A Python-based implementation of Retrieval Augmented Generation (RAG) for document question answering.

This project implements a RAG pipeline that:

• Retrieves relevant information from documents
• Uses Cohere for embeddings and language model capabilities
• Leverages Google LLMs
• Provides accurate answers to queries based on document content

• Python 3.7+
• Cohere API key
• Google API key

1. Clone the repository
2. Set up environment variables:

export COHERE_API_KEY="your-key-here"
export GOOGLE_API_KEY="your-key-here"

## Features
Use the cosine similarity function to fetch the similarity between data