177 lines
7.1 KiB
Plaintext
177 lines
7.1 KiB
Plaintext
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: <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 = {
|
||
'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 |