6c3d614973
* add OCRReader, MathPixReader and ExcelReader * update test case for ocr reader * reformat * minor fix
98 lines
3.0 KiB
Python
98 lines
3.0 KiB
Python
from pathlib import Path
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from typing import List
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from uuid import uuid4
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import requests
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from llama_index.readers.base import BaseReader
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from kotaemon.documents import Document
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from .utils.table import (
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extract_tables_from_csv_string,
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get_table_from_ocr,
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strip_special_chars_markdown,
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)
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DEFAULT_OCR_ENDPOINT = "http://127.0.0.1:8000/v2/ai/infer/"
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class OCRReader(BaseReader):
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def __init__(self, endpoint: str = DEFAULT_OCR_ENDPOINT):
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"""Init the OCR reader with OCR endpoint (FullOCR pipeline)
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Args:
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endpoint: URL to FullOCR endpoint. Defaults to OCR_ENDPOINT.
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"""
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super().__init__()
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self.ocr_endpoint = endpoint
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def load_data(
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self,
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file: Path,
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**kwargs,
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) -> List[Document]:
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# create input params for the requests
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content = open(file, "rb")
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files = {"input": content}
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data = {"job_id": uuid4()}
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# init list of output documents
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documents = []
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all_table_csv_list = []
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all_non_table_texts = []
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# call the API from FullOCR endpoint
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if "response_content" in kwargs:
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# overriding response content if specified
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results = kwargs["response_content"]
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else:
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# call original API
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resp = requests.post(url=self.ocr_endpoint, files=files, data=data)
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results = resp.json()["result"]
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for _id, each in enumerate(results):
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csv_content = each["csv_string"]
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table = each["json"]["table"]
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ocr = each["json"]["ocr"]
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# using helper function to extract list of table texts from FullOCR output
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table_texts = get_table_from_ocr(ocr, table)
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# extract the formatted CSV table from specified text
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csv_list, non_table_text = extract_tables_from_csv_string(
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csv_content, table_texts
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)
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all_table_csv_list.extend([(csv, _id) for csv in csv_list])
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all_non_table_texts.append((non_table_text, _id))
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# create output Document with metadata from table
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documents = [
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Document(
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text=strip_special_chars_markdown(csv),
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metadata={
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"table_origin": csv,
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"type": "table",
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"page_label": page_id + 1,
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"source": file.name,
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},
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metadata_template="",
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metadata_seperator="",
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)
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for csv, page_id in all_table_csv_list
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]
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# create Document from non-table text
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documents.extend(
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[
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Document(
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text=non_table_text,
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metadata={
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"page_label": page_id + 1,
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"source": file.name,
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},
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)
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for non_table_text, page_id in all_non_table_texts
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]
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)
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return documents
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