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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>
59 lines
1.5 KiB
Markdown
59 lines
1.5 KiB
Markdown
# Phoenix Tracing: Production Guide (Python)
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**CRITICAL: Configure batching, data masking, and span filtering for production deployment.**
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## Metadata
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| Attribute | Value |
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|-----------|-------|
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| Priority | Critical - production readiness |
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| Impact | Security, Performance |
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| Setup Time | 5-15 min |
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## Batch Processing
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**Enable batch processing for production efficiency.** Batching reduces network overhead by sending spans in groups rather than individually.
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## Data Masking (PII Protection)
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**Environment variables:**
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```bash
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export OPENINFERENCE_HIDE_INPUTS=true # Hide input.value
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export OPENINFERENCE_HIDE_OUTPUTS=true # Hide output.value
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export OPENINFERENCE_HIDE_INPUT_MESSAGES=true # Hide LLM input messages
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export OPENINFERENCE_HIDE_OUTPUT_MESSAGES=true # Hide LLM output messages
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export OPENINFERENCE_HIDE_INPUT_IMAGES=true # Hide image content
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export OPENINFERENCE_HIDE_INPUT_TEXT=true # Hide embedding text
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export OPENINFERENCE_BASE64_IMAGE_MAX_LENGTH=10000 # Limit image size
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```
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**Python TraceConfig:**
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```python
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from phoenix.otel import register
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from openinference.instrumentation import TraceConfig
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config = TraceConfig(
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hide_inputs=True,
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hide_outputs=True,
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hide_input_messages=True
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)
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register(trace_config=config)
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```
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**Precedence:** Code > Environment variables > Defaults
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---
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## Span Filtering
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**Suppress specific code blocks:**
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```python
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from phoenix.otel import suppress_tracing
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with suppress_tracing():
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internal_logging() # No spans generated
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```
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