How to use retrieval augmented generation with ChromaDB and Mistral

Retrieval Augmented Generation (RAG) has become a prominent technique for enhancing Large Language Models (LLMs) by enriching their outputs with contextual information from external data sources. This method allows language models to generate more accurate and contextually relevant responses by embedding meaningful external data into prompts, effectively extending the functional context length beyond typical token limits. The blog post illustrates the creation of a RAG application designed to generate click-worthy titles for Hacker News submissions by leveraging ChromaDB, a vector store that stores and queries embeddings, and the Mistral language model for generating suggestions. The process involves creating a dataset of popular titles from Hacker News, generating embeddings, and querying ChromaDB for semantically similar titles to inspire new suggestions. The post provides a step-by-step guide on setting up this system, including constructing a dataset, generating embeddings, querying a vector store, and integrating these components with an LLM to produce new title suggestions. By demonstrating this practical application, the blog aims to provide readers with a hands-on understanding of RAG and its potential to integrate LLMs with external datasets effectively.