achatbot

achatbot factory, create chat bots with llm(tools), asr, tts, vad, ocr, detect object etc..

Project Structure

Feature

demo

# need GOOGLE_API_KEY in environment variables
# default use language English

# websit
python -m demo.content_parser_tts instruct-content-tts \
    "https://en.wikipedia.org/wiki/Large_language_model"

python -m demo.content_parser_tts instruct-content-tts \
    --role-tts-voices zh-CN-YunjianNeural \
    --role-tts-voices zh-CN-XiaoxiaoNeural \
    --language zh \
    "https://en.wikipedia.org/wiki/Large_language_model"

# pdf
# https://www.apple.com/ios/ios-18/pdf/iOS_18_All_New_Features_Sept_2024.pdf
python -m demo.content_parser_tts instruct-content-tts \
    "/Users/wuyong/Desktop/iOS_18_All_New_Features_Sept_2024.pdf"

python -m demo.content_parser_tts instruct-content-tts \
    --role-tts-voices zh-CN-YunjianNeural \
    --role-tts-voices zh-CN-XiaoxiaoNeural \
    --language zh \
    "/Users/wuyong/Desktop/iOS_18_All_New_Features_Sept_2024.pdf"

cmd chat bots:
- local-terminal-chat(be/fe)
- remote-queue-chat(be/fe)
- grpc-terminal-chat(be/fe)
- grpc-speaker
- http fastapi_daily_bot_serve (with chat bots pipeline)
- bots with config see notebooks:
  - Run chat bots with colab notebook 🏃
support transport connector:
- pipe(UNIX socket),
- grpc,
- queue (redis),
- websocket
- TCP/IP socket
chat bot processors:
- aggreators(llm use, assistant message),
- ai_frameworks
  - langchain: RAG
  - llamaindex: RAG
  - autoagen: multi Agents
- realtime voice inference(RTVI),
- transport:
  - webRTC/webSocket:
    - daily: audio, video(image)
    - livekit: audio, video(image)
    - agora-audio
- ai processor: llm, tts, asr etc..
core module:
- local llm:
  - llama-cpp (support text,vision with function-call model)
  - transformers(manual, pipeline) (support text,vision:🦙,Qwen2-vl,Molmo with function-call model)
  - mlx_lm
- api llm: personal-ai(like openai api, other ai provider)
AI modules:
- functions:
  - search: search,search1,serper
  - weather: openweathermap
- speech:
  - asr: sense_voice_asr, whisper_asr, whisper_timestamped_asr, whisper_faster_asr, whisper_transformers_asr, whisper_mlx_asr, lightning_whisper_mlx_asr(!TODO), whisper_groq_asr
  - audio_stream: daily_room_audio_stream(in/out), pyaudio_stream(in/out)
  - detector: porcupine_wakeword,pyannote_vad,webrtc_vad,silero_vad,webrtc_silero_vad
  - player: stream_player
  - recorder: rms_recorder, wakeword_rms_recorder, vad_recorder, wakeword_vad_recorder
  - tts: tts_chat,tts_coqui,tts_cosy_voice,tts_edge,tts_g
  - vad_analyzer: daily_webrtc_vad_analyzer,silero_vad_analyzer
- vision
  - OCR(Optical Character Recognition):
    - PaddleOCR
    - GOT(the General OCR Theory)
  - Detector:
    - YOLO (You Only Look Once)
    - RT-DETR (RealTime End-to-End Object Detection with Transformers)
gen modules config(*.yaml, local/test/prod) from env with file: .env u also use HfArgumentParser this module's args to local cmd parse args
deploy to cloud ☁️ serverless:
- vercel (frontend ui pages)
- Cloudflare(frontend ui pages), personal ai workers
- fastapi-daily-chat-bot on cerebrium (provider aws)
- fastapi-daily-chat-bot on leptonai
- aws lambda + api Gateway
- docker -> k8s/k3s
- etc...

Service Deployment Architecture

UI (easy to deploy with github like pages)

vite-react-rtvi-web-voice rtvi web voice chat bots, diff cctv roles etc, u can diy your own role by change the system prompt with DailyRTVIGeneralBot deploy it to cloudflare pages, access https://role-chat.pages.dev/
chat-bot-rtvi-web-sandbox use this web sandbox to test config, actions with DailyRTVIGeneralBot
ui/educator-client deploy it to cloudflare pages, access https://educator-client.pages.dev/
ui/web-client-ui deploy it to cloudflare pages, access https://chat-client-weedge.pages.dev/
vite-react-web-vision deploy it to cloudflare pages, access https://vision-weedge.pages.dev/

Server Deploy (CD)

deploy/leptonai 👍🏻
deploy/cerebrium/fastapi-daily-chat-bot :)
deploy/aws/fastapi-daily-chat-bot :|
deploy/docker/fastapi-daily-chat-bot 🏃

Install

Note

python --version >=3.10 with asyncio-task

Tip

use uv + pip to run, install the required dependencies fastly, e.g.: uv pip install achatbot uv pip install "achatbot[fastapi_bot_server]"

pypi

python3 -m venv .venv_achatbot
source .venv_achatbot/bin/activate
pip install achatbot
# optional-dependencies e.g.
pip install "achatbot[fastapi_bot_server]"

local

git clone --recursive https://github.com/ai-bot-pro/chat-bot.git
cd chat-bot
python3 -m venv .venv_achatbot
source .venv_achatbot/bin/activate
bash scripts/pypi_achatbot.sh dev
# optional-dependencies e.g.
pip install "dist/achatbot-{$version}-py3-none-any.whl[fastapi_bot_server]"

Run chat bots

Run chat bots with colab notebook

Chat Bot	optional-dependencies	Device	Pipeline Desc
daily_bot livekit_bot	e.g.: daily_room_audio_stream \| livekit_room_audio_stream, sense_voice_asr, groq \| together api llm(text), tts_edge	CPU (free, 2 cores)	e.g.: daily \| livekit room in stream -> silero (vad) -> sense_voice (asr) -> groq \| together (llm) -> edge (tts) -> daily \| livekit room out stream
generate_audio2audio	remote_queue_chat_bot_be_worker	T4(free)	e.g.: pyaudio in stream -> silero (vad) -> sense_voice (asr) -> qwen (llm) -> cosy_voice (tts) -> pyaudio out stream
daily_describe_vision_tools_bot livekit_describe_vision_tools_bot	e.g.: daily_room_audio_stream \|livekit_room_audio_stream deepgram_asr, goole_gemini, tts_edge	CPU(free, 2 cores)	e.g.: daily \|livekit room in stream -> silero (vad) -> deepgram (asr) -> google gemini -> edge (tts) -> daily \|livekit room out stream
daily_describe_vision_bot livekit_describe_vision_bot	e.g.: daily_room_audio_stream \| livekit_room_audio_stream sense_voice_asr, llm_transformers_manual_vision_qwen, tts_edge	- Qwen2-VL-2B-Instruct T4(free) - Qwen2-VL-7B-Instruct L4 - Llama-3.2-11B-Vision-Instruct L4 - allenai/Molmo-7B-D-0924 A100	e.g.: daily \| livekit room in stream -> silero (vad) -> sense_voice (asr) -> qwen-vl (llm) -> edge (tts) -> daily \| livekit room out stream
daily_chat_vision_bot livekit_chat_vision_bot	e.g.: daily_room_audio_stream \|livekit_room_audio_stream sense_voice_asr, llm_transformers_manual_vision_qwen, tts_edge	- Qwen2-VL-2B-Instruct T4(free) - Qwen2-VL-7B-Instruct L4 - Ll ama-3.2-11B-Vision-Instruct L4 - allenai/Molmo-7B-D-0924 A100	e.g.: daily \| livekit room in stream -> silero (vad) -> sense_voice (asr) -> llm answer guide qwen-vl (llm) -> edge (tts) -> daily \| livekit room out stream
daily_chat_tools_vision_bot livekit_chat_tools_vision_bot	e.g.: daily_room_audio_stream \| livekit_room_audio_stream sense_voice_asr, groq api llm(text), tools: - llm_transformers_manual_vision_qwen, tts_edge	- Qwen2-VL-2B-Instruct<br /> T4(free) - Qwen2-VL-7B-Instruct L4 - Llama-3.2-11B-Vision-Instruct L4 - allenai/Molmo-7B-D-0924 A100	e.g.: daily \| livekit room in stream -> silero (vad) -> sense_voice (asr) ->llm with tools qwen-vl -> edge (tts) -> daily \| livekit room out stream
daily_annotate_vision_bot livekit_annotate_vision_bot	e.g.: daily_room_audio_stream \| livekit_room_audio_stream vision_yolo_detector tts_edge	T4(free)	e.g.: daily \| livekit room in stream vision_yolo_detector -> edge (tts) -> daily \| livekit room out stream
daily_detect_vision_bot livekit_detect_vision_bot	e.g.: daily_room_audio_stream \| livekit_room_audio_stream vision_yolo_detector tts_edge	T4(free)	e.g.: daily \| livekit room in stream vision_yolo_detector -> edge (tts) -> daily \| livekit room out stream
daily_ocr_vision_bot livekit_ocr_vision_bot	e.g.: daily_room_audio_stream \| livekit_room_audio_stream sense_voice_asr, vision_transformers_got_ocr tts_edge	T4(free)	e.g.: daily \| livekit room in stream -> silero (vad) -> sense_voice (asr) vision_transformers_got_ocr -> edge (tts) -> daily \| livekit room out stream

Run local chat bots

Note

run src code, replace achatbot to src, don't need set ACHATBOT_PKG=1 e.g.:

TQDM_DISABLE=True \
     python -m src.cmd.local-terminal-chat.generate_audio2audio > log/std_out.log

PyAudio need install python3-pyaudio e.g. ubuntu apt-get install python3-pyaudio, macos brew install portaudio see: https://pypi.org/project/PyAudio/
llm llama-cpp-python init use cpu Pre-built Wheel to install, if want to use other lib(cuda), see: https://github.com/abetlen/llama-cpp-python#installation-configuration
install pydub need install ffmpeg see: https://www.ffmpeg.org/download.html

run pip install "achatbot[local_terminal_chat_bot]" to install dependencies to run local terminal chat bot;
create achatbot data dir in $HOME dir mkdir -p ~/.achatbot/{log,config,models,records,videos};
cp .env.example .env, and check .env, add key/value env params;

select a model ckpt to download:

vad model ckpt (default vad ckpt model use silero vad)

# vad pyannote segmentation ckpt
huggingface-cli download pyannote/segmentation-3.0  --local-dir ~/.achatbot/models/pyannote/segmentation-3.0 --local-dir-use-symlinks False

asr model ckpt (default whipser ckpt model use base size)

# asr openai whisper ckpt
wget https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt -O ~/.achatbot/models/base.pt

# asr hf openai whisper ckpt for transformers pipeline to load
huggingface-cli download openai/whisper-base  --local-dir ~/.achatbot/models/openai/whisper-base --local-dir-use-symlinks False

# asr hf faster whisper (CTranslate2)
huggingface-cli download Systran/faster-whisper-base  --local-dir ~/.achatbot/models/Systran/faster-whisper-base --local-dir-use-symlinks False

# asr SenseVoice ckpt
huggingface-cli download FunAudioLLM/SenseVoiceSmall  --local-dir ~/.achatbot/models/FunAudioLLM/SenseVoiceSmall --local-dir-use-symlinks False

llm model ckpt (default llamacpp ckpt(ggml) model use qwen-2 instruct 1.5B size)

# llm llamacpp Qwen2-Instruct
huggingface-cli download Qwen/Qwen2-1.5B-Instruct-GGUF qwen2-1_5b-instruct-q8_0.gguf  --local-dir ~/.achatbot/models --local-dir-use-symlinks False

# llm llamacpp Qwen1.5-chat
huggingface-cli download Qwen/Qwen1.5-7B-Chat-GGUF qwen1_5-7b-chat-q8_0.gguf  --local-dir ~/.achatbot/models --local-dir-use-symlinks False

# llm llamacpp phi-3-mini-4k-instruct
huggingface-cli download microsoft/Phi-3-mini-4k-instruct-gguf Phi-3-mini-4k-instruct-q4.gguf --local-dir ~/.achatbot/models --local-dir-use-symlinks False

tts model ckpt (default whipser ckpt model use base size)

# tts chatTTS
huggingface-cli download 2Noise/ChatTTS  --local-dir ~/.achatbot/models/2Noise/ChatTTS --local-dir-use-symlinks False

# tts coquiTTS
huggingface-cli download coqui/XTTS-v2  --local-dir ~/.achatbot/models/coqui/XTTS-v2 --local-dir-use-symlinks False

# tts cosy voice
git lfs install
git clone https://www.modelscope.cn/iic/CosyVoice-300M.git ~/.achatbot/models/CosyVoice-300M
git clone https://www.modelscope.cn/iic/CosyVoice-300M-SFT.git ~/.achatbot/models/CosyVoice-300M-SFT
git clone https://www.modelscope.cn/iic/CosyVoice-300M-Instruct.git ~/.achatbot/models/CosyVoice-300M-Instruct
#git clone https://www.modelscope.cn/iic/CosyVoice-ttsfrd.git ~/.achatbot/models/CosyVoice-ttsfrd

run local terminal chat bot with env; e.g.

use dufault env params to run local chat bot

ACHATBOT_PKG=1 TQDM_DISABLE=True \
    python -m achatbot.cmd.local-terminal-chat.generate_audio2audio > ~/.achatbot/log/std_out.log

Run remote http fastapi daily chat bots

run pip install "achatbot[fastapi_daily_bot_server]" to install dependencies to run http fastapi daily chat bot;

run below cmd to start http server, see api docs: http://0.0.0.0:4321/docs

ACHATBOT_PKG=1 python -m achatbot.cmd.http.server.fastapi_daily_bot_serve

run chat bot processor, e.g.

run a daily langchain rag bot api, with ui/educator-client

[!NOTE] need process youtube audio save to local file with pytube, run pip install "achatbot[pytube,deep_translator]" to install dependencies and transcribe/translate to text, then chunks to vector store, and run langchain rag bot api; run data process:
ACHATBOT_PKG=1 python -m achatbot.cmd.bots.rag.data_process.youtube_audio_transcribe_to_tidb
or download processed data from hf dataset weege007/youtube_videos, then chunks to vector store .

curl -XPOST "http://0.0.0.0:4321/bot_join/chat-bot/DailyLangchainRAGBot" \
 -H "Content-Type: application/json" \
 -d $'{"config":{"llm":{"model":"llama-3.1-70b-versatile","messages":[{"role":"system","content":""}],"language":"zh"},"tts":{"tag":"cartesia_tts_processor","args":{"voice_id":"eda5bbff-1ff1-4886-8ef1-4e69a77640a0","language":"zh"}},"asr":{"tag":"deepgram_asr_processor","args":{"language":"zh","model":"nova-2"}}}}' | jq .

run a simple daily chat bot api, with ui/web-client-ui (default language: zh)

curl -XPOST "http://0.0.0.0:4321/bot_join/DailyBot" \
 -H "Content-Type: application/json" \
 -d '{}' | jq .

Run remote rpc chat bot worker

run pip install "achatbot[remote_rpc_chat_bot_be_worker]" to install dependencies to run rpc chat bot BE worker; e.g. :
- use dufault env params to run rpc chat bot BE worker

ACHATBOT_PKG=1 RUN_OP=be TQDM_DISABLE=True \
    TTS_TAG=tts_edge \
    python -m achatbot.cmd.grpc.terminal-chat.generate_audio2audio > ~/.achatbot/log/be_std_out.log

run pip install "achatbot[remote_rpc_chat_bot_fe]" to install dependencies to run rpc chat bot FE;

ACHATBOT_PKG=1 RUN_OP=fe \
    TTS_TAG=tts_edge \
    python -m achatbot.cmd.grpc.terminal-chat.generate_audio2audio > ~/.achatbot/log/fe_std_out.log

Run remote queue chat bot worker

run pip install "achatbot[remote_queue_chat_bot_be_worker]" to install dependencies to run queue chat bot worker; e.g.:

use default env params to run

ACHATBOT_PKG=1 REDIS_PASSWORD=$redis_pwd RUN_OP=be TQDM_DISABLE=True \
    python -m achatbot.cmd.remote-queue-chat.generate_audio2audio > ~/.achatbot/log/be_std_out.log

sense_voice(asr) -> qwen (llm) -> cosy_voice (tts) u can login redislabs create 30M free databases; set REDIS_HOST,REDIS_PORT and REDIS_PASSWORD to run, e.g.:

 ACHATBOT_PKG=1 RUN_OP=be \
   TQDM_DISABLE=True \
   REDIS_PASSWORD=$redis_pwd \
   REDIS_HOST=redis-14241.c256.us-east-1-2.ec2.redns.redis-cloud.com \
   REDIS_PORT=14241 \
   ASR_TAG=sense_voice_asr \
   ASR_LANG=zn \
   ASR_MODEL_NAME_OR_PATH=~/.achatbot/models/FunAudioLLM/SenseVoiceSmall \
   N_GPU_LAYERS=33 FLASH_ATTN=1 \
   LLM_MODEL_NAME=qwen \
   LLM_MODEL_PATH=~/.achatbot/models/qwen1_5-7b-chat-q8_0.gguf \
   TTS_TAG=tts_cosy_voice \
   python -m achatbot.cmd.remote-queue-chat.generate_audio2audio > ~/.achatbot/log/be_std_out.log

run pip install "achatbot[remote_queue_chat_bot_fe]" to install the required packages to run quueue chat bot frontend; e.g.:

use default env params to run (default vad_recorder)

ACHATBOT_PKG=1 RUN_OP=fe \
    REDIS_PASSWORD=$redis_pwd \
    REDIS_HOST=redis-14241.c256.us-east-1-2.ec2.redns.redis-cloud.com \
    REDIS_PORT=14241 \
    python -m achatbot.cmd.remote-queue-chat.generate_audio2audio > ~/.achatbot/log/fe_std_out.log

with wake word

ACHATBOT_PKG=1 RUN_OP=fe \
    REDIS_PASSWORD=$redis_pwd \
    REDIS_HOST=redis-14241.c256.us-east-1-2.ec2.redns.redis-cloud.com \
    REDIS_PORT=14241 \
    RECORDER_TAG=wakeword_rms_recorder \
    python -m achatbot.cmd.remote-queue-chat.generate_audio2audio > ~/.achatbot/log/fe_std_out.log

default pyaudio player stream with tts tag out sample info(rate,channels..), e.g.: (be use tts_cosy_voice out stream info)

 ACHATBOT_PKG=1 RUN_OP=fe \
     REDIS_PASSWORD=$redis_pwd \
     REDIS_HOST=redis-14241.c256.us-east-1-2.ec2.redns.redis-cloud.com \
     REDIS_PORT=14241 \
     RUN_OP=fe \
     TTS_TAG=tts_cosy_voice \
     python -m achatbot.cmd.remote-queue-chat.generate_audio2audio > ~/.achatbot/log/fe_std_out.log

remote_queue_chat_bot_be_worker in colab examples :

sense_voice(asr) -> qwen (llm) -> cosy_voice (tts):

Run remote grpc tts speaker bot

run pip install "achatbot[remote_grpc_tts_server]" to install dependencies to run grpc tts speaker bot server;

ACHATBOT_PKG=1 python -m achatbot.cmd.grpc.speaker.server.serve

run pip install "achatbot[remote_grpc_tts_client]" to install dependencies to run grpc tts speaker bot client;

ACHATBOT_PKG=1 TTS_TAG=tts_edge IS_RELOAD=1 python -m achatbot.cmd.grpc.speaker.client
ACHATBOT_PKG=1 TTS_TAG=tts_g IS_RELOAD=1 python -m achatbot.cmd.grpc.speaker.client
ACHATBOT_PKG=1 TTS_TAG=tts_coqui IS_RELOAD=1 python -m achatbot.cmd.grpc.speaker.client
ACHATBOT_PKG=1 TTS_TAG=tts_chat IS_RELOAD=1 python -m achatbot.cmd.grpc.speaker.client
ACHATBOT_PKG=1 TTS_TAG=tts_cosy_voice IS_RELOAD=1 python -m achatbot.cmd.grpc.speaker.client

Multimodal Interaction

audio (voice)

stream-stt (realtime-recorder)
audio-llm (multimode-chat)
stream-tts (realtime-(clone)-speaker)

vision (CV)

stream-ocr (realtime-object-detection)

more

Embodied Intelligence: Robots that touch the world, perceive and move

License

torchchat is released under the BSD 3 license. (Additional code in this distribution is covered by the MIT and Apache Open Source licenses.) However you may have other legal obligations that govern your use of content, such as the terms of service for third-party models.

Name		Name	Last commit message	Last commit date
Latest commit History 467 Commits
demo		demo
deploy		deploy
deps		deps
docs		docs
scripts		scripts
src		src
test		test
ui		ui
.env.example		.env.example
.gitignore		.gitignore
.gitmodules		.gitmodules
CHANGELOG.md		CHANGELOG.md
CHANGELOG.md.template		CHANGELOG.md.template
LICENSE		LICENSE
README.md		README.md
pyproject.toml		pyproject.toml
ruff.toml		ruff.toml

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achatbot

Project Structure

Feature

Service Deployment Architecture

UI (easy to deploy with github like pages)

Server Deploy (CD)

Install

pypi

local

Run chat bots

Run chat bots with colab notebook

Run local chat bots

Run remote http fastapi daily chat bots

Run remote rpc chat bot worker

Run remote queue chat bot worker

Run remote grpc tts speaker bot

Multimodal Interaction

audio (voice)

vision (CV)

more

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ai-bot-pro/achatbot

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achatbot

Project Structure

Feature

Service Deployment Architecture

UI (easy to deploy with github like pages)

Server Deploy (CD)

Install

pypi

local

Run chat bots

Run chat bots with colab notebook

Run local chat bots

Run remote http fastapi daily chat bots

Run remote rpc chat bot worker

Run remote queue chat bot worker

Run remote grpc tts speaker bot

Multimodal Interaction

audio (voice)

vision (CV)

more

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