Arize Phoenix

import os
from openai import OpenAI
from portkey_ai import PORTKEY_GATEWAY_URL, createHeaders
from arize.otel import register  # OR from phoenix.otel import register

# configure the Phoenix tracer
from openinference.instrumentation.portkey import PortkeyInstrumentor

# Step 1: Configure Arize Phoenix
tracer_provider = register(
    space_id="your-space-id",
    api_key="your-arize-api-key",
    project_name="portkey-production"
)

# Step 2: Enable Portkey instrumentation
PortkeyInstrumentor().instrument(tracer_provider=tracer_provider)

# Step 3: Configure Portkey's Advanced AI Gateway
advanced_config = {
    "strategy": {
        "mode": "loadbalance"  # Distribute load across providers
    },
    "targets": [
        {
            "provider":"@openai-vk",
            "weight": 0.7,
            "override_params": {"model": "gpt-4o"}
        },
        {
            "provider":"@anthropic-vk",
            "weight": 0.3,
            "override_params": {"model": "claude-3-opus-20240229"}
        }
    ],
    "cache": {
        "mode": "semantic",  # Intelligent caching
        "max_age": 3600
    },
    "retry": {
        "attempts": 3,
        "on_status_codes": [429, 500, 502, 503, 504]
    },
    "request_timeout": 30000
}

# Initialize Portkey-powered client
client = OpenAI(
    api_key="PORTKEY_API_KEY",
    base_url=PORTKEY_GATEWAY_URL,
    default_headers=createHeaders(
        config=advanced_config,
        metadata={
            "user_id": "user-123",
            "session_id": "session-456",
            "feature": "chat-assistant"
        }
    )
)

# Make requests through Portkey's AI Gateway
response = client.chat.completions.create(
    model="gpt-4o",  # Portkey handles provider routing
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Explain quantum computing in simple terms."}
    ],
    temperature=0.7
)

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