Fix:local variable 'response' referenced before assignment (#9230)

### What problem does this PR solve?

https://github.com/infiniflow/ragflow/issues/9227

### Type of change

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

---------

Co-authored-by: Kevin Hu <kevinhu.sh@gmail.com>

agentic_reasoning/prompts.py

@@ -20,94 +20,128 @@ BEGIN_SEARCH_RESULT = "<|begin_search_result|>"
 END_SEARCH_RESULT = "<|end_search_result|>"
 MAX_SEARCH_LIMIT = 6
 
-REASON_PROMPT = (
-        "You are a reasoning assistant with the ability to perform dataset searches to help "
-        "you answer the user's question accurately. You have special tools:\n\n"
-        f"- To perform a search: write {BEGIN_SEARCH_QUERY} your query here {END_SEARCH_QUERY}.\n"
-        f"Then, the system will search and analyze relevant content, then provide you with helpful information in the format {BEGIN_SEARCH_RESULT} ...search results... {END_SEARCH_RESULT}.\n\n"
-        f"You can repeat the search process multiple times if necessary. The maximum number of search attempts is limited to {MAX_SEARCH_LIMIT}.\n\n"
-        "Once you have all the information you need, continue your reasoning.\n\n"
-        "-- Example 1 --\n" ########################################
-        "Question: \"Are both the directors of Jaws and Casino Royale from the same country?\"\n"
-        "Assistant:\n"
-        f"    {BEGIN_SEARCH_QUERY}Who is the director of Jaws?{END_SEARCH_QUERY}\n\n"
-        "User:\n"
-        f"    {BEGIN_SEARCH_RESULT}\nThe director of Jaws is Steven Spielberg...\n{END_SEARCH_RESULT}\n\n"
-        "Continues reasoning with the new information.\n"
-        "Assistant:\n"
-        f"    {BEGIN_SEARCH_QUERY}Where is Steven Spielberg from?{END_SEARCH_QUERY}\n\n"
-        "User:\n"
-        f"    {BEGIN_SEARCH_RESULT}\nSteven Allan Spielberg is an American filmmaker...\n{END_SEARCH_RESULT}\n\n"
-        "Continues reasoning with the new information...\n\n"
-        "Assistant:\n"
-        f"    {BEGIN_SEARCH_QUERY}Who is the director of Casino Royale?{END_SEARCH_QUERY}\n\n"
-        "User:\n"
-        f"    {BEGIN_SEARCH_RESULT}\nCasino Royale is a 2006 spy film directed by Martin Campbell...\n{END_SEARCH_RESULT}\n\n"
-        "Continues reasoning with the new information...\n\n"
-        "Assistant:\n"
-        f"    {BEGIN_SEARCH_QUERY}Where is Martin Campbell from?{END_SEARCH_QUERY}\n\n"
-        "User:\n"
-        f"    {BEGIN_SEARCH_RESULT}\nMartin Campbell (born 24 October 1943) is a New Zealand film and television director...\n{END_SEARCH_RESULT}\n\n"
-        "Continues reasoning with the new information...\n\n"
-        "Assistant:\nIt's enough to answer the question\n"
+REASON_PROMPT = f"""You are an advanced reasoning agent. Your goal is to answer the user's question by breaking it down into a series of verifiable steps.
 
-        "-- Example 2 --\n" #########################################
-        "Question: \"When was the founder of craigslist born?\"\n"
-        "Assistant:\n"
-        f"    {BEGIN_SEARCH_QUERY}Who was the founder of craigslist?{END_SEARCH_QUERY}\n\n"
-        "User:\n"
-        f"    {BEGIN_SEARCH_RESULT}\nCraigslist was founded by Craig Newmark...\n{END_SEARCH_RESULT}\n\n"
-        "Continues reasoning with the new information.\n"
-        "Assistant:\n"
-        f"    {BEGIN_SEARCH_QUERY} When was Craig Newmark born?{END_SEARCH_QUERY}\n\n"
-        "User:\n"
-        f"    {BEGIN_SEARCH_RESULT}\nCraig Newmark was born on December 6, 1952...\n{END_SEARCH_RESULT}\n\n"
-        "Continues reasoning with the new information...\n\n"
-        "Assistant:\nIt's enough to answer the question\n"
-        "**Remember**:\n"
-        f"- You have a dataset to search, so you just provide a proper search query.\n"
-        f"- Use {BEGIN_SEARCH_QUERY} to request a dataset search and end with {END_SEARCH_QUERY}.\n"
-        "- The language of query MUST be as the same as 'Question' or 'search result'.\n"
-        "- If no helpful information can be found, rewrite the search query to be less and precise keywords.\n"
-        "- When done searching, continue your reasoning.\n\n"
-        'Please answer the following question. You should think step by step to solve it.\n\n'
-    )
+You have access to a powerful search tool to find information.
 
-RELEVANT_EXTRACTION_PROMPT = """**Task Instruction:**
+**Your Task:**
+1.  Analyze the user's question.
+2.  If you need information, issue a search query to find a specific fact.
+3.  Review the search results.
+4.  Repeat the search process until you have all the facts needed to answer the question.
+5.  Once you have gathered sufficient information, synthesize the facts and provide the final answer directly.
 
-    You are tasked with reading and analyzing web pages based on the following inputs: **Previous Reasoning Steps**, **Current Search Query**, and **Searched Web Pages**. Your objective is to extract relevant and helpful information for **Current Search Query** from the **Searched Web Pages** and seamlessly integrate this information into the **Previous Reasoning Steps** to continue reasoning for the original question.
+**Tool Usage:**
+- To search, you MUST write your query between the special tokens: {BEGIN_SEARCH_QUERY}your query{END_SEARCH_QUERY}.
+- The system will provide results between {BEGIN_SEARCH_RESULT}search results{END_SEARCH_RESULT}.
+- You have a maximum of {MAX_SEARCH_LIMIT} search attempts.
 
-    **Guidelines:**
+---
+**Example 1: Multi-hop Question**
 
-    1. **Analyze the Searched Web Pages:**
-    - Carefully review the content of each searched web page.
-    - Identify factual information that is relevant to the **Current Search Query** and can aid in the reasoning process for the original question.
+**Question:** "Are both the directors of Jaws and Casino Royale from the same country?"
 
-    2. **Extract Relevant Information:**
-    - Select the information from the Searched Web Pages that directly contributes to advancing the **Previous Reasoning Steps**.
-    - Ensure that the extracted information is accurate and relevant.
+**Your Thought Process & Actions:**
+First, I need to identify the director of Jaws.
+{BEGIN_SEARCH_QUERY}who is the director of Jaws?{END_SEARCH_QUERY}
+[System returns search results]
+{BEGIN_SEARCH_RESULT}
+Jaws is a 1975 American thriller film directed by Steven Spielberg.
+{END_SEARCH_RESULT}
+Okay, the director of Jaws is Steven Spielberg. Now I need to find out his nationality.
+{BEGIN_SEARCH_QUERY}where is Steven Spielberg from?{END_SEARCH_QUERY}
+[System returns search results]
+{BEGIN_SEARCH_RESULT}
+Steven Allan Spielberg is an American filmmaker. Born in Cincinnati, Ohio...
+{END_SEARCH_RESULT}
+So, Steven Spielberg is from the USA. Next, I need to find the director of Casino Royale.
+{BEGIN_SEARCH_QUERY}who is the director of Casino Royale 2006?{END_SEARCH_QUERY}
+[System returns search results]
+{BEGIN_SEARCH_RESULT}
+Casino Royale is a 2006 spy film directed by Martin Campbell.
+{END_SEARCH_RESULT}
+The director of Casino Royale is Martin Campbell. Now I need his nationality.
+{BEGIN_SEARCH_QUERY}where is Martin Campbell from?{END_SEARCH_QUERY}
+[System returns search results]
+{BEGIN_SEARCH_RESULT}
+Martin Campbell (born 24 October 1943) is a New Zealand film and television director.
+{END_SEARCH_RESULT}
+I have all the information. Steven Spielberg is from the USA, and Martin Campbell is from New Zealand. They are not from the same country.
 
-    3. **Output Format:**
-    - **If the web pages provide helpful information for current search query:** Present the information beginning with `**Final Information**` as shown below.
-    - The language of query **MUST BE** as the same as 'Search Query' or 'Web Pages'.\n"
-    **Final Information**
+Final Answer: No, the directors of Jaws and Casino Royale are not from the same country. Steven Spielberg is from the USA, and Martin Campbell is from New Zealand.
 
-    [Helpful information]
+---
+**Example 2: Simple Fact Retrieval**
 
-    - **If the web pages do not provide any helpful information for current search query:** Output the following text.
+**Question:** "When was the founder of craigslist born?"
 
-    **Final Information**
+**Your Thought Process & Actions:**
+First, I need to know who founded craigslist.
+{BEGIN_SEARCH_QUERY}who founded craigslist?{END_SEARCH_QUERY}
+[System returns search results]
+{BEGIN_SEARCH_RESULT}
+Craigslist was founded in 1995 by Craig Newmark.
+{END_SEARCH_RESULT}
+The founder is Craig Newmark. Now I need his birth date.
+{BEGIN_SEARCH_QUERY}when was Craig Newmark born?{END_SEARCH_QUERY}
+[System returns search results]
+{BEGIN_SEARCH_RESULT}
+Craig Newmark was born on December 6, 1952.
+{END_SEARCH_RESULT}
+I have found the answer.
 
-    No helpful information found.
+Final Answer: The founder of craigslist, Craig Newmark, was born on December 6, 1952.
 
-    **Inputs:**
-    - **Previous Reasoning Steps:**  
-    {prev_reasoning}
+---
+**Important Rules:**
+- **One Fact at a Time:** Decompose the problem and issue one search query at a time to find a single, specific piece of information.
+- **Be Precise:** Formulate clear and precise search queries. If a search fails, rephrase it.
+- **Synthesize at the End:** Do not provide the final answer until you have completed all necessary searches.
+- **Language Consistency:** Your search queries should be in the same language as the user's question.
 
-    - **Current Search Query:**  
-    {search_query}
+Now, begin your work. Please answer the following question by thinking step-by-step.
+"""
 
-    - **Searched Web Pages:**  
-    {document}
+RELEVANT_EXTRACTION_PROMPT = """You are a highly efficient information extraction module. Your sole purpose is to extract the single most relevant piece of information from the provided `Searched Web Pages` that directly answers the `Current Search Query`.
 
-    """
+**Your Task:**
+1.  Read the `Current Search Query` to understand what specific information is needed.
+2.  Scan the `Searched Web Pages` to find the answer to that query.
+3.  Extract only the essential, factual information that answers the query. Be concise.
+
+**Context (For Your Information Only):**
+The `Previous Reasoning Steps` are provided to give you context on the overall goal, but your primary focus MUST be on answering the `Current Search Query`. Do not use information from the previous steps in your output.
+
+**Output Format:**
+Your response must follow one of two formats precisely.
+
+1.  **If a direct and relevant answer is found:**
+    - Start your response immediately with `Final Information`.
+    - Provide only the extracted fact(s). Do not add any extra conversational text.
+
+    *Example:*
+    `Current Search Query`: Where is Martin Campbell from?
+    `Searched Web Pages`: [Long article snippet about Martin Campbell's career, which includes the sentence "Martin Campbell (born 24 October 1943) is a New Zealand film and television director..."]
+    
+    *Your Output:*
+    Final Information
+    Martin Campbell is a New Zealand film and television director.
+
+2.  **If no relevant answer that directly addresses the query is found in the web pages:**
+    - Start your response immediately with `Final Information`.
+    - Write the exact phrase: `No helpful information found.`
+
+---
+**BEGIN TASK**
+
+**Inputs:**
+
+- **Previous Reasoning Steps:**
+{prev_reasoning}
+
+- **Current Search Query:**
+{search_query}
+
+- **Searched Web Pages:**
+{document}
+"""

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");
@@ -75,11 +75,13 @@ def retrieval(tenant_id):
         for c in ranks["chunks"]:
             e, doc = DocumentService.get_by_id( c["doc_id"])
             c.pop("vector", None)
+            meta = getattr(doc, 'meta_fields', {})
+            meta["doc_id"] = c["doc_id"]
             records.append({
                 "content": c["content_with_weight"],
                 "score": c["similarity"],
                 "title": c["docnm_kwd"],
-                "metadata": getattr(doc, 'meta_fields', {})
+                "metadata": meta
             })
 
         return jsonify({"records": records})

api/db/services/canvas_service.py

@@ -173,7 +173,8 @@ def completion(tenant_id, agent_id, session_id=None, **kwargs):
     conv.message.append({"role": "assistant", "content": txt, "created_at": time.time(), "id": message_id})
     conv.reference = canvas.get_reference()
     conv.errors = canvas.error
-    API4ConversationService.append_message(conv.id, conv.to_dict())
+    conv = conv.to_dict()
+    API4ConversationService.append_message(conv["id"], conv)
 
 
 def completionOpenAI(tenant_id, agent_id, question, session_id=None, stream=True, **kwargs):

api/db/services/llm_service.py

@@ -225,6 +225,9 @@ class TenantLLMService(CommonService):
                 if llm_id == llm["llm_name"]:
                     return llm["model_type"].split(",")[-1]
 
+        for llm in LLMService.query(llm_name=llm_id):
+            return llm.model_type
+
 
 class LLMBundle:
     def __init__(self, tenant_id, llm_type, llm_name=None, lang="Chinese", **kwargs):

docs/guides/agent/agent_component_reference/agent.mdx

@@ -82,7 +82,7 @@ An integer specifying the number of previous dialogue rounds to input into the L
 This feature is used for multi-turn dialogue *only*.
 :::
 
-### Max retrieves
+### Max retries
 
 Defines the maximum number of attempts the agent will make to retry a failed task or operation before stopping or reporting failure.
 
@@ -94,6 +94,10 @@ The waiting period in seconds that the agent observes before retrying a failed t
 
 Defines the maximum number reflection rounds of the selected chat model. Defaults to 5 rounds.
 
+:::tip NOTE
+You can set the value to 1 to shorten your agent's response time.
+:::
+
 ### Output
 
 The global variable name for the output of the **Agent** component, which can be referenced by other components in the workflow.

docs/release_notes.md

@@ -34,7 +34,7 @@ From v0.20.0 onwards, Agents are no longer compatible with earlier versions, and
 
 - Unified orchestration of both Agents and Workflows.
 - A comprehensive refactor of the Agent, greatly enhancing its capabilities and usability, with support for Multi-Agent configurations, planning and reflection, and visual functionalities.
-- Fully realized MCP functionality, allowing for MCP Server import, Agents functioning as MCP Clients, and RAGFlow itself operating as an MCP Server.
+- Fully implemented MCP functionality, allowing for MCP Server import, Agents functioning as MCP Clients, and RAGFlow itself operating as an MCP Server.
 - Access to runtime logs for Agents.
 - Chat histories with Agents available through the management panel.
 - Integration of a new, more robust version of Infinity, enabling the auto-tagging functionality with Infinity as the underlying document engine.
@@ -43,7 +43,7 @@ From v0.20.0 onwards, Agents are no longer compatible with earlier versions, and
 - RAGFlow’s codebase is now mirrored on Gitee.
 - Introduction of a new model provider, Gitee AI.
 
-### New agent templates
+### New agent templates introduced
 
 - Multi-Agent based Deep Research: Collaborative Agent teamwork led by a Lead Agent with multiple Subagents, distinct from traditional workflow orchestration.
 - An intelligent Q&A chatbot leveraging internal knowledge bases, designed for customer service and training scenarios.

rag/llm/cv_model.py

@@ -59,6 +59,10 @@ class Base(ABC):
     def _image_prompt(self, text, images):
         if not images:
             return text
+
+        if isinstance(images, str):
+            images = [images]
+
         pmpt = [{"type": "text", "text": text}]
         for img in images:
             pmpt.append({
@@ -518,6 +522,7 @@ class GeminiCV(Base):
     def chat_streamly(self, system, history, gen_conf, images=[]):
         from transformers import GenerationConfig
         ans = ""
+        response = None
         try:
             response = self.model.generate_content(
                 self._form_history(system, history, images),
@@ -533,8 +538,10 @@ class GeminiCV(Base):
         except Exception as e:
             yield ans + "\n**ERROR**: " + str(e)
 
-        yield response._chunks[-1].usage_metadata.total_token_count
-
+        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):
     _FACTORY_NAME = "NVIDIA"
@@ -795,4 +802,4 @@ class GoogleCV(AnthropicCV, GeminiCV):
                 yield ans
         else:
             for ans in GeminiCV.chat_streamly(self, system, history, gen_conf, images):
-                yield ans
+                yield ans

rag/utils/redis_conn.py

@@ -227,9 +227,20 @@ class RedisDB:
         """https://redis.io/docs/latest/commands/xreadgroup/"""
         for _ in range(3):
             try:
-                group_info = self.REDIS.xinfo_groups(queue_name)
-                if not any(gi["name"] == group_name for gi in group_info):
-                    self.REDIS.xgroup_create(queue_name, group_name, id="0", mkstream=True)
+
+                try:
+                    group_info = self.REDIS.xinfo_groups(queue_name)
+                    if not any(gi["name"] == group_name for gi in group_info):
+                        self.REDIS.xgroup_create(queue_name, group_name, id="0", mkstream=True)
+                except redis.exceptions.ResponseError as e:
+                    if "no such key" in str(e).lower():
+                        self.REDIS.xgroup_create(queue_name, group_name, id="0", mkstream=True)
+                    elif "busygroup" in str(e).lower():
+                        logging.warning("Group already exists, continue.")
+                        pass
+                    else:
+                        raise
+
                 args = {
                     "groupname": group_name,
                     "consumername": consumer_name,
@@ -338,8 +349,8 @@ class RedisDB:
             logging.warning("RedisDB.delete " + str(key) + " got exception: " + str(e))
             self.__open__()
         return False
-    
-    
+
+
 REDIS_CONN = RedisDB()
 
 

rag/utils/s3_conn.py

@@ -123,7 +123,7 @@ class RAGFlowS3:
 
     @use_prefix_path
     @use_default_bucket
-    def put(self, bucket, fnm, binary):
+    def put(self, bucket, fnm, binary, **kwargs):
         logging.debug(f"bucket name {bucket}; filename :{fnm}:")
         for _ in range(1):
             try:
@@ -140,7 +140,7 @@ class RAGFlowS3:
 
     @use_prefix_path
     @use_default_bucket
-    def rm(self, bucket, fnm):
+    def rm(self, bucket, fnm, **kwargs):
         try:
             self.conn.delete_object(Bucket=bucket, Key=fnm)
         except Exception:
@@ -148,7 +148,7 @@ class RAGFlowS3:
 
     @use_prefix_path
     @use_default_bucket
-    def get(self, bucket, fnm):
+    def get(self, bucket, fnm, **kwargs):
         for _ in range(1):
             try:
                 r = self.conn.get_object(Bucket=bucket, Key=fnm)
@@ -162,7 +162,7 @@ class RAGFlowS3:
 
     @use_prefix_path
     @use_default_bucket
-    def obj_exist(self, bucket, fnm):
+    def obj_exist(self, bucket, fnm, **kwargs):
         try:
             if self.conn.head_object(Bucket=bucket, Key=fnm):
                 return True
@@ -174,7 +174,7 @@ class RAGFlowS3:
 
     @use_prefix_path
     @use_default_bucket
-    def get_presigned_url(self, bucket, fnm, expires):
+    def get_presigned_url(self, bucket, fnm, expires, **kwargs):
         for _ in range(10):
             try:
                 r = self.conn.generate_presigned_url('get_object',

web/src/hooks/logic-hooks.ts

@@ -353,7 +353,12 @@ export const useHandleMessageInputChange = () => {
 export const useSelectDerivedMessages = () => {
   const [derivedMessages, setDerivedMessages] = useState<IMessage[]>([]);
 
-  const ref = useScrollToBottom(derivedMessages);
+  const messageContainerRef = useRef<HTMLDivElement>(null);
+
+  const { scrollRef, scrollToBottom } = useScrollToBottom(
+    derivedMessages,
+    messageContainerRef,
+  );
 
   const addNewestQuestion = useCallback(
     (message: Message, answer: string = '') => {
@@ -492,7 +497,8 @@ export const useSelectDerivedMessages = () => {
   }, [setDerivedMessages]);
 
   return {
-    ref,
+    scrollRef,
+    messageContainerRef,
     derivedMessages,
     setDerivedMessages,
     addNewestQuestion,
@@ -503,6 +509,7 @@ export const useSelectDerivedMessages = () => {
     addNewestOneAnswer,
     removeMessagesAfterCurrentMessage,
     removeAllMessages,
+    scrollToBottom,
   };
 };
 

web/src/locales/en.ts

@@ -39,6 +39,13 @@ export default {
       nextPage: 'Next',
       add: 'Add',
       promptPlaceholder: `Please input or use / to quickly insert variables.`,
+      mcp: {
+        namePlaceholder: 'My MCP Server',
+        nameRequired:
+          'It must be 1–64 characters long and can only contain letters, numbers, hyphens, and underscores.',
+        urlPlaceholder: 'https://api.example.com/v1/mcp',
+        tokenPlaceholder: 'e.g. eyJhbGciOiJIUzI1Ni...',
+      },
     },
     login: {
       login: 'Sign in',

web/src/pages/agent/chat/box.tsx

@@ -22,7 +22,8 @@ import { useAwaitCompentData } from '../hooks/use-chat-logic';
 function AgentChatBox() {
   const {
     value,
-    ref,
+    scrollRef,
+    messageContainerRef,
     sendLoading,
     derivedMessages,
     handleInputChange,
@@ -59,7 +60,7 @@ function AgentChatBox() {
   return (
     <>
       <section className="flex flex-1 flex-col px-5 h-[90vh]">
-        <div className="flex-1 overflow-auto">
+        <div className="flex-1 overflow-auto" ref={messageContainerRef}>
           <div>
             {/* <Spin spinning={sendLoading}> */}
             {derivedMessages?.map((message, i) => {
@@ -106,7 +107,7 @@ function AgentChatBox() {
             })}
             {/* </Spin> */}
           </div>
-          <div ref={ref.scrollRef} />
+          <div ref={scrollRef} />
         </div>
         <NextMessageInput
           value={value}

web/src/pages/agent/chat/use-send-agent-message.ts

@@ -198,12 +198,14 @@ export const useSendAgentMessage = (
   const prologue = useGetBeginNodePrologue();
   const {
     derivedMessages,
-    ref,
+    scrollRef,
+    messageContainerRef,
     removeLatestMessage,
     removeMessageById,
     addNewestOneQuestion,
     addNewestOneAnswer,
     removeAllMessages,
+    scrollToBottom,
   } = useSelectDerivedMessages();
   const { addEventList: addEventListFun } = useContext(AgentChatLogContext);
   const {
@@ -303,7 +305,18 @@ export const useSendAgentMessage = (
       });
     }
     addNewestOneQuestion({ ...msgBody, files: fileList });
-  }, [value, done, addNewestOneQuestion, fileList, setValue, sendMessage]);
+    setTimeout(() => {
+      scrollToBottom();
+    }, 100);
+  }, [
+    value,
+    done,
+    addNewestOneQuestion,
+    fileList,
+    setValue,
+    sendMessage,
+    scrollToBottom,
+  ]);
 
   useEffect(() => {
     const { content, id } = findMessageFromList(answerList);
@@ -337,7 +350,8 @@ export const useSendAgentMessage = (
     value,
     sendLoading: !done,
     derivedMessages,
-    ref,
+    scrollRef,
+    messageContainerRef,
     handlePressEnter,
     handleInputChange,
     removeMessageById,

web/src/pages/chat/chat-container/index.tsx

@@ -1,7 +1,6 @@
 import MessageItem from '@/components/message-item';
 import { MessageType } from '@/constants/chat';
 import { Flex, Spin } from 'antd';
-import { useRef } from 'react';
 import {
   useCreateConversationBeforeUploadDocument,
   useGetFileIcon,
@@ -19,7 +18,6 @@ import {
   useFetchNextDialog,
   useGetChatSearchParams,
 } from '@/hooks/chat-hooks';
-import { useScrollToBottom } from '@/hooks/logic-hooks';
 import { useFetchUserInfo } from '@/hooks/user-setting-hooks';
 import { buildMessageUuidWithRole } from '@/utils/chat';
 import { memo } from 'react';
@@ -34,10 +32,10 @@ const ChatContainer = ({ controller }: IProps) => {
   const { data: conversation } = useFetchNextConversation();
   const { data: currentDialog } = useFetchNextDialog();
 
-  const messageContainerRef = useRef<HTMLDivElement>(null);
   const {
     value,
-    ref,
+    scrollRef,
+    messageContainerRef,
     loading,
     sendLoading,
     derivedMessages,
@@ -47,10 +45,6 @@ const ChatContainer = ({ controller }: IProps) => {
     removeMessageById,
     stopOutputMessage,
   } = useSendNextMessage(controller);
-  const { scrollRef, isAtBottom, scrollToBottom } = useScrollToBottom(
-    derivedMessages,
-    messageContainerRef,
-  );
 
   const { visible, hideModal, documentId, selectedChunk, clickDocumentButton } =
     useClickDrawer();
@@ -61,11 +55,6 @@ const ChatContainer = ({ controller }: IProps) => {
   const { createConversationBeforeUploadDocument } =
     useCreateConversationBeforeUploadDocument();
 
-  const handleSend = (msg) => {
-    // your send logic
-    setTimeout(scrollToBottom, 0);
-  };
-
   return (
     <>
       <Flex flex={1} className={styles.chatContainer} vertical>

web/src/pages/chat/hooks.ts

@@ -291,7 +291,8 @@ export const useSetConversation = () => {
 
 export const useSelectNextMessages = () => {
   const {
-    ref,
+    scrollRef,
+    messageContainerRef,
     setDerivedMessages,
     derivedMessages,
     addNewestAnswer,
@@ -335,7 +336,8 @@ export const useSelectNextMessages = () => {
   }, [conversation.message, conversationId, setDerivedMessages, isNew]);
 
   return {
-    ref,
+    scrollRef,
+    messageContainerRef,
     derivedMessages,
     loading,
     addNewestAnswer,
@@ -371,7 +373,8 @@ export const useSendNextMessage = (controller: AbortController) => {
     api.completeConversation,
   );
   const {
-    ref,
+    scrollRef,
+    messageContainerRef,
     derivedMessages,
     loading,
     addNewestAnswer,
@@ -499,7 +502,8 @@ export const useSendNextMessage = (controller: AbortController) => {
     regenerateMessage,
     sendLoading: !done,
     loading,
-    ref,
+    scrollRef,
+    messageContainerRef,
     derivedMessages,
     removeMessageById,
     stopOutputMessage,

web/src/pages/next-chats/share/index.tsx

@@ -50,7 +50,8 @@ const ChatContainer = () => {
     handleInputChange,
     value,
     sendLoading,
-    ref,
+    scrollRef,
+    messageContainerRef,
     derivedMessages,
     hasError,
     stopOutputMessage,
@@ -149,6 +150,7 @@ const ChatContainer = () => {
             className={cn(
               'flex flex-1 flex-col overflow-auto scrollbar-auto m-auto w-5/6',
             )}
+            ref={messageContainerRef}
           >
             <div>
               {derivedMessages?.map((message, i) => {
@@ -203,7 +205,7 @@ const ChatContainer = () => {
                 );
               })}
             </div>
-            <div ref={ref.scrollRef} />
+            <div ref={scrollRef} />
           </div>
           <div className="flex w-full justify-center mb-8">
             <div className="w-5/6">

web/src/pages/profile-setting/mcp/edit-mcp-form.tsx

@@ -37,20 +37,23 @@ export function useBuildFormSchema() {
     name: z
       .string()
       .min(1, {
-        message: t('common.namePlaceholder'),
+        message: t('common.mcp.namePlaceholder'),
+      })
+      .regex(/^[a-zA-Z0-9_-]{1,64}$/, {
+        message: t('common.mcp.nameRequired'),
       })
       .trim(),
     url: z
       .string()
       .url()
       .min(1, {
-        message: t('common.namePlaceholder'),
+        message: t('common.mcp.urlPlaceholder'),
       })
       .trim(),
     server_type: z
       .string()
       .min(1, {
-        message: t('common.namePlaceholder'),
+        message: t('common.pleaseSelect'),
       })
       .trim(),
     authorization_token: z.string().optional(),
@@ -89,7 +92,7 @@ export function EditMcpForm({
               <FormLabel>{t('common.name')}</FormLabel>
               <FormControl>
                 <Input
-                  placeholder={t('common.namePlaceholder')}
+                  placeholder={t('common.mcp.namePlaceholder')}
                   {...field}
                   autoComplete="off"
                 />
@@ -106,7 +109,7 @@ export function EditMcpForm({
               <FormLabel>{t('mcp.url')}</FormLabel>
               <FormControl>
                 <Input
-                  placeholder={t('common.namePlaceholder')}
+                  placeholder={t('common.mcp.urlPlaceholder')}
                   {...field}
                   autoComplete="off"
                   onChange={(e) => {
@@ -148,7 +151,7 @@ export function EditMcpForm({
               <FormLabel>Authorization Token</FormLabel>
               <FormControl>
                 <Input
-                  placeholder={t('common.namePlaceholder')}
+                  placeholder={t('common.mcp.tokenPlaceholder')}
                   {...field}
                   autoComplete="off"
                   type="password"