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‎articles/ai-services/openai/includes/use-your-data-javascript.md‎

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@@ -44,85 +44,85 @@ Your app's _package.json_ file will be updated with the dependencies.
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#### [TypeScript](#tab/typescript)
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1. Open a command prompt where you want the new project, and create a new file named `ChatWithOwnData.ts`. Copy the following code into the `ChatWithOwnData.ts` file.
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```typescript
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import "dotenv/config";
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import { AzureOpenAI } from "openai";
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import "@azure/openai/types";
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// Set the Azure and AI Search values from environment variables
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const endpoint = process.env["AZURE_OPENAI_ENDPOINT"];
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const apiKey = process.env["AZURE_OPENAI_API_KEY"];
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const searchEndpoint = process.env["AZURE_AI_SEARCH_ENDPOINT"];
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const searchKey = process.env["AZURE_AI_SEARCH_API_KEY"];
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const searchIndex = process.env["AZURE_AI_SEARCH_INDEX"];
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// Required Azure OpenAI deployment name and API version
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const deploymentName = "gpt-4";
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const apiVersion = "2024-07-01-preview";
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function getClient(): AzureOpenAI {
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return new AzureOpenAI({
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endpoint,
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apiKey: apiKey,
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deployment: deploymentName,
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apiVersion,
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});
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}
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async function main() {
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const client = getClient();
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const messages = [
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{ role: "user", content: "What are my available health plans?" },
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];
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console.log(`Message: ${messages.map((m) => m.content).join("\n")}`);
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const events = await client.chat.completions.create({
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stream: true,
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messages: [
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{
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role: "user",
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content:
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"What's the most common feedback we received from our customers about the product?",
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},
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],
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max_tokens: 128,
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model: "",
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data_sources: [
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{
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type: "azure_search",
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parameters: {
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endpoint: searchEndpoint,
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index_name: searchIndex,
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authentication: {
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type: "api_key",
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key: searchKey,
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```typescript
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import "dotenv/config";
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import { AzureOpenAI } from "openai";
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import "@azure/openai/types";
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// Set the Azure and AI Search values from environment variables
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const endpoint = process.env["AZURE_OPENAI_ENDPOINT"];
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const apiKey = process.env["AZURE_OPENAI_API_KEY"];
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const searchEndpoint = process.env["AZURE_AI_SEARCH_ENDPOINT"];
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const searchKey = process.env["AZURE_AI_SEARCH_API_KEY"];
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const searchIndex = process.env["AZURE_AI_SEARCH_INDEX"];
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// Required Azure OpenAI deployment name and API version
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const deploymentName = "gpt-4";
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const apiVersion = "2024-07-01-preview";
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function getClient(): AzureOpenAI {
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return new AzureOpenAI({
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endpoint,
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apiKey: apiKey,
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deployment: deploymentName,
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apiVersion,
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});
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}
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async function main() {
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const client = getClient();
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const messages = [
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{ role: "user", content: "What are my available health plans?" },
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];
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console.log(`Message: ${messages.map((m) => m.content).join("\n")}`);
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const events = await client.chat.completions.create({
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stream: true,
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messages: [
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{
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role: "user",
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content:
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"What's the most common feedback we received from our customers about the product?",
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},
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],
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max_tokens: 128,
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model: "",
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data_sources: [
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{
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type: "azure_search",
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parameters: {
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endpoint: searchEndpoint,
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index_name: searchIndex,
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authentication: {
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type: "api_key",
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key: searchKey,
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},
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},
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},
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},
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},
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],
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});
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let response = "";
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for await (const event of events) {
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for (const choice of event.choices) {
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const newText = choice.delta?.content;
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if (newText) {
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response += newText;
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// To see streaming results as they arrive, uncomment line below
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// console.log(newText);
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],
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});
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let response = "";
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for await (const event of events) {
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for (const choice of event.choices) {
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const newText = choice.delta?.content;
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if (newText) {
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response += newText;
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// To see streaming results as they arrive, uncomment line below
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// console.log(newText);
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}
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}
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}
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console.log(response);
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}
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}
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console.log(response);
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}
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main().catch((err) => {
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console.error("The sample encountered an error:", err);
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});
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```
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main().catch((err) => {
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console.error("The sample encountered an error:", err);
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});
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```
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The `@azure/openai/types` dependency is included to extend the OpenAI model for the `data_sources` property. This import is only necessary for TypeScript.
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@@ -141,84 +141,84 @@ The `@azure/openai/types` dependency is included to extend the OpenAI model for
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#### [JavaScript](#tab/javascript)
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1. Open a command prompt where you want the new project, and create a new file named `ChatWithOwnData.js`. Copy the following code into the `ChatWithOwnData.js` file.
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```javascript
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require("dotenv/config");
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const { AzureOpenAI } = require("openai");
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// Set the Azure and AI Search values from environment variables
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const endpoint = process.env["AZURE_OPENAI_ENDPOINT"];
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const apiKey = process.env["AZURE_OPENAI_API_KEY"];
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const searchEndpoint = process.env["AZURE_AI_SEARCH_ENDPOINT"];
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const searchKey = process.env["AZURE_AI_SEARCH_API_KEY"];
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const searchIndex = process.env["AZURE_AI_SEARCH_INDEX"];
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// Required Azure OpenAI deployment name and API version
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const deploymentName = "gpt-4";
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const apiVersion = "2024-07-01-preview";
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function getClient() {
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return new AzureOpenAI({
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endpoint,
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apiKey: apiKey,
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deployment: deploymentName,
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apiVersion,
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});
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}
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async function main() {
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const client = getClient();
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const messages = [
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{ role: "user", content: "What are my available health plans?" },
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];
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console.log(`Message: ${messages.map((m) => m.content).join("\n")}`);
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const events = await client.chat.completions.create({
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stream: true,
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messages: [
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{
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role: "user",
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content:
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"What's the most common feedback we received from our customers about the product?",
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},
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],
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max_tokens: 128,
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model: "",
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data_sources: [
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{
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type: "azure_search",
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parameters: {
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endpoint: searchEndpoint,
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index_name: searchIndex,
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authentication: {
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type: "api_key",
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key: searchKey,
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```javascript
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require("dotenv/config");
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const { AzureOpenAI } = require("openai");
148+
149+
// Set the Azure and AI Search values from environment variables
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const endpoint = process.env["AZURE_OPENAI_ENDPOINT"];
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const apiKey = process.env["AZURE_OPENAI_API_KEY"];
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const searchEndpoint = process.env["AZURE_AI_SEARCH_ENDPOINT"];
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const searchKey = process.env["AZURE_AI_SEARCH_API_KEY"];
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const searchIndex = process.env["AZURE_AI_SEARCH_INDEX"];
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// Required Azure OpenAI deployment name and API version
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const deploymentName = "gpt-4";
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const apiVersion = "2024-07-01-preview";
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function getClient() {
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return new AzureOpenAI({
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endpoint,
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apiKey: apiKey,
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deployment: deploymentName,
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apiVersion,
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});
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}
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async function main() {
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const client = getClient();
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const messages = [
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{ role: "user", content: "What are my available health plans?" },
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];
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console.log(`Message: ${messages.map((m) => m.content).join("\n")}`);
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const events = await client.chat.completions.create({
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stream: true,
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messages: [
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{
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role: "user",
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content:
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"What's the most common feedback we received from our customers about the product?",
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},
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],
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max_tokens: 128,
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model: "",
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data_sources: [
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{
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type: "azure_search",
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parameters: {
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endpoint: searchEndpoint,
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index_name: searchIndex,
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authentication: {
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type: "api_key",
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key: searchKey,
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},
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},
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},
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},
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},
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],
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});
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let response = "";
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for await (const event of events) {
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for (const choice of event.choices) {
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const newText = choice.delta?.content;
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if (newText) {
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response += newText;
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// To see streaming results as they arrive, uncomment line below
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// console.log(newText);
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],
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});
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let response = "";
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for await (const event of events) {
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for (const choice of event.choices) {
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const newText = choice.delta?.content;
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if (newText) {
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response += newText;
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// To see streaming results as they arrive, uncomment line below
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// console.log(newText);
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}
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}
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}
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console.log(response);
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}
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}
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console.log(response);
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}
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main().catch((err) => {
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console.error("The sample encountered an error:", err);
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});
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```
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main().catch((err) => {
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console.error("The sample encountered an error:", err);
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});
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```
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1. Run the application with the following command:
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