Docs: Removed /v1 from Ollama base URLs (#10067)

### What problem does this PR solve?


### Type of change

- [x] Documentation Update

agent/tools/exesql.py

@@ -53,7 +53,7 @@ class ExeSQLParam(ToolParamBase):
         self.max_records = 1024
 
     def check(self):
-        self.check_valid_value(self.db_type, "Choose DB type", ['mysql', 'postgresql', 'mariadb', 'mssql'])
+        self.check_valid_value(self.db_type, "Choose DB type", ['mysql', 'postgres', 'mariadb', 'mssql'])
         self.check_empty(self.database, "Database name")
         self.check_empty(self.username, "database username")
         self.check_empty(self.host, "IP Address")
@@ -111,7 +111,7 @@ class ExeSQL(ToolBase, ABC):
         if self._param.db_type in ["mysql", "mariadb"]:
             db = pymysql.connect(db=self._param.database, user=self._param.username, host=self._param.host,
                                  port=self._param.port, password=self._param.password)
-        elif self._param.db_type == 'postgresql':
+        elif self._param.db_type == 'postgres':
             db = psycopg2.connect(dbname=self._param.database, user=self._param.username, host=self._param.host,
                                   port=self._param.port, password=self._param.password)
         elif self._param.db_type == 'mssql':

api/apps/canvas_app.py

@@ -332,7 +332,7 @@ def test_db_connect():
         if req["db_type"] in ["mysql", "mariadb"]:
             db = MySQLDatabase(req["database"], user=req["username"], host=req["host"], port=req["port"],
                                password=req["password"])
-        elif req["db_type"] == 'postgresql':
+        elif req["db_type"] == 'postgres':
             db = PostgresqlDatabase(req["database"], user=req["username"], host=req["host"], port=req["port"],
                                     password=req["password"])
         elif req["db_type"] == 'mssql':

api/db/services/document_service.py

@@ -702,6 +702,8 @@ def queue_raptor_o_graphrag_tasks(doc, ty, priority):
 
 def get_queue_length(priority):
     group_info = REDIS_CONN.queue_info(get_svr_queue_name(priority), SVR_CONSUMER_GROUP_NAME)
+    if not group_info:
+        return 0
     return int(group_info.get("lag", 0) or 0)
 
 

deepdoc/parser/html_parser.py

@@ -37,7 +37,7 @@ TITLE_TAGS = {"h1": "#", "h2": "##", "h3": "###", "h4": "#####", "h5": "#####",
 
 
 class RAGFlowHtmlParser:
-    def __call__(self, fnm, binary=None, chunk_token_num=None):
+    def __call__(self, fnm, binary=None, chunk_token_num=512):
         if binary:
             encoding = find_codec(binary)
             txt = binary.decode(encoding, errors="ignore")

docs/guides/chat/start_chat.md

@@ -106,7 +106,7 @@ RAGFlow offers HTTP and Python APIs for you to integrate RAGFlow's capabilities
 
 You can use iframe to embed the created chat assistant into a third-party webpage:
 
-1. Before proceeding, you must [acquire an API key](../models/llm_api_key_setup.md); otherwise, an error message would appear.
+1. Before proceeding, you must [acquire an API key](../../develop/acquire_ragflow_api_key.md); otherwise, an error message would appear.
 2. Hover over an intended chat assistant **>** **Edit** to show the **iframe** window:
 
    ![chat-embed](https://raw.githubusercontent.com/infiniflow/ragflow-docs/main/images/embed_chat_into_webpage.jpg)

docs/guides/models/deploy_local_llm.mdx

@@ -91,7 +91,7 @@ In RAGFlow, click on your logo on the top right of the page **>** **Model provid
 In the popup window, complete basic settings for Ollama:
 
 1. Ensure that your model name and type match those been pulled at step 1 (Deploy Ollama using Docker). For example, (`llama3.2` and `chat`) or (`bge-m3` and `embedding`).
-2. In Ollama base URL, put the URL you found in step 2 followed by `/v1`, i.e. `http://host.docker.internal:11434/v1`, `http://localhost:11434/v1` or `http://${IP_OF_OLLAMA_MACHINE}:11434/v1`.
+2. Put in the Ollama base URL, i.e. `http://host.docker.internal:11434`, `http://localhost:11434` or `http://${IP_OF_OLLAMA_MACHINE}:11434`.
 3. OPTIONAL: Switch on the toggle under **Does it support Vision?** if your model includes an image-to-text model.
 
 

web/src/pages/agent/options.ts

@@ -2133,7 +2133,7 @@ export const QWeatherTimePeriodOptions = [
   '30d',
 ];
 
-export const ExeSQLOptions = ['mysql', 'postgresql', 'mariadb', 'mssql'].map(
+export const ExeSQLOptions = ['mysql', 'postgres', 'mariadb', 'mssql'].map(
   (x) => ({
     label: upperFirst(x),
     value: x,

web/src/pages/data-flow/options.ts

@@ -2133,7 +2133,7 @@ export const QWeatherTimePeriodOptions = [
   '30d',
 ];
 
-export const ExeSQLOptions = ['mysql', 'postgresql', 'mariadb', 'mssql'].map(
+export const ExeSQLOptions = ['mysql', 'postgres', 'mariadb', 'mssql'].map(
   (x) => ({
     label: upperFirst(x),
     value: x,

web/src/pages/flow/constant.tsx

@@ -2911,7 +2911,7 @@ export const QWeatherTimePeriodOptions = [
   '30d',
 ];
 
-export const ExeSQLOptions = ['mysql', 'postgresql', 'mariadb', 'mssql'].map(
+export const ExeSQLOptions = ['mysql', 'postgres', 'mariadb', 'mssql'].map(
   (x) => ({
     label: upperFirst(x),
     value: x,