819 lines
32 KiB
Python
819 lines
32 KiB
Python
from __future__ import annotations
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import os
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from pathlib import Path
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import sys
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import typing as t
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import nltk
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from chatchat import __version__
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from chatchat.pydantic_settings_file import *
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# chatchat 数据目录，必须通过环境变量设置。如未设置则自动使用当前目录。
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CHATCHAT_ROOT = Path(os.environ.get("CHATCHAT_ROOT", ".")).resolve()
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XF_MODELS_TYPES = {
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"text2image": {"model_family": ["stable_diffusion"]},
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"image2image": {"model_family": ["stable_diffusion"]},
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"speech2text": {"model_family": ["whisper"]},
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"text2speech": {"model_family": ["ChatTTS"]},
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}
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class BasicSettings(BaseFileSettings):
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"""
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服务器基本配置信息
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除 log_verbose/HTTPX_DEFAULT_TIMEOUT 修改后即时生效
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其它配置项修改后都需要重启服务器才能生效，服务运行期间请勿修改
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"""
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model_config = SettingsConfigDict(yaml_file=CHATCHAT_ROOT / "basic_settings.yaml")
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version: str = __version__
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"""生成该配置模板的项目代码版本，如这里的值与程序实际版本不一致，建议重建配置文件模板"""
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log_verbose: bool = False
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"""是否开启日志详细信息"""
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HTTPX_DEFAULT_TIMEOUT: float = 300
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"""httpx 请求默认超时时间（秒）。如果加载模型或对话较慢，出现超时错误，可以适当加大该值。"""
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# @computed_field
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@cached_property
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def PACKAGE_ROOT(self) -> Path:
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"""代码根目录"""
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return Path(__file__).parent
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# @computed_field
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@cached_property
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def DATA_PATH(self) -> Path:
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"""用户数据根目录"""
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p = CHATCHAT_ROOT / "data"
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return p
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# @computed_field
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@cached_property
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def IMG_DIR(self) -> Path:
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"""项目相关图片目录"""
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p = self.PACKAGE_ROOT / "img"
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return p
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# @computed_field
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@cached_property
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def NLTK_DATA_PATH(self) -> Path:
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"""nltk 模型存储路径"""
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p = self.PACKAGE_ROOT / "data/nltk_data"
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return p
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# @computed_field
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@cached_property
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def LOG_PATH(self) -> Path:
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"""日志存储路径"""
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p = self.DATA_PATH / "logs"
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return p
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# @computed_field
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@cached_property
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def MEDIA_PATH(self) -> Path:
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"""模型生成内容（图片、视频、音频等）保存位置"""
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p = self.DATA_PATH / "media"
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return p
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# @computed_field
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@cached_property
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def BASE_TEMP_DIR(self) -> Path:
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"""临时文件目录，主要用于文件对话"""
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p = self.DATA_PATH / "temp"
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(p / "openai_files").mkdir(parents=True, exist_ok=True)
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return p
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KB_ROOT_PATH: str = str(CHATCHAT_ROOT / "data/knowledge_base")
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"""知识库默认存储路径"""
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DB_ROOT_PATH: str = str(CHATCHAT_ROOT / "data/knowledge_base/info.db")
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"""数据库默认存储路径。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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SQLALCHEMY_DATABASE_URI:str = "sqlite:///" + str(CHATCHAT_ROOT / "data/knowledge_base/info.db")
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"""知识库信息数据库连接URI"""
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OPEN_CROSS_DOMAIN: bool = False
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"""API 是否开启跨域"""
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DEFAULT_BIND_HOST: str = "0.0.0.0" if sys.platform != "win32" else "127.0.0.1"
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"""
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各服务器默认绑定host。如改为"0.0.0.0"需要修改下方所有XX_SERVER的host
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Windows 下 WEBUI 自动弹出浏览器时，如果地址为 "0.0.0.0" 是无法访问的，需要手动修改地址栏
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"""
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API_SERVER: dict = {"host": DEFAULT_BIND_HOST, "port": 7861, "public_host": "127.0.0.1", "public_port": 7861}
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"""API 服务器地址。其中 public_host 用于生成云服务公网访问链接（如知识库文档链接）"""
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WEBUI_SERVER: dict = {"host": DEFAULT_BIND_HOST, "port": 8501}
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"""WEBUI 服务器地址"""
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def make_dirs(self):
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'''创建所有数据目录'''
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for p in [
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self.DATA_PATH,
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self.MEDIA_PATH,
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self.LOG_PATH,
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self.BASE_TEMP_DIR,
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]:
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p.mkdir(parents=True, exist_ok=True)
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for n in ["image", "audio", "video"]:
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(self.MEDIA_PATH / n).mkdir(parents=True, exist_ok=True)
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Path(self.KB_ROOT_PATH).mkdir(parents=True, exist_ok=True)
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class KBSettings(BaseFileSettings):
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"""知识库相关配置"""
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model_config = SettingsConfigDict(yaml_file=CHATCHAT_ROOT / "kb_settings.yaml")
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DEFAULT_KNOWLEDGE_BASE: str = "samples"
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"""默认使用的知识库"""
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DEFAULT_VS_TYPE: t.Literal["faiss", "milvus", "zilliz", "pg", "es", "relyt", "chromadb"] = "faiss"
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"""默认向量库/全文检索引擎类型"""
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CACHED_VS_NUM: int = 1
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"""缓存向量库数量（针对FAISS）"""
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CACHED_MEMO_VS_NUM: int = 10
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"""缓存临时向量库数量（针对FAISS），用于文件对话"""
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CHUNK_SIZE: int = 750
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"""知识库中单段文本长度(不适用MarkdownHeaderTextSplitter)"""
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OVERLAP_SIZE: int = 150
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"""知识库中相邻文本重合长度(不适用MarkdownHeaderTextSplitter)"""
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VECTOR_SEARCH_TOP_K: int = 3 # TODO: 与 tool 配置项重复
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"""知识库匹配向量数量"""
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SCORE_THRESHOLD: float = 2.0
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"""知识库匹配相关度阈值，取值范围在0-2之间，SCORE越小，相关度越高，取到2相当于不筛选，建议设置在0.5左右"""
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DEFAULT_SEARCH_ENGINE: t.Literal["bing", "duckduckgo", "metaphor", "searx"] = "duckduckgo"
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"""默认搜索引擎"""
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SEARCH_ENGINE_TOP_K: int = 3
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"""搜索引擎匹配结题数量"""
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ZH_TITLE_ENHANCE: bool = False
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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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PDF_OCR_THRESHOLD: t.Tuple[float, float] = (0.6, 0.6)
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"""
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PDF OCR 控制：只对宽高超过页面一定比例（图片宽/页面宽，图片高/页面高）的图片进行 OCR。
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这样可以避免 PDF 中一些小图片的干扰，提高非扫描版 PDF 处理速度
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"""
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KB_INFO: t.Dict[str, str] = {"samples": "关于本项目issue的解答"} # TODO: 都存在数据库了，这个配置项还有必要吗？
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"""每个知识库的初始化介绍，用于在初始化知识库时显示和Agent调用，没写则没有介绍，不会被Agent调用。"""
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kbs_config: t.Dict[str, t.Dict] = {
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"faiss": {},
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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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"zilliz": {
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"host": "in01-a7ce524e41e3935.ali-cn-hangzhou.vectordb.zilliz.com.cn",
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"port": "19530",
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"user": "",
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"password": "",
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"secure": True
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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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"relyt": {
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"connection_uri": "postgresql+psycopg2://postgres:postgres@127.0.0.1:7000/langchain_chatchat"
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},
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"es": {
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"scheme": "http",
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"host": "127.0.0.1",
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"port": "9200",
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"index_name": "test_index",
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"user": "",
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"password": "",
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"verify_certs": True,
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"ca_certs": None,
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"client_cert": None,
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"client_key": None
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},
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"milvus_kwargs": {
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"search_params": {
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"metric_type": "L2"
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},
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"index_params": {
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"metric_type": "L2",
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"index_type": "HNSW"
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}
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},
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"chromadb": {}
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}
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"""可选向量库类型及对应配置"""
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text_splitter_dict: t.Dict[str, t.Dict[str, t.Any]] = {
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"ChineseRecursiveTextSplitter": {
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"source": "",
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"tokenizer_name_or_path": "",
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},
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"SpacyTextSplitter": {
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"source": "huggingface",
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"tokenizer_name_or_path": "gpt2",
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},
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"RecursiveCharacterTextSplitter": {
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"source": "tiktoken",
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"tokenizer_name_or_path": "cl100k_base",
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},
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"MarkdownHeaderTextSplitter": {
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"headers_to_split_on": [
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("#", "head1"),
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("##", "head2"),
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("###", "head3"),
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("####", "head4"),
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]
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},
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}
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"""
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TextSplitter配置项，如果你不明白其中的含义，就不要修改。
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source 如果选择tiktoken则使用openai的方法 "huggingface"
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"""
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TEXT_SPLITTER_NAME: str = "ChineseRecursiveTextSplitter"
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"""TEXT_SPLITTER 名称"""
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EMBEDDING_KEYWORD_FILE: str = "embedding_keywords.txt"
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"""Embedding模型定制词语的词表文件"""
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class PlatformConfig(MyBaseModel):
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"""模型加载平台配置"""
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platform_name: str = "xinference"
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"""平台名称"""
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platform_type: t.Literal["xinference", "ollama", "oneapi", "fastchat", "openai", "custom openai"] = "xinference"
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"""平台类型"""
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api_base_url: str = "http://127.0.0.1:9997/v1"
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"""openai api url"""
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api_key: str = "EMPTY"
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"""api key if available"""
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api_proxy: str = ""
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"""API 代理"""
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api_concurrencies: int = 5
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"""该平台单模型最大并发数"""
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auto_detect_model: bool = False
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"""是否自动获取平台可用模型列表。设为 True 时下方不同模型类型可自动检测"""
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llm_models: t.Union[t.Literal["auto"], t.List[str]] = []
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"""该平台支持的大语言模型列表，auto_detect_model 设为 True 时自动检测"""
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embed_models: t.Union[t.Literal["auto"], t.List[str]] = []
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"""该平台支持的嵌入模型列表，auto_detect_model 设为 True 时自动检测"""
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text2image_models: t.Union[t.Literal["auto"], t.List[str]] = []
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"""该平台支持的图像生成模型列表，auto_detect_model 设为 True 时自动检测"""
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image2text_models: t.Union[t.Literal["auto"], t.List[str]] = []
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"""该平台支持的多模态模型列表，auto_detect_model 设为 True 时自动检测"""
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rerank_models: t.Union[t.Literal["auto"], t.List[str]] = []
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"""该平台支持的重排模型列表，auto_detect_model 设为 True 时自动检测"""
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speech2text_models: t.Union[t.Literal["auto"], t.List[str]] = []
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"""该平台支持的 STT 模型列表，auto_detect_model 设为 True 时自动检测"""
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text2speech_models: t.Union[t.Literal["auto"], t.List[str]] = []
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"""该平台支持的 TTS 模型列表，auto_detect_model 设为 True 时自动检测"""
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class ApiModelSettings(BaseFileSettings):
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"""模型配置项"""
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model_config = SettingsConfigDict(yaml_file=CHATCHAT_ROOT / "model_settings.yaml")
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DEFAULT_LLM_MODEL: str = "glm4-chat"
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"""默认选用的 LLM 名称"""
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DEFAULT_EMBEDDING_MODEL: str = "bge-m3"
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"""默认选用的 Embedding 名称"""
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Agent_MODEL: str = "" # TODO: 似乎与 LLM_MODEL_CONFIG 重复了
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"""AgentLM模型的名称 (可以不指定，指定之后就锁定进入Agent之后的Chain的模型，不指定就是 DEFAULT_LLM_MODEL)"""
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HISTORY_LEN: int = 3
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"""默认历史对话轮数"""
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MAX_TOKENS: t.Optional[int] = None # TODO: 似乎与 LLM_MODEL_CONFIG 重复了
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"""大模型最长支持的长度，如果不填写，则使用模型默认的最大长度，如果填写，则为用户设定的最大长度"""
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TEMPERATURE: float = 0.7
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"""LLM通用对话参数"""
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||
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SUPPORT_AGENT_MODELS: t.List[str] = [
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"chatglm3-6b",
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"glm-4",
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"openai-api",
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"Qwen-2",
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"qwen2-instruct",
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"gpt-3.5-turbo",
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"gpt-4o",
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]
|
||
"""支持的Agent模型"""
|
||
|
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LLM_MODEL_CONFIG: t.Dict[str, t.Dict] = {
|
||
# 意图识别不需要输出，模型后台知道就行
|
||
"preprocess_model": {
|
||
"model": "",
|
||
"temperature": 0.05,
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||
"max_tokens": 4096,
|
||
"history_len": 10,
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||
"prompt_name": "default",
|
||
"callbacks": False,
|
||
},
|
||
"llm_model": {
|
||
"model": "",
|
||
"temperature": 0.9,
|
||
"max_tokens": 4096,
|
||
"history_len": 10,
|
||
"prompt_name": "default",
|
||
"callbacks": True,
|
||
},
|
||
"action_model": {
|
||
"model": "",
|
||
"temperature": 0.01,
|
||
"max_tokens": 4096,
|
||
"history_len": 10,
|
||
"prompt_name": "ChatGLM3",
|
||
"callbacks": True,
|
||
},
|
||
"postprocess_model": {
|
||
"model": "",
|
||
"temperature": 0.01,
|
||
"max_tokens": 4096,
|
||
"history_len": 10,
|
||
"prompt_name": "default",
|
||
"callbacks": True,
|
||
},
|
||
"image_model": {
|
||
"model": "sd-turbo",
|
||
"size": "256*256",
|
||
},
|
||
}
|
||
"""
|
||
LLM模型配置，包括了不同模态初始化参数。
|
||
`model` 如果留空则自动使用 DEFAULT_LLM_MODEL
|
||
"""
|
||
|
||
MODEL_PLATFORMS: t.List[PlatformConfig] = [
|
||
PlatformConfig(**{
|
||
"platform_name": "xinference",
|
||
"platform_type": "xinference",
|
||
"api_base_url": "http://127.0.0.1:9997/v1",
|
||
"api_key": "EMPTY",
|
||
"api_concurrencies": 5,
|
||
"auto_detect_model": True,
|
||
"llm_models": [],
|
||
"embed_models": [],
|
||
"text2image_models": [],
|
||
"image2text_models": [],
|
||
"rerank_models": [],
|
||
"speech2text_models": [],
|
||
"text2speech_models": [],
|
||
}),
|
||
PlatformConfig(**{
|
||
"platform_name": "ollama",
|
||
"platform_type": "ollama",
|
||
"api_base_url": "http://127.0.0.1:11434/v1",
|
||
"api_key": "EMPTY",
|
||
"api_concurrencies": 5,
|
||
"llm_models": [
|
||
"qwen:7b",
|
||
"qwen2:7b",
|
||
],
|
||
"embed_models": [
|
||
"quentinz/bge-large-zh-v1.5",
|
||
],
|
||
}),
|
||
PlatformConfig(**{
|
||
"platform_name": "oneapi",
|
||
"platform_type": "oneapi",
|
||
"api_base_url": "http://127.0.0.1:3000/v1",
|
||
"api_key": "sk-",
|
||
"api_concurrencies": 5,
|
||
"llm_models": [
|
||
# 智谱 API
|
||
"chatglm_pro",
|
||
"chatglm_turbo",
|
||
"chatglm_std",
|
||
"chatglm_lite",
|
||
# 千问 API
|
||
"qwen-turbo",
|
||
"qwen-plus",
|
||
"qwen-max",
|
||
"qwen-max-longcontext",
|
||
# 千帆 API
|
||
"ERNIE-Bot",
|
||
"ERNIE-Bot-turbo",
|
||
"ERNIE-Bot-4",
|
||
# 星火 API
|
||
"SparkDesk",
|
||
],
|
||
"embed_models": [
|
||
# 千问 API
|
||
"text-embedding-v1",
|
||
# 千帆 API
|
||
"Embedding-V1",
|
||
],
|
||
"text2image_models": [],
|
||
"image2text_models": [],
|
||
"rerank_models": [],
|
||
"speech2text_models": [],
|
||
"text2speech_models": [],
|
||
}),
|
||
PlatformConfig(**{
|
||
"platform_name": "openai",
|
||
"platform_type": "openai",
|
||
"api_base_url": "https://api.openai.com/v1",
|
||
"api_key": "sk-proj-",
|
||
"api_concurrencies": 5,
|
||
"llm_models": [
|
||
"gpt-4o",
|
||
"gpt-3.5-turbo",
|
||
],
|
||
"embed_models": [
|
||
"text-embedding-3-small",
|
||
"text-embedding-3-large",
|
||
],
|
||
}),
|
||
]
|
||
"""模型平台配置"""
|
||
|
||
|
||
class ToolSettings(BaseFileSettings):
|
||
"""Agent 工具配置项"""
|
||
model_config = SettingsConfigDict(yaml_file=CHATCHAT_ROOT / "tool_settings.yaml",
|
||
json_file=CHATCHAT_ROOT / "tool_settings.json",
|
||
extra="allow")
|
||
|
||
search_local_knowledgebase: dict = {
|
||
"use": False,
|
||
"top_k": 3,
|
||
"score_threshold": 2.0,
|
||
"conclude_prompt": {
|
||
"with_result": '<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>\n"
|
||
"<已知信息>{{ context }}</已知信息>\n"
|
||
"<问题>{{ question }}</问题>\n",
|
||
"without_result": "请你根据我的提问回答我的问题:\n"
|
||
"{{ question }}\n"
|
||
"请注意，你必须在回答结束后强调，你的回答是根据你的经验回答而不是参考资料回答的。\n",
|
||
},
|
||
}
|
||
'''本地知识库工具配置项'''
|
||
|
||
search_internet: dict = {
|
||
"use": False,
|
||
"search_engine_name": "zhipu_search",
|
||
"search_engine_config": {
|
||
"bing": {
|
||
"bing_search_url": "https://api.bing.microsoft.com/v7.0/search",
|
||
"bing_key": "",
|
||
},
|
||
"metaphor": {
|
||
"metaphor_api_key": "",
|
||
"split_result": False,
|
||
"chunk_size": 500,
|
||
"chunk_overlap": 0,
|
||
},
|
||
"duckduckgo": {},
|
||
"searx": {
|
||
"host": "https://metasearx.com",
|
||
"engines": [],
|
||
"categories": [],
|
||
"language": "zh-CN",
|
||
},
|
||
"tavily":{
|
||
"tavily_api_key": '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',
|
||
"include_answer": True,
|
||
"search_depth": "advanced",
|
||
"include_raw_content": True,
|
||
"max_results": 1
|
||
},
|
||
"zhipu_search":{
|
||
"zhipu_api_key": ""
|
||
}
|
||
},
|
||
"top_k": 1,
|
||
"verbose": "Origin",
|
||
"conclude_prompt": "<指令>这是搜索到的互联网信息，请你根据这些信息进行提取并有调理，简洁的回答问题,不得包含有重复的词汇或句子。如果无法从中得到答案，请说 “无法搜索到能回答问题的内容”。 "
|
||
"</指令>\n<已知信息>{{ context }}</已知信息>\n"
|
||
"<问题>\n"
|
||
"{{ question }}\n"
|
||
"</问题>\n",
|
||
}
|
||
'''搜索引擎工具配置项。推荐自己部署 searx 搜索引擎，国内使用最方便。'''
|
||
|
||
arxiv: dict = {
|
||
"use": False,
|
||
}
|
||
|
||
weather_check: dict = {
|
||
"use": False,
|
||
"api_key": "",
|
||
}
|
||
'''心知天气（https://www.seniverse.com/）工具配置项'''
|
||
|
||
search_youtube: dict = {
|
||
"use": False,
|
||
}
|
||
|
||
wolfram: dict = {
|
||
"use": False,
|
||
"appid": "",
|
||
}
|
||
|
||
calculate: dict = {
|
||
"use": False,
|
||
}
|
||
'''numexpr 数学计算工具配置项'''
|
||
|
||
text2images: dict = {
|
||
"use": False,
|
||
"model": "sd-turbo",
|
||
"size": "256*256",
|
||
}
|
||
'''图片生成工具配置项。model 必须是在 model_settings.yaml/MODEL_PLATFORMS 中配置过的。'''
|
||
|
||
text2sql: dict = {
|
||
# 该工具需单独指定使用的大模型，与用户前端选择使用的模型无关
|
||
"model_name": "qwen-plus",
|
||
"use": False,
|
||
# SQLAlchemy连接字符串，支持的数据库有：
|
||
# crate、duckdb、googlesql、mssql、mysql、mariadb、oracle、postgresql、sqlite、clickhouse、prestodb
|
||
# 不同的数据库请查阅SQLAlchemy用法，修改sqlalchemy_connect_str，配置对应的数据库连接，如sqlite为sqlite:///数据库文件路径，下面示例为mysql
|
||
# 如提示缺少对应数据库的驱动，请自行通过poetry安装
|
||
"sqlalchemy_connect_str": "mysql+pymysql://用户名:密码@主机地址/数据库名称",
|
||
# 务必评估是否需要开启read_only,开启后会对sql语句进行检查，请确认text2sql.py中的intercept_sql拦截器是否满足你使用的数据库只读要求
|
||
# 优先推荐从数据库层面对用户权限进行限制
|
||
"read_only": False,
|
||
# 限定返回的行数
|
||
"top_k": 50,
|
||
# 是否返回中间步骤
|
||
"return_intermediate_steps": True,
|
||
# 如果想指定特定表，请填写表名称，如["sys_user","sys_dept"]，不填写走智能判断应该使用哪些表
|
||
"table_names": [],
|
||
# 对表名进行额外说明，辅助大模型更好的判断应该使用哪些表，尤其是SQLDatabaseSequentialChain模式下,是根据表名做的预测，很容易误判。
|
||
"table_comments": {
|
||
# 如果出现大模型选错表的情况，可尝试根据实际情况填写表名和说明
|
||
# "tableA":"这是一个用户表，存储了用户的基本信息",
|
||
# "tableB":"角色表",
|
||
},
|
||
}
|
||
'''
|
||
text2sql使用建议
|
||
1、因大模型生成的sql可能与预期有偏差，请务必在测试环境中进行充分测试、评估；
|
||
2、生产环境中，对于查询操作，由于不确定查询效率，推荐数据库采用主从数据库架构，让text2sql连接从数据库，防止可能的慢查询影响主业务；
|
||
3、对于写操作应保持谨慎，如不需要写操作，设置read_only为True,最好再从数据库层面收回数据库用户的写权限，防止用户通过自然语言对数据库进行修改操作；
|
||
4、text2sql与大模型在意图理解、sql转换等方面的能力有关，可切换不同大模型进行测试；
|
||
5、数据库表名、字段名应与其实际作用保持一致、容易理解，且应对数据库表名、字段进行详细的备注说明，帮助大模型更好理解数据库结构；
|
||
6、若现有数据库表名难于让大模型理解，可配置下面table_comments字段，补充说明某些表的作用。
|
||
'''
|
||
|
||
amap: dict = {
|
||
"use": False,
|
||
"api_key": "高德地图 API KEY",
|
||
}
|
||
'''高德地图、天气相关工具配置项。'''
|
||
|
||
text2promql: dict = {
|
||
"use": False,
|
||
# <your_prometheus_ip>:<your_prometheus_port>
|
||
"prometheus_endpoint": "http://127.0.0.1:9090",
|
||
# <your_prometheus_username>
|
||
"username": "",
|
||
# <your_prometheus_password>
|
||
"password": "",
|
||
}
|
||
'''
|
||
text2promql 使用建议
|
||
1、因大模型生成的 promql 可能与预期有偏差, 请务必在测试环境中进行充分测试、评估;
|
||
2、text2promql 与大模型在意图理解、metric 选择、promql 转换等方面的能力有关, 可切换不同大模型进行测试;
|
||
3、当前仅支持 单prometheus 查询, 后续考虑支持 多prometheus 查询.
|
||
'''
|
||
|
||
url_reader: dict = {
|
||
"use": False,
|
||
"timeout": "10000",
|
||
}
|
||
'''URL内容阅读（https://r.jina.ai/）工具配置项
|
||
请确保部署的网络环境良好，以免造成超时等问题'''
|
||
|
||
|
||
|
||
class PromptSettings(BaseFileSettings):
|
||
"""Prompt 模板.除 Agent 模板使用 f-string 外，其它均使用 jinja2 格式"""
|
||
|
||
model_config = SettingsConfigDict(yaml_file=CHATCHAT_ROOT / "prompt_settings.yaml",
|
||
json_file=CHATCHAT_ROOT / "prompt_settings.json",
|
||
extra="allow")
|
||
|
||
preprocess_model: dict = {
|
||
"default": (
|
||
"你只要回复0 和 1 ，代表不需要使用工具。以下几种问题不需要使用工具:\n"
|
||
"1. 需要联网查询的内容\n"
|
||
"2. 需要计算的内容\n"
|
||
"3. 需要查询实时性的内容\n"
|
||
"如果我的输入满足这几种情况，返回1。其他输入，请你回复0，你只要返回一个数字\n"
|
||
"这是我的问题:"
|
||
),
|
||
}
|
||
"""意图识别用模板"""
|
||
|
||
llm_model: dict = {
|
||
"default": "{{input}}",
|
||
"with_history": (
|
||
"The following is a friendly conversation between a human and an AI.\n"
|
||
"The AI is talkative and provides lots of specific details from its context.\n"
|
||
"If the AI does not know the answer to a question, it truthfully says it does not know.\n\n"
|
||
"Current conversation:\n"
|
||
"{{history}}\n"
|
||
"Human: {{input}}\n"
|
||
"AI:"
|
||
),
|
||
}
|
||
'''普通 LLM 用模板'''
|
||
|
||
rag: dict = {
|
||
"default": (
|
||
"【指令】根据已知信息，简洁和专业的来回答问题,不得包含有重复的词汇或句子。"
|
||
"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。\n\n"
|
||
"【已知信息】{{context}}\n\n"
|
||
"【问题】{{question}}\n"
|
||
),
|
||
"empty": (
|
||
"请你回答我的问题:\n"
|
||
"{{question}}"
|
||
),
|
||
}
|
||
'''RAG 用模板，可用于知识库问答、文件对话、搜索引擎对话'''
|
||
|
||
action_model: dict = {
|
||
"GPT-4": (
|
||
"Answer the following questions as best you can. You have access to the following tools:\n"
|
||
"The way you use the tools is by specifying a json blob.\n"
|
||
"Specifically, this json should have a `action` key (with the name of the tool to use) and a `action_input` key (with the input to the tool going here).\n"
|
||
'The only values that should be in the "action" field are: {tool_names}\n'
|
||
"The $JSON_BLOB should only contain a SINGLE action, do NOT return a list of multiple actions. Here is an example of a valid $JSON_BLOB:\n"
|
||
"```\n\n"
|
||
"{{{{\n"
|
||
' "action": $TOOL_NAME,\n'
|
||
' "action_input": $INPUT\n'
|
||
"}}}}\n"
|
||
"```\n\n"
|
||
"ALWAYS use the following format:\n"
|
||
"Question: the input question you must answer\n"
|
||
"Thought: you should always think about what to do\n"
|
||
"Action:\n"
|
||
"```\n\n"
|
||
"$JSON_BLOB"
|
||
"```\n\n"
|
||
"Observation: the result of the action\n"
|
||
"... (this Thought/Action/Observation can repeat N times)\n"
|
||
"Thought: I now know the final answer\n"
|
||
"Final Answer: the final answer to the original input question\n"
|
||
"Begin! Reminder to always use the exact characters `Final Answer` when responding.\n"
|
||
"Question:{input}\n"
|
||
"Thought:{agent_scratchpad}\n"
|
||
),
|
||
"ChatGLM3": (
|
||
"You can answer using the tools.Respond to the human as helpfully and accurately as possible.\n"
|
||
"You have access to the following tools:\n"
|
||
"{tools}\n"
|
||
"Use a json blob to specify a tool by providing an action key (tool name)\n"
|
||
"and an action_input key (tool input).\n"
|
||
'Valid "action" values: "Final Answer" or [{tool_names}]\n'
|
||
"Provide only ONE action per $JSON_BLOB, as shown:\n\n"
|
||
"```\n"
|
||
"{{{{\n"
|
||
' "action": $TOOL_NAME,\n'
|
||
' "action_input": $INPUT\n'
|
||
"}}}}\n"
|
||
"```\n\n"
|
||
"Follow this format:\n\n"
|
||
"Question: input question to answer\n"
|
||
"Thought: consider previous and subsequent steps\n"
|
||
"Action:\n"
|
||
"```\n"
|
||
"$JSON_BLOB\n"
|
||
"```\n"
|
||
"Observation: action result\n"
|
||
"... (repeat Thought/Action/Observation N times)\n"
|
||
"Thought: I know what to respond\n"
|
||
"Action:\n"
|
||
"```\n"
|
||
"{{{{\n"
|
||
' "action": "Final Answer",\n'
|
||
' "action_input": "Final response to human"\n'
|
||
"}}}}\n"
|
||
"Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary.\n"
|
||
"Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation:.\n"
|
||
"Question: {input}\n\n"
|
||
"{agent_scratchpad}\n"
|
||
),
|
||
"qwen": (
|
||
"Answer the following questions as best you can. You have access to the following APIs:\n\n"
|
||
"{tools}\n\n"
|
||
"Use the following format:\n\n"
|
||
"Question: the input question you must answer\n"
|
||
"Thought: you should always think about what to do\n"
|
||
"Action: the action to take, should be one of [{tool_names}]\n"
|
||
"Action Input: the input to the action\n"
|
||
"Observation: the result of the action\n"
|
||
"... (this Thought/Action/Action Input/Observation can be repeated zero or more times)\n"
|
||
"Thought: I now know the final answer\n"
|
||
"Final Answer: the final answer to the original input question\n\n"
|
||
"Format the Action Input as a JSON object.\n\n"
|
||
"Begin!\n\n"
|
||
"Question: {input}\n\n"
|
||
"{agent_scratchpad}\n\n"
|
||
"Important: After the last Observation, you must always add a Final Answer "
|
||
"summarizing the result. Do not skip this step."
|
||
),
|
||
"structured-chat-agent": (
|
||
"Respond to the human as helpfully and accurately as possible. You have access to the following tools:\n\n"
|
||
"{tools}\n\n"
|
||
"Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).\n\n"
|
||
'Valid "action" values: "Final Answer" or {tool_names}\n\n'
|
||
"Provide only ONE action per $JSON_BLOB, as shown:\n\n"
|
||
'```\n{{\n "action": $TOOL_NAME,\n "action_input": $INPUT\n}}\n```\n\n'
|
||
"Follow this format:\n\n"
|
||
"Question: input question to answer\n"
|
||
"Thought: consider previous and subsequent steps\n"
|
||
"Action:\n```\n$JSON_BLOB\n```\n"
|
||
"Observation: action result\n"
|
||
"... (repeat Thought/Action/Observation N times)\n"
|
||
"Thought: I know what to respond\n"
|
||
'Action:\n```\n{{\n "action": "Final Answer",\n "action_input": "Final response to human"\n}}\n\n'
|
||
"Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation\n"
|
||
"{input}\n\n"
|
||
"{agent_scratchpad}\n\n"
|
||
# '(reminder to respond in a JSON blob no matter what)')
|
||
),
|
||
}
|
||
"""Agent 模板"""
|
||
|
||
postprocess_model: dict = {
|
||
"default": "{{input}}",
|
||
}
|
||
"""后处理模板"""
|
||
|
||
|
||
class SettingsContainer:
|
||
CHATCHAT_ROOT = CHATCHAT_ROOT
|
||
|
||
basic_settings: BasicSettings = settings_property(BasicSettings())
|
||
kb_settings: KBSettings = settings_property(KBSettings())
|
||
model_settings: ApiModelSettings = settings_property(ApiModelSettings())
|
||
tool_settings: ToolSettings = settings_property(ToolSettings())
|
||
prompt_settings: PromptSettings = settings_property(PromptSettings())
|
||
|
||
def createl_all_templates(self):
|
||
self.basic_settings.create_template_file(write_file=True)
|
||
self.kb_settings.create_template_file(write_file=True)
|
||
self.model_settings.create_template_file(sub_comments={
|
||
"MODEL_PLATFORMS": {"model_obj": PlatformConfig(),
|
||
"is_entire_comment": True}},
|
||
write_file=True)
|
||
self.tool_settings.create_template_file(write_file=True, file_format="yaml", model_obj=ToolSettings())
|
||
self.prompt_settings.create_template_file(write_file=True, file_format="yaml")
|
||
|
||
def set_auto_reload(self, flag: bool=True):
|
||
self.basic_settings.auto_reload = flag
|
||
self.kb_settings.auto_reload = flag
|
||
self.model_settings.auto_reload = flag
|
||
self.tool_settings.auto_reload = flag
|
||
self.prompt_settings.auto_reload = flag
|
||
|
||
|
||
Settings = SettingsContainer()
|
||
nltk.data.path.append(str(Settings.basic_settings.NLTK_DATA_PATH))
|
||
|
||
|
||
if __name__ == "__main__":
|
||
Settings.createl_all_templates()
|