Fix retrieval API error and add multi-kb search (#1928)

### What problem does this PR solve?
Type of change
 Bug Fix (Import necessary class for retrieval API )
 New Feature (Add multi-KB search to retrieval API)

api/apps/api_app.py

 import re
 from datetime import datetime, timedelta
 from flask import request, Response
+from api.db.services.llm_service import TenantLLMService
 from flask_login import login_required, current_user
 
-from api.db import FileType, ParserType, FileSource
+from api.db import FileType, LLMType, ParserType, FileSource
 from api.db.db_models import APIToken, API4Conversation, Task, File
 from api.db.services import duplicate_name
 from api.db.services.api_service import APITokenService, API4ConversationService
 from itsdangerous import URLSafeTimedSerializer
 
 from api.utils.file_utils import filename_type, thumbnail
+from rag.nlp import keyword_extraction
 from rag.utils.minio_conn import MINIO
 
 from api.db.services.canvas_service import CanvasTemplateService, UserCanvasService
             data=False, retmsg='Token is not valid!"', retcode=RetCode.AUTHENTICATION_ERROR)
 
     req = request.json
-    kb_id = req.get("kb_id")
+    kb_ids = req.get("kb_id",[])
     doc_ids = req.get("doc_ids", [])
     question = req.get("question")
     page = int(req.get("page", 1))
     top = int(req.get("top_k", 1024))
 
     try:
-        e, kb = KnowledgebaseService.get_by_id(kb_id)
-        if not e:
-            return get_data_error_result(retmsg="Knowledgebase not found!")
+        kbs = KnowledgebaseService.get_by_ids(kb_ids)
+        embd_nms = list(set([kb.embd_id for kb in kbs]))
+        if len(embd_nms) != 1:
+            return get_json_result(
+                data=False, retmsg='Knowledge bases use different embedding models or does not exist."', retcode=RetCode.AUTHENTICATION_ERROR)
 
         embd_mdl = TenantLLMService.model_instance(
-            kb.tenant_id, LLMType.EMBEDDING.value, llm_name=kb.embd_id)
-
+            kbs[0].tenant_id, LLMType.EMBEDDING.value, llm_name=kbs[0].embd_id)
         rerank_mdl = None
         if req.get("rerank_id"):
             rerank_mdl = TenantLLMService.model_instance(
-                kb.tenant_id, LLMType.RERANK.value, llm_name=req["rerank_id"])
-
+            kbs[0].tenant_id, LLMType.RERANK.value, llm_name=req["rerank_id"])
         if req.get("keyword", False):
-            chat_mdl = TenantLLMService.model_instance(kb.tenant_id, LLMType.CHAT)
+            chat_mdl = TenantLLMService.model_instance(kbs[0].tenant_id, LLMType.CHAT)
             question += keyword_extraction(chat_mdl, question)
-
-        ranks = retrievaler.retrieval(question, embd_mdl, kb.tenant_id, [kb_id], page, size,
-                                      similarity_threshold, vector_similarity_weight, top,
-                                      doc_ids, rerank_mdl=rerank_mdl)
+        ranks = retrievaler.retrieval(question, embd_mdl, kbs[0].tenant_id, kb_ids, page, size,
+            similarity_threshold, vector_similarity_weight, top,
+            doc_ids, rerank_mdl=rerank_mdl)
         for c in ranks["chunks"]:
             if "vector" in c:
                 del c["vector"]
-
         return get_json_result(data=ranks)
     except Exception as e:
         if str(e).find("not_found") > 0:
             return get_json_result(data=False, retmsg=f'No chunk found! Check the chunk status please!',
                                    retcode=RetCode.DATA_ERROR)
-        return server_error_response(e)
+        return server_error_response(e)