Learn how to build a conversational agent using LangChain agents and GPT-3.5 in this comprehensive tutorial video. Explore the process of creating a chatbot that leverages vector search retrieval to find relevant snippets from Lex Fridman's podcast and uses them as context for the language model. Dive into topics such as tools and agents in LangChain, data preparation, vector store initialization, and the implementation of a RetrievalQA chain. Follow along as the instructor demonstrates how to create a Lex Fridman DB tool, initialize a LangChain conversational agent, and set up conversational memory prompts. Conclude the tutorial by testing a conversation with the newly created Lex agent and gain practical insights into building advanced AI-powered chatbots.
Overview
Syllabus
Building conversational agents in LangChain
Tools and Agents in LangChain
Notebook setup and prerequisites
Data preparation
Initialize LangChain vector store
Initializing everything needed by agent
Using RetrievalQA chain in LangChain
Creating Lex Fridman DB tool
Initializing a LangChain conversational agent
Conversational memory prompt
Testing a conversation with the Lex agent
Taught by
James Briggs
