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Merge pull request #618 from pipecat-ai/aleix/examples-switch-to-llm-context

Browse Source 
examples: use OpenAILLMContext in all the examples
This commit is contained in:
Aleix Conchillo Flaqué
2024-10-19 18:24:39 -07:00
committed by GitHub
parent 4f66e5d55f f971dbe027
commit 3556c9ce0f
36 changed files with 358 additions and 416 deletions
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5
CHANGELOG.md
View File

@@ -33,6 +33,11 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
- Renamed `OpenAILLMServiceRealtimeBeta` to `OpenAIRealtimeBetaLLMService` to
match other services.
### Deprecated
- `LLMUserResponseAggregator` and `LLMAssistantResponseAggregator` are
mostly deprecated, use `OpenAILLMContext` instead.
- The `vad` package is now deprecated and `audio.vad` should be used
instead. The `avd` package will get removed in a future release.

13
examples/canonical-metrics/bot.py
View File

@@ -19,10 +19,7 @@ from pipecat.frames.frames import EndFrame, LLMMessagesFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.llm_response import (
LLMAssistantResponseAggregator,
LLMUserResponseAggregator,
)
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.processors.audio.audio_buffer_processor import AudioBufferProcessor
from pipecat.services.canonical import CanonicalMetricsService
from pipecat.services.elevenlabs import ElevenLabsTTSService
@@ -92,8 +89,8 @@ async def main():
},
]
user_response = LLMUserResponseAggregator()
assistant_response = LLMAssistantResponseAggregator()
context = OpenAILLMContext(messages)
context_aggregator = llm.create_context_aggregator(context)
"""
CanonicalMetrics uses AudioBufferProcessor under the hood to buffer the audio. On
@@ -113,13 +110,13 @@ async def main():
pipeline = Pipeline(
[
transport.input(), # microphone
user_response,
context_aggregator.user(),
llm,
tts,
transport.output(),
audio_buffer_processor, # captures audio into a buffer
canonical, # uploads audio buffer to Canonical AI for metrics
assistant_response,
context_aggregator.assistant(),
]
)

35
examples/chatbot-audio-recording/bot.py
View File

@@ -9,6 +9,8 @@ import os
import sys
import aiohttp
import datetime
import wave
from dotenv import load_dotenv
from loguru import logger
from runner import configure
@@ -18,10 +20,7 @@ from pipecat.frames.frames import EndFrame, LLMMessagesFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.llm_response import (
LLMAssistantResponseAggregator,
LLMUserResponseAggregator,
)
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.processors.audio.audio_buffer_processor import AudioBufferProcessor
from pipecat.services.elevenlabs import ElevenLabsTTSService
from pipecat.services.openai import OpenAILLMService
@@ -33,6 +32,20 @@ logger.remove(0)
logger.add(sys.stderr, level="DEBUG")
async def save_audio(audiobuffer):
if audiobuffer.has_audio():
merged_audio = audiobuffer.merge_audio_buffers()
filename = f"conversation_recording{datetime.datetime.now().strftime('%Y%m%d_%H%M%S')}.wav"
with wave.open(filename, "wb") as wf:
wf.setnchannels(2)
wf.setsampwidth(2)
wf.setframerate(audiobuffer._sample_rate)
wf.writeframes(merged_audio)
print(f"Merged audio saved to {filename}")
else:
print("No audio data to save")
async def main():
async with aiohttp.ClientSession() as session:
(room_url, token) = await configure(session)
@@ -90,19 +103,19 @@ async def main():
},
]
user_response = LLMUserResponseAggregator()
assistant_response = LLMAssistantResponseAggregator()
context = OpenAILLMContext(messages)
context_aggregator = llm.create_context_aggregator(context)
audiobuffer = AudioBufferProcessor()
pipeline = Pipeline(
[
transport.input(), # microphone
user_response,
context_aggregator.user(),
llm,
tts,
transport.output(),
audiobuffer, # used to buffer the audio in the pipeline
assistant_response,
context_aggregator.assistant(),
]
)
@@ -117,11 +130,7 @@ async def main():
async def on_participant_left(transport, participant, reason):
print(f"Participant left: {participant}")
await task.queue_frame(EndFrame())
@transport.event_handler("on_call_state_updated")
async def on_call_state_updated(transport, state):
if state == "left":
await task.queue_frame(EndFrame())
await save_audio(audiobuffer)
runner = PipelineRunner()

13
examples/deployment/flyio-example/bot.py
View File

@@ -7,11 +7,8 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.llm_response import (
LLMAssistantResponseAggregator,
LLMUserResponseAggregator,
)
from pipecat.frames.frames import LLMMessagesFrame, EndFrame
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.openai import OpenAILLMService
from pipecat.services.elevenlabs import ElevenLabsTTSService
from pipecat.transports.services.daily import DailyParams, DailyTransport
@@ -60,17 +57,17 @@ async def main(room_url: str, token: str):
},
]
tma_in = LLMUserResponseAggregator(messages)
tma_out = LLMAssistantResponseAggregator(messages)
context = OpenAILLMContext(messages)
context_aggregator = llm.create_context_aggregator(context)
pipeline = Pipeline(
[
transport.input(),
tma_in,
context_aggregator.user(),
llm,
tts,
transport.output(),
tma_out,
context_aggregator.assistant(),
]
)

13
examples/dialin-chatbot/bot_daily.py
View File

@@ -7,11 +7,8 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.llm_response import (
LLMAssistantResponseAggregator,
LLMUserResponseAggregator,
)
from pipecat.frames.frames import LLMMessagesFrame, EndFrame
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.elevenlabs import ElevenLabsTTSService
from pipecat.services.openai import OpenAILLMService
from pipecat.transports.services.daily import DailyParams, DailyTransport, DailyDialinSettings
@@ -66,17 +63,17 @@ async def main(room_url: str, token: str, callId: str, callDomain: str):
},
]
tma_in = LLMUserResponseAggregator(messages)
tma_out = LLMAssistantResponseAggregator(messages)
context = OpenAILLMContext(messages)
context_aggregator = llm.create_context_aggregator(context)
pipeline = Pipeline(
[
transport.input(),
tma_in,
context_aggregator.user(),
llm,
tts,
transport.output(),
tma_out,
context_aggregator.assistant(),
]
)

6
examples/dialin-chatbot/bot_runner.py
View File

@@ -108,11 +108,9 @@ async def _create_daily_room(room_url, callId, callDomain=None, vendor="daily"):
# Spawn a new agent, and join the user session
# Note: this is mostly for demonstration purposes (refer to 'deployment' in docs)
if vendor == "daily":
bot_proc = f"python3 - m bot_daily - u {room.url} - t {token} - i {
callId} - d {callDomain}"
bot_proc = f"python3 -m bot_daily -u {room.url} -t {token} -i {callId} -d {callDomain}"
else:
bot_proc = f"python3 - m bot_twilio - u {room.url} - t {
token} - i {callId} - s {room.config.sip_endpoint}"
bot_proc = f"python3 -m bot_twilio -u {room.url} -t {token} -i {callId} -s {room.config.sip_endpoint}"
try:
subprocess.Popen(

13
examples/dialin-chatbot/bot_twilio.py
View File

@@ -7,11 +7,8 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.llm_response import (
LLMAssistantResponseAggregator,
LLMUserResponseAggregator,
)
from pipecat.frames.frames import LLMMessagesFrame, EndFrame
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.elevenlabs import ElevenLabsTTSService
from pipecat.services.openai import OpenAILLMService
from pipecat.transports.services.daily import DailyParams, DailyTransport
@@ -69,17 +66,17 @@ async def main(room_url: str, token: str, callId: str, sipUri: str):
},
]
tma_in = LLMUserResponseAggregator(messages)
tma_out = LLMAssistantResponseAggregator(messages)
context = OpenAILLMContext(messages)
context_aggregator = llm.create_context_aggregator(context)
pipeline = Pipeline(
[
transport.input(),
tma_in,
context_aggregator.user(),
llm,
tts,
transport.output(),
tma_out,
context_aggregator.assistant(),
]
)

14
examples/foundational/06-listen-and-respond.py
View File

@@ -20,10 +20,7 @@ from pipecat.metrics.metrics import (
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineTask
from pipecat.processors.aggregators.llm_response import (
LLMAssistantResponseAggregator,
LLMUserResponseAggregator,
)
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.services.cartesia import CartesiaTTSService
from pipecat.services.openai import OpenAILLMService
@@ -92,18 +89,19 @@ async def main():
"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 converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
},
]
tma_in = LLMUserResponseAggregator(messages)
tma_out = LLMAssistantResponseAggregator(messages)
context = OpenAILLMContext(messages)
context_aggregator = llm.create_context_aggregator(context)
pipeline = Pipeline(
[
transport.input(),
tma_in,
context_aggregator.user(),
llm,
tts,
ml,
transport.output(),
tma_out,
context_aggregator.assistant(),
]
)

13
examples/foundational/06a-image-sync.py
View File

@@ -16,10 +16,7 @@ from pipecat.frames.frames import Frame, OutputImageRawFrame, SystemFrame, TextF
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineTask
from pipecat.processors.aggregators.llm_response import (
LLMAssistantResponseAggregator,
LLMUserResponseAggregator,
)
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.services.cartesia import CartesiaHttpTTSService
from pipecat.services.openai import OpenAILLMService
@@ -105,8 +102,8 @@ async def main():
},
]
tma_in = LLMUserResponseAggregator(messages)
tma_out = LLMAssistantResponseAggregator(messages)
context = OpenAILLMContext(messages)
context_aggregator = llm.create_context_aggregator(context)
image_sync_aggregator = ImageSyncAggregator(
os.path.join(os.path.dirname(__file__), "assets", "speaking.png"),
@@ -117,11 +114,11 @@ async def main():
[
transport.input(),
image_sync_aggregator,
tma_in,
context_aggregator.user(),
llm,
tts,
transport.output(),
tma_out,
context_aggregator.assistant(),
]
)

21
examples/foundational/07-interruptible-vad.py
View File

@@ -13,11 +13,8 @@ from pipecat.frames.frames import LLMMessagesFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.llm_response import (
LLMAssistantResponseAggregator,
LLMUserResponseAggregator,
)
from pipecat.processors.audio.vad.silero import SileroVAD
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.cartesia import CartesiaTTSService
from pipecat.services.openai import OpenAILLMService
from pipecat.transports.services.daily import DailyParams, DailyTransport
@@ -65,18 +62,18 @@ async def main():
},
]
tma_in = LLMUserResponseAggregator(messages)
tma_out = LLMAssistantResponseAggregator(messages)
context = OpenAILLMContext(messages)
context_aggregator = llm.create_context_aggregator(context)
pipeline = Pipeline(
[
transport.input(), # Transport user input
transport.input(),
vad,
tma_in, # User responses
llm, # LLM
tts, # TTS
transport.output(), # Transport bot output
tma_out, # Assistant spoken responses
context_aggregator.user(),
llm,
tts,
transport.output(),
context_aggregator.assistant(),
]
)

13
examples/foundational/07-interruptible.py
View File

@@ -14,10 +14,7 @@ from pipecat.frames.frames import LLMMessagesFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.llm_response import (
LLMAssistantResponseAggregator,
LLMUserResponseAggregator,
)
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.cartesia import CartesiaTTSService
from pipecat.services.openai import OpenAILLMService
from pipecat.transports.services.daily import DailyParams, DailyTransport
@@ -64,17 +61,17 @@ async def main():
},
]
tma_in = LLMUserResponseAggregator(messages)
tma_out = LLMAssistantResponseAggregator(messages)
context = OpenAILLMContext(messages)
context_aggregator = llm.create_context_aggregator(context)
pipeline = Pipeline(
[
transport.input(), # Transport user input
tma_in, # User responses
context_aggregator.user(), # User responses
llm, # LLM
tts, # TTS
transport.output(), # Transport bot output
tma_out, # Assistant spoken responses
context_aggregator.assistant(), # Assistant spoken responses
]
)

13
examples/foundational/07c-interruptible-deepgram.py
View File

@@ -18,10 +18,7 @@ from pipecat.frames.frames import LLMMessagesFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.llm_response import (
LLMAssistantResponseAggregator,
LLMUserResponseAggregator,
)
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.deepgram import DeepgramSTTService, DeepgramTTSService
from pipecat.services.openai import OpenAILLMService
from pipecat.transports.services.daily import DailyParams, DailyTransport
@@ -61,18 +58,18 @@ async def main():
},
]
tma_in = LLMUserResponseAggregator(messages)
tma_out = LLMAssistantResponseAggregator(messages)
context = OpenAILLMContext(messages)
context_aggregator = llm.create_context_aggregator(context)
pipeline = Pipeline(
[
transport.input(), # Transport user input
stt, # STT
tma_in, # User responses
context_aggregator.user(), # User responses
llm, # LLM
tts, # TTS
transport.output(), # Transport bot output
tma_out, # Assistant spoken responses
context_aggregator.assistant(), # Assistant spoken responses
]
)

13
examples/foundational/07d-interruptible-elevenlabs.py
View File

@@ -11,6 +11,7 @@ import sys
import aiohttp
from dotenv import load_dotenv
from loguru import logger
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
@@ -18,10 +19,6 @@ from pipecat.frames.frames import LLMMessagesFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.llm_response import (
LLMAssistantResponseAggregator,
LLMUserResponseAggregator,
)
from pipecat.services.elevenlabs import ElevenLabsTTSService
from pipecat.services.openai import OpenAILLMService
from pipecat.transports.services.daily import DailyParams, DailyTransport
@@ -62,17 +59,17 @@ async def main():
},
]
tma_in = LLMUserResponseAggregator(messages)
tma_out = LLMAssistantResponseAggregator(messages)
context = OpenAILLMContext(messages)
context_aggregator = llm.create_context_aggregator(context)
pipeline = Pipeline(
[
transport.input(), # Transport user input
tma_in, # User responses
context_aggregator.user(), # User responses
llm, # LLM
tts, # TTS
transport.output(), # Transport bot output
tma_out, # Assistant spoken responses
context_aggregator.assistant(), # Assistant spoken responses
]
)

13
examples/foundational/07e-interruptible-playht.py
View File

@@ -18,10 +18,7 @@ from pipecat.frames.frames import LLMMessagesFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.llm_response import (
LLMAssistantResponseAggregator,
LLMUserResponseAggregator,
)
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.openai import OpenAILLMService
from pipecat.services.playht import PlayHTTTSService
from pipecat.transcriptions.language import Language
@@ -66,17 +63,17 @@ async def main():
},
]
tma_in = LLMUserResponseAggregator(messages)
tma_out = LLMAssistantResponseAggregator(messages)
context = OpenAILLMContext(messages)
context_aggregator = llm.create_context_aggregator(context)
pipeline = Pipeline(
[
transport.input(), # Transport user input
tma_in, # User responses
context_aggregator.user(), # User responses
llm, # LLM
tts, # TTS
transport.output(), # Transport bot output
tma_out, # Assistant spoken responses
context_aggregator.assistant(), # Assistant spoken responses
]
)

13
examples/foundational/07f-interruptible-azure.py
View File

@@ -14,10 +14,7 @@ from pipecat.frames.frames import LLMMessagesFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.llm_response import (
LLMAssistantResponseAggregator,
LLMUserResponseAggregator,
)
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.azure import AzureLLMService, AzureSTTService, AzureTTSService
from pipecat.transports.services.daily import DailyParams, DailyTransport
@@ -74,18 +71,18 @@ async def main():
},
]
tma_in = LLMUserResponseAggregator(messages)
tma_out = LLMAssistantResponseAggregator(messages)
context = OpenAILLMContext(messages)
context_aggregator = llm.create_context_aggregator(context)
pipeline = Pipeline(
[
transport.input(), # Transport user input
stt, # STT
tma_in, # User responses
context_aggregator.user(), # User responses
llm, # LLM
tts, # TTS
transport.output(), # Transport bot output
tma_out, # Assistant spoken responses
context_aggregator.assistant(), # Assistant spoken responses
]
)

13
examples/foundational/07g-interruptible-openai-tts.py
View File

@@ -11,6 +11,7 @@ import sys
import aiohttp
from dotenv import load_dotenv
from loguru import logger
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
@@ -18,10 +19,6 @@ from pipecat.frames.frames import LLMMessagesFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.llm_response import (
LLMAssistantResponseAggregator,
LLMUserResponseAggregator,
)
from pipecat.services.openai import OpenAILLMService, OpenAITTSService
from pipecat.transports.services.daily import DailyParams, DailyTransport
@@ -59,17 +56,17 @@ async def main():
},
]
tma_in = LLMUserResponseAggregator(messages)
tma_out = LLMAssistantResponseAggregator(messages)
context = OpenAILLMContext(messages)
context_aggregator = llm.create_context_aggregator(context)
pipeline = Pipeline(
[
transport.input(), # Transport user input
tma_in, # User responses
context_aggregator.user(), # User responses
llm, # LLM
tts, # TTS
transport.output(), # Transport bot output
tma_out, # Assistant spoken responses
context_aggregator.assistant(), # Assistant spoken responses
]
)

14
examples/foundational/07h-interruptible-openpipe.py
View File

@@ -14,10 +14,7 @@ from pipecat.frames.frames import LLMMessagesFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.llm_response import (
LLMAssistantResponseAggregator,
LLMUserResponseAggregator,
)
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.cartesia import CartesiaTTSService
from pipecat.services.openpipe import OpenPipeLLMService
from pipecat.transports.services.daily import DailyParams, DailyTransport
@@ -70,17 +67,18 @@ async def main():
"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 converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
},
]
tma_in = LLMUserResponseAggregator(messages)
tma_out = LLMAssistantResponseAggregator(messages)
context = OpenAILLMContext(messages)
context_aggregator = llm.create_context_aggregator(context)
pipeline = Pipeline(
[
transport.input(), # Transport user input
tma_in, # User responses
context_aggregator.user(), # User responses
llm, # LLM
tts, # TTS
transport.output(), # Transport bot output
tma_out, # Assistant spoken responses
context_aggregator.assistant(), # Assistant spoken responses
]
)

13
examples/foundational/07i-interruptible-xtts.py
View File

@@ -14,10 +14,7 @@ from pipecat.frames.frames import LLMMessagesFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.llm_response import (
LLMAssistantResponseAggregator,
LLMUserResponseAggregator,
)
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.openai import OpenAILLMService
from pipecat.services.xtts import XTTSService
from pipecat.transports.services.daily import DailyParams, DailyTransport
@@ -66,17 +63,17 @@ async def main():
},
]
tma_in = LLMUserResponseAggregator(messages)
tma_out = LLMAssistantResponseAggregator(messages)
context = OpenAILLMContext(messages)
context_aggregator = llm.create_context_aggregator(context)
pipeline = Pipeline(
[
transport.input(), # Transport user input
tma_in, # User responses
context_aggregator.user(), # User responses
llm, # LLM
tts, # TTS
transport.output(), # Transport bot output
tma_out, # Assistant spoken responses
context_aggregator.assistant(), # Assistant spoken responses
]
)

13
examples/foundational/07j-interruptible-gladia.py
View File

@@ -14,10 +14,7 @@ from pipecat.frames.frames import LLMMessagesFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.llm_response import (
LLMAssistantResponseAggregator,
LLMUserResponseAggregator,
)
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.cartesia import CartesiaTTSService
from pipecat.services.gladia import GladiaSTTService
from pipecat.services.openai import OpenAILLMService
@@ -69,18 +66,18 @@ async def main():
},
]
tma_in = LLMUserResponseAggregator(messages)
tma_out = LLMAssistantResponseAggregator(messages)
context = OpenAILLMContext(messages)
context_aggregator = llm.create_context_aggregator(context)
pipeline = Pipeline(
[
transport.input(), # Transport user input
stt, # STT
tma_in, # User responses
context_aggregator.user(), # User responses
llm, # LLM
tts, # TTS
transport.output(), # Transport bot output
tma_out, # Assistant spoken responses
context_aggregator.assistant(), # Assistant spoken responses
]
)

13
examples/foundational/07k-interruptible-lmnt.py
View File

@@ -14,10 +14,7 @@ from pipecat.frames.frames import LLMMessagesFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.llm_response import (
LLMAssistantResponseAggregator,
LLMUserResponseAggregator,
)
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.lmnt import LmntTTSService
from pipecat.services.openai import OpenAILLMService
from pipecat.transports.services.daily import DailyParams, DailyTransport
@@ -62,17 +59,17 @@ async def main():
},
]
tma_in = LLMUserResponseAggregator(messages)
tma_out = LLMAssistantResponseAggregator(messages)
context = OpenAILLMContext(messages)
context_aggregator = llm.create_context_aggregator(context)
pipeline = Pipeline(
[
transport.input(), # Transport user input
tma_in, # User responses
context_aggregator.user(), # User respones
llm, # LLM
tts, # TTS
transport.output(), # Transport bot output
tma_out, # Assistant spoken responses
context_aggregator.assistant(), # Assistant spoken responses
]
)

2
examples/foundational/07l-interruptible-together.py
View File

@@ -52,7 +52,7 @@ async def main():
llm = TogetherLLMService(
api_key=os.getenv("TOGETHER_API_KEY"),
model=os.getenv("TOGETHER_MODEL"),
model="meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo",
params=TogetherLLMService.InputParams(
temperature=1.0,
top_p=0.9,

13
examples/foundational/07m-interruptible-aws.py
View File

@@ -18,10 +18,7 @@ from pipecat.frames.frames import LLMMessagesFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.llm_response import (
LLMAssistantResponseAggregator,
LLMUserResponseAggregator,
)
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.aws import AWSTTSService
from pipecat.services.deepgram import DeepgramSTTService
from pipecat.services.openai import OpenAILLMService
@@ -69,18 +66,18 @@ async def main():
},
]
tma_in = LLMUserResponseAggregator(messages)
tma_out = LLMAssistantResponseAggregator(messages)
context = OpenAILLMContext(messages)
context_aggregator = llm.create_context_aggregator(context)
pipeline = Pipeline(
[
transport.input(), # Transport user input
stt, # STT
tma_in, # User responses
context_aggregator.user(), # User responses
llm, # LLM
tts, # TTS
transport.output(), # Transport bot output
tma_out, # Assistant spoken responses
context_aggregator.assistant(), # Assistant spoken responses
]
)

13
examples/foundational/07n-interruptible-google.py
View File

@@ -18,10 +18,7 @@ from pipecat.frames.frames import LLMMessagesFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.llm_response import (
LLMAssistantResponseAggregator,
LLMUserResponseAggregator,
)
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.deepgram import DeepgramSTTService
from pipecat.services.google import GoogleTTSService
from pipecat.services.openai import OpenAILLMService
@@ -66,18 +63,18 @@ async def main():
},
]
tma_in = LLMUserResponseAggregator(messages)
tma_out = LLMAssistantResponseAggregator(messages)
context = OpenAILLMContext(messages)
context_aggregator = llm.create_context_aggregator(context)
pipeline = Pipeline(
[
transport.input(), # Transport user input
stt, # STT
tma_in, # User responses
context_aggregator.user(), # User respones
llm, # LLM
tts, # TTS
transport.output(), # Transport bot output
tma_out, # Assistant spoken responses
context_aggregator.assistant(), # Assistant spoken responses
]
)

16
examples/foundational/10-wake-phrase.py
View File

@@ -10,14 +10,11 @@ import os
import sys
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.processors.filters.wake_check_filter import WakeCheckFilter
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.llm_response import (
LLMAssistantResponseAggregator,
LLMUserResponseAggregator,
)
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.processors.filters.wake_check_filter import WakeCheckFilter
from pipecat.services.cartesia import CartesiaTTSService
from pipecat.services.openai import OpenAILLMService
from pipecat.transports.services.daily import DailyParams, DailyTransport
@@ -65,18 +62,19 @@ async def main():
]
hey_robot_filter = WakeCheckFilter(["hey robot", "hey, robot"])
tma_in = LLMUserResponseAggregator(messages)
tma_out = LLMAssistantResponseAggregator(messages)
context = OpenAILLMContext(messages)
context_aggregator = llm.create_context_aggregator(context)
pipeline = Pipeline(
[
transport.input(), # Transport user input
hey_robot_filter, # Filter out speech not directed at the robot
tma_in, # User responses
context_aggregator.user(), # User responses
llm, # LLM
tts, # TTS
transport.output(), # Transport bot output
tma_out, # Assistant spoken responses
context_aggregator.assistant(), # Assistant spoken responses
]
)

13
examples/foundational/11-sound-effects.py
View File

@@ -20,10 +20,7 @@ from pipecat.frames.frames import (
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineTask
from pipecat.processors.aggregators.llm_response import (
LLMUserResponseAggregator,
LLMAssistantResponseAggregator,
)
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.processors.logger import FrameLogger
from pipecat.services.cartesia import CartesiaHttpTTSService
@@ -113,8 +110,8 @@ async def main():
},
]
tma_in = LLMUserResponseAggregator(messages)
tma_out = LLMAssistantResponseAggregator(messages)
context = OpenAILLMContext(messages)
context_aggregator = llm.create_context_aggregator(context)
out_sound = OutboundSoundEffectWrapper()
in_sound = InboundSoundEffectWrapper()
fl = FrameLogger("LLM Out")
@@ -123,7 +120,7 @@ async def main():
pipeline = Pipeline(
[
transport.input(),
tma_in,
context_aggregator.user(),
in_sound,
fl2,
llm,
@@ -131,7 +128,7 @@ async def main():
tts,
out_sound,
transport.output(),
tma_out,
context_aggregator.assistant(),
]
)

15
examples/foundational/16-gpu-container-local-bot.py
View File

@@ -18,10 +18,7 @@ from pipecat.frames.frames import LLMMessagesFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.llm_response import (
LLMAssistantResponseAggregator,
LLMUserResponseAggregator,
)
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.deepgram import DeepgramTTSService
from pipecat.services.openai import OpenAILLMService
from pipecat.transports.services.daily import (
@@ -75,17 +72,17 @@ async def main():
},
]
tma_in = LLMUserResponseAggregator(messages)
tma_out = LLMAssistantResponseAggregator(messages)
context = OpenAILLMContext(messages)
context_aggregator = llm.create_context_aggregator(context)
pipeline = Pipeline(
[
transport.input(), # Transport user input
tma_in, # User responses
context_aggregator.user(),
llm, # LLM
tts, # TTS
transport.output(), # Transport bot output
tma_out, # Assistant spoken responses
context_aggregator.assistant(),
]
)
@@ -123,7 +120,7 @@ async def main():
)
)
# And push to the pipeline for the Daily transport.output to send
await tma_in.push_frame(
await task.queue_frame(
DailyTransportMessageFrame(
message={"latency-pong-pipeline-delivery": {"ts": ts}},
participant_id=sender,

13
examples/foundational/17-detect-user-idle.py
View File

@@ -14,10 +14,7 @@ from pipecat.frames.frames import LLMMessagesFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.llm_response import (
LLMAssistantResponseAggregator,
LLMUserResponseAggregator,
)
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.processors.user_idle_processor import UserIdleProcessor
from pipecat.services.cartesia import CartesiaTTSService
from pipecat.services.openai import OpenAILLMService
@@ -65,8 +62,8 @@ async def main():
},
]
tma_in = LLMUserResponseAggregator(messages)
tma_out = LLMAssistantResponseAggregator(messages)
context = OpenAILLMContext(messages)
context_aggregator = llm.create_context_aggregator(context)
async def user_idle_callback(user_idle: UserIdleProcessor):
messages.append(
@@ -83,11 +80,11 @@ async def main():
[
transport.input(), # Transport user input
user_idle, # Idle user check-in
tma_in, # User responses
context_aggregator.user(),
llm, # LLM
tts, # TTS
transport.output(), # Transport bot output
tma_out, # Assistant spoken responses
context_aggregator.assistant(),
]
)

8
examples/moondream-chatbot/bot.py
View File

@@ -28,7 +28,7 @@ from pipecat.pipeline.parallel_pipeline import ParallelPipeline
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineTask
from pipecat.processors.aggregators.llm_response import LLMUserResponseAggregator
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.processors.aggregators.sentence import SentenceAggregator
from pipecat.processors.aggregators.vision_image_frame import VisionImageFrameAggregator
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
@@ -182,17 +182,19 @@ async def main():
},
]
ura = LLMUserResponseAggregator(messages)
context = OpenAILLMContext(messages)
context_aggregator = llm.create_context_aggregator(context)
pipeline = Pipeline(
[
transport.input(),
ura,
context_aggregator.user(),
llm,
ParallelPipeline([sa, ir, va, moondream], [tf, imgf]),
tts,
ta,
transport.output(),
context_aggregator.assistant(),
]
)

13
examples/simple-chatbot/bot.py
View File

@@ -15,10 +15,6 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.llm_response import (
LLMAssistantResponseAggregator,
LLMUserResponseAggregator,
)
from pipecat.frames.frames import (
OutputImageRawFrame,
SpriteFrame,
@@ -27,6 +23,7 @@ from pipecat.frames.frames import (
TTSAudioRawFrame,
TTSStoppedFrame,
)
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.services.elevenlabs import ElevenLabsTTSService
from pipecat.services.openai import OpenAILLMService
@@ -143,20 +140,20 @@ async def main():
},
]
user_response = LLMUserResponseAggregator()
assistant_response = LLMAssistantResponseAggregator()
context = OpenAILLMContext(messages)
context_aggregator = llm.create_context_aggregator(context)
ta = TalkingAnimation()
pipeline = Pipeline(
[
transport.input(),
user_response,
context_aggregator.user(),
llm,
tts,
ta,
transport.output(),
assistant_response,
context_aggregator.assistant(),
]
)

335
examples/storytelling-chatbot/frontend/package-lock.json generated
View File

@@ -299,9 +299,9 @@
}
},
"node_modules/@next/env": {
"version": "14.2.14",
"resolved": "https://registry.npmjs.org/@next/env/-/env-14.2.14.tgz",
"integrity": "sha512-/0hWQfiaD5//LvGNgc8PjvyqV50vGK0cADYzaoOOGN8fxzBn3iAiaq3S0tCRnFBldq0LVveLcxCTi41ZoYgAgg=="
"version": "14.2.15",
"resolved": "https://registry.npmjs.org/@next/env/-/env-14.2.15.tgz",
"integrity": "sha512-S1qaj25Wru2dUpcIZMjxeMVSwkt8BK4dmWHHiBuRstcIyOsMapqT4A4jSB6onvqeygkSSmOkyny9VVx8JIGamQ=="
},
"node_modules/@next/eslint-plugin-next": {
"version": "14.1.4",
@@ -313,9 +313,9 @@
}
},
"node_modules/@next/swc-darwin-arm64": {
"version": "14.2.14",
"resolved": "https://registry.npmjs.org/@next/swc-darwin-arm64/-/swc-darwin-arm64-14.2.14.tgz",
"integrity": "sha512-bsxbSAUodM1cjYeA4o6y7sp9wslvwjSkWw57t8DtC8Zig8aG8V6r+Yc05/9mDzLKcybb6EN85k1rJDnMKBd9Gw==",
"version": "14.2.15",
"resolved": "https://registry.npmjs.org/@next/swc-darwin-arm64/-/swc-darwin-arm64-14.2.15.tgz",
"integrity": "sha512-Rvh7KU9hOUBnZ9TJ28n2Oa7dD9cvDBKua9IKx7cfQQ0GoYUwg9ig31O2oMwH3wm+pE3IkAQ67ZobPfEgurPZIA==",
"cpu": [
"arm64"
],
@@ -328,9 +328,9 @@
}
},
"node_modules/@next/swc-darwin-x64": {
"version": "14.2.14",
"resolved": "https://registry.npmjs.org/@next/swc-darwin-x64/-/swc-darwin-x64-14.2.14.tgz",
"integrity": "sha512-cC9/I+0+SK5L1k9J8CInahduTVWGMXhQoXFeNvF0uNs3Bt1Ub0Azb8JzTU9vNCr0hnaMqiWu/Z0S1hfKc3+dww==",
"version": "14.2.15",
"resolved": "https://registry.npmjs.org/@next/swc-darwin-x64/-/swc-darwin-x64-14.2.15.tgz",
"integrity": "sha512-5TGyjFcf8ampZP3e+FyCax5zFVHi+Oe7sZyaKOngsqyaNEpOgkKB3sqmymkZfowy3ufGA/tUgDPPxpQx931lHg==",
"cpu": [
"x64"
],
@@ -343,9 +343,9 @@
}
},
"node_modules/@next/swc-linux-arm64-gnu": {
"version": "14.2.14",
"resolved": "https://registry.npmjs.org/@next/swc-linux-arm64-gnu/-/swc-linux-arm64-gnu-14.2.14.tgz",
"integrity": "sha512-RMLOdA2NU4O7w1PQ3Z9ft3PxD6Htl4uB2TJpocm+4jcllHySPkFaUIFacQ3Jekcg6w+LBaFvjSPthZHiPmiAUg==",
"version": "14.2.15",
"resolved": "https://registry.npmjs.org/@next/swc-linux-arm64-gnu/-/swc-linux-arm64-gnu-14.2.15.tgz",
"integrity": "sha512-3Bwv4oc08ONiQ3FiOLKT72Q+ndEMyLNsc/D3qnLMbtUYTQAmkx9E/JRu0DBpHxNddBmNT5hxz1mYBphJ3mfrrw==",
"cpu": [
"arm64"
],
@@ -358,9 +358,9 @@
}
},
"node_modules/@next/swc-linux-arm64-musl": {
"version": "14.2.14",
"resolved": "https://registry.npmjs.org/@next/swc-linux-arm64-musl/-/swc-linux-arm64-musl-14.2.14.tgz",
"integrity": "sha512-WgLOA4hT9EIP7jhlkPnvz49iSOMdZgDJVvbpb8WWzJv5wBD07M2wdJXLkDYIpZmCFfo/wPqFsFR4JS4V9KkQ2A==",
"version": "14.2.15",
"resolved": "https://registry.npmjs.org/@next/swc-linux-arm64-musl/-/swc-linux-arm64-musl-14.2.15.tgz",
"integrity": "sha512-k5xf/tg1FBv/M4CMd8S+JL3uV9BnnRmoe7F+GWC3DxkTCD9aewFRH1s5rJ1zkzDa+Do4zyN8qD0N8c84Hu96FQ==",
"cpu": [
"arm64"
],
@@ -373,9 +373,9 @@
}
},
"node_modules/@next/swc-linux-x64-gnu": {
"version": "14.2.14",
"resolved": "https://registry.npmjs.org/@next/swc-linux-x64-gnu/-/swc-linux-x64-gnu-14.2.14.tgz",
"integrity": "sha512-lbn7svjUps1kmCettV/R9oAvEW+eUI0lo0LJNFOXoQM5NGNxloAyFRNByYeZKL3+1bF5YE0h0irIJfzXBq9Y6w==",
"version": "14.2.15",
"resolved": "https://registry.npmjs.org/@next/swc-linux-x64-gnu/-/swc-linux-x64-gnu-14.2.15.tgz",
"integrity": "sha512-kE6q38hbrRbKEkkVn62reLXhThLRh6/TvgSP56GkFNhU22TbIrQDEMrO7j0IcQHcew2wfykq8lZyHFabz0oBrA==",
"cpu": [
"x64"
],
@@ -388,9 +388,9 @@
}
},
"node_modules/@next/swc-linux-x64-musl": {
"version": "14.2.14",
"resolved": "https://registry.npmjs.org/@next/swc-linux-x64-musl/-/swc-linux-x64-musl-14.2.14.tgz",
"integrity": "sha512-7TcQCvLQ/hKfQRgjxMN4TZ2BRB0P7HwrGAYL+p+m3u3XcKTraUFerVbV3jkNZNwDeQDa8zdxkKkw2els/S5onQ==",
"version": "14.2.15",
"resolved": "https://registry.npmjs.org/@next/swc-linux-x64-musl/-/swc-linux-x64-musl-14.2.15.tgz",
"integrity": "sha512-PZ5YE9ouy/IdO7QVJeIcyLn/Rc4ml9M2G4y3kCM9MNf1YKvFY4heg3pVa/jQbMro+tP6yc4G2o9LjAz1zxD7tQ==",
"cpu": [
"x64"
],
@@ -403,9 +403,9 @@
}
},
"node_modules/@next/swc-win32-arm64-msvc": {
"version": "14.2.14",
"resolved": "https://registry.npmjs.org/@next/swc-win32-arm64-msvc/-/swc-win32-arm64-msvc-14.2.14.tgz",
"integrity": "sha512-8i0Ou5XjTLEje0oj0JiI0Xo9L/93ghFtAUYZ24jARSeTMXLUx8yFIdhS55mTExq5Tj4/dC2fJuaT4e3ySvXU1A==",
"version": "14.2.15",
"resolved": "https://registry.npmjs.org/@next/swc-win32-arm64-msvc/-/swc-win32-arm64-msvc-14.2.15.tgz",
"integrity": "sha512-2raR16703kBvYEQD9HNLyb0/394yfqzmIeyp2nDzcPV4yPjqNUG3ohX6jX00WryXz6s1FXpVhsCo3i+g4RUX+g==",
"cpu": [
"arm64"
],
@@ -418,9 +418,9 @@
}
},
"node_modules/@next/swc-win32-ia32-msvc": {
"version": "14.2.14",
"resolved": "https://registry.npmjs.org/@next/swc-win32-ia32-msvc/-/swc-win32-ia32-msvc-14.2.14.tgz",
"integrity": "sha512-2u2XcSaDEOj+96eXpyjHjtVPLhkAFw2nlaz83EPeuK4obF+HmtDJHqgR1dZB7Gb6V/d55FL26/lYVd0TwMgcOQ==",
"version": "14.2.15",
"resolved": "https://registry.npmjs.org/@next/swc-win32-ia32-msvc/-/swc-win32-ia32-msvc-14.2.15.tgz",
"integrity": "sha512-fyTE8cklgkyR1p03kJa5zXEaZ9El+kDNM5A+66+8evQS5e/6v0Gk28LqA0Jet8gKSOyP+OTm/tJHzMlGdQerdQ==",
"cpu": [
"ia32"
],
@@ -433,9 +433,9 @@
}
},
"node_modules/@next/swc-win32-x64-msvc": {
"version": "14.2.14",
"resolved": "https://registry.npmjs.org/@next/swc-win32-x64-msvc/-/swc-win32-x64-msvc-14.2.14.tgz",
"integrity": "sha512-MZom+OvZ1NZxuRovKt1ApevjiUJTcU2PmdJKL66xUPaJeRywnbGGRWUlaAOwunD6dX+pm83vj979NTC8QXjGWg==",
"version": "14.2.15",
"resolved": "https://registry.npmjs.org/@next/swc-win32-x64-msvc/-/swc-win32-x64-msvc-14.2.15.tgz",
"integrity": "sha512-SzqGbsLsP9OwKNUG9nekShTwhj6JSB9ZLMWQ8g1gG6hdE5gQLncbnbymrwy2yVmH9nikSLYRYxYMFu78Ggp7/g==",
"cpu": [
"x64"
],
@@ -990,83 +990,83 @@
"dev": true
},
"node_modules/@sentry-internal/feedback": {
"version": "7.119.0",
"resolved": "https://registry.npmjs.org/@sentry-internal/feedback/-/feedback-7.119.0.tgz",
"integrity": "sha512-om8TkAU5CQGO8nkmr7qsSBVkP+/vfeS4JgtW3sjoTK0fhj26+DljR6RlfCGWtYQdPSP6XV7atcPTjbSnsmG9FQ==",
"version": "7.119.2",
"resolved": "https://registry.npmjs.org/@sentry-internal/feedback/-/feedback-7.119.2.tgz",
"integrity": "sha512-bnR1yJWVBZfXGx675nMXE8hCXsxluCBfIFy9GQT8PTN/urxpoS9cGz+5F7MA7Xe3Q06/7TT0Mz3fcDvjkqTu3Q==",
"dependencies": {
"@sentry/core": "7.119.0",
"@sentry/types": "7.119.0",
"@sentry/utils": "7.119.0"
"@sentry/core": "7.119.2",
"@sentry/types": "7.119.2",
"@sentry/utils": "7.119.2"
},
"engines": {
"node": ">=12"
}
},
"node_modules/@sentry-internal/replay-canvas": {
"version": "7.119.0",
"resolved": "https://registry.npmjs.org/@sentry-internal/replay-canvas/-/replay-canvas-7.119.0.tgz",
"integrity": "sha512-NL02VQx6ekPxtVRcsdp1bp5Tb5w6vnfBKSIfMKuDRBy5A10Uc3GSoy/c3mPyHjOxB84452A+xZSx6bliEzAnuA==",
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"resolved": "https://registry.npmjs.org/picocolors/-/picocolors-1.1.0.tgz",
"integrity": "sha512-TQ92mBOW0l3LeMeyLV6mzy/kWr8lkd/hp3mTg7wYK7zJhuBStmGMBG0BdeDZS/dZx1IukaX6Bk11zcln25o1Aw=="
"version": "1.1.1",
"resolved": "https://registry.npmjs.org/picocolors/-/picocolors-1.1.1.tgz",
"integrity": "sha512-xceH2snhtb5M9liqDsmEw56le376mTZkEX/jEb/RxNFyegNul7eNslCXP9FDj/Lcu0X8KEyMceP2ntpaHrDEVA=="
},
"node_modules/picomatch": {
"version": "2.3.1",
@@ -4817,15 +4822,15 @@
"integrity": "sha512-dYnhHh0nJoMfnkZs6GmmhFknAGRrLznOu5nc9ML+EJxGvrx6H7teuevqVqCuPcPK//3eDrrjQhehXVx9cnkGdw=="
},
"node_modules/regexp.prototype.flags": {
"version": "1.5.2",
"resolved": "https://registry.npmjs.org/regexp.prototype.flags/-/regexp.prototype.flags-1.5.2.tgz",
"integrity": "sha512-NcDiDkTLuPR+++OCKB0nWafEmhg/Da8aUPLPMQbK+bxKKCm1/S5he+AqYa4PlMCVBalb4/yxIRub6qkEx5yJbw==",
"version": "1.5.3",
"resolved": "https://registry.npmjs.org/regexp.prototype.flags/-/regexp.prototype.flags-1.5.3.tgz",
"integrity": "sha512-vqlC04+RQoFalODCbCumG2xIOvapzVMHwsyIGM/SIE8fRhFFsXeH8/QQ+s0T0kDAhKc4k30s73/0ydkHQz6HlQ==",
"dev": true,
"dependencies": {
"call-bind": "^1.0.6",
"call-bind": "^1.0.7",
"define-properties": "^1.2.1",
"es-errors": "^1.3.0",
"set-function-name": "^2.0.1"
"set-function-name": "^2.0.2"
},
"engines": {
"node": ">= 0.4"
@@ -5169,13 +5174,17 @@
}
},
"node_modules/string.prototype.includes": {
"version": "2.0.0",
"resolved": "https://registry.npmjs.org/string.prototype.includes/-/string.prototype.includes-2.0.0.tgz",
"integrity": "sha512-E34CkBgyeqNDcrbU76cDjL5JLcVrtSdYq0MEh/B10r17pRP4ciHLwTgnuLV8Ay6cgEMLkcBkFCKyFZ43YldYzg==",
"version": "2.0.1",
"resolved": "https://registry.npmjs.org/string.prototype.includes/-/string.prototype.includes-2.0.1.tgz",
"integrity": "sha512-o7+c9bW6zpAdJHTtujeePODAhkuicdAryFsfVKwA+wGw89wJ4GTY484WTucM9hLtDEOpOvI+aHnzqnC5lHp4Rg==",
"dev": true,
"dependencies": {
"define-properties": "^1.1.3",
"es-abstract": "^1.17.5"
"call-bind": "^1.0.7",
"define-properties": "^1.2.1",
"es-abstract": "^1.23.3"
},
"engines": {
"node": ">= 0.4"
}
},
"node_modules/string.prototype.matchall": {
@@ -5374,18 +5383,18 @@
}
},
"node_modules/tailwind-merge": {
"version": "2.5.2",
"resolved": "https://registry.npmjs.org/tailwind-merge/-/tailwind-merge-2.5.2.tgz",
"integrity": "sha512-kjEBm+pvD+6eAwzJL2Bi+02/9LFLal1Gs61+QB7HvTfQQ0aXwC5LGT8PEt1gS0CWKktKe6ysPTAy3cBC5MeiIg==",
"version": "2.5.4",
"resolved": "https://registry.npmjs.org/tailwind-merge/-/tailwind-merge-2.5.4.tgz",
"integrity": "sha512-0q8cfZHMu9nuYP/b5Shb7Y7Sh1B7Nnl5GqNr1U+n2p6+mybvRtayrQ+0042Z5byvTA8ihjlP8Odo8/VnHbZu4Q==",
"funding": {
"type": "github",
"url": "https://github.com/sponsors/dcastil"
}
},
"node_modules/tailwindcss": {
"version": "3.4.13",
"resolved": "https://registry.npmjs.org/tailwindcss/-/tailwindcss-3.4.13.tgz",
"integrity": "sha512-KqjHOJKogOUt5Bs752ykCeiwvi0fKVkr5oqsFNt/8px/tA8scFPIlkygsf6jXrfCqGHz7VflA6+yytWuM+XhFw==",
"version": "3.4.14",
"resolved": "https://registry.npmjs.org/tailwindcss/-/tailwindcss-3.4.14.tgz",
"integrity": "sha512-IcSvOcTRcUtQQ7ILQL5quRDg7Xs93PdJEk1ZLbhhvJc7uj/OAhYOnruEiwnGgBvUtaUAJ8/mhSw1o8L2jCiENA==",
"dependencies": {
"@alloc/quick-lru": "^5.2.0",
"arg": "^5.0.2",
@@ -5501,9 +5510,9 @@
}
},
"node_modules/tslib": {
"version": "2.7.0",
"resolved": "https://registry.npmjs.org/tslib/-/tslib-2.7.0.tgz",
"integrity": "sha512-gLXCKdN1/j47AiHiOkJN69hJmcbGTHI0ImLmbYLHykhgeN0jVGola9yVjFgzCUklsZQMW55o+dW7IXv3RCXDzA=="
"version": "2.8.0",
"resolved": "https://registry.npmjs.org/tslib/-/tslib-2.8.0.tgz",
"integrity": "sha512-jWVzBLplnCmoaTr13V9dYbiQ99wvZRd0vNWaDRg+aVYRcjDF3nDksxFDE/+fkXnKhpnUUkmx5pK/v8mCtLVqZA=="
},
"node_modules/type-check": {
"version": "0.4.0",
@@ -5603,9 +5612,9 @@
}
},
"node_modules/typescript": {
"version": "5.6.2",
"resolved": "https://registry.npmjs.org/typescript/-/typescript-5.6.2.tgz",
"integrity": "sha512-NW8ByodCSNCwZeghjN3o+JX5OFH0Ojg6sadjEKY4huZ52TqbJTJnDo5+Tw98lSy63NZvi4n+ez5m2u5d4PkZyw==",
"version": "5.6.3",
"resolved": "https://registry.npmjs.org/typescript/-/typescript-5.6.3.tgz",
"integrity": "sha512-hjcS1mhfuyi4WW8IWtjP7brDrG2cuDZukyrYrSauoXGNgx0S7zceP07adYkJycEr56BOUTNPzbInooiN3fn1qw==",
"dev": true,
"bin": {
"tsc": "bin/tsc",
@@ -5917,9 +5926,9 @@
"dev": true
},
"node_modules/yaml": {
"version": "2.5.1",
"resolved": "https://registry.npmjs.org/yaml/-/yaml-2.5.1.tgz",
"integrity": "sha512-bLQOjaX/ADgQ20isPJRvF0iRUHIxVhYvr53Of7wGcWlO2jvtUlH5m87DsmulFVxRpNLOnI4tB6p/oh8D7kpn9Q==",
"version": "2.6.0",
"resolved": "https://registry.npmjs.org/yaml/-/yaml-2.6.0.tgz",
"integrity": "sha512-a6ae//JvKDEra2kdi1qzCyrJW/WZCgFi8ydDV+eXExl95t+5R+ijnqHJbz9tmMh8FUjx3iv2fCQ4dclAQlO2UQ==",
"bin": {
"yaml": "bin.mjs"
},

2
examples/storytelling-chatbot/frontend/package.json
View File

@@ -17,7 +17,7 @@
"class-variance-authority": "^0.7.0",
"clsx": "^2.1.1",
"framer-motion": "^11.9.0",
"next": "^14.2.14",
"next": "^14.2.15",
"react": "^18.3.1",
"react-dom": "^18.3.1",
"recoil": "^0.7.7",

13
examples/storytelling-chatbot/src/bot.py
View File

@@ -14,10 +14,7 @@ from pipecat.frames.frames import EndFrame, LLMMessagesFrame, StopTaskFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineTask
from pipecat.processors.aggregators.llm_response import (
LLMAssistantResponseAggregator,
LLMUserResponseAggregator,
)
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.elevenlabs import ElevenLabsTTSService
from pipecat.services.fal import FalImageGenService
from pipecat.services.openai import OpenAILLMService
@@ -82,8 +79,8 @@ async def main(room_url, token=None):
story_pages = []
# We need aggregators to keep track of user and LLM responses
llm_responses = LLMAssistantResponseAggregator(message_history)
user_responses = LLMUserResponseAggregator(message_history)
context = OpenAILLMContext(message_history)
context_aggregator = llm_service.create_context_aggregator(context)
# -------------- Processors ------------- #
@@ -126,13 +123,13 @@ async def main(room_url, token=None):
main_pipeline = Pipeline(
[
transport.input(),
user_responses,
context_aggregator.user(),
llm_service,
story_processor,
image_processor,
tts_service,
transport.output(),
llm_responses,
context_aggregator.assistant(),
]
)

13
examples/studypal/studypal.py
View File

@@ -13,10 +13,7 @@ from pipecat.frames.frames import LLMMessagesFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.llm_response import (
LLMAssistantResponseAggregator,
LLMUserResponseAggregator,
)
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.cartesia import CartesiaTTSService
from pipecat.services.openai import OpenAILLMService
from pipecat.transports.services.daily import DailyParams, DailyTransport
@@ -150,17 +147,17 @@ Your task is to help the user understand and learn from this article in 2 senten
},
]
tma_in = LLMUserResponseAggregator(messages)
tma_out = LLMAssistantResponseAggregator(messages)
context = OpenAILLMContext(messages)
context_aggregator = llm.create_context_aggregator(context)
pipeline = Pipeline(
[
transport.input(),
tma_in,
context_aggregator.user(),
llm,
tts,
transport.output(),
tma_out,
context_aggregator.assistant(),
]
)

13
examples/twilio-chatbot/bot.py
View File

@@ -6,10 +6,7 @@ from pipecat.frames.frames import EndFrame, LLMMessagesFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.llm_response import (
LLMAssistantResponseAggregator,
LLMUserResponseAggregator,
)
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.cartesia import CartesiaTTSService
from pipecat.services.openai import OpenAILLMService
from pipecat.services.deepgram import DeepgramSTTService
@@ -58,18 +55,18 @@ async def run_bot(websocket_client, stream_sid):
},
]
tma_in = LLMUserResponseAggregator(messages)
tma_out = LLMAssistantResponseAggregator(messages)
context = OpenAILLMContext(messages)
context_aggregator = llm.create_context_aggregator(context)
pipeline = Pipeline(
[
transport.input(), # Websocket input from client
stt, # Speech-To-Text
tma_in, # User responses
context_aggregator.user(),
llm, # LLM
tts, # Text-To-Speech
transport.output(), # Websocket output to client
tma_out, # LLM responses
context_aggregator.assistant(),
]
)

13
examples/websocket-server/bot.py
View File

@@ -13,10 +13,7 @@ from pipecat.frames.frames import LLMMessagesFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineTask
from pipecat.processors.aggregators.llm_response import (
LLMAssistantResponseAggregator,
LLMUserResponseAggregator,
)
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.cartesia import CartesiaTTSService
from pipecat.services.deepgram import DeepgramSTTService
from pipecat.services.openai import OpenAILLMService
@@ -62,18 +59,18 @@ async def main():
},
]
tma_in = LLMUserResponseAggregator(messages)
tma_out = LLMAssistantResponseAggregator(messages)
context = OpenAILLMContext(messages)
context_aggregator = llm.create_context_aggregator(context)
pipeline = Pipeline(
[
transport.input(), # Websocket input from client
stt, # Speech-To-Text
tma_in, # User responses
context_aggregator.user(),
llm, # LLM
tts, # Text-To-Speech
transport.output(), # Websocket output to client
tma_out, # LLM responses
context_aggregator.assistant(),
]
)

2
src/pipecat/processors/frameworks/langchain.py
View File

@@ -39,7 +39,7 @@ class LangchainProcessor(FrameProcessor):
await super().process_frame(frame, direction)
if isinstance(frame, LLMMessagesFrame):
# Messages are accumulated by the `LLMUserResponseAggregator` in a list of messages.
# Messages are accumulated on the context as a list of messages.
# The last one by the human is the one we want to send to the LLM.
logger.debug(f"Got transcription frame {frame}")
text: str = frame.messages[-1]["content"]