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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-evaluation.py
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@@ -24,9 +24,9 @@ from pipecat.processors.aggregators.llm_response_universal import (
from pipecat.processors.audio.vad_processor import VADProcessor
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.groq.llm import GroqLLMService
from pipecat.services.groq.llm import GroqLLMService, GroqLLMSettings
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.transports.base_transport import BaseTransport, TransportParams
from pipecat.transports.daily.transport import DailyParams
@@ -62,31 +62,24 @@ 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
),
)
openai_llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
openai_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.",
},
]
openai_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.",
)
groq_llm = GroqLLMService(
api_key=os.getenv("GROQ_API_KEY"), model="meta-llama/llama-4-maverick-17b-128e-instruct"
api_key=os.getenv("GROQ_API_KEY"),
settings=GroqLLMSettings(model="meta-llama/llama-4-maverick-17b-128e-instruct"),
system_instruction="You are a very helpful assistant. Your goal is to demonstrate your capabilities in detail in a creative and helpful way.",
)
groq_messages = [
{
"role": "system",
"content": "You are a very helpful assistant. Your goal is to demonstrate your capabilities in detail in a creative and helpful way.",
},
]
openai_context = LLMContext(openai_messages)
groq_context = LLMContext(groq_messages)
openai_context = LLMContext()
groq_context = LLMContext()
# We use an external VADProcessor because the UserTurnProcessor is shared
# across multiple parallel aggregators. The VADProcessor emits
@@ -147,11 +140,11 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
async def on_client_connected(transport, client):
logger.info(f"Client connected")
# Kick off the conversation.
openai_messages.append(
{"role": "system", "content": "Please introduce yourself to the user."}
openai_context.add_message(
{"role": "user", "content": "Please introduce yourself to the user."}
)
groq_messages.append(
{"role": "system", "content": "Please introduce yourself to the user."}
groq_context.add_message(
{"role": "user", "content": "Please introduce yourself to the user."}
)
await task.queue_frames([LLMRunFrame()])