This Python script demonstrates how to interact with multiple AI models from different providers using their respective APIs.

llm-util.py

import os
from dotenv import load_dotenv
from openai import OpenAI
from groq import Groq
import anthropic
import google.generativeai as genai

# Use this pip install command: 
# python3 -m pip install openai groq anthropic google-generativeai python-dotenv

# You will need to create a .env file in the same directory with the following variables:

# OPENAI_API_KEY        --> https://platform.openai.com/api-keys
# GROQ_API_KEY          --> https://console.groq.com/keys  
# ANTHROPIC_API_KEY    --> https://console.anthropic.com/api-keys
# GOOGLE_API_KEY       --> https://aistudio.google.com/app/apikey
#
#  For Example: 
#
#OPENAI_API_KEY=<your-openai-api-key>
#GROQ_API_KEY=<your-groq-api-key>
#ANTHROPIC_API_KEY=<your-anthropic-api-key>
#GOOGLE_API_KEY=<your-google-api-key>


load_dotenv()

# -----( OPENAI )-----------------------------------------------

def generate_openai_response(
        model="gpt-4o", 
        system_role="You are a helpful assistant", 
        user_prompt="How are you today? Please introduce yourself.", 
        temperature=1, 
        max_tokens=1000, 
        top_p=1, 
        frequency_penalty=0, 
        presence_penalty=0
        ):
    
    client = OpenAI()
    response = client.chat.completions.create(
        model=model,
        messages=[
            {
                "role": "system",
                "content": [
                    {
                        "type": "text",
                        "text": system_role
                    }
                ]
            },
            {
                "role": "user",
                "content": [
                    {
                        "type": "text",
                        "text": user_prompt
                    }
                ]
            }
        ],
        temperature=temperature,
        max_tokens=max_tokens,
        top_p=top_p,
        frequency_penalty=frequency_penalty,
        presence_penalty=presence_penalty
    )
    return response.choices[0].message.content

# -----( GROQ )-----------------------------------------------

def generate_groq_response(
        model="llama3-70b-8192", 
        system_role="You are a helpful assistant", 
        user_prompt="How are you today? Please introduce yourself.", 
        temperature=0.7, 
        max_tokens=1000
        ):
    
    client = Groq(
        api_key=os.environ.get("GROQ_API_KEY"),
    )
    
    chat_completion = client.chat.completions.create(
        messages=[
            {"role": "system", "content": system_role},
            {"role": "user", "content": user_prompt}
        ],
        model=model,
        temperature=temperature,
        max_tokens=max_tokens,
    )
    return chat_completion.choices[0].message.content


# -----( ANTHROPIC )-----------------------------------------------

def generate_anthropic_response(
        model="claude-3-opus-20240229", 
        system_role="You are a helpful assistant", 
        user_prompt="How are you today? Please introduce yourself.", 
        temperature=0.7, 
        max_tokens=1000
        ):
    
    client = anthropic.Anthropic(
        api_key=os.environ.get("ANTHROPIC_API_KEY"),
    )

    message = client.messages.create(
        model=model,
        max_tokens=max_tokens,
        temperature=temperature,
        system=system_role,
        messages=[
            {"role": "user", "content": user_prompt}
        ]
    )

    return message.content[0].text

# -----( GEMINI )-----------------------------------------------

def generate_gemini_response(
        model="gemini-pro", 
        system_role="You are a helpful assistant", 
        user_prompt="How are you today? Please introduce yourself.", 
        temperature=1, 
        max_tokens=1000
        ):
    
    
    model = genai.GenerativeModel('gemini-1.5-flash')
    response = model.generate_content(
        user_prompt,
        generation_config=genai.types.GenerationConfig(
            # Only one candidate for now.
            candidate_count=1,
            max_output_tokens=max_tokens,
            temperature=temperature
        )
    )

    return response.text


if __name__ == "__main__":
    print("\n")
    print("-" * 80)
    response = generate_openai_response()
    print(response)
    print("-" * 80)
    response = generate_groq_response()
    print(response)
    print("-" * 80)
    response = generate_anthropic_response()
    print(response)
    print("-" * 80)
    response = generate_gemini_response()
    print(response)
    print("-" * 80)