traceAI-litellm 0.1.10

OpenTelemetry instrumentation for LiteLLM

Litellm OpenTelemetry Integration

Overview

This integration provides support for using OpenTelemetry with the Litellm framework. It enables tracing and monitoring of applications built with Litellm.

Installation

1. Install traceAI Litellm

pip install pip install traceAI-litellm

Set Environment Variables

Set up your environment variables to authenticate with FutureAGI

import os

os.environ["FI_API_KEY"] = FI_API_KEY
os.environ["FI_SECRET_KEY"] = FI_SECRET_KEY

Quickstart

Register Tracer Provider

Set up the trace provider to establish the observability pipeline. The trace provider:

from fi_instrumentation import register
from fi_instrumentation.fi_types import ProjectType

trace_provider = register(
    project_type=ProjectType.OBSERVE,
    project_name="litellm_app"
)

Configure Litellm Instrumentation

Instrument the Litellm client to enable telemetry collection. This step ensures that all interactions with the Litellm SDK are tracked and monitored.

from traceai_litellm import LiteLLMInstrumentor

LiteLLMInstrumentor().instrument(tracer_provider=trace_provider)

Create Litellm Components

Set up your Litellm client with built-in observability.

import asyncio
import litellm

async def run_examples():
    # Simple single message completion call
    litellm.completion(
        model="gpt-3.5-turbo",
        messages=[{"content": "What's the capital of China?", "role": "user"}],
    )

    # Multiple message conversation completion call with added param
    litellm.completion(
        model="gpt-3.5-turbo",
        messages=[
            {"content": "Hello, I want to bake a cake", "role": "user"},
            {
                "content": "Hello, I can pull up some recipes for cakes.",
                "role": "assistant",
            },
            {"content": "No actually I want to make a pie", "role": "user"},
        ],
        temperature=0.7,
    )

    # Multiple message conversation acompletion call with added params
    await litellm.acompletion(
        model="gpt-3.5-turbo",
        messages=[
            {"content": "Hello, I want to bake a cake", "role": "user"},
            {
                "content": "Hello, I can pull up some recipes for cakes.",
                "role": "assistant",
            },
            {"content": "No actually I want to make a pie", "role": "user"},
        ],
        temperature=0.7,
        max_tokens=20,
    )

    # Completion with retries
    litellm.completion_with_retries(
        model="gpt-3.5-turbo",
        messages=[{"content": "What's the highest grossing film ever", "role": "user"}],
    )

    # Embedding call
    litellm.embedding(
        model="text-embedding-ada-002", input=["good morning from litellm"]
    )

    # Asynchronous embedding call
    await litellm.aembedding(
        model="text-embedding-ada-002", input=["good morning from litellm"]
    )

    # Image generation call
    litellm.image_generation(model="dall-e-2", prompt="cute baby otter")

    # Asynchronous image generation call
    await litellm.aimage_generation(model="dall-e-2", prompt="cute baby otter")

asyncio.run(run_examples())