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Author SHA1 Message Date
WangCham
f8979056eb fix: optimize configuration function 2026-03-28 09:43:49 +08:00
276 changed files with 9467 additions and 19854 deletions

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@@ -1,5 +1,5 @@
name: 漏洞反馈
description: 【供中文用户】报错或漏洞请使用这个模板创建不使用此模板创建的异常、漏洞相关issue将被直接关闭。由于自己操作不当/不甚了解所用技术栈引起的网络连接问题恕无法解决,请勿提 issue。容器间网络连接问题参考文档 https://link.langbot.app/zh/docs/network
description: 【供中文用户】报错或漏洞请使用这个模板创建不使用此模板创建的异常、漏洞相关issue将被直接关闭。由于自己操作不当/不甚了解所用技术栈引起的网络连接问题恕无法解决,请勿提 issue。容器间网络连接问题参考文档 https://docs.langbot.app/zh/workshop/network-details.html
title: "[Bug]: "
labels: ["bug?"]
body:

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@@ -1,5 +1,5 @@
name: Bug report
description: Report bugs or vulnerabilities using this template. For container network connection issues, refer to the documentation https://link.langbot.app/en/docs/network
description: Report bugs or vulnerabilities using this template. For container network connection issues, refer to the documentation https://docs.langbot.app/en/workshop/network-details.html
title: "[Bug]: "
labels: ["bug?"]
body:

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@@ -43,10 +43,10 @@ jobs:
run: |
cd /tmp/langbot_build_web/web
npm install
npx vite build
npm run build
- name: Package Output
run: |
cp -r /tmp/langbot_build_web/web/dist ./web
cp -r /tmp/langbot_build_web/web/out ./web
- name: Upload Artifact
uses: actions/upload-artifact@v4
with:

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@@ -1,25 +0,0 @@
name: Check i18n Keys
on:
push:
branches:
- main
- master
jobs:
check-i18n:
name: Check i18n Key Consistency
runs-on: ubuntu-latest
permissions:
contents: read
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Setup Node.js
uses: actions/setup-node@v4
with:
node-version: '20'
- name: Check i18n keys against en-US reference
run: node web/scripts/check-i18n.mjs

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@@ -29,8 +29,8 @@ jobs:
npm install -g pnpm
pnpm install
pnpm build
mkdir -p ../src/langbot/web/dist
cp -r dist ../src/langbot/web/
mkdir -p ../src/langbot/web/out
cp -r out ../src/langbot/web/
- name: Install the latest version of uv
uses: astral-sh/setup-uv@v6

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@@ -1,171 +0,0 @@
name: Test Migrations
on:
push:
branches:
- main
- master
- dev
paths:
- 'src/langbot/pkg/persistence/**'
- 'src/langbot/pkg/entity/persistence/**'
pull_request:
types: [opened, synchronize, reopened, ready_for_review]
paths:
- 'src/langbot/pkg/persistence/**'
- 'src/langbot/pkg/entity/persistence/**'
jobs:
test-migrations-sqlite:
name: Migrations (SQLite)
runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: '3.12'
- name: Install uv
uses: astral-sh/setup-uv@v4
- name: Install dependencies
run: uv sync --dev
- name: Test Alembic upgrade (SQLite)
run: |
uv run python -c "
import asyncio
from sqlalchemy.ext.asyncio import create_async_engine
from langbot.pkg.entity.persistence.base import Base
from langbot.pkg.persistence.alembic_runner import run_alembic_upgrade, run_alembic_stamp, get_alembic_current
async def main():
engine = create_async_engine('sqlite+aiosqlite:///test_migrations.db')
# Create all tables (simulates existing DB)
async with engine.begin() as conn:
await conn.run_sync(Base.metadata.create_all)
# Stamp baseline
await run_alembic_stamp(engine, '0001_baseline')
rev = await get_alembic_current(engine)
assert rev == '0001_baseline', f'Expected 0001_baseline, got {rev}'
print(f'Stamped: {rev}')
# Upgrade to head
await run_alembic_upgrade(engine, 'head')
rev = await get_alembic_current(engine)
print(f'After upgrade: {rev}')
assert rev is not None, 'Expected a revision after upgrade'
# Verify idempotent
await run_alembic_upgrade(engine, 'head')
rev2 = await get_alembic_current(engine)
assert rev2 == rev, f'Expected {rev}, got {rev2}'
print(f'Idempotent check passed: {rev2}')
# Fresh DB: upgrade from scratch
engine2 = create_async_engine('sqlite+aiosqlite:///test_migrations_fresh.db')
async with engine2.begin() as conn:
await conn.run_sync(Base.metadata.create_all)
await run_alembic_upgrade(engine2, 'head')
rev3 = await get_alembic_current(engine2)
print(f'Fresh DB upgrade: {rev3}')
assert rev3 is not None
print('All SQLite migration tests passed!')
asyncio.run(main())
"
test-migrations-postgres:
name: Migrations (PostgreSQL)
runs-on: ubuntu-latest
services:
postgres:
image: postgres:16
env:
POSTGRES_USER: langbot
POSTGRES_PASSWORD: langbot
POSTGRES_DB: langbot_test
ports:
- 5432:5432
options: >-
--health-cmd="pg_isready -U langbot"
--health-interval=5s
--health-timeout=5s
--health-retries=5
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: '3.12'
- name: Install uv
uses: astral-sh/setup-uv@v4
- name: Install dependencies
run: uv sync --dev
- name: Test Alembic upgrade (PostgreSQL)
run: |
uv run python -c "
import asyncio
from sqlalchemy.ext.asyncio import create_async_engine
from langbot.pkg.entity.persistence.base import Base
from langbot.pkg.persistence.alembic_runner import run_alembic_upgrade, run_alembic_stamp, get_alembic_current
DB_URL = 'postgresql+asyncpg://langbot:langbot@localhost:5432/langbot_test'
async def main():
engine = create_async_engine(DB_URL)
# Create all tables
async with engine.begin() as conn:
await conn.run_sync(Base.metadata.create_all)
# Stamp baseline
await run_alembic_stamp(engine, '0001_baseline')
rev = await get_alembic_current(engine)
assert rev == '0001_baseline', f'Expected 0001_baseline, got {rev}'
print(f'Stamped: {rev}')
# Upgrade to head
await run_alembic_upgrade(engine, 'head')
rev = await get_alembic_current(engine)
print(f'After upgrade: {rev}')
assert rev is not None
# Verify idempotent
await run_alembic_upgrade(engine, 'head')
rev2 = await get_alembic_current(engine)
assert rev2 == rev, f'Expected {rev}, got {rev2}'
print(f'Idempotent check passed: {rev2}')
# Fresh DB: drop all and upgrade from scratch
engine2 = create_async_engine(DB_URL.replace('langbot_test', 'langbot_fresh'))
# Create fresh database
from sqlalchemy import text
async with engine.connect() as conn:
await conn.execute(text('COMMIT'))
await conn.execute(text('CREATE DATABASE langbot_fresh'))
async with engine2.begin() as conn:
await conn.run_sync(Base.metadata.create_all)
await run_alembic_upgrade(engine2, 'head')
rev3 = await get_alembic_current(engine2)
print(f'Fresh DB upgrade: {rev3}')
assert rev3 is not None
print('All PostgreSQL migration tests passed!')
asyncio.run(main())
"

3
.gitignore vendored
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@@ -52,6 +52,3 @@ src/langbot/web/
/dist
/build
*.egg-info
# Next.js build cache (legacy)
web/.next/

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@@ -70,7 +70,7 @@ Plugin Runtime automatically starts each installed plugin and interacts through
- type: must be a specific type, such as feat (new feature), fix (bug fix), docs (documentation), style (code style), refactor (refactoring), perf (performance optimization), etc.
- scope: the scope of the commit, such as the package name, the file name, the function name, the class name, the module name, etc.
- subject: the subject of the commit, such as the description of the commit, the reason for the commit, the impact of the commit, etc.
- LangBot uses [Alembic](https://alembic.sqlalchemy.org/) to manage database migrations, supporting both SQLite and PostgreSQL. Migration files are located in `src/langbot/pkg/persistence/alembic/versions/`. If you changed the definition of database entities (ORM models), generate a new migration script by running `uv run python -m langbot.pkg.persistence.alembic_runner autogenerate "description of your change"` in the project root (requires `data/config.yaml` to exist). Review and edit the generated script before committing. Migrations are executed automatically on LangBot startup. For data migrations (e.g. modifying JSON field content), you need to manually add the migration code in the generated script.
- If you changed the definition of database entities, please update the migration file in `src/langbot/pkg/persistence/migrations/` and update the constants.py file in `src/langbot/pkg/utils/constants.py` with the new migration number.
## Some Principles

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@@ -4,7 +4,7 @@ WORKDIR /app
COPY web ./web
RUN cd web && npm install && npx vite build
RUN cd web && npm install && npm run build
FROM python:3.12.7-slim
@@ -12,7 +12,7 @@ WORKDIR /app
COPY . .
COPY --from=node /app/web/dist ./web/dist
COPY --from=node /app/web/out ./web/out
RUN apt update \
&& apt install gcc -y \

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@@ -19,9 +19,9 @@ English / [简体中文](README_CN.md) / [繁體中文](README_TW.md) / [日本
[![GitHub stars](https://img.shields.io/github/stars/langbot-app/LangBot?style=social)](https://github.com/langbot-app/LangBot/stargazers)
<a href="https://langbot.app">Website</a>
<a href="https://link.langbot.app/en/docs/features">Features</a>
<a href="https://link.langbot.app/en/docs/guide">Docs</a>
<a href="https://link.langbot.app/en/docs/api">API</a>
<a href="https://docs.langbot.app/en/insight/features">Features</a>
<a href="https://docs.langbot.app/en/insight/guide">Docs</a>
<a href="https://docs.langbot.app/en/tags/readme">API</a>
<a href="https://space.langbot.app/cloud">Cloud</a>
<a href="https://space.langbot.app">Plugin Market</a>
<a href="https://langbot.featurebase.app/roadmap">Roadmap</a>
@@ -45,7 +45,7 @@ LangBot is an **open-source, production-grade platform** for building AI-powered
- **Web Management Panel** — Configure, manage, and monitor your bots through an intuitive browser interface. No YAML editing required.
- **Multi-Pipeline Architecture** — Different bots for different scenarios, with comprehensive monitoring and exception handling.
[→ Learn more about all features](https://link.langbot.app/en/docs/features)
[→ Learn more about all features](https://docs.langbot.app/en/insight/features)
---
@@ -76,7 +76,7 @@ docker compose up -d
[![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/en-US/templates/ZKTBDH)
[![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)
**More options:** [Docker](https://link.langbot.app/en/docs/docker) · [Manual](https://link.langbot.app/en/docs/manual-deploy) · [BTPanel](https://link.langbot.app/en/docs/bt-panel) · [Kubernetes](./docker/README_K8S.md)
**More options:** [Docker](https://docs.langbot.app/en/deploy/langbot/docker) · [Manual](https://docs.langbot.app/en/deploy/langbot/manual) · [BTPanel](https://docs.langbot.app/en/deploy/langbot/one-click/bt) · [Kubernetes](./docker/README_K8S.md)
---
@@ -124,7 +124,7 @@ docker compose up -d
| [接口 AI](https://jiekou.ai/) | Gateway | ✅ |
| [302.AI](https://share.302.ai/SuTG99) | Gateway | ✅ |
[→ View all integrations](https://link.langbot.app/en/docs/features)
[→ View all integrations](https://docs.langbot.app/en/insight/features)
---

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@@ -21,9 +21,9 @@
[![star](https://gitcode.com/RockChinQ/LangBot/star/badge.svg)](https://gitcode.com/RockChinQ/LangBot)
<a href="https://langbot.app">官网</a>
<a href="https://link.langbot.app/zh/docs/features">特性</a>
<a href="https://link.langbot.app/zh/docs/guide">文档</a>
<a href="https://link.langbot.app/zh/docs/api">API</a>
<a href="https://docs.langbot.app/zh/insight/features.html">特性</a>
<a href="https://docs.langbot.app/zh/insight/guide.html">文档</a>
<a href="https://docs.langbot.app/zh/tags/readme.html">API</a>
<a href="https://space.langbot.app/cloud">Cloud</a>
<a href="https://space.langbot.app">插件市场</a>
<a href="https://langbot.featurebase.app/roadmap">路线图</a>
@@ -45,7 +45,7 @@ LangBot 是一个**开源的生产级平台**,用于构建 AI 驱动的即时
- **Web 管理面板** — 通过浏览器直观地配置、管理和监控机器人,无需手动编辑配置文件。
- **多流水线架构** — 不同机器人用于不同场景,具备全面的监控和异常处理能力。
[→ 了解更多功能特性](https://link.langbot.app/zh/docs/features)
[→ 了解更多功能特性](https://docs.langbot.app/zh/insight/features.html)
---
@@ -76,7 +76,7 @@ docker compose up -d
[![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/zh-CN/templates/ZKTBDH)
[![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)
**更多方式:** [Docker](https://link.langbot.app/zh/docs/docker) · [手动部署](https://link.langbot.app/zh/docs/manual-deploy) · [宝塔面板](https://link.langbot.app/zh/docs/bt-panel) · [Kubernetes](./docker/README_K8S.md)
**更多方式:** [Docker](https://docs.langbot.app/zh/deploy/langbot/docker.html) · [手动部署](https://docs.langbot.app/zh/deploy/langbot/manual.html) · [宝塔面板](https://docs.langbot.app/zh/deploy/langbot/one-click/bt.html) · [Kubernetes](./docker/README_K8S.md)
---
@@ -125,7 +125,7 @@ docker compose up -d
| [小马算力](https://www.tokenpony.cn/453z1) | 聚合平台 | ✅ |
| [百宝箱Tbox](https://www.tbox.cn/open) | 智能体平台 | ✅ |
[→ 查看完整集成列表](https://link.langbot.app/zh/docs/features)
[→ 查看完整集成列表](https://docs.langbot.app/zh/insight/features.html)
### TTS语音合成

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@@ -19,9 +19,9 @@
[![GitHub stars](https://img.shields.io/github/stars/langbot-app/LangBot?style=social)](https://github.com/langbot-app/LangBot/stargazers)
<a href="https://langbot.app">Inicio</a>
<a href="https://link.langbot.app/en/docs/features">Características</a>
<a href="https://link.langbot.app/en/docs/guide">Documentación</a>
<a href="https://link.langbot.app/en/docs/api">API</a>
<a href="https://docs.langbot.app/en/insight/features.html">Características</a>
<a href="https://docs.langbot.app/en/insight/guide.html">Documentación</a>
<a href="https://docs.langbot.app/en/tags/readme.html">API</a>
<a href="https://space.langbot.app">Mercado de Plugins</a>
<a href="https://langbot.featurebase.app/roadmap">Hoja de Ruta</a>
@@ -44,7 +44,7 @@ LangBot es una **plataforma de código abierto y grado de producción** para con
- **Panel de Gestión Web** — Configure, gestione y monitoree sus bots a través de una interfaz de navegador intuitiva. Sin necesidad de editar YAML.
- **Arquitectura Multi-Pipeline** — Diferentes bots para diferentes escenarios, con monitoreo completo y manejo de excepciones.
[→ Conocer más sobre todas las funcionalidades](https://link.langbot.app/en/docs/features)
[→ Conocer más sobre todas las funcionalidades](https://docs.langbot.app/en/insight/features.html)
---
@@ -75,7 +75,7 @@ docker compose up -d
[![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/en-US/templates/ZKTBDH)
[![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)
**Más opciones:** [Docker](https://link.langbot.app/en/docs/docker) · [Manual](https://link.langbot.app/en/docs/manual-deploy) · [BTPanel](https://link.langbot.app/en/docs/bt-panel) · [Kubernetes](./docker/README_K8S.md)
**Más opciones:** [Docker](https://docs.langbot.app/en/deploy/langbot/docker.html) · [Manual](https://docs.langbot.app/en/deploy/langbot/manual.html) · [BTPanel](https://docs.langbot.app/en/deploy/langbot/one-click/bt.html) · [Kubernetes](./docker/README_K8S.md)
---
@@ -123,7 +123,7 @@ docker compose up -d
| [接口 AI](https://jiekou.ai/) | Pasarela | ✅ |
| [302.AI](https://share.302.ai/SuTG99) | Pasarela | ✅ |
[→ Ver todas las integraciones](https://link.langbot.app/en/docs/features)
[→ Ver todas las integraciones](https://docs.langbot.app/en/insight/features.html)
---

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@@ -19,9 +19,9 @@
[![GitHub stars](https://img.shields.io/github/stars/langbot-app/LangBot?style=social)](https://github.com/langbot-app/LangBot/stargazers)
<a href="https://langbot.app">Accueil</a>
<a href="https://link.langbot.app/en/docs/features">Fonctionnalités</a>
<a href="https://link.langbot.app/en/docs/guide">Documentation</a>
<a href="https://link.langbot.app/en/docs/api">API</a>
<a href="https://docs.langbot.app/en/insight/features.html">Fonctionnalités</a>
<a href="https://docs.langbot.app/en/insight/guide.html">Documentation</a>
<a href="https://docs.langbot.app/en/tags/readme.html">API</a>
<a href="https://space.langbot.app">Marché des Plugins</a>
<a href="https://langbot.featurebase.app/roadmap">Feuille de Route</a>
@@ -44,7 +44,7 @@ LangBot est une **plateforme open-source de niveau production** pour créer des
- **Panneau de Gestion Web** — Configurez, gérez et surveillez vos bots via une interface navigateur intuitive. Aucune édition de YAML requise.
- **Architecture Multi-Pipeline** — Différents bots pour différents scénarios, avec surveillance complète et gestion des exceptions.
[→ En savoir plus sur toutes les fonctionnalités](https://link.langbot.app/en/docs/features)
[→ En savoir plus sur toutes les fonctionnalités](https://docs.langbot.app/en/insight/features.html)
---
@@ -75,7 +75,7 @@ docker compose up -d
[![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/en-US/templates/ZKTBDH)
[![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)
**Plus d'options :** [Docker](https://link.langbot.app/en/docs/docker) · [Manuel](https://link.langbot.app/en/docs/manual-deploy) · [BTPanel](https://link.langbot.app/en/docs/bt-panel) · [Kubernetes](./docker/README_K8S.md)
**Plus d'options :** [Docker](https://docs.langbot.app/en/deploy/langbot/docker.html) · [Manuel](https://docs.langbot.app/en/deploy/langbot/manual.html) · [BTPanel](https://docs.langbot.app/en/deploy/langbot/one-click/bt.html) · [Kubernetes](./docker/README_K8S.md)
---
@@ -123,7 +123,7 @@ docker compose up -d
| [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | Plateforme GPU | ✅ |
| [ShengSuanYun](https://www.shengsuanyun.com/?from=CH_KYIPP758) | Plateforme GPU | ✅ |
[→ Voir toutes les intégrations](https://link.langbot.app/en/docs/features)
[→ Voir toutes les intégrations](https://docs.langbot.app/en/insight/features.html)
---

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@@ -19,9 +19,9 @@
[![GitHub stars](https://img.shields.io/github/stars/langbot-app/LangBot?style=social)](https://github.com/langbot-app/LangBot/stargazers)
<a href="https://langbot.app">ホーム</a>
<a href="https://link.langbot.app/ja/docs/features">機能</a>
<a href="https://link.langbot.app/ja/docs/guide">ドキュメント</a>
<a href="https://link.langbot.app/ja/docs/api">API</a>
<a href="https://docs.langbot.app/ja/insight/features.html">機能</a>
<a href="https://docs.langbot.app/ja/insight/guide.html">ドキュメント</a>
<a href="https://docs.langbot.app/ja/tags/readme.html">API</a>
<a href="https://space.langbot.app">プラグインマーケット</a>
<a href="https://langbot.featurebase.app/roadmap">ロードマップ</a>
@@ -44,7 +44,7 @@ LangBot は、AI搭載のインスタントメッセージングボットを構
- **Web管理パネル** — 直感的なブラウザインターフェースからボットの設定、管理、監視が可能。YAML編集は不要。
- **マルチパイプラインアーキテクチャ** — 異なるシナリオに異なるボットを配置し、包括的な監視と例外処理を実現。
[→ すべての機能について詳しく見る](https://link.langbot.app/ja/docs/features)
[→ すべての機能について詳しく見る](https://docs.langbot.app/ja/insight/features.html)
---
@@ -75,7 +75,7 @@ docker compose up -d
[![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/en-US/templates/ZKTBDH)
[![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)
**その他:** [Docker](https://link.langbot.app/en/docs/docker) · [手動デプロイ](https://link.langbot.app/en/docs/manual-deploy) · [BTPanel](https://link.langbot.app/en/docs/bt-panel) · [Kubernetes](./docker/README_K8S.md)
**その他:** [Docker](https://docs.langbot.app/en/deploy/langbot/docker.html) · [手動デプロイ](https://docs.langbot.app/en/deploy/langbot/manual.html) · [BTPanel](https://docs.langbot.app/en/deploy/langbot/one-click/bt.html) · [Kubernetes](./docker/README_K8S.md)
---
@@ -123,7 +123,7 @@ docker compose up -d
| [接口 AI](https://jiekou.ai/) | ゲートウェイ | ✅ |
| [302.AI](https://share.302.ai/SuTG99) | ゲートウェイ | ✅ |
[→ すべての統合を表示](https://link.langbot.app/en/docs/features)
[→ すべての統合を表示](https://docs.langbot.app/en/insight/features.html)
---

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@@ -19,9 +19,9 @@
[![GitHub stars](https://img.shields.io/github/stars/langbot-app/LangBot?style=social)](https://github.com/langbot-app/LangBot/stargazers)
<a href="https://langbot.app">홈</a>
<a href="https://link.langbot.app/en/docs/features">기능</a>
<a href="https://link.langbot.app/en/docs/guide">문서</a>
<a href="https://link.langbot.app/en/docs/api">API</a>
<a href="https://docs.langbot.app/en/insight/features.html">기능</a>
<a href="https://docs.langbot.app/en/insight/guide.html">문서</a>
<a href="https://docs.langbot.app/en/tags/readme.html">API</a>
<a href="https://space.langbot.app">플러그인 마켓</a>
<a href="https://langbot.featurebase.app/roadmap">로드맵</a>
@@ -44,7 +44,7 @@ LangBot은 AI 기반 인스턴트 메시징 봇을 구축하기 위한 **오픈
- **웹 관리 패널** — 직관적인 브라우저 인터페이스로 봇을 구성, 관리 및 모니터링. YAML 편집 불필요.
- **멀티 파이프라인 아키텍처** — 다양한 시나리오에 맞는 다양한 봇 구성, 종합 모니터링 및 예외 처리.
[→ 모든 기능 자세히 보기](https://link.langbot.app/en/docs/features)
[→ 모든 기능 자세히 보기](https://docs.langbot.app/en/insight/features.html)
---
@@ -75,7 +75,7 @@ docker compose up -d
[![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/en-US/templates/ZKTBDH)
[![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)
**더 많은 옵션:** [Docker](https://link.langbot.app/en/docs/docker) · [수동 배포](https://link.langbot.app/en/docs/manual-deploy) · [BTPanel](https://link.langbot.app/en/docs/bt-panel) · [Kubernetes](./docker/README_K8S.md)
**더 많은 옵션:** [Docker](https://docs.langbot.app/en/deploy/langbot/docker.html) · [수동 배포](https://docs.langbot.app/en/deploy/langbot/manual.html) · [BTPanel](https://docs.langbot.app/en/deploy/langbot/one-click/bt.html) · [Kubernetes](./docker/README_K8S.md)
---
@@ -123,7 +123,7 @@ docker compose up -d
| [接口 AI](https://jiekou.ai/) | 게이트웨이 | ✅ |
| [302.AI](https://share.302.ai/SuTG99) | 게이트웨이 | ✅ |
[→ 모든 통합 보기](https://link.langbot.app/en/docs/features)
[→ 모든 통합 보기](https://docs.langbot.app/en/insight/features.html)
---

View File

@@ -19,9 +19,9 @@
[![GitHub stars](https://img.shields.io/github/stars/langbot-app/LangBot?style=social)](https://github.com/langbot-app/LangBot/stargazers)
<a href="https://langbot.app">Главная</a>
<a href="https://link.langbot.app/en/docs/features">Возможности</a>
<a href="https://link.langbot.app/en/docs/guide">Документация</a>
<a href="https://link.langbot.app/en/docs/api">API</a>
<a href="https://docs.langbot.app/en/insight/features.html">Возможности</a>
<a href="https://docs.langbot.app/en/insight/guide.html">Документация</a>
<a href="https://docs.langbot.app/en/tags/readme.html">API</a>
<a href="https://space.langbot.app">Магазин плагинов</a>
<a href="https://langbot.featurebase.app/roadmap">Дорожная карта</a>
@@ -44,7 +44,7 @@ LangBot — это **платформа с открытым исходным к
- **Веб-панель управления** — Настраивайте, управляйте и мониторьте ваших ботов через интуитивный браузерный интерфейс. Ручное редактирование YAML не требуется.
- **Мультиконвейерная архитектура** — Разные боты для разных сценариев с комплексным мониторингом и обработкой исключений.
[→ Подробнее обо всех возможностях](https://link.langbot.app/en/docs/features)
[→ Подробнее обо всех возможностях](https://docs.langbot.app/en/insight/features.html)
---
@@ -75,7 +75,7 @@ docker compose up -d
[![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/en-US/templates/ZKTBDH)
[![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)
**Другие варианты:** [Docker](https://link.langbot.app/en/docs/docker) · [Ручная установка](https://link.langbot.app/en/docs/manual-deploy) · [BTPanel](https://link.langbot.app/en/docs/bt-panel) · [Kubernetes](./docker/README_K8S.md)
**Другие варианты:** [Docker](https://docs.langbot.app/en/deploy/langbot/docker.html) · [Ручная установка](https://docs.langbot.app/en/deploy/langbot/manual.html) · [BTPanel](https://docs.langbot.app/en/deploy/langbot/one-click/bt.html) · [Kubernetes](./docker/README_K8S.md)
---
@@ -123,7 +123,7 @@ docker compose up -d
| [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | Платформа GPU | ✅ |
| [ShengSuanYun](https://www.shengsuanyun.com/?from=CH_KYIPP758) | Платформа GPU | ✅ |
[→ Смотреть все интеграции](https://link.langbot.app/en/docs/features)
[→ Смотреть все интеграции](https://docs.langbot.app/en/insight/features.html)
---

View File

@@ -21,9 +21,9 @@
[![star](https://gitcode.com/RockChinQ/LangBot/star/badge.svg)](https://gitcode.com/RockChinQ/LangBot)
<a href="https://langbot.app">官網</a>
<a href="https://link.langbot.app/zh/docs/features">特性</a>
<a href="https://link.langbot.app/zh/docs/guide">文件</a>
<a href="https://link.langbot.app/zh/docs/api">API</a>
<a href="https://docs.langbot.app/zh/insight/features.html">特性</a>
<a href="https://docs.langbot.app/zh/insight/guide.html">文件</a>
<a href="https://docs.langbot.app/zh/tags/readme.html">API</a>
<a href="https://space.langbot.app">外掛市場</a>
<a href="https://langbot.featurebase.app/roadmap">路線圖</a>
@@ -46,7 +46,7 @@ LangBot 是一個**開源的生產級平台**,用於建構 AI 驅動的即時
- **Web 管理面板** — 透過瀏覽器直觀地配置、管理和監控機器人,無需手動編輯設定檔。
- **多流水線架構** — 不同機器人用於不同場景,具備全面的監控和異常處理能力。
[→ 了解更多功能特性](https://link.langbot.app/zh/docs/features)
[→ 了解更多功能特性](https://docs.langbot.app/zh/insight/features.html)
---
@@ -77,7 +77,7 @@ docker compose up -d
[![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/zh-CN/templates/ZKTBDH)
[![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)
**更多方式:** [Docker](https://link.langbot.app/zh/docs/docker) · [手動部署](https://link.langbot.app/zh/docs/manual-deploy) · [寶塔面板](https://link.langbot.app/zh/docs/bt-panel) · [Kubernetes](./docker/README_K8S.md)
**更多方式:** [Docker](https://docs.langbot.app/zh/deploy/langbot/docker.html) · [手動部署](https://docs.langbot.app/zh/deploy/langbot/manual.html) · [寶塔面板](https://docs.langbot.app/zh/deploy/langbot/one-click/bt.html) · [Kubernetes](./docker/README_K8S.md)
---
@@ -139,7 +139,7 @@ docker compose up -d
|-----------|------|
| 阿里雲百煉 | [外掛](https://github.com/Thetail001/LangBot_BailianTextToImagePlugin) |
[→ 查看完整整合列表](https://link.langbot.app/zh/docs/features)
[→ 查看完整整合列表](https://docs.langbot.app/zh/insight/features.html)
---

View File

@@ -19,9 +19,9 @@
[![GitHub stars](https://img.shields.io/github/stars/langbot-app/LangBot?style=social)](https://github.com/langbot-app/LangBot/stargazers)
<a href="https://langbot.app">Trang chủ</a>
<a href="https://link.langbot.app/en/docs/features">Tính năng</a>
<a href="https://link.langbot.app/en/docs/guide">Tài liệu</a>
<a href="https://link.langbot.app/en/docs/api">API</a>
<a href="https://docs.langbot.app/en/insight/features.html">Tính năng</a>
<a href="https://docs.langbot.app/en/insight/guide.html">Tài liệu</a>
<a href="https://docs.langbot.app/en/tags/readme.html">API</a>
<a href="https://space.langbot.app">Chợ Plugin</a>
<a href="https://langbot.featurebase.app/roadmap">Lộ trình</a>
@@ -44,7 +44,7 @@ LangBot là một **nền tảng mã nguồn mở, cấp sản xuất** để x
- **Bảng quản lý Web** — Cấu hình, quản lý và giám sát bot thông qua giao diện trình duyệt trực quan. Không cần chỉnh sửa YAML.
- **Kiến trúc đa Pipeline** — Các bot khác nhau cho các kịch bản khác nhau, với giám sát toàn diện và xử lý ngoại lệ.
[→ Tìm hiểu thêm về tất cả tính năng](https://link.langbot.app/en/docs/features)
[→ Tìm hiểu thêm về tất cả tính năng](https://docs.langbot.app/en/insight/features.html)
---
@@ -75,7 +75,7 @@ docker compose up -d
[![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/en-US/templates/ZKTBDH)
[![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)
**Thêm tùy chọn:** [Docker](https://link.langbot.app/en/docs/docker) · [Thủ công](https://link.langbot.app/en/docs/manual-deploy) · [BTPanel](https://link.langbot.app/en/docs/bt-panel) · [Kubernetes](./docker/README_K8S.md)
**Thêm tùy chọn:** [Docker](https://docs.langbot.app/en/deploy/langbot/docker.html) · [Thủ công](https://docs.langbot.app/en/deploy/langbot/manual.html) · [BTPanel](https://docs.langbot.app/en/deploy/langbot/one-click/bt.html) · [Kubernetes](./docker/README_K8S.md)
---
@@ -123,7 +123,7 @@ docker compose up -d
| [接口 AI](https://jiekou.ai/) | Cổng | ✅ |
| [302.AI](https://share.302.ai/SuTG99) | Cổng | ✅ |
[→ Xem tất cả tích hợp](https://link.langbot.app/en/docs/features)
[→ Xem tất cả tích hợp](https://docs.langbot.app/en/insight/features.html)
---

View File

@@ -312,7 +312,7 @@ spec:
### 参考资源
- [LangBot 官方文档](https://docs.langbot.app)
- [Docker 部署文档](https://link.langbot.app/zh/docs/docker)
- [Docker 部署文档](https://docs.langbot.app/zh/deploy/langbot/docker.html)
- [Kubernetes 官方文档](https://kubernetes.io/docs/)
---
@@ -625,5 +625,5 @@ spec:
### References
- [LangBot Official Documentation](https://docs.langbot.app)
- [Docker Deployment Guide](https://link.langbot.app/zh/docs/docker)
- [Docker Deployment Guide](https://docs.langbot.app/zh/deploy/langbot/docker.html)
- [Kubernetes Official Documentation](https://kubernetes.io/docs/)

View File

@@ -34,4 +34,4 @@ services:
networks:
langbot_network:
driver: bridge
driver: bridge

View File

@@ -1,6 +1,6 @@
[project]
name = "langbot"
version = "4.9.6"
version = "4.9.4"
description = "Production-grade platform for building agentic IM bots"
readme = "README.md"
license-files = ["LICENSE"]
@@ -8,7 +8,7 @@ requires-python = ">=3.11,<4.0"
dependencies = [
"aiocqhttp>=1.4.4",
"aiofiles>=24.1.0",
"aiohttp>=3.13.4",
"aiohttp>=3.11.18",
"aioshutil>=1.5",
"aiosqlite>=0.21.0",
"anthropic>=0.51.0",
@@ -16,7 +16,7 @@ dependencies = [
"async-lru>=2.0.5",
"certifi>=2025.4.26",
"colorlog~=6.6.0",
"cryptography>=46.0.7",
"cryptography>=44.0.3",
"dashscope>=1.25.10",
"dingtalk-stream>=0.24.0",
"discord-py>=2.5.2",
@@ -27,7 +27,7 @@ dependencies = [
"nakuru-project-idk>=0.0.2.1",
"ollama>=0.4.8",
"openai>1.0.0",
"pillow>=12.2.0",
"pillow>=11.2.1",
"psutil>=7.0.0",
"pycryptodome>=3.22.0",
"pydantic>2.0",
@@ -39,7 +39,6 @@ dependencies = [
"quart-cors>=0.8.0",
"requests>=2.32.3",
"slack-sdk>=3.35.0",
"alembic>=1.15.0",
"sqlalchemy[asyncio]>=2.0.40",
"sqlmodel>=0.0.24",
"telegramify-markdown>=0.5.1",
@@ -50,7 +49,7 @@ dependencies = [
"pip>=25.1.1",
"ruff>=0.11.9",
"pre-commit>=4.2.0",
"uv>=0.11.6",
"uv>=0.7.11",
"mypy>=1.16.0",
"PyPDF2>=3.0.1",
"python-docx>=1.1.0",
@@ -61,15 +60,11 @@ dependencies = [
"ebooklib>=0.18",
"html2text>=2024.2.26",
"langchain>=0.2.0",
"langchain-core>=1.2.28",
"langsmith>=0.7.31",
"python-multipart>=0.0.26",
"Mako>=1.3.11",
"langchain-text-splitters>=1.1.2",
"langchain-text-splitters>=0.0.1",
"chromadb>=1.0.0,<2.0.0",
"qdrant-client (>=1.15.1,<2.0.0)",
"pyseekdb==1.1.0.post3",
"langbot-plugin==0.3.8",
"langbot-plugin==0.3.5",
"asyncpg>=0.30.0",
"line-bot-sdk>=3.19.0",
"tboxsdk>=0.0.10",
@@ -116,12 +111,12 @@ requires = ["setuptools>=61.0", "wheel"]
build-backend = "setuptools.build_meta"
[tool.setuptools]
package-data = { "langbot" = ["templates/**", "pkg/provider/modelmgr/requesters/*", "pkg/platform/sources/*", "web/dist/**", "pkg/persistence/alembic/**"] }
package-data = { "langbot" = ["templates/**", "pkg/provider/modelmgr/requesters/*", "pkg/platform/sources/*", "web/out/**"] }
[dependency-groups]
dev = [
"pre-commit>=4.2.0",
"pytest>=9.0.3",
"pytest>=8.4.1",
"pytest-asyncio>=1.0.0",
"pytest-cov>=7.0.0",
"ruff>=0.11.9",

View File

@@ -1,3 +1,3 @@
"""LangBot - Production-grade platform for building agentic IM bots"""
__version__ = '4.9.6'
__version__ = '4.9.4'

View File

@@ -182,88 +182,6 @@ class DingTalkClient:
for handler in self._message_handlers[msg_type]:
await handler(event)
async def _parse_quoted_message(self, replied_msg: dict) -> dict:
"""Parse the quoted/replied message and extract its content.
Args:
replied_msg: The repliedMsg object from DingTalk message
Returns:
A dict containing the quoted message info with keys:
- message_id: The original message ID
- msg_type: The message type (text, file, picture, audio, etc.)
- content: The text content (if any)
- file_url: The file download URL (if file type)
- file_name: The file name (if file type)
- picture: The picture base64 (if picture type)
- audio: The audio base64 (if audio type)
"""
quote_info = {
'message_id': replied_msg.get('msgId', ''),
'msg_type': replied_msg.get('msgType', ''),
'sender_id': replied_msg.get('senderId', ''),
}
msg_type = replied_msg.get('msgType', '')
content = replied_msg.get('content', {})
# Handle content as string (JSON) or dict
if isinstance(content, str):
try:
content = json.loads(content)
except (json.JSONDecodeError, TypeError):
content = {}
if msg_type == 'text':
# Text message
if isinstance(content, dict):
quote_info['content'] = content.get('content', '')
else:
quote_info['content'] = str(content)
elif msg_type == 'file':
# File message
download_code = content.get('downloadCode')
file_name = content.get('fileName')
if download_code and file_name:
try:
quote_info['file_url'] = await self.get_file_url(download_code)
quote_info['file_name'] = file_name
except Exception as e:
if self.logger:
await self.logger.error(f'Failed to get quoted file URL: {e}')
elif msg_type == 'picture':
# Picture message
download_code = content.get('downloadCode')
if download_code:
try:
quote_info['picture'] = await self.download_image(download_code)
except Exception as e:
if self.logger:
await self.logger.error(f'Failed to download quoted image: {e}')
elif msg_type == 'audio':
# Audio message
download_code = content.get('downloadCode')
if download_code:
try:
quote_info['audio'] = await self.get_audio_url(download_code)
except Exception as e:
if self.logger:
await self.logger.error(f'Failed to get quoted audio: {e}')
elif msg_type == 'richText':
# Rich text message - extract text content
rich_text = content.get('richText', [])
texts = []
for item in rich_text:
if 'text' in item and item['text'] != '\n':
texts.append(item['text'])
quote_info['content'] = '\n'.join(texts)
return quote_info
async def get_message(self, incoming_message: dingtalk_stream.chatbot.ChatbotMessage):
try:
# print(json.dumps(incoming_message.to_dict(), indent=4, ensure_ascii=False))
@@ -275,15 +193,6 @@ class DingTalkClient:
elif str(incoming_message.conversation_type) == '2':
message_data['conversation_type'] = 'GroupMessage'
# Check for quoted/replied message
raw_data = incoming_message.to_dict()
text_data = raw_data.get('text', {})
if isinstance(text_data, dict) and text_data.get('isReplyMsg'):
replied_msg = text_data.get('repliedMsg', {})
if replied_msg:
quote_info = await self._parse_quoted_message(replied_msg)
message_data['QuotedMessage'] = quote_info
if incoming_message.message_type == 'richText':
data = incoming_message.rich_text_content.to_dict()
@@ -359,25 +268,7 @@ class DingTalkClient:
message_data['Type'] = 'image'
elif incoming_message.message_type == 'audio':
raw_content = incoming_message.to_dict().get('content', {})
# 兼容处理:如果 content 仍为 JSON 字符串则进行解析
if isinstance(raw_content, str):
try:
raw_content = json.loads(raw_content)
except (json.JSONDecodeError, TypeError):
raw_content = {}
if self.logger:
await self.logger.info(f'DingTalk audio raw content: {json.dumps(raw_content, ensure_ascii=False)}')
# 提取钉钉自带的语音转写文字Powered by Qwen
recognition = raw_content.get('recognition', '')
if recognition:
message_data['Content'] = recognition
download_code = raw_content.get('downloadCode')
if download_code:
message_data['Audio'] = await self.get_audio_url(download_code)
message_data['Audio'] = await self.get_audio_url(incoming_message.to_dict()['content']['downloadCode'])
message_data['Type'] = 'audio'
elif incoming_message.message_type == 'file':

View File

@@ -47,22 +47,6 @@ class DingTalkEvent(dict):
def conversation(self):
return self.get('conversation_type', '')
@property
def quoted_message(self) -> Optional[Dict[str, Any]]:
"""Get the quoted/replied message info if this is a reply message.
Returns:
A dict containing:
- message_id: The original message ID
- msg_type: The message type (text, file, picture, audio, etc.)
- content: The text content (if any)
- file_url: The file download URL (if file type)
- file_name: The file name (if file type)
- picture: The picture base64 (if picture type)
- audio: The audio base64 (if audio type)
"""
return self.get('QuotedMessage')
def __getattr__(self, key: str) -> Optional[Any]:
"""
允许通过属性访问数据中的任意字段。

View File

@@ -64,25 +64,16 @@ class StreamSession:
# 缓存最近一次片段,处理重试或超时兜底
last_chunk: Optional[StreamChunk] = None
# 反馈 ID用于接收用户点赞/点踩反馈
feedback_id: Optional[str] = None
class StreamSessionManager:
"""管理 stream 会话的生命周期,并负责队列的生产消费。"""
# Sessions with registered feedback_ids use a longer TTL to survive the
# full like → cancel → dislike feedback flow. Must align with the adapter's
# _stream_to_monitoring_msg TTL (wecombot.py).
_FEEDBACK_SESSION_TTL = 600 # 10 minutes
def __init__(self, logger: EventLogger, ttl: int = 60) -> None:
self.logger = logger
self.ttl = ttl # 超时时间(秒),超过该时间未被访问的会话会被清理由 cleanup
self._sessions: dict[str, StreamSession] = {} # stream_id -> StreamSession 映射
self._msg_index: dict[str, str] = {} # msgid -> stream_id 映射,便于流水线根据消息 ID 找到会话
self._feedback_index: dict[str, str] = {} # feedback_id -> stream_id 映射
def get_stream_id_by_msg(self, msg_id: str) -> Optional[str]:
if not msg_id:
@@ -92,32 +83,6 @@ class StreamSessionManager:
def get_session(self, stream_id: str) -> Optional[StreamSession]:
return self._sessions.get(stream_id)
def get_session_by_feedback_id(self, feedback_id: str) -> Optional[StreamSession]:
"""根据 feedback_id 查找会话。
Args:
feedback_id: 企业微信反馈事件中的反馈 ID。
Returns:
Optional[StreamSession]: 找到的会话实例,未找到返回 None。
"""
if not feedback_id:
return None
stream_id = self._feedback_index.get(feedback_id)
if stream_id:
return self._sessions.get(stream_id)
return None
def register_feedback_id(self, stream_id: str, feedback_id: str) -> None:
"""注册 feedback_id 与 stream_id 的映射。
Args:
stream_id: 企业微信流式会话 ID。
feedback_id: 反馈 ID。
"""
if feedback_id and stream_id:
self._feedback_index[feedback_id] = stream_id
def create_or_get(self, msg_json: dict[str, Any]) -> tuple[StreamSession, bool]:
"""根据企业微信回调创建或获取会话。
@@ -219,17 +184,11 @@ class StreamSessionManager:
session.last_access = time.time()
def cleanup(self) -> None:
"""定期清理过期会话,防止队列与映射无上限累积。
已注册 feedback_id 的会话使用更长的 TTL确保用户在点赞/取消/点踩流程中
不会因为 session 被提前清除而丢失上下文信息。
"""
"""定期清理过期会话,防止队列与映射无上限累积。"""
now = time.time()
expired: list[str] = []
for stream_id, session in self._sessions.items():
# Sessions with registered feedback_ids use a longer TTL
effective_ttl = self._FEEDBACK_SESSION_TTL if session.feedback_id else self.ttl
if now - session.last_access > effective_ttl:
if now - session.last_access > self.ttl:
expired.append(stream_id)
for stream_id in expired:
@@ -239,9 +198,6 @@ class StreamSessionManager:
msg_id = session.msg_id
if msg_id and self._msg_index.get(msg_id) == stream_id:
self._msg_index.pop(msg_id, None)
# Clean up feedback index for expired sessions
if session.feedback_id:
self._feedback_index.pop(session.feedback_id, None)
def _decrypt_file(encrypted_data: bytes, aes_key_str: str) -> bytes:
@@ -448,10 +404,10 @@ async def parse_wecom_bot_message(
}
if voice_info.get('content'):
message_data['content'] = voice_info.get('content')
# if (message_data['voice'].get('filesize') or 0) <= max_inline_file_size:
# voice_base64 = await _safe_download_as_data_uri(download_url, per_msg_aeskey)
# if voice_base64:
# message_data['voice']['base64'] = voice_base64
if (message_data['voice'].get('filesize') or 0) <= max_inline_file_size:
voice_base64 = await _safe_download_as_data_uri(download_url, per_msg_aeskey)
if voice_base64:
message_data['voice']['base64'] = voice_base64
elif msg_type == 'video':
video_info = msg_json.get('video', {}) or {}
download_url = video_info.get('url')
@@ -463,12 +419,10 @@ async def parse_wecom_bot_message(
'md5sum': video_info.get('md5sum') or video_info.get('md5'),
'filename': video_info.get('filename') or video_info.get('name'),
}
# if (video_data.get('filesize') or 0) <= max_inline_file_size:
# video_base64 = await _safe_download_as_data_uri(download_url, per_msg_aeskey)
# if video_base64:
# video_data['base64'] = video_base64
# 应为需要解密但是目前暂时不能下载到内部进行解密所以先将下载链接拼接aeskey返回给用户由插件去处理该链接的下载和解密逻辑
video_data['download_url'] = download_url + f'?aeskey={per_msg_aeskey}'
if (video_data.get('filesize') or 0) <= max_inline_file_size:
video_base64 = await _safe_download_as_data_uri(download_url, per_msg_aeskey)
if video_base64:
video_data['base64'] = video_base64
message_data['video'] = video_data
elif msg_type == 'file':
file_info = msg_json.get('file', {}) or {}
@@ -482,15 +436,12 @@ async def parse_wecom_bot_message(
'download_url': download_url,
'extra': file_info,
}
# if (file_data.get('filesize') or 0) <= max_inline_file_size:
# file_bytes, dl_filename = await _safe_download(download_url, per_msg_aeskey)
# if file_bytes:
# file_data['base64'] = _bytes_to_data_uri(file_bytes)
# if dl_filename and not file_data.get('filename'):
# file_data['filename'] = dl_filename
# 应为需要解密但是目前暂时不能下载到内部进行解密所以先将下载链接拼接aeskey返回给用户由插件去处理该链接的下载和解密逻辑
file_data['download_url'] = download_url + f'?aeskey={per_msg_aeskey}'
if (file_data.get('filesize') or 0) <= max_inline_file_size:
file_bytes, dl_filename = await _safe_download(download_url, per_msg_aeskey)
if file_bytes:
file_data['base64'] = _bytes_to_data_uri(file_bytes)
if dl_filename and not file_data.get('filename'):
file_data['filename'] = dl_filename
message_data['file'] = file_data
elif msg_type == 'link':
message_data['link'] = msg_json.get('link', {})
@@ -606,120 +557,6 @@ async def parse_wecom_bot_message(
if msg_json.get('aibotid'):
message_data['aibotid'] = msg_json.get('aibotid', '')
# Handle quote (referenced message) - important for group chat file references
quote_info = msg_json.get('quote')
if quote_info:
quote_data: dict[str, Any] = {}
quote_type = quote_info.get('msgtype', '')
quote_data['msgtype'] = quote_type
if quote_type == 'text':
quote_data['content'] = quote_info.get('text', {}).get('content', '')
elif quote_type == 'image':
img_info = quote_info.get('image', {})
img_url = img_info.get('url', '')
img_aeskey = img_info.get('aeskey', '')
base64_data = await _safe_download_as_data_uri(img_url, img_aeskey)
if base64_data:
quote_data['picurl'] = base64_data
quote_data['images'] = [base64_data]
elif quote_type == 'file':
file_info = quote_info.get('file', {}) or {}
download_url = file_info.get('url') or file_info.get('fileurl')
item_aeskey = file_info.get('aeskey', '')
file_data = {
'filename': file_info.get('filename') or file_info.get('name'),
'filesize': file_info.get('filesize') or file_info.get('size'),
'md5sum': file_info.get('md5sum') or file_info.get('md5'),
'sdkfileid': file_info.get('sdkfileid') or file_info.get('fileid'),
'download_url': download_url,
'extra': file_info,
}
# Same as private chat: append aeskey to download_url for plugin processing
if download_url and item_aeskey:
file_data['download_url'] = download_url + f'?aeskey={item_aeskey}'
quote_data['file'] = file_data
elif quote_type == 'voice':
voice_info = quote_info.get('voice', {}) or {}
download_url = voice_info.get('url')
item_aeskey = voice_info.get('aeskey', '')
voice_data = {
'url': download_url,
'md5sum': voice_info.get('md5sum') or voice_info.get('md5'),
'filesize': voice_info.get('filesize') or voice_info.get('size'),
'sdkfileid': voice_info.get('sdkfileid') or voice_info.get('fileid'),
}
if voice_info.get('content'):
quote_data['content'] = voice_info.get('content')
# Same as private chat: append aeskey to url for plugin processing
if download_url and item_aeskey:
voice_data['url'] = download_url + f'?aeskey={item_aeskey}'
quote_data['voice'] = voice_data
elif quote_type == 'video':
video_info = quote_info.get('video', {}) or {}
download_url = video_info.get('url')
item_aeskey = video_info.get('aeskey', '')
video_data = {
'url': download_url,
'filesize': video_info.get('filesize') or video_info.get('size'),
'sdkfileid': video_info.get('sdkfileid') or video_info.get('fileid'),
'md5sum': video_info.get('md5sum') or video_info.get('md5'),
'filename': video_info.get('filename') or video_info.get('name'),
}
# Same as private chat: append aeskey to download_url for plugin processing
if download_url and item_aeskey:
video_data['download_url'] = download_url + f'?aeskey={item_aeskey}'
quote_data['video'] = video_data
elif quote_type == 'link':
quote_data['link'] = quote_info.get('link', {})
link = quote_data['link']
title = link.get('title', '')
desc = link.get('description') or link.get('digest', '')
quote_data['content'] = '\n'.join(filter(None, [title, desc]))
elif quote_type == 'mixed':
# Handle mixed type in quote (text + images + files etc.)
items = quote_info.get('mixed', {}).get('msg_item', [])
texts = []
images = []
files = []
for item in items:
item_type = item.get('msgtype')
if item_type == 'text':
texts.append(item.get('text', {}).get('content', ''))
elif item_type == 'image':
img_info = item.get('image', {})
img_url = img_info.get('url')
img_aeskey = img_info.get('aeskey', '')
base64_data = await _safe_download_as_data_uri(img_url, img_aeskey)
if base64_data:
images.append(base64_data)
elif item_type == 'file':
file_info = item.get('file', {}) or {}
download_url = file_info.get('url') or file_info.get('fileurl')
item_aeskey = file_info.get('aeskey', '')
file_data = {
'filename': file_info.get('filename') or file_info.get('name'),
'filesize': file_info.get('filesize') or file_info.get('size'),
'md5sum': file_info.get('md5sum') or file_info.get('md5'),
'sdkfileid': file_info.get('sdkfileid') or file_info.get('fileid'),
'download_url': download_url,
'extra': file_info,
}
# Same as private chat: append aeskey to download_url for plugin processing
if download_url and item_aeskey:
file_data['download_url'] = download_url + f'?aeskey={item_aeskey}'
files.append(file_data)
if texts:
quote_data['content'] = ' '.join(texts)
if images:
quote_data['images'] = images
quote_data['picurl'] = images[0]
if files:
quote_data['files'] = files
quote_data['file'] = files[0]
message_data['quote'] = quote_data
return message_data
@@ -760,27 +597,14 @@ class WecomBotClient:
self.stream_sessions = StreamSessionManager(logger=logger)
self.stream_poll_timeout = 0.5
self._feedback_callback: Optional[Callable] = None
def set_feedback_callback(self, callback: Callable) -> None:
"""设置反馈回调函数。
Args:
callback: 反馈回调函数,签名: async def callback(feedback_id, feedback_type, feedback_content, inaccurate_reasons, session)
"""
self._feedback_callback = callback
@staticmethod
def _build_stream_payload(
stream_id: str, content: str, finish: bool, feedback_id: Optional[str] = None
) -> dict[str, Any]:
def _build_stream_payload(stream_id: str, content: str, finish: bool) -> dict[str, Any]:
"""按照企业微信协议拼装返回报文。
Args:
stream_id: 企业微信会话 ID。
content: 推送的文本内容。
finish: 是否为最终片段。
feedback_id: 反馈 ID用于接收用户点赞/点踩反馈。
Returns:
dict[str, Any]: 可直接加密返回的 payload。
@@ -788,16 +612,13 @@ class WecomBotClient:
Example:
组装 `{'msgtype': 'stream', 'stream': {'id': 'sid', ...}}` 结构。
"""
stream_payload = {
'id': stream_id,
'finish': finish,
'content': content,
}
if feedback_id:
stream_payload['feedback'] = {'id': feedback_id}
return {
'msgtype': 'stream',
'stream': stream_payload,
'stream': {
'id': stream_id,
'finish': finish,
'content': content,
},
}
async def _encrypt_and_reply(self, payload: dict[str, Any], nonce: str) -> tuple[Response, int]:
@@ -853,14 +674,9 @@ class WecomBotClient:
"""
session, is_new = self.stream_sessions.create_or_get(msg_json)
feedback_id = str(uuid.uuid4())
session.feedback_id = feedback_id
self.stream_sessions.register_feedback_id(session.stream_id, feedback_id)
message_data = await self.get_message(msg_json)
if message_data:
message_data['stream_id'] = session.stream_id
message_data['feedback_id'] = feedback_id
try:
event = wecombotevent.WecomBotEvent(message_data)
except Exception:
@@ -869,7 +685,7 @@ class WecomBotClient:
if is_new:
asyncio.create_task(self._dispatch_event(event))
payload = self._build_stream_payload(session.stream_id, '', False, feedback_id)
payload = self._build_stream_payload(session.stream_id, '', False)
return await self._encrypt_and_reply(payload, nonce)
async def _handle_post_followup_response(self, msg_json: dict[str, Any], nonce: str) -> tuple[Response, int]:
@@ -994,81 +810,11 @@ class WecomBotClient:
msg_json = json.loads(decrypted_xml)
event = msg_json.get('event', {})
event_type = event.get('eventtype', '')
if event_type == 'feedback_event':
return await self._handle_feedback_event(msg_json, nonce)
if msg_json.get('msgtype') == 'stream':
return await self._handle_post_followup_response(msg_json, nonce)
return await self._handle_post_initial_response(msg_json, nonce)
async def _handle_feedback_event(self, msg_json: dict[str, Any], nonce: str) -> tuple[Response, int]:
"""处理企业微信用户反馈事件(点赞/点踩)。
Args:
msg_json: 解密后的企业微信反馈事件 JSON。
nonce: 企业微信回调参数 nonce。
Returns:
Tuple[Response, int]: Quart Response 及状态码。
Note:
企业微信协议要求:反馈事件目前仅支持回复空包。
"""
try:
feedback_event = msg_json.get('event', {}).get('feedback_event', {})
feedback_id = feedback_event.get('id', '')
feedback_type = feedback_event.get('type', 0)
feedback_content = feedback_event.get('content', '')
inaccurate_reasons = feedback_event.get('inaccurate_reason_list', [])
await self.logger.info(
f'收到用户反馈事件: feedback_id={feedback_id}, type={feedback_type}, '
f'content={feedback_content}, reasons={inaccurate_reasons}'
)
session = self.stream_sessions.get_session_by_feedback_id(feedback_id)
if session:
await self.logger.info(
f'反馈关联到会话: stream_id={session.stream_id}, msg_id={session.msg_id}, user_id={session.user_id}'
)
else:
await self.logger.warning(f'未找到 feedback_id={feedback_id} 对应的会话,仍将记录反馈')
# Dispatch feedback event regardless of session availability
for handler in self._message_handlers.get('feedback', []):
try:
await handler(
feedback_id=feedback_id,
feedback_type=feedback_type,
feedback_content=feedback_content,
inaccurate_reasons=inaccurate_reasons,
session=session,
)
except Exception:
await self.logger.error(traceback.format_exc())
if self._feedback_callback:
try:
await self._feedback_callback(
feedback_id=feedback_id,
feedback_type=feedback_type,
feedback_content=feedback_content,
inaccurate_reasons=inaccurate_reasons,
session=session,
)
except Exception:
await self.logger.error(traceback.format_exc())
except Exception:
await self.logger.error(traceback.format_exc())
return await self._encrypt_and_reply({}, nonce)
async def get_message(self, msg_json):
return await parse_wecom_bot_message(msg_json, self.EnCodingAESKey, self.logger)
@@ -1137,15 +883,6 @@ class WecomBotClient:
return decorator
def on_feedback(self):
def decorator(func: Callable):
if 'feedback' not in self._message_handlers:
self._message_handlers['feedback'] = []
self._message_handlers['feedback'].append(func)
return func
return decorator
async def download_url_to_base64(self, download_url, encoding_aes_key):
data, _filename = await download_encrypted_file(download_url, encoding_aes_key, self.logger)
if data:

View File

@@ -133,24 +133,3 @@ class WecomBotEvent(dict):
AI Bot ID
"""
return self.get('aibotid', '')
@property
def feedback_id(self) -> str:
"""
反馈 ID用于关联用户点赞/点踩反馈
"""
return self.get('feedback_id', '')
@property
def stream_id(self) -> str:
"""
流式消息 ID
"""
return self.get('stream_id', '')
@property
def quote(self):
"""
引用消息信息(群聊中用户引用其他消息时返回)
"""
return self.get('quote', {})

View File

@@ -20,7 +20,7 @@ from typing import Any, Callable, Optional
import aiohttp
from langbot.libs.wecom_ai_bot_api import wecombotevent
from langbot.libs.wecom_ai_bot_api.api import parse_wecom_bot_message, StreamSession
from langbot.libs.wecom_ai_bot_api.api import parse_wecom_bot_message
from langbot.pkg.platform.logger import EventLogger
DEFAULT_WS_URL = 'wss://openws.work.weixin.qq.com'
@@ -96,12 +96,6 @@ class WecomBotWsClient:
self._stream_ids: dict[str, str] = {} # msg_id -> req_id|stream_id
# Dedup: skip sending when content hasn't changed
self._stream_last_content: dict[str, str] = {} # msg_id -> last content sent
# Stream session info for feedback tracking
self._stream_sessions: dict[str, dict] = {} # msg_id -> session info
# Feedback tracking: feedback_id -> session info
self._feedback_sessions: dict[str, dict] = {} # feedback_id -> {msg_id, user_id, chat_id, stream_id, req_id}
# msg_id -> feedback_id (for associating feedback with message)
self._msg_feedback_ids: dict[str, str] = {} # msg_id -> feedback_id
# ── Public API ──────────────────────────────────────────────────
@@ -170,27 +164,12 @@ class WecomBotWsClient:
return decorator
def on_feedback(self) -> Callable:
"""Decorator to register a feedback event handler.
Same interface as WecomBotClient.on_feedback for compatibility.
"""
def decorator(func: Callable):
if 'feedback' not in self._message_handlers:
self._message_handlers['feedback'] = []
self._message_handlers['feedback'].append(func)
return func
return decorator
async def reply_stream(
self,
req_id: str,
stream_id: str,
content: str,
finish: bool = False,
feedback_id: str = '',
) -> Optional[dict]:
"""Send a streaming reply frame.
@@ -199,22 +178,17 @@ class WecomBotWsClient:
stream_id: The stream ID for this streaming session.
content: The content to send (supports Markdown).
finish: Whether this is the final chunk.
feedback_id: Optional feedback ID for receiving user feedback (like/dislike).
Returns:
The ACK frame dict, or None on failure.
"""
stream_payload = {
'id': stream_id,
'finish': finish,
'content': content,
}
if feedback_id:
stream_payload['feedback'] = {'id': feedback_id}
body = {
'msgtype': 'stream',
'stream': stream_payload,
'stream': {
'id': stream_id,
'finish': finish,
'content': content,
},
}
return await self._send_reply(req_id, body)
@@ -279,23 +253,11 @@ class WecomBotWsClient:
# Skip sending if content hasn't changed (e.g. during tool call argument streaming)
if not is_final and content == self._stream_last_content.get(msg_id):
return True
# Generate feedback_id for final chunk
feedback_id = ''
if is_final:
feedback_id = _generate_req_id('feedback')
self._msg_feedback_ids[msg_id] = feedback_id
# Store session info for feedback tracking
session_info = self._stream_sessions.get(msg_id)
if session_info:
self._feedback_sessions[feedback_id] = session_info
await self.reply_stream(req_id, stream_id, content, finish=is_final, feedback_id=feedback_id)
await self.reply_stream(req_id, stream_id, content, finish=is_final)
self._stream_last_content[msg_id] = content
if is_final:
self._stream_ids.pop(msg_id, None)
self._stream_last_content.pop(msg_id, None)
self._stream_sessions.pop(msg_id, None)
return True
except Exception:
await self.logger.error(f'Failed to push stream chunk: {traceback.format_exc()}')
@@ -483,15 +445,6 @@ class WecomBotWsClient:
msg_id = message_data.get('msgid', '')
if msg_id:
self._stream_ids[msg_id] = f'{req_id}|{stream_id}'
# Store session info for feedback tracking
self._stream_sessions[msg_id] = {
'req_id': req_id,
'stream_id': stream_id,
'msg_id': msg_id,
'user_id': message_data.get('userid', ''),
'chat_id': message_data.get('chatid', ''),
'chat_type': message_data.get('type', 'single'),
}
message_data['stream_id'] = stream_id
message_data['req_id'] = req_id
@@ -501,7 +454,7 @@ class WecomBotWsClient:
await self.logger.error(f'Error in message callback: {traceback.format_exc()}')
async def _handle_event_callback(self, frame: dict):
"""Handle an incoming event callback frame (enter_chat, template_card_event, feedback_event, disconnected_event)."""
"""Handle an incoming event callback frame (enter_chat, template_card_event, etc.)."""
try:
body = frame.get('body', {})
req_id = frame.get('headers', {}).get('req_id', '')
@@ -526,54 +479,14 @@ class WecomBotWsClient:
if body.get('chatid'):
message_data['chatid'] = body.get('chatid', '')
if event_type == 'feedback_event':
feedback_event = event_info.get('feedback_event', {})
feedback_id = feedback_event.get('id', '')
feedback_type = feedback_event.get('type', 0)
feedback_content = feedback_event.get('content', '')
inaccurate_reasons = feedback_event.get('inaccurate_reason_list', [])
await self.logger.info(
f'收到用户反馈事件: feedback_id={feedback_id}, type={feedback_type}, '
f'content={feedback_content}, reasons={inaccurate_reasons}'
)
# Look up session by feedback_id
session_info = self._feedback_sessions.get(feedback_id)
session = None
if session_info:
session = StreamSession(
stream_id=session_info.get('stream_id', ''),
msg_id=session_info.get('msg_id', ''),
chat_id=session_info.get('chat_id') or None,
user_id=session_info.get('user_id') or None,
feedback_id=feedback_id,
)
await self.logger.info(
f'反馈关联到会话: stream_id={session.stream_id}, msg_id={session.msg_id}, user_id={session.user_id}'
)
else:
await self.logger.warning(f'未找到 feedback_id={feedback_id} 对应的会话')
for handler in self._message_handlers.get('feedback', []):
try:
await handler(
feedback_id=feedback_id,
feedback_type=feedback_type,
feedback_content=feedback_content,
inaccurate_reasons=inaccurate_reasons,
session=session,
)
except Exception:
await self.logger.error(f'Error in feedback handler: {traceback.format_exc()}')
return
event = wecombotevent.WecomBotEvent(message_data)
# Dispatch to event-specific handlers
if event_type in self._message_handlers:
for handler in self._message_handlers[event_type]:
await handler(event)
# Also dispatch to generic 'event' handlers
if 'event' in self._message_handlers:
for handler in self._message_handlers['event']:
await handler(event)

View File

@@ -456,31 +456,6 @@ class MonitoringRouterGroup(group.RouterGroup):
'platform',
'user_id',
]
elif export_type == 'feedback':
data = await self.ap.monitoring_service.export_feedback(
bot_ids=bot_ids if bot_ids else None,
pipeline_ids=pipeline_ids if pipeline_ids else None,
start_time=start_time,
end_time=end_time,
limit=limit,
)
headers = [
'id',
'timestamp',
'feedback_id',
'feedback_type',
'feedback_content',
'inaccurate_reasons',
'bot_id',
'bot_name',
'pipeline_id',
'pipeline_name',
'session_id',
'message_id',
'stream_id',
'user_id',
'platform',
]
else:
return self.error(message=f'Invalid export type: {export_type}', code=400)
@@ -511,63 +486,3 @@ class MonitoringRouterGroup(group.RouterGroup):
)
return response, 200
@self.route('/feedback/stats', methods=['GET'], auth_type=group.AuthType.USER_TOKEN)
async def get_feedback_stats() -> str:
"""Get feedback statistics"""
# Parse query parameters
bot_ids = quart.request.args.getlist('botId')
pipeline_ids = quart.request.args.getlist('pipelineId')
start_time_str = quart.request.args.get('startTime')
end_time_str = quart.request.args.get('endTime')
# Parse datetime
start_time = parse_iso_datetime(start_time_str)
end_time = parse_iso_datetime(end_time_str)
stats = await self.ap.monitoring_service.get_feedback_stats(
bot_ids=bot_ids if bot_ids else None,
pipeline_ids=pipeline_ids if pipeline_ids else None,
start_time=start_time,
end_time=end_time,
)
return self.success(data=stats)
@self.route('/feedback', methods=['GET'], auth_type=group.AuthType.USER_TOKEN)
async def get_feedback() -> str:
"""Get feedback list"""
# Parse query parameters
bot_ids = quart.request.args.getlist('botId')
pipeline_ids = quart.request.args.getlist('pipelineId')
feedback_type_str = quart.request.args.get('feedbackType')
start_time_str = quart.request.args.get('startTime')
end_time_str = quart.request.args.get('endTime')
limit = int(quart.request.args.get('limit', 100))
offset = int(quart.request.args.get('offset', 0))
# Parse datetime
start_time = parse_iso_datetime(start_time_str)
end_time = parse_iso_datetime(end_time_str)
# Parse feedback type
feedback_type = int(feedback_type_str) if feedback_type_str else None
feedback_list, total = await self.ap.monitoring_service.get_feedback_list(
bot_ids=bot_ids if bot_ids else None,
pipeline_ids=pipeline_ids if pipeline_ids else None,
feedback_type=feedback_type,
start_time=start_time,
end_time=end_time,
limit=limit,
offset=offset,
)
return self.success(
data={
'feedback': feedback_list,
'total': total,
'limit': limit,
'offset': offset,
}
)

View File

@@ -265,8 +265,6 @@ class PluginsRouterGroup(group.RouterGroup):
return self.http_status(400, -1, 'Missing asset_url parameter')
ctx = taskmgr.TaskContext.new()
ctx.metadata['plugin_name'] = f'{owner}/{repo}'
ctx.metadata['install_source'] = 'github'
install_info = {
'asset_url': asset_url,
'owner': owner,
@@ -297,17 +295,12 @@ class PluginsRouterGroup(group.RouterGroup):
data = await quart.request.json
plugin_author = data.get('plugin_author', '')
plugin_name = data.get('plugin_name', '')
ctx = taskmgr.TaskContext.new()
ctx.metadata['plugin_name'] = f'{plugin_author}/{plugin_name}'
ctx.metadata['install_source'] = 'marketplace'
wrapper = self.ap.task_mgr.create_user_task(
self.ap.plugin_connector.install_plugin(PluginInstallSource.MARKETPLACE, data, task_context=ctx),
kind='plugin-operation',
name='plugin-install-marketplace',
label=f'Installing plugin from marketplace {plugin_author}/{plugin_name}',
label=f'Installing plugin from marketplace ...{data}',
context=ctx,
)
@@ -330,13 +323,11 @@ class PluginsRouterGroup(group.RouterGroup):
}
ctx = taskmgr.TaskContext.new()
ctx.metadata['plugin_name'] = file.filename or 'local plugin'
ctx.metadata['install_source'] = 'local'
wrapper = self.ap.task_mgr.create_user_task(
self.ap.plugin_connector.install_plugin(PluginInstallSource.LOCAL, data, task_context=ctx),
kind='plugin-operation',
name='plugin-install-local',
label=f'Installing plugin from local {file.filename}',
label=f'Installing plugin from local ...{file.filename}',
context=ctx,
)

View File

@@ -97,51 +97,3 @@ class EmbeddingModelsRouterGroup(group.RouterGroup):
await self.ap.embedding_models_service.test_embedding_model(model_uuid, json_data)
return self.success()
@group.group_class('models/rerank', '/api/v1/provider/models/rerank')
class RerankModelsRouterGroup(group.RouterGroup):
async def initialize(self) -> None:
@self.route('', methods=['GET', 'POST'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
async def _() -> str:
if quart.request.method == 'GET':
provider_uuid = quart.request.args.get('provider_uuid')
if provider_uuid:
return self.success(
data={
'models': await self.ap.rerank_models_service.get_rerank_models_by_provider(provider_uuid)
}
)
return self.success(data={'models': await self.ap.rerank_models_service.get_rerank_models()})
elif quart.request.method == 'POST':
json_data = await quart.request.json
model_uuid = await self.ap.rerank_models_service.create_rerank_model(json_data)
return self.success(data={'uuid': model_uuid})
@self.route('/<model_uuid>', methods=['GET', 'PUT', 'DELETE'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
async def _(model_uuid: str) -> str:
if quart.request.method == 'GET':
model = await self.ap.rerank_models_service.get_rerank_model(model_uuid)
if model is None:
return self.http_status(404, -1, 'model not found')
return self.success(data={'model': model})
elif quart.request.method == 'PUT':
json_data = await quart.request.json
await self.ap.rerank_models_service.update_rerank_model(model_uuid, json_data)
return self.success()
elif quart.request.method == 'DELETE':
await self.ap.rerank_models_service.delete_rerank_model(model_uuid)
return self.success()
@self.route('/<model_uuid>/test', methods=['POST'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
async def _(model_uuid: str) -> str:
json_data = await quart.request.json
await self.ap.rerank_models_service.test_rerank_model(model_uuid, json_data)
return self.success()

View File

@@ -15,7 +15,6 @@ class ModelProvidersRouterGroup(group.RouterGroup):
counts = await self.ap.provider_service.get_provider_model_counts(provider['uuid'])
provider['llm_count'] = counts['llm_count']
provider['embedding_count'] = counts['embedding_count']
provider['rerank_count'] = counts['rerank_count']
return self.success(data={'providers': providers})
elif quart.request.method == 'POST':
json_data = await quart.request.json
@@ -33,7 +32,6 @@ class ModelProvidersRouterGroup(group.RouterGroup):
counts = await self.ap.provider_service.get_provider_model_counts(provider_uuid)
provider['llm_count'] = counts['llm_count']
provider['embedding_count'] = counts['embedding_count']
provider['rerank_count'] = counts['rerank_count']
return self.success(data={'provider': provider})
elif quart.request.method == 'PUT':
json_data = await quart.request.json
@@ -45,12 +43,3 @@ class ModelProvidersRouterGroup(group.RouterGroup):
return self.success()
except ValueError as e:
return self.http_status(400, -1, str(e))
@self.route('/<provider_uuid>/scan-models', methods=['GET'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
async def _(provider_uuid: str) -> str:
try:
model_type = quart.request.args.get('type')
result = await self.ap.provider_service.scan_provider_models(provider_uuid, model_type)
return self.success(data=result)
except ValueError as e:
return self.http_status(400, -1, str(e))

View File

@@ -1,45 +0,0 @@
from __future__ import annotations
from ... import group
@group.group_class('tools', '/api/v1/tools')
class ToolsRouterGroup(group.RouterGroup):
async def initialize(self) -> None:
@self.route('', methods=['GET'], auth_type=group.AuthType.USER_TOKEN)
async def _() -> str:
"""获取所有可用工具列表"""
tools = await self.ap.tool_mgr.get_all_tools()
tool_list = []
for tool in tools:
tool_list.append(
{
'name': tool.name,
'description': tool.description,
'human_desc': tool.human_desc,
'parameters': tool.parameters,
}
)
return self.success(data={'tools': tool_list})
@self.route('/<tool_name>', methods=['GET'], auth_type=group.AuthType.USER_TOKEN)
async def _(tool_name: str) -> str:
"""获取特定工具详情"""
tools = await self.ap.tool_mgr.get_all_tools()
for tool in tools:
if tool.name == tool_name:
return self.success(
data={
'tool': {
'name': tool.name,
'description': tool.description,
'human_desc': tool.human_desc,
'parameters': tool.parameters,
}
}
)
return self.http_status(404, -1, f'Tool not found: {tool_name}')

View File

@@ -1,11 +1,7 @@
import json
import quart
import sqlalchemy
from .. import group
from .....utils import constants
from .....entity.persistence.metadata import Metadata
@group.group_class('system', '/api/v1/system')
@@ -13,24 +9,6 @@ class SystemRouterGroup(group.RouterGroup):
async def initialize(self) -> None:
@self.route('/info', methods=['GET'], auth_type=group.AuthType.NONE)
async def _() -> str:
# Read wizard_status and wizard_progress from metadata table
wizard_status = 'none'
wizard_progress = None
try:
result = await self.ap.persistence_mgr.execute_async(
sqlalchemy.select(Metadata).where(Metadata.key.in_(['wizard_status', 'wizard_progress']))
)
for row in result:
if row.key == 'wizard_status':
wizard_status = row.value
elif row.key == 'wizard_progress':
try:
wizard_progress = json.loads(row.value)
except (json.JSONDecodeError, TypeError):
wizard_progress = None
except Exception:
pass
return self.success(
data={
'version': constants.semantic_version,
@@ -49,83 +27,17 @@ class SystemRouterGroup(group.RouterGroup):
'disable_models_service', False
),
'limitation': self.ap.instance_config.data.get('system', {}).get('limitation', {}),
'wizard_status': wizard_status,
'wizard_progress': wizard_progress,
}
)
@self.route('/wizard/completed', methods=['POST'], auth_type=group.AuthType.USER_TOKEN)
async def _() -> str:
"""Mark wizard status in metadata table and clear progress.
Accepts JSON body: { "status": "skipped" | "completed" }
"""
data = await quart.request.get_json(silent=True) or {}
status = data.get('status', 'completed')
if status not in ('skipped', 'completed'):
return self.http_status(400, 400, f'Invalid wizard status: {status}')
try:
result = await self.ap.persistence_mgr.execute_async(
sqlalchemy.select(Metadata).where(Metadata.key == 'wizard_status')
)
if result.first():
await self.ap.persistence_mgr.execute_async(
sqlalchemy.update(Metadata).where(Metadata.key == 'wizard_status').values(value=status)
)
else:
await self.ap.persistence_mgr.execute_async(
sqlalchemy.insert(Metadata).values(key='wizard_status', value=status)
)
# Clear wizard progress when wizard is completed/skipped
await self.ap.persistence_mgr.execute_async(
sqlalchemy.delete(Metadata).where(Metadata.key == 'wizard_progress')
)
except Exception as e:
return self.http_status(500, 500, f'Failed to update wizard status: {e}')
return self.success(data={})
@self.route('/wizard/progress', methods=['PUT'], auth_type=group.AuthType.USER_TOKEN)
async def _() -> str:
"""Save wizard progress to metadata table.
Accepts JSON body with wizard state fields:
{ "step": int, "selected_adapter": str|null, "created_bot_uuid": str|null,
"bot_saved": bool, "selected_runner": str|null }
"""
data = await quart.request.get_json(silent=True) or {}
progress_json = json.dumps(data, ensure_ascii=False)
try:
result = await self.ap.persistence_mgr.execute_async(
sqlalchemy.select(Metadata).where(Metadata.key == 'wizard_progress')
)
if result.first():
await self.ap.persistence_mgr.execute_async(
sqlalchemy.update(Metadata).where(Metadata.key == 'wizard_progress').values(value=progress_json)
)
else:
await self.ap.persistence_mgr.execute_async(
sqlalchemy.insert(Metadata).values(key='wizard_progress', value=progress_json)
)
except Exception as e:
return self.http_status(500, 500, f'Failed to save wizard progress: {e}')
return self.success(data={})
@self.route('/tasks', methods=['GET'], auth_type=group.AuthType.USER_TOKEN)
async def _() -> str:
task_type = quart.request.args.get('type')
task_kind = quart.request.args.get('kind')
if task_type == '':
task_type = None
if task_kind == '':
task_kind = None
return self.success(data=self.ap.task_mgr.get_tasks_dict(task_type, task_kind))
return self.success(data=self.ap.task_mgr.get_tasks_dict(task_type))
@self.route('/tasks/<task_id>', methods=['GET'], auth_type=group.AuthType.USER_TOKEN)
async def _(task_id: str) -> str:

View File

@@ -105,24 +105,23 @@ class HTTPController:
):
if os.path.exists(os.path.join(frontend_path, path + '.html')):
path += '.html'
elif not path.startswith('api/'):
# SPA fallback: serve index.html for all non-API, non-static routes
# so that React Router can handle client-side routing (Vite SPA).
# For /home/* sub-routes, first try parent .html files (pre-rendered pages).
if path.startswith('home/'):
segments = path.rstrip('/').split('/')
for i in range(len(segments) - 1, 0, -1):
parent_path = '/'.join(segments[:i]) + '.html'
if os.path.exists(os.path.join(frontend_path, parent_path)):
response = await quart.send_from_directory(
frontend_path, parent_path, mimetype='text/html'
)
response.headers['Cache-Control'] = 'no-cache, no-store, must-revalidate'
response.headers['Pragma'] = 'no-cache'
response.headers['Expires'] = '0'
return response
elif path.startswith('home/'):
# SPA fallback for /home/* sub-routes.
# Entity detail views use query params (e.g. /home/bots?id=uuid),
# so the pre-rendered list page is served directly via path + '.html'.
# This fallback handles any remaining unmatched sub-paths.
segments = path.rstrip('/').split('/')
# Fallback to index.html for SPA client-side routing
# Walk up parent segments looking for matching .html files
for i in range(len(segments) - 1, 0, -1):
parent_path = '/'.join(segments[:i]) + '.html'
if os.path.exists(os.path.join(frontend_path, parent_path)):
response = await quart.send_from_directory(frontend_path, parent_path, mimetype='text/html')
response.headers['Cache-Control'] = 'no-cache, no-store, must-revalidate'
response.headers['Pragma'] = 'no-cache'
response.headers['Expires'] = '0'
return response
# Final fallback to index.html for /home/* routes
response = await quart.send_from_directory(frontend_path, 'index.html', mimetype='text/html')
response.headers['Cache-Control'] = 'no-cache, no-store, must-revalidate'
response.headers['Pragma'] = 'no-cache'

View File

@@ -367,162 +367,3 @@ class EmbeddingModelsService:
input_text=['Hello, world!'],
extra_args={},
)
class RerankModelsService:
ap: app.Application
def __init__(self, ap: app.Application) -> None:
self.ap = ap
async def get_rerank_models(self) -> list[dict]:
"""Get all rerank models with provider info"""
result = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_model.RerankModel))
models = result.all()
providers_result = await self.ap.persistence_mgr.execute_async(
sqlalchemy.select(persistence_model.ModelProvider)
)
providers = {p.uuid: p for p in providers_result.all()}
models_list = []
for model in models:
model_dict = self.ap.persistence_mgr.serialize_model(persistence_model.RerankModel, model)
provider = providers.get(model.provider_uuid)
if provider:
provider_dict = self.ap.persistence_mgr.serialize_model(persistence_model.ModelProvider, provider)
model_dict['provider'] = _parse_provider_api_keys(provider_dict)
models_list.append(model_dict)
return models_list
async def get_rerank_models_by_provider(self, provider_uuid: str) -> list[dict]:
"""Get rerank models by provider UUID"""
result = await self.ap.persistence_mgr.execute_async(
sqlalchemy.select(persistence_model.RerankModel).where(
persistence_model.RerankModel.provider_uuid == provider_uuid
)
)
models = result.all()
return [self.ap.persistence_mgr.serialize_model(persistence_model.RerankModel, m) for m in models]
async def create_rerank_model(self, model_data: dict, preserve_uuid: bool = False) -> str:
"""Create a new rerank model"""
if not preserve_uuid:
model_data['uuid'] = str(uuid.uuid4())
if 'provider' in model_data:
provider_data = model_data.pop('provider')
if provider_data.get('uuid'):
model_data['provider_uuid'] = provider_data['uuid']
else:
provider_uuid = await self.ap.provider_service.find_or_create_provider(
requester=provider_data.get('requester', ''),
base_url=provider_data.get('base_url', ''),
api_keys=provider_data.get('api_keys', []),
)
model_data['provider_uuid'] = provider_uuid
await self.ap.persistence_mgr.execute_async(
sqlalchemy.insert(persistence_model.RerankModel).values(**model_data)
)
runtime_provider = self.ap.model_mgr.provider_dict.get(model_data['provider_uuid'])
if runtime_provider is None:
raise Exception('provider not found')
runtime_rerank_model = await self.ap.model_mgr.load_rerank_model_with_provider(
persistence_model.RerankModel(**model_data),
runtime_provider,
)
self.ap.model_mgr.rerank_models.append(runtime_rerank_model)
return model_data['uuid']
async def get_rerank_model(self, model_uuid: str) -> dict | None:
"""Get a single rerank model with provider info"""
result = await self.ap.persistence_mgr.execute_async(
sqlalchemy.select(persistence_model.RerankModel).where(persistence_model.RerankModel.uuid == model_uuid)
)
model = result.first()
if model is None:
return None
model_dict = self.ap.persistence_mgr.serialize_model(persistence_model.RerankModel, model)
provider_result = await self.ap.persistence_mgr.execute_async(
sqlalchemy.select(persistence_model.ModelProvider).where(
persistence_model.ModelProvider.uuid == model.provider_uuid
)
)
provider = provider_result.first()
if provider:
provider_dict = self.ap.persistence_mgr.serialize_model(persistence_model.ModelProvider, provider)
model_dict['provider'] = _parse_provider_api_keys(provider_dict)
return model_dict
async def update_rerank_model(self, model_uuid: str, model_data: dict) -> None:
"""Update an existing rerank model"""
if 'uuid' in model_data:
del model_data['uuid']
if 'provider' in model_data:
provider_data = model_data.pop('provider')
if provider_data.get('uuid'):
model_data['provider_uuid'] = provider_data['uuid']
else:
provider_uuid = await self.ap.provider_service.find_or_create_provider(
requester=provider_data.get('requester', ''),
base_url=provider_data.get('base_url', ''),
api_keys=provider_data.get('api_keys', []),
)
model_data['provider_uuid'] = provider_uuid
await self.ap.persistence_mgr.execute_async(
sqlalchemy.update(persistence_model.RerankModel)
.where(persistence_model.RerankModel.uuid == model_uuid)
.values(**model_data)
)
await self.ap.model_mgr.remove_rerank_model(model_uuid)
runtime_provider = self.ap.model_mgr.provider_dict.get(model_data['provider_uuid'])
if runtime_provider is None:
raise Exception('provider not found')
runtime_rerank_model = await self.ap.model_mgr.load_rerank_model_with_provider(
persistence_model.RerankModel(**model_data),
runtime_provider,
)
self.ap.model_mgr.rerank_models.append(runtime_rerank_model)
async def delete_rerank_model(self, model_uuid: str) -> None:
"""Delete a rerank model"""
await self.ap.persistence_mgr.execute_async(
sqlalchemy.delete(persistence_model.RerankModel).where(persistence_model.RerankModel.uuid == model_uuid)
)
await self.ap.model_mgr.remove_rerank_model(model_uuid)
async def test_rerank_model(self, model_uuid: str, model_data: dict) -> None:
"""Test a rerank model"""
runtime_rerank_model: model_requester.RuntimeRerankModel | None = None
if model_uuid != '_':
for model in self.ap.model_mgr.rerank_models:
if model.model_entity.uuid == model_uuid:
runtime_rerank_model = model
break
if runtime_rerank_model is None:
raise Exception('model not found')
else:
runtime_rerank_model = await self.ap.model_mgr.init_temporary_runtime_rerank_model(model_data)
await runtime_rerank_model.provider.invoke_rerank(
model=runtime_rerank_model,
query='What is artificial intelligence?',
documents=[
'Artificial intelligence is a branch of computer science.',
'The weather is nice today.',
],
)

View File

@@ -1183,314 +1183,3 @@ class MonitoringService:
}
for row in rows
]
# ========== Feedback Methods ==========
async def record_feedback(
self,
feedback_id: str,
feedback_type: int,
feedback_content: str | None = None,
inaccurate_reasons: list[str] | None = None,
bot_id: str | None = None,
bot_name: str | None = None,
pipeline_id: str | None = None,
pipeline_name: str | None = None,
session_id: str | None = None,
message_id: str | None = None,
stream_id: str | None = None,
user_id: str | None = None,
platform: str | None = None,
) -> str:
"""Record user feedback (like/dislike) from AI Bot conversation.
Args:
feedback_id: Unique feedback identifier from platform (e.g., WeChat Work)
feedback_type: 1 = like (thumbs up), 2 = dislike (thumbs down)
feedback_content: Optional user feedback text
inaccurate_reasons: List of reasons for inaccurate response (for dislike)
bot_id: Bot ID
bot_name: Bot name
pipeline_id: Pipeline ID
pipeline_name: Pipeline name
session_id: Session ID
message_id: Message ID
stream_id: Stream ID (for WeChat Work streaming messages)
user_id: User ID
platform: Platform name (e.g., 'wecom')
Returns:
The record ID
"""
import json
now = datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None)
reasons_json = json.dumps(inaccurate_reasons, ensure_ascii=False) if inaccurate_reasons else None
MonitoringFeedback = persistence_monitoring.MonitoringFeedback
# Handle cancel feedback (type=3): delete existing record
if feedback_type == 3:
await self.ap.persistence_mgr.execute_async(
sqlalchemy.delete(MonitoringFeedback).where(MonitoringFeedback.feedback_id == feedback_id)
)
return None
# Check if record with this feedback_id already exists
existing_result = await self.ap.persistence_mgr.execute_async(
sqlalchemy.select(MonitoringFeedback).where(MonitoringFeedback.feedback_id == feedback_id)
)
existing_row = existing_result.first()
if existing_row:
# UPDATE existing record
existing = existing_row[0] if isinstance(existing_row, tuple) else existing_row
await self.ap.persistence_mgr.execute_async(
sqlalchemy.update(MonitoringFeedback)
.where(MonitoringFeedback.feedback_id == feedback_id)
.values(
timestamp=now,
feedback_type=feedback_type,
feedback_content=feedback_content,
inaccurate_reasons=reasons_json,
bot_id=bot_id or existing.bot_id,
bot_name=bot_name or existing.bot_name,
pipeline_id=pipeline_id or existing.pipeline_id,
pipeline_name=pipeline_name or existing.pipeline_name,
session_id=session_id or existing.session_id,
message_id=message_id or existing.message_id,
stream_id=stream_id or existing.stream_id,
user_id=user_id or existing.user_id,
platform=platform or existing.platform,
)
)
return existing.id
else:
# INSERT new record with IntegrityError defense
record_id = str(uuid.uuid4())
record_data = {
'id': record_id,
'timestamp': now,
'feedback_id': feedback_id,
'feedback_type': feedback_type,
'feedback_content': feedback_content,
'inaccurate_reasons': reasons_json,
'bot_id': bot_id,
'bot_name': bot_name,
'pipeline_id': pipeline_id,
'pipeline_name': pipeline_name,
'session_id': session_id,
'message_id': message_id,
'stream_id': stream_id,
'user_id': user_id,
'platform': platform,
}
try:
await self.ap.persistence_mgr.execute_async(sqlalchemy.insert(MonitoringFeedback).values(record_data))
return record_id
except Exception:
# UNIQUE constraint conflict (concurrent feedback for same feedback_id)
await self.ap.persistence_mgr.execute_async(
sqlalchemy.update(MonitoringFeedback)
.where(MonitoringFeedback.feedback_id == feedback_id)
.values(
timestamp=now,
feedback_type=feedback_type,
feedback_content=feedback_content,
inaccurate_reasons=reasons_json,
)
)
return feedback_id
async def get_feedback_stats(
self,
bot_ids: list[str] | None = None,
pipeline_ids: list[str] | None = None,
start_time: datetime.datetime | None = None,
end_time: datetime.datetime | None = None,
) -> dict:
"""Get feedback statistics.
Returns:
Dictionary with total likes, dislikes, and breakdown by bot/pipeline
"""
conditions = []
if bot_ids:
conditions.append(persistence_monitoring.MonitoringFeedback.bot_id.in_(bot_ids))
if pipeline_ids:
conditions.append(persistence_monitoring.MonitoringFeedback.pipeline_id.in_(pipeline_ids))
if start_time:
conditions.append(persistence_monitoring.MonitoringFeedback.timestamp >= start_time)
if end_time:
conditions.append(persistence_monitoring.MonitoringFeedback.timestamp <= end_time)
# Get total likes (feedback_type = 1)
likes_query = sqlalchemy.select(sqlalchemy.func.count(persistence_monitoring.MonitoringFeedback.id)).where(
persistence_monitoring.MonitoringFeedback.feedback_type == 1
)
if conditions:
likes_query = likes_query.where(sqlalchemy.and_(*conditions))
likes_result = await self.ap.persistence_mgr.execute_async(likes_query)
total_likes = likes_result.scalar() or 0
# Get total dislikes (feedback_type = 2)
dislikes_query = sqlalchemy.select(sqlalchemy.func.count(persistence_monitoring.MonitoringFeedback.id)).where(
persistence_monitoring.MonitoringFeedback.feedback_type == 2
)
if conditions:
dislikes_query = dislikes_query.where(sqlalchemy.and_(*conditions))
dislikes_result = await self.ap.persistence_mgr.execute_async(dislikes_query)
total_dislikes = dislikes_result.scalar() or 0
# Get total feedback count
total_query = sqlalchemy.select(sqlalchemy.func.count(persistence_monitoring.MonitoringFeedback.id))
if conditions:
total_query = total_query.where(sqlalchemy.and_(*conditions))
total_result = await self.ap.persistence_mgr.execute_async(total_query)
total_feedback = total_result.scalar() or 0
# Calculate satisfaction rate
satisfaction_rate = (total_likes / total_feedback * 100) if total_feedback > 0 else 0
# Get feedback by bot
bot_stats_query = sqlalchemy.select(
persistence_monitoring.MonitoringFeedback.bot_id,
persistence_monitoring.MonitoringFeedback.bot_name,
sqlalchemy.func.count(persistence_monitoring.MonitoringFeedback.id).label('total'),
sqlalchemy.func.sum(
sqlalchemy.case((persistence_monitoring.MonitoringFeedback.feedback_type == 1, 1), else_=0)
).label('likes'),
sqlalchemy.func.sum(
sqlalchemy.case((persistence_monitoring.MonitoringFeedback.feedback_type == 2, 1), else_=0)
).label('dislikes'),
).group_by(
persistence_monitoring.MonitoringFeedback.bot_id,
persistence_monitoring.MonitoringFeedback.bot_name,
)
if conditions:
bot_stats_query = bot_stats_query.where(sqlalchemy.and_(*conditions))
bot_stats_result = await self.ap.persistence_mgr.execute_async(bot_stats_query)
bot_stats = [
{
'bot_id': row.bot_id,
'bot_name': row.bot_name,
'total': row.total,
'likes': row.likes or 0,
'dislikes': row.dislikes or 0,
}
for row in bot_stats_result.all()
]
return {
'total_feedback': total_feedback,
'total_likes': total_likes,
'total_dislikes': total_dislikes,
'satisfaction_rate': round(satisfaction_rate, 2),
'by_bot': bot_stats,
}
async def get_feedback_list(
self,
bot_ids: list[str] | None = None,
pipeline_ids: list[str] | None = None,
feedback_type: int | None = None,
start_time: datetime.datetime | None = None,
end_time: datetime.datetime | None = None,
limit: int = 100,
offset: int = 0,
) -> tuple[list[dict], int]:
"""Get feedback list with filters."""
conditions = []
if bot_ids:
conditions.append(persistence_monitoring.MonitoringFeedback.bot_id.in_(bot_ids))
if pipeline_ids:
conditions.append(persistence_monitoring.MonitoringFeedback.pipeline_id.in_(pipeline_ids))
if feedback_type is not None:
conditions.append(persistence_monitoring.MonitoringFeedback.feedback_type == feedback_type)
if start_time:
conditions.append(persistence_monitoring.MonitoringFeedback.timestamp >= start_time)
if end_time:
conditions.append(persistence_monitoring.MonitoringFeedback.timestamp <= end_time)
# Get total count
count_query = sqlalchemy.select(sqlalchemy.func.count(persistence_monitoring.MonitoringFeedback.id))
if conditions:
count_query = count_query.where(sqlalchemy.and_(*conditions))
count_result = await self.ap.persistence_mgr.execute_async(count_query)
total = count_result.scalar() or 0
# Get feedback list
query = sqlalchemy.select(persistence_monitoring.MonitoringFeedback).order_by(
persistence_monitoring.MonitoringFeedback.timestamp.desc()
)
if conditions:
query = query.where(sqlalchemy.and_(*conditions))
query = query.limit(limit).offset(offset)
result = await self.ap.persistence_mgr.execute_async(query)
rows = result.all()
return (
[
self.ap.persistence_mgr.serialize_model(
persistence_monitoring.MonitoringFeedback, row[0] if isinstance(row, tuple) else row
)
for row in rows
],
total,
)
async def export_feedback(
self,
bot_ids: list[str] | None = None,
pipeline_ids: list[str] | None = None,
start_time: datetime.datetime | None = None,
end_time: datetime.datetime | None = None,
limit: int = 100000,
) -> list[dict]:
"""Export feedback as list of dictionaries for CSV conversion."""
conditions = []
if bot_ids:
conditions.append(persistence_monitoring.MonitoringFeedback.bot_id.in_(bot_ids))
if pipeline_ids:
conditions.append(persistence_monitoring.MonitoringFeedback.pipeline_id.in_(pipeline_ids))
if start_time:
conditions.append(persistence_monitoring.MonitoringFeedback.timestamp >= start_time)
if end_time:
conditions.append(persistence_monitoring.MonitoringFeedback.timestamp <= end_time)
query = sqlalchemy.select(persistence_monitoring.MonitoringFeedback).order_by(
persistence_monitoring.MonitoringFeedback.timestamp.desc()
)
if conditions:
query = query.where(sqlalchemy.and_(*conditions))
query = query.limit(limit)
result = await self.ap.persistence_mgr.execute_async(query)
rows = result.all()
return [
{
'id': row[0].id if isinstance(row, tuple) else row.id,
'timestamp': self._format_timestamp(row[0].timestamp if isinstance(row, tuple) else row.timestamp),
'feedback_id': row[0].feedback_id if isinstance(row, tuple) else row.feedback_id,
'feedback_type': 'like'
if (row[0].feedback_type if isinstance(row, tuple) else row.feedback_type) == 1
else 'dislike',
'feedback_content': row[0].feedback_content if isinstance(row, tuple) else row.feedback_content,
'inaccurate_reasons': row[0].inaccurate_reasons if isinstance(row, tuple) else row.inaccurate_reasons,
'bot_id': row[0].bot_id if isinstance(row, tuple) else row.bot_id,
'bot_name': row[0].bot_name if isinstance(row, tuple) else row.bot_name,
'pipeline_id': row[0].pipeline_id if isinstance(row, tuple) else row.pipeline_id,
'pipeline_name': row[0].pipeline_name if isinstance(row, tuple) else row.pipeline_name,
'session_id': row[0].session_id if isinstance(row, tuple) else row.session_id,
'message_id': row[0].message_id if isinstance(row, tuple) else row.message_id,
'stream_id': row[0].stream_id if isinstance(row, tuple) else row.stream_id,
'user_id': row[0].user_id if isinstance(row, tuple) else row.user_id,
'platform': row[0].platform if isinstance(row, tuple) else row.platform,
}
for row in rows
]

View File

@@ -1,7 +1,6 @@
from __future__ import annotations
import uuid
import traceback
import sqlalchemy
@@ -98,14 +97,6 @@ class ModelProviderService:
if embedding_result.first() is not None:
raise ValueError('Cannot delete provider: Embedding models still reference it')
rerank_result = await self.ap.persistence_mgr.execute_async(
sqlalchemy.select(persistence_model.RerankModel).where(
persistence_model.RerankModel.provider_uuid == provider_uuid
)
)
if rerank_result.first() is not None:
raise ValueError('Cannot delete provider: Rerank models still reference it')
await self.ap.persistence_mgr.execute_async(
sqlalchemy.delete(persistence_model.ModelProvider).where(
persistence_model.ModelProvider.uuid == provider_uuid
@@ -130,14 +121,7 @@ class ModelProviderService:
)
embedding_count = embedding_result.scalar() or 0
rerank_result = await self.ap.persistence_mgr.execute_async(
sqlalchemy.select(sqlalchemy.func.count())
.select_from(persistence_model.RerankModel)
.where(persistence_model.RerankModel.provider_uuid == provider_uuid)
)
rerank_count = rerank_result.scalar() or 0
return {'llm_count': llm_count, 'embedding_count': embedding_count, 'rerank_count': rerank_count}
return {'llm_count': llm_count, 'embedding_count': embedding_count}
async def find_or_create_provider(self, requester: str, base_url: str, api_keys: list) -> str:
"""Find existing provider or create new one"""
@@ -180,66 +164,3 @@ class ModelProviderService:
.values(api_keys=[api_key])
)
await self.ap.model_mgr.reload_provider('00000000-0000-0000-0000-000000000000')
async def scan_provider_models(self, provider_uuid: str, model_type: str | None = None) -> dict:
provider = await self.get_provider(provider_uuid)
if provider is None:
raise ValueError('provider not found')
runtime_provider = await self.ap.model_mgr.load_provider(provider)
try:
scan_result = await runtime_provider.requester.scan_models(
runtime_provider.token_mgr.get_token() if runtime_provider.token_mgr.tokens else None
)
except NotImplementedError:
raise ValueError('current provider does not support model scanning')
except Exception as exc:
self.ap.logger.warning(
f'Failed to scan models for provider {provider_uuid}: {exc}\n{traceback.format_exc()}'
)
raise ValueError(str(exc)) from exc
if isinstance(scan_result, dict):
scanned_models = scan_result.get('models', [])
debug_info = scan_result.get('debug')
else:
scanned_models = scan_result
debug_info = None
llm_models = await self.ap.llm_model_service.get_llm_models_by_provider(provider_uuid)
embedding_models = await self.ap.embedding_models_service.get_embedding_models_by_provider(provider_uuid)
existing_llm_names = {model['name'] for model in llm_models}
existing_embedding_names = {model['name'] for model in embedding_models}
filtered_models = []
for model in scanned_models:
scanned_type = model.get('type', 'llm')
if model_type and scanned_type != model_type:
continue
model_name = model.get('name') or model.get('id')
if not model_name:
continue
filtered_models.append(
{
'id': model.get('id', model_name),
'name': model_name,
'type': scanned_type,
'abilities': model.get('abilities', []),
'display_name': model.get('display_name'),
'description': model.get('description'),
'context_length': model.get('context_length'),
'owned_by': model.get('owned_by'),
'input_modalities': model.get('input_modalities', []),
'output_modalities': model.get('output_modalities', []),
'already_added': (
model_name in existing_embedding_names
if scanned_type == 'embedding'
else model_name in existing_llm_names
),
}
)
return {'models': filtered_models, 'debug': debug_info}

View File

@@ -65,8 +65,8 @@ class UserService:
user_obj = result_list[0]
# Check if this user has a local password set
if not user_obj.password:
# Check if this is a Space account
if user_obj.account_type == 'space':
raise ValueError('请使用 Space 账户登录')
ph = argon2.PasswordHasher()
@@ -108,8 +108,9 @@ class UserService:
if user_obj is None:
raise ValueError('User not found')
if not user_obj.password:
raise ValueError('No local password set, please set a password first')
# Space accounts cannot change password locally
if user_obj.account_type == 'space':
raise ValueError('Space account cannot change password locally')
ph.verify(user_obj.password, current_password)

View File

@@ -133,8 +133,6 @@ class Application:
embedding_models_service: model_service.EmbeddingModelsService = None
rerank_models_service: model_service.RerankModelsService = None
provider_service: provider_service.ModelProviderService = None
pipeline_service: pipeline_service.PipelineService = None

View File

@@ -61,9 +61,6 @@ class BuildAppStage(stage.BootingStage):
embedding_models_service_inst = model_service.EmbeddingModelsService(ap)
ap.embedding_models_service = embedding_models_service_inst
rerank_models_service_inst = model_service.RerankModelsService(ap)
ap.rerank_models_service = rerank_models_service_inst
provider_service_inst = provider_service.ModelProviderService(ap)
ap.provider_service = provider_service_inst

View File

@@ -80,12 +80,8 @@ def _apply_env_overrides_to_config(cfg: dict) -> dict:
if i == len(keys) - 1:
# At the final key
if key in current:
if isinstance(current[key], list):
# Convert comma-separated string to list
# e.g., SYSTEM__DISABLED_ADAPTERS="aiocqhttp,dingtalk"
current[key] = [item.strip() for item in env_value.split(',') if item.strip()]
elif isinstance(current[key], dict):
# Skip dict types
if isinstance(current[key], (dict, list)):
# Skip dict and list types
pass
else:
# Valid scalar value - convert and set it

View File

@@ -17,13 +17,9 @@ class TaskContext:
log: str
"""Log"""
metadata: dict
"""Structured metadata for progress reporting"""
def __init__(self):
self.current_action = 'default'
self.log = ''
self.metadata = {}
def _log(self, msg: str):
self.log += msg + '\n'
@@ -42,7 +38,7 @@ class TaskContext:
self._log(f'{datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")} | {self.current_action} | {msg}')
def to_dict(self) -> dict:
return {'current_action': self.current_action, 'log': self.log, 'metadata': self.metadata}
return {'current_action': self.current_action, 'log': self.log}
@staticmethod
def new() -> TaskContext:
@@ -215,14 +211,9 @@ class AsyncTaskManager:
def get_tasks_dict(
self,
type: str = None,
kind: str = None,
) -> dict:
return {
'tasks': [
t.to_dict()
for t in self.tasks
if (type is None or t.task_type == type) and (kind is None or t.kind == kind)
],
'tasks': [t.to_dict() for t in self.tasks if type is None or t.task_type == type],
'id_index': TaskWrapper._id_index,
}

View File

@@ -17,23 +17,11 @@ class I18nString(pydantic.BaseModel):
"""英文"""
zh_Hans: typing.Optional[str] = None
"""简体中文"""
zh_Hant: typing.Optional[str] = None
"""繁体中文"""
"""中文"""
ja_JP: typing.Optional[str] = None
"""日文"""
th_TH: typing.Optional[str] = None
"""泰文"""
vi_VN: typing.Optional[str] = None
"""越南文"""
es_ES: typing.Optional[str] = None
"""西班牙文"""
def to_dict(self) -> dict:
"""转换为字典"""
dic = {}
@@ -41,16 +29,8 @@ class I18nString(pydantic.BaseModel):
dic['en_US'] = self.en_US
if self.zh_Hans is not None:
dic['zh_Hans'] = self.zh_Hans
if self.zh_Hant is not None:
dic['zh_Hant'] = self.zh_Hant
if self.ja_JP is not None:
dic['ja_JP'] = self.ja_JP
if self.th_TH is not None:
dic['th_TH'] = self.th_TH
if self.vi_VN is not None:
dic['vi_VN'] = self.vi_VN
if self.es_ES is not None:
dic['es_ES'] = self.es_ES
return dic

View File

@@ -16,7 +16,6 @@ class Bot(Base):
enable = sqlalchemy.Column(sqlalchemy.Boolean, nullable=False, default=False)
use_pipeline_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
use_pipeline_uuid = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
pipeline_routing_rules = sqlalchemy.Column(sqlalchemy.JSON, nullable=False, server_default='[]')
created_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, server_default=sqlalchemy.func.now())
updated_at = sqlalchemy.Column(
sqlalchemy.DateTime,

View File

@@ -59,22 +59,3 @@ class EmbeddingModel(Base):
server_default=sqlalchemy.func.now(),
onupdate=sqlalchemy.func.now(),
)
class RerankModel(Base):
"""Rerank model"""
__tablename__ = 'rerank_models'
uuid = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True, unique=True)
name = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
provider_uuid = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
extra_args = sqlalchemy.Column(sqlalchemy.JSON, nullable=False, default={})
prefered_ranking = sqlalchemy.Column(sqlalchemy.Integer, nullable=False, default=0)
created_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, server_default=sqlalchemy.func.now())
updated_at = sqlalchemy.Column(
sqlalchemy.DateTime,
nullable=False,
server_default=sqlalchemy.func.now(),
onupdate=sqlalchemy.func.now(),
)

View File

@@ -106,26 +106,3 @@ class MonitoringEmbeddingCall(Base):
session_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True)
message_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True)
call_type = sqlalchemy.Column(sqlalchemy.String(50), nullable=True) # embedding, retrieve
class MonitoringFeedback(Base):
"""User feedback records (like/dislike) from AI Bot conversations"""
__tablename__ = 'monitoring_feedback'
id = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True)
timestamp = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, index=True)
feedback_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, unique=True, index=True)
feedback_type = sqlalchemy.Column(sqlalchemy.Integer, nullable=False) # 1=like, 2=dislike
feedback_content = sqlalchemy.Column(sqlalchemy.Text, nullable=True) # User feedback text
inaccurate_reasons = sqlalchemy.Column(sqlalchemy.Text, nullable=True) # JSON list of inaccurate reasons
# Context fields
bot_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True)
bot_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
pipeline_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True)
pipeline_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
session_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True)
message_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True)
stream_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True)
user_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
platform = sqlalchemy.Column(sqlalchemy.String(255), nullable=True) # e.g., wecom

View File

@@ -1,51 +0,0 @@
"""Alembic environment for LangBot.
This env.py is designed to be called programmatically (not via CLI).
It supports both SQLite and PostgreSQL.
The sync connection is passed via config attributes by the runner.
"""
from __future__ import annotations
from alembic import context
from sqlalchemy.engine import Connection
from langbot.pkg.entity.persistence.base import Base
target_metadata = Base.metadata
def run_migrations_offline() -> None:
"""Run migrations in 'offline' mode — emit SQL without a live connection."""
url = context.config.get_main_option('sqlalchemy.url')
context.configure(
url=url,
target_metadata=target_metadata,
literal_binds=True,
dialect_opts={'paramstyle': 'named'},
)
with context.begin_transaction():
context.run_migrations()
def run_migrations_online() -> None:
"""Run migrations with a live sync connection passed via config attributes."""
connection: Connection = context.config.attributes.get('connection')
if connection is None:
raise RuntimeError('connection not provided in alembic config attributes')
context.configure(
connection=connection,
target_metadata=target_metadata,
# render_as_batch=True is critical for SQLite ALTER TABLE support
render_as_batch=True,
)
with context.begin_transaction():
context.run_migrations()
if context.is_offline_mode():
run_migrations_offline()
else:
run_migrations_online()

View File

@@ -1,24 +0,0 @@
# Alembic script.py.mako — template for auto-generated revisions
"""${message}
Revision ID: ${up_revision}
Revises: ${down_revision | comma,n}
Create Date: ${create_date}
"""
from alembic import op
import sqlalchemy as sa
${imports if imports else ""}
# revision identifiers
revision = ${repr(up_revision)}
down_revision = ${repr(down_revision)}
branch_labels = ${repr(branch_labels)}
depends_on = ${repr(depends_on)}
def upgrade() -> None:
${upgrades if upgrades else "pass"}
def downgrade() -> None:
${downgrades if downgrades else "pass"}

View File

@@ -1,24 +0,0 @@
"""baseline: stamp existing schema (db version 25)
This is a no-op migration that marks the starting point for Alembic.
All tables already exist via create_all() + legacy DBMigration system.
Revision ID: 0001_baseline
Revises: None
Create Date: 2026-04-08
"""
revision = '0001_baseline'
down_revision = None
branch_labels = None
depends_on = None
def upgrade() -> None:
# No-op: existing schema is already at database_version=25
# This revision serves as the Alembic baseline.
pass
def downgrade() -> None:
pass

View File

@@ -1,62 +0,0 @@
"""example: sample migration demonstrating Alembic patterns
This is a SAMPLE showing how to write migrations that work
seamlessly across SQLite and PostgreSQL. Delete or adapt as needed.
Revision ID: 0002_sample
Revises: 0001_baseline
Create Date: 2026-04-08
Patterns demonstrated:
1. Schema change (add column) — works on both DBs via render_as_batch
2. Data migration (read + modify JSON) — pure SQLAlchemy, no dialect branching
"""
revision = '0002_sample'
down_revision = '0001_baseline'
branch_labels = None
depends_on = None
def upgrade() -> None:
"""
EXAMPLE: Uncomment to use. This shows the patterns.
# --- Pattern 1: Schema change (add/drop column) ---
# render_as_batch=True in env.py makes this work on SQLite too.
#
# op.add_column('pipelines', sa.Column('description', sa.String(512), server_default=''))
# --- Pattern 2: Data migration (read + modify JSON field) ---
# No if/else for sqlite vs postgres needed!
#
# conn = op.get_bind()
# rows = conn.execute(sa.text("SELECT uuid, config FROM pipelines")).fetchall()
# for row in rows:
# config = json.loads(row[1]) if isinstance(row[1], str) else row[1]
# # Modify the config
# config.setdefault('ai', {}).setdefault('some_new_key', 'default_value')
# conn.execute(
# sa.text("UPDATE pipelines SET config = :cfg WHERE uuid = :uuid"),
# {"cfg": json.dumps(config), "uuid": row[0]}
# )
# --- Pattern 3: Create a new table ---
#
# op.create_table(
# 'audit_log',
# sa.Column('id', sa.Integer, primary_key=True, autoincrement=True),
# sa.Column('action', sa.String(255), nullable=False),
# sa.Column('detail', sa.Text),
# sa.Column('created_at', sa.DateTime, server_default=sa.func.now()),
# )
"""
pass
def downgrade() -> None:
"""
# op.drop_column('pipelines', 'description')
# op.drop_table('audit_log')
"""
pass

View File

@@ -1,35 +0,0 @@
"""add rerank_models table
Revision ID: 0003_add_rerank_models
Revises: 0002_sample
Create Date: 2026-04-19
"""
import sqlalchemy as sa
from alembic import op
revision = '0003_add_rerank_models'
down_revision = '0002_sample'
branch_labels = None
depends_on = None
def upgrade() -> None:
# Check if table already exists (may have been created by create_all())
conn = op.get_bind()
inspector = sa.inspect(conn)
if 'rerank_models' not in inspector.get_table_names():
op.create_table(
'rerank_models',
sa.Column('uuid', sa.String(255), primary_key=True, unique=True),
sa.Column('name', sa.String(255), nullable=False),
sa.Column('provider_uuid', sa.String(255), nullable=False),
sa.Column('extra_args', sa.JSON, nullable=False, server_default='{}'),
sa.Column('prefered_ranking', sa.Integer, nullable=False, server_default='0'),
sa.Column('created_at', sa.DateTime, nullable=False, server_default=sa.func.now()),
sa.Column('updated_at', sa.DateTime, nullable=False, server_default=sa.func.now()),
)
def downgrade() -> None:
op.drop_table('rerank_models')

View File

@@ -1,150 +0,0 @@
"""Programmatic Alembic runner for LangBot.
Usage from async code:
from langbot.pkg.persistence.alembic_runner import run_alembic_upgrade
await run_alembic_upgrade(async_engine)
CLI usage (autogenerate):
python -m langbot.pkg.persistence.alembic_runner autogenerate "add description column"
python -m langbot.pkg.persistence.alembic_runner upgrade
python -m langbot.pkg.persistence.alembic_runner current
"""
from __future__ import annotations
import os
from typing import TYPE_CHECKING
from alembic.config import Config
from alembic import command
from alembic.runtime.migration import MigrationContext
if TYPE_CHECKING:
from sqlalchemy.ext.asyncio import AsyncEngine
from sqlalchemy.engine import Connection
_ALEMBIC_DIR = os.path.join(os.path.dirname(__file__), 'alembic')
def _build_config(connection: Connection) -> Config:
"""Build an Alembic Config with sync connection attached."""
cfg = Config()
cfg.set_main_option('script_location', _ALEMBIC_DIR)
cfg.attributes['connection'] = connection
return cfg
def _do_upgrade(connection: Connection, revision: str = 'head') -> None:
"""Synchronous upgrade — runs inside run_sync."""
cfg = _build_config(connection)
command.upgrade(cfg, revision)
def _do_stamp(connection: Connection, revision: str = 'head') -> None:
"""Synchronous stamp — runs inside run_sync."""
cfg = _build_config(connection)
command.stamp(cfg, revision)
def _do_get_current(connection: Connection) -> str | None:
"""Get current alembic revision synchronously."""
ctx = MigrationContext.configure(connection)
return ctx.get_current_revision()
def _do_autogenerate(connection: Connection, message: str = 'auto migration') -> None:
"""Synchronous autogenerate — runs inside run_sync."""
cfg = _build_config(connection)
command.revision(cfg, message=message, autogenerate=True)
async def run_alembic_upgrade(async_engine: AsyncEngine, revision: str = 'head') -> None:
"""Run Alembic upgrade to the given revision."""
async with async_engine.connect() as conn:
await conn.run_sync(_do_upgrade, revision)
await conn.commit()
async def run_alembic_stamp(async_engine: AsyncEngine, revision: str = 'head') -> None:
"""Stamp the database with a revision without running migrations."""
async with async_engine.connect() as conn:
await conn.run_sync(_do_stamp, revision)
await conn.commit()
async def get_alembic_current(async_engine: AsyncEngine) -> str | None:
"""Get current alembic revision, or None if not stamped."""
async with async_engine.connect() as conn:
return await conn.run_sync(_do_get_current)
async def run_alembic_autogenerate(async_engine: AsyncEngine, message: str = 'auto migration') -> None:
"""Compare ORM models against DB schema and generate a migration script."""
async with async_engine.connect() as conn:
await conn.run_sync(_do_autogenerate, message)
# CLI entrypoint: python -m langbot.pkg.persistence.alembic_runner <command> [args]
if __name__ == '__main__':
import sys
import asyncio
def _get_engine():
"""Create engine from data/config.yaml or default SQLite."""
from sqlalchemy.ext.asyncio import create_async_engine
try:
import yaml
with open('data/config.yaml') as f:
config = yaml.safe_load(f)
db_cfg = config.get('database', {})
db_type = db_cfg.get('use', 'sqlite')
if db_type == 'postgresql':
pg = db_cfg.get('postgresql', {})
url = (
f'postgresql+asyncpg://{pg.get("user", "postgres")}:{pg.get("password", "postgres")}'
f'@{pg.get("host", "127.0.0.1")}:{pg.get("port", 5432)}/{pg.get("database", "postgres")}'
)
else:
path = db_cfg.get('sqlite', {}).get('path', 'data/langbot.db')
url = f'sqlite+aiosqlite:///{path}'
except Exception:
url = 'sqlite+aiosqlite:///data/langbot.db'
return create_async_engine(url)
def main():
if len(sys.argv) < 2:
print('Usage: python -m langbot.pkg.persistence.alembic_runner <command> [args]')
print('Commands:')
print(' autogenerate "message" — Generate migration from ORM model diff')
print(' upgrade [revision] — Upgrade database (default: head)')
print(' stamp [revision] — Stamp revision without running (default: head)')
print(' current — Show current revision')
sys.exit(1)
cmd = sys.argv[1]
engine = _get_engine()
if cmd == 'autogenerate':
msg = sys.argv[2] if len(sys.argv) > 2 else 'auto migration'
asyncio.run(run_alembic_autogenerate(engine, msg))
print(f'Migration generated: {msg}')
elif cmd == 'upgrade':
rev = sys.argv[2] if len(sys.argv) > 2 else 'head'
asyncio.run(run_alembic_upgrade(engine, rev))
print(f'Upgraded to: {rev}')
elif cmd == 'stamp':
rev = sys.argv[2] if len(sys.argv) > 2 else 'head'
asyncio.run(run_alembic_stamp(engine, rev))
print(f'Stamped: {rev}')
elif cmd == 'current':
rev = asyncio.run(get_alembic_current(engine))
print(f'Current revision: {rev}')
else:
print(f'Unknown command: {cmd}')
sys.exit(1)
main()

View File

@@ -76,9 +76,6 @@ class PersistenceManager:
self.ap.logger.info(f'Successfully upgraded database to version {last_migration_number}.')
# Run Alembic migrations (new migration system)
await self._run_alembic_migrations()
await self.write_space_model_providers()
async def create_tables(self):
@@ -138,28 +135,6 @@ class PersistenceManager:
# =================================
async def _run_alembic_migrations(self):
"""Run Alembic-based migrations after legacy migrations complete."""
from . import alembic_runner
engine = self.get_db_engine()
try:
current_rev = await alembic_runner.get_alembic_current(engine)
if current_rev is None:
# First time: stamp baseline so Alembic knows existing schema is up-to-date
self.ap.logger.info('Alembic: no revision found, stamping baseline...')
await alembic_runner.run_alembic_stamp(engine, '0001_baseline')
current_rev = '0001_baseline'
# Upgrade to head
await alembic_runner.run_alembic_upgrade(engine, 'head')
self.ap.logger.info('Alembic migrations completed.')
except Exception as e:
self.ap.logger.error(f'Alembic migration failed: {e}', exc_info=True)
raise
async def execute_async(self, *args, **kwargs) -> sqlalchemy.engine.cursor.CursorResult:
async with self.get_db_engine().connect() as conn:
result = await conn.execute(*args, **kwargs)

View File

@@ -1,15 +0,0 @@
import sqlalchemy
from .. import migration
@migration.migration_class(25)
class DBMigrateBotPipelineRoutingRules(migration.DBMigration):
"""Add pipeline_routing_rules column to bots table"""
async def upgrade(self):
sql_text = sqlalchemy.text("ALTER TABLE bots ADD COLUMN pipeline_routing_rules JSON NOT NULL DEFAULT '[]'")
await self.ap.persistence_mgr.execute_async(sql_text)
async def downgrade(self):
sql_text = sqlalchemy.text('ALTER TABLE bots DROP COLUMN pipeline_routing_rules')
await self.ap.persistence_mgr.execute_async(sql_text)

View File

@@ -37,7 +37,6 @@ class PendingMessage:
message_chain: platform_message.MessageChain
adapter: abstract_platform_adapter.AbstractMessagePlatformAdapter
pipeline_uuid: typing.Optional[str]
routed_by_rule: bool = False
timestamp: float = field(default_factory=time.time)
@@ -126,7 +125,6 @@ class MessageAggregator:
message_chain: platform_message.MessageChain,
adapter: abstract_platform_adapter.AbstractMessagePlatformAdapter,
pipeline_uuid: typing.Optional[str] = None,
routed_by_rule: bool = False,
) -> None:
"""Add a message to the aggregation buffer
@@ -147,7 +145,6 @@ class MessageAggregator:
message_chain=message_chain,
adapter=adapter,
pipeline_uuid=pipeline_uuid,
routed_by_rule=routed_by_rule,
)
return
@@ -162,7 +159,6 @@ class MessageAggregator:
message_chain=message_chain,
adapter=adapter,
pipeline_uuid=pipeline_uuid,
routed_by_rule=routed_by_rule,
)
force_flush = False
@@ -221,7 +217,6 @@ class MessageAggregator:
message_chain=msg.message_chain,
adapter=msg.adapter,
pipeline_uuid=msg.pipeline_uuid,
routed_by_rule=msg.routed_by_rule,
)
return
@@ -236,7 +231,6 @@ class MessageAggregator:
message_chain=merged_msg.message_chain,
adapter=merged_msg.adapter,
pipeline_uuid=merged_msg.pipeline_uuid,
routed_by_rule=merged_msg.routed_by_rule,
)
def _merge_messages(self, messages: list[PendingMessage]) -> PendingMessage:

View File

@@ -63,14 +63,6 @@ class Controller:
pipeline = await self.ap.pipeline_mgr.get_pipeline_by_uuid(pipeline_uuid)
if pipeline:
await pipeline.run(selected_query)
else:
self.ap.logger.warning(
f'Pipeline {pipeline_uuid} not found for query {selected_query.query_id}, query dropped'
)
else:
self.ap.logger.warning(
f'No pipeline_uuid for query {selected_query.query_id}, query dropped'
)
async with self.ap.query_pool:
(await self.ap.sess_mgr.get_session(selected_query))._semaphore.release()

View File

@@ -297,9 +297,6 @@ class RuntimePipeline:
)
# Store message_id in query variables for LLM call monitoring
query.variables['_monitoring_message_id'] = message_id
# Notify adapter so it can map platform-specific IDs to monitoring message ID
if hasattr(query.adapter, 'on_monitoring_message_created'):
await query.adapter.on_monitoring_message_created(query, message_id)
except Exception as e:
self.ap.logger.error(f'Failed to record query start: {e}')
@@ -326,9 +323,6 @@ class RuntimePipeline:
event_ctx = await self.ap.plugin_connector.emit_event(event_obj, bound_plugins)
if event_ctx.is_prevented_default():
self.ap.logger.debug(
f'MessageReceived event prevented default for query {query.query_id}, pipeline={pipeline_name}'
)
return
self.ap.logger.debug(f'Processing query {query.query_id}')

View File

@@ -41,7 +41,6 @@ class QueryPool:
message_chain: platform_message.MessageChain,
adapter: abstract_platform_adapter.AbstractMessagePlatformAdapter,
pipeline_uuid: typing.Optional[str] = None,
routed_by_rule: bool = False,
) -> pipeline_query.Query:
async with self.condition:
query_id = self.query_id_counter
@@ -53,7 +52,7 @@ class QueryPool:
sender_id=sender_id,
message_event=message_event,
message_chain=message_chain,
variables={'_routed_by_rule': routed_by_rule},
variables={},
resp_messages=[],
resp_message_chain=[],
adapter=adapter,

View File

@@ -160,6 +160,7 @@ class PreProcessor(stage.PipelineStage):
elif me.url:
content_list.append(provider_message.ContentElement.from_file_url(me.url, 'voice'))
elif isinstance(me, platform_message.File):
# if me.url is not None:
content_list.append(provider_message.ContentElement.from_file_url(me.url, me.name))
elif isinstance(me, platform_message.Quote) and quote_msg:
for msg in me.origin:
@@ -171,15 +172,6 @@ class PreProcessor(stage.PipelineStage):
):
if msg.base64 is not None:
content_list.append(provider_message.ContentElement.from_image_base64(msg.base64))
elif isinstance(msg, platform_message.File):
content_list.append(provider_message.ContentElement.from_file_url(msg.url, msg.name))
elif isinstance(msg, platform_message.Voice):
if msg.base64:
content_list.append(
provider_message.ContentElement.from_file_base64(msg.base64, 'voice.silk')
)
elif msg.url:
content_list.append(provider_message.ContentElement.from_file_url(msg.url, 'voice'))
query.variables['user_message_text'] = plain_text

View File

@@ -61,9 +61,6 @@ class ChatMessageHandler(handler.MessageHandler):
yield entities.StageProcessResult(result_type=entities.ResultType.CONTINUE, new_query=query)
else:
self.ap.logger.debug(
f'NormalMessageReceived event prevented default for query {query.query_id} without reply'
)
yield entities.StageProcessResult(result_type=entities.ResultType.INTERRUPT, new_query=query)
else:
if event_ctx.event.user_message_alter is not None:
@@ -208,7 +205,6 @@ class ChatMessageHandler(handler.MessageHandler):
'model_name': model_name,
'version': constants.semantic_version,
'instance_id': constants.instance_id,
'edition': constants.edition,
'pipeline_plugins': pipeline_plugins,
'error': locals().get('error_info', None),
'timestamp': datetime.utcnow().isoformat(),

View File

@@ -37,10 +37,6 @@ class GroupRespondRuleCheckStage(stage.PipelineStage):
if query.launcher_type.value != 'group': # 只处理群消息
return entities.StageProcessResult(result_type=entities.ResultType.CONTINUE, new_query=query)
# 通过路由规则明确指定的流水线,跳过群响应规则检查
if query.variables and query.variables.get('_routed_by_rule', False):
return entities.StageProcessResult(result_type=entities.ResultType.CONTINUE, new_query=query)
rules = query.pipeline_config['trigger']['group-respond-rules']
use_rule = rules

View File

@@ -1,8 +1,6 @@
from __future__ import annotations
import asyncio
import json
import re
import traceback
import sqlalchemy
@@ -11,7 +9,6 @@ from ..core import app, entities as core_entities, taskmgr
from ..discover import engine
from ..entity.persistence import bot as persistence_bot
from ..entity.persistence import pipeline as persistence_pipeline
from ..entity.errors import platform as platform_errors
@@ -54,148 +51,6 @@ class RuntimeBot:
self.task_context = taskmgr.TaskContext()
self.logger = logger
@staticmethod
def _match_operator(actual: str, operator: str, expected: str) -> bool:
"""Evaluate a single operator condition."""
if operator == 'eq':
return actual == expected
elif operator == 'neq':
return actual != expected
elif operator == 'contains':
return expected in actual
elif operator == 'not_contains':
return expected not in actual
elif operator == 'starts_with':
return actual.startswith(expected)
elif operator == 'regex':
try:
return bool(re.search(expected, actual))
except re.error:
return False
return False
PIPELINE_DISCARD = '__discard__'
PIPELINE_DISCARD_DISPLAY_NAME = 'Discarded'
def resolve_pipeline_uuid(
self,
launcher_type: str,
launcher_id: str,
message_text: str,
message_element_types: list[str] | None = None,
) -> tuple[str | None, bool]:
"""Resolve pipeline UUID based on routing rules.
Rules are evaluated in order; first match wins.
Falls back to use_pipeline_uuid if no rule matches.
Rule types:
- launcher_type: session type ("person" / "group")
- launcher_id: session / group id
- message_content: message text content
- message_has_element: message contains element of given type
(Image, Voice, File, Forward, Face, At, AtAll, Quote)
Operators: eq (has), neq (doesn't have)
Operators: eq, neq, contains, not_contains, starts_with, regex
When pipeline_uuid is ``__discard__``, the message should be
silently dropped by the caller.
Returns:
tuple: (pipeline_uuid, routed_by_rule) - routed_by_rule is True
when a routing rule matched, False when falling back to default.
"""
rules = self.bot_entity.pipeline_routing_rules or []
element_type_set = set(message_element_types or [])
for rule in rules:
rule_type = rule.get('type')
operator = rule.get('operator', 'eq')
rule_value = rule.get('value', '')
target_uuid = rule.get('pipeline_uuid')
if not rule_type or not target_uuid:
continue
if rule_type == 'launcher_type':
if self._match_operator(launcher_type, operator, rule_value):
return target_uuid, True
elif rule_type == 'launcher_id':
if self._match_operator(str(launcher_id), operator, str(rule_value)):
return target_uuid, True
elif rule_type == 'message_content':
if self._match_operator(message_text, operator, rule_value):
return target_uuid, True
elif rule_type == 'message_has_element':
has_element = rule_value in element_type_set
if operator == 'eq' and has_element:
return target_uuid, True
elif operator == 'neq' and not has_element:
return target_uuid, True
return self.bot_entity.use_pipeline_uuid, False
async def _record_discarded_message(
self,
launcher_type: provider_session.LauncherTypes,
launcher_id: str | int,
sender_id: str | int,
message_event: platform_events.MessageEvent,
message_chain: platform_message.MessageChain,
) -> None:
"""Record a discarded message in the monitoring system."""
try:
if hasattr(message_chain, 'model_dump'):
message_content = json.dumps(message_chain.model_dump(), ensure_ascii=False)
else:
message_content = str(message_chain)
sender_name = None
if hasattr(message_event, 'sender'):
if hasattr(message_event.sender, 'nickname'):
sender_name = message_event.sender.nickname
elif hasattr(message_event.sender, 'member_name'):
sender_name = message_event.sender.member_name
# Use the same session_id format as monitoring_helper.py
session_id = f'{launcher_type}_{launcher_id}'
platform = launcher_type.value if hasattr(launcher_type, 'value') else str(launcher_type)
await self.ap.monitoring_service.record_message(
bot_id=self.bot_entity.uuid,
bot_name=self.bot_entity.name or self.bot_entity.uuid,
pipeline_id=self.PIPELINE_DISCARD,
pipeline_name=self.PIPELINE_DISCARD_DISPLAY_NAME,
message_content=message_content,
session_id=session_id,
status='discarded',
level='info',
platform=platform,
user_id=str(sender_id),
user_name=sender_name,
)
# Ensure the session exists so the message appears in the session monitor.
# Don't overwrite pipeline info — a session may have messages from
# multiple pipelines; discarding shouldn't change the displayed pipeline.
session_updated = await self.ap.monitoring_service.update_session_activity(
session_id,
)
if not session_updated:
# No session yet (first message for this launcher was discarded).
await self.ap.monitoring_service.record_session_start(
session_id=session_id,
bot_id=self.bot_entity.uuid,
bot_name=self.bot_entity.name or self.bot_entity.uuid,
pipeline_id=self.PIPELINE_DISCARD,
pipeline_name=self.PIPELINE_DISCARD_DISPLAY_NAME,
platform=platform,
user_id=str(sender_id),
user_name=sender_name,
)
except Exception as e:
await self.logger.error(f'Failed to record discarded message: {e}')
async def initialize(self):
async def on_friend_message(
event: platform_events.FriendMessage,
@@ -227,23 +82,6 @@ class RuntimeBot:
if custom_launcher_id:
launcher_id = custom_launcher_id
message_text = str(event.message_chain)
element_types = [comp.type for comp in event.message_chain]
pipeline_uuid, routed_by_rule = self.resolve_pipeline_uuid(
'person', launcher_id, message_text, element_types
)
if pipeline_uuid == self.PIPELINE_DISCARD:
await self.logger.info('Person message discarded by routing rule')
await self._record_discarded_message(
provider_session.LauncherTypes.PERSON,
launcher_id,
event.sender.id,
event,
event.message_chain,
)
return
await self.ap.msg_aggregator.add_message(
bot_uuid=self.bot_entity.uuid,
launcher_type=provider_session.LauncherTypes.PERSON,
@@ -252,8 +90,7 @@ class RuntimeBot:
message_event=event,
message_chain=event.message_chain,
adapter=adapter,
pipeline_uuid=pipeline_uuid,
routed_by_rule=routed_by_rule,
pipeline_uuid=self.bot_entity.use_pipeline_uuid,
)
else:
await self.logger.info('Pipeline skipped for person message due to webhook response')
@@ -288,23 +125,6 @@ class RuntimeBot:
if custom_launcher_id:
launcher_id = custom_launcher_id
message_text = str(event.message_chain)
element_types = [comp.type for comp in event.message_chain]
pipeline_uuid, routed_by_rule = self.resolve_pipeline_uuid(
'group', launcher_id, message_text, element_types
)
if pipeline_uuid == self.PIPELINE_DISCARD:
await self.logger.info('Group message discarded by routing rule')
await self._record_discarded_message(
provider_session.LauncherTypes.GROUP,
launcher_id,
event.sender.id,
event,
event.message_chain,
)
return
await self.ap.msg_aggregator.add_message(
bot_uuid=self.bot_entity.uuid,
launcher_type=provider_session.LauncherTypes.GROUP,
@@ -313,8 +133,7 @@ class RuntimeBot:
message_event=event,
message_chain=event.message_chain,
adapter=adapter,
pipeline_uuid=pipeline_uuid,
routed_by_rule=routed_by_rule,
pipeline_uuid=self.bot_entity.use_pipeline_uuid,
)
else:
await self.logger.info('Pipeline skipped for group message due to webhook response')
@@ -322,50 +141,6 @@ class RuntimeBot:
self.adapter.register_listener(platform_events.FriendMessage, on_friend_message)
self.adapter.register_listener(platform_events.GroupMessage, on_group_message)
# Register feedback listener (only effective on adapters that support it)
async def on_feedback(
event: platform_events.FeedbackEvent,
adapter: abstract_platform_adapter.AbstractMessagePlatformAdapter,
):
try:
# Resolve pipeline name
pipeline_name = ''
if self.bot_entity.use_pipeline_uuid:
try:
pipeline_result = await self.ap.persistence_mgr.execute_async(
sqlalchemy.select(persistence_pipeline.LegacyPipeline.name).where(
persistence_pipeline.LegacyPipeline.uuid == self.bot_entity.use_pipeline_uuid
)
)
pipeline_row = pipeline_result.first()
if pipeline_row:
pipeline_name = pipeline_row[0]
except Exception:
pass
await self.ap.monitoring_service.record_feedback(
feedback_id=event.feedback_id,
feedback_type=event.feedback_type,
feedback_content=event.feedback_content,
inaccurate_reasons=event.inaccurate_reasons,
bot_id=self.bot_entity.uuid,
bot_name=self.bot_entity.name,
pipeline_id=self.bot_entity.use_pipeline_uuid or '',
pipeline_name=pipeline_name,
session_id=event.session_id,
message_id=event.message_id,
stream_id=event.stream_id,
user_id=event.user_id,
platform=adapter.__class__.__name__,
)
await self.logger.info(
f'Recorded feedback: feedback_id={event.feedback_id}, type={event.feedback_type}'
)
except Exception:
await self.logger.error(f'Failed to record feedback: {traceback.format_exc()}')
self.adapter.register_listener(platform_events.FeedbackEvent, on_feedback)
async def run(self):
async def exception_wrapper():
try:
@@ -421,20 +196,12 @@ class PlatformManager:
# delete all bot log images
await self.ap.storage_mgr.storage_provider.delete_dir_recursive('bot_log_images')
disabled_adapters = self.ap.instance_config.data.get('system', {}).get('disabled_adapters', []) or []
self.adapter_components = self.ap.discover.get_components_by_kind('MessagePlatformAdapter')
adapter_dict: dict[str, type[abstract_platform_adapter.AbstractMessagePlatformAdapter]] = {}
for component in self.adapter_components:
if component.metadata.name in disabled_adapters:
continue
adapter_dict[component.metadata.name] = component.get_python_component_class()
self.adapter_dict = adapter_dict
# Filter out disabled adapters from components list (for API responses)
if disabled_adapters:
self.adapter_components = [c for c in self.adapter_components if c.metadata.name not in disabled_adapters]
# initialize websocket adapter
websocket_adapter_class = self.adapter_dict['websocket']
websocket_logger = EventLogger(name='websocket-adapter', ap=self.ap)

View File

@@ -5,29 +5,19 @@ metadata:
label:
en_US: OneBot v11
zh_Hans: OneBot v11
zh_Hant: OneBot v11
description:
en_US: OneBot v11 Adapter, used for QQ bots
zh_Hans: OneBot v11 适配器,用于接入 QQ 机器人协议端,请查看文档了解使用方式
zh_Hant: OneBot v11 適配器,用於接入 QQ 機器人協定端,請查看文件了解使用方式
en_US: OneBot v11 Adapter
zh_Hans: OneBot v11 适配器,请查看文档了解使用方式
icon: onebot.png
spec:
categories:
- protocol
help_links:
zh: https://link.langbot.app/zh/platforms/aiocqhttp
en: https://link.langbot.app/en/platforms/aiocqhttp
ja: https://link.langbot.app/ja/platforms/aiocqhttp
config:
- name: host
label:
en_US: Host
zh_Hans: 主机
zh_Hant: 主機
description:
en_US: The host that OneBot v11 listens on for reverse WebSocket connections. Unless you know what you're doing, use 0.0.0.0
zh_Hans: OneBot v11 监听的反向 WS 主机,除非你知道自己在做什么,否则请写 0.0.0.0
zh_Hant: OneBot v11 監聽的反向 WS 主機,除非你知道自己在做什麼,否則請填 0.0.0.0
type: string
required: true
default: 0.0.0.0
@@ -35,11 +25,9 @@ spec:
label:
en_US: Port
zh_Hans: 端口
zh_Hant: 連接埠
description:
en_US: Port
zh_Hans: 监听的端口
zh_Hant: 監聽的連接埠
type: integer
required: true
default: 2280
@@ -47,11 +35,9 @@ spec:
label:
en_US: Access Token
zh_Hans: 访问令牌
zh_Hant: 存取令牌
description:
en_US: Custom connection token for the protocol endpoint. If the protocol endpoint is not set, don't fill it
zh_Hans: 自定义的与协议端的连接令牌,若协议端未设置,则不填
zh_Hant: 自訂的與協定端的連線令牌,若協定端未設定,則不填
type: string
required: false
default: ""

View File

@@ -71,8 +71,7 @@ class DingTalkMessageConverter(abstract_platform_adapter.AbstractMessageConverte
yiri_msg_list.append(platform_message.Image(base64=element['Picture']))
else:
# 回退到原有简单逻辑
# 对于音频消息content 来自 recognition 转写文字,在下方音频处理块中统一处理
if event.content and event.type != 'audio':
if event.content:
text_content = event.content.replace('@' + bot_name, '')
yiri_msg_list.append(platform_message.Plain(text=text_content))
if event.picture:
@@ -82,38 +81,7 @@ class DingTalkMessageConverter(abstract_platform_adapter.AbstractMessageConverte
if event.file:
yiri_msg_list.append(platform_message.File(url=event.file, name=event.name))
if event.audio:
# 优先使用钉钉自带的语音转写文字recognition字段
if event.content and event.type == 'audio':
yiri_msg_list.append(platform_message.Plain(text=event.content))
else:
yiri_msg_list.append(platform_message.Voice(base64=event.audio))
# Handle quoted/replied message - extract content as top-level components
# so that plugins like FileReader can process them the same way as direct messages
if event.quoted_message:
quote_info = event.quoted_message
msg_type = quote_info.get('msg_type', '')
# Process quoted file - add as top-level File component (same as private chat)
if msg_type == 'file' and quote_info.get('file_url'):
file_name = quote_info.get('file_name', 'file')
yiri_msg_list.append(platform_message.File(url=quote_info['file_url'], name=file_name))
# Process quoted image - add as top-level Image component
elif msg_type == 'picture' and quote_info.get('picture'):
yiri_msg_list.append(platform_message.Image(base64=quote_info['picture']))
# Process quoted audio - add as top-level Voice component
elif msg_type == 'audio' and quote_info.get('audio'):
yiri_msg_list.append(platform_message.Voice(base64=quote_info['audio']))
# Process quoted text - add as Plain text with context prefix
elif msg_type == 'text' and quote_info.get('content'):
yiri_msg_list.append(platform_message.Plain(text=f'[引用消息] {quote_info["content"]}'))
# Process quoted rich text - add as Plain text with context prefix
elif msg_type == 'richText' and quote_info.get('content'):
yiri_msg_list.append(platform_message.Plain(text=f'[引用消息] {quote_info["content"]}'))
yiri_msg_list.append(platform_message.Voice(base64=event.audio))
chain = platform_message.MessageChain(yiri_msg_list)

View File

@@ -5,25 +5,16 @@ metadata:
label:
en_US: DingTalk
zh_Hans: 钉钉
zh_Hant: 釘釘
description:
en_US: DingTalk Adapter
zh_Hans: 钉钉适配器,请查看文档了解使用方式
zh_Hant: 釘釘適配器,請查看文件了解使用方式
icon: dingtalk.svg
spec:
categories:
- china
help_links:
zh: https://link.langbot.app/zh/platforms/dingtalk
en: https://link.langbot.app/en/platforms/dingtalk
ja: https://link.langbot.app/ja/platforms/dingtalk
config:
- name: client_id
label:
en_US: Client ID
zh_Hans: 客户端ID
zh_Hant: 用戶端ID
type: string
required: true
default: ""
@@ -31,7 +22,6 @@ spec:
label:
en_US: Client Secret
zh_Hans: 客户端密钥
zh_Hant: 用戶端密鑰
type: string
required: true
default: ""
@@ -39,7 +29,6 @@ spec:
label:
en_US: Robot Code
zh_Hans: 机器人代码
zh_Hant: 機器人代碼
type: string
required: true
default: ""
@@ -47,7 +36,6 @@ spec:
label:
en_US: Robot Name
zh_Hans: 机器人名称
zh_Hant: 機器人名稱
type: string
required: true
default: ""
@@ -55,7 +43,6 @@ spec:
label:
en_US: Markdown Card
zh_Hans: 是否使用 Markdown 卡片
zh_Hant: 是否使用 Markdown 卡片
type: boolean
required: false
default: true
@@ -63,11 +50,9 @@ spec:
label:
en_US: Enable Stream Reply Mode
zh_Hans: 启用钉钉卡片流式回复模式
zh_Hant: 啟用釘釘卡片串流回覆模式
description:
en_US: If enabled, the bot will use the stream of lark reply mode
zh_Hans: 如果启用,将使用钉钉卡片流式方式来回复内容
zh_Hant: 如果啟用,將使用釘釘卡片串流方式來回覆內容
type: boolean
required: true
default: false
@@ -75,7 +60,6 @@ spec:
label:
en_US: Card Auto Layout
zh_Hans: 卡片宽屏自动布局
zh_Hant: 卡片寬螢幕自動佈局
type: boolean
required: false
default: false
@@ -83,7 +67,6 @@ spec:
label:
en_US: card template id
zh_Hans: 卡片模板ID
zh_Hant: 卡片範本ID
type: string
required: true
default: "填写你的卡片template_id"

View File

@@ -5,38 +5,16 @@ metadata:
label:
en_US: Discord
zh_Hans: Discord
zh_Hant: Discord
ja_JP: Discord
th_TH: Discord
vi_VN: Discord
es_ES: Discord
description:
en_US: Discord Adapter
zh_Hans: Discord 适配器,需要可连接 Discord 服务器的网络环境
zh_Hant: Discord 適配器,需要可連線 Discord 伺服器的網路環境
ja_JP: Discord アダプター、Discord サーバーに接続可能なネットワーク環境が必要です
th_TH: อะแดปเตอร์ Discord ต้องการสภาพแวดล้อมเครือข่ายที่สามารถเชื่อมต่อกับเซิร์ฟเวอร์ Discord ได้
vi_VN: Bộ điều hợp Discord, cần môi trường mạng có thể kết nối với máy chủ Discord
es_ES: Adaptador de Discord, requiere un entorno de red con acceso al servidor de Discord
zh_Hans: Discord 适配器,请查看文档了解使用方式
icon: discord.svg
spec:
categories:
- popular
- global
help_links:
zh: https://link.langbot.app/zh/platforms/discord
en: https://link.langbot.app/en/platforms/discord
ja: https://link.langbot.app/ja/platforms/discord
config:
- name: client_id
label:
en_US: Client ID
zh_Hans: 客户端ID
zh_Hant: 用戶端ID
ja_JP: クライアント ID
th_TH: รหัสไคลเอนต์
vi_VN: ID khách hàng
es_ES: ID de cliente
type: string
required: true
default: ""
@@ -44,11 +22,6 @@ spec:
label:
en_US: Token
zh_Hans: 令牌
zh_Hant: 令牌
ja_JP: トークン
th_TH: โทเค็น
vi_VN: Mã thông báo
es_ES: Token
type: string
required: true
default: ""

View File

@@ -5,25 +5,16 @@ metadata:
label:
en_US: KOOK
zh_Hans: KOOK
zh_Hant: KOOK
description:
en_US: KOOK Adapter (formerly KaiHeiLa)
zh_Hans: KOOK 适配器(原开黑啦),支持频道消息和私聊消息
zh_Hant: KOOK 適配器(原開黑啦),支援頻道訊息和私聊訊息
icon: kook.png
spec:
categories:
- china
help_links:
zh: https://link.langbot.app/zh/platforms/kook
en: https://link.langbot.app/en/platforms/kook
ja: https://link.langbot.app/ja/platforms/kook
config:
- name: token
label:
en_US: Bot Token
zh_Hans: 机器人令牌
zh_Hant: 機器人令牌
type: string
required: true
default: ""

View File

@@ -709,29 +709,21 @@ class LarkEventConverter(abstract_platform_adapter.AbstractEventConverter):
message_chain = await LarkMessageConverter.target2yiri(event.event.message, api_client)
# Check for quote/reply message
# Extract files/images/voice from quote and add them as top-level components
# so that plugins like FileReader can process them the same way as direct messages
quote_message_id = LarkEventConverter._extract_quote_message_id(event.event.message)
if quote_message_id:
quote_chain = await LarkEventConverter._fetch_quoted_message(quote_message_id, api_client)
if quote_chain:
# Filter out Source component from quoted chain, keep only content
quote_components = [comp for comp in quote_chain if not isinstance(comp, platform_message.Source)]
# Add quoted content as top-level components instead of wrapping in Quote
for comp in quote_components:
if isinstance(comp, platform_message.File):
# Add file as top-level component (same as direct message)
message_chain.append(comp)
elif isinstance(comp, platform_message.Image):
# Add image as top-level component
message_chain.append(comp)
elif isinstance(comp, platform_message.Voice):
# Add voice as top-level component
message_chain.append(comp)
elif isinstance(comp, platform_message.Plain):
# Add text with context prefix
message_chain.append(platform_message.Plain(text=f'[引用消息] {comp.text}'))
quote_origin = platform_message.MessageChain(
[comp for comp in quote_chain if not isinstance(comp, platform_message.Source)]
)
if quote_origin:
message_chain.append(
platform_message.Quote(
message_id=quote_message_id,
origin=quote_origin,
)
)
if event.event.message.chat_type == 'p2p':
return platform_events.FriendMessage(
@@ -787,13 +779,6 @@ class LarkAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
card_id_dict: dict[str, str] # 消息id到卡片id的映射便于创建卡片后的发送消息到指定卡片
# Monitoring message ID mapping for feedback correlation
# Temp: user Lark message ID → monitoring_message_id (populated by on_monitoring_message_created, consumed by create_message_card)
pending_monitoring_msg: dict[str, str]
# Final: reply Lark message ID → (monitoring_message_id, timestamp) (used by feedback callbacks)
reply_to_monitoring_msg: dict[str, tuple[str, float]]
_MONITORING_MAPPING_TTL = 600 # 10 minutes
seq: int # 用于在发送卡片消息中识别消息顺序直接以seq作为标识
bot_uuid: str = None # 机器人UUID
app_ticket: str = None # 商店应用用到
@@ -812,71 +797,8 @@ class LarkAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
def sync_on_message(event: lark_oapi.im.v1.P2ImMessageReceiveV1):
asyncio.create_task(on_message(event))
def sync_on_card_action(event):
try:
action_value_obj = getattr(getattr(event.event, 'action', None), 'value', {})
action_value = action_value_obj.get('feedback', '') if isinstance(action_value_obj, dict) else ''
if action_value == '有帮助':
feedback_type = 1
elif action_value == '无帮助':
feedback_type = 2
else:
from lark_oapi.event.callback.model.p2_card_action_trigger import P2CardActionTriggerResponse
return P2CardActionTriggerResponse({'toast': {'type': 'success', 'content': '操作成功'}})
operator = getattr(event.event, 'operator', None)
context = getattr(event.event, 'context', None)
user_id = getattr(operator, 'open_id', None) or getattr(operator, 'user_id', None)
open_chat_id = getattr(context, 'open_chat_id', None)
open_message_id = getattr(context, 'open_message_id', None)
if open_chat_id:
session_id = f'group_{open_chat_id}'
elif user_id:
session_id = f'person_{user_id}'
else:
session_id = None
# Resolve monitoring message ID from reply message mapping
monitoring_msg_id = None
if open_message_id and open_message_id in self.reply_to_monitoring_msg:
monitoring_msg_id = self.reply_to_monitoring_msg[open_message_id][0]
feedback_event = platform_events.FeedbackEvent(
feedback_id=getattr(event.header, 'event_id', str(uuid.uuid4())),
feedback_type=feedback_type,
feedback_content=action_value,
user_id=user_id,
session_id=session_id,
message_id=open_message_id,
stream_id=monitoring_msg_id,
source_platform_object=event,
)
if platform_events.FeedbackEvent in self.listeners:
loop = asyncio.get_event_loop()
if loop.is_running():
asyncio.create_task(self.listeners[platform_events.FeedbackEvent](feedback_event, self))
else:
loop.run_until_complete(self.listeners[platform_events.FeedbackEvent](feedback_event, self))
from lark_oapi.event.callback.model.p2_card_action_trigger import P2CardActionTriggerResponse
return P2CardActionTriggerResponse({'toast': {'type': 'success', 'content': '感谢您的反馈'}})
except Exception:
asyncio.create_task(self.logger.error(f'Error in lark card action callback: {traceback.format_exc()}'))
from lark_oapi.event.callback.model.p2_card_action_trigger import P2CardActionTriggerResponse
return P2CardActionTriggerResponse({'toast': {'type': 'error', 'content': '反馈处理失败'}})
event_handler = (
lark_oapi.EventDispatcherHandler.builder('', '')
.register_p2_im_message_receive_v1(sync_on_message)
.register_p2_card_action_trigger(sync_on_card_action)
.build()
lark_oapi.EventDispatcherHandler.builder('', '').register_p2_im_message_receive_v1(sync_on_message).build()
)
bot_account_id = config['bot_name']
@@ -891,8 +813,6 @@ class LarkAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
logger=logger,
lark_tenant_key=config.get('lark_tenant_key', ''),
card_id_dict={},
pending_monitoring_msg={},
reply_to_monitoring_msg={},
seq=1,
listeners={},
quart_app=quart_app,
@@ -1033,22 +953,6 @@ class LarkAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
is_stream = True
return is_stream
async def on_monitoring_message_created(self, query, monitoring_message_id: str):
"""Called by pipeline after monitoring message is created, to map user message ID to monitoring message ID."""
try:
user_msg_id = query.message_event.message_chain.message_id
if user_msg_id:
self.pending_monitoring_msg[user_msg_id] = monitoring_message_id
except Exception as e:
await self.logger.debug(f'Failed to map message to monitoring message: {e}')
def _cleanup_monitoring_mapping(self):
"""Remove entries older than TTL from the reply-to-monitoring mapping."""
now = time.time()
expired = [k for k, (_, ts) in self.reply_to_monitoring_msg.items() if now - ts > self._MONITORING_MAPPING_TTL]
for k in expired:
del self.reply_to_monitoring_msg[k]
async def create_card_id(self, message_id):
try:
# self.logger.debug('飞书支持stream输出,创建卡片......')
@@ -1184,7 +1088,6 @@ class LarkAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
'size': 'medium',
'icon': {'tag': 'standard_icon', 'token': 'thumbsup_outlined'},
'hover_tips': {'tag': 'plain_text', 'content': '有帮助'},
'behaviors': [{'type': 'callback', 'value': {'feedback': '有帮助'}}],
'margin': '0px 0px 0px 0px',
}
],
@@ -1208,7 +1111,6 @@ class LarkAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
'size': 'medium',
'icon': {'tag': 'standard_icon', 'token': 'thumbdown_outlined'},
'hover_tips': {'tag': 'plain_text', 'content': '无帮助'},
'behaviors': [{'type': 'callback', 'value': {'feedback': '无帮助'}}],
'margin': '0px 0px 0px 0px',
}
],
@@ -1288,18 +1190,6 @@ class LarkAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
raise Exception(
f'client.im.v1.message.reply failed, code: {response.code}, msg: {response.msg}, log_id: {response.get_log_id()}, resp: \n{json.dumps(json.loads(response.raw.content), indent=4, ensure_ascii=False)}'
)
# Transfer monitoring message mapping: user msg ID → reply msg ID
try:
user_msg_id = event.message_chain.message_id
reply_msg_id = getattr(response.data, 'message_id', None)
monitoring_msg_id = self.pending_monitoring_msg.pop(user_msg_id, None)
if reply_msg_id and monitoring_msg_id:
self.reply_to_monitoring_msg[reply_msg_id] = (monitoring_msg_id, time.time())
self._cleanup_monitoring_mapping()
except Exception as e:
asyncio.create_task(self.logger.debug(f'Failed to transfer monitoring mapping in create_message_card: {e}'))
return True
async def reply_message(
@@ -1582,58 +1472,6 @@ class LarkAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
if event.__class__ in self.listeners:
await self.listeners[event.__class__](event, self)
elif 'card.action.trigger' == type:
try:
event_data = data.get('event', {})
operator = event_data.get('operator', {})
action = event_data.get('action', {})
context_data = event_data.get('context', {})
action_value_obj = action.get('value', {})
action_value = action_value_obj.get('feedback', '') if isinstance(action_value_obj, dict) else ''
if action_value == '有帮助':
feedback_type = 1
elif action_value == '无帮助':
feedback_type = 2
else:
return {'toast': {'type': 'success', 'content': '操作成功'}}
user_id = operator.get('open_id') or operator.get('user_id')
open_chat_id = context_data.get('open_chat_id')
open_message_id = context_data.get('open_message_id')
if open_chat_id:
session_id = f'group_{open_chat_id}'
elif user_id:
session_id = f'person_{user_id}'
else:
session_id = None
# Resolve monitoring message ID from reply message mapping
monitoring_msg_id = None
if open_message_id and open_message_id in self.reply_to_monitoring_msg:
monitoring_msg_id = self.reply_to_monitoring_msg[open_message_id][0]
feedback_event = platform_events.FeedbackEvent(
feedback_id=data.get('header', {}).get('event_id', str(uuid.uuid4())),
feedback_type=feedback_type,
feedback_content=action_value,
user_id=user_id,
session_id=session_id,
message_id=open_message_id,
stream_id=monitoring_msg_id,
source_platform_object=data,
)
if platform_events.FeedbackEvent in self.listeners:
await self.listeners[platform_events.FeedbackEvent](feedback_event, self)
return {'toast': {'type': 'success', 'content': '感谢您的反馈'}}
except Exception:
await self.logger.error(f'Error in lark card action callback: {traceback.format_exc()}')
return {'toast': {'type': 'error', 'content': '反馈处理失败'}}
elif 'im.chat.member.bot.added_v1' == type:
try:
bot_added_welcome_msg = self.config.get('bot_added_welcome', '')

View File

@@ -5,30 +5,16 @@ metadata:
label:
en_US: Lark
zh_Hans: 飞书
zh_Hant: 飛書
ja_JP: Lark
description:
en_US: Lark Adapter, supports both long connection and Webhook modes. Please refer to the documentation for usage details.
zh_Hans: 飞书适配器,支持长连接和 Webhook 两种接入方式,请查看文档了解使用方式
zh_Hant: 飛書適配器,支援長連線和 Webhook 兩種接入方式,請查看文件了解使用方式
ja_JP: Lark アダプター、長期接続およびWebhookモードの両方をサポートしています。使用方法の詳細については、ドキュメントを参照してください。
en_US: Lark Adapter
zh_Hans: 飞书适配器,请查看文档了解使用方式
icon: lark.svg
spec:
categories:
- popular
- china
- global
help_links:
zh: https://link.langbot.app/zh/platforms/lark
en: https://link.langbot.app/en/platforms/lark
ja: https://link.langbot.app/ja/platforms/lark
config:
- name: app_id
label:
en_US: App ID
zh_Hans: 应用ID
zh_Hant: 應用ID
ja_JP: アプリ ID
type: string
required: true
default: ""
@@ -36,8 +22,6 @@ spec:
label:
en_US: App Secret
zh_Hans: 应用密钥
zh_Hant: 應用密鑰
ja_JP: アプリシークレット
type: string
required: true
default: ""
@@ -45,13 +29,9 @@ spec:
label:
en_US: Bot Name
zh_Hans: 机器人名称
zh_Hant: 機器人名稱
ja_JP: ボット名
description:
en_US: Must be the same as the name of the bot in Lark, otherwise the bot will not be able to receive messages in the group
zh_Hans: 必须与飞书机器人名称一致,否则机器人将无法在群内正常接收消息
zh_Hant: 必須與飛書機器人名稱一致,否則機器人將無法在群組內正常接收訊息
ja_JP: Lark のボット名と一致する必要があります。一致しない場合、グループ内でメッセージを受信できません
type: string
required: true
default: ""
@@ -59,63 +39,29 @@ spec:
label:
en_US: Enable Webhook Mode
zh_Hans: 启用Webhook模式
zh_Hant: 啟用 Webhook 模式
ja_JP: Webhook モードを有効化
description:
en_US: If enabled, the bot will use webhook mode to receive messages. Otherwise, it will use WS long connection mode
zh_Hans: 如果启用,机器人将使用 Webhook 模式接收消息。否则,将使用 WS 长连接模式
zh_Hant: 如果啟用,機器人將使用 Webhook 模式接收訊息。否則,將使用 WS 長連線模式
ja_JP: 有効にすると、ボットは Webhook モードでメッセージを受信します。無効の場合は WS 長期接続モードを使用します
type: boolean
required: true
default: false
- name: webhook_url
label:
en_US: Webhook Callback URL
zh_Hans: Webhook 回调地址
zh_Hant: Webhook 回調地址
ja_JP: Webhook コールバック URL
description:
en_US: Copy this URL and paste it into your Lark app's webhook configuration
zh_Hans: 复制此地址并粘贴到飞书应用的 Webhook 配置中
zh_Hant: 複製此地址並貼到飛書應用的 Webhook 設定中
ja_JP: この URL をコピーして Lark アプリの Webhook 設定に貼り付けてください
type: webhook-url
required: false
default: ""
show_if:
field: enable-webhook
operator: eq
value: true
- name: encrypt-key
label:
en_US: Encrypt Key
zh_Hans: 加密密钥
zh_Hant: 加密密鑰
ja_JP: 暗号化キー
description:
en_US: Only valid when webhook mode is enabled, please fill in the encrypt key
zh_Hans: 仅在启用 Webhook 模式时有效,请填写加密密钥
zh_Hant: 僅在啟用 Webhook 模式時有效,請填寫加密密鑰
ja_JP: Webhook モードが有効な場合にのみ有効です。暗号化キーを入力してください
type: string
required: true
default: ""
show_if:
field: enable-webhook
operator: eq
value: true
- name: enable-stream-reply
label:
en_US: Enable Stream Reply Mode
zh_Hans: 启用飞书流式回复模式
zh_Hant: 啟用飛書串流回覆模式
ja_JP: ストリーミング返信モードを有効化
description:
en_US: If enabled, the bot will use the stream of lark reply mode
zh_Hans: 如果启用,将使用飞书流式方式来回复内容
zh_Hant: 如果啟用,將使用飛書串流方式來回覆內容
ja_JP: 有効にすると、ボットはストリーミングモードでメッセージに返信します
type: boolean
required: true
default: false
@@ -123,40 +69,28 @@ spec:
label:
en_US: App Type
zh_Hans: 应用类型
zh_Hant: 應用類型
ja_JP: アプリタイプ
description:
en_US: Default to self-built application, refer to https://open.feishu.cn/document/platform-overveiw/overview
zh_Hans: 默认为企业自建应用,参考 https://open.feishu.cn/document/platform-overveiw/overview
zh_Hant: 預設為企業自建應用,參考 https://open.feishu.cn/document/platform-overveiw/overview
ja_JP: デフォルトはカスタムアプリです。詳細は https://open.feishu.cn/document/platform-overveiw/overview を参照してください
type: select
options:
- name: self
label:
en_US: Self-built Application
zh_Hans: 自建应用
zh_Hant: 自建應用
ja_JP: カスタムアプリ
- name: isv
label:
en_US: Store Application
zh_Hans: 商店应用
zh_Hant: 商店應用
ja_JP: ストアアプリ
required: false
default: self
- name: bot_added_welcome
label:
en_US: Bot Welcome Message
zh_Hans: 机器人进群欢迎语
zh_Hant: 機器人進群歡迎語
ja_JP: ボット参加時のウェルカムメッセージ
description:
en_US: Welcome message when the bot is added to a group, supports Markdown format
zh_Hans: 机器人进群欢迎语,支持 Markdown 格式
zh_Hant: 機器人進群歡迎語,支援 Markdown 格式
ja_JP: ボットがグループに追加された際のウェルカムメッセージ。Markdown 形式に対応しています
type: text
required: false
default: ""

View File

@@ -5,56 +5,20 @@ metadata:
label:
en_US: LINE
zh_Hans: LINE
zh_Hant: LINE
th_TH: LINE
vi_VN: LINE
es_ES: LINE
description:
en_US: LINE Adapter, requires a public URL to receive LINE message pushes, please refer to the documentation for usage details
zh_Hans: LINE适配器需要公网地址以接收 LINE 消息推送,请查看文档了解使用方式
zh_Hant: LINE 適配器,需要公網地址以接收 LINE 訊息推送,請查看文件了解使用方式
ja_JP: LINEアダプター、LINEのメッセージプッシュを受信するためにパブリックURLが必要です。使用方法の詳細については、ドキュメントを参照してください。
th_TH: อะแดปเตอร์ LINE ต้องการ URL สาธารณะเพื่อรับการแจ้งเตือนข้อความจาก LINE โปรดดูเอกสารประกอบสำหรับรายละเอียดการใช้งาน
vi_VN: Bộ điều hợp LINE, cần URL công cộng để nhận thông báo tin nhắn LINE, vui lòng xem tài liệu để biết chi tiết cách sử dụng
es_ES: Adaptador de LINE, requiere una URL pública para recibir notificaciones de mensajes de LINE, consulte la documentación para obtener detalles de uso
en_US: LINE Adapter
zh_Hans: LINE适配器请查看文档了解使用方式
ja_JP: LINEアダプター、ドキュメントを参照してください
zh_Hant: LINE適配器,請查看文檔了解使用方式
icon: line.png
spec:
categories:
- global
help_links:
zh: https://link.langbot.app/zh/platforms/line
en: https://link.langbot.app/en/platforms/line
ja: https://link.langbot.app/ja/platforms/line
config:
- name: webhook_url
label:
en_US: Webhook Callback URL
zh_Hans: Webhook 回调地址
ja_JP: Webhook コールバック URL
zh_Hant: Webhook 回調地址
th_TH: URL การเรียกกลับ Webhook
vi_VN: URL gọi lại Webhook
es_ES: URL de devolución de llamada Webhook
description:
en_US: Copy this URL and paste it into your LINE channel's webhook configuration
zh_Hans: 复制此地址并粘贴到 LINE 频道的 Webhook 配置中
ja_JP: この URL をコピーして LINE チャンネルの Webhook 設定に貼り付けてください
zh_Hant: 複製此地址並貼到 LINE 頻道的 Webhook 設定中
th_TH: คัดลอก URL นี้แล้ววางในการตั้งค่า Webhook ของช่อง LINE ของคุณ
vi_VN: Sao chép URL này và dán vào cấu hình webhook của kênh LINE của bạn
es_ES: Copie esta URL y péguela en la configuración de webhook de su canal LINE
type: webhook-url
required: false
default: ""
- name: channel_access_token
label:
en_US: Channel access token
zh_Hans: 频道访问令牌
ja_JP: チャンネルアクセストークン
zh_Hant: 頻道存取令牌
th_TH: โทเค็นการเข้าถึงช่อง
vi_VN: Mã truy cập kênh
es_ES: Token de acceso del canal
zh_Hant: 頻道訪問令牌
type: string
required: true
default: ""
@@ -63,18 +27,12 @@ spec:
en_US: Channel secret
zh_Hans: 消息密钥
ja_JP: チャンネルシークレット
zh_Hant: 息密
th_TH: รหัสลับช่อง
vi_VN: Khóa bí mật kênh
es_ES: Secreto del canal
zh_Hant: 息密
description:
en_US: Only valid when webhook mode is enabled, please fill in the encrypt key
zh_Hans: 请填写加密密钥
ja_JP: Webhookモードが有効な場合にのみ、暗号化キーを入力してください
zh_Hant: 請填寫加密密
th_TH: กรุณากรอกคีย์เข้ารหัส
vi_VN: Vui lòng điền khóa mã hóa
es_ES: Por favor, introduzca la clave de cifrado
zh_Hant: 請填寫加密密
type: string
required: true
default: ""

View File

@@ -5,44 +5,23 @@ metadata:
label:
en_US: Official Account
zh_Hans: 微信公众号
zh_Hant: 微信公眾號
description:
en_US: Official Account Adapter
zh_Hans: 微信公众号适配器,需要公网地址以接收消息推送,请查看文档了解使用方式
zh_Hant: 微信公眾號適配器,需要公網地址以接收訊息推送,請查看文件了解使用方式
zh_Hans: 微信公众号适配器,请查看文档了解使用方式
icon: officialaccount.png
spec:
categories:
- china
help_links:
zh: https://link.langbot.app/zh/platforms/officialaccount
en: https://link.langbot.app/en/platforms/officialaccount
ja: https://link.langbot.app/ja/platforms/officialaccount
config:
- name: webhook_url
label:
en_US: Webhook Callback URL
zh_Hans: Webhook 回调地址
zh_Hant: Webhook 回調地址
description:
en_US: Copy this URL and paste it into your Official Account webhook configuration
zh_Hans: 复制此地址并粘贴到微信公众号的 Webhook 配置中
zh_Hant: 複製此地址並貼到微信公眾號的 Webhook 設定中
type: webhook-url
required: false
default: ""
- name: token
label:
en_US: Token
zh_Hans: 令牌
zh_Hant: 令牌
type: string
required: true
default: ""
- name: EncodingAESKey
label:
en_US: EncodingAESKey
zh_Hans: 消息加解密密钥
zh_Hant: 訊息加解密密鑰
type: string
required: true
default: ""
@@ -50,7 +29,6 @@ spec:
label:
en_US: App ID
zh_Hans: 应用ID
zh_Hant: 應用ID
type: string
required: true
default: ""
@@ -58,7 +36,6 @@ spec:
label:
en_US: App Secret
zh_Hans: 应用密钥
zh_Hant: 應用密鑰
type: string
required: true
default: ""
@@ -66,7 +43,6 @@ spec:
label:
en_US: Mode
zh_Hans: 接入模式
zh_Hant: 接入模式
type: string
required: true
default: "drop"
@@ -74,7 +50,6 @@ spec:
label:
en_US: Loading Message
zh_Hans: 加载消息
zh_Hant: 載入訊息
type: string
required: true
default: "AI正在思考中请发送任意内容获取回复。"
@@ -82,11 +57,9 @@ spec:
label:
en_US: API Base URL
zh_Hans: API 基础 URL
zh_Hant: API 基礎 URL
description:
en_US: API Base URL, used for accessing the Official Account API. If you are deploying in an internal network environment and accessing the Official Account API through a reverse proxy, please fill in this item according to the documentation.
zh_Hans: 可选,若您部署在内网环境并通过反向代理访问微信公众号 API可根据文档修改此项
zh_Hant: 可選,若您部署在內網環境並透過反向代理存取微信公眾號 API可根據文件修改此項
type: string
required: false
default: "https://api.weixin.qq.com"

View File

@@ -4,31 +4,20 @@ metadata:
name: openclaw-weixin
label:
en_US: OpenClaw WeChat
zh_Hans: 个人微信机器人
zh_Hant: 個人微信機器人
zh_Hans: OpenClaw 微信
description:
en_US: OpenClaw WeChat adapter, supports personal WeChat via QR code login
zh_Hans: 微信官方个人助手,扫码即可登录使用
zh_Hant: 微信官方個人助手,掃碼即可登入使用
zh_Hans: OpenClaw 微信适配器,通过扫码登录支持个人微信
icon: wechat.png
spec:
categories:
- popular
- china
help_links:
zh: https://link.langbot.app/zh/platforms/openclaw_weixin
en: https://link.langbot.app/en/platforms/openclaw_weixin
ja: https://link.langbot.app/ja/platforms/openclaw_weixin
config:
- name: base_url
label:
en_US: API Base URL
zh_Hans: API 基础地址
zh_Hant: API 基礎地址
description:
en_US: The base URL of the OpenClaw WeChat backend API
zh_Hans: OpenClaw 微信后端 API 的基础地址
zh_Hant: OpenClaw 微信後端 API 的基礎地址
type: string
required: true
default: "https://ilinkai.weixin.qq.com"
@@ -36,11 +25,9 @@ spec:
label:
en_US: Token
zh_Hans: 令牌
zh_Hant: 令牌
description:
en_US: Bearer token obtained after QR code login authorization. Leave empty to trigger QR code login on startup.
zh_Hans: 扫码登录授权后获取的 Bearer 令牌。留空并保存,将在启动时输出二维码到日志,扫码后即可自动登录。
zh_Hant: 掃碼登入授權後取得的 Bearer 令牌。請留空並儲存,將在啟動時輸出 QR Code 到日誌,掃碼後即可自動登入。
zh_Hans: 扫码登录授权后获取的 Bearer 令牌。留空则启动时自动触发扫码登录。
type: string
required: false
default: ""
@@ -48,11 +35,9 @@ spec:
label:
en_US: Account ID
zh_Hans: 账号标识
zh_Hant: 帳號標識
description:
en_US: A label for this WeChat account (used for display purposes)
zh_Hans: 此微信账号的标识(用于显示)
zh_Hant: 此微信帳號的標識(用於顯示)
type: string
required: false
default: "openclaw-weixin"
@@ -60,11 +45,9 @@ spec:
label:
en_US: Poll Timeout (seconds)
zh_Hans: 轮询超时(秒)
zh_Hant: 輪詢逾時(秒)
description:
en_US: Long-poll timeout for getUpdates, the server may hold the request up to this duration
zh_Hans: getUpdates 长轮询超时时间,服务端最多持有请求的时长
zh_Hant: getUpdates 長輪詢逾時時間,伺服端最多持有請求的時長
type: integer
required: false
default: 35

View File

@@ -5,37 +5,16 @@ metadata:
label:
en_US: QQ Official API
zh_Hans: QQ 官方 API
zh_Hant: QQ 官方 API
description:
en_US: QQ Official API (Webhook)
zh_Hans: QQ 官方 API (Webhook)需要公网地址以接收消息推送,请查看文档了解使用方式
zh_Hant: QQ 官方 API (Webhook),需要公網地址以接收訊息推送,請查看文件了解使用方式
zh_Hans: QQ 官方 API (Webhook),请查看文档了解使用方式
icon: qqofficial.svg
spec:
categories:
- china
help_links:
zh: https://link.langbot.app/zh/platforms/qqofficial
en: https://link.langbot.app/en/platforms/qqofficial
ja: https://link.langbot.app/ja/platforms/qqofficial
config:
- name: webhook_url
label:
en_US: Webhook Callback URL
zh_Hans: Webhook 回调地址
zh_Hant: Webhook 回調地址
description:
en_US: Copy this URL and paste it into your QQ Official API webhook configuration
zh_Hans: 复制此地址并粘贴到 QQ 官方 API 的 Webhook 配置中
zh_Hant: 複製此地址並貼到 QQ 官方 API 的 Webhook 設定中
type: webhook-url
required: false
default: ""
- name: appid
label:
en_US: App ID
zh_Hans: 应用ID
zh_Hant: 應用ID
type: string
required: true
default: ""
@@ -43,7 +22,6 @@ spec:
label:
en_US: Secret
zh_Hans: 密钥
zh_Hant: 密鑰
type: string
required: true
default: ""
@@ -51,7 +29,6 @@ spec:
label:
en_US: Token
zh_Hans: 令牌
zh_Hant: 令牌
type: string
required: true
default: ""

View File

@@ -5,70 +5,36 @@ metadata:
label:
en_US: Satori
zh_Hans: Satori
zh_Hant: Satori
th_TH: Satori
vi_VN: Satori
es_ES: Satori
description:
en_US: SatoriAdapter
zh_Hans: Satori 协议适配器,支持多种平台的接入,请查看文档了解使用方式
zh_Hant: Satori 協定適配器,支援多種平台的接入,請查看文件了解使用方式
th_TH: อะแดปเตอร์โปรโตคอล Satori รองรับการเชื่อมต่อหลายแพลตฟอร์ม โปรดดูเอกสารประกอบสำหรับวิธีการใช้งาน
vi_VN: Bộ điều hợp giao thức Satori, hỗ trợ kết nối nhiều nền tảng, vui lòng xem tài liệu để biết cách sử dụng
es_ES: Adaptador del protocolo Satori, soporta acceso a múltiples plataformas, consulte la documentación para obtener instrucciones de uso
zh_Hans: 古明地觉协议适配器
icon: satori.png
spec:
categories:
- protocol
help_links:
zh: https://link.langbot.app/zh/platforms/satori
en: https://link.langbot.app/en/platforms/satori
ja: https://link.langbot.app/ja/platforms/satori
config:
- name: platform
label:
en_US: Platform
zh_Hans: 平台名称
zh_Hant: 平台名稱
th_TH: ชื่อแพลตฟอร์ม
vi_VN: Tên nền tảng
es_ES: Nombre de la plataforma
type: string
required: true
default: "llonebot"
description:
en_US: The platform name (e.g., llonebot, discord, telegram)
zh_Hans: 平台名称(如 llonebot, discord, telegram
zh_Hant: 平台名稱(如 llonebot、discord、telegram
th_TH: ชื่อแพลตฟอร์ม (เช่น llonebot, discord, telegram)
vi_VN: "Tên nền tảng (ví dụ: llonebot, discord, telegram)"
es_ES: El nombre de la plataforma (p. ej., llonebot, discord, telegram)
- name: host
label:
en_US: Host
zh_Hans: 主机地址
zh_Hant: 主機地址
th_TH: ที่อยู่โฮสต์
vi_VN: Địa chỉ máy chủ
es_ES: Dirección del host
type: string
required: true
default: "127.0.0.1"
description:
en_US: The host address of LLOneBot Satori server (e.g., 127.0.0.1, localhost, 192.168.1.100)
zh_Hans: LLOneBot Satori服务器的主机地址如 127.0.0.1, localhost, 192.168.1.100
zh_Hant: LLOneBot Satori 伺服器的主機地址(如 127.0.0.1、localhost、192.168.1.100
th_TH: ที่อยู่โฮสต์ของเซิร์ฟเวอร์ LLOneBot Satori (เช่น 127.0.0.1, localhost, 192.168.1.100)
vi_VN: "Địa chỉ máy chủ LLOneBot Satori (ví dụ: 127.0.0.1, localhost, 192.168.1.100)"
es_ES: La dirección del host del servidor LLOneBot Satori (p. ej., 127.0.0.1, localhost, 192.168.1.100)
- name: port
label:
en_US: Port
zh_Hans: 监听端口
zh_Hant: 監聽連接埠
th_TH: พอร์ต
vi_VN: Cổng
es_ES: Puerto
type: integer
required: true
default: 5600
@@ -76,10 +42,6 @@ spec:
label:
en_US: Satori API Endpoint
zh_Hans: Satori API 终结点
zh_Hant: Satori API 端點
th_TH: จุดปลาย Satori API
vi_VN: Điểm cuối Satori API
es_ES: Punto de acceso de la API Satori
type: string
required: true
default: "http://localhost:5600/v1"
@@ -87,10 +49,6 @@ spec:
label:
en_US: Satori WebSocket Endpoint
zh_Hans: Satori WebSocket 终结点
zh_Hant: Satori WebSocket 端點
th_TH: จุดปลาย Satori WebSocket
vi_VN: Điểm cuối Satori WebSocket
es_ES: Punto de acceso WebSocket de Satori
type: string
required: true
default: "ws://localhost:5600/v1/events"
@@ -98,10 +56,6 @@ spec:
label:
en_US: Token
zh_Hans: 令牌
zh_Hant: 令牌
th_TH: โทเค็น
vi_VN: Mã thông báo
es_ES: Token
type: string
required: true
default: ""

View File

@@ -5,58 +5,16 @@ metadata:
label:
en_US: Slack
zh_Hans: Slack
zh_Hant: Slack
ja_JP: Slack
th_TH: Slack
vi_VN: Slack
es_ES: Slack
description:
en_US: Slack Adapter
zh_Hans: Slack 适配器,需要公网地址以接收 Slack 消息推送,请查看文档了解使用方式
zh_Hant: Slack 適配器,需要公網地址以接收 Slack 訊息推送,請查看文件了解使用方式
ja_JP: Slack アダプター、Slackのメッセージプッシュを受信するためにパブリックURLが必要です。使用方法の詳細については、ドキュメントを参照してください。
th_TH: อะแดปเตอร์ Slack ต้องการที่อยู่สาธารณะเพื่อรับการแจ้งเตือนข้อความจาก Slack โปรดดูเอกสารประกอบสำหรับวิธีการใช้งาน
vi_VN: Bộ điều hợp Slack, cần địa chỉ công cộng để nhận thông báo tin nhắn từ Slack, vui lòng xem tài liệu để biết cách sử dụng
es_ES: Adaptador de Slack, requiere una dirección pública para recibir notificaciones de mensajes de Slack, consulte la documentación para obtener instrucciones de uso
zh_Hans: Slack 适配器,请查看文档了解使用方式
icon: slack.png
spec:
categories:
- popular
- global
help_links:
zh: https://link.langbot.app/zh/platforms/slack
en: https://link.langbot.app/en/platforms/slack
ja: https://link.langbot.app/ja/platforms/slack
config:
- name: webhook_url
label:
en_US: Webhook Callback URL
zh_Hans: Webhook 回调地址
zh_Hant: Webhook 回調地址
ja_JP: Webhook コールバック URL
th_TH: URL การเรียกกลับ Webhook
vi_VN: URL gọi lại Webhook
es_ES: URL de devolución de llamada Webhook
description:
en_US: Copy this URL and paste it into your Slack app's event subscription configuration
zh_Hans: 复制此地址并粘贴到 Slack 应用的事件订阅配置中
zh_Hant: 複製此地址並貼到 Slack 應用的事件訂閱設定中
ja_JP: この URL をコピーして Slack アプリのイベントサブスクリプション設定に貼り付けてください
th_TH: คัดลอก URL นี้แล้ววางในการตั้งค่าการสมัครรับเหตุการณ์ของแอป Slack ของคุณ
vi_VN: Sao chép URL này và dán vào cấu hình đăng ký sự kiện của ứng dụng Slack của bạn
es_ES: Copie esta URL y péguela en la configuración de suscripción de eventos de su aplicación Slack
type: webhook-url
required: false
default: ""
- name: bot_token
label:
en_US: Bot Token
zh_Hans: 机器人令牌
zh_Hant: 機器人令牌
ja_JP: ボットトークン
th_TH: โทเค็นบอท
vi_VN: Mã thông báo Bot
es_ES: Token del bot
type: string
required: true
default: ""
@@ -64,11 +22,6 @@ spec:
label:
en_US: signing_secret
zh_Hans: 密钥
zh_Hant: 密鑰
ja_JP: 署名シークレット
th_TH: คีย์ลายเซ็น
vi_VN: Khóa ký
es_ES: Secreto de firma
type: string
required: true
default: ""

View File

@@ -5,50 +5,23 @@ metadata:
label:
en_US: Telegram
zh_Hans: 电报
zh_Hant: Telegram
ja_JP: Telegram
th_TH: Telegram
vi_VN: Telegram
es_ES: Telegram
description:
en_US: Telegram Adapter
zh_Hans: Telegram 适配器,请查看文档了解使用方式
zh_Hant: Telegram 適配器,請查看文件了解使用方式
ja_JP: Telegram アダプター。使用方法の詳細については、ドキュメントを参照してください。
th_TH: อะแดปเตอร์ Telegram โปรดดูเอกสารประกอบสำหรับวิธีการใช้งาน
vi_VN: Bộ điều hợp Telegram, vui lòng xem tài liệu để biết cách sử dụng
es_ES: Adaptador de Telegram, consulte la documentación para obtener instrucciones de uso
zh_Hans: 电报适配器,请查看文档了解使用方式
icon: telegram.svg
spec:
categories:
- popular
- global
help_links:
zh: https://link.langbot.app/zh/platforms/telegram
en: https://link.langbot.app/en/platforms/telegram
ja: https://link.langbot.app/ja/platforms/telegram
config:
- name: token
label:
en_US: Token
zh_Hans: 令牌
zh_Hant: 令牌
ja_JP: トークン
th_TH: โทเค็น
vi_VN: Mã thông báo
es_ES: Token
type: string
required: true
default: "token_from_botfather"
default: ""
- name: markdown_card
label:
en_US: Markdown Card
zh_Hans: 是否使用 Markdown 卡片
zh_Hant: 是否使用 Markdown 卡片
ja_JP: Markdown カードを使用
th_TH: การ์ด Markdown
vi_VN: Thẻ Markdown
es_ES: Tarjeta Markdown
type: boolean
required: false
default: true
@@ -56,19 +29,9 @@ spec:
label:
en_US: Enable Stream Reply Mode
zh_Hans: 启用电报流式回复模式
zh_Hant: 啟用 Telegram 串流回覆模式
ja_JP: ストリーミング返信モードを有効化
th_TH: เปิดใช้งานโหมดตอบกลับแบบสตรีม
vi_VN: Bật chế độ trả lời trực tuyến
es_ES: Habilitar modo de respuesta en streaming
description:
en_US: If enabled, the bot will use the stream of telegram reply mode
zh_Hans: 如果启用,将使用电报流式方式来回复内容
zh_Hant: 如果啟用,將使用 Telegram 串流方式來回覆內容
ja_JP: 有効にすると、ボットはストリーミングモードでメッセージに返信します
th_TH: หากเปิดใช้งาน บอทจะใช้โหมดสตรีมของ Telegram ในการตอบกลับ
vi_VN: Nếu bật, bot sẽ sử dụng chế độ trả lời trực tuyến của Telegram
es_ES: Si está habilitado, el bot usará el modo de respuesta en streaming de Telegram
type: boolean
required: true
default: false

View File

@@ -5,21 +5,11 @@ metadata:
label:
en_US: "WebSocket Chat"
zh_Hans: "WebSocket 聊天"
zh_Hant: "WebSocket 聊天"
th_TH: "แชท WebSocket"
vi_VN: "Trò chuyện WebSocket"
es_ES: "Chat WebSocket"
description:
en_US: "WebSocket adapter for bidirectional real-time communication"
zh_Hans: "用于双向实时通信的 WebSocket 适配器"
zh_Hant: "用於雙向即時通訊的 WebSocket 適配器"
th_TH: "อะแดปเตอร์ WebSocket สำหรับการสื่อสารแบบเรียลไทม์สองทิศทาง"
vi_VN: "Bộ điều hợp WebSocket cho giao tiếp thời gian thực hai chiều"
es_ES: "Adaptador WebSocket para comunicación bidireccional en tiempo real"
icon: ""
spec:
categories:
- protocol
config: []
execution:
python:

View File

@@ -4,26 +4,17 @@ metadata:
name: wechatpad
label:
en_US: WeChatPad
zh_Hans: WeChatPad个人微信ipad
zh_Hant: WeChatPad個人微信iPad
zh_CN: WeChatPad个人微信ipad
description:
en_US: WeChatPad Adapter
zh_Hans: WeChatPad 适配器基于WeChatPad的个人微信解决方案请查看文档了解使用方式
zh_Hant: WeChatPad 適配器,基於 WeChatPad 的個人微信解決方案,請查看文件了解使用方式
zh_CN: WeChatPad 适配器
icon: wechatpad.png
spec:
categories:
- china
help_links:
zh: https://link.langbot.app/zh/platforms/wechatpad
en: https://link.langbot.app/en/platforms/wechatpad
ja: https://link.langbot.app/ja/platforms/wechatpad
config:
- name: wechatpad_url
label:
en_US: WeChatPad ERL
zh_CN: WeChatPad URL
zh_Hant: WeChatPad URL
type: string
required: true
default: ""
@@ -31,7 +22,6 @@ spec:
label:
en_US: WeChatPad_Ws
zh_CN: WeChatPad_Ws
zh_Hant: WeChatPad_Ws
type: string
required: true
default: ""
@@ -39,7 +29,6 @@ spec:
label:
en_US: Admin_Key
zh_CN: 管理员密匙
zh_Hant: 管理員密鑰
type: string
required: true
default: ""
@@ -47,7 +36,6 @@ spec:
label:
en_US: Token
zh_CN: 令牌
zh_Hant: 令牌
type: string
required: true
default: ""
@@ -55,7 +43,6 @@ spec:
label:
en_US: wxid
zh_CN: wxid
zh_Hant: wxid
type: string
required: true
default: ""

View File

@@ -5,38 +5,16 @@ metadata:
label:
en_US: WeCom
zh_Hans: 企业微信
zh_Hant: 企業微信
description:
en_US: WeCom Adapter
zh_Hans: 企业微信内部机器人,请查看文档了解使用方式
zh_Hant: 企業微信內部機器人,請查看文件了解使用方式
zh_Hans: 企业微信适配器,请查看文档了解使用方式
icon: wecom.png
spec:
categories:
- popular
- china
help_links:
zh: https://link.langbot.app/zh/platforms/wecom
en: https://link.langbot.app/en/platforms/wecom
ja: https://link.langbot.app/ja/platforms/wecom
config:
- name: webhook_url
label:
en_US: Webhook Callback URL
zh_Hans: Webhook 回调地址
zh_Hant: Webhook 回調地址
description:
en_US: Copy this URL and paste it into your WeCom app's webhook configuration
zh_Hans: 复制此地址并粘贴到企业微信应用的 Webhook 配置中
zh_Hant: 複製此地址並貼到企業微信應用的 Webhook 設定中
type: webhook-url
required: false
default: ""
- name: corpid
label:
en_US: Corpid
zh_Hans: 企业ID
zh_Hant: 企業ID
type: string
required: true
default: ""
@@ -44,7 +22,6 @@ spec:
label:
en_US: Secret
zh_Hans: 密钥 (Secret)
zh_Hant: 密鑰 (Secret)
type: string
required: true
default: ""
@@ -52,7 +29,6 @@ spec:
label:
en_US: Token
zh_Hans: 令牌 (Token)
zh_Hant: 令牌 (Token)
type: string
required: true
default: ""
@@ -60,7 +36,6 @@ spec:
label:
en_US: EncodingAESKey
zh_Hans: 消息加解密密钥 (EncodingAESKey)
zh_Hant: 訊息加解密密鑰 (EncodingAESKey)
type: string
required: true
default: ""
@@ -68,11 +43,9 @@ spec:
label:
en_US: API Base URL
zh_Hans: API 基础 URL
zh_Hant: API 基礎 URL
description:
en_US: API Base URL, used for accessing the WeCom API. If you are deploying in an internal network environment and accessing the WeCom Customer Service API through a reverse proxy, please fill in this item according to the documentation.
zh_Hans: 可选,若您部署在内网环境并通过反向代理访问企业微信 API可根据文档填写此项
zh_Hant: 可選,若您部署在內網環境並透過反向代理存取企業微信 API可根據文件填寫此項
type: string
required: false
default: "https://qyapi.weixin.qq.com/cgi-bin"

View File

@@ -1,7 +1,6 @@
from __future__ import annotations
import typing
import asyncio
import time
import traceback
import datetime
@@ -127,107 +126,6 @@ class WecomBotMessageConverter(abstract_platform_adapter.AbstractMessageConverte
if summary:
yiri_msg_list.append(platform_message.Plain(text=summary))
# Handle quoted message (引用消息) - important for group chat file references
# Extract files/images/voice from quote and add them as top-level components
# so that plugins like FileReader can process them the same way as direct messages
quote_info = event.quote or {}
if quote_info:
# Process quote text content - add as Plain for context
if quote_info.get('content'):
yiri_msg_list.append(platform_message.Plain(text=f'[引用消息] {quote_info.get("content")}'))
# Process quote images - add as top-level Image components
quote_images = quote_info.get('images', [])
if not quote_images and quote_info.get('picurl'):
quote_images = [quote_info.get('picurl')]
for img_data in quote_images:
if img_data:
yiri_msg_list.append(platform_message.Image(base64=img_data))
# Process quote file - add as top-level File component (same as private chat)
quote_file = quote_info.get('file') or {}
if quote_file:
file_url = (
quote_file.get('base64')
or quote_file.get('download_url')
or quote_file.get('url')
or quote_file.get('fileurl')
)
file_name = quote_file.get('filename') or quote_file.get('name')
file_size = quote_file.get('filesize') or quote_file.get('size')
if file_url or file_name:
file_kwargs = {}
if file_url:
file_kwargs['url'] = file_url
if file_name:
file_kwargs['name'] = file_name
if file_size is not None:
file_kwargs['size'] = file_size
try:
yiri_msg_list.append(platform_message.File(**file_kwargs))
except Exception:
yiri_msg_list.append(platform_message.Unknown(text='[quoted file unsupported]'))
# Process quote voice - add as top-level Voice/File component
quote_voice = quote_info.get('voice') or {}
if quote_voice:
voice_payload = quote_voice.get('base64') or quote_voice.get('url')
if voice_payload:
if quote_voice.get('base64') and not voice_payload.startswith('data:'):
voice_payload = f'data:audio/mpeg;base64,{quote_voice.get("base64")}'
try:
yiri_msg_list.append(platform_message.Voice(base64=voice_payload))
except Exception:
try:
voice_kwargs = {'url': voice_payload}
voice_name = quote_voice.get('filename') or quote_voice.get('name')
voice_size = quote_voice.get('filesize') or quote_voice.get('size')
if voice_name:
voice_kwargs['name'] = voice_name
if voice_size is not None:
voice_kwargs['size'] = voice_size
yiri_msg_list.append(platform_message.File(**voice_kwargs))
except Exception:
yiri_msg_list.append(platform_message.Unknown(text='[quoted voice unsupported]'))
# Process quote video - add as top-level File component
quote_video = quote_info.get('video') or {}
if quote_video:
video_payload = (
quote_video.get('base64')
or quote_video.get('url')
or quote_video.get('download_url')
or quote_video.get('fileurl')
)
if video_payload:
video_kwargs = {'url': video_payload}
video_name = quote_video.get('filename') or quote_video.get('name')
video_size = quote_video.get('filesize') or quote_video.get('size')
if video_name:
video_kwargs['name'] = video_name
if video_size is not None:
video_kwargs['size'] = video_size
try:
yiri_msg_list.append(platform_message.File(**video_kwargs))
except Exception:
yiri_msg_list.append(platform_message.Unknown(text='[quoted video unsupported]'))
# Process quote link - add as Plain text
quote_link = quote_info.get('link') or {}
if quote_link:
link_summary = '\n'.join(
filter(
None,
[
quote_link.get('title', ''),
quote_link.get('description') or quote_link.get('digest', ''),
quote_link.get('url', ''),
],
)
)
if link_summary:
yiri_msg_list.append(platform_message.Plain(text=f'[引用链接] {link_summary}'))
has_content_element = any(
not isinstance(element, (platform_message.Source, platform_message.At)) for element in yiri_msg_list
)
@@ -294,8 +192,6 @@ class WecomBotAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
_ws_mode: bool = False
bot_name: str = ''
listeners: dict = {}
_stream_to_monitoring_msg: dict = {} # Maps stream_id to (monitoring_message_id, timestamp)
_STREAM_MAPPING_TTL = 600 # 10 minutes
def __init__(self, config: dict, logger: EventLogger):
enable_webhook = config.get('enable-webhook', False)
@@ -332,9 +228,8 @@ class WecomBotAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
bot_account_id=bot_account_id,
bot_name=bot_name,
event_converter=event_converter,
listeners={},
_stream_to_monitoring_msg={},
)
self.listeners = {}
async def reply_message(
self,
@@ -416,9 +311,6 @@ class WecomBotAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
self.bot.on_message('single')(self.on_message)
elif event_type == platform_events.GroupMessage:
self.bot.on_message('group')(self.on_message)
elif event_type == platform_events.FeedbackEvent:
if hasattr(self.bot, 'on_feedback'):
self.bot.on_feedback()(self._on_feedback)
except Exception:
print(traceback.format_exc())
@@ -426,75 +318,6 @@ class WecomBotAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
"""设置 bot UUID用于生成 webhook URL"""
self.bot_uuid = bot_uuid
async def on_monitoring_message_created(self, query, monitoring_message_id: str):
"""Called by pipeline after monitoring message is created, to map stream_id to monitoring message ID."""
try:
stream_id = query.message_event.source_platform_object.stream_id
if stream_id:
self._stream_to_monitoring_msg[stream_id] = (monitoring_message_id, time.time())
self._cleanup_stream_mapping()
except Exception as e:
await self.logger.debug(f'Failed to map stream_id to monitoring message: {e}')
def _cleanup_stream_mapping(self):
"""Remove entries older than TTL from the stream_id to monitoring message mapping."""
now = time.time()
expired = [k for k, (_, ts) in self._stream_to_monitoring_msg.items() if now - ts > self._STREAM_MAPPING_TTL]
for k in expired:
del self._stream_to_monitoring_msg[k]
async def _on_feedback(self, **kwargs):
"""Handle feedback event from WeChat Work AI Bot SDK and dispatch as FeedbackEvent."""
try:
feedback_id = kwargs.get('feedback_id', '')
feedback_type = kwargs.get('feedback_type', 0)
feedback_content = kwargs.get('feedback_content', '') or None
inaccurate_reasons = kwargs.get('inaccurate_reasons', []) or None
# WeChat Work returns integer reason codes, but FeedbackEvent expects strings
if inaccurate_reasons:
inaccurate_reasons = [str(r) for r in inaccurate_reasons]
session = kwargs.get('session')
session_id = None
user_id = None
message_id = None
stream_id = None
if session:
if session.chat_id:
session_id = f'group_{session.chat_id}'
elif session.user_id:
session_id = f'person_{session.user_id}'
user_id = session.user_id
message_id = session.msg_id
stream_id = session.stream_id
# Resolve stream_id to LangBot monitoring message ID if available
monitoring_msg_id = None
if stream_id and stream_id in self._stream_to_monitoring_msg:
monitoring_msg_id = self._stream_to_monitoring_msg[stream_id][0]
await self.logger.info(
f'Feedback event: feedback_id={feedback_id}, type={feedback_type}, '
f'session_id={session_id}, user_id={user_id}, message_id={message_id}'
)
event = platform_events.FeedbackEvent(
feedback_id=feedback_id,
feedback_type=feedback_type,
feedback_content=feedback_content,
inaccurate_reasons=inaccurate_reasons,
user_id=user_id,
session_id=session_id,
message_id=message_id,
stream_id=monitoring_msg_id or stream_id,
source_platform_object=session,
)
if platform_events.FeedbackEvent in self.listeners:
await self.listeners[platform_events.FeedbackEvent](event, self)
except Exception:
await self.logger.error(f'Error in wecombot feedback callback: {traceback.format_exc()}')
async def handle_unified_webhook(self, bot_uuid: str, path: str, request):
_ws_mode = not self.config.get('enable-webhook', False)
if _ws_mode:

View File

@@ -5,25 +5,16 @@ metadata:
label:
en_US: WeComBot
zh_Hans: 企业微信智能机器人
zh_Hant: 企業微信智慧機器人
description:
en_US: WeComBot Adapter
zh_Hans: 企业微信智能机器人,支持长连接和 Webhook 两种接入方式,请查看文档了解使用方式
zh_Hant: 企業微信智慧機器人,支援長連線和 Webhook 兩種接入方式,請查看文件了解使用方式
zh_Hans: 企业微信智能机器人适配器,请查看文档了解使用方式
icon: wecombot.png
spec:
categories:
- china
help_links:
zh: https://link.langbot.app/zh/platforms/wecombot
en: https://link.langbot.app/en/platforms/wecombot
ja: https://link.langbot.app/ja/platforms/wecombot
config:
- name: BotId
label:
en_US: BotId
zh_Hans: 机器人ID (BotId)
zh_Hant: 機器人ID (BotId)
type: string
required: true
default: ""
@@ -31,7 +22,6 @@ spec:
label:
en_US: Robot Name
zh_Hans: 机器人名称
zh_Hant: 機器人名稱
type: string
required: true
default: ""
@@ -39,39 +29,19 @@ spec:
label:
en_US: Enable Webhook Mode
zh_Hans: 启用Webhook模式
zh_Hant: 啟用 Webhook 模式
description:
en_US: If enabled, the bot will use webhook mode to receive messages. Otherwise, it will use WS long connection mode
zh_Hans: 如果启用,机器人将使用 Webhook 模式接收消息。否则,将使用 WS 长连接模式
zh_Hant: 如果啟用,機器人將使用 Webhook 模式接收訊息。否則,將使用 WS 長連線模式
type: boolean
required: true
default: false
- name: webhook_url
label:
en_US: Webhook Callback URL
zh_Hans: Webhook 回调地址
zh_Hant: Webhook 回調地址
description:
en_US: Copy this URL and paste it into your WeComBot webhook configuration
zh_Hans: 复制此地址并粘贴到企业微信智能机器人的 Webhook 配置中
zh_Hant: 複製此地址並貼到企業微信智慧機器人的 Webhook 設定中
type: webhook-url
required: false
default: ""
show_if:
field: enable-webhook
operator: eq
value: true
- name: Secret
label:
en_US: Secret
zh_Hans: 机器人密钥 (Secret)
zh_Hant: 機器人密鑰 (Secret)
description:
en_US: Required for WebSocket long connection mode
zh_Hans: 使用 WS 长连接模式时必填
zh_Hant: 使用 WS 長連線模式時必填
type: string
required: false
default: ""
@@ -79,47 +49,51 @@ spec:
label:
en_US: Corpid
zh_Hans: 企业ID
zh_Hant: 企業ID
description:
en_US: Required for Webhook mode
zh_Hans: 使用 Webhook 模式时必填
zh_Hant: 使用 Webhook 模式時必填
type: string
required: false
default: ""
show_if:
field: enable-webhook
operator: eq
value: true
- name: Token
label:
en_US: Token
zh_Hans: 令牌 (Token)
zh_Hant: 令牌 (Token)
description:
en_US: Required for Webhook mode
zh_Hans: 使用 Webhook 模式时必填
zh_Hant: 使用 Webhook 模式時必填
type: string
required: false
default: ""
show_if:
field: enable-webhook
operator: eq
value: true
- name: EncodingAESKey
label:
en_US: EncodingAESKey
zh_Hans: 消息加解密密钥 (EncodingAESKey)
zh_Hant: 訊息加解密密鑰 (EncodingAESKey)
description:
en_US: Required for Webhook mode. Optional for WebSocket mode (used for file decryption)
zh_Hans: 使用 Webhook 模式时必填。WebSocket 模式下可选(用于文件解密)
zh_Hant: 使用 Webhook 模式時必填。WebSocket 模式下可選(用於檔案解密)
type: string
required: false
default: ""
show_if:
field: enable-webhook
operator: eq
value: true
- name: enable-stream-reply
label:
en_US: Enable Stream Reply
zh_Hans: 启用流式回复
zh_Hant: 啟用串流回覆
description:
en_US: If enabled, the bot will use streaming mode to reply messages
zh_Hans: 如果启用,机器人将使用流式模式回复消息
zh_Hant: 如果啟用,機器人將使用串流模式回覆訊息
type: boolean
required: false
default: true

View File

@@ -5,37 +5,16 @@ metadata:
label:
en_US: WeComCustomerService
zh_Hans: 企业微信客服
zh_Hant: 企業微信客服
description:
en_US: WeComCSAdapter
zh_Hans: 企业微信对外客服机器人,需要公网地址以接收消息推送,请查看文档了解使用方式
zh_Hant: 企業微信對外客服機器人,需要公網地址以接收訊息推送,請查看文件了解使用方式
zh_Hans: 企业微信客服适配器
icon: wecom.png
spec:
categories:
- china
help_links:
zh: https://link.langbot.app/zh/platforms/wecomcs
en: https://link.langbot.app/en/platforms/wecomcs
ja: https://link.langbot.app/ja/platforms/wecomcs
config:
- name: webhook_url
label:
en_US: Webhook Callback URL
zh_Hans: Webhook 回调地址
zh_Hant: Webhook 回調地址
description:
en_US: Copy this URL and paste it into your WeCom Customer Service webhook configuration
zh_Hans: 复制此地址并粘贴到企业微信客服的 Webhook 配置中
zh_Hant: 複製此地址並貼到企業微信客服的 Webhook 設定中
type: webhook-url
required: false
default: ""
- name: corpid
label:
en_US: Corpid
zh_Hans: 企业ID
zh_Hant: 企業ID
type: string
required: true
default: ""
@@ -43,7 +22,6 @@ spec:
label:
en_US: Secret
zh_Hans: 密钥
zh_Hant: 密鑰
type: string
required: true
default: ""
@@ -51,7 +29,6 @@ spec:
label:
en_US: Token
zh_Hans: 令牌
zh_Hant: 令牌
type: string
required: true
default: ""
@@ -59,7 +36,6 @@ spec:
label:
en_US: EncodingAESKey
zh_Hans: 消息加解密密钥
zh_Hant: 訊息加解密密鑰
type: string
required: true
default: ""
@@ -67,11 +43,9 @@ spec:
label:
en_US: API Base URL
zh_Hans: API 基础 URL
zh_Hant: API 基礎 URL
description:
en_US: API Base URL, used for accessing the WeCom API. If you are deploying in an internal network environment and accessing the WeCom Customer Service API through a reverse proxy, please fill in this item according to the documentation.
zh_Hans: 可选,若您部署在内网环境并通过反向代理访问企业微信 API可根据文档修改此项
zh_Hant: 可選,若您部署在內網環境並透過反向代理存取企業微信 API可根據文件修改此項
type: string
required: false
default: "https://qyapi.weixin.qq.com/cgi-bin"

View File

@@ -2,9 +2,6 @@
from __future__ import annotations
import asyncio
import io
import time
import zipfile
from typing import Any
import typing
import os
@@ -195,30 +192,6 @@ class PluginRuntimeConnector:
return await self.handler.ping()
def _extract_deps_metadata(
self,
file_bytes: bytes,
task_context: taskmgr.TaskContext | None,
):
"""Extract dependency count from requirements.txt inside plugin zip."""
if task_context is None:
return
try:
with zipfile.ZipFile(io.BytesIO(file_bytes)) as zf:
for name in zf.namelist():
if name.endswith('requirements.txt'):
content = zf.read(name).decode('utf-8', errors='ignore')
deps = [
line.strip()
for line in content.splitlines()
if line.strip() and not line.strip().startswith('#')
]
task_context.metadata['deps_total'] = len(deps)
task_context.metadata['deps_list'] = deps
break
except Exception:
pass
async def install_plugin(
self,
install_source: PluginInstallSource,
@@ -228,44 +201,23 @@ class PluginRuntimeConnector:
if install_source == PluginInstallSource.LOCAL:
# transfer file before install
file_bytes = install_info['plugin_file']
self._extract_deps_metadata(file_bytes, task_context)
file_key = await self.handler.send_file(file_bytes, 'lbpkg')
install_info['plugin_file_key'] = file_key
del install_info['plugin_file']
self.ap.logger.info(f'Transfered file {file_key} to plugin runtime')
elif install_source == PluginInstallSource.GITHUB:
# download and transfer file with streaming progress
# download and transfer file
try:
async with httpx.AsyncClient(
trust_env=True,
follow_redirects=True,
timeout=60,
timeout=20,
) as client:
async with client.stream('GET', install_info['asset_url']) as response:
response.raise_for_status()
total = int(response.headers.get('content-length', 0))
downloaded = 0
chunks: list[bytes] = []
start_time = time.time()
if task_context is not None:
task_context.set_current_action('downloading plugin package')
task_context.metadata['download_total'] = total
task_context.metadata['download_current'] = 0
task_context.metadata['download_speed'] = 0
async for chunk in response.aiter_bytes(chunk_size=8192):
chunks.append(chunk)
downloaded += len(chunk)
if task_context is not None:
elapsed = time.time() - start_time
task_context.metadata['download_current'] = downloaded
task_context.metadata['download_total'] = total
task_context.metadata['download_speed'] = downloaded / elapsed if elapsed > 0 else 0
file_bytes = b''.join(chunks)
self._extract_deps_metadata(file_bytes, task_context)
response = await client.get(
install_info['asset_url'],
)
response.raise_for_status()
file_bytes = response.content
file_key = await self.handler.send_file(file_bytes, 'lbpkg')
install_info['plugin_file_key'] = file_key
self.ap.logger.info(f'Transfered file {file_key} to plugin runtime')
@@ -284,11 +236,6 @@ class PluginRuntimeConnector:
if task_context is not None:
task_context.trace(trace)
# Forward structured metadata from runtime
metadata = ret.get('metadata', None)
if metadata is not None and task_context is not None:
task_context.metadata.update(metadata)
async def upgrade_plugin(
self,
plugin_author: str,

View File

@@ -9,6 +9,7 @@ from ...discover import engine
from . import token
from ...entity.persistence import model as persistence_model
from ...entity.errors import provider as provider_errors
from async_lru import alru_cache
class ModelManager:
@@ -23,8 +24,6 @@ class ModelManager:
embedding_models: list[requester.RuntimeEmbeddingModel]
rerank_models: list[requester.RuntimeRerankModel]
requester_components: list[engine.Component]
requester_dict: dict[str, type[requester.ProviderAPIRequester]]
@@ -33,7 +32,6 @@ class ModelManager:
self.ap = ap
self.llm_models = []
self.embedding_models = []
self.rerank_models = []
self.requester_components = []
self.requester_dict = {}
@@ -66,7 +64,8 @@ class ModelManager:
self.llm_models = []
self.embedding_models = []
self.rerank_models = []
# Load all providers first
self.provider_dict = {}
providers_result = await self.ap.persistence_mgr.execute_async(
sqlalchemy.select(persistence_model.ModelProvider)
@@ -111,22 +110,6 @@ class ModelManager:
except Exception as e:
self.ap.logger.error(f'Failed to load model {embedding_model.uuid}: {e}\n{traceback.format_exc()}')
# Load rerank models
result = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_model.RerankModel))
rerank_models = result.all()
for rerank_model in rerank_models:
try:
provider = self.provider_dict.get(rerank_model.provider_uuid)
if provider is None:
self.ap.logger.warning(
f'Provider {rerank_model.provider_uuid} not found for model {rerank_model.uuid}'
)
continue
runtime_rerank_model = await self.load_rerank_model_with_provider(rerank_model, provider)
self.rerank_models.append(runtime_rerank_model)
except Exception as e:
self.ap.logger.error(f'Failed to load model {rerank_model.uuid}: {e}\n{traceback.format_exc()}')
async def sync_new_models_from_space(self):
"""Sync models from Space"""
space_model_provider = await self.ap.persistence_mgr.execute_async(
@@ -229,26 +212,6 @@ class ModelManager:
return runtime_embedding_model
async def init_temporary_runtime_rerank_model(
self,
model_info: dict,
) -> requester.RuntimeRerankModel:
"""Initialize runtime rerank model from dict (for testing)"""
provider_info = model_info.get('provider', {})
runtime_provider = await self.load_provider(provider_info)
runtime_rerank_model = requester.RuntimeRerankModel(
model_entity=persistence_model.RerankModel(
uuid=model_info.get('uuid', ''),
name=model_info.get('name', ''),
provider_uuid='',
extra_args=model_info.get('extra_args', {}),
),
provider=runtime_provider,
)
return runtime_rerank_model
async def load_provider(
self, provider_info: persistence_model.ModelProvider | sqlalchemy.Row | dict
) -> requester.RuntimeProvider:
@@ -264,8 +227,7 @@ class ModelManager:
raise provider_errors.RequesterNotFoundError(provider_entity.requester)
requester_inst = self.requester_dict[provider_entity.requester](
ap=self.ap,
config={'base_url': provider_entity.base_url},
ap=self.ap, config={'base_url': provider_entity.base_url}
)
await requester_inst.initialize()
@@ -306,9 +268,6 @@ class ModelManager:
for model in self.embedding_models:
if model.provider.provider_entity.uuid == provider_uuid:
model.provider = new_runtime_provider
for model in self.rerank_models:
if model.provider.provider_entity.uuid == provider_uuid:
model.provider = new_runtime_provider
# update ref in provider dict
self.provider_dict[provider_uuid] = new_runtime_provider
@@ -345,22 +304,6 @@ class ModelManager:
return runtime_embedding_model
async def load_rerank_model_with_provider(
self,
model_info: persistence_model.RerankModel | sqlalchemy.Row,
provider: requester.RuntimeProvider,
) -> requester.RuntimeRerankModel:
"""Load rerank model with provider info"""
if isinstance(model_info, sqlalchemy.Row):
model_info = persistence_model.RerankModel(**model_info._mapping)
runtime_rerank_model = requester.RuntimeRerankModel(
model_entity=model_info,
provider=provider,
)
return runtime_rerank_model
async def load_llm_model(self, model_info: dict):
"""Load LLM model from dict (with provider info)"""
provider_info = model_info.get('provider', {})
@@ -408,6 +351,7 @@ class ModelManager:
await self.load_embedding_model_with_provider(model_entity, provider_entity)
@alru_cache(ttl=60 * 5)
async def get_model_by_uuid(self, uuid: str) -> requester.RuntimeLLMModel:
"""Get LLM model by uuid"""
for model in self.llm_models:
@@ -415,6 +359,7 @@ class ModelManager:
return model
raise ValueError(f'LLM model {uuid} not found')
@alru_cache(ttl=60 * 5)
async def get_embedding_model_by_uuid(self, uuid: str) -> requester.RuntimeEmbeddingModel:
"""Get embedding model by uuid"""
for model in self.embedding_models:
@@ -422,13 +367,6 @@ class ModelManager:
return model
raise ValueError(f'Embedding model {uuid} not found')
async def get_rerank_model_by_uuid(self, uuid: str) -> requester.RuntimeRerankModel:
"""Get rerank model by uuid"""
for model in self.rerank_models:
if model.model_entity.uuid == uuid:
return model
raise ValueError(f'Rerank model {uuid} not found')
async def remove_llm_model(self, model_uuid: str):
"""Remove LLM model"""
for model in self.llm_models:
@@ -443,13 +381,6 @@ class ModelManager:
self.embedding_models.remove(model)
return
async def remove_rerank_model(self, model_uuid: str):
"""Remove rerank model"""
for model in self.rerank_models:
if model.model_entity.uuid == model_uuid:
self.rerank_models.remove(model)
return
def get_available_requesters_info(self, model_type: str) -> list[dict]:
"""Get all available requesters"""
if model_type != '':

View File

@@ -247,40 +247,6 @@ class RuntimeProvider:
except Exception as monitor_err:
self.requester.ap.logger.error(f'[Monitoring] Failed to record embedding call: {monitor_err}')
async def invoke_rerank(
self,
model: RuntimeRerankModel,
query: str,
documents: typing.List[str],
extra_args: dict[str, typing.Any] = {},
) -> typing.List[dict]:
"""Bridge method for invoking rerank with monitoring"""
start_time = time.time()
status = 'success'
try:
result = await self.requester.invoke_rerank(
model=model,
query=query,
documents=documents,
extra_args=extra_args,
)
return result
except Exception:
status = 'error'
raise
finally:
duration_ms = int((time.time() - start_time) * 1000)
try:
self.requester.ap.logger.debug(
f'[Rerank] model={model.model_entity.name} docs={len(documents)} '
f'duration={duration_ms}ms status={status}'
)
except Exception as monitor_err:
self.requester.ap.logger.error(f'[Monitoring] Failed to record rerank call: {monitor_err}')
class RuntimeLLMModel:
"""运行时模型"""
@@ -318,24 +284,6 @@ class RuntimeEmbeddingModel:
self.provider = provider
class RuntimeRerankModel:
"""运行时 Rerank 模型"""
model_entity: persistence_model.RerankModel
"""模型数据"""
provider: RuntimeProvider
"""提供商实例"""
def __init__(
self,
model_entity: persistence_model.RerankModel,
provider: RuntimeProvider,
):
self.model_entity = model_entity
self.provider = provider
class ProviderAPIRequester(metaclass=abc.ABCMeta):
"""Provider API请求器"""
@@ -355,14 +303,6 @@ class ProviderAPIRequester(metaclass=abc.ABCMeta):
async def initialize(self):
pass
async def scan_models(self, api_key: str | None = None) -> dict[str, typing.Any] | list[dict[str, typing.Any]]:
"""Scan models supported by the provider.
The default implementation does not support scanning. Requesters that
can enumerate remote models should override this method.
"""
raise NotImplementedError('This provider does not support model scanning')
@abc.abstractmethod
async def invoke_llm(
self,
@@ -428,23 +368,3 @@ class ProviderAPIRequester(metaclass=abc.ABCMeta):
或者 tuple[typing.List[typing.List[float]], dict]: 返回 (embedding 向量, usage_info)
"""
pass
async def invoke_rerank(
self,
model: RuntimeRerankModel,
query: str,
documents: typing.List[str],
extra_args: dict[str, typing.Any] = {},
) -> typing.List[dict]:
"""调用 Rerank API
Args:
model (RuntimeRerankModel): 使用的模型信息
query (str): 查询文本
documents (typing.List[str]): 待重排序的文档列表
extra_args (dict[str, typing.Any], optional): 额外的参数. Defaults to {}.
Returns:
typing.List[dict]: [{"index": int, "relevance_score": float}, ...]
"""
raise NotImplementedError('This requester does not support rerank')

View File

@@ -25,7 +25,6 @@ spec:
support_type:
- llm
- text-embedding
- rerank
provider_category: maas
execution:
python:

View File

@@ -24,7 +24,6 @@ spec:
default: 120
support_type:
- llm
- rerank
provider_category: maas
execution:
python:

View File

@@ -31,192 +31,6 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
http_client=httpx.AsyncClient(trust_env=True, timeout=self.requester_cfg['timeout']),
)
def _mask_api_key(self, api_key: str | None) -> str:
if not api_key:
return ''
if len(api_key) <= 8:
return '****'
return f'{api_key[:4]}...{api_key[-4:]}'
def _infer_model_type(self, model_id: str) -> str:
normalized_model_id = (model_id or '').lower()
embedding_keywords = (
'embedding',
'embed',
'bge-',
'e5-',
'm3e',
'gte-',
'multilingual-e5',
'text-embedding',
)
return 'embedding' if any(keyword in normalized_model_id for keyword in embedding_keywords) else 'llm'
def _infer_model_abilities(self, item: dict[str, typing.Any], model_id: str) -> list[str]:
normalized_model_id = (model_id or '').lower()
abilities: set[str] = set()
def _flatten(value: typing.Any) -> list[str]:
if value is None:
return []
if isinstance(value, str):
return [value.lower()]
if isinstance(value, dict):
flattened: list[str] = []
for nested_value in value.values():
flattened.extend(_flatten(nested_value))
return flattened
if isinstance(value, (list, tuple, set)):
flattened: list[str] = []
for nested_value in value:
flattened.extend(_flatten(nested_value))
return flattened
return [str(value).lower()]
capability_tokens = _flatten(item.get('capabilities'))
capability_tokens.extend(_flatten(item.get('modalities')))
capability_tokens.extend(_flatten(item.get('input_modalities')))
capability_tokens.extend(_flatten(item.get('output_modalities')))
capability_tokens.extend(_flatten(item.get('supported_generation_methods')))
capability_tokens.extend(_flatten(item.get('supported_parameters')))
capability_tokens.extend(_flatten(item.get('architecture')))
combined_tokens = capability_tokens + [normalized_model_id]
vision_keywords = (
'vision',
'image',
'file',
'video',
'multimodal',
'vl',
'ocr',
'omni',
)
function_call_keywords = (
'function',
'tool',
'tools',
'tool_choice',
'tool_call',
'tool-use',
'tool_use',
)
if any(any(keyword in token for keyword in vision_keywords) for token in combined_tokens):
abilities.add('vision')
if any(any(keyword in token for keyword in function_call_keywords) for token in combined_tokens):
abilities.add('func_call')
return sorted(abilities)
def _normalize_modalities(self, value: typing.Any) -> list[str]:
normalized: list[str] = []
def _collect(item: typing.Any):
if item is None:
return
if isinstance(item, str):
for part in item.replace('->', ',').replace('+', ',').split(','):
token = part.strip().lower()
if token and token not in normalized:
normalized.append(token)
return
if isinstance(item, dict):
for nested in item.values():
_collect(nested)
return
if isinstance(item, (list, tuple, set)):
for nested in item:
_collect(nested)
return
_collect(value)
return normalized
def _extract_scan_metadata(self, item: dict[str, typing.Any], model_id: str) -> dict[str, typing.Any]:
display_name = item.get('name')
if not isinstance(display_name, str) or not display_name.strip() or display_name == model_id:
display_name = ''
description = item.get('description')
if not isinstance(description, str) or not description.strip():
description = ''
context_length = item.get('context_length')
if context_length is None and isinstance(item.get('top_provider'), dict):
context_length = item['top_provider'].get('context_length')
if not isinstance(context_length, int):
try:
context_length = int(context_length) if context_length is not None else None
except (TypeError, ValueError):
context_length = None
input_modalities = self._normalize_modalities(item.get('input_modalities'))
output_modalities = self._normalize_modalities(item.get('output_modalities'))
if isinstance(item.get('architecture'), dict):
if not input_modalities:
input_modalities = self._normalize_modalities(item['architecture'].get('input_modalities'))
if not output_modalities:
output_modalities = self._normalize_modalities(item['architecture'].get('output_modalities'))
owned_by = item.get('owned_by')
if not isinstance(owned_by, str) or not owned_by.strip():
owned_by = ''
return {
'display_name': display_name or None,
'description': description or None,
'context_length': context_length,
'owned_by': owned_by or None,
'input_modalities': input_modalities,
'output_modalities': output_modalities,
}
async def scan_models(self, api_key: str | None = None) -> dict[str, typing.Any]:
headers = {}
if api_key:
headers['Authorization'] = f'Bearer {api_key}'
models_url = f'{self.requester_cfg["base_url"].rstrip("/")}/models'
async with httpx.AsyncClient(trust_env=True, timeout=self.requester_cfg['timeout']) as client:
response = await client.get(models_url, headers=headers)
response.raise_for_status()
payload = response.json()
models = []
for item in payload.get('data', []):
model_id = item.get('id')
if not model_id:
continue
models.append(
{
'id': model_id,
'name': model_id,
'type': self._infer_model_type(model_id),
'abilities': self._infer_model_abilities(item, model_id),
**self._extract_scan_metadata(item, model_id),
}
)
models.sort(key=lambda item: (item['type'] != 'llm', item['name'].lower()))
return {
'models': models,
'debug': {
'request': {
'method': 'GET',
'url': models_url,
'headers': {
'Authorization': f'Bearer {self._mask_api_key(api_key)}' if api_key else '',
},
},
'response': payload,
},
}
async def _req(
self,
args: dict,
@@ -615,88 +429,3 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
raise errors.RequesterError(f'请求过于频繁或余额不足: {e.message}')
except openai.APIError as e:
raise errors.RequesterError(f'请求错误: {e.message}')
async def invoke_rerank(
self,
model: requester.RuntimeRerankModel,
query: str,
documents: typing.List[str],
extra_args: dict[str, typing.Any] = {},
) -> typing.List[dict]:
"""Standard /rerank endpoint (Jina/Cohere/SiliconFlow/Voyage/DashScope compatible)
Supports extra_args from model.extra_args:
- rerank_url: full URL override (e.g. "https://dashscope.aliyuncs.com/compatible-api/v1/reranks")
- rerank_path: path override appended to base_url (e.g. "reranks" instead of default "rerank")
- Any other fields are merged into the request payload.
"""
api_key = model.provider.token_mgr.get_token()
base_url = self.requester_cfg.get('base_url', '').rstrip('/')
timeout = self.requester_cfg.get('timeout', 120)
merged_args = {}
if model.model_entity.extra_args:
merged_args.update(model.model_entity.extra_args)
if extra_args:
merged_args.update(extra_args)
rerank_url = merged_args.pop('rerank_url', None)
rerank_path = merged_args.pop('rerank_path', 'rerank')
if not rerank_url:
rerank_url = f'{base_url}/{rerank_path}'
headers = {
'Content-Type': 'application/json',
'Authorization': f'Bearer {api_key}',
}
payload = {
'model': model.model_entity.name,
'query': query,
'documents': documents[:64],
'top_n': min(len(documents), 64),
}
if merged_args:
payload.update(merged_args)
try:
async with httpx.AsyncClient(trust_env=True, timeout=timeout) as client:
resp = await client.post(rerank_url, headers=headers, json=payload)
resp.raise_for_status()
data = resp.json()
results = self._parse_rerank_response(data)
if results:
scores = [r.get('relevance_score', 0.0) for r in results]
min_score = min(scores)
max_score = max(scores)
if max_score - min_score > 1e-6:
for r in results:
r['relevance_score'] = (r['relevance_score'] - min_score) / (max_score - min_score)
return results
except httpx.HTTPStatusError as e:
raise errors.RequesterError(f'Rerank request failed: {e.response.status_code} - {e.response.text}')
except httpx.TimeoutException:
raise errors.RequesterError('Rerank request timed out')
except Exception as e:
raise errors.RequesterError(f'Rerank request error: {str(e)}')
@staticmethod
def _parse_rerank_response(data: dict) -> typing.List[dict]:
"""Parse rerank response from various providers.
Handles:
- Jina/Cohere/SiliconFlow: {"results": [{"index", "relevance_score"}]}
- Voyage AI: {"data": [{"index", "relevance_score"}]}
- DashScope: {"output": {"results": [{"index", "relevance_score"}]}}
"""
if 'results' in data:
return data['results']
if 'data' in data:
return data['data']
if 'output' in data and isinstance(data['output'], dict):
return data['output'].get('results', [])
return []

View File

@@ -25,7 +25,6 @@ spec:
support_type:
- llm
- text-embedding
- rerank
provider_category: manufacturer
execution:
python:

View File

@@ -1,8 +0,0 @@
<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 128 128" id="Chroma--Streamline-Svg-Logos" height="128" width="128">
<desc>
Chroma Streamline Icon: https://streamlinehq.com
</desc>
<path fill="#ffde2d" d="M84.88839999999999 104.10666666666665c23.0732 0 41.77773333333333 -17.956266666666664 41.77773333333333 -40.10653333333333 0 -22.150266666666667 -18.70453333333333 -40.10653333333333 -41.77773333333333 -40.10653333333333 -23.0732 0 -41.77773333333333 17.956266666666664 -41.77773333333333 40.10653333333333 0 22.150266666666667 18.70453333333333 40.10653333333333 41.77773333333333 40.10653333333333Z" stroke-width="1.3333"></path>
<path fill="#327eff" d="M43.111066666666666 104.10666666666665c23.0732 0 41.77773333333333 -17.956266666666664 41.77773333333333 -40.10653333333333 0 -22.150266666666667 -18.70453333333333 -40.10653333333333 -41.77773333333333 -40.10653333333333C20.037866666666666 23.8936 1.3333333333333333 41.849866666666664 1.3333333333333333 64.00013333333334 1.3333333333333333 86.15039999999999 20.037866666666666 104.10666666666665 43.111066666666666 104.10666666666665Z" stroke-width="1.3333"></path>
<path fill="#ff6446" d="M84.88866666666667 64.00013333333334c0 22.150399999999998 -18.704666666666665 40.10626666666666 -41.778 40.10626666666666V64.00013333333334h41.778Zm-41.778 0c0 -22.150266666666667 18.70453333333333 -40.10653333333333 41.778 -40.10653333333333v40.10653333333333H43.11066666666666Z" stroke-width="1.3333"></path>
</svg>

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@@ -1,61 +0,0 @@
from __future__ import annotations
import typing
from .. import requester
REQUESTER_NAME: str = 'chroma-embedding'
class ChromaEmbedding(requester.ProviderAPIRequester):
"""Chroma built-in embedding requester.
Uses chromadb's DefaultEmbeddingFunction (all-MiniLM-L6-v2).
The embedding function runs locally using ONNX Runtime.
"""
default_config: dict[str, typing.Any] = {
'base_url': '',
}
_embedding_function = None
async def initialize(self):
try:
from chromadb.utils import embedding_functions
except ImportError:
raise ImportError('chromadb is not installed. Install it with: pip install chromadb')
self._embedding_function = embedding_functions.DefaultEmbeddingFunction()
async def invoke_llm(
self,
query,
model: requester.RuntimeLLMModel,
messages: typing.List,
funcs: typing.List = None,
extra_args: dict[str, typing.Any] = {},
remove_think: bool = False,
):
raise NotImplementedError('Chroma embedding does not support LLM inference')
async def invoke_embedding(
self,
model: requester.RuntimeEmbeddingModel,
input_text: typing.List[str],
extra_args: dict[str, typing.Any] = {},
) -> typing.List[typing.List[float]]:
"""Generate embeddings using Chroma's DefaultEmbeddingFunction."""
if self._embedding_function is None:
await self.initialize()
try:
result = self._embedding_function(input_text)
# DefaultEmbeddingFunction returns list of ndarray, convert for JSON
if isinstance(result, list):
return [item.tolist() if hasattr(item, 'tolist') else item for item in result]
return result.tolist() if hasattr(result, 'tolist') else result
except Exception as e:
from .. import errors
raise errors.RequesterError(f'Chroma embedding failed: {str(e)}')

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@@ -1,21 +0,0 @@
apiVersion: v1
kind: LLMAPIRequester
metadata:
name: chroma-embedding
label:
en_US: Chroma Embedding
zh_Hans: Chroma 嵌入
description:
en_US: Chroma built-in embedding model (all-MiniLM-L6-v2), runs locally using ONNX Runtime. First-time use will download model files automatically.
zh_Hans: 使用 Chroma 内置嵌入模型 (all-MiniLM-L6-v2),基于 ONNX Runtime 本地运行。首次使用时将自动下载模型文件。
ja_JP: Chroma 組み込み埋め込みモデル (all-MiniLM-L6-v2) を使用します。ONNX Runtime でローカル実行。初回使用時にモデルファイルが自動ダウンロードされます。
icon: chroma.svg
spec:
config: []
support_type:
- text-embedding
provider_category: builtin
execution:
python:
path: ./chromaembed.py
attr: ChromaEmbedding

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@@ -1 +0,0 @@
<svg height="1em" style="flex:none;line-height:1" viewBox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><title>Cohere</title><path clip-rule="evenodd" d="M8.128 14.099c.592 0 1.77-.033 3.398-.703 1.897-.781 5.672-2.2 8.395-3.656 1.905-1.018 2.74-2.366 2.74-4.18A4.56 4.56 0 0018.1 1H7.549A6.55 6.55 0 001 7.55c0 3.617 2.745 6.549 7.128 6.549z" fill="#39594D" fill-rule="evenodd"></path><path clip-rule="evenodd" d="M9.912 18.61a4.387 4.387 0 012.705-4.052l3.323-1.38c3.361-1.394 7.06 1.076 7.06 4.715a5.104 5.104 0 01-5.105 5.104l-3.597-.001a4.386 4.386 0 01-4.386-4.387z" fill="#D18EE2" fill-rule="evenodd"></path><path d="M4.776 14.962A3.775 3.775 0 001 18.738v.489a3.776 3.776 0 007.551 0v-.49a3.775 3.775 0 00-3.775-3.775z" fill="#FF7759"></path></svg>

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@@ -1,31 +0,0 @@
apiVersion: v1
kind: LLMAPIRequester
metadata:
name: cohere-rerank
label:
en_US: Cohere
zh_Hans: Cohere
icon: cohere.svg
spec:
config:
- name: base_url
label:
en_US: Base URL
zh_Hans: 基础 URL
type: string
required: true
default: https://api.cohere.com/v2
- name: timeout
label:
en_US: Timeout
zh_Hans: 超时时间
type: integer
required: true
default: 120
support_type:
- rerank
provider_category: manufacturer
execution:
python:
path: ./chatcmpl.py
attr: OpenAIChatCompletions

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@@ -1,7 +1,6 @@
from __future__ import annotations
import typing
import httpx
from . import chatcmpl
@@ -21,68 +20,6 @@ class GeminiChatCompletions(chatcmpl.OpenAIChatCompletions):
'timeout': 120,
}
async def scan_models(self, api_key: str | None = None) -> dict[str, typing.Any]:
models_url = 'https://generativelanguage.googleapis.com/v1beta/models'
params = {'key': api_key} if api_key else {}
all_models: list[dict[str, typing.Any]] = []
next_page_token = ''
last_payload: dict[str, typing.Any] = {}
async with httpx.AsyncClient(trust_env=True, timeout=self.requester_cfg['timeout']) as client:
while True:
request_params = dict(params)
if next_page_token:
request_params['pageToken'] = next_page_token
response = await client.get(models_url, params=request_params)
response.raise_for_status()
payload = response.json()
last_payload = payload
for item in payload.get('models', []):
model_name = item.get('name', '')
model_id = model_name.replace('models/', '', 1)
if not model_id:
continue
supported_methods = item.get('supportedGenerationMethods', []) or []
if 'embedContent' in supported_methods and 'generateContent' not in supported_methods:
model_type = 'embedding'
else:
model_type = 'llm'
all_models.append(
{
'id': model_id,
'name': model_id,
'type': model_type,
'abilities': self._infer_model_abilities(item, model_id),
'display_name': item.get('displayName') or None,
'description': item.get('description') or None,
'context_length': item.get('inputTokenLimit'),
'input_modalities': self._normalize_modalities(item.get('inputModalities')),
'output_modalities': self._normalize_modalities(item.get('outputModalities')),
}
)
next_page_token = payload.get('nextPageToken', '')
if not next_page_token:
break
all_models.sort(key=lambda item: (item['type'] != 'llm', item['name'].lower()))
return {
'models': all_models,
'debug': {
'request': {
'method': 'GET',
'url': models_url,
'query': {'key': self._mask_api_key(api_key)} if api_key else {},
},
'response': last_payload,
},
}
async def _closure_stream(
self,
query: pipeline_query.Query,

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@@ -25,7 +25,6 @@ spec:
support_type:
- llm
- text-embedding
- rerank
provider_category: maas
execution:
python:

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@@ -1 +0,0 @@
<svg fill="currentColor" fill-rule="evenodd" height="1em" style="flex:none;line-height:1" viewBox="0 0 24 24" width="1em" xmlns="http://www.w3.org/2000/svg"><title>Jina</title><path d="M6.608 21.416a4.608 4.608 0 100-9.217 4.608 4.608 0 000 9.217zM20.894 2.015c.614 0 1.106.492 1.106 1.106v9.002c0 5.13-4.148 9.309-9.217 9.37v-9.355l-.03-9.032c0-.614.491-1.106 1.106-1.106h7.158l-.123.015z"></path></svg>

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@@ -1,31 +0,0 @@
apiVersion: v1
kind: LLMAPIRequester
metadata:
name: jina-rerank
label:
en_US: Jina
zh_Hans: Jina
icon: jina.svg
spec:
config:
- name: base_url
label:
en_US: Base URL
zh_Hans: 基础 URL
type: string
required: true
default: https://api.jina.ai/v1
- name: timeout
label:
en_US: Timeout
zh_Hans: 超时时间
type: integer
required: true
default: 120
support_type:
- rerank
provider_category: manufacturer
execution:
python:
path: ./chatcmpl.py
attr: OpenAIChatCompletions

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