244 lines
9.3 KiB
Plaintext
244 lines
9.3 KiB
Plaintext
import os
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import logging
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import torch
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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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# os.environ["OPENAI_PROXY"] = "socks5h://127.0.0.1:13659"
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os.environ["API_BASE_URL"] = "http://openai.com/v1/chat/completions"
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os.environ["OPENAI_API_KEY"] = ""
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os.environ["DUCKDUCKGO_PROXY"] = "socks5://127.0.0.1:13659"
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os.environ["BAIDU_OCR_API_KEY"] = ""
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os.environ["BAIDU_OCR_SECRET_KEY"] = ""
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import platform
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system_name = platform.system()
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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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"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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}
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LOCAL_MODEL_DIR = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "embedding_models")
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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()}
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# 选用的 Embedding 名称
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EMBEDDING_ENGINE = 'openai'
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EMBEDDING_MODEL = "text2vec-base"
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# Embedding 模型运行设备
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EMBEDDING_DEVICE = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
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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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"chatglm-6b-int4": {
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"local_model_path": "THUDM/chatglm2-6b-int4/",
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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-int4": {
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"local_model_path": "THUDM/chatglm2-6b-int4",
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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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"vicuna-13b-hf": {
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"local_model_path": "",
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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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# 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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"gpt-3.5-turbo": {
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"local_model_path": "gpt-3.5-turbo",
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"api_base_url": os.environ.get("API_BASE_URL"),
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"api_key": os.environ.get("OPENAI_API_KEY")
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},
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}
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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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llm_model_dict_c = {}
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for k, v in llm_model_dict.items():
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v_c = {}
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for kk, vv in v.items():
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if k=="local_model_path":
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v_c[kk] = f"/home/user/chatbot/llm_models/{vv}" if is_running_in_docker() else f"{LOCAL_LLM_MODEL_DIR}/{vv}"
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else:
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v_c[kk] = vv
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llm_model_dict_c[k] = v_c
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llm_model_dict = llm_model_dict_c
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# LLM 名称
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LLM_MODEL = "gpt-3.5-turbo"
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USE_FASTCHAT = "gpt" not in LLM_MODEL # 判断是否进行fastchat
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# LLM 运行设备
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LLM_DEVICE = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
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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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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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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__))), "sources/docs")
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# NEBULA_DATA存储路径
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NELUBA_PATH = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "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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# 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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# 基于本地知识问答的提示词模版
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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。
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【已知信息】{context}
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【问题】{question}"""
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# 基于本地代码知识问答的提示词模版
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CODE_PROMPT_TEMPLATE = """【指令】根据已知信息来回答问题。
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【已知信息】{context}
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【问题】{question}"""
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# 代码解释模版
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CODE_INTERPERT_TEMPLATE = '''{code}
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解释一下这段代码'''
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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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