# 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:**
```json
{
"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:**
```json
{
"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
```json
{
"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
```json
{
"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]"
}
```
