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]"
}