Enhance conversation history and runtime variable management

- Update ConversationRecorder to include source and nodeId metadata in transcripts for better context tracking.
- Introduce optional variable handling in DynamicVariableStore, allowing for unset variables to be rendered as empty without raising errors.
- Refactor WorkflowBrain to apply turn configurations and manage interaction policies dynamically, improving agent responsiveness.
- Implement tests to ensure proper handling of updated session variables and workflow metadata in various scenarios.

backend/services/pipecat/pipeline.py

@@ -10,6 +10,7 @@ import asyncio
 import base64
 from collections.abc import Callable
 from io import BytesIO
+from typing import Any
 from uuid import uuid4
 
 from loguru import logger
@@ -50,6 +51,7 @@ from pipecat.frames.frames import (
     TTSSpeakFrame,
     UserImageRawFrame,
     UserImageRequestFrame,
+    VADParamsUpdateFrame,
 )
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.llm_switcher import LLMSwitcher
@@ -58,7 +60,6 @@ from pipecat.pipeline.worker import PipelineParams, PipelineWorker
 from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import (
     LLMAssistantAggregator,
-    LLMUserAggregator,
     LLMUserAggregatorParams,
 )
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
@@ -72,8 +73,10 @@ from pipecat.turns.user_mute.function_call_user_mute_strategy import (
     FunctionCallUserMuteStrategy,
 )
 from services.pipecat.turn_config import (
+    ConfigurableLLMUserAggregator,
     create_user_turn_strategies,
     create_vad_analyzer,
+    create_vad_params,
 )
 from pipecat.utils.time import time_now_iso8601
 from pipecat.workers.runner import WorkerRunner
@@ -794,7 +797,7 @@ async def run_pipeline(
     current_llm_service = llm
     if cfg.type == "workflow":
         llm, llm_services, current_llm_service = _workflow_llm_switcher(cfg, llm)
-    user_aggregator = LLMUserAggregator(
+    user_aggregator = ConfigurableLLMUserAggregator(
         context,
         params=LLMUserAggregatorParams(
             vad_analyzer=create_vad_analyzer(cfg.turnConfig),
@@ -1063,6 +1066,31 @@ async def run_pipeline(
                 )
             )
 
+    current_enable_interrupt = cfg.enableInterrupt
+    current_turn_config = dict(cfg.turnConfig)
+
+    async def apply_workflow_turn_config(
+        enable_interrupt: bool,
+        turn_config: dict[str, Any],
+    ) -> None:
+        """Apply one Agent's interaction policy before its next user turn."""
+        nonlocal current_enable_interrupt, current_turn_config
+        normalized = dict(turn_config or {})
+        if (
+            current_enable_interrupt == enable_interrupt
+            and current_turn_config == normalized
+        ):
+            return
+        await user_aggregator.apply_turn_strategies(
+            normalized,
+            enable_interruptions=enable_interrupt,
+        )
+        await worker.queue_frame(
+            VADParamsUpdateFrame(params=create_vad_params(normalized))
+        )
+        current_enable_interrupt = enable_interrupt
+        current_turn_config = normalized
+
     async def queue_transcript(role: str, content: str, timestamp: str) -> None:
         if content:
             await worker.queue_frame(
@@ -1107,6 +1135,7 @@ async def run_pipeline(
             switch_services=switch_workflow_services,
             set_knowledge_scope=knowledge_retrieval.set_scope,
             set_input_enabled=lambda enabled: input_state.__setitem__("enabled", enabled),
+            apply_turn_config=apply_workflow_turn_config,
             flow_global_functions=flow_global_functions,
         ),
     )