RAG (Retrieval-Augmented Generation) Implementation Using Google Gemini & FAISS
RAG (Retrieval-Augmented Generation) is one of the most important concepts in modern AI applications. It combines:
Retrieval systems (searching relevant information)
Large Language Models (LLMs) (generating intelligent responses)
Instead of depending only on the LLM’s training knowledge, RAG allows the AI to search custom documents and answer questions from them.
Your project uses:
LangChain
Google Gemini API
HuggingFace Embeddings
FAISS Vector Database
to create a simple RAG chatbot inside Google Colab.
