kplam
2ab8b56a01
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
180 lines
6.4 KiB
Python
180 lines
6.4 KiB
Python
"""LLM 客户端抽象层
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提供统一的 `generate(prompt, json_mode)` 接口，底层实现可切换：
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- OllamaLLMClient：走 Ollama HTTP /api/generate（本地或远程 Ollama 服务）
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- OpenAICompatibleLLMClient：走 OpenAI 兼容 /v1/chat/completions（OpenAI / DeepSeek / 智谱 / Qwen API 等）
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工厂 `create_llm_client(purpose)` 根据 runtime_settings 选择实现与参数。
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所有客户端共享同一接口，业务侧无需感知底层协议差异。
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"""
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from __future__ import annotations
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from typing import Literal, Protocol, runtime_checkable
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import httpx
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import structlog
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from app.config import settings
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from app.core.runtime_settings import LlmProviderConfig, get_runtime_settings
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from app.services.ollama import OllamaClient
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logger = structlog.get_logger()
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LlmPurpose = Literal["summarize", "query", "classify"]
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@runtime_checkable
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class LLMClient(Protocol):
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"""LLM 客户端统一接口"""
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async def generate(self, prompt: str, json_mode: bool = False) -> str:
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"""生成文本
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Args:
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prompt: 输入提示词
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json_mode: True 时约束输出为 JSON（不支持时降级为普通生成）
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Returns:
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生成的文本内容
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"""
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...
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async def is_available(self) -> bool:
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"""检查服务是否可用"""
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...
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class OllamaLLMClient:
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"""Ollama HTTP API 客户端（包装现有 OllamaClient，便于统一接口）"""
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def __init__(self, base_url: str, model: str, timeout: float = 120.0) -> None:
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self._inner = OllamaClient(base_url=base_url, model=model, timeout=timeout)
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async def generate(self, prompt: str, json_mode: bool = False) -> str:
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return await self._inner.generate(prompt, json_mode=json_mode)
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async def is_available(self) -> bool:
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return await self._inner.is_available()
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class OpenAICompatibleLLMClient:
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"""OpenAI 兼容 chat/completions 客户端
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适用于 OpenAI 官方 API、DeepSeek、智谱 ChatGLM、Qwen DashScope、Moonshot 等
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所有兼容 OpenAI /v1/chat/completions 协议的服务。
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"""
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def __init__(self, base_url: str, api_key: str, model: str, timeout: float = 120.0, temperature: float = 0.3) -> None:
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self.base_url = base_url.rstrip("/")
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self.api_key = api_key
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self.model = model
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self.timeout = timeout
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self.temperature = temperature
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async def generate(self, prompt: str, json_mode: bool = False) -> str:
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url = f"{self.base_url}/chat/completions"
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payload: dict = {
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"model": self.model,
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"messages": [{"role": "user", "content": prompt}],
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"temperature": self.temperature,
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"stream": False,
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}
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if json_mode:
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payload["response_format"] = {"type": "json_object"}
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headers = {
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"Authorization": f"Bearer {self.api_key}",
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"Content-Type": "application/json",
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}
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async with httpx.AsyncClient(timeout=self.timeout) as client:
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resp = await client.post(url, json=payload, headers=headers)
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resp.raise_for_status()
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data = resp.json()
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# OpenAI 标准响应结构：choices[0].message.content
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choices = data.get("choices") or []
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if not choices:
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logger.warning("OpenAI 兼容响应无 choices", model=self.model, raw_keys=list(data.keys()))
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return ""
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message = choices[0].get("message") or {}
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content = message.get("content") or ""
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logger.debug("OpenAI 兼容生成完成", model=self.model, output_length=len(content))
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return content
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async def is_available(self) -> bool:
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"""简单探测：调 /models 列表接口（多数 OpenAI 兼容服务支持）"""
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try:
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async with httpx.AsyncClient(timeout=5.0) as client:
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resp = await client.get(
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f"{self.base_url}/models",
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headers={"Authorization": f"Bearer {self.api_key}"},
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)
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return resp.status_code == 200
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except httpx.HTTPError:
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return False
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# ---------------------------------------------------------------------------- #
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# 工厂
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# ---------------------------------------------------------------------------- #
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# 进程级客户端缓存（避免每请求新建 httpx client）
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_client_cache: dict[str, LLMClient] = {}
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def _resolve_provider_config(purpose: LlmPurpose) -> LlmProviderConfig:
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"""从 runtime_settings 取指定用途的 LLM 配置"""
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rt = get_runtime_settings()
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return getattr(rt.models, purpose)
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def _build_client(purpose: LlmPurpose) -> LLMClient:
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"""按 runtime_settings 构造 LLM 客户端"""
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cfg = _resolve_provider_config(purpose)
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if cfg.provider == "ollama":
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base_url = cfg.base_url or settings.ollama_base_url
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model = cfg.model or settings.ollama_model
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return OllamaLLMClient(base_url=base_url, model=model, timeout=cfg.timeout)
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if cfg.provider == "openai_compatible":
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base_url = cfg.base_url or settings.openai_base_url
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api_key = cfg.api_key or settings.openai_api_key
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# openai_compatible 默认模型：若 cfg.model 为空，退化到 OpenAI 通用 chat 模型
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model = cfg.model or "gpt-4o-mini"
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if not api_key:
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logger.warning("OpenAI 兼容 provider 缺少 api_key，调用大概率会失败", purpose=purpose)
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return OpenAICompatibleLLMClient(
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base_url=base_url, api_key=api_key, model=model, timeout=cfg.timeout, temperature=cfg.temperature
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)
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raise ValueError(f"未知 LLM provider: {cfg.provider}")
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def create_llm_client(purpose: LlmPurpose, *, use_cache: bool = True) -> LLMClient:
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"""创建指定用途的 LLM 客户端
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Args:
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purpose: 用途（summarize / query / classify）
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use_cache: True 时复用进程级客户端单例（默认）；False 每次新建（测试用）
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Returns:
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LLMClient 实例
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"""
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if not use_cache:
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return _build_client(purpose)
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cache_key = f"{purpose}"
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if cache_key not in _client_cache:
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_client_cache[cache_key] = _build_client(purpose)
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return _client_cache[cache_key]
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def invalidate_llm_client_cache(purpose: LlmPurpose | None = None) -> None:
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"""清除客户端缓存（runtime_settings 更新后调用，确保后续读取新配置）"""
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if purpose is None:
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_client_cache.clear()
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else:
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_client_cache.pop(purpose, None)
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