Week 3.1: Tool Calling

Introduction

Gain insights into how tool calling transforms interactions with LLMs, enabling structured outputs like dictionaries for enhanced API interaction, code execution, and JSON object creation.

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: Run Environment Setup Script

Run the environment setup script:

python3 setup_env.py

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/openai_function_call.py

Using Tools Inside tool_lib

To use any tools inside the tool_lib, you need to attach them to an agent. Follow these steps:

1. Copy the Function from tool_lib to Your Agent Script.

2. Annotate the copied function with the @tool decorator.

3. Add the annotated function to your agent's toolkit.

4. Bind the toolkit to the agent executor to make the tool available for use within the agent's workflow.