Release 0.5.14 (#608)

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

.github/workflows/e2e.yml

@@ -4,10 +4,12 @@ on:
     branches: [main]
     paths-ignore:
       - "python/llama-index-server/**"
+      - ".github/workflows/*llama_index_server.yml"
   pull_request:
     branches: [main]
     paths-ignore:
       - "python/llama-index-server/**"
+      - ".github/workflows/*llama_index_server.yml"
 
 jobs:
   e2e-python:
@@ -21,6 +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
@@ -68,6 +71,7 @@ jobs:
           LLAMA_CLOUD_API_KEY: ${{ secrets.LLAMA_CLOUD_API_KEY }}
           FRAMEWORK: ${{ matrix.frameworks }}
           DATASOURCE: ${{ matrix.datasources }}
+          TEMPLATE_TYPE: ${{ matrix.template-types }}
           PYTHONIOENCODING: utf-8
           PYTHONLEGACYWINDOWSSTDIO: utf-8
         working-directory: packages/create-llama
@@ -75,7 +79,7 @@ jobs:
       - uses: actions/upload-artifact@v4
         if: always()
         with:
-          name: playwright-report-python-${{ matrix.os }}-${{ matrix.frameworks }}-${{ matrix.datasources }}
+          name: playwright-report-python-${{ matrix.os }}-${{ matrix.frameworks }}-${{ matrix.datasources }}-${{ matrix.template-types }}
           path: packages/create-llama/playwright-report/
           overwrite: true
           retention-days: 30
@@ -91,6 +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
@@ -138,12 +143,13 @@ 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
         if: always()
         with:
-          name: playwright-report-typescript-${{ matrix.os }}-${{ matrix.frameworks }}-${{ matrix.datasources }}-node${{ matrix.node-version }}
+          name: playwright-report-typescript-${{ matrix.os }}-${{ matrix.frameworks }}-${{ matrix.datasources }}-node${{ matrix.node-version }}-${{ matrix.template-types }}
           path: packages/create-llama/playwright-report/
           overwrite: true
           retention-days: 30

.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:

.github/workflows/release_llama_index_server.yml

@@ -22,7 +22,8 @@ jobs:
         working-directory: ./python/llama-index-server
     if: |
       github.event_name == 'push' && 
-      !startsWith(github.ref, 'refs/heads/release/llama-index-server-v')
+      !startsWith(github.ref, 'refs/heads/release/llama-index-server-v') &&
+      !contains(github.event.head_commit.message, 'Release: llama-index-server v')
 
     steps:
       - name: Checkout Repository
@@ -30,17 +31,19 @@ jobs:
         with:
           fetch-depth: 0
 
+      - name: Install uv
+        uses: astral-sh/setup-uv@v5
+        with:
+          enable-cache: true
+
       - name: Set up Python
         uses: actions/setup-python@v5
         with:
           python-version: "3.11"
 
-      - name: Install Poetry
-        run: |
-          curl -sSL https://install.python-poetry.org | python3 -
-
       - name: Install dependencies
-        run: poetry install
+        shell: bash
+        run: uv sync --all-extras --dev
 
       - name: Setup Git
         run: |
@@ -48,15 +51,17 @@ jobs:
           git config --global user.name "github-actions[bot]"
 
       - name: Bump patch version
+        shell: bash
         run: |
-          poetry version patch
+          uvx --from=toml-cli toml set --toml-path=pyproject.toml project.version $(uvx --from=toml-cli toml get --toml-path=pyproject.toml project.version | awk -F. '{$NF = $NF + 1;}1' OFS=.)
           git add pyproject.toml
-          git commit -m "chore(release): bump version to $(poetry version -s)"
+          git commit -m "chore(release): bump llama-index-server version to $(uvx --from=toml-cli toml get --toml-path=pyproject.toml project.version)"
 
       - name: Get current version
         id: get_version
+        shell: bash
         run: |
-          version=$(poetry version -s)
+          version=$(uvx --from=toml-cli toml get --toml-path=pyproject.toml project.version)
           echo "current_version=${version}" >> "$GITHUB_OUTPUT"
 
       - name: Create Release PR
@@ -91,31 +96,34 @@ jobs:
       - name: Checkout Repository
         uses: actions/checkout@v4
 
+      - name: Install uv
+        uses: astral-sh/setup-uv@v5
+        with:
+          enable-cache: true
+
       - name: Set up Python
         uses: actions/setup-python@v5
         with:
           python-version: "3.11"
 
-      - name: Install Poetry
-        run: |
-          curl -sSL https://install.python-poetry.org | python3 -
-
       - name: Install dependencies
-        run: poetry install
+        shell: bash
+        run: uv sync --all-extras
 
       - name: Get current version
         id: get_version
+        shell: bash
         run: |
-          version=$(poetry version -s)
+          version=$(uvx --from=toml-cli toml get --toml-path=pyproject.toml project.version)
           echo "current_version=${version}" >> "$GITHUB_OUTPUT"
 
-      - name: Build and publish to PyPI
-        uses: JRubics/poetry-publish@v2.1
-        with:
-          python_version: "3.11"
-          pypi_token: ${{ secrets.PYPI_TOKEN }}
-          package_directory: "python/llama-index-server"
-          poetry_install_options: "--without dev"
+      - name: Build package
+        shell: bash
+        run: uv build --no-sources
+
+      - name: Publish to PyPI
+        shell: bash
+        run: uv publish --token ${{ secrets.PYPI_TOKEN }}
 
       - name: Create GitHub Release
         uses: softprops/action-gh-release@v2

.github/workflows/test_llama_index_server.yml

@@ -4,7 +4,6 @@ on:
   pull_request:
 
 env:
-  POETRY_VERSION: "1.8.3"
   PYTHON_VERSION: "3.9"
 
 jobs:
@@ -21,29 +20,23 @@ jobs:
     steps:
       - uses: actions/checkout@v4
 
-      - name: Install Poetry
-        run: pipx install poetry==${{ env.POETRY_VERSION }}
+      - name: Install uv
+        uses: astral-sh/setup-uv@v5
+        with:
+          enable-cache: true
 
-      - name: Set up python ${{ matrix.python-version }}
+      - name: Set up Python ${{ matrix.python-version }}
         uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
-          cache: "poetry"
-
-      - name: Configure Poetry
-        run: |
-          poetry config virtualenvs.create true
-          poetry config virtualenvs.in-project true
-          poetry env use python
 
       - name: Install dependencies
         shell: bash
-        run: poetry install --with dev
+        run: uv sync --all-extras --dev
 
       - name: Run unit tests
         shell: bash
-        run: |
-          poetry run pytest tests
+        run: uv run pytest tests
 
   type-check:
     name: Type Check
@@ -54,28 +47,23 @@ jobs:
     steps:
       - uses: actions/checkout@v4
 
-      - name: Install Poetry
-        run: pipx install poetry==${{ env.POETRY_VERSION }}
+      - name: Install uv
+        uses: astral-sh/setup-uv@v5
+        with:
+          enable-cache: true
 
       - name: Set up Python
         uses: actions/setup-python@v5
         with:
           python-version: ${{ env.PYTHON_VERSION }}
-          cache: "poetry"
-
-      - name: Configure Poetry
-        run: |
-          poetry config virtualenvs.create true
-          poetry config virtualenvs.in-project true
-          poetry env use python
 
       - name: Install dependencies
         shell: bash
-        run: poetry install --with dev
+        run: uv sync --all-extras --dev
 
       - name: Run mypy
         shell: bash
-        run: poetry run mypy llama_index
+        run: uv run mypy llama_index
 
   build:
     needs: [unit-test, type-check]
@@ -85,25 +73,25 @@ jobs:
         working-directory: python/llama-index-server
     steps:
       - uses: actions/checkout@v4
-      - name: Install Poetry
-        run: pipx install poetry==${{ env.POETRY_VERSION }}
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v5
+        with:
+          enable-cache: true
+
       - name: Set up Python
         uses: actions/setup-python@v5
         with:
           python-version: ${{ env.PYTHON_VERSION }}
-      - name: Clear python cache
+
+      - name: Install build package
         shell: bash
-        run: poetry cache clear --all pypi
-      - name: Build package
-        shell: bash
-        run: poetry build
-      - name: Test installing built package
-        shell: bash
-        run: python -m pip install .
+        run: uv sync --all-extras
+
       - name: Test import
         shell: bash
-        working-directory: ${{ vars.RUNNER_TEMP }}
-        run: python -c "from llama_index.server import LlamaIndexServer"
+        run: uv run python -c "from llama_index.server import LlamaIndexServer"
+
       - name: Upload artifact
         uses: actions/upload-artifact@v4
         with:

.husky/pre-commit

@@ -1,3 +1,4 @@
 pnpm format
 pnpm lint
-uvx ruff format --check packages/create-llama/templates/
+uvx ruff check .
+uvx ruff format . --check 

.prettierignore

@@ -0,0 +1,17 @@
+node_modules/
+pnpm-lock.yaml
+lib/
+dist/
+cache/
+build/
+.next/
+out/
+packages/server/server/
+**/playwright-report/
+**/test-results/
+
+# Python
+python/
+**/*.mypy_cache/**
+**/*.venv/**
+**/*.ruff_cache/**

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/)

eslint.config.mjs

@@ -18,6 +18,21 @@ export default tseslint.config(
     },
   },
   {
+    files: ["packages/create-llama/**"],
+    rules: {
+      "max-params": ["error", 4],
+      "prefer-const": "error",
+      "no-empty": "off",
+      "no-extra-boolean-cast": "off",
+      "@typescript-eslint/no-explicit-any": "off",
+      "@typescript-eslint/no-unused-vars": "off",
+      "@typescript-eslint/no-empty-object-type": "off",
+      "@typescript-eslint/no-wrapper-object-types": "off",
+      "@typescript-eslint/ban-ts-comment": "off",
+    },
+  },
+  {
+    files: ["packages/server/**"],
     rules: {
       "no-irregular-whitespace": "off",
       "@typescript-eslint/no-unused-vars": "off",
@@ -31,7 +46,12 @@ export default tseslint.config(
   },
   {
     ignores: [
+      "python/**",
+      "**/*.mypy_cache/**",
+      "**/*.venv/**",
+      "**/*.ruff_cache/**",
       "**/dist/**",
+      "**/e2e/cache/**",
       "**/lib/*",
       "**/.next/**",
       "**/out/**",

package.json

@@ -1,39 +1,55 @@
 {
-	"name": "create-llama-monorepo",
-	"version": "1.0.0",
-	"private": true,
-	"description": "Monorepo for create-llama",
-	"keywords": [
-		"rag",
-		"llamaindex"
-	],
-	"repository": {
-		"type": "git",
-		"url": "https://github.com/run-llama/create-llama"
-	},
-	"license": "MIT",
-	"workspaces": [
-		"packages/*"
-	],
-	"scripts": {
-		"prepare": "husky",
-		"new-snapshot": "pnpm -r build && changeset version --snapshot",
-		"new-version": "pnpm -r build && changeset version",
-		"release": "pnpm -r build && changeset publish",
-		"release-snapshot": "pnpm -r build && changeset publish --tag snapshot",
-		"build": "pnpm -r build",
-		"e2e": "pnpm -r e2e",
-		"dev": "pnpm -r dev",
-		"format": "pnpm -r format",
-		"format:write": "pnpm -r format:write",
-		"lint": "pnpm -r lint"
-	},
-	"devDependencies": {
-		"@changesets/cli": "^2.27.1",
-		"husky": "^9.0.10"
-	},
-	"packageManager": "pnpm@9.0.5",
-	"engines": {
-		"node": ">=16.14.0"
-	}
+  "name": "create-llama-monorepo",
+  "version": "1.0.0",
+  "private": true,
+  "description": "Monorepo for create-llama",
+  "keywords": [
+    "rag",
+    "llamaindex"
+  ],
+  "repository": {
+    "type": "git",
+    "url": "https://github.com/run-llama/create-llama"
+  },
+  "license": "MIT",
+  "workspaces": [
+    "packages/*"
+  ],
+  "scripts": {
+    "dev": "pnpm -r dev",
+    "build": "pnpm -r build",
+    "e2e": "pnpm -r e2e",
+    "lint": "eslint .",
+    "format": "prettier --ignore-unknown --cache --check .",
+    "format:write": "prettier --ignore-unknown --write .",
+    "prepare": "husky",
+    "new-snapshot": "pnpm -r build && changeset version --snapshot",
+    "new-version": "pnpm -r build && changeset version",
+    "release": "pnpm -r build && changeset publish",
+    "release-snapshot": "pnpm -r build && changeset publish --tag snapshot"
+  },
+  "devDependencies": {
+    "@changesets/cli": "^2.27.1",
+    "bunchee": "6.4.0",
+    "husky": "^9.0.10",
+    "lint-staged": "^15.2.11",
+    "typescript-eslint": "^8.18.0",
+    "globals": "^15.12.0",
+    "eslint": "9.22.0",
+    "@eslint/js": "^9.25.0",
+    "eslint-config-next": "^15.1.0",
+    "eslint-config-prettier": "^9.1.0",
+    "eslint-plugin-react": "7.37.2",
+    "prettier": "^3.4.2",
+    "prettier-plugin-organize-imports": "^4.1.0",
+    "prettier-plugin-tailwindcss": "^0.6.11",
+    "typescript": "^5.7.3",
+    "@types/node": "^22.9.0",
+    "@types/react": "^19",
+    "@types/react-dom": "^19"
+  },
+  "packageManager": "pnpm@9.0.5",
+  "engines": {
+    "node": ">=16.14.0"
+  }
 }

packages/create-llama/.eslintrc

@@ -1,12 +0,0 @@
-{
-  "extends": [
-    "prettier"
-  ],
-  "rules": {
-    "max-params": [
-      "error",
-      4
-    ],
-    "prefer-const": "error",
-  },
-}

packages/create-llama/.prettierignore

@@ -1,6 +0,0 @@
-apps/docs/i18n
-apps/docs/docs/api
-pnpm-lock.yaml
-lib/
-dist/
-.docusaurus/

packages/create-llama/CHANGELOG.md

@@ -1,5 +1,29 @@
 # create-llama
 
+## 0.5.14
+
+### Patch Changes
+
+- 1df8cfb: Split artifacts use case to document generator and code generator
+- 1b5a519: chore: improve dev experience with nodemon
+- b3eb0ba: Fix typing check issue
+- 556f33c: fix chromadb dependency issue
+- 2451539: fix: remove dead generated ai code
+- 7a70390: Deprecate pro mode
+
+## 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
+
+- 241d82a: Add artifacts use case (python)
+
 ## 0.5.11
 
 ### Patch Changes

packages/create-llama/create-app.ts

@@ -1,4 +1,3 @@
-/* eslint-disable import/no-extraneous-dependencies */
 import path from "path";
 import { green, yellow } from "picocolors";
 import { tryGitInit } from "./helpers/git";

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/shared/llamaindexserver_template.spec.ts

@@ -1,4 +1,3 @@
-/* eslint-disable turbo/no-undeclared-env-vars */
 import { expect, test } from "@playwright/test";
 import { ChildProcess } from "child_process";
 import fs from "fs";

packages/create-llama/e2e/shared/reflex_template.spec.ts

@@ -1,4 +1,3 @@
-/* eslint-disable turbo/no-undeclared-env-vars */
 import { expect, test } from "@playwright/test";
 import { ChildProcess } from "child_process";
 import fs from "fs";

packages/create-llama/e2e/shared/streaming_template.spec.ts

@@ -1,4 +1,3 @@
-/* eslint-disable turbo/no-undeclared-env-vars */
 import { expect, test } from "@playwright/test";
 import { ChildProcess } from "child_process";
 import fs from "fs";

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/e2e/utils.ts

@@ -67,8 +67,8 @@ export async function runCreateLlama({
   ].join("-");
 
   // Handle different data source types
-  let dataSourceArgs = [];
-  if (dataSource.includes("--web-source" || "--db-source")) {
+  const dataSourceArgs = [];
+  if (dataSource.includes("--web-source")) {
     const webSource = dataSource.split(" ")[1];
     dataSourceArgs.push("--web-source", webSource);
   } else if (dataSource.includes("--db-source")) {

packages/create-llama/helpers/copy.ts

@@ -1,4 +1,3 @@
-/* eslint-disable import/no-extraneous-dependencies */
 import { async as glob } from "fast-glob";
 import fs from "fs";
 import path from "path";

packages/create-llama/helpers/env-variables.ts

@@ -181,7 +181,7 @@ const getVectorDBEnvs = (
             ]
           : []),
       ];
-    case "chroma":
+    case "chroma": {
       const envs = [
         {
           name: "CHROMA_COLLECTION",
@@ -206,6 +206,7 @@ Otherwise, use CHROMA_HOST and CHROMA_PORT config above`,
         });
       }
       return envs;
+    }
     case "weaviate":
       return [
         {

packages/create-llama/helpers/git.ts

@@ -1,4 +1,3 @@
-/* eslint-disable import/no-extraneous-dependencies */
 import { execSync } from "child_process";
 import fs from "fs";
 import path from "path";

packages/create-llama/helpers/install.ts

@@ -1,4 +1,3 @@
-/* eslint-disable import/no-extraneous-dependencies */
 import spawn from "cross-spawn";
 import { yellow } from "picocolors";
 import type { PackageManager } from "./get-pkg-manager";

packages/create-llama/helpers/is-folder-empty.ts

@@ -1,4 +1,3 @@
-/* eslint-disable import/no-extraneous-dependencies */
 import fs from "fs";
 import path from "path";
 import { blue, green } from "picocolors";

packages/create-llama/helpers/poetry.ts

@@ -1,4 +1,3 @@
-/* eslint-disable import/no-extraneous-dependencies */
 import { execSync } from "child_process";
 import fs from "fs";
 

packages/create-llama/helpers/providers/index.ts

@@ -28,7 +28,7 @@ export async function askModelConfig({
 }: ModelConfigQuestionsParams): Promise<ModelConfig> {
   let modelProvider: ModelProvider = DEFAULT_MODEL_PROVIDER;
   if (askModels) {
-    let choices = [
+    const choices = [
       { title: "OpenAI", value: "openai" },
       { title: "Groq", value: "groq" },
       { title: "Ollama", value: "ollama" },

packages/create-llama/helpers/python.ts

@@ -31,6 +31,7 @@ const getAdditionalDependencies = (
   tools?: Tool[],
   templateType?: TemplateType,
   observability?: TemplateObservability,
+  // eslint-disable-next-line max-params
 ) => {
   const dependencies: Dependency[] = [];
 
@@ -93,6 +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": {
@@ -562,15 +567,15 @@ const installLlamaIndexServerTemplate = async ({
     process.exit(1);
   }
 
-  await copy("workflow.py", path.join(root, "app"), {
+  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) {
@@ -601,7 +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

@@ -57,7 +57,9 @@ export type TemplateUseCase =
   | "form_filling"
   | "extractor"
   | "contract_review"
-  | "agentic_rag";
+  | "agentic_rag"
+  | "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/helpers/validate-pkg.ts

@@ -1,4 +1,3 @@
-// eslint-disable-next-line import/no-extraneous-dependencies
 import validateProjectName from "validate-npm-package-name";
 
 export function validateNpmName(name: string): {

packages/create-llama/index.ts

@@ -1,4 +1,3 @@
-/* eslint-disable import/no-extraneous-dependencies */
 import { execSync } from "child_process";
 import { Command } from "commander";
 import fs from "fs";
@@ -197,7 +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": "0.5.11",
+  "version": "0.5.14",
   "description": "Create LlamaIndex-powered apps with one command",
   "keywords": [
     "rag",
@@ -31,9 +31,6 @@
     "e2e": "playwright test",
     "e2e:python": "playwright test e2e/shared e2e/python",
     "e2e:typescript": "playwright test e2e/shared e2e/typescript",
-    "format": "prettier --ignore-unknown --cache --check .",
-    "format:write": "prettier --ignore-unknown --write .",
-    "lint": "eslint . --ignore-pattern dist --ignore-pattern e2e/cache",
     "pack-install": "bash ./scripts/pack.sh"
   },
   "dependencies": {
@@ -66,10 +63,6 @@
     "yaml": "2.4.1"
   },
   "devDependencies": {
-    "eslint": "^8.56.0",
-    "eslint-config-prettier": "^8.10.0",
-    "prettier": "^3.2.5",
-    "prettier-plugin-organize-imports": "^3.2.4",
     "@playwright/test": "^1.41.1",
     "@vercel/ncc": "0.38.1",
     "rimraf": "^5.0.5",

packages/create-llama/playwright.config.ts

@@ -1,4 +1,3 @@
-/* eslint-disable turbo/no-undeclared-env-vars */
 import { defineConfig, devices } from "@playwright/test";
 
 export default defineConfig({

packages/create-llama/prettier.config.js

@@ -1,3 +0,0 @@
-module.exports = {
-  plugins: ["prettier-plugin-organize-imports"],
-};

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

@@ -6,7 +6,12 @@ import { ModelConfig, TemplateFramework } from "../helpers/types";
 import { PureQuestionArgs, QuestionResults } from "./types";
 import { askPostInstallAction, questionHandlers } from "./utils";
 
-type AppType = "agentic_rag" | "financial_report" | "deep_research";
+type AppType =
+  | "agentic_rag"
+  | "financial_report"
+  | "deep_research"
+  | "code_generator"
+  | "document_generator";
 
 type SimpleAnswers = {
   appType: AppType;
@@ -42,6 +47,16 @@ export const askSimpleQuestions = async (
           description:
             "Researches and analyzes provided documents from multiple perspectives, generating a comprehensive report with citations to support key findings and insights.",
         },
+        {
+          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.",
+        },
       ],
     },
     questionHandlers,
@@ -52,35 +67,35 @@ export const askSimpleQuestions = async (
 
   let useLlamaCloud = false;
 
-  if (appType !== "extractor" && appType !== "contract_review") {
-    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
@@ -111,10 +126,10 @@ const convertAnswers = async (
   args: PureQuestionArgs,
   answers: SimpleAnswers,
 ): Promise<QuestionResults> => {
-  const MODEL_GPT4o: ModelConfig = {
+  const MODEL_GPT41: ModelConfig = {
     provider: "openai",
     apiKey: args.openAiKey,
-    model: "gpt-4o",
+    model: "gpt-4.1",
     embeddingModel: "text-embedding-3-large",
     dimensions: 1536,
     isConfigured(): boolean {
@@ -135,13 +150,25 @@ const convertAnswers = async (
       template: "llamaindexserver",
       dataSources: EXAMPLE_10K_SEC_FILES,
       tools: getTools(["interpreter", "document_generator"]),
-      modelConfig: MODEL_GPT4o,
+      modelConfig: MODEL_GPT41,
     },
     deep_research: {
       template: "llamaindexserver",
       dataSources: EXAMPLE_10K_SEC_FILES,
       tools: [],
-      modelConfig: MODEL_GPT4o,
+      modelConfig: MODEL_GPT41,
+    },
+    code_generator: {
+      template: "llamaindexserver",
+      dataSources: [],
+      tools: [],
+      modelConfig: MODEL_GPT41,
+    },
+    document_generator: {
+      template: "llamaindexserver",
+      dataSources: [],
+      tools: [],
+      modelConfig: MODEL_GPT41,
     },
   };
 

packages/create-llama/templates/components/engines/typescript/agent/tools/interpreter.ts

@@ -191,7 +191,7 @@ export class InterpreterTool implements BaseTool<InterpreterParameter> {
           case "png":
           case "jpeg":
           case "svg":
-          case "pdf":
+          case "pdf": {
             const { filename } = this.saveToDisk(data, ext);
             output.push({
               type: ext as InterpreterExtraType,
@@ -199,6 +199,7 @@ export class InterpreterTool implements BaseTool<InterpreterParameter> {
               url: this.getFileUrl(filename),
             });
             break;
+          }
           default:
             output.push({
               type: ext as InterpreterExtraType,

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/html/chat/chat-avatar.tsx

@@ -6,7 +6,7 @@ import { Message } from "./chat-messages";
 export default function ChatAvatar(message: Message) {
   if (message.role === "user") {
     return (
-      <div className="flex h-8 w-8 shrink-0 select-none items-center justify-center rounded-md border shadow bg-background">
+      <div className="bg-background flex h-8 w-8 shrink-0 select-none items-center justify-center rounded-md border shadow">
         <svg
           xmlns="http://www.w3.org/2000/svg"
           viewBox="0 0 256 256"
@@ -20,7 +20,7 @@ export default function ChatAvatar(message: Message) {
   }
 
   return (
-    <div className="flex h-8 w-8 shrink-0 select-none items-center justify-center rounded-md border  bg-black text-white">
+    <div className="flex h-8 w-8 shrink-0 select-none items-center justify-center rounded-md border bg-black text-white">
       <Image
         className="rounded-md"
         src="/llama.png"

packages/create-llama/templates/components/ui/html/chat/chat-input.tsx

@@ -23,20 +23,20 @@ export default function ChatInput(props: ChatInputProps) {
     <>
       <form
         onSubmit={props.handleSubmit}
-        className="flex items-start justify-between w-full max-w-5xl p-4 bg-white rounded-xl shadow-xl gap-4"
+        className="flex w-full max-w-5xl items-start justify-between gap-4 rounded-xl bg-white p-4 shadow-xl"
       >
         <input
           autoFocus
           name="message"
           placeholder="Type a message"
-          className="w-full p-4 rounded-xl shadow-inner flex-1"
+          className="w-full flex-1 rounded-xl p-4 shadow-inner"
           value={props.input}
           onChange={props.handleInputChange}
         />
         <button
           disabled={props.isLoading}
           type="submit"
-          className="p-4 text-white rounded-xl shadow-xl bg-gradient-to-r from-cyan-500 to-sky-500 disabled:opacity-50 disabled:cursor-not-allowed"
+          className="rounded-xl bg-gradient-to-r from-cyan-500 to-sky-500 p-4 text-white shadow-xl disabled:cursor-not-allowed disabled:opacity-50"
         >
           Send message
         </button>

packages/create-llama/templates/components/ui/html/chat/chat-item.tsx

@@ -7,7 +7,7 @@ export default function ChatItem(message: Message) {
   return (
     <div className="flex items-start gap-4 pt-5">
       <ChatAvatar {...message} />
-      <p className="break-words whitespace-pre-wrap">{message.content}</p>
+      <p className="whitespace-pre-wrap break-words">{message.content}</p>
     </div>
   );
 }

packages/create-llama/templates/components/ui/html/chat/chat-messages.tsx

@@ -39,7 +39,7 @@ export default function ChatMessages({
 
   return (
     <div
-      className="flex-1 w-full max-w-5xl p-4 bg-white rounded-xl shadow-xl overflow-auto"
+      className="w-full max-w-5xl flex-1 overflow-auto rounded-xl bg-white p-4 shadow-xl"
       ref={scrollableChatContainerRef}
     >
       <div className="flex flex-col gap-5 divide-y">

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

@@ -97,7 +97,7 @@ export default function Component({ events }) {
       case "pending":
         return <Clock className="h-4 w-4 text-gray-400" />;
       case "inprogress":
-        return <Loader2 className="h-4 w-4 text-blue-500 animate-spin" />;
+        return <Loader2 className="h-4 w-4 animate-spin text-blue-500" />;
       case "done":
         return <CheckCircle className="h-4 w-4 text-green-500" />;
       case "error":
@@ -140,9 +140,9 @@ export default function Component({ events }) {
   };
 
   return (
-    <div className="w-full max-w-4xl mx-auto space-y-6 p-4">
+    <div className="mx-auto w-full max-w-4xl space-y-6 p-4">
       {/* Header */}
-      <div className="flex items-center justify-between mb-6">
+      <div className="mb-6 flex items-center justify-between">
         <h1 className="text-2xl font-bold">DeepResearch Workflow</h1>
         <div className="flex items-center space-x-2">
           <Badge
@@ -188,7 +188,7 @@ export default function Component({ events }) {
         className={cn(
           "border-2 transition-all duration-300",
           retrieve?.state === "inprogress"
-            ? "border-blue-500 shadow-blue-100 shadow-lg"
+            ? "border-blue-500 shadow-lg shadow-blue-100"
             : retrieve?.state === "done"
               ? "border-green-500"
               : retrieve?.state === "error"
@@ -231,7 +231,7 @@ export default function Component({ events }) {
           className={cn(
             "border-2 transition-all duration-300",
             analyze?.state === "inprogress"
-              ? "border-blue-500 shadow-blue-100 shadow-lg"
+              ? "border-blue-500 shadow-lg shadow-blue-100"
               : analyze?.state === "done"
                 ? "border-green-500"
                 : analyze?.state === "error"
@@ -288,9 +288,9 @@ export default function Component({ events }) {
                   key={answer.id}
                   value={answer.id}
                   className={cn(
-                    "mb-4 border rounded-lg overflow-hidden",
+                    "mb-4 overflow-hidden rounded-lg border",
                     answer.state === "inprogress"
-                      ? "border-blue-500 shadow-blue-100 shadow-sm"
+                      ? "border-blue-500 shadow-sm shadow-blue-100"
                       : answer.state === "done"
                         ? "border-green-100"
                         : answer.state === "error"
@@ -309,7 +309,7 @@ export default function Component({ events }) {
                       <Badge
                         variant="outline"
                         className={cn(
-                          "ml-auto flex items-center space-x-1 shrink-0",
+                          "ml-auto flex shrink-0 items-center space-x-1",
                           answer.state === "inprogress"
                             ? "text-blue-500"
                             : answer.state === "done"
@@ -327,7 +327,7 @@ export default function Component({ events }) {
                   <AccordionContent className="px-4 pb-4 pt-1">
                     <div
                       className={cn(
-                        "p-3 rounded-md",
+                        "rounded-md p-3",
                         answer.state === "done"
                           ? "bg-green-50"
                           : answer.state === "inprogress"

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

@@ -0,0 +1,375 @@
+import re
+import time
+from typing import Any, Literal, Optional, Union
+
+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,
+    StartEvent,
+    StopEvent,
+    Workflow,
+    step,
+)
+from llama_index.server.api.models import (
+    Artifact,
+    ArtifactEvent,
+    ArtifactType,
+    ChatRequest,
+    CodeArtifactData,
+    UIEvent,
+)
+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
+    file_name: Optional[str] = None
+    requirement: str
+
+
+class PlanEvent(Event):
+    user_msg: str
+    context: Optional[str] = None
+
+
+class GenerateArtifactEvent(Event):
+    requirement: Requirement
+
+
+class SynthesizeAnswerEvent(Event):
+    pass
+
+
+class UIEventData(BaseModel):
+    """
+    Event data for updating workflow status to the UI.
+    """
+
+    state: Literal["plan", "generate", "completed"] = Field(
+        description="The current state of the workflow. "
+        "plan: analyze and create a plan for the next step. "
+        "generate: generate the artifact based on the requirement from the previous step. "
+        "completed: the workflow is completed. "
+    )
+    requirement: Optional[str] = Field(
+        description="The requirement for generating the artifact. ",
+        default=None,
+    )
+
+
+class CodeArtifactWorkflow(Workflow):
+    """
+    A simple workflow that help generate/update the chat artifact (code, document)
+    e.g: Help create a NextJS app.
+         Update the generated code with the user's feedback.
+         Generate a guideline for the app,...
+    """
+
+    def __init__(
+        self,
+        llm: LLM,
+        chat_request: ChatRequest,
+        **kwargs: Any,
+    ):
+        """
+        Args:
+            llm: The LLM to use.
+            chat_request: The chat request from the chat app to use.
+        """
+        super().__init__(**kwargs)
+        self.llm = llm
+        self.chat_request = chat_request
+        self.last_artifact = get_last_artifact(chat_request)
+
+    @step
+    async def prepare_chat_history(self, ctx: Context, ev: StartEvent) -> PlanEvent:
+        user_msg = ev.user_msg
+        if user_msg is None:
+            raise ValueError("user_msg is required to run the workflow")
+        await ctx.set("user_msg", user_msg)
+        chat_history = ev.chat_history or []
+        chat_history.append(
+            ChatMessage(
+                role="user",
+                content=user_msg,
+            )
+        )
+        memory = ChatMemoryBuffer.from_defaults(
+            chat_history=chat_history,
+            llm=self.llm,
+        )
+        await ctx.set("memory", memory)
+        return PlanEvent(
+            user_msg=user_msg,
+            context=str(self.last_artifact.model_dump_json())
+            if self.last_artifact
+            else "",
+        )
+
+    @step
+    async def planning(
+        self, ctx: Context, event: PlanEvent
+    ) -> Union[GenerateArtifactEvent, SynthesizeAnswerEvent]:
+        """
+        Based on the conversation history and the user's request
+        this step will help to provide a good next step for the code or document generation.
+        """
+        ctx.write_event_to_stream(
+            UIEvent(
+                type="ui_event",
+                data=UIEventData(
+                    state="plan",
+                    requirement=None,
+                ),
+            )
+        )
+        prompt = PromptTemplate("""
+        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}
+        """).format(
+            context=""
+            if event.context is None
+            else f"## The context is: \n{event.context}\n",
+            user_msg=event.user_msg,
+        )
+        response = await self.llm.acomplete(
+            prompt=prompt,
+            formatted=True,
+        )
+        # parse the response to Requirement
+        # 1. use regex to find the json block
+        json_block = re.search(
+            r"```(?:json)?\s*([\s\S]*?)\s*```", response.text, re.IGNORECASE
+        )
+        if json_block is None:
+            raise ValueError("No JSON block found in the response.")
+        # 2. parse the json block to Requirement
+        requirement = Requirement.model_validate_json(json_block.group(1).strip())
+        ctx.write_event_to_stream(
+            UIEvent(
+                type="ui_event",
+                data=UIEventData(
+                    state="generate",
+                    requirement=requirement.requirement,
+                ),
+            )
+        )
+        # Put the planning result to the memory
+        # useful for answering step
+        memory: ChatMemoryBuffer = await ctx.get("memory")
+        memory.put(
+            ChatMessage(
+                role="assistant",
+                content=f"The plan for next step: \n{response.text}",
+            )
+        )
+        await ctx.set("memory", memory)
+        if requirement.next_step == "coding":
+            return GenerateArtifactEvent(
+                requirement=requirement,
+            )
+        else:
+            return SynthesizeAnswerEvent()
+
+    @step
+    async def generate_artifact(
+        self, ctx: Context, event: GenerateArtifactEvent
+    ) -> SynthesizeAnswerEvent:
+        """
+        Generate the code based on the user's request.
+        """
+        ctx.write_event_to_stream(
+            UIEvent(
+                type="ui_event",
+                data=UIEventData(
+                    state="generate",
+                    requirement=event.requirement.requirement,
+                ),
+            )
+        )
+        prompt = PromptTemplate("""
+         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:
+            ```
+            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:
+         {previous_artifact}
+
+         Now, i have to generate the code for the following requirement:
+         {requirement}
+         ```
+        """).format(
+            previous_artifact=self.last_artifact.model_dump_json()
+            if self.last_artifact
+            else "",
+            requirement=event.requirement,
+        )
+        response = await self.llm.acomplete(
+            prompt=prompt,
+            formatted=True,
+        )
+        # Extract the code from the response
+        language_pattern = r"```(\w+)([\s\S]*)```"
+        code_match = re.search(language_pattern, response.text)
+        if code_match is None:
+            return SynthesizeAnswerEvent()
+        else:
+            code = code_match.group(2).strip()
+        # Put the generated code to the memory
+        memory: ChatMemoryBuffer = await ctx.get("memory")
+        memory.put(
+            ChatMessage(
+                role="assistant",
+                content=f"Updated the code: \n{response.text}",
+            )
+        )
+        # To show the Canvas panel for the artifact
+        ctx.write_event_to_stream(
+            ArtifactEvent(
+                data=Artifact(
+                    type=ArtifactType.CODE,
+                    created_at=int(time.time()),
+                    data=CodeArtifactData(
+                        language=event.requirement.language or "",
+                        file_name=event.requirement.file_name or "",
+                        code=code,
+                    ),
+                ),
+            )
+        )
+        return SynthesizeAnswerEvent()
+
+    @step
+    async def synthesize_answer(
+        self, ctx: Context, event: SynthesizeAnswerEvent
+    ) -> StopEvent:
+        """
+        Synthesize the answer.
+        """
+        memory: ChatMemoryBuffer = await ctx.get("memory")
+        chat_history = memory.get()
+        chat_history.append(
+            ChatMessage(
+                role="system",
+                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.
+                """,
+            )
+        )
+        response_stream = await self.llm.astream_chat(
+            messages=chat_history,
+        )
+        ctx.write_event_to_stream(
+            UIEvent(
+                type="ui_event",
+                data=UIEventData(
+                    state="completed",
+                ),
+            )
+        )
+        return StopEvent(result=response_stream)

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

@@ -0,0 +1,66 @@
+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 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.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/document_generator/workflow.py

@@ -0,0 +1,347 @@
+import re
+import time
+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 (
+    Context,
+    Event,
+    StartEvent,
+    StopEvent,
+    Workflow,
+    step,
+)
+from llama_index.server.api.models import (
+    Artifact,
+    ArtifactEvent,
+    ArtifactType,
+    ChatRequest,
+    DocumentArtifactData,
+    UIEvent,
+)
+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
+    requirement: str
+
+
+class PlanEvent(Event):
+    user_msg: str
+    context: Optional[str] = None
+
+
+class GenerateArtifactEvent(Event):
+    requirement: DocumentRequirement
+
+
+class SynthesizeAnswerEvent(Event):
+    requirement: DocumentRequirement
+    generated_artifact: str
+
+
+class UIEventData(BaseModel):
+    """
+    Event data for updating workflow status to the UI.
+    """
+
+    state: Literal["plan", "generate", "completed"] = Field(
+        description="The current state of the workflow. "
+        "plan: analyze and create a plan for the next step. "
+        "generate: generate the artifact based on the requirement from the previous step. "
+        "completed: the workflow is completed. "
+    )
+    requirement: Optional[str] = Field(
+        description="The requirement for generating the artifact. ",
+        default=None,
+    )
+
+
+class DocumentArtifactWorkflow(Workflow):
+    """
+    A workflow to help generate or update document artifacts (e.g., Markdown or HTML documents).
+    Example use cases: Generate a project guideline, update documentation with user feedback, etc.
+    """
+
+    def __init__(
+        self,
+        llm: LLM,
+        chat_request: ChatRequest,
+        **kwargs: Any,
+    ):
+        """
+        Args:
+            llm: The LLM to use.
+            chat_request: The chat request from the chat app to use.
+        """
+        super().__init__(**kwargs)
+        self.llm = llm
+        self.chat_request = chat_request
+        self.last_artifact = get_last_artifact(chat_request)
+
+    @step
+    async def prepare_chat_history(self, ctx: Context, ev: StartEvent) -> PlanEvent:
+        user_msg = ev.user_msg
+        if user_msg is None:
+            raise ValueError("user_msg is required to run the workflow")
+        await ctx.set("user_msg", user_msg)
+        chat_history = ev.chat_history or []
+        chat_history.append(
+            ChatMessage(
+                role="user",
+                content=user_msg,
+            )
+        )
+        memory = ChatMemoryBuffer.from_defaults(
+            chat_history=chat_history,
+            llm=self.llm,
+        )
+        await ctx.set("memory", memory)
+        return PlanEvent(
+            user_msg=user_msg,
+            context=str(self.last_artifact.model_dump_json())
+            if self.last_artifact
+            else "",
+        )
+
+    @step
+    async def planning(self, ctx: Context, event: PlanEvent) -> GenerateArtifactEvent:
+        """
+        Based on the conversation history and the user's request,
+        this step will provide a clear requirement for the next document generation or update.
+        """
+        ctx.write_event_to_stream(
+            UIEvent(
+                type="ui_event",
+                data=UIEventData(
+                    state="plan",
+                    requirement=None,
+                ),
+            )
+        )
+        prompt = PromptTemplate("""
+         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}
+        """).format(
+            context=""
+            if event.context is None
+            else f"## The context is: \n{event.context}\n",
+            user_msg=event.user_msg,
+        )
+        response = await self.llm.acomplete(
+            prompt=prompt,
+            formatted=True,
+        )
+        # parse the response to DocumentRequirement
+        json_block = re.search(r"```json([\s\S]*)```", response.text)
+        if json_block is None:
+            raise ValueError("No json block found in the response")
+        requirement = DocumentRequirement.model_validate_json(
+            json_block.group(1).strip()
+        )
+
+        # Put the planning result to the memory
+        memory: ChatMemoryBuffer = await ctx.get("memory")
+        memory.put(
+            ChatMessage(
+                role="assistant",
+                content=f"Planning for the document generation: \n{response.text}",
+            )
+        )
+        await ctx.set("memory", memory)
+        ctx.write_event_to_stream(
+            UIEvent(
+                type="ui_event",
+                data=UIEventData(
+                    state="generate",
+                    requirement=requirement.requirement,
+                ),
+            )
+        )
+        return GenerateArtifactEvent(
+            requirement=requirement,
+        )
+
+    @step
+    async def generate_artifact(
+        self, ctx: Context, event: GenerateArtifactEvent
+    ) -> SynthesizeAnswerEvent:
+        """
+        Generate or update the document based on the user's request.
+        """
+        ctx.write_event_to_stream(
+            UIEvent(
+                type="ui_event",
+                data=UIEventData(
+                    state="generate",
+                    requirement=event.requirement.requirement,
+                ),
+            )
+        )
+        prompt = PromptTemplate("""
+         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:
+         {previous_artifact}
+
+         Now, please generate the document for the following requirement:
+         {requirement}
+         """).format(
+            previous_artifact=self.last_artifact.model_dump_json()
+            if self.last_artifact
+            else "",
+            requirement=event.requirement,
+        )
+        response = await self.llm.acomplete(
+            prompt=prompt,
+            formatted=True,
+        )
+        # Extract the document from the response
+        language_pattern = r"```(markdown|html)([\s\S]*)```"
+        doc_match = re.search(language_pattern, response.text)
+        if doc_match is None:
+            return SynthesizeAnswerEvent(
+                requirement=event.requirement,
+                generated_artifact="There is no change to the document. "
+                + response.text.strip(),
+            )
+        content = doc_match.group(2).strip()
+        doc_type = doc_match.group(1)
+        # Put the generated document to the memory
+        memory: ChatMemoryBuffer = await ctx.get("memory")
+        memory.put(
+            ChatMessage(
+                role="assistant",
+                content=f"Generated document: \n{response.text}",
+            )
+        )
+        # To show the Canvas panel for the artifact
+        ctx.write_event_to_stream(
+            ArtifactEvent(
+                data=Artifact(
+                    type=ArtifactType.DOCUMENT,
+                    created_at=int(time.time()),
+                    data=DocumentArtifactData(
+                        title=event.requirement.title,
+                        content=content,
+                        type=doc_type,  # type: ignore
+                    ),
+                ),
+            )
+        )
+        return SynthesizeAnswerEvent(
+            requirement=event.requirement,
+            generated_artifact=response.text,
+        )
+
+    @step
+    async def synthesize_answer(
+        self, ctx: Context, event: SynthesizeAnswerEvent
+    ) -> StopEvent:
+        """
+        Synthesize the answer for the user.
+        """
+        memory: ChatMemoryBuffer = await ctx.get("memory")
+        chat_history = memory.get()
+        chat_history.append(
+            ChatMessage(
+                role="system",
+                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.
+                """,
+            )
+        )
+        response_stream = await self.llm.astream_chat(
+            messages=chat_history,
+        )
+        ctx.write_event_to_stream(
+            UIEvent(
+                type="ui_event",
+                data=UIEventData(
+                    state="completed",
+                    requirement=event.requirement.requirement,
+                ),
+            )
+        )
+        return StopEvent(result=response_stream)

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 +1,347 @@
+import { extractLastArtifact } from "@llamaindex/server";
+import { ChatMemoryBuffer, MessageContent, Settings } from "llamaindex";
+
+import {
+  agentStreamEvent,
+  createStatefulMiddleware,
+  createWorkflow,
+  startAgentEvent,
+  stopAgentEvent,
+  workflowEvent,
+} from "@llamaindex/workflow";
+
+import { z } from "zod";
+
+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 workflowFactory(reqBody: any) {
+  const llm = Settings.llm;
+
+  const { withState, getContext } = createStatefulMiddleware(() => {
+    return {
+      memory: new ChatMemoryBuffer({ llm }),
+      lastArtifact: extractLastArtifact(reqBody),
+    };
+  });
+  const workflow = withState(createWorkflow());
+
+  workflow.handle([startAgentEvent], async ({ data }) => {
+    const { userInput, chatHistory = [] } = data;
+    // 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.set(chatHistory);
+    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 +1,328 @@
+import { extractLastArtifact } from "@llamaindex/server";
+import { ChatMemoryBuffer, MessageContent, Settings } from "llamaindex";
+
+import {
+  agentStreamEvent,
+  createStatefulMiddleware,
+  createWorkflow,
+  startAgentEvent,
+  stopAgentEvent,
+  workflowEvent,
+} from "@llamaindex/workflow";
+
+import { z } from "zod";
+
+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 workflowFactory(reqBody: any) {
+  const llm = Settings.llm;
+
+  const { withState, getContext } = createStatefulMiddleware(() => {
+    return {
+      memory: new ChatMemoryBuffer({ llm }),
+      lastArtifact: extractLastArtifact(reqBody),
+    };
+  });
+  const workflow = withState(createWorkflow());
+
+  workflow.handle([startAgentEvent], async ({ data }) => {
+    const { userInput, chatHistory = [] } = data;
+    // 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.set(chatHistory);
+    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/astra/generate.ts

@@ -1,4 +1,3 @@
-/* eslint-disable turbo/no-undeclared-env-vars */
 import { AstraDBVectorStore } from "@llamaindex/astra";
 import * as dotenv from "dotenv";
 import { VectorStoreIndex, storageContextFromDefaults } from "llamaindex";

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

@@ -1,4 +1,3 @@
-/* eslint-disable turbo/no-undeclared-env-vars */
 import { AstraDBVectorStore } from "@llamaindex/astra";
 import { VectorStoreIndex } from "llamaindex";
 import { checkRequiredEnvVars } from "./shared";

packages/create-llama/templates/components/vectordbs/typescript/chroma/generate.ts

@@ -1,4 +1,3 @@
-/* eslint-disable turbo/no-undeclared-env-vars */
 import { ChromaVectorStore } from "@llamaindex/chroma";
 import * as dotenv from "dotenv";
 import { VectorStoreIndex, storageContextFromDefaults } from "llamaindex";

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

@@ -1,4 +1,3 @@
-/* eslint-disable turbo/no-undeclared-env-vars */
 import { ChromaVectorStore } from "@llamaindex/chroma";
 import { VectorStoreIndex } from "llamaindex";
 import { checkRequiredEnvVars } from "./shared";

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/vectordbs/typescript/milvus/generate.ts

@@ -1,4 +1,3 @@
-/* eslint-disable turbo/no-undeclared-env-vars */
 import { MilvusVectorStore } from "@llamaindex/milvus";
 import * as dotenv from "dotenv";
 import { VectorStoreIndex, storageContextFromDefaults } from "llamaindex";

packages/create-llama/templates/components/vectordbs/typescript/mongo/generate.ts

@@ -1,4 +1,3 @@
-/* eslint-disable turbo/no-undeclared-env-vars */
 import { MongoDBAtlasVectorSearch } from "@llamaindex/mongodb";
 import * as dotenv from "dotenv";
 import { storageContextFromDefaults, VectorStoreIndex } from "llamaindex";

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

@@ -1,4 +1,3 @@
-/* eslint-disable turbo/no-undeclared-env-vars */
 import { MongoDBAtlasVectorSearch } from "@llamaindex/mongodb";
 import { VectorStoreIndex } from "llamaindex";
 import { MongoClient } from "mongodb";

packages/create-llama/templates/components/vectordbs/typescript/pinecone/generate.ts

@@ -1,4 +1,3 @@
-/* eslint-disable turbo/no-undeclared-env-vars */
 import { PineconeVectorStore } from "@llamaindex/pinecone";
 import * as dotenv from "dotenv";
 import { VectorStoreIndex, storageContextFromDefaults } from "llamaindex";

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

@@ -1,4 +1,3 @@
-/* eslint-disable turbo/no-undeclared-env-vars */
 import { PineconeVectorStore } from "@llamaindex/pinecone";
 import { VectorStoreIndex } from "llamaindex";
 import { checkRequiredEnvVars } from "./shared";

packages/create-llama/templates/components/vectordbs/typescript/qdrant/generate.ts

@@ -1,4 +1,3 @@
-/* eslint-disable turbo/no-undeclared-env-vars */
 import { QdrantVectorStore } from "@llamaindex/qdrant";
 import * as dotenv from "dotenv";
 import { VectorStoreIndex, storageContextFromDefaults } from "llamaindex";

packages/create-llama/templates/components/vectordbs/typescript/weaviate/generate.ts

@@ -1,4 +1,3 @@
-/* eslint-disable turbo/no-undeclared-env-vars */
 import { WeaviateVectorStore } from "@llamaindex/weaviate";
 import * as dotenv from "dotenv";
 import { VectorStoreIndex, storageContextFromDefaults } from "llamaindex";

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/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/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>=0.1.14,<0.2.0",
+    "llama-index-server>=0.1.15,<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": "^0.2.0",
+    "@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/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/create-llama/templates/types/streaming/nextjs/app/components/ui/chat/tools/artifact.tsx

@@ -96,8 +96,9 @@ export function Artifact({
 
   useEffect(() => {
     // auto trigger code execution
-    !result && fetchArtifactResult();
-    // eslint-disable-next-line react-hooks/exhaustive-deps
+    if (!result) {
+      fetchArtifactResult();
+    }
   }, []);
 
   if (!artifact || version === undefined) return null;
@@ -284,7 +285,6 @@ function InterpreterOutput({ outputUrls }: { outputUrls: OutputUrl[] }) {
         <li key={url.url}>
           <div className="mt-4">
             {isImageFile(url.filename) ? (
-              // eslint-disable-next-line @next/next/no-img-element
               <img src={url.url} alt={url.filename} className="my-4 w-1/2" />
             ) : (
               <a

packages/create-llama/templates/types/streaming/nextjs/app/components/ui/chat/tools/chat-tools.tsx

@@ -51,18 +51,21 @@ function ChatTools({
   }
 
   switch (toolCall.name) {
-    case "get_weather_information":
+    case "get_weather_information": {
       const weatherData = toolOutput.output as unknown as WeatherData;
       return <WeatherCard data={weatherData} />;
-    case "artifact":
+    }
+    case "artifact": {
       return (
         <Artifact
           artifact={toolOutput.output as CodeArtifact}
           version={artifactVersion}
         />
       );
-    default:
+    }
+    default: {
       return null;
+    }
   }
 }
 

packages/server/.prettierignore

@@ -1,7 +0,0 @@
-lib/
-dist/
-server/
-next/.next/
-next/out/
-node_modules/
-build/

packages/server/CHANGELOG.md

@@ -1,5 +1,21 @@
 # @llamaindex/server
 
+## 0.2.1
+
+### Patch Changes
+
+- f072308: feat: add dev mode UI
+
+## 0.2.0
+
+### Minor Changes
+
+- 0384268: Use the new workflow engine and deprecate the old one.
+
+### Patch Changes
+
+- d9f9e3c: chore: bump chat-ui to support code editor & document editor
+
 ## 0.1.7
 
 ### Patch Changes

packages/server/README.md

@@ -4,10 +4,10 @@ LlamaIndexServer is a Next.js-based application that allows you to quickly launc
 
 ## Features
 
-- Serving a workflow as a chatbot
+- Add a sophisticated chatbot UI to your LlamaIndex workflow
+- Edit code and document artifacts in an OpenAI Canvas-style UI
+- Extendable UI components for events and headers
 - Built on Next.js for high performance and easy API development
-- Optional built-in chat UI with extendable UI components
-- Prebuilt development code
 
 ## Installation
 
@@ -21,9 +21,11 @@ Create an `index.ts` file and add the following code:
 
 ```ts
 import { LlamaIndexServer } from "@llamaindex/server";
+import { openai } from "@llamaindex/openai";
+import { agent } from "@llamaindex/workflow";
 import { wiki } from "@llamaindex/tools"; // or any other tool
 
-const createWorkflow = () => agent({ tools: [wiki()] });
+const createWorkflow = () => agent({ tools: [wiki()], llm: openai("gpt-4o") });
 
 new LlamaIndexServer({
   workflow: createWorkflow,
@@ -34,6 +36,8 @@ new LlamaIndexServer({
 }).start();
 ```
 
+The `createWorkflow` function is a factory function that creates an [Agent Workflow](https://ts.llamaindex.ai/docs/llamaindex/modules/agents/agent_workflow) with a tool that retrieves information from Wikipedia in this case. For more details, read about the [Workflow factory contract](#workflow-factory-contract).
+
 ## Running the Server
 
 In the same directory as `index.ts`, run the following command to start the server:
@@ -54,16 +58,75 @@ curl -X POST "http://localhost:3000/api/chat" -H "Content-Type: application/json
 
 The `LlamaIndexServer` accepts the following configuration options:
 
-- `workflow`: A callable function that creates a workflow instance for each request
+- `workflow`: A callable function that creates a workflow instance for each request. See [Workflow factory contract](#workflow-factory-contract) for more details.
 - `uiConfig`: An object to configure the chat UI containing the following properties:
   - `appTitle`: The title of the application (default: `"LlamaIndex App"`)
   - `starterQuestions`: List of starter questions for the chat UI (default: `[]`)
   - `componentsDir`: The directory for custom UI components rendering events emitted by the workflow. The default is undefined, which does not render custom UI components.
   - `llamaCloudIndexSelector`: Whether to show the LlamaCloud index selector in the chat UI (requires `LLAMA_CLOUD_API_KEY` to be set in the environment variables) (default: `false`)
+  - `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/src/workflow.ts`. Please start server in dev mode (`npm run dev`) to use see this reload feature enabled.
 
 LlamaIndexServer accepts all the configuration options from Nextjs Custom Server such as `port`, `hostname`, `dev`, etc.
 See all Nextjs Custom Server options [here](https://nextjs.org/docs/app/building-your-application/configuring/custom-server).
 
+## Workflow factory contract
+
+The `workflow` provided will be called for each chat request to initialize a new workflow instance. The contract of the generated workflow must be the same as for the [Agent Workflow](https://ts.llamaindex.ai/docs/llamaindex/modules/agents/agent_workflow).
+
+This means that the workflow must handle a `startAgentEvent` event, which is the entry point of the workflow and contains the following information in it's `data` property:
+
+```typescript
+{
+  userInput: MessageContent;
+  chatHistory?: ChatMessage[] | undefined;
+};
+```
+
+The `userInput` is the latest user message and the `chatHistory` is the list of messages exchanged between the user and the workflow so far.
+
+Furthermore, the workflow must stop with a `stopAgentEvent` event to mark the end of the workflow. In between, the workflow can emit [UI events](##AI-generated-UI-Components) to render custom UI components and [Artifact events](##Sending-Artifacts-to-the-UI) to send structured data like generated documents or code snippets to the UI.
+
+```ts
+import {
+  createStatefulMiddleware,
+  createWorkflow,
+  startAgentEvent,
+} from "@llamaindex/workflow";
+import { ChatMemoryBuffer, type ChatMessage, Settings } from "llamaindex";
+import { openai } from "@llamaindex/openai";
+import { wiki } from "@llamaindex/tools";
+
+Settings.llm = openai("gpt-4o");
+
+export const workflowFactory = async () => {
+  const workflow = createWorkflow();
+
+  workflow.handle([startAgentEvent], async ({ data }) => {
+    const { state, sendEvent } = getContext();
+    const messages = data.chatHistory;
+
+    const toolCallResponse = await chatWithTools(
+      Settings.llm,
+      [wiki()],
+      messages,
+    );
+
+    // using result from tool call and use `sendEvent` to emit the next event...
+  });
+
+  // define more workflow handling logic here...
+
+  // Finally stop with a `stopAgentEvent` event to mark the end of the workflow.
+  // return stopAgentEvent.with({
+  //   result: "This is the end!",
+  // });
+
+  return workflow;
+};
+```
+
+To generate sophisticated examples of workflows, you best use the [create-llama](https://github.com/run-llama/create-llama) project.
+
 ## AI-generated UI Components
 
 The LlamaIndex server provides support for rendering workflow events using custom UI components, allowing you to extend and customize the chat interface.
@@ -137,6 +200,66 @@ new LlamaIndexServer({
 }).start();
 ```
 
+## Sending Artifacts to the UI
+
+In addition to UI events for custom components, LlamaIndex Server supports a special `ArtifactEvent` to send structured data like generated documents or code snippets to the UI. These artifacts are displayed in a dedicated "Canvas" panel in the chat interface.
+
+### Artifact Event Structure
+
+To send an artifact, your workflow needs to emit an event with `type: "artifact"`. The `data` payload of this event should include:
+
+- `type`: A string indicating the type of artifact (e.g., `"document"`, `"code"`).
+- `created_at`: A timestamp (e.g., `Date.now()`) indicating when the artifact was created.
+- `data`: An object containing the specific details of the artifact. The structure of this object depends on the artifact `type`.
+
+### Defining and Sending an ArtifactEvent
+
+First, define your artifact event using `workflowEvent` from `@llamaindex/workflow`:
+
+```typescript
+import { workflowEvent } from "@llamaindex/workflow";
+
+// Example for a document artifact
+const artifactEvent = workflowEvent<{
+  type: "artifact"; // Must be "artifact"
+  data: {
+    type: "document"; // Custom type for your artifact (e.g., "document", "code")
+    created_at: number;
+    data: {
+      // Specific data for the document artifact type
+      title: string;
+      content: string;
+      type: "markdown" | "html"; // document format
+    };
+  };
+}>();
+```
+
+Then, within your workflow logic, use `sendEvent` (obtained from `getContext()`) to emit the event:
+
+```typescript
+// Assuming 'sendEvent' is available in your workflow handler
+// and 'documentDetails' contains the content for the artifact.
+
+sendEvent(
+  artifactEvent.with({
+    type: "artifact", // This top-level type must be "artifact"
+    data: {
+      type: "document", // This is your specific artifact type
+      created_at: Date.now(),
+      data: {
+        title: "My Generated Document",
+        content: "# Hello World
+This is a markdown document.",
+        type: "markdown",
+      },
+    },
+  }),
+);
+```
+
+This will send the artifact to the LlamaIndex Server UI, where it will be rendered in the [ChatCanvasPanel](/packages/server/next/app/components/ui/chat/canvas/panel.tsx) by a renderer depending on the artifact type. For type `document` this is using the [DocumentArtifactViewer](https://github.com/run-llama/chat-ui/blob/bacb75fc6edceacf742fba18632404a2483b5a81/packages/chat-ui/src/chat/canvas/artifacts/document.tsx#L17).
+
 ## Default Endpoints and Features
 
 ### Chat Endpoint

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
+pnpm run dev
+```
+
+## 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: 3000,
+}).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: 3000,
+}).start();
+```
+
+Export OpenAI API key and start the server in dev mode.
+
+```bash
+export OPENAI_API_KEY=<your-openai-api-key>
+npx nodemon --exec tsx 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: 3000,
+}).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`,
+      }),
+    ],
+  });
+};