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https://github.com/RYDE-WORK/Langchain-Chatchat.git
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5d422ca9a1
改变 Embeddings 模型改为使用框架 API，不再手动加载，删除自定义 Embeddings Keyword 代码 修改依赖文件，移除 torch transformers 等重依赖 暂时移出对 loom 的集成 后续： 1、优化目录结构 2、检查合并中有无被覆盖的 0.2.10 内容
114 lines
3.2 KiB
Plaintext
114 lines
3.2 KiB
Plaintext
import os
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# 默认使用的知识库
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DEFAULT_KNOWLEDGE_BASE = "samples"
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# 默认向量库/全文检索引擎类型。可选：faiss, milvus(离线) & zilliz(在线), pgvector,全文检索引擎es
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DEFAULT_VS_TYPE = "faiss"
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# 缓存向量库数量（针对FAISS）
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CACHED_VS_NUM = 1
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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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CACHED_MEMO_VS_NUM = 10
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# 知识库中单段文本长度(不适用MarkdownHeaderTextSplitter)
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CHUNK_SIZE = 250
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# 知识库中相邻文本重合长度(不适用MarkdownHeaderTextSplitter)
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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 = 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
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SCORE_THRESHOLD = 1
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# 默认搜索引擎。可选：bing, duckduckgo, metaphor
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DEFAULT_SEARCH_ENGINE = "duckduckgo"
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# 搜索引擎匹配结题数量
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SEARCH_ENGINE_TOP_K = 3
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ZH_TITLE_ENHANCE = False
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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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KB_INFO = {
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"samples": "关于本项目issue的解答",
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}
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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
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KB_ROOT_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "knowledge_base")
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if not os.path.exists(KB_ROOT_PATH):
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os.mkdir(KB_ROOT_PATH)
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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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"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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"es": {
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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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}
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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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text_splitter_dict = {
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"ChineseRecursiveTextSplitter": {
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"source": "huggingface", # 选择tiktoken则使用openai的方法
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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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[
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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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# TEXT_SPLITTER 名称
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TEXT_SPLITTER_NAME = "ChineseRecursiveTextSplitter"
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# Embedding模型定制词语的词表文件
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EMBEDDING_KEYWORD_FILE = "embedding_keywords.txt"
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