feat: support multi agent for ts

.changeset/yellow-jokes-protect.md

@@ -0,0 +1,5 @@
+---
+"create-llama": patch
+---
+
+Add multi agents template for Typescript

e2e/multiagent_template.spec.ts

@@ -10,19 +10,19 @@ import type {
 } from "../helpers";
 import { createTestDir, runCreateLlama, type AppType } from "./utils";
 
-const templateFramework: TemplateFramework = "fastapi";
+const templateFramework: TemplateFramework = process.env.FRAMEWORK
+  ? (process.env.FRAMEWORK as TemplateFramework)
+  : "fastapi";
 const dataSource: string = "--example-file";
 const templateUI: TemplateUI = "shadcn";
 const templatePostInstallAction: TemplatePostInstallAction = "runApp";
-const appType: AppType = "--frontend";
+const appType: AppType = templateFramework === "nextjs" ? "" : "--frontend";
 const userMessage = "Write a blog post about physical standards for letters";
 
 test.describe(`Test multiagent template ${templateFramework} ${dataSource} ${templateUI} ${appType} ${templatePostInstallAction}`, async () => {
   test.skip(
-    process.platform !== "linux" ||
-      process.env.FRAMEWORK !== "fastapi" ||
-      process.env.DATASOURCE === "--no-files",
-    "The multiagent template currently only works with FastAPI and files. We also only run on Linux to speed up tests.",
+    process.platform !== "linux" || process.env.DATASOURCE === "--no-files",
+    "The multiagent template currently only works with files. We also only run on Linux to speed up tests.",
   );
   let port: number;
   let externalPort: number;

helpers/typescript.ts

@@ -33,8 +33,7 @@ export const installTSTemplate = async ({
    * Copy the template files to the target directory.
    */
   console.log("\nInitializing project with template:", template, "\n");
-  const type = template === "multiagent" ? "streaming" : template; // use nextjs streaming template for multiagent
-  const templatePath = path.join(templatesDir, "types", type, framework);
+  const templatePath = path.join(templatesDir, "types", "streaming", framework);
   const copySource = ["**"];
 
   await copy(copySource, root, {
@@ -124,6 +123,30 @@ export const installTSTemplate = async ({
     cwd: path.join(compPath, "vectordbs", "typescript", vectorDb ?? "none"),
   });
 
+  if (template === "multiagent") {
+    const multiagentPath = path.join(compPath, "multiagent", "typescript");
+
+    // copy workflow code for multiagent template
+    await copy("**", path.join(root, relativeEngineDestPath, "workflow"), {
+      parents: true,
+      cwd: path.join(multiagentPath, "workflow"),
+    });
+
+    if (framework === "nextjs") {
+      // patch route.ts file
+      await copy("**", path.join(root, relativeEngineDestPath), {
+        parents: true,
+        cwd: path.join(multiagentPath, "nextjs"),
+      });
+    } else if (framework === "express") {
+      // patch chat.controller.ts file
+      await copy("**", path.join(root, relativeEngineDestPath), {
+        parents: true,
+        cwd: path.join(multiagentPath, "express"),
+      });
+    }
+  }
+
   // copy loader component (TS only supports llama_parse and file for now)
   const loaderFolder = useLlamaParse ? "llama_parse" : "file";
   await copy("**", enginePath, {
@@ -145,6 +168,11 @@ export const installTSTemplate = async ({
     cwd: path.join(compPath, "engines", "typescript", engine),
   });
 
+  // copy settings to engine folder
+  await copy("**", enginePath, {
+    cwd: path.join(compPath, "settings", "typescript"),
+  });
+
   /**
    * Copy the selected UI files to the target directory and reference it.
    */

questions.ts

@@ -410,10 +410,7 @@ export const askQuestions = async (
     return; // early return - no further questions needed for llamapack projects
   }
 
-  if (program.template === "multiagent") {
-    // TODO: multi-agents currently only supports FastAPI
-    program.framework = preferences.framework = "fastapi";
-  } else if (program.template === "extractor") {
+  if (program.template === "extractor") {
     // Extractor template only supports FastAPI, empty data sources, and llamacloud
     // So we just use example file for extractor template, this allows user to choose vector database later
     program.dataSources = [EXAMPLE_FILE];

templates/components/multiagent/typescript/express/chat.controller.ts

@@ -0,0 +1,34 @@
+import { Message, StreamData, streamToResponse } from "ai";
+import { Request, Response } from "express";
+import { ChatMessage } from "llamaindex";
+import { createStreamTimeout } from "./llamaindex/streaming/events";
+import { createWorkflow } from "./workflow/factory";
+import { toDataStream } from "./workflow/stream";
+
+export const chat = async (req: Request, res: Response) => {
+  const vercelStreamData = new StreamData();
+  const streamTimeout = createStreamTimeout(vercelStreamData);
+  try {
+    const { messages }: { messages: Message[] } = req.body;
+    const userMessage = messages.pop();
+    if (!messages || !userMessage || userMessage.role !== "user") {
+      return res.status(400).json({
+        error:
+          "messages are required in the request body and the last message must be from the user",
+      });
+    }
+
+    const chatHistory = messages as ChatMessage[];
+    const agent = createWorkflow(chatHistory);
+    agent.run(userMessage.content);
+    const stream = toDataStream(agent.streamEvents(), vercelStreamData);
+    return streamToResponse(stream, res, {}, vercelStreamData);
+  } catch (error) {
+    console.error("[LlamaIndex]", error);
+    return res.status(500).json({
+      detail: (error as Error).message,
+    });
+  } finally {
+    clearTimeout(streamTimeout);
+  }
+};

templates/components/multiagent/typescript/nextjs/route.ts

@@ -0,0 +1,75 @@
+import { initObservability } from "@/app/observability";
+import { StopEvent } from "@llamaindex/core/workflow";
+import { Message, StreamData, StreamingTextResponse } from "ai";
+import { ChatMessage, ChatResponseChunk } from "llamaindex";
+import { NextRequest, NextResponse } from "next/server";
+import { initSettings } from "./engine/settings";
+import { createStreamTimeout } from "./llamaindex/streaming/events";
+import { createWorkflow } from "./workflow/factory";
+import { toDataStream } from "./workflow/stream";
+import { AgentRunEvent } from "./workflow/type";
+
+initObservability();
+initSettings();
+
+export const runtime = "nodejs";
+export const dynamic = "force-dynamic";
+
+export async function POST(request: NextRequest) {
+  // Init Vercel AI StreamData and timeout
+  const vercelStreamData = new StreamData();
+  const streamTimeout = createStreamTimeout(vercelStreamData);
+
+  try {
+    const body = await request.json();
+    const { messages, data }: { messages: Message[]; data?: any } = body;
+    const userMessage = messages.pop();
+    if (!messages || !userMessage || userMessage.role !== "user") {
+      return NextResponse.json(
+        {
+          error:
+            "messages are required in the request body and the last message must be from the user",
+        },
+        { status: 400 },
+      );
+    }
+
+    const chatHistory = messages as ChatMessage[];
+    const agent = createWorkflow(chatHistory);
+    const result = agent.run<AsyncGenerator<ChatResponseChunk>>(
+      userMessage.content,
+    );
+    // start consuming the agent events in the background -> move to stream.ts
+    void (async () => {
+      for await (const event of agent.streamEvents()) {
+        // Add the event to vercelStreamData
+        if (event instanceof AgentRunEvent) {
+          const { name, msg } = event.data;
+          vercelStreamData.appendMessageAnnotation({
+            type: "agent",
+            data: { agent: name, text: msg },
+          });
+        }
+      }
+    })();
+    // TODO: fix type in agent.run in LITS
+    const stream = toDataStream(
+      result as unknown as Promise<
+        StopEvent<AsyncGenerator<ChatResponseChunk>>
+      >,
+    );
+    return new StreamingTextResponse(stream, {}, vercelStreamData);
+  } catch (error) {
+    console.error("[LlamaIndex]", error);
+    return NextResponse.json(
+      {
+        detail: (error as Error).message,
+      },
+      {
+        status: 500,
+      },
+    );
+  } finally {
+    clearTimeout(streamTimeout);
+  }
+}

templates/components/multiagent/typescript/workflow/agents.ts

@@ -0,0 +1,51 @@
+import { ChatMessage, QueryEngineTool } from "llamaindex";
+import { getDataSource } from "../engine";
+import { FunctionCallingAgent } from "./single-agent";
+
+const getQueryEngineTool = async () => {
+  const index = await getDataSource();
+  if (!index) {
+    throw new Error(
+      "StorageContext is empty - call 'npm run generate' to generate the storage first.",
+    );
+  }
+
+  const topK = process.env.TOP_K ? parseInt(process.env.TOP_K) : undefined;
+  return new QueryEngineTool({
+    queryEngine: index.asQueryEngine({
+      similarityTopK: topK,
+    }),
+    metadata: {
+      name: "query_index",
+      description: `Use this tool to retrieve information about the text corpus from the index.`,
+    },
+  });
+};
+
+export const createResearcher = async (chatHistory: ChatMessage[]) => {
+  return new FunctionCallingAgent({
+    name: "researcher",
+    tools: [await getQueryEngineTool()],
+    systemPrompt:
+      "You are a researcher agent. You are given a researching task. You must use your tools to complete the research.",
+    chatHistory,
+  });
+};
+
+export const createWriter = (chatHistory: ChatMessage[]) => {
+  return new FunctionCallingAgent({
+    name: "writer",
+    systemPrompt:
+      "You are an expert in writing blog posts. You are given a task to write a blog post. Don't make up any information yourself.",
+    chatHistory,
+  });
+};
+
+export const createReviewer = (chatHistory: ChatMessage[]) => {
+  return new FunctionCallingAgent({
+    name: "reviewer",
+    systemPrompt:
+      "You are an expert in reviewing blog posts. You are given a task to review a blog post. Review the post for logical inconsistencies, ask critical questions, and provide suggestions for improvement. Furthermore, proofread the post for grammar and spelling errors. Only if the post is good enough for publishing, then you MUST return 'The post is good.'. In all other cases return your review.",
+    chatHistory,
+  });
+};

templates/components/multiagent/typescript/workflow/factory.ts

@@ -0,0 +1,133 @@
+import {
+  Context,
+  StartEvent,
+  StopEvent,
+  Workflow,
+  WorkflowEvent,
+} from "@llamaindex/core/workflow";
+import { ChatMessage, ChatResponseChunk } from "llamaindex";
+import { createResearcher, createReviewer, createWriter } from "./agents";
+import { AgentInput, AgentRunEvent } from "./type";
+
+const TIMEOUT = 360 * 1000;
+const MAX_ATTEMPTS = 2;
+
+class ResearchEvent extends WorkflowEvent<{ input: string }> {}
+class WriteEvent extends WorkflowEvent<{
+  input: string;
+  isGood: boolean;
+}> {}
+class ReviewEvent extends WorkflowEvent<{ input: string }> {}
+
+export const createWorkflow = (chatHistory: ChatMessage[]) => {
+  const runAgent = async (
+    context: Context,
+    agent: Workflow,
+    input: AgentInput,
+  ) => {
+    const run = agent.run(new StartEvent({ input }));
+    for await (const event of agent.streamEvents()) {
+      if (event.data instanceof AgentRunEvent) {
+        context.writeEventToStream(event.data);
+      }
+    }
+    return await run;
+  };
+
+  const start = async (context: Context, ev: StartEvent) => {
+    context.set("task", ev.data.input);
+    return new ResearchEvent({
+      input: `Research for this task: ${ev.data.input}`,
+    });
+  };
+
+  const research = async (context: Context, ev: ResearchEvent) => {
+    const researcher = await createResearcher(chatHistory);
+    const researchRes = await runAgent(context, researcher, {
+      message: ev.data.input,
+    });
+    const researchResult = researchRes.data.result;
+    return new WriteEvent({
+      input: `Write a blog post given this task: ${context.get("task")} using this research content: ${researchResult}`,
+      isGood: false,
+    });
+  };
+
+  const write = async (context: Context, ev: WriteEvent) => {
+    context.set("attempts", context.get("attempts", 0) + 1);
+    const tooManyAttempts = context.get("attempts") > MAX_ATTEMPTS;
+    if (tooManyAttempts) {
+      context.writeEventToStream(
+        new AgentRunEvent({
+          name: "writer",
+          msg: `Too many attempts (${MAX_ATTEMPTS}) to write the blog post. Proceeding with the current version.`,
+        }),
+      );
+    }
+
+    if (ev.data.isGood || tooManyAttempts) {
+      // TODO: the text is already written, so we don't need to run the writer again
+      const writer = createWriter(chatHistory);
+      const writeRes = (await runAgent(context, writer, {
+        message: ev.data.input,
+        streaming: true,
+      })) as unknown as StopEvent<AsyncGenerator<ChatResponseChunk>>;
+
+      return writeRes; // stop the workflow
+    }
+
+    const writer = createWriter(chatHistory);
+    const writeRes = await runAgent(context, writer, {
+      message: ev.data.input,
+    });
+    const writeResult = writeRes.data.result;
+    context.set("result", writeResult); // store the last result
+    return new ReviewEvent({ input: writeResult });
+  };
+
+  const review = async (context: Context, ev: ReviewEvent) => {
+    const reviewer = createReviewer(chatHistory);
+    const reviewRes = await reviewer.run(
+      new StartEvent<AgentInput>({ input: { message: ev.data.input } }),
+    );
+    const reviewResult = reviewRes.data.result;
+    const oldContent = context.get("result");
+    const postIsGood = reviewResult.toLowerCase().includes("post is good");
+    context.writeEventToStream(
+      new AgentRunEvent({
+        name: "reviewer",
+        msg: `The post is ${postIsGood ? "" : "not "}good enough for publishing. Sending back to the writer${
+          postIsGood ? " for publication." : "."
+        }`,
+      }),
+    );
+    if (postIsGood) {
+      return new WriteEvent({
+        input: `You're blog post is ready for publication. Please respond with just the blog post. Blog post: \`\`\`${oldContent}\`\`\``,
+        isGood: true,
+      });
+    }
+
+    return new WriteEvent({
+      input: `Improve the writing of a given blog post by using a given review.
+            Blog post:
+            \`\`\`
+            ${oldContent}
+            \`\`\`
+
+            Review:
+            \`\`\`
+            ${reviewResult}
+            \`\`\``,
+      isGood: false,
+    });
+  };
+
+  const workflow = new Workflow({ timeout: TIMEOUT, validate: true });
+  workflow.addStep(StartEvent, start, { outputs: ResearchEvent });
+  workflow.addStep(ResearchEvent, research, { outputs: WriteEvent });
+  workflow.addStep(WriteEvent, write, { outputs: [ReviewEvent, StopEvent] });
+  workflow.addStep(ReviewEvent, review, { outputs: WriteEvent });
+
+  return workflow;
+};