RAG Customer Support Chatbot

📌 Overview

This project is a Customer Support RAG Chatbot that can be accessed through streamlit web applications.

Users can Ask questions about products, the purchase process, returning goods, etc. Questioning everything that related to the system of purchase. And the added value is Chatbot remember previous Conversation.

🚀 Features

• PDF-based Knowledge Base: Extracts relevant information from uploaded PDFs.
• RAG Architecture: Combines retrieval and generation for better responses.
• Conversational Memory: Stores chat history using LangChain memory in ChromaDB, allowing the chatbot to remember previous interactions.
• Uses LangChain, Hugging Face embeddings, and ChromaDB for retrieval.
• Frontend built with Streamlit for a smooth user experience.

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🛠️ Tech Stack

• Chatbot RAG: LangChain, Groq API, ChromaDB, Hugging Face embeddings
• Frontend: Streamlit
• PDF Processing: PyPDFLoader

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🏗️ Installation & Setup

Clone the Repository

git clone https://github.com/renaldiangsar/Customer-Support-RAG.git
cd Customer-Support-RAG

Create a Virtual Environment & Install Dependencies

# open command prompt and run
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

Run the Streamlit

# open command prompt and run
streamlit run app.py

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

Don't forget to give your api in .env file

• open .env file an set your groq and huggingface api

🛠️ Customization & Improvements

• Use a different LLM model (e.g., GPT-4, LLaMA, or local models) for customization.
• Improved response generation using fine-tuned models.

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📝 Future Enhancements

• Add multilingual support for Conversation.
• Support multiple type file, not just pdf format. Try file.txt with many Question Answer

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Visual

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I hope i can do better in my next project. 🎉