Retrieval Augmented Generation (RAGs), explained simply:
Imagine having a ChatGPT-like interface that connects to your own knowledge base to answer questions. That's exactly what RAG provides! Today, I'll break down each component needed to build a RAG application, and by the end, I'll share a working project with you.
Custom Knowledge Base:
A custom knowledge base is a collection of relevant and current information that forms the backbone of a RAG system. This could be a database, a set of documents, or a mix of both.
Chunking:
Chunking refers to the process of dividing large text into smaller, manageable pieces. This helps the text fit within the input size limits of the embedding model, improving retrieval efficiency. A well-designed chunking strategy can significantly boost the performance of your RAG system.
Embeddings & Embedding Model:
Embeddings are a method for turning text data into numerical vectors, which can then be used in machine learning models. The embedding model is responsible for converting the text into these vectors.
Vector Databases:
These are collections of pre-computed vector representations of text data, designed for quick retrieval and similarity searches. They support operations like CRUD (Create, Read, Update, Delete), metadata filtering, and horizontal scaling.
User Chat Interface:
This is a user-friendly interface that allows people to interact with the RAG system by inputting queries and receiving answers. The user’s query is converted into an embedding, which is then used to retrieve the relevant information from the vector database.
Prompt Template:
This involves creating a suitable prompt for the RAG system, which could be a combination of the user’s query and the custom knowledge base. This prompt is then fed into a large language model (LLM) to generate the final response.