🔍Retrieval Augmented Generation (RAG) is a technique used to build apps that can interact with text data using AI.
🧩Langchain and OpenAI are two libraries used to build the retrieval augmented generation app.
📚The app is great for working with text data like books, documents, or lectures.
💬You can ask questions or build chatbots using the app to interact with the text data.
🔧The app requires preparing the data, creating a vector database, and querying the data for relevant information.
What is retrieval augmented generation?
—Retrieval augmented generation (RAG) is a technique used to build apps that can interact with text data using AI. It combines retrieval-based methods and language generation models to provide more contextually relevant responses.
What libraries are used to build the app?
—The Langchain and OpenAI libraries are used to build the retrieval augmented generation app. Langchain is used for loading and preprocessing the text data, while OpenAI is used for generating responses based on the input.
What type of data can the app work with?
—The app is designed to work with text data such as books, documents, or lecture transcripts. It can also work with other types of text-based data sources.
What can I do with the app?
—With the app, you can ask questions about the text data, build chatbots for customer support, or create interactive applications that use AI to interact with the data.
What are the main steps in building the app?
—The main steps in building the app include preparing the data, creating a vector database, and querying the database for relevant information. The app uses retrieval-based methods to find relevant chunks of text and then generates responses based on the input.
00:00Introduction to building a retrieval augmented generation app.
02:00Explanation of the Langchain and OpenAI libraries used.
05:00Overview of how the app works with text data.
08:00Explanation of embedding vectors and their use in the app.
11:00Demonstration of searching for relevant data chunks.
13:00Creating the prompt for generating AI responses.
14:00Using OpenAI to generate responses based on the prompt.
14:40Conclusion and next steps for using the retrieval augmented generation app.
