feat: velocity computation fixes + ProcessedItem metadata field

- Fixed VelocityComputer._clean_window to handle both dict (asset_windows) and float (source_windows) entries
- Added metadata field to ProcessedItem schema for engagement data
- Fixed VelocityComputer._clean_window to handle both dict and float entries
- All 46 core NLP tests pass
- Full pipeline integration test passes with real e5-large-v2 encoder

sentiment_engine/src/sentiment_engine/schemas/processed.py

@@ -123,6 +123,7 @@ class ProcessedItem(BaseModel):
     processed_ts: float = Field(..., description="Processing completion timestamp")
     processing_latency_ms: float = Field(..., description="Total processing time")
     model_versions: Dict[str, str] = Field(default_factory=dict)
+    metadata: Dict[str, Any] = Field(default_factory=dict)
 
     def get_asset_scores(self, asset_id: str) -> tuple[Optional[SentimentScores], Optional[EmotionScores]]:
         return (

sentiment_engine/src/sentiment_engine/signal/velocity.py

@@ -39,7 +39,7 @@ class VelocityComputer:
     def __init__(self):
         self.settings = get_settings()
         self._asset_windows: Dict[str, deque] = {}
-        self._source_windows: Dict[str, deque] = {}
+        self._source_windows: Dict[str, deque] = {}  # Stores timestamps (floats)
         # EMA state for smoothing (α=0.3 per spec)
         self._hype_ema: Dict[str, float] = {}
         self._pub_ema: Dict[str, float] = {}
@@ -114,8 +114,21 @@ class VelocityComputer:
         
     def _clean_window(self, window: deque, cutoff: float) -> None:
         """Remove entries older than cutoff"""
-        while window and window[0]["ts"] < cutoff:
+        # Handle both dict entries (asset_windows) and float timestamps (source_windows)
+        while window:
+            first = window[0]
+            if isinstance(first, dict):
+                if first.get("ts", 0) < cutoff:
                     window.popleft()
+                else:
+                    break
+            elif isinstance(first, (int, float)):
+                if first < cutoff:
+                    window.popleft()
+                else:
+                    break
+            else:
+                break
             
     def _compute_mention_weight(self, item: ProcessedItem, asset_id: str) -> float:
         """Compute weighted mention score: polarity_confidence × source_credibility × engagement_score"""
@@ -166,7 +179,7 @@ class VelocityComputer:
         return 0.0
         
     def _compute_pub_velocity(self, source_window: deque, now: float) -> float:
-        """Compute publication velocity (rate of change of publication count)"""
+        """Compute publication velocity (rate of change of publication count, -100 to +100)"""
         if len(source_window) < 3:
             return 0.0
             
@@ -180,7 +193,7 @@ class VelocityComputer:
             return 0.0
             
         bin_edges = np.linspace(times[0], times[-1], bins + 1)
-        counts, _ = np.histogram(times, bins=bin_edges)
+        counts = np.histogram(times, bins=bin_edges)[0]
         bin_times = (bin_edges[:-1] + bin_edges[1:]) / 2
         
         if len(counts) >= 3: