Charlie Doern fb553f3430 feat: implement query_metrics ... query_metrics currently has no implementation, meaning once a metric is emitted there is no way in llama stack to query it from the store. implement query_metrics for the meta_reference provider which follows a similar style to `query_traces`, using the trace_store to format an SQL query and execute it in this case the parameters for the query are `metric.METRIC_NAME, start_time, and end_time`. this required client side changes since the client had no `query_metrics` or any associated resources, so any tests here will fail but I will provider manual execution logs for the new tests I am adding order the metrics by timestamp. Additionally add `unit` to the `MetricDataPoint` class since this adds much more context to the metric being queried. these metrics can also be aggregated via a `granularity` parameter. This was pre-defined as a string like: `1m, 1h, 1d` where metrics occuring in same timespan specified are aggregated together. Signed-off-by: Charlie Doern <cdoern@redhat.com>		2025-08-22 15:47:04 -04:00
..
__init__.py	chore: remove nested imports (#2515)	2025-06-26 08:01:05 +05:30
telemetry.py	feat: implement query_metrics	2025-08-22 15:47:04 -04:00