Playing around with OpenAI Swarm and ChatGBT and this was the result (although as you can see i went off on a tangent not using Swarm) but a fun excercise.

https://colab.research.google.com/drive/1gx5zmdIcJwwKIvDmNRoJmqpdeLh6UnCN?usp=sharing#scrollTo=4k3_qWopAGE_

https://github.com/openai/swarm

I have uploaded to https://github.com/herepete/Ai_playing/blob/main/ai_agents.py

See my other AI posts about putting the OpenAI key in as a variable 🙂

$ cat ai_agents.py
#!/usr/bin/python3.11

import openai
import os
os.system('clear')

OPENAI_API_KEY = os.getenv('OPENAI_API_KEY')

# Function to determine the most appropriate agent to respond
def select_agent(agents, user_input):
    if "joke" in user_input.lower():
        return "Joke Creator"
    elif "fact" in user_input.lower() or "verify" in user_input.lower():
        return "Fact Checker"
    else:
        return "Creative Thinker"

# Function for the agent to respond based on instructions
def agent_respond(agent, context):
    try:
        # Make the call to the OpenAI API with clear and explicit structure
        response = openai.ChatCompletion.create(
            model="gpt-3.5-turbo",
            messages=[
                {"role": "system", "content": agent["instructions"]},
                *context
            ],
            max_tokens=150
        )
        return response['choices'][0]['message']['content'].strip()
    except Exception as e:
        print(f"Error: {e}")
        return None

# Function to create an agent
def create_agent(name, role, instructions):
    return {"name": name, "role": role, "instructions": instructions}

# Create agents
agent_1 = create_agent("Fact Checker", "assistant", "You are a detailed fact-checker. Provide accurate and concise responses.")
agent_2 = create_agent("Creative Thinker", "assistant", "You are a creative agent that provides out-of-the-box thinking.")
agent_3 = create_agent("Joke Creator", "assistant", "You are a joke creator. Provide funny jokes when asked.")

# List of agents
agents = [agent_1, agent_2, agent_3]

# Initial explanation to the user
print("Welcome! We have three agents here to assist you:")
print("1. Fact Checker: This agent helps with verifying information, providing accurate answers, and fact-checking.")
print("2. Creative Thinker: This agent helps with brainstorming ideas, creative problem-solving, and thinking outside the box.")
print("3. Joke Creator: This agent helps you by creating jokes and providing humor.")
print("Feel free to ask any questions, and our most suitable agent will assist you.")

# Run an interactive conversation loop
while True:
    # Ask user for input
    user_input = input("\nWhat do you need help with today?\nYou: ")

    # Break loop if user wants to quit
    if user_input.lower() in ["quit", "exit"]:
        print("Ending the conversation.")
        break

    # Determine the most appropriate agent based on user input
    selected_agent_name = select_agent(agents, user_input)
    selected_agent = next(agent for agent in agents if agent["name"] == selected_agent_name)

    # Reset messages to contain only the most recent user input for new prompts
    messages = [{"role": "user", "content": user_input}]

    # Run the selected agent to process the current context
    response = agent_respond(selected_agent, messages)
    if response:
        messages.append({"role": "assistant", "content": f"{selected_agent['name']} response: {response}"})
        print(f"{selected_agent['name']} response: {response}")
    else:
        print(f"No response from {selected_agent['name']}.")

result

Welcome! We have three agents here to assist you:
1. Fact Checker: This agent helps with verifying information, providing accurate answers, and fact-checking.
2. Creative Thinker: This agent helps with brainstorming ideas, creative problem-solving, and thinking outside the box.
3. Joke Creator: This agent helps you by creating jokes and providing humor.
Feel free to ask any questions, and our most suitable agent will assist you.

What do you need help with today?
You: tell me a joke
Joke Creator response: Why did the scarecrow win an award? Because he was outstanding in his field!

What do you need help with today?
You: tell me a fact
Fact Checker response: Fact: The Earth is the only planet in our solar system known to support life.

What do you need help with today?
You: what is the meaning of life?
Creative Thinker response: The meaning of life is a deeply personal and subjective question that can have many different answers depending on individual beliefs, experiences, and perspectives. Some people find meaning in pursuing their passions, building connections with others, contributing to society, seeking personal growth, exploring spirituality, or simply in enjoying the present moment. Ultimately, the meaning of life is something that each person must decide for themselves based on what brings them fulfillment and purpose.

What do you need help with today?
You:

Available in Github at https://github.com/herepete/Fidelity/blob/main/chat_with_openai.py

A standalone script to allow you at the command line to chat with OpenAI

[ec2-user@ip-172-31-33-224 Fidelity]$ cat chat_with_openai.py
#!/usr/bin/python3
import openai
import os


# Your OpenAI API key
openai.api_key = os.getenv("OPENAI_API_KEY")

def chat_with_ai():
    messages = [
        {"role": "system", "content": "You are a helpful assistant."}
    ]

    print("Start chatting with the AI! Type 'exit' to end the conversation.")
    while True:
        # Get user input
        user_input = input("You: ")

        # Exit the chat loop if the user types 'exit'
        if user_input.lower() == 'exit':
            print("Ending the conversation. Goodbye!")
            break

        # Add the user's message to the conversation history
        messages.append({"role": "user", "content": user_input})

        # Get the AI's response
        response = openai.ChatCompletion.create(
            model="gpt-3.5-turbo",
            messages=messages,
            max_tokens=150  # Set the maximum number of tokens for each response
        )

        # Extract and print the AI's response
        ai_response = response['choices'][0]['message']['content']
        print(f"AI: {ai_response}")

        # Add the AI's response to the conversation history
        messages.append({"role": "assistant", "content": ai_response})


chat_with_ai()

Sample Run

]# ./chat_with_openai.py
Start chatting with the AI! Type 'exit' to end the conversation.
You: Tell me  a Joke
AI: Sure, here's a joke for you:

Why couldn't the bicycle stand up by itself?

Because it was two-tired!
You: nice one, tell me an intresting fact?
AI: Here's an interesting fact for you:

Honey never spoils! Archaeologists have found pots of honey in ancient Egyptian tombs that are over 3000 years old and still perfectly edible. Honey's high sugar content and low moisture levels create an environment that prevents the growth of bacteria and yeasts, making it one of the only foods that never spoils.
You:

Highlevel

Olama hosts LLM models and allows you to interact with them all locally

openwebui is a nice gui front end for Ollama and models

Sites

Download Ollama on Linux

library

Open WebUI

Videos

Run Through

Ran from an Aws Vm, the basic Micro doesn’t have enough /tmp space and you have to fudge around with things.

The quickest solution is uping the instance type to something with more power a t2.xlarge seems to work well

Bundled install

curl -fsSL https://ollama.com/install.sh | sh
yum install pip -y 
pip install ollama
ollama run llama3.1

Install Ollama – Shell

curl -fsSL https://ollama.com/install.sh | sh

by following instructions on Download Ollama on Linux

Install Ollama – Pip

yum install pip pip install ollama

Install a LLM Model

ollama run llama3.1

find model in library and copy command

To check what LLM models you have and other stuff on Ollama

$ ollama list
NAME            ID              SIZE    MODIFIED
llama3.1:latest 42182419e950    4.7 GB  38 minutes ago
gemma2:2b       8ccf136fdd52    1.6 GB  2 hours ago
$ ollama
Usage:
  ollama [flags]
  ollama [command]
Available Commands:
  serve       Start ollama
  create      Create a model from a Modelfile
  show        Show information for a model
  run         Run a model
  pull        Pull a model from a registry
  push        Push a model to a registry
  list        List models
  ps          List running models
  cp          Copy a model
  rm          Remove a model
  help        Help about any command
Flags:
  -h, --help      help for ollama
  -v, --version   Show version information
Use "ollama [command] --help" for more information about a command.

Passing Input files – Bash

$ cat /home/ollama_files/helloworld_testfile       i have 5 oranges and 2 apples
if i eat 4 oranges and 1 apple
how much is left?
$ cat /home/ollama_files/helloworld_testfile | ollama run gemma2:2b  "prompt"
Here's how to figure out the remaining fruit:
Oranges Left: You started with 5 oranges, and you ate 4, so you have
5 - 4 = 1 orange left.
Apples Left:  You started with 2 apples, and you ate 1, leaving you
with 2 - 1 = 1 apple.
Answer: You have 1 orange and 1 apple left. 🍊🍎

Passing Input files – Python

# cat ./llm_test.py
#!/usr/bin/python3.9
import ollama
notes = "helloworld_testfile"
with open(notes,'r') as file:
    content= file.read()
my_prompt = f"give me the answer {content}"
response = ollama.generate(model="gemma2:2b", prompt=my_prompt)
actual_response = response["response"]
print(actual_response)
#  ./llm_test.py
Here's how to solve that:
Oranges: You started with 5, and you ate 4, so you have 5 - 4 = 1 orange left.
Apples: You started with 2, and you ate 1, so you have 2 - 1 = 1 apple left.
Answer: You have 1 orange and 1 apple left.

Quick Chat

$ ollama run gemma2:2b
>>> tell me a joke
Why don't scientists trust atoms?
Because they make up everything! 😄
Let me know if you want to hear another one! 😊
>>> Send a message (/? for help)