添加关键词提出代码

dbgpt/app/apps/utils/filter.py

@@ -4,7 +4,10 @@ import re
 from fastapi import Request, Depends
 from dbgpt.app.apps.core.database import db_getter
 from sqlalchemy.ext.asyncio import AsyncSession
+
 from dbgpt.app.apps.vadmin.word.crud import SensitiveDal
+from dbgpt.app.apps.vadmin.media.crud import MediaDal,QuestionDal
+
 from dbgpt.app.apps.vadmin.auth.utils.current import AllUserAuth, FullAdminAuth, OpenAuth
 from dbgpt.app.apps.vadmin.auth.utils.validation.auth import Auth
 
@@ -13,9 +16,9 @@ class DFAFilter():
     Use DFA to keep algorithm perform constantly
     敏感词过滤
 
-    >>> f = DFAFilter()
-    >>> f.add("sexy")
-    >>> f.filter("hello sexy baby")
+    #>>> f = DFAFilter()
+    #>>> f.add("sexy")
+    #>>> f.filter("hello sexy baby")
     hello **** baby
     '''
 
@@ -52,10 +55,43 @@ class DFAFilter():
             for keyword in f:
                 self.add(keyword.strip())
 
-    #从数据库中加载 敏感词
-    async def parse_from_db(self, db: AsyncSession):
+    #从数据库中加载 图片资源
+    async def parse_picture_from_db(self, db: AsyncSession):
+        #db: AsyncSession = Depends(db_getter)
+        print('---------parse_picture_from_db--load-------------')
+        # 获取数据库中所有的图片--->添加到词库内存中
+        images_dic = {'page': 1, 'limit': 0, 'v_order': None, 'v_order_field': None, 'type': 1, 'group_id': None}
+        images_datas, count = await MediaDal(db).get_datas(**images_dic, v_return_count=True)
+        print(f"-----图片库列表为:---->:{images_datas}")
+        for imagedata in images_datas:
+            self.add(imagedata.get('key_word'))
+
+    #从数据库中加载 问答对
+    async def parse_question_from_db(self, db: AsyncSession):
+        #db: AsyncSession = Depends(db_getter)
+        print('---------parse_question_from_db--load-------------')
+        # 获取数据库中所有的问答对--->添加到词库内存中
+        question_dic = {'page': 1, 'limit': 0, 'v_order': None, 'v_order_field': None}
+        question_datas, count = await QuestionDal(db).get_datas(**question_dic, v_return_count=True)
+        print(f"-----问答对库列表为:---->:{question_datas}")
+        for questiondata in question_datas:
+            self.add(questiondata.get('key_word'))
+
+    #从数据库中加载 视频资源
+    async def parse_video_from_db(self, db: AsyncSession):
         #db: AsyncSession = Depends(db_getter)
-        print('---------sensitive-load-------------')
+        print('---------parse_video_from_db--load-------------')
+        # 获取数据库中所有的视频--->添加到词库内存中
+        video_dic = {'page': 1, 'limit': 0, 'v_order': None, 'v_order_field': None, 'type': 2, 'group_id': None}
+        video_datas, count = await MediaDal(db).get_datas(**video_dic, v_return_count=True)
+        print(f"-----视频库列表为:---->:{video_datas}")
+        for videodata in video_datas:
+            self.add(videodata.get('key_word'))
+
+    # 从数据库中加载 敏感词
+    async def parse_from_db(self, db: AsyncSession):
+        # db: AsyncSession = Depends(db_getter)
+        print('---------parse_from_db-load-------------')
         sdl = SensitiveDal(db)
         datas = await sdl.get_sensitives()
         for keyword in datas:
@@ -94,8 +130,19 @@ class DFAFilter():
         # return 返回三个参数
         return ''.join(ret), is_sensitive, matched_sensitives
 
+#初始化全局对象
+#初始化-敏感词
 mydfafiter = DFAFilter()
 
+#初始化-图片关键词
+mydfafiter_picture = DFAFilter()
+
+#初始化-视频关键词
+mydfafiter_video = DFAFilter()
+
+#初始化-问答对关键词
+mydfafiter_question = DFAFilter()
+
 """
 if __name__ == "__main__":
     gfw = DFAFilter()

dbgpt/app/apps/vadmin/keywordsviews.py

@@ -3,50 +3,43 @@ import json
 from fastapi import APIRouter, Depends, Body, UploadFile, Form, Request
 
 from dbgpt.app.apps.utils.file.file_manage import FileManage
-from dbgpt.app.apps.utils.response import SuccessResponse,ErrorResponse
+from dbgpt.app.apps.utils.response import SuccessResponse, ErrorResponse
 from dbgpt.app.apps.vadmin.auth.utils.validation.auth import Auth
-from dbgpt.app.apps.vadmin.media import schemas, crud
-from dbgpt.app.apps.vadmin.media.params.media_list import MediaListParams, GroupListParams, MediaEditParams, QuestionListParams, \
+from dbgpt.app.apps.vadmin.media import schemas
+from dbgpt.app.apps.vadmin.media.crud import MediaDal,QuestionDal
+from dbgpt.app.apps.vadmin.media.params.media_list import MediaListParams, GroupListParams, MediaEditParams, \
+    QuestionListParams, \
     QuestionEditParams, CorrelationListParams
 from dbgpt.app.apps.core.dependencies import IdList
 
+from dbgpt.app.apps.vadmin.chathistory.crud import ChatHistoryDal
+from dbgpt.app.apps.vadmin.chathistory.schemas.chathistory import ChatHistorySchemas
+
 from dbgpt.app.apps.vadmin.auth.utils.current import AllUserAuth, FullAdminAuth, OpenAuth
 from dbgpt.app.apps.vadmin.auth.utils.validation.auth import Auth
+from sqlalchemy.ext.asyncio import AsyncSession
 
 from typing import Any, Dict, Generic, Optional, TypeVar
 from dbgpt._private.pydantic import BaseModel, ConfigDict, Field, model_to_dict
 
 from dbgpt.app.apps.utils.spach_keywords import my_spacy_nlp
-from dbgpt.app.apps.utils.filter import mydfafiter
+from dbgpt.app.apps.utils.filter import mydfafiter, mydfafiter_picture, mydfafiter_question, mydfafiter_video
+from dbgpt.serve.conversation.api.schemas import MessageVo, ServeRequest, ServerResponse
+from dbgpt.app.openapi.api_view_model import (
+    ChatSceneVo,
+    ConversationVo,
+    Result,
+)
 
 router = APIRouter()
 
-class ConversationVo(BaseModel):
-    model_config = ConfigDict(protected_namespaces=())
-    user_input: str = ""
-
-@router.post("/get_spacy_keywords", summary="资源列表(图片、视频)")
-async def get_spacy_keywords(dialogue: ConversationVo = Body(), auth: Auth = Depends(OpenAuth())):
-
-    print(f"用户输入的问题：{dialogue.user_input} ")
-    print('----------------begin---------------->')
-
-    # 从数据库中加载 并且初始化敏感词-->到内存中
-    # await mydfafiter.parse_from_db(auth.db)
-
-    #先判断敏感词
-    dfa_result, is_sensitive,matched_sensitives = mydfafiter.filter(dialogue.user_input, "*")
-    print(dfa_result)
-
-    if is_sensitive:
-        print('用户输入有敏感词')
-        result = {'code': 200, 'message': 'success', 'data': [{'type': 3, 'word_name': matched_sensitives, 'is_sensitive': 1, 'user_input':dfa_result}]}
-        return SuccessResponse(result) #返回type=3
-
-    #没有敏感词的时候,查找是否有相关图片 或者 视频
-    doc = my_spacy_nlp.nlp(dialogue.user_input)
 
+def get_key_words(user_input: str) -> list:
+    """
+    接受一个字符串输入,提取关键词,并返回其中的单词列表。
+    """
     words = []
+    doc = my_spacy_nlp.nlp(user_input)
     # examine the top-ranked phrases in the document
     for phrase in doc._.phrases:
         # logger.info(f"----1--->:{phrase.rank}--->:{phrase.count}")
@@ -54,16 +47,40 @@ async def get_spacy_keywords(dialogue: ConversationVo = Body(), auth: Auth = Dep
         words.append(phrase.chunks[0])
 
     print(words)
+    return words
 
-    if len(words) > 0:
-        print(words[0])
+def get_key_words_nlp(user_input: str) -> list:
+    """
+    另外一种算法提取关键词，比上面的算法更加准确
+    """
+
+    words = []
+    dfa_result, is_sensitive, matched_medias = mydfafiter_picture.filter(user_input, "*")
+    #print(matched_medias)
+    for phrase in matched_medias:
+        words.append(phrase)
+
+    dfa_result2, is_sensitive2, matched_medias2 = mydfafiter_question.filter(user_input, "*")
+    # print(matched_medias2)
+    for phrase2 in matched_medias2:
+        words.append(phrase2)
 
-        #取出匹配到的关键词，获取数据库中的图片
-        images_dic = {'page': 1, 'limit': 10, 'v_order': None, 'v_order_field': None, 'type': 1, 'group_id': None,
-                 'file_name': ('like', words[0])}
+    dfa_result3, is_sensitive3, matched_medias3 = mydfafiter_video.filter(user_input, "*")
+    # print(matched_medias3)
+    for phrase3 in matched_medias3:
+        words.append(phrase3)
 
-        images_datas, count = await crud.MediaDal(auth.db).get_datas(**images_dic, v_return_count=True)
-        #print(f"-----查询到的图片为:---->:{images_datas}")
+    #print(words)
+    return words
+
+
+async def get_media_datas(conv_uid: str, words: str, db: AsyncSession) -> list:
+    # 取出匹配到的关键词，获取数据库中的图片
+    images_dic = {'page': 1, 'limit': 0, 'v_order': None, 'v_order_field': None, 'type': 1, 'group_id': None,
+                  'key_word': ('like', words)}
+
+    images_datas, count = await MediaDal(db).get_datas(**images_dic, v_return_count=True)
+    print(f"-----查询到的图片为:---->:{images_datas}")
     result = []
     for data in images_datas:
         json_image = {'type': 1, 'file_name': data.get('file_name'), 'key_word': data.get('key_word'),
@@ -71,35 +88,105 @@ async def get_spacy_keywords(dialogue: ConversationVo = Body(), auth: Auth = Dep
         result.append(json_image)
 
     # 取出匹配到的关键词，获取数据库中的视频
-        video_dic = {'page': 1, 'limit': 10, 'v_order': None, 'v_order_field': None, 'type': 2, 'group_id': None,
-                 'file_name': ('like', words[0])}
-
-        video_datas, count = await crud.MediaDal(auth.db).get_datas(**video_dic, v_return_count=True)
-        #print(f"-----查询到的视频为:---->:{video_datas}")
-        for data in video_datas:
-            json_video = {'type': 2, 'file_name': data.get('file_name'), 'key_word': data.get('key_word'),
-                          'local_path': data.get('local_path'), 'remote_path': data.get('remote_path')}
+    video_dic = {'page': 1, 'limit': 0, 'v_order': None, 'v_order_field': None, 'type': 2, 'group_id': None,
+                 'key_word': ('like', words)}
+
+    video_datas, count = await MediaDal(db).get_datas(**video_dic, v_return_count=True)
+    print(f"-----查询到的视频为:---->:{video_datas}")
+    for videodata in video_datas:
+        json_video = {'type': 2, 'file_name': videodata.get('file_name'), 'key_word': videodata.get('key_word'),
+                      'local_path': videodata.get('local_path'), 'remote_path': videodata.get('remote_path')}
         result.append(json_video)
 
-        return SuccessResponse(result)
-    else:
-        print(f"-----没有找到需要查询的内容:---->")
-        return ErrorResponse("没有找到需要查询的内容")
-
-@router.get("/load_parse_from_db", summary="加载敏感词")
-async def load_parse_from_db(auth: Auth = Depends(OpenAuth())):
-    # 从数据库中加载 并且初始化敏感词-->到内存中
-    await mydfafiter.parse_from_db(auth.db)
-    return SuccessResponse("sensitive load OK")
-
-
-
-
+    #匹配到的问答对有
+    question_dic = {'page': 1, 'limit': 0, 'v_order': None, 'v_order_field': None,'key_word': ('like', words)}
+    question_datas, count = await QuestionDal(db).get_datas(**question_dic, v_return_count=True)
+    print(f"-----查询到的问答对为:---->:{question_datas}")
+    for questiondata in question_datas:
+        json_question = {'type': 4, 'title': questiondata.get('title'), 'key_word': questiondata.get('key_word'),
+                         'answer': questiondata.get('answer')}
+        result.append(json_question)
+
+    #保存到聊天历史资源数据库中
+    if len(result) > 0:
+        print(f"-----保存数据的时候打印会话ID:---->:{conv_uid}")
+        #保存到聊天历史资料表中
+        json_string = json.dumps(result) #转换为字符串
+        simi_data = ChatHistorySchemas()
+        simi_data.conv_uid = conv_uid
+        simi_data.message_medias = json_string
+        await ChatHistoryDal(db).create_data(data=simi_data)
+
+    return result
+
+async def get_media_datas_all(conv_uid: str, default_model: str, db: AsyncSession, messages:list) -> list:
+    """
+    根据会话ID-->获取聊天历史的图片或视频资源
+    """
+    ret_media_datas = []
+
+    #获取聊天历史资源数据
+    history_dic = {'page': 1, 'limit': 10, 'v_order': None, 'v_order_field': None, 'conv_uid': conv_uid,
+                   'message_medias': ('like', None)}
+    datas, count = await ChatHistoryDal(db).get_datas(**history_dic, v_return_count=True)
+
+    json_data = [{"type": 1, "file_name": "723629348SJfHgjzD.png"}]  # 传入列表
+    if count > 0:
+        history_datas = datas[0].get('message_medias')
+        json_data = json.loads(history_datas)
+
+    print(f"----将字符串转换为json格式---->:{json_data}")
+
+    for msg in messages:
+        # 根据历史聊天记录msg
+        ret_media_datas.append(
+            MessageVo(
+                role=msg.type,
+                context=msg.content,
+                order=msg.round_index,
+                model_name=default_model,
+                extra=json_data,
+            )
+        )
+
+    return ret_media_datas
 
+@router.post("/get_spacy_keywords", summary="资源列表(图片、视频)")
+async def get_spacy_keywords(dialogue: ConversationVo = Body(), auth: Auth = Depends(OpenAuth())):
+    print(f"用户输入的问题：{dialogue.user_input} ")
+    print('----------------begin---------------->')
 
+    # 从数据库中加载 并且初始化敏感词-->到内存中
+    # await mydfafiter.parse_from_db(auth.db)
 
+    #先判断敏感词
+    dfa_result, is_sensitive, matched_sensitives = mydfafiter.filter(dialogue.user_input, "*")
+    print(dfa_result)
 
+    if is_sensitive:
+        print('用户输入有敏感词')
+        result = {'code': 200, 'message': 'success',
+                  'data': [{'type': 3, 'word_name': matched_sensitives, 'is_sensitive': 1, 'user_input': dfa_result}]}
+        return SuccessResponse(result)  #返回type=3
 
+    #没有敏感词的时候,查找是否有相关图片 或者 视频
+    #words = get_key_words(dialogue.user_input)
+    words = get_key_words_nlp(dialogue.user_input) #100%匹配算法 | 只取匹配到的第一个
+    if len(words) > 0:
+        print(f"----匹配到的关键词--->:{words[0]}")
+        result = await get_media_datas(dialogue.conv_uid, words[0], auth.db)
+        return SuccessResponse(result)
+    else:
+        print(f"-----没有找到需要查询的内容:---->")
+        return ErrorResponse("没有找到需要查询的内容")
 
 
+@router.get("/load_parse_from_db", summary="加载敏感词和资源关键词")
+async def load_parse_from_db(auth: Auth = Depends(OpenAuth())):
+    # 从数据库中加载 并且初始化敏感词,图片,视频,问答对-->到内存中
+    await mydfafiter.parse_from_db(auth.db)
+    await mydfafiter_picture.parse_picture_from_db(auth.db)
+    await mydfafiter_video.parse_video_from_db(auth.db)
+    await mydfafiter_question.parse_question_from_db(auth.db)
 
+    return SuccessResponse("media and sensitive all load OK")

dbgpt/app/dbgpt_server.py

@@ -45,7 +45,7 @@ from dbgpt.util.utils import (
 
 from dbgpt.app.apps.utils.tools import import_modules
 from dbgpt.app.apps.utils.spach_keywords import my_spacy_nlp
-from dbgpt.app.apps.utils.filter import mydfafiter
+from dbgpt.app.apps.utils.filter import mydfafiter, mydfafiter_picture, mydfafiter_question, mydfafiter_video
 
 REQUEST_LOG_RECORD = False
 MIDDLEWARES = [
@@ -104,6 +104,7 @@ def mount_routers(app: FastAPI):
     from dbgpt.app.apps.vadmin.media.views import router as media_views
     from dbgpt.app.apps.vadmin.word.views import router as word_views
     from dbgpt.app.apps.vadmin.keywordsviews import router as keywords_views
+    from dbgpt.app.apps.vadmin.chathistory.views import router as chathistory_views
 
     app.include_router(api_v1, prefix="/api", tags=["Chat"])
     app.include_router(api_v2, prefix="/api", tags=["ChatV2"])
@@ -119,6 +120,7 @@ def mount_routers(app: FastAPI):
     app.include_router(media_views, prefix="/api/v2", tags=["System"])
     app.include_router(word_views, prefix="/api/v2/vadmin/word", tags=["Word"])
     app.include_router(keywords_views, prefix="/api/v2/vadmin", tags=["vadmin"])
+    app.include_router(chathistory_views, prefix="/api/v2/vadmin", tags=["chathistory"])
 
 
 def mount_static_files(app: FastAPI):

dbgpt/app/openapi/api_v1/api_v1.py

@@ -46,6 +46,7 @@ from dbgpt.util.tracer import SpanType, root_tracer
 from dbgpt.app.apps.utils.filter import mydfafiter
 from dbgpt.app.apps.vadmin.auth.utils.current import AllUserAuth, FullAdminAuth, OpenAuth
 from dbgpt.app.apps.vadmin.auth.utils.validation.auth import Auth
+from sqlalchemy.ext.asyncio import AsyncSession
 
 
 router = APIRouter()
@@ -263,6 +264,7 @@ async def params_load(
     user_name: Optional[str] = None,
     sys_code: Optional[str] = None,
     doc_file: UploadFile = File(...),
+    auth: Auth = Depends(OpenAuth()),
 ):
     logger.info(f"params_load: {conv_uid},{chat_mode},{model_name}")
     try:
@@ -287,11 +289,19 @@ async def params_load(
             resp = await chat.prepare()
 
         # Refresh messages
-        return Result.succ(get_hist_messages(conv_uid))
+        #ret_his_msg = get_hist_messages(conv_uid)
+        ret_his_msg = await get_hist_messages_datas(conv_uid, auth.db)
+        return Result.succ(ret_his_msg)
     except Exception as e:
         logger.error("excel load error!", e)
         return Result.failed(code="E000X", msg=f"File Load Error {str(e)}")
 
+async def get_hist_messages_datas(conv_uid: str, db: AsyncSession):
+    from dbgpt.serve.conversation.serve import Service as ConversationService
+
+    instance: ConversationService = ConversationService.get_instance(CFG.SYSTEM_APP)
+    result = await instance.get_history_messages2(db, {"conv_uid": conv_uid})
+    return result
 
 def get_hist_messages(conv_uid: str):
     from dbgpt.serve.conversation.serve import Service as ConversationService
@@ -360,7 +370,7 @@ async def chat_completions(
     }
 
     # 从数据库中加载 并且初始化敏感词-->到内存中
-    await mydfafiter.parse_from_db(auth.db)
+    # await mydfafiter.parse_from_db(auth.db)
 
     # 先判断敏感词
     dfa_result, is_sensitive, matched_sensitives = mydfafiter.filter(dialogue.user_input, "*")

dbgpt/app/openapi/api_v2.py

@@ -99,7 +99,7 @@ async def chat_completions(
     }
 
     # 从数据库中加载 并且初始化敏感词-->到内存中
-    await mydfafiter.parse_from_db(auth.db)
+    # await mydfafiter.parse_from_db(auth.db)
 
     # 先判断敏感词
     dfa_result, is_sensitive, matched_sensitives = mydfafiter.filter(request.user_input, "*")

dbgpt/serve/conversation/api/endpoints.py

@@ -12,6 +12,9 @@ from dbgpt.util import PaginationResult
 from ..config import APP_NAME, SERVE_APP_NAME, SERVE_SERVICE_COMPONENT_NAME, ServeConfig
 from ..service.service import Service
 from .schemas import MessageVo, ServeRequest, ServerResponse
+from dbgpt.app.apps.vadmin.auth.utils.current import AllUserAuth, FullAdminAuth, OpenAuth
+from dbgpt.app.apps.vadmin.auth.utils.validation.auth import Auth
+from sqlalchemy.ext.asyncio import AsyncSession
 
 router = APIRouter()
 
@@ -207,9 +210,13 @@ async def list_latest_conv(
     response_model=Result[List[MessageVo]],
     dependencies=[Depends(check_api_key)],
 )
-async def get_history_messages(con_uid: str, service: Service = Depends(get_service)):
+async def get_history_messages(con_uid: str,
+                               service: Service = Depends(get_service),
+                               auth: Auth = Depends(OpenAuth())):
     """Get the history messages of a conversation"""
-    return Result.succ(service.get_history_messages(ServeRequest(conv_uid=con_uid)))
+    #result = service.get_history_messages(ServeRequest(conv_uid=con_uid))
+    result = await service.get_history_messages2(auth.db, ServeRequest(conv_uid=con_uid))
+    return Result.succ(result)
 
 
 def init_endpoints(system_app: SystemApp) -> None:

dbgpt/serve/conversation/api/schemas.py

 # Define your Pydantic schemas here
-from typing import Any, Dict, Optional
+from typing import Optional, List, Dict, Any
 
 from dbgpt._private.pydantic import BaseModel, ConfigDict, Field, model_to_dict
 
@@ -149,6 +149,14 @@ class MessageVo(BaseModel):
         ],
     )
 
+    extra: Optional[List[Dict[str, Any]]] = Field(  # 修改为列表类型
+        default=None,
+        description="A field to store additional JSON data.",
+        examples=[
+            [{"type": 1, "file_name": "111.png"}],
+        ],
+    )
+
     def to_dict(self, **kwargs) -> Dict[str, Any]:
         """Convert the model to a dictionary"""
         return model_to_dict(self, **kwargs)

dbgpt/serve/conversation/service/service.py

@@ -17,6 +17,8 @@ from dbgpt.util.pagination_utils import PaginationResult
 from ..api.schemas import MessageVo, ServeRequest, ServerResponse
 from ..config import SERVE_CONFIG_KEY_PREFIX, SERVE_SERVICE_COMPONENT_NAME, ServeConfig
 from ..models.models import ServeDao, ServeEntity
+from sqlalchemy.ext.asyncio import AsyncSession
+from dbgpt.app.apps.vadmin.keywordsviews import get_media_datas_all
 
 
 class Service(BaseService[ServeEntity, ServeRequest, ServerResponse]):
@@ -178,6 +180,19 @@ class Service(BaseService[ServeEntity, ServeRequest, ServerResponse]):
         """
         return self.dao.get_conv_by_page(request, page, page_size)
 
+    async def get_history_messages2(
+        self, db: AsyncSession, request: Union[ServeRequest, Dict[str, Any]]
+    ) -> List[MessageVo]:
+
+        print(f"------------历史记录request-------------->:{request}")
+
+        conv: StorageConversation = self.create_storage_conv(request)
+        messages = _append_view_messages(conv.messages)
+        # 查找是否有相关图片 或者 视频
+        result = await get_media_datas_all(request.conv_uid, self.config.default_model, db, messages)
+        print(f"------------历史记录result-------------->:{result}")
+        return result
+
     def get_history_messages(
         self, request: Union[ServeRequest, Dict[str, Any]]
     ) -> List[MessageVo]:
@@ -199,6 +214,7 @@ class Service(BaseService[ServeEntity, ServeRequest, ServerResponse]):
                     context=msg.content,
                     order=msg.round_index,
                     model_name=self.config.default_model,
+                    extra="----====---->",
                 )
             )
         return result

start

 #!/bin/bash
 echo Starting...
-if [ ! -d datamining ] ; then
-mkdir datamining
-fi
-if [ ! -d jfxt ] ; then
-mkdir jfxt
-fi
-
-export LC_ALL=C
-
-echo "`pwd`" >> ./pwd.txt
 
 nohup ./BufferServer > /dev/null 2>&1 & 
 sleep 1
-nohup ./LogicServer > /dev/null 2>&1 &
-sleep 1
-nohup ./GatewayServer > /dev/null 2>&1 &
+
+#加载一下敏感词接口
+#http://192.168.11.46:5670/api/v2/vadmin/load_parse_from_db
+
 echo Done!
\ No newline at end of file