mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-29 12:54:35 +00:00
100 lines
3.1 KiB
Python
100 lines
3.1 KiB
Python
|
import pytest
|
||
|
|
from openai import OpenAI
|
||
|
|
from utils import *
|
||
|
|
|
||
|
|
server = ServerPreset.bert_bge_small()
|
||
|
|
|
||
|
|
EPSILON = 1e-3
|
||
|
|
|
||
|
|
@pytest.fixture(scope="module", autouse=True)
|
||
|
|
def create_server():
|
||
|
|
global server
|
||
|
|
server = ServerPreset.bert_bge_small()
|
||
|
|
|
||
|
|
|
||
|
|
def test_embedding_single():
|
||
|
|
global server
|
||
|
|
server.start()
|
||
|
|
res = server.make_request("POST", "/embeddings", data={
|
||
|
|
"input": "I believe the meaning of life is",
|
||
|
|
})
|
||
|
|
assert res.status_code == 200
|
||
|
|
assert len(res.body['data']) == 1
|
||
|
|
assert 'embedding' in res.body['data'][0]
|
||
|
|
assert len(res.body['data'][0]['embedding']) > 1
|
||
|
|
|
||
|
|
# make sure embedding vector is normalized
|
||
|
|
assert abs(sum([x ** 2 for x in res.body['data'][0]['embedding']]) - 1) < EPSILON
|
||
|
|
|
||
|
|
|
||
|
|
def test_embedding_multiple():
|
||
|
|
global server
|
||
|
|
server.start()
|
||
|
|
res = server.make_request("POST", "/embeddings", data={
|
||
|
|
"input": [
|
||
|
|
"I believe the meaning of life is",
|
||
|
|
"Write a joke about AI from a very long prompt which will not be truncated",
|
||
|
|
"This is a test",
|
||
|
|
"This is another test",
|
||
|
|
],
|
||
|
|
})
|
||
|
|
assert res.status_code == 200
|
||
|
|
assert len(res.body['data']) == 4
|
||
|
|
for d in res.body['data']:
|
||
|
|
assert 'embedding' in d
|
||
|
|
assert len(d['embedding']) > 1
|
||
|
|
|
||
|
|
|
||
|
|
def test_embedding_openai_library_single():
|
||
|
|
global server
|
||
|
|
server.start()
|
||
|
|
client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}")
|
||
|
|
res = client.embeddings.create(model="text-embedding-3-small", input="I believe the meaning of life is")
|
||
|
|
assert len(res.data) == 1
|
||
|
|
assert len(res.data[0].embedding) > 1
|
||
|
|
|
||
|
|
|
||
|
|
def test_embedding_openai_library_multiple():
|
||
|
|
global server
|
||
|
|
server.start()
|
||
|
|
client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}")
|
||
|
|
res = client.embeddings.create(model="text-embedding-3-small", input=[
|
||
|
|
"I believe the meaning of life is",
|
||
|
|
"Write a joke about AI from a very long prompt which will not be truncated",
|
||
|
|
"This is a test",
|
||
|
|
"This is another test",
|
||
|
|
])
|
||
|
|
assert len(res.data) == 4
|
||
|
|
for d in res.data:
|
||
|
|
assert len(d.embedding) > 1
|
||
|
|
|
||
|
|
|
||
|
|
def test_embedding_error_prompt_too_long():
|
||
|
|
global server
|
||
|
|
server.start()
|
||
|
|
res = server.make_request("POST", "/embeddings", data={
|
||
|
|
"input": "This is a test " * 512,
|
||
|
|
})
|
||
|
|
assert res.status_code != 200
|
||
|
|
assert "too large" in res.body["error"]["message"]
|
||
|
|
|
||
|
|
|
||
|
|
def test_same_prompt_give_same_result():
|
||
|
|
server.start()
|
||
|
|
res = server.make_request("POST", "/embeddings", data={
|
||
|
|
"input": [
|
||
|
|
"I believe the meaning of life is",
|
||
|
|
"I believe the meaning of life is",
|
||
|
|
"I believe the meaning of life is",
|
||
|
|
"I believe the meaning of life is",
|
||
|
|
"I believe the meaning of life is",
|
||
|
|
],
|
||
|
|
})
|
||
|
|
assert res.status_code == 200
|
||
|
|
assert len(res.body['data']) == 5
|
||
|
|
for i in range(1, len(res.body['data'])):
|
||
|
|
v0 = res.body['data'][0]['embedding']
|
||
|
|
vi = res.body['data'][i]['embedding']
|
||
|
|
for x, y in zip(v0, vi):
|
||
|
|
assert abs(x - y) < EPSILON
|