文档更新

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

.dockerignore

@@ -1,9 +1,9 @@
 # Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm
 # Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
-# github actions
-.git
+# github acions
 .github/
 .*ignore
+.git/
 # User-specific stuff
 .idea/
 # Byte-compiled / optimized / DLL files
@@ -15,10 +15,10 @@ env/
 venv*/
 ENV/
 .conda/
+README*.md
 dashboard/
 data/
 changelogs/
 tests/
 .ruff_cache/
-.astrbot
-astrbot.lock
+.astrbot

.env

@@ -0,0 +1 @@
+ASTRBOT_ROOT = ./data

.env.example

@@ -0,0 +1,2 @@
+# ASTRBOT 数据目录
+# ASTRBOT_ROOT = ./data

.github/ISSUE_TEMPLATE/PLUGIN_PUBLISH.yml

@@ -16,7 +16,7 @@ body:
 
         请将插件信息填写到下方的 JSON 代码块中。其中 `tags`（插件标签）和 `social_link`（社交链接）选填。
 
-        不熟悉 JSON ？可以从 [此站](https://plugins.astrbot.app) 右下角提交。
+        不熟悉 JSON ？可以从 [此处](https://plugins.astrbot.app/submit) 生成 JSON ，生成后记得复制粘贴过来.
 
   - type: textarea
     id: plugin-info

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -1,44 +1,46 @@
-name: '🐛 Report Bug / 报告 Bug'
+name: '🐛 报告 Bug'
 title: '[Bug]'
-description: Submit bug report to help us improve. / 提交报告帮助我们改进。
+description: 提交报告帮助我们改进。
 labels: [ 'bug' ]
 body:
   - type: markdown
     attributes:
       value: |
-        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。
+        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: What happened / 发生了什么
-      description: Description
+      label: 发生了什么
+      description: 描述你遇到的异常
       placeholder: >
-        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。
+        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: Reproduce / 如何复现？
+      label: 如何复现？
       description: >
-        The steps to reproduce the issue. / 复现该问题的步骤
+        复现该问题的步骤
       placeholder: >
-        Example: 1. Open '...'
+        如: 1. 打开 '...'
     validations:
       required: true
 
   - type: textarea
     attributes:
-      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
+      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。
       placeholder: >
-        Example: 4.5.7 Docker, 3.1.7 Windows Launcher
+        如: 3.1.8 Docker, 3.1.7 Windows启动器
     validations:
       required: true
 
   - type: dropdown
     attributes:
-      label: OS
+      label: 操作系统
       description: |
-        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？
+        你在哪个操作系统上遇到了这个问题？
       multiple: false
       options:
         - 'Windows'
@@ -51,30 +53,30 @@ body:
 
   - type: textarea
     attributes:
-      label: Logs / 报错日志
+      label: 报错日志
       description: >
-        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。
+        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: >
-        Please provide a complete error log or screenshot. / 请提供完整的报错日志或截图。
+        请提供完整的报错日志或截图。
     validations:
       required: true
 
   - type: checkboxes
     attributes:
-      label: Are you willing to submit a PR? / 你愿意提交 PR 吗？
+      label: 你愿意提交 PR 吗？
       description: >
-        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。
+        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: Yes!
+        - label: 是的，我愿意提交 PR!
 
   - type: checkboxes
     attributes:
       label: Code of Conduct
       options:
         - label: >
-            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)。
+            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)。
           required: true
 
   - type: markdown
     attributes:
-      value: "Thank you for filling out our form! / 感谢您填写我们的表单！"
+      value: "感谢您填写我们的表单！"

.github/PULL_REQUEST_TEMPLATE.md

@@ -1,25 +1,44 @@
-<!--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）-->
+<!-- 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)-->
 
 ### 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?-->
 
-- [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 -->
+### 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...).-->
 
 ### Screenshots or Test Results / 运行截图或测试结果
 
-<!--Please paste screenshots, GIFs, or test logs here as evidence of executing the "Verification Steps" to prove this change is effective.-->
 <!--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.
 
 ---
 
 ### Checklist / 检查清单
 
-<!--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。-->
+<!--If merged, your code will serve tens of thousands of users! Please double-check the following items before submitting.-->
 
 - [ ] 😊 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**.

.github/copilot-instructions.md

@@ -1,63 +1,63 @@
-# AstrBot Development Instructions
+# AstrBot 开发指南
 
-AstrBot is a multi-platform LLM chatbot and development framework written in Python with a Vue.js dashboard. It supports multiple messaging platforms (QQ, Telegram, Discord, etc.) and various LLM providers (OpenAI, Anthropic, Google Gemini, etc.).
+AstrBot 是一个使用 Python 编写、配备 Vue.js 仪表盘的多平台 LLM 聊天机器人开发框架。它支持多个消息平台（QQ、Telegram、Discord 等）和多种 LLM 提供商（OpenAI、Anthropic、Google Gemini 等）。
 
-Always reference these instructions first and fallback to search or bash commands only when you encounter unexpected information that does not match the info here.
+始终优先参考这些指南，仅在遇到与此处信息不符的意外情况时才回退到搜索或 bash 命令。
 
-## Working Effectively
+## 高效工作
 
-### Bootstrap and Install Dependencies
-- **Python 3.10+ required** - Check `.python-version` file
-- Install UV package manager: `pip install uv`
-- Install project dependencies: `uv sync` -- takes 6-7 minutes. NEVER CANCEL. Set timeout to 10+ minutes.
-- Create required directories: `mkdir -p data/plugins data/config data/temp`
+### 引导和安装依赖
+- **需要 Python 3.10+** - 检查 `.python-version` 文件
+- 安装 UV 包管理器：`pip install uv`
+- 安装项目依赖：`uv sync` -- 很快几分钟。绝不要取消。设置超时时间为 10+ 分钟。
+- 创建必需的目录：`mkdir -p data/plugins data/config data/temp`
 
-### Running the Application
-- Run main application: `uv run main.py` -- starts in ~3 seconds
-- Application creates WebUI on http://localhost:6185 (default credentials: `astrbot`/`astrbot`)
-- Application loads plugins automatically from `packages/` and `data/plugins/` directories
+### 运行应用程序
+- 运行主应用程序：`uv run main.py` -- 约 3 秒启动
+- 应用程序在 http://localhost:6185 创建 WebUI（默认凭据：`astrbot`/`astrbot`）
+- 应用程序自动从 `packages/` 和 `data/plugins/` 目录加载插件
 
-### Dashboard Build (Vue.js/Node.js)
-- **Prerequisites**: Node.js 20+ and npm 10+ required
-- Navigate to dashboard: `cd dashboard`
-- Install dashboard dependencies: `npm install` -- takes 2-3 minutes. NEVER CANCEL. Set timeout to 5+ minutes.
-- Build dashboard: `npm run build` -- takes 25-30 seconds. NEVER CANCEL.
-- Dashboard creates optimized production build in `dashboard/dist/`
+### 仪表盘构建（Vue.js/Node.js）
+- **前置要求**：需要 Node.js 20+ 和 npm 10+
+- 导航到仪表盘：`cd dashboard`
+- 安装仪表盘依赖：`npm install` -- 需要 2-3 分钟。绝不要取消。设置超时时间为 5+ 分钟。
+- 构建仪表盘：`npm run build` -- 需要 25-30 秒。绝不要取消。
+- 仪表盘在 `dashboard/dist/` 创建优化的生产构建
 
-### Testing
-- Do not generate test files for now.
+### 测试
+- 暂时不要生成测试文件。
 
-### Code Quality and Linting
-- Install ruff linter: `uv add --dev ruff`
-- Check code style: `uv run ruff check .` -- takes <1 second
-- Check formatting: `uv run ruff format --check .` -- takes <1 second
-- Fix formatting: `uv run ruff format .`
-- **ALWAYS** run `uv run ruff check .` and `uv run ruff format .` before committing changes
+### 代码质量和检查
+- 安装 ruff 检查器：`uv add --dev ruff`
+- 检查代码风格：`uv run ruff check .` -- 耗时 <1 秒
+- 检查格式：`uv run ruff format --check .` -- 耗时 <1 秒
+- 修复格式：`uv run ruff format .`
+- **始终**在提交更改前运行 `uv run ruff check .` 和 `uv run ruff format .`
 
-### Plugin Development
-- Plugins load from `packages/` (built-in) and `data/plugins/` (user-installed)
-- Plugin system supports function tools and message handlers
-- Key plugins: python_interpreter, web_searcher, astrbot, reminder, session_controller
+### 插件开发
+- 插件从 `packages/`（内置）和 `data/plugins/`（用户安装）加载
+- 插件系统支持函数工具和消息处理器
+- 关键插件：python_interpreter、web_searcher、astrbot、reminder、session_controller
 
-### Common Issues and Workarounds
-- **Dashboard download fails**: Known issue with "division by zero" error - application still works
-- **Import errors in tests**: Ensure `uv run` is used to run tests in proper environment
-=- **Build timeouts**: Always set appropriate timeouts (10+ minutes for uv sync, 5+ minutes for npm install)
+### 常见问题和解决方法
+- **仪表盘下载失败**：已知的"除以零"错误问题 - 应用程序仍可正常工作
+- **测试中的导入错误**：确保使用 `uv run` 在适当的环境中运行测试
+- **构建超时**：始终设置适当的超时时间（uv sync 为 10+ 分钟，npm install 为 5+ 分钟）
 
-## CI/CD Integration
-- GitHub Actions workflows in `.github/workflows/`
-- Docker builds supported via `Dockerfile`
-- Pre-commit hooks enforce ruff formatting and linting
+## CI/CD 集成
+- GitHub Actions 工作流在 `.github/workflows/` 中
+- 通过 `Dockerfile` 支持 Docker 构建
+- Pre-commit 钩子强制执行 ruff 格式化和检查
 
-## Docker Support
-- Primary deployment method: `docker run soulter/astrbot:latest`
-- Compose file available: `compose.yml`
-- Exposes ports: 6185 (WebUI), 6195 (WeChat), 6199 (QQ), etc.
-- Volume mount required: `./data:/AstrBot/data`
+## Docker 支持
+- 主要部署方法：`docker run soulter/astrbot:latest`
+- 可用的 Compose 文件：`compose.yml`
+- 暴露端口：6185（WebUI）、6195（WeChat）、6199（QQ）等
+- 需要挂载卷：`./data:/AstrBot/data`
 
-## Multi-language Support
-- Documentation in Chinese (README.md), English (README_en.md), Japanese (README_ja.md)
-- UI supports internationalization
-- Default language is Chinese
+## 多语言支持
+- 文档包括中文（README.md）、英文（README_en.md）、日文（README_ja.md）
+- UI 支持国际化
+- 默认语言为中文
 
-Remember: This is a production chatbot framework with real users. Always test thoroughly and ensure changes don't break existing functionality.
+请记住：这是一个有真实用户的生产聊天机器人框架。始终进行彻底测试，确保更改不会破坏现有功能。

.github/workflows/auto_release.yml

@@ -13,7 +13,7 @@ jobs:
       contents: write
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v5
 
       - name: Dashboard Build
         run: |
@@ -70,7 +70,7 @@ jobs:
     needs: build-and-publish-to-github-release
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v5
 
       - name: Set up Python
         uses: actions/setup-python@v6

.github/workflows/code-format.yml

@@ -12,7 +12,7 @@ jobs:
     
     steps:
     - name: Checkout code
-      uses: actions/checkout@v6
+      uses: actions/checkout@v5
       
     - name: Set up Python
       uses: actions/setup-python@v6

.github/workflows/codeql.yml

@@ -56,7 +56,7 @@ jobs:
         # your codebase is analyzed, see https://docs.github.com/en/code-security/code-scanning/creating-an-advanced-setup-for-code-scanning/codeql-code-scanning-for-compiled-languages
     steps:
     - name: Checkout repository
-      uses: actions/checkout@v6
+      uses: actions/checkout@v5
 
     # Initializes the CodeQL tools for scanning.
     - name: Initialize CodeQL

.github/workflows/coverage_test.yml

@@ -17,7 +17,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout
-        uses: actions/checkout@v6
+        uses: actions/checkout@v5
         with:
           fetch-depth: 0
 

.github/workflows/dashboard_ci.yml

@@ -11,7 +11,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v5
 
       - name: Setup Node.js
         uses: actions/setup-node@v6

.github/workflows/docker-image.yml

@@ -3,125 +3,18 @@ name: Docker Image CI/CD
 on:
   push:
     tags:
-      - "v*"
-  schedule:
-    # Run at 00:00 UTC every day
-    - cron: "0 0 * * *"
+      - 'v*'
   workflow_dispatch:
 
 jobs:
-  build-nightly-image:
-    if: github.event_name == 'schedule'
+  publish-docker:
     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@v6
+      - name: Pull The Codes
+        uses: actions/checkout@v5
         with:
-          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@v6
-        with:
-          fetch-depth: 1
-          fetch-tag: true
+          fetch-depth: 0  # Must be 0 so we can fetch tags
 
       - name: Get latest tag (only on manual trigger)
         id: get-latest-tag
@@ -134,22 +27,21 @@ jobs:
         if: github.event_name == 'workflow_dispatch'
         run: git checkout ${{ steps.get-latest-tag.outputs.latest_tag }}
 
-      - name: Compute release metadata
-        id: release-meta
+      - name: Check if version is pre-release
+        id: check-prerelease
         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#refs/tags/}"
+            version="${{ github.ref_name }}"
           fi
           if [[ "$version" == *"beta"* ]] || [[ "$version" == *"alpha"* ]]; then
             echo "is_prerelease=true" >> $GITHUB_OUTPUT
-            echo "Version $version marked as pre-release"
+            echo "Version $version is a pre-release, will not push latest tag"
           else
             echo "is_prerelease=false" >> $GITHUB_OUTPUT
-            echo "Version $version marked as stable"
+            echo "Version $version is a stable release, will push latest tag"
           fi
-          echo "version=$version" >> $GITHUB_OUTPUT
 
       - name: Build Dashboard
         run: |
@@ -175,24 +67,23 @@ 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: ${{ env.GHCR_OWNER }}
+          username: Soulter
           password: ${{ secrets.GHCR_GITHUB_TOKEN }}
 
-      - name: Build and Push Release Image
+      - name: Build and Push Docker to DockerHub and Github GHCR
         uses: docker/build-push-action@v6
         with:
           context: .
           platforms: linux/amd64,linux/arm64
           push: true
           tags: |
-            ${{ 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) || '' }}
+            ${{ 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 }}
 
       - name: Post build notifications
-        run: echo "Release Docker image has been built and pushed successfully"
+        run: echo "Docker image has been built and pushed successfully"

.gitignore

@@ -34,7 +34,6 @@ dashboard/node_modules/
 dashboard/dist/
 package-lock.json
 package.json
-yarn.lock
 
 # Operating System
 **/.DS_Store

Dockerfile

@@ -12,21 +12,19 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
     ca-certificates \
     bash \
     ffmpeg \
-    curl \
-    gnupg \
-    git \
     && apt-get clean \
-    && rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
+    && rm -rf /var/lib/apt/lists/*
 
-RUN apt-get update && apt-get install -y curl gnupg \
-    && curl -fsSL https://deb.nodesource.com/setup_lts.x | bash - \
-    && apt-get install -y nodejs
+RUN apt-get update && apt-get install -y curl gnupg && \
+    curl -fsSL https://deb.nodesource.com/setup_lts.x | bash - && \
+    apt-get install -y nodejs && \
+    rm -rf /var/lib/apt/lists/*
 
-RUN python -m pip install uv \
-    && echo "3.11" > .python-version
+RUN python -m pip install uv
 RUN uv pip install -r requirements.txt --no-cache-dir --system
 RUN uv pip install socksio uv pilk --no-cache-dir --system
 
 EXPOSE 6185
+EXPOSE 6186
 
-CMD ["python", "main.py"]
+CMD [ "python", "main.py" ]

Dockerfile_with_node

@@ -0,0 +1,35 @@
+FROM python:3.10-slim
+
+WORKDIR /AstrBot
+
+COPY . /AstrBot/
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    gcc \
+    build-essential \
+    python3-dev \
+    libffi-dev \
+    libssl-dev \
+    curl \
+    unzip \
+    ca-certificates \
+    bash \
+    && apt-get clean \
+    && rm -rf /var/lib/apt/lists/*
+
+# Installation of Node.js
+ENV NVM_DIR="/root/.nvm"
+RUN curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.40.2/install.sh | bash && \
+    . "$NVM_DIR/nvm.sh" && \
+    nvm install 22 && \
+    nvm use 22
+RUN /bin/bash -c ". \"$NVM_DIR/nvm.sh\" && node -v && npm -v"
+
+RUN python -m pip install uv
+RUN uv pip install -r requirements.txt --no-cache-dir --system
+RUN uv pip install socksio uv pyffmpeg --no-cache-dir --system
+
+EXPOSE 6185
+EXPOSE 6186
+
+CMD ["python", "main.py"]

README.md

@@ -8,7 +8,7 @@
 
 <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>
-<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>
+<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=1" alt="Featured｜HelloGitHub" style="width: 250px; height: 54px;" width="250" height="54" /></a>
 </div>
 
 <br>
@@ -32,7 +32,7 @@
 <a href="https://github.com/AstrBotDevs/AstrBot/issues">问题提交</a>
 </div>
 
-AstrBot 是一个开源的一站式 Agent 聊天机器人平台，可无缝接入主流即时通讯软件，为个人、开发者和团队打造可靠、可扩展的对话式智能基础设施。无论是个人 AI 伙伴、智能客服、自动化助手，还是企业知识库，AstrBot 都能在你的即时通讯软件平台的工作流中快速构建生产可用的 AI 应用。
+AstrBot 是一个开源的一站式 Agent 聊天机器人平台及开发框架。
 
 ## 主要功能
 
@@ -42,7 +42,7 @@ AstrBot 是一个开源的一站式 Agent 聊天机器人平台，可无缝接
 4. **插件扩展**。深度优化的插件机制，支持[开发插件](https://astrbot.app/dev/plugin.html)扩展功能，社区插件生态丰富。
 5. **WebUI**。可视化配置和管理机器人，功能齐全。
 
-## 部署方式 
+## 部署方式
 
 #### Docker 部署(推荐 🥳)
 
@@ -119,73 +119,83 @@ uv run main.py
 
 <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>
 
-## 支持的消息平台
+## ⚡ 消息平台支持情况
 
 **官方维护**
 
-- QQ (官方平台 & OneBot)
-- Telegram
-- 企微应用 & 企微智能机器人
-- 微信客服 & 微信公众号
-- 飞书
-- 钉钉
-- Slack
-- Discord
-- Satori
-- Misskey
-- Whatsapp (将支持)
-- LINE (将支持)
+| 平台    | 支持性 |
+| -------- | ------- |
+| QQ(官方平台) | ✔    |
+| QQ(OneBot)      | ✔    |
+| Telegram   | ✔    |
+| 企微应用    | ✔    |
+| 企微智能机器人 | ✔ |
+| 微信客服    | ✔    |
+| 微信公众号    | ✔    |
+| 飞书   | ✔    |
+| 钉钉   | ✔    |
+| Slack   | ✔    |
+| Discord   | ✔    |
+| Satori   | ✔    |
+| Misskey   | ✔    |
+| Whatsapp | 将支持 |
+| LINE | 将支持 |
 
 **社区维护**
 
-- [KOOK](https://github.com/wuyan1003/astrbot_plugin_kook_adapter)
-- [VoceChat](https://github.com/HikariFroya/astrbot_plugin_vocechat)
-- [Bilibili 私信](https://github.com/Hina-Chat/astrbot_plugin_bilibili_adapter)
-- [wxauto](https://github.com/luosheng520qaq/wxauto-repost-onebotv11)
+| 平台    | 支持性 |
+| -------- | ------- |
+| [KOOK](https://github.com/wuyan1003/astrbot_plugin_kook_adapter)   | ✔    |
+| [VoceChat](https://github.com/HikariFroya/astrbot_plugin_vocechat)   | ✔    |
+| [Bilibili 私信](https://github.com/Hina-Chat/astrbot_plugin_bilibili_adapter)   | ✔    |
+| [wxauto](https://github.com/luosheng520qaq/wxauto-repost-onebotv11)   | ✔    |
 
-## 支持的模型服务
+## ⚡ 提供商支持情况
 
 **大模型服务**
 
-- OpenAI 及兼容服务
-- Anthropic
-- Google Gemini
-- Moonshot AI
-- 智谱 AI
-- DeepSeek
-- Ollama (本地部署)
-- LM Studio (本地部署)
-- [优云智算](https://www.compshare.cn/?ytag=GPU_YY-gh_astrbot&referral_code=FV7DcGowN4hB5UuXKgpE74)
-- [302.AI](https://share.302.ai/rr1M3l)
-- [小马算力](https://www.tokenpony.cn/3YPyf)
-- [硅基流动](https://docs.siliconflow.cn/cn/usercases/use-siliconcloud-in-astrbot)
-- [PPIO 派欧云](https://ppio.com/user/register?invited_by=AIOONE)
-- ModelScope
-- OneAPI
-
-**LLMOps 平台**
-
-- Dify
-- 阿里云百炼应用
-- Coze
+| 名称  | 支持性 | 备注 |
+| -------- | ------- | ------- |
+| OpenAI | ✔    | 支持任何兼容 OpenAI API 的服务 |
+| Anthropic | ✔    |  |
+| Google Gemini | ✔   |  |
+| Moonshot AI | ✔   |  |
+| 智谱 AI | ✔   |  |
+| DeepSeek | ✔   |  |
+| Ollama | ✔     | 本地部署 DeepSeek 等开源语言模型 |
+| LM Studio | ✔    | 本地部署 DeepSeek 等开源语言模型 |
+| [优云智算](https://www.compshare.cn/?ytag=GPU_YY-gh_astrbot&referral_code=FV7DcGowN4hB5UuXKgpE74) | ✔  |  |
+| [302.AI](https://share.302.ai/rr1M3l) | ✔   |  |
+| [小马算力](https://www.tokenpony.cn/3YPyf) | ✔   |  |
+| 硅基流动 | ✔    |  |
+| PPIO 派欧云 | ✔    |  |
+| ModelScope | ✔    |  |
+| OneAPI | ✔    |  |
+| Dify | ✔    |  |
+| 阿里云百炼应用 | ✔    |  |
+| Coze | ✔    |  |
 
 **语音转文本服务**
 
-- OpenAI Whisper
-- SenseVoice
+| 名称  | 支持性 | 备注 |
+| -------- | ------- | ------- |
+| Whisper | ✔    | 支持 API、本地部署 |
+| SenseVoice | ✔   | 本地部署 |
 
 **文本转语音服务**
 
-- OpenAI TTS
-- Gemini TTS
-- GPT-Sovits-Inference
-- GPT-Sovits
-- FishAudio
-- Edge TTS
-- 阿里云百炼 TTS
-- Azure TTS
-- Minimax TTS
-- 火山引擎 TTS
+| 名称  | 支持性 | 备注 |
+| -------- | ------- | ------- |
+| OpenAI TTS | ✔    |  |
+| Gemini TTS | ✔    |  |
+| GSVI | ✔    | GPT-Sovits-Inference |
+| GPT-SoVITs | ✔     | GPT-Sovits |
+| FishAudio | ✔    | |
+| Edge TTS | ✔    | Edge 浏览器的免费 TTS |
+| 阿里云百炼 TTS | ✔    |  |
+| Azure TTS | ✔   | |
+| Minimax TTS | ✔   | |
+| 火山引擎 TTS | ✔   | |
 
 ## ❤️ 贡献
 
@@ -219,7 +229,7 @@ pre-commit install
 
 ## ⭐ Star History
 
-> [!TIP]
+> [!TIP] 
 > 如果本项目对您的生活 / 工作产生了帮助，或者您关注本项目的未来发展，请给项目 Star，这是我们维护这个开源项目的动力 <3
 
 <div align="center">

README_en.md

@@ -1,233 +1,182 @@
-![AstrBot-Logo-Simplified](https://github.com/user-attachments/assets/ffd99b6b-3272-4682-beaa-6fe74250f7d9)
+<p align="center">
+    
+![6e1279651f16d7fdf4727558b72bbaf1](https://github.com/user-attachments/assets/ead4c551-fc3c-48f7-a6f7-afbfdb820512)
 
 </p>
 
 <div align="center">
 
-<br>
+_✨ Easy-to-use Multi-platform LLM Chatbot & Development Framework ✨_
 
-<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>
-<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>
 
-<br>
+[![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)
 
-<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>
-
-<br>
-
-<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>
+<a href="https://github.com/AstrBotDevs/AstrBot/issues">Issue Tracking</a>
 </div>
 
-AstrBot is an open-source all-in-one Agent chatbot platform and development framework.
+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.
 
-## Key Features
+## ✨ Key Features
 
-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.
+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.
 
-## Deployment Methods
+> [!TIP]
+> Dashboard Demo: [https://demo.astrbot.app/](https://demo.astrbot.app/)  
+> Username: `astrbot`, Password: `astrbot` (LLM not configured for chat page)
 
-#### Docker Deployment (Recommended 🥳)
+## ✨ Deployment
 
-We recommend deploying AstrBot using Docker or Docker Compose.
+#### Docker Deployment
 
-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).
+See docs: [Deploy with Docker](https://astrbot.app/deploy/astrbot/docker.html#docker-deployment)
 
-#### BT-Panel Deployment
+#### Windows Installer
 
-AstrBot has partnered with BT-Panel and is now available in their marketplace.
+Requires Python (>3.10). See docs: [Windows Installer Guide](https://astrbot.app/deploy/astrbot/windows.html)
 
-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.
+#### Replit Deployment
 
 [![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 deployment method.
-
-Please refer to the official documentation: [CasaOS Deployment](https://astrbot.app/deploy/astrbot/casaos.html).
+Community-contributed method.  
+See docs: [CasaOS Deployment](https://astrbot.app/deploy/astrbot/casaos.html)
 
 #### Manual Deployment
 
-First, install uv:
+See docs: [Source Code Deployment](https://astrbot.app/deploy/astrbot/cli.html)
 
-```bash
-pip install uv
-```
+## ⚡ Platform Support
 
-Install AstrBot via Git Clone:
+| 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             | -                   |
 
-```bash
-git clone https://github.com/AstrBotDevs/AstrBot && cd AstrBot
-uv run main.py
-```
+## Provider Support Status
 
-Or refer to the official documentation: [Deploy AstrBot from Source](https://astrbot.app/deploy/astrbot/cli.html).
+| 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                                  |
 
-## 🌍 Community
-
-### QQ Groups
-
-- Group 1: 322154837
-- Group 3: 630166526
-- Group 5: 822130018
-- Group 6: 753075035
-- Developer Group: 975206796
-
-### Telegram Group
-
-<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>
-
-### Discord Server
-
-<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>
-
-## Supported Messaging Platforms
-
-**Officially Maintained**
-
-- 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)
-
-**Community Maintained**
-
-- [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)
-
-## Supported Model Services
-
-**LLM Services**
-
-- 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
-
-**LLMOps Platforms**
-
-- Dify
-- Alibaba Cloud Bailian Applications
-- Coze
-
-**Speech-to-Text Services**
-
-- OpenAI Whisper
-- SenseVoice
-
-**Text-to-Speech Services**
-
-- OpenAI TTS
-- Gemini TTS
-- GPT-Sovits-Inference
-- GPT-Sovits
-- FishAudio
-- Edge TTS
-- Alibaba Cloud Bailian TTS
-- Azure TTS
-- Minimax TTS
-- Volcano Engine TTS
-
-## ❤️ Contributing
-
-Issues and Pull Requests are always welcome! Feel free to submit your changes to this project :)
-
-### 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
+# 🦌 Roadmap
 
 > [!TIP]
-> 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
+> Suggestions welcome via Issues <3
 
-<div align="center">
+- [ ] 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
 
-[![Star History Chart](https://api.star-history.com/svg?repos=astrbotdevs/astrbot&type=Date)](https://star-history.com/#astrbotdevs/astrbot&Date)
+## ❤️ Contributions
+
+All Issues/PRs welcome! Simply submit your changes to this project :)
+
+For major features, please discuss via Issues first.
+
+## 🌟 Support
+
+- Star this project!
+- Support via [Afdian](https://afdian.com/a/soulter)
+- WeChat support: [QR Code](https://drive.soulter.top/f/pYfA/d903f4fa49a496fda3f16d2be9e023b5.png)
+
+## ✨ Demos
+
+> [!NOTE]
+> Code executor file I/O currently tested with Napcat(QQ)/Lagrange(QQ)
+
+<div align='center'>
+
+<img src="https://github.com/user-attachments/assets/4ee688d9-467d-45c8-99d6-368f9a8a92d8" width="600">
+
+_✨ Docker-based Sandboxed Code Executor (Beta) ✨_
+
+<img src="https://github.com/user-attachments/assets/0378f407-6079-4f64-ae4c-e97ab20611d2" height=500>
+
+_✨ Multimodal Input, Web Search, Text-to-Image ✨_
+
+<img src="https://github.com/user-attachments/assets/8ec12797-e70f-460a-959e-48eca39ca2bb" height=100>
+
+_✨ Natural Language TODO Lists ✨_
+
+<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>
+
+_✨ Plugin System Showcase ✨_
+
+<img src="https://github.com/user-attachments/assets/592a8630-14c7-4e06-b496-9c0386e4f36c" width=600>
+
+_✨ Web Dashboard ✨_
+
+![webchat](https://drive.soulter.top/f/vlsA/ezgif-5-fb044b2542.gif)
+
+_✨ Built-in Web Chat Interface ✨_
 
 </div>
 
-</details>
+## ⭐ Star History
+
+> [!TIP] 
+> If this project helps you, please give it a star <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)
+
+</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
+    -->
+
 
 _私は、高性能ですから!_
+

README_ja.md

@@ -1,233 +1,167 @@
-![AstrBot-Logo-Simplified](https://github.com/user-attachments/assets/ffd99b6b-3272-4682-beaa-6fe74250f7d9)
+<p align="center">
+
+![6e1279651f16d7fdf4727558b72bbaf1](https://github.com/user-attachments/assets/ead4c551-fc3c-48f7-a6f7-afbfdb820512)
 
 </p>
 
 <div align="center">
 
-<br>
+_✨ 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 ✨_
 
-<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>
-<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>
+
+[![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>
 </div>
 
-<br>
+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。
 
-<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>
+## ✨ 主な機能
 
-<br>
+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。
 
-<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>
+> [!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 😭）
 
-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。
+## ✨ 使用方法
 
-## 主な機能
+#### Docker デプロイ
 
-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。
+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。
 
-## デプロイ方法
+#### Windows ワンクリックインストーラーのデプロイ
 
-#### Docker デプロイ（推奨 🥳）
+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 / 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。
+#### Replit デプロイ
 
 [![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
 
-コミュニティ貢献によるデプロイ方法。
+コミュニティが提供するデプロイ方法です。
 
-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。
+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。
 
 #### 手動デプロイ
 
-まず uv をインストールします:
+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。
 
-```bash
-pip install uv
-```
+## ⚡ メッセージプラットフォームのサポート状況
 
-Git Clone で AstrBot をインストール:
+| 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 | - |
 
-```bash
-git clone https://github.com/AstrBotDevs/AstrBot && cd AstrBot
-uv run main.py
-```
+# 🦌 今後のロードマップ
 
-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。
+> [!TIP]
+> Issue でさらに多くの提案を歓迎します <3
 
-## 🌍 コミュニティ
+- [ ] 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
 
-### QQ グループ
+## ❤️ 貢献
 
-- 1群:322154837
-- 3群:630166526
-- 5群:822130018
-- 6群:753075035
-- 開発者群:975206796
+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 :)
 
-### Telegram グループ
+新機能の追加については、まず Issue で議論してください。
 
-<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>
+## 🌟 サポート
 
-### Discord サーバー
+- 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~
 
-<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>
+## ✨ デモ
 
-## サポートされているメッセージプラットフォーム
+> [!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
 
-**公式メンテナンス**
+<div align='center'>
 
-- 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)
+<img src="https://github.com/user-attachments/assets/4ee688d9-467d-45c8-99d6-368f9a8a92d8" width="600">
 
-**コミュニティメンテナンス**
+_✨ 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）✨_
 
-- [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)
+<img src="https://github.com/user-attachments/assets/0378f407-6079-4f64-ae4c-e97ab20611d2" height=500>
 
-## サポートされているモデルサービス
+_✨ 多モーダル、ウェブ検索、長文の画像変換（設定可能）✨_
 
-**大規模言語モデルサービス**
+<img src="https://github.com/user-attachments/assets/8ec12797-e70f-460a-959e-48eca39ca2bb" height=100>
 
-- 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
+_✨ 自然言語タスク ✨_
 
-**LLMOps プラットフォーム**
+<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>
 
-- Dify
-- Alibaba Cloud 百炼アプリケーション
-- Coze
+_✨ プラグインシステム - 一部のプラグインの展示 ✨_
 
-**音声認識サービス**
+<img src="https://github.com/user-attachments/assets/592a8630-14c7-4e06-b496-9c0386e4f36c" width="600">
 
-- OpenAI Whisper
-- SenseVoice
+_✨ 管理パネル ✨_
 
-**音声合成サービス**
+![webchat](https://drive.soulter.top/f/vlsA/ezgif-5-fb044b2542.gif)
 
-- OpenAI TTS
-- Gemini TTS
-- GPT-Sovits-Inference
-- GPT-Sovits
-- FishAudio
-- Edge TTS
-- Alibaba Cloud 百炼 TTS
-- Azure TTS
-- Minimax TTS
-- Volcano Engine TTS
+_✨ 内蔵 Web Chat、オンラインでボットと対話 ✨_
 
-## ❤️ 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
+</div>
 
 ## ⭐ 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
+> 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
 
 <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=soulter/astrbot&type=Date)](https://star-history.com/#soulter/astrbot&Date)
 
 </div>
 
-</details>
+## 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
+    -->
 
 _私は、高性能ですから!_

astrbot/__main__.py

@@ -0,0 +1,97 @@
+import argparse
+import asyncio
+import mimetypes
+import os
+import sys
+
+from astrbot.core import LogBroker, LogManager, db_helper, logger
+from astrbot.core.config.default import VERSION
+from astrbot.core.initial_loader import InitialLoader
+from astrbot.core.utils.io import download_dashboard, get_dashboard_version
+from astrbot_api import LOGO, IAstrbotPaths
+from astrbot_sdk import sync_base_container
+
+AstrbotPaths: type[IAstrbotPaths] = sync_base_container.get(type[IAstrbotPaths])
+
+def check_env() -> None:
+    if not (sys.version_info.major == 3 and sys.version_info.minor >= 10):
+        logger.error("请使用 Python3.10+ 运行本项目。")
+        exit()
+
+    # os.makedirs("data/config", exist_ok=True)
+    # os.makedirs("data/plugins", exist_ok=True)
+    # os.makedirs("data/temp", exist_ok=True)
+
+    # 针对问题 #181 的临时解决方案
+    mimetypes.add_type("text/javascript", ".js")
+    mimetypes.add_type("text/javascript", ".mjs")
+    mimetypes.add_type("application/json", ".json")
+
+
+async def check_dashboard_files(webui_dir: str | None = None):
+    """下载管理面板文件"""
+    # 指定webui目录
+    if webui_dir:
+        if os.path.exists(webui_dir):
+            logger.info(f"使用指定的 WebUI 目录: {webui_dir}")
+            return webui_dir
+        logger.warning(f"指定的 WebUI 目录 {webui_dir} 不存在，将使用默认逻辑。")
+
+    data_dist_path = str(AstrbotPaths.astrbot_root / "dist")
+    if os.path.exists(data_dist_path):
+        v = await get_dashboard_version()
+        if v is not None:
+            # 存在文件
+            if v == f"v{VERSION}":
+                logger.info("WebUI 版本已是最新。")
+            else:
+                logger.warning(
+                    f"检测到 WebUI 版本 ({v}) 与当前 AstrBot 版本 (v{VERSION}) 不符。",
+                )
+        return data_dist_path
+
+    logger.info(
+        "开始下载管理面板文件...高峰期（晚上）可能导致较慢的速度。如多次下载失败，请前往 https://github.com/AstrBotDevs/AstrBot/releases/latest 下载 dist.zip，并将其中的 dist 文件夹解压至 data 目录下。",
+    )
+
+    try:
+        await download_dashboard(version=f"v{VERSION}", latest=False)
+    except Exception as e:
+        logger.critical(f"下载管理面板文件失败: {e}。")
+        return None
+
+    logger.info("管理面板下载完成。")
+    return data_dist_path
+
+
+def main():
+    parser = argparse.ArgumentParser(description="AstrBot")
+    parser.add_argument(
+        "--webui-dir",
+        type=str,
+        help="指定 WebUI 静态文件目录路径",
+        default=None,
+    )
+    args = parser.parse_args()
+
+    check_env()
+
+    # 启动日志代理
+    log_broker = LogBroker()
+    LogManager.set_queue_handler(logger, log_broker)
+
+    # 检查仪表板文件
+    webui_dir = asyncio.run(check_dashboard_files(args.webui_dir))
+
+    db = db_helper
+
+    # 打印 logo
+    logger.info(LOGO)
+
+    core_lifecycle = InitialLoader(db, log_broker)
+    core_lifecycle.webui_dir = webui_dir
+    asyncio.run(core_lifecycle.start())
+
+
+if __name__ == "__main__":
+    main()

astrbot/api/all.py

@@ -36,8 +36,7 @@ from astrbot.core.star.config import *
 
 
 # provider
-from astrbot.core.provider import Provider, ProviderMetaData
-from astrbot.core.db.po import Personality
+from astrbot.core.provider import Provider, Personality, ProviderMetaData
 
 # platform
 from astrbot.core.platform import (

astrbot/api/provider/__init__.py

@@ -1,5 +1,4 @@
-from astrbot.core.db.po import Personality
-from astrbot.core.provider import Provider, STTProvider
+from astrbot.core.provider import Personality, Provider, STTProvider
 from astrbot.core.provider.entities import (
     LLMResponse,
     ProviderMetaData,

astrbot/cli/__init__.py

@@ -1 +1 @@
-__version__ = "4.7.3"
+"""AstrBot CLI入口"""

astrbot/cli/__main__.py

@@ -1,27 +1,22 @@
 """AstrBot CLI入口"""
 
 import sys
+from importlib.metadata import version
 
 import click
 
-from . import __version__
+from astrbot_api import LOGO
+
 from .commands import conf, init, plug, run
 
-logo_tmpl = r"""
-     ___           _______.___________..______      .______     ______   .___________.
-    /   \         /       |           ||   _  \     |   _  \   /  __  \  |           |
-   /  ^  \       |   (----`---|  |----`|  |_)  |    |  |_)  | |  |  |  | `---|  |----`
-  /  /_\  \       \   \       |  |     |      /     |   _  <  |  |  |  |     |  |
- /  _____  \  .----)   |      |  |     |  |\  \----.|  |_)  | |  `--'  |     |  |
-/__/     \__\ |_______/       |__|     | _| `._____||______/   \______/      |__|
-"""
+__version__ = version("astrbot")
 
 
 @click.group()
 @click.version_option(__version__, prog_name="AstrBot")
 def cli() -> None:
     """The AstrBot CLI"""
-    click.echo(logo_tmpl)
+    click.echo(LOGO)
     click.echo("Welcome to AstrBot CLI!")
     click.echo(f"AstrBot CLI version: {__version__}")
 

astrbot/cli/commands/cmd_plug.py

@@ -3,6 +3,11 @@ import shutil
 from pathlib import Path
 
 import click
+from astrbot_api.abc import IAstrbotPaths
+
+from astrbot_sdk import sync_base_container
+
+AstrbotPaths: type[IAstrbotPaths] = sync_base_container.get(type[IAstrbotPaths])
 
 from ..utils import (
     PluginStatus,
@@ -47,8 +52,7 @@ def display_plugins(plugins, title=None, color=None):
 @click.argument("name")
 def new(name: str):
     """创建新插件"""
-    base_path = _get_data_path()
-    plug_path = base_path / "plugins" / name
+    plug_path = AstrbotPaths.getPaths(name).plugins
 
     if plug_path.exists():
         raise click.ClickException(f"插件 {name} 已存在")

astrbot/cli/utils/basic.py

@@ -1,22 +1,31 @@
+import warnings
 from pathlib import Path
 
 import click
+from astrbot_api.abc import IAstrbotPaths
 
+from astrbot_sdk import sync_base_container
+
+AstrbotPaths = sync_base_container.get(type[IAstrbotPaths])
 
 def check_astrbot_root(path: str | Path) -> bool:
     """检查路径是否为 AstrBot 根目录"""
-    if not isinstance(path, Path):
-        path = Path(path)
-    if not path.exists() or not path.is_dir():
-        return False
-    if not (path / ".astrbot").exists():
-        return False
-    return True
+    warnings.warn(
+        "请使用 AstrbotPaths 类代替本模块中的函数",
+        DeprecationWarning,
+        stacklevel=2,
+    )
+    return AstrbotPaths.is_root(Path(path))
 
 
 def get_astrbot_root() -> Path:
     """获取Astrbot根目录路径"""
-    return Path.cwd()
+    warnings.warn(
+        "请使用 AstrbotPaths 类代替本模块中的函数",
+        DeprecationWarning,
+        stacklevel=2,
+    )
+    return AstrbotPaths.astrbot_root
 
 
 async def check_dashboard(astrbot_root: Path) -> None:

astrbot/core/__init__.py

@@ -9,10 +9,6 @@ from astrbot.core.utils.shared_preferences import SharedPreferences
 from astrbot.core.utils.t2i.renderer import HtmlRenderer
 
 from .log import LogBroker, LogManager  # noqa
-from .utils.astrbot_path import get_astrbot_data_path
-
-# 初始化数据存储文件夹
-os.makedirs(get_astrbot_data_path(), exist_ok=True)
 
 DEMO_MODE = os.getenv("DEMO_MODE", False)
 

astrbot/core/agent/mcp_client.py

@@ -4,14 +4,6 @@ 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
@@ -20,24 +12,21 @@ 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(
-        "Warning: Missing 'mcp' dependency, MCP services will be unavailable."
-    )
+    logger.warning("警告: 缺少依赖库 'mcp'，将无法使用 MCP 服务。")
 
 try:
     from mcp.client.streamable_http import streamablehttp_client
 except (ModuleNotFoundError, ImportError):
     logger.warning(
-        "Warning: Missing 'mcp' dependency or MCP library version too old, Streamable HTTP connection unavailable.",
+        "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。",
     )
 
 
 def _prepare_config(config: dict) -> dict:
-    """Prepare configuration, handle nested format"""
+    """准备配置，处理嵌套格式"""
     if config.get("mcpServers"):
         first_key = next(iter(config["mcpServers"]))
         config = config["mcpServers"][first_key]
@@ -46,7 +35,7 @@ def _prepare_config(config: dict) -> dict:
 
 
 async def _quick_test_mcp_connection(config: dict) -> tuple[bool, str]:
-    """Quick test MCP server connectivity"""
+    """快速测试 MCP 服务器可达性"""
     import aiohttp
 
     cfg = _prepare_config(config.copy())
@@ -61,7 +50,7 @@ async def _quick_test_mcp_connection(config: dict) -> tuple[bool, str]:
         elif "type" in cfg:
             transport_type = cfg["type"]
         else:
-            raise Exception("MCP connection config missing transport or type field")
+            raise Exception("MCP 连接配置缺少 transport 或 type 字段")
 
         async with aiohttp.ClientSession() as session:
             if transport_type == "streamable_http":
@@ -102,7 +91,7 @@ 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"Connection timeout: {timeout} seconds"
+        return False, f"连接超时: {timeout}秒"
     except Exception as e:
         return False, f"{e!s}"
 
@@ -112,7 +101,6 @@ 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
@@ -120,32 +108,22 @@ class MCPClient:
         self.server_errlogs: list[str] = []
         self.running_event = asyncio.Event()
 
-        # 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
-
     async def connect_to_server(self, mcp_server_config: dict, name: str):
-        """Connect to MCP server
+        """连接到 MCP 服务器
 
-        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.
+        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。
 
         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):
-            # Handle MCP service error logs
+            # 处理 MCP 服务的错误日志
             print(f"MCP Server {name} Error: {msg}")
             self.server_errlogs.append(msg)
 
@@ -159,7 +137,7 @@ class MCPClient:
             elif "type" in cfg:
                 transport_type = cfg["type"]
             else:
-                raise Exception("MCP connection config missing transport or type field")
+                raise Exception("MCP 连接配置缺少 transport 或 type 字段")
 
             if transport_type != "streamable_http":
                 # SSE transport method
@@ -215,7 +193,7 @@ class MCPClient:
             )
 
             def callback(msg: str):
-                # Handle MCP service error logs
+                # 处理 MCP 服务的错误日志
                 self.server_errlogs.append(msg)
 
             stdio_transport = await self.exit_stack.enter_async_context(
@@ -244,120 +222,10 @@ 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 including old exit stacks from reconnections"""
-        # 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()
-
-        # Set running_event first to unblock any waiting tasks
-        self.running_event.set()
+        """Clean up resources"""
+        await self.exit_stack.aclose()
+        self.running_event.set()  # Set the running event to indicate cleanup is done
 
 
 class MCPTool(FunctionTool, Generic[TContext]):
@@ -378,8 +246,14 @@ class MCPTool(FunctionTool, Generic[TContext]):
     async def call(
         self, context: ContextWrapper[TContext], **kwargs
     ) -> mcp.types.CallToolResult:
-        return await self.mcp_client.call_tool_with_reconnect(
-            tool_name=self.mcp_tool.name,
+        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,
             arguments=kwargs,
-            read_timeout_seconds=timedelta(seconds=context.tool_call_timeout),
+            read_timeout_seconds=timedelta(
+                seconds=context.tool_call_timeout,
+            ),
         )
+        return res

astrbot/core/agent/message.py

@@ -3,7 +3,7 @@
 
 from typing import Any, ClassVar, Literal, cast
 
-from pydantic import BaseModel, GetCoreSchemaHandler, model_validator
+from pydantic import BaseModel, GetCoreSchemaHandler
 from pydantic_core import core_schema
 
 
@@ -76,7 +76,7 @@ class ImageURLPart(ContentPart):
         """The ID of the image, to allow LLMs to distinguish different images."""
 
     type: str = "image_url"
-    image_url: ImageURL
+    image_url: str
 
 
 class AudioURLPart(ContentPart):
@@ -119,13 +119,6 @@ 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):
@@ -145,39 +138,22 @@ class Message(BaseModel):
         "tool",
     ]
 
-    content: str | list[ContentPart] | None = None
+    content: str | list[ContentPart]
     """The content of the message."""
 
-    tool_calls: list[ToolCall] | list[dict] | None = None
-    """The tool calls of the message."""
-
-    tool_call_id: str | None = None
-    """The ID of the tool call."""
-
-    @model_validator(mode="after")
-    def check_content_required(self):
-        # assistant + tool_calls is not None: allow content to be None
-        if self.role == "assistant" and self.tool_calls is not None:
-            return self
-
-        # other all cases: content is required
-        if self.content is None:
-            raise ValueError(
-                "content is required unless role='assistant' and tool_calls is not None"
-            )
-        return self
-
 
 class AssistantMessageSegment(Message):
     """A message segment from the assistant."""
 
     role: Literal["assistant"] = "assistant"
+    tool_calls: list[ToolCall] | list[dict] | None = None
 
 
 class ToolCallMessageSegment(Message):
     """A message segment representing a tool call."""
 
     role: Literal["tool"] = "tool"
+    tool_call_id: str
 
 
 class UserMessageSegment(Message):

astrbot/core/agent/run_context.py

@@ -1,21 +1,16 @@
+from dataclasses import dataclass
 from typing import Any, Generic
 
-from pydantic import Field
-from pydantic.dataclasses import dataclass
 from typing_extensions import TypeVar
 
-from .message import Message
-
 TContext = TypeVar("TContext", default=Any)
 
 
-@dataclass(config={"arbitrary_types_allowed": True})
+@dataclass
 class ContextWrapper(Generic[TContext]):
     """A context for running an agent, which can be used to pass additional data or state."""
 
     context: TContext
-    messages: list[Message] = Field(default_factory=list)
-    """This field stores the llm message context for the agent run, agent runners will maintain this field automatically."""
     tool_call_timeout: int = 60  # Default tool call timeout in seconds
 
 

astrbot/core/agent/runners/base.py

@@ -2,12 +2,13 @@ import abc
 import typing as T
 from enum import Enum, auto
 
-from astrbot import logger
+from astrbot.core.provider import Provider
 from astrbot.core.provider.entities import LLMResponse
 
 from ..hooks import BaseAgentRunHooks
 from ..response import AgentResponse
 from ..run_context import ContextWrapper, TContext
+from ..tool_executor import BaseFunctionToolExecutor
 
 
 class AgentState(Enum):
@@ -23,7 +24,9 @@ class BaseAgentRunner(T.Generic[TContext]):
     @abc.abstractmethod
     async def reset(
         self,
+        provider: Provider,
         run_context: ContextWrapper[TContext],
+        tool_executor: BaseFunctionToolExecutor[TContext],
         agent_hooks: BaseAgentRunHooks[TContext],
         **kwargs: T.Any,
     ) -> None:
@@ -37,13 +40,6 @@ class BaseAgentRunner(T.Generic[TContext]):
         """Process a single step of the agent."""
         ...
 
-    @abc.abstractmethod
-    async def step_until_done(
-        self, max_step: int
-    ) -> T.AsyncGenerator[AgentResponse, None]:
-        """Process steps until the agent is done."""
-        ...
-
     @abc.abstractmethod
     def done(self) -> bool:
         """Check if the agent has completed its task.
@@ -57,9 +53,3 @@ class BaseAgentRunner(T.Generic[TContext]):
         This method should be called after the agent is done.
         """
         ...
-
-    def _transition_state(self, new_state: AgentState) -> None:
-        """Transition the agent state."""
-        if self._state != new_state:
-            logger.debug(f"Agent state transition: {self._state} -> {new_state}")
-            self._state = new_state

astrbot/core/agent/runners/coze/coze_agent_runner.py

@@ -1,367 +0,0 @@
-import base64
-import json
-import sys
-import typing as T
-
-import astrbot.core.message.components as Comp
-from astrbot import logger
-from astrbot.core import sp
-from astrbot.core.message.message_event_result import MessageChain
-from astrbot.core.provider.entities import (
-    LLMResponse,
-    ProviderRequest,
-)
-
-from ...hooks import BaseAgentRunHooks
-from ...response import AgentResponseData
-from ...run_context import ContextWrapper, TContext
-from ..base import AgentResponse, AgentState, BaseAgentRunner
-from .coze_api_client import CozeAPIClient
-
-if sys.version_info >= (3, 12):
-    from typing import override
-else:
-    from typing_extensions import override
-
-
-class CozeAgentRunner(BaseAgentRunner[TContext]):
-    """Coze Agent Runner"""
-
-    @override
-    async def reset(
-        self,
-        request: ProviderRequest,
-        run_context: ContextWrapper[TContext],
-        agent_hooks: BaseAgentRunHooks[TContext],
-        provider_config: dict,
-        **kwargs: T.Any,
-    ) -> None:
-        self.req = request
-        self.streaming = kwargs.get("streaming", False)
-        self.final_llm_resp = None
-        self._state = AgentState.IDLE
-        self.agent_hooks = agent_hooks
-        self.run_context = run_context
-
-        self.api_key = provider_config.get("coze_api_key", "")
-        if not self.api_key:
-            raise Exception("Coze API Key 不能为空。")
-        self.bot_id = provider_config.get("bot_id", "")
-        if not self.bot_id:
-            raise Exception("Coze Bot ID 不能为空。")
-        self.api_base: str = provider_config.get("coze_api_base", "https://api.coze.cn")
-
-        if not isinstance(self.api_base, str) or not self.api_base.startswith(
-            ("http://", "https://"),
-        ):
-            raise Exception(
-                "Coze API Base URL 格式不正确，必须以 http:// 或 https:// 开头。",
-            )
-
-        self.timeout = provider_config.get("timeout", 120)
-        if isinstance(self.timeout, str):
-            self.timeout = int(self.timeout)
-        self.auto_save_history = provider_config.get("auto_save_history", True)
-
-        # 创建 API 客户端
-        self.api_client = CozeAPIClient(api_key=self.api_key, api_base=self.api_base)
-
-        # 会话相关缓存
-        self.file_id_cache: dict[str, dict[str, str]] = {}
-
-    @override
-    async def step(self):
-        """
-        执行 Coze Agent 的一个步骤
-        """
-        if not self.req:
-            raise ValueError("Request is not set. Please call reset() first.")
-
-        if self._state == AgentState.IDLE:
-            try:
-                await self.agent_hooks.on_agent_begin(self.run_context)
-            except Exception as e:
-                logger.error(f"Error in on_agent_begin hook: {e}", exc_info=True)
-
-        # 开始处理，转换到运行状态
-        self._transition_state(AgentState.RUNNING)
-
-        try:
-            # 执行 Coze 请求并处理结果
-            async for response in self._execute_coze_request():
-                yield response
-        except Exception as e:
-            logger.error(f"Coze 请求失败：{str(e)}")
-            self._transition_state(AgentState.ERROR)
-            self.final_llm_resp = LLMResponse(
-                role="err", completion_text=f"Coze 请求失败：{str(e)}"
-            )
-            yield AgentResponse(
-                type="err",
-                data=AgentResponseData(
-                    chain=MessageChain().message(f"Coze 请求失败：{str(e)}")
-                ),
-            )
-        finally:
-            await self.api_client.close()
-
-    @override
-    async def step_until_done(
-        self, max_step: int = 30
-    ) -> T.AsyncGenerator[AgentResponse, None]:
-        while not self.done():
-            async for resp in self.step():
-                yield resp
-
-    async def _execute_coze_request(self):
-        """执行 Coze 请求的核心逻辑"""
-        prompt = self.req.prompt or ""
-        session_id = self.req.session_id or "unknown"
-        image_urls = self.req.image_urls or []
-        contexts = self.req.contexts or []
-        system_prompt = self.req.system_prompt
-
-        # 用户ID参数
-        user_id = session_id
-
-        # 获取或创建会话ID
-        conversation_id = await sp.get_async(
-            scope="umo",
-            scope_id=user_id,
-            key="coze_conversation_id",
-            default="",
-        )
-
-        # 构建消息
-        additional_messages = []
-
-        if system_prompt:
-            if not self.auto_save_history or not conversation_id:
-                additional_messages.append(
-                    {
-                        "role": "system",
-                        "content": system_prompt,
-                        "content_type": "text",
-                    },
-                )
-
-        # 处理历史上下文
-        if not self.auto_save_history and contexts:
-            for ctx in contexts:
-                if isinstance(ctx, dict) and "role" in ctx and "content" in ctx:
-                    # 处理上下文中的图片
-                    content = ctx["content"]
-                    if isinstance(content, list):
-                        # 多模态内容，需要处理图片
-                        processed_content = []
-                        for item in content:
-                            if isinstance(item, dict):
-                                if item.get("type") == "text":
-                                    processed_content.append(item)
-                                elif item.get("type") == "image_url":
-                                    # 处理图片上传
-                                    try:
-                                        image_data = item.get("image_url", {})
-                                        url = image_data.get("url", "")
-                                        if url:
-                                            file_id = (
-                                                await self._download_and_upload_image(
-                                                    url, session_id
-                                                )
-                                            )
-                                            processed_content.append(
-                                                {
-                                                    "type": "file",
-                                                    "file_id": file_id,
-                                                    "file_url": url,
-                                                }
-                                            )
-                                    except Exception as e:
-                                        logger.warning(f"处理上下文图片失败: {e}")
-                                        continue
-
-                        if processed_content:
-                            additional_messages.append(
-                                {
-                                    "role": ctx["role"],
-                                    "content": processed_content,
-                                    "content_type": "object_string",
-                                }
-                            )
-                    else:
-                        # 纯文本内容
-                        additional_messages.append(
-                            {
-                                "role": ctx["role"],
-                                "content": content,
-                                "content_type": "text",
-                            }
-                        )
-
-        # 构建当前消息
-        if prompt or image_urls:
-            if image_urls:
-                # 多模态
-                object_string_content = []
-                if prompt:
-                    object_string_content.append({"type": "text", "text": prompt})
-
-                for url in image_urls:
-                    # the url is a base64 string
-                    try:
-                        image_data = base64.b64decode(url)
-                        file_id = await self.api_client.upload_file(image_data)
-                        object_string_content.append(
-                            {
-                                "type": "image",
-                                "file_id": file_id,
-                            }
-                        )
-                    except Exception as e:
-                        logger.warning(f"处理图片失败 {url}: {e}")
-                        continue
-
-                if object_string_content:
-                    content = json.dumps(object_string_content, ensure_ascii=False)
-                    additional_messages.append(
-                        {
-                            "role": "user",
-                            "content": content,
-                            "content_type": "object_string",
-                        }
-                    )
-            elif prompt:
-                # 纯文本
-                additional_messages.append(
-                    {
-                        "role": "user",
-                        "content": prompt,
-                        "content_type": "text",
-                    },
-                )
-
-        # 执行 Coze API 请求
-        accumulated_content = ""
-        message_started = False
-
-        async for chunk in self.api_client.chat_messages(
-            bot_id=self.bot_id,
-            user_id=user_id,
-            additional_messages=additional_messages,
-            conversation_id=conversation_id,
-            auto_save_history=self.auto_save_history,
-            stream=True,
-            timeout=self.timeout,
-        ):
-            event_type = chunk.get("event")
-            data = chunk.get("data", {})
-
-            if event_type == "conversation.chat.created":
-                if isinstance(data, dict) and "conversation_id" in data:
-                    await sp.put_async(
-                        scope="umo",
-                        scope_id=user_id,
-                        key="coze_conversation_id",
-                        value=data["conversation_id"],
-                    )
-
-            if event_type == "conversation.message.delta":
-                # 增量消息
-                content = data.get("content", "")
-                if not content and "delta" in data:
-                    content = data["delta"].get("content", "")
-                if not content and "text" in data:
-                    content = data.get("text", "")
-
-                if content:
-                    accumulated_content += content
-                    message_started = True
-
-                    # 如果是流式响应，发送增量数据
-                    if self.streaming:
-                        yield AgentResponse(
-                            type="streaming_delta",
-                            data=AgentResponseData(
-                                chain=MessageChain().message(content)
-                            ),
-                        )
-
-            elif event_type == "conversation.message.completed":
-                # 消息完成
-                logger.debug("Coze message completed")
-                message_started = True
-
-            elif event_type == "conversation.chat.completed":
-                # 对话完成
-                logger.debug("Coze chat completed")
-                break
-
-            elif event_type == "error":
-                # 错误处理
-                error_msg = data.get("msg", "未知错误")
-                error_code = data.get("code", "UNKNOWN")
-                logger.error(f"Coze 出现错误: {error_code} - {error_msg}")
-                raise Exception(f"Coze 出现错误: {error_code} - {error_msg}")
-
-        if not message_started and not accumulated_content:
-            logger.warning("Coze 未返回任何内容")
-            accumulated_content = ""
-
-        # 创建最终响应
-        chain = MessageChain(chain=[Comp.Plain(accumulated_content)])
-        self.final_llm_resp = LLMResponse(role="assistant", result_chain=chain)
-        self._transition_state(AgentState.DONE)
-
-        try:
-            await self.agent_hooks.on_agent_done(self.run_context, self.final_llm_resp)
-        except Exception as e:
-            logger.error(f"Error in on_agent_done hook: {e}", exc_info=True)
-
-        # 返回最终结果
-        yield AgentResponse(
-            type="llm_result",
-            data=AgentResponseData(chain=chain),
-        )
-
-    async def _download_and_upload_image(
-        self,
-        image_url: str,
-        session_id: str | None = None,
-    ) -> str:
-        """下载图片并上传到 Coze，返回 file_id"""
-        import hashlib
-
-        # 计算哈希实现缓存
-        cache_key = hashlib.md5(image_url.encode("utf-8")).hexdigest()
-
-        if session_id:
-            if session_id not in self.file_id_cache:
-                self.file_id_cache[session_id] = {}
-
-            if cache_key in self.file_id_cache[session_id]:
-                file_id = self.file_id_cache[session_id][cache_key]
-                logger.debug(f"[Coze] 使用缓存的 file_id: {file_id}")
-                return file_id
-
-        try:
-            image_data = await self.api_client.download_image(image_url)
-            file_id = await self.api_client.upload_file(image_data)
-
-            if session_id:
-                self.file_id_cache[session_id][cache_key] = file_id
-                logger.debug(f"[Coze] 图片上传成功并缓存，file_id: {file_id}")
-
-            return file_id
-
-        except Exception as e:
-            logger.error(f"处理图片失败 {image_url}: {e!s}")
-            raise Exception(f"处理图片失败: {e!s}")
-
-    @override
-    def done(self) -> bool:
-        """检查 Agent 是否已完成工作"""
-        return self._state in (AgentState.DONE, AgentState.ERROR)
-
-    @override
-    def get_final_llm_resp(self) -> LLMResponse | None:
-        return self.final_llm_resp

astrbot/core/agent/runners/dashscope/dashscope_agent_runner.py

@@ -1,403 +0,0 @@
-import asyncio
-import functools
-import queue
-import re
-import sys
-import threading
-import typing as T
-
-from dashscope import Application
-from dashscope.app.application_response import ApplicationResponse
-
-import astrbot.core.message.components as Comp
-from astrbot.core import logger, sp
-from astrbot.core.message.message_event_result import MessageChain
-from astrbot.core.provider.entities import (
-    LLMResponse,
-    ProviderRequest,
-)
-
-from ...hooks import BaseAgentRunHooks
-from ...response import AgentResponseData
-from ...run_context import ContextWrapper, TContext
-from ..base import AgentResponse, AgentState, BaseAgentRunner
-
-if sys.version_info >= (3, 12):
-    from typing import override
-else:
-    from typing_extensions import override
-
-
-class DashscopeAgentRunner(BaseAgentRunner[TContext]):
-    """Dashscope Agent Runner"""
-
-    @override
-    async def reset(
-        self,
-        request: ProviderRequest,
-        run_context: ContextWrapper[TContext],
-        agent_hooks: BaseAgentRunHooks[TContext],
-        provider_config: dict,
-        **kwargs: T.Any,
-    ) -> None:
-        self.req = request
-        self.streaming = kwargs.get("streaming", False)
-        self.final_llm_resp = None
-        self._state = AgentState.IDLE
-        self.agent_hooks = agent_hooks
-        self.run_context = run_context
-
-        self.api_key = provider_config.get("dashscope_api_key", "")
-        if not self.api_key:
-            raise Exception("阿里云百炼 API Key 不能为空。")
-        self.app_id = provider_config.get("dashscope_app_id", "")
-        if not self.app_id:
-            raise Exception("阿里云百炼 APP ID 不能为空。")
-        self.dashscope_app_type = provider_config.get("dashscope_app_type", "")
-        if not self.dashscope_app_type:
-            raise Exception("阿里云百炼 APP 类型不能为空。")
-
-        self.variables: dict = provider_config.get("variables", {}) or {}
-        self.rag_options: dict = provider_config.get("rag_options", {})
-        self.output_reference = self.rag_options.get("output_reference", False)
-        self.rag_options = self.rag_options.copy()
-        self.rag_options.pop("output_reference", None)
-
-        self.timeout = provider_config.get("timeout", 120)
-        if isinstance(self.timeout, str):
-            self.timeout = int(self.timeout)
-
-    def has_rag_options(self):
-        """判断是否有 RAG 选项
-
-        Returns:
-            bool: 是否有 RAG 选项
-
-        """
-        if self.rag_options and (
-            len(self.rag_options.get("pipeline_ids", [])) > 0
-            or len(self.rag_options.get("file_ids", [])) > 0
-        ):
-            return True
-        return False
-
-    @override
-    async def step(self):
-        """
-        执行 Dashscope Agent 的一个步骤
-        """
-        if not self.req:
-            raise ValueError("Request is not set. Please call reset() first.")
-
-        if self._state == AgentState.IDLE:
-            try:
-                await self.agent_hooks.on_agent_begin(self.run_context)
-            except Exception as e:
-                logger.error(f"Error in on_agent_begin hook: {e}", exc_info=True)
-
-        # 开始处理，转换到运行状态
-        self._transition_state(AgentState.RUNNING)
-
-        try:
-            # 执行 Dashscope 请求并处理结果
-            async for response in self._execute_dashscope_request():
-                yield response
-        except Exception as e:
-            logger.error(f"阿里云百炼请求失败：{str(e)}")
-            self._transition_state(AgentState.ERROR)
-            self.final_llm_resp = LLMResponse(
-                role="err", completion_text=f"阿里云百炼请求失败：{str(e)}"
-            )
-            yield AgentResponse(
-                type="err",
-                data=AgentResponseData(
-                    chain=MessageChain().message(f"阿里云百炼请求失败：{str(e)}")
-                ),
-            )
-
-    @override
-    async def step_until_done(
-        self, max_step: int = 30
-    ) -> T.AsyncGenerator[AgentResponse, None]:
-        while not self.done():
-            async for resp in self.step():
-                yield resp
-
-    def _consume_sync_generator(
-        self, response: T.Any, response_queue: queue.Queue
-    ) -> None:
-        """在线程中消费同步generator,将结果放入队列
-
-        Args:
-            response: 同步generator对象
-            response_queue: 用于传递数据的队列
-
-        """
-        try:
-            if self.streaming:
-                for chunk in response:
-                    response_queue.put(("data", chunk))
-            else:
-                response_queue.put(("data", response))
-        except Exception as e:
-            response_queue.put(("error", e))
-        finally:
-            response_queue.put(("done", None))
-
-    async def _process_stream_chunk(
-        self, chunk: ApplicationResponse, output_text: str
-    ) -> tuple[str, list | None, AgentResponse | None]:
-        """处理流式响应的单个chunk
-
-        Args:
-            chunk: Dashscope响应chunk
-            output_text: 当前累积的输出文本
-
-        Returns:
-            (更新后的output_text, doc_references, AgentResponse或None)
-
-        """
-        logger.debug(f"dashscope stream chunk: {chunk}")
-
-        if chunk.status_code != 200:
-            logger.error(
-                f"阿里云百炼请求失败: request_id={chunk.request_id}, code={chunk.status_code}, message={chunk.message}, 请参考文档：https://help.aliyun.com/zh/model-studio/developer-reference/error-code",
-            )
-            self._transition_state(AgentState.ERROR)
-            error_msg = (
-                f"阿里云百炼请求失败: message={chunk.message} code={chunk.status_code}"
-            )
-            self.final_llm_resp = LLMResponse(
-                role="err",
-                result_chain=MessageChain().message(error_msg),
-            )
-            return (
-                output_text,
-                None,
-                AgentResponse(
-                    type="err",
-                    data=AgentResponseData(chain=MessageChain().message(error_msg)),
-                ),
-            )
-
-        chunk_text = chunk.output.get("text", "") or ""
-        # RAG 引用脚标格式化
-        chunk_text = re.sub(r"<ref>\[(\d+)\]</ref>", r"[\1]", chunk_text)
-
-        response = None
-        if chunk_text:
-            output_text += chunk_text
-            response = AgentResponse(
-                type="streaming_delta",
-                data=AgentResponseData(chain=MessageChain().message(chunk_text)),
-            )
-
-        # 获取文档引用
-        doc_references = chunk.output.get("doc_references", None)
-
-        return output_text, doc_references, response
-
-    def _format_doc_references(self, doc_references: list) -> str:
-        """格式化文档引用为文本
-
-        Args:
-            doc_references: 文档引用列表
-
-        Returns:
-            格式化后的引用文本
-
-        """
-        ref_parts = []
-        for ref in doc_references:
-            ref_title = (
-                ref.get("title", "") if ref.get("title") else ref.get("doc_name", "")
-            )
-            ref_parts.append(f"{ref['index_id']}. {ref_title}\n")
-        ref_str = "".join(ref_parts)
-        return f"\n\n回答来源:\n{ref_str}"
-
-    async def _build_request_payload(
-        self, prompt: str, session_id: str, contexts: list, system_prompt: str
-    ) -> dict:
-        """构建请求payload
-
-        Args:
-            prompt: 用户输入
-            session_id: 会话ID
-            contexts: 上下文列表
-            system_prompt: 系统提示词
-
-        Returns:
-            请求payload字典
-
-        """
-        conversation_id = await sp.get_async(
-            scope="umo",
-            scope_id=session_id,
-            key="dashscope_conversation_id",
-            default="",
-        )
-        # 获得会话变量
-        payload_vars = self.variables.copy()
-        session_var = await sp.get_async(
-            scope="umo",
-            scope_id=session_id,
-            key="session_variables",
-            default={},
-        )
-        payload_vars.update(session_var)
-
-        if (
-            self.dashscope_app_type in ["agent", "dialog-workflow"]
-            and not self.has_rag_options()
-        ):
-            # 支持多轮对话的
-            p = {
-                "app_id": self.app_id,
-                "api_key": self.api_key,
-                "prompt": prompt,
-                "biz_params": payload_vars or None,
-                "stream": self.streaming,
-                "incremental_output": True,
-            }
-            if conversation_id:
-                p["session_id"] = conversation_id
-            return p
-        else:
-            # 不支持多轮对话的
-            payload = {
-                "app_id": self.app_id,
-                "prompt": prompt,
-                "api_key": self.api_key,
-                "biz_params": payload_vars or None,
-                "stream": self.streaming,
-                "incremental_output": True,
-            }
-            if self.rag_options:
-                payload["rag_options"] = self.rag_options
-            return payload
-
-    async def _handle_streaming_response(
-        self, response: T.Any, session_id: str
-    ) -> T.AsyncGenerator[AgentResponse, None]:
-        """处理流式响应
-
-        Args:
-            response: Dashscope 流式响应 generator
-
-        Yields:
-            AgentResponse 对象
-
-        """
-        response_queue = queue.Queue()
-        consumer_thread = threading.Thread(
-            target=self._consume_sync_generator,
-            args=(response, response_queue),
-            daemon=True,
-        )
-        consumer_thread.start()
-
-        output_text = ""
-        doc_references = None
-
-        while True:
-            try:
-                item_type, item_data = await asyncio.get_event_loop().run_in_executor(
-                    None, response_queue.get, True, 1
-                )
-            except queue.Empty:
-                continue
-
-            if item_type == "done":
-                break
-            elif item_type == "error":
-                raise item_data
-            elif item_type == "data":
-                chunk = item_data
-                assert isinstance(chunk, ApplicationResponse)
-
-                (
-                    output_text,
-                    chunk_doc_refs,
-                    response,
-                ) = await self._process_stream_chunk(chunk, output_text)
-
-                if response:
-                    if response.type == "err":
-                        yield response
-                        return
-                    yield response
-
-                if chunk_doc_refs:
-                    doc_references = chunk_doc_refs
-
-                if chunk.output.session_id:
-                    await sp.put_async(
-                        scope="umo",
-                        scope_id=session_id,
-                        key="dashscope_conversation_id",
-                        value=chunk.output.session_id,
-                    )
-
-        # 添加 RAG 引用
-        if self.output_reference and doc_references:
-            ref_text = self._format_doc_references(doc_references)
-            output_text += ref_text
-
-            if self.streaming:
-                yield AgentResponse(
-                    type="streaming_delta",
-                    data=AgentResponseData(chain=MessageChain().message(ref_text)),
-                )
-
-        # 创建最终响应
-        chain = MessageChain(chain=[Comp.Plain(output_text)])
-        self.final_llm_resp = LLMResponse(role="assistant", result_chain=chain)
-        self._transition_state(AgentState.DONE)
-
-        try:
-            await self.agent_hooks.on_agent_done(self.run_context, self.final_llm_resp)
-        except Exception as e:
-            logger.error(f"Error in on_agent_done hook: {e}", exc_info=True)
-
-        # 返回最终结果
-        yield AgentResponse(
-            type="llm_result",
-            data=AgentResponseData(chain=chain),
-        )
-
-    async def _execute_dashscope_request(self):
-        """执行 Dashscope 请求的核心逻辑"""
-        prompt = self.req.prompt or ""
-        session_id = self.req.session_id or "unknown"
-        image_urls = self.req.image_urls or []
-        contexts = self.req.contexts or []
-        system_prompt = self.req.system_prompt
-
-        # 检查图片输入
-        if image_urls:
-            logger.warning("阿里云百炼暂不支持图片输入，将自动忽略图片内容。")
-
-        # 构建请求payload
-        payload = await self._build_request_payload(
-            prompt, session_id, contexts, system_prompt
-        )
-
-        if not self.streaming:
-            payload["incremental_output"] = False
-
-        # 发起请求
-        partial = functools.partial(Application.call, **payload)
-        response = await asyncio.get_event_loop().run_in_executor(None, partial)
-
-        async for resp in self._handle_streaming_response(response, session_id):
-            yield resp
-
-    @override
-    def done(self) -> bool:
-        """检查 Agent 是否已完成工作"""
-        return self._state in (AgentState.DONE, AgentState.ERROR)
-
-    @override
-    def get_final_llm_resp(self) -> LLMResponse | None:
-        return self.final_llm_resp

astrbot/core/agent/runners/dify/dify_agent_runner.py

@@ -1,336 +0,0 @@
-import base64
-import os
-import sys
-import typing as T
-
-import astrbot.core.message.components as Comp
-from astrbot.core import logger, sp
-from astrbot.core.message.message_event_result import MessageChain
-from astrbot.core.provider.entities import (
-    LLMResponse,
-    ProviderRequest,
-)
-from astrbot.core.utils.astrbot_path import get_astrbot_data_path
-from astrbot.core.utils.io import download_file
-
-from ...hooks import BaseAgentRunHooks
-from ...response import AgentResponseData
-from ...run_context import ContextWrapper, TContext
-from ..base import AgentResponse, AgentState, BaseAgentRunner
-from .dify_api_client import DifyAPIClient
-
-if sys.version_info >= (3, 12):
-    from typing import override
-else:
-    from typing_extensions import override
-
-
-class DifyAgentRunner(BaseAgentRunner[TContext]):
-    """Dify Agent Runner"""
-
-    @override
-    async def reset(
-        self,
-        request: ProviderRequest,
-        run_context: ContextWrapper[TContext],
-        agent_hooks: BaseAgentRunHooks[TContext],
-        provider_config: dict,
-        **kwargs: T.Any,
-    ) -> None:
-        self.req = request
-        self.streaming = kwargs.get("streaming", False)
-        self.final_llm_resp = None
-        self._state = AgentState.IDLE
-        self.agent_hooks = agent_hooks
-        self.run_context = run_context
-
-        self.api_key = provider_config.get("dify_api_key", "")
-        self.api_base = provider_config.get("dify_api_base", "https://api.dify.ai/v1")
-        self.api_type = provider_config.get("dify_api_type", "chat")
-        self.workflow_output_key = provider_config.get(
-            "dify_workflow_output_key",
-            "astrbot_wf_output",
-        )
-        self.dify_query_input_key = provider_config.get(
-            "dify_query_input_key",
-            "astrbot_text_query",
-        )
-        self.variables: dict = provider_config.get("variables", {}) or {}
-        self.timeout = provider_config.get("timeout", 60)
-        if isinstance(self.timeout, str):
-            self.timeout = int(self.timeout)
-
-        self.api_client = DifyAPIClient(self.api_key, self.api_base)
-
-    @override
-    async def step(self):
-        """
-        执行 Dify Agent 的一个步骤
-        """
-        if not self.req:
-            raise ValueError("Request is not set. Please call reset() first.")
-
-        if self._state == AgentState.IDLE:
-            try:
-                await self.agent_hooks.on_agent_begin(self.run_context)
-            except Exception as e:
-                logger.error(f"Error in on_agent_begin hook: {e}", exc_info=True)
-
-        # 开始处理，转换到运行状态
-        self._transition_state(AgentState.RUNNING)
-
-        try:
-            # 执行 Dify 请求并处理结果
-            async for response in self._execute_dify_request():
-                yield response
-        except Exception as e:
-            logger.error(f"Dify 请求失败：{str(e)}")
-            self._transition_state(AgentState.ERROR)
-            self.final_llm_resp = LLMResponse(
-                role="err", completion_text=f"Dify 请求失败：{str(e)}"
-            )
-            yield AgentResponse(
-                type="err",
-                data=AgentResponseData(
-                    chain=MessageChain().message(f"Dify 请求失败：{str(e)}")
-                ),
-            )
-        finally:
-            await self.api_client.close()
-
-    @override
-    async def step_until_done(
-        self, max_step: int = 30
-    ) -> T.AsyncGenerator[AgentResponse, None]:
-        while not self.done():
-            async for resp in self.step():
-                yield resp
-
-    async def _execute_dify_request(self):
-        """执行 Dify 请求的核心逻辑"""
-        prompt = self.req.prompt or ""
-        session_id = self.req.session_id or "unknown"
-        image_urls = self.req.image_urls or []
-        system_prompt = self.req.system_prompt
-
-        conversation_id = await sp.get_async(
-            scope="umo",
-            scope_id=session_id,
-            key="dify_conversation_id",
-            default="",
-        )
-        result = ""
-
-        # 处理图片上传
-        files_payload = []
-        for image_url in image_urls:
-            # image_url is a base64 string
-            try:
-                image_data = base64.b64decode(image_url)
-                file_response = await self.api_client.file_upload(
-                    file_data=image_data,
-                    user=session_id,
-                    mime_type="image/png",
-                    file_name="image.png",
-                )
-                logger.debug(f"Dify 上传图片响应：{file_response}")
-                if "id" not in file_response:
-                    logger.warning(
-                        f"上传图片后得到未知的 Dify 响应：{file_response}，图片将忽略。"
-                    )
-                    continue
-                files_payload.append(
-                    {
-                        "type": "image",
-                        "transfer_method": "local_file",
-                        "upload_file_id": file_response["id"],
-                    }
-                )
-            except Exception as e:
-                logger.warning(f"上传图片失败：{e}")
-                continue
-
-        # 获得会话变量
-        payload_vars = self.variables.copy()
-        # 动态变量
-        session_var = await sp.get_async(
-            scope="umo",
-            scope_id=session_id,
-            key="session_variables",
-            default={},
-        )
-        payload_vars.update(session_var)
-        payload_vars["system_prompt"] = system_prompt
-
-        # 处理不同的 API 类型
-        match self.api_type:
-            case "chat" | "agent" | "chatflow":
-                if not prompt:
-                    prompt = "请描述这张图片。"
-
-                async for chunk in self.api_client.chat_messages(
-                    inputs={
-                        **payload_vars,
-                    },
-                    query=prompt,
-                    user=session_id,
-                    conversation_id=conversation_id,
-                    files=files_payload,
-                    timeout=self.timeout,
-                ):
-                    logger.debug(f"dify resp chunk: {chunk}")
-                    if chunk["event"] == "message" or chunk["event"] == "agent_message":
-                        result += chunk["answer"]
-                        if not conversation_id:
-                            await sp.put_async(
-                                scope="umo",
-                                scope_id=session_id,
-                                key="dify_conversation_id",
-                                value=chunk["conversation_id"],
-                            )
-                            conversation_id = chunk["conversation_id"]
-
-                        # 如果是流式响应，发送增量数据
-                        if self.streaming and chunk["answer"]:
-                            yield AgentResponse(
-                                type="streaming_delta",
-                                data=AgentResponseData(
-                                    chain=MessageChain().message(chunk["answer"])
-                                ),
-                            )
-                    elif chunk["event"] == "message_end":
-                        logger.debug("Dify message end")
-                        break
-                    elif chunk["event"] == "error":
-                        logger.error(f"Dify 出现错误：{chunk}")
-                        raise Exception(
-                            f"Dify 出现错误 status: {chunk['status']} message: {chunk['message']}"
-                        )
-
-            case "workflow":
-                async for chunk in self.api_client.workflow_run(
-                    inputs={
-                        self.dify_query_input_key: prompt,
-                        "astrbot_session_id": session_id,
-                        **payload_vars,
-                    },
-                    user=session_id,
-                    files=files_payload,
-                    timeout=self.timeout,
-                ):
-                    logger.debug(f"dify workflow resp chunk: {chunk}")
-                    match chunk["event"]:
-                        case "workflow_started":
-                            logger.info(
-                                f"Dify 工作流(ID: {chunk['workflow_run_id']})开始运行。"
-                            )
-                        case "node_finished":
-                            logger.debug(
-                                f"Dify 工作流节点(ID: {chunk['data']['node_id']} Title: {chunk['data'].get('title', '')})运行结束。"
-                            )
-                        case "text_chunk":
-                            if self.streaming and chunk["data"]["text"]:
-                                yield AgentResponse(
-                                    type="streaming_delta",
-                                    data=AgentResponseData(
-                                        chain=MessageChain().message(
-                                            chunk["data"]["text"]
-                                        )
-                                    ),
-                                )
-                        case "workflow_finished":
-                            logger.info(
-                                f"Dify 工作流(ID: {chunk['workflow_run_id']})运行结束"
-                            )
-                            logger.debug(f"Dify 工作流结果：{chunk}")
-                            if chunk["data"]["error"]:
-                                logger.error(
-                                    f"Dify 工作流出现错误：{chunk['data']['error']}"
-                                )
-                                raise Exception(
-                                    f"Dify 工作流出现错误：{chunk['data']['error']}"
-                                )
-                            if self.workflow_output_key not in chunk["data"]["outputs"]:
-                                raise Exception(
-                                    f"Dify 工作流的输出不包含指定的键名：{self.workflow_output_key}"
-                                )
-                            result = chunk
-            case _:
-                raise Exception(f"未知的 Dify API 类型：{self.api_type}")
-
-        if not result:
-            logger.warning("Dify 请求结果为空，请查看 Debug 日志。")
-
-        # 解析结果
-        chain = await self.parse_dify_result(result)
-
-        # 创建最终响应
-        self.final_llm_resp = LLMResponse(role="assistant", result_chain=chain)
-        self._transition_state(AgentState.DONE)
-
-        try:
-            await self.agent_hooks.on_agent_done(self.run_context, self.final_llm_resp)
-        except Exception as e:
-            logger.error(f"Error in on_agent_done hook: {e}", exc_info=True)
-
-        # 返回最终结果
-        yield AgentResponse(
-            type="llm_result",
-            data=AgentResponseData(chain=chain),
-        )
-
-    async def parse_dify_result(self, chunk: dict | str) -> MessageChain:
-        """解析 Dify 的响应结果"""
-        if isinstance(chunk, str):
-            # Chat
-            return MessageChain(chain=[Comp.Plain(chunk)])
-
-        async def parse_file(item: dict):
-            match item["type"]:
-                case "image":
-                    return Comp.Image(file=item["url"], url=item["url"])
-                case "audio":
-                    # 仅支持 wav
-                    temp_dir = os.path.join(get_astrbot_data_path(), "temp")
-                    path = os.path.join(temp_dir, f"{item['filename']}.wav")
-                    await download_file(item["url"], path)
-                    return Comp.Image(file=item["url"], url=item["url"])
-                case "video":
-                    return Comp.Video(file=item["url"])
-                case _:
-                    return Comp.File(name=item["filename"], file=item["url"])
-
-        output = chunk["data"]["outputs"][self.workflow_output_key]
-        chains = []
-        if isinstance(output, str):
-            # 纯文本输出
-            chains.append(Comp.Plain(output))
-        elif isinstance(output, list):
-            # 主要适配 Dify 的 HTTP 请求结点的多模态输出
-            for item in output:
-                # handle Array[File]
-                if (
-                    not isinstance(item, dict)
-                    or item.get("dify_model_identity", "") != "__dify__file__"
-                ):
-                    chains.append(Comp.Plain(str(output)))
-                    break
-        else:
-            chains.append(Comp.Plain(str(output)))
-
-        # scan file
-        files = chunk["data"].get("files", [])
-        for item in files:
-            comp = await parse_file(item)
-            chains.append(comp)
-
-        return MessageChain(chain=chains)
-
-    @override
-    def done(self) -> bool:
-        """检查 Agent 是否已完成工作"""
-        return self._state in (AgentState.DONE, AgentState.ERROR)
-
-    @override
-    def get_final_llm_resp(self) -> LLMResponse | None:
-        return self.final_llm_resp

astrbot/core/agent/runners/tool_loop_agent_runner.py

@@ -23,7 +23,7 @@ from astrbot.core.provider.entities import (
 from astrbot.core.provider.provider import Provider
 
 from ..hooks import BaseAgentRunHooks
-from ..message import AssistantMessageSegment, Message, ToolCallMessageSegment
+from ..message import AssistantMessageSegment, ToolCallMessageSegment
 from ..response import AgentResponseData
 from ..run_context import ContextWrapper, TContext
 from ..tool_executor import BaseFunctionToolExecutor
@@ -55,19 +55,11 @@ class ToolLoopAgentRunner(BaseAgentRunner[TContext]):
         self.agent_hooks = agent_hooks
         self.run_context = run_context
 
-        messages = []
-        # append existing messages in the run context
-        for msg in request.contexts:
-            messages.append(Message.model_validate(msg))
-        if request.prompt is not None:
-            m = await request.assemble_context()
-            messages.append(Message.model_validate(m))
-        if request.system_prompt:
-            messages.insert(
-                0,
-                Message(role="system", content=request.system_prompt),
-            )
-        self.run_context.messages = messages
+    def _transition_state(self, new_state: AgentState) -> None:
+        """转换 Agent 状态"""
+        if self._state != new_state:
+            logger.debug(f"Agent state transition: {self._state} -> {new_state}")
+            self._state = new_state
 
     async def _iter_llm_responses(self) -> T.AsyncGenerator[LLMResponse, None]:
         """Yields chunks *and* a final LLMResponse."""
@@ -104,22 +96,13 @@ class ToolLoopAgentRunner(BaseAgentRunner[TContext]):
                         type="streaming_delta",
                         data=AgentResponseData(chain=llm_response.result_chain),
                     )
-                elif llm_response.completion_text:
+                else:
                     yield AgentResponse(
                         type="streaming_delta",
                         data=AgentResponseData(
                             chain=MessageChain().message(llm_response.completion_text),
                         ),
                     )
-                elif llm_response.reasoning_content:
-                    yield AgentResponse(
-                        type="streaming_delta",
-                        data=AgentResponseData(
-                            chain=MessageChain(type="reasoning").message(
-                                llm_response.reasoning_content,
-                            ),
-                        ),
-                    )
                 continue
             llm_resp_result = llm_response
             break  # got final response
@@ -147,13 +130,6 @@ class ToolLoopAgentRunner(BaseAgentRunner[TContext]):
             # 如果没有工具调用，转换到完成状态
             self.final_llm_resp = llm_resp
             self._transition_state(AgentState.DONE)
-            # record the final assistant message
-            self.run_context.messages.append(
-                Message(
-                    role="assistant",
-                    content=llm_resp.completion_text or "",
-                ),
-            )
             try:
                 await self.agent_hooks.on_agent_done(self.run_context, llm_resp)
             except Exception as e:
@@ -180,16 +156,13 @@ class ToolLoopAgentRunner(BaseAgentRunner[TContext]):
                 yield AgentResponse(
                     type="tool_call",
                     data=AgentResponseData(
-                        chain=MessageChain(type="tool_call").message(
-                            f"🔨 调用工具: {tool_call_name}"
-                        ),
+                        chain=MessageChain().message(f"🔨 调用工具: {tool_call_name}"),
                     ),
                 )
             async for result in self._handle_function_tools(self.req, llm_resp):
                 if isinstance(result, list):
                     tool_call_result_blocks = result
                 elif isinstance(result, MessageChain):
-                    result.type = "tool_call_result"
                     yield AgentResponse(
                         type="tool_call_result",
                         data=AgentResponseData(chain=result),
@@ -202,23 +175,8 @@ class ToolLoopAgentRunner(BaseAgentRunner[TContext]):
                 ),
                 tool_calls_result=tool_call_result_blocks,
             )
-            # record the assistant message with tool calls
-            self.run_context.messages.extend(
-                tool_calls_result.to_openai_messages_model()
-            )
-
             self.req.append_tool_calls_result(tool_calls_result)
 
-    async def step_until_done(
-        self, max_step: int
-    ) -> T.AsyncGenerator[AgentResponse, None]:
-        """Process steps until the agent is done."""
-        step_count = 0
-        while not self.done() and step_count < max_step:
-            step_count += 1
-            async for resp in self.step():
-                yield resp
-
     async def _handle_function_tools(
         self,
         req: ProviderRequest,

astrbot/core/agent/tool.py

@@ -4,13 +4,12 @@ from typing import Any, Generic
 import jsonschema
 import mcp
 from deprecated import deprecated
-from pydantic import Field, model_validator
+from pydantic import model_validator
 from pydantic.dataclasses import dataclass
 
 from .run_context import ContextWrapper, TContext
 
 ParametersType = dict[str, Any]
-ToolExecResult = str | mcp.types.CallToolResult
 
 
 @dataclass
@@ -56,14 +55,23 @@ class FunctionTool(ToolSchema, Generic[TContext]):
     def __repr__(self):
         return f"FuncTool(name={self.name}, parameters={self.parameters}, description={self.description})"
 
-    async def call(self, context: ContextWrapper[TContext], **kwargs) -> ToolExecResult:
+    def __dict__(self) -> dict[str, Any]:
+        return {
+            "name": self.name,
+            "parameters": self.parameters,
+            "description": self.description,
+            "active": self.active,
+        }
+
+    async def call(
+        self, context: ContextWrapper[TContext], **kwargs
+    ) -> str | mcp.types.CallToolResult:
         """Run the tool with the given arguments. The handler field has priority."""
         raise NotImplementedError(
             "FunctionTool.call() must be implemented by subclasses or set a handler."
         )
 
 
-@dataclass
 class ToolSet:
     """A set of function tools that can be used in function calling.
 
@@ -71,7 +79,8 @@ class ToolSet:
     convert the tools to different API formats (OpenAI, Anthropic, Google GenAI).
     """
 
-    tools: list[FunctionTool] = Field(default_factory=list)
+    def __init__(self, tools: list[FunctionTool] | None = None):
+        self.tools: list[FunctionTool] = tools or []
 
     def empty(self) -> bool:
         """Check if the tool set is empty."""

astrbot/core/astr_agent_context.py

@@ -1,19 +1,14 @@
-from pydantic import Field
-from pydantic.dataclasses import dataclass
+from dataclasses import dataclass
 
-from astrbot.core.agent.run_context import ContextWrapper
 from astrbot.core.platform.astr_message_event import AstrMessageEvent
-from astrbot.core.star.context import Context
+from astrbot.core.provider import Provider
+from astrbot.core.provider.entities import ProviderRequest
 
 
-@dataclass(config={"arbitrary_types_allowed": True})
+@dataclass
 class AstrAgentContext:
-    context: Context
-    """The star context instance"""
+    provider: Provider
+    first_provider_request: ProviderRequest
+    curr_provider_request: ProviderRequest
+    streaming: bool
     event: AstrMessageEvent
-    """The message event associated with the agent context."""
-    extra: dict[str, str] = Field(default_factory=dict)
-    """Customized extra data."""
-
-
-AgentContextWrapper = ContextWrapper[AstrAgentContext]

astrbot/core/astr_agent_hooks.py

@@ -1,36 +0,0 @@
-from typing import Any
-
-from mcp.types import CallToolResult
-
-from astrbot.core.agent.hooks import BaseAgentRunHooks
-from astrbot.core.agent.run_context import ContextWrapper
-from astrbot.core.agent.tool import FunctionTool
-from astrbot.core.astr_agent_context import AstrAgentContext
-from astrbot.core.pipeline.context_utils import call_event_hook
-from astrbot.core.star.star_handler import EventType
-
-
-class MainAgentHooks(BaseAgentRunHooks[AstrAgentContext]):
-    async def on_agent_done(self, run_context, llm_response):
-        # 执行事件钩子
-        await call_event_hook(
-            run_context.context.event,
-            EventType.OnLLMResponseEvent,
-            llm_response,
-        )
-
-    async def on_tool_end(
-        self,
-        run_context: ContextWrapper[AstrAgentContext],
-        tool: FunctionTool[Any],
-        tool_args: dict | None,
-        tool_result: CallToolResult | None,
-    ):
-        run_context.context.event.clear_result()
-
-
-class EmptyAgentHooks(BaseAgentRunHooks[AstrAgentContext]):
-    pass
-
-
-MAIN_AGENT_HOOKS = MainAgentHooks()

astrbot/core/astr_agent_run_util.py

@@ -1,80 +0,0 @@
-import traceback
-from collections.abc import AsyncGenerator
-
-from astrbot.core import logger
-from astrbot.core.agent.runners.tool_loop_agent_runner import ToolLoopAgentRunner
-from astrbot.core.astr_agent_context import AstrAgentContext
-from astrbot.core.message.message_event_result import (
-    MessageChain,
-    MessageEventResult,
-    ResultContentType,
-)
-
-AgentRunner = ToolLoopAgentRunner[AstrAgentContext]
-
-
-async def run_agent(
-    agent_runner: AgentRunner,
-    max_step: int = 30,
-    show_tool_use: bool = True,
-    stream_to_general: bool = False,
-    show_reasoning: bool = False,
-) -> AsyncGenerator[MessageChain | None, None]:
-    step_idx = 0
-    astr_event = agent_runner.run_context.context.event
-    while step_idx < max_step:
-        step_idx += 1
-        try:
-            async for resp in agent_runner.step():
-                if astr_event.is_stopped():
-                    return
-                if resp.type == "tool_call_result":
-                    msg_chain = resp.data["chain"]
-                    if msg_chain.type == "tool_direct_result":
-                        # tool_direct_result 用于标记 llm tool 需要直接发送给用户的内容
-                        await astr_event.send(resp.data["chain"])
-                        continue
-                    # 对于其他情况，暂时先不处理
-                    continue
-                elif resp.type == "tool_call":
-                    if agent_runner.streaming:
-                        # 用来标记流式响应需要分节
-                        yield MessageChain(chain=[], type="break")
-                    if show_tool_use:
-                        await astr_event.send(resp.data["chain"])
-                    continue
-
-                if stream_to_general and resp.type == "streaming_delta":
-                    continue
-
-                if stream_to_general or not agent_runner.streaming:
-                    content_typ = (
-                        ResultContentType.LLM_RESULT
-                        if resp.type == "llm_result"
-                        else ResultContentType.GENERAL_RESULT
-                    )
-                    astr_event.set_result(
-                        MessageEventResult(
-                            chain=resp.data["chain"].chain,
-                            result_content_type=content_typ,
-                        ),
-                    )
-                    yield
-                    astr_event.clear_result()
-                elif resp.type == "streaming_delta":
-                    chain = resp.data["chain"]
-                    if chain.type == "reasoning" and not show_reasoning:
-                        # display the reasoning content only when configured
-                        continue
-                    yield resp.data["chain"]  # MessageChain
-            if agent_runner.done():
-                break
-
-        except Exception as e:
-            logger.error(traceback.format_exc())
-            err_msg = f"\n\nAstrBot 请求失败。\n错误类型: {type(e).__name__}\n错误信息: {e!s}\n\n请在控制台查看和分享错误详情。\n"
-            if agent_runner.streaming:
-                yield MessageChain().message(err_msg)
-            else:
-                astr_event.set_result(MessageEventResult().message(err_msg))
-            return

astrbot/core/astr_agent_tool_exec.py

@@ -1,246 +0,0 @@
-import asyncio
-import inspect
-import traceback
-import typing as T
-
-import mcp
-
-from astrbot import logger
-from astrbot.core.agent.handoff import HandoffTool
-from astrbot.core.agent.mcp_client import MCPTool
-from astrbot.core.agent.run_context import ContextWrapper
-from astrbot.core.agent.tool import FunctionTool, ToolSet
-from astrbot.core.agent.tool_executor import BaseFunctionToolExecutor
-from astrbot.core.astr_agent_context import AstrAgentContext
-from astrbot.core.message.message_event_result import (
-    CommandResult,
-    MessageChain,
-    MessageEventResult,
-)
-from astrbot.core.provider.register import llm_tools
-
-
-class FunctionToolExecutor(BaseFunctionToolExecutor[AstrAgentContext]):
-    @classmethod
-    async def execute(cls, tool, run_context, **tool_args):
-        """执行函数调用。
-
-        Args:
-            event (AstrMessageEvent): 事件对象, 当 origin 为 local 时必须提供。
-            **kwargs: 函数调用的参数。
-
-        Returns:
-            AsyncGenerator[None | mcp.types.CallToolResult, None]
-
-        """
-        if isinstance(tool, HandoffTool):
-            async for r in cls._execute_handoff(tool, run_context, **tool_args):
-                yield r
-            return
-
-        elif isinstance(tool, MCPTool):
-            async for r in cls._execute_mcp(tool, run_context, **tool_args):
-                yield r
-            return
-
-        else:
-            async for r in cls._execute_local(tool, run_context, **tool_args):
-                yield r
-            return
-
-    @classmethod
-    async def _execute_handoff(
-        cls,
-        tool: HandoffTool,
-        run_context: ContextWrapper[AstrAgentContext],
-        **tool_args,
-    ):
-        input_ = tool_args.get("input")
-
-        # make toolset for the agent
-        tools = tool.agent.tools
-        if tools:
-            toolset = ToolSet()
-            for t in tools:
-                if isinstance(t, str):
-                    _t = llm_tools.get_func(t)
-                    if _t:
-                        toolset.add_tool(_t)
-                elif isinstance(t, FunctionTool):
-                    toolset.add_tool(t)
-        else:
-            toolset = None
-
-        ctx = run_context.context.context
-        event = run_context.context.event
-        umo = event.unified_msg_origin
-        prov_id = await ctx.get_current_chat_provider_id(umo)
-        llm_resp = await ctx.tool_loop_agent(
-            event=event,
-            chat_provider_id=prov_id,
-            prompt=input_,
-            system_prompt=tool.agent.instructions,
-            tools=toolset,
-            max_steps=30,
-            run_hooks=tool.agent.run_hooks,
-        )
-        yield mcp.types.CallToolResult(
-            content=[mcp.types.TextContent(type="text", text=llm_resp.completion_text)]
-        )
-
-    @classmethod
-    async def _execute_local(
-        cls,
-        tool: FunctionTool,
-        run_context: ContextWrapper[AstrAgentContext],
-        **tool_args,
-    ):
-        event = run_context.context.event
-        if not event:
-            raise ValueError("Event must be provided for local function tools.")
-
-        is_override_call = False
-        for ty in type(tool).mro():
-            if "call" in ty.__dict__ and ty.__dict__["call"] is not FunctionTool.call:
-                is_override_call = True
-                break
-
-        # 检查 tool 下有没有 run 方法
-        if not tool.handler and not hasattr(tool, "run") and not is_override_call:
-            raise ValueError("Tool must have a valid handler or override 'run' method.")
-
-        awaitable = None
-        method_name = ""
-        if tool.handler:
-            awaitable = tool.handler
-            method_name = "decorator_handler"
-        elif is_override_call:
-            awaitable = tool.call
-            method_name = "call"
-        elif hasattr(tool, "run"):
-            awaitable = getattr(tool, "run")
-            method_name = "run"
-        if awaitable is None:
-            raise ValueError("Tool must have a valid handler or override 'run' method.")
-
-        wrapper = call_local_llm_tool(
-            context=run_context,
-            handler=awaitable,
-            method_name=method_name,
-            **tool_args,
-        )
-        while True:
-            try:
-                resp = await asyncio.wait_for(
-                    anext(wrapper),
-                    timeout=run_context.tool_call_timeout,
-                )
-                if resp is not None:
-                    if isinstance(resp, mcp.types.CallToolResult):
-                        yield resp
-                    else:
-                        text_content = mcp.types.TextContent(
-                            type="text",
-                            text=str(resp),
-                        )
-                        yield mcp.types.CallToolResult(content=[text_content])
-                else:
-                    # NOTE: Tool 在这里直接请求发送消息给用户
-                    # TODO: 是否需要判断 event.get_result() 是否为空?
-                    # 如果为空,则说明没有发送消息给用户,并且返回值为空,将返回一个特殊的 TextContent,其内容如"工具没有返回内容"
-                    if res := run_context.context.event.get_result():
-                        if res.chain:
-                            try:
-                                await event.send(
-                                    MessageChain(
-                                        chain=res.chain,
-                                        type="tool_direct_result",
-                                    )
-                                )
-                            except Exception as e:
-                                logger.error(
-                                    f"Tool 直接发送消息失败: {e}",
-                                    exc_info=True,
-                                )
-                    yield None
-            except asyncio.TimeoutError:
-                raise Exception(
-                    f"tool {tool.name} execution timeout after {run_context.tool_call_timeout} seconds.",
-                )
-            except StopAsyncIteration:
-                break
-
-    @classmethod
-    async def _execute_mcp(
-        cls,
-        tool: FunctionTool,
-        run_context: ContextWrapper[AstrAgentContext],
-        **tool_args,
-    ):
-        res = await tool.call(run_context, **tool_args)
-        if not res:
-            return
-        yield res
-
-
-async def call_local_llm_tool(
-    context: ContextWrapper[AstrAgentContext],
-    handler: T.Callable[..., T.Awaitable[T.Any]],
-    method_name: str,
-    *args,
-    **kwargs,
-) -> T.AsyncGenerator[T.Any, None]:
-    """执行本地 LLM 工具的处理函数并处理其返回结果"""
-    ready_to_call = None  # 一个协程或者异步生成器
-
-    trace_ = None
-
-    event = context.context.event
-
-    try:
-        if method_name == "run" or method_name == "decorator_handler":
-            ready_to_call = handler(event, *args, **kwargs)
-        elif method_name == "call":
-            ready_to_call = handler(context, *args, **kwargs)
-        else:
-            raise ValueError(f"未知的方法名: {method_name}")
-    except ValueError as e:
-        logger.error(f"调用本地 LLM 工具时出错: {e}", exc_info=True)
-    except TypeError:
-        logger.error("处理函数参数不匹配，请检查 handler 的定义。", exc_info=True)
-    except Exception as e:
-        trace_ = traceback.format_exc()
-        logger.error(f"调用本地 LLM 工具时出错: {e}\n{trace_}")
-
-    if not ready_to_call:
-        return
-
-    if inspect.isasyncgen(ready_to_call):
-        _has_yielded = False
-        try:
-            async for ret in ready_to_call:
-                # 这里逐步执行异步生成器, 对于每个 yield 返回的 ret, 执行下面的代码
-                # 返回值只能是 MessageEventResult 或者 None（无返回值）
-                _has_yielded = True
-                if isinstance(ret, (MessageEventResult, CommandResult)):
-                    # 如果返回值是 MessageEventResult, 设置结果并继续
-                    event.set_result(ret)
-                    yield
-                else:
-                    # 如果返回值是 None, 则不设置结果并继续
-                    # 继续执行后续阶段
-                    yield ret
-            if not _has_yielded:
-                # 如果这个异步生成器没有执行到 yield 分支
-                yield
-        except Exception as e:
-            logger.error(f"Previous Error: {trace_}")
-            raise e
-    elif inspect.iscoroutine(ready_to_call):
-        # 如果只是一个协程, 直接执行
-        ret = await ready_to_call
-        if isinstance(ret, (MessageEventResult, CommandResult)):
-            event.set_result(ret)
-            yield
-        else:
-            yield ret

astrbot/core/config/astrbot_config.py

@@ -3,11 +3,15 @@ import json
 import logging
 import os
 
-from astrbot.core.utils.astrbot_path import get_astrbot_data_path
+from astrbot_api.abc import IAstrbotPaths
+
+from astrbot_sdk import sync_base_container
 
 from .default import DEFAULT_CONFIG, DEFAULT_VALUE_MAP
 
-ASTRBOT_CONFIG_PATH = os.path.join(get_astrbot_data_path(), "cmd_config.json")
+AstrbotPaths: type[IAstrbotPaths] = sync_base_container.get(type[IAstrbotPaths])
+
+ASTRBOT_CONFIG_PATH = str(AstrbotPaths.astrbot_root / "cmd_config.json")
 logger = logging.getLogger("astrbot")
 
 

astrbot/core/config/default.py

@@ -1,11 +1,17 @@
 """如需修改配置，请在 `data/cmd_config.json` 中修改或者在管理面板中可视化修改。"""
 
-import os
+from importlib.metadata import version
 
-from astrbot.core.utils.astrbot_path import get_astrbot_data_path
+from astrbot_api.abc import IAstrbotPaths
 
-VERSION = "4.7.3"
-DB_PATH = os.path.join(get_astrbot_data_path(), "data_v4.db")
+from astrbot_sdk import sync_base_container
+
+AstrbotPaths: type[IAstrbotPaths] = sync_base_container.get(type[IAstrbotPaths])
+
+# 警告,请使用version函数获取版本,此变量兼容保留
+VERSION = version("astrbot")
+
+DB_PATH = str(AstrbotPaths.astrbot_root / "data_v4.db")
 
 # 默认配置
 DEFAULT_CONFIG = {
@@ -68,11 +74,7 @@ DEFAULT_CONFIG = {
         "dequeue_context_length": 1,
         "streaming_response": False,
         "show_tool_use_status": False,
-        "agent_runner_type": "local",
-        "dify_agent_runner_provider_id": "",
-        "coze_agent_runner_provider_id": "",
-        "dashscope_agent_runner_provider_id": "",
-        "unsupported_streaming_strategy": "realtime_segmenting",
+        "streaming_segmented": False,
         "max_agent_step": 30,
         "tool_call_timeout": 60,
     },
@@ -90,7 +92,6 @@ DEFAULT_CONFIG = {
         "group_icl_enable": False,
         "group_message_max_cnt": 300,
         "image_caption": False,
-        "image_caption_provider_id": "",
         "active_reply": {
             "enable": False,
             "method": "possibility_reply",
@@ -142,20 +143,10 @@ 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,
 }
 
 
-"""
-AstrBot v3 时代的配置元数据，目前仅承担以下功能：
-
-1. 保存配置时，配置项的类型验证
-2. WebUI 展示提供商和平台适配器模版
-
-WebUI 的配置文件在 `CONFIG_METADATA_3` 中。
-
-未来将会逐步淘汰此配置元数据。
-"""
+# 配置项的中文描述、值类型
 CONFIG_METADATA_2 = {
     "platform_group": {
         "metadata": {
@@ -648,7 +639,7 @@ CONFIG_METADATA_2 = {
                             },
                             "words_count_threshold": {
                                 "type": "int",
-                                "hint": "分段回复的字数上限。只有字数小于此值的消息才会被分段，超过此值的长消息将直接发送（不分段）。默认为 150",
+                                "hint": "超过这个字数的消息不会被分段回复。默认为 150",
                             },
                             "regex": {
                                 "type": "string",
@@ -755,7 +746,6 @@ CONFIG_METADATA_2 = {
                         "api_base": "https://api.openai.com/v1",
                         "timeout": 120,
                         "model_config": {"model": "gpt-4o-mini", "temperature": 0.4},
-                        "custom_headers": {},
                         "custom_extra_body": {},
                         "modalities": ["text", "image", "tool_use"],
                         "hint": "也兼容所有与 OpenAI API 兼容的服务。",
@@ -771,7 +761,6 @@ CONFIG_METADATA_2 = {
                         "api_base": "",
                         "timeout": 120,
                         "model_config": {"model": "gpt-4o-mini", "temperature": 0.4},
-                        "custom_headers": {},
                         "custom_extra_body": {},
                         "modalities": ["text", "image", "tool_use"],
                     },
@@ -785,7 +774,6 @@ CONFIG_METADATA_2 = {
                         "api_base": "https://api.x.ai/v1",
                         "timeout": 120,
                         "model_config": {"model": "grok-2-latest", "temperature": 0.4},
-                        "custom_headers": {},
                         "custom_extra_body": {},
                         "xai_native_search": False,
                         "modalities": ["text", "image", "tool_use"],
@@ -817,7 +805,6 @@ CONFIG_METADATA_2 = {
                         "key": ["ollama"],  # ollama 的 key 默认是 ollama
                         "api_base": "http://localhost:11434/v1",
                         "model_config": {"model": "llama3.1-8b", "temperature": 0.4},
-                        "custom_headers": {},
                         "custom_extra_body": {},
                         "modalities": ["text", "image", "tool_use"],
                     },
@@ -832,7 +819,6 @@ CONFIG_METADATA_2 = {
                         "model_config": {
                             "model": "llama-3.1-8b",
                         },
-                        "custom_headers": {},
                         "custom_extra_body": {},
                         "modalities": ["text", "image", "tool_use"],
                     },
@@ -849,7 +835,6 @@ CONFIG_METADATA_2 = {
                             "model": "gemini-1.5-flash",
                             "temperature": 0.4,
                         },
-                        "custom_headers": {},
                         "custom_extra_body": {},
                         "modalities": ["text", "image", "tool_use"],
                     },
@@ -891,24 +876,6 @@ CONFIG_METADATA_2 = {
                         "api_base": "https://api.deepseek.com/v1",
                         "timeout": 120,
                         "model_config": {"model": "deepseek-chat", "temperature": 0.4},
-                        "custom_headers": {},
-                        "custom_extra_body": {},
-                        "modalities": ["text", "tool_use"],
-                    },
-                    "Groq": {
-                        "id": "groq_default",
-                        "provider": "groq",
-                        "type": "groq_chat_completion",
-                        "provider_type": "chat_completion",
-                        "enable": True,
-                        "key": [],
-                        "api_base": "https://api.groq.com/openai/v1",
-                        "timeout": 120,
-                        "model_config": {
-                            "model": "openai/gpt-oss-20b",
-                            "temperature": 0.4,
-                        },
-                        "custom_headers": {},
                         "custom_extra_body": {},
                         "modalities": ["text", "tool_use"],
                     },
@@ -922,7 +889,6 @@ CONFIG_METADATA_2 = {
                         "api_base": "https://api.302.ai/v1",
                         "timeout": 120,
                         "model_config": {"model": "gpt-4.1-mini", "temperature": 0.4},
-                        "custom_headers": {},
                         "custom_extra_body": {},
                         "modalities": ["text", "image", "tool_use"],
                     },
@@ -939,7 +905,6 @@ CONFIG_METADATA_2 = {
                             "model": "deepseek-ai/DeepSeek-V3",
                             "temperature": 0.4,
                         },
-                        "custom_headers": {},
                         "custom_extra_body": {},
                         "modalities": ["text", "image", "tool_use"],
                     },
@@ -956,7 +921,6 @@ CONFIG_METADATA_2 = {
                             "model": "deepseek/deepseek-r1",
                             "temperature": 0.4,
                         },
-                        "custom_headers": {},
                         "custom_extra_body": {},
                     },
                     "小马算力": {
@@ -972,7 +936,6 @@ CONFIG_METADATA_2 = {
                             "model": "kimi-k2-instruct-0905",
                             "temperature": 0.7,
                         },
-                        "custom_headers": {},
                         "custom_extra_body": {},
                     },
                     "优云智算": {
@@ -987,7 +950,6 @@ CONFIG_METADATA_2 = {
                         "model_config": {
                             "model": "moonshotai/Kimi-K2-Instruct",
                         },
-                        "custom_headers": {},
                         "custom_extra_body": {},
                         "modalities": ["text", "image", "tool_use"],
                     },
@@ -1001,7 +963,6 @@ CONFIG_METADATA_2 = {
                         "timeout": 120,
                         "api_base": "https://api.moonshot.cn/v1",
                         "model_config": {"model": "moonshot-v1-8k", "temperature": 0.4},
-                        "custom_headers": {},
                         "custom_extra_body": {},
                         "modalities": ["text", "image", "tool_use"],
                     },
@@ -1017,15 +978,13 @@ CONFIG_METADATA_2 = {
                         "model_config": {
                             "model": "glm-4-flash",
                         },
-                        "custom_headers": {},
-                        "custom_extra_body": {},
                         "modalities": ["text", "image", "tool_use"],
                     },
                     "Dify": {
                         "id": "dify_app_default",
                         "provider": "dify",
                         "type": "dify",
-                        "provider_type": "agent_runner",
+                        "provider_type": "chat_completion",
                         "enable": True,
                         "dify_api_type": "chat",
                         "dify_api_key": "",
@@ -1039,20 +998,20 @@ CONFIG_METADATA_2 = {
                     "Coze": {
                         "id": "coze",
                         "provider": "coze",
-                        "provider_type": "agent_runner",
+                        "provider_type": "chat_completion",
                         "type": "coze",
                         "enable": True,
                         "coze_api_key": "",
                         "bot_id": "",
                         "coze_api_base": "https://api.coze.cn",
                         "timeout": 60,
-                        # "auto_save_history": True,
+                        "auto_save_history": True,
                     },
                     "阿里云百炼应用": {
                         "id": "dashscope",
                         "provider": "dashscope",
                         "type": "dashscope",
-                        "provider_type": "agent_runner",
+                        "provider_type": "chat_completion",
                         "enable": True,
                         "dashscope_app_type": "agent",
                         "dashscope_api_key": "",
@@ -1075,7 +1034,6 @@ CONFIG_METADATA_2 = {
                         "timeout": 120,
                         "api_base": "https://api-inference.modelscope.cn/v1",
                         "model_config": {"model": "Qwen/Qwen3-32B", "temperature": 0.4},
-                        "custom_headers": {},
                         "custom_extra_body": {},
                         "modalities": ["text", "image", "tool_use"],
                     },
@@ -1088,7 +1046,6 @@ CONFIG_METADATA_2 = {
                         "key": [],
                         "api_base": "https://api.fastgpt.in/api/v1",
                         "timeout": 60,
-                        "custom_headers": {},
                         "custom_extra_body": {},
                     },
                     "Whisper(API)": {
@@ -1101,7 +1058,7 @@ CONFIG_METADATA_2 = {
                         "api_base": "",
                         "model": "whisper-1",
                     },
-                    "Whisper(Local)": {
+                    "Whisper(本地加载)": {
                         "hint": "启用前请 pip 安装 openai-whisper 库（N卡用户大约下载 2GB，主要是 torch 和 cuda，CPU 用户大约下载 1 GB），并且安装 ffmpeg。否则将无法正常转文字。",
                         "provider": "openai",
                         "type": "openai_whisper_selfhost",
@@ -1110,7 +1067,7 @@ CONFIG_METADATA_2 = {
                         "id": "whisper_selfhost",
                         "model": "tiny",
                     },
-                    "SenseVoice(Local)": {
+                    "SenseVoice(本地加载)": {
                         "hint": "启用前请 pip 安装 funasr、funasr_onnx、torchaudio、torch、modelscope、jieba 库（默认使用CPU，大约下载 1 GB），并且安装 ffmpeg。否则将无法正常转文字。",
                         "type": "sensevoice_stt_selfhost",
                         "provider": "sensevoice",
@@ -1145,7 +1102,7 @@ CONFIG_METADATA_2 = {
                         "pitch": "+0Hz",
                         "timeout": 20,
                     },
-                    "GSV TTS(Local)": {
+                    "GSV TTS(本地加载)": {
                         "id": "gsv_tts",
                         "enable": False,
                         "provider": "gpt_sovits",
@@ -1322,19 +1279,6 @@ CONFIG_METADATA_2 = {
                         "timeout": 20,
                         "launch_model_if_not_running": False,
                     },
-                    "阿里云百炼重排序": {
-                        "id": "bailian_rerank",
-                        "type": "bailian_rerank",
-                        "provider": "bailian",
-                        "provider_type": "rerank",
-                        "enable": True,
-                        "rerank_api_key": "",
-                        "rerank_api_base": "https://dashscope.aliyuncs.com/api/v1/services/rerank/text-rerank/text-rerank",
-                        "rerank_model": "qwen3-rerank",
-                        "timeout": 30,
-                        "return_documents": False,
-                        "instruct": "",
-                    },
                     "Xinference STT": {
                         "id": "xinference_stt",
                         "type": "xinference_stt",
@@ -1369,16 +1313,6 @@ CONFIG_METADATA_2 = {
                         "description": "重排序模型名称",
                         "type": "string",
                     },
-                    "return_documents": {
-                        "description": "是否在排序结果中返回文档原文",
-                        "type": "bool",
-                        "hint": "默认值false，以减少网络传输开销。",
-                    },
-                    "instruct": {
-                        "description": "自定义排序任务类型说明",
-                        "type": "string",
-                        "hint": "仅在使用 qwen3-rerank 模型时生效。建议使用英文撰写。",
-                    },
                     "launch_model_if_not_running": {
                         "description": "模型未运行时自动启动",
                         "type": "bool",
@@ -1393,12 +1327,6 @@ CONFIG_METADATA_2 = {
                         "render_type": "checkbox",
                         "hint": "模型支持的模态。如所填写的模型不支持图像，请取消勾选图像。",
                     },
-                    "custom_headers": {
-                        "description": "自定义添加请求头",
-                        "type": "dict",
-                        "items": {},
-                        "hint": "此处添加的键值对将被合并到 OpenAI SDK 的 default_headers 中，用于自定义 HTTP 请求头。值必须为字符串。",
-                    },
                     "custom_extra_body": {
                         "description": "自定义请求体参数",
                         "type": "dict",
@@ -1921,6 +1849,7 @@ CONFIG_METADATA_2 = {
                     "enable": {
                         "description": "启用",
                         "type": "bool",
+                        "hint": "是否启用。",
                     },
                     "key": {
                         "description": "API Key",
@@ -2047,25 +1976,15 @@ CONFIG_METADATA_2 = {
                     "show_tool_use_status": {
                         "type": "bool",
                     },
-                    "unsupported_streaming_strategy": {
-                        "type": "string",
-                    },
-                    "agent_runner_type": {
-                        "type": "string",
-                    },
-                    "dify_agent_runner_provider_id": {
-                        "type": "string",
-                    },
-                    "coze_agent_runner_provider_id": {
-                        "type": "string",
-                    },
-                    "dashscope_agent_runner_provider_id": {
-                        "type": "string",
+                    "streaming_segmented": {
+                        "type": "bool",
                     },
                     "max_agent_step": {
+                        "description": "工具调用轮数上限",
                         "type": "int",
                     },
                     "tool_call_timeout": {
+                        "description": "工具调用超时时间（秒）",
                         "type": "int",
                     },
                 },
@@ -2110,9 +2029,6 @@ CONFIG_METADATA_2 = {
                     "image_caption": {
                         "type": "bool",
                     },
-                    "image_caption_provider_id": {
-                        "type": "string",
-                    },
                     "image_caption_prompt": {
                         "type": "string",
                     },
@@ -2196,93 +2112,39 @@ 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"},
         },
     },
 }
 
 
-"""
-v4.7.0 之后，name, description, hint 等字段已经实现 i18n 国际化。国际化资源文件位于：
-
-- dashboard/src/i18n/locales/en-US/features/config-metadata.json
-- dashboard/src/i18n/locales/zh-CN/features/config-metadata.json
-
-如果在此文件中添加了新的配置字段，请务必同步更新上述两个国际化资源文件。
-"""
 CONFIG_METADATA_3 = {
     "ai_group": {
         "name": "AI 配置",
         "metadata": {
-            "agent_runner": {
-                "description": "Agent 执行方式",
-                "hint": "选择 AI 对话的执行器，默认为 AstrBot 内置 Agent 执行器，可使用 AstrBot 内的知识库、人格、工具调用功能。如果不打算接入 Dify 或 Coze 等第三方 Agent 执行器，不需要修改此节。",
+            "ai": {
+                "description": "模型",
                 "type": "object",
                 "items": {
                     "provider_settings.enable": {
-                        "description": "启用",
+                        "description": "启用大语言模型聊天",
                         "type": "bool",
-                        "hint": "AI 对话总开关",
                     },
-                    "provider_settings.agent_runner_type": {
-                        "description": "执行器",
-                        "type": "string",
-                        "options": ["local", "dify", "coze", "dashscope"],
-                        "labels": ["内置 Agent", "Dify", "Coze", "阿里云百炼应用"],
-                        "condition": {
-                            "provider_settings.enable": True,
-                        },
-                    },
-                    "provider_settings.coze_agent_runner_provider_id": {
-                        "description": "Coze Agent 执行器提供商 ID",
-                        "type": "string",
-                        "_special": "select_agent_runner_provider:coze",
-                        "condition": {
-                            "provider_settings.agent_runner_type": "coze",
-                            "provider_settings.enable": True,
-                        },
-                    },
-                    "provider_settings.dify_agent_runner_provider_id": {
-                        "description": "Dify Agent 执行器提供商 ID",
-                        "type": "string",
-                        "_special": "select_agent_runner_provider:dify",
-                        "condition": {
-                            "provider_settings.agent_runner_type": "dify",
-                            "provider_settings.enable": True,
-                        },
-                    },
-                    "provider_settings.dashscope_agent_runner_provider_id": {
-                        "description": "阿里云百炼应用 Agent 执行器提供商 ID",
-                        "type": "string",
-                        "_special": "select_agent_runner_provider:dashscope",
-                        "condition": {
-                            "provider_settings.agent_runner_type": "dashscope",
-                            "provider_settings.enable": True,
-                        },
-                    },
-                },
-            },
-            "ai": {
-                "description": "模型",
-                "hint": "当使用非内置 Agent 执行器时，默认聊天模型和默认图片转述模型可能会无效，但某些插件会依赖此配置项来调用 AI 能力。",
-                "type": "object",
-                "items": {
                     "provider_settings.default_provider_id": {
                         "description": "默认聊天模型",
                         "type": "string",
                         "_special": "select_provider",
-                        "hint": "留空时使用第一个模型",
+                        "hint": "留空时使用第一个模型。",
                     },
                     "provider_settings.default_image_caption_provider_id": {
                         "description": "默认图片转述模型",
                         "type": "string",
                         "_special": "select_provider",
-                        "hint": "留空代表不使用，可用于非多模态模型",
+                        "hint": "留空代表不使用。可用于不支持视觉模态的聊天模型。",
                     },
                     "provider_stt_settings.enable": {
                         "description": "启用语音转文本",
                         "type": "bool",
-                        "hint": "STT 总开关",
+                        "hint": "STT 总开关。",
                     },
                     "provider_stt_settings.provider_id": {
                         "description": "默认语音转文本模型",
@@ -2296,11 +2158,12 @@ CONFIG_METADATA_3 = {
                     "provider_tts_settings.enable": {
                         "description": "启用文本转语音",
                         "type": "bool",
-                        "hint": "TTS 总开关",
+                        "hint": "TTS 总开关。当关闭时，会话启用 TTS 也不会生效。",
                     },
                     "provider_tts_settings.provider_id": {
                         "description": "默认文本转语音模型",
                         "type": "string",
+                        "hint": "用户也可使用 /provider 单独选择会话的 TTS 模型。",
                         "_special": "select_provider_tts",
                         "condition": {
                             "provider_tts_settings.enable": True,
@@ -2311,9 +2174,6 @@ CONFIG_METADATA_3 = {
                         "type": "text",
                     },
                 },
-                "condition": {
-                    "provider_settings.enable": True,
-                },
             },
             "persona": {
                 "description": "人格",
@@ -2325,10 +2185,6 @@ CONFIG_METADATA_3 = {
                         "_special": "select_persona",
                     },
                 },
-                "condition": {
-                    "provider_settings.agent_runner_type": "local",
-                    "provider_settings.enable": True,
-                },
             },
             "knowledgebase": {
                 "description": "知识库",
@@ -2351,15 +2207,6 @@ 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。",
-                    },
-                },
-                "condition": {
-                    "provider_settings.agent_runner_type": "local",
-                    "provider_settings.enable": True,
                 },
             },
             "websearch": {
@@ -2397,10 +2244,6 @@ CONFIG_METADATA_3 = {
                         "type": "bool",
                     },
                 },
-                "condition": {
-                    "provider_settings.agent_runner_type": "local",
-                    "provider_settings.enable": True,
-                },
             },
             "others": {
                 "description": "其他配置",
@@ -2409,83 +2252,54 @@ CONFIG_METADATA_3 = {
                     "provider_settings.display_reasoning_text": {
                         "description": "显示思考内容",
                         "type": "bool",
-                        "condition": {
-                            "provider_settings.agent_runner_type": "local",
-                        },
                     },
                     "provider_settings.identifier": {
                         "description": "用户识别",
                         "type": "bool",
-                        "hint": "启用后，会在提示词前包含用户 ID 信息。",
                     },
                     "provider_settings.group_name_display": {
                         "description": "显示群名称",
                         "type": "bool",
-                        "hint": "启用后，在支持的平台(OneBot v11)上会在提示词前包含群名称信息。",
+                        "hint": "启用后，在支持的平台(aiocqhttp)上会在 prompt 中包含群名称信息。",
                     },
                     "provider_settings.datetime_system_prompt": {
                         "description": "现实世界时间感知",
                         "type": "bool",
-                        "hint": "启用后，会在系统提示词中附带当前时间信息。",
-                        "condition": {
-                            "provider_settings.agent_runner_type": "local",
-                        },
                     },
                     "provider_settings.show_tool_use_status": {
                         "description": "输出函数调用状态",
                         "type": "bool",
-                        "condition": {
-                            "provider_settings.agent_runner_type": "local",
-                        },
                     },
                     "provider_settings.max_agent_step": {
                         "description": "工具调用轮数上限",
                         "type": "int",
-                        "condition": {
-                            "provider_settings.agent_runner_type": "local",
-                        },
                     },
                     "provider_settings.tool_call_timeout": {
                         "description": "工具调用超时时间（秒）",
                         "type": "int",
-                        "condition": {
-                            "provider_settings.agent_runner_type": "local",
-                        },
                     },
                     "provider_settings.streaming_response": {
-                        "description": "流式输出",
+                        "description": "流式回复",
                         "type": "bool",
                     },
-                    "provider_settings.unsupported_streaming_strategy": {
-                        "description": "不支持流式回复的平台",
-                        "type": "string",
-                        "options": ["realtime_segmenting", "turn_off"],
-                        "hint": "选择在不支持流式回复的平台上的处理方式。实时分段回复会在系统接收流式响应检测到诸如标点符号等分段点时，立即发送当前已接收的内容",
-                        "labels": ["实时分段回复", "关闭流式回复"],
-                        "condition": {
-                            "provider_settings.streaming_response": True,
-                        },
+                    "provider_settings.streaming_segmented": {
+                        "description": "不支持流式回复的平台采取分段输出",
+                        "type": "bool",
                     },
                     "provider_settings.max_context_length": {
                         "description": "最多携带对话轮数",
                         "type": "int",
-                        "hint": "超出这个数量时丢弃最旧的部分，一轮聊天记为 1 条，-1 为不限制",
-                        "condition": {
-                            "provider_settings.agent_runner_type": "local",
-                        },
+                        "hint": "超出这个数量时丢弃最旧的部分，一轮聊天记为 1 条。-1 为不限制。",
                     },
                     "provider_settings.dequeue_context_length": {
                         "description": "丢弃对话轮数",
                         "type": "int",
-                        "hint": "超出最多携带对话轮数时, 一次丢弃的聊天轮数",
-                        "condition": {
-                            "provider_settings.agent_runner_type": "local",
-                        },
+                        "hint": "超出最多携带对话轮数时, 一次丢弃的聊天轮数。",
                     },
                     "provider_settings.wake_prefix": {
                         "description": "LLM 聊天额外唤醒前缀 ",
                         "type": "string",
-                        "hint": "如果唤醒前缀为 /, 额外聊天唤醒前缀为 chat，则需要 /chat 才会触发 LLM 请求",
+                        "hint": "如果唤醒前缀为 `/`, 额外聊天唤醒前缀为 `chat`，则需要 `/chat` 才会触发 LLM 请求。默认为空。",
                     },
                     "provider_settings.prompt_prefix": {
                         "description": "用户提示词",
@@ -2497,9 +2311,6 @@ CONFIG_METADATA_3 = {
                         "type": "bool",
                     },
                 },
-                "condition": {
-                    "provider_settings.enable": True,
-                },
             },
         },
     },
@@ -2789,16 +2600,7 @@ CONFIG_METADATA_3 = {
                     "provider_ltm_settings.image_caption": {
                         "description": "自动理解图片",
                         "type": "bool",
-                        "hint": "需要设置群聊图片转述模型。",
-                    },
-                    "provider_ltm_settings.image_caption_provider_id": {
-                        "description": "群聊图片转述模型",
-                        "type": "string",
-                        "_special": "select_provider",
-                        "hint": "用于群聊上下文感知的图片理解，与默认图片转述模型分开配置。",
-                        "condition": {
-                            "provider_ltm_settings.image_caption": True,
-                        },
+                        "hint": "需要设置默认图片转述模型。",
                     },
                     "provider_ltm_settings.active_reply.enable": {
                         "description": "主动回复",

astrbot/core/config/i18n_utils.py

@@ -1,110 +0,0 @@
-"""
-配置元数据国际化工具
-
-提供配置元数据的国际化键转换功能
-"""
-
-from typing import Any
-
-
-class ConfigMetadataI18n:
-    """配置元数据国际化转换器"""
-
-    @staticmethod
-    def _get_i18n_key(group: str, section: str, field: str, attr: str) -> str:
-        """
-        生成国际化键
-
-        Args:
-            group: 配置组，如 'ai_group', 'platform_group'
-            section: 配置节，如 'agent_runner', 'general'
-            field: 字段名，如 'enable', 'default_provider'
-            attr: 属性类型，如 'description', 'hint', 'labels'
-
-        Returns:
-            国际化键，格式如: 'ai_group.agent_runner.enable.description'
-        """
-        if field:
-            return f"{group}.{section}.{field}.{attr}"
-        else:
-            return f"{group}.{section}.{attr}"
-
-    @staticmethod
-    def convert_to_i18n_keys(metadata: dict[str, Any]) -> dict[str, Any]:
-        """
-        将配置元数据转换为使用国际化键
-
-        Args:
-            metadata: 原始配置元数据字典
-
-        Returns:
-            使用国际化键的配置元数据字典
-        """
-        result = {}
-
-        for group_key, group_data in metadata.items():
-            group_result = {
-                "name": f"{group_key}.name",
-                "metadata": {},
-            }
-
-            for section_key, section_data in group_data.get("metadata", {}).items():
-                section_result = {
-                    "description": f"{group_key}.{section_key}.description",
-                    "type": section_data.get("type"),
-                }
-
-                # 复制其他属性
-                for key in ["items", "condition", "_special", "invisible"]:
-                    if key in section_data:
-                        section_result[key] = section_data[key]
-
-                # 处理 hint
-                if "hint" in section_data:
-                    section_result["hint"] = f"{group_key}.{section_key}.hint"
-
-                # 处理 items 中的字段
-                if "items" in section_data and isinstance(section_data["items"], dict):
-                    items_result = {}
-                    for field_key, field_data in section_data["items"].items():
-                        # 处理嵌套的点号字段名（如 provider_settings.enable）
-                        field_name = field_key
-
-                        field_result = {}
-
-                        # 复制基本属性
-                        for attr in [
-                            "type",
-                            "condition",
-                            "_special",
-                            "invisible",
-                            "options",
-                        ]:
-                            if attr in field_data:
-                                field_result[attr] = field_data[attr]
-
-                        # 转换文本属性为国际化键
-                        if "description" in field_data:
-                            field_result["description"] = (
-                                f"{group_key}.{section_key}.{field_name}.description"
-                            )
-
-                        if "hint" in field_data:
-                            field_result["hint"] = (
-                                f"{group_key}.{section_key}.{field_name}.hint"
-                            )
-
-                        if "labels" in field_data:
-                            field_result["labels"] = (
-                                f"{group_key}.{section_key}.{field_name}.labels"
-                            )
-
-                        items_result[field_key] = field_result
-
-                    section_result["items"] = items_result
-
-                group_result["metadata"][section_key] = section_result
-
-            result[group_key] = group_result
-
-        return result

astrbot/core/core_lifecycle.py

@@ -16,12 +16,12 @@ import time
 import traceback
 from asyncio import Queue
 
-from astrbot.api import logger, sp
-from astrbot.core import LogBroker
+from astrbot.core import LogBroker, logger, sp
 from astrbot.core.astrbot_config_mgr import AstrBotConfigManager
 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.knowledge_base.kb_mgr import KnowledgeBaseManager
 from astrbot.core.persona_mgr import PersonaManager
 from astrbot.core.pipeline.scheduler import PipelineContext, PipelineScheduler
@@ -33,7 +33,6 @@ from astrbot.core.star.context import Context
 from astrbot.core.star.star_handler import EventType, star_handlers_registry, star_map
 from astrbot.core.umop_config_router import UmopConfigRouter
 from astrbot.core.updator import AstrBotUpdator
-from astrbot.core.utils.migra_helper import migra
 
 from . import astrbot_config, html_renderer
 from .event_bus import EventBus
@@ -97,16 +96,11 @@ class AstrBotCoreLifecycle:
             sp=sp,
         )
 
-        # apply migration
+        # 4.5 to 4.6 migration for umop_config_router
         try:
-            await migra(
-                self.db,
-                self.astrbot_config_mgr,
-                self.umop_config_router,
-                self.astrbot_config_mgr,
-            )
+            await migrate_45_to_46(self.astrbot_config_mgr, self.umop_config_router)
         except Exception as e:
-            logger.error(f"AstrBot migration failed: {e!s}")
+            logger.error(f"Migration from version 4.5 to 4.6 failed: {e!s}")
             logger.error(traceback.format_exc())
 
         # 初始化事件队列

astrbot/core/db/__init__.py

@@ -13,7 +13,6 @@ from astrbot.core.db.po import (
     ConversationV2,
     Persona,
     PlatformMessageHistory,
-    PlatformSession,
     PlatformStat,
     Preference,
     Stats,
@@ -184,7 +183,7 @@ class BaseDatabase(abc.ABC):
         user_id: str,
         offset_sec: int = 86400,
     ) -> None:
-        """Delete platform message history records newer than the specified offset."""
+        """Delete platform message history records older than the specified offset."""
         ...
 
     @abc.abstractmethod
@@ -314,51 +313,3 @@ 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."""
-        ...

astrbot/core/db/migration/migra_webchat_session.py

@@ -1,131 +0,0 @@
-"""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_1"
-    )
-    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) != "bot")
-                .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_1", 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_1", True)
-
-    except Exception as e:
-        logger.error(f"迁移过程中发生错误: {e}", exc_info=True)
-        raise

astrbot/core/db/po.py

@@ -3,7 +3,13 @@ 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):
@@ -12,7 +18,7 @@ class PlatformStat(SQLModel, table=True):
     Note: In astrbot v4, we moved `platform` table to here.
     """
 
-    __tablename__ = "platform_stats"  # type: ignore
+    __tablename__ = "platform_stats"
 
     id: int = Field(primary_key=True, sa_column_kwargs={"autoincrement": True})
     timestamp: datetime = Field(nullable=False)
@@ -31,7 +37,7 @@ class PlatformStat(SQLModel, table=True):
 
 
 class ConversationV2(SQLModel, table=True):
-    __tablename__ = "conversations"  # type: ignore
+    __tablename__ = "conversations"
 
     inner_conversation_id: int = Field(
         primary_key=True,
@@ -68,7 +74,7 @@ class Persona(SQLModel, table=True):
     It can be used to customize the behavior of LLMs.
     """
 
-    __tablename__ = "personas"  # type: ignore
+    __tablename__ = "personas"
 
     id: int | None = Field(
         primary_key=True,
@@ -98,7 +104,7 @@ class Persona(SQLModel, table=True):
 class Preference(SQLModel, table=True):
     """This class represents preferences for bots."""
 
-    __tablename__ = "preferences"  # type: ignore
+    __tablename__ = "preferences"
 
     id: int | None = Field(
         default=None,
@@ -134,7 +140,7 @@ class PlatformMessageHistory(SQLModel, table=True):
     or platform-specific messages.
     """
 
-    __tablename__ = "platform_message_history"  # type: ignore
+    __tablename__ = "platform_message_history"
 
     id: int | None = Field(
         primary_key=True,
@@ -155,55 +161,13 @@ 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: str(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"  # type: ignore
+    __tablename__ = "attachments"
 
     inner_attachment_id: int | None = Field(
         primary_key=True,

astrbot/core/db/sqlite.py

@@ -1,7 +1,7 @@
 import asyncio
 import threading
 import typing as T
-from datetime import datetime, timedelta, timezone
+from datetime import datetime, timedelta
 
 from sqlalchemy.ext.asyncio import AsyncSession
 from sqlmodel import col, delete, desc, func, or_, select, text, update
@@ -12,7 +12,6 @@ from astrbot.core.db.po import (
     ConversationV2,
     Persona,
     PlatformMessageHistory,
-    PlatformSession,
     PlatformStat,
     Preference,
     SQLModel,
@@ -413,7 +412,7 @@ class SQLiteDatabase(BaseDatabase):
         user_id,
         offset_sec=86400,
     ):
-        """Delete platform message history records newer than the specified offset."""
+        """Delete platform message history records older than the specified offset."""
         async with self.get_db() as session:
             session: AsyncSession
             async with session.begin():
@@ -423,7 +422,7 @@ 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,
                     ),
                 )
 
@@ -710,101 +709,3 @@ 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,
-                    ),
-                )

astrbot/core/exceptions.py

@@ -1,9 +0,0 @@
-from __future__ import annotations
-
-
-class AstrBotError(Exception):
-    """Base exception for all AstrBot errors."""
-
-
-class ProviderNotFoundError(AstrBotError):
-    """Raised when a specified provider is not found."""

astrbot/core/knowledge_base/kb_helper.py

@@ -1,7 +1,4 @@
-import asyncio
 import json
-import re
-import time
 import uuid
 from pathlib import Path
 
@@ -11,98 +8,12 @@ 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 (
-    Provider as LLMProvider,
-)
+from astrbot.core.provider.provider import EmbeddingProvider, RerankProvider
 
 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:
@@ -189,7 +100,7 @@ class KBHelper:
     async def upload_document(
         self,
         file_name: str,
-        file_content: bytes | None,
+        file_content: bytes,
         file_type: str,
         chunk_size: int = 512,
         chunk_overlap: int = 50,
@@ -197,7 +108,6 @@ 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）
 
@@ -220,63 +130,46 @@ 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:
-            chunks_text = []
+            # 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
             saved_media = []
-
-            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,
+            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,
+            )
             contents = []
             metadatas = []
             for idx, chunk_text in enumerate(chunks_text):
@@ -312,7 +205,7 @@ class KBHelper:
                 kb_id=self.kb.kb_id,
                 doc_name=file_name,
                 file_type=file_type,
-                file_size=file_size,
+                file_size=len(file_content),
                 # file_path=str(file_path),
                 file_path="",
                 chunk_count=len(chunks_text),
@@ -466,177 +359,3 @@ 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)

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 KBDocument, KnowledgeBase
+from .models import KnowledgeBase
 from .retrieval.manager import RetrievalManager, RetrievalResult
 from .retrieval.rank_fusion import RankFusion
 from .retrieval.sparse_retriever import SparseRetriever
@@ -284,47 +284,3 @@ 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,
-        )

astrbot/core/knowledge_base/parsers/url_parser.py

@@ -1,103 +0,0 @@
-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)

astrbot/core/knowledge_base/prompts.py

@@ -1,65 +0,0 @@
-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>
-"""

astrbot/core/message/components.py

@@ -21,20 +21,20 @@ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 SOFTWARE.
 """
 
-import asyncio
 import base64
 import json
 import os
 import uuid
 from enum import Enum
 
+from astrbot_api.abc import IAstrbotPaths
 from pydantic.v1 import BaseModel
 
 from astrbot.core import astrbot_config, file_token_service, logger
-from astrbot.core.utils.astrbot_path import get_astrbot_data_path
 from astrbot.core.utils.io import download_file, download_image_by_url, file_to_base64
+from astrbot_sdk import sync_base_container
 
-
+AstrbotPaths: type[IAstrbotPaths] = sync_base_container.get(type[IAstrbotPaths])
 class ComponentType(str, Enum):
     # Basic Segment Types
     Plain = "Plain"  # plain text message
@@ -153,8 +153,7 @@ class Record(BaseMessageComponent):
         if self.file.startswith("base64://"):
             bs64_data = self.file.removeprefix("base64://")
             image_bytes = base64.b64decode(bs64_data)
-            temp_dir = os.path.join(get_astrbot_data_path(), "temp")
-            file_path = os.path.join(temp_dir, f"{uuid.uuid4()}.jpg")
+            file_path = str(AstrbotPaths.astrbot_root / "temp" / f"{uuid.uuid4()}.jpg")
             with open(file_path, "wb") as f:
                 f.write(image_bytes)
             return os.path.abspath(file_path)
@@ -242,8 +241,9 @@ class Video(BaseMessageComponent):
         if url and url.startswith("file:///"):
             return url[8:]
         if url and url.startswith("http"):
-            download_dir = os.path.join(get_astrbot_data_path(), "temp")
-            video_file_path = os.path.join(download_dir, f"{uuid.uuid4().hex}")
+            video_file_path = str(
+                AstrbotPaths.astrbot_root / "temp" / f"{uuid.uuid4().hex}"
+            )
             await download_file(url, video_file_path)
             if os.path.exists(video_file_path):
                 return os.path.abspath(video_file_path)
@@ -442,8 +442,9 @@ class Image(BaseMessageComponent):
         if url.startswith("base64://"):
             bs64_data = url.removeprefix("base64://")
             image_bytes = base64.b64decode(bs64_data)
-            temp_dir = os.path.join(get_astrbot_data_path(), "temp")
-            image_file_path = os.path.join(temp_dir, f"{uuid.uuid4()}.jpg")
+            image_file_path = str(
+                AstrbotPaths.astrbot_root / "temp" / f"{uuid.uuid4()}.jpg"
+            )
             with open(image_file_path, "wb") as f:
                 f.write(image_bytes)
             return os.path.abspath(image_file_path)
@@ -527,7 +528,7 @@ class Reply(BaseMessageComponent):
 
 
 class Poke(BaseMessageComponent):
-    type: str = ComponentType.Poke
+    type = ComponentType.Poke
     id: int | None = 0
     qq: int | None = 0
 
@@ -654,33 +655,19 @@ class File(BaseMessageComponent):
 
     @property
     def file(self) -> str:
-        """获取文件路径，如果文件不存在但有URL，则同步下载文件
+        """获取本地文件路径（仅返回已存在的文件）
+
+        ⚠️ 警告：此属性不会自动下载文件！
+        - 如果文件已存在，返回绝对路径
+        - 如果只有 URL 没有本地文件，返回空字符串
+        - 需要下载文件请使用 `await get_file()` 方法
 
         Returns:
-            str: 文件路径
+            str: 文件的绝对路径，如果文件不存在则返回空字符串
 
         """
         if self.file_ and os.path.exists(self.file_):
             return os.path.abspath(self.file_)
-
-        if self.url:
-            try:
-                loop = asyncio.get_event_loop()
-                if loop.is_running():
-                    logger.warning(
-                        "不可以在异步上下文中同步等待下载! "
-                        "这个警告通常发生于某些逻辑试图通过 <File>.file 获取文件消息段的文件内容。"
-                        "请使用 await get_file() 代替直接获取 <File>.file 字段",
-                    )
-                    return ""
-                # 等待下载完成
-                loop.run_until_complete(self._download_file())
-
-                if self.file_ and os.path.exists(self.file_):
-                    return os.path.abspath(self.file_)
-            except Exception as e:
-                logger.error(f"文件下载失败: {e}")
-
         return ""
 
     @file.setter
@@ -714,15 +701,18 @@ class File(BaseMessageComponent):
 
         if self.url:
             await self._download_file()
-            return os.path.abspath(self.file_)
+            if self.file_:
+                return os.path.abspath(self.file_)
 
         return ""
 
     async def _download_file(self):
         """下载文件"""
-        download_dir = os.path.join(get_astrbot_data_path(), "temp")
+        if not self.url:
+            raise ValueError("No URL provided for download")
+        download_dir = str(AstrbotPaths.astrbot_root / "temp")
         os.makedirs(download_dir, exist_ok=True)
-        file_path = os.path.join(download_dir, f"{uuid.uuid4().hex}")
+        file_path = str(AstrbotPaths.astrbot_root / "temp" / f"{uuid.uuid4().hex}")
         await download_file(self.url, file_path)
         self.file_ = os.path.abspath(file_path)
 

astrbot/core/pipeline/context.py

@@ -3,7 +3,7 @@ from dataclasses import dataclass
 from astrbot.core.config import AstrBotConfig
 from astrbot.core.star import PluginManager
 
-from .context_utils import call_event_hook, call_handler
+from .context_utils import call_event_hook, call_handler, call_local_llm_tool
 
 
 @dataclass
@@ -15,3 +15,4 @@ class PipelineContext:
     astrbot_config_id: str
     call_handler = call_handler
     call_event_hook = call_event_hook
+    call_local_llm_tool = call_local_llm_tool

astrbot/core/pipeline/context_utils.py

@@ -3,6 +3,8 @@ import traceback
 import typing as T
 
 from astrbot import logger
+from astrbot.core.agent.run_context import ContextWrapper
+from astrbot.core.astr_agent_context import AstrAgentContext
 from astrbot.core.message.message_event_result import CommandResult, MessageEventResult
 from astrbot.core.platform.astr_message_event import AstrMessageEvent
 from astrbot.core.star.star import star_map
@@ -105,3 +107,66 @@ async def call_event_hook(
             return True
 
     return event.is_stopped()
+
+
+async def call_local_llm_tool(
+    context: ContextWrapper[AstrAgentContext],
+    handler: T.Callable[..., T.Awaitable[T.Any]],
+    method_name: str,
+    *args,
+    **kwargs,
+) -> T.AsyncGenerator[T.Any, None]:
+    """执行本地 LLM 工具的处理函数并处理其返回结果"""
+    ready_to_call = None  # 一个协程或者异步生成器
+
+    trace_ = None
+
+    event = context.context.event
+
+    try:
+        if method_name == "run" or method_name == "decorator_handler":
+            ready_to_call = handler(event, *args, **kwargs)
+        elif method_name == "call":
+            ready_to_call = handler(context, *args, **kwargs)
+        else:
+            raise ValueError(f"未知的方法名: {method_name}")
+    except ValueError as e:
+        logger.error(f"调用本地 LLM 工具时出错: {e}", exc_info=True)
+    except TypeError:
+        logger.error("处理函数参数不匹配，请检查 handler 的定义。", exc_info=True)
+    except Exception as e:
+        trace_ = traceback.format_exc()
+        logger.error(f"调用本地 LLM 工具时出错: {e}\n{trace_}")
+
+    if not ready_to_call:
+        return
+
+    if inspect.isasyncgen(ready_to_call):
+        _has_yielded = False
+        try:
+            async for ret in ready_to_call:
+                # 这里逐步执行异步生成器, 对于每个 yield 返回的 ret, 执行下面的代码
+                # 返回值只能是 MessageEventResult 或者 None（无返回值）
+                _has_yielded = True
+                if isinstance(ret, (MessageEventResult, CommandResult)):
+                    # 如果返回值是 MessageEventResult, 设置结果并继续
+                    event.set_result(ret)
+                    yield
+                else:
+                    # 如果返回值是 None, 则不设置结果并继续
+                    # 继续执行后续阶段
+                    yield ret
+            if not _has_yielded:
+                # 如果这个异步生成器没有执行到 yield 分支
+                yield
+        except Exception as e:
+            logger.error(f"Previous Error: {trace_}")
+            raise e
+    elif inspect.iscoroutine(ready_to_call):
+        # 如果只是一个协程, 直接执行
+        ret = await ready_to_call
+        if isinstance(ret, (MessageEventResult, CommandResult)):
+            event.set_result(ret)
+            yield
+        else:
+            yield ret

astrbot/core/pipeline/process_stage/method/agent_request.py

@@ -1,48 +0,0 @@
-from collections.abc import AsyncGenerator
-
-from astrbot.core import logger
-from astrbot.core.platform.astr_message_event import AstrMessageEvent
-from astrbot.core.star.session_llm_manager import SessionServiceManager
-
-from ...context import PipelineContext
-from ..stage import Stage
-from .agent_sub_stages.internal import InternalAgentSubStage
-from .agent_sub_stages.third_party import ThirdPartyAgentSubStage
-
-
-class AgentRequestSubStage(Stage):
-    async def initialize(self, ctx: PipelineContext) -> None:
-        self.ctx = ctx
-        self.config = ctx.astrbot_config
-
-        self.bot_wake_prefixs: list[str] = self.config["wake_prefix"]
-        self.prov_wake_prefix: str = self.config["provider_settings"]["wake_prefix"]
-        for bwp in self.bot_wake_prefixs:
-            if self.prov_wake_prefix.startswith(bwp):
-                logger.info(
-                    f"识别 LLM 聊天额外唤醒前缀 {self.prov_wake_prefix} 以机器人唤醒前缀 {bwp} 开头，已自动去除。",
-                )
-                self.prov_wake_prefix = self.prov_wake_prefix[len(bwp) :]
-
-        agent_runner_type = self.config["provider_settings"]["agent_runner_type"]
-        if agent_runner_type == "local":
-            self.agent_sub_stage = InternalAgentSubStage()
-        else:
-            self.agent_sub_stage = ThirdPartyAgentSubStage()
-        await self.agent_sub_stage.initialize(ctx)
-
-    async def process(self, event: AstrMessageEvent) -> AsyncGenerator[None, None]:
-        if not self.ctx.astrbot_config["provider_settings"]["enable"]:
-            logger.debug(
-                "This pipeline does not enable AI capability, skip processing."
-            )
-            return
-
-        if not SessionServiceManager.should_process_llm_request(event):
-            logger.debug(
-                f"The session {event.unified_msg_origin} has disabled AI capability, skipping processing."
-            )
-            return
-
-        async for resp in self.agent_sub_stage.process(event, self.prov_wake_prefix):
-            yield resp

astrbot/core/pipeline/process_stage/method/agent_sub_stages/internal.py

@@ -1,464 +0,0 @@
-"""本地 Agent 模式的 LLM 调用 Stage"""
-
-import asyncio
-import copy
-import json
-from collections.abc import AsyncGenerator
-
-from astrbot.core import logger
-from astrbot.core.agent.tool import ToolSet
-from astrbot.core.astr_agent_context import AstrAgentContext
-from astrbot.core.conversation_mgr import Conversation
-from astrbot.core.message.components import Image
-from astrbot.core.message.message_event_result import (
-    MessageChain,
-    MessageEventResult,
-    ResultContentType,
-)
-from astrbot.core.platform.astr_message_event import AstrMessageEvent
-from astrbot.core.provider import Provider
-from astrbot.core.provider.entities import (
-    LLMResponse,
-    ProviderRequest,
-)
-from astrbot.core.star.star_handler import EventType, star_map
-from astrbot.core.utils.metrics import Metric
-from astrbot.core.utils.session_lock import session_lock_manager
-
-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 ....context import PipelineContext, call_event_hook
-from ...stage import Stage
-from ...utils import KNOWLEDGE_BASE_QUERY_TOOL, retrieve_knowledge_base
-
-
-class InternalAgentSubStage(Stage):
-    async def initialize(self, ctx: PipelineContext) -> None:
-        self.ctx = ctx
-        conf = ctx.astrbot_config
-        settings = conf["provider_settings"]
-        self.max_context_length = settings["max_context_length"]  # int
-        self.dequeue_context_length: int = min(
-            max(1, settings["dequeue_context_length"]),
-            self.max_context_length - 1,
-        )
-        self.streaming_response: bool = settings["streaming_response"]
-        self.unsupported_streaming_strategy: str = settings[
-            "unsupported_streaming_strategy"
-        ]
-        self.max_step: int = settings.get("max_agent_step", 30)
-        self.tool_call_timeout: int = settings.get("tool_call_timeout", 60)
-        if isinstance(self.max_step, bool):  # workaround: #2622
-            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)
-
-        self.conv_manager = ctx.plugin_manager.context.conversation_manager
-
-    def _select_provider(self, event: AstrMessageEvent):
-        """选择使用的 LLM 提供商"""
-        sel_provider = event.get_extra("selected_provider")
-        _ctx = self.ctx.plugin_manager.context
-        if sel_provider and isinstance(sel_provider, str):
-            provider = _ctx.get_provider_by_id(sel_provider)
-            if not provider:
-                logger.error(f"未找到指定的提供商: {sel_provider}。")
-            return provider
-
-        return _ctx.get_using_provider(umo=event.unified_msg_origin)
-
-    async def _get_session_conv(self, event: AstrMessageEvent) -> Conversation:
-        umo = event.unified_msg_origin
-        conv_mgr = self.conv_manager
-
-        # 获取对话上下文
-        cid = await conv_mgr.get_curr_conversation_id(umo)
-        if not cid:
-            cid = await conv_mgr.new_conversation(umo, event.get_platform_id())
-        conversation = await conv_mgr.get_conversation(umo, cid)
-        if not conversation:
-            cid = await conv_mgr.new_conversation(umo, event.get_platform_id())
-            conversation = await conv_mgr.get_conversation(umo, cid)
-        if not conversation:
-            raise RuntimeError("无法创建新的对话。")
-        return conversation
-
-    async def _apply_kb(
-        self,
-        event: AstrMessageEvent,
-        req: ProviderRequest,
-    ):
-        """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)
-
-    def _truncate_contexts(
-        self,
-        contexts: list[dict],
-    ) -> list[dict]:
-        """截断上下文列表，确保不超过最大长度"""
-        if self.max_context_length == -1:
-            return contexts
-
-        if len(contexts) // 2 <= self.max_context_length:
-            return contexts
-
-        truncated_contexts = contexts[
-            -(self.max_context_length - self.dequeue_context_length + 1) * 2 :
-        ]
-        # 找到第一个role 为 user 的索引，确保上下文格式正确
-        index = next(
-            (
-                i
-                for i, item in enumerate(truncated_contexts)
-                if item.get("role") == "user"
-            ),
-            None,
-        )
-        if index is not None and index > 0:
-            truncated_contexts = truncated_contexts[index:]
-
-        return truncated_contexts
-
-    def _modalities_fix(
-        self,
-        provider: Provider,
-        req: ProviderRequest,
-    ):
-        """检查提供商的模态能力，清理请求中的不支持内容"""
-        if req.image_urls:
-            provider_cfg = provider.provider_config.get("modalities", ["image"])
-            if "image" not in provider_cfg:
-                logger.debug(f"用户设置提供商 {provider} 不支持图像，清空图像列表。")
-                req.image_urls = []
-        if req.func_tool:
-            provider_cfg = provider.provider_config.get("modalities", ["tool_use"])
-            # 如果模型不支持工具使用，但请求中包含工具列表，则清空。
-            if "tool_use" not in provider_cfg:
-                logger.debug(
-                    f"用户设置提供商 {provider} 不支持工具使用，清空工具列表。",
-                )
-                req.func_tool = None
-
-    def _plugin_tool_fix(
-        self,
-        event: AstrMessageEvent,
-        req: ProviderRequest,
-    ):
-        """根据事件中的插件设置，过滤请求中的工具列表"""
-        if event.plugins_name is not None and req.func_tool:
-            new_tool_set = ToolSet()
-            for tool in req.func_tool.tools:
-                mp = tool.handler_module_path
-                if not mp:
-                    continue
-                plugin = star_map.get(mp)
-                if not plugin:
-                    continue
-                if plugin.name in event.plugins_name or plugin.reserved:
-                    new_tool_set.add_tool(tool)
-            req.func_tool = new_tool_set
-
-    async def _handle_webchat(
-        self,
-        event: AstrMessageEvent,
-        req: ProviderRequest,
-        prov: Provider,
-    ):
-        """处理 WebChat 平台的特殊情况，包括第一次 LLM 对话时总结对话内容生成 title"""
-        if not req.conversation:
-            return
-        conversation = await self.conv_manager.get_conversation(
-            event.unified_msg_origin,
-            req.conversation.cid,
-        )
-        if conversation and not req.conversation.title:
-            messages = json.loads(conversation.history)
-            latest_pair = messages[-2:]
-            if not latest_pair:
-                return
-            content = latest_pair[0].get("content", "")
-            if isinstance(content, list):
-                # 多模态
-                text_parts = []
-                for item in content:
-                    if isinstance(item, dict):
-                        if item.get("type") == "text":
-                            text_parts.append(item.get("text", ""))
-                        elif item.get("type") == "image":
-                            text_parts.append("[图片]")
-                    elif isinstance(item, str):
-                        text_parts.append(item)
-                cleaned_text = "User: " + " ".join(text_parts).strip()
-            elif isinstance(content, str):
-                cleaned_text = "User: " + content.strip()
-            else:
-                return
-            logger.debug(f"WebChat 对话标题生成请求，清理后的文本: {cleaned_text}")
-            llm_resp = await prov.text_chat(
-                system_prompt="You are expert in summarizing user's query.",
-                prompt=(
-                    f"Please summarize the following query of user:\n"
-                    f"{cleaned_text}\n"
-                    "Only output the summary within 10 words, DO NOT INCLUDE any other text."
-                    "You must use the same language as the user."
-                    "If you think the dialog is too short to summarize, only output a special mark: `<None>`"
-                ),
-            )
-            if llm_resp and llm_resp.completion_text:
-                title = llm_resp.completion_text.strip()
-                if not title or "<None>" in title:
-                    return
-                await self.conv_manager.update_conversation_title(
-                    unified_msg_origin=event.unified_msg_origin,
-                    title=title,
-                    conversation_id=req.conversation.cid,
-                )
-
-    async def _save_to_history(
-        self,
-        event: AstrMessageEvent,
-        req: ProviderRequest,
-        llm_response: LLMResponse | None,
-    ):
-        if (
-            not req
-            or not req.conversation
-            or not llm_response
-            or llm_response.role != "assistant"
-        ):
-            return
-
-        if not llm_response.completion_text and not req.tool_calls_result:
-            logger.debug("LLM 响应为空，不保存记录。")
-            return
-
-        if req.contexts is None:
-            req.contexts = []
-
-        # 历史上下文
-        messages = copy.deepcopy(req.contexts)
-        # 这一轮对话请求的用户输入
-        messages.append(await req.assemble_context())
-        # 这一轮对话的 LLM 响应
-        if req.tool_calls_result:
-            if not isinstance(req.tool_calls_result, list):
-                messages.extend(req.tool_calls_result.to_openai_messages())
-            elif isinstance(req.tool_calls_result, list):
-                for tcr in req.tool_calls_result:
-                    messages.extend(tcr.to_openai_messages())
-        messages.append({"role": "assistant", "content": llm_response.completion_text})
-        messages = list(filter(lambda item: "_no_save" not in item, messages))
-        await self.conv_manager.update_conversation(
-            event.unified_msg_origin,
-            req.conversation.cid,
-            history=messages,
-        )
-
-    def _fix_messages(self, messages: list[dict]) -> list[dict]:
-        """验证并且修复上下文"""
-        fixed_messages = []
-        for message in messages:
-            if message.get("role") == "tool":
-                # tool block 前面必须要有 user 和 assistant block
-                if len(fixed_messages) < 2:
-                    # 这种情况可能是上下文被截断导致的
-                    # 我们直接将之前的上下文都清空
-                    fixed_messages = []
-                else:
-                    fixed_messages.append(message)
-            else:
-                fixed_messages.append(message)
-        return fixed_messages
-
-    async def process(
-        self, event: AstrMessageEvent, provider_wake_prefix: str
-    ) -> AsyncGenerator[None, None]:
-        req: ProviderRequest | None = None
-
-        provider = self._select_provider(event)
-        if provider is None:
-            return
-        if not isinstance(provider, Provider):
-            logger.error(f"选择的提供商类型无效({type(provider)})，跳过 LLM 请求处理。")
-            return
-
-        streaming_response = self.streaming_response
-        if (enable_streaming := event.get_extra("enable_streaming")) is not None:
-            streaming_response = bool(enable_streaming)
-
-        logger.debug("ready to request llm provider")
-        async with session_lock_manager.acquire_lock(event.unified_msg_origin):
-            logger.debug("acquired session lock for llm request")
-            if event.get_extra("provider_request"):
-                req = event.get_extra("provider_request")
-                assert isinstance(req, ProviderRequest), (
-                    "provider_request 必须是 ProviderRequest 类型。"
-                )
-
-                if req.conversation:
-                    req.contexts = json.loads(req.conversation.history)
-
-            else:
-                req = ProviderRequest()
-                req.prompt = ""
-                req.image_urls = []
-                if sel_model := event.get_extra("selected_model"):
-                    req.model = sel_model
-                if provider_wake_prefix and not event.message_str.startswith(
-                    provider_wake_prefix
-                ):
-                    return
-
-                req.prompt = event.message_str[len(provider_wake_prefix) :]
-                # func_tool selection 现在已经转移到 packages/astrbot 插件中进行选择。
-                # req.func_tool = self.ctx.plugin_manager.context.get_llm_tool_manager()
-                for comp in event.message_obj.message:
-                    if isinstance(comp, Image):
-                        image_path = await comp.convert_to_file_path()
-                        req.image_urls.append(image_path)
-
-                conversation = await self._get_session_conv(event)
-                req.conversation = conversation
-                req.contexts = json.loads(conversation.history)
-
-                event.set_extra("provider_request", req)
-
-            if not req.prompt and not req.image_urls:
-                return
-
-            # call event hook
-            if await call_event_hook(event, EventType.OnLLMRequestEvent, req):
-                return
-
-            # apply knowledge base feature
-            await self._apply_kb(event, req)
-
-            # fix contexts json str
-            if isinstance(req.contexts, str):
-                req.contexts = json.loads(req.contexts)
-
-            # truncate contexts to fit max length
-            if req.contexts:
-                req.contexts = self._truncate_contexts(req.contexts)
-                self._fix_messages(req.contexts)
-
-            # session_id
-            if not req.session_id:
-                req.session_id = event.unified_msg_origin
-
-            # check provider modalities, if provider does not support image/tool_use, clear them in request.
-            self._modalities_fix(provider, req)
-
-            # filter tools, only keep tools from this pipeline's selected plugins
-            self._plugin_tool_fix(event, req)
-
-            stream_to_general = (
-                self.unsupported_streaming_strategy == "turn_off"
-                and not event.platform_meta.support_streaming_message
-            )
-            # 备份 req.contexts
-            backup_contexts = copy.deepcopy(req.contexts)
-
-            # run agent
-            agent_runner = AgentRunner()
-            logger.debug(
-                f"handle provider[id: {provider.provider_config['id']}] request: {req}",
-            )
-            astr_agent_ctx = AstrAgentContext(
-                context=self.ctx.plugin_manager.context,
-                event=event,
-            )
-            await agent_runner.reset(
-                provider=provider,
-                request=req,
-                run_context=AgentContextWrapper(
-                    context=astr_agent_ctx,
-                    tool_call_timeout=self.tool_call_timeout,
-                ),
-                tool_executor=FunctionToolExecutor(),
-                agent_hooks=MAIN_AGENT_HOOKS,
-                streaming=streaming_response,
-            )
-
-            if streaming_response and not stream_to_general:
-                # 流式响应
-                event.set_result(
-                    MessageEventResult()
-                    .set_result_content_type(ResultContentType.STREAMING_RESULT)
-                    .set_async_stream(
-                        run_agent(
-                            agent_runner,
-                            self.max_step,
-                            self.show_tool_use,
-                            show_reasoning=self.show_reasoning,
-                        ),
-                    ),
-                )
-                yield
-                if agent_runner.done():
-                    if final_llm_resp := agent_runner.get_final_llm_resp():
-                        if final_llm_resp.completion_text:
-                            chain = (
-                                MessageChain()
-                                .message(final_llm_resp.completion_text)
-                                .chain
-                            )
-                        elif final_llm_resp.result_chain:
-                            chain = final_llm_resp.result_chain.chain
-                        else:
-                            chain = MessageChain().chain
-                        event.set_result(
-                            MessageEventResult(
-                                chain=chain,
-                                result_content_type=ResultContentType.STREAMING_FINISH,
-                            ),
-                        )
-            else:
-                async for _ in run_agent(
-                    agent_runner,
-                    self.max_step,
-                    self.show_tool_use,
-                    stream_to_general,
-                    show_reasoning=self.show_reasoning,
-                ):
-                    yield
-
-            # 恢复备份的 contexts
-            req.contexts = backup_contexts
-
-            await self._save_to_history(event, req, agent_runner.get_final_llm_resp())
-
-        # 异步处理 WebChat 特殊情况
-        if event.get_platform_name() == "webchat":
-            asyncio.create_task(self._handle_webchat(event, req, provider))
-
-        asyncio.create_task(
-            Metric.upload(
-                llm_tick=1,
-                model_name=agent_runner.provider.get_model(),
-                provider_type=agent_runner.provider.meta().type,
-            ),
-        )

astrbot/core/pipeline/process_stage/method/agent_sub_stages/third_party.py

@@ -1,205 +0,0 @@
-import asyncio
-from collections.abc import AsyncGenerator
-from typing import TYPE_CHECKING
-
-from astrbot.core import astrbot_config, logger
-from astrbot.core.agent.runners.coze.coze_agent_runner import CozeAgentRunner
-from astrbot.core.agent.runners.dashscope.dashscope_agent_runner import (
-    DashscopeAgentRunner,
-)
-from astrbot.core.agent.runners.dify.dify_agent_runner import DifyAgentRunner
-from astrbot.core.message.components import Image
-from astrbot.core.message.message_event_result import (
-    MessageChain,
-    MessageEventResult,
-    ResultContentType,
-)
-
-if TYPE_CHECKING:
-    from astrbot.core.agent.runners.base import BaseAgentRunner
-from astrbot.core.platform.astr_message_event import AstrMessageEvent
-from astrbot.core.provider.entities import (
-    ProviderRequest,
-)
-from astrbot.core.star.star_handler import EventType
-from astrbot.core.utils.metrics import Metric
-
-from .....astr_agent_context import AgentContextWrapper, AstrAgentContext
-from .....astr_agent_hooks import MAIN_AGENT_HOOKS
-from ....context import PipelineContext, call_event_hook
-from ...stage import Stage
-
-AGENT_RUNNER_TYPE_KEY = {
-    "dify": "dify_agent_runner_provider_id",
-    "coze": "coze_agent_runner_provider_id",
-    "dashscope": "dashscope_agent_runner_provider_id",
-}
-
-
-async def run_third_party_agent(
-    runner: "BaseAgentRunner",
-    stream_to_general: bool = False,
-) -> AsyncGenerator[MessageChain | None, None]:
-    """
-    运行第三方 agent runner 并转换响应格式
-    类似于 run_agent 函数，但专门处理第三方 agent runner
-    """
-    try:
-        async for resp in runner.step_until_done(max_step=30):  # type: ignore[misc]
-            if resp.type == "streaming_delta":
-                if stream_to_general:
-                    continue
-                yield resp.data["chain"]
-            elif resp.type == "llm_result":
-                if stream_to_general:
-                    yield resp.data["chain"]
-    except Exception as e:
-        logger.error(f"Third party agent runner error: {e}")
-        err_msg = (
-            f"\nAstrBot 请求失败。\n错误类型: {type(e).__name__}\n"
-            f"错误信息: {e!s}\n\n请在控制台查看和分享错误详情。\n"
-        )
-        yield MessageChain().message(err_msg)
-
-
-class ThirdPartyAgentSubStage(Stage):
-    async def initialize(self, ctx: PipelineContext) -> None:
-        self.ctx = ctx
-        self.conf = ctx.astrbot_config
-        self.runner_type = self.conf["provider_settings"]["agent_runner_type"]
-        self.prov_id = self.conf["provider_settings"].get(
-            AGENT_RUNNER_TYPE_KEY.get(self.runner_type, ""),
-            "",
-        )
-        settings = ctx.astrbot_config["provider_settings"]
-        self.streaming_response: bool = settings["streaming_response"]
-        self.unsupported_streaming_strategy: str = settings[
-            "unsupported_streaming_strategy"
-        ]
-
-    async def process(
-        self, event: AstrMessageEvent, provider_wake_prefix: str
-    ) -> AsyncGenerator[None, None]:
-        req: ProviderRequest | None = None
-
-        if provider_wake_prefix and not event.message_str.startswith(
-            provider_wake_prefix
-        ):
-            return
-
-        self.prov_cfg: dict = next(
-            (p for p in astrbot_config["provider"] if p["id"] == self.prov_id),
-            {},
-        )
-        if not self.prov_id:
-            logger.error("没有填写 Agent Runner 提供商 ID，请前往配置页面配置。")
-            return
-        if not self.prov_cfg:
-            logger.error(
-                f"Agent Runner 提供商 {self.prov_id} 配置不存在，请前往配置页面修改配置。"
-            )
-            return
-
-        # make provider request
-        req = ProviderRequest()
-        req.session_id = event.unified_msg_origin
-        req.prompt = event.message_str[len(provider_wake_prefix) :]
-        for comp in event.message_obj.message:
-            if isinstance(comp, Image):
-                image_path = await comp.convert_to_base64()
-                req.image_urls.append(image_path)
-
-        if not req.prompt and not req.image_urls:
-            return
-
-        # call event hook
-        if await call_event_hook(event, EventType.OnLLMRequestEvent, req):
-            return
-
-        if self.runner_type == "dify":
-            runner = DifyAgentRunner[AstrAgentContext]()
-        elif self.runner_type == "coze":
-            runner = CozeAgentRunner[AstrAgentContext]()
-        elif self.runner_type == "dashscope":
-            runner = DashscopeAgentRunner[AstrAgentContext]()
-        else:
-            raise ValueError(
-                f"Unsupported third party agent runner type: {self.runner_type}",
-            )
-
-        astr_agent_ctx = AstrAgentContext(
-            context=self.ctx.plugin_manager.context,
-            event=event,
-        )
-
-        streaming_response = self.streaming_response
-        if (enable_streaming := event.get_extra("enable_streaming")) is not None:
-            streaming_response = bool(enable_streaming)
-
-        stream_to_general = (
-            self.unsupported_streaming_strategy == "turn_off"
-            and not event.platform_meta.support_streaming_message
-        )
-
-        await runner.reset(
-            request=req,
-            run_context=AgentContextWrapper(
-                context=astr_agent_ctx,
-                tool_call_timeout=60,
-            ),
-            agent_hooks=MAIN_AGENT_HOOKS,
-            provider_config=self.prov_cfg,
-            streaming=streaming_response,
-        )
-
-        if streaming_response and not stream_to_general:
-            # 流式响应
-            event.set_result(
-                MessageEventResult()
-                .set_result_content_type(ResultContentType.STREAMING_RESULT)
-                .set_async_stream(
-                    run_third_party_agent(
-                        runner,
-                        stream_to_general=False,
-                    ),
-                ),
-            )
-            yield
-            if runner.done():
-                final_resp = runner.get_final_llm_resp()
-                if final_resp and final_resp.result_chain:
-                    event.set_result(
-                        MessageEventResult(
-                            chain=final_resp.result_chain.chain or [],
-                            result_content_type=ResultContentType.STREAMING_FINISH,
-                        ),
-                    )
-        else:
-            # 非流式响应或转换为普通响应
-            async for _ in run_third_party_agent(
-                runner,
-                stream_to_general=stream_to_general,
-            ):
-                yield
-
-            final_resp = runner.get_final_llm_resp()
-
-            if not final_resp or not final_resp.result_chain:
-                logger.warning("Agent Runner 未返回最终结果。")
-                return
-
-            event.set_result(
-                MessageEventResult(
-                    chain=final_resp.result_chain.chain or [],
-                    result_content_type=ResultContentType.LLM_RESULT,
-                ),
-            )
-            yield
-
-        asyncio.create_task(
-            Metric.upload(
-                llm_tick=1,
-                model_name=self.runner_type,
-                provider_type=self.runner_type,
-            ),
-        )

astrbot/core/pipeline/process_stage/method/llm_request.py

@@ -0,0 +1,723 @@
+"""本地 Agent 模式的 LLM 调用 Stage"""
+
+import asyncio
+import copy
+import json
+import traceback
+from collections.abc import AsyncGenerator
+from typing import Any
+
+from mcp.types import CallToolResult
+
+from astrbot.core import logger
+from astrbot.core.agent.handoff import HandoffTool
+from astrbot.core.agent.hooks import BaseAgentRunHooks
+from astrbot.core.agent.mcp_client import MCPTool
+from astrbot.core.agent.run_context import ContextWrapper
+from astrbot.core.agent.runners.tool_loop_agent_runner import ToolLoopAgentRunner
+from astrbot.core.agent.tool import FunctionTool, ToolSet
+from astrbot.core.agent.tool_executor import BaseFunctionToolExecutor
+from astrbot.core.astr_agent_context import AstrAgentContext
+from astrbot.core.conversation_mgr import Conversation
+from astrbot.core.message.components import Image
+from astrbot.core.message.message_event_result import (
+    MessageChain,
+    MessageEventResult,
+    ResultContentType,
+)
+from astrbot.core.platform.astr_message_event import AstrMessageEvent
+from astrbot.core.provider import Provider
+from astrbot.core.provider.entities import (
+    LLMResponse,
+    ProviderRequest,
+)
+from astrbot.core.provider.register import llm_tools
+from astrbot.core.star.session_llm_manager import SessionServiceManager
+from astrbot.core.star.star_handler import EventType, star_map
+from astrbot.core.utils.metrics import Metric
+
+from ...context import PipelineContext, call_event_hook, call_local_llm_tool
+from ..stage import Stage
+from ..utils import inject_kb_context
+
+try:
+    import mcp
+except (ModuleNotFoundError, ImportError):
+    logger.warning("警告: 缺少依赖库 'mcp'，将无法使用 MCP 服务。")
+
+
+AgentContextWrapper = ContextWrapper[AstrAgentContext]
+AgentRunner = ToolLoopAgentRunner[AstrAgentContext]
+
+
+class FunctionToolExecutor(BaseFunctionToolExecutor[AstrAgentContext]):
+    @classmethod
+    async def execute(cls, tool, run_context, **tool_args):
+        """执行函数调用。
+
+        Args:
+            event (AstrMessageEvent): 事件对象, 当 origin 为 local 时必须提供。
+            **kwargs: 函数调用的参数。
+
+        Returns:
+            AsyncGenerator[None | mcp.types.CallToolResult, None]
+
+        """
+        if isinstance(tool, HandoffTool):
+            async for r in cls._execute_handoff(tool, run_context, **tool_args):
+                yield r
+            return
+
+        elif isinstance(tool, MCPTool):
+            async for r in cls._execute_mcp(tool, run_context, **tool_args):
+                yield r
+            return
+
+        else:
+            async for r in cls._execute_local(tool, run_context, **tool_args):
+                yield r
+            return
+
+    @classmethod
+    async def _execute_handoff(
+        cls,
+        tool: HandoffTool,
+        run_context: ContextWrapper[AstrAgentContext],
+        **tool_args,
+    ):
+        input_ = tool_args.get("input", "agent")
+        agent_runner = AgentRunner()
+
+        # make toolset for the agent
+        tools = tool.agent.tools
+        if tools:
+            toolset = ToolSet()
+            for t in tools:
+                if isinstance(t, str):
+                    _t = llm_tools.get_func(t)
+                    if _t:
+                        toolset.add_tool(_t)
+                elif isinstance(t, FunctionTool):
+                    toolset.add_tool(t)
+        else:
+            toolset = None
+
+        request = ProviderRequest(
+            prompt=input_,
+            system_prompt=tool.description or "",
+            image_urls=[],  # 暂时不传递原始 agent 的上下文
+            contexts=[],  # 暂时不传递原始 agent 的上下文
+            func_tool=toolset,
+        )
+        astr_agent_ctx = AstrAgentContext(
+            provider=run_context.context.provider,
+            first_provider_request=run_context.context.first_provider_request,
+            curr_provider_request=request,
+            streaming=run_context.context.streaming,
+            event=run_context.context.event,
+        )
+
+        event = run_context.context.event
+
+        logger.debug(f"正在将任务委托给 Agent: {tool.agent.name}, input: {input_}")
+        await event.send(
+            MessageChain().message("✨ 正在将任务委托给 Agent: " + tool.agent.name),
+        )
+
+        await agent_runner.reset(
+            provider=run_context.context.provider,
+            request=request,
+            run_context=AgentContextWrapper(
+                context=astr_agent_ctx,
+                tool_call_timeout=run_context.tool_call_timeout,
+            ),
+            tool_executor=FunctionToolExecutor(),
+            agent_hooks=tool.agent.run_hooks or BaseAgentRunHooks[AstrAgentContext](),
+            streaming=run_context.context.streaming,
+        )
+
+        async for _ in run_agent(agent_runner, 15, True):
+            pass
+
+        if agent_runner.done():
+            llm_response = agent_runner.get_final_llm_resp()
+
+            if not llm_response:
+                text_content = mcp.types.TextContent(
+                    type="text",
+                    text=f"error when deligate task to {tool.agent.name}",
+                )
+                yield mcp.types.CallToolResult(content=[text_content])
+                return
+
+            logger.debug(
+                f"Agent  {tool.agent.name} 任务完成, response: {llm_response.completion_text}",
+            )
+
+            result = (
+                f"Agent {tool.agent.name} respond with: {llm_response.completion_text}\n\n"
+                "Note: If the result is error or need user provide more information, please provide more information to the agent(you can ask user for more information first)."
+            )
+
+            text_content = mcp.types.TextContent(
+                type="text",
+                text=result,
+            )
+            yield mcp.types.CallToolResult(content=[text_content])
+        else:
+            text_content = mcp.types.TextContent(
+                type="text",
+                text=f"error when deligate task to {tool.agent.name}",
+            )
+            yield mcp.types.CallToolResult(content=[text_content])
+        return
+
+    @classmethod
+    async def _execute_local(
+        cls,
+        tool: FunctionTool,
+        run_context: ContextWrapper[AstrAgentContext],
+        **tool_args,
+    ):
+        event = run_context.context.event
+        if not event:
+            raise ValueError("Event must be provided for local function tools.")
+
+        is_override_call = False
+        for ty in type(tool).mro():
+            if "call" in ty.__dict__ and ty.__dict__["call"] is not FunctionTool.call:
+                logger.debug(f"Found call in: {ty}")
+                is_override_call = True
+                break
+
+        # 检查 tool 下有没有 run 方法
+        if not tool.handler and not hasattr(tool, "run") and not is_override_call:
+            raise ValueError("Tool must have a valid handler or override 'run' method.")
+
+        awaitable = None
+        method_name = ""
+        if tool.handler:
+            awaitable = tool.handler
+            method_name = "decorator_handler"
+        elif is_override_call:
+            awaitable = tool.call
+            method_name = "call"
+        elif hasattr(tool, "run"):
+            awaitable = getattr(tool, "run")
+            method_name = "run"
+        if awaitable is None:
+            raise ValueError("Tool must have a valid handler or override 'run' method.")
+
+        wrapper = call_local_llm_tool(
+            context=run_context,
+            handler=awaitable,
+            method_name=method_name,
+            **tool_args,
+        )
+        while True:
+            try:
+                resp = await asyncio.wait_for(
+                    anext(wrapper),
+                    timeout=run_context.tool_call_timeout,
+                )
+                if resp is not None:
+                    if isinstance(resp, mcp.types.CallToolResult):
+                        yield resp
+                    else:
+                        text_content = mcp.types.TextContent(
+                            type="text",
+                            text=str(resp),
+                        )
+                        yield mcp.types.CallToolResult(content=[text_content])
+                else:
+                    # NOTE: Tool 在这里直接请求发送消息给用户
+                    # TODO: 是否需要判断 event.get_result() 是否为空?
+                    # 如果为空,则说明没有发送消息给用户,并且返回值为空,将返回一个特殊的 TextContent,其内容如"工具没有返回内容"
+                    if res := run_context.context.event.get_result():
+                        if res.chain:
+                            try:
+                                await event.send(
+                                    MessageChain(
+                                        chain=res.chain,
+                                        type="tool_direct_result",
+                                    )
+                                )
+                            except Exception as e:
+                                logger.error(
+                                    f"Tool 直接发送消息失败: {e}",
+                                    exc_info=True,
+                                )
+                    yield None
+            except asyncio.TimeoutError:
+                raise Exception(
+                    f"tool {tool.name} execution timeout after {run_context.tool_call_timeout} seconds.",
+                )
+            except StopAsyncIteration:
+                break
+
+    @classmethod
+    async def _execute_mcp(
+        cls,
+        tool: FunctionTool,
+        run_context: ContextWrapper[AstrAgentContext],
+        **tool_args,
+    ):
+        res = await tool.call(run_context, **tool_args)
+        if not res:
+            return
+        yield res
+
+
+class MainAgentHooks(BaseAgentRunHooks[AstrAgentContext]):
+    async def on_agent_done(self, run_context, llm_response):
+        # 执行事件钩子
+        await call_event_hook(
+            run_context.context.event,
+            EventType.OnLLMResponseEvent,
+            llm_response,
+        )
+
+    async def on_tool_end(
+        self,
+        run_context: ContextWrapper[AstrAgentContext],
+        tool: FunctionTool[Any],
+        tool_args: dict | None,
+        tool_result: CallToolResult | None,
+    ):
+        run_context.context.event.clear_result()
+
+
+MAIN_AGENT_HOOKS = MainAgentHooks()
+
+
+async def run_agent(
+    agent_runner: AgentRunner,
+    max_step: int = 30,
+    show_tool_use: bool = True,
+) -> AsyncGenerator[MessageChain, None]:
+    step_idx = 0
+    astr_event = agent_runner.run_context.context.event
+    while step_idx < max_step:
+        step_idx += 1
+        try:
+            async for resp in agent_runner.step():
+                if astr_event.is_stopped():
+                    return
+                if resp.type == "tool_call_result":
+                    msg_chain = resp.data["chain"]
+                    if msg_chain.type == "tool_direct_result":
+                        # tool_direct_result 用于标记 llm tool 需要直接发送给用户的内容
+                        resp.data["chain"].type = "tool_call_result"
+                        await astr_event.send(resp.data["chain"])
+                        continue
+                    # 对于其他情况，暂时先不处理
+                    continue
+                elif resp.type == "tool_call":
+                    if agent_runner.streaming:
+                        # 用来标记流式响应需要分节
+                        yield MessageChain(chain=[], type="break")
+                    if show_tool_use or astr_event.get_platform_name() == "webchat":
+                        resp.data["chain"].type = "tool_call"
+                        await astr_event.send(resp.data["chain"])
+                    continue
+
+                if not agent_runner.streaming:
+                    content_typ = (
+                        ResultContentType.LLM_RESULT
+                        if resp.type == "llm_result"
+                        else ResultContentType.GENERAL_RESULT
+                    )
+                    astr_event.set_result(
+                        MessageEventResult(
+                            chain=resp.data["chain"].chain,
+                            result_content_type=content_typ,
+                        ),
+                    )
+                    yield
+                    astr_event.clear_result()
+                elif resp.type == "streaming_delta":
+                    yield resp.data["chain"]  # MessageChain
+            if agent_runner.done():
+                break
+
+        except Exception as e:
+            logger.error(traceback.format_exc())
+            err_msg = f"\n\nAstrBot 请求失败。\n错误类型: {type(e).__name__}\n错误信息: {e!s}\n\n请在控制台查看和分享错误详情。\n"
+            if agent_runner.streaming:
+                yield MessageChain().message(err_msg)
+            else:
+                astr_event.set_result(MessageEventResult().message(err_msg))
+            return
+
+
+class LLMRequestSubStage(Stage):
+    async def initialize(self, ctx: PipelineContext) -> None:
+        self.ctx = ctx
+        conf = ctx.astrbot_config
+        settings = conf["provider_settings"]
+        self.bot_wake_prefixs: list[str] = conf["wake_prefix"]  # list
+        self.provider_wake_prefix: str = settings["wake_prefix"]  # str
+        self.max_context_length = settings["max_context_length"]  # int
+        self.dequeue_context_length: int = min(
+            max(1, settings["dequeue_context_length"]),
+            self.max_context_length - 1,
+        )
+        self.streaming_response: bool = settings["streaming_response"]
+        self.max_step: int = settings.get("max_agent_step", 30)
+        self.tool_call_timeout: int = settings.get("tool_call_timeout", 60)
+        if isinstance(self.max_step, bool):  # workaround: #2622
+            self.max_step = 30
+        self.show_tool_use: bool = settings.get("show_tool_use_status", True)
+
+        for bwp in self.bot_wake_prefixs:
+            if self.provider_wake_prefix.startswith(bwp):
+                logger.info(
+                    f"识别 LLM 聊天额外唤醒前缀 {self.provider_wake_prefix} 以机器人唤醒前缀 {bwp} 开头，已自动去除。",
+                )
+                self.provider_wake_prefix = self.provider_wake_prefix[len(bwp) :]
+
+        self.conv_manager = ctx.plugin_manager.context.conversation_manager
+
+    def _select_provider(self, event: AstrMessageEvent):
+        """选择使用的 LLM 提供商"""
+        sel_provider = event.get_extra("selected_provider")
+        _ctx = self.ctx.plugin_manager.context
+        if sel_provider and isinstance(sel_provider, str):
+            provider = _ctx.get_provider_by_id(sel_provider)
+            if not provider:
+                logger.error(f"未找到指定的提供商: {sel_provider}。")
+            return provider
+
+        return _ctx.get_using_provider(umo=event.unified_msg_origin)
+
+    async def _get_session_conv(self, event: AstrMessageEvent) -> Conversation:
+        umo = event.unified_msg_origin
+        conv_mgr = self.conv_manager
+
+        # 获取对话上下文
+        cid = await conv_mgr.get_curr_conversation_id(umo)
+        if not cid:
+            cid = await conv_mgr.new_conversation(umo, event.get_platform_id())
+        conversation = await conv_mgr.get_conversation(umo, cid)
+        if not conversation:
+            cid = await conv_mgr.new_conversation(umo, event.get_platform_id())
+            conversation = await conv_mgr.get_conversation(umo, cid)
+        if not conversation:
+            raise RuntimeError("无法创建新的对话。")
+        return conversation
+
+    async def process(
+        self,
+        event: AstrMessageEvent,
+        _nested: bool = False,
+    ) -> None | AsyncGenerator[None, None]:
+        req: ProviderRequest | None = None
+
+        if not self.ctx.astrbot_config["provider_settings"]["enable"]:
+            logger.debug("未启用 LLM 能力，跳过处理。")
+            return
+
+        # 检查会话级别的LLM启停状态
+        if not SessionServiceManager.should_process_llm_request(event):
+            logger.debug(f"会话 {event.unified_msg_origin} 禁用了 LLM，跳过处理。")
+            return
+
+        provider = self._select_provider(event)
+        if provider is None:
+            return
+        if not isinstance(provider, Provider):
+            logger.error(f"选择的提供商类型无效({type(provider)})，跳过 LLM 请求处理。")
+            return
+
+        if event.get_extra("provider_request"):
+            req = event.get_extra("provider_request")
+            assert isinstance(req, ProviderRequest), (
+                "provider_request 必须是 ProviderRequest 类型。"
+            )
+
+            if req.conversation:
+                req.contexts = json.loads(req.conversation.history)
+
+        else:
+            req = ProviderRequest(prompt="", image_urls=[])
+            if sel_model := event.get_extra("selected_model"):
+                req.model = sel_model
+            if self.provider_wake_prefix:
+                if not event.message_str.startswith(self.provider_wake_prefix):
+                    return
+            req.prompt = event.message_str[len(self.provider_wake_prefix) :]
+            # func_tool selection 现在已经转移到 packages/astrbot 插件中进行选择。
+            # req.func_tool = self.ctx.plugin_manager.context.get_llm_tool_manager()
+            for comp in event.message_obj.message:
+                if isinstance(comp, Image):
+                    image_path = await comp.convert_to_file_path()
+                    req.image_urls.append(image_path)
+
+            conversation = await self._get_session_conv(event)
+            req.conversation = conversation
+            req.contexts = json.loads(conversation.history)
+
+            event.set_extra("provider_request", req)
+
+        if not req.prompt and not req.image_urls:
+            return
+
+        # 应用知识库
+        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}")
+
+        # 执行请求 LLM 前事件钩子。
+        if await call_event_hook(event, EventType.OnLLMRequestEvent, req):
+            return
+
+        if isinstance(req.contexts, str):
+            req.contexts = json.loads(req.contexts)
+
+        # max context length
+        if (
+            self.max_context_length != -1  # -1 为不限制
+            and len(req.contexts) // 2 > self.max_context_length
+        ):
+            logger.debug("上下文长度超过限制，将截断。")
+            req.contexts = req.contexts[
+                -(self.max_context_length - self.dequeue_context_length + 1) * 2 :
+            ]
+            # 找到第一个role 为 user 的索引，确保上下文格式正确
+            index = next(
+                (
+                    i
+                    for i, item in enumerate(req.contexts)
+                    if item.get("role") == "user"
+                ),
+                None,
+            )
+            if index is not None and index > 0:
+                req.contexts = req.contexts[index:]
+
+        # session_id
+        if not req.session_id:
+            req.session_id = event.unified_msg_origin
+
+        # fix messages
+        req.contexts = self.fix_messages(req.contexts)
+
+        # check provider modalities
+        # 如果提供商不支持图像/工具使用，但请求中包含图像/工具列表，则清空。图片转述等的检测和调用发生在这之前，因此这里可以这样处理。
+        if req.image_urls:
+            provider_cfg = provider.provider_config.get("modalities", ["image"])
+            if "image" not in provider_cfg:
+                logger.debug(f"用户设置提供商 {provider} 不支持图像，清空图像列表。")
+                req.image_urls = []
+        if req.func_tool:
+            provider_cfg = provider.provider_config.get("modalities", ["tool_use"])
+            # 如果模型不支持工具使用，但请求中包含工具列表，则清空。
+            if "tool_use" not in provider_cfg:
+                logger.debug(
+                    f"用户设置提供商 {provider} 不支持工具使用，清空工具列表。",
+                )
+                req.func_tool = None
+        # 插件可用性设置
+        if event.plugins_name is not None and req.func_tool:
+            new_tool_set = ToolSet()
+            for tool in req.func_tool.tools:
+                mp = tool.handler_module_path
+                if not mp:
+                    continue
+                plugin = star_map.get(mp)
+                if not plugin:
+                    continue
+                if plugin.name in event.plugins_name or plugin.reserved:
+                    new_tool_set.add_tool(tool)
+            req.func_tool = new_tool_set
+
+        # 备份 req.contexts
+        backup_contexts = copy.deepcopy(req.contexts)
+
+        # run agent
+        agent_runner = AgentRunner()
+        logger.debug(
+            f"handle provider[id: {provider.provider_config['id']}] request: {req}",
+        )
+        astr_agent_ctx = AstrAgentContext(
+            provider=provider,
+            first_provider_request=req,
+            curr_provider_request=req,
+            streaming=self.streaming_response,
+            event=event,
+        )
+        await agent_runner.reset(
+            provider=provider,
+            request=req,
+            run_context=AgentContextWrapper(
+                context=astr_agent_ctx,
+                tool_call_timeout=self.tool_call_timeout,
+            ),
+            tool_executor=FunctionToolExecutor(),
+            agent_hooks=MAIN_AGENT_HOOKS,
+            streaming=self.streaming_response,
+        )
+
+        if self.streaming_response:
+            # 流式响应
+            event.set_result(
+                MessageEventResult()
+                .set_result_content_type(ResultContentType.STREAMING_RESULT)
+                .set_async_stream(
+                    run_agent(agent_runner, self.max_step, self.show_tool_use),
+                ),
+            )
+            yield
+            if agent_runner.done():
+                if final_llm_resp := agent_runner.get_final_llm_resp():
+                    if final_llm_resp.completion_text:
+                        chain = (
+                            MessageChain().message(final_llm_resp.completion_text).chain
+                        )
+                    elif final_llm_resp.result_chain:
+                        chain = final_llm_resp.result_chain.chain
+                    else:
+                        chain = MessageChain().chain
+                    event.set_result(
+                        MessageEventResult(
+                            chain=chain,
+                            result_content_type=ResultContentType.STREAMING_FINISH,
+                        ),
+                    )
+        else:
+            async for _ in run_agent(agent_runner, self.max_step, self.show_tool_use):
+                yield
+
+        # 恢复备份的 contexts
+        req.contexts = backup_contexts
+
+        await self._save_to_history(event, req, agent_runner.get_final_llm_resp())
+
+        # 异步处理 WebChat 特殊情况
+        if event.get_platform_name() == "webchat":
+            asyncio.create_task(self._handle_webchat(event, req, provider))
+
+        asyncio.create_task(
+            Metric.upload(
+                llm_tick=1,
+                model_name=agent_runner.provider.get_model(),
+                provider_type=agent_runner.provider.meta().type,
+            ),
+        )
+
+    async def _handle_webchat(
+        self,
+        event: AstrMessageEvent,
+        req: ProviderRequest,
+        prov: Provider,
+    ):
+        """处理 WebChat 平台的特殊情况，包括第一次 LLM 对话时总结对话内容生成 title"""
+        if not req.conversation:
+            return
+        conversation = await self.conv_manager.get_conversation(
+            event.unified_msg_origin,
+            req.conversation.cid,
+        )
+        if conversation and not req.conversation.title:
+            messages = json.loads(conversation.history)
+            latest_pair = messages[-2:]
+            if not latest_pair:
+                return
+            content = latest_pair[0].get("content", "")
+            if isinstance(content, list):
+                # 多模态
+                text_parts = []
+                for item in content:
+                    if isinstance(item, dict):
+                        if item.get("type") == "text":
+                            text_parts.append(item.get("text", ""))
+                        elif item.get("type") == "image":
+                            text_parts.append("[图片]")
+                    elif isinstance(item, str):
+                        text_parts.append(item)
+                cleaned_text = "User: " + " ".join(text_parts).strip()
+            elif isinstance(content, str):
+                cleaned_text = "User: " + content.strip()
+            else:
+                return
+            logger.debug(f"WebChat 对话标题生成请求，清理后的文本: {cleaned_text}")
+            llm_resp = await prov.text_chat(
+                system_prompt="You are expert in summarizing user's query.",
+                prompt=(
+                    f"Please summarize the following query of user:\n"
+                    f"{cleaned_text}\n"
+                    "Only output the summary within 10 words, DO NOT INCLUDE any other text."
+                    "You must use the same language as the user."
+                    "If you think the dialog is too short to summarize, only output a special mark: `<None>`"
+                ),
+            )
+            if llm_resp and llm_resp.completion_text:
+                logger.debug(
+                    f"WebChat 对话标题生成响应: {llm_resp.completion_text.strip()}",
+                )
+                title = llm_resp.completion_text.strip()
+                if not title or "<None>" in title:
+                    return
+                await self.conv_manager.update_conversation_title(
+                    unified_msg_origin=event.unified_msg_origin,
+                    title=title,
+                    conversation_id=req.conversation.cid,
+                )
+
+    async def _save_to_history(
+        self,
+        event: AstrMessageEvent,
+        req: ProviderRequest,
+        llm_response: LLMResponse | None,
+    ):
+        if (
+            not req
+            or not req.conversation
+            or not llm_response
+            or llm_response.role != "assistant"
+        ):
+            return
+
+        if not llm_response.completion_text and not req.tool_calls_result:
+            logger.debug("LLM 响应为空，不保存记录。")
+            return
+
+        # 历史上下文
+        messages = copy.deepcopy(req.contexts)
+        # 这一轮对话请求的用户输入
+        messages.append(await req.assemble_context())
+        # 这一轮对话的 LLM 响应
+        if req.tool_calls_result:
+            if not isinstance(req.tool_calls_result, list):
+                messages.extend(req.tool_calls_result.to_openai_messages())
+            elif isinstance(req.tool_calls_result, list):
+                for tcr in req.tool_calls_result:
+                    messages.extend(tcr.to_openai_messages())
+        messages.append({"role": "assistant", "content": llm_response.completion_text})
+        messages = list(filter(lambda item: "_no_save" not in item, messages))
+        await self.conv_manager.update_conversation(
+            event.unified_msg_origin,
+            req.conversation.cid,
+            history=messages,
+        )
+
+    def fix_messages(self, messages: list[dict]) -> list[dict]:
+        """验证并且修复上下文"""
+        fixed_messages = []
+        for message in messages:
+            if message.get("role") == "tool":
+                # tool block 前面必须要有 user 和 assistant block
+                if len(fixed_messages) < 2:
+                    # 这种情况可能是上下文被截断导致的
+                    # 我们直接将之前的上下文都清空
+                    fixed_messages = []
+                else:
+                    fixed_messages.append(message)
+            else:
+                fixed_messages.append(message)
+        return fixed_messages

astrbot/core/pipeline/process_stage/method/star_request.py

@@ -24,7 +24,7 @@ class StarRequestSubStage(Stage):
     async def process(
         self,
         event: AstrMessageEvent,
-    ) -> AsyncGenerator[None, None]:
+    ) -> None | AsyncGenerator[None, None]:
         activated_handlers: list[StarHandlerMetadata] = event.get_extra(
             "activated_handlers",
         )

astrbot/core/pipeline/process_stage/stage.py

@@ -1,12 +1,13 @@
 from collections.abc import AsyncGenerator
 
+from astrbot.core import logger
 from astrbot.core.platform.astr_message_event import AstrMessageEvent
 from astrbot.core.provider.entities import ProviderRequest
 from astrbot.core.star.star_handler import StarHandlerMetadata
 
 from ..context import PipelineContext
 from ..stage import Stage, register_stage
-from .method.agent_request import AgentRequestSubStage
+from .method.llm_request import LLMRequestSubStage
 from .method.star_request import StarRequestSubStage
 
 
@@ -16,12 +17,9 @@ class ProcessStage(Stage):
         self.ctx = ctx
         self.config = ctx.astrbot_config
         self.plugin_manager = ctx.plugin_manager
+        self.llm_request_sub_stage = LLMRequestSubStage()
+        await self.llm_request_sub_stage.initialize(ctx)
 
-        # initialize agent sub stage
-        self.agent_sub_stage = AgentRequestSubStage()
-        await self.agent_sub_stage.initialize(ctx)
-
-        # initialize star request sub stage
         self.star_request_sub_stage = StarRequestSubStage()
         await self.star_request_sub_stage.initialize(ctx)
 
@@ -41,7 +39,7 @@ class ProcessStage(Stage):
                     # Handler 的 LLM 请求
                     event.set_extra("provider_request", resp)
                     _t = False
-                    async for _ in self.agent_sub_stage.process(event):
+                    async for _ in self.llm_request_sub_stage.process(event):
                         _t = True
                         yield
                     if not _t:
@@ -62,5 +60,12 @@ class ProcessStage(Stage):
             if (
                 event.get_result() and not event.get_result().is_stopped()
             ) or not event.get_result():
-                async for _ in self.agent_sub_stage.process(event):
+                # 事件没有终止传播
+                provider = self.ctx.plugin_manager.context.get_using_provider()
+
+                if not provider:
+                    logger.info("未找到可用的 LLM 提供商，请先前往配置服务提供商。")
+                    return
+
+                async for _ in self.llm_request_sub_stage.process(event):
                     yield

astrbot/core/pipeline/process_stage/utils.py

@@ -1,64 +1,23 @@
-from pydantic import Field
-from pydantic.dataclasses import dataclass
-
 from astrbot.api import logger, sp
-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
+from astrbot.core.provider.entities import ProviderRequest
+
+from ..context import PipelineContext
 
 
-@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,
+async def inject_kb_context(
     umo: str,
-    context: Context,
-) -> str | None:
+    p_ctx: PipelineContext,
+    req: ProviderRequest,
+) -> 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 = context.kb_manager
-    config = context.get_config(umo=umo)
+    kb_mgr = p_ctx.plugin_manager.context.kb_manager
 
     # 1. 优先读取会话级配置
     session_config = await sp.session_get(umo, "kb_config", default={})
@@ -95,18 +54,18 @@ async def retrieve_knowledge_base(
 
         logger.debug(f"[知识库] 使用会话级配置，知识库数量: {len(kb_names)}")
     else:
-        kb_names = config.get("kb_names", [])
-        top_k = config.get("kb_final_top_k", 5)
+        kb_names = p_ctx.astrbot_config.get("kb_names", [])
+        top_k = p_ctx.astrbot_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 = config.get("kb_fusion_top_k", 20)
+    top_k_fusion = p_ctx.astrbot_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=query,
+        query=req.prompt,
         kb_names=kb_names,
         top_k_fusion=top_k_fusion,
         top_m_final=top_k,
@@ -119,7 +78,4 @@ async def retrieve_knowledge_base(
     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")
-        return formatted
-
-
-KNOWLEDGE_BASE_QUERY_TOOL = KnowledgeBaseQueryTool()
+        req.system_prompt = f"{formatted}\n\n{req.system_prompt or ''}"

astrbot/core/pipeline/respond/stage.py

@@ -1,7 +1,6 @@
 import asyncio
 import math
 import random
-from collections.abc import AsyncGenerator
 
 import astrbot.core.message.components as Comp
 from astrbot.core import logger
@@ -10,6 +9,7 @@ from astrbot.core.message.message_event_result import MessageChain, ResultConten
 from astrbot.core.platform.astr_message_event import AstrMessageEvent
 from astrbot.core.star.star_handler import EventType
 from astrbot.core.utils.path_util import path_Mapping
+from astrbot.core.utils.session_lock import session_lock_manager
 
 from ..context import PipelineContext, call_event_hook
 from ..stage import Stage, register_stage
@@ -152,7 +152,7 @@ class RespondStage(Stage):
     async def process(
         self,
         event: AstrMessageEvent,
-    ) -> None | AsyncGenerator[None, None]:
+    ) -> None:
         result = event.get_result()
         if result is None:
             return
@@ -168,15 +168,12 @@ class RespondStage(Stage):
                 logger.warning("async_stream 为空，跳过发送。")
                 return
             # 流式结果直接交付平台适配器处理
-            realtime_segmenting = (
-                self.config.get("provider_settings", {}).get(
-                    "unsupported_streaming_strategy",
-                    "realtime_segmenting",
-                )
-                == "realtime_segmenting"
+            use_fallback = self.config.get("provider_settings", {}).get(
+                "streaming_segmented",
+                False,
             )
             logger.info(f"应用流式输出({event.get_platform_id()})")
-            await event.send_streaming(result.async_stream, realtime_segmenting)
+            await event.send_streaming(result.async_stream, use_fallback)
             return
         if len(result.chain) > 0:
             # 检查路径映射
@@ -220,20 +217,21 @@ class RespondStage(Stage):
                         f"实际消息链为空, 跳过发送阶段。header_chain: {header_comps}, actual_chain: {result.chain}",
                     )
                     return
-                for comp in result.chain:
-                    i = await self._calc_comp_interval(comp)
-                    await asyncio.sleep(i)
-                    try:
-                        if comp.type in need_separately:
-                            await event.send(MessageChain([comp]))
-                        else:
-                            await event.send(MessageChain([*header_comps, comp]))
-                            header_comps.clear()
-                    except Exception as e:
-                        logger.error(
-                            f"发送消息链失败: chain = {MessageChain([comp])}, error = {e}",
-                            exc_info=True,
-                        )
+                async with session_lock_manager.acquire_lock(event.unified_msg_origin):
+                    for comp in result.chain:
+                        i = await self._calc_comp_interval(comp)
+                        await asyncio.sleep(i)
+                        try:
+                            if comp.type in need_separately:
+                                await event.send(MessageChain([comp]))
+                            else:
+                                await event.send(MessageChain([*header_comps, comp]))
+                                header_comps.clear()
+                        except Exception as e:
+                            logger.error(
+                                f"发送消息链失败: chain = {MessageChain([comp])}, error = {e}",
+                                exc_info=True,
+                            )
             else:
                 if all(
                     comp.type in {ComponentType.Reply, ComponentType.At}

astrbot/core/pipeline/result_decorate/stage.py

@@ -161,21 +161,11 @@ class ResultDecorateStage(Stage):
                                 # 不分段回复
                                 new_chain.append(comp)
                                 continue
-                            try:
-                                split_response = re.findall(
-                                    self.regex,
-                                    comp.text,
-                                    re.DOTALL | re.MULTILINE,
-                                )
-                            except re.error:
-                                logger.error(
-                                    f"分段回复正则表达式错误，使用默认分段方式: {traceback.format_exc()}",
-                                )
-                                split_response = re.findall(
-                                    r".*?[。？！~…]+|.+$",
-                                    comp.text,
-                                    re.DOTALL | re.MULTILINE,
-                                )
+                            split_response = re.findall(
+                                self.regex,
+                                comp.text,
+                                re.DOTALL | re.MULTILINE,
+                            )
                             if not split_response:
                                 new_chain.append(comp)
                                 continue

astrbot/core/platform/platform_metadata.py

@@ -16,6 +16,3 @@ class PlatformMetadata:
     """显示在 WebUI 配置页中的平台名称，如空则是 name"""
     logo_path: str | None = None
     """平台适配器的 logo 文件路径（相对于插件目录）"""
-
-    support_streaming_message: bool = True
-    """平台是否支持真实流式传输"""

astrbot/core/platform/register.py

@@ -14,7 +14,6 @@ def register_platform_adapter(
     default_config_tmpl: dict | None = None,
     adapter_display_name: str | None = None,
     logo_path: str | None = None,
-    support_streaming_message: bool = True,
 ):
     """用于注册平台适配器的带参装饰器。
 
@@ -43,7 +42,6 @@ def register_platform_adapter(
             default_config_tmpl=default_config_tmpl,
             adapter_display_name=adapter_display_name,
             logo_path=logo_path,
-            support_streaming_message=support_streaming_message,
         )
         platform_registry.append(pm)
         platform_cls_map[adapter_name] = cls

astrbot/core/platform/sources/aiocqhttp/aiocqhttp_platform_adapter.py

@@ -29,7 +29,6 @@ from .aiocqhttp_message_event import AiocqhttpMessageEvent
 @register_platform_adapter(
     "aiocqhttp",
     "适用于 OneBot V11 标准的消息平台适配器，支持反向 WebSockets。",
-    support_streaming_message=False,
 )
 class AiocqhttpAdapter(Platform):
     def __init__(
@@ -50,7 +49,6 @@ class AiocqhttpAdapter(Platform):
             name="aiocqhttp",
             description="适用于 OneBot 标准的消息平台适配器，支持反向 WebSockets。",
             id=self.config.get("id"),
-            support_streaming_message=False,
         )
 
         self.bot = CQHttp(
@@ -109,7 +107,7 @@ class AiocqhttpAdapter(Platform):
         )
         await super().send_by_session(session, message_chain)
 
-    async def convert_message(self, event: Event) -> AstrBotMessage | None:
+    async def convert_message(self, event: Event) -> AstrBotMessage:
         logger.debug(f"[aiocqhttp] RawMessage {event}")
 
         if event["post_type"] == "message":
@@ -224,7 +222,7 @@ class AiocqhttpAdapter(Platform):
             err = f"aiocqhttp: 无法识别的消息类型: {event.message!s}，此条消息将被忽略。如果您在使用 go-cqhttp，请将其配置文件中的 message.post-format 更改为 array。"
             logger.critical(err)
             try:
-                await self.bot.send(event, err)
+                self.bot.send(event, err)
             except BaseException as e:
                 logger.error(f"回复消息失败: {e}")
             return None

astrbot/core/platform/sources/dingtalk/dingtalk_adapter.py

@@ -37,9 +37,7 @@ class MyEventHandler(dingtalk_stream.EventHandler):
         return AckMessage.STATUS_OK, "OK"
 
 
-@register_platform_adapter(
-    "dingtalk", "钉钉机器人官方 API 适配器", support_streaming_message=False
-)
+@register_platform_adapter("dingtalk", "钉钉机器人官方 API 适配器")
 class DingtalkPlatformAdapter(Platform):
     def __init__(
         self,
@@ -76,14 +74,6 @@ class DingtalkPlatformAdapter(Platform):
         )
         self.client_ = client  # 用于 websockets 的 client
 
-    def _id_to_sid(self, dingtalk_id: str | None) -> str | None:
-        if not dingtalk_id:
-            return dingtalk_id
-        prefix = "$:LWCP_v1:$"
-        if dingtalk_id.startswith(prefix):
-            return dingtalk_id[len(prefix) :]
-        return dingtalk_id
-
     async def send_by_session(
         self,
         session: MessageSesion,
@@ -96,7 +86,6 @@ class DingtalkPlatformAdapter(Platform):
             name="dingtalk",
             description="钉钉机器人官方 API 适配器",
             id=self.config.get("id"),
-            support_streaming_message=False,
         )
 
     async def convert_msg(
@@ -113,10 +102,10 @@ class DingtalkPlatformAdapter(Platform):
             else MessageType.FRIEND_MESSAGE
         )
         abm.sender = MessageMember(
-            user_id=self._id_to_sid(message.sender_id),
+            user_id=message.sender_id,
             nickname=message.sender_nick,
         )
-        abm.self_id = self._id_to_sid(message.chatbot_user_id)
+        abm.self_id = message.chatbot_user_id
         abm.message_id = message.message_id
         abm.raw_message = message
 
@@ -124,8 +113,8 @@ class DingtalkPlatformAdapter(Platform):
             # 处理所有被 @ 的用户（包括机器人自己，因 at_users 已包含）
             if message.at_users:
                 for user in message.at_users:
-                    if id := self._id_to_sid(user.dingtalk_id):
-                        abm.message.append(At(qq=id))
+                    if user.dingtalk_id:
+                        abm.message.append(At(qq=user.dingtalk_id))
             abm.group_id = message.conversation_id
             if self.unique_session:
                 abm.session_id = abm.sender.user_id
@@ -250,7 +239,7 @@ class DingtalkPlatformAdapter(Platform):
 
     async def terminate(self):
         def monkey_patch_close():
-            raise KeyboardInterrupt("Graceful shutdown")
+            raise Exception("Graceful shutdown")
 
         self.client_.open_connection = monkey_patch_close
         await self.client_.websocket.close(code=1000, reason="Graceful shutdown")

astrbot/core/platform/sources/discord/discord_platform_adapter.py

@@ -34,9 +34,7 @@ else:
 
 
 # 注册平台适配器
-@register_platform_adapter(
-    "discord", "Discord 适配器 (基于 Pycord)", support_streaming_message=False
-)
+@register_platform_adapter("discord", "Discord 适配器 (基于 Pycord)")
 class DiscordPlatformAdapter(Platform):
     def __init__(
         self,
@@ -92,7 +90,7 @@ class DiscordPlatformAdapter(Platform):
         )
         message_obj.self_id = self.client_self_id
         message_obj.session_id = session.session_id
-        message_obj.message = message_chain.chain
+        message_obj.message = message_chain
 
         # 创建临时事件对象来发送消息
         temp_event = DiscordPlatformEvent(
@@ -113,7 +111,6 @@ class DiscordPlatformAdapter(Platform):
             "Discord 适配器",
             id=self.config.get("id"),
             default_config_tmpl=self.config,
-            support_streaming_message=False,
         )
 
     @override

astrbot/core/platform/sources/discord/discord_platform_event.py

@@ -1,7 +1,6 @@
 import asyncio
 import base64
-import binascii
-from collections.abc import AsyncGenerator
+import sys
 from io import BytesIO
 from pathlib import Path
 
@@ -21,6 +20,11 @@ from astrbot.api.platform import AstrBotMessage, At, PlatformMetadata
 from .client import DiscordBotClient
 from .components import DiscordEmbed, DiscordView
 
+if sys.version_info >= (3, 12):
+    from typing import override
+else:
+    from typing_extensions import override
+
 
 # 自定义Discord视图组件（兼容旧版本）
 class DiscordViewComponent(BaseMessageComponent):
@@ -44,6 +48,7 @@ class DiscordPlatformEvent(AstrMessageEvent):
         self.client = client
         self.interaction_followup_webhook = interaction_followup_webhook
 
+    @override
     async def send(self, message: MessageChain):
         """发送消息到Discord平台"""
         # 解析消息链为 Discord 所需的对象
@@ -92,21 +97,6 @@ class DiscordPlatformEvent(AstrMessageEvent):
 
         await super().send(message)
 
-    async def send_streaming(
-        self, generator: AsyncGenerator[MessageChain, None], use_fallback: bool = False
-    ):
-        buffer = None
-        async for chain in generator:
-            if not buffer:
-                buffer = chain
-            else:
-                buffer.chain.extend(chain.chain)
-        if not buffer:
-            return None
-        buffer.squash_plain()
-        await self.send(buffer)
-        return await super().send_streaming(generator, use_fallback)
-
     async def _get_channel(self) -> discord.abc.Messageable | None:
         """获取当前事件对应的频道对象"""
         try:
@@ -193,7 +183,7 @@ class DiscordPlatformEvent(AstrMessageEvent):
                                 BytesIO(img_bytes),
                                 filename=filename or "image.png",
                             )
-                        except (ValueError, TypeError, binascii.Error):
+                        except (ValueError, TypeError, base64.binascii.Error):
                             logger.debug(
                                 f"[Discord] 裸 Base64 解码失败，作为本地路径处理: {file_content}",
                             )

astrbot/core/platform/sources/lark/lark_adapter.py

@@ -23,9 +23,7 @@ from ...register import register_platform_adapter
 from .lark_event import LarkMessageEvent
 
 
-@register_platform_adapter(
-    "lark", "飞书机器人官方 API 适配器", support_streaming_message=False
-)
+@register_platform_adapter("lark", "飞书机器人官方 API 适配器")
 class LarkPlatformAdapter(Platform):
     def __init__(
         self,
@@ -117,7 +115,6 @@ class LarkPlatformAdapter(Platform):
             name="lark",
             description="飞书机器人官方 API 适配器",
             id=self.config.get("id"),
-            support_streaming_message=False,
         )
 
     async def convert_msg(self, event: lark.im.v1.P2ImMessageReceiveV1):

astrbot/core/platform/sources/misskey/misskey_adapter.py

@@ -45,9 +45,7 @@ MAX_FILE_UPLOAD_COUNT = 16
 DEFAULT_UPLOAD_CONCURRENCY = 3
 
 
-@register_platform_adapter(
-    "misskey", "Misskey 平台适配器", support_streaming_message=False
-)
+@register_platform_adapter("misskey", "Misskey 平台适配器")
 class MisskeyPlatformAdapter(Platform):
     def __init__(
         self,
@@ -122,7 +120,6 @@ class MisskeyPlatformAdapter(Platform):
             description="Misskey 平台适配器",
             id=self.config.get("id", "misskey"),
             default_config_tmpl=default_config,
-            support_streaming_message=False,
         )
 
     async def run(self):

astrbot/core/platform/sources/satori/satori_adapter.py

@@ -29,7 +29,8 @@ from astrbot.core.platform.astr_message_event import MessageSession
 
 
 @register_platform_adapter(
-    "satori", "Satori 协议适配器", support_streaming_message=False
+    "satori",
+    "Satori 协议适配器",
 )
 class SatoriPlatformAdapter(Platform):
     def __init__(
@@ -59,7 +60,6 @@ class SatoriPlatformAdapter(Platform):
             name="satori",
             description="Satori 通用协议适配器",
             id=self.config["id"],
-            support_streaming_message=False,
         )
 
         self.ws: ClientConnection | None = None

astrbot/core/platform/sources/slack/slack_adapter.py

@@ -30,7 +30,6 @@ from .slack_event import SlackMessageEvent
 @register_platform_adapter(
     "slack",
     "适用于 Slack 的消息平台适配器，支持 Socket Mode 和 Webhook Mode。",
-    support_streaming_message=False,
 )
 class SlackAdapter(Platform):
     def __init__(
@@ -69,7 +68,6 @@ class SlackAdapter(Platform):
             name="slack",
             description="适用于 Slack 的消息平台适配器，支持 Socket Mode 和 Webhook Mode。",
             id=self.config.get("id"),
-            support_streaming_message=False,
         )
 
         # 初始化 Slack Web Client
@@ -84,7 +82,7 @@ class SlackAdapter(Platform):
         session: MessageSesion,
         message_chain: MessageChain,
     ):
-        blocks, text = await SlackMessageEvent._parse_slack_blocks(
+        blocks, text = SlackMessageEvent._parse_slack_blocks(
             message_chain=message_chain,
             web_client=self.web_client,
         )

astrbot/core/platform/sources/webchat/webchat_adapter.py

@@ -5,6 +5,8 @@ import uuid
 from collections.abc import Awaitable, Callable
 from typing import Any
 
+from astrbot_api.abc import IAstrbotPaths
+
 from astrbot import logger
 from astrbot.core.message.components import Image, Plain, Record
 from astrbot.core.message.message_event_result import MessageChain
@@ -16,12 +18,13 @@ from astrbot.core.platform import (
     PlatformMetadata,
 )
 from astrbot.core.platform.astr_message_event import MessageSesion
-from astrbot.core.utils.astrbot_path import get_astrbot_data_path
+from astrbot_sdk import sync_base_container
 
 from ...register import register_platform_adapter
 from .webchat_event import WebChatMessageEvent
 from .webchat_queue_mgr import WebChatQueueMgr, webchat_queue_mgr
 
+AstrbotPaths: type[IAstrbotPaths] = sync_base_container.get(type[IAstrbotPaths])
 
 class QueueListener:
     def __init__(self, webchat_queue_mgr: WebChatQueueMgr, callback: Callable) -> None:
@@ -79,7 +82,7 @@ class WebChatAdapter(Platform):
         self.config = platform_config
         self.settings = platform_settings
         self.unique_session = platform_settings["unique_session"]
-        self.imgs_dir = os.path.join(get_astrbot_data_path(), "webchat", "imgs")
+        self.imgs_dir = str(AstrbotPaths.astrbot_root / "webchat" / "imgs")
         os.makedirs(self.imgs_dir, exist_ok=True)
 
         self.metadata = PlatformMetadata(
@@ -163,9 +166,6 @@ class WebChatAdapter(Platform):
         _, _, payload = message.raw_message  # type: ignore
         message_event.set_extra("selected_provider", payload.get("selected_provider"))
         message_event.set_extra("selected_model", payload.get("selected_model"))
-        message_event.set_extra(
-            "enable_streaming", payload.get("enable_streaming", True)
-        )
 
         self.commit_event(message_event)
 

astrbot/core/platform/sources/webchat/webchat_event.py

@@ -109,7 +109,6 @@ class WebChatMessageEvent(AstrMessageEvent):
 
     async def send_streaming(self, generator, use_fallback: bool = False):
         final_data = ""
-        reasoning_content = ""
         cid = self.session_id.split("!")[-1]
         web_chat_back_queue = webchat_queue_mgr.get_or_create_back_queue(cid)
         async for chain in generator:
@@ -125,22 +124,16 @@ class WebChatMessageEvent(AstrMessageEvent):
                 )
                 final_data = ""
                 continue
-
-            r = await WebChatMessageEvent._send(
+            final_data += await WebChatMessageEvent._send(
                 chain,
                 session_id=self.session_id,
                 streaming=True,
             )
-            if chain.type == "reasoning":
-                reasoning_content += chain.get_plain_text()
-            else:
-                final_data += r
 
         await web_chat_back_queue.put(
             {
                 "type": "complete",  # complete means we return the final result
                 "data": final_data,
-                "reasoning": reasoning_content,
                 "streaming": True,
                 "cid": cid,
             },

astrbot/core/platform/sources/wechatpadpro/wechatpadpro_adapter.py

@@ -8,10 +8,14 @@ import traceback
 import aiohttp
 import anyio
 import websockets
+from astrbot_api.abc import IAstrbotPaths
 
 from astrbot import logger
 from astrbot.api.message_components import At, Image, Plain, Record
 from astrbot.api.platform import Platform, PlatformMetadata
+from astrbot_sdk import sync_base_container
+
+AstrbotPaths: type[IAstrbotPaths] = sync_base_container.get(type[IAstrbotPaths])
 from astrbot.core.message.message_event_result import MessageChain
 from astrbot.core.platform.astr_message_event import MessageSesion
 from astrbot.core.platform.astrbot_message import (
@@ -19,7 +23,6 @@ from astrbot.core.platform.astrbot_message import (
     MessageMember,
     MessageType,
 )
-from astrbot.core.utils.astrbot_path import get_astrbot_data_path
 
 from ...register import register_platform_adapter
 from .wechatpadpro_message_event import WeChatPadProMessageEvent
@@ -32,9 +35,7 @@ except ImportError as e:
     )
 
 
-@register_platform_adapter(
-    "wechatpadpro", "WeChatPadPro 消息平台适配器", support_streaming_message=False
-)
+@register_platform_adapter("wechatpadpro", "WeChatPadPro 消息平台适配器")
 class WeChatPadProAdapter(Platform):
     def __init__(
         self,
@@ -53,7 +54,6 @@ class WeChatPadProAdapter(Platform):
             name="wechatpadpro",
             description="WeChatPadPro 消息平台适配器",
             id=self.config.get("id", "wechatpadpro"),
-            support_streaming_message=False,
         )
 
         # 保存配置信息
@@ -71,9 +71,8 @@ class WeChatPadProAdapter(Platform):
         self.base_url = f"http://{self.host}:{self.port}"
         self.auth_key = None  # 用于保存生成的授权码
         self.wxid = None  # 用于保存登录成功后的 wxid
-        self.credentials_file = os.path.join(
-            get_astrbot_data_path(),
-            "wechatpadpro_credentials.json",
+        self.credentials_file = str(
+            AstrbotPaths.astrbot_root / "wechatpadpro_credentials.json"
         )  # 持久化文件路径
         self.ws_handle_task = None
 
@@ -158,8 +157,8 @@ class WeChatPadProAdapter(Platform):
         }
         try:
             # 确保数据目录存在
-            data_dir = os.path.dirname(self.credentials_file)
-            os.makedirs(data_dir, exist_ok=True)
+            config_dir = AstrbotPaths.astrbot_root / "config"
+            config_dir.mkdir(parents=True, exist_ok=True)
             with open(self.credentials_file, "w") as f:
                 json.dump(credentials, f)
         except Exception as e:
@@ -790,10 +789,10 @@ class WeChatPadProAdapter(Platform):
             voice_bs64_data = voice_resp.get("Data", {}).get("Base64", None)
             if voice_bs64_data:
                 voice_bs64_data = base64.b64decode(voice_bs64_data)
-                temp_dir = os.path.join(get_astrbot_data_path(), "temp")
-                file_path = os.path.join(
-                    temp_dir,
-                    f"wechatpadpro_voice_{abm.message_id}.silk",
+                file_path = str(
+                    AstrbotPaths.astrbot_root
+                    / "temp"
+                    / f"wechatpadpro_voice_{abm.message_id}.silk"
                 )
 
                 async with await anyio.open_file(file_path, "wb") as f:

astrbot/core/platform/sources/wechatpadpro/wechatpadpro_message_event.py

@@ -1,7 +1,6 @@
 import asyncio
 import base64
 import io
-from collections.abc import AsyncGenerator
 from typing import TYPE_CHECKING
 
 import aiohttp
@@ -51,21 +50,6 @@ class WeChatPadProMessageEvent(AstrMessageEvent):
                     await self._send_voice(session, comp)
         await super().send(message)
 
-    async def send_streaming(
-        self, generator: AsyncGenerator[MessageChain, None], use_fallback: bool = False
-    ):
-        buffer = None
-        async for chain in generator:
-            if not buffer:
-                buffer = chain
-            else:
-                buffer.chain.extend(chain.chain)
-        if not buffer:
-            return None
-        buffer.squash_plain()
-        await self.send(buffer)
-        return await super().send_streaming(generator, use_fallback)
-
     async def _send_image(self, session: aiohttp.ClientSession, comp: Image):
         b64 = await comp.convert_to_base64()
         raw = self._validate_base64(b64)

astrbot/core/platform/sources/wecom/wecom_adapter.py

@@ -110,7 +110,7 @@ class WecomServer:
         await self.shutdown_event.wait()
 
 
-@register_platform_adapter("wecom", "wecom 适配器", support_streaming_message=False)
+@register_platform_adapter("wecom", "wecom 适配器")
 class WecomPlatformAdapter(Platform):
     def __init__(
         self,
@@ -196,7 +196,6 @@ class WecomPlatformAdapter(Platform):
             "wecom",
             "wecom 适配器",
             id=self.config.get("id", "wecom"),
-            support_streaming_message=False,
         )
 
     @override

astrbot/core/platform/sources/wecom_ai_bot/WXBizJsonMsgCrypt.py

@@ -10,7 +10,7 @@ import base64
 import hashlib
 import json
 import logging
-import secrets
+import random
 import socket
 import struct
 import time
@@ -139,12 +139,6 @@ class PKCS7Encoder:
 class Prpcrypt:
     """提供接收和推送给企业微信消息的加解密接口"""
 
-    # 16位随机字符串的范围常量
-    # randbelow(RANDOM_RANGE) 返回 [0, 8999999999999999]（两端都包含，即包含0和8999999999999999）
-    # 加上 MIN_RANDOM_VALUE 后得到 [1000000000000000, 9999999999999999]（两端都包含）即16位数字
-    MIN_RANDOM_VALUE = 1000000000000000  # 最小值: 1000000000000000 (16位)
-    RANDOM_RANGE = 9000000000000000  # 范围大小: 确保最大值为 9999999999999999 (16位)
-
     def __init__(self, key):
         # self.key = base64.b64decode(key+"=")
         self.key = key
@@ -213,9 +207,7 @@ class Prpcrypt:
         """随机生成16位字符串
         @return: 16位字符串
         """
-        return str(
-            secrets.randbelow(self.RANDOM_RANGE) + self.MIN_RANDOM_VALUE
-        ).encode()
+        return str(random.randint(1000000000000000, 9999999999999999)).encode()
 
 
 class WXBizJsonMsgCrypt:

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
+from .wecomai_queue_mgr import WecomAIQueueMgr, wecomai_queue_mgr
 from .wecomai_server import WecomAIBotServer
 from .wecomai_utils import (
     WecomAIBotConstants,
@@ -144,12 +144,9 @@ 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(
-            self.queue_mgr,
+            wecomai_queue_mgr,
             self._handle_queued_message,
         )
 
@@ -192,7 +189,7 @@ class WecomAIBotAdapter(Platform):
                     stream_id,
                     session_id,
                 )
-                self.queue_mgr.set_pending_response(stream_id, callback_params)
+                wecomai_queue_mgr.set_pending_response(stream_id, callback_params)
 
                 resp = WecomAIBotStreamMessageBuilder.make_text_stream(
                     stream_id,
@@ -210,7 +207,7 @@ class WecomAIBotAdapter(Platform):
         elif msgtype == "stream":
             # wechat server is requesting for updates of a stream
             stream_id = message_data["stream"]["id"]
-            if not self.queue_mgr.has_back_queue(stream_id):
+            if not wecomai_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
@@ -225,7 +222,7 @@ class WecomAIBotAdapter(Platform):
                     callback_params["timestamp"],
                 )
                 return resp
-            queue = self.queue_mgr.get_or_create_back_queue(stream_id)
+            queue = wecomai_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}",
@@ -245,9 +242,10 @@ class WecomAIBotAdapter(Platform):
                 elif msg["type"] == "end":
                     # stream end
                     finish = True
-                    self.queue_mgr.remove_queues(stream_id)
+                    wecomai_queue_mgr.remove_queues(stream_id)
                     break
-
+                else:
+                    pass
             logger.debug(
                 f"Aggregated content: {latest_plain_content}, image: {len(image_base64)}, finish: {finish}",
             )
@@ -315,8 +313,8 @@ class WecomAIBotAdapter(Platform):
         session_id: str,
     ):
         """将消息放入队列进行异步处理"""
-        input_queue = self.queue_mgr.get_or_create_queue(stream_id)
-        _ = self.queue_mgr.get_or_create_back_queue(stream_id)
+        input_queue = wecomai_queue_mgr.get_or_create_queue(stream_id)
+        _ = wecomai_queue_mgr.get_or_create_back_queue(stream_id)
         message_payload = {
             "message_data": message_data,
             "callback_params": callback_params,
@@ -455,7 +453,6 @@ 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)

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 WecomAIQueueMgr
+from .wecomai_queue_mgr import wecomai_queue_mgr
 
 
 class WecomAIBotMessageEvent(AstrMessageEvent):
@@ -21,7 +21,6 @@ class WecomAIBotMessageEvent(AstrMessageEvent):
         platform_meta,
         session_id: str,
         api_client: WecomAIBotAPIClient,
-        queue_mgr: WecomAIQueueMgr,
     ):
         """初始化消息事件
 
@@ -35,16 +34,14 @@ 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 = queue_mgr.get_or_create_back_queue(stream_id)
+        back_queue = wecomai_queue_mgr.get_or_create_back_queue(stream_id)
 
         if not message_chain:
             await back_queue.put(
@@ -97,7 +94,7 @@ 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, self.queue_mgr)
+        await WecomAIBotMessageEvent._send(message, stream_id)
         await super().send(message)
 
     async def send_streaming(self, generator, use_fallback=False):
@@ -108,7 +105,7 @@ 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 = self.queue_mgr.get_or_create_back_queue(stream_id)
+        back_queue = wecomai_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 = ""
@@ -137,7 +134,6 @@ class WecomAIBotMessageEvent(AstrMessageEvent):
             final_data += await WecomAIBotMessageEvent._send(
                 chain,
                 stream_id=stream_id,
-                queue_mgr=self.queue_mgr,
                 streaming=True,
             )
 

astrbot/core/platform/sources/wecom_ai_bot/wecomai_queue_mgr.py

@@ -151,3 +151,7 @@ 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()

astrbot/core/platform/sources/wecom_ai_bot/wecomai_utils.py

@@ -5,7 +5,7 @@
 import asyncio
 import base64
 import hashlib
-import secrets
+import random
 import string
 from typing import Any
 
@@ -53,7 +53,7 @@ def generate_random_string(length: int = 10) -> str:
 
     """
     letters = string.ascii_letters + string.digits
-    return "".join(secrets.choice(letters) for _ in range(length))
+    return "".join(random.choice(letters) for _ in range(length))
 
 
 def calculate_image_md5(image_data: bytes) -> str:

astrbot/core/platform/sources/weixin_official_account/weixin_offacc_adapter.py

@@ -113,9 +113,7 @@ class WecomServer:
         await self.shutdown_event.wait()
 
 
-@register_platform_adapter(
-    "weixin_official_account", "微信公众平台 适配器", support_streaming_message=False
-)
+@register_platform_adapter("weixin_official_account", "微信公众平台 适配器")
 class WeixinOfficialAccountPlatformAdapter(Platform):
     def __init__(
         self,
@@ -197,7 +195,6 @@ class WeixinOfficialAccountPlatformAdapter(Platform):
             "weixin_official_account",
             "微信公众平台 适配器",
             id=self.config.get("id", "weixin_official_account"),
-            support_streaming_message=False,
         )
 
     @override

astrbot/core/provider/__init__.py

@@ -1,4 +1,4 @@
 from .entities import ProviderMetaData
-from .provider import Provider, STTProvider
+from .provider import Personality, Provider, STTProvider
 
-__all__ = ["Provider", "ProviderMetaData", "STTProvider"]
+__all__ = ["Personality", "Provider", "ProviderMetaData", "STTProvider"]

astrbot/core/provider/entities.py

@@ -30,31 +30,18 @@ class ProviderType(enum.Enum):
 
 
 @dataclass
-class ProviderMeta:
-    """The basic metadata of a provider instance."""
-
-    id: str
-    """the unique id of the provider instance that user configured"""
-    model: str | None
-    """the model name of the provider instance currently used"""
+class ProviderMetaData:
     type: str
-    """the name of the provider adapter, such as openai, ollama"""
-    provider_type: ProviderType = ProviderType.CHAT_COMPLETION
-    """the capability type of the provider adapter"""
-
-
-@dataclass
-class ProviderMetaData(ProviderMeta):
-    """The metadata of a provider adapter for registration."""
-
+    """提供商适配器名称，如 openai, ollama"""
     desc: str = ""
-    """the short description of the provider adapter"""
+    """提供商适配器描述"""
+    provider_type: ProviderType = ProviderType.CHAT_COMPLETION
     cls_type: Any = None
-    """the class type of the provider adapter"""
+
     default_config_tmpl: dict | None = None
-    """the default configuration template of the provider adapter"""
+    """平台的默认配置模板"""
     provider_display_name: str | None = None
-    """the display name of the provider shown in the WebUI configuration page; if empty, the type is used"""
+    """显示在 WebUI 配置页中的提供商名称，如空则是 type"""
 
 
 @dataclass
@@ -73,20 +60,12 @@ class ToolCallsResult:
         ]
         return ret
 
-    def to_openai_messages_model(
-        self,
-    ) -> list[AssistantMessageSegment | ToolCallMessageSegment]:
-        return [
-            self.tool_calls_info,
-            *self.tool_calls_result,
-        ]
-
 
 @dataclass
 class ProviderRequest:
-    prompt: str | None = None
+    prompt: str
     """提示词"""
-    session_id: str | None = ""
+    session_id: str = ""
     """会话 ID"""
     image_urls: list[str] = field(default_factory=list)
     """图片 URL 列表"""
@@ -202,30 +181,25 @@ class ProviderRequest:
 @dataclass
 class LLMResponse:
     role: str
-    """The role of the message, e.g., assistant, tool, err"""
+    """角色, assistant, tool, err"""
     result_chain: MessageChain | None = None
-    """A chain of message components representing the text completion from LLM."""
+    """返回的消息链"""
     tools_call_args: list[dict[str, Any]] = field(default_factory=list)
-    """Tool call arguments."""
+    """工具调用参数"""
     tools_call_name: list[str] = field(default_factory=list)
-    """Tool call names."""
+    """工具调用名称"""
     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."""
+    """工具调用 ID"""
 
     raw_completion: (
         ChatCompletion | GenerateContentResponse | AnthropicMessage | None
     ) = None
-    """The raw completion response from the LLM provider."""
+    _new_record: dict[str, Any] | None = None
 
     _completion_text: str = ""
-    """The plain text of the completion."""
 
     is_chunk: bool = False
-    """Indicates if the response is a chunked response."""
+    """是否是流式输出的单个 Chunk"""
 
     def __init__(
         self,
@@ -235,11 +209,11 @@ 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
         | None = None,
+        _new_record: dict[str, Any] | None = None,
         is_chunk: bool = False,
     ):
         """初始化 LLMResponse
@@ -259,8 +233,6 @@ 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
@@ -268,8 +240,8 @@ 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._new_record = _new_record
         self.is_chunk = is_chunk
 
     @property
@@ -294,19 +266,16 @@ 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):
-            payload = {
-                "id": self.tools_call_ids[idx],
-                "function": {
-                    "name": self.tools_call_name[idx],
-                    "arguments": json.dumps(tool_call_arg),
+            ret.append(
+                {
+                    "id": self.tools_call_ids[idx],
+                    "function": {
+                        "name": self.tools_call_name[idx],
+                        "arguments": json.dumps(tool_call_arg),
+                    },
+                    "type": "function",
                 },
-                "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]:
@@ -320,10 +289,6 @@ 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

astrbot/core/provider/func_tool_manager.py

@@ -1,7 +1,6 @@
 from __future__ import annotations
 
 import asyncio
-import copy
 import json
 import os
 from collections.abc import Awaitable, Callable
@@ -25,16 +24,7 @@ SUPPORTED_TYPES = [
     "boolean",
 ]  # json schema 支持的数据类型
 
-PY_TO_JSON_TYPE = {
-    "int": "number",
-    "float": "number",
-    "bool": "boolean",
-    "str": "string",
-    "dict": "object",
-    "list": "array",
-    "tuple": "array",
-    "set": "array",
-}
+
 # alias
 FuncTool = FunctionTool
 
@@ -116,7 +106,7 @@ class FunctionToolManager:
     def spec_to_func(
         self,
         name: str,
-        func_args: list[dict],
+        func_args: list,
         desc: str,
         handler: Callable[..., Awaitable[Any]],
     ) -> FuncTool:
@@ -125,9 +115,10 @@ class FunctionToolManager:
             "properties": {},
         }
         for param in func_args:
-            p = copy.deepcopy(param)
-            p.pop("name", None)
-            params["properties"][param["name"]] = p
+            params["properties"][param["name"]] = {
+                "type": param["type"],
+                "description": param["description"],
+            }
         return FuncTool(
             name=name,
             parameters=params,
@@ -280,22 +271,19 @@ 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 client.cleanup()
+                await self.mcp_client_dict[name].cleanup()
+                self.mcp_client_dict.pop(name)
             except Exception as e:
                 logger.error(f"清空 MCP 客户端资源 {name}: {e}。")
-            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}")
+            # 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]:

astrbot/core/provider/manager.py

@@ -1,7 +1,7 @@
 import asyncio
 import traceback
 
-from astrbot.core import astrbot_config, logger, sp
+from astrbot.core import logger, sp
 from astrbot.core.astrbot_config_mgr import AstrBotConfigManager
 from astrbot.core.db import BaseDatabase
 
@@ -24,7 +24,6 @@ class ProviderManager:
         db_helper: BaseDatabase,
         persona_mgr: PersonaManager,
     ):
-        self.reload_lock = asyncio.Lock()
         self.persona_mgr = persona_mgr
         self.acm = acm
         config = acm.confs["default"]
@@ -227,9 +226,6 @@ class ProviderManager:
 
     async def load_provider(self, provider_config: dict):
         if not provider_config["enable"]:
-            logger.info(f"Provider {provider_config['id']} is disabled, skipping")
-            return
-        if provider_config.get("provider_type", "") == "agent_runner":
             return
 
         logger.info(
@@ -245,12 +241,18 @@ class ProviderManager:
                     )
                 case "zhipu_chat_completion":
                     from .sources.zhipu_source import ProviderZhipu as ProviderZhipu
-                case "groq_chat_completion":
-                    from .sources.groq_source import ProviderGroq as ProviderGroq
                 case "anthropic_chat_completion":
                     from .sources.anthropic_source import (
                         ProviderAnthropic as ProviderAnthropic,
                     )
+                case "dify":
+                    from .sources.dify_source import ProviderDify as ProviderDify
+                case "coze":
+                    from .sources.coze_source import ProviderCoze as ProviderCoze
+                case "dashscope":
+                    from .sources.dashscope_source import (
+                        ProviderDashscope as ProviderDashscope,
+                    )
                 case "googlegenai_chat_completion":
                     from .sources.gemini_source import (
                         ProviderGoogleGenAI as ProviderGoogleGenAI,
@@ -327,10 +329,6 @@ class ProviderManager:
                     from .sources.xinference_rerank_source import (
                         XinferenceRerankProvider as XinferenceRerankProvider,
                     )
-                case "bailian_rerank":
-                    from .sources.bailian_rerank_source import (
-                        BailianRerankProvider as BailianRerankProvider,
-                    )
         except (ImportError, ModuleNotFoundError) as e:
             logger.critical(
                 f"加载 {provider_config['type']}({provider_config['id']}) 提供商适配器失败：{e}。可能是因为有未安装的依赖。",
@@ -356,8 +354,6 @@ class ProviderManager:
                 logger.error(f"无法找到 {provider_metadata.type} 的类")
                 return
 
-            provider_metadata.id = provider_config["id"]
-
             if provider_metadata.provider_type == ProviderType.SPEECH_TO_TEXT:
                 # STT 任务
                 inst = cls_type(provider_config, self.provider_settings)
@@ -398,6 +394,7 @@ class ProviderManager:
                 inst = cls_type(
                     provider_config,
                     self.provider_settings,
+                    self.selected_default_persona,
                 )
 
                 if getattr(inst, "initialize", None):
@@ -436,46 +433,40 @@ class ProviderManager:
             )
 
     async def reload(self, provider_config: dict):
-        async with self.reload_lock:
-            await self.terminate_provider(provider_config["id"])
-            if provider_config["enable"]:
-                await self.load_provider(provider_config)
+        await self.terminate_provider(provider_config["id"])
+        if provider_config["enable"]:
+            await self.load_provider(provider_config)
 
-            # 和配置文件保持同步
-            self.providers_config = astrbot_config["provider"]
-            config_ids = [provider["id"] for provider in self.providers_config]
-            logger.info(f"providers in user's config: {config_ids}")
-            for key in list(self.inst_map.keys()):
-                if key not in config_ids:
-                    await self.terminate_provider(key)
+        # 和配置文件保持同步
+        config_ids = [provider["id"] for provider in self.providers_config]
+        logger.debug(f"providers in user's config: {config_ids}")
+        for key in list(self.inst_map.keys()):
+            if key not in config_ids:
+                await self.terminate_provider(key)
 
-            if len(self.provider_insts) == 0:
-                self.curr_provider_inst = None
-            elif self.curr_provider_inst is None and len(self.provider_insts) > 0:
-                self.curr_provider_inst = self.provider_insts[0]
-                logger.info(
-                    f"自动选择 {self.curr_provider_inst.meta().id} 作为当前提供商适配器。",
-                )
+        if len(self.provider_insts) == 0:
+            self.curr_provider_inst = None
+        elif self.curr_provider_inst is None and len(self.provider_insts) > 0:
+            self.curr_provider_inst = self.provider_insts[0]
+            logger.info(
+                f"自动选择 {self.curr_provider_inst.meta().id} 作为当前提供商适配器。",
+            )
 
-            if len(self.stt_provider_insts) == 0:
-                self.curr_stt_provider_inst = None
-            elif (
-                self.curr_stt_provider_inst is None and len(self.stt_provider_insts) > 0
-            ):
-                self.curr_stt_provider_inst = self.stt_provider_insts[0]
-                logger.info(
-                    f"自动选择 {self.curr_stt_provider_inst.meta().id} 作为当前语音转文本提供商适配器。",
-                )
+        if len(self.stt_provider_insts) == 0:
+            self.curr_stt_provider_inst = None
+        elif self.curr_stt_provider_inst is None and len(self.stt_provider_insts) > 0:
+            self.curr_stt_provider_inst = self.stt_provider_insts[0]
+            logger.info(
+                f"自动选择 {self.curr_stt_provider_inst.meta().id} 作为当前语音转文本提供商适配器。",
+            )
 
-            if len(self.tts_provider_insts) == 0:
-                self.curr_tts_provider_inst = None
-            elif (
-                self.curr_tts_provider_inst is None and len(self.tts_provider_insts) > 0
-            ):
-                self.curr_tts_provider_inst = self.tts_provider_insts[0]
-                logger.info(
-                    f"自动选择 {self.curr_tts_provider_inst.meta().id} 作为当前文本转语音提供商适配器。",
-                )
+        if len(self.tts_provider_insts) == 0:
+            self.curr_tts_provider_inst = None
+        elif self.curr_tts_provider_inst is None and len(self.tts_provider_insts) > 0:
+            self.curr_tts_provider_inst = self.tts_provider_insts[0]
+            logger.info(
+                f"自动选择 {self.curr_tts_provider_inst.meta().id} 作为当前文本转语音提供商适配器。",
+            )
 
     def get_insts(self):
         return self.provider_insts

astrbot/core/provider/provider.py

@@ -1,18 +1,28 @@
 import abc
 import asyncio
 from collections.abc import AsyncGenerator
+from dataclasses import dataclass
 
 from astrbot.core.agent.message import Message
 from astrbot.core.agent.tool import ToolSet
+from astrbot.core.db.po import Personality
 from astrbot.core.provider.entities import (
     LLMResponse,
-    ProviderMeta,
+    ProviderType,
     RerankResult,
     ToolCallsResult,
 )
 from astrbot.core.provider.register import provider_cls_map
 
 
+@dataclass
+class ProviderMeta:
+    id: str
+    model: str
+    type: str
+    provider_type: ProviderType
+
+
 class AbstractProvider(abc.ABC):
     """Provider Abstract Class"""
 
@@ -33,15 +43,15 @@ class AbstractProvider(abc.ABC):
         """Get the provider metadata"""
         provider_type_name = self.provider_config["type"]
         meta_data = provider_cls_map.get(provider_type_name)
-        if not meta_data:
-            raise ValueError(f"Provider type {provider_type_name} not registered")
-        meta = ProviderMeta(
-            id=self.provider_config.get("id", "default"),
+        provider_type = meta_data.provider_type if meta_data else None
+        if provider_type is None:
+            raise ValueError(f"Cannot find provider type: {provider_type_name}")
+        return ProviderMeta(
+            id=self.provider_config["id"],
             model=self.get_model(),
             type=provider_type_name,
-            provider_type=meta_data.provider_type,
+            provider_type=provider_type,
         )
-        return meta
 
 
 class Provider(AbstractProvider):
@@ -51,10 +61,15 @@ class Provider(AbstractProvider):
         self,
         provider_config: dict,
         provider_settings: dict,
+        default_persona: Personality | None = None,
     ) -> None:
         super().__init__(provider_config)
+
         self.provider_settings = provider_settings
 
+        self.curr_personality = default_persona
+        """维护了当前的使用的 persona，即人格。可能为 None"""
+
     @abc.abstractmethod
     def get_current_key(self) -> str:
         raise NotImplementedError

astrbot/core/provider/register.py

@@ -36,8 +36,6 @@ def register_provider_adapter(
                 default_config_tmpl["id"] = provider_type_name
 
         pm = ProviderMetaData(
-            id="default",  # will be replaced when instantiated
-            model=None,
             type=provider_type_name,
             desc=desc,
             provider_type=provider_type,

astrbot/core/provider/sources/anthropic_source.py

@@ -25,10 +25,12 @@ class ProviderAnthropic(Provider):
         self,
         provider_config,
         provider_settings,
+        default_persona=None,
     ) -> None:
         super().__init__(
             provider_config,
             provider_settings,
+            default_persona,
         )
 
         self.chosen_api_key: str = ""
@@ -290,7 +292,7 @@ class ProviderAnthropic(Provider):
         try:
             llm_response = await self._query(payloads, func_tool)
         except Exception as e:
-            # logger.error(f"发生了错误。Provider 配置如下: {model_config}")
+            logger.error(f"发生了错误。Provider 配置如下: {model_config}")
             raise e
 
         return llm_response

astrbot/core/provider/sources/azure_tts_source.py

@@ -1,8 +1,8 @@
 import asyncio
 import hashlib
 import json
+import random
 import re
-import secrets
 import time
 import uuid
 from pathlib import Path
@@ -54,9 +54,7 @@ class OTTSProvider:
     async def _generate_signature(self) -> str:
         await self._sync_time()
         timestamp = int(time.time()) + self.time_offset
-        nonce = "".join(
-            secrets.choice("abcdefghijklmnopqrstuvwxyz0123456789") for _ in range(10)
-        )
+        nonce = "".join(random.choices("abcdefghijklmnopqrstuvwxyz0123456789", k=10))
         path = re.sub(r"^https?://[^/]+", "", self.api_url) or "/"
         return f"{timestamp}-{nonce}-0-{hashlib.md5(f'{path}-{timestamp}-{nonce}-0-{self.skey}'.encode()).hexdigest()}"
 

astrbot/core/provider/sources/bailian_rerank_source.py

@@ -1,236 +0,0 @@
-import os
-
-import aiohttp
-
-from astrbot import logger
-
-from ..entities import ProviderType, RerankResult
-from ..provider import RerankProvider
-from ..register import register_provider_adapter
-
-
-class BailianRerankError(Exception):
-    """百炼重排序服务异常基类"""
-
-    pass
-
-
-class BailianAPIError(BailianRerankError):
-    """百炼API返回错误"""
-
-    pass
-
-
-class BailianNetworkError(BailianRerankError):
-    """百炼网络请求错误"""
-
-    pass
-
-
-@register_provider_adapter(
-    "bailian_rerank", "阿里云百炼文本排序适配器", provider_type=ProviderType.RERANK
-)
-class BailianRerankProvider(RerankProvider):
-    """阿里云百炼文本重排序适配器."""
-
-    def __init__(self, provider_config: dict, provider_settings: dict) -> None:
-        super().__init__(provider_config, provider_settings)
-        self.provider_config = provider_config
-        self.provider_settings = provider_settings
-
-        # API配置
-        self.api_key = provider_config.get("rerank_api_key") or os.getenv(
-            "DASHSCOPE_API_KEY", ""
-        )
-        if not self.api_key:
-            raise ValueError("阿里云百炼 API Key 不能为空。")
-
-        self.model = provider_config.get("rerank_model", "qwen3-rerank")
-        self.timeout = provider_config.get("timeout", 30)
-        self.return_documents = provider_config.get("return_documents", False)
-        self.instruct = provider_config.get("instruct", "")
-
-        self.base_url = provider_config.get(
-            "rerank_api_base",
-            "https://dashscope.aliyuncs.com/api/v1/services/rerank/text-rerank/text-rerank",
-        )
-
-        # 设置HTTP客户端
-        headers = {
-            "Authorization": f"Bearer {self.api_key}",
-            "Content-Type": "application/json",
-        }
-
-        self.client = aiohttp.ClientSession(
-            headers=headers, timeout=aiohttp.ClientTimeout(total=self.timeout)
-        )
-
-        # 设置模型名称
-        self.set_model(self.model)
-
-        logger.info(f"AstrBot 百炼 Rerank 初始化完成。模型: {self.model}")
-
-    def _build_payload(
-        self, query: str, documents: list[str], top_n: int | None
-    ) -> dict:
-        """构建请求载荷
-
-        Args:
-            query: 查询文本
-            documents: 文档列表
-            top_n: 返回前N个结果，如果为None则返回所有结果
-
-        Returns:
-            请求载荷字典
-        """
-        base = {"model": self.model, "input": {"query": query, "documents": documents}}
-
-        params = {
-            k: v
-            for k, v in [
-                ("top_n", top_n if top_n is not None and top_n > 0 else None),
-                ("return_documents", True if self.return_documents else None),
-                (
-                    "instruct",
-                    self.instruct
-                    if self.instruct and self.model == "qwen3-rerank"
-                    else None,
-                ),
-            ]
-            if v is not None
-        }
-
-        if params:
-            base["parameters"] = params
-
-        return base
-
-    def _parse_results(self, data: dict) -> list[RerankResult]:
-        """解析API响应结果
-
-        Args:
-            data: API响应数据
-
-        Returns:
-            重排序结果列表
-
-        Raises:
-            BailianAPIError: API返回错误
-            KeyError: 结果缺少必要字段
-        """
-        # 检查响应状态
-        if data.get("code", "200") != "200":
-            raise BailianAPIError(
-                f"百炼 API 错误: {data.get('code')} – {data.get('message', '')}"
-            )
-
-        results = data.get("output", {}).get("results", [])
-        if not results:
-            logger.warning(f"百炼 Rerank 返回空结果: {data}")
-            return []
-
-        # 转换为RerankResult对象，使用.get()避免KeyError
-        rerank_results = []
-        for idx, result in enumerate(results):
-            try:
-                index = result.get("index", idx)
-                relevance_score = result.get("relevance_score", 0.0)
-
-                if relevance_score is None:
-                    logger.warning(f"结果 {idx} 缺少 relevance_score，使用默认值 0.0")
-                    relevance_score = 0.0
-
-                rerank_result = RerankResult(
-                    index=index, relevance_score=relevance_score
-                )
-                rerank_results.append(rerank_result)
-            except Exception as e:
-                logger.warning(f"解析结果 {idx} 时出错: {e}, result={result}")
-                continue
-
-        return rerank_results
-
-    def _log_usage(self, data: dict) -> None:
-        """记录使用量信息
-
-        Args:
-            data: API响应数据
-        """
-        tokens = data.get("usage", {}).get("total_tokens", 0)
-        if tokens > 0:
-            logger.debug(f"百炼 Rerank 消耗 Token: {tokens}")
-
-    async def rerank(
-        self,
-        query: str,
-        documents: list[str],
-        top_n: int | None = None,
-    ) -> list[RerankResult]:
-        """
-        对文档进行重排序
-
-        Args:
-            query: 查询文本
-            documents: 待排序的文档列表
-            top_n: 返回前N个结果，如果为None则使用配置中的默认值
-
-        Returns:
-            重排序结果列表
-        """
-        if not documents:
-            logger.warning("文档列表为空，返回空结果")
-            return []
-
-        if not query.strip():
-            logger.warning("查询文本为空，返回空结果")
-            return []
-
-        # 检查限制
-        if len(documents) > 500:
-            logger.warning(
-                f"文档数量({len(documents)})超过限制(500)，将截断前500个文档"
-            )
-            documents = documents[:500]
-
-        try:
-            # 构建请求载荷，如果top_n为None则返回所有重排序结果
-            payload = self._build_payload(query, documents, top_n)
-
-            logger.debug(
-                f"百炼 Rerank 请求: query='{query[:50]}...', 文档数量={len(documents)}"
-            )
-
-            # 发送请求
-            async with self.client.post(self.base_url, json=payload) as response:
-                response.raise_for_status()
-                response_data = await response.json()
-
-                # 解析结果并记录使用量
-                results = self._parse_results(response_data)
-                self._log_usage(response_data)
-
-                logger.debug(f"百炼 Rerank 成功返回 {len(results)} 个结果")
-
-                return results
-
-        except aiohttp.ClientError as e:
-            error_msg = f"网络请求失败: {e}"
-            logger.error(f"百炼 Rerank 网络请求失败: {e}")
-            raise BailianNetworkError(error_msg) from e
-        except BailianRerankError:
-            raise
-        except Exception as e:
-            error_msg = f"重排序失败: {e}"
-            logger.error(f"百炼 Rerank 处理失败: {e}")
-            raise BailianRerankError(error_msg) from e
-
-    async def terminate(self) -> None:
-        """关闭HTTP客户端会话."""
-        if self.client:
-            logger.info("关闭 百炼 Rerank 客户端会话")
-            try:
-                await self.client.close()
-            except Exception as e:
-                logger.error(f"关闭 百炼 Rerank 客户端时出错: {e}")
-            finally:
-                self.client = None

astrbot/core/provider/sources/coze_source.py

@@ -0,0 +1,652 @@
+import base64
+import hashlib
+import json
+import os
+from collections.abc import AsyncGenerator
+
+import astrbot.core.message.components as Comp
+from astrbot import logger
+from astrbot.api.provider import Provider
+from astrbot.core.message.message_event_result import MessageChain
+from astrbot.core.provider.entities import LLMResponse
+
+from ..register import register_provider_adapter
+from .coze_api_client import CozeAPIClient
+
+
+@register_provider_adapter("coze", "Coze (扣子) 智能体适配器")
+class ProviderCoze(Provider):
+    def __init__(
+        self,
+        provider_config,
+        provider_settings,
+        default_persona=None,
+    ) -> None:
+        super().__init__(
+            provider_config,
+            provider_settings,
+            default_persona,
+        )
+        self.api_key = provider_config.get("coze_api_key", "")
+        if not self.api_key:
+            raise Exception("Coze API Key 不能为空。")
+        self.bot_id = provider_config.get("bot_id", "")
+        if not self.bot_id:
+            raise Exception("Coze Bot ID 不能为空。")
+        self.api_base: str = provider_config.get("coze_api_base", "https://api.coze.cn")
+
+        if not isinstance(self.api_base, str) or not self.api_base.startswith(
+            ("http://", "https://"),
+        ):
+            raise Exception(
+                "Coze API Base URL 格式不正确，必须以 http:// 或 https:// 开头。",
+            )
+
+        self.timeout = provider_config.get("timeout", 120)
+        if isinstance(self.timeout, str):
+            self.timeout = int(self.timeout)
+        self.auto_save_history = provider_config.get("auto_save_history", True)
+        self.conversation_ids: dict[str, str] = {}
+        self.file_id_cache: dict[str, dict[str, str]] = {}
+
+        # 创建 API 客户端
+        self.api_client = CozeAPIClient(api_key=self.api_key, api_base=self.api_base)
+
+    def _generate_cache_key(self, data: str, is_base64: bool = False) -> str:
+        """生成统一的缓存键
+
+        Args:
+            data: 图片数据或路径
+            is_base64: 是否是 base64 数据
+
+        Returns:
+            str: 缓存键
+
+        """
+        try:
+            if is_base64 and data.startswith("data:image/"):
+                try:
+                    header, encoded = data.split(",", 1)
+                    image_bytes = base64.b64decode(encoded)
+                    cache_key = hashlib.md5(image_bytes).hexdigest()
+                    return cache_key
+                except Exception:
+                    cache_key = hashlib.md5(encoded.encode("utf-8")).hexdigest()
+                    return cache_key
+            elif data.startswith(("http://", "https://")):
+                # URL图片，使用URL作为缓存键
+                cache_key = hashlib.md5(data.encode("utf-8")).hexdigest()
+                return cache_key
+            else:
+                clean_path = (
+                    data.split("_")[0]
+                    if "_" in data and len(data.split("_")) >= 3
+                    else data
+                )
+
+                if os.path.exists(clean_path):
+                    with open(clean_path, "rb") as f:
+                        file_content = f.read()
+                    cache_key = hashlib.md5(file_content).hexdigest()
+                    return cache_key
+                cache_key = hashlib.md5(clean_path.encode("utf-8")).hexdigest()
+                return cache_key
+
+        except Exception as e:
+            cache_key = hashlib.md5(data.encode("utf-8")).hexdigest()
+            logger.debug(f"[Coze] 异常文件缓存键: {cache_key}, error={e}")
+            return cache_key
+
+    async def _upload_file(
+        self,
+        file_data: bytes,
+        session_id: str | None = None,
+        cache_key: str | None = None,
+    ) -> str:
+        """上传文件到 Coze 并返回 file_id"""
+        # 使用 API 客户端上传文件
+        file_id = await self.api_client.upload_file(file_data)
+
+        # 缓存 file_id
+        if session_id and cache_key:
+            if session_id not in self.file_id_cache:
+                self.file_id_cache[session_id] = {}
+            self.file_id_cache[session_id][cache_key] = file_id
+            logger.debug(f"[Coze] 图片上传成功并缓存，file_id: {file_id}")
+
+        return file_id
+
+    async def _download_and_upload_image(
+        self,
+        image_url: str,
+        session_id: str | None = None,
+    ) -> str:
+        """下载图片并上传到 Coze，返回 file_id"""
+        # 计算哈希实现缓存
+        cache_key = self._generate_cache_key(image_url) if session_id else None
+
+        if session_id and cache_key:
+            if session_id not in self.file_id_cache:
+                self.file_id_cache[session_id] = {}
+
+            if cache_key in self.file_id_cache[session_id]:
+                file_id = self.file_id_cache[session_id][cache_key]
+                return file_id
+
+        try:
+            image_data = await self.api_client.download_image(image_url)
+
+            file_id = await self._upload_file(image_data, session_id, cache_key)
+
+            if session_id and cache_key:
+                self.file_id_cache[session_id][cache_key] = file_id
+
+            return file_id
+
+        except Exception as e:
+            logger.error(f"处理图片失败 {image_url}: {e!s}")
+            raise Exception(f"处理图片失败: {e!s}")
+
+    async def _process_context_images(
+        self,
+        content: str | list,
+        session_id: str,
+    ) -> str:
+        """处理上下文中的图片内容，将 base64 图片上传并替换为 file_id"""
+        try:
+            if isinstance(content, str):
+                return content
+
+            processed_content = []
+            if session_id not in self.file_id_cache:
+                self.file_id_cache[session_id] = {}
+
+            for item in content:
+                if not isinstance(item, dict):
+                    processed_content.append(item)
+                    continue
+                if item.get("type") == "text":
+                    processed_content.append(item)
+                elif item.get("type") == "image_url":
+                    # 处理图片逻辑
+                    if "file_id" in item:
+                        # 已经有 file_id
+                        logger.debug(f"[Coze] 图片已有file_id: {item['file_id']}")
+                        processed_content.append(item)
+                    else:
+                        # 获取图片数据
+                        image_data = ""
+                        if "image_url" in item and isinstance(item["image_url"], dict):
+                            image_data = item["image_url"].get("url", "")
+                        elif "data" in item:
+                            image_data = item.get("data", "")
+                        elif "url" in item:
+                            image_data = item.get("url", "")
+
+                        if not image_data:
+                            continue
+                        # 计算哈希用于缓存
+                        cache_key = self._generate_cache_key(
+                            image_data,
+                            is_base64=image_data.startswith("data:image/"),
+                        )
+
+                        # 检查缓存
+                        if cache_key in self.file_id_cache[session_id]:
+                            file_id = self.file_id_cache[session_id][cache_key]
+                            processed_content.append(
+                                {"type": "image", "file_id": file_id},
+                            )
+                        else:
+                            # 上传图片并缓存
+                            if image_data.startswith("data:image/"):
+                                # base64 处理
+                                _, encoded = image_data.split(",", 1)
+                                image_bytes = base64.b64decode(encoded)
+                                file_id = await self._upload_file(
+                                    image_bytes,
+                                    session_id,
+                                    cache_key,
+                                )
+                            elif image_data.startswith(("http://", "https://")):
+                                # URL 图片
+                                file_id = await self._download_and_upload_image(
+                                    image_data,
+                                    session_id,
+                                )
+                                # 为URL图片也添加缓存
+                                self.file_id_cache[session_id][cache_key] = file_id
+                            elif os.path.exists(image_data):
+                                # 本地文件
+                                with open(image_data, "rb") as f:
+                                    image_bytes = f.read()
+                                file_id = await self._upload_file(
+                                    image_bytes,
+                                    session_id,
+                                    cache_key,
+                                )
+                            else:
+                                logger.warning(
+                                    f"无法处理的图片格式: {image_data[:50]}...",
+                                )
+                                continue
+
+                            processed_content.append(
+                                {"type": "image", "file_id": file_id},
+                            )
+
+            result = json.dumps(processed_content, ensure_ascii=False)
+            return result
+        except Exception as e:
+            logger.error(f"处理上下文图片失败: {e!s}")
+            if isinstance(content, str):
+                return content
+            return json.dumps(content, ensure_ascii=False)
+
+    async def text_chat(
+        self,
+        prompt: str,
+        session_id=None,
+        image_urls=None,
+        func_tool=None,
+        contexts=None,
+        system_prompt=None,
+        tool_calls_result=None,
+        model=None,
+        **kwargs,
+    ) -> LLMResponse:
+        """文本对话, 内部使用流式接口实现非流式
+
+        Args:
+            prompt (str): 用户提示词
+            session_id (str): 会话ID
+            image_urls (List[str]): 图片URL列表
+            func_tool (FuncCall): 函数调用工具(不支持)
+            contexts (List): 上下文列表
+            system_prompt (str): 系统提示语
+            tool_calls_result (ToolCallsResult | List[ToolCallsResult]): 工具调用结果(不支持)
+            model (str): 模型名称(不支持)
+
+        Returns:
+            LLMResponse: LLM响应对象
+
+        """
+        accumulated_content = ""
+        final_response = None
+
+        async for llm_response in self.text_chat_stream(
+            prompt=prompt,
+            session_id=session_id,
+            image_urls=image_urls,
+            func_tool=func_tool,
+            contexts=contexts,
+            system_prompt=system_prompt,
+            tool_calls_result=tool_calls_result,
+            model=model,
+            **kwargs,
+        ):
+            if llm_response.is_chunk:
+                if llm_response.completion_text:
+                    accumulated_content += llm_response.completion_text
+            else:
+                final_response = llm_response
+
+        if final_response:
+            return final_response
+
+        if accumulated_content:
+            chain = MessageChain(chain=[Comp.Plain(accumulated_content)])
+            return LLMResponse(role="assistant", result_chain=chain)
+        return LLMResponse(role="assistant", completion_text="")
+
+    async def text_chat_stream(
+        self,
+        prompt: str,
+        session_id=None,
+        image_urls=None,
+        func_tool=None,
+        contexts=None,
+        system_prompt=None,
+        tool_calls_result=None,
+        model=None,
+        **kwargs,
+    ) -> AsyncGenerator[LLMResponse, None]:
+        """流式对话接口"""
+        # 用户ID参数(参考文档, 可以自定义)
+        user_id = session_id or kwargs.get("user", "default_user")
+
+        # 获取或创建会话ID
+        conversation_id = self.conversation_ids.get(user_id)
+
+        # 构建消息
+        additional_messages = []
+
+        if system_prompt:
+            if not self.auto_save_history or not conversation_id:
+                additional_messages.append(
+                    {
+                        "role": "system",
+                        "content": system_prompt,
+                        "content_type": "text",
+                    },
+                )
+
+        contexts = self._ensure_message_to_dicts(contexts)
+        if not self.auto_save_history and contexts:
+            # 如果关闭了自动保存历史，传入上下文
+            for ctx in contexts:
+                if isinstance(ctx, dict) and "role" in ctx and "content" in ctx:
+                    content = ctx["content"]
+                    content_type = ctx.get("content_type", "text")
+
+                    # 处理可能包含图片的上下文
+                    if (
+                        content_type == "object_string"
+                        or (isinstance(content, str) and content.startswith("["))
+                        or (
+                            isinstance(content, list)
+                            and any(
+                                isinstance(item, dict)
+                                and item.get("type") == "image_url"
+                                for item in content
+                            )
+                        )
+                    ):
+                        processed_content = await self._process_context_images(
+                            content,
+                            user_id,
+                        )
+                        additional_messages.append(
+                            {
+                                "role": ctx["role"],
+                                "content": processed_content,
+                                "content_type": "object_string",
+                            },
+                        )
+                    else:
+                        # 纯文本
+                        additional_messages.append(
+                            {
+                                "role": ctx["role"],
+                                "content": (
+                                    content
+                                    if isinstance(content, str)
+                                    else json.dumps(content, ensure_ascii=False)
+                                ),
+                                "content_type": "text",
+                            },
+                        )
+                else:
+                    logger.info(f"[Coze] 跳过格式不正确的上下文: {ctx}")
+
+        if prompt or image_urls:
+            if image_urls:
+                # 多模态
+                object_string_content = []
+                if prompt:
+                    object_string_content.append({"type": "text", "text": prompt})
+
+                for url in image_urls:
+                    try:
+                        if url.startswith(("http://", "https://")):
+                            # 网络图片
+                            file_id = await self._download_and_upload_image(
+                                url,
+                                user_id,
+                            )
+                        else:
+                            # 本地文件或 base64
+                            if url.startswith("data:image/"):
+                                # base64
+                                _, encoded = url.split(",", 1)
+                                image_data = base64.b64decode(encoded)
+                                cache_key = self._generate_cache_key(
+                                    url,
+                                    is_base64=True,
+                                )
+                                file_id = await self._upload_file(
+                                    image_data,
+                                    user_id,
+                                    cache_key,
+                                )
+                            # 本地文件
+                            elif os.path.exists(url):
+                                with open(url, "rb") as f:
+                                    image_data = f.read()
+                                # 用文件路径和修改时间来缓存
+                                file_stat = os.stat(url)
+                                cache_key = self._generate_cache_key(
+                                    f"{url}_{file_stat.st_mtime}_{file_stat.st_size}",
+                                    is_base64=False,
+                                )
+                                file_id = await self._upload_file(
+                                    image_data,
+                                    user_id,
+                                    cache_key,
+                                )
+                            else:
+                                logger.warning(f"图片文件不存在: {url}")
+                                continue
+
+                            object_string_content.append(
+                                {
+                                    "type": "image",
+                                    "file_id": file_id,
+                                },
+                            )
+                    except Exception as e:
+                        logger.error(f"处理图片失败 {url}: {e!s}")
+                        continue
+
+                if object_string_content:
+                    content = json.dumps(object_string_content, ensure_ascii=False)
+                    additional_messages.append(
+                        {
+                            "role": "user",
+                            "content": content,
+                            "content_type": "object_string",
+                        },
+                    )
+            # 纯文本
+            elif prompt:
+                additional_messages.append(
+                    {
+                        "role": "user",
+                        "content": prompt,
+                        "content_type": "text",
+                    },
+                )
+
+        try:
+            accumulated_content = ""
+            message_started = False
+
+            async for chunk in self.api_client.chat_messages(
+                bot_id=self.bot_id,
+                user_id=user_id,
+                additional_messages=additional_messages,
+                conversation_id=conversation_id,
+                auto_save_history=self.auto_save_history,
+                stream=True,
+                timeout=self.timeout,
+            ):
+                event_type = chunk.get("event")
+                data = chunk.get("data", {})
+
+                if event_type == "conversation.chat.created":
+                    if isinstance(data, dict) and "conversation_id" in data:
+                        self.conversation_ids[user_id] = data["conversation_id"]
+
+                elif event_type == "conversation.message.delta":
+                    if isinstance(data, dict):
+                        content = data.get("content", "")
+                        if not content and "delta" in data:
+                            content = data["delta"].get("content", "")
+                        if not content and "text" in data:
+                            content = data.get("text", "")
+
+                        if content:
+                            message_started = True
+                            accumulated_content += content
+                            yield LLMResponse(
+                                role="assistant",
+                                completion_text=content,
+                                is_chunk=True,
+                            )
+
+                elif event_type == "conversation.message.completed":
+                    if isinstance(data, dict):
+                        msg_type = data.get("type")
+                        if msg_type == "answer" and data.get("role") == "assistant":
+                            final_content = data.get("content", "")
+                            if not accumulated_content and final_content:
+                                chain = MessageChain(chain=[Comp.Plain(final_content)])
+                                yield LLMResponse(
+                                    role="assistant",
+                                    result_chain=chain,
+                                    is_chunk=False,
+                                )
+
+                elif event_type == "conversation.chat.completed":
+                    if accumulated_content:
+                        chain = MessageChain(chain=[Comp.Plain(accumulated_content)])
+                        yield LLMResponse(
+                            role="assistant",
+                            result_chain=chain,
+                            is_chunk=False,
+                        )
+                    break
+
+                elif event_type == "done":
+                    break
+
+                elif event_type == "error":
+                    error_msg = (
+                        data.get("message", "未知错误")
+                        if isinstance(data, dict)
+                        else str(data)
+                    )
+                    logger.error(f"Coze 流式响应错误: {error_msg}")
+                    yield LLMResponse(
+                        role="err",
+                        completion_text=f"Coze 错误: {error_msg}",
+                        is_chunk=False,
+                    )
+                    break
+
+            if not message_started and not accumulated_content:
+                yield LLMResponse(
+                    role="assistant",
+                    completion_text="LLM 未响应任何内容。",
+                    is_chunk=False,
+                )
+            elif message_started and accumulated_content:
+                chain = MessageChain(chain=[Comp.Plain(accumulated_content)])
+                yield LLMResponse(
+                    role="assistant",
+                    result_chain=chain,
+                    is_chunk=False,
+                )
+
+        except Exception as e:
+            logger.error(f"Coze 流式请求失败: {e!s}")
+            yield LLMResponse(
+                role="err",
+                completion_text=f"Coze 流式请求失败: {e!s}",
+                is_chunk=False,
+            )
+
+    async def forget(self, session_id: str):
+        """清空指定会话的上下文"""
+        user_id = session_id
+        conversation_id = self.conversation_ids.get(user_id)
+
+        if user_id in self.file_id_cache:
+            self.file_id_cache.pop(user_id, None)
+
+        if not conversation_id:
+            return True
+
+        try:
+            response = await self.api_client.clear_context(conversation_id)
+
+            if "code" in response and response["code"] == 0:
+                self.conversation_ids.pop(user_id, None)
+                return True
+            logger.warning(f"清空 Coze 会话上下文失败: {response}")
+            return False
+
+        except Exception as e:
+            logger.error(f"清空 Coze 会话失败: {e!s}")
+            return False
+
+    async def get_current_key(self):
+        """获取当前API Key"""
+        return self.api_key
+
+    async def set_key(self, key: str):
+        """设置新的API Key"""
+        raise NotImplementedError("Coze 适配器不支持设置 API Key。")
+
+    async def get_models(self):
+        """获取可用模型列表"""
+        return [f"bot_{self.bot_id}"]
+
+    def get_model(self):
+        """获取当前模型"""
+        return f"bot_{self.bot_id}"
+
+    def set_model(self, model: str):
+        """设置模型（在Coze中是Bot ID）"""
+        if model.startswith("bot_"):
+            self.bot_id = model[4:]
+        else:
+            self.bot_id = model
+
+    async def get_human_readable_context(
+        self,
+        session_id: str,
+        page: int = 1,
+        page_size: int = 10,
+    ):
+        """获取人类可读的上下文历史"""
+        user_id = session_id
+        conversation_id = self.conversation_ids.get(user_id)
+
+        if not conversation_id:
+            return []
+
+        try:
+            data = await self.api_client.get_message_list(
+                conversation_id=conversation_id,
+                order="desc",
+                limit=page_size,
+                offset=(page - 1) * page_size,
+            )
+
+            if data.get("code") != 0:
+                logger.warning(f"获取 Coze 消息历史失败: {data}")
+                return []
+
+            messages = data.get("data", {}).get("messages", [])
+
+            readable_history = []
+            for msg in messages:
+                role = msg.get("role", "unknown")
+                content = msg.get("content", "")
+                msg_type = msg.get("type", "")
+
+                if role == "user":
+                    readable_history.append(f"用户: {content}")
+                elif role == "assistant" and msg_type == "answer":
+                    readable_history.append(f"助手: {content}")
+
+            return readable_history
+
+        except Exception as e:
+            logger.error(f"获取 Coze 消息历史失败: {e!s}")
+            return []
+
+    async def terminate(self):
+        """清理资源"""
+        await self.api_client.close()

astrbot/core/provider/sources/dashscope_source.py

@@ -0,0 +1,209 @@
+import asyncio
+import functools
+import re
+
+from dashscope import Application
+from dashscope.app.application_response import ApplicationResponse
+
+from astrbot.core import logger, sp
+from astrbot.core.message.message_event_result import MessageChain
+
+from .. import Personality, Provider
+from ..entities import LLMResponse
+from ..register import register_provider_adapter
+from .openai_source import ProviderOpenAIOfficial
+
+
+@register_provider_adapter("dashscope", "Dashscope APP 适配器。")
+class ProviderDashscope(ProviderOpenAIOfficial):
+    def __init__(
+        self,
+        provider_config: dict,
+        provider_settings: dict,
+        default_persona: Personality | None = None,
+    ) -> None:
+        Provider.__init__(
+            self,
+            provider_config,
+            provider_settings,
+            default_persona,
+        )
+        self.api_key = provider_config.get("dashscope_api_key", "")
+        if not self.api_key:
+            raise Exception("阿里云百炼 API Key 不能为空。")
+        self.app_id = provider_config.get("dashscope_app_id", "")
+        if not self.app_id:
+            raise Exception("阿里云百炼 APP ID 不能为空。")
+        self.dashscope_app_type = provider_config.get("dashscope_app_type", "")
+        if not self.dashscope_app_type:
+            raise Exception("阿里云百炼 APP 类型不能为空。")
+        self.model_name = "dashscope"
+        self.variables: dict = provider_config.get("variables", {})
+        self.rag_options: dict = provider_config.get("rag_options", {})
+        self.output_reference = self.rag_options.get("output_reference", False)
+        self.rag_options = self.rag_options.copy()
+        self.rag_options.pop("output_reference", None)
+
+        self.timeout = provider_config.get("timeout", 120)
+        if isinstance(self.timeout, str):
+            self.timeout = int(self.timeout)
+
+    def has_rag_options(self):
+        """判断是否有 RAG 选项
+
+        Returns:
+            bool: 是否有 RAG 选项
+
+        """
+        if self.rag_options and (
+            len(self.rag_options.get("pipeline_ids", [])) > 0
+            or len(self.rag_options.get("file_ids", [])) > 0
+        ):
+            return True
+        return False
+
+    async def text_chat(
+        self,
+        prompt: str,
+        session_id=None,
+        image_urls=None,
+        func_tool=None,
+        contexts=None,
+        system_prompt=None,
+        model=None,
+        **kwargs,
+    ) -> LLMResponse:
+        if image_urls is None:
+            image_urls = []
+        if contexts is None:
+            contexts = []
+        # 获得会话变量
+        payload_vars = self.variables.copy()
+        # 动态变量
+        session_var = await sp.session_get(session_id, "session_variables", default={})
+        payload_vars.update(session_var)
+
+        if (
+            self.dashscope_app_type in ["agent", "dialog-workflow"]
+            and not self.has_rag_options()
+        ):
+            # 支持多轮对话的
+            new_record = {"role": "user", "content": prompt}
+            if image_urls:
+                logger.warning("阿里云百炼暂不支持图片输入，将自动忽略图片内容。")
+            contexts_no_img = await self._remove_image_from_context(contexts)
+            context_query = [*contexts_no_img, new_record]
+            if system_prompt:
+                context_query.insert(0, {"role": "system", "content": system_prompt})
+            for part in context_query:
+                if "_no_save" in part:
+                    del part["_no_save"]
+            # 调用阿里云百炼 API
+            payload = {
+                "app_id": self.app_id,
+                "api_key": self.api_key,
+                "messages": context_query,
+                "biz_params": payload_vars or None,
+            }
+            partial = functools.partial(
+                Application.call,
+                **payload,
+            )
+            response = await asyncio.get_event_loop().run_in_executor(None, partial)
+        else:
+            # 不支持多轮对话的
+            # 调用阿里云百炼 API
+            payload = {
+                "app_id": self.app_id,
+                "prompt": prompt,
+                "api_key": self.api_key,
+                "biz_params": payload_vars or None,
+            }
+            if self.rag_options:
+                payload["rag_options"] = self.rag_options
+            partial = functools.partial(
+                Application.call,
+                **payload,
+            )
+            response = await asyncio.get_event_loop().run_in_executor(None, partial)
+
+        assert isinstance(response, ApplicationResponse)
+
+        logger.debug(f"dashscope resp: {response}")
+
+        if response.status_code != 200:
+            logger.error(
+                f"阿里云百炼请求失败: request_id={response.request_id}, code={response.status_code}, message={response.message}, 请参考文档：https://help.aliyun.com/zh/model-studio/developer-reference/error-code",
+            )
+            return LLMResponse(
+                role="err",
+                result_chain=MessageChain().message(
+                    f"阿里云百炼请求失败: message={response.message} code={response.status_code}",
+                ),
+            )
+
+        output_text = response.output.get("text", "") or ""
+        # RAG 引用脚标格式化
+        output_text = re.sub(r"<ref>\[(\d+)\]</ref>", r"[\1]", output_text)
+        if self.output_reference and response.output.get("doc_references", None):
+            ref_parts = []
+            for ref in response.output.get("doc_references", []) or []:
+                ref_title = (
+                    ref.get("title", "")
+                    if ref.get("title")
+                    else ref.get("doc_name", "")
+                )
+                ref_parts.append(f"{ref['index_id']}. {ref_title}\n")
+            ref_str = "".join(ref_parts)
+            output_text += f"\n\n回答来源:\n{ref_str}"
+
+        llm_response = LLMResponse("assistant")
+        llm_response.result_chain = MessageChain().message(output_text)
+
+        return llm_response
+
+    async def text_chat_stream(
+        self,
+        prompt,
+        session_id=None,
+        image_urls=...,
+        func_tool=None,
+        contexts=...,
+        system_prompt=None,
+        tool_calls_result=None,
+        model=None,
+        **kwargs,
+    ):
+        # raise NotImplementedError("This method is not implemented yet.")
+        # 调用 text_chat 模拟流式
+        llm_response = await self.text_chat(
+            prompt=prompt,
+            session_id=session_id,
+            image_urls=image_urls,
+            func_tool=func_tool,
+            contexts=contexts,
+            system_prompt=system_prompt,
+            tool_calls_result=tool_calls_result,
+        )
+        llm_response.is_chunk = True
+        yield llm_response
+        llm_response.is_chunk = False
+        yield llm_response
+
+    async def forget(self, session_id):
+        return True
+
+    async def get_current_key(self):
+        return self.api_key
+
+    async def set_key(self, key):
+        raise Exception("阿里云百炼 适配器不支持设置 API Key。")
+
+    async def get_models(self):
+        return [self.get_model()]
+
+    async def get_human_readable_context(self, session_id, page, page_size):
+        raise Exception("暂不支持获得 阿里云百炼 的历史消息记录。")
+
+    async def terminate(self):
+        pass

astrbot/core/provider/sources/dashscope_tts.py

@@ -15,7 +15,11 @@ except (
 ):  # pragma: no cover - older dashscope versions without Qwen TTS support
     MultiModalConversation = None
 
-from astrbot.core.utils.astrbot_path import get_astrbot_data_path
+from astrbot_api.abc import IAstrbotPaths
+
+from astrbot_sdk import sync_base_container
+
+AstrbotPaths: type[IAstrbotPaths] = sync_base_container.get(type[IAstrbotPaths])
 
 from ..entities import ProviderType
 from ..provider import TTSProvider
@@ -45,7 +49,7 @@ class ProviderDashscopeTTSAPI(TTSProvider):
         if not model:
             raise RuntimeError("Dashscope TTS model is not configured.")
 
-        temp_dir = os.path.join(get_astrbot_data_path(), "temp")
+        temp_dir = str(AstrbotPaths.astrbot_root / "temp")
         os.makedirs(temp_dir, exist_ok=True)
 
         if self._is_qwen_tts_model(model):

astrbot/core/provider/sources/dify_source.py

@@ -0,0 +1,287 @@
+import os
+
+import astrbot.core.message.components as Comp
+from astrbot.core import logger, sp
+from astrbot.core.message.message_event_result import MessageChain
+from astrbot.core.utils.astrbot_path import get_astrbot_data_path
+from astrbot.core.utils.dify_api_client import DifyAPIClient
+from astrbot.core.utils.io import download_file, download_image_by_url
+
+from .. import Provider
+from ..entities import LLMResponse
+from ..register import register_provider_adapter
+
+
+@register_provider_adapter("dify", "Dify APP 适配器。")
+class ProviderDify(Provider):
+    def __init__(
+        self,
+        provider_config,
+        provider_settings,
+        default_persona=None,
+    ) -> None:
+        super().__init__(
+            provider_config,
+            provider_settings,
+            default_persona,
+        )
+        self.api_key = provider_config.get("dify_api_key", "")
+        if not self.api_key:
+            raise Exception("Dify API Key 不能为空。")
+        api_base = provider_config.get("dify_api_base", "https://api.dify.ai/v1")
+        self.api_type = provider_config.get("dify_api_type", "")
+        if not self.api_type:
+            raise Exception("Dify API 类型不能为空。")
+        self.model_name = "dify"
+        self.workflow_output_key = provider_config.get(
+            "dify_workflow_output_key",
+            "astrbot_wf_output",
+        )
+        self.dify_query_input_key = provider_config.get(
+            "dify_query_input_key",
+            "astrbot_text_query",
+        )
+        if not self.dify_query_input_key:
+            self.dify_query_input_key = "astrbot_text_query"
+        if not self.workflow_output_key:
+            self.workflow_output_key = "astrbot_wf_output"
+        self.variables: dict = provider_config.get("variables", {})
+        self.timeout = provider_config.get("timeout", 120)
+        if isinstance(self.timeout, str):
+            self.timeout = int(self.timeout)
+        self.conversation_ids = {}
+        """记录当前 session id 的对话 ID"""
+
+        self.api_client = DifyAPIClient(self.api_key, api_base)
+
+    async def text_chat(
+        self,
+        prompt: str,
+        session_id=None,
+        image_urls=None,
+        func_tool=None,
+        contexts=None,
+        system_prompt=None,
+        tool_calls_result=None,
+        model=None,
+        **kwargs,
+    ) -> LLMResponse:
+        if image_urls is None:
+            image_urls = []
+        result = ""
+        session_id = session_id or kwargs.get("user") or "unknown"  # 1734
+        conversation_id = self.conversation_ids.get(session_id, "")
+
+        files_payload = []
+        for image_url in image_urls:
+            image_path = (
+                await download_image_by_url(image_url)
+                if image_url.startswith("http")
+                else image_url
+            )
+            file_response = await self.api_client.file_upload(
+                image_path,
+                user=session_id,
+            )
+            logger.debug(f"Dify 上传图片响应：{file_response}")
+            if "id" not in file_response:
+                logger.warning(
+                    f"上传图片后得到未知的 Dify 响应：{file_response}，图片将忽略。",
+                )
+                continue
+            files_payload.append(
+                {
+                    "type": "image",
+                    "transfer_method": "local_file",
+                    "upload_file_id": file_response["id"],
+                },
+            )
+
+        # 获得会话变量
+        payload_vars = self.variables.copy()
+        # 动态变量
+        session_var = await sp.session_get(session_id, "session_variables", default={})
+        payload_vars.update(session_var)
+        payload_vars["system_prompt"] = system_prompt
+
+        try:
+            match self.api_type:
+                case "chat" | "agent" | "chatflow":
+                    if not prompt:
+                        prompt = "请描述这张图片。"
+
+                    async for chunk in self.api_client.chat_messages(
+                        inputs={
+                            **payload_vars,
+                        },
+                        query=prompt,
+                        user=session_id,
+                        conversation_id=conversation_id,
+                        files=files_payload,
+                        timeout=self.timeout,
+                    ):
+                        logger.debug(f"dify resp chunk: {chunk}")
+                        if (
+                            chunk["event"] == "message"
+                            or chunk["event"] == "agent_message"
+                        ):
+                            result += chunk["answer"]
+                            if not conversation_id:
+                                self.conversation_ids[session_id] = chunk[
+                                    "conversation_id"
+                                ]
+                                conversation_id = chunk["conversation_id"]
+                        elif chunk["event"] == "message_end":
+                            logger.debug("Dify message end")
+                            break
+                        elif chunk["event"] == "error":
+                            logger.error(f"Dify 出现错误：{chunk}")
+                            raise Exception(
+                                f"Dify 出现错误 status: {chunk['status']} message: {chunk['message']}",
+                            )
+
+                case "workflow":
+                    async for chunk in self.api_client.workflow_run(
+                        inputs={
+                            self.dify_query_input_key: prompt,
+                            "astrbot_session_id": session_id,
+                            **payload_vars,
+                        },
+                        user=session_id,
+                        files=files_payload,
+                        timeout=self.timeout,
+                    ):
+                        match chunk["event"]:
+                            case "workflow_started":
+                                logger.info(
+                                    f"Dify 工作流(ID: {chunk['workflow_run_id']})开始运行。",
+                                )
+                            case "node_finished":
+                                logger.debug(
+                                    f"Dify 工作流节点(ID: {chunk['data']['node_id']} Title: {chunk['data'].get('title', '')})运行结束。",
+                                )
+                            case "workflow_finished":
+                                logger.info(
+                                    f"Dify 工作流(ID: {chunk['workflow_run_id']})运行结束",
+                                )
+                                logger.debug(f"Dify 工作流结果：{chunk}")
+                                if chunk["data"]["error"]:
+                                    logger.error(
+                                        f"Dify 工作流出现错误：{chunk['data']['error']}",
+                                    )
+                                    raise Exception(
+                                        f"Dify 工作流出现错误：{chunk['data']['error']}",
+                                    )
+                                if (
+                                    self.workflow_output_key
+                                    not in chunk["data"]["outputs"]
+                                ):
+                                    raise Exception(
+                                        f"Dify 工作流的输出不包含指定的键名：{self.workflow_output_key}",
+                                    )
+                                result = chunk
+                case _:
+                    raise Exception(f"未知的 Dify API 类型：{self.api_type}")
+        except Exception as e:
+            logger.error(f"Dify 请求失败：{e!s}")
+            return LLMResponse(role="err", completion_text=f"Dify 请求失败：{e!s}")
+
+        if not result:
+            logger.warning("Dify 请求结果为空，请查看 Debug 日志。")
+
+        chain = await self.parse_dify_result(result)
+
+        return LLMResponse(role="assistant", result_chain=chain)
+
+    async def text_chat_stream(
+        self,
+        prompt,
+        session_id=None,
+        image_urls=...,
+        func_tool=None,
+        contexts=...,
+        system_prompt=None,
+        tool_calls_result=None,
+        model=None,
+        **kwargs,
+    ):
+        # raise NotImplementedError("This method is not implemented yet.")
+        # 调用 text_chat 模拟流式
+        llm_response = await self.text_chat(
+            prompt=prompt,
+            session_id=session_id,
+            image_urls=image_urls,
+            func_tool=func_tool,
+            contexts=contexts,
+            system_prompt=system_prompt,
+            tool_calls_result=tool_calls_result,
+        )
+        llm_response.is_chunk = True
+        yield llm_response
+        llm_response.is_chunk = False
+        yield llm_response
+
+    async def parse_dify_result(self, chunk: dict | str) -> MessageChain:
+        if isinstance(chunk, str):
+            # Chat
+            return MessageChain(chain=[Comp.Plain(chunk)])
+
+        async def parse_file(item: dict):
+            match item["type"]:
+                case "image":
+                    return Comp.Image(file=item["url"], url=item["url"])
+                case "audio":
+                    # 仅支持 wav
+                    temp_dir = os.path.join(get_astrbot_data_path(), "temp")
+                    path = os.path.join(temp_dir, f"{item['filename']}.wav")
+                    await download_file(item["url"], path)
+                    return Comp.Image(file=item["url"], url=item["url"])
+                case "video":
+                    return Comp.Video(file=item["url"])
+                case _:
+                    return Comp.File(name=item["filename"], file=item["url"])
+
+        output = chunk["data"]["outputs"][self.workflow_output_key]
+        chains = []
+        if isinstance(output, str):
+            # 纯文本输出
+            chains.append(Comp.Plain(output))
+        elif isinstance(output, list):
+            # 主要适配 Dify 的 HTTP 请求结点的多模态输出
+            for item in output:
+                # handle Array[File]
+                if (
+                    not isinstance(item, dict)
+                    or item.get("dify_model_identity", "") != "__dify__file__"
+                ):
+                    chains.append(Comp.Plain(str(output)))
+                    break
+        else:
+            chains.append(Comp.Plain(str(output)))
+
+        # scan file
+        files = chunk["data"].get("files", [])
+        for item in files:
+            comp = await parse_file(item)
+            chains.append(comp)
+
+        return MessageChain(chain=chains)
+
+    async def forget(self, session_id):
+        self.conversation_ids[session_id] = ""
+        return True
+
+    async def get_current_key(self):
+        return self.api_key
+
+    async def set_key(self, key):
+        raise Exception("Dify 适配器不支持设置 API Key。")
+
+    async def get_models(self):
+        return [self.get_model()]
+
+    async def get_human_readable_context(self, session_id, page, page_size):
+        raise Exception("暂不支持获得 Dify 的历史消息记录。")
+
+    async def terminate(self):
+        await self.api_client.close()