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* Add 9 Arize LLM observability skills Add skills for Arize AI platform covering trace export, instrumentation, datasets, experiments, evaluators, AI provider integrations, annotations, prompt optimization, and deep linking to the Arize UI. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Add 3 Phoenix AI observability skills Add skills for Phoenix (Arize open-source) covering CLI debugging, LLM evaluation workflows, and OpenInference tracing/instrumentation. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Ignoring intentional bad spelling * Fix CI: remove .DS_Store from generated skills README and add codespell ignore Remove .DS_Store artifact from winmd-api-search asset listing in generated README.skills.md so it matches the CI Linux build output. Add queston to codespell ignore list (intentional misspelling example in arize-dataset skill). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Add arize-ax and phoenix plugins Bundle the 9 Arize skills into an arize-ax plugin and the 3 Phoenix skills into a phoenix plugin for easier installation as single packages. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Fix skill folder structures to match source repos Move arize supporting files from references/ to root level and rename phoenix references/ to rules/ to exactly match the original source repository folder structures. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Fixing file locations * Fixing readme --------- Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
1.7 KiB
1.7 KiB
Phoenix Tracing: Custom Metadata (Python)
Add custom attributes to spans for richer observability.
Install
pip install openinference-instrumentation
Session
from openinference.instrumentation import using_session
with using_session(session_id="my-session-id"):
# Spans get: "session.id" = "my-session-id"
...
User
from openinference.instrumentation import using_user
with using_user("my-user-id"):
# Spans get: "user.id" = "my-user-id"
...
Metadata
from openinference.instrumentation import using_metadata
with using_metadata({"key": "value", "experiment_id": "exp_123"}):
# Spans get: "metadata" = '{"key": "value", "experiment_id": "exp_123"}'
...
Tags
from openinference.instrumentation import using_tags
with using_tags(["tag_1", "tag_2"]):
# Spans get: "tag.tags" = '["tag_1", "tag_2"]'
...
Combined (using_attributes)
from openinference.instrumentation import using_attributes
with using_attributes(
session_id="my-session-id",
user_id="my-user-id",
metadata={"environment": "production"},
tags=["prod", "v2"],
prompt_template="Answer: {question}",
prompt_template_version="v1.0",
prompt_template_variables={"question": "What is Phoenix?"},
):
# All attributes applied to spans in this context
...
On a Single Span
span.set_attribute("metadata", json.dumps({"key": "value"}))
span.set_attribute("user.id", "user_123")
span.set_attribute("session.id", "session_456")
As Decorators
All context managers can be used as decorators:
@using_session(session_id="my-session-id")
@using_user("my-user-id")
@using_metadata({"env": "prod"})
def my_function():
...