Retrieval Augmented Generation (RAG) is a technique that combines an information retrieval component with a text generator model. RAG can be fine-tuned and its internal knowledge can be modified in an efficient manner and without needing retraining of the entire model.  RAG takes an input and retrieves a set of relevant/supporting documents given a source (e.g., Wikipedia). The documents are concatenated as context with the original input prompt and fed to the text generator which produces the final output. RAG is used to improve the quality of generative AI by allowing large language model (LLMs) to access external knowledge to supplement their internal representation of information. RAG provides timeliness, context, and accuracy grounded in evidence to generative AI, going beyond what the LLM itself can provide. RAG has two phases: retrieval and content generation. In the retrieval phase, algorithms search for and retrieve snippets of information relevant to the user’s prompt or q

  Vector databases are becoming increasingly popular for building AI-powered applications, including LLM apps. In this tutorial, we will cover the basics of vector databases, how they are used, their benefits, and their implementation in Python for LLM. What is a Vector Database? A vector database is a type of database that stores data as numeric vectors in a coordinate space. This allows similarities between vectors to be calculated via operations like cosine similarity. By encoding data as vectors, developers can leverage the mathematical properties of vector spaces to achieve fast similarity search across very large datasets How are Vector Databases Used? Vector databases are used to enable fast similarity search and scale across data points. For LLM apps, vector indexes can simplify architecture over full-text search. Developers can build AI-powered applications in Python on vector databases by encoding data as vectors and using them to search for similar data points. Benefits of