648 lines
27 KiB
Python
648 lines
27 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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File: IntentRecognition.py
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Author: oyyz
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Date: 2025-05-13
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Description: 意图分类、改写核心逻辑
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"""
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import logging
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import os
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from langchain.output_parsers import PydanticOutputParser
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import json
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from typing import List, Tuple, Dict, Any, Optional
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import re
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import jieba
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import time
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from .PromptTemplates import (classification_prompt, query_rewrite_prompt,
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extract_nouns_prompt, classification_info,
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slot_filling_prompt)
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from .Multi_PromptTemplates import (
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intent_and_slot_prompt, output_example,
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generate_slot_mapping_doc, query_rewrite_prompt_pro,
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)
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from .DataModels import (
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Classification, QueryRewrite, Term, TermList,
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SoftwareFunctionSlots, SoftwareTroubleShootingSlots, ProfessionalConsultingSlots,
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DataProblemSlots, FileExtensionConsultingSlots, SoftwareLockSlots,
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InstallationDownloadSlots, ProblemDiagnosisSlots, OtherSlots, IntentAndSlotResult
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)
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from .ProfessionalNounVector import ProfessionalNounRetriever
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from rag2_0.tool.ModelTool import XinferenceReRankerModel, OpenAiLLM, SiliconFlowReRankerModel
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class IntentRecognizer:
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"""
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意图识别和问题改写类
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"""
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def __init__(self, api_key: str = None, base_url: str = None, model_name: str = "gpt-3.5-turbo", vector_index_dir: str = None):
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"""
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初始化意图识别器
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Args:
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api_key: OpenAI API密钥，如果为None则从环境变量获取
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base_url: OpenAI API基础URL，如果为None则使用默认URL
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model_name: 要使用的模型名称
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vector_index_dir: 向量索引目录，如果为None则使用默认目录
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"""
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# 初始化LLM
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llm_params = {
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"temperature": 0.2, # 降低随机性，使结果更确定
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"model": model_name
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}
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# 如果提供了API密钥，则使用提供的密钥
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if api_key:
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llm_params["api_key"] = api_key
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# 如果提供了自定义URL，则使用提供的URL
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if base_url:
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llm_params["base_url"] = base_url
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self._llm = OpenAiLLM(**llm_params)
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# 加载suffix关键词
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self._suffix_keywords = self._load_suffix_keywords()
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# 初始化向量检索器
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self._noun_retriever = ProfessionalNounRetriever(api_key=api_key, index_dir=vector_index_dir)
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def _load_suffix_keywords(self, filepath: str = None) -> List[str]:
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"""
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加载后缀关键词列表
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Args:
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filepath: 后缀关键词文件路径，默认为None使用默认路径
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Returns:
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后缀关键词列表
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"""
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try:
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# 如果未指定路径，使用默认路径
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if filepath is None:
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current_dir = os.path.dirname(os.path.abspath(__file__))
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filepath = os.path.join(current_dir, "..", "..", "data", "nouns", "suffix_keywords.json")
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# 读取JSON文件
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with open(filepath, "r", encoding="utf-8") as f:
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suffix_data = json.load(f)
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# 添加额外的固定后缀
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return suffix_data
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except Exception as e:
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raise RuntimeError(f"加载后缀关键词失败: {e}") from e
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def _classify_intent(self, query: str, conversation_context: str = "",
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chat_history: List[Dict[str, str]] = None,
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previous_slots: Dict[str, Any] = None) -> Classification:
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"""
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对用户输入进行意图分类
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Args:
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content: 用户输入内容
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keywords: 匹配到的关键词列表
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rewrite: 重写的问题
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Returns:
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分类结果
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"""
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classification_parser = PydanticOutputParser(pydantic_object=Classification)
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formatted_prompt = classification_prompt.format(user_input=query,
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classification_info=classification_info,
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output_format=classification_parser.get_format_instructions(),
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conversation_context=conversation_context,
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chat_history=json.dumps(chat_history, ensure_ascii=False))
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# 调用LLM
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response = self._llm.invoke(formatted_prompt, False)
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# 解析输出
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try:
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# 尝试直接解析JSON响应
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parsed_output = classification_parser.parse(response.content.strip())
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return parsed_output
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except Exception as e:
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raise RuntimeError(f"解析分类结果时出错: {e}") from e
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def _tokenize_with_jieba(self, query: str) -> List[str]:
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"""
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使用jieba分词器对查询进行分词
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Args:
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query: 用户查询
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Returns:
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分词后的词语列表
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"""
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# 使用jieba进行分词
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seg_list = jieba.cut(query, cut_all=False)
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# 过滤掉停用词和标点符号
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filtered_tokens = []
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for token in seg_list:
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# 过滤掉空格和标点符号
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if token.strip() and not re.match(r'^[^\w\s]$', token):
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filtered_tokens.append(token)
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return filtered_tokens
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def _extract_keywords_with_llm(self, query: str, use_jieba: bool = False) -> List[Term]:
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"""
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使用LLM从用户查询中提取专业关键词
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Args:
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query: 用户查询
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use_jieba: 是否使用jieba分词辅助提取关键词
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Returns:
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提取的术语列表
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"""
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try:
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# 如果使用jieba分词
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if use_jieba:
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# 先使用jieba分词
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tokens = self._tokenize_with_jieba(query)
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# 构建术语列表
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terms = []
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for token in tokens:
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if len(token) > 1: # 过滤掉单字词
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terms.append(Term(name=token, synonymous=[], description=""))
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return terms
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else:
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# 使用LLM提取关键词
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# 准备提示词
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formatted_prompt = extract_nouns_prompt.replace("{content}", query)
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terms_list_parser = PydanticOutputParser(pydantic_object=TermList)
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formatted_prompt = formatted_prompt.replace("{output_format}", terms_list_parser.get_format_instructions())
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# 调用LLM
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response = self._llm.invoke(formatted_prompt, False)
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# 尝试使用Pydantic解析器解析TermList
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parsed_output = terms_list_parser.parse(response.content)
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return parsed_output.terms
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except Exception as e:
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raise RuntimeError(f"无法解析LLM关键词提取响应: {e}") from e
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def _rerank_matched_terms(self, query_key: str, matched_terms: set, top_k: int = 2, rerank_score:float = 0.6) -> List[Term]:
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"""
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对召回的专业术语进行重排序，按与用户查询的相关性排序
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Args:
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query: 用户查询
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matched_terms: 匹配到的专业术语集合
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query_keys: 用户查询中提取的关键词列表
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Returns:
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重排序后的专业术语列表
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"""
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if not matched_terms:
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return []
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if len(matched_terms) <= top_k:
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return list(matched_terms)
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try:
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# 将每个术语转换为可用于重排序的文本表示
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# term_texts = ["名称:" + term.name + "|" + "同义词:" + ";".join(term.synonymous) + "|" + "描述:" + term.description for term in matched_terms]
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term_texts = ["名称:" + term.name + "|" + "同义词:" + ";".join(term.synonymous) for term in matched_terms]
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# 使用重排序模型
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xinference_reranker = XinferenceReRankerModel()
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rerank_results = xinference_reranker.rerank(query_key, term_texts, top_k=top_k)
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# 将matched_terms转换为列表以便按索引访问
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matched_terms_list = list(matched_terms)
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# 根据重排序结果获取排序后的术语列表
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reranked_terms = [matched_terms_list[result["index"]] for result in rerank_results if result["score"] >= rerank_score]
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return reranked_terms
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except Exception as e:
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raise RuntimeError(f"_rerank_matched_terms重排失败：{e}") from e
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def _match_keywords(self, query: str, use_jieba: bool = False) -> Tuple[TermList, List[str]]:
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"""
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从用户问题中匹配关键词，结合LLM提取和向量检索
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Args:
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query: 用户问题
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use_jieba: 是否使用jieba分词辅助提取关键词
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Returns:
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匹配到的关键词列表
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"""
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start_time = time.time()
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query_keys=[]
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# 步骤1: 使用LLM提取查询中的关键词
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try:
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llm_start_time = time.time()
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extracted_terms = self._extract_keywords_with_llm(query, use_jieba)
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for term in extracted_terms:
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query_keys.append(term.name)
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llm_end_time = time.time()
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llm_time = llm_end_time - llm_start_time
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except Exception as e:
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raise RuntimeError(f"LLM关键词提取失败: {e}") from e
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matched_terms = [] # 存储匹配到的Term对象
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# 步骤2: 使用向量检索找到相似的专业名词
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try:
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vector_start_time = time.time()
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# 对matched_terms中的每个关键字进行向量检索
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for current_key in query_keys:
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vector_results = self._noun_retriever.query(current_key, top_k=5, use_intersection=False)
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current_key_terms = set()
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# 添加向量检索结果
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for result in vector_results:
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if isinstance(result.get('synonymous', []), str):
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result['synonymous'] = result['synonymous'].split(';')
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term = Term(
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name=result.get('name'),
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synonymous=result.get('synonymous', []),
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description=result.get('description', '')
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)
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current_key_terms.add(term)
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if len(current_key_terms) > 0:
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reranked_terms = self._rerank_matched_terms(current_key, current_key_terms)
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matched_terms.extend(reranked_terms)
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vector_end_time = time.time()
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vector_time = vector_end_time - vector_start_time
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except Exception as e:
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raise RuntimeError(f"向量检索关键词时出错: {e}") from e
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# 提取所有Term对象的名称并排序
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# 将set类型的matched_terms转换为TermList类型
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term_list = TermList(terms=list(matched_terms))
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end_time = time.time()
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total_time = end_time - start_time
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# 输出整合的时间日志
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logging.info(f"关键词匹配耗时统计 - 总耗时: {total_time:.2f}秒, 问题关键词提取: {llm_time:.2f}秒, 向量检索+重排序: {vector_time:.2f}秒")
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return term_list, query_keys
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def _rewrite_query(self, query: str, keywords: TermList, query_keys:List[str], chat_history: List[Dict[str, str]] = None, context: str = "") -> QueryRewrite:
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"""
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对用户问题进行改写
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Args:
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query: 用户原始问题
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keywords: 匹配到的关键词列表
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query_keys: 用户查询中提取的关键词列表
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Returns:
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改写结果
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"""
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rewrite_start_time = time.time()
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# 准备问题改写提示
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# terms_dict = [term.model_dump(exclude={"description"}) for term in keywords.terms]
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terms_dict = [term.model_dump() for term in keywords.terms]
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keywords_str = json.dumps(terms_dict, ensure_ascii=False)
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query_rewrite_parser = PydanticOutputParser(pydantic_object=QueryRewrite)
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# formatted_prompt = query_rewrite_prompt.format(query=query,
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# output_format=query_rewrite_parser.get_format_instructions(),
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# keywords=keywords_str)
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formatted_prompt = query_rewrite_prompt_pro.format(query=query,
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output_format=query_rewrite_parser.get_format_instructions(),
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keywords=keywords_str,
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chat_history=chat_history,
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context=context)
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# 调用LLM
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response = self._llm.invoke(formatted_prompt, False)
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# 解析输出
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try:
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# 尝试直接解析JSON响应
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parsed_output = query_rewrite_parser.parse(response.content)
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rewrite_end_time = time.time()
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rewrite_time = rewrite_end_time - rewrite_start_time
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logging.info(f"问题改写耗时统计 - 总耗时: {rewrite_time:.2f}秒")
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return parsed_output
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except Exception as e:
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raise RuntimeError(f"解析问题改写结果时出错: {e}") from e
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def _judge_define_suffix(self, input_str: str) -> Tuple[bool, List[str]]:
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"""
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判断输入字符串是否包含定义的后缀，并返回所有匹配到的后缀名列表
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Args:
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input_str: 输入字符串
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Returns:
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Tuple[bool, List[str]]: (是否包含定义的后缀, 匹配到的后缀名列表)
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"""
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# 构建正则表达式模式，匹配大小写不敏感且前面可能带有.
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pattern = r'(?:\.?)(' + '|'.join(re.escape(field.get('name')) for field in self._suffix_keywords) + r')'
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# 使用 re.IGNORECASE 标志来忽略大小写，findall找到所有匹配
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matches = re.finditer(pattern, input_str, re.IGNORECASE)
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matched_suffixes = [match.group(1) for match in matches]
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return bool(matched_suffixes), matched_suffixes
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def process_query(self, query: str, conversation_context: str = "",
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chat_history: List[Dict[str, str]] = None,
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previous_slots: Dict[str, Any] = None,
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use_jieba: bool = False) -> Dict[str, Any]:
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"""
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处理用户问题的完整流程
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Args:
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query: 用户原始问题
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conversation_context: 会话背景信息
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chat_history: 历史对话记录，格式为[{"user": "content"}, {"assistant": "content"}]
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previous_slots: 历史槽位信息
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use_jieba: 是否使用jieba分词辅助提取关键词
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Returns:
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包含分类、关键词、改写和槽位填充结果的字典
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"""
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# 是否是扩展名
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# is_suffix, matched_suffixes = self._judge_define_suffix(query)
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# if is_suffix:
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# # 将所有匹配到的后缀名作为Term添加到结果中
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# suffix_terms = []
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# for suffix in matched_suffixes:
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# term_dict = next((item for item in self._suffix_keywords if item['name'].lower() == suffix.lower()), None)
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# if term_dict:
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# suffix_term = Term(
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# name=term_dict.get('name'),
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# synonymous=term_dict.get('synonymous', []),
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# description=json.dumps(term_dict.get('description', ''), ensure_ascii=False)
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# )
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# suffix_terms.append(suffix_term)
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# return Classification(vertical_classification="安装下载", sub_classification="查询"), TermList(terms=suffix_terms), QueryRewrite(rewrite=query), matched_suffixes
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if chat_history is None:
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chat_history = []
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if previous_slots is None:
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previous_slots = {}
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# 步骤1: 匹配关键词
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keywords_terms, query_keys = self._match_keywords(query, use_jieba)
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# 步骤2: 问题改写
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rewrite = self._rewrite_query(
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query=query,
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keywords=keywords_terms,
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query_keys=query_keys,
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chat_history=chat_history,
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context=conversation_context
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)
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# 步骤3: 进行意图识别和槽位填充
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result = self._process_intent_and_slot(rewrite.rewrite, conversation_context, chat_history, previous_slots)
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result.update({"keywords": keywords_terms.model_dump(),
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"rewrite": rewrite.model_dump(),
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"query_keys": query_keys})
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return result
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# # 步骤3: 进行意图分类
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# classification = self._classify_intent(query)
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# # 步骤4: 进行槽位填充
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# # 如果是有效分类，进行槽位填充
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# slot_filling_result = {}
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# if classification.vertical_classification not in ["其他", "闲聊"] and classification.sub_classification not in ["其他", "闲聊"]:
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# slot_filling_result = self._fill_slots(rewrite.rewrite, classification)
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# return {
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# "classification": classification.model_dump(),
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# "keywords": keywords_terms.model_dump(),
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# "rewrite": rewrite.model_dump(),
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# "query_keys": query_keys,
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# "slot_filling": slot_filling_result
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# }
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def _fill_slots(self, query: str, classification: Classification, conversation_context: str = "",
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chat_history: List[Dict[str, str]] = None,
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previous_slots: Dict[str, Any] = None,) -> Dict[str, Any]:
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"""
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根据分类结果对问题进行槽位填充
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Args:
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query: 用户原始问题
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classification: 意图分类结果
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keywords: 匹配的关键词列表
|
|
|
|
Returns:
|
|
填充后的槽位数据模型
|
|
"""
|
|
# 根据分类结果选择对应的数据模型
|
|
slot_model = self._get_slot_model(classification)
|
|
if not slot_model:
|
|
raise RuntimeError("未找到匹配的槽位模型")
|
|
|
|
# 使用LLM进行槽位填充
|
|
filled_slots = self._fill_slots_with_llm(query, classification, slot_model, conversation_context, chat_history, previous_slots)
|
|
|
|
# 检查必填槽位是否都已填充
|
|
is_complete, missing_slots = filled_slots.check_required_slots()
|
|
|
|
return {
|
|
"is_complete": is_complete,
|
|
"missing_slots": missing_slots,
|
|
"filled_data": filled_slots.model_dump()
|
|
}
|
|
|
|
def _get_slot_model(self, classification: Classification) -> Optional[type]:
|
|
"""
|
|
根据分类结果获取对应的槽位模型类，用于统一提示词处理
|
|
|
|
Args:
|
|
classification: 意图分类结果
|
|
|
|
Returns:
|
|
对应的槽位模型类
|
|
"""
|
|
# 软件问题
|
|
if classification.vertical_classification == "软件问题":
|
|
if classification.sub_classification == "软件功能":
|
|
return SoftwareFunctionSlots
|
|
elif classification.sub_classification == "故障排查":
|
|
return SoftwareTroubleShootingSlots
|
|
|
|
# 业务问题
|
|
elif classification.vertical_classification == "业务问题":
|
|
if classification.sub_classification == "专业咨询":
|
|
return ProfessionalConsultingSlots
|
|
elif classification.sub_classification == "数据问题":
|
|
return DataProblemSlots
|
|
|
|
# 安装下载注册
|
|
elif classification.vertical_classification == "安装下载注册":
|
|
if classification.sub_classification == "后缀名咨询":
|
|
return FileExtensionConsultingSlots
|
|
elif classification.sub_classification == "软件锁类":
|
|
return SoftwareLockSlots
|
|
elif classification.sub_classification == "安装下载类":
|
|
return InstallationDownloadSlots
|
|
elif classification.sub_classification == "问题排查类":
|
|
return ProblemDiagnosisSlots
|
|
|
|
# 其他
|
|
elif classification.vertical_classification == "其他":
|
|
return OtherSlots
|
|
|
|
return None
|
|
|
|
def _fill_slots_with_llm(self, query: str,
|
|
classification: Classification,
|
|
slot_model_class: type,
|
|
conversation_context: str = "",
|
|
chat_history: List[Dict[str, str]] = None,
|
|
previous_slots: Dict[str, Any] = None) -> Any:
|
|
"""
|
|
使用LLM进行槽位填充
|
|
|
|
Args:
|
|
query: 用户原始问题
|
|
classification: 意图分类结果
|
|
slot_model_class: 槽位模型类
|
|
|
|
Returns:
|
|
填充后的槽位数据模型实例
|
|
"""
|
|
# 准备提示词
|
|
slot_parser = PydanticOutputParser(pydantic_object=slot_model_class)
|
|
|
|
formatted_prompt = slot_filling_prompt.format(
|
|
query=query,
|
|
vertical_classification=classification.vertical_classification,
|
|
sub_classification=classification.sub_classification,
|
|
output_format=slot_parser.get_format_instructions(),
|
|
conversation_context=conversation_context,
|
|
chat_history=json.dumps(chat_history,ensure_ascii=False),
|
|
previous_slots=json.dumps(previous_slots,ensure_ascii=False),
|
|
)
|
|
|
|
# 调用LLM
|
|
response = self._llm.invoke(formatted_prompt, False)
|
|
|
|
try:
|
|
# 尝试解析LLM响应
|
|
parsed_output = slot_parser.parse(response.content)
|
|
return parsed_output
|
|
except Exception as e:
|
|
# 如果解析失败，创建一个空的模型实例
|
|
empty_instance = slot_model_class()
|
|
return empty_instance
|
|
|
|
def _process_intent_and_slot(self, user_input: str, conversation_context: str = "",
|
|
chat_history: List[Dict[str, str]] = None,
|
|
previous_slots: Dict[str, Any] = None) -> Dict[str, Any]:
|
|
"""
|
|
使用统一提示词同时进行意图识别和槽位填充
|
|
|
|
Args:
|
|
user_input: 当前用户输入
|
|
conversation_context: 会话背景信息
|
|
chat_history: 历史对话记录，格式为[{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]
|
|
previous_slots: 历史槽位信息
|
|
|
|
Returns:
|
|
包含意图分类和槽位填充结果的字典
|
|
"""
|
|
# 初始化默认值
|
|
if chat_history is None:
|
|
chat_history = []
|
|
|
|
if previous_slots is None:
|
|
previous_slots = {}
|
|
|
|
# 生成槽位映射文档
|
|
slot_mapping_doc = generate_slot_mapping_doc()
|
|
|
|
# 准备提示词
|
|
parser = PydanticOutputParser(pydantic_object=IntentAndSlotResult)
|
|
formatted_prompt = intent_and_slot_prompt.format(
|
|
conversation_context=conversation_context,
|
|
chat_history=json.dumps(chat_history, ensure_ascii=False),
|
|
previous_slots=json.dumps(previous_slots, ensure_ascii=False),
|
|
user_input=user_input,
|
|
slot_mapping_doc=slot_mapping_doc,
|
|
output_format=parser.get_format_instructions(),
|
|
classification_info=classification_info
|
|
)
|
|
|
|
# 调用LLM
|
|
llm_start_time = time.time()
|
|
response = self._llm.invoke(formatted_prompt + output_example, False)
|
|
llm_end_time = time.time()
|
|
llm_time = llm_end_time - llm_start_time
|
|
|
|
|
|
try:
|
|
# 解析LLM响应为JSON
|
|
result_json = parser.parse(response.content)
|
|
classification=result_json.classification
|
|
slot_filling=result_json.slots
|
|
is_complete, missing_slots = slot_filling.check_required_slots()
|
|
expected_slot_model = self._get_slot_model(classification)
|
|
|
|
# 添加容错处理，发生概率较低，但仍需处理
|
|
if expected_slot_model is None:
|
|
# 添加容错处理，应对LLM返回错误分类信息,一级分类跟二级分类错乱
|
|
# 重新分类
|
|
classify_start_time = time.time()
|
|
classification = self._classify_intent(user_input, conversation_context, chat_history, previous_slots)
|
|
classify_end_time = time.time()
|
|
classify_time = classify_end_time - classify_start_time
|
|
# logging.info(f"重新分类耗时: {classify_time:.2f}秒")
|
|
|
|
fill_start_time = time.time()
|
|
fill_slots = self._fill_slots(user_input, classification, conversation_context, chat_history, previous_slots)
|
|
fill_end_time = time.time()
|
|
fill_time = fill_end_time - fill_start_time
|
|
all_time=fill_end_time-llm_start_time
|
|
logging.info(f"总耗时：{all_time:.2f}秒,首次槽位+分类:{llm_time:.2f}秒, 重新分类耗时: {classify_time:.2f}秒, 重新槽位填充耗时: {fill_time:.2f}秒")
|
|
|
|
result = {
|
|
"classification": classification.model_dump(),
|
|
"slot_filling": fill_slots
|
|
}
|
|
logging.warning(f"重新分类与槽点填充")
|
|
return result
|
|
elif expected_slot_model.__name__ != type(slot_filling).__name__:
|
|
# 添加容错处理，应对LLM槽位与分类不匹配。重新填充槽位
|
|
fill_start_time = time.time()
|
|
slot_filling = self._fill_slots(user_input, classification)
|
|
fill_end_time = time.time()
|
|
fill_time = fill_end_time - fill_start_time
|
|
all_time=fill_end_time-llm_start_time
|
|
logging.info(f"总耗时：{all_time:.2f}秒,首次槽位+分类:{llm_time:.2f}秒, 重新槽位填充耗时: {fill_time:.2f}秒")
|
|
|
|
result = {
|
|
"classification": classification.model_dump(),
|
|
"slot_filling": slot_filling
|
|
}
|
|
logging.warning(f"重新填充槽点")
|
|
return result
|
|
|
|
logging.info(f"意图识别+槽位LLM调用耗时: {llm_time:.2f}秒")
|
|
|
|
# 构建最终结果
|
|
result = {
|
|
"classification": classification.model_dump(),
|
|
"slot_filling": {
|
|
"is_complete": is_complete,
|
|
"missing_slots": missing_slots,
|
|
"filled_data": slot_filling.model_dump()
|
|
}
|
|
}
|
|
|
|
return result
|
|
|
|
except Exception as e:
|
|
logging.error(f"process_intent_and_slot error:{e}")
|
|
raise RuntimeError(f"process_intent_and_slot error:{e}") from e |