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Author SHA1 Message Date
Soulter 4fd26814cb Merge remote-tracking branch 'origin/master' into feat/tauri-app 2025-12-18 18:46:46 +08:00
Soulter 1c090299b1 feat: tauri app 2025-11-10 15:11:59 +08:00
162 changed files with 17223 additions and 5640 deletions
+79
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@@ -0,0 +1,79 @@
name: Build Desktop App
on:
push:
tags:
- 'v*'
workflow_dispatch:
jobs:
build:
strategy:
fail-fast: false
matrix:
platform: [macos-latest, ubuntu-latest, windows-latest]
runs-on: ${{ matrix.platform }}
steps:
- uses: actions/checkout@v4
- name: Setup Python
uses: actions/setup-python@v5
with:
python-version: '3.10'
- name: Setup Node.js
uses: actions/setup-node@v4
with:
node-version: 20
- name: Install Rust
uses: dtolnay/rust-toolchain@stable
- name: Install dependencies (Ubuntu)
if: matrix.platform == 'ubuntu-latest'
run: |
sudo apt-get update
sudo apt-get install -y libgtk-3-dev libwebkit2gtk-4.0-dev libappindicator3-dev librsvg2-dev patchelf
- name: Install Python dependencies
run: |
pip install uv
uv sync
- name: Build Python backend with Nuitka
run: |
pip install nuitka
python build_nuitka.py
- name: Install Node dependencies
working-directory: ./dashboard
run: npm install
- name: Build Tauri app
working-directory: ./dashboard
run: npm run tauri:build
- name: Upload artifacts (macOS)
if: matrix.platform == 'macos-latest'
uses: actions/upload-artifact@v4
with:
name: astrbot-macos
path: dashboard/src-tauri/target/release/bundle/dmg/*.dmg
- name: Upload artifacts (Windows)
if: matrix.platform == 'windows-latest'
uses: actions/upload-artifact@v4
with:
name: astrbot-windows
path: dashboard/src-tauri/target/release/bundle/msi/*.msi
- name: Upload artifacts (Linux)
if: matrix.platform == 'ubuntu-latest'
uses: actions/upload-artifact@v4
with:
name: astrbot-linux
path: |
dashboard/src-tauri/target/release/bundle/deb/*.deb
dashboard/src-tauri/target/release/bundle/appimage/*.AppImage
+2
View File
@@ -32,6 +32,7 @@ tests/astrbot_plugin_openai
# Dashboard
dashboard/node_modules/
dashboard/dist/
dashboard/src-tauri/target
package-lock.json
package.json
yarn.lock
@@ -48,5 +49,6 @@ astrbot.lock
chroma
venv/*
pytest.ini
build/
AGENTS.md
IFLOW.md
+287
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@@ -0,0 +1,287 @@
# AstrBot 桌面应用构建指南
本指南介绍如何使用 Nuitka 将 Python 后端打包并集成到 Tauri 桌面应用中。
## 前置要求
### 系统要求
- Python 3.10+
- Node.js 20+
- Rust (通过 rustup 安装)
- UV 包管理器
### macOS 额外要求
- Xcode Command Line Tools: `xcode-select --install`
### Linux 额外要求
```bash
sudo apt-get install -y libgtk-3-dev libwebkit2gtk-4.0-dev \
libappindicator3-dev librsvg2-dev patchelf
```
### Windows 额外要求
- Visual Studio 2019+ with C++ build tools
- Windows 10 SDK
## 构建步骤
### 1. 安装 Python 依赖
```bash
pip install uv
uv sync
```
### 2. 安装 Nuitka
```bash
pip install nuitka
```
### 3. 构建 Python 后端
```bash
python build_nuitka.py
```
这会使用 Nuitka 将 `main.py` 编译为独立可执行文件,输出到 `build/nuitka/` 目录。
**注意**: Nuitka 编译过程可能需要 10-30 分钟,取决于您的系统性能。
### 4. 安装前端依赖
```bash
cd dashboard
npm install
```
### 5. 构建 Tauri 应用
```bash
npm run tauri:build
```
构建脚本会自动:
1. 运行 `build_nuitka.py` 编译 Python 后端
2. 将编译好的可执行文件复制到 `src-tauri/resources/` 目录
3. 构建 Tauri 应用并打包所有资源
### 6. 查找构建产物
构建完成后,您可以在以下位置找到安装包:
- **macOS**: `dashboard/src-tauri/target/release/bundle/dmg/AstrBot_*.dmg`
- **Windows**: `dashboard/src-tauri/target/release/bundle/msi/AstrBot_*.msi`
- **Linux**:
- `dashboard/src-tauri/target/release/bundle/deb/astrbot_*.deb`
- `dashboard/src-tauri/target/release/bundle/appimage/astrbot_*.AppImage`
## 开发模式
在开发时,您可能不想每次都完整编译 Python 后端。
### 仅开发 Tauri + Vue
```bash
cd dashboard
npm run tauri:dev
```
这会启动开发服务器,但不会自动启动 Python 后端。您需要手动运行:
```bash
uv run main.py
```
### 测试完整集成
如果您想测试 Tauri 自动启动 Python 后端的功能:
1. 先编译一次 Python 后端:
```bash
python build_nuitka.py
```
2. 手动复制到资源目录:
```bash
# macOS
cp -r build/nuitka/main.app dashboard/src-tauri/resources/astrbot-backend.app
# Windows
copy build\nuitka\main.exe dashboard\src-tauri\resources\astrbot-backend.exe
# Linux
cp build/nuitka/main.bin dashboard/src-tauri/resources/astrbot-backend
```
3. 运行开发模式:
```bash
cd dashboard
npm run tauri:dev
```
## Nuitka 构建选项说明
`build_nuitka.py` 脚本使用以下关键选项:
- `--standalone`: 创建包含所有依赖的独立目录
- `--onefile`: 将所有内容打包到单个可执行文件
- `--follow-imports`: 自动跟踪所有 Python 导入
- `--include-package`: 明确包含特定包
- `--include-data-dir`: 包含数据目录(插件、配置等)
### 自定义构建
如果您需要修改构建选项,编辑 `build_nuitka.py`:
```python
# 添加更多要包含的包
include_packages = [
"astrbot",
"your_custom_package",
# ...
]
# 添加更多数据目录
data_includes = [
"data/config",
"your_custom_data",
# ...
]
```
## 常见问题
### 1. Nuitka 编译失败
**问题**: 编译时出现 "module not found" 错误
**解决方案**: 在 `build_nuitka.py` 中添加缺失的包到 `include_packages` 列表
### 2. 运行时找不到资源文件
**问题**: 应用启动后提示找不到配置文件或插件
**解决方案**: 确保在 `build_nuitka.py` 中使用 `--include-data-dir` 包含了所有必要的数据目录
### 3. macOS 安全警告
**问题**: macOS 提示"应用来自未知开发者"
**解决方案**:
```bash
# 临时解除限制
sudo spctl --master-disable
# 或者为特定应用授权
xattr -cr /Applications/AstrBot.app
```
对于生产发布,您需要:
1. 注册 Apple Developer 账号
2. 对应用进行代码签名
3. 提交公证 (Notarization)
### 4. Windows Defender 报毒
**问题**: Windows Defender 或其他杀毒软件报毒
**解决方案**:
- 这是 Nuitka 打包程序的常见问题
- 可以使用 `--windows-company-name``--windows-product-name` 添加元数据
- 对于生产发布,需要购买代码签名证书
### 5. Linux 依赖问题
**问题**: 在某些 Linux 发行版上缺少共享库
**解决方案**: 使用 AppImage 格式,它包含所有依赖:
```bash
# 构建时会自动生成 AppImage
npm run tauri:build
```
## 优化构建大小
默认的 `--onefile` 模式会生成较大的可执行文件。如果需要减小体积:
1. 移除不需要的包
2. 使用 `--standalone` 而不是 `--onefile`
3. 排除不必要的数据文件
修改 `build_nuitka.py`:
```python
# 移除 --onefile,使用 --standalone
nuitka_cmd = [
sys.executable,
"-m", "nuitka",
"--standalone", # 只使用 standalone
# "--onefile", # 注释掉 onefile
# ...
]
```
## CI/CD 集成
项目已配置 GitHub Actions 工作流 (`.github/workflows/build-app.yml`),可以自动为所有平台构建应用。
推送标签时自动触发:
```bash
git tag v4.5.7
git push origin v4.5.7
```
或手动触发:
在 GitHub Actions 页面选择 "Build Desktop App" 工作流并点击 "Run workflow"
## 发布清单
在发布新版本前:
- [ ] 更新版本号
- `pyproject.toml` - Python 项目版本
- `dashboard/package.json` - Node 项目版本
- `dashboard/src-tauri/Cargo.toml` - Rust 项目版本
- `dashboard/src-tauri/tauri.conf.json` - Tauri 配置版本
- [ ] 运行代码检查
```bash
uv run ruff check .
uv run ruff format .
```
- [ ] 本地测试构建
```bash
python build_nuitka.py
cd dashboard && npm run tauri:build
```
- [ ] 测试安装包
- 安装生成的安装包
- 验证应用启动
- 验证 Python 后端自动启动
- 测试核心功能
- [ ] 创建发布标签
```bash
git tag -a v4.5.7 -m "Release v4.5.7"
git push origin v4.5.7
```
## 技术架构
```
┌─────────────────────────────────────┐
│ Tauri Desktop App │
│ (Rust + WebView) │
│ │
│ ┌─────────────────────────────┐ │
│ │ Vue.js Dashboard │ │
│ │ (Frontend UI) │ │
│ └─────────────────────────────┘ │
│ │
│ ┌─────────────────────────────┐ │
│ │ Python Backend │ │
│ │ (Nuitka Compiled) │ │
│ │ - AstrBot Core │ │
│ │ - Plugins │ │
│ │ - API Server │ │
│ └─────────────────────────────┘ │
│ │
│ HTTP/WebSocket │
│ localhost:6185 │
└─────────────────────────────────────┘
```
## 参考资源
- [Nuitka 文档](https://nuitka.net/doc/user-manual.html)
- [Tauri 文档](https://tauri.app/v1/guides/)
- [AstrBot 文档](https://astrbot.fun)
+1 -1
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@@ -1,4 +1,4 @@
![astrbot-banner-xmas](https://github.com/user-attachments/assets/bf2341de-ec7a-45a7-a04a-02ad36450e99)
![AstrBot-Logo-Simplified](https://github.com/user-attachments/assets/ffd99b6b-3272-4682-beaa-6fe74250f7d9)
<div align="center">
+1 -1
View File
@@ -1 +1 @@
__version__ = "4.10.2"
__version__ = "4.9.2"
@@ -76,20 +76,12 @@ class ToolLoopAgentRunner(BaseAgentRunner[TContext]):
async def _iter_llm_responses(self) -> T.AsyncGenerator[LLMResponse, None]:
"""Yields chunks *and* a final LLMResponse."""
payload = {
"contexts": self.run_context.messages, # list[Message]
"func_tool": self.req.func_tool,
"model": self.req.model, # NOTE: in fact, this arg is None in most cases
"session_id": self.req.session_id,
"extra_user_content_parts": self.req.extra_user_content_parts, # list[ContentPart]
}
if self.streaming:
stream = self.provider.text_chat_stream(**payload)
stream = self.provider.text_chat_stream(**self.req.__dict__)
async for resp in stream: # type: ignore
yield resp
else:
yield await self.provider.text_chat(**payload)
yield await self.provider.text_chat(**self.req.__dict__)
@override
async def step(self):
@@ -173,7 +165,7 @@ class ToolLoopAgentRunner(BaseAgentRunner[TContext]):
self.run_context.messages.append(
Message(
role="assistant",
content=llm_resp.completion_text or "*No response*",
content=llm_resp.completion_text or "",
),
)
try:
@@ -238,25 +230,6 @@ class ToolLoopAgentRunner(BaseAgentRunner[TContext]):
async for resp in self.step():
yield resp
# 如果循环结束了但是 agent 还没有完成,说明是达到了 max_step
if not self.done():
logger.warning(
f"Agent reached max steps ({max_step}), forcing a final response."
)
# 拔掉所有工具
if self.req:
self.req.func_tool = None
# 注入提示词
self.run_context.messages.append(
Message(
role="user",
content="工具调用次数已达到上限,请停止使用工具,并根据已经收集到的信息,对你的任务和发现进行总结,然后直接回复用户。",
)
)
# 再执行最后一步
async for resp in self.step():
yield resp
async def _handle_function_tools(
self,
req: ProviderRequest,
@@ -403,33 +376,35 @@ class ToolLoopAgentRunner(BaseAgentRunner[TContext]):
),
)
# yield the last tool call result
if tool_call_result_blocks:
last_tcr_content = str(tool_call_result_blocks[-1].content)
yield MessageChain(
type="tool_call_result",
chain=[
Json(
data={
"id": func_tool_id,
"ts": time.time(),
"result": last_tcr_content,
}
)
],
)
elif resp is None:
# Tool 直接请求发送消息给用户
# 这里我们将直接结束 Agent Loop。
# 发送消息逻辑在 ToolExecutor 中处理了。
logger.warning(
f"{func_tool_name} 没有没有返回值或者将结果直接发送给用户。"
f"{func_tool_name} 没有没有返回值或者将结果直接发送给用户,此工具调用不会被记录到历史中"
)
self._transition_state(AgentState.DONE)
self.stats.end_time = time.time()
tool_call_result_blocks.append(
ToolCallMessageSegment(
role="tool",
tool_call_id=func_tool_id,
content="*工具没有返回值或者将结果直接发送给了用户*",
),
)
else:
# 不应该出现其他类型
logger.warning(
f"Tool 返回了不支持的类型: {type(resp)}",
)
tool_call_result_blocks.append(
ToolCallMessageSegment(
role="tool",
tool_call_id=func_tool_id,
content="*工具返回了不支持的类型,请告诉用户检查这个工具的定义和实现。*",
),
f"Tool 返回了不支持的类型: {type(resp)},将忽略",
)
try:
@@ -451,22 +426,6 @@ class ToolLoopAgentRunner(BaseAgentRunner[TContext]):
),
)
# yield the last tool call result
if tool_call_result_blocks:
last_tcr_content = str(tool_call_result_blocks[-1].content)
yield MessageChain(
type="tool_call_result",
chain=[
Json(
data={
"id": func_tool_id,
"ts": time.time(),
"result": last_tcr_content,
}
)
],
)
# 处理函数调用响应
if tool_call_result_blocks:
yield tool_call_result_blocks
+1 -19
View File
@@ -2,7 +2,6 @@ import traceback
from collections.abc import AsyncGenerator
from astrbot.core import logger
from astrbot.core.agent.message import Message
from astrbot.core.agent.runners.tool_loop_agent_runner import ToolLoopAgentRunner
from astrbot.core.astr_agent_context import AstrAgentContext
from astrbot.core.message.components import Json
@@ -25,25 +24,8 @@ async def run_agent(
) -> AsyncGenerator[MessageChain | None, None]:
step_idx = 0
astr_event = agent_runner.run_context.context.event
while step_idx < max_step + 1:
while step_idx < max_step:
step_idx += 1
if step_idx == max_step + 1:
logger.warning(
f"Agent reached max steps ({max_step}), forcing a final response."
)
if not agent_runner.done():
# 拔掉所有工具
if agent_runner.req:
agent_runner.req.func_tool = None
# 注入提示词
agent_runner.run_context.messages.append(
Message(
role="user",
content="工具调用次数已达到上限,请停止使用工具,并根据已经收集到的信息,对你的任务和发现进行总结,然后直接回复用户。",
)
)
try:
async for resp in agent_runner.step():
if astr_event.is_stopped():
+4 -34
View File
@@ -209,42 +209,12 @@ async def call_local_llm_tool(
else:
raise ValueError(f"未知的方法名: {method_name}")
except ValueError as e:
raise Exception(f"Tool execution ValueError: {e}") from e
except TypeError as e:
# 获取函数的签名(包括类型),除了第一个 event/context 参数。
try:
sig = inspect.signature(handler)
params = list(sig.parameters.values())
# 跳过第一个参数(event 或 context
if params:
params = params[1:]
param_strs = []
for param in params:
param_str = param.name
if param.annotation != inspect.Parameter.empty:
# 获取类型注解的字符串表示
if isinstance(param.annotation, type):
type_str = param.annotation.__name__
else:
type_str = str(param.annotation)
param_str += f": {type_str}"
if param.default != inspect.Parameter.empty:
param_str += f" = {param.default!r}"
param_strs.append(param_str)
handler_param_str = (
", ".join(param_strs) if param_strs else "(no additional parameters)"
)
except Exception:
handler_param_str = "(unable to inspect signature)"
raise Exception(
f"Tool handler parameter mismatch, please check the handler definition. Handler parameters: {handler_param_str}"
) from 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()
raise Exception(f"Tool execution error: {e}. Traceback: {trace_}") from e
logger.error(f"调用本地 LLM 工具时出错: {e}\n{trace_}")
if not ready_to_call:
return
+205 -139
View File
@@ -1,11 +1,10 @@
"""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 typing import Any, TypedDict
from astrbot.core.utils.astrbot_path import get_astrbot_data_path
VERSION = "4.10.2"
VERSION = "4.9.2"
DB_PATH = os.path.join(get_astrbot_data_path(), "data_v4.db")
WEBHOOK_SUPPORTED_PLATFORMS = [
@@ -62,8 +61,7 @@ DEFAULT_CONFIG = {
"ignore_bot_self_message": False,
"ignore_at_all": False,
},
"provider_sources": [], # provider sources
"provider": [], # models from provider_sources
"provider": [],
"provider_settings": {
"enable": True,
"default_provider_id": "",
@@ -173,22 +171,6 @@ DEFAULT_CONFIG = {
}
class ChatProviderTemplate(TypedDict):
id: str
provider_source_id: str
model: str
modalities: list
custom_extra_body: dict[str, Any]
CHAT_PROVIDER_TEMPLATE = {
"id": "",
"provide_source_id": "",
"model": "",
"modalities": [],
"custom_extra_body": {},
}
"""
AstrBot v3 时代的配置元数据,目前仅承担以下功能:
@@ -862,7 +844,6 @@ CONFIG_METADATA_2 = {
"metadata": {
"provider": {
"type": "list",
# provider sources templates
"config_template": {
"OpenAI": {
"id": "openai",
@@ -873,10 +854,107 @@ CONFIG_METADATA_2 = {
"key": [],
"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。",
},
"Google Gemini": {
"id": "google_gemini",
"Azure OpenAI": {
"id": "azure",
"provider": "azure",
"type": "openai_chat_completion",
"provider_type": "chat_completion",
"enable": True,
"api_version": "2024-05-01-preview",
"key": [],
"api_base": "",
"timeout": 120,
"model_config": {"model": "gpt-4o-mini", "temperature": 0.4},
"custom_headers": {},
"custom_extra_body": {},
"modalities": ["text", "image", "tool_use"],
},
"xAI": {
"id": "xai",
"provider": "xai",
"type": "openai_chat_completion",
"provider_type": "chat_completion",
"enable": True,
"key": [],
"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"],
},
"Anthropic": {
"hint": "注意Claude系列模型的温度调节范围为0到1.0,超出可能导致报错",
"id": "claude",
"provider": "anthropic",
"type": "anthropic_chat_completion",
"provider_type": "chat_completion",
"enable": True,
"key": [],
"api_base": "https://api.anthropic.com/v1",
"timeout": 120,
"model_config": {
"model": "claude-3-5-sonnet-latest",
"max_tokens": 4096,
"temperature": 0.2,
},
"modalities": ["text", "image", "tool_use"],
},
"Ollama": {
"hint": "启用前请确保已正确安装并运行 Ollama 服务端,Ollama默认不带鉴权,无需修改key",
"id": "ollama_default",
"provider": "ollama",
"type": "openai_chat_completion",
"provider_type": "chat_completion",
"enable": True,
"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"],
},
"LM Studio": {
"id": "lm_studio",
"provider": "lm_studio",
"type": "openai_chat_completion",
"provider_type": "chat_completion",
"enable": True,
"key": ["lmstudio"],
"api_base": "http://localhost:1234/v1",
"model_config": {
"model": "llama-3.1-8b",
},
"custom_headers": {},
"custom_extra_body": {},
"modalities": ["text", "image", "tool_use"],
},
"Gemini(OpenAI兼容)": {
"id": "gemini_default",
"provider": "google",
"type": "openai_chat_completion",
"provider_type": "chat_completion",
"enable": True,
"key": [],
"api_base": "https://generativelanguage.googleapis.com/v1beta/openai/",
"timeout": 120,
"model_config": {
"model": "gemini-3-flash-preview",
"temperature": 0.4,
},
"custom_headers": {},
"custom_extra_body": {},
"modalities": ["text", "image", "tool_use"],
},
"Gemini": {
"id": "gemini_default",
"provider": "google",
"type": "googlegenai_chat_completion",
"provider_type": "chat_completion",
@@ -884,6 +962,10 @@ CONFIG_METADATA_2 = {
"key": [],
"api_base": "https://generativelanguage.googleapis.com/",
"timeout": 120,
"model_config": {
"model": "gemini-3-flash-preview",
"temperature": 0.4,
},
"gm_resp_image_modal": False,
"gm_native_search": False,
"gm_native_coderunner": False,
@@ -895,42 +977,10 @@ CONFIG_METADATA_2 = {
"dangerous_content": "BLOCK_MEDIUM_AND_ABOVE",
},
"gm_thinking_config": {"budget": 0, "level": "HIGH"},
},
"Anthropic": {
"id": "anthropic",
"provider": "anthropic",
"type": "anthropic_chat_completion",
"provider_type": "chat_completion",
"enable": True,
"key": [],
"api_base": "https://api.anthropic.com/v1",
"timeout": 120,
},
"Moonshot": {
"id": "moonshot",
"provider": "moonshot",
"type": "openai_chat_completion",
"provider_type": "chat_completion",
"enable": True,
"key": [],
"timeout": 120,
"api_base": "https://api.moonshot.cn/v1",
"custom_headers": {},
},
"xAI": {
"id": "xai",
"provider": "xai",
"type": "openai_chat_completion",
"provider_type": "chat_completion",
"enable": True,
"key": [],
"api_base": "https://api.x.ai/v1",
"timeout": 120,
"custom_headers": {},
"xai_native_search": False,
"modalities": ["text", "image", "tool_use"],
},
"DeepSeek": {
"id": "deepseek",
"id": "deepseek_default",
"provider": "deepseek",
"type": "openai_chat_completion",
"provider_type": "chat_completion",
@@ -938,75 +988,13 @@ CONFIG_METADATA_2 = {
"key": [],
"api_base": "https://api.deepseek.com/v1",
"timeout": 120,
"model_config": {"model": "deepseek-chat", "temperature": 0.4},
"custom_headers": {},
},
"Zhipu": {
"id": "zhipu",
"provider": "zhipu",
"type": "zhipu_chat_completion",
"provider_type": "chat_completion",
"enable": True,
"key": [],
"timeout": 120,
"api_base": "https://open.bigmodel.cn/api/paas/v4/",
"custom_headers": {},
},
"Azure OpenAI": {
"id": "azure_openai",
"provider": "azure",
"type": "openai_chat_completion",
"provider_type": "chat_completion",
"enable": True,
"api_version": "2024-05-01-preview",
"key": [],
"api_base": "",
"timeout": 120,
"custom_headers": {},
},
"Ollama": {
"id": "ollama",
"provider": "ollama",
"type": "openai_chat_completion",
"provider_type": "chat_completion",
"enable": True,
"key": ["ollama"], # ollama 的 key 默认是 ollama
"api_base": "http://127.0.0.1:11434/v1",
"custom_headers": {},
},
"LM Studio": {
"id": "lm_studio",
"provider": "lm_studio",
"type": "openai_chat_completion",
"provider_type": "chat_completion",
"enable": True,
"key": ["lmstudio"],
"api_base": "http://127.0.0.1:1234/v1",
"custom_headers": {},
},
"ModelStack": {
"id": "modelstack",
"provider": "modelstack",
"type": "openai_chat_completion",
"provider_type": "chat_completion",
"enable": True,
"key": [],
"api_base": "https://modelstack.app/v1",
"timeout": 120,
"custom_headers": {},
},
"Gemini_OpenAI_API": {
"id": "google_gemini_openai",
"provider": "google",
"type": "openai_chat_completion",
"provider_type": "chat_completion",
"enable": True,
"key": [],
"api_base": "https://generativelanguage.googleapis.com/v1beta/openai/",
"timeout": 120,
"custom_headers": {},
"custom_extra_body": {},
"modalities": ["text", "tool_use"],
},
"Groq": {
"id": "groq",
"id": "groq_default",
"provider": "groq",
"type": "groq_chat_completion",
"provider_type": "chat_completion",
@@ -1014,7 +1002,13 @@ CONFIG_METADATA_2 = {
"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"],
},
"302.AI": {
"id": "302ai",
@@ -1025,9 +1019,12 @@ CONFIG_METADATA_2 = {
"key": [],
"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"],
},
"SiliconFlow": {
"硅基流动": {
"id": "siliconflow",
"provider": "siliconflow",
"type": "openai_chat_completion",
@@ -1036,9 +1033,15 @@ CONFIG_METADATA_2 = {
"key": [],
"timeout": 120,
"api_base": "https://api.siliconflow.cn/v1",
"model_config": {
"model": "deepseek-ai/DeepSeek-V3",
"temperature": 0.4,
},
"custom_headers": {},
"custom_extra_body": {},
"modalities": ["text", "image", "tool_use"],
},
"PPIO": {
"PPIO派欧云": {
"id": "ppio",
"provider": "ppio",
"type": "openai_chat_completion",
@@ -1047,9 +1050,14 @@ CONFIG_METADATA_2 = {
"key": [],
"api_base": "https://api.ppinfra.com/v3/openai",
"timeout": 120,
"model_config": {
"model": "deepseek/deepseek-r1",
"temperature": 0.4,
},
"custom_headers": {},
"custom_extra_body": {},
},
"TokenPony": {
"小马算力": {
"id": "tokenpony",
"provider": "tokenpony",
"type": "openai_chat_completion",
@@ -1058,9 +1066,14 @@ CONFIG_METADATA_2 = {
"key": [],
"api_base": "https://api.tokenpony.cn/v1",
"timeout": 120,
"model_config": {
"model": "kimi-k2-instruct-0905",
"temperature": 0.7,
},
"custom_headers": {},
"custom_extra_body": {},
},
"Compshare": {
"优云智算": {
"id": "compshare",
"provider": "compshare",
"type": "openai_chat_completion",
@@ -1069,18 +1082,42 @@ CONFIG_METADATA_2 = {
"key": [],
"api_base": "https://api.modelverse.cn/v1",
"timeout": 120,
"model_config": {
"model": "moonshotai/Kimi-K2-Instruct",
},
"custom_headers": {},
"custom_extra_body": {},
"modalities": ["text", "image", "tool_use"],
},
"ModelScope": {
"id": "modelscope",
"provider": "modelscope",
"Kimi": {
"id": "moonshot",
"provider": "moonshot",
"type": "openai_chat_completion",
"provider_type": "chat_completion",
"enable": True,
"key": [],
"timeout": 120,
"api_base": "https://api-inference.modelscope.cn/v1",
"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"],
},
"智谱 AI": {
"id": "zhipu_default",
"provider": "zhipu",
"type": "zhipu_chat_completion",
"provider_type": "chat_completion",
"enable": True,
"key": [],
"timeout": 120,
"api_base": "https://open.bigmodel.cn/api/paas/v4/",
"model_config": {
"model": "glm-4-flash",
},
"custom_headers": {},
"custom_extra_body": {},
"modalities": ["text", "image", "tool_use"],
},
"Dify": {
"id": "dify_app_default",
@@ -1095,6 +1132,7 @@ CONFIG_METADATA_2 = {
"dify_query_input_key": "astrbot_text_query",
"variables": {},
"timeout": 60,
"hint": "请确保你在 AstrBot 里设置的 APP 类型和 Dify 里面创建的应用的类型一致!",
},
"Coze": {
"id": "coze",
@@ -1125,6 +1163,20 @@ CONFIG_METADATA_2 = {
"variables": {},
"timeout": 60,
},
"ModelScope": {
"id": "modelscope",
"provider": "modelscope",
"type": "openai_chat_completion",
"provider_type": "chat_completion",
"enable": True,
"key": [],
"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"],
},
"FastGPT": {
"id": "fastgpt",
"provider": "fastgpt",
@@ -1148,6 +1200,7 @@ CONFIG_METADATA_2 = {
"model": "whisper-1",
},
"Whisper(Local)": {
"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 cudaCPU 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",
"provider_type": "speech_to_text",
@@ -1156,6 +1209,7 @@ CONFIG_METADATA_2 = {
"model": "tiny",
},
"SenseVoice(Local)": {
"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",
"provider_type": "speech_to_text",
@@ -1177,6 +1231,7 @@ CONFIG_METADATA_2 = {
"timeout": "20",
},
"Edge TTS": {
"hint": "提示:使用这个服务前需要安装有 ffmpeg,并且可以直接在终端调用 ffmpeg 指令。",
"id": "edge_tts",
"provider": "microsoft",
"type": "edge_tts",
@@ -1392,10 +1447,6 @@ CONFIG_METADATA_2 = {
},
},
"items": {
"provider_source_id": {
"invisible": True,
"type": "string",
},
"xai_native_search": {
"description": "启用原生搜索功能",
"type": "bool",
@@ -1964,6 +2015,7 @@ CONFIG_METADATA_2 = {
"id": {
"description": "ID",
"type": "string",
"hint": "模型提供商名字。",
},
"type": {
"description": "模型提供商种类",
@@ -1983,15 +2035,29 @@ CONFIG_METADATA_2 = {
"description": "API Key",
"type": "list",
"items": {"type": "string"},
"hint": "提供商 API Key。",
},
"api_base": {
"description": "API Base URL",
"type": "string",
"hint": "API Base URL 请在模型提供商处获得。如出现 404 报错,尝试在地址末尾加上 /v1",
},
"model": {
"description": "模型 ID",
"type": "string",
"hint": "模型名称,如 gpt-4o-mini, deepseek-chat。",
"model_config": {
"description": "模型配置",
"type": "object",
"items": {
"model": {
"description": "模型名称",
"type": "string",
"hint": "模型名称,如 gpt-4o-mini, deepseek-chat。",
},
"max_tokens": {
"description": "模型最大输出长度(tokens",
"type": "int",
},
"temperature": {"description": "温度", "type": "float"},
"top_p": {"description": "Top P值", "type": "float"},
},
},
"dify_api_key": {
"description": "API Key",
-3
View File
@@ -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.llm_metadata import update_llm_metadata
from astrbot.core.utils.migra_helper import migra
from . import astrbot_config, html_renderer
@@ -186,8 +185,6 @@ class AstrBotCoreLifecycle:
# 初始化关闭控制面板的事件
self.dashboard_shutdown_event = asyncio.Event()
asyncio.create_task(update_llm_metadata())
def _load(self) -> None:
"""加载事件总线和任务并初始化."""
# 创建一个异步任务来执行事件总线的 dispatch() 方法
@@ -321,12 +321,7 @@ class InternalAgentSubStage(Stage):
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 or "*No response*",
}
)
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,
@@ -385,25 +385,10 @@ class AiocqhttpAdapter(Platform):
logger.error(f"获取 @ 用户信息失败: {e},此消息段将被忽略。")
message_str += "".join(at_parts)
elif t == "markdown":
text = m["data"].get("markdown") or m["data"].get("content", "")
abm.message.append(Plain(text=text))
message_str += text
else:
for m in m_group:
try:
if t not in ComponentTypes:
logger.warning(
f"不支持的消息段类型,已忽略: {t}, data={m['data']}"
)
continue
a = ComponentTypes[t](**m["data"])
abm.message.append(a)
except Exception as e:
logger.exception(
f"消息段解析失败: type={t}, data={m['data']}. {e}"
)
continue
a = ComponentTypes[t](**m["data"])
abm.message.append(a)
abm.timestamp = int(time.time())
abm.message_str = message_str
+9 -32
View File
@@ -14,7 +14,6 @@ import astrbot.core.message.components as Comp
from astrbot import logger
from astrbot.core.agent.message import (
AssistantMessageSegment,
ContentPart,
ToolCall,
ToolCallMessageSegment,
)
@@ -93,8 +92,6 @@ class ProviderRequest:
"""会话 ID"""
image_urls: list[str] = field(default_factory=list)
"""图片 URL 列表"""
extra_user_content_parts: list[ContentPart] = field(default_factory=list)
"""额外的用户消息内容部分列表,用于在用户消息后添加额外的内容块(如系统提醒、指令等)。"""
func_tool: ToolSet | None = None
"""可用的函数工具"""
contexts: list[dict] = field(default_factory=list)
@@ -169,23 +166,13 @@ class ProviderRequest:
async def assemble_context(self) -> dict:
"""将请求(prompt 和 image_urls)包装成 OpenAI 的消息格式。"""
# 构建内容块列表
content_blocks = []
# 1. 用户原始发言(OpenAI 建议:用户发言在前)
if self.prompt and self.prompt.strip():
content_blocks.append({"type": "text", "text": self.prompt})
elif self.image_urls:
# 如果没有文本但有图片,添加占位文本
content_blocks.append({"type": "text", "text": "[图片]"})
# 2. 额外的内容块(系统提醒、指令等)
if self.extra_user_content_parts:
for part in self.extra_user_content_parts:
content_blocks.append(part.model_dump())
# 3. 图片内容
if self.image_urls:
user_content = {
"role": "user",
"content": [
{"type": "text", "text": self.prompt if self.prompt else "[图片]"},
],
}
for image_url in self.image_urls:
if image_url.startswith("http"):
image_path = await download_image_by_url(image_url)
@@ -198,21 +185,11 @@ class ProviderRequest:
if not image_data:
logger.warning(f"图片 {image_url} 得到的结果为空,将忽略。")
continue
content_blocks.append(
user_content["content"].append(
{"type": "image_url", "image_url": {"url": image_data}},
)
# 只有当只有一个来自 prompt 的文本块且没有额外内容块时,才降级为简单格式以保持向后兼容
if (
len(content_blocks) == 1
and content_blocks[0]["type"] == "text"
and not self.extra_user_content_parts
and not self.image_urls
):
return {"role": "user", "content": content_blocks[0]["text"]}
# 否则返回多模态格式
return {"role": "user", "content": content_blocks}
return user_content
return {"role": "user", "content": self.prompt}
async def _encode_image_bs64(self, image_url: str) -> str:
"""将图片转换为 base64"""
+93 -203
View File
@@ -1,5 +1,4 @@
import asyncio
import copy
import traceback
from typing import Protocol, runtime_checkable
@@ -33,12 +32,10 @@ class ProviderManager:
persona_mgr: PersonaManager,
):
self.reload_lock = asyncio.Lock()
self.resource_lock = asyncio.Lock()
self.persona_mgr = persona_mgr
self.acm = acm
config = acm.confs["default"]
self.providers_config: list = config["provider"]
self.provider_sources_config: list = config.get("provider_sources", [])
self.provider_settings: dict = config["provider_settings"]
self.provider_stt_settings: dict = config.get("provider_stt_settings", {})
self.provider_tts_settings: dict = config.get("provider_tts_settings", {})
@@ -151,7 +148,6 @@ class ProviderManager:
"""
provider = None
provider_id = None
if umo:
provider_id = sp.get(
f"provider_perf_{provider_type.value}",
@@ -189,12 +185,6 @@ class ProviderManager:
)
else:
raise ValueError(f"Unknown provider type: {provider_type}")
if not provider and provider_id:
logger.warning(
f"没有找到 ID 为 {provider_id} 的提供商,这可能是由于您修改了提供商(模型)ID 导致的。"
)
return provider
async def initialize(self):
@@ -261,136 +251,7 @@ class ProviderManager:
# 初始化 MCP Client 连接
asyncio.create_task(self.llm_tools.init_mcp_clients(), name="init_mcp_clients")
def dynamic_import_provider(self, type: str):
"""动态导入提供商适配器模块
Args:
type (str): 提供商请求类型。
Raises:
ImportError: 如果提供商类型未知或无法导入对应模块,则抛出异常。
"""
match type:
case "openai_chat_completion":
from .sources.openai_source import (
ProviderOpenAIOfficial as ProviderOpenAIOfficial,
)
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 "googlegenai_chat_completion":
from .sources.gemini_source import (
ProviderGoogleGenAI as ProviderGoogleGenAI,
)
case "sensevoice_stt_selfhost":
from .sources.sensevoice_selfhosted_source import (
ProviderSenseVoiceSTTSelfHost as ProviderSenseVoiceSTTSelfHost,
)
case "openai_whisper_api":
from .sources.whisper_api_source import (
ProviderOpenAIWhisperAPI as ProviderOpenAIWhisperAPI,
)
case "openai_whisper_selfhost":
from .sources.whisper_selfhosted_source import (
ProviderOpenAIWhisperSelfHost as ProviderOpenAIWhisperSelfHost,
)
case "xinference_stt":
from .sources.xinference_stt_provider import (
ProviderXinferenceSTT as ProviderXinferenceSTT,
)
case "openai_tts_api":
from .sources.openai_tts_api_source import (
ProviderOpenAITTSAPI as ProviderOpenAITTSAPI,
)
case "edge_tts":
from .sources.edge_tts_source import (
ProviderEdgeTTS as ProviderEdgeTTS,
)
case "gsv_tts_selfhost":
from .sources.gsv_selfhosted_source import (
ProviderGSVTTS as ProviderGSVTTS,
)
case "gsvi_tts_api":
from .sources.gsvi_tts_source import (
ProviderGSVITTS as ProviderGSVITTS,
)
case "fishaudio_tts_api":
from .sources.fishaudio_tts_api_source import (
ProviderFishAudioTTSAPI as ProviderFishAudioTTSAPI,
)
case "dashscope_tts":
from .sources.dashscope_tts import (
ProviderDashscopeTTSAPI as ProviderDashscopeTTSAPI,
)
case "azure_tts":
from .sources.azure_tts_source import (
AzureTTSProvider as AzureTTSProvider,
)
case "minimax_tts_api":
from .sources.minimax_tts_api_source import (
ProviderMiniMaxTTSAPI as ProviderMiniMaxTTSAPI,
)
case "volcengine_tts":
from .sources.volcengine_tts import (
ProviderVolcengineTTS as ProviderVolcengineTTS,
)
case "gemini_tts":
from .sources.gemini_tts_source import (
ProviderGeminiTTSAPI as ProviderGeminiTTSAPI,
)
case "openai_embedding":
from .sources.openai_embedding_source import (
OpenAIEmbeddingProvider as OpenAIEmbeddingProvider,
)
case "gemini_embedding":
from .sources.gemini_embedding_source import (
GeminiEmbeddingProvider as GeminiEmbeddingProvider,
)
case "vllm_rerank":
from .sources.vllm_rerank_source import (
VLLMRerankProvider as VLLMRerankProvider,
)
case "xinference_rerank":
from .sources.xinference_rerank_source import (
XinferenceRerankProvider as XinferenceRerankProvider,
)
case "bailian_rerank":
from .sources.bailian_rerank_source import (
BailianRerankProvider as BailianRerankProvider,
)
def get_merged_provider_config(self, provider_config: dict) -> dict:
"""获取 provider 配置和 provider_source 配置合并后的结果
Returns:
dict: 合并后的 provider 配置,key 为 provider idvalue 为合并后的配置字典
"""
pc = copy.deepcopy(provider_config)
provider_source_id = pc.get("provider_source_id", "")
if provider_source_id:
provider_source = None
for ps in self.provider_sources_config:
if ps.get("id") == provider_source_id:
provider_source = ps
break
if provider_source:
# 合并配置,provider 的配置优先级更高
merged_config = {**provider_source, **pc}
# 保持 id 为 provider 的 id,而不是 source 的 id
merged_config["id"] = pc["id"]
pc = merged_config
return pc
async def load_provider(self, provider_config: dict):
# 如果 provider_source_id 存在且不为空,则从 provider_sources 中找到对应的配置并合并
provider_config = self.get_merged_provider_config(provider_config)
if not provider_config["enable"]:
logger.info(f"Provider {provider_config['id']} is disabled, skipping")
return
@@ -403,7 +264,99 @@ class ProviderManager:
# 动态导入
try:
self.dynamic_import_provider(provider_config["type"])
match provider_config["type"]:
case "openai_chat_completion":
from .sources.openai_source import (
ProviderOpenAIOfficial as ProviderOpenAIOfficial,
)
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 "googlegenai_chat_completion":
from .sources.gemini_source import (
ProviderGoogleGenAI as ProviderGoogleGenAI,
)
case "sensevoice_stt_selfhost":
from .sources.sensevoice_selfhosted_source import (
ProviderSenseVoiceSTTSelfHost as ProviderSenseVoiceSTTSelfHost,
)
case "openai_whisper_api":
from .sources.whisper_api_source import (
ProviderOpenAIWhisperAPI as ProviderOpenAIWhisperAPI,
)
case "openai_whisper_selfhost":
from .sources.whisper_selfhosted_source import (
ProviderOpenAIWhisperSelfHost as ProviderOpenAIWhisperSelfHost,
)
case "xinference_stt":
from .sources.xinference_stt_provider import (
ProviderXinferenceSTT as ProviderXinferenceSTT,
)
case "openai_tts_api":
from .sources.openai_tts_api_source import (
ProviderOpenAITTSAPI as ProviderOpenAITTSAPI,
)
case "edge_tts":
from .sources.edge_tts_source import (
ProviderEdgeTTS as ProviderEdgeTTS,
)
case "gsv_tts_selfhost":
from .sources.gsv_selfhosted_source import (
ProviderGSVTTS as ProviderGSVTTS,
)
case "gsvi_tts_api":
from .sources.gsvi_tts_source import (
ProviderGSVITTS as ProviderGSVITTS,
)
case "fishaudio_tts_api":
from .sources.fishaudio_tts_api_source import (
ProviderFishAudioTTSAPI as ProviderFishAudioTTSAPI,
)
case "dashscope_tts":
from .sources.dashscope_tts import (
ProviderDashscopeTTSAPI as ProviderDashscopeTTSAPI,
)
case "azure_tts":
from .sources.azure_tts_source import (
AzureTTSProvider as AzureTTSProvider,
)
case "minimax_tts_api":
from .sources.minimax_tts_api_source import (
ProviderMiniMaxTTSAPI as ProviderMiniMaxTTSAPI,
)
case "volcengine_tts":
from .sources.volcengine_tts import (
ProviderVolcengineTTS as ProviderVolcengineTTS,
)
case "gemini_tts":
from .sources.gemini_tts_source import (
ProviderGeminiTTSAPI as ProviderGeminiTTSAPI,
)
case "openai_embedding":
from .sources.openai_embedding_source import (
OpenAIEmbeddingProvider as OpenAIEmbeddingProvider,
)
case "gemini_embedding":
from .sources.gemini_embedding_source import (
GeminiEmbeddingProvider as GeminiEmbeddingProvider,
)
case "vllm_rerank":
from .sources.vllm_rerank_source import (
VLLMRerankProvider as VLLMRerankProvider,
)
case "xinference_rerank":
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。",
@@ -546,7 +499,6 @@ class ProviderManager:
# 和配置文件保持同步
self.providers_config = astrbot_config["provider"]
self.provider_sources_config = astrbot_config.get("provider_sources", [])
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()):
@@ -618,68 +570,6 @@ class ProviderManager:
)
del self.inst_map[provider_id]
async def delete_provider(
self, provider_id: str | None = None, provider_source_id: str | None = None
):
"""Delete provider and/or provider source from config and terminate the instances. Config will be saved after deletion."""
async with self.resource_lock:
# delete from config
target_prov_ids = []
if provider_id:
target_prov_ids.append(provider_id)
else:
for prov in self.providers_config:
if prov.get("provider_source_id") == provider_source_id:
target_prov_ids.append(prov.get("id"))
config = self.acm.default_conf
for tpid in target_prov_ids:
await self.terminate_provider(tpid)
config["provider"] = [
prov for prov in config["provider"] if prov.get("id") != tpid
]
config.save_config()
logger.info(f"Provider {target_prov_ids} 已从配置中删除。")
async def update_provider(self, origin_provider_id: str, new_config: dict):
"""Update provider config and reload the instance. Config will be saved after update."""
async with self.resource_lock:
npid = new_config.get("id", None)
if not npid:
raise ValueError("New provider config must have an 'id' field")
config = self.acm.default_conf
for provider in config["provider"]:
if (
provider.get("id", None) == npid
and provider.get("id", None) != origin_provider_id
):
raise ValueError(f"Provider ID {npid} already exists")
# update config
for idx, provider in enumerate(config["provider"]):
if provider.get("id", None) == origin_provider_id:
config["provider"][idx] = new_config
break
else:
raise ValueError(f"Provider ID {origin_provider_id} not found")
config.save_config()
# reload instance
await self.reload(new_config)
async def create_provider(self, new_config: dict):
"""Add new provider config and load the instance. Config will be saved after addition."""
async with self.resource_lock:
npid = new_config.get("id", None)
if not npid:
raise ValueError("New provider config must have an 'id' field")
config = self.acm.default_conf
for provider in config["provider"]:
if provider.get("id", None) == npid:
raise ValueError(f"Provider ID {npid} already exists")
# add to config
config["provider"].append(new_config)
config.save_config()
# load instance
await self.load_provider(new_config)
async def terminate(self):
for provider_inst in self.provider_insts:
if hasattr(provider_inst, "terminate"):
+1 -5
View File
@@ -4,7 +4,7 @@ import os
from collections.abc import AsyncGenerator
from typing import TypeAlias, Union
from astrbot.core.agent.message import ContentPart, Message
from astrbot.core.agent.message import Message
from astrbot.core.agent.tool import ToolSet
from astrbot.core.provider.entities import (
LLMResponse,
@@ -103,7 +103,6 @@ class Provider(AbstractProvider):
system_prompt: str | None = None,
tool_calls_result: ToolCallsResult | list[ToolCallsResult] | None = None,
model: str | None = None,
extra_user_content_parts: list[ContentPart] | None = None,
**kwargs,
) -> LLMResponse:
"""获得 LLM 的文本对话结果。会使用当前的模型进行对话。
@@ -115,7 +114,6 @@ class Provider(AbstractProvider):
tools: tool set
contexts: 上下文,和 prompt 二选一使用
tool_calls_result: 回传给 LLM 的工具调用结果。参考: https://platform.openai.com/docs/guides/function-calling
extra_user_content_parts: 额外的用户内容块列表,用于在用户消息后添加额外的文本块(如系统提醒、指令等)
kwargs: 其他参数
Notes:
@@ -135,7 +133,6 @@ class Provider(AbstractProvider):
system_prompt: str | None = None,
tool_calls_result: ToolCallsResult | list[ToolCallsResult] | None = None,
model: str | None = None,
extra_user_content_parts: list[ContentPart] | None = None,
**kwargs,
) -> AsyncGenerator[LLMResponse, None]:
"""获得 LLM 的流式文本对话结果。会使用当前的模型进行对话。在生成的最后会返回一次完整的结果。
@@ -147,7 +144,6 @@ class Provider(AbstractProvider):
tools: tool set
contexts: 上下文,和 prompt 二选一使用
tool_calls_result: 回传给 LLM 的工具调用结果。参考: https://platform.openai.com/docs/guides/function-calling
extra_user_content_parts: 额外的用户内容块列表,用于在用户消息后添加额外的文本块(如系统提醒、指令等)
kwargs: 其他参数
Notes:
+47 -125
View File
@@ -11,7 +11,6 @@ from anthropic.types.usage import Usage
from astrbot import logger
from astrbot.api.provider import Provider
from astrbot.core.agent.message import ContentPart
from astrbot.core.provider.entities import LLMResponse, TokenUsage
from astrbot.core.provider.func_tool_manager import ToolSet
from astrbot.core.utils.io import download_image_by_url
@@ -48,7 +47,7 @@ class ProviderAnthropic(Provider):
base_url=self.base_url,
)
self.set_model(provider_config.get("model", "unknown"))
self.set_model(provider_config["model_config"]["model"])
def _prepare_payload(self, messages: list[dict]):
"""准备 Anthropic API 的请求 payload
@@ -131,11 +130,7 @@ class ProviderAnthropic(Provider):
if tool_list := tools.get_func_desc_anthropic_style():
payloads["tools"] = tool_list
extra_body = self.provider_config.get("custom_extra_body", {})
completion = await self.client.messages.create(
**payloads, stream=False, extra_body=extra_body
)
completion = await self.client.messages.create(**payloads, stream=False)
assert isinstance(completion, Message)
logger.debug(f"completion: {completion}")
@@ -178,13 +173,11 @@ class ProviderAnthropic(Provider):
# 用于累积最终结果
final_text = ""
final_tool_calls = []
id = None
usage = TokenUsage()
extra_body = self.provider_config.get("custom_extra_body", {})
async with self.client.messages.stream(
**payloads, extra_body=extra_body
) as stream:
async with self.client.messages.stream(**payloads) as stream:
assert isinstance(stream, anthropic.AsyncMessageStream)
async for event in stream:
if event.type == "message_start":
@@ -297,16 +290,13 @@ class ProviderAnthropic(Provider):
system_prompt=None,
tool_calls_result=None,
model=None,
extra_user_content_parts=None,
**kwargs,
) -> LLMResponse:
if contexts is None:
contexts = []
new_record = None
if prompt is not None:
new_record = await self.assemble_context(
prompt, image_urls, extra_user_content_parts
)
new_record = await self.assemble_context(prompt, image_urls)
context_query = self._ensure_message_to_dicts(contexts)
if new_record:
context_query.append(new_record)
@@ -328,9 +318,10 @@ class ProviderAnthropic(Provider):
system_prompt, new_messages = self._prepare_payload(context_query)
model = model or self.get_model()
model_config = self.provider_config.get("model_config", {})
model_config["model"] = model or self.get_model()
payloads = {"messages": new_messages, "model": model}
payloads = {"messages": new_messages, **model_config}
# Anthropic has a different way of handling system prompts
if system_prompt:
@@ -340,6 +331,7 @@ 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}")
raise e
return llm_response
@@ -354,16 +346,13 @@ class ProviderAnthropic(Provider):
system_prompt=None,
tool_calls_result=None,
model=None,
extra_user_content_parts=None,
**kwargs,
):
if contexts is None:
contexts = []
new_record = None
if prompt is not None:
new_record = await self.assemble_context(
prompt, image_urls, extra_user_content_parts
)
new_record = await self.assemble_context(prompt, image_urls)
context_query = self._ensure_message_to_dicts(contexts)
if new_record:
context_query.append(new_record)
@@ -384,9 +373,10 @@ class ProviderAnthropic(Provider):
system_prompt, new_messages = self._prepare_payload(context_query)
model = model or self.get_model()
model_config = self.provider_config.get("model_config", {})
model_config["model"] = model or self.get_model()
payloads = {"messages": new_messages, "model": model}
payloads = {"messages": new_messages, **model_config}
# Anthropic has a different way of handling system prompts
if system_prompt:
@@ -395,116 +385,48 @@ class ProviderAnthropic(Provider):
async for llm_response in self._query_stream(payloads, func_tool):
yield llm_response
async def assemble_context(
self,
text: str,
image_urls: list[str] | None = None,
extra_user_content_parts: list[ContentPart] | None = None,
):
async def assemble_context(self, text: str, image_urls: list[str] | None = None):
"""组装上下文,支持文本和图片"""
if not image_urls:
return {"role": "user", "content": text}
content = []
content.append({"type": "text", "text": text})
# 1. 用户原始发言(OpenAI 建议:用户发言在前)
if text:
content.append({"type": "text", "text": text})
elif image_urls:
# 如果没有文本但有图片,添加占位文本
content.append({"type": "text", "text": "[图片]"})
elif extra_user_content_parts:
# 如果只有额外内容块,也需要添加占位文本
content.append({"type": "text", "text": " "})
for image_url in image_urls:
if image_url.startswith("http"):
image_path = await download_image_by_url(image_url)
image_data = await self.encode_image_bs64(image_path)
elif image_url.startswith("file:///"):
image_path = image_url.replace("file:///", "")
image_data = await self.encode_image_bs64(image_path)
else:
image_data = await self.encode_image_bs64(image_url)
# 2. 额外的内容块(系统提醒、指令等)
if extra_user_content_parts:
for block in extra_user_content_parts:
block_type = block.get("type")
if not image_data:
logger.warning(f"图片 {image_url} 得到的结果为空,将忽略。")
continue
if block_type == "text":
# 文本直接添加
content.append(block)
# Get mime type for the image
mime_type, _ = guess_type(image_url)
if not mime_type:
mime_type = "image/jpeg" # Default to JPEG if can't determine
elif block_type == "image_url":
# 转换 OpenAI 格式的图片为 Anthropic 格式
image_url_data = block.get("image_url", {})
if isinstance(image_url_data, dict):
url = image_url_data.get("url", "")
else:
# 兼容直接传 URL 字符串的情况
url = str(image_url_data)
if url and url.startswith("data:"):
try:
# 提取 MIME 类型和 base64 数据
mime_type = url.split(":")[1].split(";")[0]
base64_data = (
url.split("base64,")[1] if "base64," in url else url
)
content.append(
{
"type": "image",
"source": {
"type": "base64",
"media_type": mime_type,
"data": base64_data,
},
}
)
except Exception as e:
logger.warning(f"转换 image_url 到 Anthropic 格式失败: {e}")
else:
logger.warning(f"image_url 不是有效的 data URI: {url[:50]}...")
else:
# 其他类型(如 audio_urlAnthropic 不支持,记录警告
logger.debug(f"Anthropic 不支持的内容类型 '{block_type}',已忽略")
# 3. 图片内容
if image_urls:
for image_url in image_urls:
if image_url.startswith("http"):
image_path = await download_image_by_url(image_url)
image_data = await self.encode_image_bs64(image_path)
elif image_url.startswith("file:///"):
image_path = image_url.replace("file:///", "")
image_data = await self.encode_image_bs64(image_path)
else:
image_data = await self.encode_image_bs64(image_url)
if not image_data:
logger.warning(f"图片 {image_url} 得到的结果为空,将忽略。")
continue
# Get mime type for the image
mime_type, _ = guess_type(image_url)
if not mime_type:
mime_type = "image/jpeg" # Default to JPEG if can't determine
content.append(
{
"type": "image",
"source": {
"type": "base64",
"media_type": mime_type,
"data": (
image_data.split("base64,")[1]
if "base64," in image_data
else image_data
),
},
content.append(
{
"type": "image",
"source": {
"type": "base64",
"media_type": mime_type,
"data": (
image_data.split("base64,")[1]
if "base64," in image_data
else image_data
),
},
)
},
)
# 如果只有主文本且没有额外内容块和图片,返回简单格式以保持向后兼容
if (
text
and not extra_user_content_parts
and not image_urls
and len(content) == 1
and content[0]["type"] == "text"
):
return {"role": "user", "content": content[0]["text"]}
# 否则返回多模态格式
return {"role": "user", "content": content}
async def encode_image_bs64(self, image_url: str) -> str:
+20 -72
View File
@@ -13,7 +13,6 @@ from google.genai.errors import APIError
import astrbot.core.message.components as Comp
from astrbot import logger
from astrbot.api.provider import Provider
from astrbot.core.agent.message import ContentPart
from astrbot.core.message.message_event_result import MessageChain
from astrbot.core.provider.entities import LLMResponse, TokenUsage
from astrbot.core.provider.func_tool_manager import ToolSet
@@ -69,7 +68,7 @@ class ProviderGoogleGenAI(Provider):
self.api_base = self.api_base[:-1]
self._init_client()
self.set_model(provider_config.get("model", "unknown"))
self.set_model(provider_config["model_config"]["model"])
self._init_safety_settings()
def _init_client(self) -> None:
@@ -139,7 +138,7 @@ class ProviderGoogleGenAI(Provider):
modalities = ["TEXT"]
tool_list: list[types.Tool] | None = []
model_name = cast(str, payloads.get("model", self.get_model()))
model_name = payloads.get("model", self.get_model())
native_coderunner = self.provider_config.get("gm_native_coderunner", False)
native_search = self.provider_config.get("gm_native_search", False)
url_context = self.provider_config.get("gm_url_context", False)
@@ -200,16 +199,7 @@ class ProviderGoogleGenAI(Provider):
# oper thinking config
thinking_config = None
if model_name in [
"gemini-2.5-pro",
"gemini-2.5-pro-preview",
"gemini-2.5-flash",
"gemini-2.5-flash-preview",
"gemini-2.5-flash-lite",
"gemini-2.5-flash-lite-preview",
"gemini-robotics-er-1.5-preview",
"gemini-live-2.5-flash-preview-native-audio-09-2025",
]:
if model_name.startswith("gemini-2.5"):
# The thinkingBudget parameter, introduced with the Gemini 2.5 series
thinking_budget = self.provider_config.get("gm_thinking_config", {}).get(
"budget", 0
@@ -218,14 +208,7 @@ class ProviderGoogleGenAI(Provider):
thinking_config = types.ThinkingConfig(
thinking_budget=thinking_budget,
)
elif model_name in [
"gemini-3-pro",
"gemini-3-pro-preview",
"gemini-3-flash",
"gemini-3-flash-preview",
"gemini-3-flash-lite",
"gemini-3-flash-lite-preview",
]:
elif model_name.startswith("gemini-3"):
# The thinkingLevel parameter, recommended for Gemini 3 models and onwards
# Gemini 2.5 series models don't support thinkingLevel; use thinkingBudget instead.
thinking_level = self.provider_config.get("gm_thinking_config", {}).get(
@@ -681,16 +664,13 @@ class ProviderGoogleGenAI(Provider):
system_prompt=None,
tool_calls_result=None,
model=None,
extra_user_content_parts=None,
**kwargs,
) -> LLMResponse:
if contexts is None:
contexts = []
new_record = None
if prompt is not None:
new_record = await self.assemble_context(
prompt, image_urls, extra_user_content_parts
)
new_record = await self.assemble_context(prompt, image_urls)
context_query = self._ensure_message_to_dicts(contexts)
if new_record:
context_query.append(new_record)
@@ -709,9 +689,10 @@ class ProviderGoogleGenAI(Provider):
for tcr in tool_calls_result:
context_query.extend(tcr.to_openai_messages())
model = model or self.get_model()
model_config = self.provider_config.get("model_config", {})
model_config["model"] = model or self.get_model()
payloads = {"messages": context_query, "model": model}
payloads = {"messages": context_query, **model_config}
retry = 10
keys = self.api_keys.copy()
@@ -736,16 +717,13 @@ class ProviderGoogleGenAI(Provider):
system_prompt=None,
tool_calls_result=None,
model=None,
extra_user_content_parts=None,
**kwargs,
) -> AsyncGenerator[LLMResponse, None]:
if contexts is None:
contexts = []
new_record = None
if prompt is not None:
new_record = await self.assemble_context(
prompt, image_urls, extra_user_content_parts
)
new_record = await self.assemble_context(prompt, image_urls)
context_query = self._ensure_message_to_dicts(contexts)
if new_record:
context_query.append(new_record)
@@ -764,9 +742,10 @@ class ProviderGoogleGenAI(Provider):
for tcr in tool_calls_result:
context_query.extend(tcr.to_openai_messages())
model = model or self.get_model()
model_config = self.provider_config.get("model_config", {})
model_config["model"] = model or self.get_model()
payloads = {"messages": context_query, "model": model}
payloads = {"messages": context_query, **model_config}
retry = 10
keys = self.api_keys.copy()
@@ -804,33 +783,13 @@ class ProviderGoogleGenAI(Provider):
self.chosen_api_key = key
self._init_client()
async def assemble_context(
self,
text: str,
image_urls: list[str] | None = None,
extra_user_content_parts: list[ContentPart] | None = None,
):
async def assemble_context(self, text: str, image_urls: list[str] | None = None):
"""组装上下文。"""
# 构建内容块列表
content_blocks = []
# 1. 用户原始发言(OpenAI 建议:用户发言在前)
if text:
content_blocks.append({"type": "text", "text": text})
elif image_urls:
# 如果没有文本但有图片,添加占位文本
content_blocks.append({"type": "text", "text": "[图片]"})
elif extra_user_content_parts:
# 如果只有额外内容块,也需要添加占位文本
content_blocks.append({"type": "text", "text": " "})
# 2. 额外的内容块(系统提醒、指令等)
if extra_user_content_parts:
for part in extra_user_content_parts:
content_blocks.append(part.model_dump())
# 3. 图片内容
if image_urls:
user_content = {
"role": "user",
"content": [{"type": "text", "text": text if text else "[图片]"}],
}
for image_url in image_urls:
if image_url.startswith("http"):
image_path = await download_image_by_url(image_url)
@@ -843,25 +802,14 @@ class ProviderGoogleGenAI(Provider):
if not image_data:
logger.warning(f"图片 {image_url} 得到的结果为空,将忽略。")
continue
content_blocks.append(
user_content["content"].append(
{
"type": "image_url",
"image_url": {"url": image_data},
},
)
# 如果只有主文本且没有额外内容块和图片,返回简单格式以保持向后兼容
if (
text
and not extra_user_content_parts
and not image_urls
and len(content_blocks) == 1
and content_blocks[0]["type"] == "text"
):
return {"role": "user", "content": content_blocks[0]["text"]}
# 否则返回多模态格式
return {"role": "user", "content": content_blocks}
return user_content
return {"role": "user", "content": text}
async def encode_image_bs64(self, image_url: str) -> str:
"""将图片转换为 base64"""
+14 -46
View File
@@ -17,7 +17,7 @@ from openai.types.completion_usage import CompletionUsage
import astrbot.core.message.components as Comp
from astrbot import logger
from astrbot.api.provider import Provider
from astrbot.core.agent.message import ContentPart, Message
from astrbot.core.agent.message import Message
from astrbot.core.agent.tool import ToolSet
from astrbot.core.message.message_event_result import MessageChain
from astrbot.core.provider.entities import LLMResponse, TokenUsage, ToolCallsResult
@@ -69,7 +69,8 @@ class ProviderOpenAIOfficial(Provider):
self.client.chat.completions.create,
).parameters.keys()
model = provider_config.get("model", "unknown")
model_config = provider_config.get("model_config", {})
model = model_config.get("model", "unknown")
self.set_model(model)
self.reasoning_key = "reasoning_content"
@@ -348,7 +349,6 @@ class ProviderOpenAIOfficial(Provider):
system_prompt: str | None = None,
tool_calls_result: ToolCallsResult | list[ToolCallsResult] | None = None,
model: str | None = None,
extra_user_content_parts: list[ContentPart] | None = None,
**kwargs,
) -> tuple:
"""准备聊天所需的有效载荷和上下文"""
@@ -356,9 +356,7 @@ class ProviderOpenAIOfficial(Provider):
contexts = []
new_record = None
if prompt is not None:
new_record = await self.assemble_context(
prompt, image_urls, extra_user_content_parts
)
new_record = await self.assemble_context(prompt, image_urls)
context_query = self._ensure_message_to_dicts(contexts)
if new_record:
context_query.append(new_record)
@@ -377,9 +375,10 @@ class ProviderOpenAIOfficial(Provider):
for tcr in tool_calls_result:
context_query.extend(tcr.to_openai_messages())
model = model or self.get_model()
model_config = self.provider_config.get("model_config", {})
model_config["model"] = model or self.get_model()
payloads = {"messages": context_query, "model": model}
payloads = {"messages": context_query, **model_config}
# xAI origin search tool inject
self._maybe_inject_xai_search(payloads, **kwargs)
@@ -479,7 +478,6 @@ class ProviderOpenAIOfficial(Provider):
system_prompt=None,
tool_calls_result=None,
model=None,
extra_user_content_parts=None,
**kwargs,
) -> LLMResponse:
payloads, context_query = await self._prepare_chat_payload(
@@ -489,7 +487,6 @@ class ProviderOpenAIOfficial(Provider):
system_prompt,
tool_calls_result,
model=model,
extra_user_content_parts=extra_user_content_parts,
**kwargs,
)
@@ -544,7 +541,6 @@ class ProviderOpenAIOfficial(Provider):
system_prompt=None,
tool_calls_result=None,
model=None,
extra_user_content_parts=None,
**kwargs,
) -> AsyncGenerator[LLMResponse, None]:
"""流式对话,与服务商交互并逐步返回结果"""
@@ -555,7 +551,6 @@ class ProviderOpenAIOfficial(Provider):
system_prompt,
tool_calls_result,
model=model,
extra_user_content_parts=extra_user_content_parts,
**kwargs,
)
@@ -631,29 +626,13 @@ class ProviderOpenAIOfficial(Provider):
self,
text: str,
image_urls: list[str] | None = None,
extra_user_content_parts: list[ContentPart] | None = None,
) -> dict:
"""组装成符合 OpenAI 格式的 role 为 user 的消息段"""
# 构建内容块列表
content_blocks = []
# 1. 用户原始发言(OpenAI 建议:用户发言在前)
if text:
content_blocks.append({"type": "text", "text": text})
elif image_urls:
# 如果没有文本但有图片,添加占位文本
content_blocks.append({"type": "text", "text": "[图片]"})
elif extra_user_content_parts:
# 如果只有额外内容块,也需要添加占位文本
content_blocks.append({"type": "text", "text": " "})
# 2. 额外的内容块(系统提醒、指令等)
if extra_user_content_parts:
for part in extra_user_content_parts:
content_blocks.append(part.model_dump())
# 3. 图片内容
if image_urls:
user_content = {
"role": "user",
"content": [{"type": "text", "text": text if text else "[图片]"}],
}
for image_url in image_urls:
if image_url.startswith("http"):
image_path = await download_image_by_url(image_url)
@@ -666,25 +645,14 @@ class ProviderOpenAIOfficial(Provider):
if not image_data:
logger.warning(f"图片 {image_url} 得到的结果为空,将忽略。")
continue
content_blocks.append(
user_content["content"].append(
{
"type": "image_url",
"image_url": {"url": image_data},
},
)
# 如果只有主文本且没有额外内容块和图片,返回简单格式以保持向后兼容
if (
text
and not extra_user_content_parts
and not image_urls
and len(content_blocks) == 1
and content_blocks[0]["type"] == "text"
):
return {"role": "user", "content": content_blocks[0]["text"]}
# 否则返回多模态格式
return {"role": "user", "content": content_blocks}
return user_content
return {"role": "user", "content": text}
async def encode_image_bs64(self, image_url: str) -> str:
"""将图片转换为 base64"""
+10 -57
View File
@@ -4,7 +4,7 @@ from collections import defaultdict
from dataclasses import dataclass, field
from typing import Any
from astrbot.core import db_helper, logger
from astrbot.core import db_helper
from astrbot.core.db.po import CommandConfig
from astrbot.core.star.filter.command import CommandFilter
from astrbot.core.star.filter.command_group import CommandGroupFilter
@@ -90,7 +90,6 @@ async def toggle_command(handler_full_name: str, enabled: bool) -> CommandDescri
async def rename_command(
handler_full_name: str,
new_fragment: str,
aliases: list[str] | None = None,
) -> CommandDescriptor:
descriptor = _build_descriptor_by_full_name(handler_full_name)
if not descriptor:
@@ -100,24 +99,9 @@ async def rename_command(
if not new_fragment:
raise ValueError("指令名不能为空。")
# 校验主指令名
candidate_full = _compose_command(descriptor.parent_signature, new_fragment)
if _is_command_in_use(handler_full_name, candidate_full):
raise ValueError(f"指令名 '{candidate_full}' 已被其他指令占用。")
# 校验别名
if aliases:
for alias in aliases:
alias = alias.strip()
if not alias:
continue
alias_full = _compose_command(descriptor.parent_signature, alias)
if _is_command_in_use(handler_full_name, alias_full):
raise ValueError(f"别名 '{alias_full}' 已被其他指令占用。")
existing_cfg = await db_helper.get_command_config(handler_full_name)
merged_extra = dict(existing_cfg.extra_data or {}) if existing_cfg else {}
merged_extra["resolved_aliases"] = aliases or []
raise ValueError("新的指令名已被其他指令占用,请换一个名称")
config = await db_helper.upsert_command_config(
handler_full_name=handler_full_name,
@@ -130,7 +114,7 @@ async def rename_command(
conflict_key=descriptor.original_command,
resolution_strategy="manual_rename",
note=None,
extra_data=merged_extra,
extra_data=None,
auto_managed=False,
)
_bind_descriptor_with_config(descriptor, config)
@@ -208,18 +192,12 @@ def _collect_descriptors(include_sub_commands: bool) -> list[CommandDescriptor]:
"""收集指令,按需包含子指令。"""
descriptors: list[CommandDescriptor] = []
for handler in star_handlers_registry:
try:
desc = _build_descriptor(handler)
if not desc:
continue
if not include_sub_commands and desc.is_sub_command:
continue
descriptors.append(desc)
except Exception as e:
logger.warning(
f"解析指令处理函数 {handler.handler_full_name} 失败,跳过该指令。原因: {e!s}"
)
desc = _build_descriptor(handler)
if not desc:
continue
if not include_sub_commands and desc.is_sub_command:
continue
descriptors.append(desc)
return descriptors
@@ -379,27 +357,14 @@ def _apply_config_to_descriptor(
new_fragment,
)
extra = config.extra_data or {}
resolved_aliases = extra.get("resolved_aliases")
if isinstance(resolved_aliases, list):
descriptor.aliases = [str(x) for x in resolved_aliases if str(x).strip()]
def _apply_config_to_runtime(
descriptor: CommandDescriptor,
config: CommandConfig,
) -> None:
descriptor.handler.enabled = config.enabled
if descriptor.filter_ref:
if descriptor.current_fragment:
_set_filter_fragment(descriptor.filter_ref, descriptor.current_fragment)
extra = config.extra_data or {}
resolved_aliases = extra.get("resolved_aliases")
if isinstance(resolved_aliases, list):
_set_filter_aliases(
descriptor.filter_ref,
[str(x) for x in resolved_aliases if str(x).strip()],
)
if descriptor.filter_ref and descriptor.current_fragment:
_set_filter_fragment(descriptor.filter_ref, descriptor.current_fragment)
def _bind_configs_to_descriptors(
@@ -438,18 +403,6 @@ def _set_filter_fragment(
filter_ref._cmpl_cmd_names = None
def _set_filter_aliases(
filter_ref: CommandFilter | CommandGroupFilter,
aliases: list[str],
) -> None:
current_aliases = getattr(filter_ref, "alias", set())
if set(aliases) == current_aliases:
return
setattr(filter_ref, "alias", set(aliases))
if hasattr(filter_ref, "_cmpl_cmd_names"):
filter_ref._cmpl_cmd_names = None
def _is_command_in_use(
target_handler_full_name: str,
candidate_full_command: str,
+4 -4
View File
@@ -267,10 +267,6 @@ class Context:
):
"""通过 ID 获取对应的 LLM Provider。"""
prov = self.provider_manager.inst_map.get(provider_id)
if provider_id and not prov:
logger.warning(
f"没有找到 ID 为 {provider_id} 的提供商,这可能是由于您修改了提供商(模型)ID 导致的。"
)
return prov
def get_all_providers(self) -> list[Provider]:
@@ -300,6 +296,10 @@ class Context:
provider_type=ProviderType.CHAT_COMPLETION,
umo=umo,
)
if prov is None:
raise ProviderNotFoundError(
"provider not found, please choose provider first"
)
if not isinstance(prov, Provider):
raise ValueError("返回的 Provider 不是 Provider 类型")
return prov
+1 -5
View File
@@ -631,11 +631,7 @@ class PluginManager:
# 清除 pip.main 导致的多余的 logging handlers
for handler in logging.root.handlers[:]:
logging.root.removeHandler(handler)
try:
await sync_command_configs()
except Exception as e:
logger.error(f"同步指令配置失败: {e!s}")
logger.error(traceback.format_exc())
await sync_command_configs()
if not fail_rec:
return True, None
-63
View File
@@ -1,63 +0,0 @@
from typing import Literal, TypedDict
import aiohttp
from astrbot.core import logger
class LLMModalities(TypedDict):
input: list[Literal["text", "image", "audio", "video"]]
output: list[Literal["text", "image", "audio", "video"]]
class LLMLimit(TypedDict):
context: int
output: int
class LLMMetadata(TypedDict):
id: str
reasoning: bool
tool_call: bool
knowledge: str
release_date: str
modalities: LLMModalities
open_weights: bool
limit: LLMLimit
LLM_METADATAS: dict[str, LLMMetadata] = {}
async def update_llm_metadata():
url = "https://models.dev/api.json"
try:
async with aiohttp.ClientSession() as session:
async with session.get(url) as response:
data = await response.json()
global LLM_METADATAS
models = {}
for info in data.values():
for model in info.get("models", {}).values():
model_id = model.get("id")
if not model_id:
continue
models[model_id] = LLMMetadata(
id=model_id,
reasoning=model.get("reasoning", False),
tool_call=model.get("tool_call", False),
knowledge=model.get("knowledge", "none"),
release_date=model.get("release_date", ""),
modalities=model.get(
"modalities", {"input": [], "output": []}
),
open_weights=model.get("open_weights", False),
limit=model.get("limit", {"context": 0, "output": 0}),
)
# Replace the global cache in-place so references remain valid
LLM_METADATAS.clear()
LLM_METADATAS.update(models)
logger.info(f"Successfully fetched metadata for {len(models)} LLMs.")
except Exception as e:
logger.error(f"Failed to fetch LLM metadata: {e}")
return
-93
View File
@@ -32,92 +32,6 @@ def _migra_agent_runner_configs(conf: AstrBotConfig, ids_map: dict) -> None:
logger.error(traceback.format_exc())
def _migra_provider_to_source_structure(conf: AstrBotConfig) -> None:
"""
Migrate old provider structure to new provider-source separation.
Provider only keeps: id, provider_source_id, model, modalities, custom_extra_body
All other fields move to provider_sources.
"""
providers = conf.get("provider", [])
provider_sources = conf.get("provider_sources", [])
# Track if any migration happened
migrated = False
# Provider-only fields that should stay in provider
provider_only_fields = {
"id",
"provider_source_id",
"model",
"modalities",
"custom_extra_body",
"enable",
}
# Fields that should not go to source
source_exclude_fields = provider_only_fields | {"model_config"}
for provider in providers:
# Skip if already has provider_source_id
if provider.get("provider_source_id"):
continue
# Skip non-chat-completion types (they don't need source separation)
provider_type = provider.get("provider_type", "")
if provider_type != "chat_completion":
# For old types without provider_type, check type field
old_type = provider.get("type", "")
if "chat_completion" not in old_type:
continue
migrated = True
logger.info(f"Migrating provider {provider.get('id')} to new structure")
# Extract source fields from provider
source_fields = {}
for key, value in list(provider.items()):
if key not in source_exclude_fields:
source_fields[key] = value
# Create new provider_source
source_id = provider.get("id", "") + "_source"
new_source = {"id": source_id, **source_fields}
# Update provider to only keep necessary fields
provider["provider_source_id"] = source_id
# Extract model from model_config if exists
if "model_config" in provider and isinstance(provider["model_config"], dict):
model_config = provider["model_config"]
provider["model"] = model_config.get("model", "")
# Put other model_config fields into custom_extra_body
extra_body_fields = {k: v for k, v in model_config.items() if k != "model"}
if extra_body_fields:
if "custom_extra_body" not in provider:
provider["custom_extra_body"] = {}
provider["custom_extra_body"].update(extra_body_fields)
# Initialize new fields if not present
if "modalities" not in provider:
provider["modalities"] = []
if "custom_extra_body" not in provider:
provider["custom_extra_body"] = {}
# Remove fields that should be in source
keys_to_remove = [k for k in provider.keys() if k not in provider_only_fields]
for key in keys_to_remove:
del provider[key]
# Add source to provider_sources
provider_sources.append(new_source)
if migrated:
conf["provider_sources"] = provider_sources
conf.save_config()
logger.info("Provider-source structure migration completed")
async def migra(
db, astrbot_config_mgr, umop_config_router, acm: AstrBotConfigManager
) -> None:
@@ -157,10 +71,3 @@ async def migra(
for conf in acm.confs.values():
_migra_agent_runner_configs(conf, ids_map)
# Migrate providers to new structure: extract source fields to provider_sources
try:
_migra_provider_to_source_structure(astrbot_config)
except Exception as e:
logger.error(f"Migration for provider-source structure failed: {e!s}")
logger.error(traceback.format_exc())
+1 -1
View File
@@ -436,7 +436,7 @@ class ChatRoute(Route):
accumulated_parts = []
accumulated_text = ""
accumulated_reasoning = ""
# tool_calls = {}
tool_calls = {}
agent_stats = {}
except BaseException as e:
logger.exception(f"WebChat stream unexpected error: {e}", exc_info=True)
+1 -2
View File
@@ -61,13 +61,12 @@ class CommandRoute(Route):
data = await request.get_json()
handler_full_name = data.get("handler_full_name")
new_name = data.get("new_name")
aliases = data.get("aliases")
if not handler_full_name or not new_name:
return Response().error("handler_full_name 与 new_name 均为必填。").__dict__
try:
await rename_command_service(handler_full_name, new_name, aliases=aliases)
await rename_command_service(handler_full_name, new_name)
except ValueError as exc:
return Response().error(str(exc)).__dict__
+33 -303
View File
@@ -6,7 +6,7 @@ from typing import Any
from quart import request
from astrbot.core import astrbot_config, file_token_service, logger
from astrbot.core import file_token_service, logger
from astrbot.core.config.astrbot_config import AstrBotConfig
from astrbot.core.config.default import (
CONFIG_METADATA_2,
@@ -21,7 +21,6 @@ from astrbot.core.platform.register import platform_cls_map, platform_registry
from astrbot.core.provider import Provider
from astrbot.core.provider.register import provider_registry
from astrbot.core.star.star import star_registry
from astrbot.core.utils.llm_metadata import LLM_METADATAS
from astrbot.core.utils.webhook_utils import ensure_platform_webhook_config
from .route import Response, Route, RouteContext
@@ -180,157 +179,13 @@ class ConfigRoute(Route):
"/config/provider/new": ("POST", self.post_new_provider),
"/config/provider/update": ("POST", self.post_update_provider),
"/config/provider/delete": ("POST", self.post_delete_provider),
"/config/provider/template": ("GET", self.get_provider_template),
"/config/provider/check_one": ("GET", self.check_one_provider_status),
"/config/provider/list": ("GET", self.get_provider_config_list),
"/config/provider/model_list": ("GET", self.get_provider_model_list),
"/config/provider/get_embedding_dim": ("POST", self.get_embedding_dim),
"/config/provider_sources/models": (
"GET",
self.get_provider_source_models,
),
"/config/provider_sources/update": (
"POST",
self.update_provider_source,
),
"/config/provider_sources/delete": (
"POST",
self.delete_provider_source,
),
}
self.register_routes()
async def delete_provider_source(self):
"""删除 provider_source,并更新关联的 providers"""
post_data = await request.json
if not post_data:
return Response().error("缺少配置数据").__dict__
provider_source_id = post_data.get("id")
if not provider_source_id:
return Response().error("缺少 provider_source_id").__dict__
provider_sources = self.config.get("provider_sources", [])
target_idx = next(
(
i
for i, ps in enumerate(provider_sources)
if ps.get("id") == provider_source_id
),
-1,
)
if target_idx == -1:
return Response().error("未找到对应的 provider source").__dict__
# 删除 provider_source
del provider_sources[target_idx]
# 写回配置
self.config["provider_sources"] = provider_sources
# 删除引用了该 provider_source 的 providers
await self.core_lifecycle.provider_manager.delete_provider(
provider_source_id=provider_source_id
)
try:
save_config(self.config, self.config, is_core=True)
except Exception as e:
logger.error(traceback.format_exc())
return Response().error(str(e)).__dict__
return Response().ok(message="删除 provider source 成功").__dict__
async def update_provider_source(self):
"""更新或新增 provider_source,并重载关联的 providers"""
post_data = await request.json
if not post_data:
return Response().error("缺少配置数据").__dict__
new_source_config = post_data.get("config") or post_data
original_id = post_data.get("original_id")
if not original_id:
return Response().error("缺少 original_id").__dict__
if not isinstance(new_source_config, dict):
return Response().error("缺少或错误的配置数据").__dict__
# 确保配置中有 id 字段
if not new_source_config.get("id"):
new_source_config["id"] = original_id
provider_sources = self.config.get("provider_sources", [])
for ps in provider_sources:
if ps.get("id") == new_source_config["id"] and ps.get("id") != original_id:
return (
Response()
.error(
f"Provider source ID '{new_source_config['id']}' exists already, please try another ID.",
)
.__dict__
)
# 查找旧的 provider_source,若不存在则追加为新配置
target_idx = next(
(i for i, ps in enumerate(provider_sources) if ps.get("id") == original_id),
-1,
)
old_id = original_id
if target_idx == -1:
provider_sources.append(new_source_config)
else:
old_id = provider_sources[target_idx].get("id")
provider_sources[target_idx] = new_source_config
# 更新引用了该 provider_source 的 providers
affected_providers = []
for provider in self.config.get("provider", []):
if provider.get("provider_source_id") == old_id:
provider["provider_source_id"] = new_source_config["id"]
affected_providers.append(provider)
# 写回配置
self.config["provider_sources"] = provider_sources
try:
save_config(self.config, self.config, is_core=True)
except Exception as e:
logger.error(traceback.format_exc())
return Response().error(str(e)).__dict__
# 重载受影响的 providers,使新的 source 配置生效
reload_errors = []
prov_mgr = self.core_lifecycle.provider_manager
for provider in affected_providers:
try:
await prov_mgr.reload(provider)
except Exception as e:
logger.error(traceback.format_exc())
reload_errors.append(f"{provider.get('id')}: {e}")
if reload_errors:
return (
Response()
.error("更新成功,但部分提供商重载失败: " + ", ".join(reload_errors))
.__dict__
)
return Response().ok(message="更新 provider source 成功").__dict__
async def get_provider_template(self):
config_schema = {
"provider": CONFIG_METADATA_2["provider_group"]["metadata"]["provider"]
}
data = {
"config_schema": config_schema,
"providers": astrbot_config["provider"],
"provider_sources": astrbot_config["provider_sources"],
}
return Response().ok(data=data).__dict__
async def get_uc_table(self):
"""获取 UMOP 配置路由表"""
return Response().ok({"routing": self.ucr.umop_to_conf_id}).__dict__
@@ -578,25 +433,9 @@ class ConfigRoute(Route):
return Response().error("缺少参数 provider_type").__dict__
provider_type_ls = provider_type.split(",")
provider_list = []
ps = self.core_lifecycle.provider_manager.providers_config
p_source_pt = {
psrc["id"]: psrc["provider_type"]
for psrc in self.core_lifecycle.provider_manager.provider_sources_config
}
for provider in ps:
ps_id = provider.get("provider_source_id", None)
if (
ps_id
and ps_id in p_source_pt
and p_source_pt[ps_id] in provider_type_ls
):
# chat
prov = self.core_lifecycle.provider_manager.get_merged_provider_config(
provider
)
provider_list.append(prov)
elif not ps_id and provider.get("provider_type", None) in provider_type_ls:
# agent runner, embedding, etc
astrbot_config = self.core_lifecycle.astrbot_config
for provider in astrbot_config["provider"]:
if provider.get("provider_type", None) in provider_type_ls:
provider_list.append(provider)
return Response().ok(provider_list).__dict__
@@ -619,18 +458,9 @@ class ConfigRoute(Route):
try:
models = await provider.get_models()
models = models or []
metadata_map = {}
for model_id in models:
meta = LLM_METADATAS.get(model_id)
if meta:
metadata_map[model_id] = meta
ret = {
"models": models,
"provider_id": provider_id,
"model_metadata": metadata_map,
}
return Response().ok(ret).__dict__
except Exception as e:
@@ -692,104 +522,6 @@ class ConfigRoute(Route):
logger.error(traceback.format_exc())
return Response().error(f"获取嵌入维度失败: {e!s}").__dict__
async def get_provider_source_models(self):
"""获取指定 provider_source 支持的模型列表
本质上会临时初始化一个 Provider 实例调用 get_models() 获取模型列表然后销毁实例
"""
provider_source_id = request.args.get("source_id")
if not provider_source_id:
return Response().error("缺少参数 source_id").__dict__
try:
from astrbot.core.provider.register import provider_cls_map
# 从配置中查找对应的 provider_source
provider_sources = self.config.get("provider_sources", [])
provider_source = None
for ps in provider_sources:
if ps.get("id") == provider_source_id:
provider_source = ps
break
if not provider_source:
return (
Response()
.error(f"未找到 ID 为 {provider_source_id} 的 provider_source")
.__dict__
)
# 获取 provider 类型
provider_type = provider_source.get("type", None)
if not provider_type:
return Response().error("provider_source 缺少 type 字段").__dict__
try:
self.core_lifecycle.provider_manager.dynamic_import_provider(
provider_type
)
except ImportError as e:
logger.error(traceback.format_exc())
return Response().error(f"动态导入提供商适配器失败: {e!s}").__dict__
# 获取对应的 provider 类
if provider_type not in provider_cls_map:
return (
Response()
.error(f"未找到适用于 {provider_type} 的提供商适配器")
.__dict__
)
provider_metadata = provider_cls_map[provider_type]
cls_type = provider_metadata.cls_type
if not cls_type:
return Response().error(f"无法找到 {provider_type} 的类").__dict__
# 检查是否是 Provider 类型
if not issubclass(cls_type, Provider):
return (
Response()
.error(f"提供商 {provider_type} 不支持获取模型列表")
.__dict__
)
# 临时实例化 provider
inst = cls_type(provider_source, {})
# 如果有 initialize 方法,调用它
init_fn = getattr(inst, "initialize", None)
if inspect.iscoroutinefunction(init_fn):
await init_fn()
# 获取模型列表
models = await inst.get_models()
models = models or []
metadata_map = {}
for model_id in models:
meta = LLM_METADATAS.get(model_id)
if meta:
metadata_map[model_id] = meta
# 销毁实例(如果有 terminate 方法)
terminate_fn = getattr(inst, "terminate", None)
if inspect.iscoroutinefunction(terminate_fn):
await terminate_fn()
logger.info(
f"获取到 provider_source {provider_source_id} 的模型列表: {models}",
)
return (
Response()
.ok({"models": models, "model_metadata": metadata_map})
.__dict__
)
except Exception as e:
logger.error(traceback.format_exc())
return Response().error(f"获取模型列表失败: {e!s}").__dict__
async def get_platform_list(self):
"""获取所有平台的列表"""
platform_list = []
@@ -801,15 +533,7 @@ class ConfigRoute(Route):
data = await request.json
config = data.get("config", None)
conf_id = data.get("conf_id", None)
try:
# 不更新 provider_sources, provider, platform
# 这些配置有单独的接口进行更新
if conf_id == "default":
no_update_keys = ["provider_sources", "provider", "platform"]
for key in no_update_keys:
config[key] = self.acm.default_conf[key]
await self._save_astrbot_configs(config, conf_id)
await self.core_lifecycle.reload_pipeline_scheduler(conf_id)
return Response().ok(None, "保存成功~").__dict__
@@ -849,30 +573,28 @@ class ConfigRoute(Route):
async def post_new_provider(self):
new_provider_config = await request.json
self.config["provider"].append(new_provider_config)
try:
await self.core_lifecycle.provider_manager.create_provider(
new_provider_config
save_config(self.config, self.config, is_core=True)
await self.core_lifecycle.provider_manager.load_provider(
new_provider_config,
)
except Exception as e:
return Response().error(str(e)).__dict__
return Response().ok(None, "新增服务提供商配置成功").__dict__
return Response().ok(None, "新增服务提供商配置成功~").__dict__
async def post_update_platform(self):
update_platform_config = await request.json
origin_platform_id = update_platform_config.get("id", None)
platform_id = update_platform_config.get("id", None)
new_config = update_platform_config.get("config", None)
if not origin_platform_id or not new_config:
if not platform_id or not new_config:
return Response().error("参数错误").__dict__
if origin_platform_id != new_config.get("id", None):
return Response().error("机器人名称不允许修改").__dict__
# 如果是支持统一 webhook 模式的平台,且启用了统一 webhook 模式,确保有 webhook_uuid
ensure_platform_webhook_config(new_config)
for i, platform in enumerate(self.config["platform"]):
if platform["id"] == origin_platform_id:
if platform["id"] == platform_id:
self.config["platform"][i] = new_config
break
else:
@@ -887,15 +609,21 @@ class ConfigRoute(Route):
async def post_update_provider(self):
update_provider_config = await request.json
origin_provider_id = update_provider_config.get("id", None)
provider_id = update_provider_config.get("id", None)
new_config = update_provider_config.get("config", None)
if not origin_provider_id or not new_config:
if not provider_id or not new_config:
return Response().error("参数错误").__dict__
for i, provider in enumerate(self.config["provider"]):
if provider["id"] == provider_id:
self.config["provider"][i] = new_config
break
else:
return Response().error("未找到对应服务提供商").__dict__
try:
await self.core_lifecycle.provider_manager.update_provider(
origin_provider_id, new_config
)
save_config(self.config, self.config, is_core=True)
await self.core_lifecycle.provider_manager.reload(new_config)
except Exception as e:
return Response().error(str(e)).__dict__
return Response().ok(None, "更新成功,已经实时生效~").__dict__
@@ -918,17 +646,19 @@ class ConfigRoute(Route):
async def post_delete_provider(self):
provider_id = await request.json
provider_id = provider_id.get("id", "")
if not provider_id:
return Response().error("缺少参数 id").__dict__
provider_id = provider_id.get("id")
for i, provider in enumerate(self.config["provider"]):
if provider["id"] == provider_id:
del self.config["provider"][i]
break
else:
return Response().error("未找到对应服务提供商").__dict__
try:
await self.core_lifecycle.provider_manager.delete_provider(
provider_id=provider_id
)
save_config(self.config, self.config, is_core=True)
await self.core_lifecycle.provider_manager.terminate_provider(provider_id)
except Exception as e:
return Response().error(str(e)).__dict__
return Response().ok(None, "删除成功,已经实时生效").__dict__
return Response().ok(None, "删除成功,已经实时生效~").__dict__
async def get_llm_tools(self):
"""获取函数调用工具。包含了本地加载的以及 MCP 服务的工具"""
-96
View File
@@ -1,9 +1,6 @@
import os
import re
import threading
import time
import traceback
from functools import cmp_to_key
import aiohttp
import psutil
@@ -14,9 +11,7 @@ from astrbot.core.config import VERSION
from astrbot.core.core_lifecycle import AstrBotCoreLifecycle
from astrbot.core.db import BaseDatabase
from astrbot.core.db.migration.helper import check_migration_needed_v4
from astrbot.core.utils.astrbot_path import get_astrbot_path
from astrbot.core.utils.io import get_dashboard_version
from astrbot.core.utils.version_comparator import VersionComparator
from .route import Response, Route, RouteContext
@@ -35,8 +30,6 @@ class StatRoute(Route):
"/stat/start-time": ("GET", self.get_start_time),
"/stat/restart-core": ("POST", self.restart_core),
"/stat/test-ghproxy-connection": ("POST", self.test_ghproxy_connection),
"/stat/changelog": ("GET", self.get_changelog),
"/stat/changelog/list": ("GET", self.list_changelog_versions),
}
self.db_helper = db_helper
self.register_routes()
@@ -190,92 +183,3 @@ class StatRoute(Route):
except Exception as e:
logger.error(traceback.format_exc())
return Response().error(f"Error: {e!s}").__dict__
async def get_changelog(self):
"""获取指定版本的更新日志"""
try:
version = request.args.get("version")
if not version:
return Response().error("version parameter is required").__dict__
version = version.lstrip("v")
# 防止路径遍历攻击
if not re.match(r"^[a-zA-Z0-9._-]+$", version):
return Response().error("Invalid version format").__dict__
if ".." in version or "/" in version or "\\" in version:
return Response().error("Invalid version format").__dict__
filename = f"v{version}.md"
project_path = get_astrbot_path()
changelogs_dir = os.path.join(project_path, "changelogs")
changelog_path = os.path.join(changelogs_dir, filename)
# 规范化路径,防止符号链接攻击
changelog_path = os.path.realpath(changelog_path)
changelogs_dir = os.path.realpath(changelogs_dir)
# 验证最终路径在预期的 changelogs 目录内(防止路径遍历)
# 确保规范化后的路径以 changelogs_dir 开头,且是目录内的文件
changelog_path_normalized = os.path.normpath(changelog_path)
changelogs_dir_normalized = os.path.normpath(changelogs_dir)
# 检查路径是否在预期目录内(必须是目录的子文件,不能是目录本身)
expected_prefix = changelogs_dir_normalized + os.sep
if not changelog_path_normalized.startswith(expected_prefix):
logger.warning(
f"Path traversal attempt detected: {version} -> {changelog_path}",
)
return Response().error("Invalid version format").__dict__
if not os.path.exists(changelog_path):
return (
Response()
.error(f"Changelog for version {version} not found")
.__dict__
)
if not os.path.isfile(changelog_path):
return (
Response()
.error(f"Changelog for version {version} not found")
.__dict__
)
with open(changelog_path, encoding="utf-8") as f:
content = f.read()
return Response().ok({"content": content, "version": version}).__dict__
except Exception as e:
logger.error(traceback.format_exc())
return Response().error(f"Error: {e!s}").__dict__
async def list_changelog_versions(self):
"""获取所有可用的更新日志版本列表"""
try:
project_path = get_astrbot_path()
changelogs_dir = os.path.join(project_path, "changelogs")
if not os.path.exists(changelogs_dir):
return Response().ok({"versions": []}).__dict__
versions = []
for filename in os.listdir(changelogs_dir):
if filename.endswith(".md") and filename.startswith("v"):
# 提取版本号(去除 v 前缀和 .md 后缀)
version = filename[1:-3] # 去掉 "v" 和 ".md"
# 验证版本号格式
if re.match(r"^[a-zA-Z0-9._-]+$", version):
versions.append(version)
# 按版本号排序(降序,最新的在前)
# 使用项目中的 VersionComparator 进行语义化版本号排序
versions.sort(
key=cmp_to_key(
lambda v1, v2: VersionComparator.compare_version(v2, v1),
),
)
return Response().ok({"versions": versions}).__dict__
except Exception as e:
logger.error(traceback.format_exc())
return Response().error(f"Error: {e!s}").__dict__
+134
View File
@@ -0,0 +1,134 @@
#!/usr/bin/env python3
"""
Use Nuitka to build the AstrBot project into standalone executables
"""
import os
import platform
import subprocess
import sys
from pathlib import Path
def get_platform_info():
"""fetch the current platform information"""
system = platform.system()
machine = platform.machine()
return system, machine
def build_with_nuitka():
"""use Nuitka to build the project"""
system, machine = get_platform_info()
print(f"🚀 Starting build for {system} ({machine}) platform...")
# Output directory
output_dir = Path("build/nuitka")
output_dir.mkdir(parents=True, exist_ok=True)
# Base Nuitka command
nuitka_cmd = [
sys.executable,
"-m",
"nuitka",
"--standalone", # Create standalone directory
"--onefile", # Single file mode
"--follow-imports", # Follow all imports
"--enable-plugin=multiprocessing", # Enable multiprocessing support
"--output-dir=build/nuitka", # Output directory
"--quiet", # Reduce output verbosity
"--assume-yes-for-downloads", # Automatically download dependencies
"--jobs=4", # Use multiple CPU cores
]
# include specific packages
include_packages = [
"astrbot",
]
for pkg in include_packages:
nuitka_cmd.extend([f"--include-package={pkg}"])
# include data directories
# data_includes = [
# "data/config",
# "data/plugins",
# "data/temp",
# ]
# for data_dir in data_includes:
# if os.path.exists(data_dir):
# nuitka_cmd.extend([f"--include-data-dir={data_dir}={data_dir}"])
# include packages directory (built-in plugins)
# if os.path.exists("packages"):
# nuitka_cmd.extend(["--include-data-dir=packages=packages"])
# Platform specific settings
if system == "Darwin": # macOS
nuitka_cmd.extend(
[
"--macos-create-app-bundle", # Create .app bundle
"--macos-app-name=AstrBot",
]
)
# macOS icon (if exists)
icon_path = "dashboard/src-tauri/icons/icon.icns"
if os.path.exists(icon_path):
nuitka_cmd.extend([f"--macos-app-icon={icon_path}"])
elif system == "Windows":
nuitka_cmd.extend(
[
"--windows-console-mode=disable", # 无控制台窗口
]
)
# Windows icon (if exists)
icon_path = "dashboard/src-tauri/icons/icon.ico"
if os.path.exists(icon_path):
nuitka_cmd.extend([f"--windows-icon-from-ico={icon_path}"])
# Main file to compile
nuitka_cmd.append("main.py")
print(f"📦 Executing command: {' '.join(nuitka_cmd)}")
try:
subprocess.run(nuitka_cmd, check=True)
print("✅ Nuitka build successful!")
# Find the generated executable
if system == "Darwin":
built_file = list(output_dir.glob("*.app"))
if built_file:
print(f"Generated macOS app: {built_file[0]}")
elif system == "Windows":
built_file = list(output_dir.glob("*.exe"))
if built_file:
print(f"Generated Windows executable: {built_file[0]}")
else: # Linux
built_file = list(output_dir.glob("main.bin"))
if built_file:
print(f"Generated Linux executable: {built_file[0]}")
return True
except subprocess.CalledProcessError as e:
print(f"❌ Nuitka build failed: {e}")
return False
if __name__ == "__main__":
print("=" * 60)
print("AstrBot Nuitka Builder")
print("=" * 60)
# 构建
if build_with_nuitka():
print("\n" + "=" * 60)
print("🎉 Build Complete!")
print("=" * 60)
else:
print("\n" + "=" * 60)
print("❌ Build Failed")
print("=" * 60)
sys.exit(1)
+134
View File
@@ -0,0 +1,134 @@
#!/usr/bin/env python3
"""
Use PyInstaller to build the AstrBot project into standalone executables
"""
import platform
import subprocess
import sys
from pathlib import Path
def get_platform_info():
"""fetch the current platform information"""
system = platform.system()
machine = platform.machine()
return system, machine
def build_with_pyinstaller():
"""use PyInstaller to build the project"""
system, machine = get_platform_info()
print(f"🚀 Starting build for {system} ({machine}) platform...")
# Output directory
output_dir = Path("build/pyinstaller")
output_dir.mkdir(parents=True, exist_ok=True)
# Base PyInstaller command
pyinstaller_cmd = [
sys.executable,
"-m",
"PyInstaller",
"--clean", # Clean cache before build
"--noconfirm", # Replace output directory without asking
"--onefile", # Single file mode
"--distpath=build/pyinstaller/dist", # Distribution directory
"--workpath=build/pyinstaller/build", # Work directory
"--specpath=build/pyinstaller", # Spec file directory
"--name=AstrBot", # Output executable name
]
# Platform specific settings
# if system == "Darwin": # macOS
# # macOS icon (if exists)
# icon_path = "dashboard/src-tauri/icons/icon.icns"
# if os.path.exists(icon_path):
# pyinstaller_cmd.extend([f"--icon={icon_path}"])
# # Create .app bundle
# pyinstaller_cmd.extend(["--windowed"])
# elif system == "Windows":
# # Windows icon (if exists)
# icon_path = "dashboard/src-tauri/icons/icon.ico"
# if os.path.exists(icon_path):
# pyinstaller_cmd.extend([f"--icon={icon_path}"])
# # No console window
# pyinstaller_cmd.extend(["--windowed"])
# else: # Linux
# pyinstaller_cmd.extend(["--console"])
# Main file to compile
pyinstaller_cmd.append("main.py")
print(f"📦 Executing command: {' '.join(pyinstaller_cmd)}")
try:
subprocess.run(pyinstaller_cmd, check=True)
print("✅ PyInstaller build successful!")
# Find the generated executable
dist_dir = output_dir / "dist"
if system == "Darwin":
built_file = list(dist_dir.glob("AstrBot.app"))
if not built_file:
built_file = list(dist_dir.glob("AstrBot"))
if built_file:
print(f"📱 Generated macOS app: {built_file[0]}")
elif system == "Windows":
built_file = list(dist_dir.glob("AstrBot.exe"))
if built_file:
print(f"💻 Generated Windows executable: {built_file[0]}")
else: # Linux
built_file = list(dist_dir.glob("AstrBot"))
if built_file:
print(f"🐧 Generated Linux executable: {built_file[0]}")
print(f"\n📁 Output directory: {dist_dir.absolute()}")
return True
except subprocess.CalledProcessError as e:
print(f"❌ PyInstaller build failed: {e}")
return False
except Exception as e:
print(f"❌ Unexpected error: {e}")
return False
def install_pyinstaller():
"""Install PyInstaller if not already installed"""
try:
import PyInstaller
print(f"✅ PyInstaller already installed (version {PyInstaller.__version__})")
return True
except ImportError:
print("📥 PyInstaller not found, installing...")
try:
subprocess.run(
[sys.executable, "-m", "pip", "install", "pyinstaller"], check=True
)
print("✅ PyInstaller installed successfully!")
return True
except subprocess.CalledProcessError as e:
print(f"❌ Failed to install PyInstaller: {e}")
return False
if __name__ == "__main__":
print("=" * 60)
print("AstrBot PyInstaller Builder")
print("=" * 60)
# Check and install PyInstaller
if not install_pyinstaller():
sys.exit(1)
# Build
if build_with_pyinstaller():
print("\n" + "=" * 60)
print("🎉 Build Complete!")
print("=" * 60)
else:
print("\n" + "=" * 60)
print("❌ Build Failed")
print("=" * 60)
sys.exit(1)
-34
View File
@@ -1,34 +0,0 @@
## What's Changed
> 📢 在升级前,请**完整阅读**本次更新日志。
>
> **特别提醒:**
> 1. 该版本为 alpha.1 预览版本。
> 2. 本次升级**如果再降级**,会由于提供商配置的变更,导致提供商配置错乱,需要手动删除后重新添加。
> 3. 此版本 WebUI 包体相较上一个版本增加约 **193%**,共约 **9.8 MB**,升级可能会需要一些时间。
### 重构与优化
- 重构 Provider 页面和提供商的配置结构,将 Chat Provider 配置拆分为 Provider Source(提供商源)和 Provider(代表提供商源的各个模型),引入了提供商模型自动发现、模型元数据自动发现的功能,**提供更加便捷的模型添加体验**。
- ⚠️ 将 “MCP” 页面移动到了 “插件” 页面中
- ⚠️ 将 “MCP” 页面中的工具管理移动到了 “插件” -> “管理行为” 中。
- ⚠️ 将 “QQ 个人号(OneBot v11)” 机器人适配器类型更名为 “OneBot v11”,并将其 Logo 更改为 OneBot 的 Logo。
- ⚠️ AstrBot WebChat 升级为 **AstrBot ChatUI**,入口从边栏修改为顶部(右上角)切换按钮。
- 优化引用消息的逻辑,减少对模型输入缓存的破坏。
### 修复
- ‼️ 修复部分情况下,分段回复无法正常分段的问题。
- 修复处理工具返回结果的过程中,导致一些直接发送图片的工具(如生图工具)无法正确发送到用户的问题。
- 修复 WebChat 部分情况下,上一条消息文字内容增量到下一条消息的问题。
### 新增
- 支持**指令管理**,设置指令别名、解决指令冲突、查看指令详情等。入口:“插件” -> “管理行为”。
- 支持 Google Gemini 3 系列引入的 [Thinking Level](https://ai.google.dev/gemini-api/docs/thinking#thinking-levels) 配置。
- 支持记录每条 LLM 消息的耗时、Token 使用量、TTFT 数据,以及每次 Agent Loop 的各种统计数据。
- AstrBot ChatUI 支持查看每条消息的 TTFT、Token 使用量数据。
- AstrBot ChatUI 支持显示每次工具调用的耗时、参数和响应。
- AstrBot ChatUI 支持渲染 Mermaid、LateX 内容,优化了 Code Block 的显示效果(使用 Monaco Editor),并减少 DOM 更新于内存占用。(Powered by [Simon-He95/markstream-vue](https://github.com/Simon-He95/markstream-vue)
- 支持查看 Changelog 历史版本更新日志。
- 🎄
-44
View File
@@ -1,44 +0,0 @@
## What's Changed
> 📢 在升级前,请**完整阅读**本次更新日志。
>
> **特别提醒:**
> 1. 该版本为 alpha.2 预览版本。
> 2. 本次升级**如果再降级**,会由于提供商配置的变更,导致提供商配置错乱,需要手动删除后重新添加。
> 3. 此版本 WebUI 包体相较上一个版本增加约 **193%**,共约 **9.8 MB**,升级可能会需要一些时间。
## alpha.1 -> alpha.2
- 修复:“对话数据”页对话轨迹详情显示异常的问题
- 优化:当 Agent 达到最大步数时的处理。在达到最大步数后,会移除所有请求中的 tools 并告知模型根据上下文进行最终总结。
- 优化:LLM tools 执行的错误处理,减少工具调用无限循环的问题。
- 优化:ChatUI 打开模型选择菜单时,会重新获取提供商配置。
- 优化:ChatUI 新建对话并发送消息后,对话列表页自动选中该对话。
## 4.10.0 变化
### 重构与优化
- 重构 Provider 页面和提供商的配置结构,将 Chat Provider 配置拆分为 Provider Source(提供商源)和 Provider(代表提供商源的各个模型),引入了提供商模型自动发现、模型元数据自动发现的功能,**提供更加便捷的模型添加体验**。
- ⚠️ 将 “MCP” 页面移动到了 “插件” 页面中
- ⚠️ 将 “MCP” 页面中的工具管理移动到了 “插件” -> “管理行为” 中。
- ⚠️ 将 “QQ 个人号(OneBot v11)” 机器人适配器类型更名为 “OneBot v11”,并将其 Logo 更改为 OneBot 的 Logo。
- ⚠️ AstrBot WebChat 升级为 **AstrBot ChatUI**,入口从边栏修改为顶部(右上角)切换按钮。
- 优化引用消息的逻辑,减少对模型输入缓存的破坏。
### 修复
- ‼️ 修复部分情况下,分段回复无法正常分段的问题。
- 修复处理工具返回结果的过程中,导致一些直接发送图片的工具(如生图工具)无法正确发送到用户的问题。
- 修复 WebChat 部分情况下,上一条消息文字内容增量到下一条消息的问题。
### 新增
- 支持**指令管理**,设置指令别名、解决指令冲突、查看指令详情等。入口:“插件” -> “管理行为”。
- 支持 Google Gemini 3 系列引入的 [Thinking Level](https://ai.google.dev/gemini-api/docs/thinking#thinking-levels) 配置。
- 支持记录每条 LLM 消息的耗时、Token 使用量、TTFT 数据,以及每次 Agent Loop 的各种统计数据。
- AstrBot ChatUI 支持查看每条消息的 TTFT、Token 使用量数据。
- AstrBot ChatUI 支持显示每次工具调用的耗时、参数和响应。
- AstrBot ChatUI 支持渲染 Mermaid、LateX 内容,优化了 Code Block 的显示效果(使用 Monaco Editor),并减少 DOM 更新于内存占用。(Powered by [Simon-He95/markstream-vue](https://github.com/Simon-He95/markstream-vue)
- 支持查看 Changelog 历史版本更新日志。
- 🎄
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@@ -1,40 +0,0 @@
## What's Changed
> 📢 在升级前,请**完整阅读**本次更新日志。
>
> **特别提醒:**
> 1. 本次升级**如果再降级**,会由于提供商配置的变更,导致提供商配置错乱,需要手动删除后重新添加。
> 2. 此版本 WebUI 包体相较上一个版本增加约 **193%**,共约 **9.8 MB**,升级可能会需要一些时间。
> 3. **升级后请务必确保 WebUI 和 AstrBot Core 版本一致**,否则会产生预期之外的情况。(判断方法:日志中出现 `WebUI 版本已是最新。` 即为一致的版本,`检测到 WebUI 版本 (xxx) 与当前 AstrBot 版本 (xxx) 不符。` 即为不一致的版本。此版本的判断方法也可通查看 WebUI 右上角是否出现 Bot / Chat 的切换按钮控件来判断是否是新版本的 WebUI)。
> 4. 如果有任何问题请提交 [Issue](https://github.com/AstrBotDevs/AstrBot/issues) 并附带 `v4.10.0` tag。
### 重构与优化
- 重构 Provider 页面和提供商的配置结构,将 Chat Provider 配置拆分为 Provider Source(提供商源)和 Provider(代表提供商源的各个模型),引入了提供商模型自动发现、模型元数据自动发现的功能,**提供更加便捷的模型添加体验**。
- ⚠️ 将 “MCP” 页面移动到了 “插件” 页面中
- ⚠️ 将 “MCP” 页面中的工具管理移动到了 “插件” -> “管理行为” 中。
- ⚠️ 将 “QQ 个人号(OneBot v11)” 机器人适配器类型更名为 “OneBot v11”,并将其 Logo 更改为 OneBot 的 Logo。
- ⚠️ AstrBot WebChat 升级为 **AstrBot ChatUI**,入口从边栏修改为顶部(右上角)切换按钮。
- 优化引用消息的逻辑,减少对模型输入缓存的破坏。
- 优化当 Agent 达到最大步数时的处理。在达到最大步数后,会移除所有请求中的 tools 并告知模型根据上下文进行最终总结。
- 优化 LLM tools 执行的错误处理,减少工具调用无限循环的问题。
### 修复
- ‼️ 修复部分情况下,分段回复无法正常分段的问题。
- 修复处理工具返回结果的过程中,导致一些直接发送图片的工具(如生图工具)无法正确发送到用户的问题。
- 修复 WebChat 部分情况下,上一条消息文字内容增量到下一条消息的问题。
### 新增
- 支持**指令管理**,设置指令别名、解决指令冲突、查看指令详情等。入口:“插件” -> “管理行为”。
- 支持 Google Gemini 3 系列引入的 [Thinking Level](https://ai.google.dev/gemini-api/docs/thinking#thinking-levels) 配置。
- 支持记录每条 LLM 消息的耗时、Token 使用量、TTFT 数据,以及每次 Agent Loop 的各种统计数据。
- AstrBot ChatUI 支持查看每条消息的 TTFT、Token 使用量数据。
- AstrBot ChatUI 支持显示每次工具调用的耗时、参数和响应。
- AstrBot ChatUI 支持渲染 Mermaid、LateX 内容,优化了 Code Block 的显示效果(使用 Monaco Editor),并减少 DOM 更新于内存占用。(Powered by [Simon-He95/markstream-vue](https://github.com/Simon-He95/markstream-vue)
- 支持查看 Changelog 历史版本更新日志。
- 🎄
Merry Christmas!
-46
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@@ -1,46 +0,0 @@
## What's Changed
> 📢 在升级前,请**完整阅读**本次更新日志。
>
> **特别提醒:**
> 1. 本次升级**如果再降级**,会由于提供商配置的变更,导致提供商配置错乱,需要手动删除后重新添加。
> 2. 此版本 WebUI 包体相较上一个版本增加约 **193%**,共约 **9.8 MB**,升级可能会需要一些时间。
> 3. **升级后请务必确保 WebUI 和 AstrBot Core 版本一致**,否则会产生预期之外的情况。(判断方法:日志中出现 `WebUI 版本已是最新。` 即为一致的版本,`检测到 WebUI 版本 (xxx) 与当前 AstrBot 版本 (xxx) 不符。` 即为不一致的版本。此版本的判断方法也可通查看 WebUI 右上角是否出现 Bot / Chat 的切换按钮控件来判断是否是新版本的 WebUI)。
> 4. 如果有任何问题请提交 [Issue](https://github.com/AstrBotDevs/AstrBot/issues) 并附带 `v4.10.0` tag。
## 4.10.0 -> 4.10.1
- fix(core): 修复极少数情况下由于指令管理导致的 AstrBot 启动失败的问题
- fix(core): 修复当提供商源带有斜杠(“/”)时,无法删除 / 更新提供商源的问题(报错 405)
- perf(core): 优化 OneBot 适配器的消息段解析逻辑,修复部分情况下无法正确解析消息段的问题
### 重构与优化
- 重构 Provider 页面和提供商的配置结构,将 Chat Provider 配置拆分为 Provider Source(提供商源)和 Provider(代表提供商源的各个模型),引入了提供商模型自动发现、模型元数据自动发现的功能,**提供更加便捷的模型添加体验**。
- ⚠️ 将 “MCP” 页面移动到了 “插件” 页面中
- ⚠️ 将 “MCP” 页面中的工具管理移动到了 “插件” -> “管理行为” 中。
- ⚠️ 将 “QQ 个人号(OneBot v11)” 机器人适配器类型更名为 “OneBot v11”,并将其 Logo 更改为 OneBot 的 Logo。
- ⚠️ AstrBot WebChat 升级为 **AstrBot ChatUI**,入口从边栏修改为顶部(右上角)切换按钮。
- 优化引用消息的逻辑,减少对模型输入缓存的破坏。
- 优化当 Agent 达到最大步数时的处理。在达到最大步数后,会移除所有请求中的 tools 并告知模型根据上下文进行最终总结。
- 优化 LLM tools 执行的错误处理,减少工具调用无限循环的问题。
### 修复
- ‼️ 修复部分情况下,分段回复无法正常分段的问题。
- 修复处理工具返回结果的过程中,导致一些直接发送图片的工具(如生图工具)无法正确发送到用户的问题。
- 修复 WebChat 部分情况下,上一条消息文字内容增量到下一条消息的问题。
### 新增
- 支持**指令管理**,设置指令别名、解决指令冲突、查看指令详情等。入口:“插件” -> “管理行为”。
- 支持 Google Gemini 3 系列引入的 [Thinking Level](https://ai.google.dev/gemini-api/docs/thinking#thinking-levels) 配置。
- 支持记录每条 LLM 消息的耗时、Token 使用量、TTFT 数据,以及每次 Agent Loop 的各种统计数据。
- AstrBot ChatUI 支持查看每条消息的 TTFT、Token 使用量数据。
- AstrBot ChatUI 支持显示每次工具调用的耗时、参数和响应。
- AstrBot ChatUI 支持渲染 Mermaid、LateX 内容,优化了 Code Block 的显示效果(使用 Monaco Editor),并减少 DOM 更新于内存占用。(Powered by [Simon-He95/markstream-vue](https://github.com/Simon-He95/markstream-vue)
- 支持查看 Changelog 历史版本更新日志。
- 🎄
Merry Christmas!
-9
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@@ -1,9 +0,0 @@
## What's Changed
### 修复
1. ‼️‼️ 修复了由 `psutil` 新版本导致的启动时报错的问题。
### 新增
1. 插件指令管理支持管理别名。
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# AstrBot Dashboard - Tauri 桌面应用
本项目现已支持通过 Tauri 构建为桌面应用,同时保持与 Web 版本的兼容性。
## 环境要求
### 系统依赖
**macOS:**
```bash
# 安装 Xcode Command Line Tools
xcode-select --install
```
**Windows:**
- 安装 [Microsoft Visual Studio C++ Build Tools](https://visualstudio.microsoft.com/visual-cpp-build-tools/)
- 安装 [WebView2](https://developer.microsoft.com/en-us/microsoft-edge/webview2/)
**Linux (Ubuntu/Debian):**
```bash
sudo apt update
sudo apt install libwebkit2gtk-4.0-dev \
build-essential \
curl \
wget \
file \
libssl-dev \
libgtk-3-dev \
libayatana-appindicator3-dev \
librsvg2-dev
```
### Rust 环境
```bash
# 安装 Rust
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
# 验证安装
rustc --version
cargo --version
```
## 安装依赖
```bash
cd dashboard
npm install
```
## 开发模式
### Web 端开发(不变)
```bash
npm run dev
```
访问 http://localhost:3000
### 桌面端开发
```bash
npm run tauri:dev
```
这会同时启动:
1. Vite 开发服务器(端口 3000)
2. Tauri 桌面应用窗口
热重载功能正常工作,修改代码后会自动刷新。
## 构建
### Web 端构建(不变)
```bash
npm run build
```
输出目录:`dist/`
### 桌面端构建
```bash
npm run tauri:build
```
构建产物位置:
- **macOS**: `src-tauri/target/release/bundle/dmg/`
- **Windows**: `src-tauri/target/release/bundle/msi/`
- **Linux**: `src-tauri/target/release/bundle/deb/``appimage/`
## 图标设置
### 自动生成图标
准备一个至少 512x512 像素的 PNG 图标,然后运行:
```bash
npm run tauri icon path/to/your/icon.png
```
### 手动设置图标
将以下图标放入 `src-tauri/icons/` 目录:
- `32x32.png`
- `128x128.png`
- `128x128@2x.png`
- `icon.icns` (macOS)
- `icon.ico` (Windows)
## 代码兼容性
项目已配置为同时支持 Web 和桌面端,使用相同的代码库。
### 环境检测工具
`src/utils/tauri.ts` 中提供了环境检测工具:
```typescript
import { isTauri, isWeb, PlatformAPI } from '@/utils/tauri';
// 检测运行环境
if (isTauri()) {
console.log('运行在桌面应用中');
} else {
console.log('运行在浏览器中');
}
// 获取正确的 API 端点
const baseURL = PlatformAPI.getBaseURL();
```
### API 调用注意事项
- **Web 端**: 使用 Vite 代理,API 路径为 `/api/*`
- **桌面端**: 直接连接到 `http://127.0.0.1:6185`
已在 `PlatformAPI.getBaseURL()` 中处理,使用 axios 时:
```typescript
import axios from 'axios';
import { PlatformAPI } from '@/utils/tauri';
const api = axios.create({
baseURL: PlatformAPI.getBaseURL()
});
```
## 配置说明
### tauri.conf.json
主要配置项:
- `build.devPath`: 开发服务器地址(http://localhost:3000
- `build.distDir`: 构建输出目录(../dist
- `tauri.allowlist`: API 权限配置
- `tauri.windows`: 窗口配置(大小、标题等)
### 安全性
默认配置已启用必要的权限:
- 文件系统访问(限定在 APPDATA 目录)
- HTTP 请求(限定到本地后端)
- 窗口控制
- 对话框(打开/保存文件)
可在 `tauri.conf.json``allowlist` 部分调整权限。
## 后端连接
桌面应用需要后端服务运行在 `http://127.0.0.1:6185`
### 启动流程
1. 启动 AstrBot 后端:
```bash
cd /path/to/AstrBot
uv run main.py
```
2. 启动桌面应用:
```bash
cd dashboard
npm run tauri:dev
```
或直接运行打包后的应用(后端需要已启动)。
## 常见问题
### Q: 桌面应用无法连接到后端?
确保:
1. AstrBot 后端正在运行(`uv run main.py`
2. 后端监听在 `127.0.0.1:6185`
3. 防火墙未阻止连接
### Q: 图标未显示?
检查 `src-tauri/icons/` 目录中是否有所需的图标文件,或使用 `npm run tauri icon` 命令生成。
### Q: 构建失败?
- 确保已安装 Rust 和系统依赖
- 运行 `cargo clean` 清理缓存后重试
- 检查 Rust 版本(需要 1.60+
### Q: Web 端功能是否受影响?
不受影响。`npm run dev``npm run build` 的行为完全不变。
## 开发建议
1. **优先使用 Web 端开发**: 更快的热重载,更好的调试体验
2. **定期测试桌面端**: 确保跨平台兼容性
3. **使用环境检测**: 针对不同平台提供最佳体验
4. **注意 API 差异**: Web 和桌面端的某些 API 可能有差异
## 更多资源
- [Tauri 官方文档](https://tauri.app/)
- [Tauri API 参考](https://tauri.app/v1/api/js/)
- [Tauri Discord 社区](https://discord.com/invite/tauri)
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@@ -8,7 +8,7 @@
<meta name="description" content="AstrBot Dashboard" />
<link
rel="stylesheet"
href="https://fonts.googleapis.com/css2?family=Outfit&family=Poppins:wght@400;500;600;700&family=Roboto:wght@400;500;700&display=swap"
href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&family=Poppins:wght@400;500;600;700&family=Roboto:wght@400;500;700&display=swap"
/>
<title>AstrBot - 仪表盘</title>
</head>
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@@ -10,30 +10,30 @@
"build-prod": "vue-tsc --noEmit && vite build --base=/vue/free/",
"preview": "vite preview --port 5050",
"typecheck": "vue-tsc --noEmit",
"lint": "eslint . --ext .vue,.js,.jsx,.cjs,.mjs,.ts,.tsx,.cts,.mts --fix --ignore-path .gitignore"
"lint": "eslint . --ext .vue,.js,.jsx,.cjs,.mjs,.ts,.tsx,.cts,.mts --fix --ignore-path .gitignore",
"tauri": "tauri",
"tauri:dev": "tauri dev",
"tauri:build": "tauri build"
},
"dependencies": {
"@guolao/vue-monaco-editor": "^1.5.4",
"@mdit/plugin-katex": "^0.24.1",
"@tauri-apps/api": "^2.9.0",
"@tiptap/starter-kit": "2.1.7",
"@tiptap/vue-3": "2.1.7",
"apexcharts": "3.42.0",
"axios": ">=1.6.2 <1.10.0 || >1.10.0 <2.0.0",
"axios-mock-adapter": "^1.22.0",
"chance": "1.1.11",
"d3": "^7.9.0",
"date-fns": "2.30.0",
"highlight.js": "^11.11.1",
"js-md5": "^0.8.3",
"katex": "^0.16.27",
"lodash": "4.17.21",
"markstream-vue": "0.0.3-beta.7",
"mermaid": "^11.12.2",
"pinia": "2.1.6",
"marked": "^15.0.7",
"markdown-it": "^14.1.0",
"pinyin-pro": "^3.26.0",
"pinia": "2.1.6",
"remixicon": "3.5.0",
"shiki": "^3.20.0",
"stream-markdown": "^0.0.11",
"stream-monaco": "^0.0.8",
"vee-validate": "4.11.3",
"vite-plugin-vuetify": "1.0.2",
"vue": "3.3.4",
@@ -47,7 +47,9 @@
"devDependencies": {
"@mdi/font": "7.2.96",
"@rushstack/eslint-patch": "1.3.3",
"@tauri-apps/cli": "^2.9.4",
"@types/chance": "1.1.3",
"@types/markdown-it": "^14.1.2",
"@types/node": "^20.5.7",
"@vitejs/plugin-vue": "4.3.3",
"@vue/eslint-config-prettier": "8.0.0",
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# Tauri specific
src-tauri/target/
src-tauri/WixTools/
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[package]
name = "astrbot-dashboard"
version = "4.5.6"
description = "AstrBot"
authors = ["AstrBot Team"]
license = "AGPL-3.0"
repository = "https://github.com/AstrBotDevs/AstrBot"
default-run = "astrbot-dashboard"
edition = "2021"
rust-version = "1.91.0"
# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
[build-dependencies]
tauri-build = { version = "2", features = [] }
[dependencies]
serde_json = "1.0"
serde = { version = "1.0", features = ["derive"] }
tauri = { version = "2.9.2", features = ["macos-private-api", "protocol-asset"] }
tauri-plugin-opener = "2"
[features]
# this feature is used for production builds or when `devPath` points to the filesystem and the built-in dev server is disabled.
# If you use cargo directly instead of tauri's cli you can use this feature flag to switch between tauri's `dev` and `build` modes.
# DO NOT REMOVE!!
custom-protocol = [ "tauri/custom-protocol" ]
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fn main() {
tauri_build::build()
}
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{}
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<?xml version="1.0" encoding="utf-8"?>
<adaptive-icon xmlns:android="http://schemas.android.com/apk/res/android">
<foreground android:drawable="@mipmap/ic_launcher_foreground"/>
<background android:drawable="@color/ic_launcher_background"/>
</adaptive-icon>
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<?xml version="1.0" encoding="utf-8"?>
<resources>
<color name="ic_launcher_background">#fff</color>
</resources>
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+104
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// Prevents additional console window on Windows in release, DO NOT REMOVE!!
#![cfg_attr(not(debug_assertions), windows_subsystem = "windows")]
use std::process::{Child, Command};
use std::sync::Mutex;
use tauri::{AppHandle, Emitter, Listener, Manager, State};
struct BackendProcess(Mutex<Option<Child>>);
fn start_backend_process(app_handle: &AppHandle) -> Option<Child> {
#[cfg(target_os = "macos")]
let backend_path = "astrbot-backend.app/Contents/MacOS/main";
#[cfg(target_os = "windows")]
let backend_path = "astrbot-backend.exe";
#[cfg(target_os = "linux")]
let backend_path = "astrbot-backend";
// 获取资源目录
let resource_dir = match app_handle
.path()
.resource_dir()
{
Ok(dir) => dir,
Err(e) => {
eprintln!("Failed to get resource directory: {}", e);
return None;
}
};
let full_backend_path = resource_dir.join(backend_path);
println!("Starting backend process at: {:?}", full_backend_path);
match Command::new(&full_backend_path).spawn() {
Ok(child) => {
println!(
"Backend process started successfully with PID: {}",
child.id()
);
Some(child)
}
Err(e) => {
eprintln!("Failed to start backend process: {}", e);
None
}
}
}
#[tauri::command]
fn restart_backend(
app_handle: AppHandle,
backend_state: State<BackendProcess>,
) -> Result<String, String> {
let mut backend = backend_state.0.lock().unwrap();
// 停止现有进程
if let Some(mut child) = backend.take() {
let _ = child.kill();
let _ = child.wait();
}
// 启动新进程
*backend = start_backend_process(&app_handle);
if backend.is_some() {
Ok("Backend restarted successfully".to_string())
} else {
Err("Failed to restart backend".to_string())
}
}
#[cfg_attr(mobile, tauri::mobile_entry_point)]
pub fn run() {
tauri::Builder::default()
.setup(|app| {
// 启动后端进程
let backend_process = start_backend_process(app.handle());
app.manage(BackendProcess(Mutex::new(backend_process)));
Ok(())
})
.plugin(tauri_plugin_opener::init())
.invoke_handler(tauri::generate_handler![restart_backend])
.on_window_event(|window, event| {
if let tauri::WindowEvent::CloseRequested { .. } = event {
// 关闭窗口时清理后端进程
if let Some(backend_state) = window.app_handle().try_state::<BackendProcess>() {
let mut backend = backend_state.0.lock().unwrap();
if let Some(mut child) = backend.take() {
let _ = child.kill();
let _ = child.wait();
}
}
}
})
.run(tauri::generate_context!())
.expect("error while running tauri application");
}
fn main() {
run();
}
+53
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{
"$schema": "https://schema.tauri.app/config/2",
"productName": "AstrBot",
"version": "4.5.6",
"identifier": "com.astrbot.app",
"build": {
"beforeDevCommand": "pnpm dev",
"devUrl": "http://localhost:3000",
"beforeBuildCommand": "pnpm build",
"frontendDist": "../dist"
},
"app": {
"withGlobalTauri": true,
"macOSPrivateApi": true,
"windows": [
{
"title": "AstrBot",
"label": "main",
"url": "/",
"width": 1400,
"height": 900
}
],
"security": {
"csp": null,
"assetProtocol": {
"enable": true,
"scope": [
"$APPDATA/**"
]
}
}
},
"bundle": {
"active": true,
"targets": "all",
"icon": [
"icons/32x32.png",
"icons/128x128.png",
"icons/128x128@2x.png",
"icons/icon.icns",
"icons/icon.ico"
],
"resources": [
"resources/*"
]
},
"plugins": {
"fs": {
"requireLiteralLeadingDot": false
}
}
}

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