from haystack import Finder def test_faq_retriever_in_memory_store(): from haystack.database.memory import InMemoryDocumentStore from haystack.retriever.dense import EmbeddingRetriever document_store = InMemoryDocumentStore(embedding_field="embedding") documents = [ {'text': 'By running tox in the command line!', 'meta': {'name': 'How to test this library?', 'question': 'How to test this library?'}}, {'text': 'By running tox in the command line!', 'meta': {'name': 'blah blah blah', 'question': 'blah blah blah'}}, {'text': 'By running tox in the command line!', 'meta': {'name': 'blah blah blah', 'question': 'blah blah blah'}}, {'text': 'By running tox in the command line!', 'meta': {'name': 'blah blah blah', 'question': 'blah blah blah'}}, {'text': 'By running tox in the command line!', 'meta': {'name': 'blah blah blah', 'question': 'blah blah blah'}}, {'text': 'By running tox in the command line!', 'meta': {'name': 'blah blah blah', 'question': 'blah blah blah'}}, {'text': 'By running tox in the command line!', 'meta': {'name': 'blah blah blah', 'question': 'blah blah blah'}}, {'text': 'By running tox in the command line!', 'meta': {'name': 'blah blah blah', 'question': 'blah blah blah'}}, {'text': 'By running tox in the command line!', 'meta': {'name': 'blah blah blah', 'question': 'blah blah blah'}}, {'text': 'By running tox in the command line!', 'meta': {'name': 'blah blah blah', 'question': 'blah blah blah'}}, {'text': 'By running tox in the command line!', 'meta': {'name': 'blah blah blah', 'question': 'blah blah blah'}}, ] retriever = EmbeddingRetriever(document_store=document_store, embedding_model="deepset/sentence_bert", use_gpu=False) embedded = [] for doc in documents: doc['embedding'] = retriever.embed([doc['meta']['question']])[0] embedded.append(doc) document_store.write_documents(embedded) finder = Finder(reader=None, retriever=retriever) prediction = finder.get_answers_via_similar_questions(question="How to test this?", top_k_retriever=1) assert len(prediction.get('answers', [])) == 1