Quickest way to test naive RAG run with AutoRAG.
Run Llama,Qwen,Gemma,Mistral, any warm/cold LLM. No GPU req.
Interact with a chatbot that searches for information and reasons based on your queries
Generate chat responses from user input
Engage in chat conversations
Example on using Langfuse to trace Gradio applications.
Generate responses using text and images
Chat with a Qwen AI assistant
Generate code and answers with chat instructions
Interact with a Korean language and vision assistant
Chat Long COT model that uses tags
Generate text chat conversations using images and text prompts
Chat with GPT-4 using your API key
The Naive RAG Chatbot is a simple yet effective tool designed to test naive RAG (Retrieval-Augmented Generation) implementations. It allows users to upload files and interact with a document-aware AI assistant, making it an excellent choice for quick experimentation and prototyping.
• Document Upload: Easily upload files to train the AI on specific content.
• Document Awareness: The chatbot remembers and can reference uploaded documents during conversations.
• User-Friendly Interface: Intuitive design for seamless interaction with the AI.
• Integration with AutoRAG: Built-in support for AutoRAG, enabling robust retrieval and generation capabilities.
• Real-Time Responses: Get instant answers to your questions based on the uploaded documents.
• Versatile File Support: Accepts multiple file formats for flexibility in training data.
What file formats does Naive RAG Chatbot support?
The chatbot supports multiple file formats, including PDF, TXT, and DOC, ensuring flexibility in training data.
Can I use Naive RAG Chatbot for real-world applications?
While it's primarily designed for testing and prototyping, it can be adapted for lightweight real-world applications with document-aware AI needs.
How does it differ from traditional chatbots?
Naive RAG Chatbot stands out by leveraging uploaded documents to provide context-aware responses, unlike traditional chatbots that rely on predefined knowledge or internet access.