🚀 Streamlit-Based RAG Chatbot Assistant

A Streamlit-based chatbot assistant designed for RAG (Retrieval-Augmented Generation), providing precise and contextually relevant answers using Ollama, LangChain, FAISS, and PyPDF.

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📦 Installation & Setup

✅ 1. Make a directory and create a Python Virtual Environment

Ensure you have Python 3.10 installed, then run:

For Ubuntu/Linux/macOS:

mkdir rag_git
cd rag_git
python3.10 -m venv .env_rag_workshop
source .env_rag_workshop/bin/activate

✅ 2. Clone the Git Repository

git clone https://github.com/Asrix-AI/llm_rag_workshop.git
cd llm_rag_workshop

✅ 3. Install Project Dependencies

pip install -r requirements.txt

✅ 4. Get API key from langsmith

1. Create an account if you dont have
2. Sign-in to your account
3. Under Settings, create an API key, save it in your .env file Create a .env file in the root directory of the project and add the following environment variables:

LANGCHAIN_API_KEY=your_langsmith_api_key
LANGCHAIN_PROJECT=your_project_name
LANGCHAIN_ENDPOINT="https://API.smith.langchain.com"
LANGCHAIN_TRACING_V2=true

✅ 5. Running Streamlit application

streamlit run app.py

🔹 The application should now be running on http://localhost:8501.