phoenix-tracing

安装量: 807
排名: #4883

安装

npx skills add https://github.com/github/awesome-copilot --skill phoenix-tracing
Phoenix Tracing
Comprehensive guide for instrumenting LLM applications with OpenInference tracing in Phoenix. Contains reference files covering setup, instrumentation, span types, and production deployment.
When to Apply
Reference these guidelines when:
Setting up Phoenix tracing (Python or TypeScript)
Creating custom spans for LLM operations
Adding attributes following OpenInference conventions
Deploying tracing to production
Querying and analyzing trace data
Reference Categories
Priority
Category
Description
Prefix
1
Setup
Installation and configuration
setup-*
2
Instrumentation
Auto and manual tracing
instrumentation-*
3
Span Types
9 span kinds with attributes
span-*
4
Organization
Projects and sessions
projects-*
,
sessions-*
5
Enrichment
Custom metadata
metadata-*
6
Production
Batch processing, masking
production-*
7
Feedback
Annotations and evaluation
annotations-*
Quick Reference
1. Setup (START HERE)
setup-python
- Install arize-phoenix-otel, configure endpoint
setup-typescript
- Install @arizeai/phoenix-otel, configure endpoint
2. Instrumentation
instrumentation-auto-python
- Auto-instrument OpenAI, LangChain, etc.
instrumentation-auto-typescript
- Auto-instrument supported frameworks
instrumentation-manual-python
- Custom spans with decorators
instrumentation-manual-typescript
- Custom spans with wrappers
3. Span Types (with full attribute schemas)
span-llm
- LLM API calls (model, tokens, messages, cost)
span-chain
- Multi-step workflows and pipelines
span-retriever
- Document retrieval (documents, scores)
span-tool
- Function/API calls (name, parameters)
span-agent
- Multi-step reasoning agents
span-embedding
- Vector generation
span-reranker
- Document re-ranking
span-guardrail
- Safety checks
span-evaluator
- LLM evaluation
4. Organization
projects-python
/
projects-typescript
- Group traces by application
sessions-python
/
sessions-typescript
- Track conversations
5. Enrichment
metadata-python
/
metadata-typescript
- Custom attributes
6. Production (CRITICAL)
production-python
/
production-typescript
- Batch processing, PII masking
7. Feedback
annotations-overview
- Feedback concepts
annotations-python
/
annotations-typescript
- Add feedback to spans
Reference Files
fundamentals-overview
- Traces, spans, attributes basics
fundamentals-required-attributes
- Required fields per span type
fundamentals-universal-attributes
- Common attributes (user.id, session.id)
fundamentals-flattening
- JSON flattening rules
attributes-messages
- Chat message format
attributes-metadata
- Custom metadata schema
attributes-graph
- Agent workflow attributes
attributes-exceptions
- Error tracking
Common Workflows
Quick Start
setup-{lang} → instrumentation-auto-{lang} → Check Phoenix
Custom Spans
setup-{lang} → instrumentation-manual-{lang} → span-{type}
Session Tracking
sessions-{lang} for conversation grouping patterns
Production
production-{lang} for batching, masking, and deployment How to Use This Skill Navigation Patterns:

By category prefix

references/setup-*

Installation and configuration

references/instrumentation-*

Auto and manual tracing

references/span-*

Span type specifications

references/sessions-*

Session tracking

references/production-*

Production deployment

references/fundamentals-*

Core concepts

references/attributes-*

Attribute specifications

By language

references/*-python.md

Python implementations

references/*-typescript.md

TypeScript implementations

Reading Order: Start with setup-{lang} for your language Choose instrumentation-auto-{lang} OR instrumentation-manual-{lang} Reference span-{type} files as needed for specific operations See fundamentals-* files for attribute specifications References Phoenix Documentation: Phoenix Documentation OpenInference Spec Python API Documentation: Python OTEL Package - arize-phoenix-otel API reference Python Client Package - arize-phoenix-client API reference TypeScript API Documentation: TypeScript Packages - @arizeai/phoenix-otel , @arizeai/phoenix-client , and other TypeScript packages

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