Merge remote-tracking branch 'origin/master' into feat/memory

stage simple webui
delete(memory): remove memory module and its components
2025-11-21 20:23:41 +08:00 · 2025-11-21 17:59:22 +08:00 · 2025-11-21 17:34:33 +08:00 · 2025-11-21 17:25:55 +08:00 · 2025-11-20 23:46:34 +08:00 · 2025-11-20 23:41:20 +08:00
68 changed files with 6409 additions and 1990 deletions
--- a/.github/ISSUE_TEMPLATE/bug-report.yml
+++ b/.github/ISSUE_TEMPLATE/bug-report.yml
@@ -1,46 +1,44 @@
-name: '🐛 报告 Bug'
+name: '🐛 Report Bug / 报告 Bug'
 title: '[Bug]'
-description: 提交报告帮助我们改进。
+description: Submit bug report to help us improve. / 提交报告帮助我们改进。
 labels: [ 'bug' ]
 body:
  - type: markdown
    attributes:
      value: |
-        感谢您抽出时间报告问题！请准确解释您的问题。如果可能，请提供一个可复现的片段（这有助于更快地解决问题）。请注意，不详细 / 没有日志的 issue 会被直接关闭，谢谢理解。
+        Thank you for taking the time to report this issue! Please describe your problem accurately. If possible, please provide a reproducible snippet (this will help resolve the issue more quickly). Please note that issues that are not detailed or have no logs will be closed immediately. Thank you for your understanding. / 感谢您抽出时间报告问题！请准确解释您的问题。如果可能，请提供一个可复现的片段（这有助于更快地解决问题）。请注意，不详细 / 没有日志的 issue 会被直接关闭，谢谢理解。
  - type: textarea
    attributes:
-      label: 发生了什么
-      description: 描述你遇到的异常
+      label: What happened / 发生了什么
+      description: Description
      placeholder: >
-        一个清晰且具体的描述这个异常是什么。请注意，不详细 / 没有日志的 issue 会被直接关闭，谢谢理解。
+        Please provide a clear and specific description of what this exception is. Please note that issues that are not detailed or have no logs will be closed immediately. Thank you for your understanding. / 一个清晰且具体的描述这个异常是什么。请注意，不详细 / 没有日志的 issue 会被直接关闭，谢谢理解。
    validations:
      required: true

  - type: textarea
    attributes:
-      label: 如何复现？
+      label: Reproduce / 如何复现？
      description: >
-        复现该问题的步骤
+        The steps to reproduce the issue. / 复现该问题的步骤
      placeholder: >
-        如: 1. 打开 '...'
+        Example: 1. Open '...'
    validations:
      required: true

  - type: textarea
    attributes:
-      label: AstrBot 版本、部署方式（如 Windows Docker Desktop 部署）、使用的提供商、使用的消息平台适配器
-      description: >
-        请提供您的 AstrBot 版本和部署方式。
+      label: AstrBot version, deployment method (e.g., Windows Docker Desktop deployment), provider used, and messaging platform used. / AstrBot 版本、部署方式（如 Windows Docker Desktop 部署）、使用的提供商、使用的消息平台适配器
      placeholder: >
-        如: 3.1.8 Docker, 3.1.7 Windows启动器
+        Example: 4.5.7 Docker, 3.1.7 Windows Launcher
    validations:
      required: true

  - type: dropdown
    attributes:
-      label: 操作系统
+      label: OS
      description: |
-        你在哪个操作系统上遇到了这个问题？
+        On which operating system did you encounter this problem? / 你在哪个操作系统上遇到了这个问题？
      multiple: false
      options:
        - 'Windows'
@@ -53,30 +51,30 @@ body:

  - type: textarea
    attributes:
-      label: 报错日志
+      label: Logs / 报错日志
      description: >
-        如报错日志、截图等。请提供完整的 Debug 级别的日志，不要介意它很长！请注意，不详细 / 没有日志的 issue 会被直接关闭，谢谢理解。
+        Please provide complete Debug-level logs, such as error logs and screenshots. Don't worry if they're long! Please note that issues with insufficient details or no logs will be closed immediately. Thank you for your understanding. / 如报错日志、截图等。请提供完整的 Debug 级别的日志，不要介意它很长！请注意，不详细 / 没有日志的 issue 会被直接关闭，谢谢理解。
      placeholder: >
-        请提供完整的报错日志或截图。
+        Please provide a complete error log or screenshot. / 请提供完整的报错日志或截图。
    validations:
      required: true

  - type: checkboxes
    attributes:
-      label: 你愿意提交 PR 吗？
+      label: Are you willing to submit a PR? / 你愿意提交 PR 吗？
      description: >
-        这不是必需的，但我们很乐意在贡献过程中为您提供指导特别是如果你已经很好地理解了如何实现修复。
+        This is not required, but we would be happy to provide guidance during the contribution process, especially if you already have a good understanding of how to implement the fix. / 这不是必需的，但我们很乐意在贡献过程中为您提供指导特别是如果你已经很好地理解了如何实现修复。
      options:
-        - label: 是的，我愿意提交 PR!
+        - label: Yes!

  - type: checkboxes
    attributes:
      label: Code of Conduct
      options:
        - label: >
-            我已阅读并同意遵守该项目的 [行为准则](https://docs.github.com/zh/site-policy/github-terms/github-community-code-of-conduct)。
+            I have read and agree to abide by the project's [Code of Conduct](https://docs.github.com/zh/site-policy/github-terms/github-community-code-of-conduct)。
          required: true

  - type: markdown
    attributes:
-      value: "感谢您填写我们的表单！"
+      value: "Thank you for filling out our form! / 感谢您填写我们的表单！"
--- a/.github/PULL_REQUEST_TEMPLATE.md
+++ b/.github/PULL_REQUEST_TEMPLATE.md
@@ -1,44 +1,25 @@
-<!-- 如果有的话，请指定此 PR 旨在解决的 ISSUE 编号。 -->
-<!-- If applicable, please specify the ISSUE number this PR aims to resolve. -->
-
-fixes #XYZ
-
---
-
-### Motivation / 动机
-
-<!--请描述此项更改的动机：它解决了什么问题？（例如：修复了 XX 错误，添加了 YY 功能）-->
-<!--Please describe the motivation for this change: What problem does it solve? (e.g., Fixes XX bug, adds YY feature)-->
+<!--Please describe the motivation for this change: What problem does it solve? (e.g., Fixes XX issue, adds YY feature)-->
+<!--请描述此项更改的动机：它解决了什么问题？（例如：修复了 XX issue，添加了 YY 功能）-->

 ### Modifications / 改动点

 <!--请总结你的改动：哪些核心文件被修改了？实现了什么功能？-->
 <!--Please summarize your changes: What core files were modified? What functionality was implemented?-->

-### Verification Steps / 验证步骤
-
-<!--请为审查者 (Reviewer) 提供清晰、可复现的验证步骤（例如：1. 导航到... 2. 点击...）。-->
-<!--Please provide clear and reproducible verification steps for the Reviewer (e.g., 1. Navigate to... 2. Click...).-->
+- [x] This is NOT a breaking change. / 这不是一个破坏性变更。
+<!-- If your changes is a breaking change, please uncheck the checkbox above -->

 ### Screenshots or Test Results / 运行截图或测试结果

-<!--请粘贴截图、GIF 或测试日志，作为执行“验证步骤”的证据，证明此改动有效。-->
 <!--Please paste screenshots, GIFs, or test logs here as evidence of executing the "Verification Steps" to prove this change is effective.-->
-
-### Compatibility & Breaking Changes / 兼容性与破坏性变更
-
-<!--请说明此变更的兼容性：哪些是破坏性变更？哪些地方做了向后兼容处理？是否提供了数据迁移方法？-->
-<!--Please explain the compatibility of this change: What are the breaking changes? What backward-compatible measures were taken? Are data migration paths provided?-->
-
- [ ] 这是一个破坏性变更 (Breaking Change)。/ This is a breaking change.
- [ ] 这不是一个破坏性变更。/ This is NOT a breaking change.
+<!--请粘贴截图、GIF 或测试日志，作为执行“验证步骤”的证据，证明此改动有效。-->

 ---

 ### Checklist / 检查清单

-<!--如果分支被合并，您的代码将服务于数万名用户！在提交前，请核查一下几点内容。-->
 <!--If merged, your code will serve tens of thousands of users! Please double-check the following items before submitting.-->
+<!--如果分支被合并，您的代码将服务于数万名用户！在提交前，请核查一下几点内容。-->

 - [ ] 😊 如果 PR 中有新加入的功能，已经通过 Issue / 邮件等方式和作者讨论过。/ If there are new features added in the PR, I have discussed it with the authors through issues/emails, etc.
 - [ ] 👀 我的更改经过了良好的测试，**并已在上方提供了“验证步骤”和“运行截图”**。/ My changes have been well-tested, **and "Verification Steps" and "Screenshots" have been provided above**.
--- a/.github/workflows/docker-image.yml
+++ b/.github/workflows/docker-image.yml
@@ -3,18 +3,125 @@ name: Docker Image CI/CD
 on:
  push:
    tags:
-      - 'v*'
+      - "v*"
+  schedule:
+    # Run at 00:00 UTC every day
+    - cron: "0 0 * * *"
  workflow_dispatch:

 jobs:
-  publish-docker:
+  build-nightly-image:
+    if: github.event_name == 'schedule'
    runs-on: ubuntu-latest
+    env:
+      DOCKER_HUB_USERNAME: ${{ secrets.DOCKER_HUB_USERNAME }}
+      GHCR_OWNER: soulter
+      HAS_GHCR_TOKEN: ${{ secrets.GHCR_GITHUB_TOKEN != '' }}

    steps:
-      - name: Pull The Codes
+      - name: Checkout
        uses: actions/checkout@v5
        with:
-          fetch-depth: 0  # Must be 0 so we can fetch tags
+          fetch-depth: 1
+          fetch-tag: true
+
+      - name: Check for new commits today
+        if: github.event_name == 'schedule'
+        id: check-commits
+        run: |
+          # Get commits from the last 24 hours
+          commits=$(git log --since="24 hours ago" --oneline)
+          if [ -z "$commits" ]; then
+            echo "No commits in the last 24 hours, skipping build"
+            echo "has_commits=false" >> $GITHUB_OUTPUT
+          else
+            echo "Found commits in the last 24 hours:"
+            echo "$commits"
+            echo "has_commits=true" >> $GITHUB_OUTPUT
+          fi
+
+      - name: Exit if no commits
+        if: github.event_name == 'schedule' && steps.check-commits.outputs.has_commits == 'false'
+        run: exit 0
+
+      - name: Build Dashboard
+        run: |
+          cd dashboard
+          npm install
+          npm run build
+          mkdir -p dist/assets
+          echo $(git rev-parse HEAD) > dist/assets/version
+          cd ..
+          mkdir -p data
+          cp -r dashboard/dist data/
+
+      - name: Determine test image tags
+        id: test-meta
+        run: |
+          short_sha=$(echo "${GITHUB_SHA}" | cut -c1-12)
+          build_date=$(date +%Y%m%d)
+          echo "short_sha=$short_sha" >> $GITHUB_OUTPUT
+          echo "build_date=$build_date" >> $GITHUB_OUTPUT
+
+      - name: Set QEMU
+        uses: docker/setup-qemu-action@v3
+
+      - name: Set Docker Buildx
+        uses: docker/setup-buildx-action@v3
+
+      - name: Log in to DockerHub
+        uses: docker/login-action@v3
+        with:
+          username: ${{ secrets.DOCKER_HUB_USERNAME }}
+          password: ${{ secrets.DOCKER_HUB_PASSWORD }}
+
+      - name: Login to GitHub Container Registry
+        if: env.HAS_GHCR_TOKEN == 'true'
+        uses: docker/login-action@v3
+        with:
+          registry: ghcr.io
+          username: ${{ env.GHCR_OWNER }}
+          password: ${{ secrets.GHCR_GITHUB_TOKEN }}
+
+      - name: Build nightly image tags list
+        id: test-tags
+        run: |
+          TAGS="${{ env.DOCKER_HUB_USERNAME }}/astrbot:nightly-latest
+          ${{ env.DOCKER_HUB_USERNAME }}/astrbot:nightly-${{ steps.test-meta.outputs.build_date }}-${{ steps.test-meta.outputs.short_sha }}"
+          if [ "${{ env.HAS_GHCR_TOKEN }}" = "true" ]; then
+            TAGS="$TAGS
+          ghcr.io/${{ env.GHCR_OWNER }}/astrbot:nightly-latest
+          ghcr.io/${{ env.GHCR_OWNER }}/astrbot:nightly-${{ steps.test-meta.outputs.build_date }}-${{ steps.test-meta.outputs.short_sha }}"
+          fi
+          echo "tags<<EOF" >> $GITHUB_OUTPUT
+          echo "$TAGS" >> $GITHUB_OUTPUT
+          echo "EOF" >> $GITHUB_OUTPUT
+
+      - name: Build and Push Nightly Image
+        uses: docker/build-push-action@v6
+        with:
+          context: .
+          platforms: linux/amd64,linux/arm64
+          push: true
+          tags: ${{ steps.test-tags.outputs.tags }}
+
+      - name: Post build notifications
+        run: echo "Test Docker image has been built and pushed successfully"
+
+  build-release-image:
+    if: github.event_name == 'workflow_dispatch' || (github.event_name == 'push' && startsWith(github.ref, 'refs/tags/v'))
+    runs-on: ubuntu-latest
+    env:
+      DOCKER_HUB_USERNAME: ${{ secrets.DOCKER_HUB_USERNAME }}
+      GHCR_OWNER: soulter
+      HAS_GHCR_TOKEN: ${{ secrets.GHCR_GITHUB_TOKEN != '' }}
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v5
+        with:
+          fetch-depth: 1
+          fetch-tag: true

      - name: Get latest tag (only on manual trigger)
        id: get-latest-tag
@@ -27,21 +134,22 @@ jobs:
        if: github.event_name == 'workflow_dispatch'
        run: git checkout ${{ steps.get-latest-tag.outputs.latest_tag }}

-      - name: Check if version is pre-release
-        id: check-prerelease
+      - name: Compute release metadata
+        id: release-meta
        run: |
-          if [ "${{ github.event_name }}" == "workflow_dispatch" ]; then
+          if [ "${{ github.event_name }}" = "workflow_dispatch" ]; then
            version="${{ steps.get-latest-tag.outputs.latest_tag }}"
          else
-            version="${{ github.ref_name }}"
+            version="${GITHUB_REF#refs/tags/}"
          fi
          if [[ "$version" == *"beta"* ]] || [[ "$version" == *"alpha"* ]]; then
            echo "is_prerelease=true" >> $GITHUB_OUTPUT
-            echo "Version $version is a pre-release, will not push latest tag"
+            echo "Version $version marked as pre-release"
          else
            echo "is_prerelease=false" >> $GITHUB_OUTPUT
-            echo "Version $version is a stable release, will push latest tag"
+            echo "Version $version marked as stable"
          fi
+          echo "version=$version" >> $GITHUB_OUTPUT

      - name: Build Dashboard
        run: |
@@ -67,23 +175,24 @@ jobs:
          password: ${{ secrets.DOCKER_HUB_PASSWORD }}

      - name: Login to GitHub Container Registry
+        if: env.HAS_GHCR_TOKEN == 'true'
        uses: docker/login-action@v3
        with:
          registry: ghcr.io
-          username: Soulter
+          username: ${{ env.GHCR_OWNER }}
          password: ${{ secrets.GHCR_GITHUB_TOKEN }}

-      - name: Build and Push Docker to DockerHub and Github GHCR
+      - name: Build and Push Release Image
        uses: docker/build-push-action@v6
        with:
          context: .
          platforms: linux/amd64,linux/arm64
          push: true
          tags: |
-            ${{ steps.check-prerelease.outputs.is_prerelease == 'false' && format('{0}/astrbot:latest', secrets.DOCKER_HUB_USERNAME) || '' }}
-            ${{ secrets.DOCKER_HUB_USERNAME }}/astrbot:${{ github.event_name == 'workflow_dispatch' && steps.get-latest-tag.outputs.latest_tag || github.ref_name }}
-            ${{ steps.check-prerelease.outputs.is_prerelease == 'false' && 'ghcr.io/soulter/astrbot:latest' || '' }}
-            ghcr.io/soulter/astrbot:${{ github.event_name == 'workflow_dispatch' && steps.get-latest-tag.outputs.latest_tag || github.ref_name }}
+            ${{ steps.release-meta.outputs.is_prerelease == 'false' && format('{0}/astrbot:latest', env.DOCKER_HUB_USERNAME) || '' }}
+            ${{ steps.release-meta.outputs.is_prerelease == 'false' && env.HAS_GHCR_TOKEN == 'true' && format('ghcr.io/{0}/astrbot:latest', env.GHCR_OWNER) || '' }}
+            ${{ format('{0}/astrbot:{1}', env.DOCKER_HUB_USERNAME, steps.release-meta.outputs.version) }}
+            ${{ env.HAS_GHCR_TOKEN == 'true' && format('ghcr.io/{0}/astrbot:{1}', env.GHCR_OWNER, steps.release-meta.outputs.version) || '' }}

      - name: Post build notifications
-        run: echo "Docker image has been built and pushed successfully"
+        run: echo "Release Docker image has been built and pushed successfully"
--- a/README_en.md
+++ b/README_en.md
@@ -1,182 +1,233 @@
-<p align="center">
-    
-![6e1279651f16d7fdf4727558b72bbaf1](https://github.com/user-attachments/assets/ead4c551-fc3c-48f7-a6f7-afbfdb820512)
+![AstrBot-Logo-Simplified](https://github.com/user-attachments/assets/ffd99b6b-3272-4682-beaa-6fe74250f7d9)

 </p>

 <div align="center">

-_✨ Easy-to-use Multi-platform LLM Chatbot & Development Framework ✨_
+<br>

+<div>
 <a href="https://trendshift.io/repositories/12875" target="_blank"><img src="https://trendshift.io/api/badge/repositories/12875" alt="Soulter%2FAstrBot | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
-
-[![GitHub release (latest by date)](https://img.shields.io/github/v/release/AstrBotDevs/AstrBot)](https://github.com/AstrBotDevs/AstrBot/releases/latest)
-<img src="https://img.shields.io/badge/python-3.10+-blue.svg" alt="python">
-<a href="https://hub.docker.com/r/soulter/astrbot"><img alt="Docker pull" src="https://img.shields.io/docker/pulls/soulter/astrbot"/></a>
-<a  href="https://qm.qq.com/cgi-bin/qm/qr?k=wtbaNx7EioxeaqS9z7RQWVXPIxg2zYr7&jump_from=webapi&authKey=vlqnv/AV2DbJEvGIcxdlNSpfxVy+8vVqijgreRdnVKOaydpc+YSw4MctmEbr0k5"><img alt="Static Badge" src="https://img.shields.io/badge/QQ群-630166526-purple"></a>
-[![wakatime](https://wakatime.com/badge/user/915e5316-99c6-4563-a483-ef186cf000c9/project/018e705a-a1a7-409a-a849-3013485e6c8e.svg)](https://wakatime.com/badge/user/915e5316-99c6-4563-a483-ef186cf000c9/project/018e705a-a1a7-409a-a849-3013485e6c8e)
-![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.soulter.top%2Fastrbot%2Fstats&query=v&label=7%E6%97%A5%E6%B6%88%E6%81%AF%E4%B8%8A%E8%A1%8C%E9%87%8F&cacheSeconds=3600)
-[![codecov](https://codecov.io/gh/AstrBotDevs/AstrBot/graph/badge.svg?token=FF3P5967B8)](https://codecov.io/gh/AstrBotDevs/AstrBot)
-
-<a href="https://astrbot.app/">Documentation</a> ｜
-<a href="https://github.com/AstrBotDevs/AstrBot/issues">Issue Tracking</a>
+<a href="https://hellogithub.com/repository/AstrBotDevs/AstrBot" target="_blank"><img src="https://api.hellogithub.com/v1/widgets/recommend.svg?rid=d127d50cd5e54c5382328acc3bb25483&claim_uid=ZO9by7qCXgSd6Lp&t=2" alt="Featured｜HelloGitHub" style="width: 250px; height: 54px;" width="250" height="54" /></a>
 </div>

-AstrBot is a loosely coupled, asynchronous chatbot and development framework that supports multi-platform deployment, featuring an easy-to-use plugin system and comprehensive Large Language Model (LLM) integration capabilities.
+<br>

-## ✨ Key Features
+<div>
+<img src="https://img.shields.io/github/v/release/AstrBotDevs/AstrBot?style=for-the-badge&color=76bad9" href="https://github.com/AstrBotDevs/AstrBot/releases/latest">
+<img src="https://img.shields.io/badge/python-3.10+-blue.svg?style=for-the-badge&color=76bad9" alt="python">
+<a href="https://hub.docker.com/r/soulter/astrbot"><img alt="Docker pull" src="https://img.shields.io/docker/pulls/soulter/astrbot.svg?style=for-the-badge&color=76bad9"/></a>
+<a href="https://qm.qq.com/cgi-bin/qm/qr?k=wtbaNx7EioxeaqS9z7RQWVXPIxg2zYr7&jump_from=webapi&authKey=vlqnv/AV2DbJEvGIcxdlNSpfxVy+8vVqijgreRdnVKOaydpc+YSw4MctmEbr0k5"><img alt="QQ_community" src="https://img.shields.io/badge/QQ群-775869627-purple?style=for-the-badge&color=76bad9"></a>
+<a href="https://t.me/+hAsD2Ebl5as3NmY1"><img alt="Telegram_community" src="https://img.shields.io/badge/Telegram-AstrBot-purple?style=for-the-badge&color=76bad9"></a>
+<img src="https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.soulter.top%2Fastrbot%2Fplugin-num&query=%24.result&suffix=%E4%B8%AA&style=for-the-badge&label=%E6%8F%92%E4%BB%B6%E5%B8%82%E5%9C%BA&cacheSeconds=3600">
+</div>

-1. **LLM Conversations** - Supports various LLMs including OpenAI API, Google Gemini, Llama, Deepseek, ChatGLM, etc. Enables local model deployment via Ollama/LLMTuner. Features multi-turn dialogues, personality contexts, multimodal capabilities (image understanding), and speech-to-text (Whisper).
-2. **Multi-platform Integration** - Supports QQ (OneBot), QQ Channels, WeChat (Gewechat), Feishu, and Telegram. Planned support for DingTalk, Discord, WhatsApp, and Xiaomi Smart Speakers. Includes rate limiting, whitelisting, keyword filtering, and Baidu content moderation.
-3. **Agent Capabilities** - Native support for code execution, natural language TODO lists, web search. Integrates with [Dify Platform](https://dify.ai/) for easy access to Dify assistants/knowledge bases/workflows.
-4. **Plugin System** - Optimized plugin mechanism with minimal development effort. Supports multiple installed plugins.
-5. **Web Dashboard** - Visual configuration management, plugin controls, logging, and WebChat interface for direct LLM interaction.
-6. **High Stability & Modularity** - Event bus and pipeline architecture ensures high modularization and loose coupling.
+<br>

-> [!TIP]
-> Dashboard Demo: [https://demo.astrbot.app/](https://demo.astrbot.app/)  
-> Username: `astrbot`, Password: `astrbot` (LLM not configured for chat page)
+<a href="https://github.com/AstrBotDevs/AstrBot/blob/master/README.md">中文</a> ｜
+<a href="https://github.com/AstrBotDevs/AstrBot/blob/master/README_ja.md">日本語</a> ｜
+<a href="https://astrbot.app/">Documentation</a> ｜
+<a href="https://blog.astrbot.app/">Blog</a> ｜
+<a href="https://astrbot.featurebase.app/roadmap">Roadmap</a> ｜
+<a href="https://github.com/AstrBotDevs/AstrBot/issues">Issue Tracker</a>
+</div>

-## ✨ Deployment
+AstrBot is an open-source all-in-one Agent chatbot platform and development framework.

-#### Docker Deployment
+## Key Features

-See docs: [Deploy with Docker](https://astrbot.app/deploy/astrbot/docker.html#docker-deployment)
+1. **LLM Conversations**. Supports integration with various large language model services. Features include multimodal capabilities, tool calling, MCP, native knowledge base, character personas, and more.
+2. **Multi-Platform Support**. Integrates with QQ, WeChat Work, WeChat Official Accounts, Feishu, Telegram, DingTalk, Discord, KOOK, and other platforms. Supports rate limiting, whitelisting, and Baidu content moderation.
+3. **Agent Capabilities**. Fully optimized agentic features including multi-turn tool calling, built-in sandboxed code executor, web search, and more.
+4. **Plugin Extensions**. Deeply optimized plugin mechanism supporting [plugin development](https://astrbot.app/dev/plugin.html) to extend functionality, with a rich community plugin ecosystem.
+5. **Web UI**. Visual configuration and management of your bot with comprehensive features.

-#### Windows Installer
+## Deployment Methods

-Requires Python (>3.10). See docs: [Windows Installer Guide](https://astrbot.app/deploy/astrbot/windows.html)
+#### Docker Deployment (Recommended 🥳)

-#### Replit Deployment
+We recommend deploying AstrBot using Docker or Docker Compose.
+
+Please refer to the official documentation: [Deploy AstrBot with Docker](https://astrbot.app/deploy/astrbot/docker.html#%E4%BD%BF%E7%94%A8-docker-%E9%83%A8%E7%BD%B2-astrbot).
+
+#### BT-Panel Deployment
+
+AstrBot has partnered with BT-Panel and is now available in their marketplace.
+
+Please refer to the official documentation: [BT-Panel Deployment](https://astrbot.app/deploy/astrbot/btpanel.html).
+
+#### 1Panel Deployment
+
+AstrBot has been officially listed on the 1Panel marketplace.
+
+Please refer to the official documentation: [1Panel Deployment](https://astrbot.app/deploy/astrbot/1panel.html).
+
+#### Deploy on RainYun
+
+AstrBot has been officially listed on RainYun's cloud application platform with one-click deployment.
+
+[![Deploy on RainYun](https://rainyun-apps.cn-nb1.rains3.com/materials/deploy-on-rainyun-en.svg)](https://app.rainyun.com/apps/rca/store/5994?ref=NjU1ODg0)
+
+#### Deploy on Replit
+
+Community-contributed deployment method.

 [![Run on Repl.it](https://repl.it/badge/github/AstrBotDevs/AstrBot)](https://repl.it/github/AstrBotDevs/AstrBot)

+#### Windows One-Click Installer
+
+Please refer to the official documentation: [Deploy AstrBot with Windows One-Click Installer](https://astrbot.app/deploy/astrbot/windows.html).
+
 #### CasaOS Deployment

-Community-contributed method.  
-See docs: [CasaOS Deployment](https://astrbot.app/deploy/astrbot/casaos.html)
+Community-contributed deployment method.
+
+Please refer to the official documentation: [CasaOS Deployment](https://astrbot.app/deploy/astrbot/casaos.html).

 #### Manual Deployment

-See docs: [Source Code Deployment](https://astrbot.app/deploy/astrbot/cli.html)
+First, install uv:

-## ⚡ Platform Support
+```bash
+pip install uv
+```

-| Platform                                                       | Status | Details             | Message Types       |
-| -------------------------------------------------------------- | ------ | ------------------- | ------------------- |
-| QQ (Official Bot)                                              | ✔      | Private/Group chats | Text, Images        |
-| QQ (OneBot)                                                    | ✔      | Private/Group chats | Text, Images, Voice |
-| WeChat (Personal)                                              | ✔      | Private/Group chats | Text, Images, Voice |
-| [Telegram](https://github.com/AstrBotDevs/AstrBot_plugin_telegram) | ✔      | Private/Group chats | Text, Images        |
-| [WeChat Work](https://github.com/AstrBotDevs/AstrBot_plugin_wecom) | ✔      | Private chats       | Text, Images, Voice |
-| Feishu                                                         | ✔      | Group chats         | Text, Images        |
-| WeChat Open Platform                                           | 🚧      | Planned             | -                   |
-| Discord                                                        | 🚧      | Planned             | -                   |
-| WhatsApp                                                       | 🚧      | Planned             | -                   |
-| Xiaomi Speakers                                                | 🚧      | Planned             | -                   |
+Install AstrBot via Git Clone:

-## Provider Support Status
+```bash
+git clone https://github.com/AstrBotDevs/AstrBot && cd AstrBot
+uv run main.py
+```

-| Name                      | Support | Type                   | Notes                                                                 |
-|---------------------------|---------|------------------------|-----------------------------------------------------------------------|
-| OpenAI API                | ✔       | Text Generation        | Supports all OpenAI API-compatible services including DeepSeek, Google Gemini, GLM, Moonshot, Alibaba Cloud Bailian, Silicon Flow, xAI, etc. |
-| Claude API                | ✔       | Text Generation        |                                                                       |
-| Google Gemini API         | ✔       | Text Generation        |                                                                       |
-| Dify                      | ✔       | LLMOps                 |                                                                       |
-| DashScope (Alibaba Cloud) | ✔       | LLMOps                 |                                                                       |
-| Ollama                    | ✔       | Model Loader           | Local deployment for open-source LLMs (DeepSeek, Llama, etc.)         |
-| LM Studio                 | ✔       | Model Loader           | Local deployment for open-source LLMs (DeepSeek, Llama, etc.)         |
-| LLMTuner                  | ✔       | Model Loader           | Local loading of fine-tuned models (e.g. LoRA)                        |
-| OneAPI                    | ✔       | LLM Distribution       |                                                                       |
-| Whisper                   | ✔       | Speech-to-Text         | Supports API and local deployment                                    |
-| SenseVoice                | ✔       | Speech-to-Text         | Local deployment                                                     |
-| OpenAI TTS API            | ✔       | Text-to-Speech         |                                                                       |
-| Fishaudio                 | ✔       | Text-to-Speech         | Project involving GPT-Sovits author                                  |
+Or refer to the official documentation: [Deploy AstrBot from Source](https://astrbot.app/deploy/astrbot/cli.html).

-# 🦌 Roadmap
+## 🌍 Community

-> [!TIP]
-> Suggestions welcome via Issues <3
+### QQ Groups

- [ ] Ensure feature parity across all platform adapters
- [ ] Optimize plugin APIs
- [ ] Add default TTS services (e.g., GPT-Sovits)
- [ ] Enhance chat features with persistent memory
- [ ] i18n Planning
+- Group 1: 322154837
+- Group 3: 630166526
+- Group 5: 822130018
+- Group 6: 753075035
+- Developer Group: 975206796

-## ❤️ Contributions
+### Telegram Group

-All Issues/PRs welcome! Simply submit your changes to this project :)
+<a href="https://t.me/+hAsD2Ebl5as3NmY1"><img alt="Telegram_community" src="https://img.shields.io/badge/Telegram-AstrBot-purple?style=for-the-badge&color=76bad9"></a>

-For major features, please discuss via Issues first.
+### Discord Server

-## 🌟 Support
+<a href="https://discord.gg/hAVk6tgV36"><img alt="Discord_community" src="https://img.shields.io/badge/Discord-AstrBot-purple?style=for-the-badge&color=76bad9"></a>

- Star this project!
- Support via [Afdian](https://afdian.com/a/soulter)
- WeChat support: [QR Code](https://drive.soulter.top/f/pYfA/d903f4fa49a496fda3f16d2be9e023b5.png)
+## Supported Messaging Platforms

-## ✨ Demos
+**Officially Maintained**

-> [!NOTE]
-> Code executor file I/O currently tested with Napcat(QQ)/Lagrange(QQ)
+- QQ (Official Platform & OneBot)
+- Telegram
+- WeChat Work Application & WeChat Work Intelligent Bot
+- WeChat Customer Service & WeChat Official Accounts
+- Feishu (Lark)
+- DingTalk
+- Slack
+- Discord
+- Satori
+- Misskey
+- WhatsApp (Coming Soon)
+- LINE (Coming Soon)

-<div align='center'>
+**Community Maintained**

-<img src="https://github.com/user-attachments/assets/4ee688d9-467d-45c8-99d6-368f9a8a92d8" width="600">
+- [KOOK](https://github.com/wuyan1003/astrbot_plugin_kook_adapter)
+- [VoceChat](https://github.com/HikariFroya/astrbot_plugin_vocechat)
+- [Bilibili Direct Messages](https://github.com/Hina-Chat/astrbot_plugin_bilibili_adapter)
+- [wxauto](https://github.com/luosheng520qaq/wxauto-repost-onebotv11)

-_✨ Docker-based Sandboxed Code Executor (Beta) ✨_
+## Supported Model Services

-<img src="https://github.com/user-attachments/assets/0378f407-6079-4f64-ae4c-e97ab20611d2" height=500>
+**LLM Services**

-_✨ Multimodal Input, Web Search, Text-to-Image ✨_
+- OpenAI and Compatible Services
+- Anthropic
+- Google Gemini
+- Moonshot AI
+- Zhipu AI
+- DeepSeek
+- Ollama (Self-hosted)
+- LM Studio (Self-hosted)
+- [CompShare](https://www.compshare.cn/?ytag=GPU_YY-gh_astrbot&referral_code=FV7DcGowN4hB5UuXKgpE74)
+- [302.AI](https://share.302.ai/rr1M3l)
+- [TokenPony](https://www.tokenpony.cn/3YPyf)
+- [SiliconFlow](https://docs.siliconflow.cn/cn/usecases/use-siliconcloud-in-astrbot)
+- [PPIO Cloud](https://ppio.com/user/register?invited_by=AIOONE)
+- ModelScope
+- OneAPI

-<img src="https://github.com/user-attachments/assets/8ec12797-e70f-460a-959e-48eca39ca2bb" height=100>
+**LLMOps Platforms**

-_✨ Natural Language TODO Lists ✨_
+- Dify
+- Alibaba Cloud Bailian Applications
+- Coze

-<img src="https://github.com/user-attachments/assets/e137a9e1-340a-4bf2-bb2b-771132780735" height=150>
-<img src="https://github.com/user-attachments/assets/480f5e82-cf6a-4955-a869-0d73137aa6e1" height=150>
+**Speech-to-Text Services**

-_✨ Plugin System Showcase ✨_
+- OpenAI Whisper
+- SenseVoice

-<img src="https://github.com/user-attachments/assets/592a8630-14c7-4e06-b496-9c0386e4f36c" width=600>
+**Text-to-Speech Services**

-_✨ Web Dashboard ✨_
+- OpenAI TTS
+- Gemini TTS
+- GPT-Sovits-Inference
+- GPT-Sovits
+- FishAudio
+- Edge TTS
+- Alibaba Cloud Bailian TTS
+- Azure TTS
+- Minimax TTS
+- Volcano Engine TTS

-![webchat](https://drive.soulter.top/f/vlsA/ezgif-5-fb044b2542.gif)
+## ❤️ Contributing

-_✨ Built-in Web Chat Interface ✨_
+Issues and Pull Requests are always welcome! Feel free to submit your changes to this project :)

-</div>
+### How to Contribute
+
+You can contribute by reviewing issues or helping with pull request reviews. Any issues or PRs are welcome to encourage community participation. Of course, these are just suggestions—you can contribute in any way you like. For adding new features, please discuss through an Issue first.
+
+### Development Environment
+
+AstrBot uses `ruff` for code formatting and linting.
+
+```bash
+git clone https://github.com/AstrBotDevs/AstrBot
+pip install pre-commit
+pre-commit install
+```
+
+## ❤️ Special Thanks
+
+Special thanks to all Contributors and plugin developers for their contributions to AstrBot ❤️
+
+<a href="https://github.com/AstrBotDevs/AstrBot/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=AstrBotDevs/AstrBot" />
+</a>
+
+Additionally, the birth of this project would not have been possible without the help of the following open-source projects:
+
+- [NapNeko/NapCatQQ](https://github.com/NapNeko/NapCatQQ) - The amazing cat framework

 ## ⭐ Star History

 > [!TIP]
-> If this project helps you, please give it a star <3
+> If this project has helped you in your life or work, or if you're interested in its future development, please give the project a Star. It's the driving force behind maintaining this open-source project <3

 <div align="center">

-[![Star History Chart](https://api.star-history.com/svg?repos=AstrBotDevs/AstrBot&type=Date)](https://star-history.com/#AstrBotDevs/AstrBot&Date)
+[![Star History Chart](https://api.star-history.com/svg?repos=astrbotdevs/astrbot&type=Date)](https://star-history.com/#astrbotdevs/astrbot&Date)

 </div>

-## Disclaimer
-
-1. Licensed under `AGPL-v3`.
-2. WeChat integration uses [Gewechat](https://github.com/Devo919/Gewechat). Use at your own risk with non-critical accounts.
-3. Users must comply with local laws and regulations.
-
-<!-- ## ✨ ATRI [Beta]
-
-Available as plugin: [astrbot_plugin_atri](https://github.com/AstrBotDevs/AstrBot_plugin_atri)
-
-1. Qwen1.5-7B-Chat Lora model fine-tuned with ATRI character data
-2. Long-term memory
-3. Meme understanding & responses
-4. TTS integration
-    -->
-
+</details>

 _私は、高性能ですから!_
-
--- a/README_ja.md
+++ b/README_ja.md
@@ -1,167 +1,233 @@
-<p align="center">
-
-![6e1279651f16d7fdf4727558b72bbaf1](https://github.com/user-attachments/assets/ead4c551-fc3c-48f7-a6f7-afbfdb820512)
+![AstrBot-Logo-Simplified](https://github.com/user-attachments/assets/ffd99b6b-3272-4682-beaa-6fe74250f7d9)

 </p>

 <div align="center">

-_✨ 簡単に使えるマルチプラットフォーム LLM チャットボットおよび開発フレームワーク ✨_
+<br>

+<div>
 <a href="https://trendshift.io/repositories/12875" target="_blank"><img src="https://trendshift.io/api/badge/repositories/12875" alt="Soulter%2FAstrBot | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
-
-[![GitHub release (latest by date)](https://img.shields.io/github/v/release/AstrBotDevs/AstrBot)](https://github.com/AstrBotDevs/AstrBot/releases/latest)
-<img src="https://img.shields.io/badge/python-3.10+-blue.svg" alt="python">
-<a href="https://hub.docker.com/r/soulter/astrbot"><img alt="Docker pull" src="https://img.shields.io/docker/pulls/soulter/astrbot.svg"/></a>
-<img alt="Static Badge" src="https://img.shields.io/badge/QQ群-630166526-purple">
-[![wakatime](https://wakatime.com/badge/user/915e5316-99c6-4563-a483-ef186cf000c9/project/018e705a-a1a7-409a-a849-3013485e6c8e.svg)](https://wakatime.com/badge/user/915e5316-99c6-4563-a483-ef186cf000c9/project/018e705a-a1a7-409a-a849-3013485e6c8e)
-![Dynamic JSON Badge](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.soulter.top%2Fastrbot%2Fstats&query=v&label=7%E6%97%A5%E6%B6%88%E6%81%AF%E4%B8%8A%E8%A1%8C%E9%87%8F&cacheSeconds=3600)
-[![codecov](https://codecov.io/gh/AstrBotDevs/AstrBot/graph/badge.svg?token=FF3P5967B8)](https://codecov.io/gh/AstrBotDevs/AstrBot)
-
-<a href="https://astrbot.app/">ドキュメントを見る</a> ｜
-<a href="https://github.com/AstrBotDevs/AstrBot/issues">問題を報告する</a>
+<a href="https://hellogithub.com/repository/AstrBotDevs/AstrBot" target="_blank"><img src="https://api.hellogithub.com/v1/widgets/recommend.svg?rid=d127d50cd5e54c5382328acc3bb25483&claim_uid=ZO9by7qCXgSd6Lp&t=2" alt="Featured｜HelloGitHub" style="width: 250px; height: 54px;" width="250" height="54" /></a>
 </div>

-AstrBot は、疎結合、非同期、複数のメッセージプラットフォームに対応したデプロイ、使いやすいプラグインシステム、および包括的な大規模言語モデル（LLM）接続機能を備えたチャットボットおよび開発フレームワークです。
+<br>

-## ✨ 主な機能
+<div>
+<img src="https://img.shields.io/github/v/release/AstrBotDevs/AstrBot?style=for-the-badge&color=76bad9" href="https://github.com/AstrBotDevs/AstrBot/releases/latest">
+<img src="https://img.shields.io/badge/python-3.10+-blue.svg?style=for-the-badge&color=76bad9" alt="python">
+<a href="https://hub.docker.com/r/soulter/astrbot"><img alt="Docker pull" src="https://img.shields.io/docker/pulls/soulter/astrbot.svg?style=for-the-badge&color=76bad9"/></a>
+<a href="https://qm.qq.com/cgi-bin/qm/qr?k=wtbaNx7EioxeaqS9z7RQWVXPIxg2zYr7&jump_from=webapi&authKey=vlqnv/AV2DbJEvGIcxdlNSpfxVy+8vVqijgreRdnVKOaydpc+YSw4MctmEbr0k5"><img alt="QQ_community" src="https://img.shields.io/badge/QQ群-775869627-purple?style=for-the-badge&color=76bad9"></a>
+<a href="https://t.me/+hAsD2Ebl5as3NmY1"><img alt="Telegram_community" src="https://img.shields.io/badge/Telegram-AstrBot-purple?style=for-the-badge&color=76bad9"></a>
+<img src="https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fapi.soulter.top%2Fastrbot%2Fplugin-num&query=%24.result&suffix=%E4%B8%AA&style=for-the-badge&label=%E6%8F%92%E4%BB%B6%E5%B8%82%E5%9C%BA&cacheSeconds=3600">
+</div>

-1. **大規模言語モデルの対話**。OpenAI API、Google Gemini、Llama、Deepseek、ChatGLM など、さまざまな大規模言語モデルをサポートし、Ollama、LLMTuner を介してローカルにデプロイされた大規模モデルをサポートします。多輪対話、人格シナリオ、多モーダル機能を備え、画像理解、音声からテキストへの変換（Whisper）をサポートします。
-2. **複数のメッセージプラットフォームの接続**。QQ（OneBot）、QQ チャンネル、Feishu、Telegram への接続をサポートします。今後、DingTalk、Discord、WhatsApp、Xiaoai 音響をサポートする予定です。レート制限、ホワイトリスト、キーワードフィルタリング、Baidu コンテンツ監査をサポートします。
-3. **エージェント**。一部のエージェント機能をネイティブにサポートし、コードエグゼキューター、自然言語タスク、ウェブ検索などを提供します。[Dify プラットフォーム](https://dify.ai/)と連携し、Dify スマートアシスタント、ナレッジベース、Dify ワークフローを簡単に接続できます。
-4. **プラグインの拡張**。深く最適化されたプラグインメカニズムを備え、[プラグインの開発](https://astrbot.app/dev/plugin.html)をサポートし、機能を拡張できます。複数のプラグインのインストールをサポートします。
-5. **ビジュアル管理パネル**。設定の視覚的な変更、プラグイン管理、ログの表示などをサポートし、設定の難易度を低減します。WebChat を統合し、パネル上で大規模モデルと対話できます。
-6. **高い安定性と高いモジュール性**。イベントバスとパイプラインに基づくアーキテクチャ設計により、高度にモジュール化され、低結合です。
+<br>

-> [!TIP]
-> 管理パネルのオンラインデモを体験する: [https://demo.astrbot.app/](https://demo.astrbot.app/)
->
-> ユーザー名: `astrbot`, パスワード: `astrbot`。LLM が設定されていないため、チャットページで大規模モデルを使用することはできません。（デモのログインパスワードを変更しないでください 😭）
+<a href="https://github.com/AstrBotDevs/AstrBot/blob/master/README.md">中文</a> ｜
+<a href="https://github.com/AstrBotDevs/AstrBot/blob/master/README_en.md">English</a> ｜
+<a href="https://astrbot.app/">ドキュメント</a> ｜
+<a href="https://blog.astrbot.app/">Blog</a> ｜
+<a href="https://astrbot.featurebase.app/roadmap">ロードマップ</a> ｜
+<a href="https://github.com/AstrBotDevs/AstrBot/issues">Issue</a>
+</div>

-## ✨ 使用方法
+AstrBot は、オープンソースのオールインワン Agent チャットボットプラットフォーム及び開発フレームワークです。

-#### Docker デプロイ
+## 主な機能

-公式ドキュメント [Docker を使用して AstrBot をデプロイする](https://astrbot.app/deploy/astrbot/docker.html#%E4%BD%BF%E7%94%A8-docker-%E9%83%A8%E7%BD%B2-astrbot) を参照してください。
+1. **大規模言語モデル対話**。多様な大規模言語モデルサービスとの統合をサポート。マルチモーダル、ツール呼び出し、MCP、ネイティブナレッジベース、キャラクター設定などの機能を搭載。
+2. **マルチメッセージプラットフォームサポート**。QQ、WeChat Work、WeChat公式アカウント、Feishu、Telegram、DingTalk、Discord、KOOK などのプラットフォームと統合可能。レート制限、ホワイトリスト、Baidu コンテンツ審査をサポート。
+3. **Agent**。完全に最適化された Agentic 機能。マルチターンツール呼び出し、内蔵サンドボックスコード実行環境、Web 検索などの機能をサポート。
+4. **プラグイン拡張**。深く最適化されたプラグインメカニズムで、[プラグイン開発](https://astrbot.app/dev/plugin.html)による機能拡張をサポート。豊富なコミュニティプラグインエコシステム。
+5. **WebUI**。ビジュアル設定とボット管理、充実した機能。

-#### Windows ワンクリックインストーラーのデプロイ
+## デプロイ方法

-コンピュータに Python（>3.10）がインストールされている必要があります。公式ドキュメント [Windows ワンクリックインストーラーを使用して AstrBot をデプロイする](https://astrbot.app/deploy/astrbot/windows.html) を参照してください。
+#### Docker デプロイ（推奨 🥳）

-#### Replit デプロイ
+Docker / Docker Compose を使用した AstrBot のデプロイを推奨します。
+
+公式ドキュメント [Docker を使用した AstrBot のデプロイ](https://astrbot.app/deploy/astrbot/docker.html#%E4%BD%BF%E7%94%A8-docker-%E9%83%A8%E7%BD%B2-astrbot) をご参照ください。
+
+#### 宝塔パネルデプロイ
+
+AstrBot は宝塔パネルと提携し、宝塔パネルに公開されています。
+
+公式ドキュメント [宝塔パネルデプロイ](https://astrbot.app/deploy/astrbot/btpanel.html) をご参照ください。
+
+#### 1Panel デプロイ
+
+AstrBot は 1Panel 公式により 1Panel パネルに公開されています。
+
+公式ドキュメント [1Panel デプロイ](https://astrbot.app/deploy/astrbot/1panel.html) をご参照ください。
+
+#### 雨云でのデプロイ
+
+AstrBot は雨云公式によりクラウドアプリケーションプラットフォームに公開され、ワンクリックでデプロイ可能です。
+
+[![Deploy on RainYun](https://rainyun-apps.cn-nb1.rains3.com/materials/deploy-on-rainyun-en.svg)](https://app.rainyun.com/apps/rca/store/5994?ref=NjU1ODg0)
+
+#### Replit でのデプロイ
+
+コミュニティ貢献によるデプロイ方法。

 [![Run on Repl.it](https://repl.it/badge/github/AstrBotDevs/AstrBot)](https://repl.it/github/AstrBotDevs/AstrBot)

+#### Windows ワンクリックインストーラーデプロイ
+
+公式ドキュメント [Windows ワンクリックインストーラーを使用した AstrBot のデプロイ](https://astrbot.app/deploy/astrbot/windows.html) をご参照ください。
+
 #### CasaOS デプロイ

-コミュニティが提供するデプロイ方法です。
+コミュニティ貢献によるデプロイ方法。

-公式ドキュメント [ソースコードを使用して AstrBot をデプロイする](https://astrbot.app/deploy/astrbot/casaos.html) を参照してください。
+公式ドキュメント [CasaOS デプロイ](https://astrbot.app/deploy/astrbot/casaos.html) をご参照ください。

 #### 手動デプロイ

-公式ドキュメント [ソースコードを使用して AstrBot をデプロイする](https://astrbot.app/deploy/astrbot/cli.html) を参照してください。
+まず uv をインストールします:

-## ⚡ メッセージプラットフォームのサポート状況
+```bash
+pip install uv
+```

-| プラットフォーム    | サポート状況 | 詳細 | メッセージタイプ |
-| -------- | ------- | ------- | ------ |
-| QQ(公式ロボットインターフェース) | ✔    | プライベートチャット、グループチャット、QQ チャンネルプライベートチャット、グループチャット | テキスト、画像 |
-| QQ(OneBot)      | ✔    | プライベートチャット、グループチャット | テキスト、画像、音声 |
-| WeChat(個人アカウント)    | ✔    | WeChat 個人アカウントのプライベートチャット、グループチャット | テキスト、画像、音声 |
-| [Telegram](https://github.com/Soulter/astrbot_plugin_telegram)   | ✔    | プライベートチャット、グループチャット | テキスト、画像 |
-| [WeChat(企業 WeChat)](https://github.com/Soulter/astrbot_plugin_wecom)    | ✔    | プライベートチャット | テキスト、画像、音声 |
-| Feishu   | ✔    | グループチャット | テキスト、画像 |
-| WeChat 対話オープンプラットフォーム | 🚧    | 計画中 | - |
-| Discord   | 🚧    | 計画中 | - |
-| WhatsApp   | 🚧    | 計画中 | - |
-| Xiaoai 音響   | 🚧    | 計画中 | - |
+Git Clone で AstrBot をインストール:

-# 🦌 今後のロードマップ
+```bash
+git clone https://github.com/AstrBotDevs/AstrBot && cd AstrBot
+uv run main.py
+```

-> [!TIP]
-> Issue でさらに多くの提案を歓迎します <3
+または、公式ドキュメント [ソースコードから AstrBot をデプロイ](https://astrbot.app/deploy/astrbot/cli.html) をご参照ください。

- [ ] 現在のすべてのプラットフォームアダプターの機能の一貫性を確保し、改善する
- [ ] プラグインインターフェースの最適化
- [ ] GPT-Sovits などの TTS サービスをデフォルトでサポート
- [ ] "チャット強化" 部分を完成させ、永続的な記憶をサポート
- [ ] i18n の計画
+## 🌍 コミュニティ

-## ❤️ 貢献
+### QQ グループ

-Issue や Pull Request を歓迎します！このプロジェクトに変更を加えるだけです :)
+- 1群:322154837
+- 3群:630166526
+- 5群:822130018
+- 6群:753075035
+- 開発者群:975206796

-新機能の追加については、まず Issue で議論してください。
+### Telegram グループ

-## 🌟 サポート
+<a href="https://t.me/+hAsD2Ebl5as3NmY1"><img alt="Telegram_community" src="https://img.shields.io/badge/Telegram-AstrBot-purple?style=for-the-badge&color=76bad9"></a>

- このプロジェクトに Star を付けてください！
- [愛発電](https://afdian.com/a/soulter)で私をサポートしてください！
- [WeChat](https://drive.soulter.top/f/pYfA/d903f4fa49a496fda3f16d2be9e023b5.png)で私をサポートしてください~
+### Discord サーバー

-## ✨ デモ
+<a href="https://discord.gg/hAVk6tgV36"><img alt="Discord_community" src="https://img.shields.io/badge/Discord-AstrBot-purple?style=for-the-badge&color=76bad9"></a>

-> [!NOTE]
-> コードエグゼキューターのファイル入力/出力は現在 Napcat(QQ)、Lagrange(QQ) でのみテストされています
+## サポートされているメッセージプラットフォーム

-<div align='center'>
+**公式メンテナンス**

-<img src="https://github.com/user-attachments/assets/4ee688d9-467d-45c8-99d6-368f9a8a92d8" width="600">
+- QQ (公式プラットフォーム & OneBot)
+- Telegram
+- WeChat Work アプリケーション & WeChat Work インテリジェントボット
+- WeChat カスタマーサービス & WeChat 公式アカウント
+- Feishu (Lark)
+- DingTalk
+- Slack
+- Discord
+- Satori
+- Misskey
+- WhatsApp (近日対応予定)
+- LINE (近日対応予定)

-_✨ Docker ベースのサンドボックス化されたコードエグゼキューター（ベータテスト中）✨_
+**コミュニティメンテナンス**

-<img src="https://github.com/user-attachments/assets/0378f407-6079-4f64-ae4c-e97ab20611d2" height=500>
+- [KOOK](https://github.com/wuyan1003/astrbot_plugin_kook_adapter)
+- [VoceChat](https://github.com/HikariFroya/astrbot_plugin_vocechat)
+- [Bilibili ダイレクトメッセージ](https://github.com/Hina-Chat/astrbot_plugin_bilibili_adapter)
+- [wxauto](https://github.com/luosheng520qaq/wxauto-repost-onebotv11)

-_✨ 多モーダル、ウェブ検索、長文の画像変換（設定可能）✨_
+## サポートされているモデルサービス

-<img src="https://github.com/user-attachments/assets/8ec12797-e70f-460a-959e-48eca39ca2bb" height=100>
+**大規模言語モデルサービス**

-_✨ 自然言語タスク ✨_
+- OpenAI および互換サービス
+- Anthropic
+- Google Gemini
+- Moonshot AI
+- 智谱 AI
+- DeepSeek
+- Ollama (セルフホスト)
+- LM Studio (セルフホスト)
+- [優云智算](https://www.compshare.cn/?ytag=GPU_YY-gh_astrbot&referral_code=FV7DcGowN4hB5UuXKgpE74)
+- [302.AI](https://share.302.ai/rr1M3l)
+- [小馬算力](https://www.tokenpony.cn/3YPyf)
+- [硅基流動](https://docs.siliconflow.cn/cn/usercases/use-siliconcloud-in-astrbot)
+- [PPIO 派欧云](https://ppio.com/user/register?invited_by=AIOONE)
+- ModelScope
+- OneAPI

-<img src="https://github.com/user-attachments/assets/e137a9e1-340a-4bf2-bb2b-771132780735" height=150>
-<img src="https://github.com/user-attachments/assets/480f5e82-cf6a-4955-a869-0d73137aa6e1" height=150>
+**LLMOps プラットフォーム**

-_✨ プラグインシステム - 一部のプラグインの展示 ✨_
+- Dify
+- Alibaba Cloud 百炼アプリケーション
+- Coze

-<img src="https://github.com/user-attachments/assets/592a8630-14c7-4e06-b496-9c0386e4f36c" width="600">
+**音声認識サービス**

-_✨ 管理パネル ✨_
+- OpenAI Whisper
+- SenseVoice

-![webchat](https://drive.soulter.top/f/vlsA/ezgif-5-fb044b2542.gif)
+**音声合成サービス**

-_✨ 内蔵 Web Chat、オンラインでボットと対話 ✨_
+- OpenAI TTS
+- Gemini TTS
+- GPT-Sovits-Inference
+- GPT-Sovits
+- FishAudio
+- Edge TTS
+- Alibaba Cloud 百炼 TTS
+- Azure TTS
+- Minimax TTS
+- Volcano Engine TTS

-</div>
+## ❤️ コントリビューション
+
+Issue や Pull Request は大歓迎です!このプロジェクトに変更を送信してください :)
+
+### コントリビュート方法
+
+Issue を確認したり、PR(プルリクエスト)のレビューを手伝うことで貢献できます。どんな Issue や PR への参加も歓迎され、コミュニティ貢献を促進します。もちろん、これらは提案に過ぎず、どんな方法でも貢献できます。新機能の追加については、まず Issue で議論してください。
+
+### 開発環境
+
+AstrBot はコードのフォーマットとチェックに `ruff` を使用しています。
+
+```bash
+git clone https://github.com/AstrBotDevs/AstrBot
+pip install pre-commit
+pre-commit install
+```
+
+## ❤️ Special Thanks
+
+AstrBot への貢献をしていただいたすべてのコントリビューターとプラグイン開発者に特別な感謝を ❤️
+
+<a href="https://github.com/AstrBotDevs/AstrBot/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=AstrBotDevs/AstrBot" />
+</a>
+
+また、このプロジェクトの誕生は以下のオープンソースプロジェクトの助けなしには実現できませんでした:
+
+- [NapNeko/NapCatQQ](https://github.com/NapNeko/NapCatQQ) - 素晴らしい猫猫フレームワーク

 ## ⭐ Star History

 > [!TIP]
-> このプロジェクトがあなたの生活や仕事に役立った場合、またはこのプロジェクトの将来の発展に関心がある場合は、プロジェクトに Star を付けてください。これはこのオープンソースプロジェクトを維持するためのモチベーションです <3
+> このプロジェクトがあなたの生活や仕事に役立ったり、このプロジェクトの今後の発展に関心がある場合は、プロジェクトに Star をください。これがこのオープンソースプロジェクトを維持する原動力です <3

 <div align="center">

-[![Star History Chart](https://api.star-history.com/svg?repos=soulter/astrbot&type=Date)](https://star-history.com/#soulter/astrbot&Date)
+[![Star History Chart](https://api.star-history.com/svg?repos=astrbotdevs/astrbot&type=Date)](https://star-history.com/#astrbotdevs/astrbot&Date)

 </div>

-## スポンサー
-
-[<img src="https://api.gitsponsors.com/api/badge/img?id=575865240" height="20">](https://api.gitsponsors.com/api/badge/link?p=XEpbdGxlitw/RbcwiTX93UMzNK/jgDYC8NiSzamIPMoKvG2lBFmyXhSS/b0hFoWlBBMX2L5X5CxTDsUdyvcIEHTOfnkXz47UNOZvMwyt5CzbYpq0SEzsSV1OJF1cCo90qC/ZyYKYOWedal3MhZ3ikw==)
-
-## 免責事項
-
-1. このプロジェクトは `AGPL-v3` オープンソースライセンスの下で保護されています。
-2. このプロジェクトを使用する際は、現地の法律および規制を遵守してください。
-
-<!-- ## ✨ ATRI [ベータテスト]
-
-この機能はプラグインとしてロードされます。プラグインリポジトリのアドレス：[astrbot_plugin_atri](https://github.com/Soulter/astrbot_plugin_atri)
-
-1. 《ATRI ~ My Dear Moments》の主人公 ATRI のキャラクターセリフを微調整データセットとして使用した `Qwen1.5-7B-Chat Lora` 微調整モデル。
-2. 長期記憶
-3. ミームの理解と返信
-4. TTS
-    -->
+</details>

 _私は、高性能ですから!_
--- a/astrbot/core/agent/mcp_client.py
+++ b/astrbot/core/agent/mcp_client.py
@@ -4,6 +4,14 @@ from contextlib import AsyncExitStack
 from datetime import timedelta
 from typing import Generic

+from tenacity import (
+    before_sleep_log,
+    retry,
+    retry_if_exception_type,
+    stop_after_attempt,
+    wait_exponential,
+)
+
 from astrbot import logger
 from astrbot.core.agent.run_context import ContextWrapper
 from astrbot.core.utils.log_pipe import LogPipe
@@ -12,21 +20,24 @@ from .run_context import TContext
 from .tool import FunctionTool

 try:
+    import anyio
    import mcp
    from mcp.client.sse import sse_client
 except (ModuleNotFoundError, ImportError):
-    logger.warning("警告: 缺少依赖库 'mcp'，将无法使用 MCP 服务。")
+    logger.warning(
+        "Warning: Missing 'mcp' dependency, MCP services will be unavailable."
+    )

 try:
    from mcp.client.streamable_http import streamablehttp_client
 except (ModuleNotFoundError, ImportError):
    logger.warning(
-        "警告: 缺少依赖库 'mcp' 或者 mcp 库版本过低，无法使用 Streamable HTTP 连接方式。",
+        "Warning: Missing 'mcp' dependency or MCP library version too old, Streamable HTTP connection unavailable.",
    )


 def _prepare_config(config: dict) -> dict:
-    """准备配置，处理嵌套格式"""
+    """Prepare configuration, handle nested format"""
    if config.get("mcpServers"):
        first_key = next(iter(config["mcpServers"]))
        config = config["mcpServers"][first_key]
@@ -35,7 +46,7 @@ def _prepare_config(config: dict) -> dict:


 async def _quick_test_mcp_connection(config: dict) -> tuple[bool, str]:
-    """快速测试 MCP 服务器可达性"""
+    """Quick test MCP server connectivity"""
    import aiohttp

    cfg = _prepare_config(config.copy())
@@ -50,7 +61,7 @@ async def _quick_test_mcp_connection(config: dict) -> tuple[bool, str]:
        elif "type" in cfg:
            transport_type = cfg["type"]
        else:
-            raise Exception("MCP 连接配置缺少 transport 或 type 字段")
+            raise Exception("MCP connection config missing transport or type field")

        async with aiohttp.ClientSession() as session:
            if transport_type == "streamable_http":
@@ -91,7 +102,7 @@ async def _quick_test_mcp_connection(config: dict) -> tuple[bool, str]:
                    return False, f"HTTP {response.status}: {response.reason}"

    except asyncio.TimeoutError:
-        return False, f"连接超时: {timeout}秒"
+        return False, f"Connection timeout: {timeout} seconds"
    except Exception as e:
        return False, f"{e!s}"

@@ -101,6 +112,7 @@ class MCPClient:
        # Initialize session and client objects
        self.session: mcp.ClientSession | None = None
        self.exit_stack = AsyncExitStack()
+        self._old_exit_stacks: list[AsyncExitStack] = []  # Track old stacks for cleanup

        self.name: str | None = None
        self.active: bool = True
@@ -108,22 +120,32 @@ class MCPClient:
        self.server_errlogs: list[str] = []
        self.running_event = asyncio.Event()

-    async def connect_to_server(self, mcp_server_config: dict, name: str):
-        """连接到 MCP 服务器
+        # Store connection config for reconnection
+        self._mcp_server_config: dict | None = None
+        self._server_name: str | None = None
+        self._reconnect_lock = asyncio.Lock()  # Lock for thread-safe reconnection
+        self._reconnecting: bool = False  # For logging and debugging

-        如果 `url` 参数存在：
-            1. 当 transport 指定为 `streamable_http` 时，使用 Streamable HTTP 连接方式。
-            1. 当 transport 指定为 `sse` 时，使用 SSE 连接方式。
-            2. 如果没有指定，默认使用 SSE 的方式连接到 MCP 服务。
+    async def connect_to_server(self, mcp_server_config: dict, name: str):
+        """Connect to MCP server
+
+        If `url` parameter exists:
+            1. When transport is specified as `streamable_http`, use Streamable HTTP connection.
+            2. When transport is specified as `sse`, use SSE connection.
+            3. If not specified, default to SSE connection to MCP service.

        Args:
            mcp_server_config (dict): Configuration for the MCP server. See https://modelcontextprotocol.io/quickstart/server

        """
+        # Store config for reconnection
+        self._mcp_server_config = mcp_server_config
+        self._server_name = name
+
        cfg = _prepare_config(mcp_server_config.copy())

        def logging_callback(msg: str):
-            # 处理 MCP 服务的错误日志
+            # Handle MCP service error logs
            print(f"MCP Server {name} Error: {msg}")
            self.server_errlogs.append(msg)

@@ -137,7 +159,7 @@ class MCPClient:
            elif "type" in cfg:
                transport_type = cfg["type"]
            else:
-                raise Exception("MCP 连接配置缺少 transport 或 type 字段")
+                raise Exception("MCP connection config missing transport or type field")

            if transport_type != "streamable_http":
                # SSE transport method
@@ -193,7 +215,7 @@ class MCPClient:
            )

            def callback(msg: str):
-                # 处理 MCP 服务的错误日志
+                # Handle MCP service error logs
                self.server_errlogs.append(msg)

            stdio_transport = await self.exit_stack.enter_async_context(
@@ -222,10 +244,120 @@ class MCPClient:
        self.tools = response.tools
        return response

+    async def _reconnect(self) -> None:
+        """Reconnect to the MCP server using the stored configuration.
+
+        Uses asyncio.Lock to ensure thread-safe reconnection in concurrent environments.
+
+        Raises:
+            Exception: raised when reconnection fails
+        """
+        async with self._reconnect_lock:
+            # Check if already reconnecting (useful for logging)
+            if self._reconnecting:
+                logger.debug(
+                    f"MCP Client {self._server_name} is already reconnecting, skipping"
+                )
+                return
+
+            if not self._mcp_server_config or not self._server_name:
+                raise Exception("Cannot reconnect: missing connection configuration")
+
+            self._reconnecting = True
+            try:
+                logger.info(
+                    f"Attempting to reconnect to MCP server {self._server_name}..."
+                )
+
+                # Save old exit_stack for later cleanup (don't close it now to avoid cancel scope issues)
+                if self.exit_stack:
+                    self._old_exit_stacks.append(self.exit_stack)
+
+                # Mark old session as invalid
+                self.session = None
+
+                # Create new exit stack for new connection
+                self.exit_stack = AsyncExitStack()
+
+                # Reconnect using stored config
+                await self.connect_to_server(self._mcp_server_config, self._server_name)
+                await self.list_tools_and_save()
+
+                logger.info(
+                    f"Successfully reconnected to MCP server {self._server_name}"
+                )
+            except Exception as e:
+                logger.error(
+                    f"Failed to reconnect to MCP server {self._server_name}: {e}"
+                )
+                raise
+            finally:
+                self._reconnecting = False
+
+    async def call_tool_with_reconnect(
+        self,
+        tool_name: str,
+        arguments: dict,
+        read_timeout_seconds: timedelta,
+    ) -> mcp.types.CallToolResult:
+        """Call MCP tool with automatic reconnection on failure, max 2 retries.
+
+        Args:
+            tool_name: tool name
+            arguments: tool arguments
+            read_timeout_seconds: read timeout
+
+        Returns:
+            MCP tool call result
+
+        Raises:
+            ValueError: MCP session is not available
+            anyio.ClosedResourceError: raised after reconnection failure
+        """
+
+        @retry(
+            retry=retry_if_exception_type(anyio.ClosedResourceError),
+            stop=stop_after_attempt(2),
+            wait=wait_exponential(multiplier=1, min=1, max=3),
+            before_sleep=before_sleep_log(logger, logging.WARNING),
+            reraise=True,
+        )
+        async def _call_with_retry():
+            if not self.session:
+                raise ValueError("MCP session is not available for MCP function tools.")
+
+            try:
+                return await self.session.call_tool(
+                    name=tool_name,
+                    arguments=arguments,
+                    read_timeout_seconds=read_timeout_seconds,
+                )
+            except anyio.ClosedResourceError:
+                logger.warning(
+                    f"MCP tool {tool_name} call failed (ClosedResourceError), attempting to reconnect..."
+                )
+                # Attempt to reconnect
+                await self._reconnect()
+                # Reraise the exception to trigger tenacity retry
+                raise
+
+        return await _call_with_retry()
+
    async def cleanup(self):
-        """Clean up resources"""
-        await self.exit_stack.aclose()
-        self.running_event.set()  # Set the running event to indicate cleanup is done
+        """Clean up resources including old exit stacks from reconnections"""
+        # Set running_event first to unblock any waiting tasks
+        self.running_event.set()
+
+        # Close current exit stack
+        try:
+            await self.exit_stack.aclose()
+        except Exception as e:
+            logger.debug(f"Error closing current exit stack: {e}")
+
+        # Don't close old exit stacks as they may be in different task contexts
+        # They will be garbage collected naturally
+        # Just clear the list to release references
+        self._old_exit_stacks.clear()


 class MCPTool(FunctionTool, Generic[TContext]):
@@ -246,14 +378,8 @@ class MCPTool(FunctionTool, Generic[TContext]):
    async def call(
        self, context: ContextWrapper[TContext], **kwargs
    ) -> mcp.types.CallToolResult:
-        session = self.mcp_client.session
-        if not session:
-            raise ValueError("MCP session is not available for MCP function tools.")
-        res = await session.call_tool(
-            name=self.mcp_tool.name,
+        return await self.mcp_client.call_tool_with_reconnect(
+            tool_name=self.mcp_tool.name,
            arguments=kwargs,
-            read_timeout_seconds=timedelta(
-                seconds=context.tool_call_timeout,
-            ),
+            read_timeout_seconds=timedelta(seconds=context.tool_call_timeout),
        )
-        return res
--- a/astrbot/core/agent/message.py
+++ b/astrbot/core/agent/message.py
@@ -119,6 +119,13 @@ class ToolCall(BaseModel):
    """The ID of the tool call."""
    function: FunctionBody
    """The function body of the tool call."""
+    extra_content: dict[str, Any] | None = None
+    """Extra metadata for the tool call."""
+
+    def model_dump(self, **kwargs: Any) -> dict[str, Any]:
+        if self.extra_content is None:
+            kwargs.setdefault("exclude", set()).add("extra_content")
+        return super().model_dump(**kwargs)


 class ToolCallPart(BaseModel):
--- a/astrbot/core/config/default.py
+++ b/astrbot/core/config/default.py
@@ -4,7 +4,7 @@ import os

 from astrbot.core.utils.astrbot_path import get_astrbot_data_path

-VERSION = "4.5.8"
+VERSION = "4.6.0"
 DB_PATH = os.path.join(get_astrbot_data_path(), "data_v4.db")

 # 默认配置
@@ -137,6 +137,7 @@ DEFAULT_CONFIG = {
    "kb_names": [],  # 默认知识库名称列表
    "kb_fusion_top_k": 20,  # 知识库检索融合阶段返回结果数量
    "kb_final_top_k": 5,  # 知识库检索最终返回结果数量
+    "kb_agentic_mode": False,
 }


@@ -2146,6 +2147,7 @@ CONFIG_METADATA_2 = {
            "kb_names": {"type": "list", "items": {"type": "string"}},
            "kb_fusion_top_k": {"type": "int", "default": 20},
            "kb_final_top_k": {"type": "int", "default": 5},
+            "kb_agentic_mode": {"type": "bool"},
        },
    },
 }
@@ -2241,6 +2243,11 @@ CONFIG_METADATA_3 = {
                        "type": "int",
                        "hint": "从知识库中检索到的结果数量，越大可能获得越多相关信息，但也可能引入噪音。建议根据实际需求调整",
                    },
+                    "kb_agentic_mode": {
+                        "description": "Agentic 知识库检索",
+                        "type": "bool",
+                        "hint": "启用后，知识库检索将作为 LLM Tool，由模型自主决定何时调用知识库进行查询。需要模型支持函数调用能力。",
+                    },
                },
            },
            "websearch": {
--- a/astrbot/core/core_lifecycle.py
+++ b/astrbot/core/core_lifecycle.py
@@ -22,7 +22,9 @@ from astrbot.core.config.default import VERSION
 from astrbot.core.conversation_mgr import ConversationManager
 from astrbot.core.db import BaseDatabase
 from astrbot.core.db.migration.migra_45_to_46 import migrate_45_to_46
+from astrbot.core.db.migration.migra_webchat_session import migrate_webchat_session
 from astrbot.core.knowledge_base.kb_mgr import KnowledgeBaseManager
+from astrbot.core.memory.memory_manager import MemoryManager
 from astrbot.core.persona_mgr import PersonaManager
 from astrbot.core.pipeline.scheduler import PipelineContext, PipelineScheduler
 from astrbot.core.platform.manager import PlatformManager
@@ -103,6 +105,13 @@ class AstrBotCoreLifecycle:
            logger.error(f"Migration from version 4.5 to 4.6 failed: {e!s}")
            logger.error(traceback.format_exc())

+        # migration for webchat session
+        try:
+            await migrate_webchat_session(self.db)
+        except Exception as e:
+            logger.error(f"Migration for webchat session failed: {e!s}")
+            logger.error(traceback.format_exc())
+
        # 初始化事件队列
        self.event_queue = Queue()

@@ -128,6 +137,8 @@ class AstrBotCoreLifecycle:

        # 初始化知识库管理器
        self.kb_manager = KnowledgeBaseManager(self.provider_manager)
+        # 初始化记忆管理器
+        self.memory_manager = MemoryManager()

        # 初始化提供给插件的上下文
        self.star_context = Context(
@@ -141,6 +152,7 @@ class AstrBotCoreLifecycle:
            self.persona_mgr,
            self.astrbot_config_mgr,
            self.kb_manager,
+            self.memory_manager,
        )

        # 初始化插件管理器
--- a/astrbot/core/db/init.py
+++ b/astrbot/core/db/init.py
@@ -13,6 +13,7 @@ from astrbot.core.db.po import (
    ConversationV2,
    Persona,
    PlatformMessageHistory,
+    PlatformSession,
    PlatformStat,
    Preference,
    Stats,
@@ -183,7 +184,7 @@ class BaseDatabase(abc.ABC):
        user_id: str,
        offset_sec: int = 86400,
    ) -> None:
-        """Delete platform message history records older than the specified offset."""
+        """Delete platform message history records newer than the specified offset."""
        ...

    @abc.abstractmethod
@@ -313,3 +314,51 @@ class BaseDatabase(abc.ABC):
    ) -> tuple[list[dict], int]:
        """Get paginated session conversations with joined conversation and persona details, support search and platform filter."""
        ...
+
+    # ====
+    # Platform Session Management
+    # ====
+
+    @abc.abstractmethod
+    async def create_platform_session(
+        self,
+        creator: str,
+        platform_id: str = "webchat",
+        session_id: str | None = None,
+        display_name: str | None = None,
+        is_group: int = 0,
+    ) -> PlatformSession:
+        """Create a new Platform session."""
+        ...
+
+    @abc.abstractmethod
+    async def get_platform_session_by_id(
+        self, session_id: str
+    ) -> PlatformSession | None:
+        """Get a Platform session by its ID."""
+        ...
+
+    @abc.abstractmethod
+    async def get_platform_sessions_by_creator(
+        self,
+        creator: str,
+        platform_id: str | None = None,
+        page: int = 1,
+        page_size: int = 20,
+    ) -> list[PlatformSession]:
+        """Get all Platform sessions for a specific creator (username) and optionally platform."""
+        ...
+
+    @abc.abstractmethod
+    async def update_platform_session(
+        self,
+        session_id: str,
+        display_name: str | None = None,
+    ) -> None:
+        """Update a Platform session's updated_at timestamp and optionally display_name."""
+        ...
+
+    @abc.abstractmethod
+    async def delete_platform_session(self, session_id: str) -> None:
+        """Delete a Platform session by its ID."""
+        ...
--- a/astrbot/core/db/migration/migra_webchat_session.py
+++ b/astrbot/core/db/migration/migra_webchat_session.py
@@ -0,0 +1,131 @@
+"""Migration script for WebChat sessions.
+
+This migration creates PlatformSession from existing platform_message_history records.
+
+Changes:
+- Creates platform_sessions table
+- Adds platform_id field (default: 'webchat')
+- Adds display_name field
+- Session_id format: {platform_id}_{uuid}
+"""
+
+from sqlalchemy import func, select
+from sqlmodel import col
+
+from astrbot.api import logger, sp
+from astrbot.core.db import BaseDatabase
+from astrbot.core.db.po import ConversationV2, PlatformMessageHistory, PlatformSession
+
+
+async def migrate_webchat_session(db_helper: BaseDatabase):
+    """Create PlatformSession records from platform_message_history.
+
+    This migration extracts all unique user_ids from platform_message_history
+    where platform_id='webchat' and creates corresponding PlatformSession records.
+    """
+    # 检查是否已经完成迁移
+    migration_done = await db_helper.get_preference(
+        "global", "global", "migration_done_webchat_session"
+    )
+    if migration_done:
+        return
+
+    logger.info("开始执行数据库迁移（WebChat 会话迁移）...")
+
+    try:
+        async with db_helper.get_db() as session:
+            # 从 platform_message_history 创建 PlatformSession
+            query = (
+                select(
+                    col(PlatformMessageHistory.user_id),
+                    col(PlatformMessageHistory.sender_name),
+                    func.min(PlatformMessageHistory.created_at).label("earliest"),
+                    func.max(PlatformMessageHistory.updated_at).label("latest"),
+                )
+                .where(col(PlatformMessageHistory.platform_id) == "webchat")
+                .where(col(PlatformMessageHistory.sender_id) == "astrbot")
+                .group_by(col(PlatformMessageHistory.user_id))
+            )
+
+            result = await session.execute(query)
+            webchat_users = result.all()
+
+            if not webchat_users:
+                logger.info("没有找到需要迁移的 WebChat 数据")
+                await sp.put_async(
+                    "global", "global", "migration_done_webchat_session", True
+                )
+                return
+
+            logger.info(f"找到 {len(webchat_users)} 个 WebChat 会话需要迁移")
+
+            # 检查已存在的会话
+            existing_query = select(col(PlatformSession.session_id))
+            existing_result = await session.execute(existing_query)
+            existing_session_ids = {row[0] for row in existing_result.fetchall()}
+
+            # 查询 Conversations 表中的 title，用于设置 display_name
+            # 对于每个 user_id，对应的 conversation user_id 格式为: webchat:FriendMessage:webchat!astrbot!{user_id}
+            user_ids_to_query = [
+                f"webchat:FriendMessage:webchat!astrbot!{user_id}"
+                for user_id, _, _, _ in webchat_users
+            ]
+            conv_query = select(
+                col(ConversationV2.user_id), col(ConversationV2.title)
+            ).where(col(ConversationV2.user_id).in_(user_ids_to_query))
+            conv_result = await session.execute(conv_query)
+            # 创建 user_id -> title 的映射字典
+            title_map = {
+                user_id.replace("webchat:FriendMessage:webchat!astrbot!", ""): title
+                for user_id, title in conv_result.fetchall()
+            }
+
+            # 批量创建 PlatformSession 记录
+            sessions_to_add = []
+            skipped_count = 0
+
+            for user_id, sender_name, created_at, updated_at in webchat_users:
+                # user_id 就是 webchat_conv_id (session_id)
+                session_id = user_id
+
+                # sender_name 通常是 username，但可能为 None
+                creator = sender_name if sender_name else "guest"
+
+                # 检查是否已经存在该会话
+                if session_id in existing_session_ids:
+                    logger.debug(f"会话 {session_id} 已存在，跳过")
+                    skipped_count += 1
+                    continue
+
+                # 从 Conversations 表中获取 display_name
+                display_name = title_map.get(user_id)
+
+                # 创建新的 PlatformSession（保留原有的时间戳）
+                new_session = PlatformSession(
+                    session_id=session_id,
+                    platform_id="webchat",
+                    creator=creator,
+                    is_group=0,
+                    created_at=created_at,
+                    updated_at=updated_at,
+                    display_name=display_name,
+                )
+                sessions_to_add.append(new_session)
+
+            # 批量插入
+            if sessions_to_add:
+                session.add_all(sessions_to_add)
+                await session.commit()
+
+                logger.info(
+                    f"WebChat 会话迁移完成！成功迁移: {len(sessions_to_add)}, 跳过: {skipped_count}",
+                )
+            else:
+                logger.info("没有新会话需要迁移")
+
+        # 标记迁移完成
+        await sp.put_async("global", "global", "migration_done_webchat_session", True)
+
+    except Exception as e:
+        logger.error(f"迁移过程中发生错误: {e}", exc_info=True)
+        raise
--- a/astrbot/core/db/po.py
+++ b/astrbot/core/db/po.py
@@ -3,13 +3,7 @@ from dataclasses import dataclass, field
 from datetime import datetime, timezone
 from typing import TypedDict

-from sqlmodel import (
-    JSON,
-    Field,
-    SQLModel,
-    Text,
-    UniqueConstraint,
-)
+from sqlmodel import JSON, Field, SQLModel, Text, UniqueConstraint


 class PlatformStat(SQLModel, table=True):
@@ -18,7 +12,7 @@ class PlatformStat(SQLModel, table=True):
    Note: In astrbot v4, we moved `platform` table to here.
    """

-    __tablename__ = "platform_stats"
+    __tablename__ = "platform_stats"  # type: ignore

    id: int = Field(primary_key=True, sa_column_kwargs={"autoincrement": True})
    timestamp: datetime = Field(nullable=False)
@@ -37,7 +31,7 @@ class PlatformStat(SQLModel, table=True):


 class ConversationV2(SQLModel, table=True):
-    __tablename__ = "conversations"
+    __tablename__ = "conversations"  # type: ignore

    inner_conversation_id: int = Field(
        primary_key=True,
@@ -74,7 +68,7 @@ class Persona(SQLModel, table=True):
    It can be used to customize the behavior of LLMs.
    """

-    __tablename__ = "personas"
+    __tablename__ = "personas"  # type: ignore

    id: int | None = Field(
        primary_key=True,
@@ -104,7 +98,7 @@ class Persona(SQLModel, table=True):
 class Preference(SQLModel, table=True):
    """This class represents preferences for bots."""

-    __tablename__ = "preferences"
+    __tablename__ = "preferences"  # type: ignore

    id: int | None = Field(
        default=None,
@@ -140,7 +134,7 @@ class PlatformMessageHistory(SQLModel, table=True):
    or platform-specific messages.
    """

-    __tablename__ = "platform_message_history"
+    __tablename__ = "platform_message_history"  # type: ignore

    id: int | None = Field(
        primary_key=True,
@@ -161,13 +155,55 @@ class PlatformMessageHistory(SQLModel, table=True):
    )


+class PlatformSession(SQLModel, table=True):
+    """Platform session table for managing user sessions across different platforms.
+
+    A session represents a chat window for a specific user on a specific platform.
+    Each session can have multiple conversations (对话) associated with it.
+    """
+
+    __tablename__ = "platform_sessions"  # type: ignore
+
+    inner_id: int | None = Field(
+        primary_key=True,
+        sa_column_kwargs={"autoincrement": True},
+        default=None,
+    )
+    session_id: str = Field(
+        max_length=100,
+        nullable=False,
+        unique=True,
+        default_factory=lambda: f"webchat_{uuid.uuid4()}",
+    )
+    platform_id: str = Field(default="webchat", nullable=False)
+    """Platform identifier (e.g., 'webchat', 'qq', 'discord')"""
+    creator: str = Field(nullable=False)
+    """Username of the session creator"""
+    display_name: str | None = Field(default=None, max_length=255)
+    """Display name for the session"""
+    is_group: int = Field(default=0, nullable=False)
+    """0 for private chat, 1 for group chat (not implemented yet)"""
+    created_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
+    updated_at: datetime = Field(
+        default_factory=lambda: datetime.now(timezone.utc),
+        sa_column_kwargs={"onupdate": datetime.now(timezone.utc)},
+    )
+
+    __table_args__ = (
+        UniqueConstraint(
+            "session_id",
+            name="uix_platform_session_id",
+        ),
+    )
+
+
 class Attachment(SQLModel, table=True):
    """This class represents attachments for messages in AstrBot.

    Attachments can be images, files, or other media types.
    """

-    __tablename__ = "attachments"
+    __tablename__ = "attachments"  # type: ignore

    inner_attachment_id: int | None = Field(
        primary_key=True,
--- a/astrbot/core/db/sqlite.py
+++ b/astrbot/core/db/sqlite.py
@@ -1,7 +1,7 @@
 import asyncio
 import threading
 import typing as T
-from datetime import datetime, timedelta
+from datetime import datetime, timedelta, timezone

 from sqlalchemy.ext.asyncio import AsyncSession
 from sqlmodel import col, delete, desc, func, or_, select, text, update
@@ -12,6 +12,7 @@ from astrbot.core.db.po import (
    ConversationV2,
    Persona,
    PlatformMessageHistory,
+    PlatformSession,
    PlatformStat,
    Preference,
    SQLModel,
@@ -412,7 +413,7 @@ class SQLiteDatabase(BaseDatabase):
        user_id,
        offset_sec=86400,
    ):
-        """Delete platform message history records older than the specified offset."""
+        """Delete platform message history records newer than the specified offset."""
        async with self.get_db() as session:
            session: AsyncSession
            async with session.begin():
@@ -422,7 +423,7 @@ class SQLiteDatabase(BaseDatabase):
                    delete(PlatformMessageHistory).where(
                        col(PlatformMessageHistory.platform_id) == platform_id,
                        col(PlatformMessageHistory.user_id) == user_id,
-                        col(PlatformMessageHistory.created_at) < cutoff_time,
+                        col(PlatformMessageHistory.created_at) >= cutoff_time,
                    ),
                )

@@ -709,3 +710,101 @@ class SQLiteDatabase(BaseDatabase):
        t.start()
        t.join()
        return result
+
+    # ====
+    # Platform Session Management
+    # ====
+
+    async def create_platform_session(
+        self,
+        creator: str,
+        platform_id: str = "webchat",
+        session_id: str | None = None,
+        display_name: str | None = None,
+        is_group: int = 0,
+    ) -> PlatformSession:
+        """Create a new Platform session."""
+        kwargs = {}
+        if session_id:
+            kwargs["session_id"] = session_id
+
+        async with self.get_db() as session:
+            session: AsyncSession
+            async with session.begin():
+                new_session = PlatformSession(
+                    creator=creator,
+                    platform_id=platform_id,
+                    display_name=display_name,
+                    is_group=is_group,
+                    **kwargs,
+                )
+                session.add(new_session)
+                await session.flush()
+                await session.refresh(new_session)
+                return new_session
+
+    async def get_platform_session_by_id(
+        self, session_id: str
+    ) -> PlatformSession | None:
+        """Get a Platform session by its ID."""
+        async with self.get_db() as session:
+            session: AsyncSession
+            query = select(PlatformSession).where(
+                PlatformSession.session_id == session_id,
+            )
+            result = await session.execute(query)
+            return result.scalar_one_or_none()
+
+    async def get_platform_sessions_by_creator(
+        self,
+        creator: str,
+        platform_id: str | None = None,
+        page: int = 1,
+        page_size: int = 20,
+    ) -> list[PlatformSession]:
+        """Get all Platform sessions for a specific creator (username) and optionally platform."""
+        async with self.get_db() as session:
+            session: AsyncSession
+            offset = (page - 1) * page_size
+            query = select(PlatformSession).where(PlatformSession.creator == creator)
+
+            if platform_id:
+                query = query.where(PlatformSession.platform_id == platform_id)
+
+            query = (
+                query.order_by(desc(PlatformSession.updated_at))
+                .offset(offset)
+                .limit(page_size)
+            )
+            result = await session.execute(query)
+            return list(result.scalars().all())
+
+    async def update_platform_session(
+        self,
+        session_id: str,
+        display_name: str | None = None,
+    ) -> None:
+        """Update a Platform session's updated_at timestamp and optionally display_name."""
+        async with self.get_db() as session:
+            session: AsyncSession
+            async with session.begin():
+                values: dict[str, T.Any] = {"updated_at": datetime.now(timezone.utc)}
+                if display_name is not None:
+                    values["display_name"] = display_name
+
+                await session.execute(
+                    update(PlatformSession)
+                    .where(col(PlatformSession.session_id == session_id))
+                    .values(**values),
+                )
+
+    async def delete_platform_session(self, session_id: str) -> None:
+        """Delete a Platform session by its ID."""
+        async with self.get_db() as session:
+            session: AsyncSession
+            async with session.begin():
+                await session.execute(
+                    delete(PlatformSession).where(
+                        col(PlatformSession.session_id == session_id),
+                    ),
+                )
--- a/astrbot/core/db/vec_db/base.py
+++ b/astrbot/core/db/vec_db/base.py
@@ -1,11 +1,20 @@
 import abc
 from dataclasses import dataclass
+from typing import TypedDict


@dataclass
 class Result:
+    class ResultData(TypedDict):
+        id: str
+        doc_id: str
+        text: str
+        metadata: str
+        created_at: int
+        updated_at: int
+
    similarity: float
-    data: dict
+    data: ResultData | dict


 class BaseVecDB:
--- a/astrbot/core/knowledge_base/kb_helper.py
+++ b/astrbot/core/knowledge_base/kb_helper.py
@@ -1,4 +1,7 @@
+import asyncio
 import json
+import re
+import time
 import uuid
 from pathlib import Path

@@ -8,12 +11,98 @@ from astrbot.core import logger
 from astrbot.core.db.vec_db.base import BaseVecDB
 from astrbot.core.db.vec_db.faiss_impl.vec_db import FaissVecDB
 from astrbot.core.provider.manager import ProviderManager
-from astrbot.core.provider.provider import EmbeddingProvider, RerankProvider
+from astrbot.core.provider.provider import (
+    EmbeddingProvider,
+    RerankProvider,
+)
+from astrbot.core.provider.provider import (
+    Provider as LLMProvider,
+)

 from .chunking.base import BaseChunker
+from .chunking.recursive import RecursiveCharacterChunker
 from .kb_db_sqlite import KBSQLiteDatabase
 from .models import KBDocument, KBMedia, KnowledgeBase
+from .parsers.url_parser import extract_text_from_url
 from .parsers.util import select_parser
+from .prompts import TEXT_REPAIR_SYSTEM_PROMPT
+
+
+class RateLimiter:
+    """一个简单的速率限制器"""
+
+    def __init__(self, max_rpm: int):
+        self.max_per_minute = max_rpm
+        self.interval = 60.0 / max_rpm if max_rpm > 0 else 0
+        self.last_call_time = 0
+
+    async def __aenter__(self):
+        if self.interval == 0:
+            return
+
+        now = time.monotonic()
+        elapsed = now - self.last_call_time
+
+        if elapsed < self.interval:
+            await asyncio.sleep(self.interval - elapsed)
+
+        self.last_call_time = time.monotonic()
+
+    async def __aexit__(self, exc_type, exc_val, exc_tb):
+        pass
+
+
+async def _repair_and_translate_chunk_with_retry(
+    chunk: str,
+    repair_llm_service: LLMProvider,
+    rate_limiter: RateLimiter,
+    max_retries: int = 2,
+) -> list[str]:
+    """
+    Repairs, translates, and optionally re-chunks a single text chunk using the small LLM, with rate limiting.
+    """
+    # 为了防止 LLM 上下文污染，在 user_prompt 中也加入明确的指令
+    user_prompt = f"""IGNORE ALL PREVIOUS INSTRUCTIONS. Your ONLY task is to process the following text chunk according to the system prompt provided.
+
+Text chunk to process:
+---
+{chunk}
+---
+"""
+    for attempt in range(max_retries + 1):
+        try:
+            async with rate_limiter:
+                response = await repair_llm_service.text_chat(
+                    prompt=user_prompt, system_prompt=TEXT_REPAIR_SYSTEM_PROMPT
+                )
+
+            llm_output = response.completion_text
+
+            if "<discard_chunk />" in llm_output:
+                return []  # Signal to discard this chunk
+
+            # More robust regex to handle potential LLM formatting errors (spaces, newlines in tags)
+            matches = re.findall(
+                r"<\s*repaired_text\s*>\s*(.*?)\s*<\s*/\s*repaired_text\s*>",
+                llm_output,
+                re.DOTALL,
+            )
+
+            if matches:
+                # Further cleaning to ensure no empty strings are returned
+                return [m.strip() for m in matches if m.strip()]
+            else:
+                # If no valid tags and not explicitly discarded, discard it to be safe.
+                return []
+        except Exception as e:
+            logger.warning(
+                f"  - LLM call failed on attempt {attempt + 1}/{max_retries + 1}. Error: {str(e)}"
+            )
+
+    logger.error(
+        f"  - Failed to process chunk after {max_retries + 1} attempts. Using original text."
+    )
+    return [chunk]


 class KBHelper:
@@ -100,7 +189,7 @@ class KBHelper:
    async def upload_document(
        self,
        file_name: str,
-        file_content: bytes,
+        file_content: bytes | None,
        file_type: str,
        chunk_size: int = 512,
        chunk_overlap: int = 50,
@@ -108,6 +197,7 @@ class KBHelper:
        tasks_limit: int = 3,
        max_retries: int = 3,
        progress_callback=None,
+        pre_chunked_text: list[str] | None = None,
    ) -> KBDocument:
        """上传并处理文档（带原子性保证和失败清理）

@@ -130,46 +220,63 @@ class KBHelper:
        await self._ensure_vec_db()
        doc_id = str(uuid.uuid4())
        media_paths: list[Path] = []
+        file_size = 0

        # file_path = self.kb_files_dir / f"{doc_id}.{file_type}"
        # async with aiofiles.open(file_path, "wb") as f:
        #     await f.write(file_content)

        try:
-            # 阶段1: 解析文档
-            if progress_callback:
-                await progress_callback("parsing", 0, 100)
-
-            parser = await select_parser(f".{file_type}")
-            parse_result = await parser.parse(file_content, file_name)
-            text_content = parse_result.text
-            media_items = parse_result.media
-
-            if progress_callback:
-                await progress_callback("parsing", 100, 100)
-
-            # 保存媒体文件
+            chunks_text = []
            saved_media = []
-            for media_item in media_items:
-                media = await self._save_media(
-                    doc_id=doc_id,
-                    media_type=media_item.media_type,
-                    file_name=media_item.file_name,
-                    content=media_item.content,
-                    mime_type=media_item.mime_type,
+
+            if pre_chunked_text is not None:
+                # 如果提供了预分块文本，直接使用
+                chunks_text = pre_chunked_text
+                file_size = sum(len(chunk) for chunk in chunks_text)
+                logger.info(f"使用预分块文本进行上传，共 {len(chunks_text)} 个块。")
+            else:
+                # 否则，执行标准的文件解析和分块流程
+                if file_content is None:
+                    raise ValueError(
+                        "当未提供 pre_chunked_text 时，file_content 不能为空。"
+                    )
+
+                file_size = len(file_content)
+
+                # 阶段1: 解析文档
+                if progress_callback:
+                    await progress_callback("parsing", 0, 100)
+
+                parser = await select_parser(f".{file_type}")
+                parse_result = await parser.parse(file_content, file_name)
+                text_content = parse_result.text
+                media_items = parse_result.media
+
+                if progress_callback:
+                    await progress_callback("parsing", 100, 100)
+
+                # 保存媒体文件
+                for media_item in media_items:
+                    media = await self._save_media(
+                        doc_id=doc_id,
+                        media_type=media_item.media_type,
+                        file_name=media_item.file_name,
+                        content=media_item.content,
+                        mime_type=media_item.mime_type,
+                    )
+                    saved_media.append(media)
+                    media_paths.append(Path(media.file_path))
+
+                # 阶段2: 分块
+                if progress_callback:
+                    await progress_callback("chunking", 0, 100)
+
+                chunks_text = await self.chunker.chunk(
+                    text_content,
+                    chunk_size=chunk_size,
+                    chunk_overlap=chunk_overlap,
                )
-                saved_media.append(media)
-                media_paths.append(Path(media.file_path))
-
-            # 阶段2: 分块
-            if progress_callback:
-                await progress_callback("chunking", 0, 100)
-
-            chunks_text = await self.chunker.chunk(
-                text_content,
-                chunk_size=chunk_size,
-                chunk_overlap=chunk_overlap,
-            )
            contents = []
            metadatas = []
            for idx, chunk_text in enumerate(chunks_text):
@@ -205,7 +312,7 @@ class KBHelper:
                kb_id=self.kb.kb_id,
                doc_name=file_name,
                file_type=file_type,
-                file_size=len(file_content),
+                file_size=file_size,
                # file_path=str(file_path),
                file_path="",
                chunk_count=len(chunks_text),
@@ -359,3 +466,177 @@ class KBHelper:
        )

        return media
+
+    async def upload_from_url(
+        self,
+        url: str,
+        chunk_size: int = 512,
+        chunk_overlap: int = 50,
+        batch_size: int = 32,
+        tasks_limit: int = 3,
+        max_retries: int = 3,
+        progress_callback=None,
+        enable_cleaning: bool = False,
+        cleaning_provider_id: str | None = None,
+    ) -> KBDocument:
+        """从 URL 上传并处理文档（带原子性保证和失败清理）
+        Args:
+            url: 要提取内容的网页 URL
+            chunk_size: 文本块大小
+            chunk_overlap: 文本块重叠大小
+            batch_size: 批处理大小
+            tasks_limit: 并发任务限制
+            max_retries: 最大重试次数
+            progress_callback: 进度回调函数，接收参数 (stage, current, total)
+                - stage: 当前阶段 ('extracting', 'cleaning', 'parsing', 'chunking', 'embedding')
+                - current: 当前进度
+                - total: 总数
+        Returns:
+            KBDocument: 上传的文档对象
+        Raises:
+            ValueError: 如果 URL 为空或无法提取内容
+            IOError: 如果网络请求失败
+        """
+        # 获取 Tavily API 密钥
+        config = self.prov_mgr.acm.default_conf
+        tavily_keys = config.get("provider_settings", {}).get(
+            "websearch_tavily_key", []
+        )
+        if not tavily_keys:
+            raise ValueError(
+                "Error: Tavily API key is not configured in provider_settings."
+            )
+
+        # 阶段1: 从 URL 提取内容
+        if progress_callback:
+            await progress_callback("extracting", 0, 100)
+
+        try:
+            text_content = await extract_text_from_url(url, tavily_keys)
+        except Exception as e:
+            logger.error(f"Failed to extract content from URL {url}: {e}")
+            raise OSError(f"Failed to extract content from URL {url}: {e}") from e
+
+        if not text_content:
+            raise ValueError(f"No content extracted from URL: {url}")
+
+        if progress_callback:
+            await progress_callback("extracting", 100, 100)
+
+        # 阶段2: (可选)清洗内容并分块
+        final_chunks = await self._clean_and_rechunk_content(
+            content=text_content,
+            url=url,
+            progress_callback=progress_callback,
+            enable_cleaning=enable_cleaning,
+            cleaning_provider_id=cleaning_provider_id,
+            chunk_size=chunk_size,
+            chunk_overlap=chunk_overlap,
+        )
+
+        if enable_cleaning and not final_chunks:
+            raise ValueError(
+                "内容清洗后未提取到有效文本。请尝试关闭内容清洗功能，或更换更高性能的LLM模型后重试。"
+            )
+
+        # 创建一个虚拟文件名
+        file_name = url.split("/")[-1] or f"document_from_{url}"
+        if not Path(file_name).suffix:
+            file_name += ".url"
+
+        # 复用现有的 upload_document 方法，但传入预分块文本
+        return await self.upload_document(
+            file_name=file_name,
+            file_content=None,
+            file_type="url",  # 使用 'url' 作为特殊文件类型
+            chunk_size=chunk_size,
+            chunk_overlap=chunk_overlap,
+            batch_size=batch_size,
+            tasks_limit=tasks_limit,
+            max_retries=max_retries,
+            progress_callback=progress_callback,
+            pre_chunked_text=final_chunks,
+        )
+
+    async def _clean_and_rechunk_content(
+        self,
+        content: str,
+        url: str,
+        progress_callback=None,
+        enable_cleaning: bool = False,
+        cleaning_provider_id: str | None = None,
+        repair_max_rpm: int = 60,
+        chunk_size: int = 512,
+        chunk_overlap: int = 50,
+    ) -> list[str]:
+        """
+        对从 URL 获取的内容进行清洗、修复、翻译和重新分块。
+        """
+        if not enable_cleaning:
+            # 如果不启用清洗，则使用从前端传递的参数进行分块
+            logger.info(
+                f"内容清洗未启用，使用指定参数进行分块: chunk_size={chunk_size}, chunk_overlap={chunk_overlap}"
+            )
+            return await self.chunker.chunk(
+                content, chunk_size=chunk_size, chunk_overlap=chunk_overlap
+            )
+
+        if not cleaning_provider_id:
+            logger.warning(
+                "启用了内容清洗，但未提供 cleaning_provider_id，跳过清洗并使用默认分块。"
+            )
+            return await self.chunker.chunk(content)
+
+        if progress_callback:
+            await progress_callback("cleaning", 0, 100)
+
+        try:
+            # 获取指定的 LLM Provider
+            llm_provider = await self.prov_mgr.get_provider_by_id(cleaning_provider_id)
+            if not llm_provider or not isinstance(llm_provider, LLMProvider):
+                raise ValueError(
+                    f"无法找到 ID 为 {cleaning_provider_id} 的 LLM Provider 或类型不正确"
+                )
+
+            # 初步分块
+            # 优化分隔符，优先按段落分割，以获得更高质量的文本块
+            text_splitter = RecursiveCharacterChunker(
+                chunk_size=chunk_size,
+                chunk_overlap=chunk_overlap,
+                separators=["\n\n", "\n", " "],  # 优先使用段落分隔符
+            )
+            initial_chunks = await text_splitter.chunk(content)
+            logger.info(f"初步分块完成，生成 {len(initial_chunks)} 个块用于修复。")
+
+            # 并发处理所有块
+            rate_limiter = RateLimiter(repair_max_rpm)
+            tasks = [
+                _repair_and_translate_chunk_with_retry(
+                    chunk, llm_provider, rate_limiter
+                )
+                for chunk in initial_chunks
+            ]
+
+            repaired_results = await asyncio.gather(*tasks, return_exceptions=True)
+
+            final_chunks = []
+            for i, result in enumerate(repaired_results):
+                if isinstance(result, Exception):
+                    logger.warning(f"块 {i} 处理异常: {str(result)}. 回退到原始块。")
+                    final_chunks.append(initial_chunks[i])
+                elif isinstance(result, list):
+                    final_chunks.extend(result)
+
+            logger.info(
+                f"文本修复完成: {len(initial_chunks)} 个原始块 -> {len(final_chunks)} 个最终块。"
+            )
+
+            if progress_callback:
+                await progress_callback("cleaning", 100, 100)
+
+            return final_chunks
+
+        except Exception as e:
+            logger.error(f"使用 Provider '{cleaning_provider_id}' 清洗内容失败: {e}")
+            # 清洗失败，返回默认分块结果，保证流程不中断
+            return await self.chunker.chunk(content)
--- a/astrbot/core/knowledge_base/kb_mgr.py
+++ b/astrbot/core/knowledge_base/kb_mgr.py
@@ -8,7 +8,7 @@ from astrbot.core.provider.manager import ProviderManager
 from .chunking.recursive import RecursiveCharacterChunker
 from .kb_db_sqlite import KBSQLiteDatabase
 from .kb_helper import KBHelper
-from .models import KnowledgeBase
+from .models import KBDocument, KnowledgeBase
 from .retrieval.manager import RetrievalManager, RetrievalResult
 from .retrieval.rank_fusion import RankFusion
 from .retrieval.sparse_retriever import SparseRetriever
@@ -284,3 +284,47 @@ class KnowledgeBaseManager:
                await self.kb_db.close()
            except Exception as e:
                logger.error(f"关闭知识库元数据数据库失败: {e}")
+
+    async def upload_from_url(
+        self,
+        kb_id: str,
+        url: str,
+        chunk_size: int = 512,
+        chunk_overlap: int = 50,
+        batch_size: int = 32,
+        tasks_limit: int = 3,
+        max_retries: int = 3,
+        progress_callback=None,
+    ) -> KBDocument:
+        """从 URL 上传文档到指定的知识库
+
+        Args:
+            kb_id: 知识库 ID
+            url: 要提取内容的网页 URL
+            chunk_size: 文本块大小
+            chunk_overlap: 文本块重叠大小
+            batch_size: 批处理大小
+            tasks_limit: 并发任务限制
+            max_retries: 最大重试次数
+            progress_callback: 进度回调函数
+
+        Returns:
+            KBDocument: 上传的文档对象
+
+        Raises:
+            ValueError: 如果知识库不存在或 URL 为空
+            IOError: 如果网络请求失败
+        """
+        kb_helper = await self.get_kb(kb_id)
+        if not kb_helper:
+            raise ValueError(f"Knowledge base with id {kb_id} not found.")
+
+        return await kb_helper.upload_from_url(
+            url=url,
+            chunk_size=chunk_size,
+            chunk_overlap=chunk_overlap,
+            batch_size=batch_size,
+            tasks_limit=tasks_limit,
+            max_retries=max_retries,
+            progress_callback=progress_callback,
+        )
--- a/astrbot/core/knowledge_base/parsers/url_parser.py
+++ b/astrbot/core/knowledge_base/parsers/url_parser.py
@@ -0,0 +1,103 @@
+import asyncio
+
+import aiohttp
+
+
+class URLExtractor:
+    """URL 内容提取器，封装了 Tavily API 调用和密钥管理"""
+
+    def __init__(self, tavily_keys: list[str]):
+        """
+        初始化 URL 提取器
+
+        Args:
+            tavily_keys: Tavily API 密钥列表
+        """
+        if not tavily_keys:
+            raise ValueError("Error: Tavily API keys are not configured.")
+
+        self.tavily_keys = tavily_keys
+        self.tavily_key_index = 0
+        self.tavily_key_lock = asyncio.Lock()
+
+    async def _get_tavily_key(self) -> str:
+        """并发安全的从列表中获取并轮换Tavily API密钥。"""
+        async with self.tavily_key_lock:
+            key = self.tavily_keys[self.tavily_key_index]
+            self.tavily_key_index = (self.tavily_key_index + 1) % len(self.tavily_keys)
+            return key
+
+    async def extract_text_from_url(self, url: str) -> str:
+        """
+        使用 Tavily API 从 URL 提取主要文本内容。
+        这是 web_searcher 插件中 tavily_extract_web_page 方法的简化版本，
+        专门为知识库模块设计，不依赖 AstrMessageEvent。
+
+        Args:
+            url: 要提取内容的网页 URL
+
+        Returns:
+            提取的文本内容
+
+        Raises:
+            ValueError: 如果 URL 为空或 API 密钥未配置
+            IOError: 如果请求失败或返回错误
+        """
+        if not url:
+            raise ValueError("Error: url must be a non-empty string.")
+
+        tavily_key = await self._get_tavily_key()
+        api_url = "https://api.tavily.com/extract"
+        headers = {
+            "Authorization": f"Bearer {tavily_key}",
+            "Content-Type": "application/json",
+        }
+
+        payload = {
+            "urls": [url],
+            "extract_depth": "basic",  # 使用基础提取深度
+        }
+
+        try:
+            async with aiohttp.ClientSession(trust_env=True) as session:
+                async with session.post(
+                    api_url,
+                    json=payload,
+                    headers=headers,
+                    timeout=30.0,  # 增加超时时间，因为内容提取可能需要更长时间
+                ) as response:
+                    if response.status != 200:
+                        reason = await response.text()
+                        raise OSError(
+                            f"Tavily web extraction failed: {reason}, status: {response.status}"
+                        )
+
+                    data = await response.json()
+                    results = data.get("results", [])
+
+                    if not results:
+                        raise ValueError(f"No content extracted from URL: {url}")
+
+                    # 返回第一个结果的内容
+                    return results[0].get("raw_content", "")
+
+        except aiohttp.ClientError as e:
+            raise OSError(f"Failed to fetch URL {url}: {e}") from e
+        except Exception as e:
+            raise OSError(f"Failed to extract content from URL {url}: {e}") from e
+
+
+# 为了向后兼容，提供一个简单的函数接口
+async def extract_text_from_url(url: str, tavily_keys: list[str]) -> str:
+    """
+    简单的函数接口，用于从 URL 提取文本内容
+
+    Args:
+        url: 要提取内容的网页 URL
+        tavily_keys: Tavily API 密钥列表
+
+    Returns:
+        提取的文本内容
+    """
+    extractor = URLExtractor(tavily_keys)
+    return await extractor.extract_text_from_url(url)
--- a/astrbot/core/knowledge_base/prompts.py
+++ b/astrbot/core/knowledge_base/prompts.py
@@ -0,0 +1,65 @@
+TEXT_REPAIR_SYSTEM_PROMPT = """You are a meticulous digital archivist. Your mission is to reconstruct a clean, readable article from raw, noisy text chunks.
+
+**Core Task:**
+1.  **Analyze:** Examine the text chunk to separate "signal" (substantive information) from "noise" (UI elements, ads, navigation, footers).
+2.  **Process:** Clean and repair the signal. **Do not translate it.** Keep the original language.
+
+**Crucial Rules:**
+- **NEVER discard a chunk if it contains ANY valuable information.** Your primary duty is to salvage content.
+- **If a chunk contains multiple distinct topics, split them.** Enclose each topic in its own `<repaired_text>` tag.
+- Your output MUST be ONLY `<repaired_text>...</repaired_text>` tags or a single `<discard_chunk />` tag.
+
+---
+**Example 1: Chunk with Noise and Signal**
+
+*Input Chunk:*
+"Home | About | Products | **The Llama is a domesticated South American camelid.** | © 2025 ACME Corp."
+
+*Your Thought Process:*
+1.  "Home | About | Products..." and "© 2025 ACME Corp." are noise.
+2.  "The Llama is a domesticated..." is the signal.
+3.  I must extract the signal and wrap it.
+
+*Your Output:*
+<repaired_text>
+The Llama is a domesticated South American camelid.
+</repaired_text>
+
+---
+**Example 2: Chunk with ONLY Noise**
+
+*Input Chunk:*
+"Next Page > | Subscribe to our newsletter | Follow us on X"
+
+*Your Thought Process:*
+1.  This entire chunk is noise. There is no signal.
+2.  I must discard this.
+
+*Your Output:*
+<discard_chunk />
+
+---
+**Example 3: Chunk with Multiple Topics (Requires Splitting)**
+
+*Input Chunk:*
+"## Chapter 1: The Sun
+The Sun is the star at the center of the Solar System.
+
+## Chapter 2: The Moon
+The Moon is Earth's only natural satellite."
+
+*Your Thought Process:*
+1.  This chunk contains two distinct topics.
+2.  I must process them separately to maintain semantic integrity.
+3.  I will create two `<repaired_text>` blocks.
+
+*Your Output:*
+<repaired_text>
+## Chapter 1: The Sun
+The Sun is the star at the center of the Solar System.
+</repaired_text>
+<repaired_text>
+## Chapter 2: The Moon
+The Moon is Earth's only natural satellite.
+</repaired_text>
+"""
--- a/astrbot/core/memory/DESIGN.excalidraw
+++ b/astrbot/core/memory/DESIGN.excalidraw
@@ -0,0 +1,822 @@
+{
+  "type": "excalidraw",
+  "version": 2,
+  "source": "https://marketplace.visualstudio.com/items?itemName=pomdtr.excalidraw-editor",
+  "elements": [
+    {
+      "id": "l6cYurMvF69IM4Kc33Qou",
+      "type": "rectangle",
+      "x": 173.140625,
+      "y": -29.0234375,
+      "width": 92.95703125,
+      "height": 77.109375,
+      "angle": 0,
+      "strokeColor": "#1e1e1e",
+      "backgroundColor": "transparent",
+      "fillStyle": "solid",
+      "strokeWidth": 2,
+      "strokeStyle": "solid",
+      "roughness": 1,
+      "opacity": 100,
+      "groupIds": [],
+      "frameId": null,
+      "index": "a0",
+      "roundness": {
+        "type": 3
+      },
+      "seed": 1409469537,
+      "version": 91,
+      "versionNonce": 307958671,
+      "isDeleted": false,
+      "boundElements": [],
+      "updated": 1763703733605,
+      "link": null,
+      "locked": false
+    },
+    {
+      "id": "1ZvS6t8U6ihUjNU0dakgl",
+      "type": "arrow",
+      "x": 409.30859375,
+      "y": 9.6875,
+      "width": 118.2734375,
+      "height": 1.9609375,
+      "angle": 0,
+      "strokeColor": "#1e1e1e",
+      "backgroundColor": "transparent",
+      "fillStyle": "solid",
+      "strokeWidth": 2,
+      "strokeStyle": "solid",
+      "roughness": 1,
+      "opacity": 100,
+      "groupIds": [],
+      "frameId": null,
+      "index": "a1",
+      "roundness": {
+        "type": 2
+      },
+      "seed": 326508865,
+      "version": 120,
+      "versionNonce": 199367023,
+      "isDeleted": false,
+      "boundElements": null,
+      "updated": 1763703733605,
+      "link": null,
+      "locked": false,
+      "points": [
+        [
+          0,
+          0
+        ],
+        [
+          -118.2734375,
+          -1.9609375
+        ]
+      ],
+      "lastCommittedPoint": null,
+      "startBinding": null,
+      "endBinding": null,
+      "startArrowhead": null,
+      "endArrowhead": "arrow",
+      "elbowed": false
+    },
+    {
+      "id": "tfdUGiJdcMoOHGfqFHXK6",
+      "type": "text",
+      "x": 153.46875,
+      "y": -70.9765625,
+      "width": 136.4598846435547,
+      "height": 25,
+      "angle": 0,
+      "strokeColor": "#1e1e1e",
+      "backgroundColor": "transparent",
+      "fillStyle": "solid",
+      "strokeWidth": 2,
+      "strokeStyle": "solid",
+      "roughness": 1,
+      "opacity": 100,
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--- a/astrbot/core/memory/_README.md
+++ b/astrbot/core/memory/_README.md
@@ -0,0 +1,76 @@
+## Decay Score
+
+记忆衰减分数定义为：
+
+\[
+\text{decay\_score}
+= \alpha \cdot e^{-\lambda \cdot \Delta t \cdot \beta}
+
+ (1-\alpha)\cdot (1 - e^{-\gamma \cdot c})
+\]
+
+其中：
+
+ \(\Delta t\)：自上次检索以来经过的时间（天），由 `last_retrieval_at` 计算；
+ \(c\)：检索次数，对应字段 `retrieval_count`；
+ \(\alpha\)：控制时间衰减和检索次数影响的权重；
+ \(\gamma\)：控制检索次数影响的速率；
+ \(\lambda\)：控制时间衰减的速率；
+ \(\beta\)：时间衰减调节因子；
+
+\[
+\beta = \frac{1}{1 + a \cdot c}
+\]
+
+ \(a\)：控制检索次数对时间衰减影响的权重。
+
+## ADD MEMORY
+
+ LLM 通过 `astr_add_memory` 工具调用，传入记忆内容和记忆类型。
+ 生成 `mem_id = uuid4()`。
+ 从上下文中获取 `owner_id = unified_message_origin`。
+
+步骤：
+
+1. 使用 VecDB 以新记忆内容为 query，检索前 20 条相似记忆。
+2. 从中取相似度最高的前 5 条：
+   + 若相似度超过“合并阈值”（如 `sim >= merge_threshold`）：
+     + 将该条记忆视为同一记忆，使用 LLM 将旧内容与新内容合并；
+     + 在同一个 `mem_id` 上更新 MemoryDB 和 VecDB（UPDATE，而非新建）。
+   + 否则：
+     + 作为全新的记忆插入：
+       + 写入 VecDB（metadata 中包含 `mem_id`, `owner_id`）；
+       + 写入 MemoryDB 的 `memory_chunks` 表，初始化：
+         + `created_at = now`
+         + `last_retrieval_at = now`
+         + `retrieval_count = 1` 等。
+3. 对 VecDB 返回的前 20 条记忆，如果相似度高于某个“赫布阈值”（`hebb_threshold`），则：
+   + `retrieval_count += 1`
+   + `last_retrieval_at = now`
+
+这一步体现了赫布学习：与新记忆共同被激活的旧记忆会获得一次强化。
+
+## QUERY MEMORY (STATIC)
+
+ LLM 通过 `astr_query_memory` 工具调用，无参数。
+
+步骤：
+
+1. 从 MemoryDB 的 `memory_chunks` 表中查询当前用户所有活跃记忆：
+   + `SELECT * FROM memory_chunks WHERE owner_id = ? AND is_active = 1`
+2. 对每条记忆，根据 `last_retrieval_at` 和 `retrieval_count` 计算对应的 `decay_score`。
+3. 按 `decay_score` 从高到低排序，返回前 `top_k` 条记忆内容给 LLM。
+4. 对返回的这 `top_k` 条记忆：
+   + `retrieval_count += 1`
+   + `last_retrieval_at = now`
+
+## QUERY MEMORY (DYNAMIC)（暂不实现）
+
+ LLM 提供查询内容作为语义 query。
+ 使用 VecDB 检索与该 query 最相似的前 `N` 条记忆（`N > top_k`）。
+ 根据 `mem_id` 从 `memory_chunks` 中加载对应记录。
+ 对这批候选记忆计算：
+  + 语义相似度（来自 VecDB）
+  + `decay_score`
+  + 最终排序分数（例如 `w1 * sim + w2 * decay_score`）
+ 按最终排序分数从高到低返回前 `top_k` 条记忆内容，并更新它们的 `retrieval_count` 和 `last_retrieval_at`。
--- a/astrbot/core/memory/entities.py
+++ b/astrbot/core/memory/entities.py
@@ -0,0 +1,63 @@
+import uuid
+from datetime import datetime, timezone
+
+import numpy as np
+from sqlmodel import Field, MetaData, SQLModel
+
+MEMORY_TYPE_IMPORTANCE = {"persona": 1.3, "fact": 1.0, "ephemeral": 0.8}
+
+
+class BaseMemoryModel(SQLModel, table=False):
+    metadata = MetaData()
+
+
+class MemoryChunk(BaseMemoryModel, table=True):
+    """A chunk of memory stored in the system."""
+
+    __tablename__ = "memory_chunks"  # type: ignore
+
+    id: int | None = Field(
+        primary_key=True,
+        sa_column_kwargs={"autoincrement": True},
+        default=None,
+    )
+    mem_id: str = Field(
+        max_length=36,
+        nullable=False,
+        unique=True,
+        default_factory=lambda: str(uuid.uuid4()),
+        index=True,
+    )
+    fact: str = Field(nullable=False)
+    """The factual content of the memory chunk."""
+    owner_id: str = Field(max_length=255, nullable=False, index=True)
+    """The identifier of the owner (user) of the memory chunk."""
+    created_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
+    """The timestamp when the memory chunk was created."""
+    last_retrieval_at: datetime = Field(
+        default_factory=lambda: datetime.now(timezone.utc)
+    )
+    """The timestamp when the memory chunk was last retrieved."""
+    retrieval_count: int = Field(default=1, nullable=False)
+    """The number of times the memory chunk has been retrieved."""
+    memory_type: str = Field(max_length=20, nullable=False, default="fact")
+    """The type of memory (e.g., 'persona', 'fact', 'ephemeral')."""
+    is_active: bool = Field(default=True, nullable=False)
+    """Whether the memory chunk is active."""
+
+    def compute_decay_score(self, current_time: datetime) -> float:
+        """Compute the decay score of the memory chunk based on time and retrievals."""
+        # Constants for the decay formula
+        alpha = 0.5
+        gamma = 0.1
+        lambda_ = 0.05
+        a = 0.1
+
+        # Calculate delta_t in days
+        delta_t = (current_time - self.last_retrieval_at).total_seconds() / 86400
+        c = self.retrieval_count
+        beta = 1 / (1 + a * c)
+        decay_score = alpha * np.exp(-lambda_ * delta_t * beta) + (1 - alpha) * (
+            1 - np.exp(-gamma * c)
+        )
+        return decay_score * MEMORY_TYPE_IMPORTANCE.get(self.memory_type, 1.0)
--- a/astrbot/core/memory/mem_db_sqlite.py
+++ b/astrbot/core/memory/mem_db_sqlite.py
@@ -0,0 +1,174 @@
+from contextlib import asynccontextmanager
+from datetime import datetime, timezone
+from pathlib import Path
+
+from sqlalchemy import select, text, update
+from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker, create_async_engine
+from sqlmodel import col
+
+from astrbot.core import logger
+
+from .entities import BaseMemoryModel, MemoryChunk
+
+
+class MemoryDatabase:
+    def __init__(self, db_path: str = "data/astr_memory/memory.db") -> None:
+        """Initialize memory database
+
+        Args:
+            db_path: Database file path, default is data/astr_memory/memory.db
+
+        """
+        self.db_path = db_path
+        self.DATABASE_URL = f"sqlite+aiosqlite:///{db_path}"
+        self.inited = False
+
+        # Ensure directory exists
+        Path(db_path).parent.mkdir(parents=True, exist_ok=True)
+
+        # Create async engine
+        self.engine = create_async_engine(
+            self.DATABASE_URL,
+            echo=False,
+            pool_pre_ping=True,
+            pool_recycle=3600,
+        )
+
+        # Create session factory
+        self.async_session = async_sessionmaker(
+            self.engine,
+            class_=AsyncSession,
+            expire_on_commit=False,
+        )
+
+    @asynccontextmanager
+    async def get_db(self):
+        """Get database session
+
+        Usage:
+            async with mem_db.get_db() as session:
+                # Perform database operations
+                result = await session.execute(stmt)
+        """
+        async with self.async_session() as session:
+            yield session
+
+    async def initialize(self) -> None:
+        """Initialize database, create tables and configure SQLite parameters"""
+        async with self.engine.begin() as conn:
+            # Create all memory related tables
+            await conn.run_sync(BaseMemoryModel.metadata.create_all)
+
+            # Configure SQLite performance optimization parameters
+            await conn.execute(text("PRAGMA journal_mode=WAL"))
+            await conn.execute(text("PRAGMA synchronous=NORMAL"))
+            await conn.execute(text("PRAGMA cache_size=20000"))
+            await conn.execute(text("PRAGMA temp_store=MEMORY"))
+            await conn.execute(text("PRAGMA mmap_size=134217728"))
+            await conn.execute(text("PRAGMA optimize"))
+            await conn.commit()
+
+        await self._create_indexes()
+        self.inited = True
+        logger.info(f"Memory database initialized: {self.db_path}")
+
+    async def _create_indexes(self) -> None:
+        """Create indexes for memory_chunks table"""
+        async with self.get_db() as session:
+            async with session.begin():
+                # Create memory chunks table indexes
+                await session.execute(
+                    text(
+                        "CREATE INDEX IF NOT EXISTS idx_mem_mem_id "
+                        "ON memory_chunks(mem_id)",
+                    ),
+                )
+                await session.execute(
+                    text(
+                        "CREATE INDEX IF NOT EXISTS idx_mem_owner_id "
+                        "ON memory_chunks(owner_id)",
+                    ),
+                )
+                await session.execute(
+                    text(
+                        "CREATE INDEX IF NOT EXISTS idx_mem_owner_active "
+                        "ON memory_chunks(owner_id, is_active)",
+                    ),
+                )
+                await session.commit()
+
+    async def close(self) -> None:
+        """Close database connection"""
+        await self.engine.dispose()
+        logger.info(f"Memory database closed: {self.db_path}")
+
+    async def insert_memory(self, memory: MemoryChunk) -> MemoryChunk:
+        """Insert a new memory chunk"""
+        async with self.get_db() as session:
+            session.add(memory)
+            await session.commit()
+            await session.refresh(memory)
+            return memory
+
+    async def get_memory_by_id(self, mem_id: str) -> MemoryChunk | None:
+        """Get memory chunk by mem_id"""
+        async with self.get_db() as session:
+            stmt = select(MemoryChunk).where(col(MemoryChunk.mem_id) == mem_id)
+            result = await session.execute(stmt)
+            return result.scalar_one_or_none()
+
+    async def update_memory(self, memory: MemoryChunk) -> MemoryChunk:
+        """Update an existing memory chunk"""
+        async with self.get_db() as session:
+            session.add(memory)
+            await session.commit()
+            await session.refresh(memory)
+            return memory
+
+    async def get_active_memories(self, owner_id: str) -> list[MemoryChunk]:
+        """Get all active memories for a user"""
+        async with self.get_db() as session:
+            stmt = select(MemoryChunk).where(
+                col(MemoryChunk.owner_id) == owner_id,
+                col(MemoryChunk.is_active) == True,  # noqa: E712
+            )
+            result = await session.execute(stmt)
+            return list(result.scalars().all())
+
+    async def update_retrieval_stats(
+        self,
+        mem_ids: list[str],
+        current_time: datetime | None = None,
+    ) -> None:
+        """Update retrieval statistics for multiple memories"""
+        if not mem_ids:
+            return
+
+        if current_time is None:
+            current_time = datetime.now(timezone.utc)
+
+        async with self.get_db() as session:
+            async with session.begin():
+                stmt = (
+                    update(MemoryChunk)
+                    .where(col(MemoryChunk.mem_id).in_(mem_ids))
+                    .values(
+                        retrieval_count=MemoryChunk.retrieval_count + 1,
+                        last_retrieval_at=current_time,
+                    )
+                )
+                await session.execute(stmt)
+                await session.commit()
+
+    async def deactivate_memory(self, mem_id: str) -> bool:
+        """Deactivate a memory chunk"""
+        async with self.get_db() as session:
+            async with session.begin():
+                stmt = (
+                    update(MemoryChunk)
+                    .where(col(MemoryChunk.mem_id) == mem_id)
+                    .values(is_active=False)
+                )
+                result = await session.execute(stmt)
+                await session.commit()
+                return result.rowcount > 0 if result.rowcount else False  # type: ignore
--- a/astrbot/core/memory/memory_manager.py
+++ b/astrbot/core/memory/memory_manager.py
@@ -0,0 +1,281 @@
+import json
+import uuid
+from datetime import datetime, timezone
+from pathlib import Path
+
+from astrbot.core import logger
+from astrbot.core.db.vec_db.faiss_impl import FaissVecDB
+from astrbot.core.provider.provider import EmbeddingProvider
+from astrbot.core.provider.provider import Provider as LLMProvider
+
+from .entities import MemoryChunk
+from .mem_db_sqlite import MemoryDatabase
+
+MERGE_THRESHOLD = 0.85
+"""Similarity threshold for merging memories"""
+HEBB_THRESHOLD = 0.70
+"""Similarity threshold for Hebbian learning reinforcement"""
+MERGE_SYSTEM_PROMPT = """You are a memory consolidation assistant. Your task is to merge two related memory entries into a single, comprehensive memory.
+
+Input format:
+- Old memory: [existing memory content]
+- New memory: [new memory content to be integrated]
+
+Your output should be a single, concise memory that combines the essential information from both entries. Preserve specific details, update outdated information, and eliminate redundancy. Output only the merged memory content without any explanations or meta-commentary."""
+
+
+class MemoryManager:
+    """Manager for user long-term memory storage and retrieval"""
+
+    def __init__(self, memory_root_dir: str = "data/astr_memory"):
+        self.memory_root_dir = Path(memory_root_dir)
+        self.memory_root_dir.mkdir(parents=True, exist_ok=True)
+
+        self.mem_db: MemoryDatabase | None = None
+        self.vec_db: FaissVecDB | None = None
+
+        self._initialized = False
+
+    async def initialize(
+        self,
+        embedding_provider: EmbeddingProvider,
+        merge_llm_provider: LLMProvider,
+    ):
+        """Initialize memory database and vector database"""
+        # Initialize MemoryDB
+        db_path = self.memory_root_dir / "memory.db"
+        self.mem_db = MemoryDatabase(db_path.as_posix())
+        await self.mem_db.initialize()
+
+        self.embedding_provider = embedding_provider
+        self.merge_llm_provider = merge_llm_provider
+
+        # Initialize VecDB
+        doc_store_path = self.memory_root_dir / "doc.db"
+        index_store_path = self.memory_root_dir / "index.faiss"
+        self.vec_db = FaissVecDB(
+            doc_store_path=doc_store_path.as_posix(),
+            index_store_path=index_store_path.as_posix(),
+            embedding_provider=self.embedding_provider,
+        )
+        await self.vec_db.initialize()
+
+        logger.info("Memory manager initialized")
+        self._initialized = True
+
+    async def terminate(self):
+        """Close all database connections"""
+        if self.vec_db:
+            await self.vec_db.close()
+        if self.mem_db:
+            await self.mem_db.close()
+
+    async def add_memory(
+        self,
+        fact: str,
+        owner_id: str,
+        memory_type: str = "fact",
+    ) -> MemoryChunk:
+        """Add a new memory with similarity check and merge logic
+
+        Implements the ADD MEMORY workflow from _README.md:
+        1. Search for similar memories using VecDB
+        2. If similarity >= merge_threshold, merge with existing memory
+        3. Otherwise, create new memory
+        4. Apply Hebbian learning to similar memories (similarity >= hebb_threshold)
+
+        Args:
+            fact: Memory content
+            owner_id: User identifier
+            memory_type: Memory type ('persona', 'fact', 'ephemeral')
+
+        Returns:
+            The created or updated MemoryChunk
+
+        """
+        if not self.vec_db or not self.mem_db:
+            raise RuntimeError("Memory manager not initialized")
+
+        current_time = datetime.now(timezone.utc)
+
+        # Step 1: Search for similar memories
+        similar_results = await self.vec_db.retrieve(
+            query=fact,
+            k=20,
+            fetch_k=50,
+            metadata_filters={"owner_id": owner_id},
+        )
+
+        # Step 2: Check if we should merge with existing memories (top 3 similar ones)
+        merge_candidates = [
+            r for r in similar_results[:3] if r.similarity >= MERGE_THRESHOLD
+        ]
+
+        if merge_candidates:
+            # Get all candidate memories from database
+            candidate_memories: list[tuple[str, MemoryChunk]] = []
+            for candidate in merge_candidates:
+                mem_id = json.loads(candidate.data["metadata"])["mem_id"]
+                memory = await self.mem_db.get_memory_by_id(mem_id)
+                if memory:
+                    candidate_memories.append((mem_id, memory))
+
+            if candidate_memories:
+                # Use the most similar memory as the base
+                base_mem_id, base_memory = candidate_memories[0]
+
+                # Collect all facts to merge (existing candidates + new fact)
+                all_facts = [mem.fact for _, mem in candidate_memories] + [fact]
+                merged_fact = await self._merge_multiple_memories(all_facts)
+
+                # Update the base memory
+                base_memory.fact = merged_fact
+                base_memory.last_retrieval_at = current_time
+                base_memory.retrieval_count += 1
+                updated_memory = await self.mem_db.update_memory(base_memory)
+
+                # Update VecDB for base memory
+                await self.vec_db.delete(base_mem_id)
+                await self.vec_db.insert(
+                    content=merged_fact,
+                    metadata={
+                        "mem_id": base_mem_id,
+                        "owner_id": owner_id,
+                        "memory_type": memory_type,
+                    },
+                    id=base_mem_id,
+                )
+
+                # Deactivate and remove other merged memories
+                for mem_id, _ in candidate_memories[1:]:
+                    await self.mem_db.deactivate_memory(mem_id)
+                    await self.vec_db.delete(mem_id)
+
+                logger.info(
+                    f"Merged {len(candidate_memories)} memories into {base_mem_id} for user {owner_id}"
+                )
+                return updated_memory
+
+        # Step 3: Create new memory
+        mem_id = str(uuid.uuid4())
+        new_memory = MemoryChunk(
+            mem_id=mem_id,
+            fact=fact,
+            owner_id=owner_id,
+            memory_type=memory_type,
+            created_at=current_time,
+            last_retrieval_at=current_time,
+            retrieval_count=1,
+            is_active=True,
+        )
+
+        # Insert into MemoryDB
+        created_memory = await self.mem_db.insert_memory(new_memory)
+
+        # Insert into VecDB
+        await self.vec_db.insert(
+            content=fact,
+            metadata={
+                "mem_id": mem_id,
+                "owner_id": owner_id,
+                "memory_type": memory_type,
+            },
+            id=mem_id,
+        )
+
+        # Step 4: Apply Hebbian learning to similar memories
+        hebb_mem_ids = [
+            json.loads(r.data["metadata"])["mem_id"]
+            for r in similar_results
+            if r.similarity >= HEBB_THRESHOLD
+        ]
+        if hebb_mem_ids:
+            await self.mem_db.update_retrieval_stats(hebb_mem_ids, current_time)
+            logger.debug(
+                f"Applied Hebbian learning to {len(hebb_mem_ids)} memories for user {owner_id}",
+            )
+
+        logger.info(f"Created new memory {mem_id} for user {owner_id}")
+        return created_memory
+
+    async def query_memory(
+        self,
+        owner_id: str,
+        top_k: int = 5,
+    ) -> list[MemoryChunk]:
+        """Query user's memories using static retrieval with decay score ranking
+
+        Implements the QUERY MEMORY (STATIC) workflow from _README.md:
+        1. Get all active memories for user from MemoryDB
+        2. Compute decay_score for each memory
+        3. Sort by decay_score and return top_k
+        4. Update retrieval statistics for returned memories
+
+        Args:
+            owner_id: User identifier
+            top_k: Number of memories to return
+
+        Returns:
+            List of top_k MemoryChunk sorted by decay score
+        """
+        if not self.mem_db:
+            raise RuntimeError("Memory manager not initialized")
+
+        current_time = datetime.now(timezone.utc)
+
+        # Step 1: Get all active memories for user
+        all_memories = await self.mem_db.get_active_memories(owner_id)
+
+        if not all_memories:
+            return []
+
+        # Step 2-3: Compute decay scores and sort
+        memories_with_scores = [
+            (mem, mem.compute_decay_score(current_time)) for mem in all_memories
+        ]
+        memories_with_scores.sort(key=lambda x: x[1], reverse=True)
+
+        # Get top_k memories
+        top_memories = [mem for mem, _ in memories_with_scores[:top_k]]
+
+        # Step 4: Update retrieval statistics
+        mem_ids = [mem.mem_id for mem in top_memories]
+        await self.mem_db.update_retrieval_stats(mem_ids, current_time)
+
+        logger.debug(f"Retrieved {len(top_memories)} memories for user {owner_id}")
+        return top_memories
+
+    async def _merge_multiple_memories(self, facts: list[str]) -> str:
+        """Merge multiple memory facts using LLM in one call
+
+        Args:
+            facts: List of memory facts to merge
+
+        Returns:
+            Merged memory content
+        """
+        if not self.merge_llm_provider:
+            return " ".join(facts)
+
+        if len(facts) == 1:
+            return facts[0]
+
+        try:
+            # Format all facts as a numbered list
+            facts_list = "\n".join(f"{i + 1}. {fact}" for i, fact in enumerate(facts))
+            user_prompt = (
+                f"Please merge the following {len(facts)} related memory entries "
+                "into a single, comprehensive memory:"
+                f"\n{facts_list}\n\nOutput only the merged memory content."
+            )
+            response = await self.merge_llm_provider.text_chat(
+                prompt=user_prompt,
+                system_prompt=MERGE_SYSTEM_PROMPT,
+            )
+
+            merged_content = response.completion_text.strip()
+            return merged_content if merged_content else " ".join(facts)
+
+        except Exception as e:
+            logger.warning(f"Failed to merge memories with LLM: {e}, using fallback")
+            return " ".join(facts)
--- a/astrbot/core/memory/tools.py
+++ b/astrbot/core/memory/tools.py
@@ -0,0 +1,156 @@
+from pydantic import Field
+from pydantic.dataclasses import dataclass
+
+from astrbot.core.agent.tool import FunctionTool, ToolExecResult
+from astrbot.core.astr_agent_context import AstrAgentContext, ContextWrapper
+
+
+@dataclass
+class AddMemory(FunctionTool[AstrAgentContext]):
+    """Tool for adding memories to user's long-term memory storage"""
+
+    name: str = "astr_add_memory"
+    description: str = (
+        "Add a new memory to the user's long-term memory storage. "
+        "Use this tool only when the user explicitly asks you to remember something, "
+        "or when they share stable preferences, identity, or long-term goals that will be useful in future interactions."
+    )
+    parameters: dict = Field(
+        default_factory=lambda: {
+            "type": "object",
+            "properties": {
+                "fact": {
+                    "type": "string",
+                    "description": (
+                        "The concrete memory content to store, such as a user preference, "
+                        "identity detail, long-term goal, or stable profile fact."
+                    ),
+                },
+                "memory_type": {
+                    "type": "string",
+                    "enum": ["persona", "fact", "ephemeral"],
+                    "description": (
+                        "The relative importance of this memory. "
+                        "Use 'persona' for core identity or highly impactful information, "
+                        "'fact' for normal long-term preferences, "
+                        "and 'ephemeral' for minor or tentative facts."
+                    ),
+                },
+            },
+            "required": ["fact", "memory_type"],
+        }
+    )
+
+    async def call(
+        self, context: ContextWrapper[AstrAgentContext], **kwargs
+    ) -> ToolExecResult:
+        """Add a memory to long-term storage
+
+        Args:
+            context: Agent context
+            **kwargs: Must contain 'fact' and 'memory_type'
+
+        Returns:
+            ToolExecResult with success message
+
+        """
+        mm = context.context.context.memory_manager
+        fact = kwargs.get("fact")
+        memory_type = kwargs.get("memory_type", "fact")
+
+        if not fact:
+            return "Missing required parameter: fact"
+
+        try:
+            # Get owner_id from context
+            owner_id = context.context.event.unified_msg_origin
+
+            # Add memory using memory manager
+            memory = await mm.add_memory(
+                fact=fact,
+                owner_id=owner_id,
+                memory_type=memory_type,
+            )
+
+            return f"Memory added successfully (ID: {memory.mem_id})"
+
+        except Exception as e:
+            return f"Failed to add memory: {str(e)}"
+
+
+@dataclass
+class QueryMemory(FunctionTool[AstrAgentContext]):
+    """Tool for querying user's long-term memories"""
+
+    name: str = "astr_query_memory"
+    description: str = (
+        "Query the user's long-term memory storage and return the most relevant memories. "
+        "Use this tool when you need user-specific context, preferences, or past facts "
+        "that are not explicitly present in the current conversation."
+    )
+    parameters: dict = Field(
+        default_factory=lambda: {
+            "type": "object",
+            "properties": {
+                "top_k": {
+                    "type": "integer",
+                    "description": (
+                        "Maximum number of memories to retrieve after retention-based ranking. "
+                        "Typically between 3 and 10."
+                    ),
+                    "default": 5,
+                    "minimum": 1,
+                    "maximum": 20,
+                },
+            },
+            "required": [],
+        }
+    )
+
+    async def call(
+        self, context: ContextWrapper[AstrAgentContext], **kwargs
+    ) -> ToolExecResult:
+        """Query memories from long-term storage
+
+        Args:
+            context: Agent context
+            **kwargs: Optional 'top_k' parameter
+
+        Returns:
+            ToolExecResult with formatted memory list
+
+        """
+        mm = context.context.context.memory_manager
+        top_k = kwargs.get("top_k", 5)
+
+        try:
+            # Get owner_id from context
+            owner_id = context.context.event.unified_msg_origin
+
+            # Query memories using memory manager
+            memories = await mm.query_memory(
+                owner_id=owner_id,
+                top_k=top_k,
+            )
+
+            if not memories:
+                return "No memories found for this user."
+
+            # Format memories for output
+            formatted_memories = []
+            for i, mem in enumerate(memories, 1):
+                formatted_memories.append(
+                    f"{i}. [{mem.memory_type.upper()}] {mem.fact} "
+                    f"(retrieved {mem.retrieval_count} times, "
+                    f"last: {mem.last_retrieval_at.strftime('%Y-%m-%d')})"
+                )
+
+            result_text = "Retrieved memories:\n" + "\n".join(formatted_memories)
+            return result_text
+
+        except Exception as e:
+            return f"Failed to query memories: {str(e)}"
+
+
+ADD_MEMORY_TOOL = AddMemory()
+QUERY_MEMORY_TOOL = QueryMemory()
--- a/astrbot/core/pipeline/process_stage/method/llm_request.py
+++ b/astrbot/core/pipeline/process_stage/method/llm_request.py
@@ -30,9 +30,10 @@ from ....astr_agent_context import AgentContextWrapper
 from ....astr_agent_hooks import MAIN_AGENT_HOOKS
 from ....astr_agent_run_util import AgentRunner, run_agent
 from ....astr_agent_tool_exec import FunctionToolExecutor
+from ....memory.tools import ADD_MEMORY_TOOL, QUERY_MEMORY_TOOL
 from ...context import PipelineContext, call_event_hook
 from ..stage import Stage
-from ..utils import inject_kb_context
+from ..utils import KNOWLEDGE_BASE_QUERY_TOOL, retrieve_knowledge_base


 class LLMRequestSubStage(Stage):
@@ -57,6 +58,7 @@ class LLMRequestSubStage(Stage):
            self.max_step = 30
        self.show_tool_use: bool = settings.get("show_tool_use_status", True)
        self.show_reasoning = settings.get("display_reasoning_text", False)
+        self.kb_agentic_mode: bool = conf.get("kb_agentic_mode", False)

        for bwp in self.bot_wake_prefixs:
            if self.provider_wake_prefix.startswith(bwp):
@@ -95,20 +97,42 @@ class LLMRequestSubStage(Stage):
            raise RuntimeError("无法创建新的对话。")
        return conversation

-    async def _apply_kb_context(
+    async def _apply_kb(
        self,
        event: AstrMessageEvent,
        req: ProviderRequest,
    ):
-        """应用知识库上下文到请求中"""
-        try:
-            await inject_kb_context(
-                umo=event.unified_msg_origin,
-                p_ctx=self.ctx,
-                req=req,
-            )
-        except Exception as e:
-            logger.error(f"调用知识库时遇到问题: {e}")
+        """Apply knowledge base context to the provider request"""
+        if not self.kb_agentic_mode:
+            if req.prompt is None:
+                return
+            try:
+                kb_result = await retrieve_knowledge_base(
+                    query=req.prompt,
+                    umo=event.unified_msg_origin,
+                    context=self.ctx.plugin_manager.context,
+                )
+                if not kb_result:
+                    return
+                if req.system_prompt is not None:
+                    req.system_prompt += (
+                        f"\n\n[Related Knowledge Base Results]:\n{kb_result}"
+                    )
+            except Exception as e:
+                logger.error(f"Error occurred while retrieving knowledge base: {e}")
+        else:
+            if req.func_tool is None:
+                req.func_tool = ToolSet()
+            req.func_tool.add_tool(KNOWLEDGE_BASE_QUERY_TOOL)
+
+    async def _apply_memory(self, req: ProviderRequest):
+        mm = self.ctx.plugin_manager.context.memory_manager
+        if not mm or not mm._initialized:
+            return
+        if req.func_tool is None:
+            req.func_tool = ToolSet()
+        req.func_tool.add_tool(ADD_MEMORY_TOOL)
+        req.func_tool.add_tool(QUERY_MEMORY_TOOL)

    def _truncate_contexts(
        self,
@@ -356,13 +380,16 @@ class LLMRequestSubStage(Stage):
            if not req.prompt and not req.image_urls:
                return

-            # apply knowledge base context
-            await self._apply_kb_context(event, req)
-
            # call event hook
            if await call_event_hook(event, EventType.OnLLMRequestEvent, req):
                return

+            # apply knowledge base feature
+            await self._apply_kb(event, req)
+
+            # apply memory feature
+            await self._apply_memory(req)
+
            # fix contexts json str
            if isinstance(req.contexts, str):
                req.contexts = json.loads(req.contexts)
--- a/astrbot/core/pipeline/process_stage/utils.py
+++ b/astrbot/core/pipeline/process_stage/utils.py
@@ -1,23 +1,64 @@
+from pydantic import Field
+from pydantic.dataclasses import dataclass
+
 from astrbot.api import logger, sp
-from astrbot.core.provider.entities import ProviderRequest
-
-from ..context import PipelineContext
+from astrbot.core.agent.run_context import ContextWrapper
+from astrbot.core.agent.tool import FunctionTool, ToolExecResult
+from astrbot.core.astr_agent_context import AstrAgentContext
+from astrbot.core.star.context import Context


-async def inject_kb_context(
+@dataclass
+class KnowledgeBaseQueryTool(FunctionTool[AstrAgentContext]):
+    name: str = "astr_kb_search"
+    description: str = (
+        "Query the knowledge base for facts or relevant context. "
+        "Use this tool when the user's question requires factual information, "
+        "definitions, background knowledge, or previously indexed content. "
+        "Only send short keywords or a concise question as the query."
+    )
+    parameters: dict = Field(
+        default_factory=lambda: {
+            "type": "object",
+            "properties": {
+                "query": {
+                    "type": "string",
+                    "description": "A concise keyword query for the knowledge base.",
+                },
+            },
+            "required": ["query"],
+        }
+    )
+
+    async def call(
+        self, context: ContextWrapper[AstrAgentContext], **kwargs
+    ) -> ToolExecResult:
+        query = kwargs.get("query", "")
+        if not query:
+            return "error: Query parameter is empty."
+        result = await retrieve_knowledge_base(
+            query=kwargs.get("query", ""),
+            umo=context.context.event.unified_msg_origin,
+            context=context.context.context,
+        )
+        if not result:
+            return "No relevant knowledge found."
+        return result
+
+
+async def retrieve_knowledge_base(
+    query: str,
    umo: str,
-    p_ctx: PipelineContext,
-    req: ProviderRequest,
-) -> None:
+    context: Context,
+) -> str | None:
    """Inject knowledge base context into the provider request

    Args:
        umo: Unique message object (session ID)
        p_ctx: Pipeline context
-        req: Provider request
-
    """
-    kb_mgr = p_ctx.plugin_manager.context.kb_manager
+    kb_mgr = context.kb_manager
+    config = context.get_config(umo=umo)

    # 1. 优先读取会话级配置
    session_config = await sp.session_get(umo, "kb_config", default={})
@@ -54,18 +95,18 @@ async def inject_kb_context(

        logger.debug(f"[知识库] 使用会话级配置，知识库数量: {len(kb_names)}")
    else:
-        kb_names = p_ctx.astrbot_config.get("kb_names", [])
-        top_k = p_ctx.astrbot_config.get("kb_final_top_k", 5)
+        kb_names = config.get("kb_names", [])
+        top_k = config.get("kb_final_top_k", 5)
        logger.debug(f"[知识库] 使用全局配置，知识库数量: {len(kb_names)}")

-    top_k_fusion = p_ctx.astrbot_config.get("kb_fusion_top_k", 20)
+    top_k_fusion = config.get("kb_fusion_top_k", 20)

    if not kb_names:
        return

    logger.debug(f"[知识库] 开始检索知识库，数量: {len(kb_names)}, top_k={top_k}")
    kb_context = await kb_mgr.retrieve(
-        query=req.prompt,
+        query=query,
        kb_names=kb_names,
        top_k_fusion=top_k_fusion,
        top_m_final=top_k,
@@ -78,4 +119,7 @@ async def inject_kb_context(
    if formatted:
        results = kb_context.get("results", [])
        logger.debug(f"[知识库] 为会话 {umo} 注入了 {len(results)} 条相关知识块")
-        req.system_prompt = f"{formatted}\n\n{req.system_prompt or ''}"
+        return formatted
+
+
+KNOWLEDGE_BASE_QUERY_TOOL = KnowledgeBaseQueryTool()
--- a/astrbot/core/platform/sources/wecom_ai_bot/wecomai_adapter.py
+++ b/astrbot/core/platform/sources/wecom_ai_bot/wecomai_adapter.py
@@ -30,7 +30,7 @@ from .wecomai_api import (
    WecomAIBotStreamMessageBuilder,
 )
 from .wecomai_event import WecomAIBotMessageEvent
-from .wecomai_queue_mgr import WecomAIQueueMgr, wecomai_queue_mgr
+from .wecomai_queue_mgr import WecomAIQueueMgr
 from .wecomai_server import WecomAIBotServer
 from .wecomai_utils import (
    WecomAIBotConstants,
@@ -144,9 +144,12 @@ class WecomAIBotAdapter(Platform):
        # 事件循环和关闭信号
        self.shutdown_event = asyncio.Event()

+        # 队列管理器
+        self.queue_mgr = WecomAIQueueMgr()
+
        # 队列监听器
        self.queue_listener = WecomAIQueueListener(
-            wecomai_queue_mgr,
+            self.queue_mgr,
            self._handle_queued_message,
        )

@@ -189,7 +192,7 @@ class WecomAIBotAdapter(Platform):
                    stream_id,
                    session_id,
                )
-                wecomai_queue_mgr.set_pending_response(stream_id, callback_params)
+                self.queue_mgr.set_pending_response(stream_id, callback_params)

                resp = WecomAIBotStreamMessageBuilder.make_text_stream(
                    stream_id,
@@ -207,7 +210,7 @@ class WecomAIBotAdapter(Platform):
        elif msgtype == "stream":
            # wechat server is requesting for updates of a stream
            stream_id = message_data["stream"]["id"]
-            if not wecomai_queue_mgr.has_back_queue(stream_id):
+            if not self.queue_mgr.has_back_queue(stream_id):
                logger.error(f"Cannot find back queue for stream_id: {stream_id}")

                # 返回结束标志，告诉微信服务器流已结束
@@ -222,7 +225,7 @@ class WecomAIBotAdapter(Platform):
                    callback_params["timestamp"],
                )
                return resp
-            queue = wecomai_queue_mgr.get_or_create_back_queue(stream_id)
+            queue = self.queue_mgr.get_or_create_back_queue(stream_id)
            if queue.empty():
                logger.debug(
                    f"No new messages in back queue for stream_id: {stream_id}",
@@ -242,10 +245,9 @@ class WecomAIBotAdapter(Platform):
                elif msg["type"] == "end":
                    # stream end
                    finish = True
-                    wecomai_queue_mgr.remove_queues(stream_id)
+                    self.queue_mgr.remove_queues(stream_id)
                    break
-                else:
-                    pass
+
            logger.debug(
                f"Aggregated content: {latest_plain_content}, image: {len(image_base64)}, finish: {finish}",
            )
@@ -313,8 +315,8 @@ class WecomAIBotAdapter(Platform):
        session_id: str,
    ):
        """将消息放入队列进行异步处理"""
-        input_queue = wecomai_queue_mgr.get_or_create_queue(stream_id)
-        _ = wecomai_queue_mgr.get_or_create_back_queue(stream_id)
+        input_queue = self.queue_mgr.get_or_create_queue(stream_id)
+        _ = self.queue_mgr.get_or_create_back_queue(stream_id)
        message_payload = {
            "message_data": message_data,
            "callback_params": callback_params,
@@ -453,6 +455,7 @@ class WecomAIBotAdapter(Platform):
                platform_meta=self.meta(),
                session_id=message.session_id,
                api_client=self.api_client,
+                queue_mgr=self.queue_mgr,
            )

            self.commit_event(message_event)
--- a/astrbot/core/platform/sources/wecom_ai_bot/wecomai_event.py
+++ b/astrbot/core/platform/sources/wecom_ai_bot/wecomai_event.py
@@ -8,7 +8,7 @@ from astrbot.api.message_components import (
 )

 from .wecomai_api import WecomAIBotAPIClient
-from .wecomai_queue_mgr import wecomai_queue_mgr
+from .wecomai_queue_mgr import WecomAIQueueMgr


 class WecomAIBotMessageEvent(AstrMessageEvent):
@@ -21,6 +21,7 @@ class WecomAIBotMessageEvent(AstrMessageEvent):
        platform_meta,
        session_id: str,
        api_client: WecomAIBotAPIClient,
+        queue_mgr: WecomAIQueueMgr,
    ):
        """初始化消息事件

@@ -34,14 +35,16 @@ class WecomAIBotMessageEvent(AstrMessageEvent):
        """
        super().__init__(message_str, message_obj, platform_meta, session_id)
        self.api_client = api_client
+        self.queue_mgr = queue_mgr

    @staticmethod
    async def _send(
        message_chain: MessageChain,
        stream_id: str,
+        queue_mgr: WecomAIQueueMgr,
        streaming: bool = False,
    ):
-        back_queue = wecomai_queue_mgr.get_or_create_back_queue(stream_id)
+        back_queue = queue_mgr.get_or_create_back_queue(stream_id)

        if not message_chain:
            await back_queue.put(
@@ -94,7 +97,7 @@ class WecomAIBotMessageEvent(AstrMessageEvent):
            "wecom_ai_bot platform event raw_message should be a dict"
        )
        stream_id = raw.get("stream_id", self.session_id)
-        await WecomAIBotMessageEvent._send(message, stream_id)
+        await WecomAIBotMessageEvent._send(message, stream_id, self.queue_mgr)
        await super().send(message)

    async def send_streaming(self, generator, use_fallback=False):
@@ -105,7 +108,7 @@ class WecomAIBotMessageEvent(AstrMessageEvent):
            "wecom_ai_bot platform event raw_message should be a dict"
        )
        stream_id = raw.get("stream_id", self.session_id)
-        back_queue = wecomai_queue_mgr.get_or_create_back_queue(stream_id)
+        back_queue = self.queue_mgr.get_or_create_back_queue(stream_id)

        # 企业微信智能机器人不支持增量发送，因此我们需要在这里将增量内容累积起来，积累发送
        increment_plain = ""
@@ -134,6 +137,7 @@ class WecomAIBotMessageEvent(AstrMessageEvent):
            final_data += await WecomAIBotMessageEvent._send(
                chain,
                stream_id=stream_id,
+                queue_mgr=self.queue_mgr,
                streaming=True,
            )

--- a/astrbot/core/platform/sources/wecom_ai_bot/wecomai_queue_mgr.py
+++ b/astrbot/core/platform/sources/wecom_ai_bot/wecomai_queue_mgr.py
@@ -151,7 +151,3 @@ class WecomAIQueueMgr:
            "output_queues": len(self.back_queues),
            "pending_responses": len(self.pending_responses),
        }
-
-
-# 全局队列管理器实例
-wecomai_queue_mgr = WecomAIQueueMgr()
--- a/astrbot/core/provider/entities.py
+++ b/astrbot/core/provider/entities.py
@@ -211,6 +211,8 @@ class LLMResponse:
    """Tool call names."""
    tools_call_ids: list[str] = field(default_factory=list)
    """Tool call IDs."""
+    tools_call_extra_content: dict[str, dict[str, Any]] = field(default_factory=dict)
+    """Tool call extra content. tool_call_id -> extra_content dict"""
    reasoning_content: str = ""
    """The reasoning content extracted from the LLM, if any."""

@@ -233,6 +235,7 @@ class LLMResponse:
        tools_call_args: list[dict[str, Any]] | None = None,
        tools_call_name: list[str] | None = None,
        tools_call_ids: list[str] | None = None,
+        tools_call_extra_content: dict[str, dict[str, Any]] | None = None,
        raw_completion: ChatCompletion
        | GenerateContentResponse
        | AnthropicMessage
@@ -256,6 +259,8 @@ class LLMResponse:
            tools_call_name = []
        if tools_call_ids is None:
            tools_call_ids = []
+        if tools_call_extra_content is None:
+            tools_call_extra_content = {}

        self.role = role
        self.completion_text = completion_text
@@ -263,6 +268,7 @@ class LLMResponse:
        self.tools_call_args = tools_call_args
        self.tools_call_name = tools_call_name
        self.tools_call_ids = tools_call_ids
+        self.tools_call_extra_content = tools_call_extra_content
        self.raw_completion = raw_completion
        self.is_chunk = is_chunk

@@ -288,16 +294,19 @@ class LLMResponse:
        """Convert to OpenAI tool calls format. Deprecated, use to_openai_to_calls_model instead."""
        ret = []
        for idx, tool_call_arg in enumerate(self.tools_call_args):
-            ret.append(
-                {
-                    "id": self.tools_call_ids[idx],
-                    "function": {
-                        "name": self.tools_call_name[idx],
-                        "arguments": json.dumps(tool_call_arg),
-                    },
-                    "type": "function",
+            payload = {
+                "id": self.tools_call_ids[idx],
+                "function": {
+                    "name": self.tools_call_name[idx],
+                    "arguments": json.dumps(tool_call_arg),
                },
-            )
+                "type": "function",
+            }
+            if self.tools_call_extra_content.get(self.tools_call_ids[idx]):
+                payload["extra_content"] = self.tools_call_extra_content[
+                    self.tools_call_ids[idx]
+                ]
+            ret.append(payload)
        return ret

    def to_openai_to_calls_model(self) -> list[ToolCall]:
@@ -311,6 +320,10 @@ class LLMResponse:
                        name=self.tools_call_name[idx],
                        arguments=json.dumps(tool_call_arg),
                    ),
+                    # the extra_content will not serialize if it's None when calling ToolCall.model_dump()
+                    extra_content=self.tools_call_extra_content.get(
+                        self.tools_call_ids[idx]
+                    ),
                ),
            )
        return ret
--- a/astrbot/core/provider/func_tool_manager.py
+++ b/astrbot/core/provider/func_tool_manager.py
@@ -280,19 +280,22 @@ class FunctionToolManager:
    async def _terminate_mcp_client(self, name: str) -> None:
        """关闭并清理MCP客户端"""
        if name in self.mcp_client_dict:
+            client = self.mcp_client_dict[name]
            try:
                # 关闭MCP连接
-                await self.mcp_client_dict[name].cleanup()
-                self.mcp_client_dict.pop(name)
+                await client.cleanup()
            except Exception as e:
                logger.error(f"清空 MCP 客户端资源 {name}: {e}。")
-            # 移除关联的FuncTool
-            self.func_list = [
-                f
-                for f in self.func_list
-                if not (isinstance(f, MCPTool) and f.mcp_server_name == name)
-            ]
-            logger.info(f"已关闭 MCP 服务 {name}")
+            finally:
+                # Remove client from dict after cleanup attempt (successful or not)
+                self.mcp_client_dict.pop(name, None)
+                # 移除关联的FuncTool
+                self.func_list = [
+                    f
+                    for f in self.func_list
+                    if not (isinstance(f, MCPTool) and f.mcp_server_name == name)
+                ]
+                logger.info(f"已关闭 MCP 服务 {name}")

    @staticmethod
    async def test_mcp_server_connection(config: dict) -> list[str]:
--- a/astrbot/core/provider/sources/gemini_source.py
+++ b/astrbot/core/provider/sources/gemini_source.py
@@ -290,13 +290,24 @@ class ProviderGoogleGenAI(Provider):
                    parts = [types.Part.from_text(text=content)]
                    append_or_extend(gemini_contents, parts, types.ModelContent)
                elif not native_tool_enabled and "tool_calls" in message:
-                    parts = [
-                        types.Part.from_function_call(
+                    parts = []
+                    for tool in message["tool_calls"]:
+                        part = types.Part.from_function_call(
                            name=tool["function"]["name"],
                            args=json.loads(tool["function"]["arguments"]),
                        )
-                        for tool in message["tool_calls"]
-                    ]
+                        # we should set thought_signature back to part if exists
+                        # for more info about thought_signature, see:
+                        # https://ai.google.dev/gemini-api/docs/thought-signatures
+                        if "extra_content" in tool and tool["extra_content"]:
+                            ts_bs64 = (
+                                tool["extra_content"]
+                                .get("google", {})
+                                .get("thought_signature")
+                            )
+                            if ts_bs64:
+                                part.thought_signature = base64.b64decode(ts_bs64)
+                        parts.append(part)
                    append_or_extend(gemini_contents, parts, types.ModelContent)
                else:
                    logger.warning("assistant 角色的消息内容为空，已添加空格占位")
@@ -393,10 +404,15 @@ class ProviderGoogleGenAI(Provider):
                llm_response.role = "tool"
                llm_response.tools_call_name.append(part.function_call.name)
                llm_response.tools_call_args.append(part.function_call.args)
-                # gemini 返回的 function_call.id 可能为 None
-                llm_response.tools_call_ids.append(
-                    part.function_call.id or part.function_call.name,
-                )
+                # function_call.id might be None, use name as fallback
+                tool_call_id = part.function_call.id or part.function_call.name
+                llm_response.tools_call_ids.append(tool_call_id)
+                # extra_content
+                if part.thought_signature:
+                    ts_bs64 = base64.b64encode(part.thought_signature).decode("utf-8")
+                    llm_response.tools_call_extra_content[tool_call_id] = {
+                        "google": {"thought_signature": ts_bs64}
+                    }
            elif (
                part.inline_data
                and part.inline_data.mime_type
@@ -435,6 +451,7 @@ class ProviderGoogleGenAI(Provider):
                    contents=conversation,
                    config=config,
                )
+                logger.debug(f"genai result: {result}")

                if not result.candidates:
                    logger.error(f"请求失败, 返回的 candidates 为空: {result}")
--- a/astrbot/core/provider/sources/openai_source.py
+++ b/astrbot/core/provider/sources/openai_source.py
@@ -8,7 +8,7 @@ import re
 from collections.abc import AsyncGenerator

 from openai import AsyncAzureOpenAI, AsyncOpenAI
-from openai._exceptions import NotFoundError, UnprocessableEntityError
+from openai._exceptions import NotFoundError
 from openai.lib.streaming.chat._completions import ChatCompletionStreamState
 from openai.types.chat.chat_completion import ChatCompletion
 from openai.types.chat.chat_completion_chunk import ChatCompletionChunk
@@ -279,6 +279,7 @@ class ProviderOpenAIOfficial(Provider):
            args_ls = []
            func_name_ls = []
            tool_call_ids = []
+            tool_call_extra_content_dict = {}
            for tool_call in choice.message.tool_calls:
                if isinstance(tool_call, str):
                    # workaround for #1359
@@ -296,11 +297,16 @@ class ProviderOpenAIOfficial(Provider):
                        args_ls.append(args)
                        func_name_ls.append(tool_call.function.name)
                        tool_call_ids.append(tool_call.id)
+
+                        # gemini-2.5 / gemini-3 series extra_content handling
+                        extra_content = getattr(tool_call, "extra_content", None)
+                        if extra_content is not None:
+                            tool_call_extra_content_dict[tool_call.id] = extra_content
            llm_response.role = "tool"
            llm_response.tools_call_args = args_ls
            llm_response.tools_call_name = func_name_ls
            llm_response.tools_call_ids = tool_call_ids
-
+            llm_response.tools_call_extra_content = tool_call_extra_content_dict
        # specially handle finish reason
        if choice.finish_reason == "content_filter":
            raise Exception(
@@ -353,7 +359,7 @@ class ProviderOpenAIOfficial(Provider):

        payloads = {"messages": context_query, **model_config}

-        # xAI 原生搜索参数（最小侵入地在此处注入）
+        # xAI origin search tool inject
        self._maybe_inject_xai_search(payloads, **kwargs)

        return payloads, context_query
@@ -475,12 +481,6 @@ class ProviderOpenAIOfficial(Provider):
                self.client.api_key = chosen_key
                llm_response = await self._query(payloads, func_tool)
                break
-            except UnprocessableEntityError as e:
-                logger.warning(f"不可处理的实体错误：{e}，尝试删除图片。")
-                # 尝试删除所有 image
-                new_contexts = await self._remove_image_from_context(context_query)
-                payloads["messages"] = new_contexts
-                context_query = new_contexts
            except Exception as e:
                last_exception = e
                (
@@ -545,12 +545,6 @@ class ProviderOpenAIOfficial(Provider):
                async for response in self._query_stream(payloads, func_tool):
                    yield response
                break
-            except UnprocessableEntityError as e:
-                logger.warning(f"不可处理的实体错误：{e}，尝试删除图片。")
-                # 尝试删除所有 image
-                new_contexts = await self._remove_image_from_context(context_query)
-                payloads["messages"] = new_contexts
-                context_query = new_contexts
            except Exception as e:
                last_exception = e
                (
@@ -646,4 +640,3 @@ class ProviderOpenAIOfficial(Provider):
        with open(image_url, "rb") as f:
            image_bs64 = base64.b64encode(f.read()).decode("utf-8")
            return "data:image/jpeg;base64," + image_bs64
-        return ""
--- a/astrbot/core/star/context.py
+++ b/astrbot/core/star/context.py
@@ -14,6 +14,7 @@ from astrbot.core.config.astrbot_config import AstrBotConfig
 from astrbot.core.conversation_mgr import ConversationManager
 from astrbot.core.db import BaseDatabase
 from astrbot.core.knowledge_base.kb_mgr import KnowledgeBaseManager
+from astrbot.core.memory.memory_manager import MemoryManager
 from astrbot.core.message.message_event_result import MessageChain
 from astrbot.core.persona_mgr import PersonaManager
 from astrbot.core.platform import Platform
@@ -65,6 +66,7 @@ class Context:
        persona_manager: PersonaManager,
        astrbot_config_mgr: AstrBotConfigManager,
        knowledge_base_manager: KnowledgeBaseManager,
+        memory_manager: MemoryManager,
    ):
        self._event_queue = event_queue
        """事件队列。消息平台通过事件队列传递消息事件。"""
@@ -79,6 +81,7 @@ class Context:
        self.persona_manager = persona_manager
        self.astrbot_config_mgr = astrbot_config_mgr
        self.kb_manager = knowledge_base_manager
+        self.memory_manager = memory_manager

    async def llm_generate(
        self,
--- a/astrbot/dashboard/routes/init.py
+++ b/astrbot/dashboard/routes/init.py
@@ -5,6 +5,7 @@ from .conversation import ConversationRoute
 from .file import FileRoute
 from .knowledge_base import KnowledgeBaseRoute
 from .log import LogRoute
+from .memory import MemoryRoute
 from .persona import PersonaRoute
 from .plugin import PluginRoute
 from .session_management import SessionManagementRoute
@@ -21,6 +22,7 @@ __all__ = [
    "FileRoute",
    "KnowledgeBaseRoute",
    "LogRoute",
+    "MemoryRoute",
    "PersonaRoute",
    "PluginRoute",
    "SessionManagementRoute",
--- a/astrbot/dashboard/routes/chat.py
+++ b/astrbot/dashboard/routes/chat.py
@@ -10,7 +10,6 @@ from quart import g, make_response, request
 from astrbot.core import logger
 from astrbot.core.core_lifecycle import AstrBotCoreLifecycle
 from astrbot.core.db import BaseDatabase
-from astrbot.core.platform.astr_message_event import MessageSession
 from astrbot.core.platform.sources.webchat.webchat_queue_mgr import webchat_queue_mgr
 from astrbot.core.utils.astrbot_path import get_astrbot_data_path

@@ -36,11 +35,14 @@ class ChatRoute(Route):
        super().__init__(context)
        self.routes = {
            "/chat/send": ("POST", self.chat),
-            "/chat/new_conversation": ("GET", self.new_conversation),
-            "/chat/conversations": ("GET", self.get_conversations),
-            "/chat/get_conversation": ("GET", self.get_conversation),
-            "/chat/delete_conversation": ("GET", self.delete_conversation),
-            "/chat/rename_conversation": ("POST", self.rename_conversation),
+            "/chat/new_session": ("GET", self.new_session),
+            "/chat/sessions": ("GET", self.get_sessions),
+            "/chat/get_session": ("GET", self.get_session),
+            "/chat/delete_session": ("GET", self.delete_webchat_session),
+            "/chat/update_session_display_name": (
+                "POST",
+                self.update_session_display_name,
+            ),
            "/chat/get_file": ("GET", self.get_file),
            "/chat/post_image": ("POST", self.post_image),
            "/chat/post_file": ("POST", self.post_file),
@@ -53,6 +55,7 @@ class ChatRoute(Route):
        self.supported_imgs = ["jpg", "jpeg", "png", "gif", "webp"]
        self.conv_mgr = core_lifecycle.conversation_manager
        self.platform_history_mgr = core_lifecycle.platform_message_history_manager
+        self.db = db

        self.running_convs: dict[str, bool] = {}

@@ -116,11 +119,14 @@ class ChatRoute(Route):
        if "message" not in post_data and "image_url" not in post_data:
            return Response().error("Missing key: message or image_url").__dict__

-        if "conversation_id" not in post_data:
-            return Response().error("Missing key: conversation_id").__dict__
+        if "session_id" not in post_data and "conversation_id" not in post_data:
+            return (
+                Response().error("Missing key: session_id or conversation_id").__dict__
+            )

        message = post_data["message"]
-        conversation_id = post_data["conversation_id"]
+        # conversation_id = post_data["conversation_id"]
+        session_id = post_data.get("session_id", post_data.get("conversation_id"))
        image_url = post_data.get("image_url")
        audio_url = post_data.get("audio_url")
        selected_provider = post_data.get("selected_provider")
@@ -133,11 +139,11 @@ class ChatRoute(Route):
                .error("Message and image_url and audio_url are empty")
                .__dict__
            )
-        if not conversation_id:
-            return Response().error("conversation_id is empty").__dict__
+        if not session_id:
+            return Response().error("session_id is empty").__dict__

        # 追加用户消息
-        webchat_conv_id = await self._get_webchat_conv_id_from_conv_id(conversation_id)
+        webchat_conv_id = session_id

        # 获取会话特定的队列
        back_queue = webchat_queue_mgr.get_or_create_back_queue(webchat_conv_id)
@@ -245,88 +251,110 @@ class ChatRoute(Route):
        response.timeout = None  # fix SSE auto disconnect issue
        return response

-    async def _get_webchat_conv_id_from_conv_id(self, conversation_id: str) -> str:
-        """从对话 ID 中提取 WebChat 会话 ID
-
-        NOTE: 关于这里为什么要单独做一个 WebChat 的 Conversation ID 出来，这个是为了向前兼容。
-        """
-        conversation = await self.conv_mgr.get_conversation(
-            unified_msg_origin="webchat",
-            conversation_id=conversation_id,
-        )
-        if not conversation:
-            raise ValueError(f"Conversation with ID {conversation_id} not found.")
-        conv_user_id = conversation.user_id
-        webchat_session_id = MessageSession.from_str(conv_user_id).session_id
-        if "!" not in webchat_session_id:
-            raise ValueError(f"Invalid conv user ID: {conv_user_id}")
-        return webchat_session_id.split("!")[-1]
-
-    async def delete_conversation(self):
-        conversation_id = request.args.get("conversation_id")
-        if not conversation_id:
-            return Response().error("Missing key: conversation_id").__dict__
+    async def delete_webchat_session(self):
+        """Delete a Platform session and all its related data."""
+        session_id = request.args.get("session_id")
+        if not session_id:
+            return Response().error("Missing key: session_id").__dict__
        username = g.get("username", "guest")

-        # Clean up queues when deleting conversation
-        webchat_queue_mgr.remove_queues(conversation_id)
-        webchat_conv_id = await self._get_webchat_conv_id_from_conv_id(conversation_id)
-        await self.conv_mgr.delete_conversation(
-            unified_msg_origin=f"webchat:FriendMessage:webchat!{username}!{webchat_conv_id}",
-            conversation_id=conversation_id,
-        )
+        # 验证会话是否存在且属于当前用户
+        session = await self.db.get_platform_session_by_id(session_id)
+        if not session:
+            return Response().error(f"Session {session_id} not found").__dict__
+        if session.creator != username:
+            return Response().error("Permission denied").__dict__
+
+        # 删除该会话下的所有对话
+        unified_msg_origin = f"{session.platform_id}:FriendMessage:{session.platform_id}!{username}!{session_id}"
+        await self.conv_mgr.delete_conversations_by_user_id(unified_msg_origin)
+
+        # 删除消息历史
        await self.platform_history_mgr.delete(
-            platform_id="webchat",
-            user_id=webchat_conv_id,
+            platform_id=session.platform_id,
+            user_id=session_id,
            offset_sec=99999999,
        )
+
+        # 清理队列（仅对 webchat）
+        if session.platform_id == "webchat":
+            webchat_queue_mgr.remove_queues(session_id)
+
+        # 删除会话
+        await self.db.delete_platform_session(session_id)
+
        return Response().ok().__dict__

-    async def new_conversation(self):
+    async def new_session(self):
+        """Create a new Platform session (default: webchat)."""
        username = g.get("username", "guest")
-        webchat_conv_id = str(uuid.uuid4())
-        conv_id = await self.conv_mgr.new_conversation(
-            unified_msg_origin=f"webchat:FriendMessage:webchat!{username}!{webchat_conv_id}",
-            platform_id="webchat",
-            content=[],
+
+        # 获取可选的 platform_id 参数，默认为 webchat
+        platform_id = request.args.get("platform_id", "webchat")
+
+        # 创建新会话
+        session = await self.db.create_platform_session(
+            creator=username,
+            platform_id=platform_id,
+            is_group=0,
        )
-        return Response().ok(data={"conversation_id": conv_id}).__dict__

-    async def rename_conversation(self):
-        post_data = await request.json
-        if "conversation_id" not in post_data or "title" not in post_data:
-            return Response().error("Missing key: conversation_id or title").__dict__
-
-        conversation_id = post_data["conversation_id"]
-        title = post_data["title"]
-
-        await self.conv_mgr.update_conversation(
-            unified_msg_origin="webchat",  # fake
-            conversation_id=conversation_id,
-            title=title,
+        return (
+            Response()
+            .ok(
+                data={
+                    "session_id": session.session_id,
+                    "platform_id": session.platform_id,
+                }
+            )
+            .__dict__
        )
-        return Response().ok(message="重命名成功！").__dict__

-    async def get_conversations(self):
-        conversations = await self.conv_mgr.get_conversations(platform_id="webchat")
-        # remove content
-        conversations_ = []
-        for conv in conversations:
-            conv.history = None
-            conversations_.append(conv)
-        return Response().ok(data=conversations_).__dict__
+    async def get_sessions(self):
+        """Get all Platform sessions for the current user."""
+        username = g.get("username", "guest")

-    async def get_conversation(self):
-        conversation_id = request.args.get("conversation_id")
-        if not conversation_id:
-            return Response().error("Missing key: conversation_id").__dict__
+        # 获取可选的 platform_id 参数
+        platform_id = request.args.get("platform_id")

-        webchat_conv_id = await self._get_webchat_conv_id_from_conv_id(conversation_id)
+        sessions = await self.db.get_platform_sessions_by_creator(
+            creator=username,
+            platform_id=platform_id,
+            page=1,
+            page_size=100,  # 暂时返回前100个
+        )

-        # Get platform message history
+        # 转换为字典格式，并添加额外信息
+        sessions_data = []
+        for session in sessions:
+            sessions_data.append(
+                {
+                    "session_id": session.session_id,
+                    "platform_id": session.platform_id,
+                    "creator": session.creator,
+                    "display_name": session.display_name,
+                    "is_group": session.is_group,
+                    "created_at": session.created_at.astimezone().isoformat(),
+                    "updated_at": session.updated_at.astimezone().isoformat(),
+                }
+            )
+
+        return Response().ok(data=sessions_data).__dict__
+
+    async def get_session(self):
+        """Get session information and message history by session_id."""
+        session_id = request.args.get("session_id")
+        if not session_id:
+            return Response().error("Missing key: session_id").__dict__
+
+        # 获取会话信息以确定 platform_id
+        session = await self.db.get_platform_session_by_id(session_id)
+        platform_id = session.platform_id if session else "webchat"
+
+        # Get platform message history using session_id
        history_ls = await self.platform_history_mgr.get(
-            platform_id="webchat",
-            user_id=webchat_conv_id,
+            platform_id=platform_id,
+            user_id=session_id,
            page=1,
            page_size=1000,
        )
@@ -338,8 +366,37 @@ class ChatRoute(Route):
            .ok(
                data={
                    "history": history_res,
-                    "is_running": self.running_convs.get(webchat_conv_id, False),
+                    "is_running": self.running_convs.get(session_id, False),
                },
            )
            .__dict__
        )
+
+    async def update_session_display_name(self):
+        """Update a Platform session's display name."""
+        post_data = await request.json
+
+        session_id = post_data.get("session_id")
+        display_name = post_data.get("display_name")
+
+        if not session_id:
+            return Response().error("Missing key: session_id").__dict__
+        if display_name is None:
+            return Response().error("Missing key: display_name").__dict__
+
+        username = g.get("username", "guest")
+
+        # 验证会话是否存在且属于当前用户
+        session = await self.db.get_platform_session_by_id(session_id)
+        if not session:
+            return Response().error(f"Session {session_id} not found").__dict__
+        if session.creator != username:
+            return Response().error("Permission denied").__dict__
+
+        # 更新 display_name
+        await self.db.update_platform_session(
+            session_id=session_id,
+            display_name=display_name,
+        )
+
+        return Response().ok().__dict__
--- a/astrbot/dashboard/routes/knowledge_base.py
+++ b/astrbot/dashboard/routes/knowledge_base.py
@@ -48,6 +48,7 @@ class KnowledgeBaseRoute(Route):
            # 文档管理
            "/kb/document/list": ("GET", self.list_documents),
            "/kb/document/upload": ("POST", self.upload_document),
+            "/kb/document/upload/url": ("POST", self.upload_document_from_url),
            "/kb/document/upload/progress": ("GET", self.get_upload_progress),
            "/kb/document/get": ("GET", self.get_document),
            "/kb/document/delete": ("POST", self.delete_document),
@@ -1070,3 +1071,174 @@ class KnowledgeBaseRoute(Route):
            logger.error(f"删除会话知识库配置失败: {e}")
            logger.error(traceback.format_exc())
            return Response().error(f"删除会话知识库配置失败: {e!s}").__dict__
+
+    async def upload_document_from_url(self):
+        """从 URL 上传文档
+
+        Body:
+        - kb_id: 知识库 ID (必填)
+        - url: 要提取内容的网页 URL (必填)
+        - chunk_size: 分块大小 (可选, 默认512)
+        - chunk_overlap: 块重叠大小 (可选, 默认50)
+        - batch_size: 批处理大小 (可选, 默认32)
+        - tasks_limit: 并发任务限制 (可选, 默认3)
+        - max_retries: 最大重试次数 (可选, 默认3)
+
+        返回:
+        - task_id: 任务ID，用于查询上传进度和结果
+        """
+        try:
+            kb_manager = self._get_kb_manager()
+            data = await request.json
+
+            kb_id = data.get("kb_id")
+            if not kb_id:
+                return Response().error("缺少参数 kb_id").__dict__
+
+            url = data.get("url")
+            if not url:
+                return Response().error("缺少参数 url").__dict__
+
+            chunk_size = data.get("chunk_size", 512)
+            chunk_overlap = data.get("chunk_overlap", 50)
+            batch_size = data.get("batch_size", 32)
+            tasks_limit = data.get("tasks_limit", 3)
+            max_retries = data.get("max_retries", 3)
+            enable_cleaning = data.get("enable_cleaning", False)
+            cleaning_provider_id = data.get("cleaning_provider_id")
+
+            # 获取知识库
+            kb_helper = await kb_manager.get_kb(kb_id)
+            if not kb_helper:
+                return Response().error("知识库不存在").__dict__
+
+            # 生成任务ID
+            task_id = str(uuid.uuid4())
+
+            # 初始化任务状态
+            self.upload_tasks[task_id] = {
+                "status": "pending",
+                "result": None,
+                "error": None,
+            }
+
+            # 启动后台任务
+            asyncio.create_task(
+                self._background_upload_from_url_task(
+                    task_id=task_id,
+                    kb_helper=kb_helper,
+                    url=url,
+                    chunk_size=chunk_size,
+                    chunk_overlap=chunk_overlap,
+                    batch_size=batch_size,
+                    tasks_limit=tasks_limit,
+                    max_retries=max_retries,
+                    enable_cleaning=enable_cleaning,
+                    cleaning_provider_id=cleaning_provider_id,
+                ),
+            )
+
+            return (
+                Response()
+                .ok(
+                    {
+                        "task_id": task_id,
+                        "url": url,
+                        "message": "URL upload task created, processing in background",
+                    },
+                )
+                .__dict__
+            )
+
+        except ValueError as e:
+            return Response().error(str(e)).__dict__
+        except Exception as e:
+            logger.error(f"从URL上传文档失败: {e}")
+            logger.error(traceback.format_exc())
+            return Response().error(f"从URL上传文档失败: {e!s}").__dict__
+
+    async def _background_upload_from_url_task(
+        self,
+        task_id: str,
+        kb_helper,
+        url: str,
+        chunk_size: int,
+        chunk_overlap: int,
+        batch_size: int,
+        tasks_limit: int,
+        max_retries: int,
+        enable_cleaning: bool,
+        cleaning_provider_id: str | None,
+    ):
+        """后台上传URL任务"""
+        try:
+            # 初始化任务状态
+            self.upload_tasks[task_id] = {
+                "status": "processing",
+                "result": None,
+                "error": None,
+            }
+            self.upload_progress[task_id] = {
+                "status": "processing",
+                "file_index": 0,
+                "file_total": 1,
+                "file_name": f"URL: {url}",
+                "stage": "extracting",
+                "current": 0,
+                "total": 100,
+            }
+
+            # 创建进度回调函数
+            async def progress_callback(stage, current, total):
+                if task_id in self.upload_progress:
+                    self.upload_progress[task_id].update(
+                        {
+                            "status": "processing",
+                            "file_index": 0,
+                            "file_name": f"URL: {url}",
+                            "stage": stage,
+                            "current": current,
+                            "total": total,
+                        },
+                    )
+
+            # 上传文档
+            doc = await kb_helper.upload_from_url(
+                url=url,
+                chunk_size=chunk_size,
+                chunk_overlap=chunk_overlap,
+                batch_size=batch_size,
+                tasks_limit=tasks_limit,
+                max_retries=max_retries,
+                progress_callback=progress_callback,
+                enable_cleaning=enable_cleaning,
+                cleaning_provider_id=cleaning_provider_id,
+            )
+
+            # 更新任务完成状态
+            result = {
+                "task_id": task_id,
+                "uploaded": [doc.model_dump()],
+                "failed": [],
+                "total": 1,
+                "success_count": 1,
+                "failed_count": 0,
+            }
+
+            self.upload_tasks[task_id] = {
+                "status": "completed",
+                "result": result,
+                "error": None,
+            }
+            self.upload_progress[task_id]["status"] = "completed"
+
+        except Exception as e:
+            logger.error(f"后台上传URL任务 {task_id} 失败: {e}")
+            logger.error(traceback.format_exc())
+            self.upload_tasks[task_id] = {
+                "status": "failed",
+                "result": None,
+                "error": str(e),
+            }
+            if task_id in self.upload_progress:
+                self.upload_progress[task_id]["status"] = "failed"
--- a/astrbot/dashboard/routes/memory.py
+++ b/astrbot/dashboard/routes/memory.py
@@ -0,0 +1,174 @@
+"""Memory management API routes"""
+
+from quart import jsonify, request
+
+from astrbot.core import logger
+from astrbot.core.core_lifecycle import AstrBotCoreLifecycle
+from astrbot.core.db import BaseDatabase
+
+from .route import Response, Route, RouteContext
+
+
+class MemoryRoute(Route):
+    """Memory management routes"""
+
+    def __init__(
+        self,
+        context: RouteContext,
+        db: BaseDatabase,
+        core_lifecycle: AstrBotCoreLifecycle,
+    ):
+        super().__init__(context)
+        self.db = db
+        self.core_lifecycle = core_lifecycle
+        self.memory_manager = core_lifecycle.memory_manager
+        self.provider_manager = core_lifecycle.provider_manager
+        self.routes = [
+            ("/memory/status", ("GET", self.get_status)),
+            ("/memory/initialize", ("POST", self.initialize)),
+            ("/memory/update_merge_llm", ("POST", self.update_merge_llm)),
+        ]
+        self.register_routes()
+
+    async def get_status(self):
+        """Get memory system status"""
+        try:
+            is_initialized = self.memory_manager._initialized
+
+            status_data = {
+                "initialized": is_initialized,
+                "embedding_provider_id": None,
+                "merge_llm_provider_id": None,
+            }
+
+            if is_initialized:
+                # Get embedding provider info
+                if self.memory_manager.embedding_provider:
+                    status_data["embedding_provider_id"] = (
+                        self.memory_manager.embedding_provider.provider_config["id"]
+                    )
+                # Get merge LLM provider info
+                if self.memory_manager.merge_llm_provider:
+                    status_data["merge_llm_provider_id"] = (
+                        self.memory_manager.merge_llm_provider.provider_config["id"]
+                    )
+
+            return jsonify(Response().ok(status_data).__dict__)
+        except Exception as e:
+            logger.error(f"Failed to get memory status: {e}")
+            return jsonify(Response().error(str(e)).__dict__)
+
+    async def initialize(self):
+        """Initialize memory system with embedding and merge LLM providers"""
+        try:
+            data = await request.get_json()
+            embedding_provider_id = data.get("embedding_provider_id")
+            merge_llm_provider_id = data.get("merge_llm_provider_id")
+
+            if not embedding_provider_id or not merge_llm_provider_id:
+                return jsonify(
+                    Response()
+                    .error(
+                        "embedding_provider_id and merge_llm_provider_id are required"
+                    )
+                    .__dict__,
+                )
+
+            # Check if already initialized
+            if self.memory_manager._initialized:
+                return jsonify(
+                    Response()
+                    .error(
+                        "Memory system already initialized. Embedding provider cannot be changed.",
+                    )
+                    .__dict__,
+                )
+
+            # Get providers
+            embedding_provider = await self.provider_manager.get_provider_by_id(
+                embedding_provider_id,
+            )
+            merge_llm_provider = await self.provider_manager.get_provider_by_id(
+                merge_llm_provider_id,
+            )
+
+            if not embedding_provider:
+                return jsonify(
+                    Response()
+                    .error(f"Embedding provider {embedding_provider_id} not found")
+                    .__dict__,
+                )
+
+            if not merge_llm_provider:
+                return jsonify(
+                    Response()
+                    .error(f"Merge LLM provider {merge_llm_provider_id} not found")
+                    .__dict__,
+                )
+
+            # Initialize memory manager
+            await self.memory_manager.initialize(
+                embedding_provider=embedding_provider,
+                merge_llm_provider=merge_llm_provider,
+            )
+
+            logger.info(
+                f"Memory system initialized with embedding: {embedding_provider_id}, "
+                f"merge LLM: {merge_llm_provider_id}",
+            )
+
+            return jsonify(
+                Response()
+                .ok({"message": "Memory system initialized successfully"})
+                .__dict__,
+            )
+
+        except Exception as e:
+            logger.error(f"Failed to initialize memory system: {e}")
+            return jsonify(Response().error(str(e)).__dict__)
+
+    async def update_merge_llm(self):
+        """Update merge LLM provider (only allowed after initialization)"""
+        try:
+            data = await request.get_json()
+            merge_llm_provider_id = data.get("merge_llm_provider_id")
+
+            if not merge_llm_provider_id:
+                return jsonify(
+                    Response().error("merge_llm_provider_id is required").__dict__,
+                )
+
+            # Check if initialized
+            if not self.memory_manager._initialized:
+                return jsonify(
+                    Response()
+                    .error("Memory system not initialized. Please initialize first.")
+                    .__dict__,
+                )
+
+            # Get new merge LLM provider
+            merge_llm_provider = await self.provider_manager.get_provider_by_id(
+                merge_llm_provider_id,
+            )
+
+            if not merge_llm_provider:
+                return jsonify(
+                    Response()
+                    .error(f"Merge LLM provider {merge_llm_provider_id} not found")
+                    .__dict__,
+                )
+
+            # Update merge LLM provider
+            self.memory_manager.merge_llm_provider = merge_llm_provider
+
+            logger.info(f"Updated merge LLM provider to: {merge_llm_provider_id}")
+
+            return jsonify(
+                Response()
+                .ok({"message": "Merge LLM provider updated successfully"})
+                .__dict__,
+            )
+
+        except Exception as e:
+            logger.error(f"Failed to update merge LLM provider: {e}")
+            return jsonify(Response().error(str(e)).__dict__)
--- a/astrbot/dashboard/server.py
+++ b/astrbot/dashboard/server.py
@@ -79,6 +79,7 @@ class AstrBotDashboard:
        self.persona_route = PersonaRoute(self.context, db, core_lifecycle)
        self.t2i_route = T2iRoute(self.context, core_lifecycle)
        self.kb_route = KnowledgeBaseRoute(self.context, core_lifecycle)
+        self.memory_route = MemoryRoute(self.context, db, core_lifecycle)

        self.app.add_url_rule(
            "/api/plug/<path:subpath>",
--- a/changelogs/v4.6.0.md
+++ b/changelogs/v4.6.0.md
@@ -0,0 +1,23 @@
+## What's Changed
+
+1. 新增: 支持 gemini-3 系列的 thought signature ([#3698](https://github.com/AstrBotDevs/AstrBot/issues/3698))
+2. 新增: 支持知识库的 Agentic 检索功能 ([#3667](https://github.com/AstrBotDevs/AstrBot/issues/3667))
+3. 新增: 为知识库添加 URL 文档解析器 ([#3622](https://github.com/AstrBotDevs/AstrBot/issues/3622))
+4. 修复(core.platform): 修复启用多个企业微信智能机器人适配器时消息混乱的问题 ([#3693](https://github.com/AstrBotDevs/AstrBot/issues/3693))
+5. 修复: MCP Server 连接成功一段时间后，调用 mcp 工具时可能出现 `anyio.ClosedResourceError` 错误 ([#3700](https://github.com/AstrBotDevs/AstrBot/issues/3700))
+6. 新增(chat): 重构聊天组件结构并添加新功能 ([#3701](https://github.com/AstrBotDevs/AstrBot/issues/3701))
+7. 修复(dashboard.i18n): 完善缺失的英文国际化键值 ([#3699](https://github.com/AstrBotDevs/AstrBot/issues/3699))
+8. 重构: 实现 WebChat 会话管理及从版本 4.6 迁移到 4.7
+9. 持续集成(docker-build): 每日构建 Nightly 版本 Docker 镜像 ([#3120](https://github.com/AstrBotDevs/AstrBot/issues/3120))
+
+---
+
+1. feat: add supports for gemini-3 series thought signature ([#3698](https://github.com/AstrBotDevs/AstrBot/issues/3698))
+2. feat: supports knowledge base agentic search ([#3667](https://github.com/AstrBotDevs/AstrBot/issues/3667))
+3. feat: Add URL document parser for knowledge base ([#3622](https://github.com/AstrBotDevs/AstrBot/issues/3622))
+4. fix(core.platform): fix message mix-up issue when enabling multiple WeCom AI Bot adapters ([#3693](https://github.com/AstrBotDevs/AstrBot/issues/3693))
+5. fix: fix `anyio.ClosedResourceError` that may occur when calling mcp tools after a period of successful connection to MCP Server ([#3700](https://github.com/AstrBotDevs/AstrBot/issues/3700))
+6. feat(chat): refactor chat component structure and add new features ([#3701](https://github.com/AstrBotDevs/AstrBot/issues/3701))
+7. fix(dashboard.i18n): complete the missing i18n keys for en([#3699](https://github.com/AstrBotDevs/AstrBot/issues/3699))
+8. refactor: Implement WebChat session management and migration from version 4.6 to 4.7
+9. ci(docker-build): build nightly image everyday ([#3120](https://github.com/AstrBotDevs/AstrBot/issues/3120))
--- a/dashboard/src/components/chat/Chat.vue
+++ b/dashboard/src/components/chat/Chat.vue
--- a/dashboard/src/components/chat/ChatInput.vue
+++ b/dashboard/src/components/chat/ChatInput.vue
@@ -0,0 +1,283 @@
+<template>
+    <div class="input-area fade-in">
+        <div class="input-container"
+            style="width: 85%; max-width: 900px; margin: 0 auto; border: 1px solid #e0e0e0; border-radius: 24px;">
+            <textarea 
+                ref="inputField"
+                v-model="localPrompt" 
+                @keydown="handleKeyDown"
+                :disabled="disabled" 
+                placeholder="Ask AstrBot..."
+                style="width: 100%; resize: none; outline: none; border: 1px solid var(--v-theme-border); border-radius: 12px; padding: 8px 16px; min-height: 40px; font-family: inherit; font-size: 16px; background-color: var(--v-theme-surface);"></textarea>
+            <div style="display: flex; justify-content: space-between; align-items: center; padding: 0px 12px;">
+                <div style="display: flex; justify-content: flex-start; margin-top: 4px; align-items: center; gap: 8px;">
+                    <ProviderModelSelector ref="providerModelSelectorRef" />
+                    
+                    <v-tooltip :text="enableStreaming ? tm('streaming.enabled') : tm('streaming.disabled')" location="top">
+                        <template v-slot:activator="{ props }">
+                            <v-chip v-bind="props" @click="$emit('toggleStreaming')" size="x-small" class="streaming-toggle-chip">
+                                <v-icon start :icon="enableStreaming ? 'mdi-flash' : 'mdi-flash-off'" size="small"></v-icon>
+                                {{ enableStreaming ? tm('streaming.on') : tm('streaming.off') }}
+                            </v-chip>
+                        </template>
+                    </v-tooltip>
+                </div>
+                <div style="display: flex; justify-content: flex-end; margin-top: 8px; align-items: center;">
+                    <input type="file" ref="imageInputRef" @change="handleFileSelect" accept="image/*"
+                        style="display: none" multiple />
+                    <v-progress-circular v-if="disabled" indeterminate size="16" class="mr-1" width="1.5" />
+                    <v-btn @click="triggerImageInput" icon="mdi-plus" variant="text" color="deep-purple"
+                        class="add-btn" size="small" />
+                    <v-btn @click="handleRecordClick"
+                        :icon="isRecording ? 'mdi-stop-circle' : 'mdi-microphone'" variant="text"
+                        :color="isRecording ? 'error' : 'deep-purple'" class="record-btn" size="small" />
+                    <v-btn @click="$emit('send')" icon="mdi-send" variant="text" color="deep-purple"
+                        :disabled="!canSend" class="send-btn" size="small" />
+                </div>
+            </div>
+        </div>
+
+        <!-- 附件预览区 -->
+        <div class="attachments-preview" v-if="stagedImagesUrl.length > 0 || stagedAudioUrl">
+            <div v-for="(img, index) in stagedImagesUrl" :key="index" class="image-preview">
+                <img :src="img" class="preview-image" />
+                <v-btn @click="$emit('removeImage', index)" class="remove-attachment-btn" icon="mdi-close"
+                    size="small" color="error" variant="text" />
+            </div>
+
+            <div v-if="stagedAudioUrl" class="audio-preview">
+                <v-chip color="deep-purple-lighten-4" class="audio-chip">
+                    <v-icon start icon="mdi-microphone" size="small"></v-icon>
+                    {{ tm('voice.recording') }}
+                </v-chip>
+                <v-btn @click="$emit('removeAudio')" class="remove-attachment-btn" icon="mdi-close" size="small"
+                    color="error" variant="text" />
+            </div>
+        </div>
+    </div>
+</template>
+
+<script setup lang="ts">
+import { ref, computed, onMounted, onBeforeUnmount, watch } from 'vue';
+import { useModuleI18n } from '@/i18n/composables';
+import ProviderModelSelector from './ProviderModelSelector.vue';
+
+interface Props {
+    prompt: string;
+    stagedImagesUrl: string[];
+    stagedAudioUrl: string;
+    disabled: boolean;
+    enableStreaming: boolean;
+    isRecording: boolean;
+}
+
+const props = defineProps<Props>();
+
+const emit = defineEmits<{
+    'update:prompt': [value: string];
+    send: [];
+    toggleStreaming: [];
+    removeImage: [index: number];
+    removeAudio: [];
+    startRecording: [];
+    stopRecording: [];
+    pasteImage: [event: ClipboardEvent];
+    fileSelect: [files: FileList];
+}>();
+
+const { tm } = useModuleI18n('features/chat');
+
+const inputField = ref<HTMLTextAreaElement | null>(null);
+const imageInputRef = ref<HTMLInputElement | null>(null);
+const providerModelSelectorRef = ref<InstanceType<typeof ProviderModelSelector> | null>(null);
+
+const localPrompt = computed({
+    get: () => props.prompt,
+    set: (value) => emit('update:prompt', value)
+});
+
+const canSend = computed(() => {
+    return (props.prompt && props.prompt.trim()) || props.stagedImagesUrl.length > 0 || props.stagedAudioUrl;
+});
+
+// Ctrl+B 长按录音相关
+const ctrlKeyDown = ref(false);
+const ctrlKeyTimer = ref<number | null>(null);
+const ctrlKeyLongPressThreshold = 300;
+
+function handleKeyDown(e: KeyboardEvent) {
+    // Enter 发送消息
+    if (e.keyCode === 13 && !e.shiftKey) {
+        e.preventDefault();
+        if (canSend.value) {
+            emit('send');
+        }
+    }
+
+    // Ctrl+B 录音
+    if (e.ctrlKey && e.keyCode === 66) {
+        e.preventDefault();
+        if (ctrlKeyDown.value) return;
+
+        ctrlKeyDown.value = true;
+        ctrlKeyTimer.value = window.setTimeout(() => {
+            if (ctrlKeyDown.value && !props.isRecording) {
+                emit('startRecording');
+            }
+        }, ctrlKeyLongPressThreshold);
+    }
+}
+
+function handleKeyUp(e: KeyboardEvent) {
+    if (e.keyCode === 66) {
+        ctrlKeyDown.value = false;
+
+        if (ctrlKeyTimer.value) {
+            clearTimeout(ctrlKeyTimer.value);
+            ctrlKeyTimer.value = null;
+        }
+
+        if (props.isRecording) {
+            emit('stopRecording');
+        }
+    }
+}
+
+function handlePaste(e: ClipboardEvent) {
+    emit('pasteImage', e);
+}
+
+function triggerImageInput() {
+    imageInputRef.value?.click();
+}
+
+function handleFileSelect(event: Event) {
+    const target = event.target as HTMLInputElement;
+    const files = target.files;
+    if (files) {
+        emit('fileSelect', files);
+    }
+    target.value = '';
+}
+
+function handleRecordClick() {
+    if (props.isRecording) {
+        emit('stopRecording');
+    } else {
+        emit('startRecording');
+    }
+}
+
+function getCurrentSelection() {
+    return providerModelSelectorRef.value?.getCurrentSelection();
+}
+
+onMounted(() => {
+    if (inputField.value) {
+        inputField.value.addEventListener('paste', handlePaste);
+    }
+    document.addEventListener('keyup', handleKeyUp);
+});
+
+onBeforeUnmount(() => {
+    if (inputField.value) {
+        inputField.value.removeEventListener('paste', handlePaste);
+    }
+    document.removeEventListener('keyup', handleKeyUp);
+});
+
+defineExpose({
+    getCurrentSelection
+});
+</script>
+
+<style scoped>
+.input-area {
+    padding: 16px;
+    background-color: var(--v-theme-surface);
+    position: relative;
+    border-top: 1px solid var(--v-theme-border);
+    flex-shrink: 0;
+}
+
+.attachments-preview {
+    display: flex;
+    gap: 8px;
+    margin-top: 8px;
+    max-width: 900px;
+    margin: 8px auto 0;
+    flex-wrap: wrap;
+}
+
+.image-preview,
+.audio-preview {
+    position: relative;
+    display: inline-flex;
+}
+
+.preview-image {
+    width: 60px;
+    height: 60px;
+    object-fit: cover;
+    border-radius: 8px;
+    box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
+}
+
+.audio-chip {
+    height: 36px;
+    border-radius: 18px;
+}
+
+.remove-attachment-btn {
+    position: absolute;
+    top: -8px;
+    right: -8px;
+    opacity: 0.8;
+    transition: opacity 0.2s;
+}
+
+.remove-attachment-btn:hover {
+    opacity: 1;
+}
+
+.streaming-toggle-chip {
+    cursor: pointer;
+    transition: all 0.2s ease;
+    user-select: none;
+}
+
+.streaming-toggle-chip:hover {
+    opacity: 0.8;
+}
+
+.fade-in {
+    animation: fadeIn 0.3s ease-in-out;
+}
+
+@keyframes fadeIn {
+    from {
+        opacity: 0;
+        transform: translateY(10px);
+    }
+    to {
+        opacity: 1;
+        transform: translateY(0);
+    }
+}
+
+@media (max-width: 768px) {
+    .input-area {
+        padding: 0 !important;
+    }
+    
+    .input-container {
+        width: 100% !important;
+        max-width: 100% !important;
+        margin: 0 !important;
+        border-radius: 0 !important;
+        border-left: none !important;
+        border-right: none !important;
+        border-bottom: none !important;
+    }
+}
+</style>
--- a/dashboard/src/components/chat/ConversationSidebar.vue
+++ b/dashboard/src/components/chat/ConversationSidebar.vue
@@ -0,0 +1,295 @@
+<template>
+    <div class="sidebar-panel" 
+        :class="{ 
+            'sidebar-collapsed': sidebarCollapsed && !isMobile,
+            'mobile-sidebar-open': isMobile && mobileMenuOpen,
+            'mobile-sidebar': isMobile
+        }"
+        :style="{ 'background-color': isDark ? sidebarCollapsed ? '#1e1e1e' : '#2d2d2d' : sidebarCollapsed ? '#ffffff' : '#f1f4f9' }"
+        @mouseenter="handleSidebarMouseEnter" 
+        @mouseleave="handleSidebarMouseLeave">
+
+        <div style="display: flex; align-items: center; justify-content: center; padding: 16px; padding-bottom: 0px;"
+            v-if="chatboxMode">
+            <img width="50" src="@/assets/images/icon-no-shadow.svg" alt="AstrBot Logo">
+            <span v-if="!sidebarCollapsed"
+                style="font-weight: 1000; font-size: 26px; margin-left: 8px;">AstrBot</span>
+        </div>
+
+        <div class="sidebar-collapse-btn-container" v-if="!isMobile">
+            <v-btn icon class="sidebar-collapse-btn" @click="toggleSidebar" variant="text" color="deep-purple">
+                <v-icon>{{ (sidebarCollapsed || (!sidebarCollapsed && sidebarHoverExpanded)) ?
+                    'mdi-chevron-right' : 'mdi-chevron-left' }}</v-icon>
+            </v-btn>
+        </div>
+
+        <div class="sidebar-collapse-btn-container" v-if="isMobile">
+            <v-btn icon class="sidebar-collapse-btn" @click="$emit('closeMobileSidebar')" variant="text"
+                color="deep-purple">
+                <v-icon>mdi-close</v-icon>
+            </v-btn>
+        </div>
+
+        <div style="padding: 16px; padding-top: 8px;">
+            <v-btn block variant="text" class="new-chat-btn" @click="$emit('newChat')" :disabled="!currSessionId"
+                v-if="!sidebarCollapsed || isMobile" prepend-icon="mdi-plus"
+                style="background-color: transparent !important; border-radius: 4px;">{{ tm('actions.newChat') }}</v-btn>
+            <v-btn icon="mdi-plus" rounded="lg" @click="$emit('newChat')" :disabled="!currSessionId" 
+                v-if="sidebarCollapsed && !isMobile" elevation="0"></v-btn>
+        </div>
+        
+        <div v-if="!sidebarCollapsed || isMobile">
+            <v-divider class="mx-4"></v-divider>
+        </div>
+
+        <div style="overflow-y: auto; flex-grow: 1;" :class="{ 'fade-in': sidebarHoverExpanded }"
+            v-if="!sidebarCollapsed || isMobile">
+            <v-card v-if="sessions.length > 0" flat style="background-color: transparent;">
+                <v-list density="compact" nav class="conversation-list"
+                    style="background-color: transparent;" :selected="selectedSessions"
+                    @update:selected="$emit('selectConversation', $event)">
+                    <v-list-item v-for="item in sessions" :key="item.session_id" :value="item.session_id"
+                        rounded="lg" class="conversation-item" active-color="secondary">
+                        <v-list-item-title v-if="!sidebarCollapsed || isMobile" class="conversation-title">
+                            {{ item.display_name || tm('conversation.newConversation') }}
+                        </v-list-item-title>
+                        <v-list-item-subtitle v-if="!sidebarCollapsed || isMobile" class="timestamp">
+                            {{ new Date(item.updated_at).toLocaleString() }}
+                        </v-list-item-subtitle>
+
+                        <template v-if="!sidebarCollapsed || isMobile" v-slot:append>
+                            <div class="conversation-actions">
+                                <v-btn icon="mdi-pencil" size="x-small" variant="text"
+                                    class="edit-title-btn"
+                                    @click.stop="$emit('editTitle', item.session_id, item.display_name)" />
+                                <v-btn icon="mdi-delete" size="x-small" variant="text"
+                                    class="delete-conversation-btn" color="error"
+                                    @click.stop="$emit('deleteConversation', item.session_id)" />
+                            </div>
+                        </template>
+                    </v-list-item>
+                </v-list>
+            </v-card>
+
+            <v-fade-transition>
+                <div class="no-conversations" v-if="sessions.length === 0">
+                    <v-icon icon="mdi-message-text-outline" size="large" color="grey-lighten-1"></v-icon>
+                    <div class="no-conversations-text" v-if="!sidebarCollapsed || sidebarHoverExpanded || isMobile">
+                        {{ tm('conversation.noHistory') }}
+                    </div>
+                </div>
+            </v-fade-transition>
+        </div>
+    </div>
+</template>
+
+<script setup lang="ts">
+import { ref } from 'vue';
+import { useI18n, useModuleI18n } from '@/i18n/composables';
+import type { Session } from '@/composables/useSessions';
+
+interface Props {
+    sessions: Session[];
+    selectedSessions: string[];
+    currSessionId: string;
+    isDark: boolean;
+    chatboxMode: boolean;
+    isMobile: boolean;
+    mobileMenuOpen: boolean;
+}
+
+const props = defineProps<Props>();
+
+const emit = defineEmits<{
+    newChat: [];
+    selectConversation: [sessionIds: string[]];
+    editTitle: [sessionId: string, title: string];
+    deleteConversation: [sessionId: string];
+    closeMobileSidebar: [];
+}>();
+
+const { tm } = useModuleI18n('features/chat');
+const { t } = useI18n();
+
+const sidebarCollapsed = ref(true);
+const sidebarHovered = ref(false);
+const sidebarHoverTimer = ref<number | null>(null);
+const sidebarHoverExpanded = ref(false);
+const sidebarHoverDelay = 100;
+
+// 从 localStorage 读取侧边栏折叠状态
+const savedCollapsedState = localStorage.getItem('sidebarCollapsed');
+if (savedCollapsedState !== null) {
+    sidebarCollapsed.value = JSON.parse(savedCollapsedState);
+} else {
+    sidebarCollapsed.value = true;
+}
+
+function toggleSidebar() {
+    if (sidebarHoverExpanded.value) {
+        sidebarHoverExpanded.value = false;
+        return;
+    }
+    sidebarCollapsed.value = !sidebarCollapsed.value;
+    localStorage.setItem('sidebarCollapsed', JSON.stringify(sidebarCollapsed.value));
+}
+
+function handleSidebarMouseEnter() {
+    if (!sidebarCollapsed.value || props.isMobile) return;
+
+    sidebarHovered.value = true;
+    sidebarHoverTimer.value = window.setTimeout(() => {
+        if (sidebarHovered.value) {
+            sidebarHoverExpanded.value = true;
+            sidebarCollapsed.value = false;
+        }
+    }, sidebarHoverDelay);
+}
+
+function handleSidebarMouseLeave() {
+    sidebarHovered.value = false;
+
+    if (sidebarHoverTimer.value) {
+        clearTimeout(sidebarHoverTimer.value);
+        sidebarHoverTimer.value = null;
+    }
+
+    if (sidebarHoverExpanded.value) {
+        sidebarCollapsed.value = true;
+    }
+    sidebarHoverExpanded.value = false;
+}
+</script>
+
+<style scoped>
+.sidebar-panel {
+    max-width: 270px;
+    min-width: 240px;
+    display: flex;
+    flex-direction: column;
+    padding: 0;
+    border-right: 1px solid rgba(0, 0, 0, 0.04);
+    height: 100%;
+    max-height: 100%;
+    position: relative;
+    transition: all 0.3s ease;
+    overflow: hidden;
+}
+
+.sidebar-collapsed {
+    max-width: 75px;
+    min-width: 75px;
+    transition: all 0.3s ease;
+}
+
+.mobile-sidebar {
+    position: fixed;
+    top: 0;
+    left: 0;
+    bottom: 0;
+    max-width: 280px !important;
+    min-width: 280px !important;
+    transform: translateX(-100%);
+    transition: transform 0.3s ease;
+    z-index: 1000;
+}
+
+.mobile-sidebar-open {
+    transform: translateX(0) !important;
+}
+
+.sidebar-collapse-btn-container {
+    margin: 16px;
+    margin-bottom: 0px;
+    z-index: 10;
+}
+
+.sidebar-collapse-btn {
+    opacity: 0.6;
+    max-height: none;
+    overflow-y: visible;
+    padding: 0;
+}
+
+.conversation-item {
+    margin-bottom: 4px;
+    border-radius: 8px !important;
+    transition: all 0.2s ease;
+    height: auto !important;
+    min-height: 56px;
+    padding: 8px 16px !important;
+    position: relative;
+}
+
+.conversation-item:hover {
+    background-color: rgba(103, 58, 183, 0.05);
+}
+
+.conversation-item:hover .conversation-actions {
+    opacity: 1;
+    visibility: visible;
+}
+
+.conversation-actions {
+    display: flex;
+    gap: 4px;
+    opacity: 0;
+    visibility: hidden;
+    transition: all 0.2s ease;
+}
+
+.edit-title-btn,
+.delete-conversation-btn {
+    opacity: 0.7;
+    transition: opacity 0.2s ease;
+}
+
+.edit-title-btn:hover,
+.delete-conversation-btn:hover {
+    opacity: 1;
+}
+
+.conversation-title {
+    font-weight: 500;
+    font-size: 14px;
+    line-height: 1.3;
+    margin-bottom: 2px;
+    transition: opacity 0.25s ease;
+}
+
+.timestamp {
+    font-size: 11px;
+    color: var(--v-theme-secondaryText);
+    line-height: 1;
+    transition: opacity 0.25s ease;
+}
+
+.no-conversations {
+    display: flex;
+    flex-direction: column;
+    align-items: center;
+    justify-content: center;
+    height: 150px;
+    opacity: 0.6;
+    gap: 12px;
+}
+
+.no-conversations-text {
+    font-size: 14px;
+    color: var(--v-theme-secondaryText);
+    transition: opacity 0.25s ease;
+}
+
+.fade-in {
+    animation: fadeInContent 0.3s ease;
+}
+
+@keyframes fadeInContent {
+    from {
+        opacity: 0;
+    }
+    to {
+        opacity: 1;
+    }
+}
+</style>
--- a/dashboard/src/components/chat/MessageList.vue
+++ b/dashboard/src/components/chat/MessageList.vue
@@ -37,42 +37,49 @@
                    </v-avatar>
                    <div class="bot-message-content">
                        <div class="message-bubble bot-bubble">
-                            <!-- Reasoning Block (Collapsible) -->
-                            <div v-if="msg.content.reasoning && msg.content.reasoning.trim()" class="reasoning-container">
-                                <div class="reasoning-header" @click="toggleReasoning(index)">
-                                    <v-icon size="small" class="reasoning-icon">
-                                        {{ isReasoningExpanded(index) ? 'mdi-chevron-down' : 'mdi-chevron-right' }}
-                                    </v-icon>
-                                    <span class="reasoning-label">{{ tm('reasoning.thinking') }}</span>
-                                </div>
-                                <div v-if="isReasoningExpanded(index)" class="reasoning-content">
-                                    <div v-html="md.render(msg.content.reasoning)" class="markdown-content reasoning-text"></div>
-                                </div>
+                            <!-- Loading state -->
+                            <div v-if="msg.content.isLoading" class="loading-container">
+                                <span class="loading-text">{{ tm('message.loading') }}</span>
                            </div>
                            
-                            <!-- Text -->
-                            <div v-if="msg.content.message && msg.content.message.trim()"
-                                v-html="md.render(msg.content.message)" class="markdown-content"></div>
-
-                            <!-- Image -->
-                            <div class="embedded-images"
-                                v-if="msg.content.embedded_images && msg.content.embedded_images.length > 0">
-                                <div v-for="(img, imgIndex) in msg.content.embedded_images" :key="imgIndex"
-                                    class="embedded-image">
-                                    <img :src="img" class="bot-embedded-image"
-                                        @click="$emit('openImagePreview', img)" />
+                            <template v-else>
+                                <!-- Reasoning Block (Collapsible) -->
+                                <div v-if="msg.content.reasoning && msg.content.reasoning.trim()" class="reasoning-container">
+                                    <div class="reasoning-header" @click="toggleReasoning(index)">
+                                        <v-icon size="small" class="reasoning-icon">
+                                            {{ isReasoningExpanded(index) ? 'mdi-chevron-down' : 'mdi-chevron-right' }}
+                                        </v-icon>
+                                        <span class="reasoning-label">{{ tm('reasoning.thinking') }}</span>
+                                    </div>
+                                    <div v-if="isReasoningExpanded(index)" class="reasoning-content">
+                                        <div v-html="md.render(msg.content.reasoning)" class="markdown-content reasoning-text"></div>
+                                    </div>
                                </div>
-                            </div>
                                
-                            <!-- Audio -->
-                            <div class="embedded-audio" v-if="msg.content.embedded_audio">
-                                <audio controls class="audio-player">
-                                    <source :src="msg.content.embedded_audio" type="audio/wav">
-                                    {{ t('messages.errors.browser.audioNotSupported') }}
-                                </audio>
-                            </div>
+                                <!-- Text -->
+                                <div v-if="msg.content.message && msg.content.message.trim()"
+                                    v-html="md.render(msg.content.message)" class="markdown-content"></div>
+
+                                <!-- Image -->
+                                <div class="embedded-images"
+                                    v-if="msg.content.embedded_images && msg.content.embedded_images.length > 0">
+                                    <div v-for="(img, imgIndex) in msg.content.embedded_images" :key="imgIndex"
+                                        class="embedded-image">
+                                        <img :src="img" class="bot-embedded-image"
+                                            @click="$emit('openImagePreview', img)" />
+                                    </div>
+                                </div>
+
+                                <!-- Audio -->
+                                <div class="embedded-audio" v-if="msg.content.embedded_audio">
+                                    <audio controls class="audio-player">
+                                        <source :src="msg.content.embedded_audio" type="audio/wav">
+                                        {{ t('messages.errors.browser.audioNotSupported') }}
+                                    </audio>
+                                </div>
+                            </template>
                        </div>
-                        <div class="message-actions">
+                        <div class="message-actions" v-if="!msg.content.isLoading">
                            <v-btn :icon="getCopyIcon(index)" size="small" variant="text" class="copy-message-btn"
                                :class="{ 'copy-success': isCopySuccess(index) }"
                                @click="copyBotMessage(msg.content.message, index)" :title="t('core.common.copy')" />
@@ -841,6 +848,29 @@ export default {
    margin: 10px 0;
 }

+.loading-container {
+    display: flex;
+    align-items: center;
+    gap: 12px;
+    padding: 8px 0;
+    margin-top: 2px;
+}
+
+.loading-text {
+    font-size: 14px;
+    color: var(--v-theme-secondaryText);
+    animation: pulse 1.5s ease-in-out infinite;
+}
+
+@keyframes pulse {
+    0%, 100% {
+        opacity: 0.6;
+    }
+    50% {
+        opacity: 1;
+    }
+}
+
 .markdown-content blockquote {
    border-left: 4px solid var(--v-theme-secondary);
    padding-left: 16px;
--- a/dashboard/src/composables/useConversations.ts
+++ b/dashboard/src/composables/useConversations.ts
@@ -0,0 +1,145 @@
+import { ref, computed } from 'vue';
+import axios from 'axios';
+import { useRouter } from 'vue-router';
+
+export interface Conversation {
+    cid: string;
+    title: string;
+    updated_at: number;
+}
+
+export function useConversations(chatboxMode: boolean = false) {
+    const router = useRouter();
+    const conversations = ref<Conversation[]>([]);
+    const selectedConversations = ref<string[]>([]);
+    const currCid = ref('');
+    const pendingCid = ref<string | null>(null);
+
+    // 编辑标题相关
+    const editTitleDialog = ref(false);
+    const editingTitle = ref('');
+    const editingCid = ref('');
+
+    const getCurrentConversation = computed(() => {
+        if (!currCid.value) return null;
+        return conversations.value.find(c => c.cid === currCid.value);
+    });
+
+    async function getConversations() {
+        try {
+            const response = await axios.get('/api/chat/conversations');
+            conversations.value = response.data.data;
+
+            // 处理待加载的会话
+            if (pendingCid.value) {
+                const conversation = conversations.value.find(c => c.cid === pendingCid.value);
+                if (conversation) {
+                    selectedConversations.value = [pendingCid.value];
+                    pendingCid.value = null;
+                }
+            } else if (!currCid.value && conversations.value.length > 0) {
+                // 默认选择第一个会话
+                const firstConversation = conversations.value[0];
+                selectedConversations.value = [firstConversation.cid];
+            }
+        } catch (err: any) {
+            if (err.response?.status === 401) {
+                router.push('/auth/login?redirect=/chatbox');
+            }
+            console.error(err);
+        }
+    }
+
+    async function newConversation() {
+        try {
+            const response = await axios.get('/api/chat/new_conversation');
+            const cid = response.data.data.conversation_id;
+            currCid.value = cid;
+
+            // 更新 URL
+            const basePath = chatboxMode ? '/chatbox' : '/chat';
+            router.push(`${basePath}/${cid}`);
+            
+            await getConversations();
+            return cid;
+        } catch (err) {
+            console.error(err);
+            throw err;
+        }
+    }
+
+    async function deleteConversation(cid: string) {
+        try {
+            await axios.get('/api/chat/delete_conversation?conversation_id=' + cid);
+            await getConversations();
+            currCid.value = '';
+            selectedConversations.value = [];
+        } catch (err) {
+            console.error(err);
+        }
+    }
+
+    function showEditTitleDialog(cid: string, title: string) {
+        editingCid.value = cid;
+        editingTitle.value = title || '';
+        editTitleDialog.value = true;
+    }
+
+    async function saveTitle() {
+        if (!editingCid.value) return;
+
+        const trimmedTitle = editingTitle.value.trim();
+        try {
+            await axios.post('/api/chat/rename_conversation', {
+                conversation_id: editingCid.value,
+                title: trimmedTitle
+            });
+
+            // 更新本地会话标题
+            const conversation = conversations.value.find(c => c.cid === editingCid.value);
+            if (conversation) {
+                conversation.title = trimmedTitle;
+            }
+            editTitleDialog.value = false;
+        } catch (err) {
+            console.error('重命名对话失败:', err);
+        }
+    }
+
+    function updateConversationTitle(cid: string, title: string) {
+        const conversation = conversations.value.find(c => c.cid === cid);
+        if (conversation) {
+            conversation.title = title;
+        }
+    }
+
+    function newChat(closeMobileSidebar?: () => void) {
+        currCid.value = '';
+        selectedConversations.value = [];
+        
+        const basePath = chatboxMode ? '/chatbox' : '/chat';
+        router.push(basePath);
+        
+        if (closeMobileSidebar) {
+            closeMobileSidebar();
+        }
+    }
+
+    return {
+        conversations,
+        selectedConversations,
+        currCid,
+        pendingCid,
+        editTitleDialog,
+        editingTitle,
+        editingCid,
+        getCurrentConversation,
+        getConversations,
+        newConversation,
+        deleteConversation,
+        showEditTitleDialog,
+        saveTitle,
+        updateConversationTitle,
+        newChat
+    };
+}
--- a/dashboard/src/composables/useMediaHandling.ts
+++ b/dashboard/src/composables/useMediaHandling.ts
@@ -0,0 +1,104 @@
+import { ref } from 'vue';
+import axios from 'axios';
+
+export function useMediaHandling() {
+    const stagedImagesName = ref<string[]>([]);
+    const stagedImagesUrl = ref<string[]>([]);
+    const stagedAudioUrl = ref<string>('');
+    const mediaCache = ref<Record<string, string>>({});
+
+    async function getMediaFile(filename: string): Promise<string> {
+        if (mediaCache.value[filename]) {
+            return mediaCache.value[filename];
+        }
+
+        try {
+            const response = await axios.get('/api/chat/get_file', {
+                params: { filename },
+                responseType: 'blob'
+            });
+
+            const blobUrl = URL.createObjectURL(response.data);
+            mediaCache.value[filename] = blobUrl;
+            return blobUrl;
+        } catch (error) {
+            console.error('Error fetching media file:', error);
+            return '';
+        }
+    }
+
+    async function processAndUploadImage(file: File) {
+        const formData = new FormData();
+        formData.append('file', file);
+
+        try {
+            const response = await axios.post('/api/chat/post_image', formData, {
+                headers: {
+                    'Content-Type': 'multipart/form-data'
+                }
+            });
+
+            const img = response.data.data.filename;
+            stagedImagesName.value.push(img);
+            stagedImagesUrl.value.push(URL.createObjectURL(file));
+        } catch (err) {
+            console.error('Error uploading image:', err);
+        }
+    }
+
+    async function handlePaste(event: ClipboardEvent) {
+        const items = event.clipboardData?.items;
+        if (!items) return;
+
+        for (let i = 0; i < items.length; i++) {
+            if (items[i].type.indexOf('image') !== -1) {
+                const file = items[i].getAsFile();
+                if (file) {
+                    await processAndUploadImage(file);
+                }
+            }
+        }
+    }
+
+    function removeImage(index: number) {
+        const urlToRevoke = stagedImagesUrl.value[index];
+        if (urlToRevoke && urlToRevoke.startsWith('blob:')) {
+            URL.revokeObjectURL(urlToRevoke);
+        }
+
+        stagedImagesName.value.splice(index, 1);
+        stagedImagesUrl.value.splice(index, 1);
+    }
+
+    function removeAudio() {
+        stagedAudioUrl.value = '';
+    }
+
+    function clearStaged() {
+        stagedImagesName.value = [];
+        stagedImagesUrl.value = [];
+        stagedAudioUrl.value = '';
+    }
+
+    function cleanupMediaCache() {
+        Object.values(mediaCache.value).forEach(url => {
+            if (url.startsWith('blob:')) {
+                URL.revokeObjectURL(url);
+            }
+        });
+        mediaCache.value = {};
+    }
+
+    return {
+        stagedImagesName,
+        stagedImagesUrl,
+        stagedAudioUrl,
+        getMediaFile,
+        processAndUploadImage,
+        handlePaste,
+        removeImage,
+        removeAudio,
+        clearStaged,
+        cleanupMediaCache
+    };
+}
--- a/dashboard/src/composables/useMessages.ts
+++ b/dashboard/src/composables/useMessages.ts
@@ -0,0 +1,303 @@
+import { ref, reactive, type Ref } from 'vue';
+import axios from 'axios';
+import { useToast } from '@/utils/toast';
+
+export interface MessageContent {
+    type: string;
+    message: string;
+    reasoning?: string;
+    image_url?: string[];
+    audio_url?: string;
+    embedded_images?: string[];
+    embedded_audio?: string;
+    isLoading?: boolean;
+}
+
+export interface Message {
+    content: MessageContent;
+}
+
+export function useMessages(
+    currSessionId: Ref<string>,
+    getMediaFile: (filename: string) => Promise<string>,
+    updateSessionTitle: (sessionId: string, title: string) => void,
+    onSessionsUpdate: () => void
+) {
+    const messages = ref<Message[]>([]);
+    const isStreaming = ref(false);
+    const isConvRunning = ref(false);
+    const isToastedRunningInfo = ref(false);
+    const activeSSECount = ref(0);
+    const enableStreaming = ref(true);
+
+    // 从 localStorage 读取流式响应开关状态
+    const savedStreamingState = localStorage.getItem('enableStreaming');
+    if (savedStreamingState !== null) {
+        enableStreaming.value = JSON.parse(savedStreamingState);
+    }
+
+    function toggleStreaming() {
+        enableStreaming.value = !enableStreaming.value;
+        localStorage.setItem('enableStreaming', JSON.stringify(enableStreaming.value));
+    }
+
+    async function getSessionMessages(sessionId: string, router: any) {
+        if (!sessionId) return;
+
+        try {
+            const response = await axios.get('/api/chat/get_session?session_id=' + sessionId);
+            isConvRunning.value = response.data.data.is_running || false;
+            let history = response.data.data.history;
+
+            if (isConvRunning.value) {
+                if (!isToastedRunningInfo.value) {
+                    useToast().info("该会话正在运行中。", { timeout: 5000 });
+                    isToastedRunningInfo.value = true;
+                }
+
+                // 如果会话还在运行，3秒后重新获取消息
+                setTimeout(() => {
+                    getSessionMessages(currSessionId.value, router);
+                }, 3000);
+            }
+
+            // 处理历史消息中的媒体文件
+            for (let i = 0; i < history.length; i++) {
+                let content = history[i].content;
+                
+                if (content.message?.startsWith('[IMAGE]')) {
+                    let img = content.message.replace('[IMAGE]', '');
+                    const imageUrl = await getMediaFile(img);
+                    if (!content.embedded_images) {
+                        content.embedded_images = [];
+                    }
+                    content.embedded_images.push(imageUrl);
+                    content.message = '';
+                }
+
+                if (content.message?.startsWith('[RECORD]')) {
+                    let audio = content.message.replace('[RECORD]', '');
+                    const audioUrl = await getMediaFile(audio);
+                    content.embedded_audio = audioUrl;
+                    content.message = '';
+                }
+
+                if (content.image_url && content.image_url.length > 0) {
+                    for (let j = 0; j < content.image_url.length; j++) {
+                        content.image_url[j] = await getMediaFile(content.image_url[j]);
+                    }
+                }
+
+                if (content.audio_url) {
+                    content.audio_url = await getMediaFile(content.audio_url);
+                }
+            }
+            
+            messages.value = history;
+        } catch (err) {
+            console.error(err);
+        }
+    }
+
+    async function sendMessage(
+        prompt: string,
+        imageNames: string[],
+        audioName: string,
+        selectedProviderId: string,
+        selectedModelName: string
+    ) {
+        // Create user message
+        const userMessage: MessageContent = {
+            type: 'user',
+            message: prompt,
+            image_url: [],
+            audio_url: undefined
+        };
+
+        // Convert image filenames to blob URLs
+        if (imageNames.length > 0) {
+            const imagePromises = imageNames.map(name => {
+                if (!name.startsWith('blob:')) {
+                    return getMediaFile(name);
+                }
+                return Promise.resolve(name);
+            });
+            userMessage.image_url = await Promise.all(imagePromises);
+        }
+
+        // Convert audio filename to blob URL
+        if (audioName) {
+            if (!audioName.startsWith('blob:')) {
+                userMessage.audio_url = await getMediaFile(audioName);
+            } else {
+                userMessage.audio_url = audioName;
+            }
+        }
+
+        messages.value.push({ content: userMessage });
+
+        // 添加一个加载中的机器人消息占位符
+        const loadingMessage = reactive({
+            type: 'bot',
+            message: '',
+            reasoning: '',
+            isLoading: true
+        });
+        messages.value.push({ content: loadingMessage });
+
+        try {
+            activeSSECount.value++;
+            if (activeSSECount.value === 1) {
+                isConvRunning.value = true;
+            }
+
+            const response = await fetch('/api/chat/send', {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json',
+                    'Authorization': 'Bearer ' + localStorage.getItem('token')
+                },
+                body: JSON.stringify({
+                    message: prompt,
+                    session_id: currSessionId.value,
+                    image_url: imageNames,
+                    audio_url: audioName ? [audioName] : [],
+                    selected_provider: selectedProviderId,
+                    selected_model: selectedModelName,
+                    enable_streaming: enableStreaming.value
+                })
+            });
+
+            if (!response.ok) {
+                throw new Error(`HTTP error! status: ${response.status}`);
+            }
+
+            const reader = response.body!.getReader();
+            const decoder = new TextDecoder();
+            let in_streaming = false;
+            let message_obj: any = null;
+
+            isStreaming.value = true;
+
+            while (true) {
+                try {
+                    const { done, value } = await reader.read();
+                    if (done) {
+                        console.log('SSE stream completed');
+                        break;
+                    }
+
+                    const chunk = decoder.decode(value, { stream: true });
+                    const lines = chunk.split('\n\n');
+
+                    for (let i = 0; i < lines.length; i++) {
+                        let line = lines[i].trim();
+                        if (!line) continue;
+
+                        let chunk_json;
+                        try {
+                            chunk_json = JSON.parse(line.replace('data: ', ''));
+                        } catch (parseError) {
+                            console.warn('JSON解析失败:', line, parseError);
+                            continue;
+                        }
+
+                        if (!chunk_json || typeof chunk_json !== 'object' || !chunk_json.hasOwnProperty('type')) {
+                            console.warn('无效的数据对象:', chunk_json);
+                            continue;
+                        }
+
+                        if (chunk_json.type === 'error') {
+                            console.error('Error received:', chunk_json.data);
+                            continue;
+                        }
+
+                        if (chunk_json.type === 'image') {
+                            let img = chunk_json.data.replace('[IMAGE]', '');
+                            const imageUrl = await getMediaFile(img);
+                            let bot_resp: MessageContent = {
+                                type: 'bot',
+                                message: '',
+                                embedded_images: [imageUrl]
+                            };
+                            messages.value.push({ content: bot_resp });
+                        } else if (chunk_json.type === 'record') {
+                            let audio = chunk_json.data.replace('[RECORD]', '');
+                            const audioUrl = await getMediaFile(audio);
+                            let bot_resp: MessageContent = {
+                                type: 'bot',
+                                message: '',
+                                embedded_audio: audioUrl
+                            };
+                            messages.value.push({ content: bot_resp });
+                        } else if (chunk_json.type === 'plain') {
+                            const chain_type = chunk_json.chain_type || 'normal';
+                            
+                            if (!in_streaming) {
+                                // 移除加载占位符
+                                const lastMsg = messages.value[messages.value.length - 1];
+                                if (lastMsg?.content?.isLoading) {
+                                    messages.value.pop();
+                                }
+                                
+                                message_obj = reactive({
+                                    type: 'bot',
+                                    message: chain_type === 'reasoning' ? '' : chunk_json.data,
+                                    reasoning: chain_type === 'reasoning' ? chunk_json.data : '',
+                                });
+                                messages.value.push({ content: message_obj });
+                                in_streaming = true;
+                            } else {
+                                if (chain_type === 'reasoning') {
+                                    // 使用 reactive 对象，直接修改属性会触发响应式更新
+                                    message_obj.reasoning = (message_obj.reasoning || '') + chunk_json.data;
+                                } else {
+                                    message_obj.message = (message_obj.message || '') + chunk_json.data;
+                                }
+                            }
+                        } else if (chunk_json.type === 'update_title') {
+                            updateSessionTitle(chunk_json.session_id, chunk_json.data);
+                        }
+
+                        if ((chunk_json.type === 'break' && chunk_json.streaming) || !chunk_json.streaming) {
+                            in_streaming = false;
+                            if (!chunk_json.streaming) {
+                                isStreaming.value = false;
+                            }
+                        }
+                    }
+                } catch (readError) {
+                    console.error('SSE读取错误:', readError);
+                    break;
+                }
+            }
+
+            // 获取最新的会话列表
+            onSessionsUpdate();
+
+        } catch (err) {
+            console.error('发送消息失败:', err);
+            // 移除加载占位符
+            const lastMsg = messages.value[messages.value.length - 1];
+            if (lastMsg?.content?.isLoading) {
+                messages.value.pop();
+            }
+        } finally {
+            isStreaming.value = false;
+            activeSSECount.value--;
+            if (activeSSECount.value === 0) {
+                isConvRunning.value = false;
+            }
+        }
+    }
+
+    return {
+        messages,
+        isStreaming,
+        isConvRunning,
+        enableStreaming,
+        getSessionMessages,
+        sendMessage,
+        toggleStreaming
+    };
+}
--- a/dashboard/src/composables/useRecording.ts
+++ b/dashboard/src/composables/useRecording.ts
@@ -0,0 +1,74 @@
+import { ref } from 'vue';
+import axios from 'axios';
+
+export function useRecording() {
+    const isRecording = ref(false);
+    const audioChunks = ref<Blob[]>([]);
+    const mediaRecorder = ref<MediaRecorder | null>(null);
+
+    async function startRecording(onStart?: (label: string) => void) {
+        try {
+            const stream = await navigator.mediaDevices.getUserMedia({ audio: true });
+            mediaRecorder.value = new MediaRecorder(stream);
+            
+            mediaRecorder.value.ondataavailable = (event) => {
+                audioChunks.value.push(event.data);
+            };
+            
+            mediaRecorder.value.start();
+            isRecording.value = true;
+            
+            if (onStart) {
+                onStart('录音中...');
+            }
+        } catch (error) {
+            console.error('Failed to start recording:', error);
+        }
+    }
+
+    async function stopRecording(onStop?: (label: string) => void): Promise<string> {
+        return new Promise((resolve, reject) => {
+            if (!mediaRecorder.value) {
+                reject('No media recorder');
+                return;
+            }
+
+            isRecording.value = false;
+            if (onStop) {
+                onStop('聊天输入框');
+            }
+
+            mediaRecorder.value.stop();
+            mediaRecorder.value.onstop = async () => {
+                const audioBlob = new Blob(audioChunks.value, { type: 'audio/wav' });
+                audioChunks.value = [];
+
+                mediaRecorder.value?.stream.getTracks().forEach(track => track.stop());
+
+                const formData = new FormData();
+                formData.append('file', audioBlob);
+
+                try {
+                    const response = await axios.post('/api/chat/post_file', formData, {
+                        headers: {
+                            'Content-Type': 'multipart/form-data'
+                        }
+                    });
+
+                    const audio = response.data.data.filename;
+                    console.log('Audio uploaded:', audio);
+                    resolve(audio);
+                } catch (err) {
+                    console.error('Error uploading audio:', err);
+                    reject(err);
+                }
+            };
+        });
+    }
+
+    return {
+        isRecording,
+        startRecording,
+        stopRecording
+    };
+}
--- a/dashboard/src/composables/useSessions.ts
+++ b/dashboard/src/composables/useSessions.ts
@@ -0,0 +1,145 @@
+import { ref, computed } from 'vue';
+import axios from 'axios';
+import { useRouter } from 'vue-router';
+
+export interface Session {
+    session_id: string;
+    display_name: string;
+    updated_at: string;
+}
+
+export function useSessions(chatboxMode: boolean = false) {
+    const router = useRouter();
+    const sessions = ref<Session[]>([]);
+    const selectedSessions = ref<string[]>([]);
+    const currSessionId = ref('');
+    const pendingSessionId = ref<string | null>(null);
+
+    // 编辑标题相关
+    const editTitleDialog = ref(false);
+    const editingTitle = ref('');
+    const editingSessionId = ref('');
+
+    const getCurrentSession = computed(() => {
+        if (!currSessionId.value) return null;
+        return sessions.value.find(s => s.session_id === currSessionId.value);
+    });
+
+    async function getSessions() {
+        try {
+            const response = await axios.get('/api/chat/sessions');
+            sessions.value = response.data.data;
+
+            // 处理待加载的会话
+            if (pendingSessionId.value) {
+                const session = sessions.value.find(s => s.session_id === pendingSessionId.value);
+                if (session) {
+                    selectedSessions.value = [pendingSessionId.value];
+                    pendingSessionId.value = null;
+                }
+            } else if (!currSessionId.value && sessions.value.length > 0) {
+                // 默认选择第一个会话
+                const firstSession = sessions.value[0];
+                selectedSessions.value = [firstSession.session_id];
+            }
+        } catch (err: any) {
+            if (err.response?.status === 401) {
+                router.push('/auth/login?redirect=/chatbox');
+            }
+            console.error(err);
+        }
+    }
+
+    async function newSession() {
+        try {
+            const response = await axios.get('/api/chat/new_session');
+            const sessionId = response.data.data.session_id;
+            currSessionId.value = sessionId;
+
+            // 更新 URL
+            const basePath = chatboxMode ? '/chatbox' : '/chat';
+            router.push(`${basePath}/${sessionId}`);
+            
+            await getSessions();
+            return sessionId;
+        } catch (err) {
+            console.error(err);
+            throw err;
+        }
+    }
+
+    async function deleteSession(sessionId: string) {
+        try {
+            await axios.get('/api/chat/delete_session?session_id=' + sessionId);
+            await getSessions();
+            currSessionId.value = '';
+            selectedSessions.value = [];
+        } catch (err) {
+            console.error(err);
+        }
+    }
+
+    function showEditTitleDialog(sessionId: string, title: string) {
+        editingSessionId.value = sessionId;
+        editingTitle.value = title || '';
+        editTitleDialog.value = true;
+    }
+
+    async function saveTitle() {
+        if (!editingSessionId.value) return;
+
+        const trimmedTitle = editingTitle.value.trim();
+        try {
+            await axios.post('/api/chat/update_session_display_name', {
+                session_id: editingSessionId.value,
+                display_name: trimmedTitle
+            });
+
+            // 更新本地会话标题
+            const session = sessions.value.find(s => s.session_id === editingSessionId.value);
+            if (session) {
+                session.display_name = trimmedTitle;
+            }
+            editTitleDialog.value = false;
+        } catch (err) {
+            console.error('重命名会话失败:', err);
+        }
+    }
+
+    function updateSessionTitle(sessionId: string, title: string) {
+        const session = sessions.value.find(s => s.session_id === sessionId);
+        if (session) {
+            session.display_name = title;
+        }
+    }
+
+    function newChat(closeMobileSidebar?: () => void) {
+        currSessionId.value = '';
+        selectedSessions.value = [];
+        
+        const basePath = chatboxMode ? '/chatbox' : '/chat';
+        router.push(basePath);
+        
+        if (closeMobileSidebar) {
+            closeMobileSidebar();
+        }
+    }
+
+    return {
+        sessions,
+        selectedSessions,
+        currSessionId,
+        pendingSessionId,
+        editTitleDialog,
+        editingTitle,
+        editingSessionId,
+        getCurrentSession,
+        getSessions,
+        newSession,
+        deleteSession,
+        showEditTitleDialog,
+        saveTitle,
+        updateSessionTitle,
+        newChat
+    };
+}
--- a/dashboard/src/i18n/locales/en-US/core/common.json
+++ b/dashboard/src/i18n/locales/en-US/core/common.json
@@ -72,6 +72,7 @@
    "enabled": "Enabled",
    "disabled": "Disabled",
    "delete": "Delete",
+    "copy": "Copy",
    "edit": "Edit",
    "noData": "No data available"
  }
--- a/dashboard/src/i18n/locales/en-US/core/header.json
+++ b/dashboard/src/i18n/locales/en-US/core/header.json
@@ -32,7 +32,6 @@
      "issueLink": "GitHub Issues"
    },
    "tip": "💡 TIP:",
-    "tipLink": "",
    "tipContinue": "By default, the corresponding version of the WebUI files will be downloaded when switching versions. The WebUI code is located in the dashboard directory of the project, and you can use npm to build it yourself.",
    "dockerTip": "When switching versions, it will try to update both the bot main program and the dashboard. If you are using Docker deployment, you can also re-pull the image or use",
    "dockerTipLink": "watchtower",
--- a/dashboard/src/i18n/locales/en-US/core/navigation.json
+++ b/dashboard/src/i18n/locales/en-US/core/navigation.json
@@ -5,13 +5,14 @@
  "persona": "Persona",
  "toolUse": "MCP Tools",
  "config": "Config",
-  "extension": "Extensions",
  "chat": "Chat",
+  "extension": "Extensions",
  "conversation": "Conversations",
  "sessionManagement": "Session Management",
  "console": "Console",
  "alkaid": "Alkaid Lab",
  "knowledgeBase": "Knowledge Base",
+  "memory": "Long-term Memory",
  "about": "About",
  "settings": "Settings",
  "documentation": "Documentation",
--- a/dashboard/src/i18n/locales/en-US/features/chat.json
+++ b/dashboard/src/i18n/locales/en-US/features/chat.json
@@ -47,7 +47,11 @@
    "noHistory": "No conversation history",
    "systemStatus": "System Status",
    "llmService": "LLM Service",
-    "speechToText": "Speech to Text"
+    "speechToText": "Speech to Text",
+    "editDisplayName": "Edit Session Name",
+    "displayName": "Session Name",
+    "displayNameUpdated": "Session name updated",
+    "displayNameUpdateFailed": "Failed to update session name"
  },
  "modes": {
    "darkMode": "Switch to Dark Mode",
--- a/dashboard/src/i18n/locales/en-US/features/config.json
+++ b/dashboard/src/i18n/locales/en-US/features/config.json
@@ -30,7 +30,11 @@
    "configApplyError": "Configuration not applied, JSON format error.",
    "saveSuccess": "Configuration saved successfully",
    "saveError": "Failed to save configuration",
-    "loadError": "Failed to load configuration"
+    "loadError": "Failed to load configuration",
+    "deleteSuccess": "Deleted successfully",
+    "deleteError": "Failed to delete",
+    "updateSuccess": "Updated successfully",
+    "updateError": "Failed to update"
  },
  "sections": {
    "general": "General Settings",
--- a/dashboard/src/i18n/locales/en-US/features/knowledge-base/detail.json
+++ b/dashboard/src/i18n/locales/en-US/features/knowledge-base/detail.json
@@ -4,6 +4,7 @@
  "tabs": {
    "overview": "Overview",
    "documents": "Documents",
+    "retrieval": "Retrieval",
    "sessions": "Sessions",
    "settings": "Settings"
  },
@@ -49,6 +50,10 @@
    "maxSize": "Max file size: 128MB",
    "chunkSettings": "Chunk Settings",
    "batchSettings": "Batch Settings",
+    "cleaningSettings": "Cleaning Settings",
+    "enableCleaning": "Enable Content Cleaning",
+    "cleaningProvider": "Cleaning Service Provider",
+    "cleaningProviderHint": "Select an LLM provider to clean and summarize the extracted web page content",
    "chunkSize": "Chunk Size",
    "chunkSizeHint": "Number of characters per chunk (default: 512)",
    "chunkOverlap": "Chunk Overlap",
@@ -61,7 +66,37 @@
    "maxRetriesHint": "Number of times to retry a failed upload task (default: 3)",
    "cancel": "Cancel",
    "submit": "Upload",
-    "fileRequired": "Please select a file to upload"
+    "fileRequired": "Please select a file to upload",
+    "fileUpload": "File Upload",
+    "fromUrl": "From URL",
+    "urlPlaceholder": "Enter the URL of the web page to extract content from",
+    "urlRequired": "Please enter a URL",
+    "urlHint": "The main content will be automatically extracted from the target URL as a document. Currently supports {supported} pages. Before use, please ensure that the target web page allows crawler access.",
+    "beta": "Beta"
+  },
+  "retrieval": {
+    "title": "Retrieval",
+    "subtitle": "Test the knowledge base using dense and sparse retrieval methods",
+    "query": "Query",
+    "queryPlaceholder": "Enter a query...",
+    "search": "Search",
+    "searching": "Searching...",
+    "results": "Results",
+    "noResults": "No results found",
+    "tryDifferentQuery": "Try a different query",
+    "settings": "Retrieval Settings",
+    "topK": "Number of Results",
+    "topKHint": "Maximum number of results to return",
+    "enableRerank": "Enable Rerank",
+    "enableRerankHint": "Use a rerank model to improve retrieval quality",
+    "score": "Relevance Score",
+    "document": "Document",
+    "chunk": "Chunk #{index}",
+    "content": "Content",
+    "charCount": "{count} characters",
+    "searchSuccess": "Search completed, found {count} results",
+    "searchFailed": "Search failed",
+    "queryRequired": "Please enter a query"
  },
  "settings": {
    "title": "Knowledge Base Settings",
--- a/dashboard/src/i18n/locales/en-US/features/knowledge-base/document.json
+++ b/dashboard/src/i18n/locales/en-US/features/knowledge-base/document.json
@@ -22,7 +22,10 @@
    "preview": "Preview",
    "search": "Search Chunks",
    "searchPlaceholder": "Enter keywords to search chunks...",
-    "showing": "Showing"
+    "showing": "Showing",
+    "deleteConfirm": "Are you sure you want to delete this chunk?",
+    "deleteSuccess": "Chunk deleted successfully",
+    "deleteFailed": "Failed to delete chunk"
  },
  "edit": {
    "title": "Edit Chunk",
--- a/dashboard/src/i18n/locales/en-US/features/tool-use.json
+++ b/dashboard/src/i18n/locales/en-US/features/tool-use.json
@@ -96,6 +96,42 @@
    },
    "confirmDelete": "Are you sure you want to delete server {name}?"
  },
+  "syncProvider": {
+    "title": "Sync MCP Servers",
+    "subtitle": "Sync MCP server configurations from providers to local",
+    "steps": {
+      "selectProvider": "Step 1: Select Provider",
+      "configureAuth": "Step 2: Configure Authentication",
+      "syncServers": "Step 3: Sync Servers"
+    },
+    "providers": {
+      "modelscope": "ModelScope",
+      "description": "ModelScope is an open model community providing MCP servers for various machine learning and AI services"
+    },
+    "fields": {
+      "provider": "Select Provider",
+      "accessToken": "Access Token",
+      "tokenRequired": "Access token is required",
+      "tokenHint": "Please enter your ModelScope access token"
+    },
+    "buttons": {
+      "cancel": "Cancel",
+      "previous": "Previous",
+      "next": "Next",
+      "sync": "Start Sync",
+      "getToken": "Get Token"
+    },
+    "status": {
+      "selectProvider": "Please select an MCP server provider",
+      "enterToken": "Please enter the access token to continue",
+      "readyToSync": "Ready to sync server configurations"
+    },
+    "messages": {
+      "syncSuccess": "MCP servers synced successfully!",
+      "syncError": "Sync failed: {error}",
+      "tokenHelp": "How to get a ModelScope access token? Click the button on the right for instructions"
+    }
+  },
  "messages": {
    "getServersError": "Failed to get MCP server list: {error}",
    "getToolsError": "Failed to get function tools list: {error}",
--- a/dashboard/src/i18n/locales/zh-CN/core/navigation.json
+++ b/dashboard/src/i18n/locales/zh-CN/core/navigation.json
@@ -12,6 +12,7 @@
  "console": "控制台",
  "alkaid": "Alkaid",
  "knowledgeBase": "知识库",
+  "memory": "长期记忆",
  "about": "关于",
  "settings": "设置",
  "documentation": "官方文档",
--- a/dashboard/src/i18n/locales/zh-CN/features/chat.json
+++ b/dashboard/src/i18n/locales/zh-CN/features/chat.json
@@ -43,11 +43,15 @@
    "exitFullscreen": "退出全屏"
  },
  "conversation": {
-    "newConversation": "新对话",
+    "newConversation": "新的聊天",
    "noHistory": "暂无对话历史",
    "systemStatus": "系统状态",
    "llmService": "LLM 服务",
-    "speechToText": "语音转文本"
+    "speechToText": "语音转文本",
+    "editDisplayName": "编辑会话名称",
+    "displayName": "会话名称",
+    "displayNameUpdated": "会话名称已更新",
+    "displayNameUpdateFailed": "更新会话名称失败"
  },
  "modes": {
    "darkMode": "切换到夜间模式",
--- a/dashboard/src/i18n/locales/zh-CN/features/knowledge-base/detail.json
+++ b/dashboard/src/i18n/locales/zh-CN/features/knowledge-base/detail.json
@@ -50,6 +50,10 @@
    "maxSize": "最大文件大小: 128MB",
    "chunkSettings": "分块设置",
    "batchSettings": "批处理设置",
+    "cleaningSettings": "清洗设置",
+    "enableCleaning": "启用内容清洗",
+    "cleaningProvider": "清洗服务提供商",
+    "cleaningProviderHint": "选择一个 LLM 服务商来对提取的网页内容进行清洗和总结",
    "chunkSize": "分块大小",
    "chunkSizeHint": "每个文本块的字符数 (默认: 512)",
    "chunkOverlap": "分块重叠",
@@ -62,7 +66,13 @@
    "maxRetriesHint": "上传失败任务的重试次数 (默认: 3)",
    "cancel": "取消",
    "submit": "上传",
-    "fileRequired": "请选择要上传的文件"
+    "fileRequired": "请选择要上传的文件",
+    "fileUpload": "文件上传",
+    "fromUrl": "从 URL",
+    "urlPlaceholder": "请输入要提取内容的网页 URL",
+    "urlRequired": "请输入 URL",
+    "urlHint": "将自动从目标 URL 提取主要内容作为文档。目前支持 {supported} 页面，请确保目标网页允许爬虫访问。",
+    "beta": "测试版"
  },
  "retrieval": {
    "title": "知识库检索",
--- a/dashboard/src/layouts/full/vertical-sidebar/sidebarItem.ts
+++ b/dashboard/src/layouts/full/vertical-sidebar/sidebarItem.ts
@@ -48,6 +48,11 @@ const sidebarItem: menu[] = [
    icon: 'mdi-book-open-variant',
    to: '/knowledge-base',
  },
+  {
+    title: 'core.navigation.memory',
+    icon: 'mdi-brain',
+    to: '/memory',
+  },
  {
    title: 'core.navigation.chat',
    icon: 'mdi-chat',
--- a/dashboard/src/router/MainRoutes.ts
+++ b/dashboard/src/router/MainRoutes.ts
@@ -90,6 +90,11 @@ const MainRoutes = {
        }
      ]
    },
+    {
+      name: 'Memory',
+      path: '/memory',
+      component: () => import('@/views/MemoryPage.vue')
+    },

    // 旧版本的知识库路由
    {
--- a/dashboard/src/views/MemoryPage.vue
+++ b/dashboard/src/views/MemoryPage.vue
@@ -0,0 +1,358 @@
+<template>
+    <div class="memory-page">
+        <v-container fluid class="pa-0">
+            <!-- 页面标题 -->
+            <v-row class="d-flex justify-space-between align-center px-4 py-3 pb-8">
+                <div>
+                    <h1 class="text-h1 font-weight-bold mb-2">
+                        <v-icon color="black" class="me-2">mdi-brain</v-icon>{{ t('core.navigation.memory') }}
+                    </h1>
+                    <p class="text-subtitle-1 text-medium-emphasis mb-4">
+                        管理长期记忆系统的配置
+                    </p>
+                </div>
+            </v-row>
+
+            <!-- 加载状态 -->
+            <v-row v-if="loading">
+                <v-col cols="12">
+                    <v-card>
+                        <v-card-text class="text-center">
+                            <v-progress-circular indeterminate color="primary"></v-progress-circular>
+                        </v-card-text>
+                    </v-card>
+                </v-col>
+            </v-row>
+
+            <!-- 主内容 -->
+            <v-row v-else>
+                <v-col cols="12" md="8" lg="6">
+                    <v-card rounded="lg">
+                        <v-card-title class="d-flex align-center">
+                            <v-icon class="mr-2">mdi-cog</v-icon>
+                            记忆系统配置
+                        </v-card-title>
+                        <v-divider></v-divider>
+
+                        <v-card-text>
+                            <!-- 状态显示 -->
+                            <v-alert 
+                                :type="memoryStatus.initialized ? 'success' : 'info'" 
+                                variant="tonal"
+                                class="mb-4"
+                            >
+                                <div class="d-flex align-center">
+                                    <v-icon class="mr-2">
+                                        {{ memoryStatus.initialized ? 'mdi-check-circle' : 'mdi-information' }}
+                                    </v-icon>
+                                    <div>
+                                        <strong>状态：</strong>
+                                        {{ memoryStatus.initialized ? '已初始化' : '未初始化' }}
+                                    </div>
+                                </div>
+                            </v-alert>
+
+                            <!-- 未初始化时显示初始化表单 -->
+                            <div v-if="!memoryStatus.initialized">
+                                <v-form @submit.prevent="initializeMemory">
+                                    <v-select
+                                        v-model="selectedEmbeddingProvider"
+                                        :items="embeddingProviders"
+                                        item-title="text"
+                                        item-value="value"
+                                        label="Embedding 模型 *"
+                                        hint="用于生成向量表示，初始化后不可更改"
+                                        persistent-hint
+                                        class="mb-4"
+                                        required
+                                        :disabled="initializing"
+                                    ></v-select>
+
+                                    <v-select
+                                        v-model="selectedMergeLLM"
+                                        :items="llmProviders"
+                                        item-title="text"
+                                        item-value="value"
+                                        label="合并 LLM *"
+                                        hint="用于合并相似记忆，可在初始化后更改"
+                                        persistent-hint
+                                        class="mb-4"
+                                        required
+                                        :disabled="initializing"
+                                    ></v-select>
+
+                                    <v-btn
+                                        type="submit"
+                                        color="primary"
+                                        :loading="initializing"
+                                        :disabled="!selectedEmbeddingProvider || !selectedMergeLLM"
+                                        block
+                                        size="large"
+                                    >
+                                        初始化记忆系统
+                                    </v-btn>
+                                </v-form>
+                            </div>
+
+                            <!-- 已初始化时显示配置信息 -->
+                            <div v-else>
+                                <v-list>
+                                    <v-list-item>
+                                        <template v-slot:prepend>
+                                            <v-icon>mdi-vector-triangle</v-icon>
+                                        </template>
+                                        <v-list-item-title>Embedding 模型</v-list-item-title>
+                                        <v-list-item-subtitle>
+                                            {{ getProviderName(memoryStatus.embedding_provider_id) }}
+                                        </v-list-item-subtitle>
+                                    </v-list-item>
+
+                                    <v-divider class="my-2"></v-divider>
+
+                                    <v-list-item>
+                                        <template v-slot:prepend>
+                                            <v-icon>mdi-robot</v-icon>
+                                        </template>
+                                        <v-list-item-title>合并 LLM</v-list-item-title>
+                                        <v-list-item-subtitle>
+                                            {{ getProviderName(memoryStatus.merge_llm_provider_id) }}
+                                        </v-list-item-subtitle>
+                                    </v-list-item>
+                                </v-list>
+
+                                <v-divider class="my-4"></v-divider>
+
+                                <v-form @submit.prevent="updateMergeLLM">
+                                    <v-select
+                                        v-model="newMergeLLM"
+                                        :items="llmProviders"
+                                        item-title="text"
+                                        item-value="value"
+                                        label="更新合并 LLM"
+                                        hint="可以更换用于合并记忆的 LLM"
+                                        persistent-hint
+                                        class="mb-4"
+                                        :disabled="updating"
+                                    ></v-select>
+
+                                    <v-btn
+                                        type="submit"
+                                        color="primary"
+                                        :loading="updating"
+                                        :disabled="!newMergeLLM || newMergeLLM === memoryStatus.merge_llm_provider_id"
+                                        block
+                                        variant="tonal"
+                                    >
+                                        更新合并 LLM
+                                    </v-btn>
+                                </v-form>
+                            </div>
+                        </v-card-text>
+                    </v-card>
+                </v-col>
+
+                <!-- 说明卡片 -->
+                <v-col cols="12" md="4" lg="6">
+                    <v-card rounded="lg">
+                        <v-card-title class="d-flex align-center">
+                            <v-icon class="mr-2">mdi-information</v-icon>
+                            说明
+                        </v-card-title>
+                        <v-divider></v-divider>
+                        <v-card-text>
+                            <v-list density="compact">
+                                <v-list-item>
+                                    <v-list-item-title class="text-wrap">
+                                        <strong>Embedding 模型：</strong>用于将文本转换为向量，支持语义相似度搜索。
+                                        <v-chip size="x-small" color="warning" class="ml-2">不可更改</v-chip>
+                                    </v-list-item-title>
+                                </v-list-item>
+                                <v-list-item>
+                                    <v-list-item-title class="text-wrap">
+                                        <strong>合并 LLM：</strong>当检测到相似记忆时，使用此模型合并为一条记忆。
+                                        <v-chip size="x-small" color="success" class="ml-2">可更改</v-chip>
+                                    </v-list-item-title>
+                                </v-list-item>
+                                <v-list-item>
+                                    <v-list-item-title class="text-wrap">
+                                        <strong>注意：</strong>Embedding 模型一旦选择后无法更改，请谨慎选择。
+                                    </v-list-item-title>
+                                </v-list-item>
+                            </v-list>
+                        </v-card-text>
+                    </v-card>
+                </v-col>
+            </v-row>
+        </v-container>
+
+        <!-- 提示框 -->
+        <v-snackbar v-model="snackbar.show" :color="snackbar.color" :timeout="3000">
+            {{ snackbar.message }}
+        </v-snackbar>
+    </div>
+</template>
+
+<script setup lang="ts">
+import { ref, onMounted } from 'vue';
+import axios from 'axios';
+import { useI18n } from '@/i18n/composables';
+
+const { t } = useI18n();
+
+interface MemoryStatus {
+    initialized: boolean;
+    embedding_provider_id: string | null;
+    merge_llm_provider_id: string | null;
+}
+
+interface Provider {
+    value: string;
+    text: string;
+}
+
+const loading = ref(true);
+const initializing = ref(false);
+const updating = ref(false);
+
+const memoryStatus = ref<MemoryStatus>({
+    initialized: false,
+    embedding_provider_id: null,
+    merge_llm_provider_id: null,
+});
+
+const embeddingProviders = ref<Provider[]>([]);
+const llmProviders = ref<Provider[]>([]);
+
+const selectedEmbeddingProvider = ref<string>('');
+const selectedMergeLLM = ref<string>('');
+const newMergeLLM = ref<string>('');
+
+const snackbar = ref({
+    show: false,
+    message: '',
+    color: 'success',
+});
+
+const showMessage = (message: string, color: string = 'success') => {
+    snackbar.value.message = message;
+    snackbar.value.color = color;
+    snackbar.value.show = true;
+};
+
+const getProviderName = (providerId: string | null): string => {
+    if (!providerId) return '未设置';
+    const embedding = embeddingProviders.value.find(p => p.value === providerId);
+    const llm = llmProviders.value.find(p => p.value === providerId);
+    return embedding?.text || llm?.text || providerId;
+};
+
+const loadProviders = async () => {
+    try {
+        // Load embedding providers
+        const embeddingResponse = await axios.get('/api/config/provider/list', {
+            params: { provider_type: 'embedding' }
+        });
+        if (embeddingResponse.data.status === 'ok') {
+            embeddingProviders.value = (embeddingResponse.data.data || []).map((p: any) => ({
+                value: p.id,
+                text: `${p.embedding_model} (${p.id})`,
+            }));
+        }
+
+        // Load LLM providers
+        const llmResponse = await axios.get('/api/config/provider/list', {
+            params: { provider_type: 'chat_completion' }
+        });
+        if (llmResponse.data.status === 'ok') {
+            llmProviders.value = (llmResponse.data.data || []).map((p: any) => ({
+                value: p.id,
+                text: `${p?.model_config?.model} (${p.id})`,
+            }));
+        }
+    } catch (error) {
+        console.error('Failed to load providers:', error);
+        showMessage('加载提供商列表失败', 'error');
+    }
+};
+
+const loadStatus = async () => {
+    try {
+        const response = await axios.get('/api/memory/status');
+        if (response.data.status === 'ok') {
+            memoryStatus.value = response.data.data;
+            if (memoryStatus.value.merge_llm_provider_id) {
+                newMergeLLM.value = memoryStatus.value.merge_llm_provider_id;
+            }
+        }
+    } catch (error) {
+        console.error('Failed to load memory status:', error);
+        showMessage('加载记忆系统状态失败', 'error');
+    }
+};
+
+const initializeMemory = async () => {
+    if (!selectedEmbeddingProvider.value || !selectedMergeLLM.value) {
+        showMessage('请选择 Embedding 模型和合并 LLM', 'warning');
+        return;
+    }
+
+    initializing.value = true;
+    try {
+        const response = await axios.post('/api/memory/initialize', {
+            embedding_provider_id: selectedEmbeddingProvider.value,
+            merge_llm_provider_id: selectedMergeLLM.value,
+        });
+
+        if (response.data.status === 'ok') {
+            showMessage('记忆系统初始化成功', 'success');
+            await loadStatus();
+        } else {
+            showMessage(response.data.message || '初始化失败', 'error');
+        }
+    } catch (error: any) {
+        console.error('Failed to initialize memory:', error);
+        showMessage(error.response?.data?.message || '初始化失败', 'error');
+    } finally {
+        initializing.value = false;
+    }
+};
+
+const updateMergeLLM = async () => {
+    if (!newMergeLLM.value) {
+        showMessage('请选择新的合并 LLM', 'warning');
+        return;
+    }
+
+    updating.value = true;
+    try {
+        const response = await axios.post('/api/memory/update_merge_llm', {
+            merge_llm_provider_id: newMergeLLM.value,
+        });
+
+        if (response.data.status === 'ok') {
+            showMessage('合并 LLM 更新成功', 'success');
+            await loadStatus();
+        } else {
+            showMessage(response.data.message || '更新失败', 'error');
+        }
+    } catch (error: any) {
+        console.error('Failed to update merge LLM:', error);
+        showMessage(error.response?.data?.message || '更新失败', 'error');
+    } finally {
+        updating.value = false;
+    }
+};
+
+onMounted(async () => {
+    loading.value = true;
+    await Promise.all([loadProviders(), loadStatus()]);
+    loading.value = false;
+});
+</script>
+
+<style scoped>
+.memory-page {
+    min-height: 100vh;
+    padding: 8px;
+}
+</style>
--- a/dashboard/src/views/knowledge-base/components/DocumentsTab.vue
+++ b/dashboard/src/views/knowledge-base/components/DocumentsTab.vue
@@ -57,7 +57,7 @@
    </v-card>

    <!-- 上传对话框 -->
-    <v-dialog v-model="showUploadDialog" max-width="600px" persistent @after-enter="initUploadSettings">
+    <v-dialog v-model="showUploadDialog" max-width="650px" persistent @after-enter="initUploadSettings">
      <v-card>
        <v-card-title class="pa-4 d-flex align-center">
          <span class="text-h5">{{ t('upload.title') }}</span>
@@ -67,40 +67,91 @@

        <v-divider />

-        <v-card-text class="pa-6">
-          <!-- 文件选择 -->
-          <div class="upload-dropzone" :class="{ 'dragover': isDragging }" @drop.prevent="handleDrop"
-            @dragover.prevent="isDragging = true" @dragleave="isDragging = false" @click="fileInput?.click()">
-            <v-icon size="64" color="primary">mdi-cloud-upload</v-icon>
-            <p class="mt-4 text-h6">{{ t('upload.dropzone') }}</p>
-            <p class="text-caption text-medium-emphasis mt-2">{{ t('upload.supportedFormats') }}.txt, .md, .pdf, .docx,
-              .xls, .xlsx</p>
-            <p class="text-caption text-medium-emphasis">{{ t('upload.maxSize') }}</p>
-            <p class="text-caption text-medium-emphasis">最多可上传 10 个文件</p>
-            <input ref="fileInput" type="file" multiple hidden accept=".txt,.md,.pdf,.docx,.xls,.xlsx"
-              @change="handleFileSelect" />
-          </div>
+        <v-tabs v-model="uploadMode" grow class="mb-4">
+          <v-tab value="file">{{ t('upload.fileUpload') }}</v-tab>
+          <v-tab value="url">
+            {{ t('upload.fromUrl') }}
+            <v-badge color="warning" :content="t('upload.beta')" inline class="ml-2" />
+          </v-tab>
+        </v-tabs>

-          <div v-if="selectedFiles.length > 0" class="mt-4">
-            <div class="d-flex align-center justify-space-between mb-2">
-              <span class="text-subtitle-2">已选择 {{ selectedFiles.length }} 个文件</span>
-              <v-btn variant="text" size="small" @click="selectedFiles = []">清空</v-btn>
-            </div>
-            <div class="files-list">
-              <div v-for="(file, index) in selectedFiles" :key="index"
-                class="file-item pa-3 mb-2 rounded bg-surface-variant">
-                <div class="d-flex align-center justify-space-between">
-                  <div class="d-flex align-center gap-2">
-                    <v-icon>{{ getFileIcon(file.name) }}</v-icon>
-                    <div>
-                      <div class="font-weight-medium">{{ file.name }}</div>
-                      <div class="text-caption">{{ formatFileSize(file.size) }}</div>
+        <v-card-text class="pa-6 pt-2">
+          <v-window v-model="uploadMode">
+            <!-- 文件上传 -->
+            <v-window-item value="file">
+              <!-- 文件选择 -->
+              <div class="upload-dropzone" :class="{ 'dragover': isDragging }" @drop.prevent="handleDrop"
+                @dragover.prevent="isDragging = true" @dragleave="isDragging = false" @click="fileInput?.click()">
+                <v-icon size="64" color="primary">mdi-cloud-upload</v-icon>
+                <p class="mt-4 text-h6">{{ t('upload.dropzone') }}</p>
+                <p class="text-caption text-medium-emphasis mt-2">{{ t('upload.supportedFormats') }}.txt, .md, .pdf,
+                  .docx,
+                  .xls, .xlsx</p>
+                <p class="text-caption text-medium-emphasis">{{ t('upload.maxSize') }}</p>
+                <p class="text-caption text-medium-emphasis">最多可上传 10 个文件</p>
+                <input ref="fileInput" type="file" multiple hidden accept=".txt,.md,.pdf,.docx,.xls,.xlsx"
+                  @change="handleFileSelect" />
+              </div>
+
+              <div v-if="selectedFiles.length > 0" class="mt-4">
+                <div class="d-flex align-center justify-space-between mb-2">
+                  <span class="text-subtitle-2">已选择 {{ selectedFiles.length }} 个文件</span>
+                  <v-btn variant="text" size="small" @click="selectedFiles = []">清空</v-btn>
+                </div>
+                <div class="files-list">
+                  <div v-for="(file, index) in selectedFiles" :key="index"
+                    class="file-item pa-3 mb-2 rounded bg-surface-variant">
+                    <div class="d-flex align-center justify-space-between">
+                      <div class="d-flex align-center gap-2">
+                        <v-icon>{{ getFileIcon(file.name) }}</v-icon>
+                        <div>
+                          <div class="font-weight-medium">{{ file.name }}</div>
+                          <div class="text-caption">{{ formatFileSize(file.size) }}</div>
+                        </div>
+                      </div>
+                      <v-btn icon="mdi-close" variant="text" size="small" @click="removeFile(index)" />
                    </div>
                  </div>
-                  <v-btn icon="mdi-close" variant="text" size="small" @click="removeFile(index)" />
                </div>
              </div>
+            </v-window-item>
+
+            <!-- URL上传 -->
+            <v-window-item value="url" class="pt-2">
+              <!-- Tavily Key 快速配置 -->
+              <div v-if="tavilyConfigStatus === 'not_configured' || tavilyConfigStatus === 'error'" class="mb-4">
+                <v-alert :type="tavilyConfigStatus === 'error' ? 'error' : 'info'" variant="tonal" density="compact">
+                  <div class="d-flex align-center justify-space-between">
+                    <span>
+                      {{ tavilyConfigStatus === 'error' ? '检查网页搜索配置失败' : '使用此功能需要配置 Tavily Key' }}
+                    </span>
+                    <v-btn size="small" variant="flat" @click="showTavilyDialog = true">
+                      配置
+                    </v-btn>
+                  </div>
+                </v-alert>
+              </div>
+
+              <v-text-field v-model="uploadUrl" :label="t('upload.urlPlaceholder')" variant="outlined" clearable :disabled="tavilyConfigStatus === 'not_configured'"
+                autofocus :hint="t('upload.urlHint', { supported: 'HTML' })" persistent-hint />
+            </v-window-item>
+          </v-window>
+
+          <!-- 清洗设置 (仅在URL模式下显示) -->
+          <div v-if="uploadMode === 'url'" class="mt-6">
+            <div class="d-flex align-center mb-4">
+              <h3 class="text-h6">{{ t('upload.cleaningSettings') }}</h3>
            </div>
+            <v-row>
+              <v-col cols="12" sm="4">
+                <v-switch v-model="uploadSettings.enable_cleaning" :label="t('upload.enableCleaning')" color="primary" />
+              </v-col>
+              <v-col cols="12" sm="8">
+                <v-select v-model="uploadSettings.cleaning_provider_id" :items="llmProviders" item-title="id"
+                  item-value="id" :label="t('upload.cleaningProvider')" :hint="t('upload.cleaningProviderHint')"
+                  persistent-hint variant="outlined" density="compact" :disabled="!uploadSettings.enable_cleaning" />
+              </v-col>
+            </v-row>
          </div>

          <!-- 分块设置 -->
@@ -151,8 +202,8 @@
          <v-btn variant="text" @click="closeUploadDialog" :disabled="uploading">
            {{ t('upload.cancel') }}
          </v-btn>
-          <v-btn color="primary" variant="elevated" @click="uploadDocument" :loading="uploading"
-            :disabled="selectedFiles.length === 0">
+          <v-btn color="primary" variant="elevated" @click="startUpload" :loading="uploading"
+            :disabled="isUploadDisabled">
            {{ t('upload.submit') }}
          </v-btn>
        </v-card-actions>
@@ -185,11 +236,15 @@
    <v-snackbar v-model="snackbar.show" :color="snackbar.color">
      {{ snackbar.text }}
    </v-snackbar>
+
+    <!-- Tavily Key 配置对话框 -->
+    <TavilyKeyDialog v-model="showTavilyDialog" @success="onTavilyKeySet" />
  </div>
 </template>

 <script setup lang="ts">
-import { ref, onMounted, onUnmounted } from 'vue'
+import TavilyKeyDialog from './TavilyKeyDialog.vue'
+import { ref, onMounted, onUnmounted, computed } from 'vue'
 import { useRouter } from 'vue-router'
 import axios from 'axios'
 import { useModuleI18n } from '@/i18n/composables'
@@ -216,10 +271,13 @@ const selectedFiles = ref<File[]>([])
 const deleteTarget = ref<any>(null)
 const isDragging = ref(false)
 const fileInput = ref<HTMLInputElement | null>(null)
-
-// 上传进度 - 用于轮询多个任务
+const uploadMode = ref('file') // 'file' or 'url'
+const uploadUrl = ref('')
+const llmProviders = ref<any[]>([])
 const uploadingTasks = ref<Map<string, any>>(new Map())
 const progressPollingInterval = ref<number | null>(null)
+const tavilyConfigStatus = ref('loading') // 'loading', 'configured', 'not_configured', 'error'
+const showTavilyDialog = ref(false)

 const snackbar = ref({
  show: false,
@@ -239,7 +297,9 @@ const uploadSettings = ref({
  chunk_overlap: null as number | null,
  batch_size: 32,
  tasks_limit: 3,
-  max_retries: 3
+  max_retries: 3,
+  enable_cleaning: false,
+  cleaning_provider_id: null as string | null
 })

 // 初始化上传设置
@@ -249,10 +309,31 @@ const initUploadSettings = () => {
    chunk_overlap: props.kb?.chunk_overlap || null,
    batch_size: 32,
    tasks_limit: 3,
-    max_retries: 3
+    max_retries: 3,
+    enable_cleaning: false,
+    cleaning_provider_id: null
  }
 }

+const isUploadDisabled = computed(() => {
+  if (uploading.value) {
+    return true
+  }
+  if (uploadMode.value === 'file') {
+    return selectedFiles.value.length === 0
+  }
+  if (uploadMode.value === 'url') {
+    if (!uploadUrl.value) {
+      return true
+    }
+    if (uploadSettings.value.enable_cleaning && !uploadSettings.value.cleaning_provider_id) {
+      return true
+    }
+    return false
+  }
+  return true
+})
+
 // 表格列
 const headers = [
  { title: t('documents.name'), key: 'doc_name', sortable: true },
@@ -314,8 +395,17 @@ const handleDrop = (event: DragEvent) => {
  }
 }

-// 上传文档
-const uploadDocument = async () => {
+// 上传调度器
+const startUpload = async () => {
+  if (uploadMode.value === 'file') {
+    await uploadFiles()
+  } else if (uploadMode.value === 'url') {
+    await uploadFromUrl()
+  }
+}
+
+// 上传文件
+const uploadFiles = async () => {
  if (selectedFiles.value.length === 0) {
    showSnackbar(t('upload.fileRequired'), 'warning')
    return
@@ -390,6 +480,80 @@ const uploadDocument = async () => {
  }
 }

+// 从 URL 上传
+const uploadFromUrl = async () => {
+  if (!uploadUrl.value) {
+    showSnackbar(t('upload.urlRequired'), 'warning')
+    return
+  }
+
+  uploading.value = true
+
+  try {
+    const payload: any = {
+      kb_id: props.kbId,
+      url: uploadUrl.value,
+      batch_size: uploadSettings.value.batch_size,
+      tasks_limit: uploadSettings.value.tasks_limit,
+      max_retries: uploadSettings.value.max_retries
+    }
+    if (uploadSettings.value.chunk_size) {
+      payload.chunk_size = uploadSettings.value.chunk_size
+    }
+    if (uploadSettings.value.chunk_overlap) {
+      payload.chunk_overlap = uploadSettings.value.chunk_overlap
+    }
+    if (uploadSettings.value.enable_cleaning) {
+      payload.enable_cleaning = true
+      if (uploadSettings.value.cleaning_provider_id) {
+        payload.cleaning_provider_id = uploadSettings.value.cleaning_provider_id
+      }
+    }
+
+
+    const response = await axios.post('/api/kb/document/upload/url', payload)
+
+    if (response.data.status === 'ok') {
+      const result = response.data.data
+      const taskId = result.task_id
+
+      showSnackbar(`正在从 URL 后台提取内容...`, 'info')
+
+      // 添加占位条目
+      const uploadingDoc = {
+        doc_id: `uploading_${taskId}_0`,
+        doc_name: result.url,
+        file_type: 'url',
+        file_size: 0, // URL has no size
+        chunk_count: 0,
+        created_at: new Date().toISOString(),
+        uploading: true,
+        taskId: taskId,
+        uploadProgress: {
+          stage: 'waiting',
+          current: 0,
+          total: 100
+        }
+      }
+
+      documents.value = [uploadingDoc, ...documents.value]
+      closeUploadDialog()
+
+      if (taskId) {
+        startProgressPolling(taskId)
+      }
+    } else {
+      showSnackbar(response.data.message || t('documents.uploadFailed'), 'error')
+    }
+  } catch (error: any) {
+    console.error('Failed to upload from URL:', error)
+    const message = error.response?.data?.message || t('documents.uploadFailed')
+    showSnackbar(message, 'error')
+  } finally {
+    uploading.value = false
+  }
+}
+
 // 开始轮询进度
 const startProgressPolling = (taskId: string) => {
  // 如果已经在轮询，先停止
@@ -490,6 +654,8 @@ const getUploadPercentage = (item: any) => {
 const getStageText = (stage: string) => {
  const stageMap: Record<string, string> = {
    'waiting': '等待中...',
+    'extracting': '提取内容...',
+    'cleaning': '清洗内容...',
    'parsing': '解析文档...',
    'chunking': '文本分块...',
    'embedding': '生成向量...'
@@ -501,6 +667,8 @@ const getStageText = (stage: string) => {
 const closeUploadDialog = () => {
  showUploadDialog.value = false
  selectedFiles.value = []
+  uploadUrl.value = ''
+  uploadMode.value = 'file'
  initUploadSettings()
 }

@@ -551,6 +719,7 @@ const getFileIcon = (fileType: string) => {
  if (type.includes('pdf')) return 'mdi-file-pdf-box'
  if (type.includes('md') || type.includes('markdown')) return 'mdi-language-markdown'
  if (type.includes('txt')) return 'mdi-file-document-outline'
+  if (type.includes('url')) return 'mdi-link-variant'
  return 'mdi-file'
 }

@@ -559,6 +728,7 @@ const getFileColor = (fileType: string) => {
  if (type.includes('pdf')) return 'error'
  if (type.includes('md')) return 'info'
  if (type.includes('txt')) return 'success'
+  if (type.includes('url')) return 'primary'
  return 'grey'
 }

@@ -585,8 +755,53 @@ const formatDate = (dateStr: string) => {
  })
 }

+// 加载LLM providers
+const loadLlmProviders = async () => {
+  try {
+    const response = await axios.get('/api/config/provider/list', {
+      params: { provider_type: 'chat_completion' }
+    })
+    if (response.data.status === 'ok') {
+      llmProviders.value = response.data.data
+    }
+  } catch (error) {
+    console.error('Failed to load LLM providers:', error)
+  }
+}
+
+// 检查Tavily Key配置
+const checkTavilyConfig = async () => {
+  tavilyConfigStatus.value = 'loading'
+  try {
+    const response = await axios.get('/api/config/abconf', {
+      params: { id: 'default' }
+    })
+    if (response.data.status === 'ok') {
+      const config = response.data.data.config
+      const tavilyKeys = config?.provider_settings?.websearch_tavily_key
+      if (Array.isArray(tavilyKeys) && tavilyKeys.length > 0 && tavilyKeys.some(key => key.trim() !== '')) {
+        tavilyConfigStatus.value = 'configured'
+      } else {
+        tavilyConfigStatus.value = 'not_configured'
+      }
+    } else {
+      tavilyConfigStatus.value = 'error'
+    }
+  } catch (error) {
+    console.warn('Failed to check Tavily key config:', error)
+    tavilyConfigStatus.value = 'error'
+  }
+}
+
+const onTavilyKeySet = () => {
+  showSnackbar('Tavily API Key 配置成功', 'success')
+  checkTavilyConfig()
+}
+
 onMounted(() => {
  loadDocuments()
+  loadLlmProviders()
+  checkTavilyConfig()
 })

 onUnmounted(() => {
--- a/dashboard/src/views/knowledge-base/components/TavilyKeyDialog.vue
+++ b/dashboard/src/views/knowledge-base/components/TavilyKeyDialog.vue
@@ -0,0 +1,109 @@
+<template>
+  <v-dialog v-model="dialog" max-width="500px" persistent>
+    <v-card>
+      <v-card-title class="text-h5">
+        配置 Tavily API Key
+      </v-card-title>
+      <v-card-text>
+        <p class="mb-4 text-body-2 text-medium-emphasis">
+          为了使用基于网页的知识库功能，需要提供 Tavily API Key。您可以从 <a href="https://tavily.com/" target="_blank">Tavily 官网</a> 获取。
+        </p>
+        <v-text-field
+          v-model="apiKey"
+          label="Tavily API Key"
+          variant="outlined"
+          :loading="saving"
+          :error-messages="errorMessage"
+          autofocus
+          clearable
+          placeholder="tvly-..."
+        />
+      </v-card-text>
+      <v-card-actions>
+        <v-spacer />
+        <v-btn variant="text" @click="closeDialog" :disabled="saving">
+          取消
+        </v-btn>
+        <v-btn color="primary" variant="elevated" @click="saveKey" :loading="saving">
+          保存
+        </v-btn>
+      </v-card-actions>
+    </v-card>
+  </v-dialog>
+</template>
+
+<script setup lang="ts">
+import { ref, watch } from 'vue'
+import axios from 'axios'
+
+const props = defineProps<{
+  modelValue: boolean
+}>()
+
+const emit = defineEmits(['update:modelValue', 'success'])
+
+const dialog = ref(props.modelValue)
+const apiKey = ref('')
+const saving = ref(false)
+const errorMessage = ref('')
+
+watch(() => props.modelValue, (val) => {
+  dialog.value = val
+  if (val) {
+    // Reset state when dialog opens
+    apiKey.value = ''
+    errorMessage.value = ''
+    saving.value = false
+  }
+})
+
+const closeDialog = () => {
+  emit('update:modelValue', false)
+}
+
+const saveKey = async () => {
+  if (!apiKey.value.trim()) {
+    errorMessage.value = 'API Key 不能为空'
+    return
+  }
+  errorMessage.value = ''
+  saving.value = true
+  try {
+    // 1. 获取当前配置
+    const configResponse = await axios.get('/api/config/abconf', {
+      params: { id: 'default' }
+    })
+
+    if (configResponse.data.status !== 'ok') {
+      throw new Error('获取当前配置失败')
+    }
+
+    const currentConfig = configResponse.data.data.config
+
+    // 2. 更新配置
+    if (!currentConfig.provider_settings) {
+      currentConfig.provider_settings = {}
+    }
+    currentConfig.provider_settings.websearch_tavily_key = [apiKey.value.trim()]
+    // 同时将搜索提供商设置为 tavily
+    currentConfig.provider_settings.websearch_provider = 'tavily'
+
+    // 3. 保存整个配置
+    const saveResponse = await axios.post('/api/config/astrbot/update', {
+      conf_id: 'default',
+      config: currentConfig
+    })
+
+    if (saveResponse.data.status === 'ok') {
+      emit('success')
+      closeDialog()
+    } else {
+      errorMessage.value = saveResponse.data.message || '保存失败，请检查 Key 是否正确'
+    }
+  } catch (error: any) {
+    errorMessage.value = error.response?.data?.message || '保存失败，发生未知错误'
+  } finally {
+    saving.value = false
+  }
+}
+</script>
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "AstrBot"
-version = "4.5.8"
+version = "4.6.0"
 description = "Easy-to-use multi-platform LLM chatbot and development framework"
 readme = "README.md"
 requires-python = ">=3.10"
@@ -26,7 +26,7 @@ dependencies = [
  "docstring-parser>=0.16",
  "faiss-cpu==1.10.0",
  "filelock>=3.18.0",
-  "google-genai>=1.14.0",
+  "google-genai>=1.14.0, <1.51.0",
  "lark-oapi>=1.4.15",
  "lxml-html-clean>=0.4.2",
  "mcp>=1.8.0",
@@ -59,6 +59,7 @@ dependencies = [
  "jieba>=0.42.1",
  "markitdown-no-magika[docx,xls,xlsx]>=0.1.2",
  "xinference-client",
+  "tenacity>=9.1.2",
 ]

 [dependency-groups]
--- a/requirements.txt
+++ b/requirements.txt
@@ -52,3 +52,4 @@ rank-bm25>=0.2.2
 jieba>=0.42.1
 markitdown-no-magika[docx,xls,xlsx]>=0.1.2
 xinference-client
+tenacity>=9.1.2
Author	SHA1	Message	Date
Soulter	c76b7ec387	Merge remote-tracking branch 'origin/master' into feat/memory	2025-11-21 20:23:41 +08:00
Soulter	b7f3010d72	stage simple webui	2025-11-21 17:59:22 +08:00
Soulter	fbbaf1cd08	delete(memory): remove memory module and its components	2025-11-21 17:34:33 +08:00
Soulter	9c8025acce	stage	2025-11-21 17:25:55 +08:00
Soulter	4e2154feb7	fix(ci): repository name must be lowercase	2025-11-20 23:46:34 +08:00
Soulter	604958898c	chore: bump version to 4.6.0	2025-11-20 23:41:20 +08:00
Soulter	a093f5ad0a	fix(dependencies): specify upper version limit for google-genai	2025-11-20 23:32:05 +08:00
Soulter	a7e9a7f30c	fix(gemini): ensure extra_content is not empty before processing	2025-11-20 23:30:19 +08:00
Soulter	98c5466b5d	feat(chat): refactor chat component structure and add new features (#3701 ) - Introduced `ConversationSidebar.vue` for improved conversation management and sidebar functionality. - Enhanced `MessageList.vue` to handle loading states and improved message rendering. - Created new composables: `useConversations`, `useMessages`, `useMediaHandling`, `useRecording` for better code organization and reusability. - Added loading indicators and improved user experience during message processing. - Ensured backward compatibility and maintained existing functionalities.	2025-11-20 17:30:51 +08:00
Soulter	6345ac6ff8	feat(chat): refactor chat component structure and add new features (#3701 ) - Introduced `ConversationSidebar.vue` for improved conversation management and sidebar functionality. - Enhanced `MessageList.vue` to handle loading states and improved message rendering. - Created new composables: `useConversations`, `useMessages`, `useMediaHandling`, `useRecording` for better code organization and reusability. - Added loading indicators and improved user experience during message processing. - Ensured backward compatibility and maintained existing functionalities.	2025-11-20 17:29:27 +08:00
Soulter	5bcd683012	delete: remove useConversations composable	2025-11-20 17:29:27 +08:00
Soulter	eaa193c6c5	feat(chat): refactor chat component structure and add new features (#3701 ) - Introduced `ConversationSidebar.vue` for improved conversation management and sidebar functionality. - Enhanced `MessageList.vue` to handle loading states and improved message rendering. - Created new composables: `useConversations`, `useMessages`, `useMediaHandling`, `useRecording` for better code organization and reusability. - Added loading indicators and improved user experience during message processing. - Ensured backward compatibility and maintained existing functionalities.	2025-11-20 17:29:27 +08:00
Soulter	1bdcaa1318	delete: useConversations	2025-11-20 17:29:27 +08:00
Soulter	6b6c48354d	feat(chat): refactor chat component structure and add new features (#3701 ) - Introduced `ConversationSidebar.vue` for improved conversation management and sidebar functionality. - Enhanced `MessageList.vue` to handle loading states and improved message rendering. - Created new composables: `useConversations`, `useMessages`, `useMediaHandling`, `useRecording` for better code organization and reusability. - Added loading indicators and improved user experience during message processing. - Ensured backward compatibility and maintained existing functionalities.	2025-11-20 17:29:27 +08:00
Soulter	774efb2fe0	refactor: update timestamp handling in session management and chat components	2025-11-20 17:29:27 +08:00
Soulter	3ec76636f9	refactor(sqlite): remove auto-generation of session_id in insert method	2025-11-20 17:29:26 +08:00
Soulter	283810d103	feat(chat): refactor chat component structure and add new features (#3701 ) - Introduced `ConversationSidebar.vue` for improved conversation management and sidebar functionality. - Enhanced `MessageList.vue` to handle loading states and improved message rendering. - Created new composables: `useConversations`, `useMessages`, `useMediaHandling`, `useRecording` for better code organization and reusability. - Added loading indicators and improved user experience during message processing. - Ensured backward compatibility and maintained existing functionalities.	2025-11-20 17:29:26 +08:00
Soulter	81a76bc8e5	fix: anyio.ClosedResourceError when calling mcp tools (#3700 ) * fix: anyio.ClosedResourceError when calling mcp tools added reconnect mechanism fixes: 3676 * fix(mcp_client): implement thread-safe reconnection using asyncio.Lock	2025-11-20 17:29:26 +08:00
Soulter	788764be02	refactor: implement migration for WebChat sessions by creating PlatformSession records from platform_message_history	2025-11-20 17:29:26 +08:00
Soulter	802ab26934	refactor: update session handling by replacing conversation_id with session_id in chat routes and components	2025-11-20 17:29:26 +08:00
Soulter	6857c81a14	refactor: enhance PlatformSession migration by adding display_name from Conversations and improve session item styling	2025-11-20 17:29:26 +08:00
Soulter	a6ed511a30	refactor: update message history deletion logic to remove newer records based on offset	2025-11-20 17:29:26 +08:00
Soulter	44c2b58206	refactor: optimize WebChat session migration by batch inserting records	2025-11-20 17:29:26 +08:00
Soulter	0e2adab3fd	refactor: change to platform session	2025-11-20 17:29:26 +08:00
Soulter	0fe87d6b98	fix: restore migration check for version 4.7	2025-11-20 17:29:26 +08:00
Soulter	31ef3d1084	refactor: Implement WebChat session management and migration from version 4.6 to 4.7 - Added WebChatSession model for managing user sessions. - Introduced methods for creating, retrieving, updating, and deleting WebChat sessions in the database. - Updated core lifecycle to include migration from version 4.6 to 4.7, creating WebChat sessions from existing platform message history. - Refactored chat routes to support new session-based architecture, replacing conversation-related endpoints with session endpoints. - Updated frontend components to handle sessions instead of conversations, including session creation and management.	2025-11-20 17:29:26 +08:00
Soulter	5d1e9de096	Merge pull request #3678 from AstrBotDevs/refactor/webchat-session refactor: Implement WebChat session management and migration	2025-11-20 17:23:10 +08:00
Soulter	89da4eb747	Merge branch 'master' into refactor/webchat-session	2025-11-20 17:21:48 +08:00
Soulter	8899a1dee1	feat(chat): refactor chat component structure and add new features (#3701 ) - Introduced `ConversationSidebar.vue` for improved conversation management and sidebar functionality. - Enhanced `MessageList.vue` to handle loading states and improved message rendering. - Created new composables: `useConversations`, `useMessages`, `useMediaHandling`, `useRecording` for better code organization and reusability. - Added loading indicators and improved user experience during message processing. - Ensured backward compatibility and maintained existing functionalities.	2025-11-20 17:19:45 +08:00
Soulter	384a687ec3	delete: remove useConversations composable	2025-11-20 17:15:47 +08:00
Soulter	70cfdd2f8b	feat(chat): refactor chat component structure and add new features (#3701 ) - Introduced `ConversationSidebar.vue` for improved conversation management and sidebar functionality. - Enhanced `MessageList.vue` to handle loading states and improved message rendering. - Created new composables: `useConversations`, `useMessages`, `useMediaHandling`, `useRecording` for better code organization and reusability. - Added loading indicators and improved user experience during message processing. - Ensured backward compatibility and maintained existing functionalities.	2025-11-20 17:15:04 +08:00
Soulter	bdbd2f009a	delete: useConversations	2025-11-20 17:11:01 +08:00
Soulter	164e0d26e0	feat(chat): refactor chat component structure and add new features (#3701 ) - Introduced `ConversationSidebar.vue` for improved conversation management and sidebar functionality. - Enhanced `MessageList.vue` to handle loading states and improved message rendering. - Created new composables: `useConversations`, `useMessages`, `useMediaHandling`, `useRecording` for better code organization and reusability. - Added loading indicators and improved user experience during message processing. - Ensured backward compatibility and maintained existing functionalities.	2025-11-20 17:10:36 +08:00
Soulter	cb087b5ff9	refactor: update timestamp handling in session management and chat components	2025-11-20 17:02:01 +08:00
Soulter	1d3928d145	refactor(sqlite): remove auto-generation of session_id in insert method	2025-11-20 16:33:57 +08:00
Soulter	6dc3d161e7	feat(chat): refactor chat component structure and add new features (#3701 ) - Introduced `ConversationSidebar.vue` for improved conversation management and sidebar functionality. - Enhanced `MessageList.vue` to handle loading states and improved message rendering. - Created new composables: `useConversations`, `useMessages`, `useMediaHandling`, `useRecording` for better code organization and reusability. - Added loading indicators and improved user experience during message processing. - Ensured backward compatibility and maintained existing functionalities.	2025-11-20 16:30:05 +08:00
Soulter	e9805ba205	fix: anyio.ClosedResourceError when calling mcp tools (#3700 ) * fix: anyio.ClosedResourceError when calling mcp tools added reconnect mechanism fixes: 3676 * fix(mcp_client): implement thread-safe reconnection using asyncio.Lock	2025-11-20 16:24:02 +08:00
Dt8333	d5280dcd88	fix(core.platform): 修复启用多个企业微信智能机器人适配器时消息混乱的问题 (#3693 ) * fix(core.platform): 修复启用多个企业微信智能机器人适配器时消息混乱的问题移除了全局的消息队列，改为每个适配器处理自己的队列。修改相关方法适应该更改。 #3673 * chore: apply suggestions from code review Co-authored-by: sourcery-ai[bot] <58596630+sourcery-ai[bot]@users.noreply.github.com> --------- Co-authored-by: Soulter <37870767+Soulter@users.noreply.github.com> Co-authored-by: sourcery-ai[bot] <58596630+sourcery-ai[bot]@users.noreply.github.com>	2025-11-20 16:24:02 +08:00
Dt8333	67a9663eff	fix(dashboard.i18n): complete the missing i18n keys(#3699 ) #3679	2025-11-20 16:24:02 +08:00
Soulter	77dd89b8eb	feat: add supports for gemini-3 series thought signature (#3698 ) * feat: add supports for gemini-3 series thought signature * feat: refactor tools_call_extra_content to use a dictionary for better structure	2025-11-20 16:24:02 +08:00
Soulter	8e511bf14b	fix: build docker ci failed	2025-11-20 16:24:02 +08:00
Soulter	164a4226ea	feat(chat): refactor chat component structure and add new features (#3701 ) - Introduced `ConversationSidebar.vue` for improved conversation management and sidebar functionality. - Enhanced `MessageList.vue` to handle loading states and improved message rendering. - Created new composables: `useConversations`, `useMessages`, `useMediaHandling`, `useRecording` for better code organization and reusability. - Added loading indicators and improved user experience during message processing. - Ensured backward compatibility and maintained existing functionalities.	2025-11-20 16:07:09 +08:00
Soulter	6d6fefc435	fix: anyio.ClosedResourceError when calling mcp tools (#3700 ) * fix: anyio.ClosedResourceError when calling mcp tools added reconnect mechanism fixes: 3676 * fix(mcp_client): implement thread-safe reconnection using asyncio.Lock	2025-11-20 16:01:22 +08:00
Soulter	aa59532287	refactor: implement migration for WebChat sessions by creating PlatformSession records from platform_message_history	2025-11-20 15:58:27 +08:00
Soulter	b984bb2513	stage	2025-11-20 13:51:53 +08:00
Dt8333	8488c9aeab	fix(core.platform): 修复启用多个企业微信智能机器人适配器时消息混乱的问题 (#3693 ) * fix(core.platform): 修复启用多个企业微信智能机器人适配器时消息混乱的问题移除了全局的消息队列，改为每个适配器处理自己的队列。修改相关方法适应该更改。 #3673 * chore: apply suggestions from code review Co-authored-by: sourcery-ai[bot] <58596630+sourcery-ai[bot]@users.noreply.github.com> --------- Co-authored-by: Soulter <37870767+Soulter@users.noreply.github.com> Co-authored-by: sourcery-ai[bot] <58596630+sourcery-ai[bot]@users.noreply.github.com>	2025-11-19 21:44:38 +08:00
Dt8333	676f9fd4ff	fix(dashboard.i18n): complete the missing i18n keys(#3699 ) #3679	2025-11-19 21:36:34 +08:00
Soulter	1935ce4700	refactor: update session handling by replacing conversation_id with session_id in chat routes and components	2025-11-19 19:54:29 +08:00
Soulter	e760956353	refactor: enhance PlatformSession migration by adding display_name from Conversations and improve session item styling	2025-11-19 19:41:57 +08:00
Soulter	be3e5f3f8b	refactor: update message history deletion logic to remove newer records based on offset	2025-11-19 19:41:25 +08:00
Soulter	cdf617feac	refactor: optimize WebChat session migration by batch inserting records	2025-11-19 19:16:15 +08:00
Soulter	afb56cf707	feat: add supports for gemini-3 series thought signature (#3698 ) * feat: add supports for gemini-3 series thought signature * feat: refactor tools_call_extra_content to use a dictionary for better structure	2025-11-19 18:54:56 +08:00
Soulter	cd2556ab94	fix: build docker ci failed	2025-11-19 15:40:41 +08:00
Soulter	cf4a5d9ea4	refactor: change to platform session	2025-11-18 22:37:55 +08:00
Soulter	0747099cac	fix: restore migration check for version 4.7	2025-11-18 22:07:43 +08:00
Soulter	323ec29b02	refactor: Implement WebChat session management and migration from version 4.6 to 4.7 - Added WebChatSession model for managing user sessions. - Introduced methods for creating, retrieving, updating, and deleting WebChat sessions in the database. - Updated core lifecycle to include migration from version 4.6 to 4.7, creating WebChat sessions from existing platform message history. - Refactored chat routes to support new session-based architecture, replacing conversation-related endpoints with session endpoints. - Updated frontend components to handle sessions instead of conversations, including session creation and management.	2025-11-18 22:04:26 +08:00
magisk317	ae81d70685	ci(docker-build): build nightly image everyday (#3120 ) * ci: build test image on master pushes * ci: split workflows for master test and release builds * test ci * test ci * Update docker-image.yml * test ci Updated README to enhance deployment instructions. * Make GHCR publishing optional in Docker workflow * chore: Update DockerHub password secret in workflow * Update .github/workflows/docker-image.yml Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * chore: rename job to build nightly image in workflow * feat: schedule the nightly build at 0:00 am everyday, if have new commits * fix: update build-nightly-image job to trigger only on schedule events * Update fetch-depth and enable fetch-tag in workflows --------- Co-authored-by: Soulter <37870767+Soulter@users.noreply.github.com> Co-authored-by: LIghtJUNction <lightjunction.me@gmail.com> Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> Co-authored-by: Soulter <905617992@qq.com>	2025-11-18 10:47:58 +08:00
RC-CHN	270c89c12f	feat: Add URL document parser for knowledge base (#3622 ) * feat: 添加从 URL 上传文档的功能，支持进度回调和错误处理 * feat: 添加从 URL 上传文档的前端 * chore: 添加 URL 上传功能的警告提示，确保用户配置正确 * feat: 添加内容清洗功能，支持从 URL 上传文档时的清洗设置和服务提供商选择 * feat: 更新内容清洗系统提示，增强信息提取规则；添加 URL 上传功能的测试版标识 * style: format code * perf: 优化上传设置，增强 URL 上传时的禁用逻辑和清洗提供商验证 * refactor:使用自带chunking模块 * refactor: 提取prompt到单独文件 * feat: 添加 Tavily API Key 配置对话框，增强网页搜索功能的配置体验 * fix: update URL hint and warning messages for clarity in knowledge base upload settings * fix: 修复设置tavily_key的热重载问题 --------- Co-authored-by: Soulter <905617992@qq.com>	2025-11-17 19:05:14 +08:00
Soulter	c7a58252fe	feat: supports knowledge base agentic search (#3667 ) * feat: supports knowledge base agentic search * fix: correct formatting of system prompt in knowledge base results	2025-11-17 17:29:18 +08:00
Soulter	47ad8c86e5	docs: update translations of README	2025-11-17 12:50:01 +08:00
Soulter	937e879e5e	chore: revise the issue template Updated the bug report template to include English translations for all fields and improved clarity.	2025-11-17 11:35:24 +08:00
Soulter	1ecf26eead	chore: revice pr template Removed unnecessary comments and streamlined the pull request template.	2025-11-17 11:27:48 +08:00