MLflow Tracing

pip install mlflow openai opentelemetry-exporter-otlp-proto-http portkey-ai

import os

# Configure Portkey endpoint and authentication
os.environ["OTEL_EXPORTER_OTLP_ENDPOINT"] = "https://api.portkey.ai/v1/logs/otel"
os.environ["OTEL_EXPORTER_OTLP_HEADERS"] = "x-portkey-api-key=YOUR_PORTKEY_API_KEY"
os.environ["OTEL_EXPORTER_OTLP_TRACES_PROTOCOL"] = "http/protobuf"

import mlflow

# Enable the MLflow instrumentation for tracing OpenAI
mlflow.openai.autolog()

from openai import OpenAI
from portkey_ai import createHeaders

# Use Portkey's gateway for intelligent routing
client = OpenAI(
    api_key="PORTKEY_API_KEY",
    base_url="https://api.portkey.ai/v1",
    default_headers=createHeaders(
        api_key="PORTKEY_API_KEY",
        provider="@openai-prod"  # Your AI Provider slug from Model Catalog
    )
)

# Make calls through Portkey's gateway
# MLflow instruments the call, Portkey adds gateway intelligence
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Explain the importance of tracing in LLM applications"}],
    temperature=0.7
)

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

# You now get:
# 1. Automatic tracing from MLflow
# 2. Gateway features from Portkey (caching, fallbacks, routing)
# 3. Combined insights in Portkey's dashboard

import os
import mlflow
from openai import OpenAI
from portkey_ai import createHeaders

# Step 1: Configure Portkey endpoint
os.environ["OTEL_EXPORTER_OTLP_ENDPOINT"] = "https://api.portkey.ai/v1/logs/otel"
os.environ["OTEL_EXPORTER_OTLP_HEADERS"] = "x-portkey-api-key=YOUR_PORTKEY_API_KEY"
os.environ["OTEL_EXPORTER_OTLP_TRACES_PROTOCOL"] = "http/protobuf"

# Step 2: Enable MLflow instrumentation
mlflow.openai.autolog()

# Step 3: Configure Portkey Gateway
client = OpenAI(
    api_key="PORTKEY_API_KEY",
    base_url="https://api.portkey.ai/v1",
    default_headers=createHeaders(
        api_key="PORTKEY_API_KEY",
        provider="@openai-prod"
    )
)

# Step 4: Make instrumented calls
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": "You are a helpful AI assistant."},
        {"role": "user", "content": "What are the benefits of observability in production AI systems?"}
    ]
)

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