Embeddings are calls to embedding models that convert text into vector representations for semantic search, RAG pipelines, and similarity matching.
While generations track LLM conversations and spans track operations, embeddings specifically monitor vector generation:
- Search queries - Converting user input for semantic search
- Document indexing - Embedding content for retrieval
- RAG pipelines - Query and document vectorization
- Batch processing - Multiple embeddings in one call
For technical implementation details, see manual capture.
Event properties
An embedding is a single call to an embedding model to convert text into a vector representation.
Event name: $ai_embedding
Core properties
| Property | Description |
|---|---|
$ai_trace_id | The trace ID (a UUID to group related AI events together). Must contain only letters, numbers, and special characters: -, _, ~, ., @, (, ), !, ', :, | Example: d9222e05-8708-41b8-98ea-d4a21849e761 |
$ai_span_id | (Optional) Unique identifier for this embedding operation |
$ai_span_name | (Optional) Name given to this embedding operation Example: embed_user_query, index_document |
$ai_parent_id | (Optional) Parent span ID for tree-view grouping |
$ai_model | The embedding model used Example: text-embedding-3-small, text-embedding-ada-002 |
$ai_provider | The LLM provider Example: openai, cohere, voyage |
$ai_input | The text to embed Example: "Tell me a fun fact about hedgehogs" or array of strings for batch embeddings |
$ai_input_tokens | The number of tokens in the input |
$ai_latency | (Optional) The latency of the embedding call in seconds |
$ai_http_status | (Optional) The HTTP status code of the response |
$ai_base_url | (Optional) The base URL of the LLM provider Example: https://api.openai.com/v1 |
$ai_request_url | (Optional) The full URL of the request made to the embedding API Example: https://api.openai.com/v1/embeddings |
$ai_is_error | (Optional) Boolean to indicate if the request was an error |
$ai_error | (Optional) The error message or object if the embedding failed |
Cost properties
Cost properties are optional as we can automatically calculate them from model and token counts. If you want, you can provide your own cost property instead.
| Property | Description |
|---|---|
$ai_input_cost_usd | (Optional) Cost in USD for input tokens |
$ai_output_cost_usd | (Optional) Cost in USD for output tokens (usually 0 for embeddings) |
$ai_total_cost_usd | (Optional) Total cost in USD |
Example API call
Terminal