203 lines
8.0 KiB
Plaintext
203 lines
8.0 KiB
Plaintext
import os
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import logging
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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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# 是否显示详细日志
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log_verbose = False
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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 = {
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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": "shibing624/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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"text2vec-bge-large-chinese": "shibing624/text2vec-bge-large-chinese",
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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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"bge-large-zh-noinstruct": "BAAI/bge-large-zh-noinstruct",
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"piccolo-base-zh": "sensenova/piccolo-base-zh",
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"piccolo-large-zh": "sensenova/piccolo-large-zh",
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"text-embedding-ada-002": os.environ.get("OPENAI_API_KEY")
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}
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# 选用的 Embedding 名称
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EMBEDDING_MODEL = "m3e-base"
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# Embedding 模型运行设备。设为"auto"会自动检测，也可手动设定为"cuda","mps","cpu"其中之一。
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EMBEDDING_DEVICE = "auto"
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llm_model_dict = {
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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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"chatglm2-6b": {
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"local_model_path": "THUDM/chatglm2-6b",
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"api_base_url": "http://localhost:8888/v1", # URL需要与运行fastchat服务端的server_config.FSCHAT_OPENAI_API一致
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"api_key": "EMPTY"
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},
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"chatglm2-6b-32k": {
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"local_model_path": "THUDM/chatglm2-6b-32k", # "THUDM/chatglm2-6b-32k",
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"api_base_url": "http://localhost:8888/v1", # "URL需要与运行fastchat服务端的server_config.FSCHAT_OPENAI_API一致
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"api_key": "EMPTY"
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},
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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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# 如果出现WARNING: Retrying langchain.chat_models.openai.acompletion_with_retry.<locals>._completion_with_retry in
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# 4.0 seconds as it raised APIConnectionError: Error communicating with OpenAI.
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# 需要添加代理访问(正常开的代理软件可能会拦截不上)需要设置配置openai_proxy 或者 使用环境遍历OPENAI_PROXY 进行设置
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# 比如: "openai_proxy": 'http://127.0.0.1:4780'
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"gpt-3.5-turbo": {
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"api_base_url": "https://api.openai.com/v1",
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"api_key": os.environ.get("OPENAI_API_KEY"),
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"openai_proxy": os.environ.get("OPENAI_PROXY")
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},
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# 线上模型。当前支持智谱AI。
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# 如果没有设置有效的local_model_path，则认为是在线模型API。
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# 请在server_config中为每个在线API设置不同的端口
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# 具体注册及api key获取请前往 http://open.bigmodel.cn
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"chatglm-api": {
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"api_base_url": "http://127.0.0.1:8888/v1",
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"api_key": os.environ.get("ZHIPUAI_API_KEY"),
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"provider": "ChatGLMWorker",
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"version": "chatglm_pro", # 可选包括 "chatglm_lite", "chatglm_std", "chatglm_pro"
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},
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"minimax-api": {
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"api_base_url": "http://127.0.0.1:8888/v1",
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"group_id": "",
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"api_key": "",
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"is_pro": False,
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"provider": "MiniMaxWorker",
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},
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"xinghuo-api": {
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"api_base_url": "http://127.0.0.1:8888/v1",
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"APPID": "",
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"APISecret": "",
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"api_key": "",
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"is_v2": False,
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"provider": "XingHuoWorker",
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}
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}
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# LLM 名称
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LLM_MODEL = "chatglm2-6b"
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# 历史对话轮数
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HISTORY_LEN = 3
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# LLM通用对话参数
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TEMPERATURE = 0.7
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# TOP_P = 0.95 # ChatOpenAI暂不支持该参数
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# LLM 运行设备。设为"auto"会自动检测，也可手动设定为"cuda","mps","cpu"其中之一。
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LLM_DEVICE = "auto"
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# 日志存储路径
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LOG_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "logs")
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if not os.path.exists(LOG_PATH):
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os.mkdir(LOG_PATH)
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# 知识库默认存储路径
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KB_ROOT_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "knowledge_base")
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if not os.path.exists(KB_ROOT_PATH):
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os.mkdir(KB_ROOT_PATH)
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# 数据库默认存储路径。
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# 如果使用sqlite，可以直接修改DB_ROOT_PATH；如果使用其它数据库，请直接修改SQLALCHEMY_DATABASE_URI。
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DB_ROOT_PATH = os.path.join(KB_ROOT_PATH, "info.db")
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SQLALCHEMY_DATABASE_URI = f"sqlite:///{DB_ROOT_PATH}"
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# 可选向量库类型及对应配置
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kbs_config = {
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"faiss": {
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},
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"milvus": {
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"host": "127.0.0.1",
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"port": "19530",
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"user": "",
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"password": "",
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"secure": False,
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},
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"pg": {
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"connection_uri": "postgresql://postgres:postgres@127.0.0.1:5432/langchain_chatchat",
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}
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}
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# 默认向量库类型。可选：faiss, milvus, pg.
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DEFAULT_VS_TYPE = "faiss"
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# 缓存向量库数量
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CACHED_VS_NUM = 1
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# 知识库中单段文本长度
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CHUNK_SIZE = 250
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# 知识库中相邻文本重合长度
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OVERLAP_SIZE = 50
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# 知识库匹配向量数量
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VECTOR_SEARCH_TOP_K = 5
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# 知识库匹配相关度阈值，取值范围在0-1之间，SCORE越小，相关度越高，取到1相当于不筛选，建议设置在0.5左右
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SCORE_THRESHOLD = 1
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# 搜索引擎匹配结题数量
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SEARCH_ENGINE_TOP_K = 5
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# nltk 模型存储路径
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NLTK_DATA_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "nltk_data")
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# 基于本地知识问答的提示词模版（使用Jinja2语法，简单点就是用双大括号代替f-string的单大括号
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PROMPT_TEMPLATE = """<指令>根据已知信息，简洁和专业的来回答问题。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”，不允许在答案中添加编造成分，答案请使用中文。 </指令>
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<已知信息>{{ context }}</已知信息>
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<问题>{{ question }}</问题>"""
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# API 是否开启跨域，默认为False，如果需要开启，请设置为True
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# is open cross domain
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OPEN_CROSS_DOMAIN = False
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# Bing 搜索必备变量
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# 使用 Bing 搜索需要使用 Bing Subscription Key,需要在azure port中申请试用bing search
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# 具体申请方式请见
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# https://learn.microsoft.com/en-us/bing/search-apis/bing-web-search/create-bing-search-service-resource
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# 使用python创建bing api 搜索实例详见:
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# https://learn.microsoft.com/en-us/bing/search-apis/bing-web-search/quickstarts/rest/python
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BING_SEARCH_URL = "https://api.bing.microsoft.com/v7.0/search"
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# 注意不是bing Webmaster Tools的api key，
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# 此外，如果是在服务器上，报Failed to establish a new connection: [Errno 110] Connection timed out
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# 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
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BING_SUBSCRIPTION_KEY = ""
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# 是否开启中文标题加强，以及标题增强的相关配置
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# 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；
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# 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。
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ZH_TITLE_ENHANCE = False
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