Chat with Your Data Using ChatGPT (2024)
1h 12mIntermediate2024-06-11
Authors
Kesha Williams
Software Engineering Manager, Speaker, Tech Blogger
Course details
Elevate your expertise in AI-driven data analysis and customer interaction and find out how to integrate conversational AI into data interaction with ChatGPT and LangChain. In this hands-on course, distinguished tech leader and innovator Kesha Williams guides you through building a ChatGPT-powered chatbot using LangChain, focusing on key topics like prompt engineering, vector databases, data retrieval, and question answering. By the end of the course, you will understand how to develop complex LLM applications, handle vector databases, and create AI solutions that intelligently interact with and extract insights from diverse data sources.
Learning objectives
Fundamentals of ChatGPT
Fundamentals and more about LangChain
Understanding document loading and splitting
Retrieving data using embeddings and vector stores
Developing a query-processing workflow
Tracking the ChatGPT conversation, and data selection
Learning objectives
Fundamentals of ChatGPT
Fundamentals and more about LangChain
Understanding document loading and splitting
Retrieving data using embeddings and vector stores
Developing a query-processing workflow
Tracking the ChatGPT conversation, and data selection
Skills covered
LangChainChatGPTNatural Language Processing (NLP)OpenAIArtificial Intelligence (AI)Open SourceOne-Off
Concepts
0. Introduction
- 01 - Introduction to ChatGPT and LangChain
- 02 - Review the ChatGPT and LangChain project
1. Understanding Document Loading and Splitting
- 03 - Set up GitHub Codespaces and LangChain
- 04 - Explore LangChain data loaders
- 05 - Load different data types
- 06 - Implement document splitting
2. Storing Data in a Vector Database
- 07 - Generate embeddings
- 08 - Persist data to vector stores
- 09 - Challenge - Implement a vector store and load data
- 10 - Solution - Implement a vector store and load data
3. Retrieving Data from a Vector Database
- 11 - Understand semantic search
- 12 - Perform semantic search
- 13 - Challenge - Conduct semantic searching
- 14 - Solution - Conduct semantic searching
4. Implementing Retrieval Augmented Generation (RAG)
- 15 - Build prompt chains with retrievers
- 16 - Preserve conversation history
- 17 - Perform question answering
- 18 - Challenge - Chat with your data
- 19 - Solution - Chat with your data
Conclusion
- 20 - Your ChatGPT and LangChain journey