# NB: These keys are placeholders and subject to change class TraceMetadataKey: INPUTS = "mlflow.traceInputs" OUTPUTS = "mlflow.traceOutputs" SOURCE_RUN = "mlflow.sourceRun" class TraceTagKey: TRACE_NAME = "mlflow.traceName" EVAL_REQUEST_ID = "eval.requestId" # A set of reserved attribute keys class SpanAttributeKey: EXPERIMENT_ID = "mlflow.experimentId" REQUEST_ID = "mlflow.traceRequestId" INPUTS = "mlflow.spanInputs" OUTPUTS = "mlflow.spanOutputs" SPAN_TYPE = "mlflow.spanType" FUNCTION_NAME = "mlflow.spanFunctionName" START_TIME_NS = "mlflow.spanStartTimeNs" # these attributes are for standardized chat messages and tool definitions # in CHAT_MODEL and LLM spans. they are used for rendering the rich chat # display in the trace UI, as well as downstream consumers of trace data # such as evaluation CHAT_MESSAGES = "mlflow.chat.messages" CHAT_TOOLS = "mlflow.chat.tools" # This attribute is used to populate `intermediate_outputs` property of a trace data # representing intermediate outputs of the trace. This attribute is not empty only on # the root span of a trace created by the `mlflow.log_trace` API. The `intermediate_outputs` # property of the normal trace is generated by the outputs of non-root spans. INTERMEDIATE_OUTPUTS = "mlflow.trace.intermediate_outputs" # All storage backends are guaranteed to support request_metadata key/value up to 250 characters MAX_CHARS_IN_TRACE_INFO_METADATA = 250 # All storage backends are guaranteed to support tag keys up to 250 characters, # values up to 4096 characters MAX_CHARS_IN_TRACE_INFO_TAGS_KEY = 250 MAX_CHARS_IN_TRACE_INFO_TAGS_VALUE = 4096 TRUNCATION_SUFFIX = "..." # Trace request ID must have the prefix "tr-" appended to the OpenTelemetry trace ID TRACE_REQUEST_ID_PREFIX = "tr-" # Schema version of traces and spans. TRACE_SCHEMA_VERSION = 2 # Key for the trace schema version in the trace. This key is also used in # Databricks model serving to be careful when modifying it. TRACE_SCHEMA_VERSION_KEY = "mlflow.trace_schema.version" STREAM_CHUNK_EVENT_NAME_FORMAT = "mlflow.chunk.item.{index}" STREAM_CHUNK_EVENT_VALUE_KEY = "mlflow.chunk.value"