William Suffill

from huggingface_hub import InferenceClient

client = InferenceClient("meta-llama/Meta-Llama-3-70B-Instruct")

messages = [
    {
        "role": "user",
        "content": "I saw a puppy a cat and a raccoon during my bike ride in the park. What did I saw and when?",
    },
]

response_format = {
    "type": "json",
    "value": {
        "properties": {
            "location": {"type": "string"},
            "activity": {"type": "string"},
            "animals_seen": {"type": "integer", "minimum": 1, "maximum": 5},
            "animals": {"type": "array", "items": {"type": "string"}},
        },
        "required": ["location", "activity", "animals_seen", "animals"],
    },
}

response = client.chat_completion(
    messages=messages,
    response_format=response_format,
    max_tokens=500,
)

print(response.choices[0].message.content)

response = client.chat.completions.create(
     model="gpt-3.5-turbo-0125",
     messages=[...],
     response_format={"type": "json_object"}
)

async def query_web_scraper(url: str) -> dict:
    scraper = WebScraper(headless=False)
    return await scraper.query_page_content(url)

# First API call: Send the query and function description to the model
response = ollama.chat(
    model=model,
    messages=messages,
    tools=[
        {
            'type': 'function',
            'function': {
                'name': 'query_web_scraper',
                'description': 'Scrapes the content of a web page and returns the structured JSON object with titles, articles, and associated links.',
                'parameters': {
                    'type': 'object',
                    'properties': {
                        'url': {
                            'type': 'string',
                            'description': 'The URL of the web page to scrape.',
                        },
                    },
                    'required': ['url'],
                },
            },
        },
    ]
)