# # Copyright 2024 The InfiniFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import logging import binascii import os import json import re from copy import deepcopy from timeit import default_timer as timer import datetime from datetime import timedelta from api.db import LLMType, ParserType,StatusEnum from api.db.db_models import Dialog, Conversation,DB from api.db.services.common_service import CommonService from api.db.services.knowledgebase_service import KnowledgebaseService from api.db.services.llm_service import LLMService, TenantLLMService, LLMBundle from api import settings from rag.app.resume import forbidden_select_fields4resume from rag.nlp.search import index_name from rag.utils import rmSpace, num_tokens_from_string, encoder from api.utils.file_utils import get_project_base_directory from peewee import fn import threading, queue,uuid,time,array from concurrent.futures import ThreadPoolExecutor from api.db.services.ali_tts_service import (stream_manager_w_stream as stream_manager) def audio_fade_in(audio_data, fade_length): # 假设音频数据是16位单声道PCM # 将二进制数据转换为整数数组 samples = array.array('h', audio_data) # 对前fade_length个样本进行淡入处理 for i in range(fade_length): fade_factor = i / fade_length samples[i] = int(samples[i] * fade_factor) # 将整数数组转换回二进制数据 return samples.tobytes() class StreamSessionManager: def __init__(self): self.sessions = {} # {session_id: {'tts_model': obj, 'buffer': queue, 'task_queue': Queue}} self.lock = threading.Lock() self.executor = ThreadPoolExecutor(max_workers=30) # 固定大小线程池 self.gc_interval = 300 # 5分钟清理一次 5 x 60 300秒 self.gc_tts = 10 # 10s 大模型开始输出文本有可能需要比较久，2025年5 24 从3s->10s def create_session(self, tts_model,sample_rate =8000, stream_format='mp3'): session_id = str(uuid.uuid4()) with self.lock: self.sessions[session_id] = { 'tts_model': tts_model, 'buffer': queue.Queue(maxsize=300), # 线程安全队列 'task_queue': queue.Queue(), 'active': True, 'last_active': time.time(), 'audio_chunk_count':0, 'finished': threading.Event(), # 添加事件对象 'sample_rate':sample_rate, 'stream_format':stream_format, "tts_chunk_data_valid":False, "sentence_complete_event": threading.Event(), "current_processing": False # 标记是否正在处理句子 } # 启动任务处理线程 threading.Thread(target=self._process_tasks, args=(session_id,), daemon=True).start() return session_id def append_text(self, session_id, text): with self.lock: session = self.sessions.get(session_id) if not session: return # 将文本放入任务队列（非阻塞） #logging.info(f"StreamSessionManager append_text {text}") try: session['task_queue'].put(text, block=False) except queue.Full: logging.warning(f"Session {session_id} task queue full") def _process_tasks(self, session_id): """任务处理线程（每个会话独立）""" while True: session = self.sessions.get(session_id) if not session or not session['active']: break try: #logging.info(f"StreamSessionManager _process_tasks {session['task_queue'].qsize()}") # 合并多个文本块（最多等待50ms） texts = [] while len(texts) < 5: # 最大合并5个文本块 try: text = session['task_queue'].get(timeout=0.1) #logging.info(f"StreamSessionManager _process_tasks --0 {len(texts)}") texts.append(text) except queue.Empty: break if texts: session['last_active'] = time.time() # 如果有处理文本，重置活跃时间 # 提交到线程池处理 future=self.executor.submit( self._generate_audio, session_id, ' '.join(texts) # 合并文本减少请求次数 ) future.result() # 等待转换任务执行完毕 session['last_active'] = time.time() # 会话超时检查 if time.time() - session['last_active'] > self.gc_interval: self.close_session(session_id) break if time.time() - session['last_active'] > self.gc_tts: session['finished'].set() break except Exception as e: logging.error(f"Task processing error: {str(e)}") def _generate_audio1(self, session_id, text): """实际生成音频（线程池执行）""" session = self.sessions.get(session_id) if not session: return # logging.info(f"_generate_audio:{text}") first_chunk = True logging.info(f"转换开始!!! {text}") try: for chunk in session['tts_model'].tts(text,session['sample_rate'],session['stream_format']): if session['stream_format'] == 'wav': if first_chunk: chunk_len = len(chunk) if chunk_len > 2048: session['buffer'].put(audio_fade_in(chunk,1024)) else: session['buffer'].put(audio_fade_in(chunk, chunk_len)) first_chunk = False else: session['buffer'].put(chunk) else: session['buffer'].put(chunk) session['last_active'] = time.time() session['audio_chunk_count'] = session['audio_chunk_count'] + 1 if session['tts_chunk_data_valid'] is False: session['tts_chunk_data_valid'] = True #20250510 增加，表示连接TTS后台已经返回，可以通知前端了 logging.info(f"转换结束!!! {session['audio_chunk_count'] }") except Exception as e: session['buffer'].put(f"ERROR:{str(e)}") logging.info(f"--_generate_audio--error {str(e)}") def _generate_audio(self, session_id, text): """实际生成音频（顺序执行）- 用于非流式引擎""" session = self.sessions.get(session_id) if not session: return try: # 调用 TTS session['tts_model'].text_tts_call(text) # 标记完成 session['sentence_complete_event'].set() session['last_active'] = time.time() session['audio_chunk_count'] += 1 if not session['tts_chunk_data_valid']: session['tts_chunk_data_valid'] = True except Exception as e: session['buffer'].put(f"ERROR:{str(e)}".encode()) session['sentence_complete_event'].set() # 确保事件被设置 def close_session(self, session_id): with self.lock: if session_id in self.sessions: # 标记会话为不活跃 self.sessions[session_id]['active'] = False # 延迟2秒后清理资源 threading.Timer(1, self._clean_session, args=[session_id]).start() def _clean_session(self, session_id): with self.lock: if session_id in self.sessions: del self.sessions[session_id] def get_session(self, session_id): return self.sessions.get(session_id) stream_manager_bk = StreamSessionManager() class DialogService(CommonService): model = Dialog @classmethod @DB.connection_context() def get_list(cls, tenant_id, page_number, items_per_page, orderby, desc, id , name): chats = cls.model.select() if id: chats = chats.where(cls.model.id == id) if name: chats = chats.where(cls.model.name == name) chats = chats.where( (cls.model.tenant_id == tenant_id) & (cls.model.status == StatusEnum.VALID.value) ) if desc: chats = chats.order_by(cls.model.getter_by(orderby).desc()) else: chats = chats.order_by(cls.model.getter_by(orderby).asc()) chats = chats.paginate(page_number, items_per_page) return list(chats.dicts()) class ConversationService(CommonService): model = Conversation @classmethod @DB.connection_context() def get_list(cls,dialog_id,page_number, items_per_page, orderby, desc, id, name, cols=None): # 构建基础查询 print("--ConversationService get_list enter", page_number, items_per_page) # cyx query = cls.model.select().where(cls.model.dialog_id == dialog_id) # 如果指定了ID，则添加ID筛选 if id: query = query.where(cls.model.id == id) # 如果指定了名称，则添加名称筛选 if name: query = query.where(cls.model.name == name) # 如果指定了列筛选，则只选择指定的列 if cols: query = query.select(*[getattr(cls.model, col) for col in cols]) # 获取记录总数 total = query.count() # 添加排序 if desc: query = query.order_by(cls.model.getter_by(orderby).desc()) else: query = query.order_by(cls.model.getter_by(orderby).asc()) # 执行分页查询 paginated_query = query.paginate(page_number, items_per_page) data = list(paginated_query.dicts()) # logging.info("--ConversationService get_list",total, data) #cyx # 返回分页数据和记录总数 return total, data @classmethod @DB.connection_context() def query_sessions_summary(cls): # 按 id 分组，统计每个 id 的最旧记录 query = ( cls.model .select( cls.model.id, cls.model.dialog_id, cls.model.name, fn.MIN(cls.model.create_time).alias("create_time"), fn.MIN(cls.model.create_date).alias("create_date") ) .group_by(cls.model.id, cls.model.dialog_id, cls.model.name) .order_by( fn.MIN(cls.model.create_time).desc(), ) ) # 转换为字典列表返回 return list(query.dicts()) def message_fit_in(msg, max_length=4000): def count(): nonlocal msg tks_cnts = [] for m in msg: tks_cnts.append( {"role": m["role"], "count": num_tokens_from_string(m["content"])}) total = 0 for m in tks_cnts: total += m["count"] return total c = count() if c < max_length: return c, msg msg_ = [m for m in msg[:-1] if m["role"] == "system"] if len(msg) > 1: msg_.append(msg[-1]) msg = msg_ c = count() if c < max_length: return c, msg ll = num_tokens_from_string(msg_[0]["content"]) l = num_tokens_from_string(msg_[-1]["content"]) if ll / (ll + l) > 0.8: m = msg_[0]["content"] m = encoder.decode(encoder.encode(m)[:max_length - l]) msg[0]["content"] = m return max_length, msg m = msg_[1]["content"] m = encoder.decode(encoder.encode(m)[:max_length - l]) msg[1]["content"] = m return max_length, msg def llm_id2llm_type(llm_id): llm_id = llm_id.split("@")[0] fnm = os.path.join(get_project_base_directory(), "conf") llm_factories = json.load(open(os.path.join(fnm, "llm_factories.json"), "r")) for llm_factory in llm_factories["factory_llm_infos"]: for llm in llm_factory["llm"]: if llm_id == llm["llm_name"]: return llm["model_type"].strip(",")[-1] followup_seperator = "继续追问:" # cyx 2024 12 04 # 用于校验和修正语音合成的输入文本。该函数会去除非法字符、修正内容，并返回一个结果：包括是否有效和修正后的文本。 def validate_and_sanitize_tts_input(delta_ans, max_length=3000): """ 检验并修正语音合成的输入文本。 Args: delta_ans (str): 输入的待校验文本。 max_length (int): 文本允许的最大长度。 Returns: tuple: (is_valid, sanitized_text) - is_valid (bool): 文本是否有效。 - sanitized_text (str): 修正后的文本（如果无效，为空字符串）。 """ # 1. 确保输入为字符串 if not isinstance(delta_ans, str): return False, "" # 2. 去除前后空白并检查是否为空 delta_ans = delta_ans.strip() if len(delta_ans) == 0: return False, "" # 3. 替换全角符号为半角 delta_ans = re.sub(r'[？]', '?', delta_ans) # 4. 移除非法字符（仅保留中文、英文、数字及常见标点符号） delta_ans = re.sub(r'[^\u4e00-\u9fa5a-zA-Z0-9\s,.!?\'"；；。，！？：”“（）\-()]', '', delta_ans) # 5. 检查长度 if len(delta_ans) == 0 or len(delta_ans) > max_length: return False, "" # """清理流式输出中可能存在的 jsonjsonjson 及之后的内容""" """ 检查子串存在性：使用 in 关键字判断字符串 ans 是否包含子串 "jsonjsonjson"。 分割字符串：若存在，使用 split('jsonjsonjson', 1) 分割一次，1 确保只分割首个匹配项，避免后续重复子串的影响。 提取前部分：取分割后的第一个元素 [0]，即目标子串前的内容。 处理不存在情况：根据需求返回空字符串或原字符串，示例中返回空字符串。 if followup_seperator in delta_ans: # 分割字符串，取第一个部分 delta_ans = delta_ans.split(followup_seperator, 1)[0] json_markdown_separator_found = True """ """ # 方法:split 分割 假设```json 在文本的最后，去除```json 的内容 strings_split = delta_ans.split('```json', 1) ans_remove_json = strings_split[0].rstrip() if len(strings_split)>1 : found_last_json_markdown = True # 中间结果可能是 # json # "questions" # "通州起义的具体 思想基础。 pattern = r'^(.*?)json(?=.{0,10}"questions")' # 关键修改点：添加双引号 if match := re.search(pattern, ans_remove_json, flags=re.DOTALL): ans_remove_json1 =match.group(1).rstrip() found_last_json_markdown = True else: ans_remove_json1 = ans_remove_json #logging.info(f"--dale---3:1-{delta_ans} 2-{ans_remove_json1}") """ # logging.info(f"--dale---3--:{delta_ans}") # 如果通过所有检查，返回有效标志和修正后的文本 return True, delta_ans def _should_flush(text_chunk,chunk_buffer,last_flush_time): """智能判断是否需要立即生成音频""" # 规则1：遇到句子结束标点 if re.search(r'[。！？,]$', text_chunk): return True if re.search(r'(\d{4})(年|月|日|,)', text_chunk): return False # 不刷新，继续合并 # 规则2：达到最大缓冲长度（200字符） if sum(len(c) for c in chunk_buffer) >= 200: return True # 规则3：超过500ms未刷新 if time.time() - last_flush_time > 0.5: return True return False def extract_and_parse_json(llm_ans): # 匹配带 JSON 标记的代码块 json_pattern = r'```json\n(.*?)\n```' match = re.search(json_pattern, llm_ans, re.DOTALL) if not match: # 尝试匹配不带标记的 JSON 对象 json_pattern_fallback = r'\{.*\}' match = re.search(json_pattern_fallback, response_text, re.DOTALL) if not match: return None, "未检测到 JSON 数据" json_str = match.group(1) if match.group(1) else match.group(0) try: # 处理常见格式问题 json_str = json_str.strip() json_str = json_str.replace("，", ",") # 替换中文逗号 json_str = re.sub(r'//.*?\n', '', json_str) # 去除注释 # 解析 JSON parsed_data = json.loads(json_str) return parsed_data, None except json.JSONDecodeError as e: error_msg = f"JSON 解析失败：{str(e)}\n错误位置：第 {e.lineno} 列 {e.colno}" return None, error_msg except Exception as e: return None, f"解析异常：{str(e)}" import re import json def extract_clear_parse_json(llm_ans, clean_text=True): parsed_data = None error = None matches = [] cleaned_text = llm_ans # 先将json特殊分隔符去除 # 匹配带 JSON 标记的完整代码块（含内容） json_marker_pattern = r'```json.*?```' for match in re.finditer(json_marker_pattern, cleaned_text, re.DOTALL): start, end = match.start(), match.end() json_block = match.group(0) # 包含整个 ```json...``` 内容 matches.append((start, end, json_block)) # 若未找到带标记块，尝试匹配无标记的 JSON 对象 if not matches: json_object_pattern = r'\{[^{}]*\}' for match in re.finditer(json_object_pattern, cleaned_text, re.DOTALL): start, end = match.start(), match.end() json_block = match.group(0) matches.append((start, end, json_block)) # 清理文本（删除所有 JSON 块） if clean_text: # 反向删除避免位置偏移 for start, end, _ in sorted(matches, key=lambda x: x[0], reverse=True): cleaned_text = cleaned_text[:start] + cleaned_text[end:] # 提取并解析 JSON 内容 json_blocks = [] for _, _, block in matches: # 如果是带标记的块，剥离 ```json 和 ``` if block.startswith('```json'): pure_json = re.sub(r'^```json\s*|\s*```$', '', block, flags=re.DOTALL) json_blocks.append(pure_json.strip()) else: json_blocks.append(block.strip()) # 尝试解析所有 JSON 块 for json_str in json_blocks: try: json_str = re.sub(r'//.*', '', json_str) # 移除行内注释 json_str = json_str.replace("，", ",") # 处理中文逗号 parsed_data = json.loads(json_str) error = None break # 解析成功即停止 except json.JSONDecodeError as e: error = f"JSON 解析失败：{e}，位置：第 {e.lineno} 行" except Exception as e: error = f"解析异常：{str(e)}" if not json_blocks: error = "未检测到 JSON 数据" return parsed_data, cleaned_text.strip(), error # cyx 20250510 增加 生成后续可以追问的内容，输出为json格式 def generate_structured_followups(chat_mdl, answer, max_questions=5): """ 生成结构化追问建议（带JSON格式解释） :return: (JSON数据, 格式解释, 消耗tokens) """ system_prompt = """你是一位精通数据结构的博物馆教育专家，请完成以下任务： 1. 根据讲解内容生成{max_questions}个追问问题，格式为严格遵循的JSON 2. 为生成的JSON添加格式解释 3. JSON需包含问题分类和置信度 JSON格式要求： {{ "questions": [ {{ "text": "问题文本", "type": "问题类型", "confidence": 置信度(0-1) }} ], "source_analysis": {{ "main_topics": ["主要话题"], "missing_areas": ["未涉及领域"] }} }}""" user_prompt = f"""原始讲解内容： {answer} 请按以下步骤处理： 1. 分析内容的关键知识点 2. 生成{max_questions}个延伸问题 3. 生成JSON后添加格式解释 4. 用---分隔数据和解释""" gen_config = { "temperature": 0.5, "max_tokens": 800 } try: ans = chat_mdl.chat( system=system_prompt.format(max_questions=max_questions), history=[{"role": "user", "content": user_prompt}], gen_conf=gen_config ) # 分离JSON和解释 json_data,error = extract_and_parse_json(ans) if json_data is not None: # 强化JSON提取 return json_data,"解析正确" else: return {},"解析错误",tokens except json.JSONDecodeError as e: print(f"JSON解析失败: {str(e)}") return {}, "格式解析错误" except Exception as e: print(f"生成失败: {str(e)}") return {}, "生成过程异常" MAX_BUFFER_LEN = 200 # 最大缓冲长度 FLUSH_TIMEOUT = 0.5 # 强制刷新时间(秒) # 智能查找文本最佳分割点（标点/语义单位/短语边界） def find_split_position(text): """智能查找最佳分割位置""" # 优先查找句子结束符 sentence_end = list(re.finditer(r'[。！？]', text)) if sentence_end: return sentence_end[-1].end() # 其次查找自然停顿符 pause_mark = list(re.finditer(r'[，；、]', text)) if pause_mark: return pause_mark[-1].end() # 防止截断日期/数字短语 date_pattern = re.search(r'\d+(年|月|日)(?!\d)', text) if date_pattern: return date_pattern.end() return None # 管理文本缓冲区，根据语义规则动态分割并返回待处理内容,分割出语义完整的部分 def process_buffer(chunk_buffer, force_flush=False): """处理文本缓冲区，返回待发送文本和剩余缓冲区""" current_text = "".join(chunk_buffer) if not current_text: return "", [] split_pos = find_split_position(current_text) # 强制刷新逻辑 if force_flush or len(current_text) >= MAX_BUFFER_LEN: # 即使强制刷新也要尽量找合适的分割点 if split_pos is None or split_pos < len(current_text) // 2: split_pos = max(split_pos or 0, MAX_BUFFER_LEN) split_pos = min(split_pos, len(current_text)) if split_pos is not None and split_pos > 0: to_tts_text = current_text[:split_pos] remaining_text = [current_text[split_pos:]] return to_tts_text,remaining_text return None, chunk_buffer def chat(dialog, messages, stream=True, **kwargs): assert messages[-1]["role"] == "user", "The last content of this conversation is not from user." st = timer() tmp = dialog.llm_id.split("@") fid = None llm_id = tmp[0] if len(tmp)>1: fid = tmp[1] #logging.info(f"dialog_service--0 message={messages}") # cyx llm = LLMService.query(llm_name=llm_id) if not fid else LLMService.query(llm_name=llm_id, fid=fid) if not llm: llm = TenantLLMService.query(tenant_id=dialog.tenant_id, llm_name=llm_id) if not fid else \ TenantLLMService.query(tenant_id=dialog.tenant_id, llm_name=llm_id, llm_factory=fid) if not llm: raise LookupError("LLM(%s) not found" % dialog.llm_id) max_tokens = 8192 else: max_tokens = llm[0].max_tokens kbs = KnowledgebaseService.get_by_ids(dialog.kb_ids) embd_nms = list(set([kb.embd_id for kb in kbs])) if len(embd_nms) != 1: yield {"answer": "**ERROR**: Knowledge bases use different embedding models.", "reference": []} return {"answer": "**ERROR**: Knowledge bases use different embedding models.", "reference": []} is_kg = all([kb.parser_id == ParserType.KG for kb in kbs]) retr = settings.retrievaler if not is_kg else settings.kg_retrievaler questions = [m["content"] for m in messages if m["role"] == "user"][-3:] attachments = kwargs["doc_ids"].split(",") if "doc_ids" in kwargs else None if "doc_ids" in messages[-1]: attachments = messages[-1]["doc_ids"] for m in messages[:-1]: if "doc_ids" in m: attachments.extend(m["doc_ids"]) embd_mdl = LLMBundle(dialog.tenant_id, LLMType.EMBEDDING, embd_nms[0]) if not embd_mdl: raise LookupError("Embedding model(%s) not found" % embd_nms[0]) if llm_id2llm_type(dialog.llm_id) == "image2text": chat_mdl = LLMBundle(dialog.tenant_id, LLMType.IMAGE2TEXT, dialog.llm_id) else: chat_mdl = LLMBundle(dialog.tenant_id, LLMType.CHAT, dialog.llm_id) prompt_config = dialog.prompt_config field_map = KnowledgebaseService.get_field_map(dialog.kb_ids) tts_mdl = None if prompt_config.get("tts"): if kwargs.get('tts_model'): tts_mdl = LLMBundle(dialog.tenant_id, LLMType.TTS,kwargs.get('tts_model')) else: tts_mdl = LLMBundle(dialog.tenant_id, LLMType.TTS, dialog.tts_id) if not kwargs.get("voice"): # 20251007 cyx 修改，没有传入voice 参数，则不需要生成tts kwargs['tts_disable'] = True tts_sample_rate = kwargs.get("tts_sample_rate",8000) # 默认为8K tts_stream_format = kwargs.get("tts_stream_format","mp3") # 默认为mp3格式 # try to use sql if field mapping is good to go if field_map: logging.debug("Use SQL to retrieval:{}".format(questions[-1])) ans = use_sql(questions[-1], field_map, dialog.tenant_id, chat_mdl, prompt_config.get("quote", True)) if ans: yield ans return # logging.info(f"dialog_service--1 chat prompt_config{prompt_config['parameters']} {prompt_config}") # cyx for p in prompt_config["parameters"]: if p["key"] == "knowledge": continue if p["key"] not in kwargs and not p["optional"]: raise KeyError("Miss parameter: " + p["key"]) if p["key"] not in kwargs: prompt_config["system"] = prompt_config["system"].replace( "{%s}" % p["key"], " ") if len(questions) > 1 and prompt_config.get("refine_multiturn"): questions = [full_question(dialog.tenant_id, dialog.llm_id, messages)] else: questions = questions[-1:] refineQ_tm = timer() keyword_tm = timer() rerank_mdl = None if dialog.rerank_id: rerank_mdl = LLMBundle(dialog.tenant_id, LLMType.RERANK, dialog.rerank_id) for _ in range(len(questions) // 2): questions.append(questions[-1]) if "knowledge" not in [p["key"] for p in prompt_config["parameters"]]: kbinfos = {"total": 0, "chunks": [], "doc_aggs": []} else: if prompt_config.get("keyword", False): questions[-1] += keyword_extraction(chat_mdl, questions[-1]) keyword_tm = timer() tenant_ids = list(set([kb.tenant_id for kb in kbs])) kbinfos = retr.retrieval(" ".join(questions), embd_mdl, tenant_ids, dialog.kb_ids, 1, dialog.top_n, dialog.similarity_threshold, dialog.vector_similarity_weight, doc_ids=attachments, top=dialog.top_k, aggs=False, rerank_mdl=rerank_mdl) knowledges = [ck["content_with_weight"] for ck in kbinfos["chunks"]] logging.debug( "{}->{}".format(" ".join(questions), "\n->".join(knowledges))) # 打印查询到的知识库信息 #logging.info( "知识库中知识--!!!:{}->{}".format(" ".join(questions), "\n->".join(knowledges))) retrieval_tm = timer() if not knowledges and prompt_config.get("empty_response"): empty_res = prompt_config["empty_response"] yield {"answer": empty_res, "reference": kbinfos, "audio_binary": tts(tts_mdl, empty_res,sample_rate=tts_sample_rate,stream_format=tts_stream_format)} return {"answer": prompt_config["empty_response"], "reference": kbinfos} kwargs["knowledge"] = "\n\n------\n\n".join(knowledges) gen_conf = dialog.llm_setting msg = [{"role": "system", "content": prompt_config["system"].format(**kwargs)}] msg.extend([{"role": m["role"], "content": re.sub(r"##\d+\$\$", "", m["content"])} for m in messages if m["role"] != "system"]) used_token_count, msg = message_fit_in(msg, int(max_tokens * 0.97)) assert len(msg) >= 2, f"message_fit_in has bug: {msg}" prompt = msg[0]["content"] prompt += "\n\n### Query:\n%s" % " ".join(questions) #logging.info(f"dialog_service--3 chat msg={msg}") # cyx if "max_tokens" in gen_conf: gen_conf["max_tokens"] = min( gen_conf["max_tokens"], max_tokens - used_token_count) def decorate_answer(answer): nonlocal prompt_config, knowledges, kwargs, kbinfos, prompt, retrieval_tm refs = [] if knowledges and (prompt_config.get("quote", True) and kwargs.get("quote", True)): answer, idx = retr.insert_citations(answer, [ck["content_ltks"] for ck in kbinfos["chunks"]], [ck["vector"] for ck in kbinfos["chunks"]], embd_mdl, tkweight=1 - dialog.vector_similarity_weight, vtweight=dialog.vector_similarity_weight) # 上述转换过程中，发现有时候会在answer中插入类似##0$$ ##1$$ 这样的字符串，需要去除 # cyx 20250407 answer = re.sub(r'##\d+\$\$', '', answer).strip() #去除##0$$类似内容 同时去除多余空格 idx = set([kbinfos["chunks"][int(i)]["doc_id"] for i in idx]) recall_docs = [ d for d in kbinfos["doc_aggs"] if d["doc_id"] in idx] if not recall_docs: recall_docs = kbinfos["doc_aggs"] kbinfos["doc_aggs"] = recall_docs refs = deepcopy(kbinfos) for c in refs["chunks"]: if c.get("vector"): del c["vector"] if answer.lower().find("invalid key") >= 0 or answer.lower().find("invalid api") >= 0: answer += " Please set LLM API-Key in 'User Setting -> Model Providers -> API-Key'" done_tm = timer() prompt += "\n\n### Elapsed\n - Refine Question: %.1f ms\n - Keywords: %.1f ms\n - Retrieval: %.1f ms\n - LLM: %.1f ms" % ( (refineQ_tm - st) * 1000, (keyword_tm - refineQ_tm) * 1000, (retrieval_tm - keyword_tm) * 1000, (done_tm - retrieval_tm) * 1000) #return {"answer": answer, "prompt": prompt,"reference": refs } # cyx 增加 20250510 生成后续追问的内容 # cyx 修改 20250422 不向前端发送prompt 和 refs ，增加发送 finished 标志 return {"answer": answer, "finished":True,"reference":""} if stream: last_ans = "" answer = "" audio_url = None tts_session_id = None if not kwargs.get('tts_disable'): # 创建TTS会话（提前初始化） tts_session_id = stream_manager.create_session(tts_mdl,sample_rate=tts_sample_rate,stream_format=tts_stream_format, voice = kwargs.get('tts_model')) tts_session = stream_manager.get_session(tts_session_id) audio_url = f"/tts_stream/{tts_session_id}" send_tts_url = False chunk_buffer = [] # 新增文本缓冲 last_flush_time = time.time() # 初始化时间戳 # 下面优先处理知识库中没有找到相关内容 cyx 20250323 修改 if not kwargs["knowledge"] or kwargs["knowledge"] =="" or len(kwargs["knowledge"]) < 4: if not kwargs.get('tts_disable'): stream_manager.append_text(tts_session_id, "未找到相关内容") yield { "answer": "未找到相关内容", "delta_ans": "未找到相关内容", "session_id": tts_session_id, "reference": {}, "audio_stream_url": audio_url, "sample_rate": tts_sample_rate, "stream_format": tts_stream_format, } else: for ans in chat_mdl.chat_streamly(prompt, msg[1:], gen_conf): answer = ans delta_ans = ans[len(last_ans):] if num_tokens_from_string(delta_ans) < 24: continue last_ans = answer # yield {"answer": answer, "reference": {}, "audio_binary": tts(tts_mdl, delta_ans)} # cyx 2024 12 04 修正delta_ans 为空 ,调用tts 出错 tts_input_is_valid, sanitized_text = validate_and_sanitize_tts_input(delta_ans) # cyx 2025 01 18 前端传入tts_disable 参数，就不生成tts 音频给前端,即:没有audio_binary if kwargs.get('tts_disable'): tts_input_is_valid =False if tts_input_is_valid : # 缓冲文本直到遇到标点 chunk_buffer.append(sanitized_text) # 处理缓冲区内容 while True: # 判断是否需要强制刷新 force = time.time() - last_flush_time > FLUSH_TIMEOUT to_send, remaining = process_buffer(chunk_buffer, force_flush=force) if not to_send: break # 发送有效内容 stream_manager.append_text(tts_session_id, to_send) chunk_buffer = remaining last_flush_time = time.time() """ if tts_input_is_valid: yield {"answer": answer, "delta_ans": sanitized_text, "reference": {}, "audio_binary": tts(tts_mdl, sanitized_text)} else: yield {"answer": answer, "delta_ans": sanitized_text, "reference": {}} """ # 首块返回音频URL if send_tts_url is False and not kwargs.get('tts_disable'): if tts_session['tts_chunk_data_valid'] is True: yield { "answer": answer, "delta_ans": sanitized_text, "session_id": tts_session_id, "reference": {}, "audio_stream_url": audio_url, "sample_rate":tts_sample_rate, "stream_format":tts_stream_format, } send_tts_url = True # 发送一次tts url 给前端即可，不能重复发送 logging.info(f"--chat retur tts url {audio_url}") else: yield {"answer": answer, "delta_ans": sanitized_text,"reference": {}} delta_ans = answer[len(last_ans):] if delta_ans: # stream_manager.append_text(tts_session_id, delta_ans) # yield {"answer": answer, "reference": {}, "audio_binary": tts(tts_mdl, delta_ans)} # cyx 2024 12 04 修正delta_ans 为空 调用tts 出错 tts_input_is_valid, sanitized_text = validate_and_sanitize_tts_input(delta_ans) if kwargs.get('tts_disable'): # cyx 2025 01 18 前端传入tts_disable 参数，就不生成tts 音频给前端,即:没有audio_binary tts_input_is_valid = False if tts_input_is_valid : # 20250221 修改,在后端生成音频数据 chunk_buffer.append(sanitized_text) to_send, remaining = process_buffer(chunk_buffer, force_flush=force) if to_send: stream_manager.append_text(tts_session_id, to_send) yield {"answer": answer, "delta_ans": sanitized_text, "reference": {}} """ if tts_input_is_valid: yield {"answer": answer, "delta_ans": sanitized_text,"reference": {}, "audio_binary": tts(tts_mdl, sanitized_text)} else: yield {"answer": answer, "delta_ans": sanitized_text,"reference": {}} """ yield decorate_answer(answer) else: answer = chat_mdl.chat(prompt, msg[1:], gen_conf) logging.debug("User: {}|Assistant: {}".format( msg[-1]["content"], answer)) res = decorate_answer(answer) if kwargs.get('tts_disable'): # cyx 2025 01 18 前端传入tts_disable 参数，就不生成tts 音频给前端,即:没有audio_binary tts_input_is_valid = False else: res["audio_binary"] = tts(tts_mdl, answer,tts_sample_rate,tts_stream_format) yield res def use_sql(question, field_map, tenant_id, chat_mdl, quota=True): sys_prompt = "你是一个DBA。你需要这对以下表的字段结构，根据用户的问题列表，写出最后一个问题对应的SQL。" user_promt = """ 表名：{}； 数据库表字段说明如下： {} 问题如下： {} 请写出SQL, 且只要SQL，不要有其他说明及文字。 """.format( index_name(tenant_id), "\n".join([f"{k}: {v}" for k, v in field_map.items()]), question ) tried_times = 0 def get_table(): nonlocal sys_prompt, user_promt, question, tried_times sql = chat_mdl.chat(sys_prompt, [{"role": "user", "content": user_promt}], { "temperature": 0.06}) logging.debug(f"{question} ==> {user_promt} get SQL: {sql}") sql = re.sub(r"[\r\n]+", " ", sql.lower()) sql = re.sub(r".*select ", "select ", sql.lower()) sql = re.sub(r" +", " ", sql) sql = re.sub(r"([;；]|```).*", "", sql) if sql[:len("select ")] != "select ": return None, None if not re.search(r"((sum|avg|max|min)\(|group by )", sql.lower()): if sql[:len("select *")] != "select *": sql = "select doc_id,docnm_kwd," + sql[6:] else: flds = [] for k in field_map.keys(): if k in forbidden_select_fields4resume: continue if len(flds) > 11: break flds.append(k) sql = "select doc_id,docnm_kwd," + ",".join(flds) + sql[8:] logging.debug(f"{question} get SQL(refined): {sql}") tried_times += 1 return settings.retrievaler.sql_retrieval(sql, format="json"), sql tbl, sql = get_table() if tbl is None: return None if tbl.get("error") and tried_times <= 2: user_promt = """ 表名：{}； 数据库表字段说明如下： {} 问题如下： {} 你上一次给出的错误SQL如下： {} 后台报错如下： {} 请纠正SQL中的错误再写一遍，且只要SQL，不要有其他说明及文字。 """.format( index_name(tenant_id), "\n".join([f"{k}: {v}" for k, v in field_map.items()]), question, sql, tbl["error"] ) tbl, sql = get_table() logging.debug("TRY it again: {}".format(sql)) logging.debug("GET table: {}".format(tbl)) if tbl.get("error") or len(tbl["rows"]) == 0: return None docid_idx = set([ii for ii, c in enumerate( tbl["columns"]) if c["name"] == "doc_id"]) docnm_idx = set([ii for ii, c in enumerate( tbl["columns"]) if c["name"] == "docnm_kwd"]) clmn_idx = [ii for ii in range( len(tbl["columns"])) if ii not in (docid_idx | docnm_idx)] # compose markdown table clmns = "|" + "|".join([re.sub(r"(/.*|（[^（）]+）)", "", field_map.get(tbl["columns"][i]["name"], tbl["columns"][i]["name"])) for i in clmn_idx]) + ("|Source|" if docid_idx and docid_idx else "|") line = "|" + "|".join(["------" for _ in range(len(clmn_idx))]) + \ ("|------|" if docid_idx and docid_idx else "") rows = ["|" + "|".join([rmSpace(str(r[i])) for i in clmn_idx]).replace("None", " ") + "|" for r in tbl["rows"]] rows = [r for r in rows if re.sub(r"[ |]+", "", r)] if quota: rows = "\n".join([r + f" ##{ii}$$ |" for ii, r in enumerate(rows)]) else: rows = "\n".join([r + f" ##{ii}$$ |" for ii, r in enumerate(rows)]) rows = re.sub(r"T[0-9]{2}:[0-9]{2}:[0-9]{2}(\.[0-9]+Z)?\|", "|", rows) if not docid_idx or not docnm_idx: logging.warning("SQL missing field: " + sql) return { "answer": "\n".join([clmns, line, rows]), "reference": {"chunks": [], "doc_aggs": []}, "prompt": sys_prompt } docid_idx = list(docid_idx)[0] docnm_idx = list(docnm_idx)[0] doc_aggs = {} for r in tbl["rows"]: if r[docid_idx] not in doc_aggs: doc_aggs[r[docid_idx]] = {"doc_name": r[docnm_idx], "count": 0} doc_aggs[r[docid_idx]]["count"] += 1 return { "answer": "\n".join([clmns, line, rows]), "reference": {"chunks": [{"doc_id": r[docid_idx], "docnm_kwd": r[docnm_idx]} for r in tbl["rows"]], "doc_aggs": [{"doc_id": did, "doc_name": d["doc_name"], "count": d["count"]} for did, d in doc_aggs.items()]}, "prompt": sys_prompt } def relevant(tenant_id, llm_id, question, contents: list): if llm_id2llm_type(llm_id) == "image2text": chat_mdl = LLMBundle(tenant_id, LLMType.IMAGE2TEXT, llm_id) else: chat_mdl = LLMBundle(tenant_id, LLMType.CHAT, llm_id) prompt = """ You are a grader assessing relevance of a retrieved document to a user question. It does not need to be a stringent test. The goal is to filter out erroneous retrievals. If the document contains keyword(s) or semantic meaning related to the user question, grade it as relevant. Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question. No other words needed except 'yes' or 'no'. """ if not contents:return False contents = "Documents: \n" + " - ".join(contents) contents = f"Question: {question}\n" + contents if num_tokens_from_string(contents) >= chat_mdl.max_length - 4: contents = encoder.decode(encoder.encode(contents)[:chat_mdl.max_length - 4]) ans = chat_mdl.chat(prompt, [{"role": "user", "content": contents}], {"temperature": 0.01}) if ans.lower().find("yes") >= 0: return True return False def rewrite(tenant_id, llm_id, question): if llm_id2llm_type(llm_id) == "image2text": chat_mdl = LLMBundle(tenant_id, LLMType.IMAGE2TEXT, llm_id) else: chat_mdl = LLMBundle(tenant_id, LLMType.CHAT, llm_id) prompt = """ You are an expert at query expansion to generate a paraphrasing of a question. I can't retrieval relevant information from the knowledge base by using user's question directly. You need to expand or paraphrase user's question by multiple ways such as using synonyms words/phrase, writing the abbreviation in its entirety, adding some extra descriptions or explanations, changing the way of expression, translating the original question into another language (English/Chinese), etc. And return 5 versions of question and one is from translation. Just list the question. No other words are needed. """ ans = chat_mdl.chat(prompt, [{"role": "user", "content": question}], {"temperature": 0.8}) return ans def keyword_extraction(chat_mdl, content, topn=3): prompt = f""" Role: You're a text analyzer. Task: extract the most important keywords/phrases of a given piece of text content. Requirements: - Summarize the text content, and give top {topn} important keywords/phrases. - The keywords MUST be in language of the given piece of text content. - The keywords are delimited by ENGLISH COMMA. - Keywords ONLY in output. ### Text Content {content} """ msg = [ {"role": "system", "content": prompt}, {"role": "user", "content": "Output: "} ] _, msg = message_fit_in(msg, chat_mdl.max_length) kwd = chat_mdl.chat(prompt, msg[1:], {"temperature": 0.2}) if isinstance(kwd, tuple): kwd = kwd[0] if kwd.find("**ERROR**") >=0: return "" return kwd def question_proposal(chat_mdl, content, topn=3): prompt = f""" Role: You're a text analyzer. Task: propose {topn} questions about a given piece of text content. Requirements: - Understand and summarize the text content, and propose top {topn} important questions. - The questions SHOULD NOT have overlapping meanings. - The questions SHOULD cover the main content of the text as much as possible. - The questions MUST be in language of the given piece of text content. - One question per line. - Question ONLY in output. ### Text Content {content} """ msg = [ {"role": "system", "content": prompt}, {"role": "user", "content": "Output: "} ] _, msg = message_fit_in(msg, chat_mdl.max_length) kwd = chat_mdl.chat(prompt, msg[1:], {"temperature": 0.2}) if isinstance(kwd, tuple): kwd = kwd[0] if kwd.find("**ERROR**") >= 0: return "" return kwd def full_question(tenant_id, llm_id, messages): if llm_id2llm_type(llm_id) == "image2text": chat_mdl = LLMBundle(tenant_id, LLMType.IMAGE2TEXT, llm_id) else: chat_mdl = LLMBundle(tenant_id, LLMType.CHAT, llm_id) conv = [] for m in messages: if m["role"] not in ["user", "assistant"]: continue conv.append("{}: {}".format(m["role"].upper(), m["content"])) conv = "\n".join(conv) today = datetime.date.today().isoformat() yesterday = (datetime.date.today() - timedelta(days=1)).isoformat() tomorrow = (datetime.date.today() + timedelta(days=1)).isoformat() prompt = f""" Role: A helpful assistant Task and steps: 1. Generate a full user question that would follow the conversation. 2. If the user's question involves relative date, you need to convert it into absolute date based on the current date, which is {today}. For example: 'yesterday' would be converted to {yesterday}. Requirements & Restrictions: - Text generated MUST be in the same language of the original user's question. - If the user's latest question is completely, don't do anything, just return the original question. - DON'T generate anything except a refined question. ###################### -Examples- ###################### # Example 1 ## Conversation USER: What is the name of Donald Trump's father? ASSISTANT: Fred Trump. USER: And his mother? ############### Output: What's the name of Donald Trump's mother? ------------ # Example 2 ## Conversation USER: What is the name of Donald Trump's father? ASSISTANT: Fred Trump. USER: And his mother? ASSISTANT: Mary Trump. User: What's her full name? ############### Output: What's the full name of Donald Trump's mother Mary Trump? ------------ # Example 3 ## Conversation USER: What's the weather today in London? ASSISTANT: Cloudy. USER: What's about tomorrow in Rochester? ############### Output: What's the weather in Rochester on {tomorrow}? ###################### # Real Data ## Conversation {conv} ############### """ ans = chat_mdl.chat(prompt, [{"role": "user", "content": "Output: "}], {"temperature": 0.2}) return ans if ans.find("**ERROR**") < 0 else messages[-1]["content"] def tts(tts_mdl, text,sample_rate=8000,stream_format = "mp3"): if not tts_mdl or not text: return bin = b"" for chunk in tts_mdl.tts(text,sample_rate,stream_format): bin += chunk return binascii.hexlify(bin).decode("utf-8") def ask(question, kb_ids, tenant_id): kbs = KnowledgebaseService.get_by_ids(kb_ids) tenant_ids = [kb.tenant_id for kb in kbs] embd_nms = list(set([kb.embd_id for kb in kbs])) is_kg = all([kb.parser_id == ParserType.KG for kb in kbs]) retr = settings.retrievaler if not is_kg else settings.kg_retrievaler embd_mdl = LLMBundle(tenant_id, LLMType.EMBEDDING, embd_nms[0]) chat_mdl = LLMBundle(tenant_id, LLMType.CHAT) max_tokens = chat_mdl.max_length kbinfos = retr.retrieval(question, embd_mdl, tenant_ids, kb_ids, 1, 12, 0.1, 0.3, aggs=False) knowledges = [ck["content_with_weight"] for ck in kbinfos["chunks"]] used_token_count = 0 for i, c in enumerate(knowledges): used_token_count += num_tokens_from_string(c) if max_tokens * 0.97 < used_token_count: knowledges = knowledges[:i] break prompt = """ Role: You're a smart assistant. Your name is Miss R. Task: Summarize the information from knowledge bases and answer user's question. Requirements and restriction: - DO NOT make things up, especially for numbers. - If the information from knowledge is irrelevant with user's question, JUST SAY: Sorry, no relevant information provided. - Answer with markdown format text. - Answer in language of user's question. - DO NOT make things up, especially for numbers. ### Information from knowledge bases %s The above is information from knowledge bases. """%"\n".join(knowledges) msg = [{"role": "user", "content": question}] def decorate_answer(answer): nonlocal knowledges, kbinfos, prompt answer, idx = retr.insert_citations(answer, [ck["content_ltks"] for ck in kbinfos["chunks"]], [ck["vector"] for ck in kbinfos["chunks"]], embd_mdl, tkweight=0.7, vtweight=0.3) idx = set([kbinfos["chunks"][int(i)]["doc_id"] for i in idx]) recall_docs = [ d for d in kbinfos["doc_aggs"] if d["doc_id"] in idx] if not recall_docs: recall_docs = kbinfos["doc_aggs"] kbinfos["doc_aggs"] = recall_docs refs = deepcopy(kbinfos) for c in refs["chunks"]: if c.get("vector"): del c["vector"] if answer.lower().find("invalid key") >= 0 or answer.lower().find("invalid api") >= 0: answer += " Please set LLM API-Key in 'User Setting -> Model Providers -> API-Key'" return {"answer": answer, "reference": refs} answer = "" for ans in chat_mdl.chat_streamly(prompt, msg, {"temperature": 0.1}): answer = ans yield {"answer": answer, "reference": {}} yield decorate_answer(answer)