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6ae07f0fd8
* Always load InferenceService * Add BM25 openapi test * Add warning to development.yaml config (#7051)
123 lines
3.6 KiB
Python
123 lines
3.6 KiB
Python
import pytest
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from .helpers.collection_setup import drop_collection
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from .helpers.helpers import request_with_validation
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@pytest.fixture(autouse=True)
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def setup(collection_name):
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bm25_collection_setup(collection_name=collection_name)
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yield
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drop_collection(collection_name=collection_name)
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# Sentences from public domain sci-fi books
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TEXTS = [
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# Meaningful texts
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"I must not fear. Fear is the mind-killer.",
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"All animals are equal, but some animals are more equal than others.",
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"It was a pleasure to burn.",
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"The sky above the port was the color of television, tuned to a dead channel.",
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"In the beginning, the universe was created."
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" This has made a lot of people very angry and been widely regarded as a bad move.",
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"It's a truth universally acknowledged that a zombie in possession of brains must be in want of more brains.",
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"War is peace. Freedom is slavery. Ignorance is strength.",
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"We're not in Infinity; we're in the suburbs.",
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"I was a thousand times more evil than thou!",
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"History is merely a list of surprises... It can only prepare us to be surprised yet again.",
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# 20 Texts with a lot of stop words
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"The a an and or but",
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"A an the",
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"I am you are he she it we they",
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"Is are was were",
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"Do does did",
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"Have has had",
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"Can could",
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"I am is not the you are",
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"I is not the you are",
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]
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PROCESSED_TEXTS = list(map(
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lambda text: text.lower().replace('.', '').replace(',', '').replace('!', '').replace(';', '').split(' '),
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TEXTS
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))
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def bm25_collection_setup(
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collection_name='test_collection',
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):
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response = request_with_validation(
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api='/collections/{collection_name}',
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method="DELETE",
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path_params={'collection_name': collection_name},
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)
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assert response.ok
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response = request_with_validation(
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api='/collections/{collection_name}',
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method="PUT",
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path_params={'collection_name': collection_name},
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body={
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"sparse_vectors": {
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"text": {
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}
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}
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}
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)
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assert response.ok
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response = request_with_validation(
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api='/collections/{collection_name}',
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method="GET",
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path_params={'collection_name': collection_name},
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)
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assert response.ok
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response = request_with_validation(
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api='/collections/{collection_name}/points',
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method="PUT",
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path_params={'collection_name': collection_name},
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query_params={'wait': 'true'},
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body={
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"points": [
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{
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"id": idx,
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"vector": {
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"text": {
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"model": "qdrant/bm25",
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"text": TEXTS[idx]
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}
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},
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"payload": {
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"text": TEXTS[idx]
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}
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}
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for idx in range(len(PROCESSED_TEXTS))
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]
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}
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)
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assert response.ok
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def test_collection_request_with_idf(collection_name):
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queries = ["I should not fear", "It's universally acknowledged"]
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for query in queries:
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response = request_with_validation(
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api='/collections/{collection_name}/points/query',
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method="POST",
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path_params={'collection_name': collection_name},
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body={
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"query": {
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"model": "qdrant/bm25",
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"text": query,
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},
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"using": "text",
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"limit": 2
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}
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)
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assert response.ok
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assert len(response.json()['result']) == 1
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