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2 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
| 8bdb1c5594 | |||
| 8ed543ba67 |
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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!
|
||||
@@ -0,0 +1,10 @@
|
||||
# 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!
|
||||
@@ -0,0 +1,3 @@
|
||||
LLAMA_CLOUD_API_KEY="llx-***"
|
||||
OPENAI_API_KEY="sk-***"
|
||||
PIPELINE_NAME="my-pipeline"
|
||||
@@ -0,0 +1,131 @@
|
||||
# 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
|
||||
@@ -0,0 +1,15 @@
|
||||
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,
|
||||
]);
|
||||
@@ -0,0 +1,48 @@
|
||||
{
|
||||
"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"
|
||||
}
|
||||
}
|
||||
Generated
+1770
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,48 @@
|
||||
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: ${error}`);
|
||||
}
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
|
||||
main().catch(console.error);
|
||||
@@ -0,0 +1,8 @@
|
||||
import { createConsola } from "consola";
|
||||
import type { ConsolaInstance } from "consola";
|
||||
|
||||
export const logger: ConsolaInstance = createConsola({
|
||||
formatOptions: {
|
||||
date: false,
|
||||
},
|
||||
});
|
||||
@@ -0,0 +1,56 @@
|
||||
import { generateText } from "ai";
|
||||
import { openai } from "@ai-sdk/openai";
|
||||
import { NodeWithScore, MetadataMode } from "llamaindex";
|
||||
import * as readline from "readline/promises";
|
||||
import figlet from "figlet";
|
||||
import pc from "picocolors";
|
||||
|
||||
export async function renderLogo(): Promise<void> {
|
||||
const logoText = figlet.textSync("LlamaChat", {
|
||||
font: "ANSI Shadow",
|
||||
horizontalLayout: "default",
|
||||
verticalLayout: "default",
|
||||
width: 100,
|
||||
whitespaceBreak: true,
|
||||
});
|
||||
|
||||
// Add some styling with picocolors
|
||||
const styledLogo = pc.bold(pc.yellowBright(logoText));
|
||||
|
||||
// Add some padding/margin
|
||||
console.log("\n");
|
||||
console.log(styledLogo);
|
||||
console.log(pc.gray("─".repeat(60)));
|
||||
console.log("\n");
|
||||
}
|
||||
|
||||
export async function consoleInput(): Promise<string> {
|
||||
const rl = readline.createInterface({
|
||||
input: process.stdin,
|
||||
output: process.stdout,
|
||||
});
|
||||
|
||||
const answer = await rl.question(pc.cyanBright("You✨:"));
|
||||
rl.close();
|
||||
return answer;
|
||||
}
|
||||
|
||||
export async function retrievalAugmentedGeneration(
|
||||
nodes: NodeWithScore[],
|
||||
prompt: string,
|
||||
): Promise<string> {
|
||||
let mainText: string = "";
|
||||
|
||||
for (const node of nodes) {
|
||||
mainText += `\t{information: '${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;
|
||||
}
|
||||
@@ -0,0 +1,22 @@
|
||||
{
|
||||
"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"]
|
||||
}
|
||||
@@ -0,0 +1,124 @@
|
||||
# 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
|
||||
Binary file not shown.
@@ -0,0 +1,15 @@
|
||||
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,
|
||||
]);
|
||||
@@ -0,0 +1,47 @@
|
||||
{
|
||||
"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"
|
||||
}
|
||||
}
|
||||
Generated
+1758
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,34 @@
|
||||
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: ${error}`);
|
||||
}
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
|
||||
main().catch(console.error);
|
||||
@@ -0,0 +1,8 @@
|
||||
import { createConsola } from "consola";
|
||||
import type { ConsolaInstance } from "consola";
|
||||
|
||||
export const logger: ConsolaInstance = createConsola({
|
||||
formatOptions: {
|
||||
date: false,
|
||||
},
|
||||
});
|
||||
@@ -0,0 +1,51 @@
|
||||
import { generateText } from "ai";
|
||||
import { openai } from "@ai-sdk/openai";
|
||||
import { Document } from "llamaindex";
|
||||
import * as readline from "readline/promises";
|
||||
import figlet from "figlet";
|
||||
import pc from "picocolors";
|
||||
|
||||
export async function renderLogo(): Promise<void> {
|
||||
const logoText = figlet.textSync("LlamaParse Demo", {
|
||||
font: "ANSI Shadow",
|
||||
horizontalLayout: "default",
|
||||
verticalLayout: "default",
|
||||
width: 100,
|
||||
whitespaceBreak: true,
|
||||
});
|
||||
|
||||
// Add some styling with picocolors
|
||||
const styledLogo = pc.bold(pc.magentaBright(logoText));
|
||||
|
||||
// Add some padding/margin
|
||||
console.log("\n");
|
||||
console.log(styledLogo);
|
||||
console.log(pc.gray("─".repeat(60)));
|
||||
console.log("\n");
|
||||
}
|
||||
|
||||
export async function consoleInput(): Promise<string> {
|
||||
const rl = readline.createInterface({
|
||||
input: process.stdin,
|
||||
output: process.stdout,
|
||||
});
|
||||
|
||||
const answer = await rl.question("Path to your file: ");
|
||||
rl.close();
|
||||
return answer;
|
||||
}
|
||||
|
||||
export async function generateSummary(documents: Document[]): Promise<string> {
|
||||
let mainText: string = "";
|
||||
|
||||
for (const document of documents) {
|
||||
mainText += `${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;
|
||||
}
|
||||
@@ -0,0 +1,22 @@
|
||||
{
|
||||
"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"]
|
||||
}
|
||||
Reference in New Issue
Block a user