Build custom LLM agents using LangChain|Tutorial:100

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### Tutorial Overview

In this tutorial, we will learn how to build custom LLM (Large Language Model) agents using LangChain and the GPT-4 model. Custom agents are essential when developing large-scale applications that require specific tasks not possible with a standard LLM. We will use LangChain's built-in tools to create custom agents and test their capabilities.

### What You Will Learn

- How to use LangChain's `chat_openai` to set up a GPT-4 model.
- How to use LangChain's `load_tools` to load pre-defined tools such as `llm-math` and `wikipedia`.
- How to initialize an agent with specific tools.
- How to create and test custom agents to perform tasks like mathematical calculations and Wikipedia searches.

### Why Custom Agents?

Custom agents allow you to extend the functionality of your LLM by integrating specific tools. For instance, you might need your agent to perform mathematical operations or fetch information from external sources like Wikipedia. These tasks require custom solutions beyond the standard capabilities of LLMs.

### Step-by-Step Guide

1. **Import Necessary Modules**: We will import modules from LangChain, including `chat_openai`, `load_tools`, `initialize_agent`, and `AgentType`.

2. **Set Up LLM**: Configure the `chat_openai` with the GPT-4 model and set the temperature.

3. **Load Tools**: Load tools such as `llm-math` and `wikipedia` using `load_tools`.

4. **Initialize Agent**: Use `initialize_agent` to create an agent with the loaded tools and configure it with `AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION`.

5. **Test the Agent**: We'll ask the agent various questions, from simple mathematical calculations to fetching information from Wikipedia, to see how it performs.

### Example Questions

Here are some example questions we'll ask our custom agent:

1. **Mathematical Calculation**:
- "If you have a rectangular room with dimensions 10m x 7m, what is the area?"
- "A circular flower bed has a radius of 5m. What is the area?"

2. **Wikipedia Search**:
- "What information do you have for the company OpenAI?"
- "Give some information on Virat Kohli, the Indian cricket player."

### Conclusion

By the end of this tutorial, you will have a solid understanding of how to build and utilize custom LLM agents using LangChain and GPT-4. This knowledge will be beneficial for developing advanced applications requiring specific functionalities.

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