307 lines
11 KiB
Python
307 lines
11 KiB
Python
import os
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from pathlib import Path
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import re
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# 默认使用的知识库
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# DEFAULT_KNOWLEDGE_BASE = "t_policy_total_bge_v1"
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SELF_KNOWLEDGE_BASE = re.compile(r'^p_.*') # 个人知识库名称
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DEFAULT_KNOWLEDGE_BASE = "t_policy_total_bge_new_v2"
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DEFAULT_POLICY_BASE = DEFAULT_KNOWLEDGE_BASE #"t_policy_total_bge_new_v2"
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DEFAULT_POLICY_BASE_NAME = "政策库"
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DEFAULT_REPORT_BASE1 = DEFAULT_KNOWLEDGE_BASE # "t_strategy_report_bge_v2"
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DEFAULT_REPORT_BASE = DEFAULT_KNOWLEDGE_BASE #"gydemo_report_v2"
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DEFAULT_REPORT_BASE_NAME = "报告库"
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DEFAULT_JOURNAL_BASE = DEFAULT_KNOWLEDGE_BASE #"t_journal_article_bge_v1"
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DEFAULT_JOURNAL_BASE_NAME = "期刊论文库"
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GY_NEWS_BASE = DEFAULT_KNOWLEDGE_BASE #"gydemo_news_v2"
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GY_NEWS_BASE_NAME = "冶金行业新闻库"
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GY_REPORT_BASE = DEFAULT_KNOWLEDGE_BASE #"gydemo_report_v2"
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GY_REPORT_BASE_NAME = "冶金行业报告库"
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GY_JOURNAL_BASE = DEFAULT_KNOWLEDGE_BASE #"gy_demo_journal_v3"
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GY_JOURNAL_BASE_NAME = "冶金专业知识库"
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OLD_POLICY_BASE = ['t_policy_total_bge_v1','t_policy_total_bge_new_v1']
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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, chromadb 全文检索引擎es
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DEFAULT_VS_TYPE = "milvus"
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# 新增冶金系列知识库常量
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YJ_CH_JOURNAL_BASE = DEFAULT_KNOWLEDGE_BASE #"yj_ch_journal_bge_v1_recover"
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YJ_CH_JOURNAL_BASE_NAME = "冶金中文期刊库"
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YJ_NEWS_BASE = DEFAULT_KNOWLEDGE_BASE #"yj_news_bge_v1_recover"
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YJ_NEWS_BASE_NAME = "冶金新闻库（2024年以及之前）"
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YJ_FOR_JOURNAL_BASE = DEFAULT_KNOWLEDGE_BASE #"yj_for_journal_bge_v1_recover"
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YJ_FOR_JOURNAL_BASE_NAME = "冶金外文期刊库"
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YJ_OA_JOURNAL_BASE = DEFAULT_KNOWLEDGE_BASE #"yj_oa_journal_bge_v2_recover"
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YJ_OA_JOURNAL_BASE_NAME = "冶金OA期刊库"
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YJ_POLICYS_BASE = DEFAULT_KNOWLEDGE_BASE #"yj_policys_bge_v1_recover"
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YJ_POLICYS_BASE_NAME = "冶金政策库"
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# 专门的冶金新闻/期刊/报告可在此追加，如有需要
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YJ_BASE_NAME = [YJ_CH_JOURNAL_BASE_NAME, YJ_NEWS_BASE_NAME, YJ_FOR_JOURNAL_BASE_NAME, YJ_OA_JOURNAL_BASE_NAME, YJ_POLICYS_BASE_NAME]
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CH_BASE_NAME = (
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DEFAULT_POLICY_BASE_NAME,
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DEFAULT_JOURNAL_BASE_NAME,
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GY_NEWS_BASE_NAME,
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GY_REPORT_BASE_NAME,
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GY_JOURNAL_BASE_NAME,
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DEFAULT_REPORT_BASE_NAME,
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)
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EN_BASE_NAME = [
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DEFAULT_POLICY_BASE,
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DEFAULT_REPORT_BASE,
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DEFAULT_REPORT_BASE1,
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DEFAULT_JOURNAL_BASE,
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GY_NEWS_BASE,
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GY_REPORT_BASE,
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GY_JOURNAL_BASE,
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YJ_CH_JOURNAL_BASE,
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YJ_NEWS_BASE,
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YJ_FOR_JOURNAL_BASE,
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YJ_OA_JOURNAL_BASE,
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YJ_POLICYS_BASE,
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]
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GY_BASE_NAME = [GY_NEWS_BASE,GY_REPORT_BASE,GY_JOURNAL_BASE]
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OLD_JOURNAL_BASE = ['t_journal_article_bge_v0']
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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 = 5
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# 知识库匹配的距离阈值，一般取值范围在0-1之间，SCORE越小，距离越小从而相关度越高。
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# 但有用户报告遇到过匹配分值超过1的情况，为了兼容性默认设为1，在WEBUI中调整范围为0-2
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SCORE_THRESHOLD = 1.0
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# zsj：增加重复文档相似度阈值，0-1取值，阈值越大越相似
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DUPLICATE_THRESHOLD = 0.98
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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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DEFAULT_SEARCH_ENGINE = "duckduckgo" # 本地未部署 KGO 搜索时用 duckduckgo；自建搜索后再改为 kgo
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kgo_search_url = r"http://192.168.203.21:8326/search/search" # 若部署 KGO 搜索服务可改端口
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kgo_professional_search_url = r"http://192.168.203.21:8326/search/professionalSearch"
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# 画图接口
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realistic_url = r"http://127.0.0.1:5000/generate"
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ink_url = r"http://127.0.0.1:5000/generate-image"
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# mysql配置
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ck_mysql_config = {
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"host": "127.0.0.1",
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"port": 33306,
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"user": "root",
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"password": "1234567890",
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"database": "chat_gpt_yj",
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"charset": "utf8mb4",
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}
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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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DOWNLOAD_HOST_CK = r"http://127.0.0.1:8099/chat_web_backend"
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KB_ROOT_PATH2 = Path(__file__).resolve().parent.parent / "knowledge_base"
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# 相关度对比接口
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similarity_url = r"http://127.0.0.1:5000/similar"
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similarity_score = 0.4 # 知识库搜索相关度阈值
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similarity_internet = 0.5 # 互联网搜索相关度阈值
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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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# metaphor搜索需要KEY
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METAPHOR_API_KEY = "e09d3cdd-e7e1-41d7-a419-6b298002d921"
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# 心知天气 API KEY，用于天气Agent。申请：https://www.seniverse.com/
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SENIVERSE_API_KEY = "STNmmw0iUKB96PNpJ"
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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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# 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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PDF_OCR_THRESHOLD = (0.6, 0.6)
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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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"知识库名称": "知识库介绍",
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"samples": "关于本项目issue的解答",
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}
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# 个人知识库配置
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SELF_SCORE_THRESHOLD = 1.9
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SELF_TOP_K = 5
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SELF_TEMPERATURE = 0.3
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SELF_MAX_TOKENS = 8192
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SELF_USE_RERANKER = False # 使用milvus不需要rerank，因为milvus已经给召回结果添加了评分排序
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GENERATED_IMAGES_BASE_PATH = "/home/albert/workspaces/modelSpaces/models/text_to_pic/generated_images"
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IMAGE_SERVER_URL_TEMPLATE = "http://127.0.0.1:8099/chat_web_backend/get-image?file_name={}"
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KB_CHAT_TEMP_DIR = "/home/albert/workspaces/modelSpaces/models/tmp"
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# 谷歌浏览器存放地址 页面数据抓取
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CHROME_DIR = "/home/albert/workspaces/modelSpaces/models/chrome"
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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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# PDF→Markdown 外部微服务（file_converter.pdf_to_html）。
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# 客户端切勿使用 http://0.0.0.0:...：0.0.0.0 仅用于 bind，作为请求 URL 时 Host 异常，服务端常返回 403。
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def _normalize_pdf_convert_api_url(raw: str) -> str:
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u = (raw or "").strip() or "http://127.0.0.1:6006/convert/"
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u = u.replace("0.0.0.0", "127.0.0.1")
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return u
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PDF_CONVERT_API_URL = _normalize_pdf_convert_api_url(os.environ.get("PDF_CONVERT_API_URL", "http://127.0.0.1:6006/convert/"))
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# 传给转换服务的 pdf_path 相对此目录；需与微服务进程能读到的知识库根路径一致（不一致时设环境变量 PDF_CONVERT_KB_ROOT）
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PDF_CONVERT_KB_ROOT = os.path.abspath(os.environ.get("PDF_CONVERT_KB_ROOT", KB_ROOT_PATH))
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def get_pdf_convert_api_url() -> str:
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"""
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在发起 HTTP 请求前调用（勿仅用模块级 PDF_CONVERT_API_URL）：
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优先读当前进程的 PDF_CONVERT_API_URL 环境变量，避免子进程/旧 .pyc 仍缓存 http://0.0.0.0:6006。
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"""
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env = os.environ.get("PDF_CONVERT_API_URL", "").strip()
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base = env if env else PDF_CONVERT_API_URL
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return _normalize_pdf_convert_api_url(str(base))
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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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"db_name": "default",
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"secure": False,
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},
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"zilliz": {
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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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"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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"milvus_kwargs":{
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"search_params":{"metric_type": "L2"}, #在此处增加search_params
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"index_params":{"metric_type": "L2","index_type": "HNSW"} # 在此处增加index_params
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},
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"chromadb": {}
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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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"ChineseRecursiveParagraphSplitter": {
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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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"ChineseRecursiveTextSplitter": {
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#"source": "huggingface", # 选择tiktoken则使用openai的方法
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"source": "no_tokenizer", # 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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# "GCYMarkdownTextSplitter": {
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"MarkdownTextSplitter": {
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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_MAP = {
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# "GCYMarkdownTextSplitter": ['.md'],
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"MarkdownTextSplitter": ['.md'],
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"ChineseRecursiveTextSplitter": ['.docx', '.doc', '.html'],
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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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# 在新增知识库常量定义之后，扩充 CH_BASE_NAME 与 EN_BASE_NAME
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CH_BASE_NAME = CH_BASE_NAME + (
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YJ_CH_JOURNAL_BASE_NAME,
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YJ_NEWS_BASE_NAME,
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YJ_FOR_JOURNAL_BASE_NAME,
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YJ_OA_JOURNAL_BASE_NAME,
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YJ_POLICYS_BASE_NAME,
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)
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EN_BASE_NAME.extend([
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YJ_CH_JOURNAL_BASE,
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YJ_NEWS_BASE,
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YJ_FOR_JOURNAL_BASE,
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YJ_OA_JOURNAL_BASE,
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YJ_POLICYS_BASE,
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])
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# ********** 中国钢铁行业动态库（新增） **********
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# STEEL_KB = "steel_kb"
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STEEL_KB = DEFAULT_KNOWLEDGE_BASE #"steel_kb_token_chunk"
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STEEL_KB_NAME = "中国钢铁行业动态库"
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# 更新中文名元组和英文名列表
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CH_BASE_NAME = CH_BASE_NAME + (STEEL_KB_NAME,)
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EN_BASE_NAME.append(STEEL_KB)
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