Project Overview —Python Retrieval-Augmented Generation (RAG) Assistant for Document-Based Question Answering using LangChain, ChromaDB, and Groq

This project implements a Retrieval-Augmented Generation (RAG) AI assistant that enables intelligent question-answering over custom text documents.
The system indexes documents in a vector database and retrieves relevant context to generate accurate, context-aware responses using Large Language Models (LLMs).

The goal of this project is to provide a fast, scalable, and modular AI assistant suitable for knowledge bases, research documents, and enterprise documentation workflows.

The assistant is built using LangChain for orchestration, ChromaDB for vector storage, and Groq for high-speed LLM inference.

Key Features

📄Document Processing

The documents are automatically loaded, parsed, and prepared as text documents.

🔍Semantic Search

For efficiency, Uses embedding-based similarity search to retrieve the most relevant document segments.

💬 Conversational AI

Generates context-aware responses by combining retrieved document context with an LLM.

⚡ High Performance Retrieval

ChromaDB ensures fast vector similarity search and scalable storage.

🚀 Installation Prerequisites

Python 3.8 or higher
pip package manager

Clone the repository:

• Create a virtual environment (recommended)

• Install dependencies:
  

• Configure API keys:

Create a .env file at the project root:

💻 Usage

Run the assistant:

Code Example

Here's how the RAG system works:

🧠 Code Workflow Explanation

The system follows these steps:

1. Load documents from the data/ directory
2. Split documents into smaller chunks
3. Convert chunks into vector embeddings
4. Store vectors in ChromaDB
5. Retrieve relevant chunks for user queries
6. Generate responses using an LLM

This architecture ensures accurate responses grounded in your documents.

Adding Your Own Documents

Simply place your .txt files in the data/ folder:

The system will automatically index them.

📁 Project Structure

🛠️ Technologies Used

🔧 Configuration

Model Selection

You can change the LLM model directly in app.py to experiment with different Groq-supported models.

📊 How It Works

• Document Loading – Load text files

• Text Splitting – Chunk documents into manageable segments

• Embedding – Convert text into vector embeddings

• Vector Storage – Store embeddings in ChromaDB

• User Query – Accept natural language questions

• Retrieval – Fetch relevant document chunks

• Generation – Produce grounded responses using LLM

🙏 Acknowledgments

• Thanks to the LangChain community for the amazing framework,
• HuggingFace for providing open-source embeddings,
• Groq for fast LLM inference.
• Ready Tensor Team

📧 Contact
Project Link: https://github.com/mohamadlamg/Ready-tensor-RAG-assistant

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