Integração com o Azure AI Agents

import os
from azure.ai.inference import ChatCompletionsClient
from azure.core.credentials import AzureKeyCredential

client = ChatCompletionsClient(
    endpoint="https://router.toqan.ai/eu/v3/router",
    credential=AzureKeyCredential(os.environ["TOQAN_API_KEY"]),
)

import os
from azure.ai.inference import ChatCompletionsClient
from azure.ai.inference.models import SystemMessage, UserMessage
from azure.core.credentials import AzureKeyCredential

client = ChatCompletionsClient(
    endpoint="https://router.toqan.ai/eu/v3/router",
    credential=AzureKeyCredential(os.environ["TOQAN_API_KEY"]),
)

response = client.complete(
    model="openai/gpt-5.6-sol",
    messages=[
        SystemMessage(content="You are a helpful research assistant. Answer questions concisely and accurately."),
        UserMessage(content="What are the three most important factors when evaluating an LLM for production use?"),
    ],
)

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

import os
import json
from azure.ai.inference import ChatCompletionsClient
from azure.ai.inference.models import (
    SystemMessage,
    UserMessage,
    AssistantMessage,
    ToolMessage,
    ChatCompletionsToolDefinition,
    FunctionDefinition,
)
from azure.core.credentials import AzureKeyCredential

client = ChatCompletionsClient(
    endpoint="https://router.toqan.ai/eu/v3/router",
    credential=AzureKeyCredential(os.environ["TOQAN_API_KEY"]),
)

tools = [
    ChatCompletionsToolDefinition(
        function=FunctionDefinition(
            name="get_weather",
            description="Get the current weather for a given location.",
            parameters={
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and country, e.g. Amsterdam, NL",
                    }
                },
                "required": ["location"],
            },
        )
    )
]


def get_weather(location: str) -> str:
    data = {
        "amsterdam, nl": "Cloudy, 14°C",
        "london, uk": "Rainy, 11°C",
        "san francisco, us": "Sunny, 18°C",
    }
    return data.get(location.lower(), f"No weather data for {location}")


messages = [
    SystemMessage(content="You are a weather assistant. Always use get_weather to look up weather."),
    UserMessage(content="What's the weather in Amsterdam and London?"),
]

# Agent loop — run until no tool calls remain
while True:
    response = client.complete(
        model="openai/gpt-5.6-sol",
        messages=messages,
        tools=tools,
    )

    choice = response.choices[0]

    if choice.finish_reason == "tool_calls":
        messages.append(AssistantMessage(tool_calls=choice.message.tool_calls))
        for tool_call in choice.message.tool_calls:
            args = json.loads(tool_call.function.arguments)
            result = get_weather(args["location"])
            messages.append(ToolMessage(tool_call_id=tool_call.id, content=result))
    else:
        print(choice.message.content)
        break

import os
from azure.ai.inference import ChatCompletionsClient
from azure.ai.inference.models import SystemMessage, UserMessage
from azure.core.credentials import AzureKeyCredential

client = ChatCompletionsClient(
    endpoint="https://router.toqan.ai/eu/v3/router",
    credential=AzureKeyCredential(os.environ["TOQAN_API_KEY"]),
)

messages = [
    SystemMessage(content="You are a helpful assistant."),
    UserMessage(content="Explain transformer architectures briefly."),
]

# Use Claude
response = client.complete(model="anthropic/claude-sonnet-5", messages=messages)

# Use Gemini
response = client.complete(model="google/gemini-2.5-flash", messages=messages)

# Use GPT-5.6 Sol
response = client.complete(model="openai/gpt-5.6-sol", messages=messages)

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

import os
from azure.core.settings import settings
from azure.core.tracing.ext.opentelemetry_span import OpenTelemetrySpan
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry import trace

settings.tracing_implementation = OpenTelemetrySpan

tracer_provider = TracerProvider()
tracer_provider.add_span_processor(
    BatchSpanProcessor(
        OTLPSpanExporter(
            endpoint="https://router.toqan.ai/eu/v2/otel/v1/traces",
            headers={"Authorization": f"Bearer {os.environ['TOQAN_API_KEY']}"},
        )
    )
)
trace.set_tracer_provider(tracer_provider)
tracer = trace.get_tracer(__name__)

import os
from azure.core.settings import settings
from azure.core.tracing.ext.opentelemetry_span import OpenTelemetrySpan
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry import trace
from azure.ai.agents import AgentsClient
from azure.identity import DefaultAzureCredential

settings.tracing_implementation = OpenTelemetrySpan

tracer_provider = TracerProvider()
tracer_provider.add_span_processor(
    BatchSpanProcessor(
        OTLPSpanExporter(
            endpoint="https://router.toqan.ai/eu/v2/otel/v1/traces",
            headers={"Authorization": f"Bearer {os.environ['TOQAN_API_KEY']}"},
        )
    )
)
trace.set_tracer_provider(tracer_provider)
tracer = trace.get_tracer(__name__)

with AgentsClient(
    endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
    credential=DefaultAzureCredential()
) as agents_client:
    with tracer.start_as_current_span("azure-agent-workflow"):
        agent = agents_client.create_agent(
            model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
            name="Research Assistant",
            instructions="You are a helpful research assistant."
        )
        thread = agents_client.threads.create()
        agents_client.messages.create(
            thread_id=thread.id,
            role="user",
            content="Summarize the key advantages of transformer architectures."
        )
        run = agents_client.runs.create_and_process(thread_id=thread.id, agent_id=agent.id)
        print(f"Run status: {run.status}")
        agents_client.delete_agent(agent.id)

tracer_provider.force_flush()