feat: LangGraph工作流核心 — Agent状态/节点/图 + 验证服务 + 知识库

agent/ state.py: AgentState TypedDict（20字段含意图/压缩/会话/撤销） nodes.py: 17个节点函数（生成/修改/验证/纠错/意图分类/压缩/撤销/重置） graph.py: 17节点状态图，8意图路由分发验证服务 validation_service/ main.py: FastAPI服务，lxml XSD验证 + 结构化检查（字段引用/SQL/尺寸）数据 data/ sample_templates/: 4个JRXML示例模板 corrections/: 3个错误修正案例脚本 scripts/ init_kb.py: Chroma知识库初始化
2026-05-14 23:21:10 +08:00
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+"""LangGraph JRXML 生成工作流的节点函数。"""
+
+import copy
+import json
+import os
+import re
+from datetime import datetime, timezone
+from typing import Dict
+
+from dotenv import load_dotenv
+
+from agent.state import AgentState
+from backend.embeddings import get_embeddings
+from backend.llm import get_llm
+from backend.validation import validate_jrxml
+
+load_dotenv()
+
+MAX_RETRY = int(os.getenv("MAX_RETRY", "3"))
+CONTEXT_MAX_TOKENS = int(os.getenv("CONTEXT_MAX_TOKENS", "6000"))
+CONTEXT_KEEP_RECENT = int(os.getenv("CONTEXT_KEEP_RECENT", "4"))
+HISTORY_MAX_SNAPSHOTS = int(os.getenv("HISTORY_MAX_SNAPSHOTS", "10"))
+
+# ============================================================
+# 意图分类提示词（约 180 tokens，控制在 200 token 以内）
+# ============================================================
+INTENT_CLASSIFY_PROMPT = """你是意图分类器。根据用户输入判断意图，只输出意图名称。
+
+当前有报表：{has_report}
+用户输入：{user_input}
+
+可选意图：
+- initial_generation（新建报表，或无报表时的任何需求）
+- modify_report（修改当前已有报表）
+- preview_report（预览/查看当前报表）
+- export_pdf（导出PDF文件）
+- export_jrxml（下载/导出/保存JRXML文件）
+- undo_modification（撤销/回退上一步修改）
+- consult_question（咨询JasperReports相关知识或使用问题）
+- reset_session（清空/重置/重新开始）
+
+意图名称："""
+
+# ============================================================
+# 咨询回答提示词
+# ============================================================
+CONSULT_PROMPT = """你是 JasperReports 专家。用简洁清晰的中文回答用户关于 JasperReports 的问题。
+
+用户问题：{question}
+
+直接回答："""
+
+# ============================================================
+# 原有提示词（不变）
+# ============================================================
+INITIAL_GENERATION_PROMPT = """你是一位资深 JasperReports 工程师。根据以下参考模板和用户需求，生成一个完整、可编译的 JRXML 文件。
+JRXML 必须兼容 JasperReports 7.0.6 schema。
+
+关键规则：
+- 只输出 JRXML 代码，不要解释，不要 markdown 标记。
+- 报表正文中使用的每个字段必须在 <field name="..."> 部分中声明。
+- 根元素为 <jasperReport>，包含正确的 xmlns 属性。
+- 包含 <queryString>，在 <![CDATA[...]]> 中包含 SQL 查询。
+- 确保所有交叉引用（字段名称、band 元素）保持一致。
+
+参考模板和组件：
+{context}
+
+用户需求：
+{user_request}
+"""
+
+MODIFICATION_PROMPT = """你是一位资深 JasperReports 工程师。用户想要修改一个现有的、可编译的 JRXML 报表。精确应用请求的更改到当前 JRXML 并输出完整修改后的 JRXML。
+
+关键规则：
+- 只输出完整修改后的 JRXML 代码，不要解释，不要 markdown 标记。
+- 保留所有未被更改的现有结构。
+- 结果必须继续与 JasperReports 7.0.6 兼容。
+- 报表正文中使用的每个字段必须在 <field> 部分中声明。
+- 如果添加新字段，正确声明它们。
+- 确保 <queryString> 是 <![CDATA[...]]> 中有效的 SQL。
+
+当前 JRXML：
+{current_jrxml}
+
+对话历史：
+{conversation_history}
+
+用户的修改请求：
+{modification_request}
+"""
+
+CORRECTION_PROMPT = """你是一位资深 JasperReports 工程师。你生成的 JRXML 文件编译失败。分析错误并修复 JRXML。
+
+关键规则：
+- 只输出完整修复后的 JRXML 代码，不要解释，不要 markdown 标记。
+- JRXML 必须与 JasperReports 7.0.6 兼容。
+- 解决下面列出的特定错误。
+
+当前 JRXML（带错误）：
+{current_jrxml}
+
+编译错误：
+{error_msg}
+
+错误的自然语言解释：
+{explanation}
+
+立即生成修正后的 JRXML：
+"""
+
+EXPLAIN_PROMPT = """你是一位 JasperReports 专家。用普通非技术语言解释以下 JRXML 编译错误，让业务用户能够理解。
+
+错误消息：
+{error_msg}
+
+当前 JRXML 片段（前 80 行）：
+{jrxml_snippet}
+
+用 2-3 句话解释哪里出了问题以及如何修复：
+"""
+
+COMPRESSION_PROMPT = """你是一个信息压缩助手。以下是用户与报表生成助手之间的历史对话记录，请将其压缩为一份简洁的摘要（不超过200字）。
+
+摘要必须保留以下关键信息：
+- 用户提出的所有报表需求点（字段、标题、分组、汇总等）
+- 用户提出的所有修改要求及其顺序
+- 当前报表的核心结构（字段列表、标题、分组方式）
+- 任何特殊要求或约束条件
+
+只输出摘要文本，不要添加任何解释或标记。
+
+对话记录：
+{conversation_text}
+"""
+
+
+# ============================================================
+# 核心工作流节点
+# ============================================================
+
+def process_input(state: AgentState) -> Dict:
+    """记录用户输入到对话历史，重置本轮请求状态。"""
+    user_input = state.get("user_input", "")
+
+    # 维护全量对话历史
+    full_history = state.get("full_conversation_history", [])
+    full_history.append({"role": "user", "content": user_input, "ts": _now_iso()})
+    state["full_conversation_history"] = full_history
+
+    # 维护工作对话历史
+    conv_history = state.get("conversation_history", [])
+    conv_history.append({"role": "user", "content": user_input})
+    state["conversation_history"] = conv_history
+
+    # 重置本轮请求字段
+    state["retry_count"] = 0
+    state["user_modification_request"] = user_input
+
+    return state
+
+
+def save_state_snapshot(state: AgentState) -> Dict:
+    """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。"""
+    snapshots = state.get("history_states", [])
+    if not isinstance(snapshots, list):
+        snapshots = []
+
+    snapshot = {
+        "current_jrxml": state.get("current_jrxml", ""),
+        "final_jrxml": state.get("final_jrxml", ""),
+        "status": state.get("status", ""),
+        "conversation_history": copy.deepcopy(state.get("conversation_history", [])),
+        "user_modification_request": state.get("user_modification_request", ""),
+        "intent": state.get("intent", ""),
+    }
+    snapshots.append(snapshot)
+
+    max_snap = HISTORY_MAX_SNAPSHOTS
+    if len(snapshots) > max_snap:
+        snapshots = snapshots[-max_snap:]
+
+    state["history_states"] = snapshots
+    return state
+
+
+def classify_intent(state: AgentState) -> Dict:
+    """使用 LLM 对用户输入进行意图分类（8 种意图）。"""
+    user_input = state.get("user_input", "")
+    has_report = "是" if state.get("current_jrxml", "").strip() else "否"
+
+    intent = "initial_generation"
+    try:
+        llm = get_llm()
+        prompt = INTENT_CLASSIFY_PROMPT.format(
+            has_report=has_report,
+            user_input=user_input[:500],
+        )
+        resp = llm.invoke(prompt)
+        raw = resp.content.strip().lower()
+
+        valid_intents = [
+            "initial_generation", "modify_report", "preview_report",
+            "export_pdf", "export_jrxml", "undo_modification",
+            "consult_question", "reset_session",
+        ]
+        for vi in valid_intents:
+            if vi in raw:
+                intent = vi
+                break
+        else:
+            # 兜底：有报表 → modify_report，无报表 → initial_generation
+            intent = "modify_report" if has_report == "是" else "initial_generation"
+    except Exception:
+        intent = "modify_report" if has_report == "是" else "initial_generation"
+
+    state["intent"] = intent
+    return state
+
+
+def handle_consult(state: AgentState) -> Dict:
+    """处理咨询类问题：调用 LLM 直接回答，不走报表生成流程。"""
+    user_input = state.get("user_input", "")
+    try:
+        llm = get_llm()
+        prompt = CONSULT_PROMPT.format(question=user_input)
+        resp = llm.invoke(prompt)
+        answer = resp.content.strip()
+    except Exception:
+        answer = "抱歉，暂时无法处理您的问题，请稍后再试。"
+
+    state["consult_answer"] = answer
+    state["conversation_history"].append({"role": "assistant", "content": answer})
+    state["full_conversation_history"].append(
+        {"role": "assistant", "content": answer, "ts": _now_iso()}
+    )
+    return state
+
+
+def handle_undo(state: AgentState) -> Dict:
+    """撤销上一步修改：从 history_states 恢复最近一个快照。"""
+    snapshots = state.get("history_states", [])
+    if not isinstance(snapshots, list) or not snapshots:
+        state["conversation_history"].append(
+            {"role": "assistant", "content": "没有可撤销的操作。"}
+        )
+        return state
+
+    prev = snapshots.pop()
+    state["history_states"] = snapshots
+
+    state["current_jrxml"] = prev.get("current_jrxml", "")
+    state["final_jrxml"] = prev.get("final_jrxml", "")
+    state["status"] = prev.get("status", "")
+    state["conversation_history"] = prev.get("conversation_history", [])
+    state["user_modification_request"] = prev.get("user_modification_request", "")
+
+    state["conversation_history"].append(
+        {"role": "assistant", "content": "已撤销上一步修改，恢复到之前的状态。"}
+    )
+    state["full_conversation_history"].append(
+        {"role": "assistant", "content": "已撤销上一步修改。", "ts": _now_iso()}
+    )
+    return state
+
+
+def handle_reset(state: AgentState) -> Dict:
+    """重置当前会话：清空报表相关状态，保留会话信息。"""
+    state["current_jrxml"] = ""
+    state["final_jrxml"] = ""
+    state["status"] = ""
+    state["error_msg"] = ""
+    state["natural_explanation"] = ""
+    state["user_modification_request"] = ""
+    state["retrieved_context"] = ""
+    state["retry_count"] = 0
+    state["compressed_history"] = ""
+    state["history_states"] = []
+    state["intent"] = "initial_generation"
+
+    state["conversation_history"] = []
+    state["conversation_history"].append(
+        {"role": "assistant", "content": "会话已重置，请描述您要创建的新报表。"}
+    )
+    state["full_conversation_history"].append(
+        {"role": "assistant", "content": "会话已重置。", "ts": _now_iso()}
+    )
+    return state
+
+
+def count_tokens(state: AgentState) -> int:
+    """使用 tiktoken（gpt-4o 编码器）计算当前上下文 token 数量。"""
+    try:
+        import tiktoken
+        enc = tiktoken.encoding_for_model("gpt-4o")
+    except Exception:
+        # 回退方案：中英文混合场景下，近似 1 token ≈ 2.5 个字符
+        text = json.dumps({
+            "history": state.get("conversation_history", [])[-CONTEXT_KEEP_RECENT:],
+            "jrxml": state.get("current_jrxml", ""),
+            "compressed": state.get("compressed_history", ""),
+        }, ensure_ascii=False)
+        return len(text) // 2.5
+
+    text = json.dumps({
+        "history": state.get("conversation_history", [])[-CONTEXT_KEEP_RECENT:],
+        "jrxml": state.get("current_jrxml", ""),
+        "compressed": state.get("compressed_history", ""),
+    }, ensure_ascii=False)
+    return len(enc.encode(text))
+
+
+def manage_context(state: AgentState) -> Dict:
+    """当 token 数量超过阈值时，压缩较早的对话轮次。"""
+    token_count = count_tokens(state)
+    state["current_token_count"] = token_count
+
+    if token_count <= CONTEXT_MAX_TOKENS:
+        return state
+
+    full_history = state.get("full_conversation_history", [])
+    if len(full_history) <= CONTEXT_KEEP_RECENT:
+        return state
+
+    # 最近N轮保留完整，更早的轮次送去压缩
+    recent = full_history[-CONTEXT_KEEP_RECENT:]
+    older = full_history[:-CONTEXT_KEEP_RECENT]
+
+    if not older:
+        return state
+
+    conv_text = json.dumps(older, ensure_ascii=False, indent=2)
+
+    try:
+        llm = get_llm()
+        prompt = COMPRESSION_PROMPT.format(conversation_text=conv_text)
+        resp = llm.invoke(prompt)
+        new_compressed = resp.content.strip()[:300]
+    except Exception:
+        new_compressed = _simple_compress(older)
+
+    # 合并已有压缩与新压缩
+    existing = state.get("compressed_history", "")
+    if existing:
+        state["compressed_history"] = f"{existing}\n---\n{new_compressed}"
+    else:
+        state["compressed_history"] = new_compressed
+
+    state["conversation_history"] = list(recent)
+    state["current_token_count"] = count_tokens(state)
+    return state
+
+
+def load_session_node(state: AgentState) -> Dict:
+    """在请求开始时从磁盘加载会话状态。"""
+    session_id = state.get("session_id", "")
+    if not session_id:
+        return state
+
+    try:
+        from backend.session import load_session
+        data = load_session(session_id)
+        if data and data.get("agent_state"):
+            saved = data["agent_state"]
+            # 恢复核心字段（不覆盖当前请求的 user_input / stage）
+            for key in ("conversation_history", "full_conversation_history",
+                        "current_jrxml", "final_jrxml", "compressed_history",
+                        "session_name", "created_at", "history_states"):
+                if key in saved and key not in ("user_input", "stage"):
+                    state[key] = saved[key]
+            state["session_name"] = data.get("session_name", "")
+            state["created_at"] = data.get("created_at", "")
+    except Exception:
+        pass
+    return state
+
+
+def save_session_node(state: AgentState) -> Dict:
+    """将当前代理状态持久化到磁盘。"""
+    session_id = state.get("session_id", "")
+    if not session_id:
+        return state
+
+    try:
+        from backend.session import save_session
+        persistable = {}
+        for key in ("conversation_history", "full_conversation_history",
+                    "current_jrxml", "final_jrxml", "compressed_history",
+                    "status", "error_msg", "history_states"):
+            if key in state:
+                persistable[key] = state[key]
+        persistable["updated_at"] = _now_iso()
+
+        session_name = state.get("session_name", "")
+        if not session_name and state.get("conversation_history"):
+            first_user = next(
+                (m["content"][:50] for m in state["conversation_history"]
+                 if m.get("role") == "user"), "")
+            if first_user:
+                session_name = first_user
+
+        save_session(session_id, persistable, session_name)
+        if not state.get("session_name"):
+            state["session_name"] = session_name
+        state["updated_at"] = persistable["updated_at"]
+    except Exception:
+        pass
+    return state
+
+
+def _simple_compress(messages: list[dict]) -> str:
+    """当 LLM 不可用时，基于简单规则的压缩回退方案。"""
+    points = []
+    for m in messages:
+        if m.get("role") == "user":
+            points.append(f"用户提问：{m['content'][:100]}")
+    return "; ".join(points[-10:])
+
+
+def _now_iso() -> str:
+    return datetime.now(timezone.utc).isoformat()
+
+
+def retrieve(state: AgentState) -> Dict:
+    """在 Chroma 中搜索相关的 JRXML 模板和组件。"""
+    try:
+        embeddings = get_embeddings()
+        from langchain_chroma import Chroma
+
+        persist_dir = os.getenv("CHROMA_PERSIST_DIR", "./db/chroma")
+        if not os.path.exists(persist_dir) or not os.listdir(persist_dir):
+            state["retrieved_context"] = ""
+            return state
+
+        vectorstore = Chroma(
+            embedding_function=embeddings,
+            persist_directory=persist_dir,
+        )
+        user_input = state.get("user_input", "")
+        docs = vectorstore.similarity_search(user_input, k=5)
+        context_parts = []
+        for d in docs:
+            context_parts.append(d.page_content)
+        state["retrieved_context"] = "\n\n---\n\n".join(context_parts)
+    except Exception:
+        state["retrieved_context"] = ""
+    return state
+
+
+def generate(state: AgentState) -> Dict:
+    """根据用户需求和检索到的上下文生成初始 JRXML。"""
+    llm = get_llm()
+    prompt = INITIAL_GENERATION_PROMPT.format(
+        context=state.get("retrieved_context", ""),
+        user_request=state.get("user_input", ""),
+    )
+    resp = llm.invoke(prompt)
+    jrxml = _extract_jrxml(resp.content)
+    state["current_jrxml"] = jrxml
+    state["conversation_history"].append({"role": "assistant", "content": jrxml})
+    return state
+
+
+def modify_jrxml(state: AgentState) -> Dict:
+    """根据用户的修改请求修改现有 JRXML。"""
+    llm = get_llm()
+    # 构建对话上下文：压缩摘要 + 最近对话
+    compressed = state.get("compressed_history", "")
+    recent = state.get("conversation_history", [])[-6:]
+    conv_parts = []
+    if compressed:
+        conv_parts.append(f"[早期对话摘要]\n{compressed}")
+    conv_parts.append(json.dumps(recent, ensure_ascii=False, indent=2))
+    conv_text = "\n\n---\n\n".join(conv_parts)
+
+    prompt = MODIFICATION_PROMPT.format(
+        current_jrxml=state.get("current_jrxml", ""),
+        conversation_history=conv_text,
+        modification_request=state.get("user_modification_request", ""),
+    )
+    resp = llm.invoke(prompt)
+    jrxml = _extract_jrxml(resp.content)
+    state["current_jrxml"] = jrxml
+    state["conversation_history"].append(
+        {
+            "role": "user",
+            "content": state.get("user_modification_request", ""),
+        }
+    )
+    state["conversation_history"].append({"role": "assistant", "content": jrxml})
+    state["full_conversation_history"] = (
+        list(state.get("full_conversation_history", [])) +
+        [
+            {"role": "user", "content": state.get("user_modification_request", ""), "ts": _now_iso()},
+            {"role": "assistant", "content": jrxml, "ts": _now_iso()},
+        ]
+    )
+    state["retry_count"] = 0
+    return state
+
+
+def validate(state: AgentState) -> Dict:
+    """根据 FastAPI 验证服务验证当前 JRXML。"""
+    jrxml = state.get("current_jrxml", "")
+    if not jrxml:
+        state["status"] = "fail"
+        state["error_msg"] = "没有 JRXML 内容可供验证。"
+        return state
+
+    result = validate_jrxml(jrxml)
+    state["status"] = "pass" if result.get("valid") else "fail"
+    state["error_msg"] = result.get("error", "")
+    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 = EXPLAIN_PROMPT.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。"""
+    llm = get_llm()
+    prompt = CORRECTION_PROMPT.format(
+        current_jrxml=state.get("current_jrxml", ""),
+        error_msg=state.get("error_msg", ""),
+        explanation=state.get("natural_explanation", ""),
+    )
+    resp = llm.invoke(prompt)
+    jrxml = _extract_jrxml(resp.content)
+    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。"""
+    state["final_jrxml"] = state.get("current_jrxml", "")
+    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:
+        return m.group(1).strip()
+
+    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
+
+    return text