mirror of
https://github.com/deepset-ai/haystack.git
synced 2025-10-31 09:49:48 +00:00
39 lines
2.2 KiB
Python
39 lines
2.2 KiB
Python
from haystack import Finder
|
|
|
|
|
|
def test_faq_retriever_in_memory_store(monkeypatch):
|
|
monkeypatch.setenv("EMBEDDING_FIELD_NAME", "embedding")
|
|
|
|
from haystack.database.memory import InMemoryDocumentStore
|
|
from haystack.retriever.elasticsearch import EmbeddingRetriever
|
|
|
|
document_store = InMemoryDocumentStore()
|
|
|
|
documents = [
|
|
{'name': 'How to test this library?', 'text': 'By running tox in the command line!', 'meta': {'question': 'How to test this library?'}},
|
|
{'name': 'blah blah blah', 'text': 'By running tox in the command line!', 'meta': {'question': 'blah blah blah'}},
|
|
{'name': 'blah blah blah', 'text': 'By running tox in the command line!', 'meta': {'question': 'blah blah blah'}},
|
|
{'name': 'blah blah blah', 'text': 'By running tox in the command line!', 'meta': {'question': 'blah blah blah'}},
|
|
{'name': 'blah blah blah', 'text': 'By running tox in the command line!', 'meta': {'question': 'blah blah blah'}},
|
|
{'name': 'blah blah blah', 'text': 'By running tox in the command line!', 'meta': {'question': 'blah blah blah'}},
|
|
{'name': 'blah blah blah', 'text': 'By running tox in the command line!', 'meta': {'question': 'blah blah blah'}},
|
|
{'name': 'blah blah blah', 'text': 'By running tox in the command line!', 'meta': {'question': 'blah blah blah'}},
|
|
{'name': 'blah blah blah', 'text': 'By running tox in the command line!', 'meta': {'question': 'blah blah blah'}},
|
|
{'name': 'blah blah blah', 'text': 'By running tox in the command line!', 'meta': {'question': 'blah blah blah'}},
|
|
{'name': 'blah blah blah', 'text': 'By running tox in the command line!', 'meta': {'question': 'blah blah blah'}},
|
|
]
|
|
|
|
retriever = EmbeddingRetriever(document_store=document_store, embedding_model="deepset/sentence_bert", gpu=False)
|
|
|
|
embedded = []
|
|
for doc in documents:
|
|
doc['embedding'] = retriever.create_embedding([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
|