Observability for AutoGen with Opik

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# First we will configure the OpenTelemetry
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from opentelemetry import trace
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from opentelemetry.exporter.otlp.proto.http.trace_exporter import (
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    OTLPSpanExporter
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)
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from opentelemetry.sdk.resources import Resource
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from opentelemetry.sdk.trace import TracerProvider
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from opentelemetry.sdk.trace.export import BatchSpanProcessor
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from opentelemetry.instrumentation.openai import OpenAIInstrumentor
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def setup_telemetry():
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    """Configure OpenTelemetry with HTTP exporter"""
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    # Create a resource with service name and other metadata
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    resource = Resource.create({
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        "service.name": "autogen-demo",
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        "service.version": "1.0.0",
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        "deployment.environment": "development"
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    })
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    # Create TracerProvider with the resource
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    provider = TracerProvider(resource=resource)
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    # Create BatchSpanProcessor with OTLPSpanExporter
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    processor = BatchSpanProcessor(
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        OTLPSpanExporter()
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    )
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    provider.add_span_processor(processor)
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    # Set the TracerProvider
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    trace.set_tracer_provider(provider)
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    # Instrument OpenAI calls
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    OpenAIInstrumentor().instrument()
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# Now we can define and call the Agent
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import asyncio
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from autogen_agentchat.agents import AssistantAgent
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from autogen_agentchat.ui import Console
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from autogen_ext.models.openai import OpenAIChatCompletionClient
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# Define a model client. You can use other model client that implements
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# the `ChatCompletionClient` interface.
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model_client = OpenAIChatCompletionClient(
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    model="gpt-4o",
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    # api_key="YOUR_API_KEY",
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)
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# Define a simple function tool that the agent can use.
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# For this example, we use a fake weather tool for demonstration purposes.
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async def get_weather(city: str) -> str:
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    """Get the weather for a given city."""
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    return f"The weather in {city} is 73 degrees and Sunny."
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# Define an AssistantAgent with the model, tool, system message, and reflection
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# enabled. The system message instructs the agent via natural language.
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agent = AssistantAgent(
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    name="weather_agent",
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    model_client=model_client,
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    tools=[get_weather],
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    system_message="You are a helpful assistant.",
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    reflect_on_tool_use=True,
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    model_client_stream=True,  # Enable streaming tokens from the model client.
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)
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# Run the agent and stream the messages to the console.
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async def main() -> None:
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    tracer = trace.get_tracer(__name__)
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    with tracer.start_as_current_span("agent_conversation") as span:
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        task = "What is the weather in New York?"
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        span.set_attribute("input", task) # Manually log the question
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        res = await Console(agent.run_stream(task=task))
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        # Manually log the response
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        span.set_attribute("output", res.messages[-1].content)
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        # Close the connection to the model client.
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        await model_client.close()
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if __name__ == "__main__":
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    setup_telemetry()
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    asyncio.run(main())