Building a Retrieval Augmented Generation (RAG) Application with LangChain and Cohere

I've explored how to build a Retrieval Augmented Generation (RAG) application using LangChain and Cohere. RAG architecture are a type of language model that can retrieve and incorporate relevant information from external sources to generate more informed and context-aware responses.

LangChain is a powerful Python library that simplifies the process of building applications with large language models (LLMs) like GPT-3, BERT, and others. It provides a modular and extensible framework for building applications that can leverage the power of LLMs for various tasks, including question answering, text generation, and more.

Cohere, on the other hand, is an AI company that provides access to powerful language models through their API. In this example, we'll be using Cohere's "command-r" model, which is a retrieval-augmented language model specifically designed for question answering tasks.

References:

1. LangChain Documentation: https://python.langchain.com/en/latest/index.html
2. Cohere Documentation: https://docs.cohere.ai/
3. Retrieval Augmented Generation (RAG) Paper: https://arxiv.org/abs/2005.11401
4. https://learn.microsoft.com/en-us/azure/search/retrieval-augmented-generation-overview