feat: 完成全量功能开发，包括前端管理后台与后端服务优化
此提交实现了完整的知识库管理系统： 1. 新增Vue3 + Antd Vue前端管理后台，包含登录、文档管理、检索、类目设置等完整页面 2. 重构后端LLM调用抽象层，支持Ollama与OpenAI兼容服务动态切换 3. 调整默认嵌入模型配置为本地bge-m3模式 4. 优化入库任务去重逻辑与缓存清理机制 5. 完善Docker镜像构建与docker-compose部署配置 6. 修复多项测试用例与兼容性问题 7. 新增运行时配置API，支持动态调整系统参数
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"""LLM 工厂与客户端单元测试：mock httpx 验证 Ollama / OpenAICompatible 两条路径 + 缓存
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通过 monkeypatch 替换 `httpx.AsyncClient` 为伪造的上下文管理器，避免真实网络请求。
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"""
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import httpx
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import pytest
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from unittest.mock import AsyncMock
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from app.core import runtime_settings as rs
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from app.services import llm as llm_mod
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from app.services.llm import (
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LLMClient,
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OllamaLLMClient,
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OpenAICompatibleLLMClient,
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create_llm_client,
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invalidate_llm_client_cache,
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)
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# ---------------------------------------------------------------------------- #
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# httpx 伪造工具
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# ---------------------------------------------------------------------------- #
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class _FakeResponse:
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def __init__(self, status_code: int = 200, json_data: dict | None = None) -> None:
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self.status_code = status_code
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self._json = json_data or {}
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def raise_for_status(self) -> None:
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if self.status_code >= 400:
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raise httpx.HTTPStatusError(
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f"HTTP {self.status_code}", request=httpx.Request("POST", "http://x"), response=self
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)
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def json(self) -> dict:
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return self._json
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class _FakeAsyncClient:
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"""伪造 httpx.AsyncClient 上下文管理器
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用法：把 _FakeAsyncClient.next_response 设为期望响应，构造后 post/get 返回该响应。
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每次 __init__ 记录构造参数到 _FakeAsyncClient.last_kwargs。
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"""
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next_response: _FakeResponse | None = None
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last_kwargs: dict | None = None
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last_instances: list["_FakeAsyncClient"] = []
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def __init__(self, *args, **kwargs) -> None:
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self.post = AsyncMock()
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self.get = AsyncMock()
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if _FakeAsyncClient.next_response is not None:
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self.post.return_value = _FakeAsyncClient.next_response
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self.get.return_value = _FakeAsyncClient.next_response
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_FakeAsyncClient.last_kwargs = kwargs
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_FakeAsyncClient.last_instances.append(self)
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async def __aenter__(self) -> "_FakeAsyncClient":
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return self
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async def __aexit__(self, *args) -> bool:
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return False
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@classmethod
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def reset(cls) -> None:
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cls.next_response = None
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cls.last_kwargs = None
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cls.last_instances = []
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@pytest.fixture
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def patch_httpx(monkeypatch: pytest.MonkeyPatch):
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"""替换 httpx.AsyncClient 为伪造客户端"""
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_FakeAsyncClient.reset()
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monkeypatch.setattr(llm_mod.httpx, "AsyncClient", _FakeAsyncClient)
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# ollama 模块内 OllamaClient 也用 httpx.AsyncClient
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from app.services import ollama as ollama_mod
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monkeypatch.setattr(ollama_mod.httpx, "AsyncClient", _FakeAsyncClient)
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yield _FakeAsyncClient
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_FakeAsyncClient.reset()
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@pytest.fixture
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def isolated_settings_path(tmp_path, monkeypatch: pytest.MonkeyPatch):
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path = tmp_path / "runtime_settings.json"
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monkeypatch.setenv("RUNTIME_SETTINGS_PATH", str(path))
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rs._runtime_settings = None
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invalidate_llm_client_cache()
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yield path
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rs._runtime_settings = None
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invalidate_llm_client_cache()
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# ---------------------------------------------------------------------------- #
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# OllamaLLMClient
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# ---------------------------------------------------------------------------- #
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class TestOllamaLLMClient:
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async def test_generate_returns_response_field(self, patch_httpx):
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"""Ollama 响应：取 data['response'] 字段"""
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patch_httpx.next_response = _FakeResponse(
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json_data={"response": "你好世界", "model": "qwen2.5:1.5b"}
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)
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client = OllamaLLMClient(base_url="http://ollama:11434", model="qwen2.5:1.5b")
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out = await client.generate("prompt")
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assert out == "你好世界"
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# 校验请求 url 与 payload
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fake = patch_httpx.last_instances[-1]
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fake.post.assert_awaited_once()
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call_args = fake.post.await_args
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assert call_args.args[0] == "http://ollama:11434/api/generate"
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payload = call_args.kwargs["json"]
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assert payload["model"] == "qwen2.5:1.5b"
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assert payload["prompt"] == "prompt"
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assert payload["stream"] is False
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async def test_generate_json_mode_adds_format(self, patch_httpx):
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patch_httpx.next_response = _FakeResponse(json_data={"response": "{}"})
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client = OllamaLLMClient(base_url="http://o:11434", model="m")
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await client.generate("p", json_mode=True)
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payload = patch_httpx.last_instances[-1].post.await_args.kwargs["json"]
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assert payload["format"] == "json"
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async def test_is_available_true(self, patch_httpx):
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patch_httpx.next_response = _FakeResponse(status_code=200)
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client = OllamaLLMClient(base_url="http://o:11434", model="m")
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assert await client.is_available() is True
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async def test_is_available_false_on_http_error(self, monkeypatch):
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"""httpx 抛 HTTPError 时 OllamaClient 内部捕获返回 False，OllamaLLMClient 透传"""
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class _RaisingClient:
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async def __aenter__(self):
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return self
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async def __aexit__(self, *args):
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return False
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async def get(self, *args, **kwargs):
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raise httpx.HTTPError("conn refused")
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from app.services import ollama as ollama_mod
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monkeypatch.setattr(ollama_mod.httpx, "AsyncClient", lambda *a, **kw: _RaisingClient())
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client = OllamaLLMClient(base_url="http://o:11434", model="m")
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assert await client.is_available() is False
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# ---------------------------------------------------------------------------- #
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# OpenAICompatibleLLMClient
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# ---------------------------------------------------------------------------- #
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class TestOpenAICompatibleClient:
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async def test_generate_returns_choices_content(self, patch_httpx):
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patch_httpx.next_response = _FakeResponse(
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json_data={"choices": [{"message": {"content": "答案"}}]}
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)
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client = OpenAICompatibleLLMClient(
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base_url="https://api.openai.com/v1", api_key="sk-x", model="gpt-4o-mini"
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)
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out = await client.generate("hello")
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assert out == "答案"
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fake = patch_httpx.last_instances[-1]
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call_args = fake.post.await_args
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assert call_args.args[0] == "https://api.openai.com/v1/chat/completions"
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payload = call_args.kwargs["json"]
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assert payload["model"] == "gpt-4o-mini"
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assert payload["messages"] == [{"role": "user", "content": "hello"}]
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assert payload["stream"] is False
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# Authorization header
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headers = call_args.kwargs["headers"]
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assert headers["Authorization"] == "Bearer sk-x"
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async def test_generate_json_mode_adds_response_format(self, patch_httpx):
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patch_httpx.next_response = _FakeResponse(
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json_data={"choices": [{"message": {"content": "{}"}}]}
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)
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client = OpenAICompatibleLLMClient(
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base_url="https://api.openai.com/v1", api_key="sk-x", model="m"
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)
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await client.generate("p", json_mode=True)
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payload = patch_httpx.last_instances[-1].post.await_args.kwargs["json"]
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assert payload["response_format"] == {"type": "json_object"}
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async def test_generate_empty_choices_returns_empty(self, patch_httpx):
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patch_httpx.next_response = _FakeResponse(json_data={"choices": []})
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client = OpenAICompatibleLLMClient(
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base_url="https://api.openai.com/v1", api_key="sk-x", model="m"
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)
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assert await client.generate("p") == ""
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async def test_is_available_true(self, patch_httpx):
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patch_httpx.next_response = _FakeResponse(status_code=200)
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client = OpenAICompatibleLLMClient(
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base_url="https://api.openai.com/v1", api_key="sk-x", model="m"
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)
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assert await client.is_available() is True
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async def test_is_available_false_on_http_error(self, monkeypatch):
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"""httpx 抛 HTTPError 时返回 False"""
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client = OpenAICompatibleLLMClient(
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base_url="https://api.openai.com/v1", api_key="sk-x", model="m"
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)
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class _Raiser:
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async def __aenter__(self):
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raise httpx.HTTPError("conn refused")
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async def __aexit__(self, *args):
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return False
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monkeypatch.setattr(llm_mod.httpx, "AsyncClient", lambda *a, **kw: _Raiser())
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assert await client.is_available() is False
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# ---------------------------------------------------------------------------- #
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# 工厂
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# ---------------------------------------------------------------------------- #
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class TestFactory:
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def test_ollama_provider_returns_ollama_client(self, isolated_settings_path):
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"""runtime_settings 默认 provider=ollama，工厂返回 OllamaLLMClient"""
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# 确保配置走 ollama（默认即 ollama）
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client = create_llm_client("summarize", use_cache=False)
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assert isinstance(client, OllamaLLMClient)
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def test_openai_compatible_provider_returns_openai_client(
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self, isolated_settings_path
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):
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rs.update_runtime_settings(
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{"models": {"query": {"provider": "openai_compatible", "api_key": "sk-x", "model": "gpt-4o-mini"}}}
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)
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client = create_llm_client("query", use_cache=False)
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assert isinstance(client, OpenAICompatibleLLMClient)
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assert client.model == "gpt-4o-mini"
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assert client.api_key == "sk-x"
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def test_openai_compatible_falls_back_to_settings(self, isolated_settings_path):
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"""openai_compatible 时 base_url/api_key 为空用 settings 默认；model 为空用 gpt-4o-mini"""
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# 显式置空 model 以触发工厂默认值（env fallback 会预填 ollama_model）
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rs.update_runtime_settings(
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{"models": {"classify": {"provider": "openai_compatible", "model": ""}}}
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)
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from app.config import settings
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client = create_llm_client("classify", use_cache=False)
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assert isinstance(client, OpenAICompatibleLLMClient)
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assert client.api_key == settings.openai_api_key
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assert client.model == "gpt-4o-mini" # openai_compatible 默认模型
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def test_cache_returns_same_instance(self, isolated_settings_path):
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"""use_cache=True 时同 purpose 复用单例"""
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c1 = create_llm_client("summarize")
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c2 = create_llm_client("summarize")
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assert c1 is c2
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def test_cache_different_purposes_different_instances(self, isolated_settings_path):
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c1 = create_llm_client("summarize")
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c2 = create_llm_client("query")
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assert c1 is not c2
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def test_no_cache_returns_new_instance_each_call(self, isolated_settings_path):
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c1 = create_llm_client("summarize", use_cache=False)
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c2 = create_llm_client("summarize", use_cache=False)
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assert c1 is not c2
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def test_invalidate_clears_cache(self, isolated_settings_path):
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c1 = create_llm_client("summarize")
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invalidate_llm_client_cache()
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c2 = create_llm_client("summarize")
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assert c1 is not c2
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def test_invalidate_single_purpose(self, isolated_settings_path):
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c_sum = create_llm_client("summarize")
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c_qry = create_llm_client("query")
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invalidate_llm_client_cache("summarize")
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# summarize 已清，query 未清
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assert create_llm_client("summarize") is not c_sum
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assert create_llm_client("query") is c_qry
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def test_client_implements_protocol(self, isolated_settings_path):
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"""OllamaLLMClient 与 OpenAICompatibleLLMClient 都满足 LLMClient Protocol"""
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c1 = create_llm_client("summarize", use_cache=False)
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assert isinstance(c1, LLMClient)
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rs.update_runtime_settings(
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{"models": {"summarize": {"provider": "openai_compatible", "api_key": "k"}}}
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)
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c2 = create_llm_client("summarize", use_cache=False)
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assert isinstance(c2, LLMClient)
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