根据知识库名称，获取资源

dbgpt/app/apps/vadmin/keywordsviews.py

@@ -6,7 +6,8 @@ from dbgpt.app.apps.utils.file.file_manage import FileManage
 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
-from dbgpt.app.apps.vadmin.media.crud import MediaDal,QuestionDal
+from dbgpt.app.apps.vadmin.media.crud import MediaDal,QuestionDal,CorrelationDal
+from dbgpt.app.apps.vadmin.media.models import VadminCorrelation
 from dbgpt.app.apps.vadmin.media.params.media_list import MediaListParams, GroupListParams, MediaEditParams, \
     QuestionListParams, \
     QuestionEditParams, CorrelationListParams
@@ -30,6 +31,7 @@ from dbgpt.app.openapi.api_view_model import (
     ConversationVo,
     Result,
 )
+from sqlalchemy import BinaryExpression
 
 router = APIRouter()
 
@@ -73,6 +75,82 @@ def get_key_words_nlp(user_input: str) -> list:
     #print(words)
     return words
 
+async def get_media_datas_by(conv_uid: str, words: str, db: AsyncSession, knownledge: str) -> list:
+    # 去拿出group_id
+    datas = []
+    if knownledge != None:
+        datas, count = await CorrelationDal(db).get_datas(name=knownledge, v_return_count=True)
+
+    if len(datas) > 0:
+        result = []
+        corrdata = datas[0]
+        image_datas = corrdata.get('image_group')
+        for image_groups in image_datas:
+            image_groupid = image_groups.get('group_id')
+            print(f"===========>image_groupid:{image_groupid}")
+            # 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': image_groupid,
+                          '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}")
+
+            for data in images_datas:
+                json_image = {'type': 1, 'file_name': data.get('file_name'), 'key_word': data.get('key_word'),
+                              'local_path': data.get('local_path'), 'remote_path': data.get('remote_path')}
+                result.append(json_image)
+
+        video_datas = corrdata.get('video_group')
+        for video_groups in video_datas:
+            video_groupid = video_groups.get('group_id')
+            print(f"===========>video_groupid:{video_groupid}")
+            # 取出匹配到的关键词，获取数据库中的视频
+            video_dic = {'page': 1, 'limit': 0, 'v_order': None, 'v_order_field': None, 'type': 2,
+                         'group_id': video_groupid,
+                         '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)
+
+        question_datas = corrdata.get('question_group')
+        for question_groups in question_datas:
+            question_groupid = question_groups.get('group_id')
+            print(f"===========>question_groupid:{question_groupid}")
+            # 匹配到的问答对有
+            question_dic = {'page': 1, 'limit': 0, 'v_order': None, 'v_order_field': None,
+                            'group_id': question_groupid,
+                            '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
+    else:
+        #return await get_media_datas(conv_uid, words, db)
+        result = []
+        return result
 
 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
@@ -153,12 +231,9 @@ async def get_media_datas_all(conv_uid: str, default_model: str, db: AsyncSessio
 
 @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(f"用户输入的问题：{dialogue.user_input} -- 选择的知识库为:{dialogue.select_param}")
     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)
@@ -173,7 +248,7 @@ async def get_spacy_keywords(dialogue: ConversationVo = Body(), auth: Auth = Dep
     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"---算法1-匹配到的关键词--->:{words[0]}")
-        result = await get_media_datas(dialogue.conv_uid, words[0], auth.db)
+        result = await get_media_datas_by(dialogue.conv_uid, words[0], auth.db, dialogue.select_param)
         return SuccessResponse(result)
     else:
         print(f"---算法2-begin--->")
@@ -181,7 +256,7 @@ async def get_spacy_keywords(dialogue: ConversationVo = Body(), auth: Auth = Dep
         words2 = get_key_words(dialogue.user_input)
         if len(words2) > 0:
             print(f"---算法2-匹配到的关键词--->:{words[0]}")
-            result = await get_media_datas(dialogue.conv_uid, words2[0], auth.db)
+            result = await get_media_datas_by(dialogue.conv_uid, words2[0], auth.db, dialogue.select_param)
             return SuccessResponse(result)
         else:
             print(f"-----没有找到需要查询的内容:---->")

dbgpt/app/openapi/api_v1/api_v1.py

@@ -359,6 +359,7 @@ async def chat_completions(
     flow_service: FlowService = Depends(get_chat_flow),
     auth: Auth = Depends(OpenAuth()),
 ):
+    #dialogue.select_param 这个参数为 : 知识库名称
     logger.info(
         f"chat_completions:{dialogue.chat_mode},{dialogue.select_param},{dialogue.model_name}"
     )

start

 #!/bin/bash
 echo Starting...
 
-nohup ./BufferServer > /dev/null 2>&1 & 
-sleep 1
+nohup dbgpt start webserver --port 6006 > /dev/null 2>&1 & 
+sleep 10
 
 #加载一下敏感词接口
-#http://192.168.11.46:5670/api/v2/vadmin/load_parse_from_db
+curl http://127.0.0.1:6006/api/v2/vadmin/load_parse_from_db
 
 echo Done!
\ No newline at end of file