Building a Simple Q&A Chatbot with RAG and LangChain.
Create a question-answering system that can understand, and answer questions based on PDF documents (like course materials or company documentation).
The system should use LangChain and RAG (RetrievalAugmented Generation) to provide accurate answers based on the document content.
Project Goals

1. Build a working prototype that can:
   A. Read and process PDF documents
   B. Answer questions based on the document content
   C. Provide relevant responses using RAG architecture
   
2. Learn key concepts:
   A. Document processing with LangChain
   B. Vector embeddings
   C. RAG architecture
   D. LLM integration
   

• Python 3.9+ installed
  

To install dependancies:

pip install -r requirements.txt

To run:

streamlit run app.py

Update secrets.toml file in .streamlit

QDRANT_API_KEY = YOUR API KEY
QDRANT_URL=YOUR QDRANT URL
GEMINI_API_KEY = YOUR API KEY