# # 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 from concurrent.futures import ThreadPoolExecutor 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 def create_session(self, tts_model): session_id = str(uuid.uuid4()) with self.lock: self.sessions[session_id] = { 'tts_model': tts_model, 'buffer': queue.Queue(maxsize=100), # 线程安全队列 'task_queue': queue.Queue(), 'active': True, 'last_active': time.time(), 'audio_chunk_count':0 } # 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） 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: # 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=0.05) texts.append(text) except queue.Empty: break if texts: # 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 # 会话超时检查 if time.time() - session['last_active'] > self.gc_interval: self.close_session(session_id) break except Exception as e: logging.error(f"Task processing error: {str(e)}") def _generate_audio(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}") try: for chunk in session['tts_model'].tts(text): session['buffer'].put(chunk) session['last_active'] = time.time() session['audio_chunk_count'] = session['audio_chunk_count'] + 1 logging.info(f"转换结束!!! {session['audio_chunk_count'] }") except Exception as e: session['buffer'].put(f"ERROR:{str(e)}") 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30秒后清理资源 threading.Timer(10, 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] stream_manager = 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] # 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, "" # 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 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() # 避免拆分常见短语 for phrase in ["青少年", "博物馆", "参观"]: idx = text.rfind(phrase) if idx != -1 and idx + len(phrase) <= len(text): return idx + len(phrase) 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: return current_text[:split_pos], [current_text[split_pos:]] return "", 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] 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) # 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))) 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)} 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) 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) 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, "reference": refs, "prompt": prompt} if stream: last_ans = "" answer = "" # 创建TTS会话（提前初始化） tts_session_id = stream_manager.create_session(tts_mdl) audio_url = f"/tts_stream/{tts_session_id}" first_chunk = True chunk_buffer = [] # 新增文本缓冲 last_flush_time = time.time() # 初始化时间戳 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 first_chunk: yield { "answer": answer, "delta_ans": sanitized_text, "audio_stream_url": audio_url, "session_id": tts_session_id, "reference": {} } first_chunk = False 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) stream_manager.append_text(tts_session_id, ''.join(chunk_buffer)) 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) 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): if not tts_mdl or not text: return bin = b"" for chunk in tts_mdl.tts(text): 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)