feat: Generate NEXT_PUBLIC_CHAT_API for NextJS backend to specify alternative backend

.changeset/perfect-days-smoke.md

@@ -0,0 +1,5 @@
+---
+"create-llama": patch
+---
+
+Generate NEXT_PUBLIC_CHAT_API for NextJS backend to specify alternative backend

.eslintrc

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

.github/workflows/e2e.yml

@@ -1,15 +1,12 @@
-name: E2E Tests for create-llama package
+name: E2E Tests
 on:
   push:
     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"
+
+env:
+  POETRY_VERSION: "1.6.1"
 
 jobs:
   e2e-python:
@@ -35,10 +32,10 @@ jobs:
         with:
           python-version: ${{ matrix.python-version }}
 
-      - name: Install uv
-        run: curl -LsSf https://astral.sh/uv/install.sh | sh
-      - name: Add uv to PATH # Ensure uv is available in subsequent steps
-        run: echo "$HOME/.cargo/bin" >> $GITHUB_PATH
+      - name: Install Poetry
+        uses: snok/install-poetry@v1
+        with:
+          version: ${{ env.POETRY_VERSION }}
 
       - uses: pnpm/action-setup@v3
 
@@ -53,15 +50,15 @@ jobs:
 
       - name: Install Playwright Browsers
         run: pnpm exec playwright install --with-deps
-        working-directory: packages/create-llama
+        working-directory: .
 
       - name: Build create-llama
         run: pnpm run build
-        working-directory: packages/create-llama
+        working-directory: .
 
       - name: Install
         run: pnpm run pack-install
-        working-directory: packages/create-llama
+        working-directory: .
 
       - name: Run Playwright tests for Python
         run: pnpm run e2e:python
@@ -70,16 +67,13 @@ jobs:
           LLAMA_CLOUD_API_KEY: ${{ secrets.LLAMA_CLOUD_API_KEY }}
           FRAMEWORK: ${{ matrix.frameworks }}
           DATASOURCE: ${{ matrix.datasources }}
-          PYTHONIOENCODING: utf-8
-          PYTHONLEGACYWINDOWSSTDIO: utf-8
-        working-directory: packages/create-llama
+        working-directory: .
 
-      - uses: actions/upload-artifact@v4
+      - uses: actions/upload-artifact@v3
         if: always()
         with:
-          name: playwright-report-python-${{ matrix.os }}-${{ matrix.frameworks }}-${{ matrix.datasources }}
-          path: packages/create-llama/playwright-report/
-          overwrite: true
+          name: playwright-report-python
+          path: ./playwright-report/
           retention-days: 30
 
   e2e-typescript:
@@ -88,10 +82,10 @@ jobs:
     strategy:
       fail-fast: true
       matrix:
-        node-version: [20, 22]
+        node-version: [18, 20]
         python-version: ["3.11"]
         os: [macos-latest, windows-latest, ubuntu-22.04]
-        frameworks: ["nextjs"]
+        frameworks: ["nextjs", "express"]
         datasources: ["--no-files", "--example-file", "--llamacloud"]
     defaults:
       run:
@@ -105,10 +99,10 @@ jobs:
         with:
           python-version: ${{ matrix.python-version }}
 
-      - name: Install uv
-        run: curl -LsSf https://astral.sh/uv/install.sh | sh
-      - name: Add uv to PATH # Ensure uv is available in subsequent steps
-        run: echo "$HOME/.cargo/bin" >> $GITHUB_PATH
+      - name: Install Poetry
+        uses: snok/install-poetry@v1
+        with:
+          version: ${{ env.POETRY_VERSION }}
 
       - uses: pnpm/action-setup@v3
 
@@ -123,15 +117,15 @@ jobs:
 
       - name: Install Playwright Browsers
         run: pnpm exec playwright install --with-deps
-        working-directory: packages/create-llama
+        working-directory: .
 
       - name: Build create-llama
         run: pnpm run build
-        working-directory: packages/create-llama
+        working-directory: .
 
       - name: Install
         run: pnpm run pack-install
-        working-directory: packages/create-llama
+        working-directory: .
 
       - name: Run Playwright tests for TypeScript
         run: pnpm run e2e:typescript
@@ -140,12 +134,11 @@ jobs:
           LLAMA_CLOUD_API_KEY: ${{ secrets.LLAMA_CLOUD_API_KEY }}
           FRAMEWORK: ${{ matrix.frameworks }}
           DATASOURCE: ${{ matrix.datasources }}
-        working-directory: packages/create-llama
+        working-directory: .
 
-      - uses: actions/upload-artifact@v4
+      - uses: actions/upload-artifact@v3
         if: always()
         with:
-          name: playwright-report-typescript-${{ matrix.os }}-${{ matrix.frameworks }}-${{ matrix.datasources }}-node${{ matrix.node-version }}
-          path: packages/create-llama/playwright-report/
-          overwrite: true
+          name: playwright-report-typescript
+          path: ./playwright-report/
           retention-days: 30

.github/workflows/lint_on_push_or_pull.yml

@@ -31,21 +31,12 @@ 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:
           args: "format --check"
-          src: "python/llama-index-server"
 
       - name: Run Python lint
         uses: chartboost/ruff-action@v1
         with:
           args: "check"
-          src: "python/llama-index-server"

.github/workflows/release_llama_index_server.yml

@@ -1,138 +0,0 @@
-name: Release llama-index-server
-
-on:
-  push:
-    branches:
-      - main
-    paths:
-      - "python/llama-index-server/**"
-      - ".github/workflows/release_llama_index_server.yml"
-  pull_request:
-    types:
-      - closed
-
-concurrency: ${{ github.workflow }}-${{ github.ref }}
-
-jobs:
-  release:
-    name: Create Release PR
-    runs-on: ubuntu-latest
-    defaults:
-      run:
-        working-directory: ./python/llama-index-server
-    if: |
-      github.event_name == 'push' && 
-      !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
-        uses: actions/checkout@v4
-        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 dependencies
-        shell: bash
-        run: uv sync --all-extras --dev
-
-      - name: Setup Git
-        run: |
-          git config --global user.email "github-actions[bot]@users.noreply.github.com"
-          git config --global user.name "github-actions[bot]"
-
-      - name: Bump patch version
-        shell: bash
-        run: |
-          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 llama-index-server version to $(uvx --from=toml-cli toml get --toml-path=pyproject.toml project.version)"
-
-      - name: Get current version
-        id: get_version
-        shell: bash
-        run: |
-          version=$(uvx --from=toml-cli toml get --toml-path=pyproject.toml project.version)
-          echo "current_version=${version}" >> "$GITHUB_OUTPUT"
-
-      - name: Create Release PR
-        uses: peter-evans/create-pull-request@v6
-        with:
-          token: ${{ secrets.GITHUB_TOKEN }}
-          commit-message: "Release: llama-index-server v${{ steps.get_version.outputs.current_version }}"
-          title: "Release: llama-index-server v${{ steps.get_version.outputs.current_version }}"
-          body: |
-            This PR was automatically created to release a new version of the llama-index-server package.
-
-            Version: ${{ steps.get_version.outputs.current_version }}
-
-            Please review the changes and merge to trigger the release.
-          branch: release/llama-index-server-v${{ steps.get_version.outputs.current_version }}
-          base: main
-          labels: release, llama-index-server
-
-  publish:
-    name: Publish to PyPI
-    runs-on: ubuntu-latest
-    defaults:
-      run:
-        working-directory: ./python/llama-index-server
-    if: |
-      github.event_name == 'pull_request' && 
-      github.event.pull_request.merged == true && 
-      startsWith(github.event.pull_request.title, 'Release: llama-index-server') &&
-      startsWith(github.event.pull_request.head.ref, 'release/llama-index-server-v')
-
-    steps:
-      - 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 dependencies
-        shell: bash
-        run: uv sync --all-extras
-
-      - name: Get current version
-        id: get_version
-        shell: bash
-        run: |
-          version=$(uvx --from=toml-cli toml get --toml-path=pyproject.toml project.version)
-          echo "current_version=${version}" >> "$GITHUB_OUTPUT"
-
-      - name: Build package
-        shell: bash
-        run: uv build --no-sources
-
-      - name: Publish to PyPI
-        shell: bash
-        run: uv publish --token ${{ secrets.PYPI_TOKEN }}
-
-      - name: Create GitHub Release
-        uses: softprops/action-gh-release@v2
-        with:
-          tag_name: llama-index-server-v${{ steps.get_version.outputs.current_version }}
-          name: "llama-index-server v${{ steps.get_version.outputs.current_version }}"
-          body: |
-            Release of llama-index-server v${{ steps.get_version.outputs.current_version }}
-          draft: false
-          prerelease: false
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/test_llama_index_server.yml

@@ -1,99 +0,0 @@
-name: Build Package
-
-on:
-  pull_request:
-
-env:
-  PYTHON_VERSION: "3.9"
-
-jobs:
-  unit-test:
-    name: Unit Tests
-    runs-on: ${{ matrix.os }}
-    defaults:
-      run:
-        working-directory: python/llama-index-server
-    strategy:
-      matrix:
-        os: [ubuntu-latest, windows-latest]
-        python-version: ["3.9"]
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@v5
-        with:
-          enable-cache: true
-
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v5
-        with:
-          python-version: ${{ matrix.python-version }}
-
-      - name: Install dependencies
-        shell: bash
-        run: uv sync --all-extras --dev
-
-      - name: Run unit tests
-        shell: bash
-        run: uv run pytest tests
-
-  type-check:
-    name: Type Check
-    runs-on: ubuntu-latest
-    defaults:
-      run:
-        working-directory: python/llama-index-server
-    steps:
-      - 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: ${{ env.PYTHON_VERSION }}
-
-      - name: Install dependencies
-        shell: bash
-        run: uv sync --all-extras --dev
-
-      - name: Run mypy
-        shell: bash
-        run: uv run mypy llama_index
-
-  build:
-    needs: [unit-test, type-check]
-    runs-on: ubuntu-latest
-    defaults:
-      run:
-        working-directory: python/llama-index-server
-    steps:
-      - 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: ${{ env.PYTHON_VERSION }}
-
-      - name: Install build package
-        shell: bash
-        run: uv sync --all-extras
-
-      - name: Test import
-        shell: bash
-        run: uv run python -c "from llama_index.server import LlamaIndexServer"
-
-      - name: Upload artifact
-        uses: actions/upload-artifact@v4
-        with:
-          name: llama-index-server
-          path: python/llama-index-server/dist/

.gitignore

@@ -6,6 +6,9 @@ node_modules
 .pnpm-store
 .pnp.js
 
+# testing
+coverage
+
 # next.js
 .next/
 out/
@@ -31,9 +34,24 @@ yarn-error.log*
 dist/
 lib/
 
+# e2e
+.cache
+test-results/
+playwright-report/
+blob-report/
+playwright/.cache/
+.tsbuildinfo
+e2e/cache
+
 # intellij
 **/.idea
 
+# Python
+.mypy_cache/
+
+# build artifacts
+create-llama-*.tgz
+
 # vscode
 .vscode
 !.vscode/settings.json

.husky/pre-commit

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

.prettierignore

@@ -1,15 +1,6 @@
-node_modules/
+apps/docs/i18n
+apps/docs/docs/api
 pnpm-lock.yaml
 lib/
 dist/
-cache/
-build/
-.next/
-out/
-packages/server/server/
-
-# Python
-python/
-**/*.mypy_cache/**
-**/*.venv/**
-**/*.ruff_cache/**
+.docusaurus/

CHANGELOG.md

@@ -1,196 +1,5 @@
 # create-llama
 
-## 0.5.13
-
-### Patch Changes
-
-- f4ca602: Add artifact use case for Typescript template
-- f4ca602: Update typescript use cases to use the new workflow engine
-
-## 0.5.12
-
-### Patch Changes
-
-- 241d82a: Add artifacts use case (python)
-
-## 0.5.11
-
-### Patch Changes
-
-- 3960618: chore: create-llama monorepo
-- 8fe5fc2: chore: add llamaindex server package
-
-## 0.5.10
-
-### Patch Changes
-
-- 0a2e12a: Use uv as the default package manager
-
-## 0.5.9
-
-### Patch Changes
-
-- 4bc53ac: Bump new chat ui and update deep research component
-- 4bc53ac: Support generate UI for deep research use case (Typescript)
-
-## 0.5.8
-
-### Patch Changes
-
-- 765181a: chore: test typescript e2e with node 20 and 22
-
-## 0.5.7
-
-### Patch Changes
-
-- 5988657: chore: bump llmaindex
-
-## 0.5.6
-
-### Patch Changes
-
-- d363ced: Bump llamaindex server packages
-
-## 0.5.5
-
-### Patch Changes
-
-- ee85320: The default custom deep research component does not work.
-
-## 0.5.4
-
-### Patch Changes
-
-- 7c3b279: Support code generation of event components using an LLM (Python)
-
-## 0.5.3
-
-### Patch Changes
-
-- 76ec360: Update templates to use new chat ui config
-
-## 0.5.2
-
-### Patch Changes
-
-- c9f8f8d: Use custom component for deep research use case
-
-## 0.5.1
-
-### Patch Changes
-
-- 08b3e07: Simplify the local index code.
-
-## 0.5.0
-
-### Minor Changes
-
-- 54c9e2f: Simplified generated code using LlamaIndexServer
-
-### Patch Changes
-
-- 0e4ecfa: fix: add trycatch for generating error
-- ee69ce7: bump: chat-ui and tailwind v4
-
-## 0.4.0
-
-### Minor Changes
-
-- 61204a1: chore: bump LITS 0.9
-
-### Patch Changes
-
-- 9e723c3: Standardize the code of the workflow use case (Python)
-- d5da55b: feat: add components.json to use CLI
-- c1552eb: chore: move wikipedia tool to create-llama
-
-## 0.3.28
-
-### Patch Changes
-
-- 4e06714: Fix the error: Unable to view file sources due to CORS.
-
-## 0.3.27
-
-### Patch Changes
-
-- b4e41aa: Add deep research over own documents use case (Python)
-
-## 0.3.26
-
-### Patch Changes
-
-- f73d46b: Fix missing copy of the multiagent code
-
-## 0.3.25
-
-### Patch Changes
-
-- 5450096: bump: react 19 stable
-
-## 0.3.24
-
-### Patch Changes
-
-- a84743c: Change --agents paramameter to --use-case
-- a84743c: Add LlamaCloud support for Reflex templates
-- a7a6592: Fix the npm issue on the full-stack Python template
-- fc5e56e: bump: code interpreter v1
-
-## 0.3.23
-
-### Patch Changes
-
-- 9077cae: Add contract review use case (Python)
-
-## 0.3.22
-
-### Patch Changes
-
-- 25667d4: Make OpenAPI spec usable by custom GPTs
-
-## 0.3.21
-
-### Patch Changes
-
-- 95227a7: Add query endpoint
-
-## 0.3.20
-
-### Patch Changes
-
-- 27d2499: Bump the LlamaCloud library and fix breaking changes (Python).
-
-## 0.3.19
-
-### Patch Changes
-
-- f9a057d: Add support multimodal indexes (e.g. from LlamaCloud)
-- aedd73d: bump: chat-ui
-
-## 0.3.18
-
-### Patch Changes
-
-- fe90a7e: chore: bump ai v4
-- 02b2473: Show streaming errors in Python, optimize system prompts for tool usage and set the weather tool as default for the Agentic RAG use case
-- 63e961e: Use auto_routed retriever mode for LlamaCloudIndex
-
-## 0.3.17
-
-### Patch Changes
-
-- 28c8808: Add fly.io deployment
-- 0a7dfcf: Generate NEXT_PUBLIC_CHAT_API for NextJS backend to specify alternative backend
-
-## 0.3.16
-
-### Patch Changes
-
-- 8b371d8: Set pydantic version to <2.10 to avoid incompatibility with llama-index.
-- 30fe269: Deactive duckduckgo tool for TS
-- 30fe269: Replace DuckDuckGo by Wikipedia tool for agentic template
-
 ## 0.3.15
 
 ### Patch Changes

README.md

@@ -55,7 +55,7 @@ Then re-start your app. Remember you'll need to re-run `generate` if you add new
 If you're using the Python backend, you can trigger indexing of your data by calling:
 
 ```bash
-uv run generate
+poetry run generate
 ```
 
 ## Customizing the AI models
@@ -130,11 +130,4 @@ Pro mode is ideal for developers who want fine-grained control over their projec
 - [TS/JS docs](https://ts.llamaindex.ai/)
 - [Python docs](https://docs.llamaindex.ai/en/stable/)
 
-## LlamaIndex Server
-
-The generated code is using the LlamaIndex Server, which serves LlamaIndex Workflows and Agent Workflows via an API server. See the following docs for more information:
-
-- [LlamaIndex Server For TypeScript](./packages/server/README.md)
-- [LlamaIndex Server For Python](./python/llama-index-server/README.md)
-
 Inspired by and adapted from [create-next-app](https://github.com/vercel/next.js/tree/canary/packages/create-next-app)

create-app.ts

@@ -1,3 +1,4 @@
+/* eslint-disable import/no-extraneous-dependencies */
 import path from "path";
 import { green, yellow } from "picocolors";
 import { tryGitInit } from "./helpers/git";
@@ -9,9 +10,9 @@ import { makeDir } from "./helpers/make-dir";
 import terminalLink from "terminal-link";
 import type { InstallTemplateArgs, TemplateObservability } from "./helpers";
 import { installTemplate } from "./helpers";
+import { writeDevcontainer } from "./helpers/devcontainer";
 import { templatesDir } from "./helpers/dir";
 import { toolsRequireConfig } from "./helpers/tools";
-import { configVSCode } from "./helpers/vscode";
 
 export type InstallAppArgs = Omit<
   InstallTemplateArgs,
@@ -38,7 +39,7 @@ export async function createApp({
   tools,
   useLlamaParse,
   observability,
-  useCase,
+  agents,
 }: InstallAppArgs): Promise<void> {
   const root = path.resolve(appPath);
 
@@ -83,13 +84,13 @@ export async function createApp({
     tools,
     useLlamaParse,
     observability,
-    useCase,
+    agents,
   };
 
   // Install backend
   await installTemplate({ ...args, backend: true });
 
-  if (frontend && framework === "fastapi" && template !== "llamaindexserver") {
+  if (frontend && framework === "fastapi") {
     // install frontend
     const frontendRoot = path.join(root, ".frontend");
     await makeDir(frontendRoot);
@@ -101,7 +102,7 @@ export async function createApp({
     });
   }
 
-  await configVSCode(root, templatesDir, framework);
+  await writeDevcontainer(root, templatesDir, framework, frontend);
 
   process.chdir(root);
   if (tryGitInit(root)) {
@@ -109,7 +110,7 @@ export async function createApp({
     console.log();
   }
 
-  if (toolsRequireConfig(tools) && template !== "llamaindexserver") {
+  if (toolsRequireConfig(tools)) {
     const configFile =
       framework === "fastapi" ? "config/tools.yaml" : "config/tools.json";
     console.log(

e2e/python/resolve_dependencies.spec.ts

@@ -195,47 +195,32 @@ async function createAndCheckLlamaProject({
   const pyprojectPath = path.join(projectPath, "pyproject.toml");
   expect(fs.existsSync(pyprojectPath)).toBeTruthy();
 
-  // Modify environment for the command
-  const commandEnv = {
+  const env = {
     ...process.env,
+    POETRY_VIRTUALENVS_IN_PROJECT: "true",
   };
 
-  console.log("Running uv venv...");
+  // Run poetry install
   try {
-    const { stdout: venvStdout, stderr: venvStderr } = await execAsync(
-      "uv venv",
-      { cwd: projectPath, env: commandEnv },
+    const { stdout: installStdout, stderr: installStderr } = await execAsync(
+      "poetry install",
+      { cwd: projectPath, env },
     );
-    console.log("uv venv stdout:", venvStdout);
-    console.error("uv venv stderr:", venvStderr);
+    console.log("poetry install stdout:", installStdout);
+    console.error("poetry install stderr:", installStderr);
   } catch (error) {
-    console.error("Error running uv venv:", error);
-    throw error; // Re-throw error to fail the test
+    console.error("Error running poetry install:", error);
+    throw error;
   }
 
-  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 ....");
+  // Run poetry run mypy
   try {
     const { stdout: mypyStdout, stderr: mypyStderr } = await execAsync(
-      "uv run mypy .",
-      { cwd: projectPath, env: commandEnv },
+      "poetry run mypy .",
+      { cwd: projectPath, env },
     );
-    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
+    console.log("poetry run mypy stdout:", mypyStdout);
+    console.error("poetry run mypy stderr:", mypyStderr);
   } catch (error) {
     console.error("Error running mypy:", error);
     throw error;

e2e/shared/extractor_template.spec.ts

@@ -0,0 +1,60 @@
+/* eslint-disable turbo/no-undeclared-env-vars */
+import { expect, test } from "@playwright/test";
+import { ChildProcess } from "child_process";
+import fs from "fs";
+import path from "path";
+import { TemplateFramework } from "../../helpers";
+import { createTestDir, runCreateLlama } from "../utils";
+
+const templateFramework: TemplateFramework = process.env.FRAMEWORK
+  ? (process.env.FRAMEWORK as TemplateFramework)
+  : "fastapi";
+const dataSource: string = process.env.DATASOURCE
+  ? process.env.DATASOURCE
+  : "--example-file";
+
+// The extractor template currently only works with FastAPI and files (and not on Windows)
+if (
+  process.platform !== "win32" &&
+  templateFramework === "fastapi" &&
+  dataSource === "--example-file"
+) {
+  test.describe("Test extractor template", async () => {
+    let appPort: number;
+    let name: string;
+    let appProcess: ChildProcess;
+    let cwd: string;
+
+    // Create extractor app
+    test.beforeAll(async () => {
+      cwd = await createTestDir();
+      appPort = Math.floor(Math.random() * 10000) + 10000;
+      const result = await runCreateLlama({
+        cwd,
+        templateType: "extractor",
+        templateFramework: "fastapi",
+        dataSource: "--example-file",
+        vectorDb: "none",
+        port: appPort,
+        postInstallAction: "runApp",
+      });
+      name = result.projectName;
+      appProcess = result.appProcess;
+    });
+
+    test.afterAll(async () => {
+      appProcess.kill();
+    });
+
+    test("App folder should exist", async () => {
+      const dirExists = fs.existsSync(path.join(cwd, name));
+      expect(dirExists).toBeTruthy();
+    });
+    test("Frontend should have a title", async ({ page }) => {
+      await page.goto(`http://localhost:${appPort}`);
+      await expect(page.getByText("Built by LlamaIndex")).toBeVisible({
+        timeout: 2000 * 60,
+      });
+    });
+  });
+}

e2e/shared/multiagent_template.spec.ts

@@ -1,3 +1,4 @@
+/* eslint-disable turbo/no-undeclared-env-vars */
 import { expect, test } from "@playwright/test";
 import { ChildProcess } from "child_process";
 import fs from "fs";
@@ -15,17 +16,15 @@ const templateFramework: TemplateFramework = process.env.FRAMEWORK
 const dataSource: string = "--example-file";
 const templateUI: TemplateUI = "shadcn";
 const templatePostInstallAction: TemplatePostInstallAction = "runApp";
-const appType: AppType = "--frontend";
+const appType: AppType = templateFramework === "fastapi" ? "--frontend" : "";
 const userMessage = "Write a blog post about physical standards for letters";
-const templateUseCases = ["financial_report", "agentic_rag", "deep_research"];
+const templateAgents = ["financial_report", "blog", "form_filling"];
 
-for (const useCase of templateUseCases) {
-  test.describe(`Test use case ${useCase} ${templateFramework} ${dataSource} ${templateUI} ${appType} ${templatePostInstallAction}`, async () => {
+for (const agents of templateAgents) {
+  test.describe(`Test multiagent template ${agents} ${templateFramework} ${dataSource} ${templateUI} ${appType} ${templatePostInstallAction}`, async () => {
     test.skip(
-      process.platform !== "linux" ||
-        process.env.DATASOURCE === "--no-files" ||
-        templateFramework === "express",
-      "The llamaindexserver template currently only works with nextjs, fastapi. We also only run on Linux to speed up tests.",
+      process.platform !== "linux" || process.env.DATASOURCE === "--no-files",
+      "The multiagent template currently only works with files. We also only run on Linux to speed up tests.",
     );
     let port: number;
     let cwd: string;
@@ -39,7 +38,7 @@ for (const useCase of templateUseCases) {
       cwd = await createTestDir();
       const result = await runCreateLlama({
         cwd,
-        templateType: "llamaindexserver",
+        templateType: "multiagent",
         templateFramework,
         dataSource,
         vectorDb,
@@ -47,7 +46,7 @@ for (const useCase of templateUseCases) {
         postInstallAction: templatePostInstallAction,
         templateUI,
         appType,
-        useCase,
+        agents,
       });
       name = result.projectName;
       appProcess = result.appProcess;
@@ -64,9 +63,7 @@ for (const useCase of templateUseCases) {
           templateFramework === "express",
       );
       await page.goto(`http://localhost:${port}`);
-      await expect(page.getByText("Built by LlamaIndex")).toBeVisible({
-        timeout: 5 * 60 * 1000,
-      });
+      await expect(page.getByText("Built by LlamaIndex")).toBeVisible();
     });
 
     test("Frontend should be able to submit a message and receive the start of a streamed response", async ({
@@ -74,10 +71,10 @@ for (const useCase of templateUseCases) {
     }) => {
       test.skip(
         templatePostInstallAction !== "runApp" ||
-          useCase === "financial_report" ||
-          useCase === "deep_research" ||
+          agents === "financial_report" ||
+          agents === "form_filling" ||
           templateFramework === "express",
-        "Skip chat tests for financial report and deep research.",
+        "Skip chat tests for financial report and form filling.",
       );
       await page.goto(`http://localhost:${port}`);
       await page.fill("form textarea", userMessage);
@@ -89,12 +86,6 @@ for (const useCase of templateUseCases) {
       await page.click("form button[type=submit]");
 
       const response = await responsePromise;
-      console.log(`Response status: ${response.status()}`);
-      const responseBody = await response
-        .text()
-        .catch((e) => `Error reading body: ${e}`);
-      console.log(`Response body: ${responseBody}`);
-
       expect(response.ok()).toBeTruthy();
     });
 

e2e/shared/streaming_template.spec.ts

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

e2e/typescript/resolve_dependencies.spec.ts

@@ -74,7 +74,7 @@ test.describe("Test resolve TS dependencies", () => {
     // Install dependencies using pnpm
     try {
       const { stderr: installStderr } = await execAsync(
-        "pnpm install --prefer-offline --ignore-workspace",
+        "pnpm install --prefer-offline",
         {
           cwd: appDir,
         },

e2e/utils.ts

@@ -33,7 +33,7 @@ export type RunCreateLlamaOptions = {
   tools?: string;
   useLlamaParse?: boolean;
   observability?: string;
-  useCase?: string;
+  agents?: string;
 };
 
 export async function runCreateLlama({
@@ -51,7 +51,7 @@ export async function runCreateLlama({
   tools,
   useLlamaParse,
   observability,
-  useCase,
+  agents,
 }: RunCreateLlamaOptions): Promise<CreateLlamaResult> {
   if (!process.env.OPENAI_API_KEY || !process.env.LLAMA_CLOUD_API_KEY) {
     throw new Error(
@@ -67,8 +67,8 @@ export async function runCreateLlama({
   ].join("-");
 
   // Handle different data source types
-  const dataSourceArgs = [];
-  if (dataSource.includes("--web-source")) {
+  let dataSourceArgs = [];
+  if (dataSource.includes("--web-source" || "--db-source")) {
     const webSource = dataSource.split(" ")[1];
     dataSourceArgs.push("--web-source", webSource);
   } else if (dataSource.includes("--db-source")) {
@@ -88,7 +88,7 @@ export async function runCreateLlama({
     ...dataSourceArgs,
     "--vector-db",
     vectorDb,
-    "--use-npm",
+    "--use-pnpm",
     "--port",
     port,
     "--post-install-action",
@@ -113,13 +113,8 @@ export async function runCreateLlama({
   if (observability) {
     commandArgs.push("--observability", observability);
   }
-  if (
-    (templateType === "multiagent" ||
-      templateType === "reflex" ||
-      templateType === "llamaindexserver") &&
-    useCase
-  ) {
-    commandArgs.push("--use-case", useCase);
+  if (templateType === "multiagent" && agents) {
+    commandArgs.push("--agents", agents);
   }
 
   const command = commandArgs.join(" ");

eslint.config.mjs

@@ -1,62 +0,0 @@
-import eslint from "@eslint/js";
-import eslintConfigPrettier from "eslint-config-prettier";
-import globals from "globals";
-import tseslint from "typescript-eslint";
-
-export default tseslint.config(
-  eslint.configs.recommended,
-  ...tseslint.configs.recommended,
-  eslintConfigPrettier,
-  {
-    languageOptions: {
-      ecmaVersion: 2022,
-      sourceType: "module",
-      globals: {
-        ...globals.browser,
-        ...globals.node,
-      },
-    },
-  },
-  {
-    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",
-      "@typescript-eslint/no-explicit-any": [
-        "error",
-        {
-          ignoreRestArgs: true,
-        },
-      ],
-    },
-  },
-  {
-    ignores: [
-      "python/**",
-      "**/*.mypy_cache/**",
-      "**/*.venv/**",
-      "**/*.ruff_cache/**",
-      "**/dist/**",
-      "**/e2e/cache/**",
-      "**/lib/*",
-      "**/.next/**",
-      "**/out/**",
-      "**/node_modules/**",
-      "**/build/**",
-    ],
-  },
-);

helpers/copy.ts

@@ -1,3 +1,4 @@
+/* eslint-disable import/no-extraneous-dependencies */
 import { async as glob } from "fast-glob";
 import fs from "fs";
 import path from "path";
@@ -60,9 +61,6 @@ export const assetRelocator = (name: string) => {
     case "README-template.md": {
       return "README.md";
     }
-    case "vscode_settings.json": {
-      return "settings.json";
-    }
     default: {
       return name;
     }

helpers/datasources.ts

@@ -18,7 +18,6 @@ export const EXAMPLE_10K_SEC_FILES: TemplateDataSource[] = [
       url: new URL(
         "https://s2.q4cdn.com/470004039/files/doc_earnings/2023/q4/filing/_10-K-Q4-2023-As-Filed.pdf",
       ),
-      filename: "apple_10k_report.pdf",
     },
   },
   {
@@ -27,31 +26,10 @@ export const EXAMPLE_10K_SEC_FILES: TemplateDataSource[] = [
       url: new URL(
         "https://ir.tesla.com/_flysystem/s3/sec/000162828024002390/tsla-20231231-gen.pdf",
       ),
-      filename: "tesla_10k_report.pdf",
     },
   },
 ];
 
-export const EXAMPLE_GDPR: TemplateDataSource = {
-  type: "file",
-  config: {
-    url: new URL(
-      "https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32016R0679",
-    ),
-    filename: "gdpr.pdf",
-  },
-};
-
-export const AI_REPORTS: TemplateDataSource = {
-  type: "file",
-  config: {
-    url: new URL(
-      "https://www.europarl.europa.eu/RegData/etudes/ATAG/2024/760392/EPRS_ATA(2024)760392_EN.pdf",
-    ),
-    filename: "EPRS_ATA_2024_760392_EN.pdf",
-  },
-};
-
 export function getDataSources(
   files?: string,
   exampleFile?: boolean,

helpers/devcontainer.ts

@@ -1,6 +1,5 @@
 import fs from "fs";
 import path from "path";
-import { assetRelocator, copy } from "./copy";
 import { TemplateFramework } from "./types";
 
 function renderDevcontainerContent(
@@ -30,6 +29,7 @@ export const writeDevcontainer = async (
   root: string,
   templatesDir: string,
   framework: TemplateFramework,
+  frontend: boolean,
 ) => {
   const devcontainerDir = path.join(root, ".devcontainer");
   if (fs.existsSync(devcontainerDir)) {
@@ -46,25 +46,3 @@ export const writeDevcontainer = async (
     devcontainerContent,
   );
 };
-
-export const copyVSCodeSettings = async (
-  root: string,
-  templatesDir: string,
-) => {
-  const vscodeDir = path.join(root, ".vscode");
-  await copy("vscode_settings.json", vscodeDir, {
-    cwd: templatesDir,
-    rename: assetRelocator,
-  });
-};
-
-export const configVSCode = async (
-  root: string,
-  templatesDir: string,
-  framework: TemplateFramework,
-) => {
-  await writeDevcontainer(root, templatesDir, framework);
-  if (framework === "fastapi") {
-    await copyVSCodeSettings(root, templatesDir);
-  }
-};

helpers/env-variables.ts

@@ -13,12 +13,6 @@ import {
 
 import { TSYSTEMS_LLMHUB_API_URL } from "./providers/llmhub";
 
-const DEFAULT_SYSTEM_PROMPT =
-  "You are a helpful assistant who helps users with their questions.";
-
-const DATA_SOURCES_PROMPT =
-  "You have access to a knowledge base including the facts that you should start with to find the answer for the user question. Use the query engine tool to retrieve the facts from the knowledge base.";
-
 export type EnvVar = {
   name?: string;
   description?: string;
@@ -44,7 +38,6 @@ const renderEnvVar = (envVars: EnvVar[]): string => {
 const getVectorDBEnvs = (
   vectorDb?: TemplateVectorDB,
   framework?: TemplateFramework,
-  template?: TemplateType,
 ): EnvVar[] => {
   if (!vectorDb || !framework) {
     return [];
@@ -169,7 +162,7 @@ const getVectorDBEnvs = (
           description:
             "The organization ID for the LlamaCloud project (uses default organization if not specified)",
         },
-        ...(framework === "nextjs" && template !== "llamaindexserver"
+        ...(framework === "nextjs"
           ? // activate index selector per default (not needed for non-NextJS backends as it's handled by createFrontendEnvFile)
             [
               {
@@ -181,7 +174,7 @@ const getVectorDBEnvs = (
             ]
           : []),
       ];
-    case "chroma": {
+    case "chroma":
       const envs = [
         {
           name: "CHROMA_COLLECTION",
@@ -206,7 +199,6 @@ Otherwise, use CHROMA_HOST and CHROMA_PORT config above`,
         });
       }
       return envs;
-    }
     case "weaviate":
       return [
         {
@@ -225,15 +217,13 @@ Otherwise, use CHROMA_HOST and CHROMA_PORT config above`,
         },
       ];
     default:
-      return template !== "llamaindexserver"
-        ? [
-            {
-              name: "STORAGE_CACHE_DIR",
-              description: "The directory to store the local storage cache.",
-              value: ".cache",
-            },
-          ]
-        : [];
+      return [
+        {
+          name: "STORAGE_CACHE_DIR",
+          description: "The directory to store the local storage cache.",
+          value: ".cache",
+        },
+      ];
   }
 };
 
@@ -386,42 +376,38 @@ const getModelEnvs = (modelConfig: ModelConfig): EnvVar[] => {
 
 const getFrameworkEnvs = (
   framework: TemplateFramework,
-  template: TemplateType,
   port?: number,
 ): EnvVar[] => {
   const sPort = port?.toString() || "8000";
-  const result: EnvVar[] =
-    template !== "llamaindexserver"
-      ? [
-          {
-            name: "FILESERVER_URL_PREFIX",
-            description:
-              "FILESERVER_URL_PREFIX is the URL prefix of the server storing the images generated by the interpreter.",
-            value:
-              framework === "nextjs"
-                ? // FIXME: if we are using nextjs, port should be 3000
-                  "http://localhost:3000/api/files"
-                : `http://localhost:${sPort}/api/files`,
-          },
-        ]
-      : [];
+  const result: EnvVar[] = [
+    {
+      name: "FILESERVER_URL_PREFIX",
+      description:
+        "FILESERVER_URL_PREFIX is the URL prefix of the server storing the images generated by the interpreter.",
+      value:
+        framework === "nextjs"
+          ? // FIXME: if we are using nextjs, port should be 3000
+            "http://localhost:3000/api/files"
+          : `http://localhost:${sPort}/api/files`,
+    },
+  ];
   if (framework === "fastapi") {
     result.push(
       ...[
         {
           name: "APP_HOST",
-          description: "The address to start the FastAPI app.",
+          description: "The address to start the backend app.",
           value: "0.0.0.0",
         },
         {
           name: "APP_PORT",
-          description: "The port to start the FastAPI app.",
+          description: "The port to start the backend app.",
           value: sPort,
         },
       ],
     );
   }
-  if (framework === "nextjs" && template !== "llamaindexserver") {
+  if (framework === "nextjs") {
     result.push({
       name: "NEXT_PUBLIC_CHAT_API",
       description:
@@ -463,6 +449,9 @@ const getSystemPromptEnv = (
   dataSources?: TemplateDataSource[],
   template?: TemplateType,
 ): EnvVar[] => {
+  const defaultSystemPrompt =
+    "You are a helpful assistant who helps users with their questions.";
+
   const systemPromptEnv: EnvVar[] = [];
   // build tool system prompt by merging all tool system prompts
   // multiagent template doesn't need system prompt
@@ -477,12 +466,9 @@ const getSystemPromptEnv = (
       }
     });
 
-    const systemPrompt =
-      '"' +
-      DEFAULT_SYSTEM_PROMPT +
-      (dataSources?.length ? `\n${DATA_SOURCES_PROMPT}` : "") +
-      (toolSystemPrompt ? `\n${toolSystemPrompt}` : "") +
-      '"';
+    const systemPrompt = toolSystemPrompt
+      ? `\"${toolSystemPrompt}\"`
+      : defaultSystemPrompt;
 
     systemPromptEnv.push({
       name: "SYSTEM_PROMPT",
@@ -533,7 +519,7 @@ Here is the conversation history
 ---------------------
 {conversation}
 ---------------------
-Given the conversation history, please give me 3 questions that user might ask next!
+Given the conversation history, please give me 3 questions that you might ask next!
 Your answer should be wrapped in three sticks which follows the following format:
 \`\`\`
 <question 1>
@@ -577,41 +563,25 @@ export const createBackendEnvFile = async (
     | "port"
     | "tools"
     | "observability"
-    | "useLlamaParse"
   >,
 ) => {
   // Init env values
   const envFileName = ".env";
   const envVars: EnvVar[] = [
-    ...(opts.useLlamaParse
-      ? [
-          {
-            name: "LLAMA_CLOUD_API_KEY",
-            description: `The Llama Cloud API key.`,
-            value: opts.llamaCloudKey,
-          },
-        ]
-      : []),
-    ...getVectorDBEnvs(opts.vectorDb, opts.framework, opts.template),
-    ...getToolEnvs(opts.tools),
-    ...getFrameworkEnvs(opts.framework, opts.template, opts.port),
+    {
+      name: "LLAMA_CLOUD_API_KEY",
+      description: `The Llama Cloud API key.`,
+      value: opts.llamaCloudKey,
+    },
     // Add environment variables of each component
-    ...(opts.template === "llamaindexserver"
-      ? [
-          {
-            name: "OPENAI_API_KEY",
-            description: "The OpenAI API key to use.",
-            value: opts.modelConfig.apiKey,
-          },
-        ]
-      : [
-          // don't use this stuff for llama-indexserver
-          ...getModelEnvs(opts.modelConfig),
-          ...getEngineEnvs(),
-          ...getTemplateEnvs(opts.template),
-          ...getObservabilityEnvs(opts.observability),
-          ...getSystemPromptEnv(opts.tools, opts.dataSources, opts.template),
-        ]),
+    ...getModelEnvs(opts.modelConfig),
+    ...getEngineEnvs(),
+    ...getVectorDBEnvs(opts.vectorDb, opts.framework),
+    ...getFrameworkEnvs(opts.framework, opts.port),
+    ...getToolEnvs(opts.tools),
+    ...getTemplateEnvs(opts.template),
+    ...getObservabilityEnvs(opts.observability),
+    ...getSystemPromptEnv(opts.tools, opts.dataSources, opts.template),
   ];
   // Render and write env file
   const content = renderEnvVar(envVars);

helpers/git.ts

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

helpers/index.ts

@@ -1,7 +1,7 @@
 import { callPackageManager } from "./install";
 
 import path from "path";
-import picocolors, { cyan } from "picocolors";
+import { cyan } from "picocolors";
 
 import fsExtra from "fs-extra";
 import { writeLoadersConfig } from "./datasources";
@@ -9,6 +9,7 @@ import { createBackendEnvFile, createFrontendEnvFile } from "./env-variables";
 import { PackageManager } from "./get-pkg-manager";
 import { installLlamapackProject } from "./llama-pack";
 import { makeDir } from "./make-dir";
+import { isHavingPoetryLockFile, tryPoetryRun } from "./poetry";
 import { installPythonTemplate } from "./python";
 import { downloadAndExtractRepo } from "./repo";
 import { ConfigFileType, writeToolsConfig } from "./tools";
@@ -21,7 +22,6 @@ import {
   TemplateVectorDB,
 } from "./types";
 import { installTSTemplate } from "./typescript";
-import { isHavingUvLockFile, tryUvRun } from "./uv";
 
 const checkForGenerateScript = (
   modelConfig: ModelConfig,
@@ -41,11 +41,7 @@ const checkForGenerateScript = (
     missingSettings.push("your LLAMA_CLOUD_API_KEY");
   }
 
-  if (
-    vectorDb !== undefined &&
-    vectorDb !== "none" &&
-    vectorDb !== "llamacloud"
-  ) {
+  if (vectorDb !== "none" && vectorDb !== "llamacloud") {
     missingSettings.push("your Vector DB environment variables");
   }
 
@@ -64,7 +60,7 @@ async function generateContextData(
   if (packageManager) {
     const runGenerate = `${cyan(
       framework === "fastapi"
-        ? "uv run generate"
+        ? "poetry run generate"
         : `${packageManager} run generate`,
     )}`;
 
@@ -78,21 +74,15 @@ async function generateContextData(
     if (!missingSettings.length) {
       // If all the required environment variables are set, run the generate script
       if (framework === "fastapi") {
-        if (isHavingUvLockFile()) {
+        if (isHavingPoetryLockFile()) {
           console.log(`Running ${runGenerate} to generate the context data.`);
-          const result = tryUvRun("generate");
+          const result = tryPoetryRun("poetry run generate");
           if (!result) {
             console.log(`Failed to run ${runGenerate}.`);
             process.exit(1);
           }
           console.log(`Generated context data`);
           return;
-        } else {
-          console.log(
-            picocolors.yellow(
-              `\nWarning: uv.lock not found. Dependency installation might be incomplete. Skipping context generation.\nIf dependencies were installed, try running '${runGenerate}' manually.\n`,
-            ),
-          );
         }
       } else {
         console.log(`Running ${runGenerate} to generate the context data.`);
@@ -102,14 +92,14 @@ async function generateContextData(
     }
 
     const settingsMessage = `After setting ${missingSettings.join(" and ")}, run ${runGenerate} to generate the context data.`;
-    console.log(picocolors.yellow(`\n${settingsMessage}\n\n`));
+    console.log(`\n${settingsMessage}\n\n`);
   }
 }
 
 const downloadFile = async (url: string, destPath: string) => {
   const response = await fetch(url);
   const fileBuffer = await response.arrayBuffer();
-  await fsExtra.writeFile(destPath, new Uint8Array(fileBuffer));
+  await fsExtra.writeFile(destPath, Buffer.from(fileBuffer));
 };
 
 const prepareContextData = async (
@@ -128,8 +118,7 @@ const prepareContextData = async (
       const destPath = path.join(
         root,
         "data",
-        dataSourceConfig.filename ??
-          path.basename(dataSourceConfig.url.toString()),
+        path.basename(dataSourceConfig.url.toString()),
       );
       await downloadFile(dataSourceConfig.url.toString(), destPath);
     } else {
@@ -176,17 +165,6 @@ export const installTemplate = async (
 
   if (props.framework === "fastapi") {
     await installPythonTemplate(props);
-  } else {
-    await installTSTemplate(props);
-  }
-
-  // write configurations
-  if (props.template !== "llamaindexserver") {
-    await writeToolsConfig(
-      props.root,
-      props.tools,
-      props.framework === "fastapi" ? ConfigFileType.YAML : ConfigFileType.JSON,
-    );
     if (props.vectorDb !== "llamacloud") {
       // write loaders configuration (currently Python only)
       // not needed for LlamaCloud as it has its own loaders
@@ -196,13 +174,26 @@ export const installTemplate = async (
         props.useLlamaParse,
       );
     }
+  } else {
+    await installTSTemplate(props);
   }
 
+  // write tools configuration
+  await writeToolsConfig(
+    props.root,
+    props.tools,
+    props.framework === "fastapi" ? ConfigFileType.YAML : ConfigFileType.JSON,
+  );
+
   if (props.backend) {
     // This is a backend, so we need to copy the test data and create the env file.
 
     // Copy the environment file to the target directory.
-    if (props.template !== "community" && props.template !== "llamapack") {
+    if (
+      props.template === "streaming" ||
+      props.template === "multiagent" ||
+      props.template === "extractor"
+    ) {
       await createBackendEnvFile(props.root, props);
     }
 

helpers/install.ts

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

helpers/is-folder-empty.ts

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

helpers/llama-pack.ts

@@ -143,6 +143,6 @@ export const installLlamapackProject = async ({
   await copyData({ root });
   await installLlamapackExample({ root, llamapack });
   if (postInstallAction === "runApp" || postInstallAction === "dependencies") {
-    installPythonDependencies();
+    installPythonDependencies({ noRoot: true });
   }
 };

helpers/poetry.ts

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

helpers/providers/index.ts

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

helpers/python.ts

@@ -3,16 +3,15 @@ import path from "path";
 import { cyan, red } from "picocolors";
 import { parse, stringify } from "smol-toml";
 import terminalLink from "terminal-link";
-import { isUvAvailable, tryUvSync } from "./uv";
 
 import { assetRelocator, copy } from "./copy";
 import { templatesDir } from "./dir";
+import { isPoetryAvailable, tryPoetryInstall } from "./poetry";
 import { Tool } from "./tools";
 import {
   InstallTemplateArgs,
   ModelConfig,
   TemplateDataSource,
-  TemplateObservability,
   TemplateType,
   TemplateVectorDB,
 } from "./types";
@@ -30,8 +29,6 @@ const getAdditionalDependencies = (
   dataSources?: TemplateDataSource[],
   tools?: Tool[],
   templateType?: TemplateType,
-  observability?: TemplateObservability,
-  // eslint-disable-next-line max-params
 ) => {
   const dependencies: Dependency[] = [];
 
@@ -40,21 +37,21 @@ const getAdditionalDependencies = (
     case "mongo": {
       dependencies.push({
         name: "llama-index-vector-stores-mongodb",
-        version: ">=0.3.2,<0.4.0",
+        version: "^0.3.1",
       });
       break;
     }
     case "pg": {
       dependencies.push({
         name: "llama-index-vector-stores-postgres",
-        version: ">=0.3.2,<0.4.0",
+        version: "^0.2.5",
       });
       break;
     }
     case "pinecone": {
       dependencies.push({
         name: "llama-index-vector-stores-pinecone",
-        version: ">=0.4.1,<0.5.0",
+        version: "^0.2.1",
         constraints: {
           python: ">=3.11,<3.13",
         },
@@ -64,25 +61,25 @@ const getAdditionalDependencies = (
     case "milvus": {
       dependencies.push({
         name: "llama-index-vector-stores-milvus",
-        version: ">=0.3.0,<0.4.0",
+        version: "^0.2.0",
       });
       dependencies.push({
         name: "pymilvus",
-        version: ">=2.4.4,<3.0.0",
+        version: "2.4.4",
       });
       break;
     }
     case "astra": {
       dependencies.push({
         name: "llama-index-vector-stores-astra-db",
-        version: ">=0.4.0,<0.5.0",
+        version: "^0.2.0",
       });
       break;
     }
     case "qdrant": {
       dependencies.push({
         name: "llama-index-vector-stores-qdrant",
-        version: ">=0.4.0,<0.5.0",
+        version: "^0.3.0",
         constraints: {
           python: ">=3.11,<3.13",
         },
@@ -92,21 +89,21 @@ const getAdditionalDependencies = (
     case "chroma": {
       dependencies.push({
         name: "llama-index-vector-stores-chroma",
-        version: ">=0.4.0,<0.5.0",
+        version: "^0.2.0",
       });
       break;
     }
     case "weaviate": {
       dependencies.push({
         name: "llama-index-vector-stores-weaviate",
-        version: ">=1.2.3,<2.0.0",
+        version: "^1.1.1",
       });
       break;
     }
     case "llamacloud":
       dependencies.push({
         name: "llama-index-indices-managed-llama-cloud",
-        version: ">=0.6.3,<0.7.0",
+        version: "^0.3.1",
       });
       break;
   }
@@ -119,28 +116,28 @@ const getAdditionalDependencies = (
         case "file":
           dependencies.push({
             name: "docx2txt",
-            version: ">=0.8,<0.9",
+            version: "^0.8",
           });
           break;
         case "web":
           dependencies.push({
             name: "llama-index-readers-web",
-            version: ">=0.3.0,<0.4.0",
+            version: "^0.2.2",
           });
           break;
         case "db":
           dependencies.push({
             name: "llama-index-readers-database",
-            version: ">=0.3.0,<0.4.0",
+            version: "^0.2.0",
           });
           dependencies.push({
             name: "pymysql",
-            version: ">=1.1.0,<2.0.0",
+            version: "^1.1.0",
             extras: ["rsa"],
           });
           dependencies.push({
             name: "psycopg2-binary",
-            version: ">=2.9.9,<3.0.0",
+            version: "^2.9.9",
           });
           break;
       }
@@ -159,122 +156,155 @@ const getAdditionalDependencies = (
     case "ollama":
       dependencies.push({
         name: "llama-index-llms-ollama",
-        version: ">=0.5.0,<0.6.0",
+        version: "0.3.0",
       });
       dependencies.push({
         name: "llama-index-embeddings-ollama",
-        version: ">=0.6.0,<0.7.0",
+        version: "0.3.0",
       });
       break;
     case "openai":
       if (templateType !== "multiagent") {
         dependencies.push({
           name: "llama-index-llms-openai",
-          version: ">=0.3.2,<0.4.0",
+          version: "^0.2.0",
         });
         dependencies.push({
           name: "llama-index-embeddings-openai",
-          version: ">=0.3.1,<0.4.0",
+          version: "^0.2.3",
         });
         dependencies.push({
           name: "llama-index-agent-openai",
-          version: ">=0.4.0,<0.5.0",
+          version: "^0.3.0",
         });
       }
       break;
     case "groq":
+      // Fastembed==0.2.0 does not support python3.13 at the moment
+      // Fixed the python version less than 3.13
+      dependencies.push({
+        name: "python",
+        version: "^3.11,<3.13",
+      });
       dependencies.push({
         name: "llama-index-llms-groq",
-        version: ">=0.3.0,<0.4.0",
+        version: "0.2.0",
       });
       dependencies.push({
         name: "llama-index-embeddings-fastembed",
-        version: ">=0.3.0,<0.4.0",
+        version: "^0.2.0",
       });
       break;
     case "anthropic":
+      // Fastembed==0.2.0 does not support python3.13 at the moment
+      // Fixed the python version less than 3.13
+      dependencies.push({
+        name: "python",
+        version: "^3.11,<3.13",
+      });
       dependencies.push({
         name: "llama-index-llms-anthropic",
-        version: ">=0.6.0,<0.7.0",
+        version: "0.3.0",
       });
       dependencies.push({
         name: "llama-index-embeddings-fastembed",
-        version: ">=0.3.0,<0.4.0",
+        version: "^0.2.0",
       });
       break;
     case "gemini":
       dependencies.push({
         name: "llama-index-llms-gemini",
-        version: ">=0.4.0,<0.5.0",
+        version: "0.3.4",
       });
       dependencies.push({
         name: "llama-index-embeddings-gemini",
-        version: ">=0.3.0,<0.4.0",
+        version: "^0.2.0",
       });
       break;
     case "mistral":
       dependencies.push({
         name: "llama-index-llms-mistralai",
-        version: ">=0.4.0,<0.5.0",
+        version: "0.2.1",
       });
       dependencies.push({
         name: "llama-index-embeddings-mistralai",
-        version: ">=0.3.0,<0.4.0",
+        version: "0.2.0",
       });
       break;
     case "azure-openai":
       dependencies.push({
         name: "llama-index-llms-azure-openai",
-        version: ">=0.3.0,<0.4.0",
+        version: "0.2.0",
       });
       dependencies.push({
         name: "llama-index-embeddings-azure-openai",
-        version: ">=0.3.0,<0.4.0",
+        version: "0.2.4",
       });
       break;
     case "huggingface":
       dependencies.push({
         name: "llama-index-llms-huggingface",
-        version: ">=0.5.0,<0.6.0",
+        version: "^0.3.5",
       });
       dependencies.push({
         name: "llama-index-embeddings-huggingface",
-        version: ">=0.5.0,<0.6.0",
+        version: "^0.3.1",
       });
       dependencies.push({
         name: "optimum",
-        version: ">=1.23.3,<2.0.0",
+        version: "^1.23.3",
         extras: ["onnxruntime"],
       });
       break;
     case "t-systems":
       dependencies.push({
         name: "llama-index-agent-openai",
-        version: ">=0.4.0,<0.5.0",
+        version: "0.3.0",
       });
       dependencies.push({
         name: "llama-index-llms-openai-like",
-        version: ">=0.3.0,<0.4.0",
+        version: "0.2.0",
       });
       break;
   }
 
-  if (observability && observability !== "none") {
-    if (observability === "traceloop") {
-      dependencies.push({
-        name: "traceloop-sdk",
-        version: ">=0.15.11,<0.16.0",
-      });
+  return dependencies;
+};
+
+const mergePoetryDependencies = (
+  dependencies: Dependency[],
+  existingDependencies: Record<string, Omit<Dependency, "name"> | string>,
+) => {
+  for (const dependency of dependencies) {
+    let value = existingDependencies[dependency.name] ?? {};
+
+    // default string value is equal to attribute "version"
+    if (typeof value === "string") {
+      value = { version: value };
     }
-    if (observability === "llamatrace") {
-      dependencies.push({
-        name: "llama-index-callbacks-arize-phoenix",
-        version: ">=0.3.0,<0.4.0",
-      });
+
+    value.version = dependency.version ?? value.version;
+    value.extras = dependency.extras ?? value.extras;
+
+    // Merge constraints if they exist
+    if (dependency.constraints) {
+      value = { ...value, ...dependency.constraints };
+    }
+
+    if (value.version === undefined) {
+      throw new Error(
+        `Dependency "${dependency.name}" is missing attribute "version"!`,
+      );
+    }
+
+    // Serialize as object if there are any additional properties
+    if (Object.keys(value).length > 1) {
+      existingDependencies[dependency.name] = value;
+    } else {
+      // Otherwise, serialize just the version string
+      existingDependencies[dependency.name] = value.version;
     }
   }
-
-  return dependencies;
 };
 
 const copyRouterCode = async (root: string, tools: Tool[]) => {
@@ -299,100 +329,19 @@ export const addDependencies = async (
     // Parse toml file
     const file = path.join(projectDir, FILENAME);
     const fileContent = await fs.readFile(file, "utf8");
-    let fileParsed: any;
-    try {
-      fileParsed = parse(fileContent);
-    } catch (parseError) {
-      console.error(`Error parsing ${FILENAME}:`, parseError);
-      throw new Error(
-        `Failed to parse ${FILENAME}. Please ensure it's valid TOML.`,
-      );
-    }
+    const fileParsed = parse(fileContent);
 
-    // Ensure [project] and [project.dependencies] exist
-    if (!fileParsed.project) {
-      fileParsed.project = {};
-    }
-    if (
-      !fileParsed.project.dependencies ||
-      !Array.isArray(fileParsed.project.dependencies)
-    ) {
-      // If dependencies exist but aren't an array, log a warning or error.
-      // For now, we'll overwrite it, assuming the intent is to use the standard array format.
-      console.warn(
-        `[project.dependencies] in ${FILENAME} is not an array. It will be overwritten.`,
-      );
-      fileParsed.project.dependencies = [];
-    }
-
-    const existingDependencies: string[] = fileParsed.project.dependencies;
-    const addedDeps: string[] = [];
-    const updatedDeps: string[] = [];
-
-    // Add or update dependencies
-    for (const newDep of dependencies) {
-      let depString = newDep.name;
-      if (newDep.extras && newDep.extras.length > 0) {
-        depString += `[${newDep.extras.join(",")}]`;
-      }
-      if (newDep.version) {
-        depString += newDep.version;
-      }
-
-      let found = false;
-      for (let i = 0; i < existingDependencies.length; i++) {
-        const existingDepNameMatch =
-          existingDependencies[i].match(/^([a-zA-Z0-9._-]+)/);
-        if (
-          existingDepNameMatch &&
-          existingDepNameMatch[1].toLowerCase() === depString.toLowerCase()
-        ) {
-          // Found existing dependency, update it
-          if (existingDependencies[i] !== depString) {
-            updatedDeps.push(`${existingDependencies[i]} -> ${depString}`);
-            existingDependencies[i] = depString;
-          }
-          found = true;
-          break;
-        }
-      }
-
-      if (!found) {
-        // Add new dependency
-        existingDependencies.push(depString);
-        addedDeps.push(depString);
-      }
-      // Handle python version constraints separately (if any)
-      if (newDep.constraints?.python) {
-        if (
-          !fileParsed.project["requires-python"] ||
-          fileParsed.project["requires-python"] !== newDep.constraints.python
-        ) {
-          // This simple overwrite might not be ideal; merging constraints is complex.
-          // For now, let's just set it if the new dependency has one.
-          console.log(
-            `Setting requires-python = "${newDep.constraints.python}" from dependency ${newDep.name}`,
-          );
-          fileParsed.project["requires-python"] = newDep.constraints.python;
-        }
-      }
-    }
+    // Modify toml dependencies
+    const tool = fileParsed.tool as any;
+    const existingDependencies = tool.poetry.dependencies;
+    mergePoetryDependencies(dependencies, existingDependencies);
 
     // Write toml file
     const newFileContent = stringify(fileParsed);
     await fs.writeFile(file, newFileContent);
 
-    if (addedDeps.length > 0) {
-      console.log(`\nAdded dependencies to ${cyan(FILENAME)}:`);
-      addedDeps.forEach((dep) => console.log(`  ${dep}`));
-    }
-    if (updatedDeps.length > 0) {
-      console.log(`\nUpdated dependencies in ${cyan(FILENAME)}:`);
-      updatedDeps.forEach((dep) => console.log(`  ${dep}`));
-    }
-    if (addedDeps.length > 0 || updatedDeps.length > 0) {
-      console.log(""); // Newline for spacing
-    }
+    const dependenciesString = dependencies.map((d) => d.name).join(", ");
+    console.log(`\nAdded ${dependenciesString} to ${cyan(FILENAME)}\n`);
   } catch (error) {
     console.log(
       `Error while updating dependencies for Poetry project file ${FILENAME}\n`,
@@ -401,16 +350,18 @@ export const addDependencies = async (
   }
 };
 
-export const installPythonDependencies = () => {
-  if (isUvAvailable()) {
+export const installPythonDependencies = (
+  { noRoot }: { noRoot: boolean } = { noRoot: false },
+) => {
+  if (isPoetryAvailable()) {
     console.log(
-      `Installing Python dependencies using uv. This may take a while...`,
+      `Installing python dependencies using poetry. This may take a while...`,
     );
-    const installSuccessful = tryUvSync();
+    const installSuccessful = tryPoetryInstall(noRoot);
     if (!installSuccessful) {
       console.error(
         red(
-          "Installing dependencies using uv failed. Please check the error log above and ensure uv is installed correctly.",
+          "Installing dependencies using poetry failed. Please check error log above and try running create-llama again.",
         ),
       );
       process.exit(1);
@@ -418,34 +369,53 @@ export const installPythonDependencies = () => {
   } else {
     console.error(
       red(
-        `uv is not available in the current environment. Please check ${terminalLink(
-          "uv Installation",
-          `https://github.com/astral-sh/uv#installation`,
-        )} to install uv first, then run create-llama again.`,
+        `Poetry is not available in the current environment. Please check ${terminalLink(
+          "Poetry Installation",
+          `https://python-poetry.org/docs/#installation`,
+        )} to install poetry first, then run create-llama again.`,
       ),
     );
     process.exit(1);
   }
 };
 
-const installLegacyPythonTemplate = async ({
+export const installPythonTemplate = async ({
   root,
   template,
+  framework,
   vectorDb,
   dataSources,
   tools,
-  useCase,
+  postInstallAction,
   observability,
+  modelConfig,
+  agents,
 }: Pick<
   InstallTemplateArgs,
   | "root"
+  | "framework"
   | "template"
   | "vectorDb"
   | "dataSources"
   | "tools"
-  | "useCase"
+  | "postInstallAction"
   | "observability"
+  | "modelConfig"
+  | "agents"
 >) => {
+  console.log("\nInitializing Python project with template:", template, "\n");
+  let templatePath;
+  if (template === "extractor") {
+    templatePath = path.join(templatesDir, "types", "extractor", framework);
+  } else {
+    templatePath = path.join(templatesDir, "types", "streaming", framework);
+  }
+  await copy("**", root, {
+    parents: true,
+    cwd: templatePath,
+    rename: assetRelocator,
+  });
+
   const compPath = path.join(templatesDir, "components");
   const enginePath = path.join(root, "app", "engine");
 
@@ -502,40 +472,65 @@ const installLegacyPythonTemplate = async ({
       cwd: path.join(compPath, "engines", "python", engine),
     });
 
+    // Copy agent code
+    if (template === "multiagent") {
+      if (agents) {
+        await copy("**", path.join(root), {
+          parents: true,
+          cwd: path.join(compPath, "agents", "python", agents),
+          rename: assetRelocator,
+        });
+      } else {
+        console.log(
+          red(
+            "There is no agent selected for multi-agent template. Please pick an agent to use via --agents flag.",
+          ),
+        );
+        process.exit(1);
+      }
+    }
+
     // Copy router code
     await copyRouterCode(root, tools ?? []);
   }
 
-  // Copy multiagents overrides
   if (template === "multiagent") {
+    // Copy multi-agent code
     await copy("**", path.join(root), {
+      parents: true,
       cwd: path.join(compPath, "multiagent", "python"),
+      rename: assetRelocator,
     });
   }
 
-  if (template === "multiagent" || template === "reflex") {
-    if (useCase) {
-      const sourcePath =
-        template === "multiagent"
-          ? path.join(compPath, "agents", "python", useCase)
-          : path.join(compPath, "reflex", useCase);
+  console.log("Adding additional dependencies");
 
-      await copy("**", path.join(root), {
-        parents: true,
-        cwd: sourcePath,
-        rename: assetRelocator,
-      });
-    } else {
-      console.log(
-        red(
-          `There is no use case selected for ${template} template. Please pick a use case to use via --use-case flag.`,
-        ),
-      );
-      process.exit(1);
-    }
-  }
+  const addOnDependencies = getAdditionalDependencies(
+    modelConfig,
+    vectorDb,
+    dataSources,
+    tools,
+    template,
+  );
 
   if (observability && observability !== "none") {
+    if (observability === "traceloop") {
+      addOnDependencies.push({
+        name: "traceloop-sdk",
+        version: "^0.15.11",
+      });
+    }
+
+    if (observability === "llamatrace") {
+      addOnDependencies.push({
+        name: "llama-index-callbacks-arize-phoenix",
+        version: "^0.2.1",
+        constraints: {
+          python: ">=3.11,<3.13",
+        },
+      });
+    }
+
     const templateObservabilityPath = path.join(
       templatesDir,
       "components",
@@ -547,137 +542,15 @@ const installLegacyPythonTemplate = async ({
       cwd: templateObservabilityPath,
     });
   }
-};
-
-const installLlamaIndexServerTemplate = async ({
-  root,
-  useCase,
-  useLlamaParse,
-}: Pick<InstallTemplateArgs, "root" | "useCase" | "useLlamaParse">) => {
-  if (!useCase) {
-    console.log(
-      red(
-        `There is no use case selected. Please pick a use case to use via --use-case flag.`,
-      ),
-    );
-    process.exit(1);
-  }
-
-  await copy("*.py", path.join(root, "app"), {
-    parents: true,
-    cwd: path.join(templatesDir, "components", "workflows", "python", useCase),
-  });
-
-  // Copy custom UI component code
-  await copy(`*`, path.join(root, "components"), {
-    parents: true,
-    cwd: path.join(templatesDir, "components", "ui", "workflows", useCase),
-  });
-
-  if (useLlamaParse) {
-    await copy("index.py", path.join(root, "app"), {
-      parents: true,
-      cwd: path.join(
-        templatesDir,
-        "components",
-        "vectordbs",
-        "llamaindexserver",
-        "llamacloud",
-        "python",
-      ),
-    });
-    // TODO: Consider moving generate.py to app folder.
-    await copy("generate.py", path.join(root), {
-      parents: true,
-      cwd: path.join(
-        templatesDir,
-        "components",
-        "vectordbs",
-        "llamaindexserver",
-        "llamacloud",
-        "python",
-      ),
-    });
-  }
-  // Copy README.md
-  await copy("README-template.md", path.join(root), {
-    parents: true,
-    cwd: path.join(templatesDir, "components", "workflows", "python", useCase),
-    rename: assetRelocator,
-  });
-};
-
-export const installPythonTemplate = async ({
-  appName,
-  root,
-  template,
-  framework,
-  vectorDb,
-  postInstallAction,
-  modelConfig,
-  dataSources,
-  tools,
-  useLlamaParse,
-  useCase,
-  observability,
-}: Pick<
-  InstallTemplateArgs,
-  | "appName"
-  | "root"
-  | "template"
-  | "framework"
-  | "vectorDb"
-  | "postInstallAction"
-  | "modelConfig"
-  | "dataSources"
-  | "tools"
-  | "useLlamaParse"
-  | "useCase"
-  | "observability"
->) => {
-  console.log("\nInitializing Python project with template:", template, "\n");
-  let templatePath;
-  if (template === "reflex") {
-    templatePath = path.join(templatesDir, "types", "reflex");
-  } else {
-    templatePath = path.join(templatesDir, "types", template, framework);
-  }
-  await copy("**", root, {
-    parents: true,
-    cwd: templatePath,
-    rename: assetRelocator,
-  });
-
-  if (template === "llamaindexserver") {
-    await installLlamaIndexServerTemplate({
-      root,
-      useCase,
-      useLlamaParse,
-    });
-  } else {
-    await installLegacyPythonTemplate({
-      root,
-      template,
-      vectorDb,
-      dataSources,
-      tools,
-      useCase,
-      observability,
-    });
-  }
-
-  console.log("Adding additional dependencies");
-  const addOnDependencies = getAdditionalDependencies(
-    modelConfig,
-    vectorDb,
-    dataSources,
-    tools,
-    template,
-  );
 
   await addDependencies(root, addOnDependencies);
 
   if (postInstallAction === "runApp" || postInstallAction === "dependencies") {
     installPythonDependencies();
   }
+
+  // Copy deployment files for python
+  await copy("**", root, {
+    cwd: path.join(compPath, "deployments", "python"),
+  });
 };

helpers/run-app.ts

@@ -1,5 +1,5 @@
 import { SpawnOptions, spawn } from "child_process";
-import { TemplateFramework, TemplateType } from "./types";
+import { TemplateFramework } from "./types";
 
 const createProcess = (
   command: string,
@@ -34,24 +34,14 @@ export function runReflexApp(appPath: string, port: number) {
     "--frontend-port",
     port.toString(),
   ];
-  return createProcess("uv", commandArgs, {
+  return createProcess("poetry", commandArgs, {
     stdio: "inherit",
     cwd: appPath,
   });
 }
 
-export function runFastAPIApp(
-  appPath: string,
-  port: number,
-  template: TemplateType,
-) {
-  let commandArgs: string[];
-  if (template === "streaming") {
-    commandArgs = ["run", "dev"];
-  } else {
-    commandArgs = ["run", "fastapi", "dev", "--port", `${port}`];
-  }
-  return createProcess("uv", commandArgs, {
+export function runFastAPIApp(appPath: string, port: number) {
+  return createProcess("poetry", ["run", "dev"], {
     stdio: "inherit",
     cwd: appPath,
     env: { ...process.env, APP_PORT: `${port}` },
@@ -68,22 +58,22 @@ export function runTSApp(appPath: string, port: number) {
 
 export async function runApp(
   appPath: string,
-  template: TemplateType,
+  template: string,
   framework: TemplateFramework,
   port?: number,
 ): Promise<void> {
   try {
     // Start the app
     const defaultPort =
-      framework === "nextjs" || template === "reflex" ? 3000 : 8000;
+      framework === "nextjs" || template === "extractor" ? 3000 : 8000;
 
     const appRunner =
-      template === "reflex"
+      template === "extractor"
         ? runReflexApp
         : framework === "fastapi"
           ? runFastAPIApp
           : runTSApp;
-    await appRunner(appPath, port || defaultPort, template);
+    await appRunner(appPath, port || defaultPort);
   } catch (error) {
     console.error("Failed to run app:", error);
     throw error;

helpers/tools.ts

@@ -41,7 +41,7 @@ export const supportedTools: Tool[] = [
     dependencies: [
       {
         name: "llama-index-tools-google",
-        version: ">=0.3.0,<0.4.0",
+        version: "^0.2.0",
       },
     ],
     supportedFrameworks: ["fastapi"],
@@ -62,16 +62,17 @@ export const supportedTools: Tool[] = [
     dependencies: [
       {
         name: "duckduckgo-search",
-        version: ">=6.3.5,<7.0.0",
+        version: "^6.3.5",
       },
     ],
-    supportedFrameworks: ["fastapi"], // TODO: Re-enable this tool once the duck-duck-scrape TypeScript library works again
+    supportedFrameworks: ["fastapi", "nextjs", "express"],
     type: ToolType.LOCAL,
     envVars: [
       {
         name: TOOL_SYSTEM_PROMPT_ENV_VAR,
         description: "System prompt for DuckDuckGo search tool.",
-        value: `You have access to the duckduckgo search tool. Use it to get information from the web to answer user questions.
+        value: `You are a DuckDuckGo search agent. 
+You can use the duckduckgo search tool to get information from the web to answer user questions.
 For better results, you can specify the region parameter to get results from a specific region but it's optional.`,
       },
     ],
@@ -82,11 +83,18 @@ For better results, you can specify the region parameter to get results from a s
     dependencies: [
       {
         name: "llama-index-tools-wikipedia",
-        version: ">=0.3.0,<0.4.0",
+        version: "^0.2.0",
       },
     ],
     supportedFrameworks: ["fastapi", "express", "nextjs"],
     type: ToolType.LLAMAHUB,
+    envVars: [
+      {
+        name: TOOL_SYSTEM_PROMPT_ENV_VAR,
+        description: "System prompt for wiki tool.",
+        value: `You are a Wikipedia agent. You help users to get information from Wikipedia.`,
+      },
+    ],
   },
   {
     display: "Weather",
@@ -94,6 +102,13 @@ For better results, you can specify the region parameter to get results from a s
     dependencies: [],
     supportedFrameworks: ["fastapi", "express", "nextjs"],
     type: ToolType.LOCAL,
+    envVars: [
+      {
+        name: TOOL_SYSTEM_PROMPT_ENV_VAR,
+        description: "System prompt for weather tool.",
+        value: `You are a weather forecast agent. You help users to get the weather forecast for a given location.`,
+      },
+    ],
   },
   {
     display: "Document generator",
@@ -102,11 +117,11 @@ For better results, you can specify the region parameter to get results from a s
     dependencies: [
       {
         name: "xhtml2pdf",
-        version: ">=0.2.14,<0.3.0",
+        version: "^0.2.14",
       },
       {
         name: "markdown",
-        version: ">=3.7.0,<4.0.0",
+        version: "^3.7",
       },
     ],
     type: ToolType.LOCAL,
@@ -124,7 +139,7 @@ For better results, you can specify the region parameter to get results from a s
     dependencies: [
       {
         name: "e2b_code_interpreter",
-        version: ">=1.1.1,<1.2.0",
+        version: "0.0.11b38",
       },
     ],
     supportedFrameworks: ["fastapi", "express", "nextjs"],
@@ -155,7 +170,7 @@ For better results, you can specify the region parameter to get results from a s
     dependencies: [
       {
         name: "e2b_code_interpreter",
-        version: ">=1.1.1,<1.2.0",
+        version: "0.0.11b38",
       },
     ],
     supportedFrameworks: ["fastapi", "express", "nextjs"],
@@ -184,7 +199,7 @@ For better results, you can specify the region parameter to get results from a s
       },
       {
         name: "jsonschema",
-        version: ">=4.22.0,<5.0.0",
+        version: "^4.22.0",
       },
       {
         name: "llama-index-tools-requests",
@@ -196,6 +211,14 @@ For better results, you can specify the region parameter to get results from a s
     },
     supportedFrameworks: ["fastapi", "express", "nextjs"],
     type: ToolType.LOCAL,
+    envVars: [
+      {
+        name: TOOL_SYSTEM_PROMPT_ENV_VAR,
+        description: "System prompt for openapi action tool.",
+        value:
+          "You are an OpenAPI action agent. You help users to make requests to the provided OpenAPI schema.",
+      },
+    ],
   },
   {
     display: "Image Generator",
@@ -208,6 +231,11 @@ For better results, you can specify the region parameter to get results from a s
         description:
           "STABILITY_API_KEY key is required to run image generator. Get it here: https://platform.stability.ai/account/keys",
       },
+      {
+        name: TOOL_SYSTEM_PROMPT_ENV_VAR,
+        description: "System prompt for image generator tool.",
+        value: `You are an image generator agent. You help users to generate images using the Stability API.`,
+      },
     ],
   },
   {
@@ -247,11 +275,11 @@ For better results, you can specify the region parameter to get results from a s
     dependencies: [
       {
         name: "pandas",
-        version: ">=2.2.3,<3.0.0",
+        version: "^2.2.3",
       },
       {
         name: "tabulate",
-        version: ">=0.9.0,<1.0.0",
+        version: "^0.9.0",
       },
     ],
   },
@@ -325,16 +353,9 @@ export const writeToolsConfig = async (
       yaml.stringify(configContent),
     );
   } else {
-    // For Typescript, we treat llamahub tools as local tools
-    const tsConfigContent = {
-      local: {
-        ...configContent.local,
-        ...configContent.llamahub,
-      },
-    };
     await fs.writeFile(
       path.join(configPath, "tools.json"),
-      JSON.stringify(tsConfigContent, null, 2),
+      JSON.stringify(configContent, null, 2),
     );
   }
 };

helpers/types.ts

@@ -20,12 +20,11 @@ export type ModelConfig = {
   isConfigured(): boolean;
 };
 export type TemplateType =
+  | "extractor"
   | "streaming"
   | "community"
   | "llamapack"
-  | "multiagent"
-  | "reflex"
-  | "llamaindexserver";
+  | "multiagent";
 export type TemplateFramework = "nextjs" | "express" | "fastapi";
 export type TemplateUI = "html" | "shadcn";
 export type TemplateVectorDB =
@@ -50,24 +49,14 @@ export type TemplateDataSource = {
 };
 export type TemplateDataSourceType = "file" | "web" | "db";
 export type TemplateObservability = "none" | "traceloop" | "llamatrace";
-export type TemplateUseCase =
-  | "financial_report"
-  | "blog"
-  | "deep_research"
-  | "form_filling"
-  | "extractor"
-  | "contract_review"
-  | "agentic_rag"
-  | "artifacts";
+export type TemplateAgents = "financial_report" | "blog" | "form_filling";
 // Config for both file and folder
 export type FileSourceConfig =
   | {
       path: string;
-      filename?: string;
     }
   | {
       url: URL;
-      filename?: string;
     };
 export type WebSourceConfig = {
   baseUrl?: string;
@@ -110,5 +99,5 @@ export interface InstallTemplateArgs {
   postInstallAction?: TemplatePostInstallAction;
   tools?: Tool[];
   observability?: TemplateObservability;
-  useCase?: TemplateUseCase;
+  agents?: TemplateAgents;
 }

helpers/typescript.ts

@@ -6,106 +6,43 @@ import { assetRelocator, copy } from "../helpers/copy";
 import { callPackageManager } from "../helpers/install";
 import { templatesDir } from "./dir";
 import { PackageManager } from "./get-pkg-manager";
-import { InstallTemplateArgs, ModelProvider, TemplateVectorDB } from "./types";
+import { InstallTemplateArgs } from "./types";
 
-const installLlamaIndexServerTemplate = async ({
-  root,
-  useCase,
-  vectorDb,
-}: Pick<InstallTemplateArgs, "root" | "useCase" | "vectorDb">) => {
-  if (!useCase) {
-    console.log(
-      red(
-        `There is no use case selected. Please pick a use case to use via --use-case flag.`,
-      ),
-    );
-    process.exit(1);
-  }
-
-  if (!vectorDb) {
-    console.log(
-      red(
-        `There is no vector db selected. Please pick a vector db to use via --vector-db flag.`,
-      ),
-    );
-    process.exit(1);
-  }
-
-  await copy("*.ts", path.join(root, "src", "app"), {
-    parents: true,
-    cwd: path.join(
-      templatesDir,
-      "components",
-      "workflows",
-      "typescript",
-      useCase,
-    ),
-  });
-
-  // copy workflow UI components to output/components folder
-  await copy("*", path.join(root, "components"), {
-    parents: true,
-    cwd: path.join(templatesDir, "components", "ui", "workflows", useCase),
-  });
-
-  if (vectorDb === "llamacloud") {
-    await copy("generate.ts", path.join(root, "src"), {
-      parents: true,
-      cwd: path.join(
-        templatesDir,
-        "components",
-        "vectordbs",
-        "llamaindexserver",
-        "llamacloud",
-        "typescript",
-      ),
-    });
-
-    await copy("index.ts", path.join(root, "src", "app"), {
-      parents: true,
-      cwd: path.join(
-        templatesDir,
-        "components",
-        "vectordbs",
-        "llamaindexserver",
-        "llamacloud",
-        "typescript",
-      ),
-      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,
-  });
-};
-
-const installLegacyTSTemplate = async ({
+/**
+ * Install a LlamaIndex internal template to a given `root` directory.
+ */
+export const installTSTemplate = async ({
+  appName,
   root,
+  packageManager,
+  isOnline,
   template,
-  backend,
   framework,
   ui,
   vectorDb,
+  postInstallAction,
+  backend,
   observability,
   tools,
   dataSources,
   useLlamaParse,
-  useCase,
-  modelConfig,
-  relativeEngineDestPath,
-}: InstallTemplateArgs & {
-  backend: boolean;
-  relativeEngineDestPath: string;
-}) => {
+  agents,
+}: InstallTemplateArgs & { backend: boolean }) => {
+  console.log(bold(`Using ${packageManager}.`));
+
+  /**
+   * Copy the template files to the target directory.
+   */
+  console.log("\nInitializing project with template:", template, "\n");
+  const templatePath = path.join(templatesDir, "types", "streaming", framework);
+  const copySource = ["**"];
+
+  await copy(copySource, root, {
+    parents: true,
+    cwd: templatePath,
+    rename: assetRelocator,
+  });
+
   /**
    * If next.js is used, update its configuration if necessary
    */
@@ -160,6 +97,10 @@ const installLegacyTSTemplate = async ({
   }
 
   const compPath = path.join(templatesDir, "components");
+  const relativeEngineDestPath =
+    framework === "nextjs"
+      ? path.join("app", "api", "chat")
+      : path.join("src", "controllers");
   const enginePath = path.join(root, relativeEngineDestPath, "engine");
 
   // copy llamaindex code for TS templates
@@ -190,16 +131,16 @@ const installLegacyTSTemplate = async ({
       cwd: path.join(multiagentPath, "workflow"),
     });
 
-    // Copy use case code for multiagent template
-    if (useCase) {
-      console.log("\nCopying use case:", useCase, "\n");
-      const useCasePath = path.join(compPath, "agents", "typescript", useCase);
-      const useCaseCodePath = path.join(useCasePath, "workflow");
+    // Copy agents use case code for multiagent template
+    if (agents) {
+      console.log("\nCopying agent:", agents, "\n");
+      const useCasePath = path.join(compPath, "agents", "typescript", agents);
+      const agentsCodePath = path.join(useCasePath, "workflow");
 
-      // Copy use case codes
+      // Copy agent codes
       await copy("**", path.join(root, relativeEngineDestPath, "workflow"), {
         parents: true,
-        cwd: useCaseCodePath,
+        cwd: agentsCodePath,
         rename: assetRelocator,
       });
 
@@ -212,7 +153,7 @@ const installLegacyTSTemplate = async ({
     } else {
       console.log(
         red(
-          `There is no use case selected for ${template} template. Please pick a use case to use via --use-case flag.`,
+          "There is no agent selected for multi-agent template. Please pick an agent to use via --agents flag.",
         ),
       );
       process.exit(1);
@@ -240,12 +181,6 @@ const installLegacyTSTemplate = async ({
     cwd: path.join(compPath, "loaders", "typescript", loaderFolder),
   });
 
-  // copy provider settings
-  await copy("**", enginePath, {
-    parents: true,
-    cwd: path.join(compPath, "providers", "typescript", modelConfig.provider),
-  });
-
   // Select and copy engine code based on data sources and tools
   let engine;
   tools = tools ?? [];
@@ -294,75 +229,6 @@ const installLegacyTSTemplate = async ({
     await fs.rm(path.join(root, "app", "api"), { recursive: true });
     await fs.rm(path.join(root, "config"), { recursive: true, force: true });
   }
-};
-
-/**
- * Install a LlamaIndex internal template to a given `root` directory.
- */
-export const installTSTemplate = async ({
-  appName,
-  root,
-  packageManager,
-  isOnline,
-  template,
-  framework,
-  ui,
-  vectorDb,
-  postInstallAction,
-  backend,
-  observability,
-  tools,
-  dataSources,
-  useLlamaParse,
-  useCase,
-  modelConfig,
-}: InstallTemplateArgs & { backend: boolean }) => {
-  console.log(bold(`Using ${packageManager}.`));
-
-  /**
-   * Copy the template files to the target directory.
-   */
-  console.log("\nInitializing project with template:", template, "\n");
-  const templatePath = path.join(templatesDir, "types", template, framework);
-  const copySource = ["**"];
-
-  await copy(copySource, root, {
-    parents: true,
-    cwd: templatePath,
-    rename: assetRelocator,
-  });
-
-  const relativeEngineDestPath =
-    framework === "nextjs"
-      ? path.join("app", "api", "chat")
-      : path.join("src", "controllers");
-
-  if (template === "llamaindexserver") {
-    await installLlamaIndexServerTemplate({
-      root,
-      useCase,
-      vectorDb,
-    });
-  } else {
-    await installLegacyTSTemplate({
-      appName,
-      root,
-      packageManager,
-      isOnline,
-      template,
-      backend,
-      framework,
-      ui,
-      vectorDb,
-      observability,
-      tools,
-      dataSources,
-      useLlamaParse,
-      useCase,
-      modelConfig,
-      relativeEngineDestPath,
-    });
-  }
 
   const packageJson = await updatePackageJson({
     root,
@@ -373,9 +239,6 @@ export const installTSTemplate = async ({
     ui,
     observability,
     vectorDb,
-    backend,
-    modelConfig,
-    template,
   });
 
   if (
@@ -384,68 +247,11 @@ export const installTSTemplate = async ({
   ) {
     await installTSDependencies(packageJson, packageManager, isOnline);
   }
-};
 
-const providerDependencies: {
-  [key in ModelProvider]?: Record<string, string>;
-} = {
-  openai: {
-    "@llamaindex/openai": "^0.2.0",
-  },
-  gemini: {
-    "@llamaindex/google": "^0.2.0",
-  },
-  ollama: {
-    "@llamaindex/ollama": "^0.1.0",
-  },
-  mistral: {
-    "@llamaindex/mistral": "^0.2.0",
-  },
-  "azure-openai": {
-    "@llamaindex/openai": "^0.2.0",
-  },
-  groq: {
-    "@llamaindex/groq": "^0.0.61",
-    "@llamaindex/huggingface": "^0.1.0", // groq uses huggingface as default embedding model
-  },
-  anthropic: {
-    "@llamaindex/anthropic": "^0.3.0",
-    "@llamaindex/huggingface": "^0.1.0", // anthropic uses huggingface as default embedding model
-  },
-};
-
-const vectorDbDependencies: Record<TemplateVectorDB, Record<string, string>> = {
-  astra: {
-    "@llamaindex/astra": "^0.0.5",
-  },
-  chroma: {
-    "@llamaindex/chroma": "^0.0.5",
-  },
-  llamacloud: {},
-  milvus: {
-    "@zilliz/milvus2-sdk-node": "^2.4.6",
-    "@llamaindex/milvus": "^0.1.0",
-  },
-  mongo: {
-    mongodb: "6.7.0",
-    "@llamaindex/mongodb": "^0.0.5",
-  },
-  none: {},
-  pg: {
-    pg: "^8.12.0",
-    pgvector: "^0.2.0",
-    "@llamaindex/postgres": "^0.0.33",
-  },
-  pinecone: {
-    "@llamaindex/pinecone": "^0.0.5",
-  },
-  qdrant: {
-    "@qdrant/js-client-rest": "^1.11.0",
-    "@llamaindex/qdrant": "^0.1.0",
-  },
-  weaviate: {
-    "@llamaindex/weaviate": "^0.0.5",
-  },
+  // Copy deployment files for typescript
+  await copy("**", root, {
+    cwd: path.join(compPath, "deployments", "typescript"),
+  });
 };
 
 async function updatePackageJson({
@@ -457,9 +263,6 @@ async function updatePackageJson({
   ui,
   observability,
   vectorDb,
-  backend,
-  modelConfig,
-  template,
 }: Pick<
   InstallTemplateArgs,
   | "root"
@@ -469,11 +272,8 @@ async function updatePackageJson({
   | "ui"
   | "observability"
   | "vectorDb"
-  | "modelConfig"
-  | "template"
 > & {
   relativeEngineDestPath: string;
-  backend: boolean;
 }): Promise<any> {
   const packageJsonFile = path.join(root, "package.json");
   const packageJson: any = JSON.parse(
@@ -482,7 +282,7 @@ async function updatePackageJson({
   packageJson.name = appName;
   packageJson.version = "0.1.0";
 
-  if (relativeEngineDestPath && template !== "llamaindexserver") {
+  if (relativeEngineDestPath) {
     // TODO: move script to {root}/scripts for all frameworks
     // add generate script if using context engine
     packageJson.scripts = {
@@ -513,25 +313,32 @@ async function updatePackageJson({
     };
   }
 
-  if (backend) {
+  if (vectorDb === "pg") {
     packageJson.dependencies = {
       ...packageJson.dependencies,
-      "@llamaindex/readers": "^3.0.0",
+      pg: "^8.12.0",
+      pgvector: "^0.2.0",
     };
+  }
 
-    if (vectorDb && vectorDb in vectorDbDependencies) {
-      packageJson.dependencies = {
-        ...packageJson.dependencies,
-        ...vectorDbDependencies[vectorDb],
-      };
-    }
+  if (vectorDb === "qdrant") {
+    packageJson.dependencies = {
+      ...packageJson.dependencies,
+      "@qdrant/js-client-rest": "^1.11.0",
+    };
+  }
+  if (vectorDb === "mongo") {
+    packageJson.dependencies = {
+      ...packageJson.dependencies,
+      mongodb: "^6.7.0",
+    };
+  }
 
-    if (modelConfig.provider && modelConfig.provider in providerDependencies) {
-      packageJson.dependencies = {
-        ...packageJson.dependencies,
-        ...providerDependencies[modelConfig.provider],
-      };
-    }
+  if (vectorDb === "milvus") {
+    packageJson.dependencies = {
+      ...packageJson.dependencies,
+      "@zilliz/milvus2-sdk-node": "^2.4.6",
+    };
   }
 
   if (observability === "traceloop") {

helpers/validate-pkg.ts

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

index.ts

@@ -1,3 +1,4 @@
+/* eslint-disable import/no-extraneous-dependencies */
 import { execSync } from "child_process";
 import { Command } from "commander";
 import fs from "fs";
@@ -201,10 +202,10 @@ const program = new Command(packageJson.name)
     false,
   )
   .option(
-    "--use-case <useCase>",
+    "--agents <agents>",
     `
 
-  Select which use case to use for the multi-agent template (e.g: financial_report, blog).
+  Select which agents to use for the multi-agent template (e.g: financial_report, blog).
 `,
   )
   .allowUnknownOption()
@@ -214,7 +215,7 @@ const options = program.opts();
 
 if (
   process.argv.includes("--no-llama-parse") ||
-  options.template === "reflex"
+  options.template === "extractor"
 ) {
   options.useLlamaParse = false;
 }

package.json

@@ -1,52 +1,83 @@
 {
-  "name": "create-llama-monorepo",
-  "version": "1.0.0",
-  "private": true,
-  "description": "Monorepo for create-llama",
+  "name": "create-llama",
+  "version": "0.3.15",
+  "description": "Create LlamaIndex-powered apps with one command",
   "keywords": [
     "rag",
-    "llamaindex"
+    "llamaindex",
+    "next.js"
   ],
   "repository": {
     "type": "git",
-    "url": "https://github.com/run-llama/create-llama"
+    "url": "https://github.com/run-llama/create-llama",
+    "directory": "packages/create-llama"
   },
   "license": "MIT",
-  "workspaces": [
-    "packages/*"
+  "bin": {
+    "create-llama": "./dist/index.js"
+  },
+  "files": [
+    "dist"
   ],
   "scripts": {
-    "dev": "pnpm -r dev",
-    "build": "pnpm -r build",
-    "e2e": "pnpm -r e2e",
-    "lint": "eslint .",
+    "build": "bash ./scripts/build.sh",
+    "build:ncc": "pnpm run clean && ncc build ./index.ts -o ./dist/ --minify --no-cache --no-source-map-register",
+    "clean": "rimraf --glob ./dist ./templates/**/__pycache__ ./templates/**/node_modules ./templates/**/poetry.lock",
+    "dev": "ncc build ./index.ts -w -o dist/",
+    "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",
+    "new-snapshot": "pnpm run build && changeset version --snapshot",
+    "new-version": "pnpm run build && changeset version",
+    "pack-install": "bash ./scripts/pack.sh",
     "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"
+    "release": "pnpm run build && changeset publish",
+    "release-snapshot": "pnpm run build && changeset publish --tag snapshot"
+  },
+  "dependencies": {
+    "@types/async-retry": "1.4.2",
+    "@types/ci-info": "2.0.0",
+    "@types/cross-spawn": "6.0.0",
+    "@types/fs-extra": "11.0.4",
+    "@types/node": "^20.11.7",
+    "@types/prompts": "2.0.1",
+    "@types/tar": "6.1.5",
+    "@types/validate-npm-package-name": "3.0.0",
+    "async-retry": "1.3.1",
+    "async-sema": "3.0.1",
+    "ci-info": "github:watson/ci-info#f43f6a1cefff47fb361c88cf4b943fdbcaafe540",
+    "commander": "12.1.0",
+    "cross-spawn": "7.0.3",
+    "fast-glob": "3.3.1",
+    "fs-extra": "11.2.0",
+    "global-agent": "^3.0.0",
+    "got": "10.7.0",
+    "ollama": "^0.5.0",
+    "ora": "^8.0.1",
+    "picocolors": "1.0.0",
+    "prompts": "2.4.2",
+    "smol-toml": "^1.1.4",
+    "tar": "6.1.15",
+    "terminal-link": "^3.0.0",
+    "update-check": "1.5.4",
+    "validate-npm-package-name": "3.0.0",
+    "yaml": "2.4.1"
   },
   "devDependencies": {
     "@changesets/cli": "^2.27.1",
-    "bunchee": "6.4.0",
+    "@playwright/test": "^1.41.1",
+    "@vercel/ncc": "0.38.1",
+    "eslint": "^8.56.0",
+    "eslint-config-prettier": "^8.10.0",
     "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"
+    "prettier": "^3.2.5",
+    "prettier-plugin-organize-imports": "^3.2.4",
+    "rimraf": "^5.0.5",
+    "typescript": "^5.3.3",
+    "wait-port": "^1.1.0"
   },
   "packageManager": "pnpm@9.0.5",
   "engines": {

packages/create-llama/.gitignore

@@ -1,65 +0,0 @@
-# See https://help.github.com/articles/ignoring-files/ for more about ignoring files.
-
-# dependencies
-node_modules
-.pnp
-.pnpm-store
-.pnp.js
-
-# testing
-coverage
-.coverage
-
-# next.js
-.next/
-out/
-build
-
-# misc
-.DS_Store
-*.pem
-
-# debug
-npm-debug.log*
-yarn-debug.log*
-yarn-error.log*
-
-# local env files
-.env
-.env.local
-.env.development.local
-.env.test.local
-.env.production.local
-
-# build
-dist/
-lib/
-
-# e2e
-.cache
-test-results/
-playwright-report/
-blob-report/
-playwright/.cache/
-.tsbuildinfo
-e2e/cache
-
-# intellij
-**/.idea
-
-# Python
-.mypy_cache/
-venv/
-.venv/
-dist/
-.__pycache__
-__pycache__
-.python-version
-.ui
-
-# build artifacts
-create-llama-*.tgz
-
-# copied from root
-README.md
-LICENSE.md

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

@@ -1,63 +0,0 @@
-import { expect, test } from "@playwright/test";
-import { ChildProcess } from "child_process";
-import fs from "fs";
-import path from "path";
-import { TemplateFramework, TemplateUseCase } from "../../helpers";
-import { createTestDir, runCreateLlama } from "../utils";
-
-const templateFramework: TemplateFramework = process.env.FRAMEWORK
-  ? (process.env.FRAMEWORK as TemplateFramework)
-  : "fastapi";
-const dataSource: string = process.env.DATASOURCE
-  ? process.env.DATASOURCE
-  : "--example-file";
-const templateUseCases: TemplateUseCase[] = ["extractor", "contract_review"];
-
-// The reflex template currently only works with FastAPI and files (and not on Windows)
-if (
-  process.platform !== "win32" &&
-  templateFramework === "fastapi" &&
-  dataSource === "--example-file"
-) {
-  for (const useCase of templateUseCases) {
-    test.describe(`Test reflex template ${useCase} ${templateFramework} ${dataSource}`, async () => {
-      let appPort: number;
-      let name: string;
-      let appProcess: ChildProcess;
-      let cwd: string;
-
-      // Create reflex app
-      test.beforeAll(async () => {
-        cwd = await createTestDir();
-        appPort = Math.floor(Math.random() * 10000) + 10000;
-        const result = await runCreateLlama({
-          cwd,
-          templateType: "reflex",
-          templateFramework: "fastapi",
-          dataSource: "--example-file",
-          vectorDb: "none",
-          port: appPort,
-          postInstallAction: "runApp",
-          useCase,
-        });
-        name = result.projectName;
-        appProcess = result.appProcess;
-      });
-
-      test.afterAll(async () => {
-        appProcess.kill();
-      });
-
-      test("App folder should exist", async () => {
-        const dirExists = fs.existsSync(path.join(cwd, name));
-        expect(dirExists).toBeTruthy();
-      });
-      test("Frontend should have a title", async ({ page }) => {
-        await page.goto(`http://localhost:${appPort}`);
-        await expect(page.getByText("Built by LlamaIndex")).toBeVisible({
-          timeout: 2000 * 60,
-        });
-      });
-    });
-  }
-}

packages/create-llama/helpers/uv.ts

@@ -1,42 +0,0 @@
-// Migrate poetry to uv
-import { execSync } from "child_process";
-import fs from "fs";
-import { red } from "picocolors";
-
-export function isUvAvailable(): boolean {
-  try {
-    execSync("uv --version", { stdio: "ignore" });
-    return true;
-  } catch (_) {}
-  return false;
-}
-
-export function tryUvSync(): boolean {
-  try {
-    console.log("Syncing environment with pyproject.toml...");
-    execSync(`uv sync`, {
-      stdio: "inherit",
-    });
-    return true;
-  } catch (_) {}
-  return false;
-}
-
-export function tryUvRun(command: string): boolean {
-  try {
-    // Use uv run <command>
-    execSync(`uv run ${command}`, { stdio: "inherit" });
-    return true;
-  } catch (error) {
-    console.error(red(`Failed to run ${command}. Error: ${error}`));
-    return false;
-  }
-}
-
-export function isHavingUvLockFile(): boolean {
-  try {
-    // Check if uv.lock exists in the current directory
-    return fs.existsSync("uv.lock");
-  } catch (_) {}
-  return false;
-}

packages/create-llama/package.json

@@ -1,76 +0,0 @@
-{
-  "name": "create-llama",
-  "version": "0.5.13",
-  "description": "Create LlamaIndex-powered apps with one command",
-  "keywords": [
-    "rag",
-    "llamaindex",
-    "next.js"
-  ],
-  "repository": {
-    "type": "git",
-    "url": "https://github.com/run-llama/create-llama",
-    "directory": "packages/create-llama"
-  },
-  "license": "MIT",
-  "bin": {
-    "create-llama": "./dist/index.js"
-  },
-  "files": [
-    "dist",
-    "README.md",
-    "LICENSE.md"
-  ],
-  "scripts": {
-    "copy": "cp -r ../../README.md ../../LICENSE.md .",
-    "build": "bash ./scripts/build.sh",
-    "build:ncc": "pnpm run clean && ncc build ./index.ts -o ./dist/ --minify --no-cache --no-source-map-register",
-    "postbuild": "pnpm run copy",
-    "clean": "rimraf --glob ./dist ./templates/**/__pycache__ ./templates/**/node_modules ./templates/**/poetry.lock",
-    "dev": "ncc build ./index.ts -w -o dist/",
-    "e2e": "playwright test",
-    "e2e:python": "playwright test e2e/shared e2e/python",
-    "e2e:typescript": "playwright test e2e/shared e2e/typescript",
-    "pack-install": "bash ./scripts/pack.sh"
-  },
-  "dependencies": {
-    "@types/async-retry": "1.4.2",
-    "@types/ci-info": "2.0.0",
-    "@types/cross-spawn": "6.0.0",
-    "@types/fs-extra": "11.0.4",
-    "@types/node": "^20.11.7",
-    "@types/prompts": "2.4.2",
-    "@types/tar": "6.1.5",
-    "@types/validate-npm-package-name": "3.0.0",
-    "async-retry": "1.3.1",
-    "async-sema": "3.0.1",
-    "ci-info": "github:watson/ci-info#f43f6a1cefff47fb361c88cf4b943fdbcaafe540",
-    "commander": "12.1.0",
-    "cross-spawn": "7.0.3",
-    "fast-glob": "3.3.1",
-    "fs-extra": "11.2.0",
-    "global-agent": "^3.0.0",
-    "got": "10.7.0",
-    "ollama": "^0.5.0",
-    "ora": "^8.0.1",
-    "picocolors": "1.0.0",
-    "prompts": "2.4.2",
-    "smol-toml": "^1.1.4",
-    "tar": "6.1.15",
-    "terminal-link": "^3.0.0",
-    "update-check": "1.5.4",
-    "validate-npm-package-name": "3.0.0",
-    "yaml": "2.4.1"
-  },
-  "devDependencies": {
-    "@playwright/test": "^1.41.1",
-    "@vercel/ncc": "0.38.1",
-    "rimraf": "^5.0.5",
-    "typescript": "^5.3.3",
-    "wait-port": "^1.1.0"
-  },
-  "packageManager": "pnpm@9.0.5",
-  "engines": {
-    "node": ">=16.14.0"
-  }
-}

packages/create-llama/questions/simple.ts

@@ -1,180 +0,0 @@
-import prompts from "prompts";
-import { EXAMPLE_10K_SEC_FILES, EXAMPLE_FILE } from "../helpers/datasources";
-import { askModelConfig } from "../helpers/providers";
-import { getTools } from "../helpers/tools";
-import { ModelConfig, TemplateFramework } from "../helpers/types";
-import { PureQuestionArgs, QuestionResults } from "./types";
-import { askPostInstallAction, questionHandlers } from "./utils";
-
-type AppType =
-  | "agentic_rag"
-  | "financial_report"
-  | "deep_research"
-  | "artifacts";
-
-type SimpleAnswers = {
-  appType: AppType;
-  language: TemplateFramework;
-  useLlamaCloud: boolean;
-  llamaCloudKey?: string;
-};
-
-export const askSimpleQuestions = async (
-  args: PureQuestionArgs,
-): Promise<QuestionResults> => {
-  const { appType } = await prompts(
-    {
-      type: "select",
-      name: "appType",
-      message: "What use case do you want to build?",
-      choices: [
-        {
-          title: "Agentic RAG",
-          value: "agentic_rag",
-          description:
-            "Chatbot that answers questions based on provided documents.",
-        },
-        {
-          title: "Financial Report",
-          value: "financial_report",
-          description:
-            "Agent that analyzes data and generates visualizations by using a code interpreter.",
-        },
-        {
-          title: "Deep Research",
-          value: "deep_research",
-          description:
-            "Researches and analyzes provided documents from multiple perspectives, generating a comprehensive report with citations to support key findings and insights.",
-        },
-        {
-          title: "Artifacts",
-          value: "artifacts",
-          description:
-            "Build your own Vercel's v0 or OpenAI's canvas-styled UI.",
-        },
-      ],
-    },
-    questionHandlers,
-  );
-
-  let language: TemplateFramework = "fastapi";
-  let llamaCloudKey = args.llamaCloudKey;
-
-  let useLlamaCloud = false;
-
-  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(
-    {
-      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
-    const { llamaCloudKey: newLlamaCloudKey } = await prompts(
-      {
-        type: "text",
-        name: "llamaCloudKey",
-        message:
-          "Please provide your LlamaCloud API key (leave blank to skip):",
-      },
-      questionHandlers,
-    );
-    llamaCloudKey = newLlamaCloudKey || process.env.LLAMA_CLOUD_API_KEY;
-  }
-
-  const results = await convertAnswers(args, {
-    appType,
-    language,
-    useLlamaCloud,
-    llamaCloudKey,
-  });
-
-  results.postInstallAction = await askPostInstallAction(results);
-  return results;
-};
-
-const convertAnswers = async (
-  args: PureQuestionArgs,
-  answers: SimpleAnswers,
-): Promise<QuestionResults> => {
-  const MODEL_GPT41: ModelConfig = {
-    provider: "openai",
-    apiKey: args.openAiKey,
-    model: "gpt-4.1",
-    embeddingModel: "text-embedding-3-large",
-    dimensions: 1536,
-    isConfigured(): boolean {
-      return !!args.openAiKey;
-    },
-  };
-  const lookup: Record<
-    AppType,
-    Pick<QuestionResults, "template" | "tools" | "dataSources" | "useCase"> & {
-      modelConfig?: ModelConfig;
-    }
-  > = {
-    agentic_rag: {
-      template: "llamaindexserver",
-      dataSources: [EXAMPLE_FILE],
-    },
-    financial_report: {
-      template: "llamaindexserver",
-      dataSources: EXAMPLE_10K_SEC_FILES,
-      tools: getTools(["interpreter", "document_generator"]),
-      modelConfig: MODEL_GPT41,
-    },
-    deep_research: {
-      template: "llamaindexserver",
-      dataSources: EXAMPLE_10K_SEC_FILES,
-      tools: [],
-      modelConfig: MODEL_GPT41,
-    },
-    artifacts: {
-      template: "llamaindexserver",
-      dataSources: [],
-      tools: [],
-      modelConfig: MODEL_GPT41,
-    },
-  };
-
-  const results = lookup[answers.appType];
-  return {
-    framework: answers.language,
-    useCase: answers.appType,
-    ui: "shadcn",
-    llamaCloudKey: answers.llamaCloudKey,
-    useLlamaParse: answers.useLlamaCloud,
-    vectorDb: answers.useLlamaCloud ? "llamacloud" : "none",
-    ...results,
-    modelConfig:
-      results.modelConfig ??
-      (await askModelConfig({
-        openAiKey: args.openAiKey,
-        askModels: args.askModels ?? false,
-        framework: answers.language,
-      })),
-    frontend: true,
-  };
-};

packages/create-llama/templates/components/agents/python/deep_research/README-template.md

@@ -1,47 +0,0 @@
-This is a [LlamaIndex](https://www.llamaindex.ai/) multi-agents project using [Workflows](https://docs.llamaindex.ai/en/stable/understanding/workflows/).
-
-## Getting Started
-
-First, setup the environment with poetry:
-
-> **_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. (E.g. you might need to configure an `OPENAI_API_KEY` if you're using OpenAI as model provider).
-Second, generate the embeddings of the documents in the `./data` directory:
-
-```shell
-uv run generate
-```
-
-Third, run the development server:
-
-```shell
-uv run dev
-```
-
-## Use Case: Deep Research over own documents
-
-The workflow performs deep research by retrieving and analyzing documents from the [data](./data) directory from multiple perspectives. The project includes a sample PDF about AI investment in 2024 to help you get started. You can also add your own documents by placing them in the data directory and running the generate script again to index them.
-
-After starting the server, go to [http://localhost:8000](http://localhost:8000) and send a message to the agent to write a blog post.
-E.g: "AI investment in 2024"
-
-To update the workflow, you can edit the [deep_research.py](./app/workflows/deep_research.py) file.
-
-By default, the workflow retrieves 10 results from your documents. To customize the amount of information covered in the answer, you can adjust the `TOP_K` environment variable in the `.env` file. A higher value will retrieve more results from your documents, potentially providing more comprehensive answers.
-
-## Deployments
-
-For production deployments, check the [DEPLOY.md](DEPLOY.md) file.
-
-## 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.
-  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/agents/python/deep_research/app/workflows/__init__.py

@@ -1,3 +0,0 @@
-from .deep_research import create_workflow
-
-__all__ = ["create_workflow"]

packages/create-llama/templates/components/agents/python/deep_research/app/workflows/agents.py

@@ -1,183 +0,0 @@
-from typing import List, Literal, Optional
-
-from llama_index.core.base.llms.types import (
-    CompletionResponse,
-    CompletionResponseAsyncGen,
-)
-from llama_index.core.memory.simple_composable_memory import SimpleComposableMemory
-from llama_index.core.prompts import PromptTemplate
-from llama_index.core.schema import MetadataMode, Node, NodeWithScore
-from llama_index.core.settings import Settings
-from pydantic import BaseModel, Field
-
-
-class AnalysisDecision(BaseModel):
-    decision: Literal["research", "write", "cancel"] = Field(
-        description="Whether to continue research, write a report, or cancel the research after several retries"
-    )
-    research_questions: Optional[List[str]] = Field(
-        description="""
-        If the decision is to research, provide a list of questions to research that related to the user request.
-        Maximum 3 questions. Set to null or empty if writing a report or cancel the research.
-        """,
-        default_factory=list,
-    )
-    cancel_reason: Optional[str] = Field(
-        description="The reason for cancellation if the decision is to cancel research.",
-        default=None,
-    )
-
-
-async def plan_research(
-    memory: SimpleComposableMemory,
-    context_nodes: List[Node],
-    user_request: str,
-    total_questions: int,
-) -> AnalysisDecision:
-    analyze_prompt = """
-      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>
-      """
-    # Manually craft the prompt to avoid LLM hallucination
-    enhanced_prompt = ""
-    if total_questions == 0:
-        # Avoid writing a report without any research context
-        enhanced_prompt = """
-        
-        The student has no questions to research. Let start by asking some questions.
-        """
-    elif total_questions > 6:
-        # Avoid asking too many questions (when the data is not ready for writing a report)
-        enhanced_prompt = f"""
-
-        The student has researched {total_questions} questions. Should cancel the research if the context is not enough to write a report.
-        """
-
-    conversation_context = "\n".join(
-        [f"{message.role}: {message.content}" for message in memory.get_all()]
-    )
-    context_str = "\n".join(
-        [node.get_content(metadata_mode=MetadataMode.LLM) for node in context_nodes]
-    )
-    res = await Settings.llm.astructured_predict(
-        output_cls=AnalysisDecision,
-        prompt=PromptTemplate(template=analyze_prompt),
-        user_request=user_request,
-        context_str=context_str,
-        conversation_context=conversation_context,
-        enhanced_prompt=enhanced_prompt,
-    )
-    return res
-
-
-async def research(
-    question: str,
-    context_nodes: List[NodeWithScore],
-) -> str:
-    prompt = """
-    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}
-    """
-    context_str = "\n".join(
-        [_get_text_node_content_for_citation(node) for node in context_nodes]
-    )
-    res = await Settings.llm.acomplete(
-        prompt=prompt.format(question=question, context_str=context_str),
-    )
-    return res.text
-
-
-async def write_report(
-    memory: SimpleComposableMemory,
-    user_request: str,
-    stream: bool = False,
-) -> CompletionResponse | CompletionResponseAsyncGen:
-    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.
-
-    <User request>
-    {user_request}
-    </User request>
-
-    <Research context>
-    {research_context}
-    </Research context>
-
-    Now, write a report addressing the user request based on the research provided following the format and guidelines above.
-    """
-    research_context = "\n".join(
-        [f"{message.role}: {message.content}" for message in memory.get_all()]
-    )
-
-    llm_complete_func = (
-        Settings.llm.astream_complete if stream else Settings.llm.acomplete
-    )
-
-    res = await llm_complete_func(
-        prompt=report_prompt.format(
-            user_request=user_request,
-            research_context=research_context,
-        ),
-    )
-    return res
-
-
-def _get_text_node_content_for_citation(node: NodeWithScore) -> str:
-    """
-    Construct node content for LLM with citation flag.
-    """
-    node_id = node.node.node_id
-    content = f"<Citation id='{node_id}'>\n{node.get_content(metadata_mode=MetadataMode.LLM)}</Citation id='{node_id}'>"
-    return content

packages/create-llama/templates/components/agents/python/deep_research/app/workflows/deep_research.py

@@ -1,328 +0,0 @@
-import logging
-import os
-import uuid
-from typing import Any, Dict, List, Optional
-
-from llama_index.core.indices.base import BaseIndex
-from llama_index.core.memory import ChatMemoryBuffer
-from llama_index.core.memory.simple_composable_memory import SimpleComposableMemory
-from llama_index.core.schema import Node
-from llama_index.core.types import ChatMessage, MessageRole
-from llama_index.core.workflow import (
-    Context,
-    StartEvent,
-    StopEvent,
-    Workflow,
-    step,
-)
-
-from app.engine.index import IndexConfig, get_index
-from app.workflows.agents import plan_research, research, write_report
-from app.workflows.events import SourceNodesEvent
-from app.workflows.models import (
-    CollectAnswersEvent,
-    DataEvent,
-    PlanResearchEvent,
-    ReportEvent,
-    ResearchEvent,
-)
-
-logger = logging.getLogger("uvicorn")
-logger.setLevel(logging.INFO)
-
-
-def create_workflow(
-    params: Optional[Dict[str, Any]] = None,
-    **kwargs,
-) -> Workflow:
-    index_config = IndexConfig(**params)
-    index = get_index(index_config)
-    if index is None:
-        raise ValueError(
-            "Index is not found. Try run generation script to create the index first."
-        )
-
-    return DeepResearchWorkflow(
-        index=index,
-        timeout=120.0,
-    )
-
-
-class DeepResearchWorkflow(Workflow):
-    """
-    A workflow to research and analyze documents from multiple perspectives and write a comprehensive report.
-
-    Requirements:
-    - An indexed documents containing the knowledge base related to the topic
-
-    Steps:
-    1. Retrieve information from the knowledge base
-    2. Analyze the retrieved information and provide questions for answering
-    3. Answer the questions
-    4. Write the report based on the research results
-    """
-
-    memory: SimpleComposableMemory
-    context_nodes: List[Node]
-    index: BaseIndex
-    user_request: str
-    stream: bool = True
-
-    def __init__(
-        self,
-        index: BaseIndex,
-        **kwargs,
-    ):
-        super().__init__(**kwargs)
-        self.index = index
-        self.context_nodes = []
-        self.memory = SimpleComposableMemory.from_defaults(
-            primary_memory=ChatMemoryBuffer.from_defaults(),
-        )
-
-    @step
-    async def retrieve(self, ctx: Context, ev: StartEvent) -> PlanResearchEvent:
-        """
-        Initiate the workflow: memory, tools, agent
-        """
-        self.stream = ev.get("stream", True)
-        self.user_request = ev.get("user_msg")
-        chat_history = ev.get("chat_history")
-        if chat_history is not None:
-            self.memory.put_messages(chat_history)
-
-        await ctx.set("total_questions", 0)
-
-        # Add user message to memory
-        self.memory.put_messages(
-            messages=[
-                ChatMessage(
-                    role=MessageRole.USER,
-                    content=self.user_request,
-                )
-            ]
-        )
-        ctx.write_event_to_stream(
-            DataEvent(
-                type="deep_research_event",
-                data={
-                    "event": "retrieve",
-                    "state": "inprogress",
-                },
-            )
-        )
-        retriever = self.index.as_retriever(
-            similarity_top_k=int(os.getenv("TOP_K", 10)),
-        )
-        nodes = retriever.retrieve(self.user_request)
-        self.context_nodes.extend(nodes)
-        ctx.write_event_to_stream(
-            DataEvent(
-                type="deep_research_event",
-                data={
-                    "event": "retrieve",
-                    "state": "done",
-                },
-            )
-        )
-        # Send source nodes to the stream
-        # Use SourceNodesEvent to display source nodes in the UI.
-        ctx.write_event_to_stream(
-            SourceNodesEvent(
-                nodes=nodes,
-            )
-        )
-        return PlanResearchEvent()
-
-    @step
-    async def analyze(
-        self, ctx: Context, ev: PlanResearchEvent
-    ) -> ResearchEvent | ReportEvent | StopEvent:
-        """
-        Analyze the retrieved information
-        """
-        logger.info("Analyzing the retrieved information")
-        ctx.write_event_to_stream(
-            DataEvent(
-                type="deep_research_event",
-                data={
-                    "event": "analyze",
-                    "state": "inprogress",
-                },
-            )
-        )
-        total_questions = await ctx.get("total_questions")
-        res = await plan_research(
-            memory=self.memory,
-            context_nodes=self.context_nodes,
-            user_request=self.user_request,
-            total_questions=total_questions,
-        )
-        if res.decision == "cancel":
-            ctx.write_event_to_stream(
-                DataEvent(
-                    type="deep_research_event",
-                    data={
-                        "event": "analyze",
-                        "state": "done",
-                    },
-                )
-            )
-            return StopEvent(
-                result=res.cancel_reason,
-            )
-        elif res.decision == "write":
-            # Writing a report without any research context is not allowed.
-            # It's a LLM hallucination.
-            if total_questions == 0:
-                ctx.write_event_to_stream(
-                    DataEvent(
-                        type="deep_research_event",
-                        data={
-                            "event": "analyze",
-                            "state": "done",
-                        },
-                    )
-                )
-                return StopEvent(
-                    result="Sorry, I have a problem when analyzing the retrieved information. Please try again.",
-                )
-
-            self.memory.put(
-                message=ChatMessage(
-                    role=MessageRole.ASSISTANT,
-                    content="No more idea to analyze. We should report the answers.",
-                )
-            )
-            ctx.send_event(ReportEvent())
-        else:
-            total_questions += len(res.research_questions)
-            await ctx.set("total_questions", total_questions)  # For tracking
-            await ctx.set(
-                "waiting_questions", len(res.research_questions)
-            )  # For waiting questions to be answered
-            self.memory.put(
-                message=ChatMessage(
-                    role=MessageRole.ASSISTANT,
-                    content="We need to find answers to the following questions:\n"
-                    + "\n".join(res.research_questions),
-                )
-            )
-            for question in res.research_questions:
-                question_id = str(uuid.uuid4())
-                ctx.write_event_to_stream(
-                    DataEvent(
-                        type="deep_research_event",
-                        data={
-                            "event": "answer",
-                            "state": "pending",
-                            "id": question_id,
-                            "question": question,
-                            "answer": None,
-                        },
-                    )
-                )
-                ctx.send_event(
-                    ResearchEvent(
-                        question_id=question_id,
-                        question=question,
-                        context_nodes=self.context_nodes,
-                    )
-                )
-        ctx.write_event_to_stream(
-            DataEvent(
-                type="deep_research_event",
-                data={
-                    "event": "analyze",
-                    "state": "done",
-                },
-            )
-        )
-        return None
-
-    @step(num_workers=2)
-    async def answer(self, ctx: Context, ev: ResearchEvent) -> CollectAnswersEvent:
-        """
-        Answer the question
-        """
-        ctx.write_event_to_stream(
-            DataEvent(
-                type="deep_research_event",
-                data={
-                    "event": "answer",
-                    "state": "inprogress",
-                    "id": ev.question_id,
-                    "question": ev.question,
-                },
-            )
-        )
-        try:
-            answer = await research(
-                context_nodes=ev.context_nodes,
-                question=ev.question,
-            )
-        except Exception as e:
-            logger.error(f"Error answering question {ev.question}: {e}")
-            answer = f"Got error when answering the question: {ev.question}"
-        ctx.write_event_to_stream(
-            DataEvent(
-                type="deep_research_event",
-                data={
-                    "event": "answer",
-                    "state": "done",
-                    "id": ev.question_id,
-                    "question": ev.question,
-                    "answer": answer,
-                },
-            )
-        )
-
-        return CollectAnswersEvent(
-            question_id=ev.question_id,
-            question=ev.question,
-            answer=answer,
-        )
-
-    @step
-    async def collect_answers(
-        self, ctx: Context, ev: CollectAnswersEvent
-    ) -> PlanResearchEvent:
-        """
-        Collect answers to all questions
-        """
-        num_questions = await ctx.get("waiting_questions")
-        results = ctx.collect_events(
-            ev,
-            expected=[CollectAnswersEvent] * num_questions,
-        )
-        if results is None:
-            return None
-        for result in results:
-            self.memory.put(
-                message=ChatMessage(
-                    role=MessageRole.ASSISTANT,
-                    content=f"<Question>{result.question}</Question>\n<Answer>{result.answer}</Answer>",
-                )
-            )
-        await ctx.set("waiting_questions", 0)
-        self.memory.put(
-            message=ChatMessage(
-                role=MessageRole.ASSISTANT,
-                content="Researched all the questions. Now, i need to analyze if it's ready to write a report or need to research more.",
-            )
-        )
-        return PlanResearchEvent()
-
-    @step
-    async def report(self, ctx: Context, ev: ReportEvent) -> StopEvent:
-        """
-        Report the answers
-        """
-        res = await write_report(
-            memory=self.memory,
-            user_request=self.user_request,
-            stream=self.stream,
-        )
-        return StopEvent(
-            result=res,
-        )

packages/create-llama/templates/components/agents/python/deep_research/app/workflows/models.py

@@ -1,43 +0,0 @@
-from typing import List, Literal, Optional
-
-from llama_index.core.schema import NodeWithScore
-from llama_index.core.workflow import Event
-from pydantic import BaseModel
-
-
-# Workflow events
-class PlanResearchEvent(Event):
-    pass
-
-
-class ResearchEvent(Event):
-    question_id: str
-    question: str
-    context_nodes: List[NodeWithScore]
-
-
-class CollectAnswersEvent(Event):
-    question_id: str
-    question: str
-    answer: str
-
-
-class ReportEvent(Event):
-    pass
-
-
-# Events that are streamed to the frontend and rendered there
-class DeepResearchEventData(BaseModel):
-    event: Literal["retrieve", "analyze", "answer"]
-    state: Literal["pending", "inprogress", "done", "error"]
-    id: Optional[str] = None
-    question: Optional[str] = None
-    answer: Optional[str] = None
-
-
-class DataEvent(Event):
-    type: Literal["deep_research_event"]
-    data: DeepResearchEventData
-
-    def to_response(self):
-        return self.model_dump()

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

@@ -1,28 +0,0 @@
-import { ChatMessage, ToolCallLLM } from "llamaindex";
-import { getTool } from "../engine/tools";
-import { FinancialReportWorkflow } from "./fin-report";
-import { getQueryEngineTool } from "./tools";
-
-const TIMEOUT = 360 * 1000;
-
-export async function createWorkflow(options: {
-  chatHistory: ChatMessage[];
-  llm?: ToolCallLLM;
-}) {
-  const queryEngineTool = await getQueryEngineTool();
-  const codeInterpreterTool = await getTool("interpreter");
-  const documentGeneratorTool = await getTool("document_generator");
-
-  if (!queryEngineTool || !codeInterpreterTool || !documentGeneratorTool) {
-    throw new Error("One or more required tools are not defined");
-  }
-
-  return new FinancialReportWorkflow({
-    chatHistory: options.chatHistory,
-    queryEngineTool,
-    codeInterpreterTool,
-    documentGeneratorTool,
-    llm: options.llm,
-    timeout: TIMEOUT,
-  });
-}

packages/create-llama/templates/components/engines/python/agent/tools/query_engine.py

@@ -1,187 +0,0 @@
-import os
-from typing import Any, Dict, List, Optional, Sequence
-
-from llama_index.core import get_response_synthesizer
-from llama_index.core.base.base_query_engine import BaseQueryEngine
-from llama_index.core.base.response.schema import RESPONSE_TYPE, Response
-from llama_index.core.multi_modal_llms import MultiModalLLM
-from llama_index.core.prompts.base import BasePromptTemplate
-from llama_index.core.prompts.default_prompt_selectors import (
-    DEFAULT_TEXT_QA_PROMPT_SEL,
-)
-from llama_index.core.query_engine.multi_modal import _get_image_and_text_nodes
-from llama_index.core.response_synthesizers.base import BaseSynthesizer, QueryTextType
-from llama_index.core.schema import (
-    ImageNode,
-    NodeWithScore,
-)
-from llama_index.core.tools.query_engine import QueryEngineTool
-from llama_index.core.types import RESPONSE_TEXT_TYPE
-
-from app.settings import get_multi_modal_llm
-
-
-def create_query_engine(index, **kwargs) -> BaseQueryEngine:
-    """
-    Create a query engine for the given index.
-
-    Args:
-        index: The index to create a query engine for.
-        params (optional): Additional parameters for the query engine, e.g: similarity_top_k
-    """
-
-    top_k = int(os.getenv("TOP_K", 0))
-    if top_k != 0 and kwargs.get("filters") is None:
-        kwargs["similarity_top_k"] = top_k
-    multimodal_llm = get_multi_modal_llm()
-    if multimodal_llm:
-        kwargs["response_synthesizer"] = MultiModalSynthesizer(
-            multimodal_model=multimodal_llm,
-        )
-
-    # If index is index is LlamaCloudIndex
-    # use auto_routed mode for better query results
-    if index.__class__.__name__ == "LlamaCloudIndex":
-        if kwargs.get("retrieval_mode") is None:
-            kwargs["retrieval_mode"] = "auto_routed"
-        if multimodal_llm:
-            kwargs["retrieve_image_nodes"] = True
-    return index.as_query_engine(**kwargs)
-
-
-def get_query_engine_tool(
-    index,
-    name: Optional[str] = None,
-    description: Optional[str] = None,
-    **kwargs,
-) -> QueryEngineTool:
-    """
-    Get a query engine tool for the given index.
-
-    Args:
-        index: The index to create a query engine for.
-        name (optional): The name of the tool.
-        description (optional): The description of the tool.
-    """
-    if name is None:
-        name = "query_index"
-    if description is None:
-        description = (
-            "Use this tool to retrieve information about the text corpus from an index."
-        )
-    query_engine = create_query_engine(index, **kwargs)
-    return QueryEngineTool.from_defaults(
-        query_engine=query_engine,
-        name=name,
-        description=description,
-    )
-
-
-class MultiModalSynthesizer(BaseSynthesizer):
-    """
-    A synthesizer that summarizes text nodes and uses a multi-modal LLM to generate a response.
-    """
-
-    def __init__(
-        self,
-        multimodal_model: MultiModalLLM,
-        response_synthesizer: Optional[BaseSynthesizer] = None,
-        text_qa_template: Optional[BasePromptTemplate] = None,
-        *args,
-        **kwargs,
-    ):
-        super().__init__(*args, **kwargs)
-        self._multi_modal_llm = multimodal_model
-        self._response_synthesizer = response_synthesizer or get_response_synthesizer()
-        self._text_qa_template = text_qa_template or DEFAULT_TEXT_QA_PROMPT_SEL
-
-    def _get_prompts(self, **kwargs) -> Dict[str, Any]:
-        return {
-            "text_qa_template": self._text_qa_template,
-        }
-
-    def _update_prompts(self, prompts: Dict[str, Any]) -> None:
-        if "text_qa_template" in prompts:
-            self._text_qa_template = prompts["text_qa_template"]
-
-    async def aget_response(
-        self,
-        *args,
-        **response_kwargs: Any,
-    ) -> RESPONSE_TEXT_TYPE:
-        return await self._response_synthesizer.aget_response(*args, **response_kwargs)
-
-    def get_response(self, *args, **kwargs) -> RESPONSE_TEXT_TYPE:
-        return self._response_synthesizer.get_response(*args, **kwargs)
-
-    async def asynthesize(
-        self,
-        query: QueryTextType,
-        nodes: List[NodeWithScore],
-        additional_source_nodes: Optional[Sequence[NodeWithScore]] = None,
-        **response_kwargs: Any,
-    ) -> RESPONSE_TYPE:
-        image_nodes, text_nodes = _get_image_and_text_nodes(nodes)
-
-        if len(image_nodes) == 0:
-            return await self._response_synthesizer.asynthesize(query, text_nodes)
-
-        # Summarize the text nodes to avoid exceeding the token limit
-        text_response = str(
-            await self._response_synthesizer.asynthesize(query, text_nodes)
-        )
-
-        fmt_prompt = self._text_qa_template.format(
-            context_str=text_response,
-            query_str=query.query_str,  # type: ignore
-        )
-
-        llm_response = await self._multi_modal_llm.acomplete(
-            prompt=fmt_prompt,
-            image_documents=[
-                image_node.node
-                for image_node in image_nodes
-                if isinstance(image_node.node, ImageNode)
-            ],
-        )
-
-        return Response(
-            response=str(llm_response),
-            source_nodes=nodes,
-            metadata={"text_nodes": text_nodes, "image_nodes": image_nodes},
-        )
-
-    def synthesize(
-        self,
-        query: QueryTextType,
-        nodes: List[NodeWithScore],
-        additional_source_nodes: Optional[Sequence[NodeWithScore]] = None,
-        **response_kwargs: Any,
-    ) -> RESPONSE_TYPE:
-        image_nodes, text_nodes = _get_image_and_text_nodes(nodes)
-
-        if len(image_nodes) == 0:
-            return self._response_synthesizer.synthesize(query, text_nodes)
-
-        # Summarize the text nodes to avoid exceeding the token limit
-        text_response = str(self._response_synthesizer.synthesize(query, text_nodes))
-
-        fmt_prompt = self._text_qa_template.format(
-            context_str=text_response,
-            query_str=query.query_str,  # type: ignore
-        )
-
-        llm_response = self._multi_modal_llm.complete(
-            prompt=fmt_prompt,
-            image_documents=[
-                image_node.node
-                for image_node in image_nodes
-                if isinstance(image_node.node, ImageNode)
-            ],
-        )
-
-        return Response(
-            response=str(llm_response),
-            source_nodes=nodes,
-            metadata={"text_nodes": text_nodes, "image_nodes": image_nodes},
-        )

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

@@ -1,57 +0,0 @@
-import {
-  BaseQueryEngine,
-  CloudRetrieveParams,
-  LlamaCloudIndex,
-  MetadataFilters,
-  QueryEngineTool,
-  VectorStoreIndex,
-} from "llamaindex";
-import { generateFilters } from "../queryFilter";
-
-interface QueryEngineParams {
-  documentIds?: string[];
-  topK?: number;
-}
-
-export function createQueryEngineTool(
-  index: VectorStoreIndex | LlamaCloudIndex,
-  params?: QueryEngineParams,
-  name?: string,
-  description?: string,
-): QueryEngineTool {
-  return new QueryEngineTool({
-    queryEngine: createQueryEngine(index, params),
-    metadata: {
-      name: name || "query_engine",
-      description:
-        description ||
-        `Use this tool to retrieve information about the text corpus from an index.`,
-    },
-  });
-}
-
-function createQueryEngine(
-  index: VectorStoreIndex | LlamaCloudIndex,
-  params?: QueryEngineParams,
-): BaseQueryEngine {
-  const baseQueryParams = {
-    similarityTopK:
-      params?.topK ??
-      (process.env.TOP_K ? parseInt(process.env.TOP_K) : undefined),
-  };
-
-  if (index instanceof LlamaCloudIndex) {
-    return index.asQueryEngine({
-      ...baseQueryParams,
-      retrieval_mode: "auto_routed",
-      preFilters: generateFilters(
-        params?.documentIds || [],
-      ) as CloudRetrieveParams["filters"],
-    });
-  }
-
-  return index.asQueryEngine({
-    ...baseQueryParams,
-    preFilters: generateFilters(params?.documentIds || []) as MetadataFilters,
-  });
-}

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

@@ -1,60 +0,0 @@
-import type { JSONSchemaType } from "ajv";
-import type { BaseTool, ToolMetadata } from "llamaindex";
-import { default as wiki } from "wikipedia";
-
-type WikipediaParameter = {
-  query: string;
-  lang?: string;
-};
-
-export type WikipediaToolParams = {
-  metadata?: ToolMetadata<JSONSchemaType<WikipediaParameter>>;
-};
-
-const DEFAULT_META_DATA: ToolMetadata<JSONSchemaType<WikipediaParameter>> = {
-  name: "wikipedia_tool",
-  description: "A tool that uses a query engine to search Wikipedia.",
-  parameters: {
-    type: "object",
-    properties: {
-      query: {
-        type: "string",
-        description: "The query to search for",
-      },
-      lang: {
-        type: "string",
-        description: "The language to search in",
-        nullable: true,
-      },
-    },
-    required: ["query"],
-  },
-};
-
-export class WikipediaTool implements BaseTool<WikipediaParameter> {
-  private readonly DEFAULT_LANG = "en";
-  metadata: ToolMetadata<JSONSchemaType<WikipediaParameter>>;
-
-  constructor(params?: WikipediaToolParams) {
-    this.metadata = params?.metadata || DEFAULT_META_DATA;
-  }
-
-  async loadData(
-    page: string,
-    lang: string = this.DEFAULT_LANG,
-  ): Promise<string> {
-    wiki.setLang(lang);
-    const pageResult = await wiki.page(page, { autoSuggest: false });
-    const content = await pageResult.content();
-    return content;
-  }
-
-  async call({
-    query,
-    lang = this.DEFAULT_LANG,
-  }: WikipediaParameter): Promise<string> {
-    const searchResult = await wiki.search(query);
-    if (searchResult.results.length === 0) return "No search results.";
-    return await this.loadData(searchResult.results[0].title, lang);
-  }
-}

packages/create-llama/templates/components/multiagent/python/app/api/callbacks/base.py

@@ -1,32 +0,0 @@
-import logging
-from abc import ABC, abstractmethod
-from typing import Any
-
-logger = logging.getLogger("uvicorn")
-
-
-class EventCallback(ABC):
-    """
-    Base class for event callbacks during event streaming.
-    """
-
-    async def run(self, event: Any) -> Any:
-        """
-        Called for each event in the stream.
-        Default behavior: pass through the event unchanged.
-        """
-        return event
-
-    async def on_complete(self, final_response: str) -> Any:
-        """
-        Called when the stream is complete.
-        Default behavior: return None.
-        """
-        return None
-
-    @abstractmethod
-    def from_default(self, *args, **kwargs) -> "EventCallback":
-        """
-        Create a new instance of the processor from default values.
-        """
-        pass

packages/create-llama/templates/components/multiagent/python/app/api/callbacks/llamacloud.py

@@ -1,42 +0,0 @@
-import logging
-from typing import Any, List
-
-from fastapi import BackgroundTasks
-from llama_index.core.schema import NodeWithScore
-
-from app.api.callbacks.base import EventCallback
-
-logger = logging.getLogger("uvicorn")
-
-
-class LlamaCloudFileDownload(EventCallback):
-    """
-    Processor for handling LlamaCloud file downloads from source nodes.
-    Only work if LlamaCloud service code is available.
-    """
-
-    def __init__(self, background_tasks: BackgroundTasks):
-        self.background_tasks = background_tasks
-
-    async def run(self, event: Any) -> Any:
-        if hasattr(event, "to_response"):
-            event_response = event.to_response()
-            if event_response.get("type") == "sources" and hasattr(event, "nodes"):
-                await self._process_response_nodes(event.nodes)
-        return event
-
-    async def _process_response_nodes(self, source_nodes: List[NodeWithScore]):
-        try:
-            from app.engine.service import LLamaCloudFileService  # type: ignore
-
-            LLamaCloudFileService.download_files_from_nodes(
-                source_nodes, self.background_tasks
-            )
-        except ImportError:
-            pass
-
-    @classmethod
-    def from_default(
-        cls, background_tasks: BackgroundTasks
-    ) -> "LlamaCloudFileDownload":
-        return cls(background_tasks=background_tasks)

packages/create-llama/templates/components/multiagent/python/app/api/callbacks/next_question.py

@@ -1,34 +0,0 @@
-import logging
-from typing import Any
-
-from app.api.callbacks.base import EventCallback
-from app.api.routers.models import ChatData
-from app.api.services.suggestion import NextQuestionSuggestion
-
-logger = logging.getLogger("uvicorn")
-
-
-class SuggestNextQuestions(EventCallback):
-    """Processor for generating next question suggestions."""
-
-    def __init__(self, chat_data: ChatData):
-        self.chat_data = chat_data
-        self.accumulated_text = ""
-
-    async def on_complete(self, final_response: str) -> Any:
-        if final_response == "":
-            return None
-
-        questions = await NextQuestionSuggestion.suggest_next_questions(
-            self.chat_data.messages, final_response
-        )
-        if questions:
-            return {
-                "type": "suggested_questions",
-                "data": questions,
-            }
-        return None
-
-    @classmethod
-    def from_default(cls, chat_data: ChatData) -> "SuggestNextQuestions":
-        return cls(chat_data=chat_data)

packages/create-llama/templates/components/multiagent/python/app/api/callbacks/stream_handler.py

@@ -1,66 +0,0 @@
-import logging
-from typing import List, Optional
-
-from llama_index.core.workflow.handler import WorkflowHandler
-
-from app.api.callbacks.base import EventCallback
-
-logger = logging.getLogger("uvicorn")
-
-
-class StreamHandler:
-    """
-    Streams events from a workflow handler through a chain of callbacks.
-    """
-
-    def __init__(
-        self,
-        workflow_handler: WorkflowHandler,
-        callbacks: Optional[List[EventCallback]] = None,
-    ):
-        self.workflow_handler = workflow_handler
-        self.callbacks = callbacks or []
-        self.accumulated_text = ""
-
-    def vercel_stream(self):
-        """Create a streaming response with Vercel format."""
-        from app.api.routers.vercel_response import VercelStreamResponse
-
-        return VercelStreamResponse(stream_handler=self)
-
-    async def cancel_run(self):
-        """Cancel the workflow handler."""
-        await self.workflow_handler.cancel_run()
-
-    async def stream_events(self):
-        """Stream events through the processor chain."""
-        try:
-            async for event in self.workflow_handler.stream_events():
-                # Process the event through each processor
-                for callback in self.callbacks:
-                    event = await callback.run(event)
-                yield event
-
-            # After all events are processed, call on_complete for each callback
-            for callback in self.callbacks:
-                result = await callback.on_complete(self.accumulated_text)
-                if result:
-                    yield result
-
-        except Exception as e:
-            # Make sure to cancel the workflow on error
-            await self.workflow_handler.cancel_run()
-            raise e
-
-    async def accumulate_text(self, text: str):
-        """Accumulate text from the workflow handler."""
-        self.accumulated_text += text
-
-    @classmethod
-    def from_default(
-        cls,
-        handler: WorkflowHandler,
-        callbacks: Optional[List[EventCallback]] = None,
-    ) -> "StreamHandler":
-        """Create a new instance with the given workflow handler and callbacks."""
-        return cls(workflow_handler=handler, callbacks=callbacks)

packages/create-llama/templates/components/multiagent/python/app/api/routers/vercel_response.py

@@ -1,99 +0,0 @@
-import asyncio
-import json
-import logging
-from typing import AsyncGenerator
-
-from fastapi.responses import StreamingResponse
-from llama_index.core.agent.workflow.workflow_events import AgentStream
-from llama_index.core.workflow import StopEvent
-
-from app.api.callbacks.stream_handler import StreamHandler
-
-logger = logging.getLogger("uvicorn")
-
-
-class VercelStreamResponse(StreamingResponse):
-    """
-    Converts preprocessed events into Vercel-compatible streaming response format.
-    """
-
-    TEXT_PREFIX = "0:"
-    DATA_PREFIX = "8:"
-    ERROR_PREFIX = "3:"
-
-    def __init__(
-        self,
-        stream_handler: StreamHandler,
-        *args,
-        **kwargs,
-    ):
-        self.handler = stream_handler
-        super().__init__(content=self.content_generator())
-
-    async def content_generator(self):
-        """Generate Vercel-formatted content from preprocessed events."""
-        stream_started = False
-        try:
-            async for event in self.handler.stream_events():
-                if not stream_started:
-                    # Start the stream with an empty message
-                    stream_started = True
-                    yield self.convert_text("")
-
-                # Handle different types of events
-                if isinstance(event, (AgentStream, StopEvent)):
-                    async for chunk in self._stream_text(event):
-                        await self.handler.accumulate_text(chunk)
-                        yield self.convert_text(chunk)
-                elif isinstance(event, dict):
-                    yield self.convert_data(event)
-                elif hasattr(event, "to_response"):
-                    event_response = event.to_response()
-                    yield self.convert_data(event_response)
-                else:
-                    yield self.convert_data(event.model_dump())
-
-        except asyncio.CancelledError:
-            logger.warning("Client cancelled the request!")
-            await self.handler.cancel_run()
-        except Exception as e:
-            logger.error(f"Error in stream response: {e}")
-            yield self.convert_error(str(e))
-            await self.handler.cancel_run()
-
-    async def _stream_text(
-        self, event: AgentStream | StopEvent
-    ) -> AsyncGenerator[str, None]:
-        """
-        Accept stream text from either AgentStream or StopEvent with string or AsyncGenerator result
-        """
-        if isinstance(event, AgentStream):
-            yield self.convert_text(event.delta)
-        elif isinstance(event, StopEvent):
-            if isinstance(event.result, str):
-                yield event.result
-            elif isinstance(event.result, AsyncGenerator):
-                async for chunk in event.result:
-                    if isinstance(chunk, str):
-                        yield chunk
-                    elif hasattr(chunk, "delta"):
-                        yield chunk.delta
-
-    @classmethod
-    def convert_text(cls, token: str) -> str:
-        """Convert text event to Vercel format."""
-        # Escape newlines and double quotes to avoid breaking the stream
-        token = json.dumps(token)
-        return f"{cls.TEXT_PREFIX}{token}\n"
-
-    @classmethod
-    def convert_data(cls, data: dict) -> str:
-        """Convert data event to Vercel format."""
-        data_str = json.dumps(data)
-        return f"{cls.DATA_PREFIX}[{data_str}]\n"
-
-    @classmethod
-    def convert_error(cls, error: str) -> str:
-        """Convert error event to Vercel format."""
-        error_str = json.dumps(error)
-        return f"{cls.ERROR_PREFIX}{error_str}\n"

packages/create-llama/templates/components/providers/typescript/anthropic/provider.ts

@@ -1,19 +0,0 @@
-import {
-  ALL_AVAILABLE_ANTHROPIC_MODELS,
-  Anthropic,
-} from "@llamaindex/anthropic";
-import { HuggingFaceEmbedding } from "@llamaindex/huggingface";
-import { Settings } from "llamaindex";
-
-export function setupProvider() {
-  const embedModelMap: Record<string, string> = {
-    "all-MiniLM-L6-v2": "Xenova/all-MiniLM-L6-v2",
-    "all-mpnet-base-v2": "Xenova/all-mpnet-base-v2",
-  };
-  Settings.llm = new Anthropic({
-    model: process.env.MODEL as keyof typeof ALL_AVAILABLE_ANTHROPIC_MODELS,
-  });
-  Settings.embedModel = new HuggingFaceEmbedding({
-    modelType: embedModelMap[process.env.EMBEDDING_MODEL!],
-  });
-}

packages/create-llama/templates/components/providers/typescript/azure-openai/provider.ts

@@ -1,49 +0,0 @@
-import { OpenAI, OpenAIEmbedding } from "@llamaindex/openai";
-import { Settings } from "llamaindex";
-
-export function setupProvider() {
-  // Map Azure OpenAI model names to OpenAI model names (only for TS)
-  const AZURE_OPENAI_MODEL_MAP: Record<string, string> = {
-    "gpt-35-turbo": "gpt-3.5-turbo",
-    "gpt-35-turbo-16k": "gpt-3.5-turbo-16k",
-    "gpt-4o": "gpt-4o",
-    "gpt-4": "gpt-4",
-    "gpt-4-32k": "gpt-4-32k",
-    "gpt-4-turbo": "gpt-4-turbo",
-    "gpt-4-turbo-2024-04-09": "gpt-4-turbo",
-    "gpt-4-vision-preview": "gpt-4-vision-preview",
-    "gpt-4-1106-preview": "gpt-4-1106-preview",
-    "gpt-4o-2024-05-13": "gpt-4o-2024-05-13",
-  };
-
-  const azureConfig = {
-    apiKey: process.env.AZURE_OPENAI_KEY,
-    endpoint: process.env.AZURE_OPENAI_ENDPOINT,
-    apiVersion:
-      process.env.AZURE_OPENAI_API_VERSION || process.env.OPENAI_API_VERSION,
-  };
-
-  Settings.llm = new OpenAI({
-    model:
-      AZURE_OPENAI_MODEL_MAP[process.env.MODEL ?? "gpt-35-turbo"] ??
-      "gpt-3.5-turbo",
-    maxTokens: process.env.LLM_MAX_TOKENS
-      ? Number(process.env.LLM_MAX_TOKENS)
-      : undefined,
-    azure: {
-      ...azureConfig,
-      deployment: process.env.AZURE_OPENAI_LLM_DEPLOYMENT,
-    },
-  });
-
-  Settings.embedModel = new OpenAIEmbedding({
-    model: process.env.EMBEDDING_MODEL,
-    dimensions: process.env.EMBEDDING_DIM
-      ? parseInt(process.env.EMBEDDING_DIM)
-      : undefined,
-    azure: {
-      ...azureConfig,
-      deployment: process.env.AZURE_OPENAI_EMBEDDING_DEPLOYMENT,
-    },
-  });
-}

packages/create-llama/templates/components/providers/typescript/gemini/provider.ts

@@ -1,16 +0,0 @@
-import {
-  Gemini,
-  GEMINI_EMBEDDING_MODEL,
-  GEMINI_MODEL,
-  GeminiEmbedding,
-} from "@llamaindex/google";
-import { Settings } from "llamaindex";
-
-export function setupProvider() {
-  Settings.llm = new Gemini({
-    model: process.env.MODEL as GEMINI_MODEL,
-  });
-  Settings.embedModel = new GeminiEmbedding({
-    model: process.env.EMBEDDING_MODEL as GEMINI_EMBEDDING_MODEL,
-  });
-}

packages/create-llama/templates/components/providers/typescript/groq/provider.ts

@@ -1,18 +0,0 @@
-import { Groq } from "@llamaindex/groq";
-import { HuggingFaceEmbedding } from "@llamaindex/huggingface";
-import { Settings } from "llamaindex";
-
-export function setupProvider() {
-  const embedModelMap: Record<string, string> = {
-    "all-MiniLM-L6-v2": "Xenova/all-MiniLM-L6-v2",
-    "all-mpnet-base-v2": "Xenova/all-mpnet-base-v2",
-  };
-
-  Settings.llm = new Groq({
-    model: process.env.MODEL!,
-  });
-
-  Settings.embedModel = new HuggingFaceEmbedding({
-    modelType: embedModelMap[process.env.EMBEDDING_MODEL!],
-  });
-}

packages/create-llama/templates/components/providers/typescript/mistral/provider.ts

@@ -1,16 +0,0 @@
-import {
-  ALL_AVAILABLE_MISTRAL_MODELS,
-  MistralAI,
-  MistralAIEmbedding,
-  MistralAIEmbeddingModelType,
-} from "@llamaindex/mistral";
-import { Settings } from "llamaindex";
-
-export function setupProvider() {
-  Settings.llm = new MistralAI({
-    model: process.env.MODEL as keyof typeof ALL_AVAILABLE_MISTRAL_MODELS,
-  });
-  Settings.embedModel = new MistralAIEmbedding({
-    model: process.env.EMBEDDING_MODEL as MistralAIEmbeddingModelType,
-  });
-}

packages/create-llama/templates/components/providers/typescript/ollama/provider.ts

@@ -1,16 +0,0 @@
-import { Ollama, OllamaEmbedding } from "@llamaindex/ollama";
-import { Settings } from "llamaindex";
-
-export function setupProvider() {
-  const config = {
-    host: process.env.OLLAMA_BASE_URL ?? "http://127.0.0.1:11434",
-  };
-  Settings.llm = new Ollama({
-    model: process.env.MODEL ?? "",
-    config,
-  });
-  Settings.embedModel = new OllamaEmbedding({
-    model: process.env.EMBEDDING_MODEL ?? "",
-    config,
-  });
-}

packages/create-llama/templates/components/providers/typescript/openai/provider.ts

@@ -1,17 +0,0 @@
-import { OpenAI, OpenAIEmbedding } from "@llamaindex/openai";
-import { Settings } from "llamaindex";
-
-export function setupProvider() {
-  Settings.llm = new OpenAI({
-    model: process.env.MODEL ?? "gpt-4o-mini",
-    maxTokens: process.env.LLM_MAX_TOKENS
-      ? Number(process.env.LLM_MAX_TOKENS)
-      : undefined,
-  });
-  Settings.embedModel = new OpenAIEmbedding({
-    model: process.env.EMBEDDING_MODEL,
-    dimensions: process.env.EMBEDDING_DIM
-      ? parseInt(process.env.EMBEDDING_DIM)
-      : undefined,
-  });
-}

packages/create-llama/templates/components/reflex/contract_review/README-template.md

@@ -1,59 +0,0 @@
-This is a [LlamaIndex](https://www.llamaindex.ai/) project using [Reflex](https://reflex.dev/) bootstrapped with [`create-llama`](https://github.com/run-llama/LlamaIndexTS/tree/main/packages/create-llama) featuring automated contract review and compliance analysis use case.
-
-## Getting Started
-
-First, setup the environment with poetry:
-
-> **_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. (E.g. you might need to configure an `OPENAI_API_KEY` if you're using OpenAI as model provider).
-
-Second, generate the embeddings of the example document in the `./data` directory:
-
-```shell
-uv run generate
-```
-
-Third, start app with `reflex` command:
-
-```shell
-uv run reflex run
-```
-
-To deploy the application, refer to the Reflex deployment guide: https://reflex.dev/docs/hosting/deploy-quick-start/
-
-### UI
-
-The application provides an interactive web interface accessible at http://localhost:3000 for testing the contract review workflow.
-
-To get started:
-
-1. Upload a contract document:
-
-   - Use the provided [example_vendor_agreement.md](./example_vendor_agreement.md) for testing
-   - Or upload your own document (supported formats: PDF, TXT, Markdown, DOCX)
-
-2. Review Process:
-   - The system will automatically analyze your document against compliance guidelines
-   - By default, it uses [GDPR](./data/gdpr.pdf) as the compliance benchmark
-   - Custom guidelines can be used by adding your policy documents to the `./data` directory and running `uv run generate` to update the embeddings
-
-The interface will display the analysis results for the compliance of the contract document.
-
-### Development
-
-You can start editing the backend workflow by modifying the [`ContractReviewWorkflow`](./app/services/contract_reviewer.py).
-
-For UI, you can start looking at the [`AppState`](./app/ui/states/app.py) code and navigating to the appropriate components.
-
-## Learn More
-
-To learn more about LlamaIndex, take a look at the following resources:
-
-- [LlamaIndex Documentation](https://docs.llamaindex.ai) - learn about LlamaIndex.
-
-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/reflex/contract_review/app/config.py

@@ -1,47 +0,0 @@
-DATA_DIR = "data"
-UPLOADED_DIR = "output/uploaded"
-
-# Workflow prompts
-CONTRACT_EXTRACT_PROMPT = """\
-You are given contract data below. \
-Please extract out relevant information from the contract into the defined schema - the schema is defined as a function call.\
-
-{contract_data}
-"""
-
-CONTRACT_MATCH_PROMPT = """\
-Given the following contract clause and the corresponding relevant guideline text, evaluate the compliance \
-and provide a JSON object that matches the ClauseComplianceCheck schema.
-
-**Contract Clause:**
-{clause_text}
-
-**Matched Guideline Text(s):**
-{guideline_text}
-"""
-
-
-COMPLIANCE_REPORT_SYSTEM_PROMPT = """\
-You are a compliance reporting assistant. Your task is to generate a final compliance report \
-based on the results of clause compliance checks against \
-a given set of guidelines. 
-
-Analyze the provided compliance results and produce a structured report according to the specified schema. 
-Ensure that if there are no noncompliant clauses, the report clearly indicates full compliance.
-"""
-
-COMPLIANCE_REPORT_USER_PROMPT = """\
-A set of clauses within a contract were checked against GDPR compliance guidelines for the following vendor: {vendor_name}. 
-The set of noncompliant clauses are given below.
-
-Each section includes:
-- **Clause:** The exact text of the contract clause.
-- **Guideline:** The relevant GDPR guideline text.
-- **Compliance Status:** Should be `False` for noncompliant clauses.
-- **Notes:** Additional information or explanations.
-
-{compliance_results}
-
-Based on the above compliance results, generate a final compliance report following the `ComplianceReport` schema below. 
-If there are no noncompliant clauses, the report should indicate that the contract is fully compliant.
-"""

packages/create-llama/templates/components/reflex/contract_review/app/models.py

@@ -1,85 +0,0 @@
-from typing import List, Optional
-
-from pydantic import BaseModel, Field
-
-
-class ContractClause(BaseModel):
-    clause_text: str = Field(..., description="The exact text of the clause.")
-    mentions_data_processing: bool = Field(
-        False,
-        description="True if the clause involves personal data collection or usage.",
-    )
-    mentions_data_transfer: bool = Field(
-        False,
-        description="True if the clause involves transferring personal data, especially to third parties or across borders.",
-    )
-    requires_consent: bool = Field(
-        False,
-        description="True if the clause explicitly states that user consent is needed for data activities.",
-    )
-    specifies_purpose: bool = Field(
-        False,
-        description="True if the clause specifies a clear purpose for data handling or transfer.",
-    )
-    mentions_safeguards: bool = Field(
-        False,
-        description="True if the clause mentions security measures or other safeguards for data.",
-    )
-
-
-class ContractExtraction(BaseModel):
-    vendor_name: Optional[str] = Field(
-        None, description="The vendor's name if identifiable."
-    )
-    effective_date: Optional[str] = Field(
-        None, description="The effective date of the agreement, if available."
-    )
-    governing_law: Optional[str] = Field(
-        None, description="The governing law of the contract, if stated."
-    )
-    clauses: List[ContractClause] = Field(
-        ..., description="List of extracted clauses and their relevant indicators."
-    )
-
-
-class GuidelineMatch(BaseModel):
-    guideline_text: str = Field(
-        ...,
-        description="The single most relevant guideline excerpt related to this clause.",
-    )
-    similarity_score: float = Field(
-        ...,
-        description="Similarity score indicating how closely the guideline matches the clause, e.g., between 0 and 1.",
-    )
-    relevance_explanation: Optional[str] = Field(
-        None, description="Brief explanation of why this guideline is relevant."
-    )
-
-
-class ClauseComplianceCheck(BaseModel):
-    clause_text: str = Field(
-        ..., description="The exact text of the clause from the contract."
-    )
-    matched_guideline: Optional[GuidelineMatch] = Field(
-        None, description="The most relevant guideline extracted via vector retrieval."
-    )
-    compliant: bool = Field(
-        ...,
-        description="Indicates whether the clause is considered compliant with the referenced guideline.",
-    )
-    notes: Optional[str] = Field(
-        None, description="Additional commentary or recommendations."
-    )
-
-
-class ComplianceReport(BaseModel):
-    vendor_name: Optional[str] = Field(
-        None, description="The vendor's name if identified from the contract."
-    )
-    overall_compliant: bool = Field(
-        ..., description="Indicates if the contract is considered overall compliant."
-    )
-    summary_notes: str = Field(
-        ...,
-        description="Always give a general summary or recommendations for achieving full compliance.",
-    )

packages/create-llama/templates/components/reflex/contract_review/app/services/contract_reviewer.py

@@ -1,360 +0,0 @@
-import logging
-import os
-import uuid
-from enum import Enum
-from pathlib import Path
-from typing import List
-
-from app.config import (
-    COMPLIANCE_REPORT_SYSTEM_PROMPT,
-    COMPLIANCE_REPORT_USER_PROMPT,
-    CONTRACT_EXTRACT_PROMPT,
-    CONTRACT_MATCH_PROMPT,
-)
-from app.engine.index import get_index
-from app.models import (
-    ClauseComplianceCheck,
-    ComplianceReport,
-    ContractClause,
-    ContractExtraction,
-)
-from llama_index.core import SimpleDirectoryReader
-from llama_index.core.llms import LLM
-from llama_index.core.prompts import ChatPromptTemplate
-from llama_index.core.retrievers import BaseRetriever
-from llama_index.core.settings import Settings
-from llama_index.core.workflow import (
-    Context,
-    Event,
-    StartEvent,
-    StopEvent,
-    Workflow,
-    step,
-)
-
-logger = logging.getLogger(__name__)
-
-
-def get_workflow():
-    index = get_index()
-    if index is None:
-        raise RuntimeError(
-            "Index not found! Please run `uv run generate` to populate an index first."
-        )
-    return ContractReviewWorkflow(
-        guideline_retriever=index.as_retriever(),
-        llm=Settings.llm,
-        verbose=True,
-        timeout=120,
-    )
-
-
-class Step(Enum):
-    PARSE_CONTRACT = "parse_contract"
-    ANALYZE_CLAUSES = "analyze_clauses"
-    HANDLE_CLAUSE = "handle_clause"
-    GENERATE_REPORT = "generate_report"
-
-
-class ContractExtractionEvent(Event):
-    contract_extraction: ContractExtraction
-
-
-class MatchGuidelineEvent(Event):
-    request_id: str
-    clause: ContractClause
-    vendor_name: str
-
-
-class MatchGuidelineResultEvent(Event):
-    result: ClauseComplianceCheck
-
-
-class GenerateReportEvent(Event):
-    match_results: List[ClauseComplianceCheck]
-
-
-class LogEvent(Event):
-    msg: str
-    step: Step
-    data: dict = {}
-    is_step_completed: bool = False
-
-
-class ContractReviewWorkflow(Workflow):
-    """Contract review workflow."""
-
-    def __init__(
-        self,
-        guideline_retriever: BaseRetriever,
-        llm: LLM | None = None,
-        similarity_top_k: int = 20,
-        **kwargs,
-    ) -> None:
-        """Init params."""
-        super().__init__(**kwargs)
-
-        self.guideline_retriever = guideline_retriever
-
-        self.llm = llm or Settings.llm
-        self.similarity_top_k = similarity_top_k
-
-        # if not exists, create
-        out_path = Path("output") / "workflow_output"
-        if not out_path.exists():
-            out_path.mkdir(parents=True, exist_ok=True)
-            os.chmod(str(out_path), 0o0777)
-        self.output_dir = out_path
-
-    @step
-    async def parse_contract(
-        self, ctx: Context, ev: StartEvent
-    ) -> ContractExtractionEvent:
-        """Parse the contract."""
-        uploaded_contract_path = Path(ev.contract_path)
-        contract_file_name = uploaded_contract_path.name
-        # Set contract file name in context
-        await ctx.set("contract_file_name", contract_file_name)
-
-        # Parse and read the contract to documents
-        docs = SimpleDirectoryReader(
-            input_files=[str(uploaded_contract_path)]
-        ).load_data()
-        ctx.write_event_to_stream(
-            LogEvent(
-                msg=f"Loaded document: {contract_file_name}",
-                step=Step.PARSE_CONTRACT,
-                data={
-                    "saved_path": str(uploaded_contract_path),
-                    "parsed_data": None,
-                },
-            )
-        )
-
-        # Parse the contract into a structured model
-        # See the ContractExtraction model for information we want to extract
-        ctx.write_event_to_stream(
-            LogEvent(
-                msg="Extracting information from the document",
-                step=Step.PARSE_CONTRACT,
-                data={
-                    "saved_path": str(uploaded_contract_path),
-                    "parsed_data": None,
-                },
-            )
-        )
-        prompt = ChatPromptTemplate.from_messages([("user", CONTRACT_EXTRACT_PROMPT)])
-        contract_extraction = await self.llm.astructured_predict(
-            ContractExtraction,
-            prompt,
-            contract_data="\n".join(
-                [d.get_content(metadata_mode="all") for d in docs]  # type: ignore
-            ),
-        )
-        if not isinstance(contract_extraction, ContractExtraction):
-            raise ValueError(f"Invalid extraction from contract: {contract_extraction}")
-
-        # save output template to file
-        contract_extraction_path = Path(f"{self.output_dir}/{contract_file_name}.json")
-        with open(contract_extraction_path, "w") as fp:
-            fp.write(contract_extraction.model_dump_json())
-
-        ctx.write_event_to_stream(
-            LogEvent(
-                msg="Extracted successfully",
-                step=Step.PARSE_CONTRACT,
-                is_step_completed=True,
-                data={
-                    "saved_path": str(contract_extraction_path),
-                    "parsed_data": contract_extraction.model_dump_json(),
-                },
-            )
-        )
-
-        return ContractExtractionEvent(contract_extraction=contract_extraction)
-
-    @step
-    async def dispatch_guideline_match(  # type: ignore
-        self, ctx: Context, ev: ContractExtractionEvent
-    ) -> MatchGuidelineEvent:
-        """For each clause in the contract, find relevant guidelines.
-
-        Use a map-reduce pattern, send each parsed clause as a MatchGuidelineEvent.
-        """
-        await ctx.set("num_clauses", len(ev.contract_extraction.clauses))
-        await ctx.set("vendor_name", ev.contract_extraction.vendor_name)
-
-        for clause in ev.contract_extraction.clauses:
-            request_id = str(uuid.uuid4())
-            ctx.send_event(
-                MatchGuidelineEvent(
-                    request_id=request_id,
-                    clause=clause,
-                    vendor_name=ev.contract_extraction.vendor_name or "Not identified",
-                )
-            )
-        ctx.write_event_to_stream(
-            LogEvent(
-                msg=f"Created {len(ev.contract_extraction.clauses)} tasks for analyzing with the guidelines",
-                step=Step.ANALYZE_CLAUSES,
-            )
-        )
-
-    @step
-    async def handle_guideline_match(
-        self, ctx: Context, ev: MatchGuidelineEvent
-    ) -> MatchGuidelineResultEvent:
-        """Handle matching clause against guideline."""
-        ctx.write_event_to_stream(
-            LogEvent(
-                msg=f"Handling clause for request {ev.request_id}",
-                step=Step.HANDLE_CLAUSE,
-                data={
-                    "request_id": ev.request_id,
-                    "clause_text": ev.clause.clause_text,
-                    "is_compliant": None,
-                },
-            )
-        )
-
-        # retrieve matching guideline
-        query = f"""\
-Find the relevant guideline from {ev.vendor_name} that aligns with the following contract clause:
-
-{ev.clause.clause_text}
-"""
-        guideline_docs = self.guideline_retriever.retrieve(query)
-        guideline_text = "\n\n".join([g.get_content() for g in guideline_docs])
-
-        # extract compliance from contract into a structured model
-        # see ClauseComplianceCheck model for the schema
-        prompt = ChatPromptTemplate.from_messages([("user", CONTRACT_MATCH_PROMPT)])
-        compliance_output = await self.llm.astructured_predict(
-            ClauseComplianceCheck,
-            prompt,
-            clause_text=ev.clause.model_dump_json(),
-            guideline_text=guideline_text,
-        )
-
-        if not isinstance(compliance_output, ClauseComplianceCheck):
-            raise ValueError(f"Invalid compliance check: {compliance_output}")
-
-        ctx.write_event_to_stream(
-            LogEvent(
-                msg=f"Completed compliance check for request {ev.request_id}",
-                step=Step.HANDLE_CLAUSE,
-                is_step_completed=True,
-                data={
-                    "request_id": ev.request_id,
-                    "clause_text": ev.clause.clause_text,
-                    "is_compliant": compliance_output.compliant,
-                    "result": compliance_output,
-                },
-            )
-        )
-
-        return MatchGuidelineResultEvent(result=compliance_output)
-
-    @step
-    async def gather_guideline_match(
-        self, ctx: Context, ev: MatchGuidelineResultEvent
-    ) -> GenerateReportEvent | None:
-        """Handle matching clause against guideline."""
-        num_clauses = await ctx.get("num_clauses")
-        events = ctx.collect_events(ev, [MatchGuidelineResultEvent] * num_clauses)
-        if events is None:
-            return None
-
-        match_results = [e.result for e in events]
-        # save match results
-        contract_file_name = await ctx.get("contract_file_name")
-        match_results_path = Path(
-            f"{self.output_dir}/match_results_{contract_file_name}.jsonl"
-        )
-        with open(match_results_path, "w") as fp:
-            for mr in match_results:
-                fp.write(mr.model_dump_json() + "\n")
-
-        ctx.write_event_to_stream(
-            LogEvent(
-                msg=f"Processed {len(match_results)} clauses",
-                step=Step.ANALYZE_CLAUSES,
-                is_step_completed=True,
-                data={"saved_path": str(match_results_path)},
-            )
-        )
-        return GenerateReportEvent(match_results=[e.result for e in events])
-
-    @step
-    async def generate_output(self, ctx: Context, ev: GenerateReportEvent) -> StopEvent:
-        ctx.write_event_to_stream(
-            LogEvent(
-                msg="Generating Compliance Report",
-                step=Step.GENERATE_REPORT,
-                data={"is_completed": False},
-            )
-        )
-
-        # if all clauses are compliant, return a compliant result
-        non_compliant_results = [r for r in ev.match_results if not r.compliant]
-
-        # generate compliance results string
-        result_tmpl = """
-1. **Clause**: {clause}
-2. **Guideline:** {guideline}
-3. **Compliance Status:** {compliance_status}
-4. **Notes:** {notes}
-"""
-        non_compliant_strings = []
-        for nr in non_compliant_results:
-            non_compliant_strings.append(
-                result_tmpl.format(
-                    clause=nr.clause_text,
-                    guideline=nr.matched_guideline.guideline_text
-                    if nr.matched_guideline is not None
-                    else "No relevant guideline found",
-                    compliance_status=nr.compliant,
-                    notes=nr.notes,
-                )
-            )
-        non_compliant_str = "\n\n".join(non_compliant_strings)
-
-        prompt = ChatPromptTemplate.from_messages(
-            [
-                ("system", COMPLIANCE_REPORT_SYSTEM_PROMPT),
-                ("user", COMPLIANCE_REPORT_USER_PROMPT),
-            ]
-        )
-        compliance_report = await self.llm.astructured_predict(
-            ComplianceReport,
-            prompt,
-            compliance_results=non_compliant_str,
-            vendor_name=await ctx.get("vendor_name"),
-        )
-
-        # Save compliance report to file
-        contract_file_name = await ctx.get("contract_file_name")
-        compliance_report_path = Path(
-            f"{self.output_dir}/report_{contract_file_name}.json"
-        )
-        with open(compliance_report_path, "w") as fp:
-            fp.write(compliance_report.model_dump_json())
-
-        ctx.write_event_to_stream(
-            LogEvent(
-                msg=f"Compliance report saved to {compliance_report_path}",
-                step=Step.GENERATE_REPORT,
-                is_step_completed=True,
-                data={
-                    "saved_path": str(compliance_report_path),
-                    "result": compliance_report,
-                },
-            )
-        )
-
-        return StopEvent(
-            result={
-                "report": compliance_report,
-                "non_compliant_results": non_compliant_results,
-            }
-        )

packages/create-llama/templates/components/reflex/contract_review/app/ui/components/__init__.py

@@ -1,9 +0,0 @@
-from .upload import upload_component
-from .workflow import guideline_component, load_contract_component, report_component
-
-__all__ = [
-    "upload_component",
-    "load_contract_component",
-    "guideline_component",
-    "report_component",
-]

packages/create-llama/templates/components/reflex/contract_review/app/ui/components/shared/__init__.py

@@ -1,5 +0,0 @@
-from .card import card_component
-
-__all__ = [
-    "card_component",
-]

packages/create-llama/templates/components/reflex/contract_review/app/ui/components/shared/card.py

@@ -1,20 +0,0 @@
-import reflex as rx
-
-
-def card_component(
-    title: str,
-    children: rx.Component,
-    show_loading: bool = False,
-) -> rx.Component:
-    return rx.card(
-        rx.hstack(
-            rx.cond(show_loading, rx.spinner(size="2")),
-            rx.text(title, size="4"),
-            align_items="center",
-            gap="2",
-        ),
-        rx.divider(orientation="horizontal"),
-        rx.container(children),
-        width="100%",
-        background_color="var(--gray-3)",
-    )

packages/create-llama/templates/components/reflex/contract_review/app/ui/components/upload.py

@@ -1,30 +0,0 @@
-import reflex as rx
-
-from app.ui.components.shared import card_component
-from app.ui.states.app import AppState
-
-
-def upload_component() -> rx.Component:
-    return card_component(
-        title="Upload",
-        children=rx.container(
-            rx.vstack(
-                rx.upload(
-                    rx.vstack(
-                        rx.text("Drag and drop files here or click to select files"),
-                    ),
-                    on_drop=AppState.handle_upload(
-                        rx.upload_files(upload_id="upload1")
-                    ),
-                    id="upload1",
-                    border="1px dotted rgb(107,99,246)",
-                    padding="1rem",
-                ),
-                rx.cond(
-                    AppState.uploaded_file != None,  # noqa: E711
-                    rx.text(AppState.uploaded_file.file_name),  # type: ignore
-                    rx.text("No file uploaded"),
-                ),
-            ),
-        ),
-    )

packages/create-llama/templates/components/reflex/contract_review/app/ui/components/workflow/__init__.py

@@ -1,9 +0,0 @@
-from .guideline import guideline_component
-from .load import load_contract_component
-from .report import report_component
-
-__all__ = [
-    "guideline_component",
-    "load_contract_component",
-    "report_component",
-]

packages/create-llama/templates/components/reflex/contract_review/app/ui/components/workflow/guideline.py

@@ -1,113 +0,0 @@
-from typing import List
-
-import reflex as rx
-
-from app.models import ClauseComplianceCheck
-from app.ui.components.shared import card_component
-from app.ui.states.workflow import GuidelineData, GuidelineHandlerState, GuidelineState
-
-
-def guideline_handler_component(item: List) -> rx.Component:
-    _id: str = item[0]
-    status: GuidelineData = item[1]
-
-    return rx.hover_card.root(
-        rx.hover_card.trigger(
-            rx.card(
-                rx.stack(
-                    rx.container(
-                        rx.cond(
-                            ~status.is_completed,
-                            rx.spinner(size="1"),
-                            rx.cond(
-                                status.is_compliant,
-                                rx.icon(tag="check", color="green"),
-                                rx.icon(tag="x", color="red"),
-                            ),
-                        ),
-                    ),
-                    rx.flex(
-                        rx.text(status.clause_text, size="1"),
-                    ),
-                ),
-            ),
-        ),
-        rx.hover_card.content(
-            rx.cond(
-                status.is_completed,
-                guideline_result_component(status.result),  # type: ignore
-                rx.spinner(size="1"),
-            ),
-            side="right",
-        ),
-    )
-
-
-def guideline_result_component(result: ClauseComplianceCheck) -> rx.Component:
-    return rx.inset(
-        rx.table.root(
-            rx.table.header(
-                rx.table.row(
-                    rx.table.cell("Clause"),
-                    rx.table.cell("Guideline"),
-                ),
-            ),
-            rx.table.body(
-                rx.table.row(
-                    # rx.table.cell("Clause"),
-                    rx.table.cell(
-                        rx.text(result.clause_text, size="1"),
-                    ),  # type: ignore
-                    rx.table.cell(
-                        rx.text(result.matched_guideline.guideline_text, size="1"),  # type: ignore
-                    ),
-                ),
-            ),
-        ),
-        rx.container(
-            rx.cond(
-                result.compliant,
-                rx.text(
-                    result.notes,  # type: ignore
-                    size="2",
-                    color="green",
-                ),
-                rx.text(
-                    result.notes,  # type: ignore
-                    size="2",
-                    color="red",
-                ),
-            )
-        ),
-    )
-
-
-def guideline_component() -> rx.Component:
-    return rx.cond(
-        GuidelineState.is_started,
-        card_component(
-            title="Analyze the document with provided guidelines",
-            children=rx.vstack(
-                rx.vstack(
-                    rx.foreach(
-                        GuidelineState.log,
-                        lambda log: rx.box(
-                            rx.text(log["msg"]),
-                        ),
-                    ),
-                ),
-                rx.cond(
-                    GuidelineHandlerState.has_data(),  # type: ignore
-                    rx.grid(
-                        rx.foreach(
-                            GuidelineHandlerState.data,
-                            guideline_handler_component,
-                        ),
-                        columns="2",
-                        spacing="1",
-                    ),
-                ),
-            ),
-            show_loading=GuidelineState.is_running,
-        ),
-    )

packages/create-llama/templates/components/reflex/contract_review/app/ui/components/workflow/load.py

@@ -1,22 +0,0 @@
-import reflex as rx
-
-from app.ui.components.shared import card_component
-from app.ui.states.workflow import ContractLoaderState
-
-
-def load_contract_component() -> rx.Component:
-    return rx.cond(
-        ContractLoaderState.is_started,
-        card_component(
-            title="Parse contract",
-            children=rx.vstack(
-                rx.foreach(
-                    ContractLoaderState.log,
-                    lambda log: rx.box(
-                        rx.text(log["msg"]),
-                    ),
-                ),
-            ),
-            show_loading=ContractLoaderState.is_running,
-        ),
-    )

packages/create-llama/templates/components/reflex/contract_review/app/ui/components/workflow/report.py

@@ -1,48 +0,0 @@
-import reflex as rx
-
-from app.ui.components.shared import card_component
-from app.ui.states.workflow import ReportState
-
-
-def report_component() -> rx.Component:
-    return rx.cond(
-        ReportState.is_running,
-        card_component(
-            title="Conclusion",
-            show_loading=~ReportState.is_completed,  # type: ignore
-            children=rx.cond(
-                ReportState.is_completed,
-                rx.vstack(
-                    rx.box(
-                        rx.inset(
-                            rx.table.root(
-                                rx.table.body(
-                                    rx.table.row(
-                                        rx.table.cell("Vendor"),
-                                        rx.table.cell(ReportState.result.vendor_name),  # type: ignore
-                                    ),
-                                    rx.table.row(
-                                        rx.table.cell("Overall Compliance"),
-                                        rx.table.cell(
-                                            rx.cond(
-                                                ReportState.result.overall_compliant,
-                                                rx.text("Compliant", color="green"),
-                                                rx.text("Non-compliant", color="red"),
-                                            )
-                                        ),
-                                    ),
-                                    rx.table.row(
-                                        rx.table.cell("Summary Notes"),
-                                        rx.table.cell(ReportState.result.summary_notes),  # type: ignore
-                                    ),
-                                ),
-                            ),
-                        )
-                    ),
-                ),
-                rx.vstack(
-                    rx.text("Analyzing compliance results for final conclusion..."),
-                ),
-            ),
-        ),
-    )

packages/create-llama/templates/components/reflex/contract_review/app/ui/pages/index.py

@@ -1,46 +0,0 @@
-import reflex as rx
-
-from app.ui.components import (
-    guideline_component,
-    load_contract_component,
-    report_component,
-    upload_component,
-)
-from app.ui.templates import template
-
-
-@template(
-    route="/",
-    title="Structure extractor",
-)
-def index() -> rx.Component:
-    """The main index page."""
-    return rx.vstack(
-        rx.vstack(
-            rx.heading("Built by LlamaIndex", size="6"),
-            rx.text(
-                "Upload a contract to start the review process.",
-            ),
-            background_color="var(--gray-3)",
-            align_items="left",
-            justify_content="left",
-            width="100%",
-            padding="1rem",
-        ),
-        rx.container(
-            rx.vstack(
-                # Upload
-                upload_component(),
-                # Workflow
-                rx.vstack(
-                    load_contract_component(),
-                    guideline_component(),
-                    report_component(),
-                    width="100%",
-                ),
-            ),
-            width="100%",
-            padding="1rem",
-        ),
-        width="100%",
-    )

packages/create-llama/templates/components/reflex/contract_review/app/ui/states/__init__.py

@@ -1,15 +0,0 @@
-from .app import AppState
-from .workflow import (
-    ContractLoaderState,
-    GuidelineHandlerState,
-    GuidelineState,
-    ReportState,
-)
-
-__all__ = [
-    "AppState",
-    "ContractLoaderState",
-    "GuidelineHandlerState",
-    "GuidelineState",
-    "ReportState",
-]

packages/create-llama/templates/components/reflex/contract_review/app/ui/states/app.py

@@ -1,94 +0,0 @@
-import logging
-import os
-from typing import List, Optional
-
-import reflex as rx
-
-from app.config import UPLOADED_DIR
-from app.services.contract_reviewer import LogEvent, Step, get_workflow
-from app.ui.states.workflow import (
-    ContractLoaderState,
-    GuidelineHandlerState,
-    GuidelineState,
-    ReportState,
-)
-
-logger = logging.getLogger(__name__)
-
-
-class UploadedFile(rx.Base):
-    file_name: str
-    size: int
-
-
-class AppState(rx.State):
-    """
-    Whole main state for the app.
-    Handle for file upload, trigger workflow and produce workflow events.
-    """
-
-    uploaded_file: Optional[UploadedFile] = None
-
-    @rx.event
-    async def handle_upload(self, files: List[rx.UploadFile]):
-        if len(files) > 1:
-            yield rx.toast.error(
-                "You can only upload one file at a time", position="top-center"
-            )
-            return
-        try:
-            file = files[0]
-            upload_data = await file.read()
-            outfile = os.path.join(UPLOADED_DIR, file.filename)
-            with open(outfile, "wb") as f:
-                f.write(upload_data)
-            self.uploaded_file = UploadedFile(
-                file_name=file.filename, size=len(upload_data)
-            )
-            yield AppState.reset_workflow
-            yield AppState.trigger_workflow
-        except Exception as e:
-            yield rx.toast.error(str(e), position="top-center")
-
-    @rx.event
-    def reset_workflow(self):
-        yield ContractLoaderState.reset_state
-        yield GuidelineState.reset_state
-        yield GuidelineHandlerState.reset_state
-        yield ReportState.reset_state
-
-    @rx.event(background=True)
-    async def trigger_workflow(self):
-        """
-        Trigger backend to start reviewing the contract in a loop.
-        Get the event from the loop and update the state.
-        """
-        if self.uploaded_file is None:
-            yield rx.toast.error("No file uploaded", position="top-center")
-        else:
-            uploaded_file_path = os.path.join(
-                UPLOADED_DIR, self.uploaded_file.file_name
-            )
-
-            try:
-                workflow = get_workflow()
-                handler = workflow.run(
-                    contract_path=uploaded_file_path,
-                )
-                async for event in handler.stream_events():
-                    if isinstance(event, LogEvent):
-                        match event.step:
-                            case Step.PARSE_CONTRACT:
-                                yield ContractLoaderState.add_log(event)
-                            case Step.ANALYZE_CLAUSES:
-                                yield GuidelineState.add_log(event)
-                            case Step.HANDLE_CLAUSE:
-                                yield GuidelineHandlerState.add_log(event)
-                            case Step.GENERATE_REPORT:
-                                yield ReportState.add_log(event)
-                # Wait for workflow completion and propagate any exceptions
-                _ = await handler
-            except Exception as e:
-                logger.error(f"Error in trigger_workflow: {e}")
-                yield rx.toast.error(str(e), position="top-center")
-                yield AppState.reset_workflow

packages/create-llama/templates/components/reflex/contract_review/app/ui/states/workflow.py

@@ -1,137 +0,0 @@
-from typing import Any, Dict, List, Optional
-
-import reflex as rx
-from pydantic import BaseModel
-
-from app.models import ClauseComplianceCheck, ComplianceReport
-from app.services.contract_reviewer import LogEvent
-from rxconfig import config as rx_config
-
-
-class ContractLoaderState(rx.State):
-    is_running: bool = False
-    is_started: bool = False
-    log: List[Dict[str, Any]] = []
-
-    @rx.event
-    async def add_log(self, log: LogEvent):
-        if not self.is_started:
-            yield ContractLoaderState.start
-        self.log.append(log.model_dump())
-        if log.is_step_completed:
-            yield ContractLoaderState.stop
-
-    def has_log(self):
-        return len(self.log) > 0
-
-    @rx.event
-    def start(self):
-        self.is_running = True
-        self.is_started = True
-
-    @rx.event
-    def stop(self):
-        self.is_running = False
-
-    @rx.event
-    def reset_state(self):
-        self.is_running = False
-        self.is_started = False
-        self.log = []
-
-
-class GuidelineState(rx.State):
-    is_running: bool = False
-    is_started: bool = False
-    log: List[Dict[str, Any]] = []
-
-    @rx.event
-    def add_log(self, log: LogEvent):
-        if not self.is_started:
-            yield GuidelineState.start
-        self.log.append(log.model_dump())
-        if log.is_step_completed:
-            yield GuidelineState.stop
-
-    def has_log(self):
-        return len(self.log) > 0
-
-    @rx.event
-    def reset_state(self):
-        self.is_running = False
-        self.is_started = False
-        self.log = []
-
-    @rx.event
-    def start(self):
-        self.is_running = True
-        self.is_started = True
-
-    @rx.event
-    def stop(self):
-        self.is_running = False
-
-
-class GuidelineData(BaseModel):
-    is_completed: bool
-    is_compliant: Optional[bool]
-    clause_text: Optional[str]
-    result: Optional[ClauseComplianceCheck] = None
-
-
-class GuidelineHandlerState(rx.State):
-    data: Dict[str, GuidelineData] = {}
-
-    def has_data(self):
-        return len(self.data) > 0
-
-    @rx.event
-    def add_log(self, log: LogEvent):
-        _id = log.data.get("request_id")
-        if _id is None:
-            return
-        is_compliant = log.data.get("is_compliant", None)
-        self.data[_id] = GuidelineData(
-            is_completed=log.is_step_completed,
-            is_compliant=is_compliant,
-            clause_text=log.data.get("clause_text", None),
-            result=log.data.get("result", None),
-        )
-        if log.is_step_completed:
-            yield self.stop(request_id=_id)
-
-    @rx.event
-    def reset_state(self):
-        self.data = {}
-
-    @rx.event
-    def stop(self, request_id: str):
-        # Update the item in the data to be completed
-        self.data[request_id].is_completed = True
-
-
-class ReportState(rx.State):
-    is_running: bool = False
-    is_completed: bool = False
-    saved_path: str = ""
-    result: Optional[ComplianceReport] = None
-
-    @rx.var()
-    def download_url(self) -> str:
-        return f"{rx_config.api_url}/api/download/{self.saved_path}"
-
-    @rx.event
-    def add_log(self, log: LogEvent):
-        if not self.is_running:
-            self.is_running = True
-        if log.is_step_completed:
-            self.is_completed = True
-            self.saved_path = log.data.get("saved_path")
-            self.result = log.data.get("result")
-
-    @rx.event
-    def reset_state(self):
-        self.is_running = False
-        self.is_completed = False
-        self.saved_path = ""
-        self.result = None

packages/create-llama/templates/components/reflex/contract_review/example_vendor_agreement.md

@@ -1,142 +0,0 @@
-# ACME Vendor Agreement
-
-**Effective Date:** January 1, 2024
-
-## Parties:
-
-- **Client:** LlamaCo ("Client")
-- **Vendor:** ACME Office Supply, Inc. ("Vendor")
-
-## 1. Overview
-
-This Vendor Agreement ("Agreement") sets forth the terms and conditions under which ACME Office Supply, Inc. will provide office supplies, consumables, related goods ("Products"), and associated data processing services to LlamaCo.
-
-## 2. Definitions
-
-- **Personal Data:** Any information relating to an identified or identifiable natural person ('data subject').
-- **Processing:** Any operation performed on Personal Data, including collection, storage, modification, transfer, or deletion.
-- **Data Controller:** LlamaCo, who determines the purposes and means of processing Personal Data.
-- **Data Processor:** ACME Office Supply, Inc., who processes Personal Data on behalf of the Controller.
-
-## 3. Data Protection and Privacy
-
-### 3.1 Scope of Processing
-
-Vendor shall process Personal Data only:
-
-- To fulfill orders and manage deliveries
-- To provide customer support services
-- To maintain business records
-- To comply with legal obligations
-
-### 3.2 Data Subject Rights
-
-Vendor shall:
-
-- Respond to data subject requests within 30 days
-- Provide data in a structured, commonly used format
-- Implement measures to facilitate data portability
-- Assist with data subject rights requests at no additional cost
-
-### 3.3 Data Transfers and Storage
-
-- Vendor maintains primary data centers in the United States
-- Vendor may transfer data to any country where it maintains operations
-- No prior notification required for new data storage locations
-- Vendor will rely on its standard data transfer mechanisms
-- Data may be processed by staff operating outside the EEA
-
-### 3.4 Subprocessors
-
-- Vendor may engage subprocessors without prior Client approval
-- Subprocessors may be located in any jurisdiction globally
-- Notice of new subprocessors provided within 30 days of engagement
-- Client has no right to object to new subprocessors
-
-## 4. Security Measures
-
-### 4.1 Technical and Organizational Measures
-
-Vendor shall implement appropriate measures including:
-
-- Encryption of Personal Data in transit and at rest
-- Access controls and authentication
-- Regular security testing and assessments
-- Employee training on data protection
-- Incident response procedures
-
-### 4.2 Data Breaches
-
-Vendor shall:
-
-- Notify Client of any Personal Data breach within 72 hours
-- Provide details necessary to meet regulatory requirements
-- Cooperate with Client's breach investigation
-- Maintain records of all data breaches
-
-## 5. Data Retention
-
-### 5.1 Retention Period
-
-- Personal Data retained only as long as necessary
-- Standard retention period of 3 years after last transaction
-- Deletion of Personal Data upon written request
-- Backup copies retained for maximum of 6 months
-
-### 5.2 Termination
-
-Upon termination of services:
-
-- Return all Personal Data in standard format
-- Delete existing copies within 30 days
-- Provide written confirmation of deletion
-- Cease all processing activities
-
-## 6. Compliance and Audit
-
-### 6.1 Documentation
-
-Vendor shall maintain:
-
-- Records of all processing activities
-- Security measure documentation
-- Data transfer mechanisms
-- Subprocessor agreements
-
-### 6.2 Audits
-
-- Annual compliance audits permitted
-- 30 days notice required for audits
-- Vendor to provide necessary documentation
-- Client bears reasonable audit costs
-
-## 7. Liability and Indemnification
-
-### 7.1 Liability
-
-- Vendor liable for data protection violations
-- Reasonable compensation for damages
-- Coverage for regulatory fines where applicable
-- Joint liability as required by law
-
-## 8. Governing Law
-
-This Agreement shall be governed by the laws of Ireland, without regard to its conflict of laws principles.
-
----
-
-IN WITNESS WHEREOF, the parties have executed this Agreement as of the Effective Date.
-
-**LlamaCo**
-
-By: **_
-Name: [Authorized Representative]  
-Title: [Title]  
-Date: _**
-
-**ACME Office Supply, Inc.**
-
-By: **_  
-Name: [Authorized Representative]  
-Title: [Title]  
-Date: _**