# -*- coding: utf-8 -*- """推理后端实现 —— 本地 70B 模型接入与推理封装（issue #44）。 在 `gateway.InferenceBackend` 抽象之上交付**真实可用的本地后端**： - OpenAI 兼容接口（vLLM / TGI 等本地推理服务，`/v1/chat/completions`）， 仅用标准库 urllib，无第三方依赖； - 参数化：endpoint / model / timeout / max_tokens / temperature / context 引用注入； - **数据不出厂**（PRD 5.4）：敏感/核心内容走本地后端，云端仅接收脱敏内容； - 未配置 endpoint 时进入 dry-run 占位模式（保持与旧 LocalBackend 一致的 可测试行为，供端到端演示与联调）。 业务代码只依赖 `gateway.InferenceBackend.generate(prompt, context)`， 切换后端 = 换实现（见 `LLMGateway(local=...)`）。 """ from __future__ import annotations import json import os import time import urllib.request from typing import Callable, Optional, Sequence from .gateway import InferenceBackend class Local70BBackend(InferenceBackend): """本地 70B 推理后端（OpenAI 兼容 vLLM/TGI，参数化）。""" name = "local-70b" def __init__( self, endpoint: str = "", model: str = "iaop-local-70b", timeout_seconds: float = 60.0, max_tokens: int = 1024, temperature: float = 0.1, echo_context: bool = True, ) -> None: self.endpoint = (endpoint or "").rstrip("/") self.model = model self.timeout = float(timeout_seconds) self.max_tokens = int(max_tokens) self.temperature = float(temperature) self.echo_context = echo_context # ------------------------------------------------------------------ def generate(self, prompt: str, context: Sequence[str]) -> str: """根据 prompt 与 RAG 上下文生成回答。 - 未配置 endpoint：dry-run 占位（回显 prompt 前 40 字符 + 来源引用）； - 已配置：调用本地 OpenAI 兼容服务（/v1/chat/completions）。 """ if not self.endpoint: return self._dry_run(prompt, context) payload = { "model": self.model, "messages": [ {"role": "system", "content": self._system_prompt(context)}, {"role": "user", "content": prompt}, ], "max_tokens": self.max_tokens, "temperature": self.temperature, } body = self._post_json("/v1/chat/completions", payload) try: return body["choices"][0]["message"]["content"] except (KeyError, IndexError, TypeError): raise RuntimeError( f"本地推理服务响应格式异常: {str(body)[:200]}") # ------------------------------------------------------------------ def _system_prompt(self, context: Sequence[str]) -> str: """把 RAG 引用注入 system 提示（引用溯源，PRD 5.4）。""" refs = "\n".join(f"- {c}" for c in (context or [])) base = "你是工业 AI 助手。回答须基于给定资料并标注来源。" return f"{base}\n参考资料：\n{refs}" if refs else base def _dry_run(self, prompt: str, context: Sequence[str]) -> str: head = f"[本地70B占位] {prompt[:40]}" if self.echo_context: for i, src in enumerate(context[:3], 1): head += f"\n[来源: {src}]" return head def _post_json(self, path: str, payload: dict) -> dict: """向后端推理服务发起 JSON POST（标准库 urllib）。""" url = self.endpoint + path data = json.dumps(payload).encode("utf-8") req = urllib.request.Request( url, data=data, headers={"Content-Type": "application/json"}) with urllib.request.urlopen(req, timeout=self.timeout) as resp: raw = resp.read().decode("utf-8") return json.loads(raw) if raw else {} def health(self) -> dict: """后端健康信息（本地推理服务可探测 /health）。""" base = { "backend": self.name, "model": self.model, "endpoint": self.endpoint or "(dry-run)", } if not self.endpoint: base["status"] = "dry-run" return base try: started = time.monotonic() with urllib.request.urlopen( self.endpoint + "/health", timeout=self.timeout) as resp: base["status"] = "ok" if resp.status == 200 else f"http-{resp.status}" base["latency_ms"] = round((time.monotonic() - started) * 1000, 2) except Exception as exc: # noqa: BLE001 - 健康探测失败仅记录 base["status"] = f"error: {exc}" return base class CloudApiBackend(InferenceBackend): """云端 API 推理后端（Qwen / DeepSeek 等 OpenAI 兼容）—— issue #45。 **安全网关约束（PRD 5.4）**： - 仅接收 **DLP 放行**的脱敏/通用内容（上游 `LLMGateway` 主编排出站检查 + cloud 分支输出 DLP 复查）； - API Key 从**环境变量**读取（`api_key_env`），不硬编码、不落日志； - 可选 `safety_checker` 出站复查钩子（fail-closed：复查拒绝 → 拦截占位， 不调用上游）。 """ name = "cloud-api" def __init__( self, endpoint: str = "", api_key_env: str = "", model: str = "deepseek-chat", timeout_seconds: float = 60.0, max_tokens: int = 1024, temperature: float = 0.1, safety_checker: Optional[Callable[[str], bool]] = None, ) -> None: self.endpoint = (endpoint or "").rstrip("/") self.api_key_env = api_key_env self.model = model self.timeout = float(timeout_seconds) self.max_tokens = int(max_tokens) self.temperature = float(temperature) # 出站安全复查：返回 False 即拦截（fail-closed） self.safety_checker = safety_checker self._api_key = os.environ.get(api_key_env, "") if api_key_env else "" # ------------------------------------------------------------------ def generate(self, prompt: str, context: Sequence[str]) -> str: """生成回答。安全网关：safety_checker 拒绝 → 拦截占位，不调用上游。""" if self.safety_checker is not None and not self.safety_checker(prompt): return "[云端安全网关拦截] 出站复查未通过，已拦截（数据不出厂）。" if not self.endpoint: return self._dry_run(prompt, context) payload = { "model": self.model, "messages": [ {"role": "system", "content": self._system_prompt(context)}, {"role": "user", "content": prompt}, ], "max_tokens": self.max_tokens, "temperature": self.temperature, } body = self._post_json("/v1/chat/completions", payload) try: return body["choices"][0]["message"]["content"] except (KeyError, IndexError, TypeError): raise RuntimeError( f"云端 API 响应格式异常: {str(body)[:200]}") # ------------------------------------------------------------------ def _system_prompt(self, context: Sequence[str]) -> str: refs = "\n".join(f"- {c}" for c in (context or [])) base = "你是工业 AI 助手。回答须基于给定资料并标注来源。" return f"{base}\n参考资料：\n{refs}" if refs else base def _dry_run(self, prompt: str, context: Sequence[str]) -> str: head = f"[云端API占位] {prompt[:40]}" for i, src in enumerate(context[:3], 1): head += f"\n[来源: {src}]" if self.safety_checker is not None: head += "\n[安全网关: 已复查放行]" return head def _post_json(self, path: str, payload: dict) -> dict: """向后端推理服务发起 JSON POST（Bearer 认证，Key 来自环境变量）。""" url = self.endpoint + path data = json.dumps(payload).encode("utf-8") headers = {"Content-Type": "application/json"} if self._api_key: headers["Authorization"] = f"Bearer {self._api_key}" req = urllib.request.Request(url, data=data, headers=headers) with urllib.request.urlopen(req, timeout=self.timeout) as resp: raw = resp.read().decode("utf-8") return json.loads(raw) if raw else {} def health(self) -> dict: """后端健康信息（含安全网关状态，不含密钥）。""" return { "backend": self.name, "model": self.model, "endpoint": self.endpoint or "(dry-run)", "api_key_configured": bool(self._api_key), "safety_checker": self.safety_checker is not None, "status": "dry-run" if not self.endpoint else "configured", }