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from haystack.nodes import TfidfRetriever
from haystack.document_stores import InMemoryDocumentStore
import spacy
import re
from spacy.matcher import Matcher
from markdown import markdown
from annotated_text import annotation
from haystack.schema import Document
from typing import List, Text, Tuple
from typing_extensions import Literal
from utils.preprocessing import processingpipeline
from utils.streamlitcheck import check_streamlit
import logging
try:
from termcolor import colored
except:
pass
try:
import streamlit as st
except ImportError:
logging.info("Streamlit not installed")
def runLexicalPreprocessingPipeline(file_name:str,file_path:str,
split_by: Literal["sentence", "word"] = 'word',
split_length:int = 80, split_overlap:int = 0,
remove_punc:bool = False,)->List[Document]:
"""
creates the pipeline and runs the preprocessing pipeline,
the params for pipeline are fetched from paramconfig. As lexical doesnt gets
affected by overlap, threfore split_overlap = 0 in default paramconfig and
split_by = word.
Params
------------
file_name: filename, in case of streamlit application use
st.session_state['filename']
file_path: filepath, in case of streamlit application use
st.session_state['filepath']
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.
split_overlap: Number of words or sentences that overlap when creating
the paragraphs. This is done as one sentence or 'some words' make sense
when read in together with others. Therefore the overlap is used.
splititng of text.
removePunc: to remove all Punctuation including ',' and '.' or not
Return
--------------
List[Document]: When preprocessing pipeline is run, the output dictionary
has four objects. For the lexicaal search using TFIDFRetriever we
need to use the List of Haystack Document, which can be fetched by
key = 'documents' on output.
"""
lexical_processing_pipeline = processingpipeline()
output_lexical_pre = lexical_processing_pipeline.run(file_paths = file_path,
params= {"FileConverter": {"file_path": file_path, \
"file_name": file_name},
"UdfPreProcessor": {"remove_punc": remove_punc, \
"split_by": split_by, \
"split_length":split_length,\
"split_overlap": split_overlap}})
return output_lexical_pre
def tokenize_lexical_query(query:str)-> List[str]:
"""
Removes the stop words from query and returns the list of important keywords
in query. For the lexical search the relevent paragraphs in document are
retreived using TfIDFretreiver from Haystack. However to highlight these
keywords we need the tokenized form of query.
Params
--------
query: string which represents either list of keywords user is looking for
or a query in form of Question.
Return
-----------
token_list: list of important keywords in the query.
"""
nlp = spacy.load("en_core_web_sm")
token_list = [token.text.lower() for token in nlp(query)
if not (token.is_stop or token.is_punct)]
return token_list
def runSpacyMatcher(token_list:List[str], document:Text
)->Tuple[List[List[int]],spacy.tokens.doc.Doc]:
"""
Using the spacy in backend finds the keywords in the document using the
Matcher class from spacy. We can alternatively use the regex, but spacy
finds all keywords in serialized manner which helps in annotation of answers.
Params
-------
token_list: this is token list which tokenize_lexical_query function returns
document: text in which we need to find the tokens
Return
--------
matches: List of [start_index, end_index] in the spacydoc(at word level not
character) for the keywords in token list.
spacydoc: the keyword index in the spacydoc are at word level and not character,
therefore to allow the annotator to work seamlessly we return the spacydoc.
"""
nlp = spacy.load("en_core_web_sm")
spacydoc = nlp(document)
matcher = Matcher(nlp.vocab)
token_pattern = [[{"LOWER":token}] for token in token_list]
matcher.add(",".join(token_list), token_pattern)
spacymatches = matcher(spacydoc)
# getting start and end index in spacydoc so that annotator can work seamlessly
matches = []
for match_id, start, end in spacymatches:
matches = matches + [[start, end]]
return matches, spacydoc
def runRegexMatcher(token_list:List[str], document:Text):
"""
Using the regex in backend finds the keywords in the document.
Params
-------
token_list: this is token list which tokenize_lexical_query function returns
document: text in which we need to find the tokens
Return
--------
matches: List of [start_index, end_index] in the document for the keywords
in token list at character level.
document: the keyword index returned by regex are at character level,
therefore to allow the annotator to work seamlessly we return the text back.
"""
matches = []
for token in token_list:
matches = (matches +
[[val.start(), val.start() +
len(token)] for val in re.finditer(token, document)])
return matches, document
def spacyAnnotator(matches: List[List[int]], document:spacy.tokens.doc.Doc):
"""
This is spacy Annotator and needs spacy.doc
Annotates the text in the document defined by list of [start index, end index]
Example: "How are you today", if document type is text, matches = [[0,3]]
will give answer = "How", however in case we used the spacy matcher then the
matches = [[0,3]] will give answer = "How are you". However if spacy is used
to find "How" then the matches = [[0,1]] for the string defined above.
Params
-----------
matches: As mentioned its list of list. Example [[0,1],[10,13]]
document: document which needs to be indexed.
Return
--------
will send the output to either app front end using streamlit or
write directly to output screen.
"""
start = 0
annotated_text = ""
for match in matches:
start_idx = match[0]
end_idx = match[1]
if check_streamlit():
annotated_text = (annotated_text + document[start:start_idx].text
+ str(annotation(body=document[start_idx:end_idx].text,
label="ANSWER", background="#964448", color='#ffffff')))
else:
annotated_text = (annotated_text + document[start:start_idx].text
+ colored(document[start_idx:end_idx].text,
"green", attrs = ['bold']))
start = end_idx
annotated_text = annotated_text + document[end_idx:].text
if check_streamlit():
st.write(
markdown(annotated_text),
unsafe_allow_html=True,
)
else:
print(annotated_text)
def lexical_search(query:Text, documents:List[Document],top_k:int):
"""
Performs the Lexical search on the List of haystack documents which is
returned by preprocessing Pipeline.
Params
-------
query: Keywords that need to be searche in documents.
documents: List of Haystack documents returned by preprocessing pipeline.
top_k: Number of Top results to be fetched.
"""
document_store = InMemoryDocumentStore()
document_store.write_documents(documents)
# Haystack Retriever works with document stores only.
retriever = TfidfRetriever(document_store)
results = retriever.retrieve(query=query, top_k = top_k)
query_tokens = tokenize_lexical_query(query)
flag = True
for count, result in enumerate(results):
matches, doc = runSpacyMatcher(query_tokens,result.content)
if len(matches) != 0:
if flag:
flag = False
if check_streamlit():
st.markdown("##### Top few lexical search (TFIDF) hits #####")
else:
print("Top few lexical search (TFIDF) hits")
if check_streamlit():
st.write("Result {}".format(count+1))
else:
print("Results {}".format(count +1))
spacyAnnotator(matches, doc)
if flag:
if check_streamlit():
st.info("🤔 No relevant result found. Please try another keyword.")
else:
print("No relevant result found. Please try another keyword.") |