utils/semantic_search.py

from haystack.nodes import TransformersQueryClassifier
from haystack.nodes import EmbeddingRetriever, FARMReader
from haystack.nodes.base import BaseComponent
from haystack.document_stores import InMemoryDocumentStore
import configparser
import streamlit as st
from markdown import markdown
from annotated_text import annotation
from haystack.schema import Document
from typing import List, Text
from utils.preprocessing import processingpipeline
from haystack.pipelines import Pipeline

config = configparser.ConfigParser()
config.read_file(open('paramconfig.cfg'))

class QueryCheck(BaseComponent):

    outgoing_edges = 1

    def run(self, query):

        query_classifier =  TransformersQueryClassifier(model_name_or_path=
                            "shahrukhx01/bert-mini-finetune-question-detection")

        
        result = query_classifier.run(query=query)

        if result[1] == "output_1":
            output = {"query":query,
                       "query_type": 'question/statement'}
        else:
            output = {"query": "find all issues related to {}".format(query),
                      "query_type": 'statements/keyword'}

        return output, "output_1"
    
    def run_batch(self, query):
        pass

def runSemanticPreprocessingPipeline()->List[Document]:
    """
    creates the pipeline and runs the preprocessing pipeline, 
    the params for pipeline are fetched from paramconfig

    Return
    --------------
    List[Document]: When preprocessing pipeline is run, the output dictionary 
    has four objects. For the Haysatck implementation of semantic search we, 
    need to use the List of Haystack Document, which can be fetched by 
    key = 'documents' on output.

    """
    file_path = st.session_state['filepath']
    file_name = st.session_state['filename']
    semantic_processing_pipeline = processingpipeline()
    split_by = config.get('semantic_search','SPLIT_BY')
    split_length = int(config.get('semantic_search','SPLIT_LENGTH'))
    split_overlap = int(config.get('semantic_search','SPLIT_OVERLAP'))

    output_semantic_pre = semantic_processing_pipeline.run(file_paths = file_path, 
                            params= {"FileConverter": {"file_path": file_path, \
                                        "file_name": file_name}, 
                                        "UdfPreProcessor": {"removePunc": False, \
                                            "split_by": split_by, \
                                            "split_length":split_length,\
                                            "split_overlap": split_overlap}})

    return output_semantic_pre['documents']


def semanticSearchPipeline(documents, show_answers = False):
    document_store = InMemoryDocumentStore()
    document_store.write_documents(documents)

    embedding_model = config.get('semantic_search','RETRIEVER')
    embedding_model_format = config.get('semantic_search','RETRIEVER_FORMAT')
    embedding_layer = int(config.get('semantic_search','RETRIEVER_EMB_LAYER'))
    retriever_top_k = int(config.get('semantic_search','RETRIEVER_TOP_K'))
    

    
    querycheck = QueryCheck()
    retriever = EmbeddingRetriever(
                document_store=document_store,
                embedding_model=embedding_model,top_k = retriever_top_k,
                emb_extraction_layer=embedding_layer, scale_score =True,
                model_format=embedding_model_format, use_gpu = True)
    document_store.update_embeddings(retriever)
    

    semanticsearch_pipeline = Pipeline()
    semanticsearch_pipeline.add_node(component = querycheck, name = "QueryCheck",
                                    inputs = ["Query"])
    semanticsearch_pipeline.add_node(component = retriever, name = "EmbeddingRetriever",
                                    inputs = ["QueryCheck.output_1"])
    if show_answers == True:
        reader_model = config.get('semantic_search','READER')
        reader_top_k = retriever_top_k
        reader = FARMReader(model_name_or_path=reader_model,
                        top_k = reader_top_k, use_gpu=True)
    
        semanticsearch_pipeline.add_node(component = reader, name = "FARMReader",
                                        inputs= ["EmbeddingRetriever"])
    
    return semanticsearch_pipeline, document_store

def semanticsearchAnnotator(matches: List[List[int]], document):
    """
    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.

    """
    start = 0
    annotated_text = ""
    for match in matches:
        start_idx = match[0]
        end_idx = match[1]
        annotated_text = (annotated_text + document[start:start_idx]
                          + str(annotation(body=document[start_idx:end_idx],
                         label="ANSWER", background="#964448", color='#ffffff')))
        start = end_idx
    
    annotated_text = annotated_text + document[end_idx:]
    
    st.write(
            markdown(annotated_text),
            unsafe_allow_html=True,
        )


def semantic_search(query:Text,documents:List[Document],show_answers = False):
    """
    Performs the Lexical search on the List of haystack documents which is 
    returned by preprocessing Pipeline.
    """
    threshold = 0.4
    semanticsearch_pipeline, doc_store = semanticSearchPipeline(documents, 
                                                    show_answers=show_answers)
    results = semanticsearch_pipeline.run(query = query)
    
    
    if show_answers == False:
        results = results['documents']
        for i,queryhit in enumerate(results):
                            
            if queryhit.score > threshold:
                st.write("\t {}: \t {}".format(i+1, queryhit.content.replace("\n", " ")))
                st.markdown("---")
        
    else:
        
        for answer in results['answers']:
            st.write(answer)
            # matches = []
            # doc = []
            if answer.score >0.01:
                temp = answer.to_dict()
                start_idx = temp['offsets_in_document'][0]['start']
                end_idx = temp['offsets_in_document'][0]['end']

                # matches.append([start_idx,end_idx])
                # doc.append(doc_store.get_document_by_id(temp['document_id']).content)
                match = [[start_idx,end_idx]]
                doc = doc_store.get_document_by_id(temp['document_id']).content
                semanticsearchAnnotator(match,doc)