ragflow/rag/app/naive.py

#
#  Copyright 2025 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 re
from functools import reduce
from io import BytesIO
from timeit import default_timer as timer

from docx import Document
from docx.image.exceptions import InvalidImageStreamError, UnexpectedEndOfFileError, UnrecognizedImageError
from markdown import markdown
from PIL import Image
from tika import parser

from api.db import LLMType
from api.db.services.llm_service import LLMBundle
from deepdoc.parser import DocxParser, ExcelParser, HtmlParser, JsonParser, MarkdownParser, PdfParser, TxtParser
from deepdoc.parser.pdf_parser import PlainParser, VisionParser
from rag.nlp import concat_img, find_codec, naive_merge, naive_merge_docx, rag_tokenizer, tokenize_chunks, tokenize_chunks_docx, tokenize_table
from rag.utils import num_tokens_from_string


class Docx(DocxParser):
    def __init__(self):
        pass

    def get_picture(self, document, paragraph):
        img = paragraph._element.xpath('.//pic:pic')
        if not img:
            return None
        img = img[0]
        embed = img.xpath('.//a:blip/@r:embed')[0]
        related_part = document.part.related_parts[embed]
        try:
            image_blob = related_part.image.blob
        except UnrecognizedImageError:
            logging.info("Unrecognized image format. Skipping image.")
            return None
        except UnexpectedEndOfFileError:
            logging.info("EOF was unexpectedly encountered while reading an image stream. Skipping image.")
            return None
        except InvalidImageStreamError:
            logging.info("The recognized image stream appears to be corrupted. Skipping image.")
            return None
        try:
            image = Image.open(BytesIO(image_blob)).convert('RGB')
            return image
        except Exception:
            return None

    def __clean(self, line):
        line = re.sub(r"\u3000", " ", line).strip()
        return line

    def __call__(self, filename, binary=None, from_page=0, to_page=100000):
        self.doc = Document(
            filename) if not binary else Document(BytesIO(binary))
        pn = 0
        lines = []
        last_image = None
        for p in self.doc.paragraphs:
            if pn > to_page:
                break
            if from_page <= pn < to_page:
                if p.text.strip():
                    if p.style and p.style.name == 'Caption':
                        former_image = None
                        if lines and lines[-1][1] and lines[-1][2] != 'Caption':
                            former_image = lines[-1][1].pop()
                        elif last_image:
                            former_image = last_image
                            last_image = None
                        lines.append((self.__clean(p.text), [former_image], p.style.name))
                    else:
                        current_image = self.get_picture(self.doc, p)
                        image_list = [current_image]
                        if last_image:
                            image_list.insert(0, last_image)
                            last_image = None
                        lines.append((self.__clean(p.text), image_list, p.style.name if p.style else ""))
                else:
                    if current_image := self.get_picture(self.doc, p):
                        if lines:
                            lines[-1][1].append(current_image)
                        else:
                            last_image = current_image
            for run in p.runs:
                if 'lastRenderedPageBreak' in run._element.xml:
                    pn += 1
                    continue
                if 'w:br' in run._element.xml and 'type="page"' in run._element.xml:
                    pn += 1
        new_line = [(line[0], reduce(concat_img, line[1]) if line[1] else None) for line in lines]

        tbls = []
        for tb in self.doc.tables:
            html = "<table>"
            for r in tb.rows:
                html += "<tr>"
                i = 0
                while i < len(r.cells):
                    span = 1
                    c = r.cells[i]
                    for j in range(i + 1, len(r.cells)):
                        if c.text == r.cells[j].text:
                            span += 1
                            i = j
                        else:
                            break
                    i += 1
                    html += f"<td>{c.text}</td>" if span == 1 else f"<td colspan='{span}'>{c.text}</td>"
                html += "</tr>"
            html += "</table>"
            tbls.append(((None, html), ""))
        return new_line, tbls


class Pdf(PdfParser):
    def __init__(self):
        super().__init__()

    def __call__(self, filename, binary=None, from_page=0,
                 to_page=100000, zoomin=3, callback=None):
        start = timer()
        first_start = start
        callback(msg="OCR started")
        self.__images__(
            filename if not binary else binary,
            zoomin,
            from_page,
            to_page,
            callback
        )
        callback(msg="OCR finished ({:.2f}s)".format(timer() - start))
        logging.info("OCR({}~{}): {:.2f}s".format(from_page, to_page, timer() - start))

        start = timer()
        self._layouts_rec(zoomin)
        callback(0.63, "Layout analysis ({:.2f}s)".format(timer() - start))

        start = timer()
        self._table_transformer_job(zoomin)
        callback(0.65, "Table analysis ({:.2f}s)".format(timer() - start))

        start = timer()
        self._text_merge()
        callback(0.67, "Text merged ({:.2f}s)".format(timer() - start))
        tbls = self._extract_table_figure(True, zoomin, True, True)
        # self._naive_vertical_merge()
        self._concat_downward()
        # self._filter_forpages()

        logging.info("layouts cost: {}s".format(timer() - first_start))
        return [(b["text"], self._line_tag(b, zoomin))
                for b in self.boxes], tbls


class Markdown(MarkdownParser):
    def __call__(self, filename, binary=None):
        if binary:
            encoding = find_codec(binary)
            txt = binary.decode(encoding, errors="ignore")
        else:
            with open(filename, "r") as f:
                txt = f.read()
        remainder, tables = self.extract_tables_and_remainder(f'{txt}\n')
        sections = []
        tbls = []
        for sec in remainder.split("\n"):
            if num_tokens_from_string(sec) > 3 * self.chunk_token_num:
                sections.append((sec[:int(len(sec) / 2)], ""))
                sections.append((sec[int(len(sec) / 2):], ""))
            else:
                if sec.strip().find("#") == 0:
                    sections.append((sec, ""))
                elif sections and sections[-1][0].strip().find("#") == 0:
                    sec_, _ = sections.pop(-1)
                    sections.append((sec_ + "\n" + sec, ""))
                else:
                    sections.append((sec, ""))

        for table in tables:
            tbls.append(((None, markdown(table, extensions=['markdown.extensions.tables'])), ""))
        return sections, tbls


def chunk(filename, binary=None, from_page=0, to_page=100000,
          lang="Chinese", callback=None, **kwargs):
    """
        Supported file formats are docx, pdf, excel, txt.
        This method apply the naive ways to chunk files.
        Successive text will be sliced into pieces using 'delimiter'.
        Next, these successive pieces are merge into chunks whose token number is no more than 'Max token number'.
    """

    is_english = lang.lower() == "english"  # is_english(cks)
    parser_config = kwargs.get(
        "parser_config", {
            "chunk_token_num": 128, "delimiter": "\n!?。；！？", "layout_recognize": "DeepDOC"})
    doc = {
        "docnm_kwd": filename,
        "title_tks": rag_tokenizer.tokenize(re.sub(r"\.[a-zA-Z]+$", "", filename))
    }
    doc["title_sm_tks"] = rag_tokenizer.fine_grained_tokenize(doc["title_tks"])
    res = []
    pdf_parser = None
    if re.search(r"\.docx$", filename, re.IGNORECASE):
        callback(0.1, "Start to parse.")
        sections, tables = Docx()(filename, binary)
        res = tokenize_table(tables, doc, is_english)  # just for table

        callback(0.8, "Finish parsing.")
        st = timer()

        chunks, images = naive_merge_docx(
            sections, int(parser_config.get(
                "chunk_token_num", 128)), parser_config.get(
                "delimiter", "\n!?。；！？"))

        if kwargs.get("section_only", False):
            return chunks

        res.extend(tokenize_chunks_docx(chunks, doc, is_english, images))
        logging.info("naive_merge({}): {}".format(filename, timer() - st))
        return res

    elif re.search(r"\.pdf$", filename, re.IGNORECASE):
        layout_recognizer = parser_config.get("layout_recognize", "DeepDOC")

        if layout_recognizer == "DeepDOC":
            pdf_parser = Pdf()
        elif layout_recognizer == "Plain Text":
            pdf_parser = PlainParser()
        else:
            vision_model = LLMBundle(kwargs["tenant_id"], LLMType.IMAGE2TEXT, llm_name=layout_recognizer, lang=lang)
            pdf_parser = VisionParser(vision_model=vision_model, **kwargs)

        sections, tables = pdf_parser(filename if not binary else binary, from_page=from_page, to_page=to_page,
                                      callback=callback)
        res = tokenize_table(tables, doc, is_english)

    elif re.search(r"\.(csv|xlsx?)$", filename, re.IGNORECASE):
        callback(0.1, "Start to parse.")
        excel_parser = ExcelParser()
        if parser_config.get("html4excel"):
            sections = [(_, "") for _ in excel_parser.html(binary, 12) if _]
        else:
            sections = [(_, "") for _ in excel_parser(binary) if _]

    elif re.search(r"\.(txt|py|js|java|c|cpp|h|php|go|ts|sh|cs|kt|sql)$", filename, re.IGNORECASE):
        callback(0.1, "Start to parse.")
        sections = TxtParser()(filename, binary,
                               parser_config.get("chunk_token_num", 128),
                               parser_config.get("delimiter", "\n!?;。；！？"))
        callback(0.8, "Finish parsing.")

    elif re.search(r"\.(md|markdown)$", filename, re.IGNORECASE):
        callback(0.1, "Start to parse.")
        sections, tables = Markdown(int(parser_config.get("chunk_token_num", 128)))(filename, binary)
        res = tokenize_table(tables, doc, is_english)
        callback(0.8, "Finish parsing.")

    elif re.search(r"\.(htm|html)$", filename, re.IGNORECASE):
        callback(0.1, "Start to parse.")
        sections = HtmlParser()(filename, binary)
        sections = [(_, "") for _ in sections if _]
        callback(0.8, "Finish parsing.")

    elif re.search(r"\.json$", filename, re.IGNORECASE):
        callback(0.1, "Start to parse.")
        chunk_token_num = int(parser_config.get("chunk_token_num", 128))
        sections = JsonParser(chunk_token_num)(binary)
        sections = [(_, "") for _ in sections if _]
        callback(0.8, "Finish parsing.")

    elif re.search(r"\.doc$", filename, re.IGNORECASE):
        callback(0.1, "Start to parse.")
        binary = BytesIO(binary)
        doc_parsed = parser.from_buffer(binary)
        if doc_parsed.get('content', None) is not None:
            sections = doc_parsed['content'].split('\n')
            sections = [(_, "") for _ in sections if _]
            callback(0.8, "Finish parsing.")
        else:
            callback(0.8, f"tika.parser got empty content from {filename}.")
            logging.warning(f"tika.parser got empty content from {filename}.")
            return []

    else:
        raise NotImplementedError(
            "file type not supported yet(pdf, xlsx, doc, docx, txt supported)")

    st = timer()
    chunks = naive_merge(
        sections, int(parser_config.get(
            "chunk_token_num", 128)), parser_config.get(
            "delimiter", "\n!?。；！？"))
    if kwargs.get("section_only", False):
        return chunks

    res.extend(tokenize_chunks(chunks, doc, is_english, pdf_parser))
    logging.info("naive_merge({}): {}".format(filename, timer() - st))
    return res


if __name__ == "__main__":
    import sys

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

    chunk(sys.argv[1], from_page=0, to_page=10, callback=dummy)