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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>
1.8 KiB
1.8 KiB
Flattening Convention
OpenInference flattens nested data structures into dot-notation attributes for database compatibility, OpenTelemetry compatibility, and simple querying.
Flattening Rules
Objects → Dot Notation
{ llm: { model_name: "gpt-4", token_count: { prompt: 10, completion: 20 } } }
// becomes
{ "llm.model_name": "gpt-4", "llm.token_count.prompt": 10, "llm.token_count.completion": 20 }
Arrays → Zero-Indexed Notation
{ llm: { input_messages: [{ role: "user", content: "Hi" }] } }
// becomes
{ "llm.input_messages.0.message.role": "user", "llm.input_messages.0.message.content": "Hi" }
Message Convention: .message. segment required
llm.input_messages.{index}.message.{field}
llm.input_messages.0.message.tool_calls.0.tool_call.function.name
Complete Example
// Original
{
openinference: { span: { kind: "LLM" } },
llm: {
model_name: "claude-3-5-sonnet-20241022",
invocation_parameters: { temperature: 0.7, max_tokens: 1000 },
input_messages: [{ role: "user", content: "Tell me a joke" }],
output_messages: [{ role: "assistant", content: "Why did the chicken cross the road?" }],
token_count: { prompt: 5, completion: 10, total: 15 }
}
}
// Flattened (stored in Phoenix spans.attributes JSONB)
{
"openinference.span.kind": "LLM",
"llm.model_name": "claude-3-5-sonnet-20241022",
"llm.invocation_parameters": "{\"temperature\": 0.7, \"max_tokens\": 1000}",
"llm.input_messages.0.message.role": "user",
"llm.input_messages.0.message.content": "Tell me a joke",
"llm.output_messages.0.message.role": "assistant",
"llm.output_messages.0.message.content": "Why did the chicken cross the road?",
"llm.token_count.prompt": 5,
"llm.token_count.completion": 10,
"llm.token_count.total": 15
}