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awesome-copilot/skills/phoenix-tracing/references/fundamentals-universal-attributes.md
Jim Bennett d79183139a Add Arize and Phoenix LLM observability skills (#1204)
* 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

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Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-02 09:58:55 +11:00

2.3 KiB

Universal Attributes

This document covers attributes that can be used on any span kind in OpenInference.

Overview

These attributes can be used on any span kind to provide additional context, tracking, and metadata.

Input/Output

Attribute Type Description
input.value String Input to the operation (prompt, query, document)
input.mime_type String MIME type (e.g., "text/plain", "application/json")
output.value String Output from the operation (response, vector, result)
output.mime_type String MIME type of output

Why Capture I/O?

Always capture input/output for evaluation-ready spans:

  • Phoenix evaluators (faithfulness, relevance, Q&A correctness) require input.value and output.value
  • Phoenix UI displays I/O prominently in trace views for debugging
  • Enables exporting I/O for creating fine-tuning datasets
  • Provides complete context for analyzing agent behavior

Example attributes:

{
  "openinference.span.kind": "CHAIN",
  "input.value": "What is the weather?",
  "input.mime_type": "text/plain",
  "output.value": "I don't have access to weather data.",
  "output.mime_type": "text/plain"
}

See language-specific implementation:

  • TypeScript: instrumentation-manual-typescript.md
  • Python: instrumentation-manual-python.md

Session and User Tracking

Attribute Type Description
session.id String Session identifier for grouping related traces
user.id String User identifier for per-user analysis

Example:

{
  "openinference.span.kind": "LLM",
  "session.id": "session_abc123",
  "user.id": "user_xyz789"
}

Metadata

Attribute Type Description
metadata string JSON-serialized object of key-value pairs

Example:

{
  "openinference.span.kind": "LLM",
  "metadata": "{\"environment\": \"production\", \"model_version\": \"v2.1\", \"cost_center\": \"engineering\"}"
}