I Built an AI Financial Advisor in 10 Minutes using LangChain with Chain of Thought & ReAct
Learn how to leverage the power of custom tools to create specialized AI agents. This video walks through building a cool financial assistant bot using LangChain.
The Power of Custom Tools
Increased flexibility, control, and specialization for your AI agents
Customize for your specific needs - tweak existing tools or build new capabilities
Control your agent's behavior based on the tools you provide
Specialize for different domains like finance
Adding Custom Tools
Create a class subclassing BaseTool and implement a _run method
Use the @tool decorator to convert a function into a tool
Building a Financial Assistant
Tools for stock data, analysis, news, and recommendations
AI uses Large Language Models and prompt engineering
Plan and Execute and Chain of Thought allow the AI to reason and decompose problems
See It In Action
Test on sample questions and watch the step-by-step reasoning
Builds sophisticated responses leveraging the custom tools
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0:00 - 0:54 Intro - Introducing the topic of Financial AGI and custom tools for LangChain agents.
0:55 - 2:00 Why Custom Tools - Explaining benefits like flexibility, control, and specialization.
2:00 - 3:30 Adding Tools - Overview of how to create custom tools by subclassing BaseTool. Mentions @tool decorator.
3:30 - 4:15 Demo Intro - Transitioning to demo of building a financial assistant bot.
4:15 - 6:00 Demo Part 1 - Walkthrough of stock data, analysis, and news tools. How AI uses LLM and prompt engineering.
6:00 - 7:30 Demo Part 2 - Testing the bot on sample questions and showing the step-by-step reasoning. Analyzing more complex questions.
7:30 - 8:30 Demo Part 3 - Demo of resilience when query is too complex. Gracefully handling edge cases.