Fix: Unified terminology to "Pipeline" and optimized related component logic. #9869 (#10394)

### What problem does this PR solve?

Fix: Unified terminology to "Pipeline" and optimized related component
logic. #9869

- Added logic to clear pipeline_id when parseType changes in the chunk
method dialog.
- Fixed an issue in the Tooltip form component that prevented clicks
from triggering saves.
### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)

.github/workflows/release.yml

@@ -25,7 +25,7 @@ jobs:
       - name: Check out code
         uses: actions/checkout@v4
         with:
-          token: ${{ secrets.GITHUB_TOKEN }}  # Use the secret as an environment variable
+          token: ${{ secrets.MY_GITHUB_TOKEN }}  # Use the secret as an environment variable
           fetch-depth: 0
           fetch-tags: true
 
@@ -69,7 +69,7 @@ jobs:
         # https://github.com/actions/upload-release-asset has been replaced by https://github.com/softprops/action-gh-release
         uses: softprops/action-gh-release@v2
         with:
-          token: ${{ secrets.GITHUB_TOKEN }}  # Use the secret as an environment variable
+          token: ${{ secrets.MY_GITHUB_TOKEN }}  # Use the secret as an environment variable
           prerelease: ${{ env.PRERELEASE }}
           tag_name: ${{ env.RELEASE_TAG }}
           # The body field does not support environment variable substitution directly.

.github/workflows/tests.yml

@@ -34,10 +34,12 @@ jobs:
       # https://github.com/hmarr/debug-action
       #- uses: hmarr/debug-action@v2
 
-      - name: Ensure workspace ownership
+      - name: Show who triggered this workflow
         run: |
           echo "Workflow triggered by ${{ github.event_name }}"
-          echo "chown -R $USER $GITHUB_WORKSPACE" && sudo chown -R $USER $GITHUB_WORKSPACE
+
+      - name: Ensure workspace ownership
+        run: echo "chown -R $USER $GITHUB_WORKSPACE" && sudo chown -R $USER $GITHUB_WORKSPACE
 
       # https://github.com/actions/checkout/issues/1781
       - name: Check out code
@@ -46,44 +48,6 @@ jobs:
           fetch-depth: 0
           fetch-tags: true
 
-      - name: Check workflow duplication
-        if: ${{ !cancelled() && !failure() && (github.event_name != 'pull_request' || contains(github.event.pull_request.labels.*.name, 'ci')) }}
-        run: |
-          if [[ ${{ github.event_name }} != 'pull_request' ]]; then
-            HEAD=$(git rev-parse HEAD)
-            # Find a PR that introduced a given commit
-            gh auth login --with-token <<< "${{ secrets.GITHUB_TOKEN }}"
-            PR_NUMBER=$(gh pr list --search ${HEAD} --state merged --json number --jq .[0].number)
-            echo "HEAD=${HEAD}"
-            echo "PR_NUMBER=${PR_NUMBER}"
-            if [[ -n ${PR_NUMBER} ]]; then
-              PR_SHA_FP=${RUNNER_WORKSPACE_PREFIX}/artifacts/${GITHUB_REPOSITORY}/PR_${PR_NUMBER}
-              if [[ -f ${PR_SHA_FP} ]]; then
-                read -r PR_SHA PR_RUN_ID < "${PR_SHA_FP}"
-                # Calculate the hash of the current workspace content
-                HEAD_SHA=$(git rev-parse HEAD^{tree})
-                if [[ ${HEAD_SHA} == ${PR_SHA} ]]; then
-                  echo "Cancel myself since the workspace content hash is the same with PR #${PR_NUMBER} merged. See ${GITHUB_SERVER_URL}/${GITHUB_REPOSITORY}/actions/runs/${PR_RUN_ID} for details."
-                  gh run cancel ${GITHUB_RUN_ID}
-                  while true; do
-                    status=$(gh run view ${GITHUB_RUN_ID} --json status -q .status)
-                    [ "$status" = "completed" ] && break
-                    sleep 5
-                  done
-                  exit 1
-                fi
-              fi
-            fi
-          else
-            PR_NUMBER=${{ github.event.pull_request.number }}
-            PR_SHA_FP=${RUNNER_WORKSPACE_PREFIX}/artifacts/${GITHUB_REPOSITORY}/PR_${PR_NUMBER}
-            # Calculate the hash of the current workspace content
-            PR_SHA=$(git rev-parse HEAD^{tree})
-            echo "PR #${PR_NUMBER} workspace content hash: ${PR_SHA}"
-            mkdir -p ${RUNNER_WORKSPACE_PREFIX}/artifacts/${GITHUB_REPOSITORY}
-            echo "${PR_SHA} ${GITHUB_RUN_ID}" > ${PR_SHA_FP}
-          fi
-
       # https://github.com/astral-sh/ruff-action
       - name: Static check with Ruff
         uses: astral-sh/ruff-action@v3
@@ -95,11 +59,11 @@ jobs:
         run: |
           RUNNER_WORKSPACE_PREFIX=${RUNNER_WORKSPACE_PREFIX:-$HOME}
           sudo docker pull ubuntu:22.04
-          sudo DOCKER_BUILDKIT=1 docker build --build-arg LIGHTEN=1 --build-arg NEED_MIRROR=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim .
+          sudo docker build --progress=plain --build-arg LIGHTEN=1 --build-arg NEED_MIRROR=1 -f Dockerfile -t infiniflow/ragflow:nightly-slim .
 
       - name: Build ragflow:nightly
         run: |
-          sudo DOCKER_BUILDKIT=1 docker build --build-arg NEED_MIRROR=1 -f Dockerfile -t infiniflow/ragflow:nightly .
+          sudo docker build --progress=plain --build-arg NEED_MIRROR=1 -f Dockerfile -t infiniflow/ragflow:nightly .
 
       - name: Start ragflow:nightly-slim
         run: |

README.md

@@ -341,13 +341,11 @@ docker build --platform linux/amd64 -f Dockerfile -t infiniflow/ragflow:nightly
 5. If your operating system does not have jemalloc, please install it as follows:
 
    ```bash
-   # Ubuntu
+   # ubuntu
    sudo apt-get install libjemalloc-dev
-   # CentOS
+   # centos
    sudo yum install jemalloc
-   # OpenSUSE
+   # mac
-   sudo zypper install jemalloc
-   # macOS
    sudo brew install jemalloc
    ```
 

admin/admin_client.py

@@ -1,19 +1,3 @@
-#
-#  Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-
 import argparse
 import base64
 
@@ -406,22 +390,6 @@ class AdminCLI:
         service_id: int = command['number']
         print(f"Showing service: {service_id}")
 
-        url = f'http://{self.host}:{self.port}/api/v1/admin/services/{service_id}'
-        response = requests.get(url, auth=HTTPBasicAuth(self.admin_account, self.admin_password))
-        res_json = response.json()
-        if response.status_code == 200:
-            res_data = res_json['data']
-            if res_data['alive']:
-                print(f"Service {res_data['service_name']} is alive. Detail:")
-                if isinstance(res_data['message'], str):
-                    print(res_data['message'])
-                else:
-                    self._print_table_simple(res_data['message'])
-            else:
-                print(f"Service {res_data['service_name']} is down. Detail: {res_data['message']}")
-        else:
-            print(f"Fail to show service, code: {res_json['code']}, message: {res_json['message']}")
-
     def _handle_restart_service(self, command):
         service_id: int = command['number']
         print(f"Restart service {service_id}")

admin/admin_server.py

@@ -1,18 +1,3 @@
-#
-#  Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
 
 import os
 import signal

admin/auth.py

@@ -1,26 +1,9 @@
-#
-#  Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-
-
 import logging
 import uuid
 from functools import wraps
 from flask import request, jsonify
 
-from api.common.exceptions import AdminException
+from exceptions import AdminException
 from api.db.init_data import encode_to_base64
 from api.db.services import UserService
 

admin/config.py

@@ -1,20 +1,3 @@
-#
-#  Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-
-
 import logging
 import threading
 from enum import Enum
@@ -49,11 +32,9 @@ class BaseConfig(BaseModel):
     host: str
     port: int
     service_type: str
-    detail_func_name: str
 
     def to_dict(self) -> dict[str, Any]:
-        return {'id': self.id, 'name': self.name, 'host': self.host, 'port': self.port,
+        return {'id': self.id, 'name': self.name, 'host': self.host, 'port': self.port, 'service_type': self.service_type}
-                'service_type': self.service_type}
 
 
 class MetaConfig(BaseConfig):
@@ -228,8 +209,7 @@ def load_configurations(config_path: str) -> list[BaseConfig]:
                 name: str = f'ragflow_{ragflow_count}'
                 host: str = v['host']
                 http_port: int = v['http_port']
-                config = RAGFlowServerConfig(id=id_count, name=name, host=host, port=http_port,
+                config = RAGFlowServerConfig(id=id_count, name=name, host=host, port=http_port, service_type="ragflow_server")
-                                             service_type="ragflow_server", detail_func_name="check_ragflow_server_alive")
                 configurations.append(config)
                 id_count += 1
             case "es":
@@ -242,8 +222,7 @@ def load_configurations(config_path: str) -> list[BaseConfig]:
                 password: str = v.get('password')
                 config = ElasticsearchConfig(id=id_count, name=name, host=host, port=port, service_type="retrieval",
                                              retrieval_type="elasticsearch",
-                                             username=username, password=password,
+                                             username=username, password=password)
-                                             detail_func_name="get_es_cluster_stats")
                 configurations.append(config)
                 id_count += 1
 
@@ -255,7 +234,7 @@ def load_configurations(config_path: str) -> list[BaseConfig]:
                 port = int(parts[1])
                 database: str = v.get('db_name', 'default_db')
                 config = InfinityConfig(id=id_count, name=name, host=host, port=port, service_type="retrieval", retrieval_type="infinity",
-                                        db_name=database, detail_func_name="get_infinity_status")
+                                        db_name=database)
                 configurations.append(config)
                 id_count += 1
             case "minio":
@@ -267,7 +246,7 @@ def load_configurations(config_path: str) -> list[BaseConfig]:
                 user = v.get('user')
                 password = v.get('password')
                 config = MinioConfig(id=id_count, name=name, host=host, port=port, user=user, password=password, service_type="file_store",
-                                     store_type="minio", detail_func_name="check_minio_alive")
+                                     store_type="minio")
                 configurations.append(config)
                 id_count += 1
             case "redis":
@@ -279,7 +258,7 @@ def load_configurations(config_path: str) -> list[BaseConfig]:
                 password = v.get('password')
                 db: int = v.get('db')
                 config = RedisConfig(id=id_count, name=name, host=host, port=port, password=password, database=db,
-                                     service_type="message_queue", mq_type="redis", detail_func_name="get_redis_info")
+                                     service_type="message_queue", mq_type="redis")
                 configurations.append(config)
                 id_count += 1
             case "mysql":
@@ -289,7 +268,7 @@ def load_configurations(config_path: str) -> list[BaseConfig]:
                 username = v.get('user')
                 password = v.get('password')
                 config = MySQLConfig(id=id_count, name=name, host=host, port=port, username=username, password=password,
-                                     service_type="meta_data", meta_type="mysql", detail_func_name="get_mysql_status")
+                                     service_type="meta_data", meta_type="mysql")
                 configurations.append(config)
                 id_count += 1
             case "admin":

admin/models.py

@@ -1,15 +0,0 @@
-#
-#  Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#

admin/responses.py

@@ -1,31 +1,12 @@
-#
-#  Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-
-
 from flask import jsonify
 
-
+def success_response(data=None, message="Success", code = 0):
-def success_response(data=None, message="Success", code=0):
     return jsonify({
         "code": code,
         "message": message,
         "data": data
     }), 200
 
-
 def error_response(message="Error", code=-1, data=None):
     return jsonify({
         "code": code,

admin/routes.py

@@ -1,26 +1,9 @@
-#
-#  Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-
-
 from flask import Blueprint, request
 
 from auth import login_verify
 from responses import success_response, error_response
 from services import UserMgr, ServiceMgr, UserServiceMgr
-from api.common.exceptions import AdminException
+from exceptions import AdminException
 
 admin_bp = Blueprint('admin', __name__, url_prefix='/api/v1/admin')
 
@@ -119,7 +102,6 @@ def alter_user_activate_status(username):
     except Exception as e:
         return error_response(str(e), 500)
 
-
 @admin_bp.route('/users/<username>', methods=['GET'])
 @login_verify
 def get_user_details(username):
@@ -132,7 +114,6 @@ def get_user_details(username):
     except Exception as e:
         return error_response(str(e), 500)
 
-
 @admin_bp.route('/users/<username>/datasets', methods=['GET'])
 @login_verify
 def get_user_datasets(username):

admin/services.py

@@ -1,20 +1,3 @@
-#
-#  Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-
-
 import re
 from werkzeug.security import check_password_hash
 from api.db import ActiveEnum
@@ -24,20 +7,16 @@ from api.db.services.canvas_service import UserCanvasService
 from api.db.services.user_service import TenantService
 from api.db.services.knowledgebase_service import KnowledgebaseService
 from api.utils.crypt import decrypt
-from api.utils import health_utils
+from exceptions import AdminException, UserAlreadyExistsError, UserNotFoundError
-
-from api.common.exceptions import AdminException, UserAlreadyExistsError, UserNotFoundError
 from config import SERVICE_CONFIGS
 
-
 class UserMgr:
     @staticmethod
     def get_all_users():
         users = UserService.get_all_users()
         result = []
         for user in users:
-            result.append({'email': user.email, 'nickname': user.nickname, 'create_date': user.create_date,
+            result.append({'email': user.email, 'nickname': user.nickname, 'create_date': user.create_date, 'is_active': user.is_active})
-                           'is_active': user.is_active})
         return result
 
     @staticmethod
@@ -131,7 +110,6 @@ class UserMgr:
         UserService.update_user(usr.id, {"is_active": target_status})
         return f"Turn {_activate_status} user activate status successfully!"
 
-
 class UserServiceMgr:
 
     @staticmethod
@@ -170,7 +148,6 @@ class UserServiceMgr:
             'canvas_category': r['canvas_category']
         } for r in res]
 
-
 class ServiceMgr:
 
     @staticmethod
@@ -187,22 +164,7 @@ class ServiceMgr:
 
     @staticmethod
     def get_service_details(service_id: int):
-        service_id = int(service_id)
+        raise AdminException("get_service_details: not implemented")
-        configs = SERVICE_CONFIGS.configs
-        service_config_mapping = {
-            c.id: {
-                'name': c.name,
-                'detail_func_name': c.detail_func_name
-            } for c in configs
-        }
-        service_info = service_config_mapping.get(service_id, {})
-        if not service_info:
-            raise AdminException(f"Invalid service_id: {service_id}")
-
-        detail_func = getattr(health_utils, service_info.get('detail_func_name'))
-        res = detail_func()
-        res.update({'service_name': service_info.get('name')})
-        return res
 
     @staticmethod
     def shutdown_service(service_id: int):

agent/canvas.py

@@ -203,6 +203,7 @@ class Canvas(Graph):
             self.history = []
             self.retrieval = []
             self.memory = []
+
         for k in self.globals.keys():
             if isinstance(self.globals[k], str):
                 self.globals[k] = ""
@@ -291,6 +292,7 @@ class Canvas(Graph):
                     "thoughts": self.get_component_thoughts(self.path[i])
                 })
             _run_batch(idx, to)
+
             # post processing of components invocation
             for i in range(idx, to):
                 cpn = self.get_component(self.path[i])
@@ -391,6 +393,7 @@ class Canvas(Graph):
                 self.path = path
                 yield decorate("user_inputs", {"inputs": another_inputs, "tips": tips})
                 return
+
         self.path = self.path[:idx]
         if not self.error:
             yield decorate("workflow_finished",

agent/component/agent_with_tools.py

@@ -346,11 +346,3 @@ Respond immediately with your final comprehensive answer.
 
         return "Error occurred."
 
-    def reset(self, temp=False):
-        """
-        Reset all tools if they have a reset method. This avoids errors for tools like MCPToolCallSession.
-        """
-        for k, cpn in self.tools.items():
-            if hasattr(cpn, "reset") and callable(cpn.reset):
-                cpn.reset()
-

agent/component/invoke.py

@@ -19,12 +19,11 @@ import os
 import re
 import time
 from abc import ABC
-
 import requests
 
-from agent.component.base import ComponentBase, ComponentParamBase
 from api.utils.api_utils import timeout
 from deepdoc.parser import HtmlParser
+from agent.component.base import ComponentBase, ComponentParamBase
 
 
 class InvokeParam(ComponentParamBase):
@@ -44,11 +43,11 @@ class InvokeParam(ComponentParamBase):
         self.datatype = "json"  # New parameter to determine data posting type
 
     def check(self):
-        self.check_valid_value(self.method.lower(), "Type of content from the crawler", ["get", "post", "put"])
+        self.check_valid_value(self.method.lower(), "Type of content from the crawler", ['get', 'post', 'put'])
         self.check_empty(self.url, "End point URL")
         self.check_positive_integer(self.timeout, "Timeout time in second")
         self.check_boolean(self.clean_html, "Clean HTML")
-        self.check_valid_value(self.datatype.lower(), "Data post type", ["json", "formdata"])  # Check for valid datapost value
+        self.check_valid_value(self.datatype.lower(), "Data post type", ['json', 'formdata'])  # Check for valid datapost value
 
 
 class Invoke(ComponentBase, ABC):
@@ -64,18 +63,6 @@ class Invoke(ComponentBase, ABC):
                 args[para["key"]] = self._canvas.get_variable_value(para["ref"])
 
         url = self._param.url.strip()
-
-        def replace_variable(match):
-            var_name = match.group(1)
-            try:
-                value = self._canvas.get_variable_value(var_name)
-                return str(value or "")
-            except Exception:
-                return ""
-
-        # {base_url} or {component_id@variable_name}
-        url = re.sub(r"\{([a-zA-Z_][a-zA-Z0-9_.@-]*)\}", replace_variable, url)
-
         if url.find("http") != 0:
             url = "http://" + url
 
@@ -88,32 +75,52 @@ class Invoke(ComponentBase, ABC):
             proxies = {"http": self._param.proxy, "https": self._param.proxy}
 
         last_e = ""
-        for _ in range(self._param.max_retries + 1):
+        for _ in range(self._param.max_retries+1):
             try:
-                if method == "get":
+                if method == 'get':
-                    response = requests.get(url=url, params=args, headers=headers, proxies=proxies, timeout=self._param.timeout)
+                    response = requests.get(url=url,
+                                            params=args,
+                                            headers=headers,
+                                            proxies=proxies,
+                                            timeout=self._param.timeout)
                     if self._param.clean_html:
                         sections = HtmlParser()(None, response.content)
                         self.set_output("result", "\n".join(sections))
                     else:
                         self.set_output("result", response.text)
 
-                if method == "put":
+                if method == 'put':
-                    if self._param.datatype.lower() == "json":
+                    if self._param.datatype.lower() == 'json':
-                        response = requests.put(url=url, json=args, headers=headers, proxies=proxies, timeout=self._param.timeout)
+                        response = requests.put(url=url,
+                                                json=args,
+                                                headers=headers,
+                                                proxies=proxies,
+                                                timeout=self._param.timeout)
                     else:
-                        response = requests.put(url=url, data=args, headers=headers, proxies=proxies, timeout=self._param.timeout)
+                        response = requests.put(url=url,
+                                                data=args,
+                                                headers=headers,
+                                                proxies=proxies,
+                                                timeout=self._param.timeout)
                     if self._param.clean_html:
                         sections = HtmlParser()(None, response.content)
                         self.set_output("result", "\n".join(sections))
                     else:
                         self.set_output("result", response.text)
 
-                if method == "post":
+                if method == 'post':
-                    if self._param.datatype.lower() == "json":
+                    if self._param.datatype.lower() == 'json':
-                        response = requests.post(url=url, json=args, headers=headers, proxies=proxies, timeout=self._param.timeout)
+                        response = requests.post(url=url,
+                                                 json=args,
+                                                 headers=headers,
+                                                 proxies=proxies,
+                                                 timeout=self._param.timeout)
                     else:
-                        response = requests.post(url=url, data=args, headers=headers, proxies=proxies, timeout=self._param.timeout)
+                        response = requests.post(url=url,
+                                                 data=args,
+                                                 headers=headers,
+                                                 proxies=proxies,
+                                                 timeout=self._param.timeout)
                     if self._param.clean_html:
                         self.set_output("result", "\n".join(sections))
                     else:

agent/tools/code_exec.py

@@ -156,8 +156,8 @@ class CodeExec(ToolBase, ABC):
             self.set_output("_ERROR", "construct code request error: " + str(e))
 
         try:
-            resp = requests.post(url=f"http://{settings.SANDBOX_HOST}:9385/run", json=code_req, timeout=int(os.environ.get("COMPONENT_EXEC_TIMEOUT", 10*60)))
+            resp = requests.post(url=f"http://{settings.SANDBOX_HOST}:9385/run", json=code_req, timeout=os.environ.get("COMPONENT_EXEC_TIMEOUT", 10*60))
-            logging.info(f"http://{settings.SANDBOX_HOST}:9385/run,  code_req: {code_req}, resp.status_code {resp.status_code}:")
+            logging.info(f"http://{settings.SANDBOX_HOST}:9385/run", code_req, resp.status_code)
             if resp.status_code != 200:
                 resp.raise_for_status()
             body = resp.json()

agent/tools/exesql.py

@@ -53,12 +53,11 @@ class ExeSQLParam(ToolParamBase):
         self.max_records = 1024
 
     def check(self):
-        self.check_valid_value(self.db_type, "Choose DB type", ['mysql', 'postgres', 'mariadb', 'mssql', 'IBM DB2', 'trino'])
+        self.check_valid_value(self.db_type, "Choose DB type", ['mysql', 'postgres', 'mariadb', 'mssql', 'IBM DB2'])
         self.check_empty(self.database, "Database name")
         self.check_empty(self.username, "database username")
         self.check_empty(self.host, "IP Address")
         self.check_positive_integer(self.port, "IP Port")
-        if self.db_type != "trino":
         self.check_empty(self.password, "Database password")
         self.check_positive_integer(self.max_records, "Maximum number of records")
         if self.database == "rag_flow":
@@ -124,45 +123,6 @@ class ExeSQL(ToolBase, ABC):
                     r'PWD=' + self._param.password
             )
             db = pyodbc.connect(conn_str)
-        elif self._param.db_type == 'trino':
-            try:
-                import trino
-                from trino.auth import BasicAuthentication
-            except Exception:
-                raise Exception("Missing dependency 'trino'. Please install: pip install trino")
-
-            def _parse_catalog_schema(db: str):
-                if not db:
-                    return None, None
-                if "." in db:
-                    c, s = db.split(".", 1)
-                elif "/" in db:
-                    c, s = db.split("/", 1)
-                else:
-                    c, s = db, "default"
-                return c, s
-
-            catalog, schema = _parse_catalog_schema(self._param.database)
-            if not catalog:
-                raise Exception("For Trino, `database` must be 'catalog.schema' or at least 'catalog'.")
-
-            http_scheme = "https" if os.environ.get("TRINO_USE_TLS", "0") == "1" else "http"
-            auth = None
-            if http_scheme == "https" and self._param.password:
-                auth = BasicAuthentication(self._param.username, self._param.password)
-
-            try:
-                db = trino.dbapi.connect(
-                    host=self._param.host,
-                    port=int(self._param.port or 8080),
-                    user=self._param.username or "ragflow",
-                    catalog=catalog,
-                    schema=schema or "default",
-                    http_scheme=http_scheme,
-                    auth=auth
-                )
-            except Exception as e:
-                raise Exception("Database Connection Failed! \n" + str(e))
         elif self._param.db_type == 'IBM DB2':
             import ibm_db
             conn_str = (

agent/tools/pubmed.py

@@ -85,7 +85,13 @@ class PubMed(ToolBase, ABC):
                 self._retrieve_chunks(pubmedcnt.findall("PubmedArticle"),
                                       get_title=lambda child: child.find("MedlineCitation").find("Article").find("ArticleTitle").text,
                                       get_url=lambda child: "https://pubmed.ncbi.nlm.nih.gov/" + child.find("MedlineCitation").find("PMID").text,
-                                      get_content=lambda child: self._format_pubmed_content(child),)
+                                      get_content=lambda child: child.find("MedlineCitation") \
+                                                                    .find("Article") \
+                                                                    .find("Abstract") \
+                                                                    .find("AbstractText").text \
+                                                                    if child.find("MedlineCitation")\
+                                                                            .find("Article").find("Abstract")  \
+                                                                    else "No abstract available")
                 return self.output("formalized_content")
             except Exception as e:
                 last_e = e
@@ -98,50 +104,5 @@ class PubMed(ToolBase, ABC):
 
         assert False, self.output()
 
-    def _format_pubmed_content(self, child):
-        """Extract structured reference info from PubMed XML"""
-        def safe_find(path):
-            node = child
-            for p in path.split("/"):
-                if node is None:
-                    return None
-                node = node.find(p)
-            return node.text if node is not None and node.text else None
-
-        title = safe_find("MedlineCitation/Article/ArticleTitle") or "No title"
-        abstract = safe_find("MedlineCitation/Article/Abstract/AbstractText") or "No abstract available"
-        journal = safe_find("MedlineCitation/Article/Journal/Title") or "Unknown Journal"
-        volume = safe_find("MedlineCitation/Article/Journal/JournalIssue/Volume") or "-"
-        issue = safe_find("MedlineCitation/Article/Journal/JournalIssue/Issue") or "-"
-        pages = safe_find("MedlineCitation/Article/Pagination/MedlinePgn") or "-"
-
-        # Authors
-        authors = []
-        for author in child.findall(".//AuthorList/Author"):
-            lastname = safe_find("LastName") or ""
-            forename = safe_find("ForeName") or ""
-            fullname = f"{forename} {lastname}".strip()
-            if fullname:
-                authors.append(fullname)
-        authors_str = ", ".join(authors) if authors else "Unknown Authors"
-
-        # DOI
-        doi = None
-        for eid in child.findall(".//ArticleId"):
-            if eid.attrib.get("IdType") == "doi":
-                doi = eid.text
-                break
-
-        return (
-            f"Title: {title}\n"
-            f"Authors: {authors_str}\n"
-            f"Journal: {journal}\n"
-            f"Volume: {volume}\n"
-            f"Issue: {issue}\n"
-            f"Pages: {pages}\n"
-            f"DOI: {doi or '-'}\n"
-            f"Abstract: {abstract.strip()}"
-        )
-
     def thoughts(self) -> str:
         return "Looking for scholarly papers on `{}`,” prioritising reputable sources.".format(self.get_input().get("query", "-_-!"))

agent/tools/retrieval.py

@@ -57,7 +57,6 @@ class RetrievalParam(ToolParamBase):
         self.empty_response = ""
         self.use_kg = False
         self.cross_languages = []
-        self.toc_enhance = False
 
     def check(self):
         self.check_decimal_float(self.similarity_threshold, "[Retrieval] Similarity threshold")
@@ -122,7 +121,7 @@ class Retrieval(ToolBase, ABC):
 
         if kbs:
             query = re.sub(r"^user[:：\s]*", "", query, flags=re.IGNORECASE)
-            kbinfos = settings.retriever.retrieval(
+            kbinfos = settings.retrievaler.retrieval(
                 query,
                 embd_mdl,
                 [kb.tenant_id for kb in kbs],
@@ -135,13 +134,8 @@ class Retrieval(ToolBase, ABC):
                 rerank_mdl=rerank_mdl,
                 rank_feature=label_question(query, kbs),
             )
-            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)
-                if cks:
-                    kbinfos["chunks"] = cks
             if self._param.use_kg:
-                ck = settings.kg_retriever.retrieval(query,
+                ck = settings.kg_retrievaler.retrieval(query,
                                                        [kb.tenant_id for kb in kbs],
                                                        kb_ids,
                                                        embd_mdl,
@@ -152,7 +146,7 @@ class Retrieval(ToolBase, ABC):
             kbinfos = {"chunks": [], "doc_aggs": []}
 
         if self._param.use_kg and kbs:
-            ck = settings.kg_retriever.retrieval(query, [kb.tenant_id for kb in kbs], filtered_kb_ids, embd_mdl, LLMBundle(kbs[0].tenant_id, LLMType.CHAT))
+            ck = settings.kg_retrievaler.retrieval(query, [kb.tenant_id for kb in kbs], filtered_kb_ids, embd_mdl, LLMBundle(kbs[0].tenant_id, LLMType.CHAT))
             if ck["content_with_weight"]:
                 ck["content"] = ck["content_with_weight"]
                 del ck["content_with_weight"]

agent/tools/searxng.py

@@ -85,7 +85,7 @@ class SearXNG(ToolBase, ABC):
             self.set_output("formalized_content", "")
             return ""
 
-        searxng_url = (getattr(self._param, "searxng_url", "") or kwargs.get("searxng_url") or "").strip()
+        searxng_url = (kwargs.get("searxng_url") or getattr(self._param, "searxng_url", "") or "").strip()
         # In try-run, if no URL configured, just return empty instead of raising
         if not searxng_url:
             self.set_output("formalized_content", "")

api/apps/api_app.py

@@ -536,7 +536,7 @@ def list_chunks():
             )
         kb_ids = KnowledgebaseService.get_kb_ids(tenant_id)
 
-        res = settings.retriever.chunk_list(doc_id, tenant_id, kb_ids)
+        res = settings.retrievaler.chunk_list(doc_id, tenant_id, kb_ids)
         res = [
             {
                 "content": res_item["content_with_weight"],
@@ -884,7 +884,7 @@ 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 = settings.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, highlight= highlight,
                                                rank_feature=label_question(question, kbs))

api/apps/canvas_app.py

@@ -409,49 +409,6 @@ def test_db_connect():
             ibm_db.fetch_assoc(stmt)
             ibm_db.close(conn)
             return get_json_result(data="Database Connection Successful!")
-        elif req["db_type"] == 'trino':
-            def _parse_catalog_schema(db: str):
-                if not db:
-                    return None, None
-                if "." in db:
-                    c, s = db.split(".", 1)
-                elif "/" in db:
-                    c, s = db.split("/", 1)
-                else:
-                    c, s = db, "default"
-                return c, s
-            try:
-                import trino
-                import os
-                from trino.auth import BasicAuthentication
-            except Exception:
-                return server_error_response("Missing dependency 'trino'. Please install: pip install trino")
-
-            catalog, schema = _parse_catalog_schema(req["database"])
-            if not catalog:
-                return server_error_response("For Trino, 'database' must be 'catalog.schema' or at least 'catalog'.")
-            
-            http_scheme = "https" if os.environ.get("TRINO_USE_TLS", "0") == "1" else "http"
-
-            auth = None
-            if http_scheme == "https" and req.get("password"):
-                auth = BasicAuthentication(req.get("username") or "ragflow", req["password"])
-
-            conn = trino.dbapi.connect(
-                host=req["host"],
-                port=int(req["port"] or 8080),
-                user=req["username"] or "ragflow",
-                catalog=catalog,
-                schema=schema or "default",
-                http_scheme=http_scheme,
-                auth=auth
-            )
-            cur = conn.cursor()
-            cur.execute("SELECT 1")
-            cur.fetchall()
-            cur.close()
-            conn.close()
-            return get_json_result(data="Database Connection Successful!")
         else:
             return server_error_response("Unsupported database type.")
         if req["db_type"] != 'mssql':

api/apps/chunk_app.py

@@ -60,7 +60,7 @@ 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=True)
+        sres = settings.retrievaler.search(query, search.index_name(tenant_id), kb_ids, highlight=True)
         res = {"total": sres.total, "chunks": [], "doc": doc.to_dict()}
         for id in sres.ids:
             d = {
@@ -346,7 +346,7 @@ 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 = settings.retrievaler.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,
@@ -354,7 +354,7 @@ def retrieval_test():
                                rank_feature=labels
                                )
         if use_kg:
-            ck = settings.kg_retriever.retrieval(question,
+            ck = settings.kg_retrievaler.retrieval(question,
                                                    tenant_ids,
                                                    kb_ids,
                                                    embd_mdl,
@@ -384,7 +384,7 @@ 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 = settings.retrievaler.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"]

api/apps/document_app.py

@@ -24,7 +24,6 @@ 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, VALID_TASK_STATUS, FileSource, FileType, ParserType, TaskStatus
 from api.db.db_models import File, Task
@@ -69,10 +68,8 @@ def upload():
     e, kb = KnowledgebaseService.get_by_id(kb_id)
     if not e:
         raise LookupError("Can't find this knowledgebase!")
-    if not check_kb_team_permission(kb, current_user.id):
-        return get_json_result(data=False, message="No authorization.", code=settings.RetCode.AUTHENTICATION_ERROR)
-
     err, files = FileService.upload_document(kb, file_objs, current_user.id)
+
     if err:
         return get_json_result(data=files, message="\n".join(err), code=settings.RetCode.SERVER_ERROR)
 
@@ -97,8 +94,6 @@ def web_crawl():
     e, kb = KnowledgebaseService.get_by_id(kb_id)
     if not e:
         raise LookupError("Can't find this knowledgebase!")
-    if check_kb_team_permission(kb, current_user.id):
-        return get_json_result(data=False, message="No authorization.", code=settings.RetCode.AUTHENTICATION_ERROR)
 
     blob = html2pdf(url)
     if not blob:
@@ -557,8 +552,8 @@ def get(doc_id):
 @login_required
 @validate_request("doc_id")
 def change_parser():
-
     req = request.json
+
     if not DocumentService.accessible(req["doc_id"], current_user.id):
         return get_json_result(data=False, message="No authorization.", code=settings.RetCode.AUTHENTICATION_ERROR)
 
@@ -582,7 +577,7 @@ def change_parser():
                 settings.docStoreConn.delete({"doc_id": doc.id}, search.index_name(tenant_id), doc.kb_id)
 
     try:
-        if "pipeline_id" in req and req["pipeline_id"] != "":
+        if "pipeline_id" in req:
             if doc.pipeline_id == req["pipeline_id"]:
                 return get_json_result(data=True)
             DocumentService.update_by_id(doc.id, {"pipeline_id": req["pipeline_id"]})

api/apps/file_app.py

@@ -21,7 +21,6 @@ import flask
 from flask import request
 from flask_login import login_required, current_user
 
-from api.common.check_team_permission import check_file_team_permission
 from api.db.services.document_service import DocumentService
 from api.db.services.file2document_service import File2DocumentService
 from api.utils.api_utils import server_error_response, get_data_error_result, validate_request
@@ -247,7 +246,7 @@ def rm():
                 return get_data_error_result(message="File or Folder not found!")
             if not file.tenant_id:
                 return get_data_error_result(message="Tenant not found!")
-            if not check_file_team_permission(file, current_user.id):
+            if file.tenant_id != current_user.id:
                 return get_json_result(data=False, message='No authorization.', code=settings.RetCode.AUTHENTICATION_ERROR)
             if file.source_type == FileSource.KNOWLEDGEBASE:
                 continue
@@ -295,7 +294,7 @@ def rename():
         e, file = FileService.get_by_id(req["file_id"])
         if not e:
             return get_data_error_result(message="File not found!")
-        if not check_file_team_permission(file, current_user.id):
+        if file.tenant_id != current_user.id:
             return get_json_result(data=False, message='No authorization.', code=settings.RetCode.AUTHENTICATION_ERROR)
         if file.type != FileType.FOLDER.value \
             and pathlib.Path(req["name"].lower()).suffix != pathlib.Path(
@@ -333,7 +332,7 @@ def get(file_id):
         e, file = FileService.get_by_id(file_id)
         if not e:
             return get_data_error_result(message="Document not found!")
-        if not check_file_team_permission(file, current_user.id):
+        if file.tenant_id != current_user.id:
             return get_json_result(data=False, message='No authorization.', code=settings.RetCode.AUTHENTICATION_ERROR)
 
         blob = STORAGE_IMPL.get(file.parent_id, file.location)
@@ -374,7 +373,7 @@ def move():
                 return get_data_error_result(message="File or Folder not found!")
             if not file.tenant_id:
                 return get_data_error_result(message="Tenant not found!")
-            if not check_file_team_permission(file, current_user.id):
+            if file.tenant_id != current_user.id:
                 return get_json_result(data=False, message='No authorization.', code=settings.RetCode.AUTHENTICATION_ERROR)
         fe, _ = FileService.get_by_id(parent_id)
         if not fe:

api/apps/kb_app.py

@@ -36,10 +36,8 @@ from api import settings
 from rag.nlp import search
 from api.constants import DATASET_NAME_LIMIT
 from rag.settings import PAGERANK_FLD
-from rag.utils.redis_conn import REDIS_CONN
 from rag.utils.storage_factory import STORAGE_IMPL
 
-
 @manager.route('/create', methods=['post'])  # noqa: F821
 @login_required
 @validate_request("name")
@@ -283,7 +281,7 @@ 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 += settings.retrievaler.all_tags(tenant["tenant_id"], [kb_id])
     return get_json_result(data=tags)
 
 
@@ -302,7 +300,7 @@ 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 += settings.retrievaler.all_tags(tenant["tenant_id"], kb_ids)
     return get_json_result(data=tags)
 
 
@@ -363,7 +361,7 @@ def knowledge_graph(kb_id):
     obj = {"graph": {}, "mind_map": {}}
     if not settings.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 = settings.retrievaler.search(req, search.index_name(kb.tenant_id), [kb_id])
     if not len(sres.ids):
         return get_json_result(data=obj)
 
@@ -761,25 +759,18 @@ def delete_kb_task():
     match pipeline_task_type:
         case PipelineTaskType.GRAPH_RAG:
             settings.docStoreConn.delete({"knowledge_graph_kwd": ["graph", "subgraph", "entity", "relation"]}, search.index_name(kb.tenant_id), kb_id)
-            kb_task_id_field = "graphrag_task_id"
+            kb_task_id = "graphrag_task_id"
-            task_id = kb.graphrag_task_id
             kb_task_finish_at = "graphrag_task_finish_at"
         case PipelineTaskType.RAPTOR:
-            kb_task_id_field = "raptor_task_id"
+            kb_task_id = "raptor_task_id"
-            task_id = kb.raptor_task_id
             kb_task_finish_at = "raptor_task_finish_at"
         case PipelineTaskType.MINDMAP:
-            kb_task_id_field = "mindmap_task_id"
+            kb_task_id = "mindmap_task_id"
-            task_id = kb.mindmap_task_id
             kb_task_finish_at = "mindmap_task_finish_at"
         case _:
             return get_error_data_result(message="Internal Error: Invalid task type")
 
-    def cancel_task(task_id):
+    ok = KnowledgebaseService.update_by_id(kb_id, {kb_task_id: "", kb_task_finish_at: None})
-        REDIS_CONN.set(f"{task_id}-cancel", "x")
-    cancel_task(task_id)
-
-    ok = KnowledgebaseService.update_by_id(kb_id, {kb_task_id_field: "", kb_task_finish_at: None})
     if not ok:
         return server_error_response(f"Internal error: cannot delete task {pipeline_task_type}")
 

api/apps/plugin_app.py

@@ -1,26 +1,8 @@
-#
-#  Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-
-
 from flask import Response
 from flask_login import login_required
 from api.utils.api_utils import get_json_result
 from plugin import GlobalPluginManager
 
-
 @manager.route('/llm_tools', methods=['GET'])  # noqa: F821
 @login_required
 def llm_tools() -> Response:

api/apps/sdk/agent.py

@@ -25,7 +25,6 @@ from api.utils.api_utils import get_data_error_result, get_error_data_result, ge
 from api.utils.api_utils import get_result
 from flask import request
 
-
 @manager.route('/agents', methods=['GET'])  # noqa: F821
 @token_required
 def list_agents(tenant_id):
@@ -42,7 +41,7 @@ def list_agents(tenant_id):
         desc = False
     else:
         desc = True
-    canvas = UserCanvasService.get_list(tenant_id, page_number, items_per_page, orderby, desc, id, title)
+    canvas = UserCanvasService.get_list(tenant_id,page_number,items_per_page,orderby,desc,id,title)
     return get_result(data=canvas)
 
 

api/apps/sdk/dataset.py

@@ -215,8 +215,7 @@ def delete(tenant_id):
                     continue
                 kb_id_instance_pairs.append((kb_id, kb))
             if len(error_kb_ids) > 0:
-                return get_error_permission_result(
+                return get_error_permission_result(message=f"""User '{tenant_id}' lacks permission for datasets: '{", ".join(error_kb_ids)}'""")
-                    message=f"""User '{tenant_id}' lacks permission for datasets: '{", ".join(error_kb_ids)}'""")
 
         errors = []
         success_count = 0
@@ -233,8 +232,7 @@ def delete(tenant_id):
                     ]
                 )
                 File2DocumentService.delete_by_document_id(doc.id)
-            FileService.filter_delete(
+            FileService.filter_delete([File.source_type == FileSource.KNOWLEDGEBASE, File.type == "folder", File.name == kb.name])
-                [File.source_type == FileSource.KNOWLEDGEBASE, File.type == "folder", File.name == kb.name])
             if not KnowledgebaseService.delete_by_id(kb_id):
                 errors.append(f"Delete dataset error for {kb_id}")
                 continue
@@ -331,8 +329,7 @@ def update(tenant_id, dataset_id):
     try:
         kb = KnowledgebaseService.get_or_none(id=dataset_id, tenant_id=tenant_id)
         if kb is None:
-            return get_error_permission_result(
+            return get_error_permission_result(message=f"User '{tenant_id}' lacks permission for dataset '{dataset_id}'")
-                message=f"User '{tenant_id}' lacks permission for dataset '{dataset_id}'")
 
         if req.get("parser_config"):
             req["parser_config"] = deep_merge(kb.parser_config, req["parser_config"])
@@ -344,8 +341,7 @@ def update(tenant_id, dataset_id):
             del req["parser_config"]
 
         if "name" in req and req["name"].lower() != kb.name.lower():
-            exists = KnowledgebaseService.get_or_none(name=req["name"], tenant_id=tenant_id,
+            exists = KnowledgebaseService.get_or_none(name=req["name"], tenant_id=tenant_id, status=StatusEnum.VALID.value)
-                                                      status=StatusEnum.VALID.value)
             if exists:
                 return get_error_data_result(message=f"Dataset name '{req['name']}' already exists")
 
@@ -353,8 +349,7 @@ def update(tenant_id, dataset_id):
             if not req["embd_id"]:
                 req["embd_id"] = kb.embd_id
             if kb.chunk_num != 0 and req["embd_id"] != kb.embd_id:
-                return get_error_data_result(
+                return get_error_data_result(message=f"When chunk_num ({kb.chunk_num}) > 0, embedding_model must remain {kb.embd_id}")
-                    message=f"When chunk_num ({kb.chunk_num}) > 0, embedding_model must remain {kb.embd_id}")
             ok, err = verify_embedding_availability(req["embd_id"], tenant_id)
             if not ok:
                 return err
@@ -364,12 +359,10 @@ 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"]},
+                settings.docStoreConn.update({"kb_id": kb.id}, {PAGERANK_FLD: req["pagerank"]}, search.index_name(kb.tenant_id), kb.id)
-                                             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},
+                settings.docStoreConn.update({"exists": PAGERANK_FLD}, {"remove": PAGERANK_FLD}, search.index_name(kb.tenant_id), kb.id)
-                                             search.index_name(kb.tenant_id), kb.id)
 
         if not KnowledgebaseService.update_by_id(kb.id, req):
             return get_error_data_result(message="Update dataset error.(Database error)")
@@ -461,7 +454,7 @@ def list_datasets(tenant_id):
                 return get_error_permission_result(message=f"User '{tenant_id}' lacks permission for dataset '{name}'")
 
         tenants = TenantService.get_joined_tenants_by_user_id(tenant_id)
-        kbs, total = KnowledgebaseService.get_list(
+        kbs = KnowledgebaseService.get_list(
             [m["tenant_id"] for m in tenants],
             tenant_id,
             args["page"],
@@ -475,15 +468,14 @@ def list_datasets(tenant_id):
         response_data_list = []
         for kb in kbs:
             response_data_list.append(remap_dictionary_keys(kb))
-        return get_result(data=response_data_list, total=total)
+        return get_result(data=response_data_list)
     except OperationalError as e:
         logging.exception(e)
         return get_error_data_result(message="Database operation failed")
 
-
 @manager.route('/datasets/<dataset_id>/knowledge_graph', methods=['GET'])  # noqa: F821
 @token_required
-def knowledge_graph(tenant_id, dataset_id):
+def knowledge_graph(tenant_id,dataset_id):
     if not KnowledgebaseService.accessible(dataset_id, tenant_id):
         return get_result(
             data=False,
@@ -499,7 +491,7 @@ def knowledge_graph(tenant_id, dataset_id):
     obj = {"graph": {}, "mind_map": {}}
     if not settings.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 = settings.retrievaler.search(req, search.index_name(kb.tenant_id), [dataset_id])
     if not len(sres.ids):
         return get_result(data=obj)
 
@@ -515,16 +507,14 @@ def knowledge_graph(tenant_id, dataset_id):
     if "nodes" in obj["graph"]:
         obj["graph"]["nodes"] = sorted(obj["graph"]["nodes"], key=lambda x: x.get("pagerank", 0), reverse=True)[:256]
         if "edges" in obj["graph"]:
-            node_id_set = {o["id"] for o in obj["graph"]["nodes"]}
+            node_id_set = { o["id"] for o in obj["graph"]["nodes"] }
-            filtered_edges = [o for o in obj["graph"]["edges"] if
+            filtered_edges = [o for o in obj["graph"]["edges"] if o["source"] != o["target"] and o["source"] in node_id_set and o["target"] in node_id_set]
-                              o["source"] != o["target"] and o["source"] in node_id_set and o["target"] in node_id_set]
             obj["graph"]["edges"] = sorted(filtered_edges, key=lambda x: x.get("weight", 0), reverse=True)[:128]
     return get_result(data=obj)
 
-
 @manager.route('/datasets/<dataset_id>/knowledge_graph', methods=['DELETE'])  # noqa: F821
 @token_required
-def delete_knowledge_graph(tenant_id, dataset_id):
+def delete_knowledge_graph(tenant_id,dataset_id):
     if not KnowledgebaseService.accessible(dataset_id, tenant_id):
         return get_result(
             data=False,
@@ -532,7 +522,6 @@ def delete_knowledge_graph(tenant_id, dataset_id):
             code=settings.RetCode.AUTHENTICATION_ERROR
         )
     _, kb = KnowledgebaseService.get_by_id(dataset_id)
-    settings.docStoreConn.delete({"knowledge_graph_kwd": ["graph", "subgraph", "entity", "relation"]},
+    settings.docStoreConn.delete({"knowledge_graph_kwd": ["graph", "subgraph", "entity", "relation"]}, search.index_name(kb.tenant_id), dataset_id)
-                                 search.index_name(kb.tenant_id), dataset_id)
 
     return get_result(data=True)

api/apps/sdk/dify_retrieval.py

@@ -1,4 +1,4 @@
-#
+    #
 #  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
 #
 #  Licensed under the Apache License, Version 2.0 (the "License");
@@ -31,89 +31,6 @@ from api.db.services.dialog_service import meta_filter, convert_conditions
 @apikey_required
 @validate_request("knowledge_id", "query")
 def retrieval(tenant_id):
-    """
-    Dify-compatible retrieval API
-    ---
-    tags:
-      - SDK
-    security:
-      - ApiKeyAuth: []
-    parameters:
-      - in: body
-        name: body
-        required: true
-        schema:
-          type: object
-          required:
-            - knowledge_id
-            - query
-          properties:
-            knowledge_id:
-              type: string
-              description: Knowledge base ID
-            query:
-              type: string
-              description: Query text
-            use_kg:
-              type: boolean
-              description: Whether to use knowledge graph
-              default: false
-            retrieval_setting:
-              type: object
-              description: Retrieval configuration
-              properties:
-                score_threshold:
-                  type: number
-                  description: Similarity threshold
-                  default: 0.0
-                top_k:
-                  type: integer
-                  description: Number of results to return
-                  default: 1024
-            metadata_condition:
-              type: object
-              description: Metadata filter condition
-              properties:
-                conditions:
-                  type: array
-                  items:
-                    type: object
-                    properties:
-                      name:
-                        type: string
-                        description: Field name
-                      comparison_operator:
-                        type: string
-                        description: Comparison operator
-                      value:
-                        type: string
-                        description: Field value
-    responses:
-      200:
-        description: Retrieval succeeded
-        schema:
-          type: object
-          properties:
-            records:
-              type: array
-              items:
-                type: object
-                properties:
-                  content:
-                    type: string
-                    description: Content text
-                  score:
-                    type: number
-                    description: Similarity score
-                  title:
-                    type: string
-                    description: Document title
-                  metadata:
-                    type: object
-                    description: Metadata info
-      404:
-        description: Knowledge base or document not found
-    """
     req = request.json
     question = req["query"]
     kb_id = req["knowledge_id"]
@@ -121,7 +38,7 @@ def retrieval(tenant_id):
     retrieval_setting = req.get("retrieval_setting", {})
     similarity_threshold = float(retrieval_setting.get("score_threshold", 0.0))
     top = int(retrieval_setting.get("top_k", 1024))
-    metadata_condition = req.get("metadata_condition", {})
+    metadata_condition = req.get("metadata_condition",{})
     metas = DocumentService.get_meta_by_kbs([kb_id])
  
     doc_ids = []
@@ -133,12 +50,12 @@ def retrieval(tenant_id):
 
         embd_mdl = LLMBundle(kb.tenant_id, LLMType.EMBEDDING.value, llm_name=kb.embd_id)
         print(metadata_condition)
-        # print("after", convert_conditions(metadata_condition))
+        print("after",convert_conditions(metadata_condition))
         doc_ids.extend(meta_filter(metas, convert_conditions(metadata_condition)))
-        # print("doc_ids", doc_ids)
+        print("doc_ids",doc_ids)
         if not doc_ids and metadata_condition is not None:
             doc_ids = ['-999']
-        ranks = settings.retriever.retrieval(
+        ranks = settings.retrievaler.retrieval(
             question,
             embd_mdl,
             kb.tenant_id,
@@ -153,7 +70,7 @@ def retrieval(tenant_id):
         )
 
         if use_kg:
-            ck = settings.kg_retriever.retrieval(question,
+            ck = settings.kg_retrievaler.retrieval(question,
                                                    [tenant_id],
                                                    [kb_id],
                                                    embd_mdl,
@@ -163,7 +80,7 @@ def retrieval(tenant_id):
 
         records = []
         for c in ranks["chunks"]:
-            e, doc = DocumentService.get_by_id(c["doc_id"])
+            e, doc = DocumentService.get_by_id( c["doc_id"])
             c.pop("vector", None)
             meta = getattr(doc, 'meta_fields', {})
             meta["doc_id"] = c["doc_id"]
@@ -183,3 +100,5 @@ def retrieval(tenant_id):
             )
         logging.exception(e)
         return build_error_result(message=str(e), code=settings.RetCode.SERVER_ERROR)
+
+

api/apps/sdk/doc.py

@@ -982,7 +982,7 @@ def list_chunks(tenant_id, dataset_id, document_id):
         _ = 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)
+        sres = settings.retrievaler.search(query, search.index_name(tenant_id), [dataset_id], emb_mdl=None, highlight=True)
         res["total"] = sres.total
         for id in sres.ids:
             d = {
@@ -1446,7 +1446,7 @@ def retrieval_test(tenant_id):
             chat_mdl = LLMBundle(kb.tenant_id, LLMType.CHAT)
             question += keyword_extraction(chat_mdl, question)
 
-        ranks = settings.retriever.retrieval(
+        ranks = settings.retrievaler.retrieval(
             question,
             embd_mdl,
             tenant_ids,
@@ -1462,7 +1462,7 @@ def retrieval_test(tenant_id):
             rank_feature=label_question(question, kbs),
         )
         if use_kg:
-            ck = settings.kg_retriever.retrieval(question, [k.tenant_id for k in kbs], kb_ids, embd_mdl, LLMBundle(kb.tenant_id, LLMType.CHAT))
+            ck = settings.kg_retrievaler.retrieval(question, [k.tenant_id for k in kbs], kb_ids, embd_mdl, LLMBundle(kb.tenant_id, LLMType.CHAT))
             if ck["content_with_weight"]:
                 ranks["chunks"].insert(0, ck)
 

api/apps/sdk/files.py

@@ -1,20 +1,3 @@
-#
-#  Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-
-
 import pathlib
 import re
 
@@ -34,7 +17,6 @@ from api.utils.api_utils import get_json_result
 from api.utils.file_utils import filename_type
 from rag.utils.storage_factory import STORAGE_IMPL
 
-
 @manager.route('/file/upload', methods=['POST']) # noqa: F821
 @token_required
 def upload(tenant_id):
@@ -115,14 +97,12 @@ def upload(tenant_id):
                 e, file = FileService.get_by_id(file_id_list[len_id_list - 1])
                 if not e:
                     return get_json_result(data=False, message="Folder not found!", code=404)
-                last_folder = FileService.create_folder(file, file_id_list[len_id_list - 1], file_obj_names,
+                last_folder = FileService.create_folder(file, file_id_list[len_id_list - 1], file_obj_names, len_id_list)
-                                                        len_id_list)
             else:
                 e, file = FileService.get_by_id(file_id_list[len_id_list - 2])
                 if not e:
                     return get_json_result(data=False, message="Folder not found!", code=404)
-                last_folder = FileService.create_folder(file, file_id_list[len_id_list - 2], file_obj_names,
+                last_folder = FileService.create_folder(file, file_id_list[len_id_list - 2], file_obj_names, len_id_list)
-                                                        len_id_list)
 
             filetype = filename_type(file_obj_names[file_len - 1])
             location = file_obj_names[file_len - 1]
@@ -562,8 +542,7 @@ def rename(tenant_id):
         if not e:
             return get_json_result(message="File not found!", code=404)
 
-        if file.type != FileType.FOLDER.value and pathlib.Path(req["name"].lower()).suffix != pathlib.Path(
+        if file.type != FileType.FOLDER.value and pathlib.Path(req["name"].lower()).suffix != pathlib.Path(file.name.lower()).suffix:
-                file.name.lower()).suffix:
             return get_json_result(data=False, message="The extension of file can't be changed", code=400)
 
         for existing_file in FileService.query(name=req["name"], pf_id=file.parent_id):
@@ -585,7 +564,7 @@ def rename(tenant_id):
 
 @manager.route('/file/get/<file_id>', methods=['GET']) # noqa: F821
 @token_required
-def get(tenant_id, file_id):
+def get(tenant_id,file_id):
     """
     Download a file.
     ---
@@ -690,7 +669,6 @@ def move(tenant_id):
     except Exception as e:
         return server_error_response(e)
 
-
 @manager.route('/file/convert', methods=['POST'])  # noqa: F821
 @token_required
 def convert(tenant_id):

api/apps/sdk/session.py

@@ -36,8 +36,7 @@ from api.db.services.llm_service import LLMBundle
 from api.db.services.search_service import SearchService
 from api.db.services.user_service import UserTenantService
 from api.utils import get_uuid
-from api.utils.api_utils import check_duplicate_ids, get_data_openai, get_error_data_result, get_json_result, \
+from api.utils.api_utils import check_duplicate_ids, get_data_openai, get_error_data_result, get_json_result, get_result, server_error_response, token_required, validate_request
-    get_result, server_error_response, token_required, validate_request
 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
@@ -89,8 +88,7 @@ def create_agent_session(tenant_id, agent_id):
     canvas.reset()
 
     cvs.dsl = json.loads(str(canvas))
-    conv = {"id": session_id, "dialog_id": cvs.id, "user_id": user_id,
+    conv = {"id": session_id, "dialog_id": cvs.id, "user_id": user_id, "message": [{"role": "assistant", "content": canvas.get_prologue()}], "source": "agent", "dsl": cvs.dsl}
-            "message": [{"role": "assistant", "content": canvas.get_prologue()}], "source": "agent", "dsl": cvs.dsl}
     API4ConversationService.save(**conv)
     conv["agent_id"] = conv.pop("dialog_id")
     return get_result(data=conv)
@@ -281,7 +279,7 @@ def chat_completion_openai_like(tenant_id, chat_id):
                     reasoning_match = re.search(r"<think>(.*?)</think>", answer, flags=re.DOTALL)
                     if reasoning_match:
                         reasoning_part = reasoning_match.group(1)
-                        content_part = answer[reasoning_match.end():]
+                        content_part = answer[reasoning_match.end() :]
                     else:
                         reasoning_part = ""
                         content_part = answer
@@ -326,8 +324,7 @@ def chat_completion_openai_like(tenant_id, chat_id):
             response["choices"][0]["delta"]["content"] = None
             response["choices"][0]["delta"]["reasoning_content"] = None
             response["choices"][0]["finish_reason"] = "stop"
-            response["usage"] = {"prompt_tokens": len(prompt), "completion_tokens": token_used,
+            response["usage"] = {"prompt_tokens": len(prompt), "completion_tokens": token_used, "total_tokens": len(prompt) + token_used}
-                                 "total_tokens": len(prompt) + token_used}
             if need_reference:
                 response["choices"][0]["delta"]["reference"] = chunks_format(last_ans.get("reference", []))
                 response["choices"][0]["delta"]["final_content"] = last_ans.get("answer", "")
@@ -562,8 +559,7 @@ def list_agent_session(tenant_id, agent_id):
         desc = True
     # dsl defaults to True in all cases except for False and false
     include_dsl = request.args.get("dsl") != "False" and request.args.get("dsl") != "false"
-    total, convs = API4ConversationService.get_list(agent_id, tenant_id, page_number, items_per_page, orderby, desc, id,
+    total, convs = API4ConversationService.get_list(agent_id, tenant_id, page_number, items_per_page, orderby, desc, id, user_id, include_dsl)
-                                                    user_id, include_dsl)
     if not convs:
         return get_result(data=[])
     for conv in convs:
@@ -585,8 +581,7 @@ def list_agent_session(tenant_id, agent_id):
                 if message_num != 0 and messages[message_num]["role"] != "user":
                     chunk_list = []
                     # Add boundary and type checks to prevent KeyError
-                    if chunk_num < len(conv["reference"]) and conv["reference"][chunk_num] is not None and isinstance(
+                    if chunk_num < len(conv["reference"]) and conv["reference"][chunk_num] is not None and isinstance(conv["reference"][chunk_num], dict) and "chunks" in conv["reference"][chunk_num]:
-                            conv["reference"][chunk_num], dict) and "chunks" in conv["reference"][chunk_num]:
                         chunks = conv["reference"][chunk_num]["chunks"]
                         for chunk in chunks:
                             # Ensure chunk is a dictionary before calling get method
@@ -644,16 +639,13 @@ def delete(tenant_id, chat_id):
 
     if errors:
         if success_count > 0:
-            return get_result(data={"success_count": success_count, "errors": errors},
+            return get_result(data={"success_count": success_count, "errors": errors}, message=f"Partially deleted {success_count} sessions with {len(errors)} errors")
-                              message=f"Partially deleted {success_count} sessions with {len(errors)} errors")
         else:
             return get_error_data_result(message="; ".join(errors))
 
     if duplicate_messages:
         if success_count > 0:
-            return get_result(
+            return get_result(message=f"Partially deleted {success_count} sessions with {len(duplicate_messages)} errors", data={"success_count": success_count, "errors": duplicate_messages})
-                message=f"Partially deleted {success_count} sessions with {len(duplicate_messages)} errors",
-                data={"success_count": success_count, "errors": duplicate_messages})
         else:
             return get_error_data_result(message=";".join(duplicate_messages))
 
@@ -699,16 +691,13 @@ def delete_agent_session(tenant_id, agent_id):
 
     if errors:
         if success_count > 0:
-            return get_result(data={"success_count": success_count, "errors": errors},
+            return get_result(data={"success_count": success_count, "errors": errors}, message=f"Partially deleted {success_count} sessions with {len(errors)} errors")
-                              message=f"Partially deleted {success_count} sessions with {len(errors)} errors")
         else:
             return get_error_data_result(message="; ".join(errors))
 
     if duplicate_messages:
         if success_count > 0:
-            return get_result(
+            return get_result(message=f"Partially deleted {success_count} sessions with {len(duplicate_messages)} errors", data={"success_count": success_count, "errors": duplicate_messages})
-                message=f"Partially deleted {success_count} sessions with {len(duplicate_messages)} errors",
-                data={"success_count": success_count, "errors": duplicate_messages})
         else:
             return get_error_data_result(message=";".join(duplicate_messages))
 
@@ -741,9 +730,7 @@ def ask_about(tenant_id):
             for ans in ask(req["question"], req["kb_ids"], uid):
                 yield "data:" + json.dumps({"code": 0, "message": "", "data": ans}, ensure_ascii=False) + "\n\n"
         except Exception as e:
-            yield "data:" + json.dumps(
+            yield "data:" + json.dumps({"code": 500, "message": str(e), "data": {"answer": "**ERROR**: " + str(e), "reference": []}}, ensure_ascii=False) + "\n\n"
-                {"code": 500, "message": str(e), "data": {"answer": "**ERROR**: " + str(e), "reference": []}},
-                ensure_ascii=False) + "\n\n"
         yield "data:" + json.dumps({"code": 0, "message": "", "data": True}, ensure_ascii=False) + "\n\n"
 
     resp = Response(stream(), mimetype="text/event-stream")
@@ -895,9 +882,7 @@ def begin_inputs(agent_id):
         return get_error_data_result(f"Can't find agent by ID: {agent_id}")
 
     canvas = Canvas(json.dumps(cvs.dsl), objs[0].tenant_id)
-    return get_result(
+    return get_result(data={"title": cvs.title, "avatar": cvs.avatar, "inputs": canvas.get_component_input_form("begin"), "prologue": canvas.get_prologue(), "mode": canvas.get_mode()})
-        data={"title": cvs.title, "avatar": cvs.avatar, "inputs": canvas.get_component_input_form("begin"),
-              "prologue": canvas.get_prologue(), "mode": canvas.get_mode()})
 
 
 @manager.route("/searchbots/ask", methods=["POST"])  # noqa: F821
@@ -926,9 +911,7 @@ def ask_about_embedded():
             for ans in ask(req["question"], req["kb_ids"], uid, search_config=search_config):
                 yield "data:" + json.dumps({"code": 0, "message": "", "data": ans}, ensure_ascii=False) + "\n\n"
         except Exception as e:
-            yield "data:" + json.dumps(
+            yield "data:" + json.dumps({"code": 500, "message": str(e), "data": {"answer": "**ERROR**: " + str(e), "reference": []}}, ensure_ascii=False) + "\n\n"
-                {"code": 500, "message": str(e), "data": {"answer": "**ERROR**: " + str(e), "reference": []}},
-                ensure_ascii=False) + "\n\n"
         yield "data:" + json.dumps({"code": 0, "message": "", "data": True}, ensure_ascii=False) + "\n\n"
 
     resp = Response(stream(), mimetype="text/event-stream")
@@ -995,8 +978,7 @@ def retrieval_test_embedded():
                     tenant_ids.append(tenant.tenant_id)
                     break
             else:
-                return get_json_result(data=False, message="Only owner of knowledgebase authorized for this operation.",
+                return get_json_result(data=False, message="Only owner of knowledgebase authorized for this operation.", code=settings.RetCode.OPERATING_ERROR)
-                                       code=settings.RetCode.OPERATING_ERROR)
 
         e, kb = KnowledgebaseService.get_by_id(kb_ids[0])
         if not e:
@@ -1016,13 +998,11 @@ def retrieval_test_embedded():
             question += keyword_extraction(chat_mdl, question)
 
         labels = label_question(question, [kb])
-        ranks = settings.retriever.retrieval(
+        ranks = settings.retrievaler.retrieval(
-            question, embd_mdl, tenant_ids, kb_ids, page, size, similarity_threshold, vector_similarity_weight, top,
+            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
-            doc_ids, rerank_mdl=rerank_mdl, highlight=req.get("highlight"), rank_feature=labels
         )
         if use_kg:
-            ck = settings.kg_retriever.retrieval(question, tenant_ids, kb_ids, embd_mdl,
+            ck = settings.kg_retrievaler.retrieval(question, tenant_ids, kb_ids, embd_mdl, LLMBundle(kb.tenant_id, LLMType.CHAT))
-                                                 LLMBundle(kb.tenant_id, LLMType.CHAT))
             if ck["content_with_weight"]:
                 ranks["chunks"].insert(0, ck)
 
@@ -1033,8 +1013,7 @@ def retrieval_test_embedded():
         return get_json_result(data=ranks)
     except Exception as e:
         if str(e).find("not_found") > 0:
-            return get_json_result(data=False, message="No chunk found! Check the chunk status please!",
+            return get_json_result(data=False, message="No chunk found! Check the chunk status please!", code=settings.RetCode.DATA_ERROR)
-                                   code=settings.RetCode.DATA_ERROR)
         return server_error_response(e)
 
 
@@ -1103,8 +1082,7 @@ def detail_share_embedded():
             if SearchService.query(tenant_id=tenant.tenant_id, id=search_id):
                 break
         else:
-            return get_json_result(data=False, message="Has no permission for this operation.",
+            return get_json_result(data=False, message="Has no permission for this operation.", code=settings.RetCode.OPERATING_ERROR)
-                                   code=settings.RetCode.OPERATING_ERROR)
 
         search = SearchService.get_detail(search_id)
         if not search:

api/apps/system_app.py

@@ -162,7 +162,7 @@ def status():
         task_executors = REDIS_CONN.smembers("TASKEXE")
         now = datetime.now().timestamp()
         for task_executor_id in task_executors:
-            heartbeats = REDIS_CONN.zrangebyscore(task_executor_id, now - 60 * 30, now)
+            heartbeats = REDIS_CONN.zrangebyscore(task_executor_id, now - 60*30, now)
             heartbeats = [json.loads(heartbeat) for heartbeat in heartbeats]
             task_executor_heartbeats[task_executor_id] = heartbeats
     except Exception:
@@ -178,11 +178,6 @@ def healthz():
     return jsonify(result), (200 if all_ok else 500)
 
 
-@manager.route("/ping", methods=["GET"]) # noqa: F821
-def ping():
-    return "pong", 200
-
-
 @manager.route("/new_token", methods=["POST"])  # noqa: F821
 @login_required
 def new_token():
@@ -274,8 +269,7 @@ def token_list():
         objs = [o.to_dict() for o in objs]
         for o in objs:
             if not o["beta"]:
-                o["beta"] = generate_confirmation_token(generate_confirmation_token(tenants[0].tenant_id)).replace(
+                o["beta"] = generate_confirmation_token(generate_confirmation_token(tenants[0].tenant_id)).replace("ragflow-", "")[:32]
-                    "ragflow-", "")[:32]
                 APITokenService.filter_update([APIToken.tenant_id == tenant_id, APIToken.token == o["token"]], o)
         return get_json_result(data=objs)
     except Exception as e:

api/apps/tenant_app.py

@@ -70,8 +70,7 @@ def create(tenant_id):
             return get_data_error_result(message=f"{invite_user_email} is already in the team.")
         if user_tenant_role == UserTenantRole.OWNER:
             return get_data_error_result(message=f"{invite_user_email} is the owner of the team.")
-        return get_data_error_result(
+        return get_data_error_result(message=f"{invite_user_email} is in the team, but the role: {user_tenant_role} is invalid.")
-            message=f"{invite_user_email} is in the team, but the role: {user_tenant_role} is invalid.")
 
     UserTenantService.save(
         id=get_uuid(),
@@ -133,8 +132,7 @@ def tenant_list():
 @login_required
 def agree(tenant_id):
     try:
-        UserTenantService.filter_update([UserTenant.tenant_id == tenant_id, UserTenant.user_id == current_user.id],
+        UserTenantService.filter_update([UserTenant.tenant_id == tenant_id, UserTenant.user_id == current_user.id], {"role": UserTenantRole.NORMAL})
-                                        {"role": UserTenantRole.NORMAL})
         return get_json_result(data=True)
     except Exception as e:
         return server_error_response(e)

api/common/check_team_permission.py

@@ -1,59 +0,0 @@
-#
-#  Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-#
-
-
-from api.db import TenantPermission
-from api.db.db_models import File, Knowledgebase
-from api.db.services.file_service import FileService
-from api.db.services.knowledgebase_service import KnowledgebaseService
-from api.db.services.user_service import TenantService
-
-
-def check_kb_team_permission(kb: dict | Knowledgebase, other: str) -> bool:
-    kb = kb.to_dict() if isinstance(kb, Knowledgebase) else kb
-
-    kb_tenant_id = kb["tenant_id"]
-
-    if kb_tenant_id == other:
-        return True
-
-    if kb["permission"] != TenantPermission.TEAM:
-        return False
-
-    joined_tenants = TenantService.get_joined_tenants_by_user_id(other)
-    return any(tenant["tenant_id"] == kb_tenant_id for tenant in joined_tenants)
-
-
-def check_file_team_permission(file: dict | File, other: str) -> bool:
-    file = file.to_dict() if isinstance(file, File) else file
-
-    file_tenant_id = file["tenant_id"]
-    if file_tenant_id == other:
-        return True
-
-    file_id = file["id"]
-
-    kb_ids = [kb_info["kb_id"] for kb_info in FileService.get_kb_id_by_file_id(file_id)]
-
-    for kb_id in kb_ids:
-        ok, kb = KnowledgebaseService.get_by_id(kb_id)
-        if not ok:
-            continue
-
-        if check_kb_team_permission(kb, other):
-            return True
-
-    return False

api/db/db_models.py

@@ -641,7 +641,7 @@ class TenantLLM(DataBaseModel):
     llm_factory = CharField(max_length=128, null=False, help_text="LLM factory name", index=True)
     model_type = CharField(max_length=128, null=True, help_text="LLM, Text Embedding, Image2Text, ASR", index=True)
     llm_name = CharField(max_length=128, null=True, help_text="LLM name", default="", index=True)
-    api_key = TextField(null=True, help_text="API KEY")
+    api_key = CharField(max_length=2048, null=True, help_text="API KEY", index=True)
     api_base = CharField(max_length=255, null=True, help_text="API Base")
     max_tokens = IntegerField(default=8192, index=True)
     used_tokens = IntegerField(default=0, index=True)
@@ -1142,8 +1142,4 @@ def migrate_db():
         migrate(migrator.add_column("knowledgebase", "mindmap_task_finish_at", CharField(null=True)))
     except Exception:
         pass
-    try:
-        migrate(migrator.alter_column_type("tenant_llm", "api_key", TextField(null=True, help_text="API KEY")))
-    except Exception:
-        pass
     logging.disable(logging.NOTSET)

api/db/services/dialog_service.py

@@ -370,7 +370,7 @@ def chat(dialog, messages, stream=True, **kwargs):
         chat_mdl.bind_tools(toolcall_session, tools)
     bind_models_ts = timer()
 
-    retriever = settings.retriever
+    retriever = settings.retrievaler
     questions = [m["content"] for m in messages if m["role"] == "user"][-3:]
     attachments = kwargs["doc_ids"].split(",") if "doc_ids" in kwargs else []
     if "doc_ids" in messages[-1]:
@@ -466,17 +466,13 @@ def chat(dialog, messages, stream=True, **kwargs):
                     rerank_mdl=rerank_mdl,
                     rank_feature=label_question(" ".join(questions), kbs),
                 )
-                if prompt_config.get("toc_enhance"):
-                    cks = retriever.retrieval_by_toc(" ".join(questions), kbinfos["chunks"], tenant_ids, chat_mdl, dialog.top_n)
-                    if cks:
-                        kbinfos["chunks"] = cks
             if prompt_config.get("tavily_api_key"):
                 tav = Tavily(prompt_config["tavily_api_key"])
                 tav_res = tav.retrieve_chunks(" ".join(questions))
                 kbinfos["chunks"].extend(tav_res["chunks"])
                 kbinfos["doc_aggs"].extend(tav_res["doc_aggs"])
             if prompt_config.get("use_kg"):
-                ck = settings.kg_retriever.retrieval(" ".join(questions), tenant_ids, dialog.kb_ids, embd_mdl,
+                ck = settings.kg_retrievaler.retrieval(" ".join(questions), tenant_ids, dialog.kb_ids, embd_mdl,
                                                        LLMBundle(dialog.tenant_id, LLMType.CHAT))
                 if ck["content_with_weight"]:
                     kbinfos["chunks"].insert(0, ck)
@@ -662,7 +658,7 @@ 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 settings.retrievaler.sql_retrieval(sql, format="json"), sql
 
     tbl, sql = get_table()
     if tbl is None:
@@ -756,7 +752,7 @@ 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 = settings.retrievaler if not is_knowledge_graph else settings.kg_retrievaler
 
     embd_mdl = LLMBundle(tenant_id, LLMType.EMBEDDING, embedding_list[0])
     chat_mdl = LLMBundle(tenant_id, LLMType.CHAT, chat_llm_name)
@@ -852,7 +848,7 @@ def gen_mindmap(question, kb_ids, tenant_id, search_config={}):
             if not doc_ids:
                 doc_ids = None
 
-    ranks = settings.retriever.retrieval(
+    ranks = settings.retrievaler.retrieval(
         question=question,
         embd_mdl=embd_mdl,
         tenant_ids=tenant_ids,

api/db/services/knowledgebase_service.py

@@ -379,7 +379,6 @@ class KnowledgebaseService(CommonService):
         #     name: Optional name filter
         # Returns:
         #     List of knowledge bases
-        #     Total count of knowledge bases
         kbs = cls.model.select()
         if id:
             kbs = kbs.where(cls.model.id == id)
@@ -391,7 +390,6 @@ class KnowledgebaseService(CommonService):
                 cls.model.tenant_id == user_id))
             & (cls.model.status == StatusEnum.VALID.value)
         )
-
         if desc:
             kbs = kbs.order_by(cls.model.getter_by(orderby).desc())
         else:
@@ -399,7 +397,7 @@ class KnowledgebaseService(CommonService):
 
         kbs = kbs.paginate(page_number, items_per_page)
 
-        return list(kbs.dicts()), kbs.count()
+        return list(kbs.dicts())
 
     @classmethod
     @DB.connection_context()

api/db/services/mcp_server_service.py

@@ -33,8 +33,7 @@ class MCPServerService(CommonService):
 
     @classmethod
     @DB.connection_context()
-    def get_servers(cls, tenant_id: str, id_list: list[str] | None, page_number, items_per_page, orderby, desc,
+    def get_servers(cls, tenant_id: str, id_list: list[str] | None, page_number, items_per_page, orderby, desc, keywords):
-                    keywords):
         """Retrieve all MCP servers associated with a tenant.
 
         This method fetches all MCP servers for a given tenant, ordered by creation time.

api/db/services/search_service.py

@@ -94,8 +94,7 @@ class SearchService(CommonService):
         query = (
             cls.model.select(*fields)
             .join(User, on=(cls.model.tenant_id == User.id))
-            .where(((cls.model.tenant_id.in_(joined_tenant_ids)) | (cls.model.tenant_id == user_id)) & (
+            .where(((cls.model.tenant_id.in_(joined_tenant_ids)) | (cls.model.tenant_id == user_id)) & (cls.model.status == StatusEnum.VALID.value))
-                        cls.model.status == StatusEnum.VALID.value))
         )
 
         if keywords:

api/db/services/task_service.py

@@ -343,7 +343,6 @@ def queue_tasks(doc: dict, bucket: str, name: str, priority: int):
         - Task digests are calculated for optimization and reuse
         - Previous task chunks may be reused if available
     """
-
     def new_task():
         return {
             "id": get_uuid(),

api/db/services/tenant_llm_service.py

@@ -57,10 +57,8 @@ class TenantLLMService(CommonService):
     @classmethod
     @DB.connection_context()
     def get_my_llms(cls, tenant_id):
-        fields = [cls.model.llm_factory, LLMFactories.logo, LLMFactories.tags, cls.model.model_type, cls.model.llm_name,
+        fields = [cls.model.llm_factory, LLMFactories.logo, LLMFactories.tags, cls.model.model_type, cls.model.llm_name, cls.model.used_tokens]
-                  cls.model.used_tokens]
+        objs = cls.model.select(*fields).join(LLMFactories, on=(cls.model.llm_factory == LLMFactories.name)).where(cls.model.tenant_id == tenant_id, ~cls.model.api_key.is_null()).dicts()
-        objs = cls.model.select(*fields).join(LLMFactories, on=(cls.model.llm_factory == LLMFactories.name)).where(
-            cls.model.tenant_id == tenant_id, ~cls.model.api_key.is_null()).dicts()
 
         return list(objs)
 
@@ -124,8 +122,7 @@ class TenantLLMService(CommonService):
                     model_config = {"llm_factory": llm[0].fid, "api_key": "", "llm_name": mdlnm, "api_base": ""}
             if not model_config:
                 if mdlnm == "flag-embedding":
-                    model_config = {"llm_factory": "Tongyi-Qianwen", "api_key": "", "llm_name": llm_name,
+                    model_config = {"llm_factory": "Tongyi-Qianwen", "api_key": "", "llm_name": llm_name, "api_base": ""}
-                                    "api_base": ""}
                 else:
                     if not mdlnm:
                         raise LookupError(f"Type of {llm_type} model is not set.")
@@ -140,33 +137,27 @@ class TenantLLMService(CommonService):
         if llm_type == LLMType.EMBEDDING.value:
             if model_config["llm_factory"] not in EmbeddingModel:
                 return
-            return EmbeddingModel[model_config["llm_factory"]](model_config["api_key"], model_config["llm_name"],
+            return EmbeddingModel[model_config["llm_factory"]](model_config["api_key"], model_config["llm_name"], base_url=model_config["api_base"])
-                                                               base_url=model_config["api_base"])
 
         if llm_type == LLMType.RERANK:
             if model_config["llm_factory"] not in RerankModel:
                 return
-            return RerankModel[model_config["llm_factory"]](model_config["api_key"], model_config["llm_name"],
+            return RerankModel[model_config["llm_factory"]](model_config["api_key"], model_config["llm_name"], base_url=model_config["api_base"])
-                                                            base_url=model_config["api_base"])
 
         if llm_type == LLMType.IMAGE2TEXT.value:
             if model_config["llm_factory"] not in CvModel:
                 return
-            return CvModel[model_config["llm_factory"]](model_config["api_key"], model_config["llm_name"], lang,
+            return CvModel[model_config["llm_factory"]](model_config["api_key"], model_config["llm_name"], lang, base_url=model_config["api_base"], **kwargs)
-                                                        base_url=model_config["api_base"], **kwargs)
 
         if llm_type == LLMType.CHAT.value:
             if model_config["llm_factory"] not in ChatModel:
                 return
-            return ChatModel[model_config["llm_factory"]](model_config["api_key"], model_config["llm_name"],
+            return ChatModel[model_config["llm_factory"]](model_config["api_key"], model_config["llm_name"], base_url=model_config["api_base"], **kwargs)
-                                                          base_url=model_config["api_base"], **kwargs)
 
         if llm_type == LLMType.SPEECH2TEXT:
             if model_config["llm_factory"] not in Seq2txtModel:
                 return
-            return Seq2txtModel[model_config["llm_factory"]](key=model_config["api_key"],
+            return Seq2txtModel[model_config["llm_factory"]](key=model_config["api_key"], model_name=model_config["llm_name"], lang=lang, base_url=model_config["api_base"])
-                                                             model_name=model_config["llm_name"], lang=lang,
-                                                             base_url=model_config["api_base"])
         if llm_type == LLMType.TTS:
             if model_config["llm_factory"] not in TTSModel:
                 return
@@ -203,14 +194,11 @@ class TenantLLMService(CommonService):
         try:
             num = (
                 cls.model.update(used_tokens=cls.model.used_tokens + used_tokens)
-                .where(cls.model.tenant_id == tenant_id, cls.model.llm_name == llm_name,
+                .where(cls.model.tenant_id == tenant_id, cls.model.llm_name == llm_name, cls.model.llm_factory == llm_factory if llm_factory else True)
-                       cls.model.llm_factory == llm_factory if llm_factory else True)
                 .execute()
             )
         except Exception:
-            logging.exception(
+            logging.exception("TenantLLMService.increase_usage got exception,Failed to update used_tokens for tenant_id=%s, llm_name=%s", tenant_id, llm_name)
-                "TenantLLMService.increase_usage got exception,Failed to update used_tokens for tenant_id=%s, llm_name=%s",
-                tenant_id, llm_name)
             return 0
 
         return num
@@ -218,9 +206,7 @@ class TenantLLMService(CommonService):
     @classmethod
     @DB.connection_context()
     def get_openai_models(cls):
-        objs = cls.model.select().where((cls.model.llm_factory == "OpenAI"),
+        objs = cls.model.select().where((cls.model.llm_factory == "OpenAI"), ~(cls.model.llm_name == "text-embedding-3-small"), ~(cls.model.llm_name == "text-embedding-3-large")).dicts()
-                                        ~(cls.model.llm_name == "text-embedding-3-small"),
-                                        ~(cls.model.llm_name == "text-embedding-3-large")).dicts()
         return list(objs)
 
     @classmethod
@@ -264,8 +250,7 @@ class LLM4Tenant:
         langfuse_keys = TenantLangfuseService.filter_by_tenant(tenant_id=tenant_id)
         self.langfuse = None
         if langfuse_keys:
-            langfuse = Langfuse(public_key=langfuse_keys.public_key, secret_key=langfuse_keys.secret_key,
+            langfuse = Langfuse(public_key=langfuse_keys.public_key, secret_key=langfuse_keys.secret_key, host=langfuse_keys.host)
-                                host=langfuse_keys.host)
             if langfuse.auth_check():
                 self.langfuse = langfuse
                 trace_id = self.langfuse.create_trace_id()

api/db/services/user_canvas_version.py

@@ -2,10 +2,10 @@ from api.db.db_models import UserCanvasVersion, DB
 from api.db.services.common_service import CommonService
 from peewee import DoesNotExist
 
-
 class UserCanvasVersionService(CommonService):
     model = UserCanvasVersion
     
+    
     @classmethod
     @DB.connection_context()
     def list_by_canvas_id(cls, user_canvas_id):
@@ -46,8 +46,7 @@ class UserCanvasVersionService(CommonService):
     @DB.connection_context()
     def delete_all_versions(cls, user_canvas_id):
         try:
-            user_canvas_version = cls.model.select().where(cls.model.user_canvas_id == user_canvas_id).order_by(
+            user_canvas_version = cls.model.select().where(cls.model.user_canvas_id == user_canvas_id).order_by(cls.model.create_time.desc())
-                cls.model.create_time.desc())
             if user_canvas_version.count() > 20:
                 delete_ids = []
                 for i in range(20, user_canvas_version.count()):
@@ -59,3 +58,6 @@ class UserCanvasVersionService(CommonService):
             return None
         except Exception:
             return None
+
+
+

api/settings.py

@@ -65,8 +65,8 @@ OAUTH_CONFIG = None
 DOC_ENGINE = None
 docStoreConn = None
 
-retriever = None
+retrievaler = None
-kg_retriever = None
+kg_retrievaler = None
 
 # user registration switch
 REGISTER_ENABLED = 1
@@ -174,7 +174,7 @@ def init_settings():
 
     OAUTH_CONFIG = get_base_config("oauth", {})
 
-    global DOC_ENGINE, docStoreConn, retriever, kg_retriever
+    global DOC_ENGINE, docStoreConn, retrievaler, kg_retrievaler
     DOC_ENGINE = os.environ.get("DOC_ENGINE", "elasticsearch")
     # DOC_ENGINE = os.environ.get('DOC_ENGINE', "opensearch")
     lower_case_doc_engine = DOC_ENGINE.lower()
@@ -187,10 +187,10 @@ def init_settings():
     else:
         raise Exception(f"Not supported doc engine: {DOC_ENGINE}")
 
-    retriever = search.Dealer(docStoreConn)
+    retrievaler = search.Dealer(docStoreConn)
     from graphrag import search as kg_search
 
-    kg_retriever = kg_search.KGSearch(docStoreConn)
+    kg_retrievaler = kg_search.KGSearch(docStoreConn)
 
     if int(os.environ.get("SANDBOX_ENABLED", "0")):
         global SANDBOX_HOST

api/utils/api_utils.py

@@ -51,13 +51,15 @@ from api import settings
 from api.constants import REQUEST_MAX_WAIT_SEC, REQUEST_WAIT_SEC
 from api.db import ActiveEnum
 from api.db.db_models import APIToken
+from api.db.services import UserService
+from api.db.services.llm_service import LLMService
+from api.db.services.tenant_llm_service import TenantLLMService
 from api.utils.json import CustomJSONEncoder, json_dumps
 from api.utils import get_uuid
 from rag.utils.mcp_tool_call_conn import MCPToolCallSession, close_multiple_mcp_toolcall_sessions
 
 requests.models.complexjson.dumps = functools.partial(json.dumps, cls=CustomJSONEncoder)
 
-
 def serialize_for_json(obj):
     """
     Recursively serialize objects to make them JSON serializable.
@@ -83,7 +85,6 @@ def serialize_for_json(obj):
         # Fallback: convert to string representation
         return str(obj)
 
-
 def request(**kwargs):
     sess = requests.Session()
     stream = kwargs.pop("stream", sess.stream)
@@ -104,8 +105,7 @@ def request(**kwargs):
                         settings.HTTP_APP_KEY.encode("ascii"),
                         prepped.path_url.encode("ascii"),
                         prepped.body if kwargs.get("json") else b"",
-                        urlencode(sorted(kwargs["data"].items()), quote_via=quote, safe="-._~").encode(
+                        urlencode(sorted(kwargs["data"].items()), quote_via=quote, safe="-._~").encode("ascii") if kwargs.get("data") and isinstance(kwargs["data"], dict) else b"",
-                            "ascii") if kwargs.get("data") and isinstance(kwargs["data"], dict) else b"",
                     ]
                 ),
                 "sha1",
@@ -127,7 +127,7 @@ def request(**kwargs):
 def get_exponential_backoff_interval(retries, full_jitter=False):
     """Calculate the exponential backoff wait time."""
     # Will be zero if factor equals 0
-    countdown = min(REQUEST_MAX_WAIT_SEC, REQUEST_WAIT_SEC * (2 ** retries))
+    countdown = min(REQUEST_MAX_WAIT_SEC, REQUEST_WAIT_SEC * (2**retries))
     # Full jitter according to
     # https://aws.amazon.com/blogs/architecture/exponential-backoff-and-jitter/
     if full_jitter:
@@ -158,12 +158,11 @@ def server_error_response(e):
     if len(e.args) > 1:
         try:
             serialized_data = serialize_for_json(e.args[1])
-            return get_json_result(code=settings.RetCode.EXCEPTION_ERROR, message=repr(e.args[0]), data=serialized_data)
+            return get_json_result(code= settings.RetCode.EXCEPTION_ERROR, message=repr(e.args[0]), data=serialized_data)
         except Exception:
             return get_json_result(code=settings.RetCode.EXCEPTION_ERROR, message=repr(e.args[0]), data=None)
     if repr(e).find("index_not_found_exception") >= 0:
-        return get_json_result(code=settings.RetCode.EXCEPTION_ERROR,
+        return get_json_result(code=settings.RetCode.EXCEPTION_ERROR, message="No chunk found, please upload file and parse it.")
-                               message="No chunk found, please upload file and parse it.")
 
     return get_json_result(code=settings.RetCode.EXCEPTION_ERROR, message=repr(e))
 
@@ -208,8 +207,7 @@ def validate_request(*args, **kwargs):
                 if no_arguments:
                     error_string += "required argument are missing: {}; ".format(",".join(no_arguments))
                 if error_arguments:
-                    error_string += "required argument values: {}".format(
+                    error_string += "required argument values: {}".format(",".join(["{}={}".format(a[0], a[1]) for a in error_arguments]))
-                        ",".join(["{}={}".format(a[0], a[1]) for a in error_arguments]))
                 return get_json_result(code=settings.RetCode.ARGUMENT_ERROR, message=error_string)
             return func(*_args, **_kwargs)
 
@@ -224,8 +222,7 @@ def not_allowed_parameters(*params):
             input_arguments = flask_request.json or flask_request.form.to_dict()
             for param in params:
                 if param in input_arguments:
-                    return get_json_result(code=settings.RetCode.ARGUMENT_ERROR,
+                    return get_json_result(code=settings.RetCode.ARGUMENT_ERROR, message=f"Parameter {param} isn't allowed")
-                                           message=f"Parameter {param} isn't allowed")
             return f(*args, **kwargs)
 
         return wrapper
@@ -236,14 +233,12 @@ def not_allowed_parameters(*params):
 def active_required(f):
     @wraps(f)
     def wrapper(*args, **kwargs):
-        from api.db.services import UserService
         user_id = current_user.id
         usr = UserService.filter_by_id(user_id)
         # check is_active
         if not usr or not usr.is_active == ActiveEnum.ACTIVE.value:
             return get_json_result(code=settings.RetCode.FORBIDDEN, message="User isn't active, please activate first.")
         return f(*args, **kwargs)
-
     return wrapper
 
 
@@ -264,7 +259,7 @@ def send_file_in_mem(data, filename):
     return send_file(f, as_attachment=True, attachment_filename=filename)
 
 
-def get_json_result(code: settings.RetCode = settings.RetCode.SUCCESS, message="success", data=None):
+def get_json_result(code=settings.RetCode.SUCCESS, message="success", data=None):
     response = {"code": code, "message": message, "data": data}
     return jsonify(response)
 
@@ -319,7 +314,7 @@ def construct_result(code=settings.RetCode.DATA_ERROR, message="data is missing"
     return jsonify(response)
 
 
-def construct_json_result(code: settings.RetCode = settings.RetCode.SUCCESS, message="success", data=None):
+def construct_json_result(code=settings.RetCode.SUCCESS, message="success", data=None):
     if data is None:
         return jsonify({"code": code, "message": message})
     else:
@@ -352,36 +347,24 @@ def token_required(func):
         token = authorization_list[1]
         objs = APIToken.query(token=token)
         if not objs:
-            return get_json_result(data=False, message="Authentication error: API key is invalid!",
+            return get_json_result(data=False, message="Authentication error: API key is invalid!", code=settings.RetCode.AUTHENTICATION_ERROR)
-                                   code=settings.RetCode.AUTHENTICATION_ERROR)
         kwargs["tenant_id"] = objs[0].tenant_id
         return func(*args, **kwargs)
 
     return decorated_function
 
 
-def get_result(code=settings.RetCode.SUCCESS, message="", data=None, total=None):
+def get_result(code=settings.RetCode.SUCCESS, message="", data=None):
-    """
+    if code == 0:
-    Standard API response format:
-    {
-        "code": 0,
-        "data": [...],        # List or object, backward compatible
-        "total": 47,          # Optional field for pagination
-        "message": "..."      # Error or status message
-    }
-    """
-    response = {"code": code}
-
-    if code == settings.RetCode.SUCCESS:
         if data is not None:
-            response["data"] = data
+            response = {"code": code, "data": data}
-        if total is not None:
-            response["total_datasets"] = total
         else:
-        response["message"] = message or "Error"
+            response = {"code": code}
-
+    else:
+        response = {"code": code, "message": message}
     return jsonify(response)
 
+
 def get_error_data_result(
     message="Sorry! Data missing!",
     code=settings.RetCode.DATA_ERROR,
@@ -419,8 +402,7 @@ def get_parser_config(chunk_method, parser_config):
 
     # Define default configurations for each chunking method
     key_mapping = {
-        "naive": {"chunk_token_num": 512, "delimiter": r"\n", "html4excel": False, "layout_recognize": "DeepDOC",
+        "naive": {"chunk_token_num": 512, "delimiter": r"\n", "html4excel": False, "layout_recognize": "DeepDOC", "raptor": {"use_raptor": False}, "graphrag": {"use_graphrag": False}},
-                  "raptor": {"use_raptor": False}, "graphrag": {"use_graphrag": False}},
         "qa": {"raptor": {"use_raptor": False}, "graphrag": {"use_graphrag": False}},
         "tag": None,
         "resume": None,
@@ -542,8 +524,6 @@ def check_duplicate_ids(ids, id_type="item"):
 
 
 def verify_embedding_availability(embd_id: str, tenant_id: str) -> tuple[bool, Response | None]:
-    from api.db.services.llm_service import LLMService
-    from api.db.services.tenant_llm_service import TenantLLMService
     """
     Verifies availability of an embedding model for a specific tenant.
 
@@ -582,9 +562,7 @@ def verify_embedding_availability(embd_id: str, tenant_id: str) -> tuple[bool, R
         in_llm_service = bool(LLMService.query(llm_name=llm_name, fid=llm_factory, model_type="embedding"))
 
         tenant_llms = TenantLLMService.get_my_llms(tenant_id=tenant_id)
-        is_tenant_model = any(
+        is_tenant_model = any(llm["llm_name"] == llm_name and llm["llm_factory"] == llm_factory and llm["model_type"] == "embedding" for llm in tenant_llms)
-            llm["llm_name"] == llm_name and llm["llm_factory"] == llm_factory and llm["model_type"] == "embedding" for
-            llm in tenant_llms)
 
         is_builtin_model = embd_id in settings.BUILTIN_EMBEDDING_MODELS
         if not (is_builtin_model or is_tenant_model or in_llm_service):
@@ -815,9 +793,7 @@ async def is_strong_enough(chat_model, embedding_model):
                 _ = await trio.to_thread.run_sync(lambda: embedding_model.encode(["Are you strong enough!?"]))
         if chat_model:
             with trio.fail_after(30):
-                res = await trio.to_thread.run_sync(lambda: chat_model.chat("Nothing special.", [{"role": "user",
+                res = await trio.to_thread.run_sync(lambda: chat_model.chat("Nothing special.", [{"role": "user", "content": "Are you strong enough!?"}], {}))
-                                                                                                  "content": "Are you strong enough!?"}],
-                                                                            {}))
             if res.find("**ERROR**") >= 0:
                 raise Exception(res)
 

api/utils/common.py

@@ -21,26 +21,3 @@ def string_to_bytes(string):
 
 def bytes_to_string(byte):
     return byte.decode(encoding="utf-8")
-
-
-def convert_bytes(size_in_bytes: int) -> str:
-    """
-    Format size in bytes.
-    """
-    if size_in_bytes == 0:
-        return "0 B"
-
-    units = ['B', 'KB', 'MB', 'GB', 'TB', 'PB']
-    i = 0
-    size = float(size_in_bytes)
-
-    while size >= 1024 and i < len(units) - 1:
-        size /= 1024
-        i += 1
-
-    if i == 0 or size >= 100:
-        return f"{size:.0f} {units[i]}"
-    elif size >= 10:
-        return f"{size:.1f} {units[i]}"
-    else:
-        return f"{size:.2f} {units[i]}"

api/utils/health_utils.py

@@ -13,17 +13,14 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
-import os
+
-import requests
+
 from timeit import default_timer as timer
 
 from api import settings
 from api.db.db_models import DB
-from rag import settings as rag_settings
 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
 
 
 def _ok_nok(ok: bool) -> str:
@@ -68,96 +65,6 @@ def check_storage() -> tuple[bool, dict]:
         return False, {"elapsed": f"{(timer() - st) * 1000.0:.1f}", "error": str(e)}
 
 
-def get_es_cluster_stats() -> dict:
-    doc_engine = os.getenv('DOC_ENGINE', 'elasticsearch')
-    if doc_engine != 'elasticsearch':
-        raise Exception("Elasticsearch is not in use.")
-    try:
-        return {
-            "alive": True,
-            "message": ESConnection().get_cluster_stats()
-        }
-    except Exception as e:
-        return {
-            "alive": False,
-            "message": f"error: {str(e)}",
-        }
-
-
-def get_infinity_status():
-    doc_engine = os.getenv('DOC_ENGINE', 'elasticsearch')
-    if doc_engine != 'infinity':
-        raise Exception("Infinity is not in use.")
-    try:
-        return {
-            "alive": True,
-            "message": InfinityConnection().health()
-        }
-    except Exception as e:
-        return {
-            "alive": False,
-            "message": f"error: {str(e)}",
-        }
-
-
-def get_mysql_status():
-    try:
-        cursor = DB.execute_sql("SHOW PROCESSLIST;")
-        res_rows = cursor.fetchall()
-        headers = ['id', 'user', 'host', 'db', 'command', 'time', 'state', 'info']
-        cursor.close()
-        return {
-            "alive": True,
-            "message": [dict(zip(headers, r)) for r in res_rows]
-        }
-    except Exception as e:
-        return {
-            "alive": False,
-            "message": f"error: {str(e)}",
-        }
-
-
-def check_minio_alive():
-    start_time = timer()
-    try:
-        response = requests.get(f'http://{rag_settings.MINIO["host"]}/minio/health/live')
-        if response.status_code == 200:
-            return {'alive': True, "message": f"Confirm elapsed: {(timer() - start_time) * 1000.0:.1f} ms."}
-        else:
-            return {'alive': False, "message": f"Confirm elapsed: {(timer() - start_time) * 1000.0:.1f} ms."}
-    except Exception as e:
-        return {
-            "alive": False,
-            "message": f"error: {str(e)}",
-        }
-
-
-def get_redis_info():
-    try:
-        return {
-            "alive": True,
-            "message": REDIS_CONN.info()
-        }
-    except Exception as e:
-        return {
-            "alive": False,
-            "message": f"error: {str(e)}",
-        }
-
-
-def check_ragflow_server_alive():
-    start_time = timer()
-    try:
-        response = requests.get(f'http://{settings.HOST_IP}:{settings.HOST_PORT}/v1/system/ping')
-        if response.status_code == 200:
-            return {'alive': True, "message": f"Confirm elapsed: {(timer() - start_time) * 1000.0:.1f} ms."}
-        else:
-            return {'alive': False, "message": f"Confirm elapsed: {(timer() - start_time) * 1000.0:.1f} ms."}
-    except Exception as e:
-        return {
-            "alive": False,
-            "message": f"error: {str(e)}",
-        }
 
 
 def run_health_checks() -> tuple[dict, bool]:

conf/llm_factories.json

@@ -2816,13 +2816,6 @@
             "tags": "LLM,TEXT EMBEDDING,TEXT RE-RANK,IMAGE2TEXT",
             "status": "1",
             "llm": [
-                {
-                    "llm_name":"THUDM/GLM-4.1V-9B-Thinking",
-                    "tags":"LLM,CHAT,IMAGE2TEXT, 64k",
-                    "max_tokens":64000,
-                    "model_type":"chat",
-                    "is_tools": false
-                },
                 {
                     "llm_name": "Qwen/Qwen3-Embedding-8B",
                     "tags": "TEXT EMBEDDING,TEXT RE-RANK,32k",
@@ -3152,6 +3145,13 @@
                     "model_type": "chat",
                     "is_tools": true
                 },
+                {
+                    "llm_name": "Qwen/Qwen2-1.5B-Instruct",
+                    "tags": "LLM,CHAT,32k",
+                    "max_tokens": 32000,
+                    "model_type": "chat",
+                    "is_tools": true
+                },
                 {
                     "llm_name": "Pro/Qwen/Qwen2.5-Coder-7B-Instruct",
                     "tags": "LLM,CHAT,32k",
@@ -3159,6 +3159,13 @@
                     "model_type": "chat",
                     "is_tools": false
                 },
+                {
+                    "llm_name": "Pro/Qwen/Qwen2-VL-7B-Instruct",
+                    "tags": "LLM,CHAT,IMAGE2TEXT,32k",
+                    "max_tokens": 32000,
+                    "model_type": "image2text",
+                    "is_tools": false
+                },
                 {
                     "llm_name": "Pro/Qwen/Qwen2.5-7B-Instruct",
                     "tags": "LLM,CHAT,32k",
@@ -3526,13 +3533,6 @@
                     "model_type": "chat",
                     "is_tools": true
                 },
-                {
-                    "llm_name": "claude-sonnet-4-5-20250929",
-                    "tags": "LLM,CHAT,IMAGE2TEXT,200k",
-                    "max_tokens": 204800,
-                    "model_type": "chat",
-                    "is_tools": true
-                },
                 {
                     "llm_name": "claude-sonnet-4-20250514",
                     "tags": "LLM,CHAT,IMAGE2TEXT,200k",
@@ -4862,280 +4862,6 @@
                     "max_tokens": 8000,
                     "model_type": "chat",
                     "is_tools": true
-                },
-                {
-                    "llm_name": "LongCat-Flash-Thinking",
-                    "tags": "LLM,CHAT,8000",
-                    "max_tokens": 8000,
-                    "model_type": "chat",
-                    "is_tools": true
-                }
-            ]
-        },
-        {
-            "name": "DeerAPI",
-            "logo": "",
-            "tags": "LLM,TEXT EMBEDDING,IMAGE2TEXT",
-            "status": "1",
-            "llm": [
-                {
-                    "llm_name": "gpt-5-chat-latest",
-                    "tags": "LLM,CHAT,400k",
-                    "max_tokens": 400000,
-                    "model_type": "chat",
-                    "is_tools": true
-                },
-                {
-                    "llm_name": "chatgpt-4o-latest",
-                    "tags": "LLM,CHAT,128k",
-                    "max_tokens": 128000,
-                    "model_type": "chat",
-                    "is_tools": true
-                },
-                {
-                    "llm_name": "gpt-5-mini",
-                    "tags": "LLM,CHAT,400k",
-                    "max_tokens": 400000,
-                    "model_type": "chat",
-                    "is_tools": true
-                },
-                {
-                    "llm_name": "gpt-5-nano",
-                    "tags": "LLM,CHAT,400k",
-                    "max_tokens": 400000,
-                    "model_type": "chat",
-                    "is_tools": true
-                },
-                {
-                    "llm_name": "gpt-5",
-                    "tags": "LLM,CHAT,400k",
-                    "max_tokens": 400000,
-                    "model_type": "chat",
-                    "is_tools": true
-                },
-                {
-                    "llm_name": "gpt-4.1-mini",
-                    "tags": "LLM,CHAT,1M",
-                    "max_tokens": 1047576,
-                    "model_type": "chat",
-                    "is_tools": true
-                },
-                {
-                    "llm_name": "gpt-4.1-nano",
-                    "tags": "LLM,CHAT,1M",
-                    "max_tokens": 1047576,
-                    "model_type": "chat",
-                    "is_tools": true
-                },
-                {
-                    "llm_name": "gpt-4.1",
-                    "tags": "LLM,CHAT,1M",
-                    "max_tokens": 1047576,
-                    "model_type": "chat",
-                    "is_tools": true
-                },
-                {
-                    "llm_name": "gpt-4o-mini",
-                    "tags": "LLM,CHAT,128k",
-                    "max_tokens": 128000,
-                    "model_type": "chat",
-                    "is_tools": true
-                },
-                {
-                    "llm_name": "o4-mini-2025-04-16",
-                    "tags": "LLM,CHAT,200k",
-                    "max_tokens": 200000,
-                    "model_type": "chat",
-                    "is_tools": true
-                },
-                {
-                    "llm_name": "o3-pro-2025-06-10",
-                    "tags": "LLM,CHAT,200k",
-                    "max_tokens": 200000,
-                    "model_type": "chat",
-                    "is_tools": true
-                },
-                {
-                    "llm_name": "claude-opus-4-1-20250805",
-                    "tags": "LLM,CHAT,200k,IMAGE2TEXT",
-                    "max_tokens": 200000,
-                    "model_type": "image2text",
-                    "is_tools": true
-                },
-                {
-                    "llm_name": "claude-opus-4-1-20250805-thinking",
-                    "tags": "LLM,CHAT,200k,IMAGE2TEXT",
-                    "max_tokens": 200000,
-                    "model_type": "image2text",
-                    "is_tools": true
-                },
-                {
-                    "llm_name": "claude-sonnet-4-20250514",
-                    "tags": "LLM,CHAT,200k,IMAGE2TEXT",
-                    "max_tokens": 200000,
-                    "model_type": "image2text",
-                    "is_tools": true
-                },
-                {
-                    "llm_name": "claude-sonnet-4-20250514-thinking",
-                    "tags": "LLM,CHAT,200k,IMAGE2TEXT",
-                    "max_tokens": 200000,
-                    "model_type": "image2text",
-                    "is_tools": true
-                },
-                {
-                    "llm_name": "claude-3-7-sonnet-latest",
-                    "tags": "LLM,CHAT,200k",
-                    "max_tokens": 200000,
-                    "model_type": "chat",
-                    "is_tools": true
-                },
-                {
-                    "llm_name": "claude-3-5-haiku-latest",
-                    "tags": "LLM,CHAT,200k",
-                    "max_tokens": 200000,
-                    "model_type": "chat",
-                    "is_tools": true
-                },
-                {
-                    "llm_name": "gemini-2.5-pro",
-                    "tags": "LLM,CHAT,1M,IMAGE2TEXT",
-                    "max_tokens": 1000000,
-                    "model_type": "image2text",
-                    "is_tools": true
-                },
-                {
-                    "llm_name": "gemini-2.5-flash",
-                    "tags": "LLM,CHAT,1M,IMAGE2TEXT",
-                    "max_tokens": 1000000,
-                    "model_type": "image2text",
-                    "is_tools": true
-                },
-                {
-                    "llm_name": "gemini-2.5-flash-lite",
-                    "tags": "LLM,CHAT,1M,IMAGE2TEXT",
-                    "max_tokens": 1000000,
-                    "model_type": "image2text",
-                    "is_tools": true
-                },
-                {
-                    "llm_name": "gemini-2.0-flash",
-                    "tags": "LLM,CHAT,1M,IMAGE2TEXT",
-                    "max_tokens": 1000000,
-                    "model_type": "image2text",
-                    "is_tools": true
-                },
-                {
-                    "llm_name": "grok-4-0709",
-                    "tags": "LLM,CHAT,131k",
-                    "max_tokens": 131072,
-                    "model_type": "chat",
-                    "is_tools": true
-                },
-                {
-                    "llm_name": "grok-3",
-                    "tags": "LLM,CHAT,131k",
-                    "max_tokens": 131072,
-                    "model_type": "chat",
-                    "is_tools": true
-                },
-                {
-                    "llm_name": "grok-3-mini",
-                    "tags": "LLM,CHAT,131k",
-                    "max_tokens": 131072,
-                    "model_type": "chat",
-                    "is_tools": true
-                },
-                {
-                    "llm_name": "grok-2-image-1212",
-                    "tags": "LLM,CHAT,32k,IMAGE2TEXT",
-                    "max_tokens": 32768,
-                    "model_type": "image2text",
-                    "is_tools": true
-                },
-                {
-                    "llm_name": "deepseek-v3.1",
-                    "tags": "LLM,CHAT,64k",
-                    "max_tokens": 64000,
-                    "model_type": "chat",
-                    "is_tools": true
-                },
-                {
-                    "llm_name": "deepseek-v3",
-                    "tags": "LLM,CHAT,64k",
-                    "max_tokens": 64000,
-                    "model_type": "chat",
-                    "is_tools": true
-                },
-                {
-                    "llm_name": "deepseek-r1-0528",
-                    "tags": "LLM,CHAT,164k",
-                    "max_tokens": 164000,
-                    "model_type": "chat",
-                    "is_tools": true
-                },
-                {
-                    "llm_name": "deepseek-chat",
-                    "tags": "LLM,CHAT,32k",
-                    "max_tokens": 32000,
-                    "model_type": "chat",
-                    "is_tools": true
-                },
-                {
-                    "llm_name": "deepseek-reasoner",
-                    "tags": "LLM,CHAT,64k",
-                    "max_tokens": 64000,
-                    "model_type": "chat",
-                    "is_tools": true
-                },
-                {
-                    "llm_name": "qwen3-30b-a3b",
-                    "tags": "LLM,CHAT,128k",
-                    "max_tokens": 128000,
-                    "model_type": "chat",
-                    "is_tools": true
-                },
-                {
-                    "llm_name": "qwen3-coder-plus-2025-07-22",
-                    "tags": "LLM,CHAT,128k",
-                    "max_tokens": 128000,
-                    "model_type": "chat",
-                    "is_tools": true
-                },
-                {
-                    "llm_name": "text-embedding-ada-002",
-                    "tags": "TEXT EMBEDDING,8K",
-                    "max_tokens": 8191,
-                    "model_type": "embedding",
-                    "is_tools": false
-                },
-                {
-                    "llm_name": "text-embedding-3-small",
-                    "tags": "TEXT EMBEDDING,8K",
-                    "max_tokens": 8191,
-                    "model_type": "embedding",
-                    "is_tools": false
-                },
-                {
-                    "llm_name": "text-embedding-3-large",
-                    "tags": "TEXT EMBEDDING,8K",
-                    "max_tokens": 8191,
-                    "model_type": "embedding",
-                    "is_tools": false
-                },
-                {
-                    "llm_name": "whisper-1",
-                    "tags": "SPEECH2TEXT",
-                    "max_tokens": 26214400,
-                    "model_type": "speech2text",
-                    "is_tools": false
-                },
-                {
-                    "llm_name": "tts-1",
-                    "tags": "TTS",
-                    "max_tokens": 2048,
-                    "model_type": "tts",
-                    "is_tools": false
                 }
             ]
         }

conf/os_mapping.json

@@ -200,61 +200,6 @@
           }
         }
       },
-      {
-        "knn_vector": {
-          "match": "*_2048_vec",
-          "mapping": {
-            "type": "knn_vector",
-            "index": true,
-            "space_type": "cosinesimil",
-            "dimension": 2048
-          }
-        }
-      },
-      {
-        "knn_vector": {
-          "match": "*_4096_vec",
-          "mapping": {
-            "type": "knn_vector",
-            "index": true,
-            "space_type": "cosinesimil",
-            "dimension": 4096
-          }
-        }
-      },
-      {
-        "knn_vector": {
-          "match": "*_6144_vec",
-          "mapping": {
-            "type": "knn_vector",
-            "index": true,
-            "space_type": "cosinesimil",
-            "dimension": 6144
-          }
-        }
-      },
-      {
-        "knn_vector": {
-          "match": "*_8192_vec",
-          "mapping": {
-            "type": "knn_vector",
-            "index": true,
-            "space_type": "cosinesimil",
-            "dimension": 8192
-          }
-        }
-      },
-      {
-        "knn_vector": {
-          "match": "*_10240_vec",
-          "mapping": {
-            "type": "knn_vector",
-            "index": true,
-            "space_type": "cosinesimil",
-            "dimension": 10240
-          }
-        }
-      },
       {
         "binary": {
           "match": "*_bin",

deepdoc/parser/markdown_parser.py

@@ -17,6 +17,7 @@
 
 import re
 
+import mistune
 from markdown import markdown
 
 
@@ -116,6 +117,8 @@ class MarkdownElementExtractor:
     def __init__(self, markdown_content):
         self.markdown_content = markdown_content
         self.lines = markdown_content.split("\n")
+        self.ast_parser = mistune.create_markdown(renderer="ast")
+        self.ast_nodes = self.ast_parser(markdown_content)
 
     def extract_elements(self):
         """Extract individual elements (headers, code blocks, lists, etc.)"""

deepdoc/parser/pdf_parser.py

@@ -15,13 +15,11 @@
 #
 
 import logging
-import math
 import os
 import random
 import re
 import sys
 import threading
-from collections import Counter, defaultdict
 from copy import deepcopy
 from io import BytesIO
 from timeit import default_timer as timer
@@ -351,78 +349,9 @@ class RAGFlowPdfParser:
             self.boxes[i]["top"] += self.page_cum_height[self.boxes[i]["page_number"] - 1]
             self.boxes[i]["bottom"] += self.page_cum_height[self.boxes[i]["page_number"] - 1]
 
-    def _assign_column(self, boxes, zoomin=3):
+    def _text_merge(self):
-        if not boxes:
-            return boxes
-
-        if all("col_id" in b for b in boxes):
-            return boxes
-
-        by_page = defaultdict(list)
-        for b in boxes:
-            by_page[b["page_number"]].append(b)
-
-        page_info = {}  # pg -> dict(page_w, left_edge, cand_cols)
-        counter = Counter()
-
-        for pg, bxs in by_page.items():
-            if not bxs:
-                page_info[pg] = {"page_w": 1.0, "left_edge": 0.0, "cand": 1}
-                counter[1] += 1
-                continue
-
-            if hasattr(self, "page_images") and self.page_images and len(self.page_images) >= pg:
-                page_w = self.page_images[pg - 1].size[0] / max(1, zoomin)
-                left_edge = 0.0
-            else:
-                xs0 = [box["x0"] for box in bxs]
-                xs1 = [box["x1"] for box in bxs]
-                left_edge = float(min(xs0))
-                page_w = max(1.0, float(max(xs1) - left_edge))
-
-            widths = [max(1.0, (box["x1"] - box["x0"])) for box in bxs]
-            median_w = float(np.median(widths)) if widths else 1.0
-
-            raw_cols = int(page_w / max(1.0, median_w))
-
-            # cand = raw_cols if (raw_cols >= 2 and median_w < page_w / raw_cols * 0.8) else 1
-            cand = raw_cols
-
-            page_info[pg] = {"page_w": page_w, "left_edge": left_edge, "cand": cand}
-            counter[cand] += 1
-
-            logging.info(f"[Page {pg}] median_w={median_w:.2f}, page_w={page_w:.2f}, raw_cols={raw_cols}, cand={cand}")
-
-        global_cols = counter.most_common(1)[0][0]
-        logging.info(f"Global column_num decided by majority: {global_cols}")
-
-        for pg, bxs in by_page.items():
-            if not bxs:
-                continue
-
-            page_w = page_info[pg]["page_w"]
-            left_edge = page_info[pg]["left_edge"]
-
-            if global_cols == 1:
-                for box in bxs:
-                    box["col_id"] = 0
-                continue
-
-            for box in bxs:
-                w = box["x1"] - box["x0"]
-                if w >= 0.8 * page_w:
-                    box["col_id"] = 0
-                    continue
-                cx = 0.5 * (box["x0"] + box["x1"])
-                norm_cx = (cx - left_edge) / page_w
-                norm_cx = max(0.0, min(norm_cx, 0.999999))
-                box["col_id"] = int(min(global_cols - 1, norm_cx * global_cols))
-
-        return boxes
-
-    def _text_merge(self, zoomin=3):
         # merge adjusted boxes
-        bxs = self._assign_column(self.boxes, zoomin)
+        bxs = self.boxes
 
         def end_with(b, txt):
             txt = txt.strip()
@@ -438,15 +367,9 @@ class RAGFlowPdfParser:
         while i < len(bxs) - 1:
             b = bxs[i]
             b_ = bxs[i + 1]
-
-            if b["page_number"] != b_["page_number"] or b.get("col_id") != b_.get("col_id"):
-                i += 1
-                continue
-
             if b.get("layoutno", "0") != b_.get("layoutno", "1") or b.get("layout_type", "") in ["table", "figure", "equation"]:
                 i += 1
                 continue
-
             if abs(self._y_dis(b, b_)) < self.mean_height[bxs[i]["page_number"] - 1] / 3:
                 # merge
                 bxs[i]["x1"] = b_["x1"]
@@ -456,49 +379,50 @@ class RAGFlowPdfParser:
                 bxs.pop(i + 1)
                 continue
             i += 1
+            continue
+
+            dis_thr = 1
+            dis = b["x1"] - b_["x0"]
+            if b.get("layout_type", "") != "text" or b_.get("layout_type", "") != "text":
+                if end_with(b, "，") or start_with(b_, "（，"):
+                    dis_thr = -8
+                else:
+                    i += 1
+                    continue
+
+            if abs(self._y_dis(b, b_)) < self.mean_height[bxs[i]["page_number"] - 1] / 5 and dis >= dis_thr and b["x1"] < b_["x1"]:
+                # merge
+                bxs[i]["x1"] = b_["x1"]
+                bxs[i]["top"] = (b["top"] + b_["top"]) / 2
+                bxs[i]["bottom"] = (b["bottom"] + b_["bottom"]) / 2
+                bxs[i]["text"] += b_["text"]
+                bxs.pop(i + 1)
+                continue
+            i += 1
         self.boxes = bxs
 
     def _naive_vertical_merge(self, zoomin=3):
-        bxs = self._assign_column(self.boxes, zoomin)
+        import math
+        bxs = Recognizer.sort_Y_firstly(self.boxes, np.median(self.mean_height) / 3)
 
-        grouped = defaultdict(list)
+        column_width = np.median([b["x1"] - b["x0"] for b in self.boxes])
-        for b in bxs:
+        if not column_width or math.isnan(column_width):
-            grouped[(b["page_number"], b.get("col_id", 0))].append(b)
+            column_width = self.mean_width[0]
-
+        self.column_num = int(self.page_images[0].size[0] / zoomin / column_width)
-        merged_boxes = []
+        if column_width < self.page_images[0].size[0] / zoomin / self.column_num:
-        for (pg, col), bxs in grouped.items():
+            logging.info("Multi-column................... {} {}".format(column_width, self.page_images[0].size[0] / zoomin / self.column_num))
-            bxs = sorted(bxs, key=lambda x: (x["top"], x["x0"]))
+            self.boxes = self.sort_X_by_page(self.boxes, column_width / self.column_num)
-            if not bxs:
-                continue
-
-            mh = self.mean_height[pg - 1] if self.mean_height else np.median([b["bottom"] - b["top"] for b in bxs]) or 10
 
         i = 0
         while i + 1 < len(bxs):
             b = bxs[i]
             b_ = bxs[i + 1]
-
             if b["page_number"] < b_["page_number"] and re.match(r"[0-9  •一—-]+$", b["text"]):
                 bxs.pop(i)
                 continue
-
             if not b["text"].strip():
                 bxs.pop(i)
                 continue
-
-                if not b["text"].strip() or b.get("layoutno") != b_.get("layoutno"):
-                    i += 1
-                    continue
-
-                if b_["top"] - b["bottom"] > mh * 1.5:
-                    i += 1
-                    continue
-
-                overlap = max(0, min(b["x1"], b_["x1"]) - max(b["x0"], b_["x0"]))
-                if overlap / max(1, min(b["x1"] - b["x0"], b_["x1"] - b_["x0"])) < 0.3:
-                    i += 1
-                    continue
-
             concatting_feats = [
                 b["text"].strip()[-1] in ",;:'\"，、‘“；：-",
                 len(b["text"].strip()) > 1 and b["text"].strip()[-2] in ",;:'\"，‘“、；：",
@@ -525,39 +449,13 @@ class RAGFlowPdfParser:
                 )
                 i += 1
                 continue
-
+            # merge up and down
-                b["text"] = (b["text"].rstrip() + " " + b_["text"].lstrip()).strip()
             b["bottom"] = b_["bottom"]
+            b["text"] += b_["text"]
             b["x0"] = min(b["x0"], b_["x0"])
             b["x1"] = max(b["x1"], b_["x1"])
             bxs.pop(i + 1)
-
+        self.boxes = bxs
-            merged_boxes.extend(bxs)
-
-        self.boxes = sorted(merged_boxes, key=lambda x: (x["page_number"], x.get("col_id", 0), x["top"]))
-
-    def _final_reading_order_merge(self, zoomin=3):
-        if not self.boxes:
-            return
-
-        self.boxes = self._assign_column(self.boxes, zoomin=zoomin)
-
-        pages = defaultdict(lambda: defaultdict(list))
-        for b in self.boxes:
-            pg = b["page_number"]
-            col = b.get("col_id", 0)
-            pages[pg][col].append(b)
-
-        for pg in pages:
-            for col in pages[pg]:
-                pages[pg][col].sort(key=lambda x: (x["top"], x["x0"]))
-
-        new_boxes = []
-        for pg in sorted(pages.keys()):
-            for col in sorted(pages[pg].keys()):
-                new_boxes.extend(pages[pg][col])
-
-        self.boxes = new_boxes
 
     def _concat_downward(self, concat_between_pages=True):
         self.boxes = Recognizer.sort_Y_firstly(self.boxes, 0)
@@ -1099,7 +997,7 @@ class RAGFlowPdfParser:
                 self.__ocr(i + 1, img, chars, zoomin, id)
 
             if callback and i % 6 == 5:
-                callback((i + 1) * 0.6 / len(self.page_images))
+                callback(prog=(i + 1) * 0.6 / len(self.page_images), msg="")
 
         async def __img_ocr_launcher():
             def __ocr_preprocess():
@@ -1150,7 +1048,7 @@ class RAGFlowPdfParser:
 
     def parse_into_bboxes(self, fnm, callback=None, zoomin=3):
         start = timer()
-        self.__images__(fnm, zoomin, callback=callback)
+        self.__images__(fnm, zoomin)
         if callback:
             callback(0.40, "OCR finished ({:.2f}s)".format(timer() - start))
 
@@ -1176,6 +1074,7 @@ class RAGFlowPdfParser:
 
         def insert_table_figures(tbls_or_figs, layout_type):
             def min_rectangle_distance(rect1, rect2):
+                import math
                 pn1, left1, right1, top1, bottom1 = rect1
                 pn2, left2, right2, top2, bottom2 = rect2
                 if right1 >= left2 and right2 >= left1 and bottom1 >= top2 and bottom2 >= top1:
@@ -1192,39 +1091,27 @@ class RAGFlowPdfParser:
                     dy = top1 - bottom2
                 else:
                     dy = 0
-                return math.sqrt(dx * dx + dy * dy)  # + (pn2-pn1)*10000
+                return math.sqrt(dx*dx + dy*dy)# + (pn2-pn1)*10000
 
             for (img, txt), poss in tbls_or_figs:
                 bboxes = [(i, (b["page_number"], b["x0"], b["x1"], b["top"], b["bottom"])) for i, b in enumerate(self.boxes)]
-                dists = [
+                dists = [(min_rectangle_distance((pn, left, right, top+self.page_cum_height[pn], bott+self.page_cum_height[pn]), rect),i) for i, rect in bboxes for pn, left, right, top, bott in poss]
-                    (min_rectangle_distance((pn, left, right, top + self.page_cum_height[pn], bott + self.page_cum_height[pn]), rect), i) for i, rect in bboxes for pn, left, right, top, bott in poss
-                ]
                 min_i = np.argmin(dists, axis=0)[0]
                 min_i, rect = bboxes[dists[min_i][-1]]
                 if isinstance(txt, list):
                     txt = "\n".join(txt)
                 pn, left, right, top, bott = poss[0]
-                if self.boxes[min_i]["bottom"] < top + self.page_cum_height[pn]:
+                if self.boxes[min_i]["bottom"] < top+self.page_cum_height[pn]:
                     min_i += 1
-                self.boxes.insert(
+                self.boxes.insert(min_i, {
-                    min_i,
+                    "page_number": pn+1, "x0": left, "x1": right, "top": top+self.page_cum_height[pn], "bottom": bott+self.page_cum_height[pn], "layout_type": layout_type, "text": txt, "image": img,
-                    {
+                    "positions": [[pn+1, int(left), int(right), int(top), int(bott)]]
-                        "page_number": pn + 1,
+                })
-                        "x0": left,
-                        "x1": right,
-                        "top": top + self.page_cum_height[pn],
-                        "bottom": bott + self.page_cum_height[pn],
-                        "layout_type": layout_type,
-                        "text": txt,
-                        "image": img,
-                        "positions": [[pn + 1, int(left), int(right), int(top), int(bott)]],
-                    },
-                )
 
         for b in self.boxes:
             b["position_tag"] = self._line_tag(b, zoomin)
             b["image"] = self.crop(b["position_tag"], zoomin)
-            b["positions"] = [[pos[0][-1] + 1, *pos[1:]] for pos in RAGFlowPdfParser.extract_positions(b["position_tag"])]
+            b["positions"] = [[pos[0][-1]+1, *pos[1:]] for pos in RAGFlowPdfParser.extract_positions(b["position_tag"])]
 
         insert_table_figures(tbls, "table")
         insert_table_figures(figs, "figure")
@@ -1242,7 +1129,7 @@ class RAGFlowPdfParser:
         for tag in re.findall(r"@@[0-9-]+\t[0-9.\t]+##", txt):
             pn, left, right, top, bottom = tag.strip("#").strip("@").split("\t")
             left, right, top, bottom = float(left), float(right), float(top), float(bottom)
-            poss.append(([int(p) - 1 for p in pn.split("-")], left, right, top, bottom))
+            poss.append(([int(p) - 1 for p in pn.split("-")], int(left), int(right), int(top), int(bottom)))
         return poss
 
     def crop(self, text, ZM=3, need_position=False):
@@ -1387,16 +1274,12 @@ class VisionParser(RAGFlowPdfParser):
                 prompt=vision_llm_describe_prompt(page=pdf_page_num + 1),
                 callback=callback,
             )
-
             if kwargs.get("callback"):
                 kwargs["callback"](idx * 1.0 / len(self.page_images), f"Processed: {idx + 1}/{len(self.page_images)}")
 
             if text:
                 width, height = self.page_images[idx].size
-                all_docs.append((
+                all_docs.append((text, f"{pdf_page_num + 1} 0 {width / zoomin} 0 {height / zoomin}"))
-                    text,
-                    f"@@{pdf_page_num + 1}\t{0.0:.1f}\t{width / zoomin:.1f}\t{0.0:.1f}\t{height / zoomin:.1f}##"
-                ))
         return all_docs, []
 
 

deepdoc/vision/ocr.py

@@ -84,8 +84,7 @@ def load_model(model_dir, nm, device_id: int | None = None):
     def cuda_is_available():
         try:
             import torch
-            target_id = 0 if device_id is None else device_id
+            if torch.cuda.is_available() and torch.cuda.device_count() > device_id:
-            if torch.cuda.is_available() and torch.cuda.device_count() > target_id:
                 return True
         except Exception:
             return False
@@ -101,13 +100,10 @@ def load_model(model_dir, nm, device_id: int | None = None):
     # Shrink GPU memory after execution
     run_options = ort.RunOptions()
     if cuda_is_available():
-        gpu_mem_limit_mb = int(os.environ.get("OCR_GPU_MEM_LIMIT_MB", "2048"))
-        arena_strategy = os.environ.get("OCR_ARENA_EXTEND_STRATEGY", "kNextPowerOfTwo")
-        provider_device_id = 0 if device_id is None else device_id
         cuda_provider_options = {
-            "device_id": provider_device_id, # Use specific GPU
+            "device_id": device_id, # Use specific GPU
-            "gpu_mem_limit": max(gpu_mem_limit_mb, 0) * 1024 * 1024,
+            "gpu_mem_limit": 512 * 1024 * 1024, # Limit gpu memory
-            "arena_extend_strategy": arena_strategy,  # gpu memory allocation strategy
+            "arena_extend_strategy": "kNextPowerOfTwo",  # gpu memory allocation strategy
         }
         sess = ort.InferenceSession(
             model_file_path,
@@ -115,8 +111,8 @@ def load_model(model_dir, nm, device_id: int | None = None):
             providers=['CUDAExecutionProvider'],
             provider_options=[cuda_provider_options]
             )
-        run_options.add_run_config_entry("memory.enable_memory_arena_shrinkage", "gpu:" + str(provider_device_id))
+        run_options.add_run_config_entry("memory.enable_memory_arena_shrinkage", "gpu:" + str(device_id))
-        logging.info(f"load_model {model_file_path} uses GPU (device {provider_device_id}, gpu_mem_limit={cuda_provider_options['gpu_mem_limit']}, arena_strategy={arena_strategy})")
+        logging.info(f"load_model {model_file_path} uses GPU")
     else:
         sess = ort.InferenceSession(
             model_file_path,

docker/docker-compose-base.yml

@@ -77,7 +77,7 @@ services:
     container_name: ragflow-infinity
     profiles:
       - infinity
-    image: infiniflow/infinity:v0.6.0-dev7
+    image: infiniflow/infinity:v0.6.0-dev5
     volumes:
       - infinity_data:/var/infinity
       - ./infinity_conf.toml:/infinity_conf.toml

docs/develop/mcp/launch_mcp_server.md

@@ -98,7 +98,7 @@ Where:
 
 - `mcp-host`: The MCP server's host address.
 - `mcp-port`: The MCP server's listening port.
-- `mcp-base-url`: The address of the running RAGFlow server.
+- `mcp-base_url`: The address of the running RAGFlow server.
 - `mcp-script-path`: The file path to the MCP server’s main script.
 - `mcp-mode`: The launch mode.
   - `self-host`: (default) self-host mode.

docs/guides/dataset/set_metadata.md

@@ -21,10 +21,6 @@ Ensure that your metadata is in JSON format; otherwise, your updates will not be
 
 ![Input metadata](https://raw.githubusercontent.com/infiniflow/ragflow-docs/main/images/input_metadata.jpg)
 
-## Related APIs
-
-[Retrieve chunks](../../references/http_api_reference.md#retrieve-chunks)
-
 ## Frequently asked questions
 
 ### Can I set metadata for multiple documents at once?

docs/guides/manage_users_and_services.md

@@ -1,360 +0,0 @@
-# Admin CLI and Admin Service
-
-
-
-The Admin CLI and Admin Service form a client-server architectural suite for RAGflow system administration. The Admin CLI serves as an interactive command-line interface that receives instructions and displays execution results from the Admin Service in real-time. This duo enables real-time monitoring of system operational status, supporting visibility into RAGflow Server services and dependent components including MySQL, Elasticsearch, Redis, and MinIO. In administrator mode, they provide user management capabilities that allow viewing users and performing critical operations—such as user creation, password updates, activation status changes, and comprehensive user data deletion—even when corresponding web interface functionalities are disabled.
-
-
-
-## Starting the Admin Service
-
-1. Before start Admin Service, please make sure RAGFlow system is already started.
-2. Switch to ragflow/ directory and run the service script:
-
-```bash
-source .venv/bin/activate
-export PYTHONPATH=$(pwd)
-python admin/admin_server.py
-```
-
-The service will start and listen for incoming connections from the CLI on the configured port. Default port is 9381.
-
-
-
-## Using the Admin CLI
-
-1. Ensure the Admin Service is running.
-2. Launch the CLI client:
-
-```bash
-source .venv/bin/activate
-export PYTHONPATH=$(pwd)
-python admin/admin_client.py -h 0.0.0.0 -p 9381
-```
-
-Enter superuser's password to login. Default password is `admin`.
-
-
-
-## Supported Commands
-
-Commands are case-insensitive and must be terminated with a semicolon(;).
-
-### Service manage commands
-
- `LIST SERVICES;`
-
-- Lists all available services within the RAGFlow system.
-
-- [Example](#example-list-services)
-
-`SHOW SERVICE <id>;`
-
-- Shows detailed status information for the service identified by <id>.
-- [Example](#example-show-service)
-
-### User Management Commands
-
-`LIST USERS;`
-
-- Lists all users known to the system.
-- [Example](#example-list-users)
-
-`SHOW USER <username>;`
-
-- Shows details and permissions for the user specified by **email**. The username must be enclosed in single or double quotes.
-- [Example](#example-show-user)
-
-`CREATE USER <username> <password>;`
-
-- Create user by username and password. The username and password must be enclosed in single or double quotes.
-- [Example](#example-create-user)
-
-`DROP USER <username>;`
-
-- Removes the specified user from the system. Use with caution.
-- [Example](#example-drop-user)
-
-`ALTER USER PASSWORD <username> <new_password>;`
-
-- Changes the password for the specified user.
-- [Example](#example-alter-user-password)
-
-`ALTER USER ACTIVE <username> <on/off>;`
-
-- Changes the user to active or inactive.
-- [Example](#example-alter-user-active)
-
-### Data and Agent Commands
-
-`LIST DATASETS OF <username>;`
-
-- Lists the datasets associated with the specified user.
-- [Example](#example-list-datasets-of-user)
-
-`LIST AGENTS OF <username>;`
-
-- Lists the agents associated with the specified user.
-- [Example](#example-list-agents-of-user)
-
-### Meta-Commands
-
-- \? or \help
-  Shows help information for the available commands.
-- \q or \quit
-  Exits the CLI application.
-- [Example](#example-meta-commands)
-
-### Examples
-
-<span id="example-list-services"></span>
-
-- List all available services.
-
-```
-admin> list services;
-command: list services;
-Listing all services
-+-------------------------------------------------------------------------------------------+-----------+----+---------------+-------+----------------+
-| extra                                                                                     | host      | id | name          | port  | service_type   |
-+-------------------------------------------------------------------------------------------+-----------+----+---------------+-------+----------------+
-| {}                                                                                        | 0.0.0.0   | 0  | ragflow_0     | 9380  | ragflow_server |
-| {'meta_type': 'mysql', 'password': 'infini_rag_flow', 'username': 'root'}                 | localhost | 1  | mysql         | 5455  | meta_data      |
-| {'password': 'infini_rag_flow', 'store_type': 'minio', 'user': 'rag_flow'}                | localhost | 2  | minio         | 9000  | file_store     |
-| {'password': 'infini_rag_flow', 'retrieval_type': 'elasticsearch', 'username': 'elastic'} | localhost | 3  | elasticsearch | 1200  | retrieval      |
-| {'db_name': 'default_db', 'retrieval_type': 'infinity'}                                   | localhost | 4  | infinity      | 23817 | retrieval      |
-| {'database': 1, 'mq_type': 'redis', 'password': 'infini_rag_flow'}                        | localhost | 5  | redis         | 6379  | message_queue  |
-+-------------------------------------------------------------------------------------------+-----------+----+---------------+-------+----------------+
-
-```
-
-<span id="example-show-service"></span>
-
-- Show ragflow_server.
-
-```
-admin> show service 0;
-command: show service 0;
-Showing service: 0
-Service ragflow_0 is alive. Detail:
-Confirm elapsed: 26.0 ms.
-```
-
-- Show mysql.
-
-```
-admin> show service 1;
-command: show service 1;
-Showing service: 1
-Service mysql is alive. Detail:
-+---------+----------+------------------+------+------------------+------------------------+-------+-----------------+
-| command | db       | host             | id   | info             | state                  | time  | user            |
-+---------+----------+------------------+------+------------------+------------------------+-------+-----------------+
-| Daemon  | None     | localhost        | 5    | None             | Waiting on empty queue | 16111 | event_scheduler |
-| Sleep   | rag_flow | 172.18.0.1:40046 | 1610 | None             |                        | 2     | root            |
-| Query   | rag_flow | 172.18.0.1:35882 | 1629 | SHOW PROCESSLIST | init                   | 0     | root            |
-+---------+----------+------------------+------+------------------+------------------------+-------+-----------------+
-```
-
-- Show minio.
-
-```
-admin> show service 2;
-command: show service 2;
-Showing service: 2
-Service minio is alive. Detail:
-Confirm elapsed: 2.1 ms.
-```
-
-- Show elasticsearch.
-
-```
-admin> show service 3;
-command: show service 3;
-Showing service: 3
-Service elasticsearch is alive. Detail:
-+----------------+------+--------------+---------+----------------+--------------+---------------+--------------+------------------------------+----------------------------+-----------------+-------+---------------+---------+-------------+---------------------+--------+------------+--------------------+
-| cluster_name   | docs | docs_deleted | indices | indices_shards | jvm_heap_max | jvm_heap_used | jvm_versions | mappings_deduplicated_fields | mappings_deduplicated_size | mappings_fields | nodes | nodes_version | os_mem  | os_mem_used | os_mem_used_percent | status | store_size | total_dataset_size |
-+----------------+------+--------------+---------+----------------+--------------+---------------+--------------+------------------------------+----------------------------+-----------------+-------+---------------+---------+-------------+---------------------+--------+------------+--------------------+
-| docker-cluster | 717  | 86           | 37      | 42             | 3.76 GB      | 1.74 GB       | 21.0.1+12-29 | 6575                         | 48.0 KB                    | 8521            | 1     | ['8.11.3']    | 7.52 GB | 4.55 GB     | 61                  | green  | 4.60 MB    | 4.60 MB            |
-+----------------+------+--------------+---------+----------------+--------------+---------------+--------------+------------------------------+----------------------------+-----------------+-------+---------------+---------+-------------+---------------------+--------+------------+--------------------+
-```
-
-- Show infinity.
-
-```
-admin> show service 4;
-command: show service 4;
-Showing service: 4
-Fail to show service, code: 500, message: Infinity is not in use.
-```
-
-- Show redis.
-
-```
-admin> show service 5;
-command: show service 5;
-Showing service: 5
-Service redis is alive. Detail:
-+-----------------+-------------------+---------------------------+-------------------------+---------------+-------------+--------------------------+---------------------+-------------+
-| blocked_clients | connected_clients | instantaneous_ops_per_sec | mem_fragmentation_ratio | redis_version | server_mode | total_commands_processed | total_system_memory | used_memory |
-+-----------------+-------------------+---------------------------+-------------------------+---------------+-------------+--------------------------+---------------------+-------------+
-| 0               | 2                 | 1                         | 10.41                   | 7.2.4         | standalone  | 10446                    | 30.84G              | 1.10M       |
-+-----------------+-------------------+---------------------------+-------------------------+---------------+-------------+--------------------------+---------------------+-------------+
-```
-
-<span id="example-list-users"></span>
-
-- List all user.
-
-```
-admin> list users;
-command: list users;
-Listing all users
-+-------------------------------+----------------------+-----------+----------+
-| create_date                   | email                | is_active | nickname |
-+-------------------------------+----------------------+-----------+----------+
-| Mon, 22 Sep 2025 10:59:04 GMT | admin@ragflow.io     | 1         | admin    |
-| Sun, 14 Sep 2025 17:36:27 GMT | lynn_inf@hotmail.com | 1         | Lynn     |
-+-------------------------------+----------------------+-----------+----------+
-```
-
-<span id="example-show-user"></span>
-
-- Show specified user.
-
-```
-admin> show user "admin@ragflow.io";
-command: show user "admin@ragflow.io";
-Showing user: admin@ragflow.io
-+-------------------------------+------------------+-----------+--------------+------------------+--------------+----------+-----------------+---------------+--------+-------------------------------+
-| create_date                   | email            | is_active | is_anonymous | is_authenticated | is_superuser | language | last_login_time | login_channel | status | update_date                   |
-+-------------------------------+------------------+-----------+--------------+------------------+--------------+----------+-----------------+---------------+--------+-------------------------------+
-| Mon, 22 Sep 2025 10:59:04 GMT | admin@ragflow.io | 1         | 0            | 1                | True         | Chinese  | None            | None          | 1      | Mon, 22 Sep 2025 10:59:04 GMT |
-+-------------------------------+------------------+-----------+--------------+------------------+--------------+----------+-----------------+---------------+--------+-------------------------------+
-```
-
-<span id="example-create-user"></span>
-
-- Create new user.
-
-```
-admin> create user "example@ragflow.io" "psw";
-command: create user "example@ragflow.io" "psw";
-Create user: example@ragflow.io, password: psw, role: user
-+----------------------------------+--------------------+----------------------------------+--------------+---------------+----------+
-| access_token                     | email              | id                               | is_superuser | login_channel | nickname |
-+----------------------------------+--------------------+----------------------------------+--------------+---------------+----------+
-| 5cdc6d1e9df111f099b543aee592c6bf | example@ragflow.io | 5cdc6ca69df111f099b543aee592c6bf | False        | password      |          |
-+----------------------------------+--------------------+----------------------------------+--------------+---------------+----------+
-```
-
-<span id="example-alter-user-password"></span>
-
-- Alter user password.
-
-```
-admin> alter user password "example@ragflow.io" "newpsw";
-command: alter user password "example@ragflow.io" "newpsw";
-Alter user: example@ragflow.io, password: newpsw
-Password updated successfully!
-```
-
-<span id="example-alter-user-active"></span>
-
-- Alter user active, turn off.
-
-```
-admin> alter user active "example@ragflow.io" off;
-command: alter user active "example@ragflow.io" off;
-Alter user example@ragflow.io activate status, turn off.
-Turn off user activate status successfully!
-```
-
-<span id="example-drop-user"></span>
-
-- Drop user.
-
-```
-admin> Drop user "example@ragflow.io";
-command: Drop user "example@ragflow.io";
-Drop user: example@ragflow.io
-Successfully deleted user. Details:
-Start to delete owned tenant.
-- Deleted 2 tenant-LLM records.
-- Deleted 0 langfuse records.
-- Deleted 1 tenant.
-- Deleted 1 user-tenant records.
-- Deleted 1 user.
-Delete done!
-```
-
-Delete user's data at the same time.
-
-<span id="example-list-datasets-of-user"></span>
-
-- List the specified user's dataset.
-
-```
-admin> list datasets of "lynn_inf@hotmail.com";
-command: list datasets of "lynn_inf@hotmail.com";
-Listing all datasets of user: lynn_inf@hotmail.com
-+-----------+-------------------------------+---------+----------+---------------+------------+--------+-----------+-------------------------------+
-| chunk_num | create_date                   | doc_num | language | name          | permission | status | token_num | update_date                   |
-+-----------+-------------------------------+---------+----------+---------------+------------+--------+-----------+-------------------------------+
-| 29        | Mon, 15 Sep 2025 11:56:59 GMT | 12      | Chinese  | test_dataset  | me         | 1      | 12896     | Fri, 19 Sep 2025 17:50:58 GMT |
-| 4         | Sun, 28 Sep 2025 11:49:31 GMT | 6       | Chinese  | dataset_share | team       | 1      | 1121      | Sun, 28 Sep 2025 14:41:03 GMT |
-+-----------+-------------------------------+---------+----------+---------------+------------+--------+-----------+-------------------------------+
-```
-
-<span id="example-list-agents-of-user"></span>
-
-- List the specified user's agents.
-
-```
-admin> list agents of "lynn_inf@hotmail.com";
-command: list agents of "lynn_inf@hotmail.com";
-Listing all agents of user: lynn_inf@hotmail.com
-+-----------------+-------------+------------+-----------------+
-| canvas_category | canvas_type | permission | title           |
-+-----------------+-------------+------------+-----------------+
-| agent_canvas    | None        | team       | research_helper |
-+-----------------+-------------+------------+-----------------+
-```
-
-<span id="example-meta-commands"></span>
-
-- Show help information.
-
-```
-admin> \help
-command: \help
-
-Commands:
-  LIST SERVICES
-  SHOW SERVICE <service>
-  STARTUP SERVICE <service>
-  SHUTDOWN SERVICE <service>
-  RESTART SERVICE <service>
-  LIST USERS
-  SHOW USER <user>
-  DROP USER <user>
-  CREATE USER <user> <password>
-  ALTER USER PASSWORD <user> <new_password>
-  ALTER USER ACTIVE <user> <on/off>
-  LIST DATASETS OF <user>
-  LIST AGENTS OF <user>
-
-Meta Commands:
-  \?, \h, \help     Show this help
-  \q, \quit, \exit   Quit the CLI
-```
-
-- Exit
-
-```
-admin> \q
-command: \q
-Goodbye!
-```
-

docs/references/http_api_reference.md

@@ -830,8 +830,7 @@ Success:
             "update_time": 1728533243536,
             "vector_similarity_weight": 0.3
         }
-    ],
+    ]
-    "total": 1
 }
 ```
 
@@ -1281,7 +1280,7 @@ Success:
                 "update_time": 1728897061948
             }
         ],
-        "total_datasets": 1
+        "total": 1
     }
 }
 ```
@@ -1823,21 +1822,7 @@ curl --request POST \
      {
           "question": "What is advantage of ragflow?",
           "dataset_ids": ["b2a62730759d11ef987d0242ac120004"],
-          "document_ids": ["77df9ef4759a11ef8bdd0242ac120004"],
+          "document_ids": ["77df9ef4759a11ef8bdd0242ac120004"]
-          "metadata_condition": {
-            "conditions": [
-              {
-                "name": "author",
-                "comparison_operator": "=",
-                "value": "Toby"
-              },
-              {
-                "name": "url",
-                "comparison_operator": "not contains",
-                "value": "amd"
-              }
-            ]
-          }
      }'
 ```
 
@@ -1872,25 +1857,7 @@ curl --request POST \
 - `"cross_languages"`: (*Body parameter*) `list[string]`  
   The languages that should be translated into, in order to achieve keywords retrievals in different languages.
 - `"metadata_condition"`: (*Body parameter*), `object`  
-  The metadata condition used for filtering chunks:  
+  The metadata condition for filtering chunks.
-  - `"conditions"`: (*Body parameter*), `array`  
-    A list of metadata filter conditions.  
-    - `"name"`: `string` - The metadata field name to filter by, e.g., `"author"`, `"company"`, `"url"`. Ensure this parameter before use. See [Set metadata](../guides/dataset/set_metadata.md) for details.
-    - `comparison_operator`: `string` - The comparison operator. Can be one of: 
-      - `"contains"`
-      - `"not contains"`
-      - `"start with"`
-      - `"empty"`
-      - `"not empty"`
-      - `"="`
-      - `"≠"`
-      - `">"`
-      - `"<"`
-      - `"≥"`
-      - `"≤"`
-    - `"value"`: `string` - The value to compare.
-
-
 #### Response
 
 Success:

docs/references/supported_models.mdx

@@ -33,7 +33,7 @@ A complete list of models supported by RAGFlow, which will continue to expand.
 | Jina                  |                    | :heavy_check_mark: | :heavy_check_mark: |                    |                    |                    |
 | LeptonAI              | :heavy_check_mark: |                    |                    |                    |                    |                    |
 | LocalAI               | :heavy_check_mark: | :heavy_check_mark: |                    | :heavy_check_mark: |                    |                    |
-| LM-Studio             | :heavy_check_mark: | :heavy_check_mark: |                    | :heavy_check_mark: |                    |                    |
+| LM-Studio             | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |                    |                    |
 | MiniMax               | :heavy_check_mark: |                    |                    |                    |                    |                    |
 | Mistral               | :heavy_check_mark: | :heavy_check_mark: |                    |                    |                    |                    |
 | ModelScope            | :heavy_check_mark: |                    |                    |                    |                    |                    |
@@ -66,7 +66,6 @@ A complete list of models supported by RAGFlow, which will continue to expand.
 | DeepInfra             | :heavy_check_mark: | :heavy_check_mark: |                    |                    | :heavy_check_mark: | :heavy_check_mark: |
 | 302.AI                | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |                    |                    |
 | CometAPI              | :heavy_check_mark: | :heavy_check_mark: |                    |                    |                    |                    |
-| DeerAPI               | :heavy_check_mark: | :heavy_check_mark: |                    | :heavy_check_mark: |                    | :heavy_check_mark: |
 
 ```mdx-code-block
 </APITable>

download_deps.py

@@ -6,6 +6,7 @@
 # dependencies = [
 #   "huggingface-hub",
 #   "nltk",
+#   "argparse",
 # ]
 # ///
 
@@ -16,7 +17,7 @@ import os
 import urllib.request
 import argparse
 
-def get_urls(use_china_mirrors=False) -> list[Union[str, list[str]]]:
+def get_urls(use_china_mirrors=False) -> Union[str, list[str]]:
     if use_china_mirrors:
         return [
             "http://mirrors.tuna.tsinghua.edu.cn/ubuntu/pool/main/o/openssl/libssl1.1_1.1.1f-1ubuntu2_amd64.deb",

graphrag/entity_resolution.py

@@ -60,7 +60,7 @@ class EntityResolution(Extractor):
         self._llm = llm_invoker
         self._resolution_prompt = ENTITY_RESOLUTION_PROMPT
         self._record_delimiter_key = "record_delimiter"
-        self._entity_index_delimiter_key = "entity_index_delimiter"
+        self._entity_index_dilimiter_key = "entity_index_delimiter"
         self._resolution_result_delimiter_key = "resolution_result_delimiter"
         self._input_text_key = "input_text"
 
@@ -77,7 +77,7 @@ class EntityResolution(Extractor):
             **prompt_variables,
             self._record_delimiter_key: prompt_variables.get(self._record_delimiter_key)
                                         or DEFAULT_RECORD_DELIMITER,
-            self._entity_index_delimiter_key: prompt_variables.get(self._entity_index_delimiter_key)
+            self._entity_index_dilimiter_key: prompt_variables.get(self._entity_index_dilimiter_key)
                                               or DEFAULT_ENTITY_INDEX_DELIMITER,
             self._resolution_result_delimiter_key: prompt_variables.get(self._resolution_result_delimiter_key)
                                                    or DEFAULT_RESOLUTION_RESULT_DELIMITER,
@@ -185,7 +185,7 @@ class EntityResolution(Extractor):
         result = self._process_results(len(candidate_resolution_i[1]), response,
                                        self.prompt_variables.get(self._record_delimiter_key,
                                                             DEFAULT_RECORD_DELIMITER),
-                                       self.prompt_variables.get(self._entity_index_delimiter_key,
+                                       self.prompt_variables.get(self._entity_index_dilimiter_key,
                                                             DEFAULT_ENTITY_INDEX_DELIMITER),
                                        self.prompt_variables.get(self._resolution_result_delimiter_key,
                                                             DEFAULT_RESOLUTION_RESULT_DELIMITER))

graphrag/general/index.py

@@ -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 settings.retrievaler.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 settings.retrievaler.chunk_list(
             doc_id,
             tenant_id,
             [kb_id],

graphrag/general/smoke.py

@@ -62,7 +62,7 @@ async def main():
 
     chunks = [
         d["content_with_weight"]
-        for d in settings.retriever.chunk_list(
+        for d in settings.retrievaler.chunk_list(
             args.doc_id,
             args.tenant_id,
             [kb_id],

graphrag/light/smoke.py

@@ -63,7 +63,7 @@ async def main():
 
     chunks = [
         d["content_with_weight"]
-        for d in settings.retriever.chunk_list(
+        for d in settings.retrievaler.chunk_list(
             args.doc_id,
             args.tenant_id,
             [kb_id],

graphrag/utils.py

@@ -92,7 +92,10 @@ def dict_has_keys_with_types(data: dict, expected_fields: list[tuple[str, type]]
 
 def get_llm_cache(llmnm, txt, history, genconf):
     hasher = xxhash.xxh64()
-    hasher.update((str(llmnm)+str(txt)+str(history)+str(genconf)).encode("utf-8"))
+    hasher.update(str(llmnm).encode("utf-8"))
+    hasher.update(str(txt).encode("utf-8"))
+    hasher.update(str(history).encode("utf-8"))
+    hasher.update(str(genconf).encode("utf-8"))
 
     k = hasher.hexdigest()
     bin = REDIS_CONN.get(k)
@@ -103,7 +106,11 @@ def get_llm_cache(llmnm, txt, history, genconf):
 
 def set_llm_cache(llmnm, txt, v, history, genconf):
     hasher = xxhash.xxh64()
-    hasher.update((str(llmnm)+str(txt)+str(history)+str(genconf)).encode("utf-8"))
+    hasher.update(str(llmnm).encode("utf-8"))
+    hasher.update(str(txt).encode("utf-8"))
+    hasher.update(str(history).encode("utf-8"))
+    hasher.update(str(genconf).encode("utf-8"))
+
     k = hasher.hexdigest()
     REDIS_CONN.set(k, v.encode("utf-8"), 24 * 3600)
 
@@ -334,7 +341,7 @@ 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 = settings.retrievaler.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:
@@ -391,7 +398,7 @@ 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: settings.retrievaler.search(conds, search.index_name(tenant_id), [kb_id]))
     doc_ids = []
     if res.total == 0:
         return doc_ids
@@ -402,7 +409,7 @@ 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(settings.retrievaler.search, conds, search.index_name(tenant_id), [kb_id])
     if not res.total == 0:
         for id in res.ids:
             try:
@@ -555,7 +562,7 @@ 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: settings.retrievaler.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:

helm/values.yaml

@@ -96,7 +96,7 @@ ragflow:
 infinity:
   image:
     repository: infiniflow/infinity
-    tag: v0.6.0-dev7
+    tag: v0.6.0-dev5
     pullPolicy: IfNotPresent
     pullSecrets: []
   storage:

pyproject.toml

@@ -46,7 +46,7 @@ dependencies = [
     "html-text==0.6.2",
     "httpx[socks]==0.27.2",
     "huggingface-hub>=0.25.0,<0.26.0",
-    "infinity-sdk==0.6.0.dev7",
+    "infinity-sdk==0.6.0.dev5",
     "infinity-emb>=0.0.66,<0.0.67",
     "itsdangerous==2.1.2",
     "json-repair==0.35.0",
@@ -119,7 +119,7 @@ dependencies = [
     "graspologic>=3.4.1,<4.0.0",
     "mini-racer>=0.12.4,<0.13.0",
     "pyodbc>=5.2.0,<6.0.0",
-    "pyicu>=2.15.3,<3.0.0",
+    "pyicu>=2.13.1,<3.0.0",
     "flasgger>=0.9.7.1,<0.10.0",
     "xxhash>=3.5.0,<4.0.0",
     "trio>=0.29.0",
@@ -133,8 +133,6 @@ dependencies = [
     "litellm>=1.74.15.post1",
     "flask-mail>=0.10.0",
     "lark>=1.2.2",
-    "mammoth>=1.11.0",
-    "markdownify>=1.2.0",
 ]
 
 [project.optional-dependencies]
@@ -161,7 +159,7 @@ test = [
 ]
 
 [[tool.uv.index]]
-url = "https://pypi.tuna.tsinghua.edu.cn/simple"
+url = "https://mirrors.aliyun.com/pypi/simple"
 
 [tool.setuptools]
 packages = [

rag/app/naive.py

@@ -256,49 +256,6 @@ class Docx(DocxParser):
             tbls.append(((None, html), ""))
         return new_line, tbls
 
-    def to_markdown(self, filename=None, binary=None, inline_images: bool = True):
-        """
-        This function uses mammoth, licensed under the BSD 2-Clause License.
-        """
-
-        import base64
-        import uuid
-
-        import mammoth
-        from markdownify import markdownify
-
-        docx_file = BytesIO(binary) if binary else open(filename, "rb")
-
-        def _convert_image_to_base64(image):
-            try:
-                with image.open() as image_file:
-                    image_bytes = image_file.read()
-                encoded = base64.b64encode(image_bytes).decode("utf-8")
-                base64_url = f"data:{image.content_type};base64,{encoded}"
-
-                alt_name = "image"
-                alt_name = f"img_{uuid.uuid4().hex[:8]}"
-
-                return {"src": base64_url, "alt": alt_name}
-            except Exception as e:
-                logging.warning(f"Failed to convert image to base64: {e}")
-                return {"src": "", "alt": "image"}
-
-        try:
-            if inline_images:
-                result = mammoth.convert_to_html(docx_file, convert_image=mammoth.images.img_element(_convert_image_to_base64))
-            else:
-                result = mammoth.convert_to_html(docx_file)
-
-            html = result.value
-
-            markdown_text = markdownify(html)
-            return markdown_text
-
-        finally:
-            if not binary:
-                docx_file.close()
-
 
 class Pdf(PdfParser):
     def __init__(self):
@@ -328,7 +285,7 @@ class Pdf(PdfParser):
         callback(0.65, "Table analysis ({:.2f}s)".format(timer() - start))
 
         start = timer()
-        self._text_merge(zoomin=zoomin)
+        self._text_merge()
         callback(0.67, "Text merged ({:.2f}s)".format(timer() - start))
 
         if separate_tables_figures:
@@ -340,7 +297,6 @@ class Pdf(PdfParser):
             tbls = self._extract_table_figure(True, zoomin, True, True)
             self._naive_vertical_merge()
             self._concat_downward()
-            self._final_reading_order_merge()
             # self._filter_forpages()
             logging.info("layouts cost: {}s".format(timer() - first_start))
             return [(b["text"], self._line_tag(b, zoomin)) for b in self.boxes], tbls

rag/app/tag.py

@@ -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 = settings.retrievaler.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 = settings.retrievaler.tag_query(question,
                                               list(set([kb.tenant_id for kb in tag_kbs])),
                                               tag_kb_ids,
                                               all_tags,

rag/benchmark.py

@@ -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 = settings.retrievaler.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}")

rag/flow/chunker/__init__.py

@@ -12,27 +12,4 @@
 #  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
-#
 
-
-class AdminException(Exception):
-    def __init__(self, message, code=400):
-        super().__init__(message)
-        self.type = "admin"
-        self.code = code
-        self.message = message
-
-
-class UserNotFoundError(AdminException):
-    def __init__(self, username):
-        super().__init__(f"User '{username}' not found", 404)
-
-
-class UserAlreadyExistsError(AdminException):
-    def __init__(self, username):
-        super().__init__(f"User '{username}' already exists", 409)
-
-
-class CannotDeleteAdminError(AdminException):
-    def __init__(self):
-        super().__init__("Cannot delete admin account", 403)

rag/flow/chunker/chunker.py

@@ -0,0 +1,299 @@
+#
+#  Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+import json
+import random
+import trio
+from api.db import LLMType
+from api.db.services.llm_service import LLMBundle
+from deepdoc.parser.pdf_parser import RAGFlowPdfParser
+from graphrag.utils import chat_limiter, get_llm_cache, set_llm_cache
+from rag.flow.base import ProcessBase, ProcessParamBase
+from rag.flow.chunker.schema import ChunkerFromUpstream
+from rag.nlp import naive_merge, naive_merge_with_images, concat_img
+from rag.prompts.prompts import keyword_extraction, question_proposal, detect_table_of_contents, \
+    table_of_contents_index, toc_transformer
+from rag.utils import num_tokens_from_string
+
+
+class ChunkerParam(ProcessParamBase):
+    def __init__(self):
+        super().__init__()
+        self.method_options = [
+            # General
+            "general",
+            "onetable",
+            # Customer Service
+            "q&a",
+            "manual",
+            # Recruitment
+            "resume",
+            # Education & Research
+            "book",
+            "paper",
+            "laws",
+            "presentation",
+            "toc" # table of contents
+            # Other
+            # "Tag" # TODO: Other method
+        ]
+        self.method = "general"
+        self.chunk_token_size = 512
+        self.delimiter = "\n"
+        self.overlapped_percent = 0
+        self.page_rank = 0
+        self.auto_keywords = 0
+        self.auto_questions = 0
+        self.tag_sets = []
+        self.llm_setting = {"llm_id": "", "lang": "Chinese"}
+
+    def check(self):
+        self.check_valid_value(self.method.lower(), "Chunk method abnormal.", self.method_options)
+        self.check_positive_integer(self.chunk_token_size, "Chunk token size.")
+        self.check_nonnegative_number(self.page_rank, "Page rank value: (0, 10]")
+        self.check_nonnegative_number(self.auto_keywords, "Auto-keyword value: (0, 10]")
+        self.check_nonnegative_number(self.auto_questions, "Auto-question value: (0, 10]")
+        self.check_decimal_float(self.overlapped_percent, "Overlapped percentage: [0, 1)")
+
+    def get_input_form(self) -> dict[str, dict]:
+        return {}
+
+
+class Chunker(ProcessBase):
+    component_name = "Chunker"
+
+    def _general(self, from_upstream: ChunkerFromUpstream):
+        self.callback(random.randint(1, 5) / 100.0, "Start to chunk via `General`.")
+        if from_upstream.output_format in ["markdown", "text", "html"]:
+            if from_upstream.output_format == "markdown":
+                payload = from_upstream.markdown_result
+            elif from_upstream.output_format == "text":
+                payload = from_upstream.text_result
+            else:  # == "html"
+                payload = from_upstream.html_result
+
+            if not payload:
+                payload = ""
+
+            cks = naive_merge(
+                payload,
+                self._param.chunk_token_size,
+                self._param.delimiter,
+                self._param.overlapped_percent,
+            )
+            return [{"text": c} for c in cks]
+
+        # json
+        sections, section_images = [], []
+        for o in from_upstream.json_result or []:
+            sections.append((o.get("text", ""), o.get("position_tag", "")))
+            section_images.append(o.get("image"))
+
+        chunks, images = naive_merge_with_images(
+            sections,
+            section_images,
+            self._param.chunk_token_size,
+            self._param.delimiter,
+            self._param.overlapped_percent,
+        )
+
+        return [
+            {
+                "text": RAGFlowPdfParser.remove_tag(c),
+                "image": img,
+                "positions": RAGFlowPdfParser.extract_positions(c),
+            }
+            for c, img in zip(chunks, images)
+        ]
+
+    def _q_and_a(self, from_upstream: ChunkerFromUpstream):
+        pass
+
+    def _resume(self, from_upstream: ChunkerFromUpstream):
+        pass
+
+    def _manual(self, from_upstream: ChunkerFromUpstream):
+        pass
+
+    def _table(self, from_upstream: ChunkerFromUpstream):
+        pass
+
+    def _paper(self, from_upstream: ChunkerFromUpstream):
+        pass
+
+    def _book(self, from_upstream: ChunkerFromUpstream):
+        pass
+
+    def _laws(self, from_upstream: ChunkerFromUpstream):
+        pass
+
+    def _presentation(self, from_upstream: ChunkerFromUpstream):
+        pass
+
+    def _one(self, from_upstream: ChunkerFromUpstream):
+        pass
+
+    def _toc(self, from_upstream: ChunkerFromUpstream):
+        self.callback(random.randint(1, 5) / 100.0, "Start to chunk via `ToC`.")
+        if from_upstream.output_format in ["markdown", "text", "html"]:
+            return
+
+        # json
+        sections, section_images, page_1024, tc_arr = [], [], [""], [0]
+        for o in from_upstream.json_result or []:
+            txt = o.get("text", "")
+            tc = num_tokens_from_string(txt)
+            page_1024[-1] += "\n" + txt
+            tc_arr[-1] += tc
+            if tc_arr[-1] > 1024:
+                page_1024.append("")
+                tc_arr.append(0)
+            sections.append((o.get("text", ""), o.get("position_tag", "")))
+            section_images.append(o.get("image"))
+            print(len(sections), o)
+
+        llm_setting = self._param.llm_setting
+        chat_mdl = LLMBundle(self._canvas._tenant_id, LLMType.CHAT, llm_name=llm_setting["llm_id"], lang=llm_setting["lang"])
+        self.callback(random.randint(5, 15) / 100.0, "Start to detect table of contents...")
+        toc_secs = detect_table_of_contents(page_1024, chat_mdl)
+        if toc_secs:
+            self.callback(random.randint(25, 35) / 100.0, "Start to extract table of contents...")
+            toc_arr = toc_transformer(toc_secs, chat_mdl)
+            toc_arr = [it for it in toc_arr if it.get("structure")]
+            print(json.dumps(toc_arr, ensure_ascii=False, indent=2), flush=True)
+            self.callback(random.randint(35, 75) / 100.0, "Start to link table of contents...")
+            toc_arr = table_of_contents_index(toc_arr, [t for t,_ in sections], chat_mdl)
+            for i in range(len(toc_arr)-1):
+                if not toc_arr[i].get("indices"):
+                    continue
+
+                for j in range(i+1, len(toc_arr)):
+                    if toc_arr[j].get("indices"):
+                        if toc_arr[j]["indices"][0] - toc_arr[i]["indices"][-1] > 1:
+                            toc_arr[i]["indices"].extend([x for x in range(toc_arr[i]["indices"][-1]+1, toc_arr[j]["indices"][0])])
+                        break
+            # put all sections ahead of toc_arr[0] into it
+            # for i in range(len(toc_arr)):
+            #     if toc_arr[i].get("indices") and toc_arr[i]["indices"][0]:
+            #         toc_arr[i]["indices"] = [x for x in range(toc_arr[i]["indices"][-1]+1)]
+            #         break
+            # put all sections after toc_arr[-1] into it
+            for i in range(len(toc_arr)-1, -1, -1):
+                if toc_arr[i].get("indices") and toc_arr[i]["indices"][-1]:
+                    toc_arr[i]["indices"] = [x for x in range(toc_arr[i]["indices"][0], len(sections))]
+                    break
+            print(">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\n", json.dumps(toc_arr, ensure_ascii=False, indent=2), flush=True)
+
+            chunks, images = [], []
+            for it in toc_arr:
+                if not it.get("indices"):
+                    continue
+                txt = ""
+                img = None
+                for i in it["indices"]:
+                    idx = i
+                    txt += "\n" + sections[idx][0] + "\t" + sections[idx][1]
+                    if img and section_images[idx]:
+                        img = concat_img(img, section_images[idx])
+                    elif section_images[idx]:
+                        img = section_images[idx]
+
+                it["indices"] = []
+                if not txt:
+                    continue
+                it["indices"] = [len(chunks)]
+                print(it, "KKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK\n", txt)
+                chunks.append(txt)
+                images.append(img)
+            self.callback(1, "Done")
+            return [
+                {
+                    "text": RAGFlowPdfParser.remove_tag(c),
+                    "image": img,
+                    "positions": RAGFlowPdfParser.extract_positions(c),
+                }
+                for c, img in zip(chunks, images)
+            ]
+
+        self.callback(message="No table of contents detected.")
+
+
+    async def _invoke(self, **kwargs):
+        function_map = {
+            "general": self._general,
+            "q&a": self._q_and_a,
+            "resume": self._resume,
+            "manual": self._manual,
+            "table": self._table,
+            "paper": self._paper,
+            "book": self._book,
+            "laws": self._laws,
+            "presentation": self._presentation,
+            "one": self._one,
+        }
+
+        try:
+            from_upstream = ChunkerFromUpstream.model_validate(kwargs)
+        except Exception as e:
+            self.set_output("_ERROR", f"Input error: {str(e)}")
+            return
+
+        chunks = function_map[self._param.method](from_upstream)
+        llm_setting = self._param.llm_setting
+
+        async def auto_keywords():
+            nonlocal chunks, llm_setting
+            chat_mdl = LLMBundle(self._canvas._tenant_id, LLMType.CHAT, llm_name=llm_setting["llm_id"], lang=llm_setting["lang"])
+
+            async def doc_keyword_extraction(chat_mdl, ck, topn):
+                cached = get_llm_cache(chat_mdl.llm_name, ck["text"], "keywords", {"topn": topn})
+                if not cached:
+                    async with chat_limiter:
+                        cached = await trio.to_thread.run_sync(lambda: keyword_extraction(chat_mdl, ck["text"], topn))
+                    set_llm_cache(chat_mdl.llm_name, ck["text"], cached, "keywords", {"topn": topn})
+                if cached:
+                    ck["keywords"] = cached.split(",")
+
+            async with trio.open_nursery() as nursery:
+                for ck in chunks:
+                    nursery.start_soon(doc_keyword_extraction, chat_mdl, ck, self._param.auto_keywords)
+
+        async def auto_questions():
+            nonlocal chunks, llm_setting
+            chat_mdl = LLMBundle(self._canvas._tenant_id, LLMType.CHAT, llm_name=llm_setting["llm_id"], lang=llm_setting["lang"])
+
+            async def doc_question_proposal(chat_mdl, d, topn):
+                cached = get_llm_cache(chat_mdl.llm_name, ck["text"], "question", {"topn": topn})
+                if not cached:
+                    async with chat_limiter:
+                        cached = await trio.to_thread.run_sync(lambda: question_proposal(chat_mdl, ck["text"], topn))
+                    set_llm_cache(chat_mdl.llm_name, ck["text"], cached, "question", {"topn": topn})
+                if cached:
+                    d["questions"] = cached.split("\n")
+
+            async with trio.open_nursery() as nursery:
+                for ck in chunks:
+                    nursery.start_soon(doc_question_proposal, chat_mdl, ck, self._param.auto_questions)
+
+        async with trio.open_nursery() as nursery:
+            if self._param.auto_questions:
+                nursery.start_soon(auto_questions)
+            if self._param.auto_keywords:
+                nursery.start_soon(auto_keywords)
+
+        if self._param.page_rank:
+            for ck in chunks:
+                ck["page_rank"] = self._param.page_rank
+
+        self.set_output("chunks", chunks)

rag/flow/chunker/schema.py

@@ -0,0 +1,37 @@
+#
+#  Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+from typing import Any, Literal
+
+from pydantic import BaseModel, ConfigDict, Field
+
+
+class ChunkerFromUpstream(BaseModel):
+    created_time: float | None = Field(default=None, alias="_created_time")
+    elapsed_time: float | None = Field(default=None, alias="_elapsed_time")
+
+    name: str
+    file: dict | None = Field(default=None)
+
+    output_format: Literal["json", "markdown", "text", "html"] | None = Field(default=None)
+
+    json_result: list[dict[str, Any]] | None = Field(default=None, alias="json")
+    markdown_result: str | None = Field(default=None, alias="markdown")
+    text_result: str | None = Field(default=None, alias="text")
+    html_result: list[str] | None = Field(default=None, alias="html")
+
+    model_config = ConfigDict(populate_by_name=True, extra="forbid")
+
+    # def to_dict(self, *, exclude_none: bool = True) -> dict:
+    #     return self.model_dump(by_alias=True, exclude_none=exclude_none)

rag/flow/parser/parser.py

@@ -52,7 +52,6 @@ class ParserParam(ProcessParamBase):
             ],
             "word": [
                 "json",
-                "markdown",
             ],
             "slides": [
                 "json",
@@ -184,6 +183,8 @@ class ParserParam(ProcessParamBase):
         audio_config = self.setups.get("audio", "")
         if audio_config:
             self.check_empty(audio_config.get("llm_id"), "Audio VLM")
+            audio_language = audio_config.get("lang", "")
+            self.check_empty(audio_language, "Language")
 
         email_config = self.setups.get("email", "")
         if email_config:
@@ -246,15 +247,13 @@ class Parser(ProcessBase):
         conf = self._param.setups["word"]
         self.set_output("output_format", conf["output_format"])
         docx_parser = Docx()
-
-        if conf.get("output_format") == "json":
         sections, tbls = docx_parser(name, binary=blob)
         sections = [{"text": section[0], "image": section[1]} for section in sections if section]
         sections.extend([{"text": tb, "image": None} for ((_,tb), _) in tbls])
+        # json
+        assert conf.get("output_format") == "json", "have to be json for doc"
+        if conf.get("output_format") == "json":
             self.set_output("json", sections)
-        elif conf.get("output_format") == "markdown":
-            markdown_text = docx_parser.to_markdown(name, binary=blob)
-            self.set_output("markdown", markdown_text)
 
     def _slides(self, name, blob):
         from deepdoc.parser.ppt_parser import RAGFlowPptParser as ppt_parser
@@ -346,13 +345,15 @@ class Parser(ProcessBase):
 
         conf = self._param.setups["audio"]
         self.set_output("output_format", conf["output_format"])
+
+        lang = conf["lang"]
         _, ext = os.path.splitext(name)
         with tempfile.NamedTemporaryFile(suffix=ext) as tmpf:
             tmpf.write(blob)
             tmpf.flush()
             tmp_path = os.path.abspath(tmpf.name)
 
-            seq2txt_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.SPEECH2TEXT)
+            seq2txt_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.SPEECH2TEXT, lang=lang)
             txt = seq2txt_mdl.transcription(tmp_path)
 
             self.set_output("text", txt)
@@ -362,7 +363,6 @@ class Parser(ProcessBase):
 
         email_content = {}
         conf = self._param.setups["email"]
-        self.set_output("output_format", conf["output_format"])
         target_fields = conf["fields"]
 
         _, ext = os.path.splitext(name)
@@ -400,8 +400,8 @@ class Parser(ProcessBase):
 
                 _add_content(msg, msg.get_content_type())
 
-                email_content["text"] = "\n".join(body_text)
+                email_content["text"] = body_text
-                email_content["text_html"] = "\n".join(body_html)
+                email_content["text_html"] = body_html
             # get attachment
             if "attachments" in target_fields:
                 attachments = []
@@ -411,7 +411,7 @@ class Parser(ProcessBase):
                         dispositions = content_disposition.strip().split(";")
                         if dispositions[0].lower() == "attachment":
                             filename = part.get_filename()
-                            payload = part.get_payload(decode=True).decode(part.get_content_charset())
+                            payload = part.get_payload(decode=True)
                             attachments.append({
                                 "filename": filename,
                                 "payload": payload,
@@ -439,16 +439,15 @@ class Parser(ProcessBase):
             }
             # get body
             if "body" in target_fields:
-                email_content["text"] = msg.body[0] if isinstance(msg.body, list) and msg.body else msg.body
+                email_content["text"] = msg.body  # usually empty. try text_html instead
-                if not email_content["text"] and msg.htmlBody:
+                email_content["text_html"] = msg.htmlBody
-                    email_content["text"] = msg.htmlBody[0] if isinstance(msg.htmlBody, list) and msg.htmlBody else msg.htmlBody
             # get attachments
             if "attachments" in target_fields:
                 attachments = []
                 for t in msg.attachments:
                     attachments.append({
                         "filename": t.name,
-                        "payload": t.data.decode("utf-8")
+                        "payload": t.data  # binary
                     })
                 email_content["attachments"] = attachments
 

rag/flow/splitter/schema.py

@@ -25,7 +25,7 @@ class SplitterFromUpstream(BaseModel):
     file: dict | None = Field(default=None)
     chunks: list[dict[str, Any]] | None = Field(default=None)
 
-    output_format: Literal["json", "markdown", "text", "html", "chunks"] | None = Field(default=None)
+    output_format: Literal["json", "markdown", "text", "html"] | None = Field(default=None)
 
     json_result: list[dict[str, Any]] | None = Field(default=None, alias="json")
     markdown_result: str | None = Field(default=None, alias="markdown")

rag/flow/tokenizer/tokenizer.py

@@ -126,7 +126,7 @@ class Tokenizer(ProcessBase):
                     if ck.get("summary"):
                         ck["content_ltks"] = rag_tokenizer.tokenize(str(ck["summary"]))
                         ck["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(ck["content_ltks"])
-                    elif ck.get("text"):
+                    else:
                         ck["content_ltks"] = rag_tokenizer.tokenize(ck["text"])
                         ck["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(ck["content_ltks"])
                     if i % 100 == 99:
@@ -155,8 +155,6 @@ class Tokenizer(ProcessBase):
                 for i, ck in enumerate(chunks):
                     ck["title_tks"] = rag_tokenizer.tokenize(re.sub(r"\.[a-zA-Z]+$", "", from_upstream.name))
                     ck["title_sm_tks"] = rag_tokenizer.fine_grained_tokenize(ck["title_tks"])
-                    if not ck.get("text"):
-                        continue
                     ck["content_ltks"] = rag_tokenizer.tokenize(ck["text"])
                     ck["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(ck["content_ltks"])
                     if i % 100 == 99:

rag/llm/chat_model.py

@@ -132,7 +132,7 @@ class Base(ABC):
             "tool_choice",
             "logprobs",
             "top_logprobs",
-            "extra_headers"
+            "extra_headers",
         }
 
         gen_conf = {k: v for k, v in gen_conf.items() if k in allowed_conf}
@@ -141,22 +141,6 @@ class Base(ABC):
 
     def _chat(self, history, gen_conf, **kwargs):
         logging.info("[HISTORY]" + json.dumps(history, ensure_ascii=False, indent=2))
-        if self.model_name.lower().find("qwq") >= 0:
-            logging.info(f"[INFO] {self.model_name} detected as reasoning model, using _chat_streamly")
-
-            final_ans = ""
-            tol_token = 0
-            for delta, tol in self._chat_streamly(history, gen_conf, with_reasoning=False, **kwargs):
-                if delta.startswith("<think>") or delta.endswith("</think>"):
-                    continue
-                final_ans += delta
-                tol_token = tol
-
-            if len(final_ans.strip()) == 0:
-                final_ans = "**ERROR**: Empty response from reasoning model"
-
-            return final_ans.strip(), tol_token
-
         if self.model_name.lower().find("qwen3") >= 0:
             kwargs["extra_body"] = {"enable_thinking": False}
 
@@ -1182,43 +1166,13 @@ class GoogleChat(Base):
         else:
             if "max_tokens" in gen_conf:
                 gen_conf["max_output_tokens"] = gen_conf["max_tokens"]
-                del gen_conf["max_tokens"]
             for k in list(gen_conf.keys()):
                 if k not in ["temperature", "top_p", "max_output_tokens"]:
                     del gen_conf[k]
         return gen_conf
 
-    def _get_thinking_config(self, gen_conf):
-        """Extract and create ThinkingConfig from gen_conf.
-
-        Default behavior for Vertex AI Generative Models: thinking_budget=0 (disabled)
-        unless explicitly specified by the user. This does not apply to Claude models.
-
-        Users can override by setting thinking_budget in gen_conf/llm_setting:
-        - 0: Disabled (default)
-        - 1-24576: Manual budget
-        - -1: Auto (model decides)
-        """
-        # Claude models don't support ThinkingConfig
-        if "claude" in self.model_name:
-            gen_conf.pop("thinking_budget", None)
-            return None
-
-        # For Vertex AI Generative Models, default to thinking disabled
-        thinking_budget = gen_conf.pop("thinking_budget", 0)
-
-        if thinking_budget is not None:
-            try:
-                import vertexai.generative_models as glm  # type: ignore
-                return glm.ThinkingConfig(thinking_budget=thinking_budget)
-            except Exception:
-                pass
-        return None
-
     def _chat(self, history, gen_conf={}, **kwargs):
         system = history[0]["content"] if history and history[0]["role"] == "system" else ""
-        thinking_config = self._get_thinking_config(gen_conf)
-        gen_conf = self._clean_conf(gen_conf)
         if "claude" in self.model_name:
             response = self.client.messages.create(
                 model=self.model_name,
@@ -1251,9 +1205,6 @@ class GoogleChat(Base):
                     }
                 ]
 
-        if thinking_config:
-            response = self.client.generate_content(hist, generation_config=gen_conf, thinking_config=thinking_config)
-        else:
         response = self.client.generate_content(hist, generation_config=gen_conf)
         ans = response.text
         return ans, response.usage_metadata.total_token_count
@@ -1283,13 +1234,9 @@ class GoogleChat(Base):
 
             yield total_tokens
         else:
-            response = None
-            total_tokens = 0
             self.client._system_instruction = system
-            thinking_config = self._get_thinking_config(gen_conf)
             if "max_tokens" in gen_conf:
                 gen_conf["max_output_tokens"] = gen_conf["max_tokens"]
-                del gen_conf["max_tokens"]
             for k in list(gen_conf.keys()):
                 if k not in ["temperature", "top_p", "max_output_tokens"]:
                     del gen_conf[k]
@@ -1297,26 +1244,18 @@ class GoogleChat(Base):
                 if "role" in item and item["role"] == "assistant":
                     item["role"] = "model"
                 if "content" in item:
-                    item["parts"] = [
+                    item["parts"] = item.pop("content")
-                        {
-                            "text": item.pop("content"),
-                        }
-                    ]
             ans = ""
             try:
-                if thinking_config:
+                response = self.model.generate_content(history, generation_config=gen_conf, stream=True)
-                    response = self.client.generate_content(history, generation_config=gen_conf, thinking_config=thinking_config, stream=True)
-                else:
-                    response = self.client.generate_content(history, generation_config=gen_conf, stream=True)
                 for resp in response:
                     ans = resp.text
-                    total_tokens += num_tokens_from_string(ans)
                     yield ans
 
             except Exception as e:
                 yield ans + "\n**ERROR**: " + str(e)
 
-            yield total_tokens
+            yield response._chunks[-1].usage_metadata.total_token_count
 
 
 class GPUStackChat(Base):
@@ -1336,14 +1275,6 @@ class TokenPonyChat(Base):
         if not base_url:
             base_url = "https://ragflow.vip-api.tokenpony.cn/v1"
 
-class DeerAPIChat(Base):
-    _FACTORY_NAME = "DeerAPI"
-
-    def __init__(self, key, model_name, base_url="https://api.deerapi.com/v1", **kwargs):
-        if not base_url:
-            base_url = "https://api.deerapi.com/v1"
-        super().__init__(key, model_name, base_url, **kwargs)
-
 
 class LiteLLMBase(ABC):
     _FACTORY_NAME = [

rag/llm/cv_model.py

@@ -26,7 +26,7 @@ from openai.lib.azure import AzureOpenAI
 from zhipuai import ZhipuAI
 from rag.nlp import is_english
 from rag.prompts.generator import vision_llm_describe_prompt
-from rag.utils import num_tokens_from_string, total_token_count_from_response
+from rag.utils import num_tokens_from_string
 
 
 class Base(ABC):
@@ -125,7 +125,7 @@ class Base(ABC):
             b64 = base64.b64encode(data).decode("utf-8")
             return f"data:{mime};base64,{b64}"
         with BytesIO() as buffered:
-            fmt = "jpeg"
+            fmt = "JPEG"
             try:
                 image.save(buffered, format="JPEG")
             except Exception:
@@ -133,10 +133,10 @@ class Base(ABC):
                 buffered.seek(0)
                 buffered.truncate()
                 image.save(buffered, format="PNG")
-                fmt = "png"
+                fmt = "PNG"
             data = buffered.getvalue()
             b64 = base64.b64encode(data).decode("utf-8")
-            mime = f"image/{fmt}"
+            mime = f"image/{fmt.lower()}"
         return f"data:{mime};base64,{b64}"
 
     def prompt(self, b64):
@@ -178,7 +178,7 @@ class GptV4(Base):
             model=self.model_name,
             messages=self.prompt(b64),
         )
-        return res.choices[0].message.content.strip(), total_token_count_from_response(res)
+        return res.choices[0].message.content.strip(), res.usage.total_tokens
 
     def describe_with_prompt(self, image, prompt=None):
         b64 = self.image2base64(image)
@@ -186,7 +186,7 @@ class GptV4(Base):
             model=self.model_name,
             messages=self.vision_llm_prompt(b64, prompt),
         )
-        return res.choices[0].message.content.strip(),total_token_count_from_response(res)
+        return res.choices[0].message.content.strip(), res.usage.total_tokens
 
 
 class AzureGptV4(GptV4):
@@ -522,10 +522,11 @@ class GeminiCV(Base):
         )
         b64 = self.image2base64(image)
         with BytesIO(base64.b64decode(b64)) as bio:
-            with open(bio) as img:
+            img = open(bio)
             input = [prompt, img]
             res = self.model.generate_content(input)
-                return res.text, total_token_count_from_response(res)
+            img.close()
+            return res.text, res.usage_metadata.total_token_count
 
     def describe_with_prompt(self, image, prompt=None):
         from PIL.Image import open
@@ -533,10 +534,11 @@ class GeminiCV(Base):
         b64 = self.image2base64(image)
         vision_prompt = prompt if prompt else vision_llm_describe_prompt()
         with BytesIO(base64.b64decode(b64)) as bio:
-            with open(bio) as img:
+            img = open(bio)
             input = [vision_prompt, img]
             res = self.model.generate_content(input)
-                return res.text, total_token_count_from_response(res)
+            img.close()
+            return res.text, res.usage_metadata.total_token_count
 
     def chat(self, system, history, gen_conf, images=[]):
         generation_config = dict(temperature=gen_conf.get("temperature", 0.3), top_p=gen_conf.get("top_p", 0.7))
@@ -545,7 +547,7 @@ class GeminiCV(Base):
                 self._form_history(system, history, images),
                 generation_config=generation_config)
             ans = response.text
-            return ans, total_token_count_from_response(ans)
+            return ans, response.usage_metadata.total_token_count
         except Exception as e:
             return "**ERROR**: " + str(e), 0
 
@@ -568,7 +570,10 @@ class GeminiCV(Base):
         except Exception as e:
             yield ans + "\n**ERROR**: " + str(e)
 
-        yield total_token_count_from_response(response)
+        if response and hasattr(response, "usage_metadata") and hasattr(response.usage_metadata, "total_token_count"):
+            yield response.usage_metadata.total_token_count
+        else:
+            yield 0
 
 
 class NvidiaCV(Base):
@@ -614,7 +619,7 @@ class NvidiaCV(Base):
         response = response.json()
         return (
             response["choices"][0]["message"]["content"].strip(),
-            total_token_count_from_response(response),
+            response["usage"]["total_tokens"],
         )
 
     def _request(self, msg, gen_conf={}):
@@ -637,7 +642,7 @@ class NvidiaCV(Base):
         response = self._request(vision_prompt)
         return (
             response["choices"][0]["message"]["content"].strip(),
-            total_token_count_from_response(response)
+            response["usage"]["total_tokens"],
         )
 
     def chat(self, system, history, gen_conf, images=[], **kwargs):
@@ -645,7 +650,7 @@ class NvidiaCV(Base):
             response = self._request(self._form_history(system, history, images), gen_conf)
             return (
                 response["choices"][0]["message"]["content"].strip(),
-                total_token_count_from_response(response)
+                response["usage"]["total_tokens"],
             )
         except Exception as e:
             return "**ERROR**: " + str(e), 0
@@ -656,7 +661,7 @@ class NvidiaCV(Base):
             response = self._request(self._form_history(system, history, images), gen_conf)
             cnt = response["choices"][0]["message"]["content"]
             if "usage" in response and "total_tokens" in response["usage"]:
-                total_tokens +=  total_token_count_from_response(response)
+                total_tokens += response["usage"]["total_tokens"]
             for resp in cnt:
                 yield resp
         except Exception as e:

rag/llm/embedding_model.py

@@ -33,7 +33,7 @@ from zhipuai import ZhipuAI
 from api import settings
 from api.utils.file_utils import get_home_cache_dir
 from api.utils.log_utils import log_exception
-from rag.utils import num_tokens_from_string, truncate
+from rag.utils import num_tokens_from_string, truncate, total_token_count_from_response
 
 
 class Base(ABC):
@@ -52,15 +52,7 @@ class Base(ABC):
         raise NotImplementedError("Please implement encode method!")
 
     def total_token_count(self, resp):
-        try:
+        return total_token_count_from_response(resp)
-            return resp.usage.total_tokens
-        except Exception:
-            pass
-        try:
-            return resp["usage"]["total_tokens"]
-        except Exception:
-            pass
-        return 0
 
 
 class DefaultEmbedding(Base):
@@ -944,6 +936,7 @@ class GiteeEmbed(SILICONFLOWEmbed):
             base_url = "https://ai.gitee.com/v1/embeddings"
         super().__init__(key, model_name, base_url)
 
+
 class DeepInfraEmbed(OpenAIEmbed):
     _FACTORY_NAME = "DeepInfra"
 
@@ -969,11 +962,3 @@ class CometAPIEmbed(OpenAIEmbed):
         if not base_url:
             base_url = "https://api.cometapi.com/v1"
         super().__init__(key, model_name, base_url)
-
-class DeerAPIEmbed(OpenAIEmbed):
-    _FACTORY_NAME = "DeerAPI"
-
-    def __init__(self, key, model_name, base_url="https://api.deerapi.com/v1"):
-        if not base_url:
-            base_url = "https://api.deerapi.com/v1"
-        super().__init__(key, model_name, base_url)

rag/llm/sequence2txt_model.py

@@ -244,12 +244,3 @@ class CometAPISeq2txt(Base):
             base_url = "https://api.cometapi.com/v1"
         self.client = OpenAI(api_key=key, base_url=base_url)
         self.model_name = model_name
-        
-class DeerAPISeq2txt(Base):
-    _FACTORY_NAME = "DeerAPI"
-
-    def __init__(self, key, model_name="whisper-1", base_url="https://api.deerapi.com/v1", **kwargs):
-        if not base_url:
-            base_url = "https://api.deerapi.com/v1"
-        self.client = OpenAI(api_key=key, base_url=base_url)
-        self.model_name = model_name

rag/llm/tts_model.py

@@ -402,11 +402,3 @@ class CometAPITTS(OpenAITTS):
         if not base_url:
             base_url = "https://api.cometapi.com/v1"
         super().__init__(key, model_name, base_url, **kwargs)
-        
-class DeerAPITTS(OpenAITTS):
-    _FACTORY_NAME = "DeerAPI"
-
-    def __init__(self, key, model_name, base_url="https://api.deerapi.com/v1", **kwargs):
-        if not base_url:
-            base_url = "https://api.deerapi.com/v1"
-        super().__init__(key, model_name, base_url, **kwargs)

rag/nlp/__init__.py

@@ -613,13 +613,13 @@ def naive_merge(sections: str | list, chunk_token_num=128, delimiter="\n。；
     dels = get_delimiters(delimiter)
     for sec, pos in sections:
         if num_tokens_from_string(sec) < chunk_token_num:
-            add_chunk("\n"+sec, pos)
+            add_chunk(sec, pos)
             continue
         split_sec = re.split(r"(%s)" % dels, sec, flags=re.DOTALL)
         for sub_sec in split_sec:
             if re.match(f"^{dels}$", sub_sec):
                 continue
-            add_chunk("\n"+sub_sec, pos)
+            add_chunk(sub_sec, pos)
 
     return cks
 
@@ -669,13 +669,13 @@ def naive_merge_with_images(texts, images, chunk_token_num=128, delimiter="\n。
             for sub_sec in split_sec:
                 if re.match(f"^{dels}$", sub_sec):
                     continue
-                add_chunk("\n"+sub_sec, image, text_pos)
+                add_chunk(sub_sec, image, text_pos)
         else:
             split_sec = re.split(r"(%s)" % dels, text)
             for sub_sec in split_sec:
                 if re.match(f"^{dels}$", sub_sec):
                     continue
-                add_chunk("\n"+sub_sec, image)
+                add_chunk(sub_sec, image)
 
     return cks, result_images
 
@@ -757,7 +757,7 @@ def naive_merge_docx(sections, chunk_token_num=128, delimiter="\n。；！？"):
         for sub_sec in split_sec:
             if re.match(f"^{dels}$", sub_sec):
                 continue
-            add_chunk("\n"+sub_sec, image,"")
+            add_chunk(sub_sec, image,"")
         line = ""
 
     if line:
@@ -765,7 +765,7 @@ def naive_merge_docx(sections, chunk_token_num=128, delimiter="\n。；！？"):
         for sub_sec in split_sec:
             if re.match(f"^{dels}$", sub_sec):
                 continue
-            add_chunk("\n"+sub_sec, image,"")
+            add_chunk(sub_sec, image,"")
 
     return cks, images
 

rag/nlp/search.py

@@ -13,14 +13,12 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #
-import json
 import logging
 import re
 import math
 from collections import OrderedDict
 from dataclasses import dataclass
 
-from rag.prompts.generator import relevant_chunks_with_toc
 from rag.settings import TAG_FLD, PAGERANK_FLD
 from rag.utils import rmSpace, get_float
 from rag.nlp import rag_tokenizer, query
@@ -516,63 +514,3 @@ class Dealer:
         tag_fea = sorted([(a, round(0.1*(c + 1) / (cnt + S) / max(1e-6, all_tags.get(a, 0.0001)))) for a, c in aggs],
                          key=lambda x: x[1] * -1)[:topn_tags]
         return {a.replace(".", "_"): max(1, c) for a, c in tag_fea}
-
-    def retrieval_by_toc(self, query:str, chunks:list[dict], tenant_ids:list[str], chat_mdl, topn: int=6):
-        if not chunks:
-            return []
-        idx_nms = [index_name(tid) for tid in tenant_ids]
-        ranks, doc_id2kb_id = {}, {}
-        for ck in chunks:
-            if ck["doc_id"] not in ranks:
-                ranks[ck["doc_id"]] = 0
-            ranks[ck["doc_id"]] += ck["similarity"]
-            doc_id2kb_id[ck["doc_id"]] = ck["kb_id"]
-        doc_id = sorted(ranks.items(), key=lambda x: x[1]*-1.)[0][0]
-        kb_ids = [doc_id2kb_id[doc_id]]
-        es_res = self.dataStore.search(["content_with_weight"], [], {"doc_id": doc_id, "toc_kwd": "toc"}, [], OrderByExpr(), 0, 128, idx_nms,
-                                       kb_ids)
-        toc = []
-        dict_chunks = self.dataStore.getFields(es_res, ["content_with_weight"])
-        for _, doc in dict_chunks.items():
-            try:
-                toc.extend(json.loads(doc["content_with_weight"]))
-            except Exception as e:
-                logging.exception(e)
-        if not toc:
-            return chunks
-
-        ids = relevant_chunks_with_toc(query, toc, chat_mdl, topn*2)
-        if not ids:
-            return chunks
-        
-        vector_size = 1024
-        id2idx = {ck["chunk_id"]: i for i, ck in enumerate(chunks)}
-        for cid, sim in ids:
-            if cid in id2idx:
-                chunks[id2idx[cid]]["similarity"] += sim
-                continue
-            chunk = self.dataStore.get(cid, idx_nms, kb_ids)
-            d = {
-                "chunk_id": cid,
-                "content_ltks": chunk["content_ltks"],
-                "content_with_weight": chunk["content_with_weight"],
-                "doc_id": doc_id,
-                "docnm_kwd": chunk.get("docnm_kwd", ""),
-                "kb_id": chunk["kb_id"],
-                "important_kwd": chunk.get("important_kwd", []),
-                "image_id": chunk.get("img_id", ""),
-                "similarity": sim,
-                "vector_similarity": sim,
-                "term_similarity": sim,
-                "vector": [0.0] * vector_size,
-                "positions": chunk.get("position_int", []),
-                "doc_type_kwd": chunk.get("doc_type_kwd", "")
-            }
-            for k in chunk.keys():
-                if k[-4:] == "_vec":
-                    d["vector"] = chunk[k]
-                    vector_size = len(chunk[k])
-                    break
-            chunks.append(d)
-
-        return sorted(chunks, key=lambda x:x["similarity"]*-1)[:topn]

rag/prompts/assign_toc_levels.md

@@ -1,53 +0,0 @@
-You are given a JSON array of TOC(tabel of content) items. Each item has at least {"title": string} and may include an existing title hierarchical level.
-
-Task
-- For each item, assign a depth label using Arabic numerals only: top-level = 1, second-level = 2, third-level = 3, etc.
-- Multiple items may share the same depth (e.g., many 1s, many 2s).
-- Do not use dotted numbering (no 1.1/1.2). Use a single digit string per item indicating its depth only.
-- Preserve the original item order exactly. Do not insert, delete, or reorder.
-- Decide levels yourself to keep a coherent hierarchy. Keep peers at the same depth.
-
-Output
-- Return a valid JSON array only (no extra text).
-- Each element must be {"level": "1|2|3", "title": <original title string>}.
-- title must be the original title string.
-
-Examples
-
-Example A (chapters with sections)
-Input:
-["Chapter 1 Methods", "Section 1 Definition", "Section 2 Process", "Chapter 2 Experiment"]
-
-Output:
-[
-  {"level":"1","title":"Chapter 1 Methods"},
-  {"level":"2","title":"Section 1 Definition"},
-  {"level":"2","title":"Section 2 Process"},
-  {"level":"1","title":"Chapter 2 Experiment"}
-]
-
-Example B (parts with chapters)
-Input:
-["Part I Theory", "Chapter 1 Basics", "Chapter 2 Methods", "Part II Applications", "Chapter 3 Case Studies"]
-
-Output:
-[
-  {"level":"1","title":"Part I Theory"},
-  {"level":"2","title":"Chapter 1 Basics"},
-  {"level":"2","title":"Chapter 2 Methods"},
-  {"level":"1","title":"Part II Applications"},
-  {"level":"2","title":"Chapter 3 Case Studies"}
-]
-
-Example C (plain headings)
-Input:
-["Introduction", "Background and Motivation", "Related Work", "Methodology", "Evaluation"]
-
-Output:
-[
-  {"level":"1","title":"Introduction"},
-  {"level":"2","title":"Background and Motivation"},
-  {"level":"2","title":"Related Work"},
-  {"level":"1","title":"Methodology"},
-  {"level":"1","title":"Evaluation"}
-]

rag/prompts/generator.py

@@ -21,9 +21,7 @@ from copy import deepcopy
 from typing import Tuple
 import jinja2
 import json_repair
-import trio
 from api.utils import hash_str2int
-from rag.nlp import rag_tokenizer
 from rag.prompts.template import load_prompt
 from rag.settings import TAG_FLD
 from rag.utils import encoder, num_tokens_from_string
@@ -31,7 +29,7 @@ from rag.utils import encoder, num_tokens_from_string
 
 STOP_TOKEN="<|STOP|>"
 COMPLETE_TASK="complete_task"
-INPUT_UTILIZATION = 0.5
+
 
 def get_value(d, k1, k2):
     return d.get(k1, d.get(k2))
@@ -441,18 +439,12 @@ def gen_meta_filter(chat_mdl, meta_data:dict, query: str) -> list:
     return []
 
 
-def gen_json(system_prompt:str, user_prompt:str, chat_mdl, gen_conf = None):
+def gen_json(system_prompt:str, user_prompt:str, chat_mdl):
-    from graphrag.utils import get_llm_cache, set_llm_cache
-    cached = get_llm_cache(chat_mdl.llm_name, system_prompt, user_prompt, gen_conf)
-    if cached:
-        return json_repair.loads(cached)
     _, msg = message_fit_in(form_message(system_prompt, user_prompt), chat_mdl.max_length)
-    ans = chat_mdl.chat(msg[0]["content"], msg[1:],gen_conf=gen_conf)
+    ans = chat_mdl.chat(msg[0]["content"], msg[1:])
     ans = re.sub(r"(^.*</think>|```json\n|```\n*$)", "", ans, flags=re.DOTALL)
     try:
-        res = json_repair.loads(ans)
+        return json_repair.loads(ans)
-        set_llm_cache(chat_mdl.llm_name, system_prompt, ans, user_prompt, gen_conf)
-        return res
     except Exception:
         logging.exception(f"Loading json failure: {ans}")
 
@@ -657,140 +649,4 @@ def toc_transformer(toc_pages, chat_mdl):
     return last_complete
 
 
-TOC_LEVELS = load_prompt("assign_toc_levels")
-def assign_toc_levels(toc_secs, chat_mdl, gen_conf = {"temperature": 0.2}):
-    if not toc_secs:
-        return []
-    return gen_json(
-        PROMPT_JINJA_ENV.from_string(TOC_LEVELS).render(),
-        str(toc_secs),
-        chat_mdl,
-        gen_conf
-    )
 
-
-TOC_FROM_TEXT_SYSTEM = load_prompt("toc_from_text_system")
-TOC_FROM_TEXT_USER = load_prompt("toc_from_text_user")
-# Generate TOC from text chunks with text llms
-async def gen_toc_from_text(txt_info: dict, chat_mdl, callback=None):
-    try:
-        ans = gen_json(
-            PROMPT_JINJA_ENV.from_string(TOC_FROM_TEXT_SYSTEM).render(),
-            PROMPT_JINJA_ENV.from_string(TOC_FROM_TEXT_USER).render(text="\n".join([json.dumps(d, ensure_ascii=False) for d in txt_info["chunks"]])),
-            chat_mdl,
-            gen_conf={"temperature": 0.0, "top_p": 0.9}
-        )
-        print(ans, "::::::::::::::::::::::::::::::::::::", flush=True)
-        txt_info["toc"] = ans if ans else []
-        if callback:
-            callback(msg="")
-    except Exception as e:
-        logging.exception(e)
-
-
-def split_chunks(chunks, max_length: int):
-    """
-    Pack chunks into batches according to max_length, returning [{"id": idx, "text": chunk_text}, ...].
-    Do not split a single chunk, even if it exceeds max_length.
-    """
-
-    result = []
-    batch, batch_tokens = [], 0
-
-    for idx, chunk in enumerate(chunks):
-        t = num_tokens_from_string(chunk)
-        if batch_tokens + t > max_length:
-            result.append(batch)
-            batch, batch_tokens = [], 0
-        batch.append({idx: chunk})
-        batch_tokens += t
-    if batch:
-        result.append(batch)
-    return result
-
-
-async def run_toc_from_text(chunks, chat_mdl, callback=None):
-    input_budget = int(chat_mdl.max_length * INPUT_UTILIZATION) - num_tokens_from_string(
-        TOC_FROM_TEXT_USER + TOC_FROM_TEXT_SYSTEM
-    )
-
-    input_budget =  1024 if input_budget > 1024 else input_budget
-    chunk_sections = split_chunks(chunks, input_budget)
-    titles = []
-
-    chunks_res = []
-    async with trio.open_nursery() as nursery:
-        for i, chunk in enumerate(chunk_sections):
-            if not chunk:
-                continue
-            chunks_res.append({"chunks": chunk})
-            nursery.start_soon(gen_toc_from_text, chunks_res[-1], chat_mdl, callback)
-
-    for chunk in chunks_res:
-        titles.extend(chunk.get("toc", []))
-
-    print(titles, ">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>")
-        
-    # Filter out entries with title == -1
-    prune = len(titles) > 512
-    max_len = 12 if prune else 22
-    filtered = []
-    for x in titles:
-        if not x.get("title") or x["title"] == "-1":
-            continue
-        if len(rag_tokenizer.tokenize(x["title"]).split(" ")) > max_len:
-            continue
-        if re.match(r"[0-9,.()/ -]+$", x["title"]):
-            continue
-        filtered.append(x)
-
-    logging.info(f"\n\nFiltered TOC sections:\n{filtered}")
-
-    # Generate initial level (level/title)
-    raw_structure = [x.get("title", "") for x in filtered]
-
-    # Assign hierarchy levels using LLM
-    toc_with_levels = assign_toc_levels(raw_structure, chat_mdl, {"temperature": 0.0, "top_p": 0.9})
-
-    # Merge structure and content (by index)
-    prune = len(toc_with_levels) > 512
-    max_lvl = sorted([t.get("level", "0") for t in toc_with_levels])[-1]
-    merged = []
-    for _ , (toc_item, src_item) in enumerate(zip(toc_with_levels, filtered)):
-        if prune and toc_item.get("level", "0") >= max_lvl:
-            continue
-        merged.append({
-            "level": toc_item.get("level", "0"),
-            "title": toc_item.get("title", ""),
-            "chunk_id": src_item.get("chunk_id", ""),
-        })
-
-    return merged
-
-
-TOC_RELEVANCE_SYSTEM = load_prompt("toc_relevance_system")
-TOC_RELEVANCE_USER = load_prompt("toc_relevance_user")
-def relevant_chunks_with_toc(query: str, toc:list[dict], chat_mdl, topn: int=6):
-    import numpy as np
-    try:
-        ans = gen_json(
-            PROMPT_JINJA_ENV.from_string(TOC_RELEVANCE_SYSTEM).render(),
-            PROMPT_JINJA_ENV.from_string(TOC_RELEVANCE_USER).render(query=query, toc_json="[\n%s\n]\n"%"\n".join([json.dumps({"level": d["level"], "title":d["title"]}, ensure_ascii=False) for d in toc])),
-            chat_mdl,
-            gen_conf={"temperature": 0.0, "top_p": 0.9}
-        )
-        print(ans, "::::::::::::::::::::::::::::::::::::", flush=True)
-        id2score = {}
-        for ti, sc in zip(toc, ans):
-            if not isinstance(sc, dict) or sc.get("score", -1) < 1:
-                continue
-            for id in ti.get("ids", []):
-                if id not in id2score:
-                    id2score[id] = []
-                id2score[id].append(sc["score"]/5.)
-        for id in id2score.keys():
-            id2score[id] = np.mean(id2score[id])
-        return [(id, sc) for id, sc in list(id2score.items()) if sc>=0.3][:topn]
-    except Exception as e:
-        logging.exception(e)
-    return []

rag/prompts/toc_from_text_system.md

@@ -1,119 +0,0 @@
-You are a robust Table-of-Contents (TOC) extractor.
-
-GOAL
-Given a dictionary of chunks {"<chunk_ID>": chunk_text}, extract TOC-like headings and return a strict JSON array of objects:
-[
-  {"title": "", "chunk_id": ""},
-  ...
-]
-
-FIELDS
-- "title": the heading text (clean, no page numbers or leader dots).
-  - If any part of a chunk has no valid heading, output that part as {"title":"-1", ...}.
-- "chunk_id": the chunk ID (string).
-  - One chunk can yield multiple JSON objects in order (unmatched text + one or more headings).
-
-RULES
-1) Preserve input chunk order strictly.
-2) If a chunk contains multiple headings, expand them in order:
-   - Pre-heading narrative → {"title":"-1","chunk_id":"<chunk_ID>"}
-   - Then each heading → {"title":"...","chunk_id":"<chunk_ID>"}
-3) Do not merge outputs across chunks; each object refers to exactly one chunk ID.
-4) "title" must be non-empty (or exactly "-1"). "chunk_id" must be a string (chunk ID).
-5) When ambiguous, prefer "-1" unless the text strongly looks like a heading.
-
-HEADING DETECTION (cues, not hard rules)
-- Appears near line start, short isolated phrase, often followed by content.
-- May contain separators: — —— - : ： · •
-- Numbering styles:
-  • 第[一二三四五六七八九十百]+(篇|章|节|条)
-  • [(（]?[一二三四五六七八九十]+[)）]?
-  • [(（]?[①②③④⑤⑥⑦⑧⑨⑩][)）]?
-  • ^\d+(\.\d+)*[)．.]?\s*
-  • ^[IVXLCDM]+[).]
-  • ^[A-Z][).]
-- Canonical section cues (general only):
-  Common heading indicators include words such as:
-  "Overview", "Introduction", "Background", "Purpose", "Scope", "Definition",
-  "Method", "Procedure", "Result", "Discussion", "Summary", "Conclusion",
-  "Appendix", "Reference", "Annex", "Acknowledgment", "Disclaimer".
-  These are soft cues, not strict requirements.
-- Length restriction:
-  • Chinese heading: ≤25 characters
-  • English heading: ≤80 characters
-- Exclude long narrative sentences, continuous prose, or bullet-style lists → output as "-1".
-
-OUTPUT FORMAT
-- Return ONLY a valid JSON array of {"title","content"} objects.
-- No reasoning or commentary.
-
-EXAMPLES
-
-Example 1 — No heading
-Input:
-[{"0": "Copyright page · Publication info (ISBN 123-456). All rights reserved."}, ...]
-Output:
-[
-  {"title":"-1","chunk_id":"0"},
-  ...
-]
-
-Example 2 — One heading
-Input:
-[{"1": "Chapter 1: General Provisions This chapter defines the overall rules…"}, ...]
-Output:
-[
-  {"title":"Chapter 1: General Provisions","chunk_id":"1"},
-  ...
-]
-
-Example 3 — Narrative + heading
-Input:
-[{"2": "This paragraph introduces the background and goals. Section 2: Definitions Key terms are explained…"}, ...]
-Output:
-[
-  {"title":"Section 2: Definitions","chunk_id":"2"},
-  ...
-]
-
-Example 4 — Multiple headings in one chunk
-Input:
-[{"3": "Declarations and Commitments (I) Party B commits… (II) Party C commits… Appendix A Data Specification"}, ...]
-Output:
-[
-  {"title":"Declarations and Commitments","chunk_id":"3"},
-  {"title":"(I) Party B commits","chunk_id":"3"},
-  {"title":"(II) Party C commits","chunk_id":"3"},
-  {"title":"Appendix A Data Specification","chunk_id":"3"},
-  ...
-]
-
-Example 5 — Numbering styles
-Input:
-[{"4": "1. Scope: Defines boundaries. 2) Definitions: Terms used. III) Methods Overview."}, ...]
-Output:
-[
-  {"title":"1. Scope","chunk_id":"4"},
-  {"title":"2) Definitions","chunk_id":"4"},
-  {"title":"III) Methods Overview","chunk_id":"4"},
-  ...
-]
-
-Example 6 — Long list (NOT headings)
-Input:
-{"5": "Item list: apples, bananas, strawberries, blueberries, mangos, peaches"}, ...]
-Output:
-[
-  {"title":"-1","chunk_id":"5"},
-  ...
-]
-
-Example 7 — Mixed Chinese/English
-Input:
-{"6": "（出版信息略）This standard follows industry practices. Chapter 1: Overview 摘要… 第2节：术语与缩略语"}, ...]
-Output:
-[
-  {"title":"Chapter 1: Overview","chunk_id":"6"},
-  {"title":"第2节：术语与缩略语","chunk_id":"6"},
-  ...
-]

rag/prompts/toc_from_text_user.md

@@ -1,8 +0,0 @@
-OUTPUT FORMAT
-- Return ONLY the JSON array.
-- Use double quotes.
-- No extra commentary.
-- Keep language of "title" the same as the input.
-
-INPUT
-{{text}}

rag/prompts/toc_relevance_system.md

@@ -1,118 +0,0 @@
-# System Prompt: TOC Relevance Evaluation
-
-You are an expert logical reasoning assistant specializing in hierarchical Table of Contents (TOC) relevance evaluation.
-
-## GOAL
-You will receive:
-1. A JSON list of TOC items, each with fields:
-   ```json
-   {
-     "level": <integer>,   // e.g., 1, 2, 3
-     "title": <string>     // section title
-   }
-   ```
-2. A user query (natural language question).
-
-You must assign a **relevance score** (integer) to every TOC entry, based on how related its `title` is to the `query`.
-
----
-
-## RULES
-
-### Scoring System
-- 5 → highly relevant (directly answers or matches the query intent)
-- 3 → somewhat related (same topic or partially overlaps)
-- 1 → weakly related (vague or tangential)
-- 0 → no clear relation
-- -1 → explicitly irrelevant or contradictory
-
-### Hierarchy Traversal
-- The TOC is hierarchical: smaller `level` = higher layer (e.g., level 1 is top-level, level 2 is a subsection).
-- You must traverse in **hierarchical order** — interpret the structure based on levels (1 > 2 > 3).
-- If a high-level item (level 1) is strongly related (score 5), its child items (level 2, 3) are likely relevant too.
-- If a high-level item is unrelated (-1 or 0), its deeper children are usually less relevant unless the titles clearly match the query.
-- Lower (deeper) levels provide more specific content; prefer assigning higher scores if they directly match the query.
-
-### Output Format
-Return a **JSON array**, preserving the input order but adding a new key `"score"`:
-
-```json
-[
-  {"level": 1, "title": "Introduction", "score": 0},
-  {"level": 2, "title": "Definition of Sustainability", "score": 5}
-]
-```
-
-### Constraints
-- Output **only the JSON array** — no explanations or reasoning text.
-
-### EXAMPLES
-
-#### Example 1
-Input TOC:
-[
-  {"level": 1, "title": "Machine Learning Overview"},
-  {"level": 2, "title": "Supervised Learning"},
-  {"level": 2, "title": "Unsupervised Learning"},
-  {"level": 3, "title": "Applications of Deep Learning"}
-]
-
-Query:
-"How is deep learning used in image classification?"
-
-Output:
-[
-  {"level": 1, "title": "Machine Learning Overview", "score": 3},
-  {"level": 2, "title": "Supervised Learning", "score": 3},
-  {"level": 2, "title": "Unsupervised Learning", "score": 0},
-  {"level": 3, "title": "Applications of Deep Learning", "score": 5}
-]
-
----
-
-#### Example 2
-Input TOC:
-[
-  {"level": 1, "title": "Marketing Basics"},
-  {"level": 2, "title": "Consumer Behavior"},
-  {"level": 2, "title": "Digital Marketing"},
-  {"level": 3, "title": "Social Media Campaigns"},
-  {"level": 3, "title": "SEO Optimization"}
-]
-
-Query:
-"What are the best online marketing methods?"
-
-Output:
-[
-  {"level": 1, "title": "Marketing Basics", "score": 3},
-  {"level": 2, "title": "Consumer Behavior", "score": 1},
-  {"level": 2, "title": "Digital Marketing", "score": 5},
-  {"level": 3, "title": "Social Media Campaigns", "score": 5},
-  {"level": 3, "title": "SEO Optimization", "score": 5}
-]
-
----
-
-#### Example 3
-Input TOC:
-[
-  {"level": 1, "title": "Physics Overview"},
-  {"level": 2, "title": "Classical Mechanics"},
-  {"level": 3, "title": "Newton’s Laws"},
-  {"level": 2, "title": "Thermodynamics"},
-  {"level": 3, "title": "Entropy and Heat Transfer"}
-]
-
-Query:
-"What is entropy?"
-
-Output:
-[
-  {"level": 1, "title": "Physics Overview", "score": 3},
-  {"level": 2, "title": "Classical Mechanics", "score": 0},
-  {"level": 3, "title": "Newton’s Laws", "score": -1},
-  {"level": 2, "title": "Thermodynamics", "score": 5},
-  {"level": 3, "title": "Entropy and Heat Transfer", "score": 5}
-]
-

rag/prompts/toc_relevance_user.md

@@ -1,17 +0,0 @@
-# User Prompt: TOC Relevance Evaluation
-
-You will now receive:
-1. A JSON list of TOC items (each with `level` and `title`)
-2. A user query string.
-
-Traverse the TOC hierarchically based on level numbers and assign scores (5,3,1,0,-1) according to the rules in the system prompt.  
-Output **only** the JSON array with the added `"score"` field.
-
----
-
-**Input TOC:**
-{{ toc_json }}
-
-**Query:**
-{{ query }}
-

rag/svr/task_executor.py

@@ -32,7 +32,7 @@ from api.utils.log_utils import init_root_logger, get_project_base_directory
 from graphrag.general.index import run_graphrag_for_kb
 from graphrag.utils import get_llm_cache, set_llm_cache, get_tags_from_cache, set_tags_to_cache
 from rag.flow.pipeline import Pipeline
-from rag.prompts.generator import keyword_extraction, question_proposal, content_tagging, run_toc_from_text
+from rag.prompts import keyword_extraction, question_proposal, content_tagging
 import logging
 import os
 from datetime import datetime
@@ -370,38 +370,6 @@ async def build_chunks(task, progress_callback):
                 nursery.start_soon(doc_question_proposal, chat_mdl, d, task["parser_config"]["auto_questions"])
         progress_callback(msg="Question generation {} chunks completed in {:.2f}s".format(len(docs), timer() - st))
 
-    if task["parser_id"].lower() == "naive" and task["parser_config"].get("toc_extraction", False):
-        progress_callback(msg="Start to generate table of content ...")
-        chat_mdl = LLMBundle(task["tenant_id"], LLMType.CHAT, llm_name=task["llm_id"], lang=task["language"])
-        docs = sorted(docs, key=lambda d:(
-            d.get("page_num_int", 0)[0] if isinstance(d.get("page_num_int", 0), list) else d.get("page_num_int", 0),
-            d.get("top_int", 0)[0] if isinstance(d.get("top_int", 0), list) else d.get("top_int", 0)
-        ))
-        toc: list[dict] = await run_toc_from_text([d["content_with_weight"] for d in docs], chat_mdl, progress_callback)
-        logging.info("------------ T O C -------------\n"+json.dumps(toc, ensure_ascii=False, indent='  '))
-        ii = 0
-        while ii < len(toc):
-            try:
-                idx = int(toc[ii]["chunk_id"])
-                del toc[ii]["chunk_id"]
-                toc[ii]["ids"] = [docs[idx]["id"]]
-                if ii == len(toc) -1:
-                    break
-                for jj in range(idx+1, int(toc[ii+1]["chunk_id"])+1):
-                    toc[ii]["ids"].append(docs[jj]["id"])
-            except Exception as e:
-                logging.exception(e)
-            ii += 1
-
-        if toc:
-            d = copy.deepcopy(docs[-1])
-            d["content_with_weight"] = json.dumps(toc, ensure_ascii=False)
-            d["toc_kwd"] = "toc"
-            d["available_int"] = 0
-            d["page_num_int"] = 100000000
-            d["id"] = xxhash.xxh64((d["content_with_weight"] + str(d["doc_id"])).encode("utf-8", "surrogatepass")).hexdigest()
-            docs.append(d)
-
     if task["kb_parser_config"].get("tag_kb_ids", []):
         progress_callback(msg="Start to tag for every chunk ...")
         kb_ids = task["kb_parser_config"]["tag_kb_ids"]
@@ -412,7 +380,7 @@ 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 = settings.retrievaler.all_tags_in_portion(tenant_id, kb_ids, S)
             set_tags_to_cache(kb_ids, all_tags)
         else:
             all_tags = json.loads(all_tags)
@@ -425,7 +393,7 @@ 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 settings.retrievaler.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)
@@ -677,7 +645,7 @@ 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 settings.retrievaler.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])))
@@ -691,7 +659,7 @@ async def run_raptor_for_kb(row, kb_parser_config, chat_mdl, embd_mdl, vector_si
         raptor_config["threshold"],
     )
     original_length = len(chunks)
-    chunks = await raptor(chunks, kb_parser_config["raptor"]["random_seed"], callback)
+    chunks = await raptor(chunks, row["kb_parser_config"]["raptor"]["random_seed"], callback)
     doc = {
         "doc_id": fake_doc_id,
         "kb_id": [str(row["kb_id"])],
@@ -814,22 +782,8 @@ async def do_handle_task(task):
 
         kb_parser_config = kb.parser_config
         if not kb_parser_config.get("raptor", {}).get("use_raptor", False):
-            kb_parser_config.update(
-                {
-                    "raptor": {
-                        "use_raptor": True,
-                        "prompt": "Please summarize the following paragraphs. Be careful with the numbers, do not make things up. Paragraphs as following:\n      {cluster_content}\nThe above is the content you need to summarize.",
-                        "max_token": 256,
-                        "threshold": 0.1,
-                        "max_cluster": 64,
-                        "random_seed": 0,
-                    },
-                }
-            )
-            if not KnowledgebaseService.update_by_id(kb.id, {"parser_config":kb_parser_config}):
             progress_callback(prog=-1.0, msg="Internal error: Invalid RAPTOR configuration")
             return
-
         # bind LLM for raptor
         chat_model = LLMBundle(task_tenant_id, LLMType.CHAT, llm_name=task_llm_id, lang=task_language)
         # run RAPTOR
@@ -852,26 +806,9 @@ async def do_handle_task(task):
 
         kb_parser_config = kb.parser_config
         if not kb_parser_config.get("graphrag", {}).get("use_graphrag", False):
-            kb_parser_config.update(
-                {
-                    "graphrag": {
-                        "use_graphrag": True,
-                        "entity_types": [
-                            "organization",
-                            "person",
-                            "geo",
-                            "event",
-                            "category",
-                        ],
-                        "method": "light",
-                    }
-                }
-            )
-            if not KnowledgebaseService.update_by_id(kb.id, {"parser_config":kb_parser_config}):
             progress_callback(prog=-1.0, msg="Internal error: Invalid GraphRAG configuration")
             return
 
-
         graphrag_conf = kb_parser_config.get("graphrag", {})
         start_ts = timer()
         chat_model = LLMBundle(task_tenant_id, LLMType.CHAT, llm_name=task_llm_id, lang=task_language)

rag/utils/__init__.py

@@ -95,12 +95,6 @@ def total_token_count_from_response(resp):
         except Exception:
             pass
 
-    if hasattr(resp, "usage_metadata") and hasattr(resp.usage_metadata, "total_tokens"):
-        try:
-            return resp.usage_metadata.total_tokens
-        except Exception:
-            pass
-
     if 'usage' in resp and 'total_tokens' in resp['usage']:
         try:
             return resp["usage"]["total_tokens"]

rag/utils/es_conn.py

@@ -28,7 +28,6 @@ from rag import settings
 from rag.settings import TAG_FLD, PAGERANK_FLD
 from rag.utils import singleton, get_float
 from api.utils.file_utils import get_project_base_directory
-from api.utils.common import convert_bytes
 from rag.utils.doc_store_conn import DocStoreConnection, MatchExpr, OrderByExpr, MatchTextExpr, MatchDenseExpr, \
     FusionExpr
 from rag.nlp import is_english, rag_tokenizer
@@ -580,52 +579,3 @@ class ESConnection(DocStoreConnection):
                 break
         logger.error(f"ESConnection.sql timeout for {ATTEMPT_TIME} times!")
         return None
-
-    def get_cluster_stats(self):
-        """
-        curl -XGET "http://{es_host}/_cluster/stats" -H "kbn-xsrf: reporting" to view raw stats.
-        """
-        raw_stats = self.es.cluster.stats()
-        logger.debug(f"ESConnection.get_cluster_stats: {raw_stats}")
-        try:
-            res = {
-                'cluster_name': raw_stats['cluster_name'],
-                'status': raw_stats['status']
-            }
-            indices_status = raw_stats['indices']
-            res.update({
-                'indices': indices_status['count'],
-                'indices_shards': indices_status['shards']['total']
-            })
-            doc_info = indices_status['docs']
-            res.update({
-                'docs': doc_info['count'],
-                'docs_deleted': doc_info['deleted']
-            })
-            store_info = indices_status['store']
-            res.update({
-                'store_size': convert_bytes(store_info['size_in_bytes']),
-                'total_dataset_size': convert_bytes(store_info['total_data_set_size_in_bytes'])
-            })
-            mappings_info = indices_status['mappings']
-            res.update({
-                'mappings_fields': mappings_info['total_field_count'],
-                'mappings_deduplicated_fields': mappings_info['total_deduplicated_field_count'],
-                'mappings_deduplicated_size': convert_bytes(mappings_info['total_deduplicated_mapping_size_in_bytes'])
-            })
-            node_info = raw_stats['nodes']
-            res.update({
-                'nodes': node_info['count']['total'],
-                'nodes_version': node_info['versions'],
-                'os_mem': convert_bytes(node_info['os']['mem']['total_in_bytes']),
-                'os_mem_used': convert_bytes(node_info['os']['mem']['used_in_bytes']),
-                'os_mem_used_percent': node_info['os']['mem']['used_percent'],
-                'jvm_versions': node_info['jvm']['versions'][0]['vm_version'],
-                'jvm_heap_used': convert_bytes(node_info['jvm']['mem']['heap_used_in_bytes']),
-                'jvm_heap_max': convert_bytes(node_info['jvm']['mem']['heap_max_in_bytes'])
-            })
-            return res
-
-        except Exception as e:
-            logger.exception(f"ESConnection.get_cluster_stats: {e}")
-            return None

rag/utils/infinity_conn.py

@@ -30,7 +30,6 @@ from rag.settings import PAGERANK_FLD, TAG_FLD
 from rag.utils import singleton
 import pandas as pd
 from api.utils.file_utils import get_project_base_directory
-from rag.nlp import is_english
 
 from rag.utils.doc_store_conn import (
     DocStoreConnection,
@@ -41,8 +40,7 @@ from rag.utils.doc_store_conn import (
     OrderByExpr,
 )
 
-logger = logging.getLogger("ragflow.infinity_conn")
+logger = logging.getLogger('ragflow.infinity_conn')
-
 
 def field_keyword(field_name: str):
         # The "docnm_kwd" field is always a string, not list.
@@ -50,7 +48,6 @@ def field_keyword(field_name: str):
             return True
         return False
 
-
 def equivalent_condition_to_str(condition: dict, table_instance=None) -> str | None:
     assert "_id" not in condition
     clmns = {}
@@ -77,7 +74,7 @@ def equivalent_condition_to_str(condition: dict, table_instance=None) -> str | N
                 inCond = list()
                 for item in v:
                     if isinstance(item, str):
-                        item = item.replace("'", "''")
+                        item = item.replace("'","''")
                     inCond.append(f"filter_fulltext('{k}', '{item}')")
                 if inCond:
                     strInCond = " or ".join(inCond)
@@ -89,7 +86,7 @@ def equivalent_condition_to_str(condition: dict, table_instance=None) -> str | N
             inCond = list()
             for item in v:
                 if isinstance(item, str):
-                    item = item.replace("'", "''")
+                    item = item.replace("'","''")
                     inCond.append(f"'{item}'")
                 else:
                     inCond.append(str(item))
@@ -118,10 +115,10 @@ def concat_dataframes(df_list: list[pd.DataFrame], selectFields: list[str]) -> p
     
     schema = []
     for field_name in selectFields:
-        if field_name == "score()":  # Workaround: fix schema is changed to score()
+        if field_name == 'score()': # Workaround: fix schema is changed to score()
-            schema.append("SCORE")
+            schema.append('SCORE')
-        elif field_name == "similarity()":  # Workaround: fix schema is changed to similarity()
+        elif field_name == 'similarity()': # Workaround: fix schema is changed to similarity()
-            schema.append("SIMILARITY")
+            schema.append('SIMILARITY')
         else:
             schema.append(field_name)
     return pd.DataFrame(columns=schema)
@@ -161,7 +158,9 @@ class InfinityConnection(DocStoreConnection):
 
     def _migrate_db(self, inf_conn):
         inf_db = inf_conn.create_database(self.dbName, ConflictType.Ignore)
-        fp_mapping = os.path.join(get_project_base_directory(), "conf", "infinity_mapping.json")
+        fp_mapping = os.path.join(
+            get_project_base_directory(), "conf", "infinity_mapping.json"
+        )
         if not os.path.exists(fp_mapping):
             raise Exception(f"Mapping file not found at {fp_mapping}")
         schema = json.load(open(fp_mapping))
@@ -179,12 +178,16 @@ class InfinityConnection(DocStoreConnection):
                     continue
                 res = inf_table.add_columns({field_name: field_info})
                 assert res.error_code == infinity.ErrorCode.OK
-                logger.info(f"INFINITY added following column to table {table_name}: {field_name} {field_info}")
+                logger.info(
+                    f"INFINITY added following column to table {table_name}: {field_name} {field_info}"
+                )
                 if field_info["type"] != "varchar" or "analyzer" not in field_info:
                     continue
                 inf_table.create_index(
                     f"text_idx_{field_name}",
-                    IndexInfo(field_name, IndexType.FullText, {"ANALYZER": field_info["analyzer"]}),
+                    IndexInfo(
+                        field_name, IndexType.FullText, {"ANALYZER": field_info["analyzer"]}
+                    ),
                     ConflictType.Ignore,
                 )
 
@@ -218,7 +221,9 @@ class InfinityConnection(DocStoreConnection):
         inf_conn = self.connPool.get_conn()
         inf_db = inf_conn.create_database(self.dbName, ConflictType.Ignore)
 
-        fp_mapping = os.path.join(get_project_base_directory(), "conf", "infinity_mapping.json")
+        fp_mapping = os.path.join(
+            get_project_base_directory(), "conf", "infinity_mapping.json"
+        )
         if not os.path.exists(fp_mapping):
             raise Exception(f"Mapping file not found at {fp_mapping}")
         schema = json.load(open(fp_mapping))
@@ -248,11 +253,15 @@ class InfinityConnection(DocStoreConnection):
                 continue
             inf_table.create_index(
                 f"text_idx_{field_name}",
-                IndexInfo(field_name, IndexType.FullText, {"ANALYZER": field_info["analyzer"]}),
+                IndexInfo(
+                    field_name, IndexType.FullText, {"ANALYZER": field_info["analyzer"]}
+                ),
                 ConflictType.Ignore,
             )
         self.connPool.release_conn(inf_conn)
-        logger.info(f"INFINITY created table {table_name}, vector size {vectorSize}")
+        logger.info(
+            f"INFINITY created table {table_name}, vector size {vectorSize}"
+        )
 
     def deleteIdx(self, indexName: str, knowledgebaseId: str):
         table_name = f"{indexName}_{knowledgebaseId}"
@@ -279,8 +288,7 @@ class InfinityConnection(DocStoreConnection):
     """
 
     def search(
-        self,
+            self, selectFields: list[str],
-        selectFields: list[str],
             highlightFields: list[str],
             condition: dict,
             matchExprs: list[MatchExpr],
@@ -290,10 +298,10 @@ class InfinityConnection(DocStoreConnection):
             indexNames: str | list[str],
             knowledgebaseIds: list[str],
             aggFields: list[str] = [],
-        rank_feature: dict | None = None,
+            rank_feature: dict | None = None
     ) -> tuple[pd.DataFrame, int]:
         """
-        BUG: Infinity returns empty for a highlight field if the query string doesn't use that field.
+        TODO: Infinity doesn't provide highlight
         """
         if isinstance(indexNames, str):
             indexNames = indexNames.split(",")
@@ -430,7 +438,9 @@ class InfinityConnection(DocStoreConnection):
                                 matchExpr.extra_options.copy(),
                             )
                         elif isinstance(matchExpr, FusionExpr):
-                            builder = builder.fusion(matchExpr.method, matchExpr.topn, matchExpr.fusion_params)
+                            builder = builder.fusion(
+                                matchExpr.method, matchExpr.topn, matchExpr.fusion_params
+                            )
                 else:
                     if filter_cond and len(filter_cond) > 0:
                         builder.filter(filter_cond)
@@ -445,13 +455,15 @@ class InfinityConnection(DocStoreConnection):
         self.connPool.release_conn(inf_conn)
         res = concat_dataframes(df_list, output)
         if matchExprs:
-            res["Sum"] = res[score_column] + res[PAGERANK_FLD]
+            res['Sum'] = res[score_column] + res[PAGERANK_FLD]
-            res = res.sort_values(by="Sum", ascending=False).reset_index(drop=True).drop(columns=["Sum"])
+            res = res.sort_values(by='Sum', ascending=False).reset_index(drop=True).drop(columns=['Sum'])
             res = res.head(limit)
         logger.debug(f"INFINITY search final result: {str(res)}")
         return res, total_hits_count
 
-    def get(self, chunkId: str, indexName: str, knowledgebaseIds: list[str]) -> dict | None:
+    def get(
+            self, chunkId: str, indexName: str, knowledgebaseIds: list[str]
+    ) -> dict | None:
         inf_conn = self.connPool.get_conn()
         db_instance = inf_conn.get_database(self.dbName)
         df_list = list()
@@ -464,7 +476,8 @@ class InfinityConnection(DocStoreConnection):
             try:
                 table_instance = db_instance.get_table(table_name)
             except Exception:
-                logger.warning(f"Table not found: {table_name}, this knowledge base isn't created in Infinity. Maybe it is created in other document engine.")
+                logger.warning(
+                    f"Table not found: {table_name}, this knowledge base isn't created in Infinity. Maybe it is created in other document engine.")
                 continue
             kb_res, _ = table_instance.output(["*"]).filter(f"id = '{chunkId}'").to_df()
             logger.debug(f"INFINITY get table: {str(table_list)}, result: {str(kb_res)}")
@@ -474,7 +487,9 @@ class InfinityConnection(DocStoreConnection):
         res_fields = self.getFields(res, res.columns.tolist())
         return res_fields.get(chunkId, None)
 
-    def insert(self, documents: list[dict], indexName: str, knowledgebaseId: str = None) -> list[str]:
+    def insert(
+            self, documents: list[dict], indexName: str, knowledgebaseId: str = None
+    ) -> list[str]:
         inf_conn = self.connPool.get_conn()
         db_instance = inf_conn.get_database(self.dbName)
         table_name = f"{indexName}_{knowledgebaseId}"
@@ -517,7 +532,7 @@ class InfinityConnection(DocStoreConnection):
                         d[k] = v
                 elif re.search(r"_feas$", k):
                     d[k] = json.dumps(v)
-                elif k == "kb_id":
+                elif k == 'kb_id':
                     if isinstance(d[k], list):
                         d[k] = d[k][0]  # since d[k] is a list, but we need a str
                 elif k == "position_int":
@@ -546,14 +561,16 @@ class InfinityConnection(DocStoreConnection):
         logger.debug(f"INFINITY inserted into {table_name} {str_ids}.")
         return []
 
-    def update(self, condition: dict, newValue: dict, indexName: str, knowledgebaseId: str) -> bool:
+    def update(
+            self, condition: dict, newValue: dict, indexName: str, knowledgebaseId: str
+    ) -> bool:
         # if 'position_int' in newValue:
         #     logger.info(f"update position_int: {newValue['position_int']}")
         inf_conn = self.connPool.get_conn()
         db_instance = inf_conn.get_database(self.dbName)
         table_name = f"{indexName}_{knowledgebaseId}"
         table_instance = db_instance.get_table(table_name)
-        # if "exists" in condition:
+        #if "exists" in condition:
         #    del condition["exists"]
         
         clmns = {}
@@ -570,7 +587,7 @@ class InfinityConnection(DocStoreConnection):
                     newValue[k] = v
             elif re.search(r"_feas$", k):
                 newValue[k] = json.dumps(v)
-            elif k == "kb_id":
+            elif k == 'kb_id':
                 if isinstance(newValue[k], list):
                     newValue[k] = newValue[k][0]  # since d[k] is a list, but we need a str
             elif k == "position_int":
@@ -598,7 +615,7 @@ class InfinityConnection(DocStoreConnection):
         remove_opt = {}     # "[k,new_value]": [id_to_update, ...]
         if removeValue:
             col_to_remove = list(removeValue.keys())
-            row_to_opt = table_instance.output(col_to_remove + ["id"]).filter(filter).to_df()
+            row_to_opt = table_instance.output(col_to_remove + ['id']).filter(filter).to_df()
             logger.debug(f"INFINITY search table {str(table_name)}, filter {filter}, result: {str(row_to_opt[0])}")
             row_to_opt = self.getFields(row_to_opt, col_to_remove)
             for id, old_v in row_to_opt.items():
@@ -615,7 +632,7 @@ class InfinityConnection(DocStoreConnection):
         logger.debug(f"INFINITY update table {table_name}, filter {filter}, newValue {newValue}.")
         for update_kv, ids in remove_opt.items():
             k, v = json.loads(update_kv)
-            table_instance.update(filter + " AND id in ({0})".format(",".join([f"'{id}'" for id in ids])), {k: "###".join(v)})
+            table_instance.update(filter + " AND id in ({0})".format(",".join([f"'{id}'" for id in ids])), {k:"###".join(v)})
                 
         table_instance.update(filter, newValue)
         self.connPool.release_conn(inf_conn)
@@ -628,7 +645,9 @@ class InfinityConnection(DocStoreConnection):
         try:
             table_instance = db_instance.get_table(table_name)
         except Exception:
-            logger.warning(f"Skipped deleting from table {table_name} since the table doesn't exist.")
+            logger.warning(
+                f"Skipped deleting from table {table_name} since the table doesn't exist."
+            )
             return 0
         filter = equivalent_condition_to_str(condition, table_instance)
         logger.debug(f"INFINITY delete table {table_name}, filter {filter}.")
@@ -656,34 +675,32 @@ class InfinityConnection(DocStoreConnection):
         if not fields:
             return {}
         fieldsAll = fields.copy()
-        fieldsAll.append("id")
+        fieldsAll.append('id')
         column_map = {col.lower(): col for col in res.columns}
-        matched_columns = {column_map[col.lower()]: col for col in set(fieldsAll) if col.lower() in column_map}
+        matched_columns = {column_map[col.lower()]:col for col in set(fieldsAll) if col.lower() in column_map}
         none_columns = [col for col in set(fieldsAll) if col.lower() not in column_map]
 
         res2 = res[matched_columns.keys()]
         res2 = res2.rename(columns=matched_columns)
-        res2.drop_duplicates(subset=["id"], inplace=True)
+        res2.drop_duplicates(subset=['id'], inplace=True)
 
         for column in res2.columns:
             k = column.lower()
             if field_keyword(k):
-                res2[column] = res2[column].apply(lambda v: [kwd for kwd in v.split("###") if kwd])
+                res2[column] = res2[column].apply(lambda v:[kwd for kwd in v.split("###") if kwd])
             elif re.search(r"_feas$", k):
                 res2[column] = res2[column].apply(lambda v: json.loads(v) if v else {})
             elif k == "position_int":
-
                 def to_position_int(v):
                     if v:
-                        arr = [int(hex_val, 16) for hex_val in v.split("_")]
+                        arr = [int(hex_val, 16) for hex_val in v.split('_')]
-                        v = [arr[i : i + 5] for i in range(0, len(arr), 5)]
+                        v = [arr[i:i + 5] for i in range(0, len(arr), 5)]
                     else:
                         v = []
                     return v
-
                 res2[column] = res2[column].apply(to_position_int)
             elif k in ["page_num_int", "top_int"]:
-                res2[column] = res2[column].apply(lambda v: [int(hex_val, 16) for hex_val in v.split("_")] if v else [])
+                res2[column] = res2[column].apply(lambda v:[int(hex_val, 16) for hex_val in v.split('_')] if v else [])
             else:
                 pass
         for column in none_columns:
@@ -702,35 +719,23 @@ class InfinityConnection(DocStoreConnection):
         for i in range(num_rows):
             id = column_id[i]
             txt = res[fieldnm][i]
-            if re.search(r"<em>[^<>]+</em>", txt, flags=re.IGNORECASE | re.MULTILINE):
-                ans[id] = txt
-                continue
             txt = re.sub(r"[\r\n]", " ", txt, flags=re.IGNORECASE | re.MULTILINE)
             txts = []
             for t in re.split(r"[.?!;\n]", txt):
-                if is_english([t]):
                 for w in keywords:
                     t = re.sub(
-                            r"(^|[ .?/'\"\(\)!,:;-])(%s)([ .?/'\"\(\)!,:;-])" % re.escape(w),
+                        r"(^|[ .?/'\"\(\)!,:;-])(%s)([ .?/'\"\(\)!,:;-])"
+                        % re.escape(w),
                         r"\1<em>\2</em>\3",
                         t,
                         flags=re.IGNORECASE | re.MULTILINE,
                     )
-                else:
+                if not re.search(
-                    for w in sorted(keywords, key=len, reverse=True):
+                        r"<em>[^<>]+</em>", t, flags=re.IGNORECASE | re.MULTILINE
-                        t = re.sub(
+                ):
-                            re.escape(w),
-                            f"<em>{w}</em>",
-                            t,
-                            flags=re.IGNORECASE | re.MULTILINE,
-                        )
-                if not re.search(r"<em>[^<>]+</em>", t, flags=re.IGNORECASE | re.MULTILINE):
                     continue
                 txts.append(t)
-            if txts:
             ans[id] = "...".join(txts)
-            else:
-                ans[id] = txt
         return ans
 
     def getAggregation(self, res: tuple[pd.DataFrame, int] | pd.DataFrame, fieldnm: str):

rag/utils/redis_conn.py

@@ -91,20 +91,6 @@ class RedisDB:
         if self.REDIS.get(a) == b:
             return True
 
-    def info(self):
-        info = self.REDIS.info()
-        return {
-            'redis_version': info["redis_version"],
-            'server_mode': info["server_mode"],
-            'used_memory': info["used_memory_human"],
-            'total_system_memory': info["total_system_memory_human"],
-            'mem_fragmentation_ratio': info["mem_fragmentation_ratio"],
-            'connected_clients': info["connected_clients"],
-            'blocked_clients': info["blocked_clients"],
-            'instantaneous_ops_per_sec': info["instantaneous_ops_per_sec"],
-            'total_commands_processed': info["total_commands_processed"]
-        }
-
     def is_alive(self):
         return self.REDIS is not None
 

sdk/python/ragflow_sdk/modules/session.py

@@ -33,52 +33,35 @@ class Session(Base):
                 self.__session_type = "agent"
         super().__init__(rag, res_dict)
 
-
+    def ask(self, question="", stream=True, **kwargs):
-    def ask(self, question="", stream=False, **kwargs):
-        """
-        Ask a question to the session. If stream=True, yields Message objects as they arrive (SSE streaming).
-        If stream=False, returns a single Message object for the final answer.
-        """
         if self.__session_type == "agent":
             res = self._ask_agent(question, stream)
         elif self.__session_type == "chat":
             res = self._ask_chat(question, stream, **kwargs)
-        else:
-            raise Exception(f"Unknown session type: {self.__session_type}")
 
         if stream:
-            for line in res.iter_lines(decode_unicode=True):
+            for line in res.iter_lines():
-                if not line:
+                line = line.decode("utf-8")
-                    continue  # Skip empty lines
+                if line.startswith("{"):
-                line = line.strip()
+                    json_data = json.loads(line)
-
+                    raise Exception(json_data["message"])
-                if line.startswith("data:"):
+                if not line.startswith("data:"):
-                    content = line[len("data:"):].strip()
+                    continue
-                    if content == "[DONE]":
+                json_data = json.loads(line[5:])
-                        break  # End of stream
+                if json_data["data"] is True or json_data["data"].get("running_status"):
-                else:
+                    continue
-                    content = line
+                message = self._structure_answer(json_data)
-
+                yield message
-                try:
-                    json_data = json.loads(content)
-                except json.JSONDecodeError:
-                    continue  # Skip lines that are not valid JSON
-
-                event = json_data.get("event")
-                if event == "message":
-                    yield self._structure_answer(json_data)
-                elif event == "message_end":
-                    return  # End of message stream
         else:
             try:
-                json_data = res.json()
+                json_data = json.loads(res.text)
             except ValueError:
                 raise Exception(f"Invalid response {res}")
-            yield self._structure_answer(json_data["data"])
+            return self._structure_answer(json_data)
         
 
     def _structure_answer(self, json_data):
-        answer = json_data["data"]["content"]
+        answer = json_data["data"]["answer"]
         reference = json_data["data"].get("reference", {})
         temp_dict = {
             "content": answer,

web/.storybook/main.ts

@@ -3,7 +3,6 @@ import path from 'path';
 
 const config: StorybookConfig = {
   stories: ['../src/**/*.mdx', '../src/**/*.stories.@(js|jsx|mjs|ts|tsx)'],
-  staticDirs: ['../public'],
   addons: [
     '@storybook/addon-webpack5-compiler-swc',
     '@storybook/addon-docs',

web/.storybook/preview.ts

@@ -1,7 +1,6 @@
 import '@/locales/config';
 import type { Preview } from '@storybook/react-webpack5';
 import { createElement } from 'react';
-import '../public/iconfont.js';
 import { TooltipProvider } from '../src/components/ui/tooltip';
 
 import '../tailwind.css';

web/public/iconfont.js

@@ -59,10 +59,6 @@
   `<symbol id="icon-GitHub" viewBox="0 0 1024 1024">
         <path d="M512 42.666667C252.714667 42.666667 42.666667 252.714667 42.666667 512c0 207.658667 134.357333 383.104 320.896 445.269333 23.466667 4.096 32.256-9.941333 32.256-22.272 0-11.178667-0.554667-48.128-0.554667-87.424-117.930667 21.717333-148.437333-28.757333-157.824-55.125333-5.290667-13.525333-28.16-55.168-48.085333-66.304-16.426667-8.832-39.936-30.506667-0.597334-31.104 36.949333-0.597333 63.36 34.005333 72.149334 48.128 42.24 70.954667 109.696 51.029333 136.704 38.698667 4.096-30.506667 16.426667-51.029333 29.909333-62.762667-104.448-11.733333-213.546667-52.224-213.546667-231.765333 0-51.029333 18.176-93.269333 48.128-126.122667-4.736-11.733333-21.162667-59.818667 4.693334-124.373333 0 0 39.296-12.288 129.024 48.128a434.901333 434.901333 0 0 1 117.333333-15.829334c39.936 0 79.829333 5.248 117.333333 15.829334 89.770667-61.013333 129.066667-48.128 129.066667-48.128 25.813333 64.554667 9.429333 112.64 4.736 124.373333 29.909333 32.853333 48.085333 74.538667 48.085333 126.122667 0 180.138667-109.696 220.032-214.144 231.765333 17.024 14.677333 31.701333 42.837333 31.701334 86.826667 0 62.762667-0.597333 113.237333-0.597334 129.066666 0 12.330667 8.789333 26.965333 32.256 22.272C846.976 895.104 981.333333 719.104 981.333333 512c0-259.285333-210.005333-469.333333-469.333333-469.333333z"></path>
    </symbol>` +
-  `<symbol id="icon-more" viewBox="0 0 1024 1024">
-    <path d="M0 0h1024v1024H0z"  opacity=".01"></path>
-    <path d="M867.072 141.184H156.032a32 32 0 0 0 0 64h711.04a32 32 0 0 0 0-64z m0.832 226.368H403.2a32 32 0 0 0 0 64h464.704a32 32 0 0 0 0-64zM403.2 573.888h464.704a32 32 0 0 1 0 64H403.2a32 32 0 0 1 0-64z m464.704 226.368H156.864a32 32 0 0 0 0 64h711.04a32 32 0 0 0 0-64zM137.472 367.552v270.336l174.528-122.24-174.528-148.096z" ></path>
-</symbol>` +
   '</svg>'),
   ((h) => {
     var a = (l = (l = document.getElementsByTagName('script'))[

web/src/assets/svg/llm/deerapi.svg

@@ -1,5 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<!-- Generated by Pixelmator Pro 3.5.5 -->
-<svg width="24" height="24" viewBox="0 0 24 24" xmlns="http://www.w3.org/2000/svg">
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