Week 3.2: Agents

Introduction

Use a large language model to select a sequence of actions and act as a reasoning engine to determine which actions to take and in what order, in order to build agents using the ReACT framework.

Prerequisites

Before you begin, ensure you have met the following requirements:

You have installed Python 3.6+.

Setup Instructions

Step 1: Clone the Repository

First, clone the repository to your local machine using the following command:

git clone [repository-url]
cd [repository-name]

Step 2: Create a Python Virtual Environment

Create a virtual environment using venv:

python3 -m venv venv

Step 3: Activate the Virtual Environment

Activate the virtual environment:

On Windows:
```
venv\Scripts\activate
```
On MacOS/Linux:
```
source venv/bin/activate
```

Step 4: Install Required Packages

Install the required packages using pip:

pip install -r requirements.txt

Step 5: Create a `.env` File

Create a .env file in the root directory of the project. Use the .env.sample file as a reference:

cp .env.sample .env

Step 6: Update `.env` File

Open the .env file and update the key values as necessary.

Step 7: Export the Variables Inside Your Environment

Run the environment setup script:

export OPENAI_API_KEY=[your-key-here]
export TAVILY_API_KEY=[your-key-here]
export LANGCHAIN_API_KEY=[your-key-here]
export LANGCHAIN_TRACING_V2=true
export LANGCHAIN_PROJECT=default

Usage

Running In-Class Examples

To run any in-class examples, execute the specific file directly from the command line. For example:

python3 in_class_examples/[file-name]

Blueavian9/ai-school-week-3.2-agents