    "\n",
    "export_embeddings_to_parquet(path_to_chromadb, collection_name, output_parquet_file)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-02T03:55:35.337205Z",
     "start_time": "2024-08-01T21:29:05.165170Z"
    }
   },
   "id": "4c04eeafb792a7bd"
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metadata saved to ./metadata.yaml\n"
     ]
    }
   ],
   "source": [
    "# Generate metadata in venomx format\n",
    "metadata = {\n",
    "    'description': 'Embeddings of the Monarch KG nodes, generated using curategpt and the nodes.tsv file from the Monarch KG version 2024-07-12',\n",
    "    'model': {\n",
    "        'name': 'text-embedding-ada-002'\n",
    "    },\n",
    "    'dataset': {\n",
    "        'name': 'Monarch KG 2024-07-12',\n",
    "        'url': 'https://data.monarchinitiative.org/monarch-kg/2024-07-12/'\n",
    "    }\n",
    "}\n",
    "\n",
    "# Save the metadata to a YAML file\n",
    "metadata_file_path = './metadata.yaml'\n",
    "with open(metadata_file_path, 'w') as f:\n",
    "    yaml.dump(metadata, f)\n",
    "\n",
    "print(f\"Metadata saved to {metadata_file_path}\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-02T11:22:21.170816Z",
     "start_time": "2024-08-02T11:22:21.161180Z"
    }
   },
   "id": "e4573dbb4c2cc72b"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [
    {
     "data": {
      "text/plain": "monarch_text_embeddings.parquet:   0%|          | 0.00/9.93G [00:00<?, ?B/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "0a53be0630394f5b913470726d32f526"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Upload to Hugging Face\n",
    "repo_id = \"biomedical-translator/monarch_kg_node_text_embeddings\"\n",
    "create_repo(repo_id, repo_type=\"dataset\")\n",
    "\n",
    "this_notebook_path = \"index-monarch-kg.ipynb\"\n",
    "\n",
    "api = HfApi()\n",
    "files_to_upload = [output_parquet_file, metadata_file_path, this_notebook_path]\n",
    "\n",
    "for file in files_to_upload:\n",
    "    api.upload_file(\n",
    "        path_or_fileobj=file,\n",
    "        path_in_repo=file,\n",
    "        repo_id=repo_id,\n",
    "        repo_type=\"dataset\"\n",
    "    )\n",
    "\n",
    "print(f\"Files uploaded to Hugging Face in repository: {repo_id}\")"
   ],
   "metadata": {
    "collapsed": false,
    "is_executing": true,
    "ExecuteTime": {
     "start_time": "2024-08-02T11:52:43.155295Z"
    }
   },
   "id": "d3fcdcba15078167"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   },
   "id": "af213f49b772ace7"
  }
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