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from __future__ import annotations
import os
from pathlib import Path
import sys
import typing as t
import nltk
from chatchat import __version__
from chatchat . pydantic_settings_file import *
# 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。
CHATCHAT_ROOT = Path ( os . environ . get ( " CHATCHAT_ROOT " , " . " ) ) . resolve ( )
XF_MODELS_TYPES = {
" text2image " : { " model_family " : [ " stable_diffusion " ] } ,
" image2image " : { " model_family " : [ " stable_diffusion " ] } ,
" speech2text " : { " model_family " : [ " whisper " ] } ,
" text2speech " : { " model_family " : [ " ChatTTS " ] } ,
}
class BasicSettings ( BaseFileSettings ) :
"""
服务器基本配置信息
除 log_verbose / HTTPX_DEFAULT_TIMEOUT 修改后即时生效
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
"""
model_config = SettingsConfigDict ( yaml_file = CHATCHAT_ROOT / " basic_settings.yaml " )
version : str = __version__
""" 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 """
log_verbose : bool = False
""" 是否开启日志详细信息 """
HTTPX_DEFAULT_TIMEOUT : float = 300
""" 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。 """
# @computed_field
@cached_property
def PACKAGE_ROOT ( self ) - > Path :
""" 代码根目录 """
return Path ( __file__ ) . parent
# @computed_field
@cached_property
def DATA_PATH ( self ) - > Path :
""" 用户数据根目录 """
p = CHATCHAT_ROOT / " data "
return p
# @computed_field
@cached_property
def IMG_DIR ( self ) - > Path :
""" 项目相关图片目录 """
p = self . PACKAGE_ROOT / " img "
return p
# @computed_field
@cached_property
def NLTK_DATA_PATH ( self ) - > Path :
""" nltk 模型存储路径 """
p = self . PACKAGE_ROOT / " data/nltk_data "
return p
# @computed_field
@cached_property
def LOG_PATH ( self ) - > Path :
""" 日志存储路径 """
p = self . DATA_PATH / " logs "
return p
# @computed_field
@cached_property
def MEDIA_PATH ( self ) - > Path :
""" 模型生成内容（图片、视频、音频等）保存位置 """
p = self . DATA_PATH / " media "
return p
# @computed_field
@cached_property
def BASE_TEMP_DIR ( self ) - > Path :
""" 临时文件目录，主要用于文件对话 """
p = self . DATA_PATH / " temp "
( p / " openai_files " ) . mkdir ( parents = True , exist_ok = True )
return p
KB_ROOT_PATH : str = str ( CHATCHAT_ROOT / " data/knowledge_base " )
""" 知识库默认存储路径 """
DB_ROOT_PATH : str = str ( CHATCHAT_ROOT / " data/knowledge_base/info.db " )
""" 数据库默认存储路径。如果使用sqlite， ； ， """
SQLALCHEMY_DATABASE_URI : str = " sqlite:/// " + str ( CHATCHAT_ROOT / " data/knowledge_base/info.db " )
""" 知识库信息数据库连接URI """
OPEN_CROSS_DOMAIN : bool = False
""" API 是否开启跨域 """
DEFAULT_BIND_HOST : str = " 0.0.0.0 " if sys . platform != " win32 " else " 127.0.0.1 "
"""
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
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
"""
API_SERVER : dict = { " host " : DEFAULT_BIND_HOST , " port " : 7861 , " public_host " : " 127.0.0.1 " , " public_port " : 7861 }
""" 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） """
WEBUI_SERVER : dict = { " host " : DEFAULT_BIND_HOST , " port " : 8501 }
""" WEBUI 服务器地址 """
def make_dirs ( self ) :
''' 创建所有数据目录 '''
for p in [
self . DATA_PATH ,
self . MEDIA_PATH ,
self . LOG_PATH ,
self . BASE_TEMP_DIR ,
] :
p . mkdir ( parents = True , exist_ok = True )
for n in [ " image " , " audio " , " video " ] :
( self . MEDIA_PATH / n ) . mkdir ( parents = True , exist_ok = True )
Path ( self . KB_ROOT_PATH ) . mkdir ( parents = True , exist_ok = True )
class KBSettings ( BaseFileSettings ) :
""" 知识库相关配置 """
model_config = SettingsConfigDict ( yaml_file = CHATCHAT_ROOT / " kb_settings.yaml " )
DEFAULT_KNOWLEDGE_BASE : str = " samples "
""" 默认使用的知识库 """
DEFAULT_VS_TYPE : t . Literal [ " faiss " , " milvus " , " zilliz " , " pg " , " es " , " relyt " , " chromadb " ] = " faiss "
""" 默认向量库/全文检索引擎类型 """
CACHED_VS_NUM : int = 1
""" 缓存向量库数量（ ） """
CACHED_MEMO_VS_NUM : int = 10
""" 缓存临时向量库数量（ ） ， """
CHUNK_SIZE : int = 750
""" 知识库中单段文本长度(不适用MarkdownHeaderTextSplitter) """
OVERLAP_SIZE : int = 150
""" 知识库中相邻文本重合长度(不适用MarkdownHeaderTextSplitter) """
VECTOR_SEARCH_TOP_K : int = 3 # TODO: 与 tool 配置项重复
""" 知识库匹配向量数量 """
SCORE_THRESHOLD : float = 2.0
""" 知识库匹配相关度阈值， ， ， ， ， """
DEFAULT_SEARCH_ENGINE : t . Literal [ " bing " , " duckduckgo " , " metaphor " , " searx " ] = " duckduckgo "
""" 默认搜索引擎 """
SEARCH_ENGINE_TOP_K : int = 3
""" 搜索引擎匹配结题数量 """
ZH_TITLE_ENHANCE : bool = False
""" 是否开启中文标题加强，以及标题增强的相关配置 """
PDF_OCR_THRESHOLD : t . Tuple [ float , float ] = ( 0.6 , 0.6 )
"""
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 。
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
"""
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？
""" 每个知识库的初始化介绍， ， ， """
kbs_config : t . Dict [ str , t . Dict ] = {
" faiss " : { } ,
" milvus " : {
" host " : " 127.0.0.1 " ,
" port " : " 19530 " ,
" user " : " " ,
" password " : " " ,
" secure " : False
} ,
" zilliz " : {
" host " : " in01-a7ce524e41e3935.ali-cn-hangzhou.vectordb.zilliz.com.cn " ,
" port " : " 19530 " ,
" user " : " " ,
" password " : " " ,
" secure " : True
} ,
" pg " : {
" connection_uri " : " postgresql://postgres:postgres@127.0.0.1:5432/langchain_chatchat "
} ,
" relyt " : {
" connection_uri " : " postgresql+psycopg2://postgres:postgres@127.0.0.1:7000/langchain_chatchat "
} ,
" es " : {
" scheme " : " http " ,
" host " : " 127.0.0.1 " ,
" port " : " 9200 " ,
" index_name " : " test_index " ,
" user " : " " ,
" password " : " " ,
" verify_certs " : True ,
" ca_certs " : None ,
" client_cert " : None ,
" client_key " : None
} ,
" milvus_kwargs " : {
" search_params " : {
" metric_type " : " L2 "
} ,
" index_params " : {
" metric_type " : " L2 " ,
" index_type " : " HNSW "
}
} ,
" chromadb " : { }
}
""" 可选向量库类型及对应配置 """
text_splitter_dict : t . Dict [ str , t . Dict [ str , t . Any ] ] = {
" ChineseRecursiveTextSplitter " : {
" source " : " " ,
" tokenizer_name_or_path " : " " ,
} ,
" SpacyTextSplitter " : {
" source " : " huggingface " ,
" tokenizer_name_or_path " : " gpt2 " ,
} ,
" RecursiveCharacterTextSplitter " : {
" source " : " tiktoken " ,
" tokenizer_name_or_path " : " cl100k_base " ,
} ,
" MarkdownHeaderTextSplitter " : {
" headers_to_split_on " : [
( " # " , " head1 " ) ,
( " ## " , " head2 " ) ,
( " ### " , " head3 " ) ,
( " #### " , " head4 " ) ,
]
} ,
}
"""
TextSplitter配置项 ， 如果你不明白其中的含义 ， 就不要修改 。
source 如果选择tiktoken则使用openai的方法 " huggingface "
"""
TEXT_SPLITTER_NAME : str = " ChineseRecursiveTextSplitter "
""" TEXT_SPLITTER 名称 """
EMBEDDING_KEYWORD_FILE : str = " embedding_keywords.txt "
""" Embedding模型定制词语的词表文件 """
class PlatformConfig ( MyBaseModel ) :
""" 模型加载平台配置 """
platform_name : str = " xinference "
""" 平台名称 """
platform_type : t . Literal [ " xinference " , " ollama " , " oneapi " , " fastchat " , " openai " , " custom openai " ] = " xinference "
""" 平台类型 """
api_base_url : str = " http://127.0.0.1:9997/v1 "
""" openai api url """
api_key : str = " EMPTY "
""" api key if available """
api_proxy : str = " "
""" API 代理 """
api_concurrencies : int = 5
""" 该平台单模型最大并发数 """
auto_detect_model : bool = False
""" 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 """
llm_models : t . Union [ t . Literal [ " auto " ] , t . List [ str ] ] = [ ]
""" 该平台支持的大语言模型列表， """
embed_models : t . Union [ t . Literal [ " auto " ] , t . List [ str ] ] = [ ]
""" 该平台支持的嵌入模型列表， """
text2image_models : t . Union [ t . Literal [ " auto " ] , t . List [ str ] ] = [ ]
""" 该平台支持的图像生成模型列表， """
image2text_models : t . Union [ t . Literal [ " auto " ] , t . List [ str ] ] = [ ]
""" 该平台支持的多模态模型列表， """
rerank_models : t . Union [ t . Literal [ " auto " ] , t . List [ str ] ] = [ ]
""" 该平台支持的重排模型列表， """
speech2text_models : t . Union [ t . Literal [ " auto " ] , t . List [ str ] ] = [ ]
""" 该平台支持的 STT 模型列表， """
text2speech_models : t . Union [ t . Literal [ " auto " ] , t . List [ str ] ] = [ ]
""" 该平台支持的 TTS 模型列表， """
class ApiModelSettings ( BaseFileSettings ) :
""" 模型配置项 """
model_config = SettingsConfigDict ( yaml_file = CHATCHAT_ROOT / " model_settings.yaml " )
DEFAULT_LLM_MODEL : str = " glm4-chat "
""" 默认选用的 LLM 名称 """
DEFAULT_EMBEDDING_MODEL : str = " bge-m3 "
""" 默认选用的 Embedding 名称 """
Agent_MODEL : str = " " # TODO: 似乎与 LLM_MODEL_CONFIG 重复了
""" AgentLM模型的名称 (可以不指定， ， """
HISTORY_LEN : int = 3
""" 默认历史对话轮数 """
MAX_TOKENS : t . Optional [ int ] = None # TODO: 似乎与 LLM_MODEL_CONFIG 重复了
""" 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 """
TEMPERATURE : float = 0.7
""" LLM通用对话参数 """
SUPPORT_AGENT_MODELS : t . List [ str ] = [
" chatglm3-6b " ,
" glm-4 " ,
" openai-api " ,
" Qwen-2 " ,
" qwen2-instruct " ,
" gpt-3.5-turbo " ,
" gpt-4o " ,
]
""" 支持的Agent模型 """
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 ,
" max_tokens " : 4096 ,
" history_len " : 10 ,
" 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 ,
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" search_engine_name " : " tavily " ,
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" 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 " ,
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} ,
" tavily " : {
" tavily_api_key " : ' 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 ' ,
" include_answer " : True ,
" search_depth " : " advanced " ,
" include_raw_content " : True ,
" max_results " : 1
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}
} ,
" top_k " : 5 ,
" verbose " : " Origin " ,
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" 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”。 "
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" </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（ '''
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， ：
# crate、duckdb、googlesql、mssql、mysql、mariadb、oracle、postgresql、sqlite、clickhouse、prestodb
# 不同的数据库请查阅SQLAlchemy用法， ， ， ，
# 如提示缺少对应数据库的驱动，
" 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，
# 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， ，
" 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（
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， ， ， \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 " : (
2025-03-19 08:18:06 +08:00
" 【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。 "
2024-12-20 16:04:03 +08:00
" 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 "
) ,
" 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 ( )
