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Overview
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This course delves into the concept of agents as solutions to enhance Large Language Models (LLMs). By exploring what agents are, how they function, and how to implement them using the LangChain library, learners will acquire the skills to supercharge LLMs. The course covers initializing various tools, creating custom and prebuilt tools, and implementing different types of agents within LangChain. The teaching method involves practical demonstrations and examples in Python. This course is intended for individuals interested in advancing their understanding of artificial intelligence, natural language processing, and enhancing LLM capabilities.
Syllabus
Why LLMs need tools
What are agents?
LangChain agents in Python
Initializing a calculator tool
Initializing a LangChain agent
Asking our agent some questions
Adding more tools to agents
Custom and prebuilt tools
Francisco's definition of agents
Creating a SQL DB tool
Zero shot ReAct agents in LangChain
Conversational ReAct agent in LangChain
ReAct docstore agent in LangChain
Self-ask with search agent
Final thoughts on LangChain agents
Taught by
James Briggs