Manage the App
How to define your inference set(s), launch a session, open the UI in your notebook or browser, and close your session when you're done
Define Your Inferences
To define inferences, you must load your data into a pandas dataframe and create a matching schema. If you have a dataframe prim_df
and a matching prim_schema
, you can define inferences named "primary" with
prim_ds = px.Inferences(prim_df, prim_schema, "primary")
If you additionally have a dataframe ref_df
and a matching ref_schema
, you can define a inference set named "reference" with
ref_ds = px.Inferences(ref_df, ref_schema, "reference")
See Corpus Data if you have corpus data for an Information Retrieval use case.
Launch the App
Use phoenix.launch_app
to start your Phoenix session in the background. You can launch Phoenix with zero, one, or two inference sets.
No Inferences
session = px.launch_app()
Run Phoenix in the background to collect OpenInference traces emitted by your instrumented LLM application.
Single Inference Set
session = px.launch_app(ds)
Analyze a single cohort of data, e.g., only training data.
Check model performance and data quality, but not drift.
Primary and Reference Inference Sets
session = px.launch_app(prim_ds, ref_ds)
Compare cohorts of data, e.g., training vs. production.
Analyze drift in addition to model performance and data quality.
Primary and Corpus Inference Sets
session = px.launch_app(query_ds, corpus=corpus_ds)
Compare a query inference set to a corpus dataset to analyze your retrieval-augmented generation applications.
Open the UI
You can view and interact with the Phoenix UI either directly in your notebook or in a separate browser tab or window.
In a notebook cell, run
session.url
Copy and paste the output URL into a new browser tab or window.
Close the App
When you're done using Phoenix, gracefully shut down your running background session with
px.close_app()
Last updated
Was this helpful?