File size: 18,314 Bytes
63da636 a4bf4e8 4a20529 cc5c327 a4bf4e8 7d78a3b cc5c327 99ae6d0 a4bf4e8 99ae6d0 7d78a3b 99ae6d0 49a314a 63da636 ed0fd13 99ae6d0 ed0fd13 49a314a 63da636 cc5c327 63da636 ed0fd13 99ae6d0 63da636 4a20529 63da636 cc5c327 63da636 cc5c327 7d78a3b 1d3978a 99ae6d0 7d78a3b 99ae6d0 1d3978a a4bf4e8 1d3978a 99ae6d0 a4bf4e8 1d3978a a4bf4e8 1d3978a 7d78a3b 1d3978a 2bccbcb 7d78a3b 1d3978a 99ae6d0 7d78a3b 99ae6d0 7d78a3b 99ae6d0 7d78a3b 99ae6d0 7d78a3b 99ae6d0 a4bf4e8 99ae6d0 7d78a3b ed0fd13 7d78a3b ed0fd13 7d78a3b ed0fd13 7d78a3b ed0fd13 99ae6d0 3a88079 2918b13 3a88079 2918b13 7d78a3b 99ae6d0 3a88079 99ae6d0 3a88079 7d78a3b 3a88079 b941115 a4bf4e8 ed0fd13 99ae6d0 a4bf4e8 99ae6d0 fc3b461 7d78a3b fc3b461 7d78a3b fc3b461 7d78a3b fc3b461 99ae6d0 fc3b461 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 |
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
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,
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']
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: emebedding 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 frm 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)
# if check_streamlit:
# 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], 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. 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.
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.
"""
document_store = createDocumentStore(documents)
# if check_streamlit:
# if 'retriever' in st.session_state:
# # if st.session_state['retriever']:
# retriever = st.session_state['retriever']
# else:
# if embedding_model:
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)
# st.session_state['retriever'] = retriever
# else:
# logging.warning("no streamlit enviornment found, neither embedding model \
# provided")
# return
# elif embedding_model:
# 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)
# retriever.document_store = document_store
querycheck = QueryCheck()
# if check_streamlit:
# if 'reader' in st.session_state:
# reader = st.session_state['reader']
# else:
# if reader_model:
reader = FARMReader(model_name_or_path=reader_model,
top_k = reader_top_k, use_gpu=True)
# st.session_state['reader'] = reader
# elif reader_model:
# 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
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 |