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* 更新上agent提示词代码 * 更新部分文档,修复了issue中提到的bge匹配超过1 的bug * 按需修改 * 解决了部分最新用户用依赖的bug,加了两个工具,移除google工具 * Agent大幅度优化 1. 修改了UI界面 (1)高亮所有没有进行agent对齐的模型, (2)优化输出体验和逻辑,使用markdown 2. 降低天气工具使用门槛 3. 依赖更新 (1) vllm 更新到0.2.0,增加了一些参数 (2) torch 建议更新到2.1 (3)pydantic不要更新到1.10.12 * 更新了一些注释 * 修改部分依赖,修改了部分wiki用图 --------- Co-authored-by: zR <zRzRzRzRzRzRzR>
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import os | ||
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# 默认向量库类型。可选:faiss, milvus, pg. | ||
DEFAULT_VS_TYPE = "faiss" | ||
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# 缓存向量库数量(针对FAISS) | ||
CACHED_VS_NUM = 1 | ||
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# 知识库中单段文本长度(不适用MarkdownHeaderTextSplitter) | ||
CHUNK_SIZE = 250 | ||
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# 知识库中相邻文本重合长度(不适用MarkdownHeaderTextSplitter) | ||
OVERLAP_SIZE = 50 | ||
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# 知识库匹配向量数量 | ||
VECTOR_SEARCH_TOP_K = 3 | ||
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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 | ||
SCORE_THRESHOLD = 1 | ||
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# 搜索引擎匹配结题数量 | ||
SEARCH_ENGINE_TOP_K = 3 | ||
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# Bing 搜索必备变量 | ||
# 使用 Bing 搜索需要使用 Bing Subscription Key,需要在azure port中申请试用bing search | ||
# 具体申请方式请见 | ||
# https://learn.microsoft.com/en-us/bing/search-apis/bing-web-search/create-bing-search-service-resource | ||
# 使用python创建bing api 搜索实例详见: | ||
# https://learn.microsoft.com/en-us/bing/search-apis/bing-web-search/quickstarts/rest/python | ||
BING_SEARCH_URL = "https://api.bing.microsoft.com/v7.0/search" | ||
# 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 | ||
# 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 | ||
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 | ||
# 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; | ||
# 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。 | ||
ZH_TITLE_ENHANCE = False | ||
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# 通常情况下不需要更改以下内容 | ||
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# 知识库默认存储路径 | ||
KB_ROOT_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "knowledge_base") | ||
if not os.path.exists(KB_ROOT_PATH): | ||
os.mkdir(KB_ROOT_PATH) | ||
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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。 | ||
DB_ROOT_PATH = os.path.join(KB_ROOT_PATH, "info.db") | ||
SQLALCHEMY_DATABASE_URI = f"sqlite:///{DB_ROOT_PATH}" | ||
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# 可选向量库类型及对应配置 | ||
kbs_config = { | ||
"faiss": { | ||
}, | ||
"milvus": { | ||
"host": "127.0.0.1", | ||
"port": "19530", | ||
"user": "", | ||
"password": "", | ||
"secure": False, | ||
}, | ||
"pg": { | ||
"connection_uri": "postgresql://postgres:postgres@127.0.0.1:5432/langchain_chatchat", | ||
} | ||
} | ||
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# TextSplitter配置项,如果你不明白其中的含义,就不要修改。 | ||
text_splitter_dict = { | ||
"ChineseRecursiveTextSplitter": { | ||
"source": "huggingface", ## 选择tiktoken则使用openai的方法 | ||
"tokenizer_name_or_path": "gpt2", | ||
}, | ||
"SpacyTextSplitter": { | ||
"source": "huggingface", | ||
"tokenizer_name_or_path": "", | ||
}, | ||
"RecursiveCharacterTextSplitter": { | ||
"source": "tiktoken", | ||
"tokenizer_name_or_path": "cl100k_base", | ||
}, | ||
"MarkdownHeaderTextSplitter": { | ||
"headers_to_split_on": | ||
[ | ||
("#", "head1"), | ||
("##", "head2"), | ||
("###", "head3"), | ||
("####", "head4"), | ||
] | ||
}, | ||
} | ||
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# TEXT_SPLITTER 名称 | ||
TEXT_SPLITTER_NAME = "ChineseRecursiveTextSplitter" |
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