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Update examples

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This commit is contained in:
Mark Backman
2026-03-04 22:55:51 -05:00
parent 197885773f
commit 039a8f6c7d
207 changed files with 1241 additions and 852 deletions
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43
examples/foundational/53-concurrent-llm-rtvi-ignored-sources.py
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@@ -31,7 +31,7 @@ from pipecat.processors.audio.vad_processor import VADProcessor
from pipecat.processors.frameworks.rtvi import RTVIObserverParams
from pipecat.runner.types import RunnerArguments
from pipecat.runner.utils import create_transport
from pipecat.services.cartesia.tts import CartesiaTTSService
from pipecat.services.cartesia.tts import CartesiaTTSService, CartesiaTTSSettings
from pipecat.services.deepgram.stt import DeepgramSTTService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.transports.base_transport import BaseTransport, TransportParams
@@ -67,40 +67,27 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
tts = CartesiaTTSService(
api_key=os.getenv("CARTESIA_API_KEY"),
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
settings=CartesiaTTSSettings(
voice="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
),
)
# Main LLM — drives the conversation. Its RTVI events reach the client.
main_llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
main_messages = [
{
"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.",
},
]
main_llm = OpenAILLMService(
api_key=os.getenv("OPENAI_API_KEY"),
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.",
)
# Evaluator LLM — silently grades the user's message in the background.
# Its RTVI events will be suppressed so the client is unaware of this branch.
evaluator_llm = OpenAILLMService(
api_key=os.getenv("OPENAI_API_KEY"),
name="EvaluatorLLM",
system_instruction="You are a silent quality evaluator. When given a user message, respond with a single JSON object: {'score': <1-5>, 'reason': '<brief reason>'}. Do not respond conversationally.",
)
evaluator_messages = [
{
"role": "system",
"content": (
"You are a silent quality evaluator. When given a user message, "
"respond with a single JSON object: "
'{"score": <1-5>, "reason": "<brief reason>"}. '
"Do not respond conversationally."
),
},
]
main_context = LLMContext(main_messages)
evaluator_context = LLMContext(evaluator_messages)
main_context = LLMContext()
evaluator_context = LLMContext()
# We use an external VADProcessor because the UserTurnProcessor is shared
# across multiple parallel aggregators. The VADProcessor emits
@@ -163,10 +150,12 @@ 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")
main_messages.append(
{"role": "system", "content": "Please introduce yourself to the user."}
main_context.add_message(
{"role": "user", "content": "Please introduce yourself to the user."}
)
evaluator_context.add_message(
{"role": "user", "content": "Ready to evaluate user messages."}
)
evaluator_messages.append({"role": "system", "content": "Ready to evaluate user messages."})
await task.queue_frames([LLMRunFrame()])
@transport.event_handler("on_client_disconnected")