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rename env variables (Azure#37646)
### Packages impacted by this PR ### Issues associated with this PR ### Describe the problem that is addressed by this PR ### rename env varibales AZURE_AI_PROJECT_ENDPOINT to FOUNDRY_PROJECT_ENDPOINT MODEL_DEPLOYMENT_NAME to FOUNDRY_MODEL_NAME AZURE_AI_AGENT_NAME to FOUNDRY_AGENT_NAME AZURE_AI_MODEL_DEPLOYMENT_NAME to FOUNDRY_MODEL_NAME AZURE_AI_CHAT_MODEL_DEPLOYMENT_NAME to MEMORY_STORE_CHAT_MODEL_DEPLOYMENT_NAME AZURE_AI_EMBEDDING_MODEL_DEPLOYMENT_NAME to MEMORY_STORE_EMBEDDING_MODEL_DEPLOYMENT_NAME ### What are the possible designs available to address the problem? If there are more than one possible design, why was the one in this PR chosen? ### Are there test cases added in this PR? _(If not, why?)_ ### Provide a list of related PRs _(if any)_ ### Command used to generate this PR:**_(Applicable only to SDK release request PRs)_ ### Checklists - [ ] Added impacted package name to the issue description - [ ] Does this PR needs any fixes in the SDK Generator?** _(If so, create an Issue in the [Autorest/typescript](https://github.com/Azure/autorest.typescript) repository and link it here)_ - [ ] Added a changelog (if necessary)
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‎sdk/ai/ai-projects/README.md‎

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@@ -37,7 +37,7 @@ The client library uses version `v1` of the Microsoft Foundry [data plane REST A
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[Product documentation](https://aka.ms/azsdk/azure-ai-projects-v2/product-doc)
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| [Samples](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/ai/ai-projects/samples)
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| [Package (npm)](https://www.npmjs.com/package/@azure/ai-projects)
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| [API reference documentation](https://learn.microsoft.com/javascript/api/overview/azure/ai-projects-readme?view=azure-node-preview)
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| [API reference documentation](https://learn.microsoft.com/javascript/api/overview/azure/ai-projects-readme?view=azure-node-latest)
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| [SDK source code](https://github.com/Azure/azure-sdk-for-js/tree/main/sdk/ai/ai-projects)
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## Table of contents
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- [LTS versions of Node.js](https://github.com/nodejs/release#release-schedule)
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- An [Azure subscription][azure_sub].
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- A [project in Microsoft Foundry](https://learn.microsoft.com/azure/ai-studio/how-to/create-projects).
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- The project endpoint URL of the form `https://your-ai-services-account-name.services.ai.azure.com/api/projects/your-project-name`. It can be found in your Microsoft Foundry Project overview page. Below we will assume the environment variable `AZURE_AI_PROJECT_ENDPOINT` was defined to hold this value.
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- The project endpoint URL of the form `https://your-ai-services-account-name.services.ai.azure.com/api/projects/your-project-name`. It can be found in your Microsoft Foundry Project overview page. Below we will assume the environment variable `FOUNDRY_PROJECT_ENDPOINT` was defined to hold this value.
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### Authorization
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Entra ID is the only authentication method supported at the moment by the client.
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To construct an `AIProjectsClient`, the `projectEndpoint` can be fetched from [projectEndpoint][ai_project_client_endpoint]. Below we will assume the environment variable `AZURE_AI_PROJECT_ENDPOINT` was defined to hold this value:
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To construct an `AIProjectsClient`, the `projectEndpoint` can be fetched from [projectEndpoint][ai_project_client_endpoint]. Below we will assume the environment variable `FOUNDRY_PROJECT_ENDPOINT` was defined to hold this value:
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```ts snippet:setup
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import { AIProjectClient } from "@azure/ai-projects";
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import { DefaultAzureCredential } from "@azure/identity";
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const projectEndpoint = process.env["AZURE_AI_PROJECT_ENDPOINT"] || "<project endpoint string>";
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const projectEndpoint = process.env["FOUNDRY_PROJECT_ENDPOINT"] || "<project endpoint string>";
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project = new AIProjectClient(projectEndpoint, new DefaultAzureCredential());
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```
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```ts snippet:agent-memory-search
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const memoryStoreName = "AgentMemoryStore";
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const embeddingModelDeployment =
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process.env["AZURE_AI_EMBEDDING_MODEL_DEPLOYMENT_NAME"] || "<embedding model>";
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process.env["MEMORY_STORE_EMBEDDING_MODEL_DEPLOYMENT_NAME"] || "<embedding model>";
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const scope = "user_123";
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const memoryStore = await project.beta.memoryStores.create(
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memoryStoreName,

‎sdk/ai/ai-projects/sample.env‎

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AZURE_AI_PROJECT_ENDPOINT="<project connection string from Microsoft Foundry>"
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AZURE_AI_CHAT_MODEL_DEPLOYMENT_NAME="<memory chat model deployment name>"
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AZURE_AI_EMBEDDING_MODEL_DEPLOYMENT_NAME="<memory embedding model deployment name>"
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MODEL_DEPLOYMENT_NAME="<model deployment name>"
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FOUNDRY_PROJECT_ENDPOINT="<project connection string from Microsoft Foundry>"
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FOUNDRY_PROJECT_DEFAULT_ENDPOINT="<default project connection string from Microsoft Foundry>"
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FOUNDRY_MODEL_NAME="<model deployment name>"
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FOUNDRY_AGENT_NAME="<agent name>"
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MODEL_API_KEY="<model api key>"
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MODEL_ENDPOINT="<model endpoint url>"
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MODEL_PUBLISHER="<model publisher name>"

‎sdk/ai/ai-projects/samples-dev/agents/agentBasic.ts‎

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import { AIProjectClient } from "@azure/ai-projects";
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import "dotenv/config";
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const projectEndpoint = process.env["AZURE_AI_PROJECT_ENDPOINT"] || "<project endpoint>";
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const deploymentName = process.env["MODEL_DEPLOYMENT_NAME"] || "<model deployment name>";
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const projectEndpoint = process.env["FOUNDRY_PROJECT_ENDPOINT"] || "<project endpoint>";
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const deploymentName = process.env["FOUNDRY_MODEL_NAME"] || "<model deployment name>";
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export async function main(): Promise<void> {
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// Create AI Project client

‎sdk/ai/ai-projects/samples-dev/agents/agentBasicWithDefaultProject.ts‎

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import { AIProjectClient } from "@azure/ai-projects";
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import "dotenv/config";
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const projectEndpoint = process.env["AZURE_AI_PROJECT_DEFAULT_ENDPOINT"] || "<project endpoint>";
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const deploymentName = process.env["MODEL_DEPLOYMENT_NAME"] || "<model deployment name>";
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const projectEndpoint = process.env["FOUNDRY_PROJECT_DEFAULT_ENDPOINT"] || "<project endpoint>";
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const deploymentName = process.env["FOUNDRY_MODEL_NAME"] || "<model deployment name>";
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export async function main(): Promise<void> {
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// Create AI Project client

‎sdk/ai/ai-projects/samples-dev/agents/agentCodeInterpreter.ts‎

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import { AIProjectClient } from "@azure/ai-projects";
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import "dotenv/config";
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const projectEndpoint = process.env["AZURE_AI_PROJECT_ENDPOINT"] || "<project endpoint>";
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const deploymentName = process.env["MODEL_DEPLOYMENT_NAME"] || "<model deployment name>";
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const projectEndpoint = process.env["FOUNDRY_PROJECT_ENDPOINT"] || "<project endpoint>";
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const deploymentName = process.env["FOUNDRY_MODEL_NAME"] || "<model deployment name>";
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export async function main(): Promise<void> {
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// Create AI Project client

‎sdk/ai/ai-projects/samples-dev/agents/agentConversationCurd.ts‎

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import "dotenv/config";
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const projectEndpoint = process.env["AZURE_AI_PROJECT_ENDPOINT"] || "<project endpoint>";
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const projectEndpoint = process.env["FOUNDRY_PROJECT_ENDPOINT"] || "<project endpoint>";
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async function main(): Promise<void> {
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const project = new AIProjectClient(projectEndpoint, new DefaultAzureCredential());

‎sdk/ai/ai-projects/samples-dev/agents/agentCurd.ts‎

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import { AIProjectClient } from "@azure/ai-projects";
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import "dotenv/config";
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const projectEndpoint = process.env["AZURE_AI_PROJECT_ENDPOINT"] || "<project endpoint>";
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const deploymentName = process.env["MODEL_DEPLOYMENT_NAME"] || "<model deployment name>";
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const projectEndpoint = process.env["FOUNDRY_PROJECT_ENDPOINT"] || "<project endpoint>";
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const deploymentName = process.env["FOUNDRY_MODEL_NAME"] || "<model deployment name>";
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async function main(): Promise<void> {
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const credential = new DefaultAzureCredential();

‎sdk/ai/ai-projects/samples-dev/agents/agentFunctionTool.ts‎

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import { AIProjectClient } from "@azure/ai-projects";
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import "dotenv/config";
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const projectEndpoint = process.env["AZURE_AI_PROJECT_ENDPOINT"] || "<project endpoint>";
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const deploymentName = process.env["MODEL_DEPLOYMENT_NAME"] || "<model deployment name>";
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const projectEndpoint = process.env["FOUNDRY_PROJECT_ENDPOINT"] || "<project endpoint>";
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const deploymentName = process.env["FOUNDRY_MODEL_NAME"] || "<model deployment name>";
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/**
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* Define a function tool for the model to use

‎sdk/ai/ai-projects/samples-dev/agents/agentStreamEvents.ts‎

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import { AIProjectClient } from "@azure/ai-projects";
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import "dotenv/config";
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const projectEndpoint = process.env["AZURE_AI_PROJECT_ENDPOINT"] || "<project endpoint>";
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const deploymentName = process.env["MODEL_DEPLOYMENT_NAME"] || "<model deployment name>";
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const projectEndpoint = process.env["FOUNDRY_PROJECT_ENDPOINT"] || "<project endpoint>";
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const deploymentName = process.env["FOUNDRY_MODEL_NAME"] || "<model deployment name>";
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export async function main(): Promise<void> {
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// Create AI Project client

‎sdk/ai/ai-projects/samples-dev/agents/agentStructureOutput.ts‎

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import { AIProjectClient } from "@azure/ai-projects";
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import "dotenv/config";
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const projectEndpoint = process.env["AZURE_AI_PROJECT_ENDPOINT"] || "<project endpoint>";
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const deploymentName = process.env["MODEL_DEPLOYMENT_NAME"] || "<model deployment name>";
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const projectEndpoint = process.env["FOUNDRY_PROJECT_ENDPOINT"] || "<project endpoint>";
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const deploymentName = process.env["FOUNDRY_MODEL_NAME"] || "<model deployment name>";
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export async function main(): Promise<void> {
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// Create AI Project client

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