RAGify - Your Documents, Now with AI Superpowers!

Ever wished you could chat with your documents? With RAGify, now you can!

I am thrilled to share my latest project, RAGify - a tool that transforms static documents into an interactive Q&A system using Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs).

What does RAGify do?

• Lets you chat with your documents (like PDFs) without compromising data privacy.
• Works with both on-device and cloud-based LLMs.
• Is fully customizable to suit your specific needs.

What is "Blunder Mifflin"?

To showcase RAGify, I created a fictional company, "Blunder Mifflin," complete with its own employee handbook.

RAGify turns this handbook into an AI assistant that can answer questions about company policies, work-from-home rules, and even the "Prank Protocol."

You can access this chatbot here.

Can I use this on my own documents?

Of course you can with a few simple tweaks. That is the best part - that you can build this too!

RAGify is not using any groundbreaking new tech - it is just combining existing tools in a useful way.

I have made all the code available, along with a working demo, so you can see exactly how it is done.

Link to the code and explanations.

Why did I build RAGify?

My goal with RAGify is to demystify this type of AI application.

Whether you are a developer looking to experiment or a business leader trying to understand how such a system works, RAGify shows you how accessible this technology really is.

How RAGify works?

graph TD
    subgraph User
    A[Input Query]
    H[Get Answer]
    end
    subgraph Knowledge Base
    I[Document Storage]
    J[Vector Embeddings]
    end
    subgraph RAG System
    B[Embed Query]
    C[Vector Search]
    D[Retrieve Relevant Texts]
    E[Create Prompt]
    end
    subgraph LLM
    F[Process Prompt]
    G[Generate Response]
    end
    A --> B
    I --> J
    J --> C
    B --> C
    C --> D
    D --> E
    E --> F
    F --> G
    G --> H

here is a simple overview of the diagram:

1. The user asks a question.
2. The RAG system converts this question into a numerical format (vector) that computers can understand and compare easily.
3. It then searches through a database of pre-converted document vectors to find the most relevant information.
4. The system retrieves the actual text of these relevant documents.
5. It combines the user's question with this relevant information to create a detailed prompt.
6. This prompt is sent to an AI (the LLM), which processes it and generates a response.
7. Finally, the user receives this response as their answer.