wrapper fixes

examples/foundational/55zzq-update-settings-sarvam-llm.py

@@ -23,7 +23,6 @@ from pipecat.processors.aggregators.llm_response_universal import (
 )
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.openai.base_llm import OpenAILLMSettings
 from pipecat.services.sarvam.llm import SarvamLLMService
 from pipecat.services.sarvam.stt import SarvamSTTService
 from pipecat.services.sarvam.tts import SarvamTTSService
@@ -60,34 +59,24 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info("Starting bot")
 
     stt = SarvamSTTService(
-        model="saaras:v3",
+        settings=SarvamSTTService.Settings(model="saaras:v3"),
         api_key=_require_env("SARVAM_API_KEY"),
     )
 
     tts = SarvamTTSService(
-        model="bulbul:v3",
+        settings=SarvamTTSService.Settings(model="bulbul:v3"),
         api_key=_require_env("SARVAM_API_KEY"),
     )
 
     llm = SarvamLLMService(
         api_key=_require_env("SARVAM_API_KEY"),
-        model="sarvam-30b",
+        settings=SarvamLLMService.Settings(model="sarvam-30b"),
+        system_instruction=(
+            "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way."
+        ),
     )
 
-    messages: list[Any] = [
-        {
-            "role": "system",
-            "content": (
-                "You are a helpful LLM in a WebRTC call. Your goal is to "
-                "demonstrate your capabilities in a succinct way. Your output "
-                "will be spoken aloud, so avoid special characters that can't "
-                "easily be spoken, such as emojis or bullet points. Respond to "
-                "what the user said in a creative and helpful way."
-            ),
-        },
-    ]
-
-    context = LLMContext(messages)
+    context = LLMContext()
     user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
         context,
         user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
@@ -117,12 +106,14 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     @transport.event_handler("on_client_connected")
     async def on_client_connected(transport, client):
         logger.info("Client connected")
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+        context.add_message({"role": "user", "content": "Please introduce yourself to the user."})
         await task.queue_frames([LLMRunFrame()])
 
         await asyncio.sleep(10)
         logger.info("Updating Sarvam LLM settings: temperature=0.1")
-        await task.queue_frame(LLMUpdateSettingsFrame(delta=OpenAILLMSettings(temperature=0.1)))
+        await task.queue_frame(
+            LLMUpdateSettingsFrame(delta=SarvamLLMService.Settings(temperature=0.1))
+        )
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):