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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 files 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(remove_numeric_tables=True)
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):
""" 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
"""
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
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