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8efb28e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 | import pytest
from utils import *
server = ServerPreset.jina_reranker_tiny()
@pytest.fixture(autouse=True)
def create_server():
global server
server = ServerPreset.jina_reranker_tiny()
TEST_DOCUMENTS = [
"A machine is a physical system that uses power to apply forces and control movement to perform an action. The term is commonly applied to artificial devices, such as those employing engines or motors, but also to natural biological macromolecules, such as molecular machines.",
"Learning is the process of acquiring new understanding, knowledge, behaviors, skills, values, attitudes, and preferences. The ability to learn is possessed by humans, non-human animals, and some machines; there is also evidence for some kind of learning in certain plants.",
"Machine learning is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions.",
"Paris, capitale de la France, est une grande ville européenne et un centre mondial de l'art, de la mode, de la gastronomie et de la culture. Son paysage urbain du XIXe siècle est traversé par de larges boulevards et la Seine."
]
def test_rerank():
global server
server.start()
res = server.make_request("POST", "/rerank", data={
"query": "Machine learning is",
"documents": TEST_DOCUMENTS,
})
assert res.status_code == 200
assert len(res.body["results"]) == 4
most_relevant = res.body["results"][0]
least_relevant = res.body["results"][0]
for doc in res.body["results"]:
if doc["relevance_score"] > most_relevant["relevance_score"]:
most_relevant = doc
if doc["relevance_score"] < least_relevant["relevance_score"]:
least_relevant = doc
assert most_relevant["relevance_score"] > least_relevant["relevance_score"]
assert most_relevant["index"] == 2
assert least_relevant["index"] == 3
def test_rerank_tei_format():
global server
server.start()
res = server.make_request("POST", "/rerank", data={
"query": "Machine learning is",
"texts": TEST_DOCUMENTS,
})
assert res.status_code == 200
assert len(res.body) == 4
most_relevant = res.body[0]
least_relevant = res.body[0]
for doc in res.body:
if doc["score"] > most_relevant["score"]:
most_relevant = doc
if doc["score"] < least_relevant["score"]:
least_relevant = doc
assert most_relevant["score"] > least_relevant["score"]
assert most_relevant["index"] == 2
assert least_relevant["index"] == 3
@pytest.mark.parametrize("documents", [
[],
None,
123,
[1, 2, 3],
])
def test_invalid_rerank_req(documents):
global server
server.start()
res = server.make_request("POST", "/rerank", data={
"query": "Machine learning is",
"documents": documents,
})
assert res.status_code == 400
assert "error" in res.body
@pytest.mark.parametrize(
"query,doc1,doc2,n_tokens",
[
("Machine learning is", "A machine", "Learning is", 19),
("Which city?", "Machine learning is ", "Paris, capitale de la", 26),
]
)
def test_rerank_usage(query, doc1, doc2, n_tokens):
global server
server.start()
res = server.make_request("POST", "/rerank", data={
"query": query,
"documents": [
doc1,
doc2,
]
})
assert res.status_code == 200
assert res.body['usage']['prompt_tokens'] == res.body['usage']['total_tokens']
assert res.body['usage']['prompt_tokens'] == n_tokens
@pytest.mark.parametrize("top_n,expected_len", [
(None, len(TEST_DOCUMENTS)), # no top_n parameter
(2, 2),
(4, 4),
(99, len(TEST_DOCUMENTS)), # higher than available docs
])
def test_rerank_top_n(top_n, expected_len):
global server
server.start()
data = {
"query": "Machine learning is",
"documents": TEST_DOCUMENTS,
}
if top_n is not None:
data["top_n"] = top_n
res = server.make_request("POST", "/rerank", data=data)
assert res.status_code == 200
assert len(res.body["results"]) == expected_len
@pytest.mark.parametrize("top_n,expected_len", [
(None, len(TEST_DOCUMENTS)), # no top_n parameter
(2, 2),
(4, 4),
(99, len(TEST_DOCUMENTS)), # higher than available docs
])
def test_rerank_tei_top_n(top_n, expected_len):
global server
server.start()
data = {
"query": "Machine learning is",
"texts": TEST_DOCUMENTS,
}
if top_n is not None:
data["top_n"] = top_n
res = server.make_request("POST", "/rerank", data=data)
assert res.status_code == 200
assert len(res.body) == expected_len
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