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import json

import os

from concurrent.futures import ThreadPoolExecutor

import azure.identity

import openai

from dotenv import load_dotenv

# Setup the OpenAI client to use either Azure, OpenAI.com, or Ollama API

load_dotenv(override=True)

API_HOST = os.getenv("API_HOST", "github")

if API_HOST == "azure":

token_provider = azure.identity.get_bearer_token_provider(

azure.identity.DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default"

)

client = openai.OpenAI(

base_url=os.environ["AZURE_OPENAI_ENDPOINT"],

api_key=token_provider,

)

MODEL_NAME = os.environ["AZURE_OPENAI_CHAT_DEPLOYMENT"]

elif API_HOST == "ollama":

client = openai.OpenAI(base_url=os.environ["OLLAMA_ENDPOINT"], api_key="nokeyneeded")

MODEL_NAME = os.environ["OLLAMA_MODEL"]

elif API_HOST == "github":

client = openai.OpenAI(base_url="https://models.github.ai/inference", api_key=os.environ["GITHUB_TOKEN"])

MODEL_NAME = os.getenv("GITHUB_MODEL", "openai/gpt-4o")

else:

client = openai.OpenAI(api_key=os.environ["OPENAI_KEY"])

MODEL_NAME = os.environ["OPENAI_MODEL"]

tools = [

{

"type": "function",

"function": {

"name": "lookup_weather",

"description": "Lookup the weather for a given city name or zip code.",

"parameters": {

"type": "object",

"properties": {

"city_name": {

"type": "string",

"description": "The city name",

},

"zip_code": {

"type": "string",

"description": "The zip code",

},

},

"additionalProperties": False,

},

},

},

{

"type": "function",

"function": {

"name": "lookup_movies",

"description": "Lookup movies playing in a given city name or zip code.",

"parameters": {

"type": "object",

"properties": {

"city_name": {

"type": "string",

"description": "The city name",

},

"zip_code": {

"type": "string",

"description": "The zip code",

},

},

"additionalProperties": False,

},

},

},

]

# ---------------------------------------------------------------------------

# Tool (function) implementations

# ---------------------------------------------------------------------------

def lookup_weather(city_name: str | None = None, zip_code: str | None = None) -> str:

"""Looks up the weather for given city_name and zip_code."""

location = city_name or zip_code or "unknown"

# In a real implementation, call an external weather API here.

return {

"location": location,

"condition": "rain showers",

"rain_mm_last_24h": 7,

"recommendation": "Good day for indoor activities if you dislike drizzle.",

}

def lookup_movies(city_name: str | None = None, zip_code: str | None = None) -> str:

"""Returns a list of movies playing in the given location."""

location = city_name or zip_code or "unknown"

# A real implementation could query a cinema listings API.

return {

"location": location,

"movies": [

{"title": "The Quantum Reef", "rating": "PG-13"},

{"title": "Storm Over Harbour Bay", "rating": "PG"},

{"title": "Midnight Koala", "rating": "R"},

],

}

messages = [

{"role": "system", "content": "You are a tourism chatbot."},

{"role": "user", "content": "is it rainy enough in sydney to watch movies and which ones are on?"},

]

response = client.chat.completions.create(

model=MODEL_NAME,

messages=messages,

tools=tools,

tool_choice="auto",

)

print(f"Response from {MODEL_NAME} on {API_HOST}: \n")

# Map function names to actual functions

available_functions = {

"lookup_weather": lookup_weather,

"lookup_movies": lookup_movies,

}

# Execute all tool calls in parallel using ThreadPoolExecutor

if response.choices[0].message.tool_calls:

tool_calls = response.choices[0].message.tool_calls

print(f"Model requested {len(tool_calls)} tool call(s):\n")

# Add the assistant's message (with tool calls) to the conversation

messages.append(response.choices[0].message)

with ThreadPoolExecutor() as executor:

# Submit all tool calls to the thread pool

futures = []

for tool_call in tool_calls:

function_name = tool_call.function.name

arguments = json.loads(tool_call.function.arguments)

print(f"Tool request: {function_name}({arguments})")

if function_name in available_functions:

future = executor.submit(available_functions[function_name], **arguments)

futures.append((tool_call, function_name, future))

# Add each tool result to the conversation

for tool_call, function_name, future in futures:

result = future.result()

messages.append({"role": "tool", "tool_call_id": tool_call.id, "content": json.dumps(result)})

# Get final response from the model with all tool results

final_response = client.chat.completions.create(model=MODEL_NAME, messages=messages, tools=tools)

print("Assistant:")

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

else:

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