[全量] 初始化项目代码、配置、文档及Agent协同harness

langchain-chat/tests/test_online_api.py

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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。",
+        ],
+        worker_name=worker,
+    )
+
+    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)