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fb553f3430
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> |
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| .. | ||
| apis | ||
| cli | ||
| core | ||
| distributions | ||
| models | ||
| providers | ||
| strong_typing | ||
| testing | ||
| ui | ||
| __init__.py | ||
| env.py | ||
| log.py | ||
| schema_utils.py | ||