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）
140 lines
4.9 KiB
Python
140 lines
4.9 KiB
Python
"""
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Agent 编排层 —— 调度四层架构完成分析
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Layer 1: Planner 意图规划
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Layer 2: Explorer 自适应探索
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Layer 3: Insight 异常洞察
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Layer 4: Context 上下文记忆
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"""
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from typing import Optional
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from config import DB_PATH, MAX_EXPLORATION_ROUNDS
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from schema_extractor import extract_schema, schema_to_text
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from sandbox_executor import SandboxExecutor
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from planner import Planner
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from explorer import Explorer
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from insights import InsightEngine, quick_detect
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from reporter import ReportGenerator
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from context import ContextManager
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class DataAnalysisAgent:
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"""
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数据分析 Agent
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四层架构：
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1. Planner - 理解用户意图，生成分析计划
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2. Explorer - 基于计划多轮迭代探索
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3. Insights - 从结果中检测异常、输出主动洞察
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4. Context - 管理多轮对话上下文
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Agent 负责编排这四层，从问题到报告。
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"""
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def __init__(self, db_path: str):
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# 数据层
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self.db_path = db_path
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self.schema = extract_schema(db_path)
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self.schema_text = schema_to_text(self.schema)
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self.executor = SandboxExecutor(db_path)
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# 四层组件
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self.planner = Planner()
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self.explorer = Explorer(self.executor)
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self.insight_engine = InsightEngine()
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self.reporter = ReportGenerator()
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self.context = ContextManager()
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def analyze(self, question: str, max_rounds: Optional[int] = None) -> str:
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"""
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完整分析流程
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Args:
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question: 用户分析问题
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max_rounds: 最大探索轮数（默认用配置值）
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Returns:
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格式化的分析报告
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"""
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max_rounds = max_rounds or MAX_EXPLORATION_ROUNDS
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print(f"\n{'='*60}")
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print(f"📊 {question}")
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print(f"{'='*60}")
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# ── Layer 0: 检查上下文 ──────────────────────────
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prev_context = self.context.get_context_for(question)
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if prev_context:
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print("📎 发现历史分析上下文，将结合之前的发现")
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# ── Layer 1: 意图规划 ────────────────────────────
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print("\n🎯 [Layer 1] 意图规划...")
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plan = self.planner.plan(question, self.schema_text)
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analysis_type = plan.get("analysis_type", "unknown")
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dimensions = plan.get("dimensions", [])
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rationale = plan.get("rationale", "")
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print(f" 类型: {analysis_type}")
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print(f" 维度: {', '.join(dimensions) if dimensions else '自动发现'}")
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print(f" 理由: {rationale[:80]}{'...' if len(rationale) > 80 else ''}")
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# ── Layer 2: 自适应探索 ──────────────────────────
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print(f"\n🔍 [Layer 2] 自适应探索 (最多 {max_rounds} 轮)...")
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steps = self.explorer.explore(plan, self.schema_text, max_rounds=max_rounds)
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successful = sum(1 for s in steps if s.success)
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print(f"\n 完成: {len(steps)} 轮, {successful} 条成功查询")
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# ── Layer 3: 异常洞察 ────────────────────────────
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print("\n🔎 [Layer 3] 异常洞察...")
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# 先做规则检测
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rule_alerts = quick_detect(steps)
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for alert in rule_alerts:
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print(f" {alert}")
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# 再做 LLM 深度分析
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insights = self.insight_engine.analyze(steps, question)
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if insights:
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print(f" 发现 {len(insights)} 条洞察")
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for insight in insights:
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print(f" {insight}")
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else:
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print(" 未发现异常")
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# ── 生成报告 ────────────────────────────────────
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print("\n📝 正在生成报告...")
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report = self.reporter.generate(question, plan, steps, insights)
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# 追加主动洞察
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if insights:
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insight_text = self.insight_engine.format_insights(insights)
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report += f"\n\n---\n\n{insight_text}"
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# ── Layer 4: 记录上下文 ──────────────────────────
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self.context.add_session(
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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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return report
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def get_schema(self) -> str:
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"""获取 Schema 文本"""
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return self.schema_text
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def get_history(self) -> str:
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"""获取分析历史摘要"""
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return self.context.get_history_summary()
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def get_audit(self) -> str:
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"""获取执行审计日志"""
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return self.executor.get_execution_summary()
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def clear_history(self):
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"""清空分析历史"""
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self.context.clear()
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