awesome-copilot/skills/phoenix-tracing/references/span-embedding.md

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.text for 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]"
}