AI Agent From Scratch

Let's face it, AI agents are complicated. Most tutorials make them even more so. If you're intrigued by how AI agents operate behind the scenes and want to build your own from scratch without relying on intermediary libraries like LangChain or Llama Index, you're in the right place. In this video, I'll show you how to use the OpenAI Assistant API to unlock the agent framework and equip your agent with search capabilities to unleash its full potential.

What are AI Agents?

Using the OpenAI Assistant API takes the guesswork and complexity out of building AI agents. Unlike other solutions requiring intermediary libraries, the Assistant API streamlines the process, allowing you to create powerful agents without dealing with intricate steps and dependencies. This code framework is already set up for you by OpenAI. You just need to understand the components behind creating these assistants.

OpenAI Assistant API Basics

An agent is powered by a large language model (LLM), such as GPT-3.5 or GPT-4 Turbo. This is the brain behind the agent, enabling it to take a query, reason about it, decide if it needs to use a specific tool, and then use that tool to achieve the desired result. The OpenAI Assistant framework provides three types of tools: Code Interpreter, File Search, and Function Calling.

Agent Tools and Capabilities

• Prompt: Instructions for your agent, similar to a job description for an employee.
• Threads: A collection of messages forming a conversation history.
• Files: Documents like PDFs, Word docs, and TXT files that provide domain-specific knowledge.
• Tools: Tasks the agent can perform, such as code interpretation, file searching, and function calling.

Integrating Perplexity API for Search

In the video, I demonstrate how to equip your AI agent with internet search capabilities using the Perplexity API. This allows the agent to search the web, compile findings, and summarize them into concise answers. Additionally, I show how to create a tool for downloading search results into your local drive.

Agent Interaction Process

When a user interacts with the agent, it starts a thread, collects messages, and derives context. For example, if the user asks about retirement planning, the agent can use the Code Interpreter tool to perform necessary calculations and provide a detailed response. The video also covers creating custom tools for your agent using Python.

Tutorial Setup: Google Colab and Streamlit

I walk you through setting up the OpenAI Library in Google Colab and creating tools using the function calling feature. I also demonstrate building a front-end interface with Streamlit, allowing interactive agent capabilities through a chat interface.

Live Demonstration: Copa America Schedule Search

Watch the agent use the Perplexity API to search for the 2024 Copa America schedule, showcasing its ability to retrieve and organize information dynamically.

Full Video: Watch the full tutorial here

Join the AI Agent Challenge: Become part of our Skool community and take the AI Agent Challenge here.

Book a Consultation: Need personalized help? Book a consultation with me here.