Deprecate expect_stripped_words option from LLMAssistantAggregatorParams, when used with the newer LLMAssistantAggregator, which now handles word spacing automatically.

This commit does not change how it works in the older `LLMAssistantContextAggregator`.

examples/foundational/19-openai-realtime.py

@@ -187,12 +187,7 @@ Remember, your responses should be short. Just one or two sentences, usually. Re
         tools,
     )
 
-    context_aggregator = LLMContextAggregatorPair(
-        context,
-        # `expect_stripped_words=False` needed when OpenAI Realtime used with
-        # "audio" modality (the default)
-        assistant_params=LLMAssistantAggregatorParams(expect_stripped_words=False),
-    )
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/19a-azure-realtime.py

@@ -175,12 +175,7 @@ Remember, your responses should be short. Just one or two sentences, usually. Re
         tools,
     )
 
-    context_aggregator = LLMContextAggregatorPair(
-        context,
-        # `expect_stripped_words=False` needed when OpenAI Realtime used with
-        # "audio" modality (the default)
-        assistant_params=LLMAssistantAggregatorParams(expect_stripped_words=False),
-    )
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/26a-gemini-live-transcription.py

@@ -92,12 +92,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             #     },
         ],
     )
-    context_aggregator = LLMContextAggregatorPair(
-        context,
-        # `expect_stripped_words=False` needed when Gemini Live used with AUDIO
-        #  modality (the default)
-        assistant_params=LLMAssistantAggregatorParams(expect_stripped_words=False),
-    )
+    context_aggregator = LLMContextAggregatorPair(context)
 
     transcript = TranscriptProcessor()
 

examples/foundational/26b-gemini-live-function-calling.py

@@ -144,12 +144,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     context = LLMContext(
         [{"role": "user", "content": "Say hello."}],
     )
-    context_aggregator = LLMContextAggregatorPair(
-        context,
-        # `expect_stripped_words=False` needed when Gemini Live used with AUDIO
-        #  modality (the default)
-        assistant_params=LLMAssistantAggregatorParams(expect_stripped_words=False),
-    )
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/26c-gemini-live-video.py

@@ -75,12 +75,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             },
         ],
     )
-    context_aggregator = LLMContextAggregatorPair(
-        context,
-        # `expect_stripped_words=False` needed when Gemini Live used with AUDIO
-        #  modality (the default)
-        assistant_params=LLMAssistantAggregatorParams(expect_stripped_words=False),
-    )
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/26e-gemini-live-google-search.py

@@ -100,12 +100,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
             }
         ],
     )
-    context_aggregator = LLMContextAggregatorPair(
-        context,
-        # `expect_stripped_words=False` needed when Gemini Live used with AUDIO
-        #  modality (the default)
-        assistant_params=LLMAssistantAggregatorParams(expect_stripped_words=False),
-    )
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/26f-gemini-live-files-api.py

@@ -164,12 +164,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         )
 
     # Create context aggregator
-    context_aggregator = LLMContextAggregatorPair(
-        context,
-        # `expect_stripped_words=False` needed when Gemini Live used with AUDIO
-        #  modality (the default)
-        assistant_params=LLMAssistantAggregatorParams(expect_stripped_words=False),
-    )
+    context_aggregator = LLMContextAggregatorPair(context)
 
     # Build the pipeline
     pipeline = Pipeline(

examples/foundational/26g-gemini-live-groundingMetadata.py

@@ -127,12 +127,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     # Set up conversation context and management
     context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(
-        context,
-        # `expect_stripped_words=False` needed when Gemini Live used with AUDIO
-        #  modality (the default)
-        assistant_params=LLMAssistantAggregatorParams(expect_stripped_words=False),
-    )
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/26h-gemini-live-vertex-function-calling.py

@@ -140,12 +140,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
 
     context = LLMContext([{"role": "user", "content": "Say hello."}])
-    context_aggregator = LLMContextAggregatorPair(
-        context,
-        # `expect_stripped_words=False` needed when Gemini Live used with AUDIO
-        #  modality (the default)
-        assistant_params=LLMAssistantAggregatorParams(expect_stripped_words=False),
-    )
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/26i-gemini-live-graceful-end.py

@@ -157,12 +157,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     context = LLMContext(
         [{"role": "user", "content": "Say hello."}],
     )
-    context_aggregator = LLMContextAggregatorPair(
-        context,
-        # `expect_stripped_words=False` needed when Gemini Live used with AUDIO
-        #  modality (the default)
-        assistant_params=LLMAssistantAggregatorParams(expect_stripped_words=False),
-    )
+    context_aggregator = LLMContextAggregatorPair(context)
 
     pipeline = Pipeline(
         [

examples/foundational/46-video-processing.py

@@ -111,12 +111,7 @@ async def run_bot(pipecat_transport):
     ]
 
     context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(
-        context,
-        # `expect_stripped_words=False` needed when Gemini Live used with AUDIO
-        #  modality (the default)
-        assistant_params=LLMAssistantAggregatorParams(expect_stripped_words=False),
-    )
+    context_aggregator = LLMContextAggregatorPair(context)
 
     # RTVI events for Pipecat client UI
     rtvi = RTVIProcessor()