feat: vendor midstream and sublinear-time-solver libraries (#109)

Add ruvnet/midstream (AIMDS real-time inference) and
ruvnet/sublinear-time-solver (sublinear optimization algorithms)
as vendored dependencies under vendor/.

vendor/midstream/hyprstream-main/examples/client/python/tests/test_client.py

@@ -0,0 +1,77 @@
+"""Tests for the Hyprstream client."""
+
+import time
+import pytest
+import pandas as pd
+from hyprstream_client import MetricsClient, MetricRecord
+
+@pytest.fixture
+def client():
+    """Create a client fixture for tests."""
+    client = MetricsClient()
+    client.connect()
+    yield client
+    client.disconnect()
+
+def test_set_single_metric(client):
+    """Test setting a single metric."""
+    metric = MetricRecord(
+        metric_id="test_metric_1",
+        timestamp=int(time.time() * 1e9),
+        value_running_window_sum=10.0,
+        value_running_window_avg=2.0,
+        value_running_window_count=5
+    )
+    client.set_metric(metric)
+    
+    # Query back the metric
+    df = client.query_metrics(metric_ids=["test_metric_1"])
+    assert not df.empty
+    assert len(df) == 1
+    assert df.iloc[0]["metric_id"] == "test_metric_1"
+    assert df.iloc[0]["value_running_window_avg"] == 2.0
+
+def test_set_metrics_batch(client):
+    """Test setting multiple metrics in a batch."""
+    batch_metrics = [
+        MetricRecord(
+            metric_id=f"test_metric_{i}",
+            timestamp=int(time.time() * 1e9),
+            value_running_window_sum=float(i * 10),
+            value_running_window_avg=float(i),
+            value_running_window_count=10
+        )
+        for i in range(2, 5)
+    ]
+    client.set_metrics_batch(batch_metrics)
+    
+    # Query back the metrics
+    df = client.query_metrics(metric_ids=["test_metric_2", "test_metric_3", "test_metric_4"])
+    assert not df.empty
+    assert len(df) == 3
+    assert set(df["metric_id"]) == {"test_metric_2", "test_metric_3", "test_metric_4"}
+
+def test_query_time_window(client):
+    """Test querying metrics within a time window."""
+    # Insert a metric
+    current_time = int(time.time() * 1e9)
+    metric = MetricRecord(
+        metric_id="test_window_metric",
+        timestamp=current_time,
+        value_running_window_sum=10.0,
+        value_running_window_avg=2.0,
+        value_running_window_count=5
+    )
+    client.set_metric(metric)
+    
+    # Query with different windows
+    df = client.get_metrics_window(60)  # Last minute
+    assert not df.empty
+    assert "test_window_metric" in df["metric_id"].values
+    
+    df = client.query_metrics(
+        from_timestamp=current_time - (3600 * 1e9),  # Last hour
+        to_timestamp=current_time
+    )
+    assert not df.empty
+    assert "test_window_metric" in df["metric_id"].values