from haystack.nodes.base import BaseComponent from haystack.schema import Document from haystack.nodes import PDFToTextOCRConverter, PDFToTextConverter from haystack.nodes import TextConverter, DocxToTextConverter, PreProcessor from typing import Callable, Dict, List, Optional, Text, Tuple, Union from typing_extensions import Literal import pandas as pd import logging import re import string from haystack.pipelines import Pipeline import configparser config = configparser.ConfigParser() config.read_file(open('paramconfig.cfg')) top_k = int(config.get('lexical_search','TOP_K')) def useOCR(file_path: str)-> Text: """ Converts image pdfs into text, Using the Farm-haystack[OCR] Params ---------- file_path: file_path of uploade file, returned by add_upload function in uploadAndExample.py Returns the text file as string. """ converter = PDFToTextOCRConverter(remove_numeric_tables=True, valid_languages=["eng"]) docs = converter.convert(file_path=file_path, meta=None) return docs[0].content class FileConverter(BaseComponent): """ Wrapper class to convert uploaded document into text by calling appropriate Converter class, will use internally haystack PDFToTextOCR in case of image pdf. Cannot use the FileClassifier from haystack as its doesnt has any label/output class for image. 1. https://haystack.deepset.ai/pipeline_nodes/custom-nodes 2. https://docs.haystack.deepset.ai/docs/file_converters 3. https://github.com/deepset-ai/haystack/tree/main/haystack/nodes/file_converter 4. https://docs.haystack.deepset.ai/reference/file-converters-api """ outgoing_edges = 1 def run(self, file_name: str , file_path: str, encoding: Optional[str]=None, id_hash_keys: Optional[List[str]] = None, ) -> Tuple[dict,str]: """ this is required method to invoke the component in the pipeline implementation. Params ---------- file_name: name of file file_path: file_path of uploade file, returned by add_upload function in uploadAndExample.py See the links provided in Class docstring/description to see other params Return --------- output: dictionary, with key as identifier and value could be anything we need to return. In this case its the List of Hasyatck Document output_1: As there is only one outgoing edge, we pass 'output_1' string """ try: if file_name.endswith('.pdf'): converter = PDFToTextConverter(remove_numeric_tables=True) if file_name.endswith('.txt'): converter = TextConverter(remove_numeric_tables=True) if file_name.endswith('.docx'): converter = DocxToTextConverter() except Exception as e: logging.error(e) return documents = [] document = converter.convert( file_path=file_path, meta=None, encoding=encoding, id_hash_keys=id_hash_keys )[0] text = document.content # if file is image pdf then it will have {'content': "\x0c\x0c\x0c\x0c"} # subsitute this substring with '',and check if content is empty string text = re.sub(r'\x0c', '', text) documents.append(Document(content=text, meta={"name": file_name}, id_hash_keys=id_hash_keys)) # check if text is empty and apply pdfOCR converter. for i in documents: if i.content == "": logging.info("Using OCR") i.content = useOCR(file_path) logging.info('file conversion succesful') output = {'documents': documents} return output, 'output_1' def run_batch(): """ we dont have requirement to process the multiple files in one go therefore nothing here, however to use the custom node we need to have this method for the class. """ return def basic(s, removePunc:bool = False): """ Performs basic cleaning of text. Params ---------- s: string to be processed removePunc: to remove all Punctuation including ',' and '.' or not Returns: processed string: see comments in the source code for more info """ # Remove URLs s = re.sub(r'^https?:\/\/.*[\r\n]*', ' ', s, flags=re.MULTILINE) s = re.sub(r"http\S+", " ", s) # Remove new line characters s = re.sub('\n', ' ', s) # Remove punctuations if removePunc == True: translator = str.maketrans(' ', ' ', string.punctuation) s = s.translate(translator) # Remove distracting single quotes and dotted pattern s = re.sub("\'", " ", s) s = s.replace("..","") return s.strip() class UdfPreProcessor(BaseComponent): """ class to preprocess the document returned by FileConverter. It will check for splitting strategy and splits the document by word or sentences and then synthetically create the paragraphs. 1. https://docs.haystack.deepset.ai/docs/preprocessor 2. https://docs.haystack.deepset.ai/reference/preprocessor-api 3. https://github.com/deepset-ai/haystack/tree/main/haystack/nodes/preprocessor """ outgoing_edges = 1 # split_overlap_word = int(config.get('preprocessor','SPLIT_OVERLAP_WORD')) # split_overlap_sentence = int(config.get('preprocessor','SPLIT_OVERLAP_SENTENCE')) def run(self, documents:List[Document], removePunc:bool, split_by: Literal["sentence", "word"] = 'sentence', split_length:int = 2, split_overlap = 0): """ this is required method to invoke the component in the pipeline implementation. Params ---------- documents: documents from the output dictionary returned by Fileconverter removePunc: to remove all Punctuation including ',' and '.' or not split_by: document splitting strategy either as word or sentence split_length: when synthetically creating the paragrpahs from document, it defines the length of paragraph. Return --------- output: dictionary, with key as identifier and value could be anything we need to return. In this case the output will contain 4 objects the paragraphs text list as List, Haystack document, Dataframe and one raw text file. output_1: As there is only one outgoing edge, we pass 'output_1' string """ if split_by == 'sentence': split_respect_sentence_boundary = False # split_overlap=self.split_overlap_sentence else: split_respect_sentence_boundary = True # split_overlap= self.split_overlap_word preprocessor = PreProcessor( clean_empty_lines=True, clean_whitespace=True, clean_header_footer=True, split_by=split_by, split_length=split_length, split_respect_sentence_boundary= split_respect_sentence_boundary, split_overlap=split_overlap, # will add page number only in case of PDF not for text/docx file. add_page_number=True ) for i in documents: docs_processed = preprocessor.process([i]) for item in docs_processed: item.content = basic(item.content, removePunc= removePunc) df = pd.DataFrame(docs_processed) all_text = " ".join(df.content.to_list()) para_list = df.content.to_list() logging.info('document split into {} paragraphs'.format(len(para_list))) output = {'documents': docs_processed, 'dataframe': df, 'text': all_text, 'paraList': para_list } return output, "output_1" def run_batch(): """ we dont have requirement to process the multiple files in one go therefore nothing here, however to use the custom node we need to have this method for the class. """ return def processingpipeline(): """ Returns the preprocessing pipeline. Will use FileConverter and UdfPreProcesor from utils. """ preprocessing_pipeline = Pipeline() fileconverter = FileConverter() customPreprocessor = UdfPreProcessor() preprocessing_pipeline.add_node(component=fileconverter, name="FileConverter", inputs=["File"]) preprocessing_pipeline.add_node(component = customPreprocessor, name ='UdfPreProcessor', inputs=["FileConverter"]) return preprocessing_pipeline