feat: Add form filling use case for TS

.changeset/metal-cherries-sin.md

@@ -0,0 +1,5 @@
+---
+"create-llama": patch
+---
+
+Optimize Typescript multi-agent code

.changeset/serious-suits-turn.md

@@ -0,0 +1,5 @@
+---
+"create-llama": patch
+---
+
+Add form filling use case (Typescript)

e2e/shared/multiagent_template.spec.ts

@@ -27,8 +27,8 @@ for (const agents of templateAgents) {
       "The multiagent template currently only works with files. We also only run on Linux to speed up tests.",
     );
     test.skip(
-      agents === "form_filling" && templateFramework !== "fastapi",
-      "Form filling is currently only supported with FastAPI.",
+      agents === "form_filling" && templateFramework === "express",
+      "Form filling is not supported with Express.",
     );
     let port: number;
     let externalPort: number;

helpers/typescript.ts

@@ -144,7 +144,7 @@ export const installTSTemplate = async ({
         agents,
       );
 
-      await copy("**", path.join(root, relativeEngineDestPath, "workflow"), {
+      await copy("**", path.join(root), {
         parents: true,
         cwd: agentsCodePath,
         rename: assetRelocator,

questions/simple.ts

@@ -52,22 +52,19 @@ export const askSimpleQuestions = async (
   let useLlamaCloud = false;
 
   if (appType !== "extractor") {
-    // TODO: Add TS support for form filling use case
-    if (appType !== "form_filling") {
-      const { language: newLanguage } = await prompts(
-        {
-          type: "select",
-          name: "language",
-          message: "What language do you want to use?",
-          choices: [
-            { title: "Python (FastAPI)", value: "fastapi" },
-            { title: "Typescript (NextJS)", value: "nextjs" },
-          ],
-        },
-        questionHandlers,
-      );
-      language = newLanguage;
-    }
+    const { language: newLanguage } = await prompts(
+      {
+        type: "select",
+        name: "language",
+        message: "What language do you want to use?",
+        choices: [
+          { title: "Python (FastAPI)", value: "fastapi" },
+          { title: "Typescript (NextJS)", value: "nextjs" },
+        ],
+      },
+      questionHandlers,
+    );
+    language = newLanguage;
 
     const { useLlamaCloud: newUseLlamaCloud } = await prompts(
       {

templates/components/agents/typescript/financial_report/app/api/chat/workflow/factory.ts

@@ -0,0 +1,82 @@
+import { WorkflowEvent } from "@llamaindex/core/workflow";
+import { Message } from "ai";
+import { ChatMessage, ToolCallLLM } from "llamaindex";
+import { getAnnotations } from "../llamaindex/streaming/annotations";
+import { FinancialReportWorkflow } from "./finReport";
+import { getAvailableTools } from "./tools";
+
+const TIMEOUT = 360 * 1000;
+
+class ResearchEvent extends WorkflowEvent<{ input: string }> {}
+class AnalyzeEvent extends WorkflowEvent<{ input: string }> {}
+class ReportEvent extends WorkflowEvent<{ input: string }> {}
+
+const prepareChatHistory = (chatHistory: Message[]): ChatMessage[] => {
+  // By default, the chat history only contains the assistant and user messages
+  // all the agents messages are stored in annotation data which is not visible to the LLM
+
+  const MAX_AGENT_MESSAGES = 10;
+  const agentAnnotations = getAnnotations<{ agent: string; text: string }>(
+    chatHistory,
+    { role: "assistant", type: "agent" },
+  ).slice(-MAX_AGENT_MESSAGES);
+
+  const agentMessages = agentAnnotations
+    .map(
+      (annotation) =>
+        `\n<${annotation.data.agent}>\n${annotation.data.text}\n</${annotation.data.agent}>`,
+    )
+    .join("\n");
+
+  const agentContent = agentMessages
+    ? "Here is the previous conversation of agents:\n" + agentMessages
+    : "";
+
+  if (agentContent) {
+    const agentMessage: ChatMessage = {
+      role: "assistant",
+      content: agentContent,
+    };
+    return [
+      ...chatHistory.slice(0, -1),
+      agentMessage,
+      chatHistory.slice(-1)[0],
+    ] as ChatMessage[];
+  }
+  return chatHistory as ChatMessage[];
+};
+
+export async function createWorkflow(options: {
+  chatHistory: Message[];
+  llm?: ToolCallLLM;
+  writeEvents?: boolean;
+}) {
+  const chatHistoryWithAgentMessages = prepareChatHistory(options.chatHistory);
+
+  const tools = await getAvailableTools();
+  const retrieverTools = tools.filter((tool) =>
+    tool.metadata.name.startsWith("retriever"),
+  );
+  const documentGeneratorTool = tools.find(
+    (tool) => tool.metadata.name === "document_generator",
+  );
+  const codeInterpreterTool = tools.find(
+    (tool) => tool.metadata.name === "interpreter",
+  );
+
+  if (!documentGeneratorTool || !codeInterpreterTool || !retrieverTools) {
+    throw new Error(
+      "These tools are required: document_generator, code_interpreter, retriever",
+    );
+  }
+
+  return new FinancialReportWorkflow({
+    chatHistory: chatHistoryWithAgentMessages,
+    queryEngineTools: retrieverTools,
+    codeInterpreterTool,
+    documentGeneratorTool,
+    llm: options.llm,
+    writeEvents: options.writeEvents,
+    timeout: TIMEOUT,
+  });
+}