LlamaIndex

Arize has first-class support for LlamaIndex applications. After instrumentation, you will have a full trace of every part of your LLM application, including input, embeddings, retrieval, functions, and output messages.

We follow a standardized format for how a trace data should be structured using openinference, which is our open source package based on OpenTelemetry.

Use our code block below to get started using our LlamaIndexInstrumentor.

Python

The following code snippet showcases how to automatically instrument your LLM application.

# Import open-telemetry dependencies
from arize_otel import register_otel, Endpoints

# Setup OTEL via our convenience function
register_otel(
    endpoints = Endpoints.ARIZE,
    space_key = "your-space-key", # in app space settings page
    api_key = "your-api-key", # in app space settings page
    model_id = "your-model-id", # name this to whatever you would like
)

# Import the automatic instrumentor from OpenInference
from openinference.instrumentation.llama_index import LlamaIndexInstrumentor

# Finish automatic instrumentation
LlamaIndexInstrumentor().instrument()

Now start asking questions to your LLM app and watch the traces being collected by Arize. For more in-detail demonstration, check our Colab tutorial:

Google Colaboratory

Tutorial on instrumenting a LlamaIndex application and sending traces to Arize

Javascript (coming soon)