mirror of
https://github.com/deepset-ai/haystack.git
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214 lines
8.4 KiB
Python
214 lines
8.4 KiB
Python
import json
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import logging
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import time
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from datetime import datetime
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from typing import Any, Dict, List, Optional
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import elasticapm
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from fastapi import APIRouter
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from fastapi import HTTPException
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from haystack import Finder
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from rest_api.config import DB_HOST, DB_PORT, DB_USER, DB_PW, DB_INDEX, DB_INDEX_FEEDBACK, DEFAULT_TOP_K_READER, \
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ES_CONN_SCHEME, TEXT_FIELD_NAME, SEARCH_FIELD_NAME, EMBEDDING_DIM, EMBEDDING_FIELD_NAME, EXCLUDE_META_DATA_FIELDS, \
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RETRIEVER_TYPE, EMBEDDING_MODEL_PATH, USE_GPU, READER_MODEL_PATH, BATCHSIZE, CONTEXT_WINDOW_SIZE, \
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TOP_K_PER_CANDIDATE, NO_ANS_BOOST, READER_CAN_HAVE_NO_ANSWER, MAX_PROCESSES, MAX_SEQ_LEN, DOC_STRIDE, \
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CONCURRENT_REQUEST_PER_WORKER, FAQ_QUESTION_FIELD_NAME, EMBEDDING_MODEL_FORMAT, READER_TYPE, READER_TOKENIZER, \
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GPU_NUMBER, NAME_FIELD_NAME, VECTOR_SIMILARITY_METRIC, CREATE_INDEX, LOG_LEVEL, UPDATE_EXISTING_DOCUMENTS
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from rest_api.controller.request import Question
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from rest_api.controller.response import Answers, AnswersToIndividualQuestion
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from rest_api.controller.utils import RequestLimiter
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from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
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from haystack.reader.base import BaseReader
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from haystack.reader.farm import FARMReader
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from haystack.reader.transformers import TransformersReader
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from haystack.retriever.base import BaseRetriever
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from haystack.retriever.sparse import ElasticsearchRetriever, ElasticsearchFilterOnlyRetriever
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from haystack.retriever.dense import EmbeddingRetriever
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logger = logging.getLogger('haystack')
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logger.setLevel(LOG_LEVEL)
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router = APIRouter()
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# Init global components: DocumentStore, Retriever, Reader, Finder
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document_store = ElasticsearchDocumentStore(
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host=DB_HOST,
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port=DB_PORT,
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username=DB_USER,
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password=DB_PW,
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index=DB_INDEX,
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label_index=DB_INDEX_FEEDBACK,
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scheme=ES_CONN_SCHEME,
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ca_certs=False,
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verify_certs=False,
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text_field=TEXT_FIELD_NAME,
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name_field=NAME_FIELD_NAME,
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search_fields=SEARCH_FIELD_NAME,
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embedding_dim=EMBEDDING_DIM,
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embedding_field=EMBEDDING_FIELD_NAME,
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excluded_meta_data=EXCLUDE_META_DATA_FIELDS, # type: ignore
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faq_question_field=FAQ_QUESTION_FIELD_NAME,
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create_index=CREATE_INDEX,
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update_existing_documents=UPDATE_EXISTING_DOCUMENTS,
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similarity=VECTOR_SIMILARITY_METRIC
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)
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if RETRIEVER_TYPE == "EmbeddingRetriever":
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retriever = EmbeddingRetriever(
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document_store=document_store,
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embedding_model=EMBEDDING_MODEL_PATH,
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model_format=EMBEDDING_MODEL_FORMAT,
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use_gpu=USE_GPU
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) # type: BaseRetriever
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elif RETRIEVER_TYPE == "ElasticsearchRetriever":
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retriever = ElasticsearchRetriever(document_store=document_store)
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elif RETRIEVER_TYPE is None or RETRIEVER_TYPE == "ElasticsearchFilterOnlyRetriever":
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retriever = ElasticsearchFilterOnlyRetriever(document_store=document_store)
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else:
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raise ValueError(f"Could not load Retriever of type '{RETRIEVER_TYPE}'. "
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f"Please adjust RETRIEVER_TYPE to one of: "
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f"'EmbeddingRetriever', 'ElasticsearchRetriever', 'ElasticsearchFilterOnlyRetriever', None"
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f"OR modify rest_api/search.py to support your retriever"
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)
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if READER_MODEL_PATH: # for extractive doc-qa
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if READER_TYPE == "TransformersReader":
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use_gpu = -1 if not USE_GPU else GPU_NUMBER
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reader = TransformersReader(
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model_name_or_path=READER_MODEL_PATH,
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use_gpu=use_gpu,
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context_window_size=CONTEXT_WINDOW_SIZE,
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return_no_answers=READER_CAN_HAVE_NO_ANSWER,
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tokenizer=READER_TOKENIZER
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) # type: Optional[BaseReader]
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elif READER_TYPE == "FARMReader":
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reader = FARMReader(
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model_name_or_path=READER_MODEL_PATH,
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batch_size=BATCHSIZE,
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use_gpu=USE_GPU,
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context_window_size=CONTEXT_WINDOW_SIZE,
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top_k_per_candidate=TOP_K_PER_CANDIDATE,
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no_ans_boost=NO_ANS_BOOST,
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num_processes=MAX_PROCESSES,
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max_seq_len=MAX_SEQ_LEN,
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doc_stride=DOC_STRIDE,
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) # type: Optional[BaseReader]
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else:
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raise ValueError(f"Could not load Reader of type '{READER_TYPE}'. "
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f"Please adjust READER_TYPE to one of: "
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f"'FARMReader', 'TransformersReader', None"
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)
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else:
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reader = None # don't need one for pure FAQ matching
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FINDERS = {1: Finder(reader=reader, retriever=retriever)}
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#############################################
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# Endpoints
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#############################################
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doc_qa_limiter = RequestLimiter(CONCURRENT_REQUEST_PER_WORKER)
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@router.post("/models/{model_id}/doc-qa", response_model=Answers, response_model_exclude_unset=True)
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def doc_qa(model_id: int, question_request: Question):
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with doc_qa_limiter.run():
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start_time = time.time()
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finder = FINDERS.get(model_id, None)
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if not finder:
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raise HTTPException(
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status_code=404, detail=f"Could not get Finder with ID {model_id}. Available IDs: {list(FINDERS.keys())}"
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)
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results = search_documents(finder, question_request, start_time)
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return {"results": results}
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@router.post("/models/{model_id}/faq-qa", response_model=Answers, response_model_exclude_unset=True)
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def faq_qa(model_id: int, request: Question):
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finder = FINDERS.get(model_id, None)
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if not finder:
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raise HTTPException(
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status_code=404, detail=f"Could not get Finder with ID {model_id}. Available IDs: {list(FINDERS.keys())}"
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)
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results = []
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for question in request.questions:
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if request.filters:
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# put filter values into a list and remove filters with null value
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filters = {}
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for key, values in request.filters.items():
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if values is None:
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continue
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if not isinstance(values, list):
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values = [values]
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filters[key] = values
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logger.info(f" [{datetime.now()}] Request: {request}")
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else:
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filters = {}
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result = finder.get_answers_via_similar_questions(
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question=question, top_k_retriever=request.top_k_retriever, filters=filters,
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)
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results.append(result)
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elasticapm.set_custom_context({"results": results})
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logger.info(json.dumps({"request": request.dict(), "results": results}))
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return {"results": results}
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@router.post("/models/{model_id}/query", response_model=Dict[str, Any], response_model_exclude_unset=True)
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def query(model_id: int, query_request: Dict[str, Any], top_k_reader: int = DEFAULT_TOP_K_READER):
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with doc_qa_limiter.run():
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start_time = time.time()
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finder = FINDERS.get(model_id, None)
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if not finder:
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raise HTTPException(
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status_code=404, detail=f"Could not get Finder with ID {model_id}. Available IDs: {list(FINDERS.keys())}"
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)
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question_request = Question.from_elastic_query_dsl(query_request, top_k_reader)
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answers = search_documents(finder, question_request, start_time)
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response: Dict[str, Any] = {}
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if answers and len(answers) > 0:
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response = AnswersToIndividualQuestion.to_elastic_response_dsl(dict(answers[0]))
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return response
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def search_documents(finder, question_request, start_time) -> List[AnswersToIndividualQuestion]:
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results = []
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for question in question_request.questions:
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if question_request.filters:
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# put filter values into a list and remove filters with null value
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filters = {}
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for key, values in question_request.filters.items():
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if values is None:
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continue
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if not isinstance(values, list):
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values = [values]
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filters[key] = values
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logger.info(f" [{datetime.now()}] Request: {question_request}")
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else:
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filters = {}
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result = finder.get_answers(
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question=question,
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top_k_retriever=question_request.top_k_retriever,
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top_k_reader=question_request.top_k_reader,
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filters=filters,
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)
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results.append(result)
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elasticapm.set_custom_context({"results": results})
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end_time = time.time()
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logger.info(
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json.dumps({"request": question_request.dict(), "results": results,
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"time": f"{(end_time - start_time):.2f}"}))
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return results
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