codefuse-chatbot/configs/model_config.py.example

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import os
import sys
import logging
import torch
import openai
import base64
import json
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 Exception as e:
OPENAI_API_BASE = "https://api.openai.com/v1"
logger.error(e)
pass
try:
cur_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)))
with open(os.path.join(cur_dir, "local_config.json"), "r") as f:
update_config = json.load(f)
except:
update_config = {}
# add your openai key
os.environ["API_BASE_URL"] = os.environ.get("API_BASE_URL") or update_config.get("API_BASE_URL") or OPENAI_API_BASE
os.environ["OPENAI_API_KEY"] = os.environ.get("OPENAI_API_KEY") or update_config.get("OPENAI_API_KEY") or "sk-xx"
openai.api_key = os.environ["OPENAI_API_KEY"]
# os.environ["OPENAI_PROXY"] = "socks5h://127.0.0.1:13659"
os.environ["DUCKDUCKGO_PROXY"] = os.environ.get("DUCKDUCKGO_PROXY") or update_config.get("DUCKDUCKGO_PROXY") or "socks5h://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 = os.environ.get("EMBEDDING_ENGINE") or update_config.get("EMBEDDING_ENGINE") or 'model' # openai or model
EMBEDDING_MODEL = os.environ.get("EMBEDDING_MODEL") or update_config.get("EMBEDDING_MODEL") or "text2vec-base"
LLM_MODEL = os.environ.get("LLM_MODEL") or "gpt-3.5-turbo"
LLM_MODELs = [LLM_MODEL]
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 = json.loads(os.environ.get("embedding_model_dict")) if os.environ.get("embedding_model_dict") else {}
embedding_model_dict = embedding_model_dict or update_config.get("embedding_model_dict")
embedding_model_dict = embedding_model_dict or {
"ernie-tiny": "nghuyong/ernie-3.0-nano-zh",
"ernie-base": "nghuyong/ernie-3.0-base-zh",
"text2vec-base": "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 = json.loads(os.environ.get("ONLINE_LLM_MODEL")) if os.environ.get("ONLINE_LLM_MODEL") else {}
ONLINE_LLM_MODEL = ONLINE_LLM_MODEL or update_config.get("ONLINE_LLM_MODEL")
ONLINE_LLM_MODEL = ONLINE_LLM_MODEL or {
# 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": OPENAI_API_BASE, # "https://api.openai.com/v1",
"api_key": "",
"openai_proxy": "",
},
"example": {
"version": "gpt-3.5-turbo", # 采用openai接口做示例
"api_base_url": OPENAI_API_BASE, # "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 = json.loads(os.environ.get("llm_model_dict")) if os.environ.get("llm_model_dict") else {}
llm_model_dict = llm_model_dict or update_config.get("llm_model_dict")
llm_model_dict = llm_model_dict or {
"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: <urllib3.connection.HTTPSConnection object at 0x000001FE4BDB85E0>:
# 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 = json.loads(os.environ.get("VLLM_MODEL_DICT")) if os.environ.get("VLLM_MODEL_DICT") else {}
VLLM_MODEL_DICT = VLLM_MODEL_DICT or update_config.get("VLLM_MODEL_DICT")
VLLM_MODEL_DICT = VLLM_MODEL_DICT or {
'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