feat: implement query_metrics (#3074)

# What does this PR do?

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` and any other matchers if they are provided.

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 provide 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.


depends on
https://github.com/llamastack/llama-stack-client-python/pull/260

## Test Plan

```
import time
import uuid


def create_http_client():
    from llama_stack_client import LlamaStackClient

    return LlamaStackClient(base_url="http://localhost:8321")


client = create_http_client()

response = client.telemetry.query_metrics(metric_name="total_tokens", start_time=0)
print(response)
```

```
╰─ python3.12 ~/telemetry.py
INFO:httpx:HTTP Request: POST http://localhost:8322/v1/telemetry/metrics/total_tokens "HTTP/1.1 200 OK"
[TelemetryQueryMetricsResponse(data=None, metric='total_tokens', labels=[], values=[{'timestamp': 1753999514, 'value': 34.0, 'unit': 'tokens'}, {'timestamp': 1753999816, 'value': 34.0, 'unit': 'tokens'}, {'timestamp': 1753999881, 'value': 34.0, 'unit': 'tokens'}, {'timestamp': 1753999956, 'value': 34.0, 'unit': 'tokens'}, {'timestamp': 1754000200, 'value': 34.0, 'unit': 'tokens'}, {'timestamp': 1754000419, 'value': 36.0, 'unit': 'tokens'}, {'timestamp': 1754000714, 'value': 36.0, 'unit': 'tokens'}, {'timestamp': 1754000876, 'value': 36.0, 'unit': 'tokens'}, {'timestamp': 1754000908, 'value': 34.0, 'unit': 'tokens'}, {'timestamp': 1754001309, 'value': 584.0, 'unit': 'tokens'}, {'timestamp': 1754001311, 'value': 138.0, 'unit': 'tokens'}, {'timestamp': 1754001316, 'value': 349.0, 'unit': 'tokens'}, {'timestamp': 1754001318, 'value': 133.0, 'unit': 'tokens'}, {'timestamp': 1754001320, 'value': 133.0, 'unit': 'tokens'}, {'timestamp': 1754001341, 'value': 923.0, 'unit': 'tokens'}, {'timestamp': 1754001350, 'value': 354.0, 'unit': 'tokens'}, {'timestamp': 1754001462, 'value': 417.0, 'unit': 'tokens'}, {'timestamp': 1754001464, 'value': 158.0, 'unit': 'tokens'}, {'timestamp': 1754001475, 'value': 697.0, 'unit': 'tokens'}, {'timestamp': 1754001477, 'value': 133.0, 'unit': 'tokens'}, {'timestamp': 1754001479, 'value': 133.0, 'unit': 'tokens'}, {'timestamp': 1754001489, 'value': 298.0, 'unit': 'tokens'}, {'timestamp': 1754001541, 'value': 615.0, 'unit': 'tokens'}, {'timestamp': 1754001543, 'value': 119.0, 'unit': 'tokens'}, {'timestamp': 1754001548, 'value': 310.0, 'unit': 'tokens'}, {'timestamp': 1754001549, 'value': 133.0, 'unit': 'tokens'}, {'timestamp': 1754001551, 'value': 133.0, 'unit': 'tokens'}, {'timestamp': 1754001568, 'value': 714.0, 'unit': 'tokens'}, {'timestamp': 1754001800, 'value': 437.0, 'unit': 'tokens'}, {'timestamp': 1754001802, 'value': 200.0, 'unit': 'tokens'}, {'timestamp': 1754001806, 'value': 262.0, 'unit': 'tokens'}, {'timestamp': 1754001808, 'value': 133.0, 'unit': 'tokens'}, {'timestamp': 1754001810, 'value': 133.0, 'unit': 'tokens'}, {'timestamp': 1754001816, 'value': 82.0, 'unit': 'tokens'}, {'timestamp': 1754001923, 'value': 61.0, 'unit': 'tokens'}, {'timestamp': 1754001929, 'value': 391.0, 'unit': 'tokens'}, {'timestamp': 1754001939, 'value': 598.0, 'unit': 'tokens'}, {'timestamp': 1754001941, 'value': 133.0, 'unit': 'tokens'}, {'timestamp': 1754001942, 'value': 133.0, 'unit': 'tokens'}, {'timestamp': 1754001952, 'value': 252.0, 'unit': 'tokens'}, {'timestamp': 1754002053, 'value': 251.0, 'unit': 'tokens'}, {'timestamp': 1754002059, 'value': 375.0, 'unit': 'tokens'}, {'timestamp': 1754002062, 'value': 244.0, 'unit': 'tokens'}, {'timestamp': 1754002064, 'value': 111.0, 'unit': 'tokens'}, {'timestamp': 1754002065, 'value': 133.0, 'unit': 'tokens'}, {'timestamp': 1754002083, 'value': 719.0, 'unit': 'tokens'}, {'timestamp': 1754002302, 'value': 279.0, 'unit': 'tokens'}, {'timestamp': 1754002306, 'value': 218.0, 'unit': 'tokens'}, {'timestamp': 1754002308, 'value': 198.0, 'unit': 'tokens'}, {'timestamp': 1754002309, 'value': 69.0, 'unit': 'tokens'}, {'timestamp': 1754002311, 'value': 133.0, 'unit': 'tokens'}, {'timestamp': 1754002324, 'value': 481.0, 'unit': 'tokens'}, {'timestamp': 1754003161, 'value': 579.0, 'unit': 'tokens'}, {'timestamp': 1754003161, 'value': 69.0, 'unit': 'tokens'}, {'timestamp': 1754003169, 'value': 499.0, 'unit': 'tokens'}, {'timestamp': 1754003171, 'value': 133.0, 'unit': 'tokens'}, {'timestamp': 1754003173, 'value': 133.0, 'unit': 'tokens'}, {'timestamp': 1754003185, 'value': 422.0, 'unit': 'tokens'}, {'timestamp': 1754003448, 'value': 579.0, 'unit': 'tokens'}, {'timestamp': 1754003453, 'value': 422.0, 'unit': 'tokens'}, {'timestamp': 1754003589, 'value': 579.0, 'unit': 'tokens'}, {'timestamp': 1754003609, 'value': 279.0, 'unit': 'tokens'}, {'timestamp': 1754003614, 'value': 481.0, 'unit': 'tokens'}, {'timestamp': 1754003706, 'value': 303.0, 'unit': 'tokens'}, {'timestamp': 1754003706, 'value': 51.0, 'unit': 'tokens'}, {'timestamp': 1754003713, 'value': 426.0, 'unit': 'tokens'}, {'timestamp': 1754003714, 'value': 70.0, 'unit': 'tokens'}, {'timestamp': 1754003715, 'value': 133.0, 'unit': 'tokens'}, {'timestamp': 1754003724, 'value': 225.0, 'unit': 'tokens'}, {'timestamp': 1754004226, 'value': 516.0, 'unit': 'tokens'}, {'timestamp': 1754004228, 'value': 127.0, 'unit': 'tokens'}, {'timestamp': 1754004232, 'value': 281.0, 'unit': 'tokens'}, {'timestamp': 1754004234, 'value': 133.0, 'unit': 'tokens'}, {'timestamp': 1754004236, 'value': 133.0, 'unit': 'tokens'}, {'timestamp': 1754004244, 'value': 206.0, 'unit': 'tokens'}, {'timestamp': 1754004683, 'value': 338.0, 'unit': 'tokens'}, {'timestamp': 1754004690, 'value': 481.0, 'unit': 'tokens'}, {'timestamp': 1754004692, 'value': 124.0, 'unit': 'tokens'}, {'timestamp': 1754004692, 'value': 65.0, 'unit': 'tokens'}, {'timestamp': 1754004694, 'value': 133.0, 'unit': 'tokens'}, {'timestamp': 1754004703, 'value': 211.0, 'unit': 'tokens'}, {'timestamp': 1754004743, 'value': 338.0, 'unit': 'tokens'}, {'timestamp': 1754004749, 'value': 211.0, 'unit': 'tokens'}, {'timestamp': 1754005566, 'value': 481.0, 'unit': 'tokens'}, {'timestamp': 1754006101, 'value': 159.0, 'unit': 'tokens'}, {'timestamp': 1754006105, 'value': 272.0, 'unit': 'tokens'}, {'timestamp': 1754006109, 'value': 308.0, 'unit': 'tokens'}, {'timestamp': 1754006110, 'value': 61.0, 'unit': 'tokens'}, {'timestamp': 1754006112, 'value': 133.0, 'unit': 'tokens'}, {'timestamp': 1754006130, 'value': 705.0, 'unit': 'tokens'}, {'timestamp': 1754051825, 'value': 454.0, 'unit': 'tokens'}, {'timestamp': 1754051827, 'value': 152.0, 'unit': 'tokens'}, {'timestamp': 1754051834, 'value': 481.0, 'unit': 'tokens'}, {'timestamp': 1754051835, 'value': 55.0, 'unit': 'tokens'}, {'timestamp': 1754051837, 'value': 133.0, 'unit': 'tokens'}, {'timestamp': 1754051845, 'value': 102.0, 'unit': 'tokens'}, {'timestamp': 1754099929, 'value': 36.0, 'unit': 'tokens'}, {'timestamp': 1754510050, 'value': 598.0, 'unit': 'tokens'}, {'timestamp': 1754510052, 'value': 160.0, 'unit': 'tokens'}, {'timestamp': 1754510064, 'value': 725.0, 'unit': 'tokens'}, {'timestamp': 1754510065, 'value': 133.0, 'unit': 'tokens'}, {'timestamp': 1754510067, 'value': 133.0, 'unit': 'tokens'}, {'timestamp': 1754510083, 'value': 535.0, 'unit': 'tokens'}, {'timestamp': 1754596582, 'value': 36.0, 'unit': 'tokens'}])]
```

adding tests for each currently documented metric in llama stack using
this new function. attached is also some manual testing


integrations tests passing locally with replay mode and the linked
client changes:
<img width="1907" height="529" alt="Screenshot 2025-08-08 at 2 49 14 PM"
src="https://github.com/user-attachments/assets/d482ab06-dcff-4f0c-a1f1-f870670ee9bc"
/>

---------

Signed-off-by: Charlie Doern <cdoern@redhat.com>

tests/integration/recordings/responses/f6857bcea729.json

@@ -0,0 +1,39 @@
+{
+  "request": {
+    "method": "POST",
+    "url": "http://localhost:11434/api/generate",
+    "headers": {},
+    "body": {
+      "model": "llama3.2:3b",
+      "raw": true,
+      "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nTest metrics generation 2<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
+      "options": {
+        "temperature": 0.0
+      },
+      "stream": false
+    },
+    "endpoint": "/api/generate",
+    "model": "llama3.2:3b"
+  },
+  "response": {
+    "body": {
+      "__type__": "ollama._types.GenerateResponse",
+      "__data__": {
+        "model": "llama3.2:3b",
+        "created_at": "2025-08-11T15:56:13.082679Z",
+        "done": true,
+        "done_reason": "stop",
+        "total_duration": 2606245291,
+        "load_duration": 9979708,
+        "prompt_eval_count": 21,
+        "prompt_eval_duration": 23000000,
+        "eval_count": 321,
+        "eval_duration": 2572000000,
+        "response": "Here are some test metrics that can be used to evaluate the performance of a system:\n\n1. **Accuracy**: Measures how close the predicted values are to the actual values.\n2. **Precision**: Measures the proportion of true positives among all positive predictions made by the model.\n3. **Recall**: Measures the proportion of true positives among all actual positive instances.\n4. **F1-score**: The harmonic mean of precision and recall, providing a balanced measure of both.\n5. **Mean Squared Error (MSE)**: Measures the average squared difference between predicted and actual values.\n6. **Mean Absolute Error (MAE)**: Measures the average absolute difference between predicted and actual values.\n7. **Root Mean Squared Percentage Error (RMSPE)**: A variation of MSE that expresses errors as a percentage of the actual value.\n8. **Coefficient of Determination (R-squared, R2)**: Measures how well the model explains the variance in the data.\n9. **Mean Absolute Percentage Error (MAPE)**: Measures the average absolute percentage difference between predicted and actual values.\n10. **Mean Squared Logarithmic Error (MSLE)**: A variation of MSE that is more suitable for skewed distributions.\n\nThese metrics can be used to evaluate different aspects of a system's performance, such as:\n\n* Classification models: accuracy, precision, recall, F1-score\n* Regression models: MSE, MAE, RMSPE, R2\n* Time series forecasting: MAPE, MSLE\n\nNote that the choice of metric depends on the specific problem and data.",
+        "thinking": null,
+        "context": null
+      }
+    },
+    "is_streaming": false
+  }
+}