561 lines
26 KiB
Python
561 lines
26 KiB
Python
#
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# Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import logging
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import binascii
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import time
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from functools import partial
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import re
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from copy import deepcopy
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from timeit import default_timer as timer
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from agentic_reasoning import DeepResearcher
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from api.db import LLMType, ParserType, StatusEnum
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from api.db.db_models import Dialog, DB
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from api.db.services.common_service import CommonService
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from api.db.services.knowledgebase_service import KnowledgebaseService
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from api.db.services.llm_service import TenantLLMService, LLMBundle
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from api import settings
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from rag.app.resume import forbidden_select_fields4resume
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from rag.app.tag import label_question
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from rag.nlp.search import index_name
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from rag.prompts import kb_prompt, message_fit_in, llm_id2llm_type, keyword_extraction, full_question, chunks_format
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from rag.utils import rmSpace, num_tokens_from_string
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from rag.utils.tavily_conn import Tavily
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class DialogService(CommonService):
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model = Dialog
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@classmethod
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@DB.connection_context()
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def get_list(cls, tenant_id,
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page_number, items_per_page, orderby, desc, id, name):
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chats = cls.model.select()
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if id:
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chats = chats.where(cls.model.id == id)
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if name:
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chats = chats.where(cls.model.name == name)
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chats = chats.where(
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(cls.model.tenant_id == tenant_id)
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& (cls.model.status == StatusEnum.VALID.value)
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)
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if desc:
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chats = chats.order_by(cls.model.getter_by(orderby).desc())
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else:
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chats = chats.order_by(cls.model.getter_by(orderby).asc())
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chats = chats.paginate(page_number, items_per_page)
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return list(chats.dicts())
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def chat_solo(dialog, messages, stream=True):
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if llm_id2llm_type(dialog.llm_id) == "image2text":
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chat_mdl = LLMBundle(dialog.tenant_id, LLMType.IMAGE2TEXT, dialog.llm_id)
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else:
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chat_mdl = LLMBundle(dialog.tenant_id, LLMType.CHAT, dialog.llm_id)
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prompt_config = dialog.prompt_config
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tts_mdl = None
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if prompt_config.get("tts"):
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tts_mdl = LLMBundle(dialog.tenant_id, LLMType.TTS)
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msg = [{"role": m["role"], "content": re.sub(r"##\d+\$\$", "", m["content"])}
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for m in messages if m["role"] != "system"]
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if stream:
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last_ans = ""
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for ans in chat_mdl.chat_streamly(prompt_config.get("system", ""), msg, dialog.llm_setting):
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answer = ans
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delta_ans = ans[len(last_ans):]
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if num_tokens_from_string(delta_ans) < 16:
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continue
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last_ans = answer
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yield {"answer": answer, "reference": {}, "audio_binary": tts(tts_mdl, delta_ans), "prompt":"", "created_at": time.time()}
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else:
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answer = chat_mdl.chat(prompt_config.get("system", ""), msg, dialog.llm_setting)
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user_content = msg[-1].get("content", "[content not available]")
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logging.debug("User: {}|Assistant: {}".format(user_content, answer))
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yield {"answer": answer, "reference": {}, "audio_binary": tts(tts_mdl, answer), "prompt": "", "created_at": time.time()}
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def chat(dialog, messages, stream=True, **kwargs):
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# 确保最后一条消息来自用户，否则抛出异常
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assert messages[-1]["role"] == "user", "The last content of this conversation is not from user."
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# 如果对话没有关联知识库，则调用chat_solo函数进行简单对话处理
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if not dialog.kb_ids:
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for ans in chat_solo(dialog, messages, stream):
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yield ans
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return
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# 记录聊天开始时间，用于后续性能分析
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chat_start_ts = timer()
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# 根据对话配置的LLM类型获取模型配置
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if llm_id2llm_type(dialog.llm_id) == "image2text":
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llm_model_config = TenantLLMService.get_model_config(dialog.tenant_id, LLMType.IMAGE2TEXT, dialog.llm_id)
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else:
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llm_model_config = TenantLLMService.get_model_config(dialog.tenant_id, LLMType.CHAT, dialog.llm_id)
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# 获取模型支持的最大token数，默认为8192
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max_tokens = llm_model_config.get("max_tokens", 8192)
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# 记录检查LLM配置的时间点
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check_llm_ts = timer()
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# 获取对话关联的所有知识库
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kbs = KnowledgebaseService.get_by_ids(dialog.kb_ids)
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# 提取所有知识库使用的嵌入模型ID，并确保它们使用相同的嵌入模型
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embedding_list = list(set([kb.embd_id for kb in kbs]))
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if len(embedding_list) != 1:
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# 如果知识库使用了不同的嵌入模型，返回错误信息
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yield {"answer": "**ERROR**: Knowledge bases use different embedding models.", "reference": []}
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return {"answer": "**ERROR**: Knowledge bases use different embedding models.", "reference": []}
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# 获取嵌入模型名称
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embedding_model_name = embedding_list[0]
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# 获取检索器实例
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retriever = settings.retrievaler
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# 提取最近3条用户消息作为问题上下文
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questions = [m["content"] for m in messages if m["role"] == "user"][-3:]
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# 处理附件文档ID
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attachments = kwargs["doc_ids"].split(",") if "doc_ids" in kwargs else None
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if "doc_ids" in messages[-1]:
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attachments = messages[-1]["doc_ids"]
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# 记录创建检索器的时间点
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create_retriever_ts = timer()
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# 初始化嵌入模型
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embd_mdl = LLMBundle(dialog.tenant_id, LLMType.EMBEDDING, embedding_model_name)
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# 如果嵌入模型不存在，抛出异常
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if not embd_mdl:
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raise LookupError("Embedding model(%s) not found" % embedding_model_name)
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# 记录绑定嵌入模型的时间点
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bind_embedding_ts = timer()
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# 初始化聊天模型
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if llm_id2llm_type(dialog.llm_id) == "image2text":
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chat_mdl = LLMBundle(dialog.tenant_id, LLMType.IMAGE2TEXT, dialog.llm_id)
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else:
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chat_mdl = LLMBundle(dialog.tenant_id, LLMType.CHAT, dialog.llm_id)
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# 记录绑定LLM模型的时间点
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bind_llm_ts = timer()
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# 获取提示词配置
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prompt_config = dialog.prompt_config
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# 获取字段映射，用于SQL检索
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field_map = KnowledgebaseService.get_field_map(dialog.kb_ids)
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tts_mdl = None
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# 初始化文本转语音模型（如果配置中启用）
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if prompt_config.get("tts"):
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tts_mdl = LLMBundle(dialog.tenant_id, LLMType.TTS)
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# try to use sql if field mapping is good to go
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# 尝试使用SQL检索（如果有字段映射）
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if field_map:
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logging.debug("Use SQL to retrieval:{}".format(questions[-1]))
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ans = use_sql(questions[-1], field_map, dialog.tenant_id, chat_mdl, prompt_config.get("quote", True))
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if ans:
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# 如果SQL检索成功，直接返回结果
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yield ans
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return
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# 处理提示词配置中的参数
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for p in prompt_config["parameters"]:
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# 跳过knowledge参数，它将在后面处理
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if p["key"] == "knowledge":
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continue
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# 检查必需参数是否提供
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if p["key"] not in kwargs and not p["optional"]:
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raise KeyError("Miss parameter: " + p["key"])
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# 如果参数未提供且为可选，则在提示词中替换为空格
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if p["key"] not in kwargs:
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prompt_config["system"] = prompt_config["system"].replace(
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"{%s}" % p["key"], " ")
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# 处理多轮对话开启，并且问题数量大于1
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if len(questions) > 1 and prompt_config.get("refine_multiturn"):
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# 使用full_question函数将多轮对话合并为一个完整问题
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questions = [full_question(dialog.tenant_id, dialog.llm_id, messages)]
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else:
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# 否则只使用最后一个问题
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questions = questions[-1:]
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# 记录问题精炼的时间点
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refine_question_ts = timer()
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# 初始化重排序模型（如果配置）
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rerank_mdl = None
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if dialog.rerank_id:
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rerank_mdl = LLMBundle(dialog.tenant_id, LLMType.RERANK, dialog.rerank_id)
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# 记录绑定重排序模型的时间点
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bind_reranker_ts = timer()
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generate_keyword_ts = bind_reranker_ts
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# 初始化思考过程和知识库信息
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thought = ""
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kbinfos = {"total": 0, "chunks": [], "doc_aggs": []}
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# 检查是否需要知识库检索
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if "knowledge" not in [p["key"] for p in prompt_config["parameters"]]:
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knowledges = []
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else:
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# 如果启用了关键词提取，则增强问题
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if prompt_config.get("keyword", False):
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questions[-1] += keyword_extraction(chat_mdl, questions[-1])
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generate_keyword_ts = timer()
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# 获取所有知识库的表ID
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tenant_ids = list(set([kb.tenant_id for kb in kbs]))
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knowledges = []
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# 如果启用了推理功能，使用DeepResearcher进行深度推理
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if prompt_config.get("reasoning", False):
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reasoner = DeepResearcher(chat_mdl,
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prompt_config,
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partial(retriever.retrieval, embd_mdl=embd_mdl, tenant_ids=tenant_ids, kb_ids=dialog.kb_ids, page=1, page_size=dialog.top_n, similarity_threshold=0.2, vector_similarity_weight=0.3))
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# 执行推理过程
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for think in reasoner.thinking(kbinfos, " ".join(questions)):
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if isinstance(think, str):
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thought = think
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knowledges = [t for t in think.split("\n") if t]
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elif stream:
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yield think
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else:
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# 使用标准检索方法
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kbinfos = retriever.retrieval(" ".join(questions), embd_mdl, tenant_ids, dialog.kb_ids, 1, dialog.top_n,
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dialog.similarity_threshold,
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dialog.vector_similarity_weight,
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doc_ids=attachments,
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top=dialog.top_k, aggs=False, rerank_mdl=rerank_mdl,
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rank_feature=label_question(" ".join(questions), kbs)
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)
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# 如果配置了Tavily API，使用外部搜索增强检索结果
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if prompt_config.get("tavily_api_key"):
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tav = Tavily(prompt_config["tavily_api_key"])
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tav_res = tav.retrieve_chunks(" ".join(questions))
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kbinfos["chunks"].extend(tav_res["chunks"])
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kbinfos["doc_aggs"].extend(tav_res["doc_aggs"])
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# 如果启用了知识图谱，使用知识图谱检索结果
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if prompt_config.get("use_kg"):
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ck = settings.kg_retrievaler.retrieval(" ".join(questions),
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tenant_ids,
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dialog.kb_ids,
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embd_mdl,
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LLMBundle(dialog.tenant_id, LLMType.CHAT))
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if ck["content_with_weight"]:
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kbinfos["chunks"].insert(0, ck)
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# 将检索到的知识格式化为提示词
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knowledges = kb_prompt(kbinfos, max_tokens)
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# 记录检索到的知识
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logging.debug(
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"{}->{}".format(" ".join(questions), "\n->".join(knowledges)))
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# 记录检索完成的时间点
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retrieval_ts = timer()
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# 如果没有检索到知识且配置了空响应，则返回空响应
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if not knowledges and prompt_config.get("empty_response"):
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empty_res = prompt_config["empty_response"]
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yield {"answer": empty_res, "reference": kbinfos, "audio_binary": tts(tts_mdl, empty_res)}
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return {"answer": prompt_config["empty_response"], "reference": kbinfos}
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# 将检索到的知识添加到kwargs中，用于格式化系统提示词
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kwargs["knowledge"] = "\n------\n" + "\n\n------\n\n".join(knowledges)
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# 获取LLM生成配置
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gen_conf = dialog.llm_setting
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# 构建消息列表，包括系统提示词和用户消息
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msg = [{"role": "system", "content": prompt_config["system"].format(**kwargs)}]
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msg.extend([{"role": m["role"], "content": re.sub(r"##\d+\$\$", "", m["content"])}
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for m in messages if m["role"] != "system"])
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# 确保消息列表不超过模型最大token限制
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used_token_count, msg = message_fit_in(msg, int(max_tokens * 0.97))
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assert len(msg) >= 2, f"message_fit_in has bug: {msg}"
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prompt = msg[0]["content"]
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# 调整生成配置中的max_tokens，确保不超过模型限制
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if "max_tokens" in gen_conf:
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gen_conf["max_tokens"] = min(
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gen_conf["max_tokens"],
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max_tokens - used_token_count)
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def decorate_answer(answer):
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nonlocal prompt_config, knowledges, kwargs, kbinfos, prompt, retrieval_ts, questions
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refs = []
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# 处理思考过程，如果答案包含</think>标签
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ans = answer.split("</think>")
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think = ""
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if len(ans) == 2:
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think = ans[0] + "</think>"
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answer = ans[1]
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# 如果有知识且启用了引用功能，添加引用
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if knowledges and (prompt_config.get("quote", True) and kwargs.get("quote", True)):
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answer, idx = retriever.insert_citations(answer,
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[ck["content_ltks"]
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for ck in kbinfos["chunks"]],
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[ck["vector"]
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for ck in kbinfos["chunks"]],
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embd_mdl,
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tkweight=1 - dialog.vector_similarity_weight,
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vtweight=dialog.vector_similarity_weight)
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idx = set([kbinfos["chunks"][int(i)]["doc_id"] for i in idx])
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recall_docs = [
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d for d in kbinfos["doc_aggs"] if d["doc_id"] in idx]
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if not recall_docs:
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recall_docs = kbinfos["doc_aggs"]
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kbinfos["doc_aggs"] = recall_docs
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refs = deepcopy(kbinfos)
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for c in refs["chunks"]:
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if c.get("vector"):
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del c["vector"]
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if answer.lower().find("invalid key") >= 0 or answer.lower().find("invalid api") >= 0:
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answer += " Please set LLM API-Key in 'User Setting -> Model providers -> API-Key'"
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# 记录完成时间
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finish_chat_ts = timer()
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# 计算各阶段耗时
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total_time_cost = (finish_chat_ts - chat_start_ts) * 1000
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check_llm_time_cost = (check_llm_ts - chat_start_ts) * 1000
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create_retriever_time_cost = (create_retriever_ts - check_llm_ts) * 1000
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bind_embedding_time_cost = (bind_embedding_ts - create_retriever_ts) * 1000
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bind_llm_time_cost = (bind_llm_ts - bind_embedding_ts) * 1000
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refine_question_time_cost = (refine_question_ts - bind_llm_ts) * 1000
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bind_reranker_time_cost = (bind_reranker_ts - refine_question_ts) * 1000
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generate_keyword_time_cost = (generate_keyword_ts - bind_reranker_ts) * 1000
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retrieval_time_cost = (retrieval_ts - generate_keyword_ts) * 1000
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generate_result_time_cost = (finish_chat_ts - retrieval_ts) * 1000
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prompt += "\n\n### Query:\n%s" % " ".join(questions)
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prompt = f"{prompt}\n\n - Total: {total_time_cost:.1f}ms\n - Check LLM: {check_llm_time_cost:.1f}ms\n - Create retriever: {create_retriever_time_cost:.1f}ms\n - Bind embedding: {bind_embedding_time_cost:.1f}ms\n - Bind LLM: {bind_llm_time_cost:.1f}ms\n - Tune question: {refine_question_time_cost:.1f}ms\n - Bind reranker: {bind_reranker_time_cost:.1f}ms\n - Generate keyword: {generate_keyword_time_cost:.1f}ms\n - Retrieval: {retrieval_time_cost:.1f}ms\n - Generate answer: {generate_result_time_cost:.1f}ms"
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return {"answer": think+answer, "reference": refs, "prompt": re.sub(r"\n", " \n", prompt), "created_at": time.time()}
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# 根据stream参数决定是流式输出还是批量输出
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if stream:
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# 流式输出模式
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last_ans = ""
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answer = ""
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# 调用LLM的流式生成接口
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for ans in chat_mdl.chat_streamly(prompt, msg[1:], gen_conf):
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# 如果有思考过程，从回答中移除思考部分
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if thought:
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ans = re.sub(r"<think>.*</think>", "", ans, flags=re.DOTALL)
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answer = ans
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# 计算增量回答（与上次输出相比新增的内容）
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delta_ans = ans[len(last_ans):]
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# 如果增量内容太少，跳过本次输出
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if num_tokens_from_string(delta_ans) < 16:
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continue
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last_ans = answer
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# 生成包含回答、引用和音频的输出
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yield {"answer": thought+answer, "reference": {}, "audio_binary": tts(tts_mdl, delta_ans)}
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delta_ans = answer[len(last_ans):]
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# 处理最后一部分增量内容
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if delta_ans:
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yield {"answer": thought+answer, "reference": {}, "audio_binary": tts(tts_mdl, delta_ans)}
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# 最后输出装饰后的完整回答（包含引用和性能指标）
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yield decorate_answer(thought+answer)
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else:
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# 非流式输出模式
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answer = chat_mdl.chat(prompt, msg[1:], gen_conf)
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user_content = msg[-1].get("content", "[content not available]")
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logging.debug("User: {}|Assistant: {}".format(user_content, answer))
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res = decorate_answer(answer)
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res["audio_binary"] = tts(tts_mdl, answer)
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yield res
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def use_sql(question, field_map, tenant_id, chat_mdl, quota=True):
|
||
sys_prompt = "You are a Database Administrator. You need to check the fields of the following tables based on the user's list of questions and write the SQL corresponding to the last question."
|
||
user_prompt = """
|
||
Table name: {};
|
||
Table of database fields are as follows:
|
||
{}
|
||
|
||
Question are as follows:
|
||
{}
|
||
Please write the SQL, only SQL, without any other explanations or text.
|
||
""".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_prompt, question, tried_times
|
||
sql = chat_mdl.chat(sys_prompt, [{"role": "user", "content": user_prompt}], {
|
||
"temperature": 0.06})
|
||
logging.debug(f"{question} ==> {user_prompt} 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_prompt = """
|
||
Table name: {};
|
||
Table of database fields are as follows:
|
||
{}
|
||
|
||
Question are as follows:
|
||
{}
|
||
Please write the SQL, only SQL, without any other explanations or text.
|
||
|
||
|
||
The SQL error you provided last time is as follows:
|
||
{}
|
||
|
||
Error issued by database as follows:
|
||
{}
|
||
|
||
Please correct the error and write SQL again, only SQL, without any other explanations or text.
|
||
""".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"])
|
||
doc_name_idx = set([ii for ii, c in enumerate(
|
||
tbl["columns"]) if c["name"] == "docnm_kwd"])
|
||
column_idx = [ii for ii in range(
|
||
len(tbl["columns"])) if ii not in (docid_idx | doc_name_idx)]
|
||
|
||
# compose Markdown table
|
||
columns = "|" + "|".join([re.sub(r"(/.*|（[^（）]+）)", "", field_map.get(tbl["columns"][i]["name"],
|
||
tbl["columns"][i]["name"])) for i in
|
||
column_idx]) + ("|Source|" if docid_idx and docid_idx else "|")
|
||
|
||
line = "|" + "|".join(["------" for _ in range(len(column_idx))]) + \
|
||
("|------|" if docid_idx and docid_idx else "")
|
||
|
||
rows = ["|" +
|
||
"|".join([rmSpace(str(r[i])) for i in column_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 doc_name_idx:
|
||
logging.warning("SQL missing field: " + sql)
|
||
return {
|
||
"answer": "\n".join([columns, line, rows]),
|
||
"reference": {"chunks": [], "doc_aggs": []},
|
||
"prompt": sys_prompt
|
||
}
|
||
|
||
docid_idx = list(docid_idx)[0]
|
||
doc_name_idx = list(doc_name_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[doc_name_idx], "count": 0}
|
||
doc_aggs[r[docid_idx]]["count"] += 1
|
||
return {
|
||
"answer": "\n".join([columns, line, rows]),
|
||
"reference": {"chunks": [{"doc_id": r[docid_idx], "docnm_kwd": r[doc_name_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 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)
|
||
embedding_list = list(set([kb.embd_id for kb in kbs]))
|
||
|
||
is_knowledge_graph = all([kb.parser_id == ParserType.KG for kb in kbs])
|
||
retriever = settings.retrievaler if not is_knowledge_graph else settings.kg_retrievaler
|
||
|
||
embd_mdl = LLMBundle(tenant_id, LLMType.EMBEDDING, embedding_list[0])
|
||
chat_mdl = LLMBundle(tenant_id, LLMType.CHAT)
|
||
max_tokens = chat_mdl.max_length
|
||
tenant_ids = list(set([kb.tenant_id for kb in kbs]))
|
||
kbinfos = retriever.retrieval(question, embd_mdl, tenant_ids, kb_ids,
|
||
1, 12, 0.1, 0.3, aggs=False,
|
||
rank_feature=label_question(question, kbs)
|
||
)
|
||
knowledges = kb_prompt(kbinfos, max_tokens)
|
||
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 = retriever.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": chunks_format(refs)}
|
||
|
||
answer = ""
|
||
for ans in chat_mdl.chat_streamly(prompt, msg, {"temperature": 0.1}):
|
||
answer = ans
|
||
yield {"answer": answer, "reference": {}}
|
||
yield decorate_answer(answer)
|
||
|
||
|