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
| 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_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 | Optional - If not provided, costs will be calculated automatically from token counts |
$ai_input_cost_usd | Optional The cost in USD of the input tokens |
$ai_output_cost_usd | Optional The cost in USD of the output tokens (usually 0 for embeddings) |
$ai_total_cost_usd | Optional The total cost in USD |
| Span/Trace Properties | |
$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 |
Example
Terminal