bump server packages

* Add UI components and static assets for chat interface

* feat: Add simple chat app example with FastAPI integration

* fix: update default workflow file path and improve error handling

* update doc

* change to file_path

* include changes from #614

* fix mypy

* support devmode for backend ts server

* Revert "support devmode for backend ts server"

This reverts commit bd943fd8c1.

* fix: polling should work when server not yet started

* bump chat-ui to fix syntax highlight issue

* fix: missing language for code editor

* enhance UI with shadow overlay

* enhance doc

* fix minor UI bugs

* enhance doc

* remove unessesary debug log

* fix wrong check

* increase delay time before trigger polling

* feat: support dev mode for backend ts server (#616)

* feat: support dev mode for backend ts server

* update message

* validate typescript file

* fix: format

* use temp file to avoid server restart

* fix format

* use npx tsc

* remove typescript deps

---------

Co-authored-by: thucpn <thucsh2@gmail.com>
Co-authored-by: Thuc Pham <51660321+thucpn@users.noreply.github.com>

.changeset/calm-women-repair.md

@@ -0,0 +1,5 @@
+---
+"create-llama": patch
+---
+
+Split artifacts use case to document generator and code generator

.changeset/cuddly-cherries-trade.md

@@ -0,0 +1,5 @@
+---
+"create-llama": patch
+---
+
+chore: improve dev experience with nodemon

.changeset/fifty-badgers-jog.md

@@ -0,0 +1,5 @@
+---
+"create-llama": patch
+---
+
+Fix typing check issue

.changeset/five-buses-bake.md

@@ -0,0 +1,5 @@
+---
+"create-llama": patch
+---
+
+fix chromadb dependency issue

.changeset/olive-mangos-fly.md

@@ -0,0 +1,5 @@
+---
+"@llamaindex/server": patch
+---
+
+feat: add dev mode UI

.changeset/open-deer-sink.md

@@ -0,0 +1,5 @@
+---
+"create-llama": patch
+---
+
+Enable dev mode that allows updating code directly in the UI

.changeset/silent-ladybugs-hear.md

@@ -0,0 +1,5 @@
+---
+"create-llama": patch
+---
+
+fix: remove dead generated ai code

.changeset/tired-regions-notice.md

@@ -0,0 +1,5 @@
+---
+"create-llama": patch
+---
+
+Deprecate pro mode

.github/workflows/e2e.yml

@@ -23,6 +23,7 @@ jobs:
         os: [macos-latest, windows-latest, ubuntu-22.04]
         frameworks: ["fastapi"]
         datasources: ["--no-files", "--example-file", "--llamacloud"]
+        template-types: ["streaming", "llamaindexserver"]
     defaults:
       run:
         shell: bash
@@ -70,6 +71,7 @@ jobs:
           LLAMA_CLOUD_API_KEY: ${{ secrets.LLAMA_CLOUD_API_KEY }}
           FRAMEWORK: ${{ matrix.frameworks }}
           DATASOURCE: ${{ matrix.datasources }}
+          TEMPLATE_TYPE: ${{ matrix.template-types }}
           PYTHONIOENCODING: utf-8
           PYTHONLEGACYWINDOWSSTDIO: utf-8
         working-directory: packages/create-llama
@@ -93,6 +95,7 @@ jobs:
         os: [macos-latest, windows-latest, ubuntu-22.04]
         frameworks: ["nextjs"]
         datasources: ["--no-files", "--example-file", "--llamacloud"]
+        template-types: ["streaming", "llamaindexserver"]
     defaults:
       run:
         shell: bash
@@ -140,6 +143,7 @@ jobs:
           LLAMA_CLOUD_API_KEY: ${{ secrets.LLAMA_CLOUD_API_KEY }}
           FRAMEWORK: ${{ matrix.frameworks }}
           DATASOURCE: ${{ matrix.datasources }}
+          TEMPLATE_TYPE: ${{ matrix.template-types }}
         working-directory: packages/create-llama
 
       - uses: actions/upload-artifact@v4

.github/workflows/lint_on_push_or_pull.yml

@@ -31,6 +31,13 @@ jobs:
       - name: Run Prettier
         run: pnpm run format
 
+      - name: Run build
+        run: pnpm run build
+
+      - name: Run Typecheck for examples
+        run: pnpm run typecheck
+        working-directory: packages/server/examples
+
       - name: Run Python format check
         uses: chartboost/ruff-action@v1
         with:

.prettierignore

@@ -7,6 +7,8 @@ build/
 .next/
 out/
 packages/server/server/
+**/playwright-report/
+**/test-results/
 
 # Python
 python/

README.md

@@ -106,25 +106,6 @@ Ok to proceed? (y) y
 You can also pass command line arguments to set up a new project
 non-interactively. For a list of the latest options, call `create-llama --help`.
 
-### Running in pro mode
-
-If you prefer more advanced customization options, you can run `create-llama` in pro mode using the `--pro` flag.
-
-In pro mode, instead of selecting a predefined use case, you'll be prompted to select each technical component of your project. This allows for greater flexibility in customizing your project, including:
-
-- **Vector Store**: Choose from a variety of vector stores for keeping your documents, including MongoDB, Pinecone, Weaviate, Qdrant and Chroma.
-- **Tools**: Choose from a variety of agent tools (functions called by the LLM), such as:
-  - Code Interpreter: Executes Python code in a secure Jupyter notebook environment
-  - Artifact Code Generator: Generates code artifacts that can be run in a sandbox
-  - OpenAPI Action: Facilitates requests to a provided OpenAPI schema
-  - Image Generator: Creates images based on text descriptions
-  - Web Search: Performs web searches to retrieve up-to-date information
-- **Data Sources**: Integrate various data sources into your chat application, including local files, websites, or database-retrieved data.
-- **Backend Options**: Besides using Next.js or FastAPI, you can also select to use Express for a more traditional Node.js application.
-- **Observability**: Choose from a variety of LLM observability tools, including LlamaTrace and Traceloop.
-
-Pro mode is ideal for developers who want fine-grained control over their project's configuration and are comfortable with more technical setup options.
-
 ## LlamaIndex Documentation
 
 - [TS/JS docs](https://ts.llamaindex.ai/)

packages/create-llama/CHANGELOG.md

@@ -1,5 +1,12 @@
 # create-llama
 
+## 0.5.13
+
+### Patch Changes
+
+- f4ca602: Add artifact use case for Typescript template
+- f4ca602: Update typescript use cases to use the new workflow engine
+
 ## 0.5.12
 
 ### Patch Changes

packages/create-llama/e2e/python/resolve_dependencies.spec.ts

@@ -3,7 +3,7 @@ import { exec } from "child_process";
 import fs from "fs";
 import path from "path";
 import util from "util";
-import { TemplateFramework, TemplateVectorDB } from "../../helpers/types";
+import { TemplateFramework, TemplateType, TemplateUseCase, TemplateVectorDB } from "../../helpers/types";
 import { RunCreateLlamaOptions, createTestDir, runCreateLlama } from "../utils";
 
 const execAsync = util.promisify(exec);
@@ -11,123 +11,193 @@ const execAsync = util.promisify(exec);
 const templateFramework: TemplateFramework = process.env.FRAMEWORK
   ? (process.env.FRAMEWORK as TemplateFramework)
   : "fastapi";
+const templateType: TemplateType = process.env.TEMPLATE_TYPE
+  ? (process.env.TEMPLATE_TYPE as TemplateType)
+  : "llamaindexserver";
+const useCases: TemplateUseCase[] = [
+  "agentic_rag",
+  "deep_research",
+  "financial_report",
+  "code_generator",
+  "document_generator",
+];
 const dataSource: string = process.env.DATASOURCE
   ? process.env.DATASOURCE
   : "--example-file";
 
-// TODO: add support for other templates
+test.describe("Mypy check", () => {
+  test.describe.configure({ retries: 0 });
 
-if (
-  dataSource === "--example-file" // XXX: this test provides its own data source - only trigger it on one data source (usually the CI matrix will trigger multiple data sources)
-) {
-  // vectorDBs, tools, and data source combinations to test
-  const vectorDbs: TemplateVectorDB[] = [
-    "mongo",
-    "pg",
-    "pinecone",
-    "milvus",
-    "astra",
-    "qdrant",
-    "chroma",
-    "weaviate",
-  ];
+    // Test for streaming template
+    test.describe("StreamingTemplate", () => {
+      test.skip(templateType !== "streaming", `skipping streaming test for ${templateType}`);
+      if (
+        dataSource === "--example-file" // XXX: this test provides its own data source - only trigger it on one data source (usually the CI matrix will trigger multiple data sources)
+      ) {
+        // vectorDBs, tools, and data source combinations to test
+        const vectorDbs: TemplateVectorDB[] = [
+          "mongo",
+          "pg",
+          "pinecone",
+          "milvus",
+          "astra",
+          "qdrant",
+          "chroma",
+          "weaviate",
+        ];
+        const toolOptions = [
+          "wikipedia.WikipediaToolSpec",
+          "google.GoogleSearchToolSpec",
+          "document_generator",
+          "artifact",
+        ];
 
-  const toolOptions = [
-    "wikipedia.WikipediaToolSpec",
-    "google.GoogleSearchToolSpec",
-    "document_generator",
-    "artifact",
-  ];
+        const dataSources = [
+          "--example-file",
+          "--web-source https://www.example.com",
+          "--db-source mysql+pymysql://user:pass@localhost:3306/mydb",
+        ];
 
-  const dataSources = [
-    "--example-file",
-    "--web-source https://www.example.com",
-    "--db-source mysql+pymysql://user:pass@localhost:3306/mydb",
-  ];
+        const observabilityOptions = ["llamatrace", "traceloop"];
 
-  const observabilityOptions = ["llamatrace", "traceloop"];
+        // Test vector databases
+        for (const vectorDb of vectorDbs) {
+          test(`vectorDB: ${vectorDb} ${templateType}`, async () => {
+            const cwd = await createTestDir();
+            const { pyprojectPath } = await createAndCheckLlamaProject({
+              options: {
+                cwd,
+                templateType: "streaming",
+                templateFramework,
+                dataSource: "--example-file",
+                vectorDb,
+                tools: "none",
+                port: 3000,
+                postInstallAction: "none",
+                templateUI: undefined,
+                appType: "--no-frontend",
+                llamaCloudProjectName: undefined,
+                llamaCloudIndexName: undefined,
+                observability: undefined,
+              },
+            });
 
-  test.describe("Mypy check", () => {
-    test.describe.configure({ retries: 0 });
+            const pyprojectContent = fs.readFileSync(pyprojectPath, "utf-8");
+            if (vectorDb !== "none") {
+              if (vectorDb === "pg") {
+                expect(pyprojectContent).toContain(
+                  "llama-index-vector-stores-postgres",
+                );
+              } else {
+                expect(pyprojectContent).toContain(
+                  `llama-index-vector-stores-${vectorDb}`,
+                );
+              }
+            }
+          });
+        }
 
-    // Test vector databases
-    for (const vectorDb of vectorDbs) {
-      test(`Mypy check for vectorDB: ${vectorDb}`, async () => {
+        // // Test tools
+        for (const tool of toolOptions) {
+          test(`tool: ${tool} ${templateType}`, async () => {
+            const cwd = await createTestDir();
+            const { pyprojectPath } = await createAndCheckLlamaProject({
+              options: {
+                cwd,
+                templateType: "streaming",
+                templateFramework,
+                dataSource: "--example-file",
+                vectorDb: "none",
+                tools: tool,
+                port: 3000,
+                postInstallAction: "none",
+                templateUI: undefined,
+                appType: "--no-frontend",
+                llamaCloudProjectName: undefined,
+                llamaCloudIndexName: undefined,
+                observability: undefined,
+              },
+            });
+
+            const pyprojectContent = fs.readFileSync(pyprojectPath, "utf-8");
+            if (tool === "wikipedia.WikipediaToolSpec") {
+              expect(pyprojectContent).toContain("wikipedia");
+            }
+            if (tool === "google.GoogleSearchToolSpec") {
+              expect(pyprojectContent).toContain("google");
+            }
+          });
+        }
+
+        // // Test data sources
+        for (const dataSource of dataSources) {
+          test(`data source: ${dataSource} ${templateType}`, async () => {
+          const dataSourceType = dataSource.split(" ")[0];
+            const cwd = await createTestDir();
+            const { pyprojectPath } = await createAndCheckLlamaProject({
+              options: {
+                cwd,
+                templateType: "streaming",
+                templateFramework,
+                dataSource,
+                vectorDb: "none",
+                tools: "none",
+                port: 3000,
+                postInstallAction: "none",
+                templateUI: undefined,
+                appType: "--no-frontend",
+                llamaCloudProjectName: undefined,
+                llamaCloudIndexName: undefined,
+                observability: undefined,
+              },
+            });
+
+            const pyprojectContent = fs.readFileSync(pyprojectPath, "utf-8");
+            if (dataSource.includes("--web-source")) {
+              expect(pyprojectContent).toContain("llama-index-readers-web");
+            }
+            if (dataSource.includes("--db-source")) {
+              expect(pyprojectContent).toContain("llama-index-readers-database");
+            }
+          });
+        }
+
+        // Test observability options
+        for (const observability of observabilityOptions) {
+          test.describe(`observability: ${observability} ${templateType}`, async () => {
+            const cwd = await createTestDir();
+
+            const { pyprojectPath } = await createAndCheckLlamaProject({
+              options: {
+                cwd,
+                templateType: "streaming",
+                templateFramework,
+                dataSource: "--example-file",
+                vectorDb: "none",
+                tools: "none",
+                port: 3000,
+                postInstallAction: "none",
+                templateUI: undefined,
+                appType: "--no-frontend",
+                llamaCloudProjectName: undefined,
+                llamaCloudIndexName: undefined,
+                observability,
+              },
+            });
+          });
+        }
+      }
+    });
+
+    test.describe("LlamaIndexServer", async () => {
+      test.skip(templateType !== "llamaindexserver", `skipping llamaindexserver test for ${templateType}`);
+      test.skip(dataSource !== "--example-file", `skipping llamaindexserver test for ${dataSource}`);
+      for (const useCase of useCases) {
         const cwd = await createTestDir();
-        const { pyprojectPath } = await createAndCheckLlamaProject({
+        await createAndCheckLlamaProject({
           options: {
             cwd,
-            templateType: "streaming",
-            templateFramework,
-            dataSource: "--example-file",
-            vectorDb,
-            tools: "none",
-            port: 3000,
-            postInstallAction: "none",
-            templateUI: undefined,
-            appType: "--no-frontend",
-            llamaCloudProjectName: undefined,
-            llamaCloudIndexName: undefined,
-            observability: undefined,
-          },
-        });
-
-        const pyprojectContent = fs.readFileSync(pyprojectPath, "utf-8");
-        if (vectorDb !== "none") {
-          if (vectorDb === "pg") {
-            expect(pyprojectContent).toContain(
-              "llama-index-vector-stores-postgres",
-            );
-          } else {
-            expect(pyprojectContent).toContain(
-              `llama-index-vector-stores-${vectorDb}`,
-            );
-          }
-        }
-      });
-    }
-
-    // Test tools
-    for (const tool of toolOptions) {
-      test(`Mypy check for tool: ${tool}`, async () => {
-        const cwd = await createTestDir();
-        const { pyprojectPath } = await createAndCheckLlamaProject({
-          options: {
-            cwd,
-            templateType: "streaming",
-            templateFramework,
-            dataSource: "--example-file",
-            vectorDb: "none",
-            tools: tool,
-            port: 3000,
-            postInstallAction: "none",
-            templateUI: undefined,
-            appType: "--no-frontend",
-            llamaCloudProjectName: undefined,
-            llamaCloudIndexName: undefined,
-            observability: undefined,
-          },
-        });
-
-        const pyprojectContent = fs.readFileSync(pyprojectPath, "utf-8");
-        if (tool === "wikipedia.WikipediaToolSpec") {
-          expect(pyprojectContent).toContain("wikipedia");
-        }
-        if (tool === "google.GoogleSearchToolSpec") {
-          expect(pyprojectContent).toContain("google");
-        }
-      });
-    }
-
-    // Test data sources
-    for (const dataSource of dataSources) {
-      const dataSourceType = dataSource.split(" ")[0];
-      test(`Mypy check for data source: ${dataSourceType}`, async () => {
-        const cwd = await createTestDir();
-        const { pyprojectPath } = await createAndCheckLlamaProject({
-          options: {
-            cwd,
-            templateType: "streaming",
+            templateType: "llamaindexserver",
             templateFramework,
             dataSource,
             vectorDb: "none",
@@ -139,110 +209,77 @@ if (
             llamaCloudProjectName: undefined,
             llamaCloudIndexName: undefined,
             observability: undefined,
+            useCase,
           },
         });
+      }
+    });
 
-        const pyprojectContent = fs.readFileSync(pyprojectPath, "utf-8");
-        if (dataSource.includes("--web-source")) {
-          expect(pyprojectContent).toContain("llama-index-readers-web");
-        }
-        if (dataSource.includes("--db-source")) {
-          expect(pyprojectContent).toContain("llama-index-readers-database");
-        }
-      });
-    }
+    async function createAndCheckLlamaProject({
+      options,
+    }: {
+      options: RunCreateLlamaOptions;
+    }): Promise<{ pyprojectPath: string; projectPath: string }> {
+      const result = await runCreateLlama(options);
+      const name = result.projectName;
+      const projectPath = path.join(options.cwd, name);
 
-    // Test observability options
-    for (const observability of observabilityOptions) {
-      test(`Mypy check for observability: ${observability}`, async () => {
-        const cwd = await createTestDir();
+      // Check if the app folder exists
+      expect(fs.existsSync(projectPath)).toBeTruthy();
 
-        const { pyprojectPath } = await createAndCheckLlamaProject({
-          options: {
-            cwd,
-            templateType: "streaming",
-            templateFramework,
-            dataSource: "--example-file",
-            vectorDb: "none",
-            tools: "none",
-            port: 3000,
-            postInstallAction: "none",
-            templateUI: undefined,
-            appType: "--no-frontend",
-            llamaCloudProjectName: undefined,
-            llamaCloudIndexName: undefined,
-            observability,
-          },
-        });
-      });
-    }
-  });
-}
+      // Check if pyproject.toml exists
+      const pyprojectPath = path.join(projectPath, "pyproject.toml");
+      expect(fs.existsSync(pyprojectPath)).toBeTruthy();
 
-async function createAndCheckLlamaProject({
-  options,
-}: {
-  options: RunCreateLlamaOptions;
-}): Promise<{ pyprojectPath: string; projectPath: string }> {
-  const result = await runCreateLlama(options);
-  const name = result.projectName;
-  const projectPath = path.join(options.cwd, name);
+      // Modify environment for the command
+      const commandEnv = {
+        ...process.env,
+      };
 
-  // Check if the app folder exists
-  expect(fs.existsSync(projectPath)).toBeTruthy();
+      console.log("Running uv venv...");
+      try {
+        const { stdout: venvStdout, stderr: venvStderr } = await execAsync(
+          "uv venv",
+          { cwd: projectPath, env: commandEnv },
+        );
+        console.log("uv venv stdout:", venvStdout);
+        console.error("uv venv stderr:", venvStderr);
+      } catch (error) {
+        console.error("Error running uv venv:", error);
+        throw error; // Re-throw error to fail the test
+      }
 
-  // Check if pyproject.toml exists
-  const pyprojectPath = path.join(projectPath, "pyproject.toml");
-  expect(fs.existsSync(pyprojectPath)).toBeTruthy();
+      console.log("Running uv sync...");
+      try {
+        const { stdout: syncStdout, stderr: syncStderr } = await execAsync(
+          "uv sync --all-extras",
+          { cwd: projectPath, env: commandEnv },
+        );
+        console.log("uv sync stdout:", syncStdout);
+        console.error("uv sync stderr:", syncStderr);
+      } catch (error) {
+        console.error("Error running uv sync:", error);
+        throw error; // Re-throw error to fail the test
+      }
 
-  // Modify environment for the command
-  const commandEnv = {
-    ...process.env,
-  };
+      console.log("Running uv run mypy ....");
+      try {
+        const { stdout: mypyStdout, stderr: mypyStderr } = await execAsync(
+          "uv run mypy .",
+          { cwd: projectPath, env: commandEnv },
+        );
+        console.log("uv run mypy stdout:", mypyStdout);
+        console.error("uv run mypy stderr:", mypyStderr);
+        // Assuming mypy success means no output or specific success message
+        // Adjust checks based on actual expected mypy output
+      } catch (error) {
+        console.error("Error running mypy:", error);
+        throw error;
+      }
 
-  console.log("Running uv venv...");
-  try {
-    const { stdout: venvStdout, stderr: venvStderr } = await execAsync(
-      "uv venv",
-      { cwd: projectPath, env: commandEnv },
-    );
-    console.log("uv venv stdout:", venvStdout);
-    console.error("uv venv stderr:", venvStderr);
-  } catch (error) {
-    console.error("Error running uv venv:", error);
-    throw error; // Re-throw error to fail the test
+      // If we reach this point without throwing an error, the test passes
+      expect(true).toBeTruthy();
+
+    return { pyprojectPath, projectPath };
   }
-
-  console.log("Running uv sync...");
-  try {
-    const { stdout: syncStdout, stderr: syncStderr } = await execAsync(
-      "uv sync --all-extras",
-      { cwd: projectPath, env: commandEnv },
-    );
-    console.log("uv sync stdout:", syncStdout);
-    console.error("uv sync stderr:", syncStderr);
-  } catch (error) {
-    console.error("Error running uv sync:", error);
-    throw error; // Re-throw error to fail the test
-  }
-
-  console.log("Running uv run mypy ....");
-  try {
-    const { stdout: mypyStdout, stderr: mypyStderr } = await execAsync(
-      "uv run mypy .",
-      { cwd: projectPath, env: commandEnv },
-    );
-    console.log("uv run mypy stdout:", mypyStdout);
-    console.error("uv run mypy stderr:", mypyStderr);
-    // Assuming mypy success means no output or specific success message
-    // Adjust checks based on actual expected mypy output
-  } catch (error) {
-    console.error("Error running mypy:", error);
-    throw error;
-  }
-
-  // If we reach this point without throwing an error, the test passes
-  expect(true).toBeTruthy();
-
-  return { pyprojectPath, projectPath };
-}
+});

packages/create-llama/e2e/typescript/resolve_dependencies.spec.ts

@@ -3,7 +3,12 @@ import { exec } from "child_process";
 import fs from "fs";
 import path from "path";
 import util from "util";
-import { TemplateFramework, TemplateVectorDB } from "../../helpers/types";
+import {
+  TemplateFramework,
+  TemplateType,
+  TemplateUseCase,
+  TemplateVectorDB,
+} from "../../helpers/types";
 import { createTestDir, runCreateLlama } from "../utils";
 
 const execAsync = util.promisify(exec);
@@ -11,6 +16,16 @@ const execAsync = util.promisify(exec);
 const templateFramework: TemplateFramework = process.env.FRAMEWORK
   ? (process.env.FRAMEWORK as TemplateFramework)
   : "nextjs";
+const templateType: TemplateType = process.env.TEMPLATE_TYPE
+  ? (process.env.TEMPLATE_TYPE as TemplateType)
+  : "llamaindexserver";
+const useCases: TemplateUseCase[] = [
+  "agentic_rag",
+  "deep_research",
+  "financial_report",
+  "code_generator",
+  "document_generator",
+];
 const dataSource: string = process.env.DATASOURCE
   ? process.env.DATASOURCE
   : "--example-file";
@@ -29,77 +44,118 @@ const vectorDbs: TemplateVectorDB[] = [
 ];
 
 test.describe("Test resolve TS dependencies", () => {
+  test.describe.configure({ retries: 0 });
+
   // Test vector DBs without LlamaParse
   for (const vectorDb of vectorDbs) {
-    const optionDescription = `vectorDb: ${vectorDb}, dataSource: ${dataSource}`;
+    const optionDescription = `templateType: ${templateType}, vectorDb: ${vectorDb}, dataSource: ${dataSource}`;
 
     test(`Vector DB test - ${optionDescription}`, async () => {
-      await runTest(vectorDb, false);
+      // skip vectordb test for llamaindexserver
+      test.skip(
+        templateType === "llamaindexserver",
+        "skipping vectorDB test for llamaindexserver",
+      );
+
+      await runTest({
+        templateType: templateType,
+        useLlamaParse: false, // Disable LlamaParse for vectorDB test
+        vectorDb: vectorDb,
+      });
     });
   }
 
-  // Test LlamaParse with vectorDB 'none'
-  test(`LlamaParse test - vectorDb: none, dataSource: ${dataSource}, llamaParse: true`, async () => {
-    await runTest("none", true);
-  });
-
-  async function runTest(
-    vectorDb: TemplateVectorDB | "none",
-    useLlamaParse: boolean,
-  ) {
-    const cwd = await createTestDir();
-
-    const result = await runCreateLlama({
-      cwd: cwd,
-      templateType: "streaming",
-      templateFramework: templateFramework,
-      dataSource: dataSource,
-      vectorDb: vectorDb,
-      port: 3000,
-      postInstallAction: "none",
-      templateUI: undefined,
-      appType: templateFramework === "nextjs" ? "" : "--no-frontend",
-      llamaCloudProjectName: undefined,
-      llamaCloudIndexName: undefined,
-      tools: undefined,
-      useLlamaParse: useLlamaParse,
-    });
-    const name = result.projectName;
-
-    // Check if the app folder exists
-    const appDir = path.join(cwd, name);
-    const dirExists = fs.existsSync(appDir);
-    expect(dirExists).toBeTruthy();
-
-    // Install dependencies using pnpm
-    try {
-      const { stderr: installStderr } = await execAsync(
-        "pnpm install --prefer-offline --ignore-workspace",
-        {
-          cwd: appDir,
-        },
-      );
-    } catch (error) {
-      console.error("Error installing dependencies:", error);
-      throw error;
-    }
-
-    // Run tsc type check and capture the output
-    try {
-      const { stdout, stderr } = await execAsync(
-        "pnpm exec tsc -b --diagnostics",
-        {
-          cwd: appDir,
-        },
-      );
-      // Check if there's any error output
-      expect(stderr).toBeFalsy();
-
-      // Log the stdout for debugging purposes
-      console.log("TypeScript type-check output:", stdout);
-    } catch (error) {
-      console.error("Error running tsc:", error);
-      throw error;
+  // No vectorDB, with LlamaParse and useCase
+  // Only need to test use case with example data source
+  if (dataSource === "--example-file") {
+    for (const useCase of useCases) {
+      const optionDescription = `templateType: ${templateType}, useCase: ${useCase}`;
+      test.describe(`useCase test - ${optionDescription}`, () => {
+        test.skip(
+          templateType === "streaming",
+          "Skipping use case test for streaming template.",
+        );
+        test(`no llamaParse - ${optionDescription}`, async () => {
+          await runTest({
+            templateType: templateType,
+            useLlamaParse: false,
+            useCase: useCase,
+          });
+        });
+        // Skipping llamacloud for the use case doesn't use index.
+        if (useCase !== "code_generator" && useCase !== "document_generator") {
+          test(`llamaParse - ${optionDescription}`, async () => {
+            await runTest({
+              templateType: templateType,
+              useLlamaParse: true,
+              useCase: useCase,
+            });
+          });
+        }
+      });
     }
   }
 });
+
+async function runTest(options: {
+  templateType: TemplateType;
+  useLlamaParse: boolean;
+  useCase?: TemplateUseCase;
+  vectorDb?: TemplateVectorDB;
+}) {
+  const cwd = await createTestDir();
+
+  const result = await runCreateLlama({
+    cwd: cwd,
+    templateType: options.templateType,
+    templateFramework: templateFramework,
+    dataSource: dataSource,
+    vectorDb: options.vectorDb ?? "none",
+    port: 3000,
+    postInstallAction: "none",
+    templateUI: undefined,
+    appType: templateFramework === "nextjs" ? "" : "--no-frontend",
+    llamaCloudProjectName: undefined,
+    llamaCloudIndexName: undefined,
+    tools: undefined,
+    useLlamaParse: options.useLlamaParse,
+    useCase: options.useCase,
+  });
+  const name = result.projectName;
+
+  // Check if the app folder exists
+  const appDir = path.join(cwd, name);
+  const dirExists = fs.existsSync(appDir);
+  expect(dirExists).toBeTruthy();
+
+  // Install dependencies using pnpm
+  try {
+    const { stderr: installStderr } = await execAsync(
+      "pnpm install --prefer-offline --ignore-workspace",
+      {
+        cwd: appDir,
+      },
+    );
+  } catch (error) {
+    console.error("Error installing dependencies:", error);
+    throw error;
+  }
+
+  // Run tsc type check and capture the output
+  try {
+    const { stdout, stderr } = await execAsync(
+      "pnpm exec tsc -b --diagnostics",
+      {
+        cwd: appDir,
+      },
+    );
+    // Check if there's any error output
+    expect(stderr).toBeFalsy();
+
+    // Log the stdout for debugging purposes
+    console.log("TypeScript type-check output:", stdout);
+  } catch (error) {
+    console.error("Error running tsc:", error);
+    throw error;
+  }
+}

packages/create-llama/helpers/python.ts

@@ -94,6 +94,10 @@ const getAdditionalDependencies = (
         name: "llama-index-vector-stores-chroma",
         version: ">=0.4.0,<0.5.0",
       });
+      dependencies.push({
+        name: "onnxruntime",
+        version: "<1.22.0",
+      });
       break;
     }
     case "weaviate": {
@@ -565,13 +569,13 @@ const installLlamaIndexServerTemplate = async ({
 
   await copy("*.py", path.join(root, "app"), {
     parents: true,
-    cwd: path.join(templatesDir, "components", "workflows", "python", useCase),
+    cwd: path.join(templatesDir, "components", "use-cases", "python", useCase),
   });
 
   // Copy custom UI component code
   await copy(`*`, path.join(root, "components"), {
     parents: true,
-    cwd: path.join(templatesDir, "components", "ui", "workflows", useCase),
+    cwd: path.join(templatesDir, "components", "ui", "use-cases", useCase),
   });
 
   if (useLlamaParse) {
@@ -602,7 +606,7 @@ const installLlamaIndexServerTemplate = async ({
   // Copy README.md
   await copy("README-template.md", path.join(root), {
     parents: true,
-    cwd: path.join(templatesDir, "components", "workflows", "python", useCase),
+    cwd: path.join(templatesDir, "components", "use-cases", "python", useCase),
     rename: assetRelocator,
   });
 };

packages/create-llama/helpers/types.ts

@@ -58,7 +58,8 @@ export type TemplateUseCase =
   | "extractor"
   | "contract_review"
   | "agentic_rag"
-  | "artifacts";
+  | "code_generator"
+  | "document_generator";
 // Config for both file and folder
 export type FileSourceConfig =
   | {

packages/create-llama/helpers/typescript.ts

@@ -31,23 +31,24 @@ const installLlamaIndexServerTemplate = async ({
     process.exit(1);
   }
 
-  await copy("workflow.ts", path.join(root, "src", "app"), {
-    parents: true,
+  await copy("**", path.join(root), {
     cwd: path.join(
       templatesDir,
       "components",
-      "workflows",
+      "use-cases",
       "typescript",
       useCase,
     ),
+    rename: assetRelocator,
   });
 
   // copy workflow UI components to output/components folder
   await copy("*", path.join(root, "components"), {
     parents: true,
-    cwd: path.join(templatesDir, "components", "ui", "workflows", useCase),
+    cwd: path.join(templatesDir, "components", "ui", "use-cases", useCase),
   });
 
+  // Override generate.ts if workflow use case doesn't use custom UI
   if (vectorDb === "llamacloud") {
     await copy("generate.ts", path.join(root, "src"), {
       parents: true,
@@ -74,18 +75,14 @@ const installLlamaIndexServerTemplate = async ({
       rename: () => "data.ts",
     });
   }
-  // Copy README.md
-  await copy("README-template.md", path.join(root), {
-    parents: true,
-    cwd: path.join(
-      templatesDir,
-      "components",
-      "workflows",
-      "typescript",
-      useCase,
-    ),
-    rename: assetRelocator,
-  });
+
+  // Simplify use case code
+  if (useCase === "code_generator" || useCase === "document_generator") {
+    // Artifact use case doesn't use index.
+    // We don't need data.ts, generate.ts
+    await fs.rm(path.join(root, "src", "app", "data.ts"));
+    // TODO: Remove generate index in generate.ts and package.json if possible
+  }
 };
 
 const installLegacyTSTemplate = async ({
@@ -390,7 +387,7 @@ const providerDependencies: {
   [key in ModelProvider]?: Record<string, string>;
 } = {
   openai: {
-    "@llamaindex/openai": "^0.2.0",
+    "@llamaindex/openai": "^0.3.7",
   },
   gemini: {
     "@llamaindex/google": "^0.2.0",
@@ -516,7 +513,7 @@ async function updatePackageJson({
   if (backend) {
     packageJson.dependencies = {
       ...packageJson.dependencies,
-      "@llamaindex/readers": "^2.0.0",
+      "@llamaindex/readers": "^3.1.3",
     };
 
     if (vectorDb && vectorDb in vectorDbDependencies) {

packages/create-llama/index.ts

@@ -196,7 +196,7 @@ const program = new Command(packageJson.name)
     "--pro",
     `
 
-  Allow interactive selection of all features.
+  Deprecated: Allow interactive selection of all features.
 `,
     false,
   )

packages/create-llama/package.json

@@ -1,6 +1,6 @@
 {
   "name": "create-llama",
-  "version": "0.5.12",
+  "version": "0.5.13",
   "description": "Create LlamaIndex-powered apps with one command",
   "keywords": [
     "rag",

packages/create-llama/questions/index.ts

@@ -1,4 +1,5 @@
 import ciInfo from "ci-info";
+import { bold, yellow } from "picocolors";
 import { getCIQuestionResults } from "./ci";
 import { askProQuestions } from "./questions";
 import { askSimpleQuestions } from "./simple";
@@ -13,6 +14,12 @@ export const askQuestions = async (
     return await getCIQuestionResults(args);
   } else if (args.pro) {
     // TODO: refactor pro questions to return a result object
+    console.log(
+      yellow(
+        `Pro mode is deprecated. Please use the new templates using the ${bold("LlamaIndexServer")} by not specifying pro mode.`,
+      ),
+    );
+
     await askProQuestions(args);
     return args as unknown as QuestionResults;
   }

packages/create-llama/questions/simple.ts

@@ -10,7 +10,8 @@ type AppType =
   | "agentic_rag"
   | "financial_report"
   | "deep_research"
-  | "artifacts";
+  | "code_generator"
+  | "document_generator";
 
 type SimpleAnswers = {
   appType: AppType;
@@ -47,10 +48,14 @@ export const askSimpleQuestions = async (
             "Researches and analyzes provided documents from multiple perspectives, generating a comprehensive report with citations to support key findings and insights.",
         },
         {
-          title: "Artifacts",
-          value: "artifacts",
-          description:
-            "Build your own Vercel's v0 or OpenAI's canvas-styled UI.",
+          title: "Code Generator",
+          value: "code_generator",
+          description: "Build a Vercel v0 styled code generator.",
+        },
+        {
+          title: "Document Generator",
+          value: "document_generator",
+          description: "Build a OpenAI canvas-styled document generator.",
         },
       ],
     },
@@ -62,35 +67,35 @@ export const askSimpleQuestions = async (
 
   let useLlamaCloud = false;
 
-  if (appType !== "artifacts") {
-    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(
+  const { language: newLanguage } = await prompts(
     {
-      type: "toggle",
-      name: "useLlamaCloud",
-      message: "Do you want to use LlamaCloud services?",
-      initial: false,
-      active: "Yes",
-      inactive: "No",
-      hint: "see https://www.llamaindex.ai/enterprise for more info",
+      type: "select",
+      name: "language",
+      message: "What language do you want to use?",
+      choices: [
+        { title: "Python (FastAPI)", value: "fastapi" },
+        { title: "Typescript (NextJS)", value: "nextjs" },
+      ],
     },
     questionHandlers,
   );
-  useLlamaCloud = newUseLlamaCloud;
+  language = newLanguage;
+
+  if (appType !== "code_generator" && appType !== "document_generator") {
+    const { useLlamaCloud: newUseLlamaCloud } = await prompts(
+      {
+        type: "toggle",
+        name: "useLlamaCloud",
+        message: "Do you want to use LlamaCloud services?",
+        initial: false,
+        active: "Yes",
+        inactive: "No",
+        hint: "see https://www.llamaindex.ai/enterprise for more info",
+      },
+      questionHandlers,
+    );
+    useLlamaCloud = newUseLlamaCloud;
+  }
 
   if (useLlamaCloud && !llamaCloudKey) {
     // Ask for LlamaCloud API key, if not set
@@ -153,7 +158,13 @@ const convertAnswers = async (
       tools: [],
       modelConfig: MODEL_GPT41,
     },
-    artifacts: {
+    code_generator: {
+      template: "llamaindexserver",
+      dataSources: [],
+      tools: [],
+      modelConfig: MODEL_GPT41,
+    },
+    document_generator: {
       template: "llamaindexserver",
       dataSources: [],
       tools: [],

packages/create-llama/templates/components/llamaindex/typescript/documents/upload.ts

@@ -1,5 +1,9 @@
-import { Document, LLamaCloudFileService, VectorStoreIndex } from "llamaindex";
-import { LlamaCloudIndex } from "llamaindex/cloud/LlamaCloudIndex";
+import {
+  Document,
+  LLamaCloudFileService,
+  LlamaCloudIndex,
+  VectorStoreIndex,
+} from "llamaindex";
 import { DocumentFile } from "../streaming/annotations";
 import { parseFile, storeFile } from "./helper";
 import { runPipeline } from "./pipeline";

packages/create-llama/templates/components/reflex/contract_review/pyproject.toml

@@ -10,6 +10,7 @@ dependencies = [
     "python-dotenv>=1.0.0",
     "pydantic<2.10",
     "llama-index>=0.12.1",
+    "llama-parse>=0.6.21,<0.7.0",
     "cachetools>=5.3.3",
     "reflex>=0.6.2.post1",
 ]

packages/create-llama/templates/components/reflex/extractor/pyproject.toml

@@ -11,6 +11,7 @@ dependencies = [
     "python-dotenv>=1.0.0",
     "pydantic<2.10",
     "llama-index>=0.12.1",
+    "llama-parse>=0.6.21,<0.7.0",
     "cachetools>=5.3.3",
     "reflex>=0.6.2.post1",
 ]

packages/create-llama/templates/components/ui/use-cases/code_generator/ui_event.jsx

@@ -113,11 +113,6 @@ function ArtifactWorkflowCard({ event }) {
               state === "plan" && "bg-blue-200",
               state === "generate" && "bg-violet-200",
             )}
-            indicatorClassName={cn(
-              "transition-all duration-500",
-              state === "plan" && "bg-blue-500",
-              state === "generate" && "bg-violet-500",
-            )}
           />
         </div>
       </Card>

packages/create-llama/templates/components/ui/use-cases/document_generator/ui_event.jsx

@@ -0,0 +1,132 @@
+import { Badge } from "@/components/ui/badge";
+import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card";
+import { Progress } from "@/components/ui/progress";
+import { Skeleton } from "@/components/ui/skeleton";
+import { cn } from "@/lib/utils";
+import { Markdown } from "@llamaindex/chat-ui/widgets";
+import { ListChecks, Loader2, Wand2 } from "lucide-react";
+import { useEffect, useState } from "react";
+
+const STAGE_META = {
+  plan: {
+    icon: ListChecks,
+    badgeText: "Step 1/2: Planning",
+    gradient: "from-blue-100 via-blue-50 to-white",
+    progress: 33,
+    iconBg: "bg-blue-100 text-blue-600",
+    badge: "bg-blue-100 text-blue-700",
+  },
+  generate: {
+    icon: Wand2,
+    badgeText: "Step 2/2: Generating",
+    gradient: "from-violet-100 via-violet-50 to-white",
+    progress: 66,
+    iconBg: "bg-violet-100 text-violet-600",
+    badge: "bg-violet-100 text-violet-700",
+  },
+};
+
+function ArtifactWorkflowCard({ event }) {
+  const [visible, setVisible] = useState(event?.state !== "completed");
+  const [fade, setFade] = useState(false);
+
+  useEffect(() => {
+    if (event?.state === "completed") {
+      setVisible(false);
+    } else {
+      setVisible(true);
+      setFade(false);
+    }
+  }, [event?.state]);
+
+  if (!event || !visible) return null;
+
+  const { state, requirement } = event;
+  const meta = STAGE_META[state];
+
+  if (!meta) return null;
+
+  return (
+    <div className="flex min-h-[180px] w-full items-center justify-center py-2">
+      <Card
+        className={cn(
+          "w-full rounded-xl shadow-md transition-all duration-500",
+          "border-0",
+          fade && "pointer-events-none opacity-0",
+          `bg-gradient-to-br ${meta.gradient}`,
+        )}
+        style={{
+          boxShadow:
+            "0 2px 12px 0 rgba(80, 80, 120, 0.08), 0 1px 3px 0 rgba(80, 80, 120, 0.04)",
+        }}
+      >
+        <CardHeader className="flex flex-row items-center gap-2 px-3 pb-1 pt-2">
+          <div
+            className={cn(
+              "flex items-center justify-center rounded-full p-1",
+              meta.iconBg,
+            )}
+          >
+            <meta.icon className="h-5 w-5" />
+          </div>
+          <CardTitle className="flex items-center gap-2 text-base font-semibold">
+            <Badge className={cn("ml-1", meta.badge, "px-2 py-0.5 text-xs")}>
+              {meta.badgeText}
+            </Badge>
+          </CardTitle>
+        </CardHeader>
+        <CardContent className="px-3 py-1">
+          {state === "plan" && (
+            <div className="flex flex-col items-center gap-2 py-2">
+              <Loader2 className="mb-1 h-6 w-6 animate-spin text-blue-400" />
+              <div className="text-center text-sm font-medium text-blue-900">
+                Analyzing your request...
+              </div>
+              <Skeleton className="mt-1 h-3 w-1/2 rounded-full" />
+            </div>
+          )}
+          {state === "generate" && (
+            <div className="flex flex-col gap-2 py-2">
+              <div className="flex items-center gap-1">
+                <Loader2 className="h-4 w-4 animate-spin text-violet-400" />
+                <span className="text-sm font-medium text-violet-900">
+                  Working on the requirement:
+                </span>
+              </div>
+              <div className="max-h-24 overflow-auto rounded-lg border border-violet-200 bg-violet-50 px-2 py-1 text-xs">
+                {requirement ? (
+                  <Markdown content={requirement} />
+                ) : (
+                  <span className="italic text-violet-400">
+                    No requirements available yet.
+                  </span>
+                )}
+              </div>
+            </div>
+          )}
+        </CardContent>
+        <div className="px-3 pb-2 pt-1">
+          <Progress
+            value={meta.progress}
+            className={cn(
+              "h-1 rounded-full bg-gray-200",
+              state === "plan" && "bg-blue-200",
+              state === "generate" && "bg-violet-200",
+            )}
+          />
+        </div>
+      </Card>
+    </div>
+  );
+}
+
+export default function Component({ events }) {
+  const aggregateEvents = () => {
+    if (!events || events.length === 0) return null;
+    return events[events.length - 1];
+  };
+
+  const event = aggregateEvents();
+
+  return <ArtifactWorkflowCard event={event} />;
+}

packages/create-llama/templates/components/use-cases/python/code_generator/README-template.md

@@ -0,0 +1,65 @@
+This is a [LlamaIndex](https://www.llamaindex.ai/) project using [Workflows](https://docs.llamaindex.ai/en/stable/understanding/workflows/).
+
+## Getting Started
+
+First, setup the environment with uv:
+
+> **_Note:_** This step is not needed if you are using the dev-container.
+
+```shell
+uv sync
+```
+
+Then check the parameters that have been pre-configured in the `.env` file in this directory.
+Make sure you have set the `OPENAI_API_KEY` for the LLM.
+
+Then, run the development server:
+
+```shell
+uv run fastapi dev
+```
+
+Then open [http://localhost:8000](http://localhost:8000) with your browser to start the chat UI.
+
+To start the app optimized for **production**, run:
+
+```
+uv run fastapi run
+```
+
+## Configure LLM and Embedding Model
+
+You can configure [LLM model](https://docs.llamaindex.ai/en/stable/module_guides/models/llms) and [embedding model](https://docs.llamaindex.ai/en/stable/module_guides/models/embeddings) in [settings.py](app/settings.py).
+
+## Use Case
+AI-powered code generator that can help you generate app with a chat interface, code editor and app preview.
+
+To update the workflow, you can modify the code in [`workflow.py`](app/workflow.py).
+
+You can start by sending an request on the [chat UI](http://localhost:8000) or you can test the `/api/chat` endpoint with the following curl request:
+
+```
+curl --location 'localhost:8000/api/chat' \
+--header 'Content-Type: application/json' \
+--data '{ "messages": [{ "role": "user", "content": "Create a report comparing the finances of Apple and Tesla" }] }'
+```
+
+## Customize the UI
+
+To customize the UI, you can start by modifying the [./components/ui_event.jsx](./components/ui_event.jsx) file.
+
+You can also generate a new code for the workflow using LLM by running the following command:
+
+```
+uv run generate_ui
+```
+
+## Learn More
+
+To learn more about LlamaIndex, take a look at the following resources:
+
+- [LlamaIndex Documentation](https://docs.llamaindex.ai) - learn about LlamaIndex.
+- [Workflows Introduction](https://docs.llamaindex.ai/en/stable/understanding/workflows/) - learn about LlamaIndex workflows.
+- [LlamaIndex Server](https://pypi.org/project/llama-index-server/)
+
+You can check out [the LlamaIndex GitHub repository](https://github.com/run-llama/llama_index) - your feedback and contributions are welcome!

packages/create-llama/templates/components/use-cases/python/code_generator/workflow.py

@@ -6,6 +6,7 @@ from llama_index.core.chat_engine.types import ChatMessage
 from llama_index.core.llms import LLM
 from llama_index.core.memory import ChatMemoryBuffer
 from llama_index.core.prompts import PromptTemplate
+from llama_index.llms.openai import OpenAI
 from llama_index.core.workflow import (
     Context,
     Event,
@@ -26,6 +27,15 @@ from llama_index.server.api.utils import get_last_artifact
 from pydantic import BaseModel, Field
 
 
+def create_workflow(chat_request: ChatRequest) -> Workflow:
+    workflow = CodeArtifactWorkflow(
+        llm=OpenAI(model="gpt-4.1"),
+        chat_request=chat_request,
+        timeout=120.0,
+    )
+    return workflow
+
+
 class Requirement(BaseModel):
     next_step: Literal["answering", "coding"]
     language: Optional[str] = None

packages/create-llama/templates/components/use-cases/python/document_generator/README-template.md

@@ -33,12 +33,9 @@ You can configure [LLM model](https://docs.llamaindex.ai/en/stable/module_guides
 
 ## Use Case
 
-We have prepared two artifact workflows:
+AI-powered document generator that can help you generate documents with a chat interface and simple markdown editor.
 
-- [Code Workflow](app/code_workflow.py): To generate code and display it in the UI like Vercel's v0.
-- [Document Workflow](app/document_workflow.py): Generate and update a document like OpenAI's canvas.
-
-Modify the factory method in [`workflow.py`](app/workflow.py) to decide which artifact workflow to use. Without any changes the Code Workflow is used.
+To update the workflow, you can modify the code in [`workflow.py`](app/workflow.py).
 
 You can start by sending an request on the [chat UI](http://localhost:8000) or you can test the `/api/chat` endpoint with the following curl request:
 

packages/create-llama/templates/components/use-cases/python/document_generator/workflow.py

@@ -4,6 +4,7 @@ from typing import Any, Literal, Optional
 
 from llama_index.core.chat_engine.types import ChatMessage
 from llama_index.core.llms import LLM
+from llama_index.llms.openai import OpenAI
 from llama_index.core.memory import ChatMemoryBuffer
 from llama_index.core.prompts import PromptTemplate
 from llama_index.core.workflow import (
@@ -26,6 +27,15 @@ from llama_index.server.api.utils import get_last_artifact
 from pydantic import BaseModel, Field
 
 
+def create_workflow(chat_request: ChatRequest) -> Workflow:
+    workflow = DocumentArtifactWorkflow(
+        llm=OpenAI(model="gpt-4.1"),
+        chat_request=chat_request,
+        timeout=120.0,
+    )
+    return workflow
+
+
 class DocumentRequirement(BaseModel):
     type: Literal["markdown", "html"]
     title: str

packages/create-llama/templates/components/use-cases/typescript/agentic_rag/src/app/workflow.ts

@@ -1,4 +1,4 @@
-import { agent } from "llamaindex";
+import { agent } from "@llamaindex/workflow";
 import { getIndex } from "./data";
 
 export const workflowFactory = async (reqBody: any) => {

packages/create-llama/templates/components/use-cases/typescript/agentic_rag/src/generate.ts

@@ -0,0 +1,39 @@
+import { SimpleDirectoryReader } from "@llamaindex/readers/directory";
+import "dotenv/config";
+import { storageContextFromDefaults, VectorStoreIndex } from "llamaindex";
+import { initSettings } from "./app/settings";
+
+async function generateDatasource() {
+  console.log(`Generating storage context...`);
+  // Split documents, create embeddings and store them in the storage context
+  const storageContext = await storageContextFromDefaults({
+    persistDir: "storage",
+  });
+  // load documents from current directory into an index
+  const reader = new SimpleDirectoryReader();
+  const documents = await reader.loadData("data");
+
+  await VectorStoreIndex.fromDocuments(documents, {
+    storageContext,
+  });
+  console.log("Storage context successfully generated.");
+}
+
+(async () => {
+  const args = process.argv.slice(2);
+  const command = args[0];
+
+  initSettings();
+
+  if (command === "ui") {
+    console.error("This project doesn't use any custom UI.");
+    return;
+  } else {
+    if (command !== "datasource") {
+      console.error(
+        `Unrecognized command: ${command}. Generating datasource by default.`,
+      );
+    }
+    await generateDatasource();
+  }
+})();

packages/create-llama/templates/components/use-cases/typescript/code_generator/README-template.md

@@ -0,0 +1,53 @@
+This is a [LlamaIndex](https://www.llamaindex.ai/) project bootstrapped with [`create-llama`](https://github.com/run-llama/LlamaIndexTS/tree/main/packages/create-llama).
+
+## Getting Started
+
+First, install the dependencies:
+
+```
+npm install
+```
+
+Third, run the development server:
+
+```
+npm run dev
+```
+
+Open [http://localhost:3000](http://localhost:3000) with your browser to see the chat UI.
+
+## Configure LLM and Embedding Model
+
+You can configure [LLM model](https://ts.llamaindex.ai/docs/llamaindex/modules/llms) in the [settings file](src/app/settings.ts).
+
+## Custom UI Components
+
+We have a custom component located in `components/ui_event.jsx`. This is used to display the state of artifact workflows in UI. You can regenerate a new UI component from the workflow event schema by running the following command:
+
+```
+npm run generate:ui
+```
+
+## Use Case
+
+AI-powered code generator that can help you generate app with a chat interface, code editor and app preview.
+
+To update the workflow, you can modify the code in [`workflow.ts`](app/workflow.ts).
+
+You can start by sending an request on the [chat UI](http://localhost:3000) or you can test the `/api/chat` endpoint with the following curl request:
+
+```shell
+curl --location 'localhost:3000/api/chat' \
+--header 'Content-Type: application/json' \
+--data '{ "messages": [{ "role": "user", "content": "Compare the financial performance of Apple and Tesla" }] }'
+```
+
+## Learn More
+
+To learn more about LlamaIndex, take a look at the following resources:
+
+- [LlamaIndex Documentation](https://docs.llamaindex.ai) - learn about LlamaIndex (Python features).
+- [LlamaIndexTS Documentation](https://ts.llamaindex.ai/docs/llamaindex) - learn about LlamaIndex (Typescript features).
+- [Workflows Introduction](https://ts.llamaindex.ai/docs/llamaindex/modules/workflows) - learn about LlamaIndexTS workflows.
+
+You can check out [the LlamaIndexTS GitHub repository](https://github.com/run-llama/LlamaIndexTS) - your feedback and contributions are welcome!

packages/create-llama/templates/components/use-cases/typescript/code_generator/src/app/workflow.ts

@@ -0,0 +1,357 @@
+import { extractLastArtifact } from "@llamaindex/server";
+import { ChatMemoryBuffer, LLM, MessageContent, Settings } from "llamaindex";
+
+import {
+  agentStreamEvent,
+  createStatefulMiddleware,
+  createWorkflow,
+  startAgentEvent,
+  stopAgentEvent,
+  workflowEvent,
+} from "@llamaindex/workflow";
+
+import { z } from "zod";
+
+export const workflowFactory = async (reqBody: any) => {
+  const workflow = createCodeArtifactWorkflow(reqBody);
+
+  return workflow;
+};
+
+export const RequirementSchema = z.object({
+  next_step: z.enum(["answering", "coding"]),
+  language: z.string().nullable().optional(),
+  file_name: z.string().nullable().optional(),
+  requirement: z.string(),
+});
+
+export type Requirement = z.infer<typeof RequirementSchema>;
+
+export const UIEventSchema = z.object({
+  type: z.literal("ui_event"),
+  data: z.object({
+    state: z
+      .enum(["plan", "generate", "completed"])
+      .describe(
+        "The current state of the workflow: 'plan', 'generate', or 'completed'.",
+      ),
+    requirement: z
+      .string()
+      .optional()
+      .describe(
+        "An optional requirement creating or updating a code, if applicable.",
+      ),
+  }),
+});
+
+export type UIEvent = z.infer<typeof UIEventSchema>;
+const planEvent = workflowEvent<{
+  userInput: MessageContent;
+  context?: string | undefined;
+}>();
+
+const generateArtifactEvent = workflowEvent<{
+  requirement: Requirement;
+}>();
+
+const synthesizeAnswerEvent = workflowEvent<object>();
+
+const uiEvent = workflowEvent<UIEvent>();
+
+const artifactEvent = workflowEvent<{
+  type: "artifact";
+  data: {
+    type: "code";
+    created_at: number;
+    data: {
+      language: string;
+      file_name: string;
+      code: string;
+    };
+  };
+}>();
+
+export function createCodeArtifactWorkflow(reqBody: any, llm?: LLM) {
+  if (!llm) {
+    llm = Settings.llm;
+  }
+  const { withState, getContext } = createStatefulMiddleware(() => {
+    return {
+      memory: new ChatMemoryBuffer({
+        llm,
+        chatHistory: reqBody.chatHistory,
+      }),
+      lastArtifact: extractLastArtifact(reqBody),
+    };
+  });
+  const workflow = withState(createWorkflow());
+
+  workflow.handle([startAgentEvent], async ({ data: { userInput } }) => {
+    // Prepare chat history
+    const { state } = getContext();
+    // Put user input to the memory
+    if (!userInput) {
+      throw new Error("Missing user input to start the workflow");
+    }
+    state.memory.put({
+      role: "user",
+      content: userInput,
+    });
+    return planEvent.with({
+      userInput: userInput,
+    });
+  });
+
+  workflow.handle([planEvent], async ({ data: planData }) => {
+    const { sendEvent } = getContext();
+    const { state } = getContext();
+    sendEvent(
+      uiEvent.with({
+        type: "ui_event",
+        data: {
+          state: "plan",
+        },
+      }),
+    );
+    const user_msg = planData.userInput;
+    const context = planData.context
+      ? `## The context is: \n${planData.context}\n`
+      : "";
+    const prompt = `
+You are a product analyst responsible for analyzing the user's request and providing the next step for code or document generation.
+You are helping user with their code artifact. To update the code, you need to plan a coding step.
+
+Follow these instructions:
+1. Carefully analyze the conversation history and the user's request to determine what has been done and what the next step should be.
+2. The next step must be one of the following two options:
+    - "coding": To make the changes to the current code.
+    - "answering": If you don't need to update the current code or need clarification from the user.
+Important: Avoid telling the user to update the code themselves, you are the one who will update the code (by planning a coding step).
+3. If the next step is "coding", you may specify the language ("typescript" or "python") and file_name if known, otherwise set them to null. 
+4. The requirement must be provided clearly what is the user request and what need to be done for the next step in details
+    as precise and specific as possible, don't be stingy with in the requirement.
+5. If the next step is "answering", set language and file_name to null, and the requirement should describe what to answer or explain to the user.
+6. Be concise; only return the requirements for the next step.
+7. The requirements must be in the following format:
+    \`\`\`json
+    {
+        "next_step": "answering" | "coding",
+        "language": "typescript" | "python" | null,
+        "file_name": string | null,
+        "requirement": string
+    }
+    \`\`\`
+
+## Example 1:
+User request: Create a calculator app.
+You should return:
+\`\`\`json
+{
+    "next_step": "coding",
+    "language": "typescript",
+    "file_name": "calculator.tsx",
+    "requirement": "Generate code for a calculator app that has a simple UI with a display and button layout. The display should show the current input and the result. The buttons should include basic operators, numbers, clear, and equals. The calculation should work correctly."
+}
+\`\`\`
+
+## Example 2:
+User request: Explain how the game loop works.
+Context: You have already generated the code for a snake game.
+You should return:
+\`\`\`json
+{
+    "next_step": "answering",
+    "language": null,
+    "file_name": null,
+    "requirement": "The user is asking about the game loop. Explain how the game loop works."
+}
+\`\`\`
+
+${context}
+
+Now, plan the user's next step for this request:
+${user_msg}
+`;
+
+    const response = await llm.complete({
+      prompt,
+    });
+    // parse the response to Requirement
+    // 1. use regex to find the json block
+    const jsonBlock = response.text.match(/```json\s*([\s\S]*?)\s*```/);
+    if (!jsonBlock) {
+      throw new Error("No JSON block found in the response.");
+    }
+    const requirement = RequirementSchema.parse(JSON.parse(jsonBlock[1]));
+    state.memory.put({
+      role: "assistant",
+      content: `The plan for next step: \n${response.text}`,
+    });
+
+    if (requirement.next_step === "coding") {
+      return generateArtifactEvent.with({
+        requirement,
+      });
+    } else {
+      return synthesizeAnswerEvent.with({});
+    }
+  });
+
+  workflow.handle([generateArtifactEvent], async ({ data: planData }) => {
+    const { sendEvent } = getContext();
+    const { state } = getContext();
+
+    sendEvent(
+      uiEvent.with({
+        type: "ui_event",
+        data: {
+          state: "generate",
+          requirement: planData.requirement.requirement,
+        },
+      }),
+    );
+
+    const previousArtifact = state.lastArtifact
+      ? JSON.stringify(state.lastArtifact)
+      : "There is no previous artifact";
+    const requirementText = planData.requirement.requirement;
+
+    const prompt = `
+        You are a skilled developer who can help user with coding.
+        You are given a task to generate or update a code for a given requirement.
+
+        ## Follow these instructions:
+        **1. Carefully read the user's requirements.** 
+           If any details are ambiguous or missing, make reasonable assumptions and clearly reflect those in your output.
+           If the previous code is provided:
+           + Carefully analyze the code with the request to make the right changes.
+           + Avoid making a lot of changes from the previous code if the request is not to write the code from scratch again.
+        **2. For code requests:**
+           - If the user does not specify a framework or language, default to a React component using the Next.js framework.
+           - For Next.js, use Shadcn UI components, Typescript, @types/node, @types/react, @types/react-dom, PostCSS, and TailwindCSS.
+           The import pattern should be:
+           \`\`\`typescript
+           import { ComponentName } from "@/components/ui/component-name"
+           import { Markdown } from "@llamaindex/chat-ui"
+           import { cn } from "@/lib/utils"
+           \`\`\`
+           - Ensure the code is idiomatic, production-ready, and includes necessary imports.
+           - Only generate code relevant to the user's request—do not add extra boilerplate.
+        **3. Don't be verbose on response**
+           - No other text or comments only return the code which wrapped by \`\`\`language\`\`\` block.
+           - If the user's request is to update the code, only return the updated code.
+        **4. Only the following languages are allowed: "typescript", "python".**
+        **5. If there is no code to update, return the reason without any code block.**
+           
+        ## Example:
+        \`\`\`typescript
+        import React from "react";
+        import { Button } from "@/components/ui/button";
+        import { cn } from "@/lib/utils";
+
+        export default function MyComponent() {
+        return (
+           <div className="flex flex-col items-center justify-center h-screen">
+              <Button>Click me</Button>
+           </div>
+        );
+        }
+        \`\`\`
+
+        The previous code is:
+        {previousArtifact}
+
+        Now, i have to generate the code for the following requirement:
+        {requirement}
+      `
+      .replace("{previousArtifact}", previousArtifact)
+      .replace("{requirement}", requirementText);
+
+    const response = await llm.complete({
+      prompt,
+    });
+
+    // Extract the code from the response
+    const codeMatch = response.text.match(/```(\w+)([\s\S]*)```/);
+    if (!codeMatch) {
+      return synthesizeAnswerEvent.with({});
+    }
+
+    const code = codeMatch[2].trim();
+
+    // Put the generated code to the memory
+    state.memory.put({
+      role: "assistant",
+      content: `Updated the code: \n${response.text}`,
+    });
+
+    // To show the Canvas panel for the artifact
+    sendEvent(
+      artifactEvent.with({
+        type: "artifact",
+        data: {
+          type: "code",
+          created_at: Date.now(),
+          data: {
+            language: planData.requirement.language || "",
+            file_name: planData.requirement.file_name || "",
+            code,
+          },
+        },
+      }),
+    );
+
+    return synthesizeAnswerEvent.with({});
+  });
+
+  workflow.handle([synthesizeAnswerEvent], async () => {
+    const { sendEvent } = getContext();
+    const { state } = getContext();
+
+    const chatHistory = await state.memory.getMessages();
+    const messages = [
+      ...chatHistory,
+      {
+        role: "system" as const,
+        content: `
+        You are a helpful assistant who is responsible for explaining the work to the user.
+        Based on the conversation history, provide an answer to the user's question. 
+        The user has access to the code so avoid mentioning the whole code again in your response.
+      `,
+      },
+    ];
+
+    const responseStream = await llm.chat({
+      messages,
+      stream: true,
+    });
+
+    sendEvent(
+      uiEvent.with({
+        type: "ui_event",
+        data: {
+          state: "completed",
+        },
+      }),
+    );
+
+    let response = "";
+    for await (const chunk of responseStream) {
+      response += chunk.delta;
+      sendEvent(
+        agentStreamEvent.with({
+          delta: chunk.delta,
+          response: "",
+          currentAgentName: "assistant",
+          raw: chunk,
+        }),
+      );
+    }
+
+    return stopAgentEvent.with({
+      result: response,
+    });
+  });
+
+  return workflow;
+}

packages/create-llama/templates/components/use-cases/typescript/deep_research/README-template.md

@@ -31,7 +31,7 @@ You can configure [LLM model](https://ts.llamaindex.ai/docs/llamaindex/modules/l
 
 ## Custom UI Components
 
-For Deep Research, we have a custom component located in `components/deep_research_event.jsx`. This is used to display the results of the deep research workflow in a more user-friendly way
+For Deep Research, we have a custom component located in `components/ui_event.jsx`. This is used to display the results of the deep research workflow in a more user-friendly way
 
 ### Generate a new UI Component from workflow event
 

packages/create-llama/templates/components/use-cases/typescript/deep_research/src/app/workflow.ts

@@ -0,0 +1,416 @@
+import { toSourceEvent } from "@llamaindex/server";
+import {
+  agentStreamEvent,
+  createStatefulMiddleware,
+  createWorkflow,
+  startAgentEvent,
+  stopAgentEvent,
+  workflowEvent,
+} from "@llamaindex/workflow";
+import {
+  ChatMemoryBuffer,
+  LlamaCloudIndex,
+  MessageContent,
+  Metadata,
+  MetadataMode,
+  NodeWithScore,
+  PromptTemplate,
+  Settings,
+  VectorStoreIndex,
+  extractText,
+} from "llamaindex";
+import { randomUUID } from "node:crypto";
+import { z } from "zod";
+import { getIndex } from "./data";
+
+// workflow factory
+export const workflowFactory = async (reqBody: any) => {
+  const index = await getIndex(reqBody?.data);
+  return getWorkflow(index);
+};
+
+// workflow configs
+const MAX_QUESTIONS = 6; // max number of questions to research, research will stop when this number is reached
+const TOP_K = 10; // number of nodes to retrieve from the vector store
+
+const createPlanResearchPrompt = new PromptTemplate({
+  template: `
+You are a professor who is guiding a researcher to research a specific request/problem.
+Your task is to decide on a research plan for the researcher.
+
+The possible actions are:
++ Provide a list of questions for the researcher to investigate, with the purpose of clarifying the request.
++ Write a report if the researcher has already gathered enough research on the topic and can resolve the initial request.
++ Cancel the research if most of the answers from researchers indicate there is insufficient information to research the request. Do not attempt more than 3 research iterations or too many questions.
+
+The workflow should be:
++ Always begin by providing some initial questions for the researcher to investigate.
++ Analyze the provided answers against the initial topic/request. If the answers are insufficient to resolve the initial request, provide additional questions for the researcher to investigate.
++ If the answers are sufficient to resolve the initial request, instruct the researcher to write a report.
+
+Here are the context: 
+<Collected information>
+{context_str}
+</Collected information>
+
+<Conversation context>
+{conversation_context}
+</Conversation context>
+
+{enhanced_prompt}
+
+Now, provide your decision in the required format for this user request:
+<User request>
+{user_request}
+</User request>
+`,
+  templateVars: [
+    "context_str",
+    "conversation_context",
+    "enhanced_prompt",
+    "user_request",
+  ],
+});
+
+const researchPrompt = new PromptTemplate({
+  template: `
+You are a researcher who is in the process of answering the question.
+The purpose is to answer the question based on the collected information, without using prior knowledge or making up any new information.
+Always add citations to the sentence/point/paragraph using the id of the provided content.
+The citation should follow this format: [citation:id] where id is the id of the content.
+
+E.g:
+If we have a context like this:
+<Citation id='abc-xyz'>
+Baby llama is called cria
+</Citation id='abc-xyz'>
+
+And your answer uses the content, then the citation should be:
+- Baby llama is called cria [citation:abc-xyz]
+
+ Here is the provided context for the question:
+<Collected information>
+{context_str}
+</Collected information>
+
+No prior knowledge, just use the provided context to answer the question: {question}
+`,
+  templateVars: ["context_str", "question"],
+});
+
+const WRITE_REPORT_PROMPT = `
+You are a researcher writing a report based on a user request and the research context.
+You have researched various perspectives related to the user request.
+The report should provide a comprehensive outline covering all important points from the researched perspectives.
+Create a well-structured outline for the research report that covers all the answers.
+
+# IMPORTANT when writing in markdown format:
++ Use tables or figures where appropriate to enhance presentation.
++ Preserve all citation syntax (the \`[citation:id]()\` parts in the provided context). Keep these citations in the final report - no separate reference section is needed.
++ Do not add links, a table of contents, or a references section to the report.
+`;
+
+// workflow events
+type ResearchQuestion = { questionId: string; question: string };
+type ResearchResult = ResearchQuestion & { answer: string };
+
+// class PlanResearchEvent extends WorkflowEvent<{}> {}
+const planResearchEvent = workflowEvent<{}>();
+const researchEvent = workflowEvent<ResearchQuestion>();
+const reportEvent = workflowEvent<{}>();
+
+export const UIEventSchema = z
+  .object({
+    event: z
+      .enum(["retrieve", "analyze", "answer"])
+      .describe(
+        "The type of event. DeepResearch has 3 main stages:\n1. retrieve: Retrieve the context from the vector store\n2. analyze: Analyze the context and generate a research questions to answer\n3. answer: Answer the provided questions. Each question has a unique id, when the state is done, the event will have the answer for the question.",
+      ),
+    state: z
+      .enum(["pending", "inprogress", "done", "error"])
+      .describe("The state for each event"),
+    id: z.string().optional().describe("The id of the question"),
+    question: z
+      .string()
+      .optional()
+      .describe("The question generated by the LLM"),
+    answer: z.string().optional().describe("The answer generated by the LLM"),
+  })
+  .describe("DeepResearchEvent");
+
+type UIEventData = z.infer<typeof UIEventSchema>;
+
+const uiEvent = workflowEvent<{
+  type: "ui_event";
+  data: UIEventData;
+}>();
+
+// workflow definition
+export function getWorkflow(index: VectorStoreIndex | LlamaCloudIndex) {
+  const retriever = index.asRetriever({ similarityTopK: TOP_K });
+  const { withState, getContext } = createStatefulMiddleware(() => {
+    return {
+      memory: new ChatMemoryBuffer({
+        llm: Settings.llm,
+        chatHistory: [],
+      }),
+      contextNodes: [] as NodeWithScore<Metadata>[],
+      userRequest: "" as MessageContent,
+      totalQuestions: 0,
+      researchResults: [] as ResearchResult[],
+    };
+  });
+  const workflow = withState(createWorkflow());
+
+  workflow.handle([startAgentEvent], async ({ data }) => {
+    const { userInput, chatHistory = [] } = data;
+    const { sendEvent, state } = getContext();
+    if (!userInput) throw new Error("Invalid input");
+
+    state.memory.set(chatHistory);
+    state.memory.put({ role: "user", content: userInput });
+    state.userRequest = userInput;
+    sendEvent(
+      uiEvent.with({
+        type: "ui_event",
+        data: {
+          event: "retrieve",
+          state: "inprogress",
+        },
+      }),
+    );
+
+    const retrievedNodes = await retriever.retrieve({ query: userInput });
+
+    sendEvent(toSourceEvent(retrievedNodes));
+    sendEvent(
+      uiEvent.with({
+        type: "ui_event",
+        data: { event: "retrieve", state: "done" },
+      }),
+    );
+
+    state.contextNodes.push(...retrievedNodes);
+
+    return planResearchEvent.with({});
+  });
+
+  workflow.handle([planResearchEvent], async ({ data }) => {
+    const { sendEvent, state, stream } = getContext();
+
+    sendEvent(
+      uiEvent.with({
+        type: "ui_event",
+        data: { event: "analyze", state: "inprogress" },
+      }),
+    );
+
+    const { decision, researchQuestions, cancelReason } =
+      await createResearchPlan(
+        state.memory,
+        state.contextNodes
+          .map((node) => node.node.getContent(MetadataMode.NONE))
+          .join("\n"),
+        enhancedPrompt(state.totalQuestions),
+        state.userRequest,
+      );
+
+    sendEvent(
+      uiEvent.with({
+        type: "ui_event",
+        data: { event: "analyze", state: "done" },
+      }),
+    );
+    if (decision === "cancel") {
+      sendEvent(
+        uiEvent.with({
+          type: "ui_event",
+          data: { event: "analyze", state: "done" },
+        }),
+      );
+      return agentStreamEvent.with({
+        delta: cancelReason ?? "Research cancelled without any reason.",
+        response: cancelReason ?? "Research cancelled without any reason.",
+        currentAgentName: "",
+        raw: null,
+      });
+    }
+    if (decision === "research" && researchQuestions.length > 0) {
+      state.totalQuestions += researchQuestions.length;
+      state.memory.put({
+        role: "assistant",
+        content:
+          "We need to find answers to the following questions:\n" +
+          researchQuestions.join("\n"),
+      });
+      researchQuestions.forEach(({ questionId: id, question }) => {
+        sendEvent(
+          uiEvent.with({
+            type: "ui_event",
+            data: { event: "answer", state: "pending", id, question },
+          }),
+        );
+        sendEvent(researchEvent.with({ questionId: id, question }));
+      });
+      const events = await stream
+        .until(() => state.researchResults.length === researchQuestions.length)
+        .toArray();
+      return planResearchEvent.with({});
+    }
+    state.memory.put({
+      role: "assistant",
+      content: "No more idea to analyze. We should report the answers.",
+    });
+    sendEvent(
+      uiEvent.with({
+        type: "ui_event",
+        data: { event: "analyze", state: "done" },
+      }),
+    );
+    return reportEvent.with({});
+  });
+
+  workflow.handle([researchEvent], async ({ data }) => {
+    const { sendEvent, state } = getContext();
+    const { questionId, question } = data;
+
+    sendEvent(
+      uiEvent.with({
+        type: "ui_event",
+        data: {
+          event: "answer",
+          state: "inprogress",
+          id: questionId,
+          question,
+        },
+      }),
+    );
+
+    const answer = await answerQuestion(
+      contextStr(state.contextNodes),
+      question,
+    );
+    state.researchResults.push({ questionId, question, answer });
+
+    state.memory.put({
+      role: "assistant",
+      content: `<Question>${question}</Question>\n<Answer>${answer}</Answer>`,
+    });
+
+    sendEvent(
+      uiEvent.with({
+        type: "ui_event",
+        data: {
+          event: "answer",
+          state: "done",
+          id: questionId,
+          question,
+          answer,
+        },
+      }),
+    );
+  });
+
+  workflow.handle([reportEvent], async ({ data }) => {
+    const { sendEvent, state } = getContext();
+    const chatHistory = await state.memory.getAllMessages();
+    const messages = chatHistory.concat([
+      {
+        role: "system",
+        content: WRITE_REPORT_PROMPT,
+      },
+      {
+        role: "user",
+        content:
+          "Write a report addressing the user request based on the research provided the context",
+      },
+    ]);
+
+    const stream = await Settings.llm.chat({ messages, stream: true });
+    let response = "";
+    for await (const chunk of stream) {
+      response += chunk.delta;
+      sendEvent(
+        agentStreamEvent.with({
+          delta: chunk.delta,
+          response,
+          currentAgentName: "",
+          raw: stream,
+        }),
+      );
+    }
+    return stopAgentEvent.with({
+      result: response,
+    });
+  });
+
+  return workflow;
+}
+
+const createResearchPlan = async (
+  memory: ChatMemoryBuffer,
+  contextStr: string,
+  enhancedPrompt: string,
+  userRequest: MessageContent,
+) => {
+  const chatHistory = await memory.getMessages();
+
+  const conversationContext = chatHistory
+    .map((message) => `${message.role}: ${message.content}`)
+    .join("\n");
+
+  const prompt = createPlanResearchPrompt.format({
+    context_str: contextStr,
+    conversation_context: conversationContext,
+    enhanced_prompt: enhancedPrompt,
+    user_request: extractText(userRequest),
+  });
+
+  const responseFormat = z.object({
+    decision: z.enum(["research", "write", "cancel"]),
+    researchQuestions: z.array(z.string()),
+    cancelReason: z.string().optional(),
+  });
+
+  const result = await Settings.llm.complete({ prompt, responseFormat });
+  const plan = JSON.parse(result.text) as z.infer<typeof responseFormat>;
+
+  return {
+    ...plan,
+    researchQuestions: plan.researchQuestions.map((question) => ({
+      questionId: randomUUID(),
+      question,
+    })),
+  };
+};
+
+const contextStr = (contextNodes: NodeWithScore<Metadata>[]) => {
+  return contextNodes
+    .map((node) => {
+      const nodeId = node.node.id_;
+      const nodeContent = node.node.getContent(MetadataMode.NONE);
+      return `<Citation id='${nodeId}'>\n${nodeContent}</Citation id='${nodeId}'>`;
+    })
+    .join("\n");
+};
+
+const enhancedPrompt = (totalQuestions: number) => {
+  if (totalQuestions === 0) {
+    return "The student has no questions to research. Let start by providing some questions for the student to research.";
+  }
+
+  if (totalQuestions >= MAX_QUESTIONS) {
+    return `The student has researched ${totalQuestions} questions. Should proceeding writing report or cancel the research if the answers are not enough to write a report.`;
+  }
+
+  return "";
+};
+
+const answerQuestion = async (contextStr: string, question: string) => {
+  const prompt = researchPrompt.format({
+    context_str: contextStr,
+    question,
+  });
+  const result = await Settings.llm.complete({ prompt });
+  return result.text;
+};

packages/create-llama/templates/components/use-cases/typescript/document_generator/README-template.md

@@ -0,0 +1,53 @@
+This is a [LlamaIndex](https://www.llamaindex.ai/) project bootstrapped with [`create-llama`](https://github.com/run-llama/LlamaIndexTS/tree/main/packages/create-llama).
+
+## Getting Started
+
+First, install the dependencies:
+
+```
+npm install
+```
+
+Third, run the development server:
+
+```
+npm run dev
+```
+
+Open [http://localhost:3000](http://localhost:3000) with your browser to see the chat UI.
+
+## Configure LLM and Embedding Model
+
+You can configure [LLM model](https://ts.llamaindex.ai/docs/llamaindex/modules/llms) in the [settings file](src/app/settings.ts).
+
+## Custom UI Components
+
+We have a custom component located in `components/ui_event.jsx`. This is used to display the state of artifact workflows in UI. You can regenerate a new UI component from the workflow event schema by running the following command:
+
+```
+npm run generate:ui
+```
+
+## Use Case
+
+AI-powered document generator that can help you generate documents with a chat interface and simple markdown editor.
+
+To update the workflow, you can modify the code in [`workflow.ts`](app/workflow.ts).
+
+You can start by sending an request on the [chat UI](http://localhost:3000) or you can test the `/api/chat` endpoint with the following curl request:
+
+```shell
+curl --location 'localhost:3000/api/chat' \
+--header 'Content-Type: application/json' \
+--data '{ "messages": [{ "role": "user", "content": "Compare the financial performance of Apple and Tesla" }] }'
+```
+
+## Learn More
+
+To learn more about LlamaIndex, take a look at the following resources:
+
+- [LlamaIndex Documentation](https://docs.llamaindex.ai) - learn about LlamaIndex (Python features).
+- [LlamaIndexTS Documentation](https://ts.llamaindex.ai/docs/llamaindex) - learn about LlamaIndex (Typescript features).
+- [Workflows Introduction](https://ts.llamaindex.ai/docs/llamaindex/modules/workflows) - learn about LlamaIndexTS workflows.
+
+You can check out [the LlamaIndexTS GitHub repository](https://github.com/run-llama/LlamaIndexTS) - your feedback and contributions are welcome!

packages/create-llama/templates/components/use-cases/typescript/document_generator/src/app/workflow.ts

@@ -0,0 +1,338 @@
+import { extractLastArtifact } from "@llamaindex/server";
+import { ChatMemoryBuffer, LLM, MessageContent, Settings } from "llamaindex";
+
+import {
+  agentStreamEvent,
+  createStatefulMiddleware,
+  createWorkflow,
+  startAgentEvent,
+  stopAgentEvent,
+  workflowEvent,
+} from "@llamaindex/workflow";
+
+import { z } from "zod";
+
+export const workflowFactory = async (reqBody: any) => {
+  const workflow = createDocumentArtifactWorkflow(reqBody);
+
+  return workflow;
+};
+
+export const DocumentRequirementSchema = z.object({
+  type: z.enum(["markdown", "html"]),
+  title: z.string(),
+  requirement: z.string(),
+});
+
+export type DocumentRequirement = z.infer<typeof DocumentRequirementSchema>;
+
+export const UIEventSchema = z.object({
+  type: z.literal("ui_event"),
+  data: z.object({
+    state: z
+      .enum(["plan", "generate", "completed"])
+      .describe(
+        "The current state of the workflow: 'plan', 'generate', or 'completed'.",
+      ),
+    requirement: z
+      .string()
+      .optional()
+      .describe(
+        "An optional requirement creating or updating a document, if applicable.",
+      ),
+  }),
+});
+
+export type UIEvent = z.infer<typeof UIEventSchema>;
+
+const planEvent = workflowEvent<{
+  userInput: MessageContent;
+  context?: string | undefined;
+}>();
+
+const generateArtifactEvent = workflowEvent<{
+  requirement: DocumentRequirement;
+}>();
+
+const synthesizeAnswerEvent = workflowEvent<{
+  requirement: DocumentRequirement;
+  generatedArtifact: string;
+}>();
+
+const uiEvent = workflowEvent<UIEvent>();
+
+const artifactEvent = workflowEvent<{
+  type: "artifact";
+  data: {
+    type: "document";
+    created_at: number;
+    data: {
+      title: string;
+      content: string;
+      type: "markdown" | "html";
+    };
+  };
+}>();
+
+export function createDocumentArtifactWorkflow(reqBody: any, llm?: LLM) {
+  if (!llm) {
+    llm = Settings.llm;
+  }
+  const { withState, getContext } = createStatefulMiddleware(() => {
+    return {
+      memory: new ChatMemoryBuffer({
+        llm,
+        chatHistory: reqBody.chatHistory,
+      }),
+      lastArtifact: extractLastArtifact(reqBody),
+    };
+  });
+  const workflow = withState(createWorkflow());
+
+  workflow.handle([startAgentEvent], async ({ data: { userInput } }) => {
+    // Prepare chat history
+    const { state } = getContext();
+    // Put user input to the memory
+    if (!userInput) {
+      throw new Error("Missing user input to start the workflow");
+    }
+    state.memory.put({
+      role: "user",
+      content: userInput,
+    });
+    return planEvent.with({
+      userInput,
+      context: state.lastArtifact
+        ? JSON.stringify(state.lastArtifact)
+        : undefined,
+    });
+  });
+
+  workflow.handle([planEvent], async ({ data: planData }) => {
+    const { sendEvent } = getContext();
+    const { state } = getContext();
+    sendEvent(
+      uiEvent.with({
+        type: "ui_event",
+        data: {
+          state: "plan",
+        },
+      }),
+    );
+    const user_msg = planData.userInput;
+    const context = planData.context
+      ? `## The context is: \n${planData.context}\n`
+      : "";
+    const prompt = `
+         You are a documentation analyst responsible for analyzing the user's request and providing requirements for document generation or update.
+         Follow these instructions:
+         1. Carefully analyze the conversation history and the user's request to determine what has been done and what the next step should be.
+         2. From the user's request, provide requirements for the next step of the document generation or update.
+         3. Do not be verbose; only return the requirements for the next step of the document generation or update.
+         4. Only the following document types are allowed: "markdown", "html".
+         5. The requirement should be in the following format:
+            \`\`\`json
+            {
+                "type": "markdown" | "html",
+                "title": string,
+                "requirement": string
+            }
+            \`\`\`
+
+         ## Example:
+         User request: Create a project guideline document.
+         You should return:
+         \`\`\`json
+         {
+             "type": "markdown",
+             "title": "Project Guideline",
+             "requirement": "Generate a Markdown document that outlines the project goals, deliverables, and timeline. Include sections for introduction, objectives, deliverables, and timeline."
+         }
+         \`\`\`
+
+         User request: Add a troubleshooting section to the guideline.
+         You should return:
+         \`\`\`json
+         {
+             "type": "markdown",
+             "title": "Project Guideline",
+             "requirement": "Add a 'Troubleshooting' section at the end of the document with common issues and solutions."
+         }
+         \`\`\`
+
+         ${context}
+
+         Now, please plan for the user's request:
+         ${user_msg}
+        `;
+
+    const response = await llm.complete({
+      prompt,
+    });
+    // Parse the response to DocumentRequirement
+    const jsonBlock = response.text.match(/```json\s*([\s\S]*?)\s*```/);
+    if (!jsonBlock) {
+      throw new Error("No JSON block found in the response.");
+    }
+    const requirement = DocumentRequirementSchema.parse(
+      JSON.parse(jsonBlock[1]),
+    );
+    state.memory.put({
+      role: "assistant",
+      content: `Planning for the document generation: \n${response.text}`,
+    });
+    return generateArtifactEvent.with({
+      requirement,
+    });
+  });
+
+  workflow.handle(
+    [generateArtifactEvent],
+    async ({ data: { requirement } }) => {
+      const { sendEvent } = getContext();
+      const { state } = getContext();
+
+      sendEvent(
+        uiEvent.with({
+          type: "ui_event",
+          data: {
+            state: "generate",
+            requirement: requirement.requirement,
+          },
+        }),
+      );
+
+      const previousArtifact = state.lastArtifact
+        ? JSON.stringify(state.lastArtifact)
+        : "";
+      const requirementStr = JSON.stringify(requirement);
+
+      const prompt = `
+         You are a skilled technical writer who can help users with documentation.
+         You are given a task to generate or update a document for a given requirement.
+
+         ## Follow these instructions:
+         **1. Carefully read the user's requirements.**
+            If any details are ambiguous or missing, make reasonable assumptions and clearly reflect those in your output.
+            If the previous document is provided:
+            + Carefully analyze the document with the request to make the right changes.
+            + Avoid making unnecessary changes from the previous document if the request is not to rewrite it from scratch.
+         **2. For document requests:**
+            - If the user does not specify a type, default to Markdown.
+            - Ensure the document is clear, well-structured, and grammatically correct.
+            - Only generate content relevant to the user's request—do not add extra boilerplate.
+         **3. Do not be verbose in your response.**
+            - No other text or comments; only return the document content wrapped by the appropriate code block (\`\`\`markdown or \`\`\`html).
+            - If the user's request is to update the document, only return the updated document.
+         **4. Only the following types are allowed: "markdown", "html".**
+         **5. If there is no change to the document, return the reason without any code block.**
+
+         ## Example:
+         \`\`\`markdown
+         # Project Guideline
+         
+         ## Introduction
+         ...
+         \`\`\`
+
+         The previous content is:
+         ${previousArtifact}
+
+         Now, please generate the document for the following requirement:
+         ${requirementStr}
+      `;
+
+      const response = await llm.complete({
+        prompt,
+      });
+
+      // Extract the document from the response
+      const docMatch = response.text.match(/```(markdown|html)([\s\S]*)```/);
+      const generatedContent = response.text;
+
+      if (docMatch) {
+        const content = docMatch[2].trim();
+        const docType = docMatch[1] as "markdown" | "html";
+
+        // Put the generated document to the memory
+        state.memory.put({
+          role: "assistant",
+          content: `Generated document: \n${response.text}`,
+        });
+
+        // To show the Canvas panel for the artifact
+        sendEvent(
+          artifactEvent.with({
+            type: "artifact",
+            data: {
+              type: "document",
+              created_at: Date.now(),
+              data: {
+                title: requirement.title,
+                content: content,
+                type: docType,
+              },
+            },
+          }),
+        );
+      }
+
+      return synthesizeAnswerEvent.with({
+        requirement,
+        generatedArtifact: generatedContent,
+      });
+    },
+  );
+
+  workflow.handle([synthesizeAnswerEvent], async ({ data }) => {
+    const { sendEvent } = getContext();
+    const { state } = getContext();
+
+    const chatHistory = await state.memory.getMessages();
+    const messages = [
+      ...chatHistory,
+      {
+        role: "system" as const,
+        content: `
+                Your responsibility is to explain the work to the user.
+                If there is no document to update, explain the reason.
+                If the document is updated, just summarize what changed. Don't need to include the whole document again in the response.
+                `,
+      },
+    ];
+
+    const responseStream = await llm.chat({
+      messages,
+      stream: true,
+    });
+
+    sendEvent(
+      uiEvent.with({
+        type: "ui_event",
+        data: {
+          state: "completed",
+          requirement: data.requirement.requirement,
+        },
+      }),
+    );
+
+    let response = "";
+    for await (const chunk of responseStream) {
+      response += chunk.delta;
+      sendEvent(
+        agentStreamEvent.with({
+          delta: chunk.delta,
+          response: "",
+          currentAgentName: "assistant",
+          raw: chunk,
+        }),
+      );
+    }
+
+    return stopAgentEvent.with({
+      result: response,
+    });
+  });
+
+  return workflow;
+}

packages/create-llama/templates/components/use-cases/typescript/financial_report/src/app/workflow.ts

@@ -0,0 +1,318 @@
+import { toAgentRunEvent, toSourceEvent } from "@llamaindex/server";
+import {
+  callTools,
+  chatWithTools,
+  documentGenerator,
+  interpreter,
+} from "@llamaindex/tools";
+import {
+  agentStreamEvent,
+  createStatefulMiddleware,
+  createWorkflow,
+  startAgentEvent,
+  stopAgentEvent,
+  workflowEvent,
+} from "@llamaindex/workflow";
+import {
+  BaseToolWithCall,
+  ChatMemoryBuffer,
+  ChatMessage,
+  Metadata,
+  NodeWithScore,
+  Settings,
+  ToolCall,
+  ToolCallLLM,
+} from "llamaindex";
+import { getIndex } from "./data";
+
+export async function workflowFactory(reqBody: any) {
+  const index = await getIndex(reqBody?.data);
+
+  const queryEngineTool = index.queryTool({
+    metadata: {
+      name: "query_document",
+      description: `This tool can retrieve information about Apple and Tesla financial data`,
+    },
+    includeSourceNodes: true,
+  });
+
+  if (!process.env.E2B_API_KEY) {
+    throw new Error("E2B_API_KEY is required to use the code interpreter tool");
+  }
+
+  const codeInterpreterTool = interpreter({
+    apiKey: process.env.E2B_API_KEY!,
+  });
+  const documentGeneratorTool = documentGenerator();
+
+  return getWorkflow(
+    queryEngineTool,
+    codeInterpreterTool,
+    documentGeneratorTool,
+  );
+}
+
+// workflow events
+const inputEvent = workflowEvent<{ input: ChatMessage[] }>();
+const researchEvent = workflowEvent<{ toolCalls: ToolCall[] }>();
+const analyzeEvent = workflowEvent<{ input: ChatMessage | ToolCall[] }>();
+const reportGenerationEvent = workflowEvent<{ toolCalls: ToolCall[] }>();
+
+const DEFAULT_SYSTEM_PROMPT = `
+You are a financial analyst who are given a set of tools to help you.
+It's good to using appropriate tools for the user request and always use the information from the tools, don't make up anything yourself.
+For the query engine tool, you should break down the user request into a list of queries and call the tool with the queries.
+`;
+
+// workflow definition
+export function getWorkflow(
+  queryEngineTool: BaseToolWithCall,
+  codeInterpreterTool: BaseToolWithCall,
+  documentGeneratorTool: BaseToolWithCall,
+) {
+  const llm = Settings.llm as ToolCallLLM;
+  if (!llm.supportToolCall) {
+    throw new Error("LLM is not a ToolCallLLM");
+  }
+  const { withState, getContext } = createStatefulMiddleware(() => ({
+    memory: new ChatMemoryBuffer({ llm, chatHistory: [] }),
+  }));
+
+  const workflow = withState(createWorkflow());
+
+  // Add steps
+  workflow.handle([startAgentEvent], async ({ data }) => {
+    const { state } = getContext();
+    const { userInput, chatHistory = [] } = data;
+    if (!userInput) throw new Error("Invalid input");
+
+    state.memory.set(chatHistory);
+
+    state.memory.put({ role: "system", content: DEFAULT_SYSTEM_PROMPT });
+
+    state.memory.put({ role: "user", content: userInput });
+
+    const messages = await state.memory.getMessages();
+    return inputEvent.with({ input: messages });
+  });
+
+  workflow.handle([inputEvent], async ({ data }) => {
+    const { sendEvent, state } = getContext();
+    const chatHistory = data.input;
+
+    const tools = [codeInterpreterTool, documentGeneratorTool, queryEngineTool];
+
+    const toolCallResponse = await chatWithTools(llm, tools, chatHistory);
+
+    if (!toolCallResponse.hasToolCall()) {
+      const generator = toolCallResponse.responseGenerator;
+      let response = "";
+      if (generator) {
+        for await (const chunk of generator) {
+          response += chunk.delta;
+          sendEvent(
+            agentStreamEvent.with({
+              delta: chunk.delta,
+              response,
+              currentAgentName: "LLM", // Or derive from context if needed
+              raw: chunk.raw,
+            }),
+          );
+        }
+      }
+      return stopAgentEvent.with({ result: response });
+    }
+
+    if (toolCallResponse.hasMultipleTools()) {
+      state.memory.put({
+        role: "assistant",
+        content:
+          "Calling different tools is not allowed. Please only use multiple calls of the same tool.",
+      });
+      const newChatHistory = await state.memory.getMessages();
+      return inputEvent.with({ input: newChatHistory });
+    }
+
+    // Put the LLM tool call message into the memory
+    // And trigger the next step according to the tool call
+    if (toolCallResponse.toolCallMessage) {
+      state.memory.put(toolCallResponse.toolCallMessage);
+    }
+    const toolName = toolCallResponse.getToolNames()[0];
+    switch (toolName) {
+      case codeInterpreterTool.metadata.name:
+        return analyzeEvent.with({
+          input: toolCallResponse.toolCalls,
+        });
+      case documentGeneratorTool.metadata.name:
+        return reportGenerationEvent.with({
+          toolCalls: toolCallResponse.toolCalls,
+        });
+      default:
+        if (queryEngineTool.metadata.name === toolName) {
+          return researchEvent.with({
+            toolCalls: toolCallResponse.toolCalls,
+          });
+        }
+        throw new Error(`Unknown tool: ${toolName}`);
+    }
+  });
+
+  workflow.handle([researchEvent], async ({ data }) => {
+    const { sendEvent, state } = getContext();
+    sendEvent(
+      toAgentRunEvent({
+        agent: "Researcher",
+        text: "Researching data",
+        type: "text",
+      }),
+    );
+
+    const { toolCalls } = data;
+
+    const toolMsgs = await callTools({
+      tools: [queryEngineTool],
+      toolCalls,
+      writeEvent: (text, step) => {
+        sendEvent(
+          toAgentRunEvent({
+            agent: "Researcher",
+            text,
+            type: toolCalls.length > 1 ? "progress" : "text",
+            current: step,
+            total: toolCalls.length,
+          }),
+        );
+      },
+    });
+    for (const toolMsg of toolMsgs) {
+      state.memory.put(toolMsg);
+    }
+
+    const sourcesNodes: NodeWithScore<Metadata>[] = toolMsgs
+      .map((msg) => (msg.options as any)?.toolResult?.result?.sourceNodes)
+      .flat()
+      .filter(Boolean);
+
+    if (sourcesNodes.length > 0) {
+      sendEvent(toSourceEvent(sourcesNodes));
+    }
+
+    // Send a message indicating research is done, triggering analysis
+    return analyzeEvent.with({
+      input: {
+        role: "assistant",
+        content:
+          "I have finished researching the data, please analyze the data.",
+      },
+    });
+  });
+
+  /**
+   * Analyze a research result or a tool call for code interpreter from the LLM
+   */
+  workflow.handle([analyzeEvent], async ({ data }) => {
+    const { sendEvent, state } = getContext();
+    sendEvent(
+      toAgentRunEvent({
+        agent: "Analyst",
+        text: "Analyzing data",
+        type: "text",
+      }),
+    );
+    // Request by workflow LLM, input is a list of tool calls
+    let toolCalls: ToolCall[] = [];
+    if (Array.isArray(data.input)) {
+      toolCalls = data.input;
+    } else {
+      // Requested by Researcher, input is a ChatMessage
+      // We start new LLM chat specifically for analyzing the data
+      const analysisPrompt = `
+      You are an expert in analyzing financial data.
+      You are given a set of financial data to analyze. Your task is to analyze the financial data and return a report.
+      Your response should include a detailed analysis of the financial data, including any trends, patterns, or insights that you find.
+      Construct the analysis in textual format; including tables would be great!
+      Don't need to synthesize the data, just analyze and provide your findings.
+      `;
+
+      // Clone the current chat history
+      // Add the analysis system prompt and the message from the researcher
+      const currentChatHistory = await state.memory.getMessages();
+      const newChatHistory = [
+        ...currentChatHistory,
+        { role: "system", content: analysisPrompt },
+        data.input, // This is the ChatMessage from the research step
+      ];
+      const toolCallResponse = await chatWithTools(
+        llm,
+        [codeInterpreterTool],
+        newChatHistory as ChatMessage[],
+      );
+
+      if (!toolCallResponse.hasToolCall()) {
+        // If no tool call needed for analysis, put the response directly
+        state.memory.put(await toolCallResponse.asFullResponse());
+        const finalChatHistory = await state.memory.getMessages();
+        return inputEvent.with({ input: finalChatHistory });
+      } else {
+        state.memory.put(toolCallResponse.toolCallMessage!);
+        toolCalls = toolCallResponse.toolCalls;
+      }
+    }
+
+    // Call the code interpreter tools if needed
+    if (toolCalls.length > 0) {
+      const toolMsgs = await callTools({
+        tools: [codeInterpreterTool],
+        toolCalls,
+        writeEvent: (text, step) => {
+          sendEvent(
+            toAgentRunEvent({
+              agent: "Analyst",
+              text,
+              type: toolCalls.length > 1 ? "progress" : "text",
+              current: step,
+              total: toolCalls.length,
+            }),
+          );
+        },
+      });
+      for (const toolMsg of toolMsgs) {
+        state.memory.put(toolMsg);
+      }
+    }
+
+    const finalChatHistory = await state.memory.getMessages();
+    // After analysis (or tool calls for analysis), trigger the next LLM input cycle
+    return inputEvent.with({ input: finalChatHistory });
+  });
+
+  workflow.handle([reportGenerationEvent], async ({ data }) => {
+    const { sendEvent, state } = getContext();
+    const { toolCalls } = data;
+
+    const toolMsgs = await callTools({
+      tools: [documentGeneratorTool],
+      toolCalls,
+      writeEvent: (text, step) => {
+        sendEvent(
+          toAgentRunEvent({
+            agent: "Reporter",
+            text,
+            type: toolCalls.length > 1 ? "progress" : "text",
+            current: step,
+            total: toolCalls.length,
+          }),
+        );
+      },
+    });
+    for (const toolMsg of toolMsgs) {
+      state.memory.put(toolMsg);
+    }
+    const chatHistory = await state.memory.getMessages();
+    // After report generation, trigger the next LLM input cycle
+    return inputEvent.with({ input: chatHistory });
+  });
+
+  return workflow;
+}

packages/create-llama/templates/components/use-cases/typescript/financial_report/src/generate.ts

@@ -0,0 +1,39 @@
+import { SimpleDirectoryReader } from "@llamaindex/readers/directory";
+import "dotenv/config";
+import { storageContextFromDefaults, VectorStoreIndex } from "llamaindex";
+import { initSettings } from "./app/settings";
+
+async function generateDatasource() {
+  console.log(`Generating storage context...`);
+  // Split documents, create embeddings and store them in the storage context
+  const storageContext = await storageContextFromDefaults({
+    persistDir: "storage",
+  });
+  // load documents from current directory into an index
+  const reader = new SimpleDirectoryReader();
+  const documents = await reader.loadData("data");
+
+  await VectorStoreIndex.fromDocuments(documents, {
+    storageContext,
+  });
+  console.log("Storage context successfully generated.");
+}
+
+(async () => {
+  const args = process.argv.slice(2);
+  const command = args[0];
+
+  initSettings();
+
+  if (command === "ui") {
+    console.error("This project doesn't use any custom UI.");
+    return;
+  } else {
+    if (command !== "datasource") {
+      console.error(
+        `Unrecognized command: ${command}. Generating datasource by default.`,
+      );
+    }
+    await generateDatasource();
+  }
+})();

packages/create-llama/templates/components/vectordbs/llamaindexserver/llamacloud/typescript/index.ts

@@ -1,4 +1,4 @@
-import { LlamaCloudIndex } from "llamaindex/cloud/LlamaCloudIndex";
+import { LlamaCloudIndex } from "llamaindex";
 
 type LlamaCloudDataSourceParams = {
   llamaCloudPipeline?: {

packages/create-llama/templates/components/vectordbs/typescript/llamacloud/index.ts

@@ -1,4 +1,4 @@
-import { LlamaCloudIndex } from "llamaindex/cloud/LlamaCloudIndex";
+import { LlamaCloudIndex } from "llamaindex";
 
 type LlamaCloudDataSourceParams = {
   llamaCloudPipeline?: {

packages/create-llama/templates/components/workflows/python/artifacts/workflow.py

@@ -1,15 +0,0 @@
-from app.code_workflow import CodeArtifactWorkflow
-
-# from app.document_workflow import DocumentArtifactWorkflow to generate documents
-from llama_index.core.workflow import Workflow
-from llama_index.llms.openai import OpenAI
-from llama_index.server.api.models import ChatRequest
-
-
-def create_workflow(chat_request: ChatRequest) -> Workflow:
-    workflow = CodeArtifactWorkflow(
-        llm=OpenAI(model="gpt-4.1"),
-        chat_request=chat_request,
-        timeout=120.0,
-    )
-    return workflow

packages/create-llama/templates/components/workflows/typescript/deep_research/workflow.ts

@@ -1,447 +0,0 @@
-import { toSourceEvent, toStreamGenerator } from "@llamaindex/server";
-import {
-  AgentInputData,
-  AgentWorkflowContext,
-  ChatMemoryBuffer,
-  ChatResponseChunk,
-  HandlerContext,
-  LlamaCloudIndex,
-  Metadata,
-  MetadataMode,
-  NodeWithScore,
-  PromptTemplate,
-  Settings,
-  StartEvent,
-  StopEvent as StopEventBase,
-  ToolCallLLM,
-  VectorStoreIndex,
-  Workflow,
-  WorkflowEvent,
-} from "llamaindex";
-import { randomUUID } from "node:crypto";
-import { z } from "zod";
-import { getIndex } from "./data";
-
-// workflow factory
-export const workflowFactory = async (reqBody: any) => {
-  const index = await getIndex(reqBody?.data);
-  return new DeepResearchWorkflow(index);
-};
-
-// workflow configs
-const MAX_QUESTIONS = 6; // max number of questions to research, research will stop when this number is reached
-const TIMEOUT = 360; // timeout in seconds
-const TOP_K = 10; // number of nodes to retrieve from the vector store
-
-const createPlanResearchPrompt = new PromptTemplate({
-  template: `
-You are a professor who is guiding a researcher to research a specific request/problem.
-Your task is to decide on a research plan for the researcher.
-
-The possible actions are:
-+ Provide a list of questions for the researcher to investigate, with the purpose of clarifying the request.
-+ Write a report if the researcher has already gathered enough research on the topic and can resolve the initial request.
-+ Cancel the research if most of the answers from researchers indicate there is insufficient information to research the request. Do not attempt more than 3 research iterations or too many questions.
-
-The workflow should be:
-+ Always begin by providing some initial questions for the researcher to investigate.
-+ Analyze the provided answers against the initial topic/request. If the answers are insufficient to resolve the initial request, provide additional questions for the researcher to investigate.
-+ If the answers are sufficient to resolve the initial request, instruct the researcher to write a report.
-
-Here are the context: 
-<Collected information>
-{context_str}
-</Collected information>
-
-<Conversation context>
-{conversation_context}
-</Conversation context>
-
-{enhanced_prompt}
-
-Now, provide your decision in the required format for this user request:
-<User request>
-{user_request}
-</User request>
-`,
-  templateVars: [
-    "context_str",
-    "conversation_context",
-    "enhanced_prompt",
-    "user_request",
-  ],
-});
-
-const researchPrompt = new PromptTemplate({
-  template: `
-You are a researcher who is in the process of answering the question.
-The purpose is to answer the question based on the collected information, without using prior knowledge or making up any new information.
-Always add citations to the sentence/point/paragraph using the id of the provided content.
-The citation should follow this format: [citation:id] where id is the id of the content.
-
-E.g:
-If we have a context like this:
-<Citation id='abc-xyz'>
-Baby llama is called cria
-</Citation id='abc-xyz'>
-
-And your answer uses the content, then the citation should be:
-- Baby llama is called cria [citation:abc-xyz]
-
- Here is the provided context for the question:
-<Collected information>
-{context_str}
-</Collected information>
-
-No prior knowledge, just use the provided context to answer the question: {question}
-`,
-  templateVars: ["context_str", "question"],
-});
-
-const WRITE_REPORT_PROMPT = `
-You are a researcher writing a report based on a user request and the research context.
-You have researched various perspectives related to the user request.
-The report should provide a comprehensive outline covering all important points from the researched perspectives.
-Create a well-structured outline for the research report that covers all the answers.
-
-# IMPORTANT when writing in markdown format:
-+ Use tables or figures where appropriate to enhance presentation.
-+ Preserve all citation syntax (the \`[citation:id]()\` parts in the provided context). Keep these citations in the final report - no separate reference section is needed.
-+ Do not add links, a table of contents, or a references section to the report.
-`;
-
-// workflow events
-type ResearchQuestion = { questionId: string; question: string };
-type ResearchResult = ResearchQuestion & { answer: string };
-
-class PlanResearchEvent extends WorkflowEvent<{}> {}
-class ResearchEvent extends WorkflowEvent<ResearchQuestion[]> {}
-class ReportEvent extends WorkflowEvent<{}> {}
-class StopEvent extends StopEventBase<AsyncGenerator<ChatResponseChunk>> {}
-
-export const UIEventSchema = z
-  .object({
-    event: z
-      .enum(["retrieve", "analyze", "answer"])
-      .describe(
-        "The type of event. DeepResearch has 3 main stages:\n1. retrieve: Retrieve the context from the vector store\n2. analyze: Analyze the context and generate a research questions to answer\n3. answer: Answer the provided questions. Each question has a unique id, when the state is done, the event will have the answer for the question.",
-      ),
-    state: z
-      .enum(["pending", "inprogress", "done", "error"])
-      .describe("The state for each event"),
-    id: z.string().optional().describe("The id of the question"),
-    question: z
-      .string()
-      .optional()
-      .describe("The question generated by the LLM"),
-    answer: z.string().optional().describe("The answer generated by the LLM"),
-  })
-  .describe("DeepResearchEvent");
-
-type UIEventData = z.infer<typeof UIEventSchema>;
-
-class UIEvent extends WorkflowEvent<{
-  type: "ui_event";
-  data: UIEventData;
-}> {}
-
-// workflow definition
-class DeepResearchWorkflow extends Workflow<
-  AgentWorkflowContext,
-  AgentInputData,
-  string
-> {
-  #llm = Settings.llm as ToolCallLLM;
-  #index?: VectorStoreIndex | LlamaCloudIndex;
-
-  userRequest: string = "";
-  totalQuestions: number = 0;
-  contextNodes: NodeWithScore<Metadata>[] = [];
-  memory: ChatMemoryBuffer = new ChatMemoryBuffer({ llm: Settings.llm });
-
-  constructor(index: VectorStoreIndex | LlamaCloudIndex) {
-    super({ timeout: TIMEOUT });
-    this.#index = index;
-    this.addWorkflowSteps();
-  }
-
-  addWorkflowSteps() {
-    this.addStep(
-      {
-        inputs: [StartEvent<AgentInputData>],
-        outputs: [PlanResearchEvent],
-      },
-      this.handleStartWorkflow,
-    );
-    this.addStep(
-      {
-        inputs: [PlanResearchEvent],
-        outputs: [ResearchEvent, ReportEvent, StopEvent],
-      },
-      this.handlePlanResearch,
-    );
-    this.addStep(
-      {
-        inputs: [ResearchEvent],
-        outputs: [PlanResearchEvent],
-      },
-      this.handleResearch,
-    );
-    this.addStep(
-      {
-        inputs: [ReportEvent],
-        outputs: [StopEvent],
-      },
-      this.handleReport,
-    );
-  }
-
-  async initWorkflow(data: AgentInputData) {
-    const { userInput, chatHistory = [] } = data;
-    if (!userInput) throw new Error("Invalid input");
-
-    this.userRequest = userInput;
-
-    await this.memory.set(chatHistory);
-    await this.memory.put({ role: "user", content: userInput });
-  }
-
-  handleStartWorkflow = async (
-    ctx: HandlerContext<AgentWorkflowContext>,
-    ev: StartEvent<AgentInputData>,
-  ): Promise<PlanResearchEvent> => {
-    await this.initWorkflow(ev.data);
-
-    ctx.sendEvent(
-      new UIEvent({
-        type: "ui_event",
-        data: { event: "retrieve", state: "inprogress" },
-      }),
-    );
-
-    const retrievedNodes = await this.retriever.retrieve(this.userRequest);
-
-    ctx.sendEvent(toSourceEvent(retrievedNodes));
-
-    ctx.sendEvent(
-      new UIEvent({
-        type: "ui_event",
-        data: { event: "retrieve", state: "done" },
-      }),
-    );
-
-    this.contextNodes = retrievedNodes;
-
-    return new PlanResearchEvent({});
-  };
-
-  handlePlanResearch = async (
-    ctx: HandlerContext<AgentWorkflowContext>,
-    ev: PlanResearchEvent,
-  ): Promise<ResearchEvent | ReportEvent | StopEvent> => {
-    ctx.sendEvent(
-      new UIEvent({
-        type: "ui_event",
-        data: { event: "analyze", state: "inprogress" },
-      }),
-    );
-
-    const { decision, researchQuestions, cancelReason } =
-      await this.createResearchPlan();
-
-    // Stop workflow due to decision from LLM
-    if (decision === "cancel") {
-      ctx.sendEvent(
-        new UIEvent({
-          type: "ui_event",
-          data: { event: "analyze", state: "done" },
-        }),
-      );
-      return new StopEvent(
-        toStreamGenerator(
-          cancelReason ?? "Research cancelled without any reason.",
-        ),
-      );
-    }
-
-    // Trigger research from generated questions
-    if (decision === "research") {
-      this.memory.put({
-        role: "assistant",
-        content:
-          "We need to find answers to the following questions:\n" +
-          researchQuestions.join("\n"),
-      });
-
-      researchQuestions.forEach(({ questionId: id, question }) => {
-        ctx.sendEvent(
-          new UIEvent({
-            type: "ui_event",
-            data: { event: "answer", state: "pending", id, question },
-          }),
-        );
-      });
-
-      return new ResearchEvent(researchQuestions);
-    }
-
-    // Resarch done, start writing report
-    this.memory.put({
-      role: "assistant",
-      content: "No more idea to analyze. We should report the answers.",
-    });
-
-    ctx.sendEvent(
-      new UIEvent({
-        type: "ui_event",
-        data: { event: "analyze", state: "done" },
-      }),
-    );
-
-    return new ReportEvent({});
-  };
-
-  handleResearch = async (
-    ctx: HandlerContext<AgentWorkflowContext>,
-    ev: ResearchEvent,
-  ): Promise<PlanResearchEvent> => {
-    const researchQuestions = ev.data;
-
-    // Answer questions in parallel
-    const researchResults: ResearchResult[] = await Promise.all(
-      researchQuestions.map(async ({ questionId: id, question }) => {
-        ctx.sendEvent(
-          new UIEvent({
-            type: "ui_event",
-            data: { event: "answer", state: "inprogress", id, question },
-          }),
-        );
-
-        const answer = await this.answerQuestion(question);
-
-        ctx.sendEvent(
-          new UIEvent({
-            type: "ui_event",
-            data: { event: "answer", state: "done", id, question, answer },
-          }),
-        );
-
-        return { questionId: id, question, answer };
-      }),
-    );
-
-    // Save answers to memory
-    researchResults.forEach(({ question, answer }) => {
-      this.memory.put({
-        role: "assistant",
-        content: `<Question>${question}</Question>\n<Answer>${answer}</Answer>`,
-      });
-    });
-
-    this.memory.put({
-      role: "assistant",
-      content:
-        "Researched all the questions. Now, I need to analyze if it's ready to write a report or need to research more.",
-    });
-
-    this.totalQuestions += researchResults.length;
-
-    return new PlanResearchEvent({});
-  };
-
-  handleReport = async (
-    ctx: HandlerContext<AgentWorkflowContext>,
-    ev: ReportEvent,
-  ): Promise<StopEvent> => {
-    const chatHistory = await this.memory.getAllMessages();
-
-    const messages = chatHistory.concat([
-      {
-        role: "system",
-        content: WRITE_REPORT_PROMPT,
-      },
-      {
-        role: "user",
-        content:
-          "Write a report addressing the user request based on the research provided the context",
-      },
-    ]);
-
-    const stream = await this.llm.chat({ messages, stream: true });
-
-    return new StopEvent(toStreamGenerator(stream));
-  };
-
-  get llm() {
-    if (!this.#llm.supportToolCall) throw new Error("LLM is not a ToolCallLLM");
-    return this.#llm;
-  }
-
-  get retriever() {
-    if (!this.#index) throw new Error("Index is not initialized");
-    return this.#index.asRetriever({ similarityTopK: TOP_K });
-  }
-
-  get contextStr() {
-    return this.contextNodes
-      .map((node) => {
-        const nodeId = node.node.id_;
-        const nodeContent = node.node.getContent(MetadataMode.NONE);
-        return `<Citation id='${nodeId}'>\n${nodeContent}</Citation id='${nodeId}'>`;
-      })
-      .join("\n");
-  }
-
-  get enhancedPrompt() {
-    if (this.totalQuestions === 0) {
-      return "The student has no questions to research. Let start by asking some questions.";
-    }
-
-    if (this.totalQuestions > MAX_QUESTIONS) {
-      return `The student has researched ${this.totalQuestions} questions. Should cancel the research if the context is not enough to write a report.`;
-    }
-
-    return "";
-  }
-
-  async createResearchPlan() {
-    const chatHistory = await this.memory.getMessages();
-
-    const conversationContext = chatHistory
-      .map((message) => `${message.role}: ${message.content}`)
-      .join("\n");
-
-    const prompt = createPlanResearchPrompt.format({
-      context_str: this.contextStr,
-      conversation_context: conversationContext,
-      enhanced_prompt: this.enhancedPrompt,
-      user_request: this.userRequest,
-    });
-
-    const responseFormat = z.object({
-      decision: z.enum(["research", "write", "cancel"]),
-      researchQuestions: z.array(z.string()),
-      cancelReason: z.string().optional(),
-    });
-
-    const result = await this.llm.complete({ prompt, responseFormat });
-    const plan = JSON.parse(result.text) as z.infer<typeof responseFormat>;
-
-    return {
-      ...plan,
-      researchQuestions: plan.researchQuestions.map((question) => ({
-        questionId: randomUUID(),
-        question,
-      })),
-    };
-  }
-
-  async answerQuestion(question: string) {
-    const prompt = researchPrompt.format({
-      context_str: this.contextStr,
-      question,
-    });
-    const result = await this.llm.complete({ prompt });
-    return result.text;
-  }
-}

packages/create-llama/templates/components/workflows/typescript/financial_report/workflow.ts

@@ -1,396 +0,0 @@
-import { toAgentRunEvent, toSourceEvent } from "@llamaindex/server";
-import {
-  callTools,
-  chatWithTools,
-  documentGenerator,
-  interpreter,
-} from "@llamaindex/tools";
-import {
-  AgentInputData,
-  AgentWorkflowContext,
-  BaseToolWithCall,
-  ChatMemoryBuffer,
-  ChatMessage,
-  ChatResponseChunk,
-  HandlerContext,
-  Metadata,
-  NodeWithScore,
-  Settings,
-  StartEvent,
-  StopEvent,
-  ToolCall,
-  ToolCallLLM,
-  Workflow,
-  WorkflowEvent,
-} from "llamaindex";
-import { getIndex } from "./data";
-
-const TIMEOUT = 360 * 1000;
-
-export async function workflowFactory(reqBody: any) {
-  const index = await getIndex(reqBody?.data);
-
-  const queryEngineTool = index.queryTool({
-    metadata: {
-      name: "query_document",
-      description: `This tool can retrieve information about Apple and Tesla financial data`,
-    },
-    includeSourceNodes: true,
-  });
-
-  if (!process.env.E2B_API_KEY) {
-    throw new Error("E2B_API_KEY is required to use the code interpreter tool");
-  }
-
-  const codeInterpreterTool = interpreter({
-    apiKey: process.env.E2B_API_KEY!,
-  });
-  const documentGeneratorTool = documentGenerator();
-
-  return new FinancialReportWorkflow({
-    queryEngineTool,
-    codeInterpreterTool,
-    documentGeneratorTool,
-    timeout: TIMEOUT,
-  });
-}
-
-// Create a custom event type
-class InputEvent extends WorkflowEvent<{ input: ChatMessage[] }> {}
-
-class ResearchEvent extends WorkflowEvent<{
-  toolCalls: ToolCall[];
-}> {}
-
-class AnalyzeEvent extends WorkflowEvent<{
-  input: ChatMessage | ToolCall[];
-}> {}
-
-class ReportGenerationEvent extends WorkflowEvent<{
-  toolCalls: ToolCall[];
-}> {}
-
-const DEFAULT_SYSTEM_PROMPT = `
-You are a financial analyst who are given a set of tools to help you.
-It's good to using appropriate tools for the user request and always use the information from the tools, don't make up anything yourself.
-For the query engine tool, you should break down the user request into a list of queries and call the tool with the queries.
-`;
-
-class FinancialReportWorkflow extends Workflow<
-  AgentWorkflowContext,
-  AgentInputData,
-  string
-> {
-  llm: ToolCallLLM;
-  memory: ChatMemoryBuffer;
-  queryEngineTool: BaseToolWithCall;
-  codeInterpreterTool: BaseToolWithCall;
-  documentGeneratorTool: BaseToolWithCall;
-  systemPrompt?: string;
-
-  constructor(options: {
-    queryEngineTool: BaseToolWithCall;
-    codeInterpreterTool: BaseToolWithCall;
-    documentGeneratorTool: BaseToolWithCall;
-    systemPrompt?: string;
-    verbose?: boolean;
-    timeout?: number;
-  }) {
-    super({
-      verbose: options?.verbose ?? false,
-      timeout: options?.timeout ?? 360,
-    });
-
-    this.llm = Settings.llm as ToolCallLLM;
-    if (!this.llm.supportToolCall) {
-      throw new Error("LLM is not a ToolCallLLM");
-    }
-    this.systemPrompt = options.systemPrompt ?? DEFAULT_SYSTEM_PROMPT;
-    this.queryEngineTool = options.queryEngineTool;
-    this.codeInterpreterTool = options.codeInterpreterTool;
-
-    this.documentGeneratorTool = options.documentGeneratorTool;
-    this.memory = new ChatMemoryBuffer({ llm: this.llm, chatHistory: [] });
-
-    // Add steps
-    this.addStep(
-      {
-        inputs: [StartEvent<AgentInputData>],
-        outputs: [InputEvent],
-      },
-      this.prepareChatHistory,
-    );
-
-    this.addStep(
-      {
-        inputs: [InputEvent],
-        outputs: [
-          InputEvent,
-          ResearchEvent,
-          AnalyzeEvent,
-          ReportGenerationEvent,
-          StopEvent,
-        ],
-      },
-      this.handleLLMInput,
-    );
-
-    this.addStep(
-      {
-        inputs: [ResearchEvent],
-        outputs: [AnalyzeEvent],
-      },
-      this.handleResearch,
-    );
-
-    this.addStep(
-      {
-        inputs: [AnalyzeEvent],
-        outputs: [InputEvent],
-      },
-      this.handleAnalyze,
-    );
-
-    this.addStep(
-      {
-        inputs: [ReportGenerationEvent],
-        outputs: [InputEvent],
-      },
-      this.handleReportGeneration,
-    );
-  }
-
-  prepareChatHistory = async (
-    ctx: HandlerContext<AgentWorkflowContext>,
-    ev: StartEvent<AgentInputData>,
-  ): Promise<InputEvent> => {
-    const { userInput, chatHistory = [] } = ev.data;
-    if (!userInput) throw new Error("Invalid input");
-
-    this.memory.set(chatHistory);
-
-    if (this.systemPrompt) {
-      this.memory.put({ role: "system", content: this.systemPrompt });
-    }
-
-    this.memory.put({ role: "user", content: userInput });
-
-    const messages = await this.memory.getMessages();
-    return new InputEvent({ input: messages });
-  };
-
-  handleLLMInput = async (
-    ctx: HandlerContext<AgentWorkflowContext>,
-    ev: InputEvent,
-  ): Promise<
-    | InputEvent
-    | ResearchEvent
-    | AnalyzeEvent
-    | ReportGenerationEvent
-    | StopEvent<AsyncGenerator<ChatResponseChunk, any, any> | undefined>
-  > => {
-    const chatHistory = ev.data.input;
-
-    const tools = [
-      this.codeInterpreterTool,
-      this.documentGeneratorTool,
-      this.queryEngineTool,
-    ];
-
-    const toolCallResponse = await chatWithTools(this.llm, tools, chatHistory);
-
-    if (!toolCallResponse.hasToolCall()) {
-      return new StopEvent(toolCallResponse.responseGenerator);
-    }
-
-    if (toolCallResponse.hasMultipleTools()) {
-      this.memory.put({
-        role: "assistant",
-        content:
-          "Calling different tools is not allowed. Please only use multiple calls of the same tool.",
-      });
-      const chatHistory = await this.memory.getMessages();
-      return new InputEvent({ input: chatHistory });
-    }
-
-    // Put the LLM tool call message into the memory
-    // And trigger the next step according to the tool call
-    if (toolCallResponse.toolCallMessage) {
-      this.memory.put(toolCallResponse.toolCallMessage);
-    }
-    const toolName = toolCallResponse.getToolNames()[0];
-    switch (toolName) {
-      case this.codeInterpreterTool.metadata.name:
-        return new AnalyzeEvent({
-          input: toolCallResponse.toolCalls,
-        });
-      case this.documentGeneratorTool.metadata.name:
-        return new ReportGenerationEvent({
-          toolCalls: toolCallResponse.toolCalls,
-        });
-      default:
-        if (this.queryEngineTool.metadata.name === toolName) {
-          return new ResearchEvent({
-            toolCalls: toolCallResponse.toolCalls,
-          });
-        }
-        throw new Error(`Unknown tool: ${toolName}`);
-    }
-  };
-
-  handleResearch = async (
-    ctx: HandlerContext<AgentWorkflowContext>,
-    ev: ResearchEvent,
-  ): Promise<AnalyzeEvent> => {
-    ctx.sendEvent(
-      toAgentRunEvent({
-        agent: "Researcher",
-        text: "Researching data",
-        type: "text",
-      }),
-    );
-
-    const { toolCalls } = ev.data;
-
-    const toolMsgs = await callTools({
-      tools: [this.queryEngineTool],
-      toolCalls,
-      writeEvent: (text, step) => {
-        ctx.sendEvent(
-          toAgentRunEvent({
-            agent: "Researcher",
-            text,
-            type: toolCalls.length > 1 ? "progress" : "text",
-            current: step,
-            total: toolCalls.length,
-          }),
-        );
-      },
-    });
-    for (const toolMsg of toolMsgs) {
-      this.memory.put(toolMsg);
-    }
-
-    const sourcesNodes: NodeWithScore<Metadata>[] = toolMsgs
-      .map((msg) => (msg.options as any)?.toolResult?.result?.sourceNodes)
-      .flat()
-      .filter(Boolean);
-
-    if (sourcesNodes.length > 0) {
-      ctx.sendEvent(toSourceEvent(sourcesNodes));
-    }
-
-    return new AnalyzeEvent({
-      input: {
-        role: "assistant",
-        content:
-          "I have finished researching the data, please analyze the data.",
-      },
-    });
-  };
-
-  /**
-   * Analyze a research result or a tool call for code interpreter from the LLM
-   */
-  handleAnalyze = async (
-    ctx: HandlerContext<AgentWorkflowContext>,
-    ev: AnalyzeEvent,
-  ): Promise<InputEvent> => {
-    ctx.sendEvent(
-      toAgentRunEvent({
-        agent: "Analyst",
-        text: "Analyzing data",
-        type: "text",
-      }),
-    );
-    // Request by workflow LLM, input is a list of tool calls
-    let toolCalls: ToolCall[] = [];
-    if (Array.isArray(ev.data.input)) {
-      toolCalls = ev.data.input;
-    } else {
-      // Requested by Researcher, input is a ChatMessage
-      // We start new LLM chat specifically for analyzing the data
-      const analysisPrompt = `
-      You are an expert in analyzing financial data.
-      You are given a set of financial data to analyze. Your task is to analyze the financial data and return a report.
-      Your response should include a detailed analysis of the financial data, including any trends, patterns, or insights that you find.
-      Construct the analysis in textual format; including tables would be great!
-      Don't need to synthesize the data, just analyze and provide your findings.
-      `;
-
-      // Clone the current chat history
-      // Add the analysis system prompt and the message from the researcher
-      const chatHistory = await this.memory.getMessages();
-      const newChatHistory = [
-        ...chatHistory,
-        { role: "system", content: analysisPrompt },
-        ev.data.input,
-      ];
-      const toolCallResponse = await chatWithTools(
-        this.llm,
-        [this.codeInterpreterTool],
-        newChatHistory as ChatMessage[],
-      );
-
-      if (!toolCallResponse.hasToolCall()) {
-        this.memory.put(await toolCallResponse.asFullResponse());
-        const chatHistory = await this.memory.getMessages();
-        return new InputEvent({ input: chatHistory });
-      } else {
-        this.memory.put(toolCallResponse.toolCallMessage!);
-        toolCalls = toolCallResponse.toolCalls;
-      }
-    }
-
-    // Call the tools
-    const toolMsgs = await callTools({
-      tools: [this.codeInterpreterTool],
-      toolCalls,
-      writeEvent: (text, step) => {
-        ctx.sendEvent(
-          toAgentRunEvent({
-            agent: "Analyst",
-            text,
-            type: toolCalls.length > 1 ? "progress" : "text",
-            current: step,
-            total: toolCalls.length,
-          }),
-        );
-      },
-    });
-    for (const toolMsg of toolMsgs) {
-      this.memory.put(toolMsg);
-    }
-
-    const chatHistory = await this.memory.getMessages();
-    return new InputEvent({ input: chatHistory });
-  };
-
-  handleReportGeneration = async (
-    ctx: HandlerContext<AgentWorkflowContext>,
-    ev: ReportGenerationEvent,
-  ): Promise<InputEvent> => {
-    const { toolCalls } = ev.data;
-
-    const toolMsgs = await callTools({
-      tools: [this.documentGeneratorTool],
-      toolCalls,
-      writeEvent: (text, step) => {
-        ctx.sendEvent(
-          toAgentRunEvent({
-            agent: "Reporter",
-            text,
-            type: toolCalls.length > 1 ? "progress" : "text",
-            current: step,
-            total: toolCalls.length,
-          }),
-        );
-      },
-    });
-    for (const toolMsg of toolMsgs) {
-      this.memory.put(toolMsg);
-    }
-    const chatHistory = await this.memory.getMessages();
-    return new InputEvent({ input: chatHistory });
-  };
-}

packages/create-llama/templates/types/llamaindexserver/fastapi/app/settings.py

@@ -1,8 +1,12 @@
+import os
+
 from llama_index.core import Settings
 from llama_index.embeddings.openai import OpenAIEmbedding
 from llama_index.llms.openai import OpenAI
 
 
 def init_settings():
+    if os.getenv("OPENAI_API_KEY") is None:
+        raise RuntimeError("OPENAI_API_KEY is missing in environment variables")
     Settings.llm = OpenAI(model="gpt-4o-mini")
     Settings.embed_model = OpenAIEmbedding(model="text-embedding-3-small")

packages/create-llama/templates/types/llamaindexserver/fastapi/generate.py

@@ -51,7 +51,7 @@ def generate_ui_for_workflow():
     # and run the generate_ui_for_workflow function with the imported model.
     # Make sure the output filename of the generated UI component matches the event type (here `ui_event`)
     try:
-        from app.workflow import UIEventData
+        from app.workflow import UIEventData  # type: ignore
     except ImportError:
         raise ImportError("Couldn't generate UI component for the current workflow.")
     from llama_index.server.gen_ui import generate_event_component

packages/create-llama/templates/types/llamaindexserver/fastapi/main.py

@@ -17,8 +17,10 @@ def create_app():
         ui_config=UIConfig(
             component_dir=COMPONENT_DIR,
             app_title="Chat App",
+            dev_mode=True,  # Please disable this in production
         ),
         logger=logger,
+        env="dev",
     )
     # You can also add custom FastAPI routes to app
     app.add_api_route("/api/health", lambda: {"message": "OK"}, status_code=200)

packages/create-llama/templates/types/llamaindexserver/fastapi/pyproject.toml

@@ -12,7 +12,7 @@ dependencies = [
     "pydantic<2.10",
     "aiostream>=0.5.2,<0.6.0",
     "llama-index-core>=0.12.28,<0.13.0",
-    "llama-index-server>=0.1.15,<0.2.0",
+    "llama-index-server>=0.1.16,<0.2.0",
 ]
 
 [project.optional-dependencies]

packages/create-llama/templates/types/llamaindexserver/nextjs/nodemon.json

@@ -0,0 +1,6 @@
+{
+  "watch": ["src/**/*.ts"],
+  "exec": "nodemon --exec tsx src/index.ts",
+  "ext": "js ts",
+  "ignore": ["src/app/workflow_*.ts"]
+}

packages/create-llama/templates/types/llamaindexserver/nextjs/package.json

@@ -5,21 +5,22 @@
     "generate": "tsx src/generate.ts datasource",
     "generate:datasource": "tsx src/generate.ts datasource",
     "generate:ui": "tsx src/generate.ts ui",
-    "dev": "tsx watch src/index.ts",
+    "dev": "nodemon",
     "start": "tsx src/index.ts"
   },
   "dependencies": {
-    "@llamaindex/openai": "0.2.0",
-    "@llamaindex/readers": "^2.0.0",
-    "@llamaindex/server": "0.1.5",
-    "@llamaindex/tools": "0.0.4",
+    "@llamaindex/openai": "^0.3.7",
+    "@llamaindex/server": "^0.2.1",
+    "@llamaindex/workflow": "^1.1.2",
+    "@llamaindex/tools": "^0.0.10",
+    "llamaindex": "^0.10.6",
     "dotenv": "^16.4.7",
-    "zod": "^3.23.8",
-    "llamaindex": "0.10.2"
+    "zod": "^3.23.8"
   },
   "devDependencies": {
     "@types/node": "^20.10.3",
     "tsx": "^4.7.2",
-    "typescript": "^5.3.2"
+    "typescript": "^5.3.2",
+    "nodemon": "^3.1.10"
   }
 }

packages/create-llama/templates/types/llamaindexserver/nextjs/src/app/settings.ts

@@ -3,7 +3,7 @@ import { Settings } from "llamaindex";
 
 export function initSettings() {
   Settings.llm = new OpenAI({
-    model: "gpt-4o-mini",
+    model: "gpt-4.1",
   });
   Settings.embedModel = new OpenAIEmbedding({
     model: "text-embedding-3-small",

packages/create-llama/templates/types/llamaindexserver/nextjs/src/index.ts

@@ -10,5 +10,6 @@ new LlamaIndexServer({
   uiConfig: {
     appTitle: "LlamaIndex App",
     componentsDir: "components",
+    devMode: true,
   },
 }).start();

packages/create-llama/templates/types/streaming/fastapi/pyproject.toml

@@ -9,6 +9,7 @@ readme = "README.md"
 requires-python = ">=3.11,<3.14"
 dependencies = [
     "llama-index>=0.12.1",
+    "llama-parse>=0.6.21,<0.7.0",
     "fastapi[standard]>=0.109.1",
     "uvicorn>=0.23.2",
     "python-dotenv>=1.0.0",

packages/server/examples/README.md

@@ -0,0 +1,12 @@
+# LlamaIndex Server Examples
+
+This directory contains examples of how to use the LlamaIndex Server.
+
+## Running the examples
+
+```bash
+export OPENAI_API_KEY=your_openai_api_key
+npx tsx simple-workflow/calculator.ts
+```
+
+## Open browser at http://localhost:3000

packages/server/examples/agentic-rag/index.ts

@@ -0,0 +1,43 @@
+import { LlamaIndexServer } from "@llamaindex/server";
+import { agent } from "@llamaindex/workflow";
+import {
+  Document,
+  OpenAI,
+  OpenAIEmbedding,
+  Settings,
+  VectorStoreIndex,
+} from "llamaindex";
+
+Settings.llm = new OpenAI({
+  model: "gpt-4o-mini",
+});
+
+Settings.embedModel = new OpenAIEmbedding({
+  model: "text-embedding-3-small",
+});
+
+export const workflowFactory = async () => {
+  const index = await VectorStoreIndex.fromDocuments([
+    new Document({ text: "The dog is brown" }),
+    new Document({ text: "The dog is yellow" }),
+  ]);
+
+  const queryEngineTool = index.queryTool({
+    metadata: {
+      name: "query_document",
+      description: `This tool can retrieve information in documents`,
+    },
+    includeSourceNodes: true,
+  });
+
+  return agent({ tools: [queryEngineTool] });
+};
+
+new LlamaIndexServer({
+  workflow: workflowFactory,
+  uiConfig: {
+    appTitle: "LlamaIndex App",
+    starterQuestions: ["What is the color of the dog?"],
+  },
+  port: 4100,
+}).start();

packages/server/examples/devmode/README.md

@@ -0,0 +1,21 @@
+This example shows how to use the dev mode of the server.
+
+First, we need to set `devMode` to `true` in the `uiConfig` of the server.
+
+```ts
+new LlamaIndexServer({
+  workflow: workflowFactory,
+  uiConfig: {
+    appTitle: "Calculator",
+    devMode: true,
+  },
+  port: 6000,
+}).start();
+```
+
+Export OpenAI API key and start the server in dev mode.
+
+```bash
+export OPENAI_API_KEY=<your-openai-api-key>
+npx tsx watch index.ts
+```

packages/server/examples/devmode/index.ts

@@ -0,0 +1,15 @@
+import { LlamaIndexServer } from "@llamaindex/server";
+import { workflowFactory } from "./src/app/workflow";
+
+new LlamaIndexServer({
+  workflow: workflowFactory,
+  uiConfig: {
+    appTitle: "Calculator",
+    devMode: true,
+    starterQuestions: [
+      "What is the weather in Tokyo?",
+      "What is the weather in New York?",
+    ],
+  },
+  port: 6005,
+}).start();

packages/server/examples/devmode/src/app/workflow.ts

@@ -0,0 +1,16 @@
+import { agent } from "@llamaindex/workflow";
+import { tool } from "llamaindex";
+import { z } from "zod";
+
+export const workflowFactory = async () => {
+  return agent({
+    tools: [
+      tool({
+        name: "weather",
+        description: "Get the weather in a specific city",
+        parameters: z.object({ city: z.string() }),
+        execute: ({ city }) => `The weather in ${city} is sunny`,
+      }),
+    ],
+  });
+};

packages/server/examples/package.json

@@ -0,0 +1,25 @@
+{
+  "name": "llamaindex-server-examples",
+  "version": "0.0.1",
+  "private": true,
+  "scripts": {
+    "typecheck": "tsc --noEmit",
+    "dev": "nodemon --exec tsx simple-workflow/calculator.ts"
+  },
+  "dependencies": {
+    "@llamaindex/openai": "^0.2.0",
+    "@llamaindex/readers": "^3.0.0",
+    "@llamaindex/server": "workspace:*",
+    "@llamaindex/tools": "0.0.4",
+    "@llamaindex/workflow": "1.1.0",
+    "dotenv": "^16.4.7",
+    "llamaindex": "0.10.2",
+    "zod": "^3.23.8"
+  },
+  "devDependencies": {
+    "@types/node": "^20.10.3",
+    "nodemon": "^3.1.10",
+    "tsx": "^4.7.2",
+    "typescript": "^5.3.2"
+  }
+}

packages/server/examples/simple-workflow/calculator.ts

@@ -0,0 +1,24 @@
+import { LlamaIndexServer } from "@llamaindex/server";
+import { agent } from "@llamaindex/workflow";
+import { tool } from "llamaindex";
+import { z } from "zod";
+
+const calculatorAgent = agent({
+  tools: [
+    tool({
+      name: "add",
+      description: "Adds two numbers",
+      parameters: z.object({ x: z.number(), y: z.number() }),
+      execute: ({ x, y }) => x + y,
+    }),
+  ],
+});
+
+new LlamaIndexServer({
+  workflow: () => calculatorAgent,
+  uiConfig: {
+    appTitle: "Calculator",
+    starterQuestions: ["1 + 1", "2 + 2"],
+  },
+  port: 4000,
+}).start();

packages/server/examples/tsconfig.json

@@ -0,0 +1,14 @@
+{
+  "compilerOptions": {
+    "target": "ES2022",
+    "module": "ES2022",
+    "moduleResolution": "bundler",
+    "esModuleInterop": true,
+    "forceConsistentCasingInFileNames": true,
+    "strict": true,
+    "skipLibCheck": true,
+    "outDir": "dist"
+  },
+  "include": ["**/*"],
+  "exclude": ["node_modules", "dist"]
+}

packages/server/next/app/components/ui/chat/chat-section.tsx

@@ -13,6 +13,7 @@ import CustomChatMessages from "./chat-messages";
 import { DynamicEventsErrors } from "./custom/events/dynamic-events-errors";
 import { fetchComponentDefinitions } from "./custom/events/loader";
 import { ComponentDef } from "./custom/events/types";
+import { DevModePanel } from "./dev-mode-panel";
 
 export default function ChatSection() {
   const handler = useChat({
@@ -35,12 +36,13 @@ export default function ChatSection() {
         <ChatHeader />
         <ChatUI
           handler={handler}
-          className="flex min-h-0 flex-1 flex-row justify-center gap-4 px-4 py-0"
+          className="relative flex min-h-0 flex-1 flex-row justify-center gap-4 px-4 py-0"
         >
           <ResizablePanelGroup direction="horizontal">
             <ChatSectionPanel />
             <ChatCanvasPanel />
           </ResizablePanelGroup>
+          <DevModePanel />
         </ChatUI>
       </div>
       <ChatInjection />

packages/server/next/app/components/ui/chat/dev-mode-panel.tsx

@@ -0,0 +1,270 @@
+"use client";
+
+import {
+  CodeEditor,
+  fileExtensionToEditorLang,
+} from "@llamaindex/chat-ui/widgets";
+import { AlertCircle, Loader2 } from "lucide-react";
+import { useEffect, useState } from "react";
+import { Button } from "../button";
+import { getConfig } from "../lib/utils";
+
+const API_PATH = "/api/dev/files/workflow";
+const POLLING_TIMEOUT = 30_000; // 30 seconds
+
+type WorkflowFile = {
+  last_modified: number;
+  file_path: string;
+  content: string;
+};
+
+export function DevModePanel() {
+  const devModeEnabled = getConfig("DEV_MODE");
+  if (!devModeEnabled) return null;
+  return <DevModePanelComp />;
+}
+
+function DevModePanelComp() {
+  const [devModeOpen, setDevModeOpen] = useState(false);
+
+  const [isFetching, setIsFetching] = useState(false);
+  const [fetchingError, setFetchingError] = useState<string | null>();
+  const [workflowFile, setWorkflowFile] = useState<WorkflowFile | null>(null);
+
+  const [updatedCode, setUpdatedCode] = useState<string | null>(null);
+  const [isSaving, setIsSaving] = useState(false);
+  const [saveError, setSaveError] = useState<string | null>(null);
+
+  const [isPolling, setIsPolling] = useState(false);
+  const [pollingError, setPollingError] = useState<string | null>(null);
+
+  async function fetchWorkflowCode() {
+    try {
+      setIsFetching(true);
+      const response = await fetch(API_PATH);
+      const data = await response.json();
+
+      if (!response.ok) {
+        throw new Error(data?.detail ?? "Unknown error");
+      }
+
+      setWorkflowFile(data);
+      setFetchingError(null);
+    } catch (error) {
+      const errorMessage =
+        error instanceof Error ? error.message : "Unknown error";
+      setFetchingError(errorMessage);
+      console.warn("Error fetching workflow code:", error);
+    } finally {
+      setIsFetching(false);
+    }
+  }
+
+  async function restartingWorkflow() {
+    if (!workflowFile) return;
+
+    const initialLastModified = workflowFile.last_modified;
+    setIsPolling(true);
+    setPollingError(null);
+
+    const pollStartTime = Date.now();
+
+    // interval refetching the updated workflow code
+    const poll = async () => {
+      if (Date.now() - pollStartTime > POLLING_TIMEOUT) {
+        setPollingError(
+          `Server not responding after ${POLLING_TIMEOUT / 1000} seconds.`,
+        );
+        return;
+      }
+
+      try {
+        const pollResponse = await fetch(API_PATH);
+        const pollData = (await pollResponse.json()) as WorkflowFile;
+        if (pollData.last_modified !== initialLastModified) {
+          setWorkflowFile(pollData);
+          setUpdatedCode(pollData.content);
+          setIsPolling(false);
+          setPollingError(null);
+          setDevModeOpen(false);
+        } else {
+          setTimeout(poll, 2000);
+        }
+      } catch (error) {
+        console.info("Polling error", error);
+        setTimeout(poll, 2000);
+      }
+    };
+
+    setTimeout(poll, 2000);
+  }
+
+  const handleResetCode = () => {
+    setUpdatedCode(workflowFile?.content ?? null);
+    setSaveError(null);
+  };
+
+  const handleSaveCode = async () => {
+    if (!workflowFile) return;
+
+    try {
+      setIsSaving(true);
+      const response = await fetch(API_PATH, {
+        method: "PUT",
+        headers: {
+          Accept: "application/json",
+          "Content-Type": "application/json",
+        },
+        body: JSON.stringify({
+          content: updatedCode,
+          file_path: workflowFile.file_path,
+        }),
+      });
+      const data = await response.json();
+      if (!response.ok) {
+        throw new Error(data?.detail ?? "Unknown error");
+      }
+      setSaveError(null);
+      await restartingWorkflow();
+    } catch (error) {
+      console.warn("Error saving workflow code:", error);
+      setSaveError(
+        error instanceof Error
+          ? error.message
+          : "Unknown error happened when saving workflow code",
+      );
+    } finally {
+      setIsSaving(false);
+    }
+  };
+
+  useEffect(() => {
+    if (devModeOpen) {
+      fetchWorkflowCode();
+    }
+  }, [devModeOpen]);
+
+  const codeEditorLanguage = fileExtensionToEditorLang(
+    workflowFile?.file_path.split(".").pop() ?? "",
+  );
+
+  return (
+    <>
+      <Button
+        onClick={() => setDevModeOpen(!devModeOpen)}
+        className="fixed right-2 top-1/2 origin-right -translate-y-1/2 rotate-90 transform rounded-l-md shadow-md transition-transform hover:-translate-x-1"
+      >
+        Dev Mode
+      </Button>
+
+      {isPolling && (
+        <div className="fixed inset-0 z-50 flex flex-col items-center justify-center bg-black/50 backdrop-blur-sm">
+          {!pollingError && (
+            <>
+              <Loader2 className="mb-4 h-16 w-16 animate-spin text-white" />
+              <p className="text-lg font-semibold text-white">
+                Applying changes and restarting server...
+              </p>
+              <p className="mt-2 text-sm text-slate-300">
+                Please wait for a while then you can start chatting with the
+                updated workflow.
+              </p>
+            </>
+          )}
+          {pollingError && (
+            <div className="bg-destructive/20 text-destructive-foreground mt-4 max-w-md rounded-md p-4 text-center">
+              <div className="mb-2 flex items-center justify-center gap-2">
+                <AlertCircle className="shrink-0" size={16} />
+                <h6 className="text-sm font-medium">Server Starting Error</h6>
+              </div>
+              <p className="text-sm">{pollingError}</p>
+
+              <p className="text-sm">
+                Please reload the page and check server logs.
+              </p>
+            </div>
+          )}
+        </div>
+      )}
+
+      <div
+        className={`border-border fixed right-0 top-0 z-10 h-full w-full border-l shadow-xl transition-all duration-300 ease-in-out ${
+          devModeOpen ? "translate-x-0 bg-black/50" : "translate-x-full"
+        }`}
+        onClick={() => setDevModeOpen(false)}
+      >
+        <div
+          className={`bg-background ml-auto flex h-full w-[800px] flex-col p-4`}
+          onClick={(e) => e.stopPropagation()}
+        >
+          <div className="mb-4 flex items-center justify-between">
+            <div>
+              <h2 className="text-xl font-bold">Workflow Editor</h2>
+              <p className="text-muted-foreground text-sm">
+                {isFetching ? (
+                  "Loading..."
+                ) : workflowFile ? (
+                  <>
+                    Edit the code of <b>{workflowFile.file_path}</b> and save to
+                    apply changes to your workflow.
+                  </>
+                ) : (
+                  ""
+                )}
+              </p>
+            </div>
+            <Button
+              variant="outline"
+              size="sm"
+              onClick={() => setDevModeOpen(false)}
+            >
+              Close
+            </Button>
+          </div>
+          <div className="flex-1 overflow-auto">
+            {fetchingError ? (
+              <div className="bg-destructive/10 text-destructive/70 mb-4 flex items-center gap-2 rounded-md p-4">
+                <AlertCircle className="shrink-0" size={16} />
+                <p className="text-sm font-medium">{fetchingError}</p>
+              </div>
+            ) : (
+              <CodeEditor
+                code={updatedCode ?? workflowFile?.content ?? ""}
+                onChange={setUpdatedCode}
+                language={codeEditorLanguage}
+              />
+            )}
+          </div>
+          <div className="mt-4 flex flex-col">
+            {saveError && (
+              <div className="bg-destructive/10 text-destructive/70 mb-4 rounded-md p-4">
+                <div className="mb-2 flex items-center gap-2">
+                  <AlertCircle className="shrink-0" size={16} />
+                  <h6 className="text-sm font-medium">Error Saving Code</h6>
+                </div>
+                <p className="whitespace-pre-wrap text-sm">{saveError}</p>
+              </div>
+            )}
+
+            <div className="flex justify-end gap-2">
+              <Button
+                variant="outline"
+                className="mr-2"
+                onClick={handleResetCode}
+              >
+                Reset Code
+              </Button>
+              <Button
+                onClick={handleSaveCode}
+                disabled={isSaving || !updatedCode || !workflowFile}
+              >
+                Save & Restart Server
+                {isSaving && <Loader2 className="ml-2 h-4 w-4 animate-spin" />}
+              </Button>
+            </div>
+          </div>
+        </div>
+      </div>
+    </>
+  );
+}

packages/server/package.json

@@ -55,7 +55,7 @@
     "@babel/traverse": "^7.27.0",
     "@babel/types": "^7.27.0",
     "@hookform/resolvers": "^5.0.1",
-    "@llamaindex/chat-ui": "0.4.3",
+    "@llamaindex/chat-ui": "0.4.4",
     "@radix-ui/react-accordion": "^1.2.3",
     "@radix-ui/react-alert-dialog": "^1.1.7",
     "@radix-ui/react-aspect-ratio": "^1.1.3",

packages/server/src/handlers/files.ts

@@ -1,7 +1,9 @@
+import { exec } from "child_process";
 import fs from "fs";
 import type { IncomingMessage, ServerResponse } from "http";
+import path from "path";
 import { promisify } from "util";
-import { sendJSONResponse } from "../utils/request";
+import { parseRequestBody, sendJSONResponse } from "../utils/request";
 
 export const handleServeFiles = async (
   req: IncomingMessage,
@@ -21,3 +23,84 @@ export const handleServeFiles = async (
     return sendJSONResponse(res, 404, { error: "File not found" });
   }
 };
+
+const DEFAULT_WORKFLOW_FILE_PATH = "src/app/workflow.ts"; // TODO: we can make it as a parameter in server later
+
+export const getWorkflowFile = async (
+  req: IncomingMessage,
+  res: ServerResponse,
+  filePath: string = DEFAULT_WORKFLOW_FILE_PATH,
+) => {
+  const fileExists = await promisify(fs.exists)(filePath);
+  if (!fileExists) {
+    return sendJSONResponse(res, 404, {
+      detail: `Dev mode is currently in beta. It only supports updating workflow file at ${DEFAULT_WORKFLOW_FILE_PATH}`,
+    });
+  }
+
+  const content = await promisify(fs.readFile)(filePath, "utf-8");
+  const last_modified = fs.statSync(filePath).mtime.getTime();
+  sendJSONResponse(res, 200, { content, file_path: filePath, last_modified });
+};
+
+export const updateWorkflowFile = async (
+  req: IncomingMessage,
+  res: ServerResponse,
+  filePath: string = DEFAULT_WORKFLOW_FILE_PATH,
+) => {
+  const body = await parseRequestBody(req);
+  const { content } = body as { content: string };
+
+  const fileExists = await promisify(fs.exists)(filePath);
+  if (!fileExists) {
+    return sendJSONResponse(res, 404, {
+      detail: `Dev mode is currently in beta. It only supports updating workflow file at ${DEFAULT_WORKFLOW_FILE_PATH}`,
+    });
+  }
+
+  try {
+    const resolvedFilePath = path.resolve(DEFAULT_WORKFLOW_FILE_PATH);
+    const result = await validateTypeScriptFile(resolvedFilePath, content);
+
+    if (!result.isValid) {
+      return sendJSONResponse(res, 400, {
+        detail: result.errors.join("\n"),
+      });
+    }
+
+    await promisify(fs.writeFile)(filePath, content);
+    sendJSONResponse(res, 200, { content });
+  } catch (error) {
+    console.error("Error updating workflow file:", error);
+    sendJSONResponse(res, 500, { error: "Failed to update workflow file" });
+  }
+};
+
+// use typescript package to validate the file syntax and imports
+async function validateTypeScriptFile(filePath: string, content: string) {
+  // Update workflow file directly will cause the server restart immediately.
+  // So we create a temporary file with the same content in the same directory as the workflow file
+  // This file will be used to validate the file syntax and imports. It will be deleted after validation.
+  const tempFilePath = path.join(
+    path.dirname(filePath),
+    `workflow_${Date.now()}.ts`,
+  );
+  fs.writeFileSync(tempFilePath, content);
+
+  const errors = [];
+  try {
+    const tscCommand = `npx tsc ${tempFilePath} --noEmit --skipLibCheck true`;
+    await promisify(exec)(tscCommand);
+  } catch (error) {
+    const errorMessage = (error as { stdout: string })?.stdout;
+    errors.push(errorMessage);
+  } finally {
+    // Clean up temporary file
+    if (fs.existsSync(tempFilePath)) fs.unlinkSync(tempFilePath);
+  }
+
+  return {
+    isValid: errors.length === 0,
+    errors: errors,
+  };
+}

packages/server/src/server.ts

@@ -9,8 +9,13 @@ import { promisify } from "util";
 import { handleChat } from "./handlers/chat";
 import { getLlamaCloudConfig } from "./handlers/cloud";
 import { getComponents } from "./handlers/components";
-import { handleServeFiles } from "./handlers/files";
+import {
+  getWorkflowFile,
+  handleServeFiles,
+  updateWorkflowFile,
+} from "./handlers/files";
 import type { LlamaIndexServerOptions } from "./types";
+
 const nextDir = path.join(__dirname, "..", "server");
 const configFile = path.join(__dirname, "..", "server", "public", "config.js");
 const dev = process.env.NODE_ENV !== "production";
@@ -44,6 +49,7 @@ export class LlamaIndexServer {
         ? "/api/chat/config/llamacloud"
         : undefined;
     const componentsApi = this.componentsDir ? "/api/components" : undefined;
+    const devMode = uiConfig?.devMode ?? false;
 
     // content in javascript format
     const content = `
@@ -52,7 +58,8 @@ export class LlamaIndexServer {
         APP_TITLE: ${JSON.stringify(appTitle)},
         LLAMA_CLOUD_API: ${JSON.stringify(llamaCloudApi)},
         STARTER_QUESTIONS: ${JSON.stringify(starterQuestions)},
-        COMPONENTS_API: ${JSON.stringify(componentsApi)}
+        COMPONENTS_API: ${JSON.stringify(componentsApi)},
+        DEV_MODE: ${JSON.stringify(devMode)}
       }
     `;
     fs.writeFileSync(configFile, content);
@@ -96,6 +103,14 @@ export class LlamaIndexServer {
         return getLlamaCloudConfig(req, res);
       }
 
+      if (pathname === "/api/dev/files/workflow" && req.method === "GET") {
+        return getWorkflowFile(req, res);
+      }
+
+      if (pathname === "/api/dev/files/workflow" && req.method === "PUT") {
+        return updateWorkflowFile(req, res);
+      }
+
       const handle = this.app.getRequestHandler();
       handle(req, res, parsedUrl);
     });

packages/server/src/types.ts

@@ -17,6 +17,7 @@ export type UIConfig = {
   starterQuestions?: string[];
   componentsDir?: string;
   llamaCloudIndexSelector?: boolean;
+  devMode?: boolean;
 };
 
 export type LlamaIndexServerOptions = NextAppOptions & {

pnpm-workspace.yaml

@@ -1,2 +1,3 @@
 packages:
   - "packages/*"
+  - "packages/server/examples"

python/llama-index-server/README.md

@@ -83,6 +83,7 @@ The LlamaIndexServer accepts the following configuration parameters:
   - `ui_path`: Path for downloaded UI static files (default: ".ui")
   - `component_dir`: The directory for custom UI components rendering events emitted by the workflow. The default is None, which does not render custom UI components.
   - `llamacloud_index_selector`: Whether to show the LlamaCloud index selector in the chat UI (default: False). Requires `LLAMA_CLOUD_API_KEY` to be set.
+  - `dev_mode`: When enabled, you can update workflow code in the UI and see the changes immediately. It's currently in beta and only supports updating workflow code at `app/workflow.py`. You might also need to set `env="dev"` and start the server with the reload feature enabled.
 - `verbose`: Enable verbose logging
 - `api_prefix`: API route prefix (default: "/api")
 - `server_url`: The deployment URL of the server (default is None)
@@ -120,6 +121,21 @@ In development mode (`env="dev"`), the server:
 - Automatically includes the chat UI
 - Provides more verbose logging
 
+### Workflow Editor (Beta)
+
+In development mode, you can set `dev_mode` to `True` in the UI configuration to enable the workflow editor, which allows you to edit the workflow code directly in the browser.
+
+```python
+app = LlamaIndexServer(
+    workflow_factory=create_workflow,
+    env="dev",
+    ui_config={"dev_mode": True},
+)
+```
+
+**Note**: The workflow editor is currently in beta and only supports updating LlamaIndexServer projects created with [create-llama](https://github.com/run-llama/create-llama/). You also need to start the server via `fastapi dev` so that the server can hot reload the workflow code.
+
+
 ## API Endpoints
 
 The server provides the following default endpoints:

python/llama-index-server/examples/artifact/components/ui_event.jsx

@@ -113,11 +113,6 @@ function ArtifactWorkflowCard({ event }) {
               state === "plan" && "bg-blue-200",
               state === "generate" && "bg-violet-200",
             )}
-            indicatorClassName={cn(
-              "transition-all duration-500",
-              state === "plan" && "bg-blue-500",
-              state === "generate" && "bg-violet-500",
-            )}
           />
         </div>
       </Card>

python/llama-index-server/examples/simple-agent/README.md

@@ -0,0 +1,38 @@
+# A simple chat app
+
+This guide explains how to set up and use the LlamaIndex server with a simple chatbot agent.
+
+## Prerequisites
+
+- [uv](https://github.com/astral-sh/uv) installed (a fast Python package manager and runner)
+- An OpenAI API key
+
+## Steps
+
+1. **Set the OpenAI API Key**
+
+   Export your OpenAI API key as an environment variable:
+
+   ```sh
+   export OPENAI_API_KEY=your_openai_api_key_here
+   ```
+
+2. **Run the Server Using uv**
+
+   Start the server with the following command:
+
+   ```sh
+   uv run workflow.py
+   ```
+
+   This will launch the FastAPI server using the workflow defined in `main.py`.
+
+3. **Access the Application**
+
+   Open your browser and go to:
+
+   ```
+   http://localhost:8000
+   ```
+
+   You will see the LlamaIndex chat app UI, where you can interact with the agent.

python/llama-index-server/examples/simple-agent/app/workflow.py

@@ -0,0 +1,14 @@
+from typing import Optional
+
+from llama_index.core.agent.workflow import AgentWorkflow
+from llama_index.core.settings import Settings
+from llama_index.llms.openai import OpenAI
+from llama_index.server.api.models import ChatRequest
+
+
+def create_workflow(chat_request: Optional[ChatRequest] = None) -> AgentWorkflow:
+    return AgentWorkflow.from_tools_or_functions(
+        tools_or_functions=[],
+        llm=Settings.llm or OpenAI(model="gpt-4o-mini"),
+        system_prompt="You are a helpful assistant that can tell a joke about Llama.",
+    )

python/llama-index-server/examples/simple-agent/main.py

@@ -0,0 +1,29 @@
+from app.workflow import create_workflow
+from fastapi import FastAPI
+
+from llama_index.server import LlamaIndexServer, UIConfig
+
+
+def create_app() -> FastAPI:
+    app = LlamaIndexServer(
+        workflow_factory=create_workflow,
+        ui_config=UIConfig(
+            app_title="Artifact",
+            starter_questions=[
+                "Tell me a funny joke.",
+                "Tell me some jokes about AI.",
+            ],
+            component_dir="components",
+            dev_mode=True,  # To show the dev UI, should disable this in production
+        ),
+    )
+    return app
+
+
+app = create_app()
+
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run("main:app", host="0.0.0.0", port=8000, reload=True)

python/llama-index-server/llama_index/server/api/routers/__init__.py

@@ -1,4 +1,9 @@
 from llama_index.server.api.routers.chat import chat_router
 from llama_index.server.api.routers.ui import custom_components_router
+from llama_index.server.api.routers.dev import dev_router
 
-__all__ = ["chat_router", "custom_components_router"]
+__all__ = [
+    "chat_router",
+    "custom_components_router",
+    "dev_router",
+]

python/llama-index-server/llama_index/server/api/routers/dev.py

@@ -0,0 +1,102 @@
+import os
+import tempfile
+
+from fastapi import APIRouter, HTTPException
+from pydantic import BaseModel, Field
+
+from llama_index.server.settings import server_settings
+from llama_index.server.utils.workflow_validation import validate_workflow_file
+
+
+class WorkflowFile(BaseModel):
+    last_modified: int
+    file_path: str = Field(
+        default="app/workflow.py",
+        description="Relative path to the workflow file",
+    )
+    content: str
+
+
+class WorkflowFileUpdate(BaseModel):
+    content: str
+    file_path: str = Field(
+        default="app/workflow.py",
+        description="Relative path to the workflow file",
+    )
+
+
+class WorkflowValidationResult(BaseModel):
+    valid: bool
+    error: str
+
+
+def dev_router() -> APIRouter:
+    # Use a prefix here to avoid conflicts with other routers
+    # but we probably don't need to do this
+    router = APIRouter(prefix="/dev", tags=["dev"])
+
+    default_workflow_file_path = "app/workflow.py"
+
+    @router.get("/files/workflow")
+    async def get_workflow_file() -> WorkflowFile:
+        """
+        Fetch the current workflow code
+        """
+        # Check if the file exists
+        if not os.path.exists(default_workflow_file_path):
+            raise HTTPException(
+                status_code=400,
+                detail="Dev mode is currently in beta. It only supports updating workflow file at 'app/workflow.py'",
+            )
+        stat = os.stat(default_workflow_file_path)
+        with open(default_workflow_file_path, "r") as f:
+            return WorkflowFile(
+                last_modified=int(stat.st_mtime),
+                file_path=default_workflow_file_path,
+                content=f.read(),
+            )
+
+    @router.post("/files/workflow/validate")
+    async def validate_workflow(file: WorkflowFileUpdate) -> WorkflowValidationResult:
+        """
+        Validate the current workflow code
+        """
+        try:
+            if file.file_path != default_workflow_file_path:
+                raise HTTPException(
+                    status_code=400, detail=f"Updating {file.file_path} is not allowed"
+                )
+            validate_workflow_file(
+                workflow_content=file.content,
+                factory_signature=server_settings.workflow_factory_signature,
+            )
+            return WorkflowValidationResult(valid=True, error="")
+        except Exception as e:
+            raise HTTPException(status_code=400, detail=str(e))
+
+    @router.put("/files/workflow")
+    async def put_workflow_file(update: WorkflowFileUpdate) -> None:
+        """
+        Update the current workflow code
+        """
+        # Validations
+        if update.file_path != default_workflow_file_path:
+            raise HTTPException(
+                status_code=400, detail=f"Updating {update.file_path} is not allowed"
+            )
+        with tempfile.NamedTemporaryFile("w", suffix=".py", delete=False) as tmp:
+            tmp.write(update.content)
+            tmp_path = tmp.name
+        try:
+            # Validate workflow file using the actual callable name from the workflow_factory
+            factory_func_name = server_settings.workflow_factory_signature
+            validate_workflow_file(
+                workflow_path=tmp_path, factory_signature=factory_func_name
+            )
+        except Exception as e:
+            raise HTTPException(status_code=400, detail=str(e))
+        # If all checks pass, overwrite the real file
+        with open(default_workflow_file_path, "w") as f:
+            f.write(update.content)
+
+    return router

python/llama-index-server/llama_index/server/server.py

@@ -10,7 +10,11 @@ from fastapi.staticfiles import StaticFiles
 from pydantic import BaseModel, Field
 
 from llama_index.core.workflow import Workflow
-from llama_index.server.api.routers import chat_router, custom_components_router
+from llama_index.server.api.routers import (
+    chat_router,
+    custom_components_router,
+    dev_router,
+)
 from llama_index.server.chat_ui import download_chat_ui
 from llama_index.server.settings import server_settings
 
@@ -33,6 +37,9 @@ class UIConfig(BaseModel):
     component_dir: Optional[str] = Field(
         default=None, description="The directory to custom UI components code"
     )
+    dev_mode: bool = Field(
+        default=False, description="Whether to enable the UI dev mode"
+    )
 
     def get_config_content(self) -> str:
         return json.dumps(
@@ -46,6 +53,7 @@ class UIConfig(BaseModel):
                 "COMPONENTS_API": f"{server_settings.api_url}/components"
                 if self.component_dir
                 else None,
+                "DEV_MODE": self.dev_mode,
             },
             indent=2,
         )
@@ -100,24 +108,33 @@ class LlamaIndexServer(FastAPI):
             server_settings.set_url(server_url)
         if api_prefix:
             server_settings.set_api_prefix(api_prefix)
-
-        if self.use_default_routers:
-            self.add_default_routers()
+        server_settings.set_workflow_factory(workflow_factory.__name__)
 
         if str(env).lower() == "dev":
             self.allow_cors("*")
             if self.ui_config.enabled is None:
                 self.ui_config.enabled = True
-
+        else:
+            if self.ui_config.enabled and self.ui_config.dev_mode:
+                raise ValueError(
+                    "UI dev mode requires the environment variable for LlamaIndexServer to be set to 'dev' and start the FastAPI app in dev mode."
+                )
         if self.ui_config.enabled is None:
             self.ui_config.enabled = False
 
+        # Routers
+        if self.use_default_routers:
+            self.add_default_routers()
+
+        # Should mount ui at the end
         if self.ui_config.enabled:
             self.mount_ui()
 
     # Default routers
     def add_default_routers(self) -> None:
         self.add_chat_router()
+        if self.ui_config.enabled and self.ui_config.dev_mode:
+            self.include_router(dev_router(), prefix=server_settings.api_prefix)
         self.mount_data_dir()
         self.mount_output_dir()
 

python/llama-index-server/llama_index/server/settings.py

@@ -11,6 +11,10 @@ class ServerSettings(BaseSettings):
         default="/api",
         description="The prefix for the API endpoints",
     )
+    workflow_factory_signature: str = Field(
+        default="",
+        description="The signature of the workflow factory function",
+    )
 
     @property
     def file_server_url_prefix(self) -> str:
@@ -40,6 +44,9 @@ class ServerSettings(BaseSettings):
         self.api_prefix = v
         self.validate_api_prefix(v)  # type: ignore
 
+    def set_workflow_factory(self, v: str) -> None:
+        self.workflow_factory_signature = v
+
     class Config:
         env_file_encoding = "utf-8"
 

python/llama-index-server/llama_index/server/utils/workflow_validation.py

@@ -0,0 +1,54 @@
+"""
+Utilities for validating workflow.py files (syntax and import checks only).
+"""
+
+import ast
+import importlib.util
+from typing import Optional
+
+
+def validate_workflow_file(
+    workflow_path: Optional[str] = None,
+    workflow_content: Optional[str] = None,
+    factory_signature: Optional[str] = None,
+) -> None:
+    """
+    Validate that the workflow file is syntactically correct, can be imported, and defines a callable factory function with the given name.
+    Raises an exception if invalid.
+    """
+    if workflow_path is None and workflow_content is None:
+        raise ValueError("Either workflow_path or workflow_content must be provided")
+
+    # 1. Syntax check
+    if workflow_path is not None:
+        with open(workflow_path, "r") as f:
+            content = f.read()
+    else:
+        if workflow_content is None:
+            raise ValueError(
+                "workflow_content must be provided if workflow_path is not specified"
+            )
+        content = workflow_content
+    try:
+        ast.parse(content)
+    except SyntaxError as e:
+        raise ValueError(f"Syntax error in workflow: {e}")
+
+    # 2. Import check (will catch missing modules, etc.)
+    spec = importlib.util.spec_from_file_location("workflow", workflow_path)
+    if spec is None or spec.loader is None:
+        raise ValueError(f"Could not load module specification for {workflow_path}")
+
+    mod = importlib.util.module_from_spec(spec)
+    try:
+        spec.loader.exec_module(mod)
+    except Exception as e:
+        raise ValueError(f"Import error: {e}")
+
+    # 3. Contract validation: require the given factory function name
+    if factory_signature:
+        if not hasattr(mod, factory_signature):
+            raise ValueError(f"Missing required function: '{factory_signature}'")
+        obj = getattr(mod, factory_signature)
+        if not callable(obj):
+            raise ValueError(f"'{factory_signature}' is not callable")