Nosana: OllamaでOpenAI GPT-OSS:20Bを使用する

%pip install ollama python-dotenv

import os
from dotenv import load_dotenv
import ollama
from IPython.display import display, Markdown

# Load environment variables from .env file
load_dotenv()

# Get the remote server URL from environment variables
ollama_host = os.getenv("NOSANA_BASE_URL")

def short_link(link):
    if link and len(link) > 10:
        return link[:20] + '...' + link[-20:]
    return link

if not ollama_host:
    print("OLLAMA_HOST environment variable not found!")
    print("Please create a .env file and add your remote server URL.")
else:
    print(f"Connecting to remote Ollama server at: {short_link(ollama_host)}")

# You can create a client explicitly, which is good practice
client = ollama.Client(host=ollama_host)

model_name = 'gpt-oss:20b'

try:
    display(Markdown(f"Pulling the '{model_name}' model. This may take a while..."))
    client.pull(model_name)
    display(Markdown("Model pulled successfully!"))
except Exception as e:
    display(Markdown(f"Error: {e}"))

response = client.generate(
    model=model_name,
    prompt='Explain the concept of a Large Language Model in one sentence.'
)

print(response['response'])

stream = client.generate(
    model=model_name,
    prompt='Write a short story about a robot who discovers music in 50 words.',
    stream=True
)

for chunk in stream:
    print(chunk['response'], end='', flush=True)

messages = [
    {
        'role': 'user',
        'content': 'What is the most important programming language for AI development? Explain in 50 words.'
    }
]

chat_response = client.chat(model=model_name, messages=messages)
display(Markdown(chat_response['message']['content']))

import json
import requests

def get_current_weather(city: str):
    """Get the current weather in a given city using Open-Meteo API"""
    # Geocoding to get latitude and longitude
    geo_url = f"https://geocoding-api.open-meteo.com/v1/search?name={city}&count=1"
    geo_resp = requests.get(geo_url)
    geo_data = geo_resp.json()
    if not geo_data.get("results"):
        return json.dumps({"city": city, "temperature": "unknown", "unit": "celsius"})
    lat = geo_data["results"][0]["latitude"]
    lon = geo_data["results"][0]["longitude"]
    # Get current weather
    weather_url = f"https://api.open-meteo.com/v1/forecast?latitude={lat}&longitude={lon}¤t_weather=true"
    weather_resp = requests.get(weather_url)
    weather_data = weather_resp.json()
    temp = weather_data.get("current_weather", {}).get("temperature")
    if temp is None:
        return json.dumps({"city": city, "temperature": "unknown", "unit": "celsius"})
    return json.dumps({"city": city, "temperature": temp, "unit": "celsius"})

tools = [
    {
        'type': 'function',
        'function': {
            'name': 'get_current_weather',
            'description': 'Get the current weather in a given city',
            'parameters': {
                'type': 'object',
                'properties': {
                    'city': {
                        'type': 'string',
                        'description': 'The city, e.g., San Francisco',
                    },
                },
                'required': ['city'],
            },
        },
    },
]

messages = [{'role': 'user', 'content': 'What is the weather like in Singapore?'}]

# First, let the model decide which tool to call
response = client.chat(
    model=model_name,
    messages=messages,
    tools=tools,
)

messages.append(response['message'])

# Then, execute the tool and send the result back to the model
if response['message'].get('tool_calls'):
    tool_call = response['message']['tool_calls'][0]
    function_name = tool_call['function']['name']
    function_args = tool_call['function']['arguments']  # Already a dict
    
    # Call the function
    function_response = get_current_weather(city=function_args.get('city'))
    
    messages.append(
        {
            'role': 'tool',
            'content': function_response,
        }
    )
    
    # Get the final response from the model
    final_response = client.chat(model=model_name, messages=messages)
    print(final_response['message']['content'])