refactor code (#583)

### What problem does this PR solve?

### Type of change

- [x] Refactoring

api/apps/api_app.py

 from itsdangerous import URLSafeTimedSerializer
 
 from api.utils.file_utils import filename_type, thumbnail
-from rag.utils import MINIO
+from rag.utils.minio_conn import MINIO
 
 
 def generate_confirmation_token(tenent_id):

api/apps/chunk_app.py

 
 from rag.app.qa import rmPrefix, beAdoc
 from rag.nlp import search, huqie
-from rag.utils import ELASTICSEARCH, rmSpace
+from rag.utils.es_conn import ELASTICSEARCH
+from rag.utils import rmSpace
 from api.db import LLMType, ParserType
 from api.db.services.knowledgebase_service import KnowledgebaseService
 from api.db.services.llm_service import TenantLLMService

api/apps/document_app.py

 from api.db.services.file2document_service import File2DocumentService
 from api.db.services.file_service import FileService
 from rag.nlp import search
-from rag.utils import ELASTICSEARCH
+from rag.utils.es_conn import ELASTICSEARCH
 from api.db.services import duplicate_name
 from api.db.services.knowledgebase_service import KnowledgebaseService
 from api.utils.api_utils import server_error_response, get_data_error_result, validate_request

api/apps/file2document_app.py

 from api.settings import RetCode
 from api.utils.api_utils import get_json_result
 from rag.nlp import search
-from rag.utils import ELASTICSEARCH
+from rag.utils.es_conn import ELASTICSEARCH
 
 
 @manager.route('/convert', methods=['POST'])

api/apps/file_app.py

 from api.utils.api_utils import get_json_result
 from api.utils.file_utils import filename_type
 from rag.nlp import search
-from rag.utils import ELASTICSEARCH
+from rag.utils.es_conn import ELASTICSEARCH
 from rag.utils.minio_conn import MINIO
 
 

api/apps/kb_app.py

 from api.settings import stat_logger, RetCode
 from api.utils.api_utils import get_json_result
 from rag.nlp import search
-from rag.utils import ELASTICSEARCH
+from rag.utils.es_conn import ELASTICSEARCH
 
 
 @manager.route('/create', methods=['post'])

api/db/services/document_service.py

 from peewee import Expression
 
 from elasticsearch_dsl import Q
-from rag.utils import ELASTICSEARCH
+from rag.utils.es_conn import ELASTICSEARCH
 from rag.utils.minio_conn import MINIO
 from rag.nlp import search
 

api/settings.py

 database_logger = getLogger("database")
 chat_logger = getLogger("chat")
 
-from rag.utils import ELASTICSEARCH
+from rag.utils.es_conn import ELASTICSEARCH
 from rag.nlp import search
 from api.utils import get_base_config, decrypt_database_config
 

deepdoc/parser/__init__.py

 
 
-from .pdf_parser import HuParser as PdfParser, PlainParser
-from .docx_parser import HuDocxParser as DocxParser
-from .excel_parser import HuExcelParser as ExcelParser
-from .ppt_parser import HuPptParser as PptParser
+from .pdf_parser import RAGFlowPdfParser as PdfParser, PlainParser
+from .docx_parser import RAGFlowDocxParser as DocxParser
+from .excel_parser import RAGFlowExcelParser as ExcelParser
+from .ppt_parser import RAGFlowPptParser as PptParser

deepdoc/parser/docx_parser.py

 from io import BytesIO
 
 
-class HuDocxParser:
+class RAGFlowDocxParser:
 
     def __extract_table_content(self, tb):
         df = []

deepdoc/parser/excel_parser.py

 from rag.nlp import find_codec
 
 
-class HuExcelParser:
+class RAGFlowExcelParser:
     def html(self, fnm):
         if isinstance(fnm, str):
             wb = load_workbook(fnm)
 
 
 if __name__ == "__main__":
-    psr = HuExcelParser()
+    psr = RAGFlowExcelParser()
     psr(sys.argv[1])

deepdoc/parser/pdf_parser.py

 logging.getLogger("pdfminer").setLevel(logging.WARNING)
 
 
-class HuParser:
+class RAGFlowPdfParser:
     def __init__(self):
         self.ocr = OCR()
         if hasattr(self, "model_speciess"):

deepdoc/parser/ppt_parser.py

 from pptx import Presentation
 
 
-class HuPptParser(object):
+class RAGFlowPptParser(object):
     def __init__(self):
         super().__init__()
 

deepdoc/vision/t_ocr.py

 #  limitations under the License.
 #
 
-from deepdoc.vision.seeit import draw_box
-from deepdoc.vision import OCR, init_in_out
-import argparse
-import numpy as np
 import os
 import sys
 sys.path.insert(
                 os.path.abspath(__file__)),
             '../../')))
 
+from deepdoc.vision.seeit import draw_box
+from deepdoc.vision import OCR, init_in_out
+import argparse
+import numpy as np
+
 
 def main(args):
     ocr = OCR()

deepdoc/vision/t_recognizer.py

 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
-
-from deepdoc.vision.seeit import draw_box
-from deepdoc.vision import Recognizer, LayoutRecognizer, TableStructureRecognizer, OCR, init_in_out
-from api.utils.file_utils import get_project_base_directory
-import argparse
-import os
-import sys
-import re
-
-import numpy as np
-
+import os, sys
 sys.path.insert(
     0,
     os.path.abspath(
                 os.path.abspath(__file__)),
             '../../')))
 
+from deepdoc.vision.seeit import draw_box
+from deepdoc.vision import Recognizer, LayoutRecognizer, TableStructureRecognizer, OCR, init_in_out
+from api.utils.file_utils import get_project_base_directory
+import argparse
+import re
+import numpy as np
+
 
 def main(args):
     images, outputs = init_in_out(args)

rag/llm/__init__.py

     "Ollama": OllamaEmbed,
     "OpenAI": OpenAIEmbed,
     "Xinference": XinferenceEmbed,
-    "Tongyi-Qianwen": HuEmbedding, #QWenEmbed,
+    "Tongyi-Qianwen": DefaultEmbedding, #QWenEmbed,
     "ZHIPU-AI": ZhipuEmbed,
     "FastEmbed": FastEmbed,
     "Youdao": YoudaoEmbed

rag/llm/embedding_model.py

         raise NotImplementedError("Please implement encode method!")
 
 
-class HuEmbedding(Base):
+class DefaultEmbedding(Base):
     def __init__(self, *args, **kwargs):
         """
         If you have trouble downloading HuggingFace models, -_^ this might help!!

rag/nlp/huchunk.py

-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-import re
-import os
-import copy
-import base64
-import magic
-from dataclasses import dataclass
-from typing import List
-import numpy as np
-from io import BytesIO
-
-
-class HuChunker:
-
-    @dataclass
-    class Fields:
-        text_chunks: List = None
-        table_chunks: List = None
-
-    def __init__(self):
-        self.MAX_LVL = 12
-        self.proj_patt = [
-            (r"第[零一二三四五六七八九十百]+章", 1),
-            (r"第[零一二三四五六七八九十百]+[条节]", 2),
-            (r"[零一二三四五六七八九十百]+[、 　]", 3),
-            (r"[\(（][零一二三四五六七八九十百]+[）\)]", 4),
-            (r"[0-9]+(、|\.[　 ]|\.[^0-9])", 5),
-            (r"[0-9]+\.[0-9]+(、|[ 　]|[^0-9])", 6),
-            (r"[0-9]+\.[0-9]+\.[0-9]+(、|[ 　]|[^0-9])", 7),
-            (r"[0-9]+\.[0-9]+\.[0-9]+\.[0-9]+(、|[ 　]|[^0-9])", 8),
-            (r".{,48}[：:?？]@", 9),
-            (r"[0-9]+）", 10),
-            (r"[\(（][0-9]+[）\)]", 11),
-            (r"[零一二三四五六七八九十百]+是", 12),
-            (r"[⚫•➢✓ ]", 12)
-        ]
-        self.lines = []
-
-    def _garbage(self, txt):
-        patt = [
-            r"(在此保证|不得以任何形式翻版|请勿传阅|仅供内部使用|未经事先书面授权)",
-            r"(版权(归本公司)*所有|免责声明|保留一切权力|承担全部责任|特别声明|报告中涉及)",
-            r"(不承担任何责任|投资者的通知事项：|任何机构和个人|本报告仅为|不构成投资)",
-            r"(不构成对任何个人或机构投资建议|联系其所在国家|本报告由从事证券交易)",
-            r"(本研究报告由|「认可投资者」|所有研究报告均以|请发邮件至)",
-            r"(本报告仅供|市场有风险，投资需谨慎|本报告中提及的)",
-            r"(本报告反映|此信息仅供|证券分析师承诺|具备证券投资咨询业务资格)",
-            r"^(时间|签字|签章)[:：]",
-            r"(参考文献|目录索引|图表索引)",
-            r"[ ]*年[ ]+月[ ]+日",
-            r"^(中国证券业协会|[0-9]+年[0-9]+月[0-9]+日)$",
-            r"\.{10,}",
-            r"(———————END|帮我转发|欢迎收藏|快来关注我吧)"
-        ]
-        return any([re.search(p, txt) for p in patt])
-
-    def _proj_match(self, line):
-        for p, j in self.proj_patt:
-            if re.match(p, line):
-                return j
-        return
-
-    def _does_proj_match(self):
-        mat = [None for _ in range(len(self.lines))]
-        for i in range(len(self.lines)):
-            mat[i] = self._proj_match(self.lines[i])
-        return mat
-
-    def naive_text_chunk(self, text, ti="", MAX_LEN=612):
-        if text:
-            self.lines = [l.strip().replace(u'\u3000', u' ')
-                          .replace(u'\xa0', u'')
-                          for l in text.split("\n\n")]
-            self.lines = [l for l in self.lines if not self._garbage(l)]
-            self.lines = [re.sub(r"([ ]+| )", " ", l)
-                          for l in self.lines if l]
-        if not self.lines:
-            return []
-        arr = self.lines
-
-        res = [""]
-        i = 0
-        while i < len(arr):
-            a = arr[i]
-            if not a:
-                i += 1
-                continue
-            if len(a) > MAX_LEN:
-                a_ = a.split("\n")
-                if len(a_) >= 2:
-                    arr.pop(i)
-                    for j in range(2, len(a_) + 1):
-                        if len("\n".join(a_[:j])) >= MAX_LEN:
-                            arr.insert(i, "\n".join(a_[:j - 1]))
-                            arr.insert(i + 1, "\n".join(a_[j - 1:]))
-                            break
-                    else:
-                        assert False, f"Can't split: {a}"
-                    continue
-
-            if len(res[-1]) < MAX_LEN / 3:
-                res[-1] += "\n" + a
-            else:
-                res.append(a)
-            i += 1
-
-        if ti:
-            for i in range(len(res)):
-                if res[i].find("——来自") >= 0:
-                    continue
-                res[i] += f"\t——来自“{ti}”"
-
-        return res
-
-    def _merge(self):
-        # merge continuous same level text
-        lines = [self.lines[0]] if self.lines else []
-        for i in range(1, len(self.lines)):
-            if self.mat[i] == self.mat[i - 1] \
-               and len(lines[-1]) < 256 \
-               and len(self.lines[i]) < 256:
-                lines[-1] += "\n" + self.lines[i]
-                continue
-            lines.append(self.lines[i])
-        self.lines = lines
-        self.mat = self._does_proj_match()
-        return self.mat
-
-    def text_chunks(self, text):
-        if text:
-            self.lines = [l.strip().replace(u'\u3000', u' ')
-                          .replace(u'\xa0', u'')
-                          for l in re.split(r"[\r\n]", text)]
-            self.lines = [l for l in self.lines if not self._garbage(l)]
-            self.lines = [l for l in self.lines if l]
-        self.mat = self._does_proj_match()
-        mat = self._merge()
-
-        tree = []
-        for i in range(len(self.lines)):
-            tree.append({"proj": mat[i],
-                         "children": [],
-                         "read": False})
-        # find all children
-        for i in range(len(self.lines) - 1):
-            if tree[i]["proj"] is None:
-                continue
-            ed = i + 1
-            while ed < len(tree) and (tree[ed]["proj"] is None or
-                                      tree[ed]["proj"] > tree[i]["proj"]):
-                ed += 1
-
-            nxt = tree[i]["proj"] + 1
-            st = set([p["proj"] for p in tree[i + 1: ed] if p["proj"]])
-            while nxt not in st:
-                nxt += 1
-                if nxt > self.MAX_LVL:
-                    break
-            if nxt <= self.MAX_LVL:
-                for j in range(i + 1, ed):
-                    if tree[j]["proj"] is not None:
-                        break
-                    tree[i]["children"].append(j)
-                for j in range(i + 1, ed):
-                    if tree[j]["proj"] != nxt:
-                        continue
-                    tree[i]["children"].append(j)
-            else:
-                for j in range(i + 1, ed):
-                    tree[i]["children"].append(j)
-
-        # get DFS combinations, find all the paths to leaf
-        paths = []
-
-        def dfs(i, path):
-            nonlocal tree, paths
-            path.append(i)
-            tree[i]["read"] = True
-            if len(self.lines[i]) > 256:
-                paths.append(path)
-                return
-            if not tree[i]["children"]:
-                if len(path) > 1 or len(self.lines[i]) >= 32:
-                    paths.append(path)
-                return
-            for j in tree[i]["children"]:
-                dfs(j, copy.deepcopy(path))
-
-        for i, t in enumerate(tree):
-            if t["read"]:
-                continue
-            dfs(i, [])
-
-        # concat txt on the path for all paths
-        res = []
-        lines = np.array(self.lines)
-        for p in paths:
-            if len(p) < 2:
-                tree[p[0]]["read"] = False
-                continue
-            txt = "\n".join(lines[p[:-1]]) + "\n" + lines[p[-1]]
-            res.append(txt)
-        # concat continuous orphans
-        assert len(tree) == len(lines)
-        ii = 0
-        while ii < len(tree):
-            if tree[ii]["read"]:
-                ii += 1
-                continue
-            txt = lines[ii]
-            e = ii + 1
-            while e < len(tree) and not tree[e]["read"] and len(txt) < 256:
-                txt += "\n" + lines[e]
-                e += 1
-            res.append(txt)
-            ii = e
-
-        # if the node has not been read, find its daddy
-        def find_daddy(st):
-            nonlocal lines, tree
-            proj = tree[st]["proj"]
-            if len(self.lines[st]) > 512:
-                return [st]
-            if proj is None:
-                proj = self.MAX_LVL + 1
-            for i in range(st - 1, -1, -1):
-                if tree[i]["proj"] and tree[i]["proj"] < proj:
-                    a = [st] + find_daddy(i)
-                    return a
-            return []
-
-        return res
-
-
-class PdfChunker(HuChunker):
-
-    def __init__(self, pdf_parser):
-        self.pdf = pdf_parser
-        super().__init__()
-
-    def tableHtmls(self, pdfnm):
-        _, tbls = self.pdf(pdfnm, return_html=True)
-        res = []
-        for img, arr in tbls:
-            if arr[0].find("<table>") < 0:
-                continue
-            buffered = BytesIO()
-            if img:
-                img.save(buffered, format="JPEG")
-            img_str = base64.b64encode(
-                buffered.getvalue()).decode('utf-8') if img else ""
-            res.append({"table": arr[0], "image": img_str})
-        return res
-
-    def html(self, pdfnm):
-        txts, tbls = self.pdf(pdfnm, return_html=True)
-        res = []
-        txt_cks = self.text_chunks(txts)
-        for txt, img in [(self.pdf.remove_tag(c), self.pdf.crop(c))
-                         for c in txt_cks]:
-            buffered = BytesIO()
-            if img:
-                img.save(buffered, format="JPEG")
-            img_str = base64.b64encode(
-                buffered.getvalue()).decode('utf-8') if img else ""
-            res.append({"table": "<p>%s</p>" % txt.replace("\n", "<br/>"),
-                        "image": img_str})
-
-        for img, arr in tbls:
-            if not arr:
-                continue
-            buffered = BytesIO()
-            if img:
-                img.save(buffered, format="JPEG")
-            img_str = base64.b64encode(
-                buffered.getvalue()).decode('utf-8') if img else ""
-            res.append({"table": arr[0], "image": img_str})
-
-        return res
-
-    def __call__(self, pdfnm, return_image=True, naive_chunk=False):
-        flds = self.Fields()
-        text, tbls = self.pdf(pdfnm)
-        fnm = pdfnm
-        txt_cks = self.text_chunks(text) if not naive_chunk else \
-            self.naive_text_chunk(text, ti=fnm if isinstance(fnm, str) else "")
-        flds.text_chunks = [(self.pdf.remove_tag(c),
-                             self.pdf.crop(c) if return_image else None) for c in txt_cks]
-
-        flds.table_chunks = [(arr, img if return_image else None)
-                             for img, arr in tbls]
-        return flds
-
-
-class DocxChunker(HuChunker):
-
-    def __init__(self, doc_parser):
-        self.doc = doc_parser
-        super().__init__()
-
-    def _does_proj_match(self):
-        mat = []
-        for s in self.styles:
-            s = s.split(" ")[-1]
-            try:
-                mat.append(int(s))
-            except Exception as e:
-                mat.append(None)
-        return mat
-
-    def _merge(self):
-        i = 1
-        while i < len(self.lines):
-            if self.mat[i] == self.mat[i - 1] \
-               and len(self.lines[i - 1]) < 256 \
-               and len(self.lines[i]) < 256:
-                self.lines[i - 1] += "\n" + self.lines[i]
-                self.styles.pop(i)
-                self.lines.pop(i)
-                self.mat.pop(i)
-                continue
-            i += 1
-        self.mat = self._does_proj_match()
-        return self.mat
-
-    def __call__(self, fnm):
-        flds = self.Fields()
-        flds.title = os.path.splitext(
-            os.path.basename(fnm))[0] if isinstance(
-            fnm, type("")) else ""
-        secs, tbls = self.doc(fnm)
-        self.lines = [l for l, s in secs]
-        self.styles = [s for l, s in secs]
-
-        txt_cks = self.text_chunks("")
-        flds.text_chunks = [(t, None) for t in txt_cks if not self._garbage(t)]
-        flds.table_chunks = [(tb, None) for tb in tbls for t in tb if t]
-        return flds
-
-
-class ExcelChunker(HuChunker):
-
-    def __init__(self, excel_parser):
-        self.excel = excel_parser
-        super().__init__()
-
-    def __call__(self, fnm):
-        flds = self.Fields()
-        flds.text_chunks = [(t, None) for t in self.excel(fnm)]
-        flds.table_chunks = []
-        return flds
-
-
-class PptChunker(HuChunker):
-
-    def __init__(self):
-        super().__init__()
-
-    def __extract(self, shape):
-        if shape.shape_type == 19:
-            tb = shape.table
-            rows = []
-            for i in range(1, len(tb.rows)):
-                rows.append("; ".join([tb.cell(
-                    0, j).text + ": " + tb.cell(i, j).text for j in range(len(tb.columns)) if tb.cell(i, j)]))
-            return "\n".join(rows)
-
-        if shape.has_text_frame:
-            return shape.text_frame.text
-
-        if shape.shape_type == 6:
-            texts = []
-            for p in shape.shapes:
-                t = self.__extract(p)
-                if t:
-                    texts.append(t)
-            return "\n".join(texts)
-
-    def __call__(self, fnm):
-        from pptx import Presentation
-        ppt = Presentation(fnm) if isinstance(
-            fnm, str) else Presentation(
-            BytesIO(fnm))
-        txts = []
-        for slide in ppt.slides:
-            texts = []
-            for shape in slide.shapes:
-                txt = self.__extract(shape)
-                if txt:
-                    texts.append(txt)
-            txts.append("\n".join(texts))
-
-        import aspose.slides as slides
-        import aspose.pydrawing as drawing
-        imgs = []
-        with slides.Presentation(BytesIO(fnm)) as presentation:
-            for slide in presentation.slides:
-                buffered = BytesIO()
-                slide.get_thumbnail(
-                    0.5, 0.5).save(
-                    buffered, drawing.imaging.ImageFormat.jpeg)
-                imgs.append(buffered.getvalue())
-        assert len(imgs) == len(
-            txts), "Slides text and image do not match: {} vs. {}".format(len(imgs), len(txts))
-
-        flds = self.Fields()
-        flds.text_chunks = [(txts[i], imgs[i]) for i in range(len(txts))]
-        flds.table_chunks = []
-
-        return flds
-
-
-class TextChunker(HuChunker):
-
-    @dataclass
-    class Fields:
-        text_chunks: List = None
-        table_chunks: List = None
-
-    def __init__(self):
-        super().__init__()
-
-    @staticmethod
-    def is_binary_file(file_path):
-        mime = magic.Magic(mime=True)
-        if isinstance(file_path, str):
-            file_type = mime.from_file(file_path)
-        else:
-            file_type = mime.from_buffer(file_path)
-        if 'text' in file_type:
-            return False
-        else:
-            return True
-
-    def __call__(self, fnm):
-        flds = self.Fields()
-        if self.is_binary_file(fnm):
-            return flds
-        txt = ""
-        if isinstance(fnm, str):
-            with open(fnm, "r") as f:
-                txt = f.read()
-        else:
-            txt = fnm.decode("utf-8")
-        flds.text_chunks = [(c, None) for c in self.naive_text_chunk(txt)]
-        flds.table_chunks = []
-        return flds
-
-
-if __name__ == "__main__":
-    import sys
-    sys.path.append(os.path.dirname(__file__) + "/../")
-    if sys.argv[1].split(".")[-1].lower() == "pdf":
-        from deepdoc.parser import PdfParser
-        ckr = PdfChunker(PdfParser())
-    if sys.argv[1].split(".")[-1].lower().find("doc") >= 0:
-        from deepdoc.parser import DocxParser
-        ckr = DocxChunker(DocxParser())
-    if sys.argv[1].split(".")[-1].lower().find("xlsx") >= 0:
-        from deepdoc.parser import ExcelParser
-        ckr = ExcelChunker(ExcelParser())
-
-    # ckr.html(sys.argv[1])
-    print(ckr(sys.argv[1]))

rag/nlp/synonym.py

         try:
             self.dictionary = json.load(open(path, 'r'))
         except Exception as e:
-            logging.warning("Missing synonym.json")
+            logging.warn("Missing synonym.json")
             self.dictionary = {}
 
         if not redis:
             logging.warning(
-                "Real-time synonym is disabled, since no redis connection.")
+                "Realtime synonym is disabled, since no redis connection.")
         if not len(self.dictionary.keys()):
             logging.warning(f"Fail to load synonym")
 

rag/svr/cache_file_svr.py

 
 from api.db.db_models import close_connection
 from api.db.services.task_service import TaskService
-from rag.utils import MINIO
+from rag.utils.minio_conn import MINIO
 from rag.utils.redis_conn import REDIS_CONN
 
 

rag/svr/task_broker.py

 from api.db.services.file_service import FileService
 from api.db.services.task_service import TaskService
 from deepdoc.parser import PdfParser
-from deepdoc.parser.excel_parser import HuExcelParser
+from deepdoc.parser.excel_parser import RAGFlowExcelParser
 from rag.settings import cron_logger
-from rag.utils import MINIO
+from rag.utils.minio_conn import MINIO
 from rag.utils import findMaxTm
 import pandas as pd
 from api.db import FileType, TaskStatus
                         tsks.append(task)
 
             elif r["parser_id"] == "table":
-                rn = HuExcelParser.row_number(
+                rn = RAGFlowExcelParser.row_number(
                     r["name"], file_bin)
                 for i in range(0, rn, 3000):
                     task = new_task()

rag/svr/task_executor.py

 from functools import partial
 
 from api.db.services.file2document_service import File2DocumentService
-from rag.utils import MINIO
+from rag.utils.minio_conn import MINIO
 from api.db.db_models import close_connection
 from rag.settings import database_logger
 from rag.settings import cron_logger, DOC_MAXIMUM_SIZE
 from elasticsearch_dsl import Q
 from multiprocessing.context import TimeoutError
 from api.db.services.task_service import TaskService
-from rag.utils import ELASTICSEARCH
+from rag.utils.es_conn import ELASTICSEARCH
 from timeit import default_timer as timer
 from rag.utils import rmSpace, findMaxTm
 

rag/utils/__init__.py

     return _singleton
 
 
-from .minio_conn import MINIO
-from .es_conn import ELASTICSEARCH
-
 def rmSpace(txt):
     txt = re.sub(r"([^a-z0-9.,]) +([^ ])", r"\1\2", txt, flags=re.IGNORECASE)
     return re.sub(r"([^ ]) +([^a-z0-9.,])", r"\1\2", txt, flags=re.IGNORECASE)

rag/utils/es_conn.py

 
 
 @singleton
-class HuEs:
+class ESConnection:
     def __init__(self):
         self.info = {}
         self.conn()
             scroll_size = len(page['hits']['hits'])
 
 
-ELASTICSEARCH = HuEs()
+ELASTICSEARCH = ESConnection()

rag/utils/minio_conn.py

 
 
 @singleton
-class HuMinio(object):
+class RAGFlowMinio(object):
     def __init__(self):
         self.conn = None
         self.__open__()
                 time.sleep(1)
         return
 
-MINIO = HuMinio()
+
+MINIO = RAGFlowMinio()
+
 
 if __name__ == "__main__":
-    conn = HuMinio()
+    conn = RAGFlowMinio()
     fnm = "/opt/home/kevinhu/docgpt/upload/13/11-408.jpg"
     from PIL import Image
     img = Image.open(fnm)