Upload airbnb_embed.ipynb

airbnb_embed.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### How to generate the dataset\n",
+    "\n",
+    "1. Use PyGen.AI with prompt: \"Generate code to read dataset bstraehle/airbnb-san-francisco-202403 from Hugging Face. Process line by line to embed field 'description' using OpenAI model 'text-embedding-3-small', then append the line and embedded field 'description_embedding' to file 'c:\\temp\\airbnb-san-francisco-202403-embed.jsonl'.\n",
+    "2. Replace hard-coded OpenAI API key with getpass\n",
+    "3. Fix embedding code, see https://platform.openai.com/docs/guides/embeddings/how-to-get-embeddings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install openai transformers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Execution time: ~34 minutes\n",
+    "\n",
+    "from datasets import load_dataset\n",
+    "from openai import OpenAI\n",
+    "import json, getpass\n",
+    "\n",
+    "dataset = load_dataset(\"bstraehle/airbnb-san-francisco-202403\")\n",
+    "\n",
+    "client = OpenAI(api_key = getpass.getpass(\"OpenAI API Key\"))\n",
+    "\n",
+    "def embed_text(text):\n",
+    "    \"\"\"Function to embed text using OpenAI's text-embedding-3-small model.\"\"\"\n",
+    "    response = client.embeddings.create(\n",
+    "        input=text,\n",
+    "        model=\"text-embedding-3-small\"\n",
+    "    )\n",
+    "    return response.data[0].embedding\n",
+    "\n",
+    "def process_dataset(dataset):\n",
+    "    \"\"\"Process each row in the dataset, embed 'description', and write to a new file.\"\"\"\n",
+    "    with open(\"c:\\\\temp\\\\airbnb-san-francisco-202403-embed.jsonl\", \"w\") as f:\n",
+    "        for item in dataset:\n",
+    "            description = item[\"description\"]\n",
+    "\n",
+    "            if description:\n",
+    "                embedding = embed_text(description)\n",
+    "                new_item = {**item, \"description_embedding\": embedding}\n",
+    "                f.write(json.dumps(new_item) + '\\n')\n",
+    "\n",
+    "process_dataset(dataset['train'])\n",
+    "\n",
+    "print(\"Processing completed.\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}