What is a Retriever?

Retrieval augmented generation (RAG) enhances language models by incorporating a retriever and a database to reduce hallucinations and improve response quality without retraining the models. The process involves using an embedding model to convert database documents into vector representations, which are then matched with a query converted to a vector to retrieve relevant documents. This approach is exemplified by a tutorial on building a fully open-source retriever using LlamaIndex and Nomic Embed, the latter being an open-source model surpassing OpenAI Ada's performance. Open-source models like Nomic Embed offer complete auditability and adaptability, crucial for safe AI deployment in high-impact fields like defense and finance, and prevent vendor lock-in associated with closed-source models. The tutorial demonstrates setting up a retriever using LlamaIndex and the Nomic Embed model to handle document and query embeddings, thereby enabling effective search capabilities within RAG systems.