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Adding SingleStoreDB as a vector database with Python notebook (#402)
* SingleStoreDB folder * Readme * Readme with notebook * S2DB notebook * Update README.md * Delete OpenAI_wikipedia semantic_search.ipynb * Add files via upload * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md
This commit is contained in:
arno756
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"cells": [
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{
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"cell_type": "markdown",
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"id": "c2b98618",
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"metadata": {},
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"source": [
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"# Intro\n",
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"This notebook is an example on how you can use SingleStoreDB vector storage and functions to build an interactive Q&A application with ChatGPT. If you start a [Trial](https://www.singlestore.com/cloud-trial/) in SingleStoreDB, you can find the same notebook in our sample notebooks with native connection."
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]
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},
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{
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"cell_type": "markdown",
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"id": "55b58478",
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"metadata": {},
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"source": [
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"## First let's talk directly to ChatGPT and try and get back a response"
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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": 2,
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"id": "661cd7c3",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.1.2\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3.11 -m pip install --upgrade pip\u001b[0m\n"
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]
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}
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],
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"source": [
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"!pip install openai --quiet"
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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": 54,
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"id": "61468873",
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"metadata": {},
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"outputs": [],
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"source": [
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"import openai\n",
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"\n",
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"# models\n",
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"EMBEDDING_MODEL = \"text-embedding-ada-002\"\n",
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"GPT_MODEL = \"gpt-3.5-turbo\""
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]
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},
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{
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"cell_type": "markdown",
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"id": "3778d23e",
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"metadata": {},
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"source": [
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"## Let's connect to OpenAI and see the result we get when asking for a date beyond 2021"
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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": 5,
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"id": "3f654b3f",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"I'm sorry, I cannot provide information about events that have not occurred yet. The Winter Olympics 2022 will be held in Beijing, China from February 4 to 20, 2022. The curling events will take place during this time and the results will not be known until after the competition has concluded.\n"
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]
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}
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],
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"source": [
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"openai.api_key = 'OPENAI API KEY'\n",
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"\n",
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"response = openai.ChatCompletion.create(\n",
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" model=GPT_MODEL,\n",
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" messages=[\n",
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" {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n",
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" {\"role\": \"user\", \"content\": \"Who won the gold medal for curling in Olymics 2022?\"},\n",
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" ]\n",
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")\n",
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"\n",
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"print(response['choices'][0]['message']['content'])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a9c15d6d",
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"metadata": {},
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"source": [
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"# Get the data about Winter Olympics and provide the information to ChatGPT as context"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c5247835",
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"metadata": {},
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"source": [
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"## 1. Setup"
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]
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},
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"cell_type": "code",
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"execution_count": 33,
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"id": "0948696c",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.1.2\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3.11 -m pip install --upgrade pip\u001b[0m\n"
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]
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}
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],
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"source": [
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"!pip install matplotlib plotly.express scikit-learn tabulate tiktoken wget --quiet"
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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": 34,
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"id": "1e36f5d8",
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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 os\n",
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"import wget\n",
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"import ast"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ba9b8ae2",
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"metadata": {},
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"source": [
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"## Step 1 - Grab the data from CSV and prepare it"
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]
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"cell_type": "code",
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"execution_count": 8,
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"id": "ce3897b4",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"File downloaded successfully.\n"
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]
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}
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],
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"source": [
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"# download pre-chunked text and pre-computed embeddings\n",
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"# this file is ~200 MB, so may take a minute depending on your connection speed\n",
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"embeddings_path = \"https://cdn.openai.com/API/examples/data/winter_olympics_2022.csv\"\n",
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"file_path = \"winter_olympics_2022.csv\"\n",
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"\n",
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"if not os.path.exists(file_path):\n",
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" wget.download(embeddings_path, file_path)\n",
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" print(\"File downloaded successfully.\")\n",
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"else:\n",
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" print(\"File already exists in the local file system.\")"
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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": 9,
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"id": "082e9545",
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"metadata": {},
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"outputs": [],
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"source": [
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"df = pd.read_csv(\n",
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" \"winter_olympics_2022.csv\"\n",
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")\n",
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"\n",
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"# convert embeddings from CSV str type back to list type\n",
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"df['embedding'] = df['embedding'].apply(ast.literal_eval)"
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]
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},
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"cell_type": "code",
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"execution_count": null,
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"id": "1768fa60",
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"metadata": {},
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"outputs": [],
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"source": [
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"df"
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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": 11,
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"id": "37791a10",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"<class 'pandas.core.frame.DataFrame'>\n",
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"RangeIndex: 6059 entries, 0 to 6058\n",
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"Data columns (total 2 columns):\n",
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" # Column Non-Null Count Dtype \n",
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"--- ------ -------------- ----- \n",
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" 0 text 6059 non-null object\n",
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" 1 embedding 6059 non-null object\n",
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"dtypes: object(2)\n",
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"memory usage: 94.8+ KB\n"
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]
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}
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],
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"source": [
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"df.info(show_counts=True)"
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]
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},
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"cell_type": "markdown",
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"id": "c4e7feb6",
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"metadata": {},
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"source": [
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"## 2. Set up SingleStore DB"
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]
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"cell_type": "code",
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"execution_count": 62,
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"id": "81571781",
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"metadata": {},
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"outputs": [],
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"source": [
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"import singlestoredb as s2\n",
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"\n",
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"conn = s2.connect(\"<user>:<Password>@<host>:3306/\")\n",
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"\n",
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"cur = conn.cursor()"
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]
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"cell_type": "code",
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"execution_count": 70,
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"id": "e1b3fc6f",
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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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"1"
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]
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},
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"execution_count": 70,
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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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"# Create database\n",
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"stmt = \"\"\"\n",
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" CREATE DATABASE IF NOT EXISTS winter_wikipedia2;\n",
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"\"\"\"\n",
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"\n",
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"cur.execute(stmt)"
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]
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"cell_type": "code",
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"execution_count": 71,
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"id": "e49c728c",
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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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"0"
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]
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},
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"execution_count": 71,
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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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"#create table\n",
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"stmt = \"\"\"\n",
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"CREATE TABLE IF NOT EXISTS winter_wikipedia2.winter_olympics_2022 (\n",
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" id INT PRIMARY KEY,\n",
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" text TEXT CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci,\n",
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" embedding BLOB\n",
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");\"\"\"\n",
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"\n",
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"cur.execute(stmt)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8f10e57e",
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"metadata": {},
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"source": [
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"## 3. Populate the Table with our dataframe df and use JSON_ARRAY_PACK to compact it"
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]
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{
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"cell_type": "code",
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"execution_count": 72,
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"id": "98424a33",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"CPU times: user 8.79 s, sys: 4.63 s, total: 13.4 s\n",
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"Wall time: 11min 4s\n"
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]
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}
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],
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"source": [
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"%%time\n",
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"\n",
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"# Prepare the statement\n",
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"stmt = \"\"\"\n",
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" INSERT INTO winter_wikipedia2.winter_olympics_2022 (\n",
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" id,\n",
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" text,\n",
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" embedding\n",
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" )\n",
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" VALUES (\n",
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" %s,\n",
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" %s,\n",
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" JSON_ARRAY_PACK_F64(%s)\n",
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" )\n",
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"\"\"\"\n",
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"\n",
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"# Convert the DataFrame to a NumPy record array\n",
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"record_arr = df.to_records(index=True)\n",
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"\n",
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"# Set the batch size\n",
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"batch_size = 1000\n",
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"\n",
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"# Iterate over the rows of the record array in batches\n",
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"for i in range(0, len(record_arr), batch_size):\n",
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" batch = record_arr[i:i+batch_size]\n",
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" values = [(row[0], row[1], str(row[2])) for row in batch]\n",
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" cur.executemany(stmt, values)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3afeb4ec",
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"metadata": {},
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"source": [
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"## 4. Do a semantic search with the same question from above and use the response to send to OpenAI again\n"
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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": 73,
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"id": "b2b79750",
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"metadata": {},
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"outputs": [],
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"source": [
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"from openai.embeddings_utils import get_embedding\n",
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"\n",
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"def strings_ranked_by_relatedness(\n",
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" query: str,\n",
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" df: pd.DataFrame,\n",
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" relatedness_fn=lambda x, y: 1 - spatial.distance.cosine(x, y),\n",
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" top_n: int = 100\n",
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") -> tuple:\n",
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" \"\"\"Returns a list of strings and relatednesses, sorted from most related to least.\"\"\"\n",
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"\n",
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" # Get the embedding of the query.\n",
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||||||
|
" query_embedding_response = get_embedding(query, EMBEDDING_MODEL)\n",
|
||||||
|
"\n",
|
||||||
|
" # Create the SQL statement.\n",
|
||||||
|
" stmt = \"\"\"\n",
|
||||||
|
" SELECT\n",
|
||||||
|
" text,\n",
|
||||||
|
" DOT_PRODUCT_F64(JSON_ARRAY_PACK_F64(%s), embedding) AS score\n",
|
||||||
|
" FROM winter_wikipedia2.winter_olympics_2022\n",
|
||||||
|
" ORDER BY score DESC\n",
|
||||||
|
" LIMIT %s\n",
|
||||||
|
" \"\"\"\n",
|
||||||
|
"\n",
|
||||||
|
" # Execute the SQL statement.\n",
|
||||||
|
" results = cur.execute(stmt, [str(query_embedding_response), top_n])\n",
|
||||||
|
"\n",
|
||||||
|
" # Fetch the results\n",
|
||||||
|
" results = cur.fetchall()\n",
|
||||||
|
" \n",
|
||||||
|
" strings = []\n",
|
||||||
|
" relatednesses = []\n",
|
||||||
|
"\n",
|
||||||
|
" for row in results:\n",
|
||||||
|
" strings.append(row[0])\n",
|
||||||
|
" relatednesses.append(row[1])\n",
|
||||||
|
"\n",
|
||||||
|
" # Return the results.\n",
|
||||||
|
" return strings[:top_n], relatednesses[:top_n]"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"id": "804f2659",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"from tabulate import tabulate\n",
|
||||||
|
"\n",
|
||||||
|
"strings, relatednesses = strings_ranked_by_relatedness(\n",
|
||||||
|
" \"curling gold medal\",\n",
|
||||||
|
" df,\n",
|
||||||
|
" top_n=5\n",
|
||||||
|
")\n",
|
||||||
|
"\n",
|
||||||
|
"for string, relatedness in zip(strings, relatednesses):\n",
|
||||||
|
" print(f\"{relatedness=:.3f}\")\n",
|
||||||
|
" print(tabulate([[string]], headers=['Result'], tablefmt='fancy_grid'))"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "3a03fd7f",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## 5. Send the right context to ChatGPT for a more accurate answer"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 75,
|
||||||
|
"id": "13265651",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"import tiktoken\n",
|
||||||
|
"\n",
|
||||||
|
"def num_tokens(text: str, model: str = GPT_MODEL) -> int:\n",
|
||||||
|
" \"\"\"Return the number of tokens in a string.\"\"\"\n",
|
||||||
|
" encoding = tiktoken.encoding_for_model(model)\n",
|
||||||
|
" return len(encoding.encode(text))\n",
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"def query_message(\n",
|
||||||
|
" query: str,\n",
|
||||||
|
" df: pd.DataFrame,\n",
|
||||||
|
" model: str,\n",
|
||||||
|
" token_budget: int\n",
|
||||||
|
") -> str:\n",
|
||||||
|
" \"\"\"Return a message for GPT, with relevant source texts pulled from SingleStoreDB.\"\"\"\n",
|
||||||
|
" strings, relatednesses = strings_ranked_by_relatedness(query, df, \"winter_olympics_2022\")\n",
|
||||||
|
" introduction = 'Use the below articles on the 2022 Winter Olympics to answer the subsequent question. If the answer cannot be found in the articles, write \"I could not find an answer.\"'\n",
|
||||||
|
" question = f\"\\n\\nQuestion: {query}\"\n",
|
||||||
|
" message = introduction\n",
|
||||||
|
" for string in strings:\n",
|
||||||
|
" next_article = f'\\n\\nWikipedia article section:\\n\"\"\"\\n{string}\\n\"\"\"'\n",
|
||||||
|
" if (\n",
|
||||||
|
" num_tokens(message + next_article + question, model=model)\n",
|
||||||
|
" > token_budget\n",
|
||||||
|
" ):\n",
|
||||||
|
" break\n",
|
||||||
|
" else:\n",
|
||||||
|
" message += next_article\n",
|
||||||
|
" return message + question\n",
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"def ask(\n",
|
||||||
|
" query: str,\n",
|
||||||
|
" df: pd.DataFrame = df,\n",
|
||||||
|
" model: str = GPT_MODEL,\n",
|
||||||
|
" token_budget: int = 4096 - 500,\n",
|
||||||
|
" print_message: bool = False,\n",
|
||||||
|
") -> str:\n",
|
||||||
|
" \"\"\"Answers a query using GPT and a table of relevant texts and embeddings in SingleStoreDB.\"\"\"\n",
|
||||||
|
" message = query_message(query, df, model=model, token_budget=token_budget)\n",
|
||||||
|
" if print_message:\n",
|
||||||
|
" print(message)\n",
|
||||||
|
" messages = [\n",
|
||||||
|
" {\"role\": \"system\", \"content\": \"You answer questions about the 2022 Winter Olympics.\"},\n",
|
||||||
|
" {\"role\": \"user\", \"content\": message},\n",
|
||||||
|
" ]\n",
|
||||||
|
" response = openai.ChatCompletion.create(\n",
|
||||||
|
" model=model,\n",
|
||||||
|
" messages=messages,\n",
|
||||||
|
" temperature=0\n",
|
||||||
|
" )\n",
|
||||||
|
" response_message = response[\"choices\"][0][\"message\"][\"content\"]\n",
|
||||||
|
" return response_message"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "c9128b90",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## 6. Get an answer from Chat GPT"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 76,
|
||||||
|
"id": "d295286a",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"(\"There were three curling events at the 2022 Winter Olympics: men's, women's, \"\n",
|
||||||
|
" 'and mixed doubles. The gold medalists for each event are:\\n'\n",
|
||||||
|
" '\\n'\n",
|
||||||
|
" \"- Men's: Sweden (Niklas Edin, Oskar Eriksson, Rasmus Wranå, Christoffer \"\n",
|
||||||
|
" 'Sundgren, Daniel Magnusson)\\n'\n",
|
||||||
|
" \"- Women's: Great Britain (Eve Muirhead, Vicky Wright, Jennifer Dodds, Hailey \"\n",
|
||||||
|
" 'Duff, Mili Smith)\\n'\n",
|
||||||
|
" '- Mixed doubles: Italy (Stefania Constantini, Amos Mosaner)')\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"from pprint import pprint\n",
|
||||||
|
"\n",
|
||||||
|
"answer = ask('Who won the gold medal for curling in Olymics 2022?')\n",
|
||||||
|
"\n",
|
||||||
|
"pprint(answer)"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"metadata": {
|
||||||
|
"kernelspec": {
|
||||||
|
"display_name": "Python 3.11.0 64-bit",
|
||||||
|
"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": "3.11.3"
|
||||||
|
},
|
||||||
|
"vscode": {
|
||||||
|
"interpreter": {
|
||||||
|
"hash": "5c7b89af1651d0b8571dde13640ecdccf7d5a6204171d6ab33e7c296e100e08a"
|
||||||
|
}
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 5
|
||||||
|
}
|
||||||
15
examples/vector_databases/SingleStoreDB/README.md
Normal file
15
examples/vector_databases/SingleStoreDB/README.md
Normal file
@ -0,0 +1,15 @@
|
|||||||
|
**[SingleStoreDB](https://singlestore.com)** has first-class support for vector search through our [Vector Functions](https://docs.singlestore.com/managed-service/en/reference/sql-reference/vector-functions.html). Our vector database subsystem, first made available in 2017 and subsequently enhanced, allows extremely fast nearest-neighbor search to find objects that are semantically similar, easily using SQL.
|
||||||
|
|
||||||
|
SingleStoreDB supports vectors and vector similarity search using dot_product (for cosine similarity) and euclidean_distance functions. These functions are used by our customers for applications including face recognition, visual product photo search and text-based semantic search. With the explosion of generative AI technology, these capabilities form a firm foundation for text-based AI chatbots.
|
||||||
|
|
||||||
|
But remember, SingleStoreDB is a high-performance, scalable, modern SQL DBMS that supports multiple data models including structured data, semi-structured data based on JSON, time-series, full text, spatial, key-value and of course vector data. Start powering your next intelligent application with SingleStoreDB today!
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
## Example
|
||||||
|
|
||||||
|
This folder contains examples of using SingleStoreDB and OpenAI together. We will keep adding more scenarios so stay tuned!
|
||||||
|
|
||||||
|
| Name | Description |
|
||||||
|
| --- | --- |
|
||||||
|
| [OpenAI wikipedia semantic search](./OpenAI_wikipedia_semantic_search.ipynb) | Improve ChatGPT accuracy through SingleStoreDB semantic Search in QA |
|
||||||
Loading…
x
Reference in New Issue
Block a user