from haystack.nodes import TransformersQueryClassifier from haystack.nodes import EmbeddingRetriever, FARMReader from haystack.nodes.base import BaseComponent from haystack.document_stores import InMemoryDocumentStore from markdown import markdown from annotated_text import annotation from haystack.schema import Document from typing import List, Text from typing_extensions import Literal 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") @st.cache(allow_output_mutation=True) def loadQueryClassifier(): """ retuns the haystack query classifier model model = shahrukhx01/bert-mini-finetune-question-detection """ 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. Ability to determine the statements is not so good, therefore the chances statement also get modified. Ex: "List water related issues" will be identified by the model as keywords, and therefore it be processed as "find all issues related to 'list all water related issues'". This is one shortcoming but is igonred for now, as semantic search will not get affected a lot, by this. 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 @st.cache(allow_output_mutation=True) def runSemanticPreprocessingPipeline(file_path, file_name, split_by: Literal["sentence", "word"] = 'sentence', split_respect_sentence_boundary = False, split_length:int = 2, split_overlap = 0, removePunc = False)->List[Document]: """ creates the pipeline and runs the preprocessing pipeline. 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'] 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. split_respect_sentence_boundary: Used when using 'word' strategy for splititng of text. 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() output_semantic_pre = semantic_processing_pipeline.run(file_paths = file_path, params= {"FileConverter": {"file_path": file_path, \ "file_name": file_name}, "UdfPreProcessor": {"removePunc": removePunc, \ "split_by": split_by, \ "split_length":split_length,\ "split_overlap": split_overlap, "split_respect_sentence_boundary":split_respect_sentence_boundary}}) return output_semantic_pre @st.cache(hash_funcs={"builtins.SwigPyObject": lambda _: None},allow_output_mutation=True) def loadRetriever(embedding_model:Text = None, embedding_model_format:Text = None, embedding_layer:int = None, retriever_top_k:int = 10, document_store:InMemoryDocumentStore = None): """ Returns the Retriever model based on params provided. 1. https://docs.haystack.deepset.ai/docs/retriever#embedding-retrieval-recommended 2. https://www.sbert.net/examples/applications/semantic-search/README.html 3. https://github.com/deepset-ai/haystack/blob/main/haystack/nodes/retriever/dense.py Params --------- embedding_model: Name of the model to be used for embedding. Check the links provided in documentation embedding_model_format: check the github link of Haystack provided in documentation embedding_layer: check the github link of Haystack provided in documentation retriever_top_k: Number of Top results to be returned by retriever document_store: InMemoryDocumentStore, write haystack Document list to DocumentStore and pass the same to function call. Can be done using createDocumentStore from utils. Return ------- retriever: embedding model """ logging.info("loading retriever") if document_store is None: logging.warning("Retriever initialization requires the DocumentStore") return 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) if check_streamlit: 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'): """ Creates the InMemory Document Store from haystack list of Documents. It is mandatory component for Retriever to work in Haystack frame work. Params ------- documents: List of haystack document. If using the preprocessing pipeline, can be fetched key = 'documents; on output of preprocessing pipeline. similarity: scoring function, can be either 'cosine' or 'dot_product' Return ------- document_store: InMemory Document Store object type. """ document_store = InMemoryDocumentStore(similarity = similarity) document_store.write_documents(documents) return document_store @st.cache(hash_funcs={"builtins.SwigPyObject": lambda _: None},allow_output_mutation=True) def semanticSearchPipeline(documents:List[Document], embedding_model:Text = None, embedding_model_format:Text = None, embedding_layer:int = None, retriever_top_k:int = 10, reader_model:str = None, reader_top_k:int = 10): """ creates the semantic search pipeline and document Store object from the list of haystack documents. 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. 1. https://docs.haystack.deepset.ai/docs/retriever#embedding-retrieval-recommended 2. https://www.sbert.net/examples/applications/semantic-search/README.html 3. https://github.com/deepset-ai/haystack/blob/main/haystack/nodes/retriever/dense.py 4. https://docs.haystack.deepset.ai/docs/reader Params ---------- documents: list of Haystack Documents, returned by preprocessig pipeline. embedding_model: Name of the model to be used for embedding. Check the links provided in documentation embedding_model_format: check the github link of Haystack provided in documentation embedding_layer: check the github link of Haystack provided in documentation retriever_top_k: Number of Top results to be returned by retriever reader_model: Name of the model to be used for Reader node in hasyatck Pipeline. Check the links provided in documentation reader_top_k: Reader will use retrieved results to further find better matches. As purpose here is to use reader to extract context, the value is same as retriever_top_k. Return --------- semanticsearch_pipeline: Haystack Pipeline object, with all the necessary nodes [QueryCheck, Retriever, Reader] document_store: As retriever can work only with Haystack Document Store, the list of document returned by preprocessing pipeline are fed into to get InMemmoryDocumentStore object type, with retriever updating the embedding embeddings of each paragraph in document store. """ document_store = createDocumentStore(documents) retriever = loadRetriever(embedding_model = embedding_model, embedding_model_format=embedding_model_format, embedding_layer=embedding_layer, retriever_top_k= retriever_top_k, document_store = document_store) document_store.update_embeddings(retriever) querycheck = QueryCheck() 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 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],embedding_model:Text, embedding_model_format:Text, embedding_layer:int, reader_model:str, retriever_top_k:int = 10, reader_top_k:int = 10): """ 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, embedding_model= embedding_model, embedding_layer= embedding_layer, embedding_model_format= embedding_model_format, reader_model= reader_model, retriever_top_k= retriever_top_k, reader_top_k= reader_top_k) results = semanticsearch_pipeline.run(query = query) if check_streamlit: st.markdown("##### Top few semantic search results #####") else: print("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 if check_streamlit: st.write("Result {}".format(i+1)) else: print("Result {}".format(i+1)) semanticsearchAnnotator(match, doc)