Compare commits
1 Commits
v0.0.92
...
hush/aggre
| Author | SHA1 | Date | |
|---|---|---|---|
|
0a163201ea |
22
.github/workflows/publish.yaml
vendored
22
.github/workflows/publish.yaml
vendored
@@ -5,25 +5,25 @@ on:
|
||||
inputs:
|
||||
gitref:
|
||||
type: string
|
||||
description: 'what git tag to build (e.g. v0.0.74)'
|
||||
description: "what git tag to build (e.g. v0.0.74)"
|
||||
required: true
|
||||
|
||||
jobs:
|
||||
build:
|
||||
name: 'Build and upload wheels'
|
||||
name: "Build and upload wheels"
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- name: Checkout repo
|
||||
uses: actions/checkout@v4
|
||||
with:
|
||||
ref: ${{ github.event.inputs.gitref }}
|
||||
|
||||
|
||||
- name: Install uv
|
||||
uses: astral-sh/setup-uv@v3
|
||||
with:
|
||||
version: 'latest'
|
||||
version: "latest"
|
||||
- name: Set up Python
|
||||
run: uv python install 3.12
|
||||
run: uv python install 3.10
|
||||
- name: Install development dependencies
|
||||
run: uv sync --group dev
|
||||
- name: Build project
|
||||
@@ -35,9 +35,9 @@ jobs:
|
||||
path: ./dist
|
||||
|
||||
publish-to-pypi:
|
||||
name: 'Publish to PyPI'
|
||||
name: "Publish to PyPI"
|
||||
runs-on: ubuntu-latest
|
||||
needs: [build]
|
||||
needs: [ build ]
|
||||
environment:
|
||||
name: pypi
|
||||
url: https://pypi.org/p/pipecat-ai
|
||||
@@ -56,12 +56,12 @@ jobs:
|
||||
print-hash: true
|
||||
|
||||
publish-to-test-pypi:
|
||||
name: 'Publish to Test PyPI'
|
||||
name: "Publish to Test PyPI"
|
||||
runs-on: ubuntu-latest
|
||||
needs: [build]
|
||||
needs: [ build ]
|
||||
environment:
|
||||
name: testpypi
|
||||
url: https://test.pypi.org/p/pipecat-ai
|
||||
url: https://pypi.org/p/pipecat-ai
|
||||
permissions:
|
||||
id-token: write
|
||||
steps:
|
||||
@@ -70,7 +70,7 @@ jobs:
|
||||
with:
|
||||
name: wheels
|
||||
path: ./dist
|
||||
- name: Publish to Test PyPI
|
||||
- name: Publish to PyPI
|
||||
uses: pypa/gh-action-pypi-publish@release/v1
|
||||
with:
|
||||
verbose: true
|
||||
|
||||
12
.github/workflows/publish_test.yaml
vendored
12
.github/workflows/publish_test.yaml
vendored
@@ -4,7 +4,7 @@ on: workflow_dispatch
|
||||
|
||||
jobs:
|
||||
build:
|
||||
name: 'Build and upload wheels'
|
||||
name: "Build and upload wheels"
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- name: Checkout repo
|
||||
@@ -15,9 +15,9 @@ jobs:
|
||||
- name: Install uv
|
||||
uses: astral-sh/setup-uv@v3
|
||||
with:
|
||||
version: 'latest'
|
||||
version: "latest"
|
||||
- name: Set up Python
|
||||
run: uv python install 3.12
|
||||
run: uv python install 3.10
|
||||
- name: Install development dependencies
|
||||
run: uv sync --group dev
|
||||
- name: Build project
|
||||
@@ -29,12 +29,12 @@ jobs:
|
||||
path: ./dist
|
||||
|
||||
publish-to-test-pypi:
|
||||
name: 'Publish to Test PyPI'
|
||||
name: "Publish to Test PyPI"
|
||||
runs-on: ubuntu-latest
|
||||
needs: [build]
|
||||
environment:
|
||||
name: testpypi
|
||||
url: https://test.pypi.org/p/pipecat-ai
|
||||
url: https://pypi.org/p/pipecat-ai
|
||||
permissions:
|
||||
id-token: write
|
||||
steps:
|
||||
@@ -43,7 +43,7 @@ jobs:
|
||||
with:
|
||||
name: wheels
|
||||
path: ./dist
|
||||
- name: Publish to Test PyPI
|
||||
- name: Publish to PyPI
|
||||
uses: pypa/gh-action-pypi-publish@release/v1
|
||||
with:
|
||||
verbose: true
|
||||
|
||||
580
CHANGELOG.md
580
CHANGELOG.md
@@ -5,574 +5,6 @@ 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).
|
||||
|
||||
## [0.0.92] - 2025-10-31 🎃 "The Haunted Edition" 👻
|
||||
|
||||
### Added
|
||||
|
||||
- Added a new `DeepgramHttpTTSService`, which delivers a meaningful reduction
|
||||
in latency when compared to the `DeepgramTTSService`.
|
||||
|
||||
- Add support for `speaking_rate` input parameter in `GoogleHttpTTSService`.
|
||||
|
||||
- Added `enable_speaker_diarization` and `enable_language_identification` to
|
||||
`SonioxSTTService`.
|
||||
|
||||
- Added `SpeechmaticsTTSService`, which uses Speechmatic's TTS API. Updated
|
||||
examples 07a\* to use the new TTS service.
|
||||
|
||||
- Added support for including images or audio to LLM context messages using
|
||||
`LLMContext.create_image_message()` or `LLMContext.create_image_url_message()`
|
||||
(not all LLMs support URLs) and `LLMContext.create_audio_message()`. For
|
||||
example, when creating `LLMMessagesAppendFrame`:
|
||||
|
||||
```python
|
||||
message = LLMContext.create_image_message(image=..., size= ...)
|
||||
await self.push_frame(LLMMessagesAppendFrame(messages=[message], run_llm=True))
|
||||
```
|
||||
|
||||
- New event handlers for the `DeepgramFluxSTTService`: `on_start_of_turn`,
|
||||
`on_turn_resumed`, `on_end_of_turn`, `on_eager_end_of_turn`, `on_update`.
|
||||
|
||||
- Added `generation_config` parameter support to `CartesiaTTSService` and
|
||||
`CartesiaHttpTTSService` for Cartesia Sonic-3 models. Includes a new
|
||||
`GenerationConfig` class with `volume` (0.5-2.0), `speed` (0.6-1.5),
|
||||
and `emotion` (60+ options) parameters for fine-grained speech generation
|
||||
control.
|
||||
|
||||
- Expanded support for univeral `LLMContext` to `OpenAIRealtimeLLMService`.
|
||||
As a reminder, the context-setup pattern when using `LLMContext` is:
|
||||
|
||||
```python
|
||||
context = LLMContext(messages, tools)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
```
|
||||
|
||||
(Note that even though `OpenAIRealtimeLLMService` now supports the universal
|
||||
`LLMContext`, it is not meant to be swapped out for another LLM service at
|
||||
runtime with `LLMSwitcher`.)
|
||||
|
||||
Note: `TranscriptionFrame`s and `InterimTranscriptionFrame`s now go upstream
|
||||
from `OpenAIRealtimeLLMService`, so if you're using `TranscriptProcessor`,
|
||||
say, you'll want to adjust accordingly:
|
||||
|
||||
```python
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(),
|
||||
context_aggregator.user(),
|
||||
|
||||
# BEFORE
|
||||
llm,
|
||||
transcript.user(),
|
||||
|
||||
# AFTER
|
||||
transcript.user(),
|
||||
llm,
|
||||
|
||||
transport.output(),
|
||||
transcript.assistant(),
|
||||
context_aggregator.assistant(),
|
||||
]
|
||||
)
|
||||
```
|
||||
|
||||
Also worth noting: whether or not you use the new context-setup pattern with
|
||||
`OpenAIRealtimeLLMService`, some types have changed under the hood:
|
||||
|
||||
```python
|
||||
## BEFORE:
|
||||
|
||||
# Context aggregator type
|
||||
context_aggregator: OpenAIContextAggregatorPair
|
||||
|
||||
# Context frame type
|
||||
frame: OpenAILLMContextFrame
|
||||
|
||||
# Context type
|
||||
context: OpenAIRealtimeLLMContext
|
||||
# or
|
||||
context: OpenAILLMContext
|
||||
|
||||
## AFTER:
|
||||
|
||||
# Context aggregator type
|
||||
context_aggregator: LLMContextAggregatorPair
|
||||
|
||||
# Context frame type
|
||||
frame: LLMContextFrame
|
||||
|
||||
# Context type
|
||||
context: LLMContext
|
||||
```
|
||||
|
||||
Also note that `RealtimeMessagesUpdateFrame` and
|
||||
`RealtimeFunctionCallResultFrame` have been deprecated, since they're no
|
||||
longer used by `OpenAIRealtimeLLMService`. OpenAI Realtime now works more
|
||||
like other LLM services in Pipecat, relying on updates to its context, pushed
|
||||
by context aggregators, to update its internal state. Listen for
|
||||
`LLMContextFrame`s for context updates.
|
||||
|
||||
Finally, `LLMTextFrame`s are no longer pushed from `OpenAIRealtimeLLMService`
|
||||
when it's configured with `output_modalities=['audio']`. If you need
|
||||
to process its output, listen for `TTSTextFrame`s instead.
|
||||
|
||||
- Expanded support for universal `LLMContext` to `GeminiLiveLLMService`.
|
||||
As a reminder, the context-setup pattern when using `LLMContext` is:
|
||||
|
||||
```python
|
||||
context = LLMContext(messages, tools)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
```
|
||||
|
||||
(Note that even though `GeminiLiveLLMService` now supports the universal
|
||||
`LLMContext`, it is not meant to be swapped out for another LLM service at
|
||||
runtime with `LLMSwitcher`.)
|
||||
|
||||
Worth noting: whether or not you use the new context-setup pattern with
|
||||
`GeminiLiveLLMService`, some types have changed under the hood:
|
||||
|
||||
```python
|
||||
## BEFORE:
|
||||
|
||||
# Context aggregator type
|
||||
context_aggregator: GeminiLiveContextAggregatorPair
|
||||
|
||||
# Context frame type
|
||||
frame: OpenAILLMContextFrame
|
||||
|
||||
# Context type
|
||||
context: GeminiLiveLLMContext
|
||||
# or
|
||||
context: OpenAILLMContext
|
||||
|
||||
## AFTER:
|
||||
|
||||
# Context aggregator type
|
||||
context_aggregator: LLMContextAggregatorPair
|
||||
|
||||
# Context frame type
|
||||
frame: LLMContextFrame
|
||||
|
||||
# Context type
|
||||
context: LLMContext
|
||||
```
|
||||
|
||||
Also note that `LLMTextFrame`s are no longer pushed from `GeminiLiveLLMService`
|
||||
when it's configured with `modalities=GeminiModalities.AUDIO`. If you need
|
||||
to process its output, listen for `TTSTextFrame`s instead.
|
||||
|
||||
### Changed
|
||||
|
||||
- The development runner's `/start` endpoint now supports passing
|
||||
`dailyRoomProperties` and `dailyMeetingTokenProperties` in the request body
|
||||
when `createDailyRoom` is true. Properties are validated against the
|
||||
`DailyRoomProperties` and `DailyMeetingTokenProperties` types respectively
|
||||
and passed to Daily's room and token creation APIs.
|
||||
|
||||
- `UserImageRawFrame` new fields `append_to_context` and `text`. The
|
||||
`append_to_context` field indicates if this image and text should be added to
|
||||
the LLM context (by the LLM assistant aggregator). The `text` field, if set,
|
||||
might also guide the LLM or the vision service on how to analyze the image.
|
||||
|
||||
- `UserImageRequestFrame` new fiels `append_to_context` and `text`. Both fields
|
||||
will be used to set the same fields on the captured `UserImageRawFrame`.
|
||||
|
||||
- `UserImageRequestFrame` don't require function call name and ID anymore.
|
||||
|
||||
- Updated `MoondreamService` to process `UserImageRawFrame`.
|
||||
|
||||
- `VisionService` expects `UserImageRawFrame` in order to analyze images.
|
||||
|
||||
- `DailyTransport` triggers `on_error` event if transcription can't be started
|
||||
or stopped.
|
||||
|
||||
- `DailyTransport` updates: `start_dialout()` now returns two values:
|
||||
`session_id` and `error`. `start_recording()` now returns two values:
|
||||
`stream_id` and `error`.
|
||||
|
||||
- Updated `daily-python` to 0.21.0.
|
||||
|
||||
- `SimliVideoService` now accepts `api_key` and `face_id` parameters directly,
|
||||
with optional `params` for `max_session_length` and `max_idle_time`
|
||||
configuration, aligning with other Pipecat service patterns.
|
||||
|
||||
- Updated the default model to `sonic-3` for `CartesiaTTSService` and
|
||||
`CartesiaHttpTTSService`.
|
||||
|
||||
- `FunctionFilter` now has a `filter_system_frames` arg, which controls whether
|
||||
or not SystemFrames are filtered.
|
||||
|
||||
- Upgraded `aws_sdk_bedrock_runtime` to v0.1.1 to resolve potential CPU issues
|
||||
when running `AWSNovaSonicLLMService`.
|
||||
|
||||
### Deprecated
|
||||
|
||||
- The `expect_stripped_words` parameter of `LLMAssistantAggregatorParams` is
|
||||
ignored when used with the newer `LLMAssistantAggregator`, which now handles
|
||||
word spacing automatically.
|
||||
|
||||
- `LLMService.request_image_frame()` is deprecated, push a
|
||||
`UserImageRequestFrame` instead.
|
||||
|
||||
- `UserResponseAggregator` is deprecated and will be removed in a future version.
|
||||
|
||||
- The `send_transcription_frames` argument to `OpenAIRealtimeLLMService` is
|
||||
deprecated. Transcription frames are now always sent. They go upstream, to be
|
||||
handled by the user context aggregator. See "Added" section for details.
|
||||
|
||||
- Types in `pipecat.services.openai.realtime.context` and
|
||||
`pipecat.services.openai.realtime.frames` are deprecated, as they're no
|
||||
longer used by `OpenAIRealtimeLLMService`. See "Added" section for details.
|
||||
|
||||
- `SimliVideoService` `simli_config` parameter is deprecated. Use `api_key` and
|
||||
`face_id` parameters instead.
|
||||
|
||||
### Removed
|
||||
|
||||
- Removed `enable_non_final_tokens` and `max_non_final_tokens_duration_ms` from
|
||||
`SonioxSTTService`.
|
||||
|
||||
- Removed the `aiohttp_session` arg from `SarvamTTSService` as it's no longer
|
||||
used.
|
||||
|
||||
### Fixed
|
||||
|
||||
- Fixed a `PipelineTask` issue that was causing an idle timeout for frames that
|
||||
were being generated but not reaching the end of the pipeline. Since the exact
|
||||
point when frames are discarded is unknown, we now monitor pipeline frames
|
||||
using an observer. If the observer detects frames are being generated, it will
|
||||
prevent the pipeline from being considered idle.
|
||||
|
||||
- Fixed an issue in `HumeTTSService` that was only using Octave 2, which does
|
||||
not support the `description` field. Now, if a description is provided, it
|
||||
switches to Octave 1.
|
||||
|
||||
- Fixed an issue where `DailyTransport` would timeout prematurely on join and on
|
||||
leave.
|
||||
|
||||
- Fixed an issue in the runner where starting a DailyTransport room via
|
||||
`/start` didn't support using the `DAILY_SAMPLE_ROOM_URL` env var.
|
||||
|
||||
- Fixed an issue in `ServiceSwitcher` where the `STTService`s would result in
|
||||
all STT services producing `TranscriptionFrame`s.
|
||||
|
||||
### Other
|
||||
|
||||
- Updated all vision 12-series foundational examples to load images from a file.
|
||||
|
||||
- Added 14-series video examples for different services. These new examples
|
||||
request an image from the user camera through a function call.
|
||||
|
||||
## [0.0.91] - 2025-10-21
|
||||
|
||||
### Added
|
||||
|
||||
- It is now possible to start a bot from the `/start` endpoint when using the
|
||||
runner Daily's transport. This follows the Pipecat Cloud format with
|
||||
`createDailyRoom` and `body` fields in the POST request body.
|
||||
|
||||
- Added an ellipsis character (`…`) to the end of sentence detection in the
|
||||
string utils.
|
||||
|
||||
- Expanded support for universal `LLMContext` to `AWSNovaSonicLLMService`.
|
||||
As a reminder, the context-setup pattern when using `LLMContext` is:
|
||||
|
||||
```python
|
||||
context = LLMContext(messages, tools)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
```
|
||||
|
||||
(Note that even though `AWSNovaSonicLLMService` now supports the universal
|
||||
`LLMContext`, it is not meant to be swapped out for another LLM service at
|
||||
runtime with `LLMSwitcher`.)
|
||||
|
||||
Worth noting: whether or not you use the new context-setup pattern with
|
||||
`AWSNovaSonicLLMService`, some types have changed under the hood:
|
||||
|
||||
```python
|
||||
## BEFORE:
|
||||
|
||||
# Context aggregator type
|
||||
context_aggregator: AWSNovaSonicContextAggregatorPair
|
||||
|
||||
# Context frame type
|
||||
frame: OpenAILLMContextFrame
|
||||
|
||||
# Context type
|
||||
context: AWSNovaSonicLLMContext
|
||||
# or
|
||||
context: OpenAILLMContext
|
||||
|
||||
## AFTER:
|
||||
|
||||
# Context aggregator type
|
||||
context_aggregator: LLMContextAggregatorPair
|
||||
|
||||
# Context frame type
|
||||
frame: LLMContextFrame
|
||||
|
||||
# Context type
|
||||
context: LLMContext
|
||||
```
|
||||
|
||||
- Added support for `bulbul:v3` model in `SarvamTTSService` and
|
||||
`SarvamHttpTTSService`.
|
||||
|
||||
- Added `keyterms_prompt` parameter to `AssemblyAIConnectionParams`.
|
||||
|
||||
- Added `speech_model` parameter to `AssemblyAIConnectionParams` to access the
|
||||
multilingual model.
|
||||
|
||||
- Added support for trickle ICE to the `SmallWebRTCTransport`.
|
||||
|
||||
- Added support for updating `OpenAITTSService` settings (`instructions` and
|
||||
`speed`) at runtime via `TTSUpdateSettingsFrame`.
|
||||
|
||||
- Added `--whatsapp` flag to runner to better surface WhatsApp transport logs.
|
||||
|
||||
- Added `on_connected` and `on_disconnected` events to TTS and STT
|
||||
websocket-based services.
|
||||
|
||||
- Added an `aggregate_sentences` arg in `ElevenLabsHttpTTSService`, where the
|
||||
default value is True.
|
||||
|
||||
- Added a `room_properties` arg to the Daily runner's `configure()` method,
|
||||
allowing `DailyRoomProperties` to be provided.
|
||||
|
||||
- The runner `--folder` argument now supports downloading files from
|
||||
subdirectories.
|
||||
|
||||
### Changed
|
||||
|
||||
- `RunnerArguments` now include the `body` field, so there's no need to add it
|
||||
to subclasses. Also, all `RunnerArguments` fields are now keyword-only.
|
||||
|
||||
- `CartesiaSTTService` now inherits from `WebsocketSTTService`.
|
||||
|
||||
- Package upgrades:
|
||||
|
||||
- `daily-python` upgraded to 0.20.0.
|
||||
- `openai` upgraded to support up to 2.x.x.
|
||||
- `openpipe` upgraded to support up to 5.x.x.
|
||||
|
||||
- `SpeechmaticsSTTService` updated dependencies for `speechmatics-rt>=0.5.0`.
|
||||
|
||||
### Deprecated
|
||||
|
||||
- The `send_transcription_frames` argument to `AWSNovaSonicLLMService` is
|
||||
deprecated. Transcription frames are now always sent. They go upstream, to be
|
||||
handled by the user context aggregator. See "Added" section for details.
|
||||
|
||||
- Types in `pipecat.services.aws.nova_sonic.context` are deprecated, as they're
|
||||
no longer used by `AWSNovaSonicLLMService`. See "Added" section for
|
||||
details.
|
||||
|
||||
### Fixed
|
||||
|
||||
- Fixed an issue where the `RTVIProcessor` was sending duplicate
|
||||
`UserStartedSpeakingFrame` and `UserStoppedSpeakingFrame` messages.
|
||||
|
||||
- Fixed an issue in `AWSBedrockLLMService` where both `temperature` and `top_p`
|
||||
were always sent together, causing conflicts with models like Claude Sonnet 4.5
|
||||
that don't allow both parameters simultaneously. The service now only includes
|
||||
inference parameters that are explicitly set, and `InputParams` defaults have
|
||||
been changed to `None` to rely on AWS Bedrock's built-in model defaults.
|
||||
|
||||
- Fixed an issue in `RivaSegmentedSTTService` where a runtime error occurred due
|
||||
to a mismatch in the `_handle_transcription` method's signature.
|
||||
|
||||
- Fixed multiple pipeline task cancellation issues. `asyncio.CancelledError` is
|
||||
now handled properly in `PipelineTask` making it possible to cancel an asyncio
|
||||
task that it's executing a `PipelineRunner` cleanly. Also,
|
||||
`PipelineTask.cancel()` does not block anymore waiting for the `CancelFrame`
|
||||
to reach the end of the pipeline (going back to the behavior in < 0.0.83).
|
||||
|
||||
- Fixed an issue in `ElevenLabsTTSService` and `ElevenLabsHttpTTSService` where
|
||||
the Flash models would split words, resulting in a space being inserted
|
||||
between words.
|
||||
|
||||
- Fixed an issue where audio filters' `stop()` would not be called when using
|
||||
`CancelFrame`.
|
||||
|
||||
- Fixed an issue in `ElevenLabsHttpTTSService`, where
|
||||
`apply_text_normalization` was incorrectly set as a query parameter. It's now
|
||||
being added as a request parameter.
|
||||
|
||||
- Fixed an issue where `RimeHttpTTSService` and `PiperTTSService` could generate
|
||||
incorrectly 16-bit aligned audio frames, potentially leading to internal
|
||||
errors or static audio.
|
||||
|
||||
- Fixed an issue in `SpeechmaticsSTTService` where `AdditionalVocabEntry` items
|
||||
needed to have `sounds_like` for the session to start.
|
||||
|
||||
### Other
|
||||
|
||||
- Added foundational example `47-sentry-metrics.py`, demonstrating how to use the
|
||||
`SentryMetrics` processor.
|
||||
|
||||
- Added foundational example `14x-function-calling-openpipe.py`.
|
||||
|
||||
## [0.0.90] - 2025-10-10
|
||||
|
||||
### Added
|
||||
|
||||
- Added audio filter `KrispVivaFilter` using the Krisp VIVA SDK.
|
||||
|
||||
- Added `--folder` argument to the runner, allowing files saved in that folder
|
||||
to be downloaded from `http://HOST:PORT/file/FILE`.
|
||||
|
||||
- Added `GeminiLiveVertexLLMService`, for accessing Gemini Live via Google
|
||||
Vertex AI.
|
||||
|
||||
- Added some new configuration options to `GeminiLiveLLMService`:
|
||||
|
||||
- `thinking`
|
||||
- `enable_affective_dialog`
|
||||
- `proactivity`
|
||||
|
||||
Note that these new configuration options require using a newer model than
|
||||
the default, like "gemini-2.5-flash-native-audio-preview-09-2025". The last
|
||||
two require specifying `http_options=HttpOptions(api_version="v1alpha")`.
|
||||
|
||||
- Added `on_pipeline_error` event to `PipelineTask`. This event will get fired
|
||||
when an `ErrorFrame` is pushed (use `FrameProcessor.push_error()`).
|
||||
|
||||
```python
|
||||
@task.event_handler("on_pipeline_error")
|
||||
async def on_pipeline_error(task: PipelineTask, frame: ErrorFrame):
|
||||
...
|
||||
```
|
||||
|
||||
- Added a `service_tier` `InputParam` to the `BaseOpenAILLMService`. This
|
||||
parameter can influence the latency of the response. For example `"priority"`
|
||||
will result in faster completions, but in exchange for a higher price.
|
||||
|
||||
### Changed
|
||||
|
||||
- Updated `GeminiLiveLLMService` to use the `google-genai` library rather than
|
||||
use WebSockets directly.
|
||||
|
||||
### Deprecated
|
||||
|
||||
- `LivekitFrameSerializer` is now deprecated. Use `LiveKitTransport` instead.
|
||||
|
||||
- `pipecat.service.openai_realtime` is now deprecated, use
|
||||
`pipecat.services.openai.realtime` instead or
|
||||
`pipecat.services.azure.realtime` for Azure Realtime.
|
||||
|
||||
- `pipecat.service.aws_nova_sonic` is now deprecated, use
|
||||
`pipecat.services.aws.nova_sonic` instead.
|
||||
|
||||
- `GeminiMultimodalLiveLLMService` is now deprecated, use
|
||||
`GeminiLiveLLMService`.
|
||||
|
||||
### Fixed
|
||||
|
||||
- Fixed a `GoogleVertexLLMService` issue that would generate an error if no
|
||||
token information was returned.
|
||||
|
||||
- `GeminiLiveLLMService` will now end gracefully (i.e. after the bot has
|
||||
finished) upon receiving an `EndFrame`.
|
||||
|
||||
- `GeminiLiveLLMService` will try to seamlessly reconnect when it loses its
|
||||
connection.
|
||||
|
||||
## [0.0.89] - 2025-10-07
|
||||
|
||||
### Fixed
|
||||
|
||||
- Reverted a change introduced in 0.0.88 that was causing pipelines to be frozen
|
||||
when using interruption strategies and processors that block interruption
|
||||
frames (e.g. `STTMuteFilter`).
|
||||
|
||||
## [0.0.88] - 2025-10-07
|
||||
|
||||
### Added
|
||||
|
||||
- Added support for Nano Banana models to `GoogleLLMService`. For example, you
|
||||
can now use the `gemini-2.5-flash-image` model to generate images.
|
||||
|
||||
- Added `HumeTTSService` for text-to-speech synthesis using Hume AI's expressive
|
||||
voice models. Provides high-quality, emotionally expressive speech synthesis
|
||||
with support for various voice models. Includes example in
|
||||
`examples/foundational/07ad-interruptible-hume.py`. Use with:
|
||||
`uv pip install pipecat-ai[hume]`.
|
||||
|
||||
### Changed
|
||||
|
||||
- Updated default `GoogleLLMService` model to `gemini-2.5-flash`.
|
||||
|
||||
### Deprecated
|
||||
|
||||
- PlayHT is shutting down their API on December 31st, 2025. As a result,
|
||||
`PlayHTTTSService` and `PlayHTHttpTTSService` are deprecated and will be
|
||||
removed in a future version.
|
||||
|
||||
### Fixed
|
||||
|
||||
- Fixed an issue with `AWSNovaSonicLLMService` where the client wouldn't
|
||||
connect due to a breaking change in the AWS dependency chain.
|
||||
|
||||
- `PermissionError` is now caught if NLTK's `punkt_tab` can't be downloaded.
|
||||
|
||||
- Fixed an issue that would cause wrong user/assistant context ordering when
|
||||
using interruption strategies.
|
||||
|
||||
- Fixed RTVI incoming message handling, broken in 0.0.87.
|
||||
|
||||
## [0.0.87] - 2025-10-02
|
||||
|
||||
### Added
|
||||
|
||||
- Added `WebsocketSTTService` base class for websocket-based STT services.
|
||||
Combines STT functionality with websocket connectivity, providing automatic
|
||||
error handling and reconnection capabilities with exponential backoff.
|
||||
|
||||
- Added `DeepgramFluxSTTService` for real-time speech recognition using
|
||||
Deepgram's Flux WebSocket API. Flux understands conversational flow and
|
||||
automatically handles turn-taking.
|
||||
|
||||
- Added RTVI messages for user/bot audio levels and system logs.
|
||||
|
||||
- Include OpenAI-based LLM services cached tokens to `MetricsFrame`.
|
||||
|
||||
### Changed
|
||||
|
||||
- Updated the default model for `AnthropicLLMService` to
|
||||
`claude-sonnet-4-5-20250929`.
|
||||
|
||||
### Deprecated
|
||||
|
||||
- `DailyTransportMessageFrame` and `DailyTransportMessageUrgentFrame` are
|
||||
deprecated, use `DailyOutputTransportMessageFrame` and
|
||||
`DailyOutputTransportMessageUrgentFrame` respectively instead.
|
||||
|
||||
- `LiveKitTransportMessageFrame` and `LiveKitTransportMessageUrgentFrame` are
|
||||
deprecated, use `LiveKitOutputTransportMessageFrame` and
|
||||
`LiveKitOutputTransportMessageUrgentFrame` respectively instead.
|
||||
|
||||
- `TransportMessageFrame` and `TransportMessageUrgentFrame` are deprecated, use
|
||||
`OutputTransportMessageFrame` and `OutputTransportMessageUrgentFrame`
|
||||
respectively instead.
|
||||
|
||||
- `InputTransportMessageUrgentFrame` is deprecated, use
|
||||
`InputTransportMessageFrame` instead.
|
||||
|
||||
- `DailyUpdateRemoteParticipantsFrame` is deprecated and will be removed in a
|
||||
future version. Instead, create your own custom frame and handle it in the
|
||||
`@transport.output().event_handler("on_after_push_frame")` event handler or a
|
||||
custom processor.
|
||||
|
||||
## Fixed
|
||||
|
||||
- Fixed an issue in `AWSBedrockLLMService` where timeout exceptions weren't
|
||||
being detected.
|
||||
|
||||
- Fixed a `PipelineTask` issue that could prevent the application to exit if
|
||||
`task.cancel()` was called when the task was already finished.
|
||||
|
||||
- Fixed an issue where local SmartTurn was not being ran in a separate thread.
|
||||
|
||||
## [0.0.86] - 2025-09-24
|
||||
|
||||
### Added
|
||||
@@ -1403,8 +835,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
|
||||
|
||||
### Added
|
||||
|
||||
- Added `SonioxSTTService` using Soniox's STT websocket API.
|
||||
|
||||
- Added `enable_emulated_vad_interruptions` to `LLMUserAggregatorParams`.
|
||||
When user speech is emulated (e.g. when a transcription is received but
|
||||
VAD doesn't detect speech), this parameter controls whether the emulated
|
||||
@@ -1896,7 +1326,7 @@ quality and critical bugs impacting `ParallelPipelines` functionality.**
|
||||
- Added `session_token` parameter to `AWSNovaSonicLLMService`.
|
||||
|
||||
- Added Gemini Multimodal Live File API for uploading, fetching, listing, and
|
||||
deleting files. See `26f-gemini-live-files-api.py` for example usage.
|
||||
deleting files. See `26f-gemini-multimodal-live-files-api.py` for example usage.
|
||||
|
||||
### Changed
|
||||
|
||||
@@ -3902,7 +3332,7 @@ stt = DeepgramSTTService(..., live_options=LiveOptions(model="nova-2-general"))
|
||||
- Added the new modalities option and helper function to set Gemini output
|
||||
modalities.
|
||||
|
||||
- Added `examples/foundational/26d-gemini-live-text.py` which is
|
||||
- Added `examples/foundational/26d-gemini-multimodal-live-text.py` which is
|
||||
using Gemini as TEXT modality and using another TTS provider for TTS process.
|
||||
|
||||
### Changed
|
||||
@@ -4089,9 +3519,9 @@ stt = DeepgramSTTService(..., live_options=LiveOptions(model="nova-2-general"))
|
||||
- Added new foundational examples for `GeminiMultimodalLiveLLMService`:
|
||||
|
||||
- `26-gemini-multimodal-live.py`
|
||||
- `26a-gemini-live-transcription.py`
|
||||
- `26b-gemini-live-video.py`
|
||||
- `26c-gemini-live-video.py`
|
||||
- `26a-gemini-multimodal-live-transcription.py`
|
||||
- `26b-gemini-multimodal-live-video.py`
|
||||
- `26c-gemini-multimodal-live-video.py`
|
||||
|
||||
- Added `SimliVideoService`. This is an integration for Simli AI avatars.
|
||||
(see https://www.simli.com)
|
||||
|
||||
@@ -1,336 +0,0 @@
|
||||
# Community Integrations Guide
|
||||
|
||||
Pipecat welcomes community-maintained integrations! As our ecosystem grows, we've established a process for any developer to create and maintain their own service integrations while ensuring discoverability for the Pipecat community.
|
||||
|
||||
## Overview
|
||||
|
||||
**What we support:** Community-maintained integrations that live in separate repositories and are maintained by their authors.
|
||||
|
||||
**What we don't do:** The Pipecat team does not code review, test, or maintain community integrations. We provide guidance and list approved integrations for discoverability.
|
||||
|
||||
**Why this approach:** This allows the community to move quickly while keeping the Pipecat core team focused on maintaining the framework itself.
|
||||
|
||||
## Submitting your Integration
|
||||
|
||||
To be listed as an official community integration, follow these steps:
|
||||
|
||||
### Step 1: Build Your Integration
|
||||
|
||||
Create your integration following the patterns and examples shown in the "Integration Patterns and Examples" section below.
|
||||
|
||||
### Step 2: Set Up Your Repository
|
||||
|
||||
Your repository must contain these components:
|
||||
|
||||
- **Source code** - Complete implementation following Pipecat patterns
|
||||
- **Foundational example** - Single file example showing basic usage (see [Pipecat examples](https://github.com/pipecat-ai/pipecat/tree/main/examples/foundational))
|
||||
- **README.md** - Must include:
|
||||
|
||||
- Introduction and explanation of your integration
|
||||
- Installation instructions
|
||||
- Usage instructions with Pipecat Pipeline
|
||||
- How to run your example
|
||||
- Pipecat version compatibility (e.g., "Tested with Pipecat v0.0.86")
|
||||
- Company attribution: If you work for the company providing the service, please mention this in your README. This helps build confidence that the integration will be actively maintained.
|
||||
|
||||
- **LICENSE** - Permissive license (BSD-2 like Pipecat, or equivalent open source terms)
|
||||
- **Code documentation** - Source code with docstrings (we recommend following [Pipecat's docstring conventions](https://github.com/pipecat-ai/pipecat/blob/main/CONTRIBUTING.md#docstring-conventions))
|
||||
- **Changelog** - Maintain a changelog for version updates
|
||||
|
||||
### Step 3: Join Discord
|
||||
|
||||
Join our Discord: https://discord.gg/pipecat
|
||||
|
||||
### Step 4: Submit for Listing
|
||||
|
||||
Submit a pull request to add your integration to our [Community Integrations documentation page](https://docs.pipecat.ai/server/services/community-integrations).
|
||||
|
||||
**To submit:**
|
||||
|
||||
1. Fork the [Pipecat docs repository](https://github.com/pipecat-ai/docs)
|
||||
2. Edit the file `server/services/community-integrations.mdx`
|
||||
3. Add your integration to the appropriate service category table with:
|
||||
- Service name
|
||||
- Link to your repository
|
||||
- Maintainer GitHub username(s)
|
||||
4. Include a link to your demo video (approx 30-60 seconds) in your PR description showing:
|
||||
- Core functionality of your integration
|
||||
- Handling of an interruption (if applicable to service type)
|
||||
5. Submit your pull request
|
||||
|
||||
Once your PR is submitted, post in the `#community-integrations` Discord channel to let us know.
|
||||
|
||||
## Integration Patterns and Examples
|
||||
|
||||
### STT (Speech-to-Text) Services
|
||||
|
||||
#### Websocket-based Services
|
||||
|
||||
**Base class:** `STTService`
|
||||
|
||||
**Examples:**
|
||||
|
||||
- [DeepgramSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/deepgram/stt.py)
|
||||
- [SpeechmaticsSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/speechmatics/stt.py)
|
||||
|
||||
#### File-based Services
|
||||
|
||||
**Base class:** `SegmentedSTTService`
|
||||
|
||||
**Examples:**
|
||||
|
||||
- [RivaSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/riva/stt.py)
|
||||
- [FalSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/fal/stt.py)
|
||||
|
||||
#### Key requirements:
|
||||
|
||||
- STT services should push `InterimTranscriptionFrames` and `TranscriptionFrames`
|
||||
- If confidence values are available, filter for values >50% confidence
|
||||
|
||||
### LLM (Large Language Model) Services
|
||||
|
||||
#### OpenAI-Compatible Services
|
||||
|
||||
**Base class:** `OpenAILLMService`
|
||||
|
||||
**Examples:**
|
||||
|
||||
- [AzureLLMService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/azure/llm.py)
|
||||
- [GrokLLMService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/grok/llm.py) - Shows overriding the base class where needed
|
||||
|
||||
#### Non-OpenAI Compatible Services
|
||||
|
||||
**Requires:** Full implementation
|
||||
|
||||
**Examples:**
|
||||
|
||||
- [AnthropicLLMService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/anthropic/llm.py)
|
||||
- [GoogleLLMService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/google/llm.py)
|
||||
|
||||
#### Key requirements:
|
||||
|
||||
- **Frame sequence:** Output must follow this frame sequence pattern:
|
||||
|
||||
- `LLMFullResponseStartFrame` - Signals the start of an LLM response
|
||||
- `LLMTextFrame` - Contains LLM content, typically streamed as tokens
|
||||
- `LLMFullResponseEndFrame` - Signals the end of an LLM response
|
||||
|
||||
- **Context aggregation:** Implement context aggregation to collect user and assistant content:
|
||||
- Aggregators come in pairs with a `user()` instance and `assistant()` instance
|
||||
- Context must adhere to the `LLMContext` universal format
|
||||
- Aggregators should handle adding messages, function calls, and images to the context
|
||||
|
||||
### TTS (Text-to-Speech) Services
|
||||
|
||||
#### AudioContextWordTTSService
|
||||
|
||||
**Use for:** Websocket-based services supporting word/timestamp alignment
|
||||
|
||||
**Example:**
|
||||
|
||||
- [CartesiaTTSService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/cartesia/tts.py)
|
||||
|
||||
#### InterruptibleTTSService
|
||||
|
||||
**Use for:** Websocket-based services without word/timestamp alignment, requiring disconnection on interruption
|
||||
|
||||
**Example:**
|
||||
|
||||
- [SarvamTTSService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/sarvam/tts.py)
|
||||
|
||||
#### WordTTSService
|
||||
|
||||
**Use for:** HTTP-based services supporting word/timestamp alignment
|
||||
|
||||
**Example:**
|
||||
|
||||
- [ElevenLabsHttpTTSService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/elevenlabs/tts.py)
|
||||
|
||||
#### TTSService
|
||||
|
||||
**Use for:** HTTP-based services without word/timestamp alignment
|
||||
|
||||
**Example:**
|
||||
|
||||
- [GoogleHttpTTSService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/google/tts.py)
|
||||
|
||||
#### Key requirements:
|
||||
|
||||
- For websocket services, use asyncio WebSocket implementation (required for v13+ support)
|
||||
- Handle idle service timeouts with keepalives
|
||||
- TTSServices push both audio (`TTSRawAudioFrame`) and text (`TTSTextFrame`) frames
|
||||
|
||||
### Telephony Serializers
|
||||
|
||||
Pipecat supports telephony provider integration using websocket connections to exchange MediaStreams. These services use a FrameSerializer to serialize and deserialize inputs from the FastAPIWebsocketTransport.
|
||||
|
||||
**Examples:**
|
||||
|
||||
- [Twilio](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/serializers/twilio.py)
|
||||
- [Telnyx](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/serializers/telnyx.py)
|
||||
|
||||
#### Key requirements:
|
||||
|
||||
- Include hang-up functionality using the provider's native API, ideally using `aiohttp`
|
||||
- Support DTMF (dual-tone multi-frequency) events if the provider supports them:
|
||||
- Deserialize DTMF events from the provider's protocol to `InputDTMFFrame`
|
||||
- Use `KeypadEntry` enum for valid keypad entries (0-9, \*, #, A-D)
|
||||
- Handle invalid DTMF digits gracefully by returning `None`
|
||||
|
||||
### Image Generation Services
|
||||
|
||||
**Base class:** `ImageGenService`
|
||||
|
||||
**Examples:**
|
||||
|
||||
- [FalImageGenService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/fal/image.py)
|
||||
- [GoogleImageGenService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/google/image.py)
|
||||
|
||||
#### Key requirements:
|
||||
|
||||
- Must implement `run_image_gen` method returning an `AsyncGenerator`
|
||||
|
||||
### Vision Services
|
||||
|
||||
Vision services process images and provide analysis such as descriptions, object detection, or visual question answering.
|
||||
|
||||
**Base class:** `VisionService`
|
||||
|
||||
**Example:**
|
||||
|
||||
- [MoondreamVisionService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/moondream/vision.py)
|
||||
|
||||
#### Key requirements:
|
||||
|
||||
- Must implement `run_vision` method that takes an `LLMContext` and returns an `AsyncGenerator[Frame, None]`
|
||||
- The method processes the latest image in the context and yields frames with analysis results
|
||||
- Typically yields `TextFrame` objects containing descriptions or answers
|
||||
|
||||
## Implementation Guidelines
|
||||
|
||||
### Naming Conventions
|
||||
|
||||
- **STT:** `VendorSTTService`
|
||||
- **LLM:** `VendorLLMService`
|
||||
- **TTS:**
|
||||
- Websocket: `VendorTTSService`
|
||||
- HTTP: `VendorHttpTTSService`
|
||||
- **Image:** `VendorImageGenService`
|
||||
- **Vision:** `VendorVisionService`
|
||||
- **Telephony:** `VendorFrameSerializer`
|
||||
|
||||
### Metrics Support
|
||||
|
||||
Enable metrics in your service:
|
||||
|
||||
```python
|
||||
def can_generate_metrics(self) -> bool:
|
||||
"""Check if this service can generate processing metrics.
|
||||
|
||||
Returns:
|
||||
True, as this service supports metrics.
|
||||
"""
|
||||
return True
|
||||
```
|
||||
|
||||
### Dynamic Settings Updates
|
||||
|
||||
STT, LLM, and TTS services support `ServiceUpdateSettingsFrame` for dynamic configuration changes. The base STTService has an `_update_settings()` method that handles settings, and the private `_settings` `Dict` is used to store settings and provide access to the subclass.
|
||||
|
||||
```python
|
||||
async def set_language(self, language: Language):
|
||||
"""Set the recognition language and reconnect.
|
||||
|
||||
Args:
|
||||
language: The language to use for speech recognition.
|
||||
"""
|
||||
logger.info(f"Switching STT language to: [{language}]")
|
||||
self._settings["language"] = language
|
||||
await self._disconnect()
|
||||
await self._connect()
|
||||
```
|
||||
|
||||
Note that, in this example, Deepgram requires the websocket connection be disconnected and reconnected to reinitialize the service with the new value. Consider if your service requires reconnection.
|
||||
|
||||
### Sample Rate Handling
|
||||
|
||||
Sample rates are set via PipelineParams and passed to each frame processor at initialization. The pattern is to _not_ set the sample rate value in the constructor of a given service. Instead, use the `start()` method to initialize sample rates from the frame:
|
||||
|
||||
```python
|
||||
async def start(self, frame: StartFrame):
|
||||
"""Start the service."""
|
||||
await super().start(frame)
|
||||
self._settings["output_format"]["sample_rate"] = self.sample_rate
|
||||
await self._connect()
|
||||
```
|
||||
|
||||
Note that `self.sample_rate` is a `@property` set in the TTSService base class, which provides access to the private sample rate value obtained from the StartFrame.
|
||||
|
||||
### Tracing Decorators
|
||||
|
||||
Use Pipecat's tracing decorators:
|
||||
|
||||
- **STT:** `@traced_stt` - decorate a function that handles `transcript`, `is_final`, `language` as args
|
||||
- **LLM:** `@traced_llm` - decorate the `_process_context()` method
|
||||
- **TTS:** `@traced_tts` - decorate the `run_tts()` method
|
||||
|
||||
## Best Practices
|
||||
|
||||
### Packaging and Distribution
|
||||
|
||||
- Use [uv](https://docs.astral.sh/uv/) for packaging (encouraged)
|
||||
- Consider releasing to PyPI for easier installation
|
||||
- Follow semantic versioning principles
|
||||
- Maintain a changelog
|
||||
|
||||
### HTTP Communication
|
||||
|
||||
For REST-based communication, use aiohttp. Pipecat includes this as a required dependency, so using it prevents adding an additional dependency to your integration.
|
||||
|
||||
### Error Handling
|
||||
|
||||
- Wrap API calls in appropriate try/catch blocks
|
||||
- Handle rate limits and network failures gracefully
|
||||
- Provide meaningful error messages
|
||||
- When errors occur, raise exceptions AND push `ErrorFrame`s to notify the pipeline:
|
||||
|
||||
```python
|
||||
from pipecat.frames.frames import ErrorFrame
|
||||
|
||||
try:
|
||||
# Your API call
|
||||
result = await self._make_api_call()
|
||||
except Exception as e:
|
||||
# Push error frame to pipeline
|
||||
await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
|
||||
# Raise or handle as appropriate
|
||||
raise
|
||||
```
|
||||
|
||||
### Testing
|
||||
|
||||
- Your foundational example serves as a valuable integration-level test
|
||||
- Unit tests are nice to have. As the Pipecat teams provides better guidance, we will encourage unit testing more
|
||||
|
||||
## Disclaimer
|
||||
|
||||
Community integrations are community-maintained and not officially supported by the Pipecat team. Users should evaluate these integrations independently. The Pipecat team reserves the right to remove listings that become unmaintained or problematic.
|
||||
|
||||
## Staying Up to Date
|
||||
|
||||
Pipecat evolves rapidly to support the latest AI technologies and patterns. While we strive to minimize breaking changes, they do occur as the framework matures.
|
||||
|
||||
**We strongly recommend:**
|
||||
|
||||
- Join our Discord at https://discord.gg/pipecat and monitor the `#announcements` channel for release notifications
|
||||
- Follow our changelog: https://github.com/pipecat-ai/pipecat/blob/main/CHANGELOG.md
|
||||
- Test your integration against new Pipecat releases promptly
|
||||
- Update your README with the last tested Pipecat version
|
||||
|
||||
This helps ensure your integration remains compatible and your users have clear expectations about version support.
|
||||
|
||||
## Questions?
|
||||
|
||||
Join our Discord community at https://discord.gg/pipecat and post in the `#community-integrations` channel for guidance and support.
|
||||
|
||||
For additional questions, you can also reach out to us at pipecat-ai@daily.co.
|
||||
@@ -1,9 +1,5 @@
|
||||
## Contributing to Pipecat
|
||||
|
||||
**Want to add a new service integration?**
|
||||
We encourage community-maintained integrations! Please see our [Community Integration Guide](COMMUNITY_INTEGRATIONS.md) for the process and requirements.
|
||||
|
||||
**Want to contribute to Pipecat core?**
|
||||
We welcome contributions of all kinds! Your help is appreciated. Follow these steps to get involved:
|
||||
|
||||
1. **Fork this repository**: Start by forking the Pipecat Documentation repository to your GitHub account.
|
||||
|
||||
143
README.md
143
README.md
@@ -3,7 +3,6 @@
|
||||
</div></h1>
|
||||
|
||||
[](https://pypi.org/project/pipecat-ai)  [](https://codecov.io/gh/pipecat-ai/pipecat) [](https://docs.pipecat.ai) [](https://discord.gg/pipecat) [](https://deepwiki.com/pipecat-ai/pipecat)
|
||||
[](https://getmanta.ai/pipecat)
|
||||
|
||||
# 🎙️ Pipecat: Real-Time Voice & Multimodal AI Agents
|
||||
|
||||
@@ -20,6 +19,10 @@
|
||||
- **Business Agents** – customer intake, support bots, guided flows
|
||||
- **Complex Dialog Systems** – design logic with structured conversations
|
||||
|
||||
🧭 Looking to build structured conversations? Check out [Pipecat Flows](https://github.com/pipecat-ai/pipecat-flows) for managing complex conversational states and transitions.
|
||||
|
||||
🔍 Looking for help debugging your pipeline and processors? Check out [Whisker](https://github.com/pipecat-ai/whisker), a real-time Pipecat debugger.
|
||||
|
||||
## 🧠 Why Pipecat?
|
||||
|
||||
- **Voice-first**: Integrates speech recognition, text-to-speech, and conversation handling
|
||||
@@ -27,38 +30,40 @@
|
||||
- **Composable Pipelines**: Build complex behavior from modular components
|
||||
- **Real-Time**: Ultra-low latency interaction with different transports (e.g. WebSockets or WebRTC)
|
||||
|
||||
## 🌐 Pipecat Ecosystem
|
||||
## 📱 Client SDKs
|
||||
|
||||
### 📱 Client SDKs
|
||||
You can connect to Pipecat from any platform using our official SDKs:
|
||||
|
||||
Building client applications? You can connect to Pipecat from any platform using our official SDKs:
|
||||
|
||||
<a href="https://docs.pipecat.ai/client/js/introduction">JavaScript</a> | <a href="https://docs.pipecat.ai/client/react/introduction">React</a> | <a href="https://docs.pipecat.ai/client/react-native/introduction">React Native</a> |
|
||||
<a href="https://docs.pipecat.ai/client/ios/introduction">Swift</a> | <a href="https://docs.pipecat.ai/client/android/introduction">Kotlin</a> | <a href="https://docs.pipecat.ai/client/c++/introduction">C++</a> | <a href="https://github.com/pipecat-ai/pipecat-esp32">ESP32</a>
|
||||
|
||||
### 🧭 Structured conversations
|
||||
|
||||
Looking to build structured conversations? Check out [Pipecat Flows](https://github.com/pipecat-ai/pipecat-flows) for managing complex conversational states and transitions.
|
||||
|
||||
### 🪄 Beautiful UIs
|
||||
|
||||
Want to build beautiful and engaging experiences? Checkout the [Voice UI Kit](https://github.com/pipecat-ai/voice-ui-kit), a collection of components, hooks and templates for building voice AI applications quickly.
|
||||
|
||||
### 🛠️ Create and deploy projects
|
||||
|
||||
Create a new project in under a minute with the [Pipecat CLI](https://github.com/pipecat-ai/pipecat-cli). Then use the CLI to monitor and deploy your agent to production.
|
||||
|
||||
### 🔍 Debugging
|
||||
|
||||
Looking for help debugging your pipeline and processors? Check out [Whisker](https://github.com/pipecat-ai/whisker), a real-time Pipecat debugger.
|
||||
|
||||
### 🖥️ Terminal
|
||||
|
||||
Love terminal applications? Check out [Tail](https://github.com/pipecat-ai/tail), a terminal dashboard for Pipecat.
|
||||
|
||||
### 📺️ Pipecat TV Channel
|
||||
|
||||
Catch new features, interviews, and how-tos on our [Pipecat TV](https://www.youtube.com/playlist?list=PLzU2zoMTQIHjqC3v4q2XVSR3hGSzwKFwH) channel.
|
||||
<table>
|
||||
<tr>
|
||||
<td>
|
||||
<img src="https://cdn.jsdelivr.net/gh/devicons/devicon/icons/javascript/javascript-original.svg" width="40" height="40" alt="JavaScript"/>
|
||||
<a href="https://docs.pipecat.ai/client/js/introduction">JavaScript</a>
|
||||
</td>
|
||||
<td>
|
||||
<img src="https://cdn.jsdelivr.net/gh/devicons/devicon/icons/react/react-original.svg" width="40" height="40" alt="React"/>
|
||||
<a href="https://docs.pipecat.ai/client/react/introduction">React</a>
|
||||
</td>
|
||||
<td>
|
||||
<img src="https://cdn.jsdelivr.net/gh/devicons/devicon/icons/react/react-original.svg" width="40" height="40" alt="React Native"/>
|
||||
<a href="https://docs.pipecat.ai/client/react-native/introduction">React Native</a>
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>
|
||||
<img src="https://cdn.jsdelivr.net/gh/devicons/devicon/icons/swift/swift-original.svg" width="40" height="40" alt="Swift"/>
|
||||
<a href="https://docs.pipecat.ai/client/ios/introduction">Swift</a>
|
||||
</td>
|
||||
<td>
|
||||
<img src="https://cdn.jsdelivr.net/gh/devicons/devicon/icons/kotlin/kotlin-original.svg" width="40" height="40" alt="Kotlin"/>
|
||||
<a href="https://docs.pipecat.ai/client/android/introduction">Kotlin</a>
|
||||
</td>
|
||||
<td>
|
||||
<img src="https://cdn.jsdelivr.net/gh/devicons/devicon/icons/cplusplus/cplusplus-original.svg" width="40" height="40" alt="JavaScript"/>
|
||||
<a href="https://docs.pipecat.ai/client/c++/introduction">C++</a>
|
||||
</td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
## 🎬 See it in action
|
||||
|
||||
@@ -67,24 +72,24 @@ Catch new features, interviews, and how-tos on our [Pipecat TV](https://www.yout
|
||||
<a href="https://github.com/pipecat-ai/pipecat-examples/tree/main/storytelling-chatbot"><img src="https://raw.githubusercontent.com/pipecat-ai/pipecat-examples/main/storytelling-chatbot/image.png" width="400" /></a>
|
||||
<br/>
|
||||
<a href="https://github.com/pipecat-ai/pipecat-examples/tree/main/translation-chatbot"><img src="https://raw.githubusercontent.com/pipecat-ai/pipecat-examples/main/translation-chatbot/image.png" width="400" /></a>
|
||||
<a href="https://github.com/pipecat-ai/pipecat/blob/main/examples/foundational/12-describe-video.py"><img src="https://github.com/pipecat-ai/pipecat/blob/main/examples/foundational/assets/moondream.png" width="400" /></a>
|
||||
<a href="https://github.com/pipecat-ai/pipecat-examples/tree/main/moondream-chatbot"><img src="https://raw.githubusercontent.com/pipecat-ai/pipecat-examples/main/moondream-chatbot/image.png" width="400" /></a>
|
||||
</p>
|
||||
|
||||
## 🧩 Available services
|
||||
|
||||
| Category | Services |
|
||||
| ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| Speech-to-Text | [AssemblyAI](https://docs.pipecat.ai/server/services/stt/assemblyai), [AWS](https://docs.pipecat.ai/server/services/stt/aws), [Azure](https://docs.pipecat.ai/server/services/stt/azure), [Cartesia](https://docs.pipecat.ai/server/services/stt/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/stt/elevenlabs), [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), [NVIDIA Riva](https://docs.pipecat.ai/server/services/stt/riva), [OpenAI (Whisper)](https://docs.pipecat.ai/server/services/stt/openai), [SambaNova (Whisper)](https://docs.pipecat.ai/server/services/stt/sambanova), [Soniox](https://docs.pipecat.ai/server/services/stt/soniox), [Speechmatics](https://docs.pipecat.ai/server/services/stt/speechmatics), [Ultravox](https://docs.pipecat.ai/server/services/stt/ultravox), [Whisper](https://docs.pipecat.ai/server/services/stt/whisper) |
|
||||
| LLMs | [Anthropic](https://docs.pipecat.ai/server/services/llm/anthropic), [AWS](https://docs.pipecat.ai/server/services/llm/aws), [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), [Mistral](https://docs.pipecat.ai/server/services/llm/mistral), [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), [Qwen](https://docs.pipecat.ai/server/services/llm/qwen), [SambaNova](https://docs.pipecat.ai/server/services/llm/sambanova) [Together AI](https://docs.pipecat.ai/server/services/llm/together) |
|
||||
| Text-to-Speech | [Async](https://docs.pipecat.ai/server/services/tts/asyncai), [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), [Groq](https://docs.pipecat.ai/server/services/tts/groq), [Hume](https://docs.pipecat.ai/server/services/tts/hume), [Inworld](https://docs.pipecat.ai/server/services/tts/inworld), [LMNT](https://docs.pipecat.ai/server/services/tts/lmnt), [MiniMax](https://docs.pipecat.ai/server/services/tts/minimax), [Neuphonic](https://docs.pipecat.ai/server/services/tts/neuphonic), [NVIDIA Riva](https://docs.pipecat.ai/server/services/tts/riva), [OpenAI](https://docs.pipecat.ai/server/services/tts/openai), [Piper](https://docs.pipecat.ai/server/services/tts/piper), [PlayHT](https://docs.pipecat.ai/server/services/tts/playht), [Rime](https://docs.pipecat.ai/server/services/tts/rime), [Sarvam](https://docs.pipecat.ai/server/services/tts/sarvam), [Speechmatics](https://docs.pipecat.ai/server/services/tts/speechmatics), [XTTS](https://docs.pipecat.ai/server/services/tts/xtts) |
|
||||
| Speech-to-Speech | [AWS Nova Sonic](https://docs.pipecat.ai/server/services/s2s/aws), [Gemini Multimodal Live](https://docs.pipecat.ai/server/services/s2s/gemini), [OpenAI Realtime](https://docs.pipecat.ai/server/services/s2s/openai) |
|
||||
| Transport | [Daily (WebRTC)](https://docs.pipecat.ai/server/services/transport/daily), [FastAPI Websocket](https://docs.pipecat.ai/server/services/transport/fastapi-websocket), [SmallWebRTCTransport](https://docs.pipecat.ai/server/services/transport/small-webrtc), [WebSocket Server](https://docs.pipecat.ai/server/services/transport/websocket-server), Local |
|
||||
| Serializers | [Plivo](https://docs.pipecat.ai/server/utilities/serializers/plivo), [Twilio](https://docs.pipecat.ai/server/utilities/serializers/twilio), [Telnyx](https://docs.pipecat.ai/server/utilities/serializers/telnyx) |
|
||||
| Video | [HeyGen](https://docs.pipecat.ai/server/services/video/heygen), [Tavus](https://docs.pipecat.ai/server/services/video/tavus), [Simli](https://docs.pipecat.ai/server/services/video/simli) |
|
||||
| Memory | [mem0](https://docs.pipecat.ai/server/services/memory/mem0) |
|
||||
| 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) |
|
||||
| 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), [ai-coustics](https://docs.pipecat.ai/server/utilities/audio/aic-filter) |
|
||||
| Analytics & Metrics | [OpenTelemetry](https://docs.pipecat.ai/server/utilities/opentelemetry), [Sentry](https://docs.pipecat.ai/server/services/analytics/sentry) |
|
||||
| Category | Services |
|
||||
| ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| Speech-to-Text | [AssemblyAI](https://docs.pipecat.ai/server/services/stt/assemblyai), [AWS](https://docs.pipecat.ai/server/services/stt/aws), [Azure](https://docs.pipecat.ai/server/services/stt/azure), [Cartesia](https://docs.pipecat.ai/server/services/stt/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/stt/elevenlabs), [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), [NVIDIA Riva](https://docs.pipecat.ai/server/services/stt/riva), [OpenAI (Whisper)](https://docs.pipecat.ai/server/services/stt/openai), [SambaNova (Whisper)](https://docs.pipecat.ai/server/services/stt/sambanova), [Soniox](https://docs.pipecat.ai/server/services/stt/soniox), [Speechmatics](https://docs.pipecat.ai/server/services/stt/speechmatics), [Ultravox](https://docs.pipecat.ai/server/services/stt/ultravox), [Whisper](https://docs.pipecat.ai/server/services/stt/whisper) |
|
||||
| LLMs | [Anthropic](https://docs.pipecat.ai/server/services/llm/anthropic), [AWS](https://docs.pipecat.ai/server/services/llm/aws), [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), [Mistral](https://docs.pipecat.ai/server/services/llm/mistral), [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), [Qwen](https://docs.pipecat.ai/server/services/llm/qwen), [SambaNova](https://docs.pipecat.ai/server/services/llm/sambanova) [Together AI](https://docs.pipecat.ai/server/services/llm/together) |
|
||||
| Text-to-Speech | [Async](https://docs.pipecat.ai/server/services/tts/asyncai), [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), [Groq](https://docs.pipecat.ai/server/services/tts/groq), [Inworld](https://docs.pipecat.ai/server/services/tts/inworld), [LMNT](https://docs.pipecat.ai/server/services/tts/lmnt), [MiniMax](https://docs.pipecat.ai/server/services/tts/minimax), [Neuphonic](https://docs.pipecat.ai/server/services/tts/neuphonic), [NVIDIA Riva](https://docs.pipecat.ai/server/services/tts/riva), [OpenAI](https://docs.pipecat.ai/server/services/tts/openai), [Piper](https://docs.pipecat.ai/server/services/tts/piper), [PlayHT](https://docs.pipecat.ai/server/services/tts/playht), [Rime](https://docs.pipecat.ai/server/services/tts/rime), [Sarvam](https://docs.pipecat.ai/server/services/tts/sarvam), [XTTS](https://docs.pipecat.ai/server/services/tts/xtts) |
|
||||
| Speech-to-Speech | [AWS Nova Sonic](https://docs.pipecat.ai/server/services/s2s/aws), [Gemini Multimodal Live](https://docs.pipecat.ai/server/services/s2s/gemini), [OpenAI Realtime](https://docs.pipecat.ai/server/services/s2s/openai) |
|
||||
| Transport | [Daily (WebRTC)](https://docs.pipecat.ai/server/services/transport/daily), [FastAPI Websocket](https://docs.pipecat.ai/server/services/transport/fastapi-websocket), [SmallWebRTCTransport](https://docs.pipecat.ai/server/services/transport/small-webrtc), [WebSocket Server](https://docs.pipecat.ai/server/services/transport/websocket-server), Local |
|
||||
| Serializers | [Plivo](https://docs.pipecat.ai/server/utilities/serializers/plivo), [Twilio](https://docs.pipecat.ai/server/utilities/serializers/twilio), [Telnyx](https://docs.pipecat.ai/server/utilities/serializers/telnyx) |
|
||||
| Video | [HeyGen](https://docs.pipecat.ai/server/services/video/heygen), [Tavus](https://docs.pipecat.ai/server/services/video/tavus), [Simli](https://docs.pipecat.ai/server/services/video/simli) |
|
||||
| Memory | [mem0](https://docs.pipecat.ai/server/services/memory/mem0) |
|
||||
| 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) |
|
||||
| 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), [ai-coustics](https://docs.pipecat.ai/server/utilities/audio/aic-filter) |
|
||||
| Analytics & Metrics | [OpenTelemetry](https://docs.pipecat.ai/server/utilities/opentelemetry), [Sentry](https://docs.pipecat.ai/server/services/analytics/sentry) |
|
||||
|
||||
📚 [View full services documentation →](https://docs.pipecat.ai/server/services/supported-services)
|
||||
|
||||
@@ -179,6 +184,54 @@ Run a specific test suite:
|
||||
uv run pytest tests/test_name.py
|
||||
```
|
||||
|
||||
### Setting up your editor
|
||||
|
||||
This project uses strict [PEP 8](https://peps.python.org/pep-0008/) formatting via [Ruff](https://github.com/astral-sh/ruff).
|
||||
|
||||
#### Emacs
|
||||
|
||||
You can use [use-package](https://github.com/jwiegley/use-package) to install [emacs-lazy-ruff](https://github.com/christophermadsen/emacs-lazy-ruff) package and configure `ruff` arguments:
|
||||
|
||||
```elisp
|
||||
(use-package lazy-ruff
|
||||
:ensure t
|
||||
:hook ((python-mode . lazy-ruff-mode))
|
||||
:config
|
||||
(setq lazy-ruff-format-command "ruff format")
|
||||
(setq lazy-ruff-check-command "ruff check --select I"))
|
||||
```
|
||||
|
||||
`ruff` was installed in the `venv` environment described before, so you should be able to use [pyvenv-auto](https://github.com/ryotaro612/pyvenv-auto) to automatically load that environment inside Emacs.
|
||||
|
||||
```elisp
|
||||
(use-package pyvenv-auto
|
||||
:ensure t
|
||||
:defer t
|
||||
:hook ((python-mode . pyvenv-auto-run)))
|
||||
```
|
||||
|
||||
#### Visual Studio Code
|
||||
|
||||
Install the
|
||||
[Ruff](https://marketplace.visualstudio.com/items?itemName=charliermarsh.ruff) extension. Then edit the user settings (_Ctrl-Shift-P_ `Open User Settings (JSON)`) and set it as the default Python formatter, and enable formatting on save:
|
||||
|
||||
```json
|
||||
"[python]": {
|
||||
"editor.defaultFormatter": "charliermarsh.ruff",
|
||||
"editor.formatOnSave": true
|
||||
}
|
||||
```
|
||||
|
||||
#### PyCharm
|
||||
|
||||
`ruff` was installed in the `venv` environment described before, now to enable autoformatting on save, go to `File` -> `Settings` -> `Tools` -> `File Watchers` and add a new watcher with the following settings:
|
||||
|
||||
1. **Name**: `Ruff formatter`
|
||||
2. **File type**: `Python`
|
||||
3. **Working directory**: `$ContentRoot$`
|
||||
4. **Arguments**: `format $FilePath$`
|
||||
5. **Program**: `$PyInterpreterDirectory$/ruff`
|
||||
|
||||
## 🤝 Contributing
|
||||
|
||||
We welcome contributions from the community! Whether you're fixing bugs, improving documentation, or adding new features, here's how you can help:
|
||||
|
||||
@@ -1,5 +0,0 @@
|
||||
# Security Policy
|
||||
|
||||
## Reporting a Vulnerability
|
||||
|
||||
Please email `disclosures@daily.co`.
|
||||
@@ -50,7 +50,6 @@ autodoc_mock_imports = [
|
||||
# Krisp - has build issues on some platforms
|
||||
"pipecat_ai_krisp",
|
||||
"krisp",
|
||||
"krisp_audio",
|
||||
# System-specific GUI libraries
|
||||
"_tkinter",
|
||||
"tkinter",
|
||||
|
||||
180
env.example
180
env.example
@@ -4,9 +4,6 @@ AICOUSTICS_LICENSE_KEY=...
|
||||
# Anthropic
|
||||
ANTHROPIC_API_KEY=...
|
||||
|
||||
# Assembly AI
|
||||
ASSEMBLYAI_API_KEY=...
|
||||
|
||||
# Async
|
||||
ASYNCAI_API_KEY=...
|
||||
ASYNCAI_VOICE_ID=...
|
||||
@@ -24,19 +21,12 @@ AZURE_CHATGPT_API_KEY=...
|
||||
AZURE_CHATGPT_ENDPOINT=https://...
|
||||
AZURE_CHATGPT_MODEL=...
|
||||
|
||||
AZURE_REALTIME_API_KEY=...
|
||||
AZURE_REALTIME_BASE_URL=...
|
||||
|
||||
AZURE_DALLE_API_KEY=...
|
||||
AZURE_DALLE_ENDPOINT=https://...
|
||||
AZURE_DALLE_MODEL=...
|
||||
|
||||
# Cartesia
|
||||
CARTESIA_API_KEY=...
|
||||
CARTESIA_VOICE_ID=...
|
||||
|
||||
# Cerebras
|
||||
CEREBRAS_API_KEY=...
|
||||
|
||||
# Daily
|
||||
DAILY_API_KEY=...
|
||||
@@ -45,75 +35,39 @@ DAILY_SAMPLE_ROOM_URL=https://...
|
||||
# Deepgram
|
||||
DEEPGRAM_API_KEY=...
|
||||
|
||||
# DeepSeek
|
||||
DEEPSEEK_API_KEY=...
|
||||
|
||||
# ElevenLabs
|
||||
ELEVENLABS_API_KEY=...
|
||||
ELEVENLABS_VOICE_ID=...
|
||||
|
||||
# Neuphonic
|
||||
NEUPHONIC_API_KEY=...
|
||||
|
||||
# Fal
|
||||
FAL_KEY=...
|
||||
|
||||
# Fireworks
|
||||
FIREWORKS_API_KEY=...
|
||||
|
||||
# Fish Audio
|
||||
FISH_API_KEY=...
|
||||
|
||||
# Gladia
|
||||
GLADIA_API_KEY=...
|
||||
GLADIA_REGION=...
|
||||
|
||||
# Google
|
||||
GOOGLE_API_KEY=...
|
||||
GOOGLE_VERTEX_TEST_CREDENTIALS=...
|
||||
GOOGLE_CLOUD_PROJECT_ID=...
|
||||
GOOGLE_CLOUD_LOCATION=...
|
||||
GOOGLE_TEST_CREDENTIALS=...
|
||||
|
||||
# Grok
|
||||
GROK_API_KEY=...
|
||||
|
||||
# Groq
|
||||
GROQ_API_KEY=...
|
||||
|
||||
# Heygen
|
||||
HEYGEN_API_KEY=...
|
||||
|
||||
# Hume
|
||||
HUME_API_KEY=...
|
||||
HUME_VOICE_ID=...
|
||||
|
||||
# Inworld
|
||||
INWORLD_API_KEY=...
|
||||
|
||||
# Krisp
|
||||
KRISP_MODEL_PATH=...
|
||||
|
||||
# Krisp Viva
|
||||
KRISP_VIVA_MODEL_PATH=...
|
||||
|
||||
# LiveKit
|
||||
LIVEKIT_API_KEY=...
|
||||
LIVEKIT_API_SECRET=...
|
||||
GOOGLE_VERTEX_TEST_CREDENTIALS=...
|
||||
|
||||
# LMNT
|
||||
LMNT_API_KEY=...
|
||||
LMNT_VOICE_ID=...
|
||||
|
||||
# MiniMax
|
||||
MINIMAX_API_KEY=...
|
||||
MINIMAX_GROUP_ID=...
|
||||
# Perplexity
|
||||
PERPLEXITY_API_KEY=...
|
||||
|
||||
# Mistral
|
||||
MISTRAL_API_KEY=...
|
||||
|
||||
# Neuphonic
|
||||
NEUPHONIC_API_KEY=...
|
||||
|
||||
# NVIDIA
|
||||
NVIDIA_API_KEY=...
|
||||
# PlayHT
|
||||
PLAYHT_USER_ID=...
|
||||
PLAYHT_API_KEY=...
|
||||
|
||||
# OpenAI
|
||||
OPENAI_API_KEY=...
|
||||
@@ -121,73 +75,83 @@ OPENAI_API_KEY=...
|
||||
# OpenPipe
|
||||
OPENPIPE_API_KEY=...
|
||||
|
||||
# OpenRouter
|
||||
OPENROUTER_API_KEY=...
|
||||
|
||||
# Perplexity
|
||||
PERPLEXITY_API_KEY=...
|
||||
|
||||
# Picovoice Koala
|
||||
KOALA_ACCESS_KEY=...
|
||||
|
||||
# Piper
|
||||
PIPER_BASE_URL=...
|
||||
|
||||
# PlayHT
|
||||
PLAYHT_USER_ID=...
|
||||
PLAYHT_API_KEY=...
|
||||
|
||||
# Plivo
|
||||
PLIVO_AUTH_ID=...
|
||||
PLIVO_AUTH_TOKEN=...
|
||||
|
||||
# Qwen
|
||||
QWEN_API_KEY=...
|
||||
|
||||
# Rime
|
||||
RIME_API_KEY=...
|
||||
RIME_VOICE_ID=...
|
||||
|
||||
# SambaNova
|
||||
SAMBANOVA_API_KEY=...
|
||||
|
||||
# Sarvam AI
|
||||
SARVAM_API_KEY=...
|
||||
|
||||
# Sentry
|
||||
SENTRY_DSN=...
|
||||
# Tavus
|
||||
TAVUS_API_KEY=...
|
||||
TAVUS_REPLICA_ID=...
|
||||
TAVUS_PERSONA_ID=...
|
||||
|
||||
# Simli
|
||||
SIMLI_API_KEY=...
|
||||
SIMLI_FACE_ID=...
|
||||
|
||||
# Smart turn
|
||||
LOCAL_SMART_TURN_MODEL_PATH=...
|
||||
FAL_SMART_TURN_API_KEY=...
|
||||
# Krisp
|
||||
KRISP_MODEL_PATH=...
|
||||
|
||||
# Soniox
|
||||
SONIOX_API_KEY=...
|
||||
# DeepSeek
|
||||
DEEPSEEK_API_KEY=...
|
||||
|
||||
# Speechmatics
|
||||
SPEECHMATICS_API_KEY=...
|
||||
# Groq
|
||||
GROQ_API_KEY=...
|
||||
|
||||
# Tavus
|
||||
TAVUS_API_KEY=...
|
||||
TAVUS_REPLICA_ID=...
|
||||
# Grok
|
||||
GROK_API_KEY=...
|
||||
|
||||
# Telnyx
|
||||
TELNYX_API_KEY=...
|
||||
TELNYX_ACCOUNT_SID=...
|
||||
# Inworld
|
||||
INWORLD_API_KEY=...
|
||||
|
||||
# Together.ai
|
||||
TOGETHER_API_KEY=...
|
||||
|
||||
# Cerebras
|
||||
CEREBRAS_API_KEY=...
|
||||
|
||||
# Fish Audio
|
||||
FISH_API_KEY=...
|
||||
|
||||
# Assembly AI
|
||||
ASSEMBLYAI_API_KEY=...
|
||||
|
||||
# OpenRouter
|
||||
OPENROUTER_API_KEY=...
|
||||
|
||||
# Piper
|
||||
PIPER_BASE_URL=...
|
||||
|
||||
# Smart turn
|
||||
LOCAL_SMART_TURN_MODEL_PATH=...
|
||||
FAL_SMART_TURN_API_KEY=...
|
||||
|
||||
# Twilio
|
||||
TWILIO_ACCOUNT_SID=...
|
||||
TWILIO_AUTH_TOKEN=...
|
||||
|
||||
# WhatsApp
|
||||
WHATSAPP_TOKEN=...
|
||||
WHATSAPP_WEBHOOK_VERIFICATION_TOKEN=...
|
||||
WHATSAPP_PHONE_NUMBER_ID=...
|
||||
WHATSAPP_APP_SECRET=...
|
||||
# MiniMax
|
||||
MINIMAX_API_KEY=...
|
||||
MINIMAX_GROUP_ID=...
|
||||
|
||||
# Sarvam AI
|
||||
SARVAM_API_KEY=...
|
||||
|
||||
# Soniox
|
||||
SONIOX_API_KEY=
|
||||
|
||||
# Speechmatics
|
||||
SPEECHMATICS_API_KEY=...
|
||||
|
||||
# SambaNova
|
||||
SAMBANOVA_API_KEY=...
|
||||
|
||||
# Sentry
|
||||
SENTRY_DSN=...
|
||||
|
||||
# Heygen
|
||||
HEYGEN_API_KEY=...
|
||||
|
||||
# Mistral
|
||||
MISTRAL_API_KEY=...
|
||||
|
||||
# NVIDIA
|
||||
NVIDIA_API_KEY=...
|
||||
|
||||
# Qwen
|
||||
QWEN_API_KEY=...
|
||||
|
||||
@@ -25,7 +25,7 @@ from pipecat.processors.aggregators.llm_response_universal import LLMContextAggr
|
||||
from pipecat.runner.daily import configure
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.transports.daily.transport import DailyParams, DailyTransport
|
||||
from pipecat.transports.daily.transport import DailyLogLevel, DailyParams, DailyTransport
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
@@ -49,6 +49,7 @@ async def main():
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
)
|
||||
transport.set_log_level(DailyLogLevel.Info)
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
|
||||
@@ -21,8 +21,8 @@ from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.cartesia.stt import CartesiaSTTService
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
@@ -58,7 +58,7 @@ transport_params = {
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
stt = CartesiaSTTService(api_key=os.getenv("CARTESIA_API_KEY"))
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
|
||||
@@ -6,7 +6,6 @@
|
||||
|
||||
import os
|
||||
|
||||
import aiohttp
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
@@ -21,10 +20,10 @@ from pipecat.processors.aggregators.llm_response import (
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
|
||||
from pipecat.services.openai.base_llm import BaseOpenAILLMService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.services.speechmatics.stt import SpeechmaticsSTTService
|
||||
from pipecat.services.speechmatics.tts import SpeechmaticsTTSService
|
||||
from pipecat.transcriptions.language import Language
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
@@ -52,127 +51,121 @@ transport_params = {
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
"""Speechmatics STT and TTS Service Example
|
||||
"""Speechmatics STT Service Example
|
||||
|
||||
This example demonstrates using Speechmatics Speech-to-Text and Text-to-Speech services
|
||||
with speaker diarization and intelligent speaker management. Key features:
|
||||
This example demonstrates using Speechmatics Speech-to-Text service with speaker diarization and intelligent speaker management. Key features:
|
||||
|
||||
1. Speaker Diarization (STT)
|
||||
1. Speaker Diarization
|
||||
- Automatically identifies and distinguishes between different speakers
|
||||
- First speaker is identified as 'S1', others get subsequent IDs
|
||||
- Uses `enable_diarization` parameter to manage speaker detection
|
||||
|
||||
2. Smart Speaker Control (STT)
|
||||
2. Smart Speaker Control
|
||||
- `focus_speakers` parameter lets you target specific speakers (e.g. ["S1"])
|
||||
- Other speakers will be wrapped in PASSIVE tags
|
||||
- Only processes speech from focused speakers
|
||||
- Words from all speakers are wrapped with XML tags for clear speaker identification
|
||||
- Other speakers' speech only sent when focused speaker is active
|
||||
|
||||
3. Voice Activity Detection (STT)
|
||||
3. Voice Activity Detection
|
||||
- Built-in VAD using `enable_vad` parameter
|
||||
- Remove `vad_analyzer` from `transport` config to use module's VAD
|
||||
- Emits speaker started/stopped events
|
||||
|
||||
4. Text-to-Speech (TTS)
|
||||
- Low latency streaming audio synthesis
|
||||
- Multiple voice options available including `sarah`, `theo`, and `megan`
|
||||
|
||||
5. Configuration Options
|
||||
4. Configuration Options
|
||||
- `operating_point` parameter defaults to `ENHANCED` for optimal accuracy
|
||||
- Configurable `end_of_utterance_silence_trigger` (default 0.5s)
|
||||
- Customizable speaker formatting
|
||||
- Additional diarization settings available
|
||||
|
||||
For detailed information:
|
||||
- STT: https://docs.speechmatics.com/rt-api-ref
|
||||
- TTS: https://docs.speechmatics.com/text-to-speech/quickstart
|
||||
For detailed information about operating points and configuration:
|
||||
https://docs.speechmatics.com/rt-api-ref
|
||||
"""
|
||||
|
||||
logger.info(f"Starting bot")
|
||||
async with aiohttp.ClientSession() as session:
|
||||
stt = SpeechmaticsSTTService(
|
||||
api_key=os.getenv("SPEECHMATICS_API_KEY"),
|
||||
params=SpeechmaticsSTTService.InputParams(
|
||||
language=Language.EN,
|
||||
enable_vad=True,
|
||||
enable_diarization=True,
|
||||
focus_speakers=["S1"],
|
||||
end_of_utterance_silence_trigger=0.5,
|
||||
speaker_active_format="<{speaker_id}>{text}</{speaker_id}>",
|
||||
speaker_passive_format="<PASSIVE><{speaker_id}>{text}</{speaker_id}></PASSIVE>",
|
||||
|
||||
stt = SpeechmaticsSTTService(
|
||||
api_key=os.getenv("SPEECHMATICS_API_KEY"),
|
||||
params=SpeechmaticsSTTService.InputParams(
|
||||
language=Language.EN,
|
||||
enable_vad=True,
|
||||
enable_diarization=True,
|
||||
focus_speakers=["S1"],
|
||||
end_of_utterance_silence_trigger=0.5,
|
||||
speaker_active_format="<{speaker_id}>{text}</{speaker_id}>",
|
||||
speaker_passive_format="<PASSIVE><{speaker_id}>{text}</{speaker_id}></PASSIVE>",
|
||||
),
|
||||
)
|
||||
|
||||
tts = ElevenLabsTTSService(
|
||||
api_key=os.getenv("ELEVENLABS_API_KEY"),
|
||||
voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
|
||||
model="eleven_turbo_v2_5",
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
params=BaseOpenAILLMService.InputParams(temperature=0.75),
|
||||
)
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": (
|
||||
"You are a helpful British assistant called Alfred. "
|
||||
"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. "
|
||||
"Always include punctuation in your responses. "
|
||||
"Give very short replies - do not give longer replies unless strictly necessary. "
|
||||
"Respond to what the user said in a concise, funny, creative and helpful way. "
|
||||
"Use `<Sn/>` tags to identify different speakers - do not use tags in your replies. "
|
||||
"Do not respond to speakers within `<PASSIVE/>` tags unless explicitly asked to. "
|
||||
),
|
||||
)
|
||||
},
|
||||
]
|
||||
|
||||
tts = SpeechmaticsTTSService(
|
||||
api_key=os.getenv("SPEECHMATICS_API_KEY"),
|
||||
voice_id="sarah",
|
||||
aiohttp_session=session,
|
||||
)
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(aggregation_timeout=0.005),
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
params=BaseOpenAILLMService.InputParams(temperature=0.75),
|
||||
)
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": (
|
||||
"You are a helpful British assistant called Sarah. "
|
||||
"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. "
|
||||
"Always include punctuation in your responses. "
|
||||
"Give very short replies - do not give longer replies unless strictly necessary. "
|
||||
"Respond to what the user said in a concise, funny, creative and helpful way. "
|
||||
"Use `<Sn/>` tags to identify different speakers - do not use tags in your replies. "
|
||||
"Do not respond to speakers within `<PASSIVE/>` tags unless explicitly asked to. "
|
||||
),
|
||||
},
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt,
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(aggregation_timeout=0.005),
|
||||
)
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt,
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
messages.append({"role": "system", "content": "Say a short hello to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
messages.append({"role": "system", "content": "Say a short hello to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
await runner.run(task)
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
|
||||
@@ -6,7 +6,6 @@
|
||||
|
||||
import os
|
||||
|
||||
import aiohttp
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
@@ -25,10 +24,10 @@ from pipecat.processors.aggregators.llm_response import (
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.elevenlabs.tts import ElevenLabsTTSService
|
||||
from pipecat.services.openai.base_llm import BaseOpenAILLMService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.services.speechmatics.stt import SpeechmaticsSTTService
|
||||
from pipecat.services.speechmatics.tts import SpeechmaticsTTSService
|
||||
from pipecat.transcriptions.language import Language
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
@@ -62,106 +61,100 @@ transport_params = {
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
"""Run example using Speechmatics STT and TTS.
|
||||
"""Run example using Speechmatics STT.
|
||||
|
||||
This example demonstrates a complete Speechmatics integration with both Speech-to-Text
|
||||
and Text-to-Speech services:
|
||||
This example will use diarization within our STT service and output the words spoken by
|
||||
each individual speaker and wrap them with XML tags for the LLM to process. Note the
|
||||
instructions in the system context for the LLM. This greatly improves the conversation
|
||||
experience by allowing the LLM to understand who is speaking in a multi-party call.
|
||||
|
||||
STT Features:
|
||||
- Diarization to identify and distinguish between different speakers
|
||||
- Words spoken by each speaker are wrapped with XML tags for LLM processing
|
||||
- System context instructions help the LLM understand multi-party conversations
|
||||
- ENHANCED operating point by default for optimal accuracy
|
||||
By default, this example will use our ENHANCED operating point, which is optimized for
|
||||
high accuracy. You can change this by setting the `operating_point` parameter to a different
|
||||
value.
|
||||
|
||||
TTS Features:
|
||||
- Low latency streaming audio synthesis
|
||||
- Multiple voice options available including `sarah`, `theo`, and `megan`
|
||||
|
||||
For more information:
|
||||
- STT: https://docs.speechmatics.com/rt-api-ref
|
||||
- TTS: https://docs.speechmatics.com/text-to-speech/quickstart
|
||||
For more information on operating points, see the Speechmatics documentation:
|
||||
https://docs.speechmatics.com/rt-api-ref
|
||||
"""
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
async with aiohttp.ClientSession() as session:
|
||||
stt = SpeechmaticsSTTService(
|
||||
api_key=os.getenv("SPEECHMATICS_API_KEY"),
|
||||
params=SpeechmaticsSTTService.InputParams(
|
||||
language=Language.EN,
|
||||
enable_diarization=True,
|
||||
end_of_utterance_silence_trigger=0.5,
|
||||
speaker_active_format="<{speaker_id}>{text}</{speaker_id}>",
|
||||
stt = SpeechmaticsSTTService(
|
||||
api_key=os.getenv("SPEECHMATICS_API_KEY"),
|
||||
params=SpeechmaticsSTTService.InputParams(
|
||||
language=Language.EN,
|
||||
enable_diarization=True,
|
||||
end_of_utterance_silence_trigger=0.5,
|
||||
speaker_active_format="<{speaker_id}>{text}</{speaker_id}>",
|
||||
),
|
||||
)
|
||||
|
||||
tts = ElevenLabsTTSService(
|
||||
api_key=os.getenv("ELEVENLABS_API_KEY"),
|
||||
voice_id=os.getenv("ELEVENLABS_VOICE_ID"),
|
||||
model="eleven_turbo_v2_5",
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
params=BaseOpenAILLMService.InputParams(temperature=0.75),
|
||||
)
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": (
|
||||
"You are a helpful British assistant called Alfred. "
|
||||
"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. "
|
||||
"Always include punctuation in your responses. "
|
||||
"Give very short replies - do not give longer replies unless strictly necessary. "
|
||||
"Respond to what the user said in a concise, funny, creative and helpful way. "
|
||||
"Use `<Sn/>` tags to identify different speakers - do not use tags in your replies."
|
||||
),
|
||||
)
|
||||
},
|
||||
]
|
||||
|
||||
tts = SpeechmaticsTTSService(
|
||||
api_key=os.getenv("SPEECHMATICS_API_KEY"),
|
||||
voice_id="sarah",
|
||||
aiohttp_session=session,
|
||||
)
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(aggregation_timeout=0.005),
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
params=BaseOpenAILLMService.InputParams(temperature=0.75),
|
||||
)
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": (
|
||||
"You are a helpful British assistant called Sarah. "
|
||||
"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. "
|
||||
"Always include punctuation in your responses. "
|
||||
"Give very short replies - do not give longer replies unless strictly necessary. "
|
||||
"Respond to what the user said in a concise, funny, creative and helpful way. "
|
||||
"Use `<Sn/>` tags to identify different speakers - do not use tags in your replies."
|
||||
),
|
||||
},
|
||||
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
|
||||
]
|
||||
)
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(aggregation_timeout=0.005),
|
||||
)
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
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
|
||||
]
|
||||
)
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
messages.append({"role": "system", "content": "Say a short hello to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
messages.append({"role": "system", "content": "Say a short hello to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
await runner.run(task)
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
|
||||
@@ -1,138 +0,0 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import os
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIObserver, RTVIProcessor
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.hume.tts import HUME_SAMPLE_RATE, HumeTTSService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
tts = HumeTTSService(
|
||||
api_key=os.getenv("HUME_API_KEY"),
|
||||
# Replace with your Hume voice ID
|
||||
voice_id="f898a92e-685f-43fa-985b-a46920f0650b",
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
rtvi = RTVIProcessor(config=RTVIConfig(config=[]))
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
rtvi,
|
||||
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(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
audio_out_sample_rate=HUME_SAMPLE_RATE,
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
observers=[RTVIObserver(rtvi)],
|
||||
)
|
||||
|
||||
@rtvi.event_handler("on_client_ready")
|
||||
async def on_client_ready(rtvi):
|
||||
await rtvi.set_bot_ready()
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
@@ -1,122 +0,0 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
|
||||
import os
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_response_universal import (
|
||||
LLMContext,
|
||||
LLMContextAggregatorPair,
|
||||
)
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.deepgram.flux.stt import DeepgramFluxSTTService
|
||||
from pipecat.services.deepgram.tts import DeepgramTTSService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
stt = DeepgramFluxSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-2-andromeda-en")
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(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(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
@stt.event_handler("on_update")
|
||||
async def on_deepgram_flux_update(stt, transcript):
|
||||
logger.debug(f"On deeggram flux update: {transcript}")
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
@@ -1,132 +0,0 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
|
||||
import os
|
||||
|
||||
import aiohttp
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.deepgram.tts import DeepgramHttpTTSService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
async with aiohttp.ClientSession() as session:
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
tts = DeepgramHttpTTSService(
|
||||
api_key=os.getenv("DEEPGRAM_API_KEY"),
|
||||
voice="aura-2-andromeda-en",
|
||||
aiohttp_session=session,
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(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(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
@@ -23,6 +23,7 @@ from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.elevenlabs.stt import ElevenLabsSTTService
|
||||
from pipecat.services.elevenlabs.tts import ElevenLabsHttpTTSService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
|
||||
@@ -67,8 +67,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
llm = AWSBedrockLLMService(
|
||||
aws_region="us-west-2",
|
||||
model="us.anthropic.claude-haiku-4-5-20251001-v1:0",
|
||||
params=AWSBedrockLLMService.InputParams(temperature=0.8),
|
||||
model="us.anthropic.claude-3-5-haiku-20241022-v1:0",
|
||||
params=AWSBedrockLLMService.InputParams(temperature=0.8, latency="optimized"),
|
||||
)
|
||||
|
||||
messages = [
|
||||
|
||||
@@ -1,151 +0,0 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
"""
|
||||
A conversational AI bot using Gemini for both LLM, STT and TTS.
|
||||
|
||||
This example demonstrates how to use Gemini's image generation capabilities.
|
||||
|
||||
Features showcased:
|
||||
- Gemini LLM for conversation and image generation
|
||||
- Google TTS and STT
|
||||
|
||||
Run with:
|
||||
python examples/foundational/07n-interruptible-gemini-image.py
|
||||
|
||||
Make sure to set your environment variables:
|
||||
export GOOGLE_API_KEY=your_api_key_here
|
||||
"""
|
||||
|
||||
import os
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.google.llm import GoogleLLMService
|
||||
from pipecat.services.google.stt import GoogleSTTService
|
||||
from pipecat.services.google.tts import GoogleTTSService
|
||||
from pipecat.transcriptions.language import Language
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
video_out_enabled=True,
|
||||
video_out_width=1024,
|
||||
video_out_height=1024,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
video_out_enabled=True,
|
||||
video_out_width=1024,
|
||||
video_out_height=1024,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
stt = GoogleSTTService(
|
||||
params=GoogleSTTService.InputParams(languages=Language.EN_US),
|
||||
credentials=os.getenv("GOOGLE_TEST_CREDENTIALS"),
|
||||
)
|
||||
|
||||
tts = GoogleTTSService(
|
||||
voice_id="en-US-Chirp3-HD-Charon",
|
||||
params=GoogleTTSService.InputParams(language=Language.EN_US),
|
||||
credentials=os.getenv("GOOGLE_TEST_CREDENTIALS"),
|
||||
)
|
||||
|
||||
llm = GoogleLLMService(
|
||||
api_key=os.getenv("GOOGLE_API_KEY"),
|
||||
model="gemini-2.5-flash-image",
|
||||
)
|
||||
|
||||
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 = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt, # STT
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # Gemini TTS
|
||||
transport.output(), # Transport bot output
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation with a styled introduction
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
@@ -1,129 +0,0 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
|
||||
import os
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.filters.krisp_viva_filter import KrispVivaFilter
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.deepgram.tts import DeepgramTTSService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
audio_in_filter=KrispVivaFilter(),
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
audio_in_filter=KrispVivaFilter(),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
audio_in_filter=KrispVivaFilter(),
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
tts = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"), voice="aura-helios-en")
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(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(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
147
examples/foundational/08-bots-arguing.py
Normal file
147
examples/foundational/08-bots-arguing.py
Normal file
@@ -0,0 +1,147 @@
|
||||
import asyncio
|
||||
import logging
|
||||
import os
|
||||
from typing import Tuple
|
||||
|
||||
import aiohttp
|
||||
from dotenv import load_dotenv
|
||||
|
||||
from pipecat.frames.frames import AudioFrame, EndFrame, ImageFrame, LLMContextFrame, TextFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.processors.aggregators import SentenceAggregator
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.daily import configure
|
||||
from pipecat.services.azure import AzureLLMService, AzureTTSService
|
||||
from pipecat.services.elevenlabs import ElevenLabsTTSService
|
||||
from pipecat.services.fal import FalImageGenService
|
||||
from pipecat.transports.daily.transport import DailyTransport
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s")
|
||||
logger = logging.getLogger("pipecat")
|
||||
logger.setLevel(logging.DEBUG)
|
||||
|
||||
|
||||
async def main():
|
||||
async with aiohttp.ClientSession() as session:
|
||||
(room_url, _) = await configure(session)
|
||||
|
||||
transport = DailyTransport(
|
||||
room_url,
|
||||
None,
|
||||
"Respond bot",
|
||||
duration_minutes=10,
|
||||
mic_enabled=True,
|
||||
mic_sample_rate=16000,
|
||||
camera_enabled=True,
|
||||
camera_width=1024,
|
||||
camera_height=1024,
|
||||
)
|
||||
|
||||
llm = AzureLLMService(
|
||||
api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
|
||||
endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
|
||||
model=os.getenv("AZURE_CHATGPT_MODEL"),
|
||||
)
|
||||
tts1 = AzureTTSService(
|
||||
api_key=os.getenv("AZURE_SPEECH_API_KEY"),
|
||||
region=os.getenv("AZURE_SPEECH_REGION"),
|
||||
)
|
||||
tts2 = ElevenLabsTTSService(
|
||||
api_key=os.getenv("ELEVENLABS_API_KEY"),
|
||||
voice_id="jBpfuIE2acCO8z3wKNLl",
|
||||
)
|
||||
dalle = FalImageGenService(
|
||||
params=FalImageGenService.InputParams(image_size="1024x1024"),
|
||||
aiohttp_session=session,
|
||||
key=os.getenv("FAL_KEY"),
|
||||
)
|
||||
|
||||
bot1_messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a stern librarian. You strongly believe that a hot dog is a sandwich. Start by stating this fact in a few sentences, then be prepared to debate this with the user. You shouldn't ever compromise on the fundamental truth that a hot dog is a sandwich. Your responses should only be a few sentences long.",
|
||||
},
|
||||
]
|
||||
bot2_messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a silly cat, and you strongly believe that a hot dog is not a sandwich. Debate this with the user, only responding with a few sentences. Don't ever accept that a hot dog is a sandwich.",
|
||||
},
|
||||
]
|
||||
|
||||
async def get_text_and_audio(messages) -> Tuple[str, bytearray]:
|
||||
"""This function streams text from the LLM and uses the TTS service to convert
|
||||
that text to speech as it's received.
|
||||
"""
|
||||
source_queue = asyncio.Queue()
|
||||
sink_queue = asyncio.Queue()
|
||||
sentence_aggregator = SentenceAggregator()
|
||||
pipeline = Pipeline([llm, sentence_aggregator, tts1], source_queue, sink_queue)
|
||||
|
||||
await source_queue.put(LLMContextFrame(LLMContext(messages)))
|
||||
await source_queue.put(EndFrame())
|
||||
await pipeline.run_pipeline()
|
||||
|
||||
message = ""
|
||||
all_audio = bytearray()
|
||||
while sink_queue.qsize():
|
||||
frame = sink_queue.get_nowait()
|
||||
if isinstance(frame, TextFrame):
|
||||
message += frame.text
|
||||
elif isinstance(frame, AudioFrame):
|
||||
all_audio.extend(frame.audio)
|
||||
|
||||
return (message, all_audio)
|
||||
|
||||
async def get_bot1_statement():
|
||||
message, audio = await get_text_and_audio(bot1_messages)
|
||||
|
||||
bot1_messages.append({"role": "assistant", "content": message})
|
||||
bot2_messages.append({"role": "user", "content": message})
|
||||
|
||||
return audio
|
||||
|
||||
async def get_bot2_statement():
|
||||
message, audio = await get_text_and_audio(bot2_messages)
|
||||
|
||||
bot2_messages.append({"role": "assistant", "content": message})
|
||||
bot1_messages.append({"role": "user", "content": message})
|
||||
|
||||
return audio
|
||||
|
||||
async def argue():
|
||||
for i in range(100):
|
||||
print(f"In iteration {i}")
|
||||
|
||||
bot1_description = "A woman conservatively dressed as a librarian in a library surrounded by books, cartoon, serious, highly detailed"
|
||||
|
||||
(audio1, image_data1) = await asyncio.gather(
|
||||
get_bot1_statement(), dalle.run_image_gen(bot1_description)
|
||||
)
|
||||
await transport.send_queue.put(
|
||||
[
|
||||
ImageFrame(image_data1[1], image_data1[2]),
|
||||
AudioFrame(audio1),
|
||||
]
|
||||
)
|
||||
|
||||
bot2_description = "A cat dressed in a hot dog costume, cartoon, bright colors, funny, highly detailed"
|
||||
|
||||
(audio2, image_data2) = await asyncio.gather(
|
||||
get_bot2_statement(), dalle.run_image_gen(bot2_description)
|
||||
)
|
||||
await transport.send_queue.put(
|
||||
[
|
||||
ImageFrame(image_data2[1], image_data2[2]),
|
||||
AudioFrame(audio2),
|
||||
]
|
||||
)
|
||||
|
||||
await asyncio.gather(transport.run(), argue())
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
@@ -1,141 +0,0 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
|
||||
import os
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
from PIL import Image
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
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. You are also able to describe images.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(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(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
|
||||
if not runner_args.body:
|
||||
script_dir = os.path.dirname(__file__)
|
||||
runner_args.body = {
|
||||
"image_path": os.path.join(script_dir, "assets", "cat.jpg"),
|
||||
"question": "Describe this image",
|
||||
}
|
||||
|
||||
image_path = runner_args.body["image_path"]
|
||||
question = runner_args.body["question"]
|
||||
|
||||
# Kick off the conversation.
|
||||
image = Image.open(image_path)
|
||||
message = LLMContext.create_image_message(
|
||||
image=image.tobytes(),
|
||||
format="RGB",
|
||||
size=image.size,
|
||||
text=question,
|
||||
)
|
||||
messages.append(message)
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
180
examples/foundational/12-describe-video.py
Normal file
180
examples/foundational/12-describe-video.py
Normal file
@@ -0,0 +1,180 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import os
|
||||
from typing import Optional
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import (
|
||||
Frame,
|
||||
LLMContextFrame,
|
||||
TextFrame,
|
||||
TTSSpeakFrame,
|
||||
UserImageRawFrame,
|
||||
UserImageRequestFrame,
|
||||
)
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.user_response import UserResponseAggregator
|
||||
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import (
|
||||
create_transport,
|
||||
get_transport_client_id,
|
||||
maybe_capture_participant_camera,
|
||||
)
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.moondream.vision import MoondreamService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
class UserImageRequester(FrameProcessor):
|
||||
"""Converts incoming text into requests for user images."""
|
||||
|
||||
def __init__(self, participant_id: Optional[str] = None):
|
||||
super().__init__()
|
||||
self._participant_id = participant_id
|
||||
|
||||
def set_participant_id(self, participant_id: str):
|
||||
self._participant_id = participant_id
|
||||
|
||||
async def process_frame(self, frame: Frame, direction: FrameDirection):
|
||||
await super().process_frame(frame, direction)
|
||||
|
||||
if self._participant_id and isinstance(frame, TextFrame):
|
||||
await self.push_frame(
|
||||
UserImageRequestFrame(self._participant_id, context=frame.text),
|
||||
FrameDirection.UPSTREAM,
|
||||
)
|
||||
else:
|
||||
await self.push_frame(frame, direction)
|
||||
|
||||
|
||||
class UserImageProcessor(FrameProcessor):
|
||||
"""Converts incoming user images into context frames."""
|
||||
|
||||
async def process_frame(self, frame: Frame, direction: FrameDirection):
|
||||
await super().process_frame(frame, direction)
|
||||
|
||||
if isinstance(frame, UserImageRawFrame):
|
||||
if frame.request and frame.request.context:
|
||||
context = LLMContext()
|
||||
context.add_image_frame_message(
|
||||
image=frame.image,
|
||||
text=frame.request.context,
|
||||
size=frame.size,
|
||||
format=frame.format,
|
||||
)
|
||||
frame = LLMContextFrame(context)
|
||||
await self.push_frame(frame)
|
||||
else:
|
||||
await self.push_frame(frame, direction)
|
||||
|
||||
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
video_in_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
video_in_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
user_response = UserResponseAggregator()
|
||||
|
||||
# Initialize the image requester without setting the participant ID yet
|
||||
image_requester = UserImageRequester()
|
||||
|
||||
image_processor = UserImageProcessor()
|
||||
|
||||
# If you run into weird description, try with use_cpu=True
|
||||
moondream = MoondreamService()
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(),
|
||||
stt,
|
||||
user_response,
|
||||
image_requester,
|
||||
image_processor,
|
||||
moondream,
|
||||
tts,
|
||||
transport.output(),
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected: {client}")
|
||||
|
||||
await maybe_capture_participant_camera(transport, client)
|
||||
|
||||
# Set the participant ID in the image requester
|
||||
client_id = get_transport_client_id(transport, client)
|
||||
image_requester.set_participant_id(client_id)
|
||||
|
||||
# Welcome message
|
||||
await task.queue_frame(TTSSpeakFrame("Hi there! Feel free to ask me about what I see."))
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
@@ -5,23 +5,29 @@
|
||||
#
|
||||
|
||||
import os
|
||||
from typing import Optional
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.adapters.schemas.function_schema import FunctionSchema
|
||||
from pipecat.adapters.schemas.tools_schema import ToolsSchema
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame, UserImageRequestFrame
|
||||
from pipecat.frames.frames import (
|
||||
Frame,
|
||||
LLMContextFrame,
|
||||
TextFrame,
|
||||
TTSSpeakFrame,
|
||||
UserImageRawFrame,
|
||||
UserImageRequestFrame,
|
||||
)
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.processors.frame_processor import FrameDirection
|
||||
from pipecat.processors.aggregators.user_response import UserResponseAggregator
|
||||
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import (
|
||||
create_transport,
|
||||
@@ -31,37 +37,53 @@ from pipecat.runner.utils import (
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.google.llm import GoogleLLMService
|
||||
from pipecat.services.llm_service import FunctionCallParams
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
async def fetch_user_image(params: FunctionCallParams):
|
||||
"""Fetch the user image and push it to the LLM.
|
||||
class UserImageRequester(FrameProcessor):
|
||||
"""Converts incoming text into requests for user images."""
|
||||
|
||||
When called, this function pushes a UserImageRequestFrame upstream to the
|
||||
transport. As a result, the transport will request the user image and push a
|
||||
UserImageRawFrame downstream which will be added to the context by the LLM
|
||||
assistant aggregator.
|
||||
"""
|
||||
user_id = params.arguments["user_id"]
|
||||
question = params.arguments["question"]
|
||||
logger.debug(f"Requesting image with user_id={user_id}, question={question}")
|
||||
def __init__(self, participant_id: Optional[str] = None):
|
||||
super().__init__()
|
||||
self._participant_id = participant_id
|
||||
|
||||
# Request a user image frame and indicate that it should be added to the
|
||||
# context.
|
||||
await params.llm.push_frame(
|
||||
UserImageRequestFrame(user_id=user_id, text=question, append_to_context=True),
|
||||
FrameDirection.UPSTREAM,
|
||||
)
|
||||
def set_participant_id(self, participant_id: str):
|
||||
self._participant_id = participant_id
|
||||
|
||||
await params.result_callback(None)
|
||||
async def process_frame(self, frame: Frame, direction: FrameDirection):
|
||||
await super().process_frame(frame, direction)
|
||||
|
||||
# Instead of None, it's possible to also provide a tool call answer to
|
||||
# tell the LLM that we are grabbing the image to analyze.
|
||||
# await params.result_callback({"result": "Image is being captured."})
|
||||
if self._participant_id and isinstance(frame, TextFrame):
|
||||
await self.push_frame(
|
||||
UserImageRequestFrame(self._participant_id, context=frame.text),
|
||||
FrameDirection.UPSTREAM,
|
||||
)
|
||||
else:
|
||||
await self.push_frame(frame, direction)
|
||||
|
||||
|
||||
class UserImageProcessor(FrameProcessor):
|
||||
"""Converts incoming user images into context frames."""
|
||||
|
||||
async def process_frame(self, frame: Frame, direction: FrameDirection):
|
||||
await super().process_frame(frame, direction)
|
||||
|
||||
if isinstance(frame, UserImageRawFrame):
|
||||
if frame.request and frame.request.context:
|
||||
context = LLMContext()
|
||||
context.add_image_frame_message(
|
||||
image=frame.image,
|
||||
text=frame.request.context,
|
||||
size=frame.size,
|
||||
format=frame.format,
|
||||
)
|
||||
frame = LLMContextFrame(context)
|
||||
await self.push_frame(frame)
|
||||
else:
|
||||
await self.push_frame(frame, direction)
|
||||
|
||||
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
@@ -88,53 +110,33 @@ transport_params = {
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
user_response = UserResponseAggregator()
|
||||
|
||||
# Initialize the image requester without setting the participant ID yet
|
||||
image_requester = UserImageRequester()
|
||||
|
||||
image_processor = UserImageProcessor()
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
# Google Gemini model for vision analysis
|
||||
google = GoogleLLMService(model="gemini-2.0-flash-001", 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
|
||||
)
|
||||
|
||||
# Google Gemini model for vision analysis
|
||||
llm = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"))
|
||||
llm.register_function("fetch_user_image", fetch_user_image)
|
||||
|
||||
fetch_image_function = FunctionSchema(
|
||||
name="fetch_user_image",
|
||||
description="Called when the user requests a description of their camera feed",
|
||||
properties={
|
||||
"user_id": {
|
||||
"type": "string",
|
||||
"description": "The ID of the user to grab the image from",
|
||||
},
|
||||
"question": {
|
||||
"type": "string",
|
||||
"description": "The question that the user is asking about the image",
|
||||
},
|
||||
},
|
||||
required=["user_id", "question"],
|
||||
)
|
||||
tools = ToolsSchema(standard_tools=[fetch_image_function])
|
||||
|
||||
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. You are able to describe images from the user camera.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages, tools)
|
||||
context_aggregator = LLMContextAggregatorPair(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
|
||||
transport.input(),
|
||||
stt,
|
||||
user_response,
|
||||
image_requester,
|
||||
image_processor,
|
||||
google,
|
||||
tts,
|
||||
transport.output(),
|
||||
]
|
||||
)
|
||||
|
||||
@@ -155,15 +157,10 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
# Set the participant ID in the image requester
|
||||
client_id = get_transport_client_id(transport, client)
|
||||
image_requester.set_participant_id(client_id)
|
||||
|
||||
# Kick off the conversation.
|
||||
messages.append(
|
||||
{
|
||||
"role": "system",
|
||||
"content": f"Please introduce yourself to the user. Use '{client_id}' as the user ID during function calls.",
|
||||
}
|
||||
)
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
# Welcome message
|
||||
await task.queue_frame(TTSSpeakFrame("Hi there! Feel free to ask me about what I see."))
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
@@ -1,148 +0,0 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
|
||||
import os
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
from PIL import Image
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.aws.llm import AWSBedrockLLMService
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
llm = AWSBedrockLLMService(
|
||||
aws_region="us-west-2",
|
||||
model="us.anthropic.claude-3-7-sonnet-20250219-v1:0",
|
||||
# Note: usually, prefer providing latency="optimized" param.
|
||||
# Here we can't because AWS Bedrock doesn't support it for Claude 3.7,
|
||||
# which we need for image input.
|
||||
params=AWSBedrockLLMService.InputParams(temperature=0.8),
|
||||
)
|
||||
|
||||
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. You are also able to describe images.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(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(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
|
||||
if not runner_args.body:
|
||||
script_dir = os.path.dirname(__file__)
|
||||
runner_args.body = {
|
||||
"image_path": os.path.join(script_dir, "assets", "cat.jpg"),
|
||||
"question": "Describe this image",
|
||||
}
|
||||
|
||||
image_path = runner_args.body["image_path"]
|
||||
question = runner_args.body["question"]
|
||||
|
||||
# Kick off the conversation.
|
||||
image = Image.open(image_path)
|
||||
message = LLMContext.create_image_message(
|
||||
image=image.tobytes(),
|
||||
format="RGB",
|
||||
size=image.size,
|
||||
text=question,
|
||||
)
|
||||
messages.append(message)
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
@@ -4,9 +4,8 @@
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import io
|
||||
import os
|
||||
import re
|
||||
from typing import Optional
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
@@ -17,17 +16,24 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import (
|
||||
Frame,
|
||||
LLMRunFrame,
|
||||
MetricsFrame,
|
||||
LLMContextFrame,
|
||||
TextFrame,
|
||||
TTSSpeakFrame,
|
||||
UserImageRawFrame,
|
||||
UserImageRequestFrame,
|
||||
)
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.processors.aggregators.user_response import UserResponseAggregator
|
||||
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.runner.utils import (
|
||||
create_transport,
|
||||
get_transport_client_id,
|
||||
maybe_capture_participant_camera,
|
||||
)
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
@@ -37,41 +43,46 @@ from pipecat.transports.daily.transport import DailyParams
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
def format_metrics(metrics, indent=0):
|
||||
lines = []
|
||||
tab = "\t" * indent
|
||||
class UserImageRequester(FrameProcessor):
|
||||
"""Converts incoming text into requests for user images."""
|
||||
|
||||
for metric in metrics:
|
||||
lines.append(tab + type(metric).__name__)
|
||||
for field, value in vars(metric).items():
|
||||
if hasattr(value, "__dict__") and not isinstance(
|
||||
value, (str, int, float, bool, type(None))
|
||||
):
|
||||
lines.append(f"{tab}\t{field}={type(value).__name__}")
|
||||
for k, v in vars(value).items():
|
||||
lines.append(f"{tab}\t\t{k}={repr(v)}")
|
||||
else:
|
||||
lines.append(f"{tab}\t{field}={repr(value)}")
|
||||
def __init__(self, participant_id: Optional[str] = None):
|
||||
super().__init__()
|
||||
self._participant_id = participant_id
|
||||
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
class MetricsFrameLogger(FrameProcessor):
|
||||
"""MetricsFrameLogger formats and logs all MetericsFrames"""
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
def set_participant_id(self, participant_id: str):
|
||||
self._participant_id = participant_id
|
||||
|
||||
async def process_frame(self, frame: Frame, direction: FrameDirection):
|
||||
await super().process_frame(frame, direction)
|
||||
|
||||
if isinstance(frame, MetricsFrame):
|
||||
logger.info(f"{frame.name}\n {format_metrics(frame.data)}")
|
||||
if self._participant_id and isinstance(frame, TextFrame):
|
||||
await self.push_frame(
|
||||
UserImageRequestFrame(self._participant_id, context=frame.text),
|
||||
FrameDirection.UPSTREAM,
|
||||
)
|
||||
else:
|
||||
await self.push_frame(frame, direction)
|
||||
|
||||
# ALWAYS push all frames
|
||||
|
||||
class UserImageProcessor(FrameProcessor):
|
||||
"""Converts incoming user images into context frames."""
|
||||
|
||||
async def process_frame(self, frame: Frame, direction: FrameDirection):
|
||||
await super().process_frame(frame, direction)
|
||||
|
||||
if isinstance(frame, UserImageRawFrame):
|
||||
if frame.request and frame.request.context:
|
||||
context = LLMContext()
|
||||
context.add_image_frame_message(
|
||||
image=frame.image,
|
||||
text=frame.request.context,
|
||||
size=frame.size,
|
||||
format=frame.format,
|
||||
)
|
||||
frame = LLMContextFrame(context)
|
||||
await self.push_frame(frame)
|
||||
else:
|
||||
# SUPER IMPORTANT: always push every frame!
|
||||
await self.push_frame(frame, direction)
|
||||
|
||||
|
||||
@@ -82,13 +93,14 @@ transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
video_in_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
video_out_enabled=True,
|
||||
video_in_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
@@ -98,37 +110,33 @@ transport_params = {
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
user_response = UserResponseAggregator()
|
||||
|
||||
# Initialize the image requester without setting the participant ID yet
|
||||
image_requester = UserImageRequester()
|
||||
|
||||
image_processor = UserImageProcessor()
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
# OpenAI GPT-4o for vision analysis
|
||||
openai = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
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 = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
metrics_frame_processor = MetricsFrameLogger()
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(),
|
||||
stt,
|
||||
context_aggregator.user(),
|
||||
llm,
|
||||
user_response,
|
||||
image_requester,
|
||||
image_processor,
|
||||
openai,
|
||||
tts,
|
||||
transport.output(),
|
||||
context_aggregator.assistant(),
|
||||
metrics_frame_processor, # pretty print metrics frames
|
||||
]
|
||||
)
|
||||
|
||||
@@ -144,9 +152,15 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected: {client}")
|
||||
# Kick off the conversation.
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
await maybe_capture_participant_camera(transport, client)
|
||||
|
||||
# Set the participant ID in the image requester
|
||||
client_id = get_transport_client_id(transport, client)
|
||||
image_requester.set_participant_id(client_id)
|
||||
|
||||
# Welcome message
|
||||
await task.queue_frame(TTSSpeakFrame("Hi there! Feel free to ask me about what I see."))
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
@@ -1,141 +0,0 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
|
||||
import os
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
from PIL import Image
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.google.llm import GoogleLLMService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
llm = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"))
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way. You are also able to describe images.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(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(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
|
||||
if not runner_args.body:
|
||||
script_dir = os.path.dirname(__file__)
|
||||
runner_args.body = {
|
||||
"image_path": os.path.join(script_dir, "assets", "cat.jpg"),
|
||||
"question": "Describe this image",
|
||||
}
|
||||
|
||||
image_path = runner_args.body["image_path"]
|
||||
question = runner_args.body["question"]
|
||||
|
||||
# Kick off the conversation.
|
||||
image = Image.open(image_path)
|
||||
message = LLMContext.create_image_message(
|
||||
image=image.tobytes(),
|
||||
format="RGB",
|
||||
size=image.size,
|
||||
text=question,
|
||||
)
|
||||
messages.append(message)
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
@@ -4,25 +4,36 @@
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
|
||||
import os
|
||||
from typing import Optional
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
from PIL import Image
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.frames.frames import (
|
||||
Frame,
|
||||
LLMContextFrame,
|
||||
TextFrame,
|
||||
TTSSpeakFrame,
|
||||
UserImageRawFrame,
|
||||
UserImageRequestFrame,
|
||||
)
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.processors.aggregators.user_response import UserResponseAggregator
|
||||
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.runner.utils import (
|
||||
create_transport,
|
||||
get_transport_client_id,
|
||||
maybe_capture_participant_camera,
|
||||
)
|
||||
from pipecat.services.anthropic.llm import AnthropicLLMService
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
@@ -32,6 +43,49 @@ from pipecat.transports.daily.transport import DailyParams
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
class UserImageRequester(FrameProcessor):
|
||||
"""Converts incoming text into requests for user images."""
|
||||
|
||||
def __init__(self, participant_id: Optional[str] = None):
|
||||
super().__init__()
|
||||
self._participant_id = participant_id
|
||||
|
||||
def set_participant_id(self, participant_id: str):
|
||||
self._participant_id = participant_id
|
||||
|
||||
async def process_frame(self, frame: Frame, direction: FrameDirection):
|
||||
await super().process_frame(frame, direction)
|
||||
|
||||
if self._participant_id and isinstance(frame, TextFrame):
|
||||
await self.push_frame(
|
||||
UserImageRequestFrame(self._participant_id, context=frame.text),
|
||||
FrameDirection.UPSTREAM,
|
||||
)
|
||||
else:
|
||||
await self.push_frame(frame, direction)
|
||||
|
||||
|
||||
class UserImageProcessor(FrameProcessor):
|
||||
"""Converts incoming user images into context frames."""
|
||||
|
||||
async def process_frame(self, frame: Frame, direction: FrameDirection):
|
||||
await super().process_frame(frame, direction)
|
||||
|
||||
if isinstance(frame, UserImageRawFrame):
|
||||
if frame.request and frame.request.context:
|
||||
context = LLMContext()
|
||||
context.add_image_frame_message(
|
||||
image=frame.image,
|
||||
text=frame.request.context,
|
||||
size=frame.size,
|
||||
format=frame.format,
|
||||
)
|
||||
frame = LLMContextFrame(context)
|
||||
await self.push_frame(frame)
|
||||
else:
|
||||
await self.push_frame(frame, direction)
|
||||
|
||||
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
@@ -39,12 +93,14 @@ transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
video_in_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
video_in_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
@@ -54,34 +110,33 @@ transport_params = {
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
user_response = UserResponseAggregator()
|
||||
|
||||
# Initialize the image requester without setting the participant ID yet
|
||||
image_requester = UserImageRequester()
|
||||
|
||||
image_processor = UserImageProcessor()
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
# Anthropic for vision analysis
|
||||
anthropic = AnthropicLLMService(api_key=os.getenv("ANTHROPIC_API_KEY"))
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
llm = AnthropicLLMService(api_key=os.getenv("ANTHROPIC_API_KEY"))
|
||||
|
||||
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. You are also able to describe images.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(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
|
||||
transport.input(),
|
||||
stt,
|
||||
user_response,
|
||||
image_requester,
|
||||
image_processor,
|
||||
anthropic,
|
||||
tts,
|
||||
transport.output(),
|
||||
]
|
||||
)
|
||||
|
||||
@@ -96,28 +151,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
logger.info(f"Client connected: {client}")
|
||||
|
||||
if not runner_args.body:
|
||||
script_dir = os.path.dirname(__file__)
|
||||
runner_args.body = {
|
||||
"image_path": os.path.join(script_dir, "assets", "cat.jpg"),
|
||||
"question": "Describe this image",
|
||||
}
|
||||
await maybe_capture_participant_camera(transport, client)
|
||||
|
||||
image_path = runner_args.body["image_path"]
|
||||
question = runner_args.body["question"]
|
||||
# Set the participant ID in the image requester
|
||||
client_id = get_transport_client_id(transport, client)
|
||||
image_requester.set_participant_id(client_id)
|
||||
|
||||
# Kick off the conversation.
|
||||
image = Image.open(image_path)
|
||||
message = LLMContext.create_image_message(
|
||||
image=image.tobytes(),
|
||||
format="RGB",
|
||||
size=image.size,
|
||||
text=question,
|
||||
)
|
||||
messages.append(message)
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
# Welcome message
|
||||
await task.queue_frame(TTSSpeakFrame("Hi there! Feel free to ask me about what I see."))
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
@@ -1,122 +0,0 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
|
||||
import os
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
from PIL import Image
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import UserImageRawFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.moondream.vision import MoondreamService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
vision = MoondreamService()
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
vision, # Vision
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
|
||||
if not runner_args.body:
|
||||
script_dir = os.path.dirname(__file__)
|
||||
runner_args.body = {
|
||||
"image_path": os.path.join(script_dir, "assets", "cat.jpg"),
|
||||
"question": "Describe this image",
|
||||
}
|
||||
|
||||
image_path = runner_args.body["image_path"]
|
||||
question = runner_args.body["question"]
|
||||
|
||||
# Describe the image.
|
||||
image = Image.open(image_path)
|
||||
await task.queue_frames(
|
||||
[
|
||||
UserImageRawFrame(
|
||||
image=image.tobytes(),
|
||||
format="RGB",
|
||||
size=image.size,
|
||||
text=question,
|
||||
)
|
||||
]
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
@@ -5,23 +5,29 @@
|
||||
#
|
||||
|
||||
import os
|
||||
from typing import Optional
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.adapters.schemas.function_schema import FunctionSchema
|
||||
from pipecat.adapters.schemas.tools_schema import ToolsSchema
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame, UserImageRequestFrame
|
||||
from pipecat.frames.frames import (
|
||||
Frame,
|
||||
LLMContextFrame,
|
||||
TextFrame,
|
||||
TTSSpeakFrame,
|
||||
UserImageRawFrame,
|
||||
UserImageRequestFrame,
|
||||
)
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.processors.frame_processor import FrameDirection
|
||||
from pipecat.processors.aggregators.user_response import UserResponseAggregator
|
||||
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import (
|
||||
create_transport,
|
||||
@@ -31,37 +37,54 @@ from pipecat.runner.utils import (
|
||||
from pipecat.services.aws.llm import AWSBedrockLLMService
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.llm_service import FunctionCallParams
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
async def fetch_user_image(params: FunctionCallParams):
|
||||
"""Fetch the user image and push it to the LLM.
|
||||
class UserImageRequester(FrameProcessor):
|
||||
"""Converts incoming text into requests for user images."""
|
||||
|
||||
When called, this function pushes a UserImageRequestFrame upstream to the
|
||||
transport. As a result, the transport will request the user image and push a
|
||||
UserImageRawFrame downstream which will be added to the context by the LLM
|
||||
assistant aggregator.
|
||||
"""
|
||||
user_id = params.arguments["user_id"]
|
||||
question = params.arguments["question"]
|
||||
logger.debug(f"Requesting image with user_id={user_id}, question={question}")
|
||||
def __init__(self, participant_id: Optional[str] = None):
|
||||
super().__init__()
|
||||
self._participant_id = participant_id
|
||||
|
||||
# Request a user image frame and indicate that it should be added to the
|
||||
# context.
|
||||
await params.llm.push_frame(
|
||||
UserImageRequestFrame(user_id=user_id, text=question, append_to_context=True),
|
||||
FrameDirection.UPSTREAM,
|
||||
)
|
||||
def set_participant_id(self, participant_id: str):
|
||||
self._participant_id = participant_id
|
||||
|
||||
await params.result_callback(None)
|
||||
async def process_frame(self, frame: Frame, direction: FrameDirection):
|
||||
await super().process_frame(frame, direction)
|
||||
|
||||
# Instead of None, it's possible to also provide a tool call answer to
|
||||
# tell the LLM that we are grabbing the image to analyze.
|
||||
# await params.result_callback({"result": "Image is being captured."})
|
||||
if self._participant_id and isinstance(frame, TextFrame):
|
||||
await self.push_frame(
|
||||
UserImageRequestFrame(self._participant_id, context=frame.text),
|
||||
FrameDirection.UPSTREAM,
|
||||
)
|
||||
else:
|
||||
await self.push_frame(frame, direction)
|
||||
|
||||
|
||||
class UserImageProcessor(FrameProcessor):
|
||||
"""Converts incoming user images into context frames."""
|
||||
|
||||
async def process_frame(self, frame: Frame, direction: FrameDirection):
|
||||
await super().process_frame(frame, direction)
|
||||
|
||||
if isinstance(frame, UserImageRawFrame):
|
||||
if frame.request and frame.request.context:
|
||||
# Note: AWS Bedrock does not yet support the universal LLMContext
|
||||
context = LLMContext()
|
||||
context.add_image_frame_message(
|
||||
image=frame.image,
|
||||
text=frame.request.context,
|
||||
size=frame.size,
|
||||
format=frame.format,
|
||||
)
|
||||
frame = LLMContextFrame(context)
|
||||
await self.push_frame(frame)
|
||||
else:
|
||||
await self.push_frame(frame, direction)
|
||||
|
||||
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
@@ -88,15 +111,17 @@ transport_params = {
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
user_response = UserResponseAggregator()
|
||||
|
||||
# Initialize the image requester without setting the participant ID yet
|
||||
image_requester = UserImageRequester()
|
||||
|
||||
image_processor = UserImageProcessor()
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
# AWS for vision analysis
|
||||
llm = AWSBedrockLLMService(
|
||||
aws = AWSBedrockLLMService(
|
||||
aws_region="us-west-2",
|
||||
model="us.anthropic.claude-3-7-sonnet-20250219-v1:0",
|
||||
# Note: usually, prefer providing latency="optimized" param.
|
||||
@@ -104,44 +129,22 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
# which we need for image input.
|
||||
params=AWSBedrockLLMService.InputParams(temperature=0.8),
|
||||
)
|
||||
llm.register_function("fetch_user_image", fetch_user_image)
|
||||
|
||||
fetch_image_function = FunctionSchema(
|
||||
name="fetch_user_image",
|
||||
description="Called when the user requests a description of their camera feed",
|
||||
properties={
|
||||
"user_id": {
|
||||
"type": "string",
|
||||
"description": "The ID of the user to grab the image from",
|
||||
},
|
||||
"question": {
|
||||
"type": "string",
|
||||
"description": "The question that the user is asking about the image",
|
||||
},
|
||||
},
|
||||
required=["user_id", "question"],
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
tools = ToolsSchema(standard_tools=[fetch_image_function])
|
||||
|
||||
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. You are able to describe images from the user camera.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages, tools)
|
||||
context_aggregator = LLMContextAggregatorPair(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
|
||||
transport.input(),
|
||||
stt,
|
||||
user_response,
|
||||
image_requester,
|
||||
image_processor,
|
||||
aws,
|
||||
tts,
|
||||
transport.output(),
|
||||
]
|
||||
)
|
||||
|
||||
@@ -162,15 +165,10 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
# Set the participant ID in the image requester
|
||||
client_id = get_transport_client_id(transport, client)
|
||||
image_requester.set_participant_id(client_id)
|
||||
|
||||
# Kick off the conversation.
|
||||
messages.append(
|
||||
{
|
||||
"role": "system",
|
||||
"content": f"Please introduce yourself to the user. Use '{client_id}' as the user ID during function calls.",
|
||||
}
|
||||
)
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
# Welcome message
|
||||
await task.queue_frame(TTSSpeakFrame("Hi there! Feel free to ask me about what I see."))
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
@@ -48,7 +48,10 @@ transport_params = {
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
stt = CartesiaSTTService(api_key=os.getenv("CARTESIA_API_KEY"))
|
||||
stt = CartesiaSTTService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
base_url=os.getenv("CARTESIA_BASE_URL"),
|
||||
)
|
||||
|
||||
tl = TranscriptionLogger()
|
||||
|
||||
|
||||
@@ -4,6 +4,8 @@
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
|
||||
import asyncio
|
||||
import os
|
||||
|
||||
from dotenv import load_dotenv
|
||||
@@ -15,13 +17,12 @@ from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame, UserImageRequestFrame
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.processors.frame_processor import FrameDirection
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import (
|
||||
create_transport,
|
||||
@@ -38,30 +39,34 @@ from pipecat.transports.daily.transport import DailyParams
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
async def fetch_user_image(params: FunctionCallParams):
|
||||
"""Fetch the user image and push it to the LLM.
|
||||
# Global variable to store the client ID
|
||||
client_id = ""
|
||||
|
||||
When called, this function pushes a UserImageRequestFrame upstream to the
|
||||
transport. As a result, the transport will request the user image and push a
|
||||
UserImageRawFrame downstream which will be added to the context by the LLM
|
||||
assistant aggregator.
|
||||
"""
|
||||
user_id = params.arguments["user_id"]
|
||||
|
||||
async def get_weather(params: FunctionCallParams):
|
||||
location = params.arguments["location"]
|
||||
await params.result_callback(f"The weather in {location} is currently 72 degrees and sunny.")
|
||||
|
||||
|
||||
async def get_image(params: FunctionCallParams):
|
||||
question = params.arguments["question"]
|
||||
logger.debug(f"Requesting image with user_id={user_id}, question={question}")
|
||||
logger.debug(f"Requesting image with user_id={client_id}, question={question}")
|
||||
|
||||
# Request a user image frame and indicate that it should be added to the
|
||||
# context.
|
||||
await params.llm.push_frame(
|
||||
UserImageRequestFrame(user_id=user_id, text=question, append_to_context=True),
|
||||
FrameDirection.UPSTREAM,
|
||||
# Request the image frame
|
||||
await params.llm.request_image_frame(
|
||||
user_id=client_id,
|
||||
function_name=params.function_name,
|
||||
tool_call_id=params.tool_call_id,
|
||||
text_content=question,
|
||||
)
|
||||
|
||||
await params.result_callback(None)
|
||||
# Wait a short time for the frame to be processed
|
||||
await asyncio.sleep(0.5)
|
||||
|
||||
# Instead of None, it's possible to also provide a tool call answer to
|
||||
# tell the LLM that we are grabbing the image to analyze.
|
||||
# await params.result_callback({"result": "Image is being captured."})
|
||||
# Return a result to complete the function call
|
||||
await params.result_callback(
|
||||
f"I've captured an image from your camera and I'm analyzing what you asked about: {question}"
|
||||
)
|
||||
|
||||
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
@@ -95,32 +100,70 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
# Anthropic for vision analysis
|
||||
llm = AnthropicLLMService(api_key=os.getenv("ANTHROPIC_API_KEY"))
|
||||
llm.register_function("fetch_user_image", fetch_user_image)
|
||||
llm = AnthropicLLMService(
|
||||
api_key=os.getenv("ANTHROPIC_API_KEY"),
|
||||
model="claude-3-7-sonnet-latest",
|
||||
params=AnthropicLLMService.InputParams(enable_prompt_caching=True),
|
||||
)
|
||||
llm.register_function("get_weather", get_weather)
|
||||
llm.register_function("get_image", get_image)
|
||||
|
||||
fetch_image_function = FunctionSchema(
|
||||
name="fetch_user_image",
|
||||
description="Called when the user requests a description of their camera feed",
|
||||
weather_function = FunctionSchema(
|
||||
name="get_weather",
|
||||
description="Get the current weather",
|
||||
properties={
|
||||
"user_id": {
|
||||
"location": {
|
||||
"type": "string",
|
||||
"description": "The ID of the user to grab the image from",
|
||||
},
|
||||
"question": {
|
||||
"type": "string",
|
||||
"description": "The question that the user is asking about the image",
|
||||
"description": "The city and state, e.g. San Francisco, CA",
|
||||
},
|
||||
},
|
||||
required=["user_id", "question"],
|
||||
required=["location"],
|
||||
)
|
||||
tools = ToolsSchema(standard_tools=[fetch_image_function])
|
||||
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.
|
||||
|
||||
Your response will be turned into speech so use only simple words and punctuation.
|
||||
|
||||
You have access to two tools: get_weather and get_image.
|
||||
|
||||
You can respond to questions about the weather using the get_weather tool.
|
||||
|
||||
You can answer questions about the user's video stream using the get_image tool. Some examples of phrases that \
|
||||
indicate you should use the get_image tool are:
|
||||
- What do you see?
|
||||
- What's in the video?
|
||||
- Can you describe the video?
|
||||
- Tell me about what you see.
|
||||
- Tell me something interesting about what you see.
|
||||
- What's happening in the video?
|
||||
|
||||
If you need to use a tool, simply use the tool. Do not tell the user the tool you are using. Be brief and concise.
|
||||
"""
|
||||
|
||||
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. You are able to describe images from the user camera.",
|
||||
"content": [
|
||||
{
|
||||
"type": "text",
|
||||
"text": system_prompt,
|
||||
}
|
||||
],
|
||||
},
|
||||
{"role": "user", "content": "Start the conversation by introducing yourself."},
|
||||
]
|
||||
|
||||
context = LLMContext(messages, tools)
|
||||
@@ -130,11 +173,11 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt, # STT
|
||||
context_aggregator.user(), # User responses
|
||||
context_aggregator.user(), # User speech to text
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
context_aggregator.assistant(), # Assistant spoken responses and tool context
|
||||
]
|
||||
)
|
||||
|
||||
@@ -153,16 +196,10 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
await maybe_capture_participant_camera(transport, client)
|
||||
|
||||
# Set the participant ID in the image requester
|
||||
global client_id
|
||||
client_id = get_transport_client_id(transport, client)
|
||||
|
||||
# Kick off the conversation.
|
||||
messages.append(
|
||||
{
|
||||
"role": "system",
|
||||
"content": f"Please introduce yourself to the user. Use '{client_id}' as the user ID during function calls.",
|
||||
}
|
||||
)
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
@@ -1,190 +0,0 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import os
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.adapters.schemas.function_schema import FunctionSchema
|
||||
from pipecat.adapters.schemas.tools_schema import ToolsSchema
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame, UserImageRequestFrame
|
||||
from pipecat.pipeline.parallel_pipeline import ParallelPipeline
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.processors.frame_processor import FrameDirection
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import (
|
||||
create_transport,
|
||||
get_transport_client_id,
|
||||
maybe_capture_participant_camera,
|
||||
)
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.llm_service import FunctionCallParams
|
||||
from pipecat.services.moondream.vision import MoondreamService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
async def fetch_user_image(params: FunctionCallParams):
|
||||
"""Fetch the user image.
|
||||
|
||||
When called, this function pushes a UserImageRequestFrame upstream to the
|
||||
transport. As a result, the transport will request the user image and push a
|
||||
UserImageRawFrame downstream.
|
||||
"""
|
||||
user_id = params.arguments["user_id"]
|
||||
question = params.arguments["question"]
|
||||
logger.debug(f"Requesting image with user_id={user_id}, question={question}")
|
||||
|
||||
# Request a user image frame. In this case, we don't want the requested
|
||||
# image to be added to the context because we will process it with
|
||||
# Moondream.
|
||||
await params.llm.push_frame(
|
||||
UserImageRequestFrame(user_id=user_id, text=question, append_to_context=False),
|
||||
FrameDirection.UPSTREAM,
|
||||
)
|
||||
|
||||
await params.result_callback(None)
|
||||
|
||||
# Instead of None, it's possible to also provide a tool call answer to
|
||||
# tell the LLM that we are grabbing the image to analyze.
|
||||
# await params.result_callback({"result": "Image is being captured."})
|
||||
|
||||
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
video_in_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
video_in_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
llm.register_function("fetch_user_image", fetch_user_image)
|
||||
|
||||
fetch_image_function = FunctionSchema(
|
||||
name="fetch_user_image",
|
||||
description="Called when the user requests a description of their camera feed",
|
||||
properties={
|
||||
"user_id": {
|
||||
"type": "string",
|
||||
"description": "The ID of the user to grab the image from",
|
||||
},
|
||||
"question": {
|
||||
"type": "string",
|
||||
"description": "The question that the user is asking about the image",
|
||||
},
|
||||
},
|
||||
required=["user_id", "question"],
|
||||
)
|
||||
tools = ToolsSchema(standard_tools=[fetch_image_function])
|
||||
|
||||
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. You are able to describe images from the user camera.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages, tools)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
# If you run into weird description, try with use_cpu=True
|
||||
moondream = MoondreamService()
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt, # STT
|
||||
context_aggregator.user(), # User responses
|
||||
ParallelPipeline(
|
||||
[llm], # LLM
|
||||
[moondream],
|
||||
),
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected: {client}")
|
||||
|
||||
await maybe_capture_participant_camera(transport, client)
|
||||
|
||||
# Set the participant ID in the image requester
|
||||
client_id = get_transport_client_id(transport, client)
|
||||
|
||||
# Kick off the conversation.
|
||||
messages.append(
|
||||
{
|
||||
"role": "system",
|
||||
"content": f"Please introduce yourself to the user. Use '{client_id}' as the user ID during function calls.",
|
||||
}
|
||||
)
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
@@ -1,186 +0,0 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
|
||||
import os
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.adapters.schemas.function_schema import FunctionSchema
|
||||
from pipecat.adapters.schemas.tools_schema import ToolsSchema
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame, UserImageRequestFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.processors.frame_processor import FrameDirection
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import (
|
||||
create_transport,
|
||||
get_transport_client_id,
|
||||
maybe_capture_participant_camera,
|
||||
)
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.llm_service import FunctionCallParams
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
async def fetch_user_image(params: FunctionCallParams):
|
||||
"""Fetch the user image and push it to the LLM.
|
||||
|
||||
When called, this function pushes a UserImageRequestFrame upstream to the
|
||||
transport. As a result, the transport will request the user image and push a
|
||||
UserImageRawFrame downstream which will be added to the context by the LLM
|
||||
assistant aggregator.
|
||||
"""
|
||||
user_id = params.arguments["user_id"]
|
||||
question = params.arguments["question"]
|
||||
logger.debug(f"Requesting image with user_id={user_id}, question={question}")
|
||||
|
||||
# Request a user image frame and indicate that it should be added to the
|
||||
# context.
|
||||
await params.llm.push_frame(
|
||||
UserImageRequestFrame(user_id=user_id, text=question, append_to_context=True),
|
||||
FrameDirection.UPSTREAM,
|
||||
)
|
||||
|
||||
await params.result_callback(None)
|
||||
|
||||
# Instead of None, it's possible to also provide a tool call answer to
|
||||
# tell the LLM that we are grabbing the image to analyze.
|
||||
# await params.result_callback({"result": "Image is being captured."})
|
||||
|
||||
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
video_in_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
video_in_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
llm.register_function("fetch_user_image", fetch_user_image)
|
||||
|
||||
fetch_image_function = FunctionSchema(
|
||||
name="fetch_user_image",
|
||||
description="Called when the user requests a description of their camera feed",
|
||||
properties={
|
||||
"user_id": {
|
||||
"type": "string",
|
||||
"description": "The ID of the user to grab the image from",
|
||||
},
|
||||
"question": {
|
||||
"type": "string",
|
||||
"description": "The question that the user is asking about the image",
|
||||
},
|
||||
},
|
||||
required=["user_id", "question"],
|
||||
)
|
||||
tools = ToolsSchema(standard_tools=[fetch_image_function])
|
||||
|
||||
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. You are able to describe images from the user camera.",
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages, tools)
|
||||
context_aggregator = LLMContextAggregatorPair(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(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
|
||||
await maybe_capture_participant_camera(transport, client)
|
||||
|
||||
client_id = get_transport_client_id(transport, client)
|
||||
|
||||
# Kick off the conversation.
|
||||
messages.append(
|
||||
{
|
||||
"role": "system",
|
||||
"content": f"Please introduce yourself to the user. Use '{client_id}' as the user ID during function calls.",
|
||||
}
|
||||
)
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
@@ -4,8 +4,9 @@
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
|
||||
import asyncio
|
||||
import os
|
||||
import time
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
@@ -16,31 +17,56 @@ from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame, TTSSpeakFrame
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.runner.utils import (
|
||||
create_transport,
|
||||
get_transport_client_id,
|
||||
maybe_capture_participant_camera,
|
||||
)
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.llm_service import FunctionCallParams
|
||||
from pipecat.services.openpipe.llm import OpenPipeLLMService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
async def fetch_weather_from_api(params: FunctionCallParams):
|
||||
await params.result_callback({"conditions": "nice", "temperature": "75"})
|
||||
# Global variable to store the client ID
|
||||
client_id = ""
|
||||
|
||||
|
||||
async def fetch_restaurant_recommendation(params: FunctionCallParams):
|
||||
await params.result_callback({"name": "The Golden Dragon"})
|
||||
async def get_weather(params: FunctionCallParams):
|
||||
location = params.arguments["location"]
|
||||
await params.result_callback(f"The weather in {location} is currently 72 degrees and sunny.")
|
||||
|
||||
|
||||
async def get_image(params: FunctionCallParams):
|
||||
question = params.arguments["question"]
|
||||
logger.debug(f"Requesting image with user_id={client_id}, question={question}")
|
||||
|
||||
# Request the image frame
|
||||
await params.llm.request_image_frame(
|
||||
user_id=client_id,
|
||||
function_name=params.function_name,
|
||||
tool_call_id=params.tool_call_id,
|
||||
text_content=question,
|
||||
)
|
||||
|
||||
# Wait a short time for the frame to be processed
|
||||
await asyncio.sleep(0.5)
|
||||
|
||||
# Return a result to complete the function call
|
||||
await params.result_callback(
|
||||
f"I've captured an image from your camera and I'm analyzing what you asked about: {question}"
|
||||
)
|
||||
|
||||
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
@@ -50,18 +76,14 @@ transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
video_in_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
video_in_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
@@ -78,24 +100,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
timestamp = int(time.time())
|
||||
llm = OpenPipeLLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
openpipe_api_key=os.getenv("OPENPIPE_API_KEY"),
|
||||
tags={"conversation_id": f"pipecat-{timestamp}"},
|
||||
)
|
||||
|
||||
# You can also register a function_name of None to get all functions
|
||||
# sent to the same callback with an additional function_name parameter.
|
||||
llm.register_function("get_current_weather", fetch_weather_from_api)
|
||||
llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
|
||||
|
||||
@llm.event_handler("on_function_calls_started")
|
||||
async def on_function_calls_started(service, function_calls):
|
||||
await tts.queue_frame(TTSSpeakFrame("Let me check on that."))
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
llm.register_function("get_weather", get_weather)
|
||||
llm.register_function("get_image", get_image)
|
||||
|
||||
weather_function = FunctionSchema(
|
||||
name="get_current_weather",
|
||||
name="get_weather",
|
||||
description="Get the current weather",
|
||||
properties={
|
||||
"location": {
|
||||
@@ -108,26 +118,41 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
"description": "The temperature unit to use. Infer this from the user's location.",
|
||||
},
|
||||
},
|
||||
required=["location", "format"],
|
||||
)
|
||||
restaurant_function = FunctionSchema(
|
||||
name="get_restaurant_recommendation",
|
||||
description="Get a restaurant recommendation",
|
||||
properties={
|
||||
"location": {
|
||||
"type": "string",
|
||||
"description": "The city and state, e.g. San Francisco, CA",
|
||||
},
|
||||
},
|
||||
required=["location"],
|
||||
)
|
||||
tools = ToolsSchema(standard_tools=[weather_function, restaurant_function])
|
||||
|
||||
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.",
|
||||
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.
|
||||
|
||||
Your response will be turned into speech so use only simple words and punctuation.
|
||||
|
||||
You have access to two tools: get_weather and get_image.
|
||||
|
||||
You can respond to questions about the weather using the get_weather tool.
|
||||
|
||||
You can answer questions about the user's video stream using the get_image tool. Some examples of phrases that \
|
||||
indicate you should use the get_image tool are:
|
||||
- What do you see?
|
||||
- What's in the video?
|
||||
- Can you describe the video?
|
||||
- Tell me about what you see.
|
||||
- Tell me something interesting about what you see.
|
||||
- What's happening in the video?
|
||||
"""
|
||||
messages = [
|
||||
{"role": "system", "content": system_prompt},
|
||||
]
|
||||
|
||||
context = LLMContext(messages, tools)
|
||||
@@ -157,6 +182,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
|
||||
await maybe_capture_participant_camera(transport, client)
|
||||
|
||||
global client_id
|
||||
client_id = get_transport_client_id(transport, client)
|
||||
|
||||
# Kick off the conversation.
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@@ -76,8 +76,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
llm = GoogleVertexLLMService(
|
||||
credentials=os.getenv("GOOGLE_VERTEX_TEST_CREDENTIALS"),
|
||||
project_id=os.getenv("GOOGLE_CLOUD_PROJECT_ID"),
|
||||
location=os.getenv("GOOGLE_CLOUD_LOCATION"),
|
||||
params=GoogleVertexLLMService.InputParams(
|
||||
project_id=os.getenv("GOOGLE_CLOUD_PROJECT_ID"),
|
||||
),
|
||||
)
|
||||
# You can aslo register a function_name of None to get all functions
|
||||
# sent to the same callback with an additional function_name parameter.
|
||||
|
||||
@@ -79,8 +79,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
llm = AWSBedrockLLMService(
|
||||
aws_region="us-west-2",
|
||||
model="us.anthropic.claude-haiku-4-5-20251001-v1:0",
|
||||
params=AWSBedrockLLMService.InputParams(temperature=0.8),
|
||||
model="us.anthropic.claude-3-5-haiku-20241022-v1:0",
|
||||
params=AWSBedrockLLMService.InputParams(temperature=0.8, latency="optimized"),
|
||||
)
|
||||
|
||||
# You can also register a function_name of None to get all functions
|
||||
|
||||
@@ -26,11 +26,7 @@ from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.deepgram.tts import DeepgramTTSService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import (
|
||||
DailyOutputTransportMessageFrame,
|
||||
DailyOutputTransportMessageUrgentFrame,
|
||||
DailyParams,
|
||||
)
|
||||
from pipecat.transports.daily.transport import DailyParams, DailyTransportMessageFrame
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
@@ -132,14 +128,14 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.debug(f"Received latency ping app message: {message}")
|
||||
ts = message["latency-ping"]["ts"]
|
||||
# Send immediately
|
||||
await task.queue_frame(
|
||||
DailyOutputTransportMessageUrgentFrame(
|
||||
transport.output().send_message(
|
||||
DailyTransportMessageFrame(
|
||||
message={"latency-pong-msg-handler": {"ts": ts}}, participant_id=sender
|
||||
)
|
||||
)
|
||||
# And push to the pipeline for the Daily transport.output to send
|
||||
await task.queue_frame(
|
||||
DailyOutputTransportMessageFrame(
|
||||
DailyTransportMessageFrame(
|
||||
message={"latency-pong-pipeline-delivery": {"ts": ts}},
|
||||
participant_id=sender,
|
||||
)
|
||||
|
||||
@@ -5,7 +5,6 @@
|
||||
#
|
||||
|
||||
|
||||
import asyncio
|
||||
import os
|
||||
from datetime import datetime
|
||||
|
||||
@@ -15,27 +14,24 @@ from loguru import logger
|
||||
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 LLMRunFrame, LLMSetToolsFrame, TranscriptionMessage
|
||||
from pipecat.frames.frames import LLMRunFrame, TranscriptionMessage
|
||||
from pipecat.observers.loggers.transcription_log_observer import TranscriptionLogObserver
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response import LLMAssistantAggregatorParams
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
|
||||
from pipecat.processors.transcript_processor import TranscriptProcessor
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.llm_service import FunctionCallParams
|
||||
from pipecat.services.openai.realtime.events import (
|
||||
AudioConfiguration,
|
||||
AudioInput,
|
||||
from pipecat.services.openai_realtime import (
|
||||
InputAudioNoiseReduction,
|
||||
InputAudioTranscription,
|
||||
OpenAIRealtimeLLMService,
|
||||
SemanticTurnDetection,
|
||||
SessionProperties,
|
||||
)
|
||||
from pipecat.services.openai.realtime.llm import OpenAIRealtimeLLMService
|
||||
from pipecat.services.openai_realtime.events import AudioConfiguration, AudioInput
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
@@ -55,18 +51,6 @@ async def fetch_weather_from_api(params: FunctionCallParams):
|
||||
)
|
||||
|
||||
|
||||
async def get_news(params: FunctionCallParams):
|
||||
await params.result_callback(
|
||||
{
|
||||
"news": [
|
||||
"Massive UFO currently hovering above New York City",
|
||||
"Stock markets reach all-time highs",
|
||||
"Living dinosaur species discovered in the Amazon rainforest",
|
||||
],
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
async def fetch_restaurant_recommendation(params: FunctionCallParams):
|
||||
await params.result_callback({"name": "The Golden Dragon"})
|
||||
|
||||
@@ -88,13 +72,6 @@ weather_function = FunctionSchema(
|
||||
required=["location", "format"],
|
||||
)
|
||||
|
||||
get_news_function = FunctionSchema(
|
||||
name="get_news",
|
||||
description="Get the current news.",
|
||||
properties={},
|
||||
required=[],
|
||||
)
|
||||
|
||||
restaurant_function = FunctionSchema(
|
||||
name="get_restaurant_recommendation",
|
||||
description="Get a restaurant recommendation",
|
||||
@@ -162,6 +139,10 @@ even if you're asked about them.
|
||||
You are participating in a voice conversation. Keep your responses concise, short, and to the point
|
||||
unless specifically asked to elaborate on a topic.
|
||||
|
||||
You have access to the following tools:
|
||||
- get_current_weather: Get the current weather for a given location.
|
||||
- get_restaurant_recommendation: Get a restaurant recommendation for a given location.
|
||||
|
||||
Remember, your responses should be short. Just one or two sentences, usually. Respond in English.""",
|
||||
)
|
||||
|
||||
@@ -175,26 +156,25 @@ Remember, your responses should be short. Just one or two sentences, usually. Re
|
||||
# llm.register_function(None, fetch_weather_from_api)
|
||||
llm.register_function("get_current_weather", fetch_weather_from_api)
|
||||
llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
|
||||
llm.register_function("get_news", get_news)
|
||||
|
||||
transcript = TranscriptProcessor()
|
||||
|
||||
# Create a standard OpenAI LLM context object using the normal messages format. The
|
||||
# OpenAIRealtimeLLMService will convert this internally to messages that the
|
||||
# openai WebSocket API can understand.
|
||||
context = LLMContext(
|
||||
context = OpenAILLMContext(
|
||||
[{"role": "user", "content": "Say hello!"}],
|
||||
tools,
|
||||
)
|
||||
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
context_aggregator = llm.create_context_aggregator(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
context_aggregator.user(),
|
||||
transcript.user(), # LLM pushes TranscriptionFrames upstream
|
||||
llm, # LLM
|
||||
transcript.user(), # Placed after the LLM, as LLM pushes TranscriptionFrames downstream
|
||||
transport.output(), # Transport bot output
|
||||
transcript.assistant(), # After the transcript output, to time with the audio output
|
||||
context_aggregator.assistant(),
|
||||
@@ -217,13 +197,6 @@ Remember, your responses should be short. Just one or two sentences, usually. Re
|
||||
# Kick off the conversation.
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
# Add a new tool at runtime after a delay.
|
||||
await asyncio.sleep(15)
|
||||
new_tools = ToolsSchema(
|
||||
standard_tools=[weather_function, restaurant_function, get_news_function]
|
||||
)
|
||||
await task.queue_frames([LLMSetToolsFrame(tools=new_tools)])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
|
||||
@@ -18,19 +18,16 @@ from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response import LLMAssistantAggregatorParams
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.azure.realtime.llm import AzureRealtimeLLMService
|
||||
from pipecat.services.llm_service import FunctionCallParams
|
||||
from pipecat.services.openai.realtime.events import (
|
||||
AudioConfiguration,
|
||||
AudioInput,
|
||||
from pipecat.services.openai_realtime import (
|
||||
AzureRealtimeLLMService,
|
||||
InputAudioTranscription,
|
||||
SessionProperties,
|
||||
)
|
||||
from pipecat.services.openai_realtime.events import AudioConfiguration, AudioInput
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
@@ -157,10 +154,10 @@ Remember, your responses should be short. Just one or two sentences, usually. Re
|
||||
llm.register_function("get_current_weather", fetch_weather_from_api)
|
||||
llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
|
||||
|
||||
# Create a standard LLM context object using the normal messages format. The
|
||||
# Create a standard OpenAI LLM context object using the normal messages format. The
|
||||
# OpenAIRealtimeBetaLLMService will convert this internally to messages that the
|
||||
# openai WebSocket API can understand.
|
||||
context = LLMContext(
|
||||
context = OpenAILLMContext(
|
||||
[{"role": "user", "content": "Say hello!"}],
|
||||
# [{"role": "user", "content": [{"type": "text", "text": "Say hello!"}]}],
|
||||
# [
|
||||
@@ -175,7 +172,7 @@ Remember, your responses should be short. Just one or two sentences, usually. Re
|
||||
tools,
|
||||
)
|
||||
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
context_aggregator = llm.create_context_aggregator(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
|
||||
@@ -18,22 +18,20 @@ from pipecat.frames.frames import LLMRunFrame, TranscriptionMessage
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
|
||||
from pipecat.processors.transcript_processor import TranscriptProcessor
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.cartesia import CartesiaTTSService
|
||||
from pipecat.services.llm_service import FunctionCallParams
|
||||
from pipecat.services.openai.realtime.events import (
|
||||
AudioConfiguration,
|
||||
AudioInput,
|
||||
from pipecat.services.openai_realtime import (
|
||||
InputAudioNoiseReduction,
|
||||
InputAudioTranscription,
|
||||
OpenAIRealtimeLLMService,
|
||||
SemanticTurnDetection,
|
||||
SessionProperties,
|
||||
)
|
||||
from pipecat.services.openai.realtime.llm import OpenAIRealtimeLLMService
|
||||
from pipecat.services.openai_realtime.events import AudioConfiguration, AudioInput
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
@@ -170,20 +168,20 @@ Remember, your responses should be short. Just one or two sentences, usually. Re
|
||||
# Create a standard OpenAI LLM context object using the normal messages format. The
|
||||
# OpenAIRealtimeLLMService will convert this internally to messages that the
|
||||
# openai WebSocket API can understand.
|
||||
context = LLMContext(
|
||||
context = OpenAILLMContext(
|
||||
[{"role": "user", "content": "Say hello!"}],
|
||||
tools,
|
||||
)
|
||||
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
context_aggregator = llm.create_context_aggregator(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
context_aggregator.user(),
|
||||
transcript.user(), # LLM pushes TranscriptionFrames upstream
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transcript.user(), # Placed after the LLM, as LLM pushes TranscriptionFrames downstream
|
||||
transport.output(), # Transport bot output
|
||||
transcript.assistant(), # After the transcript output, to time with the audio output
|
||||
context_aggregator.assistant(),
|
||||
|
||||
@@ -13,27 +13,25 @@ from datetime import datetime
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
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 LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.processors.aggregators.openai_llm_context import (
|
||||
OpenAILLMContext,
|
||||
)
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.llm_service import FunctionCallParams
|
||||
from pipecat.services.openai.realtime.events import (
|
||||
AudioConfiguration,
|
||||
AudioInput,
|
||||
from pipecat.services.openai_realtime import (
|
||||
InputAudioTranscription,
|
||||
OpenAIRealtimeLLMService,
|
||||
SessionProperties,
|
||||
TurnDetection,
|
||||
)
|
||||
from pipecat.services.openai.realtime.llm import OpenAIRealtimeLLMService
|
||||
from pipecat.services.openai_realtime.events import AudioConfiguration, AudioInput
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
@@ -70,11 +68,11 @@ async def save_conversation(params: FunctionCallParams):
|
||||
timestamp = datetime.now().strftime("%Y-%m-%d_%H:%M:%S")
|
||||
filename = f"{BASE_FILENAME}{timestamp}.json"
|
||||
logger.debug(
|
||||
f"writing conversation to {filename}\n{json.dumps(params.context.get_messages(), indent=4)}"
|
||||
f"writing conversation to {filename}\n{json.dumps(params.context.messages, indent=4)}"
|
||||
)
|
||||
try:
|
||||
with open(filename, "w") as file:
|
||||
messages = params.context.get_messages()
|
||||
messages = params.context.get_messages_for_persistent_storage()
|
||||
# remove the last message, which is the instruction we just gave to save the conversation
|
||||
messages.pop()
|
||||
json.dump(messages, file, indent=2)
|
||||
@@ -91,10 +89,6 @@ async def load_conversation(params: FunctionCallParams):
|
||||
with open(filename, "r") as file:
|
||||
params.context.set_messages(json.load(file))
|
||||
await params.llm.reset_conversation()
|
||||
# NOTE: we manually create a response here rather than relying
|
||||
# on the function callback to trigger one since we've reset the
|
||||
# conversation so the remote service doesn't know about the
|
||||
# in-progress tool call.
|
||||
await params.llm._create_response()
|
||||
except Exception as e:
|
||||
await params.result_callback({"success": False, "error": str(e)})
|
||||
@@ -102,12 +96,14 @@ async def load_conversation(params: FunctionCallParams):
|
||||
asyncio.create_task(_reset())
|
||||
|
||||
|
||||
tools = ToolsSchema(
|
||||
standard_tools=[
|
||||
FunctionSchema(
|
||||
name="get_current_weather",
|
||||
description="Get the current weather",
|
||||
properties={
|
||||
tools = [
|
||||
{
|
||||
"type": "function",
|
||||
"name": "get_current_weather",
|
||||
"description": "Get the current weather",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"location": {
|
||||
"type": "string",
|
||||
"description": "The city and state, e.g. San Francisco, CA",
|
||||
@@ -118,33 +114,45 @@ tools = ToolsSchema(
|
||||
"description": "The temperature unit to use. Infer this from the users location.",
|
||||
},
|
||||
},
|
||||
required=["location", "format"],
|
||||
),
|
||||
FunctionSchema(
|
||||
name="save_conversation",
|
||||
description="Save the current conversatione. Use this function to persist the current conversation to external storage.",
|
||||
properties={},
|
||||
required=[],
|
||||
),
|
||||
FunctionSchema(
|
||||
name="get_saved_conversation_filenames",
|
||||
description="Get a list of saved conversation histories. Returns a list of filenames. Each filename includes a date and timestamp. Each file is conversation history that can be loaded into this session.",
|
||||
properties={},
|
||||
required=[],
|
||||
),
|
||||
FunctionSchema(
|
||||
name="load_conversation",
|
||||
description="Load a conversation history. Use this function to load a conversation history into the current session.",
|
||||
properties={
|
||||
"required": ["location", "format"],
|
||||
},
|
||||
},
|
||||
{
|
||||
"type": "function",
|
||||
"name": "save_conversation",
|
||||
"description": "Save the current conversatione. Use this function to persist the current conversation to external storage.",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {},
|
||||
"required": [],
|
||||
},
|
||||
},
|
||||
{
|
||||
"type": "function",
|
||||
"name": "get_saved_conversation_filenames",
|
||||
"description": "Get a list of saved conversation histories. Returns a list of filenames. Each filename includes a date and timestamp. Each file is conversation history that can be loaded into this session.",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {},
|
||||
"required": [],
|
||||
},
|
||||
},
|
||||
{
|
||||
"type": "function",
|
||||
"name": "load_conversation",
|
||||
"description": "Load a conversation history. Use this function to load a conversation history into the current session.",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"filename": {
|
||||
"type": "string",
|
||||
"description": "The filename of the conversation history to load.",
|
||||
}
|
||||
},
|
||||
required=["filename"],
|
||||
),
|
||||
]
|
||||
)
|
||||
"required": ["filename"],
|
||||
},
|
||||
},
|
||||
]
|
||||
|
||||
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
@@ -215,8 +223,8 @@ Remember, your responses should be short. Just one or two sentences, usually."""
|
||||
llm.register_function("get_saved_conversation_filenames", get_saved_conversation_filenames)
|
||||
llm.register_function("load_conversation", load_conversation)
|
||||
|
||||
context = LLMContext([{"role": "user", "content": "Say hello!"}], tools)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
context = OpenAILLMContext([], tools)
|
||||
context_aggregator = llm.create_context_aggregator(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
|
||||
@@ -72,6 +72,7 @@ async def save_conversation(params: FunctionCallParams):
|
||||
)
|
||||
try:
|
||||
with open(filename, "w") as file:
|
||||
# todo: extract 'system' into the first message in the list
|
||||
messages = params.context.get_messages()
|
||||
# remove the last message, which is the instruction we just gave to save the conversation
|
||||
messages.pop()
|
||||
|
||||
@@ -90,6 +90,7 @@ async def save_conversation(params: FunctionCallParams):
|
||||
)
|
||||
try:
|
||||
with open(filename, "w") as file:
|
||||
# todo: extract 'system' into the first message in the list
|
||||
messages = params.context.get_messages()
|
||||
# remove the last message (the instruction to save the context)
|
||||
messages.pop()
|
||||
|
||||
@@ -20,12 +20,10 @@ from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.aws.nova_sonic.llm import AWSNovaSonicLLMService
|
||||
from pipecat.services.aws_nova_sonic.aws import AWSNovaSonicLLMService
|
||||
from pipecat.services.llm_service import FunctionCallParams
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
@@ -77,7 +75,7 @@ async def save_conversation(params: FunctionCallParams):
|
||||
filename = f"{BASE_FILENAME}{timestamp}.json"
|
||||
try:
|
||||
with open(filename, "w") as file:
|
||||
messages = params.context.get_messages()
|
||||
messages = params.context.get_messages_for_persistent_storage()
|
||||
# remove the last few messages. in reverse order, they are:
|
||||
# - the in progress save tool call
|
||||
# - the invocation of the save tool call
|
||||
@@ -225,13 +223,13 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
llm.register_function("get_saved_conversation_filenames", get_saved_conversation_filenames)
|
||||
llm.register_function("load_conversation", load_conversation)
|
||||
|
||||
context = LLMContext(
|
||||
context = OpenAILLMContext(
|
||||
messages=[
|
||||
{"role": "system", "content": f"{system_instruction}"},
|
||||
],
|
||||
tools=tools,
|
||||
)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
context_aggregator = llm.create_context_aggregator(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
|
||||
@@ -17,7 +17,7 @@ from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.google.gemini_live.llm import GeminiLiveLLMService
|
||||
from pipecat.services.gemini_multimodal_live.gemini import GeminiMultimodalLiveLLMService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
@@ -65,7 +65,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
Respond to what the user said in a creative and helpful way.
|
||||
"""
|
||||
|
||||
llm = GeminiLiveLLMService(
|
||||
llm = GeminiMultimodalLiveLLMService(
|
||||
api_key=os.getenv("GOOGLE_API_KEY"),
|
||||
system_instruction=system_instruction,
|
||||
voice_id="Puck", # Aoede, Charon, Fenrir, Kore, Puck
|
||||
@@ -16,13 +16,11 @@ from pipecat.frames.frames import LLMRunFrame, TranscriptionMessage
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response import LLMAssistantAggregatorParams
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
|
||||
from pipecat.processors.transcript_processor import TranscriptProcessor
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.google.gemini_live.llm import GeminiLiveLLMService
|
||||
from pipecat.services.gemini_multimodal_live.gemini import GeminiMultimodalLiveLLMService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
@@ -67,14 +65,14 @@ transport_params = {
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
llm = GeminiLiveLLMService(
|
||||
llm = GeminiMultimodalLiveLLMService(
|
||||
api_key=os.getenv("GOOGLE_API_KEY"),
|
||||
voice_id="Aoede", # Puck, Charon, Kore, Fenrir, Aoede
|
||||
# system_instruction="Talk like a pirate."
|
||||
# inference_on_context_initialization=False,
|
||||
)
|
||||
|
||||
context = LLMContext(
|
||||
context = OpenAILLMContext(
|
||||
[
|
||||
{
|
||||
"role": "user",
|
||||
@@ -92,7 +90,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
# },
|
||||
],
|
||||
)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
context_aggregator = llm.create_context_aggregator(context)
|
||||
|
||||
transcript = TranscriptProcessor()
|
||||
|
||||
@@ -19,12 +19,10 @@ from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response import LLMAssistantAggregatorParams
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.google.gemini_live.llm import GeminiLiveLLMService
|
||||
from pipecat.services.gemini_multimodal_live.gemini import GeminiMultimodalLiveLLMService
|
||||
from pipecat.services.llm_service import FunctionCallParams
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
@@ -124,15 +122,12 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
required=["location"],
|
||||
)
|
||||
search_tool = {"google_search": {}}
|
||||
# KNOWN ISSUE: If using GeminiVertexLiveLLMService, it appears
|
||||
# you cannot use the "google_search" tool alongside other tools.
|
||||
# See https://github.com/googleapis/python-genai/issues/941.
|
||||
tools = ToolsSchema(
|
||||
standard_tools=[weather_function, restaurant_function],
|
||||
custom_tools={AdapterType.GEMINI: [search_tool]},
|
||||
)
|
||||
|
||||
llm = GeminiLiveLLMService(
|
||||
llm = GeminiMultimodalLiveLLMService(
|
||||
api_key=os.getenv("GOOGLE_API_KEY"),
|
||||
system_instruction=system_instruction,
|
||||
tools=tools,
|
||||
@@ -141,10 +136,10 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
llm.register_function("get_current_weather", fetch_weather_from_api)
|
||||
llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
|
||||
|
||||
context = LLMContext(
|
||||
context = OpenAILLMContext(
|
||||
[{"role": "user", "content": "Say hello."}],
|
||||
)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
context_aggregator = llm.create_context_aggregator(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
@@ -17,16 +17,14 @@ from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response import LLMAssistantAggregatorParams
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import (
|
||||
create_transport,
|
||||
maybe_capture_participant_camera,
|
||||
maybe_capture_participant_screen,
|
||||
)
|
||||
from pipecat.services.google.gemini_live.llm import GeminiLiveLLMService
|
||||
from pipecat.services.gemini_multimodal_live.gemini import GeminiMultimodalLiveLLMService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
|
||||
@@ -60,14 +58,14 @@ transport_params = {
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
llm = GeminiLiveLLMService(
|
||||
llm = GeminiMultimodalLiveLLMService(
|
||||
api_key=os.getenv("GOOGLE_API_KEY"),
|
||||
voice_id="Aoede", # Puck, Charon, Kore, Fenrir, Aoede
|
||||
# system_instruction="Talk like a pirate."
|
||||
# inference_on_context_initialization=False,
|
||||
)
|
||||
|
||||
context = LLMContext(
|
||||
context = OpenAILLMContext(
|
||||
[
|
||||
{
|
||||
"role": "user",
|
||||
@@ -75,7 +73,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
},
|
||||
],
|
||||
)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
context_aggregator = llm.create_context_aggregator(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
@@ -16,14 +16,13 @@ from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.google.gemini_live.llm import (
|
||||
GeminiLiveLLMService,
|
||||
GeminiModalities,
|
||||
from pipecat.services.gemini_multimodal_live.gemini import (
|
||||
GeminiMultimodalLiveLLMService,
|
||||
GeminiMultimodalModalities,
|
||||
InputParams,
|
||||
)
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
@@ -81,15 +80,11 @@ transport_params = {
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
# KNOWN ISSUE: If using GeminiLiveVertexLLMService, you cannot specify a
|
||||
# modality other than AUDIO (at least not if using the service's default
|
||||
# model, which is a native audio model:
|
||||
# https://cloud.google.com/vertex-ai/generative-ai/docs/live-api/tools#native-audio).
|
||||
llm = GeminiLiveLLMService(
|
||||
llm = GeminiMultimodalLiveLLMService(
|
||||
api_key=os.getenv("GOOGLE_API_KEY"),
|
||||
system_instruction=SYSTEM_INSTRUCTION,
|
||||
tools=[{"google_search": {}}, {"code_execution": {}}],
|
||||
params=InputParams(modalities=GeminiModalities.TEXT),
|
||||
params=InputParams(modalities=GeminiMultimodalModalities.TEXT),
|
||||
)
|
||||
|
||||
# Optionally, you can set the response modalities via a function
|
||||
@@ -110,8 +105,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
# Set up conversation context and management
|
||||
# The context_aggregator will automatically collect conversation context
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
context = OpenAILLMContext(messages)
|
||||
context_aggregator = llm.create_context_aggregator(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
@@ -16,12 +16,10 @@ from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response import LLMAssistantAggregatorParams
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.google.gemini_live.llm import GeminiLiveLLMService
|
||||
from pipecat.services.gemini_multimodal_live.gemini import GeminiMultimodalLiveLLMService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
@@ -85,14 +83,14 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
# Initialize the Gemini Multimodal Live model
|
||||
llm = GeminiLiveLLMService(
|
||||
llm = GeminiMultimodalLiveLLMService(
|
||||
api_key=os.getenv("GOOGLE_API_KEY"),
|
||||
voice_id="Puck", # Aoede, Charon, Fenrir, Kore, Puck
|
||||
system_instruction=system_instruction,
|
||||
tools=tools,
|
||||
)
|
||||
|
||||
context = LLMContext(
|
||||
context = OpenAILLMContext(
|
||||
[
|
||||
{
|
||||
"role": "user",
|
||||
@@ -100,7 +98,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
}
|
||||
],
|
||||
)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
context_aggregator = llm.create_context_aggregator(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
@@ -16,12 +16,12 @@ from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response import LLMAssistantAggregatorParams
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.google.gemini_live.llm import GeminiLiveLLMService
|
||||
from pipecat.services.gemini_multimodal_live.gemini import (
|
||||
GeminiMultimodalLiveLLMService,
|
||||
)
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
@@ -110,7 +110,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
"""
|
||||
|
||||
# Initialize Gemini service with File API support
|
||||
llm = GeminiLiveLLMService(
|
||||
llm = GeminiMultimodalLiveLLMService(
|
||||
api_key=os.getenv("GOOGLE_API_KEY"),
|
||||
system_instruction=system_instruction,
|
||||
voice_id="Charon", # Aoede, Charon, Fenrir, Kore, Puck
|
||||
@@ -131,7 +131,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
mime_type = "text/plain"
|
||||
|
||||
# Create context with file reference
|
||||
context = LLMContext(
|
||||
context = OpenAILLMContext(
|
||||
[
|
||||
{
|
||||
"role": "user",
|
||||
@@ -154,7 +154,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
except Exception as e:
|
||||
logger.error(f"Error uploading file: {e}")
|
||||
# Continue with a basic context if file upload fails
|
||||
context = LLMContext(
|
||||
context = OpenAILLMContext(
|
||||
[
|
||||
{
|
||||
"role": "user",
|
||||
@@ -164,7 +164,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
)
|
||||
|
||||
# Create context aggregator
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
context_aggregator = llm.create_context_aggregator(context)
|
||||
|
||||
# Build the pipeline
|
||||
pipeline = Pipeline(
|
||||
@@ -9,15 +9,13 @@ from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import Frame, LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response import LLMAssistantAggregatorParams
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
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.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.gemini_multimodal_live.gemini import GeminiMultimodalLiveLLMService
|
||||
from pipecat.services.google.frames import LLMSearchResponseFrame
|
||||
from pipecat.services.google.gemini_live.llm import GeminiLiveLLMService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
@@ -107,7 +105,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
custom_tools={AdapterType.GEMINI: [{"google_search": {}}, {"code_execution": {}}]},
|
||||
)
|
||||
|
||||
llm = GeminiLiveLLMService(
|
||||
llm = GeminiMultimodalLiveLLMService(
|
||||
api_key=os.getenv("GOOGLE_API_KEY"),
|
||||
system_instruction=SYSTEM_INSTRUCTION,
|
||||
voice_id="Charon", # Aoede, Charon, Fenrir, Kore, Puck
|
||||
@@ -126,8 +124,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
]
|
||||
|
||||
# Set up conversation context and management
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
context = OpenAILLMContext(messages)
|
||||
context_aggregator = llm.create_context_aggregator(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
@@ -1,189 +0,0 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
|
||||
import os
|
||||
from datetime import datetime
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.adapters.schemas.function_schema import FunctionSchema
|
||||
from pipecat.adapters.schemas.tools_schema import ToolsSchema
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response import LLMAssistantAggregatorParams
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.google.gemini_live.llm_vertex import GeminiLiveVertexLLMService
|
||||
from pipecat.services.llm_service import FunctionCallParams
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
async def fetch_weather_from_api(params: FunctionCallParams):
|
||||
temperature = 75 if params.arguments["format"] == "fahrenheit" else 24
|
||||
await params.result_callback(
|
||||
{
|
||||
"conditions": "nice",
|
||||
"temperature": temperature,
|
||||
"format": params.arguments["format"],
|
||||
"timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"),
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
async def fetch_restaurant_recommendation(params: FunctionCallParams):
|
||||
await params.result_callback({"name": "The Golden Dragon"})
|
||||
|
||||
|
||||
system_instruction = """
|
||||
You are a helpful assistant who can answer questions and use tools.
|
||||
|
||||
You have three tools available to you:
|
||||
1. get_current_weather: Use this tool to get the current weather in a specific location.
|
||||
2. get_restaurant_recommendation: Use this tool to get a restaurant recommendation in a specific location.
|
||||
"""
|
||||
|
||||
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
# set stop_secs to something roughly similar to the internal setting
|
||||
# of the Multimodal Live api, just to align events. This doesn't really
|
||||
# matter because we can only use the Multimodal Live API's phrase
|
||||
# endpointing, for now.
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.5)),
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
# set stop_secs to something roughly similar to the internal setting
|
||||
# of the Multimodal Live api, just to align events. This doesn't really
|
||||
# matter because we can only use the Multimodal Live API's phrase
|
||||
# endpointing, for now.
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.5)),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
# set stop_secs to something roughly similar to the internal setting
|
||||
# of the Multimodal Live api, just to align events. This doesn't really
|
||||
# matter because we can only use the Multimodal Live API's phrase
|
||||
# endpointing, for now.
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.5)),
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
weather_function = FunctionSchema(
|
||||
name="get_current_weather",
|
||||
description="Get the current weather",
|
||||
properties={
|
||||
"location": {
|
||||
"type": "string",
|
||||
"description": "The city and state, e.g. San Francisco, CA",
|
||||
},
|
||||
"format": {
|
||||
"type": "string",
|
||||
"enum": ["celsius", "fahrenheit"],
|
||||
"description": "The temperature unit to use. Infer this from the user's location.",
|
||||
},
|
||||
},
|
||||
required=["location", "format"],
|
||||
)
|
||||
restaurant_function = FunctionSchema(
|
||||
name="get_restaurant_recommendation",
|
||||
description="Get a restaurant recommendation",
|
||||
properties={
|
||||
"location": {
|
||||
"type": "string",
|
||||
"description": "The city and state, e.g. San Francisco, CA",
|
||||
},
|
||||
},
|
||||
required=["location"],
|
||||
)
|
||||
# KNOWN ISSUE: If using GeminiVertexLiveLLMService, it appears
|
||||
# you cannot use the "google_search" tool alongside other tools.
|
||||
# See https://github.com/googleapis/python-genai/issues/941.
|
||||
tools = ToolsSchema(standard_tools=[weather_function, restaurant_function])
|
||||
|
||||
llm = GeminiLiveVertexLLMService(
|
||||
credentials=os.getenv("GOOGLE_VERTEX_TEST_CREDENTIALS"),
|
||||
project_id=os.getenv("GOOGLE_CLOUD_PROJECT_ID"),
|
||||
location=os.getenv("GOOGLE_CLOUD_LOCATION"),
|
||||
system_instruction=system_instruction,
|
||||
voice_id="Puck", # Aoede, Charon, Fenrir, Kore, Puck
|
||||
tools=tools,
|
||||
)
|
||||
|
||||
llm.register_function("get_current_weather", fetch_weather_from_api)
|
||||
llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
|
||||
|
||||
context = LLMContext([{"role": "user", "content": "Say hello."}])
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(),
|
||||
context_aggregator.user(),
|
||||
llm,
|
||||
transport.output(),
|
||||
context_aggregator.assistant(),
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
@@ -1,206 +0,0 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import os
|
||||
from datetime import datetime
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.adapters.schemas.function_schema import FunctionSchema
|
||||
from pipecat.adapters.schemas.tools_schema import AdapterType, ToolsSchema
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import EndTaskFrame, LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response import LLMAssistantAggregatorParams
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.processors.frame_processor import FrameDirection
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.google.gemini_live.llm import GeminiLiveLLMService
|
||||
from pipecat.services.llm_service import FunctionCallParams
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
async def fetch_weather_from_api(params: FunctionCallParams):
|
||||
temperature = 75 if params.arguments["format"] == "fahrenheit" else 24
|
||||
await params.result_callback(
|
||||
{
|
||||
"conditions": "nice",
|
||||
"temperature": temperature,
|
||||
"format": params.arguments["format"],
|
||||
"timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"),
|
||||
}
|
||||
)
|
||||
|
||||
|
||||
async def fetch_restaurant_recommendation(params: FunctionCallParams):
|
||||
await params.result_callback({"name": "The Golden Dragon"})
|
||||
|
||||
|
||||
async def end_conversation(params: FunctionCallParams):
|
||||
await params.result_callback({"success": True})
|
||||
await params.llm.push_frame(EndTaskFrame(), FrameDirection.UPSTREAM)
|
||||
|
||||
|
||||
system_instruction = """
|
||||
You are a helpful assistant who can answer questions and use tools.
|
||||
|
||||
You have three tools available to you:
|
||||
1. get_current_weather: Use this tool to get the current weather in a specific location.
|
||||
2. get_restaurant_recommendation: Use this tool to get a restaurant recommendation in a specific location.
|
||||
3. end_conversation: Use this tool to gracefully end the conversation.
|
||||
|
||||
After you've responded to the user three times, do two things, in order:
|
||||
1. Politely let them know that that's all the time you have today and say goodbye.
|
||||
2. *WITHOUT WAITING FOR THE USER TO RESPOND*, call the end_conversation tool to gracefully end the conversation.
|
||||
"""
|
||||
|
||||
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
# set stop_secs to something roughly similar to the internal setting
|
||||
# of the Multimodal Live api, just to align events. This doesn't really
|
||||
# matter because we can only use the Multimodal Live API's phrase
|
||||
# endpointing, for now.
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.5)),
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
# set stop_secs to something roughly similar to the internal setting
|
||||
# of the Multimodal Live api, just to align events. This doesn't really
|
||||
# matter because we can only use the Multimodal Live API's phrase
|
||||
# endpointing, for now.
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.5)),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
# set stop_secs to something roughly similar to the internal setting
|
||||
# of the Multimodal Live api, just to align events. This doesn't really
|
||||
# matter because we can only use the Multimodal Live API's phrase
|
||||
# endpointing, for now.
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.5)),
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
weather_function = FunctionSchema(
|
||||
name="get_current_weather",
|
||||
description="Get the current weather",
|
||||
properties={
|
||||
"location": {
|
||||
"type": "string",
|
||||
"description": "The city and state, e.g. San Francisco, CA",
|
||||
},
|
||||
"format": {
|
||||
"type": "string",
|
||||
"enum": ["celsius", "fahrenheit"],
|
||||
"description": "The temperature unit to use. Infer this from the user's location.",
|
||||
},
|
||||
},
|
||||
required=["location", "format"],
|
||||
)
|
||||
restaurant_function = FunctionSchema(
|
||||
name="get_restaurant_recommendation",
|
||||
description="Get a restaurant recommendation",
|
||||
properties={
|
||||
"location": {
|
||||
"type": "string",
|
||||
"description": "The city and state, e.g. San Francisco, CA",
|
||||
},
|
||||
},
|
||||
required=["location"],
|
||||
)
|
||||
end_conversation_function = FunctionSchema(
|
||||
name="end_conversation",
|
||||
description="Gracefully end the conversation",
|
||||
properties={},
|
||||
required=[],
|
||||
)
|
||||
search_tool = {"google_search": {}}
|
||||
tools = ToolsSchema(
|
||||
standard_tools=[weather_function, restaurant_function, end_conversation_function],
|
||||
custom_tools={AdapterType.GEMINI: [search_tool]},
|
||||
)
|
||||
|
||||
llm = GeminiLiveLLMService(
|
||||
api_key=os.getenv("GOOGLE_API_KEY"),
|
||||
system_instruction=system_instruction,
|
||||
tools=tools,
|
||||
)
|
||||
|
||||
llm.register_function("get_current_weather", fetch_weather_from_api)
|
||||
llm.register_function("get_restaurant_recommendation", fetch_restaurant_recommendation)
|
||||
llm.register_function("end_conversation", end_conversation)
|
||||
|
||||
context = LLMContext(
|
||||
[{"role": "user", "content": "Say hello."}],
|
||||
)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(),
|
||||
context_aggregator.user(),
|
||||
llm,
|
||||
transport.output(),
|
||||
context_aggregator.assistant(),
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
@@ -9,6 +9,7 @@ import os
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
from simli import SimliConfig
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
@@ -65,12 +66,11 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",
|
||||
voice_id="a167e0f3-df7e-4d52-a9c3-f949145efdab",
|
||||
)
|
||||
|
||||
simli_ai = SimliVideoService(
|
||||
api_key=os.getenv("SIMLI_API_KEY"),
|
||||
face_id="cace3ef7-a4c4-425d-a8cf-a5358eb0c427",
|
||||
SimliConfig(os.getenv("SIMLI_API_KEY"), os.getenv("SIMLI_FACE_ID")),
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o-mini")
|
||||
|
||||
@@ -29,6 +29,10 @@ from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
from pipecat.utils.string import match_endofsentence
|
||||
|
||||
logger.info("Loading Whisker debugger...")
|
||||
from pipecat_whisker import WhiskerObserver
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
@@ -52,6 +56,8 @@ class TranscriptHandler:
|
||||
"""
|
||||
self.messages: List[TranscriptionMessage] = []
|
||||
self.output_file: Optional[str] = output_file
|
||||
self._current_user_sentence = ""
|
||||
self._current_assistant_sentence = ""
|
||||
logger.debug(
|
||||
f"TranscriptHandler initialized {'with output_file=' + output_file if output_file else 'with log output only'}"
|
||||
)
|
||||
@@ -78,11 +84,29 @@ class TranscriptHandler:
|
||||
except Exception as e:
|
||||
logger.error(f"Error saving transcript message to file: {e}")
|
||||
|
||||
async def _save_sentence(self, role: str, content: str, timestamp: Optional[str] = None):
|
||||
"""Save a complete sentence as a transcript message.
|
||||
|
||||
Args:
|
||||
role: The role (user/assistant)
|
||||
content: The complete sentence content
|
||||
timestamp: Optional timestamp
|
||||
"""
|
||||
# Cast role to the appropriate literal type
|
||||
message_role = "user" if role == "user" else "assistant"
|
||||
sentence_message = TranscriptionMessage(
|
||||
role=message_role, content=content.strip(), timestamp=timestamp
|
||||
)
|
||||
self.messages.append(sentence_message)
|
||||
await self.save_message(sentence_message)
|
||||
|
||||
async def on_transcript_update(
|
||||
self, processor: TranscriptProcessor, frame: TranscriptionUpdateFrame
|
||||
):
|
||||
"""Handle new transcript messages.
|
||||
|
||||
Aggregates messages into complete sentences before saving them using match_endofsentence.
|
||||
|
||||
Args:
|
||||
processor: The TranscriptProcessor that emitted the update
|
||||
frame: TranscriptionUpdateFrame containing new messages
|
||||
@@ -90,8 +114,31 @@ class TranscriptHandler:
|
||||
logger.debug(f"Received transcript update with {len(frame.messages)} new messages")
|
||||
|
||||
for msg in frame.messages:
|
||||
self.messages.append(msg)
|
||||
await self.save_message(msg)
|
||||
# Accumulate text for the appropriate role
|
||||
if msg.role == "user":
|
||||
self._current_user_sentence += msg.content + " "
|
||||
# Check if we have a complete sentence
|
||||
if match_endofsentence(self._current_user_sentence):
|
||||
await self._save_sentence("user", self._current_user_sentence, msg.timestamp)
|
||||
self._current_user_sentence = ""
|
||||
elif msg.role == "assistant":
|
||||
self._current_assistant_sentence += msg.content + " "
|
||||
# Check if we have a complete sentence
|
||||
if match_endofsentence(self._current_assistant_sentence):
|
||||
await self._save_sentence(
|
||||
"assistant", self._current_assistant_sentence, msg.timestamp
|
||||
)
|
||||
self._current_assistant_sentence = ""
|
||||
|
||||
async def finalize_partial_sentences(self):
|
||||
"""Save any remaining partial sentences when the conversation ends."""
|
||||
if self._current_user_sentence.strip():
|
||||
await self._save_sentence("user", self._current_user_sentence)
|
||||
self._current_user_sentence = ""
|
||||
|
||||
if self._current_assistant_sentence.strip():
|
||||
await self._save_sentence("assistant", self._current_assistant_sentence)
|
||||
self._current_assistant_sentence = ""
|
||||
|
||||
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
@@ -160,12 +207,16 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
]
|
||||
)
|
||||
|
||||
# Create Whisker debugger observer
|
||||
whisker = WhiskerObserver(pipeline)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
observers=[whisker],
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
@@ -183,6 +234,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
# Finalize any partial sentences before canceling
|
||||
await transcript_handler.finalize_partial_sentences()
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
@@ -206,14 +206,6 @@ async def bot(runner_args: RunnerArguments):
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
if not os.getenv("NASA_API_KEY"):
|
||||
logger.error(
|
||||
f"Please set NASA_API_KEY environment variable for this example. See https://api.nasa.gov"
|
||||
)
|
||||
import sys
|
||||
|
||||
sys.exit(1)
|
||||
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
|
||||
@@ -141,14 +141,6 @@ async def bot(runner_args: RunnerArguments):
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
if not os.getenv("MCP_RUN_SSE_URL"):
|
||||
logger.error(
|
||||
f"Please set MCP_RUN_SSE_URL environment variable for this example. See https://mcp.run"
|
||||
)
|
||||
import sys
|
||||
|
||||
sys.exit(1)
|
||||
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
|
||||
@@ -219,14 +219,6 @@ async def bot(runner_args: RunnerArguments):
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
if not os.getenv("NASA_API_KEY") or not os.getenv("MCP_RUN_SSE_URL"):
|
||||
logger.error(
|
||||
f"Please set NASA_API_KEY and MCP_RUN_SSE_URL environment variables. See https://api.nasa.gov and https://mcp.run"
|
||||
)
|
||||
import sys
|
||||
|
||||
sys.exit(1)
|
||||
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
|
||||
@@ -145,14 +145,6 @@ async def bot(runner_args: RunnerArguments):
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
if not os.getenv("GITHUB_PERSONAL_ACCESS_TOKEN"):
|
||||
logger.error(
|
||||
f"Please set GITHUB_PERSONAL_ACCESS_TOKEN environment variable for this example."
|
||||
)
|
||||
import sys
|
||||
|
||||
sys.exit(1)
|
||||
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
|
||||
@@ -18,11 +18,10 @@ from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.aws.nova_sonic.llm import AWSNovaSonicLLMService
|
||||
from pipecat.services.aws_nova_sonic import AWSNovaSonicLLMService
|
||||
from pipecat.services.llm_service import FunctionCallParams
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
@@ -120,7 +119,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
llm.register_function("get_current_weather", fetch_weather_from_api)
|
||||
|
||||
# Set up context and context management.
|
||||
context = LLMContext(
|
||||
# AWSNovaSonicService will adapt OpenAI LLM context objects with standard message format to
|
||||
# what's expected by Nova Sonic.
|
||||
context = OpenAILLMContext(
|
||||
messages=[
|
||||
{"role": "system", "content": f"{system_instruction}"},
|
||||
{
|
||||
@@ -130,7 +131,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
],
|
||||
tools=tools,
|
||||
)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
context_aggregator = llm.create_context_aggregator(context)
|
||||
|
||||
# Build the pipeline
|
||||
pipeline = Pipeline(
|
||||
|
||||
@@ -15,14 +15,12 @@ from pipecat.frames.frames import Frame, InputImageRawFrame, LLMRunFrame, Output
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response import LLMAssistantAggregatorParams
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
|
||||
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
|
||||
from pipecat.processors.frameworks.rtvi import RTVIObserver, RTVIProcessor
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.google.gemini_live.llm import GeminiLiveLLMService
|
||||
from pipecat.services.gemini_multimodal_live import GeminiMultimodalLiveLLMService
|
||||
from pipecat.transports.base_transport import TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams, DailyTransport
|
||||
|
||||
@@ -96,7 +94,7 @@ Respond to what the user said in a creative and helpful way. Keep your responses
|
||||
|
||||
|
||||
async def run_bot(pipecat_transport):
|
||||
llm = GeminiLiveLLMService(
|
||||
llm = GeminiMultimodalLiveLLMService(
|
||||
api_key=os.getenv("GOOGLE_API_KEY"),
|
||||
voice_id="Puck", # Aoede, Charon, Fenrir, Kore, Puck
|
||||
transcribe_user_audio=True,
|
||||
@@ -110,8 +108,8 @@ async def run_bot(pipecat_transport):
|
||||
}
|
||||
]
|
||||
|
||||
context = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
context = OpenAILLMContext(messages)
|
||||
context_aggregator = llm.create_context_aggregator(context)
|
||||
|
||||
# RTVI events for Pipecat client UI
|
||||
rtvi = RTVIProcessor()
|
||||
|
||||
@@ -1,142 +0,0 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import os
|
||||
|
||||
import sentry_sdk
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.processors.metrics.sentry import SentryMetrics
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
# Initialize Sentry
|
||||
sentry_sdk.init(
|
||||
dsn=os.getenv("SENTRY_DSN"),
|
||||
traces_sample_rate=1.0,
|
||||
)
|
||||
|
||||
stt = DeepgramSTTService(
|
||||
api_key=os.getenv("DEEPGRAM_API_KEY"),
|
||||
metrics=SentryMetrics(),
|
||||
)
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
metrics=SentryMetrics(),
|
||||
)
|
||||
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
metrics=SentryMetrics(),
|
||||
)
|
||||
|
||||
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 = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt,
|
||||
context_aggregator.user(), # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
@@ -1,153 +0,0 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import asyncio
|
||||
import os
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
|
||||
from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
|
||||
from pipecat.audio.vad.silero import SileroVADAnalyzer
|
||||
from pipecat.audio.vad.vad_analyzer import VADParams
|
||||
from pipecat.frames.frames import LLMRunFrame, ManuallySwitchServiceFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.service_switcher import ServiceSwitcher, ServiceSwitcherStrategyManual
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.cartesia.stt import CartesiaSTTService
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.deepgram.stt import DeepgramSTTService
|
||||
from pipecat.services.deepgram.tts import DeepgramTTSService
|
||||
from pipecat.services.google.llm import GoogleLLMService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
# We store functions so objects (e.g. SileroVADAnalyzer) don't get
|
||||
# instantiated. The function will be called when the desired transport gets
|
||||
# selected.
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"twilio": lambda: FastAPIWebsocketParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
"webrtc": lambda: TransportParams(
|
||||
audio_in_enabled=True,
|
||||
audio_out_enabled=True,
|
||||
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
|
||||
turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info(f"Starting bot")
|
||||
|
||||
stt_cartesia = CartesiaSTTService(api_key=os.getenv("CARTESIA_API_KEY"))
|
||||
stt_deepgram = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
stt_switcher = ServiceSwitcher(
|
||||
services=[stt_cartesia, stt_deepgram], strategy_type=ServiceSwitcherStrategyManual
|
||||
)
|
||||
|
||||
tts_cartesia = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",
|
||||
)
|
||||
tts_deepgram = DeepgramTTSService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
tts_switcher = ServiceSwitcher(
|
||||
services=[tts_cartesia, tts_deepgram], strategy_type=ServiceSwitcherStrategyManual
|
||||
)
|
||||
|
||||
llm_openai = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
llm_google = GoogleLLMService(api_key=os.getenv("GOOGLE_API_KEY"))
|
||||
llm_switcher = ServiceSwitcher(
|
||||
services=[llm_openai, llm_google], strategy_type=ServiceSwitcherStrategyManual
|
||||
)
|
||||
|
||||
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 = LLMContext(messages)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt_switcher,
|
||||
context_aggregator.user(), # User responses
|
||||
llm_switcher, # LLM
|
||||
tts_switcher, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
context_aggregator.assistant(), # Assistant spoken responses
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
params=PipelineParams(
|
||||
enable_metrics=True,
|
||||
enable_usage_metrics=True,
|
||||
),
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info(f"Client connected")
|
||||
# Kick off the conversation.
|
||||
messages.append({"role": "system", "content": "Please introduce yourself to the user."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
await asyncio.sleep(15)
|
||||
print(f"Switching to {stt_deepgram}")
|
||||
await task.queue_frames([ManuallySwitchServiceFrame(service=stt_deepgram)])
|
||||
await asyncio.sleep(15)
|
||||
print(f"Switching to {llm_google}")
|
||||
await task.queue_frames([ManuallySwitchServiceFrame(service=llm_google)])
|
||||
await asyncio.sleep(15)
|
||||
print(f"Switching to {tts_deepgram}")
|
||||
await task.queue_frames([ManuallySwitchServiceFrame(service=tts_deepgram)])
|
||||
|
||||
@transport.event_handler("on_client_disconnected")
|
||||
async def on_client_disconnected(transport, client):
|
||||
logger.info(f"Client disconnected")
|
||||
await task.cancel()
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
@@ -105,7 +105,7 @@ uv run 07-interruptible.py -t twilio -x NGROK_HOST_NAME
|
||||
### Vision & Multimodal
|
||||
|
||||
- **[12a-describe-video-gemini-flash.py](./12a-describe-video-gemini-flash.py)**: Bot describes user's video (Video input, Multimodal LLMs)
|
||||
- **[26c-gemini-live-video.py](./26c-gemini-live-video.py)**: Gemini with video input (Streaming video, Function calls)
|
||||
- **[26c-gemini-multimodal-live-video.py](./26c-gemini-multimodal-live-video.py)**: Gemini with video input (Streaming video, Function calls)
|
||||
|
||||
### Voice & Language
|
||||
|
||||
|
||||
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|
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@@ -73,13 +73,13 @@ Transform your local bot into a production-ready service. Pipecat Cloud handles
|
||||
|
||||
1. [Sign up for Pipecat Cloud](https://pipecat.daily.co/sign-up).
|
||||
|
||||
2. Install the Pipecat CLI:
|
||||
2. Install the Pipecat Cloud CLI:
|
||||
|
||||
```bash
|
||||
uv tool install pipecat-ai-cli
|
||||
uv add pipecatcloud
|
||||
```
|
||||
|
||||
> 💡 Tip: You can run the `pipecat` CLI using the `pc` alias.
|
||||
> 💡 Tip: You can run the `pipecatcloud` CLI using the `pcc` alias.
|
||||
|
||||
3. Set up Docker for building your bot image:
|
||||
|
||||
@@ -113,22 +113,12 @@ secret_set = "quickstart-secrets"
|
||||
|
||||
> 💡 Tip: [Set up `image_credentials`](https://docs.pipecat.ai/deployment/pipecat-cloud/fundamentals/secrets#image-pull-secrets) in your TOML file for authenticated image pulls
|
||||
|
||||
### Log in to Pipecat Cloud
|
||||
|
||||
To start using the CLI, authenticate to Pipecat Cloud:
|
||||
|
||||
```bash
|
||||
pipecat cloud auth login
|
||||
```
|
||||
|
||||
You'll be presented with a link that you can click to authenticate your client.
|
||||
|
||||
### Configure secrets
|
||||
|
||||
Upload your API keys to Pipecat Cloud's secure storage:
|
||||
|
||||
```bash
|
||||
pipecat cloud secrets set quickstart-secrets --file .env
|
||||
uv run pcc secrets set quickstart-secrets --file .env
|
||||
```
|
||||
|
||||
This creates a secret set called `quickstart-secrets` (matching your TOML file) and uploads all your API keys from `.env`.
|
||||
@@ -138,13 +128,13 @@ This creates a secret set called `quickstart-secrets` (matching your TOML file)
|
||||
Build your Docker image and push to Docker Hub:
|
||||
|
||||
```bash
|
||||
pipecat cloud docker build-push
|
||||
uv run pcc docker build-push
|
||||
```
|
||||
|
||||
Deploy to Pipecat Cloud:
|
||||
|
||||
```bash
|
||||
pipecat cloud deploy
|
||||
uv run pcc deploy
|
||||
```
|
||||
|
||||
### Connect to your agent
|
||||
|
||||
@@ -1,11 +1,6 @@
|
||||
agent_name = "quickstart"
|
||||
image = "your_username/quickstart:0.1"
|
||||
secret_set = "quickstart-secrets"
|
||||
agent_profile = "agent-1x"
|
||||
|
||||
# RECOMMENDED: Set an image pull secret:
|
||||
# https://docs.pipecat.ai/deployment/pipecat-cloud/fundamentals/secrets#image-pull-secrets
|
||||
# image_credentials = "your_image_pull_secret"
|
||||
|
||||
[scaling]
|
||||
min_agents = 1
|
||||
|
||||
@@ -4,14 +4,13 @@ version = "0.1.0"
|
||||
description = "Quickstart example for building voice AI bots with Pipecat"
|
||||
requires-python = ">=3.10"
|
||||
dependencies = [
|
||||
"pipecat-ai[webrtc,daily,silero,deepgram,openai,cartesia,local-smart-turn-v3,runner]",
|
||||
"pipecat-ai-cli"
|
||||
"pipecat-ai[webrtc,daily,silero,deepgram,openai,cartesia,local-smart-turn-v3,runner]>=0.0.85",
|
||||
"pipecatcloud>=0.2.4"
|
||||
]
|
||||
|
||||
[dependency-groups]
|
||||
dev = [
|
||||
"pyright>=1.1.404,<2",
|
||||
"ruff>=0.12.11,<1",
|
||||
"ruff~=0.12.1",
|
||||
]
|
||||
|
||||
[tool.ruff]
|
||||
|
||||
@@ -34,7 +34,7 @@ dependencies = [
|
||||
"pyloudnorm~=0.1.1",
|
||||
"resampy~=0.4.3",
|
||||
"soxr~=0.5.0",
|
||||
"openai>=1.74.0,<3",
|
||||
"openai>=1.74.0,<=1.99.1",
|
||||
# Pinning numba to resolve package dependencies
|
||||
"numba==0.61.2",
|
||||
"wait_for2>=0.4.1; python_version<'3.12'",
|
||||
@@ -50,24 +50,23 @@ anthropic = [ "anthropic~=0.49.0" ]
|
||||
assemblyai = [ "pipecat-ai[websockets-base]" ]
|
||||
asyncai = [ "pipecat-ai[websockets-base]" ]
|
||||
aws = [ "aioboto3~=15.0.0", "pipecat-ai[websockets-base]" ]
|
||||
aws-nova-sonic = [ "aws_sdk_bedrock_runtime~=0.1.1; python_version>='3.12'" ]
|
||||
aws-nova-sonic = [ "aws_sdk_bedrock_runtime~=0.0.2; python_version>='3.12'" ]
|
||||
azure = [ "azure-cognitiveservices-speech~=1.42.0"]
|
||||
cartesia = [ "cartesia~=2.0.3", "pipecat-ai[websockets-base]" ]
|
||||
cerebras = []
|
||||
deepseek = []
|
||||
daily = [ "daily-python~=0.21.0" ]
|
||||
daily = [ "daily-python~=0.19.9" ]
|
||||
deepgram = [ "deepgram-sdk~=4.7.0" ]
|
||||
elevenlabs = [ "pipecat-ai[websockets-base]" ]
|
||||
fal = [ "fal-client~=0.5.9" ]
|
||||
fireworks = []
|
||||
fish = [ "ormsgpack~=1.7.0", "pipecat-ai[websockets-base]" ]
|
||||
gladia = [ "pipecat-ai[websockets-base]" ]
|
||||
google = [ "google-cloud-speech>=2.33.0,<3", "google-cloud-texttospeech>=2.31.0,<3", "google-genai>=1.41.0,<2", "pipecat-ai[websockets-base]" ]
|
||||
google = [ "google-cloud-speech~=2.32.0", "google-cloud-texttospeech~=2.26.0", "google-genai~=1.24.0", "pipecat-ai[websockets-base]" ]
|
||||
grok = []
|
||||
groq = [ "groq~=0.23.0" ]
|
||||
gstreamer = [ "pygobject~=3.50.0" ]
|
||||
heygen = [ "livekit>=1.0.13", "pipecat-ai[websockets-base]" ]
|
||||
hume = [ "hume>=0.11.2" ]
|
||||
inworld = []
|
||||
krisp = [ "pipecat-ai-krisp~=0.4.0" ]
|
||||
koala = [ "pvkoala~=2.0.3" ]
|
||||
@@ -84,7 +83,7 @@ nim = []
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||||
neuphonic = [ "pipecat-ai[websockets-base]" ]
|
||||
noisereduce = [ "noisereduce~=3.0.3" ]
|
||||
openai = [ "pipecat-ai[websockets-base]" ]
|
||||
openpipe = [ "openpipe>=4.50.0,<6" ]
|
||||
openpipe = [ "openpipe~=4.50.0" ]
|
||||
openrouter = []
|
||||
perplexity = []
|
||||
playht = [ "pipecat-ai[websockets-base]" ]
|
||||
@@ -102,7 +101,7 @@ silero = [ "onnxruntime>=1.20.1,<2" ]
|
||||
simli = [ "simli-ai~=0.1.10"]
|
||||
soniox = [ "pipecat-ai[websockets-base]" ]
|
||||
soundfile = [ "soundfile~=0.13.0" ]
|
||||
speechmatics = [ "speechmatics-rt>=0.5.0" ]
|
||||
speechmatics = [ "speechmatics-rt>=0.4.0" ]
|
||||
strands = [ "strands-agents>=1.9.1,<2" ]
|
||||
tavus=[]
|
||||
together = []
|
||||
|
||||
@@ -10,10 +10,9 @@ import os
|
||||
import re
|
||||
import time
|
||||
import wave
|
||||
from dataclasses import dataclass
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
from typing import Any, List, Optional, Tuple
|
||||
from typing import List, Optional, Tuple
|
||||
|
||||
import aiofiles
|
||||
from deepgram import LiveOptions
|
||||
@@ -35,8 +34,7 @@ from pipecat.frames.frames import EndTaskFrame, LLMRunFrame, OutputImageRawFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineParams, PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
|
||||
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
|
||||
from pipecat.processors.audio.audio_buffer_processor import AudioBufferProcessor
|
||||
from pipecat.processors.frame_processor import FrameDirection
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
@@ -54,14 +52,6 @@ EVAL_TIMEOUT_SECS = 120
|
||||
EvalPrompt = str | Tuple[str, ImageFile]
|
||||
|
||||
|
||||
@dataclass
|
||||
class EvalConfig:
|
||||
prompt: EvalPrompt
|
||||
eval: str
|
||||
eval_speaks_first: bool = False
|
||||
runner_args_body: Optional[Any] = None
|
||||
|
||||
|
||||
class EvalRunner:
|
||||
def __init__(
|
||||
self,
|
||||
@@ -102,7 +92,9 @@ class EvalRunner:
|
||||
async def run_eval(
|
||||
self,
|
||||
example_file: str,
|
||||
eval_config: EvalConfig,
|
||||
prompt: EvalPrompt,
|
||||
eval: str,
|
||||
user_speaks_first: bool = False,
|
||||
):
|
||||
if not re.match(self._pattern, example_file):
|
||||
return
|
||||
@@ -119,8 +111,10 @@ class EvalRunner:
|
||||
|
||||
try:
|
||||
tasks = [
|
||||
asyncio.create_task(run_example_pipeline(script_path, eval_config)),
|
||||
asyncio.create_task(run_eval_pipeline(self, example_file, eval_config)),
|
||||
asyncio.create_task(run_example_pipeline(script_path)),
|
||||
asyncio.create_task(
|
||||
run_eval_pipeline(self, example_file, prompt, eval, user_speaks_first)
|
||||
),
|
||||
]
|
||||
_, pending = await asyncio.wait(tasks, timeout=EVAL_TIMEOUT_SECS)
|
||||
if pending:
|
||||
@@ -182,7 +176,7 @@ class EvalRunner:
|
||||
return os.path.join(self._recordings_dir, f"{base_name}.wav")
|
||||
|
||||
|
||||
async def run_example_pipeline(script_path: Path, eval_config: EvalConfig):
|
||||
async def run_example_pipeline(script_path: Path):
|
||||
room_url = os.getenv("DAILY_SAMPLE_ROOM_URL")
|
||||
|
||||
module = load_module_from_path(script_path)
|
||||
@@ -201,7 +195,6 @@ async def run_example_pipeline(script_path: Path, eval_config: EvalConfig):
|
||||
|
||||
runner_args = RunnerArguments()
|
||||
runner_args.pipeline_idle_timeout_secs = PIPELINE_IDLE_TIMEOUT_SECS
|
||||
runner_args.body = eval_config.runner_args_body
|
||||
|
||||
await module.run_bot(transport, runner_args)
|
||||
|
||||
@@ -209,7 +202,9 @@ async def run_example_pipeline(script_path: Path, eval_config: EvalConfig):
|
||||
async def run_eval_pipeline(
|
||||
eval_runner: EvalRunner,
|
||||
example_file: str,
|
||||
eval_config: EvalConfig,
|
||||
prompt: EvalPrompt,
|
||||
eval: str,
|
||||
user_speaks_first: bool = False,
|
||||
):
|
||||
logger.info(f"Starting eval bot")
|
||||
|
||||
@@ -266,16 +261,17 @@ async def run_eval_pipeline(
|
||||
# Load example prompt depending on image.
|
||||
example_prompt = ""
|
||||
example_image: Optional[ImageFile] = None
|
||||
if isinstance(eval_config.prompt, str):
|
||||
example_prompt = eval_config.prompt
|
||||
elif isinstance(eval_config.prompt, tuple):
|
||||
example_prompt, example_image = eval_config.prompt
|
||||
if isinstance(prompt, str):
|
||||
example_prompt = prompt
|
||||
elif isinstance(prompt, tuple):
|
||||
example_prompt, example_image = prompt
|
||||
|
||||
eval_prompt = f"The answer is correct if it matches: {eval}."
|
||||
common_system_prompt = (
|
||||
"The user might say things other than the answer and that's allowed. "
|
||||
f"You should only call the eval function when the user: {eval_config.eval}"
|
||||
f"You should only call the eval function with your assessment when the user actually answers the question. {eval_prompt}"
|
||||
)
|
||||
if eval_config.eval_speaks_first:
|
||||
if user_speaks_first:
|
||||
system_prompt = f"You are an LLM eval, be extremly brief. You will start the conversation by saying: '{example_prompt}'. {common_system_prompt}"
|
||||
else:
|
||||
system_prompt = f"You are an LLM eval, be extremly brief. Your goal is to first ask one question: {example_prompt}. {common_system_prompt}"
|
||||
@@ -287,8 +283,8 @@ async def run_eval_pipeline(
|
||||
},
|
||||
]
|
||||
|
||||
context = LLMContext(messages, tools)
|
||||
context_aggregator = LLMContextAggregatorPair(context)
|
||||
context = OpenAILLMContext(messages, tools)
|
||||
context_aggregator = llm.create_context_aggregator(context)
|
||||
|
||||
audio_buffer = AudioBufferProcessor()
|
||||
|
||||
@@ -333,9 +329,9 @@ async def run_eval_pipeline(
|
||||
|
||||
# Default behavior is for the bot to speak first
|
||||
# If the eval bot speaks first, we append the prompt to the messages
|
||||
if eval_config.eval_speaks_first:
|
||||
if user_speaks_first:
|
||||
messages.append(
|
||||
{"role": "user", "content": f"Start by saying this exactly: '{eval_config.prompt}'"}
|
||||
{"role": "user", "content": f"Start by saying this exactly: '{prompt}'"}
|
||||
)
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
|
||||
|
||||
@@ -11,7 +11,7 @@ from datetime import datetime, timezone
|
||||
from pathlib import Path
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from eval import EvalConfig, EvalRunner
|
||||
from eval import EvalRunner
|
||||
from loguru import logger
|
||||
from PIL import Image
|
||||
from utils import check_env_variables
|
||||
@@ -24,184 +24,179 @@ ASSETS_DIR = SCRIPT_DIR / "assets"
|
||||
|
||||
FOUNDATIONAL_DIR = SCRIPT_DIR.parent.parent / "examples" / "foundational"
|
||||
|
||||
EVAL_SIMPLE_MATH = EvalConfig(
|
||||
prompt="A simple math addition.",
|
||||
eval="The user answers the math addition correctly.",
|
||||
# Speaking order constants
|
||||
USER_SPEAKS_FIRST = True
|
||||
BOT_SPEAKS_FIRST = False
|
||||
|
||||
# Math
|
||||
PROMPT_SIMPLE_MATH = "A simple math addition."
|
||||
EVAL_SIMPLE_MATH = "Correct math addition."
|
||||
|
||||
# Weather
|
||||
PROMPT_WEATHER = "What's the weather in San Francisco?"
|
||||
EVAL_WEATHER = (
|
||||
"Something specific about the current weather in San Francisco, including the degrees."
|
||||
)
|
||||
|
||||
EVAL_WEATHER = EvalConfig(
|
||||
prompt="What's the weather in San Francisco?",
|
||||
eval="The user says something specific about the current weather in San Francisco, including the degrees.",
|
||||
)
|
||||
# Online search
|
||||
PROMPT_ONLINE_SEARCH = "What's the date right now in London?"
|
||||
EVAL_ONLINE_SEARCH = f"Today is {datetime.now(timezone.utc).strftime('%B %d, %Y')}."
|
||||
|
||||
EVAL_ONLINE_SEARCH = EvalConfig(
|
||||
prompt="What's the date right now in London?",
|
||||
eval=f"The user says today is {datetime.now(timezone.utc).strftime('%B %d, %Y')} in London.",
|
||||
)
|
||||
# Switch language
|
||||
PROMPT_SWITCH_LANGUAGE = "Say something in Spanish."
|
||||
EVAL_SWITCH_LANGUAGE = "The user is now talking in Spanish."
|
||||
|
||||
EVAL_SWITCH_LANGUAGE = EvalConfig(
|
||||
prompt="Say something in Spanish.",
|
||||
eval="The user talks in Spanish.",
|
||||
)
|
||||
|
||||
EVAL_VISION_CAMERA = EvalConfig(
|
||||
prompt=("Briefly describe what you see.", Image.open(ASSETS_DIR / "cat.jpg")),
|
||||
eval="The user provides a cat description.",
|
||||
)
|
||||
|
||||
|
||||
def EVAL_VISION_IMAGE(*, eval_speaks_first: bool = False):
|
||||
return EvalConfig(
|
||||
prompt="Briefly describe this image.",
|
||||
eval="The user provides a cat description.",
|
||||
eval_speaks_first=eval_speaks_first,
|
||||
runner_args_body={
|
||||
"image_path": ASSETS_DIR / "cat.jpg",
|
||||
"question": "Briefly describe this image.",
|
||||
},
|
||||
)
|
||||
|
||||
|
||||
EVAL_VOICEMAIL = EvalConfig(
|
||||
prompt="Please leave a message.",
|
||||
eval="The user leaves a voicemail message.",
|
||||
eval_speaks_first=True,
|
||||
)
|
||||
|
||||
EVAL_CONVERSATION = EvalConfig(
|
||||
prompt="Hello, this is Mark.",
|
||||
eval="The user replies with a greeting.",
|
||||
eval_speaks_first=True,
|
||||
)
|
||||
# Vision
|
||||
PROMPT_VISION = ("What do you see?", Image.open(ASSETS_DIR / "cat.jpg"))
|
||||
EVAL_VISION = "A cat description."
|
||||
|
||||
# Voicemail
|
||||
PROMPT_VOICEMAIL = "Please leave a message after the beep."
|
||||
EVAL_VOICEMAIL = "Assess the conversation and determine if it is a voicemail."
|
||||
PROMPT_CONVERSATION = "Hello, this is Mark."
|
||||
EVAL_CONVERSATION = "A start of a conversation, not a voicemail."
|
||||
|
||||
TESTS_07 = [
|
||||
# 07 series
|
||||
("07-interruptible.py", EVAL_SIMPLE_MATH),
|
||||
("07-interruptible-cartesia-http.py", EVAL_SIMPLE_MATH),
|
||||
("07a-interruptible-speechmatics.py", EVAL_SIMPLE_MATH),
|
||||
("07aa-interruptible-soniox.py", EVAL_SIMPLE_MATH),
|
||||
("07ab-interruptible-inworld-http.py", EVAL_SIMPLE_MATH),
|
||||
("07ac-interruptible-asyncai.py", EVAL_SIMPLE_MATH),
|
||||
("07ac-interruptible-asyncai-http.py", EVAL_SIMPLE_MATH),
|
||||
("07b-interruptible-langchain.py", EVAL_SIMPLE_MATH),
|
||||
("07c-interruptible-deepgram.py", EVAL_SIMPLE_MATH),
|
||||
("07c-interruptible-deepgram-flux.py", EVAL_SIMPLE_MATH),
|
||||
("07c-interruptible-deepgram-http.py", EVAL_SIMPLE_MATH),
|
||||
("07d-interruptible-elevenlabs.py", EVAL_SIMPLE_MATH),
|
||||
("07d-interruptible-elevenlabs-http.py", EVAL_SIMPLE_MATH),
|
||||
("07f-interruptible-azure.py", EVAL_SIMPLE_MATH),
|
||||
("07g-interruptible-openai.py", EVAL_SIMPLE_MATH),
|
||||
("07h-interruptible-openpipe.py", EVAL_SIMPLE_MATH),
|
||||
("07j-interruptible-gladia.py", EVAL_SIMPLE_MATH),
|
||||
("07k-interruptible-lmnt.py", EVAL_SIMPLE_MATH),
|
||||
("07l-interruptible-groq.py", EVAL_SIMPLE_MATH),
|
||||
("07m-interruptible-aws.py", EVAL_SIMPLE_MATH),
|
||||
("07m-interruptible-aws-strands.py", EVAL_WEATHER),
|
||||
("07n-interruptible-gemini.py", EVAL_SIMPLE_MATH),
|
||||
("07n-interruptible-google.py", EVAL_SIMPLE_MATH),
|
||||
("07o-interruptible-assemblyai.py", EVAL_SIMPLE_MATH),
|
||||
("07q-interruptible-rime.py", EVAL_SIMPLE_MATH),
|
||||
("07q-interruptible-rime-http.py", EVAL_SIMPLE_MATH),
|
||||
("07r-interruptible-riva-nim.py", EVAL_SIMPLE_MATH),
|
||||
("07s-interruptible-google-audio-in.py", EVAL_SIMPLE_MATH),
|
||||
("07t-interruptible-fish.py", EVAL_SIMPLE_MATH),
|
||||
("07v-interruptible-neuphonic.py", EVAL_SIMPLE_MATH),
|
||||
("07v-interruptible-neuphonic-http.py", EVAL_SIMPLE_MATH),
|
||||
("07w-interruptible-fal.py", EVAL_SIMPLE_MATH),
|
||||
("07y-interruptible-minimax.py", EVAL_SIMPLE_MATH),
|
||||
("07z-interruptible-sarvam.py", EVAL_SIMPLE_MATH),
|
||||
("07ae-interruptible-hume.py", EVAL_SIMPLE_MATH),
|
||||
("07-interruptible.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07-interruptible-cartesia-http.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07a-interruptible-speechmatics.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07aa-interruptible-soniox.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07ab-interruptible-inworld-http.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07ac-interruptible-asyncai.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07ac-interruptible-asyncai-http.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07b-interruptible-langchain.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07c-interruptible-deepgram.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07d-interruptible-elevenlabs.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
(
|
||||
"07d-interruptible-elevenlabs-http.py",
|
||||
PROMPT_SIMPLE_MATH,
|
||||
EVAL_SIMPLE_MATH,
|
||||
BOT_SPEAKS_FIRST,
|
||||
),
|
||||
("07e-interruptible-playht.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07e-interruptible-playht-http.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07f-interruptible-azure.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07g-interruptible-openai.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07h-interruptible-openpipe.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07j-interruptible-gladia.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07k-interruptible-lmnt.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07l-interruptible-groq.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07m-interruptible-aws.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07m-interruptible-aws-strands.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
("07n-interruptible-gemini.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07n-interruptible-google.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07o-interruptible-assemblyai.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07q-interruptible-rime.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07q-interruptible-rime-http.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07r-interruptible-riva-nim.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
(
|
||||
"07s-interruptible-google-audio-in.py",
|
||||
PROMPT_SIMPLE_MATH,
|
||||
EVAL_SIMPLE_MATH,
|
||||
BOT_SPEAKS_FIRST,
|
||||
),
|
||||
("07t-interruptible-fish.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07v-interruptible-neuphonic.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07v-interruptible-neuphonic-http.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07w-interruptible-fal.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07y-interruptible-minimax.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
("07z-interruptible-sarvam.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
# Needs a local XTTS docker instance running.
|
||||
# ("07i-interruptible-xtts.py", EVAL_SIMPLE_MATH),
|
||||
# ("07i-interruptible-xtts.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
# Needs a Krisp license.
|
||||
# ("07p-interruptible-krisp.py", EVAL_SIMPLE_MATH),
|
||||
# ("07p-interruptible-krisp.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
# Needs GPU resources.
|
||||
# ("07u-interruptible-ultravox.py", EVAL_SIMPLE_MATH),
|
||||
# ("07u-interruptible-ultravox.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
]
|
||||
|
||||
TESTS_12 = [
|
||||
("12-describe-image-openai.py", EVAL_VISION_IMAGE(eval_speaks_first=True)),
|
||||
("12a-describe-image-anthropic.py", EVAL_VISION_IMAGE(eval_speaks_first=True)),
|
||||
("12b-describe-image-aws.py", EVAL_VISION_IMAGE(eval_speaks_first=True)),
|
||||
("12c-describe-image-gemini-flash.py", EVAL_VISION_IMAGE(eval_speaks_first=True)),
|
||||
("12d-describe-image-moondream.py", EVAL_VISION_IMAGE()),
|
||||
("12-describe-video.py", PROMPT_VISION, EVAL_VISION, BOT_SPEAKS_FIRST),
|
||||
("12a-describe-video-gemini-flash.py", PROMPT_VISION, EVAL_VISION, BOT_SPEAKS_FIRST),
|
||||
("12b-describe-video-gpt-4o.py", PROMPT_VISION, EVAL_VISION, BOT_SPEAKS_FIRST),
|
||||
("12c-describe-video-anthropic.py", PROMPT_VISION, EVAL_VISION, BOT_SPEAKS_FIRST),
|
||||
]
|
||||
|
||||
TESTS_14 = [
|
||||
("14-function-calling.py", EVAL_WEATHER),
|
||||
("14a-function-calling-anthropic.py", EVAL_WEATHER),
|
||||
("14e-function-calling-google.py", EVAL_WEATHER),
|
||||
("14f-function-calling-groq.py", EVAL_WEATHER),
|
||||
("14g-function-calling-grok.py", EVAL_WEATHER),
|
||||
("14h-function-calling-azure.py", EVAL_WEATHER),
|
||||
("14i-function-calling-fireworks.py", EVAL_WEATHER),
|
||||
("14j-function-calling-nim.py", EVAL_WEATHER),
|
||||
("14k-function-calling-cerebras.py", EVAL_WEATHER),
|
||||
("14m-function-calling-openrouter.py", EVAL_WEATHER),
|
||||
("14n-function-calling-perplexity.py", EVAL_WEATHER),
|
||||
("14p-function-calling-gemini-vertex-ai.py", EVAL_WEATHER),
|
||||
("14q-function-calling-qwen.py", EVAL_WEATHER),
|
||||
("14r-function-calling-aws.py", EVAL_WEATHER),
|
||||
("14v-function-calling-openai.py", EVAL_WEATHER),
|
||||
("14w-function-calling-mistral.py", EVAL_WEATHER),
|
||||
("14x-function-calling-openpipe.py", EVAL_WEATHER),
|
||||
# Video
|
||||
("14d-function-calling-anthropic-video.py", EVAL_VISION_CAMERA),
|
||||
("14d-function-calling-aws-video.py", EVAL_VISION_CAMERA),
|
||||
("14d-function-calling-gemini-flash-video.py", EVAL_VISION_CAMERA),
|
||||
("14d-function-calling-moondream-video.py", EVAL_VISION_CAMERA),
|
||||
("14d-function-calling-openai-video.py", EVAL_VISION_CAMERA),
|
||||
("14-function-calling.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
("14a-function-calling-anthropic.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
("14b-function-calling-anthropic-video.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
("14d-function-calling-video.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
("14e-function-calling-google.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
("14f-function-calling-groq.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
("14g-function-calling-grok.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
("14h-function-calling-azure.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
("14i-function-calling-fireworks.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
("14j-function-calling-nim.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
("14k-function-calling-cerebras.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
("14m-function-calling-openrouter.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
("14n-function-calling-perplexity.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
("14p-function-calling-gemini-vertex-ai.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
("14q-function-calling-qwen.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
("14r-function-calling-aws.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
("14v-function-calling-openai.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
("14w-function-calling-mistral.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
# Currently not working.
|
||||
# ("14c-function-calling-together.py", EVAL_WEATHER),
|
||||
# ("14l-function-calling-deepseek.py", EVAL_WEATHER),
|
||||
# ("14o-function-calling-gemini-openai-format.py", EVAL_WEATHER),
|
||||
# ("14c-function-calling-together.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
# ("14l-function-calling-deepseek.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
# ("14o-function-calling-gemini-openai-format.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
]
|
||||
|
||||
TESTS_15 = [
|
||||
("15a-switch-languages.py", EVAL_SWITCH_LANGUAGE),
|
||||
("15a-switch-languages.py", PROMPT_SWITCH_LANGUAGE, EVAL_SWITCH_LANGUAGE, BOT_SPEAKS_FIRST),
|
||||
]
|
||||
|
||||
TESTS_19 = [
|
||||
("19-openai-realtime.py", EVAL_WEATHER),
|
||||
("19-openai-realtime-beta.py", EVAL_WEATHER),
|
||||
# OpenAI Realtime not released on Azure yet
|
||||
# ("19a-azure-realtime.py", EVAL_WEATHER),
|
||||
("19a-azure-realtime-beta.py", EVAL_WEATHER),
|
||||
("19b-openai-realtime-text.py", EVAL_WEATHER),
|
||||
("19b-openai-realtime-beta-text.py", EVAL_WEATHER),
|
||||
("19-openai-realtime-beta.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
("19a-azure-realtime-beta.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
("19b-openai-realtime-text.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
("19b-openai-realtime-beta-text.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
|
||||
]
|
||||
|
||||
TESTS_21 = [
|
||||
("21a-tavus-video-service.py", EVAL_SIMPLE_MATH),
|
||||
("21a-tavus-video-service.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
]
|
||||
|
||||
TESTS_26 = [
|
||||
("26-gemini-live.py", EVAL_SIMPLE_MATH),
|
||||
("26a-gemini-live-transcription.py", EVAL_SIMPLE_MATH),
|
||||
("26b-gemini-live-function-calling.py", EVAL_WEATHER),
|
||||
("26c-gemini-live-video.py", EVAL_SIMPLE_MATH),
|
||||
("26e-gemini-live-google-search.py", EVAL_ONLINE_SEARCH),
|
||||
("26h-gemini-live-vertex-function-calling.py", EVAL_WEATHER),
|
||||
("26-gemini-multimodal-live.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
(
|
||||
"26a-gemini-multimodal-live-transcription.py",
|
||||
PROMPT_SIMPLE_MATH,
|
||||
EVAL_SIMPLE_MATH,
|
||||
BOT_SPEAKS_FIRST,
|
||||
),
|
||||
(
|
||||
"26b-gemini-multimodal-live-function-calling.py",
|
||||
PROMPT_WEATHER,
|
||||
EVAL_WEATHER,
|
||||
BOT_SPEAKS_FIRST,
|
||||
),
|
||||
("26c-gemini-multimodal-live-video.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
(
|
||||
"26e-gemini-multimodal-google-search.py",
|
||||
PROMPT_ONLINE_SEARCH,
|
||||
EVAL_ONLINE_SEARCH,
|
||||
BOT_SPEAKS_FIRST,
|
||||
),
|
||||
# Currently not working.
|
||||
# ("26d-gemini-live-text.py", EVAL_SIMPLE_MATH),
|
||||
# ("26d-gemini-multimodal-live-text.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
]
|
||||
|
||||
TESTS_27 = [
|
||||
("27-simli-layer.py", EVAL_SIMPLE_MATH),
|
||||
("27-simli-layer.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
]
|
||||
|
||||
TESTS_40 = [
|
||||
("40-aws-nova-sonic.py", EVAL_SIMPLE_MATH),
|
||||
("40-aws-nova-sonic.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
]
|
||||
|
||||
TESTS_43 = [
|
||||
("43a-heygen-video-service.py", EVAL_SIMPLE_MATH),
|
||||
("43a-heygen-video-service.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
|
||||
]
|
||||
|
||||
TESTS_44 = [
|
||||
("44-voicemail-detection.py", EVAL_VOICEMAIL),
|
||||
("44-voicemail-detection.py", EVAL_CONVERSATION),
|
||||
("44-voicemail-detection.py", PROMPT_VOICEMAIL, EVAL_VOICEMAIL, USER_SPEAKS_FIRST),
|
||||
("44-voicemail-detection.py", PROMPT_CONVERSATION, EVAL_CONVERSATION, USER_SPEAKS_FIRST),
|
||||
]
|
||||
|
||||
TESTS = [
|
||||
@@ -239,9 +234,9 @@ async def main(args: argparse.Namespace):
|
||||
|
||||
# Parse test config: (test, prompt, eval, user_speaks_first)
|
||||
for test_config in TESTS:
|
||||
test, eval_config = test_config
|
||||
test, prompt, eval, user_speaks_first = test_config
|
||||
|
||||
await runner.run_eval(test, eval_config)
|
||||
await runner.run_eval(test, prompt, eval, user_speaks_first)
|
||||
|
||||
runner.print_results()
|
||||
|
||||
|
||||
@@ -22,12 +22,9 @@ class AdapterType(Enum):
|
||||
|
||||
Parameters:
|
||||
GEMINI: Google Gemini adapter - currently the only service supporting custom tools.
|
||||
SHIM: Backward compatibility shim for creating ToolsSchemas from lists of tools in
|
||||
any format, used by LLMContext.from_openai_context.
|
||||
"""
|
||||
|
||||
GEMINI = "gemini" # that is the only service where we are able to add custom tools for now
|
||||
SHIM = "shim" # for use as backward compatibility shim for creating ToolsSchemas from list of tools in any format
|
||||
|
||||
|
||||
class ToolsSchema:
|
||||
|
||||
@@ -110,7 +110,7 @@ class AnthropicLLMAdapter(BaseLLMAdapter[AnthropicLLMInvocationParams]):
|
||||
system = NOT_GIVEN
|
||||
messages = []
|
||||
|
||||
# First, map messages using self._from_universal_context_message(m)
|
||||
# first, map messages using self._from_universal_context_message(m)
|
||||
try:
|
||||
messages = [self._from_universal_context_message(m) for m in universal_context_messages]
|
||||
except Exception as e:
|
||||
@@ -245,25 +245,13 @@ class AnthropicLLMAdapter(BaseLLMAdapter[AnthropicLLMInvocationParams]):
|
||||
item["text"] = "(empty)"
|
||||
# handle image_url -> image conversion
|
||||
if item["type"] == "image_url":
|
||||
if item["image_url"]["url"].startswith("data:"):
|
||||
item["type"] = "image"
|
||||
item["source"] = {
|
||||
"type": "base64",
|
||||
"media_type": "image/jpeg",
|
||||
"data": item["image_url"]["url"].split(",")[1],
|
||||
}
|
||||
del item["image_url"]
|
||||
elif item["image_url"]["url"].startswith("http"):
|
||||
item["type"] = "image"
|
||||
item["source"] = {
|
||||
"type": "url",
|
||||
"url": item["image_url"]["url"],
|
||||
}
|
||||
del item["image_url"]
|
||||
else:
|
||||
url = item["image_url"]["url"]
|
||||
logger.warning(f"Unsupported 'image_url': {url}")
|
||||
|
||||
item["type"] = "image"
|
||||
item["source"] = {
|
||||
"type": "base64",
|
||||
"media_type": "image/jpeg",
|
||||
"data": item["image_url"]["url"].split(",")[1],
|
||||
}
|
||||
del item["image_url"]
|
||||
# In the case where there's a single image in the list (like what
|
||||
# would result from a UserImageRawFrame), ensure that the image
|
||||
# comes before text, as recommended by Anthropic docs
|
||||
|
||||
@@ -6,47 +6,13 @@
|
||||
|
||||
"""AWS Nova Sonic LLM adapter for Pipecat."""
|
||||
|
||||
import copy
|
||||
import json
|
||||
from dataclasses import dataclass
|
||||
from enum import Enum
|
||||
from typing import Any, Dict, List, Optional, TypedDict
|
||||
|
||||
from loguru import logger
|
||||
from typing import Any, Dict, List, TypedDict
|
||||
|
||||
from pipecat.adapters.base_llm_adapter import BaseLLMAdapter
|
||||
from pipecat.adapters.schemas.function_schema import FunctionSchema
|
||||
from pipecat.adapters.schemas.tools_schema import AdapterType, ToolsSchema
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext, LLMContextMessage
|
||||
|
||||
|
||||
class Role(Enum):
|
||||
"""Roles supported in AWS Nova Sonic conversations.
|
||||
|
||||
Parameters:
|
||||
SYSTEM: System-level messages (not used in conversation history).
|
||||
USER: Messages sent by the user.
|
||||
ASSISTANT: Messages sent by the assistant.
|
||||
TOOL: Messages sent by tools (not used in conversation history).
|
||||
"""
|
||||
|
||||
SYSTEM = "SYSTEM"
|
||||
USER = "USER"
|
||||
ASSISTANT = "ASSISTANT"
|
||||
TOOL = "TOOL"
|
||||
|
||||
|
||||
@dataclass
|
||||
class AWSNovaSonicConversationHistoryMessage:
|
||||
"""A single message in AWS Nova Sonic conversation history.
|
||||
|
||||
Parameters:
|
||||
role: The role of the message sender (USER or ASSISTANT only).
|
||||
text: The text content of the message.
|
||||
"""
|
||||
|
||||
role: Role # only USER and ASSISTANT
|
||||
text: str
|
||||
from pipecat.adapters.schemas.tools_schema import ToolsSchema
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
|
||||
|
||||
class AWSNovaSonicLLMInvocationParams(TypedDict):
|
||||
@@ -55,9 +21,7 @@ class AWSNovaSonicLLMInvocationParams(TypedDict):
|
||||
This is a placeholder until support for universal LLMContext machinery is added for AWS Nova Sonic.
|
||||
"""
|
||||
|
||||
system_instruction: Optional[str]
|
||||
messages: List[AWSNovaSonicConversationHistoryMessage]
|
||||
tools: List[Dict[str, Any]]
|
||||
pass
|
||||
|
||||
|
||||
class AWSNovaSonicLLMAdapter(BaseLLMAdapter[AWSNovaSonicLLMInvocationParams]):
|
||||
@@ -70,7 +34,7 @@ class AWSNovaSonicLLMAdapter(BaseLLMAdapter[AWSNovaSonicLLMInvocationParams]):
|
||||
@property
|
||||
def id_for_llm_specific_messages(self) -> str:
|
||||
"""Get the identifier used in LLMSpecificMessage instances for AWS Nova Sonic."""
|
||||
return "aws-nova-sonic"
|
||||
raise NotImplementedError("Universal LLMContext is not yet supported for AWS Nova Sonic.")
|
||||
|
||||
def get_llm_invocation_params(self, context: LLMContext) -> AWSNovaSonicLLMInvocationParams:
|
||||
"""Get AWS Nova Sonic-specific LLM invocation parameters from a universal LLM context.
|
||||
@@ -83,13 +47,7 @@ class AWSNovaSonicLLMAdapter(BaseLLMAdapter[AWSNovaSonicLLMInvocationParams]):
|
||||
Returns:
|
||||
Dictionary of parameters for invoking AWS Nova Sonic's LLM API.
|
||||
"""
|
||||
messages = self._from_universal_context_messages(self.get_messages(context))
|
||||
return {
|
||||
"system_instruction": messages.system_instruction,
|
||||
"messages": messages.messages,
|
||||
# NOTE: LLMContext's tools are guaranteed to be a ToolsSchema (or NOT_GIVEN)
|
||||
"tools": self.from_standard_tools(context.tools) or [],
|
||||
}
|
||||
raise NotImplementedError("Universal LLMContext is not yet supported for AWS Nova Sonic.")
|
||||
|
||||
def get_messages_for_logging(self, context) -> List[Dict[str, Any]]:
|
||||
"""Get messages from a universal LLM context in a format ready for logging about AWS Nova Sonic.
|
||||
@@ -104,75 +62,7 @@ class AWSNovaSonicLLMAdapter(BaseLLMAdapter[AWSNovaSonicLLMInvocationParams]):
|
||||
Returns:
|
||||
List of messages in a format ready for logging about AWS Nova Sonic.
|
||||
"""
|
||||
return self._from_universal_context_messages(self.get_messages(context)).messages
|
||||
|
||||
@dataclass
|
||||
class ConvertedMessages:
|
||||
"""Container for Google-formatted messages converted from universal context."""
|
||||
|
||||
messages: List[AWSNovaSonicConversationHistoryMessage]
|
||||
system_instruction: Optional[str] = None
|
||||
|
||||
def _from_universal_context_messages(
|
||||
self, universal_context_messages: List[LLMContextMessage]
|
||||
) -> ConvertedMessages:
|
||||
system_instruction = None
|
||||
messages = []
|
||||
|
||||
# Bail if there are no messages
|
||||
if not universal_context_messages:
|
||||
return self.ConvertedMessages()
|
||||
|
||||
universal_context_messages = copy.deepcopy(universal_context_messages)
|
||||
|
||||
# If we have a "system" message as our first message, let's pull that out into "instruction"
|
||||
if universal_context_messages[0].get("role") == "system":
|
||||
system = universal_context_messages.pop(0)
|
||||
content = system.get("content")
|
||||
if isinstance(content, str):
|
||||
system_instruction = content
|
||||
elif isinstance(content, list):
|
||||
system_instruction = content[0].get("text")
|
||||
if system_instruction:
|
||||
self._system_instruction = system_instruction
|
||||
|
||||
# Process remaining messages to fill out conversation history.
|
||||
# Nova Sonic supports "user" and "assistant" messages in history.
|
||||
for universal_context_message in universal_context_messages:
|
||||
message = self._from_universal_context_message(universal_context_message)
|
||||
if message:
|
||||
messages.append(message)
|
||||
|
||||
return self.ConvertedMessages(messages=messages, system_instruction=system_instruction)
|
||||
|
||||
def _from_universal_context_message(self, message) -> AWSNovaSonicConversationHistoryMessage:
|
||||
"""Convert standard message format to Nova Sonic format.
|
||||
|
||||
Args:
|
||||
message: Standard message dictionary to convert.
|
||||
|
||||
Returns:
|
||||
Nova Sonic conversation history message, or None if not convertible.
|
||||
"""
|
||||
role = message.get("role")
|
||||
if message.get("role") == "user" or message.get("role") == "assistant":
|
||||
content = message.get("content")
|
||||
if isinstance(message.get("content"), list):
|
||||
content = ""
|
||||
for c in message.get("content"):
|
||||
if c.get("type") == "text":
|
||||
content += " " + c.get("text")
|
||||
else:
|
||||
logger.error(
|
||||
f"Unhandled content type in context message: {c.get('type')} - {message}"
|
||||
)
|
||||
# There won't be content if this is an assistant tool call entry.
|
||||
# We're ignoring those since they can't be loaded into AWS Nova Sonic conversation
|
||||
# history
|
||||
if content:
|
||||
return AWSNovaSonicConversationHistoryMessage(role=Role[role.upper()], text=content)
|
||||
# NOTE: we're ignoring messages with role "tool" since they can't be loaded into AWS Nova
|
||||
# Sonic conversation history
|
||||
raise NotImplementedError("Universal LLMContext is not yet supported for AWS Nova Sonic.")
|
||||
|
||||
@staticmethod
|
||||
def _to_aws_nova_sonic_function_format(function: FunctionSchema) -> Dict[str, Any]:
|
||||
@@ -210,18 +100,4 @@ class AWSNovaSonicLLMAdapter(BaseLLMAdapter[AWSNovaSonicLLMInvocationParams]):
|
||||
List of dictionaries in AWS Nova Sonic function format.
|
||||
"""
|
||||
functions_schema = tools_schema.standard_tools
|
||||
standard_tools = [
|
||||
self._to_aws_nova_sonic_function_format(func) for func in functions_schema
|
||||
]
|
||||
|
||||
# For backward compatibility, AWS Nova Sonic can still be used with
|
||||
# tools in dict format, even though it always uses `LLMContext` under
|
||||
# the hood (via `LLMContext.from_openai_context()`).
|
||||
# To support this behavior, we use "shimmed" custom tools here.
|
||||
# (We maintain this backward compatibility because users aren't
|
||||
# *knowingly* opting into the new `LLMContext`.)
|
||||
shimmed_tools = []
|
||||
if tools_schema.custom_tools:
|
||||
shimmed_tools = tools_schema.custom_tools.get(AdapterType.SHIM, [])
|
||||
|
||||
return standard_tools + shimmed_tools
|
||||
return [self._to_aws_nova_sonic_function_format(func) for func in functions_schema]
|
||||
|
||||
@@ -107,7 +107,7 @@ class AWSBedrockLLMAdapter(BaseLLMAdapter[AWSBedrockLLMInvocationParams]):
|
||||
system = None
|
||||
messages = []
|
||||
|
||||
# First, map messages using self._from_universal_context_message(m)
|
||||
# first, map messages using self._from_universal_context_message(m)
|
||||
try:
|
||||
messages = [self._from_universal_context_message(m) for m in universal_context_messages]
|
||||
except Exception as e:
|
||||
@@ -256,22 +256,15 @@ class AWSBedrockLLMAdapter(BaseLLMAdapter[AWSBedrockLLMInvocationParams]):
|
||||
new_content.append({"text": text_content})
|
||||
# handle image_url -> image conversion
|
||||
if item["type"] == "image_url":
|
||||
if item["image_url"]["url"].startswith("data:"):
|
||||
new_item = {
|
||||
"image": {
|
||||
"format": "jpeg",
|
||||
"source": {
|
||||
"bytes": base64.b64decode(
|
||||
item["image_url"]["url"].split(",")[1]
|
||||
)
|
||||
},
|
||||
}
|
||||
new_item = {
|
||||
"image": {
|
||||
"format": "jpeg",
|
||||
"source": {
|
||||
"bytes": base64.b64decode(item["image_url"]["url"].split(",")[1])
|
||||
},
|
||||
}
|
||||
new_content.append(new_item)
|
||||
else:
|
||||
url = item["image_url"]["url"]
|
||||
logger.warning(f"Unsupported 'image_url': {url}")
|
||||
|
||||
}
|
||||
new_content.append(new_item)
|
||||
# In the case where there's a single image in the list (like what
|
||||
# would result from a UserImageRawFrame), ensure that the image
|
||||
# comes before text
|
||||
|
||||
@@ -8,8 +8,8 @@
|
||||
|
||||
import base64
|
||||
import json
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Any, Dict, List, Optional, Tuple, TypedDict
|
||||
from dataclasses import dataclass
|
||||
from typing import Any, Dict, List, Optional, TypedDict
|
||||
|
||||
from loguru import logger
|
||||
from openai import NotGiven
|
||||
@@ -24,7 +24,13 @@ from pipecat.processors.aggregators.llm_context import (
|
||||
)
|
||||
|
||||
try:
|
||||
from google.genai.types import Blob, Content, FileData, FunctionCall, FunctionResponse, Part
|
||||
from google.genai.types import (
|
||||
Blob,
|
||||
Content,
|
||||
FunctionCall,
|
||||
FunctionResponse,
|
||||
Part,
|
||||
)
|
||||
except ModuleNotFoundError as e:
|
||||
logger.error(f"Exception: {e}")
|
||||
logger.error("In order to use Google AI, you need to `pip install pipecat-ai[google]`.")
|
||||
@@ -81,11 +87,9 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
|
||||
Includes both converted standard tools and any custom Gemini-specific tools.
|
||||
"""
|
||||
functions_schema = tools_schema.standard_tools
|
||||
formatted_standard_tools = (
|
||||
[{"function_declarations": [func.to_default_dict() for func in functions_schema]}]
|
||||
if functions_schema
|
||||
else []
|
||||
)
|
||||
formatted_standard_tools = [
|
||||
{"function_declarations": [func.to_default_dict() for func in functions_schema]}
|
||||
]
|
||||
custom_gemini_tools = []
|
||||
if tools_schema.custom_tools:
|
||||
custom_gemini_tools = tools_schema.custom_tools.get(AdapterType.GEMINI, [])
|
||||
@@ -127,28 +131,6 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
|
||||
messages: List[Content]
|
||||
system_instruction: Optional[str] = None
|
||||
|
||||
@dataclass
|
||||
class MessageConversionResult:
|
||||
"""Result of converting a single universal context message to Google format.
|
||||
|
||||
Either content (a Google Content object) or a system instruction string
|
||||
is guaranteed to be set.
|
||||
|
||||
Also returns a tool call ID to name mapping for any tool calls
|
||||
discovered in the message.
|
||||
"""
|
||||
|
||||
content: Optional[Content] = None
|
||||
system_instruction: Optional[str] = None
|
||||
tool_call_id_to_name_mapping: Dict[str, str] = field(default_factory=dict)
|
||||
|
||||
@dataclass
|
||||
class MessageConversionParams:
|
||||
"""Parameters for converting a single universal context message to Google format."""
|
||||
|
||||
already_have_system_instruction: bool
|
||||
tool_call_id_to_name_mapping: Dict[str, str]
|
||||
|
||||
def _from_universal_context_messages(
|
||||
self, universal_context_messages: List[LLMContextMessage]
|
||||
) -> ConvertedMessages:
|
||||
@@ -172,26 +154,24 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
|
||||
"""
|
||||
system_instruction = None
|
||||
messages = []
|
||||
tool_call_id_to_name_mapping = {}
|
||||
|
||||
# Process each message, preserving Google-formatted messages and converting others
|
||||
for message in universal_context_messages:
|
||||
result = self._from_universal_context_message(
|
||||
message,
|
||||
params=self.MessageConversionParams(
|
||||
already_have_system_instruction=bool(system_instruction),
|
||||
tool_call_id_to_name_mapping=tool_call_id_to_name_mapping,
|
||||
),
|
||||
)
|
||||
# Each result is either a Content or a system instruction
|
||||
if result.content:
|
||||
messages.append(result.content)
|
||||
elif result.system_instruction:
|
||||
system_instruction = result.system_instruction
|
||||
if isinstance(message, LLMSpecificMessage):
|
||||
# Assume that LLMSpecificMessage wraps a message in Google format
|
||||
messages.append(message.message)
|
||||
continue
|
||||
|
||||
# Merge tool call ID to name mapping
|
||||
if result.tool_call_id_to_name_mapping:
|
||||
tool_call_id_to_name_mapping.update(result.tool_call_id_to_name_mapping)
|
||||
# Convert standard format to Google format
|
||||
converted = self._from_standard_message(
|
||||
message, already_have_system_instruction=bool(system_instruction)
|
||||
)
|
||||
if isinstance(converted, Content):
|
||||
# Regular (non-system) message
|
||||
messages.append(converted)
|
||||
else:
|
||||
# System instruction
|
||||
system_instruction = converted
|
||||
|
||||
# Check if we only have function-related messages (no regular text)
|
||||
has_regular_messages = any(
|
||||
@@ -211,16 +191,9 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
|
||||
|
||||
return self.ConvertedMessages(messages=messages, system_instruction=system_instruction)
|
||||
|
||||
def _from_universal_context_message(
|
||||
self, message: LLMContextMessage, *, params: MessageConversionParams
|
||||
) -> MessageConversionResult:
|
||||
if isinstance(message, LLMSpecificMessage):
|
||||
return self.MessageConversionResult(content=message.message)
|
||||
return self._from_standard_message(message, params=params)
|
||||
|
||||
def _from_standard_message(
|
||||
self, message: LLMStandardMessage, *, params: MessageConversionParams
|
||||
) -> MessageConversionResult:
|
||||
self, message: LLMStandardMessage, already_have_system_instruction: bool
|
||||
) -> Content | str:
|
||||
"""Convert standard universal context message to Google Content object.
|
||||
|
||||
Handles conversion of text, images, and function calls to Google's
|
||||
@@ -230,11 +203,10 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
|
||||
Args:
|
||||
message: Message in standard universal context format.
|
||||
already_have_system_instruction: Whether we already have a system instruction
|
||||
params: Parameters for conversion.
|
||||
|
||||
Returns:
|
||||
MessageConversionResult containing either a Content object or a
|
||||
system instruction string.
|
||||
Content object with role and parts, or a plain string for system
|
||||
messages.
|
||||
|
||||
Examples:
|
||||
Standard text message::
|
||||
@@ -268,49 +240,38 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
|
||||
Converts to Google Content with::
|
||||
|
||||
Content(
|
||||
role="user",
|
||||
role="model",
|
||||
parts=[Part(function_call=FunctionCall(name="search", args={"query": "test"}))]
|
||||
)
|
||||
"""
|
||||
role = message["role"]
|
||||
content = message.get("content", [])
|
||||
|
||||
if role == "system":
|
||||
if params.already_have_system_instruction:
|
||||
if already_have_system_instruction:
|
||||
role = "user" # Convert system message to user role if we already have a system instruction
|
||||
else:
|
||||
system_instruction: str = None
|
||||
# System instructions are returned as plain text
|
||||
if isinstance(content, str):
|
||||
system_instruction = content
|
||||
return content
|
||||
elif isinstance(content, list):
|
||||
# If content is a list, we assume it's a list of text parts, per the standard
|
||||
system_instruction = " ".join(
|
||||
part["text"] for part in content if part.get("type") == "text"
|
||||
)
|
||||
if system_instruction:
|
||||
return self.MessageConversionResult(system_instruction=system_instruction)
|
||||
return " ".join(part["text"] for part in content if part.get("type") == "text")
|
||||
elif role == "assistant":
|
||||
role = "model"
|
||||
|
||||
parts = []
|
||||
tool_call_id_to_name_mapping = {}
|
||||
|
||||
if message.get("tool_calls"):
|
||||
for tc in message["tool_calls"]:
|
||||
id = tc["id"]
|
||||
name = tc["function"]["name"]
|
||||
tool_call_id_to_name_mapping[id] = name
|
||||
parts.append(
|
||||
Part(
|
||||
function_call=FunctionCall(
|
||||
id=id,
|
||||
name=name,
|
||||
name=tc["function"]["name"],
|
||||
args=json.loads(tc["function"]["arguments"]),
|
||||
)
|
||||
)
|
||||
)
|
||||
elif role == "tool":
|
||||
role = "user"
|
||||
role = "model"
|
||||
try:
|
||||
response = json.loads(message["content"])
|
||||
if isinstance(response, dict):
|
||||
@@ -321,18 +282,10 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
|
||||
# Response might not be JSON-deserializable.
|
||||
# This occurs with a UserImageFrame, for example, where we get a plain "COMPLETED" string.
|
||||
response_dict = {"value": message["content"]}
|
||||
|
||||
# Get function name from mapping using tool_call_id, or fallback
|
||||
tool_call_id = message.get("tool_call_id")
|
||||
function_name = "tool_call_result" # Default fallback
|
||||
if tool_call_id and tool_call_id in params.tool_call_id_to_name_mapping:
|
||||
function_name = params.tool_call_id_to_name_mapping[tool_call_id]
|
||||
|
||||
parts.append(
|
||||
Part(
|
||||
function_response=FunctionResponse(
|
||||
id=tool_call_id,
|
||||
name=function_name,
|
||||
name="tool_call_result", # seems to work to hard-code the same name every time
|
||||
response=response_dict,
|
||||
)
|
||||
)
|
||||
@@ -343,7 +296,7 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
|
||||
for c in content:
|
||||
if c["type"] == "text":
|
||||
parts.append(Part(text=c["text"]))
|
||||
elif c["type"] == "image_url" and c["image_url"]["url"].startswith("data:"):
|
||||
elif c["type"] == "image_url":
|
||||
parts.append(
|
||||
Part(
|
||||
inline_data=Blob(
|
||||
@@ -352,25 +305,9 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
|
||||
)
|
||||
)
|
||||
)
|
||||
elif c["type"] == "image_url":
|
||||
url = c["image_url"]["url"]
|
||||
logger.warning(f"Unsupported 'image_url': {url}")
|
||||
elif c["type"] == "input_audio":
|
||||
input_audio = c["input_audio"]
|
||||
audio_bytes = base64.b64decode(input_audio["data"])
|
||||
parts.append(Part(inline_data=Blob(mime_type="audio/wav", data=audio_bytes)))
|
||||
elif c["type"] == "file_data":
|
||||
file_data = c["file_data"]
|
||||
parts.append(
|
||||
Part(
|
||||
file_data=FileData(
|
||||
mime_type=file_data.get("mime_type"),
|
||||
file_uri=file_data.get("file_uri"),
|
||||
)
|
||||
)
|
||||
)
|
||||
|
||||
return self.MessageConversionResult(
|
||||
content=Content(role=role, parts=parts),
|
||||
tool_call_id_to_name_mapping=tool_call_id_to_name_mapping,
|
||||
)
|
||||
return Content(role=role, parts=parts)
|
||||
|
||||
@@ -6,18 +6,12 @@
|
||||
|
||||
"""OpenAI Realtime LLM adapter for Pipecat."""
|
||||
|
||||
import copy
|
||||
import json
|
||||
from dataclasses import dataclass
|
||||
from typing import Any, Dict, List, Optional, TypedDict
|
||||
|
||||
from loguru import logger
|
||||
from typing import Any, Dict, List, TypedDict
|
||||
|
||||
from pipecat.adapters.base_llm_adapter import BaseLLMAdapter
|
||||
from pipecat.adapters.schemas.function_schema import FunctionSchema
|
||||
from pipecat.adapters.schemas.tools_schema import AdapterType, ToolsSchema
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext, LLMContextMessage
|
||||
from pipecat.services.openai.realtime import events
|
||||
from pipecat.adapters.schemas.tools_schema import ToolsSchema
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
|
||||
|
||||
class OpenAIRealtimeLLMInvocationParams(TypedDict):
|
||||
@@ -26,9 +20,7 @@ class OpenAIRealtimeLLMInvocationParams(TypedDict):
|
||||
This is a placeholder until support for universal LLMContext machinery is added for OpenAI Realtime.
|
||||
"""
|
||||
|
||||
system_instruction: Optional[str]
|
||||
messages: List[events.ConversationItem]
|
||||
tools: List[Dict[str, Any]]
|
||||
pass
|
||||
|
||||
|
||||
class OpenAIRealtimeLLMAdapter(BaseLLMAdapter):
|
||||
@@ -41,7 +33,7 @@ class OpenAIRealtimeLLMAdapter(BaseLLMAdapter):
|
||||
@property
|
||||
def id_for_llm_specific_messages(self) -> str:
|
||||
"""Get the identifier used in LLMSpecificMessage instances for OpenAI Realtime."""
|
||||
return "openai-realtime"
|
||||
raise NotImplementedError("Universal LLMContext is not yet supported for OpenAI Realtime.")
|
||||
|
||||
def get_llm_invocation_params(self, context: LLMContext) -> OpenAIRealtimeLLMInvocationParams:
|
||||
"""Get OpenAI Realtime-specific LLM invocation parameters from a universal LLM context.
|
||||
@@ -54,13 +46,7 @@ class OpenAIRealtimeLLMAdapter(BaseLLMAdapter):
|
||||
Returns:
|
||||
Dictionary of parameters for invoking OpenAI Realtime's API.
|
||||
"""
|
||||
messages = self._from_universal_context_messages(self.get_messages(context))
|
||||
return {
|
||||
"system_instruction": messages.system_instruction,
|
||||
"messages": messages.messages,
|
||||
# NOTE: LLMContext's tools are guaranteed to be a ToolsSchema (or NOT_GIVEN)
|
||||
"tools": self.from_standard_tools(context.tools) or [],
|
||||
}
|
||||
raise NotImplementedError("Universal LLMContext is not yet supported for OpenAI Realtime.")
|
||||
|
||||
def get_messages_for_logging(self, context) -> List[Dict[str, Any]]:
|
||||
"""Get messages from a universal LLM context in a format ready for logging about OpenAI Realtime.
|
||||
@@ -75,124 +61,7 @@ class OpenAIRealtimeLLMAdapter(BaseLLMAdapter):
|
||||
Returns:
|
||||
List of messages in a format ready for logging about OpenAI Realtime.
|
||||
"""
|
||||
# NOTE: this is the same as in OpenAIAdapter, as that's what it was
|
||||
# prior to a refactor. Worth noting that for OpenAI Realtime
|
||||
# specifically, not everything handled here is necessarily supported
|
||||
# (or supported yet).
|
||||
msgs = []
|
||||
for message in self.get_messages(context):
|
||||
msg = copy.deepcopy(message)
|
||||
if "content" in msg:
|
||||
if isinstance(msg["content"], list):
|
||||
for item in msg["content"]:
|
||||
if item["type"] == "image_url":
|
||||
if item["image_url"]["url"].startswith("data:image/"):
|
||||
item["image_url"]["url"] = "data:image/..."
|
||||
if item["type"] == "input_audio":
|
||||
item["input_audio"]["data"] = "..."
|
||||
if "mime_type" in msg and msg["mime_type"].startswith("image/"):
|
||||
msg["data"] = "..."
|
||||
msgs.append(msg)
|
||||
return msgs
|
||||
|
||||
@dataclass
|
||||
class ConvertedMessages:
|
||||
"""Container for OpenAI-formatted messages converted from universal context."""
|
||||
|
||||
messages: List[events.ConversationItem]
|
||||
system_instruction: Optional[str] = None
|
||||
|
||||
def _from_universal_context_messages(
|
||||
self, universal_context_messages: List[LLMContextMessage]
|
||||
) -> ConvertedMessages:
|
||||
# We can't load a long conversation history into the openai realtime api yet. (The API/model
|
||||
# forgets that it can do audio, if you do a series of `conversation.item.create` calls.) So
|
||||
# our general strategy until this is fixed is just to put everything into a first "user"
|
||||
# message as a single input.
|
||||
|
||||
if not universal_context_messages:
|
||||
return self.ConvertedMessages(messages=[])
|
||||
|
||||
messages = copy.deepcopy(universal_context_messages)
|
||||
system_instruction = None
|
||||
|
||||
# If we have a "system" message as our first message, let's pull that out into session
|
||||
# "instructions"
|
||||
if messages[0].get("role") == "system":
|
||||
system = messages.pop(0)
|
||||
content = system.get("content")
|
||||
if isinstance(content, str):
|
||||
system_instruction = content
|
||||
elif isinstance(content, list):
|
||||
system_instruction = content[0].get("text")
|
||||
if not messages:
|
||||
return self.ConvertedMessages(messages=[], system_instruction=system_instruction)
|
||||
|
||||
# If we have just a single "user" item, we can just send it normally
|
||||
if len(messages) == 1 and messages[0].get("role") == "user":
|
||||
return self.ConvertedMessages(
|
||||
messages=[self._from_universal_context_message(messages[0])],
|
||||
system_instruction=system_instruction,
|
||||
)
|
||||
|
||||
# Otherwise, let's pack everything into a single "user" message with a bit of
|
||||
# explanation for the LLM
|
||||
intro_text = """
|
||||
This is a previously saved conversation. Please treat this conversation history as a
|
||||
starting point for the current conversation."""
|
||||
|
||||
trailing_text = """
|
||||
This is the end of the previously saved conversation. Please continue the conversation
|
||||
from here. If the last message is a user instruction or question, act on that instruction
|
||||
or answer the question. If the last message is an assistant response, simple say that you
|
||||
are ready to continue the conversation."""
|
||||
|
||||
return self.ConvertedMessages(
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"type": "message",
|
||||
"content": [
|
||||
{
|
||||
"type": "input_text",
|
||||
"text": "\n\n".join(
|
||||
[intro_text, json.dumps(messages, indent=2), trailing_text]
|
||||
),
|
||||
}
|
||||
],
|
||||
}
|
||||
],
|
||||
system_instruction=system_instruction,
|
||||
)
|
||||
|
||||
def _from_universal_context_message(
|
||||
self, message: LLMContextMessage
|
||||
) -> events.ConversationItem:
|
||||
if message.get("role") == "user":
|
||||
content = message.get("content")
|
||||
if isinstance(message.get("content"), list):
|
||||
content = ""
|
||||
for c in message.get("content"):
|
||||
if c.get("type") == "text":
|
||||
content += " " + c.get("text")
|
||||
else:
|
||||
logger.error(
|
||||
f"Unhandled content type in context message: {c.get('type')} - {message}"
|
||||
)
|
||||
return events.ConversationItem(
|
||||
role="user",
|
||||
type="message",
|
||||
content=[events.ItemContent(type="input_text", text=content)],
|
||||
)
|
||||
if message.get("role") == "assistant" and message.get("tool_calls"):
|
||||
tc = message.get("tool_calls")[0]
|
||||
return events.ConversationItem(
|
||||
type="function_call",
|
||||
call_id=tc["id"],
|
||||
name=tc["function"]["name"],
|
||||
arguments=tc["function"]["arguments"],
|
||||
)
|
||||
logger.error(f"Unhandled message type in _from_universal_context_message: {message}")
|
||||
raise NotImplementedError("Universal LLMContext is not yet supported for OpenAI Realtime.")
|
||||
|
||||
@staticmethod
|
||||
def _to_openai_realtime_function_format(function: FunctionSchema) -> Dict[str, Any]:
|
||||
@@ -225,18 +94,4 @@ class OpenAIRealtimeLLMAdapter(BaseLLMAdapter):
|
||||
List of function definitions in OpenAI Realtime format.
|
||||
"""
|
||||
functions_schema = tools_schema.standard_tools
|
||||
standard_tools = [
|
||||
self._to_openai_realtime_function_format(func) for func in functions_schema
|
||||
]
|
||||
|
||||
# For backward compatibility, OpenAI Realtime can still be used with
|
||||
# tools in dict format, even though it always uses `LLMContext` under
|
||||
# the hood (via `LLMContext.from_openai_context()`).
|
||||
# To support this behavior, we use "shimmed" custom tools here.
|
||||
# (We maintain this backward compatibility because users aren't
|
||||
# *knowingly* opting into the new `LLMContext`.)
|
||||
shimmed_tools = []
|
||||
if tools_schema.custom_tools:
|
||||
shimmed_tools = tools_schema.custom_tools.get(AdapterType.SHIM, [])
|
||||
|
||||
return standard_tools + shimmed_tools
|
||||
return [self._to_openai_realtime_function_format(func) for func in functions_schema]
|
||||
|
||||
@@ -1,193 +0,0 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
"""Krisp noise reduction audio filter for Pipecat.
|
||||
|
||||
This module provides an audio filter implementation using Krisp VIVA SDK.
|
||||
"""
|
||||
|
||||
import os
|
||||
|
||||
import numpy as np
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.audio.filters.base_audio_filter import BaseAudioFilter
|
||||
from pipecat.frames.frames import FilterControlFrame, FilterEnableFrame
|
||||
|
||||
try:
|
||||
import krisp_audio
|
||||
except ModuleNotFoundError as e:
|
||||
logger.error(f"Exception: {e}")
|
||||
logger.error("In order to use the Krisp filter, you need to install krisp_audio.")
|
||||
raise Exception(f"Missing module: {e}")
|
||||
|
||||
|
||||
def _log_callback(log_message, log_level):
|
||||
logger.info(f"[{log_level}] {log_message}")
|
||||
|
||||
|
||||
class KrispVivaFilter(BaseAudioFilter):
|
||||
"""Audio filter using the Krisp VIVA SDK.
|
||||
|
||||
Provides real-time noise reduction for audio streams using Krisp's
|
||||
proprietary noise suppression algorithms. This filter requires a
|
||||
valid Krisp model file to operate.
|
||||
|
||||
Supported sample rates:
|
||||
- 8000 Hz
|
||||
- 16000 Hz
|
||||
- 24000 Hz
|
||||
- 32000 Hz
|
||||
- 44100 Hz
|
||||
- 48000 Hz
|
||||
"""
|
||||
|
||||
# Initialize Krisp Audio SDK globally
|
||||
krisp_audio.globalInit("", _log_callback, krisp_audio.LogLevel.Off)
|
||||
SDK_VERSION = krisp_audio.getVersion()
|
||||
logger.debug(
|
||||
f"Krisp Audio Python SDK Version: {SDK_VERSION.major}."
|
||||
f"{SDK_VERSION.minor}.{SDK_VERSION.patch}"
|
||||
)
|
||||
|
||||
SAMPLE_RATES = {
|
||||
8000: krisp_audio.SamplingRate.Sr8000Hz,
|
||||
16000: krisp_audio.SamplingRate.Sr16000Hz,
|
||||
24000: krisp_audio.SamplingRate.Sr24000Hz,
|
||||
32000: krisp_audio.SamplingRate.Sr32000Hz,
|
||||
44100: krisp_audio.SamplingRate.Sr44100Hz,
|
||||
48000: krisp_audio.SamplingRate.Sr48000Hz,
|
||||
}
|
||||
|
||||
FRAME_SIZE_MS = 10 # Krisp requires audio frames of 10ms duration for processing.
|
||||
|
||||
def __init__(self, model_path: str = None, noise_suppression_level: int = 100) -> None:
|
||||
"""Initialize the Krisp noise reduction filter.
|
||||
|
||||
Args:
|
||||
model_path: Path to the Krisp model file (.kef extension).
|
||||
If None, uses KRISP_VIVA_MODEL_PATH environment variable.
|
||||
noise_suppression_level: Noise suppression level.
|
||||
|
||||
Raises:
|
||||
ValueError: If model_path is not provided and KRISP_VIVA_MODEL_PATH is not set.
|
||||
Exception: If model file doesn't have .kef extension.
|
||||
FileNotFoundError: If model file doesn't exist.
|
||||
"""
|
||||
super().__init__()
|
||||
|
||||
# Set model path, checking environment if not specified
|
||||
self._model_path = model_path or os.getenv("KRISP_VIVA_MODEL_PATH")
|
||||
if not self._model_path:
|
||||
logger.error("Model path is not provided and KRISP_VIVA_MODEL_PATH is not set.")
|
||||
raise ValueError("Model path for KrispAudioProcessor must be provided.")
|
||||
|
||||
if not self._model_path.endswith(".kef"):
|
||||
raise Exception("Model is expected with .kef extension")
|
||||
|
||||
if not os.path.isfile(self._model_path):
|
||||
raise FileNotFoundError(f"Model file not found: {self._model_path}")
|
||||
|
||||
self._filtering = True
|
||||
self._session = None
|
||||
self._samples_per_frame = None
|
||||
self._noise_suppression_level = noise_suppression_level
|
||||
|
||||
# Audio buffer to accumulate samples for complete frames
|
||||
self._audio_buffer = bytearray()
|
||||
|
||||
def _int_to_sample_rate(self, sample_rate):
|
||||
"""Convert integer sample rate to krisp_audio SamplingRate enum.
|
||||
|
||||
Args:
|
||||
sample_rate: Sample rate as integer
|
||||
|
||||
Returns:
|
||||
krisp_audio.SamplingRate enum value
|
||||
|
||||
Raises:
|
||||
ValueError: If sample rate is not supported
|
||||
"""
|
||||
if sample_rate not in self.SAMPLE_RATES:
|
||||
raise ValueError("Unsupported sample rate")
|
||||
return self.SAMPLE_RATES[sample_rate]
|
||||
|
||||
async def start(self, sample_rate: int):
|
||||
"""Initialize the Krisp processor with the transport's sample rate.
|
||||
|
||||
Args:
|
||||
sample_rate: The sample rate of the input transport in Hz.
|
||||
"""
|
||||
model_info = krisp_audio.ModelInfo()
|
||||
model_info.path = self._model_path
|
||||
|
||||
nc_cfg = krisp_audio.NcSessionConfig()
|
||||
nc_cfg.inputSampleRate = self._int_to_sample_rate(sample_rate)
|
||||
nc_cfg.inputFrameDuration = krisp_audio.FrameDuration.Fd10ms
|
||||
nc_cfg.outputSampleRate = nc_cfg.inputSampleRate
|
||||
nc_cfg.modelInfo = model_info
|
||||
|
||||
self._samples_per_frame = int((sample_rate * self.FRAME_SIZE_MS) / 1000)
|
||||
self._session = krisp_audio.NcInt16.create(nc_cfg)
|
||||
|
||||
async def stop(self):
|
||||
"""Clean up the Krisp processor when stopping."""
|
||||
self._session = None
|
||||
|
||||
async def process_frame(self, frame: FilterControlFrame):
|
||||
"""Process control frames to enable/disable filtering.
|
||||
|
||||
Args:
|
||||
frame: The control frame containing filter commands.
|
||||
"""
|
||||
if isinstance(frame, FilterEnableFrame):
|
||||
self._filtering = frame.enable
|
||||
|
||||
async def filter(self, audio: bytes) -> bytes:
|
||||
"""Apply Krisp noise reduction to audio data.
|
||||
|
||||
Args:
|
||||
audio: Raw audio data as bytes to be filtered.
|
||||
|
||||
Returns:
|
||||
Noise-reduced audio data as bytes.
|
||||
"""
|
||||
if not self._filtering:
|
||||
return audio
|
||||
|
||||
# Add incoming audio to our buffer
|
||||
self._audio_buffer.extend(audio)
|
||||
|
||||
# Calculate how many complete frames we can process
|
||||
total_samples = len(self._audio_buffer) // 2 # 2 bytes per int16 sample
|
||||
num_complete_frames = total_samples // self._samples_per_frame
|
||||
|
||||
if num_complete_frames == 0:
|
||||
# Not enough samples for a complete frame yet, return empty
|
||||
return b""
|
||||
|
||||
# Calculate how many bytes we need for complete frames
|
||||
complete_samples_count = num_complete_frames * self._samples_per_frame
|
||||
bytes_to_process = complete_samples_count * 2 # 2 bytes per sample
|
||||
|
||||
# Extract the bytes we can process
|
||||
audio_to_process = bytes(self._audio_buffer[:bytes_to_process])
|
||||
|
||||
# Remove processed bytes from buffer, keep the remainder
|
||||
self._audio_buffer = self._audio_buffer[bytes_to_process:]
|
||||
|
||||
# Process the complete frames
|
||||
samples = np.frombuffer(audio_to_process, dtype=np.int16)
|
||||
frames = samples.reshape(-1, self._samples_per_frame)
|
||||
processed_samples = np.empty_like(samples)
|
||||
|
||||
for i, frame in enumerate(frames):
|
||||
cleaned_frame = self._session.process(frame, self._noise_suppression_level)
|
||||
processed_samples[i * self._samples_per_frame : (i + 1) * self._samples_per_frame] = (
|
||||
cleaned_frame
|
||||
)
|
||||
|
||||
return processed_samples.tobytes()
|
||||
@@ -14,8 +14,6 @@ from abc import ABC, abstractmethod
|
||||
from enum import Enum
|
||||
from typing import Optional, Tuple
|
||||
|
||||
from pydantic import BaseModel
|
||||
|
||||
from pipecat.metrics.metrics import MetricsData
|
||||
|
||||
|
||||
@@ -31,12 +29,6 @@ class EndOfTurnState(Enum):
|
||||
INCOMPLETE = 2
|
||||
|
||||
|
||||
class BaseTurnParams(BaseModel):
|
||||
"""Base class for turn analyzer parameters."""
|
||||
|
||||
pass
|
||||
|
||||
|
||||
class BaseTurnAnalyzer(ABC):
|
||||
"""Abstract base class for analyzing user end of turn.
|
||||
|
||||
@@ -86,7 +78,7 @@ class BaseTurnAnalyzer(ABC):
|
||||
|
||||
@property
|
||||
@abstractmethod
|
||||
def params(self) -> BaseTurnParams:
|
||||
def params(self):
|
||||
"""Get the current turn analyzer parameters.
|
||||
|
||||
Returns:
|
||||
|
||||
@@ -11,17 +11,15 @@ machine learning models to determine when a user has finished speaking, going
|
||||
beyond simple silence-based detection.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import time
|
||||
from abc import abstractmethod
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
from typing import Any, Dict, Optional, Tuple
|
||||
|
||||
import numpy as np
|
||||
from loguru import logger
|
||||
from pydantic import BaseModel
|
||||
|
||||
from pipecat.audio.turn.base_turn_analyzer import BaseTurnAnalyzer, BaseTurnParams, EndOfTurnState
|
||||
from pipecat.audio.turn.base_turn_analyzer import BaseTurnAnalyzer, EndOfTurnState
|
||||
from pipecat.metrics.metrics import MetricsData, SmartTurnMetricsData
|
||||
|
||||
# Default timing parameters
|
||||
@@ -31,7 +29,7 @@ MAX_DURATION_SECONDS = 8 # Max allowed segment duration
|
||||
USE_ONLY_LAST_VAD_SEGMENT = True
|
||||
|
||||
|
||||
class SmartTurnParams(BaseTurnParams):
|
||||
class SmartTurnParams(BaseModel):
|
||||
"""Configuration parameters for smart turn analysis.
|
||||
|
||||
Parameters:
|
||||
@@ -79,9 +77,6 @@ class BaseSmartTurn(BaseTurnAnalyzer):
|
||||
self._speech_triggered = False
|
||||
self._silence_ms = 0
|
||||
self._speech_start_time = 0
|
||||
# Thread executor that will run the model. We only need one thread per
|
||||
# analyzer because one analyzer just handles one audio stream.
|
||||
self._executor = ThreadPoolExecutor(max_workers=1)
|
||||
|
||||
@property
|
||||
def speech_triggered(self) -> bool:
|
||||
@@ -156,10 +151,7 @@ class BaseSmartTurn(BaseTurnAnalyzer):
|
||||
Tuple containing the end-of-turn state and optional metrics data
|
||||
from the ML model analysis.
|
||||
"""
|
||||
loop = asyncio.get_running_loop()
|
||||
state, result = await loop.run_in_executor(
|
||||
self._executor, self._process_speech_segment, self._audio_buffer
|
||||
)
|
||||
state, result = await self._process_speech_segment(self._audio_buffer)
|
||||
if state == EndOfTurnState.COMPLETE or USE_ONLY_LAST_VAD_SEGMENT:
|
||||
self._clear(state)
|
||||
logger.debug(f"End of Turn result: {state}")
|
||||
@@ -177,7 +169,9 @@ class BaseSmartTurn(BaseTurnAnalyzer):
|
||||
self._speech_start_time = 0
|
||||
self._silence_ms = 0
|
||||
|
||||
def _process_speech_segment(self, audio_buffer) -> Tuple[EndOfTurnState, Optional[MetricsData]]:
|
||||
async def _process_speech_segment(
|
||||
self, audio_buffer
|
||||
) -> Tuple[EndOfTurnState, Optional[MetricsData]]:
|
||||
"""Process accumulated audio segment using ML model."""
|
||||
state = EndOfTurnState.INCOMPLETE
|
||||
|
||||
@@ -209,7 +203,7 @@ class BaseSmartTurn(BaseTurnAnalyzer):
|
||||
if len(segment_audio) > 0:
|
||||
start_time = time.perf_counter()
|
||||
try:
|
||||
result = self._predict_endpoint(segment_audio)
|
||||
result = await self._predict_endpoint(segment_audio)
|
||||
state = (
|
||||
EndOfTurnState.COMPLETE
|
||||
if result["prediction"] == 1
|
||||
@@ -255,6 +249,6 @@ class BaseSmartTurn(BaseTurnAnalyzer):
|
||||
return state, result_data
|
||||
|
||||
@abstractmethod
|
||||
def _predict_endpoint(self, audio_array: np.ndarray) -> Dict[str, Any]:
|
||||
async def _predict_endpoint(self, audio_array: np.ndarray) -> Dict[str, Any]:
|
||||
"""Predict end-of-turn using ML model from audio data."""
|
||||
pass
|
||||
|
||||
@@ -104,15 +104,11 @@ class HttpSmartTurnAnalyzer(BaseSmartTurn):
|
||||
logger.error(f"Failed to send raw request to Daily Smart Turn: {e}")
|
||||
raise Exception("Failed to send raw request to Daily Smart Turn.")
|
||||
|
||||
def _predict_endpoint(self, audio_array: np.ndarray) -> Dict[str, Any]:
|
||||
async def _predict_endpoint(self, audio_array: np.ndarray) -> Dict[str, Any]:
|
||||
"""Predict end-of-turn using remote HTTP ML service."""
|
||||
try:
|
||||
serialized_array = self._serialize_array(audio_array)
|
||||
loop = asyncio.get_running_loop()
|
||||
future = asyncio.run_coroutine_threadsafe(
|
||||
self._send_raw_request(serialized_array), loop
|
||||
)
|
||||
return future.result()
|
||||
return await self._send_raw_request(serialized_array)
|
||||
except Exception as e:
|
||||
logger.error(f"Smart turn prediction failed: {str(e)}")
|
||||
# Return an incomplete prediction when a failure occurs
|
||||
|
||||
@@ -64,7 +64,7 @@ class LocalSmartTurnAnalyzer(BaseSmartTurn):
|
||||
self._turn_model.eval()
|
||||
logger.debug("Loaded Local Smart Turn")
|
||||
|
||||
def _predict_endpoint(self, audio_array: np.ndarray) -> Dict[str, Any]:
|
||||
async def _predict_endpoint(self, audio_array: np.ndarray) -> Dict[str, Any]:
|
||||
"""Predict end-of-turn using local PyTorch model."""
|
||||
inputs = self._turn_processor(
|
||||
audio_array,
|
||||
|
||||
@@ -73,7 +73,7 @@ class LocalSmartTurnAnalyzerV2(BaseSmartTurn):
|
||||
self._turn_model.eval()
|
||||
logger.debug("Loaded Local Smart Turn v2")
|
||||
|
||||
def _predict_endpoint(self, audio_array: np.ndarray) -> Dict[str, Any]:
|
||||
async def _predict_endpoint(self, audio_array: np.ndarray) -> Dict[str, Any]:
|
||||
"""Predict end-of-turn using local PyTorch model."""
|
||||
inputs = self._turn_processor(
|
||||
audio_array,
|
||||
|
||||
@@ -77,7 +77,7 @@ class LocalSmartTurnAnalyzerV3(BaseSmartTurn):
|
||||
|
||||
logger.debug("Loaded Local Smart Turn v3")
|
||||
|
||||
def _predict_endpoint(self, audio_array: np.ndarray) -> Dict[str, Any]:
|
||||
async def _predict_endpoint(self, audio_array: np.ndarray) -> Dict[str, Any]:
|
||||
"""Predict end-of-turn using local ONNX model."""
|
||||
|
||||
def truncate_audio_to_last_n_seconds(audio_array, n_seconds=8, sample_rate=16000):
|
||||
|
||||
@@ -11,9 +11,7 @@ data structures for voice activity detection in audio streams. Includes state
|
||||
management, parameter configuration, and audio analysis framework.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
from abc import ABC, abstractmethod
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
from enum import Enum
|
||||
from typing import Optional
|
||||
|
||||
@@ -86,10 +84,6 @@ class VADAnalyzer(ABC):
|
||||
self._smoothing_factor = 0.2
|
||||
self._prev_volume = 0
|
||||
|
||||
# Thread executor that will run the model. We only need one thread per
|
||||
# analyzer because one analyzer just handles one audio stream.
|
||||
self._executor = ThreadPoolExecutor(max_workers=1)
|
||||
|
||||
@property
|
||||
def sample_rate(self) -> int:
|
||||
"""Get the current sample rate.
|
||||
@@ -171,7 +165,7 @@ class VADAnalyzer(ABC):
|
||||
volume = calculate_audio_volume(audio, self.sample_rate)
|
||||
return exp_smoothing(volume, self._prev_volume, self._smoothing_factor)
|
||||
|
||||
async def analyze_audio(self, buffer: bytes) -> VADState:
|
||||
def analyze_audio(self, buffer) -> VADState:
|
||||
"""Analyze audio buffer and return current VAD state.
|
||||
|
||||
Processes incoming audio data, maintains internal state, and determines
|
||||
@@ -183,12 +177,6 @@ class VADAnalyzer(ABC):
|
||||
Returns:
|
||||
Current VAD state after processing the buffer.
|
||||
"""
|
||||
loop = asyncio.get_running_loop()
|
||||
state = await loop.run_in_executor(self._executor, self._run_analyzer, buffer)
|
||||
return state
|
||||
|
||||
def _run_analyzer(self, buffer: bytes) -> VADState:
|
||||
"""Analyze audio buffer and return current VAD state."""
|
||||
self._vad_buffer += buffer
|
||||
|
||||
num_required_bytes = self._vad_frames_num_bytes
|
||||
|
||||
@@ -672,7 +672,7 @@ class TTSSpeakFrame(DataFrame):
|
||||
|
||||
|
||||
@dataclass
|
||||
class OutputTransportMessageFrame(DataFrame):
|
||||
class TransportMessageFrame(DataFrame):
|
||||
"""Frame containing transport-specific message data.
|
||||
|
||||
Parameters:
|
||||
@@ -685,32 +685,6 @@ class OutputTransportMessageFrame(DataFrame):
|
||||
return f"{self.name}(message: {self.message})"
|
||||
|
||||
|
||||
@dataclass
|
||||
class TransportMessageFrame(OutputTransportMessageFrame):
|
||||
"""Frame containing transport-specific message data.
|
||||
|
||||
.. deprecated:: 0.0.87
|
||||
This frame is deprecated and will be removed in a future version.
|
||||
Instead, use `OutputTransportMessageFrame`.
|
||||
|
||||
Parameters:
|
||||
message: The transport message payload.
|
||||
"""
|
||||
|
||||
def __post_init__(self):
|
||||
super().__post_init__()
|
||||
import warnings
|
||||
|
||||
with warnings.catch_warnings():
|
||||
warnings.simplefilter("always")
|
||||
warnings.warn(
|
||||
"TransportMessageFrame is deprecated and will be removed in a future version. "
|
||||
"Instead, use OutputTransportMessageFrame.",
|
||||
DeprecationWarning,
|
||||
stacklevel=2,
|
||||
)
|
||||
|
||||
|
||||
@dataclass
|
||||
class DTMFFrame:
|
||||
"""Base class for DTMF (Dual-Tone Multi-Frequency) keypad frames.
|
||||
@@ -1118,8 +1092,8 @@ class STTMuteFrame(SystemFrame):
|
||||
|
||||
|
||||
@dataclass
|
||||
class InputTransportMessageFrame(SystemFrame):
|
||||
"""Frame for transport messages received from external sources.
|
||||
class TransportMessageUrgentFrame(SystemFrame):
|
||||
"""Frame for urgent transport messages that need immediate processing.
|
||||
|
||||
Parameters:
|
||||
message: The urgent transport message payload.
|
||||
@@ -1132,92 +1106,46 @@ class InputTransportMessageFrame(SystemFrame):
|
||||
|
||||
|
||||
@dataclass
|
||||
class InputTransportMessageUrgentFrame(InputTransportMessageFrame):
|
||||
class InputTransportMessageUrgentFrame(TransportMessageUrgentFrame):
|
||||
"""Frame for transport messages received from external sources.
|
||||
|
||||
.. deprecated:: 0.0.87
|
||||
This frame is deprecated and will be removed in a future version.
|
||||
Instead, use `InputTransportMessageFrame`.
|
||||
This frame wraps incoming transport messages to distinguish them from outgoing
|
||||
urgent transport messages (TransportMessageUrgentFrame), preventing infinite
|
||||
message loops in the transport layer. It inherits the message payload from
|
||||
TransportMessageFrame while marking the message as having been received
|
||||
rather than generated locally.
|
||||
|
||||
Parameters:
|
||||
message: The urgent transport message payload.
|
||||
Used by transport implementations to properly handle bidirectional message
|
||||
flow without creating feedback loops.
|
||||
"""
|
||||
|
||||
def __post_init__(self):
|
||||
super().__post_init__()
|
||||
import warnings
|
||||
|
||||
with warnings.catch_warnings():
|
||||
warnings.simplefilter("always")
|
||||
warnings.warn(
|
||||
"InputTransportMessageUrgentFrame is deprecated and will be removed in a future version. "
|
||||
"Instead, use InputTransportMessageFrame.",
|
||||
DeprecationWarning,
|
||||
stacklevel=2,
|
||||
)
|
||||
|
||||
|
||||
@dataclass
|
||||
class OutputTransportMessageUrgentFrame(SystemFrame):
|
||||
"""Frame for urgent transport messages that need to be sent immediately.
|
||||
|
||||
Parameters:
|
||||
message: The urgent transport message payload.
|
||||
"""
|
||||
|
||||
message: Any
|
||||
|
||||
def __str__(self):
|
||||
return f"{self.name}(message: {self.message})"
|
||||
|
||||
|
||||
@dataclass
|
||||
class TransportMessageUrgentFrame(OutputTransportMessageUrgentFrame):
|
||||
"""Frame for urgent transport messages that need to be sent immediately.
|
||||
|
||||
.. deprecated:: 0.0.87
|
||||
This frame is deprecated and will be removed in a future version.
|
||||
Instead, use `OutputTransportMessageUrgentFrame`.
|
||||
|
||||
Parameters:
|
||||
message: The urgent transport message payload.
|
||||
"""
|
||||
|
||||
def __post_init__(self):
|
||||
super().__post_init__()
|
||||
import warnings
|
||||
|
||||
with warnings.catch_warnings():
|
||||
warnings.simplefilter("always")
|
||||
warnings.warn(
|
||||
"TransportMessageUrgentFrame is deprecated and will be removed in a future version. "
|
||||
"Instead, use OutputTransportMessageFrame.",
|
||||
DeprecationWarning,
|
||||
stacklevel=2,
|
||||
)
|
||||
pass
|
||||
|
||||
|
||||
@dataclass
|
||||
class UserImageRequestFrame(SystemFrame):
|
||||
"""Frame requesting an image from a specific user.
|
||||
|
||||
A frame to request an image from the given user. The request might come with
|
||||
a text that can be later used to describe the requested image.
|
||||
A frame to request an image from the given user. The frame might be
|
||||
generated by a function call in which case the corresponding fields will be
|
||||
properly set.
|
||||
|
||||
Parameters:
|
||||
user_id: Identifier of the user to request image from.
|
||||
text: An optional text associated to the image request.
|
||||
append_to_context: Whether the requested image should be appended to the LLM context.
|
||||
context: Optional context for the image request.
|
||||
function_name: Name of function that generated this request (if any).
|
||||
tool_call_id: Tool call ID if generated by function call.
|
||||
video_source: Specific video source to capture from.
|
||||
"""
|
||||
|
||||
user_id: str
|
||||
text: Optional[str] = None
|
||||
append_to_context: Optional[bool] = None
|
||||
context: Optional[Any] = None
|
||||
function_name: Optional[str] = None
|
||||
tool_call_id: Optional[str] = None
|
||||
video_source: Optional[str] = None
|
||||
|
||||
def __str__(self):
|
||||
return f"{self.name}(user: {self.user_id}, text: {self.text}, append_to_context: {self.append_to_context}, {self.video_source})"
|
||||
return f"{self.name}(user: {self.user_id}, video_source: {self.video_source}, function: {self.function_name}, request: {self.tool_call_id})"
|
||||
|
||||
|
||||
@dataclass
|
||||
@@ -1291,17 +1219,15 @@ class UserImageRawFrame(InputImageRawFrame):
|
||||
|
||||
Parameters:
|
||||
user_id: Identifier of the user who provided this image.
|
||||
text: An optional text associated to this image.
|
||||
append_to_context: Whether the requested image should be appended to the LLM context.
|
||||
request: The original image request frame if this is a response.
|
||||
"""
|
||||
|
||||
user_id: str = ""
|
||||
text: Optional[str] = None
|
||||
append_to_context: Optional[bool] = None
|
||||
request: Optional[UserImageRequestFrame] = None
|
||||
|
||||
def __str__(self):
|
||||
pts = format_pts(self.pts)
|
||||
return f"{self.name}(pts: {pts}, user: {self.user_id}, source: {self.transport_source}, size: {self.size}, format: {self.format}, text: {self.text}, append_to_context: {self.append_to_context})"
|
||||
return f"{self.name}(pts: {pts}, user: {self.user_id}, source: {self.transport_source}, size: {self.size}, format: {self.format}, request: {self.request})"
|
||||
|
||||
|
||||
@dataclass
|
||||
|
||||
@@ -14,41 +14,20 @@ from pipecat.services.llm_service import LLMService
|
||||
|
||||
|
||||
class LLMSwitcher(ServiceSwitcher[StrategyType]):
|
||||
"""A pipeline that switches between different LLMs at runtime.
|
||||
|
||||
Example::
|
||||
|
||||
llm_switcher = LLMSwitcher(
|
||||
llms=[openai_llm, anthropic_llm],
|
||||
strategy_type=ServiceSwitcherStrategyManual
|
||||
)
|
||||
"""
|
||||
"""A pipeline that switches between different LLMs at runtime."""
|
||||
|
||||
def __init__(self, llms: List[LLMService], strategy_type: Type[StrategyType]):
|
||||
"""Initialize the service switcher with a list of LLMs and a switching strategy.
|
||||
|
||||
Args:
|
||||
llms: List of LLM services to switch between.
|
||||
strategy_type: The strategy class to use for switching between LLMs.
|
||||
"""
|
||||
"""Initialize the service switcher with a list of LLMs and a switching strategy."""
|
||||
super().__init__(llms, strategy_type)
|
||||
|
||||
@property
|
||||
def llms(self) -> List[LLMService]:
|
||||
"""Get the list of LLMs managed by this switcher.
|
||||
|
||||
Returns:
|
||||
List of LLM services managed by this switcher.
|
||||
"""
|
||||
"""Get the list of LLMs managed by this switcher."""
|
||||
return self.services
|
||||
|
||||
@property
|
||||
def active_llm(self) -> Optional[LLMService]:
|
||||
"""Get the currently active LLM.
|
||||
|
||||
Returns:
|
||||
The currently active LLM service, or None if no LLM is active.
|
||||
"""
|
||||
"""Get the currently active LLM, if any."""
|
||||
return self.strategy.active_service
|
||||
|
||||
async def run_inference(self, context: LLMContext) -> Optional[str]:
|
||||
|
||||
@@ -70,15 +70,11 @@ class PipelineRunner(BaseObject):
|
||||
"""
|
||||
logger.debug(f"Runner {self} started running {task}")
|
||||
self._tasks[task.name] = task
|
||||
|
||||
# PipelineTask handles asyncio.CancelledError to shutdown the pipeline
|
||||
# properly and re-raises it in case there's more cleanup to do.
|
||||
params = PipelineTaskParams(loop=self._loop)
|
||||
try:
|
||||
params = PipelineTaskParams(loop=self._loop)
|
||||
await task.run(params)
|
||||
except asyncio.CancelledError:
|
||||
pass
|
||||
|
||||
await self._cancel()
|
||||
del self._tasks[task.name]
|
||||
|
||||
# Cleanup base object.
|
||||
|
||||
@@ -21,22 +21,10 @@ from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
|
||||
|
||||
|
||||
class ServiceSwitcherStrategy:
|
||||
"""Base class for service switching strategies.
|
||||
|
||||
Note:
|
||||
Strategy classes are instantiated internally by ServiceSwitcher.
|
||||
Developers should pass the strategy class (not an instance) to ServiceSwitcher.
|
||||
"""
|
||||
"""Base class for service switching strategies."""
|
||||
|
||||
def __init__(self, services: List[FrameProcessor]):
|
||||
"""Initialize the service switcher strategy with a list of services.
|
||||
|
||||
Note:
|
||||
This is called internally by ServiceSwitcher. Do not instantiate directly.
|
||||
|
||||
Args:
|
||||
services: List of frame processors to switch between.
|
||||
"""
|
||||
"""Initialize the service switcher strategy with a list of services."""
|
||||
self.services = services
|
||||
self.active_service: Optional[FrameProcessor] = None
|
||||
|
||||
@@ -58,24 +46,10 @@ class ServiceSwitcherStrategyManual(ServiceSwitcherStrategy):
|
||||
|
||||
This strategy allows the user to manually select which service is active.
|
||||
The initial active service is the first one in the list.
|
||||
|
||||
Example::
|
||||
|
||||
stt_switcher = ServiceSwitcher(
|
||||
services=[stt_1, stt_2],
|
||||
strategy_type=ServiceSwitcherStrategyManual
|
||||
)
|
||||
"""
|
||||
|
||||
def __init__(self, services: List[FrameProcessor]):
|
||||
"""Initialize the manual service switcher strategy with a list of services.
|
||||
|
||||
Note:
|
||||
This is called internally by ServiceSwitcher. Do not instantiate directly.
|
||||
|
||||
Args:
|
||||
services: List of frame processors to switch between.
|
||||
"""
|
||||
"""Initialize the manual service switcher strategy with a list of services."""
|
||||
super().__init__(services)
|
||||
self.active_service = services[0] if services else None
|
||||
|
||||
@@ -111,12 +85,7 @@ class ServiceSwitcher(ParallelPipeline, Generic[StrategyType]):
|
||||
"""A pipeline that switches between different services at runtime."""
|
||||
|
||||
def __init__(self, services: List[FrameProcessor], strategy_type: Type[StrategyType]):
|
||||
"""Initialize the service switcher with a list of services and a switching strategy.
|
||||
|
||||
Args:
|
||||
services: List of frame processors to switch between.
|
||||
strategy_type: The strategy class to use for switching between services.
|
||||
"""
|
||||
"""Initialize the service switcher with a list of services and a switching strategy."""
|
||||
strategy = strategy_type(services)
|
||||
super().__init__(*self._make_pipeline_definitions(services, strategy))
|
||||
self.services = services
|
||||
@@ -131,20 +100,14 @@ class ServiceSwitcher(ParallelPipeline, Generic[StrategyType]):
|
||||
active_service: FrameProcessor,
|
||||
direction: FrameDirection,
|
||||
):
|
||||
"""Initialize the service switcher filter with a strategy and direction.
|
||||
|
||||
Args:
|
||||
wrapped_service: The service that this filter wraps.
|
||||
active_service: The currently active service.
|
||||
direction: The direction of frame flow to filter.
|
||||
"""
|
||||
self._wrapped_service = wrapped_service
|
||||
self._active_service = active_service
|
||||
"""Initialize the service switcher filter with a strategy and direction."""
|
||||
|
||||
async def filter(_: Frame) -> bool:
|
||||
return self._wrapped_service == self._active_service
|
||||
|
||||
super().__init__(filter, direction, filter_system_frames=True)
|
||||
super().__init__(filter, direction)
|
||||
self._wrapped_service = wrapped_service
|
||||
self._active_service = active_service
|
||||
|
||||
async def process_frame(self, frame, direction):
|
||||
"""Process a frame through the filter, handling special internal filter-updating frames."""
|
||||
|
||||
@@ -12,7 +12,9 @@ including heartbeats, idle detection, and observer integration.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
from typing import Any, AsyncIterable, Dict, Iterable, List, Optional, Tuple, Type
|
||||
import time
|
||||
from collections import deque
|
||||
from typing import Any, AsyncIterable, Deque, Dict, Iterable, List, Optional, Tuple, Type
|
||||
|
||||
from loguru import logger
|
||||
from pydantic import BaseModel, ConfigDict, Field
|
||||
@@ -29,6 +31,7 @@ from pipecat.frames.frames import (
|
||||
ErrorFrame,
|
||||
Frame,
|
||||
HeartbeatFrame,
|
||||
InputAudioRawFrame,
|
||||
InterruptionFrame,
|
||||
InterruptionTaskFrame,
|
||||
MetricsFrame,
|
||||
@@ -38,7 +41,7 @@ from pipecat.frames.frames import (
|
||||
UserSpeakingFrame,
|
||||
)
|
||||
from pipecat.metrics.metrics import ProcessingMetricsData, TTFBMetricsData
|
||||
from pipecat.observers.base_observer import BaseObserver, FramePushed
|
||||
from pipecat.observers.base_observer import BaseObserver
|
||||
from pipecat.observers.turn_tracking_observer import TurnTrackingObserver
|
||||
from pipecat.pipeline.base_task import BasePipelineTask, PipelineTaskParams
|
||||
from pipecat.pipeline.pipeline import Pipeline, PipelineSink, PipelineSource
|
||||
@@ -56,43 +59,6 @@ IDLE_TIMEOUT_SECS = 300
|
||||
CANCEL_TIMEOUT_SECS = 20.0
|
||||
|
||||
|
||||
class IdleFrameObserver(BaseObserver):
|
||||
"""Idle timeout observer.
|
||||
|
||||
This observer waits for specific frames being generated in the pipeline. If
|
||||
the frames are generated the given asyncio event is set. If the event is not
|
||||
set it means the pipeline is probably idle.
|
||||
|
||||
"""
|
||||
|
||||
def __init__(self, *, idle_event: asyncio.Event, idle_timeout_frames: Tuple[Type[Frame], ...]):
|
||||
"""Initialize the observer.
|
||||
|
||||
Args:
|
||||
idle_event: The event to set if the idle timeout frames are being pushed.
|
||||
idle_timeout_frames: A tuple with the frames that should set the event when received
|
||||
"""
|
||||
super().__init__()
|
||||
self._idle_event = idle_event
|
||||
self._idle_timeout_frames = idle_timeout_frames
|
||||
self._processed_frames = set()
|
||||
|
||||
async def on_push_frame(self, data: FramePushed):
|
||||
"""Callback executed when a frame is pushed in the pipeline.
|
||||
|
||||
Args:
|
||||
data: The frame push event data.
|
||||
"""
|
||||
# Skip already processed frames
|
||||
if data.frame.id in self._processed_frames:
|
||||
return
|
||||
|
||||
self._processed_frames.add(data.frame.id)
|
||||
|
||||
if isinstance(data.frame, StartFrame) or isinstance(data.frame, self._idle_timeout_frames):
|
||||
self._idle_event.set()
|
||||
|
||||
|
||||
class PipelineParams(BaseModel):
|
||||
"""Configuration parameters for pipeline execution.
|
||||
|
||||
@@ -166,16 +132,12 @@ class PipelineTask(BasePipelineTask):
|
||||
|
||||
- on_pipeline_finished: Called after the pipeline has reached any terminal state.
|
||||
This includes:
|
||||
|
||||
- StopFrame: pipeline was stopped (processors keep connections open)
|
||||
- EndFrame: pipeline ended normally
|
||||
- CancelFrame: pipeline was cancelled
|
||||
|
||||
Use this event for cleanup, logging, or post-processing tasks. Users can inspect
|
||||
the frame if they need to handle specific cases.
|
||||
|
||||
- on_pipeline_error: Called when an error occurs with ErrorFrame
|
||||
|
||||
Example::
|
||||
|
||||
@task.event_handler("on_frame_reached_upstream")
|
||||
@@ -186,17 +148,9 @@ class PipelineTask(BasePipelineTask):
|
||||
async def on_pipeline_idle_timeout(task):
|
||||
...
|
||||
|
||||
@task.event_handler("on_pipeline_started")
|
||||
async def on_pipeline_started(task, frame):
|
||||
...
|
||||
|
||||
@task.event_handler("on_pipeline_finished")
|
||||
async def on_pipeline_finished(task, frame):
|
||||
...
|
||||
|
||||
@task.event_handler("on_pipeline_error")
|
||||
async def on_pipeline_error(task, frame):
|
||||
...
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
@@ -251,6 +205,7 @@ class PipelineTask(BasePipelineTask):
|
||||
self._conversation_id = conversation_id
|
||||
self._enable_tracing = enable_tracing and is_tracing_available()
|
||||
self._enable_turn_tracking = enable_turn_tracking
|
||||
self._idle_timeout_frames = idle_timeout_frames
|
||||
self._idle_timeout_secs = idle_timeout_secs
|
||||
if self._params.observers:
|
||||
import warnings
|
||||
@@ -285,24 +240,16 @@ class PipelineTask(BasePipelineTask):
|
||||
# This queue is the queue used to push frames to the pipeline.
|
||||
self._push_queue = asyncio.Queue()
|
||||
self._process_push_task: Optional[asyncio.Task] = None
|
||||
|
||||
# This is the heartbeat queue. When a heartbeat frame is received in the
|
||||
# down queue we add it to the heartbeat queue for processing.
|
||||
self._heartbeat_queue = asyncio.Queue()
|
||||
self._heartbeat_push_task: Optional[asyncio.Task] = None
|
||||
self._heartbeat_monitor_task: Optional[asyncio.Task] = None
|
||||
|
||||
# This is the idle event. When selected frames are pushed from any
|
||||
# processor we consider the pipeline is not idle. We use an observer
|
||||
# which will be listening any part of the pipeline.
|
||||
self._idle_event = asyncio.Event()
|
||||
# This is the idle queue. When frames are received downstream they are
|
||||
# put in the queue. If no frame is received the pipeline is considered
|
||||
# idle.
|
||||
self._idle_queue = asyncio.Queue()
|
||||
self._idle_monitor_task: Optional[asyncio.Task] = None
|
||||
if self._idle_timeout_secs:
|
||||
idle_frame_observer = IdleFrameObserver(
|
||||
idle_event=self._idle_event,
|
||||
idle_timeout_frames=idle_timeout_frames,
|
||||
)
|
||||
observers.append(idle_frame_observer)
|
||||
|
||||
# This event is used to indicate the StartFrame has been received at the
|
||||
# end of the pipeline.
|
||||
@@ -312,9 +259,6 @@ class PipelineTask(BasePipelineTask):
|
||||
# StopFrame) has been received at the end of the pipeline.
|
||||
self._pipeline_end_event = asyncio.Event()
|
||||
|
||||
# This event is set when the pipeline truly finishes.
|
||||
self._pipeline_finished_event = asyncio.Event()
|
||||
|
||||
# This is the final pipeline. It is composed of a source processor,
|
||||
# followed by the user pipeline, and ending with a sink processor. The
|
||||
# source allows us to receive and react to upstream frames, and the sink
|
||||
@@ -344,7 +288,6 @@ class PipelineTask(BasePipelineTask):
|
||||
self._register_event_handler("on_pipeline_ended")
|
||||
self._register_event_handler("on_pipeline_cancelled")
|
||||
self._register_event_handler("on_pipeline_finished")
|
||||
self._register_event_handler("on_pipeline_error")
|
||||
|
||||
@property
|
||||
def params(self) -> PipelineParams:
|
||||
@@ -447,9 +390,12 @@ class PipelineTask(BasePipelineTask):
|
||||
await self.queue_frame(EndFrame())
|
||||
|
||||
async def cancel(self):
|
||||
"""Request the running pipeline to cancel."""
|
||||
if not self._finished:
|
||||
await self._cancel()
|
||||
"""Immediately stop the running pipeline.
|
||||
|
||||
Cancels all running tasks and stops frame processing without
|
||||
waiting for completion.
|
||||
"""
|
||||
await self._cancel()
|
||||
|
||||
async def run(self, params: PipelineTaskParams):
|
||||
"""Start and manage the pipeline execution until completion or cancellation.
|
||||
@@ -459,38 +405,51 @@ class PipelineTask(BasePipelineTask):
|
||||
"""
|
||||
if self.has_finished():
|
||||
return
|
||||
|
||||
# Setup processors.
|
||||
await self._setup(params)
|
||||
|
||||
# Create all main tasks and wait for the main push task. This is the
|
||||
# task that pushes frames to the very beginning of our pipeline (i.e. to
|
||||
# our controlled source processor).
|
||||
await self._create_tasks()
|
||||
|
||||
cleanup_pipeline = True
|
||||
try:
|
||||
# Wait for pipeline to finish.
|
||||
await self._wait_for_pipeline_finished()
|
||||
# Setup processors.
|
||||
await self._setup(params)
|
||||
|
||||
# Create all main tasks and wait of the main push task. This is the
|
||||
# task that pushes frames to the very beginning of our pipeline (our
|
||||
# controlled source processor).
|
||||
push_task = await self._create_tasks()
|
||||
await push_task
|
||||
|
||||
# We have already cleaned up the pipeline inside the task.
|
||||
cleanup_pipeline = False
|
||||
|
||||
# Pipeline has finished nicely.
|
||||
self._finished = True
|
||||
except asyncio.CancelledError:
|
||||
logger.debug(f"Pipeline task {self} got cancelled from outside...")
|
||||
# We have been cancelled from outside, let's just cancel everything.
|
||||
await self._cancel()
|
||||
# Wait again for pipeline to finish. This time we have really
|
||||
# cancelled, so it should really finish.
|
||||
await self._wait_for_pipeline_finished()
|
||||
# Re-raise in case there's more cleanup to do.
|
||||
# Raise exception back to the pipeline runner so it can cancel this
|
||||
# task properly.
|
||||
raise
|
||||
finally:
|
||||
# We can reach this point for different reasons:
|
||||
#
|
||||
# 1. The pipeline task has finished (try case).
|
||||
# 2. By an asyncio task cancellation (except case).
|
||||
logger.debug(f"Pipeline task {self} is finishing...")
|
||||
await self._cancel_tasks()
|
||||
if self._check_dangling_tasks:
|
||||
self._print_dangling_tasks()
|
||||
self._finished = True
|
||||
logger.debug(f"Pipeline task {self} has finished")
|
||||
# 1. The task has finished properly (e.g. `EndFrame`).
|
||||
# 2. By calling `PipelineTask.cancel()`.
|
||||
# 3. By asyncio task cancellation.
|
||||
#
|
||||
# Case (1) will execute the code below without issues because
|
||||
# `self._finished` is true.
|
||||
#
|
||||
# Case (2) will execute the code below without issues because
|
||||
# `self._cancelled` is true.
|
||||
#
|
||||
# Case (3) will raise the exception above (because we are cancelling
|
||||
# the asyncio task). This will be then captured by the
|
||||
# `PipelineRunner` which will call `PipelineTask.cancel()` and
|
||||
# therefore becoming case (2).
|
||||
if self._finished or self._cancelled:
|
||||
logger.debug(f"Pipeline task {self} is finishing cleanup...")
|
||||
await self._cancel_tasks()
|
||||
await self._cleanup(cleanup_pipeline)
|
||||
if self._check_dangling_tasks:
|
||||
self._print_dangling_tasks()
|
||||
self._finished = True
|
||||
logger.debug(f"Pipeline task {self} has finished")
|
||||
|
||||
async def queue_frame(self, frame: Frame):
|
||||
"""Queue a single frame to be pushed down the pipeline.
|
||||
@@ -518,7 +477,19 @@ class PipelineTask(BasePipelineTask):
|
||||
if not self._cancelled:
|
||||
logger.debug(f"Cancelling pipeline task {self}")
|
||||
self._cancelled = True
|
||||
await self.queue_frame(CancelFrame())
|
||||
cancel_frame = CancelFrame()
|
||||
# Make sure everything is cleaned up downstream. This is sent
|
||||
# out-of-band from the main streaming task which is what we want since
|
||||
# we want to cancel right away.
|
||||
await self._pipeline.queue_frame(cancel_frame)
|
||||
# Wait for CancelFrame to make it through the pipeline.
|
||||
await self._wait_for_pipeline_end(cancel_frame)
|
||||
# Only cancel the push task, we don't want to be able to process any
|
||||
# other frame after cancel. Everything else will be cancelled in
|
||||
# run().
|
||||
if self._process_push_task:
|
||||
await self._task_manager.cancel_task(self._process_push_task)
|
||||
self._process_push_task = None
|
||||
|
||||
async def _create_tasks(self):
|
||||
"""Create and start all pipeline processing tasks."""
|
||||
@@ -573,7 +544,7 @@ class PipelineTask(BasePipelineTask):
|
||||
|
||||
async def _maybe_cancel_idle_task(self):
|
||||
"""Cancel idle monitoring task if it is running."""
|
||||
if self._idle_monitor_task:
|
||||
if self._idle_timeout_secs and self._idle_monitor_task:
|
||||
await self._task_manager.cancel_task(self._idle_monitor_task)
|
||||
self._idle_monitor_task = None
|
||||
|
||||
@@ -620,17 +591,6 @@ class PipelineTask(BasePipelineTask):
|
||||
|
||||
self._pipeline_end_event.clear()
|
||||
|
||||
# We are really done.
|
||||
self._pipeline_finished_event.set()
|
||||
|
||||
async def _wait_for_pipeline_finished(self):
|
||||
await self._pipeline_finished_event.wait()
|
||||
self._pipeline_finished_event.clear()
|
||||
# Make sure we wait for the main task to complete.
|
||||
if self._process_push_task:
|
||||
await self._process_push_task
|
||||
self._process_push_task = None
|
||||
|
||||
async def _setup(self, params: PipelineTaskParams):
|
||||
"""Set up the pipeline task and all processors."""
|
||||
mgr_params = TaskManagerParams(loop=params.loop)
|
||||
@@ -733,11 +693,12 @@ class PipelineTask(BasePipelineTask):
|
||||
logger.debug(f"{self}: received interruption task frame {frame}")
|
||||
await self._pipeline.queue_frame(InterruptionFrame())
|
||||
elif isinstance(frame, ErrorFrame):
|
||||
await self._call_event_handler("on_pipeline_error", frame)
|
||||
if frame.fatal:
|
||||
logger.error(f"A fatal error occurred: {frame}")
|
||||
# Cancel all tasks downstream.
|
||||
await self.queue_frame(CancelFrame())
|
||||
# Tell the task we should stop.
|
||||
await self.queue_frame(StopTaskFrame())
|
||||
else:
|
||||
logger.warning(f"{self}: Something went wrong: {frame}")
|
||||
|
||||
@@ -749,6 +710,10 @@ class PipelineTask(BasePipelineTask):
|
||||
processors have handled the EndFrame and therefore we can exit the task
|
||||
cleanly.
|
||||
"""
|
||||
# Queue received frame to the idle queue so we can monitor idle
|
||||
# pipelines.
|
||||
await self._idle_queue.put(frame)
|
||||
|
||||
if isinstance(frame, self._reached_downstream_types):
|
||||
await self._call_event_handler("on_frame_reached_downstream", frame)
|
||||
|
||||
@@ -811,10 +776,33 @@ class PipelineTask(BasePipelineTask):
|
||||
Note: Heartbeats are excluded from idle detection.
|
||||
"""
|
||||
running = True
|
||||
last_frame_time = 0
|
||||
|
||||
while running:
|
||||
try:
|
||||
await asyncio.wait_for(self._idle_event.wait(), timeout=self._idle_timeout_secs)
|
||||
self._idle_event.clear()
|
||||
frame = await asyncio.wait_for(
|
||||
self._idle_queue.get(), timeout=self._idle_timeout_secs
|
||||
)
|
||||
|
||||
if isinstance(frame, StartFrame) or isinstance(frame, self._idle_timeout_frames):
|
||||
# If we find a StartFrame or one of the frames that prevents a
|
||||
# time out we update the time.
|
||||
last_frame_time = time.time()
|
||||
else:
|
||||
# If we find any other frame we check if the pipeline is
|
||||
# idle by checking the last time we received one of the
|
||||
# valid frames.
|
||||
diff_time = time.time() - last_frame_time
|
||||
if diff_time >= self._idle_timeout_secs:
|
||||
running = await self._idle_timeout_detected()
|
||||
# Reset `last_frame_time` so we don't trigger another
|
||||
# immediate idle timeout if we are not cancelling. For
|
||||
# example, we might want to force the bot to say goodbye
|
||||
# and then clean nicely with an `EndFrame`.
|
||||
last_frame_time = time.time()
|
||||
|
||||
self._idle_queue.task_done()
|
||||
|
||||
except asyncio.TimeoutError:
|
||||
running = await self._idle_timeout_detected()
|
||||
|
||||
@@ -826,7 +814,7 @@ class PipelineTask(BasePipelineTask):
|
||||
"""
|
||||
# If we are cancelling, just exit the task.
|
||||
if self._cancelled:
|
||||
return False
|
||||
return True
|
||||
|
||||
logger.warning("Idle timeout detected.")
|
||||
await self._call_event_handler("on_idle_timeout")
|
||||
|
||||
@@ -129,7 +129,7 @@ class TaskObserver(BaseObserver):
|
||||
for proxy in self._proxies:
|
||||
await proxy.cleanup()
|
||||
|
||||
async def on_process_frame(self, data: FrameProcessed):
|
||||
async def on_process_frame(self, data: FramePushed):
|
||||
"""Queue frame data for all managed observers.
|
||||
|
||||
Args:
|
||||
@@ -189,7 +189,7 @@ class TaskObserver(BaseObserver):
|
||||
if isinstance(data, FramePushed):
|
||||
if on_push_frame_deprecated:
|
||||
await observer.on_push_frame(
|
||||
data.source, data.destination, data.frame, data.direction, data.timestamp
|
||||
data.src, data.dst, data.frame, data.direction, data.timestamp
|
||||
)
|
||||
else:
|
||||
await observer.on_push_frame(data)
|
||||
|
||||
@@ -16,9 +16,8 @@ service-specific adapter.
|
||||
|
||||
import base64
|
||||
import io
|
||||
import wave
|
||||
from dataclasses import dataclass
|
||||
from typing import TYPE_CHECKING, Any, List, Optional, TypeAlias, Union
|
||||
from typing import Any, List, Optional, TypeAlias, Union
|
||||
|
||||
from loguru import logger
|
||||
from openai._types import NOT_GIVEN as OPEN_AI_NOT_GIVEN
|
||||
@@ -29,12 +28,9 @@ from openai.types.chat import (
|
||||
)
|
||||
from PIL import Image
|
||||
|
||||
from pipecat.adapters.schemas.tools_schema import AdapterType, ToolsSchema
|
||||
from pipecat.adapters.schemas.tools_schema import ToolsSchema
|
||||
from pipecat.frames.frames import AudioRawFrame
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
|
||||
|
||||
# "Re-export" types from OpenAI that we're using as universal context types.
|
||||
# NOTE: if universal message types need to someday diverge from OpenAI's, we
|
||||
# should consider managing our own definitions. But we should do so carefully,
|
||||
@@ -69,34 +65,6 @@ class LLMContext:
|
||||
and content formatting.
|
||||
"""
|
||||
|
||||
@staticmethod
|
||||
def from_openai_context(openai_context: "OpenAILLMContext") -> "LLMContext":
|
||||
"""Create a universal LLM context from an OpenAI-specific context.
|
||||
|
||||
NOTE: this should only be used internally, for facilitating migration
|
||||
from OpenAILLMContext to LLMContext. New user code should use
|
||||
LLMContext directly.
|
||||
|
||||
Args:
|
||||
openai_context: The OpenAI LLM context to convert.
|
||||
|
||||
Returns:
|
||||
New LLMContext instance with converted messages and settings.
|
||||
"""
|
||||
# Convert tools to ToolsSchema if needed.
|
||||
# If the tools are already a ToolsSchema, this is a no-op.
|
||||
# Otherwise, we wrap them in a shim ToolsSchema.
|
||||
converted_tools = openai_context.tools
|
||||
if isinstance(converted_tools, list):
|
||||
converted_tools = ToolsSchema(
|
||||
standard_tools=[], custom_tools={AdapterType.SHIM: converted_tools}
|
||||
)
|
||||
return LLMContext(
|
||||
messages=openai_context.get_messages(),
|
||||
tools=converted_tools,
|
||||
tool_choice=openai_context.tool_choice,
|
||||
)
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
messages: Optional[List[LLMContextMessage]] = None,
|
||||
@@ -114,129 +82,6 @@ class LLMContext:
|
||||
self._tools: ToolsSchema | NotGiven = LLMContext._normalize_and_validate_tools(tools)
|
||||
self._tool_choice: LLMContextToolChoice | NotGiven = tool_choice
|
||||
|
||||
@staticmethod
|
||||
def create_image_url_message(
|
||||
*,
|
||||
role: str = "user",
|
||||
url: str,
|
||||
text: Optional[str] = None,
|
||||
) -> LLMContextMessage:
|
||||
"""Create a context message containing an image URL.
|
||||
|
||||
Args:
|
||||
role: The role of this message (defaults to "user").
|
||||
url: The URL of the image.
|
||||
text: Optional text to include with the image.
|
||||
"""
|
||||
content = []
|
||||
if text:
|
||||
content.append({"type": "text", "text": text})
|
||||
|
||||
content.append({"type": "image_url", "image_url": {"url": url}})
|
||||
|
||||
return {"role": role, "content": content}
|
||||
|
||||
@staticmethod
|
||||
def create_image_message(
|
||||
*,
|
||||
role: str = "user",
|
||||
format: str,
|
||||
size: tuple[int, int],
|
||||
image: bytes,
|
||||
text: Optional[str] = None,
|
||||
) -> LLMContextMessage:
|
||||
"""Create a context message containing an image.
|
||||
|
||||
Args:
|
||||
role: The role of this message (defaults to "user").
|
||||
format: Image format (e.g., 'RGB', 'RGBA').
|
||||
size: Image dimensions as (width, height) tuple.
|
||||
image: Raw image bytes.
|
||||
text: Optional text to include with the image.
|
||||
"""
|
||||
buffer = io.BytesIO()
|
||||
Image.frombytes(format, size, image).save(buffer, format="JPEG")
|
||||
encoded_image = base64.b64encode(buffer.getvalue()).decode("utf-8")
|
||||
url = f"data:image/jpeg;base64,{encoded_image}"
|
||||
|
||||
return LLMContext.create_image_url_message(role=role, url=url, text=text)
|
||||
|
||||
@staticmethod
|
||||
def create_audio_message(
|
||||
*, role: str = "user", audio_frames: list[AudioRawFrame], text: str = "Audio follows"
|
||||
) -> LLMContextMessage:
|
||||
"""Create a context message containing audio.
|
||||
|
||||
Args:
|
||||
role: The role of this message (defaults to "user").
|
||||
audio_frames: List of audio frame objects to include.
|
||||
text: Optional text to include with the audio.
|
||||
"""
|
||||
sample_rate = audio_frames[0].sample_rate
|
||||
num_channels = audio_frames[0].num_channels
|
||||
|
||||
content = []
|
||||
content.append({"type": "text", "text": text})
|
||||
data = b"".join(frame.audio for frame in audio_frames)
|
||||
|
||||
with io.BytesIO() as buffer:
|
||||
with wave.open(buffer, "wb") as wf:
|
||||
wf.setsampwidth(2)
|
||||
wf.setnchannels(num_channels)
|
||||
wf.setframerate(sample_rate)
|
||||
wf.writeframes(data)
|
||||
|
||||
encoded_audio = base64.b64encode(buffer.getvalue()).decode("utf-8")
|
||||
|
||||
content.append(
|
||||
{
|
||||
"type": "input_audio",
|
||||
"input_audio": {"data": encoded_audio, "format": "wav"},
|
||||
}
|
||||
)
|
||||
|
||||
return {"role": role, "content": content}
|
||||
|
||||
@property
|
||||
def messages(self) -> List[LLMContextMessage]:
|
||||
"""Get the current messages list.
|
||||
|
||||
NOTE: This is equivalent to calling `get_messages()` with no filter. If
|
||||
you want to filter out LLM-specific messages that don't pertain to your
|
||||
LLM, use `get_messages()` directly.
|
||||
|
||||
Returns:
|
||||
List of conversation messages.
|
||||
"""
|
||||
return self.get_messages()
|
||||
|
||||
def get_messages_for_persistent_storage(self) -> List[LLMContextMessage]:
|
||||
"""Get messages suitable for persistent storage.
|
||||
|
||||
NOTE: the only reason this method exists is because we're "silently"
|
||||
switching from OpenAILLMContext to LLMContext under the hood in some
|
||||
services and don't want to trip up users who may have been relying on
|
||||
this method, which is part of the public API of OpenAILLMContext but
|
||||
doesn't need to be for LLMContext.
|
||||
|
||||
.. deprecated::
|
||||
Use `get_messages()` instead.
|
||||
|
||||
Returns:
|
||||
List of conversation messages.
|
||||
"""
|
||||
import warnings
|
||||
|
||||
with warnings.catch_warnings():
|
||||
warnings.simplefilter("always")
|
||||
warnings.warn(
|
||||
"get_messages_for_persistent_storage() is deprecated, use get_messages() instead.",
|
||||
DeprecationWarning,
|
||||
stacklevel=2,
|
||||
)
|
||||
|
||||
return self.get_messages()
|
||||
|
||||
def get_messages(self, llm_specific_filter: Optional[str] = None) -> List[LLMContextMessage]:
|
||||
"""Get the current messages list.
|
||||
|
||||
@@ -244,8 +89,7 @@ class LLMContext:
|
||||
llm_specific_filter: Optional filter to return LLM-specific
|
||||
messages for the given LLM, in addition to the standard
|
||||
messages. If messages end up being filtered, an error will be
|
||||
logged; this is intended to catch accidental use of
|
||||
incompatible LLM-specific messages.
|
||||
logged.
|
||||
|
||||
Returns:
|
||||
List of conversation messages.
|
||||
@@ -322,7 +166,7 @@ class LLMContext:
|
||||
self._tool_choice = tool_choice
|
||||
|
||||
def add_image_frame_message(
|
||||
self, *, format: str, size: tuple[int, int], image: bytes, text: Optional[str] = None
|
||||
self, *, format: str, size: tuple[int, int], image: bytes, text: str = None
|
||||
):
|
||||
"""Add a message containing an image frame.
|
||||
|
||||
@@ -332,8 +176,17 @@ class LLMContext:
|
||||
image: Raw image bytes.
|
||||
text: Optional text to include with the image.
|
||||
"""
|
||||
message = LLMContext.create_image_message(format=format, size=size, image=image, text=text)
|
||||
self.add_message(message)
|
||||
buffer = io.BytesIO()
|
||||
Image.frombytes(format, size, image).save(buffer, format="JPEG")
|
||||
encoded_image = base64.b64encode(buffer.getvalue()).decode("utf-8")
|
||||
|
||||
content = []
|
||||
if text:
|
||||
content.append({"type": "text", "text": text})
|
||||
content.append(
|
||||
{"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{encoded_image}"}},
|
||||
)
|
||||
self.add_message({"role": "user", "content": content})
|
||||
|
||||
def add_audio_frames_message(
|
||||
self, *, audio_frames: list[AudioRawFrame], text: str = "Audio follows"
|
||||
@@ -344,8 +197,66 @@ class LLMContext:
|
||||
audio_frames: List of audio frame objects to include.
|
||||
text: Optional text to include with the audio.
|
||||
"""
|
||||
message = LLMContext.create_audio_message(audio_frames=audio_frames, text=text)
|
||||
self.add_message(message)
|
||||
if not audio_frames:
|
||||
return
|
||||
|
||||
sample_rate = audio_frames[0].sample_rate
|
||||
num_channels = audio_frames[0].num_channels
|
||||
|
||||
content = []
|
||||
content.append({"type": "text", "text": text})
|
||||
data = b"".join(frame.audio for frame in audio_frames)
|
||||
data = bytes(
|
||||
self._create_wav_header(
|
||||
sample_rate,
|
||||
num_channels,
|
||||
16,
|
||||
len(data),
|
||||
)
|
||||
+ data
|
||||
)
|
||||
encoded_audio = base64.b64encode(data).decode("utf-8")
|
||||
content.append(
|
||||
{
|
||||
"type": "input_audio",
|
||||
"input_audio": {"data": encoded_audio, "format": "wav"},
|
||||
}
|
||||
)
|
||||
self.add_message({"role": "user", "content": content})
|
||||
|
||||
def _create_wav_header(self, sample_rate, num_channels, bits_per_sample, data_size):
|
||||
"""Create a WAV file header for audio data.
|
||||
|
||||
Args:
|
||||
sample_rate: Audio sample rate in Hz.
|
||||
num_channels: Number of audio channels.
|
||||
bits_per_sample: Bits per audio sample.
|
||||
data_size: Size of audio data in bytes.
|
||||
|
||||
Returns:
|
||||
WAV header as a bytearray.
|
||||
"""
|
||||
# RIFF chunk descriptor
|
||||
header = bytearray()
|
||||
header.extend(b"RIFF") # ChunkID
|
||||
header.extend((data_size + 36).to_bytes(4, "little")) # ChunkSize: total size - 8
|
||||
header.extend(b"WAVE") # Format
|
||||
# "fmt " sub-chunk
|
||||
header.extend(b"fmt ") # Subchunk1ID
|
||||
header.extend((16).to_bytes(4, "little")) # Subchunk1Size (16 for PCM)
|
||||
header.extend((1).to_bytes(2, "little")) # AudioFormat (1 for PCM)
|
||||
header.extend(num_channels.to_bytes(2, "little")) # NumChannels
|
||||
header.extend(sample_rate.to_bytes(4, "little")) # SampleRate
|
||||
# Calculate byte rate and block align
|
||||
byte_rate = sample_rate * num_channels * (bits_per_sample // 8)
|
||||
block_align = num_channels * (bits_per_sample // 8)
|
||||
header.extend(byte_rate.to_bytes(4, "little")) # ByteRate
|
||||
header.extend(block_align.to_bytes(2, "little")) # BlockAlign
|
||||
header.extend(bits_per_sample.to_bytes(2, "little")) # BitsPerSample
|
||||
# "data" sub-chunk
|
||||
header.extend(b"data") # Subchunk2ID
|
||||
header.extend(data_size.to_bytes(4, "little")) # Subchunk2Size
|
||||
return header
|
||||
|
||||
@staticmethod
|
||||
def _normalize_and_validate_tools(tools: ToolsSchema | NotGiven) -> ToolsSchema | NotGiven:
|
||||
|
||||
@@ -89,9 +89,7 @@ class LLMAssistantAggregatorParams:
|
||||
|
||||
Parameters:
|
||||
expect_stripped_words: Whether to expect and handle stripped words
|
||||
in text frames by adding spaces between tokens. This parameter is
|
||||
ignored when used with the newer LLMAssistantAggregator, which
|
||||
handles word spacing automatically.
|
||||
in text frames by adding spaces between tokens.
|
||||
"""
|
||||
|
||||
expect_stripped_words: bool = True
|
||||
|
||||
@@ -13,8 +13,6 @@ LLM processing, and text-to-speech components in conversational AI pipelines.
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import warnings
|
||||
from abc import abstractmethod
|
||||
from typing import Any, Dict, List, Literal, Optional, Set
|
||||
|
||||
from loguru import logger
|
||||
@@ -66,7 +64,6 @@ from pipecat.processors.aggregators.llm_response import (
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
|
||||
from pipecat.utils.string import concatenate_aggregated_text
|
||||
from pipecat.utils.time import time_now_iso8601
|
||||
|
||||
|
||||
@@ -90,7 +87,7 @@ class LLMContextAggregator(FrameProcessor):
|
||||
self._context = context
|
||||
self._role = role
|
||||
|
||||
self._aggregation: List[str] = []
|
||||
self._aggregation: str = ""
|
||||
|
||||
@property
|
||||
def messages(self) -> List[LLMContextMessage]:
|
||||
@@ -170,20 +167,7 @@ class LLMContextAggregator(FrameProcessor):
|
||||
|
||||
async def reset(self):
|
||||
"""Reset the aggregation state."""
|
||||
self._aggregation = []
|
||||
|
||||
@abstractmethod
|
||||
async def push_aggregation(self):
|
||||
"""Push the current aggregation downstream."""
|
||||
pass
|
||||
|
||||
def aggregation_string(self) -> str:
|
||||
"""Get the current aggregation as a string.
|
||||
|
||||
Returns:
|
||||
The concatenated aggregation string.
|
||||
"""
|
||||
return concatenate_aggregated_text(self._aggregation)
|
||||
self._aggregation = ""
|
||||
|
||||
|
||||
class LLMUserAggregator(LLMContextAggregator):
|
||||
@@ -222,6 +206,8 @@ class LLMUserAggregator(LLMContextAggregator):
|
||||
self._turn_params: Optional[SmartTurnParams] = None
|
||||
|
||||
if "aggregation_timeout" in kwargs:
|
||||
import warnings
|
||||
|
||||
with warnings.catch_warnings():
|
||||
warnings.simplefilter("always")
|
||||
warnings.warn(
|
||||
@@ -298,12 +284,6 @@ class LLMUserAggregator(LLMContextAggregator):
|
||||
await self._handle_llm_messages_update(frame)
|
||||
elif isinstance(frame, LLMSetToolsFrame):
|
||||
self.set_tools(frame.tools)
|
||||
# Push the LLMSetToolsFrame as well, since speech-to-speech LLM
|
||||
# services (like OpenAI Realtime) may need to know about tool
|
||||
# changes; unlike text-based LLM services they won't just "pick up
|
||||
# the change" on the next LLM run, as the LLM is continuously
|
||||
# running.
|
||||
await self.push_frame(frame, direction)
|
||||
elif isinstance(frame, LLMSetToolChoiceFrame):
|
||||
self.set_tool_choice(frame.tool_choice)
|
||||
elif isinstance(frame, SpeechControlParamsFrame):
|
||||
@@ -315,13 +295,13 @@ class LLMUserAggregator(LLMContextAggregator):
|
||||
|
||||
async def _process_aggregation(self):
|
||||
"""Process the current aggregation and push it downstream."""
|
||||
aggregation = self.aggregation_string()
|
||||
aggregation = self._aggregation
|
||||
await self.reset()
|
||||
self._context.add_message({"role": self.role, "content": aggregation})
|
||||
frame = LLMContextFrame(self._context)
|
||||
await self.push_frame(frame)
|
||||
|
||||
async def push_aggregation(self):
|
||||
async def _push_aggregation(self):
|
||||
"""Push the current aggregation based on interruption strategies and conditions."""
|
||||
if len(self._aggregation) > 0:
|
||||
if self.interruption_strategies and self._bot_speaking:
|
||||
@@ -363,7 +343,7 @@ class LLMUserAggregator(LLMContextAggregator):
|
||||
"""
|
||||
|
||||
async def should_interrupt(strategy: BaseInterruptionStrategy):
|
||||
await strategy.append_text(self.aggregation_string())
|
||||
await strategy.append_text(self._aggregation)
|
||||
return await strategy.should_interrupt()
|
||||
|
||||
return any([await should_interrupt(s) for s in self._interruption_strategies])
|
||||
@@ -412,7 +392,7 @@ class LLMUserAggregator(LLMContextAggregator):
|
||||
# pushing the aggregation as we will probably get a final transcription.
|
||||
if len(self._aggregation) > 0:
|
||||
if not self._seen_interim_results:
|
||||
await self.push_aggregation()
|
||||
await self._push_aggregation()
|
||||
# Handles the case where both the user and the bot are not speaking,
|
||||
# and the bot was previously speaking before the user interruption.
|
||||
# So in this case we are resetting the aggregation timer
|
||||
@@ -433,7 +413,7 @@ class LLMUserAggregator(LLMContextAggregator):
|
||||
if not text.strip():
|
||||
return
|
||||
|
||||
self._aggregation.append(text)
|
||||
self._aggregation += f" {text}" if self._aggregation else text
|
||||
# We just got a final result, so let's reset interim results.
|
||||
self._seen_interim_results = False
|
||||
# Reset aggregation timer.
|
||||
@@ -491,7 +471,7 @@ class LLMUserAggregator(LLMContextAggregator):
|
||||
await self._maybe_emulate_user_speaking()
|
||||
except asyncio.TimeoutError:
|
||||
if not self._user_speaking:
|
||||
await self.push_aggregation()
|
||||
await self._push_aggregation()
|
||||
|
||||
# If we are emulating VAD we still need to send the user stopped
|
||||
# speaking frame.
|
||||
@@ -558,31 +538,23 @@ class LLMAssistantAggregator(LLMContextAggregator):
|
||||
Args:
|
||||
context: The OpenAI LLM context for conversation storage.
|
||||
params: Configuration parameters for aggregation behavior.
|
||||
**kwargs: Additional arguments.
|
||||
**kwargs: Additional arguments. Supports deprecated 'expect_stripped_words'.
|
||||
"""
|
||||
super().__init__(context=context, role="assistant", **kwargs)
|
||||
self._params = params or LLMAssistantAggregatorParams()
|
||||
|
||||
if "expect_stripped_words" in kwargs:
|
||||
import warnings
|
||||
|
||||
with warnings.catch_warnings():
|
||||
warnings.simplefilter("always")
|
||||
warnings.warn(
|
||||
"Parameter 'expect_stripped_words' is deprecated. "
|
||||
"LLMAssistantAggregator now handles word spacing automatically.",
|
||||
"Parameter 'expect_stripped_words' is deprecated, use 'params' instead.",
|
||||
DeprecationWarning,
|
||||
)
|
||||
|
||||
self._params.expect_stripped_words = kwargs["expect_stripped_words"]
|
||||
|
||||
if params and not params.expect_stripped_words:
|
||||
with warnings.catch_warnings():
|
||||
warnings.simplefilter("always")
|
||||
warnings.warn(
|
||||
"params.expect_stripped_words is deprecated. "
|
||||
"LLMAssistantAggregator now handles word spacing automatically.",
|
||||
DeprecationWarning,
|
||||
)
|
||||
|
||||
self._started = 0
|
||||
self._function_calls_in_progress: Dict[str, Optional[FunctionCallInProgressFrame]] = {}
|
||||
self._context_updated_tasks: Set[asyncio.Task] = set()
|
||||
@@ -632,20 +604,20 @@ class LLMAssistantAggregator(LLMContextAggregator):
|
||||
await self._handle_function_call_result(frame)
|
||||
elif isinstance(frame, FunctionCallCancelFrame):
|
||||
await self._handle_function_call_cancel(frame)
|
||||
elif isinstance(frame, UserImageRawFrame):
|
||||
elif isinstance(frame, UserImageRawFrame) and frame.request and frame.request.tool_call_id:
|
||||
await self._handle_user_image_frame(frame)
|
||||
elif isinstance(frame, BotStoppedSpeakingFrame):
|
||||
await self.push_aggregation()
|
||||
await self._push_aggregation()
|
||||
await self.push_frame(frame, direction)
|
||||
else:
|
||||
await self.push_frame(frame, direction)
|
||||
|
||||
async def push_aggregation(self):
|
||||
async def _push_aggregation(self):
|
||||
"""Push the current assistant aggregation with timestamp."""
|
||||
if not self._aggregation:
|
||||
return
|
||||
|
||||
aggregation = self.aggregation_string()
|
||||
aggregation = self._aggregation.strip()
|
||||
await self.reset()
|
||||
|
||||
if aggregation:
|
||||
@@ -672,7 +644,7 @@ class LLMAssistantAggregator(LLMContextAggregator):
|
||||
await self.push_context_frame(FrameDirection.UPSTREAM)
|
||||
|
||||
async def _handle_interruptions(self, frame: InterruptionFrame):
|
||||
await self.push_aggregation()
|
||||
await self._push_aggregation()
|
||||
self._started = 0
|
||||
await self.reset()
|
||||
|
||||
@@ -783,19 +755,30 @@ class LLMAssistantAggregator(LLMContextAggregator):
|
||||
message["content"] = result
|
||||
|
||||
async def _handle_user_image_frame(self, frame: UserImageRawFrame):
|
||||
if not frame.append_to_context:
|
||||
logger.debug(
|
||||
f"{self} UserImageRawFrame: [{frame.request.function_name}:{frame.request.tool_call_id}]"
|
||||
)
|
||||
|
||||
if frame.request.tool_call_id not in self._function_calls_in_progress:
|
||||
logger.warning(
|
||||
f"UserImageRawFrame tool_call_id [{frame.request.tool_call_id}] is not running"
|
||||
)
|
||||
return
|
||||
|
||||
logger.debug(f"{self} Appending UserImageRawFrame to LLM context (size: {frame.size})")
|
||||
del self._function_calls_in_progress[frame.request.tool_call_id]
|
||||
|
||||
# Update context with the image frame
|
||||
self._update_function_call_result(
|
||||
frame.request.function_name, frame.request.tool_call_id, "COMPLETED"
|
||||
)
|
||||
self._context.add_image_frame_message(
|
||||
format=frame.format,
|
||||
size=frame.size,
|
||||
image=frame.image,
|
||||
text=frame.text,
|
||||
text=frame.request.context,
|
||||
)
|
||||
|
||||
await self.push_aggregation()
|
||||
await self._push_aggregation()
|
||||
await self.push_context_frame(FrameDirection.UPSTREAM)
|
||||
|
||||
async def _handle_llm_start(self, _: LLMFullResponseStartFrame):
|
||||
@@ -803,17 +786,16 @@ class LLMAssistantAggregator(LLMContextAggregator):
|
||||
|
||||
async def _handle_llm_end(self, _: LLMFullResponseEndFrame):
|
||||
self._started -= 1
|
||||
await self.push_aggregation()
|
||||
await self._push_aggregation()
|
||||
|
||||
async def _handle_text(self, frame: TextFrame):
|
||||
if not self._started:
|
||||
return
|
||||
|
||||
# Make sure we really have text (spaces count, too!)
|
||||
if len(frame.text) == 0:
|
||||
return
|
||||
|
||||
self._aggregation.append(frame.text)
|
||||
if self._params.expect_stripped_words:
|
||||
self._aggregation += f" {frame.text}" if self._aggregation else frame.text
|
||||
else:
|
||||
self._aggregation += frame.text
|
||||
|
||||
def _context_updated_task_finished(self, task: asyncio.Task):
|
||||
self._context_updated_tasks.discard(task)
|
||||
|
||||
@@ -12,14 +12,14 @@ in conversational pipelines.
|
||||
"""
|
||||
|
||||
from pipecat.frames.frames import TextFrame
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.llm_response_universal import LLMUserAggregator
|
||||
from pipecat.processors.aggregators.llm_response import LLMUserContextAggregator
|
||||
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
|
||||
|
||||
|
||||
class UserResponseAggregator(LLMUserAggregator):
|
||||
class UserResponseAggregator(LLMUserContextAggregator):
|
||||
"""Aggregates user responses into TextFrame objects.
|
||||
|
||||
This aggregator extends LLMUserAggregator to specifically handle
|
||||
This aggregator extends LLMUserContextAggregator to specifically handle
|
||||
user input by collecting text responses and outputting them as TextFrame
|
||||
objects when the aggregation is complete.
|
||||
"""
|
||||
@@ -27,23 +27,10 @@ class UserResponseAggregator(LLMUserAggregator):
|
||||
def __init__(self, **kwargs):
|
||||
"""Initialize the user response aggregator.
|
||||
|
||||
.. deprecated:: 0.0.92
|
||||
`UserResponseAggregator` is deprecated and will be removed in a future version.
|
||||
|
||||
Args:
|
||||
**kwargs: Additional arguments passed to parent LLMUserAggregator.
|
||||
**kwargs: Additional arguments passed to parent LLMUserContextAggregator.
|
||||
"""
|
||||
super().__init__(context=LLMContext(), **kwargs)
|
||||
|
||||
import warnings
|
||||
|
||||
with warnings.catch_warnings():
|
||||
warnings.simplefilter("always")
|
||||
warnings.warn(
|
||||
"`UserResponseAggregator` is deprecated and will be removed in a future version.",
|
||||
DeprecationWarning,
|
||||
stacklevel=2,
|
||||
)
|
||||
super().__init__(context=OpenAILLMContext(), **kwargs)
|
||||
|
||||
async def push_aggregation(self):
|
||||
"""Push the aggregated user response as a TextFrame.
|
||||
|
||||
@@ -12,7 +12,7 @@ allowing for flexible frame filtering logic in processing pipelines.
|
||||
|
||||
from typing import Awaitable, Callable
|
||||
|
||||
from pipecat.frames.frames import CancelFrame, EndFrame, Frame, StartFrame, SystemFrame
|
||||
from pipecat.frames.frames import EndFrame, Frame, SystemFrame
|
||||
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
|
||||
|
||||
|
||||
@@ -28,7 +28,6 @@ class FunctionFilter(FrameProcessor):
|
||||
self,
|
||||
filter: Callable[[Frame], Awaitable[bool]],
|
||||
direction: FrameDirection = FrameDirection.DOWNSTREAM,
|
||||
filter_system_frames: bool = False,
|
||||
):
|
||||
"""Initialize the function filter.
|
||||
|
||||
@@ -37,32 +36,22 @@ class FunctionFilter(FrameProcessor):
|
||||
frame should pass through, False otherwise.
|
||||
direction: The direction to apply filtering. Only frames moving in
|
||||
this direction will be filtered. Defaults to DOWNSTREAM.
|
||||
filter_system_frames: Whether to filter system frames. Defaults to False.
|
||||
"""
|
||||
super().__init__()
|
||||
self._filter = filter
|
||||
self._direction = direction
|
||||
self._filter_system_frames = filter_system_frames
|
||||
|
||||
#
|
||||
# Frame processor
|
||||
#
|
||||
|
||||
# Ignore system frames, end frames and frames that are not following the
|
||||
# direction of this gate
|
||||
def _should_passthrough_frame(self, frame, direction):
|
||||
"""Check if a frame should pass through without filtering."""
|
||||
# Always passthrough frames in the wrong direction
|
||||
if direction != self._direction:
|
||||
return True
|
||||
|
||||
# Always passthrough lifecycle frames
|
||||
if isinstance(frame, (StartFrame, EndFrame, CancelFrame)):
|
||||
return True
|
||||
|
||||
# If not filtering system frames, passthrough all other system frames
|
||||
if not self._filter_system_frames and isinstance(frame, SystemFrame):
|
||||
return True
|
||||
|
||||
return False
|
||||
# Ignore system frames, end frames and frames that are not following the
|
||||
# direction of this gate
|
||||
return isinstance(frame, (SystemFrame, EndFrame)) or direction != self._direction
|
||||
|
||||
async def process_frame(self, frame: Frame, direction: FrameDirection):
|
||||
"""Process a frame through the filter.
|
||||
|
||||
@@ -877,8 +877,6 @@ class FrameProcessor(BaseObject):
|
||||
|
||||
"""
|
||||
while True:
|
||||
(frame, direction, callback) = await self.__input_queue.get()
|
||||
|
||||
if self.__should_block_system_frames and self.__input_event:
|
||||
logger.trace(f"{self}: system frame processing paused")
|
||||
await self.__input_event.wait()
|
||||
@@ -886,6 +884,8 @@ class FrameProcessor(BaseObject):
|
||||
self.__should_block_system_frames = False
|
||||
logger.trace(f"{self}: system frame processing resumed")
|
||||
|
||||
(frame, direction, callback) = await self.__input_queue.get()
|
||||
|
||||
if isinstance(frame, SystemFrame):
|
||||
await self.__process_frame(frame, direction, callback)
|
||||
elif self.__process_queue:
|
||||
@@ -900,8 +900,6 @@ class FrameProcessor(BaseObject):
|
||||
async def __process_frame_task_handler(self):
|
||||
"""Handle non-system frames from the process queue."""
|
||||
while True:
|
||||
(frame, direction, callback) = await self.__process_queue.get()
|
||||
|
||||
if self.__should_block_frames and self.__process_event:
|
||||
logger.trace(f"{self}: frame processing paused")
|
||||
await self.__process_event.wait()
|
||||
@@ -909,6 +907,8 @@ class FrameProcessor(BaseObject):
|
||||
self.__should_block_frames = False
|
||||
logger.trace(f"{self}: frame processing resumed")
|
||||
|
||||
(frame, direction, callback) = await self.__process_queue.get()
|
||||
|
||||
await self.__process_frame(frame, direction, callback)
|
||||
|
||||
self.__process_queue.task_done()
|
||||
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user