Move system_instruction into LLMSettings
Add `system_instruction` field to `LLMSettings` so it is runtime-updatable via settings. For Google (GoogleLLMService, GoogleVertexLLMService), deprecate the init-time arg since it was already shipped. For Anthropic, AWS Bedrock, and OpenAI, remove the init-time arg entirely since it was never shipped. Add system instruction prepend logic to `build_chat_completion_params` overrides in Cerebras, SambaNova, Fireworks, Mistral, and Perplexity, which build params from scratch rather than calling `super()`. Still need to handle realtime services (OpenAI Realtime, Grok Realtime, Gemini Live).
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@@ -25,7 +25,7 @@ from pipecat.runner.utils import create_transport
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from pipecat.services.cartesia.tts import CartesiaTTSService, CartesiaTTSSettings
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from pipecat.services.deepgram.stt import DeepgramSTTService
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from pipecat.services.google.llm import GoogleLLMSettings
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from pipecat.services.google.llm_vertex import GoogleVertexLLMService
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from pipecat.services.google.llm_vertex import GoogleVertexLLMService, GoogleVertexLLMSettings
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from pipecat.transports.base_transport import BaseTransport, TransportParams
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from pipecat.transports.daily.transport import DailyParams
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from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
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@@ -64,7 +64,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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credentials=os.getenv("GOOGLE_VERTEX_TEST_CREDENTIALS"),
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project_id=os.getenv("GOOGLE_CLOUD_PROJECT_ID"),
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location=os.getenv("GOOGLE_CLOUD_LOCATION"),
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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.",
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settings=GoogleVertexLLMSettings(
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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.",
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),
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)
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context = LLMContext()
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