diff --git a/agent/tools/retrieval.py b/agent/tools/retrieval.py
index 22909a28a..93a50c0c3 100644
--- a/agent/tools/retrieval.py
+++ b/agent/tools/retrieval.py
@@ -25,6 +25,7 @@ from api.db.services.dialog_service import meta_filter
 from api.db.services.knowledgebase_service import KnowledgebaseService
 from api.db.services.llm_service import LLMBundle
 from api import settings
+from common import globals
 from common.connection_utils import timeout
 from rag.app.tag import label_question
 from rag.prompts.generator import cross_languages, kb_prompt, gen_meta_filter
@@ -170,7 +171,7 @@ class Retrieval(ToolBase, ABC):
 
         if kbs:
             query = re.sub(r"^user[:：\s]*", "", query, flags=re.IGNORECASE)
-            kbinfos = settings.retriever.retrieval(
+            kbinfos = globals.retriever.retrieval(
                 query,
                 embd_mdl,
                 [kb.tenant_id for kb in kbs],
@@ -186,7 +187,7 @@ class Retrieval(ToolBase, ABC):
             )
             if self._param.toc_enhance:
                 chat_mdl = LLMBundle(self._canvas._tenant_id, LLMType.CHAT)
-                cks = settings.retriever.retrieval_by_toc(query, kbinfos["chunks"], [kb.tenant_id for kb in kbs], chat_mdl, self._param.top_n)
+                cks = globals.retriever.retrieval_by_toc(query, kbinfos["chunks"], [kb.tenant_id for kb in kbs], chat_mdl, self._param.top_n)
                 if cks:
                     kbinfos["chunks"] = cks
             if self._param.use_kg:
diff --git a/api/apps/api_app.py b/api/apps/api_app.py
index 306b54268..40341c5f7 100644
--- a/api/apps/api_app.py
+++ b/api/apps/api_app.py
@@ -32,7 +32,6 @@ from api.db.services.file_service import FileService
 from api.db.services.knowledgebase_service import KnowledgebaseService
 from api.db.services.task_service import queue_tasks, TaskService
 from api.db.services.user_service import UserTenantService
-from api import settings
 from common.misc_utils import get_uuid
 from common.constants import RetCode, VALID_TASK_STATUS, LLMType, ParserType, FileSource
 from api.utils.api_utils import server_error_response, get_data_error_result, get_json_result, validate_request, \
@@ -48,6 +47,7 @@ from api.db.services.canvas_service import UserCanvasService
 from agent.canvas import Canvas
 from functools import partial
 from pathlib import Path
+from common import globals
 
 
 @manager.route('/new_token', methods=['POST'])  # noqa: F821
@@ -538,7 +538,7 @@ def list_chunks():
             )
         kb_ids = KnowledgebaseService.get_kb_ids(tenant_id)
 
-        res = settings.retriever.chunk_list(doc_id, tenant_id, kb_ids)
+        res = globals.retriever.chunk_list(doc_id, tenant_id, kb_ids)
         res = [
             {
                 "content": res_item["content_with_weight"],
@@ -564,7 +564,7 @@ def get_chunk(chunk_id):
     try:
         tenant_id = objs[0].tenant_id
         kb_ids = KnowledgebaseService.get_kb_ids(tenant_id)
-        chunk = settings.docStoreConn.get(chunk_id, search.index_name(tenant_id), kb_ids)
+        chunk = globals.docStoreConn.get(chunk_id, search.index_name(tenant_id), kb_ids)
         if chunk is None:
             return server_error_response(Exception("Chunk not found"))
         k = []
@@ -886,7 +886,7 @@ def retrieval():
         if req.get("keyword", False):
             chat_mdl = LLMBundle(kbs[0].tenant_id, LLMType.CHAT)
             question += keyword_extraction(chat_mdl, question)
-        ranks = settings.retriever.retrieval(question, embd_mdl, kbs[0].tenant_id, kb_ids, page, size,
+        ranks = globals.retriever.retrieval(question, embd_mdl, kbs[0].tenant_id, kb_ids, page, size,
                                                similarity_threshold, vector_similarity_weight, top,
                                                doc_ids, rerank_mdl=rerank_mdl, highlight= highlight,
                                                rank_feature=label_question(question, kbs))
diff --git a/api/apps/canvas_app.py b/api/apps/canvas_app.py
index 96147d65c..eb3ee956a 100644
--- a/api/apps/canvas_app.py
+++ b/api/apps/canvas_app.py
@@ -25,7 +25,6 @@ from flask import request, Response
 from flask_login import login_required, current_user
 
 from agent.component import LLM
-from api import settings
 from api.db import CanvasCategory, FileType
 from api.db.services.canvas_service import CanvasTemplateService, UserCanvasService, API4ConversationService
 from api.db.services.document_service import DocumentService
@@ -46,6 +45,7 @@ from api.utils.file_utils import filename_type, read_potential_broken_pdf
 from rag.flow.pipeline import Pipeline
 from rag.nlp import search
 from rag.utils.redis_conn import REDIS_CONN
+from common import globals
 
 
 @manager.route('/templates', methods=['GET'])  # noqa: F821
@@ -192,8 +192,8 @@ def rerun():
     if 0 < doc["progress"] < 1:
         return get_data_error_result(message=f"`{doc['name']}` is processing...")
 
-    if settings.docStoreConn.indexExist(search.index_name(current_user.id), doc["kb_id"]):
-        settings.docStoreConn.delete({"doc_id": doc["id"]}, search.index_name(current_user.id), doc["kb_id"])
+    if globals.docStoreConn.indexExist(search.index_name(current_user.id), doc["kb_id"]):
+        globals.docStoreConn.delete({"doc_id": doc["id"]}, search.index_name(current_user.id), doc["kb_id"])
     doc["progress_msg"] = ""
     doc["chunk_num"] = 0
     doc["token_num"] = 0
diff --git a/api/apps/chunk_app.py b/api/apps/chunk_app.py
index 89e59400b..0f5211240 100644
--- a/api/apps/chunk_app.py
+++ b/api/apps/chunk_app.py
@@ -36,6 +36,7 @@ from rag.prompts.generator import gen_meta_filter, cross_languages, keyword_extr
 from rag.settings import PAGERANK_FLD
 from common.string_utils import remove_redundant_spaces
 from common.constants import RetCode, LLMType, ParserType
+from common import globals
 
 
 @manager.route('/list', methods=['POST'])  # noqa: F821
@@ -60,7 +61,7 @@ def list_chunk():
         }
         if "available_int" in req:
             query["available_int"] = int(req["available_int"])
-        sres = settings.retriever.search(query, search.index_name(tenant_id), kb_ids, highlight=["content_ltks"])
+        sres = globals.retriever.search(query, search.index_name(tenant_id), kb_ids, highlight=["content_ltks"])
         res = {"total": sres.total, "chunks": [], "doc": doc.to_dict()}
         for id in sres.ids:
             d = {
@@ -98,7 +99,7 @@ def get():
             return get_data_error_result(message="Tenant not found!")
         for tenant in tenants:
             kb_ids = KnowledgebaseService.get_kb_ids(tenant.tenant_id)
-            chunk = settings.docStoreConn.get(chunk_id, search.index_name(tenant.tenant_id), kb_ids)
+            chunk = globals.docStoreConn.get(chunk_id, search.index_name(tenant.tenant_id), kb_ids)
             if chunk:
                 break
         if chunk is None:
@@ -170,7 +171,7 @@ def set():
         v, c = embd_mdl.encode([doc.name, req["content_with_weight"] if not d.get("question_kwd") else "\n".join(d["question_kwd"])])
         v = 0.1 * v[0] + 0.9 * v[1] if doc.parser_id != ParserType.QA else v[1]
         d["q_%d_vec" % len(v)] = v.tolist()
-        settings.docStoreConn.update({"id": req["chunk_id"]}, d, search.index_name(tenant_id), doc.kb_id)
+        globals.docStoreConn.update({"id": req["chunk_id"]}, d, search.index_name(tenant_id), doc.kb_id)
         return get_json_result(data=True)
     except Exception as e:
         return server_error_response(e)
@@ -186,7 +187,7 @@ def switch():
         if not e:
             return get_data_error_result(message="Document not found!")
         for cid in req["chunk_ids"]:
-            if not settings.docStoreConn.update({"id": cid},
+            if not globals.docStoreConn.update({"id": cid},
                                                 {"available_int": int(req["available_int"])},
                                                 search.index_name(DocumentService.get_tenant_id(req["doc_id"])),
                                                 doc.kb_id):
@@ -206,7 +207,7 @@ def rm():
         e, doc = DocumentService.get_by_id(req["doc_id"])
         if not e:
             return get_data_error_result(message="Document not found!")
-        if not settings.docStoreConn.delete({"id": req["chunk_ids"]},
+        if not globals.docStoreConn.delete({"id": req["chunk_ids"]},
                                             search.index_name(DocumentService.get_tenant_id(req["doc_id"])),
                                             doc.kb_id):
             return get_data_error_result(message="Chunk deleting failure")
@@ -270,7 +271,7 @@ def create():
         v, c = embd_mdl.encode([doc.name, req["content_with_weight"] if not d["question_kwd"] else "\n".join(d["question_kwd"])])
         v = 0.1 * v[0] + 0.9 * v[1]
         d["q_%d_vec" % len(v)] = v.tolist()
-        settings.docStoreConn.insert([d], search.index_name(tenant_id), doc.kb_id)
+        globals.docStoreConn.insert([d], search.index_name(tenant_id), doc.kb_id)
 
         DocumentService.increment_chunk_num(
             doc.id, doc.kb_id, c, 1, 0)
@@ -346,7 +347,7 @@ def retrieval_test():
             question += keyword_extraction(chat_mdl, question)
 
         labels = label_question(question, [kb])
-        ranks = settings.retriever.retrieval(question, embd_mdl, tenant_ids, kb_ids, page, size,
+        ranks = globals.retriever.retrieval(question, embd_mdl, tenant_ids, kb_ids, page, size,
                                float(req.get("similarity_threshold", 0.0)),
                                float(req.get("vector_similarity_weight", 0.3)),
                                top,
@@ -385,7 +386,7 @@ def knowledge_graph():
         "doc_ids": [doc_id],
         "knowledge_graph_kwd": ["graph", "mind_map"]
     }
-    sres = settings.retriever.search(req, search.index_name(tenant_id), kb_ids)
+    sres = globals.retriever.search(req, search.index_name(tenant_id), kb_ids)
     obj = {"graph": {}, "mind_map": {}}
     for id in sres.ids[:2]:
         ty = sres.field[id]["knowledge_graph_kwd"]
diff --git a/api/apps/document_app.py b/api/apps/document_app.py
index 3d6a02710..b31535efd 100644
--- a/api/apps/document_app.py
+++ b/api/apps/document_app.py
@@ -23,7 +23,6 @@ import flask
 from flask import request
 from flask_login import current_user, login_required
 
-from api import settings
 from api.common.check_team_permission import check_kb_team_permission
 from api.constants import FILE_NAME_LEN_LIMIT, IMG_BASE64_PREFIX
 from api.db import VALID_FILE_TYPES, FileType
@@ -49,6 +48,7 @@ from api.utils.web_utils import CONTENT_TYPE_MAP, html2pdf, is_valid_url
 from deepdoc.parser.html_parser import RAGFlowHtmlParser
 from rag.nlp import search, rag_tokenizer
 from rag.utils.storage_factory import STORAGE_IMPL
+from common import globals
 
 
 @manager.route("/upload", methods=["POST"])  # noqa: F821
@@ -367,7 +367,7 @@ def change_status():
                 continue
 
             status_int = int(status)
-            if not settings.docStoreConn.update({"doc_id": doc_id}, {"available_int": status_int}, search.index_name(kb.tenant_id), doc.kb_id):
+            if not globals.docStoreConn.update({"doc_id": doc_id}, {"available_int": status_int}, search.index_name(kb.tenant_id), doc.kb_id):
                 result[doc_id] = {"error": "Database error (docStore update)!"}
             result[doc_id] = {"status": status}
         except Exception as e:
@@ -432,8 +432,8 @@ def run():
             DocumentService.update_by_id(id, info)
             if req.get("delete", False):
                 TaskService.filter_delete([Task.doc_id == id])
-                if settings.docStoreConn.indexExist(search.index_name(tenant_id), doc.kb_id):
-                    settings.docStoreConn.delete({"doc_id": id}, search.index_name(tenant_id), doc.kb_id)
+                if globals.docStoreConn.indexExist(search.index_name(tenant_id), doc.kb_id):
+                    globals.docStoreConn.delete({"doc_id": id}, search.index_name(tenant_id), doc.kb_id)
 
             if str(req["run"]) == TaskStatus.RUNNING.value:
                 doc = doc.to_dict()
@@ -479,8 +479,8 @@ def rename():
             "title_tks": title_tks,
             "title_sm_tks": rag_tokenizer.fine_grained_tokenize(title_tks),
         }
-        if settings.docStoreConn.indexExist(search.index_name(tenant_id), doc.kb_id):
-            settings.docStoreConn.update(
+        if globals.docStoreConn.indexExist(search.index_name(tenant_id), doc.kb_id):
+            globals.docStoreConn.update(
                 {"doc_id": req["doc_id"]},
                 es_body,
                 search.index_name(tenant_id),
@@ -541,8 +541,8 @@ def change_parser():
             tenant_id = DocumentService.get_tenant_id(req["doc_id"])
             if not tenant_id:
                 return get_data_error_result(message="Tenant not found!")
-            if settings.docStoreConn.indexExist(search.index_name(tenant_id), doc.kb_id):
-                settings.docStoreConn.delete({"doc_id": doc.id}, search.index_name(tenant_id), doc.kb_id)
+            if globals.docStoreConn.indexExist(search.index_name(tenant_id), doc.kb_id):
+                globals.docStoreConn.delete({"doc_id": doc.id}, search.index_name(tenant_id), doc.kb_id)
 
     try:
         if "pipeline_id" in req and req["pipeline_id"] != "":
diff --git a/api/apps/kb_app.py b/api/apps/kb_app.py
index 19f5fc8fa..28c2fa31e 100644
--- a/api/apps/kb_app.py
+++ b/api/apps/kb_app.py
@@ -35,7 +35,6 @@ from api.db import VALID_FILE_TYPES
 from api.db.services.knowledgebase_service import KnowledgebaseService
 from api.db.db_models import File
 from api.utils.api_utils import get_json_result
-from api import settings
 from rag.nlp import search
 from api.constants import DATASET_NAME_LIMIT
 from rag.settings import PAGERANK_FLD
@@ -43,7 +42,7 @@ from rag.utils.redis_conn import REDIS_CONN
 from rag.utils.storage_factory import STORAGE_IMPL
 from rag.utils.doc_store_conn import OrderByExpr  
 from common.constants import RetCode, PipelineTaskType, StatusEnum, VALID_TASK_STATUS, FileSource, LLMType
-
+from common import globals
 
 @manager.route('/create', methods=['post'])  # noqa: F821
 @login_required
@@ -110,11 +109,11 @@ def update():
 
         if kb.pagerank != req.get("pagerank", 0):
             if req.get("pagerank", 0) > 0:
-                settings.docStoreConn.update({"kb_id": kb.id}, {PAGERANK_FLD: req["pagerank"]},
+                globals.docStoreConn.update({"kb_id": kb.id}, {PAGERANK_FLD: req["pagerank"]},
                                          search.index_name(kb.tenant_id), kb.id)
             else:
                 # Elasticsearch requires PAGERANK_FLD be non-zero!
-                settings.docStoreConn.update({"exists": PAGERANK_FLD}, {"remove": PAGERANK_FLD},
+                globals.docStoreConn.update({"exists": PAGERANK_FLD}, {"remove": PAGERANK_FLD},
                                          search.index_name(kb.tenant_id), kb.id)
 
         e, kb = KnowledgebaseService.get_by_id(kb.id)
@@ -226,8 +225,8 @@ def rm():
             return get_data_error_result(
                 message="Database error (Knowledgebase removal)!")
         for kb in kbs:
-            settings.docStoreConn.delete({"kb_id": kb.id}, search.index_name(kb.tenant_id), kb.id)
-            settings.docStoreConn.deleteIdx(search.index_name(kb.tenant_id), kb.id)
+            globals.docStoreConn.delete({"kb_id": kb.id}, search.index_name(kb.tenant_id), kb.id)
+            globals.docStoreConn.deleteIdx(search.index_name(kb.tenant_id), kb.id)
             if hasattr(STORAGE_IMPL, 'remove_bucket'):
                 STORAGE_IMPL.remove_bucket(kb.id)
         return get_json_result(data=True)
@@ -248,7 +247,7 @@ def list_tags(kb_id):
     tenants = UserTenantService.get_tenants_by_user_id(current_user.id)
     tags = []
     for tenant in tenants:
-        tags += settings.retriever.all_tags(tenant["tenant_id"], [kb_id])
+        tags += globals.retriever.all_tags(tenant["tenant_id"], [kb_id])
     return get_json_result(data=tags)
 
 
@@ -267,7 +266,7 @@ def list_tags_from_kbs():
     tenants = UserTenantService.get_tenants_by_user_id(current_user.id)
     tags = []
     for tenant in tenants:
-        tags += settings.retriever.all_tags(tenant["tenant_id"], kb_ids)
+        tags += globals.retriever.all_tags(tenant["tenant_id"], kb_ids)
     return get_json_result(data=tags)
 
 
@@ -284,7 +283,7 @@ def rm_tags(kb_id):
     e, kb = KnowledgebaseService.get_by_id(kb_id)
 
     for t in req["tags"]:
-        settings.docStoreConn.update({"tag_kwd": t, "kb_id": [kb_id]},
+        globals.docStoreConn.update({"tag_kwd": t, "kb_id": [kb_id]},
                                      {"remove": {"tag_kwd": t}},
                                      search.index_name(kb.tenant_id),
                                      kb_id)
@@ -303,7 +302,7 @@ def rename_tags(kb_id):
         )
     e, kb = KnowledgebaseService.get_by_id(kb_id)
 
-    settings.docStoreConn.update({"tag_kwd": req["from_tag"], "kb_id": [kb_id]},
+    globals.docStoreConn.update({"tag_kwd": req["from_tag"], "kb_id": [kb_id]},
                                      {"remove": {"tag_kwd": req["from_tag"].strip()}, "add": {"tag_kwd": req["to_tag"]}},
                                      search.index_name(kb.tenant_id),
                                      kb_id)
@@ -326,9 +325,9 @@ def knowledge_graph(kb_id):
     }
 
     obj = {"graph": {}, "mind_map": {}}
-    if not settings.docStoreConn.indexExist(search.index_name(kb.tenant_id), kb_id):
+    if not globals.docStoreConn.indexExist(search.index_name(kb.tenant_id), kb_id):
         return get_json_result(data=obj)
-    sres = settings.retriever.search(req, search.index_name(kb.tenant_id), [kb_id])
+    sres = globals.retriever.search(req, search.index_name(kb.tenant_id), [kb_id])
     if not len(sres.ids):
         return get_json_result(data=obj)
 
@@ -360,7 +359,7 @@ def delete_knowledge_graph(kb_id):
             code=RetCode.AUTHENTICATION_ERROR
         )
     _, kb = KnowledgebaseService.get_by_id(kb_id)
-    settings.docStoreConn.delete({"knowledge_graph_kwd": ["graph", "subgraph", "entity", "relation"]}, search.index_name(kb.tenant_id), kb_id)
+    globals.docStoreConn.delete({"knowledge_graph_kwd": ["graph", "subgraph", "entity", "relation"]}, search.index_name(kb.tenant_id), kb_id)
 
     return get_json_result(data=True)
 
@@ -732,13 +731,13 @@ def delete_kb_task():
             task_id = kb.graphrag_task_id
             kb_task_finish_at = "graphrag_task_finish_at"
             cancel_task(task_id)
-            settings.docStoreConn.delete({"knowledge_graph_kwd": ["graph", "subgraph", "entity", "relation"]}, search.index_name(kb.tenant_id), kb_id)
+            globals.docStoreConn.delete({"knowledge_graph_kwd": ["graph", "subgraph", "entity", "relation"]}, search.index_name(kb.tenant_id), kb_id)
         case PipelineTaskType.RAPTOR:
             kb_task_id_field = "raptor_task_id"
             task_id = kb.raptor_task_id
             kb_task_finish_at = "raptor_task_finish_at"
             cancel_task(task_id)
-            settings.docStoreConn.delete({"raptor_kwd": ["raptor"]}, search.index_name(kb.tenant_id), kb_id)
+            globals.docStoreConn.delete({"raptor_kwd": ["raptor"]}, search.index_name(kb.tenant_id), kb_id)
         case PipelineTaskType.MINDMAP:
             kb_task_id_field = "mindmap_task_id"
             task_id = kb.mindmap_task_id
@@ -850,7 +849,7 @@ def check_embedding():
     tenant_id = kb.tenant_id
 
     emb_mdl = LLMBundle(tenant_id, LLMType.EMBEDDING, embd_id)
-    samples = sample_random_chunks_with_vectors(settings.docStoreConn, tenant_id=tenant_id, kb_id=kb_id, n=n)
+    samples = sample_random_chunks_with_vectors(globals.docStoreConn, tenant_id=tenant_id, kb_id=kb_id, n=n)
 
     results, eff_sims = [], []
     for ck in samples:
diff --git a/api/apps/llm_app.py b/api/apps/llm_app.py
index c49097fac..3d9be95f7 100644
--- a/api/apps/llm_app.py
+++ b/api/apps/llm_app.py
@@ -24,7 +24,7 @@ from api.utils.api_utils import server_error_response, get_data_error_result, va
 from common.constants import StatusEnum, LLMType
 from api.db.db_models import TenantLLM
 from api.utils.api_utils import get_json_result, get_allowed_llm_factories
-from common.base64_image import test_image
+from rag.utils.base64_image import test_image
 from rag.llm import EmbeddingModel, ChatModel, RerankModel, CvModel, TTSModel
 
 
diff --git a/api/apps/sdk/dataset.py b/api/apps/sdk/dataset.py
index 20a7578c9..19ae9a60e 100644
--- a/api/apps/sdk/dataset.py
+++ b/api/apps/sdk/dataset.py
@@ -20,7 +20,6 @@ import os
 import json
 from flask import request
 from peewee import OperationalError
-from api import settings
 from api.db.db_models import File
 from api.db.services.document_service import DocumentService
 from api.db.services.file2document_service import File2DocumentService
@@ -49,6 +48,7 @@ from api.utils.validation_utils import (
 )
 from rag.nlp import search
 from rag.settings import PAGERANK_FLD
+from common import globals
 
 
 @manager.route("/datasets", methods=["POST"])  # noqa: F821
@@ -360,11 +360,11 @@ def update(tenant_id, dataset_id):
                 return get_error_argument_result(message="'pagerank' can only be set when doc_engine is elasticsearch")
 
             if req["pagerank"] > 0:
-                settings.docStoreConn.update({"kb_id": kb.id}, {PAGERANK_FLD: req["pagerank"]},
+                globals.docStoreConn.update({"kb_id": kb.id}, {PAGERANK_FLD: req["pagerank"]},
                                              search.index_name(kb.tenant_id), kb.id)
             else:
                 # Elasticsearch requires PAGERANK_FLD be non-zero!
-                settings.docStoreConn.update({"exists": PAGERANK_FLD}, {"remove": PAGERANK_FLD},
+                globals.docStoreConn.update({"exists": PAGERANK_FLD}, {"remove": PAGERANK_FLD},
                                              search.index_name(kb.tenant_id), kb.id)
 
         if not KnowledgebaseService.update_by_id(kb.id, req):
@@ -493,9 +493,9 @@ def knowledge_graph(tenant_id, dataset_id):
     }
 
     obj = {"graph": {}, "mind_map": {}}
-    if not settings.docStoreConn.indexExist(search.index_name(kb.tenant_id), dataset_id):
+    if not globals.docStoreConn.indexExist(search.index_name(kb.tenant_id), dataset_id):
         return get_result(data=obj)
-    sres = settings.retriever.search(req, search.index_name(kb.tenant_id), [dataset_id])
+    sres = globals.retriever.search(req, search.index_name(kb.tenant_id), [dataset_id])
     if not len(sres.ids):
         return get_result(data=obj)
 
@@ -528,7 +528,7 @@ def delete_knowledge_graph(tenant_id, dataset_id):
             code=RetCode.AUTHENTICATION_ERROR
         )
     _, kb = KnowledgebaseService.get_by_id(dataset_id)
-    settings.docStoreConn.delete({"knowledge_graph_kwd": ["graph", "subgraph", "entity", "relation"]},
+    globals.docStoreConn.delete({"knowledge_graph_kwd": ["graph", "subgraph", "entity", "relation"]},
                                  search.index_name(kb.tenant_id), dataset_id)
 
     return get_result(data=True)
diff --git a/api/apps/sdk/dify_retrieval.py b/api/apps/sdk/dify_retrieval.py
index 9380cc4f0..b91e5faaa 100644
--- a/api/apps/sdk/dify_retrieval.py
+++ b/api/apps/sdk/dify_retrieval.py
@@ -25,6 +25,7 @@ from api.utils.api_utils import validate_request, build_error_result, apikey_req
 from rag.app.tag import label_question
 from api.db.services.dialog_service import meta_filter, convert_conditions
 from common.constants import RetCode, LLMType
+from common import globals
 
 @manager.route('/dify/retrieval', methods=['POST'])  # noqa: F821
 @apikey_required
@@ -137,7 +138,7 @@ def retrieval(tenant_id):
         # print("doc_ids", doc_ids)
         if not doc_ids and metadata_condition is not None:
             doc_ids = ['-999']
-        ranks = settings.retriever.retrieval(
+        ranks = globals.retriever.retrieval(
             question,
             embd_mdl,
             kb.tenant_id,
diff --git a/api/apps/sdk/doc.py b/api/apps/sdk/doc.py
index f415db0cd..30d9f877c 100644
--- a/api/apps/sdk/doc.py
+++ b/api/apps/sdk/doc.py
@@ -44,6 +44,7 @@ from rag.prompts.generator import cross_languages, keyword_extraction
 from rag.utils.storage_factory import STORAGE_IMPL
 from common.string_utils import remove_redundant_spaces
 from common.constants import RetCode, LLMType, ParserType, TaskStatus, FileSource
+from common import globals
 
 MAXIMUM_OF_UPLOADING_FILES = 256
 
@@ -307,7 +308,7 @@ def update_doc(tenant_id, dataset_id, document_id):
             )
             if not e:
                 return get_error_data_result(message="Document not found!")
-            settings.docStoreConn.delete({"doc_id": doc.id}, search.index_name(tenant_id), dataset_id)
+            globals.docStoreConn.delete({"doc_id": doc.id}, search.index_name(tenant_id), dataset_id)
 
     if "enabled" in req:
         status = int(req["enabled"])
@@ -316,7 +317,7 @@ def update_doc(tenant_id, dataset_id, document_id):
                 if not DocumentService.update_by_id(doc.id, {"status": str(status)}):
                     return get_error_data_result(message="Database error (Document update)!")
 
-                settings.docStoreConn.update({"doc_id": doc.id}, {"available_int": status}, search.index_name(kb.tenant_id), doc.kb_id)
+                globals.docStoreConn.update({"doc_id": doc.id}, {"available_int": status}, search.index_name(kb.tenant_id), doc.kb_id)
                 return get_result(data=True)
             except Exception as e:
                 return server_error_response(e)
@@ -755,7 +756,7 @@ def parse(tenant_id, dataset_id):
             return get_error_data_result("Can't parse document that is currently being processed")
         info = {"run": "1", "progress": 0, "progress_msg": "", "chunk_num": 0, "token_num": 0}
         DocumentService.update_by_id(id, info)
-        settings.docStoreConn.delete({"doc_id": id}, search.index_name(tenant_id), dataset_id)
+        globals.docStoreConn.delete({"doc_id": id}, search.index_name(tenant_id), dataset_id)
         TaskService.filter_delete([Task.doc_id == id])
         e, doc = DocumentService.get_by_id(id)
         doc = doc.to_dict()
@@ -835,7 +836,7 @@ def stop_parsing(tenant_id, dataset_id):
             return get_error_data_result("Can't stop parsing document with progress at 0 or 1")
         info = {"run": "2", "progress": 0, "chunk_num": 0}
         DocumentService.update_by_id(id, info)
-        settings.docStoreConn.delete({"doc_id": doc[0].id}, search.index_name(tenant_id), dataset_id)
+        globals.docStoreConn.delete({"doc_id": doc[0].id}, search.index_name(tenant_id), dataset_id)
         success_count += 1
     if duplicate_messages:
         if success_count > 0:
@@ -968,7 +969,7 @@ def list_chunks(tenant_id, dataset_id, document_id):
 
     res = {"total": 0, "chunks": [], "doc": renamed_doc}
     if req.get("id"):
-        chunk = settings.docStoreConn.get(req.get("id"), search.index_name(tenant_id), [dataset_id])
+        chunk = globals.docStoreConn.get(req.get("id"), search.index_name(tenant_id), [dataset_id])
         if not chunk:
             return get_result(message=f"Chunk not found: {dataset_id}/{req.get('id')}", code=RetCode.NOT_FOUND)
         k = []
@@ -995,8 +996,8 @@ def list_chunks(tenant_id, dataset_id, document_id):
         res["chunks"].append(final_chunk)
         _ = Chunk(**final_chunk)
 
-    elif settings.docStoreConn.indexExist(search.index_name(tenant_id), dataset_id):
-        sres = settings.retriever.search(query, search.index_name(tenant_id), [dataset_id], emb_mdl=None, highlight=True)
+    elif globals.docStoreConn.indexExist(search.index_name(tenant_id), dataset_id):
+        sres = globals.retriever.search(query, search.index_name(tenant_id), [dataset_id], emb_mdl=None, highlight=True)
         res["total"] = sres.total
         for id in sres.ids:
             d = {
@@ -1120,7 +1121,7 @@ def add_chunk(tenant_id, dataset_id, document_id):
     v, c = embd_mdl.encode([doc.name, req["content"] if not d["question_kwd"] else "\n".join(d["question_kwd"])])
     v = 0.1 * v[0] + 0.9 * v[1]
     d["q_%d_vec" % len(v)] = v.tolist()
-    settings.docStoreConn.insert([d], search.index_name(tenant_id), dataset_id)
+    globals.docStoreConn.insert([d], search.index_name(tenant_id), dataset_id)
 
     DocumentService.increment_chunk_num(doc.id, doc.kb_id, c, 1, 0)
     # rename keys
@@ -1201,7 +1202,7 @@ def rm_chunk(tenant_id, dataset_id, document_id):
     if "chunk_ids" in req:
         unique_chunk_ids, duplicate_messages = check_duplicate_ids(req["chunk_ids"], "chunk")
         condition["id"] = unique_chunk_ids
-    chunk_number = settings.docStoreConn.delete(condition, search.index_name(tenant_id), dataset_id)
+    chunk_number = globals.docStoreConn.delete(condition, search.index_name(tenant_id), dataset_id)
     if chunk_number != 0:
         DocumentService.decrement_chunk_num(document_id, dataset_id, 1, chunk_number, 0)
     if "chunk_ids" in req and chunk_number != len(unique_chunk_ids):
@@ -1273,7 +1274,7 @@ def update_chunk(tenant_id, dataset_id, document_id, chunk_id):
         schema:
           type: object
     """
-    chunk = settings.docStoreConn.get(chunk_id, search.index_name(tenant_id), [dataset_id])
+    chunk = globals.docStoreConn.get(chunk_id, search.index_name(tenant_id), [dataset_id])
     if chunk is None:
         return get_error_data_result(f"Can't find this chunk {chunk_id}")
     if not KnowledgebaseService.accessible(kb_id=dataset_id, user_id=tenant_id):
@@ -1318,7 +1319,7 @@ def update_chunk(tenant_id, dataset_id, document_id, chunk_id):
     v, c = embd_mdl.encode([doc.name, d["content_with_weight"] if not d.get("question_kwd") else "\n".join(d["question_kwd"])])
     v = 0.1 * v[0] + 0.9 * v[1] if doc.parser_id != ParserType.QA else v[1]
     d["q_%d_vec" % len(v)] = v.tolist()
-    settings.docStoreConn.update({"id": chunk_id}, d, search.index_name(tenant_id), dataset_id)
+    globals.docStoreConn.update({"id": chunk_id}, d, search.index_name(tenant_id), dataset_id)
     return get_result()
 
 
@@ -1464,7 +1465,7 @@ def retrieval_test(tenant_id):
             chat_mdl = LLMBundle(kb.tenant_id, LLMType.CHAT)
             question += keyword_extraction(chat_mdl, question)
 
-        ranks = settings.retriever.retrieval(
+        ranks = globals.retriever.retrieval(
             question,
             embd_mdl,
             tenant_ids,
diff --git a/api/apps/sdk/session.py b/api/apps/sdk/session.py
index 2997e1f00..3d3043bc5 100644
--- a/api/apps/sdk/session.py
+++ b/api/apps/sdk/session.py
@@ -41,6 +41,7 @@ from rag.app.tag import label_question
 from rag.prompts.template import load_prompt
 from rag.prompts.generator import cross_languages, gen_meta_filter, keyword_extraction, chunks_format
 from common.constants import RetCode, LLMType, StatusEnum
+from common import globals
 
 @manager.route("/chats/<chat_id>/sessions", methods=["POST"])  # noqa: F821
 @token_required
@@ -1015,7 +1016,7 @@ def retrieval_test_embedded():
             question += keyword_extraction(chat_mdl, question)
 
         labels = label_question(question, [kb])
-        ranks = settings.retriever.retrieval(
+        ranks = globals.retriever.retrieval(
             question, embd_mdl, tenant_ids, kb_ids, page, size, similarity_threshold, vector_similarity_weight, top,
             doc_ids, rerank_mdl=rerank_mdl, highlight=req.get("highlight"), rank_feature=labels
         )
diff --git a/api/apps/system_app.py b/api/apps/system_app.py
index c1467f3d1..10b94102d 100644
--- a/api/apps/system_app.py
+++ b/api/apps/system_app.py
@@ -38,6 +38,7 @@ from timeit import default_timer as timer
 from rag.utils.redis_conn import REDIS_CONN
 from flask import jsonify
 from api.utils.health_utils import run_health_checks
+from common import globals
 
 
 @manager.route("/version", methods=["GET"])  # noqa: F821
@@ -100,7 +101,7 @@ def status():
     res = {}
     st = timer()
     try:
-        res["doc_engine"] = settings.docStoreConn.health()
+        res["doc_engine"] = globals.docStoreConn.health()
         res["doc_engine"]["elapsed"] = "{:.1f}".format((timer() - st) * 1000.0)
     except Exception as e:
         res["doc_engine"] = {
diff --git a/api/apps/user_app.py b/api/apps/user_app.py
index f8151f887..8dd4ff10e 100644
--- a/api/apps/user_app.py
+++ b/api/apps/user_app.py
@@ -58,6 +58,7 @@ from api.utils.web_utils import (
     hash_code,
     captcha_key,
 )
+from common import globals
 
 
 @manager.route("/login", methods=["POST", "GET"])  # noqa: F821
@@ -623,7 +624,7 @@ def user_register(user_id, user):
         "id": user_id,
         "name": user["nickname"] + "‘s Kingdom",
         "llm_id": settings.CHAT_MDL,
-        "embd_id": settings.EMBEDDING_MDL,
+        "embd_id": globals.EMBEDDING_MDL,
         "asr_id": settings.ASR_MDL,
         "parser_ids": settings.PARSERS,
         "img2txt_id": settings.IMAGE2TEXT_MDL,
diff --git a/api/db/init_data.py b/api/db/init_data.py
index a520f2f41..ffce4fe53 100644
--- a/api/db/init_data.py
+++ b/api/db/init_data.py
@@ -32,6 +32,7 @@ from api.db.services.user_service import TenantService, UserTenantService
 from api import settings
 from common.constants import LLMType
 from common.file_utils import get_project_base_directory
+from common import globals
 from api.common.base64 import encode_to_base64
 
 
@@ -49,7 +50,7 @@ def init_superuser():
         "id": user_info["id"],
         "name": user_info["nickname"] + "‘s Kingdom",
         "llm_id": settings.CHAT_MDL,
-        "embd_id": settings.EMBEDDING_MDL,
+        "embd_id": globals.EMBEDDING_MDL,
         "asr_id": settings.ASR_MDL,
         "parser_ids": settings.PARSERS,
         "img2txt_id": settings.IMAGE2TEXT_MDL
diff --git a/api/db/joint_services/user_account_service.py b/api/db/joint_services/user_account_service.py
index 733cb518d..79ff70714 100644
--- a/api/db/joint_services/user_account_service.py
+++ b/api/db/joint_services/user_account_service.py
@@ -38,6 +38,7 @@ from api.db.services.user_service import TenantService, UserService, UserTenantS
 from rag.utils.storage_factory import STORAGE_IMPL
 from rag.nlp import search
 from common.constants import ActiveEnum
+from common import globals
 
 def create_new_user(user_info: dict) -> dict:
     """
@@ -63,7 +64,7 @@ def create_new_user(user_info: dict) -> dict:
         "id": user_id,
         "name": user_info["nickname"] + "‘s Kingdom",
         "llm_id": settings.CHAT_MDL,
-        "embd_id": settings.EMBEDDING_MDL,
+        "embd_id": globals.EMBEDDING_MDL,
         "asr_id": settings.ASR_MDL,
         "parser_ids": settings.PARSERS,
         "img2txt_id": settings.IMAGE2TEXT_MDL,
@@ -179,7 +180,7 @@ def delete_user_data(user_id: str) -> dict:
                     )
                     done_msg += f"- Deleted {file2doc_delete_res} document-file relation records.\n"
                 # step1.1.3 delete chunk in es
-                r = settings.docStoreConn.delete({"kb_id": kb_ids},
+                r = globals.docStoreConn.delete({"kb_id": kb_ids},
                                          search.index_name(tenant_id), kb_ids)
                 done_msg += f"- Deleted {r} chunk records.\n"
                 kb_delete_res = KnowledgebaseService.delete_by_ids(kb_ids)
@@ -237,7 +238,7 @@ def delete_user_data(user_id: str) -> dict:
                     kb_doc_info = {}
                     for _tenant_id, kb_doc in kb_grouped_doc.items():
                         for _kb_id, docs in kb_doc.items():
-                            chunk_delete_res += settings.docStoreConn.delete(
+                            chunk_delete_res += globals.docStoreConn.delete(
                                 {"doc_id": [d["id"] for d in docs]},
                                 search.index_name(_tenant_id), _kb_id
                             )
diff --git a/api/db/services/dialog_service.py b/api/db/services/dialog_service.py
index ec5dc523f..6e5bab0da 100644
--- a/api/db/services/dialog_service.py
+++ b/api/db/services/dialog_service.py
@@ -44,6 +44,7 @@ from rag.prompts.generator import chunks_format, citation_prompt, cross_language
 from common.token_utils import num_tokens_from_string
 from rag.utils.tavily_conn import Tavily
 from common.string_utils import remove_redundant_spaces
+from common import globals
 
 
 class DialogService(CommonService):
@@ -371,7 +372,7 @@ def chat(dialog, messages, stream=True, **kwargs):
         chat_mdl.bind_tools(toolcall_session, tools)
     bind_models_ts = timer()
 
-    retriever = settings.retriever
+    retriever = globals.retriever
     questions = [m["content"] for m in messages if m["role"] == "user"][-3:]
     attachments = kwargs["doc_ids"].split(",") if "doc_ids" in kwargs else []
     if "doc_ids" in messages[-1]:
@@ -663,7 +664,7 @@ Please write the SQL, only SQL, without any other explanations or text.
 
         logging.debug(f"{question} get SQL(refined): {sql}")
         tried_times += 1
-        return settings.retriever.sql_retrieval(sql, format="json"), sql
+        return globals.retriever.sql_retrieval(sql, format="json"), sql
 
     tbl, sql = get_table()
     if tbl is None:
@@ -757,7 +758,7 @@ def ask(question, kb_ids, tenant_id, chat_llm_name=None, search_config={}):
     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.retriever if not is_knowledge_graph else settings.kg_retriever
+    retriever = globals.retriever if not is_knowledge_graph else settings.kg_retriever
 
     embd_mdl = LLMBundle(tenant_id, LLMType.EMBEDDING, embedding_list[0])
     chat_mdl = LLMBundle(tenant_id, LLMType.CHAT, chat_llm_name)
@@ -853,7 +854,7 @@ def gen_mindmap(question, kb_ids, tenant_id, search_config={}):
             if not doc_ids:
                 doc_ids = None
 
-    ranks = settings.retriever.retrieval(
+    ranks = globals.retriever.retrieval(
         question=question,
         embd_mdl=embd_mdl,
         tenant_ids=tenant_ids,
diff --git a/api/db/services/document_service.py b/api/db/services/document_service.py
index 5351e7d92..b8f3f1580 100644
--- a/api/db/services/document_service.py
+++ b/api/db/services/document_service.py
@@ -26,7 +26,6 @@ import trio
 import xxhash
 from peewee import fn, Case, JOIN
 
-from api import settings
 from api.constants import IMG_BASE64_PREFIX, FILE_NAME_LEN_LIMIT
 from api.db import FileType, UserTenantRole, CanvasCategory
 from api.db.db_models import DB, Document, Knowledgebase, Task, Tenant, UserTenant, File2Document, File, UserCanvas, \
@@ -42,7 +41,7 @@ from rag.settings import get_svr_queue_name, SVR_CONSUMER_GROUP_NAME
 from rag.utils.redis_conn import REDIS_CONN
 from rag.utils.storage_factory import STORAGE_IMPL
 from rag.utils.doc_store_conn import OrderByExpr
-
+from common import globals
 
 class DocumentService(CommonService):
     model = Document
@@ -309,10 +308,10 @@ class DocumentService(CommonService):
             page_size = 1000
             all_chunk_ids = []
             while True:
-                chunks = settings.docStoreConn.search(["img_id"], [], {"doc_id": doc.id}, [], OrderByExpr(),
+                chunks = globals.docStoreConn.search(["img_id"], [], {"doc_id": doc.id}, [], OrderByExpr(),
                                                       page * page_size, page_size, search.index_name(tenant_id),
                                                       [doc.kb_id])
-                chunk_ids = settings.docStoreConn.getChunkIds(chunks)
+                chunk_ids = globals.docStoreConn.getChunkIds(chunks)
                 if not chunk_ids:
                     break
                 all_chunk_ids.extend(chunk_ids)
@@ -323,19 +322,19 @@ class DocumentService(CommonService):
             if doc.thumbnail and not doc.thumbnail.startswith(IMG_BASE64_PREFIX):
                 if STORAGE_IMPL.obj_exist(doc.kb_id, doc.thumbnail):
                     STORAGE_IMPL.rm(doc.kb_id, doc.thumbnail)
-            settings.docStoreConn.delete({"doc_id": doc.id}, search.index_name(tenant_id), doc.kb_id)
+            globals.docStoreConn.delete({"doc_id": doc.id}, search.index_name(tenant_id), doc.kb_id)
 
-            graph_source = settings.docStoreConn.getFields(
-                settings.docStoreConn.search(["source_id"], [], {"kb_id": doc.kb_id, "knowledge_graph_kwd": ["graph"]}, [], OrderByExpr(), 0, 1, search.index_name(tenant_id), [doc.kb_id]), ["source_id"]
+            graph_source = globals.docStoreConn.getFields(
+                globals.docStoreConn.search(["source_id"], [], {"kb_id": doc.kb_id, "knowledge_graph_kwd": ["graph"]}, [], OrderByExpr(), 0, 1, search.index_name(tenant_id), [doc.kb_id]), ["source_id"]
             )
             if len(graph_source) > 0 and doc.id in list(graph_source.values())[0]["source_id"]:
-                settings.docStoreConn.update({"kb_id": doc.kb_id, "knowledge_graph_kwd": ["entity", "relation", "graph", "subgraph", "community_report"], "source_id": doc.id},
+                globals.docStoreConn.update({"kb_id": doc.kb_id, "knowledge_graph_kwd": ["entity", "relation", "graph", "subgraph", "community_report"], "source_id": doc.id},
                                              {"remove": {"source_id": doc.id}},
                                              search.index_name(tenant_id), doc.kb_id)
-                settings.docStoreConn.update({"kb_id": doc.kb_id, "knowledge_graph_kwd": ["graph"]},
+                globals.docStoreConn.update({"kb_id": doc.kb_id, "knowledge_graph_kwd": ["graph"]},
                                              {"removed_kwd": "Y"},
                                              search.index_name(tenant_id), doc.kb_id)
-                settings.docStoreConn.delete({"kb_id": doc.kb_id, "knowledge_graph_kwd": ["entity", "relation", "graph", "subgraph", "community_report"], "must_not": {"exists": "source_id"}},
+                globals.docStoreConn.delete({"kb_id": doc.kb_id, "knowledge_graph_kwd": ["entity", "relation", "graph", "subgraph", "community_report"], "must_not": {"exists": "source_id"}},
                                              search.index_name(tenant_id), doc.kb_id)
         except Exception:
             pass
@@ -996,10 +995,10 @@ def doc_upload_and_parse(conversation_id, file_objs, user_id):
             d["q_%d_vec" % len(v)] = v
         for b in range(0, len(cks), es_bulk_size):
             if try_create_idx:
-                if not settings.docStoreConn.indexExist(idxnm, kb_id):
-                    settings.docStoreConn.createIdx(idxnm, kb_id, len(vects[0]))
+                if not globals.docStoreConn.indexExist(idxnm, kb_id):
+                    globals.docStoreConn.createIdx(idxnm, kb_id, len(vects[0]))
                 try_create_idx = False
-            settings.docStoreConn.insert(cks[b:b + es_bulk_size], idxnm, kb_id)
+            globals.docStoreConn.insert(cks[b:b + es_bulk_size], idxnm, kb_id)
 
         DocumentService.increment_chunk_num(
             doc_id, kb.id, token_counts[doc_id], chunk_counts[doc_id], 0)
diff --git a/api/db/services/task_service.py b/api/db/services/task_service.py
index 0cc0b0a93..1fcb8e7dc 100644
--- a/api/db/services/task_service.py
+++ b/api/db/services/task_service.py
@@ -34,7 +34,7 @@ from deepdoc.parser.excel_parser import RAGFlowExcelParser
 from rag.settings import get_svr_queue_name
 from rag.utils.storage_factory import STORAGE_IMPL
 from rag.utils.redis_conn import REDIS_CONN
-from api import settings
+from common import globals
 from rag.nlp import search
 
 CANVAS_DEBUG_DOC_ID = "dataflow_x"
@@ -418,7 +418,7 @@ def queue_tasks(doc: dict, bucket: str, name: str, priority: int):
             if pre_task["chunk_ids"]:
                 pre_chunk_ids.extend(pre_task["chunk_ids"].split())
         if pre_chunk_ids:
-            settings.docStoreConn.delete({"id": pre_chunk_ids}, search.index_name(chunking_config["tenant_id"]),
+            globals.docStoreConn.delete({"id": pre_chunk_ids}, search.index_name(chunking_config["tenant_id"]),
                                          chunking_config["kb_id"])
     DocumentService.update_by_id(doc["id"], {"chunk_num": ck_num})
 
diff --git a/api/settings.py b/api/settings.py
index 12b260ae4..c82a7cfff 100644
--- a/api/settings.py
+++ b/api/settings.py
@@ -32,12 +32,12 @@ LLM = None
 LLM_FACTORY = None
 LLM_BASE_URL = None
 CHAT_MDL = ""
-EMBEDDING_MDL = ""
+# EMBEDDING_MDL = "" has been moved to common/globals.py
 RERANK_MDL = ""
 ASR_MDL = ""
 IMAGE2TEXT_MDL = ""
 CHAT_CFG = ""
-
+# EMBEDDING_CFG = "" has been moved to common/globals.py
 RERANK_CFG = ""
 ASR_CFG = ""
 IMAGE2TEXT_CFG = ""
@@ -61,10 +61,10 @@ HTTP_APP_KEY = None
 GITHUB_OAUTH = None
 FEISHU_OAUTH = None
 OAUTH_CONFIG = None
-DOC_ENGINE = None
-docStoreConn = None
+# DOC_ENGINE = None has been moved to common/globals.py
+# docStoreConn = None has been moved to common/globals.py
 
-retriever = None
+#retriever = None has been moved to common/globals.py
 kg_retriever = None
 
 # user registration switch
@@ -125,7 +125,7 @@ def init_settings():
     except Exception:
         FACTORY_LLM_INFOS = []
 
-    global CHAT_MDL, EMBEDDING_MDL, RERANK_MDL, ASR_MDL, IMAGE2TEXT_MDL
+    global CHAT_MDL, RERANK_MDL, ASR_MDL, IMAGE2TEXT_MDL
     global CHAT_CFG, RERANK_CFG, ASR_CFG, IMAGE2TEXT_CFG
 
     global API_KEY, PARSERS, HOST_IP, HOST_PORT, SECRET_KEY
@@ -135,7 +135,7 @@ def init_settings():
     )
 
     chat_entry = _parse_model_entry(LLM_DEFAULT_MODELS.get("chat_model", CHAT_MDL))
-    embedding_entry = _parse_model_entry(LLM_DEFAULT_MODELS.get("embedding_model", EMBEDDING_MDL))
+    embedding_entry = _parse_model_entry(LLM_DEFAULT_MODELS.get("embedding_model", globals.EMBEDDING_MDL))
     rerank_entry = _parse_model_entry(LLM_DEFAULT_MODELS.get("rerank_model", RERANK_MDL))
     asr_entry = _parse_model_entry(LLM_DEFAULT_MODELS.get("asr_model", ASR_MDL))
     image2text_entry = _parse_model_entry(LLM_DEFAULT_MODELS.get("image2text_model", IMAGE2TEXT_MDL))
@@ -147,7 +147,7 @@ def init_settings():
     IMAGE2TEXT_CFG = _resolve_per_model_config(image2text_entry, LLM_FACTORY, API_KEY, LLM_BASE_URL)
 
     CHAT_MDL = CHAT_CFG.get("model", "") or ""
-    EMBEDDING_MDL = os.getenv("TEI_MODEL", "BAAI/bge-small-en-v1.5") if "tei-" in os.getenv("COMPOSE_PROFILES", "") else ""
+    globals.EMBEDDING_MDL = os.getenv("TEI_MODEL", "BAAI/bge-small-en-v1.5") if "tei-" in os.getenv("COMPOSE_PROFILES", "") else ""
     RERANK_MDL = RERANK_CFG.get("model", "") or ""
     ASR_MDL = ASR_CFG.get("model", "") or ""
     IMAGE2TEXT_MDL = IMAGE2TEXT_CFG.get("model", "") or ""
@@ -169,23 +169,23 @@ def init_settings():
 
     OAUTH_CONFIG = get_base_config("oauth", {})
 
-    global DOC_ENGINE, docStoreConn, retriever, kg_retriever
-    DOC_ENGINE = os.environ.get("DOC_ENGINE", "elasticsearch")
-    # DOC_ENGINE = os.environ.get('DOC_ENGINE', "opensearch")
-    lower_case_doc_engine = DOC_ENGINE.lower()
+    global kg_retriever
+    globals.DOC_ENGINE = os.environ.get("DOC_ENGINE", "elasticsearch")
+    # globals.DOC_ENGINE = os.environ.get('DOC_ENGINE', "opensearch")
+    lower_case_doc_engine = globals.DOC_ENGINE.lower()
     if lower_case_doc_engine == "elasticsearch":
-        docStoreConn = rag.utils.es_conn.ESConnection()
+        globals.docStoreConn = rag.utils.es_conn.ESConnection()
     elif lower_case_doc_engine == "infinity":
-        docStoreConn = rag.utils.infinity_conn.InfinityConnection()
+        globals.docStoreConn = rag.utils.infinity_conn.InfinityConnection()
     elif lower_case_doc_engine == "opensearch":
-        docStoreConn = rag.utils.opensearch_conn.OSConnection()
+        globals.docStoreConn = rag.utils.opensearch_conn.OSConnection()
     else:
-        raise Exception(f"Not supported doc engine: {DOC_ENGINE}")
+        raise Exception(f"Not supported doc engine: {globals.DOC_ENGINE}")
 
-    retriever = search.Dealer(docStoreConn)
+    globals.retriever = search.Dealer(globals.docStoreConn)
     from graphrag import search as kg_search
 
-    kg_retriever = kg_search.KGSearch(docStoreConn)
+    kg_retriever = kg_search.KGSearch(globals.docStoreConn)
 
     if int(os.environ.get("SANDBOX_ENABLED", "0")):
         global SANDBOX_HOST
diff --git a/api/utils/health_utils.py b/api/utils/health_utils.py
index 3a97fc572..f86a0d728 100644
--- a/api/utils/health_utils.py
+++ b/api/utils/health_utils.py
@@ -24,6 +24,7 @@ from rag.utils.redis_conn import REDIS_CONN
 from rag.utils.storage_factory import STORAGE_IMPL
 from rag.utils.es_conn import ESConnection
 from rag.utils.infinity_conn import InfinityConnection
+from common import globals
 
 
 def _ok_nok(ok: bool) -> str:
@@ -52,7 +53,7 @@ def check_redis() -> tuple[bool, dict]:
 def check_doc_engine() -> tuple[bool, dict]:
     st = timer()
     try:
-        meta = settings.docStoreConn.health()
+        meta = globals.docStoreConn.health()
         # treat any successful call as ok
         return True, {"elapsed": f"{(timer() - st) * 1000.0:.1f}", **(meta or {})}
     except Exception as e:
diff --git a/common/globals.py b/common/globals.py
index 7e9879ef1..3f2859c74 100644
--- a/common/globals.py
+++ b/common/globals.py
@@ -14,4 +14,12 @@
 #  limitations under the License.
 #
 
-EMBEDDING_CFG = ""
\ No newline at end of file
+EMBEDDING_MDL = ""
+
+EMBEDDING_CFG = ""
+
+DOC_ENGINE = None
+
+docStoreConn = None
+
+retriever = None
\ No newline at end of file
diff --git a/graphrag/general/index.py b/graphrag/general/index.py
index 51f79f57f..9247df687 100644
--- a/graphrag/general/index.py
+++ b/graphrag/general/index.py
@@ -20,7 +20,6 @@ import os
 import networkx as nx
 import trio
 
-from api import settings
 from api.db.services.document_service import DocumentService
 from common.misc_utils import get_uuid
 from common.connection_utils import timeout
@@ -40,6 +39,7 @@ from graphrag.utils import (
 )
 from rag.nlp import rag_tokenizer, search
 from rag.utils.redis_conn import RedisDistributedLock
+from common import globals
 
 
 async def run_graphrag(
@@ -55,7 +55,7 @@ async def run_graphrag(
     start = trio.current_time()
     tenant_id, kb_id, doc_id = row["tenant_id"], str(row["kb_id"]), row["doc_id"]
     chunks = []
-    for d in settings.retriever.chunk_list(doc_id, tenant_id, [kb_id], fields=["content_with_weight", "doc_id"], sort_by_position=True):
+    for d in globals.retriever.chunk_list(doc_id, tenant_id, [kb_id], fields=["content_with_weight", "doc_id"], sort_by_position=True):
         chunks.append(d["content_with_weight"])
 
     with trio.fail_after(max(120, len(chunks) * 60 * 10) if enable_timeout_assertion else 10000000000):
@@ -170,7 +170,7 @@ async def run_graphrag_for_kb(
         chunks = []
         current_chunk = ""
 
-        for d in settings.retriever.chunk_list(
+        for d in globals.retriever.chunk_list(
             doc_id,
             tenant_id,
             [kb_id],
@@ -387,8 +387,8 @@ async def generate_subgraph(
         "removed_kwd": "N",
     }
     cid = chunk_id(chunk)
-    await trio.to_thread.run_sync(settings.docStoreConn.delete, {"knowledge_graph_kwd": "subgraph", "source_id": doc_id}, search.index_name(tenant_id), kb_id)
-    await trio.to_thread.run_sync(settings.docStoreConn.insert, [{"id": cid, **chunk}], search.index_name(tenant_id), kb_id)
+    await trio.to_thread.run_sync(globals.docStoreConn.delete, {"knowledge_graph_kwd": "subgraph", "source_id": doc_id}, search.index_name(tenant_id), kb_id)
+    await trio.to_thread.run_sync(globals.docStoreConn.insert, [{"id": cid, **chunk}], search.index_name(tenant_id), kb_id)
     now = trio.current_time()
     callback(msg=f"generated subgraph for doc {doc_id} in {now - start:.2f} seconds.")
     return subgraph
@@ -496,7 +496,7 @@ async def extract_community(
         chunks.append(chunk)
 
     await trio.to_thread.run_sync(
-        lambda: settings.docStoreConn.delete(
+        lambda: globals.docStoreConn.delete(
             {"knowledge_graph_kwd": "community_report", "kb_id": kb_id},
             search.index_name(tenant_id),
             kb_id,
@@ -504,7 +504,7 @@ async def extract_community(
     )
     es_bulk_size = 4
     for b in range(0, len(chunks), es_bulk_size):
-        doc_store_result = await trio.to_thread.run_sync(lambda: settings.docStoreConn.insert(chunks[b : b + es_bulk_size], search.index_name(tenant_id), kb_id))
+        doc_store_result = await trio.to_thread.run_sync(lambda: globals.docStoreConn.insert(chunks[b : b + es_bulk_size], search.index_name(tenant_id), kb_id))
         if doc_store_result:
             error_message = f"Insert chunk error: {doc_store_result}, please check log file and Elasticsearch/Infinity status!"
             raise Exception(error_message)
diff --git a/graphrag/general/smoke.py b/graphrag/general/smoke.py
index 7023df412..56b4863f9 100644
--- a/graphrag/general/smoke.py
+++ b/graphrag/general/smoke.py
@@ -28,6 +28,7 @@ from api.db.services.llm_service import LLMBundle
 from api.db.services.user_service import TenantService
 from graphrag.general.graph_extractor import GraphExtractor
 from graphrag.general.index import update_graph, with_resolution, with_community
+from common import globals
 
 settings.init_settings()
 
@@ -62,7 +63,7 @@ async def main():
 
     chunks = [
         d["content_with_weight"]
-        for d in settings.retriever.chunk_list(
+        for d in globals.retriever.chunk_list(
             args.doc_id,
             args.tenant_id,
             [kb_id],
diff --git a/graphrag/light/smoke.py b/graphrag/light/smoke.py
index ec83ab6cf..c83dc8a91 100644
--- a/graphrag/light/smoke.py
+++ b/graphrag/light/smoke.py
@@ -28,6 +28,7 @@ from api.db.services.llm_service import LLMBundle
 from api.db.services.user_service import TenantService
 from graphrag.general.index import update_graph
 from graphrag.light.graph_extractor import GraphExtractor
+from common import globals
 
 settings.init_settings()
 
@@ -63,7 +64,7 @@ async def main():
 
     chunks = [
         d["content_with_weight"]
-        for d in settings.retriever.chunk_list(
+        for d in globals.retriever.chunk_list(
             args.doc_id,
             args.tenant_id,
             [kb_id],
diff --git a/graphrag/search.py b/graphrag/search.py
index e54c70167..51ec23013 100644
--- a/graphrag/search.py
+++ b/graphrag/search.py
@@ -29,6 +29,7 @@ from rag.utils.doc_store_conn import OrderByExpr
 
 from rag.nlp.search import Dealer, index_name
 from common.float_utils import get_float
+from common import globals
 
 
 class KGSearch(Dealer):
@@ -334,6 +335,6 @@ if __name__ == "__main__":
     _, kb = KnowledgebaseService.get_by_id(kb_id)
     embed_bdl = LLMBundle(args.tenant_id, LLMType.EMBEDDING, kb.embd_id)
 
-    kg = KGSearch(settings.docStoreConn)
+    kg = KGSearch(globals.docStoreConn)
     print(kg.retrieval({"question": args.question, "kb_ids": [kb_id]},
                     search.index_name(kb.tenant_id), [kb_id], embed_bdl, llm_bdl))
diff --git a/graphrag/utils.py b/graphrag/utils.py
index b64a12265..c880eab9c 100644
--- a/graphrag/utils.py
+++ b/graphrag/utils.py
@@ -23,12 +23,12 @@ import trio
 import xxhash
 from networkx.readwrite import json_graph
 
-from api import settings
 from common.misc_utils import get_uuid
 from common.connection_utils import timeout
 from rag.nlp import rag_tokenizer, search
 from rag.utils.doc_store_conn import OrderByExpr
 from rag.utils.redis_conn import REDIS_CONN
+from common import globals
 
 GRAPH_FIELD_SEP = "<SEP>"
 
@@ -334,7 +334,7 @@ def get_relation(tenant_id, kb_id, from_ent_name, to_ent_name, size=1):
     ents = list(set(ents))
     conds = {"fields": ["content_with_weight"], "size": size, "from_entity_kwd": ents, "to_entity_kwd": ents, "knowledge_graph_kwd": ["relation"]}
     res = []
-    es_res = settings.retriever.search(conds, search.index_name(tenant_id), [kb_id] if isinstance(kb_id, str) else kb_id)
+    es_res = globals.retriever.search(conds, search.index_name(tenant_id), [kb_id] if isinstance(kb_id, str) else kb_id)
     for id in es_res.ids:
         try:
             if size == 1:
@@ -381,8 +381,8 @@ async def does_graph_contains(tenant_id, kb_id, doc_id):
         "knowledge_graph_kwd": ["graph"],
         "removed_kwd": "N",
     }
-    res = await trio.to_thread.run_sync(lambda: settings.docStoreConn.search(fields, [], condition, [], OrderByExpr(), 0, 1, search.index_name(tenant_id), [kb_id]))
-    fields2 = settings.docStoreConn.getFields(res, fields)
+    res = await trio.to_thread.run_sync(lambda: globals.docStoreConn.search(fields, [], condition, [], OrderByExpr(), 0, 1, search.index_name(tenant_id), [kb_id]))
+    fields2 = globals.docStoreConn.getFields(res, fields)
     graph_doc_ids = set()
     for chunk_id in fields2.keys():
         graph_doc_ids = set(fields2[chunk_id]["source_id"])
@@ -391,7 +391,7 @@ async def does_graph_contains(tenant_id, kb_id, doc_id):
 
 async def get_graph_doc_ids(tenant_id, kb_id) -> list[str]:
     conds = {"fields": ["source_id"], "removed_kwd": "N", "size": 1, "knowledge_graph_kwd": ["graph"]}
-    res = await trio.to_thread.run_sync(lambda: settings.retriever.search(conds, search.index_name(tenant_id), [kb_id]))
+    res = await trio.to_thread.run_sync(lambda: globals.retriever.search(conds, search.index_name(tenant_id), [kb_id]))
     doc_ids = []
     if res.total == 0:
         return doc_ids
@@ -402,7 +402,7 @@ async def get_graph_doc_ids(tenant_id, kb_id) -> list[str]:
 
 async def get_graph(tenant_id, kb_id, exclude_rebuild=None):
     conds = {"fields": ["content_with_weight", "removed_kwd", "source_id"], "size": 1, "knowledge_graph_kwd": ["graph"]}
-    res = await trio.to_thread.run_sync(settings.retriever.search, conds, search.index_name(tenant_id), [kb_id])
+    res = await trio.to_thread.run_sync(globals.retriever.search, conds, search.index_name(tenant_id), [kb_id])
     if not res.total == 0:
         for id in res.ids:
             try:
@@ -423,17 +423,17 @@ async def set_graph(tenant_id: str, kb_id: str, embd_mdl, graph: nx.Graph, chang
     global chat_limiter
     start = trio.current_time()
 
-    await trio.to_thread.run_sync(settings.docStoreConn.delete, {"knowledge_graph_kwd": ["graph", "subgraph"]}, search.index_name(tenant_id), kb_id)
+    await trio.to_thread.run_sync(globals.docStoreConn.delete, {"knowledge_graph_kwd": ["graph", "subgraph"]}, search.index_name(tenant_id), kb_id)
 
     if change.removed_nodes:
-        await trio.to_thread.run_sync(settings.docStoreConn.delete, {"knowledge_graph_kwd": ["entity"], "entity_kwd": sorted(change.removed_nodes)}, search.index_name(tenant_id), kb_id)
+        await trio.to_thread.run_sync(globals.docStoreConn.delete, {"knowledge_graph_kwd": ["entity"], "entity_kwd": sorted(change.removed_nodes)}, search.index_name(tenant_id), kb_id)
 
     if change.removed_edges:
 
         async def del_edges(from_node, to_node):
             async with chat_limiter:
                 await trio.to_thread.run_sync(
-                    settings.docStoreConn.delete, {"knowledge_graph_kwd": ["relation"], "from_entity_kwd": from_node, "to_entity_kwd": to_node}, search.index_name(tenant_id), kb_id
+                    globals.docStoreConn.delete, {"knowledge_graph_kwd": ["relation"], "from_entity_kwd": from_node, "to_entity_kwd": to_node}, search.index_name(tenant_id), kb_id
                 )
 
         async with trio.open_nursery() as nursery:
@@ -501,7 +501,7 @@ async def set_graph(tenant_id: str, kb_id: str, embd_mdl, graph: nx.Graph, chang
     es_bulk_size = 4
     for b in range(0, len(chunks), es_bulk_size):
         with trio.fail_after(3 if enable_timeout_assertion else 30000000):
-            doc_store_result = await trio.to_thread.run_sync(lambda: settings.docStoreConn.insert(chunks[b : b + es_bulk_size], search.index_name(tenant_id), kb_id))
+            doc_store_result = await trio.to_thread.run_sync(lambda: globals.docStoreConn.insert(chunks[b : b + es_bulk_size], search.index_name(tenant_id), kb_id))
         if b % 100 == es_bulk_size and callback:
             callback(msg=f"Insert chunks: {b}/{len(chunks)}")
         if doc_store_result:
@@ -555,7 +555,7 @@ def merge_tuples(list1, list2):
 
 
 async def get_entity_type2samples(idxnms, kb_ids: list):
-    es_res = await trio.to_thread.run_sync(lambda: settings.retriever.search({"knowledge_graph_kwd": "ty2ents", "kb_id": kb_ids, "size": 10000, "fields": ["content_with_weight"]}, idxnms, kb_ids))
+    es_res = await trio.to_thread.run_sync(lambda: globals.retriever.search({"knowledge_graph_kwd": "ty2ents", "kb_id": kb_ids, "size": 10000, "fields": ["content_with_weight"]}, idxnms, kb_ids))
 
     res = defaultdict(list)
     for id in es_res.ids:
@@ -589,10 +589,10 @@ async def rebuild_graph(tenant_id, kb_id, exclude_rebuild=None):
     bs = 256
     for i in range(0, 1024 * bs, bs):
         es_res = await trio.to_thread.run_sync(
-            lambda: settings.docStoreConn.search(flds, [], {"kb_id": kb_id, "knowledge_graph_kwd": ["subgraph"]}, [], OrderByExpr(), i, bs, search.index_name(tenant_id), [kb_id])
+            lambda: globals.docStoreConn.search(flds, [], {"kb_id": kb_id, "knowledge_graph_kwd": ["subgraph"]}, [], OrderByExpr(), i, bs, search.index_name(tenant_id), [kb_id])
         )
-        # tot = settings.docStoreConn.getTotal(es_res)
-        es_res = settings.docStoreConn.getFields(es_res, flds)
+        # tot = globals.docStoreConn.getTotal(es_res)
+        es_res = globals.docStoreConn.getFields(es_res, flds)
 
         if len(es_res) == 0:
             break
diff --git a/rag/app/tag.py b/rag/app/tag.py
index e1a675652..dbe1aac55 100644
--- a/rag/app/tag.py
+++ b/rag/app/tag.py
@@ -21,6 +21,7 @@ from copy import deepcopy
 from deepdoc.parser.utils import get_text
 from rag.app.qa import Excel
 from rag.nlp import rag_tokenizer
+from common import globals
 
 
 def beAdoc(d, q, a, eng, row_num=-1):
@@ -124,7 +125,6 @@ def chunk(filename, binary=None, lang="Chinese", callback=None, **kwargs):
 def label_question(question, kbs):
     from api.db.services.knowledgebase_service import KnowledgebaseService
     from graphrag.utils import get_tags_from_cache, set_tags_to_cache
-    from api import settings
     tags = None
     tag_kb_ids = []
     for kb in kbs:
@@ -133,14 +133,14 @@ def label_question(question, kbs):
     if tag_kb_ids:
         all_tags = get_tags_from_cache(tag_kb_ids)
         if not all_tags:
-            all_tags = settings.retriever.all_tags_in_portion(kb.tenant_id, tag_kb_ids)
+            all_tags = globals.retriever.all_tags_in_portion(kb.tenant_id, tag_kb_ids)
             set_tags_to_cache(tags=all_tags, kb_ids=tag_kb_ids)
         else:
             all_tags = json.loads(all_tags)
         tag_kbs = KnowledgebaseService.get_by_ids(tag_kb_ids)
         if not tag_kbs:
             return tags
-        tags = settings.retriever.tag_query(question,
+        tags = globals.retriever.tag_query(question,
                                               list(set([kb.tenant_id for kb in tag_kbs])),
                                               tag_kb_ids,
                                               all_tags,
diff --git a/rag/benchmark.py b/rag/benchmark.py
index 173fcd140..031dbfb64 100644
--- a/rag/benchmark.py
+++ b/rag/benchmark.py
@@ -20,10 +20,10 @@ import time
 import argparse
 from collections import defaultdict
 
+from common import globals
 from common.constants import LLMType
 from api.db.services.llm_service import LLMBundle
 from api.db.services.knowledgebase_service import KnowledgebaseService
-from api import settings
 from common.misc_utils import get_uuid
 from rag.nlp import tokenize, search
 from ranx import evaluate
@@ -52,7 +52,7 @@ class Benchmark:
         run = defaultdict(dict)
         query_list = list(qrels.keys())
         for query in query_list:
-            ranks = settings.retriever.retrieval(query, self.embd_mdl, self.tenant_id, [self.kb.id], 1, 30,
+            ranks = globals.retriever.retrieval(query, self.embd_mdl, self.tenant_id, [self.kb.id], 1, 30,
                                             0.0, self.vector_similarity_weight)
             if len(ranks["chunks"]) == 0:
                 print(f"deleted query: {query}")
@@ -77,9 +77,9 @@ class Benchmark:
     def init_index(self, vector_size: int):
         if self.initialized_index:
             return
-        if settings.docStoreConn.indexExist(self.index_name, self.kb_id):
-            settings.docStoreConn.deleteIdx(self.index_name, self.kb_id)
-        settings.docStoreConn.createIdx(self.index_name, self.kb_id, vector_size)
+        if globals.docStoreConn.indexExist(self.index_name, self.kb_id):
+            globals.docStoreConn.deleteIdx(self.index_name, self.kb_id)
+        globals.docStoreConn.createIdx(self.index_name, self.kb_id, vector_size)
         self.initialized_index = True
 
     def ms_marco_index(self, file_path, index_name):
@@ -114,13 +114,13 @@ class Benchmark:
                     docs_count += len(docs)
                     docs, vector_size = self.embedding(docs)
                     self.init_index(vector_size)
-                    settings.docStoreConn.insert(docs, self.index_name, self.kb_id)
+                    globals.docStoreConn.insert(docs, self.index_name, self.kb_id)
                     docs = []
 
         if docs:
             docs, vector_size = self.embedding(docs)
             self.init_index(vector_size)
-            settings.docStoreConn.insert(docs, self.index_name, self.kb_id)
+            globals.docStoreConn.insert(docs, self.index_name, self.kb_id)
         return qrels, texts
 
     def trivia_qa_index(self, file_path, index_name):
@@ -155,12 +155,12 @@ class Benchmark:
                     docs_count += len(docs)
                     docs, vector_size = self.embedding(docs)
                     self.init_index(vector_size)
-                    settings.docStoreConn.insert(docs,self.index_name)
+                    globals.docStoreConn.insert(docs,self.index_name)
                     docs = []
 
         docs, vector_size = self.embedding(docs)
         self.init_index(vector_size)
-        settings.docStoreConn.insert(docs, self.index_name)
+        globals.docStoreConn.insert(docs, self.index_name)
         return qrels, texts
 
     def miracl_index(self, file_path, corpus_path, index_name):
@@ -210,12 +210,12 @@ class Benchmark:
                     docs_count += len(docs)
                     docs, vector_size = self.embedding(docs)
                     self.init_index(vector_size)
-                    settings.docStoreConn.insert(docs, self.index_name)
+                    globals.docStoreConn.insert(docs, self.index_name)
                     docs = []
 
         docs, vector_size = self.embedding(docs)
         self.init_index(vector_size)
-        settings.docStoreConn.insert(docs, self.index_name)
+        globals.docStoreConn.insert(docs, self.index_name)
         return qrels, texts
 
     def save_results(self, qrels, run, texts, dataset, file_path):
diff --git a/rag/flow/hierarchical_merger/hierarchical_merger.py b/rag/flow/hierarchical_merger/hierarchical_merger.py
index ded3cbead..2cc794f4b 100644
--- a/rag/flow/hierarchical_merger/hierarchical_merger.py
+++ b/rag/flow/hierarchical_merger/hierarchical_merger.py
@@ -21,7 +21,7 @@ from functools import partial
 import trio
 
 from common.misc_utils import get_uuid
-from common.base64_image import id2image, image2id
+from rag.utils.base64_image import id2image, image2id
 from deepdoc.parser.pdf_parser import RAGFlowPdfParser
 from rag.flow.base import ProcessBase, ProcessParamBase
 from rag.flow.hierarchical_merger.schema import HierarchicalMergerFromUpstream
diff --git a/rag/flow/parser/parser.py b/rag/flow/parser/parser.py
index 28aaf600c..f147d738a 100644
--- a/rag/flow/parser/parser.py
+++ b/rag/flow/parser/parser.py
@@ -27,7 +27,7 @@ from api.db.services.file2document_service import File2DocumentService
 from api.db.services.file_service import FileService
 from api.db.services.llm_service import LLMBundle
 from common.misc_utils import get_uuid
-from common.base64_image import image2id
+from rag.utils.base64_image import image2id
 from deepdoc.parser import ExcelParser
 from deepdoc.parser.mineru_parser import MinerUParser
 from deepdoc.parser.pdf_parser import PlainParser, RAGFlowPdfParser, VisionParser
diff --git a/rag/flow/splitter/splitter.py b/rag/flow/splitter/splitter.py
index a62c44580..4a944d050 100644
--- a/rag/flow/splitter/splitter.py
+++ b/rag/flow/splitter/splitter.py
@@ -18,7 +18,7 @@ from functools import partial
 import trio
 
 from common.misc_utils import get_uuid
-from common.base64_image import id2image, image2id
+from rag.utils.base64_image import id2image, image2id
 from deepdoc.parser.pdf_parser import RAGFlowPdfParser
 from rag.flow.base import ProcessBase, ProcessParamBase
 from rag.flow.splitter.schema import SplitterFromUpstream
diff --git a/rag/llm/embedding_model.py b/rag/llm/embedding_model.py
index a4f5edf0d..fa5a0d21a 100644
--- a/rag/llm/embedding_model.py
+++ b/rag/llm/embedding_model.py
@@ -30,7 +30,6 @@ from zhipuai import ZhipuAI
 from common.log_utils import log_exception
 from common.token_utils import num_tokens_from_string, truncate
 from common import globals
-from api import settings
 import logging
 
 
@@ -74,9 +73,9 @@ class BuiltinEmbed(Base):
         embedding_cfg = globals.EMBEDDING_CFG
         if not BuiltinEmbed._model and "tei-" in os.getenv("COMPOSE_PROFILES", ""):
             with BuiltinEmbed._model_lock:
-                BuiltinEmbed._model_name = settings.EMBEDDING_MDL
-                BuiltinEmbed._max_tokens = BuiltinEmbed.MAX_TOKENS.get(settings.EMBEDDING_MDL, 500)
-                BuiltinEmbed._model = HuggingFaceEmbed(embedding_cfg["api_key"], settings.EMBEDDING_MDL, base_url=embedding_cfg["base_url"])
+                BuiltinEmbed._model_name = globals.EMBEDDING_MDL
+                BuiltinEmbed._max_tokens = BuiltinEmbed.MAX_TOKENS.get(globals.EMBEDDING_MDL, 500)
+                BuiltinEmbed._model = HuggingFaceEmbed(embedding_cfg["api_key"], globals.EMBEDDING_MDL, base_url=embedding_cfg["base_url"])
         self._model = BuiltinEmbed._model
         self._model_name = BuiltinEmbed._model_name
         self._max_tokens = BuiltinEmbed._max_tokens
diff --git a/rag/settings.py b/rag/settings.py
index 57df2ee14..dbfc36880 100644
--- a/rag/settings.py
+++ b/rag/settings.py
@@ -18,13 +18,14 @@ import logging
 from common.config_utils import get_base_config, decrypt_database_config
 from common.file_utils import get_project_base_directory
 from common.misc_utils import pip_install_torch
+from common import globals
 
 # Server
 RAG_CONF_PATH = os.path.join(get_project_base_directory(), "conf")
 
 # Get storage type and document engine from system environment variables
 STORAGE_IMPL_TYPE = os.getenv('STORAGE_IMPL', 'MINIO')
-DOC_ENGINE = os.getenv('DOC_ENGINE', 'elasticsearch')
+globals.DOC_ENGINE = os.getenv('DOC_ENGINE', 'elasticsearch')
 
 ES = {}
 INFINITY = {}
@@ -35,11 +36,11 @@ OSS = {}
 OS = {}
 
 # Initialize the selected configuration data based on environment variables to solve the problem of initialization errors due to lack of configuration
-if DOC_ENGINE == 'elasticsearch':
+if globals.DOC_ENGINE == 'elasticsearch':
     ES = get_base_config("es", {})
-elif DOC_ENGINE == 'opensearch':
+elif globals.DOC_ENGINE == 'opensearch':
     OS = get_base_config("os", {})
-elif DOC_ENGINE == 'infinity':
+elif globals.DOC_ENGINE == 'infinity':
     INFINITY = get_base_config("infinity", {"uri": "infinity:23817"})
 
 if STORAGE_IMPL_TYPE in ['AZURE_SPN', 'AZURE_SAS']:
diff --git a/rag/svr/task_executor.py b/rag/svr/task_executor.py
index 834ff59ee..e35306d4e 100644
--- a/rag/svr/task_executor.py
+++ b/rag/svr/task_executor.py
@@ -27,7 +27,7 @@ from api.db.services.canvas_service import UserCanvasService
 from api.db.services.knowledgebase_service import KnowledgebaseService
 from api.db.services.pipeline_operation_log_service import PipelineOperationLogService
 from common.connection_utils import timeout
-from common.base64_image import image2id
+from rag.utils.base64_image import image2id
 from common.log_utils import init_root_logger
 from common.config_utils import show_configs
 from graphrag.general.index import run_graphrag_for_kb
@@ -68,6 +68,7 @@ from rag.utils.redis_conn import REDIS_CONN, RedisDistributedLock
 from rag.utils.storage_factory import STORAGE_IMPL
 from graphrag.utils import chat_limiter
 from common.signal_utils import start_tracemalloc_and_snapshot, stop_tracemalloc
+from common import globals
 
 BATCH_SIZE = 64
 
@@ -349,7 +350,7 @@ async def build_chunks(task, progress_callback):
         examples = []
         all_tags = get_tags_from_cache(kb_ids)
         if not all_tags:
-            all_tags = settings.retriever.all_tags_in_portion(tenant_id, kb_ids, S)
+            all_tags = globals.retriever.all_tags_in_portion(tenant_id, kb_ids, S)
             set_tags_to_cache(kb_ids, all_tags)
         else:
             all_tags = json.loads(all_tags)
@@ -362,7 +363,7 @@ async def build_chunks(task, progress_callback):
             if task_canceled:
                 progress_callback(-1, msg="Task has been canceled.")
                 return
-            if settings.retriever.tag_content(tenant_id, kb_ids, d, all_tags, topn_tags=topn_tags, S=S) and len(d[TAG_FLD]) > 0:
+            if globals.retriever.tag_content(tenant_id, kb_ids, d, all_tags, topn_tags=topn_tags, S=S) and len(d[TAG_FLD]) > 0:
                 examples.append({"content": d["content_with_weight"], TAG_FLD: d[TAG_FLD]})
             else:
                 docs_to_tag.append(d)
@@ -423,7 +424,7 @@ def build_TOC(task, docs, progress_callback):
 
 def init_kb(row, vector_size: int):
     idxnm = search.index_name(row["tenant_id"])
-    return settings.docStoreConn.createIdx(idxnm, row.get("kb_id", ""), vector_size)
+    return globals.docStoreConn.createIdx(idxnm, row.get("kb_id", ""), vector_size)
 
 
 async def embedding(docs, mdl, parser_config=None, callback=None):
@@ -647,7 +648,7 @@ async def run_raptor_for_kb(row, kb_parser_config, chat_mdl, embd_mdl, vector_si
     chunks = []
     vctr_nm = "q_%d_vec"%vector_size
     for doc_id in doc_ids:
-        for d in settings.retriever.chunk_list(doc_id, row["tenant_id"], [str(row["kb_id"])],
+        for d in globals.retriever.chunk_list(doc_id, row["tenant_id"], [str(row["kb_id"])],
                                                  fields=["content_with_weight", vctr_nm],
                                                  sort_by_position=True):
             chunks.append((d["content_with_weight"], np.array(d[vctr_nm])))
@@ -698,7 +699,7 @@ async def delete_image(kb_id, chunk_id):
 
 async def insert_es(task_id, task_tenant_id, task_dataset_id, chunks, progress_callback):
     for b in range(0, len(chunks), DOC_BULK_SIZE):
-        doc_store_result = await trio.to_thread.run_sync(lambda: settings.docStoreConn.insert(chunks[b:b + DOC_BULK_SIZE], search.index_name(task_tenant_id), task_dataset_id))
+        doc_store_result = await trio.to_thread.run_sync(lambda: globals.docStoreConn.insert(chunks[b:b + DOC_BULK_SIZE], search.index_name(task_tenant_id), task_dataset_id))
         task_canceled = has_canceled(task_id)
         if task_canceled:
             progress_callback(-1, msg="Task has been canceled.")
@@ -715,7 +716,7 @@ async def insert_es(task_id, task_tenant_id, task_dataset_id, chunks, progress_c
             TaskService.update_chunk_ids(task_id, chunk_ids_str)
         except DoesNotExist:
             logging.warning(f"do_handle_task update_chunk_ids failed since task {task_id} is unknown.")
-            doc_store_result = await trio.to_thread.run_sync(lambda: settings.docStoreConn.delete({"id": chunk_ids}, search.index_name(task_tenant_id), task_dataset_id))
+            doc_store_result = await trio.to_thread.run_sync(lambda: globals.docStoreConn.delete({"id": chunk_ids}, search.index_name(task_tenant_id), task_dataset_id))
             async with trio.open_nursery() as nursery:
                 for chunk_id in chunk_ids:
                     nursery.start_soon(delete_image, task_dataset_id, chunk_id)
@@ -751,7 +752,7 @@ async def do_handle_task(task):
     progress_callback = partial(set_progress, task_id, task_from_page, task_to_page)
 
     # FIXME: workaround, Infinity doesn't support table parsing method, this check is to notify user
-    lower_case_doc_engine = settings.DOC_ENGINE.lower()
+    lower_case_doc_engine = globals.DOC_ENGINE.lower()
     if lower_case_doc_engine == 'infinity' and task['parser_id'].lower() == 'table':
         error_message = "Table parsing method is not supported by Infinity, please use other parsing methods or use Elasticsearch as the document engine."
         progress_callback(-1, msg=error_message)
diff --git a/common/base64_image.py b/rag/utils/base64_image.py
similarity index 100%
rename from common/base64_image.py
rename to rag/utils/base64_image.py