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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 |