ragflow/rag/app/table.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 copy
import re
from io import BytesIO
from xpinyin import Pinyin
import numpy as np
import pandas as pd
from collections import Counter

# from openpyxl import load_workbook, Workbook
from dateutil.parser import parse as datetime_parse

from api.db.services.knowledgebase_service import KnowledgebaseService
from deepdoc.parser.utils import get_text
from rag.nlp import rag_tokenizer, tokenize
from deepdoc.parser import ExcelParser


class Excel(ExcelParser):
    def __call__(self, fnm, binary=None, from_page=0, to_page=10000000000, callback=None):
        if not binary:
            wb = Excel._load_excel_to_workbook(fnm)
        else:
            wb = Excel._load_excel_to_workbook(BytesIO(binary))
        total = 0
        for sheetname in wb.sheetnames:
            total += len(list(wb[sheetname].rows))

        res, fails, done = [], [], 0
        rn = 0
        for sheetname in wb.sheetnames:
            ws = wb[sheetname]
            rows = list(ws.rows)
            if not rows:
                continue
            headers = [cell.value for cell in rows[0]]
            missed = set([i for i, h in enumerate(headers) if h is None])
            headers = [cell.value for i, cell in enumerate(rows[0]) if i not in missed]
            if not headers:
                continue
            data = []
            for i, r in enumerate(rows[1:]):
                rn += 1
                if rn - 1 < from_page:
                    continue
                if rn - 1 >= to_page:
                    break
                row = [cell.value for ii, cell in enumerate(r) if ii not in missed]
                if len(row) != len(headers):
                    fails.append(str(i))
                    continue
                data.append(row)
                done += 1
            if np.array(data).size == 0:
                continue
            res.append(pd.DataFrame(np.array(data), columns=headers))

        callback(0.3, ("Extract records: {}~{}".format(from_page + 1, min(to_page, from_page + rn)) + (f"{len(fails)} failure, line: %s..." % (",".join(fails[:3])) if fails else "")))
        return res


def trans_datatime(s):
    try:
        return datetime_parse(s.strip()).strftime("%Y-%m-%d %H:%M:%S")
    except Exception:
        pass


def trans_bool(s):
    if re.match(r"(true|yes|是|\*|✓|✔|☑|✅|√)$", str(s).strip(), flags=re.IGNORECASE):
        return "yes"
    if re.match(r"(false|no|否|⍻|×)$", str(s).strip(), flags=re.IGNORECASE):
        return "no"


def column_data_type(arr):
    arr = list(arr)
    counts = {"int": 0, "float": 0, "text": 0, "datetime": 0, "bool": 0}
    trans = {t: f for f, t in [(int, "int"), (float, "float"), (trans_datatime, "datetime"), (trans_bool, "bool"), (str, "text")]}
    float_flag = False
    for a in arr:
        if a is None:
            continue
        if re.match(r"[+-]?[0-9]+$", str(a).replace("%%", "")) and not str(a).replace("%%", "").startswith("0"):
            counts["int"] += 1
            if int(str(a)) > 2**63 - 1:
                float_flag = True
                break
        elif re.match(r"[+-]?[0-9.]{,19}$", str(a).replace("%%", "")) and not str(a).replace("%%", "").startswith("0"):
            counts["float"] += 1
        elif re.match(r"(true|yes|是|\*|✓|✔|☑|✅|√|false|no|否|⍻|×)$", str(a), flags=re.IGNORECASE):
            counts["bool"] += 1
        elif trans_datatime(str(a)):
            counts["datetime"] += 1
        else:
            counts["text"] += 1
    if float_flag:
        ty = "float"
    else:
        counts = sorted(counts.items(), key=lambda x: x[1] * -1)
        ty = counts[0][0]
    for i in range(len(arr)):
        if arr[i] is None:
            continue
        try:
            arr[i] = trans[ty](str(arr[i]))
        except Exception:
            arr[i] = None
    # if ty == "text":
    #    if len(arr) > 128 and uni / len(arr) < 0.1:
    #        ty = "keyword"
    return arr, ty


def chunk(filename, binary=None, from_page=0, to_page=10000000000, lang="Chinese", callback=None, **kwargs):
    """
    Excel and csv(txt) format files are supported.
    For csv or txt file, the delimiter between columns is TAB.
    The first line must be column headers.
    Column headers must be meaningful terms inorder to make our NLP model understanding.
    It's good to enumerate some synonyms using slash '/' to separate, and even better to
    enumerate values using brackets like 'gender/sex(male, female)'.
    Here are some examples for headers:
        1. supplier/vendor\tcolor(yellow, red, brown)\tgender/sex(male, female)\tsize(M,L,XL,XXL)
        2. 姓名/名字\t电话/手机/微信\t最高学历（高中，职高，硕士，本科，博士，初中，中技，中专，专科，专升本，MPA，MBA，EMBA）

    Every row in table will be treated as a chunk.
    """

    if re.search(r"\.xlsx?$", filename, re.IGNORECASE):
        callback(0.1, "Start to parse.")
        excel_parser = Excel()
        dfs = excel_parser(filename, binary, from_page=from_page, to_page=to_page, callback=callback)
    elif re.search(r"\.(txt|csv)$", filename, re.IGNORECASE):
        callback(0.1, "Start to parse.")
        txt = get_text(filename, binary)
        lines = txt.split("\n")
        fails = []
        headers = lines[0].split(kwargs.get("delimiter", "\t"))
        rows = []
        for i, line in enumerate(lines[1:]):
            if i < from_page:
                continue
            if i >= to_page:
                break
            row = [field for field in line.split(kwargs.get("delimiter", "\t"))]
            if len(row) != len(headers):
                fails.append(str(i))
                continue
            rows.append(row)

        callback(0.3, ("Extract records: {}~{}".format(from_page, min(len(lines), to_page)) + (f"{len(fails)} failure, line: %s..." % (",".join(fails[:3])) if fails else "")))

        dfs = [pd.DataFrame(np.array(rows), columns=headers)]

    else:
        raise NotImplementedError("file type not supported yet(excel, text, csv supported)")

    res = []
    PY = Pinyin()
    fieds_map = {"text": "_tks", "int": "_long", "keyword": "_kwd", "float": "_flt", "datetime": "_dt", "bool": "_kwd"}
    for df in dfs:
        for n in ["id", "_id", "index", "idx"]:
            if n in df.columns:
                del df[n]
        clmns = df.columns.values
        if len(clmns) != len(set(clmns)):
            col_counts = Counter(clmns)
            duplicates = [col for col, count in col_counts.items() if count > 1]
            if duplicates:
                raise ValueError(f"Duplicate column names detected: {duplicates}\nFrom: {clmns}")

        txts = list(copy.deepcopy(clmns))
        py_clmns = [PY.get_pinyins(re.sub(r"(/.*|（[^（）]+?）|\([^()]+?\))", "", str(n)), "_")[0] for n in clmns]
        clmn_tys = []
        for j in range(len(clmns)):
            cln, ty = column_data_type(df[clmns[j]])
            clmn_tys.append(ty)
            df[clmns[j]] = cln
            if ty == "text":
                txts.extend([str(c) for c in cln if c])
        clmns_map = [(py_clmns[i].lower() + fieds_map[clmn_tys[i]], str(clmns[i]).replace("_", " ")) for i in range(len(clmns))]

        eng = lang.lower() == "english"  # is_english(txts)
        for ii, row in df.iterrows():
            d = {"docnm_kwd": filename, "title_tks": rag_tokenizer.tokenize(re.sub(r"\.[a-zA-Z]+$", "", filename))}
            row_txt = []
            for j in range(len(clmns)):
                if row[clmns[j]] is None:
                    continue
                if not str(row[clmns[j]]):
                    continue
                if not isinstance(row[clmns[j]], pd.Series) and pd.isna(row[clmns[j]]):
                    continue
                fld = clmns_map[j][0]
                d[fld] = row[clmns[j]] if clmn_tys[j] != "text" else rag_tokenizer.tokenize(row[clmns[j]])
                row_txt.append("{}:{}".format(clmns[j], row[clmns[j]]))
            if not row_txt:
                continue
            tokenize(d, "; ".join(row_txt), eng)
            res.append(d)

        KnowledgebaseService.update_parser_config(kwargs["kb_id"], {"field_map": {k: v for k, v in clmns_map}})
    callback(0.35, "")

    return res


if __name__ == "__main__":
    import sys

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

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