mirror of
https://github.com/openai/openai-cookbook
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234 lines
7.2 KiB
Plaintext
234 lines
7.2 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Get embeddings from dataset\n",
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"\n",
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"This notebook gives an example on how to get embeddings from a large dataset.\n",
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"\n",
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"\n",
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"## 1. Load the dataset\n",
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"\n",
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"The dataset used in this example is [fine-food reviews](https://www.kaggle.com/snap/amazon-fine-food-reviews) from Amazon. The dataset contains a total of 568,454 food reviews Amazon users left up to October 2012. We will use a subset of this dataset, consisting of 1,000 most recent reviews for illustration purposes. The reviews are in English and tend to be positive or negative. Each review has a ProductId, UserId, Score, review title (Summary) and review body (Text).\n",
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"\n",
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"We will combine the review summary and review text into a single combined text. The model will encode this combined text and it will output a single vector embedding."
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"To run this notebook, you will need to install: pandas, openai, transformers, plotly, matplotlib, scikit-learn, torch (transformer dep), torchvision, and scipy."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import tiktoken\n",
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"\n",
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"from utils.embeddings_utils import get_embedding"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {},
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"outputs": [],
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"source": [
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"embedding_model = \"text-embedding-3-small\"\n",
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"embedding_encoding = \"cl100k_base\"\n",
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"max_tokens = 8000 # the maximum for text-embedding-3-small is 8191"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Time</th>\n",
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" <th>ProductId</th>\n",
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" <th>UserId</th>\n",
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" <th>Score</th>\n",
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" <th>Summary</th>\n",
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" <th>Text</th>\n",
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" <th>combined</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>1351123200</td>\n",
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" <td>B003XPF9BO</td>\n",
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" <td>A3R7JR3FMEBXQB</td>\n",
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" <td>5</td>\n",
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" <td>where does one start...and stop... with a tre...</td>\n",
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" <td>Wanted to save some to bring to my Chicago fam...</td>\n",
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" <td>Title: where does one start...and stop... wit...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>1351123200</td>\n",
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" <td>B003JK537S</td>\n",
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" <td>A3JBPC3WFUT5ZP</td>\n",
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" <td>1</td>\n",
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" <td>Arrived in pieces</td>\n",
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" <td>Not pleased at all. When I opened the box, mos...</td>\n",
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" <td>Title: Arrived in pieces; Content: Not pleased...</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" Time ProductId UserId Score \\\n",
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"0 1351123200 B003XPF9BO A3R7JR3FMEBXQB 5 \n",
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"1 1351123200 B003JK537S A3JBPC3WFUT5ZP 1 \n",
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"\n",
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" Summary \\\n",
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"0 where does one start...and stop... with a tre... \n",
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"1 Arrived in pieces \n",
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"\n",
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" Text \\\n",
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"0 Wanted to save some to bring to my Chicago fam... \n",
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"1 Not pleased at all. When I opened the box, mos... \n",
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"\n",
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" combined \n",
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"0 Title: where does one start...and stop... wit... \n",
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"1 Title: Arrived in pieces; Content: Not pleased... "
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]
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},
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"execution_count": 17,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# load & inspect dataset\n",
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"input_datapath = \"data/fine_food_reviews_1k.csv\" # to save space, we provide a pre-filtered dataset\n",
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"df = pd.read_csv(input_datapath, index_col=0)\n",
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"df = df[[\"Time\", \"ProductId\", \"UserId\", \"Score\", \"Summary\", \"Text\"]]\n",
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"df = df.dropna()\n",
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"df[\"combined\"] = (\n",
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" \"Title: \" + df.Summary.str.strip() + \"; Content: \" + df.Text.str.strip()\n",
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")\n",
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"df.head(2)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"1000"
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]
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},
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"execution_count": 18,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# subsample to 1k most recent reviews and remove samples that are too long\n",
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"top_n = 1000\n",
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"df = df.sort_values(\"Time\").tail(top_n * 2) # first cut to first 2k entries, assuming less than half will be filtered out\n",
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"df.drop(\"Time\", axis=1, inplace=True)\n",
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"\n",
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"encoding = tiktoken.get_encoding(embedding_encoding)\n",
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"\n",
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"# omit reviews that are too long to embed\n",
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"df[\"n_tokens\"] = df.combined.apply(lambda x: len(encoding.encode(x)))\n",
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"df = df[df.n_tokens <= max_tokens].tail(top_n)\n",
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"len(df)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 2. Get embeddings and save them for future reuse"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Ensure you have your API key set in your environment per the README: https://github.com/openai/openai-python#usage\n",
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"\n",
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"# This may take a few minutes\n",
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"df[\"embedding\"] = df.combined.apply(lambda x: get_embedding(x, model=embedding_model))\n",
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"df.to_csv(\"data/fine_food_reviews_with_embeddings_1k.csv\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"metadata": {},
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"outputs": [],
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"source": [
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"a = get_embedding(\"hi\", model=embedding_model)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "openai",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.5"
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},
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"orig_nbformat": 4,
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"vscode": {
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"interpreter": {
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"hash": "365536dcbde60510dc9073d6b991cd35db2d9bac356a11f5b64279a5e6708b97"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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