# pip install opentelemetry-sdk opentelemetry-exporter-otlp-proto-http
import json
import time

from opentelemetry import trace
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.trace import Status, StatusCode

resource = Resource.create({"service.name": "my-app"})
provider = TracerProvider(resource=resource)
provider.add_span_processor(
    BatchSpanProcessor(
        OTLPSpanExporter(endpoint="http://YOUR_SOBS_HOST/v1/traces")
    )
)
trace.set_tracer_provider(provider)
tracer = trace.get_tracer("my-app.genai")


def tracked_llm_call(user_prompt: str, model: str = "gpt-4o", provider_name: str = "openai"):
    start = time.monotonic()
    with tracer.start_as_current_span("gen_ai.chat") as span:
        span.set_attribute("gen_ai.operation.name", "chat")
        span.set_attribute("gen_ai.provider.name", provider_name)
        span.set_attribute("gen_ai.request.model", model)
        span.set_attribute(
            "gen_ai.system_instructions",
            "You are a concise observability assistant.",
        )

        input_messages = [
            {"role": "system", "content": "You are a concise observability assistant."},
            {"role": "user", "content": user_prompt},
        ]
        span.set_attribute("gen_ai.input.messages", json.dumps(input_messages, ensure_ascii=False))

        try:
            # ... your provider SDK/API call ...
            assistant_text = "..."
            tokens_in = 50
            tokens_out = 120

            output_messages = [{"role": "assistant", "content": assistant_text}]
            span.set_attribute("gen_ai.output.messages", json.dumps(output_messages, ensure_ascii=False))
            span.set_attribute("gen_ai.usage.input_tokens", tokens_in)
            span.set_attribute("gen_ai.usage.output_tokens", tokens_out)
            span.set_attribute("gen_ai.response.finish_reason", "stop")
            span.set_attribute("gen_ai.response.latency_ms", round((time.monotonic() - start) * 1000, 1))
            return assistant_text
        except Exception as exc:
            span.set_attribute("error.type", exc.__class__.__name__)
            span.set_attribute("error.message", str(exc))
            span.set_status(Status(StatusCode.ERROR, str(exc)))
            raise

# Shared HTTP transport auto instrumentation (works across OpenAI, Anthropic, Groq clients using httpx)
# pip install opentelemetry-instrumentation-httpx
from opentelemetry.instrumentation.httpx import HTTPXClientInstrumentor

HTTPXClientInstrumentor().instrument()

# Provider-specific GenAI auto instrumentation (OSS)
from openinference.instrumentation.anthropic import AnthropicInstrumentor
from openinference.instrumentation.groq import GroqInstrumentor

# pip install \
#   openinference-instrumentation-openai \
#   openinference-instrumentation-anthropic \
#   openinference-instrumentation-groq
from openinference.instrumentation.openai import OpenAIInstrumentor

OpenAIInstrumentor().instrument()
AnthropicInstrumentor().instrument()
GroqInstrumentor().instrument()

# then create your SDK clients normally (openai/anthropic/groq)