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‎articles/search/includes/quickstarts/code/complex-query-dac.js

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// This is a RAG (Retrieval Augmented Generation) implementation that:
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// 1. Takes a user query about hotels
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// 2. Searches a hotel database using Azure AI Search
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// 3. Formats the search results for the LLM
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// 4. Sends the query and formatted results to Azure OpenAI
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// 5. Returns a grounded response based only on the retrieved information
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import { SearchClient } from "@azure/search-documents";
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import { AzureOpenAI } from "openai";
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import { DefaultAzureCredential, getBearerTokenProvider } from "@azure/identity";
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function getClients() {
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const credential = new DefaultAzureCredential();
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// Search
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const azureSearchEndpoint = process.env.AZURE_SEARCH_ENDPOINT;
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const azureSearchIndexName = process.env.AZURE_SEARCH_INDEX_NAME;
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const searchClient = new SearchClient(
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azureSearchEndpoint,
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azureSearchIndexName,
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credential
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);
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// OpenAI
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const azureOpenAiEndpoint = process.env.AZURE_OPENAI_ENDPOINT;
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const azureOpenAiApiVersion = process.env.AZURE_OPENAI_VERSION;
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const azureOpenAiDeploymentName = process.env.AZURE_DEPLOYMENT_MODEL;
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const scope = "https://cognitiveservices.azure.com/.default";
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const azureADTokenProvider = getBearerTokenProvider(credential, scope);
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const options = { azureADTokenProvider, deployment: azureOpenAiDeploymentName, apiVersion: azureOpenAiApiVersion, endpoint: azureOpenAiEndpoint }
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const openaiClient = new AzureOpenAI(options);
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return { openaiClient, searchClient, modelName: azureOpenAiDeploymentName };
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}
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async function queryAISearchForSources(
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searchClient,
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query
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) {
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console.log(`Searching for: "${query}"\n`);
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const selectedFields = ["HotelName", "Description", "Address", "Rooms", "Tags"];
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const searchResults = await searchClient.search(query, {
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top: 5,
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select: selectedFields,
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queryType: "semantic",
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semanticSearchOptions: {},
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});
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return searchResults;
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}
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async function queryOpenAIForResponse(
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openaiClient,
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query,
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sourcesFormatted,
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modelName
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){
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const GROUNDED_PROMPT = `
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You are a friendly assistant that recommends hotels based on activities and amenities.
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Answer the query using only the sources provided below in a friendly and concise bulleted manner.
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Answer ONLY with the facts listed in the list of sources below.
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If there isn't enough information below, say you don't know.
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Do not generate answers that don't use the sources below.
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Query: {query}
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Sources: {sources}
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`;
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return openaiClient.chat.completions.create({
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model: modelName,
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messages: [
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{
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role: "user",
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content: GROUNDED_PROMPT.replace("{query}", query).replace("{sources}", sourcesFormatted),
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}
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],
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temperature: 0.7,
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max_tokens: 800,
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});
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}
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async function main() {
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const { openaiClient, searchClient, modelName } = getClients();
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const query = `
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Can you recommend a few hotels that offer complimentary breakfast?
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Tell me their description, address, tags, and the rate for one room that sleeps 4 people.
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`;
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const sourcesResult = await queryAISearchForSources(searchClient, query);
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let sourcesFormatted = "";
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for await (const result of sourcesResult.results) {
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// Explicitly typing result to ensure compatibility
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sourcesFormatted += JSON.stringify(result.document) + "\n";
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}
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const response = await queryOpenAIForResponse(openaiClient, query, sourcesFormatted.trim(), modelName);
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// Print the response from the chat model
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const content = response.choices[0].message.content;
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if (content) {
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console.log(content);
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} else {
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console.log("No content available in the response.");
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}
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}
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main().catch((error) => {
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console.error("An error occurred:", error);
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process.exit(1);
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});

‎articles/search/includes/quickstarts/code/complex-query-dac.ts

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// This is a RAG (Retrieval Augmented Generation) implementation that:
2+
// 1. Takes a user query about hotels
3+
// 2. Searches a hotel database using Azure AI Search
4+
// 3. Formats the search results for the LLM
5+
// 4. Sends the query and formatted results to Azure OpenAI
6+
// 5. Returns a grounded response based only on the retrieved information
7+
8+
import { SearchClient, SearchDocumentsResult } from "@azure/search-documents";
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import { AzureOpenAI } from "openai";
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import { DefaultAzureCredential, getBearerTokenProvider } from "@azure/identity";
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function getClients(): { openaiClient: AzureOpenAI; searchClient: SearchClient<{ HotelName: string; Description: string; Tags: string[] | string; Address: string; Rooms: string }>; modelName: string } {
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const credential = new DefaultAzureCredential();
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// Search
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const azureSearchEndpoint = process.env.AZURE_SEARCH_ENDPOINT!;
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const azureSearchIndexName = process.env.AZURE_SEARCH_INDEX_NAME!;
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const searchClient = new SearchClient<{ HotelName: string; Description: string; Tags: string[] | string; Address: string; Rooms: string }>(
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azureSearchEndpoint,
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azureSearchIndexName,
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credential
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);
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// OpenAI
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const azureOpenAiEndpoint = process.env.AZURE_OPENAI_ENDPOINT!;
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const azureOpenAiApiVersion = process.env.AZURE_OPENAI_VERSION!;
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const azureOpenAiDeploymentName = process.env.AZURE_DEPLOYMENT_MODEL!;
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const scope = "https://cognitiveservices.azure.com/.default";
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const azureADTokenProvider = getBearerTokenProvider(credential, scope);
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const options = { azureADTokenProvider, deployment: azureOpenAiDeploymentName, apiVersion: azureOpenAiApiVersion, endpoint: azureOpenAiEndpoint }
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const openaiClient = new AzureOpenAI(options);
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return { openaiClient, searchClient, modelName: azureOpenAiDeploymentName };
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}
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async function queryAISearchForSources(
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searchClient: SearchClient<{ HotelName: string; Description: string; Tags: string[] | string; Address: string; Rooms: string }>,
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query: string
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): Promise<SearchDocumentsResult<{ HotelName: string; Description: string; Tags: string[] | string; Address: string; Rooms: string }>> {
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console.log(`Searching for: "${query}"\n`);
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const selectedFields: readonly ["HotelName", "Description", "Address", "Rooms", "Tags"] = ["HotelName", "Description", "Address", "Rooms", "Tags"];
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const searchResults = await searchClient.search(query, {
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top: 5,
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select: selectedFields,
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queryType: "semantic",
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semanticSearchOptions: {},
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});
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return searchResults;
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}
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async function queryOpenAIForResponse(
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openaiClient: AzureOpenAI,
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query: string,
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sourcesFormatted: string,
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modelName: string
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): Promise<{ choices: { message: { content: string | null } }[] }> {
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const GROUNDED_PROMPT = `
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You are a friendly assistant that recommends hotels based on activities and amenities.
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Answer the query using only the sources provided below in a friendly and concise bulleted manner.
65+
Answer ONLY with the facts listed in the list of sources below.
66+
If there isn't enough information below, say you don't know.
67+
Do not generate answers that don't use the sources below.
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Query: {query}
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Sources: {sources}
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`;
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return openaiClient.chat.completions.create({
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model: modelName,
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messages: [
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{
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role: "user",
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content: GROUNDED_PROMPT.replace("{query}", query).replace("{sources}", sourcesFormatted),
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}
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],
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temperature: 0.7,
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max_tokens: 800,
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});
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}
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async function main(): Promise<void> {
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const { openaiClient, searchClient, modelName } = getClients();
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const query = `
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Can you recommend a few hotels that offer complimentary breakfast?
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Tell me their description, address, tags, and the rate for one room that sleeps 4 people.
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`;
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const sourcesResult = await queryAISearchForSources(searchClient, query);
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let sourcesFormatted = "";
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for await (const result of sourcesResult.results) {
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// Explicitly typing result to ensure compatibility
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sourcesFormatted += JSON.stringify(result.document) + "\n";
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}
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const response = await queryOpenAIForResponse(openaiClient, query, sourcesFormatted.trim(), modelName);
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// Print the response from the chat model
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const content = response.choices[0].message.content;
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if (content) {
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console.log(content);
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} else {
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console.log("No content available in the response.");
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}
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}
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main().catch((error) => {
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console.error("An error occurred:", error);
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process.exit(1);
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});

‎articles/search/includes/quickstarts/code/query-dac.js

Lines changed: 106 additions & 0 deletions
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// This is a RAG (Retrieval Augmented Generation) implementation that:
2+
// 1. Takes a user query about hotels
3+
// 2. Searches a hotel database using Azure AI Search
4+
// 3. Formats the search results for the LLM
5+
// 4. Sends the query and formatted results to Azure OpenAI
6+
// 5. Returns a grounded response based only on the retrieved information
7+
8+
import { SearchClient } from "@azure/search-documents";
9+
import { AzureOpenAI } from "openai";
10+
import { DefaultAzureCredential, getBearerTokenProvider } from "@azure/identity";
11+
12+
function getClients() {
13+
14+
const credential = new DefaultAzureCredential();
15+
16+
// Search
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const azureSearchEndpoint = process.env.AZURE_SEARCH_ENDPOINT;
18+
const azureSearchIndexName = process.env.AZURE_SEARCH_INDEX_NAME;
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const searchClient = new SearchClient(
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azureSearchEndpoint,
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azureSearchIndexName,
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credential
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);
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26+
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// OpenAI
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const azureOpenAiEndpoint = process.env.AZURE_OPENAI_ENDPOINT;
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const azureOpenAiApiVersion = process.env.AZURE_OPENAI_VERSION;
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const azureOpenAiDeploymentName = process.env.AZURE_DEPLOYMENT_MODEL;
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const scope = "https://cognitiveservices.azure.com/.default";
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const azureADTokenProvider = getBearerTokenProvider(credential, scope);
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const options = { azureADTokenProvider, deployment: azureOpenAiDeploymentName, apiVersion: azureOpenAiApiVersion, endpoint: azureOpenAiEndpoint }
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const openaiClient = new AzureOpenAI(options);
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return { openaiClient, searchClient, modelName: azureOpenAiDeploymentName };
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}
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async function queryAISearchForSources(searchClient, query) {
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console.log(`Searching for: "${query}"\n`);
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const searchResults = await searchClient.search(query, {
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top: 5,
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select: ["Description", "HotelName", "Tags"]
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});
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const sources = [];
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for await (const result of searchResults.results) {
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const doc = result.document;
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sources.push(
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`Hotel: ${doc.HotelName}\n` +
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`Description: ${doc.Description}\n` +
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`Tags: ${Array.isArray(doc.Tags) ? doc.Tags.join(', ') : doc.Tags}\n`
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);
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}
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const sourcesFormatted = sources.join("\n---\n");
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return sourcesFormatted;
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}
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async function queryOpenAIForResponse(openaiClient, query, sourcesFormatted, modelName) {
60+
61+
const GROUNDED_PROMPT = `
62+
You are a friendly assistant that recommends hotels based on activities and amenities.
63+
Answer the query using only the sources provided below in a friendly and concise bulleted manner.
64+
Answer ONLY with the facts listed in the list of sources below.
65+
If there isn't enough information below, say you don't know.
66+
Do not generate answers that don't use the sources below.
67+
68+
Query: {query}
69+
Sources: {sources}
70+
`;
71+
72+
return openaiClient.chat.completions.create({
73+
model: modelName,
74+
messages: [
75+
{
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role: "user",
77+
content: GROUNDED_PROMPT.replace("{query}", query).replace("{sources}", sourcesFormatted),
78+
}
79+
],
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temperature: 0.7,
81+
max_tokens: 800,
82+
});
83+
}
84+
85+
async function main() {
86+
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const { openaiClient, searchClient, modelName } = getClients();
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const query = "Can you recommend a few hotels with complimentary breakfast?";
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const sources = await queryAISearchForSources(searchClient, query);
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const response = await queryOpenAIForResponse(openaiClient, query, sources, modelName);
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// Print the response from the chat model
95+
const content = response.choices[0].message.content;
96+
if (content) {
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console.log(content);
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} else {
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console.log("No content available in the response.");
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}
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}
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main().catch((error) => {
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console.error("An error occurred:", error);
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process.exit(1);
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});

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