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style: add path to yaml

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0Xiao0
2025-04-03 18:10:32 +08:00
parent 1ee838327e
commit eb9ec3c0bf
8 changed files with 98 additions and 49 deletions
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17
sample/chroma_rerank.py
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@ -7,20 +7,23 @@ from langchain_community.vectorstores import Chroma
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.embeddings.sentence_transformer import SentenceTransformerEmbeddings
import time
from pathlib import Path
path = Path("/media/verachen/e0f7a88c-ad43-4736-8829-4d06e5ed8f4f/model/BAAI")
# chroma run --path chroma_db/ --port 8000 --host 0.0.0.0
# loader = TextLoader("/Workspace/chroma_data/粤语语料.txt",encoding="utf-8")
loader = TextLoader("/Workspace/jarvis-models/sample/RAG_zh_kiki.txt")
loader = TextLoader("./RAG_boss.txt")
documents = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=10, chunk_overlap=0, length_function=len, is_separator_regex=True,separators=['\n', '\n\n'])
docs = text_splitter.split_documents(documents)
print("len(docs)", len(docs))
ids = ["粤语语料"+str(i) for i in range(len(docs))]
embedding_model = SentenceTransformerEmbeddings(model_name='/Workspace/Models/BAAI/bge-m3', model_kwargs={"device": "cuda:0"})
client = chromadb.HttpClient(host='10.6.44.141', port=7000)
embedding_model = SentenceTransformerEmbeddings(model_name= str(path / "bge-m3"), model_kwargs={"device": "cuda:0"})
client = chromadb.HttpClient(host="localhost", port=7000)
id = "kiki"
id = "boss2"
# client.delete_collection(id)
# 插入向量(如果ids已存在，则会更新向量)
db = Chroma.from_documents(documents=docs, embedding=embedding_model, ids=ids, collection_name=id, client=client)
@ -28,13 +31,13 @@ db = Chroma.from_documents(documents=docs, embedding=embedding_model, ids=ids, c
embedding_model = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="/Workspace/Models/BAAI/bge-m3", device = "cuda:0")
embedding_model = embedding_functions.SentenceTransformerEmbeddingFunction(model_name= str(path / "bge-m3"), device = "cuda:0")
client = chromadb.HttpClient(host='10.6.44.141', port=7000)
client = chromadb.HttpClient(host='localhost', port=7000)
collection = client.get_collection(id, embedding_function=embedding_model)
reranker_model = CrossEncoder("/Workspace/Models/BAAI/bge-reranker-v2-m3", max_length=512, device = "cuda:0")
reranker_model = CrossEncoder(str(path / "bge-reranker-v2-m3"), max_length=512, device = "cuda:0")
# while True:
# usr_question = input("\n 请输入问题: ")

14
src/blackbox/asr.py
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@ -12,6 +12,9 @@ from funasr.utils.postprocess_utils import rich_transcription_postprocess
from .blackbox import Blackbox
from injector import singleton, inject
from pathlib import Path
from ..configuration import PathConf
import tempfile
import json
@ -44,12 +47,13 @@ class ASR(Blackbox):
speaker: str
@logging_time(logger=logger)
def model_init(self, sensevoice_config: SenseVoiceConf) -> None:
def model_init(self, sensevoice_config: SenseVoiceConf, path: PathConf) -> None:
config = read_yaml(".env.yaml")
self.paraformer = RapidParaformer(config)
sense_model_path = Path(path.sensevoice_model_path)
model_dir = "/model/Voice/SenseVoice/SenseVoiceSmall"
model_dir = str(sense_model_path)
self.speed = sensevoice_config.speed
self.device = sensevoice_config.device
@ -65,7 +69,7 @@ class ASR(Blackbox):
self.asr = AutoModel(
model=model_dir,
trust_remote_code=True,
remote_code= "/Workspace/SenseVoice/model.py",
remote_code= "../SenseVoice/model.py",
vad_model="fsmn-vad",
vad_kwargs={"max_single_segment_time": 30000},
device=self.device,
@ -76,8 +80,8 @@ class ASR(Blackbox):
logging.info('#### Initializing SenseVoiceASR Service in cuda:' + sensevoice_config.device + ' mode...')
@inject
def __init__(self, sensevoice_config: SenseVoiceConf, settings: dict) -> None:
self.model_init(sensevoice_config)
def __init__(self, sensevoice_config: SenseVoiceConf, settings: dict, path: PathConf) -> None:
self.model_init(sensevoice_config, path)
def __call__(self, *args, **kwargs):
return self.processing(*args, **kwargs)

6
src/blackbox/chat.py
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@ -64,7 +64,7 @@ class Chat(Blackbox):
user_stream = settings.get('stream')
user_websearch = settings.get('websearch')
llm_model = "vllm"
llm_model = "llm"
if user_context == None:
user_context = []
@ -106,9 +106,9 @@ class Chat(Blackbox):
if user_model_url is None or user_model_url.isspace() or user_model_url == "":
if llm_model != "vllm":
user_model_url = "http://10.6.80.75:23333/v1/chat/completions"
user_model_url = "http://localhost:23333/v1/chat/completions"
else:
user_model_url = "http://10.6.80.94:8000/v1/completions"
user_model_url = "http://localhost:8000/v1/completions"
if user_model_key is None or user_model_key.isspace() or user_model_key == "":
if llm_model != "vllm":

33
src/blackbox/chroma_query.py
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@ -12,22 +12,29 @@ from ..log.logging_time import logging_time
import re
from sentence_transformers import CrossEncoder
from pathlib import Path
from ..configuration import Configuration
from ..configuration import PathConf
logger = logging.getLogger
DEFAULT_COLLECTION_ID = "123"
from injector import singleton
@singleton
class ChromaQuery(Blackbox):
def __init__(self, *args, **kwargs) -> None:
# config = read_yaml(args[0])
# load chromadb and embedding model
self.embedding_model_1 = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="/Workspace/Models/BAAI/bge-large-zh-v1.5", device = "cuda:0")
self.embedding_model_2 = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="/Workspace/Models/BAAI/bge-small-en-v1.5", device = "cuda:0")
self.embedding_model_3 = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="/Workspace/Models/BAAI/bge-m3", device = "cuda:0")
self.client_1 = chromadb.HttpClient(host='10.6.44.141', port=7000)
path = PathConf(Configuration())
self.model_path = Path(path.chroma_rerank_embedding_model)
self.embedding_model_1 = embedding_functions.SentenceTransformerEmbeddingFunction(model_name=str(self.model_path / "bge-large-zh-v1.5"), device = "cuda:0")
self.embedding_model_2 = embedding_functions.SentenceTransformerEmbeddingFunction(model_name=str(self.model_path / "bge-small-en-v1.5"), device = "cuda:0")
self.embedding_model_3 = embedding_functions.SentenceTransformerEmbeddingFunction(model_name=str(self.model_path / "bge-m3"), device = "cuda:0")
self.client_1 = chromadb.HttpClient(host='localhost', port=7000)
# self.client_2 = chromadb.HttpClient(host='10.6.82.192', port=8000)
self.reranker_model_1 = CrossEncoder("/Workspace/Models/BAAI/bge-reranker-v2-m3", max_length=512, device = "cuda")
self.reranker_model_1 = CrossEncoder(str(self.model_path / "bge-reranker-v2-m3"), max_length=512, device = "cuda")
def __call__(self, *args, **kwargs):
return self.processing(*args, **kwargs)
@ -57,10 +64,10 @@ class ChromaQuery(Blackbox):
return JSONResponse(content={"error": "question is required"}, status_code=status.HTTP_400_BAD_REQUEST)
if chroma_embedding_model is None or chroma_embedding_model.isspace() or chroma_embedding_model == "":
chroma_embedding_model = "/Workspace/Models/BAAI/bge-large-zh-v1.5"
chroma_embedding_model = str(self.model_path / "bge-large-zh-v1.5")
if chroma_host is None or chroma_host.isspace() or chroma_host == "":
chroma_host = "10.6.44.141"
chroma_host = "localhost"
if chroma_port is None or chroma_port.isspace() or chroma_port == "":
chroma_port = "7000"
@ -72,7 +79,7 @@ class ChromaQuery(Blackbox):
chroma_n_results = 10
# load client and embedding model from init
if re.search(r"10.6.44.141", chroma_host) and re.search(r"7000", chroma_port):
if re.search(r"localhost", chroma_host) and re.search(r"7000", chroma_port):
client = self.client_1
else:
try:
@ -80,11 +87,11 @@ class ChromaQuery(Blackbox):
except:
return JSONResponse(content={"error": "chroma client not found"}, status_code=status.HTTP_400_BAD_REQUEST)
if re.search(r"/Workspace/Models/BAAI/bge-large-zh-v1.5", chroma_embedding_model):
if re.search(str(self.model_path / "bge-large-zh-v1.5"), chroma_embedding_model):
embedding_model = self.embedding_model_1
elif re.search(r"/Workspace/Models/BAAI/bge-small-en-v1.5", chroma_embedding_model):
elif re.search(str(self.model_path / "bge-small-en-v1.5"), chroma_embedding_model):
embedding_model = self.embedding_model_2
elif re.search(r"/Workspace/Models/BAAI/bge-m3", chroma_embedding_model):
elif re.search(str(self.model_path / "bge-m3"), chroma_embedding_model):
embedding_model = self.embedding_model_3
else:
try:
@ -123,7 +130,7 @@ class ChromaQuery(Blackbox):
final_result = str(results["documents"])
if chroma_reranker_model:
if re.search(r"/Workspace/Models/BAAI/bge-reranker-v2-m3", chroma_reranker_model):
if re.search(str(self.model_path / "bge-reranker-v2-m3"), chroma_reranker_model):
reranker_model = self.reranker_model_1
else:
try:

22
src/blackbox/chroma_upsert.py
View File

@ -21,6 +21,10 @@ import logging
from ..log.logging_time import logging_time
import re
from pathlib import Path
from ..configuration import Configuration
from ..configuration import PathConf
logger = logging.getLogger
DEFAULT_COLLECTION_ID = "123"
@ -31,9 +35,13 @@ class ChromaUpsert(Blackbox):
def __init__(self, *args, **kwargs) -> None:
# config = read_yaml(args[0])
# load embedding model
self.embedding_model_1 = SentenceTransformerEmbeddings(model_name="/Workspace/Models/BAAI/bge-large-zh-v1.5", model_kwargs={"device": "cuda"})
path = PathConf(Configuration())
self.model_path = Path(path.chroma_rerank_embedding_model)
self.embedding_model_1 = SentenceTransformerEmbeddings(model_name=str(self.model_path / "bge-large-zh-v1.5"), model_kwargs={"device": "cuda"})
# load chroma db
self.client_1 = chromadb.HttpClient(host='10.6.44.141', port=7000)
self.client_1 = chromadb.HttpClient(host='localhost', port=7000)
def __call__(self, *args, **kwargs):
return self.processing(*args, **kwargs)
@ -79,24 +87,24 @@ class ChromaUpsert(Blackbox):
chroma_collection_id = settings.get("chroma_collection_id")
if chroma_embedding_model is None or chroma_embedding_model.isspace() or chroma_embedding_model == "":
chroma_embedding_model = "/Workspace/Models/BAAI/bge-large-zh-v1.5"
chroma_embedding_model = model_name=str(self.model_path / "bge-large-zh-v1.5")
if chroma_host is None or chroma_host.isspace() or chroma_host == "":
chroma_host = "10.6.82.192"
chroma_host = "localhost"
if chroma_port is None or chroma_port.isspace() or chroma_port == "":
chroma_port = "8000"
chroma_port = "7000"
if chroma_collection_id is None or chroma_collection_id.isspace() or chroma_collection_id == "":
chroma_collection_id = "g2e"
# load client and embedding model from init
if re.search(r"10.6.82.192", chroma_host) and re.search(r"8000", chroma_port):
if re.search(r"localhost", chroma_host) and re.search(r"7000", chroma_port):
client = self.client_1
else:
client = chromadb.HttpClient(host=chroma_host, port=chroma_port)
print(f"chroma_embedding_model: {chroma_embedding_model}")
if re.search(r"/Workspace/Models/BAAI/bge-large-zh-v1.5", chroma_embedding_model):
if re.search((self.model_path / "bge-large-zh-v1.5"), chroma_embedding_model):
embedding_model = self.embedding_model_1
else:
embedding_model = SentenceTransformerEmbeddings(model_name=chroma_embedding_model, device = "cuda:0")

39
src/blackbox/tts.py
View File

@ -10,14 +10,15 @@ from ..tts.tts_service import TTService
from ..configuration import MeloConf
from ..configuration import CosyVoiceConf
from ..configuration import SovitsConf
from ..configuration import PathConf
from injector import inject
from injector import singleton
import sys,os
sys.path.append('/Workspace/CosyVoice')
sys.path.append('/Workspace/CosyVoice/third_party/Matcha-TTS')
from cosyvoice.cli.cosyvoice import CosyVoice, CosyVoice2
from cosyvoice.utils.file_utils import load_wav#, speed_change
from pathlib import Path
import soundfile as sf
import pyloudnorm as pyln
@ -100,7 +101,18 @@ class TTS(Blackbox):
print('1.#### Initializing MeloTTS Service in ' + self.melo_device + ' mode...')
@logging_time(logger=logger)
def cosyvoice_model_init(self, cosyvoice_config: CosyVoiceConf) -> None:
def cosyvoice_model_init(self, cosyvoice_config: CosyVoiceConf, path: PathConf) -> None:
cosy_path = Path(path.cosyvoice_path)
cosy_model_path = Path(path.cosyvoice_model_path)
sys.path.append(str(cosy_path))
sys.path.append(str(cosy_path / "third_party/Matcha-TTS"))
from cosyvoice.cli.cosyvoice import CosyVoice, CosyVoice2
from cosyvoice.utils.file_utils import load_wav#, speed_change
self.cosyvoice_speed = cosyvoice_config.speed
self.cosyvoice_device = cosyvoice_config.device
self.cosyvoice_language = cosyvoice_config.language
@ -113,8 +125,8 @@ class TTS(Blackbox):
if self.cosyvoice_mode == 'local':
# self.cosyvoicetts = CosyVoice('/Workspace/Models/CosyVoice/pretrained_models/CosyVoice-300M')
# self.cosyvoicetts = CosyVoice('/model/Voice/CosyVoice/pretrained_models/CosyVoice-300M')
self.cosyvoicetts = CosyVoice2('/model/Voice/CosyVoice/pretrained_models/CosyVoice2-0.5B', load_jit=True, load_onnx=False, load_trt=False)
self.prompt_speech_16k = load_wav('/Workspace/jarvis-models/Ricky-Wong-3-Mins.wav_0006003840_0006134080.wav', 16000)
self.cosyvoicetts = CosyVoice2(str (cosy_model_path / "CosyVoice2-0.5B"), load_jit=True, load_trt=False)
self.prompt_speech_16k = load_wav("./Ricky-Wong-3-Mins.wav_0006003840_0006134080.wav", 16000)
else:
self.cosyvoice_url = cosyvoice_config.url
@ -151,10 +163,10 @@ class TTS(Blackbox):
@inject
def __init__(self, melo_config: MeloConf, cosyvoice_config: CosyVoiceConf, sovits_config: SovitsConf, settings: dict) -> None:
def __init__(self, melo_config: MeloConf, cosyvoice_config: CosyVoiceConf, sovits_config: SovitsConf, settings: dict, path: PathConf) -> None:
self.tts_service = TTService("yunfeineo")
self.melo_model_init(melo_config)
self.cosyvoice_model_init(cosyvoice_config)
self.cosyvoice_model_init(cosyvoice_config, path)
self.sovits_model_init(sovits_config)
self.audio_dir = "audio_files" # 存储音频文件的目录
@ -336,7 +348,7 @@ class TTS(Blackbox):
"text_split_method": self.sovits_text_split_method,
"batch_size": self.sovits_batch_size,
"media_type": self.sovits_media_type,
"streaming_mode": self.sovits_streaming_mode
"streaming_mode": user_stream
}
if user_stream == True or str(user_stream).lower() == "true":
response = requests.get(self.sovits_url, params=message, stream=True)
@ -409,10 +421,11 @@ class TTS(Blackbox):
if text is None:
return JSONResponse(content={"error": "text is required"}, status_code=status.HTTP_400_BAD_REQUEST)
by = self.processing(text, settings=setting)
# return Response(content=by, media_type="audio/wav", headers={"Content-Disposition": "attachment; filename=audio.wav"})
if user_stream not in (True, "True", "true"):
return Response(content=by, media_type="audio/wav", headers={"Content-Disposition": "attachment; filename=audio.wav"})
print(f"tts user_stream: {type(user_stream)}")
if user_stream == True or str(user_stream).lower() == "true":
# import pdb; pdb.set_trace()
if user_stream in (True, "True", "true"):
print(f"tts user_stream22: {user_stream}")
if by.status_code == 200:
print("*"*90)

14
src/configuration.py
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@ -180,3 +180,17 @@ class VLMConf():
@inject
def __init__(self, config: Configuration) -> None:
self.urls = config.get("vlms.urls")
@singleton
class PathConf():
chroma_rerank_embedding_model: str
cosyvoice_path: str
cosyvoice_model_path: str
@inject
def __init__(self, config: Configuration) -> None:
self.chroma_rerank_embedding_model = config.get("path.chroma_rerank_embedding_model")
self.cosyvoice_path = config.get("path.cosyvoice_path")
self.cosyvoice_model_path = config.get("path.cosyvoice_model_path")
self.sensevoice_model_path = config.get("path.sensevoice_model_path")

2
src/tts/tts_service.py
View File

@ -19,7 +19,7 @@ import logging
logging.basicConfig(level=logging.INFO)
dirbaspath = __file__.split("\\")[1:-1]
dirbaspath= "/Workspace/jarvis-models/src/tts" + "/".join(dirbaspath)
dirbaspath= "./src/tts" + "/".join(dirbaspath)
config = {
'ayaka': {
'cfg': dirbaspath + '/models/ayaka.json',