Let’s Build AI Agent From Scratch
Learn Most Imp Skill with Simple Example, Every Detail Spelled Out, Step-by-Step
● What's This Article About?
The article explains how to build a simple AI agent for text analysis from scratch.
It shows how to create an AI that can understand user questions, choose the right analysis tools, and give detailed information about text.
The system uses a language model to understand what the user wants and custom-made text analysis tools.
These tools can count characters and words, find the longest words, and spot unique words in text.
The article shows how to make this AI easy to use with a tool called Streamlit.
● Why Read This Article?
Readers will learn about the basic parts of AI agents.
It shows how AI can be used in business, like looking at customer feedback or dealing with lots of documents.
The article proves that making AI doesn't always need lots of money or expert knowledge.
It's useful for business leaders, developers, and anyone interested in using AI for business.
● Let's Design
The article explains the design of the AI agent using a diagram.
It shows how the AI agent works, from setting up to giving answers to the user.
● Let's Get Cooking
- This section gives a link to the project on GitHub and explains the project structure.
● AI Agent Brain Module
Explains the main part of the AI agent that talks to the language model.
Shows how the code is organized to make it easy to use and change.
● Our Simple AI Agent Module
Describes the part of the AI that handles text analysis.
Explains how it works with different tools and gives results to users.
● Tools
Talks about the text analysis tools used by the AI agent.
Explains each tool: counting characters, finding longest words, spotting unique words, and counting words.
● Let's Setup
- Gives a link to instructions on how to set up the project.
● Let's Run
- Shows an example of the AI agent working.
● Closing Thoughts
Talks about how AI might be used in business in the future.
Suggests that AI agents might do more complex tasks in the future.
Reminds readers that the basic ideas in the article will still be important for making good AI tools.