LlamaIndex is a data framework for your LLM application. It's a powerful framework by which you can build an application that leverages RAG (retrieval-augmented generation) to super-charge an LLM with your own data. RAG is an extremely powerful LLM application model because it lets you harness the power of LLMs such as OpenAI's GPT but tuned to your data and use-case.

For LlamaIndex, tracing instrumentation is added via a callback. This callback is what is used to create spans and send them to the Phoenix collector.

Using phoenix as a callback requires an install of `llama-index-callbacks-arize-phoenix>0.1.3'

llama-index 0.10 introduced modular sub-packages. To use llama-index's one click, you must install the small integration first:

pip install 'llama-index-callbacks-arize-phoenix>0.1.3'
# Phoenix can display in real time the traces automatically
# collected from your LlamaIndex application.
import phoenix as px
# Look for a URL in the output to open the App in a browser.
# The App is initially empty, but as you proceed with the steps below,
# traces will appear automatically as your LlamaIndex application runs.

from llama_index.core import set_global_handler


# Run all of your LlamaIndex applications as usual and traces
# will be collected and displayed in Phoenix.

By adding the callback to the callback manager of LlamaIndex, we've created a one-way data connection between your LLM application and Phoenix Server.

To view the traces in Phoenix, simply open the UI in your browser.


For a fully working example of tracing with LlamaIndex, checkout our colab notebook.

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