dj
e5d4724e82
- 新增对话历史管理：MongoDB新增conversations集合，存储用户与AI的对话上下文，支持多轮对话意图延续
- 新增对话历史API（conversation.py）：GET/DELETE conversation历史、列出所有会话
- 意图解析增强：支持基于对话历史的意图识别，上下文理解更准确
- 字段提取优化：支持"提取文档中的医院数量"等自然语言模式，智能去除"文档中的"前缀
- 文档对比优化：从指令中提取文件名并精确匹配source_docs，支持"对比A和B两个文档"
- 文档摘要优化：使用LLM生成真实AI摘要而非返回原始文档预览
【Word模板填表核心功能】
- Word模板字段生成：空白Word上传后，自动从源文档（Excel/Word/TXT/MD）内容AI生成字段名
- Word模板填表（_fill_docx）：将提取数据写入Word模板表格，支持精确匹配、模糊匹配、追加新行
- 数据润色（_polish_word_filled_data）：LLM对多行Excel数据进行统计归纳（合计/平均/极值），转化为专业自然语言描述
- 段落格式输出：使用📌字段名+值段落+分隔线（灰色横线）格式，提升可读性
- 导出链打通：fill_template返回filled_file_path，export直接返回已填好的Word文件
【其他修复】
- 修复Word导出Windows文件锁问题：NamedTemporaryFile改为mkstemp+close
- 修复Word方框非法字符：扩展clean_text移除\uFFFD、□等Unicode替代符和零宽字符
- 修复文档对比"需要至少2个文档"：从指令提取具体文件名优先匹配而非取前2个
- 修复导出format硬编码：自动识别docx/xlsx格式
- Docx解析器增加备用解析方法和更完整的段落/表格/标题提取
- RAG服务新增MySQL数据源支持
295 lines
10 KiB
Python
295 lines
10 KiB
Python
"""
|
|
意图解析器模块
|
|
|
|
解析用户自然语言指令，识别意图和参数
|
|
"""
|
|
import re
|
|
import logging
|
|
from typing import Any, Dict, List, Optional, Tuple
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
class IntentParser:
|
|
"""意图解析器"""
|
|
|
|
# 意图类型定义
|
|
INTENT_EXTRACT = "extract" # 信息提取
|
|
INTENT_FILL_TABLE = "fill_table" # 填表
|
|
INTENT_SUMMARIZE = "summarize" # 摘要总结
|
|
INTENT_QUESTION = "question" # 问答
|
|
INTENT_SEARCH = "search" # 搜索
|
|
INTENT_COMPARE = "compare" # 对比分析
|
|
INTENT_TRANSFORM = "transform" # 格式转换
|
|
INTENT_EDIT = "edit" # 编辑文档
|
|
INTENT_UNKNOWN = "unknown" # 未知
|
|
|
|
# 意图关键词映射
|
|
INTENT_KEYWORDS = {
|
|
INTENT_EXTRACT: ["提取", "抽取", "获取", "找出", "查找", "识别", "找到"],
|
|
INTENT_FILL_TABLE: ["填表", "填写", "填充", "录入", "导入到表格", "填写到"],
|
|
INTENT_SUMMARIZE: ["总结", "摘要", "概括", "概述", "归纳", "提炼", "分析", "聊聊"],
|
|
INTENT_QUESTION: ["问答", "回答", "解释", "什么是", "为什么", "如何", "怎样", "多少", "几个"],
|
|
INTENT_SEARCH: ["搜索", "查找", "检索", "查询", "找"],
|
|
INTENT_COMPARE: ["对比", "比较", "差异", "区别", "不同"],
|
|
INTENT_TRANSFORM: ["转换", "转化", "变成", "转为", "导出"],
|
|
INTENT_EDIT: ["修改", "编辑", "调整", "改写", "润色", "优化"],
|
|
}
|
|
|
|
# 实体模式定义
|
|
ENTITY_PATTERNS = {
|
|
"number": [r"\d+", r"[一二三四五六七八九十百千万]+"],
|
|
"date": [r"\d{4}年", r"\d{1,2}月", r"\d{1,2}日"],
|
|
"percentage": [r"\d+(\.\d+)?%", r"\d+(\.\d+)?‰"],
|
|
"currency": [r"\d+(\.\d+)?万元", r"\d+(\.\d+)?亿元", r"\d+(\.\d+)?元"],
|
|
}
|
|
|
|
def __init__(self):
|
|
self.intent_history: List[Dict[str, Any]] = []
|
|
|
|
async def parse(self, text: str, context: Dict[str, Any] = None) -> Tuple[str, Dict[str, Any]]:
|
|
"""
|
|
解析自然语言指令
|
|
|
|
Args:
|
|
text: 用户输入的自然语言
|
|
context: 执行上下文（包含对话历史等）
|
|
|
|
Returns:
|
|
(意图类型, 参数字典)
|
|
"""
|
|
text = text.strip()
|
|
if not text:
|
|
return self.INTENT_UNKNOWN, {}
|
|
|
|
# 检查对话历史中的上下文
|
|
conversation_history = []
|
|
if context and context.get("conversation_history"):
|
|
conversation_history = context.get("conversation_history", [])
|
|
logger.info(f"解析时使用对话历史: {len(conversation_history)} 条消息")
|
|
|
|
# 记录历史
|
|
self.intent_history.append({"text": text, "intent": None})
|
|
|
|
# 识别意图（考虑对话上下文）
|
|
intent = self._recognize_intent_with_context(text, conversation_history)
|
|
|
|
# 提取参数
|
|
params = self._extract_params(text, intent)
|
|
|
|
# 更新历史
|
|
if self.intent_history:
|
|
self.intent_history[-1]["intent"] = intent
|
|
|
|
logger.info(f"意图解析: text={text[:50]}..., intent={intent}, params={params}")
|
|
|
|
return intent, params
|
|
|
|
def _recognize_intent_with_context(self, text: str, conversation_history: List[Dict[str, Any]]) -> str:
|
|
"""
|
|
基于对话历史识别意图
|
|
|
|
Args:
|
|
text: 当前用户输入
|
|
conversation_history: 对话历史
|
|
|
|
Returns:
|
|
意图类型
|
|
"""
|
|
# 如果对话历史为空，使用基础意图识别
|
|
if not conversation_history:
|
|
return self._recognize_intent(text)
|
|
|
|
# 基于历史上下文进行意图识别
|
|
# 分析最近的对话了解用户意图的延续性
|
|
last_intent = None
|
|
last_topic = None
|
|
|
|
for msg in conversation_history[-5:]: # 最多看最近5条消息
|
|
if msg.get("role") == "assistant":
|
|
last_intent = msg.get("intent")
|
|
if msg.get("intent") and msg.get("intent") != "unknown":
|
|
last_topic = msg.get("intent")
|
|
|
|
# 如果当前消息很短（如"继续"、"是的"），可能延续之前的意图
|
|
short_confirmation = ["是", "是的", "好", "继续", "ok", "好", "接着", "然后", "还有吗"]
|
|
if text.strip() in short_confirmation or len(text.strip()) <= 3:
|
|
if last_topic:
|
|
logger.info(f"简短确认，延续之前的意图: {last_topic}")
|
|
return last_topic
|
|
|
|
# 否则使用标准意图识别
|
|
return self._recognize_intent(text)
|
|
|
|
def _recognize_intent(self, text: str) -> str:
|
|
"""识别意图类型"""
|
|
intent_scores: Dict[str, float] = {}
|
|
|
|
for intent, keywords in self.INTENT_KEYWORDS.items():
|
|
score = 0
|
|
for keyword in keywords:
|
|
if keyword in text:
|
|
score += 1
|
|
if score > 0:
|
|
intent_scores[intent] = score
|
|
|
|
if not intent_scores:
|
|
return self.INTENT_UNKNOWN
|
|
|
|
# 返回得分最高的意图
|
|
return max(intent_scores, key=intent_scores.get)
|
|
|
|
def _extract_params(self, text: str, intent: str) -> Dict[str, Any]:
|
|
"""提取参数"""
|
|
params: Dict[str, Any] = {
|
|
"entities": self._extract_entities(text),
|
|
"document_refs": self._extract_document_refs(text),
|
|
"field_refs": self._extract_field_refs(text),
|
|
"template_refs": self._extract_template_refs(text),
|
|
}
|
|
|
|
# 根据意图类型提取特定参数
|
|
if intent == self.INTENT_QUESTION:
|
|
params["question"] = text
|
|
params["focus"] = self._extract_question_focus(text)
|
|
elif intent == self.INTENT_FILL_TABLE:
|
|
params["template"] = self._extract_template_info(text)
|
|
elif intent == self.INTENT_EXTRACT:
|
|
params["target_fields"] = self._extract_target_fields(text)
|
|
|
|
return params
|
|
|
|
def _extract_entities(self, text: str) -> Dict[str, List[str]]:
|
|
"""提取实体"""
|
|
entities: Dict[str, List[str]] = {}
|
|
|
|
for entity_type, patterns in self.ENTITY_PATTERNS.items():
|
|
matches = []
|
|
for pattern in patterns:
|
|
found = re.findall(pattern, text)
|
|
matches.extend(found)
|
|
if matches:
|
|
entities[entity_type] = list(set(matches))
|
|
|
|
return entities
|
|
|
|
def _extract_document_refs(self, text: str) -> List[str]:
|
|
"""提取文档引用"""
|
|
# 匹配 "文档1"、"doc1"、"第一个文档" 等
|
|
refs = []
|
|
|
|
# 数字索引: 文档1, doc1, 第1个文档
|
|
num_patterns = [
|
|
r"[文档doc]+(\d+)",
|
|
r"第(\d+)个文档",
|
|
r"第(\d+)份",
|
|
]
|
|
for pattern in num_patterns:
|
|
matches = re.findall(pattern, text.lower())
|
|
refs.extend([f"doc_{m}" for m in matches])
|
|
|
|
# "所有文档"、"全部文档"
|
|
if any(kw in text for kw in ["所有", "全部", "整个"]):
|
|
refs.append("all_docs")
|
|
|
|
return refs
|
|
|
|
def _extract_field_refs(self, text: str) -> List[str]:
|
|
"""提取字段引用"""
|
|
fields = []
|
|
|
|
# 匹配引号内的字段名
|
|
quoted = re.findall(r"['\"『「]([^'\"』」]+)['\"』」]", text)
|
|
fields.extend(quoted)
|
|
|
|
# 匹配 "xxx字段"、"xxx列" 等
|
|
field_patterns = [
|
|
r"([^\s]+)字段",
|
|
r"([^\s]+)列",
|
|
r"([^\s]+)数据",
|
|
]
|
|
for pattern in field_patterns:
|
|
matches = re.findall(pattern, text)
|
|
fields.extend(matches)
|
|
|
|
return list(set(fields))
|
|
|
|
def _extract_template_refs(self, text: str) -> List[str]:
|
|
"""提取模板引用"""
|
|
templates = []
|
|
|
|
# 匹配 "表格模板"、"Excel模板"、"表1" 等
|
|
template_patterns = [
|
|
r"([^\s]+模板)",
|
|
r"表(\d+)",
|
|
r"([^\s]+表格)",
|
|
]
|
|
for pattern in template_patterns:
|
|
matches = re.findall(pattern, text)
|
|
templates.extend(matches)
|
|
|
|
return list(set(templates))
|
|
|
|
def _extract_question_focus(self, text: str) -> Optional[str]:
|
|
"""提取问题焦点"""
|
|
# "什么是XXX"、"XXX是什么"
|
|
match = re.search(r"[什么是]([^?]+)", text)
|
|
if match:
|
|
return match.group(1).strip()
|
|
|
|
# "XXX有多少"
|
|
match = re.search(r"([^?]+)有多少", text)
|
|
if match:
|
|
return match.group(1).strip()
|
|
|
|
return None
|
|
|
|
def _extract_template_info(self, text: str) -> Optional[Dict[str, str]]:
|
|
"""提取模板信息"""
|
|
template_info: Dict[str, str] = {}
|
|
|
|
# 提取模板类型
|
|
if "excel" in text.lower() or "xlsx" in text.lower() or "电子表格" in text:
|
|
template_info["type"] = "xlsx"
|
|
elif "word" in text.lower() or "docx" in text.lower() or "文档" in text:
|
|
template_info["type"] = "docx"
|
|
|
|
return template_info if template_info else None
|
|
|
|
def _extract_target_fields(self, text: str) -> List[str]:
|
|
"""提取目标字段 - 按分隔符切分再逐段清理"""
|
|
fields = []
|
|
|
|
# 去除提取/抽取前缀
|
|
cleaned_text = re.sub(r"^(?:提取|抽取)", "", text).strip()
|
|
|
|
# 按'和'、'与'、'、'分割成多段
|
|
segments = re.split(r"[和与、]", cleaned_text)
|
|
|
|
# 常见前缀（这些不是字段名，需要去除）
|
|
prefixes = ["文档中的", "文档中", "文件中的", "文件中", "内容中的", "内容中"]
|
|
|
|
for seg in segments:
|
|
seg = seg.strip()
|
|
# 去除常见前缀
|
|
for p in prefixes:
|
|
if seg.startswith(p):
|
|
seg = seg[len(p):]
|
|
break
|
|
if seg and 2 <= len(seg) <= 20:
|
|
fields.append(seg)
|
|
|
|
return list(set(fields))
|
|
|
|
def get_intent_history(self) -> List[Dict[str, Any]]:
|
|
"""获取意图历史"""
|
|
return self.intent_history
|
|
|
|
def clear_history(self):
|
|
"""清空历史"""
|
|
self.intent_history = []
|
|
|
|
|
|
# 全局单例
|
|
intent_parser = IntentParser()
|