panda
6467fd4ae5
- OCR: EasyOCR (primary, ch_sim+en) with PaddleOCR fallback for Windows compatibility - Validation: _check_minimum_content() rejects empty-shell JRXML (no band/textField) - Retry: MAX_RETRY 3→5, exhaustion records pending_failure_context for next-turn auto-injection - Finalize: only saves jrxml_versions on pass, preserves last good final_jrxml on fail - Extract JRXML: improved empty markdown block handling and XML fragment fallback - UI: real-time node progress via placeholder updates, initial "analyzing" feedback - UI: use agent_state (full) instead of node_state (partial) for summary card routing - UI: unknown template_type now gives LLM meaningful image context instead of metadata - Docs: updated CLAUDE.md and CODE_GUIDE.md to reflect all v3 changes Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
571 lines
20 KiB
Python
571 lines
20 KiB
Python
"""LangGraph JRXML 生成工作流的节点函数。"""
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import copy
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import json
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import os
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import re
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from datetime import datetime, timezone
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from typing import Dict
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from dotenv import load_dotenv
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from agent.state import AgentState
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from backend.llm import get_llm
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from backend.validation import validate_jrxml
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from prompts.loader import load_prompt
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load_dotenv()
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MAX_RETRY = int(os.getenv("MAX_RETRY", "3"))
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CONTEXT_MAX_TOKENS = int(os.getenv("CONTEXT_MAX_TOKENS", "6000"))
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CONTEXT_KEEP_RECENT = int(os.getenv("CONTEXT_KEEP_RECENT", "4"))
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HISTORY_MAX_SNAPSHOTS = int(os.getenv("HISTORY_MAX_SNAPSHOTS", "10"))
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# ============================================================
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# 核心工作流节点
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# ============================================================
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def process_input(state: AgentState) -> Dict:
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"""记录用户输入到对话历史，重置本轮请求状态。如有上次失败上下文则自动注入。"""
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user_input = state.get("user_input", "")
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# 维护全量对话历史（始终记录原始用户消息）
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full_history = state.get("full_conversation_history", [])
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full_history.append({"role": "user", "content": user_input, "ts": _now_iso()})
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state["full_conversation_history"] = full_history
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# 自动注入上次失败上下文
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pending = state.get("pending_failure_context", {})
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if pending and pending.get("error_msg"):
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failure_note = (
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f"[系统提示] 上次生成失败，以下是失败详情，请基于此修正：\n"
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f"失败原因: {pending['error_msg']}\n"
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f"上次失败的输出:\n{pending.get('bad_jrxml', '（无输出）')}"
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)
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user_input = f"{failure_note}\n\n---\n用户新输入:\n{user_input}"
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state["pending_failure_context"] = {}
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# 维护工作对话历史
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conv_history = state.get("conversation_history", [])
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conv_history.append({"role": "user", "content": user_input})
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state["conversation_history"] = conv_history
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# 重置本轮请求字段
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state["retry_count"] = 0
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state["user_modification_request"] = user_input
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return state
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def save_state_snapshot(state: AgentState) -> Dict:
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"""保存当前状态快照到 history_states，用于撤销操作。最多保留 N 个版本。"""
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snapshots = state.get("history_states", [])
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if not isinstance(snapshots, list):
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snapshots = []
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snapshot = {
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"current_jrxml": state.get("current_jrxml", ""),
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"final_jrxml": state.get("final_jrxml", ""),
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"status": state.get("status", ""),
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"conversation_history": copy.deepcopy(state.get("conversation_history", [])),
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"user_modification_request": state.get("user_modification_request", ""),
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"intent": state.get("intent", ""),
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}
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snapshots.append(snapshot)
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max_snap = HISTORY_MAX_SNAPSHOTS
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if len(snapshots) > max_snap:
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snapshots = snapshots[-max_snap:]
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state["history_states"] = snapshots
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return state
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def classify_intent(state: AgentState) -> Dict:
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"""使用 LLM 对用户输入进行意图分类（8 种意图）。"""
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user_input = state.get("user_input", "")
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has_report = "是" if state.get("current_jrxml", "").strip() else "否"
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intent = "initial_generation"
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try:
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llm = get_llm()
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prompt = load_prompt("intent_classify").format(
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has_report=has_report,
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user_input=user_input[:500],
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)
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resp = llm.invoke(prompt)
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raw = resp.content.strip().lower()
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valid_intents = [
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"initial_generation", "modify_report", "preview_report",
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"export_pdf", "export_jrxml", "undo_modification",
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"consult_question", "reset_session",
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]
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for vi in valid_intents:
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if vi in raw:
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intent = vi
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break
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else:
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# 兜底：有报表 → modify_report，无报表 → initial_generation
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intent = "modify_report" if has_report == "是" else "initial_generation"
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except Exception:
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intent = "modify_report" if has_report == "是" else "initial_generation"
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state["intent"] = intent
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return state
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def handle_consult(state: AgentState) -> Dict:
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"""处理咨询类问题：调用 LLM 直接回答，不走报表生成流程。"""
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user_input = state.get("user_input", "")
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try:
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llm = get_llm()
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prompt = load_prompt("consult").format(question=user_input)
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resp = llm.invoke(prompt)
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answer = resp.content.strip()
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except Exception:
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answer = "抱歉，暂时无法处理您的问题，请稍后再试。"
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state["consult_answer"] = answer
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state["conversation_history"].append({"role": "assistant", "content": answer})
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state["full_conversation_history"].append(
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{"role": "assistant", "content": answer, "ts": _now_iso()}
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)
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return state
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def handle_undo(state: AgentState) -> Dict:
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"""撤销上一步修改：从 history_states 恢复最近一个快照。"""
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snapshots = state.get("history_states", [])
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if not isinstance(snapshots, list) or not snapshots:
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state["conversation_history"].append(
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{"role": "assistant", "content": "没有可撤销的操作。"}
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)
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return state
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prev = snapshots.pop()
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state["history_states"] = snapshots
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state["current_jrxml"] = prev.get("current_jrxml", "")
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state["final_jrxml"] = prev.get("final_jrxml", "")
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state["status"] = prev.get("status", "")
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state["conversation_history"] = prev.get("conversation_history", [])
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state["user_modification_request"] = prev.get("user_modification_request", "")
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state["conversation_history"].append(
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{"role": "assistant", "content": "已撤销上一步修改，恢复到之前的状态。"}
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)
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state["full_conversation_history"].append(
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{"role": "assistant", "content": "已撤销上一步修改。", "ts": _now_iso()}
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)
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return state
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def handle_reset(state: AgentState) -> Dict:
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"""重置当前会话：清空报表相关状态，保留会话信息。"""
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state["current_jrxml"] = ""
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state["final_jrxml"] = ""
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state["status"] = ""
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state["error_msg"] = ""
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state["natural_explanation"] = ""
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state["user_modification_request"] = ""
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state["retrieved_context"] = ""
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state["retry_count"] = 0
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state["compressed_history"] = ""
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state["history_states"] = []
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state["intent"] = "initial_generation"
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state["conversation_history"] = []
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state["conversation_history"].append(
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{"role": "assistant", "content": "会话已重置，请描述您要创建的新报表。"}
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)
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state["full_conversation_history"].append(
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{"role": "assistant", "content": "会话已重置。", "ts": _now_iso()}
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)
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return state
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def count_tokens(state: AgentState) -> int:
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"""使用 tiktoken（gpt-4o 编码器）计算当前上下文 token 数量。"""
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try:
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import tiktoken
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enc = tiktoken.encoding_for_model("gpt-4o")
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except Exception:
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# 回退方案：中英文混合场景下，近似 1 token ≈ 2.5 个字符
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text = json.dumps({
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"history": state.get("conversation_history", [])[-CONTEXT_KEEP_RECENT:],
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"jrxml": state.get("current_jrxml", ""),
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"compressed": state.get("compressed_history", ""),
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}, ensure_ascii=False)
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return len(text) // 2.5
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text = json.dumps({
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"history": state.get("conversation_history", [])[-CONTEXT_KEEP_RECENT:],
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"jrxml": state.get("current_jrxml", ""),
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"compressed": state.get("compressed_history", ""),
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}, ensure_ascii=False)
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return len(enc.encode(text))
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def manage_context(state: AgentState) -> Dict:
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"""当 token 数量超过阈值时，压缩较早的对话轮次。"""
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token_count = count_tokens(state)
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state["current_token_count"] = token_count
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if token_count <= CONTEXT_MAX_TOKENS:
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return state
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full_history = state.get("full_conversation_history", [])
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if len(full_history) <= CONTEXT_KEEP_RECENT:
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return state
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# 最近N轮保留完整，更早的轮次送去压缩
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recent = full_history[-CONTEXT_KEEP_RECENT:]
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older = full_history[:-CONTEXT_KEEP_RECENT]
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if not older:
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return state
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conv_text = json.dumps(older, ensure_ascii=False, indent=2)
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try:
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llm = get_llm()
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prompt = load_prompt("compression").format(conversation_text=conv_text)
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resp = llm.invoke(prompt)
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new_compressed = resp.content.strip()[:300]
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except Exception:
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new_compressed = _simple_compress(older)
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# 合并已有压缩与新压缩
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existing = state.get("compressed_history", "")
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if existing:
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state["compressed_history"] = f"{existing}\n---\n{new_compressed}"
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else:
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state["compressed_history"] = new_compressed
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state["conversation_history"] = list(recent)
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state["current_token_count"] = count_tokens(state)
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return state
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def load_session_node(state: AgentState) -> Dict:
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"""在请求开始时从磁盘加载会话状态。"""
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session_id = state.get("session_id", "")
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if not session_id:
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return state
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try:
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from backend.session import load_session
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data = load_session(session_id)
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if data and data.get("agent_state"):
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saved = data["agent_state"]
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# 恢复核心字段（不覆盖当前请求的 user_input / stage）
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for key in ("conversation_history", "full_conversation_history",
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"current_jrxml", "final_jrxml", "compressed_history",
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"session_name", "created_at", "history_states"):
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if key in saved and key not in ("user_input", "stage"):
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state[key] = saved[key]
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state["session_name"] = data.get("session_name", "")
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state["created_at"] = data.get("created_at", "")
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except Exception:
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pass
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return state
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def save_session_node(state: AgentState) -> Dict:
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"""将当前代理状态持久化到磁盘。"""
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session_id = state.get("session_id", "")
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if not session_id:
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return state
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try:
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from backend.session import save_session
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persistable = {}
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for key in ("conversation_history", "full_conversation_history",
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"current_jrxml", "final_jrxml", "compressed_history",
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"status", "error_msg", "history_states"):
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if key in state:
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persistable[key] = state[key]
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persistable["updated_at"] = _now_iso()
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session_name = state.get("session_name", "")
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if not session_name and state.get("conversation_history"):
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first_user = next(
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(m["content"][:50] for m in state["conversation_history"]
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if m.get("role") == "user"), "")
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if first_user:
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session_name = first_user
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save_session(session_id, persistable, session_name)
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if not state.get("session_name"):
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state["session_name"] = session_name
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state["updated_at"] = persistable["updated_at"]
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except Exception:
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pass
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return state
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def _simple_compress(messages: list[dict]) -> str:
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"""当 LLM 不可用时，基于简单规则的压缩回退方案。"""
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points = []
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for m in messages:
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if m.get("role") == "user":
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points.append(f"用户提问：{m['content'][:100]}")
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return "; ".join(points[-10:])
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def _now_iso() -> str:
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return datetime.now(timezone.utc).isoformat()
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def retrieve(state: AgentState) -> Dict:
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"""在 ChromaDB + 错误知识库中搜索相关的 JRXML 模板和组件。"""
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try:
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from backend.rag_adapter import search_chunks
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from backend.error_kb import search_error_cases
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user_input = state.get("user_input", "")
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context = search_chunks(user_input, k=5)
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# 如果有最近错误，同时搜索错误知识库
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error_msg = state.get("error_msg", "")
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if error_msg:
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error_context = search_error_cases(error_msg, k=2)
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if error_context:
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context = f"{context}\n\n[历史错误修正案例]\n{error_context}"
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state["retrieved_context"] = context
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except Exception:
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state["retrieved_context"] = ""
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return state
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def generate(state: AgentState) -> Dict:
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"""根据用户需求和检索到的上下文生成初始 JRXML。"""
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from langgraph.config import get_stream_writer
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writer = get_stream_writer()
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llm = get_llm()
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prompt = load_prompt("initial_generation").format(
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context=state.get("retrieved_context", ""),
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user_request=state.get("user_input", ""),
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)
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full = []
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for chunk in llm.stream(prompt):
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full.append(chunk)
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writer({"type": "stream", "node": "generate", "text": chunk})
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jrxml = _extract_jrxml("".join(full))
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state["current_jrxml"] = jrxml
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state["conversation_history"].append({"role": "assistant", "content": jrxml})
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return state
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def modify_jrxml(state: AgentState) -> Dict:
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"""根据用户的修改请求修改现有 JRXML。"""
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from langgraph.config import get_stream_writer
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writer = get_stream_writer()
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llm = get_llm()
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# 构建对话上下文：压缩摘要 + 最近对话
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compressed = state.get("compressed_history", "")
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recent = state.get("conversation_history", [])[-6:]
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conv_parts = []
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if compressed:
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conv_parts.append(f"[早期对话摘要]\n{compressed}")
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conv_parts.append(json.dumps(recent, ensure_ascii=False, indent=2))
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conv_text = "\n\n---\n\n".join(conv_parts)
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prompt = load_prompt("modification").format(
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current_jrxml=state.get("current_jrxml", ""),
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conversation_history=conv_text,
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modification_request=state.get("user_modification_request", ""),
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)
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full = []
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for chunk in llm.stream(prompt):
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full.append(chunk)
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writer({"type": "stream", "node": "modify_jrxml", "text": chunk})
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jrxml = _extract_jrxml("".join(full))
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state["current_jrxml"] = jrxml
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state["conversation_history"].append(
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{
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"role": "user",
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"content": state.get("user_modification_request", ""),
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}
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)
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state["conversation_history"].append({"role": "assistant", "content": jrxml})
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state["full_conversation_history"] = (
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list(state.get("full_conversation_history", [])) +
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[
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{"role": "user", "content": state.get("user_modification_request", ""), "ts": _now_iso()},
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{"role": "assistant", "content": jrxml, "ts": _now_iso()},
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]
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)
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state["retry_count"] = 0
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return state
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def validate(state: AgentState) -> Dict:
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"""根据 FastAPI 验证服务验证当前 JRXML。"""
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jrxml = state.get("current_jrxml", "")
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if not jrxml:
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state["status"] = "fail"
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state["error_msg"] = "没有 JRXML 内容可供验证。"
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return state
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# 过短的内容不可能是合法报表（最小骨架约 500+ 字符）
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if len(jrxml.strip()) < 200:
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state["status"] = "fail"
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state["error_msg"] = f"JRXML 内容过短（{len(jrxml.strip())} 字符），可能为不完整或空内容。"
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return state
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result = validate_jrxml(jrxml)
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state["status"] = "pass" if result.get("valid") else "fail"
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state["error_msg"] = result.get("error", "")
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# 修正成功后记录到错误知识库
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if result.get("valid") and state.get("retry_count", 0) > 0:
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case = state.get("last_error_case", {})
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if case and case.get("error_msg"):
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try:
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from backend.error_kb import record_error
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recorded = record_error(
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error_msg=case["error_msg"],
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bad_jrxml=case.get("bad_jrxml", ""),
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good_jrxml=jrxml,
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correction_prompt=case.get("correction_prompt", ""),
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retry_count=state.get("retry_count", 0),
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)
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if recorded:
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state["conversation_history"].append({
|
||
"role": "system",
|
||
"content": f"[系统] 错误案例已记录到知识库（指纹: {case['error_msg'][:40]}...）",
|
||
})
|
||
except Exception:
|
||
pass # 知识库写入不影响主流程
|
||
|
||
return state
|
||
|
||
|
||
def explain_error(state: AgentState) -> Dict:
|
||
"""生成验证错误的可读解释。"""
|
||
llm = get_llm()
|
||
jrxml = state.get("current_jrxml", "")
|
||
lines = jrxml.split("\n")[:80]
|
||
snippet = "\n".join(lines)
|
||
|
||
prompt = load_prompt("explain_error").format(
|
||
error_msg=state.get("error_msg", "未知错误"),
|
||
jrxml_snippet=snippet,
|
||
)
|
||
resp = llm.invoke(prompt)
|
||
state["natural_explanation"] = resp.content.strip()
|
||
return state
|
||
|
||
|
||
def correct_jrxml(state: AgentState) -> Dict:
|
||
"""尝试自动修正验证失败的 JRXML。"""
|
||
from langgraph.config import get_stream_writer
|
||
|
||
writer = get_stream_writer()
|
||
llm = get_llm()
|
||
prompt = load_prompt("correction").format(
|
||
current_jrxml=state.get("current_jrxml", ""),
|
||
error_msg=state.get("error_msg", ""),
|
||
explanation=state.get("natural_explanation", ""),
|
||
)
|
||
# 保存修正前状态（供 validate 判断是否写入错误知识库）
|
||
state["last_error_case"] = {
|
||
"error_msg": state.get("error_msg", ""),
|
||
"bad_jrxml": state.get("current_jrxml", ""),
|
||
"correction_prompt": prompt,
|
||
}
|
||
|
||
full = []
|
||
for chunk in llm.stream(prompt):
|
||
full.append(chunk)
|
||
writer({"type": "stream", "node": "correct_jrxml", "text": chunk})
|
||
jrxml = _extract_jrxml("".join(full))
|
||
state["current_jrxml"] = jrxml
|
||
state["retry_count"] = state.get("retry_count", 0) + 1
|
||
state["conversation_history"].append(
|
||
{"role": "assistant", "content": f"[自动修正，第 {state['retry_count']} 次尝试]\n{jrxml}"}
|
||
)
|
||
return state
|
||
|
||
|
||
def finalize(state: AgentState) -> Dict:
|
||
"""保存最终验证通过的 JRXML 并更新对话历史 + 版本记录。"""
|
||
jrxml = state.get("current_jrxml", "")
|
||
status = state.get("status", "")
|
||
|
||
if status == "pass":
|
||
state["final_jrxml"] = jrxml
|
||
if jrxml.strip():
|
||
versions = state.get("jrxml_versions", [])
|
||
if not isinstance(versions, list):
|
||
versions = []
|
||
intent = state.get("intent", "")
|
||
label_map = {
|
||
"initial_generation": "初始生成",
|
||
"modify_report": "修改",
|
||
"correct_jrxml": f"自动修正 (第{state.get('retry_count', 1)}次)",
|
||
}
|
||
versions.append({
|
||
"ts": _now_iso(),
|
||
"jrxml": jrxml,
|
||
"intent": intent,
|
||
"label": label_map.get(intent, intent),
|
||
"status": status,
|
||
})
|
||
state["jrxml_versions"] = versions
|
||
else:
|
||
# 验证未通过：不覆盖 final_jrxml，保留上一次成功的版本
|
||
retries = state.get("retry_count", 0)
|
||
error_msg = state.get("error_msg", "未知错误")
|
||
# 记录失败上下文，下次用户输入时自动注入
|
||
state["pending_failure_context"] = {
|
||
"error_msg": error_msg,
|
||
"bad_jrxml": state.get("current_jrxml", ""),
|
||
"retry_count": retries,
|
||
"ts": _now_iso(),
|
||
}
|
||
state["conversation_history"].append({
|
||
"role": "assistant",
|
||
"content": (
|
||
f"❌ 经过 {retries} 次重试后仍无法生成有效的 JRXML。\n"
|
||
f"错误: {error_msg}\n"
|
||
f"请描述您想要的修改，系统会自动加载失败上下文继续修复。"
|
||
),
|
||
})
|
||
return state
|
||
|
||
|
||
def _extract_jrxml(text: str) -> str:
|
||
"""从 LLM 响应中提取 JRXML 内容，如有 markdown 标记则去除。"""
|
||
text = text.strip()
|
||
xml_pattern = re.compile(r"```(?:xml|jrxml)?\s*([\s\S]*?)```", re.IGNORECASE)
|
||
m = xml_pattern.search(text)
|
||
if m:
|
||
content = m.group(1).strip()
|
||
if content:
|
||
return content
|
||
# markdown 代码块存在但内容为空 — 回退到直接匹配
|
||
|
||
jasper_tag = re.search(r"(<\?xml[\s\S]*?</jasperReport>)", text, re.IGNORECASE)
|
||
if jasper_tag:
|
||
return jasper_tag.group(1).strip()
|
||
|
||
if text.startswith("<?xml") or text.startswith("<jasperReport"):
|
||
return text
|
||
|
||
# 最终回退：如果文本中包含 XML 片段但没有被捕获到，尝试直接提取
|
||
# 这处理 LLM 在代码块外用自然语言"包裹"JRXML 的情况
|
||
xml_start = text.find("<?xml")
|
||
jr_end = text.lower().rfind("</jasperreport>")
|
||
if xml_start >= 0 and jr_end > xml_start:
|
||
return text[xml_start:jr_end + len("</jasperreport>")].strip()
|
||
|
||
return text
|