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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>
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2.7 KiB
EMBEDDING Spans
Purpose
EMBEDDING spans represent vector generation operations (text-to-vector conversion for semantic search).
Required Attributes
| Attribute | Type | Description | Required |
|---|---|---|---|
openinference.span.kind |
String | Must be "EMBEDDING" | Yes |
embedding.model_name |
String | Embedding model identifier | Recommended |
Attribute Reference
Single Embedding
| Attribute | Type | Description |
|---|---|---|
embedding.model_name |
String | Embedding model identifier |
embedding.text |
String | Input text to embed |
embedding.vector |
String (JSON array) | Generated embedding vector |
Example:
{
"embedding.model_name": "text-embedding-ada-002",
"embedding.text": "What is machine learning?",
"embedding.vector": "[0.023, -0.012, 0.045, ..., 0.001]"
}
Batch Embeddings
| Attribute Pattern | Type | Description |
|---|---|---|
embedding.embeddings.{i}.embedding.text |
String | Text at index i |
embedding.embeddings.{i}.embedding.vector |
String (JSON array) | Vector at index i |
Example:
{
"embedding.model_name": "text-embedding-ada-002",
"embedding.embeddings.0.embedding.text": "First document",
"embedding.embeddings.0.embedding.vector": "[0.1, 0.2, 0.3, ..., 0.5]",
"embedding.embeddings.1.embedding.text": "Second document",
"embedding.embeddings.1.embedding.vector": "[0.6, 0.7, 0.8, ..., 0.9]"
}
Vector Format
Vectors stored as JSON array strings:
- Dimensions: Typically 384, 768, 1536, or 3072
- Format:
"[0.123, -0.456, 0.789, ...]" - Precision: Usually 3-6 decimal places
Storage Considerations:
- Large vectors can significantly increase trace size
- Consider omitting vectors in production (keep
embedding.textfor debugging) - Use separate vector database for actual similarity search
Examples
Single Embedding
{
"openinference.span.kind": "EMBEDDING",
"embedding.model_name": "text-embedding-ada-002",
"embedding.text": "What is machine learning?",
"embedding.vector": "[0.023, -0.012, 0.045, ..., 0.001]",
"input.value": "What is machine learning?",
"output.value": "[0.023, -0.012, 0.045, ..., 0.001]"
}
Batch Embeddings
{
"openinference.span.kind": "EMBEDDING",
"embedding.model_name": "text-embedding-ada-002",
"embedding.embeddings.0.embedding.text": "First document",
"embedding.embeddings.0.embedding.vector": "[0.1, 0.2, 0.3]",
"embedding.embeddings.1.embedding.text": "Second document",
"embedding.embeddings.1.embedding.vector": "[0.4, 0.5, 0.6]",
"embedding.embeddings.2.embedding.text": "Third document",
"embedding.embeddings.2.embedding.vector": "[0.7, 0.8, 0.9]"
}