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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

---------

Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

2026-04-02 09:58:55 +11:00

3.4 KiB

Raw Blame History

Phoenix Tracing: Python Setup

Setup Phoenix tracing in Python with arize-phoenix-otel.

Metadata

Attribute	Value
Priority	Critical - required for all tracing
Setup Time	<5 min

Quick Start (3 lines)

from phoenix.otel import register
register(project_name="my-app", auto_instrument=True)

Connects to http://localhost:6006, auto-instruments all supported libraries.

Installation

pip install arize-phoenix-otel

Supported: Python 3.10-3.13

Configuration

Environment Variables (Recommended)

export PHOENIX_API_KEY="your-api-key"  # Required for Phoenix Cloud
export PHOENIX_COLLECTOR_ENDPOINT="http://localhost:6006"  # Or Cloud URL
export PHOENIX_PROJECT_NAME="my-app"  # Optional

Python Code

from phoenix.otel import register

tracer_provider = register(
    project_name="my-app",              # Project name
    endpoint="http://localhost:6006",   # Phoenix endpoint
    auto_instrument=True,               # Auto-instrument supported libs
    batch=True,                         # Batch processing (default: True)
)

Parameters:

project_name: Project name (overrides PHOENIX_PROJECT_NAME)
endpoint: Phoenix URL (overrides PHOENIX_COLLECTOR_ENDPOINT)
auto_instrument: Enable auto-instrumentation (default: False)
batch: Use BatchSpanProcessor (default: True, production-recommended)
protocol: "http/protobuf" (default) or "grpc"

Auto-Instrumentation

Install instrumentors for your frameworks:

pip install openinference-instrumentation-openai      # OpenAI SDK
pip install openinference-instrumentation-langchain   # LangChain
pip install openinference-instrumentation-llama-index # LlamaIndex
# ... install others as needed

Then enable auto-instrumentation:

register(project_name="my-app", auto_instrument=True)

Phoenix discovers and instruments all installed OpenInference packages automatically.

Batch Processing (Production)

Enabled by default. Configure via environment variables:

export OTEL_BSP_SCHEDULE_DELAY=5000           # Batch every 5s
export OTEL_BSP_MAX_QUEUE_SIZE=2048           # Queue 2048 spans
export OTEL_BSP_MAX_EXPORT_BATCH_SIZE=512     # Send 512 spans/batch

Link: https://opentelemetry.io/docs/specs/otel/configuration/sdk-environment-variables/

Verification

Open Phoenix UI: http://localhost:6006
Navigate to your project
Run your application
Check for traces (appear within batch delay)

Troubleshooting

No traces:

Verify PHOENIX_COLLECTOR_ENDPOINT matches Phoenix server
Set PHOENIX_API_KEY for Phoenix Cloud
Confirm instrumentors installed

Missing attributes:

Check span kind (see rules/ directory)
Verify attribute names (see rules/ directory)

Example

from phoenix.otel import register
from openai import OpenAI

# Enable tracing with auto-instrumentation
register(project_name="my-chatbot", auto_instrument=True)

# OpenAI automatically instrumented
client = OpenAI()
response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello!"}]
)

3.4 KiB Raw Blame History