116 lines
4.8 KiB
Python
116 lines
4.8 KiB
Python
import os
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import platform
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#
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system_name = platform.system()
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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(os.path.abspath(__file__))), "logs")
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# 知识库默认存储路径
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SOURCE_PATH = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "sources")
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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(os.path.abspath(__file__))), "knowledge_base")
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# 代码库默认存储路径
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CB_ROOT_PATH = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "code_base")
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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(os.path.abspath(__file__))), "nltk_data")
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# 代码存储路径
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JUPYTER_WORK_PATH = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "jupyter_work")
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# WEB_CRAWL存储路径
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WEB_CRAWL_PATH = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "knowledge_base")
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# NEBULA_DATA存储路径
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NEBULA_PATH = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "data/nebula_data")
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# 语言模型存储路径
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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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# 向量模型存储路径
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LOCAL_EM_MODEL_DIR = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "embedding_models")
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# CHROMA 存储路径
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CHROMA_PERSISTENT_PATH = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "data/chroma_data")
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for _path in [LOG_PATH, SOURCE_PATH, KB_ROOT_PATH, CB_ROOT_PATH, NLTK_DATA_PATH, JUPYTER_WORK_PATH, WEB_CRAWL_PATH, NEBULA_PATH, CHROMA_PERSISTENT_PATH, LOCAL_LLM_MODEL_DIR, LOCAL_EM_MODEL_DIR]:
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if not os.path.exists(_path):
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os.makedirs(_path, exist_ok=True)
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path_envt_dict = {
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"LOG_PATH": LOG_PATH, "SOURCE_PATH": SOURCE_PATH, "KB_ROOT_PATH": KB_ROOT_PATH,
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"NLTK_DATA_PATH":NLTK_DATA_PATH, "JUPYTER_WORK_PATH": JUPYTER_WORK_PATH,
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"WEB_CRAWL_PATH": WEB_CRAWL_PATH, "NEBULA_PATH": NEBULA_PATH,
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"CHROMA_PERSISTENT_PATH": CHROMA_PERSISTENT_PATH
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}
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for path_name, _path in path_envt_dict.items():
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os.environ[path_name] = _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 = 500
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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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# Mac 可能存在无法使用normalized_L2的问题，因此调整SCORE_THRESHOLD至 0~1100
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FAISS_NORMALIZE_L2 = True if system_name in ["Linux", "Windows"] else False
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SCORE_THRESHOLD = 1 if system_name in ["Linux", "Windows"] else 1100
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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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# 代码引擎匹配结题数量
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CODE_SEARCH_TOP_K = 1
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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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log_verbose = False |