96927a789d
- Layer 1 Planner: 意图规划，将问题转为结构化分析计划 - Layer 2 Explorer: 自适应探索循环，多轮迭代动态生成 SQL - Layer 3 InsightEngine: 异常检测 + 主动洞察 - Layer 4 ContextManager: 多轮对话上下文记忆 安全设计：AI 只看 Schema + 聚合结果，不接触原始数据。 支持任意 OpenAI 兼容 API（OpenAI / Ollama / DeepSeek / vLLM）
135 lines
4.1 KiB
Python
135 lines
4.1 KiB
Python
"""
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Layer 4: 上下文管理器
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管理多轮对话的分析上下文，让后续问题可以引用之前的发现。
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"""
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import json
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import time
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from dataclasses import dataclass, field
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from typing import Any, Optional
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from explorer import ExplorationStep
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from insights import Insight
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@dataclass
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class AnalysisSession:
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"""一次分析的完整记录"""
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question: str
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plan: dict
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steps: list[ExplorationStep]
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insights: list[Insight]
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report: str
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timestamp: float = field(default_factory=time.time)
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def summary(self) -> str:
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"""生成本次分析的摘要（供后续对话引用）"""
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parts = [f"**问题**: {self.question}"]
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if self.plan:
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parts.append(f"**分析类型**: {self.plan.get('analysis_type', 'unknown')}")
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parts.append(f"**关注维度**: {', '.join(self.plan.get('dimensions', []))}")
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# 核心发现（从成功步骤中提取）
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key_findings = []
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for step in self.steps:
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if step.success and step.rows:
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# 提取最突出的值
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top_row = step.rows[0] if step.rows else {}
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finding = f"{step.purpose}: "
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finding += ", ".join(
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f"{k}={v}" for k, v in top_row.items() if k.lower() not in ("id",)
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)
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key_findings.append(finding)
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if key_findings:
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parts.append("**核心发现**:")
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for f in key_findings[:5]:
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parts.append(f" - {f}")
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# 洞察
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if self.insights:
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parts.append("**主动洞察**:")
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for insight in self.insights[:3]:
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parts.append(f" - {insight}")
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return "\n".join(parts)
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def to_reference_text(self) -> str:
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"""生成供 LLM 使用的上下文文本"""
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return (
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f"## 之前的分析\n\n"
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f"### 问题\n{self.question}\n\n"
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f"### 摘要\n{self.summary()}\n\n"
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f"### 详细发现\n"
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+ "\n".join(
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f"- {step.purpose}: {step.row_count} 行结果"
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for step in self.steps if step.success
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)
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)
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class ContextManager:
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"""上下文管理器：管理多轮对话的分析历史"""
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def __init__(self, max_history: int = 10):
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self.sessions: list[AnalysisSession] = []
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self.max_history = max_history
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def add_session(
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self,
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question: str,
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plan: dict,
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steps: list[ExplorationStep],
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insights: list[Insight],
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report: str,
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) -> AnalysisSession:
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"""记录一次分析"""
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session = AnalysisSession(
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question=question,
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plan=plan,
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steps=steps,
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insights=insights,
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report=report,
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)
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self.sessions.append(session)
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# 保持历史大小
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if len(self.sessions) > self.max_history:
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self.sessions = self.sessions[-self.max_history:]
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return session
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def get_context_for(self, new_question: str) -> Optional[str]:
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"""
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根据新问题，从历史中找到相关上下文。
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简单实现：取最近的分析会话。
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"""
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if not self.sessions:
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return None
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# 取最近 2 轮分析的摘要
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recent = self.sessions[-2:]
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parts = []
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for session in recent:
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parts.append(session.to_reference_text())
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return "\n\n---\n\n".join(parts)
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def get_history_summary(self) -> str:
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"""获取所有历史的摘要"""
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if not self.sessions:
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return "（无历史分析）"
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lines = [f"共 {len(self.sessions)} 次分析："]
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for i, session in enumerate(self.sessions, 1):
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ts = time.strftime("%H:%M", time.localtime(session.timestamp))
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lines.append(f" {i}. [{ts}] {session.question}")
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if session.insights:
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for insight in session.insights[:2]:
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lines.append(f" {insight}")
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return "\n".join(lines)
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def clear(self):
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"""清空历史"""
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self.sessions.clear()
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