mirror of
https://git.mirrors.martin98.com/https://github.com/infiniflow/ragflow.git
synced 2025-08-13 10:39:00 +08:00
refine page ranges (#147)
This commit is contained in:
KevinHuSh
GitHub
parent
1d9a50b090
commit
71fe314955
@ -477,7 +477,7 @@ class Knowledgebase(DataBaseModel):
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vector_similarity_weight = FloatField(default=0.3)
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parser_id = CharField(max_length=32, null=False, help_text="default parser ID", default=ParserType.NAIVE.value)
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parser_config = JSONField(null=False, default={"pages":[[0,1000000]]})
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parser_config = JSONField(null=False, default={"pages":[[1,1000000]]})
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status = CharField(max_length=1, null=True, help_text="is it validate(0: wasted，1: validate)", default="1")
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def __str__(self):
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@ -492,7 +492,7 @@ class Document(DataBaseModel):
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thumbnail = TextField(null=True, help_text="thumbnail base64 string")
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kb_id = CharField(max_length=256, null=False, index=True)
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parser_id = CharField(max_length=32, null=False, help_text="default parser ID")
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parser_config = JSONField(null=False, default={"pages":[[0,1000000]]})
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parser_config = JSONField(null=False, default={"pages":[[1,1000000]]})
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source_type = CharField(max_length=128, null=False, default="local", help_text="where dose this document from")
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type = CharField(max_length=32, null=False, help_text="file extension")
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created_by = CharField(max_length=32, null=False, help_text="who created it")
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@ -1074,15 +1074,15 @@ class HuParser:
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class PlainParser(object):
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def __call__(self, filename, **kwargs):
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def __call__(self, filename, from_page=0, to_page=100000, **kwargs):
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self.outlines = []
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lines = []
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try:
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self.pdf = pdf2_read(filename if isinstance(filename, str) else BytesIO(filename))
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outlines = self.pdf.outline
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for page in self.pdf.pages:
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for page in self.pdf.pages[from_page:to_page]:
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lines.extend([t for t in page.extract_text().split("\n")])
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outlines = self.pdf.outline
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def dfs(arr, depth):
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for a in arr:
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if isinstance(a, dict):
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@ -15,6 +15,7 @@ import re
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from collections import Counter
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from copy import deepcopy
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import numpy as np
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from huggingface_hub import snapshot_download
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from api.db import ParserType
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from api.utils.file_utils import get_project_base_directory
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@ -36,7 +37,8 @@ class LayoutRecognizer(Recognizer):
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"Equation",
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]
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def __init__(self, domain):
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super().__init__(self.labels, domain, os.path.join(get_project_base_directory(), "rag/res/deepdoc/"))
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model_dir = snapshot_download(repo_id="InfiniFlow/deepdoc")
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super().__init__(self.labels, domain, model_dir)#os.path.join(get_project_base_directory(), "rag/res/deepdoc/"))
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self.garbage_layouts = ["footer", "header", "reference"]
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def __call__(self, image_list, ocr_res, scale_factor=3, thr=0.2, batch_size=16, drop=True):
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@ -30,8 +30,6 @@ class Pdf(PdfParser):
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# print(b)
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print("OCR:", timer()-start)
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self._layouts_rec(zoomin)
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callback(0.65, "Layout analysis finished.")
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print("paddle layouts:", timer() - start)
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@ -47,53 +45,8 @@ class Pdf(PdfParser):
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for b in self.boxes:
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b["text"] = re.sub(r"([\t ]|\u3000){2,}", " ", b["text"].strip())
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return [(b["text"], b.get("layout_no", ""), self.get_position(b, zoomin)) for i, b in enumerate(self.boxes)]
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return [(b["text"], b.get("layout_no", ""), self.get_position(b, zoomin)) for i, b in enumerate(self.boxes)], tbls
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# set pivot using the most frequent type of title,
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# then merge between 2 pivot
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if len(self.boxes)>0 and len(self.outlines)/len(self.boxes) > 0.1:
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max_lvl = max([lvl for _, lvl in self.outlines])
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most_level = max(0, max_lvl-1)
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levels = []
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for b in self.boxes:
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for t,lvl in self.outlines:
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tks = set([t[i]+t[i+1] for i in range(len(t)-1)])
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tks_ = set([b["text"][i]+b["text"][i+1] for i in range(min(len(t), len(b["text"])-1))])
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if len(set(tks & tks_))/max([len(tks), len(tks_), 1]) > 0.8:
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levels.append(lvl)
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break
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else:
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levels.append(max_lvl + 1)
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else:
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bull = bullets_category([b["text"] for b in self.boxes])
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most_level, levels = title_frequency(bull, [(b["text"], b.get("layout_no","")) for b in self.boxes])
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assert len(self.boxes) == len(levels)
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sec_ids = []
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sid = 0
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for i, lvl in enumerate(levels):
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if lvl <= most_level and i > 0 and lvl != levels[i-1]: sid += 1
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sec_ids.append(sid)
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#print(lvl, self.boxes[i]["text"], most_level, sid)
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sections = [(b["text"], sec_ids[i], self.get_position(b, zoomin)) for i, b in enumerate(self.boxes)]
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for (img, rows), poss in tbls:
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sections.append((rows if isinstance(rows, str) else rows[0], -1, [(p[0]+1-from_page, p[1], p[2], p[3], p[4]) for p in poss]))
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chunks = []
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last_sid = -2
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tk_cnt = 0
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for txt, sec_id, poss in sorted(sections, key=lambda x: (x[-1][0][0], x[-1][0][3], x[-1][0][1])):
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poss = "\t".join([tag(*pos) for pos in poss])
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if tk_cnt < 2048 and (sec_id == last_sid or sec_id == -1):
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if chunks:
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chunks[-1] += "\n" + txt + poss
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tk_cnt += num_tokens_from_string(txt)
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continue
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chunks.append(txt + poss)
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tk_cnt = num_tokens_from_string(txt)
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if sec_id >-1: last_sid = sec_id
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return chunks, tbls
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def chunk(filename, binary=None, from_page=0, to_page=100000, lang="Chinese", callback=None, **kwargs):
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@ -106,7 +59,8 @@ def chunk(filename, binary=None, from_page=0, to_page=100000, lang="Chinese", ca
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pdf_parser = Pdf() if kwargs.get("parser_config",{}).get("layout_recognize", True) else PlainParser()
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sections, tbls = pdf_parser(filename if not binary else binary,
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from_page=from_page, to_page=to_page, callback=callback)
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if sections and len(sections[0])<3: cks = [(t, l, [0]*5) for t, l in sections]
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if sections and len(sections[0])<3: sections = [(t, l, [[0]*5]) for t, l in sections]
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else: raise NotImplementedError("file type not supported yet(pdf supported)")
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doc = {
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"docnm_kwd": filename
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@ -131,6 +85,7 @@ def chunk(filename, binary=None, from_page=0, to_page=100000, lang="Chinese", ca
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break
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else:
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levels.append(max_lvl + 1)
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else:
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bull = bullets_category([txt for txt,_,_ in sections])
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most_level, levels = title_frequency(bull, [(txt, l) for txt, l, poss in sections])
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@ -45,7 +45,7 @@ class Pdf(PdfParser):
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for (img, rows), poss in tbls:
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sections.append((rows if isinstance(rows, str) else rows[0],
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[(p[0] + 1 - from_page, p[1], p[2], p[3], p[4]) for p in poss]))
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return [(txt, "") for txt, _ in sorted(sections, key=lambda x: (x[-1][0][0], x[-1][0][3], x[-1][0][1]))]
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return [(txt, "") for txt, _ in sorted(sections, key=lambda x: (x[-1][0][0], x[-1][0][3], x[-1][0][1]))], None
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def chunk(filename, binary=None, from_page=0, to_page=100000, lang="Chinese", callback=None, **kwargs):
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@ -56,7 +56,6 @@ def chunk(filename, binary=None, from_page=0, to_page=100000, lang="Chinese", ca
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eng = lang.lower() == "english"#is_english(cks)
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sections = []
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if re.search(r"\.docx?$", filename, re.IGNORECASE):
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callback(0.1, "Start to parse.")
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sections = [txt for txt in laws.Docx()(filename, binary) if txt]
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@ -64,7 +63,7 @@ def chunk(filename, binary=None, from_page=0, to_page=100000, lang="Chinese", ca
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elif re.search(r"\.pdf$", filename, re.IGNORECASE):
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pdf_parser = Pdf() if kwargs.get("parser_config",{}).get("layout_recognize", True) else PlainParser()
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sections = pdf_parser(filename if not binary else binary, to_page=to_page, callback=callback)
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sections, _ = pdf_parser(filename if not binary else binary, to_page=to_page, callback=callback)
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sections = [s for s, _ in sections if s]
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elif re.search(r"\.xlsx?$", filename, re.IGNORECASE):
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@ -136,7 +136,7 @@ def chunk(filename, binary=None, from_page=0, to_page=100000, lang="Chinese", ca
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"title": filename,
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"authors": " ",
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"abstract": "",
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"sections": pdf_parser(filename if not binary else binary),
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"sections": pdf_parser(filename if not binary else binary, from_page=from_page, to_page=to_page),
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"tables": []
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}
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else:
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@ -65,10 +65,10 @@ class Pdf(PdfParser):
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class PlainPdf(PlainParser):
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def __call__(self, filename, binary=None, callback=None, **kwargs):
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def __call__(self, filename, binary=None, from_page=0, to_page=100000, callback=None, **kwargs):
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self.pdf = pdf2_read(filename if not binary else BytesIO(filename))
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page_txt = []
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for page in self.pdf.pages:
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for page in self.pdf.pages[from_page: to_page]:
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page_txt.append(page.extract_text())
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callback(0.9, "Parsing finished")
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return [(txt, None) for txt in page_txt]
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@ -16,8 +16,8 @@ BULLET_PATTERN = [[
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], [
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r"第[0-9]+章",
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r"第[0-9]+节",
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r"[0-9]{,3}[\. 、]",
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r"[0-9]{,2}\.[0-9]{,2}",
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r"[0-9]{,2}[\. 、]",
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r"[0-9]{,2}\.[0-9]{,2}[^a-zA-Z/%~-]",
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r"[0-9]{,2}\.[0-9]{,2}\.[0-9]{,2}",
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r"[0-9]{,2}\.[0-9]{,2}\.[0-9]{,2}\.[0-9]{,2}",
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], [
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@ -40,13 +40,20 @@ def random_choices(arr, k):
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return random.choices(arr, k=k)
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def not_bullet(line):
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patt = [
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r"0", r"[0-9]+ +[0-9~个只-]", r"[0-9]+\.{2,}"
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]
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return any([re.match(r, line) for r in patt])
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def bullets_category(sections):
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global BULLET_PATTERN
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hits = [0] * len(BULLET_PATTERN)
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for i, pro in enumerate(BULLET_PATTERN):
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for sec in sections:
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for p in pro:
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if re.match(p, sec):
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if re.match(p, sec) and not not_bullet(sec):
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hits[i] += 1
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break
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maxium = 0
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@ -194,7 +201,7 @@ def title_frequency(bull, sections):
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for i, (txt, layout) in enumerate(sections):
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for j, p in enumerate(BULLET_PATTERN[bull]):
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if re.match(p, txt.strip()):
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if re.match(p, txt.strip()) and not not_bullet(txt):
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levels[i] = j
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break
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else:
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@ -81,21 +81,22 @@ def dispatch():
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tsks = []
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if r["type"] == FileType.PDF.value:
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if not r["parser_config"].get("layout_recognize", True):
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tsks.append(new_task())
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continue
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do_layout = r["parser_config"].get("layout_recognize", True)
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pages = PdfParser.total_page_number(r["name"], MINIO.get(r["kb_id"], r["location"]))
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page_size = r["parser_config"].get("task_page_size", 12)
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if r["parser_id"] == "paper": page_size = r["parser_config"].get("task_page_size", 22)
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if r["parser_id"] == "one": page_size = 1000000000
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if not do_layout: page_size = 1000000000
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for s,e in r["parser_config"].get("pages", [(1, 100000)]):
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s -= 1
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e = min(e, pages)
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s = max(0, s)
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e = min(e-1, pages)
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for p in range(s, e, page_size):
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task = new_task()
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task["from_page"] = p
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task["to_page"] = min(p + page_size, e)
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tsks.append(task)
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elif r["parser_id"] == "table":
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rn = HuExcelParser.row_number(r["name"], MINIO.get(r["kb_id"], r["location"]))
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for i in range(0, rn, 3000):
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@ -75,7 +75,7 @@ def set_progress(task_id, from_page=0, to_page=-1,
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if to_page > 0:
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if msg:
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msg = f"Page({from_page}~{to_page}): " + msg
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msg = f"Page({from_page+1}~{to_page+1}): " + msg
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d = {"progress_msg": msg}
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if prog is not None:
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d["progress"] = prog
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133
requirements.txt
Normal file
133
requirements.txt
Normal file
@ -0,0 +1,133 @@
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accelerate==0.27.2
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aiohttp==3.9.3
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aiosignal==1.3.1
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annotated-types==0.6.0
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anyio==4.3.0
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argon2-cffi==23.1.0
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argon2-cffi-bindings==21.2.0
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Aspose.Slides==24.2.0
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attrs==23.2.0
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blinker==1.7.0
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cachelib==0.12.0
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cachetools==5.3.3
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certifi==2024.2.2
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cffi==1.16.0
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charset-normalizer==3.3.2
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click==8.1.7
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coloredlogs==15.0.1
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cryptography==42.0.5
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dashscope==1.14.1
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datasets==2.17.1
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datrie==0.8.2
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demjson==2.2.4
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dill==0.3.8
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distro==1.9.0
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elastic-transport==8.12.0
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elasticsearch==8.12.1
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elasticsearch-dsl==8.12.0
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et-xmlfile==1.1.0
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filelock==3.13.1
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FlagEmbedding==1.2.5
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Flask==3.0.2
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Flask-Cors==4.0.0
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Flask-Login==0.6.3
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Flask-Session==0.6.0
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flatbuffers==23.5.26
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frozenlist==1.4.1
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fsspec==2023.10.0
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h11==0.14.0
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hanziconv==0.3.2
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httpcore==1.0.4
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httpx==0.27.0
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huggingface-hub==0.20.3
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humanfriendly==10.0
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idna==3.6
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install==1.3.5
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itsdangerous==2.1.2
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Jinja2==3.1.3
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joblib==1.3.2
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lxml==5.1.0
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MarkupSafe==2.1.5
|
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minio==7.2.4
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mpi4py==3.1.5
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mpmath==1.3.0
|
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multidict==6.0.5
|
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multiprocess==0.70.16
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networkx==3.2.1
|
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nltk==3.8.1
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numpy==1.26.4
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nvidia-cublas-cu12==12.1.3.1
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nvidia-cuda-cupti-cu12==12.1.105
|
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nvidia-cuda-nvrtc-cu12==12.1.105
|
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nvidia-cuda-runtime-cu12==12.1.105
|
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nvidia-cudnn-cu12==8.9.2.26
|
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nvidia-cufft-cu12==11.0.2.54
|
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nvidia-curand-cu12==10.3.2.106
|
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nvidia-cusolver-cu12==11.4.5.107
|
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nvidia-cusparse-cu12==12.1.0.106
|
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nvidia-nccl-cu12==2.19.3
|
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nvidia-nvjitlink-cu12==12.3.101
|
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nvidia-nvtx-cu12==12.1.105
|
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onnxruntime-gpu==1.17.1
|
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openai==1.12.0
|
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opencv-python==4.9.0.80
|
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openpyxl==3.1.2
|
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packaging==23.2
|
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pandas==2.2.1
|
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pdfminer.six==20221105
|
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pdfplumber==0.10.4
|
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peewee==3.17.1
|
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pillow==10.2.0
|
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protobuf==4.25.3
|
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psutil==5.9.8
|
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pyarrow==15.0.0
|
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pyarrow-hotfix==0.6
|
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pyclipper==1.3.0.post5
|
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pycparser==2.21
|
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pycryptodome==3.20.0
|
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pycryptodome-test-vectors==1.0.14
|
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pycryptodomex==3.20.0
|
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pydantic==2.6.2
|
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pydantic_core==2.16.3
|
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PyJWT==2.8.0
|
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PyMuPDF==1.23.25
|
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PyMuPDFb==1.23.22
|
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PyMySQL==1.1.0
|
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PyPDF2==3.0.1
|
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pypdfium2==4.27.0
|
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python-dateutil==2.8.2
|
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python-docx==1.1.0
|
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python-dotenv==1.0.1
|
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python-pptx==0.6.23
|
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pytz==2024.1
|
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PyYAML==6.0.1
|
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regex==2023.12.25
|
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requests==2.31.0
|
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ruamel.yaml==0.18.6
|
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ruamel.yaml.clib==0.2.8
|
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safetensors==0.4.2
|
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scikit-learn==1.4.1.post1
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scipy==1.12.0
|
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sentence-transformers==2.4.0
|
||||
shapely==2.0.3
|
||||
six==1.16.0
|
||||
sniffio==1.3.1
|
||||
StrEnum==0.4.15
|
||||
sympy==1.12
|
||||
threadpoolctl==3.3.0
|
||||
tiktoken==0.6.0
|
||||
tokenizers==0.15.2
|
||||
torch==2.2.1
|
||||
tqdm==4.66.2
|
||||
transformers==4.38.1
|
||||
triton==2.2.0
|
||||
typing_extensions==4.10.0
|
||||
tzdata==2024.1
|
||||
urllib3==2.2.1
|
||||
Werkzeug==3.0.1
|
||||
xgboost==2.0.3
|
||||
XlsxWriter==3.2.0
|
||||
xpinyin==0.7.6
|
||||
xxhash==3.4.1
|
||||
yarl==1.9.4
|
||||
zhipuai==2.0.1
|
||||
@ -193,7 +193,7 @@ const ChunkMethodModal: React.FC<IProps> = ({
|
||||
rules={[
|
||||
{
|
||||
required: true,
|
||||
message: 'Missing end page number(excluding)',
|
||||
message: 'Missing end page number(excluded)',
|
||||
},
|
||||
({ getFieldValue }) => ({
|
||||
validator(_, value) {
|
||||
|
||||
@ -120,7 +120,7 @@ export const TextMap = {
|
||||
</p><p>
|
||||
For a document, it will be treated as an entire chunk, no split at all.
|
||||
</p><p>
|
||||
If you don't trust any chunk method and the selected LLM's context length covers the document length, you can try this method.
|
||||
If you want to summarize something that needs all the context of an article and the selected LLM's context length covers the document length, you can try this method.
|
||||
</p>`,
|
||||
},
|
||||
};
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user