feat: run per node QA

api/services/workflow/qa/conversation.py

@@ -0,0 +1,109 @@
+"""Conversation building, transcript formatting, and per-node event splitting."""
+
+from collections import OrderedDict
+from datetime import datetime
+
+from pipecat.utils.enums import RealtimeFeedbackType
+
+
+def build_conversation_structure(logs: list[dict]) -> list[dict]:
+    """Transform raw call logs into a conversation structure for LLM QA analysis."""
+    if not logs:
+        return []
+
+    start_time = datetime.fromisoformat(logs[0]["timestamp"])
+
+    conversation = []
+    for event in logs:
+        if event["type"] == RealtimeFeedbackType.BOT_TEXT.value:
+            speaker = "assistant"
+            utterance_text = event["payload"]["text"]
+            try:
+                event_time = datetime.fromisoformat(event["payload"]["timestamp"])
+            except KeyError:
+                event_time = datetime.fromisoformat(event["timestamp"])
+        elif event["type"] == RealtimeFeedbackType.USER_TRANSCRIPTION.value and event[
+            "payload"
+        ].get("final", False):
+            speaker = "user"
+            utterance_text = event["payload"]["text"]
+            try:
+                event_time = datetime.fromisoformat(event["payload"]["timestamp"])
+            except KeyError:
+                event_time = datetime.fromisoformat(event["timestamp"])
+        elif event["type"] == RealtimeFeedbackType.FUNCTION_CALL_START.value:
+            speaker = "tool_call"
+            payload = event["payload"]
+            utterance_text = payload.get("function_name", "unknown")
+            event_time = datetime.fromisoformat(event["timestamp"])
+        else:
+            continue
+
+        time_from_start = (event_time - start_time).total_seconds()
+
+        conversation.append(
+            {
+                "time_from_start_seconds": round(time_from_start, 2),
+                "speaker": speaker,
+                "text": utterance_text,
+                "node_name": event.get("node_name", ""),
+                "turn": event.get("turn", 0),
+            }
+        )
+
+    return conversation
+
+
+def format_transcript(conversation: list[dict]) -> str:
+    """Format conversation structure into a readable transcript string for the LLM."""
+    lines = []
+    for entry in conversation:
+        if entry["speaker"] == "tool_call":
+            lines.append(
+                f"[{entry['time_from_start_seconds']:.1f}s] "
+                f"[tool_call]: {entry['text']}"
+            )
+        else:
+            lines.append(
+                f"[{entry['time_from_start_seconds']:.1f}s] "
+                f"{entry['speaker']}: {entry['text']}"
+            )
+    return "\n".join(lines)
+
+
+def split_events_by_node(
+    rtf_events: list[dict],
+) -> list[tuple[str, str, list[dict]]]:
+    """Split realtime_feedback_events by node_id.
+
+    Returns an ordered list of (node_id, node_name, events) tuples.
+    Only includes nodes that have conversational content (BOT_TEXT or USER_TRANSCRIPTION).
+    """
+    conversational_types = {
+        RealtimeFeedbackType.BOT_TEXT.value,
+        RealtimeFeedbackType.USER_TRANSCRIPTION.value,
+    }
+
+    # Preserve insertion order — first occurrence defines position
+    node_events: OrderedDict[str, list[dict]] = OrderedDict()
+    node_names: dict[str, str] = {}
+
+    for event in rtf_events:
+        node_id = event.get("node_id")
+        if not node_id:
+            return []  # Events lack node_id — caller should fall back
+
+        if node_id not in node_events:
+            node_events[node_id] = []
+            node_names[node_id] = event.get("node_name", "")
+
+        node_events[node_id].append(event)
+
+    # Filter to nodes with conversational content
+    result = []
+    for node_id, events in node_events.items():
+        has_conversation = any(e["type"] in conversational_types for e in events)
+        if has_conversation:
+            result.append((node_id, node_names[node_id], events))
+
+    return result