Compare commits
1 Commits
v0.0.60
...
cb/extra-l
| Author | SHA1 | Date | |
|---|---|---|---|
|
b8e2227a21 |
54
.github/workflows/coverage.yaml
vendored
54
.github/workflows/coverage.yaml
vendored
@@ -1,54 +0,0 @@
|
||||
name: coverage
|
||||
|
||||
on:
|
||||
workflow_dispatch:
|
||||
push:
|
||||
branches:
|
||||
- main
|
||||
pull_request:
|
||||
branches:
|
||||
- "**"
|
||||
paths-ignore:
|
||||
- "docs/**"
|
||||
|
||||
jobs:
|
||||
coverage:
|
||||
name: "Coverage"
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- name: Checkout repo
|
||||
uses: actions/checkout@v4
|
||||
- name: Set up Python
|
||||
id: setup_python
|
||||
uses: actions/setup-python@v4
|
||||
with:
|
||||
python-version: "3.10"
|
||||
- name: Cache virtual environment
|
||||
uses: actions/cache@v3
|
||||
with:
|
||||
# We are hashing dev-requirements.txt and test-requirements.txt which
|
||||
# contain all dependencies needed to run the tests.
|
||||
key: venv-${{ runner.os }}-${{ steps.setup_python.outputs.python-version}}-${{ hashFiles('dev-requirements.txt') }}-${{ hashFiles('test-requirements.txt') }}
|
||||
path: .venv
|
||||
- name: Install system packages
|
||||
id: install_system_packages
|
||||
run: |
|
||||
sudo apt-get install -y portaudio19-dev
|
||||
- name: Setup virtual environment
|
||||
run: |
|
||||
python -m venv .venv
|
||||
- name: Install basic Python dependencies
|
||||
run: |
|
||||
source .venv/bin/activate
|
||||
python -m pip install --upgrade pip
|
||||
pip install -r dev-requirements.txt -r test-requirements.txt
|
||||
- name: Run tests with coverage
|
||||
run: |
|
||||
source .venv/bin/activate
|
||||
coverage run
|
||||
coverage xml
|
||||
- name: Upload coverage to Codecov
|
||||
uses: codecov/codecov-action@v5
|
||||
with:
|
||||
token: ${{ secrets.CODECOV_TOKEN }}
|
||||
slug: pipecat-ai/pipecat
|
||||
15
.gitignore
vendored
15
.gitignore
vendored
@@ -32,21 +32,6 @@ fly.toml
|
||||
|
||||
# Example files
|
||||
pipecat/examples/twilio-chatbot/templates/streams.xml
|
||||
pipecat/examples/bot-ready-signalling/client/react-native/node_modules/
|
||||
pipecat/examples/bot-ready-signalling/client/react-native/.expo/
|
||||
pipecat/examples/bot-ready-signalling/client/react-native/dist/
|
||||
pipecat/examples/bot-ready-signalling/client/react-native/npm-debug.*
|
||||
pipecat/examples/bot-ready-signalling/client/react-native/*.jks
|
||||
pipecat/examples/bot-ready-signalling/client/react-native/*.p8
|
||||
pipecat/examples/bot-ready-signalling/client/react-native/*.p12
|
||||
pipecat/examples/bot-ready-signalling/client/react-native/*.key
|
||||
pipecat/examples/bot-ready-signalling/client/react-native/*.mobileprovision
|
||||
pipecat/examples/bot-ready-signalling/client/react-native/*.orig.*
|
||||
pipecat/examples/bot-ready-signalling/client/react-native/web-build/
|
||||
|
||||
# macOS
|
||||
.DS_Store
|
||||
|
||||
|
||||
# Documentation
|
||||
docs/api/_build/
|
||||
|
||||
@@ -1,8 +1,7 @@
|
||||
repos:
|
||||
- repo: https://github.com/astral-sh/ruff-pre-commit
|
||||
rev: v0.9.7
|
||||
- repo: local
|
||||
hooks:
|
||||
- id: ruff
|
||||
language_version: python3
|
||||
args: [ --select, I, ]
|
||||
- id: ruff-format
|
||||
- id: ruff-format-hook
|
||||
name: Check ruff formatting
|
||||
entry: sh scripts/pre-commit.sh
|
||||
language: system
|
||||
|
||||
679
CHANGELOG.md
679
CHANGELOG.md
@@ -5,647 +5,10 @@ All notable changes to **Pipecat** will be documented in this file.
|
||||
The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
|
||||
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
|
||||
|
||||
## [0.0.60] - 2025-03-20
|
||||
## [Unreleased]
|
||||
|
||||
### 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. 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)
|
||||
|
||||
- Added `on_frame_reached_upstream` and `on_frame_reached_downstream` event
|
||||
handlers to `PipelineTask`. Those events will be called when a frame reaches
|
||||
the beginning or end of the pipeline respectively. Note that by default, the
|
||||
event handlers will not be called unless a filter is set with
|
||||
`PipelineTask.set_reached_upstream_filter()` or
|
||||
`PipelineTask.set_reached_downstream_filter()`.
|
||||
|
||||
- Added support for Chirp voices in `GoogleTTSService`.
|
||||
|
||||
- Added a `flush_audio()` method to `FishTTSService` and `LmntTTSService`.
|
||||
|
||||
- Added a `set_language` convenience method for `GoogleSTTService`, allowing
|
||||
you to set a single language. This is in addition to the `set_languages`
|
||||
method which allows you to set a list of languages.
|
||||
|
||||
- Added `on_user_turn_audio_data` and `on_bot_turn_audio_data` to
|
||||
`AudioBufferProcessor`. This gives the ability to grab the audio of only that
|
||||
turn for both the user and the bot.
|
||||
|
||||
- Added new base class `BaseObject` which is now the base class of
|
||||
`FrameProcessor`, `PipelineRunner`, `PipelineTask` and `BaseTransport`. The
|
||||
new `BaseObject` adds supports for event handlers.
|
||||
|
||||
- Added support for a unified format for specifying function calling across all
|
||||
LLM services.
|
||||
|
||||
```python
|
||||
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"],
|
||||
)
|
||||
tools = ToolsSchema(standard_tools=[weather_function])
|
||||
```
|
||||
|
||||
- Added `speech_threshold` parameter to `GladiaSTTService`.
|
||||
|
||||
- Allow passing user (`user_kwargs`) and assistant (`assistant_kwargs`) context
|
||||
aggregator parameters when using `create_context_aggregator()`. The values are
|
||||
passed as a mapping that will then be converted to arguments.
|
||||
|
||||
- Added `speed` as an `InputParam` for both `ElevenLabsTTSService` and
|
||||
`ElevenLabsHttpTTSService`.
|
||||
|
||||
- Added new `LLMFullResponseAggregator` to aggregate full LLM completions. At
|
||||
every completion the `on_completion` event handler is triggered.
|
||||
|
||||
- Added a new frame, `RTVIServerMessageFrame`, and RTVI message
|
||||
`RTVIServerMessage` which provides a generic mechanism for sending custom
|
||||
messages from server to client. The `RTVIServerMessageFrame` is processed by
|
||||
the `RTVIObserver` and will be delivered to the client's `onServerMessage`
|
||||
callback or `ServerMessage` event.
|
||||
|
||||
- Added `GoogleLLMOpenAIBetaService` for Google LLM integration with an
|
||||
OpenAI-compatible interface. Added foundational example
|
||||
`14o-function-calling-gemini-openai-format.py`.
|
||||
|
||||
- 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()`
|
||||
instead.
|
||||
|
||||
- Removed deprecated`stt_service` parameter from `STTMuteFilter`.
|
||||
|
||||
- Removed deprecated RTVI processors, use an `RTVIObserver` instead.
|
||||
|
||||
- Removed deprecated `AWSTTSService`, use `PollyTTSService` instead.
|
||||
|
||||
- Removed deprecated field `tier` from `DailyTranscriptionSettings`, use `model`
|
||||
instead.
|
||||
|
||||
- Removed deprecated `pipecat.vad` package, use `pipecat.audio.vad` instead.
|
||||
|
||||
### 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 `<spell></spell>`
|
||||
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
|
||||
|
||||
- Added track-specific audio event `on_track_audio_data` to
|
||||
`AudioBufferProcessor` for accessing separate input and output audio tracks.
|
||||
|
||||
- Pipecat version will now be logged on every application startup. This will
|
||||
help us identify what version we are running in case of any issues.
|
||||
|
||||
- Added a new `StopFrame` which can be used to stop a pipeline task while
|
||||
keeping the frame processors running. The frame processors could then be used
|
||||
in a different pipeline. The difference between a `StopFrame` and a
|
||||
`StopTaskFrame` is that, as with `EndFrame` and `EndTaskFrame`, the
|
||||
`StopFrame` is pushed from the task and the `StopTaskFrame` is pushed upstream
|
||||
inside the pipeline by any processor.
|
||||
|
||||
- Added a new `PipelineTask` parameter `observers` that replaces the previous
|
||||
`PipelineParams.observers`.
|
||||
|
||||
- Added a new `PipelineTask` parameter `check_dangling_tasks` to enable or
|
||||
disable checking for frame processors' dangling tasks when the Pipeline
|
||||
finishes running.
|
||||
|
||||
- Added new `on_completion_timeout` event for LLM services (all OpenAI-based
|
||||
services, Anthropic and Google). Note that this event will only get triggered
|
||||
if LLM timeouts are setup and if the timeout was reached. It can be useful to
|
||||
retrigger another completion and see if the timeout was just a blip.
|
||||
|
||||
- Added new log observers `LLMLogObserver` and `TranscriptionLogObserver` that
|
||||
can be useful for debugging your pipelines.
|
||||
|
||||
- Added `room_url` property to `DailyTransport`.
|
||||
|
||||
- Added `addons` argument to `DeepgramSTTService`.
|
||||
|
||||
- Added `exponential_backoff_time()` to `utils.network` module.
|
||||
|
||||
### Changed
|
||||
|
||||
- ⚠️ `PipelineTask` now requires keyword arguments (except for the first one for
|
||||
the pipeline).
|
||||
|
||||
- Updated `PlayHTHttpTTSService` to take a `voice_engine` and `protocol` input
|
||||
in the constructor. The previous method of providing a `voice_engine` input
|
||||
that contains the engine and protocol is deprecated by PlayHT.
|
||||
|
||||
- The base `TTSService` class now strips leading newlines before sending text
|
||||
to the TTS provider. This change is to solve issues where some TTS providers,
|
||||
like Azure, would not output text due to newlines.
|
||||
|
||||
- `GrokLLMSService` now uses `grok-2` as the default model.
|
||||
|
||||
- `AnthropicLLMService` now uses `claude-3-7-sonnet-20250219` as the default
|
||||
model.
|
||||
|
||||
- `RimeHttpTTSService` needs an `aiohttp.ClientSession` to be passed to the
|
||||
constructor as all the other HTTP-based services.
|
||||
|
||||
- `RimeHttpTTSService` doesn't use a default voice anymore.
|
||||
|
||||
- `DeepgramSTTService` now uses the new `nova-3` model by default. If you want
|
||||
to use the previous model you can pass `LiveOptions(model="nova-2-general")`.
|
||||
(see https://deepgram.com/learn/introducing-nova-3-speech-to-text-api)
|
||||
|
||||
```python
|
||||
stt = DeepgramSTTService(..., live_options=LiveOptions(model="nova-2-general"))
|
||||
```
|
||||
|
||||
### Deprecated
|
||||
|
||||
- `PipelineParams.observers` is now deprecated, you the new `PipelineTask`
|
||||
parameter `observers`.
|
||||
|
||||
### Removed
|
||||
|
||||
- Remove `TransportParams.audio_out_is_live` since it was not being used at all.
|
||||
|
||||
### 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.
|
||||
|
||||
- Fixed an `AudioContextWordTTSService` issue that would cause an `EndFrame` to
|
||||
disconnect from the TTS service before audio from all the contexts was
|
||||
received. This affected services like Cartesia and Rime.
|
||||
|
||||
- Fixed an issue that was not allowing to pass an `OpenAILLMContext` to create
|
||||
`GoogleLLMService`'s context aggregators.
|
||||
|
||||
- Fixed a `ElevenLabsTTSService`, `FishAudioTTSService`, `LMNTTTSService` and
|
||||
`PlayHTTTSService` issue that was resulting in audio requested before an
|
||||
interruption being played after an interruption.
|
||||
|
||||
- Fixed `match_endofsentence` support for ellipses.
|
||||
|
||||
- Fixed an issue where `EndTaskFrame` was not triggering
|
||||
`on_client_disconnected` or closing the WebSocket in FastAPI.
|
||||
|
||||
- Fixed an issue in `DeepgramSTTService` where the `sample_rate` passed to the
|
||||
`LiveOptions` was not being used, causing the service to use the default
|
||||
sample rate of pipeline.
|
||||
|
||||
- Fixed a context aggregator issue that would not append the LLM text response
|
||||
to the context if a function call happened in the same LLM turn.
|
||||
|
||||
- Fixed an issue that was causing HTTP TTS services to push `TTSStoppedFrame`
|
||||
more than once.
|
||||
|
||||
- Fixed a `FishAudioTTSService` issue where `TTSStoppedFrame` was not being
|
||||
pushed.
|
||||
|
||||
- Fixed an issue that `start_callback` was not invoked for some LLM services.
|
||||
|
||||
- Fixed an issue that would cause `DeepgramSTTService` to stop working after an
|
||||
error occurred (e.g. sudden network loss). If the network recovered we would
|
||||
not reconnect.
|
||||
|
||||
- Fixed a `STTMuteFilter` issue that would not mute user audio frames causing
|
||||
transcriptions to be generated by the STT service.
|
||||
|
||||
### Other
|
||||
|
||||
- Added Gemini support to `examples/phone-chatbot`.
|
||||
|
||||
- Added foundational example `34-audio-recording.py` showing how to use the
|
||||
AudioBufferProcessor callbacks to save merged and track recordings.
|
||||
|
||||
## [0.0.57] - 2025-02-14
|
||||
|
||||
### Added
|
||||
|
||||
- Added new `AudioContextWordTTSService`. This is a TTS base class for TTS
|
||||
services that handling multiple separate audio requests.
|
||||
|
||||
- Added new frames `EmulateUserStartedSpeakingFrame` and
|
||||
`EmulateUserStoppedSpeakingFrame` which can be used to emulated VAD behavior
|
||||
without VAD being present or not being triggered.
|
||||
|
||||
- Added a new `audio_in_stream_on_start` field to `TransportParams`.
|
||||
|
||||
- Added a new method `start_audio_in_streaming` in the `BaseInputTransport`.
|
||||
|
||||
- This method should be used to start receiving the input audio in case the
|
||||
field `audio_in_stream_on_start` is set to `false`.
|
||||
|
||||
- Added support for the `RTVIProcessor` to handle buffered audio in `base64`
|
||||
format, converting it into InputAudioRawFrame for transport.
|
||||
|
||||
- Added support for the `RTVIProcessor` to trigger `start_audio_in_streaming`
|
||||
only after the `client-ready` message.
|
||||
|
||||
- Added new `MUTE_UNTIL_FIRST_BOT_COMPLETE` strategy to `STTMuteStrategy`. This
|
||||
strategy starts muted and remains muted until the first bot speech completes,
|
||||
ensuring the bot's first response cannot be interrupted. This complements the
|
||||
existing `FIRST_SPEECH` strategy which only mutes during the first detected
|
||||
bot speech.
|
||||
|
||||
- Added support for Google Cloud Speech-to-Text V2 through `GoogleSTTService`.
|
||||
|
||||
- Added `RimeTTSService`, a new `WordTTSService`. Updated the foundational
|
||||
example `07q-interruptible-rime.py` to use `RimeTTSService`.
|
||||
|
||||
- Added support for Groq's Whisper API through the new `GroqSTTService` and
|
||||
OpenAI's Whisper API through the new `OpenAISTTService`. Introduced a new
|
||||
base class `BaseWhisperSTTService` to handle common Whisper API
|
||||
functionality.
|
||||
|
||||
- Added `PerplexityLLMService` for Perplexity NIM API integration, with an
|
||||
OpenAI-compatible interface. Also, added foundational example
|
||||
`14n-function-calling-perplexity.py`.
|
||||
|
||||
- Added `DailyTransport.update_remote_participants()`. This allows you to update
|
||||
remote participant's settings, like their permissions or which of their
|
||||
devices are enabled. Requires that the local participant have participant
|
||||
admin permission.
|
||||
|
||||
### Changed
|
||||
|
||||
- We don't consider a colon `:` and end of sentence any more.
|
||||
|
||||
- Updated `DailyTransport` to respect the `audio_in_stream_on_start` field,
|
||||
ensuring it only starts receiving the audio input if it is enabled.
|
||||
|
||||
- Updated `FastAPIWebsocketOutputTransport` to send `TransportMessageFrame` and
|
||||
`TransportMessageUrgentFrame` to the serializer.
|
||||
|
||||
- Updated `WebsocketServerOutputTransport` to send `TransportMessageFrame` and
|
||||
`TransportMessageUrgentFrame` to the serializer.
|
||||
|
||||
- Enhanced `STTMuteConfig` to validate strategy combinations, preventing
|
||||
`MUTE_UNTIL_FIRST_BOT_COMPLETE` and `FIRST_SPEECH` from being used together
|
||||
as they handle first bot speech differently.
|
||||
|
||||
- Updated foundational example `07n-interruptible-google.py` to use all Google
|
||||
services.
|
||||
|
||||
- `RimeHttpTTSService` now uses the `mistv2` model by default.
|
||||
|
||||
- Improved error handling in `AzureTTSService` to properly detect and log
|
||||
synthesis cancellation errors.
|
||||
|
||||
- Enhanced `WhisperSTTService` with full language support and improved model
|
||||
documentation.
|
||||
|
||||
- Updated foundation example `14f-function-calling-groq.py` to use
|
||||
`GroqSTTService` for transcription.
|
||||
|
||||
- Updated `GroqLLMService` to use `llama-3.3-70b-versatile` as the default
|
||||
model.
|
||||
|
||||
- `RTVIObserver` doesn't handle `LLMSearchResponseFrame` frames anymore. For
|
||||
now, to handle those frames you need to create a `GoogleRTVIObserver`
|
||||
instead.
|
||||
|
||||
### Deprecated
|
||||
|
||||
- `STTMuteFilter` constructor's `stt_service` parameter is now deprecated and
|
||||
will be removed in a future version. The filter now manages mute state
|
||||
internally instead of querying the STT service.
|
||||
|
||||
- `RTVI.observer()` is now deprecated, instantiate an `RTVIObserver` directly
|
||||
instead.
|
||||
|
||||
- All RTVI frame processors (e.g. `RTVISpeakingProcessor`,
|
||||
`RTVIBotLLMProcessor`) are now deprecated, instantiate an `RTVIObserver`
|
||||
instead.
|
||||
|
||||
### Fixed
|
||||
|
||||
- Fixed a `FalImageGenService` issue that was causing the event loop to be
|
||||
blocked while loading the downloadded image.
|
||||
|
||||
- Fixed a `CartesiaTTSService` service issue that would cause audio overlapping
|
||||
in some cases.
|
||||
|
||||
- Fixed a websocket-based service issue (e.g. `CartesiaTTSService`) that was
|
||||
preventing a reconnection after the server disconnected cleanly, which was
|
||||
causing an inifite loop instead.
|
||||
|
||||
- Fixed a `BaseOutputTransport` issue that was causing upstream frames to no be
|
||||
pushed upstream.
|
||||
|
||||
- Fixed multiple issue where user transcriptions where not being handled
|
||||
properly. It was possible for short utterances to not trigger VAD which would
|
||||
cause user transcriptions to be ignored. It was also possible for one or more
|
||||
transcriptions to be generated after VAD in which case they would also be
|
||||
ignored.
|
||||
|
||||
- Fixed an issue that was causing `BotStoppedSpeakingFrame` to be generated too
|
||||
late. This could then cause issues unblocking `STTMuteFilter` later than
|
||||
desired.
|
||||
|
||||
- Fixed an issue that was causing `AudioBufferProcessor` to not record
|
||||
synchronized audio.
|
||||
|
||||
- Fixed an `RTVI` issue that was causing `bot-tts-text` messages to be sent
|
||||
before being processed by the output transport.
|
||||
|
||||
- Fixed an issue[#1192] in 11labs where we are trying to reconnect/disconnect
|
||||
the websocket connection even when the connection is already closed.
|
||||
|
||||
- Fixed an issue where `has_regular_messages` condition was always true in
|
||||
`GoogleLLMContext` due to `Part` having `function_call` & `function_response`
|
||||
with `None` values.
|
||||
|
||||
### Other
|
||||
|
||||
- Added new `instant-voice` example. This example showcases how to enable
|
||||
instant voice communication as soon as a user connects.
|
||||
|
||||
- Added new `local-input-select-stt` example. This examples allows you to play
|
||||
with local audio inputs by slecting them through a nice text interface.
|
||||
|
||||
## [0.0.56] - 2025-02-06
|
||||
|
||||
### Changed
|
||||
|
||||
- Use `gemini-2.0-flash-001` as the default model for `GoogleLLMSerivce`.
|
||||
|
||||
- Improved foundational examples 22b, 22c, and 22d to support function calling.
|
||||
With these base examples, `FunctionCallInProgressFrame` and
|
||||
`FunctionCallResultFrame` will no longer be blocked by the gates.
|
||||
|
||||
### Fixed
|
||||
|
||||
- Fixed a `TkLocalTransport` and `LocalAudioTransport` issues that was causing
|
||||
errors on cleanup.
|
||||
|
||||
- Fixed an issue that was causing `tests.utils` import to fail because of
|
||||
logging setup.
|
||||
|
||||
- Fixed a `SentryMetrics` issue that was preventing any metrics to be sent to
|
||||
Sentry and also was preventing from metrics frames to be pushed to the
|
||||
pipeline.
|
||||
|
||||
- Fixed an issue in `BaseOutputTransport` where incoming audio would not be
|
||||
resampled to the desired output sample rate.
|
||||
|
||||
- Fixed an issue with the `TwilioFrameSerializer` and `TelnyxFrameSerializer`
|
||||
where `twilio_sample_rate` and `telnyx_sample_rate` were incorrectly
|
||||
initialized to `audio_in_sample_rate`. Those values currently default to 8000
|
||||
and should be set manually from the serializer constructor if a different
|
||||
value is needed.
|
||||
|
||||
### Other
|
||||
|
||||
- Added a new `sentry-metrics` example.
|
||||
|
||||
## [0.0.55] - 2025-02-05
|
||||
|
||||
### Added
|
||||
|
||||
- Added a new `start_metadata` field to `PipelineParams`. The provided metadata
|
||||
will be set to the initial `StartFrame` being pushed from the `PipelineTask`.
|
||||
|
||||
- Added new fields to `PipelineParams` to control audio input and output sample
|
||||
rates for the whole pipeline. This allows controlling sample rates from a
|
||||
single place instead of having to specify sample rates in each
|
||||
service. Setting a sample rate to a service is still possible and will
|
||||
override the value from `PipelineParams`.
|
||||
|
||||
- Introduce audio resamplers (`BaseAudioResampler`). This is just a base class
|
||||
to implement audio resamplers. Currently, two implementations are provided
|
||||
`SOXRAudioResampler` and `ResampyResampler`. A new
|
||||
`create_default_resampler()` has been added (replacing the now deprecated
|
||||
`resample_audio()`).
|
||||
|
||||
- It is now possible to specify the asyncio event loop that a `PipelineTask` and
|
||||
all the processors should run on by passing it as a new argument to the
|
||||
`PipelineRunner`. This could allow running pipelines in multiple threads each
|
||||
@@ -678,9 +41,6 @@ stt = DeepgramSTTService(..., live_options=LiveOptions(model="nova-2-general"))
|
||||
|
||||
### Changed
|
||||
|
||||
- `GatedOpenAILLMContextAggregator` now require keyword arguments. Also, a new
|
||||
`start_open` argument has been added to set the initial state of the gate.
|
||||
|
||||
- Added `organization` and `project` level authentication to
|
||||
`OpenAILLMService`.
|
||||
|
||||
@@ -696,27 +56,8 @@ stt = DeepgramSTTService(..., live_options=LiveOptions(model="nova-2-general"))
|
||||
- `InputDTMFFrame` is now based on `DTMFFrame`. There's also a new
|
||||
`OutputDTMFFrame` frame.
|
||||
|
||||
### Deprecated
|
||||
|
||||
- `resample_audio()` is now deprecated, use `create_default_resampler()`
|
||||
instead.
|
||||
|
||||
### Removed
|
||||
|
||||
- `AudioBufferProcessor.reset_audio_buffers()` has been removed, use
|
||||
`AudioBufferProcessor.start_recording()` and
|
||||
`AudioBufferProcessor.stop_recording()` instead.
|
||||
|
||||
### Fixed
|
||||
|
||||
- Fixed a `AudioBufferProcessor` that would cause crackling in some recordings.
|
||||
|
||||
- Fixed an issue in `AudioBufferProcessor` where user callback would not be
|
||||
called on task cancellation.
|
||||
|
||||
- Fixed an issue in `AudioBufferProcessor` that would cause wrong silence
|
||||
padding in some cases.
|
||||
|
||||
- Fixed an issue where `ElevenLabsTTSService` messages would return a 1009
|
||||
websocket error by increasing the max message size limit to 16MB.
|
||||
|
||||
@@ -732,16 +73,6 @@ stt = DeepgramSTTService(..., live_options=LiveOptions(model="nova-2-general"))
|
||||
|
||||
### Other
|
||||
|
||||
- Improved Unit Test `run_test()` to use `PipelineTask` and
|
||||
`PipelineRunner`. There's now also some control around `StartFrame` and
|
||||
`EndFrame`. The `EndTaskFrame` has been removed since it doesn't seem
|
||||
necessary with this new approach.
|
||||
|
||||
- Updated `twilio-chatbot` with a few new features: use 8000 sample rate and
|
||||
avoid resampling, a new client useful for stress testing and testing locally
|
||||
without the need to make phone calls. Also, added audio recording on both the
|
||||
client and the server to make sure the audio sounds good.
|
||||
|
||||
- Updated examples to use `task.cancel()` to immediately exit the example when a
|
||||
participant leaves or disconnects, instead of pushing an `EndFrame`. Pushing
|
||||
an `EndFrame` causes the bot to run through everything that is internally
|
||||
@@ -771,8 +102,7 @@ stt = DeepgramSTTService(..., live_options=LiveOptions(model="nova-2-general"))
|
||||
|
||||
- Added `enable_recording` and `geo` parameters to `DailyRoomProperties`.
|
||||
|
||||
- Added `RecordingsBucketConfig` to `DailyRoomProperties` to upload recordings
|
||||
to a custom AWS bucket.
|
||||
- Added `RecordingsBucketConfig` to `DailyRoomProperties` to upload recordings to a custom AWS bucket.
|
||||
|
||||
### Changed
|
||||
|
||||
@@ -2078,7 +1408,7 @@ 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
|
||||
cancelled.
|
||||
canceled.
|
||||
|
||||
### Fixed
|
||||
|
||||
@@ -2197,9 +1527,6 @@ async def on_connected(processor):
|
||||
|
||||
### Changed
|
||||
|
||||
- `FrameSerializer.serialize()` and `FrameSerializer.deserialize()` are now
|
||||
`async`.
|
||||
|
||||
- `Filter` has been renamed to `FrameFilter` and it's now under
|
||||
`processors/filters`.
|
||||
|
||||
|
||||
46
README.md
46
README.md
@@ -2,7 +2,7 @@
|
||||
<img alt="pipecat" width="300px" height="auto" src="https://raw.githubusercontent.com/pipecat-ai/pipecat/main/pipecat.png">
|
||||
</div></h1>
|
||||
|
||||
[](https://pypi.org/project/pipecat-ai)  [](https://codecov.io/gh/pipecat-ai/pipecat) [](https://docs.pipecat.ai) [](https://discord.gg/pipecat)
|
||||
[](https://pypi.org/project/pipecat-ai)  [](https://docs.pipecat.ai) [](https://discord.gg/pipecat) <a href="https://app.commanddash.io/agent/github_pipecat-ai_pipecat"><img src="https://img.shields.io/badge/AI-Code%20Agent-EB9FDA"></a>
|
||||
|
||||
Pipecat is an open source Python framework for building voice and multimodal conversational agents. It handles the complex orchestration of AI services, network transport, audio processing, and multimodal interactions, letting you focus on creating engaging experiences.
|
||||
|
||||
@@ -55,17 +55,17 @@ pip install "pipecat-ai[option,...]"
|
||||
|
||||
### Available services
|
||||
|
||||
| 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), [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), [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 | [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]"` |
|
||||
| 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), [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), [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), [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]"` |
|
||||
| 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[openai]"` |
|
||||
| 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]"` |
|
||||
| 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]"` |
|
||||
|
||||
📚 [View full services documentation →](https://docs.pipecat.ai/server/services/supported-services)
|
||||
|
||||
@@ -149,40 +149,36 @@ Sign up [here](https://dashboard.daily.co/u/signup) and [create a room](https://
|
||||
|
||||
## Hacking on the framework itself
|
||||
|
||||
_Note: You may need to set up a virtual environment before following these instructions. From the root of the repo:_
|
||||
_Note that you may need to set up a virtual environment before following the instructions below. For instance, you might need to run the following from the root of the repo:_
|
||||
|
||||
```shell
|
||||
python3 -m venv venv
|
||||
source venv/bin/activate
|
||||
```
|
||||
|
||||
Install the development dependencies:
|
||||
From the root of this repo, run the following:
|
||||
|
||||
```shell
|
||||
pip install -r dev-requirements.txt
|
||||
```
|
||||
|
||||
Install the git pre-commit hooks (these help ensure your code follows project rules):
|
||||
This will install the necessary development dependencies. Also, make sure you install the git pre-commit hooks:
|
||||
|
||||
```shell
|
||||
pre-commit install
|
||||
```
|
||||
|
||||
Install the `pipecat-ai` package locally in editable mode:
|
||||
The hooks will just save you time when you submit a PR by making sure your code follows the project rules.
|
||||
|
||||
To use the package locally (e.g. to run sample files), run:
|
||||
|
||||
```shell
|
||||
pip install -e .
|
||||
pip install --editable ".[option,...]"
|
||||
```
|
||||
|
||||
The `-e` or `--editable` option allows you to modify the code without reinstalling.
|
||||
The `--editable` option makes sure you don't have to run `pip install` again and you can just edit the project files locally.
|
||||
|
||||
To include optional dependencies, add them to the install command. For example:
|
||||
|
||||
```shell
|
||||
pip install -e ".[daily,deepgram,cartesia,openai,silero]" # Updated for the services you're using
|
||||
```
|
||||
|
||||
If you want to use this package from another directory:
|
||||
If you want to use this package from another directory, you can run:
|
||||
|
||||
```shell
|
||||
pip install "path_to_this_repo[option,...]"
|
||||
|
||||
11
codecov.yml
11
codecov.yml
@@ -1,11 +0,0 @@
|
||||
coverage:
|
||||
range: 50..90 # coverage lower than 50 is red, higher than 90 green, between color code
|
||||
|
||||
status:
|
||||
project:
|
||||
default:
|
||||
target: auto # auto % coverage target
|
||||
threshold: 5% # allow for 5% reduction of coverage without failing
|
||||
|
||||
# do not run coverage on patch nor changes
|
||||
patch: false
|
||||
@@ -1,12 +1,11 @@
|
||||
build~=1.2.2
|
||||
coverage~=7.6.12
|
||||
grpcio-tools~=1.67.1
|
||||
grpcio-tools~=1.69.0
|
||||
pip-tools~=7.4.1
|
||||
pre-commit~=4.0.1
|
||||
pyright~=1.1.394
|
||||
pyright~=1.1.392
|
||||
pytest~=8.3.4
|
||||
pytest-asyncio~=0.25.3
|
||||
ruff~=0.9.7
|
||||
setuptools~=70.0.0
|
||||
pytest-asyncio~=0.25.2
|
||||
ruff~=0.9.1
|
||||
setuptools~=75.8.0
|
||||
setuptools_scm~=8.1.0
|
||||
python-dotenv~=1.0.1
|
||||
|
||||
@@ -18,9 +18,6 @@ AZURE_DALLE_API_KEY=...
|
||||
AZURE_DALLE_ENDPOINT=https://...
|
||||
AZURE_DALLE_MODEL=...
|
||||
|
||||
# Cartesia
|
||||
CARTESIA_API_KEY=...
|
||||
|
||||
# Daily
|
||||
DAILY_API_KEY=...
|
||||
DAILY_SAMPLE_ROOM_URL=https://...
|
||||
@@ -29,9 +26,6 @@ DAILY_SAMPLE_ROOM_URL=https://...
|
||||
ELEVENLABS_API_KEY=...
|
||||
ELEVENLABS_VOICE_ID=...
|
||||
|
||||
# Neuphonic
|
||||
NEUPHONIC_API_KEY=...
|
||||
|
||||
# Fal
|
||||
FAL_KEY=...
|
||||
|
||||
|
||||
@@ -12,7 +12,7 @@
|
||||
"@daily-co/daily-js": "0.74.0"
|
||||
},
|
||||
"devDependencies": {
|
||||
"vite": "^6.0.9"
|
||||
"vite": "^6.0.2"
|
||||
}
|
||||
},
|
||||
"node_modules/@babel/runtime": {
|
||||
@@ -1007,14 +1007,15 @@
|
||||
}
|
||||
},
|
||||
"node_modules/vite": {
|
||||
"version": "6.1.0",
|
||||
"resolved": "https://registry.npmjs.org/vite/-/vite-6.1.0.tgz",
|
||||
"integrity": "sha512-RjjMipCKVoR4hVfPY6GQTgveinjNuyLw+qruksLDvA5ktI1150VmcMBKmQaEWJhg/j6Uaf6dNCNA0AfdzUb/hQ==",
|
||||
"version": "6.0.7",
|
||||
"resolved": "https://registry.npmjs.org/vite/-/vite-6.0.7.tgz",
|
||||
"integrity": "sha512-RDt8r/7qx9940f8FcOIAH9PTViRrghKaK2K1jY3RaAURrEUbm9Du1mJ72G+jlhtG3WwodnfzY8ORQZbBavZEAQ==",
|
||||
"dev": true,
|
||||
"license": "MIT",
|
||||
"dependencies": {
|
||||
"esbuild": "^0.24.2",
|
||||
"postcss": "^8.5.1",
|
||||
"rollup": "^4.30.1"
|
||||
"postcss": "^8.4.49",
|
||||
"rollup": "^4.23.0"
|
||||
},
|
||||
"bin": {
|
||||
"vite": "bin/vite.js"
|
||||
|
||||
@@ -12,7 +12,7 @@
|
||||
"license": "ISC",
|
||||
"description": "",
|
||||
"devDependencies": {
|
||||
"vite": "^6.0.9"
|
||||
"vite": "^6.0.2"
|
||||
},
|
||||
"dependencies": {
|
||||
"@daily-co/daily-js": "0.74.0"
|
||||
|
||||
@@ -1 +0,0 @@
|
||||
22.14
|
||||
@@ -1,60 +0,0 @@
|
||||
# React Native Implementation
|
||||
|
||||
Basic implementation using the [Pipecat React Native SDK](https://docs.pipecat.ai/client/react-native/introduction).
|
||||
|
||||
## Usage
|
||||
|
||||
### Expo requirements
|
||||
|
||||
This project cannot be used with an [Expo Go](https://docs.expo.dev/workflow/expo-go/) app because [it requires custom native code](https://docs.expo.io/workflow/customizing/).
|
||||
|
||||
When a project requires custom native code or a config plugin, we need to transition from using [Expo Go](https://docs.expo.dev/workflow/expo-go/)
|
||||
to a [development build](https://docs.expo.dev/development/introduction/).
|
||||
|
||||
More details about the custom native code used by this demo can be found in [rn-daily-js-expo-config-plugin](https://github.com/daily-co/rn-daily-js-expo-config-plugin).
|
||||
|
||||
### Building remotely
|
||||
|
||||
If you do not have experience with Xcode and Android Studio builds or do not have them installed locally on your computer, you will need to follow [this guide from Expo to use EAS Build](https://docs.expo.dev/development/create-development-builds/#create-and-install-eas-build).
|
||||
|
||||
### Building locally
|
||||
|
||||
You will need to have installed locally on your computer:
|
||||
- [Xcode](https://developer.apple.com/xcode/) to build for iOS;
|
||||
- [Android Studio](https://developer.android.com/studio) to build for Android;
|
||||
|
||||
#### Install the demo dependencies
|
||||
|
||||
```bash
|
||||
# Use the version of node specified in .nvmrc
|
||||
nvm i
|
||||
|
||||
# Install dependencies
|
||||
npm i
|
||||
|
||||
# Before a native app can be compiled, the native source code must be generated.
|
||||
npx expo prebuild
|
||||
|
||||
# Configure the environment variable to connect to the local server
|
||||
cp env.example .env
|
||||
# edit .env and add your local ip address, for example: http://192.168.1.16:7860
|
||||
```
|
||||
|
||||
#### Running on Android
|
||||
|
||||
After plugging in an Android device [configured for debugging](https://developer.android.com/studio/debug/dev-options), run the following command:
|
||||
|
||||
```
|
||||
npm run android
|
||||
```
|
||||
|
||||
#### Running on iOS
|
||||
|
||||
Run the following command:
|
||||
|
||||
```
|
||||
npm run ios
|
||||
```
|
||||
|
||||
#### Connect to the server
|
||||
Use the http://localhost:5173 in your app.
|
||||
@@ -1,75 +0,0 @@
|
||||
{
|
||||
"expo": {
|
||||
"name": "bot-ready-rn",
|
||||
"slug": "bot-ready-rn",
|
||||
"version": "1.0.0",
|
||||
"orientation": "portrait",
|
||||
"icon": "./assets/icon.png",
|
||||
"userInterfaceStyle": "light",
|
||||
"splash": {
|
||||
"image": "./assets/splash.png",
|
||||
"resizeMode": "contain",
|
||||
"backgroundColor": "#ffffff"
|
||||
},
|
||||
"updates": {
|
||||
"fallbackToCacheTimeout": 0
|
||||
},
|
||||
"assetBundlePatterns": [
|
||||
"**/*"
|
||||
],
|
||||
"ios": {
|
||||
"supportsTablet": true,
|
||||
"bitcode": false,
|
||||
"bundleIdentifier": "co.daily.expo.BotReady",
|
||||
"infoPlist": {
|
||||
"UIBackgroundModes": [
|
||||
"voip"
|
||||
]
|
||||
},
|
||||
"appleTeamId": "EEBGKV9N3N"
|
||||
},
|
||||
"android": {
|
||||
"adaptiveIcon": {
|
||||
"foregroundImage": "./assets/adaptive-icon.png",
|
||||
"backgroundColor": "#FFFFFF"
|
||||
},
|
||||
"package": "co.daily.expo.BotReady",
|
||||
"permissions": [
|
||||
"android.permission.ACCESS_NETWORK_STATE",
|
||||
"android.permission.BLUETOOTH",
|
||||
"android.permission.CAMERA",
|
||||
"android.permission.INTERNET",
|
||||
"android.permission.MODIFY_AUDIO_SETTINGS",
|
||||
"android.permission.RECORD_AUDIO",
|
||||
"android.permission.SYSTEM_ALERT_WINDOW",
|
||||
"android.permission.WAKE_LOCK",
|
||||
"android.permission.FOREGROUND_SERVICE",
|
||||
"android.permission.FOREGROUND_SERVICE_CAMERA",
|
||||
"android.permission.FOREGROUND_SERVICE_MICROPHONE",
|
||||
"android.permission.FOREGROUND_SERVICE_MEDIA_PROJECTION",
|
||||
"android.permission.POST_NOTIFICATIONS"
|
||||
]
|
||||
},
|
||||
"web": {
|
||||
"favicon": "./assets/favicon.png"
|
||||
},
|
||||
"plugins": [
|
||||
"@config-plugins/react-native-webrtc",
|
||||
"@daily-co/config-plugin-rn-daily-js",
|
||||
[
|
||||
"expo-build-properties",
|
||||
{
|
||||
"android": {
|
||||
"minSdkVersion": 24,
|
||||
"compileSdkVersion": 35,
|
||||
"targetSdkVersion": 34,
|
||||
"buildToolsVersion": "35.0.0"
|
||||
},
|
||||
"ios": {
|
||||
"deploymentTarget": "15.1"
|
||||
}
|
||||
}
|
||||
]
|
||||
]
|
||||
}
|
||||
}
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 17 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 1.4 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 22 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 46 KiB |
@@ -1,7 +0,0 @@
|
||||
module.exports = function(api) {
|
||||
api.cache(true);
|
||||
return {
|
||||
presets: ['babel-preset-expo'],
|
||||
plugins: [["module:react-native-dotenv"]],
|
||||
};
|
||||
};
|
||||
@@ -1 +0,0 @@
|
||||
API_BASE_URL=http://YOUR_LOCAL_IP:7860
|
||||
@@ -1,7 +0,0 @@
|
||||
import { registerRootComponent } from "expo";
|
||||
|
||||
import App from "./src/App";
|
||||
|
||||
// registerRootComponent calls AppRegistry.registerComponent('main', () => App);
|
||||
// It also ensures that the environment is set up appropriately
|
||||
registerRootComponent(App);
|
||||
@@ -1,4 +0,0 @@
|
||||
// Learn more https://docs.expo.io/guides/customizing-metro
|
||||
const { getDefaultConfig } = require('expo/metro-config');
|
||||
|
||||
module.exports = getDefaultConfig(__dirname);
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,31 +0,0 @@
|
||||
{
|
||||
"name": "bot-ready-rn",
|
||||
"version": "1.0.0",
|
||||
"scripts": {
|
||||
"start": "expo start --dev-client",
|
||||
"android": "expo run:android --device",
|
||||
"ios": "expo run:ios --device",
|
||||
"web": "expo start --web"
|
||||
},
|
||||
"dependencies": {
|
||||
"@config-plugins/react-native-webrtc": "^10.0.0",
|
||||
"@daily-co/config-plugin-rn-daily-js": "0.0.7",
|
||||
"@daily-co/react-native-daily-js": "^0.70.0",
|
||||
"@daily-co/react-native-webrtc": "^118.0.3-daily.2",
|
||||
"@react-native-async-storage/async-storage": "1.23.1",
|
||||
"expo": "^52.0.0",
|
||||
"expo-build-properties": "~0.13.1",
|
||||
"expo-dev-client": "~5.0.5",
|
||||
"expo-splash-screen": "~0.29.16",
|
||||
"expo-status-bar": "~2.0.0",
|
||||
"react": "18.3.1",
|
||||
"react-native": "0.76.3",
|
||||
"react-native-background-timer": "^2.4.1",
|
||||
"react-native-dotenv": "^3.4.11",
|
||||
"react-native-get-random-values": "^1.11.0"
|
||||
},
|
||||
"devDependencies": {
|
||||
"@babel/core": "^7.12.9"
|
||||
},
|
||||
"private": true
|
||||
}
|
||||
@@ -1,121 +0,0 @@
|
||||
import React, { useState, useEffect } from 'react';
|
||||
import {SafeAreaView, View, Text, Button, StyleSheet, ScrollView} from 'react-native';
|
||||
import Daily from "@daily-co/react-native-daily-js";
|
||||
import { API_BASE_URL } from "@env";
|
||||
|
||||
const CallScreen = () => {
|
||||
const [connectionStatus, setConnectionStatus] = useState('Disconnected');
|
||||
const [isConnected, setIsConnected] = useState(false);
|
||||
const [callObject, setCallObject] = useState(null);
|
||||
const [logs, setLogs] = useState([]);
|
||||
|
||||
useEffect(() => {
|
||||
if (callObject) {
|
||||
setupTrackListeners(callObject);
|
||||
}
|
||||
}, [callObject]);
|
||||
|
||||
const log = (message) => {
|
||||
setLogs((prevLogs) => [...prevLogs, `${new Date().toISOString()} - ${message}`]);
|
||||
console.log(message);
|
||||
};
|
||||
|
||||
const setupTrackListeners = (callObject) => {
|
||||
callObject.on("joined-meeting", () => {
|
||||
setConnectionStatus('Connected');
|
||||
setIsConnected(true);
|
||||
log('Client connected');
|
||||
});
|
||||
callObject.on("left-meeting", () => {
|
||||
setConnectionStatus('Disconnected');
|
||||
setIsConnected(false);
|
||||
log('Client disconnected');
|
||||
});
|
||||
callObject.on("participant-left", () => {
|
||||
// When the bot leaves, we are also disconnecting from the call
|
||||
disconnect().catch((err) => {
|
||||
log(`Failed to disconnect ${err}`);
|
||||
})
|
||||
});
|
||||
// Trigger so the bot can start sending audio
|
||||
callObject.on("track-started", (evt) => {
|
||||
if (evt.track.kind === "audio" && evt.participant.local === false) {
|
||||
handleEventToConsole(evt)
|
||||
log("Sending the message that will trigger the bot to play the audio.")
|
||||
callObject.sendAppMessage("playable")
|
||||
}
|
||||
});
|
||||
callObject.on("error", (evt) => log(`Error: ${evt.error}`));
|
||||
// Other events just for awareness
|
||||
callObject.on("track-stopped", handleEventToConsole);
|
||||
callObject.on("participant-joined", handleEventToConsole);
|
||||
callObject.on("participant-updated", handleEventToConsole);
|
||||
};
|
||||
|
||||
const handleEventToConsole = (evt) => {
|
||||
log(`Received event: ${evt.action}`);
|
||||
};
|
||||
|
||||
const connect = async () => {
|
||||
try {
|
||||
const callObject = Daily.createCallObject({ subscribeToTracksAutomatically: true });
|
||||
setCallObject(callObject);
|
||||
const connectionUrl = `${API_BASE_URL}/connect`
|
||||
const res = await fetch(connectionUrl, { method: "POST", headers: { "Content-Type": "application/json" } });
|
||||
const roomInfo = await res.json();
|
||||
await callObject.join({ url: roomInfo.room_url });
|
||||
} catch (error) {
|
||||
log(`Error connecting: ${error.message}`);
|
||||
}
|
||||
};
|
||||
|
||||
const disconnect = async () => {
|
||||
if (callObject) {
|
||||
try {
|
||||
await callObject.leave();
|
||||
await callObject.destroy();
|
||||
setCallObject(null);
|
||||
} catch (error) {
|
||||
log(`Error disconnecting: ${error.message}`);
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
return (
|
||||
<SafeAreaView style={styles.safeArea}>
|
||||
<View style={styles.container}>
|
||||
<View style={styles.statusBar}>
|
||||
<Text>Status: <Text style={styles.status}>{connectionStatus}</Text></Text>
|
||||
<View style={styles.controls}>
|
||||
<Button
|
||||
title={isConnected ? "Disconnect" : "Connect"}
|
||||
onPress={isConnected ? disconnect : connect}
|
||||
/>
|
||||
</View>
|
||||
</View>
|
||||
|
||||
<View style={styles.debugPanel}>
|
||||
<Text style={styles.debugTitle}>Debug Info</Text>
|
||||
<ScrollView style={styles.debugLog}>
|
||||
{logs.map((logEntry, index) => (
|
||||
<Text key={index} style={styles.logText}>{logEntry}</Text>
|
||||
))}
|
||||
</ScrollView>
|
||||
</View>
|
||||
</View>
|
||||
</SafeAreaView>
|
||||
);
|
||||
};
|
||||
|
||||
const styles = StyleSheet.create({
|
||||
safeArea: { flex: 1, backgroundColor: '#f0f0f0', padding: 20 },
|
||||
container: { flex: 1, margin: 20 },
|
||||
statusBar: { flexDirection: 'row', justifyContent: 'space-between', alignItems: 'center', padding: 10, backgroundColor: '#fff', borderRadius: 8, marginBottom: 20 },
|
||||
status: { fontWeight: 'bold' },
|
||||
controls: { flexDirection: 'row', gap: 10 },
|
||||
debugPanel: { height: '80%', backgroundColor: '#fff', borderRadius: 8, padding: 20},
|
||||
debugTitle: { fontSize: 16, fontWeight: 'bold' },
|
||||
debugLog: { height: '100%', overflow: 'scroll', backgroundColor: '#f8f8f8', padding: 10, borderRadius: 4, fontFamily: 'monospace', fontSize: 12, lineHeight: 1.4 },
|
||||
});
|
||||
|
||||
export default CallScreen;
|
||||
@@ -6,7 +6,6 @@
|
||||
|
||||
import argparse
|
||||
import os
|
||||
from typing import Optional
|
||||
|
||||
import aiohttp
|
||||
|
||||
@@ -19,7 +18,7 @@ async def configure(aiohttp_session: aiohttp.ClientSession):
|
||||
|
||||
|
||||
async def configure_with_args(
|
||||
aiohttp_session: aiohttp.ClientSession, parser: Optional[argparse.ArgumentParser] = None
|
||||
aiohttp_session: aiohttp.ClientSession, parser: argparse.ArgumentParser | None = None
|
||||
):
|
||||
if not parser:
|
||||
parser = argparse.ArgumentParser(description="Daily AI SDK Bot Sample")
|
||||
|
||||
@@ -31,15 +31,16 @@ logger.add(sys.stderr, level="DEBUG")
|
||||
class SilenceFrame(OutputAudioRawFrame):
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
sample_rate: int,
|
||||
duration: float,
|
||||
audio: bytes = None,
|
||||
sample_rate: int = 16000,
|
||||
num_channels: int = 1,
|
||||
duration: float = 0.1,
|
||||
):
|
||||
# Initialize the parent class with the silent frame's data
|
||||
super().__init__(
|
||||
audio=self.create_silent_audio_frame(sample_rate, 1, duration).audio,
|
||||
audio=self.create_silent_audio_frame(sample_rate, num_channels, duration).audio,
|
||||
sample_rate=sample_rate,
|
||||
num_channels=1,
|
||||
num_channels=num_channels,
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
@@ -64,7 +65,7 @@ async def main():
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
runner = PipelineRunner()
|
||||
@@ -79,10 +80,7 @@ async def main():
|
||||
return
|
||||
await task.queue_frames(
|
||||
[
|
||||
SilenceFrame(
|
||||
sample_rate=task.params.audio_out_sample_rate,
|
||||
duration=0.5,
|
||||
),
|
||||
SilenceFrame(duration=0.5),
|
||||
TTSSpeakFrame(f"Hello there, how are you doing today ?"),
|
||||
EndFrame(),
|
||||
]
|
||||
|
||||
@@ -65,6 +65,7 @@ async def main():
|
||||
# English
|
||||
#
|
||||
voice_id="cgSgspJ2msm6clMCkdW9",
|
||||
aiohttp_session=session,
|
||||
#
|
||||
# Spanish
|
||||
#
|
||||
@@ -113,17 +114,16 @@ async def main():
|
||||
llm,
|
||||
tts,
|
||||
transport.output(),
|
||||
canonical, # uploads audio buffer to Canonical AI for metrics
|
||||
audio_buffer_processor, # captures audio into a buffer
|
||||
canonical, # uploads audio buffer to Canonical AI for metrics
|
||||
context_aggregator.assistant(),
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
|
||||
task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
|
||||
|
||||
@transport.event_handler("on_first_participant_joined")
|
||||
async def on_first_participant_joined(transport, participant):
|
||||
await audio_buffer_processor.start_recording()
|
||||
await transport.capture_participant_transcription(participant["id"])
|
||||
await task.queue_frames([context_aggregator.user().get_context_frame()])
|
||||
|
||||
|
||||
@@ -32,16 +32,10 @@ load_dotenv(override=True)
|
||||
logger.remove(0)
|
||||
logger.add(sys.stderr, level="DEBUG")
|
||||
|
||||
# Create the recordings directory if it doesn't exist
|
||||
os.makedirs("recordings", exist_ok=True)
|
||||
|
||||
|
||||
async def save_audio(audio: bytes, sample_rate: int, num_channels: int, name: str):
|
||||
async def save_audio(audio: bytes, sample_rate: int, num_channels: int):
|
||||
if len(audio) > 0:
|
||||
filename = os.path.join(
|
||||
"recordings",
|
||||
f"{name}_conversation_recording{datetime.datetime.now().strftime('%Y%m%d_%H%M%S')}.wav",
|
||||
)
|
||||
filename = f"conversation_recording{datetime.datetime.now().strftime('%Y%m%d_%H%M%S')}.wav"
|
||||
with io.BytesIO() as buffer:
|
||||
with wave.open(buffer, "wb") as wf:
|
||||
wf.setsampwidth(2)
|
||||
@@ -88,6 +82,7 @@ async def main():
|
||||
# English
|
||||
#
|
||||
voice_id="cgSgspJ2msm6clMCkdW9",
|
||||
aiohttp_session=session,
|
||||
#
|
||||
# Spanish
|
||||
#
|
||||
@@ -114,9 +109,8 @@ async def main():
|
||||
context = OpenAILLMContext(messages)
|
||||
context_aggregator = llm.create_context_aggregator(context)
|
||||
|
||||
# NOTE: Watch out! This will save all the conversation in memory. You
|
||||
# can pass `buffer_size` to get periodic callbacks.
|
||||
audiobuffer = AudioBufferProcessor(enable_turn_audio=True)
|
||||
# Save audio every 10 seconds.
|
||||
audiobuffer = AudioBufferProcessor(buffer_size=480000)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
@@ -130,23 +124,14 @@ async def main():
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
|
||||
task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
|
||||
|
||||
@audiobuffer.event_handler("on_audio_data")
|
||||
async def on_audio_data(buffer, audio, sample_rate, num_channels):
|
||||
await save_audio(audio, sample_rate, num_channels, "full")
|
||||
|
||||
@audiobuffer.event_handler("on_user_turn_audio_data")
|
||||
async def on_user_turn_audio_data(buffer, audio, sample_rate, num_channels):
|
||||
await save_audio(audio, sample_rate, num_channels, "user")
|
||||
|
||||
@audiobuffer.event_handler("on_bot_turn_audio_data")
|
||||
async def on_bot_turn_audio_data(buffer, audio, sample_rate, num_channels):
|
||||
await save_audio(audio, sample_rate, num_channels, "bot")
|
||||
await save_audio(audio, sample_rate, num_channels)
|
||||
|
||||
@transport.event_handler("on_first_participant_joined")
|
||||
async def on_first_participant_joined(transport, participant):
|
||||
await audiobuffer.start_recording()
|
||||
await transport.capture_participant_transcription(participant["id"])
|
||||
await task.queue_frames([context_aggregator.user().get_context_frame()])
|
||||
|
||||
|
||||
@@ -70,7 +70,7 @@ async def main(room_url: str, token: str):
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
|
||||
task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
|
||||
|
||||
@transport.event_handler("on_first_participant_joined")
|
||||
async def on_first_participant_joined(transport, participant):
|
||||
|
||||
@@ -34,7 +34,7 @@ async def main(room_url: str, token: str):
|
||||
)
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY", ""), voice_id="71a7ad14-091c-4e8e-a314-022ece01c121"
|
||||
api_key=os.getenv("CARTESIA_API_KEY", ""), voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22"
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
|
||||
@@ -62,7 +62,7 @@ async def main(room_url: str, token: str):
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
|
||||
@@ -1,94 +0,0 @@
|
||||
# Python
|
||||
__pycache__/
|
||||
*.py[cod]
|
||||
*$py.class
|
||||
*.so
|
||||
.Python
|
||||
build/
|
||||
dist/
|
||||
*.egg-info/
|
||||
*.egg
|
||||
.installed.cfg
|
||||
.eggs/
|
||||
downloads/
|
||||
lib/
|
||||
lib64/
|
||||
parts/
|
||||
sdist/
|
||||
var/
|
||||
wheels/
|
||||
share/python-wheels/
|
||||
MANIFEST
|
||||
|
||||
# Virtual Environments
|
||||
venv/
|
||||
env/
|
||||
.env
|
||||
.venv/
|
||||
ENV/
|
||||
env.bak/
|
||||
venv.bak/
|
||||
|
||||
# IDE
|
||||
.idea/
|
||||
.vscode/
|
||||
.spyderproject
|
||||
.spyproject
|
||||
.ropeproject
|
||||
|
||||
# Testing and Coverage
|
||||
.coverage
|
||||
.coverage.*
|
||||
htmlcov/
|
||||
.pytest_cache/
|
||||
.tox/
|
||||
.nox/
|
||||
.cache
|
||||
nosetests.xml
|
||||
coverage.xml
|
||||
*.cover
|
||||
.hypothesis/
|
||||
cover/
|
||||
|
||||
# Logs and Databases
|
||||
*.log
|
||||
*.db
|
||||
db.sqlite3
|
||||
db.sqlite3-journal
|
||||
pip-log.txt
|
||||
|
||||
# System Files
|
||||
.DS_Store
|
||||
Thumbs.db
|
||||
desktop.ini
|
||||
*.swp
|
||||
*.swo
|
||||
*.bak
|
||||
*.tmp
|
||||
*~
|
||||
|
||||
# Build and Documentation
|
||||
docs/_build/
|
||||
.pybuilder/
|
||||
target/
|
||||
instance/
|
||||
.webassets-cache
|
||||
.pdm.toml
|
||||
.pdm-python
|
||||
.pdm-build/
|
||||
__pypackages__/
|
||||
|
||||
# Other
|
||||
*.mo
|
||||
*.pot
|
||||
*.sage.py
|
||||
.mypy_cache/
|
||||
.dmypy.json
|
||||
dmypy.json
|
||||
.pyre/
|
||||
.pytype/
|
||||
cython_debug/
|
||||
.ipynb_checkpoints
|
||||
|
||||
# Pipecat cloud
|
||||
.pcc-deploy.toml
|
||||
@@ -1,7 +0,0 @@
|
||||
FROM dailyco/pipecat-base:latest
|
||||
|
||||
COPY ./requirements.txt requirements.txt
|
||||
|
||||
RUN pip install --no-cache-dir --upgrade -r requirements.txt
|
||||
|
||||
COPY ./bot.py bot.py
|
||||
@@ -1,196 +0,0 @@
|
||||
# Pipecat Cloud Starter Project
|
||||
|
||||
[](https://docs.pipecat.daily.co) [](https://discord.gg/dailyco)
|
||||
|
||||
A template voice agent for [Pipecat Cloud](https://www.daily.co/products/pipecat-cloud/) that demonstrates building and deploying a conversational AI agent.
|
||||
|
||||
> **For a detailed step-by-step guide, see our [Quickstart Documentation](https://docs.pipecat.daily.co/quickstart).**
|
||||
|
||||
## Prerequisites
|
||||
|
||||
- Python 3.10+
|
||||
- Linux, MacOS, or Windows Subsystem for Linux (WSL)
|
||||
- [Docker](https://www.docker.com) and a Docker repository (e.g., [Docker Hub](https://hub.docker.com))
|
||||
- A Docker Hub account (or other container registry account)
|
||||
- [Pipecat Cloud](https://pipecat.daily.co) account
|
||||
|
||||
> **Note**: If you haven't installed Docker yet, follow the official installation guides for your platform ([Linux](https://docs.docker.com/engine/install/), [Mac](https://docs.docker.com/desktop/setup/install/mac-install/), [Windows](https://docs.docker.com/desktop/setup/install/windows-install/)). For Docker Hub, [create a free account](https://hub.docker.com/signup) and log in via terminal with `docker login`.
|
||||
|
||||
## Get Started
|
||||
|
||||
### 1. Get the starter project
|
||||
|
||||
Clone the starter project from GitHub:
|
||||
|
||||
```bash
|
||||
git clone https://github.com/daily-co/pipecat-cloud-starter
|
||||
cd pipecat-cloud-starter
|
||||
```
|
||||
|
||||
### 2. Set up your Python environment
|
||||
|
||||
We recommend using a virtual environment to manage your Python dependencies.
|
||||
|
||||
```bash
|
||||
# Create a virtual environment
|
||||
python -m venv .venv
|
||||
|
||||
# Activate it
|
||||
source .venv/bin/activate # On Windows: .venv\Scripts\activate
|
||||
|
||||
# Install the Pipecat Cloud CLI
|
||||
pip install pipecatcloud
|
||||
```
|
||||
|
||||
### 3. Authenticate with Pipecat Cloud
|
||||
|
||||
```bash
|
||||
pcc auth login
|
||||
```
|
||||
|
||||
### 4. Acquire required API keys
|
||||
|
||||
This starter requires the following API keys:
|
||||
|
||||
- **OpenAI API Key**: Get from [platform.openai.com/api-keys](https://platform.openai.com/api-keys)
|
||||
- **Cartesia API Key**: Get from [play.cartesia.ai/keys](https://play.cartesia.ai/keys)
|
||||
- **Daily API Key**: Automatically provided through your Pipecat Cloud account
|
||||
|
||||
### 5. Configure to run locally (optional)
|
||||
|
||||
You can test your agent locally before deploying to Pipecat Cloud:
|
||||
|
||||
```bash
|
||||
# Set environment variables with your API keys
|
||||
export CARTESIA_API_KEY="your_cartesia_key"
|
||||
export DAILY_API_KEY="your_daily_key"
|
||||
export OPENAI_API_KEY="your_openai_key"
|
||||
```
|
||||
|
||||
> Your `DAILY_API_KEY` can be found at [https://pipecat.daily.co](https://pipecat.daily.co) under the `Settings` in the `Daily (WebRTC)` tab.
|
||||
|
||||
First install requirements:
|
||||
|
||||
```bash
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
Then, launch the bot.py script locally:
|
||||
|
||||
```bash
|
||||
LOCAL_RUN=1 python bot.py
|
||||
```
|
||||
|
||||
## Deploy & Run
|
||||
|
||||
### 1. Build and push your Docker image
|
||||
|
||||
```bash
|
||||
# Build the image (targeting ARM architecture for cloud deployment)
|
||||
docker build --platform=linux/arm64 -t my-first-agent:latest .
|
||||
|
||||
# Tag with your Docker username and version
|
||||
docker tag my-first-agent:latest your-username/my-first-agent:0.1
|
||||
|
||||
# Push to Docker Hub
|
||||
docker push your-username/my-first-agent:0.1
|
||||
```
|
||||
|
||||
### 2. Create a secret set for your API keys
|
||||
|
||||
The starter project requires API keys for OpenAI and Cartesia:
|
||||
|
||||
```bash
|
||||
# Copy the example env file
|
||||
cp env.example .env
|
||||
|
||||
# Edit .env to add your API keys:
|
||||
# CARTESIA_API_KEY=your_cartesia_key
|
||||
# OPENAI_API_KEY=your_openai_key
|
||||
|
||||
# Create a secret set from your .env file
|
||||
pcc secrets set my-first-agent-secrets --file .env
|
||||
```
|
||||
|
||||
Alternatively, you can create secrets directly via CLI:
|
||||
|
||||
```bash
|
||||
pcc secrets set my-first-agent-secrets \
|
||||
CARTESIA_API_KEY=your_cartesia_key \
|
||||
OPENAI_API_KEY=your_openai_key
|
||||
```
|
||||
|
||||
### 3. Deploy to Pipecat Cloud
|
||||
|
||||
```bash
|
||||
pcc deploy my-first-agent your-username/my-first-agent:0.1 --secrets my-first-agent-secrets
|
||||
```
|
||||
|
||||
> **Note (Optional)**: For a more maintainable approach, you can use the included `pcc-deploy.toml` file:
|
||||
>
|
||||
> ```toml
|
||||
> agent_name = "my-first-agent"
|
||||
> image = "your-username/my-first-agent:0.1"
|
||||
> secret_set = "my-first-agent-secrets"
|
||||
>
|
||||
> [scaling]
|
||||
> min_instances = 0
|
||||
> ```
|
||||
>
|
||||
> Then simply run `pcc deploy` without additional arguments.
|
||||
|
||||
> **Note**: If your repository is private, you'll need to add credentials:
|
||||
>
|
||||
> ```bash
|
||||
> # Create pull secret (you’ll be prompted for credentials)
|
||||
> pcc secrets image-pull-secret pull-secret https://index.docker.io/v1/
|
||||
>
|
||||
> # Deploy with credentials
|
||||
> pcc deploy my-first-agent your-username/my-first-agent:0.1 --credentials pull-secret
|
||||
> ```
|
||||
|
||||
### 4. Check deployment and scaling (optional)
|
||||
|
||||
By default, your agent will use "scale-to-zero" configuration, which means it may have a cold start of around 10 seconds when first used. By default, idle instances are maintained for 5 minutes before being terminated when using scale-to-zero.
|
||||
|
||||
For more responsive testing, you can scale your deployment to keep a minimum of one instance warm:
|
||||
|
||||
```bash
|
||||
# Ensure at least one warm instance is always available
|
||||
pcc deploy my-first-agent your-username/my-first-agent:0.1 --min-instances 1
|
||||
|
||||
# Check the status of your deployment
|
||||
pcc agent status my-first-agent
|
||||
```
|
||||
|
||||
By default, idle instances are maintained for 5 minutes before being terminated when using scale-to-zero.
|
||||
|
||||
### 5. Create an API key
|
||||
|
||||
```bash
|
||||
# Create a public API key for accessing your agent
|
||||
pcc organizations keys create
|
||||
|
||||
# Set it as the default key to use with your agent
|
||||
pcc organizations keys use
|
||||
```
|
||||
|
||||
### 6. Start your agent
|
||||
|
||||
```bash
|
||||
# Start a session with your agent in a Daily room
|
||||
pcc agent start my-first-agent --use-daily
|
||||
```
|
||||
|
||||
This will return a URL, which you can use to connect to your running agent.
|
||||
|
||||
## Documentation
|
||||
|
||||
For more details on Pipecat Cloud and its capabilities:
|
||||
|
||||
- [Pipecat Cloud Documentation](https://docs.pipecat.daily.co)
|
||||
- [Pipecat Project Documentation](https://docs.pipecat.ai)
|
||||
|
||||
## Support
|
||||
|
||||
Join our [Discord community](https://discord.gg/dailyco) for help and discussions.
|
||||
@@ -1,161 +0,0 @@
|
||||
#
|
||||
# Copyright (c) 2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import os
|
||||
|
||||
import aiohttp
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
from pipecatcloud.agent import DailySessionArguments
|
||||
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.frames.frames import LLMMessagesFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.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
|
||||
|
||||
# Check if we're in local development mode
|
||||
LOCAL_RUN = os.getenv("LOCAL_RUN")
|
||||
if LOCAL_RUN:
|
||||
import asyncio
|
||||
import webbrowser
|
||||
|
||||
try:
|
||||
from local_runner import configure
|
||||
except ImportError:
|
||||
logger.error("Could not import local_runner module. Local development mode may not work.")
|
||||
|
||||
# Load environment variables
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
async def main(room_url: str, token: str):
|
||||
"""Main pipeline setup and execution function.
|
||||
|
||||
Args:
|
||||
room_url: The Daily room URL
|
||||
token: The Daily room token
|
||||
"""
|
||||
logger.debug("Starting bot in room: {}", room_url)
|
||||
|
||||
transport = DailyTransport(
|
||||
room_url,
|
||||
token,
|
||||
"bot",
|
||||
DailyParams(
|
||||
audio_out_enabled=True,
|
||||
transcription_enabled=True,
|
||||
vad_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
),
|
||||
)
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"), voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22"
|
||||
)
|
||||
|
||||
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(),
|
||||
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):
|
||||
logger.info("First participant joined: {}", participant["id"])
|
||||
await transport.capture_participant_transcription(participant["id"])
|
||||
# Kick off the conversation.
|
||||
messages.append(
|
||||
{
|
||||
"role": "system",
|
||||
"content": "Please start with 'Hello World' and introduce yourself to the user.",
|
||||
}
|
||||
)
|
||||
await task.queue_frames([LLMMessagesFrame(messages)])
|
||||
|
||||
@transport.event_handler("on_participant_left")
|
||||
async def on_participant_left(transport, participant, reason):
|
||||
logger.info("Participant left: {}", participant)
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner()
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(args: DailySessionArguments):
|
||||
"""Main bot entry point compatible with the FastAPI route handler.
|
||||
|
||||
Args:
|
||||
room_url: The Daily room URL
|
||||
token: The Daily room token
|
||||
body: The configuration object from the request body
|
||||
session_id: The session ID for logging
|
||||
"""
|
||||
logger.info(f"Bot process initialized {args.room_url} {args.token}")
|
||||
|
||||
try:
|
||||
await main(args.room_url, args.token)
|
||||
logger.info("Bot process completed")
|
||||
except Exception as e:
|
||||
logger.exception(f"Error in bot process: {str(e)}")
|
||||
raise
|
||||
|
||||
|
||||
# Local development functions
|
||||
async def local_main():
|
||||
"""Function for local development testing."""
|
||||
try:
|
||||
async with aiohttp.ClientSession() as session:
|
||||
(room_url, token) = await configure(session)
|
||||
logger.warning("_")
|
||||
logger.warning("_")
|
||||
logger.warning(f"Talk to your voice agent here: {room_url}")
|
||||
logger.warning("_")
|
||||
logger.warning("_")
|
||||
webbrowser.open(room_url)
|
||||
await main(room_url, token)
|
||||
except Exception as e:
|
||||
logger.exception(f"Error in local development mode: {e}")
|
||||
|
||||
|
||||
# Local development entry point
|
||||
if LOCAL_RUN and __name__ == "__main__":
|
||||
try:
|
||||
asyncio.run(local_main())
|
||||
except Exception as e:
|
||||
logger.exception(f"Failed to run in local mode: {e}")
|
||||
@@ -1,2 +0,0 @@
|
||||
CARTESIA_API_KEY=
|
||||
OPENAI_API_KEY=
|
||||
@@ -1,46 +0,0 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import os
|
||||
|
||||
import aiohttp
|
||||
|
||||
from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper, DailyRoomParams
|
||||
|
||||
|
||||
async def configure(aiohttp_session: aiohttp.ClientSession):
|
||||
(url, token) = await configure_with_args(aiohttp_session)
|
||||
return (url, token)
|
||||
|
||||
|
||||
async def configure_with_args(aiohttp_session: aiohttp.ClientSession = None):
|
||||
key = os.getenv("DAILY_API_KEY")
|
||||
if not key:
|
||||
raise Exception(
|
||||
"No Daily API key specified. set DAILY_API_KEY in your environment to specify a Daily API key, available from https://dashboard.daily.co/developers."
|
||||
)
|
||||
|
||||
daily_rest_helper = DailyRESTHelper(
|
||||
daily_api_key=key,
|
||||
daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"),
|
||||
aiohttp_session=aiohttp_session,
|
||||
)
|
||||
|
||||
room = await daily_rest_helper.create_room(
|
||||
DailyRoomParams(properties={"enable_prejoin_ui": False})
|
||||
)
|
||||
if not room.url:
|
||||
raise HTTPException(status_code=500, detail="Failed to create room")
|
||||
|
||||
url = room.url
|
||||
|
||||
# Create a meeting token for the given room with an expiration 1 hour in
|
||||
# the future.
|
||||
expiry_time: float = 60 * 60
|
||||
|
||||
token = await daily_rest_helper.get_token(url, expiry_time)
|
||||
|
||||
return (url, token)
|
||||
@@ -1,6 +0,0 @@
|
||||
agent_name = "my-first-agent"
|
||||
image = "your-username/my-first-agent:0.1"
|
||||
secret_set = "my-first-agent-secrets"
|
||||
|
||||
[scaling]
|
||||
min_instances = 0
|
||||
@@ -1,3 +0,0 @@
|
||||
pipecatcloud
|
||||
pipecat-ai[cartesia,daily,openai,silero]>=0.0.58
|
||||
python-dotenv~=1.0.1
|
||||
@@ -36,7 +36,7 @@ async def main():
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
runner = PipelineRunner()
|
||||
|
||||
@@ -16,7 +16,8 @@ from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineTask
|
||||
from pipecat.services.cartesia import CartesiaTTSService
|
||||
from pipecat.transports.local.audio import LocalAudioTransport, LocalAudioTransportParams
|
||||
from pipecat.transports.base_transport import TransportParams
|
||||
from pipecat.transports.local.audio import LocalAudioTransport
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
@@ -25,11 +26,11 @@ logger.add(sys.stderr, level="DEBUG")
|
||||
|
||||
|
||||
async def main():
|
||||
transport = LocalAudioTransport(LocalAudioTransportParams(audio_out_enabled=True))
|
||||
transport = LocalAudioTransport(TransportParams(audio_out_enabled=True))
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
pipeline = Pipeline([tts, transport.output()])
|
||||
@@ -40,7 +41,7 @@ async def main():
|
||||
await asyncio.sleep(1)
|
||||
await task.queue_frames([TTSSpeakFrame("Hello there, how is it going!"), EndFrame()])
|
||||
|
||||
runner = PipelineRunner(handle_sigint=False if sys.platform == "win32" else True)
|
||||
runner = PipelineRunner()
|
||||
|
||||
await asyncio.gather(runner.run(task), say_something())
|
||||
|
||||
|
||||
@@ -83,7 +83,7 @@ async def main():
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
runner = PipelineRunner()
|
||||
|
||||
@@ -37,7 +37,7 @@ async def main():
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
|
||||
|
||||
@@ -18,7 +18,8 @@ from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineTask
|
||||
from pipecat.services.fal import FalImageGenService
|
||||
from pipecat.transports.local.tk import TkLocalTransport, TkTransportParams
|
||||
from pipecat.transports.base_transport import TransportParams
|
||||
from pipecat.transports.local.tk import TkLocalTransport
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
@@ -33,9 +34,7 @@ async def main():
|
||||
|
||||
transport = TkLocalTransport(
|
||||
tk_root,
|
||||
TkTransportParams(
|
||||
camera_out_enabled=True, camera_out_width=1024, camera_out_height=1024
|
||||
),
|
||||
TransportParams(camera_out_enabled=True, camera_out_width=1024, camera_out_height=1024),
|
||||
)
|
||||
|
||||
imagegen = FalImageGenService(
|
||||
|
||||
@@ -44,8 +44,7 @@ async def main():
|
||||
runner = PipelineRunner()
|
||||
|
||||
task = PipelineTask(
|
||||
Pipeline([imagegen, transport.output()]),
|
||||
params=PipelineParams(enable_metrics=True),
|
||||
Pipeline([imagegen, transport.output()]), PipelineParams(enable_metrics=True)
|
||||
)
|
||||
|
||||
@transport.event_handler("on_first_participant_joined")
|
||||
|
||||
@@ -51,6 +51,7 @@ async def main():
|
||||
)
|
||||
|
||||
elevenlabs_tts = ElevenLabsTTSService(
|
||||
aiohttp_session=session,
|
||||
api_key=os.getenv("ELEVENLABS_API_KEY"),
|
||||
voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
|
||||
)
|
||||
|
||||
@@ -87,7 +87,7 @@ async def main():
|
||||
|
||||
tts = CartesiaHttpTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
imagegen = FalImageGenService(
|
||||
|
||||
@@ -30,7 +30,8 @@ from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
|
||||
from pipecat.services.cartesia import CartesiaHttpTTSService
|
||||
from pipecat.services.fal import FalImageGenService
|
||||
from pipecat.services.openai import OpenAILLMService
|
||||
from pipecat.transports.local.tk import TkLocalTransport, TkTransportParams
|
||||
from pipecat.transports.base_transport import TransportParams
|
||||
from pipecat.transports.local.tk import TkLocalTransport, TkOutputTransport
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
@@ -97,7 +98,7 @@ async def main():
|
||||
|
||||
tts = CartesiaHttpTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
imagegen = FalImageGenService(
|
||||
@@ -151,7 +152,7 @@ async def main():
|
||||
|
||||
transport = TkLocalTransport(
|
||||
tk_root,
|
||||
TkTransportParams(
|
||||
TransportParams(
|
||||
audio_out_enabled=True,
|
||||
camera_out_enabled=True,
|
||||
camera_out_width=1024,
|
||||
|
||||
@@ -74,7 +74,7 @@ async def main():
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
|
||||
@@ -105,10 +105,7 @@ async def main():
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
PipelineParams(enable_metrics=True, enable_usage_metrics=True),
|
||||
)
|
||||
|
||||
@transport.event_handler("on_first_participant_joined")
|
||||
|
||||
@@ -27,7 +27,7 @@ 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.frame_processor import FrameDirection, FrameProcessor
|
||||
from pipecat.services.cartesia import CartesiaTTSService
|
||||
from pipecat.services.cartesia import CartesiaHttpTTSService
|
||||
from pipecat.services.openai import OpenAILLMService
|
||||
from pipecat.transports.services.daily import DailyParams, DailyTransport
|
||||
|
||||
@@ -91,9 +91,9 @@ async def main():
|
||||
),
|
||||
)
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
tts = CartesiaHttpTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
|
||||
@@ -127,7 +127,7 @@ async def main():
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
|
||||
@@ -47,7 +47,7 @@ async def main():
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
|
||||
@@ -76,7 +76,7 @@ async def main():
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
|
||||
@@ -46,7 +46,7 @@ async def main():
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
|
||||
@@ -74,7 +74,7 @@ async def main():
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
|
||||
@@ -46,7 +46,7 @@ async def main():
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
llm = AnthropicLLMService(
|
||||
@@ -79,7 +79,7 @@ async def main():
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
|
||||
@@ -64,7 +64,7 @@ async def main():
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
prompt = ChatPromptTemplate.from_messages(
|
||||
@@ -103,7 +103,7 @@ async def main():
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
|
||||
@@ -81,7 +81,7 @@ async def main():
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
|
||||
@@ -74,7 +74,7 @@ async def main():
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
|
||||
@@ -1,103 +0,0 @@
|
||||
#
|
||||
# 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.elevenlabs import ElevenLabsHttpTTSService
|
||||
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,
|
||||
transcription_enabled=True,
|
||||
vad_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
),
|
||||
)
|
||||
|
||||
tts = ElevenLabsHttpTTSService(
|
||||
api_key=os.getenv("ELEVENLABS_API_KEY", ""),
|
||||
voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
|
||||
aiohttp_session=session,
|
||||
)
|
||||
|
||||
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
|
||||
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()])
|
||||
|
||||
@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())
|
||||
@@ -74,7 +74,7 @@ async def main():
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
|
||||
@@ -75,7 +75,7 @@ async def main():
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
|
||||
@@ -77,7 +77,7 @@ async def main():
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
|
||||
@@ -83,7 +83,7 @@ async def main():
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
|
||||
@@ -18,8 +18,7 @@ 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.openai import OpenAILLMService
|
||||
from pipecat.services.rime import RimeHttpTTSService
|
||||
from pipecat.services.openai import OpenAILLMService, OpenAITTSService
|
||||
from pipecat.transports.services.daily import DailyParams, DailyTransport
|
||||
|
||||
load_dotenv(override=True)
|
||||
@@ -38,17 +37,14 @@ async def main():
|
||||
"Respond bot",
|
||||
DailyParams(
|
||||
audio_out_enabled=True,
|
||||
audio_out_sample_rate=24000,
|
||||
transcription_enabled=True,
|
||||
vad_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
),
|
||||
)
|
||||
|
||||
tts = RimeHttpTTSService(
|
||||
api_key=os.getenv("RIME_API_KEY", ""),
|
||||
voice_id="rex",
|
||||
aiohttp_session=session,
|
||||
)
|
||||
tts = OpenAITTSService(api_key=os.getenv("OPENAI_API_KEY"), voice="alloy")
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
|
||||
|
||||
@@ -75,7 +71,7 @@ async def main():
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
@@ -1,113 +0,0 @@
|
||||
#
|
||||
# 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.openai import OpenAILLMService, OpenAISTTService, OpenAITTSService
|
||||
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,
|
||||
audio_out_sample_rate=24000,
|
||||
transcription_enabled=False,
|
||||
vad_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
vad_audio_passthrough=True,
|
||||
),
|
||||
)
|
||||
|
||||
# You can use the OpenAI compatible API like Groq.
|
||||
# stt = OpenAISTTService(
|
||||
# base_url="https://api.groq.com/openai/v1",
|
||||
# api_key="gsk_***",
|
||||
# model="whisper-large-v3",
|
||||
# )
|
||||
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"), model="gpt-4o-mini-tts-latest")
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"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.",
|
||||
},
|
||||
]
|
||||
|
||||
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()])
|
||||
|
||||
@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())
|
||||
@@ -47,7 +47,7 @@ async def main():
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
timestamp = int(time.time())
|
||||
@@ -81,7 +81,7 @@ async def main():
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
|
||||
@@ -75,7 +75,7 @@ async def main():
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
|
||||
@@ -51,7 +51,7 @@ async def main():
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
|
||||
@@ -80,7 +80,7 @@ async def main():
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
|
||||
@@ -38,6 +38,7 @@ async def main():
|
||||
"Respond bot",
|
||||
DailyParams(
|
||||
audio_out_enabled=True,
|
||||
audio_out_sample_rate=24000,
|
||||
transcription_enabled=True,
|
||||
vad_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
@@ -71,7 +72,7 @@ async def main():
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
|
||||
@@ -46,7 +46,7 @@ async def main():
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
llm = TogetherLLMService(
|
||||
@@ -88,7 +88,7 @@ async def main():
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
|
||||
@@ -81,7 +81,7 @@ async def main():
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
|
||||
@@ -18,7 +18,9 @@ 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.google import GoogleLLMService, GoogleSTTService, GoogleTTSService
|
||||
from pipecat.services.deepgram import DeepgramSTTService
|
||||
from pipecat.services.google import GoogleTTSService
|
||||
from pipecat.services.openai import OpenAILLMService
|
||||
from pipecat.transcriptions.language import Language
|
||||
from pipecat.transports.services.daily import DailyParams, DailyTransport
|
||||
|
||||
@@ -38,22 +40,21 @@ async def main():
|
||||
"Respond bot",
|
||||
DailyParams(
|
||||
audio_out_enabled=True,
|
||||
audio_out_sample_rate=24000,
|
||||
vad_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
vad_audio_passthrough=True,
|
||||
),
|
||||
)
|
||||
|
||||
stt = GoogleSTTService(
|
||||
params=GoogleSTTService.InputParams(languages=Language.EN_US),
|
||||
)
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
tts = GoogleTTSService(
|
||||
voice_id="en-US-Journey-F",
|
||||
params=GoogleTTSService.InputParams(language=Language.EN_US),
|
||||
)
|
||||
|
||||
llm = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"))
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
|
||||
|
||||
messages = [
|
||||
{
|
||||
@@ -79,7 +80,7 @@ async def main():
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
|
||||
@@ -51,7 +51,7 @@ async def main():
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
|
||||
@@ -80,7 +80,7 @@ async def main():
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
|
||||
@@ -76,7 +76,7 @@ async def main():
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
|
||||
@@ -19,7 +19,7 @@ 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 import OpenAILLMService
|
||||
from pipecat.services.rime import RimeTTSService
|
||||
from pipecat.services.rime import RimeHttpTTSService
|
||||
from pipecat.transports.services.daily import DailyParams, DailyTransport
|
||||
|
||||
load_dotenv(override=True)
|
||||
@@ -44,9 +44,10 @@ async def main():
|
||||
),
|
||||
)
|
||||
|
||||
tts = RimeTTSService(
|
||||
tts = RimeHttpTTSService(
|
||||
api_key=os.getenv("RIME_API_KEY", ""),
|
||||
voice_id="rex",
|
||||
params=RimeHttpTTSService.InputParams(reduce_latency=True),
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
|
||||
@@ -74,7 +75,7 @@ async def main():
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
|
||||
@@ -74,7 +74,7 @@ async def main():
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
|
||||
task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
|
||||
|
||||
@transport.event_handler("on_first_participant_joined")
|
||||
async def on_first_participant_joined(transport, participant):
|
||||
|
||||
@@ -213,10 +213,14 @@ async def main():
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
llm = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"), model="gemini-2.0-flash-001")
|
||||
llm = GoogleLLMService(
|
||||
model="gemini-1.5-flash-latest",
|
||||
# model="gemini-exp-1114",
|
||||
api_key=os.getenv("GOOGLE_API_KEY"),
|
||||
)
|
||||
|
||||
messages = [
|
||||
{
|
||||
@@ -251,7 +255,7 @@ async def main():
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
|
||||
@@ -74,7 +74,7 @@ async def main():
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
|
||||
@@ -1,90 +0,0 @@
|
||||
#
|
||||
# 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.services.cartesia import CartesiaTTSService
|
||||
from pipecat.services.ultravox import UltravoxSTTService
|
||||
from pipecat.transports.services.daily import DailyParams, DailyTransport
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
# NOTE: This example requires GPU resources to run efficiently.
|
||||
# The Ultravox model is compute-intensive and performs best with GPU acceleration.
|
||||
# This can be deployed on cloud GPU providers like Cerebrium.ai for optimal performance.
|
||||
|
||||
logger.remove(0)
|
||||
logger.add(sys.stderr, level="DEBUG")
|
||||
|
||||
# Want to initialize the ultravox processor since it takes time to load the model and dont
|
||||
# want to load it every time the pipeline is run
|
||||
ultravox_processor = UltravoxSTTService(
|
||||
model_size="fixie-ai/ultravox-v0_4_1-llama-3_1-8b",
|
||||
hf_token=os.getenv("HF_TOKEN"),
|
||||
)
|
||||
|
||||
|
||||
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=False,
|
||||
vad_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
vad_audio_passthrough=True,
|
||||
),
|
||||
)
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.environ.get("CARTESIA_API_KEY"),
|
||||
voice_id="97f4b8fb-f2fe-444b-bb9a-c109783a857a",
|
||||
)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
ultravox_processor,
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
),
|
||||
)
|
||||
|
||||
@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())
|
||||
@@ -1,102 +0,0 @@
|
||||
#
|
||||
# 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.neuphonic import NeuphonicHttpTTSService
|
||||
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,
|
||||
transcription_enabled=True,
|
||||
vad_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
),
|
||||
)
|
||||
|
||||
tts = NeuphonicHttpTTSService(
|
||||
api_key=os.getenv("NEUPHONIC_API_KEY"),
|
||||
voice_id="fc854436-2dac-4d21-aa69-ae17b54e98eb", # Emily
|
||||
)
|
||||
|
||||
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
|
||||
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()])
|
||||
|
||||
@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())
|
||||
@@ -1,102 +0,0 @@
|
||||
#
|
||||
# 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.neuphonic import NeuphonicTTSService
|
||||
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,
|
||||
transcription_enabled=True,
|
||||
vad_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
),
|
||||
)
|
||||
|
||||
tts = NeuphonicTTSService(
|
||||
api_key=os.getenv("NEUPHONIC_API_KEY"),
|
||||
voice_id="fc854436-2dac-4d21-aa69-ae17b54e98eb", # Emily
|
||||
)
|
||||
|
||||
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
|
||||
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()])
|
||||
|
||||
@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())
|
||||
@@ -1,110 +0,0 @@
|
||||
#
|
||||
# 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())
|
||||
@@ -48,6 +48,7 @@ async def main():
|
||||
region=os.getenv("AZURE_SPEECH_REGION"),
|
||||
)
|
||||
tts2 = ElevenLabsTTSService(
|
||||
aiohttp_session=session,
|
||||
api_key=os.getenv("ELEVENLABS_API_KEY"),
|
||||
voice_id="jBpfuIE2acCO8z3wKNLl",
|
||||
)
|
||||
|
||||
@@ -21,7 +21,7 @@ from pipecat.frames.frames import (
|
||||
)
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.pipeline.task import PipelineTask
|
||||
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
|
||||
from pipecat.transports.services.daily import DailyParams, DailyTransport
|
||||
|
||||
@@ -61,6 +61,7 @@ async def main():
|
||||
"Test",
|
||||
DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_in_sample_rate=24000,
|
||||
audio_out_enabled=True,
|
||||
camera_out_enabled=True,
|
||||
camera_out_is_live=True,
|
||||
@@ -77,13 +78,7 @@ async def main():
|
||||
|
||||
runner = PipelineRunner()
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
audio_in_sample_rate=24000,
|
||||
audio_out_sample_rate=24000,
|
||||
),
|
||||
)
|
||||
task = PipelineTask(pipeline)
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
@@ -22,9 +22,10 @@ from pipecat.frames.frames import (
|
||||
)
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.pipeline.task import PipelineTask
|
||||
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
|
||||
from pipecat.transports.local.tk import TkLocalTransport, TkTransportParams
|
||||
from pipecat.transports.base_transport import TransportParams
|
||||
from pipecat.transports.local.tk import TkLocalTransport
|
||||
from pipecat.transports.services.daily import DailyParams, DailyTransport
|
||||
|
||||
load_dotenv(override=True)
|
||||
@@ -61,12 +62,12 @@ async def main():
|
||||
tk_root.title("Local Mirror")
|
||||
|
||||
daily_transport = DailyTransport(
|
||||
room_url, token, "Test", DailyParams(audio_in_enabled=True)
|
||||
room_url, token, "Test", DailyParams(audio_in_enabled=True, audio_in_sample_rate=24000)
|
||||
)
|
||||
|
||||
tk_transport = TkLocalTransport(
|
||||
tk_root,
|
||||
TkTransportParams(
|
||||
TransportParams(
|
||||
audio_out_enabled=True,
|
||||
camera_out_enabled=True,
|
||||
camera_out_is_live=True,
|
||||
@@ -81,13 +82,7 @@ async def main():
|
||||
|
||||
pipeline = Pipeline([daily_transport.input(), MirrorProcessor(), tk_transport.output()])
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
audio_in_sample_rate=24000,
|
||||
audio_out_sample_rate=24000,
|
||||
),
|
||||
)
|
||||
task = PipelineTask(pipeline)
|
||||
|
||||
async def run_tk():
|
||||
while not task.has_finished():
|
||||
|
||||
@@ -47,7 +47,7 @@ async def main():
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
|
||||
@@ -76,7 +76,7 @@ async def main():
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
|
||||
task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
|
||||
|
||||
@transport.event_handler("on_first_participant_joined")
|
||||
async def on_first_participant_joined(transport, participant):
|
||||
|
||||
@@ -100,7 +100,7 @@ async def main():
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
messages = [
|
||||
|
||||
@@ -7,7 +7,6 @@
|
||||
import asyncio
|
||||
import os
|
||||
import sys
|
||||
from typing import Optional
|
||||
|
||||
import aiohttp
|
||||
from dotenv import load_dotenv
|
||||
@@ -33,7 +32,7 @@ logger.add(sys.stderr, level="DEBUG")
|
||||
|
||||
|
||||
class UserImageRequester(FrameProcessor):
|
||||
def __init__(self, participant_id: Optional[str] = None):
|
||||
def __init__(self, participant_id: str | None = None):
|
||||
super().__init__()
|
||||
self._participant_id = participant_id
|
||||
|
||||
@@ -77,7 +76,7 @@ async def main():
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
@transport.event_handler("on_first_participant_joined")
|
||||
|
||||
@@ -7,7 +7,6 @@
|
||||
import asyncio
|
||||
import os
|
||||
import sys
|
||||
from typing import Optional
|
||||
|
||||
import aiohttp
|
||||
from dotenv import load_dotenv
|
||||
@@ -33,7 +32,7 @@ logger.add(sys.stderr, level="DEBUG")
|
||||
|
||||
|
||||
class UserImageRequester(FrameProcessor):
|
||||
def __init__(self, participant_id: Optional[str] = None):
|
||||
def __init__(self, participant_id: str | None = None):
|
||||
super().__init__()
|
||||
self._participant_id = participant_id
|
||||
|
||||
@@ -73,11 +72,13 @@ async def main():
|
||||
|
||||
vision_aggregator = VisionImageFrameAggregator()
|
||||
|
||||
google = GoogleLLMService(model="gemini-2.0-flash-001", api_key=os.getenv("GOOGLE_API_KEY"))
|
||||
google = GoogleLLMService(
|
||||
model="gemini-1.5-flash-latest", api_key=os.getenv("GOOGLE_API_KEY")
|
||||
)
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
@transport.event_handler("on_first_participant_joined")
|
||||
|
||||
@@ -7,7 +7,6 @@
|
||||
import asyncio
|
||||
import os
|
||||
import sys
|
||||
from typing import Optional
|
||||
|
||||
import aiohttp
|
||||
from dotenv import load_dotenv
|
||||
@@ -33,7 +32,7 @@ logger.add(sys.stderr, level="DEBUG")
|
||||
|
||||
|
||||
class UserImageRequester(FrameProcessor):
|
||||
def __init__(self, participant_id: Optional[str] = None):
|
||||
def __init__(self, participant_id: str | None = None):
|
||||
super().__init__()
|
||||
self._participant_id = participant_id
|
||||
|
||||
@@ -76,7 +75,7 @@ async def main():
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
@transport.event_handler("on_first_participant_joined")
|
||||
|
||||
@@ -7,7 +7,6 @@
|
||||
import asyncio
|
||||
import os
|
||||
import sys
|
||||
from typing import Optional
|
||||
|
||||
import aiohttp
|
||||
from dotenv import load_dotenv
|
||||
@@ -33,7 +32,7 @@ logger.add(sys.stderr, level="DEBUG")
|
||||
|
||||
|
||||
class UserImageRequester(FrameProcessor):
|
||||
def __init__(self, participant_id: Optional[str] = None):
|
||||
def __init__(self, participant_id: str | None = None):
|
||||
super().__init__()
|
||||
self._participant_id = participant_id
|
||||
|
||||
@@ -76,7 +75,7 @@ async def main():
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
@transport.event_handler("on_first_participant_joined")
|
||||
|
||||
@@ -16,7 +16,8 @@ from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineTask
|
||||
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
|
||||
from pipecat.services.whisper import WhisperSTTService
|
||||
from pipecat.transports.local.audio import LocalAudioTransport, LocalAudioTransportParams
|
||||
from pipecat.transports.base_transport import TransportParams
|
||||
from pipecat.transports.local.audio import LocalAudioTransport
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
@@ -33,7 +34,7 @@ class TranscriptionLogger(FrameProcessor):
|
||||
|
||||
|
||||
async def main():
|
||||
transport = LocalAudioTransport(LocalAudioTransportParams(audio_in_enabled=True))
|
||||
transport = LocalAudioTransport(TransportParams(audio_in_enabled=True))
|
||||
|
||||
stt = WhisperSTTService()
|
||||
|
||||
@@ -43,7 +44,7 @@ async def main():
|
||||
|
||||
task = PipelineTask(pipeline)
|
||||
|
||||
runner = PipelineRunner(handle_sigint=False if sys.platform == "win32" else True)
|
||||
runner = PipelineRunner()
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
@@ -11,10 +11,9 @@ 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.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
|
||||
@@ -30,8 +29,13 @@ 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):
|
||||
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"})
|
||||
|
||||
|
||||
@@ -53,33 +57,38 @@ async def main():
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
|
||||
|
||||
# You can also register a function_name of None to get all functions
|
||||
# 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)
|
||||
llm.register_function(None, fetch_weather_from_api, start_callback=start_fetch_weather)
|
||||
|
||||
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",
|
||||
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"],
|
||||
},
|
||||
},
|
||||
"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": "system",
|
||||
@@ -103,7 +112,7 @@ async def main():
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
|
||||
@@ -13,8 +13,6 @@ 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.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
@@ -53,26 +51,30 @@ async def main():
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
llm = AnthropicLLMService(
|
||||
api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-7-sonnet-latest"
|
||||
api_key=os.getenv("ANTHROPIC_API_KEY"), model="claude-3-5-sonnet-20240620"
|
||||
)
|
||||
llm.register_function("get_weather", get_weather)
|
||||
|
||||
weather_function = FunctionSchema(
|
||||
name="get_weather",
|
||||
description="Get the current weather",
|
||||
properties={
|
||||
"location": {
|
||||
"type": "string",
|
||||
"description": "The city and state, e.g. San Francisco, CA",
|
||||
tools = [
|
||||
{
|
||||
"name": "get_weather",
|
||||
"description": "Get the current weather in a given location",
|
||||
"input_schema": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"location": {
|
||||
"type": "string",
|
||||
"description": "The city and state, e.g. San Francisco, CA",
|
||||
}
|
||||
},
|
||||
"required": ["location"],
|
||||
},
|
||||
},
|
||||
required=["location"],
|
||||
)
|
||||
tools = ToolsSchema(standard_tools=[weather_function])
|
||||
}
|
||||
]
|
||||
|
||||
# todo: test with very short initial user message
|
||||
|
||||
@@ -97,13 +99,7 @@ async def main():
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
),
|
||||
)
|
||||
task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True, enable_metrics=True))
|
||||
|
||||
@transport.event_handler("on_first_participant_joined")
|
||||
async def on_first_participant_joined(transport, participant):
|
||||
|
||||
@@ -13,8 +13,6 @@ 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.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
@@ -39,12 +37,7 @@ 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,
|
||||
function_name=function_name,
|
||||
tool_call_id=tool_call_id,
|
||||
text_content=question,
|
||||
)
|
||||
await llm.request_image_frame(user_id=video_participant_id, text_content=question)
|
||||
|
||||
|
||||
async def main():
|
||||
@@ -67,40 +60,48 @@ async def main():
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
llm = AnthropicLLMService(
|
||||
api_key=os.getenv("ANTHROPIC_API_KEY"),
|
||||
model="claude-3-7-sonnet-latest",
|
||||
# model="claude-3-5-sonnet-20240620",
|
||||
model="claude-3-5-sonnet-latest",
|
||||
enable_prompt_caching_beta=True,
|
||||
)
|
||||
llm.register_function("get_weather", get_weather)
|
||||
llm.register_function("get_image", get_image)
|
||||
|
||||
weather_function = FunctionSchema(
|
||||
name="get_weather",
|
||||
description="Get the current weather",
|
||||
properties={
|
||||
"location": {
|
||||
"type": "string",
|
||||
"description": "The city and state, e.g. San Francisco, CA",
|
||||
tools = [
|
||||
{
|
||||
"name": "get_weather",
|
||||
"description": "Get the current weather in a given location",
|
||||
"input_schema": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"location": {
|
||||
"type": "string",
|
||||
"description": "The city and state, e.g. San Francisco, CA",
|
||||
}
|
||||
},
|
||||
"required": ["location"],
|
||||
},
|
||||
},
|
||||
required=["location"],
|
||||
)
|
||||
get_image_function = FunctionSchema(
|
||||
name="get_image",
|
||||
description="Get an image from the video stream.",
|
||||
properties={
|
||||
"question": {
|
||||
"type": "string",
|
||||
"description": "The question that the user is asking about the image.",
|
||||
}
|
||||
{
|
||||
"name": "get_image",
|
||||
"description": "Get an image from the video stream.",
|
||||
"input_schema": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"question": {
|
||||
"type": "string",
|
||||
"description": "The question that the user is asking about the image.",
|
||||
}
|
||||
},
|
||||
"required": ["question"],
|
||||
},
|
||||
},
|
||||
required=["question"],
|
||||
)
|
||||
tools = ToolsSchema(standard_tools=[weather_function, get_image_function])
|
||||
]
|
||||
|
||||
# todo: test with very short initial user message
|
||||
|
||||
@@ -152,13 +153,7 @@ If you need to use a tool, simply use the tool. Do not tell the user the tool yo
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
),
|
||||
)
|
||||
task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True, enable_metrics=True))
|
||||
|
||||
@transport.event_handler("on_first_participant_joined")
|
||||
async def on_first_participant_joined(transport, participant):
|
||||
|
||||
@@ -11,10 +11,9 @@ 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.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
|
||||
@@ -31,8 +30,13 @@ 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):
|
||||
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"})
|
||||
|
||||
|
||||
@@ -54,34 +58,41 @@ async def main():
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
llm = TogetherLLMService(
|
||||
api_key=os.getenv("TOGETHER_API_KEY"),
|
||||
model="meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo",
|
||||
)
|
||||
# You can also register a function_name of None to get all functions
|
||||
# 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)
|
||||
llm.register_function(None, fetch_weather_from_api, start_callback=start_fetch_weather)
|
||||
|
||||
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",
|
||||
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"],
|
||||
},
|
||||
},
|
||||
"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": "system",
|
||||
|
||||
@@ -11,10 +11,9 @@ 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.adapters.schemas.function_schema import FunctionSchema
|
||||
from pipecat.adapters.schemas.tools_schema import ToolsSchema
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
@@ -39,12 +38,7 @@ 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,
|
||||
function_name=function_name,
|
||||
tool_call_id=tool_call_id,
|
||||
text_content=question,
|
||||
)
|
||||
await llm.request_image_frame(user_id=video_participant_id, text_content=question)
|
||||
|
||||
|
||||
async def main():
|
||||
@@ -65,41 +59,54 @@ async def main():
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
|
||||
llm.register_function("get_weather", get_weather)
|
||||
llm.register_function("get_image", get_image)
|
||||
|
||||
weather_function = FunctionSchema(
|
||||
name="get_weather",
|
||||
description="Get the current weather",
|
||||
properties={
|
||||
"location": {
|
||||
"type": "string",
|
||||
"description": "The city and state, e.g. San Francisco, CA",
|
||||
tools = [
|
||||
ChatCompletionToolParam(
|
||||
type="function",
|
||||
function={
|
||||
"name": "get_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"],
|
||||
},
|
||||
},
|
||||
"format": {
|
||||
"type": "string",
|
||||
"enum": ["celsius", "fahrenheit"],
|
||||
"description": "The temperature unit to use. Infer this from the user's location.",
|
||||
),
|
||||
ChatCompletionToolParam(
|
||||
type="function",
|
||||
function={
|
||||
"name": "get_image",
|
||||
"description": "Get an image from the video stream.",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"question": {
|
||||
"type": "string",
|
||||
"description": "The question to ask the AI to generate an image of",
|
||||
},
|
||||
},
|
||||
"required": ["question"],
|
||||
},
|
||||
},
|
||||
},
|
||||
required=["location"],
|
||||
)
|
||||
get_image_function = FunctionSchema(
|
||||
name="get_image",
|
||||
description="Get an image from the video stream.",
|
||||
properties={
|
||||
"question": {
|
||||
"type": "string",
|
||||
"description": "The question that the user is asking about the image.",
|
||||
}
|
||||
},
|
||||
required=["question"],
|
||||
)
|
||||
tools = ToolsSchema(standard_tools=[weather_function, get_image_function])
|
||||
),
|
||||
]
|
||||
|
||||
system_prompt = """\
|
||||
You are a helpful assistant who converses with a user and answers questions. Respond concisely to general questions.
|
||||
@@ -146,7 +153,7 @@ 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 task.queue_frames([context_aggregator.user().get_context_frame()])
|
||||
await tts.say("Hi! Ask me about the weather in San Francisco.")
|
||||
|
||||
runner = PipelineRunner()
|
||||
|
||||
|
||||
@@ -13,10 +13,7 @@ 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
|
||||
@@ -34,7 +31,6 @@ video_participant_id = None
|
||||
|
||||
|
||||
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.")
|
||||
|
||||
@@ -42,12 +38,7 @@ 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,
|
||||
function_name=function_name,
|
||||
tool_call_id=tool_call_id,
|
||||
text_content=question,
|
||||
)
|
||||
await llm.request_image_frame(user_id=video_participant_id, text_content=question)
|
||||
|
||||
|
||||
async def main():
|
||||
@@ -68,41 +59,56 @@ async def main():
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
llm = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"), model="gemini-2.0-flash-001")
|
||||
llm = GoogleLLMService(
|
||||
model="gemini-1.5-flash-latest",
|
||||
# model="gemini-exp-1114",
|
||||
api_key=os.getenv("GOOGLE_API_KEY"),
|
||||
)
|
||||
llm.register_function("get_weather", get_weather)
|
||||
llm.register_function("get_image", get_image)
|
||||
|
||||
weather_function = FunctionSchema(
|
||||
name="get_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"],
|
||||
)
|
||||
get_image_function = FunctionSchema(
|
||||
name="get_image",
|
||||
description="Get an image from the video stream.",
|
||||
properties={
|
||||
"question": {
|
||||
"type": "string",
|
||||
"description": "The question that the user is asking about the image.",
|
||||
}
|
||||
},
|
||||
required=["question"],
|
||||
)
|
||||
tools = ToolsSchema(standard_tools=[weather_function, get_image_function])
|
||||
tools = [
|
||||
{
|
||||
"function_declarations": [
|
||||
{
|
||||
"name": "get_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"],
|
||||
},
|
||||
},
|
||||
{
|
||||
"name": "get_image",
|
||||
"description": "Get and image from the camera or video stream.",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"question": {
|
||||
"type": "string",
|
||||
"description": "The question to to use when running inference on the acquired image.",
|
||||
},
|
||||
},
|
||||
"required": ["question"],
|
||||
},
|
||||
},
|
||||
]
|
||||
}
|
||||
]
|
||||
|
||||
system_prompt = """\
|
||||
You are a helpful assistant who converses with a user and answers questions. Respond concisely to general questions.
|
||||
@@ -143,7 +149,7 @@ indicate you should use the get_image tool are:
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
|
||||
@@ -11,17 +11,16 @@ 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.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.cartesia import CartesiaTTSService
|
||||
from pipecat.services.groq import GroqLLMService, GroqSTTService
|
||||
from pipecat.services.groq import GroqLLMService
|
||||
from pipecat.services.openai import OpenAILLMContext
|
||||
from pipecat.transports.services.daily import DailyParams, DailyTransport
|
||||
|
||||
@@ -31,8 +30,13 @@ 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):
|
||||
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"})
|
||||
|
||||
|
||||
@@ -46,41 +50,48 @@ async def main():
|
||||
"Respond bot",
|
||||
DailyParams(
|
||||
audio_out_enabled=True,
|
||||
transcription_enabled=True,
|
||||
vad_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(),
|
||||
vad_audio_passthrough=True,
|
||||
),
|
||||
)
|
||||
|
||||
stt = GroqSTTService(api_key=os.getenv("GROQ_API_KEY"), model="distil-whisper-large-v3-en")
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
llm = GroqLLMService(api_key=os.getenv("GROQ_API_KEY"), model="llama-3.3-70b-versatile")
|
||||
# You can also register a function_name of None to get all functions
|
||||
llm = GroqLLMService(
|
||||
api_key=os.getenv("GROQ_API_KEY"), model="llama3-groq-70b-8192-tool-use-preview"
|
||||
)
|
||||
# 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)
|
||||
llm.register_function(None, fetch_weather_from_api, start_callback=start_fetch_weather)
|
||||
|
||||
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",
|
||||
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",
|
||||
},
|
||||
"unit": {
|
||||
"type": "string",
|
||||
"enum": ["celsius", "fahrenheit"],
|
||||
"description": "The temperature unit to use. Infer this from the users location.",
|
||||
},
|
||||
},
|
||||
"required": ["location"],
|
||||
},
|
||||
},
|
||||
"format": {
|
||||
"type": "string",
|
||||
"enum": ["celsius", "fahrenheit"],
|
||||
"description": "The temperature unit to use. Infer this from the user's location.",
|
||||
},
|
||||
},
|
||||
required=["location"],
|
||||
)
|
||||
tools = ToolsSchema(standard_tools=[weather_function])
|
||||
)
|
||||
]
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
@@ -94,7 +105,6 @@ async def main():
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(),
|
||||
stt,
|
||||
context_aggregator.user(),
|
||||
llm,
|
||||
tts,
|
||||
@@ -105,7 +115,7 @@ async def main():
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
|
||||
@@ -11,11 +11,11 @@ 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.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
|
||||
@@ -30,6 +30,12 @@ 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"})
|
||||
|
||||
@@ -52,31 +58,38 @@ async def main():
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
llm = GrokLLMService(api_key=os.getenv("GROK_API_KEY"))
|
||||
# You can also register a function_name of None to get all functions
|
||||
# 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)
|
||||
llm.register_function(None, fetch_weather_from_api, start_callback=start_fetch_weather)
|
||||
|
||||
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",
|
||||
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"],
|
||||
},
|
||||
},
|
||||
"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": "system",
|
||||
@@ -100,7 +113,7 @@ async def main():
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
|
||||
@@ -11,10 +11,9 @@ 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.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
|
||||
@@ -31,8 +30,13 @@ 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):
|
||||
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"})
|
||||
|
||||
|
||||
@@ -54,7 +58,7 @@ async def main():
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
llm = AzureLLMService(
|
||||
@@ -62,27 +66,34 @@ async def main():
|
||||
endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
|
||||
model=os.getenv("AZURE_CHATGPT_MODEL"),
|
||||
)
|
||||
# You can also register a function_name of None to get all functions
|
||||
# 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)
|
||||
llm.register_function(None, fetch_weather_from_api, start_callback=start_fetch_weather)
|
||||
|
||||
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",
|
||||
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"],
|
||||
},
|
||||
},
|
||||
"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": "system",
|
||||
@@ -106,7 +117,7 @@ async def main():
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
|
||||
@@ -11,10 +11,9 @@ 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.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
|
||||
@@ -31,8 +30,13 @@ 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):
|
||||
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"})
|
||||
|
||||
|
||||
@@ -54,34 +58,41 @@ async def main():
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
)
|
||||
|
||||
llm = FireworksLLMService(
|
||||
api_key=os.getenv("FIREWORKS_API_KEY"),
|
||||
model="accounts/fireworks/models/llama-v3p1-405b-instruct",
|
||||
model="accounts/fireworks/models/firefunction-v2",
|
||||
)
|
||||
# You can also register a function_name of None to get all functions
|
||||
# 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)
|
||||
llm.register_function(None, fetch_weather_from_api, start_callback=start_fetch_weather)
|
||||
|
||||
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",
|
||||
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"],
|
||||
},
|
||||
},
|
||||
"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": "system",
|
||||
@@ -105,7 +116,7 @@ async def main():
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
|
||||
@@ -11,10 +11,9 @@ 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.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
|
||||
@@ -31,8 +30,13 @@ 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):
|
||||
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"})
|
||||
|
||||
|
||||
@@ -54,34 +58,41 @@ async def main():
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
# text_filters=[MarkdownTextFilter()],
|
||||
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
|
||||
# text_filter=MarkdownTextFilter(),
|
||||
)
|
||||
|
||||
llm = NimLLMService(
|
||||
api_key=os.getenv("NVIDIA_API_KEY"), model="meta/llama-3.3-70b-instruct"
|
||||
)
|
||||
# You can also register a function_name of None to get all functions
|
||||
# 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)
|
||||
llm.register_function(None, fetch_weather_from_api, start_callback=start_fetch_weather)
|
||||
|
||||
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",
|
||||
tools = [
|
||||
ChatCompletionToolParam(
|
||||
type="function",
|
||||
function={
|
||||
"name": "get_current_weather",
|
||||
"description": "Returns the current weather at a location, if one is specified, and defaults to the user's location.",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"location": {
|
||||
"type": "string",
|
||||
"description": "The location to find the weather of, or if not provided, it's the default location.",
|
||||
},
|
||||
"format": {
|
||||
"type": "string",
|
||||
"enum": ["celsius", "fahrenheit"],
|
||||
"description": "Whether to use SI or USCS units (celsius or fahrenheit).",
|
||||
},
|
||||
},
|
||||
"required": ["location", "format"],
|
||||
},
|
||||
},
|
||||
"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": "system",
|
||||
@@ -105,7 +116,7 @@ async def main():
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
PipelineParams(
|
||||
allow_interruptions=True,
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user