mirror of
https://github.com/deepset-ai/haystack.git
synced 2025-08-22 07:30:54 +00:00
6bed2f509f
* fix tokenizer warning in latest transformers * change dpr arg from gpu to use_gpu * change gpu arg for EmbeddingRetriever
38 lines
2.1 KiB
Python
38 lines
2.1 KiB
Python
from haystack import Finder
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def test_faq_retriever_in_memory_store():
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from haystack.database.memory import InMemoryDocumentStore
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from haystack.retriever.dense import EmbeddingRetriever
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document_store = InMemoryDocumentStore(embedding_field="embedding")
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documents = [
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{'text': 'By running tox in the command line!', 'meta': {'name': 'How to test this library?', 'question': 'How to test this library?'}},
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{'text': 'By running tox in the command line!', 'meta': {'name': 'blah blah blah', 'question': 'blah blah blah'}},
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{'text': 'By running tox in the command line!', 'meta': {'name': 'blah blah blah', 'question': 'blah blah blah'}},
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{'text': 'By running tox in the command line!', 'meta': {'name': 'blah blah blah', 'question': 'blah blah blah'}},
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{'text': 'By running tox in the command line!', 'meta': {'name': 'blah blah blah', 'question': 'blah blah blah'}},
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{'text': 'By running tox in the command line!', 'meta': {'name': 'blah blah blah', 'question': 'blah blah blah'}},
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{'text': 'By running tox in the command line!', 'meta': {'name': 'blah blah blah', 'question': 'blah blah blah'}},
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{'text': 'By running tox in the command line!', 'meta': {'name': 'blah blah blah', 'question': 'blah blah blah'}},
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{'text': 'By running tox in the command line!', 'meta': {'name': 'blah blah blah', 'question': 'blah blah blah'}},
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{'text': 'By running tox in the command line!', 'meta': {'name': 'blah blah blah', 'question': 'blah blah blah'}},
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{'text': 'By running tox in the command line!', 'meta': {'name': 'blah blah blah', 'question': 'blah blah blah'}},
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]
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retriever = EmbeddingRetriever(document_store=document_store, embedding_model="deepset/sentence_bert", use_gpu=False)
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embedded = []
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for doc in documents:
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doc['embedding'] = retriever.embed([doc['meta']['question']])[0]
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embedded.append(doc)
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document_store.write_documents(embedded)
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finder = Finder(reader=None, retriever=retriever)
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prediction = finder.get_answers_via_similar_questions(question="How to test this?", top_k_retriever=1)
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assert len(prediction.get('answers', [])) == 1
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