7e00b027d4
Phase 3 能力: - DAG 并行执行 (workflow_engine): asyncio.gather 并行执行就绪节点 - Debate 并行 (orchestrator): for 循环改为 asyncio.gather - 粒度进度上报 (workflow_engine + tasks + websocket): Redis 推送 + DB 降级 - 工具结果缓存 (tool_manager): 确定性工具默认开启缓存 - LLM 响应缓存 (core): messages[-4:] + model 哈希，5min TTL AgentChat bug 修复 (Gitea #1-#5): - #1 SSE 降级重复空消息: fallback POST 前移除占位消息 - #2 streamTimeout 泄漏: while 正常退出后 clearTimeout - #3 loading 闪烁: final/error 事件中提前设 loading=false - #4 SSE 事件类型对齐: 确认匹配，未知类型加 console.warn - #5 retryMessage 流式残留: 重试时清理占位消息 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
143 lines
4.9 KiB
Python
143 lines
4.9 KiB
Python
"""
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Agent 工具管理器：包装已有 ToolRegistry，提供 Agent 需要的工具格式转换和执行。
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"""
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from __future__ import annotations
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import hashlib
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import json
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import logging
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from typing import Any, Dict, List, Optional
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from app.services.tool_registry import tool_registry
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logger = logging.getLogger(__name__)
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# 默认确定性工具（结果可缓存）
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_DETERMINISTIC_TOOLS = {
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"file_read", "math_calculate", "database_query",
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"json", "text", "csv", "excel", "pdf", "image",
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}
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class AgentToolManager:
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"""
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为 Agent Runtime 管理工具：
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- 将 ToolRegistry 的工具 schema 转为 OpenAI Function Calling 格式
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- 按 Agent 配置过滤（白名单/黑名单）
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- 执行工具调用并返回结果字符串
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- 工具结果缓存（Redis / 内存 fallback）
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"""
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def __init__(self, include_tools: Optional[List[str]] = None,
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exclude_tools: Optional[List[str]] = None,
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cache_enabled: bool = True,
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cache_tool_whitelist: Optional[List[str]] = None,
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cache_ttl_ms: int = 3600000):
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self._include_tools: set = set(include_tools or [])
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self._exclude_tools: set = set(exclude_tools or [])
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self._cache_enabled = cache_enabled
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self._cache_whitelist: set = set(cache_tool_whitelist or [])
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self._cache_ttl_s = max(1, int(cache_ttl_ms / 1000))
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self._cache_store: Dict[str, str] = {} # 内存 fallback
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def _is_cacheable(self, tool_name: str) -> bool:
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"""判断工具结果是否可缓存。"""
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if not self._cache_enabled:
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return False
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if self._cache_whitelist:
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return tool_name in self._cache_whitelist
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return tool_name in _DETERMINISTIC_TOOLS
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@staticmethod
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def _cache_key(name: str, args: Dict[str, Any]) -> str:
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raw = json.dumps([name, args], sort_keys=True, ensure_ascii=False)
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return f"tool:{name}:{hashlib.sha256(raw.encode()).hexdigest()[:16]}"
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async def _cache_get(self, key: str) -> Optional[str]:
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try:
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from app.core.redis_client import get_redis_client
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redis = get_redis_client()
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if redis:
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return await redis.get(key)
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except Exception:
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pass
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return self._cache_store.get(key)
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async def _cache_set(self, key: str, value: str):
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try:
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from app.core.redis_client import get_redis_client
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redis = get_redis_client()
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if redis:
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await redis.setex(key, self._cache_ttl_s, value)
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return
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except Exception:
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pass
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self._cache_store[key] = value
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def get_tool_schemas(self) -> List[Dict[str, Any]]:
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"""获取 Agent 可用的工具定义列表（OpenAI Function Calling 格式）。"""
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all_schemas = tool_registry.get_all_tool_schemas()
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if not self._include_tools and not self._exclude_tools:
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return all_schemas
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filtered = []
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for schema in all_schemas:
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name = self._extract_tool_name(schema)
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if not name:
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continue
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if self._include_tools and name not in self._include_tools:
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continue
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if name in self._exclude_tools:
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continue
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filtered.append(schema)
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return filtered
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def has_tools(self) -> bool:
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"""是否有可用工具。"""
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return len(self.get_tool_schemas()) > 0
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def tool_names(self) -> List[str]:
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"""可用工具名称列表。"""
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return [
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self._extract_tool_name(s) or "?"
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for s in self.get_tool_schemas()
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]
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async def execute(self, name: str, args: Dict[str, Any]) -> str:
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"""
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执行工具调用（带缓存）。
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优先查找内置工具，其次查找数据库自定义工具（HTTP / Code）。
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Args:
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name: 工具名称
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args: 工具参数字典
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Returns:
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工具执行结果的字符串表示
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"""
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# 缓存检查
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if self._is_cacheable(name):
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ck = self._cache_key(name, args)
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cached = await self._cache_get(ck)
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if cached is not None:
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logger.info("Agent 工具命中缓存: %s", name)
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return cached
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logger.info("Agent 执行工具: %s", name)
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result = await tool_registry.execute_tool(name, args)
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# 缓存写入
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if self._is_cacheable(name):
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ck = self._cache_key(name, args)
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await self._cache_set(ck, result)
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logger.debug("Agent 工具结果已缓存: %s", name)
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return result
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@staticmethod
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def _extract_tool_name(schema: Dict[str, Any]) -> Optional[str]:
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"""从工具 schema 中提取工具名称。"""
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fn = schema.get("function") or schema
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return fn.get("name") if isinstance(fn, dict) else None
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