ragflow/api/db/services/dialog_service.py

#
#  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 time

import json_repair
import re
from collections import defaultdict
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, DB
from api.db.services.common_service import CommonService
from api.db.services.document_service import DocumentService
from api.db.services.knowledgebase_service import KnowledgebaseService
from api.db.services.llm_service import TenantLLMService, LLMBundle
from api import settings
from graphrag.utils import get_tags_from_cache, set_tags_to_cache
from rag.app.resume import forbidden_select_fields4resume
from rag.nlp import extract_between
from rag.nlp.search import index_name
from rag.settings import TAG_FLD
from rag.utils import rmSpace, num_tokens_from_string, encoder
from api.utils.file_utils import get_project_base_directory
from rag.utils.tavily_conn import Tavily


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())


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"])
    ll2 = num_tokens_from_string(msg_[-1]["content"])
    if ll / (ll + ll2) > 0.8:
        m = msg_[0]["content"]
        m = encoder.decode(encoder.encode(m)[:max_length - ll2])
        msg[0]["content"] = m
        return max_length, msg

    m = msg_[1]["content"]
    m = encoder.decode(encoder.encode(m)[:max_length - ll2])
    msg[1]["content"] = m
    return max_length, msg


def llm_id2llm_type(llm_id):
    llm_id, _ = TenantLLMService.split_model_name_and_factory(llm_id)
    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]


def kb_prompt(kbinfos, max_tokens):
    knowledges = [ck["content_with_weight"] for ck in kbinfos["chunks"]]
    used_token_count = 0
    chunks_num = 0
    for i, c in enumerate(knowledges):
        used_token_count += num_tokens_from_string(c)
        chunks_num += 1
        if max_tokens * 0.97 < used_token_count:
            knowledges = knowledges[:i]
            logging.warning(f"Not all the retrieval into prompt: {i+1}/{len(knowledges)}")
            break

    docs = DocumentService.get_by_ids([ck["doc_id"] for ck in kbinfos["chunks"][:chunks_num]])
    docs = {d.id: d.meta_fields for d in docs}

    doc2chunks = defaultdict(lambda: {"chunks": [], "meta": []})
    for ck in kbinfos["chunks"][:chunks_num]:
        doc2chunks[ck["docnm_kwd"]]["chunks"].append((f"URL: {ck['url']}\n" if "url" in ck else "") + ck["content_with_weight"])
        doc2chunks[ck["docnm_kwd"]]["meta"] = docs.get(ck["doc_id"], {})

    knowledges = []
    for nm, cks_meta in doc2chunks.items():
        txt = f"Document: {nm} \n"
        for k, v in cks_meta["meta"].items():
            txt += f"{k}: {v}\n"
        txt += "Relevant fragments as following:\n"
        for i, chunk in enumerate(cks_meta["chunks"], 1):
            txt += f"{i}. {chunk}\n"
        knowledges.append(txt)
    return knowledges


def label_question(question, kbs):
    tags = None
    tag_kb_ids = []
    for kb in kbs:
        if kb.parser_config.get("tag_kb_ids"):
            tag_kb_ids.extend(kb.parser_config["tag_kb_ids"])
    if tag_kb_ids:
        all_tags = get_tags_from_cache(tag_kb_ids)
        if not all_tags:
            all_tags = settings.retrievaler.all_tags_in_portion(kb.tenant_id, tag_kb_ids)
            set_tags_to_cache(all_tags, tag_kb_ids)
        else:
            all_tags = json.loads(all_tags)
        tag_kbs = KnowledgebaseService.get_by_ids(tag_kb_ids)
        tags = settings.retrievaler.tag_query(question,
                                              list(set([kb.tenant_id for kb in tag_kbs])),
                                              tag_kb_ids,
                                              all_tags,
                                              kb.parser_config.get("topn_tags", 3)
                                              )
    return tags


def chat_solo(dialog, messages, stream=True):
    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
    tts_mdl = None
    if prompt_config.get("tts"):
        tts_mdl = LLMBundle(dialog.tenant_id, LLMType.TTS)
    msg = [{"role": m["role"], "content": re.sub(r"##\d+\$\$", "", m["content"])}
                for m in messages if m["role"] != "system"]
    if stream:
        last_ans = ""
        for ans in chat_mdl.chat_streamly(prompt_config.get("system", ""), msg, dialog.llm_setting):
            answer = ans
            delta_ans = ans[len(last_ans):]
            if num_tokens_from_string(delta_ans) < 16:
                continue
            last_ans = answer
            yield {"answer": answer, "reference": {}, "audio_binary": tts(tts_mdl, delta_ans), "prompt":"", "created_at": time.time()}
    else:
        answer = chat_mdl.chat(prompt_config.get("system", ""), msg, dialog.llm_setting)
        user_content = msg[-1].get("content", "[content not available]")
        logging.debug("User: {}|Assistant: {}".format(user_content, answer))
        yield {"answer": answer, "reference": {}, "audio_binary": tts(tts_mdl, answer), "prompt": "", "created_at": time.time()}


def chat(dialog, messages, stream=True, **kwargs):
    assert messages[-1]["role"] == "user", "The last content of this conversation is not from user."
    if not dialog.kb_ids:
        for ans in chat_solo(dialog, messages, stream):
            yield ans
        return

    chat_start_ts = timer()

    if llm_id2llm_type(dialog.llm_id) == "image2text":
        llm_model_config = TenantLLMService.get_model_config(dialog.tenant_id, LLMType.IMAGE2TEXT, dialog.llm_id)
    else:
        llm_model_config = TenantLLMService.get_model_config(dialog.tenant_id, LLMType.CHAT, dialog.llm_id)

    max_tokens = llm_model_config.get("max_tokens", 8192)

    check_llm_ts = timer()

    kbs = KnowledgebaseService.get_by_ids(dialog.kb_ids)
    embedding_list = list(set([kb.embd_id for kb in kbs]))
    if len(embedding_list) != 1:
        yield {"answer": "**ERROR**: Knowledge bases use different embedding models.", "reference": []}
        return {"answer": "**ERROR**: Knowledge bases use different embedding models.", "reference": []}

    embedding_model_name = embedding_list[0]

    retriever = settings.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"]

    create_retriever_ts = timer()

    embd_mdl = LLMBundle(dialog.tenant_id, LLMType.EMBEDDING, embedding_model_name)
    if not embd_mdl:
        raise LookupError("Embedding model(%s) not found" % embedding_model_name)

    bind_embedding_ts = timer()

    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)

    bind_llm_ts = timer()

    prompt_config = dialog.prompt_config
    field_map = KnowledgebaseService.get_field_map(dialog.kb_ids)
    tts_mdl = None
    if prompt_config.get("tts"):
        tts_mdl = LLMBundle(dialog.tenant_id, LLMType.TTS)
    # 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

    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:]

    refine_question_ts = timer()

    rerank_mdl = None
    if dialog.rerank_id:
        rerank_mdl = LLMBundle(dialog.tenant_id, LLMType.RERANK, dialog.rerank_id)

    bind_reranker_ts = timer()
    generate_keyword_ts = bind_reranker_ts
    thought = ""
    kbinfos = {"total": 0, "chunks": [], "doc_aggs": []}

    if "knowledge" not in [p["key"] for p in prompt_config["parameters"]]:
        knowledges = []
    else:
        if prompt_config.get("keyword", False):
            questions[-1] += keyword_extraction(chat_mdl, questions[-1])
            generate_keyword_ts = timer()

        tenant_ids = list(set([kb.tenant_id for kb in kbs]))

        knowledges = []
        if prompt_config.get("reasoning", False):
            for think in reasoning(kbinfos, " ".join(questions), chat_mdl, embd_mdl, tenant_ids, dialog.kb_ids, prompt_config, MAX_SEARCH_LIMIT=3):
                if isinstance(think, str):
                    thought = think
                    knowledges = [t for t in think.split("\n") if t]
                else:
                    yield think
        else:
            kbinfos = retriever.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,
                                          rank_feature=label_question(" ".join(questions), kbs)
                                          )
            if prompt_config.get("tavily_api_key"):
                tav = Tavily(prompt_config["tavily_api_key"])
                tav_res = tav.retrieve_chunks(" ".join(questions))
                kbinfos["chunks"].extend(tav_res["chunks"])
                kbinfos["doc_aggs"].extend(tav_res["doc_aggs"])
            if prompt_config.get("use_kg"):
                ck = settings.kg_retrievaler.retrieval(" ".join(questions),
                                                       tenant_ids,
                                                       dialog.kb_ids,
                                                       embd_mdl,
                                                       LLMBundle(dialog.tenant_id, LLMType.CHAT))
                if ck["content_with_weight"]:
                    kbinfos["chunks"].insert(0, ck)

            knowledges = kb_prompt(kbinfos, max_tokens)

    logging.debug(
        "{}->{}".format(" ".join(questions), "\n->".join(knowledges)))

    retrieval_ts = 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------\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_ts

        refs = []
        ans = answer.split("</think>")
        think = ""
        if len(ans) == 2:
            think = ans[0] + "</think>"
            answer = ans[1]
        if knowledges and (prompt_config.get("quote", True) and kwargs.get("quote", True)):
            answer, idx = retriever.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'"
        finish_chat_ts = timer()

        total_time_cost = (finish_chat_ts - chat_start_ts) * 1000
        check_llm_time_cost = (check_llm_ts - chat_start_ts) * 1000
        create_retriever_time_cost = (create_retriever_ts - check_llm_ts) * 1000
        bind_embedding_time_cost = (bind_embedding_ts - create_retriever_ts) * 1000
        bind_llm_time_cost = (bind_llm_ts - bind_embedding_ts) * 1000
        refine_question_time_cost = (refine_question_ts - bind_llm_ts) * 1000
        bind_reranker_time_cost = (bind_reranker_ts - refine_question_ts) * 1000
        generate_keyword_time_cost = (generate_keyword_ts - bind_reranker_ts) * 1000
        retrieval_time_cost = (retrieval_ts - generate_keyword_ts) * 1000
        generate_result_time_cost = (finish_chat_ts - retrieval_ts) * 1000

        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"
        return {"answer": think+answer, "reference": refs, "prompt": re.sub(r"\n", "  \n", prompt), "created_at": time.time()}

    if stream:
        last_ans = ""
        answer = ""
        for ans in chat_mdl.chat_streamly(prompt, msg[1:], gen_conf):
            if thought:
                ans = re.sub(r"<think>.*</think>", "", ans, flags=re.DOTALL)
            answer = ans
            delta_ans = ans[len(last_ans):]
            if num_tokens_from_string(delta_ans) < 16:
                continue
            last_ans = answer
            yield {"answer": thought+answer, "reference": {}, "audio_binary": tts(tts_mdl, delta_ans)}
        delta_ans = answer[len(last_ans):]
        if delta_ans:
            yield {"answer": thought+answer, "reference": {}, "audio_binary": tts(tts_mdl, delta_ans)}
        yield decorate_answer(thought+answer)
    else:
        answer = chat_mdl.chat(prompt, msg[1:], gen_conf)
        user_content = msg[-1].get("content", "[content not available]")
        logging.debug("User: {}|Assistant: {}".format(user_content, answer))
        res = decorate_answer(answer)
        res["audio_binary"] = tts(tts_mdl, answer)
        yield res


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 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]
    kwd = re.sub(r"<think>.*</think>", "", kwd, flags=re.DOTALL)
    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]
    kwd = re.sub(r"<think>.*</think>", "", kwd, flags=re.DOTALL)
    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})
    ans = re.sub(r"<think>.*</think>", "", ans, flags=re.DOTALL)
    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)
    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": refs}

    answer = ""
    for ans in chat_mdl.chat_streamly(prompt, msg, {"temperature": 0.1}):
        answer = ans
        yield {"answer": answer, "reference": {}}
    yield decorate_answer(answer)


def content_tagging(chat_mdl, content, all_tags, examples, topn=3):
    prompt = f"""
Role: You're a text analyzer. 

Task: Tag (put on some labels) to a given piece of text content based on the examples and the entire tag set.

Steps:: 
  - Comprehend the tag/label set.
  - Comprehend examples which all consist of both text content and assigned tags with relevance score in format of JSON.
  - Summarize the text content, and tag it with top {topn} most relevant tags from the set of tag/label and the corresponding relevance score.

Requirements
  - The tags MUST be from the tag set.
  - The output MUST be in JSON format only, the key is tag and the value is its relevance score.
  - The relevance score must be range from 1 to 10.
  - Keywords ONLY in output.

# TAG SET
{", ".join(all_tags)}

"""
    for i, ex in enumerate(examples):
        prompt += """
# Examples {}
### Text Content
{}

Output:
{}

        """.format(i, ex["content"], json.dumps(ex[TAG_FLD], indent=2, ensure_ascii=False))

    prompt += f"""
# Real Data
### 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": 0.5})
    if isinstance(kwd, tuple):
        kwd = kwd[0]
    kwd = re.sub(r"<think>.*</think>", "", kwd, flags=re.DOTALL)
    if kwd.find("**ERROR**") >= 0:
        raise Exception(kwd)

    try:
        return json_repair.loads(kwd)
    except json_repair.JSONDecodeError:
        try:
            result = kwd.replace(prompt[:-1], '').replace('user', '').replace('model', '').strip()
            result = '{' + result.split('{')[1].split('}')[0] + '}'
            return json_repair.loads(result)
        except Exception as e:
            logging.exception(f"JSON parsing error: {result} -> {e}")
            raise e


def reasoning(chunk_info: dict, question: str, chat_mdl: LLMBundle, embd_mdl: LLMBundle,
              tenant_ids: list[str], kb_ids: list[str], prompt_config, MAX_SEARCH_LIMIT: int = 6,
              top_n: int = 5, similarity_threshold: float = 0.4, vector_similarity_weight: float = 0.3):
    BEGIN_SEARCH_QUERY = "<|begin_search_query|>"
    END_SEARCH_QUERY = "<|end_search_query|>"
    BEGIN_SEARCH_RESULT = "<|begin_search_result|>"
    END_SEARCH_RESULT = "<|end_search_result|>"

    def rm_query_tags(line):
        pattern = re.escape(BEGIN_SEARCH_QUERY) + r"(.*?)" + re.escape(END_SEARCH_QUERY)
        return re.sub(pattern, "", line)

    def rm_result_tags(line):
        pattern = re.escape(BEGIN_SEARCH_RESULT) + r"(.*?)" + re.escape(END_SEARCH_RESULT)
        return re.sub(pattern, "", line)

    reason_prompt = (
        "You are a reasoning assistant with the ability to perform dataset searches to help "
        "you answer the user's question accurately. You have special tools:\n\n"
        f"- To perform a search: write {BEGIN_SEARCH_QUERY} your query here {END_SEARCH_QUERY}.\n"
        f"Then, the system will search and analyze relevant content, then provide you with helpful information in the format {BEGIN_SEARCH_RESULT} ...search results... {END_SEARCH_RESULT}.\n\n"
        f"You can repeat the search process multiple times if necessary. The maximum number of search attempts is limited to {MAX_SEARCH_LIMIT}.\n\n"
        "Once you have all the information you need, continue your reasoning.\n\n"
        "-- Example 1 --\n" ########################################
        "Question: \"Are both the directors of Jaws and Casino Royale from the same country?\"\n"
        "Assistant:\n"
        f"    {BEGIN_SEARCH_QUERY}Who is the director of Jaws?{END_SEARCH_QUERY}\n\n"
        "User:\n"
        f"    {BEGIN_SEARCH_RESULT}\nThe director of Jaws is Steven Spielberg...\n{END_SEARCH_RESULT}\n\n"
        "Continues reasoning with the new information.\n"
        "Assistant:\n"
        f"    {BEGIN_SEARCH_QUERY}Where is Steven Spielberg from?{END_SEARCH_QUERY}\n\n"
        "User:\n"
        f"    {BEGIN_SEARCH_RESULT}\nSteven Allan Spielberg is an American filmmaker...\n{END_SEARCH_RESULT}\n\n"
        "Continues reasoning with the new information...\n\n"
        "Assistant:\n"
        f"    {BEGIN_SEARCH_QUERY}Who is the director of Casino Royale?{END_SEARCH_QUERY}\n\n"
        "User:\n"
        f"    {BEGIN_SEARCH_RESULT}\nCasino Royale is a 2006 spy film directed by Martin Campbell...\n{END_SEARCH_RESULT}\n\n"
        "Continues reasoning with the new information...\n\n"
        "Assistant:\n"
        f"    {BEGIN_SEARCH_QUERY}Where is Martin Campbell from?{END_SEARCH_QUERY}\n\n"
        "User:\n"
        f"    {BEGIN_SEARCH_RESULT}\nMartin Campbell (born 24 October 1943) is a New Zealand film and television director...\n{END_SEARCH_RESULT}\n\n"
        "Continues reasoning with the new information...\n\n"
        "Assistant:\nIt's enough to answer the question\n"
        
        "-- Example 2 --\n" #########################################
        "Question: \"When was the founder of craigslist born?\"\n"
        "Assistant:\n"
        f"    {BEGIN_SEARCH_QUERY}Who was the founder of craigslist?{END_SEARCH_QUERY}\n\n"
        "User:\n"
        f"    {BEGIN_SEARCH_RESULT}\nCraigslist was founded by Craig Newmark...\n{END_SEARCH_RESULT}\n\n"
        "Continues reasoning with the new information.\n"
        "Assistant:\n"
        f"    {BEGIN_SEARCH_QUERY} When was Craig Newmark born?{END_SEARCH_QUERY}\n\n"
        "User:\n"
        f"    {BEGIN_SEARCH_RESULT}\nCraig Newmark was born on December 6, 1952...\n{END_SEARCH_RESULT}\n\n"
        "Continues reasoning with the new information...\n\n"
        "Assistant:\nIt's enough to answer the question\n"
        "**Remember**:\n"
        f"- You have a dataset to search, so you just provide a proper search query.\n"
        f"- Use {BEGIN_SEARCH_QUERY} to request a dataset search and end with {END_SEARCH_QUERY}.\n"
        "- The language of query MUST be as the same as 'Question' or 'search result'.\n"
        "- When done searching, continue your reasoning.\n\n"
        'Please answer the following question. You should think step by step to solve it.\n\n'
    )

    relevant_extraction_prompt = """**Task Instruction:**

    You are tasked with reading and analyzing web pages based on the following inputs: **Previous Reasoning Steps**, **Current Search Query**, and **Searched Web Pages**. Your objective is to extract relevant and helpful information for **Current Search Query** from the **Searched Web Pages** and seamlessly integrate this information into the **Previous Reasoning Steps** to continue reasoning for the original question.

    **Guidelines:**

    1. **Analyze the Searched Web Pages:**
    - Carefully review the content of each searched web page.
    - Identify factual information that is relevant to the **Current Search Query** and can aid in the reasoning process for the original question.

    2. **Extract Relevant Information:**
    - Select the information from the Searched Web Pages that directly contributes to advancing the **Previous Reasoning Steps**.
    - Ensure that the extracted information is accurate and relevant.

    3. **Output Format:**
    - **If the web pages provide helpful information for current search query:** Present the information beginning with `**Final Information**` as shown below.
    - The language of query **MUST BE** as the same as 'Search Query' or 'Web Pages'.\n"
    **Final Information**

    [Helpful information]

    - **If the web pages do not provide any helpful information for current search query:** Output the following text.

    **Final Information**

    No helpful information found.

    **Inputs:**
    - **Previous Reasoning Steps:**  
    {prev_reasoning}

    - **Current Search Query:**  
    {search_query}

    - **Searched Web Pages:**  
    {document}

    """

    executed_search_queries = []
    msg_hisotry = [{"role": "user", "content": f'Question:\"{question}\"\n'}]
    all_reasoning_steps = []
    think = "<think>"
    for ii in range(MAX_SEARCH_LIMIT + 1):
        if ii == MAX_SEARCH_LIMIT - 1:
            summary_think = f"\n{BEGIN_SEARCH_RESULT}\nThe maximum search limit is exceeded. You are not allowed to search.\n{END_SEARCH_RESULT}\n"
            yield {"answer": think + summary_think + "</think>", "reference": {}, "audio_binary": None}
            all_reasoning_steps.append(summary_think)
            msg_hisotry.append({"role": "assistant", "content": summary_think})
            break

        query_think = ""
        if msg_hisotry[-1]["role"] != "user":
            msg_hisotry.append({"role": "user", "content": "Continues reasoning with the new information.\n"})
        else:
            msg_hisotry[-1]["content"] += "\n\nContinues reasoning with the new information.\n"
        for ans in chat_mdl.chat_streamly(reason_prompt, msg_hisotry, {"temperature": 0.7}):
            ans = re.sub(r"<think>.*</think>", "", ans, flags=re.DOTALL)
            if not ans:
                continue
            query_think = ans
            yield {"answer": think + rm_query_tags(query_think) + "</think>", "reference": {}, "audio_binary": None}

        think += rm_query_tags(query_think)
        all_reasoning_steps.append(query_think)
        queries = extract_between(query_think, BEGIN_SEARCH_QUERY, END_SEARCH_QUERY)
        if not queries:
            if ii > 0:
                break
            queries = [question]

        for search_query in queries:
            logging.info(f"[THINK]Query: {ii}. {search_query}")
            msg_hisotry.append({"role": "assistant", "content": search_query})
            think += f"\n\n> {ii+1}. {search_query}\n\n"
            yield {"answer": think + "</think>", "reference": {}, "audio_binary": None}

            summary_think = ""
            # The search query has been searched in previous steps.
            if search_query in executed_search_queries:
                summary_think = f"\n{BEGIN_SEARCH_RESULT}\nYou have searched this query. Please refer to previous results.\n{END_SEARCH_RESULT}\n"
                yield {"answer": think + summary_think + "</think>", "reference": {}, "audio_binary": None}
                all_reasoning_steps.append(summary_think)
                msg_hisotry.append({"role": "user", "content": summary_think})
                think += summary_think
                continue

            truncated_prev_reasoning = ""
            for i, step in enumerate(all_reasoning_steps):
                truncated_prev_reasoning += f"Step {i + 1}: {step}\n\n"

            prev_steps = truncated_prev_reasoning.split('\n\n')
            if len(prev_steps) <= 5:
                truncated_prev_reasoning = '\n\n'.join(prev_steps)
            else:
                truncated_prev_reasoning = ''
                for i, step in enumerate(prev_steps):
                    if i == 0 or i >= len(prev_steps) - 4 or BEGIN_SEARCH_QUERY in step or BEGIN_SEARCH_RESULT in step:
                        truncated_prev_reasoning += step + '\n\n'
                    else:
                        if truncated_prev_reasoning[-len('\n\n...\n\n'):] != '\n\n...\n\n':
                            truncated_prev_reasoning += '...\n\n'
            truncated_prev_reasoning = truncated_prev_reasoning.strip('\n')

            # Retrieval procedure:
            # 1. KB search
            # 2. Web search (optional)
            # 3. KG search (optional)
            kbinfos = settings.retrievaler.retrieval(search_query, embd_mdl, tenant_ids, kb_ids, 1, top_n,
                                                     similarity_threshold,
                                                     vector_similarity_weight
                                                     )
            if prompt_config.get("tavily_api_key", "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"):
                tav = Tavily(prompt_config["tavily_api_key"])
                tav_res = tav.retrieve_chunks(" ".join(search_query))
                kbinfos["chunks"].extend(tav_res["chunks"])
                kbinfos["doc_aggs"].extend(tav_res["doc_aggs"])
            if prompt_config.get("use_kg"):
                ck = settings.kg_retrievaler.retrieval(search_query,
                                                       tenant_ids,
                                                       kb_ids,
                                                       embd_mdl,
                                                       chat_mdl)
                if ck["content_with_weight"]:
                    kbinfos["chunks"].insert(0, ck)

            # Merge chunk info for citations
            if not chunk_info["chunks"]:
                for k in chunk_info.keys():
                    chunk_info[k] = kbinfos[k]
            else:
                cids = [c["chunk_id"] for c in chunk_info["chunks"]]
                for c in kbinfos["chunks"]:
                    if c["chunk_id"] in cids:
                        continue
                    chunk_info["chunks"].append(c)
                dids = [d["doc_id"] for d in chunk_info["doc_aggs"]]
                for d in kbinfos["doc_aggs"]:
                    if d["doc_id"] in dids:
                        continue
                    chunk_info["doc_aggs"].append(d)

            think += "\n\n"
            for ans in chat_mdl.chat_streamly(
                    relevant_extraction_prompt.format(
                        prev_reasoning=truncated_prev_reasoning,
                        search_query=search_query,
                        document="\n".join(kb_prompt(kbinfos, 4096))
                    ),
                    [{"role": "user",
                     "content": f'Now you should analyze each web page and find helpful information based on the current search query "{search_query}" and previous reasoning steps.'}],
                    {"temperature": 0.7}):
                ans = re.sub(r"<think>.*</think>", "", ans, flags=re.DOTALL)
                if not ans:
                    continue
                summary_think = ans
                yield {"answer": think + rm_result_tags(summary_think) + "</think>", "reference": {}, "audio_binary": None}

            all_reasoning_steps.append(summary_think)
            msg_hisotry.append(
                {"role": "user", "content": f"\n\n{BEGIN_SEARCH_RESULT}{summary_think}{END_SEARCH_RESULT}\n\n"})
            think += rm_result_tags(summary_think)
            logging.info(f"[THINK]Summary: {ii}. {summary_think}")

    yield think + "</think>"