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Author SHA1 Message Date
Clelia (Astra) Bertelli 8bdb1c5594 chore: correct title 2025-07-31 11:13:38 +02:00
Clelia (Astra) Bertelli 8ed543ba67 feat: add ts examples (clean) 2025-07-30 20:14:28 +02:00
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# LlamaCloud Services Examples - Python
In this folder you will find several python notebooks with examples regarding:
- [LlamaParse](./parse/)
- [LlamaExtract](./extract/)
- [LlamaReport](./report/)
Follow the instructions of each notebook to get started!
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# LlamaCloud Services Examples - TypeScript
In this folder you will find two end-to-end examples on how to build applications with:
- [LlamaParse](./parse/)
- [LlamaCloud Index](./index)
In TypeScript.
Follow the instruction in each of the example sub-folders to get started!
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LLAMA_CLOUD_API_KEY="llx-***"
OPENAI_API_KEY="sk-***"
PIPELINE_NAME="my-pipeline"
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# LlamaCloud Index Demo
A TypeScript demo application showcasing the power of **LlamaCloud Index** - a fully automated document ingestion and retrieval serviced offered within [LlamaCloud](https://cloud.llamaindex.ai). This demo allows you to ask questions, retrieve relevant contextual information and generate AI-powered responses using OpenAI's GPT models.
## Table of Contents
- [Features](#features)
- [Prerequisites](#prerequisites)
- [Installation](#installation)
- [Usage](#usage)
- [Start the Demo](#start-the-demo)
- [Development Mode](#development-mode)
- [Build the Project](#build-the-project)
- [Code Quality](#code-quality)
- [Quick Commands Reference](#quick-commands-reference)
- [How It Works](#how-it-works)
- [API Dependencies](#api-dependencies)
- [Troubleshooting](#troubleshooting)
- [Common Issues](#common-issues)
- [License](#license)
- [Contributing](#contributing)
## Features
- 🤖 **RAG**: Simple-yet-effective Retrieval Augmented Generation pipeline built on top of LlamaCloud Index adn OpenAI
- 🎨 **Beautiful CLI**: Styled console interface with colors and ASCII art
-**Fast Development**: Hot reload support with watch mode
- 🛠️ **TypeScript**: Full TypeScript support with strict type checking
## Prerequisites
- Node.js (version 18 or higher)
- pnpm package manager
- OpenAI API key
- LlamaCloud API key
- An existing LlamaCloud Index pipeline
## Installation
1. Clone the repository:
```bash
git clone <repository-url>
cd llamaparse-demo
```
2. Install dependencies:
```bash
pnpm install
```
3. Set up your environment variables:
```bash
export OPENAI_API_KEY="your-openai-api-key"
export LLAMA_CLOUD_API_KEY="your-llamacloud-api-key"
export PIPELINE_NAME="your-pipeline-name"
```
4. Or write them into a `.env` file:
```env
OPENAI_API_KEY="your-openai-api-key"
LLAMA_CLOUD_API_KEY="your-llamacloud-api-key"
PIPELINE_NAME="your-pipeline-name"
```
## Usage
### Start the Demo
```bash
pnpm run start
```
The application will display a welcome screen and prompt you to start chatting!
### Development Mode
For development with hot reload:
```bash
pnpm run dev
```
### Build the Project
```bash
pnpm run build
```
### Code Quality
Format code:
```bash
pnpm run format
```
Lint code:
```bash
pnpm run lint
```
## How It Works
1. **Message Input**: Enter a message
2. **Retrieval**: Several nodes are retrieved from the LlamaCloud index you specified
3. **AI Response Generation**: The retrieved information is passed on to the AI model, along with its relevance score, and a reply to your original message is generated starting from that.
4. **Results**: View the AI-generated summary in your terminal
## Troubleshooting
### Common Issues
1. **Module Resolution Errors**: Ensure you're using Node.js 18+ and have all dependencies installed
2. **API Key Issues**: Verify your OpenAI and LlamaCloud API keys are correctly set
## License
MIT License - see the [LICENSE](../../../LICENSE) file for details.
## Contributing
1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Run `pnpm format` and `pnpm lint`
5. Submit a pull request
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import js from "@eslint/js";
import globals from "globals";
import tseslint from "typescript-eslint";
import { defineConfig } from "eslint/config";
export default defineConfig([
{
files: ["**/*.{js,mjs,cjs,ts,mts,cts}"],
plugins: { js },
extends: ["js/recommended"],
languageOptions: { globals: globals.browser },
},
{ files: ["**/*.js"], languageOptions: { sourceType: "script" } },
tseslint.configs.recommended,
]);
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{
"name": "llama-chat",
"version": "0.1.0",
"description": "Demo for LlamaCloud Index in TypeScript",
"type": "module",
"main": "index.js",
"scripts": {
"test": "echo \"There are no tests\"",
"start": "pnpm exec tsx src/index.ts",
"lint": "eslint ./src/",
"format": "prettier --write ./src/",
"build": "tsc",
"dev": "pnpm exec tsx --watch src/index.ts"
},
"keywords": [
"ai",
"rag",
"retrieval",
"pipeline",
"llms",
"chatbot"
],
"author": "LlamaIndex",
"license": "MIT",
"packageManager": "pnpm@10.12.4",
"devDependencies": {
"@eslint/js": "^9.32.0",
"@types/figlet": "^1.7.0",
"@types/node": "^24.1.0",
"@typescript-eslint/eslint-plugin": "^8.38.0",
"@typescript-eslint/parser": "^8.38.0",
"eslint": "^9.32.0",
"globals": "^16.3.0",
"jiti": "^2.5.1",
"prettier": "^3.6.2",
"typescript": "^5.8.3",
"typescript-eslint": "^8.38.0"
},
"dependencies": {
"@ai-sdk/openai": "^1.3.23",
"ai": "^4.3.19",
"consola": "^3.4.2",
"dotenv": "^17.2.1",
"figlet": "^1.8.2",
"llama-cloud-services": "link:../../../ts/llama_cloud_services",
"picocolors": "^1.1.1"
}
}
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import { LlamaCloudIndex } from "llama-cloud-services";
import { logger } from "./logger";
import pc from "picocolors";
import {
consoleInput,
retrievalAugmentedGeneration,
renderLogo,
} from "./utils";
import dotenv from "dotenv";
dotenv.config();
export async function main(): Promise<number> {
const index = new LlamaCloudIndex({
name: process.env.PIPELINE_NAME as string,
projectName: "Default",
apiKey: process.env.LLAMA_CLOUD_API_KEY, // can provide API-key in the constructor or in the env
});
const retriever = index.asRetriever({
similarityTopK: 5,
});
await renderLogo();
logger.log(
`Welcome to ${pc.bold(
pc.magentaBright("✨LlamaChat✨"),
)}, our demo for ${pc.bold(pc.green("Index🦙"))}, a ${pc.bold(
pc.cyan("LlamaCloud☁️"),
)} (https://cloud.llamaindex.ai) product!.\nType a question below, and you will get an answer!👇\nIf you wish to exit, just type ${pc.bold(
pc.gray("quit"),
)}.\n`,
);
while (true) {
const userInput = await consoleInput();
if (userInput.toLowerCase() == "quit") {
break;
}
try {
const nodes = await retriever.retrieve(userInput);
const summary = await retrievalAugmentedGeneration(nodes, userInput);
logger.log(`${pc.bold(pc.magentaBright("LlamaChat✨:"))}\n${summary}`);
} catch (error) {
logger.error(`Error processing your request: ${error}`);
}
}
return 0;
}
main().catch(console.error);
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import { createConsola } from "consola";
import type { ConsolaInstance } from "consola";
export const logger: ConsolaInstance = createConsola({
formatOptions: {
date: false,
},
});
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import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";
import { NodeWithScore, MetadataMode } from "llamaindex";
import * as readline from "readline/promises";
import figlet from "figlet";
import pc from "picocolors";
export async function renderLogo(): Promise<void> {
const logoText = figlet.textSync("LlamaChat", {
font: "ANSI Shadow",
horizontalLayout: "default",
verticalLayout: "default",
width: 100,
whitespaceBreak: true,
});
// Add some styling with picocolors
const styledLogo = pc.bold(pc.yellowBright(logoText));
// Add some padding/margin
console.log("\n");
console.log(styledLogo);
console.log(pc.gray("─".repeat(60)));
console.log("\n");
}
export async function consoleInput(): Promise<string> {
const rl = readline.createInterface({
input: process.stdin,
output: process.stdout,
});
const answer = await rl.question(pc.cyanBright("You✨:"));
rl.close();
return answer;
}
export async function retrievalAugmentedGeneration(
nodes: NodeWithScore[],
prompt: string,
): Promise<string> {
let mainText: string = "";
for (const node of nodes) {
mainText += `\t{information: '${node.node.getContent(
MetadataMode.ALL,
)}', relevanceScore: '${node.score ?? "no score"}'}\n`;
}
const { text } = await generateText({
model: openai("gpt-4.1"),
prompt: `[\n${mainText}\n]\n\nBased on the information you are given and on the relevance score of that (where -1 means no score available), answer to this user prompt: '${prompt}'`,
});
return text;
}
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{
"compilerOptions": {
"target": "ES2022",
"module": "ES2022",
"lib": ["ES2022"],
"outDir": "./dist",
"rootDir": "./src",
"strict": true,
"esModuleInterop": true,
"skipLibCheck": true,
"forceConsistentCasingInFileNames": true,
"declaration": true,
"declarationMap": true,
"sourceMap": true,
"types": ["node"],
"moduleResolution": "bundler",
"allowSyntheticDefaultImports": true,
"resolveJsonModule": true
},
"include": ["src/**/*"],
"exclude": ["node_modules", "dist"]
}
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# LlamaParse Demo
A TypeScript demo application showcasing the power of **LlamaParse** - an intelligent document parsing service from [LlamaCloud](https://cloud.llamaindex.ai). This demo allows you to parse various document formats and generate AI-powered summaries using OpenAI's GPT models.
## Table of Contents
- [Features](#features)
- [Prerequisites](#prerequisites)
- [Installation](#installation)
- [Usage](#usage)
- [Start the Demo](#start-the-demo)
- [Development Mode](#development-mode)
- [Build the Project](#build-the-project)
- [Code Quality](#code-quality)
- [Quick Commands Reference](#quick-commands-reference)
- [How It Works](#how-it-works)
- [API Dependencies](#api-dependencies)
- [Troubleshooting](#troubleshooting)
- [Common Issues](#common-issues)
- [License](#license)
- [Contributing](#contributing)
## Features
- 📄 **Document Parsing**: Parse PDFs, Word docs, and other formats using LlamaParse
- 🤖 **AI Summaries**: Generate intelligent summaries using OpenAI GPT-4
- 🎨 **Beautiful CLI**: Styled console interface with colors and ASCII art
-**Fast Development**: Hot reload support with watch mode
- 🛠️ **TypeScript**: Full TypeScript support with strict type checking
## Prerequisites
- Node.js (version 18 or higher)
- pnpm package manager
- OpenAI API key
- LlamaCloud API key
## Installation
1. Clone the repository:
```bash
git clone <repository-url>
cd llamaparse-demo
```
2. Install dependencies:
```bash
pnpm install
```
3. Set up your environment variables:
```bash
# Add your API keys to your environment
export OPENAI_API_KEY="your-openai-api-key"
export LLAMA_CLOUD_API_KEY="your-llamacloud-api-key"
```
## Usage
### Start the Demo
```bash
pnpm run start
```
The application will display a welcome screen and prompt you to enter the path to a document you'd like to process.
### Development Mode
For development with hot reload:
```bash
pnpm run dev
```
### Build the Project
```bash
pnpm run build
```
### Code Quality
Format code:
```bash
pnpm run format
```
Lint code:
```bash
pnpm run lint
```
## How It Works
1. **Document Input**: Enter the path to your document when prompted
2. **Parsing**: LlamaParse processes the document and extracts structured content
3. **AI Summary**: The extracted content is sent to OpenAI GPT-4 for summarization
4. **Results**: View the AI-generated summary in your terminal
## Troubleshooting
### Common Issues
1. **Module Resolution Errors**: Ensure you're using Node.js 18+ and have all dependencies installed
2. **API Key Issues**: Verify your OpenAI and LlamaCloud API keys are correctly set
3. **File Path Errors**: Use absolute paths or ensure relative paths are correct from the project root
## License
MIT License - see the [LICENSE](../../../LICENSE) file for details.
## Contributing
1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Run `pnpm format` and `pnpm lint`
5. Submit a pull request
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import js from "@eslint/js";
import globals from "globals";
import tseslint from "typescript-eslint";
import { defineConfig } from "eslint/config";
export default defineConfig([
{
files: ["**/*.{js,mjs,cjs,ts,mts,cts}"],
plugins: { js },
extends: ["js/recommended"],
languageOptions: { globals: globals.browser },
},
{ files: ["**/*.js"], languageOptions: { sourceType: "script" } },
tseslint.configs.recommended,
]);
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{
"name": "llamaparse-demo",
"version": "0.1.0",
"description": "Demo for LlamaParse in TypeScript",
"type": "module",
"main": "index.js",
"scripts": {
"test": "echo \"There are no tests\"",
"start": "pnpm exec tsx src/index.ts",
"lint": "eslint ./src/",
"format": "prettier --write ./src/",
"build": "tsc",
"dev": "pnpm exec tsx --watch src/index.ts"
},
"keywords": [
"ai",
"ocr",
"parsing",
"intelligent-document-processing",
"pdf",
"llms"
],
"author": "LlamaIndex",
"license": "MIT",
"packageManager": "pnpm@10.12.4",
"devDependencies": {
"@eslint/js": "^9.32.0",
"@types/figlet": "^1.7.0",
"@types/node": "^24.1.0",
"@typescript-eslint/eslint-plugin": "^8.38.0",
"@typescript-eslint/parser": "^8.38.0",
"eslint": "^9.32.0",
"globals": "^16.3.0",
"jiti": "^2.5.1",
"prettier": "^3.6.2",
"typescript": "^5.8.3",
"typescript-eslint": "^8.38.0"
},
"dependencies": {
"@ai-sdk/openai": "^1.3.23",
"ai": "^4.3.19",
"consola": "^3.4.2",
"figlet": "^1.8.2",
"llama-cloud-services": "link:../../../ts/llama_cloud_services",
"picocolors": "^1.1.1"
}
}
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import { LlamaParseReader } from "llama-cloud-services";
import { logger } from "./logger";
import pc from "picocolors";
import { consoleInput, generateSummary, renderLogo } from "./utils";
export async function main(): Promise<number> {
const reader = new LlamaParseReader({ resultType: "markdown" });
await renderLogo();
logger.log(
`Welcome to ${pc.bold(
pc.magentaBright("✨LlamaParse Demo✨"),
)}, our demo for ${pc.bold(pc.green("LlamaParse🦙"))}, a ${pc.bold(
pc.cyan("LlamaCloud☁️"),
)} (https://cloud.llamaindex.ai) product!.\nType the path to the document you would like to process below👇\nIf you wish to exit, just type ${pc.bold(
pc.gray("quit"),
)}.\n`,
);
while (true) {
const userInput = await consoleInput();
if (userInput.toLowerCase() == "quit") {
break;
}
try {
const documents = await reader.loadData(userInput);
const summary = await generateSummary(documents); // Added await here
logger.log(`${pc.bold(pc.cyan("AI-generated summary✨"))}:\n${summary}`);
} catch (error) {
logger.error(`Error processing file: ${error}`);
}
}
return 0;
}
main().catch(console.error);
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import { createConsola } from "consola";
import type { ConsolaInstance } from "consola";
export const logger: ConsolaInstance = createConsola({
formatOptions: {
date: false,
},
});
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import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";
import { Document } from "llamaindex";
import * as readline from "readline/promises";
import figlet from "figlet";
import pc from "picocolors";
export async function renderLogo(): Promise<void> {
const logoText = figlet.textSync("LlamaParse Demo", {
font: "ANSI Shadow",
horizontalLayout: "default",
verticalLayout: "default",
width: 100,
whitespaceBreak: true,
});
// Add some styling with picocolors
const styledLogo = pc.bold(pc.magentaBright(logoText));
// Add some padding/margin
console.log("\n");
console.log(styledLogo);
console.log(pc.gray("─".repeat(60)));
console.log("\n");
}
export async function consoleInput(): Promise<string> {
const rl = readline.createInterface({
input: process.stdin,
output: process.stdout,
});
const answer = await rl.question("Path to your file: ");
rl.close();
return answer;
}
export async function generateSummary(documents: Document[]): Promise<string> {
let mainText: string = "";
for (const document of documents) {
mainText += `${document.text}\n\n---\n\n`;
}
const { text } = await generateText({
model: openai("gpt-4.1"),
prompt: `</chat>\n\t<text>${mainText}</text>\n\t<instructions>Could you please generate a summary of the given text?</instructions>\n</chat>`,
});
return text;
}
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{
"compilerOptions": {
"target": "ES2022",
"module": "ES2022",
"lib": ["ES2022"],
"outDir": "./dist",
"rootDir": "./src",
"strict": true,
"esModuleInterop": true,
"skipLibCheck": true,
"forceConsistentCasingInFileNames": true,
"declaration": true,
"declarationMap": true,
"sourceMap": true,
"types": ["node"],
"moduleResolution": "bundler",
"allowSyntheticDefaultImports": true,
"resolveJsonModule": true
},
"include": ["src/**/*"],
"exclude": ["node_modules", "dist"]
}