diff --git a/CHANGELOG.md b/CHANGELOG.md index e47f1010a..e47c2560e 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -9,10 +9,97 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ### Added +- Added support in `DailyTransport` for updating remote participants' + `canReceive` permission via the `update_remote_participants()` method, by + bumping the daily-python dependency to >= 0.16.0. + +- ElevenLabs TTS services now support a sample rate of 8000. + +### Fixed + +- Fixed a `GoogleAssistantContextAggregator` issue where function calls + placeholders where not being updated when then function call result was + different from a string. + +- Fixed an issue that would cause `LLMAssistantContextAggregator` to block + processing more frames while processing a function call result. + +- Fixed an issue where the `RTVIObserver` would report two bot started and + stopped speaking events for each bot turn. + +- Fixed an issue in `UltravoxSTTService` that caused improper audio processing + and incorrect LLM frame output. + +### Other + +- Added `examples/foundational/07x-interruptible-local.py` to show how a local + transport can be used. + +## [0.0.60] - 2025-03-20 + +### Added + +- Added `default_headers` parameter to `BaseOpenAILLMService` constructor. + +### Changed + +- Rollback to `deepgram-sdk` 3.8.0 since 3.10.1 was causing connections issues. + +- Changed the default `InputAudioTranscription` model to `gpt-4o-transcribe` + for `OpenAIRealtimeBetaLLMService`. + +### Other + +- Update the `19-openai-realtime-beta.py` and `19a-azure-realtime-beta.py` + examples to use the FunctionSchema format. + +## [0.0.59] - 2025-03-20 + +### Added + +- When registering a function call it is now possible to indicate if you want + the function call to be cancelled if there's a user interruption via + `cancel_on_interruption` (defaults to False). This is now possible because + function calls are executed concurrently. + +- Added support for detecting idle pipelines. By default, if no activity has + been detected during 5 minutes, the `PipelineTask` will be automatically + cancelled. It is possible to override this behavior by passing + `cancel_on_idle_timeout=False`. It is also possible to change the default + timeout with `idle_timeout_secs` or the frames that prevent the pipeline from + being idle with `idle_timeout_frames`. Finally, an `on_idle_timeout` event + handler will be triggered if the idle timeout is reached (whether the pipeline + task is cancelled or not). + +- Added `FalSTTService`, which provides STT for Fal's Wizper API. + +- Added a `reconnect_on_error` parameter to websocket-based TTS services as well + as a `on_connection_error` event handler. The `reconnect_on_error` indicates + whether the TTS service should reconnect on error. The `on_connection_error` + will always get called if there's any error no matter the value of + `reconnect_on_error`. This allows, for example, to fallback to a different TTS + provider if something goes wrong with the current one. + +- Added new `SkipTagsAggregator` that extends `BaseTextAggregator` to aggregate + text and skips end of sentence matching if aggregated text is between + start/end tags. + +- Added new `PatternPairAggregator` that extends `BaseTextAggregator` to + identify content between matching pattern pairs in streamed text. This allows + for detection and processing of structured content like XML-style tags that + may span across multiple text chunks or sentence boundaries. + - Added new `BaseTextAggregator`. Text aggregators are used by the TTS service to aggregate LLM tokens and decide when the aggregated text should be pushed - to the TTS service. It also allows for the text to be manipulated while it's - being aggregated. + to the TTS service. They also allow for the text to be manipulated while it's + being aggregated. A text aggregator can be passed via `text_aggregator` to the + TTS service. + +- Added new `sample_rate` constructor parameter to `TavusVideoService` to allow + changing the output sample rate. + +- Added new `NeuphonicTTSService`. + (see https://neuphonic.com) - Added new `UltravoxSTTService`. (see https://github.com/fixie-ai/ultravox) @@ -88,11 +175,86 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 - Added `AzureRealtimeBetaLLMService` to support Azure's OpeanAI Realtime API. Added foundational example `19a-azure-realtime-beta.py`. +- Introduced `GoogleVertexLLMService`, a new class for integrating with Vertex AI + Gemini models. Added foundational example + `14p-function-calling-gemini-vertex-ai.py`. + +- Added support in `OpenAIRealtimeBetaLLMService` for a slate of new features: + + - The `'gpt-4o-transcribe'` input audio transcription model, along + with new `language` and `prompt` options specific to that model. + - The `input_audio_noise_reduction` session property. + + ```python + session_properties = SessionProperties( + # ... + input_audio_noise_reduction=InputAudioNoiseReduction( + type="near_field" # also supported: "far_field" + ) + # ... + ) + ``` + + - The `'semantic_vad'` `turn_detection` session property value, a more + sophisticated model for detecting when the user has stopped speaking. + - `on_conversation_item_created` and `on_conversation_item_updated` + events to `OpenAIRealtimeBetaLLMService`. + + ```python + @llm.event_handler("on_conversation_item_created") + async def on_conversation_item_created(llm, item_id, item): + # ... + + @llm.event_handler("on_conversation_item_updated") + async def on_conversation_item_updated(llm, item_id, item): + # `item` may not always be available here + # ... + ``` + + - The `retrieve_conversation_item(item_id)` method for introspecting a + conversation item on the server. + + ```python + item = await llm.retrieve_conversation_item(item_id) + ``` + ### Changed +- Updated `OpenAISTTService` to use `gpt-4o-transcribe` as the default + transcription model. + +- Updated `OpenAITTSService` to use `gpt-4o-mini-tts` as the default TTS model. + +- Function calls are now executed in tasks. This means that the pipeline will + not be blocked while the function call is being executed. + +- ⚠️ `PipelineTask` will now be automatically cancelled if no bot activity is + happening in the pipeline. There are a few settings to configure this + behavior, see `PipelineTask` documentation for more details. + +- All event handlers are now executed in separate tasks in order to prevent + blocking the pipeline. It is possible that event handlers take some time to + execute in which case the pipeline would be blocked waiting for the event + handler to complete. + +- Updated `TranscriptProcessor` to support text output from + `OpenAIRealtimeBetaLLMService`. + +- `OpenAIRealtimeBetaLLMService` and `GeminiMultimodalLiveLLMService` now push + a `TTSTextFrame`. + - Updated the default mode for `CartesiaTTSService` and `CartesiaHttpTTSService` to `sonic-2`. +### Deprecated + +- Passing a `start_callback` to `LLMService.register_function()` is now + deprecated, simply move the code from the start callback to the function call. + +- `TTSService` parameter `text_filter` is now deprecated, use `text_filters` + instead which is now a list. This allows passing multiple filters that will be + executed in order. + ### Removed - Removed deprecated `audio.resample_audio()`, use `create_default_resampler()` @@ -111,13 +273,78 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ### Fixed +- Fixed an assistant aggregator issue that could cause assistant text to be + split into multiple chunks during function calls. + +- Fixed an assistant aggregator issue that was causing assistant text to not be + added to the context during function calls. This could lead to duplications. + +- Fixed a `SegmentedSTTService` issue that was causing audio to be sent + prematurely to the STT service. Instead of analyzing the volume in this + service we rely on VAD events which use both VAD and volume. + +- Fixed a `GeminiMultimodalLiveLLMService` issue that was causing messages to be + duplicated in the context when pushing `LLMMessagesAppendFrame` frames. + +- Fixed an issue with `SegmentedSTTService` based services + (e.g. `GroqSTTService`) that was not allow audio to pass-through downstream. + +- Fixed a `CartesiaTTSService` and `RimeTTSService` issue that would consider + text between spelling out tags end of sentence. + +- Fixed a `match_endofsentence` issue that would result in floating point + numbers to be considered an end of sentence. + +- Fixed a `match_endofsentence` issue that would result in emails to be + considered an end of sentence. + +- Fixed an issue where the RTVI message `disconnect-bot` was pushing an + `EndFrame`, resulting in the pipeline not shutting down. It now pushes an + `EndTaskFrame` upstream to shutdown the pipeline. + +- Fixed an issue with the `GoogleSTTService` where stream timeouts during + periods of inactivity were causing connection failures. The service now + properly detects timeout errors and handles reconnection gracefully, + ensuring continuous operation even after periods of silence or when using an + `STTMuteFilter`. + - Fixed an issue in `RimeTTSService` where the last line of text sent didn't result in an audio output being generated. +- Fixed `OpenAIRealtimeBetaLLMService` by adding proper handling for: + - The `conversation.item.input_audio_transcription.delta` server message, + which was added server-side at some point and not handled client-side. + - Errors reported by the `response.done` server message. + ### Other +- Add foundational example `07w-interruptible-fal.py`, showing `FalSTTService`. + +- Added a new Ultravox example + `examples/foundational/07u-interruptible-ultravox.py`. + +- Added new Neuphonic examples + `examples/foundational/07v-interruptible-neuphonic.py` and + `examples/foundational/07v-interruptible-neuphonic-http.py`. + +- Added a new example `examples/foundational/36-user-email-gathering.py` to show + how to gather user emails. The example uses's Cartesia's `` + tags and Rime `spell()` function to spell out the emails for confirmation. + +- Update the `34-audio-recording.py` example to include an STT processor. + +- Added foundational example `35-voice-switching.py` showing how to use the new + `PatternPairAggregator`. This example shows how to encode information for the + LLM to instruct TTS voice changes, but this can be used to encode any + information into the LLM response, which you want to parse and use in other + parts of your application. + - Added a Pipecat Cloud deployment example to the `examples` directory. +- Removed foundational examples 28b and 28c as the TranscriptProcessor no + longer has an LLM depedency. Renamed foundational example 28a to + `28-transcript-processor.py`. + ## [0.0.58] - 2025-02-26 ### Added @@ -198,6 +425,9 @@ stt = DeepgramSTTService(..., live_options=LiveOptions(model="nova-2-general")) ### Fixed +- Fixed an issue that would cause undesired interruptions via + `EmulateUserStartedSpeakingFrame`. + - Fixed a `GoogleLLMService` that was causing an exception when sending inline audio in some cases. @@ -214,10 +444,6 @@ stt = DeepgramSTTService(..., live_options=LiveOptions(model="nova-2-general")) - Fixed `match_endofsentence` support for ellipses. -- Fixed an issue that would cause undesired interruptions via - `EmulateUserStartedSpeakingFrame` when only interim transcriptions (i.e. no - final transcriptions) where received. - - Fixed an issue where `EndTaskFrame` was not triggering `on_client_disconnected` or closing the WebSocket in FastAPI. @@ -1882,7 +2108,7 @@ async def on_connected(processor): completed. If a task is never ran `has_finished()` will return False. - `PipelineRunner` now supports SIGTERM. If received, the runner will be - canceled. + cancelled. ### Fixed diff --git a/README.md b/README.md index 822c75aee..f28005618 100644 --- a/README.md +++ b/README.md @@ -57,13 +57,13 @@ pip install "pipecat-ai[option,...]" | Category | Services | Install Command Example | | ------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------- | -| Speech-to-Text | [AssemblyAI](https://docs.pipecat.ai/server/services/stt/assemblyai), [Azure](https://docs.pipecat.ai/server/services/stt/azure), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [Gladia](https://docs.pipecat.ai/server/services/stt/gladia), [Google](https://docs.pipecat.ai/server/services/stt/google), [Groq (Whisper)](https://docs.pipecat.ai/server/services/stt/groq), [OpenAI (Whisper)](https://docs.pipecat.ai/server/services/stt/openai), [Parakeet (NVIDIA)](https://docs.pipecat.ai/server/services/stt/parakeet), [Ultravox](https://docs.pipecat.ai/server/services/stt/ultravox), [Whisper](https://docs.pipecat.ai/server/services/stt/whisper) | `pip install "pipecat-ai[deepgram]"` | +| Speech-to-Text | [AssemblyAI](https://docs.pipecat.ai/server/services/stt/assemblyai), [Azure](https://docs.pipecat.ai/server/services/stt/azure), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [Fal Wizper](https://docs.pipecat.ai/server/services/stt/fal), [Gladia](https://docs.pipecat.ai/server/services/stt/gladia), [Google](https://docs.pipecat.ai/server/services/stt/google), [Groq (Whisper)](https://docs.pipecat.ai/server/services/stt/groq), [OpenAI (Whisper)](https://docs.pipecat.ai/server/services/stt/openai), [Parakeet (NVIDIA)](https://docs.pipecat.ai/server/services/stt/parakeet), [Ultravox](https://docs.pipecat.ai/server/services/stt/ultravox), [Whisper](https://docs.pipecat.ai/server/services/stt/whisper) | `pip install "pipecat-ai[deepgram]"` | | LLMs | [Anthropic](https://docs.pipecat.ai/server/services/llm/anthropic), [Azure](https://docs.pipecat.ai/server/services/llm/azure), [Cerebras](https://docs.pipecat.ai/server/services/llm/cerebras), [DeepSeek](https://docs.pipecat.ai/server/services/llm/deepseek), [Fireworks AI](https://docs.pipecat.ai/server/services/llm/fireworks), [Gemini](https://docs.pipecat.ai/server/services/llm/gemini), [Grok](https://docs.pipecat.ai/server/services/llm/grok), [Groq](https://docs.pipecat.ai/server/services/llm/groq), [NVIDIA NIM](https://docs.pipecat.ai/server/services/llm/nim), [Ollama](https://docs.pipecat.ai/server/services/llm/ollama), [OpenAI](https://docs.pipecat.ai/server/services/llm/openai), [OpenRouter](https://docs.pipecat.ai/server/services/llm/openrouter), [Perplexity](https://docs.pipecat.ai/server/services/llm/perplexity), [Together AI](https://docs.pipecat.ai/server/services/llm/together) | `pip install "pipecat-ai[openai]"` | -| Text-to-Speech | [AWS](https://docs.pipecat.ai/server/services/tts/aws), [Azure](https://docs.pipecat.ai/server/services/tts/azure), [Cartesia](https://docs.pipecat.ai/server/services/tts/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/tts/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/tts/elevenlabs), [FastPitch (NVIDIA)](https://docs.pipecat.ai/server/services/tts/fastpitch), [Fish](https://docs.pipecat.ai/server/services/tts/fish), [Google](https://docs.pipecat.ai/server/services/tts/google), [LMNT](https://docs.pipecat.ai/server/services/tts/lmnt), [OpenAI](https://docs.pipecat.ai/server/services/tts/openai), [PlayHT](https://docs.pipecat.ai/server/services/tts/playht), [Rime](https://docs.pipecat.ai/server/services/tts/rime), [XTTS](https://docs.pipecat.ai/server/services/tts/xtts) | `pip install "pipecat-ai[cartesia]"` | +| Text-to-Speech | [AWS](https://docs.pipecat.ai/server/services/tts/aws), [Azure](https://docs.pipecat.ai/server/services/tts/azure), [Cartesia](https://docs.pipecat.ai/server/services/tts/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/tts/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/tts/elevenlabs), [FastPitch (NVIDIA)](https://docs.pipecat.ai/server/services/tts/fastpitch), [Fish](https://docs.pipecat.ai/server/services/tts/fish), [Google](https://docs.pipecat.ai/server/services/tts/google), [LMNT](https://docs.pipecat.ai/server/services/tts/lmnt), [Neuphonic](https://docs.pipecat.ai/server/services/tts/neuphonic), [OpenAI](https://docs.pipecat.ai/server/services/tts/openai), [PlayHT](https://docs.pipecat.ai/server/services/tts/playht), [Rime](https://docs.pipecat.ai/server/services/tts/rime), [XTTS](https://docs.pipecat.ai/server/services/tts/xtts) | `pip install "pipecat-ai[cartesia]"` | | Speech-to-Speech | [Gemini Multimodal Live](https://docs.pipecat.ai/server/services/s2s/gemini), [OpenAI Realtime](https://docs.pipecat.ai/server/services/s2s/openai) | `pip install "pipecat-ai[google]"` | | Transport | [Daily (WebRTC)](https://docs.pipecat.ai/server/services/transport/daily), [FastAPI Websocket](https://docs.pipecat.ai/server/services/transport/fastapi-websocket), [WebSocket Server](https://docs.pipecat.ai/server/services/transport/websocket-server), Local | `pip install "pipecat-ai[daily]"` | | Video | [Tavus](https://docs.pipecat.ai/server/services/video/tavus), [Simli](https://docs.pipecat.ai/server/services/video/simli) | `pip install "pipecat-ai[tavus,simli]"` | -| Vision & Image | [Moondream](https://docs.pipecat.ai/server/services/vision/moondream), [fal](https://docs.pipecat.ai/server/services/image-generation/fal) | `pip install "pipecat-ai[moondream]"` | +| Vision & Image | [fal](https://docs.pipecat.ai/server/services/image-generation/fal), [Google Imagen](https://docs.pipecat.ai/server/services/image-generation/fal), [Moondream](https://docs.pipecat.ai/server/services/vision/moondream) | `pip install "pipecat-ai[moondream]"` | | Audio Processing | [Silero VAD](https://docs.pipecat.ai/server/utilities/audio/silero-vad-analyzer), [Krisp](https://docs.pipecat.ai/server/utilities/audio/krisp-filter), [Koala](https://docs.pipecat.ai/server/utilities/audio/koala-filter), [Noisereduce](https://docs.pipecat.ai/server/utilities/audio/noisereduce-filter) | `pip install "pipecat-ai[silero]"` | | Analytics & Metrics | [Canonical AI](https://docs.pipecat.ai/server/services/analytics/canonical), [Sentry](https://docs.pipecat.ai/server/services/analytics/sentry) | `pip install "pipecat-ai[canonical]"` | diff --git a/dev-requirements.txt b/dev-requirements.txt index 2b55adcbe..e65c2755c 100644 --- a/dev-requirements.txt +++ b/dev-requirements.txt @@ -3,10 +3,10 @@ coverage~=7.6.12 grpcio-tools~=1.67.1 pip-tools~=7.4.1 pre-commit~=4.0.1 -pyright~=1.1.394 +pyright~=1.1.397 pytest~=8.3.4 pytest-asyncio~=0.25.3 -ruff~=0.9.7 +ruff~=0.11.1 setuptools~=70.0.0 setuptools_scm~=8.1.0 python-dotenv~=1.0.1 diff --git a/examples/foundational/07g-interruptible-openai.py b/examples/foundational/07g-interruptible-openai.py index f9cac5910..b66d0346a 100644 --- a/examples/foundational/07g-interruptible-openai.py +++ b/examples/foundational/07g-interruptible-openai.py @@ -51,16 +51,20 @@ async def main(): # api_key="gsk_***", # model="whisper-large-v3", # ) - stt = OpenAISTTService(api_key=os.getenv("OPENAI_API_KEY"), model="whisper-1") + stt = OpenAISTTService( + api_key=os.getenv("OPENAI_API_KEY"), + model="gpt-4o-transcribe-latest", + prompt="Expect words related to dogs, such as breed names.", + ) - tts = OpenAITTSService(api_key=os.getenv("OPENAI_API_KEY"), voice="alloy") + tts = OpenAITTSService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o-mini-tts-latest") llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o") 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 converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.", + "content": "You are very knowledgable about dogs. 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.", }, ] diff --git a/examples/foundational/07u-interruptible-ultravox.py b/examples/foundational/07u-interruptible-ultravox.py index 3ae4540f0..429e5a9fb 100644 --- a/examples/foundational/07u-interruptible-ultravox.py +++ b/examples/foundational/07u-interruptible-ultravox.py @@ -14,6 +14,7 @@ from loguru import logger from runner import configure from pipecat.audio.vad.silero import SileroVADAnalyzer +from pipecat.audio.vad.vad_analyzer import VADParams from pipecat.pipeline.pipeline import Pipeline from pipecat.pipeline.runner import PipelineRunner from pipecat.pipeline.task import PipelineParams, PipelineTask diff --git a/examples/foundational/07u-interruptible-neuphonic-http.py b/examples/foundational/07v-interruptible-neuphonic-http.py similarity index 100% rename from examples/foundational/07u-interruptible-neuphonic-http.py rename to examples/foundational/07v-interruptible-neuphonic-http.py diff --git a/examples/foundational/07u-interruptible-neuphonic.py b/examples/foundational/07v-interruptible-neuphonic.py similarity index 100% rename from examples/foundational/07u-interruptible-neuphonic.py rename to examples/foundational/07v-interruptible-neuphonic.py diff --git a/examples/foundational/07w-interruptible-fal.py b/examples/foundational/07w-interruptible-fal.py new file mode 100644 index 000000000..526602166 --- /dev/null +++ b/examples/foundational/07w-interruptible-fal.py @@ -0,0 +1,110 @@ +# +# Copyright (c) 2024–2025, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +import asyncio +import os +import sys + +import aiohttp +from dotenv import load_dotenv +from loguru import logger +from runner import configure + +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.openai_llm_context import OpenAILLMContext +from pipecat.services.cartesia import CartesiaTTSService +from pipecat.services.fal import FalSTTService +from pipecat.services.gladia import GladiaSTTService +from pipecat.services.openai import OpenAILLMService +from pipecat.transports.services.daily import DailyParams, DailyTransport + +load_dotenv(override=True) + +logger.remove(0) +logger.add(sys.stderr, level="DEBUG") + + +async def main(): + async with aiohttp.ClientSession() as session: + (room_url, token) = await configure(session) + + transport = DailyTransport( + room_url, + token, + "Respond bot", + DailyParams( + audio_out_enabled=True, + vad_enabled=True, + vad_analyzer=SileroVADAnalyzer(), + vad_audio_passthrough=True, + ), + ) + + stt = FalSTTService( + api_key=os.getenv("FAL_KEY"), + ) + + tts = CartesiaTTSService( + api_key=os.getenv("CARTESIA_API_KEY"), + voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady + ) + + llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o") + + 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 converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.", + }, + ] + + context = OpenAILLMContext(messages) + context_aggregator = llm.create_context_aggregator(context) + + pipeline = Pipeline( + [ + transport.input(), # Transport user input + stt, # STT + context_aggregator.user(), # User responses + llm, # LLM + tts, # TTS + transport.output(), # Transport bot output + context_aggregator.assistant(), # Assistant spoken responses + ] + ) + + task = PipelineTask( + pipeline, + params=PipelineParams( + allow_interruptions=True, + enable_metrics=True, + enable_usage_metrics=True, + report_only_initial_ttfb=True, + ), + ) + + @transport.event_handler("on_first_participant_joined") + async def on_first_participant_joined(transport, participant): + await transport.capture_participant_transcription(participant["id"]) + # Kick off the conversation. + messages.append({"role": "system", "content": "Please introduce yourself to the user."}) + await task.queue_frames([context_aggregator.user().get_context_frame()]) + + # Register an event handler to exit the application when the user leaves. + @transport.event_handler("on_participant_left") + async def on_participant_left(transport, participant, reason): + await task.cancel() + + runner = PipelineRunner() + + await runner.run(task) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/examples/foundational/07x-interruptible-local.py b/examples/foundational/07x-interruptible-local.py new file mode 100644 index 000000000..54942ad97 --- /dev/null +++ b/examples/foundational/07x-interruptible-local.py @@ -0,0 +1,91 @@ +# +# Copyright (c) 2024–2025, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +import asyncio +import os +import sys + +from dotenv import load_dotenv +from loguru import logger + +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.openai_llm_context import OpenAILLMContext +from pipecat.services.cartesia import CartesiaTTSService +from pipecat.services.deepgram import DeepgramSTTService +from pipecat.services.openai import OpenAILLMService +from pipecat.transports.local.audio import LocalAudioTransport, LocalAudioTransportParams + +load_dotenv(override=True) + +logger.remove(0) +logger.add(sys.stderr, level="DEBUG") + + +async def main(): + transport = LocalAudioTransport( + LocalAudioTransportParams( + audio_in_enabled=True, + audio_out_enabled=True, + vad_enabled=True, + vad_analyzer=SileroVADAnalyzer(), + vad_audio_passthrough=True, + ) + ) + + stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY")) + + tts = CartesiaTTSService( + api_key=os.getenv("CARTESIA_API_KEY"), + voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady + ) + + llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o") + + messages = [ + { + "role": "system", + "content": "You are a helpful LLM. 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.", + }, + ] + + context = OpenAILLMContext(messages) + context_aggregator = llm.create_context_aggregator(context) + + pipeline = Pipeline( + [ + transport.input(), # Transport user input + stt, + context_aggregator.user(), # User responses + llm, # LLM + tts, # TTS + transport.output(), # Transport bot output + context_aggregator.assistant(), # Assistant spoken responses + ] + ) + + task = PipelineTask( + pipeline, + params=PipelineParams( + allow_interruptions=True, + enable_metrics=True, + enable_usage_metrics=True, + report_only_initial_ttfb=True, + ), + ) + + messages.append({"role": "system", "content": "Please introduce yourself to the user."}) + await task.queue_frames([context_aggregator.user().get_context_frame()]) + + runner = PipelineRunner() + + await runner.run(task) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/examples/foundational/14-function-calling.py b/examples/foundational/14-function-calling.py index 1d4e37344..dddfed1d2 100644 --- a/examples/foundational/14-function-calling.py +++ b/examples/foundational/14-function-calling.py @@ -30,13 +30,8 @@ logger.remove(0) logger.add(sys.stderr, level="DEBUG") -async def start_fetch_weather(function_name, llm, context): - """Push a frame to the LLM; this is handy when the LLM response might take a while.""" - await llm.push_frame(TTSSpeakFrame("Let me check on that.")) - logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}") - - async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback): + await llm.push_frame(TTSSpeakFrame("Let me check on that.")) await result_callback({"conditions": "nice", "temperature": "75"}) @@ -62,9 +57,10 @@ async def main(): ) llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o") - # Register a function_name of None to get all functions + + # You can also register a function_name of None to get all functions # sent to the same callback with an additional function_name parameter. - llm.register_function(None, fetch_weather_from_api, start_callback=start_fetch_weather) + llm.register_function("get_current_weather", fetch_weather_from_api) weather_function = FunctionSchema( name="get_current_weather", diff --git a/examples/foundational/14a-function-calling-anthropic.py b/examples/foundational/14a-function-calling-anthropic.py index 13505550b..923b1488d 100644 --- a/examples/foundational/14a-function-calling-anthropic.py +++ b/examples/foundational/14a-function-calling-anthropic.py @@ -57,7 +57,7 @@ async def main(): ) llm = AnthropicLLMService( - api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-5-sonnet-20240620" + api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-7-sonnet-latest" ) llm.register_function("get_weather", get_weather) diff --git a/examples/foundational/14b-function-calling-anthropic-video.py b/examples/foundational/14b-function-calling-anthropic-video.py index 8c49900fa..cd9777b72 100644 --- a/examples/foundational/14b-function-calling-anthropic-video.py +++ b/examples/foundational/14b-function-calling-anthropic-video.py @@ -39,7 +39,12 @@ async def get_weather(function_name, tool_call_id, arguments, llm, context, resu async def get_image(function_name, tool_call_id, arguments, llm, context, result_callback): question = arguments["question"] - await llm.request_image_frame(user_id=video_participant_id, text_content=question) + await llm.request_image_frame( + user_id=video_participant_id, + function_name=function_name, + tool_call_id=tool_call_id, + text_content=question, + ) async def main(): @@ -67,8 +72,7 @@ async def main(): llm = AnthropicLLMService( api_key=os.getenv("ANTHROPIC_API_KEY"), - # model="claude-3-5-sonnet-20240620", - model="claude-3-5-sonnet-latest", + model="claude-3-7-sonnet-latest", enable_prompt_caching_beta=True, ) llm.register_function("get_weather", get_weather) diff --git a/examples/foundational/14c-function-calling-together.py b/examples/foundational/14c-function-calling-together.py index dd3eb714f..94d587799 100644 --- a/examples/foundational/14c-function-calling-together.py +++ b/examples/foundational/14c-function-calling-together.py @@ -31,13 +31,8 @@ logger.remove(0) logger.add(sys.stderr, level="DEBUG") -async def start_fetch_weather(function_name, llm, context): - """Push a frame to the LLM; this is handy when the LLM response might take a while.""" - await llm.push_frame(TTSSpeakFrame("Let me check on that.")) - logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}") - - async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback): + await llm.push_frame(TTSSpeakFrame("Let me check on that.")) await result_callback({"conditions": "nice", "temperature": "75"}) @@ -66,9 +61,9 @@ async def main(): api_key=os.getenv("TOGETHER_API_KEY"), model="meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo", ) - # Register a function_name of None to get all functions + # You can also register a function_name of None to get all functions # sent to the same callback with an additional function_name parameter. - llm.register_function(None, fetch_weather_from_api, start_callback=start_fetch_weather) + llm.register_function("get_current_weather", fetch_weather_from_api) weather_function = FunctionSchema( name="get_current_weather", diff --git a/examples/foundational/14d-function-calling-video.py b/examples/foundational/14d-function-calling-video.py index 6e290d55f..a7e997f9b 100644 --- a/examples/foundational/14d-function-calling-video.py +++ b/examples/foundational/14d-function-calling-video.py @@ -39,7 +39,12 @@ async def get_weather(function_name, tool_call_id, arguments, llm, context, resu async def get_image(function_name, tool_call_id, arguments, llm, context, result_callback): logger.debug(f"!!! IN get_image {video_participant_id}, {arguments}") question = arguments["question"] - await llm.request_image_frame(user_id=video_participant_id, text_content=question) + await llm.request_image_frame( + user_id=video_participant_id, + function_name=function_name, + tool_call_id=tool_call_id, + text_content=question, + ) async def main(): @@ -141,7 +146,7 @@ indicate you should use the get_image tool are: await transport.capture_participant_transcription(participant["id"]) await transport.capture_participant_video(video_participant_id, framerate=0) # Kick off the conversation. - await tts.say("Hi! Ask me about the weather in San Francisco.") + await task.queue_frames([context_aggregator.user().get_context_frame()]) runner = PipelineRunner() diff --git a/examples/foundational/14e-function-calling-gemini.py b/examples/foundational/14e-function-calling-gemini.py index bf022a65a..8448643b4 100644 --- a/examples/foundational/14e-function-calling-gemini.py +++ b/examples/foundational/14e-function-calling-gemini.py @@ -33,13 +33,8 @@ logger.add(sys.stderr, level="DEBUG") video_participant_id = None -async def start_fetch_weather(function_name, llm, context): - """Push a frame to the LLM; this is handy when the LLM response might take a while.""" - await llm.push_frame(TTSSpeakFrame("Let me check on that.")) - logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}") - - async def get_weather(function_name, tool_call_id, arguments, llm, context, result_callback): + await llm.push_frame(TTSSpeakFrame("Let me check on that.")) location = arguments["location"] await result_callback(f"The weather in {location} is currently 72 degrees and sunny.") @@ -47,7 +42,12 @@ async def get_weather(function_name, tool_call_id, arguments, llm, context, resu async def get_image(function_name, tool_call_id, arguments, llm, context, result_callback): logger.debug(f"!!! IN get_image {video_participant_id}, {arguments}") question = arguments["question"] - await llm.request_image_frame(user_id=video_participant_id, text_content=question) + await llm.request_image_frame( + user_id=video_participant_id, + function_name=function_name, + tool_call_id=tool_call_id, + text_content=question, + ) async def main(): @@ -72,7 +72,7 @@ async def main(): ) llm = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"), model="gemini-2.0-flash-001") - llm.register_function("get_weather", get_weather, start_fetch_weather) + llm.register_function("get_weather", get_weather) llm.register_function("get_image", get_image) weather_function = FunctionSchema( diff --git a/examples/foundational/14f-function-calling-groq.py b/examples/foundational/14f-function-calling-groq.py index c402ac91a..9818d185f 100644 --- a/examples/foundational/14f-function-calling-groq.py +++ b/examples/foundational/14f-function-calling-groq.py @@ -31,13 +31,8 @@ logger.remove(0) logger.add(sys.stderr, level="DEBUG") -async def start_fetch_weather(function_name, llm, context): - """Push a frame to the LLM; this is handy when the LLM response might take a while.""" - await llm.push_frame(TTSSpeakFrame("Let me check on that.")) - logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}") - - async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback): + await llm.push_frame(TTSSpeakFrame("Let me check on that.")) await result_callback({"conditions": "nice", "temperature": "75"}) @@ -65,9 +60,9 @@ async def main(): ) llm = GroqLLMService(api_key=os.getenv("GROQ_API_KEY"), model="llama-3.3-70b-versatile") - # Register a function_name of None to get all functions + # You can also register a function_name of None to get all functions # sent to the same callback with an additional function_name parameter. - llm.register_function(None, fetch_weather_from_api, start_callback=start_fetch_weather) + llm.register_function("get_current_weather", fetch_weather_from_api) weather_function = FunctionSchema( name="get_current_weather", diff --git a/examples/foundational/14g-function-calling-grok.py b/examples/foundational/14g-function-calling-grok.py index e6b822e39..e5bb2f9b3 100644 --- a/examples/foundational/14g-function-calling-grok.py +++ b/examples/foundational/14g-function-calling-grok.py @@ -16,7 +16,6 @@ from runner import configure from pipecat.adapters.schemas.function_schema import FunctionSchema from pipecat.adapters.schemas.tools_schema import ToolsSchema from pipecat.audio.vad.silero import SileroVADAnalyzer -from pipecat.frames.frames import TTSSpeakFrame from pipecat.pipeline.pipeline import Pipeline from pipecat.pipeline.runner import PipelineRunner from pipecat.pipeline.task import PipelineParams, PipelineTask @@ -31,12 +30,6 @@ logger.remove(0) logger.add(sys.stderr, level="DEBUG") -async def start_fetch_weather(function_name, llm, context): - """Push a frame to the LLM; this is handy when the LLM response might take a while.""" - await llm.push_frame(TTSSpeakFrame("Let me check on that.")) - logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}") - - async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback): await result_callback({"conditions": "nice", "temperature": "75"}) @@ -63,9 +56,9 @@ async def main(): ) llm = GrokLLMService(api_key=os.getenv("GROK_API_KEY")) - # Register a function_name of None to get all functions + # You can also register a function_name of None to get all functions # sent to the same callback with an additional function_name parameter. - llm.register_function(None, fetch_weather_from_api, start_callback=start_fetch_weather) + llm.register_function("get_current_weather", fetch_weather_from_api) weather_function = FunctionSchema( name="get_current_weather", diff --git a/examples/foundational/14h-function-calling-azure.py b/examples/foundational/14h-function-calling-azure.py index aefa1a474..6c8465832 100644 --- a/examples/foundational/14h-function-calling-azure.py +++ b/examples/foundational/14h-function-calling-azure.py @@ -31,13 +31,8 @@ logger.remove(0) logger.add(sys.stderr, level="DEBUG") -async def start_fetch_weather(function_name, llm, context): - """Push a frame to the LLM; this is handy when the LLM response might take a while.""" - await llm.push_frame(TTSSpeakFrame("Let me check on that.")) - logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}") - - async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback): + await llm.push_frame(TTSSpeakFrame("Let me check on that.")) await result_callback({"conditions": "nice", "temperature": "75"}) @@ -67,9 +62,9 @@ async def main(): endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"), model=os.getenv("AZURE_CHATGPT_MODEL"), ) - # Register a function_name of None to get all functions + # You can also register a function_name of None to get all functions # sent to the same callback with an additional function_name parameter. - llm.register_function(None, fetch_weather_from_api, start_callback=start_fetch_weather) + llm.register_function("get_current_weather", fetch_weather_from_api) weather_function = FunctionSchema( name="get_current_weather", diff --git a/examples/foundational/14i-function-calling-fireworks.py b/examples/foundational/14i-function-calling-fireworks.py index 50291615b..887e734dd 100644 --- a/examples/foundational/14i-function-calling-fireworks.py +++ b/examples/foundational/14i-function-calling-fireworks.py @@ -31,13 +31,8 @@ logger.remove(0) logger.add(sys.stderr, level="DEBUG") -async def start_fetch_weather(function_name, llm, context): - """Push a frame to the LLM; this is handy when the LLM response might take a while.""" - await llm.push_frame(TTSSpeakFrame("Let me check on that.")) - logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}") - - async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback): + await llm.push_frame(TTSSpeakFrame("Let me check on that.")) await result_callback({"conditions": "nice", "temperature": "75"}) @@ -64,11 +59,11 @@ async def main(): llm = FireworksLLMService( api_key=os.getenv("FIREWORKS_API_KEY"), - model="accounts/fireworks/models/firefunction-v2", + model="accounts/fireworks/models/llama-v3p1-405b-instruct", ) - # Register a function_name of None to get all functions + # You can also register a function_name of None to get all functions # sent to the same callback with an additional function_name parameter. - llm.register_function(None, fetch_weather_from_api, start_callback=start_fetch_weather) + llm.register_function("get_current_weather", fetch_weather_from_api) weather_function = FunctionSchema( name="get_current_weather", diff --git a/examples/foundational/14j-function-calling-nim.py b/examples/foundational/14j-function-calling-nim.py index d703d637a..c628bedf0 100644 --- a/examples/foundational/14j-function-calling-nim.py +++ b/examples/foundational/14j-function-calling-nim.py @@ -31,13 +31,8 @@ logger.remove(0) logger.add(sys.stderr, level="DEBUG") -async def start_fetch_weather(function_name, llm, context): - """Push a frame to the LLM; this is handy when the LLM response might take a while.""" - await llm.push_frame(TTSSpeakFrame("Let me check on that.")) - logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}") - - async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback): + await llm.push_frame(TTSSpeakFrame("Let me check on that.")) await result_callback({"conditions": "nice", "temperature": "75"}) @@ -60,15 +55,15 @@ async def main(): tts = CartesiaTTSService( api_key=os.getenv("CARTESIA_API_KEY"), voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady - # text_filter=MarkdownTextFilter(), + # text_filters=[MarkdownTextFilter()], ) llm = NimLLMService( api_key=os.getenv("NVIDIA_API_KEY"), model="meta/llama-3.3-70b-instruct" ) - # Register a function_name of None to get all functions + # You can also register a function_name of None to get all functions # sent to the same callback with an additional function_name parameter. - llm.register_function(None, fetch_weather_from_api, start_callback=start_fetch_weather) + llm.register_function("get_current_weather", fetch_weather_from_api) weather_function = FunctionSchema( name="get_current_weather", diff --git a/examples/foundational/14k-function-calling-cerebras.py b/examples/foundational/14k-function-calling-cerebras.py index 9b3641307..541e6f250 100644 --- a/examples/foundational/14k-function-calling-cerebras.py +++ b/examples/foundational/14k-function-calling-cerebras.py @@ -31,13 +31,8 @@ logger.remove(0) logger.add(sys.stderr, level="DEBUG") -async def start_fetch_weather(function_name, llm, context): - """Push a frame to the LLM; this is handy when the LLM response might take a while.""" - await llm.push_frame(TTSSpeakFrame("Let me check on that.")) - logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}") - - async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback): + await llm.push_frame(TTSSpeakFrame("Let me check on that.")) await result_callback({"conditions": "nice", "temperature": "75"}) @@ -63,9 +58,9 @@ async def main(): ) llm = CerebrasLLMService(api_key=os.getenv("CEREBRAS_API_KEY"), model="llama-3.3-70b") - # Register a function_name of None to get all functions + # You can also register a function_name of None to get all functions # sent to the same callback with an additional function_name parameter. - llm.register_function(None, fetch_weather_from_api, start_callback=start_fetch_weather) + llm.register_function("get_current_weather", fetch_weather_from_api) weather_function = FunctionSchema( name="get_current_weather", diff --git a/examples/foundational/14l-function-calling-deepseek.py b/examples/foundational/14l-function-calling-deepseek.py index 0360f0b84..812a6716e 100644 --- a/examples/foundational/14l-function-calling-deepseek.py +++ b/examples/foundational/14l-function-calling-deepseek.py @@ -31,13 +31,8 @@ logger.remove(0) logger.add(sys.stderr, level="DEBUG") -async def start_fetch_weather(function_name, llm, context): - """Push a frame to the LLM; this is handy when the LLM response might take a while.""" - await llm.push_frame(TTSSpeakFrame("Let me check on that.")) - logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}") - - async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback): + await llm.push_frame(TTSSpeakFrame("Let me check on that.")) await result_callback({"conditions": "nice", "temperature": "75"}) @@ -63,9 +58,9 @@ async def main(): ) llm = DeepSeekLLMService(api_key=os.getenv("DEEPSEEK_API_KEY"), model="deepseek-chat") - # Register a function_name of None to get all functions + # You can also register a function_name of None to get all functions # sent to the same callback with an additional function_name parameter. - llm.register_function(None, fetch_weather_from_api, start_callback=start_fetch_weather) + llm.register_function("get_current_weather", fetch_weather_from_api) weather_function = FunctionSchema( name="get_current_weather", diff --git a/examples/foundational/14m-function-calling-openrouter.py b/examples/foundational/14m-function-calling-openrouter.py index e816f6322..a675c8ac4 100644 --- a/examples/foundational/14m-function-calling-openrouter.py +++ b/examples/foundational/14m-function-calling-openrouter.py @@ -31,13 +31,8 @@ logger.remove(0) logger.add(sys.stderr, level="DEBUG") -async def start_fetch_weather(function_name, llm, context): - """Push a frame to the LLM; this is handy when the LLM response might take a while.""" - await llm.push_frame(TTSSpeakFrame("Let me check on that.")) - logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}") - - async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback): + await llm.push_frame(TTSSpeakFrame("Let me check on that.")) await result_callback({"conditions": "nice", "temperature": "75"}) @@ -67,9 +62,9 @@ async def main(): llm = OpenRouterLLMService( api_key=os.getenv("OPENROUTER_API_KEY"), model="openai/gpt-4o-2024-11-20" ) - # Register a function_name of None to get all functions + # You can also register a function_name of None to get all functions # sent to the same callback with an additional function_name parameter. - llm.register_function(None, fetch_weather_from_api, start_callback=start_fetch_weather) + llm.register_function("get_current_weather", fetch_weather_from_api) weather_function = FunctionSchema( name="get_current_weather", diff --git a/examples/foundational/14o-function-calling-gemini-openai-format.py b/examples/foundational/14o-function-calling-gemini-openai-format.py index a1bb53b6b..78100f0d3 100644 --- a/examples/foundational/14o-function-calling-gemini-openai-format.py +++ b/examples/foundational/14o-function-calling-gemini-openai-format.py @@ -31,13 +31,8 @@ logger.remove(0) logger.add(sys.stderr, level="DEBUG") -async def start_fetch_weather(function_name, llm, context): - """Push a frame to the LLM; this is handy when the LLM response might take a while.""" - await llm.push_frame(TTSSpeakFrame("Let me check on that.")) - logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}") - - async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback): + await llm.push_frame(TTSSpeakFrame("Let me check on that.")) await result_callback({"conditions": "nice", "temperature": "75"}) @@ -63,11 +58,9 @@ async def main(): ) llm = GoogleLLMOpenAIBetaService(api_key=os.getenv("GEMINI_API_KEY")) - # Register a function_name of None to get all functions + # You can aslo register a function_name of None to get all functions # sent to the same callback with an additional function_name parameter. - llm.register_function( - "get_current_weather", fetch_weather_from_api, start_callback=start_fetch_weather - ) + llm.register_function("get_current_weather", fetch_weather_from_api) weather_function = FunctionSchema( name="get_current_weather", diff --git a/examples/foundational/14p-function-calling-gemini-vertex-ai.py b/examples/foundational/14p-function-calling-gemini-vertex-ai.py new file mode 100644 index 000000000..8888a2922 --- /dev/null +++ b/examples/foundational/14p-function-calling-gemini-vertex-ai.py @@ -0,0 +1,130 @@ +# +# Copyright (c) 2024–2025, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +import asyncio +import os +import sys + +import aiohttp +from dotenv import load_dotenv +from loguru import logger +from runner import configure + +from pipecat.adapters.schemas.function_schema import FunctionSchema +from pipecat.adapters.schemas.tools_schema import ToolsSchema +from pipecat.audio.vad.silero import SileroVADAnalyzer +from pipecat.frames.frames import TTSSpeakFrame +from pipecat.pipeline.pipeline import Pipeline +from pipecat.pipeline.runner import PipelineRunner +from pipecat.pipeline.task import PipelineParams, PipelineTask +from pipecat.services.elevenlabs import ElevenLabsTTSService +from pipecat.services.google import GoogleVertexLLMService +from pipecat.services.openai import OpenAILLMContext +from pipecat.transports.services.daily import DailyParams, DailyTransport + +load_dotenv(override=True) + +logger.remove(0) +logger.add(sys.stderr, level="DEBUG") + + +async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback): + await llm.push_frame(TTSSpeakFrame("Let me check on that.")) + await result_callback({"conditions": "nice", "temperature": "75"}) + + +async def main(): + async with aiohttp.ClientSession() as session: + (room_url, token) = await configure(session) + + transport = DailyTransport( + room_url, + token, + "Respond bot", + DailyParams( + audio_out_enabled=True, + transcription_enabled=True, + vad_enabled=True, + vad_analyzer=SileroVADAnalyzer(), + ), + ) + + tts = ElevenLabsTTSService( + api_key=os.getenv("ELEVENLABS_API_KEY", ""), + voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""), + ) + + llm = GoogleVertexLLMService( + # credentials="", + params=GoogleVertexLLMService.InputParams( + project_id="", + ) + ) + # You can aslo register a function_name of None to get all functions + # sent to the same callback with an additional function_name parameter. + llm.register_function("get_current_weather", fetch_weather_from_api) + + weather_function = FunctionSchema( + name="get_current_weather", + description="Get the current weather", + properties={ + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", + }, + "format": { + "type": "string", + "enum": ["celsius", "fahrenheit"], + "description": "The temperature unit to use. Infer this from the user's location.", + }, + }, + required=["location", "format"], + ) + tools = ToolsSchema(standard_tools=[weather_function]) + + messages = [ + { + "role": "user", + "content": "Start a conversation with 'Hey there' to get the current weather.", + }, + ] + + context = OpenAILLMContext(messages, tools) + context_aggregator = llm.create_context_aggregator(context) + + pipeline = Pipeline( + [ + transport.input(), + context_aggregator.user(), + llm, + tts, + transport.output(), + context_aggregator.assistant(), + ] + ) + + task = PipelineTask( + pipeline, + params=PipelineParams( + allow_interruptions=True, + enable_metrics=True, + enable_usage_metrics=True, + ), + ) + + @transport.event_handler("on_first_participant_joined") + async def on_first_participant_joined(transport, participant): + await transport.capture_participant_transcription(participant["id"]) + # Kick off the conversation. + await task.queue_frames([context_aggregator.user().get_context_frame()]) + + runner = PipelineRunner() + + await runner.run(task) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/examples/foundational/19-openai-realtime-beta.py b/examples/foundational/19-openai-realtime-beta.py index 504f8fbf1..3aff14e65 100644 --- a/examples/foundational/19-openai-realtime-beta.py +++ b/examples/foundational/19-openai-realtime-beta.py @@ -14,6 +14,8 @@ from dotenv import load_dotenv from loguru import logger from runner import configure +from pipecat.adapters.schemas.function_schema import FunctionSchema +from pipecat.adapters.schemas.tools_schema import ToolsSchema from pipecat.audio.vad.silero import SileroVADAnalyzer from pipecat.audio.vad.vad_analyzer import VADParams from pipecat.pipeline.pipeline import Pipeline @@ -21,10 +23,11 @@ from pipecat.pipeline.runner import PipelineRunner from pipecat.pipeline.task import PipelineParams, PipelineTask from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext from pipecat.services.openai_realtime_beta import ( + InputAudioNoiseReduction, InputAudioTranscription, OpenAIRealtimeBetaLLMService, + SemanticTurnDetection, SessionProperties, - TurnDetection, ) from pipecat.transports.services.daily import DailyParams, DailyTransport @@ -46,28 +49,25 @@ async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context ) -tools = [ - { - "type": "function", - "name": "get_current_weather", - "description": "Get the current weather", - "parameters": { - "type": "object", - "properties": { - "location": { - "type": "string", - "description": "The city and state, e.g. San Francisco, CA", - }, - "format": { - "type": "string", - "enum": ["celsius", "fahrenheit"], - "description": "The temperature unit to use. Infer this from the users location.", - }, - }, - "required": ["location", "format"], +weather_function = FunctionSchema( + name="get_current_weather", + description="Get the current weather", + properties={ + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", }, - } -] + "format": { + "type": "string", + "enum": ["celsius", "fahrenheit"], + "description": "The temperature unit to use. Infer this from the users location.", + }, + }, + required=["location", "format"], +) + +# Create tools schema +tools = ToolsSchema(standard_tools=[weather_function]) async def main(): @@ -92,9 +92,10 @@ async def main(): input_audio_transcription=InputAudioTranscription(), # Set openai TurnDetection parameters. Not setting this at all will turn it # on by default - turn_detection=TurnDetection(silence_duration_ms=1000), + turn_detection=SemanticTurnDetection(), # Or set to False to disable openai turn detection and use transport VAD # turn_detection=False, + input_audio_noise_reduction=InputAudioNoiseReduction(type="near_field"), # tools=tools, instructions="""Your knowledge cutoff is 2023-10. You are a helpful and friendly AI. @@ -147,8 +148,8 @@ Remember, your responses should be short. Just one or two sentences, usually.""" transport.input(), # Transport user input context_aggregator.user(), llm, # LLM - context_aggregator.assistant(), transport.output(), # Transport bot output + context_aggregator.assistant(), ] ) diff --git a/examples/foundational/19a-azure-realtime-beta.py b/examples/foundational/19a-azure-realtime-beta.py index 14d034836..2eefd4ec9 100644 --- a/examples/foundational/19a-azure-realtime-beta.py +++ b/examples/foundational/19a-azure-realtime-beta.py @@ -10,11 +10,12 @@ import sys from datetime import datetime import aiohttp -import websockets from dotenv import load_dotenv from loguru import logger from runner import configure +from pipecat.adapters.schemas.function_schema import FunctionSchema +from pipecat.adapters.schemas.tools_schema import ToolsSchema from pipecat.audio.vad.silero import SileroVADAnalyzer from pipecat.audio.vad.vad_analyzer import VADParams from pipecat.pipeline.pipeline import Pipeline @@ -25,7 +26,6 @@ from pipecat.services.openai_realtime_beta import ( AzureRealtimeBetaLLMService, InputAudioTranscription, SessionProperties, - TurnDetection, ) from pipecat.transports.services.daily import DailyParams, DailyTransport @@ -47,28 +47,26 @@ async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context ) -tools = [ - { - "type": "function", - "name": "get_current_weather", - "description": "Get the current weather", - "parameters": { - "type": "object", - "properties": { - "location": { - "type": "string", - "description": "The city and state, e.g. San Francisco, CA", - }, - "format": { - "type": "string", - "enum": ["celsius", "fahrenheit"], - "description": "The temperature unit to use. Infer this from the users location.", - }, - }, - "required": ["location", "format"], +# Define weather function using standardized schema +weather_function = FunctionSchema( + name="get_current_weather", + description="Get the current weather", + properties={ + "location": { + "type": "string", + "description": "The city and state, e.g. San Francisco, CA", }, - } -] + "format": { + "type": "string", + "enum": ["celsius", "fahrenheit"], + "description": "The temperature unit to use. Infer this from the users location.", + }, + }, + required=["location", "format"], +) + +# Create tools schema +tools = ToolsSchema(standard_tools=[weather_function]) async def main(): diff --git a/examples/foundational/20d-persistent-context-gemini.py b/examples/foundational/20d-persistent-context-gemini.py index 740bdc68f..2381ea131 100644 --- a/examples/foundational/20d-persistent-context-gemini.py +++ b/examples/foundational/20d-persistent-context-gemini.py @@ -54,7 +54,12 @@ async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context async def get_image(function_name, tool_call_id, arguments, llm, context, result_callback): question = arguments["question"] - await llm.request_image_frame(user_id=video_participant_id, text_content=question) + await llm.request_image_frame( + user_id=video_participant_id, + function_name=function_name, + tool_call_id=tool_call_id, + text_content=question, + ) async def get_saved_conversation_filenames( diff --git a/examples/foundational/22b-natural-conversation-proposal.py b/examples/foundational/22b-natural-conversation-proposal.py index c97289217..d7f7eb597 100644 --- a/examples/foundational/22b-natural-conversation-proposal.py +++ b/examples/foundational/22b-natural-conversation-proposal.py @@ -199,13 +199,8 @@ class OutputGate(FrameProcessor): break -async def start_fetch_weather(function_name, llm, context): - """Push a frame to the LLM; this is handy when the LLM response might take a while.""" - await llm.push_frame(TTSSpeakFrame("Let me check on that.")) - logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}") - - async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback): + await llm.push_frame(TTSSpeakFrame("Let me check on that.")) await result_callback({"conditions": "nice", "temperature": "75"}) @@ -239,9 +234,9 @@ async def main(): # This is the regular LLM. llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o") - # Register a function_name of None to get all functions + # You can also register a function_name of None to get all functions # sent to the same callback with an additional function_name parameter. - llm.register_function(None, fetch_weather_from_api, start_callback=start_fetch_weather) + llm.register_function("get_current_weather", fetch_weather_from_api) tools = [ ChatCompletionToolParam( diff --git a/examples/foundational/22c-natural-conversation-mixed-llms.py b/examples/foundational/22c-natural-conversation-mixed-llms.py index d342a2765..7fa1bb5fa 100644 --- a/examples/foundational/22c-natural-conversation-mixed-llms.py +++ b/examples/foundational/22c-natural-conversation-mixed-llms.py @@ -403,13 +403,8 @@ class OutputGate(FrameProcessor): break -async def start_fetch_weather(function_name, llm, context): - """Push a frame to the LLM; this is handy when the LLM response might take a while.""" - await llm.push_frame(TTSSpeakFrame("Let me check on that.")) - logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}") - - async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback): + await llm.push_frame(TTSSpeakFrame("Let me check on that.")) await result_callback({"conditions": "nice", "temperature": "75"}) @@ -451,7 +446,7 @@ async def main(): ) # Register a function_name of None to get all functions # sent to the same callback with an additional function_name parameter. - llm.register_function(None, fetch_weather_from_api, start_callback=start_fetch_weather) + llm.register_function("get_current_weather", fetch_weather_from_api) tools = [ ChatCompletionToolParam( diff --git a/examples/foundational/24-stt-mute-filter.py b/examples/foundational/24-stt-mute-filter.py index 7ae6c73fa..d2b7174d1 100644 --- a/examples/foundational/24-stt-mute-filter.py +++ b/examples/foundational/24-stt-mute-filter.py @@ -30,10 +30,6 @@ logger.remove(0) logger.add(sys.stderr, level="DEBUG") -async def start_fetch_weather(function_name, llm, context): - logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}") - - async def fetch_weather_from_api(function_name, tool_call_id, args, llm, context, result_callback): # Add a delay to test interruption during function calls logger.info("Weather API call starting...") @@ -72,7 +68,7 @@ async def main(): tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en") llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o") - llm.register_function(None, fetch_weather_from_api, start_callback=start_fetch_weather) + llm.register_function("get_current_weather", fetch_weather_from_api) tools = [ ChatCompletionToolParam( diff --git a/examples/foundational/28a-transcription-processor-openai.py b/examples/foundational/28-transcription-processor.py similarity index 100% rename from examples/foundational/28a-transcription-processor-openai.py rename to examples/foundational/28-transcription-processor.py diff --git a/examples/foundational/28b-transcript-processor-anthropic.py b/examples/foundational/28b-transcript-processor-anthropic.py deleted file mode 100644 index c9f2672e0..000000000 --- a/examples/foundational/28b-transcript-processor-anthropic.py +++ /dev/null @@ -1,177 +0,0 @@ -# -# Copyright (c) 2024–2025, Daily -# -# SPDX-License-Identifier: BSD 2-Clause License -# - -import asyncio -import os -import sys -from typing import List, Optional - -import aiohttp -from dotenv import load_dotenv -from loguru import logger -from runner import configure - -from pipecat.audio.vad.silero import SileroVADAnalyzer -from pipecat.frames.frames import TranscriptionMessage, TranscriptionUpdateFrame -from pipecat.pipeline.pipeline import Pipeline -from pipecat.pipeline.runner import PipelineRunner -from pipecat.pipeline.task import PipelineParams, PipelineTask -from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext -from pipecat.processors.transcript_processor import TranscriptProcessor -from pipecat.services.anthropic import AnthropicLLMService -from pipecat.services.cartesia import CartesiaTTSService -from pipecat.services.deepgram import DeepgramSTTService -from pipecat.transports.services.daily import DailyParams, DailyTransport - -load_dotenv(override=True) - -logger.remove(0) -logger.add(sys.stderr, level="DEBUG") - - -class TranscriptHandler: - """Handles real-time transcript processing and output. - - Maintains a list of conversation messages and outputs them either to a log - or to a file as they are received. Each message includes its timestamp and role. - - Attributes: - messages: List of all processed transcript messages - output_file: Optional path to file where transcript is saved. If None, outputs to log only. - """ - - def __init__(self, output_file: Optional[str] = None): - """Initialize handler with optional file output. - - Args: - output_file: Path to output file. If None, outputs to log only. - """ - self.messages: List[TranscriptionMessage] = [] - self.output_file: Optional[str] = output_file - logger.debug( - f"TranscriptHandler initialized {'with output_file=' + output_file if output_file else 'with log output only'}" - ) - - async def save_message(self, message: TranscriptionMessage): - """Save a single transcript message. - - Outputs the message to the log and optionally to a file. - - Args: - message: The message to save - """ - timestamp = f"[{message.timestamp}] " if message.timestamp else "" - line = f"{timestamp}{message.role}: {message.content}" - - # Always log the message - logger.info(f"Transcript: {line}") - - # Optionally write to file - if self.output_file: - try: - with open(self.output_file, "a", encoding="utf-8") as f: - f.write(line + "\n") - except Exception as e: - logger.error(f"Error saving transcript message to file: {e}") - - async def on_transcript_update( - self, processor: TranscriptProcessor, frame: TranscriptionUpdateFrame - ): - """Handle new transcript messages. - - Args: - processor: The TranscriptProcessor that emitted the update - frame: TranscriptionUpdateFrame containing new messages - """ - logger.debug(f"Received transcript update with {len(frame.messages)} new messages") - - for msg in frame.messages: - self.messages.append(msg) - await self.save_message(msg) - - -async def main(): - async with aiohttp.ClientSession() as session: - (room_url, token) = await configure(session) - - transport = DailyTransport( - room_url, - None, - "Respond bot", - DailyParams( - audio_out_enabled=True, - vad_enabled=True, - vad_analyzer=SileroVADAnalyzer(), - vad_audio_passthrough=True, - ), - ) - - stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY")) - - tts = CartesiaTTSService( - api_key=os.getenv("CARTESIA_API_KEY"), - voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady - ) - - llm = AnthropicLLMService( - api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-5-sonnet-20241022" - ) - - 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 converted to audio so don't include special characters in your answers. Respond to what the user said in a creative, helpful, and brief way.", - }, - {"role": "user", "content": "Say hello."}, - ] - - context = OpenAILLMContext(messages) - context_aggregator = llm.create_context_aggregator(context) - - # Create transcript processor and handler - transcript = TranscriptProcessor() - transcript_handler = TranscriptHandler() # Output to log only - # transcript_handler = TranscriptHandler(output_file="transcript.txt") # Output to file and log - - pipeline = Pipeline( - [ - transport.input(), # Transport user input - stt, # STT - transcript.user(), # User transcripts - context_aggregator.user(), # User responses - llm, # LLM - tts, # TTS - transport.output(), # Transport bot output - transcript.assistant(), # Assistant transcripts - context_aggregator.assistant(), # Assistant spoken responses - ] - ) - - task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True)) - - @transport.event_handler("on_first_participant_joined") - async def on_first_participant_joined(transport, participant): - await transport.capture_participant_transcription(participant["id"]) - # Kick off the conversation. - await task.queue_frames([context_aggregator.user().get_context_frame()]) - - # Register event handler for transcript updates - @transcript.event_handler("on_transcript_update") - async def on_transcript_update(processor, frame): - await transcript_handler.on_transcript_update(processor, frame) - - @transport.event_handler("on_participant_left") - async def on_participant_left(transport, participant, reason): - # Stop the pipeline immediately when the participant leaves - await task.cancel() - - runner = PipelineRunner() - - await runner.run(task) - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/examples/foundational/28c-transcription-processor-gemini.py b/examples/foundational/28c-transcription-processor-gemini.py deleted file mode 100644 index 558edc76d..000000000 --- a/examples/foundational/28c-transcription-processor-gemini.py +++ /dev/null @@ -1,210 +0,0 @@ -# -# Copyright (c) 2024–2025, Daily -# -# SPDX-License-Identifier: BSD 2-Clause License -# - -import asyncio -import os -import sqlite3 -import sys -from typing import List, Optional - -import aiohttp -from dotenv import load_dotenv -from loguru import logger -from runner import configure - -from pipecat.audio.vad.silero import SileroVADAnalyzer -from pipecat.frames.frames import TranscriptionMessage, TranscriptionUpdateFrame -from pipecat.pipeline.pipeline import Pipeline -from pipecat.pipeline.runner import PipelineRunner -from pipecat.pipeline.task import PipelineParams, PipelineTask -from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext -from pipecat.processors.transcript_processor import TranscriptProcessor -from pipecat.services.cartesia import CartesiaTTSService -from pipecat.services.deepgram import DeepgramSTTService -from pipecat.services.google import GoogleLLMService -from pipecat.services.openai import OpenAILLMContext -from pipecat.transports.services.daily import DailyParams, DailyTransport - -load_dotenv(override=True) - -logger.remove(0) -logger.add(sys.stderr, level="DEBUG") - - -class TranscriptHandler: - """Handles real-time transcript processing and output. - - Maintains a list of conversation messages and outputs them either to a log - or to a file as they are received. Each message includes its timestamp and role. - - Attributes: - messages: List of all processed transcript messages - output_file: Optional path to file where transcript is saved. If None, outputs to log only. - """ - - def __init__(self, output_file: Optional[str] = None, output_db: Optional[str] = None): - """Initialize handler with optional file or database output. - - Args: - output_file: Path to output file. If None, outputs to log only. - """ - self.messages: List[TranscriptionMessage] = [] - self.output_file: Optional[str] = output_file - self.output_db: Optional[str] = output_db - - if self.output_db: - self.con = sqlite3.connect("example.db") - self.db = self.con.cursor() - - table = self.db.execute("SELECT name FROM sqlite_master WHERE name='messages'") - if not (table.fetchone()): - self.db.execute( - "CREATE TABLE messages(role TEXT, content TEXT, timestamp DATETIME DEFAULT CURRENT_TIMESTAMP )" - ) - logger.debug( - f"TranscriptHandler initialized; output file: {output_file}, output DB: {output_db}" - ) - - async def save_message(self, message: TranscriptionMessage): - """Save a single transcript message. - - Outputs the message to the log and optionally to a SQLite database or file. - - Args: - message: The message to save - """ - timestamp = f"[{message.timestamp}] " if message.timestamp else "" - line = f"{timestamp}{message.role}: {message.content}" - - # Always log the message - logger.info(f"Transcript: {line}") - - # Optionally write to file - if self.output_file: - try: - with open(self.output_file, "a", encoding="utf-8") as f: - f.write(line + "\n") - except Exception as e: - logger.error(f"Error saving transcript message to file: {e}") - - # and/or to a SQLite database - if self.output_db: - self.db.execute( - "INSERT INTO messages VALUES (?, ?, ?)", - (message.role, message.content, message.timestamp), - ) - self.con.commit() - - async def on_transcript_update( - self, processor: TranscriptProcessor, frame: TranscriptionUpdateFrame - ): - """Handle new transcript messages. - - Args: - processor: The TranscriptProcessor that emitted the update - frame: TranscriptionUpdateFrame containing new messages - """ - logger.debug(f"Received transcript update with {len(frame.messages)} new messages") - - for msg in frame.messages: - self.messages.append(msg) - await self.save_message(msg) - - -async def main(): - async with aiohttp.ClientSession() as session: - (room_url, token) = await configure(session) - - transport = DailyTransport( - room_url, - None, - "Respond bot", - DailyParams( - audio_out_enabled=True, - vad_enabled=True, - vad_analyzer=SileroVADAnalyzer(), - vad_audio_passthrough=True, - ), - ) - - stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY")) - - tts = CartesiaTTSService( - api_key=os.getenv("CARTESIA_API_KEY"), - voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady - ) - - llm = GoogleLLMService( - model="models/gemini-2.0-flash-exp", - # model="gemini-exp-1114", - api_key=os.getenv("GOOGLE_API_KEY"), - ) - - 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 converted to audio so don't include special characters in your answers. Respond to what the user said in a creative, helpful, and brief way.", - }, - {"role": "user", "content": "Say hello."}, - ] - - context = OpenAILLMContext(messages) - context_aggregator = llm.create_context_aggregator(context) - - # Create transcript processor and handler - transcript = TranscriptProcessor() - # Select a TranscriptHandler output method - # Uncomment out only one of the following lines: - transcript_handler = TranscriptHandler() # Output to log only - # transcript_handler = TranscriptHandler(output_file="transcript.txt") # Output to file and log - # transcript_handler = TranscriptHandler(output_db="example.db") # Output to SQLite DB and log - - pipeline = Pipeline( - [ - transport.input(), # Transport user input - stt, # STT - transcript.user(), # User transcripts - context_aggregator.user(), # User responses - llm, # LLM - tts, # TTS - transport.output(), # Transport bot output - transcript.assistant(), # Assistant transcripts - context_aggregator.assistant(), # Assistant spoken responses - ] - ) - - task = PipelineTask( - pipeline, - params=PipelineParams( - allow_interruptions=True, - enable_metrics=True, - enable_usage_metrics=True, - ), - ) - - @transport.event_handler("on_first_participant_joined") - async def on_first_participant_joined(transport, participant): - await transport.capture_participant_transcription(participant["id"]) - # Kick off the conversation. - await task.queue_frames([context_aggregator.user().get_context_frame()]) - - # Register event handler for transcript updates - @transcript.event_handler("on_transcript_update") - async def on_transcript_update(processor, frame): - await transcript_handler.on_transcript_update(processor, frame) - - @transport.event_handler("on_participant_left") - async def on_participant_left(transport, participant, reason): - # Stop the pipeline immediately when the participant leaves - await task.cancel() - - runner = PipelineRunner() - - await runner.run(task) - - -if __name__ == "__main__": - asyncio.run(main()) diff --git a/examples/foundational/34-audio-recording.py b/examples/foundational/34-audio-recording.py index 94877c722..24f009570 100644 --- a/examples/foundational/34-audio-recording.py +++ b/examples/foundational/34-audio-recording.py @@ -32,6 +32,7 @@ Requirements: OPENAI_API_KEY=your_openai_key CARTESIA_API_KEY=your_cartesia_key DAILY_API_KEY=your_daily_key + DEEPGRAM_API_KEY=your_deepgram_key The recordings will be saved in a 'recordings' directory with timestamps: recordings/ @@ -65,6 +66,7 @@ from pipecat.pipeline.task import PipelineParams, PipelineTask from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext from pipecat.processors.audio.audio_buffer_processor import AudioBufferProcessor from pipecat.services.cartesia import CartesiaTTSService +from pipecat.services.deepgram import DeepgramSTTService from pipecat.services.openai import OpenAILLMService from pipecat.transports.services.daily import DailyParams, DailyTransport @@ -98,13 +100,14 @@ async def main(): DailyParams( # audio_in_enabled=True, audio_out_enabled=True, - transcription_enabled=True, vad_enabled=True, vad_analyzer=SileroVADAnalyzer(), - vad_audio_passthrough=True, # Enable audio passthrough for recording + vad_audio_passthrough=True, ), ) + stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"), audio_passthrough=True) + tts = CartesiaTTSService( api_key=os.getenv("CARTESIA_API_KEY"), voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", @@ -128,6 +131,7 @@ async def main(): pipeline = Pipeline( [ transport.input(), + stt, context_aggregator.user(), llm, tts, diff --git a/examples/foundational/35-pattern-pair-voice-switching.py b/examples/foundational/35-pattern-pair-voice-switching.py new file mode 100644 index 000000000..bb9587706 --- /dev/null +++ b/examples/foundational/35-pattern-pair-voice-switching.py @@ -0,0 +1,230 @@ +# +# Copyright (c) 2024–2025, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +"""Pattern Pair Voice Switching Example with Pipecat. + +This example demonstrates how to use the PatternPairAggregator to dynamically switch +between different voices in a storytelling application. It showcases how pattern matching +can be used to control TTS behavior in streaming text from an LLM. + +The example: + 1. Sets up a storytelling bot with three distinct voices (narrator, male, female) + 2. Uses pattern pairs (name) to trigger voice switching + 3. Processes the patterns in real-time as text streams from the LLM + 4. Removes the pattern tags before sending text to TTS + +The PatternPairAggregator: + - Buffers text until complete patterns are detected + - Identifies content between start/end pattern pairs + - Triggers callbacks when patterns are matched + - Processes patterns that may span across multiple text chunks + - Returns processed text at sentence boundaries + +Example usage (run from pipecat root directory): + $ pip install "pipecat-ai[daily,openai,cartesia,silero]" + $ pip install -r dev-requirements.txt + $ python examples/foundational/35-pattern-pair-voice-switching.py + +Requirements: + - OpenAI API key (for GPT-4o) + - Cartesia API key (for text-to-speech) + - Daily API key (for video/audio transport) + + Environment variables (.env file): + OPENAI_API_KEY=your_openai_key + CARTESIA_API_KEY=your_cartesia_key + DAILY_API_KEY=your_daily_key + +Note: + This example shows one application of PatternPairAggregator (voice switching), + but the same approach can be used for various pattern-based text processing needs, + such as formatting instructions, command recognition, or structured data extraction. +""" + +import asyncio +import os +import sys + +import aiohttp +from dotenv import load_dotenv +from loguru import logger +from runner import configure + +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.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 +from pipecat.utils.text.pattern_pair_aggregator import PatternMatch, PatternPairAggregator + +load_dotenv(override=True) + +logger.remove(0) +logger.add(sys.stderr, level="DEBUG") + +# Define voice IDs +VOICE_IDS = { + "narrator": "c45bc5ec-dc68-4feb-8829-6e6b2748095d", # Narrator voice + "female": "71a7ad14-091c-4e8e-a314-022ece01c121", # Female character voice + "male": "7cf0e2b1-8daf-4fe4-89ad-f6039398f359", # Male character voice +} + + +async def main(): + async with aiohttp.ClientSession() as session: + (room_url, token) = await configure(session) + + transport = DailyTransport( + room_url, + token, + "Multi-voice storyteller", + DailyParams( + audio_out_enabled=True, + transcription_enabled=True, + vad_enabled=True, + vad_analyzer=SileroVADAnalyzer(), + ), + ) + + # Create pattern pair aggregator for voice switching + pattern_aggregator = PatternPairAggregator() + + # Add pattern for voice switching + pattern_aggregator.add_pattern_pair( + pattern_id="voice_tag", + start_pattern="", + end_pattern="", + remove_match=True, + ) + + # Register handler for voice switching + def on_voice_tag(match: PatternMatch): + voice_name = match.content.strip().lower() + if voice_name in VOICE_IDS: + voice_id = VOICE_IDS[voice_name] + tts.set_voice(voice_id) + logger.info(f"Switched to {voice_name} voice") + else: + logger.warning(f"Unknown voice: {voice_name}") + + pattern_aggregator.on_pattern_match("voice_tag", on_voice_tag) + + # Initialize TTS with narrator voice as default + tts = CartesiaTTSService( + api_key=os.getenv("CARTESIA_API_KEY"), + voice_id=VOICE_IDS["narrator"], + text_aggregator=pattern_aggregator, + ) + + # Initialize LLM + llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o") + + # System prompt for storytelling with voice switching + system_prompt = """You are an engaging storyteller that uses different voices to bring stories to life. + +You have three voices to use, but each has a specific purpose: + +narrator +This is the default narrator voice. Use this for all narration, descriptions, and non-dialogue text. + +female +Use this ONLY for direct speech by female characters (just the quoted text). + +male +Use this ONLY for direct speech by male characters (just the quoted text). + +IMPORTANT: Switch back to narrator voice immediately after character dialogue. + +Here's an EXAMPLE of correct voice usage: + +narrator +Sarah spotted her old friend across the café. She couldn't believe her eyes. + +female +"Jacob! It's been so long!" + +narrator +Sarah exclaimed, jumping up from her seat with a radiant smile. + +male +"Sarah, is it really you? I can't believe it!" + +narrator +Jacob replied, grinning widely as he walked over to her. The two friends embraced warmly, as if trying to make up for all the years spent apart. + +female +"What are you doing in town? Last I heard you were in Seattle." + +narrator +She asked, gesturing for him to join her at the table. + +FOLLOW THESE RULES: +1. Always begin with the narrator voice +2. Only use character voices for the EXACT words they speak (in quotes) +3. SWITCH BACK to narrator voice for speech tags and all other text +4. Begin by asking what kind of story the user would like to hear +5. Create engaging dialogue with distinct characters + +Remember: Use narrator voice for EVERYTHING except the actual quoted dialogue.""" + + # Set up LLM context + messages = [ + { + "role": "system", + "content": system_prompt, + }, + ] + + context = OpenAILLMContext(messages) + context_aggregator = llm.create_context_aggregator(context) + + # Create pipeline + pipeline = Pipeline( + [ + transport.input(), + context_aggregator.user(), + llm, + tts, # TTS with pattern aggregator + transport.output(), + context_aggregator.assistant(), + ] + ) + + task = PipelineTask( + pipeline, + params=PipelineParams( + allow_interruptions=True, + enable_metrics=True, + enable_usage_metrics=True, + report_only_initial_ttfb=True, + ), + ) + + @transport.event_handler("on_first_participant_joined") + async def on_first_participant_joined(transport, participant): + logger.info(f"First participant joined: {participant['id']}") + await transport.capture_participant_transcription(participant["id"]) + + # Start conversation - empty prompt to let LLM follow system instructions + await task.queue_frames([context_aggregator.user().get_context_frame()]) + + @transport.event_handler("on_participant_left") + async def on_participant_left(transport, participant, reason): + logger.info(f"Participant left: {participant['id']}") + await task.cancel() + + logger.info(f"Starting storytelling bot at: {room_url}") + logger.info("Join the room to interact with the bot!") + + runner = PipelineRunner() + await runner.run(task) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/examples/foundational/36-user-email-gathering.py b/examples/foundational/36-user-email-gathering.py new file mode 100644 index 000000000..0e76826e3 --- /dev/null +++ b/examples/foundational/36-user-email-gathering.py @@ -0,0 +1,141 @@ +# +# Copyright (c) 2024–2025, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +import asyncio +import os +import sys + +import aiohttp +from dotenv import load_dotenv +from loguru import logger +from openai.types.chat import ChatCompletionToolParam +from runner import configure + +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.services.cartesia import CartesiaTTSService +from pipecat.services.openai import OpenAILLMContext, OpenAILLMService +from pipecat.services.rime import RimeHttpTTSService +from pipecat.transports.services.daily import DailyParams, DailyTransport + +load_dotenv(override=True) + +logger.remove(0) +logger.add(sys.stderr, level="DEBUG") + + +async def store_user_emails(function_name, tool_call_id, args, llm, context, result_callback): + print(f"User emails: {args}") + + +async def main(): + async with aiohttp.ClientSession() as session: + (room_url, token) = await configure(session) + + transport = DailyTransport( + room_url, + token, + "Respond bot", + DailyParams( + audio_out_enabled=True, + transcription_enabled=True, + vad_enabled=True, + vad_analyzer=SileroVADAnalyzer(), + ), + ) + + # Cartesia offers a `` tags that we can use to ask the user + # to confirm the emails. + # (see https://docs.cartesia.ai/build-with-sonic/formatting-text-for-sonic/spelling-out-input-text) + tts = CartesiaTTSService( + api_key=os.getenv("CARTESIA_API_KEY"), + voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady + aiohttp_session=session, + ) + + # Rime offers a function `spell()` that we can use to ask the user + # to confirm the emails. + # (see https://docs.rime.ai/api-reference/spell) + # tts = RimeHttpTTSService( + # api_key=os.getenv("RIME_API_KEY", ""), + # voice_id="eva", + # aiohttp_session=session, + # ) + + llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o") + # You can aslo register a function_name of None to get all functions + # sent to the same callback with an additional function_name parameter. + llm.register_function("store_user_emails", store_user_emails) + + tools = [ + ChatCompletionToolParam( + type="function", + function={ + "name": "store_user_emails", + "description": "Store user emails when confirmed", + "parameters": { + "type": "object", + "properties": { + "emails": { + "type": "array", + "description": "The list of user emails", + "items": {"type": "string"}, + }, + }, + "required": ["emails"], + }, + }, + ) + ] + messages = [ + { + "role": "system", + # Cartesia + "content": "You need to gather a valid email or emails from the user. Your output will be converted to audio so don't include special characters in your answers. If the user provides one or more email addresses confirm them with the user. Enclose all emails with tags, for example a@a.com.", + # Rime spell() + # "content": "You need to gather a valid email or emails from the user. Your output will be converted to audio so don't include special characters in your answers. If the user provides one or more email addresses confirm them with the user. Enclose all emails with spell(), for example spell(a@a.com).", + }, + ] + + context = OpenAILLMContext(messages, tools) + context_aggregator = llm.create_context_aggregator(context) + + pipeline = Pipeline( + [ + transport.input(), + context_aggregator.user(), + llm, + tts, + transport.output(), + context_aggregator.assistant(), + ] + ) + + task = PipelineTask( + pipeline, + params=PipelineParams( + allow_interruptions=True, + enable_metrics=True, + enable_usage_metrics=True, + report_only_initial_ttfb=True, + ), + ) + + @transport.event_handler("on_first_participant_joined") + async def on_first_participant_joined(transport, participant): + await transport.capture_participant_transcription(participant["id"]) + # Kick off the conversation. + await task.queue_frames([context_aggregator.user().get_context_frame()]) + + runner = PipelineRunner() + + await runner.run(task) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/examples/moondream-chatbot/bot.py b/examples/moondream-chatbot/bot.py index 5dd88f4d1..6639c08c1 100644 --- a/examples/moondream-chatbot/bot.py +++ b/examples/moondream-chatbot/bot.py @@ -23,7 +23,6 @@ from pipecat.frames.frames import ( OutputImageRawFrame, SpriteFrame, TextFrame, - UserImageRawFrame, UserImageRequestFrame, ) from pipecat.pipeline.parallel_pipeline import ParallelPipeline diff --git a/examples/news-chatbot/server/news_bot.py b/examples/news-chatbot/server/news_bot.py index 2a389094e..b9f60200f 100644 --- a/examples/news-chatbot/server/news_bot.py +++ b/examples/news-chatbot/server/news_bot.py @@ -97,7 +97,7 @@ async def main(): tts = CartesiaTTSService( api_key=os.getenv("CARTESIA_API_KEY"), voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady - text_filter=MarkdownTextFilter(), + text_filters=[MarkdownTextFilter()], ) llm = GoogleLLMService( diff --git a/examples/patient-intake/bot.py b/examples/patient-intake/bot.py index b6f7fb941..de6ad2051 100644 --- a/examples/patient-intake/bot.py +++ b/examples/patient-intake/bot.py @@ -142,7 +142,9 @@ class IntakeProcessor: ] ) - async def start_prescriptions(self, function_name, llm, context): + async def list_prescriptions( + self, function_name, tool_call_id, args, llm, context, result_callback + ): print(f"!!! doing start prescriptions") # Move on to allergies context.set_tools( @@ -182,9 +184,12 @@ class IntakeProcessor: print(f"!!! about to await llm process frame in start prescrpitions") await llm.queue_frame(OpenAILLMContextFrame(context), FrameDirection.DOWNSTREAM) print(f"!!! past await process frame in start prescriptions") + await self.save_data(args, result_callback) - async def start_allergies(self, function_name, llm, context): - print("!!! doing start allergies") + async def list_allergies( + self, function_name, tool_call_id, args, llm, context, result_callback + ): + print("!!! doing list allergies") # Move on to conditions context.set_tools( [ @@ -221,8 +226,11 @@ class IntakeProcessor: } ) await llm.queue_frame(OpenAILLMContextFrame(context), FrameDirection.DOWNSTREAM) + await self.save_data(args, result_callback) - async def start_conditions(self, function_name, llm, context): + async def list_conditions( + self, function_name, tool_call_id, args, llm, context, result_callback + ): print("!!! doing start conditions") # Move on to visit reasons context.set_tools( @@ -260,8 +268,11 @@ class IntakeProcessor: } ) await llm.queue_frame(OpenAILLMContextFrame(context), FrameDirection.DOWNSTREAM) + await self.save_data(args, result_callback) - async def start_visit_reasons(self, function_name, llm, context): + async def list_visit_reasons( + self, function_name, tool_call_id, args, llm, context, result_callback + ): print("!!! doing start visit reasons") # move to finish call context.set_tools([]) @@ -269,8 +280,9 @@ class IntakeProcessor: {"role": "system", "content": "Now, thank the user and end the conversation."} ) await llm.queue_frame(OpenAILLMContextFrame(context), FrameDirection.DOWNSTREAM) + await self.save_data(args, result_callback) - async def save_data(self, function_name, tool_call_id, args, llm, context, result_callback): + async def save_data(self, args, result_callback): logger.info(f"!!! Saving data: {args}") # Since this is supposed to be "async", returning None from the callback # will prevent adding anything to context or re-prompting @@ -319,18 +331,10 @@ async def main(): intake = IntakeProcessor(context) llm.register_function("verify_birthday", intake.verify_birthday) - llm.register_function( - "list_prescriptions", intake.save_data, start_callback=intake.start_prescriptions - ) - llm.register_function( - "list_allergies", intake.save_data, start_callback=intake.start_allergies - ) - llm.register_function( - "list_conditions", intake.save_data, start_callback=intake.start_conditions - ) - llm.register_function( - "list_visit_reasons", intake.save_data, start_callback=intake.start_visit_reasons - ) + llm.register_function("list_prescriptions", intake.list_prescriptions) + llm.register_function("list_allergies", intake.list_allergies) + llm.register_function("list_conditions", intake.list_conditions) + llm.register_function("list_visit_reasons", intake.list_visit_reasons) fl = FrameLogger("LLM Output") diff --git a/examples/telnyx-chatbot/env.example b/examples/telnyx-chatbot/env.example index 5de3bace2..1da398649 100644 --- a/examples/telnyx-chatbot/env.example +++ b/examples/telnyx-chatbot/env.example @@ -1,3 +1,3 @@ OPENAI_API_KEY= DEEPGRAM_API_KEY= -ELEVENLABS_API_KEY= +CARTESIA_API_KEY= diff --git a/examples/telnyx-chatbot/requirements.txt b/examples/telnyx-chatbot/requirements.txt index 630a98dd6..e103e438d 100644 --- a/examples/telnyx-chatbot/requirements.txt +++ b/examples/telnyx-chatbot/requirements.txt @@ -1,4 +1,4 @@ -pipecat-ai[openai,silero,deepgram,elevenlabs] +pipecat-ai[openai,silero,deepgram,cartesia] fastapi uvicorn python-dotenv diff --git a/pyproject.toml b/pyproject.toml index 586c5c026..d2fa46dcf 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -31,7 +31,7 @@ dependencies = [ "pyloudnorm~=0.1.1", "resampy~=0.4.3", "soxr~=0.5.0", - "openai~=1.59.6" + "openai~=1.67.0" ] [project.urls] @@ -39,50 +39,51 @@ Source = "https://github.com/pipecat-ai/pipecat" Website = "https://pipecat.ai" [project.optional-dependencies] -anthropic = [ "anthropic~=0.47.2" ] -assemblyai = [ "assemblyai~=0.36.0" ] -aws = [ "boto3~=1.35.99" ] +anthropic = [ "anthropic~=0.49.0" ] +assemblyai = [ "assemblyai~=0.37.0" ] +aws = [ "boto3~=1.37.16" ] azure = [ "azure-cognitiveservices-speech~=1.42.0"] canonical = [ "aiofiles~=24.1.0" ] -cartesia = [ "cartesia~=1.3.1", "websockets~=13.1" ] +cartesia = [ "cartesia~=1.4.0", "websockets~=13.1" ] neuphonic = [ "pyneuphonic~=1.5.13", "websockets~=13.1" ] cerebras = [] deepseek = [] -daily = [ "daily-python~=0.15.0" ] +daily = [ "daily-python~=0.16.0" ] deepgram = [ "deepgram-sdk~=3.8.0" ] elevenlabs = [ "websockets~=13.1" ] -fal = [ "fal-client~=0.5.6" ] +fal = [ "fal-client~=0.5.9" ] fish = [ "ormsgpack~=1.7.0", "websockets~=13.1" ] gladia = [ "websockets~=13.1" ] -google = [ "google-cloud-speech~=2.31.0", "google-cloud-texttospeech~=2.25.0", "google-genai~=1.3.0", "google-generativeai~=0.8.4" ] +google = [ "google-cloud-speech~=2.31.1", "google-cloud-texttospeech~=2.25.1", "google-genai~=1.7.0", "google-generativeai~=0.8.4" ] grok = [] groq = [] gstreamer = [ "pygobject~=3.50.0" ] fireworks = [] krisp = [ "pipecat-ai-krisp~=0.3.0" ] koala = [ "pvkoala~=2.0.3" ] -langchain = [ "langchain~=0.3.14", "langchain-community~=0.3.14", "langchain-openai~=0.3.0" ] -livekit = [ "livekit~=0.19.1", "livekit-api~=0.8.1", "tenacity~=9.0.0" ] +langchain = [ "langchain~=0.3.20", "langchain-community~=0.3.20", "langchain-openai~=0.3.9" ] +livekit = [ "livekit~=0.22.0", "livekit-api~=0.8.2", "tenacity~=9.0.0" ] lmnt = [ "websockets~=13.1" ] local = [ "pyaudio~=0.2.14" ] moondream = [ "einops~=0.8.0", "timm~=1.0.13", "transformers~=4.48.0" ] nim = [] noisereduce = [ "noisereduce~=3.0.3" ] openai = [ "websockets~=13.1" ] -openpipe = [ "openpipe~=4.45.0" ] +openpipe = [ "openpipe~=4.48.0" ] +openrouter = [] perplexity = [] playht = [ "pyht~=0.1.12", "websockets~=13.1" ] rime = [ "websockets~=13.1" ] -riva = [ "nvidia-riva-client~=2.18.0" ] -sentry = [ "sentry-sdk~=2.20.0" ] +riva = [ "nvidia-riva-client~=2.19.0" ] +sentry = [ "sentry-sdk~=2.23.1" ] silero = [ "onnxruntime~=1.20.1" ] simli = [ "simli-ai~=0.1.10"] soundfile = [ "soundfile~=0.13.0" ] +tavus=[] together = [] ultravox = [ "transformers~=4.48.0", "vllm~=0.7.3" ] websocket = [ "websockets~=13.1", "fastapi~=0.115.6" ] whisper = [ "faster-whisper~=1.1.1" ] -openrouter = [] [tool.setuptools.packages.find] # All the following settings are optional: diff --git a/src/pipecat/frames/frames.py b/src/pipecat/frames/frames.py index 734f0ea6a..39e25fc11 100644 --- a/src/pipecat/frames/frames.py +++ b/src/pipecat/frames/frames.py @@ -391,7 +391,7 @@ class FunctionCallResultFrame(DataFrame): function_name: str tool_call_id: str - arguments: str + arguments: Any result: Any properties: Optional[FunctionCallResultProperties] = None @@ -640,7 +640,16 @@ class FunctionCallInProgressFrame(SystemFrame): function_name: str tool_call_id: str - arguments: str + arguments: Any + cancel_on_interruption: bool = False + + +@dataclass +class FunctionCallCancelFrame(SystemFrame): + """A frame to signal a function call has been cancelled.""" + + function_name: str + tool_call_id: str @dataclass @@ -660,13 +669,19 @@ class TransportMessageUrgentFrame(SystemFrame): @dataclass class UserImageRequestFrame(SystemFrame): - """A frame user to request an image from the given user.""" + """A frame to request an image from the given user. The frame might be + generated by a function call in which case the corresponding fields will be + properly set. + + """ user_id: str context: Optional[Any] = None + function_name: Optional[str] = None + tool_call_id: Optional[str] = None def __str__(self): - return f"{self.name}, user: {self.user_id}" + return f"{self.name}(user: {self.user_id}, function: {self.function_name}, request: {self.tool_call_id})" @dataclass @@ -696,10 +711,11 @@ class UserImageRawFrame(InputImageRawFrame): """An image associated to a user.""" user_id: str + request: Optional[UserImageRequestFrame] = None def __str__(self): pts = format_pts(self.pts) - return f"{self.name}(pts: {pts}, user: {self.user_id}, size: {self.size}, format: {self.format})" + return f"{self.name}(pts: {pts}, user: {self.user_id}, size: {self.size}, format: {self.format}, request: {self.request})" @dataclass diff --git a/src/pipecat/pipeline/runner.py b/src/pipecat/pipeline/runner.py index 3209fa92a..7ac07064f 100644 --- a/src/pipecat/pipeline/runner.py +++ b/src/pipecat/pipeline/runner.py @@ -40,12 +40,18 @@ class PipelineRunner(BaseObject): task.set_event_loop(self._loop) await task.run() del self._tasks[task.name] + + # Cleanup base object. + await self.cleanup() + # If we are cancelling through a signal, make sure we wait for it so # everything gets cleaned up nicely. if self._sig_task: await self._sig_task + if self._force_gc: self._gc_collect() + logger.debug(f"Runner {self} finished running {task}") async def stop_when_done(self): diff --git a/src/pipecat/pipeline/task.py b/src/pipecat/pipeline/task.py index c10b33189..8279373cb 100644 --- a/src/pipecat/pipeline/task.py +++ b/src/pipecat/pipeline/task.py @@ -5,6 +5,7 @@ # import asyncio +import time from typing import Any, AsyncIterable, Dict, Iterable, List, Optional, Tuple, Type from loguru import logger @@ -13,6 +14,7 @@ from pydantic import BaseModel, ConfigDict from pipecat.clocks.base_clock import BaseClock from pipecat.clocks.system_clock import SystemClock from pipecat.frames.frames import ( + BotSpeakingFrame, CancelFrame, CancelTaskFrame, EndFrame, @@ -20,6 +22,7 @@ from pipecat.frames.frames import ( ErrorFrame, Frame, HeartbeatFrame, + LLMFullResponseEndFrame, MetricsFrame, StartFrame, StopFrame, @@ -133,12 +136,27 @@ class PipelineTask(BaseTask): async def on_frame_reached_downstream(task, frame): ... + It also has an event handler that detects when the pipeline is idle. By + default, a pipeline is idle if no `BotSpeakingFrame` or + `LLMFullResponseEndFrame` are received within `idle_timeout_secs`. + + @task.event_handler("on_idle_timeout") + async def on_idle_timeout(task): + ... + Args: pipeline: The pipeline to execute. params: Configuration parameters for the pipeline. observers: List of observers for monitoring pipeline execution. clock: Clock implementation for timing operations. check_dangling_tasks: Whether to check for processors' tasks finishing properly. + idle_timeout_secs: Timeout (in seconds) to consider pipeline idle or + None. If a pipeline is idle the pipeline task will be cancelled + automatically. + idle_timeout_frames: A tuple with the frames that should trigger an idle + timeout if not received withing `idle_timeout_seconds`. + cancel_on_idle_timeout: Whether the pipeline task should be cancelled if + the idle timeout is reached. """ @@ -151,12 +169,21 @@ class PipelineTask(BaseTask): clock: BaseClock = SystemClock(), task_manager: Optional[BaseTaskManager] = None, check_dangling_tasks: bool = True, + idle_timeout_secs: Optional[float] = 300, + idle_timeout_frames: Tuple[Type[Frame], ...] = ( + BotSpeakingFrame, + LLMFullResponseEndFrame, + ), + cancel_on_idle_timeout: bool = True, ): super().__init__() self._pipeline = pipeline self._clock = clock self._params = params self._check_dangling_tasks = check_dangling_tasks + self._idle_timeout_secs = idle_timeout_secs + self._idle_timeout_frames = idle_timeout_frames + self._cancel_on_idle_timeout = cancel_on_idle_timeout if self._params.observers: import warnings @@ -178,6 +205,10 @@ class PipelineTask(BaseTask): # This is the heartbeat queue. When a heartbeat frame is received in the # down queue we add it to the heartbeat queue for processing. self._heartbeat_queue = asyncio.Queue() + # This is the idle queue. When frames are received downstream they are + # put in the queue. If no frame is received the pipeline is considered + # idle. + self._idle_queue = asyncio.Queue() # This event is used to indicate a finalize frame (e.g. EndFrame, # StopFrame) has been received in the down queue. self._pipeline_end_event = asyncio.Event() @@ -213,6 +244,7 @@ class PipelineTask(BaseTask): self._reached_downstream_types: Tuple[Type[Frame], ...] = () self._register_event_handler("on_frame_reached_upstream") self._register_event_handler("on_frame_reached_downstream") + self._register_event_handler("on_idle_timeout") @property def params(self) -> PipelineParams: @@ -328,19 +360,30 @@ class PipelineTask(BaseTask): self._heartbeat_monitor_handler(), f"{self}::_heartbeat_monitor_handler" ) + def _maybe_start_idle_task(self): + if self._idle_timeout_secs: + self._idle_monitor_task = self._task_manager.create_task( + self._idle_monitor_handler(), f"{self}::_idle_monitor_handler" + ) + async def _cancel_tasks(self): - await self._maybe_cancel_heartbeat_tasks() + await self._observer.stop() await self._task_manager.cancel_task(self._process_up_task) await self._task_manager.cancel_task(self._process_down_task) - await self._observer.stop() + await self._maybe_cancel_heartbeat_tasks() + await self._maybe_cancel_idle_task() async def _maybe_cancel_heartbeat_tasks(self): if self._params.enable_heartbeats: await self._task_manager.cancel_task(self._heartbeat_push_task) await self._task_manager.cancel_task(self._heartbeat_monitor_task) + async def _maybe_cancel_idle_task(self): + if self._idle_timeout_secs: + await self._task_manager.cancel_task(self._idle_monitor_task) + def _initial_metrics_frame(self) -> MetricsFrame: processors = self._pipeline.processors_with_metrics() data = [] @@ -354,6 +397,10 @@ class PipelineTask(BaseTask): self._pipeline_end_event.clear() async def _cleanup(self, cleanup_pipeline: bool): + # Cleanup base object. + await self.cleanup() + + # Cleanup pipeline processors. await self._source.cleanup() if cleanup_pipeline: await self._pipeline.cleanup() @@ -362,12 +409,13 @@ class PipelineTask(BaseTask): async def _process_push_queue(self): """This is the task that runs the pipeline for the first time by sending a StartFrame and by pushing any other frames queued by the user. It runs - until the tasks is canceled or stopped (e.g. with an EndFrame). + until the tasks is cancelled or stopped (e.g. with an EndFrame). """ self._clock.start() self._maybe_start_heartbeat_tasks() + self._maybe_start_idle_task() start_frame = StartFrame( clock=self._clock, @@ -421,12 +469,14 @@ class PipelineTask(BaseTask): # Tell the task we should stop nicely. await self.queue_frame(StopFrame()) elif isinstance(frame, ErrorFrame): - logger.error(f"Error running app: {frame}") if frame.fatal: + logger.error(f"A fatal error occurred: {frame}") # Cancel all tasks downstream. await self.queue_frame(CancelFrame()) # Tell the task we should stop. await self.queue_frame(StopTaskFrame()) + else: + logger.warning(f"Something went wrong: {frame}") self._up_queue.task_done() async def _process_down_queue(self): @@ -439,6 +489,10 @@ class PipelineTask(BaseTask): while True: frame = await self._down_queue.get() + # Queue received frame to the idle queue so we can monitor idle + # pipelines. + await self._idle_queue.put(frame) + if isinstance(frame, self._reached_downstream_types): await self._call_event_handler("on_frame_reached_downstream", frame) @@ -476,6 +530,48 @@ class PipelineTask(BaseTask): f"{self}: heartbeat frame not received for more than {wait_time} seconds" ) + async def _idle_monitor_handler(self): + """This tasks monitors activity in the pipeline. If no frames are + received (heartbeats don't count) the pipeline is considered idle. + + """ + running = True + last_frame_time = 0 + while running: + try: + frame = await asyncio.wait_for( + self._idle_queue.get(), timeout=self._idle_timeout_secs + ) + + if isinstance(frame, StartFrame) or isinstance(frame, self._idle_timeout_frames): + # If we find a StartFrame or one of the frames that prevents a + # time out we update the time. + last_frame_time = time.time() + else: + # If we find any other frame we check if the pipeline is + # idle by checking the last time we received one of the + # valid frames. + diff_time = time.time() - last_frame_time + if diff_time >= self._idle_timeout_secs: + running = await self._idle_timeout_detected() + + self._idle_queue.task_done() + except asyncio.TimeoutError: + running = await self._idle_timeout_detected() + + async def _idle_timeout_detected(self) -> bool: + """Logic for when the pipeline is idle. + + Returns: + bool: Whther the pipeline task is being cancelled or not. + """ + await self._call_event_handler("on_idle_timeout") + if self._cancel_on_idle_timeout: + logger.warning(f"Idle pipeline detected, cancelling pipeline task...") + await self.cancel() + return False + return True + def _print_dangling_tasks(self): tasks = [t.get_name() for t in self._task_manager.current_tasks()] if tasks: diff --git a/src/pipecat/processors/aggregators/llm_response.py b/src/pipecat/processors/aggregators/llm_response.py index 44d41f535..c0ef65f12 100644 --- a/src/pipecat/processors/aggregators/llm_response.py +++ b/src/pipecat/processors/aggregators/llm_response.py @@ -5,16 +5,21 @@ # import asyncio -import time from abc import abstractmethod -from typing import List, Literal +from typing import Dict, List, Literal, Set + +from loguru import logger from pipecat.frames.frames import ( + BotStoppedSpeakingFrame, CancelFrame, EmulateUserStartedSpeakingFrame, EmulateUserStoppedSpeakingFrame, EndFrame, Frame, + FunctionCallCancelFrame, + FunctionCallInProgressFrame, + FunctionCallResultFrame, InterimTranscriptionFrame, LLMFullResponseEndFrame, LLMFullResponseStartFrame, @@ -24,10 +29,12 @@ from pipecat.frames.frames import ( LLMSetToolChoiceFrame, LLMSetToolsFrame, LLMTextFrame, + OpenAILLMContextAssistantTimestampFrame, StartFrame, StartInterruptionFrame, TextFrame, TranscriptionFrame, + UserImageRawFrame, UserStartedSpeakingFrame, UserStoppedSpeakingFrame, ) @@ -36,6 +43,7 @@ from pipecat.processors.aggregators.openai_llm_context import ( OpenAILLMContextFrame, ) from pipecat.processors.frame_processor import FrameDirection, FrameProcessor +from pipecat.utils.time import time_now_iso8601 class LLMFullResponseAggregator(FrameProcessor): @@ -144,74 +152,23 @@ class BaseLLMResponseAggregator(FrameProcessor): internal messages.""" pass + @abstractmethod + async def handle_aggregation(self, aggregation: str): + """Adds the given aggregation to the aggregator. The aggregator can use + a simple list of message or a context. It doesn't not push any frames. + + """ + pass + @abstractmethod async def push_aggregation(self): + """Pushes the current aggregation. For example, iN the case of context + aggregation this might push a new context frame. + + """ pass -class LLMResponseAggregator(BaseLLMResponseAggregator): - """This is a base LLM aggregator that uses a simple list of messages to - store the conversation. It pushes `LLMMessagesFrame` as an aggregation - frame. - - """ - - def __init__( - self, - *, - messages: List[dict], - role: str = "user", - **kwargs, - ): - super().__init__(**kwargs) - - self._messages = messages - self._role = role - - self._aggregation = "" - - self.reset() - - @property - def messages(self) -> List[dict]: - return self._messages - - @property - def role(self) -> str: - return self._role - - def add_messages(self, messages): - self._messages.extend(messages) - - def set_messages(self, messages): - self.reset() - self._messages.clear() - self._messages.extend(messages) - - def set_tools(self, tools): - pass - - def set_tool_choice(self, tool_choice): - pass - - def reset(self): - self._aggregation = "" - - async def push_aggregation(self): - if len(self._aggregation) > 0: - self._messages.append({"role": self._role, "content": self._aggregation}) - - # Reset the aggregation. Reset it before pushing it down, otherwise - # if the tasks gets cancelled we won't be able to clear things up. - self._aggregation = "" - - frame = LLMMessagesFrame(self._messages) - await self.push_frame(frame) - - # Reset our accumulator state. - self.reset() - - class LLMContextResponseAggregator(BaseLLMResponseAggregator): """This is a base LLM aggregator that uses an LLM context to store the conversation. It pushes `OpenAILLMContextFrame` as an aggregation frame. @@ -259,20 +216,6 @@ class LLMContextResponseAggregator(BaseLLMResponseAggregator): def reset(self): self._aggregation = "" - async def push_aggregation(self): - if len(self._aggregation) > 0: - self._context.add_message({"role": self.role, "content": self._aggregation}) - - # Reset the aggregation. Reset it before pushing it down, otherwise - # if the tasks gets cancelled we won't be able to clear things up. - self._aggregation = "" - - frame = OpenAILLMContextFrame(self._context) - await self.push_frame(frame) - - # Reset our accumulator state. - self.reset() - class LLMUserContextAggregator(LLMContextResponseAggregator): """This is a user LLM aggregator that uses an LLM context to store the @@ -287,26 +230,26 @@ class LLMUserContextAggregator(LLMContextResponseAggregator): self, context: OpenAILLMContext, aggregation_timeout: float = 1.0, - bot_interruption_timeout: float = 2.0, **kwargs, ): super().__init__(context=context, role="user", **kwargs) self._aggregation_timeout = aggregation_timeout - self._bot_interruption_timeout = bot_interruption_timeout self._seen_interim_results = False self._user_speaking = False - self._last_user_speaking_time = 0 self._emulating_vad = False + self._waiting_for_aggregation = False self._aggregation_event = asyncio.Event() self._aggregation_task = None - self.reset() - def reset(self): super().reset() self._seen_interim_results = False + self._waiting_for_aggregation = False + + async def handle_aggregation(self, aggregation: str): + self._context.add_message({"role": self.role, "content": self._aggregation}) async def process_frame(self, frame: Frame, direction: FrameDirection): await super().process_frame(frame, direction) @@ -345,6 +288,17 @@ class LLMUserContextAggregator(LLMContextResponseAggregator): else: await self.push_frame(frame, direction) + async def push_aggregation(self): + if len(self._aggregation) > 0: + await self.handle_aggregation(self._aggregation) + + # Reset the aggregation. Reset it before pushing it down, otherwise + # if the tasks gets cancelled we won't be able to clear things up. + self.reset() + + frame = OpenAILLMContextFrame(self._context) + await self.push_frame(frame) + async def _start(self, frame: StartFrame): self._create_aggregation_task() @@ -355,12 +309,14 @@ class LLMUserContextAggregator(LLMContextResponseAggregator): await self._cancel_aggregation_task() async def _handle_user_started_speaking(self, _: UserStartedSpeakingFrame): - self._last_user_speaking_time = time.time() self._user_speaking = True + self._waiting_for_aggregation = True async def _handle_user_stopped_speaking(self, _: UserStoppedSpeakingFrame): - self._last_user_speaking_time = time.time() self._user_speaking = False + # We just stopped speaking. Let's see if there's some aggregation to + # push. If the last thing we saw is an interim transcription, let's wait + # pushing the aggregation as we will probably get a final transcription. if not self._seen_interim_results: await self.push_aggregation() @@ -413,18 +369,13 @@ class LLMUserContextAggregator(LLMContextResponseAggregator): frame we might want to interrupt the bot. """ - if not self._user_speaking: - diff_time = time.time() - self._last_user_speaking_time - if diff_time > self._bot_interruption_timeout: - # If we reach this case we received a transcription but VAD was - # not able to detect voice (e.g. when you whisper a short - # utterance). So, we need to emulate VAD (i.e. user - # start/stopped speaking). - await self.push_frame(EmulateUserStartedSpeakingFrame(), FrameDirection.UPSTREAM) - self._emulating_vad = True - - # Reset time so we don't interrupt again right away. - self._last_user_speaking_time = time.time() + if not self._user_speaking and not self._waiting_for_aggregation: + # If we reach this case we received a transcription but VAD was not + # able to detect voice (e.g. when you whisper a short + # utterance). So, we need to emulate VAD (i.e. user start/stopped + # speaking). + await self.push_frame(EmulateUserStartedSpeakingFrame(), FrameDirection.UPSTREAM) + self._emulating_vad = True class LLMAssistantContextAggregator(LLMContextResponseAggregator): @@ -438,17 +389,30 @@ class LLMAssistantContextAggregator(LLMContextResponseAggregator): super().__init__(context=context, role="assistant", **kwargs) self._expect_stripped_words = expect_stripped_words - self._started = False + self._started = 0 + self._function_calls_in_progress: Dict[str, FunctionCallInProgressFrame] = {} + self._context_updated_tasks: Set[asyncio.Task] = set() - self.reset() + async def handle_aggregation(self, aggregation: str): + self._context.add_message({"role": "assistant", "content": aggregation}) + + async def handle_function_call_in_progress(self, frame: FunctionCallInProgressFrame): + pass + + async def handle_function_call_result(self, frame: FunctionCallResultFrame): + pass + + async def handle_function_call_cancel(self, frame: FunctionCallCancelFrame): + pass + + async def handle_user_image_frame(self, frame: UserImageRawFrame): + pass async def process_frame(self, frame: Frame, direction: FrameDirection): await super().process_frame(frame, direction) if isinstance(frame, StartInterruptionFrame): - await self.push_aggregation() - # Reset anyways - self.reset() + await self._handle_interruptions(frame) await self.push_frame(frame, direction) elif isinstance(frame, LLMFullResponseStartFrame): await self._handle_llm_start(frame) @@ -464,14 +428,120 @@ class LLMAssistantContextAggregator(LLMContextResponseAggregator): self.set_tools(frame.tools) elif isinstance(frame, LLMSetToolChoiceFrame): self.set_tool_choice(frame.tool_choice) + elif isinstance(frame, FunctionCallInProgressFrame): + await self._handle_function_call_in_progress(frame) + elif isinstance(frame, FunctionCallResultFrame): + await self._handle_function_call_result(frame) + elif isinstance(frame, FunctionCallCancelFrame): + await self._handle_function_call_cancel(frame) + elif isinstance(frame, UserImageRawFrame) and frame.request and frame.request.tool_call_id: + await self._handle_user_image_frame(frame) + elif isinstance(frame, BotStoppedSpeakingFrame): + await self.push_aggregation() else: await self.push_frame(frame, direction) + async def push_aggregation(self): + if not self._aggregation: + return + + aggregation = self._aggregation.strip() + self.reset() + + if aggregation: + await self.handle_aggregation(aggregation) + + # Push context frame + await self.push_context_frame() + + # Push timestamp frame with current time + timestamp_frame = OpenAILLMContextAssistantTimestampFrame(timestamp=time_now_iso8601()) + await self.push_frame(timestamp_frame) + + async def _handle_interruptions(self, frame: StartInterruptionFrame): + await self.push_aggregation() + self._started = 0 + self.reset() + + async def _handle_function_call_in_progress(self, frame: FunctionCallInProgressFrame): + logger.debug( + f"{self} FunctionCallInProgressFrame: [{frame.function_name}:{frame.tool_call_id}]" + ) + await self.handle_function_call_in_progress(frame) + self._function_calls_in_progress[frame.tool_call_id] = frame + + async def _handle_function_call_result(self, frame: FunctionCallResultFrame): + logger.debug( + f"{self} FunctionCallResultFrame: [{frame.function_name}:{frame.tool_call_id}]" + ) + if frame.tool_call_id not in self._function_calls_in_progress: + logger.warning( + f"FunctionCallResultFrame tool_call_id [{frame.tool_call_id}] is not running" + ) + return + + del self._function_calls_in_progress[frame.tool_call_id] + + properties = frame.properties + + await self.handle_function_call_result(frame) + + # Run inference if the function call result requires it. + if frame.result: + run_llm = False + + if properties and properties.run_llm is not None: + # If the tool call result has a run_llm property, use it + run_llm = properties.run_llm + else: + # Default behavior is to run the LLM if there are no function calls in progress + run_llm = not bool(self._function_calls_in_progress) + + if run_llm: + await self.push_context_frame(FrameDirection.UPSTREAM) + + # Call the `on_context_updated` callback once the function call result + # is added to the context. Also, run this in a separate task to make + # sure we don't block the pipeline. + if properties and properties.on_context_updated: + task_name = f"{frame.function_name}:{frame.tool_call_id}:on_context_updated" + task = self.create_task(properties.on_context_updated(), task_name) + self._context_updated_tasks.add(task) + task.add_done_callback(self._context_updated_task_finished) + + async def _handle_function_call_cancel(self, frame: FunctionCallCancelFrame): + logger.debug( + f"{self} FunctionCallCancelFrame: [{frame.function_name}:{frame.tool_call_id}]" + ) + if frame.tool_call_id not in self._function_calls_in_progress: + return + + if self._function_calls_in_progress[frame.tool_call_id].cancel_on_interruption: + await self.handle_function_call_cancel(frame) + del self._function_calls_in_progress[frame.tool_call_id] + + async def _handle_user_image_frame(self, frame: UserImageRawFrame): + logger.debug( + f"{self} UserImageRawFrame: [{frame.request.function_name}:{frame.request.tool_call_id}]" + ) + + if frame.request.tool_call_id not in self._function_calls_in_progress: + logger.warning( + f"UserImageRawFrame tool_call_id [{frame.request.tool_call_id}] is not running" + ) + return + + del self._function_calls_in_progress[frame.request.tool_call_id] + + await self.handle_user_image_frame(frame) + await self.push_aggregation() + await self.push_context_frame(FrameDirection.UPSTREAM) + async def _handle_llm_start(self, _: LLMFullResponseStartFrame): - self._started = True + self._started += 1 async def _handle_llm_end(self, _: LLMFullResponseEndFrame): - self._started = False + self._started -= 1 await self.push_aggregation() async def _handle_text(self, frame: TextFrame): @@ -483,6 +553,13 @@ class LLMAssistantContextAggregator(LLMContextResponseAggregator): else: self._aggregation += frame.text + def _context_updated_task_finished(self, task: asyncio.Task): + self._context_updated_tasks.discard(task) + # The task is finished so this should exit immediately. We need to do + # this because otherwise the task manager would report a dangling task + # if we don't remove it. + asyncio.run_coroutine_threadsafe(self.wait_for_task(task), self.get_event_loop()) + class LLMUserResponseAggregator(LLMUserContextAggregator): def __init__(self, messages: List[dict] = [], **kwargs): @@ -490,18 +567,15 @@ class LLMUserResponseAggregator(LLMUserContextAggregator): async def push_aggregation(self): if len(self._aggregation) > 0: - self._context.add_message({"role": self.role, "content": self._aggregation}) + await self.handle_aggregation(self._aggregation) # Reset the aggregation. Reset it before pushing it down, otherwise # if the tasks gets cancelled we won't be able to clear things up. - self._aggregation = "" + self.reset() frame = LLMMessagesFrame(self._context.messages) await self.push_frame(frame) - # Reset our accumulator state. - self.reset() - class LLMAssistantResponseAggregator(LLMAssistantContextAggregator): def __init__(self, messages: List[dict] = [], **kwargs): @@ -509,14 +583,11 @@ class LLMAssistantResponseAggregator(LLMAssistantContextAggregator): async def push_aggregation(self): if len(self._aggregation) > 0: - self._context.add_message({"role": self.role, "content": self._aggregation}) + await self.handle_aggregation(self._aggregation) # Reset the aggregation. Reset it before pushing it down, otherwise # if the tasks gets cancelled we won't be able to clear things up. - self._aggregation = "" + self.reset() frame = LLMMessagesFrame(self._context.messages) await self.push_frame(frame) - - # Reset our accumulator state. - self.reset() diff --git a/src/pipecat/processors/aggregators/openai_llm_context.py b/src/pipecat/processors/aggregators/openai_llm_context.py index e8391d62b..948e3e101 100644 --- a/src/pipecat/processors/aggregators/openai_llm_context.py +++ b/src/pipecat/processors/aggregators/openai_llm_context.py @@ -9,9 +9,8 @@ import copy import io import json from dataclasses import dataclass -from typing import Any, Awaitable, Callable, List, Optional +from typing import Any, List, Optional -from loguru import logger from openai._types import NOT_GIVEN, NotGiven from openai.types.chat import ( ChatCompletionMessageParam, @@ -22,12 +21,7 @@ from PIL import Image from pipecat.adapters.base_llm_adapter import BaseLLMAdapter from pipecat.adapters.schemas.tools_schema import ToolsSchema -from pipecat.frames.frames import ( - AudioRawFrame, - Frame, - FunctionCallInProgressFrame, - FunctionCallResultFrame, -) +from pipecat.frames.frames import AudioRawFrame, Frame from pipecat.processors.frame_processor import FrameDirection, FrameProcessor # JSON custom encoder to handle bytes arrays so that we can log contexts @@ -52,7 +46,6 @@ class OpenAILLMContext: self._messages: List[ChatCompletionMessageParam] = messages if messages else [] self._tool_choice: ChatCompletionToolChoiceOptionParam | NotGiven = tool_choice self._tools: List[ChatCompletionToolParam] | NotGiven | ToolsSchema = tools - self._user_image_request_context = {} self._llm_adapter: Optional[BaseLLMAdapter] = None def get_llm_adapter(self) -> Optional[BaseLLMAdapter]: @@ -164,7 +157,7 @@ class OpenAILLMContext: self._tool_choice = tool_choice def set_tools(self, tools: List[ChatCompletionToolParam] | NotGiven | ToolsSchema = NOT_GIVEN): - if tools != NOT_GIVEN and len(tools) == 0: + if tools != NOT_GIVEN and isinstance(tools, list) and len(tools) == 0: tools = NOT_GIVEN self._tools = tools @@ -187,61 +180,6 @@ class OpenAILLMContext: # todo: implement for OpenAI models and others pass - async def call_function( - self, - f: Callable[ - [str, str, Any, FrameProcessor, "OpenAILLMContext", Callable[[Any], Awaitable[None]]], - Awaitable[None], - ], - *, - function_name: str, - tool_call_id: str, - arguments: str, - llm: FrameProcessor, - run_llm: bool = True, - ) -> None: - logger.info(f"Calling function {function_name} with arguments {arguments}") - # Push a SystemFrame downstream. This frame will let our assistant context aggregator - # know that we are in the middle of a function call. Some contexts/aggregators may - # not need this. But some definitely do (Anthropic, for example). - # Also push a SystemFrame upstream for use by other processors, like STTMuteFilter. - progress_frame_downstream = FunctionCallInProgressFrame( - function_name=function_name, - tool_call_id=tool_call_id, - arguments=arguments, - ) - progress_frame_upstream = FunctionCallInProgressFrame( - function_name=function_name, - tool_call_id=tool_call_id, - arguments=arguments, - ) - - # Push frame both downstream and upstream - await llm.push_frame(progress_frame_downstream, FrameDirection.DOWNSTREAM) - await llm.push_frame(progress_frame_upstream, FrameDirection.UPSTREAM) - - # Define a callback function that pushes a FunctionCallResultFrame upstream & downstream. - async def function_call_result_callback(result, *, properties=None): - result_frame_downstream = FunctionCallResultFrame( - function_name=function_name, - tool_call_id=tool_call_id, - arguments=arguments, - result=result, - properties=properties, - ) - result_frame_upstream = FunctionCallResultFrame( - function_name=function_name, - tool_call_id=tool_call_id, - arguments=arguments, - result=result, - properties=properties, - ) - - await llm.push_frame(result_frame_downstream, FrameDirection.DOWNSTREAM) - await llm.push_frame(result_frame_upstream, FrameDirection.UPSTREAM) - - await f(function_name, tool_call_id, arguments, llm, self, function_call_result_callback) - def create_wav_header(self, sample_rate, num_channels, bits_per_sample, data_size): # RIFF chunk descriptor header = bytearray() diff --git a/src/pipecat/processors/frame_processor.py b/src/pipecat/processors/frame_processor.py index 6a1669ff1..590698e7f 100644 --- a/src/pipecat/processors/frame_processor.py +++ b/src/pipecat/processors/frame_processor.py @@ -147,10 +147,13 @@ class FrameProcessor(BaseObject): await self.stop_ttfb_metrics() await self.stop_processing_metrics() - def create_task(self, coroutine: Coroutine) -> asyncio.Task: + def create_task(self, coroutine: Coroutine, name: Optional[str] = None) -> asyncio.Task: if not self._task_manager: raise Exception(f"{self} TaskManager is still not initialized.") - name = f"{self}::{coroutine.cr_code.co_name}" + if name: + name = f"{self}::{name}" + else: + name = f"{self}::{coroutine.cr_code.co_name}" return self._task_manager.create_task(coroutine, name) async def cancel_task(self, task: asyncio.Task, timeout: Optional[float] = None): @@ -164,6 +167,7 @@ class FrameProcessor(BaseObject): await self._task_manager.wait_for_task(task, timeout) async def cleanup(self): + await super().cleanup() await self.__cancel_input_task() await self.__cancel_push_task() diff --git a/src/pipecat/processors/frameworks/rtvi.py b/src/pipecat/processors/frameworks/rtvi.py index 29747a582..f782e6ea8 100644 --- a/src/pipecat/processors/frameworks/rtvi.py +++ b/src/pipecat/processors/frameworks/rtvi.py @@ -29,6 +29,7 @@ from pipecat.frames.frames import ( CancelFrame, DataFrame, EndFrame, + EndTaskFrame, ErrorFrame, Frame, FunctionCallResultFrame, @@ -439,7 +440,9 @@ class RTVIObserver(BaseObserver): if isinstance(frame, (UserStartedSpeakingFrame, UserStoppedSpeakingFrame)): await self._handle_interruptions(frame) - elif isinstance(frame, (BotStartedSpeakingFrame, BotStoppedSpeakingFrame)): + elif isinstance(frame, (BotStartedSpeakingFrame, BotStoppedSpeakingFrame)) and ( + direction == FrameDirection.UPSTREAM + ): await self._handle_bot_speaking(frame) elif isinstance(frame, (TranscriptionFrame, InterimTranscriptionFrame)): await self._handle_user_transcriptions(frame) @@ -766,7 +769,7 @@ class RTVIProcessor(FrameProcessor): update_config = RTVIUpdateConfig.model_validate(message.data) await self._handle_update_config(message.id, update_config) case "disconnect-bot": - await self.push_frame(EndFrame()) + await self.push_frame(EndTaskFrame(), FrameDirection.UPSTREAM) case "action": action = RTVIActionRun.model_validate(message.data) action_frame = RTVIActionFrame(message_id=message.id, rtvi_action_run=action) diff --git a/src/pipecat/processors/transcript_processor.py b/src/pipecat/processors/transcript_processor.py index 614fe176c..3eaff66ca 100644 --- a/src/pipecat/processors/transcript_processor.py +++ b/src/pipecat/processors/transcript_processor.py @@ -90,11 +90,62 @@ class AssistantTranscriptProcessor(BaseTranscriptProcessor): self._aggregation_start_time: Optional[str] = None async def _emit_aggregated_text(self): - """Emit aggregated text as a transcript message.""" + """Aggregates and emits text fragments as a transcript message. + + This method uses a heuristic to automatically detect whether text fragments + use pre-spacing (spaces at the beginning of fragments) or not, and applies + the appropriate joining strategy. It handles fragments from different TTS + services with different formatting patterns. + + Examples: + Pre-spaced fragments (concatenated): + ``` + TTSTextFrame: ["Hello"] + TTSTextFrame: [" there"] + TTSTextFrame: ["!"] + TTSTextFrame: [" How"] + TTSTextFrame: ["'s"] + TTSTextFrame: [" it"] + TTSTextFrame: [" going"] + TTSTextFrame: ["?"] + ``` + Result: "Hello there! How's it going?" + + Word-by-word fragments (joined with spaces): + ``` + TTSTextFrame: ["Hello"] + TTSTextFrame: ["there!"] + TTSTextFrame: ["How"] + TTSTextFrame: ["is"] + TTSTextFrame: ["it"] + TTSTextFrame: ["going?"] + ``` + Result: "Hello there! How is it going?" + """ if self._current_text_parts and self._aggregation_start_time: - content = " ".join(self._current_text_parts).strip() + # Heuristic to detect pre-spaced fragments + uses_prespacing = False + if len(self._current_text_parts) > 1: + # Check if any fragment after the first one starts with whitespace + has_spaced_parts = any( + part and part[0].isspace() for part in self._current_text_parts[1:] + ) + if has_spaced_parts: + uses_prespacing = True + + # Apply appropriate joining method + if uses_prespacing: + # Pre-spaced fragments - just concatenate + content = "".join(self._current_text_parts) + else: + # Word-by-word fragments - join with spaces + content = " ".join(self._current_text_parts) + + # Clean up any excessive whitespace + content = content.strip() + if content: - logger.debug(f"Emitting aggregated assistant message: {content}") + logger.trace(f"Emitting aggregated assistant message: {content}") message = TranscriptionMessage( role="assistant", content=content, @@ -102,7 +153,7 @@ class AssistantTranscriptProcessor(BaseTranscriptProcessor): ) await self._emit_update([message]) else: - logger.debug("No content to emit after stripping whitespace") + logger.trace("No content to emit after stripping whitespace") # Reset aggregation state self._current_text_parts = [] diff --git a/src/pipecat/services/ai_services.py b/src/pipecat/services/ai_services.py index 3fe33d69e..a78c268dd 100644 --- a/src/pipecat/services/ai_services.py +++ b/src/pipecat/services/ai_services.py @@ -8,13 +8,13 @@ import asyncio import io import wave from abc import abstractmethod -from typing import Any, AsyncGenerator, Dict, List, Mapping, Optional, Tuple, Type +from dataclasses import dataclass +from typing import Any, AsyncGenerator, Dict, List, Mapping, Optional, Sequence, Set, Tuple, Type from loguru import logger from pipecat.adapters.base_llm_adapter import BaseLLMAdapter from pipecat.adapters.services.open_ai_adapter import OpenAILLMAdapter -from pipecat.audio.utils import calculate_audio_volume, exp_smoothing from pipecat.frames.frames import ( AudioRawFrame, BotStartedSpeakingFrame, @@ -23,6 +23,9 @@ from pipecat.frames.frames import ( EndFrame, ErrorFrame, Frame, + FunctionCallCancelFrame, + FunctionCallInProgressFrame, + FunctionCallResultFrame, InterimTranscriptionFrame, LLMFullResponseEndFrame, StartFrame, @@ -38,6 +41,8 @@ from pipecat.frames.frames import ( TTSTextFrame, TTSUpdateSettingsFrame, UserImageRequestFrame, + UserStartedSpeakingFrame, + UserStoppedSpeakingFrame, VisionImageRawFrame, ) from pipecat.metrics.metrics import MetricsData @@ -137,6 +142,13 @@ class AIService(FrameProcessor): await self.push_frame(f) +@dataclass +class FunctionEntry: + function_name: Optional[str] + callback: Any # TODO(aleix): add proper typing. + cancel_on_interruption: bool + + class LLMService(AIService): """This class is a no-op but serves as a base class for LLM services.""" @@ -146,38 +158,74 @@ class LLMService(AIService): def __init__(self, **kwargs): super().__init__(**kwargs) - self._callbacks = {} + self._functions = {} self._start_callbacks = {} self._adapter = self.adapter_class() + self._function_call_tasks: Set[Tuple[asyncio.Task, str, str]] = set() + + self._register_event_handler("on_completion_timeout") def get_llm_adapter(self) -> BaseLLMAdapter: return self._adapter def create_context_aggregator( - self, context: OpenAILLMContext, *, assistant_expect_stripped_words: bool = True + self, + context: OpenAILLMContext, + *, + user_kwargs: Mapping[str, Any] = {}, + assistant_kwargs: Mapping[str, Any] = {}, ) -> Any: pass - self._register_event_handler("on_completion_timeout") + async def process_frame(self, frame: Frame, direction: FrameDirection): + await super().process_frame(frame, direction) - # TODO-CB: callback function type - def register_function(self, function_name: Optional[str], callback, start_callback=None): + if isinstance(frame, StartInterruptionFrame): + await self._handle_interruptions(frame) + + async def _handle_interruptions(self, frame: StartInterruptionFrame): + for function_name, entry in self._functions.items(): + if entry.cancel_on_interruption: + await self._cancel_function_call(function_name) + + def register_function( + self, + function_name: Optional[str], + callback: Any, + start_callback=None, + *, + cancel_on_interruption: bool = False, + ): # Registering a function with the function_name set to None will run that callback # for all functions - self._callbacks[function_name] = callback - # QUESTION FOR CB: maybe this isn't needed anymore? + self._functions[function_name] = FunctionEntry( + function_name=function_name, + callback=callback, + cancel_on_interruption=cancel_on_interruption, + ) + + # Start callbacks are now deprecated. if start_callback: + import warnings + + with warnings.catch_warnings(): + warnings.simplefilter("always") + warnings.warn( + "Parameter 'start_callback' is deprecated, just put your code on top of the actual function call instead.", + DeprecationWarning, + ) + self._start_callbacks[function_name] = start_callback def unregister_function(self, function_name: Optional[str]): - del self._callbacks[function_name] + del self._functions[function_name] if self._start_callbacks[function_name]: del self._start_callbacks[function_name] def has_function(self, function_name: str): - if None in self._callbacks.keys(): + if None in self._functions.keys(): return True - return function_name in self._callbacks.keys() + return function_name in self._functions.keys() async def call_function( self, @@ -187,36 +235,144 @@ class LLMService(AIService): function_name: str, arguments: str, run_llm: bool = True, - ) -> None: - f = None - if function_name in self._callbacks.keys(): - f = self._callbacks[function_name] - elif None in self._callbacks.keys(): - f = self._callbacks[None] - else: - return None - await self.call_start_function(context, function_name) - await context.call_function( - f, - function_name=function_name, - tool_call_id=tool_call_id, - arguments=arguments, - llm=self, - run_llm=run_llm, + ): + if not function_name in self._functions.keys() and not None in self._functions.keys(): + return + + task = self.create_task( + self._run_function_call(context, tool_call_id, function_name, arguments, run_llm) ) - # QUESTION FOR CB: maybe this isn't needed anymore? + self._function_call_tasks.add((task, tool_call_id, function_name)) + + task.add_done_callback(self._function_call_task_finished) + async def call_start_function(self, context: OpenAILLMContext, function_name: str): if function_name in self._start_callbacks.keys(): await self._start_callbacks[function_name](function_name, self, context) elif None in self._start_callbacks.keys(): return await self._start_callbacks[None](function_name, self, context) - async def request_image_frame(self, user_id: str, *, text_content: Optional[str] = None): + async def request_image_frame( + self, + user_id: str, + *, + function_name: Optional[str] = None, + tool_call_id: Optional[str] = None, + text_content: Optional[str] = None, + ): await self.push_frame( - UserImageRequestFrame(user_id=user_id, context=text_content), FrameDirection.UPSTREAM + UserImageRequestFrame( + user_id=user_id, + function_name=function_name, + tool_call_id=tool_call_id, + context=text_content, + ), + FrameDirection.UPSTREAM, ) + async def _run_function_call( + self, + context: OpenAILLMContext, + tool_call_id: str, + function_name: str, + arguments: str, + run_llm: bool = True, + ): + if function_name in self._functions.keys(): + entry = self._functions[function_name] + elif None in self._functions.keys(): + entry = self._functions[None] + else: + return + + logger.debug( + f"{self} Calling function [{function_name}:{tool_call_id}] with arguments {arguments}" + ) + + # NOTE(aleix): This needs to be removed after we remove the deprecation. + await self.call_start_function(context, function_name) + + # Push a SystemFrame downstream. This frame will let our assistant context aggregator + # know that we are in the middle of a function call. Some contexts/aggregators may + # not need this. But some definitely do (Anthropic, for example). + # Also push a SystemFrame upstream for use by other processors, like STTMuteFilter. + progress_frame_downstream = FunctionCallInProgressFrame( + function_name=function_name, + tool_call_id=tool_call_id, + arguments=arguments, + cancel_on_interruption=entry.cancel_on_interruption, + ) + progress_frame_upstream = FunctionCallInProgressFrame( + function_name=function_name, + tool_call_id=tool_call_id, + arguments=arguments, + cancel_on_interruption=entry.cancel_on_interruption, + ) + + # Push frame both downstream and upstream + await self.push_frame(progress_frame_downstream, FrameDirection.DOWNSTREAM) + await self.push_frame(progress_frame_upstream, FrameDirection.UPSTREAM) + + # Define a callback function that pushes a FunctionCallResultFrame upstream & downstream. + async def function_call_result_callback(result, *, properties=None): + result_frame_downstream = FunctionCallResultFrame( + function_name=function_name, + tool_call_id=tool_call_id, + arguments=arguments, + result=result, + properties=properties, + ) + result_frame_upstream = FunctionCallResultFrame( + function_name=function_name, + tool_call_id=tool_call_id, + arguments=arguments, + result=result, + properties=properties, + ) + + await self.push_frame(result_frame_downstream, FrameDirection.DOWNSTREAM) + await self.push_frame(result_frame_upstream, FrameDirection.UPSTREAM) + + await entry.callback( + function_name, tool_call_id, arguments, self, context, function_call_result_callback + ) + + async def _cancel_function_call(self, function_name: str): + cancelled_tasks = set() + for task, tool_call_id, name in self._function_call_tasks: + if name == function_name: + # We remove the callback because we are going to cancel the task + # now, otherwise we will be removing it from the set while we + # are iterating. + task.remove_done_callback(self._function_call_task_finished) + + logger.debug(f"{self} Cancelling function call [{name}:{tool_call_id}]...") + + await self.cancel_task(task) + + frame = FunctionCallCancelFrame( + function_name=function_name, tool_call_id=tool_call_id + ) + await self.push_frame(frame) + + logger.debug(f"{self} Function call [{name}:{tool_call_id}] has been cancelled") + + cancelled_tasks.add(task) + + # Remove all cancelled tasks from our set. + for task in cancelled_tasks: + self._function_call_task_finished(task) + + def _function_call_task_finished(self, task: asyncio.Task): + tuple_to_remove = next((t for t in self._function_call_tasks if t[0] == task), None) + if tuple_to_remove: + self._function_call_tasks.discard(tuple_to_remove) + # The task is finished so this should exit immediately. We need to + # do this because otherwise the task manager would report a dangling + # task if we don't remove it. + asyncio.run_coroutine_threadsafe(self.wait_for_task(task), self.get_event_loop()) + class TTSService(AIService): def __init__( @@ -241,6 +397,7 @@ class TTSService(AIService): # Text aggregator to aggregate incoming tokens and decide when to push to the TTS. text_aggregator: Optional[BaseTextAggregator] = None, # Text filter executed after text has been aggregated. + text_filters: Sequence[BaseTextFilter] = [], text_filter: Optional[BaseTextFilter] = None, **kwargs, ): @@ -257,7 +414,17 @@ class TTSService(AIService): self._voice_id: str = "" self._settings: Dict[str, Any] = {} self._text_aggregator: BaseTextAggregator = text_aggregator or SimpleTextAggregator() - self._text_filter: Optional[BaseTextFilter] = text_filter + self._text_filters: Sequence[BaseTextFilter] = text_filters + if text_filter: + import warnings + + with warnings.catch_warnings(): + warnings.simplefilter("always") + warnings.warn( + "Parameter 'text_filter' is deprecated, use 'text_filters' instead.", + DeprecationWarning, + ) + self._text_filters = [text_filter] self._stop_frame_task: Optional[asyncio.Task] = None self._stop_frame_queue: asyncio.Queue = asyncio.Queue() @@ -317,8 +484,9 @@ class TTSService(AIService): self.set_model_name(value) elif key == "voice": self.set_voice(value) - elif key == "text_filter" and self._text_filter: - self._text_filter.update_settings(value) + elif key == "text_filter": + for filter in self._text_filters: + filter.update_settings(value) else: logger.warning(f"Unknown setting for TTS service: {key}") @@ -353,12 +521,14 @@ class TTSService(AIService): else: await self.push_frame(frame, direction) elif isinstance(frame, TTSSpeakFrame): + # Store if we were processing text or not so we can set it back. + processing_text = self._processing_text await self._push_tts_frames(frame.text) # We pause processing incoming frames because we are sending data to # the TTS. We pause to avoid audio overlapping. await self._maybe_pause_frame_processing() await self.flush_audio() - self._processing_text = False + self._processing_text = processing_text elif isinstance(frame, TTSUpdateSettingsFrame): await self._update_settings(frame.settings) elif isinstance(frame, BotStoppedSpeakingFrame): @@ -391,8 +561,8 @@ class TTSService(AIService): async def _handle_interruption(self, frame: StartInterruptionFrame, direction: FrameDirection): self._processing_text = False self._text_aggregator.handle_interruption() - if self._text_filter: - self._text_filter.handle_interruption() + for filter in self._text_filters: + filter.handle_interruption() async def _maybe_pause_frame_processing(self): if self._processing_text and self._pause_frame_processing: @@ -427,11 +597,16 @@ class TTSService(AIService): self._processing_text = True await self.start_processing_metrics() - if self._text_filter: - self._text_filter.reset_interruption() - text = self._text_filter.filter(text) + + # Process all filter. + for filter in self._text_filters: + filter.reset_interruption() + text = filter.filter(text) + await self.process_generator(self.run_tts(text)) + await self.stop_processing_metrics() + if self._push_text_frames: # We send the original text after the audio. This way, if we are # interrupted, the text is not added to the assistant context. @@ -532,11 +707,25 @@ class WordTTSService(TTSService): class WebsocketTTSService(TTSService, WebsocketService): - """This is a base class for websocket-based TTS services.""" + """This is a base class for websocket-based TTS services. - def __init__(self, **kwargs): + If an error occurs with the websocket, an "on_connection_error" event will + be triggered: + + @tts.event_handler("on_connection_error") + async def on_connection_error(tts: TTSService, error: str): + ... + + """ + + def __init__(self, *, reconnect_on_error: bool = True, **kwargs): TTSService.__init__(self, **kwargs) - WebsocketService.__init__(self) + WebsocketService.__init__(self, reconnect_on_error=reconnect_on_error, **kwargs) + self._register_event_handler("on_connection_error") + + async def _report_error(self, error: ErrorFrame): + await self._call_event_handler("on_connection_error", error.error) + await self.push_error(error) class InterruptibleTTSService(WebsocketTTSService): @@ -573,11 +762,23 @@ class WebsocketWordTTSService(WordTTSService, WebsocketService): """This is a base class for websocket-based TTS services that support word timestamps. + If an error occurs with the websocket a "on_connection_error" event will be + triggered: + + @tts.event_handler("on_connection_error") + async def on_connection_error(tts: TTSService, error: str): + ... + """ - def __init__(self, **kwargs): + def __init__(self, *, reconnect_on_error: bool = True, **kwargs): WordTTSService.__init__(self, **kwargs) - WebsocketService.__init__(self) + WebsocketService.__init__(self, reconnect_on_error=reconnect_on_error, **kwargs) + self._register_event_handler("on_connection_error") + + async def _report_error(self, error: ErrorFrame): + await self._call_event_handler("on_connection_error", error.error) + await self.push_error(error) class InterruptibleWordTTSService(WebsocketWordTTSService): @@ -794,8 +995,6 @@ class STTService(AIService): return await self.process_generator(self.run_stt(frame.audio)) - if self._audio_passthrough: - await self.push_frame(frame, direction) async def process_frame(self, frame: Frame, direction: FrameDirection): """Processes a frame of audio data, either buffering or transcribing it.""" @@ -806,6 +1005,8 @@ class STTService(AIService): # push a TextFrame. We also push audio downstream in case someone # else needs it. await self.process_audio_frame(frame, direction) + if self._audio_passthrough: + await self.push_frame(frame, direction) elif isinstance(frame, STTUpdateSettingsFrame): await self._update_settings(frame.settings) elif isinstance(frame, STTMuteFrame): @@ -816,79 +1017,64 @@ class STTService(AIService): class SegmentedSTTService(STTService): - """SegmentedSTTService is an STTService that will detect speech and will run - speech-to-text on speech segments only, instead of a continous stream. + """SegmentedSTTService is an STTService that uses VAD events to detect + speech and will run speech-to-text on speech segments only, instead of a + continous stream. Since it uses VAD it means that VAD needs to be enabled in + the pipeline. + + This service always keeps a small audio buffer to take into account that VAD + events are delayed from when the user speech really starts. """ - def __init__( - self, - *, - min_volume: float = 0.6, - max_silence_secs: float = 0.3, - max_buffer_secs: float = 1.5, - sample_rate: Optional[int] = None, - **kwargs, - ): + def __init__(self, *, sample_rate: Optional[int] = None, **kwargs): super().__init__(sample_rate=sample_rate, **kwargs) - self._min_volume = min_volume - self._max_silence_secs = max_silence_secs - self._max_buffer_secs = max_buffer_secs self._content = None self._wave = None - self._silence_num_frames = 0 - # Volume exponential smoothing - self._smoothing_factor = 0.2 - self._prev_volume = 0 - - async def process_audio_frame(self, frame: AudioRawFrame, direction: FrameDirection): - # Try to filter out empty background noise - volume = self._get_smoothed_volume(frame) - if volume >= self._min_volume: - # If volume is high enough, write new data to wave file - self._wave.writeframes(frame.audio) - self._silence_num_frames = 0 - else: - self._silence_num_frames += frame.num_frames - self._prev_volume = volume - - # If buffer is not empty and we have enough data or there's been a long - # silence, transcribe the audio gathered so far. - silence_secs = self._silence_num_frames / self.sample_rate - buffer_secs = self._wave.getnframes() / self.sample_rate - if self._content.tell() > 0 and ( - buffer_secs > self._max_buffer_secs or silence_secs > self._max_silence_secs - ): - self._silence_num_frames = 0 - self._wave.close() - self._content.seek(0) - await self.process_generator(self.run_stt(self._content.read())) - (self._content, self._wave) = self._new_wave() + self._audio_buffer = bytearray() + self._audio_buffer_size_1s = 0 + self._user_speaking = False async def start(self, frame: StartFrame): await super().start(frame) - if not self._wave: - (self._content, self._wave) = self._new_wave() + self._audio_buffer_size_1s = self.sample_rate * 2 - async def stop(self, frame: EndFrame): - await super().stop(frame) - self._wave.close() + async def process_frame(self, frame: Frame, direction: FrameDirection): + await super().process_frame(frame, direction) - async def cancel(self, frame: CancelFrame): - await super().cancel(frame) - self._wave.close() + if isinstance(frame, UserStartedSpeakingFrame): + await self._handle_user_started_speaking(frame) + elif isinstance(frame, UserStoppedSpeakingFrame): + await self._handle_user_stopped_speaking(frame) + + async def _handle_user_started_speaking(self, frame: UserStartedSpeakingFrame): + self._user_speaking = True + + async def _handle_user_stopped_speaking(self, frame: UserStoppedSpeakingFrame): + self._user_speaking = False - def _new_wave(self): content = io.BytesIO() - ww = wave.open(content, "wb") - ww.setsampwidth(2) - ww.setnchannels(1) - ww.setframerate(self.sample_rate) - return (content, ww) + wav = wave.open(content, "wb") + wav.setsampwidth(2) + wav.setnchannels(1) + wav.setframerate(self.sample_rate) + wav.writeframes(self._audio_buffer) + wav.close() + content.seek(0) - def _get_smoothed_volume(self, frame: AudioRawFrame) -> float: - volume = calculate_audio_volume(frame.audio, frame.sample_rate) - return exp_smoothing(volume, self._prev_volume, self._smoothing_factor) + await self.process_generator(self.run_stt(content.read())) + + # Start clean. + self._audio_buffer.clear() + + async def process_audio_frame(self, frame: AudioRawFrame, direction: FrameDirection): + # If the user is speaking the audio buffer will keep growin. + self._audio_buffer += frame.audio + + # If the user is not speaking we keep just a little bit of audio. + if not self._user_speaking and len(self._audio_buffer) > self._audio_buffer_size_1s: + discarded = len(self._audio_buffer) - self._audio_buffer_size_1s + self._audio_buffer = self._audio_buffer[discarded:] class ImageGenService(AIService): diff --git a/src/pipecat/services/anthropic.py b/src/pipecat/services/anthropic.py index 10a2ab7b7..3e369075a 100644 --- a/src/pipecat/services/anthropic.py +++ b/src/pipecat/services/anthropic.py @@ -21,19 +21,16 @@ from pydantic import BaseModel, Field from pipecat.adapters.services.anthropic_adapter import AnthropicLLMAdapter from pipecat.frames.frames import ( Frame, + FunctionCallCancelFrame, FunctionCallInProgressFrame, FunctionCallResultFrame, - FunctionCallResultProperties, LLMEnablePromptCachingFrame, LLMFullResponseEndFrame, LLMFullResponseStartFrame, LLMMessagesFrame, LLMTextFrame, LLMUpdateSettingsFrame, - OpenAILLMContextAssistantTimestampFrame, - StartInterruptionFrame, UserImageRawFrame, - UserImageRequestFrame, VisionImageRawFrame, ) from pipecat.metrics.metrics import LLMTokenUsage @@ -47,7 +44,6 @@ from pipecat.processors.aggregators.openai_llm_context import ( ) from pipecat.processors.frame_processor import FrameDirection from pipecat.services.ai_services import LLMService -from pipecat.utils.time import time_now_iso8601 try: from anthropic import NOT_GIVEN, AsyncAnthropic, NotGiven @@ -60,13 +56,6 @@ except ModuleNotFoundError as e: raise Exception(f"Missing module: {e}") -# internal use only -- todo: refactor -@dataclass -class AnthropicImageMessageFrame(Frame): - user_image_raw_frame: UserImageRawFrame - text: Optional[str] = None - - @dataclass class AnthropicContextAggregatorPair: _user: "AnthropicUserContextAggregator" @@ -683,42 +672,7 @@ class AnthropicLLMContext(OpenAILLMContext): class AnthropicUserContextAggregator(LLMUserContextAggregator): - def __init__(self, context: OpenAILLMContext | AnthropicLLMContext, **kwargs): - super().__init__(context=context, **kwargs) - - async def process_frame(self, frame, direction): - await super().process_frame(frame, direction) - # Our parent method has already called push_frame(). So we can't interrupt the - # flow here and we don't need to call push_frame() ourselves. Possibly something - # to talk through (tagging @aleix). At some point we might need to refactor these - # context aggregators. - try: - if isinstance(frame, UserImageRequestFrame): - # The LLM sends a UserImageRequestFrame upstream. Cache any context provided with - # that frame so we can use it when we assemble the image message in the assistant - # context aggregator. - if frame.context: - if isinstance(frame.context, str): - self._context._user_image_request_context[frame.user_id] = frame.context - else: - logger.error( - f"Unexpected UserImageRequestFrame context type: {type(frame.context)}" - ) - del self._context._user_image_request_context[frame.user_id] - else: - if frame.user_id in self._context._user_image_request_context: - del self._context._user_image_request_context[frame.user_id] - elif isinstance(frame, UserImageRawFrame): - # Push a new AnthropicImageMessageFrame with the text context we cached - # downstream to be handled by our assistant context aggregator. This is - # necessary so that we add the message to the context in the right order. - text = self._context._user_image_request_context.get(frame.user_id) or "" - if text: - del self._context._user_image_request_context[frame.user_id] - frame = AnthropicImageMessageFrame(user_image_raw_frame=frame, text=text) - await self.push_frame(frame) - except Exception as e: - logger.error(f"Error processing frame: {e}") + pass # @@ -732,112 +686,64 @@ class AnthropicUserContextAggregator(LLMUserContextAggregator): class AnthropicAssistantContextAggregator(LLMAssistantContextAggregator): - def __init__(self, context: OpenAILLMContext | AnthropicLLMContext, **kwargs): - super().__init__(context=context, **kwargs) - self._function_call_in_progress = None - self._function_call_result = None - self._pending_image_frame_message = None + async def handle_function_call_in_progress(self, frame: FunctionCallInProgressFrame): + assistant_message = {"role": "assistant", "content": []} + assistant_message["content"].append( + { + "type": "tool_use", + "id": frame.tool_call_id, + "name": frame.function_name, + "input": frame.arguments, + } + ) + self._context.add_message(assistant_message) + self._context.add_message( + { + "role": "user", + "content": [ + { + "type": "tool_result", + "tool_use_id": frame.tool_call_id, + "content": "IN_PROGRESS", + } + ], + } + ) - async def process_frame(self, frame, direction): - await super().process_frame(frame, direction) - # See note above about not calling push_frame() here. - if isinstance(frame, StartInterruptionFrame): - self._function_call_in_progress = None - self._function_call_finished = None - elif isinstance(frame, FunctionCallInProgressFrame): - self._function_call_in_progress = frame - elif isinstance(frame, FunctionCallResultFrame): - if ( - self._function_call_in_progress - and self._function_call_in_progress.tool_call_id == frame.tool_call_id - ): - self._function_call_in_progress = None - self._function_call_result = frame - await self.push_aggregation() - else: - logger.warning( - "FunctionCallResultFrame tool_call_id != InProgressFrame tool_call_id" - ) - self._function_call_in_progress = None - self._function_call_result = None - elif isinstance(frame, AnthropicImageMessageFrame): - self._pending_image_frame_message = frame - await self.push_aggregation() + async def handle_function_call_result(self, frame: FunctionCallResultFrame): + if frame.result: + result = json.dumps(frame.result) + await self._update_function_call_result(frame.function_name, frame.tool_call_id, result) + else: + await self._update_function_call_result( + frame.function_name, frame.tool_call_id, "COMPLETED" + ) - async def push_aggregation(self): - if not ( - self._aggregation or self._function_call_result or self._pending_image_frame_message - ): - return + async def handle_function_call_cancel(self, frame: FunctionCallCancelFrame): + await self._update_function_call_result( + frame.function_name, frame.tool_call_id, "CANCELLED" + ) - run_llm = False - properties: Optional[FunctionCallResultProperties] = None + async def _update_function_call_result( + self, function_name: str, tool_call_id: str, result: Any + ): + for message in self._context.messages: + if message["role"] == "user": + for content in message["content"]: + if ( + isinstance(content, dict) + and content["type"] == "tool_result" + and content["tool_use_id"] == tool_call_id + ): + content["content"] = result - aggregation = self._aggregation.strip() - self.reset() - - try: - if aggregation: - self._context.add_message({"role": "assistant", "content": aggregation}) - - if self._function_call_result: - frame = self._function_call_result - properties = frame.properties - self._function_call_result = None - if frame.result: - assistant_message = {"role": "assistant", "content": []} - assistant_message["content"].append( - { - "type": "tool_use", - "id": frame.tool_call_id, - "name": frame.function_name, - "input": frame.arguments, - } - ) - self._context.add_message(assistant_message) - self._context.add_message( - { - "role": "user", - "content": [ - { - "type": "tool_result", - "tool_use_id": frame.tool_call_id, - "content": json.dumps(frame.result), - } - ], - } - ) - if properties and properties.run_llm is not None: - # If the tool call result has a run_llm property, use it - run_llm = properties.run_llm - else: - # Default behavior - run_llm = True - - if self._pending_image_frame_message: - frame = self._pending_image_frame_message - self._pending_image_frame_message = None - self._context.add_image_frame_message( - format=frame.user_image_raw_frame.format, - size=frame.user_image_raw_frame.size, - image=frame.user_image_raw_frame.image, - text=frame.text, - ) - run_llm = True - - if run_llm: - await self.push_context_frame(FrameDirection.UPSTREAM) - - # Emit the on_context_updated callback once the function call result is added to the context - if properties and properties.on_context_updated is not None: - await properties.on_context_updated() - - # Push context frame - await self.push_context_frame() - - # Push timestamp frame with current time - timestamp_frame = OpenAILLMContextAssistantTimestampFrame(timestamp=time_now_iso8601()) - await self.push_frame(timestamp_frame) - - except Exception as e: - logger.error(f"Error processing frame: {e}") + async def handle_user_image_frame(self, frame: UserImageRawFrame): + await self._update_function_call_result( + frame.request.function_name, frame.request.tool_call_id, "COMPLETED" + ) + self._context.add_image_frame_message( + format=frame.format, + size=frame.size, + image=frame.image, + text=frame.request.context, + ) diff --git a/src/pipecat/services/azure.py b/src/pipecat/services/azure.py index c59cd29c2..9df1a8ef1 100644 --- a/src/pipecat/services/azure.py +++ b/src/pipecat/services/azure.py @@ -686,8 +686,11 @@ class AzureSTTService(STTService): ): super().__init__(sample_rate=sample_rate, **kwargs) - self._speech_config = SpeechConfig(subscription=api_key, region=region) - self._speech_config.speech_recognition_language = language + self._speech_config = SpeechConfig( + subscription=api_key, + region=region, + speech_recognition_language=language_to_azure_language(language), + ) self._audio_stream = None self._speech_recognizer = None diff --git a/src/pipecat/services/cartesia.py b/src/pipecat/services/cartesia.py index 8b7f57c63..11796464d 100644 --- a/src/pipecat/services/cartesia.py +++ b/src/pipecat/services/cartesia.py @@ -26,6 +26,8 @@ from pipecat.frames.frames import ( from pipecat.processors.frame_processor import FrameDirection from pipecat.services.ai_services import AudioContextWordTTSService, TTSService from pipecat.transcriptions.language import Language +from pipecat.utils.text.base_text_aggregator import BaseTextAggregator +from pipecat.utils.text.skip_tags_aggregator import SkipTagsAggregator # See .env.example for Cartesia configuration needed try: @@ -89,6 +91,7 @@ class CartesiaTTSService(AudioContextWordTTSService): encoding: str = "pcm_s16le", container: str = "raw", params: InputParams = InputParams(), + text_aggregator: Optional[BaseTextAggregator] = None, **kwargs, ): # Aggregating sentences still gives cleaner-sounding results and fewer @@ -106,6 +109,7 @@ class CartesiaTTSService(AudioContextWordTTSService): push_text_frames=False, pause_frame_processing=True, sample_rate=sample_rate, + text_aggregator=text_aggregator or SkipTagsAggregator([("", "")]), **kwargs, ) @@ -183,7 +187,7 @@ class CartesiaTTSService(AudioContextWordTTSService): async def _connect(self): await self._connect_websocket() if not self._receive_task: - self._receive_task = self.create_task(self._receive_task_handler(self.push_error)) + self._receive_task = self.create_task(self._receive_task_handler(self._report_error)) async def _disconnect(self): if self._receive_task: @@ -203,6 +207,7 @@ class CartesiaTTSService(AudioContextWordTTSService): except Exception as e: logger.error(f"{self} initialization error: {e}") self._websocket = None + await self._call_event_handler("on_connection_error", f"{e}") async def _disconnect_websocket(self): try: diff --git a/src/pipecat/services/elevenlabs.py b/src/pipecat/services/elevenlabs.py index c41e5de02..68c71a144 100644 --- a/src/pipecat/services/elevenlabs.py +++ b/src/pipecat/services/elevenlabs.py @@ -102,6 +102,8 @@ def language_to_elevenlabs_language(language: Language) -> Optional[str]: def output_format_from_sample_rate(sample_rate: int) -> str: match sample_rate: + case 8000: + return "pcm_8000" case 16000: return "pcm_16000" case 22050: @@ -113,7 +115,7 @@ def output_format_from_sample_rate(sample_rate: int) -> str: logger.warning( f"ElevenLabsTTSService: No output format available for {sample_rate} sample rate" ) - return "pcm_16000" + return "pcm_24000" def build_elevenlabs_voice_settings( @@ -309,7 +311,7 @@ class ElevenLabsTTSService(InterruptibleWordTTSService): await self._connect_websocket() if not self._receive_task: - self._receive_task = self.create_task(self._receive_task_handler(self.push_error)) + self._receive_task = self.create_task(self._receive_task_handler(self._report_error)) if not self._keepalive_task: self._keepalive_task = self.create_task(self._keepalive_task_handler()) @@ -364,6 +366,7 @@ class ElevenLabsTTSService(InterruptibleWordTTSService): except Exception as e: logger.error(f"{self} initialization error: {e}") self._websocket = None + await self._call_event_handler("on_connection_error", f"{e}") async def _disconnect_websocket(self): try: diff --git a/src/pipecat/services/fal.py b/src/pipecat/services/fal.py index 7173861ab..cb39da75f 100644 --- a/src/pipecat/services/fal.py +++ b/src/pipecat/services/fal.py @@ -7,6 +7,7 @@ import asyncio import io import os +import wave from typing import AsyncGenerator, Dict, Optional, Union import aiohttp @@ -14,8 +15,10 @@ from loguru import logger from PIL import Image from pydantic import BaseModel -from pipecat.frames.frames import ErrorFrame, Frame, URLImageRawFrame -from pipecat.services.ai_services import ImageGenService +from pipecat.frames.frames import ErrorFrame, Frame, TranscriptionFrame, URLImageRawFrame +from pipecat.services.ai_services import ImageGenService, SegmentedSTTService +from pipecat.transcriptions.language import Language +from pipecat.utils.time import time_now_iso8601 try: import fal_client @@ -27,6 +30,120 @@ except ModuleNotFoundError as e: raise Exception(f"Missing module: {e}") +def language_to_fal_language(language: Language) -> Optional[str]: + """Language support for Fal's Wizper API.""" + BASE_LANGUAGES = { + Language.AF: "af", + Language.AM: "am", + Language.AR: "ar", + Language.AS: "as", + Language.AZ: "az", + Language.BA: "ba", + Language.BE: "be", + Language.BG: "bg", + Language.BN: "bn", + Language.BO: "bo", + Language.BR: "br", + Language.BS: "bs", + Language.CA: "ca", + Language.CS: "cs", + Language.CY: "cy", + Language.DA: "da", + Language.DE: "de", + Language.EL: "el", + Language.EN: "en", + Language.ES: "es", + Language.ET: "et", + Language.EU: "eu", + Language.FA: "fa", + Language.FI: "fi", + Language.FO: "fo", + Language.FR: "fr", + Language.GL: "gl", + Language.GU: "gu", + Language.HA: "ha", + Language.HE: "he", + Language.HI: "hi", + Language.HR: "hr", + Language.HT: "ht", + Language.HU: "hu", + Language.HY: "hy", + Language.ID: "id", + Language.IS: "is", + Language.IT: "it", + Language.JA: "ja", + Language.JW: "jw", + Language.KA: "ka", + Language.KK: "kk", + Language.KM: "km", + Language.KN: "kn", + Language.KO: "ko", + Language.LA: "la", + Language.LB: "lb", + Language.LN: "ln", + Language.LO: "lo", + Language.LT: "lt", + Language.LV: "lv", + Language.MG: "mg", + Language.MI: "mi", + Language.MK: "mk", + Language.ML: "ml", + Language.MN: "mn", + Language.MR: "mr", + Language.MS: "ms", + Language.MT: "mt", + Language.MY: "my", + Language.NE: "ne", + Language.NL: "nl", + Language.NN: "nn", + Language.NO: "no", + Language.OC: "oc", + Language.PA: "pa", + Language.PL: "pl", + Language.PS: "ps", + Language.PT: "pt", + Language.RO: "ro", + Language.RU: "ru", + Language.SA: "sa", + Language.SD: "sd", + Language.SI: "si", + Language.SK: "sk", + Language.SL: "sl", + Language.SN: "sn", + Language.SO: "so", + Language.SQ: "sq", + Language.SR: "sr", + Language.SU: "su", + Language.SV: "sv", + Language.SW: "sw", + Language.TA: "ta", + Language.TE: "te", + Language.TG: "tg", + Language.TH: "th", + Language.TK: "tk", + Language.TL: "tl", + Language.TR: "tr", + Language.TT: "tt", + Language.UK: "uk", + Language.UR: "ur", + Language.UZ: "uz", + Language.VI: "vi", + Language.YI: "yi", + Language.YO: "yo", + Language.ZH: "zh", + } + + result = BASE_LANGUAGES.get(language) + + # If not found in base languages, try to find the base language from a variant + if not result: + lang_str = str(language.value) + base_code = lang_str.split("-")[0].lower() + result = base_code if base_code in BASE_LANGUAGES.values() else None + + return result + + class FalImageGenService(ImageGenService): class InputParams(BaseModel): seed: Optional[int] = None @@ -84,3 +201,109 @@ class FalImageGenService(ImageGenService): frame = URLImageRawFrame(url=image_url, image=image_bytes, size=size, format=format) yield frame + + +class FalSTTService(SegmentedSTTService): + """Speech-to-text service using Fal's Wizper API. + + This service uses Fal's Wizper API to perform speech-to-text transcription on audio + segments. It inherits from SegmentedSTTService to handle audio buffering and speech detection. + + Args: + api_key: Fal API key. If not provided, will check FAL_KEY environment variable. + sample_rate: Audio sample rate in Hz. If not provided, uses the pipeline's rate. + params: Configuration parameters for the Wizper API. + **kwargs: Additional arguments passed to SegmentedSTTService. + """ + + class InputParams(BaseModel): + """Configuration parameters for Fal's Wizper API. + + Attributes: + language: Language of the audio input. Defaults to English. + task: Task to perform ('transcribe' or 'translate'). Defaults to 'transcribe'. + chunk_level: Level of chunking ('segment'). Defaults to 'segment'. + version: Version of Wizper model to use. Defaults to '3'. + """ + + language: Optional[Language] = Language.EN + task: str = "transcribe" + chunk_level: str = "segment" + version: str = "3" + + def __init__( + self, + *, + api_key: Optional[str] = None, + sample_rate: Optional[int] = None, + params: InputParams = InputParams(), + **kwargs, + ): + super().__init__( + sample_rate=sample_rate, + **kwargs, + ) + + if api_key: + os.environ["FAL_KEY"] = api_key + elif "FAL_KEY" not in os.environ: + raise ValueError( + "FAL_KEY must be provided either through api_key parameter or environment variable" + ) + + self._fal_client = fal_client.AsyncClient(key=api_key or os.getenv("FAL_KEY")) + self._settings = { + "task": params.task, + "language": self.language_to_service_language(params.language) + if params.language + else "en", + "chunk_level": params.chunk_level, + "version": params.version, + } + + def can_generate_metrics(self) -> bool: + return True + + def language_to_service_language(self, language: Language) -> Optional[str]: + return language_to_fal_language(language) + + async def set_language(self, language: Language): + logger.info(f"Switching STT language to: [{language}]") + self._settings["language"] = self.language_to_service_language(language) + + async def set_model(self, model: str): + await super().set_model(model) + logger.info(f"Switching STT model to: [{model}]") + + async def run_stt(self, audio: bytes) -> AsyncGenerator[Frame, None]: + """Transcribes an audio segment using Fal's Wizper API. + + Args: + audio: Raw audio bytes in WAV format (already converted by base class). + + Yields: + Frame: TranscriptionFrame containing the transcribed text. + + Note: + The audio is already in WAV format from the SegmentedSTTService. + Only non-empty transcriptions are yielded. + """ + try: + # Send to Fal directly (audio is already in WAV format from base class) + data_uri = fal_client.encode(audio, "audio/x-wav") + response = await self._fal_client.run( + "fal-ai/wizper", + arguments={"audio_url": data_uri, **self._settings}, + ) + + if response and "text" in response: + text = response["text"].strip() + if text: # Only yield non-empty text + logger.debug(f"Transcription: [{text}]") + yield TranscriptionFrame( + text, "", time_now_iso8601(), Language(self._settings["language"]) + ) + + except Exception as e: + logger.error(f"Fal Wizper error: {e}") + yield ErrorFrame(f"Fal Wizper error: {str(e)}") diff --git a/src/pipecat/services/fish.py b/src/pipecat/services/fish.py index 96968d6e9..9e6a8b91e 100644 --- a/src/pipecat/services/fish.py +++ b/src/pipecat/services/fish.py @@ -107,7 +107,7 @@ class FishAudioTTSService(InterruptibleTTSService): async def _connect(self): await self._connect_websocket() if not self._receive_task: - self._receive_task = self.create_task(self._receive_task_handler(self.push_error)) + self._receive_task = self.create_task(self._receive_task_handler(self._report_error)) async def _disconnect(self): if self._receive_task: @@ -132,6 +132,7 @@ class FishAudioTTSService(InterruptibleTTSService): except Exception as e: logger.error(f"Fish Audio initialization error: {e}") self._websocket = None + await self._call_event_handler("on_connection_error", f"{e}") async def _disconnect_websocket(self): try: diff --git a/src/pipecat/services/gemini_multimodal_live/gemini.py b/src/pipecat/services/gemini_multimodal_live/gemini.py index ef49df329..801af46b4 100644 --- a/src/pipecat/services/gemini_multimodal_live/gemini.py +++ b/src/pipecat/services/gemini_multimodal_live/gemini.py @@ -38,6 +38,8 @@ from pipecat.frames.frames import ( TTSAudioRawFrame, TTSStartedFrame, TTSStoppedFrame, + TTSTextFrame, + UserImageRawFrame, UserStartedSpeakingFrame, UserStoppedSpeakingFrame, ) @@ -117,10 +119,10 @@ class GeminiMultimodalLiveUserContextAggregator(OpenAIUserContextAggregator): class GeminiMultimodalLiveAssistantContextAggregator(OpenAIAssistantContextAggregator): - async def push_aggregation(self): - # We don't want to store any images in the context. Revisit this later when the API evolves. - self._pending_image_frame_message = None - await super().push_aggregation() + async def handle_user_image_frame(self, frame: UserImageRawFrame): + # We don't want to store any images in the context. Revisit this later + # when the API evolves. + pass @dataclass @@ -312,6 +314,7 @@ class GeminiMultimodalLiveLLMService(LLMService): # context.add_message({"role": "assistant", "content": [{"type": "text", "text": text}]}) await self.push_frame(LLMFullResponseStartFrame()) await self.push_frame(LLMTextFrame(text=text)) + await self.push_frame(TTSTextFrame(text=text)) await self.push_frame(LLMFullResponseEndFrame()) async def _transcribe_audio(self, audio, context): @@ -339,10 +342,8 @@ class GeminiMultimodalLiveLLMService(LLMService): async def process_frame(self, frame: Frame, direction: FrameDirection): await super().process_frame(frame, direction) - # logger.debug(f"Processing frame: {frame}") - if isinstance(frame, TranscriptionFrame): - pass + await self.push_frame(frame, direction) elif isinstance(frame, OpenAILLMContextFrame): context: GeminiMultimodalLiveContext = GeminiMultimodalLiveContext.upgrade( frame.context @@ -359,31 +360,35 @@ class GeminiMultimodalLiveLLMService(LLMService): # Support just one tool call per context frame for now tool_result_message = context.messages[-1] await self._tool_result(tool_result_message) - elif isinstance(frame, InputAudioRawFrame): await self._send_user_audio(frame) + await self.push_frame(frame, direction) elif isinstance(frame, InputImageRawFrame): await self._send_user_video(frame) + await self.push_frame(frame, direction) elif isinstance(frame, StartInterruptionFrame): await self._handle_interruption() + await self.push_frame(frame, direction) elif isinstance(frame, UserStartedSpeakingFrame): await self._handle_user_started_speaking(frame) + await self.push_frame(frame, direction) elif isinstance(frame, UserStoppedSpeakingFrame): await self._handle_user_stopped_speaking(frame) + await self.push_frame(frame, direction) elif isinstance(frame, BotStartedSpeakingFrame): # Ignore this frame. Use the serverContent API message instead - pass + await self.push_frame(frame, direction) elif isinstance(frame, BotStoppedSpeakingFrame): # ignore this frame. Use the serverContent.turnComplete API message - pass + await self.push_frame(frame, direction) elif isinstance(frame, LLMMessagesAppendFrame): await self._create_single_response(frame.messages) elif isinstance(frame, LLMUpdateSettingsFrame): await self._update_settings(frame.settings) elif isinstance(frame, LLMSetToolsFrame): await self._update_settings() - - await self.push_frame(frame, direction) + else: + await self.push_frame(frame, direction) # # websocket communication diff --git a/src/pipecat/services/google/google.py b/src/pipecat/services/google/google.py index 9d4a0a8a8..554d9cb6b 100644 --- a/src/pipecat/services/google/google.py +++ b/src/pipecat/services/google/google.py @@ -10,6 +10,7 @@ import io import json import os import time +import uuid from google.api_core.exceptions import DeadlineExceeded from openai import AsyncStream @@ -33,20 +34,22 @@ from pipecat.frames.frames import ( EndFrame, ErrorFrame, Frame, - FunctionCallResultProperties, + FunctionCallCancelFrame, + FunctionCallInProgressFrame, + FunctionCallResultFrame, InterimTranscriptionFrame, LLMFullResponseEndFrame, LLMFullResponseStartFrame, LLMMessagesFrame, LLMTextFrame, LLMUpdateSettingsFrame, - OpenAILLMContextAssistantTimestampFrame, StartFrame, TranscriptionFrame, TTSAudioRawFrame, TTSStartedFrame, TTSStoppedFrame, URLImageRawFrame, + UserImageRawFrame, VisionImageRawFrame, ) from pipecat.metrics.metrics import LLMTokenUsage @@ -71,6 +74,7 @@ try: import google.generativeai as gai from google import genai from google.api_core.client_options import ClientOptions + from google.auth.transport.requests import Request from google.cloud import speech_v2, texttospeech_v1 from google.cloud.speech_v2.types import cloud_speech from google.genai import types @@ -564,91 +568,72 @@ class GoogleUserContextAggregator(OpenAIUserContextAggregator): class GoogleAssistantContextAggregator(OpenAIAssistantContextAggregator): - async def push_aggregation(self): - if not ( - self._aggregation or self._function_call_result or self._pending_image_frame_message - ): - return + async def handle_aggregation(self, aggregation: str): + self._context.add_message(glm.Content(role="model", parts=[glm.Part(text=aggregation)])) - run_llm = False - properties: Optional[FunctionCallResultProperties] = None - - aggregation = self._aggregation.strip() - self.reset() - - try: - if aggregation: - self._context.add_message( - glm.Content(role="model", parts=[glm.Part(text=aggregation)]) - ) - - if self._function_call_result: - frame = self._function_call_result - properties = frame.properties - self._function_call_result = None - if frame.result: - logger.debug(f"FunctionCallResultFrame result: {frame.arguments}") - self._context.add_message( - glm.Content( - role="model", - parts=[ - glm.Part( - function_call=glm.FunctionCall( - name=frame.function_name, args=frame.arguments - ) - ) - ], + async def handle_function_call_in_progress(self, frame: FunctionCallInProgressFrame): + self._context.add_message( + glm.Content( + role="model", + parts=[ + glm.Part( + function_call=glm.FunctionCall( + id=frame.tool_call_id, name=frame.function_name, args=frame.arguments ) ) - response = frame.result - if isinstance(response, str): - response = {"response": response} - self._context.add_message( - glm.Content( - role="user", - parts=[ - glm.Part( - function_response=glm.FunctionResponse( - name=frame.function_name, response=response - ) - ) - ], + ], + ) + ) + self._context.add_message( + glm.Content( + role="user", + parts=[ + glm.Part( + function_response=glm.FunctionResponse( + id=frame.tool_call_id, + name=frame.function_name, + response={"response": "IN_PROGRESS"}, ) ) - if properties and properties.run_llm is not None: - # If the tool call result has a run_llm property, use it - run_llm = properties.run_llm - else: - # Default behavior is to run the LLM if there are no function calls in progress - run_llm = not bool(self._function_calls_in_progress) + ], + ) + ) - if self._pending_image_frame_message: - frame = self._pending_image_frame_message - self._pending_image_frame_message = None - self._context.add_image_frame_message( - format=frame.user_image_raw_frame.format, - size=frame.user_image_raw_frame.size, - image=frame.user_image_raw_frame.image, - text=frame.text, - ) - run_llm = True + async def handle_function_call_result(self, frame: FunctionCallResultFrame): + if frame.result: + await self._update_function_call_result( + frame.function_name, frame.tool_call_id, frame.result + ) + else: + response = {"response": "COMPLETED"} + await self._update_function_call_result( + frame.function_name, frame.tool_call_id, response + ) - if run_llm: - await self.push_context_frame(FrameDirection.UPSTREAM) + async def handle_function_call_cancel(self, frame: FunctionCallCancelFrame): + response = {"response": "CANCELLED"} + await self._update_function_call_result(frame.function_name, frame.tool_call_id, response) - # Emit the on_context_updated callback once the function call result is added to the context - if properties and properties.on_context_updated is not None: - await properties.on_context_updated() + async def _update_function_call_result( + self, function_name: str, tool_call_id: str, result: Any + ): + for message in self._context.messages: + if message.role == "user": + for part in message.parts: + if part.function_response and part.function_response.id == tool_call_id: + part.function_response.response = result - # Push context frame - await self.push_context_frame() - - # Push timestamp frame with current time - timestamp_frame = OpenAILLMContextAssistantTimestampFrame(timestamp=time_now_iso8601()) - await self.push_frame(timestamp_frame) - - except Exception as e: - logger.exception(f"Error processing frame: {e}") + async def handle_user_image_frame(self, frame: UserImageRawFrame): + response = {"response": "COMPLETED"} + await self._update_function_call_result( + frame.request.function_name, frame.request.tool_call_id, response + ) + self._context.add_image_frame_message( + format=frame.format, + size=frame.size, + image=frame.image, + text=frame.request.context, + ) @dataclass @@ -1070,7 +1055,7 @@ class GoogleLLMService(LLMService): args = type(c.function_call).to_dict(c.function_call).get("args", {}) await self.call_function( context=context, - tool_call_id="what_should_this_be", + tool_call_id=str(uuid.uuid4()), function_name=c.function_call.name, arguments=args, ) @@ -1333,6 +1318,82 @@ class GoogleLLMOpenAIBetaService(OpenAILLMService): ) +class GoogleVertexLLMService(OpenAILLMService): + """Implements inference with Google's AI models via Vertex AI while + maintaining OpenAI API compatibility. + + Reference: + https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/call-vertex-using-openai-library + + """ + + class InputParams(OpenAILLMService.InputParams): + """Input parameters specific to Vertex AI.""" + + # https://cloud.google.com/vertex-ai/generative-ai/docs/learn/locations + location: str = "us-east4" + project_id: str + + def __init__( + self, + *, + credentials: Optional[str] = None, + credentials_path: Optional[str] = None, + model: str = "google/gemini-2.0-flash-001", + params: InputParams = OpenAILLMService.InputParams(), + **kwargs, + ): + """Initializes the VertexLLMService. + Args: + credentials (Optional[str]): JSON string of service account credentials. + credentials_path (Optional[str]): Path to the service account JSON file. + model (str): Model identifier. Defaults to "google/gemini-2.0-flash-001". + params (InputParams): Vertex AI input parameters. + **kwargs: Additional arguments for OpenAILLMService. + """ + base_url = self._get_base_url(params) + self._api_key = self._get_api_token(credentials, credentials_path) + + super().__init__(api_key=self._api_key, base_url=base_url, model=model, **kwargs) + + @staticmethod + def _get_base_url(params: InputParams) -> str: + """Constructs the base URL for Vertex AI API.""" + return ( + f"https://{params.location}-aiplatform.googleapis.com/v1/" + f"projects/{params.project_id}/locations/{params.location}/endpoints/openapi" + ) + + @staticmethod + def _get_api_token(credentials: Optional[str], credentials_path: Optional[str]) -> str: + """Retrieves an authentication token using Google service account credentials. + Args: + credentials (Optional[str]): JSON string of service account credentials. + credentials_path (Optional[str]): Path to the service account JSON file. + Returns: + str: OAuth token for API authentication. + """ + creds: Optional[service_account.Credentials] = None + + if credentials: + # Parse and load credentials from JSON string + creds = service_account.Credentials.from_service_account_info( + json.loads(credentials), scopes=["https://www.googleapis.com/auth/cloud-platform"] + ) + elif credentials_path: + # Load credentials from JSON file + creds = service_account.Credentials.from_service_account_file( + credentials_path, scopes=["https://www.googleapis.com/auth/cloud-platform"] + ) + + if not creds: + raise ValueError("No valid credentials provided.") + + creds.refresh(Request()) # Ensure token is up-to-date, lifetime is 1 hour. + + return creds.token + + class GoogleTTSService(TTSService): class InputParams(BaseModel): pitch: Optional[str] = None @@ -1972,7 +2033,8 @@ class GoogleSTTService(STTService): break except Exception as e: - logger.error(f"Stream error, attempting to reconnect: {e}") + logger.warning(f"{self} Reconnecting: {e}") + await asyncio.sleep(1) # Brief delay before reconnecting self._stream_start_time = int(time.time() * 1000) continue @@ -2025,3 +2087,6 @@ class GoogleSTTService(STTService): except Exception as e: logger.error(f"Error processing Google STT responses: {e}") + + # Re-raise the exception to let it propagate (e.g. in the case of a timeout, propagate to _stream_audio to reconnect) + raise diff --git a/src/pipecat/services/grok.py b/src/pipecat/services/grok.py index cf7d74f59..faed13050 100644 --- a/src/pipecat/services/grok.py +++ b/src/pipecat/services/grok.py @@ -25,94 +25,15 @@ from pipecat.services.openai import ( ) -class GrokAssistantContextAggregator(OpenAIAssistantContextAggregator): - """Custom assistant context aggregator for Grok that handles empty content requirement.""" - - async def push_aggregation(self): - if not ( - self._aggregation or self._function_call_result or self._pending_image_frame_message - ): - return - - run_llm = False - properties: Optional[FunctionCallResultProperties] = None - - aggregation = self._aggregation.strip() - self.reset() - - try: - if aggregation: - self._context.add_message({"role": "assistant", "content": aggregation}) - - if self._function_call_result: - frame = self._function_call_result - properties = frame.properties - self._function_call_result = None - if frame.result: - # Grok requires an empty content field for function calls - self._context.add_message( - { - "role": "assistant", - "content": "", # Required by Grok - "tool_calls": [ - { - "id": frame.tool_call_id, - "function": { - "name": frame.function_name, - "arguments": json.dumps(frame.arguments), - }, - "type": "function", - } - ], - } - ) - self._context.add_message( - { - "role": "tool", - "content": json.dumps(frame.result), - "tool_call_id": frame.tool_call_id, - } - ) - if properties and properties.run_llm is not None: - # If the tool call result has a run_llm property, use it - run_llm = properties.run_llm - else: - # Default behavior is to run the LLM if there are no function calls in progress - run_llm = not bool(self._function_calls_in_progress) - - if self._pending_image_frame_message: - frame = self._pending_image_frame_message - self._pending_image_frame_message = None - self._context.add_image_frame_message( - format=frame.user_image_raw_frame.format, - size=frame.user_image_raw_frame.size, - image=frame.user_image_raw_frame.image, - text=frame.text, - ) - run_llm = True - - if run_llm: - await self.push_context_frame(FrameDirection.UPSTREAM) - - # Emit the on_context_updated callback once the function call result is added to the context - if properties and properties.on_context_updated is not None: - await properties.on_context_updated() - - await self.push_context_frame() - - except Exception as e: - logger.error(f"Error processing frame: {e}") - - @dataclass class GrokContextAggregatorPair: _user: "OpenAIUserContextAggregator" - _assistant: "GrokAssistantContextAggregator" + _assistant: "OpenAIAssistantContextAggregator" def user(self) -> "OpenAIUserContextAggregator": return self._user - def assistant(self) -> "GrokAssistantContextAggregator": + def assistant(self) -> "OpenAIAssistantContextAggregator": return self._assistant @@ -235,5 +156,5 @@ class GrokLLMService(OpenAILLMService): context.set_llm_adapter(self.get_llm_adapter()) user = OpenAIUserContextAggregator(context, **user_kwargs) - assistant = GrokAssistantContextAggregator(context, **assistant_kwargs) + assistant = OpenAIAssistantContextAggregator(context, **assistant_kwargs) return GrokContextAggregatorPair(_user=user, _assistant=assistant) diff --git a/src/pipecat/services/lmnt.py b/src/pipecat/services/lmnt.py index cfdf8e6cd..d3cc92603 100644 --- a/src/pipecat/services/lmnt.py +++ b/src/pipecat/services/lmnt.py @@ -112,7 +112,7 @@ class LmntTTSService(InterruptibleTTSService): await self._connect_websocket() if not self._receive_task: - self._receive_task = self.create_task(self._receive_task_handler(self.push_error)) + self._receive_task = self.create_task(self._receive_task_handler(self._report_error)) async def _disconnect(self): if self._receive_task: @@ -147,6 +147,7 @@ class LmntTTSService(InterruptibleTTSService): except Exception as e: logger.error(f"{self} initialization error: {e}") self._websocket = None + await self._call_event_handler("on_connection_error", f"{e}") async def _disconnect_websocket(self): """Disconnect from LMNT websocket.""" diff --git a/src/pipecat/services/neuphonic.py b/src/pipecat/services/neuphonic.py index a2ea98f42..407e54a83 100644 --- a/src/pipecat/services/neuphonic.py +++ b/src/pipecat/services/neuphonic.py @@ -49,6 +49,11 @@ def language_to_neuphonic_lang_code(language: Language) -> Optional[str]: Language.ES: "es", Language.NL: "nl", Language.AR: "ar", + Language.FR: "fr", + Language.PT: "pt", + Language.RU: "ru", + Language.HI: "HI", + Language.ZH: "zh", } result = BASE_LANGUAGES.get(language) @@ -157,7 +162,7 @@ class NeuphonicTTSService(InterruptibleTTSService): async def _connect(self): await self._connect_websocket() - self._receive_task = self.create_task(self._receive_task_handler(self.push_error)) + self._receive_task = self.create_task(self._receive_task_handler(self._report_error)) self._keepalive_task = self.create_task(self._keepalive_task_handler()) async def _disconnect(self): @@ -192,6 +197,7 @@ class NeuphonicTTSService(InterruptibleTTSService): except Exception as e: logger.error(f"{self} initialization error: {e}") self._websocket = None + await self._call_event_handler("on_connection_error", f"{e}") async def _disconnect_websocket(self): try: diff --git a/src/pipecat/services/openai.py b/src/pipecat/services/openai.py index 5a3a993aa..cb1edea72 100644 --- a/src/pipecat/services/openai.py +++ b/src/pipecat/services/openai.py @@ -27,23 +27,20 @@ from pydantic import BaseModel, Field from pipecat.frames.frames import ( ErrorFrame, Frame, + FunctionCallCancelFrame, FunctionCallInProgressFrame, FunctionCallResultFrame, - FunctionCallResultProperties, LLMFullResponseEndFrame, LLMFullResponseStartFrame, LLMMessagesFrame, LLMTextFrame, LLMUpdateSettingsFrame, - OpenAILLMContextAssistantTimestampFrame, StartFrame, - StartInterruptionFrame, TTSAudioRawFrame, TTSStartedFrame, TTSStoppedFrame, URLImageRawFrame, UserImageRawFrame, - UserImageRequestFrame, VisionImageRawFrame, ) from pipecat.metrics.metrics import LLMTokenUsage @@ -63,7 +60,6 @@ from pipecat.services.ai_services import ( ) from pipecat.services.base_whisper import BaseWhisperSTTService, Transcription from pipecat.transcriptions.language import Language -from pipecat.utils.time import time_now_iso8601 ValidVoice = Literal["alloy", "echo", "fable", "onyx", "nova", "shimmer"] @@ -116,6 +112,7 @@ class BaseOpenAILLMService(LLMService): base_url=None, organization=None, project=None, + default_headers: Mapping[str, str] | None = None, params: InputParams = InputParams(), **kwargs, ): @@ -132,10 +129,23 @@ class BaseOpenAILLMService(LLMService): } self.set_model_name(model) self._client = self.create_client( - api_key=api_key, base_url=base_url, organization=organization, project=project, **kwargs + api_key=api_key, + base_url=base_url, + organization=organization, + project=project, + default_headers=default_headers, + **kwargs, ) - def create_client(self, api_key=None, base_url=None, organization=None, project=None, **kwargs): + def create_client( + self, + api_key=None, + base_url=None, + organization=None, + project=None, + default_headers=None, + **kwargs, + ): return AsyncOpenAI( api_key=api_key, base_url=base_url, @@ -146,6 +156,7 @@ class BaseOpenAILLMService(LLMService): max_keepalive_connections=100, max_connections=1000, keepalive_expiry=None ) ), + default_headers=default_headers, ) def can_generate_metrics(self) -> bool: @@ -413,13 +424,13 @@ class OpenAIImageGenService(ImageGenService): class OpenAISTTService(BaseWhisperSTTService): - """OpenAI Whisper speech-to-text service. + """OpenAI Speech-to-Text service that generates text from audio. - Uses OpenAI's Whisper API to convert audio to text. Requires an OpenAI API key + Uses OpenAI's transcription API to convert audio to text. Requires an OpenAI API key set via the api_key parameter or OPENAI_API_KEY environment variable. Args: - model: Whisper model to use. Defaults to "whisper-1". + model: Model to use — either gpt-4o or Whisper. Defaults to "gpt-4o-transcribe". api_key: OpenAI API key. Defaults to None. base_url: API base URL. Defaults to None. language: Language of the audio input. Defaults to English. @@ -431,7 +442,7 @@ class OpenAISTTService(BaseWhisperSTTService): def __init__( self, *, - model: str = "whisper-1", + model: str = "gpt-4o-transcribe", api_key: Optional[str] = None, base_url: Optional[str] = None, language: Optional[Language] = Language.EN, @@ -472,22 +483,16 @@ class OpenAITTSService(TTSService): """OpenAI Text-to-Speech service that generates audio from text. This service uses the OpenAI TTS API to generate PCM-encoded audio at 24kHz. - When using with DailyTransport, configure the sample rate in DailyParams - as shown below: - - DailyParams( - audio_out_enabled=True, - audio_out_sample_rate=24_000, - ) Args: api_key: OpenAI API key. Defaults to None. voice: Voice ID to use. Defaults to "alloy". - model: TTS model to use ("tts-1" or "tts-1-hd"). Defaults to "tts-1". - sample_rate: Output audio sample rate in Hz. Defaults to 24000. + model: TTS model to use. Defaults to "gpt-4o-mini-tts". + sample_rate: Output audio sample rate in Hz. Defaults to None. **kwargs: Additional keyword arguments passed to TTSService. The service returns PCM-encoded audio at the specified sample rate. + """ OPENAI_SAMPLE_RATE = 24000 # OpenAI TTS always outputs at 24kHz @@ -497,7 +502,7 @@ class OpenAITTSService(TTSService): *, api_key: Optional[str] = None, voice: str = "alloy", - model: Literal["tts-1", "tts-1-hd"] = "tts-1", + model: str = "gpt-4o-mini-tts", sample_rate: Optional[int] = None, **kwargs, ): @@ -564,156 +569,67 @@ class OpenAITTSService(TTSService): logger.exception(f"{self} error generating TTS: {e}") -# internal use only -- todo: refactor -@dataclass -class OpenAIImageMessageFrame(Frame): - user_image_raw_frame: UserImageRawFrame - text: Optional[str] = None - - class OpenAIUserContextAggregator(LLMUserContextAggregator): - def __init__(self, context: OpenAILLMContext, **kwargs): - super().__init__(context=context, **kwargs) - - async def process_frame(self, frame, direction): - await super().process_frame(frame, direction) - # Our parent method has already called push_frame(). So we can't interrupt the - # flow here and we don't need to call push_frame() ourselves. - try: - if isinstance(frame, UserImageRequestFrame): - # The LLM sends a UserImageRequestFrame upstream. Cache any context provided with - # that frame so we can use it when we assemble the image message in the assistant - # context aggregator. - if frame.context: - if isinstance(frame.context, str): - self._context._user_image_request_context[frame.user_id] = frame.context - else: - logger.error( - f"Unexpected UserImageRequestFrame context type: {type(frame.context)}" - ) - del self._context._user_image_request_context[frame.user_id] - else: - if frame.user_id in self._context._user_image_request_context: - del self._context._user_image_request_context[frame.user_id] - elif isinstance(frame, UserImageRawFrame): - # Push a new OpenAIImageMessageFrame with the text context we cached - # downstream to be handled by our assistant context aggregator. This is - # necessary so that we add the message to the context in the right order. - text = self._context._user_image_request_context.get(frame.user_id) or "" - if text: - del self._context._user_image_request_context[frame.user_id] - frame = OpenAIImageMessageFrame(user_image_raw_frame=frame, text=text) - await self.push_frame(frame) - except Exception as e: - logger.error(f"Error processing frame: {e}") + pass class OpenAIAssistantContextAggregator(LLMAssistantContextAggregator): - def __init__(self, context: OpenAILLMContext, **kwargs): - super().__init__(context=context, **kwargs) - self._function_calls_in_progress = {} - self._function_call_result = None - self._pending_image_frame_message = None + async def handle_function_call_in_progress(self, frame: FunctionCallInProgressFrame): + self._context.add_message( + { + "role": "assistant", + "tool_calls": [ + { + "id": frame.tool_call_id, + "function": { + "name": frame.function_name, + "arguments": json.dumps(frame.arguments), + }, + "type": "function", + } + ], + } + ) + self._context.add_message( + { + "role": "tool", + "content": "IN_PROGRESS", + "tool_call_id": frame.tool_call_id, + } + ) - async def process_frame(self, frame, direction): - await super().process_frame(frame, direction) - # See note above about not calling push_frame() here. - if isinstance(frame, StartInterruptionFrame): - self._function_calls_in_progress.clear() - self._function_call_finished = None - elif isinstance(frame, FunctionCallInProgressFrame): - logger.debug(f"FunctionCallInProgressFrame: {frame}") - self._function_calls_in_progress[frame.tool_call_id] = frame - elif isinstance(frame, FunctionCallResultFrame): - logger.debug(f"FunctionCallResultFrame: {frame}") - if frame.tool_call_id in self._function_calls_in_progress: - del self._function_calls_in_progress[frame.tool_call_id] - self._function_call_result = frame - # TODO-CB: Kwin wants us to refactor this out of here but I REFUSE - await self.push_aggregation() - else: - logger.warning( - "FunctionCallResultFrame tool_call_id does not match any function call in progress" - ) - self._function_call_result = None - elif isinstance(frame, OpenAIImageMessageFrame): - self._pending_image_frame_message = frame - await self.push_aggregation() + async def handle_function_call_result(self, frame: FunctionCallResultFrame): + if frame.result: + result = json.dumps(frame.result) + await self._update_function_call_result(frame.function_name, frame.tool_call_id, result) + else: + await self._update_function_call_result( + frame.function_name, frame.tool_call_id, "COMPLETED" + ) - async def push_aggregation(self): - if not ( - self._aggregation or self._function_call_result or self._pending_image_frame_message - ): - return + async def handle_function_call_cancel(self, frame: FunctionCallCancelFrame): + await self._update_function_call_result( + frame.function_name, frame.tool_call_id, "CANCELLED" + ) - run_llm = False - properties: Optional[FunctionCallResultProperties] = None + async def _update_function_call_result( + self, function_name: str, tool_call_id: str, result: Any + ): + for message in self._context.messages: + if ( + message["role"] == "tool" + and message["tool_call_id"] + and message["tool_call_id"] == tool_call_id + ): + message["content"] = result - aggregation = self._aggregation.strip() - self.reset() - - try: - if aggregation: - self._context.add_message({"role": "assistant", "content": aggregation}) - - if self._function_call_result: - frame = self._function_call_result - properties = frame.properties - self._function_call_result = None - if frame.result: - self._context.add_message( - { - "role": "assistant", - "tool_calls": [ - { - "id": frame.tool_call_id, - "function": { - "name": frame.function_name, - "arguments": json.dumps(frame.arguments), - }, - "type": "function", - } - ], - } - ) - self._context.add_message( - { - "role": "tool", - "content": json.dumps(frame.result), - "tool_call_id": frame.tool_call_id, - } - ) - if properties and properties.run_llm is not None: - # If the tool call result has a run_llm property, use it - run_llm = properties.run_llm - else: - # Default behavior is to run the LLM if there are no function calls in progress - run_llm = not bool(self._function_calls_in_progress) - - if self._pending_image_frame_message: - frame = self._pending_image_frame_message - self._pending_image_frame_message = None - self._context.add_image_frame_message( - format=frame.user_image_raw_frame.format, - size=frame.user_image_raw_frame.size, - image=frame.user_image_raw_frame.image, - text=frame.text, - ) - run_llm = True - - if run_llm: - await self.push_context_frame(FrameDirection.UPSTREAM) - - # Emit the on_context_updated callback once the function call result is added to the context - if properties and properties.on_context_updated is not None: - await properties.on_context_updated() - - # Push context frame - await self.push_context_frame() - - # Push timestamp frame with current time - timestamp_frame = OpenAILLMContextAssistantTimestampFrame(timestamp=time_now_iso8601()) - await self.push_frame(timestamp_frame) - - except Exception as e: - logger.error(f"Error processing frame: {e}") + async def handle_user_image_frame(self, frame: UserImageRawFrame): + await self._update_function_call_result( + frame.request.function_name, frame.request.tool_call_id, "COMPLETED" + ) + self._context.add_image_frame_message( + format=frame.format, + size=frame.size, + image=frame.image, + text=frame.request.context, + ) diff --git a/src/pipecat/services/openai_realtime_beta/__init__.py b/src/pipecat/services/openai_realtime_beta/__init__.py index 52b00f6c8..595105d7f 100644 --- a/src/pipecat/services/openai_realtime_beta/__init__.py +++ b/src/pipecat/services/openai_realtime_beta/__init__.py @@ -1,3 +1,9 @@ from .azure import AzureRealtimeBetaLLMService -from .events import InputAudioTranscription, SessionProperties, TurnDetection +from .events import ( + InputAudioNoiseReduction, + InputAudioTranscription, + SemanticTurnDetection, + SessionProperties, + TurnDetection, +) from .openai import OpenAIRealtimeBetaLLMService diff --git a/src/pipecat/services/openai_realtime_beta/context.py b/src/pipecat/services/openai_realtime_beta/context.py index 31639dc6b..c8381976f 100644 --- a/src/pipecat/services/openai_realtime_beta/context.py +++ b/src/pipecat/services/openai_realtime_beta/context.py @@ -12,6 +12,7 @@ from loguru import logger from pipecat.frames.frames import ( Frame, + FunctionCallResultFrame, FunctionCallResultProperties, LLMMessagesUpdateFrame, LLMSetToolsFrame, @@ -174,67 +175,12 @@ class OpenAIRealtimeUserContextAggregator(OpenAIUserContextAggregator): class OpenAIRealtimeAssistantContextAggregator(OpenAIAssistantContextAggregator): - async def push_aggregation(self): - # the only thing we implement here is function calling. in all other cases, messages - # are added to the context when we receive openai realtime api events - if not self._function_call_result: - return + async def handle_function_call_result(self, frame: FunctionCallResultFrame): + await super().handle_function_call_result(frame) - properties: Optional[FunctionCallResultProperties] = None - - self.reset() - try: - run_llm = True - frame = self._function_call_result - properties = frame.properties - self._function_call_result = None - if frame.result: - # The "tool_call" message from the LLM that triggered the function call - self._context.add_message( - { - "role": "assistant", - "tool_calls": [ - { - "id": frame.tool_call_id, - "function": { - "name": frame.function_name, - "arguments": json.dumps(frame.arguments), - }, - "type": "function", - } - ], - } - ) - # The result of the function call. Need to add this both to our context here and to - # the openai realtime api context. - result_message = { - "role": "tool", - "content": json.dumps(frame.result), - "tool_call_id": frame.tool_call_id, - } - - self._context.add_message(result_message) - # The standard function callback code path pushes the FunctionCallResultFrame from the llm itself, - # so we didn't have a chance to add the result to the openai realtime api context. Let's push a - # special frame to do that. - await self.push_frame( - RealtimeFunctionCallResultFrame(result_frame=frame), FrameDirection.UPSTREAM - ) - if properties and properties.run_llm is not None: - # If the tool call result has a run_llm property, use it - run_llm = properties.run_llm - else: - # Default behavior is to run the LLM if there are no function calls in progress - run_llm = not bool(self._function_calls_in_progress) - - if run_llm: - await self.push_context_frame(FrameDirection.UPSTREAM) - - # Emit the on_context_updated callback once the function call result is added to the context - if properties and properties.on_context_updated is not None: - await properties.on_context_updated() - - await self.push_context_frame() - - except Exception as e: - logger.error(f"Error processing frame: {e}") + # The standard function callback code path pushes the FunctionCallResultFrame from the llm itself, + # so we didn't have a chance to add the result to the openai realtime api context. Let's push a + # special frame to do that. + await self.push_frame( + RealtimeFunctionCallResultFrame(result_frame=frame), FrameDirection.UPSTREAM + ) diff --git a/src/pipecat/services/openai_realtime_beta/events.py b/src/pipecat/services/openai_realtime_beta/events.py index 17ce0a6d4..c2dcb7f09 100644 --- a/src/pipecat/services/openai_realtime_beta/events.py +++ b/src/pipecat/services/openai_realtime_beta/events.py @@ -17,7 +17,29 @@ from pydantic import BaseModel, Field class InputAudioTranscription(BaseModel): - model: Optional[str] = "whisper-1" + """Configuration for audio transcription settings. + + Attributes: + model: Transcription model to use (e.g., "gpt-4o-transcribe", "whisper-1"). + language: Optional language code for transcription. + prompt: Optional transcription hint text. + """ + + model: str = "gpt-4o-transcribe" + language: Optional[str] + prompt: Optional[str] + + def __init__( + self, + model: Optional[str] = "gpt-4o-transcribe", + language: Optional[str] = None, + prompt: Optional[str] = None, + ): + super().__init__(model=model, language=language, prompt=prompt) + if self.model != "gpt-4o-transcribe" and (self.language or self.prompt): + raise ValueError( + "Fields 'language' and 'prompt' are only supported when model is 'gpt-4o-transcribe'" + ) class TurnDetection(BaseModel): @@ -27,6 +49,17 @@ class TurnDetection(BaseModel): silence_duration_ms: Optional[int] = 800 +class SemanticTurnDetection(BaseModel): + type: Optional[Literal["semantic_vad"]] = "semantic_vad" + eagerness: Optional[Literal["low", "medium", "high", "auto"]] = None + create_response: Optional[bool] = None + interrupt_response: Optional[bool] = None + + +class InputAudioNoiseReduction(BaseModel): + type: Optional[Literal["near_field", "far_field"]] + + class SessionProperties(BaseModel): modalities: Optional[List[Literal["text", "audio"]]] = None instructions: Optional[str] = None @@ -34,8 +67,11 @@ class SessionProperties(BaseModel): input_audio_format: Optional[Literal["pcm16", "g711_ulaw", "g711_alaw"]] = None output_audio_format: Optional[Literal["pcm16", "g711_ulaw", "g711_alaw"]] = None input_audio_transcription: Optional[InputAudioTranscription] = None + input_audio_noise_reduction: Optional[InputAudioNoiseReduction] = None # set turn_detection to False to disable turn detection - turn_detection: Optional[Union[TurnDetection, bool]] = Field(default=None) + turn_detection: Optional[Union[TurnDetection, SemanticTurnDetection, bool]] = Field( + default=None + ) tools: Optional[List[Dict]] = None tool_choice: Optional[Literal["auto", "none", "required"]] = None temperature: Optional[float] = None @@ -93,6 +129,7 @@ class RealtimeError(BaseModel): code: Optional[str] = "" message: str param: Optional[str] = None + event_id: Optional[str] = None # @@ -150,6 +187,11 @@ class ConversationItemDeleteEvent(ClientEvent): item_id: str +class ConversationItemRetrieveEvent(ClientEvent): + type: Literal["conversation.item.retrieve"] = "conversation.item.retrieve" + item_id: str + + class ResponseCreateEvent(ClientEvent): type: Literal["response.create"] = "response.create" response: Optional[ResponseProperties] = None @@ -193,6 +235,13 @@ class ConversationItemCreated(ServerEvent): item: ConversationItem +class ConversationItemInputAudioTranscriptionDelta(ServerEvent): + type: Literal["conversation.item.input_audio_transcription.delta"] + item_id: str + content_index: int + delta: str + + class ConversationItemInputAudioTranscriptionCompleted(ServerEvent): type: Literal["conversation.item.input_audio_transcription.completed"] item_id: str @@ -219,6 +268,11 @@ class ConversationItemDeleted(ServerEvent): item_id: str +class ConversationItemRetrieved(ServerEvent): + type: Literal["conversation.item.retrieved"] + item: ConversationItem + + class ResponseCreated(ServerEvent): type: Literal["response.created"] response: "Response" @@ -400,10 +454,12 @@ _server_event_types = { "input_audio_buffer.speech_started": InputAudioBufferSpeechStarted, "input_audio_buffer.speech_stopped": InputAudioBufferSpeechStopped, "conversation.item.created": ConversationItemCreated, + "conversation.item.input_audio_transcription.delta": ConversationItemInputAudioTranscriptionDelta, "conversation.item.input_audio_transcription.completed": ConversationItemInputAudioTranscriptionCompleted, "conversation.item.input_audio_transcription.failed": ConversationItemInputAudioTranscriptionFailed, "conversation.item.truncated": ConversationItemTruncated, "conversation.item.deleted": ConversationItemDeleted, + "conversation.item.retrieved": ConversationItemRetrieved, "response.created": ResponseCreated, "response.done": ResponseDone, "response.output_item.added": ResponseOutputItemAdded, diff --git a/src/pipecat/services/openai_realtime_beta/openai.py b/src/pipecat/services/openai_realtime_beta/openai.py index 00f8cd840..a1f4bc731 100644 --- a/src/pipecat/services/openai_realtime_beta/openai.py +++ b/src/pipecat/services/openai_realtime_beta/openai.py @@ -30,6 +30,7 @@ from pipecat.frames.frames import ( ErrorFrame, Frame, InputAudioRawFrame, + InterimTranscriptionFrame, LLMFullResponseEndFrame, LLMFullResponseStartFrame, LLMMessagesAppendFrame, @@ -43,6 +44,7 @@ from pipecat.frames.frames import ( TTSAudioRawFrame, TTSStartedFrame, TTSStoppedFrame, + TTSTextFrame, UserStartedSpeakingFrame, UserStoppedSpeakingFrame, ) @@ -114,12 +116,35 @@ class OpenAIRealtimeBetaLLMService(LLMService): self._messages_added_manually = {} self._user_and_response_message_tuple = None + self._register_event_handler("on_conversation_item_created") + self._register_event_handler("on_conversation_item_updated") + self._retrieve_conversation_item_futures = {} + def can_generate_metrics(self) -> bool: return True def set_audio_input_paused(self, paused: bool): self._audio_input_paused = paused + async def retrieve_conversation_item(self, item_id: str): + future = self.get_event_loop().create_future() + retrieval_in_flight = False + if not self._retrieve_conversation_item_futures.get(item_id): + self._retrieve_conversation_item_futures[item_id] = [] + else: + retrieval_in_flight = True + self._retrieve_conversation_item_futures[item_id].append(future) + if not retrieval_in_flight: + await self.send_client_event( + # Set event_id to "rci_{item_id}" so that we can identify an + # error later if the retrieval fails. We don't need a UUID + # suffix to the event_id because we're ensuring only one + # in-flight retrieval per item_id. (Note: "rci" = "retrieve + # conversation item") + events.ConversationItemRetrieveEvent(item_id=item_id, event_id=f"rci_{item_id}") + ) + return await future + # # standard AIService frame handling # @@ -353,8 +378,12 @@ class OpenAIRealtimeBetaLLMService(LLMService): await self._handle_evt_audio_done(evt) elif evt.type == "conversation.item.created": await self._handle_evt_conversation_item_created(evt) + elif evt.type == "conversation.item.input_audio_transcription.delta": + await self._handle_evt_input_audio_transcription_delta(evt) elif evt.type == "conversation.item.input_audio_transcription.completed": await self.handle_evt_input_audio_transcription_completed(evt) + elif evt.type == "conversation.item.retrieved": + await self._handle_conversation_item_retrieved(evt) elif evt.type == "response.done": await self._handle_evt_response_done(evt) elif evt.type == "input_audio_buffer.speech_started": @@ -364,9 +393,10 @@ class OpenAIRealtimeBetaLLMService(LLMService): elif evt.type == "response.audio_transcript.delta": await self._handle_evt_audio_transcript_delta(evt) elif evt.type == "error": - await self._handle_evt_error(evt) - # errors are fatal, so exit the receive loop - return + if not await self._maybe_handle_evt_retrieve_conversation_item_error(evt): + await self._handle_evt_error(evt) + # errors are fatal, so exit the receive loop + return async def _handle_evt_session_created(self, evt): # session.created is received right after connecting. Send a message @@ -408,6 +438,8 @@ class OpenAIRealtimeBetaLLMService(LLMService): # receive a BotStoppedSpeakingFrame from the output transport. async def _handle_evt_conversation_item_created(self, evt): + await self._call_event_handler("on_conversation_item_created", evt.item.id, evt.item) + # This will get sent from the server every time a new "message" is added # to the server's conversation state, whether we create it via the API # or the server creates it from LLM output. @@ -424,7 +456,16 @@ class OpenAIRealtimeBetaLLMService(LLMService): self._current_assistant_response = evt.item await self.push_frame(LLMFullResponseStartFrame()) + async def _handle_evt_input_audio_transcription_delta(self, evt): + if self._send_transcription_frames: + await self.push_frame( + # no way to get a language code? + InterimTranscriptionFrame(evt.delta, "", time_now_iso8601()) + ) + async def handle_evt_input_audio_transcription_completed(self, evt): + await self._call_event_handler("on_conversation_item_updated", evt.item_id, None) + if self._send_transcription_frames: await self.push_frame( # no way to get a language code? @@ -442,6 +483,12 @@ class OpenAIRealtimeBetaLLMService(LLMService): # User message without preceding conversation.item.created. Bug? logger.warning(f"Transcript for unknown user message: {evt}") + async def _handle_conversation_item_retrieved(self, evt: events.ConversationItemRetrieved): + futures = self._retrieve_conversation_item_futures.pop(evt.item.id, None) + if futures: + for future in futures: + future.set_result(evt.item) + async def _handle_evt_response_done(self, evt): # todo: figure out whether there's anything we need to do for "cancelled" events # usage metrics @@ -454,7 +501,15 @@ class OpenAIRealtimeBetaLLMService(LLMService): await self.stop_processing_metrics() await self.push_frame(LLMFullResponseEndFrame()) self._current_assistant_response = None + # error handling + if evt.response.status == "failed": + await self.push_error( + ErrorFrame(error=evt.response.status_details["error"]["message"], fatal=True) + ) + return # response content + for item in evt.response.output: + await self._call_event_handler("on_conversation_item_updated", item.id, item) pair = self._user_and_response_message_tuple if pair: user, assistant = pair @@ -471,6 +526,7 @@ class OpenAIRealtimeBetaLLMService(LLMService): async def _handle_evt_audio_transcript_delta(self, evt): if evt.delta: await self.push_frame(LLMTextFrame(evt.delta)) + await self.push_frame(TTSTextFrame(evt.delta)) async def _handle_evt_speech_started(self, evt): await self._truncate_current_audio_response() @@ -485,6 +541,22 @@ class OpenAIRealtimeBetaLLMService(LLMService): await self.push_frame(StopInterruptionFrame()) await self.push_frame(UserStoppedSpeakingFrame()) + async def _maybe_handle_evt_retrieve_conversation_item_error(self, evt: events.ErrorEvent): + """If the given error event is an error retrieving a conversation item: + - set an exception on the future that retrieve_conversation_item() is waiting on + - return true + Otherwise: + - return false + """ + if evt.error.code == "item_retrieve_invalid_item_id": + item_id = evt.error.event_id.split("_", 1)[1] # event_id is of the form "rci_{item_id}" + futures = self._retrieve_conversation_item_futures.pop(item_id, None) + if futures: + for future in futures: + future.set_exception(Exception(evt.error.message)) + return True + return False + async def _handle_evt_error(self, evt): # Errors are fatal to this connection. Send an ErrorFrame. await self.push_error(ErrorFrame(error=f"Error: {evt}", fatal=True)) @@ -507,7 +579,7 @@ class OpenAIRealtimeBetaLLMService(LLMService): arguments = json.loads(item.arguments) if self.has_function(function_name): run_llm = index == total_items - 1 - if function_name in self._callbacks.keys(): + if function_name in self._functions.keys(): await self.call_function( context=self._context, tool_call_id=tool_id, @@ -515,7 +587,7 @@ class OpenAIRealtimeBetaLLMService(LLMService): arguments=arguments, run_llm=run_llm, ) - elif None in self._callbacks.keys(): + elif None in self._functions.keys(): await self.call_function( context=self._context, tool_call_id=tool_id, diff --git a/src/pipecat/services/playht.py b/src/pipecat/services/playht.py index 80d313765..75677876f 100644 --- a/src/pipecat/services/playht.py +++ b/src/pipecat/services/playht.py @@ -160,7 +160,7 @@ class PlayHTTTSService(InterruptibleTTSService): await self._connect_websocket() if not self._receive_task: - self._receive_task = self.create_task(self._receive_task_handler(self.push_error)) + self._receive_task = self.create_task(self._receive_task_handler(self._report_error)) async def _disconnect(self): if self._receive_task: @@ -183,12 +183,14 @@ class PlayHTTTSService(InterruptibleTTSService): raise ValueError("WebSocket URL is not a string") self._websocket = await websockets.connect(self._websocket_url) - except ValueError as ve: - logger.error(f"{self} initialization error: {ve}") + except ValueError as e: + logger.error(f"{self} initialization error: {e}") self._websocket = None + await self._call_event_handler("on_connection_error", f"{e}") except Exception as e: logger.error(f"{self} initialization error: {e}") self._websocket = None + await self._call_event_handler("on_connection_error", f"{e}") async def _disconnect_websocket(self): try: diff --git a/src/pipecat/services/rime.py b/src/pipecat/services/rime.py index b2610b06c..a1a455eb5 100644 --- a/src/pipecat/services/rime.py +++ b/src/pipecat/services/rime.py @@ -27,6 +27,8 @@ from pipecat.frames.frames import ( from pipecat.processors.frame_processor import FrameDirection from pipecat.services.ai_services import AudioContextWordTTSService, TTSService from pipecat.transcriptions.language import Language +from pipecat.utils.text.base_text_aggregator import BaseTextAggregator +from pipecat.utils.text.skip_tags_aggregator import SkipTagsAggregator try: import websockets @@ -78,6 +80,7 @@ class RimeTTSService(AudioContextWordTTSService): model: str = "mistv2", sample_rate: Optional[int] = None, params: InputParams = InputParams(), + text_aggregator: Optional[BaseTextAggregator] = None, **kwargs, ): """Initialize Rime TTS service. @@ -97,6 +100,7 @@ class RimeTTSService(AudioContextWordTTSService): push_stop_frames=True, pause_frame_processing=True, sample_rate=sample_rate, + text_aggregator=text_aggregator or SkipTagsAggregator([("spell(", ")")]), **kwargs, ) @@ -167,7 +171,7 @@ class RimeTTSService(AudioContextWordTTSService): await self._connect_websocket() if not self._receive_task: - self._receive_task = self.create_task(self._receive_task_handler(self.push_error)) + self._receive_task = self.create_task(self._receive_task_handler(self._report_error)) async def _disconnect(self): """Close websocket connection and clean up tasks.""" @@ -190,6 +194,7 @@ class RimeTTSService(AudioContextWordTTSService): except Exception as e: logger.error(f"{self} initialization error: {e}") self._websocket = None + await self._call_event_handler("on_connection_error", f"{e}") async def _disconnect_websocket(self): """Close websocket connection and reset state.""" diff --git a/src/pipecat/services/tavus.py b/src/pipecat/services/tavus.py index b0ca699cb..9083dd53d 100644 --- a/src/pipecat/services/tavus.py +++ b/src/pipecat/services/tavus.py @@ -37,6 +37,7 @@ class TavusVideoService(AIService): replica_id: str, persona_id: str = "pipecat0", # Use `pipecat0` so that your TTS voice is used in place of the Tavus persona session: aiohttp.ClientSession, + sample_rate: int = 16000, **kwargs, ) -> None: super().__init__(**kwargs) @@ -44,6 +45,7 @@ class TavusVideoService(AIService): self._replica_id = replica_id self._persona_id = persona_id self._session = session + self._sample_rate = sample_rate self._conversation_id: str @@ -94,7 +96,7 @@ class TavusVideoService(AIService): async def _encode_audio_and_send(self, audio: bytes, in_rate: int, done: bool) -> None: """Encodes audio to base64 and sends it to Tavus""" if not done: - audio = await self._resampler.resample(audio, in_rate, 16000) + audio = await self._resampler.resample(audio, in_rate, self._sample_rate) audio_base64 = base64.b64encode(audio).decode("utf-8") logger.trace(f"{self}: sending {len(audio)} bytes") await self._send_audio_message(audio_base64, done=done) @@ -108,7 +110,7 @@ class TavusVideoService(AIService): elif isinstance(frame, TTSAudioRawFrame): await self._encode_audio_and_send(frame.audio, frame.sample_rate, done=False) elif isinstance(frame, TTSStoppedFrame): - await self._encode_audio_and_send(b"\x00", 16000, done=True) + await self._encode_audio_and_send(b"\x00", self._sample_rate, done=True) await self.stop_ttfb_metrics() await self.stop_processing_metrics() elif isinstance(frame, StartInterruptionFrame): @@ -137,6 +139,7 @@ class TavusVideoService(AIService): "inference_id": self._current_idx_str, "audio": audio_base64, "done": done, + "sample_rate": self._sample_rate, }, } ) diff --git a/src/pipecat/services/ultravox.py b/src/pipecat/services/ultravox.py index 40029e673..52a5d05eb 100644 --- a/src/pipecat/services/ultravox.py +++ b/src/pipecat/services/ultravox.py @@ -21,14 +21,15 @@ from pipecat.frames.frames import ( EndFrame, ErrorFrame, Frame, + LLMFullResponseEndFrame, + LLMFullResponseStartFrame, + LLMTextFrame, StartFrame, - TranscriptionFrame, UserStartedSpeakingFrame, UserStoppedSpeakingFrame, ) from pipecat.processors.frame_processor import FrameDirection from pipecat.services.ai_services import AIService -from pipecat.utils.time import time_now_iso8601 try: from transformers import AutoTokenizer @@ -339,6 +340,12 @@ class UltravoxSTTService(AIService): # Concatenate audio frames - all should be int16 now audio_data = np.concatenate(audio_arrays) + audio_int16 = audio_data # Already in int16 format + # Save int16 audio + + # Convert int16 to float32 and normalize for model input + audio_float32 = audio_int16.astype(np.float32) / 32768.0 + # Generate text using the model if self._model: try: @@ -349,11 +356,11 @@ class UltravoxSTTService(AIService): await self.start_ttfb_metrics() await self.start_processing_metrics() - async for response in self._model.generate( + async for response in self.model.generate( messages=[{"role": "user", "content": "<|audio|>\n"}], - temperature=self._temperature, - max_tokens=self._max_tokens, - audio=audio_data, + temperature=self.temperature, + max_tokens=self.max_tokens, + audio=audio_float32, ): # Stop TTFB metrics after first response await self.stop_ttfb_metrics() @@ -369,18 +376,13 @@ class UltravoxSTTService(AIService): await self.stop_processing_metrics() logger.info(f"Generated text: {full_response}") - # Create a transcription frame with the generated text - transcription = full_response.strip() - if transcription: - yield TranscriptionFrame( - user_id="", - text=transcription, - timestamp=time_now_iso8601(), - ) - else: - logger.warning("Empty transcription result") - yield ErrorFrame("Empty transcription result") + yield LLMFullResponseStartFrame() + + text_frame = LLMTextFrame(text=full_response.strip()) + yield text_frame + + yield LLMFullResponseEndFrame() except Exception as e: logger.error(f"Error generating text from model: {e}") diff --git a/src/pipecat/services/websocket_service.py b/src/pipecat/services/websocket_service.py index 19fb4ae01..2e82ddaba 100644 --- a/src/pipecat/services/websocket_service.py +++ b/src/pipecat/services/websocket_service.py @@ -19,9 +19,10 @@ from pipecat.utils.network import exponential_backoff_time class WebsocketService(ABC): """Base class for websocket-based services with reconnection logic.""" - def __init__(self): + def __init__(self, *, reconnect_on_error: bool = True, **kwargs): """Initialize websocket attributes.""" self._websocket: Optional[websockets.WebSocketClientProtocol] = None + self._reconnect_on_error = reconnect_on_error async def _verify_connection(self) -> bool: """Verify websocket connection is working. @@ -72,24 +73,29 @@ class WebsocketService(ABC): self._websocket.close_rcvd_then_sent, ) except Exception as e: - retry_count += 1 - if retry_count >= MAX_RETRIES: - message = f"{self} error receiving messages: {e}" - logger.error(message) - await report_error(ErrorFrame(message, fatal=True)) + message = f"{self} error receiving messages: {e}" + logger.error(message) + + if self._reconnect_on_error: + retry_count += 1 + if retry_count >= MAX_RETRIES: + await report_error(ErrorFrame(message, fatal=True)) + break + + logger.warning(f"{self} connection error, will retry: {e}") + await report_error(ErrorFrame(message)) + + try: + if await self._reconnect_websocket(retry_count): + retry_count = 0 # Reset counter on successful reconnection + wait_time = exponential_backoff_time(retry_count) + await asyncio.sleep(wait_time) + except Exception as reconnect_error: + logger.error(f"{self} reconnection failed: {reconnect_error}") + else: + await report_error(ErrorFrame(message)) break - logger.warning(f"{self} connection error, will retry: {e}") - - try: - if await self._reconnect_websocket(retry_count): - retry_count = 0 # Reset counter on successful reconnection - wait_time = exponential_backoff_time(retry_count) - await asyncio.sleep(wait_time) - except Exception as reconnect_error: - logger.error(f"{self} reconnection failed: {reconnect_error}") - continue - @abstractmethod async def _connect(self): """Implement service-specific connection logic. This function will diff --git a/src/pipecat/tests/utils.py b/src/pipecat/tests/utils.py index 7fcecc3ff..e2368ba09 100644 --- a/src/pipecat/tests/utils.py +++ b/src/pipecat/tests/utils.py @@ -6,7 +6,7 @@ import asyncio from dataclasses import dataclass -from typing import Any, Awaitable, Callable, Dict, Sequence, Tuple +from typing import Any, Awaitable, Callable, Dict, Optional, Sequence, Tuple from pipecat.frames.frames import ( EndFrame, @@ -80,8 +80,8 @@ async def run_test( processor: FrameProcessor, *, frames_to_send: Sequence[Frame], - expected_down_frames: Sequence[type], - expected_up_frames: Sequence[type] = [], + expected_down_frames: Optional[Sequence[type]] = None, + expected_up_frames: Optional[Sequence[type]] = None, ignore_start: bool = True, start_metadata: Dict[str, Any] = {}, send_end_frame: bool = True, @@ -101,7 +101,11 @@ async def run_test( pipeline = Pipeline([source, processor, sink]) - task = PipelineTask(pipeline, params=PipelineParams(start_metadata=start_metadata)) + task = PipelineTask( + pipeline, + params=PipelineParams(start_metadata=start_metadata), + cancel_on_idle_timeout=False, + ) async def push_frames(): # Just give a little head start to the runner. @@ -122,33 +126,35 @@ async def run_test( # Down frames # received_down_frames: Sequence[Frame] = [] - while not received_down.empty(): - frame = await received_down.get() - if not isinstance(frame, EndFrame) or not send_end_frame: - received_down_frames.append(frame) + if expected_down_frames is not None: + while not received_down.empty(): + frame = await received_down.get() + if not isinstance(frame, EndFrame) or not send_end_frame: + received_down_frames.append(frame) - print("received DOWN frames =", received_down_frames) - print("expected DOWN frames =", expected_down_frames) + print("received DOWN frames =", received_down_frames) + print("expected DOWN frames =", expected_down_frames) - assert len(received_down_frames) == len(expected_down_frames) + assert len(received_down_frames) == len(expected_down_frames) - for real, expected in zip(received_down_frames, expected_down_frames): - assert isinstance(real, expected) + for real, expected in zip(received_down_frames, expected_down_frames): + assert isinstance(real, expected) # # Up frames # received_up_frames: Sequence[Frame] = [] - while not received_up.empty(): - frame = await received_up.get() - received_up_frames.append(frame) + if expected_up_frames is not None: + while not received_up.empty(): + frame = await received_up.get() + received_up_frames.append(frame) - print("received UP frames =", received_up_frames) - print("expected UP frames =", expected_up_frames) + print("received UP frames =", received_up_frames) + print("expected UP frames =", expected_up_frames) - assert len(received_up_frames) == len(expected_up_frames) + assert len(received_up_frames) == len(expected_up_frames) - for real, expected in zip(received_up_frames, expected_up_frames): - assert isinstance(real, expected) + for real, expected in zip(received_up_frames, expected_up_frames): + assert isinstance(real, expected) return (received_down_frames, received_up_frames) diff --git a/src/pipecat/transcriptions/language.py b/src/pipecat/transcriptions/language.py index b8b9fafe9..75f714a72 100644 --- a/src/pipecat/transcriptions/language.py +++ b/src/pipecat/transcriptions/language.py @@ -54,6 +54,9 @@ class Language(StrEnum): AZ = "az" AZ_AZ = "az-AZ" + # Bashkir + BA = "ba" + # Belarusian BE = "be" @@ -66,6 +69,12 @@ class Language(StrEnum): BN_BD = "bn-BD" BN_IN = "bn-IN" + # Tibetan + BO = "bo" + + # Breton + BR = "br" + # Bosnian BS = "bs" BS_BA = "bs-BA" @@ -159,6 +168,9 @@ class Language(StrEnum): FIL = "fil" FIL_PH = "fil-PH" + # Faroese + FO = "fo" + # French FR = "fr" FR_BE = "fr-BE" @@ -178,6 +190,9 @@ class Language(StrEnum): GU = "gu" GU_IN = "gu-IN" + # Hausa + HA = "ha" + # Hebrew HE = "he" HE_IL = "he-IL" @@ -190,6 +205,9 @@ class Language(StrEnum): HR = "hr" HR_HR = "hr-HR" + # Haitian Creole + HT = "ht" + # Hungarian HU = "hu" HU_HU = "hu-HU" @@ -224,6 +242,7 @@ class Language(StrEnum): # Javanese JV = "jv" JV_ID = "jv-ID" + JW = "jw" # Fal requires for Javanese # Georgian KA = "ka" @@ -245,6 +264,15 @@ class Language(StrEnum): KO = "ko" KO_KR = "ko-KR" + # Latin + LA = "la" + + # Luxembourgish + LB = "lb" + + # Lingala + LN = "ln" + # Lao LO = "lo" LO_LA = "lo-LA" @@ -257,6 +285,9 @@ class Language(StrEnum): LV = "lv" LV_LV = "lv-LV" + # Malagasy + MG = "mg" + # Macedonian MK = "mk" MK_MK = "mk-MK" @@ -289,9 +320,10 @@ class Language(StrEnum): MY_MM = "my-MM" # Norwegian - NB = "nb" + NB = "nb" # Norwegian Bokmål NB_NO = "nb-NO" NO = "no" + NN = "nn" # Norwegian Nynorsk # Nepali NE = "ne" @@ -302,6 +334,9 @@ class Language(StrEnum): NL_BE = "nl-BE" NL_NL = "nl-NL" + # Occitan + OC = "oc" + # Odia OR = "or" OR_IN = "or-IN" @@ -331,6 +366,12 @@ class Language(StrEnum): RU = "ru" RU_RU = "ru-RU" + # Sanskrit + SA = "sa" + + # Sindhi + SD = "sd" + # Sinhala SI = "si" SI_LK = "si-LK" @@ -343,6 +384,9 @@ class Language(StrEnum): SL = "sl" SL_SI = "sl-SI" + # Shona + SN = "sn" + # Somali SO = "so" SO_SO = "so-SO" @@ -384,14 +428,23 @@ class Language(StrEnum): TE = "te" TE_IN = "te-IN" + # Tajik + TG = "tg" + # Thai TH = "th" TH_TH = "th-TH" + # Turkmen + TK = "tk" + # Turkish TR = "tr" TR_TR = "tr-TR" + # Tatar + TT = "tt" + # Ukrainian UK = "uk" UK_UA = "uk-UA" @@ -413,6 +466,12 @@ class Language(StrEnum): WUU = "wuu" WUU_CN = "wuu-CN" + # Yiddish + YI = "yi" + + # Yoruba + YO = "yo" + # Yue Chinese YUE = "yue" YUE_CN = "yue-CN" diff --git a/src/pipecat/transports/base_input.py b/src/pipecat/transports/base_input.py index 782ad1333..971dfe066 100644 --- a/src/pipecat/transports/base_input.py +++ b/src/pipecat/transports/base_input.py @@ -152,6 +152,7 @@ class BaseInputTransport(FrameProcessor): async def _handle_user_interruption(self, frame: Frame): if isinstance(frame, UserStartedSpeakingFrame): logger.debug("User started speaking") + await self.push_frame(frame) # Make sure we notify about interruptions quickly out-of-band. if self.interruptions_allowed: await self._start_interruption() @@ -161,12 +162,11 @@ class BaseInputTransport(FrameProcessor): await self.push_frame(StartInterruptionFrame()) elif isinstance(frame, UserStoppedSpeakingFrame): logger.debug("User stopped speaking") + await self.push_frame(frame) if self.interruptions_allowed: await self._stop_interruption() await self.push_frame(StopInterruptionFrame()) - await self.push_frame(frame) - # # Audio input # diff --git a/src/pipecat/transports/network/fastapi_websocket.py b/src/pipecat/transports/network/fastapi_websocket.py index bfb1e8146..937dda80c 100644 --- a/src/pipecat/transports/network/fastapi_websocket.py +++ b/src/pipecat/transports/network/fastapi_websocket.py @@ -102,11 +102,13 @@ class FastAPIWebsocketClient: class FastAPIWebsocketInputTransport(BaseInputTransport): def __init__( self, + transport: BaseTransport, client: FastAPIWebsocketClient, params: FastAPIWebsocketParams, **kwargs, ): super().__init__(params, **kwargs) + self._transport = transport self._client = client self._params = params self._receive_task = None @@ -139,6 +141,10 @@ class FastAPIWebsocketInputTransport(BaseInputTransport): await self._stop_tasks() await self._client.disconnect() + async def cleanup(self): + await super().cleanup() + await self._transport.cleanup() + async def _receive_messages(self): try: async for message in self._client.receive(): @@ -165,11 +171,14 @@ class FastAPIWebsocketInputTransport(BaseInputTransport): class FastAPIWebsocketOutputTransport(BaseOutputTransport): def __init__( self, + transport: BaseTransport, client: FastAPIWebsocketClient, params: FastAPIWebsocketParams, **kwargs, ): super().__init__(params, **kwargs) + + self._transport = transport self._client = client self._params = params @@ -194,6 +203,10 @@ class FastAPIWebsocketOutputTransport(BaseOutputTransport): await super().cancel(frame) await self._client.disconnect() + async def cleanup(self): + await super().cleanup() + await self._transport.cleanup() + async def process_frame(self, frame: Frame, direction: FrameDirection): await super().process_frame(frame, direction) @@ -266,6 +279,7 @@ class FastAPIWebsocketTransport(BaseTransport): output_name: Optional[str] = None, ): super().__init__(input_name=input_name, output_name=output_name) + self._params = params self._callbacks = FastAPIWebsocketCallbacks( @@ -278,10 +292,10 @@ class FastAPIWebsocketTransport(BaseTransport): self._client = FastAPIWebsocketClient(websocket, is_binary, self._callbacks) self._input = FastAPIWebsocketInputTransport( - self._client, self._params, name=self._input_name + self, self._client, self._params, name=self._input_name ) self._output = FastAPIWebsocketOutputTransport( - self._client, self._params, name=self._output_name + self, self._client, self._params, name=self._output_name ) # Register supported handlers. The user will only be able to register diff --git a/src/pipecat/transports/network/websocket_client.py b/src/pipecat/transports/network/websocket_client.py index eb2b5cfb8..11a000e69 100644 --- a/src/pipecat/transports/network/websocket_client.py +++ b/src/pipecat/transports/network/websocket_client.py @@ -118,9 +118,15 @@ class WebsocketClientSession: class WebsocketClientInputTransport(BaseInputTransport): - def __init__(self, session: WebsocketClientSession, params: WebsocketClientParams): + def __init__( + self, + transport: BaseTransport, + session: WebsocketClientSession, + params: WebsocketClientParams, + ): super().__init__(params) + self._transport = transport self._session = session self._params = params @@ -138,6 +144,10 @@ class WebsocketClientInputTransport(BaseInputTransport): await super().cancel(frame) await self._session.disconnect() + async def cleanup(self): + await super().cleanup() + await self._transport.cleanup() + async def on_message(self, websocket, message): frame = await self._params.serializer.deserialize(message) if not frame: @@ -149,9 +159,15 @@ class WebsocketClientInputTransport(BaseInputTransport): class WebsocketClientOutputTransport(BaseOutputTransport): - def __init__(self, session: WebsocketClientSession, params: WebsocketClientParams): + def __init__( + self, + transport: BaseTransport, + session: WebsocketClientSession, + params: WebsocketClientParams, + ): super().__init__(params) + self._transport = transport self._session = session self._params = params @@ -178,6 +194,10 @@ class WebsocketClientOutputTransport(BaseOutputTransport): await super().cancel(frame) await self._session.disconnect() + async def cleanup(self): + await super().cleanup() + await self._transport.cleanup() + async def send_message(self, frame: TransportMessageFrame | TransportMessageUrgentFrame): await self._write_frame(frame) @@ -250,12 +270,12 @@ class WebsocketClientTransport(BaseTransport): def input(self) -> WebsocketClientInputTransport: if not self._input: - self._input = WebsocketClientInputTransport(self._session, self._params) + self._input = WebsocketClientInputTransport(self, self._session, self._params) return self._input def output(self) -> WebsocketClientOutputTransport: if not self._output: - self._output = WebsocketClientOutputTransport(self._session, self._params) + self._output = WebsocketClientOutputTransport(self, self._session, self._params) return self._output async def _on_connected(self, websocket): diff --git a/src/pipecat/transports/network/websocket_server.py b/src/pipecat/transports/network/websocket_server.py index 19ebe4a45..e542342a2 100644 --- a/src/pipecat/transports/network/websocket_server.py +++ b/src/pipecat/transports/network/websocket_server.py @@ -55,6 +55,7 @@ class WebsocketServerCallbacks(BaseModel): class WebsocketServerInputTransport(BaseInputTransport): def __init__( self, + transport: BaseTransport, host: str, port: int, params: WebsocketServerParams, @@ -63,6 +64,7 @@ class WebsocketServerInputTransport(BaseInputTransport): ): super().__init__(params, **kwargs) + self._transport = transport self._host = host self._port = port self._params = params @@ -102,6 +104,10 @@ class WebsocketServerInputTransport(BaseInputTransport): await self.cancel_task(self._server_task) self._server_task = None + async def cleanup(self): + await super().cleanup() + await self._transport.cleanup() + async def _server_task_handler(self): logger.info(f"Starting websocket server on {self._host}:{self._port}") async with websockets.serve(self._client_handler, self._host, self._port) as server: @@ -163,9 +169,10 @@ class WebsocketServerInputTransport(BaseInputTransport): class WebsocketServerOutputTransport(BaseOutputTransport): - def __init__(self, params: WebsocketServerParams, **kwargs): + def __init__(self, transport: BaseTransport, params: WebsocketServerParams, **kwargs): super().__init__(params, **kwargs) + self._transport = transport self._params = params self._websocket: Optional[websockets.WebSocketServerProtocol] = None @@ -189,6 +196,10 @@ class WebsocketServerOutputTransport(BaseOutputTransport): await self._params.serializer.setup(frame) self._send_interval = (self._audio_chunk_size / self.sample_rate) / 2 + async def cleanup(self): + await super().cleanup() + await self._transport.cleanup() + async def process_frame(self, frame: Frame, direction: FrameDirection): await super().process_frame(frame, direction) @@ -283,13 +294,15 @@ class WebsocketServerTransport(BaseTransport): def input(self) -> WebsocketServerInputTransport: if not self._input: self._input = WebsocketServerInputTransport( - self._host, self._port, self._params, self._callbacks, name=self._input_name + self, self._host, self._port, self._params, self._callbacks, name=self._input_name ) return self._input def output(self) -> WebsocketServerOutputTransport: if not self._output: - self._output = WebsocketServerOutputTransport(self._params, name=self._output_name) + self._output = WebsocketServerOutputTransport( + self, self._params, name=self._output_name + ) return self._output async def _on_client_connected(self, websocket): diff --git a/src/pipecat/transports/services/daily.py b/src/pipecat/transports/services/daily.py index 873b13cd1..af7d2308c 100644 --- a/src/pipecat/transports/services/daily.py +++ b/src/pipecat/transports/services/daily.py @@ -811,9 +811,16 @@ class DailyInputTransport(BaseInputTransport): params: Configuration parameters. """ - def __init__(self, client: DailyTransportClient, params: DailyParams, **kwargs): + def __init__( + self, + transport: BaseTransport, + client: DailyTransportClient, + params: DailyParams, + **kwargs, + ): super().__init__(params, **kwargs) + self._transport = transport self._client = client self._params = params @@ -881,6 +888,7 @@ class DailyInputTransport(BaseInputTransport): async def cleanup(self): await super().cleanup() await self._client.cleanup() + await self._transport.cleanup() # # FrameProcessor @@ -890,7 +898,7 @@ class DailyInputTransport(BaseInputTransport): await super().process_frame(frame, direction) if isinstance(frame, UserImageRequestFrame): - await self.request_participant_image(frame.user_id) + await self.request_participant_image(frame) # # Frames @@ -927,16 +935,16 @@ class DailyInputTransport(BaseInputTransport): self._video_renderers[participant_id] = { "framerate": framerate, "timestamp": 0, - "render_next_frame": False, + "render_next_frame": [], } await self._client.capture_participant_video( participant_id, self._on_participant_video_frame, framerate, video_source, color_format ) - async def request_participant_image(self, participant_id: str): - if participant_id in self._video_renderers: - self._video_renderers[participant_id]["render_next_frame"] = True + async def request_participant_image(self, frame: UserImageRequestFrame): + if frame.user_id in self._video_renderers: + self._video_renderers[frame.user_id]["render_next_frame"].append(frame) async def _on_participant_video_frame(self, participant_id: str, buffer, size, format): render_frame = False @@ -945,17 +953,24 @@ class DailyInputTransport(BaseInputTransport): prev_time = self._video_renderers[participant_id]["timestamp"] framerate = self._video_renderers[participant_id]["framerate"] + # Some times we render frames because of a request. + request_frame = None + if framerate > 0: next_time = prev_time + 1 / framerate render_frame = (next_time - curr_time) < 0.1 elif self._video_renderers[participant_id]["render_next_frame"]: - self._video_renderers[participant_id]["render_next_frame"] = False + request_frame = self._video_renderers[participant_id]["render_next_frame"].pop(0) render_frame = True if render_frame: frame = UserImageRawFrame( - user_id=participant_id, image=buffer, size=size, format=format + user_id=participant_id, + request=request_frame, + image=buffer, + size=size, + format=format, ) await self.push_frame(frame) self._video_renderers[participant_id]["timestamp"] = curr_time @@ -971,9 +986,12 @@ class DailyOutputTransport(BaseOutputTransport): params: Configuration parameters. """ - def __init__(self, client: DailyTransportClient, params: DailyParams, **kwargs): + def __init__( + self, transport: BaseTransport, client: DailyTransportClient, params: DailyParams, **kwargs + ): super().__init__(params, **kwargs) + self._transport = transport self._client = client # Whether we have seen a StartFrame already. @@ -1008,6 +1026,7 @@ class DailyOutputTransport(BaseOutputTransport): async def cleanup(self): await super().cleanup() await self._client.cleanup() + await self._transport.cleanup() async def send_message(self, frame: TransportMessageFrame | TransportMessageUrgentFrame): await self._client.send_message(frame) @@ -1109,12 +1128,16 @@ class DailyTransport(BaseTransport): def input(self) -> DailyInputTransport: if not self._input: - self._input = DailyInputTransport(self._client, self._params, name=self._input_name) + self._input = DailyInputTransport( + self, self._client, self._params, name=self._input_name + ) return self._input def output(self) -> DailyOutputTransport: if not self._output: - self._output = DailyOutputTransport(self._client, self._params, name=self._output_name) + self._output = DailyOutputTransport( + self, self._client, self._params, name=self._output_name + ) return self._output # diff --git a/src/pipecat/transports/services/livekit.py b/src/pipecat/transports/services/livekit.py index 7018ea520..8ce5c885c 100644 --- a/src/pipecat/transports/services/livekit.py +++ b/src/pipecat/transports/services/livekit.py @@ -345,9 +345,17 @@ class LiveKitTransportClient: class LiveKitInputTransport(BaseInputTransport): - def __init__(self, client: LiveKitTransportClient, params: LiveKitParams, **kwargs): + def __init__( + self, + transport: BaseTransport, + client: LiveKitTransportClient, + params: LiveKitParams, + **kwargs, + ): super().__init__(params, **kwargs) + self._transport = transport self._client = client + self._audio_in_task = None self._vad_analyzer: Optional[VADAnalyzer] = params.vad_analyzer self._resampler = create_default_resampler() @@ -377,6 +385,10 @@ class LiveKitInputTransport(BaseInputTransport): if self._audio_in_task and (self._params.audio_in_enabled or self._params.vad_enabled): await self.cancel_task(self._audio_in_task) + async def cleanup(self): + await super().cleanup() + await self._transport.cleanup() + async def push_app_message(self, message: Any, sender: str): frame = LiveKitTransportMessageUrgentFrame(message=message, participant_id=sender) await self.push_frame(frame) @@ -414,8 +426,15 @@ class LiveKitInputTransport(BaseInputTransport): class LiveKitOutputTransport(BaseOutputTransport): - def __init__(self, client: LiveKitTransportClient, params: LiveKitParams, **kwargs): + def __init__( + self, + transport: BaseTransport, + client: LiveKitTransportClient, + params: LiveKitParams, + **kwargs, + ): super().__init__(params, **kwargs) + self._transport = transport self._client = client async def start(self, frame: StartFrame): @@ -433,6 +452,10 @@ class LiveKitOutputTransport(BaseOutputTransport): await super().cancel(frame) await self._client.disconnect() + async def cleanup(self): + await super().cleanup() + await self._transport.cleanup() + async def send_message(self, frame: TransportMessageFrame | TransportMessageUrgentFrame): if isinstance(frame, (LiveKitTransportMessageFrame, LiveKitTransportMessageUrgentFrame)): await self._client.send_data(frame.message.encode(), frame.participant_id) @@ -499,13 +522,15 @@ class LiveKitTransport(BaseTransport): def input(self) -> LiveKitInputTransport: if not self._input: - self._input = LiveKitInputTransport(self._client, self._params, name=self._input_name) + self._input = LiveKitInputTransport( + self, self._client, self._params, name=self._input_name + ) return self._input def output(self) -> LiveKitOutputTransport: if not self._output: self._output = LiveKitOutputTransport( - self._client, self._params, name=self._output_name + self, self._client, self._params, name=self._output_name ) return self._output @@ -574,13 +599,6 @@ class LiveKitTransport(BaseTransport): ) await self._output.send_message(frame) - async def cleanup(self): - if self._input: - await self._input.cleanup() - if self._output: - await self._output.cleanup() - await self._client.disconnect() - async def on_room_event(self, event): # Handle room events pass diff --git a/src/pipecat/utils/base_object.py b/src/pipecat/utils/base_object.py index e51eac35d..1dee24ce7 100644 --- a/src/pipecat/utils/base_object.py +++ b/src/pipecat/utils/base_object.py @@ -4,6 +4,7 @@ # SPDX-License-Identifier: BSD 2-Clause License # +import asyncio import inspect from abc import ABC from typing import Optional @@ -17,8 +18,15 @@ class BaseObject(ABC): def __init__(self, *, name: Optional[str] = None): self._id: int = obj_id() self._name = name or f"{self.__class__.__name__}#{obj_count(self)}" + + # Registered event handlers. self._event_handlers: dict = {} + # Set of tasks being executed. When a task finishes running it gets + # automatically removed from the set. When we cleanup we wait for all + # event tasks still being executed. + self._event_tasks = set() + @property def id(self) -> int: return self._id @@ -27,6 +35,12 @@ class BaseObject(ABC): def name(self) -> str: return self._name + async def cleanup(self): + if self._event_tasks: + event_names, tasks = zip(*self._event_tasks) + logger.debug(f"{self} wating on event handlers to finish {list(event_names)}...") + await asyncio.wait(tasks) + def event_handler(self, event_name: str): def decorator(handler): self.add_event_handler(event_name, handler) @@ -45,6 +59,16 @@ class BaseObject(ABC): self._event_handlers[event_name] = [] async def _call_event_handler(self, event_name: str, *args, **kwargs): + # Create the task. + task = asyncio.create_task(self._run_task(event_name, *args, **kwargs)) + + # Add it to our list of event tasks. + self._event_tasks.add((event_name, task)) + + # Remove the task from the event tasks list when the task completes. + task.add_done_callback(self._event_task_finished) + + async def _run_task(self, event_name: str, *args, **kwargs): try: for handler in self._event_handlers[event_name]: if inspect.iscoroutinefunction(handler): @@ -54,5 +78,10 @@ class BaseObject(ABC): except Exception as e: logger.exception(f"Exception in event handler {event_name}: {e}") + def _event_task_finished(self, task: asyncio.Task): + tuple_to_remove = next((t for t in self._event_tasks if t[1] == task), None) + if tuple_to_remove: + self._event_tasks.discard(tuple_to_remove) + def __str__(self): return self.name diff --git a/src/pipecat/utils/string.py b/src/pipecat/utils/string.py index 154d03174..69036a665 100644 --- a/src/pipecat/utils/string.py +++ b/src/pipecat/utils/string.py @@ -5,10 +5,12 @@ # import re +from typing import Optional, Sequence, Tuple ENDOFSENTENCE_PATTERN_STR = r""" (? str: + """Replace occurrences of a substring within a matched section of a given + text. + + Args: + text (str): The input text in which replacements will be made. + match (re.Match): A regex match object representing the section of text to modify. + old (str): The substring to be replaced. + new (str): The substring to replace `old` with. + + Returns: + str: The modified text with the specified replacements made within the matched section. + + """ + start = match.start() + end = match.end() + replacement = text[start:end].replace(old, new) + text = text[:start] + replacement + text[end:] + return text + def match_endofsentence(text: str) -> int: - match = ENDOFSENTENCE_PATTERN.search(text.rstrip()) + """Finds the position of the end of a sentence in the provided text string. + + This function processes the input text by replacing periods in email + addresses and numbers with ampersands to prevent them from being + misidentified as sentence terminals. It then searches for the end of a + sentence using a specified regex pattern. + + Args: + text (str): The input text in which to find the end of the sentence. + + Returns: + int: The position of the end of the sentence if found, otherwise 0. + + """ + text = text.rstrip() + + # Replace email dots by ampersands so we can find the end of sentence. For + # example, first.last@email.com becomes first&last@email&com. + emails = list(EMAIL_PATTERN.finditer(text)) + for email_match in emails: + text = replace_match(text, email_match, ".", "&") + + # Replace number dots by ampersands so we can find the end of sentence. + numbers = list(NUMBER_PATTERN.finditer(text)) + for number_match in numbers: + text = replace_match(text, number_match, ".", "&") + + # Match against the new text. + match = ENDOFSENTENCE_PATTERN.search(text) + return match.end() if match else 0 + + +def parse_start_end_tags( + text: str, + tags: Sequence[StartEndTags], + current_tag: Optional[StartEndTags], + current_tag_index: int, +) -> Tuple[Optional[StartEndTags], int]: + """Parses the given text to identify a pair of start/end tags. + + If a start tag was previously found (i.e. current_tags is valid), wait for + the corresponding end tag. Otherwise, wait for a start tag. + + This function will return the index in the text that we should start parsing + in the next call and the current or new tags. + + Parameters: + - text (str): The text to be parsed. + - tags (Sequence[StartEndTags]): List of tuples containing start and end tags. + - current_tags (Optional[StartEndTags]): The currently active tags, if any. + - current_tags_index (int): The current index in the text. + + Returns: + Tuple[Optional[StartEndTags], int]: A tuple containing None or the current + tag and the index of the text. + + """ + # If we are already inside a tag, check if the end tag is in the text. + if current_tag: + _, end_tag = current_tag + if end_tag in text[current_tag_index:]: + return (None, len(text)) + return (current_tag, current_tag_index) + + # Check if any start tag appears in the text + for start_tag, end_tag in tags: + start_tag_count = text[current_tag_index:].count(start_tag) + end_tag_count = text[current_tag_index:].count(end_tag) + if start_tag_count == 0 and end_tag_count == 0: + return (None, current_tag_index) + elif start_tag_count > end_tag_count: + return ((start_tag, end_tag), len(text)) + elif start_tag_count == end_tag_count: + return (None, len(text)) + + return (None, current_tag_index) diff --git a/src/pipecat/utils/test_frame_processor.py b/src/pipecat/utils/test_frame_processor.py deleted file mode 100644 index b35864497..000000000 --- a/src/pipecat/utils/test_frame_processor.py +++ /dev/null @@ -1,48 +0,0 @@ -from typing import List - -from pipecat.processors.frame_processor import FrameProcessor - - -class TestException(Exception): - pass - - -class TestFrameProcessor(FrameProcessor): - __test__ = False # Prevents pytest from collecting this class as a test - - def __init__(self, test_frames): - self.test_frames = test_frames - self._list_counter = 0 - super().__init__() - - async def process_frame(self, frame, direction): - await super().process_frame(frame, direction) - - if not self.test_frames[ - 0 - ]: # then we've run out of required frames but the generator is still going? - raise TestException(f"Oops, got an extra frame, {frame}") - if isinstance(self.test_frames[0], List): - # We need to consume frames until we see the next frame type after this - next_frame = self.test_frames[1] - if isinstance(frame, next_frame): - # we're done iterating the list I guess - print(f"TestFrameProcessor got expected list exit frame: {frame}") - # pop twice to get rid of the list, as well as the next frame - self.test_frames.pop(0) - self.test_frames.pop(0) - self.list_counter = 0 - else: - fl = self.test_frames[0] - fl_el = fl[self._list_counter % len(fl)] - if isinstance(frame, fl_el): - print(f"TestFrameProcessor got expected list frame: {frame}") - self._list_counter += 1 - else: - raise TestException(f"Inside a list, expected {fl_el} but got {frame}") - - else: - if not isinstance(frame, self.test_frames[0]): - raise TestException(f"Expected {self.test_frames[0]}, but got {frame}") - print(f"TestFrameProcessor got expected frame: {frame}") - self.test_frames.pop(0) diff --git a/src/pipecat/utils/text/pattern_pair_aggregator.py b/src/pipecat/utils/text/pattern_pair_aggregator.py new file mode 100644 index 000000000..86f87103b --- /dev/null +++ b/src/pipecat/utils/text/pattern_pair_aggregator.py @@ -0,0 +1,262 @@ +# +# Copyright (c) 2024–2025, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +import re +from typing import Callable, Optional, Tuple + +from loguru import logger + +from pipecat.utils.string import match_endofsentence +from pipecat.utils.text.base_text_aggregator import BaseTextAggregator + + +class PatternMatch: + """Represents a matched pattern pair with its content. + + A PatternMatch object is created when a complete pattern pair is found + in the text. It contains information about which pattern was matched, + the full matched text (including start and end patterns), and the + content between the patterns. + + Attributes: + pattern_id: The identifier of the matched pattern pair. + full_match: The complete text including start and end patterns. + content: The text content between the start and end patterns. + """ + + def __init__(self, pattern_id: str, full_match: str, content: str): + """Initialize a pattern match. + + Args: + pattern_id: ID of the pattern pair. + full_match: Complete matched text including start and end patterns. + content: Content between the start and end patterns. + """ + self.pattern_id = pattern_id + self.full_match = full_match + self.content = content + + def __str__(self) -> str: + """Return a string representation of the pattern match. + + Returns: + A string describing the pattern match. + """ + return f"PatternMatch(id={self.pattern_id}, content={self.content})" + + +class PatternPairAggregator(BaseTextAggregator): + """Aggregator that identifies and processes content between pattern pairs. + + This aggregator buffers text until it can identify complete pattern pairs + (defined by start and end patterns), processes the content between these + patterns using registered handlers, and returns text at sentence boundaries. + It's particularly useful for processing structured content in streaming text, + such as XML tags, markdown formatting, or custom delimiters. + + The aggregator ensures that patterns spanning multiple text chunks are + correctly identified and handles cases where patterns contain sentence + boundaries. + """ + + def __init__(self): + """Initialize the pattern pair aggregator. + + Creates an empty aggregator with no patterns or handlers registered. + """ + self._text = "" + self._patterns = {} + self._handlers = {} + + @property + def text(self) -> str: + """Get the currently buffered text. + + Returns: + The current text buffer content. + """ + return self._text + + def add_pattern_pair( + self, pattern_id: str, start_pattern: str, end_pattern: str, remove_match: bool = True + ) -> "PatternPairAggregator": + """Add a pattern pair to detect in the text. + + Registers a new pattern pair with a unique identifier. The aggregator + will look for text that starts with the start pattern and ends with + the end pattern, and treat the content between them as a match. + + Args: + pattern_id: Unique identifier for this pattern pair. + start_pattern: Pattern that marks the beginning of content. + end_pattern: Pattern that marks the end of content. + remove_match: Whether to remove the matched content from the text. + + Returns: + Self for method chaining. + """ + self._patterns[pattern_id] = { + "start": start_pattern, + "end": end_pattern, + "remove_match": remove_match, + } + return self + + def on_pattern_match( + self, pattern_id: str, handler: Callable[[PatternMatch], None] + ) -> "PatternPairAggregator": + """Register a handler for when a pattern pair is matched. + + The handler will be called whenever a complete match for the + specified pattern ID is found in the text. + + Args: + pattern_id: ID of the pattern pair to match. + handler: Function to call when pattern is matched. + The function should accept a PatternMatch object. + + Returns: + Self for method chaining. + """ + self._handlers[pattern_id] = handler + return self + + def _process_complete_patterns(self, text: str) -> Tuple[str, bool]: + """Process all complete pattern pairs in the text. + + Searches for all complete pattern pairs in the text, calls the + appropriate handlers, and optionally removes the matches. + + Args: + text: The text to process. + + Returns: + Tuple of (processed_text, was_modified) where: + - processed_text is the text after processing patterns + - was_modified indicates whether any changes were made + """ + processed_text = text + modified = False + + for pattern_id, pattern_info in self._patterns.items(): + # Escape special regex characters in the patterns + start = re.escape(pattern_info["start"]) + end = re.escape(pattern_info["end"]) + remove_match = pattern_info["remove_match"] + + # Create regex to match from start pattern to end pattern + # The .*? is non-greedy to handle nested patterns + regex = f"{start}(.*?){end}" + + # Find all matches + match_iter = re.finditer(regex, processed_text, re.DOTALL) + matches = list(match_iter) # Convert to list for safe iteration + + for match in matches: + content = match.group(1) # Content between patterns + full_match = match.group(0) # Full match including patterns + + # Create pattern match object + pattern_match = PatternMatch( + pattern_id=pattern_id, full_match=full_match, content=content + ) + + # Call the appropriate handler if registered + if pattern_id in self._handlers: + try: + self._handlers[pattern_id](pattern_match) + except Exception as e: + logger.error(f"Error in pattern handler for {pattern_id}: {e}") + + # Remove the pattern from the text if configured + if remove_match: + processed_text = processed_text.replace(full_match, "", 1) + modified = True + + return processed_text, modified + + def _has_incomplete_patterns(self, text: str) -> bool: + """Check if text contains incomplete pattern pairs. + + Determines whether the text contains any start patterns without + matching end patterns, which would indicate incomplete content. + + Args: + text: The text to check. + + Returns: + True if there are incomplete patterns, False otherwise. + """ + for pattern_id, pattern_info in self._patterns.items(): + start = pattern_info["start"] + end = pattern_info["end"] + + # Count occurrences + start_count = text.count(start) + end_count = text.count(end) + + # If there are more starts than ends, we have incomplete patterns + if start_count > end_count: + return True + + return False + + def aggregate(self, text: str) -> Optional[str]: + """Aggregate text and process pattern pairs. + + This method adds the new text to the buffer, processes any complete pattern + pairs, and returns processed text up to sentence boundaries if possible. + If there are incomplete patterns (start without matching end), it will + continue buffering text. + + Args: + text: New text to add to the buffer. + + Returns: + Processed text up to a sentence boundary, or None if more + text is needed to form a complete sentence or pattern. + """ + # Add new text to buffer + self._text += text + + # Process any complete patterns in the buffer + processed_text, modified = self._process_complete_patterns(self._text) + + # Only update the buffer if modifications were made + if modified: + self._text = processed_text + + # Check if we have incomplete patterns + if self._has_incomplete_patterns(self._text): + # Still waiting for complete patterns + return None + + # Find sentence boundary if no incomplete patterns + eos_marker = match_endofsentence(self._text) + if eos_marker: + # Extract text up to the sentence boundary + result = self._text[:eos_marker] + self._text = self._text[eos_marker:] + return result + + # No complete sentence found yet + return None + + def handle_interruption(self): + """Handle interruptions by clearing the buffer. + + Called when an interruption occurs in the processing pipeline, + to reset the state and discard any partially aggregated text. + """ + self._text = "" + + def reset(self): + """Clear the internally aggregated text. + + Resets the aggregator to its initial state, discarding any + buffered text. + """ + self._text = "" diff --git a/src/pipecat/utils/text/skip_tags_aggregator.py b/src/pipecat/utils/text/skip_tags_aggregator.py new file mode 100644 index 000000000..00129028e --- /dev/null +++ b/src/pipecat/utils/text/skip_tags_aggregator.py @@ -0,0 +1,94 @@ +# +# Copyright (c) 2024–2025, Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +from typing import Optional, Sequence + +from pipecat.utils.string import StartEndTags, match_endofsentence, parse_start_end_tags +from pipecat.utils.text.base_text_aggregator import BaseTextAggregator + + +class SkipTagsAggregator(BaseTextAggregator): + """Aggregator that prevents end of sentence matching between start/end tags. + + This aggregator buffers text until it finds an end of sentence or a start + tag. If a start tag is found the aggregator will keep aggregating text + unconditionally until the corresponding end tag is found. It's particularly + useful for processing content with custom delimiters that should prevent + text from being considered for end of sentence matching.. + + The aggregator ensures that tags spanning multiple text chunks are correctly + identified. + + """ + + def __init__(self, tags: Sequence[StartEndTags]): + """Initialize the pattern pair aggregator. + + Creates an empty aggregator with no patterns or handlers registered. + """ + self._text = "" + self._tags = tags + self._current_tag: Optional[StartEndTags] = None + self._current_tag_index: int = 0 + + @property + def text(self) -> str: + """Get the currently buffered text. + + Returns: + The current text buffer content. + """ + return self._text + + def aggregate(self, text: str) -> Optional[str]: + """Aggregate text and process pattern pairs. + + This method adds the new text to the buffer, processes any complete pattern + pairs, and returns processed text up to sentence boundaries if possible. + If there are incomplete patterns (start without matching end), it will + continue buffering text. + + Args: + text: New text to add to the buffer. + + Returns: + Processed text up to a sentence boundary, or None if more + text is needed to form a complete sentence or pattern. + """ + # Add new text to buffer + self._text += text + + (self._current_tag, self._current_tag_index) = parse_start_end_tags( + self._text, self._tags, self._current_tag, self._current_tag_index + ) + + # Find sentence boundary if no incomplete patterns + if not self._current_tag: + eos_marker = match_endofsentence(self._text) + if eos_marker: + # Extract text up to the sentence boundary + result = self._text[:eos_marker] + self._text = self._text[eos_marker:] + return result + + # No complete sentence found yet + return None + + def handle_interruption(self): + """Handle interruptions by clearing the buffer. + + Called when an interruption occurs in the processing pipeline, + to reset the state and discard any partially aggregated text. + """ + self._text = "" + + def reset(self): + """Clear the internally aggregated text. + + Resets the aggregator to its initial state, discarding any + buffered text. + """ + self._text = "" diff --git a/tests/integration/integration_azure_llm.py b/tests/integration/integration_azure_llm.py deleted file mode 100644 index 8e49e9d04..000000000 --- a/tests/integration/integration_azure_llm.py +++ /dev/null @@ -1,33 +0,0 @@ -import asyncio -import os -import unittest - -from openai.types.chat import ( - ChatCompletionSystemMessageParam, -) - -from pipecat.processors.aggregators.openai_llm_context import ( - OpenAILLMContext, - OpenAILLMContextFrame, -) -from pipecat.services.azure import AzureLLMService - -if __name__ == "__main__": - - @unittest.skip("Skip azure integration test") - async def test_chat(): - llm = AzureLLMService( - api_key=os.getenv("AZURE_CHATGPT_API_KEY"), - endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"), - model=os.getenv("AZURE_CHATGPT_MODEL"), - ) - context = OpenAILLMContext() - message: ChatCompletionSystemMessageParam = ChatCompletionSystemMessageParam( - content="Please tell the world hello.", name="system", role="system" - ) - context.add_message(message) - frame = OpenAILLMContextFrame(context) - async for s in llm.process_frame(frame): - print(s) - - asyncio.run(test_chat()) diff --git a/tests/integration/integration_ollama_llm.py b/tests/integration/integration_ollama_llm.py deleted file mode 100644 index 085500cb8..000000000 --- a/tests/integration/integration_ollama_llm.py +++ /dev/null @@ -1,28 +0,0 @@ -import asyncio -import unittest - -from openai.types.chat import ( - ChatCompletionSystemMessageParam, -) - -from pipecat.processors.aggregators.openai_llm_context import ( - OpenAILLMContext, - OpenAILLMContextFrame, -) -from pipecat.services.ollama import OLLamaLLMService - -if __name__ == "__main__": - - @unittest.skip("Skip azure integration test") - async def test_chat(): - llm = OLLamaLLMService() - context = OpenAILLMContext() - message: ChatCompletionSystemMessageParam = ChatCompletionSystemMessageParam( - content="Please tell the world hello.", name="system", role="system" - ) - context.add_message(message) - frame = OpenAILLMContextFrame(context) - async for s in llm.process_frame(frame): - print(s) - - asyncio.run(test_chat()) diff --git a/tests/integration/integration_openai_llm.py b/tests/integration/integration_openai_llm.py deleted file mode 100644 index 2c84c8882..000000000 --- a/tests/integration/integration_openai_llm.py +++ /dev/null @@ -1,128 +0,0 @@ -import asyncio -import json -import os -from typing import List - -from openai.types.chat import ( - ChatCompletionSystemMessageParam, - ChatCompletionToolParam, - ChatCompletionUserMessageParam, -) - -from pipecat.frames.frames import LLMFullResponseEndFrame, LLMFullResponseStartFrame, TextFrame -from pipecat.processors.frame_processor import FrameDirection -from pipecat.services.openai import OpenAILLMContext, OpenAILLMContextFrame, OpenAILLMService -from pipecat.utils.test_frame_processor import TestFrameProcessor - -tools = [ - ChatCompletionToolParam( - type="function", - function={ - "name": "get_current_weather", - "description": "Get the current weather", - "parameters": { - "type": "object", - "properties": { - "location": { - "type": "string", - "description": "The city and state, e.g. San Francisco, CA", - }, - "format": { - "type": "string", - "enum": ["celsius", "fahrenheit"], - "description": "The temperature unit to use. Infer this from the users location.", - }, - }, - "required": ["location", "format"], - }, - }, - ) -] - -if __name__ == "__main__": - - async def test_simple_functions(): - async def get_weather_from_api(llm, args): - return json.dumps({"conditions": "nice", "temperature": "75"}) - - api_key = os.getenv("OPENAI_API_KEY") - - llm = OpenAILLMService( - api_key=api_key or "", - model="gpt-4-1106-preview", - ) - - llm.register_function("get_current_weather", get_weather_from_api) - t = TestFrameProcessor([LLMFullResponseStartFrame, TextFrame, LLMFullResponseEndFrame]) - llm.link(t) - - context = OpenAILLMContext(tools=tools) - system_message: ChatCompletionSystemMessageParam = ChatCompletionSystemMessageParam( - content="Ask the user to ask for a weather report", name="system", role="system" - ) - user_message: ChatCompletionUserMessageParam = ChatCompletionUserMessageParam( - content="Could you tell me the weather for Boulder, Colorado", - name="user", - role="user", - ) - context.add_message(system_message) - context.add_message(user_message) - frame = OpenAILLMContextFrame(context) - await llm.process_frame(frame, FrameDirection.DOWNSTREAM) - - async def test_advanced_functions(): - async def get_weather_from_api(llm, args): - return [ - { - "role": "system", - "content": "The user has asked for live weather. Respond by telling them we don't currently support live weather for that area, but it's coming soon.", - } - ] - - api_key = os.getenv("OPENAI_API_KEY") - - llm = OpenAILLMService( - api_key=api_key or "", - model="gpt-4-1106-preview", - ) - - llm.register_function("get_current_weather", get_weather_from_api) - t = TestFrameProcessor([LLMFullResponseStartFrame, TextFrame, LLMFullResponseEndFrame]) - llm.link(t) - - context = OpenAILLMContext(tools=tools) - system_message: ChatCompletionSystemMessageParam = ChatCompletionSystemMessageParam( - content="Ask the user to ask for a weather report", name="system", role="system" - ) - user_message: ChatCompletionUserMessageParam = ChatCompletionUserMessageParam( - content="Could you tell me the weather for Boulder, Colorado", - name="user", - role="user", - ) - context.add_message(system_message) - context.add_message(user_message) - frame = OpenAILLMContextFrame(context) - await llm.process_frame(frame, FrameDirection.DOWNSTREAM) - - async def test_chat(): - api_key = os.getenv("OPENAI_API_KEY") - t = TestFrameProcessor([LLMFullResponseStartFrame, TextFrame, LLMFullResponseEndFrame]) - llm = OpenAILLMService( - api_key=api_key or "", - model="gpt-4o", - ) - llm.link(t) - context = OpenAILLMContext() - message: ChatCompletionSystemMessageParam = ChatCompletionSystemMessageParam( - content="Please tell the world hello.", name="system", role="system" - ) - context.add_message(message) - frame = OpenAILLMContextFrame(context) - await llm.process_frame(frame, FrameDirection.DOWNSTREAM) - - async def run_tests(): - await test_simple_functions() - await test_advanced_functions() - await test_chat() - - asyncio.run(run_tests()) diff --git a/tests/integration/test_integration_unified_function_calling.py b/tests/integration/test_integration_unified_function_calling.py index 88407d703..0696c531a 100644 --- a/tests/integration/test_integration_unified_function_calling.py +++ b/tests/integration/test_integration_unified_function_calling.py @@ -1,3 +1,9 @@ +# +# Copyright (c) 2024-2025 Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + import os from unittest.mock import AsyncMock @@ -6,17 +12,12 @@ from dotenv import load_dotenv from pipecat.adapters.schemas.function_schema import FunctionSchema from pipecat.adapters.schemas.tools_schema import ToolsSchema -from pipecat.frames.frames import ( - LLMFullResponseEndFrame, - LLMFullResponseStartFrame, - LLMTextFrame, -) -from pipecat.processors.frame_processor import FrameDirection +from pipecat.pipeline.pipeline import Pipeline from pipecat.services.ai_services import LLMService from pipecat.services.anthropic import AnthropicLLMService from pipecat.services.google import GoogleLLMService from pipecat.services.openai import OpenAILLMContext, OpenAILLMContextFrame, OpenAILLMService -from pipecat.utils.test_frame_processor import TestFrameProcessor +from pipecat.tests.utils import run_test load_dotenv(override=True) @@ -47,8 +48,6 @@ async def _test_llm_function_calling(llm: LLMService): mock_fetch_weather = AsyncMock() llm.register_function(None, mock_fetch_weather) - t = TestFrameProcessor([LLMFullResponseStartFrame, LLMTextFrame, LLMFullResponseEndFrame]) - llm.link(t) messages = [ { @@ -61,10 +60,14 @@ async def _test_llm_function_calling(llm: LLMService): # This is done by default inside the create_context_aggregator context.set_llm_adapter(llm.get_llm_adapter()) - frame = OpenAILLMContextFrame(context) + pipeline = Pipeline([llm]) - # This will fail if an exception is raised - await llm.process_frame(frame, FrameDirection.DOWNSTREAM) + frames_to_send = [OpenAILLMContextFrame(context)] + await run_test( + pipeline, + frames_to_send=frames_to_send, + expected_down_frames=None, + ) # Assert that the mock function was called mock_fetch_weather.assert_called_once() diff --git a/tests/test_context_aggregators.py b/tests/test_context_aggregators.py index d4b8c35ce..baee3496f 100644 --- a/tests/test_context_aggregators.py +++ b/tests/test_context_aggregators.py @@ -4,13 +4,18 @@ # SPDX-License-Identifier: BSD 2-Clause License # +import json import unittest +from typing import Any import google.ai.generativelanguage as glm from pipecat.frames.frames import ( EmulateUserStartedSpeakingFrame, EmulateUserStoppedSpeakingFrame, + FunctionCallInProgressFrame, + FunctionCallResultFrame, + FunctionCallResultProperties, InterimTranscriptionFrame, LLMFullResponseEndFrame, LLMFullResponseStartFrame, @@ -21,10 +26,7 @@ from pipecat.frames.frames import ( UserStartedSpeakingFrame, UserStoppedSpeakingFrame, ) -from pipecat.processors.aggregators.llm_response import ( - LLMAssistantContextAggregator, - LLMUserContextAggregator, -) +from pipecat.processors.aggregators.llm_response import LLMUserContextAggregator from pipecat.processors.aggregators.openai_llm_context import ( OpenAILLMContext, OpenAILLMContextFrame, @@ -44,8 +46,6 @@ from pipecat.tests.utils import SleepFrame, run_test AGGREGATION_TIMEOUT = 0.1 AGGREGATION_SLEEP = 0.15 -BOT_INTERRUPTION_TIMEOUT = 0.2 -BOT_INTERRUPTION_SLEEP = 0.25 class BaseTestUserContextAggregator: @@ -388,14 +388,13 @@ class BaseTestUserContextAggregator: aggregator = self.AGGREGATOR_CLASS( context, aggregation_timeout=AGGREGATION_TIMEOUT, - bot_interruption_timeout=BOT_INTERRUPTION_TIMEOUT, ) frames_to_send = [ UserStartedSpeakingFrame(), InterimTranscriptionFrame(text="How ", user_id="cat", timestamp=""), SleepFrame(), UserStoppedSpeakingFrame(), - SleepFrame(BOT_INTERRUPTION_SLEEP), + SleepFrame(AGGREGATION_SLEEP), InterimTranscriptionFrame(text="are you?", user_id="cat", timestamp=""), TranscriptionFrame(text="How are you?", user_id="cat", timestamp=""), SleepFrame(sleep=AGGREGATION_SLEEP), @@ -405,12 +404,10 @@ class BaseTestUserContextAggregator: UserStoppedSpeakingFrame, *self.EXPECTED_CONTEXT_FRAMES, ] - expected_up_frames = [EmulateUserStartedSpeakingFrame, EmulateUserStoppedSpeakingFrame] await run_test( aggregator, frames_to_send=frames_to_send, expected_down_frames=expected_down_frames, - expected_up_frames=expected_up_frames, ) self.check_message_content(context, 0, "How are you?") @@ -418,7 +415,7 @@ class BaseTestUserContextAggregator: class BaseTestAssistantContextAggreagator: CONTEXT_CLASS = None # To be set in subclasses AGGREGATOR_CLASS = None # To be set in subclasses - EXPECTED_CONTEXT_FRAMES = [OpenAILLMContextFrame] + EXPECTED_CONTEXT_FRAMES = None # To be set in subclasses def check_message_content(self, context: OpenAILLMContext, index: int, content: str): assert context.messages[index]["content"] == content @@ -428,6 +425,9 @@ class BaseTestAssistantContextAggreagator: ): assert context.messages[index]["content"] == content + def check_function_call_result(self, context: OpenAILLMContext, index: int, content: str): + assert json.loads(context.messages[index]["content"]) == content + async def test_empty(self): assert self.CONTEXT_CLASS is not None, "CONTEXT_CLASS must be set in a subclass" assert self.AGGREGATOR_CLASS is not None, "AGGREGATOR_CLASS must be set in a subclass" @@ -561,9 +561,76 @@ class BaseTestAssistantContextAggreagator: self.check_message_multi_content(context, 0, 0, "Hello Pipecat.") self.check_message_multi_content(context, 0, 1, "How are you?") + async def test_function_call(self): + assert self.CONTEXT_CLASS is not None, "CONTEXT_CLASS must be set in a subclass" + assert self.AGGREGATOR_CLASS is not None, "AGGREGATOR_CLASS must be set in a subclass" + + context = self.CONTEXT_CLASS() + aggregator = self.AGGREGATOR_CLASS(context) + frames_to_send = [ + FunctionCallInProgressFrame( + function_name="get_weather", + tool_call_id="1", + arguments={"location": "Los Angeles"}, + cancel_on_interruption=False, + ), + SleepFrame(), + FunctionCallResultFrame( + function_name="get_weather", + tool_call_id="1", + arguments={"location": "Los Angeles"}, + result={"conditions": "Sunny"}, + ), + ] + expected_down_frames = [] + await run_test( + aggregator, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) + self.check_function_call_result(context, -1, {"conditions": "Sunny"}) + + async def test_function_call_on_context_updated(self): + assert self.CONTEXT_CLASS is not None, "CONTEXT_CLASS must be set in a subclass" + assert self.AGGREGATOR_CLASS is not None, "AGGREGATOR_CLASS must be set in a subclass" + + context_updated = False + + async def on_context_updated(): + nonlocal context_updated + context_updated = True + + context = self.CONTEXT_CLASS() + aggregator = self.AGGREGATOR_CLASS(context) + frames_to_send = [ + FunctionCallInProgressFrame( + function_name="get_weather", + tool_call_id="1", + arguments={"location": "Los Angeles"}, + cancel_on_interruption=False, + ), + SleepFrame(), + FunctionCallResultFrame( + function_name="get_weather", + tool_call_id="1", + arguments={"location": "Los Angeles"}, + result={"conditions": "Sunny"}, + properties=FunctionCallResultProperties(on_context_updated=on_context_updated), + ), + SleepFrame(), + ] + expected_down_frames = [] + await run_test( + aggregator, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) + self.check_function_call_result(context, -1, {"conditions": "Sunny"}) + assert context_updated + # -# LLMUserContextAggregator, LLMAssistantContextAggregator +# LLMUserContextAggregator # @@ -572,13 +639,6 @@ class TestLLMUserContextAggregator(BaseTestUserContextAggregator, unittest.Isola AGGREGATOR_CLASS = LLMUserContextAggregator -class TestLLMAssistantContextAggregator( - BaseTestAssistantContextAggreagator, unittest.IsolatedAsyncioTestCase -): - CONTEXT_CLASS = OpenAILLMContext - AGGREGATOR_CLASS = LLMAssistantContextAggregator - - # # OpenAI # @@ -630,6 +690,9 @@ class TestAnthropicAssistantContextAggregator( messages = context.messages[content_index] assert messages["content"][index]["text"] == content + def check_function_call_result(self, context: OpenAILLMContext, index: int, content: Any): + assert context.messages[index]["content"][0]["content"] == json.dumps(content) + # # Google @@ -669,3 +732,7 @@ class TestGoogleAssistantContextAggregator( ): obj = glm.Content.to_dict(context.messages[index]) assert obj["parts"][0]["text"] == content + + def check_function_call_result(self, context: OpenAILLMContext, index: int, content: Any): + obj = glm.Content.to_dict(context.messages[index]) + assert obj["parts"][0]["function_response"]["response"] == content diff --git a/tests/test_pattern_pair_aggregator.py b/tests/test_pattern_pair_aggregator.py new file mode 100644 index 000000000..e1086e577 --- /dev/null +++ b/tests/test_pattern_pair_aggregator.py @@ -0,0 +1,147 @@ +# +# Copyright (c) 2024-2025 Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +import unittest +from unittest.mock import Mock + +from pipecat.utils.text.pattern_pair_aggregator import PatternMatch, PatternPairAggregator + + +class TestPatternPairAggregator(unittest.IsolatedAsyncioTestCase): + def setUp(self): + self.aggregator = PatternPairAggregator() + self.test_handler = Mock() + + # Add a test pattern + self.aggregator.add_pattern_pair( + pattern_id="test_pattern", + start_pattern="", + end_pattern="", + remove_match=True, + ) + + # Register the mock handler + self.aggregator.on_pattern_match("test_pattern", self.test_handler) + + async def test_pattern_match_and_removal(self): + # First part doesn't complete the pattern + result = self.aggregator.aggregate("Hello pattern") + self.assertIsNone(result) + self.assertEqual(self.aggregator.text, "Hello pattern") + + # Second part completes the pattern and includes an exclamation point + result = self.aggregator.aggregate(" content!") + + # Verify the handler was called with correct PatternMatch object + self.test_handler.assert_called_once() + call_args = self.test_handler.call_args[0][0] + self.assertIsInstance(call_args, PatternMatch) + self.assertEqual(call_args.pattern_id, "test_pattern") + self.assertEqual(call_args.full_match, "pattern content") + self.assertEqual(call_args.content, "pattern content") + + # The exclamation point should be treated as a sentence boundary, + # so the result should include just text up to and including "!" + self.assertEqual(result, "Hello !") + + # Next sentence should be processed separately + result = self.aggregator.aggregate(" This is another sentence.") + self.assertEqual(result, " This is another sentence.") + + # Buffer should be empty after returning a complete sentence + self.assertEqual(self.aggregator.text, "") + + async def test_incomplete_pattern(self): + # Add text with incomplete pattern + result = self.aggregator.aggregate("Hello pattern content") + + # No complete pattern yet, so nothing should be returned + self.assertIsNone(result) + + # The handler should not be called yet + self.test_handler.assert_not_called() + + # Buffer should contain the incomplete text + self.assertEqual(self.aggregator.text, "Hello pattern content") + + # Reset and confirm buffer is cleared + self.aggregator.reset() + self.assertEqual(self.aggregator.text, "") + + async def test_multiple_patterns(self): + # Set up multiple patterns and handlers + voice_handler = Mock() + emphasis_handler = Mock() + + self.aggregator.add_pattern_pair( + pattern_id="voice", start_pattern="", end_pattern="", remove_match=True + ) + + self.aggregator.add_pattern_pair( + pattern_id="emphasis", + start_pattern="", + end_pattern="", + remove_match=False, # Keep emphasis tags + ) + + self.aggregator.on_pattern_match("voice", voice_handler) + self.aggregator.on_pattern_match("emphasis", emphasis_handler) + + # Test with multiple patterns in one text block + text = "Hello female I am very excited to meet you!" + result = self.aggregator.aggregate(text) + + # Both handlers should be called with correct data + voice_handler.assert_called_once() + voice_match = voice_handler.call_args[0][0] + self.assertEqual(voice_match.pattern_id, "voice") + self.assertEqual(voice_match.content, "female") + + emphasis_handler.assert_called_once() + emphasis_match = emphasis_handler.call_args[0][0] + self.assertEqual(emphasis_match.pattern_id, "emphasis") + self.assertEqual(emphasis_match.content, "very") + + # Voice pattern should be removed, emphasis pattern should remain + self.assertEqual(result, "Hello I am very excited to meet you!") + + # Buffer should be empty + self.assertEqual(self.aggregator.text, "") + + async def test_handle_interruption(self): + # Start with incomplete pattern + result = self.aggregator.aggregate("Hello pattern") + self.assertIsNone(result) + + # Simulate interruption + self.aggregator.handle_interruption() + + # Buffer should be cleared + self.assertEqual(self.aggregator.text, "") + + # Handler should not have been called + self.test_handler.assert_not_called() + + async def test_pattern_across_sentences(self): + # Test pattern that spans multiple sentences + result = self.aggregator.aggregate("Hello This is sentence one.") + + # First sentence contains start of pattern but no end, so no complete pattern yet + self.assertIsNone(result) + + # Add second part with pattern end + result = self.aggregator.aggregate(" This is sentence two. Final sentence.") + + # Handler should be called with entire content + self.test_handler.assert_called_once() + call_args = self.test_handler.call_args[0][0] + self.assertEqual(call_args.content, "This is sentence one. This is sentence two.") + + # Pattern should be removed, resulting in text with sentences merged + self.assertEqual(result, "Hello Final sentence.") + + # Buffer should be empty + self.assertEqual(self.aggregator.text, "") diff --git a/tests/test_pipeline.py b/tests/test_pipeline.py index c3811a672..46114b5b2 100644 --- a/tests/test_pipeline.py +++ b/tests/test_pipeline.py @@ -5,6 +5,7 @@ # import asyncio +import time import unittest from pipecat.frames.frames import EndFrame, HeartbeatFrame, StartFrame, TextFrame @@ -100,7 +101,7 @@ class TestPipelineTask(unittest.IsolatedAsyncioTestCase): identity = IdentityFilter() pipeline = Pipeline([identity]) - task = PipelineTask(pipeline) + task = PipelineTask(pipeline, cancel_on_idle_timeout=False) task.set_event_loop(asyncio.get_event_loop()) task.set_reached_upstream_filter((TextFrame,)) task.set_reached_downstream_filter((TextFrame,)) @@ -123,7 +124,7 @@ class TestPipelineTask(unittest.IsolatedAsyncioTestCase): await task.queue_frame(TextFrame(text="Hello Downstream!")) try: - await asyncio.wait_for(task.run(), timeout=1.0) + await asyncio.wait_for(asyncio.shield(task.run()), timeout=1.0) except asyncio.TimeoutError: pass @@ -149,6 +150,7 @@ class TestPipelineTask(unittest.IsolatedAsyncioTestCase): heartbeats_period_secs=0.2, ), observers=[heartbeats_observer], + cancel_on_idle_timeout=False, ) task.set_event_loop(asyncio.get_event_loop()) @@ -156,7 +158,90 @@ class TestPipelineTask(unittest.IsolatedAsyncioTestCase): await task.queue_frame(TextFrame(text="Hello!")) try: - await asyncio.wait_for(task.run(), timeout=1.0) + await asyncio.wait_for(asyncio.shield(task.run()), timeout=1.0) except asyncio.TimeoutError: pass assert heartbeats_counter == expected_heartbeats + + async def test_idle_task(self): + identity = IdentityFilter() + pipeline = Pipeline([identity]) + task = PipelineTask(pipeline, idle_timeout_secs=0.2) + task.set_event_loop(asyncio.get_event_loop()) + await task.run() + assert True + + async def test_no_idle_task(self): + identity = IdentityFilter() + pipeline = Pipeline([identity]) + task = PipelineTask(pipeline, idle_timeout_secs=0.2, cancel_on_idle_timeout=False) + task.set_event_loop(asyncio.get_event_loop()) + try: + await asyncio.wait_for(asyncio.shield(task.run()), timeout=0.3) + except asyncio.TimeoutError: + assert True + else: + assert False + + async def test_idle_task_heartbeats(self): + identity = IdentityFilter() + pipeline = Pipeline([identity]) + task = PipelineTask( + pipeline, + params=PipelineParams( + enable_heartbeats=True, + heartbeats_period_secs=0.1, + ), + idle_timeout_secs=0.3, + ) + task.set_event_loop(asyncio.get_event_loop()) + await task.run() + assert True + + async def test_idle_task_event_handler(self): + identity = IdentityFilter() + pipeline = Pipeline([identity]) + task = PipelineTask(pipeline, idle_timeout_secs=0.2, cancel_on_idle_timeout=False) + task.set_event_loop(asyncio.get_event_loop()) + + idle_timeout = False + + @task.event_handler("on_idle_timeout") + async def on_idle_timeout(task: PipelineTask): + nonlocal idle_timeout + idle_timeout = True + await task.cancel() + + await task.run() + assert True + + async def test_idle_task_frames(self): + idle_timeout_secs = 0.2 + sleep_time_secs = idle_timeout_secs / 2 + + identity = IdentityFilter() + pipeline = Pipeline([identity]) + task = PipelineTask( + pipeline, + idle_timeout_secs=idle_timeout_secs, + idle_timeout_frames=(TextFrame,), + ) + task.set_event_loop(asyncio.get_event_loop()) + + async def delayed_frames(): + await asyncio.sleep(sleep_time_secs) + await task.queue_frame(TextFrame("Hello Pipecat!")) + await asyncio.sleep(sleep_time_secs) + await task.queue_frame(TextFrame("Hello Pipecat!")) + await asyncio.sleep(sleep_time_secs) + await task.queue_frame(TextFrame("Hello Pipecat!")) + + start_time = time.time() + + tasks = {asyncio.create_task(task.run()), asyncio.create_task(delayed_frames())} + + await asyncio.wait(tasks, return_when=asyncio.FIRST_COMPLETED) + + diff_time = time.time() - start_time + + self.assertGreater(diff_time, sleep_time_secs * 3) diff --git a/tests/test_skip_tags_aggregator.py b/tests/test_skip_tags_aggregator.py new file mode 100644 index 000000000..8f36c4c05 --- /dev/null +++ b/tests/test_skip_tags_aggregator.py @@ -0,0 +1,54 @@ +# +# Copyright (c) 2024-2025 Daily +# +# SPDX-License-Identifier: BSD 2-Clause License +# + +import unittest + +from pipecat.utils.text.skip_tags_aggregator import SkipTagsAggregator + + +class TestSkipTagsAggregator(unittest.IsolatedAsyncioTestCase): + def setUp(self): + self.aggregator = SkipTagsAggregator([("", "")]) + + async def test_no_tags(self): + self.aggregator.reset() + + # No tags involved, aggregate at end of sentence. + result = self.aggregator.aggregate("Hello Pipecat!") + self.assertEqual(result, "Hello Pipecat!") + self.assertEqual(self.aggregator.text, "") + + async def test_basic_tags(self): + self.aggregator.reset() + + # Tags involved, avoid aggregation during tags. + result = self.aggregator.aggregate("My email is foo@pipecat.ai.") + self.assertEqual(result, "My email is foo@pipecat.ai.") + self.assertEqual(self.aggregator.text, "") + + async def test_streaming_tags(self): + self.aggregator.reset() + + # Tags involved, stream small chunk of texts. + result = self.aggregator.aggregate("My email is foo.") + self.assertIsNone(result) + self.assertEqual(self.aggregator.text, "My email is foo.") + + result = self.aggregator.aggregate("bar@pipecat.") + self.assertIsNone(result) + self.assertEqual(self.aggregator.text, "My email is foo.bar@pipecat.") + + result = self.aggregator.aggregate("aifoo.bar@pipecat.ai.") + self.assertEqual(result, "My email is foo.bar@pipecat.ai.") + self.assertEqual(self.aggregator.text, "") diff --git a/tests/test_transcript_processor.py b/tests/test_transcript_processor.py index 1c6db277f..d13246b2c 100644 --- a/tests/test_transcript_processor.py +++ b/tests/test_transcript_processor.py @@ -235,8 +235,7 @@ class TestUserTranscriptProcessor(unittest.IsolatedAsyncioTestCase): BotStartedSpeakingFrame(), SleepFrame(sleep=0.1), TTSTextFrame(text="Hello"), - TTSTextFrame(text="world"), - TTSTextFrame(text="!"), + TTSTextFrame(text="world!"), SleepFrame(sleep=0.1), StartInterruptionFrame(), # User interrupts here BotStartedSpeakingFrame(), @@ -251,8 +250,7 @@ class TestUserTranscriptProcessor(unittest.IsolatedAsyncioTestCase): expected_down_frames = [ BotStartedSpeakingFrame, TTSTextFrame, # "Hello" - TTSTextFrame, # "world" - TTSTextFrame, # "!" + TTSTextFrame, # "world!" TranscriptionUpdateFrame, # First message (emitted due to interruption) StartInterruptionFrame, # Interruption frame comes after the update BotStartedSpeakingFrame, @@ -275,7 +273,7 @@ class TestUserTranscriptProcessor(unittest.IsolatedAsyncioTestCase): # First update should be interrupted message first_message = received_updates[0].messages[0] self.assertEqual(first_message.role, "assistant") - self.assertEqual(first_message.content, "Hello world !") + self.assertEqual(first_message.content, "Hello world!") self.assertIsNotNone(first_message.timestamp) # Second update should be new response @@ -426,3 +424,57 @@ class TestUserTranscriptProcessor(unittest.IsolatedAsyncioTestCase): self.assertEqual(received_updates[0].content, "User message") self.assertEqual(received_updates[1].role, "assistant") self.assertEqual(received_updates[1].content, "Assistant message") + + async def test_text_fragments_with_spaces(self): + """Test aggregating text fragments with various spacing patterns""" + processor = AssistantTranscriptProcessor() + + # Track received updates + received_updates = [] + + @processor.event_handler("on_transcript_update") + async def handle_update(proc, frame: TranscriptionUpdateFrame): + received_updates.append(frame) + + # Test the specific pattern shared + frames_to_send = [ + BotStartedSpeakingFrame(), + SleepFrame(sleep=0.1), + TTSTextFrame(text="Hello"), + TTSTextFrame(text=" there"), + TTSTextFrame(text="!"), + TTSTextFrame(text=" How"), + TTSTextFrame(text="'s"), + TTSTextFrame(text=" it"), + TTSTextFrame(text=" going"), + TTSTextFrame(text="?"), + BotStoppedSpeakingFrame(), + ] + + expected_down_frames = [ + BotStartedSpeakingFrame, + BotStoppedSpeakingFrame, + TTSTextFrame, + TTSTextFrame, + TTSTextFrame, + TTSTextFrame, + TTSTextFrame, + TTSTextFrame, + TTSTextFrame, + TTSTextFrame, + TranscriptionUpdateFrame, + ] + + # Run test + received_frames, _ = await run_test( + processor, + frames_to_send=frames_to_send, + expected_down_frames=expected_down_frames, + ) + + # Verify result + self.assertEqual(len(received_updates), 1) + message = received_updates[0].messages[0] + self.assertEqual(message.role, "assistant") + # Should be properly joined without extra spaces + self.assertEqual(message.content, "Hello there! How's it going?") diff --git a/tests/test_utils_string.py b/tests/test_utils_string.py index e4cdb4cb4..cabd88a36 100644 --- a/tests/test_utils_string.py +++ b/tests/test_utils_string.py @@ -6,7 +6,7 @@ import unittest -from pipecat.utils.string import match_endofsentence +from pipecat.utils.string import match_endofsentence, parse_start_end_tags class TestUtilsString(unittest.IsolatedAsyncioTestCase): @@ -18,6 +18,15 @@ class TestUtilsString(unittest.IsolatedAsyncioTestCase): assert match_endofsentence("This is a sentence...") == 21 assert match_endofsentence("This is a sentence . . .") == 24 assert match_endofsentence("This is a sentence. ..") == 22 + assert match_endofsentence("This is for Mr. and Mrs. Jones.") == 31 + assert match_endofsentence("U.S.A and U.S.A..") == 17 + assert match_endofsentence("My emails are foo@pipecat.ai and bar@pipecat.ai.") == 48 + assert match_endofsentence("My email is foo.bar@pipecat.ai.") == 31 + assert match_endofsentence("My email is spell(foo.bar@pipecat.ai).") == 38 + assert match_endofsentence("My email is foo.bar@pipecat.ai.") == 46 + assert match_endofsentence("The number pi is 3.14159.") == 25 + assert match_endofsentence("Valid scientific notation 1.23e4.") == 33 + assert match_endofsentence("Valid scientific notation 0.e4.") == 31 assert not match_endofsentence("This is not a sentence") assert not match_endofsentence("This is not a sentence,") assert not match_endofsentence("This is not a sentence, ") @@ -28,6 +37,8 @@ class TestUtilsString(unittest.IsolatedAsyncioTestCase): assert not match_endofsentence("Heute ist Dienstag, der 3.") # 3. Juli 2024 assert not match_endofsentence("America, or the U.") # U.S.A. assert not match_endofsentence("It still early, it's 3:00 a.") # 3:00 a.m. + assert not match_endofsentence("My emails are foo@pipecat.ai and bar@pipecat.ai") + assert not match_endofsentence("The number pi is 3.14159") async def test_endofsentence_zh(self): chinese_sentences = [ @@ -50,3 +61,50 @@ class TestUtilsString(unittest.IsolatedAsyncioTestCase): for i in hindi_sentences: assert match_endofsentence(i) assert not match_endofsentence("हैलो，") + + +class TestStartEndTags(unittest.IsolatedAsyncioTestCase): + async def test_empty(self): + assert parse_start_end_tags("", [], None, 0) == (None, 0) + assert parse_start_end_tags("Hello from Pipecat!", [], None, 0) == (None, 0) + + async def test_simple(self): + # (, ) + assert parse_start_end_tags("Hello from Pipecat!", [("", "")], None, 0) == ( + None, + 26, + ) + assert parse_start_end_tags("Hello from Pipecat", [("", "")], None, 0) == ( + ("", ""), + 21, + ) + assert parse_start_end_tags("Hello from Pipecat", [("", "")], None, 6) == ( + ("", ""), + 21, + ) + + # (spell(, )) + assert parse_start_end_tags("Hello from spell(Pipecat)!", [("spell(", ")")], None, 0) == ( + None, + 26, + ) + assert parse_start_end_tags("Hello from spell(Pipecat", [("spell(", ")")], None, 0) == ( + ("spell(", ")"), + 24, + ) + + async def test_multiple(self): + # (, ) + assert parse_start_end_tags( + "Hello from Pipecat! Hello World!", [("", "")], None, 0 + ) == ( + None, + 46, + ) + + assert parse_start_end_tags( + "Hello from Pipecat! Hello World", [("", "")], None, 0 + ) == ( + ("", ""), + 41, + )