configs/model_config.py

import torch.cuda
import torch.backends
import os
import logging
import uuid

LOG_FORMAT = "%(levelname) -5s %(asctime)s" "-1d: %(message)s"
logger = logging.getLogger()
logger.setLevel(logging.INFO)
logging.basicConfig(format=LOG_FORMAT)

# 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": "G:\\projects\\ChatGLM\\langchain-ChatGLM\\downloaded\\GanymedeNil_text2vec-large-chinese",
    "m3e-small": "moka-ai/m3e-small",
    "m3e-base": "moka-ai/m3e-base",
}

# Embedding model name
EMBEDDING_MODEL = "text2vec"

# Embedding running device
EMBEDDING_DEVICE = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"


# supported LLM models
# llm_model_dict 处理了loader的一些预设行为，如加载位置，模型名称，模型处理器实例
# 在以下字典中修改属性值，以指定本地 LLM 模型存储位置
# 如将 "chatglm-6b" 的 "local_model_path" 由 None 修改为 "User/Downloads/chatglm-6b"
# 此处请写绝对路径
llm_model_dict = {
    "chatglm-6b-int4-qe": {
        "name": "chatglm-6b-int4-qe",
        "pretrained_model_name": "THUDM/chatglm-6b-int4-qe",
        "local_model_path": None,
        "provides": "ChatGLM"
    },
    "chatglm-6b-int4": {
        "name": "chatglm-6b-int4",
        "pretrained_model_name": "THUDM/chatglm-6b-int4",
        "local_model_path": None,
        "provides": "ChatGLM"
    },
    "chatglm-6b-int8": {
        "name": "chatglm-6b-int8",
        "pretrained_model_name": "THUDM/chatglm-6b-int8",
        "local_model_path": None,
        "provides": "ChatGLM"
    },
    "chatglm-6b": {
        "name": "chatglm-6b",
        "pretrained_model_name": "THUDM/chatglm-6b",
        "local_model_path": None,
        "provides": "ChatGLM"
    },

    "chatyuan": {
        "name": "chatyuan",
        "pretrained_model_name": "ClueAI/ChatYuan-large-v2",
        "local_model_path": None,
        "provides": None
    },
    "moss": {
        "name": "moss",
        "pretrained_model_name": "fnlp/moss-moon-003-sft",
        "local_model_path": None,
        "provides": "MOSSLLM"
    },
    "vicuna-13b-hf": {
        "name": "vicuna-13b-hf",
        "pretrained_model_name": "vicuna-13b-hf",
        "local_model_path": None,
        "provides": "LLamaLLM"
    },

    # 通过 fastchat 调用的模型请参考如下格式
    "fastchat-chatglm-6b": {
        "name": "chatglm-6b",  # "name"修改为fastchat服务中的"model_name"
        "pretrained_model_name": "chatglm-6b",
        "local_model_path": None,
        "provides": "FastChatOpenAILLM",  # 使用fastchat api时，需保证"provides"为"FastChatOpenAILLM"
        "api_base_url": "http://localhost:8000/v1"  # "name"修改为fastchat服务中的"api_base_url"
    },

    # 通过 fastchat 调用的模型请参考如下格式
    "fastchat-vicuna-13b-hf": {
        "name": "vicuna-13b-hf",  # "name"修改为fastchat服务中的"model_name"
        "pretrained_model_name": "vicuna-13b-hf",
        "local_model_path": None,
        "provides": "FastChatOpenAILLM",  # 使用fastchat api时，需保证"provides"为"FastChatOpenAILLM"
        "api_base_url": "http://localhost:8000/v1"  # "name"修改为fastchat服务中的"api_base_url"
    },
}

# LLM 名称
LLM_MODEL = "chatglm-6b-int8"
# 量化加载8bit 模型
LOAD_IN_8BIT = False
# Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU.
BF16 = False
# 本地lora存放的位置
LORA_DIR = "loras/"

# LLM lora path，默认为空，如果有请直接指定文件夹路径
LLM_LORA_PATH = ""
USE_LORA = True if LLM_LORA_PATH else False

# LLM streaming reponse
STREAMING = True

# Use p-tuning-v2 PrefixEncoder
USE_PTUNING_V2 = False

# LLM running device
LLM_DEVICE = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"

# 知识库默认存储路径
KB_ROOT_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "knowledge_base")

# 基于上下文的prompt模版，请务必保留"{question}"和"{context}"
PROMPT_TEMPLATE = """已知信息：
{context} 

根据上述已知信息，简洁和专业的来回答用户的问题。如果无法从中得到答案，请说 “根据已知信息无法回答该问题” 或 “没有提供足够的相关信息”，不允许在答案中添加编造成分，答案请使用中文。 问题是：{question}"""

# 缓存知识库数量
CACHED_VS_NUM = 1

# 文本分句长度
SENTENCE_SIZE = 100

# 匹配后单段上下文长度
CHUNK_SIZE = 250

# 传入LLM的历史记录长度
LLM_HISTORY_LEN = 3

# 知识库检索时返回的匹配内容条数
VECTOR_SEARCH_TOP_K = 5

# 知识检索内容相关度 Score, 数值范围约为0-1100，如果为0，则不生效，经测试设置为小于500时，匹配结果更精准
VECTOR_SEARCH_SCORE_THRESHOLD = 0

NLTK_DATA_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "nltk_data")

FLAG_USER_NAME = uuid.uuid4().hex

logger.info(f"""
loading model config
llm device: {LLM_DEVICE}
embedding device: {EMBEDDING_DEVICE}
dir: {os.path.dirname(os.path.dirname(__file__))}
flagging username: {FLAG_USER_NAME}
""")

# 是否开启跨域，默认为False，如果需要开启，请设置为True
# is open cross domain
OPEN_CROSS_DOMAIN = False

# Bing 搜索必备变量
# 使用 Bing 搜索需要使用 Bing Subscription Key,需要在azure port中申请试用bing search
# 具体申请方式请见
# https://learn.microsoft.com/en-us/bing/search-apis/bing-web-search/create-bing-search-service-resource
# 使用python创建bing api 搜索实例详见:
# https://learn.microsoft.com/en-us/bing/search-apis/bing-web-search/quickstarts/rest/python
BING_SEARCH_URL = "https://api.bing.microsoft.com/v7.0/search"
# 注意不是bing Webmaster Tools的api key，

# 此外，如果是在服务器上，报Failed to establish a new connection: [Errno 110] Connection timed out
# 是因为服务器加了防火墙，需要联系管理员加白名单，如果公司的服务器的话，就别想了GG
BING_SUBSCRIPTION_KEY = ""

# 是否开启中文标题加强，以及标题增强的相关配置
# 通过增加标题判断，判断哪些文本为标题，并在metadata中进行标记；
# 然后将文本与往上一级的标题进行拼合，实现文本信息的增强。
ZH_TITLE_ENHANCE = False