panda
4b43c5d3e4
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
572 lines
20 KiB
Python
572 lines
20 KiB
Python
"""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
|