adding js package

Author: aahill

articles/ai-foundry/agents/how-to/tools/deep-research-samples.md

@@ -177,10 +177,220 @@ client.Administration.DeleteAgent(agentId: agent.Id);
 
 :::zone-end 
 
-:::zone pivot="javascript"
+:::zone pivot="typescript"
 
-```javascript
-TBD
+> [!NOTE]
+> You need the latest preview version of the `@azure/ai-projects` package.
+
+```typescript
+import type {
+  MessageTextContent,
+  ThreadMessage,
+  DeepResearchToolDefinition,
+  MessageTextUrlCitationAnnotation,
+} from "@azure/ai-agents";
+import { AgentsClient, isOutputOfType } from "@azure/ai-agents";
+import { DefaultAzureCredential } from "@azure/identity";
+
+import "dotenv/config";
+
+const projectEndpoint = process.env["PROJECT_ENDPOINT"] || "<project endpoint>";
+const modelDeploymentName = process.env["MODEL_DEPLOYMENT_NAME"] || "gpt-4o";
+const deepResearchModelDeploymentName =
+  process.env["DEEP_RESEARCH_MODEL_DEPLOYMENT_NAME"] || "gpt-4o";
+const bingConnectionId = process.env["AZURE_BING_CONNECTION_ID"] || "<connection-id>";
+
+/**
+ * Fetches and prints new agent response from the thread
+ * @param threadId - The thread ID
+ * @param client - The AgentsClient instance
+ * @param lastMessageId - The ID of the last message processed
+ * @returns The ID of the newest message, or undefined if no new message
+ */
+async function fetchAndPrintNewAgentResponse(
+  threadId: string,
+  client: AgentsClient,
+  lastMessageId?: string,
+): Promise<string | undefined> {
+  const messages = client.messages.list(threadId);
+  let latestMessage: ThreadMessage | undefined;
+  for await (const msg of messages) {
+    if (msg.role === "assistant") {
+      latestMessage = msg;
+      break;
+    }
+  }
+
+  if (!latestMessage || latestMessage.id === lastMessageId) {
+    return lastMessageId;
+  }
+
+  console.log("\nAgent response:");
+
+  // Print text content
+  for (const content of latestMessage.content) {
+    if (isOutputOfType<MessageTextContent>(content, "text")) {
+      console.log(content.text.value);
+    }
+  }
+
+  const urlCitations = getUrlCitationsFromMessage(latestMessage);
+  if (urlCitations.length > 0) {
+    console.log("\nURL Citations:");
+    for (const citation of urlCitations) {
+      console.log(`URL Citations: [${citation.title}](${citation.url})`);
+    }
+  }
+
+  return latestMessage.id;
+}
+
+/**
+ * Extracts URL citations from a thread message
+ * @param message - The thread message
+ * @returns Array of URL citations
+ */
+function getUrlCitationsFromMessage(message: ThreadMessage): Array<{ title: string; url: string }> {
+  const citations: Array<{ title: string; url: string }> = [];
+
+  for (const content of message.content) {
+    if (isOutputOfType<MessageTextContent>(content, "text")) {
+      for (const annotation of content.text.annotations) {
+        if (isOutputOfType<MessageTextUrlCitationAnnotation>(annotation, "url_citation")) {
+          citations.push({
+            title: annotation.urlCitation.title || annotation.urlCitation.url,
+            url: annotation.urlCitation.url,
+          });
+        }
+      }
+    }
+  }
+
+  return citations;
+}
+
+/**
+ * Creates a research summary from the final message
+ * @param message - The thread message containing the research results
+ * @param filepath - The file path to write the summary to
+ */
+function createResearchSummary(message: ThreadMessage): void {
+  if (!message) {
+    console.log("No message content provided, cannot create research summary.");
+    return;
+  }
+
+  let content = "";
+
+  // Write text summary
+  const textSummaries: string[] = [];
+  for (const contentItem of message.content) {
+    if (isOutputOfType<MessageTextContent>(contentItem, "text")) {
+      textSummaries.push(contentItem.text.value.trim());
+    }
+  }
+  content += textSummaries.join("\n\n");
+
+  // Write unique URL citations, if present
+  const urlCitations = getUrlCitationsFromMessage(message);
+  if (urlCitations.length > 0) {
+    content += "\n\n## References\n";
+    const seenUrls = new Set<string>();
+    for (const citation of urlCitations) {
+      if (!seenUrls.has(citation.url)) {
+        content += `- [${citation.title}](${citation.url})\n`;
+        seenUrls.add(citation.url);
+      }
+    }
+  }
+
+  // writeFileSync(filepath, content, "utf-8");
+  console.log(`Research summary created:\n${content}`);
+  // console.log(`Research summary written to '${filepath}'.`);
+}
+
+export async function main(): Promise<void> {
+  // Create an Azure AI Client
+  const client = new AgentsClient(projectEndpoint, new DefaultAzureCredential());
+
+  // Create Deep Research tool definition
+  const deepResearchTool: DeepResearchToolDefinition = {
+    type: "deep_research",
+    deepResearch: {
+      deepResearchModel: deepResearchModelDeploymentName,
+      deepResearchBingGroundingConnections: [
+        {
+          connectionId: bingConnectionId,
+        },
+      ],
+    },
+  };
+
+  // Create agent with the Deep Research tool
+  const agent = await client.createAgent(modelDeploymentName, {
+    name: "my-agent",
+    instructions: "You are a helpful Agent that assists in researching scientific topics.",
+    tools: [deepResearchTool],
+  });
+  console.log(`Created agent, ID: ${agent.id}`);
+
+  // Create thread for communication
+  const thread = await client.threads.create();
+  console.log(`Created thread, ID: ${thread.id}`);
+
+  // Create message to thread
+  const message = await client.messages.create(
+    thread.id,
+    "user",
+    "Research the current scientific understanding of orca intelligence and communication, focusing on recent (preferably past 5 years) peer-reviewed studies, comparisons with other intelligent species such as dolphins or primates, specific cognitive abilities like problem-solving and social learning, and detailed analyses of vocal and non-vocal communication systems—please include notable authors or landmark papers if applicable.",
+  );
+  console.log(`Created message, ID: ${message.id}`);
+
+  console.log("Start processing the message... this may take a few minutes to finish. Be patient!");
+
+  // Create and poll the run
+  const run = await client.runs.create(thread.id, agent.id);
+  let lastMessageId: string | undefined;
+
+  // Poll the run status
+  let currentRun = run;
+  while (currentRun.status === "queued" || currentRun.status === "in_progress") {
+    await new Promise((resolve) => setTimeout(resolve, 1000)); // Wait 1 second
+    currentRun = await client.runs.get(thread.id, run.id);
+
+    lastMessageId = await fetchAndPrintNewAgentResponse(thread.id, client, lastMessageId);
+    console.log(`Run status: ${currentRun.status}`);
+  }
+
+  console.log(`Run finished with status: ${currentRun.status}, ID: ${currentRun.id}`);
+
+  if (currentRun.status === "failed") {
+    console.log(`Run failed: ${currentRun.lastError}`);
+  }
+
+  // Fetch the final message from the agent and create a research summary
+  const messages = client.messages.list(thread.id, { order: "desc", limit: 10 });
+  let finalMessage: ThreadMessage | undefined;
+
+  for await (const msg of messages) {
+    if (msg.role === "assistant") {
+      finalMessage = msg;
+      break;
+    }
+  }
+
+  if (finalMessage) {
+    createResearchSummary(finalMessage);
+  }
+
+  // Clean-up and delete the agent once the run is finished
+  await client.deleteAgent(agent.id);
+  console.log("Deleted agent");
+}
+
+main().catch((err) => {
+  console.error("The sample encountered an error:", err);
+});
 ```
 
 :::zone-end 

zone-pivots/zone-pivot-groups.yml

@@ -1102,12 +1102,10 @@ groups:
   title: Selections
   prompt: What would you like to see?
   pivots:
-  - id: portal
-    title: Azure AI Foundry portal
   - id: csharp
     title: C#
-  - id: javascript
-    title: JavaScript
+  - id: typescript
+    title: TypeScript
   - id: python
     title: Python
 # owner: aahi