From 8fe010bbbec108098e67728aa83af64c1cb70280 Mon Sep 17 00:00:00 2001 From: 0Xiao0 <511201264@qq.com> Date: Mon, 28 Oct 2024 17:38:40 +0800 Subject: [PATCH] style: convert /home/gpu/ to / --- sample/chroma_client1.py | 12 ++++++------ sample/chroma_client_en.py | 12 ++++++------ sample/chroma_client_query.py | 12 ++++++------ sample/chroma_rerank.py | 14 +++++++------- src/blackbox/asr.py | 4 ++-- src/blackbox/chat.py | 2 +- src/blackbox/chroma_query.py | 24 ++++++++++++------------ src/blackbox/chroma_upsert.py | 8 ++++---- src/blackbox/g2e.py | 2 +- src/blackbox/tts.py | 4 ++-- src/sentiment_engine/sentiment_engine.py | 2 +- src/tts/tts_service.py | 2 +- 12 files changed, 49 insertions(+), 49 deletions(-) diff --git a/sample/chroma_client1.py b/sample/chroma_client1.py index 133613c..edce182 100644 --- a/sample/chroma_client1.py +++ b/sample/chroma_client1.py @@ -70,7 +70,7 @@ def get_all_files(folder_path): # 加载文档和拆分文档 -loader = TextLoader("/home/gpu/Workspace/jarvis-models/sample/RAG_zh.txt") +loader = TextLoader("/Workspace/jarvis-models/sample/RAG_zh.txt") documents = loader.load() @@ -84,8 +84,8 @@ ids = ["20240521_store"+str(i) for i in range(len(docs))] # 加载embedding模型和chroma server -embedding_model = SentenceTransformerEmbeddings(model_name='/home/gpu/Workspace/Models/BAAI/bge-large-zh-v1.5', model_kwargs={"device": "cuda"}) -client = chromadb.HttpClient(host='10.6.81.119', port=7000) +embedding_model = SentenceTransformerEmbeddings(model_name='/Workspace/Models/BAAI/bge-large-zh-v1.5', model_kwargs={"device": "cuda"}) +client = chromadb.HttpClient(host='192.168.0.200', port=7000) id = "g2e" #client.delete_collection(id) @@ -106,8 +106,8 @@ print("collection_number",collection_number) # # chroma 召回 # from chromadb.utils import embedding_functions -# embedding_model = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="/home/gpu/Workspace/Models/BAAI/bge-large-zh-v1.5", device = "cuda") -# client = chromadb.HttpClient(host='10.6.81.119', port=7000) +# embedding_model = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="/Workspace/Models/BAAI/bge-large-zh-v1.5", device = "cuda") +# client = chromadb.HttpClient(host='192.168.0.200', port=7000) # collection = client.get_collection("g2e", embedding_function=embedding_model) # print(collection.count()) @@ -152,7 +152,7 @@ print("collection_number",collection_number) # 'Content-Type': 'application/json', # 'Authorization': "Bearer " + key # } -# url = "http://10.6.81.119:23333/v1/chat/completions" +# url = "http://192.168.0.200:23333/v1/chat/completions" # fastchat_response = requests.post(url, json=chat_inputs, headers=header) # # print(fastchat_response.json()) diff --git a/sample/chroma_client_en.py b/sample/chroma_client_en.py index ee60034..d3e3de9 100644 --- a/sample/chroma_client_en.py +++ b/sample/chroma_client_en.py @@ -70,7 +70,7 @@ def get_all_files(folder_path): # 加载文档和拆分文档 -loader = TextLoader("/home/gpu/Workspace/jarvis-models/sample/RAG_en.txt") +loader = TextLoader("/Workspace/jarvis-models/sample/RAG_en.txt") documents = loader.load() @@ -84,8 +84,8 @@ ids = ["20240521_store"+str(i) for i in range(len(docs))] # 加载embedding模型和chroma server -embedding_model = SentenceTransformerEmbeddings(model_name='/home/gpu/Workspace/Models/BAAI/bge-small-en-v1.5', model_kwargs={"device": "cuda"}) -client = chromadb.HttpClient(host='10.6.81.119', port=7000) +embedding_model = SentenceTransformerEmbeddings(model_name='/Workspace/Models/BAAI/bge-small-en-v1.5', model_kwargs={"device": "cuda"}) +client = chromadb.HttpClient(host='192.168.0.200', port=7000) id = "g2e_english" client.delete_collection(id) @@ -106,8 +106,8 @@ print("collection_number",collection_number) # # chroma 召回 # from chromadb.utils import embedding_functions -# embedding_model = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="/home/gpu/Workspace/Models/BAAI/bge-large-zh-v1.5", device = "cuda") -# client = chromadb.HttpClient(host='10.6.81.119', port=7000) +# embedding_model = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="/Workspace/Models/BAAI/bge-large-zh-v1.5", device = "cuda") +# client = chromadb.HttpClient(host='192.168.0.200', port=7000) # collection = client.get_collection("g2e", embedding_function=embedding_model) # print(collection.count()) @@ -152,7 +152,7 @@ print("collection_number",collection_number) # 'Content-Type': 'application/json', # 'Authorization': "Bearer " + key # } -# url = "http://10.6.81.119:23333/v1/chat/completions" +# url = "http://192.168.0.200:23333/v1/chat/completions" # fastchat_response = requests.post(url, json=chat_inputs, headers=header) # # print(fastchat_response.json()) diff --git a/sample/chroma_client_query.py b/sample/chroma_client_query.py index a3935f3..cafbfcc 100644 --- a/sample/chroma_client_query.py +++ b/sample/chroma_client_query.py @@ -66,7 +66,7 @@ def get_all_files(folder_path): # 加载文档和拆分文档 -# loader = TextLoader("/home/gpu/Workspace/jarvis-models/sample/RAG_zh.txt") +# loader = TextLoader("/Workspace/jarvis-models/sample/RAG_zh.txt") # documents = loader.load() @@ -80,8 +80,8 @@ def get_all_files(folder_path): # # 加载embedding模型和chroma server -# embedding_model = SentenceTransformerEmbeddings(model_name='/home/gpu/Workspace/Models/BAAI/bge-large-zh-v1.5', model_kwargs={"device": "cuda"}) -# client = chromadb.HttpClient(host='10.6.81.119', port=7000) +# embedding_model = SentenceTransformerEmbeddings(model_name='/Workspace/Models/BAAI/bge-large-zh-v1.5', model_kwargs={"device": "cuda"}) +# client = chromadb.HttpClient(host='192.168.0.200', port=7000) # id = "g2e" # client.delete_collection(id) @@ -102,8 +102,8 @@ def get_all_files(folder_path): # chroma 召回 from chromadb.utils import embedding_functions -embedding_model = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="/home/gpu/Workspace/Models/BAAI/bge-large-zh-v1.5", device = "cuda") -client = chromadb.HttpClient(host='10.6.81.119', port=7000) +embedding_model = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="/Workspace/Models/BAAI/bge-large-zh-v1.5", device = "cuda") +client = chromadb.HttpClient(host='192.168.0.200', port=7000) collection = client.get_collection("g2e", embedding_function=embedding_model) print(collection.count()) @@ -148,7 +148,7 @@ print("time: ", time.time() - start_time) # 'Content-Type': 'application/json', # 'Authorization': "Bearer " + key # } -# url = "http://10.6.81.119:23333/v1/chat/completions" +# url = "http://192.168.0.200:23333/v1/chat/completions" # fastchat_response = requests.post(url, json=chat_inputs, headers=header) # # print(fastchat_response.json()) diff --git a/sample/chroma_rerank.py b/sample/chroma_rerank.py index b94f311..3093c85 100644 --- a/sample/chroma_rerank.py +++ b/sample/chroma_rerank.py @@ -9,16 +9,16 @@ from langchain_community.embeddings.sentence_transformer import SentenceTransfor import time # chroma run --path chroma_db/ --port 8000 --host 0.0.0.0 -# loader = TextLoader("/home/administrator/Workspace/chroma_data/粤语语料.txt",encoding="utf-8") -loader = TextLoader("/home/administrator/Workspace/jarvis-models/sample/RAG_boss.txt") +# loader = TextLoader("/Workspace/chroma_data/粤语语料.txt",encoding="utf-8") +loader = TextLoader("/Workspace/jarvis-models/sample/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='/home/administrator/Workspace/Models/BAAI/bge-m3', model_kwargs={"device": "cuda:1"}) -client = chromadb.HttpClient(host='172.16.4.7', port=7000) +embedding_model = SentenceTransformerEmbeddings(model_name='/Workspace/Models/BAAI/bge-m3', model_kwargs={"device": "cuda:1"}) +client = chromadb.HttpClient(host='192.168.0.200', port=7000) id = "boss" client.delete_collection(id) @@ -28,13 +28,13 @@ db = Chroma.from_documents(documents=docs, embedding=embedding_model, ids=ids, c -embedding_model = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="/home/administrator/Workspace/Models/BAAI/bge-m3", device = "cuda:1") +embedding_model = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="/Workspace/Models/BAAI/bge-m3", device = "cuda:1") -client = chromadb.HttpClient(host='172.16.4.7', port=7000) +client = chromadb.HttpClient(host='192.168.0.200', port=7000) collection = client.get_collection(id, embedding_function=embedding_model) -reranker_model = CrossEncoder("/home/administrator/Workspace/Models/BAAI/bge-reranker-v2-m3", max_length=512, device = "cuda:1") +reranker_model = CrossEncoder("/Workspace/Models/BAAI/bge-reranker-v2-m3", max_length=512, device = "cuda:1") while True: usr_question = input("\n 请输入问题: ") diff --git a/src/blackbox/asr.py b/src/blackbox/asr.py index 935f2f0..416009f 100644 --- a/src/blackbox/asr.py +++ b/src/blackbox/asr.py @@ -43,7 +43,7 @@ class ASR(Blackbox): config = read_yaml(".env.yaml") self.paraformer = RapidParaformer(config) - model_dir = "/home/gpu/Workspace/Models/SenseVoice/SenseVoiceSmall" + model_dir = "/Workspace/Models/SenseVoice/SenseVoiceSmall" self.speed = sensevoice_config.speed self.device = sensevoice_config.device @@ -59,7 +59,7 @@ class ASR(Blackbox): self.asr = AutoModel( model=model_dir, trust_remote_code=True, - remote_code= "/home/gpu/Workspace/SenseVoice/model.py", + remote_code= "/Workspace/SenseVoice/model.py", vad_model="fsmn-vad", vad_kwargs={"max_single_segment_time": 30000}, device=self.device, diff --git a/src/blackbox/chat.py b/src/blackbox/chat.py index a3ae892..4ae8283 100644 --- a/src/blackbox/chat.py +++ b/src/blackbox/chat.py @@ -98,7 +98,7 @@ class Chat(Blackbox): #user_presence_penalty = 0.8 if user_model_url is None or user_model_url.isspace() or user_model_url == "": - user_model_url = "http://10.6.81.119:23333/v1/chat/completions" + user_model_url = "http://192.168.0.200:23333/v1/chat/completions" if user_model_key is None or user_model_key.isspace() or user_model_key == "": user_model_key = "YOUR_API_KEY" diff --git a/src/blackbox/chroma_query.py b/src/blackbox/chroma_query.py index 449b317..ba4767a 100755 --- a/src/blackbox/chroma_query.py +++ b/src/blackbox/chroma_query.py @@ -22,12 +22,12 @@ 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="/home/administrator/Workspace/Models/BAAI/bge-large-zh-v1.5", device = "cuda:0") - self.embedding_model_2 = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="/home/administrator/Workspace/Models/BAAI/bge-small-en-v1.5", device = "cuda:0") - self.embedding_model_3 = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="/home/administrator/Workspace/Models/BAAI/bge-m3", device = "cuda:0") - self.client_1 = chromadb.HttpClient(host='172.16.4.7', port=7000) + 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='192.168.0.200', port=7000) # self.client_2 = chromadb.HttpClient(host='10.6.82.192', port=8000) - self.reranker_model_1 = CrossEncoder("/home/administrator/Workspace/Models/BAAI/bge-reranker-v2-m3", max_length=512, device = "cuda") + self.reranker_model_1 = CrossEncoder("/Workspace/Models/BAAI/bge-reranker-v2-m3", max_length=512, device = "cuda") def __call__(self, *args, **kwargs): return self.processing(*args, **kwargs) @@ -57,10 +57,10 @@ 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 = "/home/administrator/Workspace/Models/BAAI/bge-large-zh-v1.5" + 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 = "172.16.4.7" + chroma_host = "192.168.0.200" if chroma_port is None or chroma_port.isspace() or chroma_port == "": chroma_port = "7000" @@ -72,7 +72,7 @@ class ChromaQuery(Blackbox): chroma_n_results = 10 # load client and embedding model from init - if re.search(r"172.16.4.7", chroma_host) and re.search(r"7000", chroma_port): + if re.search(r"192.168.0.200", chroma_host) and re.search(r"7000", chroma_port): client = self.client_1 else: try: @@ -80,11 +80,11 @@ class ChromaQuery(Blackbox): except: return JSONResponse(content={"error": "chroma client not found"}, status_code=status.HTTP_400_BAD_REQUEST) - if re.search(r"/home/administrator/Workspace/Models/BAAI/bge-large-zh-v1.5", chroma_embedding_model): + if re.search(r"/Workspace/Models/BAAI/bge-large-zh-v1.5", chroma_embedding_model): embedding_model = self.embedding_model_1 - elif re.search(r"/home/administrator/Workspace/Models/BAAI/bge-small-en-v1.5", chroma_embedding_model): + elif re.search(r"/Workspace/Models/BAAI/bge-small-en-v1.5", chroma_embedding_model): embedding_model = self.embedding_model_2 - elif re.search(r"/home/administrator/Workspace/Models/BAAI/bge-m3", chroma_embedding_model): + elif re.search(r"/Workspace/Models/BAAI/bge-m3", chroma_embedding_model): embedding_model = self.embedding_model_3 else: try: @@ -123,7 +123,7 @@ class ChromaQuery(Blackbox): final_result = str(results["documents"]) if chroma_reranker_model: - if re.search(r"/home/administrator/Workspace/Models/BAAI/bge-reranker-v2-m3", chroma_reranker_model): + if re.search(r"/Workspace/Models/BAAI/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 5e0fdf9..c07ba7a 100755 --- a/src/blackbox/chroma_upsert.py +++ b/src/blackbox/chroma_upsert.py @@ -31,9 +31,9 @@ class ChromaUpsert(Blackbox): def __init__(self, *args, **kwargs) -> None: # config = read_yaml(args[0]) # load embedding model - self.embedding_model_1 = SentenceTransformerEmbeddings(model_name="/home/gpu/Workspace/Models/BAAI/bge-large-zh-v1.5", model_kwargs={"device": "cuda"}) + self.embedding_model_1 = SentenceTransformerEmbeddings(model_name="/Workspace/Models/BAAI/bge-large-zh-v1.5", model_kwargs={"device": "cuda"}) # load chroma db - self.client_1 = chromadb.HttpClient(host='10.6.81.119', port=7000) + self.client_1 = chromadb.HttpClient(host='192.168.0.200', port=7000) def __call__(self, *args, **kwargs): return self.processing(*args, **kwargs) @@ -79,7 +79,7 @@ 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 = "/home/gpu/Workspace/Models/BAAI/bge-large-zh-v1.5" + 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" @@ -96,7 +96,7 @@ class ChromaUpsert(Blackbox): else: client = chromadb.HttpClient(host=chroma_host, port=chroma_port) print(f"chroma_embedding_model: {chroma_embedding_model}") - if re.search(r"/home/gpu/Workspace/Models/BAAI/bge-large-zh-v1.5", chroma_embedding_model): + if re.search(r"/Workspace/Models/BAAI/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") diff --git a/src/blackbox/g2e.py b/src/blackbox/g2e.py index d192a78..d43bcc0 100755 --- a/src/blackbox/g2e.py +++ b/src/blackbox/g2e.py @@ -23,7 +23,7 @@ class G2E(Blackbox): if context == None: context = [] #url = 'http://120.196.116.194:48890/v1' - url = 'http://10.6.81.119:23333/v1' + url = 'http://192.168.0.200:23333/v1' background_prompt = '''KOMBUKIKI是一款茶饮料，目标受众 年龄：20-35岁 性别：女性 地点：一线城市、二线城市 职业：精英中产、都市白领 收入水平：中高收入，有一定消费能力 兴趣和爱好：注重健康，有运动习惯 diff --git a/src/blackbox/tts.py b/src/blackbox/tts.py index 64f6d86..247fd02 100644 --- a/src/blackbox/tts.py +++ b/src/blackbox/tts.py @@ -13,7 +13,7 @@ from injector import inject from injector import singleton import sys,os -sys.path.append('/home/gpu/Workspace/CosyVoice') +sys.path.append('/Workspace/CosyVoice') from cosyvoice.cli.cosyvoice import CosyVoice # from cosyvoice.utils.file_utils import load_wav, speed_change @@ -81,7 +81,7 @@ class TTS(Blackbox): self.cosyvoicetts = None # os.environ['CUDA_VISIBLE_DEVICES'] = str(cosyvoice_config.device) if self.cosyvoice_mode == 'local': - self.cosyvoicetts = CosyVoice('/home/gpu/Workspace/Models/CosyVoice/pretrained_models/CosyVoice-300M') + self.cosyvoicetts = CosyVoice('/Workspace/Models/CosyVoice/pretrained_models/CosyVoice-300M') else: self.cosyvoice_url = cosyvoice_config.url diff --git a/src/sentiment_engine/sentiment_engine.py b/src/sentiment_engine/sentiment_engine.py index ae52eeb..b0dfaf0 100644 --- a/src/sentiment_engine/sentiment_engine.py +++ b/src/sentiment_engine/sentiment_engine.py @@ -5,7 +5,7 @@ from transformers import BertTokenizer import numpy as np dirabspath = __file__.split("\\")[1:-1] -dirabspath= "/home/gpu/Workspace/jarvis-models/src/sentiment_engine" + "/".join(dirabspath) +dirabspath= "/Workspace/jarvis-models/src/sentiment_engine" + "/".join(dirabspath) default_path = dirabspath + "/models/paimon_sentiment.onnx" diff --git a/src/tts/tts_service.py b/src/tts/tts_service.py index 0770442..39214b5 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= "/home/gpu/Workspace/jarvis-models/src/tts" + "/".join(dirbaspath) +dirbaspath= "/Workspace/jarvis-models/src/tts" + "/".join(dirbaspath) config = { 'ayaka': { 'cfg': dirbaspath + '/models/ayaka.json',