Add Q&A and Book, fix task running bugs (#50)

api/apps/chunk_app.py

 from flask import request
 from flask_login import login_required, current_user
 from elasticsearch_dsl import Q
+
+from rag.app.qa import rmPrefix, beAdoc
 from rag.nlp import search, huqie, retrievaler
 from rag.utils import ELASTICSEARCH, rmSpace
-from api.db import LLMType
-from api.db.services.kb_service import KnowledgebaseService
+from api.db import LLMType, ParserType
+from api.db.services.knowledgebase_service import KnowledgebaseService
 from api.db.services.llm_service import TenantLLMService
 from api.db.services.user_service import UserTenantService
 from api.utils.api_utils import server_error_response, get_data_error_result, validate_request
         res["chunk_id"] = id
         k = []
         for n in res.keys():
-            if re.search(r"(_vec$|_sm_)", n):
+            if re.search(r"(_vec$|_sm_|_tks|_ltks)", n):
                 k.append(n)
-            if re.search(r"(_tks|_ltks)", n):
-                res[n] = rmSpace(res[n])
         for n in k:
             del res[n]
 
 
 @manager.route('/set', methods=['POST'])
 @login_required
-@validate_request("doc_id", "chunk_id", "content_ltks",
+@validate_request("doc_id", "chunk_id", "content_with_weight",
                   "important_kwd")
 def set():
     req = request.json
     d = {"id": req["chunk_id"]}
-    d["content_ltks"] = huqie.qie(req["content_ltks"])
+    d["content_ltks"] = huqie.qie(req["content_with_weight"])
     d["content_sm_ltks"] = huqie.qieqie(d["content_ltks"])
     d["important_kwd"] = req["important_kwd"]
     d["important_tks"] = huqie.qie(" ".join(req["important_kwd"]))
         e, doc = DocumentService.get_by_id(req["doc_id"])
         if not e:
             return get_data_error_result(retmsg="Document not found!")
+
+        if doc.parser_id == ParserType.QA:
+            arr = [t for t in re.split(r"[\n\t]", req["content_with_weight"]) if len(t)>1]
+            if len(arr) != 2: return get_data_error_result(retmsg="Q&A must be separated by TAB/ENTER key.")
+            q, a = rmPrefix(arr[0]), rmPrefix[arr[1]]
+            d = beAdoc(d, arr[0], arr[1], not any([huqie.is_chinese(t) for t in q+a]))
+
         v, c = embd_mdl.encode([doc.name, req["content_ltks"]])
-        v = 0.1 * v[0] + 0.9 * v[1]
+        v = 0.1 * v[0] + 0.9 * v[1] if doc.parser_id != ParserType.QA else v[1]
         d["q_%d_vec" % len(v)] = v.tolist()
         ELASTICSEARCH.upsert([d], search.index_name(tenant_id))
         return get_json_result(data=True)

api/apps/dialog_app.py

 from flask_login import login_required, current_user
 from api.db.services.dialog_service import DialogService
 from api.db import StatusEnum
-from api.db.services.kb_service import KnowledgebaseService
+from api.db.services.knowledgebase_service import KnowledgebaseService
 from api.db.services.user_service import TenantService
 from api.utils.api_utils import server_error_response, get_data_error_result, validate_request
 from api.utils import get_uuid

api/apps/document_app.py

 from rag.nlp import search
 from rag.utils import ELASTICSEARCH
 from api.db.services import duplicate_name
-from api.db.services.kb_service import KnowledgebaseService
+from api.db.services.knowledgebase_service import KnowledgebaseService
 from api.utils.api_utils import server_error_response, get_data_error_result, validate_request
 from api.utils import get_uuid
-from api.db import FileType
+from api.db import FileType, TaskStatus
 from api.db.services.document_service import DocumentService
 from api.settings import RetCode
 from api.utils.api_utils import get_json_result
 @manager.route('/run', methods=['POST'])
 @login_required
 @validate_request("doc_ids", "run")
-def rm():
+def run():
     req = request.json
     try:
         for id in req["doc_ids"]:
-            DocumentService.update_by_id(id, {"run": str(req["run"])})
-            if req["run"] == "2":
-                TaskService.filter_delete([Task.doc_id == id])
+            DocumentService.update_by_id(id, {"run": str(req["run"]), "progress": 0})
+            if str(req["run"]) == TaskStatus.CANCEL.value:
                 tenant_id = DocumentService.get_tenant_id(id)
                 if not tenant_id:
                     return get_data_error_result(retmsg="Tenant not found!")
         if doc.parser_id.lower() == req["parser_id"].lower():
             return get_json_result(data=True)
 
-        e = DocumentService.update_by_id(doc.id, {"parser_id": req["parser_id"], "progress":0, "progress_msg": "", "run": 1})
-        if not e:
-            return get_data_error_result(retmsg="Document not found!")
-        e = DocumentService.increment_chunk_num(doc.id, doc.kb_id, doc.token_num*-1, doc.chunk_num*-1, doc.process_duation*-1)
+        e = DocumentService.update_by_id(doc.id, {"parser_id": req["parser_id"], "progress":0, "progress_msg": ""})
         if not e:
             return get_data_error_result(retmsg="Document not found!")
+        if doc.token_num>0:
+            e = DocumentService.increment_chunk_num(doc.id, doc.kb_id, doc.token_num*-1, doc.chunk_num*-1, doc.process_duation*-1)
+            if not e:
+                return get_data_error_result(retmsg="Document not found!")
 
         return get_json_result(data=True)
     except Exception as e:

api/apps/kb_app.py

 from api.utils.api_utils import server_error_response, get_data_error_result, validate_request
 from api.utils import get_uuid, get_format_time
 from api.db import StatusEnum, UserTenantRole
-from api.db.services.kb_service import KnowledgebaseService
+from api.db.services.knowledgebase_service import KnowledgebaseService
 from api.db.db_models import Knowledgebase
 from api.settings import stat_logger, RetCode
 from api.utils.api_utils import get_json_result

api/apps/llm_app.py

 from api.utils.api_utils import server_error_response, get_data_error_result, validate_request
 from api.utils import get_uuid, get_format_time
 from api.db import StatusEnum, UserTenantRole
-from api.db.services.kb_service import KnowledgebaseService
+from api.db.services.knowledgebase_service import KnowledgebaseService
 from api.db.db_models import Knowledgebase, TenantLLM
 from api.settings import stat_logger, RetCode
 from api.utils.api_utils import get_json_result

api/db/__init__.py

     CUSTOM = 'Custom'
 
 
+class TaskStatus(StrEnum):
+    RUNNING = "1"
+    CANCEL = "2"
+    DONE = "3"
+    FAIL = "4"
+
+
 class ParserType(StrEnum):
     GENERAL = "general"
     PRESENTATION = "presentation"
     LAWS = "laws"
     MANUAL = "manual"
     PAPER = "paper"
-    RESUME = ""
-    BOOK = ""
-    QA = ""
+    RESUME = "resume"
+    BOOK = "book"
+    QA = "qa"

api/db/db_utils.py

     DB.create_tables([model])
 
 
-    for data in data_source:
-        current_time = current_timestamp()
+    for i,data in enumerate(data_source):
+        current_time = current_timestamp() + i
         current_date = timestamp_to_date(current_time)
         if 'create_time' not in data:
             data['create_time'] = current_time

api/db/services/document_service.py

 #
 from peewee import Expression
 
-from api.db import TenantPermission, FileType
+from api.db import TenantPermission, FileType, TaskStatus
 from api.db.db_models import DB, Knowledgebase, Tenant
 from api.db.db_models import Document
 from api.db.services.common_service import CommonService
-from api.db.services.kb_service import KnowledgebaseService
+from api.db.services.knowledgebase_service import KnowledgebaseService
 from api.db import StatusEnum
 
 
                 ~(cls.model.type == FileType.VIRTUAL.value),
                 cls.model.progress == 0,
                 cls.model.update_time >= tm,
+                cls.model.run == TaskStatus.RUNNING.value,
                 (Expression(cls.model.create_time, "%%", comm) == mod))\
             .order_by(cls.model.update_time.asc())\
             .paginate(1, items_per_page)

api/db/services/knowledgebase_service.py

 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
-from api.db.db_models import Knowledgebase, Document
+from api.db import StatusEnum, TenantPermission
+from api.db.db_models import Knowledgebase, DB, Tenant
 from api.db.services.common_service import CommonService
 
 
 class KnowledgebaseService(CommonService):
     model = Knowledgebase
 
+    @classmethod
+    @DB.connection_context()
+    def get_by_tenant_ids(cls, joined_tenant_ids, user_id,
+                          page_number, items_per_page, orderby, desc):
+        kbs = cls.model.select().where(
+            ((cls.model.tenant_id.in_(joined_tenant_ids) & (cls.model.permission ==
+             TenantPermission.TEAM.value)) | (cls.model.tenant_id == user_id))
+            & (cls.model.status == StatusEnum.VALID.value)
+        )
+        if desc:
+            kbs = kbs.order_by(cls.model.getter_by(orderby).desc())
+        else:
+            kbs = kbs.order_by(cls.model.getter_by(orderby).asc())
 
-class DocumentService(CommonService):
-    model = Document
+        kbs = kbs.paginate(page_number, items_per_page)
+
+        return list(kbs.dicts())
+
+    @classmethod
+    @DB.connection_context()
+    def get_detail(cls, kb_id):
+        fields = [
+            cls.model.id,
+            Tenant.embd_id,
+            cls.model.avatar,
+            cls.model.name,
+            cls.model.description,
+            cls.model.permission,
+            cls.model.doc_num,
+            cls.model.token_num,
+            cls.model.chunk_num,
+            cls.model.parser_id]
+        kbs = cls.model.select(*fields).join(Tenant, on=((Tenant.id == cls.model.tenant_id)&(Tenant.status== StatusEnum.VALID.value))).where(
+            (cls.model.id == kb_id),
+            (cls.model.status == StatusEnum.VALID.value)
+        )
+        if not kbs:
+            return
+        d = kbs[0].to_dict()
+        d["embd_id"] = kbs[0].tenant.embd_id
+        return d

api/db/services/task_service.py

-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  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.
-#
-from peewee import Expression
-from api.db.db_models import DB
-from api.db import StatusEnum, FileType
-from api.db.db_models import Task, Document, Knowledgebase, Tenant
-from api.db.services.common_service import CommonService
-
-
-class TaskService(CommonService):
-    model = Task
-
-    @classmethod
-    @DB.connection_context()
-    def get_tasks(cls, tm, mod=0, comm=1, items_per_page=64):
-        fields = [cls.model.id, cls.model.doc_id, cls.model.from_page,cls.model.to_page, Document.kb_id, Document.parser_id, Document.name, Document.type, Document.location, Document.size, Knowledgebase.tenant_id, Tenant.embd_id, Tenant.img2txt_id, Tenant.asr_id, cls.model.update_time]
-        docs = cls.model.select(*fields) \
-            .join(Document, on=(cls.model.doc_id == Document.id)) \
-            .join(Knowledgebase, on=(Document.kb_id == Knowledgebase.id)) \
-            .join(Tenant, on=(Knowledgebase.tenant_id == Tenant.id))\
-            .where(
-                Document.status == StatusEnum.VALID.value,
-                ~(Document.type == FileType.VIRTUAL.value),
-                cls.model.progress == 0,
-                cls.model.update_time >= tm,
-                (Expression(cls.model.create_time, "%%", comm) == mod))\
-            .order_by(cls.model.update_time.asc())\
-            .paginate(1, items_per_page)
-        return list(docs.dicts())
-
-
-    @classmethod
-    @DB.connection_context()
-    def do_cancel(cls, id):
-        try:
-            cls.model.get_by_id(id)
-            return False
-        except Exception as e:
-            pass
-        return True
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  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.
+#
+from peewee import Expression
+from api.db.db_models import DB
+from api.db import StatusEnum, FileType, TaskStatus
+from api.db.db_models import Task, Document, Knowledgebase, Tenant
+from api.db.services.common_service import CommonService
+from api.db.services.document_service import DocumentService
+
+
+class TaskService(CommonService):
+    model = Task
+
+    @classmethod
+    @DB.connection_context()
+    def get_tasks(cls, tm, mod=0, comm=1, items_per_page=64):
+        fields = [cls.model.id, cls.model.doc_id, cls.model.from_page,cls.model.to_page, Document.kb_id, Document.parser_id, Document.name, Document.type, Document.location, Document.size, Knowledgebase.tenant_id, Tenant.embd_id, Tenant.img2txt_id, Tenant.asr_id, cls.model.update_time]
+        docs = cls.model.select(*fields) \
+            .join(Document, on=(cls.model.doc_id == Document.id)) \
+            .join(Knowledgebase, on=(Document.kb_id == Knowledgebase.id)) \
+            .join(Tenant, on=(Knowledgebase.tenant_id == Tenant.id))\
+            .where(
+                Document.status == StatusEnum.VALID.value,
+                ~(Document.type == FileType.VIRTUAL.value),
+                cls.model.progress == 0,
+                cls.model.update_time >= tm,
+                (Expression(cls.model.create_time, "%%", comm) == mod))\
+            .order_by(cls.model.update_time.asc())\
+            .paginate(1, items_per_page)
+        return list(docs.dicts())
+
+
+    @classmethod
+    @DB.connection_context()
+    def do_cancel(cls, id):
+        try:
+            task = cls.model.get_by_id(id)
+            _, doc = DocumentService.get_by_id(task.doc_id)
+            return doc.run == TaskStatus.CANCEL.value
+        except Exception as e:
+            pass
+        return True

api/utils/file_utils.py

     if re.match(r".*\.pdf$", filename):
         return FileType.PDF.value
 
-    if re.match(r".*\.(docx|doc|ppt|pptx|yml|xml|htm|json|csv|txt|ini|xsl|wps|rtf|hlp|pages|numbers|key|md)$", filename):
+    if re.match(r".*\.(docx|doc|ppt|pptx|yml|xml|htm|json|csv|txt|ini|xls|xlsx|wps|rtf|hlp|pages|numbers|key|md)$", filename):
         return FileType.DOC.value
 
     if re.match(r".*\.(wav|flac|ape|alac|wavpack|wv|mp3|aac|ogg|vorbis|opus|mp3)$", filename):

rag/app/__init__.py

 
 from rag.nlp import stemmer, huqie
 
-
-def callback__(progress, msg, func):
-    if not func :return
-    func(progress, msg)
-
-
 BULLET_PATTERN = [[
-        r"第[零一二三四五六七八九十百]+编",
+        r"第[零一二三四五六七八九十百]+(编|部分)",
         r"第[零一二三四五六七八九十百]+章",
         r"第[零一二三四五六七八九十百]+节",
         r"第[零一二三四五六七八九十百]+条",
         r"[0-9]{,2}\.[0-9]{,2}\.[0-9]{,2}",
         r"[0-9]{,2}\.[0-9]{,2}\.[0-9]{,2}\.[0-9]{,2}",
     ], [
+        r"第[零一二三四五六七八九十百]+章",
+        r"第[零一二三四五六七八九十百]+节",
         r"[零一二三四五六七八九十百]+[ 、]",
         r"[\(（][零一二三四五六七八九十百]+[\)）]",
         r"[\(（][0-9]{,2}[\)）]",
 def is_english(texts):
     eng = 0
     for t in texts:
-        if re.match(r"[a-zA-Z]", t.strip()):
+        if re.match(r"[a-zA-Z]{2,}", t.strip()):
             eng += 1
     if eng / len(texts) > 0.8:
         return True
         d["content_sm_ltks"] = huqie.qieqie(d["content_ltks"])
 
 
+def remove_contents_table(sections, eng=False):
+    i = 0
+    while i < len(sections):
+        def get(i):
+            nonlocal sections
+            return (sections[i] if type(sections[i]) == type("") else sections[i][0]).strip()
+        if not re.match(r"(contents|目录|目次|table of contents|致谢|acknowledge)$", re.sub(r"( | |\u3000)+", "", get(i).split("@@")[0], re.IGNORECASE)):
+            i += 1
+            continue
+        sections.pop(i)
+        if i >= len(sections): break
+        prefix = get(i)[:3] if not eng else " ".join(get(i).split(" ")[:2])
+        while not prefix:
+            sections.pop(i)
+            if i >= len(sections): break
+            prefix = get(i)[:3] if not eng else " ".join(get(i).split(" ")[:2])
+        sections.pop(i)
+        if i >= len(sections) or not prefix: break
+        for j in range(i, min(i+128, len(sections))):
+            if not re.match(prefix, get(j)):
+                continue
+            for _ in range(i, j):sections.pop(i)
+            break

rag/app/book.py

+import copy
+import random
+import re
+from io import BytesIO
+from docx import Document
+import numpy as np
+from rag.app import bullets_category, BULLET_PATTERN, is_english, tokenize, remove_contents_table
+from rag.nlp import huqie
+from rag.parser.docx_parser import HuDocxParser
+from rag.parser.pdf_parser import HuParser
+
+
+class Pdf(HuParser):
+    def __call__(self, filename, binary=None, from_page=0,
+                 to_page=100000, zoomin=3, callback=None):
+        self.__images__(
+            filename if not binary else binary,
+            zoomin,
+            from_page,
+            to_page)
+        callback(0.1, "OCR finished")
+
+        from timeit import default_timer as timer
+        start = timer()
+        self._layouts_paddle(zoomin)
+        callback(0.47, "Layout analysis finished")
+        print("paddle layouts:", timer() - start)
+        self._table_transformer_job(zoomin)
+        callback(0.68, "Table analysis finished")
+        self._text_merge()
+        column_width = np.median([b["x1"] - b["x0"] for b in self.boxes])
+        self._concat_downward(concat_between_pages=False)
+        self._filter_forpages()
+        self._merge_with_same_bullet()
+        callback(0.75, "Text merging finished.")
+        tbls = self._extract_table_figure(True, zoomin, False)
+
+        callback(0.8, "Text extraction finished")
+
+        return [(b["text"] + self._line_tag(b, zoomin), b.get("layoutno","")) for b in self.boxes]
+
+
+def chunk(filename, binary=None, from_page=0, to_page=100000, callback=None):
+    doc = {
+        "docnm_kwd": filename,
+        "title_tks": huqie.qie(re.sub(r"\.[a-zA-Z]+$", "", filename))
+    }
+    doc["title_sm_tks"] = huqie.qieqie(doc["title_tks"])
+    pdf_parser = None
+    sections,tbls = [], []
+    if re.search(r"\.docx?$", filename, re.IGNORECASE):
+        callback(0.1, "Start to parse.")
+        doc_parser = HuDocxParser()
+        # TODO: table of contents need to be removed
+        sections, tbls = doc_parser(binary if binary else filename)
+        remove_contents_table(sections, eng = is_english(random.choices([t for t,_ in sections], k=200)))
+        callback(0.8, "Finish parsing.")
+    elif re.search(r"\.pdf$", filename, re.IGNORECASE):
+        pdf_parser = Pdf()
+        sections,tbls = pdf_parser(filename if not binary else binary,
+                         from_page=from_page, to_page=to_page, callback=callback)
+    elif re.search(r"\.txt$", filename, re.IGNORECASE):
+        callback(0.1, "Start to parse.")
+        txt = ""
+        if binary:txt = binary.decode("utf-8")
+        else:
+            with open(filename, "r") as f:
+                while True:
+                    l = f.readline()
+                    if not l:break
+                    txt += l
+            sections = txt.split("\n")
+        sections = [(l,"") for l in sections if l]
+        remove_contents_table(sections, eng = is_english(random.choices([t for t,_ in sections], k=200)))
+        callback(0.8, "Finish parsing.")
+    else: raise NotImplementedError("file type not supported yet(docx, pdf, txt supported)")
+
+    bull = bullets_category([b["text"] for b in random.choices([t for t,_ in sections], k=100)])
+    projs = [len(BULLET_PATTERN[bull]) + 1] * len(sections)
+    levels = [[]] * len(BULLET_PATTERN[bull]) + 2
+    for i, (txt, layout) in enumerate(sections):
+        for j, p in enumerate(BULLET_PATTERN[bull]):
+            if re.match(p, txt.strip()):
+                projs[i] = j
+                levels[j].append(i)
+                break
+        else:
+            if re.search(r"(title|head)", layout):
+                projs[i] = BULLET_PATTERN[bull]
+                levels[BULLET_PATTERN[bull]].append(i)
+            else:
+                levels[BULLET_PATTERN[bull] + 1].append(i)
+    sections = [t for t,_ in sections]
+
+    def binary_search(arr, target):
+        if target > arr[-1]: return len(arr) - 1
+        if target > arr[0]: return -1
+        s, e = 0, len(arr)
+        while e - s > 1:
+            i = (e + s) // 2
+            if target > arr[i]:
+                s = i
+                continue
+            elif target < arr[i]:
+                e = i
+                continue
+            else:
+                assert False
+        return s
+
+    cks = []
+    readed = [False] * len(sections)
+    levels = levels[::-1]
+    for i, arr in enumerate(levels):
+        for j in arr:
+            if readed[j]: continue
+            readed[j] = True
+            cks.append([j])
+            if i + 1 == len(levels) - 1: continue
+            for ii in range(i + 1, len(levels)):
+                jj = binary_search(levels[ii], j)
+                if jj < 0: break
+                if jj > cks[-1][-1]: cks[-1].pop(-1)
+                cks[-1].append(levels[ii][jj])
+
+    # is it English
+    eng = is_english(random.choices(sections, k=218))
+
+    res = []
+    # add tables
+    for img, rows in tbls:
+        bs = 10
+        de = ";" if eng else "；"
+        for i in range(0, len(rows), bs):
+            d = copy.deepcopy(doc)
+            r = de.join(rows[i:i + bs])
+            r = re.sub(r"\t——(来自| in ).*”%s" % de, "", r)
+            tokenize(d, r, eng)
+            d["image"] = img
+            res.append(d)
+    # wrap up to es documents
+    for ck in cks:
+        print("\n-".join(ck[::-1]))
+        ck = "\n".join(ck[::-1])
+        d = copy.deepcopy(doc)
+        if pdf_parser:
+            d["image"] = pdf_parser.crop(ck)
+            ck = pdf_parser.remove_tag(ck)
+        tokenize(d, ck, eng)
+        res.append(d)
+    return res
+
+
+if __name__ == "__main__":
+    import sys
+    chunk(sys.argv[1])

rag/app/laws.py

 from io import BytesIO
 from docx import Document
 import numpy as np
-from rag.app import callback__, bullets_category, BULLET_PATTERN, is_english, tokenize
+from rag.app import bullets_category, BULLET_PATTERN, is_english, tokenize
 from rag.nlp import huqie
 from rag.parser.docx_parser import HuDocxParser
 from rag.parser.pdf_parser import HuParser
             zoomin,
             from_page,
             to_page)
-        callback__(0.1, "OCR finished", callback)
+        callback(0.1, "OCR finished")
 
         from timeit import default_timer as timer
         start = timer()
         self._layouts_paddle(zoomin)
-        callback__(0.77, "Layout analysis finished", callback)
+        callback(0.77, "Layout analysis finished")
         print("paddle layouts:", timer()-start)
         bxs = self.sort_Y_firstly(self.boxes, np.median(self.mean_height) / 3)
         # is it English
             b["x1"] = max(b["x1"], b_["x1"])
             bxs.pop(i + 1)
 
-        callback__(0.8, "Text extraction finished", callback)
+        callback(0.8, "Text extraction finished")
 
         return [b["text"] + self._line_tag(b, zoomin) for b in bxs]
 
     pdf_parser = None
     sections = []
     if re.search(r"\.docx?$", filename, re.IGNORECASE):
-        callback__(0.1, "Start to parse.", callback)
+        callback(0.1, "Start to parse.")
         for txt in Docx()(filename, binary):
             sections.append(txt)
-        callback__(0.8, "Finish parsing.", callback)
+        callback(0.8, "Finish parsing.")
     elif re.search(r"\.pdf$", filename, re.IGNORECASE):
         pdf_parser = Pdf()
         for txt in pdf_parser(filename if not binary else binary,
                          from_page=from_page, to_page=to_page, callback=callback):
             sections.append(txt)
     elif re.search(r"\.txt$", filename, re.IGNORECASE):
-        callback__(0.1, "Start to parse.", callback)
+        callback(0.1, "Start to parse.")
         txt = ""
         if binary:txt = binary.decode("utf-8")
         else:
                     txt += l
             sections = txt.split("\n")
         sections = [l for l in sections if l]
-        callback__(0.8, "Finish parsing.", callback)
+        callback(0.8, "Finish parsing.")
     else: raise NotImplementedError("file type not supported yet(docx, pdf, txt supported)")
 
     # is it English
     # Remove 'Contents' part
     i = 0
     while i < len(sections):
-        if not re.match(r"(Contents|目录|目次)$", re.sub(r"( | |\u3000)+", "", sections[i].split("@@")[0])):
+        if not re.match(r"(contents|目录|目次|table of contents)$", re.sub(r"( | |\u3000)+", "", sections[i].split("@@")[0], re.IGNORECASE)):
             i += 1
             continue
         sections.pop(i)
         for j in range(i, min(i+128, len(sections))):
             if not re.match(prefix, sections[j]):
                 continue
-            for k in range(i, j):sections.pop(i)
+            for _ in range(i, j):sections.pop(i)
             break
 
     bull = bullets_category(sections)

rag/app/manual.py

 import copy
 import re
-from rag.app import callback__, tokenize
+from rag.app import tokenize
 from rag.nlp import huqie
 from rag.parser.pdf_parser import HuParser
 from rag.utils import num_tokens_from_string
             zoomin,
             from_page,
             to_page)
-        callback__(0.2, "OCR finished.", callback)
+        callback(0.2, "OCR finished.")
 
         from timeit import default_timer as timer
         start = timer()
         self._layouts_paddle(zoomin)
-        callback__(0.5, "Layout analysis finished.", callback)
+        callback(0.5, "Layout analysis finished.")
         print("paddle layouts:", timer() - start)
         self._table_transformer_job(zoomin)
-        callback__(0.7, "Table analysis finished.", callback)
+        callback(0.7, "Table analysis finished.")
         self._text_merge()
         self._concat_downward(concat_between_pages=False)
         self._filter_forpages()
-        callback__(0.77, "Text merging finished", callback)
+        callback(0.77, "Text merging finished")
         tbls = self._extract_table_figure(True, zoomin, False)
 
         # clean mess
             b["text"] = re.sub(r"([\t 　]|\u3000){2,}", " ", b["text"].strip())
 
         # merge chunks with the same bullets
-        i = 0
-        while i + 1 < len(self.boxes):
-            b = self.boxes[i]
-            b_ = self.boxes[i + 1]
-            if b["text"].strip()[0] != b_["text"].strip()[0] \
-                    or b["page_number"]!=b_["page_number"] \
-                    or b["top"] > b_["bottom"]:
-                i += 1
-                continue
-            b_["text"] = b["text"] + "\n" + b_["text"]
-            b_["x0"] = min(b["x0"], b_["x0"])
-            b_["x1"] = max(b["x1"], b_["x1"])
-            b_["top"] = b["top"]
-            self.boxes.pop(i)
+        self._merge_with_same_bullet()
+
         # merge title with decent chunk
         i = 0
         while i + 1 < len(self.boxes):
             b_["top"] = b["top"]
             self.boxes.pop(i)
 
-        callback__(0.8, "Parsing finished", callback)
+        callback(0.8, "Parsing finished")
         for b in self.boxes: print(b["text"], b.get("layoutno"))
 
         print(tbls)

rag/app/paper.py

 import copy
 import re
 from collections import Counter
-from rag.app import callback__, bullets_category, BULLET_PATTERN, is_english, tokenize
-from rag.nlp import huqie, stemmer
-from rag.parser.docx_parser import HuDocxParser
+from rag.app import tokenize
+from rag.nlp import huqie
 from rag.parser.pdf_parser import HuParser
-from nltk.tokenize import word_tokenize
 import numpy as np
 from rag.utils import num_tokens_from_string
 
             zoomin,
             from_page,
             to_page)
-        callback__(0.2, "OCR finished.", callback)
+        callback(0.2, "OCR finished.")
 
         from timeit import default_timer as timer
         start = timer()
         self._layouts_paddle(zoomin)
-        callback__(0.47, "Layout analysis finished", callback)
+        callback(0.47, "Layout analysis finished")
         print("paddle layouts:", timer() - start)
         self._table_transformer_job(zoomin)
-        callback__(0.68, "Table analysis finished", callback)
+        callback(0.68, "Table analysis finished")
         self._text_merge()
         column_width = np.median([b["x1"] - b["x0"] for b in self.boxes])
         self._concat_downward(concat_between_pages=False)
         self._filter_forpages()
-        callback__(0.75, "Text merging finished.", callback)
+        callback(0.75, "Text merging finished.")
         tbls = self._extract_table_figure(True, zoomin, False)
 
         # clean mess
                 break
         if not abstr: i = 0
 
-        callback__(0.8, "Page {}~{}: Text merging finished".format(from_page, min(to_page, self.total_page)), callback)
+        callback(0.8, "Page {}~{}: Text merging finished".format(from_page, min(to_page, self.total_page)))
         for b in self.boxes: print(b["text"], b.get("layoutno"))
         print(tbls)
 

rag/app/presentation.py

 from io import BytesIO
 from pptx import Presentation
 
-from rag.app import callback__, tokenize, is_english
+from rag.app import tokenize, is_english
 from rag.nlp import huqie
 from rag.parser.pdf_parser import HuParser
 
                 if txt: texts.append(txt)
             txts.append("\n".join(texts))
 
-        callback__(0.5, "Text extraction finished.", callback)
+        callback(0.5, "Text extraction finished.")
         import aspose.slides as slides
         import aspose.pydrawing as drawing
         imgs = []
                 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))
-        callback__(0.9, "Image extraction finished", callback)
+        callback(0.9, "Image extraction finished")
         self.is_english = is_english(txts)
         return [(txts[i], imgs[i]) for i in range(len(txts))]
 
 
     def __call__(self, filename, binary=None, from_page=0, to_page=100000, zoomin=3, callback=None):
         self.__images__(filename if not binary else binary, zoomin, from_page, to_page)
-        callback__(0.8, "Page {}~{}: OCR finished".format(from_page, min(to_page, self.total_page)), callback)
+        callback(0.8, "Page {}~{}: OCR finished".format(from_page, min(to_page, self.total_page)))
         assert len(self.boxes) == len(self.page_images), "{} vs. {}".format(len(self.boxes), len(self.page_images))
         res = []
         #################### More precisely ###################
         for i in range(len(self.boxes)):
             lines = "\n".join([b["text"] for b in self.boxes[i] if not self.__garbage(b["text"])])
             res.append((lines, self.page_images[i]))
-        callback__(0.9, "Page {}~{}: Parsing finished".format(from_page, min(to_page, self.total_page)), callback)
+        callback(0.9, "Page {}~{}: Parsing finished".format(from_page, min(to_page, self.total_page)))
         return res
 
 

rag/app/qa.py

+import random
+import re
+from io import BytesIO
+from nltk import word_tokenize
+from openpyxl import load_workbook
+from rag.app import is_english
+from rag.nlp import huqie, stemmer
+
+
+class Excel(object):
+    def __call__(self, fnm, binary=None, callback=None):
+        if not binary:
+            wb = load_workbook(fnm)
+        else:
+            wb = load_workbook(BytesIO(binary))
+        total = 0
+        for sheetname in wb.sheetnames:
+            total += len(list(wb[sheetname].rows))
+
+        res, fails = [], []
+        for sheetname in wb.sheetnames:
+            ws = wb[sheetname]
+            rows = list(ws.rows)
+            for i, r in enumerate(rows):
+                q, a = "", ""
+                for cell in r:
+                    if not cell.value: continue
+                    if not q: q = str(cell.value)
+                    elif not a: a = str(cell.value)
+                    else: break
+                if q and a: res.append((q, a))
+                else: fails.append(str(i+1))
+                if len(res) % 999 == 0:
+                    callback(len(res)*0.6/total, ("Extract Q&A: {}".format(len(res)) + (f"{len(fails)} failure, line: %s..."%(",".join(fails[:3])) if fails else "")))
+
+        callback(0.6, ("Extract Q&A: {}".format(len(res)) + (
+            f"{len(fails)} failure, line: %s..." % (",".join(fails[:3])) if fails else "")))
+        self.is_english = is_english([rmPrefix(q) for q, _ in random.choices(res, k=30) if len(q)>1])
+        return res
+
+
+def rmPrefix(txt):
+    return re.sub(r"^(问题|答案|回答|user|assistant|Q|A|Question|Answer|问|答)[\t:： ]+", "", txt.strip(), flags=re.IGNORECASE)
+
+
+def beAdoc(d, q, a, eng):
+    qprefix = "Question: " if eng else "问题："
+    aprefix = "Answer: " if eng else "回答："
+    d["content_with_weight"] = "\t".join([qprefix+rmPrefix(q), aprefix+rmPrefix(a)])
+    if eng:
+        d["content_ltks"] = " ".join([stemmer.stem(w) for w in word_tokenize(q)])
+    else:
+        d["content_ltks"] = huqie.qie(q)
+        d["content_sm_ltks"] = huqie.qieqie(d["content_ltks"])
+    return d
+
+
+def chunk(filename, binary=None,  from_page=0, to_page=100000, callback=None):
+
+    res = []
+    if re.search(r"\.xlsx?$", filename, re.IGNORECASE):
+        callback(0.1, "Start to parse.")
+        excel_parser = Excel()
+        for q,a in excel_parser(filename, binary, callback):
+            res.append(beAdoc({}, q, a, excel_parser.is_english))
+        return res
+    elif re.search(r"\.(txt|csv)$", filename, re.IGNORECASE):
+        callback(0.1, "Start to parse.")
+        txt = ""
+        if binary:
+            txt = binary.decode("utf-8")
+        else:
+            with open(filename, "r") as f:
+                while True:
+                    l = f.readline()
+                    if not l: break
+                    txt += l
+        lines = txt.split("\n")
+        eng = is_english([rmPrefix(l) for l in lines[:100]])
+        fails = []
+        for i, line in enumerate(lines):
+            arr = [l for l in line.split("\t") if len(l) > 1]
+            if len(arr) != 2:
+                fails.append(str(i))
+                continue
+            res.append(beAdoc({}, arr[0], arr[1], eng))
+            if len(res) % 999 == 0:
+                callback(len(res) * 0.6 / len(lines), ("Extract Q&A: {}".format(len(res)) + (
+                    f"{len(fails)} failure, line: %s..." % (",".join(fails[:3])) if fails else "")))
+
+        callback(0.6, ("Extract Q&A: {}".format(len(res)) + (
+            f"{len(fails)} failure, line: %s..." % (",".join(fails[:3])) if fails else "")))
+
+        return res
+
+    raise NotImplementedError("file type not supported yet(pptx, pdf supported)")
+
+
+if __name__== "__main__":
+    import sys
+    def kk(rat, ss):
+        pass
+    print(chunk(sys.argv[1], callback=kk))
+

rag/parser/pdf_parser.py

             return
         i = 0
         while i < len(self.boxes):
-            if not re.match(r"(contents|目录|目次|table of contents)$", re.sub(r"( | |\u3000)+", "", self.boxes[i]["text"].lower())):
+            if not re.match(r"(contents|目录|目次|table of contents|致谢|acknowledge)$", re.sub(r"( | |\u3000)+", "", self.boxes[i]["text"].lower())):
                 i += 1
                 continue
             eng = re.match(r"[0-9a-zA-Z :'.-]{5,}", self.boxes[i]["text"].strip())
                 for k in range(i, j): self.boxes.pop(i)
                 break
 
+    def _merge_with_same_bullet(self):
+        i = 0
+        while i + 1 < len(self.boxes):
+            b = self.boxes[i]
+            b_ = self.boxes[i + 1]
+            if b["text"].strip()[0] != b_["text"].strip()[0] \
+                    or b["text"].strip()[0].lower() in set("qwertyuopasdfghjklzxcvbnm") \
+                    or b["top"] > b_["bottom"]:
+                i += 1
+                continue
+            b_["text"] = b["text"] + "\n" + b_["text"]
+            b_["x0"] = min(b["x0"], b_["x0"])
+            b_["x1"] = max(b["x1"], b_["x1"])
+            b_["top"] = b["top"]
+            self.boxes.pop(i)
+
     def _blockType(self, b):
         patt = [
             ("^(20|19)[0-9]{2}[年/-][0-9]{1,2}[月/-][0-9]{1,2}日*$", "Dt"),

rag/svr/task_broker.py

-#
-#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
-#
-#  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 logging
-import os
-import time
-import random
-from timeit import default_timer as timer
-from api.db.db_models import Task
-from api.db.db_utils import bulk_insert_into_db
-from api.db.services.task_service import TaskService
-from rag.parser.pdf_parser import HuParser
-from rag.settings import cron_logger
-from rag.utils import MINIO
-from rag.utils import findMaxTm
-import pandas as pd
-from api.db import FileType
-from api.db.services.document_service import DocumentService
-from api.settings import database_logger
-from api.utils import get_format_time, get_uuid
-from api.utils.file_utils import get_project_base_directory
-
-
-def collect(tm):
-    docs = DocumentService.get_newly_uploaded(tm)
-    if len(docs) == 0:
-        return pd.DataFrame()
-    docs = pd.DataFrame(docs)
-    mtm = docs["update_time"].max()
-    cron_logger.info("TOTAL:{}, To:{}".format(len(docs), mtm))
-    return docs
-
-
-def set_dispatching(docid):
-    try:
-        DocumentService.update_by_id(
-            docid, {"progress": random.randint(0, 3) / 100.,
-                    "progress_msg": "Task dispatched...",
-                    "process_begin_at": get_format_time()
-                    })
-    except Exception as e:
-        cron_logger.error("set_dispatching:({}), {}".format(docid, str(e)))
-
-
-def dispatch():
-    tm_fnm = os.path.join(get_project_base_directory(), "rag/res", f"broker.tm")
-    tm = findMaxTm(tm_fnm)
-    rows = collect(tm)
-    if len(rows) == 0:
-        return
-
-    tmf = open(tm_fnm, "a+")
-    for _, r in rows.iterrows():
-        try:
-            tsks = TaskService.query(doc_id=r["id"])
-            if tsks:
-                for t in tsks:
-                    TaskService.delete_by_id(t.id)
-        except Exception as e:
-            cron_logger.error("delete task exception:" + str(e))
-
-        def new_task():
-            nonlocal r
-            return {
-                "id": get_uuid(),
-                "doc_id": r["id"]
-            }
-
-        tsks = []
-        if r["type"] == FileType.PDF.value:
-            pages = HuParser.total_page_number(r["name"], MINIO.get(r["kb_id"], r["location"]))
-            for p in range(0, pages, 10):
-                task = new_task()
-                task["from_page"] = p
-                task["to_page"] = min(p + 10, pages)
-                tsks.append(task)
-        else:
-            tsks.append(new_task())
-        print(tsks)
-        bulk_insert_into_db(Task, tsks, True)
-        set_dispatching(r["id"])
-        tmf.write(str(r["update_time"]) + "\n")
-    tmf.close()
-
-
-def update_progress():
-    docs = DocumentService.get_unfinished_docs()
-    for d in docs:
-        try:
-            tsks = TaskService.query(doc_id=d["id"], order_by=Task.create_time)
-            if not tsks:continue
-            msg = []
-            prg = 0
-            finished = True
-            bad = 0
-            for t in tsks:
-                if 0 <= t.progress < 1: finished = False
-                prg += t.progress if t.progress >= 0 else 0
-                msg.append(t.progress_msg)
-                if t.progress == -1: bad += 1
-            prg /= len(tsks)
-            if finished and bad: prg = -1
-            msg = "\n".join(msg)
-            DocumentService.update_by_id(d["id"], {"progress": prg, "progress_msg": msg, "process_duation": timer()-d["process_begin_at"].timestamp()})
-        except Exception as e:
-            cron_logger.error("fetch task exception:" + str(e))
-
-
-if __name__ == "__main__":
-    peewee_logger = logging.getLogger('peewee')
-    peewee_logger.propagate = False
-    peewee_logger.addHandler(database_logger.handlers[0])
-    peewee_logger.setLevel(database_logger.level)
-
-    while True:
-        dispatch()
-        time.sleep(3)
-        update_progress()
+#
+#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
+#
+#  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 logging
+import os
+import time
+import random
+from datetime import datetime
+from api.db.db_models import Task
+from api.db.db_utils import bulk_insert_into_db
+from api.db.services.task_service import TaskService
+from rag.parser.pdf_parser import HuParser
+from rag.settings import cron_logger
+from rag.utils import MINIO
+from rag.utils import findMaxTm
+import pandas as pd
+from api.db import FileType, TaskStatus
+from api.db.services.document_service import DocumentService
+from api.settings import database_logger
+from api.utils import get_format_time, get_uuid
+from api.utils.file_utils import get_project_base_directory
+
+
+def collect(tm):
+    docs = DocumentService.get_newly_uploaded(tm)
+    if len(docs) == 0:
+        return pd.DataFrame()
+    docs = pd.DataFrame(docs)
+    mtm = docs["update_time"].max()
+    cron_logger.info("TOTAL:{}, To:{}".format(len(docs), mtm))
+    return docs
+
+
+def set_dispatching(docid):
+    try:
+        DocumentService.update_by_id(
+            docid, {"progress": random.randint(0, 3) / 100.,
+                    "progress_msg": "Task dispatched...",
+                    "process_begin_at": get_format_time()
+                    })
+    except Exception as e:
+        cron_logger.error("set_dispatching:({}), {}".format(docid, str(e)))
+
+
+def dispatch():
+    tm_fnm = os.path.join(get_project_base_directory(), "rag/res", f"broker.tm")
+    tm = findMaxTm(tm_fnm)
+    rows = collect(tm)
+    if len(rows) == 0:
+        return
+
+    tmf = open(tm_fnm, "a+")
+    for _, r in rows.iterrows():
+        try:
+            tsks = TaskService.query(doc_id=r["id"])
+            if tsks:
+                for t in tsks:
+                    TaskService.delete_by_id(t.id)
+        except Exception as e:
+            cron_logger.error("delete task exception:" + str(e))
+
+        def new_task():
+            nonlocal r
+            return {
+                "id": get_uuid(),
+                "doc_id": r["id"]
+            }
+
+        tsks = []
+        if r["type"] == FileType.PDF.value:
+            pages = HuParser.total_page_number(r["name"], MINIO.get(r["kb_id"], r["location"]))
+            for p in range(0, pages, 10):
+                task = new_task()
+                task["from_page"] = p
+                task["to_page"] = min(p + 10, pages)
+                tsks.append(task)
+        else:
+            tsks.append(new_task())
+        print(tsks)
+        bulk_insert_into_db(Task, tsks, True)
+        set_dispatching(r["id"])
+        tmf.write(str(r["update_time"]) + "\n")
+    tmf.close()
+
+
+def update_progress():
+    docs = DocumentService.get_unfinished_docs()
+    for d in docs:
+        try:
+            tsks = TaskService.query(doc_id=d["id"], order_by=Task.create_time)
+            if not tsks:continue
+            msg = []
+            prg = 0
+            finished = True
+            bad = 0
+            status = TaskStatus.RUNNING.value
+            for t in tsks:
+                if 0 <= t.progress < 1: finished = False
+                prg += t.progress if t.progress >= 0 else 0
+                msg.append(t.progress_msg)
+                if t.progress == -1: bad += 1
+            prg /= len(tsks)
+            if finished and bad:
+                prg = -1
+                status = TaskStatus.FAIL.value
+            elif finished: status = TaskStatus.DONE.value
+
+            msg = "\n".join(msg)
+            info = {"process_duation": datetime.timestamp(datetime.now())-d["process_begin_at"].timestamp(), "run": status}
+            if prg !=0 : info["progress"] = prg
+            if msg: info["progress_msg"] = msg
+            DocumentService.update_by_id(d["id"], info)
+        except Exception as e:
+            cron_logger.error("fetch task exception:" + str(e))
+
+
+if __name__ == "__main__":
+    peewee_logger = logging.getLogger('peewee')
+    peewee_logger.propagate = False
+    peewee_logger.addHandler(database_logger.handlers[0])
+    peewee_logger.setLevel(database_logger.level)
+
+    while True:
+        dispatch()
+        time.sleep(3)
+        update_progress()

rag/svr/task_executor.py

 from functools import partial
 from timeit import default_timer as timer
 
+from elasticsearch_dsl import Q
+
 from api.db.services.task_service import TaskService
-from rag.llm import EmbeddingModel, CvModel
 from rag.settings import cron_logger, DOC_MAXIMUM_SIZE
 from rag.utils import ELASTICSEARCH
 from rag.utils import MINIO
 from io import BytesIO
 import pandas as pd
 
-from rag.app import laws, paper, presentation, manual
+from rag.app import laws, paper, presentation, manual, qa
 
 from api.db import LLMType, ParserType
 from api.db.services.document_service import DocumentService
     ParserType.PRESENTATION.value: presentation,
     ParserType.MANUAL.value: manual,
     ParserType.LAWS.value: laws,
+    ParserType.QA.value: qa,
 }
 
 
 def set_progress(task_id, from_page, to_page, prog=None, msg="Processing..."):
     cancel = TaskService.do_cancel(task_id)
     if cancel:
-        msg = "Canceled."
+        msg += " [Canceled]"
         prog = -1
 
     if to_page > 0: msg = f"Page({from_page}~{to_page}): " + msg
 
 
 def embedding(docs, mdl):
-    tts, cnts = [d["docnm_kwd"] for d in docs], [d["content_with_weight"] for d in docs]
+    tts, cnts = [d["docnm_kwd"] for d in docs if d.get("docnm_kwd")], [d["content_with_weight"] for d in docs]
     tk_count = 0
-    tts, c = mdl.encode(tts)
-    tk_count += c
+    if len(tts) == len(cnts):
+        tts, c = mdl.encode(tts)
+        tk_count += c
+
     cnts, c = mdl.encode(cnts)
     tk_count += c
-    vects = 0.1 * tts + 0.9 * cnts
+    vects = (0.1 * tts + 0.9 * cnts) if len(tts) == len(cnts) else cnts
+
     assert len(vects) == len(docs)
     for i, d in enumerate(docs):
         v = vects[i].tolist()
         callback(msg="Finished embedding! Start to build index!")
         init_kb(r)
         chunk_count = len(set([c["_id"] for c in cks]))
-        callback(1., "Done!")
         es_r = ELASTICSEARCH.bulk(cks, search.index_name(r["tenant_id"]))
         if es_r:
             callback(-1, "Index failure!")
             cron_logger.error(str(es_r))
         else:
+            if TaskService.do_cancel(r["id"]):
+                ELASTICSEARCH.deleteByQuery(Q("match", doc_id=r["doc_id"]), idxnm=search.index_name(r["tenant_id"]))
+            callback(1., "Done!")
             DocumentService.increment_chunk_num(r["doc_id"], r["kb_id"], tk_count, chunk_count, 0)
             cron_logger.info("Chunk doc({}), token({}), chunks({})".format(r["id"], tk_count, len(cks)))