Merge branch 'main' into feat/sarvam-llm-integration
This commit is contained in:
dhruvladia-sarvam
575
CHANGELOG.md
575
CHANGELOG.md
@@ -7,6 +7,581 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
|
||||
|
||||
<!-- towncrier release notes start -->
|
||||
|
||||
## [0.0.107] - 2026-03-23
|
||||
|
||||
### Added
|
||||
|
||||
- Added `frame_order` parameter to `SyncParallelPipeline`. Set
|
||||
`frame_order=FrameOrder.PIPELINE` to push synchronized output frames in
|
||||
pipeline definition order (all frames from the first pipeline, then the
|
||||
second, etc.) instead of the default arrival order.
|
||||
(PR [#4029](https://github.com/pipecat-ai/pipecat/pull/4029))
|
||||
|
||||
- Added `sync_with_audio` field to `OutputImageRawFrame`. When set to `True`,
|
||||
the output transport queues image frames with audio so they are displayed
|
||||
only after all preceding audio has been sent, enabling synchronized
|
||||
audio/image playback.
|
||||
(PR [#4029](https://github.com/pipecat-ai/pipecat/pull/4029))
|
||||
|
||||
- Added `OpenAIResponsesLLMService`, a new LLM service that uses the OpenAI
|
||||
Responses API. Supports streaming text, function calling, usage metrics, and
|
||||
out-of-band inference. Works with the universal `LLMContext` and
|
||||
`LLMContextAggregatorPair`. See
|
||||
`examples/foundational/07-interruptible-openai-responses.py` and
|
||||
`14-function-calling-openai-responses.py`.
|
||||
(PR [#4074](https://github.com/pipecat-ai/pipecat/pull/4074))
|
||||
|
||||
- Added `audio_out_auto_silence` parameter to `TransportParams` (defaults to
|
||||
`True`). When set to `False`, the transport waits for audio data instead of
|
||||
inserting silence when the output queue is empty, which is useful for
|
||||
scenarios that require uninterrupted audio playback without artificial gaps.
|
||||
(PR [#4104](https://github.com/pipecat-ai/pipecat/pull/4104))
|
||||
|
||||
### Changed
|
||||
|
||||
- Renamed tracing span attributes to align with OpenTelemetry GenAI semantic
|
||||
conventions: `gen_ai.system` to `gen_ai.provider.name`, `system` to
|
||||
`gen_ai.system_instructions`, `gen_ai.usage.cache_read_input_tokens` to
|
||||
`gen_ai.usage.cache_read.input_tokens`, and
|
||||
`gen_ai.usage.cache_creation_input_tokens` to
|
||||
`gen_ai.usage.cache_creation.input_tokens`.
|
||||
(PR [#3449](https://github.com/pipecat-ai/pipecat/pull/3449))
|
||||
|
||||
- `DeepgramSageMakerTTSService` now correctly routes audio through the base
|
||||
`TTSService` audio context queue. Audio frames are delivered via
|
||||
`append_to_audio_context()` instead of being pushed directly, enabling proper
|
||||
ordering, interruption handling, and start/stop frame lifecycle management.
|
||||
Interruptions now trigger a `Clear` message to Deepgram (flushing its text
|
||||
buffer) at the right time via `on_audio_context_interrupted`.
|
||||
(PR [#4083](https://github.com/pipecat-ai/pipecat/pull/4083))
|
||||
|
||||
- `GradiumTTSService` now sends a per-context `setup` message with
|
||||
`client_req_id` before the first text message for each TTS context, following
|
||||
Gradium's multiplexing protocol. Previously, a single setup message was sent
|
||||
at connection time without a `client_req_id`, which prevented Gradium from
|
||||
associating requests with their sessions when using `close_ws_on_eos=False`.
|
||||
(PR [#4091](https://github.com/pipecat-ai/pipecat/pull/4091))
|
||||
|
||||
### Fixed
|
||||
|
||||
- Fixed stale `system_instruction` in LLM tracing spans by reading from
|
||||
`_settings.system_instruction` instead of the removed `_system_instruction`
|
||||
attribute.
|
||||
(PR [#3449](https://github.com/pipecat-ai/pipecat/pull/3449))
|
||||
|
||||
- Fixed `SyncParallelPipeline` breaking the Whisker debugger.
|
||||
(PR [#4029](https://github.com/pipecat-ai/pipecat/pull/4029))
|
||||
|
||||
- Fixed `SyncParallelPipeline` race condition where concurrent SystemFrame
|
||||
processing (e.g. from RTVI) could corrupt sink queues and cause deadlocks.
|
||||
SystemFrames now take a fast path that passes them through without draining
|
||||
queued output.
|
||||
(PR [#4029](https://github.com/pipecat-ai/pipecat/pull/4029))
|
||||
|
||||
- Fixed TTS frame ordering so that non-system frames always arrive in correct
|
||||
order relative to the `TTSStartedFrame`/`TTSAudioRawFrame`/`TTSStoppedFrame`
|
||||
sequence. Previously these frames could race ahead of or behind audio context
|
||||
frames, producing out-of-order output downstream.
|
||||
(PR [#4075](https://github.com/pipecat-ai/pipecat/pull/4075))
|
||||
|
||||
- Fixed `SarvamTTSService` audio and error frames now route through
|
||||
`append_to_audio_context()` instead of `push_frame()`, ensuring correct
|
||||
behavior with audio contexts and interruptions.
|
||||
(PR [#4082](https://github.com/pipecat-ai/pipecat/pull/4082))
|
||||
|
||||
- Fixed audio frame ordering and interruption handling in Fish Audio, LMNT,
|
||||
Neuphonic, and Rime NonJson TTS services. These services were bypassing the
|
||||
base `TTSService` audio context serialization queue by pushing audio frames
|
||||
directly, which could cause out-of-order frames and broken interruptions
|
||||
during speech.
|
||||
(PR [#4090](https://github.com/pipecat-ai/pipecat/pull/4090))
|
||||
|
||||
- Fixed Genesys AudioHook serializer to always include the `parameters` field in
|
||||
protocol messages. The AudioHook protocol requires every message to carry a
|
||||
`parameters` object (even if empty), but `_create_message` omitted it when no
|
||||
parameters were provided. This caused clients that validate message structure
|
||||
(including the Genesys reference implementation) to reject `pong` and
|
||||
parameter-less `closed` responses, breaking server sequence tracking and
|
||||
preventing `outputVariables` from reaching the Architect flow.
|
||||
(PR [#4093](https://github.com/pipecat-ai/pipecat/pull/4093))
|
||||
|
||||
## [0.0.106] - 2026-03-18
|
||||
|
||||
### Added
|
||||
|
||||
- Added optional `service` field to `ServiceUpdateSettingsFrame` (and its
|
||||
subclasses `LLMUpdateSettingsFrame`, `TTSUpdateSettingsFrame`,
|
||||
`STTUpdateSettingsFrame`) to target a specific service instance. When
|
||||
`service` is set, only the matching service applies the settings; others
|
||||
forward the frame unchanged. This enables updating a single service when
|
||||
multiple services of the same type exist in the pipeline.
|
||||
(PR [#4004](https://github.com/pipecat-ai/pipecat/pull/4004))
|
||||
|
||||
- Added `sip_provider` and `room_geo` parameters to `configure()` in the Daily
|
||||
runner. These convenience parameters let callers specify a SIP provider name
|
||||
and geographic region directly without manually constructing
|
||||
`DailyRoomProperties` and `DailyRoomSipParams`.
|
||||
(PR [#4005](https://github.com/pipecat-ai/pipecat/pull/4005))
|
||||
|
||||
- Added `PerplexityLLMAdapter` that automatically transforms conversation
|
||||
messages to satisfy Perplexity's stricter API constraints (strict role
|
||||
alternation, no non-initial system messages, last message must be user/tool).
|
||||
Previously, certain conversation histories could cause Perplexity API errors
|
||||
that didn't occur with OpenAI (`PerplexityLLMService` subclasses
|
||||
`OpenAILLMService` since Perplexity uses an OpenAI-compatible API).
|
||||
(PR [#4009](https://github.com/pipecat-ai/pipecat/pull/4009))
|
||||
|
||||
- Added DTMF input event support to the Daily transport. Incoming DTMF tones
|
||||
are now received via Daily's `on_dtmf_event` callback and pushed into the
|
||||
pipeline as `InputDTMFFrame`, enabling bots to react to keypad presses from
|
||||
phone callers.
|
||||
(PR [#4047](https://github.com/pipecat-ai/pipecat/pull/4047))
|
||||
|
||||
- Added `WakePhraseUserTurnStartStrategy` for triggering user turns based on
|
||||
wake phrases, with support for `single_activation` mode. Deprecates
|
||||
`WakeCheckFilter`.
|
||||
(PR [#4064](https://github.com/pipecat-ai/pipecat/pull/4064))
|
||||
|
||||
- Added `default_user_turn_start_strategies()` and
|
||||
`default_user_turn_stop_strategies()` helper functions for composing custom
|
||||
strategy lists.
|
||||
(PR [#4064](https://github.com/pipecat-ai/pipecat/pull/4064))
|
||||
|
||||
### Changed
|
||||
|
||||
- Changed tool result JSON serialization to use `ensure_ascii=False`,
|
||||
preserving UTF-8 characters instead of escaping them. This reduces context
|
||||
size and token usage for non-English languages.
|
||||
(PR [#3457](https://github.com/pipecat-ai/pipecat/pull/3457))
|
||||
|
||||
- `OpenAIRealtimeSTTService`'s `noise_reduction` parameter is now part of
|
||||
`OpenAIRealtimeSTTSettings`, making it runtime-updatable via
|
||||
`STTUpdateSettingsFrame`. The direct `noise_reduction` init argument is
|
||||
deprecated as of 0.0.106.
|
||||
(PR [#3991](https://github.com/pipecat-ai/pipecat/pull/3991))
|
||||
|
||||
- Updated `sarvamai` dependency from `0.1.26a2` (alpha) to `0.1.26` (stable
|
||||
release).
|
||||
(PR [#3997](https://github.com/pipecat-ai/pipecat/pull/3997))
|
||||
|
||||
- `SimliVideoService` now extends `AIService` instead of `FrameProcessor`,
|
||||
aligning it with the HeyGen and Tavus video services. It supports
|
||||
`SimliVideoService.Settings(...)` for configuration and uses
|
||||
`start()`/`stop()`/`cancel()` lifecycle methods. Existing constructor usage
|
||||
(`api_key`, `face_id`, etc.) remains unchanged.
|
||||
(PR [#4001](https://github.com/pipecat-ai/pipecat/pull/4001))
|
||||
|
||||
- Update `pipecat-ai-small-webrtc-prebuilt` to `2.4.0`.
|
||||
(PR [#4023](https://github.com/pipecat-ai/pipecat/pull/4023))
|
||||
|
||||
- Nova Sonic assistant text transcripts are now delivered in real-time using
|
||||
speculative text events instead of delayed final text events. Previously,
|
||||
assistant text only arrived after all audio had finished playing, causing
|
||||
laggy transcripts in client UIs. Speculative text arrives before each audio
|
||||
chunk, providing text synchronized with what the bot is saying. This also
|
||||
simplifies the internal text handling by removing the interruption re-push
|
||||
hack and assistant text buffer.
|
||||
(PR [#4042](https://github.com/pipecat-ai/pipecat/pull/4042))
|
||||
|
||||
- Updated `daily-python` dependency to 0.25.0.
|
||||
(PR [#4047](https://github.com/pipecat-ai/pipecat/pull/4047))
|
||||
|
||||
- Added `enable_dialout` parameter to `configure()` in `pipecat.runner.daily`
|
||||
to support dial-out rooms. Also narrowed misleading `Optional` type hints and
|
||||
deduplicated token expiry calculation.
|
||||
(PR [#4048](https://github.com/pipecat-ai/pipecat/pull/4048))
|
||||
|
||||
- Extended `ProcessFrameResult` to stop strategies, allowing a stop strategy to
|
||||
short-circuit evaluation of subsequent strategies by returning `STOP`.
|
||||
(PR [#4064](https://github.com/pipecat-ai/pipecat/pull/4064))
|
||||
|
||||
- `GradiumSTTService` now takes both an `encoding` and `sample_rate`
|
||||
constructor argument which is assmebled in the class to form the
|
||||
`input_format`. PCM accepts `8000`, `16000`, and `24000` Hz sample rates.
|
||||
(PR [#4066](https://github.com/pipecat-ai/pipecat/pull/4066))
|
||||
|
||||
- Improved `GradiumSTTService` transcription accuracy by reworking how text
|
||||
fragments are accumulated and finalized. Previously, trailing words could be
|
||||
dropped when the server's `flushed` response arrived before all text tokens
|
||||
were delivered. The service now uses a short aggregation delay after flush to
|
||||
capture trailing tokens, producing complete utterances.
|
||||
(PR [#4066](https://github.com/pipecat-ai/pipecat/pull/4066))
|
||||
|
||||
### Deprecated
|
||||
|
||||
- `SimliVideoService.InputParams` is deprecated. Use the direct constructor
|
||||
parameters `max_session_length`, `max_idle_time`, and `enable_logging`
|
||||
instead.
|
||||
(PR [#4001](https://github.com/pipecat-ai/pipecat/pull/4001))
|
||||
|
||||
- Deprecated `LocalSmartTurnAnalyzerV2` and `LocalCoreMLSmartTurnAnalyzer`. Use
|
||||
`LocalSmartTurnAnalyzerV3` instead. Instantiating these analyzers will now
|
||||
emit a `DeprecationWarning`.
|
||||
(PR [#4012](https://github.com/pipecat-ai/pipecat/pull/4012))
|
||||
|
||||
- Deprecated `WakeCheckFilter` in favor of `WakePhraseUserTurnStartStrategy`.
|
||||
(PR [#4064](https://github.com/pipecat-ai/pipecat/pull/4064))
|
||||
|
||||
### Fixed
|
||||
|
||||
- Fixed an issue where the default model for `OpenAILLMService` and
|
||||
`AzureLLMService` was mistakenly reverted to `gpt-4o`. The defaults are now
|
||||
restored to `gpt-4.1`.
|
||||
(PR [#4000](https://github.com/pipecat-ai/pipecat/pull/4000))
|
||||
|
||||
- Fixed a race condition where `EndTaskFrame` could cause the pipeline to shut
|
||||
down before in-flight frames (e.g. LLM function call responses) finished
|
||||
processing. `EndTaskFrame` and `StopTaskFrame` now flow through the pipeline
|
||||
as `ControlFrame`s, ensuring all pending work is flushed before shutdown
|
||||
begins. `CancelTaskFrame` and `InterruptionTaskFrame` remain immediate
|
||||
(`SystemFrame`).
|
||||
(PR [#4006](https://github.com/pipecat-ai/pipecat/pull/4006))
|
||||
|
||||
- Fixed `ParallelPipeline` dropping or misordering frames during lifecycle
|
||||
synchronization. Buffered frames are now flushed in the correct order
|
||||
relative to synchronization frames (`StartFrame` goes first,
|
||||
`EndFrame`/`CancelFrame` go after), and frames added to the buffer during
|
||||
flush are also drained.
|
||||
(PR [#4007](https://github.com/pipecat-ai/pipecat/pull/4007))
|
||||
|
||||
- Fixed `TTSService` potentially canceling in-flight audio during shutdown. The
|
||||
stop sequence now waits for all queued audio contexts to finish processing
|
||||
before canceling the stop frame task.
|
||||
(PR [#4007](https://github.com/pipecat-ai/pipecat/pull/4007))
|
||||
|
||||
- Fixed `Language` enum values (e.g. `Language.ES`) not being converted to
|
||||
service-specific codes when passed via
|
||||
`settings=Service.Settings(language=Language.ES)` at init time. This caused
|
||||
API errors (e.g. 400 from Rime) because the raw enum was sent instead of the
|
||||
expected language code (e.g. `"spa"`). Runtime updates via
|
||||
`UpdateSettingsFrame` were unaffected. The fix centralizes conversion in the
|
||||
base `TTSService` and `STTService` classes so all services handle this
|
||||
consistently.
|
||||
(PR [#4024](https://github.com/pipecat-ai/pipecat/pull/4024))
|
||||
|
||||
- Fixed `DeepgramSTTService` ignoring the `base_url` scheme when using `ws://`
|
||||
or `http://`. Previously these were silently overwritten with `wss://` /
|
||||
`https://`, breaking air-gapped or private deployments that don't use TLS.
|
||||
All scheme choices (`wss://`, `https://`, `ws://`, `http://`, or bare
|
||||
hostname) are now respected.
|
||||
(PR [#4026](https://github.com/pipecat-ai/pipecat/pull/4026))
|
||||
|
||||
- Fixed `LLMSwitcher.register_function()` and `register_direct_function()` not
|
||||
accepting or forwarding the `timeout_secs` parameter.
|
||||
(PR [#4037](https://github.com/pipecat-ai/pipecat/pull/4037))
|
||||
|
||||
- Fixed empty user transcriptions in Nova Sonic causing spurious interruptions.
|
||||
Previously, an empty transcription could trigger an interruption of the
|
||||
assistant's response even though the user hadn't actually spoken.
|
||||
(PR [#4042](https://github.com/pipecat-ai/pipecat/pull/4042))
|
||||
|
||||
- Fixed `SonioxSTTService` and `OpenAIRealtimeSTTService` crash when language
|
||||
parameters contain plain strings instead of `Language` enum values.
|
||||
(PR [#4046](https://github.com/pipecat-ai/pipecat/pull/4046))
|
||||
|
||||
- Fixed premature user turn stops caused by late transcriptions arriving
|
||||
between turns. A stale transcript from the previous turn could persist into
|
||||
the next turn and trigger a stop before the current turn's real transcript
|
||||
arrived. Stop strategies are now reset at both turn start and turn stop to
|
||||
prevent state from leaking across turn boundaries.
|
||||
(PR [#4057](https://github.com/pipecat-ai/pipecat/pull/4057))
|
||||
|
||||
- Fixed raw language strings like `"de-DE"` silently failing when passed to
|
||||
TTS/STT services (e.g. ElevenLabs producing no audio). Raw strings now go
|
||||
through the same `Language` enum resolution as enum values, so regional codes
|
||||
like `"de-DE"` are properly converted to service-expected formats like
|
||||
`"de"`. Unrecognized strings log a warning instead of failing silently.
|
||||
(PR [#4058](https://github.com/pipecat-ai/pipecat/pull/4058))
|
||||
|
||||
- Fixed Deepgram STT list-type settings (`keyterm`, `keywords`, `search`,
|
||||
`redact`, `replace`) being stringified instead of passed as lists to the SDK,
|
||||
which caused them to be sent as literal strings (e.g. `"['pipecat']"`) in the
|
||||
WebSocket query params.
|
||||
(PR [#4063](https://github.com/pipecat-ai/pipecat/pull/4063))
|
||||
|
||||
- Fixed `MinWordsUserTurnStartStrategy` including text below the word threshold
|
||||
in the output by resetting aggregation when the minimum word count is not
|
||||
met.
|
||||
(PR [#4064](https://github.com/pipecat-ai/pipecat/pull/4064))
|
||||
|
||||
- Fixed audio overlap and potential dropped TTS content when multiple assistant
|
||||
turns occur in quick succession. `TTSService` now flushes remaining text
|
||||
before pausing frame processing on `LLMFullResponseEndFrame`/`EndFrame`,
|
||||
instead of pausing first.
|
||||
(PR [#4071](https://github.com/pipecat-ai/pipecat/pull/4071))
|
||||
|
||||
### Security
|
||||
|
||||
- Bumped PyJWT minimum version from 2.10.1 to 2.12.0 in the `livekit` extra to
|
||||
address CVE-2026-32597 (GHSA-752w-5fwx-jx9f), where PyJWT <= 2.11.0 accepted
|
||||
unknown `crit` header extensions.
|
||||
(PR [#4035](https://github.com/pipecat-ai/pipecat/pull/4035))
|
||||
|
||||
## [0.0.105] - 2026-03-10
|
||||
|
||||
### Added
|
||||
|
||||
- Added concurrent audio context support: `CartesiaTTSService` can now
|
||||
synthesize the next sentence while the previous one is still playing, by
|
||||
setting `pause_frame_processing=False` and routing each sentence through its
|
||||
own audio context queue.
|
||||
(PR [#3804](https://github.com/pipecat-ai/pipecat/pull/3804))
|
||||
|
||||
- Added custom video track support to Daily transport. Use
|
||||
`video_out_destinations` in `DailyParams` to publish multiple video tracks
|
||||
simultaneously, mirroring the existing `audio_out_destinations` feature.
|
||||
(PR [#3831](https://github.com/pipecat-ai/pipecat/pull/3831))
|
||||
|
||||
- Added `ServiceSwitcherStrategyFailover` that automatically switches to the
|
||||
next service when the active service reports a non-fatal error. Recovery
|
||||
policies can be implemented via the `on_service_switched` event handler.
|
||||
(PR [#3861](https://github.com/pipecat-ai/pipecat/pull/3861))
|
||||
|
||||
- Added optional `timeout_secs` parameter to `register_function()` and
|
||||
`register_direct_function()` for per-tool function call timeout control,
|
||||
overriding the global `function_call_timeout_secs` default.
|
||||
(PR [#3915](https://github.com/pipecat-ai/pipecat/pull/3915))
|
||||
|
||||
- Added `cloud-audio-only` recording option to Daily transport's
|
||||
`enable_recording` property.
|
||||
(PR [#3916](https://github.com/pipecat-ai/pipecat/pull/3916))
|
||||
|
||||
- Wired up `system_instruction` in `BaseOpenAILLMService`,
|
||||
`AnthropicLLMService`, and `AWSBedrockLLMService` so it works as a default
|
||||
system prompt, matching the behavior of the Google services. This enables
|
||||
sharing a single `LLMContext` across multiple LLM services, where each
|
||||
service provides its own system instruction independently.
|
||||
|
||||
```python
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="You are a helpful assistant.",
|
||||
)
|
||||
|
||||
context = LLMContext()
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
context.add_message({"role": "user", "content": "Please introduce yourself."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
```
|
||||
(PR [#3918](https://github.com/pipecat-ai/pipecat/pull/3918))
|
||||
|
||||
- Added `vad_threshold` parameter to `AssemblyAIConnectionParams` for
|
||||
configuring voice activity detection sensitivity in U3 Pro. Aligning this
|
||||
with external VAD thresholds (e.g., Silero VAD) prevents the "dead zone"
|
||||
where AssemblyAI transcribes speech that VAD hasn't detected yet.
|
||||
(PR [#3927](https://github.com/pipecat-ai/pipecat/pull/3927))
|
||||
|
||||
- Added `push_empty_transcripts` parameter to `BaseWhisperSTTService` and
|
||||
`OpenAISTTService` to allow empty transcripts to be pushed downstream as
|
||||
`TranscriptionFrame` instead of discarding them (the default behavior). This
|
||||
is intended for situations where VAD fires even though the user did not
|
||||
speak. In these cases, it is useful to know that nothing was transcribed so
|
||||
that the agent can resume speaking, instead of waiting longer for a
|
||||
transcription.
|
||||
(PR [#3930](https://github.com/pipecat-ai/pipecat/pull/3930))
|
||||
|
||||
- LLM services (`BaseOpenAILLMService`, `AnthropicLLMService`,
|
||||
`AWSBedrockLLMService`) now log a warning when both `system_instruction` and
|
||||
a system message in the context are set. The constructor's
|
||||
`system_instruction` takes precedence.
|
||||
(PR [#3932](https://github.com/pipecat-ai/pipecat/pull/3932))
|
||||
|
||||
- Runtime settings updates (via `STTUpdateSettingsFrame`) now work for AWS
|
||||
Transcribe, Azure, Cartesia, Deepgram, ElevenLabs Realtime, Gradium, and
|
||||
Soniox STT services. Previously, changing settings at runtime only stored the
|
||||
new values without reconnecting.
|
||||
(PR [#3946](https://github.com/pipecat-ai/pipecat/pull/3946))
|
||||
|
||||
- Exposed `on_summary_applied` event on `LLMAssistantAggregator`, allowing
|
||||
users to listen for context summarization events without accessing private
|
||||
members.
|
||||
(PR [#3947](https://github.com/pipecat-ai/pipecat/pull/3947))
|
||||
|
||||
- Deepgram Flux STT settings (`keyterm`, `eot_threshold`,
|
||||
`eager_eot_threshold`, `eot_timeout_ms`) can now be updated mid-stream via
|
||||
`STTUpdateSettingsFrame` without triggering a reconnect. The new values are
|
||||
sent to Deepgram as a Configure WebSocket message on the existing connection.
|
||||
(PR [#3953](https://github.com/pipecat-ai/pipecat/pull/3953))
|
||||
|
||||
- Added `system_instruction` parameter to `run_inference` across all LLM
|
||||
services, allowing callers to override the system prompt for one-shot
|
||||
inference calls. Used by `_generate_summary` to pass the summarization prompt
|
||||
cleanly.
|
||||
(PR [#3968](https://github.com/pipecat-ai/pipecat/pull/3968))
|
||||
|
||||
### Changed
|
||||
|
||||
- Audio context management (previously in `AudioContextTTSService`) is now
|
||||
built into `TTSService`. All WebSocket providers (`cartesia`, `elevenlabs`,
|
||||
`asyncai`, `inworld`, `rime`, `gradium`, `resembleai`) now inherit from
|
||||
`WebsocketTTSService` directly. Word-timestamp baseline is set automatically
|
||||
on the first audio chunk of each context instead of requiring each provider
|
||||
to call `start_word_timestamps()` in their receive loop.
|
||||
(PR [#3804](https://github.com/pipecat-ai/pipecat/pull/3804))
|
||||
|
||||
- Daily transport now uses `CustomVideoSource`/`CustomVideoTrack` instead of
|
||||
`VirtualCameraDevice` for the default camera output, mirroring how audio
|
||||
already works with `CustomAudioSource`/`CustomAudioTrack`.
|
||||
(PR [#3831](https://github.com/pipecat-ai/pipecat/pull/3831))
|
||||
|
||||
- ⚠️ Updated `DeepgramSTTService` to use `deepgram-sdk` v6. The `LiveOptions`
|
||||
class was removed from the SDK and is now provided by pipecat directly;
|
||||
import it from `pipecat.services.deepgram.stt` instead of `deepgram`.
|
||||
(PR [#3848](https://github.com/pipecat-ai/pipecat/pull/3848))
|
||||
|
||||
- `ServiceSwitcherStrategy` base class now provides a `handle_error()` hook for
|
||||
subclasses to implement error-based switching. `ServiceSwitcher` defaults to
|
||||
`ServiceSwitcherStrategyManual` and `strategy_type` is now optional.
|
||||
(PR [#3861](https://github.com/pipecat-ai/pipecat/pull/3861))
|
||||
|
||||
- Support for Voice Focus 2.0 models.
|
||||
- Updated `aic-sdk` to `~=2.1.0` to support Voice Focus 2.0 models.
|
||||
- Cleaned unused `ParameterFixedError` exception handling in `AICFilter`
|
||||
parameter setup.
|
||||
(PR [#3889](https://github.com/pipecat-ai/pipecat/pull/3889))
|
||||
|
||||
- `max_context_tokens` and `max_unsummarized_messages` in
|
||||
`LLMAutoContextSummarizationConfig` (and deprecated
|
||||
`LLMContextSummarizationConfig`) can now be set to `None` independently to
|
||||
disable that summarization threshold. At least one must remain set.
|
||||
(PR [#3914](https://github.com/pipecat-ai/pipecat/pull/3914))
|
||||
|
||||
- ⚠️ Removed `formatted_finals` and `word_finalization_max_wait_time` from
|
||||
`AssemblyAIConnectionParams` as these were v2 API parameters not supported in
|
||||
v3. Clarified that `format_turns` only applies to Universal-Streaming models;
|
||||
U3 Pro has automatic formatting built-in.
|
||||
(PR [#3927](https://github.com/pipecat-ai/pipecat/pull/3927))
|
||||
|
||||
- Changed `DeepgramTTSService` to send a Clear message on interruption instead
|
||||
of disconnecting and reconnecting the WebSocket, allowing the connection to
|
||||
persist throughout the session.
|
||||
(PR [#3958](https://github.com/pipecat-ai/pipecat/pull/3958))
|
||||
|
||||
- Re-added `enhancement_level` support to `AICFilter` with runtime
|
||||
`FilterEnableFrame` control, applying `ProcessorParameter.Bypass` and
|
||||
`ProcessorParameter.EnhancementLevel` together.
|
||||
(PR [#3961](https://github.com/pipecat-ai/pipecat/pull/3961))
|
||||
|
||||
- Updated `daily-python` dependency from `~=0.23.0` to `~=0.24.0`.
|
||||
(PR [#3970](https://github.com/pipecat-ai/pipecat/pull/3970))
|
||||
|
||||
- Updated `FishAudioTTSService` default model from `s1` to `s2-pro`, matching
|
||||
Fish Audio's latest recommended model for improved quality and speed.
|
||||
(PR [#3973](https://github.com/pipecat-ai/pipecat/pull/3973))
|
||||
|
||||
- `AzureSTTService` `region` parameter is now optional when `private_endpoint`
|
||||
is provided. A `ValueError` is raised if neither is given, and a warning is
|
||||
logged if both are provided (`private_endpoint` takes priority).
|
||||
(PR [#3974](https://github.com/pipecat-ai/pipecat/pull/3974))
|
||||
|
||||
### Deprecated
|
||||
|
||||
- Deprecated `AudioContextTTSService` and `AudioContextWordTTSService`.
|
||||
Subclass `WebsocketTTSService` directly instead; audio context management is
|
||||
now part of the base `TTSService`.
|
||||
- Deprecated `WordTTSService`, `WebsocketWordTTSService`, and
|
||||
`InterruptibleWordTTSService`. Word timestamp logic is now always active in
|
||||
`TTSService` and no longer needs to be opted into via a subclass.
|
||||
(PR [#3804](https://github.com/pipecat-ai/pipecat/pull/3804))
|
||||
|
||||
- Deprecated `pipecat.services.google.llm_vertex`,
|
||||
`pipecat.services.google.llm_openai`, and
|
||||
`pipecat.services.google.gemini_live.llm_vertex` modules. Use
|
||||
`pipecat.services.google.vertex.llm`, `pipecat.services.google.openai.llm`,
|
||||
and `pipecat.services.google.gemini_live.vertex.llm` instead. The old import
|
||||
paths still work but will emit a `DeprecationWarning`.
|
||||
(PR [#3980](https://github.com/pipecat-ai/pipecat/pull/3980))
|
||||
|
||||
### Removed
|
||||
|
||||
- ⚠️ Removed `supports_word_timestamps` parameter from `TTSService.__init__()`.
|
||||
Word timestamp logic is now always active. Remove this argument from any
|
||||
custom subclass `super().__init__()` calls.
|
||||
(PR [#3804](https://github.com/pipecat-ai/pipecat/pull/3804))
|
||||
|
||||
### Fixed
|
||||
|
||||
- Fixed `DeepgramSTTService` keepalive ping timeout disconnections. The
|
||||
deepgram-sdk v6 removed automatic keepalive; pipecat now sends explicit
|
||||
`KeepAlive` messages every 5 seconds, within the recommended 3–5 second
|
||||
interval before Deepgram's 10-second inactivity timeout.
|
||||
(PR [#3848](https://github.com/pipecat-ai/pipecat/pull/3848))
|
||||
|
||||
- Fixed `BufferError: Existing exports of data: object cannot be re-sized` in
|
||||
`AICFilter` caused by holding a `memoryview` on the mutable audio buffer
|
||||
across async yield points.
|
||||
(PR [#3889](https://github.com/pipecat-ai/pipecat/pull/3889))
|
||||
|
||||
- Fixed TTS context not being appended to the assistant message history when
|
||||
using `TTSSpeakFrame` with `append_to_context=True` with some TTS providers.
|
||||
(PR [#3936](https://github.com/pipecat-ai/pipecat/pull/3936))
|
||||
|
||||
- Fixed context summarization leaving orphaned tool responses in the kept
|
||||
context when tool calls were moved to the summarized portion.
|
||||
(PR [#3937](https://github.com/pipecat-ai/pipecat/pull/3937))
|
||||
|
||||
- Fixed turn completion state not resetting at end of LLM responses.
|
||||
`LLMFullResponseEndFrame` is pushed (not received) by the LLM service, so the
|
||||
mixin now handles it in `push_frame` instead of `process_frame`.
|
||||
(PR [#3956](https://github.com/pipecat-ai/pipecat/pull/3956))
|
||||
|
||||
- Fixed turn completion instructions being injected as a context system message
|
||||
instead of using `system_instruction`. This caused warning spam when
|
||||
`system_instruction` was also set and didn't persist across full context
|
||||
updates.
|
||||
(PR [#3957](https://github.com/pipecat-ai/pipecat/pull/3957))
|
||||
|
||||
- Fixed `TTSService` audio context queue getting blocked when
|
||||
`append_to_audio_context()` was called with a `None` context ID, which
|
||||
prevented subsequent audio from being delivered.
|
||||
(PR [#3958](https://github.com/pipecat-ai/pipecat/pull/3958))
|
||||
|
||||
- Fixed `on_call_state_updated` event handler in LiveKit transport receiving
|
||||
incorrect number of arguments due to redundant `self` passed to
|
||||
`_call_event_handler`.
|
||||
(PR [#3959](https://github.com/pipecat-ai/pipecat/pull/3959))
|
||||
|
||||
- Fixed OpenAI Realtime, OpenAI Realtime Beta, and Grok realtime services
|
||||
treating `conversation_already_has_active_response` as a fatal error. These
|
||||
services now log it as a non-fatal debug event when a response is already in
|
||||
progress.
|
||||
(PR [#3960](https://github.com/pipecat-ai/pipecat/pull/3960))
|
||||
|
||||
- Fixed `SmallWebRTCConnection` silently discarding messages sent before the
|
||||
data channel is open by queuing them and flushing once the channel is ready.
|
||||
A bounded queue (`MAX_MESSAGE_QUEUE_SIZE = 50`) prevents unbounded memory
|
||||
growth, and a 10-second timeout after connection clears the queue and falls
|
||||
back to discard mode if the data channel never opens.
|
||||
(PR [#3962](https://github.com/pipecat-ai/pipecat/pull/3962))
|
||||
|
||||
- Fixed `AzureSTTService` failing to initialize when `private_endpoint` is
|
||||
provided. The Azure Speech SDK's `SpeechConfig` does not accept both `region`
|
||||
and `endpoint` simultaneously, so they are now passed conditionally.
|
||||
(PR [#3967](https://github.com/pipecat-ai/pipecat/pull/3967))
|
||||
|
||||
- Fixed `GoogleLLMService` ignoring the `system_instruction` set via
|
||||
constructor or `GoogleLLMSettings` when a system message was also present in
|
||||
the context. The settings value now correctly takes priority, and a warning
|
||||
is logged when both are set.
|
||||
(PR [#3976](https://github.com/pipecat-ai/pipecat/pull/3976))
|
||||
|
||||
### Other
|
||||
|
||||
- Updated foundational examples to use `system_instruction` on LLM services
|
||||
instead of adding system messages to `LLMContext`.
|
||||
(PR [#3918](https://github.com/pipecat-ai/pipecat/pull/3918))
|
||||
|
||||
- Updated AssemblyAI turn detection example to use `keyterms_prompt` list
|
||||
format instead of `prompt` string for improved clarity.
|
||||
(PR [#3929](https://github.com/pipecat-ai/pipecat/pull/3929))
|
||||
|
||||
- Updated foundational examples and eval scripts to use `"user"` role instead
|
||||
of `"system"` when adding messages to `LLMContext`, since system prompts
|
||||
should be set via `system_instruction` on the LLM service.
|
||||
(PR [#3931](https://github.com/pipecat-ai/pipecat/pull/3931))
|
||||
|
||||
## [0.0.104] - 2026-03-02
|
||||
|
||||
### Added
|
||||
|
||||
@@ -280,17 +280,17 @@ from typing import Optional
|
||||
|
||||
class MyTTSService(TTSService):
|
||||
Settings = MyTTSSettings
|
||||
_settings: MyTTSSettings
|
||||
_settings: Settings
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
api_key: str,
|
||||
settings: Optional[MyTTSSettings] = None,
|
||||
settings: Optional[Settings] = None,
|
||||
**kwargs,
|
||||
):
|
||||
# 1. Defaults — every field has a real value (store mode).
|
||||
default_settings = MyTTSSettings(
|
||||
default_settings = self.Settings(
|
||||
model="my-model-v1",
|
||||
voice="default-voice",
|
||||
language="en",
|
||||
|
||||
@@ -65,6 +65,10 @@ claude plugin marketplace add pipecat-ai/skills
|
||||
|
||||
and install any of the available plugins.
|
||||
|
||||
### 🧩 Community Integrations
|
||||
|
||||
Build and share your own Pipecat service integrations! Browse existing [community integrations](https://docs.pipecat.ai/server/services/community-integrations) or check out our [guide](COMMUNITY_INTEGRATIONS.md) to create your own.
|
||||
|
||||
### 📺️ Pipecat TV Channel
|
||||
|
||||
Catch new features, interviews, and how-tos on our [Pipecat TV](https://www.youtube.com/playlist?list=PLzU2zoMTQIHjqC3v4q2XVSR3hGSzwKFwH) channel.
|
||||
@@ -94,6 +98,7 @@ Catch new features, interviews, and how-tos on our [Pipecat TV](https://www.yout
|
||||
| Vision & Image | [fal](https://docs.pipecat.ai/server/services/image-generation/fal), [Google Imagen](https://docs.pipecat.ai/server/services/image-generation/google-imagen), [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) |
|
||||
| Community | [Browse community integrations →](https://docs.pipecat.ai/server/services/community-integrations) |
|
||||
|
||||
📚 [View full services documentation →](https://docs.pipecat.ai/server/services/supported-services)
|
||||
|
||||
|
||||
@@ -1 +0,0 @@
|
||||
- Added concurrent audio context support: `CartesiaTTSService` can now synthesize the next sentence while the previous one is still playing, by setting `pause_frame_processing=False` and routing each sentence through its own audio context queue.
|
||||
@@ -1 +0,0 @@
|
||||
- Audio context management (previously in `AudioContextTTSService`) is now built into `TTSService`. All WebSocket providers (`cartesia`, `elevenlabs`, `asyncai`, `inworld`, `rime`, `gradium`, `resembleai`) now inherit from `WebsocketTTSService` directly. Word-timestamp baseline is set automatically on the first audio chunk of each context instead of requiring each provider to call `start_word_timestamps()` in their receive loop.
|
||||
@@ -1,2 +0,0 @@
|
||||
- Deprecated `AudioContextTTSService` and `AudioContextWordTTSService`. Subclass `WebsocketTTSService` directly instead; audio context management is now part of the base `TTSService`.
|
||||
- Deprecated `WordTTSService`, `WebsocketWordTTSService`, and `InterruptibleWordTTSService`. Word timestamp logic is now always active in `TTSService` and no longer needs to be opted into via a subclass.
|
||||
@@ -1 +0,0 @@
|
||||
- ⚠️ Removed `supports_word_timestamps` parameter from `TTSService.__init__()`. Word timestamp logic is now always active. Remove this argument from any custom subclass `super().__init__()` calls.
|
||||
@@ -1 +0,0 @@
|
||||
- Added custom video track support to Daily transport. Use `video_out_destinations` in `DailyParams` to publish multiple video tracks simultaneously, mirroring the existing `audio_out_destinations` feature.
|
||||
@@ -1 +0,0 @@
|
||||
- Daily transport now uses `CustomVideoSource`/`CustomVideoTrack` instead of `VirtualCameraDevice` for the default camera output, mirroring how audio already works with `CustomAudioSource`/`CustomAudioTrack`.
|
||||
@@ -1 +0,0 @@
|
||||
- ⚠️ Updated `DeepgramSTTService` to use `deepgram-sdk` v6. The `LiveOptions` class was removed from the SDK and is now provided by pipecat directly; import it from `pipecat.services.deepgram.stt` instead of `deepgram`.
|
||||
@@ -1 +0,0 @@
|
||||
- Fixed `DeepgramSTTService` keepalive ping timeout disconnections. The deepgram-sdk v6 removed automatic keepalive; pipecat now sends explicit `KeepAlive` messages every 5 seconds, within the recommended 3–5 second interval before Deepgram's 10-second inactivity timeout.
|
||||
@@ -1 +0,0 @@
|
||||
- Added `ServiceSwitcherStrategyFailover` that automatically switches to the next service when the active service reports a non-fatal error. Recovery policies can be implemented via the `on_service_switched` event handler.
|
||||
@@ -1 +0,0 @@
|
||||
- `ServiceSwitcherStrategy` base class now provides a `handle_error()` hook for subclasses to implement error-based switching. `ServiceSwitcher` defaults to `ServiceSwitcherStrategyManual` and `strategy_type` is now optional.
|
||||
@@ -1,3 +0,0 @@
|
||||
- Support for Voice Focus 2.0 models.
|
||||
- Updated `aic-sdk` to `~=2.1.0` to support Voice Focus 2.0 models.
|
||||
- Cleaned unused `ParameterFixedError` exception handling in `AICFilter` parameter setup.
|
||||
@@ -1 +0,0 @@
|
||||
- Fixed `BufferError: Existing exports of data: object cannot be re-sized` in `AICFilter` caused by holding a `memoryview` on the mutable audio buffer across async yield points.
|
||||
@@ -1 +0,0 @@
|
||||
- `max_context_tokens` and `max_unsummarized_messages` in `LLMAutoContextSummarizationConfig` (and deprecated `LLMContextSummarizationConfig`) can now be set to `None` independently to disable that summarization threshold. At least one must remain set.
|
||||
@@ -1 +0,0 @@
|
||||
- Added optional `timeout_secs` parameter to `register_function()` and `register_direct_function()` for per-tool function call timeout control, overriding the global `function_call_timeout_secs` default.
|
||||
@@ -1 +0,0 @@
|
||||
- Added `cloud-audio-only` recording option to Daily transport's `enable_recording` property.
|
||||
@@ -1,15 +0,0 @@
|
||||
- Wired up `system_instruction` in `BaseOpenAILLMService`, `AnthropicLLMService`, and `AWSBedrockLLMService` so it works as a default system prompt, matching the behavior of the Google services. This enables sharing a single `LLMContext` across multiple LLM services, where each service provides its own system instruction independently.
|
||||
|
||||
```python
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
system_instruction="You are a helpful assistant.",
|
||||
)
|
||||
|
||||
context = LLMContext()
|
||||
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
context.add_message({"role": "user", "content": "Please introduce yourself."})
|
||||
await task.queue_frames([LLMRunFrame()])
|
||||
```
|
||||
@@ -1 +0,0 @@
|
||||
- Updated foundational examples to use `system_instruction` on LLM services instead of adding system messages to `LLMContext`.
|
||||
@@ -1 +0,0 @@
|
||||
- Added `vad_threshold` parameter to `AssemblyAIConnectionParams` for configuring voice activity detection sensitivity in U3 Pro. Aligning this with external VAD thresholds (e.g., Silero VAD) prevents the "dead zone" where AssemblyAI transcribes speech that VAD hasn't detected yet.
|
||||
@@ -1 +0,0 @@
|
||||
- ⚠️ Removed `formatted_finals` and `word_finalization_max_wait_time` from `AssemblyAIConnectionParams` as these were v2 API parameters not supported in v3. Clarified that `format_turns` only applies to Universal-Streaming models; U3 Pro has automatic formatting built-in.
|
||||
@@ -1 +0,0 @@
|
||||
- Updated AssemblyAI turn detection example to use `keyterms_prompt` list format instead of `prompt` string for improved clarity.
|
||||
@@ -1 +0,0 @@
|
||||
- Added `push_empty_transcripts` parameter to `BaseWhisperSTTService` and `OpenAISTTService` to allow empty transcripts to be pushed downstream as `TranscriptionFrame` instead of discarding them (the default behavior). This is intended for situations where VAD fires even though the user did not speak. In these cases, it is useful to know that nothing was transcribed so that the agent can resume speaking, instead of waiting longer for a transcription.
|
||||
@@ -1 +0,0 @@
|
||||
- Updated foundational examples and eval scripts to use `"user"` role instead of `"system"` when adding messages to `LLMContext`, since system prompts should be set via `system_instruction` on the LLM service.
|
||||
@@ -1 +0,0 @@
|
||||
- LLM services (`BaseOpenAILLMService`, `AnthropicLLMService`, `AWSBedrockLLMService`) now log a warning when both `system_instruction` and a system message in the context are set. The constructor's `system_instruction` takes precedence.
|
||||
@@ -1 +0,0 @@
|
||||
- Fixed TTS context not being appended to the assistant message history when using `TTSSpeakFrame` with `append_to_context=True` with some TTS providers.
|
||||
@@ -1 +0,0 @@
|
||||
- Fixed context summarization leaving orphaned tool responses in the kept context when tool calls were moved to the summarized portion.
|
||||
@@ -1 +0,0 @@
|
||||
- Runtime settings updates (via `STTUpdateSettingsFrame`) now work for AWS Transcribe, Azure, Cartesia, Deepgram, ElevenLabs Realtime, Gradium, and Soniox STT services. Previously, changing settings at runtime only stored the new values without reconnecting.
|
||||
@@ -1 +0,0 @@
|
||||
- Exposed `on_summary_applied` event on `LLMAssistantAggregator`, allowing users to listen for context summarization events without accessing private members.
|
||||
@@ -1 +0,0 @@
|
||||
- Deepgram Flux STT settings (`keyterm`, `eot_threshold`, `eager_eot_threshold`, `eot_timeout_ms`) can now be updated mid-stream via `STTUpdateSettingsFrame` without triggering a reconnect. The new values are sent to Deepgram as a Configure WebSocket message on the existing connection.
|
||||
@@ -1 +0,0 @@
|
||||
- Fixed turn completion state not resetting at end of LLM responses. `LLMFullResponseEndFrame` is pushed (not received) by the LLM service, so the mixin now handles it in `push_frame` instead of `process_frame`.
|
||||
@@ -1 +0,0 @@
|
||||
- Fixed turn completion instructions being injected as a context system message instead of using `system_instruction`. This caused warning spam when `system_instruction` was also set and didn't persist across full context updates.
|
||||
@@ -1 +0,0 @@
|
||||
- Changed `DeepgramTTSService` to send a Clear message on interruption instead of disconnecting and reconnecting the WebSocket, allowing the connection to persist throughout the session.
|
||||
@@ -1 +0,0 @@
|
||||
- Fixed `TTSService` audio context queue getting blocked when `append_to_audio_context()` was called with a `None` context ID, which prevented subsequent audio from being delivered.
|
||||
@@ -1 +0,0 @@
|
||||
- Fixed `on_call_state_updated` event handler in LiveKit transport receiving incorrect number of arguments due to redundant `self` passed to `_call_event_handler`.
|
||||
@@ -1 +0,0 @@
|
||||
- Fixed OpenAI Realtime, OpenAI Realtime Beta, and Grok realtime services treating `conversation_already_has_active_response` as a fatal error. These services now log it as a non-fatal debug event when a response is already in progress.
|
||||
@@ -1 +0,0 @@
|
||||
- Re-added `enhancement_level` support to `AICFilter` with runtime `FilterEnableFrame` control, applying `ProcessorParameter.Bypass` and `ProcessorParameter.EnhancementLevel` together.
|
||||
@@ -1 +0,0 @@
|
||||
- Fixed `SmallWebRTCConnection` silently discarding messages sent before the data channel is open by queuing them and flushing once the channel is ready. A bounded queue (`MAX_MESSAGE_QUEUE_SIZE = 50`) prevents unbounded memory growth, and a 10-second timeout after connection clears the queue and falls back to discard mode if the data channel never opens.
|
||||
@@ -1 +0,0 @@
|
||||
- Fixed `AzureSTTService` failing to initialize when `private_endpoint` is provided. The Azure Speech SDK's `SpeechConfig` does not accept both `region` and `endpoint` simultaneously, so they are now passed conditionally.
|
||||
@@ -1 +0,0 @@
|
||||
- Added `system_instruction` parameter to `run_inference` across all LLM services, allowing callers to override the system prompt for one-shot inference calls. Used by `_generate_summary` to pass the summarization prompt cleanly.
|
||||
@@ -1 +0,0 @@
|
||||
- Updated `daily-python` dependency from `~=0.23.0` to `~=0.24.0`.
|
||||
@@ -1 +0,0 @@
|
||||
- Updated `FishAudioTTSService` default model from `s1` to `s2-pro`, matching Fish Audio's latest recommended model for improved quality and speed.
|
||||
@@ -1 +0,0 @@
|
||||
- `AzureSTTService` `region` parameter is now optional when `private_endpoint` is provided. A `ValueError` is raised if neither is given, and a warning is logged if both are provided (`private_endpoint` takes priority).
|
||||
@@ -1 +0,0 @@
|
||||
- Fixed `GoogleLLMService` ignoring the `system_instruction` set via constructor or `GoogleLLMSettings` when a system message was also present in the context. The settings value now correctly takes priority, and a warning is logged when both are set.
|
||||
@@ -47,7 +47,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are an LLM in a WebRTC session, and this is a 'hello world' demo.",
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -75,7 +75,7 @@ async def run_example(webrtc_connection: SmallWebRTCConnection):
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -58,8 +58,7 @@ async def main():
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
model="gpt-4o",
|
||||
system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -58,7 +58,7 @@ async def main():
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -16,11 +16,12 @@ from pipecat.frames.frames import (
|
||||
Frame,
|
||||
LLMContextFrame,
|
||||
LLMFullResponseStartFrame,
|
||||
OutputImageRawFrame,
|
||||
TextFrame,
|
||||
)
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.sync_parallel_pipeline import SyncParallelPipeline
|
||||
from pipecat.pipeline.sync_parallel_pipeline import FrameOrder, SyncParallelPipeline
|
||||
from pipecat.pipeline.task import PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.sentence import SentenceAggregator
|
||||
@@ -30,6 +31,7 @@ from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.cartesia.tts import CartesiaHttpTTSService
|
||||
from pipecat.services.fal.image import FalImageGenService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.services.tts_service import TextAggregationMode
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
|
||||
@@ -44,6 +46,18 @@ class MonthFrame(DataFrame):
|
||||
return f"{self.name}(month: {self.month})"
|
||||
|
||||
|
||||
class MarkImageForPlaybackSync(FrameProcessor):
|
||||
"""Marks output image frames to be synchronized with audio playback."""
|
||||
|
||||
async def process_frame(self, frame: Frame, direction: FrameDirection):
|
||||
await super().process_frame(frame, direction)
|
||||
|
||||
if isinstance(frame, OutputImageRawFrame):
|
||||
frame.sync_with_audio = True
|
||||
|
||||
await self.push_frame(frame, direction)
|
||||
|
||||
|
||||
class MonthPrepender(FrameProcessor):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
@@ -101,6 +115,10 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
settings=CartesiaHttpTTSService.Settings(
|
||||
voice="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
),
|
||||
# No need to aggregate by sentences (the default), as we already know we're getting full sentences
|
||||
# (Otherwise the service will unnecessarily wait for follow-up input to confirm the sentence is complete,
|
||||
# which, sadly, actually breaks the synchronization mechanism)
|
||||
text_aggregation_mode=TextAggregationMode.TOKEN,
|
||||
)
|
||||
|
||||
imagegen = FalImageGenService(
|
||||
@@ -119,17 +137,26 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
# that, each pipeline runs concurrently and `SyncParallelPipeline` will
|
||||
# wait for the input frame to be processed.
|
||||
#
|
||||
# We use `FrameOrder.PIPELINE` so that each synchronized batch of output
|
||||
# frames is pushed in the order the pipelines are listed: image first,
|
||||
# then audio. This ensures the transport receives the image before the
|
||||
# audio frames it should accompany.
|
||||
#
|
||||
# Note that `SyncParallelPipeline` requires the last processor in each
|
||||
# of the pipelines to be synchronous. In this case, we use
|
||||
# `CartesiaHttpTTSService` and `FalImageGenService` which make HTTP
|
||||
# `FalImageGenService` and `CartesiaHttpTTSService` which make HTTP
|
||||
# requests and wait for the response.
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
llm, # LLM
|
||||
sentence_aggregator, # Aggregates LLM output into full sentences
|
||||
SyncParallelPipeline( # Run pipelines in parallel aggregating the result
|
||||
[
|
||||
imagegen, # Generate image
|
||||
MarkImageForPlaybackSync(), # Mark image as needing sync w/audio during playback
|
||||
],
|
||||
[month_prepender, tts], # Create "Month: sentence" and output audio
|
||||
[imagegen], # Generate image
|
||||
frame_order=FrameOrder.PIPELINE,
|
||||
),
|
||||
transport.output(), # Transport output
|
||||
]
|
||||
|
||||
@@ -1,202 +0,0 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import asyncio
|
||||
import os
|
||||
import sys
|
||||
import tkinter as tk
|
||||
|
||||
import aiohttp
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.frames.frames import (
|
||||
Frame,
|
||||
LLMContextFrame,
|
||||
OutputAudioRawFrame,
|
||||
TextFrame,
|
||||
TTSAudioRawFrame,
|
||||
URLImageRawFrame,
|
||||
)
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.sync_parallel_pipeline import SyncParallelPipeline
|
||||
from pipecat.pipeline.task import PipelineTask
|
||||
from pipecat.processors.aggregators.llm_context import LLMContext
|
||||
from pipecat.processors.aggregators.sentence import SentenceAggregator
|
||||
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
|
||||
from pipecat.services.cartesia.tts import CartesiaHttpTTSService
|
||||
from pipecat.services.fal.image import FalImageGenService
|
||||
from pipecat.services.openai.llm import OpenAILLMService
|
||||
from pipecat.transports.local.tk import TkLocalTransport, TkTransportParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
logger.remove(0)
|
||||
logger.add(sys.stderr, level="DEBUG")
|
||||
|
||||
|
||||
async def main():
|
||||
async with aiohttp.ClientSession() as session:
|
||||
tk_root = tk.Tk()
|
||||
tk_root.title("Calendar")
|
||||
|
||||
runner = PipelineRunner()
|
||||
|
||||
async def get_month_data(month):
|
||||
messages = [
|
||||
{
|
||||
"role": "user",
|
||||
"content": f"Describe a nature photograph suitable for use in a calendar, for the month of {month}. Include only the image description with no preamble. Limit the description to one sentence, please.",
|
||||
}
|
||||
]
|
||||
|
||||
class ImageDescription(FrameProcessor):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.text = ""
|
||||
|
||||
async def process_frame(self, frame: Frame, direction: FrameDirection):
|
||||
await super().process_frame(frame, direction)
|
||||
|
||||
if isinstance(frame, TextFrame):
|
||||
self.text = frame.text
|
||||
await self.push_frame(frame, direction)
|
||||
|
||||
class AudioGrabber(FrameProcessor):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.audio = bytearray()
|
||||
self.frame = None
|
||||
|
||||
async def process_frame(self, frame: Frame, direction: FrameDirection):
|
||||
await super().process_frame(frame, direction)
|
||||
|
||||
if isinstance(frame, TTSAudioRawFrame):
|
||||
self.audio.extend(frame.audio)
|
||||
self.frame = OutputAudioRawFrame(
|
||||
bytes(self.audio), frame.sample_rate, frame.num_channels
|
||||
)
|
||||
await self.push_frame(frame, direction)
|
||||
|
||||
class ImageGrabber(FrameProcessor):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.frame = None
|
||||
|
||||
async def process_frame(self, frame: Frame, direction: FrameDirection):
|
||||
await super().process_frame(frame, direction)
|
||||
|
||||
if isinstance(frame, URLImageRawFrame):
|
||||
self.frame = frame
|
||||
await self.push_frame(frame, direction)
|
||||
|
||||
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
|
||||
|
||||
tts = CartesiaHttpTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
settings=CartesiaHttpTTSService.Settings(
|
||||
voice="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
),
|
||||
)
|
||||
|
||||
imagegen = FalImageGenService(
|
||||
settings=FalImageGenService.Settings(
|
||||
image_size="square_hd",
|
||||
),
|
||||
aiohttp_session=session,
|
||||
key=os.getenv("FAL_KEY"),
|
||||
)
|
||||
|
||||
sentence_aggregator = SentenceAggregator()
|
||||
|
||||
description = ImageDescription()
|
||||
|
||||
audio_grabber = AudioGrabber()
|
||||
|
||||
image_grabber = ImageGrabber()
|
||||
|
||||
# With `SyncParallelPipeline` we synchronize audio and images by
|
||||
# pushing them basically in order (e.g. I1 A1 A1 A1 I2 A2 A2 A2 A2
|
||||
# I3 A3). To do that, each pipeline runs concurrently and
|
||||
# `SyncParallelPipeline` will wait for the input frame to be
|
||||
# processed.
|
||||
#
|
||||
# Note that `SyncParallelPipeline` requires the last processor in
|
||||
# each of the pipelines to be synchronous. In this case, we use
|
||||
# `CartesiaHttpTTSService` and `FalImageGenService` which make HTTP
|
||||
# requests and wait for the response.
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
llm, # LLM
|
||||
sentence_aggregator, # Aggregates LLM output into full sentences
|
||||
description, # Store sentence
|
||||
SyncParallelPipeline(
|
||||
[tts, audio_grabber], # Generate and store audio for the given sentence
|
||||
[imagegen, image_grabber], # Generate and storeimage for the given sentence
|
||||
),
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(pipeline)
|
||||
await task.queue_frame(LLMContextFrame(LLMContext(messages)))
|
||||
await task.stop_when_done()
|
||||
|
||||
await runner.run(task)
|
||||
|
||||
return {
|
||||
"month": month,
|
||||
"text": description.text,
|
||||
"image": image_grabber.frame,
|
||||
"audio": audio_grabber.frame,
|
||||
}
|
||||
|
||||
transport = TkLocalTransport(
|
||||
tk_root,
|
||||
TkTransportParams(
|
||||
audio_out_enabled=True,
|
||||
video_out_enabled=True,
|
||||
video_out_width=1024,
|
||||
video_out_height=1024,
|
||||
),
|
||||
)
|
||||
|
||||
pipeline = Pipeline([transport.output()])
|
||||
|
||||
task = PipelineTask(pipeline)
|
||||
|
||||
# We only specify a few months as we create tasks all at once and we
|
||||
# might get rate limited otherwise.
|
||||
months: list[str] = [
|
||||
"January",
|
||||
"February",
|
||||
]
|
||||
|
||||
# We create one task per month. This will be executed concurrently.
|
||||
month_tasks = [asyncio.create_task(get_month_data(month)) for month in months]
|
||||
|
||||
# Now we wait for each month task in the order they're completed. The
|
||||
# benefit is we'll have as little delay as possible before the first
|
||||
# month, and likely no delay between months, but the months won't
|
||||
# display in order.
|
||||
async def show_images(month_tasks):
|
||||
for month_data_task in asyncio.as_completed(month_tasks):
|
||||
data = await month_data_task
|
||||
await task.queue_frames([data["image"], data["audio"]])
|
||||
|
||||
await runner.stop_when_done()
|
||||
|
||||
async def run_tk():
|
||||
while not task.has_finished():
|
||||
tk_root.update()
|
||||
tk_root.update_idletasks()
|
||||
await asyncio.sleep(0.1)
|
||||
|
||||
await asyncio.gather(runner.run(task), show_images(month_tasks), run_tk())
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
@@ -91,7 +91,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -108,7 +108,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -67,7 +67,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
125
examples/foundational/07-interruptible-openai-responses.py
Normal file
125
examples/foundational/07-interruptible-openai-responses.py
Normal file
@@ -0,0 +1,125 @@
|
||||
#
|
||||
# Copyright (c) 2024-2026, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
import os
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
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,
|
||||
LLMUserAggregatorParams,
|
||||
)
|
||||
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.responses.llm import OpenAIResponsesLLMService
|
||||
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 use lambdas to defer transport parameter creation until the transport
|
||||
# type is selected at runtime.
|
||||
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 = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
|
||||
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY"),
|
||||
settings=CartesiaTTSService.Settings(
|
||||
voice="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
),
|
||||
)
|
||||
|
||||
llm = OpenAIResponsesLLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAIResponsesLLMService.Settings(
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
context = LLMContext()
|
||||
user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
|
||||
context,
|
||||
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
|
||||
)
|
||||
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
transport.input(), # Transport user input
|
||||
stt,
|
||||
user_aggregator, # User responses
|
||||
llm, # LLM
|
||||
tts, # TTS
|
||||
transport.output(), # Transport bot output
|
||||
assistant_aggregator, # 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.
|
||||
context.add_message(
|
||||
{"role": "developer", "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()
|
||||
@@ -63,7 +63,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -113,7 +113,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
temperature=0.75,
|
||||
system_instruction="You are a helpful British assistant called Sarah. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. 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.",
|
||||
system_instruction="You are a helpful British assistant called Sarah in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. 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.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -93,7 +93,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
temperature=0.75,
|
||||
system_instruction="You are a helpful British assistant called Sarah. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. 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.",
|
||||
system_instruction="You are a helpful British assistant called Sarah in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. 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.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -80,8 +80,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
[
|
||||
(
|
||||
"system",
|
||||
"Be nice and helpful. Answer very briefly and without special characters like `#` or `*`. "
|
||||
"Your response will be synthesized to voice and those characters will create unnatural sounds.",
|
||||
"You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
MessagesPlaceholder("chat_history"),
|
||||
("human", "{input}"),
|
||||
|
||||
@@ -71,7 +71,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -68,7 +68,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -79,7 +79,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
settings=AWSBedrockLLMSettings(
|
||||
model="us.amazon.nova-pro-v1:0",
|
||||
temperature=0.8,
|
||||
system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -71,7 +71,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -65,7 +65,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -72,7 +72,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -65,7 +65,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -67,7 +67,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
|
||||
settings=AzureLLMService.Settings(
|
||||
model=os.getenv("AZURE_CHATGPT_MODEL"),
|
||||
system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -67,7 +67,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
|
||||
settings=AzureLLMService.Settings(
|
||||
model=os.getenv("AZURE_CHATGPT_MODEL"),
|
||||
system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -68,7 +68,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
openpipe_api_key=os.getenv("OPENPIPE_API_KEY"),
|
||||
tags={"conversation_id": f"pipecat-{timestamp}"},
|
||||
settings=OpenPipeLLMService.Settings(
|
||||
system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -68,7 +68,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -76,7 +76,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY", ""),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -74,7 +74,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY", ""),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -64,7 +64,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -57,8 +57,8 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
llm = GroqLLMService(
|
||||
api_key=os.getenv("GROQ_API_KEY"),
|
||||
settings=GroqLLMService.Settings(
|
||||
model="meta-llama/llama-4-maverick-17b-128e-instruct",
|
||||
system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
model="llama-3.1-8b-instant",
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -66,7 +66,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
settings=AWSBedrockLLMService.Settings(
|
||||
model="us.anthropic.claude-sonnet-4-6",
|
||||
temperature=0.8,
|
||||
system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -89,7 +89,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
settings=GoogleLLMService.Settings(
|
||||
model="gemini-2.5-flash-image",
|
||||
# model="gemini-3-pro-image-preview", # A more powerful model, but slower,
|
||||
system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -74,7 +74,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
api_key=os.getenv("GOOGLE_API_KEY"),
|
||||
model="gemini-2.5-flash",
|
||||
settings=GoogleLLMService.Settings(
|
||||
system_instruction="""You are a helpful AI assistant in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way.
|
||||
system_instruction="""You are a helpful assistant in a voice conversation.
|
||||
|
||||
IMPORTANT: You're using Gemini TTS which supports expressive markup tags. You can use these tags in your responses:
|
||||
- [sigh] - Insert a sigh sound
|
||||
@@ -91,7 +91,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
- "[whispering] Let me tell you a secret."
|
||||
- "The answer is... [long pause] ...42!"
|
||||
|
||||
Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.""",
|
||||
Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Keep responses concise. Respond to what the user said in a creative and helpful way.""",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -73,11 +73,11 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
|
||||
llm = GoogleLLMService(
|
||||
api_key=os.getenv("GOOGLE_API_KEY"),
|
||||
settings=GoogleLLMService.GoogleLLMSettings(
|
||||
settings=GoogleLLMService.Settings(
|
||||
model="gemini-2.5-flash",
|
||||
# force a certain amount of thinking if you want it
|
||||
# thinking=GoogleLLMService.ThinkingConfig(thinking_budget=4096)
|
||||
system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -77,7 +77,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
model="gemini-2.5-flash",
|
||||
# force a certain amount of thinking if you want it
|
||||
# thinking=GoogleLLMService.ThinkingConfig(thinking_budget=4096),
|
||||
system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -115,7 +115,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -67,7 +67,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -93,7 +93,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -68,7 +68,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -71,7 +71,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -64,7 +64,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -58,7 +58,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
api_key=os.getenv("NVIDIA_API_KEY"),
|
||||
settings=NvidiaLLMService.Settings(
|
||||
model="meta/llama-3.3-70b-instruct",
|
||||
system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -48,7 +48,7 @@ load_dotenv(override=True)
|
||||
|
||||
marker = "|----|"
|
||||
system_message = f"""
|
||||
You are a helpful LLM in a WebRTC call. Your goals are to be helpful and brief in your responses.
|
||||
You are a helpful LLM in a voice call. Your goals are to be helpful and brief in your responses.
|
||||
|
||||
You are expert at transcribing audio to text. You will receive a mixture of audio and text input. When
|
||||
asked to transcribe what the user said, output an exact, word-for-word transcription.
|
||||
|
||||
@@ -65,7 +65,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -69,7 +69,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -64,7 +64,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -70,7 +70,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -52,7 +52,7 @@ async def main():
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful LLM. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -71,7 +71,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -72,9 +72,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
),
|
||||
)
|
||||
|
||||
llm = SarvamLLMService(
|
||||
api_key=os.getenv("SARVAM_API_KEY"),
|
||||
settings=SarvamLLMService.Settings(
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
@@ -66,10 +66,10 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
voice="shubh",
|
||||
),
|
||||
)
|
||||
llm = SarvamLLMService(
|
||||
api_key=os.getenv("SARVAM_API_KEY"),
|
||||
settings=SarvamLLMService.Settings(
|
||||
model="sarvam-30b",
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -71,7 +71,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -69,7 +69,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -65,7 +65,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -85,7 +85,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@@ -67,7 +67,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
llm = OpenAILLMService(
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
settings=OpenAILLMService.Settings(
|
||||
system_instruction="You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
|
||||
system_instruction="You are a helpful assistant in a voice conversation. Your responses will be spoken aloud, so avoid emojis, bullet points, or other formatting that can't be spoken. Respond to what the user said in a creative, helpful, and brief way.",
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user