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57 lines
2.0 KiB
Python
57 lines
2.0 KiB
Python
from haystack.retriever.tfidf import TfidfRetriever
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from haystack.reader.farm import FARMReader
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import logging
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import pandas as pd
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pd.options.display.max_colwidth = 80
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logger = logging.getLogger(__name__)
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logging.getLogger('farm').setLevel(logging.WARNING)
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logging.getLogger('transformers').setLevel(logging.WARNING)
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class Finder:
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"""
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Finder ties together instances of the Reader and Retriever class.
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It provides an interface to predict top n answers for a given question.
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"""
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def __init__(self, reader, retriever):
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self.retriever = retriever
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self.retriever.fit()
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self.reader = reader
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def get_answers(self, question, top_k_reader=1, top_k_retriever=10, filters=None):
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"""
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Get top k answers for a given question.
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:param question: the question string
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:param top_k_reader: number of answers returned by the reader
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:param top_k_retriever: number of text units to be retrieved
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:param filters: limit scope to documents having the given tags and their corresponding values.
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The format for the dict is {"tag-1": "value-1", "tag-2": "value-2" ...}
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:return:
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"""
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# 1) Optional: reduce the search space via document tags
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if filters:
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candidate_doc_ids = self.retriever.datastore.get_document_ids_by_tags(filters)
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else:
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candidate_doc_ids = None
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# 2) Apply retriever to get fast candidate paragraphs
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paragraphs, meta_data = self.retriever.retrieve(question, top_k=top_k_retriever, candidate_doc_ids=candidate_doc_ids)
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# 3) Apply reader to get granular answer(s)
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logger.info(f"Applying the reader now to look for the answer in detail ...")
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results = self.reader.predict(question=question,
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paragrahps=paragraphs,
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meta_data_paragraphs=meta_data,
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top_k=top_k_reader)
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return results
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