Build Your Own ChatGPT in Minutes with RAGs
RAGs allows you to create and customize your own RAG pipeline and use it on your own data, all through natural language.
This means you can now set up a “ChatGPT based on your own data” without coding.
Bots created using RAGs are intelligent chatbots that combine information retrieval and text generation capabilities.
Generates more accurate and informative answers.
Easy installation:
This project is inspired by OpenAI GPTs. RAGs enable you to create and customize your own chatbot with simple natural language descriptions. This process requires no programming knowledge, just follow a few steps:
1. Describe the task: Tell RAGs what you want the robot to do, such as answer questions or summarize information.
2. Set parameters: Adjust some options on an interface, such as the amount of information to be found.
3. Interact with the robot: After setting up, you can start asking questions to the robot.
The steps to install RAGs are also simple, just download the code, install the necessary packages, and run the program. This tool is for those who want their own chatbot but don’t know how to program.
It supports multiple LLMs (Large Language Models), including models from OpenAI and Anthropic. Users can set configurations for embedded models and LLMs via natural language or manually.
Chatbots created using RAGs are intelligent chatbots that combine information retrieval and text generation capabilities.
Features and capabilities of this robot include:
- Information retrieval capabilities: Robots are able to access and search large amounts of documents and data to find information relevant to user queries. This means it can take data from external sources rather than relying solely on pre-trained knowledge.
2. High-quality answer generation: Combining the retrieved information and the built-in language model (ChatGPT), the bot is able to generate more accurate and informative answers.
3. Adaptable: Because it combines retrieval and generation, this robot is better able to handle complex problems, especially those that require real-time information or expertise.
4. Flexibility and customization: Users can customize the behavior of the robot according to their own needs, such as specifying the source of information retrieval, adjusting the detailed level of answers, etc.
5. Suitable for a variety of applications: This kind of robot is suitable for various scenarios, such as customer service, education, research assistance, etc., especially in fields that require processing large amounts of information and data.
Introduction
GitHub
