📄 Chat with Your Documents – RAG App

Retrieval-Augmented Generation (RAG) powered app to chat with your own PDF, Word, or PowerPoint files using Ollama, LangChain, and Streamlit.

Ask questions about your uploaded documents, the system retrieves relevant chunks and responds using a locally running LLM (e.g., Mistral via Ollama).

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🚀 Features

• 📁 Upload .pdf, .docx, and .pptx files
• ✂️ Smart chunking with RecursiveCharacterTextSplitter
• 🧠 Embedding via OllamaEmbeddings (Mistral)
• 🗂️ Fast in-memory vector store (no disk I/O delay)
• 🔍 Similarity search-based retrieval
• 🤖 LLM response constrained to document content
• ⚡ Streamlit UI with live Q&A

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🧱 Tech Stack

• Streamlit – Frontend interface
• LangChain – RAG pipeline components
• Ollama – Local language model inference
• Mistral – LLM used for generation
• Python 3.10+

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🛠️ Installation

Prerequisites

• Python 3.10+
• Ollama installed on your system

Step 1: Install Python Dependencies

pip install -r requirements.txt

Step 2: Install and Setup Ollama

1. Download and install Ollama from https://ollama.com/

2. Pull the Mistral model:

   ollama pull mistral

3. Start Ollama service (keep this running in a separate terminal):

   ollama serve

Step 3: Run the Application

streamlit run app.py

The app will open in your browser at http://localhost:8501

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🎯 Usage

1. Upload Documents: Drop your PDF, Word, or PowerPoint files
2. Wait for Processing: The app will chunk and embed your documents
3. Ask Questions: Type questions about your uploaded content
4. Get Answers: The AI will respond based only on your document content

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📝 Requirements

The project uses these key packages:

• streamlit>=1.28.0 - Web interface
• langchain-community>=0.0.1 - Document loaders
• langchain-text-splitters>=0.0.1 - Text chunking
• langchain-ollama>=0.1.0 - Ollama integration
• langchain-core>=0.1.0 - Core LangChain components