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Retrieval Augmented Generation with the OpenAI API & DuckDB

Packt via Coursera

Overview

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Use Retrieval-Augmented Generation (RAG) to build a chatbot for movie queries. Integrate GPT with DuckDB vector database via LangChain, leveraging embeddings from IMDb descriptions to enhance GPT's accuracy and deliver factual responses without retraining. Build an advanced movie database search bot using Retrieval-Augmented Generation (RAG). In this notebook, you'll learn to enhance GPT’s capabilities by integrating factual context through embeddings stored in DuckDB. Utilize OpenAI’s gpt-4o-mini for generating responses, and the text-embedding-3-large model for accurate context retrieval. Explore the practical application of LangChain to seamlessly interact with both GPT and the vector database. By embedding IMDb movie descriptions, you’ll create a chatbot capable of providing precise answers to movie-related queries. This hands-on project demonstrates efficient methods to improve the factual accuracy of language model outputs without retraining or fine-tuning. Create your own DataLab workbook for this code along: (https://www.datacamp.com/datalab/new?accountType=personal&_tag=workspace&workspaceId=95d9f251-73fd-41c6-b17b-0a8486695cdd&title=Retrieval%20Augmented%20Generation%20with%20the%20OpenAI%20API%20%26%20DuckDB&visibility=private&openIfExists=true) This course is ideal for intermediate learners with a background in Python, AI, and API integrations. It targets individuals who want to enhance their understanding of language models, particularly GPT, and apply them in real-world applications. Familiarity with databases and vector searches is helpful but not required. Ideal for anyone looking to improve their skills in building advanced chatbots, leveraging external data sources, and optimizing AI-driven applications. The course adopts a hands-on, project-based learning approach. You will start by importing data and creating documents, then move on to setting up DuckDB and GPT integration. Step-by-step, you’ll connect the components, allowing you to gradually build a fully functional chatbot while exploring critical concepts like cost estimation, prompt creation, and debugging. This course is based on Retrieval Augmented Generation with the OpenAI API & DuckDB, by DataLab DataLab and Vincent Vankrunkelsven. This course is licensed and distributed by Packt. All rights reserved. Packt is one of the world's most prolific publishers of cutting-edge technical content. For over two decades we've made it our mission to curate and publish the knowledge of only the very best technical experts. We focus on real-world courses that help our customers get the job done, with coverage that extends across a wide range of established and cutting-edge technical topics. If you're an individual or an organisation that embraces learning by doing, Packt is the perfect fit for you.

Syllabus

  • Retrieval Augmented Generation with the OpenAI API & DuckDB
    • This module explores how to implement Retrieval-Augmented Generation (RAG) using the OpenAI API and DuckDB. Learners will gain hands-on experience integrating GPT with vector databases, improving response accuracy, and applying data manipulation techniques in Python.

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