Research and Content Generation Project with WebRAGTool

This project implements a Retrieval Augmented Generation (RAG) system using the kaibanjs library and natural language processing tools. The goal is to create teams of agents that collaborate on research and content generation tasks, leveraging up-to-date information from the web.

Table of Contents

• Installation
• Configuration
• Project Structure
• Dependencies
• Contributing
• License

Installation

1. Clone the repository:

   git clone https://github.com/kaiban-ai/kaibanjs-web-rag-tool-demo.git
   cd kaibanjs-web-rag-tool-demo

2. Install dependencies:

   Ensure you have node and npm installed on your system.

   npm install

Configuration

Before running the project, you need to configure the necessary environment variables:

1. Environment Variables:

   Create a .env file in the root directory of the project and add your OpenAI API key:

   VITE_OPENAI_API_KEY=your_openai_api_key

Project Structure

• tool.js: Implementation of WebRAGTool, a tool for dynamically retrieving and processing web content.
• team.js: Definition of the Financial Analysis Team.

Dependencies

• Node.js and npm
• kaibanjs: Main library for creating agents and teams.
• langchain: Used for natural language processing and chain creation.
• OpenAI API: For utilizing advanced language models.

Contributing

Contributions are welcome! Please fork the repository and submit a pull request with your improvements.

License

This project is licensed under the MIT License.