Exporting Annotated Spans

This information can be used to:

build new LLM judges
form the basis for new datasets
help identify ideas for improving your application

Pulling Spans

from phoenix.client import Client

client = Client()

spans = client.spans.get_spans_dataframe(
    project_identifier="default",  # you can also pass a project id
)

If you only want the spans that contain a specific annotation, you can pass in a query that filters on annotation names, scores, or labels.

from phoenix.client import Client
from phoenix.client.types.spans import SpanQuery

client = Client()
query = SpanQuery().where("annotations['correctness']")

spans = client.spans.get_spans_dataframe(
    query=query,
    project_identifier="default",  # you can also pass a project id
)

The queries can also filter by annotation scores and labels.

from phoenix.client import Client
from phoenix.client.types.spans import SpanQuery

client = Client()
query = SpanQuery().where("annotations['correctness'].score == 1")
# query = SpanQuery().where("annotations['correctness'].label == 'correct'")

spans = client.spans.get_spans_dataframe(
    query=query,
    project_identifier="default",  # you can also pass a project id
)

To filter spans by trace-level annotations (annotations attached to the trace rather than an individual span), use trace_annotations in place of annotations. Spans are returned when the trace they belong to carries a matching annotation.

from phoenix.client import Client
from phoenix.client.types.spans import SpanQuery

client = Client()
query = SpanQuery().where("trace_annotations['quality'].score >= 0.5")
# query = SpanQuery().where("trace_annotations['quality'].label == 'good'")

spans = client.spans.get_spans_dataframe(
    query=query,
    project_identifier="default",  # you can also pass a project id
)

This spans dataframe can be used to pull associated annotations.

annotations = client.spans.get_span_annotations_dataframe(
    spans_dataframe=spans,
    project_identifier="default",
)

Instead of an input dataframe, you can also pass in a list of ids:

annotations = client.spans.get_span_annotations_dataframe(
    span_ids=list(spans.index),
    project_identifier="default",
)

The annotations and spans dataframes can be easily joined to produce a one-row-per-annotation dataframe that can be used to analyze the annotations!

annotations.join(spans, how="left")

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Exporting Annotated Spans

Pulling Spans

​Pulling Spans

Pulling Spans