Observability for Novita AI with Langfuse

This guide shows you how to integrate Novita AI with Langfuse. Novita AI’s API endpoints for chat, language and code are fully compatible with OpenAI’s API. This allows us to use the Langfuse OpenAI drop-in replacement to trace all parts of your application.

What is Novita AI? Novita AI is an AI cloud platform that helps developers easily deploy AI models through a simple API, backed by affordable and reliable GPU cloud infrastructure. You can try the Novita AI Llama 3 API Demo here.

What is Langfuse? Langfuse is an open source LLM engineering platform that helps teams trace API calls, monitor performance, and debug issues in their AI applications.

%pip install openai langfuse

import os
 
# Get keys for your project from the project settings page
# https://cloud.langfuse.com
 
os.environ["LANGFUSE_PUBLIC_KEY"] = "pk-..." 
os.environ["LANGFUSE_SECRET_KEY"] = "sk-..."
os.environ["LANGFUSE_HOST"] = "https://cloud.langfuse.com" # 🇪🇺 EU region
# os.environ["LANGFUSE_HOST"] = "https://us.cloud.langfuse.com" # 🇺🇸 US region
 
 
# Get your Novita AI API key from the project settings page
os.environ["NOVITA_API_KEY"] = "..."

# instead of import openai:
from langfuse.openai import openai
 
client = openai.OpenAI(
  api_key=os.environ.get("NOVITA_API_KEY"),
  base_url="https://api.novita.ai/v3/openai",
)

client = openai.OpenAI(
  api_key=os.environ.get("NOVITA_API_KEY"),
  base_url="https://api.novita.ai/v3/openai",
)
 
response = client.chat.completions.create(
  model="meta-llama/llama-3.1-70b-instruct",
  messages=[
    {"role": "system", "content": "Act like you are a helpful assistant."},
    {"role": "user", "content": "What are the famous attractions in San Francisco?"},
  ]
)
 
print(response.choices[0].message.content)

Interoperability with the Python SDK

You can use this integration together with the Langfuse Python SDK to add additional attributes to the trace.

from langfuse import observe, get_client
 
langfuse = get_client()
 
@observe()
def my_instrumented_function(input):
    output = my_llm_call(input)
 
    langfuse.update_current_trace(
        input=input,
        output=output,
        user_id="user_123",
        session_id="session_abc",
        tags=["agent", "my-trace"],
        metadata={"email": "[email protected]"},
        version="1.0.0"
    )
 
    return output

from langfuse import get_client
 
langfuse = get_client()
 
with langfuse.start_as_current_span(name="my-trace") as span:
    
    # Run your application here
    output = my_llm_call(input)
 
    # Pass additional attributes to the span
    span.update_trace(
        input=input,
        output=output,
        user_id="user_123",
        session_id="session_abc",
        tags=["agent", "my-trace"],
        metadata={"email": "[email protected]"},
        version="1.0.0"
        )
 
# Flush events in short-lived applications
langfuse.flush()

Next Steps

Once you have instrumented your code, you can manage, evaluate and debug your application:

Interoperability with the Python SDK

• Check the Novita AI Documentation for further details on available models and API options.
• Visit Langfuse to learn more about monitoring and tracing capabilities for your LLM applications.