From 30f5e1bb6e6dc971d10b7d77e7420f6d9a29edd5 Mon Sep 17 00:00:00 2001 From: Rainer Date: Thu, 8 Aug 2024 08:11:36 +0200 Subject: [PATCH] Add Qdrant sample --- labs/042-qdrant-rag/.gitignore | 1 + labs/042-qdrant-rag/.vscode/launch.json | 22 + labs/042-qdrant-rag/.vscode/tasks.json | 16 + labs/042-qdrant-rag/package-lock.json | 1544 +++++++++++++++++++++++ labs/042-qdrant-rag/package.json | 28 + labs/042-qdrant-rag/readme.md | 7 + labs/042-qdrant-rag/src/input.ts | 18 + labs/042-qdrant-rag/src/main.ts | 141 +++ labs/042-qdrant-rag/src/products.ts | 37 + labs/042-qdrant-rag/src/sql.ts | 38 + labs/042-qdrant-rag/tsconfig.json | 14 + 11 files changed, 1866 insertions(+) create mode 100644 labs/042-qdrant-rag/.gitignore create mode 100644 labs/042-qdrant-rag/.vscode/launch.json create mode 100644 labs/042-qdrant-rag/.vscode/tasks.json create mode 100644 labs/042-qdrant-rag/package-lock.json create mode 100644 labs/042-qdrant-rag/package.json create mode 100644 labs/042-qdrant-rag/readme.md create mode 100644 labs/042-qdrant-rag/src/input.ts create mode 100644 labs/042-qdrant-rag/src/main.ts create mode 100644 labs/042-qdrant-rag/src/products.ts create mode 100644 labs/042-qdrant-rag/src/sql.ts create mode 100644 labs/042-qdrant-rag/tsconfig.json diff --git a/labs/042-qdrant-rag/.gitignore b/labs/042-qdrant-rag/.gitignore new file mode 100644 index 0000000..4a08260 --- /dev/null +++ b/labs/042-qdrant-rag/.gitignore @@ -0,0 +1 @@ +embeddings.json \ No newline at end of file diff --git a/labs/042-qdrant-rag/.vscode/launch.json b/labs/042-qdrant-rag/.vscode/launch.json new file mode 100644 index 0000000..d876024 --- /dev/null +++ b/labs/042-qdrant-rag/.vscode/launch.json @@ -0,0 +1,22 @@ +{ + // Use IntelliSense to learn about possible attributes. + // Hover to view descriptions of existing attributes. + // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 + "version": "0.2.0", + "configurations": [ + { + "type": "node", + "request": "launch", + 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qdrant/qdrant", + "build": "tsc", + "start": "npm run build && node ./dist/main.js" + }, + "keywords": [], + "author": "", + "license": "ISC", + "devDependencies": { + "@types/mssql": "^9.1.5", + "@types/node": "^22.1.0", + "typescript": "^5.4.3" + }, + "dependencies": { + "@qdrant/js-client-rest": "^1.10.0", + "dotenv": "^16.4.5", + "mathjs": "^13.0.3", + "mssql": "^11.0.1", + "openai": "^4.29.2", + "tedious": "^18.1.0", + "winston": "^3.13.0" + } +} diff --git a/labs/042-qdrant-rag/readme.md b/labs/042-qdrant-rag/readme.md new file mode 100644 index 0000000..f25ea9e --- /dev/null +++ b/labs/042-qdrant-rag/readme.md @@ -0,0 +1,7 @@ +# Embeddings and RAG with Vector Database + +## Introduction + +This example is similar to [040-embeddings-rag-nodejs](../040-embeddings-rag-nodejs/). The difference is that in this example, we use a vector database to store the embeddings of the product models instead of keeping them in memory. + +In this example, we use the vector database [Qdrant](https://qdrant.tech/). There are many options for vector databases (see e.g. [OpenAI Cookbook](https://cookbook.openai.com/examples/vector_databases/readme)). We chose Qdrant because it is easy to run local experiments using Docker. diff --git a/labs/042-qdrant-rag/src/input.ts b/labs/042-qdrant-rag/src/input.ts new file mode 100644 index 0000000..36fe572 --- /dev/null +++ b/labs/042-qdrant-rag/src/input.ts @@ -0,0 +1,18 @@ +import readline from 'readline'; + +/** + * Read a line from the console. + */ +export function readLine(prompt: string): Promise { + return new Promise((resolve) => { + const rl = readline.createInterface({ + input: process.stdin, + output: process.stdout + }); + + rl.question(prompt, (answer: string) => { + rl.close(); + resolve(answer); + }); + }); +} diff --git a/labs/042-qdrant-rag/src/main.ts b/labs/042-qdrant-rag/src/main.ts new file mode 100644 index 0000000..75df334 --- /dev/null +++ b/labs/042-qdrant-rag/src/main.ts @@ -0,0 +1,141 @@ +import OpenAI from "openai"; +import dotenv from "dotenv"; +import winston from "winston"; +import { createConnectionPool } from "./sql.js"; +import { getProductModels } from "./products.js"; +import { readLine } from "./input.js"; +import { QdrantClient } from "@qdrant/js-client-rest"; + +dotenv.config({ path: "../../.env" }); + +const logger = winston.createLogger({ + level: process.env.LOG_LEVEL ?? "info", + format: winston.format.combine( + winston.format.colorize(), + winston.format.simple() + ), + transports: [new winston.transports.Console()], +}); + +logger.info("Creating connection pool"); +const pool = await createConnectionPool(process.env.ADVENTURE_WORKS ?? ""); +logger.info("Connection pool connected"); + +logger.info("Fetching product models"); +const products = await getProductModels(pool); +logger.info("Product models complete", { count: products.length }); + +const openai = new OpenAI({ + apiKey: process.env.OPENAI_KEY, +}); + +const client = new QdrantClient({ host: "localhost", port: 6333 }); + +// Uncomment this line to delete the collection and start from scratch +// await client.deleteCollection('products'); + +const collection = await client.collectionExists("products"); +if (!collection.exists) { + logger.info("Creating collection"); + await client.createCollection("products", { + vectors: { + size: 3072, + distance: "Dot", + }, + }); + + logger.info("Calculating embeddings"); + for (const product of products) { + const description = `# ${product.productGroupDescription2} + + ## ${product.productGroupDescription1} + + ### ${product.name} + + ${product.description} + `; + const embedding = await openai.embeddings.create({ + model: process.env.OPENAI_EMBEDDINGS ?? "", + input: description, + }); + + await client.upsert("products", { + wait: true, + points: [ + { + id: product.productModelID, + vector: embedding.data[0].embedding, + payload: { + name: product.name, + }, + }, + ], + }); + } +} + +while (true) { + const options = [ + "Do you have padles for my road bike?", + "I am looking for padels that I can ride with my regular shoes.", + "I got a voucher from your store and I want to buy new clothes for mountain biking. What can you recommend?", + ]; + console.log("\n"); + for (let i = 0; i < options.length; i++) { + console.log(`${i + 1}: ${options[i]}`); + } + let query = await readLine( + "\nYou (just press enter to exit the conversation): " + ); + if (!query) { + break; + } + const selection = parseInt(query); + if ((!isNaN(selection) && selection >= 1) || selection <= options.length) { + query = options[selection - 1]; + } + + const queryEmbedding = await openai.embeddings.create({ + model: process.env.OPENAI_EMBEDDINGS ?? "", + input: query, + }); + + let searchResult = await client.search("products", { + vector: queryEmbedding.data[0].embedding, + limit: 3, + }); + + const augmentedPrompt = `You are a helpful assistant in a bike shop. People are looking for bikes and bike parts. +Below you find relevant product models that you can recommend. ONLY use those product models. DO NOT suggest +anything else. If no product model fits, appologize that we do not have the right product for them. +If the customer asks anything not related to bikes or bike parts, tell them that you can only help with bikes and bike parts. + +=== PRODUCT MODELS + +${searchResult + .map((r) => { + const product = products.find((p) => p.productModelID === r.id); + if (product) { + return `${product.productModelID}: ${product.name} - ${product.description} (${product.productGroupDescription2} ${product.productGroupDescription1})`; + } + + return ""; + }) + .join("\n\n")}`; + + logger.info("Calling ChatGPT completions", { + prompt: augmentedPrompt, + query, + }); + const response = await openai.chat.completions.create({ + messages: [ + { role: "system", content: augmentedPrompt }, + { role: "user", content: query }, + ], + model: process.env.OPENAI_MODEL ?? "", + }); + + console.log(); + console.log(response.choices[0].message.content); +} +pool.close(); diff --git a/labs/042-qdrant-rag/src/products.ts b/labs/042-qdrant-rag/src/products.ts new file mode 100644 index 0000000..6417dd3 --- /dev/null +++ b/labs/042-qdrant-rag/src/products.ts @@ -0,0 +1,37 @@ +import sql from 'mssql'; + +export type ProductModel = { + productModelID: number; + name: string; + description: string; + productGroupDescription1: string; + productGroupDescription2: string; +} + +export async function getProductModels(pool: sql.ConnectionPool): Promise { + const result = await pool.request().query(`select pm.ProductModelID as productModelID, pm.Name as name, pd.[Description] as description, + ( + select top 1 pg.Name + from SalesLT.Product p + inner join SalesLT.ProductCategory pg on p.ProductCategoryID = pg.ProductCategoryID + where pm.ProductModelID = p.ProductModelID + ) as productGroupDescription1, + ( + select top 1 pg2.Name + from SalesLT.Product p + inner join SalesLT.ProductCategory pg on p.ProductCategoryID = pg.ProductCategoryID + inner join SalesLT.ProductCategory pg2 on pg.ParentProductCategoryID = pg2.ProductCategoryID + where pm.ProductModelID = p.ProductModelID + ) as productGroupDescription2 + from SalesLT.ProductModel pm + inner join SalesLT.ProductModelProductDescription pmpd on pmpd.ProductModelID = pm.ProductModelID and pmpd.Culture = 'en' + inner join SalesLT.ProductDescription pd on pmpd.ProductDescriptionID = pd.ProductDescriptionID + where exists ( + select top 1 1 + from SalesLT.Product p + where pm.ProductModelID = p.ProductModelID + )`); + + + return result.recordset as ProductModel[]; +} diff --git a/labs/042-qdrant-rag/src/sql.ts b/labs/042-qdrant-rag/src/sql.ts new file mode 100644 index 0000000..9e2aa2c --- /dev/null +++ b/labs/042-qdrant-rag/src/sql.ts @@ -0,0 +1,38 @@ +import sql from 'mssql'; + +/** + * Creates a connection pool to the Azure SQL Server database and connects to it + */ +export async function createConnectionPool(connectionString: string): Promise { + if (!connectionString) { + throw new Error('Connection string is required'); + } + + // Parse the connection string as we need server, user, etc. separately + const connectionStringParts = new Map( + connectionString.split(';').map(p => p.split('=') as [string, string]) + ); + let server = connectionStringParts.get('Server') ?? ''; + if (server.startsWith('tcp:')) { + server = server.substring(4); + } + if (server.endsWith(',1433')) { + server = server.substring(0, server.length - 5); + } + + const config: sql.config = { + user: connectionStringParts.get('User') ?? '', + password: connectionStringParts.get('Password') ?? '', + server: server, + database: connectionStringParts.get('Initial Catalog') ?? '', + options: { + encrypt: true, + trustServerCertificate: false + } + }; + const pool = new sql.ConnectionPool(config); + + await pool.connect(); + + return pool; +} diff --git a/labs/042-qdrant-rag/tsconfig.json b/labs/042-qdrant-rag/tsconfig.json new file mode 100644 index 0000000..b506f08 --- /dev/null +++ b/labs/042-qdrant-rag/tsconfig.json @@ -0,0 +1,14 @@ +{ + "compilerOptions": { + "target": "es2022", + "module": "NodeNext", + "moduleResolution": "NodeNext", + "rootDir": "./src", + "inlineSourceMap": true, + "outDir": "./dist", + "esModuleInterop": true, + "forceConsistentCasingInFileNames": true, + "strict": true, + "skipLibCheck": true + } +}