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 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 utils.streamlitcheck import check_streamlit from haystack.pipelines import Pipeline import logging try: from termcolor import colored except: pass try: import streamlit as st except ImportError: logging.info("Streamlit not installed") config = configparser.ConfigParser() try: config.read_file(open('paramconfig.cfg')) except Exception: logging.info("paramconfig file not found") st.info("Please place the paramconfig file in the same directory as app.py") @st.cache(allow_output_mutation=True) def loadQueryClassifier(): query_classifier = TransformersQueryClassifier(model_name_or_path= "shahrukhx01/bert-mini-finetune-question-detection") return query_classifier class QueryCheck(BaseComponent): """ Uses Query Classifier from Haystack, process the query based on query type 1. https://docs.haystack.deepset.ai/docs/query_classifier """ outgoing_edges = 1 def run(self, query): """ mandatory method to use the cusotm node. Determines the query type, if if the query is of type keyword/statement will modify it to make it more useful for sentence transoformers. """ query_classifier = loadQueryClassifier() 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(file_path, file_name)->List[Document]: """ creates the pipeline and runs the preprocessing pipeline, the params for pipeline are fetched from paramconfig 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'] 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. """ 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 @st.cache(hash_funcs={"builtins.SwigPyObject": lambda _: None},allow_output_mutation=True) def loadRetriever(embedding_model = None, embedding_model_format = None, embedding_layer = None, retriever_top_k = 10, document_store = None): logging.info("loading retriever") if document_store is None: logging.warning("Retriever initialization requires the DocumentStore") return if embedding_model is None: try: 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')) except Exception as e: logging.info(e) st.info(e) retriever = EmbeddingRetriever( embedding_model=embedding_model,top_k = retriever_top_k, document_store = document_store, emb_extraction_layer=embedding_layer, scale_score =True, model_format=embedding_model_format, use_gpu = True) st.session_state['retriever'] = retriever return retriever @st.cache(hash_funcs={"builtins.SwigPyObject": lambda _: None},allow_output_mutation=True) def createDocumentStore(documents:List[Document], similarity:str = 'cosine'): document_store = InMemoryDocumentStore(similarity = similarity) document_store.write_documents(documents) if 'retriever' in st.session_state: retriever = st.session_state['retriever'] document_store.update_embeddings(retriever) return document_store @st.cache(hash_funcs={"builtins.SwigPyObject": lambda _: None},allow_output_mutation=True) def semanticSearchPipeline(documents:List[Document]): """ creates the semantic search pipeline and document Store object from the list of haystack documents. Retriever and Reader model are read from paramconfig. The top_k for the Reader and Retirever are kept same, so that all the results returned by Retriever are used, however the context is extracted by Reader for each retrieved result. The querycheck is added as node to process the query. Params ---------- documents: list of Haystack Documents, returned by preprocessig pipeline. Return --------- semanticsearch_pipeline: Haystack Pipeline object, with all the necessary nodes [QueryCheck, Retriever, Reader] document_store: As retriever cna work only with Haystack Document Store, the list of document returned by preprocessing pipeline. """ document_store = createDocumentStore(documents) retriever = loadRetriever(document_store=document_store) document_store.update_embeddings(retriever) querycheck = QueryCheck() if 'reader' in st.session_state: reader = st.session_state['reader'] else: reader_model = config.get('semantic_search','READER') reader_top_k = int(config.get('semantic_search','RETRIEVER_TOP_K')) reader = FARMReader(model_name_or_path=reader_model, top_k = reader_top_k, use_gpu=True) st.session_state['reader'] = reader 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"]) 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] if check_streamlit(): annotated_text = (annotated_text + document[start:start_idx] + str(annotation(body=document[start_idx:end_idx], label="Context", background="#964448", color='#ffffff'))) else: annotated_text = (annotated_text + document[start:start_idx] + colored(document[start_idx:end_idx], "green", attrs = ['bold'])) start = end_idx annotated_text = annotated_text + document[end_idx:] if check_streamlit(): st.write( markdown(annotated_text), unsafe_allow_html=True, ) else: print(annotated_text) def semantic_search(query:Text,documents:List[Document]): """ Performs the Semantic 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 fo Haystack documents returned by preprocessing pipeline. """ semanticsearch_pipeline, doc_store = semanticSearchPipeline(documents) results = semanticsearch_pipeline.run(query = query) st.markdown("##### Top few semantic search results #####") for i,answer in enumerate(results['answers']): temp = answer.to_dict() start_idx = temp['offsets_in_document'][0]['start'] end_idx = temp['offsets_in_document'][0]['end'] match = [[start_idx,end_idx]] doc = doc_store.get_document_by_id(temp['document_id']).content st.write("Result {}".format(i+1)) semanticsearchAnnotator(match, doc) # if 'document_store' in st.session_state: # document_store = st.session_state['document_store'] # temp = document_store.get_all_documents() # if st.session_state['filename'] != temp[0].meta['name']: # document_store = InMemoryDocumentStore() # document_store.write_documents(documents) # if 'retriever' in st.session_state: # retriever = st.session_state['retriever'] # document_store.update_embeddings(retriever) # # querycheck = # # 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')) # # 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) # else: # embedding_model = config.get('semantic_search','RETRIEVER') # embedding_model_format = config.get('semantic_search','RETRIEVER_FORMAT') # 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) # else: # 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')) # 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) # st.session_state['retriever'] = retriever # document_store.update_embeddings(retriever) # st.session_state['document_store'] = document_store # querycheck = QueryCheck() # st.session_state['querycheck'] = querycheck # 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) # st.session_state['reader'] = reader # querycheck = QueryCheck() # 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 = Pipeline() # semanticsearch_pipeline.add_node(component = querycheck, name = "QueryCheck", # inputs = ["Query"]) # semanticsearch_pipeline.add_node(component = retriever, name = "EmbeddingRetriever", # inputs = ["QueryCheck.output_1"]) # semanticsearch_pipeline.add_node(component = reader, name = "FARMReader", # inputs= ["EmbeddingRetriever"]) # return semanticsearch_pipeline, document_store