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1
Why LLMs need tools
2
What are agents?
3
LangChain agents in Python
4
Initializing a calculator tool
5
Initializing a LangChain agent
6
Asking our agent some questions
7
Adding more tools to agents
8
Custom and prebuilt tools
9
Francisco's definition of agents
10
Creating a SQL DB tool
11
Zero shot ReAct agents in LangChain
12
Conversational ReAct agent in LangChain
13
ReAct docstore agent in LangChain
14
Self-ask with search agent
15
Final thoughts on LangChain agents
Description:
Dive deep into the world of LangChain Agents and their integration with GPT 3.5 in this comprehensive 32-minute video tutorial. Explore the limitations of Large Language Models (LLMs) and discover how agents can enhance their capabilities. Learn about the concept of agents, their functionality, and practical implementation using the LangChain library. Follow along as the tutorial demonstrates initializing calculator tools, creating LangChain agents, and posing questions to test their abilities. Gain insights into adding multiple tools, creating custom tools, and understanding various agent types such as Zero-shot ReAct, Conversational ReAct, and Self-ask with search. By the end, grasp the potential of LangChain agents in supercharging LLMs for improved performance in logic, calculation, and search tasks.

LangChain Agents Deep Dive with GPT 3.5 - LangChain

James Briggs
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