[Projects] v2 update LRO poller for memory ops

sdk/ai/ai-projects/review/ai-projects-node.api.md

@@ -1841,7 +1841,7 @@ export interface MemoryStoresOperations {
     list: (options?: MemoryStoresListMemoryStoresOptionalParams) => PagedAsyncIterableIterator<MemoryStore>;
     searchMemories: (name: string, scope: string, options?: MemoryStoresSearchMemoriesOptionalParams) => Promise<MemoryStoreSearchResponse>;
     update: (name: string, options?: MemoryStoresUpdateMemoryStoreOptionalParams) => Promise<MemoryStore>;
-    updateMemories: (name: string, scope: string, options?: MemoryStoresUpdateMemoriesOptionalParams) => PollerLike<OperationState_2<MemoryStoreUpdateResult>, MemoryStoreUpdateResult>;
+    updateMemories: (name: string, scope: string, options?: MemoryStoresUpdateMemoriesOptionalParams) => MemoryStoreUpdateMemoriesPoller;
 }
 
 // @public
@@ -1867,6 +1867,20 @@ export interface MemoryStoresUpdateMemoryStoreOptionalParams extends OperationOp
     metadata?: Record<string, string>;
 }
 
+// @public
+export type MemoryStoreUpdateMemoriesPoller = PollerLike<MemoryStoreUpdateOperationState, MemoryStoreUpdateResult> & {
+    readonly updateId?: string;
+    readonly updateStatus?: MemoryStoreUpdateStatus;
+    readonly supersededBy?: string;
+};
+
+// @public
+export type MemoryStoreUpdateOperationState = OperationState_2<MemoryStoreUpdateResult> & {
+    updateId?: string;
+    updateStatus?: MemoryStoreUpdateStatus;
+    supersededBy?: string;
+};
+
 // @public
 export interface MemoryStoreUpdateResponse {
     error?: ApiError;

sdk/ai/ai-projects/sample.env

@@ -1,4 +1,6 @@
 AZURE_AI_PROJECT_ENDPOINT="<project connection string from Microsoft Foundry>"
+AZURE_AI_CHAT_MODEL_DEPLOYMENT_NAME="<memory chat model deployment name>"
+AZURE_AI_EMBEDDING_MODEL_DEPLOYMENT_NAME="<memory embedding model deployment name>"
 MODEL_DEPLOYMENT_NAME="<model deployment name>"
 MODEL_API_KEY="<model api key>"
 MODEL_ENDPOINT="<model endpoint url>"
@@ -22,4 +24,3 @@ IMAGE_GENERATION_MODEL_DEPLOYMENT_NAME="<image generation model deployment name>
 
 MCP_PROJECT_CONNECTION_ID="<mcp project connection id>"
 TRIPADVISOR_PROJECT_CONNECTION_ID="<tripadvisor project connection id>"
-

sdk/ai/ai-projects/samples-dev/agents/tools/agentAgentToAgent.ts

@@ -19,7 +19,7 @@ import * as readline from "readline";
 import "dotenv/config";
 
 const projectEndpoint = process.env["AZURE_AI_PROJECT_ENDPOINT"] || "<project endpoint>";
-const deploymentName = process.env["AZURE_AI_MODEL_DEPLOYMENT_NAME"] || "<model deployment name>";
+const deploymentName = process.env["MODEL_DEPLOYMENT_NAME"] || "<model deployment name>";
 const a2aProjectConnectionId =
   process.env["A2A_PROJECT_CONNECTION_ID"] || "<a2a project connection id>";
 

sdk/ai/ai-projects/samples-dev/agents/tools/agentAiSearch.ts

@@ -18,7 +18,7 @@ import * as readline from "readline";
 import "dotenv/config";
 
 const projectEndpoint = process.env["AZURE_AI_PROJECT_ENDPOINT"] || "<project endpoint>";
-const deploymentName = process.env["AZURE_AI_MODEL_DEPLOYMENT_NAME"] || "<model deployment name>";
+const deploymentName = process.env["MODEL_DEPLOYMENT_NAME"] || "<model deployment name>";
 const aiSearchConnectionId =
   process.env["AZURE_AI_SEARCH_CONNECTION_ID"] || "<ai search project connection id>";
 const aiSearchIndexName = process.env["AI_SEARCH_INDEX_NAME"] || "<ai search index name>";

sdk/ai/ai-projects/samples-dev/agents/tools/agentBingCustomSearch.ts

@@ -18,7 +18,7 @@ import * as readline from "readline";
 import "dotenv/config";
 
 const projectEndpoint = process.env["AZURE_AI_PROJECT_ENDPOINT"] || "<project endpoint>";
-const deploymentName = process.env["AZURE_AI_MODEL_DEPLOYMENT_NAME"] || "<model deployment name>";
+const deploymentName = process.env["MODEL_DEPLOYMENT_NAME"] || "<model deployment name>";
 const bingCustomSearchProjectConnectionId =
   process.env["BING_CUSTOM_SEARCH_PROJECT_CONNECTION_ID"] ||
   "<bing custom search project connection id>";

sdk/ai/ai-projects/samples-dev/agents/tools/agentBingGrounding.ts

@@ -17,7 +17,7 @@ import { AIProjectClient } from "@azure/ai-projects";
 import "dotenv/config";
 
 const projectEndpoint = process.env["AZURE_AI_PROJECT_ENDPOINT"] || "<project endpoint>";
-const deploymentName = process.env["AZURE_AI_MODEL_DEPLOYMENT_NAME"] || "<model deployment name>";
+const deploymentName = process.env["MODEL_DEPLOYMENT_NAME"] || "<model deployment name>";
 const bingProjectConnectionId =
   process.env["BING_PROJECT_CONNECTION_ID"] || "<bing project connection id>";
 

sdk/ai/ai-projects/samples-dev/agents/tools/agentBrowserAutomation.ts

@@ -17,7 +17,7 @@ import { AIProjectClient } from "@azure/ai-projects";
 import "dotenv/config";
 
 const projectEndpoint = process.env["AZURE_AI_PROJECT_ENDPOINT"] || "<project endpoint>";
-const deploymentName = process.env["AZURE_AI_MODEL_DEPLOYMENT_NAME"] || "<model deployment name>";
+const deploymentName = process.env["MODEL_DEPLOYMENT_NAME"] || "<model deployment name>";
 const browserAutomationProjectConnectionId =
   process.env["BROWSER_AUTOMATION_PROJECT_CONNECTION_ID"] ||
   "<browser automation project connection id>";

sdk/ai/ai-projects/samples-dev/agents/tools/agentMemorySearch.ts

@@ -0,0 +1,174 @@
+// Copyright (c) Microsoft Corporation.
+// Licensed under the MIT License.
+
+/**
+ * This sample demonstrates how to add conversational memory to an agent by using the
+ * Memory Search tool. The agent stores memories in a memory store and can recall them
+ * in later conversations.
+ *
+ * @summary Create an agent with Memory Search, capture memories from a conversation,
+ * and retrieve them in a new conversation.
+ *
+ * @azsdk-weight 100
+ */
+
+import { DefaultAzureCredential } from "@azure/identity";
+import {
+  AIProjectClient,
+  MemoryStoreDefaultDefinition,
+  MemoryStoreDefaultOptions,
+  MemorySearchTool,
+} from "@azure/ai-projects";
+import "dotenv/config";
+
+const projectEndpoint = process.env["AZURE_AI_PROJECT_ENDPOINT"] || "<project endpoint>";
+const agentModelDeployment =
+  process.env["MODEL_DEPLOYMENT_NAME"] || "<agent model deployment name>";
+const chatModelDeployment =
+  process.env["AZURE_AI_CHAT_MODEL_DEPLOYMENT_NAME"] || "<memory chat model deployment name>";
+const embeddingModelDeployment =
+  process.env["AZURE_AI_EMBEDDING_MODEL_DEPLOYMENT_NAME"] ||
+  "<memory embedding model deployment name>";
+
+const memoryStoreName = "my_memory_store_123";
+const scope = "user_123";
+
+function delay(ms: number): Promise<void> {
+  return new Promise((resolve) => setTimeout(resolve, ms));
+}
+
+export async function main(): Promise<void> {
+  const project = new AIProjectClient(projectEndpoint, new DefaultAzureCredential());
+  const openAIClient = await project.getOpenAIClient();
+
+  let conversationId: string | undefined;
+  let followUpConversationId: string | undefined;
+  let agentVersion:
+    | {
+        name: string;
+        version: string;
+      }
+    | undefined;
+
+  try {
+    // Clean up an existing memory store if it already exists
+    try {
+      await project.memoryStores.delete(memoryStoreName);
+      console.log(`Memory store '${memoryStoreName}' deleted`);
+    } catch (error: any) {
+      if (error?.statusCode !== 404) {
+        throw error;
+      }
+    }
+
+    // Create a memory store with chat and embedding models
+    const memoryStore = await project.memoryStores.create(
+      memoryStoreName,
+      {
+        kind: "default",
+        chat_model: chatModelDeployment,
+        embedding_model: embeddingModelDeployment,
+        options: {
+          user_profile_enabled: true,
+          chat_summary_enabled: true,
+        } satisfies MemoryStoreDefaultOptions,
+      } satisfies MemoryStoreDefaultDefinition,
+      {
+        description: "Memory store for agent conversations",
+      },
+    );
+    const chatModelUsed =
+      "chat_model" in memoryStore.definition
+        ? memoryStore.definition.chat_model
+        : chatModelDeployment;
+    console.log(
+      `Created memory store: ${memoryStore.name} (${memoryStore.id}) using chat model '${chatModelUsed}'`,
+    );
+
+    // Configure Memory Search tool to attach to the agent
+    const memorySearchTool: MemorySearchTool = {
+      type: "memory_search",
+      memory_store_name: memoryStore.name,
+      scope,
+      update_delay: 1, // wait briefly after conversation inactivity before updating memories
+    };
+
+    // Create an agent that will use the Memory Search tool
+    const agent = await project.agents.createVersion("MemorySearchAgent", {
+      kind: "prompt",
+      model: agentModelDeployment,
+      instructions:
+        "You are a helpful assistant that remembers user preferences using the memory search tool.",
+      tools: [memorySearchTool],
+    });
+    agentVersion = {
+      name: agent.name,
+      version: agent.version,
+    };
+    console.log(`Agent created (id: ${agent.id}, name: ${agent.name}, version: ${agent.version})`);
+
+    // Start a conversation and provide details the agent should remember
+    const conversation = await openAIClient.conversations.create();
+    conversationId = conversation.id;
+    console.log(`Conversation started (${conversation.id}). Sending a message to seed memories...`);
+
+    const firstResponse = await openAIClient.responses.create(
+      {
+        input: "I prefer dark roast coffee and usually drink it in the morning.",
+        conversation: conversation.id,
+      },
+      {
+        body: { agent: { name: agent.name, type: "agent_reference" } },
+      },
+    );
+    console.log(`Initial response: ${firstResponse.output_text}`);
+
+    // Allow time for the memory store to update from this conversation
+    console.log("Waiting for the memory store to capture the new memory...");
+    await delay(60000);
+
+    // Create a follow-up conversation and ask the agent to recall the stored memory
+    const followUpConversation = await openAIClient.conversations.create();
+    followUpConversationId = followUpConversation.id;
+    console.log(`Follow-up conversation started (${followUpConversation.id}).`);
+
+    const followUpResponse = await openAIClient.responses.create(
+      {
+        input: "Can you remind me of my usual coffee order?",
+        conversation: followUpConversation.id,
+      },
+      {
+        body: { agent: { name: agent.name, type: "agent_reference" } },
+      },
+    );
+    console.log(`Follow-up response: ${followUpResponse.output_text}`);
+  } finally {
+    console.log("\nCleaning up resources...");
+    if (conversationId) {
+      await openAIClient.conversations.delete(conversationId);
+      console.log(`Conversation ${conversationId} deleted`);
+    }
+    if (followUpConversationId) {
+      await openAIClient.conversations.delete(followUpConversationId);
+      console.log(`Conversation ${followUpConversationId} deleted`);
+    }
+    if (agentVersion) {
+      await project.agents.deleteVersion(agentVersion.name, agentVersion.version);
+      console.log("Agent deleted");
+    }
+    try {
+      await project.memoryStores.delete(memoryStoreName);
+      console.log("Memory store deleted");
+    } catch (error: any) {
+      if (error?.statusCode !== 404) {
+        throw error;
+      }
+    }
+  }
+
+  console.log("\nMemory Search agent sample completed!");
+}
+
+main().catch((err) => {
+  console.error("The sample encountered an error:", err);
+});

sdk/ai/ai-projects/samples-dev/agents/tools/agentOpenApi.ts

@@ -25,7 +25,7 @@ import * as path from "path";
 import "dotenv/config";
 
 const projectEndpoint = process.env["AZURE_AI_PROJECT_ENDPOINT"] || "<project endpoint>";
-const deploymentName = process.env["AZURE_AI_MODEL_DEPLOYMENT_NAME"] || "<model deployment name>";
+const deploymentName = process.env["MODEL_DEPLOYMENT_NAME"] || "<model deployment name>";
 const weatherSpecPath = path.resolve(__dirname, "../assets", "weather_openapi.json");
 
 function loadOpenApiSpec(specPath: string): unknown {

sdk/ai/ai-projects/samples-dev/agents/tools/agentOpenApiConnectionAuth.ts

@@ -25,7 +25,7 @@ import * as path from "path";
 import "dotenv/config";
 
 const projectEndpoint = process.env["AZURE_AI_PROJECT_ENDPOINT"] || "<project endpoint>";
-const deploymentName = process.env["AZURE_AI_MODEL_DEPLOYMENT_NAME"] || "<model deployment name>";
+const deploymentName = process.env["MODEL_DEPLOYMENT_NAME"] || "<model deployment name>";
 const tripAdvisorProjectConnectionId =
   process.env["TRIPADVISOR_PROJECT_CONNECTION_ID"] || "<tripadvisor project connection id>";
 const tripAdvisorSpecPath = path.resolve(__dirname, "../assets", "tripadvisor_openapi.json");

sdk/ai/ai-projects/samples-dev/agents/tools/agentSharepoint.ts

@@ -17,7 +17,7 @@ import { AIProjectClient } from "@azure/ai-projects";
 import "dotenv/config";
 
 const projectEndpoint = process.env["AZURE_AI_PROJECT_ENDPOINT"] || "<project endpoint>";
-const deploymentName = process.env["AZURE_AI_MODEL_DEPLOYMENT_NAME"] || "<model deployment name>";
+const deploymentName = process.env["MODEL_DEPLOYMENT_NAME"] || "<model deployment name>";
 const sharepointProjectConnectionId =
   process.env["SHAREPOINT_PROJECT_CONNECTION_ID"] || "<sharepoint project connection id>";