Building a Simple Document Q&A System with RAG: A Step-by-Step Guide

Have you ever wanted to chat with your documents? Let's look at a document Q&A system that uses RAG (Retrieval Augmented Generation) to help you do just that. This app lets you upload documents and ask questions about them in plain English.

What Can It Do?

• Upload multiple documents (PDF, Word, Markdown)

• Ask questions in natural language

• Get detailed answers with source citations

• Process documents once and reuse them for many questions

How It Works

Let's break down the main parts of the system:

1. Document Processing

When you upload a document, the app:

• Splits it into smaller chunks that are easier to process

• Creates embeddings (numerical representations) of each chunk using the MiniLM model

• Stores these embeddings in Pinecone, a vector database

• Keeps track of which files it has processed to avoid duplicate work

2. Question Answering

When you ask a question:

• The app converts your question into an embedding

• Searches Pinecone for the most relevant document chunks

• Sends these chunks along with your question to GPT-4

• Returns an answer based on the document content

3. Key Components

The system uses several modern tools:

• Streamlit for the web interface

• LangChain for document processing and RAG pipeline

• Sentence Transformers for creating embeddings

• Pinecone for storing and searching document chunks

• OpenAI's GPT-4 for generating answers

Here's a sample of what happens when you ask a question:

# User asks: "What are the main features of the product?"

1. Question → Embedding
question_embedding = embedding_model.encode("What are the main features?")

2. Find Relevant Chunks
relevant_docs = vector_store.similarity_search(question_embedding)

3. Generate Answer
answer = llm.generate_answer(question, relevant_docs)

Technical Deep Dive

The app uses a three-step RAG process:

1. Retrieval: The system finds relevant information from your documents using semantic search. It compares the meaning of your question with stored document chunks.

2. Augmentation: It takes the retrieved chunks and adds them as context to your question. This gives the language model specific information to work with.

3. Generation: GPT-4 generates an answer using only the provided context, ensuring responses are grounded in your documents.

Smart Features

• Deduplication: The app remembers which files it has processed to avoid duplicate work

• Chunk Management: Documents are split with overlap to maintain context

• Source Tracking: Every answer comes with references to source documents

• Configurable Retrieval: You can adjust how many document chunks to use per query

Setting Up Your Own Instance

To run this app, you'll need:

• OpenAI API key (openai.com)

• Pinecone API key and index (pinecone.io)

• Python environment with required packages

Getting Started

1. Clone the repository

2. Install requirements

3. Set up your API keys

4. Run the Streamlit app

Demo : https://doc-rag.streamlit.app/

[Source Code: Github]

Technical Requirements

• Python 3.11+

• Streamlit

• LangChain

• Sentence Transformers

• Pinecone

• OpenAI API access

This Q&A system shows how modern AI tools can make document interaction more natural and efficient. By combining RAG with a user-friendly interface, we've created a practical tool for anyone who needs to quickly find information in their documents.

Would you like me to explain any part in more detail?