196 lines
8.6 KiB
Plaintext
196 lines
8.6 KiB
Plaintext
import os
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import sys
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import logging
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import torch
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import openai
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import base64
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import json
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from .utils import is_running_in_docker
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from .default_config import *
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# 日志格式
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LOG_FORMAT = "%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s: %(message)s"
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logger = logging.getLogger()
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logger.setLevel(logging.INFO)
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logging.basicConfig(format=LOG_FORMAT)
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VERSION = "v0.1.0"
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import platform
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system_name = platform.system()
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try:
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# ignore these content
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from zdatafront import client, monkey, OPENAI_API_BASE
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# patch openai sdk
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monkey.patch_openai()
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secret_key = base64.b64decode('xx').decode('utf-8')
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# zdatafront 提供的统一加密密钥
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client.aes_secret_key = secret_key
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# zdatafront 分配的业务标记
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client.visit_domain = os.environ.get("visit_domain")
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client.visit_biz = os.environ.get("visit_biz")
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client.visit_biz_line = os.environ.get("visit_biz_line")
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except Exception as e:
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OPENAI_API_BASE = "https://api.openai.com/v1"
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logger.error(e)
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pass
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try:
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cur_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)))
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with open(os.path.join(cur_dir, "local_config.json"), "r") as f:
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update_config = json.load(f)
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except:
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update_config = {}
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# add your openai key
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os.environ["API_BASE_URL"] = os.environ.get("API_BASE_URL") or update_config.get("API_BASE_URL") or OPENAI_API_BASE
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os.environ["OPENAI_API_KEY"] = os.environ.get("OPENAI_API_KEY") or update_config.get("OPENAI_API_KEY") or "sk-xx"
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openai.api_key = os.environ["OPENAI_API_KEY"]
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# os.environ["OPENAI_PROXY"] = "socks5h://127.0.0.1:13659"
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os.environ["DUCKDUCKGO_PROXY"] = os.environ.get("DUCKDUCKGO_PROXY") or update_config.get("DUCKDUCKGO_PROXY") or "socks5h://127.0.0.1:13659"
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# ignore if you dont's use baidu_ocr_api
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os.environ["BAIDU_OCR_API_KEY"] = "xx"
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os.environ["BAIDU_OCR_SECRET_KEY"] = "xx"
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os.environ["log_verbose"] = "2"
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# LLM 名称
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EMBEDDING_ENGINE = os.environ.get("EMBEDDING_ENGINE") or update_config.get("EMBEDDING_ENGINE") or 'model' # openai or model
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EMBEDDING_MODEL = os.environ.get("EMBEDDING_MODEL") or update_config.get("EMBEDDING_MODEL") or "text2vec-base"
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LLM_MODEL = os.environ.get("LLM_MODEL") or "gpt-3.5-turbo"
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LLM_MODELs = [LLM_MODEL]
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USE_FASTCHAT = "gpt" not in LLM_MODEL # 判断是否进行fastchat
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# LLM 运行设备
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LLM_DEVICE = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
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# 在以下字典中修改属性值，以指定本地embedding模型存储位置
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# 如将 "text2vec": "GanymedeNil/text2vec-large-chinese" 修改为 "text2vec": "User/Downloads/text2vec-large-chinese"
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# 此处请写绝对路径
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embedding_model_dict = json.loads(os.environ.get("embedding_model_dict")) if os.environ.get("embedding_model_dict") else {}
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embedding_model_dict = embedding_model_dict or update_config.get("embedding_model_dict")
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embedding_model_dict = embedding_model_dict or {
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"ernie-tiny": "nghuyong/ernie-3.0-nano-zh",
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"ernie-base": "nghuyong/ernie-3.0-base-zh",
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"text2vec-base": "text2vec-base-chinese",
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"text2vec": "GanymedeNil/text2vec-large-chinese",
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"text2vec-paraphrase": "shibing624/text2vec-base-chinese-paraphrase",
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"text2vec-sentence": "shibing624/text2vec-base-chinese-sentence",
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"text2vec-multilingual": "shibing624/text2vec-base-multilingual",
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"m3e-small": "moka-ai/m3e-small",
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"m3e-base": "moka-ai/m3e-base",
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"m3e-large": "moka-ai/m3e-large",
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"bge-small-zh": "BAAI/bge-small-zh",
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"bge-base-zh": "BAAI/bge-base-zh",
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"bge-large-zh": "BAAI/bge-large-zh"
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}
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# LOCAL_MODEL_DIR = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "embedding_models")
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# 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()}
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# Embedding 模型运行设备
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EMBEDDING_DEVICE = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
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ONLINE_LLM_MODEL = json.loads(os.environ.get("ONLINE_LLM_MODEL")) if os.environ.get("ONLINE_LLM_MODEL") else {}
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ONLINE_LLM_MODEL = ONLINE_LLM_MODEL or update_config.get("ONLINE_LLM_MODEL")
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ONLINE_LLM_MODEL = ONLINE_LLM_MODEL or {
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# 线上模型。请在server_config中为每个在线API设置不同的端口
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"openai-api": {
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"model_name": "gpt-3.5-turbo",
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"api_base_url": OPENAI_API_BASE, # "https://api.openai.com/v1",
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"api_key": "",
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"openai_proxy": "",
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},
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"example": {
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"version": "gpt-3.5-turbo", # 采用openai接口做示例
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"api_base_url": OPENAI_API_BASE, # "https://api.openai.com/v1",
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"api_key": "",
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"provider": "ExampleWorker",
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},
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}
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# 建议使用chat模型，不要使用base，无法获取正确输出
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llm_model_dict = json.loads(os.environ.get("llm_model_dict")) if os.environ.get("llm_model_dict") else {}
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llm_model_dict = llm_model_dict or update_config.get("llm_model_dict")
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llm_model_dict = llm_model_dict or {
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"chatglm-6b": {
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"local_model_path": "THUDM/chatglm-6b",
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"api_base_url": "http://localhost:8888/v1", # "name"修改为fastchat服务中的"api_base_url"
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"api_key": "EMPTY"
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},
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# 以下模型经过测试可接入，配置仿照上述即可
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# 'codellama_34b', 'Baichuan2-13B-Base', 'Baichuan2-13B-Chat', 'baichuan2-7b-base', 'baichuan2-7b-chat',
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# 'internlm-7b-base', 'internlm-chat-7b', 'chatglm2-6b', 'qwen-14b-base', 'qwen-14b-chat', 'qwen-1-8B-Chat',
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# 'Qwen-7B', 'Qwen-7B-Chat', 'qwen-7b-base-v1.1', 'qwen-7b-chat-v1.1', 'chatglm3-6b', 'chatglm3-6b-32k',
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# 'chatglm3-6b-base', 'Qwen-72B-Chat-Int4'
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# 调用chatgpt时如果报出： urllib3.exceptions.MaxRetryError: HTTPSConnectionPool(host='api.openai.com', port=443):
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# Max retries exceeded with url: /v1/chat/completions
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# 则需要将urllib3版本修改为1.25.11
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# 如果依然报urllib3.exceptions.MaxRetryError: HTTPSConnectionPool，则将https改为http
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# 参考https://zhuanlan.zhihu.com/p/350015032
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# 如果报出：raise NewConnectionError(
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# urllib3.exceptions.NewConnectionError: <urllib3.connection.HTTPSConnection object at 0x000001FE4BDB85E0>:
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# Failed to establish a new connection: [WinError 10060]
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# 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
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"gpt-3.5-turbo": {
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"local_model_path": "gpt-3.5-turbo",
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"api_base_url": os.environ.get("API_BASE_URL"),
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"api_key": os.environ.get("OPENAI_API_KEY")
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},
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"gpt-3.5-turbo-16k": {
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"local_model_path": "gpt-3.5-turbo-16k",
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"api_base_url": os.environ.get("API_BASE_URL"),
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"api_key": os.environ.get("OPENAI_API_KEY")
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},
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"gpt-3.5-turbo-0613": {
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"local_model_path": "gpt-3.5-turbo-0613",
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"api_base_url": os.environ.get("API_BASE_URL"),
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"api_key": os.environ.get("OPENAI_API_KEY")
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},
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"gpt-4": {
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"local_model_path": "gpt-4",
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"api_base_url": os.environ.get("API_BASE_URL"),
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"api_key": os.environ.get("OPENAI_API_KEY")
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},
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"gpt-3.5-turbo-1106": {
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"local_model_path": "gpt-3.5-turbo-1106",
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"api_base_url": os.environ.get("API_BASE_URL"),
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"api_key": os.environ.get("OPENAI_API_KEY")
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},
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}
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# 建议使用chat模型，不要使用base，无法获取正确输出
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VLLM_MODEL_DICT = json.loads(os.environ.get("VLLM_MODEL_DICT")) if os.environ.get("VLLM_MODEL_DICT") else {}
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VLLM_MODEL_DICT = VLLM_MODEL_DICT or update_config.get("VLLM_MODEL_DICT")
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VLLM_MODEL_DICT = VLLM_MODEL_DICT or {
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'chatglm2-6b': "THUDM/chatglm-6b",
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}
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# 以下模型经过测试可接入，配置仿照上述即可
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# 'codellama_34b', 'Baichuan2-13B-Base', 'Baichuan2-13B-Chat', 'baichuan2-7b-base', 'baichuan2-7b-chat',
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# 'internlm-7b-base', 'internlm-chat-7b', 'chatglm2-6b', 'qwen-14b-base', 'qwen-14b-chat', 'qwen-1-8B-Chat',
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# 'Qwen-7B', 'Qwen-7B-Chat', 'qwen-7b-base-v1.1', 'qwen-7b-chat-v1.1', 'chatglm3-6b', 'chatglm3-6b-32k',
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# 'chatglm3-6b-base', 'Qwen-72B-Chat-Int4'
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# LOCAL_LLM_MODEL_DIR = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "llm_models")
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# # 模型路径重置
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# llm_model_dict_c = {}
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# for k, v in llm_model_dict.items():
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# v_c = {}
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# for kk, vv in v.items():
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# if k=="local_model_path":
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# v_c[kk] = f"/home/user/chatbot/llm_models/{vv}" if is_running_in_docker() else f"{LOCAL_LLM_MODEL_DIR}/{vv}"
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# else:
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# v_c[kk] = vv
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# llm_model_dict_c[k] = v_c
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# llm_model_dict = llm_model_dict_c
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# #
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# VLLM_MODEL_DICT_c = {}
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# for k, v in VLLM_MODEL_DICT.items():
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# VLLM_MODEL_DICT_c[k] = f"/home/user/chatbot/llm_models/{v}" if is_running_in_docker() else f"{LOCAL_LLM_MODEL_DIR}/{v}"
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# VLLM_MODEL_DICT = VLLM_MODEL_DICT_c |