diff --git a/README.md b/README.md index f29c2fa..344e03b 100644 --- a/README.md +++ b/README.md @@ -45,14 +45,54 @@ log: time_format: "%Y-%m-%d %H:%M:%S" filename: "D:/Workspace/Logging/jarvis/jarvis-models.log" +loki: + url: "https://loki.bwgdi.com/loki/api/v1/push" + labels: + app: jarvis + env: dev + location: "gdi" + layer: models + melotts: mode: local # or docker - url: http://10.6.44.16:18080/convert/tts + url: http://10.6.44.141:18080/convert/tts speed: 0.9 - device: 'cuda' + device: 'cuda:0' language: 'ZH' speaker: 'ZH' +cosyvoicetts: + mode: local # or docker + url: http://10.6.44.141:18080/convert/tts + speed: 0.9 + device: 'cuda:0' + language: '粤语女' + speaker: 'ZH' + +sovitstts: + mode: docker + url: http://10.6.80.90:9880/tts + speed: 0.9 + device: 'cuda:0' + language: 'ZH' + speaker: 'ZH' + text_lang: "yue" + ref_audio_path: "output/slicer_opt/Ricky-Wong/Ricky-Wong-3-Mins.wav_0006003840_0006134080.wav" + prompt_lang: "yue" + prompt_text: "你失敗咗點算啊？你而家安安穩穩，點解要咁樣做呢？" + text_split_method: "cut5" + batch_size: 1 + media_type: "wav" + streaming_mode: True + +sensevoiceasr: + mode: local # or docker + url: http://10.6.44.141:18080/convert/tts + speed: 0.9 + device: 'cuda:0' + language: '粤语女' + speaker: 'ZH' + tesou: url: http://120.196.116.194:48891/chat/ @@ -91,5 +131,14 @@ blackbox: lazyloading: true vlms: - url: http://10.6.80.87:23333 + urls: + qwen_vl: http://10.6.80.87:8000 + qwen2_vl: http://10.6.80.87:23333 + qwen2_vl_72b: http://10.6.80.91:23333 + +path: +chroma_rerank_embedding_model: /media/verachen/e0f7a88c-ad43-4736-8829-4d06e5ed8f4f/model/BAAI +cosyvoice_path: /media/verachen/e0f7a88c-ad43-4736-8829-4d06e5ed8f4f/Workspace/CosyVoice +cosyvoice_model_path: /media/verachen/e0f7a88c-ad43-4736-8829-4d06e5ed8f4f/model/Voice/CosyVoice/pretrained_models +sensevoice_model_path: /media/verachen/e0f7a88c-ad43-4736-8829-4d06e5ed8f4f/model/Voice/SenseVoice/SenseVoiceSmall ``` diff --git a/requirements.txt b/requirements.txt index 58d7037..d654b8b 100644 --- a/requirements.txt +++ b/requirements.txt @@ -9,4 +9,5 @@ chromadb==0.5.0 langchain==0.1.17 langchain-community==0.0.36 sentence-transformers==2.7.0 -openai \ No newline at end of file +openai +python-logging-loki \ No newline at end of file diff --git a/sample/chroma_rerank.py b/sample/chroma_rerank.py index 09be954..af36c37 100644 --- a/sample/chroma_rerank.py +++ b/sample/chroma_rerank.py @@ -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: ") diff --git a/server.py b/server.py index c5c7990..5e46253 100644 --- a/server.py +++ b/server.py @@ -4,8 +4,12 @@ from fastapi import FastAPI, Request, status from fastapi.responses import JSONResponse, Response, StreamingResponse from src.blackbox.blackbox_factory import BlackboxFactory +from src.log.loki_config import LokiLogger +import logging + from fastapi.middleware.cors import CORSMiddleware from injector import Injector +import yaml app = FastAPI() @@ -20,16 +24,64 @@ app.add_middleware( injector = Injector() blackbox_factory = injector.get(BlackboxFactory) +with open(".env.yaml", "r") as f: + config = yaml.safe_load(f) +logger = LokiLogger( + # url=config["loki"]["url"], + # username=config["loki"]["username"], + # password=config["loki"]["password"], + tags=config["loki"]["labels"], + logger_name=__name__, + level=logging.DEBUG +).get_logger() + + +@app.exception_handler(Exception) +async def catch_all_exceptions(request: Request, exc: Exception): + """ + 捕获所有在 ASGI 应用中未被处理的异常，并记录到 Loki。 + """ + logger.error( + f"Unhandled exception in ASGI application for path: {request.url.path}, method: {request.method} - {exc}", + exc_info=True, # 必须为 True，才能获取完整的堆栈信息 + extra={ + "path": request.url.path, + "method": request.method, + "error_type": type(exc).__name__, + "error_message": str(exc) + } + ) + return JSONResponse( + status_code=500, + content={"message": "Internal Server Error", "detail": "An unexpected error occurred."} + ) + @app.post("/") @app.get("/") async def blackbox(blackbox_name: Union[str, None] = None, request: Request = None): if not blackbox_name: - return await JSONResponse(content={"error": "blackbox_name is required"}, status_code=status.HTTP_400_BAD_REQUEST) + return JSONResponse(content={"error": "blackbox_name is required"}, status_code=status.HTTP_400_BAD_REQUEST) try: box = blackbox_factory.get_blackbox(blackbox_name) - except ValueError: - return await JSONResponse(content={"error": "value error"}, status_code=status.HTTP_400_BAD_REQUEST) - return await box.fast_api_handler(request) + except ValueError as e: + logger.error(f"获取 blackbox 失败: {blackbox_name} - {e}", exc_info=True) + return JSONResponse(content={"error": "value error"}, status_code=status.HTTP_400_BAD_REQUEST) + try: + response = await box.fast_api_handler(request) + # 检查响应的状态码，如果 >= 400，则认为是错误响应并记录 + if response.status_code >= 400: + try: + decoded_content = response.body.decode('utf-8', errors='ignore') + logger.error(f"Blackbox 返回错误响应: URL={request.url}, Method={request.method}, Status={response.status_code}, Content={decoded_content}") + except Exception as body_exc: + logger.error(f"Blackbox {blackbox_name} 返回错误响应: URL={request.url}, Method={request.method}, Status={response.status_code}, 无法解析响应内容: {body_exc}") + return response + except Exception as e: + logger.error(f"Blackbox 内部抛出异常: URL={request.url}, Method={request.method}, 异常报错: {type(e).__name__}: {e}", exc_info=True) + return JSONResponse( + status_code=500, + content={"message": "Internal Server Error", "detail": "An unexpected error occurred."} + ) # @app.get("/audio/{filename}") # async def serve_audio(filename: str): diff --git a/src/blackbox/asr.py b/src/blackbox/asr.py index 00d5ac6..ee971e8 100644 --- a/src/blackbox/asr.py +++ b/src/blackbox/asr.py @@ -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) diff --git a/src/blackbox/chat.py b/src/blackbox/chat.py index 4a84761..a4c17dc 100644 --- a/src/blackbox/chat.py +++ b/src/blackbox/chat.py @@ -59,12 +59,13 @@ class Chat(Blackbox): chroma_embedding_model = settings.get("chroma_embedding_model") chroma_collection_id = settings.get("chroma_collection_id") chroma_response = '' - system_prompt = settings.get('system_prompt') + system_prompt = settings.get('system_prompt',"") user_prompt_template = settings.get('user_prompt_template') user_stream = settings.get('stream') user_websearch = settings.get('websearch') + user_thinking = settings.get('thinking', False) - llm_model = "vllm" + llm_model = "llm" if user_context == None: user_context = [] @@ -106,9 +107,9 @@ 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": @@ -120,16 +121,16 @@ class Chat(Blackbox): chroma_response = self.chroma_query(user_question, settings) print("1.Chroma_response: \n", chroma_response) - if chroma_response: + if chroma_response and isinstance(chroma_response, str): if chroma_collection_id == 'boss': user_prompt_template = "# 你的身份 #\n你是周家俊，澳门博维集团董事长。你擅长脑机接口回答。\n# OBJECTIVE（目标） #\n回答游客的提问。\n# STYLE（风格）#\n成熟稳重，回答简练。不要分条。\n# 回答方式 #\n首先自行判断下方问题与检索内容是否相关，若相关则根据检索内容总结概括相关信息进行回答；若检索内容与问题无关，则根据自身知识进行回答。\n# 回答 #\n如果检索内容与问题相关，则直接从检索内容中提炼出问题所需的信息。如果检索内容与问题不相关，则不参考检索内容，直接根据常识尝试回答问题，或者则回答：“对不起，我无法回答此问题哦。”\n # 回答限制 #\n回答内容限制总结在50字内。\n回答内容出不要出现“相关”等字眼,不要乱说或者多说,回答的内容需要与问题对应。常见的对话可以不采用检索内容，根据人物设定，直接进行回答。\n只回答与脑机接口，澳门博维集团董事长，周家俊，G2E，RELX，BO VISION相关内容，若遇到其他提问则回答：“对不起，我无法回答此问题哦。”" elif chroma_collection_id == 'g2e' or chroma_collection_id == 'kiki': user_prompt_template = "# 你的身份 #\n你是琪琪，你是康普可可的代言人，由博维开发。你擅长澳门文旅问答。\n# OBJECTIVE（目标） #\n回答游客的提问。\n# STYLE（风格）#\n像少女一般开朗活泼，回答简练。不要分条。\n# 回答方式 #\n首先自行判断下方问题与检索内容是否相关，若相关则根据检索内容总结概括相关信息进行回答；若检索内容与问题无关，则根据自身知识进行回答。\n# 回答 #\n如果检索内容与问题相关，则直接从检索内容中提炼出问题所需的信息。如果检索内容与问题不相关，则不参考检索内容，直接根据常识尝试回答问题，或者则回答：“对不起，我无法回答此问题哦。”\n# 回答限制 #\n回答内容限制总结在50字内。\n回答内容出不要出现“相关”等字眼,不要乱说或者多说,回答的内容需要与问题对应。常见的对话可以不采用检索内容，根据人物设定，直接进行回答。\n只回答与澳门文旅，博维，康普可可，琪琪，G2E，RELX，BO VISION相关内容，若遇到其他提问则回答：“对不起，我无法回答此问题哦。”" print(f"user_prompt_template: {type(user_prompt_template)}, user_question: {type(user_question)}, chroma_response: {type(chroma_response)}") - user_question = user_prompt_template + "问题: " + user_question + "。检索内容: " + chroma_response + "。" + user_question = user_prompt_template + "\n\n问题: " + user_question + "\n\n检索内容: " + chroma_response else: if llm_model != "vllm": - user_question = user_prompt_template + "问题: " + user_question + "。" + user_question = user_prompt_template + "\n\n问题: " + user_question else: user_question = user_question @@ -249,6 +250,7 @@ class Chat(Blackbox): "presence_penalty": str(user_presence_penalty), "stop": str(user_stop), "stream": user_stream, + "chat_template_kwargs": {"enable_thinking": user_thinking}, } else: chat_inputs={ @@ -267,6 +269,7 @@ class Chat(Blackbox): "presence_penalty":float( user_presence_penalty), # "stop": user_stop, "stream": user_stream, + "chat_template_kwargs": {"enable_thinking": user_thinking}, } # # 获取当前时间戳 @@ -308,6 +311,7 @@ class Chat(Blackbox): except json.JSONDecodeError: # print("---- Error in JSON parsing ----") # 打印错误信息 continue # 继续处理下一个chunk，直到解析成功 + # yield "[Response END]" else: print("*"*90) @@ -343,4 +347,4 @@ class Chat(Blackbox): return EventSourceResponse(self.processing(prompt, context, setting)) else: response_content = "".join(self.processing(prompt, context, setting)) - return JSONResponse(content={"response": response_content}, status_code=status.HTTP_200_OK) \ No newline at end of file + return JSONResponse(content={"response": response_content}, status_code=status.HTTP_200_OK) diff --git a/src/blackbox/chroma_query.py b/src/blackbox/chroma_query.py index dff10ce..f501283 100755 --- a/src/blackbox/chroma_query.py +++ b/src/blackbox/chroma_query.py @@ -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: diff --git a/src/blackbox/chroma_upsert.py b/src/blackbox/chroma_upsert.py index b4d1816..8c9e5c1 100755 --- a/src/blackbox/chroma_upsert.py +++ b/src/blackbox/chroma_upsert.py @@ -8,7 +8,7 @@ import requests import json from langchain_community.document_loaders.csv_loader import CSVLoader -from langchain_community.document_loaders import PyPDFLoader, DirectoryLoader, TextLoader, UnstructuredHTMLLoader, JSONLoader, Docx2txtLoader, UnstructuredExcelLoader +from langchain_community.document_loaders import UnstructuredMarkdownLoader, DirectoryLoader, TextLoader, UnstructuredHTMLLoader, JSONLoader, Docx2txtLoader, UnstructuredExcelLoader, UnstructuredPDFLoader from langchain_community.vectorstores import Chroma from langchain.text_splitter import RecursiveCharacterTextSplitter, CharacterTextSplitter from langchain_community.embeddings.sentence_transformer import SentenceTransformerEmbeddings @@ -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) @@ -51,45 +59,44 @@ class ChromaUpsert(Blackbox): # # chroma_query settings if "settings" in settings: chroma_embedding_model = settings["settings"].get("chroma_embedding_model") - chroma_host = settings["settings"].get("chroma_host") - chroma_port = settings["settings"].get("chroma_port") - chroma_collection_id = settings["settings"].get("chroma_collection_id") + chroma_host = settings["settings"].get("chroma_host", "localhost") + chroma_port = settings["settings"].get("chroma_port", "7000") + chroma_collection_id = settings["settings"].get("chroma_collection_id", DEFAULT_COLLECTION_ID) + user_chunk_size = settings["settings"].get("chunk_size", 256) + user_chunk_overlap = settings["settings"].get("chunk_overlap", 10) + user_separators = settings["settings"].get("separators", ["\n\n"]) else: chroma_embedding_model = settings.get("chroma_embedding_model") - chroma_host = settings.get("chroma_host") - chroma_port = settings.get("chroma_port") - chroma_collection_id = settings.get("chroma_collection_id") + chroma_host = settings.get("chroma_host", "localhost") + chroma_port = settings.get("chroma_port", "7000") + chroma_collection_id = settings.get("chroma_collection_id", DEFAULT_COLLECTION_ID) + user_chunk_size = settings.get("chunk_size", 256) + user_chunk_overlap = settings.get("chunk_overlap", 10) + user_separators = settings.get("separators", ["\n\n"]) 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" - - if chroma_host is None or chroma_host.isspace() or chroma_host == "": - chroma_host = "10.6.82.192" - - if chroma_port is None or chroma_port.isspace() or chroma_port == "": - chroma_port = "8000" - - if chroma_collection_id is None or chroma_collection_id.isspace() or chroma_collection_id == "": - chroma_collection_id = "g2e" + chroma_embedding_model = model_name=str(self.model_path / "bge-large-zh-v1.5") # 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(str(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") + embedding_model = SentenceTransformerEmbeddings(model_name=chroma_embedding_model, model_kwargs={"device": "cuda"}) response_file ='' response_string = '' if file is not None: + text_splitter = RecursiveCharacterTextSplitter(chunk_size=user_chunk_size, chunk_overlap=user_chunk_overlap, separators=user_separators) + file_type = file.split(".")[-1] print("file_type: ",file_type) if file_type == "pdf": - loader = PyPDFLoader(file) + loader = UnstructuredPDFLoader(file) elif file_type == "txt": loader = TextLoader(file) elif file_type == "csv": @@ -102,9 +109,10 @@ class ChromaUpsert(Blackbox): loader = Docx2txtLoader(file) elif file_type == "xlsx": loader = UnstructuredExcelLoader(file) + elif file_type == "md": + loader = UnstructuredMarkdownLoader(file, mode="single", strategy="fast") documents = loader.load() - text_splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=0) docs = text_splitter.split_documents(documents) @@ -148,13 +156,13 @@ class ChromaUpsert(Blackbox): if user_text is not None and user_text_ids is None: return JSONResponse(content={"error": "text_ids is required when text is provided"}, status_code=status.HTTP_400_BAD_REQUEST) - if user_file is not None: + if user_file is not None and user_file.size != 0: pdf_bytes = await user_file.read() custom_filename = user_file.filename # 获取系统的临时目录路径 safe_filename = os.path.join(tempfile.gettempdir(), os.path.basename(custom_filename)) - + print("file_path", safe_filename) with open(safe_filename, "wb") as f: f.write(pdf_bytes) else: diff --git a/src/blackbox/tts.py b/src/blackbox/tts.py index 7446190..1813f29 100644 --- a/src/blackbox/tts.py +++ b/src/blackbox/tts.py @@ -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 @@ -33,6 +34,7 @@ import numpy as np from pydub import AudioSegment import subprocess +import re def set_all_random_seed(seed): random.seed(seed) @@ -99,7 +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 @@ -107,12 +120,13 @@ class TTS(Blackbox): self.cosyvoice_url = '' self.cosyvoice_mode = cosyvoice_config.mode self.cosyvoicetts = None + self.prompt_speech_16k = None # os.environ['CUDA_VISIBLE_DEVICES'] = str(cosyvoice_config.device) 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.cosyvoicetts = CosyVoice('/model/Voice/CosyVoice/pretrained_models/CosyVoice-300M') + 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 @@ -149,16 +163,31 @@ 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 def __call__(self, *args, **kwargs): return self.processing(*args, **kwargs) + + def filter_invalid_chars(self,text): + """过滤无效字符（包括字节流）""" + invalid_keywords = ["data:", "\n", "\r", "\t", " "] + + if isinstance(text, bytes): + text = text.decode('utf-8', errors='ignore') + + for keyword in invalid_keywords: + text = text.replace(keyword, "") + + # 移除所有英文字母和符号（保留中文、标点等） + text = re.sub(r'[a-zA-Z]', '', text) + return text.strip() + @logging_time(logger=logger) def processing(self, *args, settings: dict) -> io.BytesIO: @@ -213,7 +242,8 @@ class TTS(Blackbox): elif chroma_collection_id == 'boss': if self.cosyvoice_mode == 'local': set_all_random_seed(35616313) - audio = self.cosyvoicetts.inference_sft(text, '中文男', speed=1.5, stream=False) + # audio = self.cosyvoicetts.inference_sft(text, '中文男', speed=1.5, stream=False) + audio = self.cosyvoicetts.inference_instruct2(text, '用粤语说这句话', self.prompt_speech_16k, stream=False) for i, j in enumerate(audio): f = io.BytesIO() sf.write(f, j['tts_speech'].cpu().numpy().squeeze(0), 22050, format='wav') @@ -234,12 +264,44 @@ class TTS(Blackbox): set_all_random_seed(56056558) print("*"*90) audio = self.cosyvoicetts.inference_sft(text, self.cosyvoice_language, stream=True) + # audio = self.cosyvoicetts.inference_instruct2(text, '用粤语说这句话', self.prompt_speech_16k, stream=False) + # for i, j in enumerate(audio): + # f = io.BytesIO() + # sf.write(f, j['tts_speech'].cpu().numpy().squeeze(0), 22050, format='wav') + # f.seek(0) + # print("#### CosyVoiceTTS Service consume - local : ", (time.time() - current_time)) + # return f.read() + # 打印 audio 的长度和内容结构 + # print(f"Total audio segments: {len(audio)}") + # print(f"Audio data structure: {audio}") + + # 创建一个空的列表来存储所有音频段的 NumPy 数组 + all_audio_data = [] + + # 遍历每一段音频并将它们存储到 all_audio_data 列表 for i, j in enumerate(audio): - f = io.BytesIO() - sf.write(f, j['tts_speech'].cpu().numpy().squeeze(0), 22050, format='wav') - f.seek(0) + # print(f"Processing segment {i + 1}...") + + # 打印每段音频的信息，确保其正确 + # print(f"Segment {i + 1} shape: {j['tts_speech'].shape}") + + # 直接将音频数据转换成 NumPy 数组 + audio_data = j['tts_speech'].cpu().numpy() + + # 将每个段的音频数据添加到 all_audio_data 列表 + all_audio_data.append(audio_data[0]) # 取音频的第一个通道（假设为单声道） + + # 将所有音频段的 NumPy 数组合并成一个完整的音频数组 + combined_audio_data = np.concatenate(all_audio_data, axis=0) + + # 将合并后的音频数据写入到 BytesIO 中 + f = io.BytesIO() + sf.write(f, combined_audio_data, 22050, format='wav') # 22050 为采样率，可能需要根据实际情况调整 + f.seek(0) + + # 返回合并后的音频 print("#### CosyVoiceTTS Service consume - local : ", (time.time() - current_time)) - return f.read() + return f.read() # 返回最终合并后的音频数据 else: message = { "text": text @@ -250,7 +312,8 @@ class TTS(Blackbox): elif chroma_collection_id == 'boss': if self.cosyvoice_mode == 'local': set_all_random_seed(35616313) - audio = self.cosyvoicetts.inference_sft(text, '中文男', speed=1.5, stream=False) + # audio = self.cosyvoicetts.inference_sft(text, '中文男', speed=1.5, stream=False) + audio = self.cosyvoicetts.inference_instruct2(text, '用粤语说这句话', self.prompt_speech_16k, stream=False) for i, j in enumerate(audio): f = io.BytesIO() sf.write(f, j['tts_speech'].cpu().numpy().squeeze(0), 22050, format='wav') @@ -266,6 +329,7 @@ class TTS(Blackbox): return response.content elif user_model_name == 'sovitstts': + # text = self.filter_invalid_chars(text) if chroma_collection_id == 'kiki' or chroma_collection_id is None: if self.sovits_mode == 'local': set_all_random_seed(56056558) @@ -286,9 +350,9 @@ 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: + if user_stream == True or str(user_stream).lower() == "true": response = requests.get(self.sovits_url, params=message, stream=True) print("#### SoVITS Service consume - docker : ", (time.time()-current_time)) return response @@ -299,23 +363,37 @@ class TTS(Blackbox): print("#### SoVITS Service consume - docker : ", (time.time()-current_time)) - # elif chroma_collection_id == 'boss': - # if self.cosyvoice_mode == 'local': - # set_all_random_seed(35616313) - # audio = self.cosyvoicetts.inference_sft(text, '中文男', speed=1.5, stream=False) - # for i, j in enumerate(audio): - # f = io.BytesIO() - # sf.write(f, j['tts_speech'].cpu().numpy().squeeze(0), 22050, format='wav') - # f.seek(0) - # print("#### CosyVoiceTTS Service consume - local : ", (time.time() - current_time)) - # return f.read() - # else: - # message = { - # "text": text - # } - # response = requests.post(self.cosyvoice_url, json=message) - # print("#### CosyVoiceTTS Service consume - docker : ", (time.time()-current_time)) - # return response.content + elif chroma_collection_id == 'boss': + if self.sovits_mode == 'local': + set_all_random_seed(56056558) + audio = self.cosyvoicetts.inference_sft(text, self.cosyvoice_language, stream=True) + for i, j in enumerate(audio): + f = io.BytesIO() + sf.write(f, j['tts_speech'].cpu().numpy().squeeze(0), 22050, format='wav') + f.seek(0) + print("#### CosyVoiceTTS Service consume - local : ", (time.time() - current_time)) + return f.read() + else: + message = { + "text": text, + "text_lang": self.sovits_text_lang, + "ref_audio_path": self.sovits_ref_audio_path, + "prompt_lang": self.sovits_prompt_lang, + "prompt_text": self.sovits_prompt_text, + "text_split_method": self.sovits_text_split_method, + "batch_size": self.sovits_batch_size, + "media_type": self.sovits_media_type, + "streaming_mode": user_stream + } + if user_stream == True or str(user_stream).lower() == "true": + response = requests.get(self.sovits_url, params=message, stream=True) + print("#### SoVITS Service consume - docker : ", (time.time()-current_time)) + return response + else: + response = requests.get(self.sovits_url, params=message) + print("#### SoVITS Service consume - docker : ", (time.time()-current_time)) + return response.content + print("#### SoVITS Service consume - docker : ", (time.time()-current_time)) elif user_model_name == 'man': @@ -359,9 +437,12 @@ 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: + 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)}") + # 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) def audio_stream(): @@ -405,6 +486,7 @@ class TTS(Blackbox): else: wav_filename = os.path.join(self.audio_dir, 'audio.wav') + print("8"*90) with open(wav_filename, 'wb') as f: f.write(by) diff --git a/src/blackbox/vlms.py b/src/blackbox/vlms.py index b2716e0..4d2b1ed 100644 --- a/src/blackbox/vlms.py +++ b/src/blackbox/vlms.py @@ -13,13 +13,19 @@ import requests import base64 import copy import ast + +import json + import random from time import time + import io from PIL import Image from lmdeploy.serve.openai.api_client import APIClient +from openai import OpenAI + def is_base64(value) -> bool: try: @@ -52,14 +58,14 @@ class VLMS(Blackbox): - skip_special_tokens (bool): Whether or not to remove special tokens in the decoding. Default to be True.""" self.model_dict = vlm_config.urls - self.model_url = None + self.available_models = {} self.temperature: float = 0.7 self.top_p:float = 1 self.max_tokens: (int |None) = 512 self.repetition_penalty: float = 1 self.stop: (str | List[str] |None) = ['<|endoftext|>','<|im_end|>'] - self.top_k: (int) = None + self.top_k: (int) = 40 self.ignore_eos: (bool) = False self.skip_special_tokens: (bool) = True @@ -74,7 +80,13 @@ class VLMS(Blackbox): "skip_special_tokens": self.skip_special_tokens, } - + for model, url in self.model_dict.items(): + try: + response = requests.get(url+'/health',timeout=3) + if response.status_code == 200: + self.available_models[model] = url + except Exception as e: + pass def __call__(self, *args, **kwargs): return self.processing(*args, **kwargs) @@ -82,7 +94,7 @@ class VLMS(Blackbox): data = args[0] return isinstance(data, list) - def processing(self, prompt:str | None, images:str | bytes | None, settings: dict, model_name: Optional[str] = None, user_context: List[dict] = None) -> str: + def processing(self, prompt:str | None, images:str | bytes | None, settings: dict, user_context: List[dict] = None) -> str: """ Args: prompt: a string query to the model. @@ -94,22 +106,29 @@ class VLMS(Blackbox): response: a string history: a list """ - + config: dict = { + "lmdeploy_infer":True, + "system_prompt":"", + "vlm_model_name":"", + } if settings: - for k in settings: + for k in list(settings.keys()): if k not in self.settings: print("Warning: '{}' is not a support argument and ignore this argment, check the arguments {}".format(k,self.settings.keys())) - settings.pop(k) + config[k] = settings.pop(k) tmp = copy.deepcopy(self.settings) tmp.update(settings) settings = tmp else: settings = {} + config['lmdeploy_infer'] = str(config['lmdeploy_infer']).strip().lower() == 'true' + if not prompt: prompt = '你是一个辅助机器人，请就此图做一个简短的概括性描述，包括图中的主体物品及状态，不超过50字。' if images else '你好' - # Transform the images into base64 format where openai format need. + # Transform the images into base64 format where openai url) + if images: if is_base64(images): # image as base64 str images_data = images @@ -122,43 +141,14 @@ class VLMS(Blackbox): images_data = str(base64.b64encode(res.content),'utf-8') else: images_data = None - ## AutoLoad Model - # url = 'http://10.6.80.87:8000/' + model_name + '/' - # data_input = {'model': model_name, 'prompt': prompt, 'img_data': images_data} - # data = requests.post(url, json=data_input) - # print(data.text) - # return data.text - # 'https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg' - ## Lmdeploy - # if not user_context: - # user_context = [] - ## Predefine user_context only for testing - # user_context = [{'role':'user','content':'你好，我叫康康，你是谁？'}, {'role': 'assistant', 'content': '你好！很高兴为你提供帮助。'}] - # user_context = [{ - # 'role': 'user', - # 'content': [{ - # 'type': 'text', - # 'text': '图中有什么，请描述一下', - # }, { - # 'type': 'image_url', - # 'image_url': { - # 'url': 'https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg' - # }, - # }] - # },{ - # 'role': 'assistant', - # 'content': '图片中主要展示了一只老虎，它正在绿色的草地上休息。草地上有很多可以让人坐下的地方，而且看起来相当茂盛。背景比较模糊，可能是因为老虎的影响，让整个图片的其他部分都变得不太清晰了。' - # } - # ] + ## Predefine user_context only for testing + # user_context = [{'role':'user','content':'你好，我叫康康，你是谁？'}, {'role': 'assistant', 'content': '你好！很高兴为你提供帮助。'}] + + if not user_context and config['system_prompt']: user_context = [{'role':'system','content': config['system_prompt']}] + user_context = self.keep_last_k_images(user_context,k = 2) - user_context = self.keep_last_k_images(user_context,k = 1) - if self.model_url is None: self.model_url = self._get_model_url(model_name) - - api_client = APIClient(self.model_url) - # api_client = APIClient("http://10.6.80.91:23333") - model_name = api_client.available_models[0] # Reformat input into openai format to request. if images_data: messages = user_context + [{ @@ -170,8 +160,6 @@ class VLMS(Blackbox): 'type': 'image_url', 'image_url': { # Image two 'url': - # 'https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg' - # './val_data/image_5.jpg' f"data:image/jpeg;base64,{images_data}", }, # },{ # Image one @@ -194,40 +182,56 @@ class VLMS(Blackbox): responses = '' total_token_usage = 0 # which can be used to count the cost of a query - for i,item in enumerate(api_client.chat_completions_v1(model=model_name, + model_url = self._get_model_url(config['vlm_model_name']) + + if config['lmdeploy_infer']: + api_client = APIClient(model_url) + model_name = api_client.available_models[0] + for i,item in enumerate(api_client.chat_completions_v1(model=model_name, messages=messages,stream = True, **settings, # session_id=, )): - # Stream output - print(item["choices"][0]["delta"]['content'],end='\n') - yield item["choices"][0]["delta"]['content'] - responses += item["choices"][0]["delta"]['content'] + # Stream output + yield item["choices"][0]["delta"]['content'] + responses += item["choices"][0]["delta"]['content'] - # print(item["choices"][0]["message"]['content']) - # responses += item["choices"][0]["message"]['content'] - # total_token_usage += item['usage']['total_tokens'] # 'usage': {'prompt_tokens': *, 'total_tokens': *, 'completion_tokens': *} + # print(item["choices"][0]["message"]['content']) + # responses += item["choices"][0]["message"]['content'] + # total_token_usage += item['usage']['total_tokens'] # 'usage': {'prompt_tokens': *, 'total_tokens': *, 'completion_tokens': *} + else: + api_key = "EMPTY_API_KEY" + api_client = OpenAI(api_key=api_key, base_url=model_url+'/v1') + model_name = api_client.models.list().data[0].id + for item in api_client.chat.completions.create( + model=model_name, + messages=messages, + temperature=0.8, + top_p=0.8, + stream=True): + yield(item.choices[0].delta.content) + responses += item.choices[0].delta.content + # response = api_client.chat.completions.create( + # model=model_name, + # messages=messages, + # temperature=0.8, + # top_p=0.8) + # print(response.choices[0].message.content) + # return response.choices[0].message.content + + user_context = messages + [{'role': 'assistant', 'content': responses}] self.custom_print(user_context) - # return responses, user_context + # return responses def _get_model_url(self,model_name:str | None): - available_models = {} - for model, url in self.model_dict.items(): - try: - response = requests.get(url,timeout=3) - if response.status_code == 200: - available_models[model] = url - except Exception as e: - # print(e) - pass - if not available_models: print("There are no available running models and please check your endpoint urls.") - if model_name and model_name in available_models: - return available_models[model_name] + if not self.available_models: print("There are no available running models and please check your endpoint urls.") + if model_name and model_name in self.available_models: + return self.available_models[model_name] else: - model = random.choice(list(available_models.keys())) + model = random.choice(list(self.available_models.keys())) print(f"No such model {model_name}, using {model} instead.") if model_name else print(f"Using random model {model}.") - return available_models[model] + return self.available_models[model] def _into_openai_format(self, context:List[list]) -> List[dict]: """ @@ -298,7 +302,6 @@ class VLMS(Blackbox): result.append(item) return result[::-1] - def custom_print(self, user_context: list): result = [] for item in user_context: @@ -315,18 +318,23 @@ class VLMS(Blackbox): ## TODO: add support for multiple images and support image in form-data format json_request = True try: - content_type = request.headers['content-type'] + content_type = request.headers.get('content-type', '') if content_type == 'application/json': data = await request.json() - else: + elif 'multipart/form-data' in content_type: data = await request.form() - json_request = False + json_request = False + else: + body = await request.body() + data = json.loads(body.decode("utf-8")) + except Exception as e: - return JSONResponse(content={"error": "json parse error"}, status_code=status.HTTP_400_BAD_REQUEST) + return JSONResponse(content={"error": "json parse error"}, status_code=status.HTTP_400_BAD_REQUEST) - model_name = data.get("model_name") + prompt = data.get("prompt") settings: dict = data.get('settings') + context = data.get("context") if not context: user_context = [] @@ -337,22 +345,20 @@ class VLMS(Blackbox): else: return JSONResponse(content={"error": "context format error, should be in format of list or Openai_format"}, status_code=status.HTTP_400_BAD_REQUEST) - if json_request: - img_data = data.get("img_data") + if json_request or 'multipart/form-data' not in content_type: + img_data = data.get("img_data") else: img_data = await data.get("img_data").read() if settings: settings = ast.literal_eval(settings) if prompt is None: return JSONResponse(content={'error': "Question is required"}, status_code=status.HTTP_400_BAD_REQUEST) - - # if model_name is None or model_name.isspace(): - # model_name = "Qwen-VL-Chat" - # response,_ = self.processing(prompt, img_data,settings, model_name,user_context=user_context) - - # return StreamingResponse(self.processing(prompt, img_data,settings, model_name,user_context=user_context), status_code=status.HTTP_200_OK) - return EventSourceResponse(self.processing(prompt, img_data,settings, model_name,user_context=user_context), status_code=status.HTTP_200_OK) - - # HTTP JsonResponse - response, history = self.processing(prompt, img_data,settings, model_name,user_context=user_context) - # return JSONResponse(content={"response": response}, status_code=status.HTTP_200_OK) \ No newline at end of file + streaming_output = str(settings.get('stream',False)).strip().lower() == 'true' if settings else False + if streaming_output: + # return StreamingResponse(self.processing(prompt, img_data,settings, user_context=user_context), status_code=status.HTTP_200_OK) + return EventSourceResponse(self.processing(prompt, img_data,settings, user_context=user_context), status_code=status.HTTP_200_OK) + else: + # HTTP JsonResponse + output = self.processing(prompt, img_data,settings, user_context=user_context) + response = ''.join([res for res in output]) + return JSONResponse(content={"response": response}, status_code=status.HTTP_200_OK) \ No newline at end of file diff --git a/src/configuration.py b/src/configuration.py index bac66ea..e726d69 100644 --- a/src/configuration.py +++ b/src/configuration.py @@ -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") \ No newline at end of file diff --git a/src/log/loki_config.py b/src/log/loki_config.py new file mode 100644 index 0000000..121b4a0 --- /dev/null +++ b/src/log/loki_config.py @@ -0,0 +1,102 @@ +import logging +import logging_loki +import os +from requests.auth import HTTPBasicAuth +from typing import Dict, Optional +class LokiLogger: + """ + 一个用于配置和获取Loki日志记录器的类。 + 可以在其他文件里import后直接使用。 + + 用法示例： + from loki_config import LokiLogger + + # 获取日志记录器实例 (使用默认配置或环境变量) + loki_logger_instance = LokiLogger().get_logger() + loki_logger_instance.info("这条日志会推送到Loki！") + + # 或者使用自定义配置 + my_loki_logger = LokiLogger( + url="https://your-custom-loki-url.com/loki/api/v1/push", + username="myuser", + password="mypassword", + tags={"app_name": "my-custom-app", "environment": "prod"}, + level=logging.DEBUG + ).get_logger() + my_loki_logger.debug("这条调试日志也推送到Loki。") + """ + def __init__(self, + url: Optional[str] = None, + username: Optional[str] = None, + password: Optional[str] = None, + tags: Optional[Dict[str, str]] = None, + level: int = logging.INFO, + logger_name: str = "app_loki_logger"): + """ + 初始化LokiLogger。 + + Args: + url (str, optional): Loki的推送URL。默认从环境变量LOKI_URL获取， + 如果不存在则使用"https://loki.bwgdi.com/loki/api/v1/push"。 + username (str, optional): Loki的认证用户名。默认从环境变量LOKI_USERNAME获取。 + password (str, optional): Loki的认证密码。默认从环境变量LOKI_PASSWORD获取。 + tags (Dict[str, str], optional): 发送到Loki的默认标签。例如：{"application": "my-app"}。 + 如果未提供，则默认为{"application": "default-app", "source": "python-app"}。 + level (int, optional): 日志级别（如logging.INFO, logging.DEBUG）。默认为logging.INFO。 + logger_name (str, optional): 日志记录器的名称。默认为"app_loki_logger"。 + """ + # 从环境变量获取配置，如果未通过参数提供 + self._url = url if url else os.getenv("LOKI_URL", "https://loki.bwgdi.com/loki/api/v1/push") + self._username = username if username else os.getenv("LOKI_USERNAME",'admin') + self._password = password if password else os.getenv("LOKI_PASSWORD",'admin') + self._tags = tags if tags is not None else {"app": "jarvis", "env": "dev", "location": "gdi", "layer": "models"} + + # 获取或创建指定名称的日志记录器 + self._logger = logging.getLogger(logger_name) + self._logger.setLevel(level) + + for handler in self._logger.handlers[:]: + self._logger.removeHandler(handler) + # 检查是否已存在LokiHandler，避免重复添加导致重复日志 + # 在多文件或多次初始化的情况下，同一个logger_name可能会获取到同一个logger实例 + if not any(isinstance(h, logging_loki.LokiHandler) for h in self._logger.handlers): + try: + auth = None + if self._username and self._password: + auth = HTTPBasicAuth(self._username, self._password) + + loki_handler = logging_loki.LokiHandler( + url=self._url, + tags=self._tags, + version="1", # 通常Loki API版本为1 + auth=auth + ) + self._logger.addHandler(loki_handler) + # 同时添加一个StreamHandler到控制台，以便在本地调试时也能看到日志输出 + if not any(isinstance(h, logging.StreamHandler) for h in self._logger.handlers): + self._logger.addHandler(logging.StreamHandler()) + self._logger.info(f"LokiLogger: 已成功配置Loki日志处理器，目标地址：{self._url}") + except Exception as e: + # 如果Loki配置失败，确保仍然有StreamHandler将日志输出到控制台 + if not any(isinstance(h, logging.StreamHandler) for h in self._logger.handlers): + self._logger.addHandler(logging.StreamHandler()) + self._logger.error(f"LokiLogger: 配置LokiHandler失败：{e}。将回退到控制台日志记录。", exc_info=True) + else: + # 如果LokiHandler已经存在，只确保StreamHandler存在 + if not any(isinstance(h, logging.StreamHandler) for h in self._logger.handlers): + self._logger.addHandler(logging.StreamHandler()) + self._logger.debug(f"LokiLogger: '{logger_name}' 记录器已配置LokiHandler，跳过重新配置。") + + + def get_logger(self) -> logging.Logger: + """ + 返回已配置的日志记录器实例。 + """ + self._logger.debug("LokiLogger: 获取已配置的日志记录器实例。") + return self._logger + +# 可选：如果希望有一个默认的全局LokiLogger实例 +# 你可以在这里实例化，然后在其他文件直接从这里导入 `loki_logger` +# 例如： +# DEFAULT_LOKI_LOGGER_INSTANCE = LokiLogger().get_logger() +# 然后在其他文件里： `from loki_config import DEFAULT_LOKI_LOGGER_INSTANCE as logger` diff --git a/src/tts/tts_service.py b/src/tts/tts_service.py index 39214b5..8b7c1ab 100644 --- a/src/tts/tts_service.py +++ b/src/tts/tts_service.py @@ -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',