from openai import OpenAI
import json
from typing import Dict, Any
# You should use a model that is fine tuned to do function_calling
model_id = "hermes-3-llama3.1-8b"
# Configure the OpenAI client
client = OpenAI(
api_key="your_user_id#your_api_key",
base_url="https://llm-gateway.heurist.xyz"
)
def get_coin_price(token: str) -> float:
"""
Simulates fetching the current price of a given cryptocurrency.
In a real-world scenario, this would call an actual API.
Args:
token (str): The name or symbol of the cryptocurrency.
Returns:
float: The current price of the specified cryptocurrency in USD.
"""
# For demonstration purposes, we're returning static prices
# In practice, you'd call a real-time crypto price API here
print("calling get_coin_price")
prices = {
"bitcoin": 45000.00,
"ethereum": 3000.00,
"dogecoin": 0.25,
}
return prices.get(token.lower(), 0.0)
def get_weather(city: str) -> Dict[str, Any]:
"""
Simulates fetching the current weather for a given city.
In a real-world scenario, this would call an actual weather API.
Args:
city (str): The name of the city.
Returns:
Dict[str, Any]: A dictionary containing weather information.
"""
# For demonstration purposes, we're returning static weather data
# In practice, you'd call a real weather API here
print("calling get_weather")
weathers = {
"new york": {"temperature": 20, "condition": "Cloudy"},
"london": {"temperature": 15, "condition": "Rainy"},
"tokyo": {"temperature": 25, "condition": "Sunny"},
}
return weathers.get(city.lower(), {"temperature": 0, "condition": "Unknown"})
def format_price(price: float) -> str:
"""
Formats the price with two decimal places and adds the USD symbol.
Args:
price (float): The price to format.
Returns:
str: Formatted price string.
"""
return f"${price:.2f}"
# Define the tools
tools = [
{
'type': 'function',
'function': {
'name': 'get_coin_price',
'description': 'Get the current price of a specified cryptocurrency in USD',
'parameters': {
'type': 'object',
'properties': {
'token': {
'type': 'string',
'description': 'The name or symbol of the cryptocurrency',
},
},
'required': ['token'],
},
}
},
{
'type': 'function',
'function': {
'name': 'get_weather',
'description': 'Get the current weather for a specified city',
'parameters': {
'type': 'object',
'properties': {
'city': {
'type': 'string',
'description': 'The name of the city',
},
},
'required': ['city'],
},
}
}
]
def query_llm_with_tools(prompt: str) -> str:
"""
Queries the LLM to get information about cryptocurrency prices or weather.
Args:
prompt (str): User's question about crypto prices or weather.
Returns:
str: LLM's response incorporating the requested information.
"""
messages = [{'role': 'user', 'content': prompt}]
# First API call to determine if we need to use a tool
response = client.chat.completions.create(
model=model_id,
messages=messages,
temperature=0.01,
tools=tools,
tool_choice="auto"
)
# Check if the model wants to use a tool
if response.choices[0].message.tool_calls:
tool_call = response.choices[0].message.tool_calls[0]
function_name = tool_call.function.name
function_args = json.loads(tool_call.function.arguments)
# Call the appropriate function based on the tool choice
if function_name == "get_coin_price":
result = get_coin_price(function_args['token'])
tool_response = format_price(result)
elif function_name == "get_weather":
result = get_weather(function_args['city'])
tool_response = f"Temperature: {result['temperature']}°C, Condition: {result['condition']}"
else:
tool_response = "Unknown function called"
# Add the tool response to the conversation
messages.append(response.choices[0].message)
messages.append({
'role': 'tool',
'content': tool_response,
'tool_call_id': tool_call.id
})
# Second API call to generate the final response
final_response = client.chat.completions.create(
model=model_id,
messages=messages,
temperature=0.01
)
return final_response.choices[0].message.content
else:
# If no tool was called, return the initial response
return response.choices[0].message.content
# Example usage
if __name__ == "__main__":
prompts = [
"What's the current Bitcoin price?",
"How's the weather in Tokyo?",
"Write a joke within 50 words" # This should not call any tools
]
for prompt in prompts:
print(f"User: {prompt}")
response = query_llm_with_tools(prompt)
print(f"AI: {response}\n")
