Build your RAG Chatbot

Welcome to our hands-on workshop where you'll dive into the world of building RAG-based applications! In this workshop, you'll embark on a journey through below modules, each designed to equip you with the knowledge and skills to create your very own RAG chatbot application.

Module	Description	File
🔮 LLM Generation	Using LLM with prompt engineering to solve a specific use case.	1_LLM_Generation.py
📚 Vector Database	Creating a vector database from our knowledge base (PDFs) and the process of data ingestion.	2_Vector_DB[qdrant].py
🤖 RAG Chatbot	Implementing a chatbot using RAG with the vector database and LLM for response generation.	3_RAG_Chatbot.py
🔗 RAG & LangChain	Integrating LangChain library to enhance the RAG chatbot application.	4_RAG_Chatbot_Langchain.py
🦙 Ollama Chatbot	Utilizing an open-source LLM running on our machine for generative AI tasks.	5_Ollama_Chatbot.py
📈 Advanced RAG	Optimizing RAG with intent recognition, re-ranking, mmr.	6_Advanced_RAG.py

Requirements ✅

• Python 3.7 or above
• Get an Gemini API key

Run The Application ⚙️

1. Clone the repo

git clone https://github.com/SSK-14/RAG-Chatbot.git

2. Install packages

If running for the first time,

• Create virtual environment

pip3 install virtualenv
python3 -m venv {your-venvname}
source {your-venvname}/bin/activate

• Install required libraries

pip3 install -r requirements.txt

3. Set up your API key

Set your API keys in the .env file by copying .env.tmpl

4. Running

streamlit run 1_LLM_Generation.py 

Setting up Vector database 🗃️

We will be using qdrant vector database

Use the Cloud

Qdrant Cloud Sign Up

1. Create a Cluster
2. Get API key and cluster URL

Setup in Local

First, download the latest Qdrant image from Dockerhub:

docker pull qdrant/qdrant

Then, run the service:

docker run -p 6333:6333 -p 6334:6334 \
    -v $(pwd)/qdrant_storage:/qdrant/storage:z \
    qdrant/qdrant