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Cole Medin
2
.gitattributes
vendored
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.gitattributes
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# Auto detect text files and perform LF normalization
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* text=auto
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20
1-first-agent/.env.example
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1-first-agent/.env.example
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# Rename this file to .env once you have filled in the below environment variables!
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# Get your Open AI API Key by following these instructions -
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https://help.openai.com/en/articles/4936850-where-do-i-find-my-openai-api-key
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OPENAI_API_KEY=
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# See all Open AI models you can use here -
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# https://platform.openai.com/docs/models
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# A good default to go with here is gpt-4o
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OPENAI_MODEL=
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# Get your personal Asana access token through the developer console in Asana.
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# Feel free to follow these instructions -
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# https://developers.asana.com/docs/personal-access-token
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ASANA_ACCESS_TOKEN=
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# The Asana project ID is in the URL when you visit a project in the Asana UI.
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# If your URL is https://app.asana.com/0/123456789/1212121212, then your
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# Asana project ID is 123456789
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ASANA_PROJECT_ID=
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144
1-first-agent/agents.py
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1-first-agent/agents.py
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import asana
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from asana.rest import ApiException
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from openai import OpenAI
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from dotenv import load_dotenv
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from datetime import datetime
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import json
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import os
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load_dotenv()
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client = OpenAI()
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model = os.getenv('OPENAI_MODEL', 'gpt-4o')
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configuration = asana.Configuration()
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configuration.access_token = os.getenv('ASANA_ACCESS_TOKEN', '')
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api_client = asana.ApiClient(configuration)
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tasks_api_instance = asana.TasksApi(api_client)
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def create_asana_task(task_name, due_on="today"):
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"""
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Creates a task in Asana given the name of the task and when it is due
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Example call:
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create_asana_task("Test Task", "2024-06-24")
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Args:
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task_name (str): The name of the task in Asana
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due_on (str): The date the task is due in the format YYYY-MM-DD. If not given, the current day is used
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Returns:
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str: The API response of adding the task to Asana or an error message if the API call threw an error
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"""
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if due_on == "today":
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due_on = str(datetime.now().date())
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task_body = {
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"data": {
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"name": task_name,
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"due_on": due_on,
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"projects": [os.getenv("ASANA_PROJECT_ID", "")]
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}
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}
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try:
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api_response = tasks_api_instance.create_task(task_body, {})
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return json.dumps(api_response, indent=2)
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except ApiException as e:
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return f"Exception when calling TasksApi->create_task: {e}"
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def get_tools():
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tools = [
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{
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"type": "function",
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"function": {
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"name": "create_asana_task",
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"description": "Creates a task in Asana given the name of the task and when it is due",
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"parameters": {
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"type": "object",
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"properties": {
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"task_name": {
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"type": "string",
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"description": "The name of the task in Asana"
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},
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"due_on": {
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"type": "string",
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"description": "The date the task is due in the format YYYY-MM-DD. If not given, the current day is used"
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},
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},
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"required": ["task_name"]
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},
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},
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}
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]
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return tools
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def prompt_ai(messages):
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# First, prompt the AI with the latest user message
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completion = client.chat.completions.create(
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model=model,
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messages=messages,
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tools=get_tools()
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)
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response_message = completion.choices[0].message
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tool_calls = response_message.tool_calls
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# Second, see if the AI decided it needs to invoke a tool
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if tool_calls:
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# If the AI decided to invoke a tool, invoke it
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available_functions = {
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"create_asana_task": create_asana_task
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}
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# Add the tool request to the list of messages so the AI knows later it invoked the tool
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messages.append(response_message)
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# Next, for each tool the AI wanted to call, call it and add the tool result to the list of messages
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for tool_call in tool_calls:
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function_name = tool_call.function.name
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function_to_call = available_functions[function_name]
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function_args = json.loads(tool_call.function.arguments)
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function_response = function_to_call(**function_args)
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messages.append({
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"tool_call_id": tool_call.id,
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"role": "tool",
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"name": function_name,
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"content": function_response
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})
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# Call the AI again so it can produce a response with the result of calling the tool(s)
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second_response = client.chat.completions.create(
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model=model,
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messages=messages,
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)
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return second_response.choices[0].message.content
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return response_message.content
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def main():
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messages = [
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{
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"role": "system",
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"content": f"You are a personal assistant who helps manage tasks in Asana. The current date is: {datetime.now().date()}"
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}
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]
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while True:
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user_input = input("Chat with AI (q to quit): ").strip()
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if user_input == 'q':
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break
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messages.append({"role": "user", "content": user_input})
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ai_response = prompt_ai(messages)
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print(ai_response)
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messages.append({"role": "assistant", "content": ai_response})
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if __name__ == "__main__":
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main()
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1-first-agent/requirements.txt
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1-first-agent/requirements.txt
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asana==5.0.0
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openai==1.10.0
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python-dotenv==0.13.0
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21
LICENSE
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LICENSE
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MIT License
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Copyright (c) 2024 Cole Medin
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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80
README.md
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README.md
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<div align="center">
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<a href="https://www.youtube.com/channel/UCMwVTLZIRRUyyVrkjDpn4pA">
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<img alt="AI Agents Masterclass" src="https://i.imgur.com/8Gr2pBA.png">
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<h1 align="center">AI Agents Masterclass</h1>
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</a>
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</div>
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<p align="center">
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Artificial Intelligence is the #1 thing for all developers to spend their time on now.
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The problem is, most developers aren't focusing on AI agents, which is the real way to unleash the full power of AI.
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This is why I'm creating this AI Agents Masterclass - so I can show YOU how to use AI agents to transform
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businesses and create incredibly powerful software like I've already done many times!
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Click the image or link above to go to the masterclass on YouTube.
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</p>
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<p align="center" style="margin-top: 25px">
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<a href="#what-are-ai-agents"><strong>What are AI Agents?</strong></a> ·
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<a href="#how-this-repo-works"><strong>How this Repo Works</strong></a> ·
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<a href="#instructions-to-follow-along"><strong>Instructions to Follow Along</strong></a>
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</p>
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<br/>
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## What are AI Agents?
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AI agents are simply Large Language Models that have been given the ability to interact with the outside world. They
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can do things like draft emails, book appointments in your CRM, create tasks in your task management software, and
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really anything you can dream of! I hope that everything I show here can really help you dream big
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and create incredible things with AI!
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AI agents can be very powerful without having to create a lot of code. That doesn't mean there isn't room though
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to create more complex applications to tie together many different agents to accomplish truly incredible things!
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That's where we'll be heading with this masterclass and I really look forward to it!
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Below is a very basic diagram just to get an idea of what an AI agent looks like:
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<div align="center" style="margin-top: 25px;margin-bottom:25px">
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<img width="700" alt="Trainers Ally LangGraph graph" src="https://i.imgur.com/ChRoV8W.png">
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</div>
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<br/>
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## How this Repo Works
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Each week there will be a new video for my AI Agents Masterclass! Each video will have its own folder
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in this repo, starting with [/1-first-agent/](/1-first-agent) for the first video in the masterclass
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where I create our very first AI agent!
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The code in each folder will be exactly what I used/created in the accompanying masterclass video.
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<br/>
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## Instructions to Follow Along
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The below instructions assume you already have Git, Python, and Pip installed. If you do not, you can install
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[Python + Pip from here](https://www.python.org/downloads/) and [Git from here](https://git-scm.com/).
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To follow along with any of my videos, first clone this GitHub repository, open up a terminal,
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and change your directory to the folder for the current video you are watching (example: 1st video is [/1-first-agent/](/1-first-agent)).
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The below instructions work on any OS - Windows, Linux, or Mac!
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You will need to use the environment variables defined in the .env.example file in the folder (example for the first video: [`1-first-agent/.env.example`](/1-first-agent/.env.example)) to set up your API keys and other configuration. Turn the .env.example file into a `.env` file, and supply the necessary environment variables.
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After setting up the .env file, run the below commands to create a Python virtual environment and install the necessary Python packages to run the code from the masterclass. Creating a virtual environment is optional but recommended! Creating a virtual environment for the entire masterclass is a one time thing. Make sure to run the pip install for each video though!
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```bash
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python -m venv ai-agents-masterclass
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On Windows: .\ai-agents-masterclass\Scripts\activate
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On MacOS/Linux: source ai-agents-masterclass/bin/activate
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cd 1-first-agent (or whichever folder)
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pip install -r requirements.txt
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```
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Then, you can execute the code in the folder with:
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```bash
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python [script name].py
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```
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