88 lines
3.1 KiB
Python
88 lines
3.1 KiB
Python
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import os
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import platform
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system_name = platform.system()
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executable_path = os.getcwd()
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# 日志存储路径
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LOG_PATH = os.environ.get("LOG_PATH", None) or os.path.join(executable_path, "logs")
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# 知识库默认存储路径
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SOURCE_PATH = os.environ.get("SOURCE_PATH", None) or os.path.join(executable_path, "sources")
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# 知识库默认存储路径
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KB_ROOT_PATH = os.environ.get("KB_ROOT_PATH", None) or os.path.join(executable_path, "knowledge_base")
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# 代码库默认存储路径
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CB_ROOT_PATH = os.environ.get("CB_ROOT_PATH", None) or os.path.join(executable_path, "code_base")
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# nltk 模型存储路径
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NLTK_DATA_PATH = os.environ.get("NLTK_DATA_PATH", None) or os.path.join(executable_path, "nltk_data")
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# 代码存储路径
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JUPYTER_WORK_PATH = os.environ.get("JUPYTER_WORK_PATH", None) or os.path.join(executable_path, "jupyter_work")
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# WEB_CRAWL存储路径
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WEB_CRAWL_PATH = os.environ.get("WEB_CRAWL_PATH", None) or os.path.join(executable_path, "knowledge_base")
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# NEBULA_DATA存储路径
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NELUBA_PATH = os.environ.get("NELUBA_PATH", None) or os.path.join(executable_path, "data/neluba_data")
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for _path in [LOG_PATH, SOURCE_PATH, KB_ROOT_PATH, NLTK_DATA_PATH, JUPYTER_WORK_PATH, WEB_CRAWL_PATH, NELUBA_PATH]:
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if not os.path.exists(_path):
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os.makedirs(_path, exist_ok=True)
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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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kbs_config = {
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"faiss": {
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},}
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# GENERAL SERVER CONFIG
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DEFAULT_BIND_HOST = os.environ.get("DEFAULT_BIND_HOST", None) or "127.0.0.1"
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# NEBULA SERVER CONFIG
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NEBULA_HOST = DEFAULT_BIND_HOST
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NEBULA_PORT = 9669
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NEBULA_STORAGED_PORT = 9779
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NEBULA_USER = 'root'
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NEBULA_PASSWORD = ''
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NEBULA_GRAPH_SERVER = {
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"host": DEFAULT_BIND_HOST,
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"port": NEBULA_PORT,
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"docker_port": NEBULA_PORT
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}
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# CHROMA CONFIG
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CHROMA_PERSISTENT_PATH = '/home/user/chatbot/data/chroma_data'
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# 默认向量库类型。可选：faiss, milvus, pg.
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DEFAULT_VS_TYPE = os.environ.get("DEFAULT_VS_TYPE") or "faiss"
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# 缓存向量库数量
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CACHED_VS_NUM = os.environ.get("CACHED_VS_NUM") or 1
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# 知识库中单段文本长度
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CHUNK_SIZE = os.environ.get("CHUNK_SIZE") or 500
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# 知识库中相邻文本重合长度
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OVERLAP_SIZE = os.environ.get("OVERLAP_SIZE") or 50
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# 知识库匹配向量数量
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VECTOR_SEARCH_TOP_K = os.environ.get("VECTOR_SEARCH_TOP_K") or 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 = os.environ.get("SEARCH_ENGINE_TOP_K") or 5
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# 代码引擎匹配结题数量
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CODE_SEARCH_TOP_K = os.environ.get("CODE_SEARCH_TOP_K") or 1
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