rag/app/manual.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 copy
import re

from api.db import ParserType
from rag.nlp import rag_tokenizer, tokenize, tokenize_table, add_positions, bullets_category, title_frequency, tokenize_chunks
from deepdoc.parser import PdfParser, PlainParser
from rag.utils import num_tokens_from_string


class Pdf(PdfParser):
    def __init__(self):
        self.model_speciess = ParserType.MANUAL.value
        super().__init__()

    def __call__(self, filename, binary=None, from_page=0,

                 to_page=100000, zoomin=3, callback=None):
        from timeit import default_timer as timer
        start = timer()
        callback(msg="OCR is running...")
        self.__images__(
            filename if not binary else binary,
            zoomin,
            from_page,
            to_page,
            callback
        )
        callback(msg="OCR finished.")
        # for bb in self.boxes:
        #    for b in bb:
        #        print(b)
        print("OCR:", timer() - start)

        self._layouts_rec(zoomin)
        callback(0.65, "Layout analysis finished.")
        print("layouts:", timer() - start)
        self._table_transformer_job(zoomin)
        callback(0.67, "Table analysis finished.")
        self._text_merge()
        tbls = self._extract_table_figure(True, zoomin, True, True)
        self._concat_downward()
        self._filter_forpages()
        callback(0.68, "Text merging finished")

        # clean mess
        for b in self.boxes:
            b["text"] = re.sub(r"([\t 　]|\u3000){2,}", " ", b["text"].strip())

        return [(b["text"], b.get("layout_no", ""), self.get_position(b, zoomin))
                for i, b in enumerate(self.boxes)], tbls


def chunk(filename, binary=None, from_page=0, to_page=100000,

          lang="Chinese", callback=None, **kwargs):
    """

        Only pdf is supported.

    """
    pdf_parser = None

    if re.search(r"\.pdf$", filename, re.IGNORECASE):
        pdf_parser = Pdf() if kwargs.get(
            "parser_config", {}).get(
            "layout_recognize", True) else PlainParser()
        sections, tbls = pdf_parser(filename if not binary else binary,
                                    from_page=from_page, to_page=to_page, callback=callback)
        if sections and len(sections[0]) < 3:
            sections = [(t, l, [[0] * 5]) for t, l in sections]

    else:
        raise NotImplementedError("file type not supported yet(pdf supported)")
    doc = {
        "docnm_kwd": filename
    }
    doc["title_tks"] = rag_tokenizer.tokenize(re.sub(r"\.[a-zA-Z]+$", "", doc["docnm_kwd"]))
    doc["title_sm_tks"] = rag_tokenizer.fine_grained_tokenize(doc["title_tks"])
    # is it English
    eng = lang.lower() == "english"  # pdf_parser.is_english

    # set pivot using the most frequent type of title,
    # then merge between 2 pivot
    if len(sections) > 0 and len(pdf_parser.outlines) / len(sections) > 0.1:
        max_lvl = max([lvl for _, lvl in pdf_parser.outlines])
        most_level = max(0, max_lvl - 1)
        levels = []
        for txt, _, _ in sections:
            for t, lvl in pdf_parser.outlines:
                tks = set([t[i] + t[i + 1] for i in range(len(t) - 1)])
                tks_ = set([txt[i] + txt[i + 1]
                            for i in range(min(len(t), len(txt) - 1))])
                if len(set(tks & tks_)) / max([len(tks), len(tks_), 1]) > 0.8:
                    levels.append(lvl)
                    break
            else:
                levels.append(max_lvl + 1)

    else:
        bull = bullets_category([txt for txt, _, _ in sections])
        most_level, levels = title_frequency(
            bull, [(txt, l) for txt, l, poss in sections])

    assert len(sections) == len(levels)
    sec_ids = []
    sid = 0
    for i, lvl in enumerate(levels):
        if lvl <= most_level and i > 0 and lvl != levels[i - 1]:
            sid += 1
        sec_ids.append(sid)
        # print(lvl, self.boxes[i]["text"], most_level, sid)

    sections = [(txt, sec_ids[i], poss)
                for i, (txt, _, poss) in enumerate(sections)]
    for (img, rows), poss in tbls:
        if not rows: continue
        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]))

    def tag(pn, left, right, top, bottom):
        if pn + left + right + top + bottom == 0:
            return ""
        return "@@{}\t{:.1f}\t{:.1f}\t{:.1f}\t{:.1f}##" \
            .format(pn, left, right, top, bottom)

    chunks = []
    last_sid = -2
    tk_cnt = 0
    for txt, sec_id, poss in sorted(sections, key=lambda x: (
            x[-1][0][0], x[-1][0][3], x[-1][0][1])):
        poss = "\t".join([tag(*pos) for pos in poss])
        if tk_cnt < 32 or (tk_cnt < 1024 and (sec_id == last_sid or sec_id == -1)):
            if chunks:
                chunks[-1] += "\n" + txt + poss
                tk_cnt += num_tokens_from_string(txt)
                continue
        chunks.append(txt + poss)
        tk_cnt = num_tokens_from_string(txt)
        if sec_id > -1:
            last_sid = sec_id

    res = tokenize_table(tbls, doc, eng)
    res.extend(tokenize_chunks(chunks, doc, eng, pdf_parser))
    return res


if __name__ == "__main__":
    import sys


    def dummy(prog=None, msg=""):
        pass


    chunk(sys.argv[1], callback=dummy)