refactor: import from llamaindex edge for typescript templates and vectordbs

.changeset/curly-trains-obey.md

@@ -1,5 +0,0 @@
----
-"create-llama": patch
----
-
-Remove UI question (use shadcn as default). Use `html` UI by calling create-llama with --ui html parameter

.changeset/curvy-candles-mix.md

@@ -1,5 +0,0 @@
----
-"create-llama": patch
----
-
-Update loaders and tools config to yaml format

.changeset/dry-seals-sniff.md

@@ -1,5 +0,0 @@
----
-"create-llama": patch
----
-
-Let user select multiple datasources (URLs, files and folders)

.changeset/itchy-seahorses-move.md

@@ -1,5 +0,0 @@
----
-"create-llama": patch
----
-
-Merge non-streaming and streaming template to one

.changeset/quiet-actors-help.md

@@ -1,5 +0,0 @@
----
-"create-llama": patch
----
-
-Add support for agent generation for Typescript

.changeset/shaggy-ads-fix.md

@@ -0,0 +1,5 @@
+---
+"create-llama": patch
+---
+
+Add fetching llm and embedding models from server

.changeset/tasty-hairs-wait.md

@@ -2,4 +2,4 @@
 "create-llama": patch
 ---
 
-Add Dockerfile template
+Add Milvus vector database

.github/workflows/e2e.yml

@@ -46,11 +46,14 @@ jobs:
       - name: Build create-llama
         run: pnpm run build
         working-directory: .
-      - name: Install
-        run: pnpm run pack-install
+      - name: Pack
+        run: pnpm pack --pack-destination ./output
+        working-directory: .
+      - name: Extract Pack
+        run: tar -xvzf ./output/*.tgz -C ./output
         working-directory: .
       - name: Run Playwright tests
-        run: pnpm run e2e
+        run: pnpm exec playwright test
         env:
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
         working-directory: .

.gitignore

@@ -45,6 +45,3 @@ e2e/cache
 
 # intellij
 **/.idea
-
-# build artifacts
-create-llama-*.tgz

CHANGELOG.md

@@ -1,21 +1,5 @@
 # create-llama
 
-## 0.0.31
-
-### Patch Changes
-
-- 56faee0: Added windows e2e tests
-- 60ed8fe: Added missing environment variable config for URL data source
-- 60ed8fe: Fixed tool usage by freezing llama-index package versions
-
-## 0.0.30
-
-### Patch Changes
-
-- 3af6328: Add support for llamaparse using Typescript
-- dd92b91: Add fetching llm and embedding models from server
-- bac1b43: Add Milvus vector database
-
 ## 0.0.29
 
 ### Patch Changes

CONTRIBUTING.md

@@ -1,73 +0,0 @@
-# Contributing
-
-## Getting Started
-
-Install NodeJS. Preferably v18 using nvm or n.
-
-Inside the `create-llama` directory:
-
-```
-npm i -g pnpm ts-node
-pnpm install
-```
-
-Note: we use pnpm in this repo, which has a lot of the same functionality and CLI options as npm but it does do some things better, like caching.
-
-### Building
-
-When we publish to NPM we will have a [ncc](https://github.com/vercel/ncc) compiled version of the tool. To run the build command, run
-
-```
-pnpm run build
-```
-
-### Test cases
-
-We are using a set of e2e tests to ensure that the tool works as expected.
-
-We're using [playwright](https://playwright.dev/) to run the tests.
-To install it, call:
-
-```
-pnpm exec playwright install --with-deps
-```
-
-Then you can create a global `create-llama` command (used by the e2e tests) that is linked to your local dev environment (if you update the build, you don't need to re-link):
-
-```
-pnpm link --global
-```
-
-And then finally run the tests:
-
-```
-pnpm run e2e
-```
-
-To write new test cases write them in [e2e](/e2e)
-
-## Changeset
-
-We use [changesets](https://github.com/changesets/changesets) for managing versions and changelogs. To create a new changeset, run:
-
-```
-pnpm changeset
-```
-
-Please send a descriptive changeset for each PR.
-
-## Publishing (maintainers only)
-
-To publish a new version of the library, first create a new version:
-
-```shell
-pnpm new-version
-```
-
-If everything looks good, commit the generated files and release the new version:
-
-```shell
-pnpm release
-git push # push to the main branch
-git push --tags
-```

create-app.ts

@@ -26,9 +26,11 @@ export type InstallAppArgs = Omit<
 export async function createApp({
   template,
   framework,
+  engine,
   ui,
   appPath,
   packageManager,
+  eslint,
   frontend,
   openAiKey,
   llamaCloudKey,
@@ -39,9 +41,8 @@ export async function createApp({
   vectorDb,
   externalPort,
   postInstallAction,
-  dataSources,
+  dataSource,
   tools,
-  useLlamaParse,
   observability,
 }: InstallAppArgs): Promise<void> {
   const root = path.resolve(appPath);
@@ -74,9 +75,11 @@ export async function createApp({
     root,
     template,
     framework,
+    engine,
     ui,
     packageManager,
     isOnline,
+    eslint,
     openAiKey,
     llamaCloudKey,
     model,
@@ -86,9 +89,8 @@ export async function createApp({
     vectorDb,
     externalPort,
     postInstallAction,
-    dataSources,
+    dataSource,
     tools,
-    useLlamaParse,
     observability,
   };
 
@@ -125,13 +127,11 @@ export async function createApp({
   }
 
   if (toolsRequireConfig(tools)) {
-    const configFile =
-      framework === "fastapi" ? "config/tools.yaml" : "config/tools.json";
     console.log(
       yellow(
         `You have selected tools that require configuration. Please configure them in the ${terminalLink(
-          configFile,
-          `file://${root}/${configFile}`,
+          "tools_config.json",
+          `file://${root}/tools_config.json`,
         )} file.`,
       ),
     );

e2e/basic.spec.ts

@@ -4,6 +4,7 @@ import { ChildProcess } from "child_process";
 import fs from "fs";
 import path from "path";
 import type {
+  TemplateEngine,
   TemplateFramework,
   TemplatePostInstallAction,
   TemplateType,
@@ -11,13 +12,13 @@ import type {
 } from "../helpers";
 import { createTestDir, runCreateLlama, type AppType } from "./utils";
 
-const templateTypes: TemplateType[] = ["streaming"];
+const templateTypes: TemplateType[] = ["streaming", "simple"];
 const templateFrameworks: TemplateFramework[] = [
   "nextjs",
   "express",
   "fastapi",
 ];
-const dataSources: string[] = ["--no-files", "--example-file"];
+const templateEngines: TemplateEngine[] = ["simple", "context"];
 const templateUIs: TemplateUI[] = ["shadcn", "html"];
 const templatePostInstallActions: TemplatePostInstallAction[] = [
   "none",
@@ -26,12 +27,24 @@ const templatePostInstallActions: TemplatePostInstallAction[] = [
 
 for (const templateType of templateTypes) {
   for (const templateFramework of templateFrameworks) {
-    for (const dataSource of dataSources) {
+    for (const templateEngine of templateEngines) {
       for (const templateUI of templateUIs) {
         for (const templatePostInstallAction of templatePostInstallActions) {
+          if (templateFramework === "nextjs" && templateType === "simple") {
+            // nextjs doesn't support simple templates - skip tests
+            continue;
+          }
           const appType: AppType =
-            templateFramework === "nextjs" ? "" : "--frontend";
-          test.describe(`try create-llama ${templateType} ${templateFramework} ${dataSource} ${templateUI} ${appType} ${templatePostInstallAction}`, async () => {
+            templateFramework === "express" || templateFramework === "fastapi"
+              ? templateType === "simple"
+                ? "--no-frontend" // simple templates don't have frontends
+                : "--frontend"
+              : "";
+          if (appType === "--no-frontend" && templateUI !== "html") {
+            // if there's no frontend, don't iterate over UIs
+            continue;
+          }
+          test.describe(`try create-llama ${templateType} ${templateFramework} ${templateEngine} ${templateUI} ${appType} ${templatePostInstallAction}`, async () => {
             let port: number;
             let externalPort: number;
             let cwd: string;
@@ -48,7 +61,7 @@ for (const templateType of templateTypes) {
                 cwd,
                 templateType,
                 templateFramework,
-                dataSource,
+                templateEngine,
                 templateUI,
                 vectorDb,
                 appType,
@@ -66,6 +79,7 @@ for (const templateType of templateTypes) {
             });
             test("Frontend should have a title", async ({ page }) => {
               test.skip(templatePostInstallAction !== "runApp");
+              test.skip(appType === "--no-frontend");
               await page.goto(`http://localhost:${port}`);
               await expect(page.getByText("Built by LlamaIndex")).toBeVisible();
             });
@@ -74,6 +88,7 @@ for (const templateType of templateTypes) {
               page,
             }) => {
               test.skip(templatePostInstallAction !== "runApp");
+              test.skip(appType === "--no-frontend");
               await page.goto(`http://localhost:${port}`);
               await page.fill("form input", "hello");
               const [response] = await Promise.all([
@@ -94,13 +109,14 @@ for (const templateType of templateTypes) {
               expect(response.ok()).toBeTruthy();
             });
 
-            test("Backend frameworks should response when calling non-streaming chat API", async ({
+            test("Backend should response when calling API", async ({
               request,
             }) => {
               test.skip(templatePostInstallAction !== "runApp");
-              test.skip(templateFramework === "nextjs");
+              test.skip(appType !== "--no-frontend");
+              const backendPort = appType === "" ? port : externalPort;
               const response = await request.post(
-                `http://localhost:${externalPort}/api/chat/request`,
+                `http://localhost:${backendPort}/api/chat`,
                 {
                   data: {
                     messages: [

e2e/utils.ts

@@ -4,6 +4,7 @@ import { mkdir } from "node:fs/promises";
 import * as path from "path";
 import waitPort from "wait-port";
 import {
+  TemplateEngine,
   TemplateFramework,
   TemplatePostInstallAction,
   TemplateType,
@@ -66,7 +67,7 @@ export async function runCreateLlama(
   cwd: string,
   templateType: TemplateType,
   templateFramework: TemplateFramework,
-  dataSource: string,
+  templateEngine: TemplateEngine,
   templateUI: TemplateUI,
   vectorDb: TemplateVectorDB,
   appType: AppType,
@@ -74,24 +75,32 @@ export async function runCreateLlama(
   externalPort: number,
   postInstallAction: TemplatePostInstallAction,
 ): Promise<CreateLlamaResult> {
-  if (!process.env.OPENAI_API_KEY) {
-    throw new Error("Setting OPENAI_API_KEY is mandatory to run tests");
-  }
+  const createLlama = path.join(
+    __dirname,
+    "..",
+    "output",
+    "package",
+    "dist",
+    "index.js",
+  );
+
   const name = [
     templateType,
     templateFramework,
-    dataSource,
+    templateEngine,
     templateUI,
     appType,
   ].join("-");
   const command = [
-    "create-llama",
+    "node",
+    createLlama,
     name,
     "--template",
     templateType,
     "--framework",
     templateFramework,
-    dataSource,
+    "--engine",
+    templateEngine,
     "--ui",
     templateUI,
     "--vector-db",
@@ -101,9 +110,10 @@ export async function runCreateLlama(
     "--embedding-model",
     EMBEDDING_MODEL,
     "--open-ai-key",
-    process.env.OPENAI_API_KEY,
+    process.env.OPENAI_API_KEY || "testKey",
     appType,
-    "--use-pnpm",
+    "--eslint",
+    "--use-npm",
     "--port",
     port,
     "--external-port",

helpers/datasources.ts

@@ -1,30 +0,0 @@
-import path from "path";
-import { templatesDir } from "./dir";
-import { TemplateDataSource } from "./types";
-
-export const EXAMPLE_FILE: TemplateDataSource = {
-  type: "file",
-  config: {
-    path: path.join(templatesDir, "components", "data", "101.pdf"),
-  },
-};
-
-export function getDataSources(
-  files?: string,
-  exampleFile?: boolean,
-): TemplateDataSource[] | undefined {
-  let dataSources: TemplateDataSource[] | undefined = undefined;
-  if (files) {
-    // If user specified files option, then the program should use context engine
-    dataSources = files.split(",").map((filePath) => ({
-      type: "file",
-      config: {
-        path: filePath,
-      },
-    }));
-  }
-  if (exampleFile) {
-    dataSources = [...(dataSources ? dataSources : []), EXAMPLE_FILE];
-  }
-  return dataSources;
-}

helpers/env-variables.ts

@@ -1,6 +1,7 @@
 import fs from "fs/promises";
 import path from "path";
 import {
+  FileSourceConfig,
   TemplateDataSource,
   TemplateFramework,
   TemplateVectorDB,
@@ -98,6 +99,28 @@ const getVectorDBEnvs = (vectorDb: TemplateVectorDB) => {
   }
 };
 
+const getDataSourceEnvs = (dataSource: TemplateDataSource) => {
+  switch (dataSource.type) {
+    case "web":
+      return [
+        {
+          name: "BASE_URL",
+          description: "The base URL to start web scraping.",
+        },
+        {
+          name: "URL_PREFIX",
+          description: "The prefix of the URL to start web scraping.",
+        },
+        {
+          name: "MAX_DEPTH",
+          description: "The maximum depth to scrape.",
+        },
+      ];
+    default:
+      return [];
+  }
+};
+
 export const createBackendEnvFile = async (
   root: string,
   opts: {
@@ -107,7 +130,7 @@ export const createBackendEnvFile = async (
     model?: string;
     embeddingModel?: string;
     framework?: TemplateFramework;
-    dataSources?: TemplateDataSource[];
+    dataSource?: TemplateDataSource;
     port?: number;
   },
 ) => {
@@ -126,13 +149,10 @@ export const createBackendEnvFile = async (
       description: "The OpenAI API key to use.",
       value: opts.openAiKey,
     },
-    {
-      name: "LLAMA_CLOUD_API_KEY",
-      description: `The Llama Cloud API key.`,
-      value: opts.llamaCloudKey,
-    },
     // Add vector database environment variables
     ...(opts.vectorDb ? getVectorDBEnvs(opts.vectorDb) : []),
+    // Add data source environment variables
+    ...(opts.dataSource ? getDataSourceEnvs(opts.dataSource) : []),
   ];
   let envVars: EnvVar[] = [];
   if (opts.framework === "fastapi") {
@@ -184,6 +204,13 @@ We have provided context information below.
 Given this information, please answer the question: {query_str}
 "`,
         },
+        (opts?.dataSource?.config as FileSourceConfig).useLlamaParse
+          ? {
+              name: "LLAMA_CLOUD_API_KEY",
+              description: `The Llama Cloud API key.`,
+              value: opts.llamaCloudKey,
+            }
+          : {},
       ],
     ];
   } else {

helpers/index.ts

@@ -1,9 +1,11 @@
+import { copy } from "./copy";
 import { callPackageManager } from "./install";
 
+import fs from "fs/promises";
 import path from "path";
 import { cyan } from "picocolors";
 
-import fsExtra from "fs-extra";
+import { templatesDir } from "./dir";
 import { createBackendEnvFile, createFrontendEnvFile } from "./env-variables";
 import { PackageManager } from "./get-pkg-manager";
 import { installLlamapackProject } from "./llama-pack";
@@ -25,8 +27,8 @@ async function generateContextData(
   packageManager?: PackageManager,
   openAiKey?: string,
   vectorDb?: TemplateVectorDB,
+  dataSource?: TemplateDataSource,
   llamaCloudKey?: string,
-  useLlamaParse?: boolean,
 ) {
   if (packageManager) {
     const runGenerate = `${cyan(
@@ -35,31 +37,35 @@ async function generateContextData(
         : `${packageManager} run generate`,
     )}`;
     const openAiKeyConfigured = openAiKey || process.env["OPENAI_API_KEY"];
-    const llamaCloudKeyConfigured = useLlamaParse
+    const llamaCloudKeyConfigured = (dataSource?.config as FileSourceConfig)
+      ?.useLlamaParse
       ? llamaCloudKey || process.env["LLAMA_CLOUD_API_KEY"]
       : true;
     const hasVectorDb = vectorDb && vectorDb !== "none";
-    if (openAiKeyConfigured && llamaCloudKeyConfigured && !hasVectorDb) {
-      // If all the required environment variables are set, run the generate script
-      if (framework === "fastapi") {
-        if (isHavingPoetryLockFile()) {
-          console.log(`Running ${runGenerate} to generate the context data.`);
-          const result = tryPoetryRun("python app/engine/generate.py");
-          if (!result) {
-            console.log(`Failed to run ${runGenerate}.`);
-            process.exit(1);
-          }
-          console.log(`Generated context data`);
-          return;
+    if (framework === "fastapi") {
+      if (
+        openAiKeyConfigured &&
+        llamaCloudKeyConfigured &&
+        !hasVectorDb &&
+        isHavingPoetryLockFile()
+      ) {
+        console.log(`Running ${runGenerate} to generate the context data.`);
+        const result = tryPoetryRun("python app/engine/generate.py");
+        if (!result) {
+          console.log(`Failed to run ${runGenerate}.`);
+          process.exit(1);
         }
-      } else {
+        console.log(`Generated context data`);
+        return;
+      }
+    } else {
+      if (openAiKeyConfigured && vectorDb === "none") {
         console.log(`Running ${runGenerate} to generate the context data.`);
         await callPackageManager(packageManager, true, ["run", "generate"]);
         return;
       }
     }
 
-    // generate the message of what to do to run the generate script manually
     const settings = [];
     if (!openAiKeyConfigured) settings.push("your OpenAI key");
     if (!llamaCloudKeyConfigured) settings.push("your Llama Cloud key");
@@ -73,16 +79,38 @@ async function generateContextData(
 
 const copyContextData = async (
   root: string,
-  dataSources: TemplateDataSource[],
+  dataSource?: TemplateDataSource,
 ) => {
-  for (const dataSource of dataSources) {
-    const dataSourceConfig = dataSource?.config as FileSourceConfig;
-    // Copy local data
-    const dataPath = dataSourceConfig.path;
+  const destPath = path.join(root, "data");
 
-    const destPath = path.join(root, "data", path.basename(dataPath));
-    console.log("Copying data from path:", dataPath);
-    await fsExtra.copy(dataPath, destPath);
+  const dataSourceConfig = dataSource?.config as FileSourceConfig;
+
+  // Copy file
+  if (dataSource?.type === "file") {
+    if (dataSourceConfig.path) {
+      console.log(`\nCopying file to ${cyan(destPath)}\n`);
+      await fs.mkdir(destPath, { recursive: true });
+      await fs.copyFile(
+        dataSourceConfig.path,
+        path.join(destPath, path.basename(dataSourceConfig.path)),
+      );
+    } else {
+      console.log("Missing file path in config");
+      process.exit(1);
+    }
+    return;
+  }
+
+  // Copy folder
+  if (dataSource?.type === "folder") {
+    const srcPath =
+      dataSourceConfig.path ?? path.join(templatesDir, "components", "data");
+    console.log(`\nCopying data to ${cyan(destPath)}\n`);
+    await copy("**", destPath, {
+      parents: true,
+      cwd: srcPath,
+    });
+    return;
   }
 };
 
@@ -132,16 +160,12 @@ export const installTemplate = async (
       model: props.model,
       embeddingModel: props.embeddingModel,
       framework: props.framework,
-      dataSources: props.dataSources,
+      dataSource: props.dataSource,
       port: props.externalPort,
     });
 
-    if (props.dataSources.length > 0) {
-      console.log("\nGenerating context data...\n");
-      await copyContextData(
-        props.root,
-        props.dataSources.filter((ds) => ds.type === "file"),
-      );
+    if (props.engine === "context") {
+      await copyContextData(props.root, props.dataSource);
       if (
         props.postInstallAction === "runApp" ||
         props.postInstallAction === "dependencies"
@@ -151,14 +175,14 @@ export const installTemplate = async (
           props.packageManager,
           props.openAiKey,
           props.vectorDb,
+          props.dataSource,
           props.llamaCloudKey,
-          props.useLlamaParse,
         );
       }
     }
   } else {
     // this is a frontend for a full-stack app, create .env file with model information
-    await createFrontendEnvFile(props.root, {
+    createFrontendEnvFile(props.root, {
       model: props.model,
       customApiPath: props.customApiPath,
     });

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 yaml, { Document } from "yaml";
 import { copy } from "./copy";
 import { templatesDir } from "./dir";
 import { isPoetryAvailable, tryPoetryInstall } from "./poetry";
 import { Tool } from "./tools";
 import {
+  FileSourceConfig,
   InstallTemplateArgs,
   TemplateDataSource,
   TemplateVectorDB,
-  WebSourceConfig,
 } from "./types";
 
 interface Dependency {
@@ -55,17 +54,13 @@ const getAdditionalDependencies = (
         name: "llama-index-vector-stores-milvus",
         version: "^0.1.6",
       });
-      dependencies.push({
-        name: "pymilvus",
-        version: "2.3.7",
-      });
       break;
     }
   }
 
   // Add data source dependencies
   const dataSourceType = dataSource?.type;
-  if (dataSourceType === "file") {
+  if (dataSourceType === "file" || dataSourceType === "folder") {
     // llama-index-readers-file (pdf, excel, csv) is already included in llama_index package
     dependencies.push({
       name: "docx2txt",
@@ -178,20 +173,20 @@ export const installPythonTemplate = async ({
   root,
   template,
   framework,
+  engine,
   vectorDb,
-  dataSources,
+  dataSource,
   tools,
   postInstallAction,
-  useLlamaParse,
 }: Pick<
   InstallTemplateArgs,
   | "root"
   | "framework"
   | "template"
+  | "engine"
   | "vectorDb"
-  | "dataSources"
+  | "dataSource"
   | "tools"
-  | "useLlamaParse"
   | "postInstallAction"
 >) => {
   console.log("\nInitializing Python project with template:", template, "\n");
@@ -216,10 +211,9 @@ export const installPythonTemplate = async ({
     },
   });
 
-  const compPath = path.join(templatesDir, "components");
-
-  if (dataSources.length > 0) {
+  if (engine === "context") {
     const enginePath = path.join(root, "app", "engine");
+    const compPath = path.join(templatesDir, "components");
 
     const vectorDbDirName = vectorDb ?? "none";
     const VectorDBPath = path.join(
@@ -239,14 +233,16 @@ export const installPythonTemplate = async ({
         parents: true,
         cwd: path.join(compPath, "engines", "python", "agent"),
       });
-      // Write tool configs
+      // Write tools_config.json
       const configContent: Record<string, any> = {};
       tools.forEach((tool) => {
         configContent[tool.name] = tool.config ?? {};
       });
-      const configFilePath = path.join(root, "config/tools.yaml");
-      await fs.mkdir(path.join(root, "config"), { recursive: true });
-      await fs.writeFile(configFilePath, yaml.stringify(configContent));
+      const configFilePath = path.join(root, "tools_config.json");
+      await fs.writeFile(
+        configFilePath,
+        JSON.stringify(configContent, null, 2),
+      );
     } else {
       await copy("**", enginePath, {
         parents: true,
@@ -254,78 +250,30 @@ export const installPythonTemplate = async ({
       });
     }
 
-    const loaderConfig = new Document({});
-    const loaderPath = path.join(enginePath, "loaders");
-
-    // Copy loaders to enginePath
-    await copy("**", loaderPath, {
-      parents: true,
-      cwd: path.join(compPath, "loaders", "python"),
-    });
-
-    // Generate loaders config
-    // Web loader config
-    if (dataSources.some((ds) => ds.type === "web")) {
-      const webLoaderConfig = new Document({});
-
-      // Create config for browser driver arguments
-      const driverArgNodeValue = webLoaderConfig.createNode([
-        "--no-sandbox",
-        "--disable-dev-shm-usage",
-      ]);
-      driverArgNodeValue.commentBefore =
-        " The arguments to pass to the webdriver. E.g.: add --headless to run in headless mode";
-      webLoaderConfig.set("driver_arguments", driverArgNodeValue);
-
-      // Create config for urls
-      const urlConfigs = dataSources
-        .filter((ds) => ds.type === "web")
-        .map((ds) => {
-          const dsConfig = ds.config as WebSourceConfig;
-          return {
-            base_url: dsConfig.baseUrl,
-            prefix: dsConfig.prefix,
-            depth: dsConfig.depth,
-          };
-        });
-      const urlConfigNode = webLoaderConfig.createNode(urlConfigs);
-      urlConfigNode.commentBefore = ` base_url: The URL to start crawling with
- prefix: Only crawl URLs matching the specified prefix
- depth: The maximum depth for BFS traversal
- You can add more websites by adding more entries (don't forget the - prefix from YAML)`;
-      webLoaderConfig.set("urls", urlConfigNode);
-
-      // Add web config to the loaders config
-      loaderConfig.set("web", webLoaderConfig);
-    }
-    // File loader config
-    if (dataSources.some((ds) => ds.type === "file")) {
-      // Add documentation to web loader config
-      const node = loaderConfig.createNode({
-        use_llama_parse: useLlamaParse,
+    const dataSourceType = dataSource?.type;
+    if (dataSourceType !== undefined && dataSourceType !== "none") {
+      let loaderFolder: string;
+      if (dataSourceType === "file" || dataSourceType === "folder") {
+        const dataSourceConfig = dataSource?.config as FileSourceConfig;
+        loaderFolder = dataSourceConfig.useLlamaParse ? "llama_parse" : "file";
+      } else {
+        loaderFolder = dataSourceType;
+      }
+      await copy("**", enginePath, {
+        parents: true,
+        cwd: path.join(compPath, "loaders", "python", loaderFolder),
       });
-      node.commentBefore = ` use_llama_parse: Use LlamaParse if \`true\`. Needs a \`LLAMA_CLOUD_API_KEY\` from https://cloud.llamaindex.ai set as environment variable`;
-      loaderConfig.set("file", node);
-    }
-    // Write loaders config
-    if (Object.keys(loaderConfig).length > 0) {
-      const loaderConfigPath = path.join(root, "config/loaders.yaml");
-      await fs.mkdir(path.join(root, "config"), { recursive: true });
-      await fs.writeFile(loaderConfigPath, yaml.stringify(loaderConfig));
     }
   }
 
-  const addOnDependencies = dataSources
-    .map((ds) => getAdditionalDependencies(vectorDb, ds, tools))
-    .flat();
+  const addOnDependencies = getAdditionalDependencies(
+    vectorDb,
+    dataSource,
+    tools,
+  );
   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/tools.ts

@@ -1,12 +1,10 @@
 import { red } from "picocolors";
-import { TemplateFramework } from "./types";
 
 export type Tool = {
   display: string;
   name: string;
   config?: Record<string, any>;
   dependencies?: ToolDependencies[];
-  supportedFrameworks?: Array<TemplateFramework>;
 };
 export type ToolDependencies = {
   name: string;
@@ -29,7 +27,6 @@ export const supportedTools: Tool[] = [
         version: "0.1.2",
       },
     ],
-    supportedFrameworks: ["fastapi"],
   },
   {
     display: "Wikipedia",
@@ -40,7 +37,6 @@ export const supportedTools: Tool[] = [
         version: "0.1.2",
       },
     ],
-    supportedFrameworks: ["fastapi", "express", "nextjs"],
   },
 ];
 

helpers/types.ts

@@ -1,8 +1,9 @@
 import { PackageManager } from "../helpers/get-pkg-manager";
 import { Tool } from "./tools";
 
-export type TemplateType = "streaming" | "community" | "llamapack";
+export type TemplateType = "simple" | "streaming" | "community" | "llamapack";
 export type TemplateFramework = "nextjs" | "express" | "fastapi";
+export type TemplateEngine = "simple" | "context";
 export type TemplateUI = "html" | "shadcn";
 export type TemplateVectorDB = "none" | "mongo" | "pg" | "pinecone" | "milvus";
 export type TemplatePostInstallAction =
@@ -14,18 +15,17 @@ export type TemplateDataSource = {
   type: TemplateDataSourceType;
   config: TemplateDataSourceConfig;
 };
-export type TemplateDataSourceType = "file" | "web";
+export type TemplateDataSourceType = "none" | "file" | "folder" | "web";
 export type TemplateObservability = "none" | "opentelemetry";
 // Config for both file and folder
 export type FileSourceConfig = {
-  path: string;
+  path?: string;
+  useLlamaParse?: boolean;
 };
 export type WebSourceConfig = {
   baseUrl?: string;
-  prefix?: string;
   depth?: number;
 };
-
 export type TemplateDataSourceConfig = FileSourceConfig | WebSourceConfig;
 
 export type CommunityProjectConfig = {
@@ -42,12 +42,13 @@ export interface InstallTemplateArgs {
   isOnline: boolean;
   template: TemplateType;
   framework: TemplateFramework;
+  engine: TemplateEngine;
   ui: TemplateUI;
-  dataSources: TemplateDataSource[];
+  dataSource?: TemplateDataSource;
+  eslint: boolean;
   customApiPath?: string;
   openAiKey?: string;
   llamaCloudKey?: string;
-  useLlamaParse?: boolean;
   model: string;
   embeddingModel: string;
   communityProjectConfig?: CommunityProjectConfig;

helpers/typescript.ts

@@ -6,7 +6,6 @@ import { copy } from "../helpers/copy";
 import { callPackageManager } from "../helpers/install";
 import { templatesDir } from "./dir";
 import { PackageManager } from "./get-pkg-manager";
-import { makeDir } from "./make-dir";
 import { InstallTemplateArgs } from "./types";
 
 const rename = (name: string) => {
@@ -57,15 +56,14 @@ export const installTSTemplate = async ({
   isOnline,
   template,
   framework,
+  engine,
   ui,
+  eslint,
   customApiPath,
   vectorDb,
   postInstallAction,
   backend,
   observability,
-  tools,
-  dataSources,
-  useLlamaParse,
 }: InstallTemplateArgs & { backend: boolean }) => {
   console.log(bold(`Using ${packageManager}.`));
 
@@ -75,6 +73,7 @@ export const installTSTemplate = async ({
   console.log("\nInitializing project with template:", template, "\n");
   const templatePath = path.join(templatesDir, "types", template, framework);
   const copySource = ["**"];
+  if (!eslint) copySource.push("!eslintrc.json");
 
   await copy(copySource, root, {
     parents: true,
@@ -119,7 +118,6 @@ export const installTSTemplate = async ({
     }
   }
 
-  // copy observability component
   if (observability && observability !== "none") {
     const chosenObservabilityPath = path.join(
       templatesDir,
@@ -140,65 +138,32 @@ export const installTSTemplate = async ({
   /**
    * Copy the selected chat engine files to the target directory and reference it.
    */
+  let relativeEngineDestPath;
   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");
+  if (engine && (framework === "express" || framework === "nextjs")) {
+    console.log("\nUsing chat engine:", engine, "\n");
 
-  if (dataSources.length === 0) {
-    // use simple hat engine if user neither select tools nor a data source
-    console.log("\nUsing simple chat engine\n");
-  } else {
-    if (vectorDb) {
-      // copy vector db component
+    let vectorDBFolder: string = engine;
+
+    if (engine !== "simple" && vectorDb) {
       console.log("\nUsing vector DB:", vectorDb, "\n");
-      const vectorDBPath = path.join(
-        compPath,
-        "vectordbs",
-        "typescript",
-        vectorDb,
-      );
-      await copy("**", enginePath, {
-        parents: true,
-        cwd: vectorDBPath,
-      });
+      vectorDBFolder = vectorDb;
     }
-    // copy loader component (TS only supports llama_parse and file for now)
-    let loaderFolder: string;
-    loaderFolder = useLlamaParse ? "llama_parse" : "file";
-    await copy("**", enginePath, {
-      parents: true,
-      cwd: path.join(compPath, "loaders", "typescript", loaderFolder),
-    });
-    if (tools?.length) {
-      // use agent chat engine if user selects tools
-      console.log("\nUsing agent chat engine\n");
-      await copy("**", enginePath, {
-        parents: true,
-        cwd: path.join(compPath, "engines", "typescript", "agent"),
-      });
 
-      // Write config/tools.json
-      const configContent: Record<string, any> = {};
-      tools.forEach((tool) => {
-        configContent[tool.name] = tool.config ?? {};
-      });
-      const configPath = path.join(root, "config");
-      await makeDir(configPath);
-      await fs.writeFile(
-        path.join(configPath, "tools.json"),
-        JSON.stringify(configContent, null, 2),
-      );
-    } else {
-      // use context chat engine if user does not select tools
-      console.log("\nUsing context chat engine\n");
-      await copy("**", enginePath, {
-        parents: true,
-        cwd: path.join(compPath, "engines", "typescript", "chat"),
-      });
-    }
+    const VectorDBPath = path.join(
+      compPath,
+      "vectordbs",
+      "typescript",
+      vectorDBFolder,
+    );
+    relativeEngineDestPath =
+      framework === "nextjs"
+        ? path.join("app", "api", "chat")
+        : path.join("src", "controllers");
+    await copy("**", path.join(root, relativeEngineDestPath, "engine"), {
+      parents: true,
+      cwd: VectorDBPath,
+    });
   }
 
   /**
@@ -240,7 +205,7 @@ export const installTSTemplate = async ({
     // modify the dev script to use the custom api path
   }
 
-  if (dataSources.length > 0 && relativeEngineDestPath) {
+  if (engine === "context" && relativeEngineDestPath) {
     // add generate script if using context engine
     packageJson.scripts = {
       ...packageJson.scripts,
@@ -287,6 +252,14 @@ export const installTSTemplate = async ({
     };
   }
 
+  if (!eslint) {
+    // Remove packages starting with "eslint" from devDependencies
+    packageJson.devDependencies = Object.fromEntries(
+      Object.entries(packageJson.devDependencies).filter(
+        ([key]) => !key.startsWith("eslint"),
+      ),
+    );
+  }
   await fs.writeFile(
     packageJsonFile,
     JSON.stringify(packageJson, null, 2) + os.EOL,
@@ -295,9 +268,4 @@ export const installTSTemplate = async ({
   if (postInstallAction === "runApp" || postInstallAction === "dependencies") {
     await installTSDependencies(packageJson, packageManager, isOnline);
   }
-
-  // Copy deployment files for typescript
-  await copy("**", root, {
-    cwd: path.join(compPath, "deployments", "typescript"),
-  });
 };

index.ts

@@ -1,3 +1,4 @@
+#!/usr/bin/env node
 /* eslint-disable import/no-extraneous-dependencies */
 import { execSync } from "child_process";
 import Commander from "commander";
@@ -9,7 +10,6 @@ import prompts from "prompts";
 import terminalLink from "terminal-link";
 import checkForUpdate from "update-check";
 import { createApp } from "./create-app";
-import { getDataSources } from "./helpers/datasources";
 import { getPkgManager } from "./helpers/get-pkg-manager";
 import { isFolderEmpty } from "./helpers/is-folder-empty";
 import { runApp } from "./helpers/run-app";
@@ -32,6 +32,13 @@ const program = new Commander.Command(packageJson.name)
   .action((name) => {
     projectPath = name;
   })
+  .option(
+    "--eslint",
+    `
+
+  Initialize with eslint config.
+`,
+  )
   .option(
     "--use-npm",
     `
@@ -65,6 +72,13 @@ const program = new Commander.Command(packageJson.name)
     `
 
   Select a template to bootstrap the application with.
+`,
+  )
+  .option(
+    "--engine <engine>",
+    `
+
+  Select a chat engine to bootstrap the application with.
 `,
   )
   .option(
@@ -79,13 +93,6 @@ const program = new Commander.Command(packageJson.name)
     `
   
     Specify the path to a local file or folder for chatting.
-`,
-  )
-  .option(
-    "--example-file",
-    `
-
-  Select to use an example PDF as data source.
 `,
   )
   .option(
@@ -158,7 +165,7 @@ const program = new Commander.Command(packageJson.name)
 `,
   )
   .option(
-    "--use-llama-parse",
+    "--llama-parse",
     `
     Enable LlamaParse.
 `,
@@ -182,6 +189,9 @@ const program = new Commander.Command(packageJson.name)
 if (process.argv.includes("--no-frontend")) {
   program.frontend = false;
 }
+if (process.argv.includes("--no-eslint")) {
+  program.eslint = false;
+}
 if (process.argv.includes("--tools")) {
   if (program.tools === "none") {
     program.tools = [];
@@ -190,12 +200,7 @@ if (process.argv.includes("--tools")) {
   }
 }
 if (process.argv.includes("--no-llama-parse")) {
-  program.useLlamaParse = false;
-}
-if (process.argv.includes("--no-files")) {
-  program.dataSources = [];
-} else {
-  program.dataSources = getDataSources(program.files, program.exampleFile);
+  program.llamaParse = false;
 }
 
 const packageManager = !!program.useNpm
@@ -283,9 +288,11 @@ async function run(): Promise<void> {
   await createApp({
     template: program.template,
     framework: program.framework,
+    engine: program.engine,
     ui: program.ui,
     appPath: resolvedProjectPath,
     packageManager,
+    eslint: program.eslint,
     frontend: program.frontend,
     openAiKey: program.openAiKey,
     llamaCloudKey: program.llamaCloudKey,
@@ -296,9 +303,8 @@ async function run(): Promise<void> {
     vectorDb: program.vectorDb,
     externalPort: program.externalPort,
     postInstallAction: program.postInstallAction,
-    dataSources: program.dataSources,
+    dataSource: program.dataSource,
     tools: program.tools,
-    useLlamaParse: program.useLlamaParse,
     observability: program.observability,
   });
   conf.set("preferences", preferences);

package.json

@@ -1,6 +1,6 @@
 {
   "name": "create-llama",
-  "version": "0.0.31",
+  "version": "0.0.29",
   "keywords": [
     "rag",
     "llamaindex",
@@ -24,16 +24,12 @@
     "format": "prettier --ignore-unknown --cache --check .",
     "format:write": "prettier --ignore-unknown --write .",
     "dev": "ncc build ./index.ts -w -o dist/",
-    "build": "bash ./scripts/build.sh",
-    "build:ncc": "pnpm run clean && ncc build ./index.ts -o ./dist/ --minify --no-cache --no-source-map-register",
+    "build": "npm run clean && ncc build ./index.ts -o ./dist/ --minify --no-cache --no-source-map-register",
     "lint": "eslint . --ignore-pattern dist --ignore-pattern e2e/cache",
     "e2e": "playwright test",
     "prepare": "husky",
     "release": "pnpm run build && changeset publish",
-    "new-version": "pnpm run build && changeset version",
-    "release-snapshot": "pnpm run build && changeset publish --tag snapshot",
-    "new-snapshot": "pnpm run build && changeset version --snapshot",
-    "pack-install": "bash ./scripts/pack.sh"
+    "new-version": "pnpm run build && changeset version"
   },
   "devDependencies": {
     "@playwright/test": "^1.41.1",
@@ -44,7 +40,6 @@
     "@types/prompts": "2.0.1",
     "@types/tar": "6.1.5",
     "@types/validate-npm-package-name": "3.0.0",
-    "@types/fs-extra": "11.0.4",
     "@vercel/ncc": "0.38.1",
     "async-retry": "1.3.1",
     "async-sema": "3.0.1",
@@ -70,9 +65,7 @@
     "prettier-plugin-organize-imports": "^3.2.4",
     "typescript": "^5.3.3",
     "eslint-config-prettier": "^8.10.0",
-    "ora": "^8.0.1",
-    "fs-extra": "11.2.0",
-    "yaml": "2.4.1"
+    "ora": "^8.0.1"
   },
   "engines": {
     "node": ">=16.14.0"

pnpm-lock.yaml

@@ -20,9 +20,6 @@ devDependencies:
   '@types/cross-spawn':
     specifier: 6.0.0
     version: 6.0.0
-  '@types/fs-extra':
-    specifier: 11.0.4
-    version: 11.0.4
   '@types/node':
     specifier: ^20.11.7
     version: 20.11.26
@@ -65,9 +62,6 @@ devDependencies:
   fast-glob:
     specifier: 3.3.1
     version: 3.3.1
-  fs-extra:
-    specifier: 11.2.0
-    version: 11.2.0
   got:
     specifier: 10.7.0
     version: 10.7.0
@@ -113,9 +107,6 @@ devDependencies:
   wait-port:
     specifier: ^1.1.0
     version: 1.1.0
-  yaml:
-    specifier: 2.4.1
-    version: 2.4.1
 
 packages:
 
@@ -498,23 +489,10 @@ packages:
       '@types/node': 20.11.26
     dev: true
 
-  /@types/fs-extra@11.0.4:
-    resolution: {integrity: sha512-yTbItCNreRooED33qjunPthRcSjERP1r4MqCZc7wv0u2sUkzTFp45tgUfS5+r7FrZPdmCCNflLhVSP/o+SemsQ==}
-    dependencies:
-      '@types/jsonfile': 6.1.4
-      '@types/node': 20.11.26
-    dev: true
-
   /@types/http-cache-semantics@4.0.4:
     resolution: {integrity: sha512-1m0bIFVc7eJWyve9S0RnuRgcQqF/Xd5QsUZAZeQFr1Q3/p9JWoQQEqmVy+DPTNpGXwhgIetAoYF8JSc33q29QA==}
     dev: true
 
-  /@types/jsonfile@6.1.4:
-    resolution: {integrity: sha512-D5qGUYwjvnNNextdU59/+fI+spnwtTFmyQP0h+PfIOSkNfpU6AOICUOkm4i0OnSk+NyjdPJrxCDro0sJsWlRpQ==}
-    dependencies:
-      '@types/node': 20.11.26
-    dev: true
-
   /@types/keyv@3.1.4:
     resolution: {integrity: sha512-BQ5aZNSCpj7D6K2ksrRCTmKRLEpnPvWDiLPfoGyhZ++8YtiK9d/3DBKPJgry359X/P1PfruyYwvnvwFjuEiEIg==}
     dependencies:
@@ -1459,15 +1437,6 @@ packages:
       signal-exit: 4.1.0
     dev: true
 
-  /fs-extra@11.2.0:
-    resolution: {integrity: sha512-PmDi3uwK5nFuXh7XDTlVnS17xJS7vW36is2+w3xcv8SVxiB4NyATf4ctkVY5bkSjX0Y4nbvZCq1/EjtEyr9ktw==}
-    engines: {node: '>=14.14'}
-    dependencies:
-      graceful-fs: 4.2.11
-      jsonfile: 6.1.0
-      universalify: 2.0.1
-    dev: true
-
   /fs-extra@7.0.1:
     resolution: {integrity: sha512-YJDaCJZEnBmcbw13fvdAM9AwNOJwOzrE4pqMqBq5nFiEqXUqHwlK4B+3pUw6JNvfSPtX05xFHtYy/1ni01eGCw==}
     engines: {node: '>=6 <7 || >=8'}
@@ -2013,14 +1982,6 @@ packages:
       graceful-fs: 4.2.11
     dev: true
 
-  /jsonfile@6.1.0:
-    resolution: {integrity: sha512-5dgndWOriYSm5cnYaJNhalLNDKOqFwyDB/rr1E9ZsGciGvKPs8R2xYGCacuf3z6K1YKDz182fd+fY3cn3pMqXQ==}
-    dependencies:
-      universalify: 2.0.1
-    optionalDependencies:
-      graceful-fs: 4.2.11
-    dev: true
-
   /keyv@4.5.4:
     resolution: {integrity: sha512-oxVHkHR/EJf2CNXnWxRLW6mg7JyCCUcG0DtEGmL2ctUo1PNTin1PUil+r/+4r5MpVgC/fn1kjsx7mjSujKqIpw==}
     dependencies:
@@ -3224,11 +3185,6 @@ packages:
     engines: {node: '>= 4.0.0'}
     dev: true
 
-  /universalify@2.0.1:
-    resolution: {integrity: sha512-gptHNQghINnc/vTGIk0SOFGFNXw7JVrlRUtConJRlvaw6DuX0wO5Jeko9sWrMBhh+PsYAZ7oXAiOnf/UKogyiw==}
-    engines: {node: '>= 10.0.0'}
-    dev: true
-
   /update-check@1.5.4:
     resolution: {integrity: sha512-5YHsflzHP4t1G+8WGPlvKbJEbAJGCgw+Em+dGR1KmBUbr1J36SJBqlHLjR7oob7sco5hWHGQVcr9B2poIVDDTQ==}
     dependencies:
@@ -3369,12 +3325,6 @@ packages:
     resolution: {integrity: sha512-3wdGidZyq5PB084XLES5TpOSRA3wjXAlIWMhum2kRcv/41Sn2emQ0dycQW4uZXLejwKvg6EsvbdlVL+FYEct7A==}
     dev: true
 
-  /yaml@2.4.1:
-    resolution: {integrity: sha512-pIXzoImaqmfOrL7teGUBt/T7ZDnyeGBWyXQBvOVhLkWLN37GXv8NMLK406UY6dS51JfcQHsmcW5cJ441bHg6Lg==}
-    engines: {node: '>= 14'}
-    hasBin: true
-    dev: true
-
   /yargs-parser@18.1.3:
     resolution: {integrity: sha512-o50j0JeToy/4K6OZcaQmW6lyXXKhq7csREXcDwk2omFPJEwUNOVtJKvmDr9EI1fAJZUyZcRF7kxGBWmRXudrCQ==}
     engines: {node: '>=6'}

questions.ts

@@ -8,12 +8,11 @@ import { blue, green, red } from "picocolors";
 import prompts from "prompts";
 import { InstallAppArgs } from "./create-app";
 import {
-  TemplateDataSource,
+  FileSourceConfig,
   TemplateDataSourceType,
   TemplateFramework,
 } from "./helpers";
 import { COMMUNITY_OWNER, COMMUNITY_REPO } from "./helpers/constant";
-import { EXAMPLE_FILE } from "./helpers/datasources";
 import { templatesDir } from "./helpers/dir";
 import { getAvailableLlamapackOptions } from "./helpers/llama-pack";
 import { getProjectOptions } from "./helpers/repo";
@@ -25,6 +24,8 @@ export type QuestionArgs = Omit<
   InstallAppArgs,
   "appPath" | "packageManager"
 > & {
+  files?: string;
+  llamaParse?: boolean;
   listServerModels?: boolean;
 };
 const supportedContextFileTypes = [
@@ -39,22 +40,21 @@ const MACOS_FILE_SELECTION_SCRIPT = `
 osascript -l JavaScript -e '
   a = Application.currentApplication();
   a.includeStandardAdditions = true;
-  a.chooseFile({ withPrompt: "Please select files to process:", multipleSelectionsAllowed: true }).map(file => file.toString())
+  a.chooseFile({ withPrompt: "Please select a file to process:" }).toString()
 '`;
 const MACOS_FOLDER_SELECTION_SCRIPT = `
 osascript -l JavaScript -e '
   a = Application.currentApplication();
   a.includeStandardAdditions = true;
-  a.chooseFolder({ withPrompt: "Please select folders to process:", multipleSelectionsAllowed: true }).map(folder => folder.toString())
+  a.chooseFolder({ withPrompt: "Please select a folder to process:" }).toString()
 '`;
 const WINDOWS_FILE_SELECTION_SCRIPT = `
 Add-Type -AssemblyName System.Windows.Forms
 $openFileDialog = New-Object System.Windows.Forms.OpenFileDialog
 $openFileDialog.InitialDirectory = [Environment]::GetFolderPath('Desktop')
-$openFileDialog.Multiselect = $true
 $result = $openFileDialog.ShowDialog()
 if ($result -eq 'OK') {
-  $openFileDialog.FileNames
+  $openFileDialog.FileName
 }
 `;
 const WINDOWS_FOLDER_SELECTION_SCRIPT = `
@@ -70,17 +70,21 @@ if ($dialogResult -eq [System.Windows.Forms.DialogResult]::OK)
 const defaults: QuestionArgs = {
   template: "streaming",
   framework: "nextjs",
-  ui: "shadcn",
+  engine: "simple",
+  ui: "html",
+  eslint: true,
   frontend: false,
   openAiKey: "",
   llamaCloudKey: "",
-  useLlamaParse: false,
   model: "gpt-3.5-turbo",
   embeddingModel: "text-embedding-ada-002",
   communityProjectConfig: undefined,
   llamapack: "",
   postInstallAction: "dependencies",
-  dataSources: [],
+  dataSource: {
+    type: "none",
+    config: {},
+  },
   tools: [],
 };
 
@@ -118,42 +122,24 @@ const getVectorDbChoices = (framework: TemplateFramework) => {
   return displayedChoices;
 };
 
-export const getDataSourceChoices = (
-  framework: TemplateFramework,
-  selectedDataSource: TemplateDataSource[],
-) => {
-  const choices = [];
-  if (selectedDataSource.length > 0) {
-    choices.push({
-      title: "No",
-      value: "no",
-    });
-  }
-  if (selectedDataSource === undefined || selectedDataSource.length === 0) {
-    choices.push({
+const getDataSourceChoices = (framework: TemplateFramework) => {
+  const choices = [
+    {
       title: "No data, just a simple chat",
-      value: "none",
+      value: "simple",
+    },
+    { title: "Use an example PDF", value: "exampleFile" },
+  ];
+  if (process.platform === "win32" || process.platform === "darwin") {
+    choices.push({
+      title: `Use a local file (${supportedContextFileTypes.join(", ")})`,
+      value: "localFile",
     });
     choices.push({
-      title: "Use an example PDF",
-      value: "exampleFile",
+      title: `Use a local folder`,
+      value: "localFolder",
     });
   }
-
-  choices.push(
-    {
-      title: `Use local files (${supportedContextFileTypes.join(", ")})`,
-      value: "file",
-    },
-    {
-      title:
-        process.platform === "win32"
-          ? "Use a local folder"
-          : "Use local folders",
-      value: "folder",
-    },
-  );
-
   if (framework === "fastapi") {
     choices.push({
       title: "Use website content (requires Chrome)",
@@ -187,16 +173,9 @@ const selectLocalContextData = async (type: TemplateDataSourceType) => {
         process.exit(1);
     }
     selectedPath = execSync(execScript, execOpts).toString().trim();
-    const paths =
-      process.platform === "win32"
-        ? selectedPath.split("\r\n")
-        : selectedPath.split(", ");
-
-    for (const p of paths) {
-      if (
-        fs.statSync(p).isFile() &&
-        !supportedContextFileTypes.includes(path.extname(p))
-      ) {
+    if (type === "file") {
+      const fileType = path.extname(selectedPath);
+      if (!supportedContextFileTypes.includes(fileType)) {
         console.log(
           red(
             `Please select a supported file type: ${supportedContextFileTypes}`,
@@ -205,7 +184,7 @@ const selectLocalContextData = async (type: TemplateDataSourceType) => {
         process.exit(1);
       }
     }
-    return paths;
+    return selectedPath;
   } catch (error) {
     console.log(
       red(
@@ -330,7 +309,9 @@ export const askQuestions = async (
         const openAiKeyConfigured =
           program.openAiKey || process.env["OPENAI_API_KEY"];
         // If using LlamaParse, require LlamaCloud API key
-        const llamaCloudKeyConfigured = program.useLlamaParse
+        const llamaCloudKeyConfigured = (
+          program.dataSource?.config as FileSourceConfig
+        )?.useLlamaParse
           ? program.llamaCloudKey || process.env["LLAMA_CLOUD_API_KEY"]
           : true;
         const hasVectorDb = program.vectorDb && program.vectorDb !== "none";
@@ -378,7 +359,8 @@ export const askQuestions = async (
           name: "template",
           message: "Which template would you like to use?",
           choices: [
-            { title: "Chat", value: "streaming" },
+            { title: "Chat without streaming", value: "simple" },
+            { title: "Chat with streaming", value: "streaming" },
             {
               title: `Community template from ${styledRepo}`,
               value: "community",
@@ -388,7 +370,7 @@ export const askQuestions = async (
               value: "llamapack",
             },
           ],
-          initial: 0,
+          initial: 1,
         },
         handlers,
       );
@@ -447,10 +429,13 @@ export const askQuestions = async (
       program.framework = getPrefOrDefault("framework");
     } else {
       const choices = [
-        { title: "NextJS", value: "nextjs" },
         { title: "Express", value: "express" },
         { title: "FastAPI (Python)", value: "fastapi" },
       ];
+      if (program.template === "streaming") {
+        // allow NextJS only for streaming template
+        choices.unshift({ title: "NextJS", value: "nextjs" });
+      }
 
       const { framework } = await prompts(
         {
@@ -467,7 +452,10 @@ export const askQuestions = async (
     }
   }
 
-  if (program.framework === "express" || program.framework === "fastapi") {
+  if (
+    program.template === "streaming" &&
+    (program.framework === "express" || program.framework === "fastapi")
+  ) {
     // if a backend-only framework is selected, ask whether we should create a frontend
     // (only for streaming backends)
     if (program.frontend === undefined) {
@@ -501,7 +489,25 @@ export const askQuestions = async (
 
   if (program.framework === "nextjs" || program.frontend) {
     if (!program.ui) {
-      program.ui = getPrefOrDefault("ui");
+      if (ciInfo.isCI) {
+        program.ui = getPrefOrDefault("ui");
+      } else {
+        const { ui } = await prompts(
+          {
+            type: "select",
+            name: "ui",
+            message: "Which UI would you like to use?",
+            choices: [
+              { title: "Just HTML", value: "html" },
+              { title: "Shadcn", value: "shadcn" },
+            ],
+            initial: 0,
+          },
+          handlers,
+        );
+        program.ui = ui;
+        preferences.ui = ui;
+      }
     }
   }
 
@@ -601,110 +607,131 @@ export const askQuestions = async (
     }
   }
 
-  if (!program.dataSources) {
-    if (ciInfo.isCI) {
-      program.dataSources = getPrefOrDefault("dataSources");
+  if (program.files) {
+    // If user specified files option, then the program should use context engine
+    program.engine == "context";
+    if (!fs.existsSync(program.files)) {
+      console.log("File or folder not found");
+      process.exit(1);
     } else {
-      program.dataSources = [];
-      // continue asking user for data sources if none are initially provided
-      while (true) {
-        const firstQuestion = program.dataSources.length === 0;
-        const { selectedSource } = await prompts(
-          {
-            type: "select",
-            name: "selectedSource",
-            message: firstQuestion
-              ? "Which data source would you like to use?"
-              : "Would you like to add another data source?",
-            choices: getDataSourceChoices(
-              program.framework,
-              program.dataSources,
-            ),
-            initial: firstQuestion ? 1 : 0,
-          },
-          handlers,
-        );
-
-        if (selectedSource === "no" || selectedSource === "none") {
-          // user doesn't want another data source or any data source
-          break;
-        }
-        if (selectedSource === "exampleFile") {
-          program.dataSources.push(EXAMPLE_FILE);
-        } else if (selectedSource === "file" || selectedSource === "folder") {
-          // Select local data source
-          const selectedPaths = await selectLocalContextData(selectedSource);
-          for (const p of selectedPaths) {
-            program.dataSources.push({
-              type: "file",
-              config: {
-                path: p,
-              },
-            });
-          }
-        } else if (selectedSource === "web") {
-          // Selected web data source
-          const { baseUrl } = await prompts(
-            {
-              type: "text",
-              name: "baseUrl",
-              message: "Please provide base URL of the website: ",
-              initial: "https://www.llamaindex.ai",
-              validate: (value: string) => {
-                if (!value.includes("://")) {
-                  value = `https://${value}`;
-                }
-                const urlObj = new URL(value);
-                if (
-                  urlObj.protocol !== "https:" &&
-                  urlObj.protocol !== "http:"
-                ) {
-                  return `URL=${value} has invalid protocol, only allow http or https`;
-                }
-                return true;
-              },
-            },
-            handlers,
-          );
-
-          program.dataSources.push({
-            type: "web",
-            config: {
-              baseUrl,
-              prefix: baseUrl,
-              depth: 1,
-            },
-          });
-        }
-      }
+      program.dataSource = {
+        type: fs.lstatSync(program.files).isDirectory() ? "folder" : "file",
+        config: {
+          path: program.files,
+        },
+      };
     }
   }
 
-  // Asking for LlamaParse if user selected file or folder data source
-  if (
-    program.dataSources.some((ds) => ds.type === "file") &&
-    program.useLlamaParse === undefined
-  ) {
+  if (!program.engine) {
     if (ciInfo.isCI) {
-      program.useLlamaParse = getPrefOrDefault("useLlamaParse");
-      program.llamaCloudKey = getPrefOrDefault("llamaCloudKey");
+      program.engine = getPrefOrDefault("engine");
     } else {
-      const { useLlamaParse } = await prompts(
+      const { dataSource } = await prompts(
         {
-          type: "toggle",
-          name: "useLlamaParse",
-          message:
-            "Would you like to use LlamaParse (improved parser for RAG - requires API key)?",
-          initial: false,
-          active: "yes",
-          inactive: "no",
+          type: "select",
+          name: "dataSource",
+          message: "Which data source would you like to use?",
+          choices: getDataSourceChoices(program.framework),
+          initial: 1,
         },
         handlers,
       );
-      program.useLlamaParse = useLlamaParse;
+      // Initialize with default config
+      program.dataSource = getPrefOrDefault("dataSource");
+      if (program.dataSource) {
+        switch (dataSource) {
+          case "simple":
+            program.engine = "simple";
+            program.dataSource = { type: "none", config: {} };
+            break;
+          case "exampleFile":
+            program.engine = "context";
+            // Treat example as a folder data source with no config
+            program.dataSource = { type: "folder", config: {} };
+            break;
+          case "localFile":
+            program.engine = "context";
+            program.dataSource = {
+              type: "file",
+              config: {
+                path: await selectLocalContextData("file"),
+              },
+            };
+            break;
+          case "localFolder":
+            program.engine = "context";
+            program.dataSource = {
+              type: "folder",
+              config: {
+                path: await selectLocalContextData("folder"),
+              },
+            };
+            break;
+          case "web":
+            program.engine = "context";
+            program.dataSource.type = "web";
+            break;
+        }
+      }
+    }
+  } else if (!program.dataSource) {
+    // Handle a case when engine is specified but dataSource is not
+    if (program.engine === "context") {
+      program.dataSource = {
+        type: "folder",
+        config: {},
+      };
+    } else if (program.engine === "simple") {
+      program.dataSource = {
+        type: "none",
+        config: {},
+      };
+    }
+  }
+
+  if (
+    (program.dataSource?.type === "file" ||
+      program.dataSource?.type === "folder") &&
+    program.framework === "fastapi"
+  ) {
+    if (ciInfo.isCI) {
+      program.llamaCloudKey = getPrefOrDefault("llamaCloudKey");
+    } else {
+      const dataSourceConfig = program.dataSource.config as FileSourceConfig;
+      dataSourceConfig.useLlamaParse = program.llamaParse;
+
+      // Is pdf file selected as data source or is it a folder data source
+      const askingLlamaParse =
+        dataSourceConfig.useLlamaParse === undefined &&
+        (program.dataSource.type === "folder"
+          ? true
+          : dataSourceConfig.path &&
+            path.extname(dataSourceConfig.path) === ".pdf");
+
+      // Ask if user wants to use LlamaParse
+      if (askingLlamaParse) {
+        const { useLlamaParse } = await prompts(
+          {
+            type: "toggle",
+            name: "useLlamaParse",
+            message:
+              "Would you like to use LlamaParse (improved parser for RAG - requires API key)?",
+            initial: true,
+            active: "yes",
+            inactive: "no",
+          },
+          handlers,
+        );
+        dataSourceConfig.useLlamaParse = useLlamaParse;
+        program.dataSource.config = dataSourceConfig;
+      }
 
       // Ask for LlamaCloud API key
-      if (useLlamaParse && program.llamaCloudKey === undefined) {
+      if (
+        dataSourceConfig.useLlamaParse &&
+        program.llamaCloudKey === undefined
+      ) {
         const { llamaCloudKey } = await prompts(
           {
             type: "text",
@@ -719,7 +746,39 @@ export const askQuestions = async (
     }
   }
 
-  if (program.dataSources.length > 0 && !program.vectorDb) {
+  if (program.dataSource?.type === "web" && program.framework === "fastapi") {
+    let { baseUrl } = await prompts(
+      {
+        type: "text",
+        name: "baseUrl",
+        message: "Please provide base URL of the website:",
+        initial: "https://www.llamaindex.ai",
+      },
+      handlers,
+    );
+    try {
+      if (!baseUrl.includes("://")) {
+        baseUrl = `https://${baseUrl}`;
+      }
+      const checkUrl = new URL(baseUrl);
+      if (checkUrl.protocol !== "https:" && checkUrl.protocol !== "http:") {
+        throw new Error("Invalid protocol");
+      }
+    } catch (error) {
+      console.log(
+        red(
+          "Invalid URL provided! Please provide a valid URL (e.g. https://www.llamaindex.ai)",
+        ),
+      );
+      process.exit(1);
+    }
+    program.dataSource.config = {
+      baseUrl: baseUrl,
+      depth: 1,
+    };
+  }
+
+  if (program.engine !== "simple" && !program.vectorDb) {
     if (ciInfo.isCI) {
       program.vectorDb = getPrefOrDefault("vectorDb");
     } else {
@@ -738,15 +797,15 @@ export const askQuestions = async (
     }
   }
 
-  // TODO: allow tools also without datasources
-  if (!program.tools && program.dataSources.length > 0) {
+  if (
+    !program.tools &&
+    program.framework === "fastapi" &&
+    program.engine === "context"
+  ) {
     if (ciInfo.isCI) {
       program.tools = getPrefOrDefault("tools");
     } else {
-      const options = supportedTools.filter((t) =>
-        t.supportedFrameworks?.includes(program.framework),
-      );
-      const toolChoices = options.map((tool) => ({
+      const toolChoices = supportedTools.map((tool) => ({
         title: tool.display,
         value: tool.name,
       }));
@@ -765,5 +824,30 @@ export const askQuestions = async (
     }
   }
 
+  if (program.framework !== "fastapi" && program.eslint === undefined) {
+    if (ciInfo.isCI) {
+      program.eslint = getPrefOrDefault("eslint");
+    } else {
+      const styledEslint = blue("ESLint");
+      const { eslint } = await prompts({
+        onState: onPromptState,
+        type: "toggle",
+        name: "eslint",
+        message: `Would you like to use ${styledEslint}?`,
+        initial: getPrefOrDefault("eslint"),
+        active: "Yes",
+        inactive: "No",
+      });
+      program.eslint = Boolean(eslint);
+      preferences.eslint = Boolean(eslint);
+    }
+  }
+
   await askPostInstallAction();
+
+  // TODO: consider using zod to validate the input (doesn't work like this as not every option is required)
+  // templateUISchema.parse(program.ui);
+  // templateEngineSchema.parse(program.engine);
+  // templateFrameworkSchema.parse(program.framework);
+  // templateTypeSchema.parse(program.template);``
 };

scripts/build.sh

@@ -1,12 +0,0 @@
-#!/usr/bin/env bash
-
-# build dist/index.js file
-pnpm run build:ncc
-
-# add shebang to the top of dist/index.js
-# XXX: Windows needs a space after `node` to work correctly
-# Note: ncc can handle shebang but it didn't work with Windows in our tests
-echo '#!/usr/bin/env node ' | cat - dist/index.js >temp && mv temp dist/index.js
-
-# make dist/index.js executable
-chmod +x dist/index.js

scripts/pack.sh

@@ -1,3 +0,0 @@
-#!/usr/bin/env bash
-
-pnpm pack && npm install -g $(pwd)/$(ls ./*.tgz | head -1)

templates/components/deployments/python/Dockerfile

@@ -1,26 +0,0 @@
-FROM python:3.11 as build
-
-WORKDIR /app
-
-ENV PYTHONPATH=/app
-
-# Install Poetry
-RUN curl -sSL https://install.python-poetry.org | POETRY_HOME=/opt/poetry python && \
-    cd /usr/local/bin && \
-    ln -s /opt/poetry/bin/poetry && \
-    poetry config virtualenvs.create false
-
-# Install Chromium for web loader
-# Can disable this if you don't use the web loader to reduce the image size
-RUN apt update && apt install -y chromium chromium-driver
-
-# Install dependencies
-COPY ./pyproject.toml ./poetry.lock* /app/
-RUN poetry install --no-root --no-cache --only main
-
-# ====================================
-FROM build as release
-
-COPY . .
-
-CMD ["python", "main.py"]

templates/components/deployments/typescript/Dockerfile

@@ -1,16 +0,0 @@
-FROM node:20-alpine as build
-
-WORKDIR /app
-
-# Install dependencies
-COPY package.json package-lock.* ./
-RUN npm install
-
-# Build the application
-COPY . .
-RUN npm run build
-
-# ====================================
-FROM build as release
-
-CMD ["npm", "run", "start"]

templates/components/engines/python/agent/tools.py

@@ -1,4 +1,4 @@
-import yaml
+import json
 import importlib
 
 from llama_index.core.tools.tool_spec.base import BaseToolSpec
@@ -26,8 +26,8 @@ class ToolFactory:
     @staticmethod
     def from_env() -> list[FunctionTool]:
         tools = []
-        with open("config/tools.yaml", "r") as f:
-            tool_configs = yaml.safe_load(f)
+        with open("tools_config.json", "r") as f:
+            tool_configs = json.load(f)
             for name, config in tool_configs.items():
                 tools += ToolFactory.create_tool(name, **config)
         return tools

templates/components/engines/typescript/agent/chat.ts

@@ -1,26 +0,0 @@
-import config from "@/config/tools.json";
-import { OpenAI, OpenAIAgent, QueryEngineTool, ToolFactory } from "llamaindex";
-import { STORAGE_CACHE_DIR } from "./constants.mjs";
-import { getDataSource } from "./index";
-
-export async function createChatEngine(llm: OpenAI) {
-  const index = await getDataSource(llm);
-  const queryEngine = index.asQueryEngine();
-  const queryEngineTool = new QueryEngineTool({
-    queryEngine: queryEngine,
-    metadata: {
-      name: "data_query_engine",
-      description: `A query engine for documents in storage folder: ${STORAGE_CACHE_DIR}`,
-    },
-  });
-
-  const externalTools = await ToolFactory.createTools(config);
-
-  const agent = new OpenAIAgent({
-    tools: [queryEngineTool, ...externalTools],
-    verbose: true,
-    llm,
-  });
-
-  return agent;
-}

templates/components/engines/typescript/chat/chat.ts

@@ -1,13 +0,0 @@
-import { ContextChatEngine, LLM } from "llamaindex";
-import { getDataSource } from "./index";
-
-export async function createChatEngine(llm: LLM) {
-  const index = await getDataSource(llm);
-  const retriever = index.asRetriever();
-  retriever.similarityTopK = 3;
-
-  return new ContextChatEngine({
-    chatModel: llm,
-    retriever,
-  });
-}

templates/components/loaders/python/__init__.py

@@ -1,32 +0,0 @@
-import os
-import yaml
-import importlib
-import logging
-from typing import Dict
-from app.engine.loaders.file import FileLoaderConfig, get_file_documents
-from app.engine.loaders.web import WebLoaderConfig, get_web_documents
-
-logger = logging.getLogger(__name__)
-
-
-def load_configs():
-    with open("config/loaders.yaml") as f:
-        configs = yaml.safe_load(f)
-    return configs
-
-
-def get_documents():
-    documents = []
-    config = load_configs()
-    for loader_type, loader_config in config.items():
-        logger.info(
-            f"Loading documents from loader: {loader_type}, config: {loader_config}"
-        )
-        if loader_type == "file":
-            document = get_file_documents(FileLoaderConfig(**loader_config))
-            documents.extend(document)
-        elif loader_type == "web":
-            document = get_web_documents(WebLoaderConfig(**loader_config))
-            documents.extend(document)
-
-    return documents

templates/components/loaders/python/file.py

@@ -1,37 +0,0 @@
-import os
-from llama_parse import LlamaParse
-from pydantic import BaseModel, validator
-
-
-class FileLoaderConfig(BaseModel):
-    data_dir: str = "data"
-    use_llama_parse: bool = False
-
-    @validator("data_dir")
-    def data_dir_must_exist(cls, v):
-        if not os.path.isdir(v):
-            raise ValueError(f"Directory '{v}' does not exist")
-        return v
-
-
-def llama_parse_parser():
-    if os.getenv("LLAMA_CLOUD_API_KEY") is None:
-        raise ValueError(
-            "LLAMA_CLOUD_API_KEY environment variable is not set. "
-            "Please set it in .env file or in your shell environment then run again!"
-        )
-    parser = LlamaParse(result_type="markdown", verbose=True, language="en")
-    return parser
-
-
-def get_file_documents(config: FileLoaderConfig):
-    from llama_index.core.readers import SimpleDirectoryReader
-
-    reader = SimpleDirectoryReader(
-        config.data_dir,
-        recursive=True,
-    )
-    if config.use_llama_parse:
-        parser = llama_parse_parser()
-        reader.file_extractor = {".pdf": parser}
-    return reader.load_data()

templates/components/loaders/python/file/loader.py

@@ -0,0 +1,7 @@
+from llama_index.core.readers import SimpleDirectoryReader
+
+DATA_DIR = "data"  # directory containing the documents
+
+
+def get_documents():
+    return SimpleDirectoryReader(DATA_DIR).load_data()

templates/components/loaders/python/llama_parse/loader.py

@@ -0,0 +1,17 @@
+import os
+from llama_parse import LlamaParse
+from llama_index.core import SimpleDirectoryReader
+
+DATA_DIR = "data"  # directory containing the documents
+
+
+def get_documents():
+    if os.getenv("LLAMA_CLOUD_API_KEY") is None:
+        raise ValueError(
+            "LLAMA_CLOUD_API_KEY environment variable is not set. "
+            "Please set it in .env file or in your shell environment then run again!"
+        )
+    parser = LlamaParse(result_type="markdown", verbose=True, language="en")
+
+    reader = SimpleDirectoryReader(DATA_DIR, file_extractor={".pdf": parser})
+    return reader.load_data()

templates/components/loaders/python/web.py

@@ -1,36 +0,0 @@
-import os
-import json
-from pydantic import BaseModel, Field
-
-
-class CrawlUrl(BaseModel):
-    base_url: str
-    prefix: str
-    max_depth: int = Field(default=1, ge=0)
-
-
-class WebLoaderConfig(BaseModel):
-    driver_arguments: list[str] = Field(default=None)
-    urls: list[CrawlUrl]
-
-
-def get_web_documents(config: WebLoaderConfig):
-    from llama_index.readers.web import WholeSiteReader
-    from selenium import webdriver
-    from selenium.webdriver.chrome.options import Options
-
-    options = Options()
-    driver_arguments = config.driver_arguments or []
-    for arg in driver_arguments:
-        options.add_argument(arg)
-
-    docs = []
-    for url in config.urls:
-        scraper = WholeSiteReader(
-            prefix=url.prefix,
-            max_depth=url.max_depth,
-            driver=webdriver.Chrome(options=options),
-        )
-        docs.extend(scraper.load_data(url.base_url))
-
-    return docs

templates/components/loaders/python/web/loader.py

@@ -0,0 +1,13 @@
+import os
+from llama_index.readers.web import WholeSiteReader
+
+
+def get_documents():
+    # Initialize the scraper with a prefix URL and maximum depth
+    scraper = WholeSiteReader(
+        prefix=os.environ.get("URL_PREFIX"), max_depth=int(os.environ.get("MAX_DEPTH"))
+    )
+    # Start scraping from a base URL
+    documents = scraper.load_data(base_url=os.environ.get("BASE_URL"))
+
+    return documents

templates/components/loaders/typescript/file/loader.mjs

@@ -1,9 +0,0 @@
-import { SimpleDirectoryReader } from "llamaindex";
-
-export const DATA_DIR = "./data";
-
-export async function getDocuments() {
-  return await new SimpleDirectoryReader().loadData({
-    directoryPath: DATA_DIR,
-  });
-}

templates/components/loaders/typescript/llama_parse/loader.mjs

@@ -1,19 +0,0 @@
-import {
-  FILE_EXT_TO_READER,
-  LlamaParseReader,
-  SimpleDirectoryReader,
-} from "llamaindex";
-
-export const DATA_DIR = "./data";
-
-export async function getDocuments() {
-  const reader = new SimpleDirectoryReader();
-  // Load PDFs using LlamaParseReader
-  return await reader.loadData({
-    directoryPath: DATA_DIR,
-    fileExtToReader: {
-      ...FILE_EXT_TO_READER,
-      pdf: new LlamaParseReader({ resultType: "markdown" }),
-    },
-  });
-}

templates/components/observability/typescript/opentelemetry/index.ts

@@ -1,5 +1,5 @@
+import * as LlamaIndex from "@llamaindex/edge";
 import * as traceloop from "@traceloop/node-server-sdk";
-import * as LlamaIndex from "llamaindex";
 
 export const initObservability = () => {
   traceloop.initialize({

templates/components/ui/html/chat/chat-item.tsx

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

templates/components/vectordbs/python/milvus/generate.py

@@ -8,7 +8,7 @@ from llama_index.core.storage import StorageContext
 from llama_index.core.indices import VectorStoreIndex
 from llama_index.vector_stores.milvus import MilvusVectorStore
 from app.settings import init_settings
-from app.engine.loaders import get_documents
+from app.engine.loader import get_documents
 
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger()
@@ -20,7 +20,7 @@ def generate_datasource():
     documents = get_documents()
     store = MilvusVectorStore(
         uri=os.environ["MILVUS_ADDRESS"],
-        user=os.getenv("MILVUS_USERNAME"),
+        user=os.getenv("MILVUS_USER"),
         password=os.getenv("MILVUS_PASSWORD"),
         collection_name=os.getenv("MILVUS_COLLECTION"),
         dim=int(os.getenv("MILVUS_DIMENSION", "1536")),

templates/components/vectordbs/python/milvus/index.py

@@ -12,7 +12,7 @@ def get_index():
     logger.info("Connecting to index from Milvus...")
     store = MilvusVectorStore(
         uri=os.getenv("MILVUS_ADDRESS"),
-        user=os.getenv("MILVUS_USERNAME"),
+        user=os.getenv("MILVUS_USER"),
         password=os.getenv("MILVUS_PASSWORD"),
         collection_name=os.getenv("MILVUS_COLLECTION"),
         dim=int(os.getenv("EMBEDDING_DIM", "1536")),

templates/components/vectordbs/python/mongo/generate.py

@@ -8,7 +8,7 @@ from llama_index.core.storage import StorageContext
 from llama_index.core.indices import VectorStoreIndex
 from llama_index.vector_stores.mongodb import MongoDBAtlasVectorSearch
 from app.settings import init_settings
-from app.engine.loaders import get_documents
+from app.engine.loader import get_documents
 
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger()

templates/components/vectordbs/python/none/generate.py

@@ -7,7 +7,7 @@ from llama_index.core.indices import (
     VectorStoreIndex,
 )
 from app.engine.constants import STORAGE_DIR
-from app.engine.loaders import get_documents
+from app.engine.loader import get_documents
 from app.settings import init_settings
 
 

templates/components/vectordbs/python/pg/generate.py

@@ -6,7 +6,7 @@ import logging
 from llama_index.core.indices import VectorStoreIndex
 from llama_index.core.storage import StorageContext
 
-from app.engine.loaders import get_documents
+from app.engine.loader import get_documents
 from app.settings import init_settings
 from app.engine.utils import init_pg_vector_store_from_env
 

templates/components/vectordbs/python/pinecone/generate.py

@@ -8,7 +8,7 @@ from llama_index.core.storage import StorageContext
 from llama_index.core.indices import VectorStoreIndex
 from llama_index.vector_stores.pinecone import PineconeVectorStore
 from app.settings import init_settings
-from app.engine.loaders import get_documents
+from app.engine.loader import get_documents
 
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger()

templates/components/vectordbs/typescript/milvus/generate.mjs

@@ -1,12 +1,15 @@
 /* eslint-disable turbo/no-undeclared-env-vars */
+import { VectorStoreIndex } from "@llamaindex/edge";
+import { SimpleDirectoryReader } from "@llamaindex/edge/readers/SimpleDirectoryReader";
+import { storageContextFromDefaults } from "@llamaindex/edge/storage/StorageContext";
+import { MilvusVectorStore } from "@llamaindex/edge/storage/vectorStore/MilvusVectorStore";
 import * as dotenv from "dotenv";
+
 import {
-  MilvusVectorStore,
-  VectorStoreIndex,
-  storageContextFromDefaults,
-} from "llamaindex";
-import { getDocuments } from "./loader.mjs";
-import { checkRequiredEnvVars, getMilvusClient } from "./shared.mjs";
+  STORAGE_DIR,
+  checkRequiredEnvVars,
+  getMilvusClient,
+} from "./shared.mjs";
 
 dotenv.config();
 
@@ -14,7 +17,9 @@ const collectionName = process.env.MILVUS_COLLECTION;
 
 async function loadAndIndex() {
   // load objects from storage and convert them into LlamaIndex Document objects
-  const documents = await getDocuments();
+  const documents = await new SimpleDirectoryReader().loadData({
+    directoryPath: STORAGE_DIR,
+  });
 
   // Connect to Milvus
   const milvusClient = getMilvusClient();

templates/components/vectordbs/typescript/milvus/index.ts

@@ -1,14 +1,14 @@
 import {
   ContextChatEngine,
   LLM,
-  MilvusVectorStore,
-  serviceContextFromDefaults,
   VectorStoreIndex,
-} from "llamaindex";
+  serviceContextFromDefaults,
+} from "@llamaindex/edge";
+import { MilvusVectorStore } from "@llamaindex/edge/storage/vectorStore/MilvusVectorStore";
 import {
-  checkRequiredEnvVars,
   CHUNK_OVERLAP,
   CHUNK_SIZE,
+  checkRequiredEnvVars,
   getMilvusClient,
 } from "./shared.mjs";
 

templates/components/vectordbs/typescript/milvus/shared.mjs

@@ -1,5 +1,6 @@
 import { MilvusClient } from "@zilliz/milvus2-sdk-node";
 
+export const STORAGE_DIR = "./data";
 export const CHUNK_SIZE = 512;
 export const CHUNK_OVERLAP = 20;
 

templates/components/vectordbs/typescript/mongo/generate.mjs

@@ -1,13 +1,11 @@
 /* eslint-disable turbo/no-undeclared-env-vars */
+import { VectorStoreIndex } from "@llamaindex/edge";
+import { SimpleDirectoryReader } from "@llamaindex/edge/readers/SimpleDirectoryReader";
+import { storageContextFromDefaults } from "@llamaindex/edge/storage/StorageContext";
+import { MongoDBAtlasVectorSearch } from "@llamaindex/edge/storage/vectorStore/MongoDBAtlasVectorSearch";
 import * as dotenv from "dotenv";
-import {
-  MongoDBAtlasVectorSearch,
-  VectorStoreIndex,
-  storageContextFromDefaults,
-} from "llamaindex";
 import { MongoClient } from "mongodb";
-import { getDocuments } from "./loader.mjs";
-import { checkRequiredEnvVars } from "./shared.mjs";
+import { STORAGE_DIR, checkRequiredEnvVars } from "./shared.mjs";
 
 dotenv.config();
 
@@ -21,7 +19,9 @@ async function loadAndIndex() {
   const client = new MongoClient(mongoUri);
 
   // load objects from storage and convert them into LlamaIndex Document objects
-  const documents = await getDocuments();
+  const documents = await new SimpleDirectoryReader().loadData({
+    directoryPath: STORAGE_DIR,
+  });
 
   // create Atlas as a vector store
   const vectorStore = new MongoDBAtlasVectorSearch({

templates/components/vectordbs/typescript/mongo/index.ts

@@ -2,12 +2,12 @@
 import {
   ContextChatEngine,
   LLM,
-  MongoDBAtlasVectorSearch,
-  serviceContextFromDefaults,
   VectorStoreIndex,
-} from "llamaindex";
+  serviceContextFromDefaults,
+} from "@llamaindex/edge";
+import { MongoDBAtlasVectorSearch } from "@llamaindex/edge/storage/vectorStore/MongoDBAtlasVectorSearch";
 import { MongoClient } from "mongodb";
-import { checkRequiredEnvVars, CHUNK_OVERLAP, CHUNK_SIZE } from "./shared.mjs";
+import { CHUNK_OVERLAP, CHUNK_SIZE, checkRequiredEnvVars } from "./shared.mjs";
 
 async function getDataSource(llm: LLM) {
   checkRequiredEnvVars();

templates/components/vectordbs/typescript/mongo/shared.mjs

@@ -1,3 +1,4 @@
+export const STORAGE_DIR = "./data";
 export const CHUNK_SIZE = 512;
 export const CHUNK_OVERLAP = 20;
 

templates/components/vectordbs/typescript/none/constants.mjs

@@ -1,3 +1,4 @@
+export const STORAGE_DIR = "./data";
 export const STORAGE_CACHE_DIR = "./cache";
 export const CHUNK_SIZE = 512;
 export const CHUNK_OVERLAP = 20;

templates/components/vectordbs/typescript/none/generate.mjs

@@ -1,13 +1,15 @@
-import {
-  serviceContextFromDefaults,
-  storageContextFromDefaults,
-  VectorStoreIndex,
-} from "llamaindex";
+import { VectorStoreIndex, serviceContextFromDefaults } from "@llamaindex/edge";
+import { SimpleDirectoryReader } from "@llamaindex/edge/readers/SimpleDirectoryReader";
+import { storageContextFromDefaults } from "@llamaindex/edge/storage/StorageContext";
 
 import * as dotenv from "dotenv";
 
-import { CHUNK_OVERLAP, CHUNK_SIZE, STORAGE_CACHE_DIR } from "./constants.mjs";
-import { getDocuments } from "./loader.mjs";
+import {
+  CHUNK_OVERLAP,
+  CHUNK_SIZE,
+  STORAGE_CACHE_DIR,
+  STORAGE_DIR,
+} from "./constants.mjs";
 
 // Load environment variables from local .env file
 dotenv.config();
@@ -26,7 +28,9 @@ async function generateDatasource(serviceContext) {
     const storageContext = await storageContextFromDefaults({
       persistDir: STORAGE_CACHE_DIR,
     });
-    const documents = await getDocuments();
+    const documents = await new SimpleDirectoryReader().loadData({
+      directoryPath: STORAGE_DIR,
+    });
     await VectorStoreIndex.fromDocuments(documents, {
       storageContext,
       serviceContext,

templates/components/vectordbs/typescript/none/index.ts

@@ -1,13 +1,14 @@
 import {
+  ContextChatEngine,
   LLM,
-  serviceContextFromDefaults,
-  SimpleDocumentStore,
-  storageContextFromDefaults,
   VectorStoreIndex,
-} from "llamaindex";
+  serviceContextFromDefaults,
+} from "@llamaindex/edge";
+import { storageContextFromDefaults } from "@llamaindex/edge/storage/StorageContext";
+import { SimpleDocumentStore } from "@llamaindex/edge/storage/docStore/SimpleDocumentStore";
 import { CHUNK_OVERLAP, CHUNK_SIZE, STORAGE_CACHE_DIR } from "./constants.mjs";
 
-export async function getDataSource(llm: LLM) {
+async function getDataSource(llm: LLM) {
   const serviceContext = serviceContextFromDefaults({
     llm,
     chunkSize: CHUNK_SIZE,
@@ -30,3 +31,14 @@ export async function getDataSource(llm: LLM) {
     serviceContext,
   });
 }
+
+export async function createChatEngine(llm: LLM) {
+  const index = await getDataSource(llm);
+  const retriever = index.asRetriever();
+  retriever.similarityTopK = 3;
+
+  return new ContextChatEngine({
+    chatModel: llm,
+    retriever,
+  });
+}

templates/components/vectordbs/typescript/pg/generate.mjs

@@ -1,15 +1,13 @@
 /* eslint-disable turbo/no-undeclared-env-vars */
+import { VectorStoreIndex } from "@llamaindex/edge";
+import { SimpleDirectoryReader } from "@llamaindex/edge/readers/SimpleDirectoryReader";
+import { storageContextFromDefaults } from "@llamaindex/edge/storage/StorageContext";
+import { PGVectorStore } from "@llamaindex/edge/storage/vectorStore/PGVectorStore";
 import * as dotenv from "dotenv";
 import {
-  PGVectorStore,
-  VectorStoreIndex,
-  storageContextFromDefaults,
-} from "llamaindex";
-import { getDocuments } from "./loader.mjs";
-import {
-  PGVECTOR_COLLECTION,
   PGVECTOR_SCHEMA,
   PGVECTOR_TABLE,
+  STORAGE_DIR,
   checkRequiredEnvVars,
 } from "./shared.mjs";
 
@@ -17,7 +15,9 @@ dotenv.config();
 
 async function loadAndIndex() {
   // load objects from storage and convert them into LlamaIndex Document objects
-  const documents = await getDocuments();
+  const documents = await new SimpleDirectoryReader().loadData({
+    directoryPath: STORAGE_DIR,
+  });
 
   // create postgres vector store
   const vectorStore = new PGVectorStore({
@@ -25,7 +25,7 @@ async function loadAndIndex() {
     schemaName: PGVECTOR_SCHEMA,
     tableName: PGVECTOR_TABLE,
   });
-  vectorStore.setCollection(PGVECTOR_COLLECTION);
+  vectorStore.setCollection(STORAGE_DIR);
   vectorStore.clearCollection();
 
   // create index from all the Documents

templates/components/vectordbs/typescript/pg/index.ts

@@ -2,10 +2,10 @@
 import {
   ContextChatEngine,
   LLM,
-  PGVectorStore,
   VectorStoreIndex,
   serviceContextFromDefaults,
-} from "llamaindex";
+} from "@llamaindex/edge";
+import { PGVectorStore } from "@llamaindex/edge/storage/vectorStore/PGVectorStore";
 import {
   CHUNK_OVERLAP,
   CHUNK_SIZE,

templates/components/vectordbs/typescript/pg/shared.mjs

@@ -1,4 +1,4 @@
-export const PGVECTOR_COLLECTION = "data";
+export const STORAGE_DIR = "./data";
 export const CHUNK_SIZE = 512;
 export const CHUNK_OVERLAP = 20;
 export const PGVECTOR_SCHEMA = "public";

templates/components/vectordbs/typescript/pinecone/generate.mjs

@@ -1,18 +1,18 @@
 /* eslint-disable turbo/no-undeclared-env-vars */
+import { VectorStoreIndex } from "@llamaindex/edge";
+import { SimpleDirectoryReader } from "@llamaindex/edge/readers/SimpleDirectoryReader";
+import { storageContextFromDefaults } from "@llamaindex/edge/storage/StorageContext";
+import { PineconeVectorStore } from "@llamaindex/edge/storage/vectorStore/PineconeVectorStore";
 import * as dotenv from "dotenv";
-import {
-  PineconeVectorStore,
-  VectorStoreIndex,
-  storageContextFromDefaults,
-} from "llamaindex";
-import { getDocuments } from "./loader.mjs";
-import { checkRequiredEnvVars } from "./shared.mjs";
+import { STORAGE_DIR, checkRequiredEnvVars } from "./shared.mjs";
 
 dotenv.config();
 
 async function loadAndIndex() {
   // load objects from storage and convert them into LlamaIndex Document objects
-  const documents = await getDocuments();
+  const documents = await new SimpleDirectoryReader().loadData({
+    directoryPath: STORAGE_DIR,
+  });
 
   // create vector store
   const vectorStore = new PineconeVectorStore();

templates/components/vectordbs/typescript/pinecone/index.ts

@@ -2,10 +2,10 @@
 import {
   ContextChatEngine,
   LLM,
-  PineconeVectorStore,
   VectorStoreIndex,
   serviceContextFromDefaults,
-} from "llamaindex";
+} from "@llamaindex/edge";
+import { PineconeVectorStore } from "@llamaindex/edge/storage/vectorStore/PineconeVectorStore";
 import { CHUNK_OVERLAP, CHUNK_SIZE, checkRequiredEnvVars } from "./shared.mjs";
 
 async function getDataSource(llm: LLM) {

templates/components/vectordbs/typescript/pinecone/shared.mjs

@@ -1,3 +1,4 @@
+export const STORAGE_DIR = "./data";
 export const CHUNK_SIZE = 512;
 export const CHUNK_OVERLAP = 20;
 

templates/types/simple/express/README-template.md

@@ -0,0 +1,50 @@
+This is a [LlamaIndex](https://www.llamaindex.ai/) project using [Express](https://expressjs.com/) bootstrapped with [`create-llama`](https://github.com/run-llama/LlamaIndexTS/tree/main/packages/create-llama).
+
+## Getting Started
+
+First, install the dependencies:
+
+```
+npm install
+```
+
+Second, run the development server:
+
+```
+npm run dev
+```
+
+Then call the express API endpoint `/api/chat` to see the result:
+
+```
+curl --location 'localhost:8000/api/chat' \
+--header 'Content-Type: application/json' \
+--data '{ "messages": [{ "role": "user", "content": "Hello" }] }'
+```
+
+You can start editing the API by modifying `src/controllers/chat.controller.ts`. The endpoint auto-updates as you save the file.
+
+## Production
+
+First, build the project:
+
+```
+npm run build
+```
+
+You can then run the production server:
+
+```
+NODE_ENV=production npm run start
+```
+
+> Note that the `NODE_ENV` environment variable is set to `production`. This disables CORS for all origins.
+
+## Learn More
+
+To learn more about LlamaIndex, take a look at the following resources:
+
+- [LlamaIndex Documentation](https://docs.llamaindex.ai) - learn about LlamaIndex (Python features).
+- [LlamaIndexTS Documentation](https://ts.llamaindex.ai) - learn about LlamaIndex (Typescript features).
+
+You can check out [the LlamaIndexTS GitHub repository](https://github.com/run-llama/LlamaIndexTS) - your feedback and contributions are welcome!

templates/types/simple/express/eslintrc.json

@@ -0,0 +1,3 @@
+{
+  "extends": "eslint:recommended"
+}

templates/types/simple/express/gitignore

@@ -0,0 +1,3 @@
+# local env files
+.env
+node_modules/

templates/types/simple/express/index.ts

@@ -0,0 +1,44 @@
+/* eslint-disable turbo/no-undeclared-env-vars */
+import cors from "cors";
+import "dotenv/config";
+import express, { Express, Request, Response } from "express";
+import { initObservability } from "./src/observability";
+import chatRouter from "./src/routes/chat.route";
+
+const app: Express = express();
+const port = parseInt(process.env.PORT || "8000");
+
+const env = process.env["NODE_ENV"];
+const isDevelopment = !env || env === "development";
+const prodCorsOrigin = process.env["PROD_CORS_ORIGIN"];
+
+initObservability();
+
+app.use(express.json());
+
+if (isDevelopment) {
+  console.warn("Running in development mode - allowing CORS for all origins");
+  app.use(cors());
+} else if (prodCorsOrigin) {
+  console.log(
+    `Running in production mode - allowing CORS for domain: ${prodCorsOrigin}`,
+  );
+  const corsOptions = {
+    origin: prodCorsOrigin, // Restrict to production domain
+  };
+  app.use(cors(corsOptions));
+} else {
+  console.warn("Production CORS origin not set, defaulting to no CORS.");
+}
+
+app.use(express.text());
+
+app.get("/", (req: Request, res: Response) => {
+  res.send("LlamaIndex Express Server");
+});
+
+app.use("/api/chat", chatRouter);
+
+app.listen(port, () => {
+  console.log(`⚡️[server]: Server is running at http://localhost:${port}`);
+});

templates/types/simple/express/package.json

@@ -0,0 +1,27 @@
+{
+  "name": "llama-index-express",
+  "version": "1.0.0",
+  "main": "dist/index.js",
+  "type": "module",
+  "scripts": {
+    "build": "tsup index.ts --format esm --dts",
+    "start": "node dist/index.js",
+    "dev": "concurrently \"tsup index.ts --format esm --dts --watch\" \"nodemon -q dist/index.js\""
+  },
+  "dependencies": {
+    "cors": "^2.8.5",
+    "dotenv": "^16.3.1",
+    "express": "^4.18.2",
+    "@llamaindex/edge": "^0.2.1"
+  },
+  "devDependencies": {
+    "@types/cors": "^2.8.17",
+    "@types/express": "^4.17.21",
+    "@types/node": "^20.9.5",
+    "concurrently": "^8.2.2",
+    "eslint": "^8.54.0",
+    "nodemon": "^3.0.1",
+    "tsup": "^7.3.0",
+    "typescript": "^5.3.2"
+  }
+}

templates/types/simple/express/src/controllers/chat.controller.ts

@@ -1,6 +1,6 @@
+import { ChatMessage, MessageContent, OpenAI } from "@llamaindex/edge";
 import { Request, Response } from "express";
-import { ChatMessage, MessageContent, OpenAI } from "llamaindex";
-import { createChatEngine } from "./engine/chat";
+import { createChatEngine } from "./engine";
 
 const convertMessageContent = (
   textMessage: string,
@@ -21,7 +21,7 @@ const convertMessageContent = (
   ];
 };
 
-export const chatRequest = async (req: Request, res: Response) => {
+export const chat = async (req: Request, res: Response) => {
   try {
     const { messages, data }: { messages: ChatMessage[]; data: any } = req.body;
     const userMessage = messages.pop();
@@ -48,7 +48,7 @@ export const chatRequest = async (req: Request, res: Response) => {
     // Calling LlamaIndex's ChatEngine to get a response
     const response = await chatEngine.chat({
       message: userMessageContent,
-      chatHistory: messages,
+      messages,
     });
     const result: ChatMessage = {
       role: "assistant",

templates/types/simple/express/src/controllers/engine/index.ts

@@ -1,4 +1,4 @@
-import { LLM, SimpleChatEngine } from "llamaindex";
+import { LLM, SimpleChatEngine } from "@llamaindex/edge";
 
 export async function createChatEngine(llm: LLM) {
   return new SimpleChatEngine({

templates/types/simple/express/src/observability/index.ts

@@ -0,0 +1 @@
+export const initObservability = () => {};

templates/types/simple/express/src/routes/chat.route.ts

@@ -0,0 +1,8 @@
+import express from "express";
+import { chat } from "../controllers/chat.controller";
+
+const llmRouter = express.Router();
+
+llmRouter.route("/").post(chat);
+
+export default llmRouter;

templates/types/simple/express/tsconfig.json

@@ -0,0 +1,10 @@
+{
+  "compilerOptions": {
+    "target": "es2016",
+    "esModuleInterop": true,
+    "forceConsistentCasingInFileNames": true,
+    "strict": true,
+    "skipLibCheck": true,
+    "moduleResolution": "node"
+  }
+}

templates/types/simple/fastapi/README-template.md

@@ -0,0 +1,58 @@
+This is a [LlamaIndex](https://www.llamaindex.ai/) project using [FastAPI](https://fastapi.tiangolo.com/) bootstrapped with [`create-llama`](https://github.com/run-llama/LlamaIndexTS/tree/main/packages/create-llama).
+
+## Getting Started
+
+First, setup the environment with poetry:
+
+> **_Note:_** This step is not needed if you are using the dev-container.
+
+```
+poetry install
+poetry shell
+```
+
+By default, we use the OpenAI LLM (though you can customize, see app/api/routers/chat.py). As a result you need to specify an `OPENAI_API_KEY` in an .env file in this directory.
+
+Example `backend/.env` file:
+
+```
+OPENAI_API_KEY=<openai_api_key>
+```
+
+Second, generate the embeddings of the documents in the `./data` directory (if this folder exists - otherwise, skip this step):
+
+```
+python app/engine/generate.py
+```
+
+Third, run the development server:
+
+```
+python main.py
+```
+
+Then call the API endpoint `/api/chat` to see the result:
+
+```
+curl --location 'localhost:8000/api/chat' \
+--header 'Content-Type: application/json' \
+--data '{ "messages": [{ "role": "user", "content": "Hello" }] }'
+```
+
+You can start editing the API by modifying `app/api/routers/chat.py`. The endpoint auto-updates as you save the file.
+
+Open [http://localhost:8000/docs](http://localhost:8000/docs) with your browser to see the Swagger UI of the API.
+
+The API allows CORS for all origins to simplify development. You can change this behavior by setting the `ENVIRONMENT` environment variable to `prod`:
+
+```
+ENVIRONMENT=prod python main.py
+```
+
+## 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!

templates/types/simple/fastapi/app/api/routers/chat.py

@@ -0,0 +1,54 @@
+from typing import List
+from pydantic import BaseModel
+from fastapi import APIRouter, Depends, HTTPException, status
+from llama_index.core.chat_engine.types import BaseChatEngine
+from llama_index.core.llms import ChatMessage, MessageRole
+from app.engine import get_chat_engine
+
+chat_router = r = APIRouter()
+
+
+class _Message(BaseModel):
+    role: MessageRole
+    content: str
+
+
+class _ChatData(BaseModel):
+    messages: List[_Message]
+
+
+class _Result(BaseModel):
+    result: _Message
+
+
+@r.post("")
+async def chat(
+    data: _ChatData,
+    chat_engine: BaseChatEngine = Depends(get_chat_engine),
+) -> _Result:
+    # check preconditions and get last message
+    if len(data.messages) == 0:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="No messages provided",
+        )
+    lastMessage = data.messages.pop()
+    if lastMessage.role != MessageRole.USER:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="Last message must be from user",
+        )
+    # convert messages coming from the request to type ChatMessage
+    messages = [
+        ChatMessage(
+            role=m.role,
+            content=m.content,
+        )
+        for m in data.messages
+    ]
+
+    # query chat engine
+    response = await chat_engine.achat(lastMessage.content, messages)
+    return _Result(
+        result=_Message(role=MessageRole.ASSISTANT, content=response.response)
+    )

templates/types/simple/fastapi/app/engine/__init__.py

@@ -0,0 +1,5 @@
+from llama_index.core.chat_engine import SimpleChatEngine
+
+
+def get_chat_engine():
+    return SimpleChatEngine.from_defaults()

templates/types/simple/fastapi/app/settings.py

@@ -0,0 +1,41 @@
+import os
+from typing import Dict
+from llama_index.core.settings import Settings
+from llama_index.llms.openai import OpenAI
+from llama_index.embeddings.openai import OpenAIEmbedding
+
+
+def llm_config_from_env() -> Dict:
+    from llama_index.core.constants import DEFAULT_TEMPERATURE
+
+    model = os.getenv("MODEL")
+    temperature = os.getenv("LLM_TEMPERATURE", DEFAULT_TEMPERATURE)
+    max_tokens = os.getenv("LLM_MAX_TOKENS")
+
+    config = {
+        "model": model,
+        "temperature": float(temperature),
+        "max_tokens": int(max_tokens) if max_tokens is not None else None,
+    }
+    return config
+
+
+def embedding_config_from_env() -> Dict:
+    model = os.getenv("EMBEDDING_MODEL")
+    dimension = os.getenv("EMBEDDING_DIM")
+
+    config = {
+        "model": model,
+        "dimension": int(dimension) if dimension is not None else None,
+    }
+    return config
+
+
+def init_settings():
+    llm_configs = llm_config_from_env()
+    embedding_configs = embedding_config_from_env()
+
+    Settings.llm = OpenAI(**llm_configs)
+    Settings.embed_model = OpenAIEmbedding(**embedding_configs)
+    Settings.chunk_size = int(os.getenv("CHUNK_SIZE", "1024"))
+    Settings.chunk_overlap = int(os.getenv("CHUNK_OVERLAP", "20"))

templates/types/simple/fastapi/gitignore

@@ -0,0 +1,3 @@
+__pycache__
+storage
+.env

templates/types/simple/fastapi/main.py

@@ -0,0 +1,39 @@
+from dotenv import load_dotenv
+
+load_dotenv()
+
+import logging
+import os
+import uvicorn
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from app.api.routers.chat import chat_router
+from app.settings import init_settings
+
+app = FastAPI()
+
+init_settings()
+
+environment = os.getenv("ENVIRONMENT", "dev")  # Default to 'development' if not set
+
+
+if environment == "dev":
+    logger = logging.getLogger("uvicorn")
+    logger.warning("Running in development mode - allowing CORS for all origins")
+    app.add_middleware(
+        CORSMiddleware,
+        allow_origins=["*"],
+        allow_credentials=True,
+        allow_methods=["*"],
+        allow_headers=["*"],
+    )
+
+app.include_router(chat_router, prefix="/api/chat")
+
+
+if __name__ == "__main__":
+    app_host = os.getenv("APP_HOST", "0.0.0.0")
+    app_port = int(os.getenv("APP_PORT", "8000"))
+    reload = True if environment == "dev" else False
+
+    uvicorn.run(app="main:app", host=app_host, port=app_port, reload=reload)

templates/types/simple/fastapi/pyproject.toml

@@ -0,0 +1,17 @@
+[tool.poetry]
+name = "app"
+version = "0.1.0"
+description = ""
+authors = ["Marcus Schiesser <mail@marcusschiesser.de>"]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = "^3.11,<3.12"
+fastapi = "^0.109.1"
+uvicorn = { extras = ["standard"], version = "^0.23.2" }
+python-dotenv = "^1.0.0"
+llama-index = "^0.10.7"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

templates/types/streaming/express/README-template.md

@@ -8,41 +8,21 @@ First, install the dependencies:
 npm install
 ```
 
-Second, generate the embeddings of the documents in the `./data` directory (if this folder exists - otherwise, skip this step):
-
-```
-npm run generate
-```
-
-Third, run the development server:
+Second, run the development server:
 
 ```
 npm run dev
 ```
 
-The example provides two different API endpoints:
-
-1. `/api/chat` - a streaming chat endpoint (found in `src/controllers/chat.controller.ts`)
-2. `/api/chat/request` - a non-streaming chat endpoint (found in `src/controllers/chat-request.controller.ts`)
-
-You can test the streaming endpoint with the following curl request:
+Then call the express API endpoint `/api/chat` to see the result:
 
 ```
 curl --location 'localhost:8000/api/chat' \
---header 'Content-Type: application/json' \
+--header 'Content-Type: text/plain' \
 --data '{ "messages": [{ "role": "user", "content": "Hello" }] }'
 ```
 
-And for the non-streaming endpoint run:
-
-```
-curl --location 'localhost:8000/api/chat/request' \
---header 'Content-Type: application/json' \
---data '{ "messages": [{ "role": "user", "content": "Hello" }] }'
-```
-
-You can start editing the API by modifying `src/controllers/chat.controller.ts` or `src/controllers/chat-request.controller.ts`. The endpoint auto-updates as you save the file.
-You can delete the endpoint that you're not using.
+You can start editing the API by modifying `src/controllers/chat.controller.ts`. The endpoint auto-updates as you save the file.
 
 ## Production
 
@@ -60,39 +40,6 @@ NODE_ENV=production npm run start
 
 > Note that the `NODE_ENV` environment variable is set to `production`. This disables CORS for all origins.
 
-## Using Docker
-
-1. Build an image for the Express API:
-
-```
-docker build -t <your_backend_image_name> .
-```
-
-2. Generate embeddings:
-
-Parse the data and generate the vector embeddings if the `./data` folder exists - otherwise, skip this step:
-
-```
-docker run --rm \
-  -v $(pwd)/.env:/app/.env \ # Use ENV variables and configuration from your file-system
-  -v $(pwd)/config:/app/config \
-  -v $(pwd)/data:/app/data \
-  -v $(pwd)/cache:/app/cache \ # Use your file system to store the vector database
-  <your_backend_image_name>
-  npm run generate
-```
-
-3. Start the API:
-
-```
-docker run \
-  -v $(pwd)/.env:/app/.env \ # Use ENV variables and configuration from your file-system
-  -v $(pwd)/config:/app/config \
-  -v $(pwd)/cache:/app/cache \ # Use your file system to store the vector database
-  -p 8000:8000 \
-  <your_backend_image_name>
-```
-
 ## Learn More
 
 To learn more about LlamaIndex, take a look at the following resources:

templates/types/streaming/express/eslintrc.json

@@ -1,7 +1,3 @@
 {
-  "extends": ["eslint:recommended", "prettier"],
-  "rules": {
-    "max-params": ["error", 4],
-    "prefer-const": "error"
-  }
+  "extends": "eslint:recommended"
 }

templates/types/streaming/express/package.json

@@ -1,13 +1,12 @@
 {
   "name": "llama-index-express-streaming",
   "version": "1.0.0",
-  "main": "dist/index.mjs",
+  "main": "dist/index.js",
+  "type": "module",
   "scripts": {
-    "format": "prettier --ignore-unknown --cache --check .",
-    "format:write": "prettier --ignore-unknown --write .",
     "build": "tsup index.ts --format esm --dts",
-    "start": "node dist/index.mjs",
-    "dev": "concurrently \"tsup index.ts --format esm --dts --watch\" \"nodemon -q dist/index.mjs\""
+    "start": "node dist/index.js",
+    "dev": "concurrently \"tsup index.ts --format esm --dts --watch\" \"nodemon -q dist/index.js\""
   },
   "dependencies": {
     "ai": "^2.2.25",
@@ -24,9 +23,6 @@
     "eslint": "^8.54.0",
     "nodemon": "^3.0.1",
     "tsup": "^8.0.1",
-    "typescript": "^5.3.2",
-    "prettier": "^3.2.5",
-    "prettier-plugin-organize-imports": "^3.2.4",
-    "eslint-config-prettier": "^8.10.0"
+    "typescript": "^5.3.2"
   }
 }

templates/types/streaming/express/prettier.config.js

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

templates/types/streaming/express/src/controllers/chat.controller.ts

@@ -1,7 +1,7 @@
+import { ChatMessage, MessageContent, OpenAI } from "@llamaindex/edge";
 import { streamToResponse } from "ai";
 import { Request, Response } from "express";
-import { ChatMessage, MessageContent, OpenAI } from "llamaindex";
-import { createChatEngine } from "./engine/chat";
+import { createChatEngine } from "./engine";
 import { LlamaIndexStream } from "./llamaindex-stream";
 
 const convertMessageContent = (

templates/types/streaming/express/src/controllers/engine/index.ts

@@ -1,4 +1,4 @@
-import { LLM, SimpleChatEngine } from "llamaindex";
+import { LLM, SimpleChatEngine } from "@llamaindex/edge";
 
 export async function createChatEngine(llm: LLM) {
   return new SimpleChatEngine({

templates/types/streaming/express/src/controllers/llamaindex-stream.ts

@@ -1,3 +1,4 @@
+import { Response } from "@llamaindex/edge";
 import {
   JSONValue,
   createCallbacksTransformer,
@@ -6,7 +7,6 @@ import {
   trimStartOfStreamHelper,
   type AIStreamCallbacksAndOptions,
 } from "ai";
-import { Response, StreamingAgentChatResponse } from "llamaindex";
 
 type ParserOptions = {
   image_url?: string;
@@ -52,17 +52,13 @@ function createParser(
 }
 
 export function LlamaIndexStream(
-  response: StreamingAgentChatResponse | AsyncIterable<Response>,
+  res: AsyncIterable<Response>,
   opts?: {
     callbacks?: AIStreamCallbacksAndOptions;
     parserOptions?: ParserOptions;
   },
 ): { stream: ReadableStream; data: experimental_StreamData } {
   const data = new experimental_StreamData();
-  const res =
-    response instanceof StreamingAgentChatResponse
-      ? response.response
-      : response;
   return {
     stream: createParser(res, data, opts?.parserOptions)
       .pipeThrough(createCallbacksTransformer(opts?.callbacks))

templates/types/streaming/express/src/routes/chat.route.ts

@@ -1,10 +1,8 @@
-import express, { Router } from "express";
-import { chatRequest } from "../controllers/chat-request.controller";
+import express from "express";
 import { chat } from "../controllers/chat.controller";
 
-const llmRouter: Router = express.Router();
+const llmRouter = express.Router();
 
 llmRouter.route("/").post(chat);
-llmRouter.route("/request").post(chatRequest);
 
 export default llmRouter;

templates/types/streaming/express/tsconfig.json

@@ -5,9 +5,6 @@
     "forceConsistentCasingInFileNames": true,
     "strict": true,
     "skipLibCheck": true,
-    "moduleResolution": "node",
-    "paths": {
-      "@/*": ["./*"]
-    }
+    "moduleResolution": "node"
   }
 }

templates/types/streaming/fastapi/README-template.md

@@ -11,7 +11,7 @@ poetry install
 poetry shell
 ```
 
-By default, we use the OpenAI LLM (though you can customize, see `app/settings.py`). As a result, you need to specify an `OPENAI_API_KEY` in an .env file in this directory.
+By default, we use the OpenAI LLM (though you can customize, see `app/settings.py`). As a result you need to specify an `OPENAI_API_KEY` in an .env file in this directory.
 
 Example `.env` file:
 
@@ -19,8 +19,6 @@ Example `.env` file:
 OPENAI_API_KEY=<openai_api_key>
 ```
 
-If you are using any tools or data sources, you can update their config files in the `config` folder.
-
 Second, generate the embeddings of the documents in the `./data` directory (if this folder exists - otherwise, skip this step):
 
 ```
@@ -33,12 +31,7 @@ Third, run the development server:
 python main.py
 ```
 
-The example provides two different API endpoints:
-
-1. `/api/chat` - a streaming chat endpoint
-2. `/api/chat/request` - a non-streaming chat endpoint
-
-You can test the streaming endpoint with the following curl request:
+Then call the API endpoint `/api/chat` to see the result:
 
 ```
 curl --location 'localhost:8000/api/chat' \
@@ -46,15 +39,7 @@ curl --location 'localhost:8000/api/chat' \
 --data '{ "messages": [{ "role": "user", "content": "Hello" }] }'
 ```
 
-And for the non-streaming endpoint run:
-
-```
-curl --location 'localhost:8000/api/chat/request' \
---header 'Content-Type: application/json' \
---data '{ "messages": [{ "role": "user", "content": "Hello" }] }'
-```
-
-You can start editing the API endpoints by modifying `app/api/routers/chat.py`. The endpoints auto-update as you save the file. You can delete the endpoint you're not using.
+You can start editing the API by modifying `app/api/routers/chat.py`. The endpoint auto-updates as you save the file.
 
 Open [http://localhost:8000/docs](http://localhost:8000/docs) with your browser to see the Swagger UI of the API.
 
@@ -64,40 +49,6 @@ The API allows CORS for all origins to simplify development. You can change this
 ENVIRONMENT=prod python main.py
 ```
 
-## Using Docker
-
-1. Build an image for the FastAPI app:
-
-```
-docker build -t <your_backend_image_name> .
-```
-
-2. Generate embeddings:
-
-Parse the data and generate the vector embeddings if the `./data` folder exists - otherwise, skip this step:
-
-```
-docker run \
-  --rm \
-  -v $(pwd)/.env:/app/.env \ # Use ENV variables and configuration from your file-system
-  -v $(pwd)/config:/app/config \
-  -v $(pwd)/data:/app/data \ # Use your local folder to read the data
-  -v $(pwd)/storage:/app/storage \ # Use your file system to store the vector database
-  <your_backend_image_name> \
-  python app/engine/generate.py
-```
-
-3. Start the API:
-
-```
-docker run \
-  -v $(pwd)/.env:/app/.env \ # Use ENV variables and configuration from your file-system
-  -v $(pwd)/config:/app/config \
-  -v $(pwd)/storage:/app/storage \ # Use your file system to store gea vector database
-  -p 8000:8000 \
-  <your_backend_image_name>
-```
-
 ## Learn More
 
 To learn more about LlamaIndex, take a look at the following resources:

templates/types/streaming/fastapi/app/api/routers/chat.py

@@ -5,7 +5,6 @@ from fastapi import APIRouter, Depends, HTTPException, Request, status
 from llama_index.core.chat_engine.types import BaseChatEngine
 from llama_index.core.llms import ChatMessage, MessageRole
 from app.engine import get_chat_engine
-from typing import List, Tuple
 
 chat_router = r = APIRouter()
 
@@ -19,19 +18,20 @@ class _ChatData(BaseModel):
     messages: List[_Message]
 
 
-class _Result(BaseModel):
-    result: _Message
-
-
-async def parse_chat_data(data: _ChatData) -> Tuple[str, List[ChatMessage]]:
+@r.post("")
+async def chat(
+    request: Request,
+    data: _ChatData,
+    chat_engine: BaseChatEngine = Depends(get_chat_engine),
+):
     # check preconditions and get last message
     if len(data.messages) == 0:
         raise HTTPException(
             status_code=status.HTTP_400_BAD_REQUEST,
             detail="No messages provided",
         )
-    last_message = data.messages.pop()
-    if last_message.role != MessageRole.USER:
+    lastMessage = data.messages.pop()
+    if lastMessage.role != MessageRole.USER:
         raise HTTPException(
             status_code=status.HTTP_400_BAD_REQUEST,
             detail="Last message must be from user",
@@ -44,38 +44,16 @@ async def parse_chat_data(data: _ChatData) -> Tuple[str, List[ChatMessage]]:
         )
         for m in data.messages
     ]
-    return last_message.content, messages
 
+    # query chat engine
+    response = await chat_engine.astream_chat(lastMessage.content, messages)
 
-# streaming endpoint - delete if not needed
-@r.post("")
-async def chat(
-    request: Request,
-    data: _ChatData,
-    chat_engine: BaseChatEngine = Depends(get_chat_engine),
-):
-    last_message_content, messages = await parse_chat_data(data)
-
-    response = await chat_engine.astream_chat(last_message_content, messages)
-
+    # stream response
     async def event_generator():
         async for token in response.async_response_gen():
+            # If client closes connection, stop sending events
             if await request.is_disconnected():
                 break
             yield token
 
     return StreamingResponse(event_generator(), media_type="text/plain")
-
-
-# non-streaming endpoint - delete if not needed
-@r.post("/request")
-async def chat_request(
-    data: _ChatData,
-    chat_engine: BaseChatEngine = Depends(get_chat_engine),
-) -> _Result:
-    last_message_content, messages = await parse_chat_data(data)
-
-    response = await chat_engine.achat(last_message_content, messages)
-    return _Result(
-        result=_Message(role=MessageRole.ASSISTANT, content=response.response)
-    )

templates/types/streaming/fastapi/pyproject.toml

@@ -10,9 +10,7 @@ python = "^3.11,<3.12"
 fastapi = "^0.109.1"
 uvicorn = { extras = ["standard"], version = "^0.23.2" }
 python-dotenv = "^1.0.0"
-llama-index = "0.10.15"
-llama-index-core = "0.10.15"
-llama-index-agent-openai = "0.1.5"
+llama-index = "^0.10.7"
 
 [build-system]
 requires = ["poetry-core"]

templates/types/streaming/nextjs/README-template.md

@@ -8,13 +8,7 @@ First, install the dependencies:
 npm install
 ```
 
-Second, generate the embeddings of the documents in the `./data` directory (if this folder exists - otherwise, skip this step):
-
-```
-npm run generate
-```
-
-Third, run the development server:
+Second, run the development server:
 
 ```
 npm run dev
@@ -26,41 +20,6 @@ You can start editing the page by modifying `app/page.tsx`. The page auto-update
 
 This project uses [`next/font`](https://nextjs.org/docs/basic-features/font-optimization) to automatically optimize and load Inter, a custom Google Font.
 
-## Using Docker
-
-1. Build an image for the Next.js app:
-
-```
-docker build -t <your_app_image_name> .
-```
-
-2. Generate embeddings:
-
-Parse the data and generate the vector embeddings if the `./data` folder exists - otherwise, skip this step:
-
-```
-docker run \
-  --rm \
-  -v $(pwd)/.env:/app/.env \ # Use ENV variables and configuration from your file-system
-  -v $(pwd)/config:/app/config \
-  -v $(pwd)/data:/app/data \
-  -v $(pwd)/cache:/app/cache \ # Use your file system to store the vector database
-  <your_app_image_name> \
-  npm run generate
-```
-
-3. Start the app:
-
-```
-docker run \
-  --rm \
-  -v $(pwd)/.env:/app/.env \ # Use ENV variables and configuration from your file-system
-  -v $(pwd)/config:/app/config \
-  -v $(pwd)/cache:/app/cache \ # Use your file system to store gea vector database
-  -p 3000:3000 \
-  <your_app_image_name>
-```
-
 ## Learn More
 
 To learn more about LlamaIndex, take a look at the following resources:

templates/types/streaming/nextjs/app/api/chat/engine/index.ts

@@ -0,0 +1,7 @@
+import { LLM, SimpleChatEngine } from "@llamaindex/edge";
+
+export async function createChatEngine(llm: LLM) {
+  return new SimpleChatEngine({
+    llm,
+  });
+}

templates/types/streaming/nextjs/app/api/chat/llamaindex-stream.ts

@@ -1,3 +1,4 @@
+import { Response } from "@llamaindex/edge";
 import {
   JSONValue,
   createCallbacksTransformer,
@@ -6,7 +7,6 @@ import {
   trimStartOfStreamHelper,
   type AIStreamCallbacksAndOptions,
 } from "ai";
-import { Response, StreamingAgentChatResponse } from "llamaindex";
 
 type ParserOptions = {
   image_url?: string;
@@ -52,17 +52,13 @@ function createParser(
 }
 
 export function LlamaIndexStream(
-  response: StreamingAgentChatResponse | AsyncIterable<Response>,
+  res: AsyncIterable<Response>,
   opts?: {
     callbacks?: AIStreamCallbacksAndOptions;
     parserOptions?: ParserOptions;
   },
 ): { stream: ReadableStream; data: experimental_StreamData } {
   const data = new experimental_StreamData();
-  const res =
-    response instanceof StreamingAgentChatResponse
-      ? response.response
-      : response;
   return {
     stream: createParser(res, data, opts?.parserOptions)
       .pipeThrough(createCallbacksTransformer(opts?.callbacks))

templates/types/streaming/nextjs/app/api/chat/route.ts

@@ -1,13 +1,13 @@
 import { initObservability } from "@/app/observability";
+import { ChatMessage, MessageContent, OpenAI } from "@llamaindex/edge";
 import { StreamingTextResponse } from "ai";
-import { ChatMessage, MessageContent, OpenAI } from "llamaindex";
 import { NextRequest, NextResponse } from "next/server";
-import { createChatEngine } from "./engine/chat";
+import { createChatEngine } from "./engine";
 import { LlamaIndexStream } from "./llamaindex-stream";
 
 initObservability();
 
-export const runtime = "nodejs";
+export const runtime = "edge";
 export const dynamic = "force-dynamic";
 
 const convertMessageContent = (