diff --git a/app/chat_qa/classification/general_text_extract.py b/app/chat_qa/classification/general_text_extract.py new file mode 100644 index 0000000..5157962 --- /dev/null +++ b/app/chat_qa/classification/general_text_extract.py @@ -0,0 +1,43 @@ +from langchain_core.output_parsers import StrOutputParser +from langchain_core.prompts import PromptTemplate + +from app.llm import llm + +general_prompt = """ +你是一个专业的文本改写助手。请分析输入文本，按以下规则处理： + +**规则：** +1. 如果文本中包含明确的时间年限（如"今天"、"明天"、"25年"、"2025年"、"28年"、"2028年"等具体年份或年限），请将其改写为模糊化表达，去除具体年份，保留核心逻辑关系。 +2. 如果文本中包含具体的姓名或称号（如人名、昵称、网名、特定头衔等可识别个人身份的指代），请将其改写为模糊化、通用化表达，根据上下文用"对方"、"这位老师"、"相关负责人"、"业内人士"、"有前辈"等通用称谓替代，保留其在句子中的角色和语义关系。 +3. 如果文本中**既不包含**明确的时间年限，**也不包含**任何具体的姓名或称号，则**保持原样返回**，不做任何修改。 +4. 改写时保留原文的核心语义：毕业时间、校招条件限制、社招要求、AI岗位趋势、竞争压力、简历包装等关键信息。 +5. 去除具体时间数字，用"毕业后X年"、"校招年限要求"、"当前"等相对化表达替代；去除具体姓名称号，用不指向特定个人的通用化称谓替代。 +6. 保持原文字数大致相当，不要过度扩写或删减。 + +**示例一（时间模糊化）：** + +输入："如果25年毕业，校招机会基本没有了，因为25年毕业的学生在28年已经不符合校招条件，只能走社招。社招通常要求至少一年工作经验，且今年AI校招的美术岗位在大量减少，竞争只会更大。学得很好也需要包装简历走社招，和社保没有直接关系。" + +输出："如果刚毕业不久，校招机会基本没有了，因为毕业一段时间后已经不符合校招年限条件，只能走社招。社招通常要求至少一年工作经验，且当前AI校招的美术岗位在大量减少，竞争只会更大。学得很好也需要包装简历走社招，和社保没有直接关系。" + +**示例二（时间与姓名称号同时模糊化）：** + +输入："王工之前提醒过，25届的同学最迟26年就要准备转社招，再拖下去机会更少。" + +输出："有前辈之前提醒过，刚毕业的同学最迟毕业一两年内就要准备转社招，再拖下去机会更少。" + +**请直接输出处理后的文本，不要解释，不要添加额外内容。** + +输入文本：{text} +""" + +class TextExtract: + + async def text_extract(self, text:str)->str: + if not text: + return "" + prompt_template = PromptTemplate(template=general_prompt, input_variables=["text"]) + str_output_parser = StrOutputParser() + chain = prompt_template | llm |str_output_parser + answer = await chain.ainvoke({"text": text}) + return answer diff --git a/app/chat_qa/classification/history_intent.py b/app/chat_qa/classification/history_intent.py new file mode 100644 index 0000000..e92b781 --- /dev/null +++ b/app/chat_qa/classification/history_intent.py @@ -0,0 +1,455 @@ +import os + +import asyncio +import json +import time +from datetime import datetime, timedelta +from typing import List, Dict + +from langchain_core.prompts import PromptTemplate + +from app.chat_qa.classification.text_intent import TextIntent +from app.chat_qa.classification.general_text_extract import TextExtract +from app.chat_qa.classification.standard_intent import standard_intent +from app.client.embedding_client_manager import EmbeddingClientManager, embedding_client +from app.client.milvus_client_manager import milvus_client +from app.client.mysql_client_manager import db_assistant_mysql_client_manager +from app.dto.summary_dto import SummaryDto +from app.llm import llm +from app.models.mysql import ArchiveMessages +from app.repository.archive_messages_repository import ArchiveMessagesRepository +from app.repository.milvus.intent_classification_repository import IntentClassificationRepository +from app.repository.milvus.summary_repository import SummaryRepository +from app.core.log import logger +from app.reranker_utils import get_reranker_model +import re + + +intent_prompt = """ +你是一个专业的QA提取专家。请分析以下聊天记录，提取或生成1-2个高质量的QA对。 + +## 核心规则 + +### 1. QA提取规则 +- 优先提取用户明确提出的问题及其对应的回答 +- 如果对话中没有明确的问题，根据讨论内容生成一个总结性问题 +- 答案必须基于聊天记录中的实际内容，不能编造 +- 如果某个问题在聊天记录中没有明确答案，则不生成QA +- 最多生成2个QA对，优先选择最有价值的信息 + +### 2. 单意图选择规则（关键） +**每个QA对只能有一个意图标签，不能以顿号、逗号或“和”分隔多个意图。** + +当内容可能对应多个意图时，按以下优先级选择唯一意图： + +| 优先级 | 选择规则 | 说明 | +|-------|---------|------| +| **P0** | **优先匹配已有意图标签** | 必须优先从已有意图体系（含扩展示例）中选择最匹配的标签，禁止新建与原标签语义高度重复的意图。**当销售单方面介绍课程内容（无用户明确提问）时，直接使用对应的主题标签（如“3D场景建模”、“3D角色建模”），严禁新建“XXX课程咨询”类标签。** 例：“场景线上课程介绍” → 使用“3D场景建模”，而非“场景线上课程咨询”。 | +| **P1** | **核心诉求优先** | 选择最能代表用户核心诉求的意图。例如：报名流程说明 → “报名咨询”（核心是报名咨询），而非“线上直播教学”（只是提到线上） | +| **P2** | **动作维度优先于主题维度** | 当主题和诉求冲突时，优先选择主题维度。例如：3D建模费用 → “3D建模”（主题） | +| **P3** | **直接提及优先于间接关联** | 用户明确说出的意图优先于推断出的意图 | +| **P4** | **去重合并** | 多个子诉求属于同一类别时，只输出一个意图标签 | + +### 3. 意图分类体系（必须从中选择或基于其扩充） +{intent_list} + +**扩充规则：** 仅当用户问题涉及的具体方向在已有标签中完全无法覆盖时，才可创建更细粒度的标签。创建时必须严格对比已有标签，确保无重复语义。例如：“原画好就业吗” → 可扩充为“原画就业”，因为已有标签中无“就业”与“原画”的结合且不冲突。 + +### 4. 多意图拆分规则（新增） +- 若同一段对话明确涉及多个不重叠的核心意图（如同时介绍“3D场景建模”和“就业咨询”），必须拆分为多个QA对，每个QA对聚焦一个意图。 +- 每个QA对的answer仅包含与该意图直接相关的信息，不相关的信息不得混杂其中，避免信息污染。 +- 若几个意图高度关联无法拆分（如介绍课程时自然提到费用，但核心是课程），则按单意图规则选择最核心的标签，其余信息可融入answer作为补充，但intents仍为单一核心意图。 + +## 输出格式要求 +- 输出纯JSON，不要任何解释、不要markdown标记、不要注释 +- 将中文引号 "" '' 替换为英文引号 "" '' +- 删除所有注释（// 和 /* */） +- 删除markdown代码块标记（```json ```） +- 删除JSON前后多余的文本说明 +- 修复末尾多余的逗号（如 }},] 或 }},}}） +- 确保所有字符串用双引号包裹 +- 确保布尔值是小写的 true/false，null 是小写的 +- 如果键名没有引号，给键名加上双引号 + +## 输出格式 +{{ + "qa_pairs": [ + {{ + "intents": "意图标签（只能有一个，不能并列）", + "answer": "回答内容" + }}, + {{ + "intents": "意图标签（只能有一个，不能并列）", + "answer": "回答内容" + }} + ] +}} + + +# 历史对话 +{message_str} +其中INTERNAL表示销售，EXTERNAL表示用户 + +要求： +- 一个intents对应的answer必须包含聊天记录中所有可用信息，确保信息不遗漏 +- 意图标签必须从上述分类中选择或基于其合理扩充 +- **intents字段必须是单个字符串，不能包含顿号、逗号或“和”** +- 优先选择最有价值的1-2个QA对 +- 当销售（INTERNAL）主动进行课程/产品介绍，而非回答用户（EXTERNAL）明确提问时，意图标签应选择主题维度（如：3D角色建模），而非诉求维度（如：课程咨询） +""" +from FlagEmbedding import FlagReranker + +class HistoryIntent: + + def __init__(self, archive_messages_repository: ArchiveMessagesRepository, + embedding_client: EmbeddingClientManager, + summary_repository: SummaryRepository, + reranker: FlagReranker + ): + self.archive_messages_repository = archive_messages_repository + self.embedding_client = embedding_client + self.summary_repository = summary_repository + self.reranker = reranker + + async def get_day_ms_timestamp(self, date_str: str): + """ + 输入日期字符串 如 "2025-06-10" + :return: start_ms(当日0点毫秒时间戳), end_ms(当日23:59:59毫秒时间戳) + """ + # 解析日期，生成当日 00:00:00 + day_start = datetime.strptime(date_str, "%Y-%m-%d") + # 当日最后一秒 23:59:59 + day_end = day_start + timedelta(days=1) - timedelta(seconds=1) + + # timestamp() 返回秒，*1000 转毫秒，转整数 + start_ms = int(day_start.timestamp() * 1000) + end_ms = int(day_end.timestamp() * 1000) + return start_ms, end_ms + + async def get_message_summary(self, date: str = None): + stime = time.time() + intent_repo = IntentClassificationRepository() + if date: + start_ts, end_ts = await self.get_day_ms_timestamp(date) + day_ArchiveMessages = await self.archive_messages_repository.get_message_statistics_by_date(start_date=start_ts, + end_date=end_ts) + logger.info(f"{date}共产生了{len(day_ArchiveMessages)}条会话") + else: + day_ArchiveMessages = await self.archive_messages_repository.get_message_statistics_by_date() + + + # 获取意图分类 + intent_list:list[str] = await intent_repo.get_style_intent_categories() + # 改成map + date_user_message_a_set = set() + summary_list: list[str] = [] + for idx, day_message in enumerate(day_ArchiveMessages, 1): #打印计数 + logger.info(f"处理第{idx}会话") + day_date = str(day_message.created_at) + user_a = day_message.from_user + # 找出发送人相关的接收人信息 + date_user_message_a = await self.archive_messages_repository.get_message_statistics_by_date_user(day_date, + user_a) + + # 找出发送人与接收人 相关的所有会话信息 + for date_a in date_user_message_a: + + date_user_b = date_a.to_user + # if date_user_b != "[\"wmI1AkDQAAdVhy2ENtY6fecaw9-pi4uQ\"]": + # continue + + date_user_a = date_a.from_user + date_user_b_clean = date_user_b.replace("[\"", "").replace("\"]", "") + date_user_a_append = "[\"" + date_user_a + "\"]" + # 判断只要存在就跳过 + if date_user_a + date_user_b in date_user_message_a_set or date_user_b_clean + date_user_a_append in date_user_message_a_set: + logger.info(f"已存在{date_user_a}和{date_user_b}在{day_date}对话，跳过") + continue + logger.info(f"查询{date_user_a}和{date_user_b}在{day_date}对话开始") + list1 = await self.archive_messages_repository.get_message_statistics_by_date_user(day_date, + date_user_a, + date_user_b) + + list2 = await self.archive_messages_repository.get_message_statistics_by_date_user(day_date, + date_user_b_clean, + date_user_a_append) + # 查询过的聊天账号，后续不再查询 + date_user_message_a_set.add(date_user_a + date_user_b) + + date_user_message_a_set.add(date_user_b_clean + date_user_a_append) + coro_list = list1 + list2 + coro_list.sort(key=lambda x: x.created_at, reverse=False) + logger.info(f"查询{date_user_a}和{date_user_b}对话共：{len(coro_list)}") + # 获取意图分类 + summary_res = await self.intent_message(coro_list, intent_list) + summary_list.append(summary_res) + + logger.info(f"总共：{summary_list}") + + logger.info(f"识别到意图数量：{len(summary_list)}") + + #将意图分类结果进行合并 + summary_merged :Dict[str, List[str]] = await self.merge_intents_from_content(summary_list) + logger.info(f"合并后的意图数量：{len(summary_merged)},{summary_merged}") + + check_text = TextIntent() + text_extract = TextExtract() + new_summary_merged: Dict[str, List[str]]={} + + for key, value in summary_merged.items(): + if key in standard_intent: + logger.info(f"已存在标准意图：{key}") + continue + # 重排序，取最相近的2个 + reranked_value:List[str] = await self.rerank_merged_intents(value, key) + # 检查意图, 并返回最匹配的意图 + intent_key: str = await check_text.intent_message(str(reranked_value), key, intent_list) + logger.info(f"老的意图：{key},新的意图:{intent_key}, value:{reranked_value}") + if intent_key in standard_intent: + logger.info(f"已存在标准意图：{intent_key}") + continue + key = intent_key + # 跟标准进行重排序 + std_pairs = [[key, std] for std in standard_intent] + try: + std_scores = self.reranker.compute_score(std_pairs, normalize=True) + if isinstance(std_scores, list): + max_sim = max(std_scores) if std_scores else 0.0 + else: + max_sim = std_scores or 0.0 + if max_sim > 0.9: + top_idx = std_scores.index(max_sim) if isinstance(std_scores, list) else 0 + logger.info(f"key[{key}]与标准意图[{standard_intent[top_idx]}]相似度{max_sim:.3f}>0.9，跳过") + continue + except Exception as e: + logger.warning(f"标准意图重排序失败: {e}，继续处理") + + + reranked_value_tmp: List[str] = [] + for answers in reranked_value: + # 文本改写 + answer = await text_extract.text_extract(answers) + reranked_value_tmp.append(answer) + reranked_value = reranked_value_tmp + #从milvus中获取 + milvus_content = await intent_repo.get_content_by_intent_category(key) + #合并 + merged = list(dict.fromkeys(reranked_value + milvus_content)) + if len(merged) > 2: + # 重新排序 + merged = await self.rerank_merged_intents(merged, key, top_k=1) + + for content in merged: + insert_data = [] + batch_embeddings = await self.embedding_client.aembed_documents([content]) + if batch_embeddings: + insert_data.append({ + "intent_category": key, + "content": content, + "content_vector": batch_embeddings[0] + }) + if insert_data: + await intent_repo.upsert_intent_data(insert_data) + logger.info(f"key[{key}]入库完成，upsert {len(insert_data)} 条") + + logger.info(f"运行结束：{(time.time() - stime)}") + + + async def intent_message(self, coro_list: List[ArchiveMessages], intent_list:list[str]) -> str: + + lines = [ + f"{msg.created_at} {msg.from_role}：{msg.content}" + for msg in coro_list + if not (msg.msgtype == "voice" and "[语音]" not in msg.content) + ] + message_str = "\n".join(lines) + + prompt_template = PromptTemplate(template=intent_prompt, input_variables=["message_str","intent_list"]) + chain = prompt_template | llm + result = await chain.ainvoke({"message_str": message_str,"intent_list": str(intent_list)}) + res_new = str(result.content) if hasattr( + result, 'content') else str(result) + return res_new + + async def merge_intents_from_content(self, summary_list:list[str]) -> Dict[str, List[str]]: + merged: Dict[str, List[str]] = {} + for content in summary_list: + + json_candidates = self._extract_jsons(content) + + for json_str in json_candidates: + try: + data = json.loads(json_str) + qa_pairs = data.get("qa_pairs", []) + for pair in qa_pairs: + intent = pair.get("intents", "") + answer = pair.get("answer", "") + if intent and answer and len(answer) > 6: + + if intent not in merged: + merged[intent] = [] + merged[intent].append(answer) + except (json.JSONDecodeError, AttributeError): + continue + + #logger.info(f"合并完成，共 {len(merged)} 个intent") + return merged + + def _extract_jsons(self, content: str) -> List[str]: + results = [] + start = 0 + while start < len(content): + brace_start = content.find("{", start) + if brace_start == -1: + break + depth = 0 + end = brace_start + for i in range(brace_start, len(content)): + if content[i] == "{": + depth += 1 + elif content[i] == "}": + depth -= 1 + if depth == 0: + end = i + 1 + break + if end > brace_start: + results.append(content[brace_start:end]) + start = end + else: + start = brace_start + 1 + return results + + + def _calc_completeness_score(self, answer: str, query: str) -> float: + if not answer: + return 0.0 + + length_score = min(len(answer) / 50.0, 1.0) + + punctuations = "。！？!?：:；;，," + punct_score = 1.0 if any(p in answer for p in punctuations) else 0.0 + + modal_particles = ["哦", "呢", "啊", "吧", "嘛", "呀", "啦"] + modal_penalty = 0.3 if any(answer.rstrip().endswith(m) for m in modal_particles) else 0.0 + + unique_ratio = len(set(answer)) / max(len(answer), 1) + diversity_score = min(unique_ratio / 0.4, 1.0) + + query_chars = set(query) + answer_chars = set(answer) + overlap = query_chars & answer_chars + keyword_score = min(len(overlap) / max(len(query_chars), 1), 1.0) + + score = ( + 0.30 * length_score + + 0.20 * punct_score + + 0.20 * diversity_score + + 0.20 * keyword_score + - 0.10 * modal_penalty + ) + return max(0.0, min(score, 1.0)) + + def _filter_invalid_answers(self, answers: List[str]) -> List[str]: + modal_particles = ["哦", "呢", "啊", "吧", "嘛", "呀", "啦"] + filtered = [] + for a in answers: + if len(a) < 6: + continue + stripped = a.strip() + if not stripped: + continue + if len(stripped) <= 3 and any(stripped.endswith(m) for m in modal_particles): + continue + if len(set(stripped)) < max(2, len(stripped) // 3): + continue + filtered.append(a) + return filtered + + async def rerank_merged_intents(self, intent_value: List[str], query: str, top_k: int = 2, max_rerank: int = 20) -> List[str]: + if not intent_value: + return [] + + filtered = self._filter_invalid_answers(intent_value) + if not filtered: + logger.warning(f"所有答案均被规则过滤，返回原数据前{top_k}条") + return intent_value[:top_k] + + sorted_by_len = sorted(filtered, key=lambda x: len(x), reverse=True) + rerank_candidates = sorted_by_len[:max_rerank] + + if self.reranker is None: + logger.warning("reranker不可用，直接返回前top_k条") + return rerank_candidates[:top_k] + + pairs = [[query, answer] for answer in rerank_candidates] + try: + scores = self.reranker.compute_score(pairs, normalize=True) + except Exception as e: + logger.error(f"rerank失败: {e}，直接返回前top_k条") + return rerank_candidates[:top_k] + + if not isinstance(scores, list): + scores = [scores] + + final_scores = [] + for i, answer in enumerate(rerank_candidates): + rerank_score = scores[i] if i < len(scores) else 0.0 + completeness_score = self._calc_completeness_score(answer, query) + fused = 0.7 * rerank_score + 0.3 * completeness_score + final_scores.append((answer, fused, rerank_score, completeness_score)) + + final_scores.sort(key=lambda x: x[1], reverse=True) + print(final_scores) + result = [item[0] for item in final_scores[:top_k]] + + details = ", ".join( + f"'{a[:20]}...'(融合={f:.3f}, rerank={r:.3f},完整={c:.3f})" + for a, f, r, c in final_scores[:top_k] + ) + logger.info(f"rerank完成: 过滤前{len(intent_value)}条→过滤后{len(filtered)}条→rerank{len(rerank_candidates)}条→top{top_k}: {details}") + return result + +async def build(day:str): + logger.info(f"执行日期：{day}") + db_assistant_mysql_client_manager.init() + embedding_client.init() + milvus_client.init() + try: + async with db_assistant_mysql_client_manager.session_factory() as db_assistant: + archive_messages_repository = ArchiveMessagesRepository(db_assistant) + summary_repository = SummaryRepository() + reranker = get_reranker_model() + service = HistoryIntent(archive_messages_repository, embedding_client, summary_repository, reranker) + await service.get_message_summary(day) + + # intent_value: List[str] = ['公司介绍','课程咨询','3D建模','3D场景建模','3D角色建模','2D原画','二次元风格','写实风格','动作/分镜','游戏动作/分镜','开发','UE引擎开发','3D大师课','GGAC绘殓课程','大学生扶持计划','兴趣/提升班','学习模式','线下面授教学','线上直播教学','寒暑假教学','提供服务','优势/竞争力','校区分布','退费咨询','住宿咨询','学不会怎么办','就业咨询','学历咨询'] + # pairs = [["场景建模", answer] for answer in intent_value] + # + # scores = reranker.compute_score(pairs, normalize=True) + # print(scores) + + + + finally: + await embedding_client.close() + await db_assistant_mysql_client_manager.close() + milvus_client.close() + +if __name__ == '__main__': + + start = datetime(2026, 7, 17) + end = datetime(2026, 7, 17) + + result = [] + temp = start + while temp <= end: + # 按 年-月-日 无补零格式输出 + date_format = f"{temp.year}-{temp.month}-{temp.day}" + + result.append(date_format) + temp += timedelta(days=1) + for date in result: + asyncio.run(build(date)) + + + diff --git a/app/chat_qa/classification/standard_intent.py b/app/chat_qa/classification/standard_intent.py new file mode 100644 index 0000000..da4842a --- /dev/null +++ b/app/chat_qa/classification/standard_intent.py @@ -0,0 +1 @@ +standard_intent:list[str] = ['公司介绍','课程咨询','3D建模','3D场景建模','3D角色建模','2D原画','二次元风格','写实风格','动作/分镜','游戏动作/分镜','开发','UE引擎开发','3D大师课','GGAC绘殓课程','大学生扶持计划','兴趣/提升班','学习模式','线下面授教学','线上直播教学','寒暑假教学','提供服务','优势/竞争力','校区分布','退费咨询','住宿咨询','学不会怎么办','就业咨询','学历咨询'] \ No newline at end of file diff --git a/app/chat_qa/classification/text_intent.py b/app/chat_qa/classification/text_intent.py new file mode 100644 index 0000000..3706165 --- /dev/null +++ b/app/chat_qa/classification/text_intent.py @@ -0,0 +1,107 @@ +import json +from typing import List + +from langchain_core.output_parsers import StrOutputParser +from langchain_core.prompts import PromptTemplate + +from app.llm import llm +from app.repository.milvus.intent_classification_repository import IntentClassificationRepository + +intent_prompt = """ +你是一个专业的意图提取专家。请分析以下文本内容，并结合当前已有的意图标签，判断是否符合；若不符合则生成一个新意图，符合则直接返回原意图。 + +## 输入说明 +- **文本内容**：`{text}` +- **目前的意图**：`{intent}`（可能为空） + +## 核心规则 + +### 1. 意图提取与对比规则 +- 首先从文本中提取1个核心意图（提取方法见下方“意图提取规则”）。 +- 将提取出的意图与给定的`{{intent}}`进行对比： + - 若两者**完全相同**（字符串完全一致），则最终输出意图为原意图`{{intent}}`。 + - 若两者不同，或`{{intent}}`为空，则最终输出意图为新提取的意图。 +- 无论采用原意图还是新意图，都必须来自或可扩充自“意图分类体系”。 + +### 2. 意图提取规则（用于从文本中提取核心意图） +- 若文本包含用户明确的问题，提取其核心诉求对应的意图。 +- 若文本为销售介绍/陈述内容，**聚焦于其最想传达的核心功能或核心信息**，提取总结性意图。**不得将补充说明信息（如附带提到的地点、时间等）误判为核心意图。** + - 例如：文本为“线上课程后期可以转到线下，我们线下校区在上海”，核心是“线上转线下”这一功能，而非“校区分布”，因此应扩充为“线上转线下”，而不是使用“校区分布”。 +- 意图必须基于实际内容，不编造。 +- 仅提取1个意图标签。 + +### 3. 单意图选择规则（关键） +**只能输出一个意图标签，不能以顿号、逗号或“和”分隔多个意图。** + +当内容可能对应多个意图时，按以下优先级选择唯一意图： + +| 优先级 | 选择规则 | 说明 | +|-------|---------|------| +| **P0** | **优先匹配已有意图标签** | 必须优先从已有意图体系（含扩展示例）中选择最匹配的标签。**若已有标签中无准确对应，则按扩充规则创建新标签，严禁使用仅部分相关或语义模糊的已有标签。** 当销售单方面介绍课程内容（无用户明确提问）时，直接使用对应的主题标签（如“3D场景建模”），若主题为已有标签未覆盖的功能/服务（如“线上转线下”），则应扩充新标签，不得强行归类为“校区分布”等。 | +| **P1** | **核心诉求优先** | 选择最能代表文本核心诉求的意图，**忽略次要的补充信息**。例如：文本“线上课程可转线下，校区在上海” → 核心是“线上转线下”，而非“校区分布”；报名流程说明 → “报名咨询”，而非“线上直播教学”。 | +| **P2** | **主题维度优先于动作维度** | 当主题和动作诉求冲突时，优先选择主题维度。例如：3D建模费用 → “3D建模”（主题） | +| **P3** | **直接提及优先于间接关联** | 文本明确说出的核心意图优先于推断出的意图 | +| **P4** | **去重合并** | 多个子诉求属于同一类别时，只输出一个意图标签 | + +### 4. 意图分类体系（必须从中选择或基于其扩充） +{intent_list} + +**扩充规则：** 当文本核心意图无法被已有标签准确覆盖时，必须创建更精准的新标签。创建时： +- 新标签应简洁体现核心语义（如“线上转线下”）。 +- 严格对比已有标签，确保无重复语义。 +- **示例**：文本“线上课程后期可以转到线下” → 已有标签无对应，扩充为“线上转线下”；文本“原画好就业吗” → 扩充为“原画就业”。 + +### 5. 多意图处理 +若同一段文本明确涉及多个不重叠的核心意图，仅选择最能代表核心内容的1个意图。 + +## 输出格式要求 +- 输出纯JSON，不要任何解释、不要markdown标记、不要注释 +- 确保所有字符串用双引号包裹 +- 输出格式固定为： +{{ + "intent": "最终的意图标签（原意图或新意图，只能有一个）" +}} +""" +from app.core.log import logger +class TextIntent: + + async def intent_message(self, text:str, intent:str, intent_list:list[str]) -> str: + + prompt_template = PromptTemplate(template=intent_prompt, input_variables=["text","intent","intent_list"]) + chain = prompt_template | llm + result = await chain.ainvoke({"text": text,"intent":intent,"intent_list": str(intent_list)}) + res_new = str(result.content) if hasattr( + result, 'content') else str(result) + + intent:str="" + try: + data = json.loads(res_new) + intent = data.get("intent", "") + except (json.JSONDecodeError, AttributeError): + logger.info(f"无法解析JSON：{res_new}") + return intent + + + def _extract_jsons(self, content: str) -> List[str]: + results = [] + start = 0 + while start < len(content): + brace_start = content.find("{", start) + if brace_start == -1: + break + depth = 0 + end = brace_start + for i in range(brace_start, len(content)): + if content[i] == "{": + depth += 1 + elif content[i] == "}": + depth -= 1 + if depth == 0: + end = i + 1 + break + if end > brace_start: + results.append(content[brace_start:end]) + start = end + else: + start = brace_start + 1 + return results \ No newline at end of file diff --git a/app/chat_qa_query/query_graph.py b/app/chat_qa_query/query_graph.py index d0b9664..556510b 100644 --- a/app/chat_qa_query/query_graph.py +++ b/app/chat_qa_query/query_graph.py @@ -45,7 +45,7 @@ if __name__ == '__main__': archive_messages_mysql_repository = ArchiveMessagesRepository(db_session) qa_milvus_repository = QARepository() - state: QueryQAState = QueryQAState(original_query="老师现在建模课多少钱呢？", history=['2026-07-10T15:42:13 EXTERNAL 老师现在建模课多少钱呢？\n']) + state: QueryQAState = QueryQAState(original_query="发票？", history=['2026-07-10T15:42:13 EXTERNAL 我想学角色，我不知道能不能学，好不好学\n']) context = QueryQAContext(meta_mysql_repository=archive_messages_mysql_repository, qa_milvus_repository=qa_milvus_repository ) diff --git a/app/chat_qa_query/query_nodes/node_rewrite.py b/app/chat_qa_query/query_nodes/node_rewrite.py index 844fd2b..2fcfef0 100644 --- a/app/chat_qa_query/query_nodes/node_rewrite.py +++ b/app/chat_qa_query/query_nodes/node_rewrite.py @@ -7,8 +7,9 @@ from langchain_core.prompts import PromptTemplate from app.chat_qa_query.query_qa_state import QueryQAState from app.llm import llm from app.core.log import logger +from app.repository.milvus.intent_classification_repository import IntentClassificationRepository -problem_rewriting_prompt = """ +problem_rewriting_prompt1 = """ 你是一名智能客服意图分类助手。你的任务是根据用户的历史会话和当前问题，提取用户的核心诉求，并输出标准化的意图标签。 ## 核心原则（必须严格遵守） @@ -214,7 +215,201 @@ problem_rewriting_prompt = """ 历史聊天：{history_text} 请直接输出JSON： """ +problem_rewriting_prompt = """ +你是一名智能客服意图分类助手。你的任务是根据用户的历史会话和当前问题，提取用户的核心诉求，并输出标准化的意图标签。 +## 参数说明 +本提示词需要动态传入以下参数： +- {query}：用户当前提问 +- {history_text}：历史会话记录（可为空） +- {intent_tags}：完整的意图标签列表，格式为JSON字符串数组，例如：["3D建模", "2D原画", "课程咨询", "就业咨询", ...]。**所有输出的意图标签必须严格从该列表中选择，不得自创或修改。** + +## 核心原则（必须严格遵守） + +### 1. 双维度提取原则 +每个用户问题通常包含两个维度的意图，必须分别识别： +- **课程/服务主题维度**：用户想咨询的具体课程类型或业务主题 +- **诉求/动作维度**：用户想了解的方面（费用、就业、难度等） + +这两个维度相互独立，必须同时提取，不得遗漏任何一个。最终输出的标签本身不区分维度，但模型内部需要判断每个标签的维度，以便后续优先级排序（见P3规则）。判断维度的方法：若标签名称中包含“咨询”、“怎么办”、“分布”等特征词，通常属于诉求/动作维度；否则多属于主题维度。请结合语义自行判断。 + +### 2. 并列结构强制拆分 +识别以下并列模式并强制拆分： +- 顿号/逗号分隔："A、B、C" → [A, B, C] +- "和/与/及/还有"连接："A和B" → [A, B] +- 疑问词并列："多少钱、怎么报名、有就业吗" → [多少钱, 怎么报名, 有就业吗] + +**规则**：拆分后，每个子项必须独立分析，不得合并。 + +### 3. 意图识别置信度评分体系 +每个意图标签在输出前必须经过置信度评分，**只有总分≥60分的标签才会被输出**。 + +#### 评分维度： + +| 评分维度 | 分值 | 触发条件 | 说明 | +|---------|------|---------|------| +| **直接匹配** | **60分** | 用户问题中直接出现标签关键词或同义词扩展词（见第5节） | 关键词完全匹配或同义词匹配，直接给60分，超过阈值 | +| **语义关联** | **40分** | 用户表达与标签语义高度相关，通过语义场推断 | 语义场匹配给40分，需配合上下文支持达到阈值 | +| **上下文支持** | **20分** | 历史会话或当前上下文支持该意图推断 | 与语义关联配合：40+20=60分，刚好达到阈值 | +| **历史补全** | **10分** | 意图完全依赖历史会话补全 | 仅作为辅助加分，不单独使用 | + +#### 阈值判定规则： +- **总分 ≥ 60分** → 进入候选池 +- **总分 < 60分** → 直接过滤，不进入候选池 +- 无任何意图≥60分 → 输出 `{{"intents": []}}` + +### 4. 强制截断规则（新增） +**无论识别到多少个意图，最终输出最多只返回2个意图标签。** + +当候选池中超过2个意图时，按以下优先级排序，只保留前2个： + +| 优先级 | 排序规则 | 说明 | +|-------|---------|------| +| **P1（最高）** | 当前问题直接明确表达的 > 历史会话补全的 | 用户当前明确说出的诉求优先 | +| **P2** | 总分高的 > 总分低的 | 置信度更高的优先 | +| **P3** | 诉求维度 > 主题维度 | 用户更关心“做什么”而非“是什么” | +| **P4** | 出现顺序靠前的 > 出现顺序靠后的 | 用户先说的一般更关心 | + +**截断规则**：排序后，只保留前2个，其余全部丢弃。 + +### 5. 语义同义词扩展匹配 +匹配时不仅使用标签字面本身，还必须覆盖常见的同义词扩展。以下为常见标签的同义词映射表（**当且仅当 {intent_tags} 中包含与下列标准标签完全同名的标签时，才可使用该扩展**；若传入的标签不在该表中，则根据标签字面及通用语义进行匹配，禁止编造扩展词）： + +- **就业咨询**：就业、找工作、工作推荐、就业支持、就业服务、就业保障、就业率、包就业、毕业安排、工作分配、推荐工作、就业方向、职业发展、入职、岗位推荐、能就业吗、好找工作吗、就业情况、就业数据、薪资、工资待遇、收入水平 +- **学不会怎么办**：学不会、跟不上、没基础、怕学不会、能不能学、能学会吗、学得会吗、适合我学吗、零基础能学吗、没经验能学吗、纯小白能学吗、好不好学、难不难、难吗、简单吗、容易学吗、好学吗、有难度吗、门槛高吗、有门槛吗、担心自己学不会、不知道能不能学会、怕跟不上、怕太难、上手难、学不进、听不懂、理解不了 +- **课程咨询**：多少钱、费用、价格、学费、学什么、内容、课程安排、怎么收费、贵不贵、收费标准、分期付款、优惠、折扣、教什么、学什么内容、课程体系、教学大纲、课程表、怎么报名、报名流程、入学流程、报名需要什么、报名条件、上课时间、上课周期、学多久、多长时间、几个月、课时、开班时间、什么时候开课、有班级吗、还能报名吗 +- **退费咨询**：退费、退款、退钱、退款政策、不学了能退吗、中途退学、退课、退费规则、退费流程、能退多少、退款比例、退费条件、不想学了怎么办 +- **住宿咨询**：住宿、宿舍、包住、住哪、住宿条件、有宿舍吗、包住宿吗、住宿环境、住宿费、房租、住校、外宿、租房、住宿安排、住宿费用 +- **学历咨询**：学历、文凭、证书、学位、毕业证、大专、本科、学历认证、学历证书、学位认证 +- **校区分布**：校区、地址、在哪、位置、分布、哪个城市、怎么去、交通、附近 +- **优势/竞争力**：优势、竞争力、为什么选你们、和其他机构比、口碑、排名、实力、师资、教学质量 + +（若传入的标签列表包含上述标签以外的标签，同义词匹配将仅基于标签文字及通用语义，不得臆造扩展。） + +### 6. 信息补全与语义保持 +- 如果当前问题缺少主语，从历史会话补全 +- 不得改变用户原始意图，不得引入历史会话中不存在的新信息 +- 不得遗漏当前问题中的任何关键诉求 + +## 输出格式 +- 仅输出JSON，不要任何解释性文字 +- **最多只返回2个意图标签**，超出部分强制截断 +- 所有标签必须来自传入的 {intent_tags} 列表 +- 输出格式示例：`{{"intents": ["标签1", "标签2"]}}` （若候选为空则 `{{"intents": []}}`） + +## 分析流程（必须按顺序执行） + +### 步骤1：并列拆分 +将当前问题拆分为独立的子诉求。 + +### 步骤2：双维度扫描 + 置信度评分 +对每个子诉求，扫描 {intent_tags} 中的标签，按照第3节和第5节计算总分。每个意图独立评分。 + +### 步骤3：阈值过滤 +只保留总分≥60分的意图，进入候选池。 + +### 步骤4：优先级排序（当候选池超过2个时） +按P1→P2→P3→P4规则排序。 + +### 步骤5：强制截断 +只保留排序后的前2个意图标签。 + +### 步骤6：完整性自检（输出前必须执行） +- [ ] 每个子诉求是否都经过了置信度评分？ +- [ ] 是否有总分≥60分的意图被遗漏？ +- [ ] 最终输出是否≤2个标签？ +- [ ] 截断时是否按优先级规则执行？ +- [ ] 所有输出的标签是否都在 {intent_tags} 中？ + +如果任何一项为“否”，重新分析。 + +## 分类示例（假设传入标签列表包含：3D建模、3D角色建模、课程咨询、就业咨询、住宿咨询、退费咨询、学不会怎么办） + +### 示例1（2个意图，无需截断） +历史会话：（空） +当前问题：3D建模培训的课程内容有哪些？ +分析： + - 主题维度："3D建模" → 直接匹配 = 60分 → 候选池 + - 诉求维度："课程内容" → 直接匹配"课程咨询" = 60分 → 候选池 + - 候选池共2个，无需截断 +分类结果： +{{"intents": ["3D建模", "课程咨询"]}} + +### 示例2（并列拆分 + 阈值过滤 + 截断） +历史会话：（空） +当前问题：流程、费用和就业支持 +分析： + - 拆分：[流程, 费用, 就业支持] + - "流程" → 直接匹配"课程咨询" = 60分 → 候选池 + - "费用" → 直接匹配"课程咨询" = 60分 → 候选池（去重） + - "就业支持" → 直接匹配"就业咨询" = 60分 → 候选池 + - 候选池：["课程咨询", "就业咨询"]，共2个，无需截断 +分类结果： +{{"intents": ["课程咨询", "就业咨询"]}} + +### 示例3（3个意图，强制截断为2个） +历史会话：（空） +当前问题：我想学角色，不知道能不能学，好不好学 +分析： + - 主题维度："学角色" → 直接匹配"3D角色建模" = 60分 → 候选池 + - 诉求维度1："想学" → 直接匹配"课程咨询" = 60分 → 候选池 + - 诉求维度2："不知道能不能学" → 语义关联(40) + 上下文支持(20) = 60分 → 候选池 + - 诉求维度3："好不好学" → 语义关联(40) + 上下文支持(20) = 60分 → 候选池（与上同标签，去重） + - 候选池：["3D角色建模", "课程咨询", "学不会怎么办"]，共3个，需截断 + - 排序：课程咨询(P1当前直接) > 学不会怎么办(P1当前直接) > 3D角色建模(P3主题维度) + - 截断后保留前2个 +分类结果： +{{"intents": ["课程咨询", "学不会怎么办"]}} + +### 示例4（5个意图，强制截断为2个） +历史会话：（空） +当前问题：3D建模费用、就业、住宿、退费 +分析： + - "3D建模" → 直接匹配 = 60分 → 候选池 + - "费用" → 直接匹配"课程咨询" = 60分 → 候选池 + - "就业" → 直接匹配"就业咨询" = 60分 → 候选池 + - "住宿" → 直接匹配"住宿咨询" = 60分 → 候选池 + - "退费" → 直接匹配"退费咨询" = 60分 → 候选池 + - 候选池共5个，需截断 + - 排序：课程咨询(P1当前直接,出现第2) > 就业咨询(P1当前直接,出现第3) > 住宿咨询 > 退费咨询 > 3D建模(P3主题维度) + - 截断后保留前2个 +分类结果： +{{"intents": ["课程咨询", "就业咨询"]}} + +### 示例5（历史补全被过滤 + 截断） +历史会话： +- 用户：我想学3D建模 +当前问题：多少钱、就业情况、住宿条件 +分析： + - 主题维度："3D建模"历史补全 = 10分 < 60分 → 不进入候选池 + - "多少钱" → 直接匹配"课程咨询" = 60分 → 候选池 + - "就业情况" → 直接匹配"就业咨询" = 60分 → 候选池 + - "住宿条件" → 直接匹配"住宿咨询" = 60分 → 候选池 + - 候选池：["课程咨询", "就业咨询", "住宿咨询"]，共3个，需截断 + - 排序：课程咨询(P1,出现第1) > 就业咨询(P1,出现第2) > 住宿咨询(P1,出现第3) + - 截断后保留前2个 +分类结果： +{{"intents": ["课程咨询", "就业咨询"]}} + +### 示例6（语义关联不足60分，全部被过滤） +历史会话：（空） +当前问题：这个课有意思吗 +分析： + - "有意思" → 语义关联"课程咨询" = 40分，无上下文支持 = 0分，总分40分 < 60分 → 不进入候选池 + - 候选池为空 +分类结果： +{{"intents": []}} + +--- + +## 现在请处理以下输入 + +当前问题：{query} +历史聊天：{history_text} +完整标签列表：{intent_tags} + +请直接输出JSON： +""" async def rewrite_query(state:QueryQAState): """ 问题改写 @@ -224,12 +419,13 @@ async def rewrite_query(state:QueryQAState): """ history_text = state.get("history", "") query = state.get("original_query", "") - prompt = PromptTemplate(template=problem_rewriting_prompt, input_variables=["history_text", "query"]) + prompt = PromptTemplate(template=problem_rewriting_prompt, input_variables=["history_text", "query","intent_tags"]) output = JsonOutputParser() chain = prompt | llm | output + intent_repo = IntentClassificationRepository() try: - result = await chain.ainvoke({"history_text": history_text, "query": query}) + result = await chain.ainvoke({"history_text": history_text, "query": query, "intent_tags":str(await intent_repo.get_style_intent_categories())}) rewritten_query = result.get("intents", "") logger.info(f"原问题：{query},历史会话：{history_text},改写后问题：{rewritten_query}") return {"rewritten_query": rewritten_query} diff --git a/app/conf/app_config.py b/app/conf/app_config.py index e0f6b47..58a71d3 100644 --- a/app/conf/app_config.py +++ b/app/conf/app_config.py @@ -68,6 +68,11 @@ class RedisConfig: db: int = 0 decode_responses: bool = True +@dataclass +class RerankerConfig: + model_path: str + use_fp16: bool = True + @dataclass class AppConfig: logging: LoggingConfig @@ -77,12 +82,17 @@ class AppConfig: milvus: MilvusConfig asr: ASRConfig redis: RedisConfig + reranker: RerankerConfig config_file = Path(__file__).parents[2] / 'conf' / 'app_config.yaml' +project_root = Path(__file__).parents[2] context = OmegaConf.load(config_file) schema = OmegaConf.structured(AppConfig) app_config: AppConfig = OmegaConf.to_object(OmegaConf.merge(schema, context)) +if not Path(app_config.reranker.model_path).is_absolute(): + app_config.reranker.model_path = str(project_root / app_config.reranker.model_path) + if __name__ == '__main__': print(app_config.db_assistant.host) diff --git a/app/repository/milvus/intent_classification_repository.py b/app/repository/milvus/intent_classification_repository.py index 39713d0..2b0a2c3 100644 --- a/app/repository/milvus/intent_classification_repository.py +++ b/app/repository/milvus/intent_classification_repository.py @@ -77,6 +77,49 @@ class IntentClassificationRepository: logger.error(f"意图分类数据入库失败: {str(e)}", exc_info=True) return [] + async def upsert_intent_data(self, data: List[Dict[str, Any]]) -> List[int]: + try: + created_at = int(datetime.now().timestamp()) + all_ids = [] + for item in data: + category = item.get("intent_category", "") + content = item.get("content", "") + vector = item.get("content_vector", []) + + escaped_content = content.replace("\\", "\\\\").replace('"', '\\"') + expr = f'intent_category == "{category}"' + existing = milvus_client.client.query( + collection_name=self.collection_name, + filter=expr, + output_fields=["id"], + limit=1000 + ) + if existing: + existing_ids = [e["id"] for e in existing] + milvus_client.client.delete( + collection_name=self.collection_name, + ids=existing_ids + ) + logger.info(f"删除旧记录 {len(existing_ids)} 条: category={category}") + + insert_result = milvus_client.client.insert( + collection_name=self.collection_name, + data=[{ + "intent_category": category, + "content": content, + "content_vector": vector, + "created_at": created_at + }] + ) + all_ids.extend(insert_result.get("ids", [])) + + logger.info(f"意图分类数据upsert完成，共处理 {len(data)} 条") + return all_ids + + except Exception as e: + logger.error(f"意图分类数据upsert失败: {str(e)}", exc_info=True) + return [] + async def search_by_intent_category(self, intent_category: List[str], top_k: int = 100) -> List[Dict[str, Any]]: if isinstance(intent_category, str): intent_category = [intent_category] @@ -126,4 +169,46 @@ class IntentClassificationRepository: async def get_style_intent_categories(self) -> List[str]: all_categories = await self.get_all_intent_categories() - return [c for c in all_categories if "风格" in c] \ No newline at end of file + return [c for c in all_categories] + + async def get_content_by_intent_category(self, intent_category: str) -> List[str]: + result = milvus_client.client.query( + collection_name=self.collection_name, + filter=f'intent_category == "{intent_category}"', + output_fields=["content"], + limit=1000 + ) + return [item.get("content", "") for item in result if item.get("content")] + + async def delete_today_data(self) -> int: + today_start = int(datetime.now().replace(hour=0, minute=0, second=0, microsecond=0).timestamp()) + query_result = milvus_client.client.query( + collection_name=self.collection_name, + filter=f'created_at >= {today_start}', + output_fields=["id"], + limit=10000 + ) + if not query_result: + logger.info("今日无数据可删除") + return 0 + ids_to_delete = [item["id"] for item in query_result] + milvus_client.client.delete( + collection_name=self.collection_name, + ids=ids_to_delete + ) + logger.info(f"删除今日数据 {len(ids_to_delete)} 条") + return len(ids_to_delete) + + +async def main(): + milvus_client.init() + repo = IntentClassificationRepository() + style_categories = await repo.delete_today_data() + print(style_categories) + +if __name__ == "__main__": + # today_start = int(datetime.now().replace(hour=0, minute=0, second=0, microsecond=0).timestamp()) + # print(today_start) + import asyncio + asyncio.run(main()) + diff --git a/app/reranker_utils.py b/app/reranker_utils.py new file mode 100644 index 0000000..3dd585c --- /dev/null +++ b/app/reranker_utils.py @@ -0,0 +1,24 @@ +from FlagEmbedding import FlagReranker + +from app.conf.app_config import app_config + +_reranker_model = None +_reranker_init_failed = False + + +def get_reranker_model(): + global _reranker_model, _reranker_init_failed + if _reranker_init_failed: + return None + if _reranker_model is None: + try: + _reranker_model = FlagReranker( + model_name_or_path=app_config.reranker.model_path, + device="cuda:0", + use_fp16=app_config.reranker.use_fp16 + ) + except Exception as e: + _reranker_init_failed = True + print(f"[WARNING] reranker初始化失败，将跳过rerank: {e}") + return None + return _reranker_model diff --git a/conf/app_config.yaml b/conf/app_config.yaml index 92177e3..4e79a41 100644 --- a/conf/app_config.yaml +++ b/conf/app_config.yaml @@ -45,3 +45,7 @@ redis: password: "" db: 0 decode_responses: true + +reranker: + model_path: "app/models/BAAI/bge-reranker-large" + use_fp16: true diff --git a/pyproject.toml b/pyproject.toml index d522773..f6acf5f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -9,7 +9,8 @@ dependencies = [ "asyncmy>=0.2.11", "cryptography>=48.0.1", "fastapi[standard]>=0.136.3", - "huggingface-hub>=1.18.0", + "flagembedding>=1.4.0", + 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