import os import sys import logging import torch import openai import base64 from .utils import is_running_in_docker from .default_config import * # 日志格式 LOG_FORMAT = "%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s: %(message)s" logger = logging.getLogger() logger.setLevel(logging.INFO) logging.basicConfig(format=LOG_FORMAT) VERSION = "v0.1.0" import platform system_name = platform.system() try: # ignore these content from zdatafront import client, monkey, OPENAI_API_BASE # patch openai sdk monkey.patch_openai() secret_key = base64.b64decode('xx').decode('utf-8') # zdatafront 提供的统一加密密钥 client.aes_secret_key = secret_key # zdatafront 分配的业务标记 client.visit_domain = os.environ.get("visit_domain") client.visit_biz = os.environ.get("visit_biz") client.visit_biz_line = os.environ.get("visit_biz_line") except: pass # add your openai key OPENAI_API_BASE = "https://api.openai.com/v1" os.environ["API_BASE_URL"] = OPENAI_API_BASE os.environ["OPENAI_API_KEY"] = "sk-xx" openai.api_key = "sk-xx" # os.environ["OPENAI_PROXY"] = "socks5h://127.0.0.1:13659" os.environ["DUCKDUCKGO_PROXY"] = os.environ.get("DUCKDUCKGO_PROXY") or "socks5://127.0.0.1:13659" # ignore if you dont's use baidu_ocr_api os.environ["BAIDU_OCR_API_KEY"] = "xx" os.environ["BAIDU_OCR_SECRET_KEY"] = "xx" os.environ["log_verbose"] = "2" # LLM 名称 EMBEDDING_ENGINE = 'model' # openai or model EMBEDDING_MODEL = "text2vec-base" LLM_MODEL = "gpt-3.5-turbo" LLM_MODELs = ["gpt-3.5-turbo"] USE_FASTCHAT = "gpt" not in LLM_MODEL # 判断是否进行fastchat # LLM 运行设备 LLM_DEVICE = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu" # 在以下字典中修改属性值，以指定本地embedding模型存储位置 # 如将 "text2vec": "GanymedeNil/text2vec-large-chinese" 修改为 "text2vec": "User/Downloads/text2vec-large-chinese" # 此处请写绝对路径 embedding_model_dict = { "ernie-tiny": "nghuyong/ernie-3.0-nano-zh", "ernie-base": "nghuyong/ernie-3.0-base-zh", "text2vec-base": "shibing624/text2vec-base-chinese", "text2vec": "GanymedeNil/text2vec-large-chinese", "text2vec-paraphrase": "shibing624/text2vec-base-chinese-paraphrase", "text2vec-sentence": "shibing624/text2vec-base-chinese-sentence", "text2vec-multilingual": "shibing624/text2vec-base-multilingual", "m3e-small": "moka-ai/m3e-small", "m3e-base": "moka-ai/m3e-base", "m3e-large": "moka-ai/m3e-large", "bge-small-zh": "BAAI/bge-small-zh", "bge-base-zh": "BAAI/bge-base-zh", "bge-large-zh": "BAAI/bge-large-zh" } LOCAL_MODEL_DIR = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "embedding_models") embedding_model_dict = {k: f"/home/user/chatbot/embedding_models/{v}" if is_running_in_docker() else f"{LOCAL_MODEL_DIR}/{v}" for k, v in embedding_model_dict.items()} # Embedding 模型运行设备 EMBEDDING_DEVICE = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu" ONLINE_LLM_MODEL = { # 线上模型。请在server_config中为每个在线API设置不同的端口 "openai-api": { "model_name": "gpt-3.5-turbo", "api_base_url": "https://api.openai.com/v1", "api_key": "", "openai_proxy": "", }, "example": { "version": "gpt-3.5", # 采用openai接口做示例 "api_base_url": "https://api.openai.com/v1", "api_key": "", "provider": "ExampleWorker", }, } # 建议使用chat模型，不要使用base，无法获取正确输出 llm_model_dict = { "chatglm-6b": { "local_model_path": "THUDM/chatglm-6b", "api_base_url": "http://localhost:8888/v1", # "name"修改为fastchat服务中的"api_base_url" "api_key": "EMPTY" }, # 以下模型经过测试可接入，配置仿照上述即可 # 'codellama_34b', 'Baichuan2-13B-Base', 'Baichuan2-13B-Chat', 'baichuan2-7b-base', 'baichuan2-7b-chat', # 'internlm-7b-base', 'internlm-chat-7b', 'chatglm2-6b', 'qwen-14b-base', 'qwen-14b-chat', 'qwen-1-8B-Chat', # 'Qwen-7B', 'Qwen-7B-Chat', 'qwen-7b-base-v1.1', 'qwen-7b-chat-v1.1', 'chatglm3-6b', 'chatglm3-6b-32k', # 'chatglm3-6b-base', 'Qwen-72B-Chat-Int4' # 调用chatgpt时如果报出： urllib3.exceptions.MaxRetryError: HTTPSConnectionPool(host='api.openai.com', port=443): # Max retries exceeded with url: /v1/chat/completions # 则需要将urllib3版本修改为1.25.11 # 如果依然报urllib3.exceptions.MaxRetryError: HTTPSConnectionPool，则将https改为http # 参考https://zhuanlan.zhihu.com/p/350015032 # 如果报出：raise NewConnectionError( # urllib3.exceptions.NewConnectionError: : # Failed to establish a new connection: [WinError 10060] # 则是因为内地和香港的IP都被OPENAI封了，需要切换为日本、新加坡等地 "gpt-3.5-turbo": { "local_model_path": "gpt-3.5-turbo", "api_base_url": os.environ.get("API_BASE_URL"), "api_key": os.environ.get("OPENAI_API_KEY") }, "gpt-3.5-turbo-16k": { "local_model_path": "gpt-3.5-turbo-16k", "api_base_url": os.environ.get("API_BASE_URL"), "api_key": os.environ.get("OPENAI_API_KEY") }, "gpt-3.5-turbo-0613": { "local_model_path": "gpt-3.5-turbo-0613", "api_base_url": os.environ.get("API_BASE_URL"), "api_key": os.environ.get("OPENAI_API_KEY") }, "gpt-4": { "local_model_path": "gpt-4", "api_base_url": os.environ.get("API_BASE_URL"), "api_key": os.environ.get("OPENAI_API_KEY") }, "gpt-3.5-turbo-1106": { "local_model_path": "gpt-3.5-turbo-1106", "api_base_url": os.environ.get("API_BASE_URL"), "api_key": os.environ.get("OPENAI_API_KEY") }, } # 建议使用chat模型，不要使用base，无法获取正确输出 VLLM_MODEL_DICT = { 'chatglm2-6b': "THUDM/chatglm-6b", } # 以下模型经过测试可接入，配置仿照上述即可 # 'codellama_34b', 'Baichuan2-13B-Base', 'Baichuan2-13B-Chat', 'baichuan2-7b-base', 'baichuan2-7b-chat', # 'internlm-7b-base', 'internlm-chat-7b', 'chatglm2-6b', 'qwen-14b-base', 'qwen-14b-chat', 'qwen-1-8B-Chat', # 'Qwen-7B', 'Qwen-7B-Chat', 'qwen-7b-base-v1.1', 'qwen-7b-chat-v1.1', 'chatglm3-6b', 'chatglm3-6b-32k', # 'chatglm3-6b-base', 'Qwen-72B-Chat-Int4' LOCAL_LLM_MODEL_DIR = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "llm_models") # 模型路径重置 llm_model_dict_c = {} for k, v in llm_model_dict.items(): v_c = {} for kk, vv in v.items(): if k=="local_model_path": v_c[kk] = f"/home/user/chatbot/llm_models/{vv}" if is_running_in_docker() else f"{LOCAL_LLM_MODEL_DIR}/{vv}" else: v_c[kk] = vv llm_model_dict_c[k] = v_c llm_model_dict = llm_model_dict_c # VLLM_MODEL_DICT_c = {} for k, v in VLLM_MODEL_DICT.items(): VLLM_MODEL_DICT_c[k] = f"/home/user/chatbot/llm_models/{v}" if is_running_in_docker() else f"{LOCAL_LLM_MODEL_DIR}/{v}" VLLM_MODEL_DICT = VLLM_MODEL_DICT_c