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179 Commits

Author SHA1 Message Date
Aleix Conchillo Flaqué
99d3227ff5 Merge pull request #1126 from pipecat-ai/aleix/prepare-0.0.55
update CHANGELOG for 0.0.55
2025-02-05 11:32:39 -08:00
Aleix Conchillo Flaqué
7730f59635 update CHANGELOG for 0.0.55 2025-02-05 11:30:40 -08:00
Aleix Conchillo Flaqué
ba31546c32 Merge pull request #1139 from pipecat-ai/aleix/task-start-metadata
pipeline task start metadata and unit test improvements
2025-02-05 10:51:51 -08:00
Aleix Conchillo Flaqué
a363d12d1f dev-requirements: fix conflicts because of nvidia-riva-client 2025-02-05 10:34:46 -08:00
Aleix Conchillo Flaqué
feab9c8fa2 tests: run_test() now uses PipelineTask 2025-02-05 10:34:38 -08:00
Aleix Conchillo Flaqué
61f6669926 task: allow passing StartFrame metadata via start_metadata param 2025-02-05 10:34:38 -08:00
Aleix Conchillo Flaqué
3be69908d2 Merge pull request #1131 from pipecat-ai/aleix/global-audio-sample-rates
introduce PipelineParams audio input/output sample rates
2025-02-05 08:11:25 -08:00
Aleix Conchillo Flaqué
fcb80ec330 playht: don't set sample_rate in _settings 2025-02-05 07:46:24 -08:00
Mark Backman
c9f5684e2f OpenAITTSService: Add warning about changing sample_rate 2025-02-05 10:13:46 -05:00
Mark Backman
c257fa1573 AzureTTSService, AzureHttpTTSService: add start() method 2025-02-05 10:05:19 -05:00
Mark Backman
97c55da29f PlayHTHttpTTSService: add start() method to set sample_rate 2025-02-05 09:54:41 -05:00
Aleix Conchillo Flaqué
49426aa9a1 transport(websocket): improve exception logging 2025-02-04 23:50:45 -08:00
Aleix Conchillo Flaqué
0a333c26da services(elevenlabs): warn if sample rate not supported 2025-02-04 23:50:21 -08:00
Aleix Conchillo Flaqué
75a29424ff examples(telnyx-chatbot): use cartesia so we can use 8khz 2025-02-04 23:49:50 -08:00
Filipi da Silva Fuchter
cd1b429308 Merge pull request #1133 from pipecat-ai/fixing_krisp_issue
Fixing the issue in Krisp when trying to create more than one
2025-02-04 20:44:29 -03:00
Filipi Fuchter
7f1ae4b8cc Fixing the issue in Krisp when trying to create more than one filter in the same process. 2025-02-04 20:10:56 -03:00
Aleix Conchillo Flaqué
af9fd811cd examples(moondream-chatbot): fix UserImageRequester 2025-02-04 14:37:53 -08:00
Aleix Conchillo Flaqué
69f5c9b9d3 update anthropic and openpipe versions 2025-02-04 14:37:36 -08:00
Aleix Conchillo Flaqué
ab45e481be introduce PipelineParams audio input/output sample rates 2025-02-04 14:12:56 -08:00
Aleix Conchillo Flaqué
cc54255c41 Merge pull request #1125 from pipecat-ai/aleix/twilio-chatbot-improvements 2025-02-03 11:10:33 -08:00
Aleix Conchillo Flaqué
1cdb66f889 examples(twilio-chatbot): create sample rate variable 2025-02-03 10:58:06 -08:00
Aleix Conchillo Flaqué
51a86a509c examples: multiple twilio-chatbot improvements 2025-02-03 10:36:24 -08:00
Aleix Conchillo Flaqué
824898f7b7 Merge pull request #1121 from pipecat-ai/aleix/audio-resamplers
introduce audio resamplers
2025-02-03 10:32:55 -08:00
Aleix Conchillo Flaqué
57dadb6359 audio(utils): some variable renames 2025-02-03 09:33:04 -08:00
Aleix Conchillo Flaqué
5dcdc68ef5 examples: fix 22 series initial gate state 2025-02-03 09:16:58 -08:00
Aleix Conchillo Flaqué
aafb2db620 GatedOpenAILLMContextAggregator: use keyword argument and add start_open 2025-02-03 09:16:44 -08:00
Aleix Conchillo Flaqué
f3f22cf61c AudioBufferProcessor: add start_recording()/stop_recording() 2025-02-01 11:06:58 -08:00
Aleix Conchillo Flaqué
371c2f3704 canonical: do not reset audio buffers 2025-02-01 11:06:58 -08:00
Aleix Conchillo Flaqué
1f14f62696 AudioBufferProcessor: fix audio buffer silence computation 2025-02-01 11:06:58 -08:00
Aleix Conchillo Flaqué
06449eff2c BaseAudioResampler: make resample() async 2025-02-01 11:06:58 -08:00
Aleix Conchillo Flaqué
dcfb86583d serializers: serialize()/deserialize() are now async 2025-02-01 11:06:58 -08:00
Aleix Conchillo Flaqué
cda34a1320 AudioBufferProcessor: fix user/bot audio buffers silence padding 2025-02-01 11:06:58 -08:00
Aleix Conchillo Flaqué
13611fd8e1 AudioBufferProcessor: call callback on CancelFrame 2025-02-01 11:06:58 -08:00
Aleix Conchillo Flaqué
fc89aad469 introduce audio resamplers 2025-02-01 11:06:55 -08:00
Aleix Conchillo Flaqué
6c7474e1a2 frames: add pass to DTMFFrames 2025-01-31 18:37:40 -08:00
Aleix Conchillo Flaqué
95f0dbf3f3 CHANGELOG.md: task.cancel() and EndFrame clarification 2025-01-31 18:35:35 -08:00
Aleix Conchillo Flaqué
11aeb68ddb frames: fix type s/OuputDTMFFrame/OutputDTMFFrame/ 2025-01-31 18:28:38 -08:00
Aleix Conchillo Flaqué
a43c102fc8 Merge pull request #1064 from jcbjoe/jg/additional_dtmf_frames
Added: Additional DTMF frames
2025-01-31 18:25:08 -08:00
Mark Backman
16b49bdce6 Merge pull request #1122 from pipecat-ai/mb/openai-org-id
Add organization and project level auth in OpenAILLMService
2025-01-31 14:35:26 -05:00
Mark Backman
41477c8f78 Add organization and project level auth in OpenAILLMService 2025-01-31 14:27:25 -05:00
Aleix Conchillo Flaqué
bb9a2560c3 Merge pull request #1118 from pipecat-ai/aleix/task-manager
introduce TaskManager
2025-01-31 10:24:52 -08:00
Aleix Conchillo Flaqué
002699f16c rtvi: delay creating tasks until we get StartFrame 2025-01-31 10:06:11 -08:00
chadbailey59
a17243bc1e More Storybot updates (#1116)
* initial changes for gemini storybot

* storybot updates for gemini

* more storybot updates

* interim interruptible commit

* cleanup

* cleanup

* cleanup

* first draft

* wip

* more storybot fixes

* more storybot updates WIP

* committing before changing the image prompting strategy

* wip

* prompt updating

* cleanup

* cleanup

* cleanup

* readme cleanup

* fixup
2025-01-30 20:13:18 -06:00
Aleix Conchillo Flaqué
d95819746a tests: make sure QueuedFrameProcessor push frames 2025-01-30 13:48:44 -08:00
Aleix Conchillo Flaqué
b65f32e8e1 task: start TaskObserver when tasks can be created
We have to start proxy observer tasks once we know the TaskManager has an event
loop.
2025-01-30 13:46:56 -08:00
Aleix Conchillo Flaqué
0131d0a531 examples: make sure unhandled frames are always pushed 2025-01-30 13:15:49 -08:00
Aleix Conchillo Flaqué
642affb2fe add missing super().process_frame() calls 2025-01-30 13:15:17 -08:00
Aleix Conchillo Flaqué
a145005498 SyncParallelPipeline: cleanup source/sink processors 2025-01-30 13:13:02 -08:00
Aleix Conchillo Flaqué
241f241ed9 SyncParallelPipeline: don't add source/sink processors inside pipeline 2025-01-30 13:12:37 -08:00
Aleix Conchillo Flaqué
85e572e2d8 gladia: cleanup receive messages task 2025-01-30 13:10:47 -08:00
Aleix Conchillo Flaqué
10716e8ec1 utils: protect obj_id() and obj_count() with a lock 2025-01-30 13:10:36 -08:00
Aleix Conchillo Flaqué
41d60a14cc introduce TaskManager and PipelineRunner event loop 2025-01-30 13:10:36 -08:00
Aleix Conchillo Flaqué
e69c065a86 update CHANGELOG and fix formatting 2025-01-30 08:55:29 -08:00
Aleix Conchillo Flaqué
f90c17ab30 Merge pull request #1083 from team-telnyx/creating_telnyx_chatbot
Creating telnyx chatbot
2025-01-30 08:49:20 -08:00
Aleix Conchillo Flaqué
bc4fdd587a Merge pull request #1103 from pipecat-ai/aleix/tts-service-push-silence-before-tts-stop-frame
services(tts): allow pushing silence audio before TTSStoppedFrame
2025-01-30 08:48:41 -08:00
Aleix Conchillo Flaqué
665a6017f9 services(tts): allow pushing silence audio before TTSStoppedFrame 2025-01-30 08:46:56 -08:00
Aleix Conchillo Flaqué
4119d7a115 Merge pull request #1104 from pipecat-ai/aleix/twilio-transport-message-frames
serializers(twilio): handle transport message frames
2025-01-30 08:45:55 -08:00
Aleix Conchillo Flaqué
2634b03ffa serializers(twilio): handle transport message frames 2025-01-30 08:30:09 -08:00
Aleix Conchillo Flaqué
6a50759b9f Merge pull request #1105 from pipecat-ai/aleix/websocket-client
added new websocket client transport
2025-01-30 08:28:26 -08:00
Mark Backman
7982faba67 Merge pull request #1115 from pipecat-ai/mb/elevenlabs-language-fixes
Improve ElevenLabs language checking logic
2025-01-30 10:03:22 -05:00
Mark Backman
2b4bf57c04 Improve ElevenLabs language checking logic 2025-01-30 09:52:36 -05:00
Rafal Skorski
b93e4ab9cb Formatting adjusted and the encoding selection moved from TelnyFrameSerilaizer to websocket_endpoint function in server.py 2025-01-30 12:52:30 +01:00
Dominic Stewart
c140c04b9a Merge pull request #1080 from DominicStewart/dom/voicemail-detection-bot
Add voicemail detection example
2025-01-30 09:20:12 +09:00
Dominic
a7c8d2af8e Removed extra space too 2025-01-30 09:18:29 +09:00
Dominic
f3f520a76a Removed formatting that vs code automatically adds to readme file 2025-01-30 09:17:27 +09:00
Mark Backman
5e0f42a3e0 Merge pull request #1111 from pipecat-ai/mb/gemini-restructure-messages
GoogleLLMContext: Allow _restructure_from_openai_messages to handle c…
2025-01-29 19:06:47 -05:00
Mark Backman
220ce9fd0f GoogleLLMContext: Allow _restructure_from_openai_messages to handle context frames that contain function call data and / or messages 2025-01-29 16:01:39 -05:00
Filipi da Silva Fuchter
5d0486a26f Merge pull request #1008 from pipecat-ai/cutting_initial_words
Avoid cutting off the beginning of the audio
2025-01-29 17:02:40 -03:00
Aleix Conchillo Flaqué
091258f617 improve create_task names 2025-01-29 11:11:40 -08:00
Aleix Conchillo Flaqué
2a1408eb2a transports(websocket server): remove unused variable 2025-01-29 11:11:40 -08:00
Aleix Conchillo Flaqué
6393b41d58 transports(websocket): added WebsocketClientTransport 2025-01-29 11:11:37 -08:00
Filipi Fuchter
2a5728264c Adding missing dependency to openai 2025-01-29 15:52:42 -03:00
Filipi Fuchter
2ef0735462 Adding readme to teach how to use. 2025-01-29 15:45:48 -03:00
Filipi Fuchter
80bbfff4be Merge branch 'main' into cutting_initial_words 2025-01-29 15:36:52 -03:00
Aleix Conchillo Flaqué
4ff68e66b9 Merge pull request #1110 from pipecat-ai/aleix/frame-metadata
frames: added metadata field to Frame class
2025-01-29 10:30:59 -08:00
Aleix Conchillo Flaqué
3a688840fc frames: added metadata field to Frame class 2025-01-29 09:53:21 -08:00
Aleix Conchillo Flaqué
2ca8b95bbf Merge pull request #1106 from Vaibhav159/vl_moving_test_utils_to_pipecat_package
moving test utils inside of package
2025-01-29 09:44:34 -08:00
Mark Backman
2aafc6bd1d Merge pull request #1107 from AngeloGiacco/angelo/increase-ws-connection
fix: elevenlabs tts increase websocket max message size limit to 16MB
2025-01-29 10:04:42 -05:00
Angelo Giacco
0ff9ef8707 fix: add changelog 2025-01-29 14:27:39 +00:00
Angelo Giacco
596cae994d fix: elevenlabs tts increase websocket max message size limit to 16MB 2025-01-29 13:55:27 +00:00
Dominic
9ad9cb1ff8 Cleaned up formatting 2025-01-29 17:36:08 +09:00
Dominic Stewart
60e800e9ba Merge branch 'main' into dom/voicemail-detection-bot 2025-01-29 17:30:56 +09:00
Dominic
1c8f0ed7da Finalised code and added a bit about this example to the README 2025-01-29 17:27:44 +09:00
Vaibhav159
8407a86532 moving test utils inside of package 2025-01-29 12:46:43 +05:30
Dominic
417d661d28 Updated bot_runner and bot_daily with adjustments necessary to run voicemail detection from bot_daily code 2025-01-29 16:11:45 +09:00
Aleix Conchillo Flaqué
8cd23c42fc Merge pull request #1100 from pipecat-ai/aleix/use-task-cancel-on-left-disconnected
use `task.cancel()` when participant leaves/disconnects
2025-01-28 16:02:02 -08:00
Aleix Conchillo Flaqué
0547a15695 task: allow queuing a CancelFrame to cancel the task 2025-01-28 15:59:36 -08:00
Aleix Conchillo Flaqué
3fe2124314 examples: use task.cancel() when participant leaves or disconnects 2025-01-28 15:46:20 -08:00
Aleix Conchillo Flaqué
ba358a4f0a task: cleanup processors after task finishes running 2025-01-28 15:02:25 -08:00
Aleix Conchillo Flaqué
79ef8c947d Merge pull request #1099 from pipecat-ai/aleix/daily-transport-queue-events
transports(daily): queue events until join completes
2025-01-28 14:38:25 -08:00
Aleix Conchillo Flaqué
f024476b08 transports(daily): queue events until join completes 2025-01-28 11:22:42 -08:00
Dominic
73690a13d9 Moved voicemail detection to phone-chatbot and working on that now 2025-01-28 22:31:08 +09:00
Dominic
6ebf06a6fb Removed start_terminate_call function as unnecessary 2025-01-28 10:39:10 +09:00
Dominic
2f4f779c91 Fixed a few things 2025-01-28 10:39:10 +09:00
Dominic
941ee6e5e8 Add voicemail detection example 2025-01-28 10:39:10 +09:00
Aleix Conchillo Flaqué
cd5075ed7a Merge pull request #1097 from pipecat-ai/aleix/pipecat-0.0.57
prepare CHANGELOG for 0.0.54
2025-01-27 14:56:51 -08:00
Aleix Conchillo Flaqué
6f41a667c8 prepare CHANGELOG for 0.0.54 2025-01-27 14:48:56 -08:00
Aleix Conchillo Flaqué
0b222a7eae Merge pull request #1085 from pipecat-ai/aleix/task-creation-and-cancellation
improve task creation and cancellation
2025-01-27 14:47:20 -08:00
Aleix Conchillo Flaqué
f09f4b8fc4 services(tavus): fix EndFrame and CancelFrame processing 2025-01-27 14:42:23 -08:00
Aleix Conchillo Flaqué
cca241a2b7 examples(22c): fix cancel_task call 2025-01-27 14:42:23 -08:00
Aleix Conchillo Flaqué
1489e44740 gemini(multimodal live): fix model audio queue variable 2025-01-27 14:42:23 -08:00
Aleix Conchillo Flaqué
f55f78e70e update CHANGELOG.md 2025-01-27 14:42:23 -08:00
Aleix Conchillo Flaqué
10202dc529 transports(websockets): cancel or wait for tasks to finish 2025-01-27 14:42:23 -08:00
Aleix Conchillo Flaqué
498805a34c FrameProcessor: add wait_for_task() 2025-01-27 14:42:23 -08:00
Aleix Conchillo Flaqué
509f143e1b update CHANGELOG.md 2025-01-27 14:42:23 -08:00
Aleix Conchillo Flaqué
737e4fa3bd gemini(multimodal live): connect on StartFrame 2025-01-27 14:42:23 -08:00
Aleix Conchillo Flaqué
8b5228a105 utils: move task functions to asyncio module 2025-01-27 14:42:23 -08:00
Aleix Conchillo Flaqué
6cc01bc5b0 examples: update 14 series with TTSSpeakFrame 2025-01-27 14:42:23 -08:00
Aleix Conchillo Flaqué
2a2928d96c gemini: create transcribe tasks only once 2025-01-27 14:42:23 -08:00
Aleix Conchillo Flaqué
a3a6adbd17 user_idle_processor: add missing parent cleanup() 2025-01-27 14:42:23 -08:00
Aleix Conchillo Flaqué
bf5ced18b2 fix parallel pipelines cleanup 2025-01-27 14:42:23 -08:00
Aleix Conchillo Flaqué
2eccd1b1e9 utils: update some logging levels 2025-01-27 14:42:23 -08:00
Aleix Conchillo Flaqué
9374bed878 tests: langchain fixes 2025-01-27 14:42:23 -08:00
Aleix Conchillo Flaqué
c03d0352b1 utils/tasks: added new documentation 2025-01-27 14:42:23 -08:00
Aleix Conchillo Flaqué
af90b8b4fa utils: add wait_for_task() 2025-01-27 14:42:23 -08:00
Aleix Conchillo Flaqué
0a9daa2f56 task: avoid canceling tasks more than once 2025-01-27 14:42:23 -08:00
Aleix Conchillo Flaqué
e48c0e52ef transports(daily): avoid canceling task more than once 2025-01-27 14:42:23 -08:00
Aleix Conchillo Flaqué
6bca8396d3 utils: error if we try to cancel the same task multiple times 2025-01-27 14:42:23 -08:00
Aleix Conchillo Flaqué
c2d8a45a07 runner: warn about remaining dangling tasks 2025-01-27 14:42:23 -08:00
Aleix Conchillo Flaqué
80a7f1b1e7 runner: improve signal handler task cancellation 2025-01-27 14:42:23 -08:00
Aleix Conchillo Flaqué
aff6e24560 pipeline: fix pipeline cleanup 2025-01-27 14:42:23 -08:00
Aleix Conchillo Flaqué
cb93f6b368 utils: store created tasks and add current_tasks() 2025-01-27 14:42:23 -08:00
Aleix Conchillo Flaqué
ff0bcec33a transports: improve task naming 2025-01-27 14:42:23 -08:00
Aleix Conchillo Flaqué
5885fcc230 add id and name properties 2025-01-27 14:42:23 -08:00
Aleix Conchillo Flaqué
57b186cde8 base_transport: add name and id fields 2025-01-27 14:42:23 -08:00
Aleix Conchillo Flaqué
d1a3f404a5 improve task creation and cancellation
If a FrameProcessor needs to create a task it should use
FrameProcessor.create_task() and FrameProcessor.cancel_task(). This gives
Pipecat more control over all the tasks that are created in Pipecat.

Both functions internally use the utils module: utils.create_task() and
utils.cancel_task() which should also be used outside of FrameProcessors. That
is, unless strictly necessary, we should avoid using asyncio.create_task().
2025-01-27 14:42:23 -08:00
chadbailey59
179ddbea7d Add dialout to the Daily phone example (#998)
* added dialout to daily phone example

* cleanup

* cleanup

* pre-commit hook

* Fix typo

* More explicit README instructions

---------

Co-authored-by: Mark Backman <mark@daily.co>
2025-01-27 12:21:30 -06:00
Mark Backman
86c1e6a3bd Merge pull request #1081 from pipecat-ai/mb/user-idle-add-retry
Added retry functionality and a new callback to the UserIdleProcessor
2025-01-27 10:30:45 -05:00
Mark Backman
9e9822f17d Use inspect.signature to determine which callback to use 2025-01-27 10:24:58 -05:00
Mark Backman
5f9671e2ca Added retry functionality and a new callback to the UserIdleProcessor 2025-01-27 10:24:57 -05:00
Mark Backman
aac8961ae5 Merge pull request #1078 from pipecat-ai/mb/improve-error-handling-truncate-audio
Add better error handling for OpenAIRealtimeBetaLLMService truncate errors
2025-01-27 08:54:39 -05:00
Mark Backman
3e6377346a Merge pull request #1093 from pipecat-ai/mb/update-example-6a 2025-01-26 19:43:39 -05:00
Mark Backman
9d9a622b1a Merge pull request #1094 from pipecat-ai/mb/readme-service-section 2025-01-26 19:43:12 -05:00
Mark Backman
3e9a6b6262 Merge pull request #1095 from pipecat-ai/mb/elevenlabs-lang-codes 2025-01-26 12:21:28 -05:00
Mark Backman
fb3097560f Remove eleven_multilinguagal_v2 from language code list 2025-01-26 07:17:38 -05:00
Mark Backman
ff6368add0 Update README.md
Adding a section so that table can be linked to.
2025-01-25 16:12:53 -05:00
Mark Backman
89fd03d86f Merge pull request #1090 from vengad-arrowhead/main
Adding hindi danda symbol as end of sentence marker
2025-01-25 09:36:19 -05:00
Mark Backman
0672530d6b Fix foundational example 6a to switch images when the bot is speaking 2025-01-25 08:40:42 -05:00
vengadanathan srinivasan
7a0cfc8d3d Adding hindi danda symbol as end of sentence marker 2025-01-25 14:55:51 +05:30
Mark Backman
b881dd57b3 Merge pull request #1086 from pipecat-ai/mb/fix-expiry-time-type-mismatch 2025-01-24 17:31:08 -05:00
Mark Backman
abf0d0d053 Improve token parameter construction using DailyMeetingTokenProperties 2025-01-24 17:22:31 -05:00
Mark Backman
1acdf7aff7 Fix expiry_time type validation in get_token REST API helper 2025-01-24 17:21:50 -05:00
Mark Backman
96b90abda6 Merge pull request #1082 from pipecat-ai/mb/update-function-calling-examples
Update function calling examples to push a TextFrame in the start_cal…
2025-01-24 17:21:13 -05:00
Filipi da Silva Fuchter
202a844eeb Merge pull request #1051 from pipecat-ai/gemini_grounding_metadata_rtvi
Sending Search Response to RTVI
2025-01-24 19:20:50 -03:00
Filipi Fuchter
655d56f634 Fixing pydantic validation when creating meeting token. 2025-01-24 19:15:56 -03:00
Filipi Fuchter
07c84b733b Sending Search Response to RTVI 2025-01-24 18:59:46 -03:00
Filipi da Silva Fuchter
7c52736ff6 Merge pull request #1030 from pipecat-ai/gemini_grounding_metadata
Introduce support for extracting and processing grounding metadata from GoogleLLMService.
2025-01-24 15:41:54 -03:00
Mark Backman
48ce751602 Merge pull request #1075 from Vaibhav159/vl_add_daily_meeting_token_v2
adding models to DailyRestHelper
2025-01-24 13:21:52 -05:00
Vaibhav159
1f1e2dac2b wrapping things up 2025-01-24 23:44:23 +05:30
Vaibhav159
71c2dc3d05 minor typing change 2025-01-24 23:38:44 +05:30
Vaibhav159
ef02ece662 doc string 2025-01-24 22:47:40 +05:30
Vaibhav159
d5818fad5b addressing comments 2025-01-24 22:46:54 +05:30
Rafal Skorski
9c22bd8df1 Improving read me and encoding support 2025-01-24 16:44:11 +01:00
Mark Backman
dbea86baae Update function calling examples to push a TextFrame in the start_callback 2025-01-24 10:21:08 -05:00
Vaibhav159
c5faac1cf8 adding RecordingsBucketConfig 2025-01-24 15:14:20 +05:30
Vaibhav159
e106d7a215 adding line space 2025-01-24 09:12:07 +05:30
Vaibhav159
40c1a8369a updated changelog 2025-01-24 09:11:15 +05:30
Vaibhav159
6ab2404a98 adding more properties to daily room 2025-01-24 09:10:25 +05:30
Mark Backman
e61c996a2e Merge pull request #1079 from ecdeng/patch-1
Update cartesia.py to use the new model pointer `sonic`
2025-01-23 22:15:30 -05:00
Eric Deng
2c81dc1f06 Update cartesia.py to use the new model pointer sonic instead of sonic-english
We are now using `sonic` as a pointer to the latest stable release (https://docs.cartesia.ai/build-with-sonic/models#continuous-updates). sonic-english will forever point to `sonic-2024-10-19`, which is already out of date.
2025-01-23 15:47:07 -08:00
Mark Backman
53251dcb88 Add better error handling for OpenAIRealtimeBetaLLMService truncate errors 2025-01-23 14:25:08 -05:00
Mark Backman
d4e4b12109 Merge pull request #1071 from porcelaincode/patch-1
Update runner.py
2025-01-23 13:19:22 -05:00
Mark Backman
466d26a4f2 Merge pull request #1077 from Vaibhav159/vl_fix_missing_leftover_audio
adding missing audio buffer fix
2025-01-23 13:16:41 -05:00
Vaibhav159
ef511d580d adding missing audio buffer fix 2025-01-23 23:17:49 +05:30
Vaibhav159
5957ddb038 adding missing audio buffer fix 2025-01-23 23:17:18 +05:30
Vaibhav159
799c2d14b8 adding meeting token v2 func 2025-01-23 21:40:42 +05:30
Rafal Skorski
8eef21db6e Adding telnyx serializer 2025-01-23 15:39:46 +01:00
vatsal
dee1224530 Update runner.py 2025-01-23 13:21:49 +05:30
Joe Garlick
b72504f1cb Added: Additional DTMF frames 2025-01-22 13:47:23 +00:00
Rafal Skorski
89b87289e2 elevenlabs key added to env.example 2025-01-21 17:12:27 +01:00
Rafal Skorski
e0e190a1a2 Create telnyx chat bot example application 2025-01-21 17:09:55 +01:00
Filipi Fuchter
9b61633aa0 Introduce support for extracting and processing grounding metadata from Google LLM responses. 2025-01-20 11:28:12 -03:00
Filipi Fuchter
c4c15eff39 Sending a silence frame to prevent the audio from clipping. 2025-01-16 18:30:19 -03:00
Filipi Fuchter
7efd00e0f7 Asking for the bot to send the audio only when the audio element is already on playing state. 2025-01-16 16:00:56 -03:00
Filipi Fuchter
119c0da299 Configuring a proxy so we can test from mobile 2025-01-16 11:02:53 -03:00
Filipi Fuchter
ea1323723d Handling the signalling to play the audio 2025-01-16 10:42:22 -03:00
Filipi Fuchter
d2efe27350 Improving the logs and updating status 2025-01-16 10:36:45 -03:00
Filipi Fuchter
5dc7d2a378 Creating the bot when pressing to connect. 2025-01-16 10:28:39 -03:00
Filipi Fuchter
88c540f9bc Starting to create the example signalling through app message. 2025-01-16 10:14:38 -03:00
221 changed files with 8932 additions and 1817 deletions

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@@ -5,15 +5,162 @@ All notable changes to **Pipecat** will be documented in this file.
The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
## [Unreleased]
## [0.0.55] - 2025-02-05
### Added
- Added a new `start_metadata` field to `PipelineParams`. The provided metadata
will be set to the initial `StartFrame` being pushed from the `PipelineTask`.
- Added new fields to `PipelineParams` to control audio input and output sample
rates for the whole pipeline. This allows controlling sample rates from a
single place instead of having to specify sample rates in each
service. Setting a sample rate to a service is still possible and will
override the value from `PipelineParams`.
- Introduce audio resamplers (`BaseAudioResampler`). This is just a base class
to implement audio resamplers. Currently, two implementations are provided
`SOXRAudioResampler` and `ResampyResampler`. A new
`create_default_resampler()` has been added (replacing the now deprecated
`resample_audio()`).
- It is now possible to specify the asyncio event loop that a `PipelineTask` and
all the processors should run on by passing it as a new argument to the
`PipelineRunner`. This could allow running pipelines in multiple threads each
one with its own event loop.
- Added a new `utils.TaskManager`. Instead of a global task manager we now have
a task manager per `PipelineTask`. In the previous version the task manager
was global, so running multiple simultaneous `PipelineTask`s could result in
dangling task warnings which were not actually true. In order, for all the
processors to know about the task manager, we pass it through the
`StartFrame`. This means that processors should create tasks when they receive
a `StartFrame` but not before (because they don't have a task manager yet).
- Added `TelnyxFrameSerializer` to support Telnyx calls. A full running example
has also been added to `examples/telnyx-chatbot`.
- Allow pushing silence audio frames before `TTSStoppedFrame`. This might be
useful for testing purposes, for example, passing bot audio to an STT service
which usually needs additional audio data to detect the utterance stopped.
- `TwilioSerializer` now supports transport message frames. With this we can
create Twilio emulators.
- Added a new transport: `WebsocketClientTransport`.
- Added a `metadata` field to `Frame` which makes it possible to pass custom
data to all frames.
- Added `test/utils.py` inside of pipecat package.
### Changed
- `GatedOpenAILLMContextAggregator` now require keyword arguments. Also, a new
`start_open` argument has been added to set the initial state of the gate.
- Added `organization` and `project` level authentication to
`OpenAILLMService`.
- Improved the language checking logic in `ElevenLabsTTSService` and
`ElevenLabsHttpTTSService` to properly handle language codes based on model
compatibility, with appropriate warnings when language codes cannot be
applied.
- Updated `GoogleLLMContext` to support pushing `LLMMessagesUpdateFrame`s that
contain a combination of function calls, function call responses, system
messages, or just messages.
- `InputDTMFFrame` is now based on `DTMFFrame`. There's also a new
`OutputDTMFFrame` frame.
### Deprecated
- `resample_audio()` is now deprecated, use `create_default_resampler()`
instead.
### Removed
- `AudioBufferProcessor.reset_audio_buffers()` has been removed, use
`AudioBufferProcessor.start_recording()` and
``AudioBufferProcessor.stop_recording()` instead.
### Fixed
- Fixed a `AudioBufferProcessor` that would cause crackling in some recordings.
- Fixed an issue in `AudioBufferProcessor` where user callback would not be
called on task cancellation.
- Fixed an issue in `AudioBufferProcessor` that would cause wrong silence
padding in some cases.
- Fixed an issue where `ElevenLabsTTSService` messages would return a 1009
websocket error by increasing the max message size limit to 16MB.
- Fixed a `DailyTransport` issue that would cause events to be triggered before
join finished.
- Fixed a `PipelineTask` issue that was preventing processors to be cleaned up
after cancelling the task.
- Fixed an issue where queuing a `CancelFrame` to a pipeline task would not
cause the task to finish. However, using `PipelineTask.cancel()` is still the
recommended way to cancel a task.
### Other
- Improved Unit Test `run_test()` to use `PipelineTask` and
`PipelineRunner`. There's now also some control around `StartFrame` and
`EndFrame`. The `EndTaskFrame` has been removed since it doesn't seem
necessary with this new approach.
- Updated `twilio-chatbot` with a few new features: use 8000 sample rate and
avoid resampling, a new client useful for stress testing and testing locally
without the need to make phone calls. Also, added audio recording on both the
client and the server to make sure the audio sounds good.
- Updated examples to use `task.cancel()` to immediately exit the example when a
participant leaves or disconnects, instead of pushing an `EndFrame`. Pushing
an `EndFrame` causes the bot to run through everything that is internally
queued (which could take some seconds). Note that using `task.cancel()` might
not always be the best option and pushing an `EndFrame` could still be
desirable to make sure all the pipeline is flushed.
## [0.0.54] - 2025-01-27
### Added
- In order to create tasks in Pipecat frame processors it is now recommended to
use `FrameProcessor.create_task()` (which uses the new
`utils.asyncio.create_task()`). It takes care of uncaught exceptions, task
cancellation handling and task management. To cancel or wait for a task there
is `FrameProcessor.cancel_task()` and `FrameProcessor.wait_for_task()`. All of
Pipecat processors have been updated accordingly. Also, when a pipeline runner
finishes, a warning about dangling tasks might appear, which indicates if any
of the created tasks was never cancelled or awaited for (using these new
functions).
- It is now possible to specify the period of the `PipelineTask` heartbeat
frames with `heartbeats_period_secs`.
- Added `DailyMeetingTokenProperties` and `DailyMeetingTokenParams` Pydantic models
for meeting token creation in `get_token` method of `DailyRESTHelper`.
- Added `enable_recording` and `geo` parameters to `DailyRoomProperties`.
- Added `RecordingsBucketConfig` to `DailyRoomProperties` to upload recordings to a custom AWS bucket.
### Changed
- Enhanced `UserIdleProcessor` with retry functionality and control over idle
monitoring via new callback signature `(processor, retry_count) -> bool`.
Updated the `17-detect-user-idle.py` to show how to use the `retry_count`.
- Add defensive error handling for `OpenAIRealtimeBetaLLMService`'s audio
truncation. Audio truncation errors during interruptions now log a warning
and allow the session to continue instead of throwing an exception.
- Modified `TranscriptProcessor` to use TTS text frames for more accurate assistant
transcripts. Assistant messages are now aggregated based on bot speaking boundaries
rather than LLM context, providing better handling of interruptions and partial
@@ -26,11 +173,21 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
### Fixed
- Fixed an `GeminiMultimodalLiveLLMService` issue that was preventing the user
to push initial LLM assistant messages (using `LLMMessagesAppendFrame`).
- Added missing `FrameProcessor.cleanup()` calls to `Pipeline`,
`ParallelPipeline` and `UserIdleProcessor`.
- Fixed a type error when using `voice_settings` in `ElevenLabsHttpTTSService`.
- Fixed an issue where `OpenAIRealtimeBetaLLMService` function calling resulted
in an error.
- Fixed an issue in `AudioBufferProcessor` where the last audio buffer was not
being processed, in cases where the `_user_audio_buffer` was smaller than the
buffer size.
### Performance
- Replaced audio resampling library `resampy` with `soxr`. Resampling a 2:21s
@@ -1417,6 +1574,9 @@ async def on_connected(processor):
### Changed
- `FrameSerializer.serialize()` and `FrameSerializer.deserialize()` are now
`async`.
- `Filter` has been renamed to `FrameFilter` and it's now under
`processors/filters`.

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@@ -53,7 +53,7 @@ To keep things lightweight, only the core framework is included by default. If y
pip install "pipecat-ai[option,...]"
```
Available options include:
### Available services
| Category | Services | Install Command Example |
| ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------- |
@@ -81,7 +81,7 @@ Here is a very basic Pipecat bot that greets a user when they join a real-time s
```python
import asyncio
from pipecat.frames.frames import EndFrame, TextFrame
from pipecat.frames.frames import TextFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.task import PipelineTask
from pipecat.pipeline.runner import PipelineRunner
@@ -122,7 +122,7 @@ async def main():
# Register an event handler to exit the application when the user leaves.
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
await task.queue_frame(EndFrame())
await task.cancel()
# Run the pipeline task
await runner.run(task)

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@@ -1,11 +1,11 @@
build~=1.2.2
grpcio-tools~=1.69.0
grpcio-tools~=1.67.1
pip-tools~=7.4.1
pre-commit~=4.0.1
pyright~=1.1.392
pytest~=8.3.4
pytest-asyncio~=0.25.2
ruff~=0.9.1
setuptools~=75.8.0
setuptools~=70.0.0
setuptools_scm~=8.1.0
python-dotenv~=1.0.1

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@@ -39,7 +39,7 @@ Next, follow the steps in the README for each demo.
| [Translation Chatbot](translation-chatbot) | Listens for user speech, then translates that speech to Spanish and speaks the translation back. Demonstrates multi-participant use-cases. | Deepgram, Azure, OpenAI, Daily, Daily Prebuilt UI |
| [Moondream Chatbot](moondream-chatbot) | Demonstrates how to add vision capabilities to GPT4. **Note: works best with a GPU** | Deepgram, ElevenLabs, OpenAI, Moondream, Daily, Daily Prebuilt UI |
| [Patient intake](patient-intake) | A chatbot that can call functions in response to user input. | Deepgram, ElevenLabs, OpenAI, Daily, Daily Prebuilt UI |
| [Dialin Chatbot](dialin-chatbot) | A chatbot that connects to an incoming phone call from Daily or Twilio. | Deepgram, ElevenLabs, OpenAI, Daily, Twilio |
| [Phone Chatbot](phone-chatbot) | A chatbot that connects to PSTN/SIP phone calls, powered by Daily or Twilio. | Deepgram, ElevenLabs, OpenAI, Daily, Twilio |
| [Twilio Chatbot](twilio-chatbot) | A chatbot that connects to an incoming phone call from Twilio. | Deepgram, ElevenLabs, OpenAI, Daily, Twilio |
| [studypal](studypal) | A chatbot to have a conversation about any article on the web | |
| [WebSocket Chatbot Server](websocket-server) | A real-time websocket server that handles audio streaming and bot interactions with speech-to-text and text-to-speech capabilities. | Cartesia, Deepgram, OpenAI, Websockets |

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@@ -0,0 +1,45 @@
# Bot ready signaling
A simple Pipecat example demonstrating how to handle signaling between the client and the bot,
ensuring that the bot starts sending audio only when the client is available,
thereby avoiding the risk of cutting off the beginning of the audio.
## Quick Start
### First, start the bot server:
1. Navigate to the server directory:
```bash
cd server
```
2. Create and activate a virtual environment:
```bash
python3 -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
3. Install requirements:
```bash
pip install -r requirements.txt
```
4. Copy env.example to .env and configure:
- Add your API keys
5. Start the server:
```bash
python server.py
```
### Next, connect using the client app:
For client-side setup, refer to the [JavaScript Guide](client/javascript/README.md).
## Important Note
Ensure the bot server is running before using any client implementations.
## Requirements
- Python 3.10+
- Node.js 16+ (for JavaScript)
- Daily API key
- Cartesia API key
- Modern web browser with WebRTC support

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@@ -0,0 +1,27 @@
# JavaScript Implementation
Basic implementation using the [Pipecat JavaScript SDK](https://docs.pipecat.ai/client/js/introduction).
## Setup
1. Run the bot server. See the [server README](../../README).
2. Navigate to the `client/javascript` directory:
```bash
cd client/javascript
```
3. Install dependencies:
```bash
npm install
```
4. Run the client app:
```
npm run dev
```
5. Visit http://localhost:5173 in your browser.

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@@ -0,0 +1,34 @@
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>AI Chatbot</title>
</head>
<body>
<div class="container">
<div class="status-bar">
<div class="status">
Status: <span id="connection-status">Disconnected</span>
</div>
<div class="controls">
<button id="connect-btn">Connect</button>
<button id="disconnect-btn" disabled>Disconnect</button>
</div>
</div>
<audio id="bot-audio" autoplay></audio>
<div class="debug-panel">
<h3>Debug Info</h3>
<div id="debug-log"></div>
</div>
</div>
<script type="module" src="/src/app.js"></script>
<link rel="stylesheet" href="/src/style.css">
</body>
</html>

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@@ -0,0 +1,20 @@
{
"name": "client",
"version": "1.0.0",
"main": "index.js",
"scripts": {
"dev": "vite",
"build": "vite build",
"preview": "vite preview"
},
"keywords": [],
"author": "",
"license": "ISC",
"description": "",
"devDependencies": {
"vite": "^6.0.2"
},
"dependencies": {
"@daily-co/daily-js": "0.74.0"
}
}

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@@ -0,0 +1,216 @@
/**
* Copyright (c) 20242025, Daily
*
* SPDX-License-Identifier: BSD 2-Clause License
*/
import Daily from "@daily-co/daily-js";
/**
* ChatbotClient handles the connection and media management for a real-time
* voice interaction with an AI bot.
*/
class ChatbotClient {
constructor() {
// Initialize client state
this.dailyCallObject = null;
this.setupDOMElements();
this.setupEventListeners();
}
/**
* Set up references to DOM elements and create necessary media elements
*/
setupDOMElements() {
// Get references to UI control elements
this.connectBtn = document.getElementById('connect-btn');
this.disconnectBtn = document.getElementById('disconnect-btn');
this.statusSpan = document.getElementById('connection-status');
this.debugLog = document.getElementById('debug-log');
// Create an audio element for bot's voice output
this.botAudio = document.createElement('audio');
this.botAudio.autoplay = true;
this.botAudio.playsInline = true;
document.body.appendChild(this.botAudio);
}
/**
* Set up event listeners for connect/disconnect buttons
*/
setupEventListeners() {
this.connectBtn.addEventListener('click', () => this.connect());
this.disconnectBtn.addEventListener('click', () => this.disconnect());
}
/**
* Add a timestamped message to the debug log
*/
log(message) {
const entry = document.createElement('div');
entry.textContent = `${new Date().toISOString()} - ${message}`;
// Add styling based on message type
if (message.startsWith('User: ')) {
entry.style.color = '#2196F3'; // blue for user
} else if (message.startsWith('Bot: ')) {
entry.style.color = '#4CAF50'; // green for bot
}
this.debugLog.appendChild(entry);
this.debugLog.scrollTop = this.debugLog.scrollHeight;
console.log(message);
}
/**
* Update the connection status display
*/
updateStatus(status) {
this.statusSpan.textContent = status;
this.log(`Status: ${status}`);
}
handleEventToConsole (evt) {
this.log(`Received event: ${evt.action}`);
};
/**
* Set up listeners for track events (start/stop)
* This handles new tracks being added during the session
*/
setupTrackListeners() {
if (!this.dailyCallObject) return;
this.dailyCallObject.on("joined-meeting", () => {
this.updateStatus('Connected');
this.connectBtn.disabled = true;
this.disconnectBtn.disabled = false;
this.log('Client connected');
});
this.dailyCallObject.on("track-started", (evt) => {
if (evt.track.kind === "audio" && evt.participant.local === false) {
this.log("Audio track started.")
this.setupAudioTrack(evt.track);
}
});
this.dailyCallObject.on("track-stopped", this.handleEventToConsole.bind(this));
this.dailyCallObject.on("participant-joined", this.handleEventToConsole.bind(this));
this.dailyCallObject.on("participant-updated", this.handleEventToConsole.bind(this));
this.dailyCallObject.on("participant-left", () => {
// When the bot leaves, we are also disconnecting from the call
this.disconnect()
});
this.dailyCallObject.on("left-meeting", () => {
this.updateStatus('Disconnected');
this.connectBtn.disabled = false;
this.disconnectBtn.disabled = true;
this.log('Client disconnected');
});
this.dailyCallObject.on("error", this.handleEventToConsole.bind(this));
}
/**
* Set up an audio track for playback
* Handles both initial setup and track updates
*/
setupAudioTrack(track) {
this.log(`Setting up audio track, track state: ${track.readyState}, muted: ${track.muted}`);
// Check if we're already playing this track
if (this.botAudio.srcObject) {
const oldTrack = this.botAudio.srcObject.getAudioTracks()[0];
if (oldTrack?.id === track.id) return;
}
// Create a new MediaStream with the track and set it as the audio source
this.botAudio.srcObject = new MediaStream([track]);
this.botAudio.onplaying = async (event) => {
this.log("onplaying")
this.log("Will send the audio message to play the audio at the next tick")
this.dailyCallObject.sendAppMessage("playable")
}
}
async fetchRoomInfo() {
let connectUrl = '/connect'
let res = await fetch(connectUrl, {
method: "POST",
mode: "cors",
headers: new Headers({
"Content-Type": "application/json"
}),
})
if (res.ok) {
return res.json();
}
}
/**
* Initialize and connect to the bot
* This sets up the RTVI client, initializes devices, and establishes the connection
*/
async connect() {
try {
// Initialize the client
this.dailyCallObject = Daily.createCallObject({
subscribeToTracksAutomatically: true,
});
// Set up listeners for media track events
this.setupTrackListeners();
this.log('Creating the bot...');
let roomInfo = await this.fetchRoomInfo()
// Connect to the bot
this.log('Connecting to bot...');
// Only for making debugger easier
window.callObject = this.dailyCallObject;
await this.dailyCallObject.join({
url: roomInfo.room_url,
});
this.log('Connection complete');
} catch (error) {
// Handle any errors during connection
this.log(`Error connecting: ${error.message}`);
this.log(`Error stack: ${error.stack}`);
this.updateStatus('Error');
// Clean up if there's an error
if (this.dailyCallObject) {
try {
await this.dailyCallObject.leave();
} catch (disconnectError) {
this.log(`Error during disconnect: ${disconnectError.message}`);
}
}
}
}
/**
* Disconnect from the bot and clean up media resources
*/
async disconnect() {
if (this.dailyCallObject) {
try {
// Disconnect the RTVI client
await this.dailyCallObject.leave();
await this.dailyCallObject.destroy();
this.dailyCallObject = null;
// Clean up audio
if (this.botAudio.srcObject) {
this.botAudio.srcObject.getTracks().forEach((track) => track.stop());
this.botAudio.srcObject = null;
}
} catch (error) {
this.log(`Error disconnecting: ${error.message}`);
}
}
}
}
// Initialize the client when the page loads
window.addEventListener('DOMContentLoaded', () => {
new ChatbotClient();
});

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@@ -0,0 +1,98 @@
body {
margin: 0;
padding: 20px;
font-family: Arial, sans-serif;
background-color: #f0f0f0;
}
.container {
max-width: 1200px;
margin: 0 auto;
}
.status-bar {
display: flex;
justify-content: space-between;
align-items: center;
padding: 10px;
background-color: #fff;
border-radius: 8px;
margin-bottom: 20px;
}
.controls button {
padding: 8px 16px;
margin-left: 10px;
border: none;
border-radius: 4px;
cursor: pointer;
}
#connect-btn {
background-color: #4caf50;
color: white;
}
#disconnect-btn {
background-color: #f44336;
color: white;
}
button:disabled {
opacity: 0.5;
cursor: not-allowed;
}
.main-content {
background-color: #fff;
border-radius: 8px;
padding: 20px;
margin-bottom: 20px;
}
.bot-container {
display: flex;
flex-direction: column;
align-items: center;
}
#bot-video-container {
width: 640px;
height: 360px;
background-color: #e0e0e0;
border-radius: 8px;
margin: 20px auto;
overflow: hidden;
display: flex;
align-items: center;
justify-content: center;
}
#bot-video-container video {
width: 100%;
height: 100%;
object-fit: cover;
}
.debug-panel {
background-color: #fff;
border-radius: 8px;
padding: 20px;
}
.debug-panel h3 {
margin: 0 0 10px 0;
font-size: 16px;
font-weight: bold;
}
#debug-log {
height: 200px;
overflow-y: auto;
background-color: #f8f8f8;
padding: 10px;
border-radius: 4px;
font-family: monospace;
font-size: 12px;
line-height: 1.4;
}

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@@ -0,0 +1,13 @@
import { defineConfig } from 'vite';
export default defineConfig({
server: {
proxy: {
// Proxy /api requests to the backend server
'/connect': {
target: 'http://0.0.0.0:7860', // Replace with your backend URL
changeOrigin: true,
},
},
},
});

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@@ -0,0 +1,50 @@
# Bot ready signaling Server
A FastAPI server that manages bot instances and provide endpoint for Pipecat client connections.
## Endpoints
- `POST /connect` - Pipecat client connection endpoint
## Environment Variables
Copy `env.example` to `.env` and configure:
```ini
# Required API Keys
DAILY_API_KEY= # Your Daily API key
CARTESIA_API_KEY= # Your Cartesia API key
# Optional Configuration
DAILY_API_URL= # Optional: Daily API URL (defaults to https://api.daily.co/v1)
DAILY_SAMPLE_ROOM_URL= # Optional: Fixed room URL for development
HOST= # Optional: Host address (defaults to 0.0.0.0)
FAST_API_PORT= # Optional: Port number (defaults to 7860)
```
## Running the Server
Set up and activate your virtual environment:
```bash
python3 -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
Install dependencies:
```bash
pip install -r requirements.txt
```
If you want to use the local version of `pipecat` in this repo rather than the last published version, also run:
```bash
pip install --editable "../../../[daily,cartesia,openai]"
```
Run the server:
```bash
python server.py
```

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@@ -0,0 +1,3 @@
DAILY_SAMPLE_ROOM_URL=https://yourdomain.daily.co/yourroom # (for joining the bot to the same room repeatedly for local dev)
DAILY_API_KEY=
CARTESIA_API_KEY=

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@@ -0,0 +1,4 @@
python-dotenv
fastapi[all]
uvicorn
pipecat-ai[daily,cartesia,openai]

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@@ -0,0 +1,63 @@
#
# Copyright (c) 20242025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import argparse
import os
import aiohttp
from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper
async def configure(aiohttp_session: aiohttp.ClientSession):
(url, token, _) = await configure_with_args(aiohttp_session)
return (url, token)
async def configure_with_args(
aiohttp_session: aiohttp.ClientSession, parser: argparse.ArgumentParser | None = None
):
if not parser:
parser = argparse.ArgumentParser(description="Daily AI SDK Bot Sample")
parser.add_argument(
"-u", "--url", type=str, required=False, help="URL of the Daily room to join"
)
parser.add_argument(
"-k",
"--apikey",
type=str,
required=False,
help="Daily API Key (needed to create an owner token for the room)",
)
args, unknown = parser.parse_known_args()
url = args.url or os.getenv("DAILY_SAMPLE_ROOM_URL")
key = args.apikey or os.getenv("DAILY_API_KEY")
if not url:
raise Exception(
"No Daily room specified. use the -u/--url option from the command line, or set DAILY_SAMPLE_ROOM_URL in your environment to specify a Daily room URL."
)
if not key:
raise Exception(
"No Daily API key specified. use the -k/--apikey option from the command line, or set DAILY_API_KEY in your environment to specify a Daily API key, available from https://dashboard.daily.co/developers."
)
daily_rest_helper = DailyRESTHelper(
daily_api_key=key,
daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"),
aiohttp_session=aiohttp_session,
)
# Create a meeting token for the given room with an expiration 1 hour in
# the future.
expiry_time: float = 60 * 60
token = await daily_rest_helper.get_token(url, expiry_time)
return (url, token, args)

View File

@@ -0,0 +1,147 @@
#
# Copyright (c) 2025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import argparse
import os
import subprocess
from contextlib import asynccontextmanager
from typing import Any, Dict
import aiohttp
from dotenv import load_dotenv
from fastapi import FastAPI, HTTPException, Request
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse
from pipecat.transports.services.helpers.daily_rest import DailyRESTHelper, DailyRoomParams
# Load environment variables from .env file
load_dotenv(override=True)
# Dictionary to track bot processes: {pid: (process, room_url)}
bot_procs = {}
# Store Daily API helpers
daily_helpers = {}
def cleanup():
"""Cleanup function to terminate all bot processes.
Called during server shutdown.
"""
for entry in bot_procs.values():
proc = entry[0]
proc.terminate()
proc.wait()
@asynccontextmanager
async def lifespan(app: FastAPI):
"""FastAPI lifespan manager that handles startup and shutdown tasks.
- Creates aiohttp session
- Initializes Daily API helper
- Cleans up resources on shutdown
"""
aiohttp_session = aiohttp.ClientSession()
daily_helpers["rest"] = DailyRESTHelper(
daily_api_key=os.getenv("DAILY_API_KEY", ""),
daily_api_url=os.getenv("DAILY_API_URL", "https://api.daily.co/v1"),
aiohttp_session=aiohttp_session,
)
yield
await aiohttp_session.close()
cleanup()
# Initialize FastAPI app with lifespan manager
app = FastAPI(lifespan=lifespan)
# Configure CORS to allow requests from any origin
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
async def create_room_and_token() -> tuple[str, str]:
"""Helper function to create a Daily room and generate an access token.
Returns:
tuple[str, str]: A tuple containing (room_url, token)
Raises:
HTTPException: If room creation or token generation fails
"""
room = await daily_helpers["rest"].create_room(DailyRoomParams())
if not room.url:
raise HTTPException(status_code=500, detail="Failed to create room")
token = await daily_helpers["rest"].get_token(room.url)
if not token:
raise HTTPException(status_code=500, detail=f"Failed to get token for room: {room.url}")
return room.url, token
@app.post("/connect")
async def bot_connect(request: Request) -> Dict[Any, Any]:
"""Connect endpoint that creates a room and returns connection credentials.
This endpoint is called by client to establish a connection.
Returns:
Dict[Any, Any]: Authentication bundle containing room_url and token
Raises:
HTTPException: If room creation, token generation, or bot startup fails
"""
print("Creating room for RTVI connection")
room_url, token = await create_room_and_token()
print(f"Room URL: {room_url}")
# Start the bot process
try:
bot_file = "signalling_bot"
proc = subprocess.Popen(
[f"python3 -m {bot_file} -u {room_url} -t {token}"],
shell=True,
bufsize=1,
cwd=os.path.dirname(os.path.abspath(__file__)),
)
bot_procs[proc.pid] = (proc, room_url)
except Exception as e:
raise HTTPException(status_code=500, detail=f"Failed to start subprocess: {e}")
# Return the authentication bundle in format expected by DailyTransport
return {"room_url": room_url, "token": token}
if __name__ == "__main__":
import uvicorn
# Parse command line arguments for server configuration
default_host = os.getenv("HOST", "0.0.0.0")
default_port = int(os.getenv("FAST_API_PORT", "7860"))
parser = argparse.ArgumentParser(description="Daily Travel Companion FastAPI server")
parser.add_argument("--host", type=str, default=default_host, help="Host address")
parser.add_argument("--port", type=int, default=default_port, help="Port number")
parser.add_argument("--reload", action="store_true", help="Reload code on change")
config = parser.parse_args()
# Start the FastAPI server
uvicorn.run(
"server:app",
host=config.host,
port=config.port,
reload=config.reload,
)

View File

@@ -0,0 +1,95 @@
#
# Copyright (c) 20242025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import asyncio
import os
import sys
from dataclasses import dataclass
import aiohttp
from dotenv import load_dotenv
from loguru import logger
from runner import configure
from pipecat.frames.frames import AudioRawFrame, EndFrame, OutputAudioRawFrame, TTSSpeakFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.services.cartesia import CartesiaTTSService
from pipecat.transports.services.daily import DailyParams, DailyTransport
load_dotenv(override=True)
logger.remove(0)
logger.add(sys.stderr, level="DEBUG")
@dataclass
class SilenceFrame(OutputAudioRawFrame):
def __init__(
self,
*,
sample_rate: int,
duration: float,
):
# Initialize the parent class with the silent frame's data
super().__init__(
audio=self.create_silent_audio_frame(sample_rate, 1, duration).audio,
sample_rate=sample_rate,
num_channels=1,
)
@staticmethod
def create_silent_audio_frame(
sample_rate: int, num_channels: int, duration: float
) -> AudioRawFrame:
"""Create an AudioRawFrame containing silence."""
frame_size = num_channels * 2 # 2 bytes per sample for 16-bit audio
total_frames = int(sample_rate * duration)
total_bytes = total_frames * frame_size
silent_audio = bytes(total_bytes) # Create a byte array filled with zeros
return AudioRawFrame(audio=silent_audio, sample_rate=sample_rate, num_channels=num_channels)
async def main():
async with aiohttp.ClientSession() as session:
(room_url, _) = await configure(session)
transport = DailyTransport(
room_url, None, "Say One Thing", DailyParams(audio_out_enabled=True)
)
tts = CartesiaTTSService(
api_key=os.getenv("CARTESIA_API_KEY"),
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
)
runner = PipelineRunner()
task = PipelineTask(Pipeline([tts, transport.output()]))
# Register an event handler so we can play the audio when we receive a specific message
@transport.event_handler("on_app_message")
async def on_app_message(transport, message, sender):
logger.debug(f"Received app message: {message} - {sender}")
if "playable" not in message:
return
await task.queue_frames(
[
SilenceFrame(
sample_rate=task.params.audio_out_sample_rate,
duration=0.5,
),
TTSSpeakFrame(f"Hello there, how are you doing today ?"),
EndFrame(),
]
)
await runner.run(task)
if __name__ == "__main__":
asyncio.run(main())

View File

@@ -124,17 +124,20 @@ async def main():
@transport.event_handler("on_first_participant_joined")
async def on_first_participant_joined(transport, participant):
await audio_buffer_processor.start_recording()
await transport.capture_participant_transcription(participant["id"])
await task.queue_frames([context_aggregator.user().get_context_frame()])
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
print(f"Participant left: {participant}")
await task.queue_frame(EndFrame())
await task.cancel()
@transport.event_handler("on_call_state_updated")
async def on_call_state_updated(transport, state):
if state == "left":
# Here we don't want to cancel, we just want to finish sending
# whatever is queued, so we use an EndFrame().
await task.queue_frame(EndFrame())
runner = PipelineRunner()

View File

@@ -53,4 +53,3 @@ async def configure(aiohttp_session: aiohttp.ClientSession):
token = await daily_rest_helper.get_token(url, expiry_time)
return (url, token)
return (url, token)

View File

@@ -18,7 +18,6 @@ from loguru import logger
from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import EndFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -110,8 +109,9 @@ async def main():
context = OpenAILLMContext(messages)
context_aggregator = llm.create_context_aggregator(context)
# Save audio every 10 seconds.
audiobuffer = AudioBufferProcessor(buffer_size=480000)
# NOTE: Watch out! This will save all the conversation in memory. You
# can pass `buffer_size` to get periodic callbacks.
audiobuffer = AudioBufferProcessor()
pipeline = Pipeline(
[
@@ -133,13 +133,14 @@ async def main():
@transport.event_handler("on_first_participant_joined")
async def on_first_participant_joined(transport, participant):
await audiobuffer.start_recording()
await transport.capture_participant_transcription(participant["id"])
await task.queue_frames([context_aggregator.user().get_context_frame()])
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
print(f"Participant left: {participant}")
await task.queue_frame(EndFrame())
await task.cancel()
runner = PipelineRunner()

View File

@@ -79,11 +79,13 @@ async def main(room_url: str, token: str):
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
await task.queue_frame(EndFrame())
await task.cancel()
@transport.event_handler("on_call_state_updated")
async def on_call_state_updated(transport, state):
if state == "left":
# Here we don't want to cancel, we just want to finish sending
# whatever is queued, so we use an EndFrame().
await task.queue_frame(EndFrame())
runner = PipelineRunner()

View File

@@ -5,6 +5,15 @@ import sys
from dotenv import load_dotenv
from loguru import logger
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.cartesia import CartesiaTTSService
from pipecat.services.openai import OpenAILLMService
from pipecat.transports.services.daily import DailyParams, DailyTransport
load_dotenv(override=True)
logger.remove(0)
@@ -12,16 +21,6 @@ logger.add(sys.stderr, level="DEBUG")
async def main(room_url: str, token: str):
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import EndFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.cartesia import CartesiaTTSService
from pipecat.services.openai import OpenAILLMService
from pipecat.transports.services.daily import DailyParams, DailyTransport
transport = DailyTransport(
room_url,
token,
@@ -79,7 +78,7 @@ async def main(room_url: str, token: str):
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
await task.queue_frame(EndFrame())
await task.cancel()
runner = PipelineRunner()

View File

@@ -1,85 +0,0 @@
<div align="center">
 <img alt="pipecat" width="300px" height="auto" src="image.png">
</div>
# Dialin example
Example project that demonstrates how to add phone number dialin to your Pipecat bots. We include examples for both Daily (`bot_daily.py`) and Twilio (`bot_twilio.py`), depending on who you want to use as a phone vendor.
- 🔁 Transport: Daily WebRTC
- 💬 Speech-to-Text: Deepgram via Daily transport
- 🤖 LLM: GPT4-o / OpenAI
- 🔉 Text-to-Speech: ElevenLabs
#### Should I use Daily or Twilio as a vendor?
If you're starting from scratch, using Daily to provision phone numbers alongside Daily as a transport offers some convenience (such as automatic call forwarding.)
If you already have Twilio numbers and workflows that you want to connect to your Pipecat bots, there is some additional configuration required (you'll need to create a `on_dialin_ready` and use the Twilio client to trigger the forward.)
You can read more about this, as well as see respective walkthroughs in our docs.
## Setup
```shell
# Install the requirements
pip install -r requirements.txt
# Setup your env
mv env.example .env
```
## Using Daily numbers
Run `bot_runner.py` to handle incoming HTTP requests:
`python bot_runner.py --host localhost`
Then target the following URL:
`POST /daily_start_bot`
For more configuration options, please consult Daily's API documentation.
## Using Twilio numbers
As above, but target the following URL:
`POST /twilio_start_bot`
For more configuration options, please consult Twilio's API documentation.
## Deployment example
A Dockerfile is included in this demo for convenience. Here is an example of how to build and deploy your bot to [fly.io](https://fly.io).
*Please note: This demo spawns agents as subprocesses for convenience / demonstration purposes. You would likely not want to do this in production as it would limit concurrency to available system resources. For more information on how to deploy your bots using VMs, refer to the Pipecat documentation.*
### Build the docker image
`docker build -t tag:project .`
### Launch the fly project
`mv fly.example.toml fly.toml`
`fly launch` (using the included fly.toml)
### Setup your secrets on Fly
Set the necessary secrets (found in `env.example`)
`fly secrets set DAILY_API_KEY=... OPENAI_API_KEY=... ELEVENLABS_API_KEY=... ELEVENLABS_VOICE_ID=...`
If you're using Twilio as a number vendor:
`fly secrets set TWILIO_ACCOUNT_SID=... TWILIO_AUTH_TOKEN=...`
### Deploy!
`fly deploy`
## Need to do something more advanced?
This demo covers the basics of bot telephony. If you want to know more about working with PSTN / SIP, please ping us on [Discord](https://discord.gg/pipecat).

View File

@@ -1,103 +0,0 @@
import argparse
import asyncio
import os
import sys
from dotenv import load_dotenv
from loguru import logger
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import EndFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.elevenlabs import ElevenLabsTTSService
from pipecat.services.openai import OpenAILLMService
from pipecat.transports.services.daily import DailyDialinSettings, DailyParams, DailyTransport
load_dotenv(override=True)
logger.remove(0)
logger.add(sys.stderr, level="DEBUG")
daily_api_key = os.getenv("DAILY_API_KEY", "")
daily_api_url = os.getenv("DAILY_API_URL", "https://api.daily.co/v1")
async def main(room_url: str, token: str, callId: str, callDomain: str):
# diallin_settings are only needed if Daily's SIP URI is used
# If you are handling this via Twilio, Telnyx, set this to None
# and handle call-forwarding when on_dialin_ready fires.
diallin_settings = DailyDialinSettings(call_id=callId, call_domain=callDomain)
transport = DailyTransport(
room_url,
token,
"Chatbot",
DailyParams(
api_url=daily_api_url,
api_key=daily_api_key,
dialin_settings=diallin_settings,
audio_in_enabled=True,
audio_out_enabled=True,
camera_out_enabled=False,
vad_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
transcription_enabled=True,
),
)
tts = ElevenLabsTTSService(
api_key=os.getenv("ELEVENLABS_API_KEY", ""),
voice_id=os.getenv("ELEVENLABS_VOICE_ID", ""),
)
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"), model="gpt-4o")
messages = [
{
"role": "system",
"content": "You are Chatbot, a friendly, helpful robot. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way, but keep your responses brief. Start by saying 'Oh, hello! Who dares dial me at this hour?!'.",
},
]
context = OpenAILLMContext(messages)
context_aggregator = llm.create_context_aggregator(context)
pipeline = Pipeline(
[
transport.input(),
context_aggregator.user(),
llm,
tts,
transport.output(),
context_aggregator.assistant(),
]
)
task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
@transport.event_handler("on_first_participant_joined")
async def on_first_participant_joined(transport, participant):
await transport.capture_participant_transcription(participant["id"])
await task.queue_frames([context_aggregator.user().get_context_frame()])
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
await task.queue_frame(EndFrame())
runner = PipelineRunner()
await runner.run(task)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Pipecat Simple ChatBot")
parser.add_argument("-u", type=str, help="Room URL")
parser.add_argument("-t", type=str, help="Token")
parser.add_argument("-i", type=str, help="Call ID")
parser.add_argument("-d", type=str, help="Call Domain")
config = parser.parse_args()
asyncio.run(main(config.u, config.t, config.i, config.d))

View File

@@ -13,7 +13,7 @@ from dotenv import load_dotenv
from loguru import logger
from runner import configure
from pipecat.frames.frames import EndFrame, TextFrame
from pipecat.frames.frames import TextFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineTask
@@ -53,7 +53,7 @@ async def main():
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
await task.queue_frame(EndFrame())
await task.cancel()
await runner.run(task)

View File

@@ -0,0 +1,64 @@
#
# Copyright (c) 20242025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import asyncio
import os
import sys
import aiohttp
from dotenv import load_dotenv
from loguru import logger
from runner import configure
from pipecat.frames.frames import EndFrame, TextFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.services.google import GoogleImageGenService
from pipecat.transports.services.daily import DailyParams, DailyTransport
load_dotenv(override=True)
logger.remove(0)
logger.add(sys.stderr, level="DEBUG")
async def main():
async with aiohttp.ClientSession() as session:
(room_url, _) = await configure(session)
transport = DailyTransport(
room_url,
None,
"Show a still frame image",
DailyParams(camera_out_enabled=True, camera_out_width=1024, camera_out_height=1024),
)
imagegen = GoogleImageGenService(
api_key=os.getenv("GOOGLE_API_KEY"),
)
runner = PipelineRunner()
task = PipelineTask(
Pipeline([imagegen, transport.output()]), PipelineParams(enable_metrics=True)
)
@transport.event_handler("on_first_participant_joined")
async def on_first_participant_joined(transport, participant):
await task.queue_frame(TextFrame("a cat in the style of picasso"))
await task.queue_frame(TextFrame("a dog in the style of picasso"))
await task.queue_frame(TextFrame("a fish in the style of picasso"))
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
await task.queue_frame(EndFrame())
await runner.run(task)
if __name__ == "__main__":
asyncio.run(main())

View File

@@ -14,7 +14,7 @@ from loguru import logger
from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import EndFrame, Frame, MetricsFrame
from pipecat.frames.frames import Frame, MetricsFrame
from pipecat.metrics.metrics import (
LLMUsageMetricsData,
ProcessingMetricsData,
@@ -38,6 +38,8 @@ logger.add(sys.stderr, level="DEBUG")
class MetricsLogger(FrameProcessor):
async def process_frame(self, frame: Frame, direction: FrameDirection):
await super().process_frame(frame, direction)
if isinstance(frame, MetricsFrame):
for d in frame.data:
if isinstance(d, TTFBMetricsData):
@@ -115,7 +117,7 @@ async def main():
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
await task.queue_frame(EndFrame())
await task.cancel()
runner = PipelineRunner()

View File

@@ -15,10 +15,16 @@ from PIL import Image
from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import EndFrame, Frame, OutputImageRawFrame, SystemFrame, TextFrame
from pipecat.frames.frames import (
BotStartedSpeakingFrame,
BotStoppedSpeakingFrame,
Frame,
OutputImageRawFrame,
TextFrame,
)
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineTask
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.services.cartesia import CartesiaHttpTTSService
@@ -45,7 +51,7 @@ class ImageSyncAggregator(FrameProcessor):
async def process_frame(self, frame: Frame, direction: FrameDirection):
await super().process_frame(frame, direction)
if not isinstance(frame, SystemFrame) and direction == FrameDirection.DOWNSTREAM:
if isinstance(frame, BotStartedSpeakingFrame):
await self.push_frame(
OutputImageRawFrame(
image=self._speaking_image_bytes,
@@ -53,7 +59,8 @@ class ImageSyncAggregator(FrameProcessor):
format=self._speaking_image_format,
)
)
await self.push_frame(frame)
elif isinstance(frame, BotStoppedSpeakingFrame):
await self.push_frame(
OutputImageRawFrame(
image=self._waiting_image_bytes,
@@ -61,8 +68,8 @@ class ImageSyncAggregator(FrameProcessor):
format=self._waiting_image_format,
)
)
else:
await self.push_frame(frame)
await self.push_frame(frame)
async def main():
@@ -109,16 +116,24 @@ async def main():
pipeline = Pipeline(
[
transport.input(),
image_sync_aggregator,
context_aggregator.user(),
llm,
tts,
image_sync_aggregator,
transport.output(),
context_aggregator.assistant(),
]
)
task = PipelineTask(pipeline)
task = PipelineTask(
pipeline,
PipelineParams(
allow_interruptions=True,
enable_metrics=True,
enable_usage_metrics=True,
report_only_initial_ttfb=True,
),
)
@transport.event_handler("on_first_participant_joined")
async def on_first_participant_joined(transport, participant):
@@ -128,7 +143,7 @@ async def main():
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
await task.queue_frame(EndFrame())
await task.cancel()
runner = PipelineRunner()

View File

@@ -13,7 +13,6 @@ from dotenv import load_dotenv
from loguru import logger
from runner import configure
from pipecat.frames.frames import EndFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -94,7 +93,7 @@ async def main():
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
await task.queue_frame(EndFrame())
await task.cancel()
runner = PipelineRunner()

View File

@@ -14,7 +14,6 @@ from loguru import logger
from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import EndFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -92,7 +91,7 @@ async def main():
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
await task.queue_frame(EndFrame())
await task.cancel()
runner = PipelineRunner()

View File

@@ -14,7 +14,6 @@ from loguru import logger
from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import EndFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -96,7 +95,7 @@ async def main():
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
await task.queue_frame(EndFrame())
await task.cancel()
runner = PipelineRunner()

View File

@@ -19,7 +19,7 @@ from loguru import logger
from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import EndFrame, LLMMessagesFrame
from pipecat.frames.frames import LLMMessagesFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -124,7 +124,7 @@ async def main():
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
await task.queue_frame(EndFrame())
await task.cancel()
runner = PipelineRunner()

View File

@@ -16,7 +16,6 @@ from runner import configure
from pipecat.frames.frames import (
BotInterruptionFrame,
EndFrame,
StopInterruptionFrame,
UserStartedSpeakingFrame,
UserStoppedSpeakingFrame,
@@ -106,7 +105,7 @@ async def main():
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
await task.queue_frame(EndFrame())
await task.cancel()
runner = PipelineRunner()

View File

@@ -14,7 +14,6 @@ from loguru import logger
from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import EndFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -91,7 +90,7 @@ async def main():
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
await task.queue_frame(EndFrame())
await task.cancel()
runner = PipelineRunner()

View File

@@ -14,7 +14,6 @@ from loguru import logger
from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import EndFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -92,7 +91,7 @@ async def main():
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
await task.queue_frame(EndFrame())
await task.cancel()
runner = PipelineRunner()

View File

@@ -14,14 +14,12 @@ from loguru import logger
from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import EndFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.services.openai import OpenAILLMService
from pipecat.services.playht import PlayHTHttpTTSService
from pipecat.transcriptions.language import Language
from pipecat.transports.services.daily import DailyParams, DailyTransport
load_dotenv(override=True)
@@ -94,7 +92,7 @@ async def main():
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
await task.queue_frame(EndFrame())
await task.cancel()
runner = PipelineRunner()

View File

@@ -14,7 +14,6 @@ from loguru import logger
from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import EndFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -95,7 +94,7 @@ async def main():
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
await task.queue_frame(EndFrame())
await task.cancel()
runner = PipelineRunner()

View File

@@ -14,7 +14,6 @@ from loguru import logger
from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import EndFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -101,7 +100,7 @@ async def main():
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
await task.queue_frame(EndFrame())
await task.cancel()
runner = PipelineRunner()

View File

@@ -14,7 +14,6 @@ from loguru import logger
from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import EndFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -38,7 +37,6 @@ async def main():
"Respond bot",
DailyParams(
audio_out_enabled=True,
audio_out_sample_rate=24000,
transcription_enabled=True,
vad_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
@@ -89,7 +87,7 @@ async def main():
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
await task.queue_frame(EndFrame())
await task.cancel()
runner = PipelineRunner()

View File

@@ -15,7 +15,6 @@ from loguru import logger
from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import EndFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -99,7 +98,7 @@ async def main():
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
await task.queue_frame(EndFrame())
await task.cancel()
runner = PipelineRunner()

View File

@@ -14,7 +14,6 @@ from loguru import logger
from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import EndFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -93,7 +92,7 @@ async def main():
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
await task.queue_frame(EndFrame())
await task.cancel()
runner = PipelineRunner()

View File

@@ -14,7 +14,6 @@ from loguru import logger
from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import EndFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -99,7 +98,7 @@ async def main():
# Register an event handler to exit the application when the user leaves.
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
await task.queue_frame(EndFrame())
await task.cancel()
runner = PipelineRunner()

View File

@@ -14,7 +14,6 @@ from loguru import logger
from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import EndFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -39,7 +38,6 @@ async def main():
"Respond bot",
DailyParams(
audio_out_enabled=True,
audio_out_sample_rate=24000,
transcription_enabled=True,
vad_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
@@ -90,7 +88,7 @@ async def main():
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
await task.queue_frame(EndFrame())
await task.cancel()
runner = PipelineRunner()

View File

@@ -14,7 +14,6 @@ from loguru import logger
from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import EndFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -105,7 +104,7 @@ async def main():
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
await task.queue_frame(EndFrame())
await task.cancel()
runner = PipelineRunner()

View File

@@ -14,7 +14,6 @@ from loguru import logger
from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import EndFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -99,7 +98,7 @@ async def main():
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
await task.queue_frame(EndFrame())
await task.cancel()
runner = PipelineRunner()

View File

@@ -14,7 +14,6 @@ from loguru import logger
from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import EndFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -41,7 +40,6 @@ async def main():
"Respond bot",
DailyParams(
audio_out_enabled=True,
audio_out_sample_rate=24000,
vad_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
vad_audio_passthrough=True,
@@ -98,7 +96,7 @@ async def main():
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
await task.queue_frame(EndFrame())
await task.cancel()
runner = PipelineRunner()

View File

@@ -14,7 +14,6 @@ from loguru import logger
from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import EndFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -98,7 +97,7 @@ async def main():
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
await task.queue_frame(EndFrame())
await task.cancel()
runner = PipelineRunner()

View File

@@ -14,7 +14,6 @@ from loguru import logger
from runner import configure
from pipecat.audio.filters.krisp_filter import KrispFilter
from pipecat.frames.frames import EndFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -93,7 +92,7 @@ async def main():
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
await task.queue_frame(EndFrame())
await task.cancel()
runner = PipelineRunner()

View File

@@ -14,7 +14,6 @@ from loguru import logger
from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import EndFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -93,7 +92,7 @@ async def main():
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
await task.queue_frame(EndFrame())
await task.cancel()
runner = PipelineRunner()

View File

@@ -14,7 +14,6 @@ from loguru import logger
from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import EndFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -85,7 +84,7 @@ async def main():
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
await task.queue_frame(EndFrame())
await task.cancel()
runner = PipelineRunner()

View File

@@ -17,7 +17,6 @@ from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import (
EndFrame,
Frame,
InputAudioRawFrame,
LLMFullResponseEndFrame,
@@ -271,7 +270,7 @@ async def main():
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
await task.queue_frame(EndFrame())
await task.cancel()
runner = PipelineRunner()

View File

@@ -14,7 +14,6 @@ from loguru import logger
from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import EndFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -92,7 +91,7 @@ async def main():
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
await task.queue_frame(EndFrame())
await task.cancel()
runner = PipelineRunner()

View File

@@ -21,7 +21,7 @@ from pipecat.frames.frames import (
)
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineTask
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.transports.services.daily import DailyParams, DailyTransport
@@ -61,7 +61,6 @@ async def main():
"Test",
DailyParams(
audio_in_enabled=True,
audio_in_sample_rate=24000,
audio_out_enabled=True,
camera_out_enabled=True,
camera_out_is_live=True,
@@ -78,7 +77,9 @@ async def main():
runner = PipelineRunner()
task = PipelineTask(pipeline)
task = PipelineTask(
pipeline, PipelineParams(audio_in_sample_rate=24000, audio_out_sample_rate=24000)
)
await runner.run(task)

View File

@@ -22,7 +22,7 @@ from pipecat.frames.frames import (
)
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineTask
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.transports.base_transport import TransportParams
from pipecat.transports.local.tk import TkLocalTransport
@@ -62,7 +62,7 @@ async def main():
tk_root.title("Local Mirror")
daily_transport = DailyTransport(
room_url, token, "Test", DailyParams(audio_in_enabled=True, audio_in_sample_rate=24000)
room_url, token, "Test", DailyParams(audio_in_enabled=True)
)
tk_transport = TkLocalTransport(
@@ -82,7 +82,9 @@ async def main():
pipeline = Pipeline([daily_transport.input(), MirrorProcessor(), tk_transport.output()])
task = PipelineTask(pipeline)
task = PipelineTask(
pipeline, PipelineParams(audio_in_sample_rate=24000, audio_out_sample_rate=24000)
)
async def run_tk():
while not task.has_finished():

View File

@@ -15,6 +15,7 @@ from openai.types.chat import ChatCompletionToolParam
from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import TTSSpeakFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -29,11 +30,8 @@ logger.add(sys.stderr, level="DEBUG")
async def start_fetch_weather(function_name, llm, context):
# note: we can't push a frame to the LLM here. the bot
# can interrupt itself and/or cause audio overlapping glitches.
# possible question for Aleix and Chad about what the right way
# to trigger speech is, now, with the new queues/async/sync refactors.
# await llm.push_frame(TextFrame("Let me check on that."))
"""Push a frame to the LLM; this is handy when the LLM response might take a while."""
await llm.push_frame(TTSSpeakFrame("Let me check on that."))
logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}")

View File

@@ -15,6 +15,7 @@ from openai.types.chat import ChatCompletionToolParam
from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import TTSSpeakFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineTask
@@ -30,11 +31,8 @@ logger.add(sys.stderr, level="DEBUG")
async def start_fetch_weather(function_name, llm, context):
# note: we can't push a frame to the LLM here. the bot
# can interrupt itself and/or cause audio overlapping glitches.
# possible question for Aleix and Chad about what the right way
# to trigger speech is, now, with the new queues/async/sync refactors.
# await llm.push_frame(TextFrame("Let me check on that."))
"""Push a frame to the LLM; this is handy when the LLM response might take a while."""
await llm.push_frame(TTSSpeakFrame("Let me check on that."))
logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}")

View File

@@ -15,6 +15,7 @@ from openai.types.chat import ChatCompletionToolParam
from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import TTSSpeakFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -30,11 +31,8 @@ logger.add(sys.stderr, level="DEBUG")
async def start_fetch_weather(function_name, llm, context):
# note: we can't push a frame to the LLM here. the bot
# can interrupt itself and/or cause audio overlapping glitches.
# possible question for Aleix and Chad about what the right way
# to trigger speech is, now, with the new queues/async/sync refactors.
# await llm.push_frame(TextFrame("Let me check on that."))
"""Push a frame to the LLM; this is handy when the LLM response might take a while."""
await llm.push_frame(TTSSpeakFrame("Let me check on that."))
logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}")

View File

@@ -15,6 +15,7 @@ from openai.types.chat import ChatCompletionToolParam
from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import TTSSpeakFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -30,11 +31,8 @@ logger.add(sys.stderr, level="DEBUG")
async def start_fetch_weather(function_name, llm, context):
# note: we can't push a frame to the LLM here. the bot
# can interrupt itself and/or cause audio overlapping glitches.
# possible question for Aleix and Chad about what the right way
# to trigger speech is, now, with the new queues/async/sync refactors.
# await llm.push_frame(TextFrame("Let me check on that."))
"""Push a frame to the LLM; this is handy when the LLM response might take a while."""
await llm.push_frame(TTSSpeakFrame("Let me check on that."))
logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}")

View File

@@ -15,6 +15,7 @@ from openai.types.chat import ChatCompletionToolParam
from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import TTSSpeakFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -30,11 +31,8 @@ logger.add(sys.stderr, level="DEBUG")
async def start_fetch_weather(function_name, llm, context):
# note: we can't push a frame to the LLM here. the bot
# can interrupt itself and/or cause audio overlapping glitches.
# possible question for Aleix and Chad about what the right way
# to trigger speech is, now, with the new queues/async/sync refactors.
# await llm.push_frame(TextFrame("Let me check on that."))
"""Push a frame to the LLM; this is handy when the LLM response might take a while."""
await llm.push_frame(TTSSpeakFrame("Let me check on that."))
logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}")

View File

@@ -15,6 +15,7 @@ from openai.types.chat import ChatCompletionToolParam
from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import TTSSpeakFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -30,11 +31,8 @@ logger.add(sys.stderr, level="DEBUG")
async def start_fetch_weather(function_name, llm, context):
# note: we can't push a frame to the LLM here. the bot
# can interrupt itself and/or cause audio overlapping glitches.
# possible question for Aleix and Chad about what the right way
# to trigger speech is, now, with the new queues/async/sync refactors.
# await llm.push_frame(TextFrame("Let me check on that."))
"""Push a frame to the LLM; this is handy when the LLM response might take a while."""
await llm.push_frame(TTSSpeakFrame("Let me check on that."))
logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}")

View File

@@ -15,6 +15,7 @@ from openai.types.chat import ChatCompletionToolParam
from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import TTSSpeakFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -30,11 +31,8 @@ logger.add(sys.stderr, level="DEBUG")
async def start_fetch_weather(function_name, llm, context):
# note: we can't push a frame to the LLM here. the bot
# can interrupt itself and/or cause audio overlapping glitches.
# possible question for Aleix and Chad about what the right way
# to trigger speech is, now, with the new queues/async/sync refactors.
# await llm.push_frame(TextFrame("Let me check on that."))
"""Push a frame to the LLM; this is handy when the LLM response might take a while."""
await llm.push_frame(TTSSpeakFrame("Let me check on that."))
logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}")

View File

@@ -15,6 +15,7 @@ from openai.types.chat import ChatCompletionToolParam
from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import TTSSpeakFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -30,11 +31,8 @@ logger.add(sys.stderr, level="DEBUG")
async def start_fetch_weather(function_name, llm, context):
# note: we can't push a frame to the LLM here. the bot
# can interrupt itself and/or cause audio overlapping glitches.
# possible question for Aleix and Chad about what the right way
# to trigger speech is, now, with the new queues/async/sync refactors.
# await llm.push_frame(TextFrame("Let me check on that."))
"""Push a frame to the LLM; this is handy when the LLM response might take a while."""
await llm.push_frame(TTSSpeakFrame("Let me check on that."))
logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}")
@@ -95,7 +93,7 @@ async def main():
messages = [
{
"role": "system",
"content": """You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way.
"content": """You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way.
You have one functions available:

View File

@@ -15,6 +15,7 @@ from openai.types.chat import ChatCompletionToolParam
from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import TTSSpeakFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -30,11 +31,8 @@ logger.add(sys.stderr, level="DEBUG")
async def start_fetch_weather(function_name, llm, context):
# note: we can't push a frame to the LLM here. the bot
# can interrupt itself and/or cause audio overlapping glitches.
# possible question for Aleix and Chad about what the right way
# to trigger speech is, now, with the new queues/async/sync refactors.
# await llm.push_frame(TextFrame("Let me check on that."))
"""Push a frame to the LLM; this is handy when the LLM response might take a while."""
await llm.push_frame(TTSSpeakFrame("Let me check on that."))
logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}")
@@ -95,7 +93,7 @@ async def main():
messages = [
{
"role": "system",
"content": """You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way.
"content": """You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way.
You have one functions available:

View File

@@ -15,6 +15,7 @@ from openai.types.chat import ChatCompletionToolParam
from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import TTSSpeakFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -30,11 +31,8 @@ logger.add(sys.stderr, level="DEBUG")
async def start_fetch_weather(function_name, llm, context):
# note: we can't push a frame to the LLM here. the bot
# can interrupt itself and/or cause audio overlapping glitches.
# possible question for Aleix and Chad about what the right way
# to trigger speech is, now, with the new queues/async/sync refactors.
# await llm.push_frame(TextFrame("Let me check on that."))
"""Push a frame to the LLM; this is handy when the LLM response might take a while."""
await llm.push_frame(TTSSpeakFrame("Let me check on that."))
logger.debug(f"Starting fetch_weather_from_api with function_name: {function_name}")

View File

@@ -14,7 +14,7 @@ from loguru import logger
from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import LLMMessagesFrame
from pipecat.frames.frames import EndFrame, LLMMessagesFrame, TTSSpeakFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -63,16 +63,36 @@ async def main():
context = OpenAILLMContext(messages)
context_aggregator = llm.create_context_aggregator(context)
async def user_idle_callback(user_idle: UserIdleProcessor):
messages.append(
{
"role": "system",
"content": "Ask the user if they are still there and try to prompt for some input, but be short.",
}
)
await user_idle.push_frame(LLMMessagesFrame(messages))
async def handle_user_idle(user_idle: UserIdleProcessor, retry_count: int) -> bool:
if retry_count == 1:
# First attempt: Add a gentle prompt to the conversation
messages.append(
{
"role": "system",
"content": "The user has been quiet. Politely and briefly ask if they're still there.",
}
)
await user_idle.push_frame(LLMMessagesFrame(messages))
return True
elif retry_count == 2:
# Second attempt: More direct prompt
messages.append(
{
"role": "system",
"content": "The user is still inactive. Ask if they'd like to continue our conversation.",
}
)
await user_idle.push_frame(LLMMessagesFrame(messages))
return True
else:
# Third attempt: End the conversation
await user_idle.push_frame(
TTSSpeakFrame("It seems like you're busy right now. Have a nice day!")
)
await task.queue_frame(EndFrame())
return False
user_idle = UserIdleProcessor(callback=user_idle_callback, timeout=5.0)
user_idle = UserIdleProcessor(callback=handle_user_idle, timeout=5.0)
pipeline = Pipeline(
[

View File

@@ -51,8 +51,6 @@ async def main():
out_params=GStreamerPipelineSource.OutputParams(
video_width=1280,
video_height=720,
audio_sample_rate=24000,
audio_channels=1,
),
)

View File

@@ -80,9 +80,7 @@ async def main():
"Respond bot",
DailyParams(
audio_in_enabled=True,
audio_in_sample_rate=24000,
audio_out_enabled=True,
audio_out_sample_rate=24000,
transcription_enabled=False,
vad_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.8)),

View File

@@ -177,9 +177,7 @@ async def main():
"Respond bot",
DailyParams(
audio_in_enabled=True,
audio_in_sample_rate=24000,
audio_out_enabled=True,
audio_out_sample_rate=24000,
transcription_enabled=False,
vad_enabled=True,
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.8)),

View File

@@ -14,7 +14,6 @@ from dotenv import load_dotenv
from loguru import logger
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import EndFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -89,6 +88,10 @@ async def main():
task = PipelineTask(
pipeline,
PipelineParams(
# We just use 16000 because that's what Tavus is expecting and
# we avoid resampling.
audio_in_sample_rate=16000,
audio_out_sample_rate=16000,
allow_interruptions=True,
enable_metrics=True,
enable_usage_metrics=True,
@@ -120,7 +123,7 @@ async def main():
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
await task.queue_frame(EndFrame())
await task.cancel()
runner = PipelineRunner()

View File

@@ -104,8 +104,11 @@ async def main():
)
# This processor keeps the last context and will let it through once the
# notifier is woken up.
gated_context_aggregator = GatedOpenAILLMContextAggregator(notifier)
# notifier is woken up. We start with the gate open because we send an
# initial context frame to start the conversation.
gated_context_aggregator = GatedOpenAILLMContextAggregator(
notifier=notifier, start_open=True
)
# Notify if the user hasn't said anything.
async def user_idle_notifier(frame):

View File

@@ -117,18 +117,21 @@ class CompletenessCheck(FrameProcessor):
async def process_frame(self, frame: Frame, direction: FrameDirection):
await super().process_frame(frame, direction)
if isinstance(frame, TextFrame) and frame.text == "YES":
logger.debug("Completeness check YES")
await self.push_frame(UserStoppedSpeakingFrame())
await self._notifier.notify()
elif isinstance(frame, TextFrame) and frame.text == "NO":
logger.debug("Completeness check NO")
else:
await self.push_frame(frame, direction)
class OutputGate(FrameProcessor):
def __init__(self, notifier: BaseNotifier, **kwargs):
def __init__(self, *, notifier: BaseNotifier, start_open: bool = False, **kwargs):
super().__init__(**kwargs)
self._gate_open = False
self._gate_open = start_open
self._frames_buffer = []
self._notifier = notifier
@@ -166,11 +169,10 @@ class OutputGate(FrameProcessor):
async def _start(self):
self._frames_buffer = []
self._gate_task = self.get_event_loop().create_task(self._gate_task_handler())
self._gate_task = self.create_task(self._gate_task_handler())
async def _stop(self):
self._gate_task.cancel()
await self._gate_task
await self.cancel_task(self._gate_task)
async def _gate_task_handler(self):
while True:
@@ -250,7 +252,9 @@ async def main():
# sentence, this will wake up the notifier if that happens.
user_idle = UserIdleProcessor(callback=user_idle_notifier, timeout=5.0)
bot_output_gate = OutputGate(notifier=notifier)
# We start with the gate open because we send an initial context frame
# to start the conversation.
bot_output_gate = OutputGate(notifier=notifier, start_open=True)
async def block_user_stopped_speaking(frame):
return not isinstance(frame, UserStoppedSpeakingFrame)

View File

@@ -101,12 +101,12 @@ HIGH PRIORITY SIGNALS:
Examples:
# Complete Wh-question
[{"role": "assistant", "content": "I can help you learn."},
[{"role": "assistant", "content": "I can help you learn."},
{"role": "user", "content": "What's the fastest way to learn Spanish"}]
Output: YES
# Complete Yes/No question despite STT error
[{"role": "assistant", "content": "I know about planets."},
[{"role": "assistant", "content": "I know about planets."},
{"role": "user", "content": "Is is Jupiter the biggest planet"}]
Output: YES
@@ -118,12 +118,12 @@ Output: YES
Examples:
# Direct instruction
[{"role": "assistant", "content": "I can explain many topics."},
[{"role": "assistant", "content": "I can explain many topics."},
{"role": "user", "content": "Tell me about black holes"}]
Output: YES
# Action demand
[{"role": "assistant", "content": "I can help with math."},
[{"role": "assistant", "content": "I can help with math."},
{"role": "user", "content": "Solve this equation x plus 5 equals 12"}]
Output: YES
@@ -134,12 +134,12 @@ Output: YES
Examples:
# Specific answer
[{"role": "assistant", "content": "What's your favorite color?"},
[{"role": "assistant", "content": "What's your favorite color?"},
{"role": "user", "content": "I really like blue"}]
Output: YES
# Option selection
[{"role": "assistant", "content": "Would you prefer morning or evening?"},
[{"role": "assistant", "content": "Would you prefer morning or evening?"},
{"role": "user", "content": "Morning"}]
Output: YES
@@ -153,17 +153,17 @@ MEDIUM PRIORITY SIGNALS:
Examples:
# Self-correction reaching completion
[{"role": "assistant", "content": "What would you like to know?"},
[{"role": "assistant", "content": "What would you like to know?"},
{"role": "user", "content": "Tell me about... no wait, explain how rainbows form"}]
Output: YES
# Topic change with complete thought
[{"role": "assistant", "content": "The weather is nice today."},
[{"role": "assistant", "content": "The weather is nice today."},
{"role": "user", "content": "Actually can you tell me who invented the telephone"}]
Output: YES
# Mid-sentence completion
[{"role": "assistant", "content": "Hello I'm ready."},
[{"role": "assistant", "content": "Hello I'm ready."},
{"role": "user", "content": "What's the capital of? France"}]
Output: YES
@@ -175,12 +175,12 @@ Output: YES
Examples:
# Acknowledgment
[{"role": "assistant", "content": "Should we talk about history?"},
[{"role": "assistant", "content": "Should we talk about history?"},
{"role": "user", "content": "Sure"}]
Output: YES
# Disagreement with completion
[{"role": "assistant", "content": "Is that what you meant?"},
[{"role": "assistant", "content": "Is that what you meant?"},
{"role": "user", "content": "No not really"}]
Output: YES
@@ -194,12 +194,12 @@ LOW PRIORITY SIGNALS:
Examples:
# Word repetition but complete
[{"role": "assistant", "content": "I can help with that."},
[{"role": "assistant", "content": "I can help with that."},
{"role": "user", "content": "What what is the time right now"}]
Output: YES
# Missing punctuation but complete
[{"role": "assistant", "content": "I can explain that."},
[{"role": "assistant", "content": "I can explain that."},
{"role": "user", "content": "Please tell me how computers work"}]
Output: YES
@@ -211,12 +211,12 @@ Output: YES
Examples:
# Filler words but complete
[{"role": "assistant", "content": "What would you like to know?"},
[{"role": "assistant", "content": "What would you like to know?"},
{"role": "user", "content": "Um uh how do airplanes fly"}]
Output: YES
# Thinking pause but incomplete
[{"role": "assistant", "content": "I can explain anything."},
[{"role": "assistant", "content": "I can explain anything."},
{"role": "user", "content": "Well um I want to know about the"}]
Output: NO
@@ -241,17 +241,17 @@ DECISION RULES:
Examples:
# Incomplete despite corrections
[{"role": "assistant", "content": "What would you like to know about?"},
[{"role": "assistant", "content": "What would you like to know about?"},
{"role": "user", "content": "Can you tell me about"}]
Output: NO
# Complete despite multiple artifacts
[{"role": "assistant", "content": "I can help you learn."},
[{"role": "assistant", "content": "I can help you learn."},
{"role": "user", "content": "How do you I mean what's the best way to learn programming"}]
Output: YES
# Trailing off incomplete
[{"role": "assistant", "content": "I can explain anything."},
[{"role": "assistant", "content": "I can explain anything."},
{"role": "user", "content": "I was wondering if you could tell me why"}]
Output: NO
"""
@@ -328,12 +328,14 @@ class CompletenessCheck(FrameProcessor):
await self._notifier.notify()
elif isinstance(frame, TextFrame) and frame.text == "NO":
logger.debug("!!! Completeness check NO")
else:
await self.push_frame(frame, direction)
class OutputGate(FrameProcessor):
def __init__(self, notifier: BaseNotifier, **kwargs):
def __init__(self, *, notifier: BaseNotifier, start_open: bool = False, **kwargs):
super().__init__(**kwargs)
self._gate_open = False
self._gate_open = start_open
self._frames_buffer = []
self._notifier = notifier
@@ -371,11 +373,10 @@ class OutputGate(FrameProcessor):
async def _start(self):
self._frames_buffer = []
self._gate_task = self.get_event_loop().create_task(self._gate_task_handler())
self._gate_task = self.create_task(self._gate_task_handler())
async def _stop(self):
self._gate_task.cancel()
await self._gate_task
await self.cancel_task(self._gate_task)
async def _gate_task_handler(self):
while True:
@@ -460,7 +461,9 @@ async def main():
# sentence, this will wake up the notifier if that happens.
user_idle = UserIdleProcessor(callback=user_idle_notifier, timeout=5.0)
bot_output_gate = OutputGate(notifier=notifier)
# We start with the gate open because we send an initial context frame
# to start the conversation.
bot_output_gate = OutputGate(notifier=notifier, start_open=True)
async def block_user_stopped_speaking(frame):
return not isinstance(frame, UserStoppedSpeakingFrame)

View File

@@ -44,9 +44,7 @@ from pipecat.processors.aggregators.openai_llm_context import (
)
from pipecat.processors.filters.function_filter import FunctionFilter
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.processors.user_idle_processor import UserIdleProcessor
from pipecat.services.cartesia import CartesiaTTSService
from pipecat.services.deepgram import DeepgramSTTService
from pipecat.services.google import GoogleLLMContext, GoogleLLMService
from pipecat.sync.base_notifier import BaseNotifier
from pipecat.sync.event_notifier import EventNotifier
@@ -440,11 +438,11 @@ class CompletenessCheck(FrameProcessor):
if isinstance(frame, UserStartedSpeakingFrame):
if self._idle_task:
self._idle_task.cancel()
await self.cancel_task(self._idle_task)
elif isinstance(frame, TextFrame) and frame.text.startswith("YES"):
logger.debug("Completeness check YES")
if self._idle_task:
self._idle_task.cancel()
await self.cancel_task(self._idle_task)
await self.push_frame(UserStoppedSpeakingFrame())
await self._audio_accumulator.reset()
await self._notifier.notify()
@@ -457,7 +455,9 @@ class CompletenessCheck(FrameProcessor):
else:
# logger.debug("!!! CompletenessCheck idle wait START")
self._wakeup_time = time.time() + self.wait_time
self._idle_task = self.get_event_loop().create_task(self._idle_task_handler())
self._idle_task = self.create_task(self._idle_task_handler())
else:
await self.push_frame(frame, direction)
async def _idle_task_handler(self):
try:
@@ -599,11 +599,10 @@ class OutputGate(FrameProcessor):
async def _start(self):
self._frames_buffer = []
self._gate_task = self.get_event_loop().create_task(self._gate_task_handler())
self._gate_task = self.create_task(self._gate_task_handler())
async def _stop(self):
self._gate_task.cancel()
await self._gate_task
await self.cancel_task(self._gate_task)
async def _gate_task_handler(self):
while True:
@@ -640,7 +639,6 @@ async def main():
vad_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
vad_audio_passthrough=True,
audio_in_sample_rate=16000,
),
)

View File

@@ -212,7 +212,7 @@ class InputTranscriptionFrameEmitter(FrameProcessor):
elif isinstance(frame, LLMFullResponseEndFrame):
await self.push_frame(LLMDemoTranscriptionFrame(text=self._aggregation.strip()))
self._aggregation = ""
elif isinstance(frame, MetricsFrame):
else:
await self.push_frame(frame, direction)

View File

@@ -15,6 +15,7 @@ from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.audio.vad.vad_analyzer import VADParams
from pipecat.frames.frames import LLMMessagesAppendFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -36,8 +37,6 @@ async def main():
token,
"Respond bot",
DailyParams(
audio_in_sample_rate=16000,
audio_out_sample_rate=24000,
audio_out_enabled=True,
vad_enabled=True,
vad_audio_passthrough=True,
@@ -71,6 +70,21 @@ async def main():
),
)
@transport.event_handler("on_first_participant_joined")
async def on_first_participant_joined(transport, participant):
await task.queue_frames(
[
LLMMessagesAppendFrame(
messages=[
{
"role": "assistant",
"content": "Greet the user.",
}
]
)
]
)
runner = PipelineRunner()
await runner.run(task)

View File

@@ -37,8 +37,6 @@ async def main():
token,
"Respond bot",
DailyParams(
audio_in_sample_rate=16000,
audio_out_sample_rate=24000,
audio_out_enabled=True,
vad_enabled=True,
vad_audio_passthrough=True,

View File

@@ -84,8 +84,6 @@ async def main():
token,
"Respond bot",
DailyParams(
audio_in_sample_rate=16000,
audio_out_sample_rate=24000,
audio_out_enabled=True,
vad_enabled=True,
vad_audio_passthrough=True,

View File

@@ -37,8 +37,6 @@ async def main():
token,
"Respond bot",
DailyParams(
audio_in_sample_rate=16000,
audio_out_sample_rate=24000,
audio_out_enabled=True,
vad_enabled=True,
vad_audio_passthrough=True,
@@ -47,8 +45,6 @@ async def main():
# matter because we can only use the Multimodal Live API's phrase
# endpointing, for now.
vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.5)),
start_audio_paused=True,
start_video_paused=True,
),
)

View File

@@ -15,7 +15,6 @@ from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.audio.vad.vad_analyzer import VADParams
from pipecat.frames.frames import EndFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -53,8 +52,6 @@ async def main():
token,
"Respond bot",
DailyParams(
audio_in_sample_rate=16000,
audio_out_sample_rate=24000,
audio_out_enabled=True,
vad_enabled=True,
vad_audio_passthrough=True,
@@ -124,7 +121,7 @@ async def main():
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
print(f"Participant left: {participant}")
await task.queue_frame(EndFrame())
await task.cancel()
runner = PipelineRunner()

View File

@@ -15,11 +15,7 @@ from loguru import logger
from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import (
CancelFrame,
TranscriptionMessage,
TranscriptionUpdateFrame,
)
from pipecat.frames.frames import TranscriptionMessage, TranscriptionUpdateFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -170,7 +166,7 @@ async def main():
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
# Stop the pipeline immediately when the participant leaves
await task.queue_frame(CancelFrame())
await task.cancel()
runner = PipelineRunner()

View File

@@ -15,11 +15,7 @@ from loguru import logger
from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import (
CancelFrame,
TranscriptionMessage,
TranscriptionUpdateFrame,
)
from pipecat.frames.frames import TranscriptionMessage, TranscriptionUpdateFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -170,7 +166,7 @@ async def main():
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
# Stop the pipeline immediately when the participant leaves
await task.queue_frame(CancelFrame())
await task.cancel()
runner = PipelineRunner()

View File

@@ -15,11 +15,7 @@ from loguru import logger
from runner import configure
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import (
CancelFrame,
TranscriptionMessage,
TranscriptionUpdateFrame,
)
from pipecat.frames.frames import TranscriptionMessage, TranscriptionUpdateFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
@@ -180,7 +176,7 @@ async def main():
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
# Stop the pipeline immediately when the participant leaves
await task.queue_frame(CancelFrame())
await task.cancel()
runner = PipelineRunner()

View File

@@ -38,8 +38,6 @@ load_dotenv(override=True)
logger.remove(0)
logger.add(sys.stderr, level="DEBUG")
DESIRED_SAMPLE_RATE = 16000
def generate_token(room_name: str, participant_name: str, api_key: str, api_secret: str) -> str:
token = api.AccessToken(api_key, api_secret)
@@ -114,11 +112,8 @@ async def main():
token=token,
room_name=room_name,
params=LiveKitParams(
audio_in_channels=1,
audio_in_enabled=True,
audio_out_enabled=True,
audio_in_sample_rate=DESIRED_SAMPLE_RATE,
audio_out_sample_rate=DESIRED_SAMPLE_RATE,
vad_analyzer=SileroVADAnalyzer(),
vad_enabled=True,
vad_audio_passthrough=True,
@@ -128,7 +123,6 @@ async def main():
stt = DeepgramSTTService(
api_key=os.getenv("DEEPGRAM_API_KEY"),
live_options=LiveOptions(
sample_rate=DESIRED_SAMPLE_RATE,
vad_events=True,
),
)
@@ -138,7 +132,6 @@ async def main():
tts = CartesiaTTSService(
api_key=os.getenv("CARTESIA_API_KEY"),
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
sample_rate=DESIRED_SAMPLE_RATE,
)
messages = [

View File

@@ -17,7 +17,6 @@ from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import (
BotStartedSpeakingFrame,
BotStoppedSpeakingFrame,
EndFrame,
Frame,
LLMFullResponseEndFrame,
LLMFullResponseStartFrame,
@@ -170,7 +169,7 @@ async def main():
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
await task.queue_frame(EndFrame())
await task.cancel()
runner = PipelineRunner()

View File

@@ -0,0 +1,130 @@
#
# Copyright (c) 2024, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import asyncio
import os
import sys
from pathlib import Path
import aiohttp
from dotenv import load_dotenv
from loguru import logger
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import Frame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.services.cartesia import CartesiaTTSService
from pipecat.services.deepgram import DeepgramSTTService
from pipecat.services.google import GoogleLLMService, LLMSearchResponseFrame
from pipecat.transports.services.daily import DailyParams, DailyTransport
sys.path.append(str(Path(__file__).parent.parent))
from runner import configure
load_dotenv(override=True)
logger.remove(0)
logger.add(sys.stderr, level="DEBUG")
# Function handlers for the LLM
search_tool = {"google_search_retrieval": {}}
tools = [search_tool]
system_instruction = """
You are an expert at providing the most recent news from any place. Your responses will be converted to audio, so avoid using special characters or overly complex formatting.
Always use the google search API to retrieve the latest news. You must also use it to check which day is today.
You can:
- Use the Google search API to check the current date.
- Provide the most recent and relevant news from any place by using the google search API.
- Answer any questions the user may have, ensuring your responses are accurate and concise.
Start each interaction by asking the user about which place they would like to know the information.
"""
class LLMSearchLoggerProcessor(FrameProcessor):
async def process_frame(self, frame: Frame, direction: FrameDirection):
await super().process_frame(frame, direction)
if isinstance(frame, LLMSearchResponseFrame):
print(f"LLMSearchLoggerProcessor: {frame}")
await self.push_frame(frame)
async def main():
async with aiohttp.ClientSession() as session:
(room_url, token) = await configure(session)
transport = DailyTransport(
room_url,
token,
"Latest news!",
DailyParams(
audio_out_enabled=True,
vad_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
vad_audio_passthrough=True,
),
)
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
tts = CartesiaTTSService(
api_key=os.getenv("CARTESIA_API_KEY"),
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
)
# Initialize the Gemini Multimodal Live model
llm = GoogleLLMService(
api_key=os.getenv("GOOGLE_API_KEY"),
system_instruction=system_instruction,
tools=tools,
)
context = OpenAILLMContext(
[
{
"role": "user",
"content": "Start by greeting the user warmly, introducing yourself, and mentioning the current day. Be friendly and engaging to set a positive tone for the interaction.",
}
],
)
context_aggregator = llm.create_context_aggregator(context)
llm_search_logger = LLMSearchLoggerProcessor()
pipeline = Pipeline(
[
transport.input(),
stt,
context_aggregator.user(),
llm,
llm_search_logger,
tts,
transport.output(),
context_aggregator.assistant(),
]
)
task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
@transport.event_handler("on_first_participant_joined")
async def on_first_participant_joined(transport, participant):
await task.queue_frames([context_aggregator.user().get_context_frame()])
runner = PipelineRunner()
await runner.run(task)
if __name__ == "__main__":
asyncio.run(main())

View File

@@ -106,8 +106,8 @@ class UserImageRequester(FrameProcessor):
UserImageRequestFrame(self.participant_id), FrameDirection.UPSTREAM
)
await self.push_frame(TextFrame("Describe the image in a short sentence."))
elif isinstance(frame, UserImageRawFrame):
await self.push_frame(frame)
else:
await self.push_frame(frame, direction)
class TextFilterProcessor(FrameProcessor):

51
examples/news-chatbot/.gitignore vendored Normal file
View File

@@ -0,0 +1,51 @@
# Python
__pycache__/
*.py[cod]
*$py.class
*.so
.Python
build/
dist/
*.egg-info/
.installed.cfg
*.egg
.pytest_cache/
.coverage
.coverage.*
.env
.venv
env/
venv/
ENV/
.mypy_cache/
.dmypy.json
dmypy.json
# JavaScript/Node.js
node_modules/
dist/
dist-ssr/
*.local
.env.local
.env.development.local
.env.test.local
.env.production.local
# Logs
logs/
*.log
npm-debug.log*
yarn-debug.log*
yarn-error.log*
pnpm-debug.log*
# Editor/IDE
.vscode/*
!.vscode/extensions.json
.idea/
*.swp
*.swo
.DS_Store
# Project specific
runpod.toml

View File

@@ -0,0 +1,48 @@
# News Chatbot
A simple AI-powered chatbot that leverages Gemini's real-time search capabilities in a voice AI application.
This example demonstrates Gemini's ability to query Google search in real time and return relevant responses, including links to the URLs that Gemini searched.
All the details about grounding with Google Search can be found [here](https://ai.google.dev/gemini-api/docs/grounding?lang=python).
## Quick Start
### First, start the bot server:
1. Navigate to the server directory:
```bash
cd server
```
2. Create and activate a virtual environment:
```bash
python3 -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
3. Install requirements:
```bash
pip install -r requirements.txt
```
4. Copy env.example to .env and configure:
- Add your API keys
5. Start the server:
```bash
python server.py
```
### Next, connect using the client app:
For client-side setup, refer to the [JavaScript Guide](client/javascript/README.md).
## Important Note
Ensure the bot server is running before using any client implementations.
## Requirements
- Python 3.10+
- Node.js 16+ (for JavaScript and React implementations)
- Daily API key
- Gemini API key (for Gemini bot)
- Cartesia API key
- Modern web browser with WebRTC support

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@@ -0,0 +1,27 @@
# JavaScript Implementation
Basic implementation using the [Pipecat JavaScript SDK](https://docs.pipecat.ai/client/js/introduction).
## Setup
1. Run the bot server. See the [server README](../../README).
2. Navigate to the `client/javascript` directory:
```bash
cd client/javascript
```
3. Install dependencies:
```bash
npm install
```
4. Run the client app:
```
npm run dev
```
5. Visit http://localhost:5173 in your browser.

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@@ -0,0 +1,40 @@
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>AI Chatbot</title>
</head>
<body>
<div class="container">
<div class="status-bar">
<div class="status">
Status: <span id="connection-status">Disconnected</span>
</div>
<div class="controls">
<button id="connect-btn">Connect</button>
<button id="disconnect-btn" disabled>Disconnect</button>
</div>
</div>
<div class="main-content">
<div class="bot-container">
<div id="search-result-container">
</div>
<audio id="bot-audio" autoplay></audio>
</div>
</div>
<div class="debug-panel">
<h3>Debug Info</h3>
<div id="debug-log"></div>
</div>
</div>
<script type="module" src="/src/app.js"></script>
<link rel="stylesheet" href="/src/style.css">
</body>
</html>

File diff suppressed because it is too large Load Diff

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@@ -0,0 +1,21 @@
{
"name": "client",
"version": "1.0.0",
"main": "index.js",
"scripts": {
"dev": "vite",
"build": "vite build",
"preview": "vite preview"
},
"keywords": [],
"author": "",
"license": "ISC",
"description": "",
"devDependencies": {
"vite": "^6.0.2"
},
"dependencies": {
"@pipecat-ai/client-js": "^0.3.2",
"@pipecat-ai/daily-transport": "^0.3.4"
}
}

View File

@@ -0,0 +1,341 @@
/**
* Copyright (c) 20242025, Daily
*
* SPDX-License-Identifier: BSD 2-Clause License
*/
/**
* RTVI Client Implementation
*
* This client connects to an RTVI-compatible bot server using WebRTC (via Daily).
* It handles audio/video streaming and manages the connection lifecycle.
*
* Requirements:
* - A running RTVI bot server (defaults to http://localhost:7860)
* - The server must implement the /connect endpoint that returns Daily.co room credentials
* - Browser with WebRTC support
*/
import {LogLevel, RTVIClient, RTVIClientHelper, RTVIEvent} from '@pipecat-ai/client-js';
import { DailyTransport } from '@pipecat-ai/daily-transport';
class SearchResponseHelper extends RTVIClientHelper {
constructor(contentPanel) {
super()
this.contentPanel = contentPanel
}
handleMessage(rtviMessage) {
console.log("SearchResponseHelper, received message:", rtviMessage)
if (rtviMessage.data) {
// Clear existing content
this.contentPanel.innerHTML = "";
// Create a container for all content
const contentContainer = document.createElement('div');
contentContainer.className = "content-container";
// Add the search_result
if (rtviMessage.data.search_result) {
const searchResultDiv = document.createElement('div');
searchResultDiv.className = "search-result";
searchResultDiv.textContent = rtviMessage.data.search_result;
contentContainer.appendChild(searchResultDiv);
}
// Add the sources
if (rtviMessage.data.origins) {
const sourcesDiv = document.createElement('div');
sourcesDiv.className = "sources";
const sourcesTitle = document.createElement('h3');
sourcesTitle.className = "sources-title";
sourcesTitle.textContent = "Sources:";
sourcesDiv.appendChild(sourcesTitle);
rtviMessage.data.origins.forEach(origin => {
const sourceLink = document.createElement('a');
sourceLink.className = "source-link";
sourceLink.href = origin.site_uri;
sourceLink.target = "_blank";
sourceLink.textContent = origin.site_title;
sourcesDiv.appendChild(sourceLink);
});
contentContainer.appendChild(sourcesDiv);
}
// Add the rendered_content in an iframe
if (rtviMessage.data.rendered_content) {
const iframe = document.createElement('iframe');
iframe.className = "iframe-container";
iframe.srcdoc = rtviMessage.data.rendered_content;
contentContainer.appendChild(iframe);
}
// Append the content container to the content panel
this.contentPanel.appendChild(contentContainer);
}
}
getMessageTypes() {
return ["bot-llm-search-response"]
}
}
/**
* ChatbotClient handles the connection and media management for a real-time
* voice and video interaction with an AI bot.
*/
class ChatbotClient {
constructor() {
// Initialize client state
this.rtviClient = null;
this.setupDOMElements();
this.setupEventListeners();
}
/**
* Set up references to DOM elements and create necessary media elements
*/
setupDOMElements() {
// Get references to UI control elements
this.connectBtn = document.getElementById('connect-btn');
this.disconnectBtn = document.getElementById('disconnect-btn');
this.statusSpan = document.getElementById('connection-status');
this.debugLog = document.getElementById('debug-log');
this.searchResultContainer = document.getElementById('search-result-container');
// Create an audio element for bot's voice output
this.botAudio = document.createElement('audio');
this.botAudio.autoplay = true;
this.botAudio.playsInline = true;
document.body.appendChild(this.botAudio);
}
/**
* Set up event listeners for connect/disconnect buttons
*/
setupEventListeners() {
this.connectBtn.addEventListener('click', () => this.connect());
this.disconnectBtn.addEventListener('click', () => this.disconnect());
}
/**
* Add a timestamped message to the debug log
*/
log(message) {
const entry = document.createElement('div');
entry.textContent = `${new Date().toISOString()} - ${message}`;
// Add styling based on message type
if (message.startsWith('User: ')) {
entry.style.color = '#2196F3'; // blue for user
} else if (message.startsWith('Bot: ')) {
entry.style.color = '#4CAF50'; // green for bot
}
this.debugLog.appendChild(entry);
this.debugLog.scrollTop = this.debugLog.scrollHeight;
console.log(message);
}
/**
* Update the connection status display
*/
updateStatus(status) {
this.statusSpan.textContent = status;
this.log(`Status: ${status}`);
}
/**
* Check for available media tracks and set them up if present
* This is called when the bot is ready or when the transport state changes to ready
*/
setupMediaTracks() {
if (!this.rtviClient) return;
// Get current tracks from the client
const tracks = this.rtviClient.tracks();
// Set up any available bot tracks
if (tracks.bot?.audio) {
this.setupAudioTrack(tracks.bot.audio);
}
}
/**
* Set up listeners for track events (start/stop)
* This handles new tracks being added during the session
*/
setupTrackListeners() {
if (!this.rtviClient) return;
// Listen for new tracks starting
this.rtviClient.on(RTVIEvent.TrackStarted, (track, participant) => {
// Only handle non-local (bot) tracks
if (!participant?.local && track.kind === 'audio') {
this.setupAudioTrack(track);
}
});
// Listen for tracks stopping
this.rtviClient.on(RTVIEvent.TrackStopped, (track, participant) => {
this.log(
`Track stopped event: ${track.kind} from ${
participant?.name || 'unknown'
}`
);
});
}
/**
* Set up an audio track for playback
* Handles both initial setup and track updates
*/
setupAudioTrack(track) {
this.log('Setting up audio track');
// Check if we're already playing this track
if (this.botAudio.srcObject) {
const oldTrack = this.botAudio.srcObject.getAudioTracks()[0];
if (oldTrack?.id === track.id) return;
}
// Create a new MediaStream with the track and set it as the audio source
this.botAudio.srcObject = new MediaStream([track]);
}
/**
* Initialize and connect to the bot
* This sets up the RTVI client, initializes devices, and establishes the connection
*/
async connect() {
try {
// Create a new Daily transport for WebRTC communication
const transport = new DailyTransport();
// Initialize the RTVI client with our configuration
this.rtviClient = new RTVIClient({
transport,
params: {
// The baseURL and endpoint of your bot server that the client will connect to
baseUrl: 'http://localhost:7860',
endpoints: {
connect: '/connect',
},
},
enableMic: true, // Enable microphone for user input
enableCam: false,
callbacks: {
// Handle connection state changes
onConnected: () => {
this.updateStatus('Connected');
this.connectBtn.disabled = true;
this.disconnectBtn.disabled = false;
this.log('Client connected');
},
onDisconnected: () => {
this.updateStatus('Disconnected');
this.connectBtn.disabled = false;
this.disconnectBtn.disabled = true;
this.log('Client disconnected');
},
// Handle transport state changes
onTransportStateChanged: (state) => {
this.updateStatus(`Transport: ${state}`);
this.log(`Transport state changed: ${state}`);
if (state === 'ready') {
this.setupMediaTracks();
}
},
// Handle bot connection events
onBotConnected: (participant) => {
this.log(`Bot connected: ${JSON.stringify(participant)}`);
},
onBotDisconnected: (participant) => {
this.log(`Bot disconnected: ${JSON.stringify(participant)}`);
},
onBotReady: (data) => {
this.log(`Bot ready: ${JSON.stringify(data)}`);
this.setupMediaTracks();
},
// Transcript events
onUserTranscript: (data) => {
// Only log final transcripts
if (data.final) {
this.log(`User: ${data.text}`);
}
},
onBotTranscript: (data) => {
this.log(`Bot: ${data.text}`);
},
// Error handling
onMessageError: (error) => {
console.log('Message error:', error);
},
onError: (error) => {
console.log('Error:', error);
},
},
});
//this.rtviClient.setLogLevel(LogLevel.DEBUG)
this.rtviClient.registerHelper("llm", new SearchResponseHelper(this.searchResultContainer))
// Set up listeners for media track events
this.setupTrackListeners();
// Initialize audio devices
this.log('Initializing devices...');
await this.rtviClient.initDevices();
// Connect to the bot
this.log('Connecting to bot...');
await this.rtviClient.connect();
this.log('Connection complete');
} catch (error) {
// Handle any errors during connection
this.log(`Error connecting: ${error.message}`);
this.log(`Error stack: ${error.stack}`);
this.updateStatus('Error');
// Clean up if there's an error
if (this.rtviClient) {
try {
await this.rtviClient.disconnect();
} catch (disconnectError) {
this.log(`Error during disconnect: ${disconnectError.message}`);
}
}
}
}
/**
* Disconnect from the bot and clean up media resources
*/
async disconnect() {
if (this.rtviClient) {
try {
// Disconnect the RTVI client
await this.rtviClient.disconnect();
this.rtviClient = null;
// Clean up audio
if (this.botAudio.srcObject) {
this.botAudio.srcObject.getTracks().forEach((track) => track.stop());
this.botAudio.srcObject = null;
}
// Clean up video
this.searchResultContainer.innerHTML = '';
} catch (error) {
this.log(`Error disconnecting: ${error.message}`);
}
}
}
}
// Initialize the client when the page loads
window.addEventListener('DOMContentLoaded', () => {
new ChatbotClient();
});

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@@ -0,0 +1,134 @@
body {
margin: 0;
padding: 20px;
font-family: Arial, sans-serif;
background-color: #f0f0f0;
}
.container {
max-width: 1200px;
margin: 0 auto;
}
.status-bar {
display: flex;
justify-content: space-between;
align-items: center;
padding: 10px;
background-color: #fff;
border-radius: 8px;
margin-bottom: 20px;
}
.controls button {
padding: 8px 16px;
margin-left: 10px;
border: none;
border-radius: 4px;
cursor: pointer;
}
#connect-btn {
background-color: #4caf50;
color: white;
}
#disconnect-btn {
background-color: #f44336;
color: white;
}
button:disabled {
opacity: 0.5;
cursor: not-allowed;
}
.main-content {
background-color: #fff;
border-radius: 8px;
padding: 20px;
margin-bottom: 20px;
}
.bot-container {
display: flex;
flex-direction: column;
align-items: center;
}
#search-result-container {
background-color: #e0e0e0;
padding: 20px;
width: calc(100% - 40px);
height: 450px;
overflow: auto;
}
/* Container for all content */
.content-container {
display: flex;
flex-direction: column;
gap: 20px; /* Space between elements */
font-family: Arial, sans-serif;
}
/* Styles for the search result */
.search-result {
font-size: 16px;
line-height: 1.5;
color: #333;
}
/* Styles for the sources container */
.sources {
display: flex;
flex-direction: column;
gap: 8px; /* Space between source links */
}
.sources-title {
font-size: 16px;
font-weight: bold;
color: #444;
}
/* Styles for source links */
.source-link {
text-decoration: none;
color: #1a73e8;
}
.source-link:hover {
text-decoration: underline;
}
/* Styles for the iframe container */
.iframe-container {
flex: none;
width: 100%;
height: 400px; /* Adjust height as needed */
border: none;
}
.debug-panel {
background-color: #fff;
border-radius: 8px;
padding: 20px;
}
.debug-panel h3 {
margin: 0 0 10px 0;
font-size: 16px;
font-weight: bold;
}
#debug-log {
height: 200px;
overflow-y: auto;
background-color: #f8f8f8;
padding: 10px;
border-radius: 4px;
font-family: monospace;
font-size: 12px;
line-height: 1.4;
}

View File

@@ -0,0 +1,13 @@
import { defineConfig } from 'vite';
export default defineConfig({
server: {
proxy: {
// Proxy /api requests to the backend server
'/connect': {
target: 'http://0.0.0.0:7860', // Replace with your backend URL
changeOrigin: true,
},
},
},
});

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@@ -0,0 +1,52 @@
# News Chatbot Server
A FastAPI server that manages bot instances and provide endpoint for Pipecat client connections.
## Endpoints
- `POST /connect` - Pipecat client connection endpoint
## Environment Variables
Copy `env.example` to `.env` and configure:
```ini
# Required API Keys
DAILY_API_KEY= # Your Daily API key
DEEPGRAM_API_KEY= # Your Deepgram API key
GOOGLE_API_KEY= # Your Google/Gemini API key
CARTESIA_API_KEY= # Your Cartesia API key
# Optional Configuration
DAILY_API_URL= # Optional: Daily API URL (defaults to https://api.daily.co/v1)
DAILY_SAMPLE_ROOM_URL= # Optional: Fixed room URL for development
HOST= # Optional: Host address (defaults to 0.0.0.0)
FAST_API_PORT= # Optional: Port number (defaults to 7860)
```
## Running the Server
Set up and activate your virtual environment:
```bash
python3 -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
Install dependencies:
```bash
pip install -r requirements.txt
```
If you want to use the local version of `pipecat` in this repo rather than the last published version, also run:
```bash
pip install --editable "../../../[daily,deepgram,google,cartesia,openai,silero]"
```
Run the server:
```bash
python server.py
```

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@@ -0,0 +1,5 @@
DAILY_SAMPLE_ROOM_URL=https://yourdomain.daily.co/yourroom # (for joining the bot to the same room repeatedly for local dev)
DAILY_API_KEY=
CARTESIA_API_KEY=
DEEPGRAM_API_KEY=
GOOGLE_API_KEY=

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@@ -0,0 +1,166 @@
#
# Copyright (c) 2024-2025, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#
import asyncio
import os
import sys
from pathlib import Path
import aiohttp
from dotenv import load_dotenv
from loguru import logger
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import Frame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from pipecat.processors.frameworks.rtvi import RTVIConfig, RTVIProcessor
from pipecat.services.cartesia import CartesiaTTSService
from pipecat.services.deepgram import DeepgramSTTService
from pipecat.services.google import GoogleLLMService, LLMSearchResponseFrame
from pipecat.transports.services.daily import DailyParams, DailyTransport
from pipecat.utils.text.markdown_text_filter import MarkdownTextFilter
sys.path.append(str(Path(__file__).parent.parent))
from runner import configure
load_dotenv(override=True)
logger.remove(0)
logger.add(sys.stderr, level="DEBUG")
# Function handlers for the LLM
# https://ai.google.dev/gemini-api/docs/grounding?lang=python#dynamic-retrieval
# Some queries are likely to benefit more from Grounding with Google Search than others.
# The dynamic retrieval feature gives you additional control over when to use Grounding with Google Search.
# If the dynamic retrieval mode is unspecified, Grounding with Google Search is always triggered.
# If the mode is set to dynamic, the model decides when to use grounding based on a threshold that you can configure.
# The threshold is a floating-point value in the range [0,1] and defaults to 0.3.
# If the threshold value is 0, the response is always grounded with Google Search; if it's 1, it never is.
search_tool = {
"google_search_retrieval": {
"dynamic_retrieval_config": {
"mode": "MODE_DYNAMIC",
"dynamic_threshold": 0,
} # always grounding
}
}
tools = [search_tool]
system_instruction = """
You are an expert at providing the most recent news from any place. Your responses will be converted to audio, so ensure they are formatted in plain text without special characters (e.g., *, _, -) or overly complex formatting.
Guidelines:
- Use the Google search API to retrieve the current date and provide the latest news.
- Always deliver accurate and concise responses.
- Ensure all responses are clear, using plain text only. Avoid any special characters or symbols.
Start every interaction by asking how you can assist the user.
"""
class LLMSearchLoggerProcessor(FrameProcessor):
async def process_frame(self, frame: Frame, direction: FrameDirection):
await super().process_frame(frame, direction)
if isinstance(frame, LLMSearchResponseFrame):
print(f"LLMSearchLoggerProcessor: {frame}")
await self.push_frame(frame)
async def main():
async with aiohttp.ClientSession() as session:
(room_url, token) = await configure(session)
transport = DailyTransport(
room_url,
token,
"Latest news!",
DailyParams(
audio_out_enabled=True,
vad_enabled=True,
vad_analyzer=SileroVADAnalyzer(),
vad_audio_passthrough=True,
),
)
stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
tts = CartesiaTTSService(
api_key=os.getenv("CARTESIA_API_KEY"),
voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22", # British Lady
text_filter=MarkdownTextFilter(),
)
llm = GoogleLLMService(
api_key=os.getenv("GOOGLE_API_KEY"),
system_instruction=system_instruction,
tools=tools,
)
context = OpenAILLMContext(
[
{
"role": "user",
"content": "Start by greeting the user warmly, introducing yourself, and mentioning the current day. Be friendly and engaging to set a positive tone for the interaction.",
}
],
)
context_aggregator = llm.create_context_aggregator(context)
llm_search_logger = LLMSearchLoggerProcessor()
#
# RTVI events for Pipecat client UI
#
rtvi = RTVIProcessor(config=RTVIConfig(config=[]))
pipeline = Pipeline(
[
transport.input(),
stt,
rtvi,
context_aggregator.user(),
llm,
llm_search_logger,
tts,
transport.output(),
context_aggregator.assistant(),
]
)
task = PipelineTask(
pipeline,
PipelineParams(
allow_interruptions=True,
observers=[rtvi.observer()],
),
)
@rtvi.event_handler("on_client_ready")
async def on_client_ready(rtvi):
await rtvi.set_bot_ready()
@transport.event_handler("on_first_participant_joined")
async def on_first_participant_joined(transport, participant):
await task.queue_frames([context_aggregator.user().get_context_frame()])
@transport.event_handler("on_participant_left")
async def on_participant_left(transport, participant, reason):
print(f"Participant left: {participant}")
await task.cancel()
runner = PipelineRunner()
await runner.run(task)
if __name__ == "__main__":
asyncio.run(main())

View File

@@ -0,0 +1,4 @@
python-dotenv
fastapi[all]
uvicorn
pipecat-ai[daily,google,deepgram,cartesia,silero,openai]

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