chore: publish from staged

plugins/phoenix/skills/phoenix-tracing/references/span-embedding.md

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+# 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]"
+}
+```