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import sys
from pathlib import Path
root_path = Path(__file__).parent.parent
sys.path.append(str(root_path))
from configs import ONLINE_LLM_MODEL
from server.model_workers.base import *
from server.utils import get_model_worker_config, list_config_llm_models
from pprint import pprint
import pytest
workers = []
for x in list_config_llm_models()["online"]:
if x in ONLINE_LLM_MODEL and x not in workers:
workers.append(x)
print(f"all workers to test: {workers}")
# workers = ["fangzhou-api"]
@pytest.mark.parametrize("worker", workers)
def test_chat(worker):
params = ApiChatParams(
messages = [
{"role": "user", "content": "你是谁"},
],
)
print(f"\nchat with {worker} \n")
if worker_class := get_model_worker_config(worker).get("worker_class"):
for x in worker_class().do_chat(params):
pprint(x)
assert isinstance(x, dict)
assert x["error_code"] == 0
@pytest.mark.parametrize("worker", workers)
def test_embeddings(worker):
params = ApiEmbeddingsParams(
texts = [
"LangChain-Chatchat (原 Langchain-ChatGLM): 基于 Langchain 与 ChatGLM 等大语言模型的本地知识库问答应用实现。",
"一种利用 langchain 思想实现的基于本地知识库的问答应用，目标期望建立一套对中文场景与开源模型支持友好、可离线运行的知识库问答解决方案。",
]
)
if worker_class := get_model_worker_config(worker).get("worker_class"):
if worker_class.can_embedding():
print(f"\embeddings with {worker} \n")
resp = worker_class().do_embeddings(params)
pprint(resp, depth=2)
assert resp["code"] == 200
assert "data" in resp
embeddings = resp["data"]
assert isinstance(embeddings, list) and len(embeddings) > 0
assert isinstance(embeddings[0], list) and len(embeddings[0]) > 0
assert isinstance(embeddings[0][0], float)
print("向量长度：", len(embeddings[0]))
# @pytest.mark.parametrize("worker", workers)
# def test_completion(worker):
# params = ApiCompletionParams(prompt="五十六个民族")
# print(f"\completion with {worker} \n")
# worker_class = get_model_worker_config(worker)["worker_class"]
# resp = worker_class().do_completion(params)
# pprint(resp)
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