llm/tests/conftest.py

104 lines
2.5 KiB
Python

import pytest
import sqlite_utils
import llm
from llm.plugins import pm
def pytest_configure(config):
import sys
sys._called_from_test = True
@pytest.fixture
def user_path(tmpdir):
dir = tmpdir / "llm.datasette.io"
dir.mkdir()
return dir
@pytest.fixture
def user_path_with_embeddings(user_path):
path = str(user_path / "embeddings.db")
db = sqlite_utils.Database(path)
collection = llm.Collection("demo", db, model_id="embed-demo")
collection.embed("1", "hello world")
collection.embed("2", "goodbye world")
@pytest.fixture
def templates_path(user_path):
dir = user_path / "templates"
dir.mkdir()
return dir
@pytest.fixture(autouse=True)
def env_setup(monkeypatch, user_path):
monkeypatch.setenv("LLM_USER_PATH", str(user_path))
class EmbedDemo(llm.EmbeddingModel):
model_id = "embed-demo"
batch_size = 10
def embed_batch(self, texts):
if not hasattr(self, "batch_count"):
self.batch_count = 0
self.batch_count += 1
for text in texts:
words = text.split()[:16]
embedding = [len(word) for word in words]
# Pad with 0 up to 16 words
embedding += [0] * (16 - len(embedding))
yield embedding
@pytest.fixture(autouse=True)
def register_embed_demo_model():
class EmbedDemoPlugin:
__name__ = "EmbedDemoPlugin"
@llm.hookimpl
def register_embedding_models(self, register):
register(EmbedDemo())
pm.register(EmbedDemoPlugin(), name="undo-embed-demo-plugin")
try:
yield
finally:
pm.unregister(name="undo-embed-demo-plugin")
@pytest.fixture
def mocked_openai(requests_mock):
return requests_mock.post(
"https://api.openai.com/v1/chat/completions",
json={
"model": "gpt-3.5-turbo",
"usage": {},
"choices": [{"message": {"content": "Bob, Alice, Eve"}}],
},
headers={"Content-Type": "application/json"},
)
@pytest.fixture
def mocked_localai(requests_mock):
return requests_mock.post(
"http://localai.localhost/chat/completions",
json={
"model": "orca",
"usage": {},
"choices": [{"message": {"content": "Bob, Alice, Eve"}}],
},
headers={"Content-Type": "application/json"},
)
@pytest.fixture
def collection():
collection = llm.Collection("test", model_id="embed-demo")
collection.embed(1, "hello world")
collection.embed(2, "goodbye world")
return collection