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✨ feat!(llm): 重构并升级大语言模型服务为全新 AI 智能体框架 (#2146)
* ✨ feat!(llm): 重构并升级大语言模型服务为全新 AI 智能体框架 - 【重构】将原 services/llm 重构并迁移至全新的 services/ai 架构，提供向下兼容垫片 - 【新增】引入 Agent、Team、Workflow 三大智能体与工作流编排范式 - 【新增】引入基于 RAG 的长期向量记忆与中期槽位记忆系统 - 【新增】引入基于 Docker 的安全代码执行沙箱环境 - 【新增】支持 MCP 协议，允许动态管理和调用 MCP 服务 - 【新增】引入输入输出安全合规护栏与自愈反思机制 - 【优化】重构并优化多厂商 API 适配器 (Gemini, OpenAI, DeepSeek, GLM 等) - 【优化】优化日志脱敏与 Token 预估机制 - 【移除】移除旧版 llm default 和 llm reset-key 命令，新增 llm mcp 管理命令 * 🔧 chore(deps): 更新项目依赖与配置 - 添加 mcp、jieba 和 aiodocker 依赖到配置文件及 requirements.txt - 在 pyright 配置中设置 reportMissingImports 为 none - 调整 .gitignore 中 resources 目录的忽略规则 * ♻️ refactor(tools): 重构工具终止机制并清理知识库日志输出 - 统一使用 `context.state["__end_run__"]` 替代 `EndRunResult` 控制任务结束 - 移除文件系统和向量知识库检索工具中 `ToolResult` 的 `.with_log` 调用 - 调整指令处理器（Directive）的返回值为 `tool_res.output` - 修复部分类型检查警告并优化联合类型判断语法 * ♻️ refactor(tools): 重构工具副作用指令与控制流熔断机制 - 引入 `DirectivePayload` 及 `ToolResult` 的子类以结构化表达工具副作用 - 移除通过 `context.state` 传递魔术变量的隐式控制流设计 - 重构 `DirectiveManager` 处理器接口，直接在处理器中修改 `AgentState` 并构建 `AgentRunResult` - 在 `StandardAgentExecutor` 中统一通过 `directive_manager` 调度工具返回的副作用指令 - 补全 `MessageBuilder` 中部分核心方法的文档注释 * 🐛 fix(sandbox): 修复 Docker 沙箱容器状态检测与会话清理逻辑 -【修复】修正 `is_alive` 中直接读取私有属性的问题，改用 `show()` 返回值 -【修复】解决 `execute_code` 中缓存的执行器与当前会话不一致的问题 -【优化】在清理工作区前增加容器存活检测，避免向已死容器发送请求 -【优化】创建容器时增加运行状态校验，若已停止则自动从缓存中移除并重建 -【优化】优化容器销毁和清理逻辑，静默处理容器不存在 (404) 的异常 * 📝 docs(core): 补充核心模块初始化方法的文档注释 * 🚨 auto fix by pre-commit hooks --------- Co-authored-by: webjoin111 <455457521@qq.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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parent
bdc1374848
commit
80fc5b86a7
@@ -0,0 +1,641 @@
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import asyncio
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import hashlib
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import json
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import re
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import time
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from typing import Any, ClassVar, cast
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from aiocache import SimpleMemoryCache
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import httpx
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from zhenxun.services.ai.core.exceptions import (
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ConfigurationException,
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LLMException,
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NetworkTimeoutException,
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UpstreamServerException,
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)
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from zhenxun.services.ai.core.messages import (
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AudioPart,
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AudioResponse,
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ChatRequest,
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ChatResponse,
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EmbeddingRequest,
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EmbeddingResponse,
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FilePart,
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ImagePart,
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ImageRequest,
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ImageResponse,
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RerankRequest,
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RerankResponse,
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SpeechRequest,
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TextPart,
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VideoPart,
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)
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from zhenxun.services.ai.core.models import (
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LLMContext,
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ModelCapabilities,
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ModelIdentity,
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ModelModality,
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)
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from zhenxun.services.ai.core.options import (
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GenerationConfig,
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)
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from zhenxun.services.ai.core.protocols.middleware import LLMMiddleware, NextCall
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from zhenxun.services.ai.llm.adapters.base import (
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BaseAdapter,
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RequestData,
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process_image_data,
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)
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from zhenxun.services.ai.llm.system.models import RetryConfig
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from zhenxun.services.ai.llm.system.network import (
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HealthManager,
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LLMHttpClient,
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)
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from zhenxun.services.log import logger
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from zhenxun.utils.http_utils import AsyncHttpx
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from zhenxun.utils.log_sanitizer import sanitize_for_logging
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from zhenxun.utils.pydantic_compat import (
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dump_json_safely,
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model_copy,
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model_dump,
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parse_as,
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)
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_LLM_API_CACHE = SimpleMemoryCache(namespace="zhenxun_llm_api_cache")
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class MiddlewarePipeline:
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"""中间件管线组装器"""
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def __init__(self):
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self.middlewares: list[LLMMiddleware] = []
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def add_middleware(self, middleware: LLMMiddleware) -> None:
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"""按顺序追加中间件，先加入的将处在调用链的最外层"""
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self.middlewares.append(middleware)
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def build(self, terminal_handler: NextCall[Any, Any]) -> NextCall[Any, Any]:
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handler = terminal_handler
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for middleware in reversed(self.middlewares):
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def _wrap(
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mw: LLMMiddleware[Any, Any], next_c: NextCall[Any, Any]
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) -> NextCall[Any, Any]:
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async def _handler(context: LLMContext[Any, Any]) -> Any:
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return await mw(context, next_c)
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return _handler
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handler = _wrap(middleware, handler)
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return handler
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class LLMCacheMiddleware:
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"""
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大模型极速缓存中间件：
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只在开发者显式配置了 __cache_ttl__ 时生效。
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拦截高成本的 API 网络请求，直接返回本地缓存。
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"""
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_RESPONSE_TYPE_MAP: ClassVar[dict[type, type]] = {
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ChatRequest: ChatResponse,
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EmbeddingRequest: EmbeddingResponse,
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ImageRequest: ImageResponse,
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SpeechRequest: AudioResponse,
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RerankRequest: RerankResponse,
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}
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def __init__(self, model_name: str):
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self.model_name = model_name
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def _generate_cache_key(self, context: LLMContext[Any, Any]) -> str:
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"""构造绝对纯净的请求哈希键，剔除时间戳等干扰项"""
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payload = {
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"model": self.model_name,
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"type": type(context.request).__name__,
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"request": context.request.get_cache_hash_payload(),
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}
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json_str = json.dumps(payload, sort_keys=True, ensure_ascii=False, default=str)
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return hashlib.md5(json_str.encode("utf-8")).hexdigest()
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async def __call__(
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self, context: LLMContext[Any, Any], next_call: NextCall[Any, Any]
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) -> Any:
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ttl = None
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if hasattr(context.request, "config") and context.request.config:
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ttl = getattr(context.request.config, "custom_kwargs", {}).get(
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"__cache_ttl__"
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)
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if ttl is None:
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return await next_call(context)
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cache_key = self._generate_cache_key(context)
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cached_data = await _LLM_API_CACHE.get(cache_key)
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if cached_data is not None:
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logger.debug(
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f"⚡ [LLMCache] 命中本地极速缓存 - "
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f"model: {self.model_name}, type: {type(context.request).__name__}"
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)
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response_type = self._RESPONSE_TYPE_MAP.get(type(context.request))
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if response_type:
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cached_resp = parse_as(response_type, cached_data)
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else:
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cached_resp = cached_data
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if isinstance(cached_resp, ChatResponse):
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cached_resp.usage_info = {
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"is_cache_hit": True,
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"total_tokens": 0,
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"prompt_tokens": 0,
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"completion_tokens": 0,
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"promptTokenCount": 0,
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"candidatesTokenCount": 0,
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"totalTokenCount": 0,
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}
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return cached_resp
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response = await next_call(context)
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await _LLM_API_CACHE.set(cache_key, model_dump(response), ttl=ttl)
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return response
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class FailoverAndRetryMiddleware:
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"""
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故障转移与重试中间件：
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结合了密钥轮询 (Key Selection) 与异常退避重试 (Retry) 逻辑。
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"""
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def __init__(
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self,
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retry_config: RetryConfig,
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health_manager: HealthManager,
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provider_name: str,
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api_keys: list[str],
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):
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self.retry_config = retry_config
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self.health_manager = health_manager
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self.provider_name = provider_name
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self.api_keys = api_keys
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self._failed_keys: set[str] = set()
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def _raise_with_masked_key(self, e: LLMException, api_key: str) -> None:
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"""辅助函数：掩码 API Key 并原样抛出异常，防止密钥泄露"""
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masked = f"{api_key[:8]}..." if api_key else "unknown"
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if isinstance(e.details, dict):
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e.details["api_key"] = masked
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raise e.with_traceback(None) from None
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async def __call__(
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self, context: LLMContext[Any, Any], next_call: NextCall[Any, Any]
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) -> Any:
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last_exception: Exception | None = None
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is_routed = context.request.extra.get("_is_routed_call", False)
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max_retries = 0 if is_routed else self.retry_config.max_retries
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total_attempts = max_retries + 1
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for attempt in range(total_attempts):
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selected_key = await self.health_manager.get_next_available_key(
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self.provider_name,
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self.api_keys,
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exclude_keys=self._failed_keys,
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strict_mode=is_routed,
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)
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if not selected_key:
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raise ConfigurationException(
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f"提供商 {self.provider_name} 无可用 API Key"
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)
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context.runtime_state["api_key"] = selected_key
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context.runtime_state["provider_name"] = self.provider_name
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try:
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context.runtime_state["attempt"] = attempt + 1
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return await next_call(context)
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except LLMException as e:
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last_exception = e
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await self.health_manager.record_key_failure(
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self.provider_name, selected_key, e
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)
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if e.should_rotate_key:
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self._failed_keys.add(selected_key)
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if not e.is_retryable:
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self._raise_with_masked_key(e, selected_key)
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if attempt == total_attempts - 1:
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self._raise_with_masked_key(e, selected_key)
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wait_time = self.retry_config.retry_delay
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if self.retry_config.exponential_backoff:
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wait_time *= 2**attempt
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logger.warning(
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f"请求失败，{wait_time:.2f}秒后重试"
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f" (第{attempt + 1}/{max_retries}次重试): {e}"
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)
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await asyncio.sleep(wait_time)
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except Exception as e:
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logger.error(f"非预期异常，停止重试: {e}", e=e)
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raise e.with_traceback(None) from None
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if last_exception:
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raise last_exception.with_traceback(None) from None
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raise LLMException("重试循环异常结束").with_traceback(None) from None
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class LoggingMiddleware:
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"""
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日志中间件：
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职责归位后，统一负责 HTTP Payload 的生成、安全脱敏以及完整生命周期的日志记录。
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"""
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def __init__(
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self,
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provider_name: str,
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model_name: str,
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adapter: BaseAdapter,
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identity: ModelIdentity,
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log_context: str = "Generation",
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):
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self.provider_name = provider_name
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self.model_name = model_name
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self.adapter = adapter
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self.identity = identity
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self.log_context = log_context
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async def __call__(
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self, context: LLMContext[Any, Any], next_call: NextCall[Any, Any]
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) -> Any:
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attempt = context.runtime_state.get("attempt", 1)
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api_key = context.runtime_state.get("api_key", "unknown")
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masked_key = f"{api_key[:8]}..."
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logger.info(
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f"🌐 发起LLM请求 (尝试 {attempt}) - {self.provider_name}/{self.model_name} "
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f"[{self.log_context}] Key: {masked_key}"
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)
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request_data = await self.adapter.prepare_payload(
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identity=self.identity,
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api_key=api_key,
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request=context.request,
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)
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context.runtime_state["request_data"] = request_data
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logger.debug(f"📡 请求URL: {request_data.url}")
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logger.debug(f"📋 请求头: {dict(request_data.headers)}")
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if self.identity.api_type == "smart":
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from zhenxun.services.ai.llm.adapters.factory import SmartAdapter
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smart_adapter = cast(SmartAdapter, self.adapter)
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delegate_adapter = smart_adapter._get_delegate_adapter(self.identity)
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sanitizer_req_context = f"{delegate_adapter.api_type}_request"
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else:
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sanitizer_req_context = self.adapter.log_sanitization_context
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sanitized_body = sanitize_for_logging(
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request_data.body, context=sanitizer_req_context
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)
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if request_data.files and isinstance(sanitized_body, dict):
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file_info: list[str] = []
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file_count = 0
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if isinstance(request_data.files, list):
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file_count = len(request_data.files)
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for key, value in request_data.files:
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filename = (
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value[0]
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if isinstance(value, tuple) and len(value) > 0
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else "..."
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)
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file_info.append(f"{key}='{filename}'")
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elif isinstance(request_data.files, dict):
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file_count = len(request_data.files)
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file_info = list(request_data.files.keys())
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sanitized_body["[MULTIPART_FILES]"] = f"Count: {file_count} | {file_info}"
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request_body_str = dump_json_safely(
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sanitized_body, ensure_ascii=False, indent=2
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)
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logger.debug(f"📦 请求体: {request_body_str}")
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try:
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start_time = time.monotonic()
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response = await next_call(context)
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duration = (time.monotonic() - start_time) * 1000
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logger.debug(f"🎯 LLM响应成功 [{self.log_context}] 耗时: {duration:.2f}ms")
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return response
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except Exception as e:
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raise e.with_traceback(None) from None
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class HttpExecutionMiddleware:
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"""
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终端 HTTP 执行中间件：
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只负责将上游构建好的 Payload 发送出去，并拦截纯粹的 HTTP 网络故障。
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"""
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def __init__(
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self,
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http_client: LLMHttpClient,
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identity: ModelIdentity,
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health_manager: HealthManager,
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adapter: BaseAdapter,
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):
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self.http_client = http_client
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self.identity = identity
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self.health_manager = health_manager
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self.adapter = adapter
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async def __call__(
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self, context: LLMContext[Any, Any], next_call: NextCall[Any, Any]
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) -> Any:
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api_key = context.runtime_state["api_key"]
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provider_name = self.identity.provider_name
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route_id = f"{self.identity.provider_name}/{self.identity.model_name}"
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request_data: RequestData = context.runtime_state["request_data"]
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if context.cancellation_token:
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context.cancellation_token.raise_if_cancelled()
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start_time = time.monotonic()
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try:
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method = getattr(request_data, "method", "POST").upper()
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req_kwargs = {
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"headers": request_data.headers,
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"timeout": context.request.timeout,
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}
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if method in ("POST", "PUT", "PATCH"):
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if request_data.files:
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req_kwargs["data"] = request_data.body
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req_kwargs["files"] = request_data.files
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else:
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req_kwargs["content"] = json.dumps(
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request_data.body, ensure_ascii=False
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)
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elif method == "GET" and request_data.body:
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req_kwargs["params"] = request_data.body
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post_task = asyncio.create_task(
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self.http_client.request(method, request_data.url, **req_kwargs)
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||||
)
|
||||
|
||||
if context.cancellation_token:
|
||||
context.cancellation_token.link_future(post_task)
|
||||
|
||||
raw_engine_output = await post_task
|
||||
|
||||
logger.debug(f"📥 HTTP响应状态码: {raw_engine_output.status_code}")
|
||||
if exception := self.adapter.handle_http_error(raw_engine_output):
|
||||
error_text = raw_engine_output.content.decode("utf-8", errors="ignore")
|
||||
logger.debug(f"💥 完整错误响应: {error_text}")
|
||||
raise exception.with_traceback(None) from None
|
||||
|
||||
latency = (time.monotonic() - start_time) * 1000
|
||||
await self.health_manager.record_key_success(provider_name, api_key)
|
||||
await self.health_manager.record_route_success(route_id, latency)
|
||||
|
||||
return await self.adapter.parse_payload(
|
||||
identity=self.identity,
|
||||
request=context.request,
|
||||
raw_response=raw_engine_output,
|
||||
)
|
||||
|
||||
except asyncio.CancelledError:
|
||||
logger.warning(f"网络请求已被取消: {request_data.url}")
|
||||
raise
|
||||
except httpx.TimeoutException as e:
|
||||
await self.health_manager.record_route_failure(route_id, e)
|
||||
raise NetworkTimeoutException(f"HTTP请求超时: {e}", cause=e)
|
||||
except httpx.NetworkError as e:
|
||||
await self.health_manager.record_route_failure(route_id, e)
|
||||
raise UpstreamServerException(f"网络连接中断: {e}", cause=e)
|
||||
except LLMException as e:
|
||||
if e.should_failover:
|
||||
await self.health_manager.record_route_failure(route_id, e)
|
||||
raise e.with_traceback(None) from None
|
||||
except Exception as e:
|
||||
logger.error(f"解析响应失败或发生未知错误: {e}")
|
||||
masked_key = (
|
||||
f"{api_key[:8]}...{api_key[-4:] if len(api_key) > 12 else '***'}"
|
||||
if api_key
|
||||
else "N/A"
|
||||
)
|
||||
raise UpstreamServerException(
|
||||
f"网络请求异常: {type(e).__name__} - {e}",
|
||||
details={"api_key": masked_key},
|
||||
cause=e,
|
||||
).with_traceback(None) from None
|
||||
|
||||
|
||||
class ModalityFilterMiddleware:
|
||||
"""模态过滤中间件：负责自动剔除当前模型不支持的多模态输入"""
|
||||
|
||||
def __init__(self, model_name: str, capabilities: ModelCapabilities):
|
||||
self.model_name = model_name
|
||||
self.capabilities = capabilities
|
||||
|
||||
async def __call__(
|
||||
self, context: LLMContext[Any, Any], next_call: NextCall[Any, Any]
|
||||
) -> Any:
|
||||
request = context.request
|
||||
if isinstance(request, ChatRequest):
|
||||
filtered_messages = []
|
||||
_warned_image, _warned_audio, _warned_video = False, False, False
|
||||
for msg in request.messages:
|
||||
new_content = []
|
||||
for part in msg.content:
|
||||
if (
|
||||
isinstance(part, ImagePart)
|
||||
and ModelModality.IMAGE
|
||||
not in self.capabilities.input_modalities
|
||||
):
|
||||
if not _warned_image:
|
||||
logger.warning(
|
||||
f"模型 {self.model_name} 不支持图像输入，"
|
||||
"已自动将图片替换为占位符"
|
||||
)
|
||||
_warned_image = True
|
||||
new_content.append(TextPart(text="<图片>"))
|
||||
elif (
|
||||
isinstance(part, AudioPart)
|
||||
and ModelModality.AUDIO
|
||||
not in self.capabilities.input_modalities
|
||||
):
|
||||
if not _warned_audio:
|
||||
logger.warning(
|
||||
f"模型 {self.model_name} 不支持音频输入，"
|
||||
"已自动将音频替换为占位符"
|
||||
)
|
||||
_warned_audio = True
|
||||
new_content.append(TextPart(text="<音频>"))
|
||||
elif (
|
||||
isinstance(part, VideoPart)
|
||||
and ModelModality.VIDEO
|
||||
not in self.capabilities.input_modalities
|
||||
):
|
||||
if not _warned_video:
|
||||
logger.warning(
|
||||
f"模型 {self.model_name} 不支持视频输入，"
|
||||
"已自动将视频替换为占位符"
|
||||
)
|
||||
_warned_video = True
|
||||
new_content.append(TextPart(text="<视频>"))
|
||||
elif (
|
||||
isinstance(part, FilePart)
|
||||
and ModelModality.FILE not in self.capabilities.input_modalities
|
||||
):
|
||||
new_content.append(TextPart(text="<文件>"))
|
||||
else:
|
||||
new_content.append(part)
|
||||
|
||||
filtered_messages.append(
|
||||
model_copy(msg, update={"content": new_content})
|
||||
)
|
||||
context.request = model_copy(
|
||||
request, update={"messages": filtered_messages}
|
||||
)
|
||||
return await next_call(context)
|
||||
|
||||
|
||||
class ConfigMergeMiddleware:
|
||||
"""配置合并中间件：合并覆盖配置，统一填充默认参数"""
|
||||
|
||||
def __init__(self, generation_config: GenerationConfig | None):
|
||||
self.generation_config = generation_config
|
||||
|
||||
async def __call__(
|
||||
self, context: LLMContext[Any, Any], next_call: NextCall[Any, Any]
|
||||
) -> Any:
|
||||
request = context.request
|
||||
updates = {}
|
||||
|
||||
if hasattr(request, "tools") and getattr(request, "tools", None) is not None:
|
||||
tools = getattr(request, "tools")
|
||||
updates["tools"] = (
|
||||
list(tools.values())
|
||||
if isinstance(tools, dict)
|
||||
else (tools if isinstance(tools, list) else [tools])
|
||||
)
|
||||
|
||||
if hasattr(request, "config"):
|
||||
req_config = getattr(request, "config", None)
|
||||
if isinstance(req_config, GenerationConfig) and self.generation_config:
|
||||
updates["config"] = self.generation_config.merge_with(req_config)
|
||||
elif (
|
||||
req_config is None
|
||||
and self.generation_config
|
||||
and hasattr(request, "messages")
|
||||
):
|
||||
updates["config"] = self.generation_config
|
||||
|
||||
if updates:
|
||||
context.request = model_copy(request, update=updates)
|
||||
|
||||
return await next_call(context)
|
||||
|
||||
|
||||
class ResponseRescueMiddleware:
|
||||
"""响应挽救中间件：对于没有按要求返回图片链接的模型，尝试进行正则兜底下载"""
|
||||
|
||||
async def __call__(
|
||||
self, context: LLMContext[Any, Any], next_call: NextCall[Any, Any]
|
||||
) -> Any:
|
||||
response = await next_call(context)
|
||||
request = context.request
|
||||
|
||||
if isinstance(request, ChatRequest) and isinstance(response, ChatResponse):
|
||||
gen_config = request.config
|
||||
policy = gen_config.validation_policy if gen_config else None
|
||||
should_rescue_image = policy and policy.get("require_image")
|
||||
if (
|
||||
should_rescue_image
|
||||
and not response.images
|
||||
and response.text
|
||||
and gen_config
|
||||
):
|
||||
markdown_matches = re.findall(
|
||||
r"(!?\[.*?\]\((https?://[^\)]+)\))", response.text
|
||||
)
|
||||
if markdown_matches:
|
||||
logger.info(
|
||||
f"检测到 {len(markdown_matches)} 个链接，尝试自动下载清洗。"
|
||||
)
|
||||
current_text = response.text
|
||||
other_parts = [
|
||||
p for p in response.content_parts if not isinstance(p, TextPart)
|
||||
]
|
||||
downloaded_urls = set()
|
||||
for full_tag, url in markdown_matches:
|
||||
try:
|
||||
if url not in downloaded_urls:
|
||||
content = await AsyncHttpx.get_content(url)
|
||||
processed = process_image_data(content)
|
||||
if isinstance(processed, bytes):
|
||||
img_part = ImagePart(raw=processed)
|
||||
else:
|
||||
img_part = ImagePart(path=processed)
|
||||
other_parts.append(img_part)
|
||||
downloaded_urls.add(url)
|
||||
current_text = current_text.replace(full_tag, "")
|
||||
except Exception as exc:
|
||||
logger.warning(f"自动下载图片失败: {url}, 错误: {exc}")
|
||||
response.content_parts = [
|
||||
TextPart(text=current_text.strip()),
|
||||
*other_parts,
|
||||
]
|
||||
return response
|
||||
|
||||
|
||||
class OutputValidationMiddleware:
|
||||
"""输出验证中间件：负责策略校验与自定义格式验证）"""
|
||||
|
||||
async def __call__(
|
||||
self, context: LLMContext[Any, Any], next_call: NextCall[Any, Any]
|
||||
) -> Any:
|
||||
response = await next_call(context)
|
||||
request = context.request
|
||||
|
||||
if isinstance(request, ChatRequest) and isinstance(response, ChatResponse):
|
||||
gen_config = request.config
|
||||
if not gen_config:
|
||||
return response
|
||||
|
||||
if gen_config.response_validator:
|
||||
try:
|
||||
gen_config.response_validator(response)
|
||||
except Exception as exc:
|
||||
raise LLMException(
|
||||
f"响应内容未通过自定义验证器: {exc}",
|
||||
details={"validator_error": str(exc)},
|
||||
).with_traceback(None) from None
|
||||
|
||||
policy = gen_config.validation_policy
|
||||
if policy and policy.get("require_image") and not response.images:
|
||||
prompt_had_image = any(
|
||||
isinstance(p, ImagePart)
|
||||
for msg in request.messages
|
||||
for p in msg.content
|
||||
)
|
||||
if not prompt_had_image:
|
||||
logger.debug("提示词中未包含图片，跳过要求图片返回的重试特判。")
|
||||
else:
|
||||
raise LLMException(
|
||||
"响应验证失败：要求返回图片但未找到图片数据。",
|
||||
details={"policy": policy, "text_response": response.text},
|
||||
)
|
||||
return response
|
||||
Reference in New Issue
Block a user