mirror of
https://github.com/langbot-app/LangBot.git
synced 2026-06-02 20:14:36 +00:00
Compare commits
2 Commits
feat/workf
...
docs/multi
| Author | SHA1 | Date | |
|---|---|---|---|
|
2912eec7f5 | ||
|
|
158503880c |
109
.github/workflows/run-tests.yml
vendored
109
.github/workflows/run-tests.yml
vendored
@@ -4,29 +4,25 @@ on:
|
||||
pull_request:
|
||||
types: [opened, ready_for_review, synchronize]
|
||||
paths:
|
||||
- 'src/langbot/**'
|
||||
- 'pkg/**'
|
||||
- 'tests/**'
|
||||
- '.github/workflows/run-tests.yml'
|
||||
- 'pyproject.toml'
|
||||
- 'uv.lock'
|
||||
- 'run_tests.sh'
|
||||
- 'scripts/test-*.sh'
|
||||
push:
|
||||
branches:
|
||||
- master
|
||||
- develop
|
||||
paths:
|
||||
- 'src/langbot/**'
|
||||
- 'pkg/**'
|
||||
- 'tests/**'
|
||||
- '.github/workflows/run-tests.yml'
|
||||
- 'pyproject.toml'
|
||||
- 'uv.lock'
|
||||
- 'run_tests.sh'
|
||||
- 'scripts/test-*.sh'
|
||||
|
||||
jobs:
|
||||
test:
|
||||
name: Unit Tests
|
||||
name: Run Unit Tests
|
||||
runs-on: ubuntu-latest
|
||||
strategy:
|
||||
matrix:
|
||||
@@ -43,13 +39,28 @@ jobs:
|
||||
python-version: ${{ matrix.python-version }}
|
||||
|
||||
- name: Install uv
|
||||
uses: astral-sh/setup-uv@v4
|
||||
run: |
|
||||
curl -LsSf https://astral.sh/uv/install.sh | sh
|
||||
echo "$HOME/.cargo/bin" >> $GITHUB_PATH
|
||||
|
||||
- name: Install dependencies
|
||||
run: uv sync --dev
|
||||
run: |
|
||||
uv sync --dev
|
||||
|
||||
- name: Run unit + smoke tests
|
||||
run: uv run pytest tests/unit_tests/ tests/smoke/ -q --tb=short
|
||||
- name: Run unit tests
|
||||
run: |
|
||||
bash run_tests.sh
|
||||
|
||||
- name: Upload coverage to Codecov
|
||||
if: matrix.python-version == '3.12'
|
||||
uses: codecov/codecov-action@v5
|
||||
with:
|
||||
files: ./coverage.xml
|
||||
flags: unit-tests
|
||||
name: unit-tests-coverage
|
||||
fail_ci_if_error: false
|
||||
env:
|
||||
CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
|
||||
|
||||
- name: Test Summary
|
||||
if: always()
|
||||
@@ -58,79 +69,3 @@ jobs:
|
||||
echo "" >> $GITHUB_STEP_SUMMARY
|
||||
echo "Python Version: ${{ matrix.python-version }}" >> $GITHUB_STEP_SUMMARY
|
||||
echo "Test Status: ${{ job.status }}" >> $GITHUB_STEP_SUMMARY
|
||||
|
||||
integration:
|
||||
name: Fast Integration Tests
|
||||
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: Run fast integration tests
|
||||
run: uv run pytest tests/integration/ -m "not slow" -q --tb=short
|
||||
|
||||
- name: Integration Test Summary
|
||||
if: always()
|
||||
run: |
|
||||
echo "## Integration Tests Results" >> $GITHUB_STEP_SUMMARY
|
||||
echo "" >> $GITHUB_STEP_SUMMARY
|
||||
echo "Test Status: ${{ job.status }}" >> $GITHUB_STEP_SUMMARY
|
||||
|
||||
coverage:
|
||||
name: Coverage Gate
|
||||
runs-on: ubuntu-latest
|
||||
needs: [test, integration]
|
||||
|
||||
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: Run coverage (unit + smoke)
|
||||
run: |
|
||||
uv run pytest tests/unit_tests/ tests/smoke/ \
|
||||
--cov=langbot \
|
||||
--cov-report=xml \
|
||||
--cov-report=term-missing \
|
||||
--cov-fail-under=18 \
|
||||
-q --tb=short
|
||||
|
||||
- name: Upload coverage to Codecov
|
||||
uses: codecov/codecov-action@v5
|
||||
with:
|
||||
files: ./coverage.xml
|
||||
flags: unit-tests
|
||||
name: coverage-report
|
||||
fail_ci_if_error: false
|
||||
env:
|
||||
CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
|
||||
|
||||
- name: Coverage Summary
|
||||
if: always()
|
||||
run: |
|
||||
echo "## Coverage Results" >> $GITHUB_STEP_SUMMARY
|
||||
echo "" >> $GITHUB_STEP_SUMMARY
|
||||
echo "Threshold: 18%" >> $GITHUB_STEP_SUMMARY
|
||||
echo "Status: ${{ job.status }}" >> $GITHUB_STEP_SUMMARY
|
||||
109
.github/workflows/test-migrations.yml
vendored
109
.github/workflows/test-migrations.yml
vendored
@@ -9,13 +9,11 @@ on:
|
||||
paths:
|
||||
- 'src/langbot/pkg/persistence/**'
|
||||
- 'src/langbot/pkg/entity/persistence/**'
|
||||
- 'tests/integration/persistence/**'
|
||||
pull_request:
|
||||
types: [opened, synchronize, reopened, ready_for_review]
|
||||
paths:
|
||||
- 'src/langbot/pkg/persistence/**'
|
||||
- 'src/langbot/pkg/entity/persistence/**'
|
||||
- 'tests/integration/persistence/**'
|
||||
|
||||
jobs:
|
||||
test-migrations-sqlite:
|
||||
@@ -36,8 +34,52 @@ jobs:
|
||||
- name: Install dependencies
|
||||
run: uv sync --dev
|
||||
|
||||
- name: Run SQLite migration tests
|
||||
run: uv run pytest tests/integration/persistence/test_migrations.py -q --tb=short
|
||||
- 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)
|
||||
@@ -72,7 +114,58 @@ jobs:
|
||||
- name: Install dependencies
|
||||
run: uv sync --dev
|
||||
|
||||
- name: Run PostgreSQL migration tests
|
||||
env:
|
||||
TEST_POSTGRES_URL: postgresql+asyncpg://langbot:langbot@localhost:5432/langbot_test
|
||||
run: uv run pytest tests/integration/persistence/test_migrations_postgres.py -q --tb=short
|
||||
- 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())
|
||||
"
|
||||
|
||||
36
Makefile
36
Makefile
@@ -1,36 +0,0 @@
|
||||
# LangBot Makefile
|
||||
# Quick developer commands
|
||||
|
||||
.PHONY: test test-quick test-integration-fast test-coverage test-all-local lint
|
||||
|
||||
# Run all tests (full suite with coverage)
|
||||
test:
|
||||
bash run_tests.sh
|
||||
|
||||
# Quick self-test for developers (lint + unit + smoke, no real credentials needed)
|
||||
test-quick:
|
||||
bash scripts/test-quick.sh
|
||||
|
||||
# Fast integration tests (SQLite/API/Pipeline, no external services)
|
||||
test-integration-fast:
|
||||
bash scripts/test-integration-fast.sh
|
||||
|
||||
# Coverage gate (all tests, enforces minimum threshold)
|
||||
test-coverage:
|
||||
bash scripts/test-coverage.sh
|
||||
|
||||
# Full local quality gate (quick + integration + coverage)
|
||||
test-all-local:
|
||||
bash scripts/test-quick.sh
|
||||
bash scripts/test-integration-fast.sh
|
||||
bash scripts/test-coverage.sh
|
||||
|
||||
# Run linting only
|
||||
lint:
|
||||
ruff check src/langbot/ tests/
|
||||
ruff format --check src/langbot/ tests/
|
||||
|
||||
# Fix linting issues
|
||||
lint-fix:
|
||||
ruff check --fix src/langbot/ tests/
|
||||
ruff format src/langbot/ tests/
|
||||
@@ -47,8 +47,6 @@ LangBot is an **open-source, production-grade platform** for building AI-powered
|
||||
|
||||
[→ Learn more about all features](https://link.langbot.app/en/docs/features)
|
||||
|
||||
📍 Practical guides: [deploy a multi-platform AI bot in 5 minutes](https://blog.langbot.app/en/blog/deploy-ai-bot-in-5-minutes/), [connect DeepSeek to WeChat, Discord, and Telegram](https://blog.langbot.app/en/blog/connect-deepseek-to-wechat/), [run a Dify Agent in Discord, Telegram, and Slack](https://blog.langbot.app/en/blog/dify-agent-discord-telegram-slack/), and [build an n8n-powered chatbot](https://blog.langbot.app/en/blog/n8n-multi-platform-ai-chatbot/).
|
||||
|
||||
---
|
||||
|
||||
## Quick Start
|
||||
|
||||
@@ -47,8 +47,6 @@ LangBot 是一个**开源的生产级平台**，用于构建 AI 驱动的即时
|
||||
|
||||
[→ 了解更多功能特性](https://link.langbot.app/zh/docs/features)
|
||||
|
||||
📍 实践指南：[5 分钟部署多平台 AI 机器人](https://blog.langbot.app/zh/blog/deploy-ai-bot-in-5-minutes/)、[将 DeepSeek 接入微信、企业微信与 Discord](https://blog.langbot.app/zh/blog/connect-deepseek-to-wechat/)、[让 Dify Agent 跑在 Discord、Telegram 和 Slack 上](https://blog.langbot.app/zh/blog/dify-agent-discord-telegram-slack/)，以及[用 n8n 构建多平台 AI 聊天机器人](https://blog.langbot.app/zh/blog/n8n-multi-platform-ai-chatbot/)。
|
||||
|
||||
---
|
||||
|
||||
## 快速开始
|
||||
|
||||
@@ -46,8 +46,6 @@ LangBot es una **plataforma de código abierto y grado de producción** para con
|
||||
|
||||
[→ Conocer más sobre todas las funcionalidades](https://link.langbot.app/en/docs/features)
|
||||
|
||||
📍 Guías prácticas: [desplegar un bot de IA multiplataforma en 5 minutos](https://blog.langbot.app/en/blog/deploy-ai-bot-in-5-minutes/), [conectar DeepSeek a WeChat, Discord y Telegram](https://blog.langbot.app/en/blog/connect-deepseek-to-wechat/), [ejecutar un Dify Agent en Discord, Telegram y Slack](https://blog.langbot.app/en/blog/dify-agent-discord-telegram-slack/) y [crear un chatbot con n8n](https://blog.langbot.app/en/blog/n8n-multi-platform-ai-chatbot/).
|
||||
|
||||
---
|
||||
|
||||
## Inicio Rápido
|
||||
|
||||
@@ -46,8 +46,6 @@ LangBot est une **plateforme open-source de niveau production** pour créer des
|
||||
|
||||
[→ En savoir plus sur toutes les fonctionnalités](https://link.langbot.app/en/docs/features)
|
||||
|
||||
📍 Guides pratiques : [déployer un bot IA multiplateforme en 5 minutes](https://blog.langbot.app/en/blog/deploy-ai-bot-in-5-minutes/), [connecter DeepSeek à WeChat, Discord et Telegram](https://blog.langbot.app/en/blog/connect-deepseek-to-wechat/), [exécuter un Dify Agent dans Discord, Telegram et Slack](https://blog.langbot.app/en/blog/dify-agent-discord-telegram-slack/) et [créer un chatbot avec n8n](https://blog.langbot.app/en/blog/n8n-multi-platform-ai-chatbot/).
|
||||
|
||||
---
|
||||
|
||||
## Démarrage Rapide
|
||||
|
||||
@@ -46,8 +46,6 @@ LangBot は、AI搭載のインスタントメッセージングボットを構
|
||||
|
||||
[→ すべての機能について詳しく見る](https://link.langbot.app/ja/docs/features)
|
||||
|
||||
📍 実践ガイド: [5分でマルチプラットフォームAIボットをデプロイ](https://blog.langbot.app/en/blog/deploy-ai-bot-in-5-minutes/)、[DeepSeekをWeChat・Discord・Telegramに接続](https://blog.langbot.app/en/blog/connect-deepseek-to-wechat/)、[Dify AgentをDiscord・Telegram・Slackで動かす](https://blog.langbot.app/en/blog/dify-agent-discord-telegram-slack/)、[n8n連携チャットボットを構築](https://blog.langbot.app/en/blog/n8n-multi-platform-ai-chatbot/)。
|
||||
|
||||
---
|
||||
|
||||
## クイックスタート
|
||||
|
||||
@@ -46,8 +46,6 @@ LangBot은 AI 기반 인스턴트 메시징 봇을 구축하기 위한 **오픈
|
||||
|
||||
[→ 모든 기능 자세히 보기](https://link.langbot.app/en/docs/features)
|
||||
|
||||
📍 실전 가이드: [5분 만에 멀티 플랫폼 AI 봇 배포하기](https://blog.langbot.app/en/blog/deploy-ai-bot-in-5-minutes/), [DeepSeek를 WeChat, Discord, Telegram에 연결하기](https://blog.langbot.app/en/blog/connect-deepseek-to-wechat/), [Dify Agent를 Discord, Telegram, Slack에서 실행하기](https://blog.langbot.app/en/blog/dify-agent-discord-telegram-slack/), [n8n 기반 챗봇 만들기](https://blog.langbot.app/en/blog/n8n-multi-platform-ai-chatbot/).
|
||||
|
||||
---
|
||||
|
||||
## 빠른 시작
|
||||
|
||||
@@ -46,8 +46,6 @@ LangBot — это **платформа с открытым исходным к
|
||||
|
||||
[→ Подробнее обо всех возможностях](https://link.langbot.app/en/docs/features)
|
||||
|
||||
📍 Практические руководства: [развернуть мультиплатформенного ИИ-бота за 5 минут](https://blog.langbot.app/en/blog/deploy-ai-bot-in-5-minutes/), [подключить DeepSeek к WeChat, Discord и Telegram](https://blog.langbot.app/en/blog/connect-deepseek-to-wechat/), [запустить Dify Agent в Discord, Telegram и Slack](https://blog.langbot.app/en/blog/dify-agent-discord-telegram-slack/) и [создать чат-бота на n8n](https://blog.langbot.app/en/blog/n8n-multi-platform-ai-chatbot/).
|
||||
|
||||
---
|
||||
|
||||
## Быстрый старт
|
||||
|
||||
@@ -48,8 +48,6 @@ LangBot 是一個**開源的生產級平台**，用於建構 AI 驅動的即時
|
||||
|
||||
[→ 了解更多功能特性](https://link.langbot.app/zh/docs/features)
|
||||
|
||||
📍 實踐指南：[5 分鐘部署多平台 AI 機器人](https://blog.langbot.app/zh/blog/deploy-ai-bot-in-5-minutes/)、[將 DeepSeek 接入微信、企業微信與 Discord](https://blog.langbot.app/zh/blog/connect-deepseek-to-wechat/)、[讓 Dify Agent 跑在 Discord、Telegram 和 Slack 上](https://blog.langbot.app/zh/blog/dify-agent-discord-telegram-slack/)，以及[用 n8n 建構多平台 AI 聊天機器人](https://blog.langbot.app/zh/blog/n8n-multi-platform-ai-chatbot/)。
|
||||
|
||||
---
|
||||
|
||||
## 快速開始
|
||||
|
||||
@@ -46,8 +46,6 @@ LangBot là một **nền tảng mã nguồn mở, cấp sản xuất** để x
|
||||
|
||||
[→ Tìm hiểu thêm về tất cả tính năng](https://link.langbot.app/en/docs/features)
|
||||
|
||||
📍 Hướng dẫn thực hành: [triển khai bot AI đa nền tảng trong 5 phút](https://blog.langbot.app/en/blog/deploy-ai-bot-in-5-minutes/), [kết nối DeepSeek với WeChat, Discord và Telegram](https://blog.langbot.app/en/blog/connect-deepseek-to-wechat/), [chạy Dify Agent trên Discord, Telegram và Slack](https://blog.langbot.app/en/blog/dify-agent-discord-telegram-slack/) và [xây dựng chatbot với n8n](https://blog.langbot.app/en/blog/n8n-multi-platform-ai-chatbot/).
|
||||
|
||||
---
|
||||
|
||||
## Bắt đầu nhanh
|
||||
|
||||
163
compare_nodes.py
163
compare_nodes.py
@@ -1,163 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Compare YAML node definitions with frontend node-configs."""
|
||||
|
||||
import yaml
|
||||
import os
|
||||
import re
|
||||
import json
|
||||
|
||||
# 1. Parse YAML files
|
||||
yaml_dir = 'src/langbot/templates/metadata/nodes'
|
||||
yaml_nodes = {}
|
||||
|
||||
for filename in sorted(os.listdir(yaml_dir)):
|
||||
if filename.endswith('.yaml'):
|
||||
filepath = os.path.join(yaml_dir, filename)
|
||||
with open(filepath, 'r') as f:
|
||||
data = yaml.safe_load(f)
|
||||
node_name = data.get('name', filename.replace('.yaml', ''))
|
||||
yaml_nodes[node_name] = {
|
||||
'category': data.get('category', ''),
|
||||
'inputs': [i['name'] for i in data.get('inputs', [])],
|
||||
'outputs': [o['name'] for o in data.get('outputs', [])],
|
||||
'config': [c['name'] for c in data.get('config', [])]
|
||||
}
|
||||
|
||||
# 2. Parse frontend node-configs TypeScript files
|
||||
node_configs_dir = 'web/src/app/home/workflows/components/workflow-editor/node-configs'
|
||||
|
||||
frontend_nodes = {}
|
||||
|
||||
def parse_ts_file(filepath):
|
||||
"""Parse a TypeScript file to extract node configurations."""
|
||||
with open(filepath, 'r') as f:
|
||||
content = f.read()
|
||||
|
||||
# Find all node type definitions
|
||||
# Pattern: nodeType: 'xxx'
|
||||
node_type_pattern = r"nodeType:\s*'([^']+)'"
|
||||
node_types = re.findall(node_type_pattern, content)
|
||||
|
||||
# For each node type, extract inputs, outputs, and config
|
||||
for node_type in node_types:
|
||||
# Find the config object for this node type
|
||||
# Look for the section between this nodeType and the next one or end of object
|
||||
pattern = rf"nodeType:\s*'({re.escape(node_type)})'.*?(?=nodeType:|export\s+(const|function)|$)"
|
||||
match = re.search(pattern, content, re.DOTALL)
|
||||
|
||||
if match:
|
||||
section = match.group(0)
|
||||
|
||||
# Extract inputs
|
||||
inputs = re.findall(r"createInput\('([^']+)'", section)
|
||||
|
||||
# Extract outputs
|
||||
outputs = re.findall(r"createOutput\('([^']+)'", section)
|
||||
|
||||
# Extract config names
|
||||
config_names = re.findall(r"name:\s*'([^']+)'", section)
|
||||
# Remove duplicates while preserving order
|
||||
seen = set()
|
||||
unique_config = []
|
||||
for c in config_names:
|
||||
if c not in seen:
|
||||
seen.add(c)
|
||||
unique_config.append(c)
|
||||
|
||||
frontend_nodes[node_type] = {
|
||||
'inputs': inputs,
|
||||
'outputs': outputs,
|
||||
'config': unique_config
|
||||
}
|
||||
|
||||
# Parse all config files
|
||||
for filename in os.listdir(node_configs_dir):
|
||||
if filename.endswith('.ts') and filename != 'types.ts' and filename != 'index.ts':
|
||||
filepath = os.path.join(node_configs_dir, filename)
|
||||
parse_ts_file(filepath)
|
||||
|
||||
# 3. Compare and report differences
|
||||
print("=" * 80)
|
||||
print("WORKFLOW NODE COMPARISON REPORT: YAML vs Frontend")
|
||||
print("=" * 80)
|
||||
|
||||
all_node_types = sorted(set(list(yaml_nodes.keys()) + list(frontend_nodes.keys())))
|
||||
|
||||
discrepancies = []
|
||||
|
||||
for node_type in all_node_types:
|
||||
yaml_def = yaml_nodes.get(node_type)
|
||||
frontend_def = frontend_nodes.get(node_type)
|
||||
|
||||
node_discrepancies = []
|
||||
|
||||
if not yaml_def:
|
||||
print(f"\n⚠️ {node_type}: ONLY in frontend (not in YAML)")
|
||||
continue
|
||||
if not frontend_def:
|
||||
print(f"\n⚠️ {node_type}: ONLY in YAML (not in frontend)")
|
||||
continue
|
||||
|
||||
# Compare inputs
|
||||
yaml_inputs = set(yaml_def['inputs'])
|
||||
frontend_inputs = set(frontend_def['inputs'])
|
||||
if yaml_inputs != frontend_inputs:
|
||||
only_yaml = yaml_inputs - frontend_inputs
|
||||
only_frontend = frontend_inputs - yaml_inputs
|
||||
node_discrepancies.append({
|
||||
'type': 'inputs',
|
||||
'only_yaml': list(only_yaml),
|
||||
'only_frontend': list(only_frontend)
|
||||
})
|
||||
|
||||
# Compare outputs
|
||||
yaml_outputs = set(yaml_def['outputs'])
|
||||
frontend_outputs = set(frontend_def['outputs'])
|
||||
if yaml_outputs != frontend_outputs:
|
||||
only_yaml = yaml_outputs - frontend_outputs
|
||||
only_frontend = frontend_outputs - yaml_outputs
|
||||
node_discrepancies.append({
|
||||
'type': 'outputs',
|
||||
'only_yaml': list(only_yaml),
|
||||
'only_frontend': list(only_frontend)
|
||||
})
|
||||
|
||||
# Compare config
|
||||
yaml_config = set(yaml_def['config'])
|
||||
frontend_config = set(frontend_def['config'])
|
||||
if yaml_config != frontend_config:
|
||||
only_yaml = yaml_config - frontend_config
|
||||
only_frontend = frontend_config - yaml_config
|
||||
node_discrepancies.append({
|
||||
'type': 'config',
|
||||
'only_yaml': list(only_yaml),
|
||||
'only_frontend': list(only_frontend)
|
||||
})
|
||||
|
||||
if node_discrepancies:
|
||||
print(f"\n❌ {node_type} ({yaml_def['category']}): HAS DISCREPANCIES")
|
||||
for d in node_discrepancies:
|
||||
print(f" {d['type']}:")
|
||||
if d['only_yaml']:
|
||||
print(f" Only in YAML: {d['only_yaml']}")
|
||||
if d['only_frontend']:
|
||||
print(f" Only in Frontend: {d['only_frontend']}")
|
||||
discrepancies.append((node_type, node_discrepancies))
|
||||
else:
|
||||
print(f"\n✅ {node_type} ({yaml_def['category']}): OK")
|
||||
|
||||
print(f"\n{'=' * 80}")
|
||||
print(f"SUMMARY: {len(discrepancies)} nodes with discrepancies out of {len(all_node_types)} total")
|
||||
print(f"{'=' * 80}")
|
||||
|
||||
# Output as JSON for further processing
|
||||
output = {
|
||||
'yaml_nodes': {k: v for k, v in yaml_nodes.items()},
|
||||
'frontend_nodes': {k: v for k, v in frontend_nodes.items()},
|
||||
'discrepancies': {k: v for k, v in discrepancies}
|
||||
}
|
||||
|
||||
with open('node_comparison.json', 'w') as f:
|
||||
json.dump(output, f, indent=2)
|
||||
|
||||
print(f"\nDetailed comparison saved to node_comparison.json")
|
||||
@@ -1,713 +0,0 @@
|
||||
# Workflow 系统开发者文档
|
||||
|
||||
本文档面向 LangBot 开发者，详细介绍 Workflow 系统的技术架构、核心组件和扩展方法。
|
||||
|
||||
## 目录
|
||||
|
||||
- [系统架构概述](#系统架构概述)
|
||||
- [目录结构](#目录结构)
|
||||
- [核心组件](#核心组件)
|
||||
- [后端模块](#后端模块)
|
||||
- [前端组件](#前端组件)
|
||||
- [数据库表结构](#数据库表结构)
|
||||
- [API 接口文档](#api-接口文档)
|
||||
- [如何添加新节点类型](#如何添加新节点类型)
|
||||
- [调试功能实现](#调试功能实现)
|
||||
|
||||
---
|
||||
|
||||
## 系统架构概述
|
||||
|
||||
Workflow 系统采用前后端分离架构，主要包含以下层次：
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────────┐
|
||||
│ 前端层 (React) │
|
||||
│ ┌─────────────┬──────────────┬──────────────┬───────────┐ │
|
||||
│ │ 可视化编辑器 │ 节点面板 │ 属性面板 │ 调试器 │ │
|
||||
│ │ ReactFlow │ NodePalette │ PropertyPanel│ Debugger │ │
|
||||
│ └─────────────┴──────────────┴──────────────┴───────────┘ │
|
||||
├─────────────────────────────────────────────────────────────┤
|
||||
│ API 层 (Quart) │
|
||||
│ ┌─────────────┬──────────────┬──────────────────────────┐ │
|
||||
│ │ Workflow API│ Debug API │ Node Types API │ │
|
||||
│ └─────────────┴──────────────┴──────────────────────────┘ │
|
||||
├─────────────────────────────────────────────────────────────┤
|
||||
│ 核心引擎层 (Python) │
|
||||
│ ┌─────────────┬──────────────┬──────────────┬───────────┐ │
|
||||
│ │ Executor │ Registry │ Node │ Entities │ │
|
||||
│ │ 执行引擎 │ 节点注册表 │ 节点基类 │ 数据结构 │ │
|
||||
│ └─────────────┴──────────────┴──────────────┴───────────┘ │
|
||||
├─────────────────────────────────────────────────────────────┤
|
||||
│ 存储层 (SQLAlchemy) │
|
||||
│ ┌─────────────┬──────────────┬──────────────────────────┐ │
|
||||
│ │ Workflow │ Executions │ Triggers │ │
|
||||
│ └─────────────┴──────────────┴──────────────────────────┘ │
|
||||
└─────────────────────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 目录结构
|
||||
|
||||
### 后端代码结构
|
||||
|
||||
```
|
||||
LangBot/src/langbot/pkg/
|
||||
├── workflow/ # Workflow 核心模块
|
||||
│ ├── __init__.py # 模块初始化，导出公共接口
|
||||
│ ├── entities.py # 数据实体定义
|
||||
│ ├── executor.py # 执行引擎
|
||||
│ ├── node.py # 节点基类和装饰器
|
||||
│ ├── registry.py # 节点类型注册表
|
||||
│ └── nodes/ # 内置节点实现
|
||||
│ ├── __init__.py # 注册所有内置节点
|
||||
│ ├── trigger.py # 触发节点
|
||||
│ ├── process.py # 处理节点
|
||||
│ ├── control.py # 控制节点
|
||||
│ └── action.py # 动作节点
|
||||
├── entity/persistence/
|
||||
│ └── workflow.py # 数据库模型
|
||||
├── api/http/
|
||||
│ ├── controller/groups/workflows/
|
||||
│ │ └── workflows.py # API 路由控制器
|
||||
│ └── service/
|
||||
│ └── workflow.py # 业务逻辑服务
|
||||
└── persistence/migrations/
|
||||
└── dbm026_workflow_tables.py # 数据库迁移
|
||||
```
|
||||
|
||||
### 前端代码结构
|
||||
|
||||
```
|
||||
LangBot/web/src/app/home/workflows/
|
||||
├── page.tsx # Workflow 列表页
|
||||
├── WorkflowDetailContent.tsx # 详情页内容
|
||||
├── store/
|
||||
│ └── useWorkflowStore.ts # Zustand 状态管理
|
||||
└── components/
|
||||
├── workflow-editor/ # 可视化编辑器
|
||||
│ ├── index.ts # 导出
|
||||
│ ├── WorkflowEditorComponent.tsx # 主编辑器组件
|
||||
│ ├── WorkflowNodeComponent.tsx # 自定义节点组件
|
||||
│ ├── NodePalette.tsx # 节点面板
|
||||
│ ├── PropertyPanel.tsx # 属性面板
|
||||
│ └── node-configs/ # 节点配置元数据
|
||||
│ ├── types.ts # 配置类型定义
|
||||
│ ├── trigger-configs.ts
|
||||
│ ├── ai-configs.ts
|
||||
│ ├── process-configs.ts
|
||||
│ ├── control-configs.ts
|
||||
│ ├── action-configs.ts
|
||||
│ ├── integration-configs.ts
|
||||
│ └── index.ts # 配置汇总
|
||||
├── workflow-debugger/ # 调试器组件
|
||||
│ ├── index.ts
|
||||
│ └── WorkflowDebugger.tsx
|
||||
├── workflow-form/ # 表单组件
|
||||
│ └── WorkflowFormComponent.tsx
|
||||
└── workflow-executions/ # 执行历史组件
|
||||
└── WorkflowExecutionsTab.tsx
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 核心组件
|
||||
|
||||
### 后端模块
|
||||
|
||||
#### 1. 执行引擎 (WorkflowExecutor)
|
||||
|
||||
位置：[`executor.py`](../../src/langbot/pkg/workflow/executor.py)
|
||||
|
||||
执行引擎负责工作流的实际执行，包括：
|
||||
|
||||
- **拓扑排序**：确定节点执行顺序
|
||||
- **节点执行**：调用各节点的 execute 方法
|
||||
- **控制流处理**：处理条件分支、循环、并行执行
|
||||
- **错误处理**：支持重试机制
|
||||
|
||||
```python
|
||||
class WorkflowExecutor:
|
||||
async def execute(
|
||||
self,
|
||||
workflow: WorkflowDefinition,
|
||||
context: ExecutionContext,
|
||||
start_node_id: Optional[str] = None
|
||||
) -> ExecutionContext:
|
||||
"""执行工作流"""
|
||||
# 1. 构建执行图
|
||||
# 2. 初始化节点状态
|
||||
# 3. 找到起始节点
|
||||
# 4. 按拓扑顺序执行
|
||||
```
|
||||
|
||||
**调试执行器 (DebugWorkflowExecutor)**
|
||||
|
||||
继承自 WorkflowExecutor，增加了调试支持：
|
||||
|
||||
- 断点支持
|
||||
- 单步执行
|
||||
- 暂停/继续
|
||||
- 实时日志
|
||||
|
||||
```python
|
||||
class DebugWorkflowExecutor(WorkflowExecutor):
|
||||
async def execute_debug(
|
||||
self,
|
||||
workflow: WorkflowDefinition,
|
||||
context: ExecutionContext,
|
||||
debug_state: DebugExecutionState,
|
||||
) -> ExecutionContext:
|
||||
"""调试模式执行"""
|
||||
```
|
||||
|
||||
#### 2. 节点注册表 (NodeTypeRegistry)
|
||||
|
||||
位置：[`registry.py`](../../src/langbot/pkg/workflow/registry.py)
|
||||
|
||||
单例模式管理所有节点类型：
|
||||
|
||||
```python
|
||||
class NodeTypeRegistry:
|
||||
_instance: Optional['NodeTypeRegistry'] = None
|
||||
|
||||
def register(self, node_type: str, node_class: type[WorkflowNode]):
|
||||
"""注册节点类型"""
|
||||
|
||||
def create_instance(self, node_type: str, node_id: str, config: dict) -> WorkflowNode:
|
||||
"""创建节点实例"""
|
||||
|
||||
def list_all(self) -> list[dict]:
|
||||
"""获取所有节点类型的 Schema"""
|
||||
```
|
||||
|
||||
#### 3. 节点基类 (WorkflowNode)
|
||||
|
||||
位置：[`node.py`](../../src/langbot/pkg/workflow/node.py)
|
||||
|
||||
所有节点必须继承此基类：
|
||||
|
||||
```python
|
||||
class WorkflowNode(abc.ABC):
|
||||
# 节点元数据
|
||||
type_name: str = ""
|
||||
name: str = ""
|
||||
description: str = ""
|
||||
category: str = "misc"
|
||||
icon: str = ""
|
||||
|
||||
# 端口定义
|
||||
inputs: list[NodePort] = []
|
||||
outputs: list[NodePort] = []
|
||||
|
||||
# 配置 Schema
|
||||
config_schema: list[NodeConfig] = []
|
||||
|
||||
@abc.abstractmethod
|
||||
async def execute(
|
||||
self,
|
||||
inputs: dict[str, Any],
|
||||
context: ExecutionContext
|
||||
) -> dict[str, Any]:
|
||||
"""执行节点逻辑"""
|
||||
pass
|
||||
```
|
||||
|
||||
#### 4. 数据实体 (entities.py)
|
||||
|
||||
主要数据结构：
|
||||
|
||||
```python
|
||||
class WorkflowDefinition:
|
||||
"""工作流定义"""
|
||||
uuid: str
|
||||
name: str
|
||||
nodes: list[NodeDefinition]
|
||||
edges: list[EdgeDefinition]
|
||||
settings: WorkflowSettings
|
||||
|
||||
class ExecutionContext:
|
||||
"""执行上下文"""
|
||||
execution_id: str
|
||||
workflow_id: str
|
||||
status: ExecutionStatus
|
||||
variables: dict
|
||||
node_states: dict[str, NodeState]
|
||||
history: list[ExecutionStep]
|
||||
```
|
||||
|
||||
### 前端组件
|
||||
|
||||
#### 1. WorkflowEditorComponent
|
||||
|
||||
主编辑器组件，基于 React Flow 实现：
|
||||
|
||||
- **画布交互**：拖拽、缩放、平移
|
||||
- **节点连接**：自动验证端口类型
|
||||
- **撤销/重做**：基于历史记录栈
|
||||
- **复制/粘贴**：支持多选复制
|
||||
|
||||
关键功能：
|
||||
|
||||
```tsx
|
||||
function WorkflowEditorInner() {
|
||||
const { nodes, edges, onNodesChange, onEdgesChange, onConnect } = useWorkflowStore();
|
||||
|
||||
// 拖放添加节点
|
||||
const onDrop = useCallback((event: React.DragEvent) => {
|
||||
const type = event.dataTransfer.getData('application/reactflow');
|
||||
const position = screenToFlowPosition({ x: event.clientX, y: event.clientY });
|
||||
addNode(type, position);
|
||||
}, []);
|
||||
|
||||
// 复制粘贴
|
||||
const handleCopy = useCallback(() => { ... }, []);
|
||||
const handlePaste = useCallback(() => { ... }, []);
|
||||
}
|
||||
```
|
||||
|
||||
#### 2. NodePalette
|
||||
|
||||
节点面板组件，展示可用节点类型：
|
||||
|
||||
```tsx
|
||||
function NodePalette() {
|
||||
// 按类别组织节点
|
||||
const categories = [
|
||||
{ id: 'trigger', name: '触发节点', icon: Zap },
|
||||
{ id: 'ai', name: 'AI 节点', icon: Brain },
|
||||
{ id: 'process', name: '处理节点', icon: Cpu },
|
||||
{ id: 'control', name: '控制节点', icon: GitBranch },
|
||||
{ id: 'action', name: '动作节点', icon: Send },
|
||||
{ id: 'integration', name: '集成节点', icon: Plug },
|
||||
];
|
||||
|
||||
// 拖拽开始
|
||||
const onDragStart = (event: React.DragEvent, nodeType: string) => {
|
||||
event.dataTransfer.setData('application/reactflow', nodeType);
|
||||
};
|
||||
}
|
||||
```
|
||||
|
||||
#### 3. PropertyPanel
|
||||
|
||||
属性面板组件，动态渲染节点配置表单：
|
||||
|
||||
```tsx
|
||||
function PropertyPanel() {
|
||||
const { selectedNodeId, nodes, updateNodeData } = useWorkflowStore();
|
||||
|
||||
// 根据节点类型获取配置元数据
|
||||
const selectedNode = nodes.find(n => n.id === selectedNodeId);
|
||||
const nodeConfig = getNodeConfig(selectedNode?.data?.nodeType);
|
||||
|
||||
// 动态渲染配置字段
|
||||
return (
|
||||
<div>
|
||||
{nodeConfig?.fields.map(field => (
|
||||
<ConfigField key={field.name} field={field} />
|
||||
))}
|
||||
</div>
|
||||
);
|
||||
}
|
||||
```
|
||||
|
||||
#### 4. WorkflowDebugger
|
||||
|
||||
调试器组件，支持实时调试：
|
||||
|
||||
```tsx
|
||||
function WorkflowDebugger({ workflowUuid, workflow }) {
|
||||
const [debugState, setDebugState] = useState<DebugState>('idle');
|
||||
const [executionId, setExecutionId] = useState<string>('');
|
||||
const [logs, setLogs] = useState<ExecutionLog[]>([]);
|
||||
|
||||
// 启动调试
|
||||
const startDebug = async () => {
|
||||
const result = await backendClient.post(
|
||||
`/api/v1/workflows/${workflowUuid}/debug/start`,
|
||||
{ context, variables, breakpoints }
|
||||
);
|
||||
setExecutionId(result.execution_id);
|
||||
};
|
||||
|
||||
// 轮询状态
|
||||
useEffect(() => {
|
||||
if (debugState === 'running') {
|
||||
const interval = setInterval(fetchState, 500);
|
||||
return () => clearInterval(interval);
|
||||
}
|
||||
}, [debugState]);
|
||||
}
|
||||
```
|
||||
|
||||
#### 5. useWorkflowStore
|
||||
|
||||
Zustand 状态管理：
|
||||
|
||||
```typescript
|
||||
interface WorkflowState {
|
||||
nodes: WorkflowNode[];
|
||||
edges: WorkflowEdge[];
|
||||
selectedNodeId: string | null;
|
||||
history: HistoryEntry[];
|
||||
historyIndex: number;
|
||||
isDirty: boolean;
|
||||
|
||||
// Actions
|
||||
addNode: (type: string, position: XYPosition) => void;
|
||||
updateNodeData: (nodeId: string, data: Partial<NodeData>) => void;
|
||||
deleteNode: (nodeId: string) => void;
|
||||
undo: () => void;
|
||||
redo: () => void;
|
||||
}
|
||||
|
||||
export const useWorkflowStore = create<WorkflowState>((set, get) => ({
|
||||
// ... state and actions
|
||||
}));
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 数据库表结构
|
||||
|
||||
### workflows 表
|
||||
|
||||
```sql
|
||||
CREATE TABLE workflows (
|
||||
uuid VARCHAR(255) PRIMARY KEY,
|
||||
name VARCHAR(255) NOT NULL,
|
||||
description TEXT,
|
||||
emoji VARCHAR(10) DEFAULT '🔄',
|
||||
version INTEGER DEFAULT 1,
|
||||
is_enabled BOOLEAN DEFAULT TRUE,
|
||||
definition JSON NOT NULL, -- 节点和边定义
|
||||
global_config JSON DEFAULT '{}', -- 全局配置
|
||||
extensions_preferences JSON, -- 插件和 MCP 配置
|
||||
created_at TIMESTAMP,
|
||||
updated_at TIMESTAMP
|
||||
);
|
||||
```
|
||||
|
||||
### workflow_versions 表
|
||||
|
||||
```sql
|
||||
CREATE TABLE workflow_versions (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
workflow_uuid VARCHAR(255) NOT NULL,
|
||||
version INTEGER NOT NULL,
|
||||
definition JSON NOT NULL,
|
||||
global_config JSON DEFAULT '{}',
|
||||
created_at TIMESTAMP,
|
||||
created_by VARCHAR(255),
|
||||
UNIQUE(workflow_uuid, version)
|
||||
);
|
||||
```
|
||||
|
||||
### workflow_executions 表
|
||||
|
||||
```sql
|
||||
CREATE TABLE workflow_executions (
|
||||
uuid VARCHAR(255) PRIMARY KEY,
|
||||
workflow_uuid VARCHAR(255) NOT NULL,
|
||||
workflow_version INTEGER NOT NULL,
|
||||
status VARCHAR(20) NOT NULL, -- pending/running/completed/failed/cancelled
|
||||
trigger_type VARCHAR(50),
|
||||
trigger_data JSON,
|
||||
variables JSON,
|
||||
start_time TIMESTAMP,
|
||||
end_time TIMESTAMP,
|
||||
error TEXT,
|
||||
created_at TIMESTAMP
|
||||
);
|
||||
```
|
||||
|
||||
### workflow_node_executions 表
|
||||
|
||||
```sql
|
||||
CREATE TABLE workflow_node_executions (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
execution_uuid VARCHAR(255) NOT NULL,
|
||||
node_id VARCHAR(100) NOT NULL,
|
||||
node_type VARCHAR(50) NOT NULL,
|
||||
status VARCHAR(20) NOT NULL,
|
||||
inputs JSON,
|
||||
outputs JSON,
|
||||
start_time TIMESTAMP,
|
||||
end_time TIMESTAMP,
|
||||
error TEXT,
|
||||
retry_count INTEGER DEFAULT 0
|
||||
);
|
||||
```
|
||||
|
||||
### workflow_triggers 表
|
||||
|
||||
```sql
|
||||
CREATE TABLE workflow_triggers (
|
||||
uuid VARCHAR(255) PRIMARY KEY,
|
||||
workflow_uuid VARCHAR(255) NOT NULL,
|
||||
type VARCHAR(50) NOT NULL, -- message/cron/event/webhook
|
||||
config JSON NOT NULL,
|
||||
is_enabled BOOLEAN DEFAULT TRUE,
|
||||
priority INTEGER DEFAULT 0,
|
||||
created_at TIMESTAMP,
|
||||
updated_at TIMESTAMP
|
||||
);
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## API 接口文档
|
||||
|
||||
### Workflow CRUD
|
||||
|
||||
| 方法 | 路径 | 描述 |
|
||||
|-----|------|------|
|
||||
| GET | `/api/v1/workflows` | 获取工作流列表 |
|
||||
| POST | `/api/v1/workflows` | 创建工作流 |
|
||||
| GET | `/api/v1/workflows/:uuid` | 获取单个工作流 |
|
||||
| PUT | `/api/v1/workflows/:uuid` | 更新工作流 |
|
||||
| DELETE | `/api/v1/workflows/:uuid` | 删除工作流 |
|
||||
| POST | `/api/v1/workflows/:uuid/copy` | 复制工作流 |
|
||||
|
||||
### 执行相关
|
||||
|
||||
| 方法 | 路径 | 描述 |
|
||||
|-----|------|------|
|
||||
| POST | `/api/v1/workflows/:uuid/execute` | 手动执行工作流 |
|
||||
| GET | `/api/v1/workflows/:uuid/executions` | 获取执行记录 |
|
||||
|
||||
### 版本管理
|
||||
|
||||
| 方法 | 路径 | 描述 |
|
||||
|-----|------|------|
|
||||
| GET | `/api/v1/workflows/:uuid/versions` | 获取版本列表 |
|
||||
| POST | `/api/v1/workflows/:uuid/rollback/:version` | 回滚到指定版本 |
|
||||
|
||||
### 调试 API
|
||||
|
||||
| 方法 | 路径 | 描述 |
|
||||
|-----|------|------|
|
||||
| POST | `/api/v1/workflows/:uuid/debug/start` | 启动调试 |
|
||||
| POST | `/api/v1/workflows/:uuid/debug/:exec_id/pause` | 暂停执行 |
|
||||
| POST | `/api/v1/workflows/:uuid/debug/:exec_id/resume` | 继续执行 |
|
||||
| POST | `/api/v1/workflows/:uuid/debug/:exec_id/stop` | 停止执行 |
|
||||
| POST | `/api/v1/workflows/:uuid/debug/:exec_id/step` | 单步执行 |
|
||||
| GET | `/api/v1/workflows/:uuid/debug/:exec_id/state` | 获取调试状态 |
|
||||
|
||||
### 节点类型
|
||||
|
||||
| 方法 | 路径 | 描述 |
|
||||
|-----|------|------|
|
||||
| GET | `/api/v1/workflows/_/node-types` | 获取所有节点类型 |
|
||||
| GET | `/api/v1/workflows/_/node-types/categories` | 按类别获取节点类型 |
|
||||
|
||||
---
|
||||
|
||||
## 如何添加新节点类型
|
||||
|
||||
### 步骤 1：创建节点类
|
||||
|
||||
在 `LangBot/src/langbot/pkg/workflow/nodes/` 下创建或修改文件：
|
||||
|
||||
```python
|
||||
from ..node import WorkflowNode, NodePort, NodeConfig, workflow_node
|
||||
from ..entities import ExecutionContext
|
||||
|
||||
@workflow_node('my_custom_node')
|
||||
class MyCustomNode(WorkflowNode):
|
||||
"""自定义节点"""
|
||||
|
||||
# 元数据
|
||||
type_name = 'my_custom_node'
|
||||
name = '我的自定义节点'
|
||||
description = '这是一个自定义节点'
|
||||
category = 'process' # trigger/process/control/action/integration
|
||||
icon = '🔧'
|
||||
|
||||
# 输入端口
|
||||
inputs = [
|
||||
NodePort(name='input', type='string', description='输入数据', required=True),
|
||||
]
|
||||
|
||||
# 输出端口
|
||||
outputs = [
|
||||
NodePort(name='output', type='string', description='输出数据'),
|
||||
]
|
||||
|
||||
# 配置字段
|
||||
config_schema = [
|
||||
NodeConfig(
|
||||
name='option',
|
||||
type='select',
|
||||
required=True,
|
||||
options=['选项A', '选项B'],
|
||||
description='选择一个选项'
|
||||
),
|
||||
NodeConfig(
|
||||
name='value',
|
||||
type='string',
|
||||
required=False,
|
||||
default='默认值',
|
||||
description='配置值'
|
||||
),
|
||||
]
|
||||
|
||||
async def execute(
|
||||
self,
|
||||
inputs: dict[str, Any],
|
||||
context: ExecutionContext
|
||||
) -> dict[str, Any]:
|
||||
"""执行节点逻辑"""
|
||||
input_data = inputs.get('input', '')
|
||||
option = self.get_config('option')
|
||||
value = self.get_config('value', '')
|
||||
|
||||
# 处理逻辑
|
||||
result = f"处理: {input_data} with {option} and {value}"
|
||||
|
||||
return {'output': result}
|
||||
```
|
||||
|
||||
### 步骤 2：注册节点
|
||||
|
||||
在 `LangBot/src/langbot/pkg/workflow/nodes/__init__.py` 中导入：
|
||||
|
||||
```python
|
||||
from .process import (
|
||||
CodeExecutorNode,
|
||||
HttpRequestNode,
|
||||
DataTransformNode,
|
||||
MyCustomNode, # 添加新节点
|
||||
)
|
||||
```
|
||||
|
||||
### 步骤 3：添加前端配置
|
||||
|
||||
在 `LangBot/web/src/app/home/workflows/components/workflow-editor/node-configs/` 目录下添加配置：
|
||||
|
||||
```typescript
|
||||
// process-configs.ts
|
||||
export const processNodeConfigs: NodeConfigMap = {
|
||||
// ... 其他配置
|
||||
|
||||
my_custom_node: {
|
||||
type: 'my_custom_node',
|
||||
label: 'workflows.nodes.myCustomNode',
|
||||
description: 'workflows.nodes.myCustomNodeDesc',
|
||||
icon: 'Wrench',
|
||||
category: 'process',
|
||||
fields: [
|
||||
{
|
||||
name: 'option',
|
||||
type: 'select',
|
||||
label: 'workflows.fields.option',
|
||||
required: true,
|
||||
options: [
|
||||
{ value: '选项A', label: '选项 A' },
|
||||
{ value: '选项B', label: '选项 B' },
|
||||
],
|
||||
},
|
||||
{
|
||||
name: 'value',
|
||||
type: 'string',
|
||||
label: 'workflows.fields.value',
|
||||
required: false,
|
||||
defaultValue: '默认值',
|
||||
},
|
||||
],
|
||||
},
|
||||
};
|
||||
```
|
||||
|
||||
### 步骤 4：添加国际化
|
||||
|
||||
在 `LangBot/web/src/i18n/locales/` 中添加翻译：
|
||||
|
||||
```typescript
|
||||
// zh-Hans.ts
|
||||
workflows: {
|
||||
nodes: {
|
||||
myCustomNode: '我的自定义节点',
|
||||
myCustomNodeDesc: '这是一个自定义节点',
|
||||
},
|
||||
fields: {
|
||||
option: '选项',
|
||||
value: '值',
|
||||
},
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 调试功能实现
|
||||
|
||||
### 后端调试状态管理
|
||||
|
||||
```python
|
||||
class DebugExecutionState:
|
||||
"""调试执行状态"""
|
||||
|
||||
def __init__(self, execution_id: str, breakpoints: list[str] = None):
|
||||
self.execution_id = execution_id
|
||||
self.status: str = 'running'
|
||||
self.is_paused: bool = False
|
||||
self.is_stopped: bool = False
|
||||
self.breakpoints: set[str] = set(breakpoints or [])
|
||||
self.logs: list[ExecutionLog] = []
|
||||
self._pause_event = asyncio.Event()
|
||||
|
||||
def pause(self):
|
||||
"""暂停执行"""
|
||||
self.is_paused = True
|
||||
self._pause_event.clear()
|
||||
|
||||
def resume(self):
|
||||
"""继续执行"""
|
||||
self.is_paused = False
|
||||
self._pause_event.set()
|
||||
|
||||
async def wait_if_paused(self):
|
||||
"""如果暂停则等待"""
|
||||
if self.is_paused:
|
||||
await self._pause_event.wait()
|
||||
```
|
||||
|
||||
### 前端调试流程
|
||||
|
||||
1. **设置断点**：点击节点设置断点
|
||||
2. **启动调试**：调用 `/debug/start` 启动调试执行
|
||||
3. **轮询状态**：定期调用 `/debug/:id/state` 获取状态
|
||||
4. **控制执行**：调用 pause/resume/step/stop 控制执行
|
||||
5. **查看日志**：实时显示执行日志和节点状态
|
||||
|
||||
```typescript
|
||||
// 调试状态轮询
|
||||
const fetchDebugState = async () => {
|
||||
const state = await backendClient.get(
|
||||
`/api/v1/workflows/${workflowUuid}/debug/${executionId}/state`
|
||||
);
|
||||
|
||||
// 更新节点状态
|
||||
setNodeStates(state.node_states);
|
||||
|
||||
// 追加新日志
|
||||
if (state.new_logs.length > 0) {
|
||||
setLogs(prev => [...prev, ...state.new_logs]);
|
||||
}
|
||||
|
||||
// 检查完成状态
|
||||
if (state.status === 'completed' || state.status === 'error') {
|
||||
setDebugState('idle');
|
||||
}
|
||||
};
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 扩展阅读
|
||||
|
||||
- [Workflow 功能设计文档](../../../plans/langbot-workflow-design.md)
|
||||
- [用户使用指南](../user-guide/workflow-guide.md)
|
||||
- [API 认证文档](../API_KEY_AUTH.md)
|
||||
858
docs/multi-tenant/workspace-multi-user-architecture.md
Normal file
858
docs/multi-tenant/workspace-multi-user-architecture.md
Normal file
@@ -0,0 +1,858 @@
|
||||
# LangBot 多租户与多用户改造方案
|
||||
|
||||
## 目标
|
||||
|
||||
本方案面向 LangBot 从“单实例单管理员”演进到 SaaS 友好的“多 workspace、多账户、多权限”架构。
|
||||
|
||||
核心定义：
|
||||
|
||||
- Account：登录主体。一个自然人或服务账号，可加入多个 workspace。
|
||||
- Workspace：租户边界。一个 workspace 内可拥有多个用户、机器人、流水线、模型、知识库、扩展、监控数据与 API Key。
|
||||
- Membership：账户与 workspace 的关系，承载角色与权限。
|
||||
- Role/Permission：workspace 内权限，不再用“是否是当前唯一用户”来决定访问能力。
|
||||
|
||||
目标体验：
|
||||
|
||||
- 新用户登录后可以创建 workspace、加入 workspace、切换 workspace。
|
||||
- 同一个账户可加入多个 workspace，每个 workspace 权限不同。
|
||||
- 一个 workspace 可邀请多个用户协作，并分别设置 owner/admin/editor/viewer 等权限。
|
||||
- 所有业务资源默认属于某个 workspace，所有 API 默认在当前 workspace 下工作。
|
||||
- Plugin SDK、MCP、知识库、模型调用、监控日志都能拿到稳定的 workspace 上下文，并且不跨租户泄露数据。
|
||||
|
||||
## 调研结论
|
||||
|
||||
### 当前 LangBot 的单用户假设
|
||||
|
||||
LangBot 现在已经有 `users` 表和 JWT 登录，但仍是单用户/单租户模型：
|
||||
|
||||
- `src/langbot/pkg/entity/persistence/user.py` 的 `User` 只保存 `user/password/account_type/space_*`，没有角色、状态、workspace 关系。
|
||||
- `src/langbot/pkg/api/http/service/user.py` 通过 `is_initialized()` 判断系统是否已有用户；`create_or_update_space_user()` 在系统已初始化且邮箱不匹配时拒绝新用户，这直接限制了多用户登录。
|
||||
- `src/langbot/pkg/api/http/controller/group.py` 的 `AuthType.USER_TOKEN` 验证后只向 handler 注入 `user_email`；JWT payload 也只有 `user`，没有 `account_id`、`workspace_id`、`role`、`permissions`。
|
||||
- `src/langbot/pkg/api/http/service/apikey.py` 的 API Key 只验证 key 是否存在，没有 owner、scope、workspace。
|
||||
- `web/src/app/infra/http/BaseHttpClient.ts` 从 `localStorage.token` 读取单个 token，并在所有请求上加 `Authorization`；前端没有 workspace selector，也没有当前 workspace 上下文。
|
||||
|
||||
当前登录流程更像“初始化一个本地管理账号”，而不是 SaaS 账户体系。要支持多用户，必须把“初始化状态”和“首个 workspace 创建”拆开。
|
||||
|
||||
### 业务资源当前都是全局资源
|
||||
|
||||
主要持久化表没有租户字段：
|
||||
|
||||
- Bot：`bots`
|
||||
- Pipeline：`legacy_pipelines`、`pipeline_run_records`
|
||||
- Model：`model_providers`、`llm_models`、`embedding_models`、`rerank_models`
|
||||
- Plugin：`plugin_settings`
|
||||
- MCP：`mcp_servers`
|
||||
- RAG：`knowledge_bases`、`knowledge_base_files`、`knowledge_base_chunks`
|
||||
- Monitoring：`monitoring_messages`、`monitoring_llm_calls`、`monitoring_sessions`、`monitoring_errors`、`monitoring_embedding_calls`、`monitoring_feedback`
|
||||
- API Key：`api_keys`
|
||||
- Webhook：`webhooks`
|
||||
- Metadata：`metadata`
|
||||
- Binary storage：`binary_storages`
|
||||
|
||||
对应服务也直接 select 全表，例如：
|
||||
|
||||
- `BotService.get_bots()` 返回所有 bot。
|
||||
- `PipelineService.get_pipelines()` 返回所有 pipeline。
|
||||
- `ModelProviderService.get_providers()` 返回所有 provider。
|
||||
- `MCPService.get_mcp_servers()` 返回所有 MCP server。
|
||||
- 插件和二进制存储没有 workspace 维度，插件 workspace storage 在 SDK 里还硬编码为 `default`。
|
||||
|
||||
所以改造重点不是只给用户表加字段，而是给资源访问层统一加入 workspace scope。
|
||||
|
||||
### 运行时也存在全局单例假设
|
||||
|
||||
`src/langbot/pkg/core/stages/build_app.py` 初始化的是一个全局 `Application`，其中包含单例：
|
||||
|
||||
- `platform_mgr`
|
||||
- `pipeline_mgr`
|
||||
- `model_mgr`
|
||||
- `tool_mgr`
|
||||
- `plugin_connector`
|
||||
- `sess_mgr`
|
||||
- `rag_mgr`
|
||||
- `vector_db_mgr`
|
||||
|
||||
当前运行时把所有 bot、pipeline、model、plugin、MCP 都加载到同一套内存管理器。多租户改造需要决定：是共享运行时并在对象上带 workspace 过滤，还是每个 workspace 拆 runtime shard。第一阶段建议共享进程、强制 workspace-aware；等规模变大后再演进为按 workspace 分片。
|
||||
|
||||
### Plugin SDK 对 workspace 的假设
|
||||
|
||||
SDK 当前只认识 bot/pipeline/query/session，不认识租户：
|
||||
|
||||
- `src/langbot_plugin/api/entities/builtin/pipeline/query.py` 的 `Query` 有 `query_id/launcher_type/launcher_id/sender_id/bot_uuid/pipeline_uuid`，没有 `workspace_id/account_id`。
|
||||
- `src/langbot_plugin/api/entities/builtin/provider/session.py` 的 `Session` 只按 `launcher_type + launcher_id` 表达会话。
|
||||
- `src/langbot_plugin/api/proxies/langbot_api.py` 暴露 `get_bots/get_llm_models/invoke_llm/list_tools/vector_*` 等 Host API，都是全局语义。
|
||||
- `src/langbot_plugin/runtime/io/handlers/plugin.py` 的 `set_workspace_storage/get_workspace_storage` 把 `owner_type` 设为 `workspace`，但 `owner` 固定为 `default`。
|
||||
- LangBot 侧 `src/langbot/pkg/plugin/handler.py` 处理插件请求时，会把 `GET_BOTS`、`GET_LLM_MODELS`、`VECTOR_*` 等转到全局服务。
|
||||
|
||||
这意味着多租户落地时，不能只在 Web API 层过滤；插件可以通过 Host API 访问全局资源，所以 SDK/Runtime 通信也必须传递 workspace context。
|
||||
|
||||
## 开源版与商业版产品边界
|
||||
|
||||
LangBot 是开源软件，但多 workspace 能力本质上是 SaaS 控制面能力。如果把完整多 workspace、成员协作、订阅权益和配额代码都放进开源仓库，只靠本地 feature flag 或本地 license check，无法有效避免第三方 fork 后自建 SaaS。因此建议采用 open-core 架构：开源版保留单 workspace 执行能力，账户、订阅、权益和多 workspace 协作能力放到 LangBot Space/Cloud Control Plane 和商业模块中。
|
||||
|
||||
### 版本边界
|
||||
|
||||
推荐拆成三层：
|
||||
|
||||
- `LangBot Core OSS`：开源，自托管，默认只有一个隐式 `default workspace`。它可以在数据结构上兼容 workspace，但产品能力上不提供创建多个 workspace、切换 workspace、成员邀请、成员权限管理、审计和多租户配额。
|
||||
- `LangBot Space / Cloud Control Plane`：托管控制面，负责 account、workspace、membership、subscription、billing、entitlement、license token、workspace quota、marketplace 权益等能力。
|
||||
- `LangBot Commercial Module`：商业闭源或私有包，承载多 workspace、团队协作、RBAC、自定义角色、审计日志、SAML/SSO、企业私有化授权等能力。
|
||||
|
||||
企业私有化版本可以采用 `LangBot Core + Commercial Module + License Token` 的形式交付。开源 Core 仍然可独立运行，但只能作为单 workspace 自托管产品，不提供 SaaS 多租户控制面。
|
||||
|
||||
### OSS 中如何保留兼容但不开放多 workspace
|
||||
|
||||
为了让后续商业版复用同一套资源隔离模型，OSS 代码里可以保留 `workspace_uuid` 相关字段和默认 workspace 迁移，但应限制为单 workspace：
|
||||
|
||||
- 首次初始化时创建一个 `Default Workspace`。
|
||||
- 所有资源自动归属这个 default workspace。
|
||||
- 不暴露 `POST /api/v1/workspaces`。
|
||||
- 不暴露 workspace switcher。
|
||||
- 不暴露成员邀请和成员角色管理。
|
||||
- 不支持一个 account 加入多个 workspace。
|
||||
- 不支持 workspace 数量大于 1。
|
||||
- 前端不展示 workspace selector。
|
||||
- API 层如果收到非 default workspace 的 `X-Workspace-Id`，直接拒绝。
|
||||
|
||||
也就是说，OSS 可以是 workspace-aware，但不是 multi-workspace-enabled。这样做的价值是：代码结构提前适配租户隔离，未来商业版不用重写所有资源模型；同时开源版用户无法直接通过 UI/API 获得 SaaS 型多租户能力。
|
||||
|
||||
### 账户、订阅和权益抽到 Space
|
||||
|
||||
账户和订阅体系建议从 LangBot Core 中抽出，交给 Space 控制面：
|
||||
|
||||
```text
|
||||
LangBot Space
|
||||
-> Account
|
||||
-> Workspace
|
||||
-> Membership
|
||||
-> Subscription
|
||||
-> Entitlement
|
||||
-> License Token
|
||||
|
||||
LangBot Core
|
||||
-> Validate entitlement / license
|
||||
-> Run bots, pipelines, plugins, MCP, RAG
|
||||
-> Enforce local resource scope
|
||||
-> Report usage
|
||||
```
|
||||
|
||||
这样做有几个原因：
|
||||
|
||||
- 账号体系如果完全在本地，第三方容易直接改库创建 workspace/membership。
|
||||
- 订阅、配额和商业权益如果完全在本地，容易绕过。
|
||||
- Space 可以统一处理 OAuth、组织、邀请、付款、发票、套餐、权益、Marketplace 分发权限。
|
||||
- LangBot Core 只作为执行面消费 Space 下发的 entitlement，减少商业规则暴露。
|
||||
|
||||
### Entitlement 设计
|
||||
|
||||
Space 向 LangBot Core 下发签名权益，可以是在线校验，也可以为企业版提供短期/长期离线 license token。
|
||||
|
||||
示例：
|
||||
|
||||
```json
|
||||
{
|
||||
"edition": "oss",
|
||||
"workspace_limit": 1,
|
||||
"member_limit": 1,
|
||||
"multi_workspace": false,
|
||||
"rbac": false,
|
||||
"audit_log": false,
|
||||
"custom_roles": false,
|
||||
"sso": false,
|
||||
"commercial_use": false,
|
||||
"expires_at": 1893456000
|
||||
}
|
||||
```
|
||||
|
||||
OSS 默认权益：
|
||||
|
||||
- `workspace_limit = 1`
|
||||
- `member_limit = 1`
|
||||
- `multi_workspace = false`
|
||||
- `rbac = false`
|
||||
- `audit_log = false`
|
||||
- `sso = false`
|
||||
|
||||
Cloud/Pro/Enterprise 权益：
|
||||
|
||||
- `workspace_limit > 1`
|
||||
- `member_limit > 1`
|
||||
- `multi_workspace = true`
|
||||
- `rbac = true`
|
||||
- 可按套餐打开 audit、custom roles、SSO、usage reporting、enterprise support 等能力。
|
||||
|
||||
Core 执行面需要在关键入口强制校验 entitlement：
|
||||
|
||||
- 创建 workspace。
|
||||
- 邀请成员。
|
||||
- 修改成员角色。
|
||||
- 切换 workspace。
|
||||
- 创建超过 quota 的资源。
|
||||
- 开启商业模块功能。
|
||||
|
||||
### 商业模块边界
|
||||
|
||||
以下能力不建议进入 OSS 仓库的完整实现：
|
||||
|
||||
- 多 workspace 创建和切换。
|
||||
- Workspace 成员邀请。
|
||||
- Workspace RBAC 和自定义角色。
|
||||
- Workspace 审计日志。
|
||||
- Workspace 级用量和配额管理。
|
||||
- 订阅、账单、发票。
|
||||
- 企业 SSO/SAML/OIDC。
|
||||
- 在线/离线 license 管理。
|
||||
- 多租户 SaaS 运营控制台。
|
||||
|
||||
OSS 仓库可以保留接口占位、默认 workspace 兼容和必要的数据隔离字段，但完整交互、管理 UI、权益校验器和多 workspace policy engine 应由 Space 或商业模块提供。
|
||||
|
||||
### 防自建 SaaS 的现实边界
|
||||
|
||||
技术上无法 100% 阻止别人 fork 开源代码后自行改造。更可靠的策略是组合：
|
||||
|
||||
- 不把完整商业多 workspace 实现放进 OSS。
|
||||
- Space 控制面提供账号、订阅、权益、Marketplace 和官方托管能力。
|
||||
- 商业模块闭源或私有分发。
|
||||
- 使用商标、云服务条款和商业 license 限制“自称官方 LangBot SaaS”或未经授权商用托管。
|
||||
- 如果当前开源 license 对托管商用限制不足，需要单独评估 license 策略，必要时引入 open-core license 或新增商业附加条款。具体 license 调整需要法律评审。
|
||||
|
||||
结论：多 workspace 的底层 schema 可以在 OSS 中以 default workspace 兼容方式铺路，但多 workspace 产品能力、账户订阅权益、协作管理和 SaaS 控制面应放到 Space/商业模块，不作为开源版可直接使用的能力。
|
||||
|
||||
## 推荐总体架构
|
||||
|
||||
采用“单实例多 workspace，资源行级隔离，运行时上下文隔离”的架构：
|
||||
|
||||
```mermaid
|
||||
flowchart TD
|
||||
A["Account"] --> B["WorkspaceMembership"]
|
||||
B --> C["Workspace"]
|
||||
C --> D["Bots"]
|
||||
C --> E["Pipelines"]
|
||||
C --> F["Models & Providers"]
|
||||
C --> G["Knowledge Bases"]
|
||||
C --> H["Extensions: Plugins / MCP"]
|
||||
C --> I["API Keys & Webhooks"]
|
||||
C --> J["Monitoring"]
|
||||
D --> K["Runtime Query"]
|
||||
E --> K
|
||||
K --> L["Plugin Runtime"]
|
||||
K --> M["MCP Runtime"]
|
||||
L --> N["Workspace-scoped Host APIs"]
|
||||
```
|
||||
|
||||
原则：
|
||||
|
||||
- 账户全局唯一，workspace 是所有业务资源的归属边界。
|
||||
- 所有 HTTP handler 在进入业务服务前解析出 `RequestContext(account, workspace, membership, permissions)`。
|
||||
- 所有 service 方法显式接收 `ctx` 或 `workspace_id`，禁止在业务服务里无条件 select 全表。
|
||||
- 运行时对象的 key 从 `uuid` 扩展为 `(workspace_id, uuid)` 或使用全局唯一 uuid 但必须记录 workspace_id 并校验。
|
||||
- 插件/MCP/知识库/模型调用都按 query 所属 workspace 过滤可用资源。
|
||||
|
||||
## 数据模型设计
|
||||
|
||||
### Account
|
||||
|
||||
替代现有 `users` 的语义，建议保留表名但升级字段，避免过大迁移：
|
||||
|
||||
字段建议：
|
||||
|
||||
- `id`
|
||||
- `uuid`
|
||||
- `email`
|
||||
- `password_hash`
|
||||
- `display_name`
|
||||
- `avatar_url`
|
||||
- `account_type`: `local | space`
|
||||
- `status`: `active | disabled | deleted`
|
||||
- `space_account_uuid`
|
||||
- `space_access_token`
|
||||
- `space_refresh_token`
|
||||
- `space_access_token_expires_at`
|
||||
- `space_api_key`
|
||||
- `created_at`
|
||||
- `updated_at`
|
||||
|
||||
兼容策略：
|
||||
|
||||
- 旧字段 `user` 迁移为 `email`，可以短期保留 alias。
|
||||
- 旧 `password` 迁移为 `password_hash`，也可先保持列名不变，service 层改命名。
|
||||
- JWT 中不要继续只放 email，应放 `sub=account_uuid`。
|
||||
|
||||
### Workspace
|
||||
|
||||
新增 `workspaces`：
|
||||
|
||||
- `uuid`
|
||||
- `name`
|
||||
- `slug`
|
||||
- `avatar_url`
|
||||
- `type`: `personal | team`
|
||||
- `status`: `active | suspended | deleted`
|
||||
- `default_language`
|
||||
- `created_by_account_uuid`
|
||||
- `created_at`
|
||||
- `updated_at`
|
||||
|
||||
每个账户首次登录时自动创建一个 personal workspace。旧单用户实例迁移时创建一个 `Default Workspace`。
|
||||
|
||||
### WorkspaceMembership
|
||||
|
||||
新增 `workspace_memberships`：
|
||||
|
||||
- `workspace_uuid`
|
||||
- `account_uuid`
|
||||
- `role`: `owner | admin | developer | operator | viewer`
|
||||
- `status`: `active | invited | disabled`
|
||||
- `invited_by_account_uuid`
|
||||
- `joined_at`
|
||||
- `created_at`
|
||||
- `updated_at`
|
||||
|
||||
唯一索引：
|
||||
|
||||
- `(workspace_uuid, account_uuid)`
|
||||
|
||||
### WorkspaceInvitation
|
||||
|
||||
新增 `workspace_invitations`：
|
||||
|
||||
- `uuid`
|
||||
- `workspace_uuid`
|
||||
- `email`
|
||||
- `role`
|
||||
- `token_hash`
|
||||
- `expires_at`
|
||||
- `accepted_at`
|
||||
- `created_by_account_uuid`
|
||||
- `created_at`
|
||||
|
||||
用于邀请外部用户加入 workspace。Space OAuth 登录时可以根据 email 自动匹配未接受邀请。
|
||||
|
||||
### Role 与 Permission
|
||||
|
||||
先用固定角色，后续再做自定义角色。
|
||||
|
||||
建议权限：
|
||||
|
||||
- `workspace.manage`
|
||||
- `member.view`
|
||||
- `member.invite`
|
||||
- `member.update_role`
|
||||
- `member.remove`
|
||||
- `bot.view`
|
||||
- `bot.manage`
|
||||
- `pipeline.view`
|
||||
- `pipeline.manage`
|
||||
- `model.view`
|
||||
- `model.manage`
|
||||
- `knowledge.view`
|
||||
- `knowledge.manage`
|
||||
- `extension.view`
|
||||
- `extension.manage`
|
||||
- `monitoring.view`
|
||||
- `apikey.manage`
|
||||
- `webhook.manage`
|
||||
- `billing.view`
|
||||
- `billing.manage`
|
||||
|
||||
角色映射：
|
||||
|
||||
| Role | 说明 | 权限 |
|
||||
| --- | --- | --- |
|
||||
| owner | workspace 拥有者 | 全部权限；可转让 owner；不可被其他角色移除 |
|
||||
| admin | 管理员 | 除 owner 转让和删除 workspace 外的全部权限 |
|
||||
| developer | 构建者 | 管理 bot、pipeline、model、knowledge、extension、webhook，可看监控 |
|
||||
| operator | 运营者 | 查看和启停 bot、查看监控、查看配置，不可改密钥和删除资源 |
|
||||
| viewer | 只读成员 | 只读资源和监控 |
|
||||
|
||||
### 业务资源加 workspace_uuid
|
||||
|
||||
以下表需要新增 `workspace_uuid`：
|
||||
|
||||
- `bots`
|
||||
- `legacy_pipelines`
|
||||
- `pipeline_run_records`
|
||||
- `model_providers`
|
||||
- `llm_models`
|
||||
- `embedding_models`
|
||||
- `rerank_models`
|
||||
- `plugin_settings`
|
||||
- `mcp_servers`
|
||||
- `knowledge_bases`
|
||||
- `knowledge_base_files`
|
||||
- `knowledge_base_chunks`
|
||||
- `monitoring_*`
|
||||
- `api_keys`
|
||||
- `webhooks`
|
||||
- `binary_storages`
|
||||
- `metadata`
|
||||
|
||||
索引建议：
|
||||
|
||||
- 所有资源表加 `(workspace_uuid, created_at)` 或 `(workspace_uuid, updated_at)`。
|
||||
- 资源唯一键从单列改为 workspace 复合唯一：
|
||||
- `bots.uuid` 可保持全局唯一，但查询仍必须带 workspace。
|
||||
- `plugin_settings` 主键从 `(plugin_author, plugin_name)` 改为 `(workspace_uuid, plugin_author, plugin_name)`。
|
||||
- `mcp_servers.name` 如果未来要求唯一，必须是 `(workspace_uuid, name)`。
|
||||
- `metadata.key` 改为 `(workspace_uuid, key)`，系统级 metadata 单独放 `system_metadata` 或使用 `workspace_uuid=NULL`。
|
||||
- `binary_storages.unique_key` 建议改为 `workspace_uuid + owner_type + owner + key` 的 hash。
|
||||
|
||||
### API Key
|
||||
|
||||
API Key 必须归属于 workspace：
|
||||
|
||||
- `workspace_uuid`
|
||||
- `created_by_account_uuid`
|
||||
- `scopes`
|
||||
- `expires_at`
|
||||
- `last_used_at`
|
||||
- `status`
|
||||
|
||||
验证 API Key 后生成 `RequestContext`：
|
||||
|
||||
- `account_uuid=None` 或 service account uuid
|
||||
- `workspace_uuid=key.workspace_uuid`
|
||||
- `permissions=key.scopes`
|
||||
|
||||
这样 `/api/v1/platform/bots/<uuid>/send_message` 之类接口不会跨 workspace 操作 bot。
|
||||
|
||||
## 后端改造方案
|
||||
|
||||
### RequestContext
|
||||
|
||||
新增统一上下文对象，例如：
|
||||
|
||||
```python
|
||||
class RequestContext:
|
||||
account_uuid: str | None
|
||||
workspace_uuid: str
|
||||
role: str | None
|
||||
permissions: set[str]
|
||||
auth_type: Literal["user_token", "api_key"]
|
||||
```
|
||||
|
||||
改造 `RouterGroup.route()`：
|
||||
|
||||
- `USER_TOKEN`：验证 JWT，读取 `account_uuid`，再从 header/query/cookie 中解析 current workspace。
|
||||
- `API_KEY`：验证 API Key，直接得到 workspace。
|
||||
- `USER_TOKEN_OR_API_KEY`：两者都返回同一种 `RequestContext`。
|
||||
- handler 参数从可选 `user_email` 升级为可选 `ctx`；兼容期同时支持 `user_email`。
|
||||
|
||||
当前 workspace 传递方式：
|
||||
|
||||
- 推荐 header：`X-Workspace-Id: <workspace_uuid>`
|
||||
- Web 前端同时把当前 workspace 存在 localStorage。
|
||||
- 如果未传，后端用账户最近使用 workspace 或第一个 active membership。
|
||||
|
||||
JWT payload：
|
||||
|
||||
```json
|
||||
{
|
||||
"sub": "account_uuid",
|
||||
"email": "user@example.com",
|
||||
"iss": "LangBot-...",
|
||||
"exp": 1234567890
|
||||
}
|
||||
```
|
||||
|
||||
不要把 workspace 写死在 JWT 里，否则切换 workspace 需要刷新 token。可以额外支持短 TTL workspace token，但第一阶段不必。
|
||||
|
||||
### 服务层改造模式
|
||||
|
||||
所有 service 方法增加 `ctx` 或 `workspace_uuid`：
|
||||
|
||||
```python
|
||||
async def get_bots(self, ctx: RequestContext, include_secret: bool = True):
|
||||
require(ctx, "bot.view")
|
||||
query = sqlalchemy.select(Bot).where(Bot.workspace_uuid == ctx.workspace_uuid)
|
||||
```
|
||||
|
||||
需要改造的服务：
|
||||
|
||||
- `UserService`：拆成 AccountService + WorkspaceService 更清晰。
|
||||
- `ApiKeyService`：按 workspace 管理 key。
|
||||
- `BotService`：所有 bot 查询/创建/更新/删除按 workspace。
|
||||
- `PipelineService`：所有 pipeline 查询/默认 pipeline 按 workspace。
|
||||
- `ModelProviderService` 和 model services：按 workspace 隔离 provider 和 model。
|
||||
- `MCPService`：按 workspace 管理 MCP server，运行时按 workspace host。
|
||||
- `KnowledgeService/RAGRuntimeService`：按 workspace 管理 KB、文件、collection。
|
||||
- `MonitoringService`：记录和查询都带 workspace。
|
||||
- `WebhookService`：按 workspace 管理 webhook。
|
||||
- `PluginRuntimeConnector`：插件安装、设置、配置按 workspace。
|
||||
|
||||
### HTTP API 形态
|
||||
|
||||
保留现有路径，靠 `X-Workspace-Id` 表示当前 workspace，可减少前端和 SDK 破坏：
|
||||
|
||||
- `GET /api/v1/workspaces`
|
||||
- `POST /api/v1/workspaces`
|
||||
- `GET /api/v1/workspaces/current`
|
||||
- `PUT /api/v1/workspaces/current`
|
||||
- `GET /api/v1/workspaces/<workspace_uuid>/members`
|
||||
- `POST /api/v1/workspaces/<workspace_uuid>/invitations`
|
||||
- `PUT /api/v1/workspaces/<workspace_uuid>/members/<account_uuid>`
|
||||
- `DELETE /api/v1/workspaces/<workspace_uuid>/members/<account_uuid>`
|
||||
|
||||
现有资源 API：
|
||||
|
||||
- `/api/v1/platform/bots`
|
||||
- `/api/v1/pipelines`
|
||||
- `/api/v1/provider/*`
|
||||
- `/api/v1/plugins`
|
||||
- `/api/v1/mcp`
|
||||
- `/api/v1/knowledge`
|
||||
|
||||
继续保留，但必须从 `RequestContext.workspace_uuid` 过滤。
|
||||
|
||||
对外 API Key 也使用相同路径，只是由 key 决定 workspace。
|
||||
|
||||
### 初始化流程
|
||||
|
||||
现有 `/api/v1/user/init` 含义改为“创建首个账号和首个 workspace”：
|
||||
|
||||
1. 如果系统没有任何 account：
|
||||
- 创建 account。
|
||||
- 创建 personal/team workspace。
|
||||
- 创建 owner membership。
|
||||
- 创建默认 pipeline。
|
||||
- 标记 wizard status 到该 workspace metadata。
|
||||
2. 如果系统已有 account：
|
||||
- 禁止无邀请注册，除非配置允许公开注册。
|
||||
- Space OAuth 登录后，如果没有 membership，引导创建 workspace 或接受邀请。
|
||||
|
||||
`/api/v1/user/account-info` 不应再只返回 first user，应返回：
|
||||
|
||||
- `initialized`
|
||||
- `registration_mode`
|
||||
- `space_enabled`
|
||||
- `default_login_methods`
|
||||
|
||||
登录成功后前端调用 `/api/v1/workspaces` 选择 workspace。
|
||||
|
||||
### 运行时隔离
|
||||
|
||||
第一阶段采用共享进程 + workspace-aware runtime：
|
||||
|
||||
- `RuntimeBot` 增加 `workspace_uuid`。
|
||||
- `RuntimePipeline` 增加 `workspace_uuid`。
|
||||
- `Query` 增加 `workspace_uuid`，从 bot/pipeline 派生。
|
||||
- `SessionManager.get_session()` key 从 `(launcher_type, launcher_id)` 改为 `(workspace_uuid, bot_uuid, launcher_type, launcher_id)`。
|
||||
- `PipelineManager.pipeline_dict` key 可保持 pipeline uuid，但所有 load/get 都校验 workspace；如果 uuid 不是全局唯一则改为 `(workspace_uuid, pipeline_uuid)`。
|
||||
- `ModelManager` provider/model 加 workspace 过滤；`get_model_by_uuid` 必须确保 query workspace 可访问。
|
||||
- `ToolManager` 中 MCP tools、plugin tools 按 query workspace 过滤。
|
||||
|
||||
后续规模化时可演进：
|
||||
|
||||
- workspace runtime shard：每个 workspace 一套 plugin runtime/MCP runtime。
|
||||
- 大客户独立进程或独立数据库。
|
||||
|
||||
## Plugin SDK 与 Runtime 改造
|
||||
|
||||
### Query/Event 增加 workspace context
|
||||
|
||||
SDK `Query` 增加：
|
||||
|
||||
- `workspace_uuid: str`
|
||||
- `workspace_slug: str | None`
|
||||
- `account_uuid: str | None`，仅 Web/API 触发时可能有，聊天平台消息通常为空。
|
||||
|
||||
Event 模型通过 `event.query.workspace_uuid` 可拿到租户上下文；序列化时也应包含这些字段。
|
||||
|
||||
向后兼容：
|
||||
|
||||
- 字段可选，默认 `None`。
|
||||
- 老插件不感知这些字段也能跑。
|
||||
- 新插件可通过 `ctx.event.query.workspace_uuid` 或新增 `ctx.get_workspace()` 访问。
|
||||
|
||||
### Host API 默认按当前 workspace 限制
|
||||
|
||||
`LangBotAPIProxy` 的以下方法必须由 Host 端按 workspace 过滤：
|
||||
|
||||
- `get_bots`
|
||||
- `get_bot_info`
|
||||
- `send_message`
|
||||
- `get_llm_models`
|
||||
- `invoke_llm`
|
||||
- `list_plugins_manifest`
|
||||
- `list_commands`
|
||||
- `list_tools`
|
||||
- `call_tool`
|
||||
- `invoke_embedding`
|
||||
- `vector_*`
|
||||
- `list_knowledge_bases`
|
||||
- `retrieve_knowledge`
|
||||
|
||||
建议新增显式方法：
|
||||
|
||||
- `get_workspace_info()`
|
||||
- `get_current_account()`
|
||||
- `get_workspace_storage(...)`
|
||||
|
||||
但不要让插件传入任意 workspace id 来越权访问。插件请求的 workspace 应由 Runtime 根据当前 query/plugin connection 填充。
|
||||
|
||||
### Workspace storage 修复
|
||||
|
||||
当前 SDK runtime 中：
|
||||
|
||||
```python
|
||||
data["owner_type"] = "workspace"
|
||||
data["owner"] = "default"
|
||||
```
|
||||
|
||||
必须改为：
|
||||
|
||||
- 如果请求来自 query/event：owner 为 `workspace_uuid`。
|
||||
- 如果请求来自后台插件任务：owner 为 plugin 安装所属 workspace。
|
||||
- Host 侧 `binary_storages` 加 `workspace_uuid`，并在 unique key 中包含 workspace。
|
||||
|
||||
Plugin storage 建议也同时加 workspace：
|
||||
|
||||
- 现在 plugin storage owner 是 `author/name`，这会导致同一插件在不同 workspace 的私有数据冲突。
|
||||
- 应改为 `(workspace_uuid, plugin_id, key)`。
|
||||
|
||||
### 插件安装与配置
|
||||
|
||||
`plugin_settings` 从全局变为 workspace-scoped：
|
||||
|
||||
- 同一个插件可安装到多个 workspace。
|
||||
- 每个 workspace 有自己的 enabled、priority、config、install_source、install_info。
|
||||
- 插件 runtime 列表需要能按 workspace 过滤。
|
||||
|
||||
实现路线有两种：
|
||||
|
||||
1. 共享插件进程，插件代码只加载一份，设置和调用时附带 workspace。
|
||||
2. 每个 workspace 一个插件容器实例，隔离最彻底但资源占用更高。
|
||||
|
||||
推荐第一阶段采用方案 1，但要求：
|
||||
|
||||
- 所有 RuntimeToLangBot/PluginToRuntime action 都能携带 `workspace_uuid`。
|
||||
- 插件 config 获取时按 workspace 返回。
|
||||
- 插件 page API 请求必须校验当前用户在该 workspace 有访问权限。
|
||||
|
||||
### MCP
|
||||
|
||||
MCP server 是租户资源：
|
||||
|
||||
- `mcp_servers.workspace_uuid`。
|
||||
- MCP session key 从 `server_name` 改为 `(workspace_uuid, server_name)` 或使用全局 uuid。
|
||||
- Pipeline extension preferences 中绑定 MCP server uuid 时，只能绑定同 workspace 的 server。
|
||||
- MCP tool 列表在 query 执行时按 query.workspace_uuid + pipeline 绑定关系过滤。
|
||||
|
||||
## 前端改造
|
||||
|
||||
### Workspace selector
|
||||
|
||||
Home layout 顶部或 sidebar 增加 workspace selector：
|
||||
|
||||
- 当前 workspace 名称和头像。
|
||||
- 切换 workspace 后写入 `localStorage.currentWorkspaceId`。
|
||||
- 所有请求自动带 `X-Workspace-Id`。
|
||||
- 切换后刷新 sidebar 数据和页面缓存。
|
||||
|
||||
`BaseHttpClient` request interceptor 增加：
|
||||
|
||||
```ts
|
||||
const workspaceId = localStorage.getItem("currentWorkspaceId");
|
||||
if (workspaceId) config.headers["X-Workspace-Id"] = workspaceId;
|
||||
```
|
||||
|
||||
### 用户与成员管理页面
|
||||
|
||||
新增页面：
|
||||
|
||||
- `/home/workspace/settings`
|
||||
- `/home/workspace/members`
|
||||
- `/home/workspace/invitations`
|
||||
|
||||
能力：
|
||||
|
||||
- owner/admin 邀请成员。
|
||||
- owner/admin 修改成员角色。
|
||||
- owner 移除成员、转让 owner。
|
||||
- 所有人可切换 workspace。
|
||||
- viewer/operator 在 UI 上隐藏不可操作按钮，但后端仍做权限校验。
|
||||
|
||||
### 登录与注册
|
||||
|
||||
登录后流程：
|
||||
|
||||
1. `authUser` 拿 token。
|
||||
2. `initializeUserInfo()` 获取 account info。
|
||||
3. `GET /api/v1/workspaces`。
|
||||
4. 如果没有 workspace：进入创建 workspace 向导。
|
||||
5. 如果有多个 workspace：默认进入最近使用 workspace，可切换。
|
||||
|
||||
注册页不再表达“初始化管理员账号”，而是：
|
||||
|
||||
- 首次系统启动：创建首个 owner + default workspace。
|
||||
- 后续：根据配置允许公开注册，或只能接受邀请。
|
||||
|
||||
### 旧页面影响
|
||||
|
||||
需要逐个检查这些页面的数据加载是否都依赖当前 workspace：
|
||||
|
||||
- Bots
|
||||
- Pipelines
|
||||
- Plugins/Market/MCP
|
||||
- Knowledge
|
||||
- Monitoring
|
||||
- Models dialog
|
||||
- API integration dialog
|
||||
- Wizard
|
||||
|
||||
## 迁移方案
|
||||
|
||||
### 迁移阶段 0：准备
|
||||
|
||||
- 引入 `workspaces`、`workspace_memberships`、`workspace_invitations`。
|
||||
- 给 `users` 增加 `uuid/status/display_name` 等字段。
|
||||
- 创建 `RequestContext`，但先不强制所有服务改完。
|
||||
|
||||
### 迁移阶段 1：默认 workspace
|
||||
|
||||
对现有实例执行迁移：
|
||||
|
||||
1. 创建 `Default Workspace`。
|
||||
2. 找到现有第一个 user，设为 owner。
|
||||
3. 所有已有资源写入 `workspace_uuid=default_workspace_uuid`。
|
||||
4. `metadata` 迁入 default workspace；确实全局的配置放到 `system_metadata`。
|
||||
5. `binary_storages` 中 `owner_type=workspace, owner=default` 改为 owner 为 default workspace uuid。
|
||||
6. 插件 `plugin_settings` 归入 default workspace。
|
||||
|
||||
### 迁移阶段 2：服务层强制 scope
|
||||
|
||||
- 改所有 service 查询，必须要求 `workspace_uuid`。
|
||||
- API Key 迁移为 workspace key。
|
||||
- 所有写操作必须检查权限。
|
||||
- 监控和任务查询按 workspace 过滤。
|
||||
|
||||
### 迁移阶段 3：运行时上下文
|
||||
|
||||
- `Query`、`Session`、`RuntimeBot`、`RuntimePipeline` 增加 workspace。
|
||||
- Plugin/MCP/Model/RAG runtime 全部按 workspace 过滤。
|
||||
- 修复 SDK workspace storage。
|
||||
|
||||
### 迁移阶段 4：前端多 workspace
|
||||
|
||||
- 登录后 workspace 选择。
|
||||
- Header/sidebar workspace switcher。
|
||||
- 成员和邀请管理。
|
||||
- 所有 API 请求带 `X-Workspace-Id`。
|
||||
|
||||
### 迁移阶段 5：安全收敛
|
||||
|
||||
- 添加跨 workspace 越权测试。
|
||||
- 添加 API Key scope 测试。
|
||||
- 添加插件 Host API 过滤测试。
|
||||
- 添加 MCP 和 RAG 隔离测试。
|
||||
|
||||
## 安全边界
|
||||
|
||||
必须防的场景：
|
||||
|
||||
- 用户 A 修改 URL 中 bot uuid，访问用户 B workspace 的 bot。
|
||||
- API Key 来自 workspace A，但调用 workspace B 的 bot。
|
||||
- 插件通过 `get_bots()` 枚举所有 workspace 的 bot。
|
||||
- 插件通过 `workspace_storage` 读取其它 workspace 的数据。
|
||||
- MCP server 名称相同导致 session 复用。
|
||||
- monitoring session_id 相同导致数据串租户。
|
||||
- Space OAuth 登录时，同 email 账户被错误绑定到已有本地 account。
|
||||
|
||||
建议策略：
|
||||
|
||||
- 所有资源访问都使用 `workspace_uuid + resource_id`。
|
||||
- 所有 service 方法入口做权限检查。
|
||||
- 插件 Host API 的 workspace 不信任插件入参，只信任 query/runtime connection 上下文。
|
||||
- API Key 只授予最小 scope，默认不允许成员管理。
|
||||
- owner 角色不能被普通 admin 移除或降权。
|
||||
|
||||
## 实施优先级
|
||||
|
||||
### P0：基础租户骨架
|
||||
|
||||
- Account uuid/status。
|
||||
- Workspace / Membership / Invitation。
|
||||
- RequestContext。
|
||||
- JWT 改为 account uuid。
|
||||
- 前端 current workspace header。
|
||||
|
||||
### P1：资源行级隔离
|
||||
|
||||
- Bots、Pipelines、Models、MCP、Plugins、Knowledge、Monitoring、API Keys 全部加 workspace_uuid。
|
||||
- service 查询统一加 workspace filter。
|
||||
- 权限矩阵落地。
|
||||
|
||||
### P2：运行时隔离
|
||||
|
||||
- Query、Session、RuntimeBot、RuntimePipeline 加 workspace。
|
||||
- Plugin Host API 和 MCP tools 按 workspace 过滤。
|
||||
- SDK workspace storage 从 `default` 改为真实 workspace。
|
||||
|
||||
### P3：协作体验
|
||||
|
||||
- 邀请成员。
|
||||
- 成员列表和角色管理。
|
||||
- workspace switcher。
|
||||
- 最近使用 workspace。
|
||||
|
||||
### P4：SaaS 运维增强
|
||||
|
||||
- Workspace 级用量统计。
|
||||
- Workspace 级限额：max_bots/max_pipelines/max_extensions/tokens/storage。
|
||||
- 审计日志。
|
||||
- workspace suspend/delete。
|
||||
- 可选自定义角色。
|
||||
|
||||
## 测试计划
|
||||
|
||||
后端测试：
|
||||
|
||||
- 账户可加入多个 workspace。
|
||||
- 同账户在不同 workspace 权限不同。
|
||||
- viewer 不能创建/修改资源。
|
||||
- API Key 只能访问所属 workspace。
|
||||
- 所有资源 list/get/update/delete 都不能跨 workspace。
|
||||
- 默认 workspace 迁移后旧数据可用。
|
||||
|
||||
运行时测试：
|
||||
|
||||
- 两个 workspace 使用相同 `launcher_id` 不共享 session。
|
||||
- 两个 workspace 使用相同 MCP server name 不共享 MCP session。
|
||||
- 插件 `get_bots()` 只能看到当前 workspace bot。
|
||||
- 插件 `workspace_storage` 在不同 workspace 读写隔离。
|
||||
- Pipeline 只调用当前 workspace 绑定的插件和 MCP tools。
|
||||
|
||||
前端测试：
|
||||
|
||||
- 登录后自动进入最近 workspace。
|
||||
- 切换 workspace 后列表数据变化。
|
||||
- 无权限按钮隐藏，直接调用 API 也被后端拒绝。
|
||||
- 邀请成员流程完整。
|
||||
|
||||
迁移测试：
|
||||
|
||||
- SQLite 老实例迁移。
|
||||
- PostgreSQL 老实例迁移。
|
||||
- 已有 local account 迁移为 default workspace owner。
|
||||
- 已有 Space account token 和 Space model provider API key 不丢失。
|
||||
|
||||
## 关键实现注意事项
|
||||
|
||||
- 不建议在第一版做数据库 schema-per-tenant。LangBot 当前 ORM 和运行时均以单库单表为主，先做 shared schema + workspace_uuid 成本更低。
|
||||
- 不建议每个 workspace 立即启动独立 plugin runtime。先共享 runtime，强制 action 带 workspace；大客户隔离可作为后续部署形态。
|
||||
- 不要只在前端过滤 workspace。插件、API Key、MCP、RAG 都能绕过前端，必须在后端和运行时层过滤。
|
||||
- `metadata` 要拆清楚：wizard status 属于 workspace，系统版本/迁移信息属于 system。
|
||||
- `users.user` 用 email 当主键语义不稳，应尽快引入 `account_uuid` 并让 JWT 以 uuid 为准。
|
||||
- `plugin_settings` 当前主键没有 workspace，改造时要先改主键/唯一约束，否则同插件无法在多个 workspace 配不同配置。
|
||||
|
||||
## 建议落地顺序
|
||||
|
||||
1. 新增 workspace/account/membership 表和 RequestContext。
|
||||
2. 迁移旧数据到 default workspace。
|
||||
3. 改 auth 和前端请求头，让每个请求都有 current workspace。
|
||||
4. 从最核心资源开始逐个加 scope：bot -> pipeline -> provider/model -> plugin/MCP -> knowledge -> monitoring。
|
||||
5. 改 SDK Query/Event 和 runtime storage。
|
||||
6. 上成员管理 UI 和邀请。
|
||||
7. 做越权测试和迁移测试。
|
||||
|
||||
这个顺序的好处是可以较早让主 UI 在一个 workspace 下继续工作，同时把最危险的跨租户泄露面逐步收紧。
|
||||
@@ -1,425 +0,0 @@
|
||||
# Workflow 用户指南
|
||||
|
||||
本文档帮助您了解和使用 LangBot 的 Workflow（工作流）功能，通过可视化方式构建自动化的对话处理流程。
|
||||
|
||||
## 目录
|
||||
|
||||
- [功能介绍](#功能介绍)
|
||||
- [快速入门](#快速入门)
|
||||
- [节点类型说明](#节点类型说明)
|
||||
- [编辑器使用指南](#编辑器使用指南)
|
||||
- [调试功能](#调试功能)
|
||||
- [常见问题解答](#常见问题解答)
|
||||
|
||||
---
|
||||
|
||||
## 功能介绍
|
||||
|
||||
### 什么是 Workflow？
|
||||
|
||||
Workflow（工作流）是 LangBot 提供的可视化自动化编排系统。通过拖拽节点、连接边的方式，您可以：
|
||||
|
||||
- 📝 **构建复杂的对话流程**：使用条件分支、循环等控制节点
|
||||
- 🤖 **调用 AI 能力**：集成 LLM、知识库检索、参数提取
|
||||
- 🔗 **连接外部服务**：集成 Dify、n8n、Coze 等平台
|
||||
- ⚡ **自动化任务执行**：消息触发、定时触发、Webhook 触发
|
||||
|
||||
### Workflow vs Pipeline
|
||||
|
||||
| 对比项 | Pipeline | Workflow |
|
||||
|-------|----------|----------|
|
||||
| 配置方式 | 表单配置 | 可视化拖拽 |
|
||||
| 流程控制 | 线性执行 | 支持分支、循环、并行 |
|
||||
| 适用场景 | 简单对话 | 复杂流程 |
|
||||
| 学习曲线 | 低 | 中等 |
|
||||
|
||||
---
|
||||
|
||||
## 快速入门
|
||||
|
||||
### 第一步：创建 Workflow
|
||||
|
||||
1. 在侧边栏点击 **Workflow** 进入工作流列表
|
||||
2. 点击右上角 **创建工作流** 按钮
|
||||
3. 填写基本信息：
|
||||
- **名称**：给工作流起一个描述性的名字
|
||||
- **描述**：可选，说明工作流的用途
|
||||
- **图标**：选择一个 emoji 作为标识
|
||||
|
||||
### 第二步：添加节点
|
||||
|
||||
进入编辑器后，左侧是节点面板，中间是画布区域，右侧是属性面板。
|
||||
|
||||
1. **添加触发节点**：从左侧面板拖拽一个"消息触发"节点到画布
|
||||
2. **添加 AI 节点**：拖拽一个"LLM 调用"节点
|
||||
3. **添加回复节点**：拖拽一个"回复消息"节点
|
||||
|
||||
### 第三步：连接节点
|
||||
|
||||
1. 将鼠标悬停在触发节点的输出端口（右侧小圆点）
|
||||
2. 按住鼠标拖拽到 LLM 节点的输入端口（左侧小圆点）
|
||||
3. 同样方式连接 LLM 节点和回复节点
|
||||
|
||||
```
|
||||
[消息触发] ──▶ [LLM 调用] ──▶ [回复消息]
|
||||
```
|
||||
|
||||
### 第四步：配置节点
|
||||
|
||||
点击 LLM 调用节点，在右侧属性面板配置：
|
||||
|
||||
- **运行方式**：选择"本地 Agent"
|
||||
- **系统提示词**：描述 AI 的角色和行为
|
||||
- **模型**：选择要使用的 LLM 模型
|
||||
|
||||
点击回复消息节点配置：
|
||||
|
||||
- **消息内容**：设置为 `{{nodes.llm_call.outputs.response}}`（引用 LLM 输出）
|
||||
|
||||
### 第五步：保存并绑定
|
||||
|
||||
1. 点击工具栏的 **保存** 按钮
|
||||
2. 返回 Bot 配置页面
|
||||
3. 在 Bot 的绑定设置中选择 **Workflow**，然后选择刚创建的工作流
|
||||
|
||||
恭喜！您已经创建了第一个 Workflow。
|
||||
|
||||
---
|
||||
|
||||
## 节点类型说明
|
||||
|
||||
### 触发节点 (Trigger)
|
||||
|
||||
触发节点是工作流的入口，定义何时启动执行。
|
||||
|
||||
| 节点 | 说明 | 输出 |
|
||||
|-----|------|------|
|
||||
| 消息触发 | 收到消息时触发 | message, sender_id, platform |
|
||||
| 定时触发 | 按 Cron 表达式定时触发 | timestamp |
|
||||
| Webhook 触发 | 收到 HTTP 请求时触发 | request_body, headers |
|
||||
| 事件触发 | 系统事件触发 | event_type, event_data |
|
||||
|
||||
**消息触发配置示例**：
|
||||
|
||||
```yaml
|
||||
触发条件:
|
||||
- 关键词匹配: ["帮助", "help"]
|
||||
- 平台: ["wechat", "qq"]
|
||||
```
|
||||
|
||||
### AI 节点
|
||||
|
||||
AI 节点用于调用各种 AI 能力。
|
||||
|
||||
| 节点 | 说明 | 典型用途 |
|
||||
|-----|------|---------|
|
||||
| LLM 调用 | 调用大语言模型 | 生成回复、理解意图 |
|
||||
| 问题分类器 | 对用户问题分类 | 路由到不同处理分支 |
|
||||
| 参数提取器 | 从文本提取结构化数据 | 提取订单号、日期等 |
|
||||
| 知识库检索 | 查询知识库 | RAG 增强回复 |
|
||||
|
||||
**LLM 调用配置示例**：
|
||||
|
||||
```yaml
|
||||
运行方式: 本地 Agent
|
||||
模型: gpt-4
|
||||
系统提示词: |
|
||||
你是一个友好的客服助手。
|
||||
请根据用户的问题提供帮助。
|
||||
温度: 0.7
|
||||
最大 Token 数: 2000
|
||||
```
|
||||
|
||||
### 处理节点 (Process)
|
||||
|
||||
处理节点用于数据处理和外部调用。
|
||||
|
||||
| 节点 | 说明 | 典型用途 |
|
||||
|-----|------|---------|
|
||||
| 代码执行 | 执行 Python/JavaScript 代码 | 数据处理、格式转换 |
|
||||
| HTTP 请求 | 发送 HTTP 请求 | 调用外部 API |
|
||||
| 数据转换 | JSON/模板转换 | 数据格式化 |
|
||||
|
||||
**HTTP 请求配置示例**：
|
||||
|
||||
```yaml
|
||||
URL: https://api.example.com/data
|
||||
方法: POST
|
||||
请求头:
|
||||
Content-Type: application/json
|
||||
Authorization: Bearer {{variables.api_key}}
|
||||
请求体: |
|
||||
{"query": "{{message.content}}"}
|
||||
```
|
||||
|
||||
### 控制节点 (Control)
|
||||
|
||||
控制节点用于流程控制。
|
||||
|
||||
| 节点 | 说明 | 用途 |
|
||||
|-----|------|------|
|
||||
| 条件分支 | 二选一分支 | if-else 逻辑 |
|
||||
| 多路分支 | 多选一分支 | switch-case 逻辑 |
|
||||
| 循环 | 遍历数组 | 批量处理 |
|
||||
| 并行 | 同时执行多分支 | 并发处理 |
|
||||
| 等待 | 暂停执行 | 延时处理 |
|
||||
| 合并 | 合并多个分支 | 汇总结果 |
|
||||
|
||||
**条件分支配置示例**：
|
||||
|
||||
```yaml
|
||||
条件表达式: "{{nodes.classifier.outputs.category}}" == "complaint"
|
||||
真分支: 投诉处理
|
||||
假分支: 普通咨询
|
||||
```
|
||||
|
||||
### 动作节点 (Action)
|
||||
|
||||
动作节点执行具体操作。
|
||||
|
||||
| 节点 | 说明 | 用途 |
|
||||
|-----|------|------|
|
||||
| 发送消息 | 主动发送消息 | 通知、推送 |
|
||||
| 回复消息 | 回复当前消息 | 对话回复 |
|
||||
| 存储数据 | 保存数据到存储 | 持久化 |
|
||||
| 调用 Pipeline | 调用现有 Pipeline | 复用现有流程 |
|
||||
|
||||
**回复消息配置示例**：
|
||||
|
||||
```yaml
|
||||
消息内容: |
|
||||
感谢您的咨询！
|
||||
|
||||
{{nodes.llm_call.outputs.response}}
|
||||
|
||||
如有其他问题，随时联系我。
|
||||
```
|
||||
|
||||
### 集成节点 (Integration)
|
||||
|
||||
集成节点连接外部平台。
|
||||
|
||||
| 节点 | 说明 | 平台 |
|
||||
|-----|------|------|
|
||||
| Dify 工作流 | 调用 Dify 应用 | Dify |
|
||||
| Dify 知识库 | 查询 Dify 知识库 | Dify |
|
||||
| n8n 工作流 | 调用 n8n 流程 | n8n |
|
||||
| Langflow | 调用 Langflow 流程 | Langflow |
|
||||
| Coze Bot | 调用扣子 Bot | Coze |
|
||||
|
||||
**Dify 工作流配置示例**：
|
||||
|
||||
```yaml
|
||||
API 地址: https://api.dify.ai/v1
|
||||
API Key: sk-xxxxx
|
||||
应用类型: workflow
|
||||
同步对话历史: true
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 编辑器使用指南
|
||||
|
||||
### 画布操作
|
||||
|
||||
| 操作 | 方式 |
|
||||
|-----|------|
|
||||
| 平移画布 | 按住鼠标中键/空格+左键 拖拽 |
|
||||
| 缩放画布 | 鼠标滚轮 / 工具栏按钮 |
|
||||
| 框选多个节点 | 按住 Shift + 拖拽框选 |
|
||||
| 适应视图 | 点击工具栏"适应"按钮 |
|
||||
|
||||
### 节点操作
|
||||
|
||||
| 操作 | 方式 |
|
||||
|-----|------|
|
||||
| 添加节点 | 从左侧面板拖拽到画布 |
|
||||
| 移动节点 | 点击节点拖拽 |
|
||||
| 删除节点 | 选中后按 Delete / 点击工具栏删除 |
|
||||
| 复制节点 | 选中后 Ctrl+C / 工具栏复制 |
|
||||
| 粘贴节点 | Ctrl+V / 工具栏粘贴 |
|
||||
|
||||
### 连接操作
|
||||
|
||||
| 操作 | 方式 |
|
||||
|-----|------|
|
||||
| 创建连接 | 从输出端口拖拽到输入端口 |
|
||||
| 删除连接 | 点击连接线后按 Delete |
|
||||
| 选中连接 | 点击连接线 |
|
||||
|
||||
### 快捷键
|
||||
|
||||
| 快捷键 | 功能 |
|
||||
|-------|------|
|
||||
| Ctrl + Z | 撤销 |
|
||||
| Ctrl + Shift + Z | 重做 |
|
||||
| Ctrl + C | 复制 |
|
||||
| Ctrl + V | 粘贴 |
|
||||
| Delete | 删除选中 |
|
||||
| Ctrl + S | 保存 |
|
||||
|
||||
### 工具栏功能
|
||||
|
||||
```
|
||||
[撤销] [重做] | [放大] [缩小] [适应] | [复制] [粘贴] [删除] | [保存] [调试]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 调试功能
|
||||
|
||||
### 启动调试
|
||||
|
||||
1. 点击工具栏的 **调试** 按钮
|
||||
2. 在调试面板中配置初始数据：
|
||||
- **输入消息**：模拟用户发送的消息
|
||||
- **会话 ID**：可选，用于测试会话变量
|
||||
- **变量**：设置初始变量值
|
||||
|
||||
3. 点击 **开始调试** 按钮
|
||||
|
||||
### 调试控制
|
||||
|
||||
| 按钮 | 功能 |
|
||||
|-----|------|
|
||||
| ▶️ 开始/继续 | 开始或继续执行 |
|
||||
| ⏸️ 暂停 | 暂停执行 |
|
||||
| ⏹️ 停止 | 停止执行 |
|
||||
| ⏭️ 单步 | 执行下一个节点 |
|
||||
|
||||
### 断点
|
||||
|
||||
- **设置断点**：点击节点上的断点图标
|
||||
- **断点触发**：执行到断点时自动暂停
|
||||
- **查看状态**：在暂停时查看节点的输入输出
|
||||
|
||||
### 执行日志
|
||||
|
||||
调试面板下方显示实时日志：
|
||||
|
||||
```
|
||||
[INFO] 2024-01-15 10:30:00 - Starting debug execution
|
||||
[INFO] 2024-01-15 10:30:00 - Executing node: message_trigger
|
||||
[DEBUG] 2024-01-15 10:30:00 - Node inputs: {"message": "你好"}
|
||||
[INFO] 2024-01-15 10:30:01 - Node completed in 50ms
|
||||
[INFO] 2024-01-15 10:30:01 - Executing node: llm_call
|
||||
...
|
||||
```
|
||||
|
||||
### 节点状态颜色
|
||||
|
||||
| 颜色 | 状态 |
|
||||
|-----|------|
|
||||
| 灰色 | 待执行 |
|
||||
| 蓝色 | 执行中 |
|
||||
| 绿色 | 已完成 |
|
||||
| 红色 | 失败 |
|
||||
| 黄色 | 已跳过 |
|
||||
|
||||
---
|
||||
|
||||
## 常见问题解答
|
||||
|
||||
### Q1：如何在节点间传递数据？
|
||||
|
||||
使用表达式语法引用其他节点的输出：
|
||||
|
||||
```
|
||||
{{nodes.节点ID.outputs.输出名称}}
|
||||
```
|
||||
|
||||
例如：
|
||||
- `{{nodes.llm_call.outputs.response}}` - 引用 LLM 节点的响应
|
||||
- `{{nodes.http_request.outputs.body}}` - 引用 HTTP 请求的响应体
|
||||
|
||||
### Q2：如何使用变量？
|
||||
|
||||
Workflow 支持三种变量类型：
|
||||
|
||||
1. **工作流变量**：`{{variables.变量名}}`
|
||||
2. **会话变量**：`{{conversation_variables.变量名}}`
|
||||
3. **消息上下文**：`{{message.content}}`、`{{message.sender_id}}`
|
||||
|
||||
### Q3：条件分支如何写条件表达式？
|
||||
|
||||
支持以下运算符：
|
||||
|
||||
- 比较：`==`, `!=`, `>`, `<`, `>=`, `<=`
|
||||
- 逻辑：`and`, `or`, `not`
|
||||
- 包含：`in`
|
||||
|
||||
示例：
|
||||
```python
|
||||
# 字符串比较
|
||||
"{{nodes.classifier.outputs.intent}}" == "purchase"
|
||||
|
||||
# 数值比较
|
||||
{{nodes.extractor.outputs.amount}} > 1000
|
||||
|
||||
# 包含检查
|
||||
"退款" in "{{message.content}}"
|
||||
```
|
||||
|
||||
### Q4：如何处理错误？
|
||||
|
||||
1. **节点级重试**：在节点配置中设置重试次数
|
||||
2. **全局错误处理**：在 Workflow 设置中配置错误处理策略
|
||||
3. **条件分支**：使用条件节点检查上一节点的状态
|
||||
|
||||
### Q5：如何查看执行历史？
|
||||
|
||||
1. 进入 Workflow 详情页
|
||||
2. 点击 **执行历史** 标签
|
||||
3. 查看每次执行的状态、耗时、输入输出
|
||||
|
||||
### Q6：Workflow 可以被多个 Bot 使用吗？
|
||||
|
||||
是的。一个 Workflow 可以被多个 Bot 绑定使用，但每个 Bot 只能绑定一个处理单元（Pipeline 或 Workflow）。
|
||||
|
||||
### Q7：如何复制现有的 Workflow？
|
||||
|
||||
在 Workflow 列表页，点击工作流卡片右上角的菜单，选择"复制"即可创建副本。
|
||||
|
||||
### Q8：支持版本回滚吗？
|
||||
|
||||
支持。每次保存都会创建新版本。在 Workflow 详情页可以查看版本历史并回滚到指定版本。
|
||||
|
||||
---
|
||||
|
||||
## 最佳实践
|
||||
|
||||
### 1. 合理命名
|
||||
|
||||
- 为节点和 Workflow 使用描述性名称
|
||||
- 使用统一的命名规范
|
||||
|
||||
### 2. 模块化设计
|
||||
|
||||
- 将复杂流程拆分为多个小 Workflow
|
||||
- 使用"调用 Pipeline"节点复用现有流程
|
||||
|
||||
### 3. 错误处理
|
||||
|
||||
- 为关键节点设置重试机制
|
||||
- 使用条件分支处理异常情况
|
||||
- 添加日志记录便于排查问题
|
||||
|
||||
### 4. 测试先行
|
||||
|
||||
- 使用调试功能充分测试
|
||||
- 准备多种测试场景
|
||||
- 检查边界情况
|
||||
|
||||
### 5. 性能优化
|
||||
|
||||
- 避免不必要的节点
|
||||
- 使用并行节点提高效率
|
||||
- 合理设置超时时间
|
||||
|
||||
---
|
||||
|
||||
## 更多资源
|
||||
|
||||
- [开发者文档](../development/workflow-system.md)
|
||||
- [设计文档](../../../plans/langbot-workflow-design.md)
|
||||
- [API 文档](../service-api-openapi.json)
|
||||
1468
node_comparison.json
1468
node_comparison.json
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@@ -1,6 +1,6 @@
|
||||
[project]
|
||||
name = "langbot"
|
||||
version = "4.9.7"
|
||||
version = "4.9.6"
|
||||
description = "Production-grade platform for building agentic IM bots"
|
||||
readme = "README.md"
|
||||
license-files = ["LICENSE"]
|
||||
@@ -22,7 +22,7 @@ dependencies = [
|
||||
"discord-py>=2.5.2",
|
||||
"pynacl>=1.5.0", # Required for Discord voice support
|
||||
"gewechat-client>=0.1.5",
|
||||
"lark-oapi>=1.5.5",
|
||||
"lark-oapi>=1.4.15",
|
||||
"mcp>=1.25.0",
|
||||
"nakuru-project-idk>=0.0.2.1",
|
||||
"ollama>=0.4.8",
|
||||
@@ -35,7 +35,6 @@ dependencies = [
|
||||
"python-telegram-bot>=22.0",
|
||||
"pyyaml>=6.0.2",
|
||||
"qq-botpy-rc>=1.2.1.6",
|
||||
"qrcode>=7.4",
|
||||
"quart>=0.20.0",
|
||||
"quart-cors>=0.8.0",
|
||||
"requests>=2.32.3",
|
||||
@@ -70,7 +69,7 @@ dependencies = [
|
||||
"chromadb>=1.0.0,<2.0.0",
|
||||
"qdrant-client (>=1.15.1,<2.0.0)",
|
||||
"pyseekdb==1.1.0.post3",
|
||||
"langbot-plugin @ file:///home/typer/Desktop/langbot-plugin-sdk",
|
||||
"langbot-plugin==0.3.10",
|
||||
"asyncpg>=0.30.0",
|
||||
"line-bot-sdk>=3.19.0",
|
||||
"matrix-nio>=0.25.2",
|
||||
@@ -122,7 +121,6 @@ package-data = { "langbot" = ["templates/**", "pkg/provider/modelmgr/requesters/
|
||||
|
||||
[dependency-groups]
|
||||
dev = [
|
||||
"moto>=5.2.1",
|
||||
"pre-commit>=4.2.0",
|
||||
"pytest>=9.0.3",
|
||||
"pytest-asyncio>=1.0.0",
|
||||
|
||||
@@ -4,9 +4,6 @@ python_files = test_*.py
|
||||
python_classes = Test*
|
||||
python_functions = test_*
|
||||
|
||||
# Python path for imports
|
||||
pythonpath = . tests
|
||||
|
||||
# Test paths
|
||||
testpaths = tests
|
||||
|
||||
@@ -25,9 +22,7 @@ markers =
|
||||
asyncio: mark test as async
|
||||
unit: mark test as unit test
|
||||
integration: mark test as integration test
|
||||
smoke: mark test as smoke test
|
||||
slow: mark test as slow running
|
||||
e2e: mark test as end-to-end test (requires real LangBot process)
|
||||
|
||||
# Coverage options (when using pytest-cov)
|
||||
[coverage:run]
|
||||
|
||||
@@ -1,65 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
# Coverage gate script
|
||||
# Runs all tests with coverage, enforcing minimum coverage threshold
|
||||
# Uses separate pytest invocations to avoid sys.modules pollution between test types
|
||||
|
||||
set -euo pipefail
|
||||
|
||||
echo "=== LangBot Coverage Gate ==="
|
||||
echo ""
|
||||
|
||||
# Coverage threshold (baseline from current coverage, conservative buffer)
|
||||
# Current: ~22.14%, threshold: 18%
|
||||
COVERAGE_THRESHOLD=18
|
||||
|
||||
# Create temporary directory for coverage files
|
||||
COV_DIR=$(mktemp -d)
|
||||
trap "rm -rf $COV_DIR" EXIT
|
||||
|
||||
echo "[1/3] Running unit + smoke tests with coverage..."
|
||||
uv run pytest tests/unit_tests/ tests/smoke/ \
|
||||
--cov=langbot \
|
||||
--cov-report=json:$COV_DIR/unit.json \
|
||||
--cov-report=term-missing \
|
||||
-q --tb=short
|
||||
echo ""
|
||||
|
||||
echo "[2/3] Running fast integration tests with coverage..."
|
||||
uv run pytest tests/integration/ -m "not slow" \
|
||||
--cov=langbot \
|
||||
--cov-report=json:$COV_DIR/integration.json \
|
||||
--cov-report=term-missing \
|
||||
-q --tb=short
|
||||
echo ""
|
||||
|
||||
echo "[3/3] Combining coverage reports..."
|
||||
# Use coverage combine if available, otherwise just report total
|
||||
if command -v coverage &> /dev/null; then
|
||||
# Combine JSON reports
|
||||
coverage combine --keep $COV_DIR/unit.json $COV_DIR/integration.json \
|
||||
--data-file=$COV_DIR/combined.data 2>/dev/null || true
|
||||
|
||||
coverage report --data-file=$COV_DIR/combined.data || true
|
||||
else
|
||||
echo "Note: coverage combine not available, showing individual reports above"
|
||||
fi
|
||||
|
||||
# Generate final XML report for CI (from last run)
|
||||
uv run pytest tests/unit_tests/ tests/smoke/ \
|
||||
--cov=langbot \
|
||||
--cov-report=xml:coverage.xml \
|
||||
--cov-report=term \
|
||||
--cov-fail-under=$COVERAGE_THRESHOLD \
|
||||
-q 2>/dev/null || {
|
||||
# If threshold check fails on combined, check unit+smoke baseline
|
||||
echo ""
|
||||
echo "Coverage threshold: $COVERAGE_THRESHOLD%"
|
||||
echo "Note: Full coverage requires running all test types separately"
|
||||
}
|
||||
|
||||
echo ""
|
||||
echo "=== Coverage Gate Complete ==="
|
||||
echo ""
|
||||
echo "Coverage baseline: $COVERAGE_THRESHOLD%"
|
||||
echo "Coverage report saved to coverage.xml"
|
||||
@@ -1,16 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
# Fast integration tests
|
||||
# Runs integration tests excluding slow ones (PostgreSQL, external services)
|
||||
# Uses fake runner/provider, no real credentials needed
|
||||
|
||||
set -euo pipefail
|
||||
|
||||
echo "=== LangBot Fast Integration Tests ==="
|
||||
echo ""
|
||||
|
||||
echo "Running integration tests (excluding slow)..."
|
||||
uv run pytest tests/integration/ -m "not slow" -q --tb=short
|
||||
|
||||
echo ""
|
||||
echo "=== Fast Integration Tests Complete ==="
|
||||
@@ -1,36 +0,0 @@
|
||||
#!/bin/bash
|
||||
|
||||
# Quick developer self-test command
|
||||
# Runs linting, unit tests, and smoke tests without requiring real provider keys
|
||||
# Suitable for local branch validation
|
||||
|
||||
set -euo pipefail
|
||||
|
||||
echo "=== LangBot Quick Self-Test ==="
|
||||
echo ""
|
||||
|
||||
# 1. Ruff check
|
||||
echo "[1/3] Running ruff check..."
|
||||
uv run ruff check src/langbot/ tests/ --output-format=concise || {
|
||||
echo ""
|
||||
echo "⚠ Ruff check found issues. Run 'uv run ruff check --fix' to auto-fix."
|
||||
exit 1
|
||||
}
|
||||
echo "✓ Ruff check passed"
|
||||
echo ""
|
||||
|
||||
# 2. Unit tests
|
||||
echo "[2/3] Running unit tests..."
|
||||
uv run pytest tests/unit_tests/ -q --tb=short
|
||||
echo ""
|
||||
|
||||
# 3. Smoke tests (if exists)
|
||||
echo "[3/3] Running smoke tests..."
|
||||
if [ -d "tests/smoke" ]; then
|
||||
uv run pytest tests/smoke/ -q --tb=short
|
||||
else
|
||||
echo "No smoke tests found, skipping"
|
||||
fi
|
||||
echo ""
|
||||
|
||||
echo "=== Quick Self-Test Complete ==="
|
||||
@@ -1,3 +1,3 @@
|
||||
"""LangBot - Production-grade platform for building agentic IM bots"""
|
||||
|
||||
__version__ = '4.9.7'
|
||||
__version__ = '4.9.6'
|
||||
|
||||
@@ -1,6 +1,5 @@
|
||||
import quart
|
||||
import mimetypes
|
||||
import asyncio
|
||||
from ... import group
|
||||
from langbot.pkg.utils import importutil
|
||||
|
||||
@@ -36,640 +35,3 @@ class AdaptersRouterGroup(group.RouterGroup):
|
||||
return quart.Response(
|
||||
importutil.read_resource_file_bytes(icon_path), mimetype=mimetypes.guess_type(icon_path)[0]
|
||||
)
|
||||
|
||||
# In-memory session store for active registrations
|
||||
_create_app_sessions: dict = {}
|
||||
_SESSION_TTL = 900 # 15 minutes
|
||||
|
||||
def _cleanup_expired_sessions():
|
||||
"""Remove sessions that have exceeded their TTL."""
|
||||
import time
|
||||
|
||||
now = time.time()
|
||||
expired = [sid for sid, s in _create_app_sessions.items() if now - s.get('created_at', 0) > _SESSION_TTL]
|
||||
for sid in expired:
|
||||
session = _create_app_sessions.pop(sid, None)
|
||||
if session and session.get('task') and not session['task'].done():
|
||||
session['task'].cancel()
|
||||
|
||||
@self.route('/lark/create-app', methods=['POST'])
|
||||
async def _() -> str:
|
||||
"""Start Feishu one-click app registration. Returns session_id + QR code URL."""
|
||||
import uuid
|
||||
import time
|
||||
import lark_oapi as lark
|
||||
from lark_oapi.scene.registration.errors import AppAccessDeniedError, AppExpiredError
|
||||
|
||||
_cleanup_expired_sessions()
|
||||
|
||||
session_id = str(uuid.uuid4())
|
||||
loop = asyncio.get_running_loop()
|
||||
|
||||
session = {
|
||||
'status': 'pending',
|
||||
'qr_url': None,
|
||||
'expire_at': None,
|
||||
'app_id': None,
|
||||
'app_secret': None,
|
||||
'error': None,
|
||||
'created_at': time.time(),
|
||||
}
|
||||
_create_app_sessions[session_id] = session
|
||||
|
||||
def on_qr_code(info):
|
||||
# May be called from a background thread by the SDK;
|
||||
# use call_soon_threadsafe to safely update session state.
|
||||
def _update():
|
||||
session['qr_url'] = info['url']
|
||||
session['expire_at'] = time.time() + 600 # 10 minutes
|
||||
session['status'] = 'waiting'
|
||||
|
||||
loop.call_soon_threadsafe(_update)
|
||||
|
||||
async def run_registration():
|
||||
try:
|
||||
result = await lark.aregister_app(
|
||||
on_qr_code=on_qr_code,
|
||||
source='langbot',
|
||||
)
|
||||
session['status'] = 'success'
|
||||
session['app_id'] = result['client_id']
|
||||
session['app_secret'] = result['client_secret']
|
||||
except AppAccessDeniedError:
|
||||
session['status'] = 'error'
|
||||
session['error'] = 'User denied authorization'
|
||||
except AppExpiredError:
|
||||
session['status'] = 'error'
|
||||
session['error'] = 'QR code expired'
|
||||
except Exception as e:
|
||||
session['status'] = 'error'
|
||||
session['error'] = str(e)
|
||||
|
||||
task = asyncio.create_task(run_registration())
|
||||
session['task'] = task
|
||||
|
||||
# Wait for QR code to be ready (max 10 seconds)
|
||||
for _ in range(20):
|
||||
if session['qr_url']:
|
||||
break
|
||||
await asyncio.sleep(0.5)
|
||||
|
||||
if not session['qr_url']:
|
||||
task.cancel()
|
||||
session['status'] = 'error'
|
||||
session['error'] = 'Timeout waiting for QR code'
|
||||
return self.http_status(504, -1, 'Timeout waiting for QR code')
|
||||
|
||||
return self.success(
|
||||
data={
|
||||
'session_id': session_id,
|
||||
'qr_url': session['qr_url'],
|
||||
'expire_at': session['expire_at'],
|
||||
}
|
||||
)
|
||||
|
||||
@self.route('/lark/create-app/status/<session_id>', methods=['GET'])
|
||||
async def _(session_id: str) -> str:
|
||||
"""Poll registration status."""
|
||||
session = _create_app_sessions.get(session_id)
|
||||
if not session:
|
||||
return self.http_status(404, -1, 'Session not found')
|
||||
|
||||
data = {'status': session['status']}
|
||||
|
||||
if session['status'] == 'success':
|
||||
data['app_id'] = session['app_id']
|
||||
data['app_secret'] = session['app_secret']
|
||||
_create_app_sessions.pop(session_id, None)
|
||||
elif session['status'] == 'error':
|
||||
data['error'] = session['error']
|
||||
_create_app_sessions.pop(session_id, None)
|
||||
|
||||
return self.success(data=data)
|
||||
|
||||
@self.route('/lark/create-app/<session_id>', methods=['DELETE'])
|
||||
async def _(session_id: str) -> str:
|
||||
"""Cancel and clean up a registration session."""
|
||||
session = _create_app_sessions.pop(session_id, None)
|
||||
if session and session.get('task') and not session['task'].done():
|
||||
session['task'].cancel()
|
||||
return self.success(data={})
|
||||
|
||||
# -----------------------------------------------------------------------
|
||||
# WeChat QR Code Login
|
||||
# -----------------------------------------------------------------------
|
||||
|
||||
_weixin_login_sessions: dict = {}
|
||||
_WEIXIN_SESSION_TTL = 600 # 10 minutes (3 retries × 3 min QR validity)
|
||||
|
||||
def _cleanup_expired_weixin_sessions():
|
||||
import time
|
||||
|
||||
now = time.time()
|
||||
expired = [
|
||||
sid for sid, s in _weixin_login_sessions.items() if now - s.get('created_at', 0) > _WEIXIN_SESSION_TTL
|
||||
]
|
||||
for sid in expired:
|
||||
session = _weixin_login_sessions.pop(sid, None)
|
||||
if session and session.get('task') and not session['task'].done():
|
||||
session['task'].cancel()
|
||||
|
||||
@self.route('/weixin/login', methods=['POST'])
|
||||
async def _() -> str:
|
||||
"""Start WeChat QR code login. Returns session_id + QR code data URL."""
|
||||
import uuid
|
||||
import time
|
||||
import io
|
||||
import base64
|
||||
|
||||
from langbot.libs.openclaw_weixin_api.client import OpenClawWeixinClient, DEFAULT_BASE_URL
|
||||
|
||||
_cleanup_expired_weixin_sessions()
|
||||
|
||||
session_id = str(uuid.uuid4())
|
||||
loop = asyncio.get_running_loop()
|
||||
|
||||
session = {
|
||||
'status': 'pending',
|
||||
'qr_data_url': None,
|
||||
'expire_at': None,
|
||||
'token': None,
|
||||
'base_url': None,
|
||||
'account_id': None,
|
||||
'error': None,
|
||||
'created_at': time.time(),
|
||||
}
|
||||
_weixin_login_sessions[session_id] = session
|
||||
|
||||
client = OpenClawWeixinClient(
|
||||
base_url=DEFAULT_BASE_URL,
|
||||
token='',
|
||||
)
|
||||
|
||||
async def run_login():
|
||||
try:
|
||||
import qrcode as qr_lib
|
||||
|
||||
for _attempt in range(3):
|
||||
qr_resp = await client.fetch_qrcode()
|
||||
if not qr_resp.qrcode or not qr_resp.qrcode_img_content:
|
||||
raise Exception('Failed to get QR code from server')
|
||||
|
||||
# Generate QR code image locally
|
||||
qr = qr_lib.QRCode(error_correction=qr_lib.constants.ERROR_CORRECT_L)
|
||||
qr.add_data(qr_resp.qrcode_img_content)
|
||||
qr.make(fit=True)
|
||||
img = qr.make_image(fill_color='black', back_color='white')
|
||||
buf = io.BytesIO()
|
||||
img.save(buf, format='PNG')
|
||||
b64 = base64.b64encode(buf.getvalue()).decode('utf-8')
|
||||
data_url = f'data:image/png;base64,{b64}'
|
||||
|
||||
def _update_qr():
|
||||
session['qr_data_url'] = data_url
|
||||
session['expire_at'] = time.time() + 480 # 8 minutes
|
||||
session['status'] = 'waiting'
|
||||
|
||||
loop.call_soon_threadsafe(_update_qr)
|
||||
|
||||
# Poll for scan status
|
||||
deadline = loop.time() + 180
|
||||
while loop.time() < deadline:
|
||||
try:
|
||||
status_resp = await client.poll_qrcode_status(qr_resp.qrcode)
|
||||
except Exception:
|
||||
await asyncio.sleep(2)
|
||||
continue
|
||||
|
||||
if status_resp.status == 'confirmed' and status_resp.bot_token:
|
||||
session['status'] = 'success'
|
||||
session['token'] = status_resp.bot_token
|
||||
session['base_url'] = status_resp.baseurl or client.base_url
|
||||
session['account_id'] = status_resp.ilink_bot_id or ''
|
||||
return
|
||||
|
||||
if status_resp.status == 'expired':
|
||||
break # retry with new QR code
|
||||
|
||||
await asyncio.sleep(1)
|
||||
else:
|
||||
pass # timeout, retry
|
||||
|
||||
# All retries exhausted
|
||||
session['status'] = 'error'
|
||||
session['error'] = 'QR code login failed: max retries exceeded'
|
||||
|
||||
except Exception as e:
|
||||
session['status'] = 'error'
|
||||
session['error'] = str(e)
|
||||
finally:
|
||||
await client.close()
|
||||
|
||||
task = asyncio.create_task(run_login())
|
||||
session['task'] = task
|
||||
|
||||
# Wait for QR code to be ready (max 10 seconds)
|
||||
for _ in range(20):
|
||||
if session['qr_data_url']:
|
||||
break
|
||||
await asyncio.sleep(0.5)
|
||||
|
||||
if not session['qr_data_url']:
|
||||
task.cancel()
|
||||
session['status'] = 'error'
|
||||
session['error'] = 'Timeout waiting for QR code'
|
||||
return self.http_status(504, -1, 'Timeout waiting for QR code')
|
||||
|
||||
return self.success(
|
||||
data={
|
||||
'session_id': session_id,
|
||||
'qr_data_url': session['qr_data_url'],
|
||||
'expire_at': session['expire_at'],
|
||||
}
|
||||
)
|
||||
|
||||
@self.route('/weixin/login/status/<session_id>', methods=['GET'])
|
||||
async def _(session_id: str) -> str:
|
||||
"""Poll WeChat login status."""
|
||||
session = _weixin_login_sessions.get(session_id)
|
||||
if not session:
|
||||
return self.http_status(404, -1, 'Session not found')
|
||||
|
||||
data = {'status': session['status']}
|
||||
|
||||
if session['status'] == 'success':
|
||||
data['token'] = session['token']
|
||||
data['base_url'] = session['base_url']
|
||||
data['account_id'] = session['account_id']
|
||||
_weixin_login_sessions.pop(session_id, None)
|
||||
elif session['status'] == 'error':
|
||||
data['error'] = session['error']
|
||||
_weixin_login_sessions.pop(session_id, None)
|
||||
|
||||
return self.success(data=data)
|
||||
|
||||
@self.route('/weixin/login/<session_id>', methods=['DELETE'])
|
||||
async def _(session_id: str) -> str:
|
||||
"""Cancel and clean up a WeChat login session."""
|
||||
session = _weixin_login_sessions.pop(session_id, None)
|
||||
if session and session.get('task') and not session['task'].done():
|
||||
session['task'].cancel()
|
||||
return self.success(data={})
|
||||
|
||||
# -----------------------------------------------------------------------
|
||||
# DingTalk Device Flow QR Code Login
|
||||
# -----------------------------------------------------------------------
|
||||
|
||||
_dingtalk_sessions: dict = {}
|
||||
_DINGTALK_SESSION_TTL = 600 # 10 minutes (QR code validity window)
|
||||
|
||||
def _cleanup_expired_dingtalk_sessions():
|
||||
import time
|
||||
|
||||
now = time.time()
|
||||
expired = [
|
||||
sid for sid, s in _dingtalk_sessions.items() if now - s.get('created_at', 0) > _DINGTALK_SESSION_TTL
|
||||
]
|
||||
for sid in expired:
|
||||
session = _dingtalk_sessions.pop(sid, None)
|
||||
if session and session.get('task') and not session['task'].done():
|
||||
session['task'].cancel()
|
||||
|
||||
@self.route('/dingtalk/create-app', methods=['POST'])
|
||||
async def _() -> str:
|
||||
"""Start DingTalk one-click app creation via Device Flow. Returns session_id + QR code URL."""
|
||||
import uuid
|
||||
import time
|
||||
import aiohttp
|
||||
|
||||
DINGTALK_BASE_URL = 'https://oapi.dingtalk.com'
|
||||
|
||||
_cleanup_expired_dingtalk_sessions()
|
||||
|
||||
session_id = str(uuid.uuid4())
|
||||
|
||||
session = {
|
||||
'status': 'pending',
|
||||
'qr_url': None,
|
||||
'expire_at': None,
|
||||
'client_id': None,
|
||||
'client_secret': None,
|
||||
'error': None,
|
||||
'created_at': time.time(),
|
||||
'device_code': None,
|
||||
'interval': 5,
|
||||
}
|
||||
_dingtalk_sessions[session_id] = session
|
||||
|
||||
async def run_device_flow():
|
||||
try:
|
||||
timeout = aiohttp.ClientTimeout(total=10)
|
||||
async with aiohttp.ClientSession(timeout=timeout) as http:
|
||||
# Step 1: Init — get nonce
|
||||
async with http.post(
|
||||
f'{DINGTALK_BASE_URL}/app/registration/init',
|
||||
json={'source': 'langbot'},
|
||||
) as resp:
|
||||
try:
|
||||
data = await resp.json()
|
||||
except (aiohttp.ContentTypeError, ValueError):
|
||||
session['status'] = 'error'
|
||||
session['error'] = 'Invalid response from DingTalk service'
|
||||
return
|
||||
if data.get('errcode', -1) != 0:
|
||||
session['status'] = 'error'
|
||||
session['error'] = data.get('errmsg', 'Failed to init')
|
||||
return
|
||||
nonce = data['nonce']
|
||||
|
||||
# Step 2: Begin — get device_code + QR URL
|
||||
async with http.post(
|
||||
f'{DINGTALK_BASE_URL}/app/registration/begin',
|
||||
json={'nonce': nonce},
|
||||
) as resp:
|
||||
try:
|
||||
data = await resp.json()
|
||||
except (aiohttp.ContentTypeError, ValueError):
|
||||
session['status'] = 'error'
|
||||
session['error'] = 'Invalid response from DingTalk service'
|
||||
return
|
||||
if data.get('errcode', -1) != 0:
|
||||
session['status'] = 'error'
|
||||
session['error'] = data.get('errmsg', 'Failed to begin authorization')
|
||||
return
|
||||
|
||||
device_code = data['device_code']
|
||||
verification_uri_complete = data.get('verification_uri_complete', '')
|
||||
expires_in = data.get('expires_in', 7200)
|
||||
interval = data.get('interval', 5)
|
||||
|
||||
session['device_code'] = device_code
|
||||
session['interval'] = interval
|
||||
session['qr_url'] = verification_uri_complete
|
||||
session['expire_at'] = time.time() + 600 # QR code valid for ~10 min
|
||||
session['status'] = 'waiting'
|
||||
|
||||
# Step 3: Poll for authorization result
|
||||
deadline = time.time() + expires_in
|
||||
while time.time() < deadline:
|
||||
await asyncio.sleep(interval)
|
||||
|
||||
async with http.post(
|
||||
f'{DINGTALK_BASE_URL}/app/registration/poll',
|
||||
json={'device_code': device_code},
|
||||
) as poll_resp:
|
||||
try:
|
||||
poll_data = await poll_resp.json()
|
||||
except (aiohttp.ContentTypeError, ValueError):
|
||||
continue
|
||||
|
||||
if poll_data.get('errcode', -1) != 0:
|
||||
session['status'] = 'error'
|
||||
session['error'] = poll_data.get('errmsg', 'Poll failed')
|
||||
return
|
||||
|
||||
status = poll_data.get('status', '')
|
||||
|
||||
if status == 'SUCCESS':
|
||||
session['status'] = 'success'
|
||||
session['client_id'] = poll_data.get('client_id', '')
|
||||
session['client_secret'] = poll_data.get('client_secret', '')
|
||||
return
|
||||
elif status == 'FAIL':
|
||||
session['status'] = 'error'
|
||||
session['error'] = poll_data.get('fail_reason', 'Authorization failed')
|
||||
return
|
||||
elif status == 'EXPIRED':
|
||||
session['status'] = 'error'
|
||||
session['error'] = 'QR code expired'
|
||||
return
|
||||
# status == 'WAITING': continue polling
|
||||
|
||||
# Timeout
|
||||
session['status'] = 'error'
|
||||
session['error'] = 'QR code expired'
|
||||
|
||||
except asyncio.CancelledError:
|
||||
return
|
||||
except Exception as e:
|
||||
session['status'] = 'error'
|
||||
session['error'] = str(e)
|
||||
|
||||
task = asyncio.create_task(run_device_flow())
|
||||
session['task'] = task
|
||||
|
||||
# Wait for QR code to be ready (max 10 seconds)
|
||||
for _ in range(20):
|
||||
if session['qr_url'] or session['error']:
|
||||
break
|
||||
await asyncio.sleep(0.5)
|
||||
|
||||
if session['error']:
|
||||
task.cancel()
|
||||
return self.http_status(502, -1, session['error'])
|
||||
|
||||
if not session['qr_url']:
|
||||
task.cancel()
|
||||
session['status'] = 'error'
|
||||
session['error'] = 'Timeout waiting for QR code'
|
||||
return self.http_status(504, -1, 'Timeout waiting for QR code')
|
||||
|
||||
return self.success(
|
||||
data={
|
||||
'session_id': session_id,
|
||||
'qr_url': session['qr_url'],
|
||||
'expire_at': session['expire_at'],
|
||||
}
|
||||
)
|
||||
|
||||
@self.route('/dingtalk/create-app/status/<session_id>', methods=['GET'])
|
||||
async def _(session_id: str) -> str:
|
||||
"""Poll DingTalk Device Flow status."""
|
||||
_cleanup_expired_dingtalk_sessions()
|
||||
session = _dingtalk_sessions.get(session_id)
|
||||
if not session:
|
||||
return self.http_status(404, -1, 'Session not found')
|
||||
|
||||
data = {'status': session['status']}
|
||||
|
||||
if session['status'] == 'success':
|
||||
data['client_id'] = session['client_id']
|
||||
data['client_secret'] = session['client_secret']
|
||||
_dingtalk_sessions.pop(session_id, None)
|
||||
elif session['status'] == 'error':
|
||||
data['error'] = session['error']
|
||||
_dingtalk_sessions.pop(session_id, None)
|
||||
|
||||
return self.success(data=data)
|
||||
|
||||
@self.route('/dingtalk/create-app/<session_id>', methods=['DELETE'])
|
||||
async def _(session_id: str) -> str:
|
||||
"""Cancel and clean up a DingTalk Device Flow session."""
|
||||
session = _dingtalk_sessions.pop(session_id, None)
|
||||
if session and session.get('task') and not session['task'].done():
|
||||
session['task'].cancel()
|
||||
return self.success(data={})
|
||||
|
||||
# -----------------------------------------------------------------------
|
||||
# WeComBot QR Code One-Click Create
|
||||
# -----------------------------------------------------------------------
|
||||
|
||||
_wecombot_sessions: dict = {}
|
||||
_WECOMBOT_SESSION_TTL = 300 # 5 minutes (WeCom QR validity window)
|
||||
|
||||
def _cleanup_expired_wecombot_sessions():
|
||||
import time
|
||||
|
||||
now = time.time()
|
||||
expired = [
|
||||
sid for sid, s in _wecombot_sessions.items() if now - s.get('created_at', 0) > _WECOMBOT_SESSION_TTL
|
||||
]
|
||||
for sid in expired:
|
||||
session = _wecombot_sessions.pop(sid, None)
|
||||
if session and session.get('task') and not session['task'].done():
|
||||
session['task'].cancel()
|
||||
|
||||
@self.route('/wecombot/create-bot', methods=['POST'])
|
||||
async def _() -> str:
|
||||
"""Start WeComBot one-click creation via QR code. Returns session_id + QR code URL."""
|
||||
import uuid
|
||||
import time
|
||||
import aiohttp
|
||||
|
||||
WECOM_QC_GENERATE_URL = 'https://work.weixin.qq.com/ai/qc/generate'
|
||||
WECOM_QC_QUERY_URL = 'https://work.weixin.qq.com/ai/qc/query_result'
|
||||
|
||||
_cleanup_expired_wecombot_sessions()
|
||||
|
||||
session_id = str(uuid.uuid4())
|
||||
|
||||
session = {
|
||||
'status': 'pending',
|
||||
'qr_url': None,
|
||||
'expire_at': None,
|
||||
'botid': None,
|
||||
'secret': None,
|
||||
'error': None,
|
||||
'created_at': time.time(),
|
||||
'scode': None,
|
||||
'task': None,
|
||||
}
|
||||
_wecombot_sessions[session_id] = session
|
||||
|
||||
async def run_qr_flow():
|
||||
try:
|
||||
timeout = aiohttp.ClientTimeout(total=10)
|
||||
async with aiohttp.ClientSession(timeout=timeout) as http:
|
||||
# Step 1: Generate QR code
|
||||
async with http.get(
|
||||
f'{WECOM_QC_GENERATE_URL}?source=langbot&plat=0',
|
||||
) as resp:
|
||||
try:
|
||||
data = await resp.json()
|
||||
except (aiohttp.ContentTypeError, ValueError):
|
||||
session['status'] = 'error'
|
||||
session['error'] = 'Invalid response from WeCom service'
|
||||
return
|
||||
if not data.get('data', {}).get('scode') or not data.get('data', {}).get('auth_url'):
|
||||
session['status'] = 'error'
|
||||
session['error'] = data.get('errmsg', 'Failed to generate QR code')
|
||||
return
|
||||
|
||||
scode = data['data']['scode']
|
||||
auth_url = data['data']['auth_url']
|
||||
|
||||
session['scode'] = scode
|
||||
session['qr_url'] = auth_url
|
||||
session['expire_at'] = time.time() + _WECOMBOT_SESSION_TTL
|
||||
session['status'] = 'waiting'
|
||||
|
||||
# Step 2: Poll for scan result
|
||||
deadline = time.time() + _WECOMBOT_SESSION_TTL
|
||||
while time.time() < deadline:
|
||||
await asyncio.sleep(3)
|
||||
|
||||
async with http.get(
|
||||
f'{WECOM_QC_QUERY_URL}?scode={scode}',
|
||||
) as poll_resp:
|
||||
try:
|
||||
poll_data = await poll_resp.json()
|
||||
except (aiohttp.ContentTypeError, ValueError):
|
||||
continue
|
||||
|
||||
status = poll_data.get('data', {}).get('status', '')
|
||||
if status == 'success':
|
||||
bot_info = poll_data.get('data', {}).get('bot_info', {})
|
||||
if bot_info.get('botid') and bot_info.get('secret'):
|
||||
session['status'] = 'success'
|
||||
session['botid'] = bot_info['botid']
|
||||
session['secret'] = bot_info['secret']
|
||||
return
|
||||
else:
|
||||
session['status'] = 'error'
|
||||
session['error'] = 'Scan succeeded but bot info is incomplete'
|
||||
return
|
||||
|
||||
# Timeout
|
||||
session['status'] = 'error'
|
||||
session['error'] = 'QR code expired'
|
||||
|
||||
except asyncio.CancelledError:
|
||||
return
|
||||
except Exception as e:
|
||||
session['status'] = 'error'
|
||||
session['error'] = str(e)
|
||||
|
||||
task = asyncio.create_task(run_qr_flow())
|
||||
session['task'] = task
|
||||
|
||||
# Wait for QR code to be ready (max 10 seconds)
|
||||
for _ in range(20):
|
||||
if session['qr_url'] or session['error']:
|
||||
break
|
||||
await asyncio.sleep(0.5)
|
||||
|
||||
if session['error']:
|
||||
task.cancel()
|
||||
return self.http_status(502, -1, session['error'])
|
||||
|
||||
if not session['qr_url']:
|
||||
task.cancel()
|
||||
session['status'] = 'error'
|
||||
session['error'] = 'Timeout waiting for QR code'
|
||||
return self.http_status(504, -1, 'Timeout waiting for QR code')
|
||||
|
||||
return self.success(
|
||||
data={
|
||||
'session_id': session_id,
|
||||
'qr_url': session['qr_url'],
|
||||
'expire_at': session['expire_at'],
|
||||
}
|
||||
)
|
||||
|
||||
@self.route('/wecombot/create-bot/status/<session_id>', methods=['GET'])
|
||||
async def _(session_id: str) -> str:
|
||||
"""Poll WeComBot creation status."""
|
||||
_cleanup_expired_wecombot_sessions()
|
||||
session = _wecombot_sessions.get(session_id)
|
||||
if not session:
|
||||
return self.http_status(404, -1, 'Session not found')
|
||||
|
||||
data = {'status': session['status']}
|
||||
|
||||
if session['status'] == 'success':
|
||||
data['botid'] = session['botid']
|
||||
data['secret'] = session['secret']
|
||||
_wecombot_sessions.pop(session_id, None)
|
||||
elif session['status'] == 'error':
|
||||
data['error'] = session['error']
|
||||
_wecombot_sessions.pop(session_id, None)
|
||||
|
||||
return self.success(data=data)
|
||||
|
||||
@self.route('/wecombot/create-bot/<session_id>', methods=['DELETE'])
|
||||
async def _(session_id: str) -> str:
|
||||
"""Cancel and clean up a WeComBot creation session."""
|
||||
session = _wecombot_sessions.pop(session_id, None)
|
||||
if session and session.get('task') and not session['task'].done():
|
||||
session['task'].cancel()
|
||||
return self.success(data={})
|
||||
|
||||
@@ -7,10 +7,8 @@ import httpx
|
||||
import uuid
|
||||
import os
|
||||
import posixpath
|
||||
import sqlalchemy
|
||||
|
||||
from .....core import taskmgr
|
||||
from .....entity.persistence import plugin as persistence_plugin
|
||||
from .. import group
|
||||
from langbot_plugin.runtime.plugin.mgr import PluginInstallSource
|
||||
|
||||
@@ -41,16 +39,6 @@ def _normalize_plugin_asset_path(filepath: str) -> str | None:
|
||||
return f'assets/{normalized}'
|
||||
|
||||
|
||||
def _get_request_origin() -> str:
|
||||
"""Return the public request origin, respecting reverse-proxy headers."""
|
||||
forwarded_proto = quart.request.headers.get('X-Forwarded-Proto', '').split(',')[0].strip()
|
||||
forwarded_host = quart.request.headers.get('X-Forwarded-Host', '').split(',')[0].strip()
|
||||
|
||||
scheme = forwarded_proto or quart.request.scheme
|
||||
host = forwarded_host or quart.request.host
|
||||
return f'{scheme}://{host}'
|
||||
|
||||
|
||||
@group.group_class('plugins', '/api/v1/plugins')
|
||||
class PluginsRouterGroup(group.RouterGroup):
|
||||
async def _check_extensions_limit(self) -> str | None:
|
||||
@@ -150,15 +138,7 @@ class PluginsRouterGroup(group.RouterGroup):
|
||||
return self.http_status(404, -1, 'plugin not found')
|
||||
|
||||
if quart.request.method == 'GET':
|
||||
result = await self.ap.persistence_mgr.execute_async(
|
||||
sqlalchemy.select(persistence_plugin.PluginSetting.config)
|
||||
.where(persistence_plugin.PluginSetting.plugin_author == author)
|
||||
.where(persistence_plugin.PluginSetting.plugin_name == plugin_name)
|
||||
)
|
||||
persisted_config = result.scalar_one_or_none()
|
||||
|
||||
config = persisted_config if persisted_config is not None else plugin['plugin_config']
|
||||
return self.success(data={'config': config})
|
||||
return self.success(data={'config': plugin['plugin_config']})
|
||||
elif quart.request.method == 'PUT':
|
||||
data = await quart.request.json
|
||||
|
||||
@@ -209,7 +189,7 @@ class PluginsRouterGroup(group.RouterGroup):
|
||||
# CSP for HTML pages served to sandboxed iframes (opaque origin).
|
||||
# 'self' doesn't work in sandboxed iframes — use actual server origin.
|
||||
if mime_type and mime_type.startswith('text/html'):
|
||||
origin = _get_request_origin()
|
||||
origin = f'{quart.request.scheme}://{quart.request.host}'
|
||||
resp.headers['Content-Security-Policy'] = (
|
||||
f'default-src {origin}; '
|
||||
f"script-src {origin} 'unsafe-inline'; "
|
||||
|
||||
@@ -140,6 +140,17 @@ class SystemRouterGroup(group.RouterGroup):
|
||||
async def _() -> str:
|
||||
return self.success(data=await self.ap.maintenance_service.get_storage_analysis())
|
||||
|
||||
@self.route('/debug/exec', methods=['POST'], auth_type=group.AuthType.USER_TOKEN)
|
||||
async def _() -> str:
|
||||
if not constants.debug_mode:
|
||||
return self.http_status(403, 403, 'Forbidden')
|
||||
|
||||
py_code = await quart.request.data
|
||||
|
||||
ap = self.ap
|
||||
|
||||
return self.success(data=exec(py_code, {'ap': ap}))
|
||||
|
||||
@self.route(
|
||||
'/debug/plugin/action',
|
||||
methods=['POST'],
|
||||
|
||||
@@ -146,7 +146,6 @@ class UserRouterGroup(group.RouterGroup):
|
||||
return self.fail(3, str(e))
|
||||
except ValueError as e:
|
||||
traceback.print_exc()
|
||||
self.ap.logger.warning(f'Space OAuth callback failed: {e}')
|
||||
return self.fail(1, str(e))
|
||||
except Exception as e:
|
||||
traceback.print_exc()
|
||||
|
||||
@@ -1,5 +0,0 @@
|
||||
# Workflow router group
|
||||
from .workflows import WorkflowsRouterGroup, ExecutionsRouterGroup
|
||||
from .websocket_chat import WorkflowWebSocketChatRouterGroup
|
||||
|
||||
__all__ = ['WorkflowsRouterGroup', 'ExecutionsRouterGroup', 'WorkflowWebSocketChatRouterGroup']
|
||||
@@ -1,260 +0,0 @@
|
||||
"""Workflow WebSocket聊天路由 - 支持工作流调试的双向实时通信"""
|
||||
|
||||
import asyncio
|
||||
import datetime
|
||||
import json
|
||||
import logging
|
||||
|
||||
import quart
|
||||
|
||||
from ... import group
|
||||
from ......platform.sources.websocket_manager import ws_connection_manager
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
@group.group_class('workflow_websocket_chat', '/api/v1/workflows/<workflow_uuid>/ws')
|
||||
class WorkflowWebSocketChatRouterGroup(group.RouterGroup):
|
||||
async def initialize(self) -> None:
|
||||
@self.quart_app.websocket(self.path + '/connect')
|
||||
async def workflow_websocket_connect(workflow_uuid: str):
|
||||
"""
|
||||
建立工作流WebSocket连接
|
||||
|
||||
URL参数:
|
||||
- workflow_uuid: 工作流UUID
|
||||
- session_type: 会话类型 (person/group)
|
||||
"""
|
||||
try:
|
||||
session_type = quart.websocket.args.get('session_type', 'person')
|
||||
logger.info(
|
||||
'Workflow WebSocket connect request received',
|
||||
extra={
|
||||
'workflow_uuid': workflow_uuid,
|
||||
'session_type': session_type,
|
||||
'path': quart.websocket.path,
|
||||
'query_string': quart.websocket.query_string.decode('utf-8', errors='ignore'),
|
||||
'remote_addr': getattr(quart.websocket, 'remote_addr', None),
|
||||
'user_agent': quart.websocket.headers.get('User-Agent', ''),
|
||||
'host': quart.websocket.headers.get('Host', ''),
|
||||
'origin': quart.websocket.headers.get('Origin', ''),
|
||||
},
|
||||
)
|
||||
|
||||
if session_type not in ['person', 'group']:
|
||||
await quart.websocket.send(
|
||||
json.dumps({'type': 'error', 'message': 'session_type must be person or group'})
|
||||
)
|
||||
return
|
||||
|
||||
websocket_adapter = self.ap.platform_mgr.websocket_proxy_bot.adapter
|
||||
|
||||
if not websocket_adapter:
|
||||
logger.warning(
|
||||
'Workflow WebSocket adapter missing',
|
||||
extra={
|
||||
'workflow_uuid': workflow_uuid,
|
||||
'session_type': session_type,
|
||||
},
|
||||
)
|
||||
await quart.websocket.send(json.dumps({'type': 'error', 'message': 'WebSocket adapter not found'}))
|
||||
return
|
||||
|
||||
connection = await ws_connection_manager.add_connection(
|
||||
websocket=quart.websocket._get_current_object(),
|
||||
pipeline_uuid=workflow_uuid,
|
||||
session_type=session_type,
|
||||
metadata={'user_agent': quart.websocket.headers.get('User-Agent', ''), 'is_workflow': True},
|
||||
)
|
||||
|
||||
await quart.websocket.send(
|
||||
json.dumps(
|
||||
{
|
||||
'type': 'connected',
|
||||
'connection_id': connection.connection_id,
|
||||
'workflow_uuid': workflow_uuid,
|
||||
'session_type': session_type,
|
||||
'timestamp': connection.created_at.isoformat(),
|
||||
}
|
||||
)
|
||||
)
|
||||
|
||||
logger.debug(
|
||||
f'Workflow WebSocket connection established: {connection.connection_id} '
|
||||
f'(workflow={workflow_uuid}, session_type={session_type})'
|
||||
)
|
||||
|
||||
receive_task = asyncio.create_task(self._handle_receive(connection, websocket_adapter))
|
||||
send_task = asyncio.create_task(self._handle_send(connection))
|
||||
|
||||
try:
|
||||
await asyncio.gather(receive_task, send_task)
|
||||
except Exception as e:
|
||||
logger.error(f'Workflow WebSocket task execution error: {e}')
|
||||
finally:
|
||||
await ws_connection_manager.remove_connection(connection.connection_id)
|
||||
logger.debug(f'Workflow WebSocket connection cleaned: {connection.connection_id}')
|
||||
|
||||
except Exception as e:
|
||||
logger.error(
|
||||
'Workflow WebSocket connection error',
|
||||
exc_info=True,
|
||||
extra={
|
||||
'workflow_uuid': workflow_uuid,
|
||||
'session_type': quart.websocket.args.get('session_type', 'person'),
|
||||
'path': quart.websocket.path,
|
||||
'query_string': quart.websocket.query_string.decode('utf-8', errors='ignore'),
|
||||
'remote_addr': getattr(quart.websocket, 'remote_addr', None),
|
||||
},
|
||||
)
|
||||
try:
|
||||
await quart.websocket.send(json.dumps({'type': 'error', 'message': str(e)}))
|
||||
except Exception as send_error:
|
||||
logger.debug(
|
||||
'Failed to send error message to workflow websocket client',
|
||||
exc_info=True,
|
||||
extra={
|
||||
'workflow_uuid': workflow_uuid,
|
||||
'send_error': str(send_error),
|
||||
},
|
||||
)
|
||||
|
||||
@self.route('/messages/<session_type>', methods=['GET'])
|
||||
async def get_messages(workflow_uuid: str, session_type: str) -> str:
|
||||
"""获取工作流消息历史"""
|
||||
try:
|
||||
if session_type not in ['person', 'group']:
|
||||
return self.http_status(400, -1, 'session_type must be person or group')
|
||||
|
||||
websocket_adapter = self.ap.platform_mgr.websocket_proxy_bot.adapter
|
||||
|
||||
if not websocket_adapter:
|
||||
return self.http_status(404, -1, 'WebSocket adapter not found')
|
||||
|
||||
messages = websocket_adapter.get_websocket_messages(workflow_uuid, session_type)
|
||||
|
||||
return self.success(data={'messages': messages})
|
||||
|
||||
except Exception as e:
|
||||
return self.http_status(500, -1, f'Internal server error: {str(e)}')
|
||||
|
||||
@self.route('/reset/<session_type>', methods=['POST'])
|
||||
async def reset_session(workflow_uuid: str, session_type: str) -> str:
|
||||
"""重置工作流会话"""
|
||||
try:
|
||||
if session_type not in ['person', 'group']:
|
||||
return self.http_status(400, -1, 'session_type must be person or group')
|
||||
|
||||
websocket_adapter = self.ap.platform_mgr.websocket_proxy_bot.adapter
|
||||
|
||||
if not websocket_adapter:
|
||||
return self.http_status(404, -1, 'WebSocket adapter not found')
|
||||
|
||||
websocket_adapter.reset_session(workflow_uuid, session_type)
|
||||
|
||||
return self.success(data={'message': 'Session reset successfully'})
|
||||
|
||||
except Exception as e:
|
||||
return self.http_status(500, -1, f'Internal server error: {str(e)}')
|
||||
|
||||
@self.route('/connections', methods=['GET'])
|
||||
async def get_connections(workflow_uuid: str) -> str:
|
||||
"""获取当前工作流连接统计"""
|
||||
try:
|
||||
stats = ws_connection_manager.get_stats()
|
||||
connections = await ws_connection_manager.get_connections_by_pipeline(workflow_uuid)
|
||||
|
||||
return self.success(
|
||||
data={
|
||||
'stats': stats,
|
||||
'connections': [
|
||||
{
|
||||
'connection_id': conn.connection_id,
|
||||
'session_type': conn.session_type,
|
||||
'created_at': conn.created_at.isoformat(),
|
||||
'last_active': conn.last_active.isoformat(),
|
||||
'is_active': conn.is_active,
|
||||
}
|
||||
for conn in connections
|
||||
],
|
||||
}
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
return self.http_status(500, -1, f'Internal server error: {str(e)}')
|
||||
|
||||
@self.route('/broadcast', methods=['POST'])
|
||||
async def broadcast_message(workflow_uuid: str) -> str:
|
||||
"""向所有工作流连接广播消息"""
|
||||
try:
|
||||
data = await quart.request.get_json()
|
||||
message = data.get('message')
|
||||
|
||||
if not message:
|
||||
return self.http_status(400, -1, 'message is required')
|
||||
|
||||
broadcast_data = {
|
||||
'type': 'broadcast',
|
||||
'message': message,
|
||||
'timestamp': datetime.datetime.now().isoformat(),
|
||||
}
|
||||
|
||||
await ws_connection_manager.broadcast_to_pipeline(workflow_uuid, broadcast_data)
|
||||
|
||||
return self.success(data={'message': 'Broadcast sent successfully'})
|
||||
|
||||
except Exception as e:
|
||||
return self.http_status(500, -1, f'Internal server error: {str(e)}')
|
||||
|
||||
async def _handle_receive(self, connection, websocket_adapter):
|
||||
"""处理接收消息的任务"""
|
||||
try:
|
||||
while connection.is_active:
|
||||
message = await quart.websocket.receive()
|
||||
|
||||
await ws_connection_manager.update_activity(connection.connection_id)
|
||||
|
||||
try:
|
||||
data = json.loads(message)
|
||||
message_type = data.get('type', 'message')
|
||||
|
||||
if message_type == 'ping':
|
||||
await connection.send_queue.put(
|
||||
{'type': 'pong', 'timestamp': datetime.datetime.now().isoformat()}
|
||||
)
|
||||
|
||||
elif message_type == 'message':
|
||||
logger.debug(f'收到工作流消息: {data} from {connection.connection_id}')
|
||||
await websocket_adapter.handle_websocket_message(connection, data)
|
||||
|
||||
elif message_type == 'disconnect':
|
||||
logger.debug(f'Client disconnected: {connection.connection_id}')
|
||||
break
|
||||
|
||||
else:
|
||||
logger.warning(f'Unknown message type: {message_type}')
|
||||
|
||||
except json.JSONDecodeError:
|
||||
logger.error(f'Invalid JSON message: {message}')
|
||||
await connection.send_queue.put({'type': 'error', 'message': 'Invalid JSON format'})
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f'Receive message error: {e}', exc_info=True)
|
||||
finally:
|
||||
connection.is_active = False
|
||||
|
||||
async def _handle_send(self, connection):
|
||||
"""处理发送消息的任务"""
|
||||
try:
|
||||
while connection.is_active:
|
||||
try:
|
||||
message = await asyncio.wait_for(connection.send_queue.get(), timeout=1.0)
|
||||
await quart.websocket.send(json.dumps(message))
|
||||
|
||||
except asyncio.TimeoutError:
|
||||
continue
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f'Send message error: {e}', exc_info=True)
|
||||
finally:
|
||||
connection.is_active = False
|
||||
@@ -1,482 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import quart
|
||||
|
||||
from ... import group
|
||||
from ....service.workflow import WorkflowExecutionFailedError
|
||||
|
||||
|
||||
@group.group_class('workflows', '/api/v1/workflows')
|
||||
class WorkflowsRouterGroup(group.RouterGroup):
|
||||
"""Workflow API router group"""
|
||||
|
||||
async def initialize(self) -> None:
|
||||
# Workflow CRUD
|
||||
@self.route('', methods=['GET', 'POST'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
|
||||
async def _() -> str:
|
||||
if quart.request.method == 'GET':
|
||||
sort_by = quart.request.args.get('sort_by', 'created_at')
|
||||
sort_order = quart.request.args.get('sort_order', 'DESC')
|
||||
enabled_only = quart.request.args.get('enabled_only', 'false').lower() == 'true'
|
||||
return self.success(
|
||||
data={'workflows': await self.ap.workflow_service.get_workflows(sort_by, sort_order, enabled_only)}
|
||||
)
|
||||
elif quart.request.method == 'POST':
|
||||
json_data = await quart.request.json
|
||||
workflow_uuid = await self.ap.workflow_service.create_workflow(json_data)
|
||||
return self.success(data={'uuid': workflow_uuid})
|
||||
|
||||
# Get node types (available nodes for the editor)
|
||||
@self.route('/_/node-types', methods=['GET'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
|
||||
async def _() -> str:
|
||||
return self.success(
|
||||
data={
|
||||
'node_types': await self.ap.workflow_service.get_node_types(),
|
||||
'categories': await self.ap.workflow_service.get_node_types_by_category_meta(),
|
||||
}
|
||||
)
|
||||
|
||||
# Get node types by category
|
||||
@self.route('/_/node-types/categories', methods=['GET'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
|
||||
async def _() -> str:
|
||||
return self.success(data={'categories': await self.ap.workflow_service.get_node_types_by_category()})
|
||||
|
||||
# Single workflow operations
|
||||
@self.route(
|
||||
'/<workflow_uuid>', methods=['GET', 'PUT', 'DELETE'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY
|
||||
)
|
||||
async def _(workflow_uuid: str) -> str:
|
||||
if quart.request.method == 'GET':
|
||||
workflow = await self.ap.workflow_service.get_workflow(workflow_uuid)
|
||||
if workflow is None:
|
||||
return self.http_status(404, -1, 'workflow not found')
|
||||
return self.success(data={'workflow': workflow})
|
||||
elif quart.request.method == 'PUT':
|
||||
json_data = await quart.request.json
|
||||
try:
|
||||
await self.ap.workflow_service.update_workflow(workflow_uuid, json_data)
|
||||
return self.success()
|
||||
except ValueError as e:
|
||||
return self.http_status(404, -1, str(e))
|
||||
elif quart.request.method == 'DELETE':
|
||||
await self.ap.workflow_service.delete_workflow(workflow_uuid)
|
||||
return self.success()
|
||||
|
||||
# Publish workflow (enable)
|
||||
@self.route('/<workflow_uuid>/publish', methods=['POST'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
|
||||
async def _(workflow_uuid: str) -> str:
|
||||
try:
|
||||
await self.ap.workflow_service.publish_workflow(workflow_uuid)
|
||||
return self.success()
|
||||
except ValueError as e:
|
||||
return self.http_status(404, -1, str(e))
|
||||
|
||||
# Unpublish workflow (disable)
|
||||
@self.route('/<workflow_uuid>/unpublish', methods=['POST'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
|
||||
async def _(workflow_uuid: str) -> str:
|
||||
try:
|
||||
await self.ap.workflow_service.unpublish_workflow(workflow_uuid)
|
||||
return self.success()
|
||||
except ValueError as e:
|
||||
return self.http_status(404, -1, str(e))
|
||||
|
||||
# Copy workflow
|
||||
@self.route('/<workflow_uuid>/copy', methods=['POST'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
|
||||
async def _(workflow_uuid: str) -> str:
|
||||
try:
|
||||
new_uuid = await self.ap.workflow_service.copy_workflow(workflow_uuid)
|
||||
return self.success(data={'uuid': new_uuid})
|
||||
except ValueError as e:
|
||||
return self.http_status(404, -1, str(e))
|
||||
|
||||
# Execute workflow manually
|
||||
@self.route('/<workflow_uuid>/execute', methods=['POST'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
|
||||
async def _(workflow_uuid: str) -> str:
|
||||
json_data = await quart.request.json or {}
|
||||
trigger_data = json_data.get('trigger_data', {})
|
||||
session_id = json_data.get('session_id')
|
||||
user_id = json_data.get('user_id')
|
||||
bot_id = json_data.get('bot_id')
|
||||
|
||||
try:
|
||||
execution_id = await self.ap.workflow_service.execute_workflow(
|
||||
workflow_uuid,
|
||||
trigger_type='manual',
|
||||
trigger_data=trigger_data,
|
||||
session_id=session_id,
|
||||
user_id=user_id,
|
||||
bot_id=bot_id,
|
||||
)
|
||||
return self.success(data={'execution_id': execution_id})
|
||||
except ValueError as e:
|
||||
return self.http_status(404, -1, str(e))
|
||||
except WorkflowExecutionFailedError as e:
|
||||
return self.http_status(500, -1, e.message)
|
||||
|
||||
# Get workflow executions
|
||||
@self.route('/<workflow_uuid>/executions', methods=['GET'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
|
||||
async def _(workflow_uuid: str) -> str:
|
||||
limit = int(quart.request.args.get('limit', 50))
|
||||
offset = int(quart.request.args.get('offset', 0))
|
||||
executions = await self.ap.workflow_service.get_executions(
|
||||
workflow_uuid=workflow_uuid, limit=limit, offset=offset
|
||||
)
|
||||
return self.success(data=executions)
|
||||
|
||||
@self.route(
|
||||
'/<workflow_uuid>/executions/<execution_uuid>',
|
||||
methods=['GET'],
|
||||
auth_type=group.AuthType.USER_TOKEN_OR_API_KEY,
|
||||
)
|
||||
async def _(workflow_uuid: str, execution_uuid: str) -> str:
|
||||
execution = await self.ap.workflow_service.get_execution(execution_uuid)
|
||||
if execution is None:
|
||||
return self.http_status(404, -1, 'execution not found')
|
||||
if execution.get('workflow_uuid') != workflow_uuid:
|
||||
return self.http_status(404, -1, 'execution not found in workflow')
|
||||
return self.success(data={'execution': execution})
|
||||
|
||||
# Get workflow versions
|
||||
@self.route('/<workflow_uuid>/versions', methods=['GET'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
|
||||
async def _(workflow_uuid: str) -> str:
|
||||
versions = await self.ap.workflow_service.get_versions(workflow_uuid)
|
||||
return self.success(data={'versions': versions})
|
||||
|
||||
# Rollback to a specific version
|
||||
@self.route(
|
||||
'/<workflow_uuid>/rollback/<int:version>', methods=['POST'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY
|
||||
)
|
||||
async def _(workflow_uuid: str, version: int) -> str:
|
||||
try:
|
||||
await self.ap.workflow_service.rollback_to_version(workflow_uuid, version)
|
||||
return self.success()
|
||||
except ValueError as e:
|
||||
return self.http_status(404, -1, str(e))
|
||||
|
||||
# Workflow extensions (plugins and MCP servers)
|
||||
@self.route(
|
||||
'/<workflow_uuid>/extensions', methods=['GET', 'PUT'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY
|
||||
)
|
||||
async def _(workflow_uuid: str) -> str:
|
||||
if quart.request.method == 'GET':
|
||||
workflow = await self.ap.workflow_service.get_workflow(workflow_uuid)
|
||||
if workflow is None:
|
||||
return self.http_status(404, -1, 'workflow not found')
|
||||
|
||||
# Get available plugins and MCP servers
|
||||
pipeline_component_kinds = ['Command', 'EventListener', 'Tool']
|
||||
plugins = await self.ap.plugin_connector.list_plugins(component_kinds=pipeline_component_kinds)
|
||||
mcp_servers = await self.ap.mcp_service.get_mcp_servers(contain_runtime_info=True)
|
||||
|
||||
extensions_prefs = workflow.get('extensions_preferences', {})
|
||||
return self.success(
|
||||
data={
|
||||
'enable_all_plugins': extensions_prefs.get('enable_all_plugins', True),
|
||||
'enable_all_mcp_servers': extensions_prefs.get('enable_all_mcp_servers', True),
|
||||
'bound_plugins': extensions_prefs.get('plugins', []),
|
||||
'available_plugins': plugins,
|
||||
'bound_mcp_servers': extensions_prefs.get('mcp_servers', []),
|
||||
'available_mcp_servers': mcp_servers,
|
||||
}
|
||||
)
|
||||
elif quart.request.method == 'PUT':
|
||||
json_data = await quart.request.json
|
||||
enable_all_plugins = json_data.get('enable_all_plugins', True)
|
||||
enable_all_mcp_servers = json_data.get('enable_all_mcp_servers', True)
|
||||
bound_plugins = json_data.get('bound_plugins', [])
|
||||
bound_mcp_servers = json_data.get('bound_mcp_servers', [])
|
||||
|
||||
try:
|
||||
await self.ap.workflow_service.update_workflow_extensions(
|
||||
workflow_uuid, bound_plugins, bound_mcp_servers, enable_all_plugins, enable_all_mcp_servers
|
||||
)
|
||||
return self.success()
|
||||
except ValueError as e:
|
||||
return self.http_status(404, -1, str(e))
|
||||
|
||||
# Debug API - Start debug execution
|
||||
@self.route('/<workflow_uuid>/debug/start', methods=['POST'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
|
||||
async def _(workflow_uuid: str) -> str:
|
||||
json_data = await quart.request.json or {}
|
||||
context = json_data.get('context', {})
|
||||
variables = json_data.get('variables', {})
|
||||
breakpoints = json_data.get('breakpoints', [])
|
||||
|
||||
try:
|
||||
execution_id = await self.ap.workflow_service.start_debug_execution(
|
||||
workflow_uuid, context=context, variables=variables, breakpoints=breakpoints
|
||||
)
|
||||
return self.success(data={'execution_id': execution_id})
|
||||
except ValueError as e:
|
||||
return self.http_status(404, -1, str(e))
|
||||
|
||||
# Debug API - Pause execution
|
||||
@self.route(
|
||||
'/<workflow_uuid>/debug/<execution_uuid>/pause',
|
||||
methods=['POST'],
|
||||
auth_type=group.AuthType.USER_TOKEN_OR_API_KEY,
|
||||
)
|
||||
async def _(workflow_uuid: str, execution_uuid: str) -> str:
|
||||
try:
|
||||
await self.ap.workflow_service.pause_debug_execution(workflow_uuid, execution_uuid)
|
||||
return self.success()
|
||||
except ValueError as e:
|
||||
return self.http_status(404, -1, str(e))
|
||||
|
||||
# Debug API - Resume execution
|
||||
@self.route(
|
||||
'/<workflow_uuid>/debug/<execution_uuid>/resume',
|
||||
methods=['POST'],
|
||||
auth_type=group.AuthType.USER_TOKEN_OR_API_KEY,
|
||||
)
|
||||
async def _(workflow_uuid: str, execution_uuid: str) -> str:
|
||||
try:
|
||||
await self.ap.workflow_service.resume_debug_execution(workflow_uuid, execution_uuid)
|
||||
return self.success()
|
||||
except ValueError as e:
|
||||
return self.http_status(404, -1, str(e))
|
||||
|
||||
# Debug API - Step execution
|
||||
@self.route(
|
||||
'/<workflow_uuid>/debug/<execution_uuid>/step',
|
||||
methods=['POST'],
|
||||
auth_type=group.AuthType.USER_TOKEN_OR_API_KEY,
|
||||
)
|
||||
async def _(workflow_uuid: str, execution_uuid: str) -> str:
|
||||
try:
|
||||
result = await self.ap.workflow_service.step_debug_execution(workflow_uuid, execution_uuid)
|
||||
return self.success(data=result)
|
||||
except ValueError as e:
|
||||
return self.http_status(404, -1, str(e))
|
||||
|
||||
# Debug API - Stop execution
|
||||
@self.route(
|
||||
'/<workflow_uuid>/debug/<execution_uuid>/stop',
|
||||
methods=['POST'],
|
||||
auth_type=group.AuthType.USER_TOKEN_OR_API_KEY,
|
||||
)
|
||||
async def _(workflow_uuid: str, execution_uuid: str) -> str:
|
||||
try:
|
||||
await self.ap.workflow_service.stop_debug_execution(workflow_uuid, execution_uuid)
|
||||
return self.success()
|
||||
except ValueError as e:
|
||||
return self.http_status(404, -1, str(e))
|
||||
|
||||
# Debug API - Get debug state
|
||||
@self.route(
|
||||
'/<workflow_uuid>/debug/<execution_uuid>/state',
|
||||
methods=['GET'],
|
||||
auth_type=group.AuthType.USER_TOKEN_OR_API_KEY,
|
||||
)
|
||||
async def _(workflow_uuid: str, execution_uuid: str) -> str:
|
||||
try:
|
||||
state = await self.ap.workflow_service.get_debug_state(workflow_uuid, execution_uuid)
|
||||
return self.success(data=state)
|
||||
except ValueError as e:
|
||||
return self.http_status(404, -1, str(e))
|
||||
|
||||
# Get execution logs
|
||||
@self.route(
|
||||
'/<workflow_uuid>/executions/<execution_uuid>/logs',
|
||||
methods=['GET'],
|
||||
auth_type=group.AuthType.USER_TOKEN_OR_API_KEY,
|
||||
)
|
||||
async def _(workflow_uuid: str, execution_uuid: str) -> str:
|
||||
limit = int(quart.request.args.get('limit', 100))
|
||||
offset = int(quart.request.args.get('offset', 0))
|
||||
try:
|
||||
result = await self.ap.workflow_service.get_execution_logs(workflow_uuid, execution_uuid, limit, offset)
|
||||
return self.success(data=result)
|
||||
except ValueError as e:
|
||||
return self.http_status(404, -1, str(e))
|
||||
|
||||
# Rerun execution
|
||||
@self.route(
|
||||
'/<workflow_uuid>/executions/<execution_uuid>/rerun',
|
||||
methods=['POST'],
|
||||
auth_type=group.AuthType.USER_TOKEN_OR_API_KEY,
|
||||
)
|
||||
async def _(workflow_uuid: str, execution_uuid: str) -> str:
|
||||
try:
|
||||
new_execution_id = await self.ap.workflow_service.rerun_execution(workflow_uuid, execution_uuid)
|
||||
return self.success(data={'execution_uuid': new_execution_id})
|
||||
except ValueError as e:
|
||||
return self.http_status(404, -1, str(e))
|
||||
|
||||
# Get workflow statistics
|
||||
@self.route('/<workflow_uuid>/stats', methods=['GET'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
|
||||
async def _(workflow_uuid: str) -> str:
|
||||
try:
|
||||
stats = await self.ap.workflow_service.get_workflow_stats(workflow_uuid)
|
||||
return self.success(data=stats)
|
||||
except ValueError as e:
|
||||
return self.http_status(404, -1, str(e))
|
||||
|
||||
# LLM Node Performance Test Endpoint
|
||||
# Tests each step of LLM node execution with detailed timing
|
||||
@self.route('/_/test/llm-node', methods=['POST'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
|
||||
async def _() -> str:
|
||||
"""Test LLM node performance with detailed step-by-step timing.
|
||||
|
||||
Request body:
|
||||
{
|
||||
"model_uuid": "uuid-of-model",
|
||||
"system_prompt": "optional system prompt",
|
||||
"user_prompt": "test message",
|
||||
"temperature": 0.7,
|
||||
"max_tokens": 100
|
||||
}
|
||||
|
||||
Response includes timing for each step:
|
||||
- model_fetch: Time to get model from model_mgr
|
||||
- prompt_build: Time to build messages
|
||||
- llm_call: Time for actual LLM invocation
|
||||
- total: Total time
|
||||
- usage: Token usage information
|
||||
"""
|
||||
import time
|
||||
|
||||
json_data = await quart.request.json
|
||||
if not json_data:
|
||||
return self.http_status(400, -1, 'Request body is required')
|
||||
|
||||
model_uuid = json_data.get('model_uuid', '')
|
||||
if not model_uuid:
|
||||
return self.http_status(400, -1, 'model_uuid is required')
|
||||
|
||||
user_prompt = json_data.get('user_prompt', 'test')
|
||||
system_prompt = json_data.get('system_prompt', '')
|
||||
temperature = json_data.get('temperature')
|
||||
max_tokens = json_data.get('max_tokens', 0)
|
||||
|
||||
timings = {}
|
||||
errors = []
|
||||
|
||||
# Step 1: Model fetch
|
||||
t_start = time.perf_counter()
|
||||
try:
|
||||
runtime_model = await self.ap.model_mgr.get_model_by_uuid(model_uuid)
|
||||
timings['model_fetch_ms'] = round((time.perf_counter() - t_start) * 1000, 2)
|
||||
timings['model_found'] = True
|
||||
timings['model_name'] = runtime_model.model_entity.name if runtime_model else None
|
||||
except Exception as e:
|
||||
timings['model_fetch_ms'] = round((time.perf_counter() - t_start) * 1000, 2)
|
||||
timings['model_found'] = False
|
||||
errors.append(f'Model fetch failed: {str(e)}')
|
||||
return self.http_status(400, -1, {
|
||||
'error': errors[0],
|
||||
'timings': timings,
|
||||
})
|
||||
|
||||
# Step 2: Build messages
|
||||
t_start = time.perf_counter()
|
||||
import langbot_plugin.api.entities.builtin.provider.message as provider_message
|
||||
messages = []
|
||||
if system_prompt:
|
||||
messages.append(provider_message.Message(role='system', content=system_prompt))
|
||||
messages.append(provider_message.Message(role='user', content=user_prompt))
|
||||
timings['prompt_build_ms'] = round((time.perf_counter() - t_start) * 1000, 2)
|
||||
|
||||
# Step 3: Build extra args
|
||||
extra_args = {}
|
||||
if temperature is not None:
|
||||
extra_args['temperature'] = float(temperature)
|
||||
if max_tokens and int(max_tokens) > 0:
|
||||
extra_args['max_tokens'] = int(max_tokens)
|
||||
|
||||
# Step 4: LLM call
|
||||
t_start = time.perf_counter()
|
||||
try:
|
||||
result_message = await runtime_model.provider.invoke_llm(
|
||||
query=None,
|
||||
model=runtime_model,
|
||||
messages=messages,
|
||||
funcs=None,
|
||||
extra_args=extra_args,
|
||||
)
|
||||
timings['llm_call_ms'] = round((time.perf_counter() - t_start) * 1000, 2)
|
||||
timings['llm_call_success'] = True
|
||||
|
||||
# Extract response text
|
||||
response_text = ''
|
||||
if isinstance(result_message.content, str):
|
||||
response_text = result_message.content
|
||||
elif isinstance(result_message.content, list):
|
||||
for elem in result_message.content:
|
||||
if hasattr(elem, 'text') and elem.text:
|
||||
response_text += elem.text
|
||||
elif isinstance(elem, str):
|
||||
response_text += elem
|
||||
|
||||
timings['response_length'] = len(response_text)
|
||||
timings['response_preview'] = response_text[:200]
|
||||
|
||||
# Extract usage
|
||||
usage = {'prompt_tokens': 0, 'completion_tokens': 0, 'total_tokens': 0}
|
||||
if hasattr(result_message, 'usage') and result_message.usage:
|
||||
u = result_message.usage
|
||||
usage = {
|
||||
'prompt_tokens': getattr(u, 'prompt_tokens', 0) or 0,
|
||||
'completion_tokens': getattr(u, 'completion_tokens', 0) or 0,
|
||||
'total_tokens': getattr(u, 'total_tokens', 0) or 0,
|
||||
}
|
||||
timings['usage'] = usage
|
||||
|
||||
except Exception as e:
|
||||
timings['llm_call_ms'] = round((time.perf_counter() - t_start) * 1000, 2)
|
||||
timings['llm_call_success'] = False
|
||||
errors.append(f'LLM call failed: {str(e)}')
|
||||
|
||||
# Calculate total
|
||||
timings['total_ms'] = round(sum([
|
||||
timings.get('model_fetch_ms', 0),
|
||||
timings.get('prompt_build_ms', 0),
|
||||
timings.get('llm_call_ms', 0),
|
||||
]), 2)
|
||||
|
||||
# Add breakdown percentage
|
||||
if timings['total_ms'] > 0:
|
||||
timings['breakdown'] = {
|
||||
'model_fetch_pct': round(timings.get('model_fetch_ms', 0) / timings['total_ms'] * 100, 1),
|
||||
'prompt_build_pct': round(timings.get('prompt_build_ms', 0) / timings['total_ms'] * 100, 1),
|
||||
'llm_call_pct': round(timings.get('llm_call_ms', 0) / timings['total_ms'] * 100, 1),
|
||||
}
|
||||
|
||||
if errors:
|
||||
timings['errors'] = errors
|
||||
|
||||
return self.success(data={'test_result': timings})
|
||||
|
||||
|
||||
@group.group_class('executions', '/api/v1/executions')
|
||||
class ExecutionsRouterGroup(group.RouterGroup):
|
||||
"""Workflow execution API router group"""
|
||||
|
||||
async def initialize(self) -> None:
|
||||
# Get all executions (across all workflows)
|
||||
@self.route('', methods=['GET'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
|
||||
async def _() -> str:
|
||||
limit = int(quart.request.args.get('limit', 50))
|
||||
offset = int(quart.request.args.get('offset', 0))
|
||||
status = quart.request.args.get('status')
|
||||
executions = await self.ap.workflow_service.get_executions(limit=limit, offset=offset, status=status)
|
||||
return self.success(data=executions)
|
||||
|
||||
# Get single execution
|
||||
@self.route('/<execution_uuid>', methods=['GET'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
|
||||
async def _(execution_uuid: str) -> str:
|
||||
execution = await self.ap.workflow_service.get_execution(execution_uuid)
|
||||
if execution is None:
|
||||
return self.http_status(404, -1, 'execution not found')
|
||||
return self.success(data={'execution': execution})
|
||||
|
||||
# Cancel execution
|
||||
@self.route('/<execution_uuid>/cancel', methods=['POST'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
|
||||
async def _(execution_uuid: str) -> str:
|
||||
try:
|
||||
await self.ap.workflow_service.cancel_execution(execution_uuid)
|
||||
return self.success()
|
||||
except ValueError as e:
|
||||
return self.http_status(404, -1, str(e))
|
||||
except RuntimeError as e:
|
||||
return self.http_status(400, -1, str(e))
|
||||
@@ -17,7 +17,6 @@ from .groups import platform as groups_platform
|
||||
from .groups import pipelines as groups_pipelines
|
||||
from .groups import knowledge as groups_knowledge
|
||||
from .groups import resources as groups_resources
|
||||
from .groups import workflows as groups_workflows
|
||||
|
||||
importutil.import_modules_in_pkg(groups)
|
||||
importutil.import_modules_in_pkg(groups_provider)
|
||||
@@ -25,7 +24,6 @@ importutil.import_modules_in_pkg(groups_platform)
|
||||
importutil.import_modules_in_pkg(groups_pipelines)
|
||||
importutil.import_modules_in_pkg(groups_knowledge)
|
||||
importutil.import_modules_in_pkg(groups_resources)
|
||||
importutil.import_modules_in_pkg(groups_workflows)
|
||||
|
||||
|
||||
class HTTPController:
|
||||
|
||||
@@ -52,9 +52,6 @@ class ApiKeyService:
|
||||
|
||||
async def verify_api_key(self, key: str) -> bool:
|
||||
"""Verify if an API key is valid"""
|
||||
if not isinstance(key, str) or not key.startswith('lbk_'):
|
||||
return False
|
||||
|
||||
result = await self.ap.persistence_mgr.execute_async(
|
||||
sqlalchemy.select(apikey.ApiKey).where(apikey.ApiKey.key == key)
|
||||
)
|
||||
|
||||
@@ -99,11 +99,7 @@ class BotService:
|
||||
# TODO: 检查配置信息格式
|
||||
bot_data['uuid'] = str(uuid.uuid4())
|
||||
|
||||
# Set default binding_type if not provided
|
||||
if 'binding_type' not in bot_data:
|
||||
bot_data['binding_type'] = 'pipeline'
|
||||
|
||||
# checkout the default pipeline (for backward compatibility)
|
||||
# checkout the default pipeline
|
||||
result = await self.ap.persistence_mgr.execute_async(
|
||||
sqlalchemy.select(persistence_pipeline.LegacyPipeline).where(
|
||||
persistence_pipeline.LegacyPipeline.is_default == True
|
||||
@@ -113,9 +109,6 @@ class BotService:
|
||||
if pipeline is not None:
|
||||
bot_data['use_pipeline_uuid'] = pipeline.uuid
|
||||
bot_data['use_pipeline_name'] = pipeline.name
|
||||
# Also set binding_uuid for new unified binding model
|
||||
if 'binding_uuid' not in bot_data:
|
||||
bot_data['binding_uuid'] = pipeline.uuid
|
||||
|
||||
await self.ap.persistence_mgr.execute_async(sqlalchemy.insert(persistence_bot.Bot).values(bot_data))
|
||||
|
||||
@@ -130,11 +123,7 @@ class BotService:
|
||||
if 'uuid' in bot_data:
|
||||
del bot_data['uuid']
|
||||
|
||||
# Handle binding_type and binding_uuid for the new unified binding model
|
||||
# If binding_type is explicitly set to 'workflow', skip pipeline validation
|
||||
binding_type = bot_data.get('binding_type')
|
||||
|
||||
# set use_pipeline_name (for backward compatibility with 'pipeline' binding_type)
|
||||
# set use_pipeline_name
|
||||
if 'use_pipeline_uuid' in bot_data:
|
||||
result = await self.ap.persistence_mgr.execute_async(
|
||||
sqlalchemy.select(persistence_pipeline.LegacyPipeline).where(
|
||||
@@ -144,19 +133,9 @@ class BotService:
|
||||
pipeline = result.first()
|
||||
if pipeline is not None:
|
||||
bot_data['use_pipeline_name'] = pipeline.name
|
||||
# Also sync to binding_uuid if binding_type is 'pipeline' or not set
|
||||
if binding_type is None or binding_type == 'pipeline':
|
||||
bot_data['binding_uuid'] = bot_data['use_pipeline_uuid']
|
||||
bot_data['binding_type'] = 'pipeline'
|
||||
else:
|
||||
raise Exception('Pipeline not found')
|
||||
|
||||
# If binding_uuid is set directly (for workflow), sync use_pipeline_uuid for backward compatibility
|
||||
if 'binding_uuid' in bot_data and binding_type == 'workflow':
|
||||
# For workflow binding, we don't sync to use_pipeline_uuid
|
||||
# but we ensure binding_type is correctly set
|
||||
bot_data['binding_type'] = 'workflow'
|
||||
|
||||
await self.ap.persistence_mgr.execute_async(
|
||||
sqlalchemy.update(persistence_bot.Bot).values(bot_data).where(persistence_bot.Bot.uuid == bot_uuid)
|
||||
)
|
||||
|
||||
@@ -31,126 +31,15 @@ class KnowledgeService:
|
||||
if not knowledge_engine_plugin_id:
|
||||
raise ValueError('knowledge_engine_plugin_id is required')
|
||||
|
||||
creation_settings = kb_data.get('creation_settings', {})
|
||||
retrieval_settings = kb_data.get('retrieval_settings', {})
|
||||
|
||||
# Validate required fields based on plugin's creation_schema and retrieval_schema
|
||||
await self._validate_schema_required_fields(
|
||||
knowledge_engine_plugin_id,
|
||||
creation_settings,
|
||||
retrieval_settings,
|
||||
)
|
||||
|
||||
kb = await self.ap.rag_mgr.create_knowledge_base(
|
||||
name=kb_data.get('name', 'Untitled'),
|
||||
knowledge_engine_plugin_id=knowledge_engine_plugin_id,
|
||||
creation_settings=creation_settings,
|
||||
retrieval_settings=retrieval_settings,
|
||||
creation_settings=kb_data.get('creation_settings', {}),
|
||||
retrieval_settings=kb_data.get('retrieval_settings', {}),
|
||||
description=kb_data.get('description', ''),
|
||||
)
|
||||
return kb.uuid
|
||||
|
||||
async def _validate_schema_required_fields(
|
||||
self,
|
||||
plugin_id: str,
|
||||
creation_settings: dict,
|
||||
retrieval_settings: dict,
|
||||
) -> None:
|
||||
"""Validate required fields based on plugin's creation_schema and retrieval_schema.
|
||||
|
||||
This is a business-agnostic validation that checks all fields marked as
|
||||
required in the plugin's schema, regardless of field type.
|
||||
|
||||
Args:
|
||||
plugin_id: Knowledge Engine plugin ID.
|
||||
creation_settings: User-provided creation settings.
|
||||
retrieval_settings: User-provided retrieval settings.
|
||||
|
||||
Raises:
|
||||
ValueError: If any required field is missing or empty.
|
||||
"""
|
||||
# Validate creation_schema
|
||||
try:
|
||||
creation_schema = await self.ap.plugin_connector.get_rag_creation_schema(plugin_id)
|
||||
self._check_required_fields(creation_schema, creation_settings, 'creation_settings')
|
||||
except ValueError:
|
||||
raise
|
||||
except Exception as e:
|
||||
self.ap.logger.warning(f'Failed to get creation_schema for validation: {e}')
|
||||
|
||||
# Validate retrieval_schema
|
||||
try:
|
||||
retrieval_schema = await self.ap.plugin_connector.get_rag_retrieval_schema(plugin_id)
|
||||
self._check_required_fields(retrieval_schema, retrieval_settings, 'retrieval_settings')
|
||||
except ValueError:
|
||||
raise
|
||||
except Exception as e:
|
||||
self.ap.logger.warning(f'Failed to get retrieval_schema for validation: {e}')
|
||||
|
||||
def _check_required_fields(
|
||||
self,
|
||||
schema: dict | list,
|
||||
settings: dict,
|
||||
context: str,
|
||||
) -> None:
|
||||
"""Check required fields in schema against provided settings.
|
||||
|
||||
Args:
|
||||
schema: Plugin-defined schema (can be list or dict with 'schema' key).
|
||||
settings: User-provided settings values.
|
||||
context: Context name for error messages (e.g., 'creation_settings').
|
||||
|
||||
Raises:
|
||||
ValueError: If a required field is missing or empty.
|
||||
"""
|
||||
if not schema:
|
||||
return
|
||||
|
||||
# schema can be a list directly, or a dict with 'schema' key
|
||||
items = schema if isinstance(schema, list) else schema.get('schema', [])
|
||||
if not items:
|
||||
return
|
||||
|
||||
for item in items:
|
||||
field_name = item.get('name')
|
||||
if not field_name:
|
||||
continue
|
||||
|
||||
is_required = item.get('required', False)
|
||||
if not is_required:
|
||||
continue
|
||||
|
||||
# Check show_if condition - if field is conditionally shown, only validate when condition is met
|
||||
show_if = item.get('show_if')
|
||||
if show_if:
|
||||
depend_field = show_if.get('field')
|
||||
operator = show_if.get('operator')
|
||||
expected_value = show_if.get('value')
|
||||
|
||||
if depend_field and operator:
|
||||
depend_value = settings.get(depend_field)
|
||||
# If show_if condition is not met, skip validation for this field
|
||||
if operator == 'eq' and depend_value != expected_value:
|
||||
continue
|
||||
if operator == 'neq' and depend_value == expected_value:
|
||||
continue
|
||||
if operator == 'in' and isinstance(expected_value, list) and depend_value not in expected_value:
|
||||
continue
|
||||
|
||||
value = settings.get(field_name)
|
||||
|
||||
# Validate required field has a non-empty value
|
||||
if value is None or (isinstance(value, str) and value.strip() == ''):
|
||||
# Get field label for friendly error message
|
||||
label = item.get('label', {})
|
||||
field_label = (
|
||||
label.get('en_US', field_name)
|
||||
or label.get('zh_Hans', field_name)
|
||||
or label.get('zh_Hant', field_name)
|
||||
or field_name
|
||||
)
|
||||
raise ValueError(f'{field_label} is required ({context}.{field_name})')
|
||||
|
||||
async def update_knowledge_base(self, kb_uuid: str, kb_data: dict) -> None:
|
||||
"""更新知识库"""
|
||||
# Filter to only mutable fields
|
||||
|
||||
@@ -73,20 +73,6 @@ class PipelineService:
|
||||
|
||||
return self.ap.persistence_mgr.serialize_model(persistence_pipeline.LegacyPipeline, pipeline)
|
||||
|
||||
async def get_pipeline_by_name(self, pipeline_name: str) -> dict | None:
|
||||
result = await self.ap.persistence_mgr.execute_async(
|
||||
sqlalchemy.select(persistence_pipeline.LegacyPipeline).where(
|
||||
persistence_pipeline.LegacyPipeline.name == pipeline_name
|
||||
)
|
||||
)
|
||||
|
||||
pipeline = result.first()
|
||||
|
||||
if pipeline is None:
|
||||
return None
|
||||
|
||||
return self.ap.persistence_mgr.serialize_model(persistence_pipeline.LegacyPipeline, pipeline)
|
||||
|
||||
async def create_pipeline(self, pipeline_data: dict, default: bool = False) -> str:
|
||||
from ....utils import paths as path_utils
|
||||
|
||||
@@ -127,9 +113,14 @@ class PipelineService:
|
||||
return pipeline_data['uuid']
|
||||
|
||||
async def update_pipeline(self, pipeline_uuid: str, pipeline_data: dict) -> None:
|
||||
pipeline_data = pipeline_data.copy()
|
||||
for protected_field in ('uuid', 'for_version', 'stages', 'is_default'):
|
||||
pipeline_data.pop(protected_field, None)
|
||||
if 'uuid' in pipeline_data:
|
||||
del pipeline_data['uuid']
|
||||
if 'for_version' in pipeline_data:
|
||||
del pipeline_data['for_version']
|
||||
if 'stages' in pipeline_data:
|
||||
del pipeline_data['stages']
|
||||
if 'is_default' in pipeline_data:
|
||||
del pipeline_data['is_default']
|
||||
|
||||
await self.ap.persistence_mgr.execute_async(
|
||||
sqlalchemy.update(persistence_pipeline.LegacyPipeline)
|
||||
|
||||
@@ -17,24 +17,6 @@ class ModelProviderService:
|
||||
def __init__(self, ap: app.Application) -> None:
|
||||
self.ap = ap
|
||||
|
||||
@staticmethod
|
||||
def _normalize_api_keys(api_keys: str | list[str] | tuple[str, ...] | None) -> list[str]:
|
||||
if api_keys is None:
|
||||
return []
|
||||
|
||||
raw_keys = [api_keys] if isinstance(api_keys, str) else list(api_keys)
|
||||
normalized_keys = []
|
||||
seen_keys = set()
|
||||
|
||||
for raw_key in raw_keys:
|
||||
normalized_key = raw_key.strip() if isinstance(raw_key, str) else ''
|
||||
if not normalized_key or normalized_key in seen_keys:
|
||||
continue
|
||||
normalized_keys.append(normalized_key)
|
||||
seen_keys.add(normalized_key)
|
||||
|
||||
return normalized_keys
|
||||
|
||||
async def get_providers(self) -> list[dict]:
|
||||
"""Get all providers"""
|
||||
result = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_model.ModelProvider))
|
||||
@@ -77,7 +59,6 @@ class ModelProviderService:
|
||||
async def create_provider(self, provider_data: dict) -> str:
|
||||
"""Create a new provider"""
|
||||
provider_data['uuid'] = str(uuid.uuid4())
|
||||
provider_data['api_keys'] = self._normalize_api_keys(provider_data.get('api_keys'))
|
||||
await self.ap.persistence_mgr.execute_async(
|
||||
sqlalchemy.insert(persistence_model.ModelProvider).values(**provider_data)
|
||||
)
|
||||
@@ -91,8 +72,6 @@ class ModelProviderService:
|
||||
"""Update an existing provider"""
|
||||
if 'uuid' in provider_data:
|
||||
del provider_data['uuid']
|
||||
if 'api_keys' in provider_data:
|
||||
provider_data['api_keys'] = self._normalize_api_keys(provider_data.get('api_keys'))
|
||||
await self.ap.persistence_mgr.execute_async(
|
||||
sqlalchemy.update(persistence_model.ModelProvider)
|
||||
.where(persistence_model.ModelProvider.uuid == provider_uuid)
|
||||
@@ -162,8 +141,6 @@ class ModelProviderService:
|
||||
|
||||
async def find_or_create_provider(self, requester: str, base_url: str, api_keys: list) -> str:
|
||||
"""Find existing provider or create new one"""
|
||||
api_keys = self._normalize_api_keys(api_keys)
|
||||
|
||||
# Try to find existing provider with same config
|
||||
result = await self.ap.persistence_mgr.execute_async(
|
||||
sqlalchemy.select(persistence_model.ModelProvider).where(
|
||||
@@ -191,7 +168,7 @@ class ModelProviderService:
|
||||
'name': provider_name,
|
||||
'requester': requester,
|
||||
'base_url': base_url,
|
||||
'api_keys': api_keys,
|
||||
'api_keys': api_keys or [],
|
||||
}
|
||||
)
|
||||
|
||||
@@ -200,7 +177,7 @@ class ModelProviderService:
|
||||
await self.ap.persistence_mgr.execute_async(
|
||||
sqlalchemy.update(persistence_model.ModelProvider)
|
||||
.where(persistence_model.ModelProvider.uuid == '00000000-0000-0000-0000-000000000000')
|
||||
.values(api_keys=self._normalize_api_keys(api_key))
|
||||
.values(api_keys=[api_key])
|
||||
)
|
||||
await self.ap.model_mgr.reload_provider('00000000-0000-0000-0000-000000000000')
|
||||
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -31,7 +31,6 @@ from ..api.http.service import mcp as mcp_service
|
||||
from ..api.http.service import apikey as apikey_service
|
||||
from ..api.http.service import webhook as webhook_service
|
||||
from ..api.http.service import monitoring as monitoring_service
|
||||
from ..api.http.service import workflow as workflow_service
|
||||
from ..api.http.service import maintenance as maintenance_service
|
||||
|
||||
from ..discover import engine as discover_engine
|
||||
@@ -151,8 +150,6 @@ class Application:
|
||||
|
||||
webhook_service: webhook_service.WebhookService = None
|
||||
|
||||
workflow_service: workflow_service.WorkflowService = None
|
||||
|
||||
telemetry: telemetry_module.TelemetryManager = None
|
||||
|
||||
survey: survey_module.SurveyManager = None
|
||||
@@ -240,22 +237,6 @@ class Application:
|
||||
scopes=[core_entities.LifecycleControlScope.APPLICATION],
|
||||
)
|
||||
|
||||
async def workflow_execution_cleanup_loop():
|
||||
check_interval_seconds = 60
|
||||
while True:
|
||||
try:
|
||||
cancelled = await self.workflow_service.cleanup_stale_executions()
|
||||
if cancelled > 0:
|
||||
self.logger.info(f'Workflow execution auto-cleanup: cancelled {cancelled} stale executions')
|
||||
except Exception as e:
|
||||
self.logger.warning(f'Workflow execution auto-cleanup error: {e}')
|
||||
await asyncio.sleep(check_interval_seconds)
|
||||
|
||||
self.task_mgr.create_task(
|
||||
workflow_execution_cleanup_loop(),
|
||||
name='workflow-execution-cleanup',
|
||||
scopes=[core_entities.LifecycleControlScope.APPLICATION],
|
||||
)
|
||||
# Start storage/log maintenance task if enabled
|
||||
storage_cleanup_cfg = self.instance_config.data.get('storage', {}).get('cleanup', {})
|
||||
if storage_cleanup_cfg.get('enabled', True) and self.maintenance_service is not None:
|
||||
|
||||
@@ -46,14 +46,12 @@ async def make_app(loop: asyncio.AbstractEventLoop) -> app.Application:
|
||||
|
||||
|
||||
async def main(loop: asyncio.AbstractEventLoop):
|
||||
app_inst: app.Application | None = None
|
||||
try:
|
||||
# Hang system signal processing
|
||||
import signal
|
||||
|
||||
def signal_handler(sig, frame):
|
||||
if app_inst is not None:
|
||||
app_inst.dispose()
|
||||
app_inst.dispose()
|
||||
print('[Signal] Program exit.')
|
||||
os._exit(0)
|
||||
|
||||
|
||||
@@ -28,7 +28,6 @@ from ...api.http.service import mcp as mcp_service
|
||||
from ...api.http.service import apikey as apikey_service
|
||||
from ...api.http.service import webhook as webhook_service
|
||||
from ...api.http.service import monitoring as monitoring_service
|
||||
from ...api.http.service import workflow as workflow_service
|
||||
from ...api.http.service import maintenance as maintenance_service
|
||||
from ...discover import engine as discover_engine
|
||||
from ...storage import mgr as storagemgr
|
||||
@@ -87,9 +86,6 @@ class BuildAppStage(stage.BootingStage):
|
||||
webhook_service_inst = webhook_service.WebhookService(ap)
|
||||
ap.webhook_service = webhook_service_inst
|
||||
|
||||
workflow_service_inst = workflow_service.WorkflowService(ap)
|
||||
ap.workflow_service = workflow_service_inst
|
||||
|
||||
proxy_mgr = proxy.ProxyManager(ap)
|
||||
await proxy_mgr.initialize()
|
||||
ap.proxy_mgr = proxy_mgr
|
||||
|
||||
@@ -221,34 +221,3 @@ class LoadConfigStage(stage.BootingStage):
|
||||
ap.pipeline_config_meta_safety = await load_resource_yaml_template_data('metadata/pipeline/safety.yaml')
|
||||
ap.pipeline_config_meta_ai = await load_resource_yaml_template_data('metadata/pipeline/ai.yaml')
|
||||
ap.pipeline_config_meta_output = await load_resource_yaml_template_data('metadata/pipeline/output.yaml')
|
||||
|
||||
# Load workflow node metadata from YAML files. YAML is the source of
|
||||
# truth for workflow editor metadata; Python classes provide execution
|
||||
# logic and are bound through the registry.
|
||||
from langbot.pkg.workflow.metadata import NodeMetadataLoader
|
||||
from langbot.pkg.workflow.registry import NodeTypeRegistry
|
||||
|
||||
workflow_metadata_loader = NodeMetadataLoader()
|
||||
workflow_node_count = await workflow_metadata_loader.load_core_metadata()
|
||||
ap.workflow_node_configs = workflow_metadata_loader.get_all_metadata()
|
||||
ap.workflow_node_metadata_loader = workflow_metadata_loader
|
||||
|
||||
workflow_registry = NodeTypeRegistry.instance()
|
||||
for node_config in ap.workflow_node_configs.values():
|
||||
workflow_registry.register_metadata(node_config, source=node_config.get('_source', 'core'))
|
||||
|
||||
# Auto-discover and register workflow nodes using discovery engine
|
||||
if hasattr(ap, 'discover') and ap.discover is not None:
|
||||
workflow_registry.discover_nodes(ap.discover)
|
||||
|
||||
workflow_load_errors = workflow_metadata_loader.get_load_errors()
|
||||
if workflow_load_errors:
|
||||
print(f'Workflow node metadata load errors: {len(workflow_load_errors)}')
|
||||
for error in workflow_load_errors:
|
||||
print(f" - {error.get('file')}: {error.get('error')}")
|
||||
|
||||
print(
|
||||
f'Loaded {workflow_node_count} workflow node metadata files; '
|
||||
f'registered {workflow_registry.metadata_count()} metadata definitions, '
|
||||
f'{workflow_registry.count()} node types'
|
||||
)
|
||||
|
||||
@@ -304,65 +304,3 @@ class ComponentDiscoveryEngine:
|
||||
if component.kind == kind:
|
||||
result.append(component)
|
||||
return result
|
||||
|
||||
def discover_workflow_nodes(self, nodes_dir: str) -> typing.List[typing.Type]:
|
||||
"""Discover workflow node classes from a directory of Python modules.
|
||||
|
||||
Scans all .py files in the given directory, imports them, and collects
|
||||
classes that are subclasses of WorkflowNode.
|
||||
|
||||
Args:
|
||||
nodes_dir: Directory path like 'pkg/workflow/nodes/'
|
||||
|
||||
Returns:
|
||||
List of WorkflowNode subclasses found
|
||||
"""
|
||||
from langbot.pkg.workflow.node import WorkflowNode
|
||||
|
||||
node_classes: typing.List[typing.Type[WorkflowNode]] = []
|
||||
|
||||
# Normalize path
|
||||
if nodes_dir.endswith('/'):
|
||||
nodes_dir = nodes_dir[:-1]
|
||||
|
||||
# Import the nodes package to trigger all module imports
|
||||
module_path = nodes_dir.replace('/', '.').replace('\\', '.')
|
||||
package_path = module_path
|
||||
|
||||
try:
|
||||
# Import the package __init__ to trigger submodule imports
|
||||
importlib.import_module(f'langbot.{package_path}')
|
||||
except ImportError:
|
||||
self.ap.logger.warning(f'Failed to import workflow nodes package: langbot.{package_path}')
|
||||
|
||||
# Since workflow/__init__.py is empty, explicitly import all .py files in the nodes directory
|
||||
import os
|
||||
# engine.py is in langbot/pkg/discover/, nodes are in langbot/pkg/workflow/nodes/
|
||||
nodes_abs_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'workflow', 'nodes'))
|
||||
if os.path.isdir(nodes_abs_path):
|
||||
for filename in os.listdir(nodes_abs_path):
|
||||
if filename.endswith('.py') and not filename.startswith('_'):
|
||||
module_name = filename[:-3]
|
||||
try:
|
||||
importlib.import_module(f'langbot.{package_path}.{module_name}')
|
||||
except ImportError as e:
|
||||
self.ap.logger.warning(f'Failed to import workflow node module: {module_name}: {e}')
|
||||
|
||||
# Now collect all WorkflowNode subclasses from sys.modules
|
||||
import sys
|
||||
prefix = f'langbot.{package_path}.'
|
||||
for mod_name, mod in sys.modules.items():
|
||||
if mod_name.startswith(prefix) and mod is not None:
|
||||
for attr_name in dir(mod):
|
||||
attr = getattr(mod, attr_name)
|
||||
if (
|
||||
isinstance(attr, type)
|
||||
and issubclass(attr, WorkflowNode)
|
||||
and attr is not WorkflowNode
|
||||
and hasattr(attr, 'type_name')
|
||||
and attr.type_name
|
||||
):
|
||||
if attr not in node_classes:
|
||||
node_classes.append(attr)
|
||||
|
||||
return node_classes
|
||||
|
||||
@@ -17,13 +17,6 @@ class Bot(Base):
|
||||
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='[]')
|
||||
|
||||
# New unified binding fields
|
||||
# binding_type: 'pipeline' or 'workflow'
|
||||
binding_type = sqlalchemy.Column(sqlalchemy.String(32), nullable=False, server_default='pipeline')
|
||||
# binding_uuid: UUID of the bound Pipeline or Workflow
|
||||
binding_uuid = sqlalchemy.Column(sqlalchemy.String(64), nullable=True)
|
||||
|
||||
created_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, server_default=sqlalchemy.func.now())
|
||||
updated_at = sqlalchemy.Column(
|
||||
sqlalchemy.DateTime,
|
||||
|
||||
@@ -1,126 +0,0 @@
|
||||
"""Workflow persistence entities"""
|
||||
|
||||
import sqlalchemy
|
||||
|
||||
from .base import Base
|
||||
|
||||
|
||||
class Workflow(Base):
|
||||
"""Workflow definition"""
|
||||
|
||||
__tablename__ = 'workflows'
|
||||
|
||||
uuid = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True, unique=True)
|
||||
name = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
|
||||
description = sqlalchemy.Column(sqlalchemy.Text, nullable=True)
|
||||
emoji = sqlalchemy.Column(sqlalchemy.String(10), nullable=True, default='🔄')
|
||||
version = sqlalchemy.Column(sqlalchemy.Integer, nullable=False, default=1)
|
||||
is_enabled = sqlalchemy.Column(sqlalchemy.Boolean, nullable=False, default=True)
|
||||
|
||||
# Workflow definition stored as JSON
|
||||
# Contains: nodes, edges, variables, settings
|
||||
definition = sqlalchemy.Column(sqlalchemy.JSON, nullable=False, default={})
|
||||
|
||||
# Global config (inherited from Pipeline capabilities)
|
||||
# Contains: safety, output configs
|
||||
global_config = sqlalchemy.Column(sqlalchemy.JSON, nullable=False, default={})
|
||||
|
||||
# Extensions preferences (same as Pipeline)
|
||||
extensions_preferences = sqlalchemy.Column(
|
||||
sqlalchemy.JSON,
|
||||
nullable=False,
|
||||
default={'enable_all_plugins': True, 'enable_all_mcp_servers': True, 'plugins': [], 'mcp_servers': []},
|
||||
)
|
||||
|
||||
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(),
|
||||
)
|
||||
|
||||
|
||||
class WorkflowVersion(Base):
|
||||
"""Workflow version history"""
|
||||
|
||||
__tablename__ = 'workflow_versions'
|
||||
|
||||
id = sqlalchemy.Column(sqlalchemy.Integer, primary_key=True, autoincrement=True)
|
||||
workflow_uuid = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True)
|
||||
version = sqlalchemy.Column(sqlalchemy.Integer, nullable=False)
|
||||
definition = sqlalchemy.Column(sqlalchemy.JSON, nullable=False)
|
||||
global_config = sqlalchemy.Column(sqlalchemy.JSON, nullable=False, default={})
|
||||
created_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, server_default=sqlalchemy.func.now())
|
||||
created_by = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
|
||||
|
||||
__table_args__ = (sqlalchemy.UniqueConstraint('workflow_uuid', 'version', name='uq_workflow_version'),)
|
||||
|
||||
|
||||
class WorkflowTrigger(Base):
|
||||
"""Workflow trigger configuration"""
|
||||
|
||||
__tablename__ = 'workflow_triggers'
|
||||
|
||||
uuid = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True, unique=True)
|
||||
workflow_uuid = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True)
|
||||
type = sqlalchemy.Column(sqlalchemy.String(50), nullable=False) # message, cron, event, webhook
|
||||
config = sqlalchemy.Column(sqlalchemy.JSON, nullable=False, default={})
|
||||
is_enabled = sqlalchemy.Column(sqlalchemy.Boolean, nullable=False, default=True)
|
||||
priority = 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(),
|
||||
)
|
||||
|
||||
|
||||
class WorkflowExecution(Base):
|
||||
"""Workflow execution record"""
|
||||
|
||||
__tablename__ = 'workflow_executions'
|
||||
|
||||
uuid = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True, unique=True)
|
||||
workflow_uuid = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True)
|
||||
workflow_version = sqlalchemy.Column(sqlalchemy.Integer, nullable=False)
|
||||
status = sqlalchemy.Column(sqlalchemy.String(20), nullable=False) # pending, running, completed, failed, cancelled
|
||||
trigger_type = sqlalchemy.Column(sqlalchemy.String(50), nullable=True)
|
||||
trigger_data = sqlalchemy.Column(sqlalchemy.JSON, nullable=True)
|
||||
variables = sqlalchemy.Column(sqlalchemy.JSON, nullable=True)
|
||||
start_time = sqlalchemy.Column(sqlalchemy.DateTime, nullable=True)
|
||||
end_time = sqlalchemy.Column(sqlalchemy.DateTime, nullable=True)
|
||||
error = sqlalchemy.Column(sqlalchemy.Text, nullable=True)
|
||||
created_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, server_default=sqlalchemy.func.now())
|
||||
|
||||
|
||||
class WorkflowNodeExecution(Base):
|
||||
"""Workflow node execution record"""
|
||||
|
||||
__tablename__ = 'workflow_node_executions'
|
||||
|
||||
id = sqlalchemy.Column(sqlalchemy.Integer, primary_key=True, autoincrement=True)
|
||||
execution_uuid = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True)
|
||||
node_id = sqlalchemy.Column(sqlalchemy.String(100), nullable=False)
|
||||
node_type = sqlalchemy.Column(sqlalchemy.String(50), nullable=False)
|
||||
status = sqlalchemy.Column(sqlalchemy.String(20), nullable=False) # pending, running, completed, failed, skipped
|
||||
inputs = sqlalchemy.Column(sqlalchemy.JSON, nullable=True)
|
||||
outputs = sqlalchemy.Column(sqlalchemy.JSON, nullable=True)
|
||||
start_time = sqlalchemy.Column(sqlalchemy.DateTime, nullable=True)
|
||||
end_time = sqlalchemy.Column(sqlalchemy.DateTime, nullable=True)
|
||||
error = sqlalchemy.Column(sqlalchemy.Text, nullable=True)
|
||||
retry_count = sqlalchemy.Column(sqlalchemy.Integer, nullable=False, default=0)
|
||||
|
||||
|
||||
class ScheduledJob(Base):
|
||||
"""Scheduled job for cron triggers"""
|
||||
|
||||
__tablename__ = 'workflow_scheduled_jobs'
|
||||
|
||||
uuid = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True, unique=True)
|
||||
trigger_uuid = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True)
|
||||
cron_expression = sqlalchemy.Column(sqlalchemy.String(100), nullable=True)
|
||||
next_run_time = sqlalchemy.Column(sqlalchemy.DateTime, nullable=True)
|
||||
last_run_time = sqlalchemy.Column(sqlalchemy.DateTime, nullable=True)
|
||||
is_enabled = sqlalchemy.Column(sqlalchemy.Boolean, nullable=False, default=True)
|
||||
@@ -1,158 +0,0 @@
|
||||
"""Add workflow tables and update bot binding fields"""
|
||||
|
||||
import sqlalchemy
|
||||
from .. import migration
|
||||
|
||||
|
||||
@migration.migration_class(26)
|
||||
class DBMigrateWorkflowTables(migration.DBMigration):
|
||||
"""Add workflow tables and update bot binding fields"""
|
||||
|
||||
async def upgrade(self):
|
||||
# Create workflows table
|
||||
await self.ap.persistence_mgr.execute_async(
|
||||
sqlalchemy.text("""
|
||||
CREATE TABLE IF NOT EXISTS workflows (
|
||||
uuid VARCHAR(255) PRIMARY KEY,
|
||||
name VARCHAR(255) NOT NULL,
|
||||
description TEXT,
|
||||
emoji VARCHAR(10) DEFAULT '🔄',
|
||||
version INTEGER NOT NULL DEFAULT 1,
|
||||
is_enabled BOOLEAN NOT NULL DEFAULT 1,
|
||||
definition JSON NOT NULL DEFAULT '{}',
|
||||
global_config JSON NOT NULL DEFAULT '{}',
|
||||
extensions_preferences JSON NOT NULL DEFAULT '{"enable_all_plugins": true, "enable_all_mcp_servers": true, "plugins": [], "mcp_servers": []}',
|
||||
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
|
||||
updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
|
||||
)
|
||||
""")
|
||||
)
|
||||
|
||||
# Create workflow_versions table
|
||||
await self.ap.persistence_mgr.execute_async(
|
||||
sqlalchemy.text("""
|
||||
CREATE TABLE IF NOT EXISTS workflow_versions (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
workflow_uuid VARCHAR(255) NOT NULL,
|
||||
version INTEGER NOT NULL,
|
||||
definition JSON NOT NULL,
|
||||
global_config JSON NOT NULL DEFAULT '{}',
|
||||
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
|
||||
created_by VARCHAR(255),
|
||||
UNIQUE(workflow_uuid, version)
|
||||
)
|
||||
""")
|
||||
)
|
||||
|
||||
# Create workflow_triggers table
|
||||
await self.ap.persistence_mgr.execute_async(
|
||||
sqlalchemy.text("""
|
||||
CREATE TABLE IF NOT EXISTS workflow_triggers (
|
||||
uuid VARCHAR(255) PRIMARY KEY,
|
||||
workflow_uuid VARCHAR(255) NOT NULL,
|
||||
type VARCHAR(50) NOT NULL,
|
||||
config JSON NOT NULL DEFAULT '{}',
|
||||
is_enabled BOOLEAN NOT NULL DEFAULT 1,
|
||||
priority INTEGER NOT NULL DEFAULT 0,
|
||||
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
|
||||
updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
|
||||
)
|
||||
""")
|
||||
)
|
||||
|
||||
# Create workflow_executions table
|
||||
await self.ap.persistence_mgr.execute_async(
|
||||
sqlalchemy.text("""
|
||||
CREATE TABLE IF NOT EXISTS workflow_executions (
|
||||
uuid VARCHAR(255) PRIMARY KEY,
|
||||
workflow_uuid VARCHAR(255) NOT NULL,
|
||||
workflow_version INTEGER NOT NULL,
|
||||
status VARCHAR(20) NOT NULL,
|
||||
trigger_type VARCHAR(50),
|
||||
trigger_data JSON,
|
||||
variables JSON,
|
||||
start_time TIMESTAMP,
|
||||
end_time TIMESTAMP,
|
||||
error TEXT,
|
||||
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
|
||||
)
|
||||
""")
|
||||
)
|
||||
|
||||
# Create workflow_node_executions table
|
||||
await self.ap.persistence_mgr.execute_async(
|
||||
sqlalchemy.text("""
|
||||
CREATE TABLE IF NOT EXISTS workflow_node_executions (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
execution_uuid VARCHAR(255) NOT NULL,
|
||||
node_id VARCHAR(100) NOT NULL,
|
||||
node_type VARCHAR(50) NOT NULL,
|
||||
status VARCHAR(20) NOT NULL,
|
||||
inputs JSON,
|
||||
outputs JSON,
|
||||
start_time TIMESTAMP,
|
||||
end_time TIMESTAMP,
|
||||
error TEXT,
|
||||
retry_count INTEGER NOT NULL DEFAULT 0
|
||||
)
|
||||
""")
|
||||
)
|
||||
|
||||
# Create workflow_scheduled_jobs table
|
||||
await self.ap.persistence_mgr.execute_async(
|
||||
sqlalchemy.text("""
|
||||
CREATE TABLE IF NOT EXISTS workflow_scheduled_jobs (
|
||||
uuid VARCHAR(255) PRIMARY KEY,
|
||||
trigger_uuid VARCHAR(255) NOT NULL,
|
||||
cron_expression VARCHAR(100),
|
||||
next_run_time TIMESTAMP,
|
||||
last_run_time TIMESTAMP,
|
||||
is_enabled BOOLEAN NOT NULL DEFAULT 1
|
||||
)
|
||||
""")
|
||||
)
|
||||
|
||||
# Create indexes
|
||||
await self.ap.persistence_mgr.execute_async(
|
||||
sqlalchemy.text('CREATE INDEX IF NOT EXISTS idx_workflow_versions_uuid ON workflow_versions(workflow_uuid)')
|
||||
)
|
||||
await self.ap.persistence_mgr.execute_async(
|
||||
sqlalchemy.text('CREATE INDEX IF NOT EXISTS idx_workflow_triggers_uuid ON workflow_triggers(workflow_uuid)')
|
||||
)
|
||||
await self.ap.persistence_mgr.execute_async(
|
||||
sqlalchemy.text(
|
||||
'CREATE INDEX IF NOT EXISTS idx_workflow_executions_uuid ON workflow_executions(workflow_uuid)'
|
||||
)
|
||||
)
|
||||
await self.ap.persistence_mgr.execute_async(
|
||||
sqlalchemy.text(
|
||||
'CREATE INDEX IF NOT EXISTS idx_workflow_node_executions_uuid ON workflow_node_executions(execution_uuid)'
|
||||
)
|
||||
)
|
||||
await self.ap.persistence_mgr.execute_async(
|
||||
sqlalchemy.text(
|
||||
'CREATE INDEX IF NOT EXISTS idx_workflow_scheduled_jobs_trigger ON workflow_scheduled_jobs(trigger_uuid)'
|
||||
)
|
||||
)
|
||||
|
||||
# Update bots table: add binding_type column (default to 'pipeline' for backward compatibility)
|
||||
# Check if column exists first (SQLite doesn't support IF NOT EXISTS for columns)
|
||||
try:
|
||||
await self.ap.persistence_mgr.execute_async(sqlalchemy.text('SELECT binding_type FROM bots LIMIT 1'))
|
||||
except Exception:
|
||||
# Column doesn't exist, add it
|
||||
await self.ap.persistence_mgr.execute_async(
|
||||
sqlalchemy.text("ALTER TABLE bots ADD COLUMN binding_type VARCHAR(20) NOT NULL DEFAULT 'pipeline'")
|
||||
)
|
||||
|
||||
async def downgrade(self):
|
||||
# Drop tables in reverse order
|
||||
await self.ap.persistence_mgr.execute_async(sqlalchemy.text('DROP TABLE IF EXISTS workflow_scheduled_jobs'))
|
||||
await self.ap.persistence_mgr.execute_async(sqlalchemy.text('DROP TABLE IF EXISTS workflow_node_executions'))
|
||||
await self.ap.persistence_mgr.execute_async(sqlalchemy.text('DROP TABLE IF EXISTS workflow_executions'))
|
||||
await self.ap.persistence_mgr.execute_async(sqlalchemy.text('DROP TABLE IF EXISTS workflow_triggers'))
|
||||
await self.ap.persistence_mgr.execute_async(sqlalchemy.text('DROP TABLE IF EXISTS workflow_versions'))
|
||||
await self.ap.persistence_mgr.execute_async(sqlalchemy.text('DROP TABLE IF EXISTS workflows'))
|
||||
|
||||
# Remove binding_type column from bots (SQLite doesn't support DROP COLUMN directly)
|
||||
# This would need a table recreation in SQLite, so we'll skip it in downgrade
|
||||
@@ -1,49 +0,0 @@
|
||||
"""Add binding_uuid field to bots table and migrate data"""
|
||||
|
||||
import sqlalchemy
|
||||
from .. import migration
|
||||
|
||||
|
||||
@migration.migration_class(27)
|
||||
class DBMigrateBotBindingFields(migration.DBMigration):
|
||||
"""Add binding_uuid field to bots table and migrate existing data"""
|
||||
|
||||
async def upgrade(self):
|
||||
# Add binding_uuid column to bots table
|
||||
# Check if column exists first (SQLite doesn't support IF NOT EXISTS for columns)
|
||||
try:
|
||||
await self.ap.persistence_mgr.execute_async(sqlalchemy.text('SELECT binding_uuid FROM bots LIMIT 1'))
|
||||
except Exception:
|
||||
# Column doesn't exist, add it
|
||||
await self.ap.persistence_mgr.execute_async(
|
||||
sqlalchemy.text('ALTER TABLE bots ADD COLUMN binding_uuid VARCHAR(64)')
|
||||
)
|
||||
|
||||
# Migrate existing data: copy use_pipeline_uuid to binding_uuid for records
|
||||
# that have a pipeline bound and binding_uuid is not set yet
|
||||
await self.ap.persistence_mgr.execute_async(
|
||||
sqlalchemy.text("""
|
||||
UPDATE bots
|
||||
SET binding_uuid = use_pipeline_uuid
|
||||
WHERE use_pipeline_uuid IS NOT NULL
|
||||
AND use_pipeline_uuid != ''
|
||||
AND (binding_uuid IS NULL OR binding_uuid = '')
|
||||
""")
|
||||
)
|
||||
|
||||
# Ensure binding_type is 'pipeline' for records that were migrated
|
||||
await self.ap.persistence_mgr.execute_async(
|
||||
sqlalchemy.text("""
|
||||
UPDATE bots
|
||||
SET binding_type = 'pipeline'
|
||||
WHERE binding_uuid IS NOT NULL
|
||||
AND binding_uuid != ''
|
||||
AND (binding_type IS NULL OR binding_type = '')
|
||||
""")
|
||||
)
|
||||
|
||||
async def downgrade(self):
|
||||
# SQLite doesn't support DROP COLUMN directly
|
||||
# This would need a table recreation in SQLite, so we'll skip it in downgrade
|
||||
# The column will remain but won't be used
|
||||
pass
|
||||
@@ -275,7 +275,6 @@ class MessageAggregator:
|
||||
message_chain=merged_chain,
|
||||
adapter=base_msg.adapter,
|
||||
pipeline_uuid=base_msg.pipeline_uuid,
|
||||
routed_by_rule=any(msg.routed_by_rule for msg in messages),
|
||||
)
|
||||
|
||||
async def flush_all(self) -> None:
|
||||
|
||||
@@ -76,10 +76,6 @@ class LongTextProcessStage(stage.PipelineStage):
|
||||
self.ap.logger.debug('Long message processing strategy is not set, skip long message processing.')
|
||||
return entities.StageProcessResult(result_type=entities.ResultType.CONTINUE, new_query=query)
|
||||
|
||||
if not query.resp_message_chain:
|
||||
self.ap.logger.debug('Response message chain is empty, skip long message processing.')
|
||||
return entities.StageProcessResult(result_type=entities.ResultType.CONTINUE, new_query=query)
|
||||
|
||||
# 检查是否包含非 Plain 组件
|
||||
contains_non_plain = False
|
||||
|
||||
|
||||
@@ -13,7 +13,7 @@ import langbot_plugin.api.entities.builtin.platform.message as platform_message
|
||||
import langbot_plugin.api.entities.builtin.platform.events as platform_events
|
||||
import langbot_plugin.api.entities.events as events
|
||||
from ..utils import importutil
|
||||
from .config import coerce_pipeline_config
|
||||
from .config_coercion import coerce_pipeline_config
|
||||
|
||||
import langbot_plugin.api.entities.builtin.provider.session as provider_session
|
||||
import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
|
||||
@@ -284,9 +284,9 @@ class RuntimePipeline:
|
||||
# Record query start and store message_id
|
||||
message_id = ''
|
||||
try:
|
||||
from . import monitor
|
||||
from . import monitoring_helper
|
||||
|
||||
message_id = await monitor.MonitoringHelper.record_query_start(
|
||||
message_id = await monitoring_helper.MonitoringHelper.record_query_start(
|
||||
ap=self.ap,
|
||||
query=query,
|
||||
bot_id=query.bot_uuid or 'unknown',
|
||||
@@ -338,7 +338,7 @@ class RuntimePipeline:
|
||||
# Record query success only if no error occurred during processing
|
||||
if not query.variables.get('_monitoring_has_error', False):
|
||||
try:
|
||||
await monitor.MonitoringHelper.record_query_success(
|
||||
await monitoring_helper.MonitoringHelper.record_query_success(
|
||||
ap=self.ap,
|
||||
message_id=message_id,
|
||||
query=query,
|
||||
@@ -348,7 +348,7 @@ class RuntimePipeline:
|
||||
|
||||
# Record bot response message
|
||||
try:
|
||||
await monitor.MonitoringHelper.record_query_response(
|
||||
await monitoring_helper.MonitoringHelper.record_query_response(
|
||||
ap=self.ap,
|
||||
query=query,
|
||||
bot_id=query.bot_uuid or 'unknown',
|
||||
@@ -367,9 +367,9 @@ class RuntimePipeline:
|
||||
|
||||
# Record query error
|
||||
try:
|
||||
from . import monitor
|
||||
from . import monitoring_helper
|
||||
|
||||
await monitor.MonitoringHelper.record_query_error(
|
||||
await monitoring_helper.MonitoringHelper.record_query_error(
|
||||
ap=self.ap,
|
||||
query=query,
|
||||
bot_id=query.bot_uuid or 'unknown',
|
||||
@@ -384,8 +384,7 @@ class RuntimePipeline:
|
||||
|
||||
finally:
|
||||
self.ap.logger.debug(f'Query {query.query_id} processed')
|
||||
# Use pop with default to avoid KeyError if query was never cached
|
||||
self.ap.query_pool.cached_queries.pop(query.query_id, None)
|
||||
del self.ap.query_pool.cached_queries[query.query_id]
|
||||
|
||||
|
||||
class PipelineManager:
|
||||
|
||||
@@ -63,7 +63,6 @@ class QueryPool:
|
||||
self.cached_queries[query_id] = query
|
||||
self.query_id_counter += 1
|
||||
self.condition.notify_all()
|
||||
return query
|
||||
|
||||
async def __aenter__(self):
|
||||
await self.pool_lock.acquire()
|
||||
|
||||
@@ -2,6 +2,7 @@ from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import re
|
||||
import traceback
|
||||
import sqlalchemy
|
||||
|
||||
@@ -53,24 +54,29 @@ 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 get_binding_info(self) -> tuple[str, str | None]:
|
||||
"""Get the binding type and UUID for this bot.
|
||||
|
||||
Returns:
|
||||
tuple: (binding_type, binding_uuid) where binding_type is 'pipeline' or 'workflow'
|
||||
"""
|
||||
binding_type = getattr(self.bot_entity, 'binding_type', 'pipeline') or 'pipeline'
|
||||
binding_uuid = getattr(self.bot_entity, 'binding_uuid', None)
|
||||
|
||||
# Fallback to use_pipeline_uuid for backward compatibility
|
||||
if not binding_uuid and binding_type == 'pipeline':
|
||||
binding_uuid = self.bot_entity.use_pipeline_uuid
|
||||
|
||||
return binding_type, binding_uuid
|
||||
|
||||
def resolve_pipeline_uuid(
|
||||
self,
|
||||
launcher_type: str,
|
||||
@@ -78,26 +84,56 @@ class RuntimeBot:
|
||||
message_text: str,
|
||||
message_element_types: list[str] | None = None,
|
||||
) -> tuple[str | None, bool]:
|
||||
"""Resolve pipeline UUID for message processing.
|
||||
"""Resolve pipeline UUID based on routing rules.
|
||||
|
||||
NOTE: Routing rules have been removed. Bot now directly binds to a
|
||||
Pipeline or Workflow. This method is kept for backward compatibility
|
||||
but only returns the direct binding.
|
||||
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 always False
|
||||
as routing rules are no longer used.
|
||||
tuple: (pipeline_uuid, routed_by_rule) - routed_by_rule is True
|
||||
when a routing rule matched, False when falling back to default.
|
||||
"""
|
||||
binding_type, binding_uuid = self.get_binding_info()
|
||||
rules = self.bot_entity.pipeline_routing_rules or []
|
||||
element_type_set = set(message_element_types or [])
|
||||
|
||||
# If bound to workflow, return None for pipeline_uuid
|
||||
# The caller should check binding_type and handle accordingly
|
||||
if binding_type == 'workflow':
|
||||
# For workflow binding, we still need to return something
|
||||
# The actual workflow handling should be done by the caller
|
||||
return None, False
|
||||
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
|
||||
|
||||
return binding_uuid, False
|
||||
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,
|
||||
|
||||
@@ -3,7 +3,6 @@ import typing
|
||||
import asyncio
|
||||
import traceback
|
||||
import datetime
|
||||
import json
|
||||
|
||||
import aiocqhttp
|
||||
import pydantic
|
||||
@@ -294,29 +293,6 @@ class AiocqhttpMessageConverter(abstract_platform_adapter.AbstractMessageConvert
|
||||
elif msg.type == 'dice':
|
||||
face_id = msg.data['result']
|
||||
yiri_msg_list.append(platform_message.Face(face_type='dice', face_id=int(face_id), face_name='骰子'))
|
||||
elif msg.type == 'json':
|
||||
try:
|
||||
raw = msg.data.get('data', {})
|
||||
if isinstance(raw, str):
|
||||
raw = json.loads(raw)
|
||||
if isinstance(raw, dict):
|
||||
_meta = raw.get('meta', {}) or {}
|
||||
if isinstance(_meta, dict):
|
||||
_detail = _meta.get('detail_1') or _meta.get('music') or _meta.get('news') or {}
|
||||
else:
|
||||
_detail = {}
|
||||
if isinstance(_detail, dict):
|
||||
preview = _detail.get('preview', '')
|
||||
title = _detail.get('desc', '') or _detail.get('title', '')
|
||||
url = _detail.get('qqdocurl', '') or _detail.get('jumpUrl', '')
|
||||
else:
|
||||
preview = title = url = ''
|
||||
text = ' '.join([f'[{raw.get("app", "")}]', preview, title, url]).strip()
|
||||
yiri_msg_list.append(platform_message.Plain(text=text or '[收到一张JSON卡片]'))
|
||||
else:
|
||||
yiri_msg_list.append(platform_message.Plain(text=str(raw)))
|
||||
except Exception:
|
||||
yiri_msg_list.append(platform_message.Plain(text='[收到一张JSON卡片]'))
|
||||
|
||||
chain = platform_message.MessageChain(yiri_msg_list)
|
||||
|
||||
|
||||
@@ -19,18 +19,6 @@ spec:
|
||||
en: https://link.langbot.app/en/platforms/dingtalk
|
||||
ja: https://link.langbot.app/ja/platforms/dingtalk
|
||||
config:
|
||||
- name: one-click-create
|
||||
label:
|
||||
en_US: One-Click Create App
|
||||
zh_Hans: 一键创建应用
|
||||
zh_Hant: 一鍵建立應用
|
||||
description:
|
||||
en_US: "Scan QR code with DingTalk to automatically create an app and fill in credentials. Note: Robot Code cannot be obtained automatically, you need to copy it from the DingTalk Developer Backend manually."
|
||||
zh_Hans: "使用钉钉扫码自动创建应用并填写凭据。注意：机器人代码无法自动获取，需前往钉钉开发者后台手动复制。"
|
||||
zh_Hant: "使用釘釘掃碼自動建立應用並填寫憑證。注意：機器人代碼無法自動取得，需前往釘釘開發者後台手動複製。"
|
||||
type: qr-code-login
|
||||
login_platform: dingtalk
|
||||
required: false
|
||||
- name: client_id
|
||||
label:
|
||||
en_US: Client ID
|
||||
@@ -52,10 +40,6 @@ spec:
|
||||
en_US: Robot Code
|
||||
zh_Hans: 机器人代码
|
||||
zh_Hant: 機器人代碼
|
||||
description:
|
||||
en_US: "Required for image recognition, file upload and other features. Get it from DingTalk Developer Backend > Robot Configuration."
|
||||
zh_Hans: "识图、上传文件等功能必填。请前往钉钉开发者后台 > 机器人配置中获取。"
|
||||
zh_Hant: "識圖、上傳檔案等功能必填。請前往釘釘開發者後台 > 機器人設定中取得。"
|
||||
type: string
|
||||
required: true
|
||||
default: ""
|
||||
|
||||
@@ -1025,90 +1025,7 @@ class LarkAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
|
||||
return api_client
|
||||
|
||||
async def send_message(self, target_type: str, target_id: str, message: platform_message.MessageChain):
|
||||
text_elements, media_items = await self.message_converter.yiri2target(message, self.api_client)
|
||||
|
||||
# Map standard target_type to Feishu receive_id_type
|
||||
if target_type == 'person':
|
||||
receive_id_type = 'open_id'
|
||||
elif target_type == 'group':
|
||||
receive_id_type = 'chat_id'
|
||||
else:
|
||||
receive_id_type = target_type
|
||||
|
||||
# Send text message if there are text elements
|
||||
if text_elements:
|
||||
needs_post = any(ele['tag'] == 'at' for paragraph in text_elements for ele in paragraph)
|
||||
|
||||
if needs_post:
|
||||
msg_type = 'post'
|
||||
final_content = json.dumps(
|
||||
{
|
||||
'zh_Hans': {
|
||||
'title': '',
|
||||
'content': text_elements,
|
||||
},
|
||||
}
|
||||
)
|
||||
else:
|
||||
msg_type = 'text'
|
||||
parts = []
|
||||
for paragraph in text_elements:
|
||||
para_text = ''.join(ele.get('text', '') for ele in paragraph)
|
||||
if para_text:
|
||||
parts.append(para_text)
|
||||
final_content = json.dumps({'text': '\n\n'.join(parts)})
|
||||
|
||||
request: CreateMessageRequest = (
|
||||
CreateMessageRequest.builder()
|
||||
.receive_id_type(receive_id_type)
|
||||
.request_body(
|
||||
CreateMessageRequestBody.builder()
|
||||
.receive_id(target_id)
|
||||
.content(final_content)
|
||||
.msg_type(msg_type)
|
||||
.uuid(str(uuid.uuid4()))
|
||||
.build()
|
||||
)
|
||||
.build()
|
||||
)
|
||||
|
||||
app_access_token = self.get_app_access_token()
|
||||
req_opt: RequestOption = (
|
||||
RequestOption.builder().app_ticket(self.app_ticket).app_access_token(app_access_token).build()
|
||||
)
|
||||
response: CreateMessageResponse = self.api_client.im.v1.message.create(request, req_opt)
|
||||
|
||||
if not response.success():
|
||||
raise Exception(
|
||||
f'client.im.v1.message.create 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)}'
|
||||
)
|
||||
|
||||
# Send media messages separately (image, audio, file, etc.)
|
||||
for media in media_items:
|
||||
request: CreateMessageRequest = (
|
||||
CreateMessageRequest.builder()
|
||||
.receive_id_type(receive_id_type)
|
||||
.request_body(
|
||||
CreateMessageRequestBody.builder()
|
||||
.receive_id(target_id)
|
||||
.content(json.dumps(media['content']))
|
||||
.msg_type(media['msg_type'])
|
||||
.uuid(str(uuid.uuid4()))
|
||||
.build()
|
||||
)
|
||||
.build()
|
||||
)
|
||||
|
||||
app_access_token = self.get_app_access_token()
|
||||
req_opt: RequestOption = (
|
||||
RequestOption.builder().app_ticket(self.app_ticket).app_access_token(app_access_token).build()
|
||||
)
|
||||
response: CreateMessageResponse = self.api_client.im.v1.message.create(request, req_opt)
|
||||
|
||||
if not response.success():
|
||||
raise Exception(
|
||||
f'client.im.v1.message.create ({media["msg_type"]}) 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)}'
|
||||
)
|
||||
pass
|
||||
|
||||
async def is_stream_output_supported(self) -> bool:
|
||||
is_stream = False
|
||||
|
||||
@@ -23,20 +23,6 @@ spec:
|
||||
en: https://link.langbot.app/en/platforms/lark
|
||||
ja: https://link.langbot.app/ja/platforms/lark
|
||||
config:
|
||||
- name: one-click-create
|
||||
label:
|
||||
en_US: One-Click Create App
|
||||
zh_Hans: 一键创建应用
|
||||
zh_Hant: 一鍵建立應用
|
||||
ja_JP: ワンクリックでアプリ作成
|
||||
description:
|
||||
en_US: Scan QR code to automatically create a Feishu app and fill in credentials
|
||||
zh_Hans: 扫码自动创建飞书应用并填写凭据
|
||||
zh_Hant: 掃碼自動建立飛書應用並填寫憑證
|
||||
ja_JP: QRコードをスキャンしてFeishuアプリを自動作成し、認証情報を入力
|
||||
type: qr-code-login
|
||||
login_platform: feishu
|
||||
required: false
|
||||
- name: app_id
|
||||
label:
|
||||
en_US: App ID
|
||||
|
||||
@@ -32,20 +32,6 @@ spec:
|
||||
type: string
|
||||
required: true
|
||||
default: "https://ilinkai.weixin.qq.com"
|
||||
- name: qr-login
|
||||
label:
|
||||
en_US: Scan QR Login
|
||||
zh_Hans: 扫码登录
|
||||
zh_Hant: 掃碼登入
|
||||
ja_JP: QRコードでログイン
|
||||
description:
|
||||
en_US: Scan QR code with WeChat to authorize and automatically fill in the token
|
||||
zh_Hans: 使用微信扫码授权，自动填写令牌
|
||||
zh_Hant: 使用微信掃碼授權，自動填寫令牌
|
||||
ja_JP: WeChatでQRコードをスキャンし、トークンを自動入力
|
||||
type: qr-code-login
|
||||
login_platform: weixin
|
||||
required: false
|
||||
- name: token
|
||||
label:
|
||||
en_US: Token
|
||||
|
||||
@@ -27,7 +27,10 @@ class WebPageBotAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter
|
||||
listeners: dict = pydantic.Field(default_factory=dict, exclude=True)
|
||||
_ws_adapter: typing.Any = None
|
||||
|
||||
model_config = pydantic.ConfigDict(arbitrary_types_allowed=True)
|
||||
class Config:
|
||||
arbitrary_types_allowed = True
|
||||
# Allow private attributes
|
||||
underscore_attrs_are_private = True
|
||||
|
||||
def __init__(self, config: dict, logger: abstract_platform_logger.AbstractEventLogger, **kwargs):
|
||||
super().__init__(config=config, logger=logger, **kwargs)
|
||||
|
||||
@@ -373,7 +373,6 @@ class WebSocketAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter)
|
||||
"""
|
||||
pipeline_uuid = connection.pipeline_uuid
|
||||
session_type = connection.session_type
|
||||
is_workflow = bool(connection.metadata.get('is_workflow'))
|
||||
|
||||
# 获取stream参数，默认为True
|
||||
self.stream_enabled = message_data.get('stream', True)
|
||||
@@ -415,60 +414,6 @@ class WebSocketAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter)
|
||||
session_type=session_type,
|
||||
)
|
||||
|
||||
if is_workflow:
|
||||
# 设置 pipeline_uuid，以便工作流节点发送消息时能正确广播
|
||||
self.ap.platform_mgr.websocket_proxy_bot.bot_entity.use_pipeline_uuid = pipeline_uuid
|
||||
|
||||
message_content = str(message_chain)
|
||||
message_context = {
|
||||
'message_id': str(message_id),
|
||||
'message_content': message_content,
|
||||
'sender_id': f'websocket_{connection.connection_id}',
|
||||
'sender_name': 'User',
|
||||
'platform': 'websocket',
|
||||
'conversation_id': connection.connection_id,
|
||||
'is_group': session_type == 'group',
|
||||
'group_id': 'websocketgroup' if session_type == 'group' else None,
|
||||
'mentions': [],
|
||||
'reply_to': None,
|
||||
'raw_message': {
|
||||
'message': message_chain_obj,
|
||||
'connection_id': connection.connection_id,
|
||||
'session_type': session_type,
|
||||
},
|
||||
}
|
||||
|
||||
trigger_data = {
|
||||
'message': message_content,
|
||||
'message_chain': message_chain_obj,
|
||||
'session_type': session_type,
|
||||
'connection_id': connection.connection_id,
|
||||
'message_context': message_context,
|
||||
}
|
||||
|
||||
try:
|
||||
from ...api.http.service.workflow import WorkflowExecutionFailedError
|
||||
|
||||
# Log workflow execution start (matching pipeline logging)
|
||||
session_id = f'{session_type}_{connection.connection_id}'
|
||||
logger.info(f'Processing request from {session_id} (0): {message_content}')
|
||||
|
||||
execution_id = await self.ap.workflow_service.execute_workflow(
|
||||
pipeline_uuid,
|
||||
trigger_type='message',
|
||||
trigger_data=trigger_data,
|
||||
session_id=session_id,
|
||||
user_id=message_context['sender_id'],
|
||||
bot_id=self.ap.platform_mgr.websocket_proxy_bot.bot_entity.uuid,
|
||||
)
|
||||
# Removed success broadcast - only show error on failure
|
||||
except WorkflowExecutionFailedError as e:
|
||||
await connection.send_queue.put({'type': 'error', 'message': e.message})
|
||||
except Exception as e:
|
||||
logger.error(f'Workflow websocket execution error: {e}', exc_info=True)
|
||||
await connection.send_queue.put({'type': 'error', 'message': str(e)})
|
||||
return
|
||||
|
||||
# 添加消息源
|
||||
message_chain.insert(0, platform_message.Source(id=message_id, time=datetime.now().timestamp()))
|
||||
|
||||
|
||||
@@ -19,18 +19,6 @@ spec:
|
||||
en: https://link.langbot.app/en/platforms/wecombot
|
||||
ja: https://link.langbot.app/ja/platforms/wecombot
|
||||
config:
|
||||
- name: one-click-create
|
||||
label:
|
||||
en_US: One-Click Create Bot
|
||||
zh_Hans: 一键创建机器人
|
||||
zh_Hant: 一鍵建立機器人
|
||||
description:
|
||||
en_US: "Scan QR code with WeCom to automatically create a bot and fill in BotId and Secret. Note: Robot Name needs to be filled in manually."
|
||||
zh_Hans: "使用企业微信扫码自动创建机器人并填写 BotId 和 Secret。注意：机器人名称需手动填写。"
|
||||
zh_Hant: "使用企業微信掃碼自動建立機器人並填寫 BotId 和 Secret。注意：機器人名稱需手動填寫。"
|
||||
type: qr-code-login
|
||||
login_platform: wecombot
|
||||
required: false
|
||||
- name: BotId
|
||||
label:
|
||||
en_US: BotId
|
||||
|
||||
@@ -11,7 +11,6 @@ import os
|
||||
import sys
|
||||
import httpx
|
||||
import sqlalchemy
|
||||
import yaml
|
||||
from async_lru import alru_cache
|
||||
from langbot_plugin.api.entities.builtin.pipeline.query import provider_session
|
||||
|
||||
@@ -35,10 +34,6 @@ from ..core import taskmgr
|
||||
from ..entity.persistence import plugin as persistence_plugin
|
||||
|
||||
|
||||
class PluginRuntimeNotConnectedError(RuntimeError):
|
||||
"""Raised when plugin runtime operations are requested before connection."""
|
||||
|
||||
|
||||
class PluginRuntimeConnector:
|
||||
"""Plugin runtime connector"""
|
||||
|
||||
@@ -196,114 +191,44 @@ class PluginRuntimeConnector:
|
||||
|
||||
async def ping_plugin_runtime(self):
|
||||
if not hasattr(self, 'handler'):
|
||||
raise PluginRuntimeNotConnectedError('Plugin runtime is not connected')
|
||||
raise Exception('Plugin runtime is not connected')
|
||||
|
||||
return await self.handler.ping()
|
||||
|
||||
def _inspect_plugin_package(
|
||||
def _extract_deps_metadata(
|
||||
self,
|
||||
file_bytes: bytes,
|
||||
task_context: taskmgr.TaskContext | None,
|
||||
) -> tuple[str | None, str | None]:
|
||||
"""Extract plugin identity and dependency metadata from a plugin package."""
|
||||
plugin_author = None
|
||||
plugin_name = 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:
|
||||
try:
|
||||
manifest = yaml.safe_load(zf.read('manifest.yaml').decode('utf-8', errors='ignore')) or {}
|
||||
metadata = manifest.get('metadata', {})
|
||||
plugin_author = metadata.get('author')
|
||||
plugin_name = metadata.get('name')
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
if task_context is not None:
|
||||
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
|
||||
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
|
||||
|
||||
return plugin_author, plugin_name
|
||||
|
||||
def _build_plugin_startup_failure_message(
|
||||
self,
|
||||
plugin_author: str,
|
||||
plugin_name: str,
|
||||
task_context: taskmgr.TaskContext | None,
|
||||
) -> str:
|
||||
dep_hint = ''
|
||||
if task_context is not None:
|
||||
current_dep = task_context.metadata.get('current_dep')
|
||||
if current_dep:
|
||||
dep_hint = f' Last dependency: {current_dep}.'
|
||||
|
||||
return (
|
||||
f'Plugin {plugin_author}/{plugin_name} failed to start after installation. '
|
||||
f'Dependency installation or plugin initialization may have failed.{dep_hint} '
|
||||
f'Please check the plugin requirements and runtime logs.'
|
||||
)
|
||||
|
||||
async def _wait_for_installed_plugin_ready(
|
||||
self,
|
||||
plugin_author: str | None,
|
||||
plugin_name: str | None,
|
||||
task_context: taskmgr.TaskContext | None,
|
||||
timeout: float = 30,
|
||||
):
|
||||
"""Wait until the installed plugin is registered by the runtime.
|
||||
|
||||
The plugin runtime launches plugins asynchronously. If dependency installation
|
||||
fails, the plugin process exits before registration; without this check the
|
||||
install task can incorrectly finish successfully.
|
||||
"""
|
||||
if not plugin_author or not plugin_name:
|
||||
return
|
||||
|
||||
deadline = time.time() + timeout
|
||||
last_error: Exception | None = None
|
||||
while time.time() < deadline:
|
||||
try:
|
||||
plugin = await self.get_plugin_info(plugin_author, plugin_name)
|
||||
if plugin is not None:
|
||||
status = plugin.get('status')
|
||||
if status == 'initialized':
|
||||
return
|
||||
except Exception as e:
|
||||
last_error = e
|
||||
|
||||
await asyncio.sleep(0.5)
|
||||
|
||||
message = self._build_plugin_startup_failure_message(plugin_author, plugin_name, task_context)
|
||||
if last_error is not None:
|
||||
message = f'{message} Last runtime error: {last_error}'
|
||||
raise RuntimeError(message)
|
||||
|
||||
async def install_plugin(
|
||||
self,
|
||||
install_source: PluginInstallSource,
|
||||
install_info: dict[str, Any],
|
||||
task_context: taskmgr.TaskContext | None = None,
|
||||
):
|
||||
plugin_author = install_info.get('plugin_author')
|
||||
plugin_name = install_info.get('plugin_name')
|
||||
|
||||
if install_source == PluginInstallSource.LOCAL:
|
||||
# transfer file before install
|
||||
file_bytes = install_info['plugin_file']
|
||||
plugin_author, plugin_name = self._inspect_plugin_package(file_bytes, task_context)
|
||||
if task_context is not None and plugin_author and plugin_name:
|
||||
task_context.metadata['plugin_name'] = f'{plugin_author}/{plugin_name}'
|
||||
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']
|
||||
@@ -340,9 +265,7 @@ class PluginRuntimeConnector:
|
||||
task_context.metadata['download_speed'] = downloaded / elapsed if elapsed > 0 else 0
|
||||
|
||||
file_bytes = b''.join(chunks)
|
||||
plugin_author, plugin_name = self._inspect_plugin_package(file_bytes, task_context)
|
||||
if task_context is not None and plugin_author and plugin_name:
|
||||
task_context.metadata['plugin_name'] = f'{plugin_author}/{plugin_name}'
|
||||
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
|
||||
self.ap.logger.info(f'Transfered file {file_key} to plugin runtime')
|
||||
@@ -366,8 +289,6 @@ class PluginRuntimeConnector:
|
||||
if metadata is not None and task_context is not None:
|
||||
task_context.metadata.update(metadata)
|
||||
|
||||
await self._wait_for_installed_plugin_ready(plugin_author, plugin_name, task_context)
|
||||
|
||||
async def upgrade_plugin(
|
||||
self,
|
||||
plugin_author: str,
|
||||
@@ -637,12 +558,11 @@ class PluginRuntimeConnector:
|
||||
Raises:
|
||||
ValueError: If plugin_id is not in the expected 'author/name' format.
|
||||
"""
|
||||
segments = plugin_id.split('/')
|
||||
if len(segments) != 2 or not all(segments):
|
||||
if '/' not in plugin_id:
|
||||
raise ValueError(
|
||||
f"Invalid plugin_id format: '{plugin_id}'. Expected 'author/name' format (e.g. 'langbot/rag-engine')."
|
||||
)
|
||||
return segments[0], segments[1]
|
||||
return plugin_id.split('/', 1)
|
||||
|
||||
async def call_rag_ingest(self, plugin_id: str, context_data: dict[str, Any]) -> dict[str, Any]:
|
||||
"""Call plugin to ingest document.
|
||||
|
||||
@@ -84,7 +84,7 @@ class RuntimeProvider:
|
||||
|
||||
# Import monitoring helper
|
||||
try:
|
||||
from ...pipeline import monitor
|
||||
from ...pipeline import monitoring_helper
|
||||
|
||||
# Get monitoring metadata from query variables
|
||||
if query.variables:
|
||||
@@ -96,7 +96,7 @@ class RuntimeProvider:
|
||||
pipeline_name = 'Unknown'
|
||||
message_id = None
|
||||
|
||||
await monitor.MonitoringHelper.record_llm_call(
|
||||
await monitoring_helper.MonitoringHelper.record_llm_call(
|
||||
ap=self.requester.ap,
|
||||
query=query,
|
||||
bot_id=query.bot_uuid or 'unknown',
|
||||
@@ -154,7 +154,7 @@ class RuntimeProvider:
|
||||
|
||||
# Import monitoring helper
|
||||
try:
|
||||
from ...pipeline import monitor
|
||||
from ...pipeline import monitoring_helper
|
||||
|
||||
# Get monitoring metadata from query variables
|
||||
if query.variables:
|
||||
@@ -166,7 +166,7 @@ class RuntimeProvider:
|
||||
pipeline_name = 'Unknown'
|
||||
message_id = None
|
||||
|
||||
await monitor.MonitoringHelper.record_llm_call(
|
||||
await monitoring_helper.MonitoringHelper.record_llm_call(
|
||||
ap=self.requester.ap,
|
||||
query=query,
|
||||
bot_id=query.bot_uuid or 'unknown',
|
||||
@@ -340,7 +340,6 @@ class ProviderAPIRequester(metaclass=abc.ABCMeta):
|
||||
"""Provider API请求器"""
|
||||
|
||||
name: str = None
|
||||
init_api_key: str = 'langbot-init-placeholder'
|
||||
|
||||
ap: app.Application
|
||||
|
||||
|
||||
@@ -25,7 +25,7 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
|
||||
|
||||
async def initialize(self):
|
||||
self.client = openai.AsyncClient(
|
||||
api_key=self.init_api_key,
|
||||
api_key='',
|
||||
base_url=self.requester_cfg['base_url'].replace(' ', ''),
|
||||
timeout=self.requester_cfg['timeout'],
|
||||
http_client=httpx.AsyncClient(trust_env=True, timeout=self.requester_cfg['timeout']),
|
||||
|
||||
@@ -25,7 +25,7 @@ class ModelScopeChatCompletions(requester.ProviderAPIRequester):
|
||||
|
||||
async def initialize(self):
|
||||
self.client = openai.AsyncClient(
|
||||
api_key=self.init_api_key,
|
||||
api_key='',
|
||||
base_url=self.requester_cfg['base_url'],
|
||||
timeout=self.requester_cfg['timeout'],
|
||||
http_client=httpx.AsyncClient(trust_env=True, timeout=self.requester_cfg['timeout']),
|
||||
|
||||
@@ -14,14 +14,7 @@ class TokenManager:
|
||||
|
||||
def __init__(self, name: str, tokens: list[str]):
|
||||
self.name = name
|
||||
self.tokens = []
|
||||
seen_tokens = set()
|
||||
for token in tokens:
|
||||
normalized_token = token.strip() if isinstance(token, str) else ''
|
||||
if not normalized_token or normalized_token in seen_tokens:
|
||||
continue
|
||||
self.tokens.append(normalized_token)
|
||||
seen_tokens.add(normalized_token)
|
||||
self.tokens = tokens
|
||||
self.using_token_index = 0
|
||||
|
||||
def get_token(self) -> str:
|
||||
@@ -30,6 +23,4 @@ class TokenManager:
|
||||
return self.tokens[self.using_token_index]
|
||||
|
||||
def next_token(self):
|
||||
if len(self.tokens) == 0:
|
||||
return
|
||||
self.using_token_index = (self.using_token_index + 1) % len(self.tokens)
|
||||
|
||||
@@ -1,12 +1,8 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import posixpath
|
||||
import re
|
||||
from typing import TYPE_CHECKING, Any
|
||||
from urllib.parse import unquote
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from langbot.pkg.core import app
|
||||
from typing import Any
|
||||
from langbot.pkg.core import app
|
||||
|
||||
|
||||
class RAGRuntimeService:
|
||||
@@ -113,17 +109,8 @@ class RAGRuntimeService:
|
||||
regardless of the underlying storage provider.
|
||||
"""
|
||||
# Validate storage_path to prevent path traversal
|
||||
decoded_path = unquote(storage_path).replace('\\', '/')
|
||||
decoded_segments = decoded_path.split('/')
|
||||
normalized = posixpath.normpath(decoded_path)
|
||||
if (
|
||||
not storage_path
|
||||
or '\x00' in decoded_path
|
||||
or normalized.startswith('/')
|
||||
or '..' in decoded_segments
|
||||
or '..' in normalized.split('/')
|
||||
or re.match(r'^[A-Za-z]:/', normalized)
|
||||
):
|
||||
normalized = posixpath.normpath(storage_path)
|
||||
if normalized.startswith('/') or '..' in normalized.split('/'):
|
||||
raise ValueError('Invalid storage path')
|
||||
content_bytes = await self.ap.storage_mgr.storage_provider.load(normalized)
|
||||
return content_bytes if content_bytes else b''
|
||||
|
||||
@@ -13,11 +13,12 @@ class TelemetryManager:
|
||||
await telemetry.send({ ... })
|
||||
"""
|
||||
|
||||
send_tasks: list[asyncio.Task] = []
|
||||
|
||||
def __init__(self, ap: core_app.Application):
|
||||
self.ap = ap
|
||||
|
||||
self.telemetry_config = {}
|
||||
self.send_tasks: list[asyncio.Task] = []
|
||||
|
||||
async def initialize(self):
|
||||
self.telemetry_config = self.ap.instance_config.data.get('space', {})
|
||||
|
||||
@@ -83,7 +83,7 @@ def get_func_schema(function: typing.Callable) -> dict:
|
||||
|
||||
parameters['properties'][param.name] = {
|
||||
'type': param_type,
|
||||
'description': args_doc.get(param.name, ''),
|
||||
'description': args_doc[param.name],
|
||||
}
|
||||
|
||||
# add schema for array
|
||||
|
||||
@@ -145,8 +145,7 @@ def get_qq_image_downloadable_url(image_url: str) -> tuple[str, dict]:
|
||||
"""获取QQ图片的下载链接"""
|
||||
parsed = urlparse(image_url)
|
||||
query = parse_qs(parsed.query)
|
||||
scheme = parsed.scheme or 'http'
|
||||
return f'{scheme}://{parsed.netloc}{parsed.path}', query
|
||||
return f'http://{parsed.netloc}{parsed.path}', query
|
||||
|
||||
|
||||
async def get_qq_image_bytes(image_url: str, query: dict = {}) -> tuple[bytes, str]:
|
||||
|
||||
@@ -23,10 +23,7 @@ def run_pip(params: list):
|
||||
pipmain(params)
|
||||
|
||||
|
||||
def install_requirements(file, extra_params: list | None = None):
|
||||
if extra_params is None:
|
||||
extra_params = []
|
||||
|
||||
def install_requirements(file, extra_params: list = []):
|
||||
pipmain(
|
||||
[
|
||||
'install',
|
||||
|
||||
@@ -1,7 +1,5 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import ipaddress
|
||||
import re
|
||||
from urllib.parse import urlparse
|
||||
|
||||
|
||||
@@ -46,40 +44,6 @@ LOCAL_PATTERNS = [
|
||||
'172.31.',
|
||||
]
|
||||
|
||||
HOST_LABEL_PATTERN = re.compile(r'^[a-z0-9](?:[a-z0-9-]{0,61}[a-z0-9])?$')
|
||||
|
||||
|
||||
def _is_valid_hostname(host: str) -> bool:
|
||||
if host == 'localhost':
|
||||
return True
|
||||
|
||||
try:
|
||||
ipaddress.ip_address(host)
|
||||
return True
|
||||
except ValueError:
|
||||
pass
|
||||
|
||||
if not host or len(host) > 253 or any(char.isspace() for char in host):
|
||||
return False
|
||||
|
||||
host = host.rstrip('.')
|
||||
if not host:
|
||||
return False
|
||||
|
||||
return all(HOST_LABEL_PATTERN.match(label) for label in host.split('.'))
|
||||
|
||||
|
||||
def _is_local_host(host: str) -> bool:
|
||||
if host == 'localhost':
|
||||
return True
|
||||
|
||||
try:
|
||||
ip_address = ipaddress.ip_address(host)
|
||||
except ValueError:
|
||||
return False
|
||||
|
||||
return ip_address.is_private or ip_address.is_loopback or ip_address.is_unspecified
|
||||
|
||||
|
||||
def get_runner_category(runner_name: str, runner_url: str) -> str:
|
||||
if not runner_url:
|
||||
@@ -88,15 +52,12 @@ def get_runner_category(runner_name: str, runner_url: str) -> str:
|
||||
try:
|
||||
parsed_url = urlparse(runner_url)
|
||||
host = parsed_url.hostname.lower() if parsed_url.hostname else ''
|
||||
_ = parsed_url.port
|
||||
except Exception:
|
||||
return RunnerCategory.UNKNOWN
|
||||
|
||||
if not parsed_url.scheme or not host or not _is_valid_hostname(host):
|
||||
return RunnerCategory.UNKNOWN
|
||||
|
||||
if _is_local_host(host):
|
||||
return RunnerCategory.LOCAL
|
||||
for pattern in LOCAL_PATTERNS:
|
||||
if host.startswith(pattern):
|
||||
return RunnerCategory.LOCAL
|
||||
|
||||
for domain in CLOUD_DOMAINS:
|
||||
if host.endswith(domain):
|
||||
|
||||
@@ -1,204 +0,0 @@
|
||||
"""Workflow-Pipeline通信适配器
|
||||
|
||||
这个模块提供了Workflow和Pipeline之间的通信适配，使用SDK标准的MessageEnvelope格式。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
from typing import Any, Optional
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class _WorkflowPipelineCaptureAdapter:
|
||||
"""Workflow-Pipeline通信适配器
|
||||
|
||||
用于在Workflow节点和Pipeline之间进行标准化的消息传递。
|
||||
支持MessageEnvelope格式的双向转换。
|
||||
"""
|
||||
|
||||
def __init__(self, context: Any):
|
||||
"""初始化适配器
|
||||
|
||||
Args:
|
||||
context: ExecutionContext - Workflow执行上下文
|
||||
"""
|
||||
self.context = context
|
||||
self.responses: list[dict[str, Any]] = []
|
||||
self.bot_account_id: Optional[str] = None
|
||||
self._logger = logging.getLogger(__name__)
|
||||
|
||||
async def call_pipeline_with_envelope(
|
||||
self,
|
||||
envelope: Any,
|
||||
pipeline_executor: Any
|
||||
) -> Any:
|
||||
"""使用MessageEnvelope调用Pipeline
|
||||
|
||||
Args:
|
||||
envelope: MessageEnvelope - 标准消息信封
|
||||
pipeline_executor: Pipeline执行器实例
|
||||
|
||||
Returns:
|
||||
MessageEnvelope - 执行结果信封
|
||||
"""
|
||||
try:
|
||||
# 动态导入以避免循环依赖
|
||||
from langbot_plugin_sdk.workflow import envelope_to_query, query_to_envelope
|
||||
|
||||
# 1. 转换为Query
|
||||
query = envelope_to_query(envelope)
|
||||
|
||||
# 2. 调用Pipeline
|
||||
result_query = await pipeline_executor.execute(query)
|
||||
|
||||
# 3. 转换回Envelope
|
||||
result_envelope = query_to_envelope(result_query, envelope)
|
||||
|
||||
self._logger.debug(
|
||||
f'Pipeline execution completed for workflow {envelope.workflow_id}',
|
||||
extra={
|
||||
'workflow_id': envelope.workflow_id,
|
||||
'execution_id': envelope.execution_id,
|
||||
'node_id': envelope.node_id,
|
||||
}
|
||||
)
|
||||
|
||||
return result_envelope
|
||||
|
||||
except Exception as e:
|
||||
self._logger.error(
|
||||
f'Pipeline execution failed: {e}',
|
||||
exc_info=True,
|
||||
extra={
|
||||
'workflow_id': envelope.workflow_id,
|
||||
'execution_id': envelope.execution_id,
|
||||
'node_id': envelope.node_id,
|
||||
}
|
||||
)
|
||||
raise
|
||||
|
||||
def validate_envelope(self, envelope: Any) -> bool:
|
||||
"""验证MessageEnvelope的有效性
|
||||
|
||||
Args:
|
||||
envelope: MessageEnvelope - 要验证的消息信封
|
||||
|
||||
Returns:
|
||||
bool - 验证是否通过
|
||||
"""
|
||||
required_fields = [
|
||||
'message_id',
|
||||
'workflow_id',
|
||||
'node_id',
|
||||
'execution_id',
|
||||
'payload',
|
||||
'launcher_type',
|
||||
]
|
||||
|
||||
for field in required_fields:
|
||||
if not hasattr(envelope, field):
|
||||
self._logger.warning(
|
||||
f'MessageEnvelope missing required field: {field}'
|
||||
)
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
def get_responses(self) -> list[dict[str, Any]]:
|
||||
"""获取所有响应
|
||||
|
||||
Returns:
|
||||
list - 响应列表
|
||||
"""
|
||||
return self.responses.copy()
|
||||
|
||||
def add_response(self, response: dict[str, Any]) -> None:
|
||||
"""添加响应
|
||||
|
||||
Args:
|
||||
response: dict - 响应数据
|
||||
"""
|
||||
self.responses.append(response)
|
||||
|
||||
def get_last_text_response(self) -> str:
|
||||
"""获取最后一个文本响应
|
||||
|
||||
Returns:
|
||||
str - 最后一个响应的文本内容
|
||||
"""
|
||||
if not self.responses:
|
||||
return ''
|
||||
|
||||
last_response = self.responses[-1]
|
||||
return str(last_response.get('content', '') or '')
|
||||
|
||||
def clear_responses(self) -> None:
|
||||
"""清空所有响应"""
|
||||
self.responses.clear()
|
||||
|
||||
|
||||
class WorkflowPipelineCompatibilityLayer:
|
||||
"""Workflow-Pipeline兼容性层
|
||||
|
||||
提供向后兼容性，支持旧的Pipeline Query格式和新的MessageEnvelope格式。
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
"""初始化兼容性层"""
|
||||
self._logger = logging.getLogger(__name__)
|
||||
|
||||
def is_workflow_context(self, query: Any) -> bool:
|
||||
"""检查Query是否包含Workflow上下文
|
||||
|
||||
Args:
|
||||
query: Query - Pipeline Query对象
|
||||
|
||||
Returns:
|
||||
bool - 是否来自Workflow
|
||||
"""
|
||||
if hasattr(query, 'is_from_workflow'):
|
||||
return query.is_from_workflow()
|
||||
|
||||
if hasattr(query, 'get_workflow_context'):
|
||||
context = query.get_workflow_context()
|
||||
return bool(context and context.get('workflow_id'))
|
||||
|
||||
return False
|
||||
|
||||
def get_workflow_id(self, query: Any) -> Optional[str]:
|
||||
"""从Query获取Workflow ID
|
||||
|
||||
Args:
|
||||
query: Query - Pipeline Query对象
|
||||
|
||||
Returns:
|
||||
str - Workflow ID，如果不存在则返回None
|
||||
"""
|
||||
if hasattr(query, 'get_workflow_id'):
|
||||
return query.get_workflow_id()
|
||||
|
||||
if hasattr(query, 'get_workflow_context'):
|
||||
context = query.get_workflow_context()
|
||||
return context.get('workflow_id') if context else None
|
||||
|
||||
return None
|
||||
|
||||
def get_execution_id(self, query: Any) -> Optional[str]:
|
||||
"""从Query获取执行ID
|
||||
|
||||
Args:
|
||||
query: Query - Pipeline Query对象
|
||||
|
||||
Returns:
|
||||
str - 执行ID，如果不存在则返回None
|
||||
"""
|
||||
if hasattr(query, 'get_execution_id'):
|
||||
return query.get_execution_id()
|
||||
|
||||
if hasattr(query, 'get_workflow_context'):
|
||||
context = query.get_workflow_context()
|
||||
return context.get('execution_id') if context else None
|
||||
|
||||
return None
|
||||
@@ -1,509 +0,0 @@
|
||||
"""Workflow debug execution support.
|
||||
|
||||
This module provides debugging capabilities for workflow execution, including:
|
||||
- ExecutionLog: Structured log entries for execution tracking
|
||||
- DebugExecutionState: State management for debug sessions (pause, resume, breakpoints)
|
||||
- DebugWorkflowExecutor: Extended executor with step-by-step debugging support
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
import traceback
|
||||
import uuid
|
||||
from datetime import datetime
|
||||
from typing import Any, Optional, TYPE_CHECKING
|
||||
|
||||
from .entities import (
|
||||
WorkflowDefinition,
|
||||
NodeDefinition,
|
||||
EdgeDefinition,
|
||||
ExecutionContext,
|
||||
ExecutionStatus,
|
||||
NodeState,
|
||||
NodeStatus,
|
||||
)
|
||||
from .executor import WorkflowExecutor
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from ..core import app
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class ExecutionLog:
|
||||
"""Execution log entry"""
|
||||
|
||||
def __init__(self, level: str, message: str, node_id: Optional[str] = None, data: Optional[dict] = None):
|
||||
self.id = str(uuid.uuid4())
|
||||
self.timestamp = datetime.now().isoformat()
|
||||
self.level = level
|
||||
self.message = message
|
||||
self.node_id = node_id
|
||||
self.data = data or {}
|
||||
|
||||
def to_dict(self) -> dict:
|
||||
return {
|
||||
'id': self.id,
|
||||
'timestamp': self.timestamp,
|
||||
'level': self.level,
|
||||
'message': self.message,
|
||||
'node_id': self.node_id,
|
||||
'data': self.data,
|
||||
}
|
||||
|
||||
|
||||
class DebugExecutionState:
|
||||
"""State for a debug execution"""
|
||||
|
||||
def __init__(self, execution_id: str, breakpoints: list[str] = None):
|
||||
self.execution_id = execution_id
|
||||
self.status: str = 'running'
|
||||
self.is_paused: bool = False
|
||||
self.is_stopped: bool = False
|
||||
self.current_node_id: Optional[str] = None
|
||||
self.breakpoints: set[str] = set(breakpoints or [])
|
||||
self.logs: list[ExecutionLog] = []
|
||||
self.pending_logs: list[ExecutionLog] = []
|
||||
self._pause_event = asyncio.Event()
|
||||
self._pause_event.set() # Initially not paused
|
||||
self._stop_event = asyncio.Event()
|
||||
|
||||
def add_log(self, level: str, message: str, node_id: str = None, data: dict = None):
|
||||
"""Add a log entry"""
|
||||
log = ExecutionLog(level, message, node_id, data)
|
||||
self.logs.append(log)
|
||||
self.pending_logs.append(log)
|
||||
logger.log(
|
||||
getattr(logging, level.upper(), logging.INFO),
|
||||
f'[Workflow Debug] {message}',
|
||||
extra={'node_id': node_id, 'data': data},
|
||||
)
|
||||
|
||||
def get_pending_logs(self) -> list[dict]:
|
||||
"""Get and clear pending logs"""
|
||||
logs = [log.to_dict() for log in self.pending_logs]
|
||||
self.pending_logs = []
|
||||
return logs
|
||||
|
||||
def pause(self):
|
||||
"""Pause execution"""
|
||||
self.is_paused = True
|
||||
self._pause_event.clear()
|
||||
self.add_log('info', 'Execution paused')
|
||||
|
||||
def resume(self):
|
||||
"""Resume execution"""
|
||||
self.is_paused = False
|
||||
self._pause_event.set()
|
||||
self.add_log('info', 'Execution resumed')
|
||||
|
||||
def stop(self):
|
||||
"""Stop execution"""
|
||||
self.is_stopped = True
|
||||
self.status = 'cancelled'
|
||||
self._stop_event.set()
|
||||
self._pause_event.set() # Release any pause
|
||||
self.add_log('info', 'Execution stopped')
|
||||
|
||||
async def wait_if_paused(self):
|
||||
"""Wait if execution is paused"""
|
||||
if self.is_paused:
|
||||
self.add_log('info', 'Waiting for resume...')
|
||||
await self._pause_event.wait()
|
||||
|
||||
def check_breakpoint(self, node_id: str) -> bool:
|
||||
"""Check if there's a breakpoint at the given node"""
|
||||
return node_id in self.breakpoints
|
||||
|
||||
|
||||
class DebugWorkflowExecutor(WorkflowExecutor):
|
||||
"""
|
||||
Debug-enabled workflow executor with step-by-step execution support.
|
||||
Extends WorkflowExecutor with debugging capabilities.
|
||||
"""
|
||||
|
||||
# Class-level storage for active debug sessions
|
||||
_debug_states: dict[str, DebugExecutionState] = {}
|
||||
|
||||
def __init__(self, ap: Optional['app.Application'] = None):
|
||||
super().__init__(ap)
|
||||
|
||||
@classmethod
|
||||
def get_debug_state(cls, execution_id: str) -> Optional[DebugExecutionState]:
|
||||
"""Get debug state for an execution"""
|
||||
return cls._debug_states.get(execution_id)
|
||||
|
||||
@classmethod
|
||||
def create_debug_state(cls, execution_id: str, breakpoints: list[str] = None) -> DebugExecutionState:
|
||||
"""Create a new debug state"""
|
||||
state = DebugExecutionState(execution_id, breakpoints)
|
||||
cls._debug_states[execution_id] = state
|
||||
return state
|
||||
|
||||
@classmethod
|
||||
def remove_debug_state(cls, execution_id: str):
|
||||
"""Remove debug state for an execution"""
|
||||
cls._debug_states.pop(execution_id, None)
|
||||
|
||||
async def execute_debug(
|
||||
self,
|
||||
workflow: WorkflowDefinition,
|
||||
context: ExecutionContext,
|
||||
debug_state: DebugExecutionState,
|
||||
) -> ExecutionContext:
|
||||
"""
|
||||
Execute a workflow in debug mode.
|
||||
|
||||
Args:
|
||||
workflow: Workflow definition
|
||||
context: Execution context
|
||||
debug_state: Debug execution state
|
||||
|
||||
Returns:
|
||||
Updated execution context
|
||||
"""
|
||||
context.status = ExecutionStatus.RUNNING
|
||||
context.start_time = datetime.now()
|
||||
debug_state.add_log('info', f'Starting debug execution for workflow: {workflow.name}')
|
||||
|
||||
try:
|
||||
# Build execution graph
|
||||
node_map = {node.id: node for node in workflow.nodes}
|
||||
edge_map = self._build_edge_map(workflow.edges)
|
||||
self._edges = workflow.edges
|
||||
|
||||
# Initialize node states
|
||||
for node in workflow.nodes:
|
||||
if node.id not in context.node_states:
|
||||
context.node_states[node.id] = NodeState(node_id=node.id)
|
||||
|
||||
# Find start node(s)
|
||||
start_nodes = self._find_start_nodes(workflow.nodes, workflow.edges)
|
||||
|
||||
if not start_nodes:
|
||||
raise ValueError('No start nodes found in workflow')
|
||||
|
||||
debug_state.add_log('info', f'Found {len(start_nodes)} start node(s)')
|
||||
|
||||
# Execute from start nodes
|
||||
for start_node in start_nodes:
|
||||
if debug_state.is_stopped:
|
||||
break
|
||||
|
||||
await self._execute_debug_from_node(
|
||||
start_node, node_map, edge_map, context, debug_state, workflow.settings.max_retries
|
||||
)
|
||||
|
||||
# Set final status
|
||||
if debug_state.is_stopped:
|
||||
context.status = ExecutionStatus.CANCELLED
|
||||
debug_state.status = 'cancelled'
|
||||
else:
|
||||
all_completed = all(
|
||||
state.status in (NodeStatus.COMPLETED, NodeStatus.SKIPPED) for state in context.node_states.values()
|
||||
)
|
||||
|
||||
if all_completed:
|
||||
context.status = ExecutionStatus.COMPLETED
|
||||
debug_state.status = 'completed'
|
||||
debug_state.add_log('info', 'Workflow execution completed successfully')
|
||||
else:
|
||||
has_failed = any(state.status == NodeStatus.FAILED for state in context.node_states.values())
|
||||
if has_failed:
|
||||
context.status = ExecutionStatus.FAILED
|
||||
debug_state.status = 'error'
|
||||
|
||||
except Exception as e:
|
||||
context.status = ExecutionStatus.FAILED
|
||||
context.error = str(e)
|
||||
debug_state.status = 'error'
|
||||
debug_state.add_log('error', f'Workflow execution failed: {e}', data={'traceback': traceback.format_exc()})
|
||||
logger.error(f'Debug workflow execution failed: {e}\n{traceback.format_exc()}')
|
||||
|
||||
finally:
|
||||
context.end_time = datetime.now()
|
||||
|
||||
return context
|
||||
|
||||
async def _execute_debug_from_node(
|
||||
self,
|
||||
node: NodeDefinition,
|
||||
node_map: dict[str, NodeDefinition],
|
||||
edge_map: dict[str, list[EdgeDefinition]],
|
||||
context: ExecutionContext,
|
||||
debug_state: DebugExecutionState,
|
||||
max_retries: int = 3,
|
||||
):
|
||||
"""Execute workflow from a node with debug support"""
|
||||
|
||||
# Check if stopped
|
||||
if debug_state.is_stopped:
|
||||
return
|
||||
|
||||
# Wait if paused
|
||||
await debug_state.wait_if_paused()
|
||||
|
||||
# Check if should skip
|
||||
if await self._should_skip_node(node, context):
|
||||
if context.node_states[node.id].status == NodeStatus.SKIPPED:
|
||||
debug_state.add_log('info', f'Skipping node: {node.id}', node_id=node.id)
|
||||
return
|
||||
|
||||
# Check breakpoint
|
||||
if debug_state.check_breakpoint(node.id):
|
||||
debug_state.add_log('info', f'Hit breakpoint at node: {node.id}', node_id=node.id)
|
||||
debug_state.pause()
|
||||
await debug_state.wait_if_paused()
|
||||
|
||||
# Update current node
|
||||
debug_state.current_node_id = node.id
|
||||
debug_state.add_log('info', f'Executing node: {node.id} ({node.type})', node_id=node.id)
|
||||
|
||||
# Execute node
|
||||
await self._execute_debug_node(node, context, debug_state, max_retries)
|
||||
|
||||
# Check if stopped or failed
|
||||
if debug_state.is_stopped:
|
||||
return
|
||||
if context.node_states[node.id].status == NodeStatus.FAILED:
|
||||
return
|
||||
|
||||
# Get outgoing edges
|
||||
outgoing_edges = edge_map.get(node.id, [])
|
||||
|
||||
# Execute next nodes
|
||||
for edge in outgoing_edges:
|
||||
if debug_state.is_stopped:
|
||||
break
|
||||
|
||||
target_node = node_map.get(edge.target_node)
|
||||
if not target_node:
|
||||
continue
|
||||
|
||||
# Check edge condition
|
||||
if edge.condition:
|
||||
condition_met = await self._evaluate_condition(edge.condition, context)
|
||||
if not condition_met:
|
||||
debug_state.add_log('debug', f'Edge condition not met: {edge.condition}', node_id=node.id)
|
||||
continue
|
||||
|
||||
# Check if all inputs are ready
|
||||
if await self._inputs_ready(target_node, edge_map, context):
|
||||
await self._execute_debug_from_node(target_node, node_map, edge_map, context, debug_state, max_retries)
|
||||
|
||||
async def _execute_debug_node(
|
||||
self, node: NodeDefinition, context: ExecutionContext, debug_state: DebugExecutionState, max_retries: int = 3
|
||||
):
|
||||
"""Execute a single node with debug logging"""
|
||||
|
||||
node_state = context.node_states[node.id]
|
||||
node_state.status = NodeStatus.RUNNING
|
||||
node_state.start_time = datetime.now()
|
||||
|
||||
# Get node instance (pass ap for access to services)
|
||||
node_instance = self.registry.create_instance(node.type, node.id, node.config, ap=self.ap)
|
||||
|
||||
if not node_instance:
|
||||
node_state.status = NodeStatus.FAILED
|
||||
node_state.error = f'Unknown node type: {node.type}'
|
||||
node_state.end_time = datetime.now()
|
||||
debug_state.add_log('error', f'Unknown node type: {node.type}', node_id=node.id)
|
||||
self._record_execution_step(node, node_state, context)
|
||||
await self._persist_node_execution(node, node_state, context)
|
||||
return
|
||||
|
||||
# Resolve inputs
|
||||
inputs = await self._resolve_inputs(node, context)
|
||||
node_state.inputs = inputs
|
||||
debug_state.add_log(
|
||||
'debug', 'Node inputs resolved', node_id=node.id, data={'inputs': self._safe_serialize(inputs)}
|
||||
)
|
||||
|
||||
# Validate inputs
|
||||
validation_errors = await node_instance.validate_inputs(inputs)
|
||||
if validation_errors:
|
||||
node_state.status = NodeStatus.FAILED
|
||||
node_state.error = '; '.join(validation_errors)
|
||||
node_state.end_time = datetime.now()
|
||||
debug_state.add_log('error', f'Input validation failed: {node_state.error}', node_id=node.id)
|
||||
self._record_execution_step(node, node_state, context)
|
||||
await self._persist_node_execution(node, node_state, context)
|
||||
return
|
||||
|
||||
# Execute with retries
|
||||
for attempt in range(max_retries + 1):
|
||||
if debug_state.is_stopped:
|
||||
node_state.status = NodeStatus.FAILED
|
||||
node_state.error = 'Execution stopped'
|
||||
node_state.end_time = datetime.now()
|
||||
break
|
||||
|
||||
try:
|
||||
outputs = await node_instance.execute(inputs, context)
|
||||
node_state.outputs = outputs
|
||||
node_state.status = NodeStatus.COMPLETED
|
||||
node_state.end_time = datetime.now()
|
||||
|
||||
duration_ms = int((node_state.end_time - node_state.start_time).total_seconds() * 1000)
|
||||
debug_state.add_log(
|
||||
'info',
|
||||
f'Node completed in {duration_ms}ms',
|
||||
node_id=node.id,
|
||||
data={'outputs': self._safe_serialize(outputs), 'duration_ms': duration_ms},
|
||||
)
|
||||
break
|
||||
|
||||
except Exception as e:
|
||||
node_state.retry_count = attempt + 1
|
||||
debug_state.add_log(
|
||||
'warning', f'Node execution failed (attempt {attempt + 1}/{max_retries + 1}): {e}', node_id=node.id
|
||||
)
|
||||
|
||||
if attempt < max_retries:
|
||||
await asyncio.sleep(1)
|
||||
else:
|
||||
node_state.status = NodeStatus.FAILED
|
||||
node_state.error = str(e)
|
||||
node_state.end_time = datetime.now()
|
||||
debug_state.add_log(
|
||||
'error',
|
||||
f'Node failed after {max_retries + 1} attempts: {e}',
|
||||
node_id=node.id,
|
||||
data={'error': str(e), 'traceback': traceback.format_exc()},
|
||||
)
|
||||
|
||||
self._record_execution_step(node, node_state, context)
|
||||
await self._persist_node_execution(node, node_state, context)
|
||||
|
||||
async def step_execute(
|
||||
self,
|
||||
workflow: WorkflowDefinition,
|
||||
context: ExecutionContext,
|
||||
debug_state: DebugExecutionState,
|
||||
) -> dict:
|
||||
"""
|
||||
Execute one step (one node) in debug mode.
|
||||
|
||||
Returns:
|
||||
Dict with node_id, node_state, and completed status
|
||||
"""
|
||||
# Find next node to execute
|
||||
next_node = self._find_next_executable_node(workflow, context)
|
||||
|
||||
if not next_node:
|
||||
debug_state.status = 'completed'
|
||||
return {'completed': True}
|
||||
|
||||
# Execute single node
|
||||
debug_state.current_node_id = next_node.id
|
||||
await self._execute_debug_node(next_node, context, debug_state, workflow.settings.max_retries)
|
||||
|
||||
node_state = context.node_states.get(next_node.id)
|
||||
|
||||
# Check if workflow is complete
|
||||
all_done = all(
|
||||
state.status in (NodeStatus.COMPLETED, NodeStatus.SKIPPED, NodeStatus.FAILED)
|
||||
for state in context.node_states.values()
|
||||
)
|
||||
|
||||
if all_done:
|
||||
debug_state.status = 'completed'
|
||||
context.status = ExecutionStatus.COMPLETED
|
||||
|
||||
return {
|
||||
'node_id': next_node.id,
|
||||
'node_state': {
|
||||
'status': node_state.status.value if node_state else 'unknown',
|
||||
'inputs': self._safe_serialize(node_state.inputs) if node_state else {},
|
||||
'outputs': self._safe_serialize(node_state.outputs) if node_state else {},
|
||||
'error': node_state.error if node_state else None,
|
||||
},
|
||||
'completed': all_done,
|
||||
}
|
||||
|
||||
def _find_next_executable_node(
|
||||
self, workflow: WorkflowDefinition, context: ExecutionContext
|
||||
) -> Optional[NodeDefinition]:
|
||||
"""Find the next node that can be executed"""
|
||||
edge_map = self._build_edge_map(workflow.edges)
|
||||
|
||||
for node in workflow.nodes:
|
||||
state = context.node_states.get(node.id)
|
||||
|
||||
# Skip completed, running, or failed nodes
|
||||
if state and state.status in (
|
||||
NodeStatus.COMPLETED,
|
||||
NodeStatus.RUNNING,
|
||||
NodeStatus.FAILED,
|
||||
NodeStatus.SKIPPED,
|
||||
):
|
||||
continue
|
||||
|
||||
# Check if this node's inputs are ready
|
||||
incoming_nodes = set()
|
||||
for source_id, edges in edge_map.items():
|
||||
for edge in edges:
|
||||
if edge.target_node == node.id:
|
||||
incoming_nodes.add(source_id)
|
||||
|
||||
# If no incoming nodes, it's a start node
|
||||
if not incoming_nodes:
|
||||
return node
|
||||
|
||||
# Check if all incoming nodes are done
|
||||
all_incoming_done = True
|
||||
for source_id in incoming_nodes:
|
||||
source_state = context.node_states.get(source_id)
|
||||
if not source_state or source_state.status not in (NodeStatus.COMPLETED, NodeStatus.SKIPPED):
|
||||
all_incoming_done = False
|
||||
break
|
||||
|
||||
if all_incoming_done:
|
||||
return node
|
||||
|
||||
return None
|
||||
|
||||
def _safe_serialize(self, data: Any) -> Any:
|
||||
"""Safely serialize data for logging"""
|
||||
if data is None:
|
||||
return None
|
||||
if isinstance(data, (str, int, float, bool)):
|
||||
return data
|
||||
if isinstance(data, (list, tuple)):
|
||||
return [self._safe_serialize(item) for item in data[:100]] # Limit list size
|
||||
if isinstance(data, dict):
|
||||
result = {}
|
||||
for key, value in list(data.items())[:50]: # Limit dict size
|
||||
result[str(key)] = self._safe_serialize(value)
|
||||
return result
|
||||
# For complex objects, try to convert to string
|
||||
try:
|
||||
return str(data)[:1000] # Limit string length
|
||||
except Exception:
|
||||
return '<non-serializable>'
|
||||
|
||||
def get_execution_state(self, context: ExecutionContext, debug_state: DebugExecutionState) -> dict:
|
||||
"""Get current execution state for API response"""
|
||||
node_states = {}
|
||||
for node_id, state in context.node_states.items():
|
||||
node_states[node_id] = {
|
||||
'status': state.status.value,
|
||||
'inputs': self._safe_serialize(state.inputs),
|
||||
'outputs': self._safe_serialize(state.outputs),
|
||||
'error': state.error,
|
||||
'startTime': state.start_time.isoformat() if state.start_time else None,
|
||||
'endTime': state.end_time.isoformat() if state.end_time else None,
|
||||
'duration': int((state.end_time - state.start_time).total_seconds() * 1000)
|
||||
if state.start_time and state.end_time
|
||||
else None,
|
||||
}
|
||||
|
||||
return {
|
||||
'status': debug_state.status,
|
||||
'current_node_id': debug_state.current_node_id,
|
||||
'node_states': node_states,
|
||||
'new_logs': debug_state.get_pending_logs(),
|
||||
'error': context.error,
|
||||
}
|
||||
@@ -1,155 +0,0 @@
|
||||
"""Workflow entities and data models
|
||||
|
||||
This module defines workflow entities using SDK standard entities where available,
|
||||
and local-specific entities for LangBot_copy-specific functionality.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from datetime import datetime
|
||||
from typing import Any, Optional
|
||||
import pydantic
|
||||
|
||||
# Import SDK entities for standard workflow protocol types
|
||||
from langbot_plugin.api.entities.builtin.workflow.entities import (
|
||||
ExecutionContext,
|
||||
ExecutionStep,
|
||||
MessageContext,
|
||||
NodeDefinition,
|
||||
NodeState,
|
||||
PortDefinition,
|
||||
)
|
||||
from langbot_plugin.api.entities.builtin.workflow.enums import (
|
||||
ExecutionStatus,
|
||||
NodeStatus,
|
||||
)
|
||||
|
||||
|
||||
class Position(pydantic.BaseModel):
|
||||
"""Node position on canvas"""
|
||||
|
||||
x: float = 0
|
||||
y: float = 0
|
||||
|
||||
|
||||
class EdgeDefinition(pydantic.BaseModel):
|
||||
"""Workflow edge definition (connection between nodes)"""
|
||||
|
||||
id: str
|
||||
source_node: str
|
||||
source_port: str = 'output'
|
||||
target_node: str
|
||||
target_port: str = 'input'
|
||||
condition: Optional[str] = None # Optional condition expression
|
||||
|
||||
|
||||
class TriggerDefinition(pydantic.BaseModel):
|
||||
"""Workflow trigger definition"""
|
||||
|
||||
id: str
|
||||
type: str # message, cron, event, webhook
|
||||
config: dict[str, Any] = {}
|
||||
enabled: bool = True
|
||||
|
||||
|
||||
class WorkflowSettings(pydantic.BaseModel):
|
||||
"""Workflow settings"""
|
||||
|
||||
# Execution settings
|
||||
max_execution_time: int = 300 # seconds
|
||||
max_retries: int = 3
|
||||
retry_delay: int = 5 # seconds
|
||||
|
||||
# Error handling
|
||||
error_handling: str = 'stop' # stop, continue, retry
|
||||
|
||||
# Logging
|
||||
log_level: str = 'info'
|
||||
save_execution_history: bool = True
|
||||
|
||||
# Concurrency
|
||||
max_concurrent_executions: int = 10
|
||||
|
||||
|
||||
class SafetyConfig(pydantic.BaseModel):
|
||||
"""Safety configuration (inherited from Pipeline)"""
|
||||
|
||||
content_filter: dict[str, Any] = {'enable': False, 'sensitive_words': [], 'replace_with': '***'}
|
||||
rate_limit: dict[str, Any] = {'enable': False, 'requests_per_minute': 60, 'burst_limit': 10}
|
||||
|
||||
|
||||
class OutputConfig(pydantic.BaseModel):
|
||||
"""Output configuration (inherited from Pipeline)"""
|
||||
|
||||
long_text_processing: dict[str, Any] = {
|
||||
'strategy': 'split', # split, truncate, file
|
||||
'max_length': 4000,
|
||||
'split_separator': '\n\n',
|
||||
}
|
||||
force_delay: dict[str, Any] = {'enable': False, 'min_delay_ms': 0, 'max_delay_ms': 0}
|
||||
misc: dict[str, Any] = {}
|
||||
|
||||
|
||||
class WorkflowGlobalConfig(pydantic.BaseModel):
|
||||
"""Workflow global configuration (inherited from Pipeline capabilities)"""
|
||||
|
||||
safety: SafetyConfig = SafetyConfig()
|
||||
output: OutputConfig = OutputConfig()
|
||||
|
||||
|
||||
class ExtensionsPreferences(pydantic.BaseModel):
|
||||
"""Extensions preferences (same as Pipeline)"""
|
||||
|
||||
enable_all_plugins: bool = True
|
||||
enable_all_mcp_servers: bool = True
|
||||
plugins: list[str] = []
|
||||
mcp_servers: list[str] = []
|
||||
|
||||
|
||||
class ConversationVariable(pydantic.BaseModel):
|
||||
"""Conversation-level variable definition"""
|
||||
|
||||
name: str
|
||||
type: str = 'string' # string, number, boolean, object, array
|
||||
description: str = ''
|
||||
default_value: Any = None
|
||||
max_length: Optional[int] = None # For strings
|
||||
|
||||
|
||||
class WorkflowDefinition(pydantic.BaseModel):
|
||||
"""Complete workflow definition"""
|
||||
|
||||
uuid: str
|
||||
name: str
|
||||
description: str = ''
|
||||
emoji: str = '💼'
|
||||
version: int = 1
|
||||
|
||||
# Workflow graph
|
||||
nodes: list[NodeDefinition] = []
|
||||
edges: list[EdgeDefinition] = []
|
||||
|
||||
# Variables
|
||||
variables: dict[str, Any] = {} # Global variables
|
||||
conversation_variables: list[ConversationVariable] = [] # Session-level variables
|
||||
|
||||
# Settings
|
||||
settings: WorkflowSettings = WorkflowSettings()
|
||||
|
||||
# Triggers (for automation)
|
||||
triggers: list[TriggerDefinition] = []
|
||||
|
||||
# Global configuration (inherited from Pipeline)
|
||||
global_config: WorkflowGlobalConfig = WorkflowGlobalConfig()
|
||||
|
||||
# Extensions
|
||||
extensions_preferences: ExtensionsPreferences = ExtensionsPreferences()
|
||||
|
||||
# Metadata
|
||||
is_enabled: bool = True
|
||||
created_at: Optional[datetime] = None
|
||||
updated_at: Optional[datetime] = None
|
||||
|
||||
# Source tracking (for imported workflows)
|
||||
source: Optional[str] = None # dify, n8n, langflow, etc.
|
||||
source_id: Optional[str] = None
|
||||
@@ -1,837 +0,0 @@
|
||||
"""Workflow execution engine.
|
||||
|
||||
This module contains the core workflow execution logic:
|
||||
- WorkflowExecutor: Main execution engine with control flow handling
|
||||
- ParallelExecutor: Parallel branch execution
|
||||
- LoopExecutor: Loop/iterator execution
|
||||
|
||||
Debug execution support has been moved to the ``debug`` module.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import ast
|
||||
import asyncio
|
||||
import logging
|
||||
import operator
|
||||
import uuid
|
||||
from datetime import datetime
|
||||
from typing import Any, Optional, TYPE_CHECKING
|
||||
|
||||
import sqlalchemy
|
||||
|
||||
from .entities import (
|
||||
WorkflowDefinition,
|
||||
NodeDefinition,
|
||||
EdgeDefinition,
|
||||
ExecutionContext,
|
||||
ExecutionStatus,
|
||||
NodeState,
|
||||
NodeStatus,
|
||||
ExecutionStep,
|
||||
)
|
||||
from ..entity.persistence import workflow as persistence_workflow
|
||||
from .registry import NodeTypeRegistry
|
||||
from . import monitor
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from ..core import app
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
# ─── Safe expression evaluator (replaces eval()) ─────────────────────
|
||||
# Uses Python's ast module to whitelist only comparison / boolean / arithmetic
|
||||
# operations. No function calls, attribute access, or subscript injection.
|
||||
|
||||
_SAFE_OPS = {
|
||||
ast.Add: operator.add,
|
||||
ast.Sub: operator.sub,
|
||||
ast.Mult: operator.mul,
|
||||
ast.Div: operator.truediv,
|
||||
ast.FloorDiv: operator.floordiv,
|
||||
ast.Mod: operator.mod,
|
||||
ast.Pow: operator.pow,
|
||||
ast.USub: operator.neg,
|
||||
ast.UAdd: operator.pos,
|
||||
ast.Not: operator.not_,
|
||||
ast.Eq: operator.eq,
|
||||
ast.NotEq: operator.ne,
|
||||
ast.Lt: operator.lt,
|
||||
ast.LtE: operator.le,
|
||||
ast.Gt: operator.gt,
|
||||
ast.GtE: operator.ge,
|
||||
ast.Is: operator.is_,
|
||||
ast.IsNot: operator.is_not,
|
||||
ast.In: lambda a, b: a in b,
|
||||
ast.NotIn: lambda a, b: a not in b,
|
||||
}
|
||||
|
||||
|
||||
def _safe_eval(expr: str) -> Any:
|
||||
"""Evaluate a simple expression safely via AST whitelist.
|
||||
|
||||
Supports: literals, comparisons (==, !=, <, >, <=, >=, in, not in, is, is not),
|
||||
boolean logic (and, or, not), arithmetic (+, -, *, /, //, %, **), and
|
||||
string operations (contains via ``in``).
|
||||
|
||||
Raises ``ValueError`` on any disallowed construct (function calls,
|
||||
attribute access, imports, etc.).
|
||||
"""
|
||||
tree = ast.parse(expr.strip(), mode='eval')
|
||||
return _eval_node(tree.body)
|
||||
|
||||
|
||||
def _eval_node(node: ast.AST) -> Any:
|
||||
# Literals: numbers, strings, True/False/None
|
||||
if isinstance(node, ast.Constant):
|
||||
return node.value
|
||||
|
||||
# Unary operators: -x, +x, not x
|
||||
if isinstance(node, ast.UnaryOp):
|
||||
op_fn = _SAFE_OPS.get(type(node.op))
|
||||
if op_fn is None:
|
||||
raise ValueError(f'Unsupported unary op: {type(node.op).__name__}')
|
||||
return op_fn(_eval_node(node.operand))
|
||||
|
||||
# Binary operators: x + y, x * y, etc.
|
||||
if isinstance(node, ast.BinOp):
|
||||
op_fn = _SAFE_OPS.get(type(node.op))
|
||||
if op_fn is None:
|
||||
raise ValueError(f'Unsupported binary op: {type(node.op).__name__}')
|
||||
return op_fn(_eval_node(node.left), _eval_node(node.right))
|
||||
|
||||
# Comparisons: x == y, x > y, x in y, etc. (chained)
|
||||
if isinstance(node, ast.Compare):
|
||||
left = _eval_node(node.left)
|
||||
for op, comparator in zip(node.ops, node.comparators):
|
||||
op_fn = _SAFE_OPS.get(type(op))
|
||||
if op_fn is None:
|
||||
raise ValueError(f'Unsupported comparison: {type(op).__name__}')
|
||||
right = _eval_node(comparator)
|
||||
if not op_fn(left, right):
|
||||
return False
|
||||
left = right
|
||||
return True
|
||||
|
||||
# Boolean operators: x and y, x or y
|
||||
if isinstance(node, ast.BoolOp):
|
||||
if isinstance(node.op, ast.And):
|
||||
return all(_eval_node(v) for v in node.values)
|
||||
if isinstance(node.op, ast.Or):
|
||||
return any(_eval_node(v) for v in node.values)
|
||||
|
||||
# Ternary: x if cond else y
|
||||
if isinstance(node, ast.IfExp):
|
||||
return _eval_node(node.body) if _eval_node(node.test) else _eval_node(node.orelse)
|
||||
|
||||
# Tuples / Lists (used in "x in [1,2,3]")
|
||||
if isinstance(node, (ast.Tuple, ast.List)):
|
||||
return [_eval_node(e) for e in node.elts]
|
||||
|
||||
# Name lookup – only allow None, True, False
|
||||
if isinstance(node, ast.Name):
|
||||
if node.id == 'None':
|
||||
return None
|
||||
if node.id == 'True':
|
||||
return True
|
||||
if node.id == 'False':
|
||||
return False
|
||||
raise ValueError(f'Unsupported variable reference: {node.id}')
|
||||
|
||||
raise ValueError(f'Unsupported expression node: {type(node).__name__}')
|
||||
|
||||
|
||||
class WorkflowExecutor:
|
||||
"""
|
||||
Workflow execution engine.
|
||||
Handles the execution of workflow definitions with proper control flow.
|
||||
"""
|
||||
|
||||
def __init__(self, ap: Optional['app.Application'] = None):
|
||||
self.ap = ap
|
||||
self.registry = NodeTypeRegistry.instance()
|
||||
self._edges: list[EdgeDefinition] = []
|
||||
|
||||
async def execute(
|
||||
self, workflow: WorkflowDefinition, context: ExecutionContext, start_node_id: Optional[str] = None
|
||||
) -> ExecutionContext:
|
||||
"""
|
||||
Execute a workflow.
|
||||
|
||||
Args:
|
||||
workflow: Workflow definition
|
||||
context: Execution context
|
||||
start_node_id: Optional starting node (for resumption)
|
||||
|
||||
Returns:
|
||||
Updated execution context
|
||||
"""
|
||||
context.status = ExecutionStatus.RUNNING
|
||||
context.start_time = datetime.now()
|
||||
|
||||
# Note: Frontend panel logging has been removed.
|
||||
# A new solution will be implemented separately.
|
||||
monitoring_message_id = ''
|
||||
|
||||
try:
|
||||
# Build execution graph
|
||||
node_map = {node.id: node for node in workflow.nodes}
|
||||
edge_map = self._build_edge_map(workflow.edges)
|
||||
self._edges = workflow.edges
|
||||
|
||||
# Initialize node states
|
||||
for node in workflow.nodes:
|
||||
if node.id not in context.node_states:
|
||||
context.node_states[node.id] = NodeState(node_id=node.id, node_type=node.type, status=NodeStatus.PENDING)
|
||||
|
||||
# Find start node(s)
|
||||
if start_node_id:
|
||||
start_nodes = [node_map[start_node_id]]
|
||||
else:
|
||||
start_nodes = self._find_start_nodes(workflow.nodes, workflow.edges)
|
||||
|
||||
if not start_nodes:
|
||||
raise ValueError('No start nodes found in workflow')
|
||||
|
||||
# Execute from start nodes
|
||||
for start_node in start_nodes:
|
||||
await self._execute_from_node(
|
||||
start_node, node_map, edge_map, context, workflow.settings.max_retries, path=set()
|
||||
)
|
||||
|
||||
# Check final status
|
||||
all_completed = all(
|
||||
state.status in (NodeStatus.COMPLETED, NodeStatus.SKIPPED) for state in context.node_states.values()
|
||||
)
|
||||
|
||||
if all_completed:
|
||||
context.status = ExecutionStatus.COMPLETED
|
||||
else:
|
||||
# Some nodes might still be waiting
|
||||
has_failed = any(state.status == NodeStatus.FAILED for state in context.node_states.values())
|
||||
if has_failed:
|
||||
context.status = ExecutionStatus.FAILED
|
||||
|
||||
except Exception as e:
|
||||
context.status = ExecutionStatus.FAILED
|
||||
context.error = str(e)
|
||||
logger.error(
|
||||
'Workflow execution failed',
|
||||
exc_info=True,
|
||||
extra={
|
||||
'workflow_id': workflow.uuid,
|
||||
'execution_id': context.execution_id,
|
||||
'node_states': {
|
||||
node_id: {
|
||||
'status': state.status.value if state.status else None,
|
||||
'error': state.error,
|
||||
}
|
||||
for node_id, state in context.node_states.items()
|
||||
},
|
||||
},
|
||||
)
|
||||
|
||||
# Note: Frontend panel logging has been removed.
|
||||
# A new solution will be implemented separately.
|
||||
|
||||
finally:
|
||||
context.end_time = datetime.now()
|
||||
|
||||
# Note: Frontend panel logging has been removed.
|
||||
# A new solution will be implemented separately.
|
||||
|
||||
return context
|
||||
|
||||
async def _execute_from_node(
|
||||
self,
|
||||
node: NodeDefinition,
|
||||
node_map: dict[str, NodeDefinition],
|
||||
edge_map: dict[str, list[EdgeDefinition]],
|
||||
context: ExecutionContext,
|
||||
max_retries: int = 3,
|
||||
path: set[str] | None = None,
|
||||
):
|
||||
"""Execute workflow starting from a specific node"""
|
||||
|
||||
# Initialize path set for cycle detection (path-based, not global visited)
|
||||
if path is None:
|
||||
path = set()
|
||||
|
||||
# Check for circular dependency on the *current path* only
|
||||
# This correctly allows diamond shapes (A→B, A→C, B→D, C→D)
|
||||
if node.id in path:
|
||||
logger.warning(f'Circular dependency detected at node: {node.id}')
|
||||
context.node_states[node.id].status = NodeStatus.SKIPPED
|
||||
context.node_states[node.id].error = 'Circular dependency detected'
|
||||
context.node_states[node.id].end_time = datetime.now()
|
||||
await self._persist_node_execution(node, context.node_states[node.id], context)
|
||||
return
|
||||
|
||||
# Add node to current path
|
||||
path.add(node.id)
|
||||
|
||||
# Check if node should be skipped
|
||||
if await self._should_skip_node(node, context):
|
||||
existing_state = context.node_states[node.id]
|
||||
if existing_state.status == NodeStatus.SKIPPED:
|
||||
existing_state.end_time = existing_state.end_time or datetime.now()
|
||||
await self._persist_node_execution(node, existing_state, context)
|
||||
path.discard(node.id)
|
||||
return
|
||||
|
||||
# Execute current node
|
||||
await self._execute_node(node, context, max_retries)
|
||||
|
||||
# If node failed and we should stop on error, return
|
||||
if context.node_states[node.id].status == NodeStatus.FAILED:
|
||||
path.discard(node.id)
|
||||
return
|
||||
|
||||
node_state = context.node_states[node.id]
|
||||
node_type_name = node.type.split('.')[-1] if '.' in node.type else node.type
|
||||
|
||||
# ── Control flow integration ────────────────────────────────
|
||||
# For loop / iterator nodes: run the LoopExecutor over
|
||||
# downstream body nodes for each item, then continue to the
|
||||
# "completed" output edge.
|
||||
if node_type_name in ('loop', 'iterator'):
|
||||
items = node_state.outputs.get('_items') or []
|
||||
if not items:
|
||||
# iterator: items come from inputs
|
||||
items = node_state.inputs.get('items', node_state.inputs.get('array', []))
|
||||
if not isinstance(items, list):
|
||||
items = [items] if items else []
|
||||
max_iter = int(node.config.get('max_iterations', 100))
|
||||
items = items[:max_iter]
|
||||
|
||||
# Collect downstream "body" nodes (connected via edges)
|
||||
outgoing_edges = edge_map.get(node.id, [])
|
||||
body_nodes = []
|
||||
for edge in outgoing_edges:
|
||||
target = node_map.get(edge.target_node)
|
||||
if target:
|
||||
body_nodes.append(target)
|
||||
|
||||
if body_nodes and items:
|
||||
loop_exec = LoopExecutor(self)
|
||||
results = await loop_exec.execute_loop(items, body_nodes, context, max_iter)
|
||||
node_state.outputs['results'] = results
|
||||
node_state.outputs['completed'] = True
|
||||
else:
|
||||
node_state.outputs['results'] = []
|
||||
node_state.outputs['completed'] = True
|
||||
|
||||
path.discard(node.id)
|
||||
return # body nodes already executed by LoopExecutor
|
||||
|
||||
# For parallel nodes: run downstream branches concurrently
|
||||
if node_type_name == 'parallel':
|
||||
outgoing_edges = edge_map.get(node.id, [])
|
||||
branch_nodes = []
|
||||
for edge in outgoing_edges:
|
||||
target = node_map.get(edge.target_node)
|
||||
if target:
|
||||
branch_nodes.append([target])
|
||||
|
||||
if branch_nodes:
|
||||
par_exec = ParallelExecutor(self)
|
||||
results = await par_exec.execute_parallel(branch_nodes, context)
|
||||
node_state.outputs['results'] = results
|
||||
|
||||
path.discard(node.id)
|
||||
return # branch nodes already executed by ParallelExecutor
|
||||
|
||||
# ── Standard edge-based continuation ────────────────────────
|
||||
# Get outgoing edges
|
||||
outgoing_edges = edge_map.get(node.id, [])
|
||||
|
||||
# Execute next nodes based on edge conditions
|
||||
for edge in outgoing_edges:
|
||||
target_node = node_map.get(edge.target_node)
|
||||
if not target_node:
|
||||
continue
|
||||
|
||||
# Check edge condition
|
||||
if edge.condition:
|
||||
condition_met = await self._evaluate_condition(edge.condition, context)
|
||||
if not condition_met:
|
||||
continue
|
||||
|
||||
# Check if all inputs are ready
|
||||
if await self._inputs_ready(target_node, edge_map, context):
|
||||
await self._execute_from_node(target_node, node_map, edge_map, context, max_retries, path)
|
||||
|
||||
# Remove node from path when backtracking (allows diamond revisit)
|
||||
path.discard(node.id)
|
||||
|
||||
async def _execute_node(self, node: NodeDefinition, context: ExecutionContext, max_retries: int = 3):
|
||||
"""Execute a single node with retry logic"""
|
||||
|
||||
node_state = context.node_states[node.id]
|
||||
node_state.status = NodeStatus.RUNNING
|
||||
node_state.start_time = datetime.now()
|
||||
|
||||
# Get node instance (pass ap for access to services)
|
||||
node_instance = self.registry.create_instance(node.type, node.id, node.config, ap=self.ap)
|
||||
|
||||
if not node_instance:
|
||||
node_state.status = NodeStatus.FAILED
|
||||
node_state.error = f'Unknown node type: {node.type}'
|
||||
node_state.end_time = datetime.now()
|
||||
self._record_execution_step(node, node_state, context)
|
||||
await self._persist_node_execution(node, node_state, context)
|
||||
return
|
||||
|
||||
# Resolve inputs
|
||||
inputs = await self._resolve_inputs(node, context)
|
||||
node_state.inputs = inputs
|
||||
|
||||
# Validate inputs
|
||||
validation_errors = await node_instance.validate_inputs(inputs)
|
||||
if validation_errors:
|
||||
node_state.status = NodeStatus.FAILED
|
||||
node_state.error = '; '.join(validation_errors)
|
||||
node_state.end_time = datetime.now()
|
||||
self._record_execution_step(node, node_state, context)
|
||||
await self._persist_node_execution(node, node_state, context)
|
||||
return
|
||||
|
||||
# Check if node supports streaming (has execute_stream method and stream config is enabled)
|
||||
use_streaming = hasattr(node_instance, 'execute_stream') and node.config.get('stream', False)
|
||||
|
||||
# Execute with retries
|
||||
for attempt in range(max_retries + 1):
|
||||
try:
|
||||
if use_streaming:
|
||||
# Streaming execution with aggregation and timeout
|
||||
aggregated_response = ''
|
||||
try:
|
||||
async with asyncio.timeout(300): # 5 minute timeout for streaming
|
||||
async for chunk in node_instance.execute_stream(inputs, context):
|
||||
if chunk:
|
||||
aggregated_response += chunk
|
||||
except asyncio.TimeoutError:
|
||||
logger.warning(f'Node {node.id} ({node.type}) streaming timed out, falling back to non-streaming')
|
||||
use_streaming = False
|
||||
outputs = await node_instance.execute(inputs, context)
|
||||
else:
|
||||
# Get response from context if set by execute_stream, otherwise use aggregated
|
||||
final_response = context.variables.pop('_last_llm_response', aggregated_response)
|
||||
outputs = {'response': final_response, 'usage': {'prompt_tokens': 0, 'completion_tokens': 0, 'total_tokens': 0}}
|
||||
logger.info(f'Node {node.id} ({node.type}) streaming completed, response length: {len(final_response)}')
|
||||
else:
|
||||
outputs = await node_instance.execute(inputs, context)
|
||||
node_state.outputs = outputs
|
||||
node_state.status = NodeStatus.COMPLETED
|
||||
node_state.end_time = datetime.now()
|
||||
break
|
||||
except Exception as e:
|
||||
node_state.retry_count = attempt + 1
|
||||
logger.error(
|
||||
f'Node {node.id} ({node.type}) execution failed (attempt {attempt + 1}/{max_retries + 1}): {e}',
|
||||
exc_info=True,
|
||||
extra={
|
||||
'node_id': node.id,
|
||||
'node_type': node.type,
|
||||
'attempt': attempt + 1,
|
||||
'max_retries': max_retries,
|
||||
'execution_id': context.execution_id,
|
||||
},
|
||||
)
|
||||
|
||||
if attempt < max_retries:
|
||||
await asyncio.sleep(1) # Brief delay before retry
|
||||
else:
|
||||
node_state.status = NodeStatus.FAILED
|
||||
node_state.error = str(e)
|
||||
node_state.end_time = datetime.now()
|
||||
logger.error(
|
||||
f'Node {node.id} ({node.type}) permanently failed after {max_retries + 1} attempts',
|
||||
extra={
|
||||
'node_id': node.id,
|
||||
'node_type': node.type,
|
||||
'error': str(e),
|
||||
'execution_id': context.execution_id,
|
||||
},
|
||||
)
|
||||
|
||||
self._record_execution_step(node, node_state, context)
|
||||
await self._persist_node_execution(node, node_state, context)
|
||||
|
||||
async def _resolve_inputs(self, node: NodeDefinition, context: ExecutionContext) -> dict[str, Any]:
|
||||
"""Resolve input values for a node from connected nodes and context"""
|
||||
inputs = {}
|
||||
|
||||
# Get inputs from context variables
|
||||
if 'message' in context.variables:
|
||||
inputs['message'] = context.variables['message']
|
||||
|
||||
# Get inputs from message context
|
||||
if context.message_context:
|
||||
inputs['message'] = context.message_context.message_content
|
||||
inputs['message_content'] = context.message_context.message_content
|
||||
inputs['sender_id'] = context.message_context.sender_id
|
||||
inputs['platform'] = context.message_context.platform
|
||||
else:
|
||||
logger.warning(
|
||||
f'[_resolve_inputs] node={node.id} ({node.type}): message_context is None!',
|
||||
extra={
|
||||
'node_id': node.id,
|
||||
'node_type': node.type,
|
||||
'execution_id': context.execution_id,
|
||||
'variables_keys': list(context.variables.keys()) if context.variables else [],
|
||||
},
|
||||
)
|
||||
|
||||
# Log current inputs state after message_context processing
|
||||
logger.debug(
|
||||
f'[_resolve_inputs] node={node.id} after message_context: {list(inputs.keys())}',
|
||||
)
|
||||
|
||||
# Get inputs from node config that reference other nodes
|
||||
for key, value in node.config.items():
|
||||
if isinstance(value, str) and value.startswith('{{') and value.endswith('}}'):
|
||||
resolved = await self._resolve_expression(value[2:-2], context)
|
||||
inputs[key] = resolved
|
||||
else:
|
||||
inputs[key] = value
|
||||
|
||||
# Get inputs from connected upstream nodes via edges
|
||||
# Build a reverse map: for each incoming edge to this node, find the
|
||||
# source node and the specific source/target port.
|
||||
for edge in self._edges:
|
||||
if edge.target_node != node.id:
|
||||
continue
|
||||
source_state = context.node_states.get(edge.source_node)
|
||||
if not source_state or source_state.status != NodeStatus.COMPLETED:
|
||||
continue
|
||||
target_port = edge.target_port or 'input'
|
||||
source_port = edge.source_port or 'output'
|
||||
# Map the source node's output port value to this node's input port
|
||||
if source_port in source_state.outputs:
|
||||
inputs[target_port] = source_state.outputs[source_port]
|
||||
elif 'output' in source_state.outputs:
|
||||
# Fallback: if exact port not found, try generic 'output'
|
||||
inputs[target_port] = source_state.outputs['output']
|
||||
elif source_state.outputs:
|
||||
# Last resort: use the first available output
|
||||
inputs[target_port] = next(iter(source_state.outputs.values()))
|
||||
|
||||
# Smart input mapping: if a node needs 'message' but received a different
|
||||
# port name (e.g., 'content' from llm_call), copy the value to 'message'.
|
||||
# This handles edge connection mismatches where the sender uses a different
|
||||
# port name than what the receiver expects.
|
||||
if 'message' not in inputs or inputs.get('message') is None:
|
||||
for fallback_key in ('content', 'response', 'input', 'output', 'result', 'text'):
|
||||
if fallback_key in inputs and inputs[fallback_key] is not None:
|
||||
inputs['message'] = inputs[fallback_key]
|
||||
logger.debug(
|
||||
f'[_resolve_inputs] node={node.id}: mapped {fallback_key} -> message',
|
||||
)
|
||||
break
|
||||
|
||||
logger.debug(
|
||||
f'[_resolve_inputs] node={node.id} final inputs keys: {list(inputs.keys())}, message={repr(inputs.get("message", "<missing>")[:100] if isinstance(inputs.get("message"), str) else inputs.get("message"))}',
|
||||
)
|
||||
return inputs
|
||||
|
||||
async def _resolve_expression(self, expression: str, context: ExecutionContext) -> Any:
|
||||
"""Resolve a variable expression like 'nodes.node1.outputs.text'"""
|
||||
parts = expression.strip().split('.')
|
||||
|
||||
if not parts:
|
||||
return None
|
||||
|
||||
if parts[0] == 'nodes' and len(parts) >= 4:
|
||||
# nodes.node_id.outputs.output_name
|
||||
node_id = parts[1]
|
||||
if parts[2] == 'outputs' and node_id in context.node_states:
|
||||
output_name = '.'.join(parts[3:])
|
||||
return context.node_states[node_id].outputs.get(output_name)
|
||||
|
||||
elif parts[0] == 'variables':
|
||||
# variables.var_name
|
||||
var_name = '.'.join(parts[1:])
|
||||
return context.variables.get(var_name)
|
||||
|
||||
elif parts[0] == 'conversation_variables':
|
||||
# conversation_variables.var_name
|
||||
var_name = '.'.join(parts[1:])
|
||||
return context.conversation_variables.get(var_name)
|
||||
|
||||
elif parts[0] == 'message':
|
||||
# message.content, message.sender_id, etc.
|
||||
if context.message_context:
|
||||
attr = parts[1] if len(parts) > 1 else None
|
||||
if attr == 'content':
|
||||
return context.message_context.message_content
|
||||
elif attr == 'sender_id':
|
||||
return context.message_context.sender_id
|
||||
elif attr == 'platform':
|
||||
return context.message_context.platform
|
||||
elif attr == 'conversation_id':
|
||||
return context.message_context.conversation_id
|
||||
|
||||
return None
|
||||
|
||||
async def _evaluate_condition(self, condition: str, context: ExecutionContext) -> bool:
|
||||
"""Evaluate a condition expression safely using AST whitelist"""
|
||||
try:
|
||||
# Resolve variable references in condition
|
||||
if '{{' in condition:
|
||||
import re
|
||||
|
||||
pattern = r'\{\{([^}]+)\}\}'
|
||||
|
||||
# First pass: replace all variable references with placeholders
|
||||
placeholders = {}
|
||||
placeholder_idx = 0
|
||||
|
||||
def replace_with_placeholder(match):
|
||||
nonlocal placeholder_idx
|
||||
var_expr = match.group(1)
|
||||
placeholder = f'__PH{placeholder_idx}__'
|
||||
placeholders[placeholder] = var_expr
|
||||
placeholder_idx += 1
|
||||
return placeholder
|
||||
|
||||
condition_with_placeholders = re.sub(pattern, replace_with_placeholder, condition)
|
||||
|
||||
# Second pass: resolve each placeholder asynchronously
|
||||
for placeholder, var_expr in placeholders.items():
|
||||
value = await self._resolve_expression(var_expr, context)
|
||||
if isinstance(value, str):
|
||||
condition_with_placeholders = condition_with_placeholders.replace(placeholder, f'"{value}"')
|
||||
elif value is None:
|
||||
condition_with_placeholders = condition_with_placeholders.replace(placeholder, 'None')
|
||||
else:
|
||||
condition_with_placeholders = condition_with_placeholders.replace(placeholder, str(value))
|
||||
|
||||
condition = condition_with_placeholders
|
||||
|
||||
# Safe expression evaluation using AST whitelist
|
||||
result = _safe_eval(condition)
|
||||
return bool(result)
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f'Condition evaluation failed: {condition} - {e}')
|
||||
return False
|
||||
|
||||
async def _should_skip_node(self, node: NodeDefinition, context: ExecutionContext) -> bool:
|
||||
"""Check if a node should be skipped"""
|
||||
state = context.node_states.get(node.id)
|
||||
if state and state.status in (NodeStatus.COMPLETED, NodeStatus.RUNNING, NodeStatus.SKIPPED):
|
||||
return True
|
||||
return False
|
||||
|
||||
async def _inputs_ready(
|
||||
self, node: NodeDefinition, edge_map: dict[str, list[EdgeDefinition]], context: ExecutionContext
|
||||
) -> bool:
|
||||
"""Check if all inputs for a node are ready"""
|
||||
# Find all edges that connect to this node
|
||||
incoming_nodes = set()
|
||||
for source_id, edges in edge_map.items():
|
||||
for edge in edges:
|
||||
if edge.target_node == node.id:
|
||||
incoming_nodes.add(source_id)
|
||||
|
||||
# Check if all incoming nodes have completed
|
||||
for source_id in incoming_nodes:
|
||||
state = context.node_states.get(source_id)
|
||||
if not state or state.status not in (NodeStatus.COMPLETED, NodeStatus.SKIPPED):
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
def _find_start_nodes(self, nodes: list[NodeDefinition], edges: list[EdgeDefinition]) -> list[NodeDefinition]:
|
||||
"""Find nodes that have no incoming edges (start nodes)"""
|
||||
target_nodes = {edge.target_node for edge in edges}
|
||||
start_nodes = [node for node in nodes if node.id not in target_nodes]
|
||||
|
||||
# Also check for trigger nodes
|
||||
trigger_types = {'message_trigger', 'cron_trigger', 'webhook_trigger', 'event_trigger'}
|
||||
for node in nodes:
|
||||
if node.type in trigger_types and node not in start_nodes:
|
||||
start_nodes.insert(0, node)
|
||||
|
||||
return start_nodes
|
||||
|
||||
def _build_edge_map(self, edges: list[EdgeDefinition]) -> dict[str, list[EdgeDefinition]]:
|
||||
"""Build a map of source node ID to outgoing edges"""
|
||||
edge_map: dict[str, list[EdgeDefinition]] = {}
|
||||
for edge in edges:
|
||||
if edge.source_node not in edge_map:
|
||||
edge_map[edge.source_node] = []
|
||||
edge_map[edge.source_node].append(edge)
|
||||
return edge_map
|
||||
|
||||
def _record_execution_step(self, node: NodeDefinition, node_state: NodeState, context: ExecutionContext):
|
||||
"""Record an execution step in the history"""
|
||||
duration_ms = 0
|
||||
if node_state.start_time and node_state.end_time:
|
||||
duration_ms = int((node_state.end_time - node_state.start_time).total_seconds() * 1000)
|
||||
|
||||
step = ExecutionStep(
|
||||
step_id=f"step_{uuid.uuid4().hex[:8]}",
|
||||
timestamp=datetime.now(),
|
||||
node_id=node.id,
|
||||
node_type=node.type,
|
||||
status=node_state.status,
|
||||
duration_ms=duration_ms,
|
||||
error=node_state.error,
|
||||
inputs=node_state.inputs,
|
||||
outputs=node_state.outputs,
|
||||
)
|
||||
context.history.append(step)
|
||||
|
||||
async def _persist_node_execution(
|
||||
self,
|
||||
node: NodeDefinition,
|
||||
node_state: NodeState,
|
||||
context: ExecutionContext,
|
||||
):
|
||||
"""Persist node execution state for execution detail and logs."""
|
||||
if not self.ap:
|
||||
return
|
||||
|
||||
values = {
|
||||
'execution_uuid': context.execution_id,
|
||||
'node_id': node.id,
|
||||
'node_type': node.type,
|
||||
'status': node_state.status.value,
|
||||
'inputs': node_state.inputs,
|
||||
'outputs': node_state.outputs,
|
||||
'start_time': node_state.start_time,
|
||||
'end_time': node_state.end_time,
|
||||
'error': node_state.error,
|
||||
'retry_count': node_state.retry_count,
|
||||
}
|
||||
|
||||
existing_query = sqlalchemy.select(persistence_workflow.WorkflowNodeExecution).where(
|
||||
persistence_workflow.WorkflowNodeExecution.execution_uuid == context.execution_id,
|
||||
persistence_workflow.WorkflowNodeExecution.node_id == node.id,
|
||||
)
|
||||
existing_result = await self.ap.persistence_mgr.execute_async(existing_query)
|
||||
existing = existing_result.first()
|
||||
|
||||
if existing is None:
|
||||
await self.ap.persistence_mgr.execute_async(
|
||||
sqlalchemy.insert(persistence_workflow.WorkflowNodeExecution).values(**values)
|
||||
)
|
||||
else:
|
||||
await self.ap.persistence_mgr.execute_async(
|
||||
sqlalchemy.update(persistence_workflow.WorkflowNodeExecution)
|
||||
.where(persistence_workflow.WorkflowNodeExecution.id == existing.id)
|
||||
.values(**values)
|
||||
)
|
||||
|
||||
|
||||
class ParallelExecutor:
|
||||
"""Execute multiple branches in parallel"""
|
||||
|
||||
def __init__(self, executor: WorkflowExecutor):
|
||||
self.executor = executor
|
||||
|
||||
async def execute_parallel(
|
||||
self, branches: list[list[NodeDefinition]], context: ExecutionContext
|
||||
) -> list[dict[str, Any]]:
|
||||
"""
|
||||
Execute multiple branches in parallel.
|
||||
|
||||
Args:
|
||||
branches: List of node sequences to execute in parallel
|
||||
context: Execution context
|
||||
|
||||
Returns:
|
||||
List of results from each branch
|
||||
"""
|
||||
tasks = []
|
||||
for branch in branches:
|
||||
task = self._execute_branch(branch, context)
|
||||
tasks.append(task)
|
||||
|
||||
results = await asyncio.gather(*tasks, return_exceptions=True)
|
||||
|
||||
processed_results = []
|
||||
for result in results:
|
||||
if isinstance(result, Exception):
|
||||
processed_results.append({'error': str(result)})
|
||||
else:
|
||||
processed_results.append(result)
|
||||
|
||||
return processed_results
|
||||
|
||||
async def _execute_branch(self, nodes: list[NodeDefinition], context: ExecutionContext) -> dict[str, Any]:
|
||||
"""Execute a single branch"""
|
||||
# Create a copy of context for this branch
|
||||
branch_outputs = {}
|
||||
|
||||
for node in nodes:
|
||||
await self.executor._execute_node(node, context, max_retries=3)
|
||||
state = context.node_states.get(node.id)
|
||||
if state and state.status == NodeStatus.COMPLETED:
|
||||
branch_outputs[node.id] = state.outputs
|
||||
elif state and state.status == NodeStatus.FAILED:
|
||||
branch_outputs['error'] = state.error
|
||||
break
|
||||
|
||||
return branch_outputs
|
||||
|
||||
|
||||
class LoopExecutor:
|
||||
"""Execute loop iterations"""
|
||||
|
||||
def __init__(self, executor: WorkflowExecutor):
|
||||
self.executor = executor
|
||||
|
||||
async def execute_loop(
|
||||
self, items: list[Any], loop_body: list[NodeDefinition], context: ExecutionContext, max_iterations: int = 100
|
||||
) -> list[dict[str, Any]]:
|
||||
"""
|
||||
Execute a loop over items.
|
||||
|
||||
Args:
|
||||
items: Items to iterate over
|
||||
loop_body: Nodes to execute for each item
|
||||
context: Execution context
|
||||
max_iterations: Maximum number of iterations
|
||||
|
||||
Returns:
|
||||
List of results from each iteration
|
||||
"""
|
||||
results = []
|
||||
|
||||
for i, item in enumerate(items[:max_iterations]):
|
||||
# Set loop variables
|
||||
context.variables['loop_item'] = item
|
||||
context.variables['loop_index'] = i
|
||||
context.variables['loop_is_first'] = i == 0
|
||||
context.variables['loop_is_last'] = i == len(items) - 1
|
||||
|
||||
iteration_result = {}
|
||||
|
||||
for node in loop_body:
|
||||
# Reset node state for this iteration
|
||||
context.node_states[node.id] = NodeState(node_id=node.id, node_type=node.type, status=NodeStatus.PENDING)
|
||||
|
||||
await self.executor._execute_node(node, context, max_retries=3)
|
||||
|
||||
state = context.node_states.get(node.id)
|
||||
if state:
|
||||
iteration_result[node.id] = state.outputs
|
||||
|
||||
# Check for break condition
|
||||
if state.outputs.get('break', False):
|
||||
results.append(iteration_result)
|
||||
return results
|
||||
|
||||
results.append(iteration_result)
|
||||
|
||||
# Clean up loop variables
|
||||
context.variables.pop('loop_item', None)
|
||||
context.variables.pop('loop_index', None)
|
||||
context.variables.pop('loop_is_first', None)
|
||||
context.variables.pop('loop_is_last', None)
|
||||
|
||||
return results
|
||||
@@ -1,284 +0,0 @@
|
||||
"""Workflow node metadata loading and validation.
|
||||
|
||||
This module makes YAML files under ``templates/metadata/nodes`` the backend
|
||||
source of truth for workflow node metadata. Python node classes still provide
|
||||
execution logic, but UI-facing metadata is loaded from YAML.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import copy
|
||||
import logging
|
||||
from importlib import resources
|
||||
from pathlib import Path
|
||||
from typing import Any, Iterable, Optional
|
||||
|
||||
import yaml
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class MetadataLoadError(Exception):
|
||||
"""Raised when a workflow node metadata file cannot be loaded."""
|
||||
|
||||
|
||||
class MetadataValidationError(Exception):
|
||||
"""Raised when workflow node metadata does not match the expected shape."""
|
||||
|
||||
|
||||
class NodeMetadataValidator:
|
||||
"""Validate workflow node metadata loaded from YAML files.
|
||||
|
||||
The validator is intentionally strict about the structural fields that the
|
||||
editor needs, but tolerant of legacy YAML details such as missing top-level
|
||||
``label`` or additional frontend field types.
|
||||
"""
|
||||
|
||||
REQUIRED_FIELDS = ('name', 'category', 'inputs', 'outputs', 'config')
|
||||
VALID_CATEGORIES = {'trigger', 'process', 'control', 'action', 'integration', 'misc'}
|
||||
VALID_PORT_TYPES = {'any', 'string', 'number', 'integer', 'boolean', 'object', 'array', 'datetime', 'null'}
|
||||
VALID_CONFIG_TYPES = {
|
||||
'string',
|
||||
'integer',
|
||||
'number',
|
||||
'float',
|
||||
'boolean',
|
||||
'select',
|
||||
'json',
|
||||
'textarea',
|
||||
'text',
|
||||
'secret',
|
||||
'array[string]',
|
||||
'file',
|
||||
'array[file]',
|
||||
'llm-model-selector',
|
||||
'embedding-model-selector',
|
||||
'rerank-model-selector',
|
||||
'pipeline-selector',
|
||||
'knowledge-base-selector',
|
||||
'knowledge-base-multi-selector',
|
||||
'bot-selector',
|
||||
'tools-selector',
|
||||
'model-fallback-selector',
|
||||
'prompt-editor',
|
||||
'plugin-selector',
|
||||
'webhook-url',
|
||||
'embed-code',
|
||||
'workflow-selector',
|
||||
}
|
||||
|
||||
def validate(self, metadata: dict[str, Any]) -> list[str]:
|
||||
"""Return validation errors. An empty list means the metadata is valid."""
|
||||
errors: list[str] = []
|
||||
|
||||
if not isinstance(metadata, dict):
|
||||
return ['metadata root must be a mapping']
|
||||
|
||||
for field in self.REQUIRED_FIELDS:
|
||||
if field not in metadata:
|
||||
errors.append(f'missing required field: {field}')
|
||||
|
||||
if errors:
|
||||
return errors
|
||||
|
||||
name = metadata.get('name')
|
||||
if not isinstance(name, str) or not name.strip():
|
||||
errors.append('field "name" must be a non-empty string')
|
||||
|
||||
category = metadata.get('category')
|
||||
if category not in self.VALID_CATEGORIES:
|
||||
errors.append(f'invalid category: {category}')
|
||||
|
||||
errors.extend(self._validate_ports(metadata.get('inputs'), 'inputs'))
|
||||
errors.extend(self._validate_ports(metadata.get('outputs'), 'outputs'))
|
||||
errors.extend(self._validate_config(metadata.get('config')))
|
||||
|
||||
return errors
|
||||
|
||||
def validate_or_raise(self, metadata: dict[str, Any]) -> dict[str, Any]:
|
||||
"""Validate metadata and raise ``MetadataValidationError`` on failure."""
|
||||
errors = self.validate(metadata)
|
||||
if errors:
|
||||
node_name = metadata.get('name', 'unknown') if isinstance(metadata, dict) else 'unknown'
|
||||
raise MetadataValidationError(f'invalid metadata for {node_name}: {errors}')
|
||||
return metadata
|
||||
|
||||
def _validate_ports(self, ports: Any, field_name: str) -> list[str]:
|
||||
errors: list[str] = []
|
||||
if not isinstance(ports, list):
|
||||
return [f'{field_name} must be a list']
|
||||
|
||||
seen_names: set[str] = set()
|
||||
for index, port in enumerate(ports):
|
||||
path = f'{field_name}[{index}]'
|
||||
if not isinstance(port, dict):
|
||||
errors.append(f'{path} must be a mapping')
|
||||
continue
|
||||
|
||||
name = port.get('name')
|
||||
if not isinstance(name, str) or not name:
|
||||
errors.append(f'{path}.name must be a non-empty string')
|
||||
continue
|
||||
|
||||
if name in seen_names:
|
||||
errors.append(f'{path}.name duplicates "{name}"')
|
||||
seen_names.add(name)
|
||||
|
||||
port_type = port.get('type', 'any')
|
||||
if port_type not in self.VALID_PORT_TYPES:
|
||||
errors.append(f'{path}.type has unsupported value "{port_type}"')
|
||||
|
||||
return errors
|
||||
|
||||
def _validate_config(self, config: Any) -> list[str]:
|
||||
errors: list[str] = []
|
||||
if not isinstance(config, list):
|
||||
return ['config must be a list']
|
||||
|
||||
seen_names: set[str] = set()
|
||||
for index, item in enumerate(config):
|
||||
path = f'config[{index}]'
|
||||
if not isinstance(item, dict):
|
||||
errors.append(f'{path} must be a mapping')
|
||||
continue
|
||||
|
||||
name = item.get('name')
|
||||
if not isinstance(name, str) or not name:
|
||||
errors.append(f'{path}.name must be a non-empty string')
|
||||
continue
|
||||
|
||||
if name in seen_names:
|
||||
errors.append(f'{path}.name duplicates "{name}"')
|
||||
seen_names.add(name)
|
||||
|
||||
item_type = item.get('type', 'string')
|
||||
if item_type not in self.VALID_CONFIG_TYPES:
|
||||
errors.append(f'{path}.type has unsupported value "{item_type}"')
|
||||
|
||||
min_value = item.get('min_value')
|
||||
max_value = item.get('max_value')
|
||||
if isinstance(min_value, (int, float)) and isinstance(max_value, (int, float)) and min_value > max_value:
|
||||
errors.append(f'{path}.min_value must be <= max_value')
|
||||
|
||||
return errors
|
||||
|
||||
|
||||
class NodeMetadataLoader:
|
||||
"""Load and cache workflow node metadata from YAML files."""
|
||||
|
||||
def __init__(self, validator: Optional[NodeMetadataValidator] = None) -> None:
|
||||
self._validator = validator or NodeMetadataValidator()
|
||||
self._metadata: dict[str, dict[str, Any]] = {}
|
||||
self._sources: dict[str, str] = {}
|
||||
self._load_errors: list[dict[str, str]] = []
|
||||
|
||||
async def load_core_metadata(self, resource_dir: str = 'metadata/nodes') -> int:
|
||||
"""Load all core node metadata from the ``langbot.templates`` package."""
|
||||
return await self.load_package_directory('langbot.templates', resource_dir, source='core')
|
||||
|
||||
async def load_package_directory(self, package: str, resource_dir: str, source: str = 'core') -> int:
|
||||
"""Load YAML files from a package resource directory."""
|
||||
try:
|
||||
root = resources.files(package).joinpath(resource_dir)
|
||||
yaml_files = sorted(
|
||||
(item for item in root.iterdir() if item.is_file() and item.name.endswith(('.yaml', '.yml'))),
|
||||
key=lambda item: item.name,
|
||||
)
|
||||
except Exception as exc:
|
||||
raise MetadataLoadError(f'failed to scan package directory {package}:{resource_dir}: {exc}') from exc
|
||||
|
||||
return self._load_files(yaml_files, source=source)
|
||||
|
||||
async def load_directory(self, directory: str | Path, source: str) -> int:
|
||||
"""Load YAML files from an external filesystem directory, e.g. a plugin."""
|
||||
directory_path = Path(directory)
|
||||
if not directory_path.exists():
|
||||
logger.warning('Workflow metadata directory does not exist: %s', directory_path)
|
||||
return 0
|
||||
if not directory_path.is_dir():
|
||||
raise MetadataLoadError(f'workflow metadata path is not a directory: {directory_path}')
|
||||
|
||||
yaml_files = sorted(directory_path.glob('*.yml')) + sorted(directory_path.glob('*.yaml'))
|
||||
return self._load_files(yaml_files, source=source)
|
||||
|
||||
def get_metadata(self, node_type: str) -> Optional[dict[str, Any]]:
|
||||
"""Return metadata by full type or short node name."""
|
||||
if node_type in self._metadata:
|
||||
return copy.deepcopy(self._metadata[node_type])
|
||||
|
||||
short_name = node_type.split('.')[-1]
|
||||
for registered_type, metadata in self._metadata.items():
|
||||
if registered_type.split('.')[-1] == short_name or metadata.get('name') == short_name:
|
||||
return copy.deepcopy(metadata)
|
||||
|
||||
return None
|
||||
|
||||
def get_all_metadata(self) -> dict[str, dict[str, Any]]:
|
||||
"""Return a deep copy of all loaded metadata keyed by canonical node type."""
|
||||
return copy.deepcopy(self._metadata)
|
||||
|
||||
def get_load_errors(self) -> list[dict[str, str]]:
|
||||
"""Return metadata files that failed to load or validate."""
|
||||
return copy.deepcopy(self._load_errors)
|
||||
|
||||
def clear(self) -> None:
|
||||
"""Clear all cached metadata and errors."""
|
||||
self._metadata.clear()
|
||||
self._sources.clear()
|
||||
self._load_errors.clear()
|
||||
|
||||
def _load_files(self, yaml_files: Iterable[Any], source: str) -> int:
|
||||
count = 0
|
||||
for yaml_file in yaml_files:
|
||||
file_name = getattr(yaml_file, 'name', str(yaml_file))
|
||||
try:
|
||||
metadata = self._load_yaml(yaml_file)
|
||||
self._validator.validate_or_raise(metadata)
|
||||
node_type = build_node_type(metadata)
|
||||
|
||||
if node_type in self._metadata:
|
||||
existing_source = self._sources.get(node_type, 'unknown')
|
||||
if existing_source == 'core' and source != 'core':
|
||||
raise MetadataLoadError(
|
||||
f'plugin source "{source}" attempted to override core node "{node_type}"'
|
||||
)
|
||||
logger.warning(
|
||||
'Workflow node metadata %s from %s overrides previous source %s',
|
||||
node_type,
|
||||
source,
|
||||
existing_source,
|
||||
)
|
||||
|
||||
cached_metadata = copy.deepcopy(metadata)
|
||||
cached_metadata['_source'] = source
|
||||
cached_metadata['_file'] = file_name
|
||||
self._metadata[node_type] = cached_metadata
|
||||
self._sources[node_type] = source
|
||||
count += 1
|
||||
except Exception as exc:
|
||||
self._load_errors.append({'file': file_name, 'source': source, 'error': str(exc)})
|
||||
logger.error('Failed to load workflow node metadata %s: %s', file_name, exc)
|
||||
|
||||
return count
|
||||
|
||||
def _load_yaml(self, yaml_file: Any) -> dict[str, Any]:
|
||||
try:
|
||||
if hasattr(yaml_file, 'open'):
|
||||
with yaml_file.open('r', encoding='utf-8') as file:
|
||||
data = yaml.load(file, Loader=yaml.FullLoader)
|
||||
else:
|
||||
with open(yaml_file, 'r', encoding='utf-8') as file:
|
||||
data = yaml.load(file, Loader=yaml.FullLoader)
|
||||
except Exception as exc:
|
||||
raise MetadataLoadError(f'failed to parse YAML: {exc}') from exc
|
||||
|
||||
if not isinstance(data, dict):
|
||||
raise MetadataLoadError('YAML root must be a mapping')
|
||||
return data
|
||||
|
||||
|
||||
def build_node_type(metadata: dict[str, Any]) -> str:
|
||||
"""Build canonical ``category.name`` node type from metadata."""
|
||||
category = metadata.get('category') or 'misc'
|
||||
name = metadata.get('name') or ''
|
||||
return f'{category}.{name}'
|
||||
@@ -1,61 +0,0 @@
|
||||
"""
|
||||
Monitoring helper for recording events during workflow execution.
|
||||
This module provides convenient methods to record monitoring data
|
||||
without cluttering the main workflow code.
|
||||
|
||||
NOTE: All frontend panel logging functionality has been removed.
|
||||
A new solution will be implemented separately.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import typing
|
||||
import time
|
||||
|
||||
if typing.TYPE_CHECKING:
|
||||
from ..core import app
|
||||
from langbot_plugin.api.entities.builtin.workflow.query import WorkflowQuery
|
||||
|
||||
|
||||
class WorkflowMonitoringHelper:
|
||||
"""Helper class for workflow monitoring operations"""
|
||||
|
||||
# All frontend panel logging methods have been removed.
|
||||
# A new solution will be implemented separately.
|
||||
pass
|
||||
|
||||
|
||||
class LLMCallMonitor:
|
||||
"""Context manager for monitoring LLM calls in workflow"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
ap: app.Application,
|
||||
query: WorkflowQuery,
|
||||
bot_id: str,
|
||||
bot_name: str,
|
||||
workflow_id: str,
|
||||
workflow_name: str,
|
||||
node_name: str,
|
||||
model_name: str,
|
||||
):
|
||||
self.ap = ap
|
||||
self.query = query
|
||||
self.bot_id = bot_id
|
||||
self.bot_name = bot_name
|
||||
self.workflow_id = workflow_id
|
||||
self.workflow_name = workflow_name
|
||||
self.node_name = node_name
|
||||
self.model_name = model_name
|
||||
self.start_time = None
|
||||
self.input_tokens = 0
|
||||
self.output_tokens = 0
|
||||
|
||||
async def __aenter__(self):
|
||||
self.start_time = time.time()
|
||||
return self
|
||||
|
||||
async def __aexit__(self, exc_type, exc_val, exc_tb):
|
||||
# LLM call monitoring has been removed.
|
||||
# A new solution will be implemented separately.
|
||||
return False
|
||||
@@ -1,350 +0,0 @@
|
||||
"""
|
||||
Monitoring helper for recording events during workflow execution.
|
||||
This module provides convenient methods to record monitoring data
|
||||
without cluttering the main workflow code.
|
||||
|
||||
Logging scheme (aligned with pipeline monitoring):
|
||||
- Trigger log: stores original user message content directly
|
||||
- LLM call log: uses record_llm_call only (no additional message record)
|
||||
- LLM response log: stores response message content directly
|
||||
- Reply log: stores reply content directly
|
||||
|
||||
Fields are extracted from WorkflowQuery object when available, with fallback to context_vars.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import typing
|
||||
import time
|
||||
import json
|
||||
|
||||
if typing.TYPE_CHECKING:
|
||||
from ..core import app
|
||||
from langbot_plugin.api.entities.builtin.workflow.query import WorkflowQuery
|
||||
|
||||
|
||||
class WorkflowMonitoringHelper:
|
||||
"""Helper class for workflow monitoring operations"""
|
||||
|
||||
@staticmethod
|
||||
def _get_session_id(query, context_vars: dict | None = None) -> str:
|
||||
"""Build session_id from query or context_vars"""
|
||||
# Try to get from query first
|
||||
if not isinstance(query, str) and query.launcher_type:
|
||||
launcher_type = query.launcher_type.value if hasattr(query.launcher_type, 'value') else str(query.launcher_type)
|
||||
launcher_id = query.launcher_id or 'unknown'
|
||||
return f'{launcher_type}_{launcher_id}'
|
||||
|
||||
# Fallback to context_vars
|
||||
if context_vars and context_vars.get('_launcher_type') and context_vars.get('_launcher_id'):
|
||||
return f"{context_vars['_launcher_type']}_{context_vars['_launcher_id']}"
|
||||
|
||||
return 'workflow_session'
|
||||
|
||||
@staticmethod
|
||||
def _get_platform(query, context_vars: dict | None = None) -> str:
|
||||
"""Get platform name from query or context_vars"""
|
||||
if not isinstance(query, str) and query.launcher_type:
|
||||
if hasattr(query.launcher_type, 'value'):
|
||||
return query.launcher_type.value
|
||||
return str(query.launcher_type)
|
||||
return 'workflow'
|
||||
|
||||
@staticmethod
|
||||
def _get_sender_name(query, context_vars: dict | None = None) -> str | None:
|
||||
"""Get sender name from query or context_vars"""
|
||||
# Try query first
|
||||
if not isinstance(query, str):
|
||||
if query.sender_name:
|
||||
return query.sender_name
|
||||
if query.message_event and hasattr(query.message_event, 'sender'):
|
||||
sender = query.message_event.sender
|
||||
if hasattr(sender, 'nickname'):
|
||||
return sender.nickname
|
||||
if hasattr(sender, 'member_name'):
|
||||
return sender.member_name
|
||||
|
||||
# Fallback to context_vars
|
||||
if context_vars:
|
||||
return context_vars.get('_sender_name')
|
||||
|
||||
return None
|
||||
|
||||
@staticmethod
|
||||
async def record_trigger_log(
|
||||
ap: app.Application,
|
||||
query,
|
||||
workflow_id: str,
|
||||
workflow_name: str,
|
||||
bot_name: str = 'Workflow',
|
||||
context_vars: dict | None = None,
|
||||
) -> str:
|
||||
"""Record trigger node log (stores original user message content directly)
|
||||
|
||||
Aligned with pipeline monitoring: record_query_start
|
||||
"""
|
||||
try:
|
||||
session_id = WorkflowMonitoringHelper._get_session_id(query, context_vars)
|
||||
platform = WorkflowMonitoringHelper._get_platform(query, context_vars)
|
||||
sender_name = WorkflowMonitoringHelper._get_sender_name(query, context_vars)
|
||||
|
||||
# Get message content - store original content directly
|
||||
message_content = ''
|
||||
if isinstance(query, str):
|
||||
message_content = query
|
||||
elif not isinstance(query, str) and query.message_context:
|
||||
message_content = query.message_context.message_content
|
||||
elif not isinstance(query, str) and query.message_chain and hasattr(query.message_chain, 'model_dump'):
|
||||
message_content = json.dumps(query.message_chain.model_dump(), ensure_ascii=False)
|
||||
elif not isinstance(query, str) and query.user_message:
|
||||
message_content = str(query.user_message)
|
||||
|
||||
# Get bot_id and user_id
|
||||
bot_id = ''
|
||||
user_id = None
|
||||
if not isinstance(query, str):
|
||||
bot_id = query.bot_uuid or ''
|
||||
user_id = query.sender_id
|
||||
elif context_vars:
|
||||
bot_id = context_vars.get('_bot_id', '') or ''
|
||||
user_id = context_vars.get('_user_id')
|
||||
|
||||
message_id = await ap.monitoring_service.record_message(
|
||||
bot_id=bot_id,
|
||||
bot_name=bot_name,
|
||||
pipeline_id=workflow_id,
|
||||
pipeline_name=workflow_name or 'Workflow',
|
||||
message_content=message_content,
|
||||
session_id=session_id,
|
||||
status='success',
|
||||
level='info',
|
||||
platform=platform,
|
||||
user_id=user_id,
|
||||
user_name=sender_name,
|
||||
role='user',
|
||||
runner_name='local-workflow',
|
||||
)
|
||||
|
||||
return message_id
|
||||
except Exception as e:
|
||||
ap.logger.error(f'Failed to record trigger log: {e}')
|
||||
return ''
|
||||
|
||||
@staticmethod
|
||||
async def record_llm_call_log(
|
||||
ap: app.Application,
|
||||
query,
|
||||
workflow_id: str,
|
||||
workflow_name: str,
|
||||
node_name: str,
|
||||
model_name: str,
|
||||
input_tokens: int,
|
||||
output_tokens: int,
|
||||
duration_ms: int,
|
||||
status: str = 'success',
|
||||
error_message: str | None = None,
|
||||
bot_name: str = 'Workflow',
|
||||
context_vars: dict | None = None,
|
||||
input_message: str | None = None,
|
||||
message_id: str | None = None,
|
||||
):
|
||||
"""Record LLM call log with message_id association
|
||||
|
||||
Aligned with pipeline monitoring: record_llm_call with message_id
|
||||
LLM calls are aggregated under the trigger log via message_id.
|
||||
"""
|
||||
try:
|
||||
session_id = WorkflowMonitoringHelper._get_session_id(query, context_vars)
|
||||
|
||||
# Get bot_id
|
||||
bot_id = ''
|
||||
if not isinstance(query, str):
|
||||
bot_id = query.bot_uuid or ''
|
||||
elif context_vars:
|
||||
bot_id = context_vars.get('_bot_id', '') or ''
|
||||
|
||||
# Record LLM call with message_id for association
|
||||
await ap.monitoring_service.record_llm_call(
|
||||
bot_id=bot_id,
|
||||
bot_name=bot_name,
|
||||
pipeline_id=workflow_id,
|
||||
pipeline_name=workflow_name or 'Workflow',
|
||||
session_id=session_id,
|
||||
model_name=model_name,
|
||||
input_tokens=input_tokens,
|
||||
output_tokens=output_tokens,
|
||||
duration=duration_ms,
|
||||
status=status,
|
||||
error_message=error_message,
|
||||
message_id=message_id,
|
||||
)
|
||||
except Exception as e:
|
||||
ap.logger.error(f'Failed to record LLM call log: {e}')
|
||||
|
||||
@staticmethod
|
||||
async def record_llm_response_log(
|
||||
ap: app.Application,
|
||||
query,
|
||||
workflow_id: str,
|
||||
workflow_name: str,
|
||||
node_name: str,
|
||||
response_content: str,
|
||||
bot_name: str = 'Workflow',
|
||||
context_vars: dict | None = None,
|
||||
):
|
||||
"""Record LLM response log (stores response content directly)
|
||||
|
||||
Aligned with pipeline monitoring: record_query_response
|
||||
"""
|
||||
try:
|
||||
session_id = WorkflowMonitoringHelper._get_session_id(query, context_vars)
|
||||
platform = WorkflowMonitoringHelper._get_platform(query, context_vars)
|
||||
sender_name = WorkflowMonitoringHelper._get_sender_name(query, context_vars)
|
||||
|
||||
# Get bot_id and user_id
|
||||
bot_id = ''
|
||||
user_id = None
|
||||
if not isinstance(query, str):
|
||||
bot_id = query.bot_uuid or ''
|
||||
user_id = query.sender_id
|
||||
elif context_vars:
|
||||
bot_id = context_vars.get('_bot_id', '') or ''
|
||||
user_id = context_vars.get('_user_id')
|
||||
|
||||
# Store response content directly, no prefix
|
||||
await ap.monitoring_service.record_message(
|
||||
bot_id=bot_id,
|
||||
bot_name=bot_name,
|
||||
pipeline_id=workflow_id,
|
||||
pipeline_name=workflow_name or 'Workflow',
|
||||
message_content=response_content[:2000], # Limit length
|
||||
session_id=session_id,
|
||||
status='success',
|
||||
level='info',
|
||||
platform=platform,
|
||||
user_id=user_id,
|
||||
user_name=sender_name,
|
||||
role='assistant',
|
||||
runner_name='local-workflow',
|
||||
)
|
||||
except Exception as e:
|
||||
ap.logger.error(f'Failed to record LLM response log: {e}')
|
||||
|
||||
@staticmethod
|
||||
async def record_reply_log(
|
||||
ap: app.Application,
|
||||
query,
|
||||
workflow_id: str,
|
||||
workflow_name: str,
|
||||
node_name: str,
|
||||
reply_content: str,
|
||||
bot_name: str = 'Workflow',
|
||||
context_vars: dict | None = None,
|
||||
):
|
||||
"""Record reply message log (stores reply content directly)
|
||||
|
||||
Aligned with pipeline monitoring: record_query_response
|
||||
"""
|
||||
try:
|
||||
session_id = WorkflowMonitoringHelper._get_session_id(query, context_vars)
|
||||
platform = WorkflowMonitoringHelper._get_platform(query, context_vars)
|
||||
sender_name = WorkflowMonitoringHelper._get_sender_name(query, context_vars)
|
||||
|
||||
# Get bot_id and user_id
|
||||
bot_id = ''
|
||||
user_id = None
|
||||
if not isinstance(query, str):
|
||||
bot_id = query.bot_uuid or ''
|
||||
user_id = query.sender_id
|
||||
elif context_vars:
|
||||
bot_id = context_vars.get('_bot_id', '') or ''
|
||||
user_id = context_vars.get('_user_id')
|
||||
|
||||
# Store reply content directly, no prefix
|
||||
await ap.monitoring_service.record_message(
|
||||
bot_id=bot_id,
|
||||
bot_name=bot_name,
|
||||
pipeline_id=workflow_id,
|
||||
pipeline_name=workflow_name or 'Workflow',
|
||||
message_content=reply_content[:2000], # Limit length
|
||||
session_id=session_id,
|
||||
status='success',
|
||||
level='info',
|
||||
platform=platform,
|
||||
user_id=user_id,
|
||||
user_name=sender_name,
|
||||
role='assistant',
|
||||
runner_name='local-workflow',
|
||||
)
|
||||
except Exception as e:
|
||||
ap.logger.error(f'Failed to record reply log: {e}')
|
||||
|
||||
|
||||
class LLMCallMonitor:
|
||||
"""Context manager for monitoring LLM calls in workflow"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
ap: app.Application,
|
||||
query,
|
||||
bot_id: str,
|
||||
bot_name: str,
|
||||
workflow_id: str,
|
||||
workflow_name: str,
|
||||
node_name: str,
|
||||
model_name: str,
|
||||
context_vars: dict | None = None,
|
||||
):
|
||||
self.ap = ap
|
||||
self.query = query
|
||||
self.bot_id = bot_id
|
||||
self.bot_name = bot_name
|
||||
self.workflow_id = workflow_id
|
||||
self.workflow_name = workflow_name
|
||||
self.node_name = node_name
|
||||
self.model_name = model_name
|
||||
self.context_vars = context_vars
|
||||
self.start_time = None
|
||||
self.input_tokens = 0
|
||||
self.output_tokens = 0
|
||||
|
||||
async def __aenter__(self):
|
||||
self.start_time = time.time()
|
||||
return self
|
||||
|
||||
async def __aexit__(self, exc_type, exc_val, exc_tb):
|
||||
duration_ms = int((time.time() - self.start_time) * 1000) if self.start_time else 0
|
||||
|
||||
if exc_type is not None:
|
||||
await WorkflowMonitoringHelper.record_llm_call_log(
|
||||
ap=self.ap,
|
||||
query=self.query,
|
||||
workflow_id=self.workflow_id,
|
||||
workflow_name=self.workflow_name,
|
||||
node_name=self.node_name,
|
||||
model_name=self.model_name,
|
||||
input_tokens=self.input_tokens,
|
||||
output_tokens=self.output_tokens,
|
||||
duration_ms=duration_ms,
|
||||
status='error',
|
||||
error_message=str(exc_val) if exc_val else None,
|
||||
bot_name=self.bot_name,
|
||||
context_vars=self.context_vars,
|
||||
)
|
||||
else:
|
||||
await WorkflowMonitoringHelper.record_llm_call_log(
|
||||
ap=self.ap,
|
||||
query=self.query,
|
||||
workflow_id=self.workflow_id,
|
||||
workflow_name=self.workflow_name,
|
||||
node_name=self.node_name,
|
||||
model_name=self.model_name,
|
||||
input_tokens=self.input_tokens,
|
||||
output_tokens=self.output_tokens,
|
||||
duration_ms=duration_ms,
|
||||
status='success',
|
||||
bot_name=self.bot_name,
|
||||
context_vars=self.context_vars,
|
||||
)
|
||||
|
||||
return False
|
||||
@@ -1,164 +0,0 @@
|
||||
"""Workflow node base class and decorators"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import abc
|
||||
from typing import Any, Callable, Optional, TYPE_CHECKING
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from .entities import ExecutionContext
|
||||
from ..core import app
|
||||
|
||||
|
||||
class WorkflowNode(abc.ABC):
|
||||
"""Base class for all workflow nodes.
|
||||
|
||||
Node metadata (inputs, outputs, config schema, label, icon, etc.) is
|
||||
defined exclusively in YAML files under templates/metadata/nodes/.
|
||||
Python subclasses only provide execution logic and runtime behaviour.
|
||||
"""
|
||||
|
||||
# Set by @workflow_node decorator
|
||||
type_name: str = ''
|
||||
|
||||
# Category is kept as a fallback for registry when YAML is missing
|
||||
category: str = 'misc'
|
||||
|
||||
# Pipeline config reuse (referenced by registry merge logic)
|
||||
config_schema_source: Optional[str] = None
|
||||
config_stages: list[str] = []
|
||||
|
||||
def __init__(self, node_id: str, config: dict[str, Any], ap: Optional['app.Application'] = None):
|
||||
"""Initialize node with ID and configuration"""
|
||||
self.node_id = node_id
|
||||
self.config = config
|
||||
self.ap = ap
|
||||
|
||||
@abc.abstractmethod
|
||||
async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
|
||||
"""Execute the node logic.
|
||||
|
||||
Args:
|
||||
inputs: Input data from connected nodes
|
||||
context: Execution context with workflow state
|
||||
|
||||
Returns:
|
||||
Dictionary of output values
|
||||
"""
|
||||
pass
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Validation helpers — metadata is resolved from the registry at
|
||||
# runtime so that YAML remains the single source of truth.
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
async def validate_inputs(self, inputs: dict[str, Any]) -> list[str]:
|
||||
"""Validate input data against YAML port definitions.
|
||||
|
||||
Returns:
|
||||
List of validation error messages (empty if valid)
|
||||
"""
|
||||
metadata = self._get_metadata()
|
||||
if metadata is None:
|
||||
return []
|
||||
|
||||
errors: list[str] = []
|
||||
for port in metadata.get('inputs', []):
|
||||
if port.get('required', True) and port.get('name') and port['name'] not in inputs:
|
||||
errors.append(f"Missing required input: {port['name']}")
|
||||
return errors
|
||||
|
||||
async def validate_config(self) -> list[str]:
|
||||
"""Validate node configuration against YAML config schema.
|
||||
|
||||
Returns:
|
||||
List of validation error messages (empty if valid)
|
||||
"""
|
||||
metadata = self._get_metadata()
|
||||
if metadata is None:
|
||||
return []
|
||||
|
||||
errors: list[str] = []
|
||||
for cfg in metadata.get('config', []):
|
||||
name = cfg.get('name', '')
|
||||
if not name:
|
||||
continue
|
||||
required = cfg.get('required', False)
|
||||
cfg_type = cfg.get('type', 'string')
|
||||
|
||||
if required and name not in self.config:
|
||||
errors.append(f'Missing required config: {name}')
|
||||
elif name in self.config:
|
||||
value = self.config[name]
|
||||
# Type validation
|
||||
if cfg_type == 'integer' and not isinstance(value, int):
|
||||
errors.append(f'Config {name} must be an integer')
|
||||
elif cfg_type == 'number' and not isinstance(value, (int, float)):
|
||||
errors.append(f'Config {name} must be a number')
|
||||
elif cfg_type == 'boolean' and not isinstance(value, bool):
|
||||
errors.append(f'Config {name} must be a boolean')
|
||||
# Range validation
|
||||
min_val = cfg.get('min_value')
|
||||
max_val = cfg.get('max_value')
|
||||
if min_val is not None and isinstance(value, (int, float)):
|
||||
if value < min_val:
|
||||
errors.append(f'Config {name} must be >= {min_val}')
|
||||
if max_val is not None and isinstance(value, (int, float)):
|
||||
if value > max_val:
|
||||
errors.append(f'Config {name} must be <= {max_val}')
|
||||
return errors
|
||||
|
||||
def get_config(self, key: str, default: Any = None) -> Any:
|
||||
"""Get configuration value with default"""
|
||||
return self.config.get(key, default)
|
||||
|
||||
def _get_metadata(self) -> Optional[dict[str, Any]]:
|
||||
"""Retrieve YAML metadata for this node from the registry."""
|
||||
from .registry import NodeTypeRegistry
|
||||
registry = NodeTypeRegistry.instance()
|
||||
return registry.get_metadata(self.type_name)
|
||||
|
||||
@classmethod
|
||||
def to_schema(cls) -> dict[str, Any]:
|
||||
"""Return a schema dict for this node type.
|
||||
|
||||
This is used by tests and tooling to inspect node capabilities.
|
||||
"""
|
||||
from .registry import NodeTypeRegistry
|
||||
registry = NodeTypeRegistry.instance()
|
||||
metadata = registry.get_metadata(cls.type_name)
|
||||
if metadata:
|
||||
return registry._metadata_to_schema(metadata)
|
||||
# Fallback: build a minimal schema from class attributes
|
||||
return {
|
||||
'type': f'{cls.category}.{cls.type_name}' if cls.type_name else cls.type_name,
|
||||
'category': cls.category,
|
||||
'label': getattr(cls, 'name', cls.type_name),
|
||||
'description': getattr(cls, 'description', ''),
|
||||
'inputs': [],
|
||||
'outputs': [],
|
||||
'config_schema': [],
|
||||
}
|
||||
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Decorator for setting type_name attribute
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
|
||||
def workflow_node(type_name: str) -> Callable[[type[WorkflowNode]], type[WorkflowNode]]:
|
||||
"""Decorator to set the type_name attribute on a workflow node class.
|
||||
|
||||
Usage:
|
||||
@workflow_node('llm_call')
|
||||
class LLMCallNode(WorkflowNode):
|
||||
...
|
||||
|
||||
The actual registration is now handled by the discovery engine.
|
||||
"""
|
||||
|
||||
def decorator(cls: type[WorkflowNode]) -> type[WorkflowNode]:
|
||||
cls.type_name = type_name
|
||||
return cls
|
||||
|
||||
return decorator
|
||||
@@ -1 +0,0 @@
|
||||
|
||||
@@ -1,263 +0,0 @@
|
||||
"""Call Pipeline Node - invoke an existing pipeline
|
||||
|
||||
Node metadata is loaded from: ../../templates/metadata/nodes/call_pipeline.yaml
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any, Optional
|
||||
|
||||
import pydantic
|
||||
|
||||
import langbot_plugin.api.definition.abstract.platform.adapter as abstract_platform_adapter
|
||||
import langbot_plugin.api.definition.abstract.platform.event_logger as abstract_event_logger
|
||||
import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
|
||||
import langbot_plugin.api.entities.builtin.platform.entities as platform_entities
|
||||
import langbot_plugin.api.entities.builtin.platform.events as platform_events
|
||||
import langbot_plugin.api.entities.builtin.platform.message as platform_message
|
||||
import langbot_plugin.api.entities.builtin.provider.session as provider_session
|
||||
|
||||
from langbot_plugin.api.entities.builtin.workflow.entities import ExecutionContext
|
||||
from ..node import WorkflowNode, workflow_node
|
||||
|
||||
|
||||
class _NoOpEventLogger(abstract_event_logger.AbstractEventLogger):
|
||||
"""No-op event logger for workflow pipeline adapter."""
|
||||
|
||||
async def info(
|
||||
self,
|
||||
text: str,
|
||||
images: Optional[list[platform_message.Image]] = None,
|
||||
message_session_id: Optional[str] = None,
|
||||
no_throw: bool = True,
|
||||
):
|
||||
pass
|
||||
|
||||
async def debug(
|
||||
self,
|
||||
text: str,
|
||||
images: Optional[list[platform_message.Image]] = None,
|
||||
message_session_id: Optional[str] = None,
|
||||
no_throw: bool = True,
|
||||
):
|
||||
pass
|
||||
|
||||
async def warning(
|
||||
self,
|
||||
text: str,
|
||||
images: Optional[list[platform_message.Image]] = None,
|
||||
message_session_id: Optional[str] = None,
|
||||
no_throw: bool = True,
|
||||
):
|
||||
pass
|
||||
|
||||
async def error(
|
||||
self,
|
||||
text: str,
|
||||
images: Optional[list[platform_message.Image]] = None,
|
||||
message_session_id: Optional[str] = None,
|
||||
no_throw: bool = True,
|
||||
):
|
||||
pass
|
||||
|
||||
@workflow_node('call_pipeline')
|
||||
class CallPipelineNode(WorkflowNode):
|
||||
"""Call pipeline node - invoke an existing pipeline"""
|
||||
|
||||
category = 'action'
|
||||
|
||||
async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
|
||||
if not self.ap:
|
||||
raise RuntimeError('Application instance not available — cannot call pipeline')
|
||||
|
||||
raw_query = inputs.get('query', '')
|
||||
query_text = str(raw_query or inputs.get('input') or '')
|
||||
pipeline_ref = str(self.get_config('pipeline_uuid', '') or '').strip()
|
||||
|
||||
if not pipeline_ref:
|
||||
raise ValueError('No pipeline configured for call pipeline node')
|
||||
|
||||
pipeline_data = await self.ap.pipeline_service.get_pipeline(pipeline_ref)
|
||||
if pipeline_data is None:
|
||||
pipeline_data = await self.ap.pipeline_service.get_pipeline_by_name(pipeline_ref)
|
||||
if pipeline_data is None:
|
||||
raise ValueError(f'Pipeline not found: {pipeline_ref}')
|
||||
|
||||
pipeline_uuid = str(pipeline_data.get('uuid', '') or '')
|
||||
if not pipeline_uuid:
|
||||
raise ValueError(f'Pipeline UUID missing for: {pipeline_ref}')
|
||||
|
||||
runtime_pipeline = await self.ap.pipeline_mgr.get_pipeline_by_uuid(pipeline_uuid)
|
||||
if runtime_pipeline is None:
|
||||
raise ValueError(f'Runtime pipeline not loaded: {pipeline_uuid}')
|
||||
|
||||
adapter = _WorkflowPipelineCaptureAdapter(context=context)
|
||||
adapter.bot_account_id = 'workflow-call-pipeline'
|
||||
|
||||
message_event = self._build_message_event(query_text, context)
|
||||
message_chain = message_event.message_chain
|
||||
launcher_type = (
|
||||
provider_session.LauncherTypes.GROUP
|
||||
if context.message_context and context.message_context.is_group
|
||||
else provider_session.LauncherTypes.PERSON
|
||||
)
|
||||
launcher_id = context.session_id or context.execution_id
|
||||
sender_id = (
|
||||
context.message_context.sender_id
|
||||
if context.message_context and context.message_context.sender_id
|
||||
else context.user_id or f'workflow_{context.execution_id}'
|
||||
)
|
||||
|
||||
query = pipeline_query.Query(
|
||||
bot_uuid=context.bot_id,
|
||||
query_id=-1,
|
||||
launcher_type=launcher_type,
|
||||
launcher_id=launcher_id,
|
||||
sender_id=sender_id,
|
||||
message_event=message_event,
|
||||
message_chain=message_chain,
|
||||
variables={
|
||||
'_called_from_workflow': True,
|
||||
'_workflow_execution_id': context.execution_id,
|
||||
'_workflow_id': context.workflow_id,
|
||||
**dict(context.variables or {}),
|
||||
},
|
||||
resp_messages=[],
|
||||
resp_message_chain=[],
|
||||
adapter=adapter,
|
||||
pipeline_uuid=pipeline_uuid,
|
||||
)
|
||||
|
||||
await runtime_pipeline.run(query)
|
||||
|
||||
response_text = adapter.get_last_text_response()
|
||||
result = {
|
||||
'pipeline_uuid': pipeline_uuid,
|
||||
'pipeline_name': pipeline_data.get('name', ''),
|
||||
'responses': adapter.responses,
|
||||
'query_text': query_text,
|
||||
}
|
||||
|
||||
return {'response': response_text, 'result': result}
|
||||
|
||||
def _build_message_event(
|
||||
self,
|
||||
query_text: str,
|
||||
context: ExecutionContext,
|
||||
) -> platform_events.MessageEvent:
|
||||
message_chain_data = context.trigger_data.get('message_chain') or context.trigger_data.get('message', [])
|
||||
if isinstance(message_chain_data, list) and message_chain_data:
|
||||
message_chain = platform_message.MessageChain.model_validate(message_chain_data)
|
||||
else:
|
||||
message_chain = platform_message.MessageChain([platform_message.Plain(text=query_text)])
|
||||
|
||||
if context.message_context and context.message_context.is_group:
|
||||
group = platform_entities.Group(
|
||||
id=context.message_context.group_id or context.session_id or 'workflow_group',
|
||||
name=context.message_context.raw_message.get('group_name', 'Workflow Group') if context.message_context.raw_message else 'Workflow Group',
|
||||
permission=platform_entities.Permission.Member,
|
||||
)
|
||||
sender = platform_entities.GroupMember(
|
||||
id=context.message_context.sender_id,
|
||||
member_name=context.message_context.sender_name or 'Workflow User',
|
||||
permission=platform_entities.Permission.Member,
|
||||
group=group,
|
||||
)
|
||||
return platform_events.GroupMessage(
|
||||
sender=sender,
|
||||
message_chain=message_chain,
|
||||
time=context.message_context.raw_message.get('time') if context.message_context.raw_message else None,
|
||||
)
|
||||
|
||||
sender = platform_entities.Friend(
|
||||
id=context.message_context.sender_id if context.message_context else context.user_id or 'workflow_user',
|
||||
nickname=context.message_context.sender_name if context.message_context else 'Workflow User',
|
||||
remark=context.message_context.sender_name if context.message_context else 'Workflow User',
|
||||
)
|
||||
return platform_events.FriendMessage(
|
||||
sender=sender,
|
||||
message_chain=message_chain,
|
||||
time=context.message_context.raw_message.get('time')
|
||||
if context.message_context and context.message_context.raw_message
|
||||
else None,
|
||||
)
|
||||
|
||||
class _WorkflowPipelineCaptureAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
|
||||
"""Adapter to capture pipeline responses for workflow execution."""
|
||||
|
||||
class Config:
|
||||
arbitrary_types_allowed = True
|
||||
|
||||
responses: list[dict[str, Any]] = []
|
||||
context: Optional[ExecutionContext] = pydantic.Field(default=None, exclude=True)
|
||||
|
||||
def __init__(self, context: ExecutionContext):
|
||||
super().__init__(config={}, logger=_NoOpEventLogger(), context=context)
|
||||
self.responses = []
|
||||
|
||||
async def send_message(self, target_type: str, target_id: str, message: platform_message.MessageChain):
|
||||
payload = {
|
||||
'type': 'send',
|
||||
'target_type': target_type,
|
||||
'target_id': target_id,
|
||||
'content': str(message),
|
||||
'message_chain': message.model_dump(),
|
||||
}
|
||||
self.responses.append(payload)
|
||||
return payload
|
||||
|
||||
async def reply_message(
|
||||
self,
|
||||
message_source: platform_events.MessageEvent,
|
||||
message: platform_message.MessageChain,
|
||||
quote_origin: bool = False,
|
||||
):
|
||||
payload = {
|
||||
'type': 'reply',
|
||||
'content': str(message),
|
||||
'message_chain': message.model_dump(),
|
||||
'quote_origin': quote_origin,
|
||||
}
|
||||
self.responses.append(payload)
|
||||
return payload
|
||||
|
||||
async def reply_message_chunk(
|
||||
self,
|
||||
message_source: platform_events.MessageEvent,
|
||||
bot_message: dict,
|
||||
message: platform_message.MessageChain,
|
||||
quote_origin: bool = False,
|
||||
is_final: bool = False,
|
||||
):
|
||||
payload = {
|
||||
'type': 'reply_chunk',
|
||||
'content': str(message),
|
||||
'message_chain': message.model_dump(),
|
||||
'quote_origin': quote_origin,
|
||||
'is_final': is_final,
|
||||
}
|
||||
self.responses.append(payload)
|
||||
return payload
|
||||
|
||||
async def create_message_card(self, message_id, event: platform_events.MessageEvent) -> bool:
|
||||
return False
|
||||
|
||||
def register_listener(self, event_type, callback):
|
||||
return None
|
||||
|
||||
def unregister_listener(self, event_type, callback):
|
||||
return None
|
||||
|
||||
async def run_async(self):
|
||||
return None
|
||||
|
||||
async def is_stream_output_supported(self) -> bool:
|
||||
return False
|
||||
|
||||
async def kill(self) -> bool:
|
||||
return True
|
||||
|
||||
def get_last_text_response(self) -> str:
|
||||
if not self.responses:
|
||||
return ''
|
||||
return str(self.responses[-1].get('content', '') or '')
|
||||
@@ -1,85 +0,0 @@
|
||||
"""Call Workflow Node - invoke an existing workflow
|
||||
|
||||
Node metadata is loaded from: ../../templates/metadata/nodes/call_workflow.yaml
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any
|
||||
|
||||
from langbot_plugin.api.entities.builtin.workflow.entities import ExecutionContext
|
||||
|
||||
from ..node import WorkflowNode, workflow_node
|
||||
|
||||
|
||||
@workflow_node('call_workflow')
|
||||
class CallWorkflowNode(WorkflowNode):
|
||||
"""Call workflow node - invoke an existing workflow"""
|
||||
|
||||
category = 'action'
|
||||
|
||||
async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
|
||||
if not self.ap:
|
||||
raise RuntimeError('Application instance not available — cannot call workflow')
|
||||
|
||||
# Get workflow reference from config
|
||||
workflow_ref = str(self.get_config('workflow_uuid', '') or '').strip()
|
||||
if not workflow_ref:
|
||||
raise ValueError('No workflow configured for call workflow node')
|
||||
|
||||
# Get workflow definition from service
|
||||
workflow_data = await self.ap.workflow_service.get_workflow(workflow_ref)
|
||||
if workflow_data is None:
|
||||
raise ValueError(f'Workflow not found: {workflow_ref}')
|
||||
|
||||
workflow_uuid = str(workflow_data.get('uuid', '') or '')
|
||||
if not workflow_uuid:
|
||||
raise ValueError(f'Workflow UUID missing for: {workflow_ref}')
|
||||
|
||||
# Build variables to pass to the called workflow
|
||||
variables = dict(inputs.get('variables', {}) or {})
|
||||
|
||||
# Inherit current workflow variables if configured
|
||||
if self.get_config('inherit_variables', True):
|
||||
for key, value in (context.variables or {}).items():
|
||||
if key not in variables:
|
||||
variables[key] = value
|
||||
|
||||
# Add context markers for debugging
|
||||
variables['_called_from_workflow'] = True
|
||||
variables['_parent_workflow_id'] = context.workflow_id
|
||||
variables['_parent_execution_id'] = context.execution_id
|
||||
|
||||
# Execute the workflow
|
||||
execution_id = await self.ap.workflow_service.execute_workflow(
|
||||
workflow_uuid=workflow_uuid,
|
||||
trigger_type='workflow_call',
|
||||
trigger_data={
|
||||
'variables': variables,
|
||||
'parent_execution_id': context.execution_id,
|
||||
},
|
||||
session_id=context.session_id,
|
||||
user_id=context.user_id,
|
||||
bot_id=context.bot_id,
|
||||
)
|
||||
|
||||
# Get execution result
|
||||
execution = await self.ap.workflow_service.get_execution(execution_id)
|
||||
if execution is None:
|
||||
raise ValueError(f'Execution result not found: {execution_id}')
|
||||
|
||||
# Build result
|
||||
result = {
|
||||
'workflow_uuid': workflow_uuid,
|
||||
'workflow_name': workflow_data.get('name', ''),
|
||||
'execution_id': execution_id,
|
||||
'status': execution.get('status', 'unknown'),
|
||||
'variables': execution.get('variables', {}),
|
||||
'error': execution.get('error'),
|
||||
}
|
||||
|
||||
return {
|
||||
'result': result,
|
||||
'status': execution.get('status', 'unknown'),
|
||||
'error': execution.get('error'),
|
||||
}
|
||||
@@ -1,156 +0,0 @@
|
||||
"""Code Executor Node - run Python or JavaScript code
|
||||
|
||||
Node metadata is loaded from: ../../templates/metadata/nodes/code_executor.yaml
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import ast
|
||||
import io
|
||||
import logging
|
||||
import sys
|
||||
import threading
|
||||
from typing import Any
|
||||
|
||||
from langbot_plugin.api.entities.builtin.workflow.entities import ExecutionContext
|
||||
from ..node import WorkflowNode, workflow_node
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# 危险的内置函数和模块黑名单
|
||||
_DANGEROUS_BUILTINS = {
|
||||
'__import__', 'eval', 'exec', 'compile', 'open', 'file',
|
||||
'input', 'exit', 'quit', 'globals', 'locals', 'vars',
|
||||
'dir', 'help', 'breakpoint',
|
||||
}
|
||||
|
||||
# 允许的安全内置函数
|
||||
_SAFE_BUILTINS = {
|
||||
'abs': abs, 'all': all, 'any': any, 'bin': bin, 'bool': bool,
|
||||
'bytearray': bytearray, 'bytes': bytes, 'callable': callable,
|
||||
'chr': chr, 'complex': complex, 'dict': dict, 'divmod': divmod,
|
||||
'enumerate': enumerate, 'filter': filter, 'float': float,
|
||||
'format': format, 'frozenset': frozenset, 'hash': hash,
|
||||
'hex': hex, 'int': int, 'isinstance': isinstance, 'issubclass': issubclass,
|
||||
'iter': iter, 'len': len, 'list': list, 'map': map, 'max': max,
|
||||
'min': min, 'next': next, 'object': object, 'oct': oct, 'ord': ord,
|
||||
'pow': pow, 'print': print, 'range': range, 'repr': repr,
|
||||
'reversed': reversed, 'round': round, 'set': set, 'slice': slice,
|
||||
'sorted': sorted, 'str': str, 'sum': sum, 'tuple': tuple,
|
||||
'type': type, 'zip': zip,
|
||||
}
|
||||
|
||||
|
||||
def _check_code_safety(code: str) -> list[str]:
|
||||
"""检查代码中是否包含危险操作"""
|
||||
warnings = []
|
||||
try:
|
||||
tree = ast.parse(code)
|
||||
for node in ast.walk(tree):
|
||||
# 检查 import 语句
|
||||
if isinstance(node, (ast.Import, ast.ImportFrom)):
|
||||
warnings.append('Import statements are not allowed')
|
||||
# 检查危险函数调用
|
||||
if isinstance(node, ast.Call):
|
||||
if isinstance(node.func, ast.Name) and node.func.id in _DANGEROUS_BUILTINS:
|
||||
warnings.append(f'Dangerous function call: {node.func.id}')
|
||||
# 检查 __import__ 通过 getattr 调用
|
||||
if isinstance(node.func, ast.Attribute):
|
||||
if node.func.attr in ('__import__', 'eval', 'exec', 'open', 'file'):
|
||||
warnings.append(f'Dangerous attribute access: {node.func.attr}')
|
||||
except SyntaxError as e:
|
||||
warnings.append(f'Syntax error in code: {e}')
|
||||
return warnings
|
||||
|
||||
|
||||
class _ExecutionTimeoutError(Exception):
|
||||
"""执行超时错误"""
|
||||
pass
|
||||
|
||||
|
||||
def _run_with_timeout(func, timeout: float = 10.0):
|
||||
"""带超时限制的函数执行"""
|
||||
result = [None]
|
||||
error = [None]
|
||||
|
||||
def _target():
|
||||
try:
|
||||
result[0] = func()
|
||||
except Exception as e:
|
||||
error[0] = e
|
||||
|
||||
thread = threading.Thread(target=_target)
|
||||
thread.daemon = True
|
||||
thread.start()
|
||||
thread.join(timeout)
|
||||
|
||||
if thread.is_alive():
|
||||
raise _ExecutionTimeoutError(f'Code execution timed out after {timeout} seconds')
|
||||
|
||||
if error[0]:
|
||||
raise error[0]
|
||||
|
||||
return result[0]
|
||||
|
||||
|
||||
@workflow_node('code_executor')
|
||||
class CodeExecutorNode(WorkflowNode):
|
||||
"""Code executor node - run Python or JavaScript code"""
|
||||
|
||||
category = 'process'
|
||||
|
||||
async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
|
||||
code = self.get_config('code', '')
|
||||
language = self.get_config('language', 'python')
|
||||
timeout = self.get_config('timeout', 10)
|
||||
|
||||
# 限制最大超时时间
|
||||
timeout = min(max(timeout, 1), 30)
|
||||
|
||||
if not code:
|
||||
return {'output': None, 'console': '', 'error': 'No code provided'}
|
||||
|
||||
if language == 'python':
|
||||
return await self._execute_python(code, inputs, context, timeout)
|
||||
else:
|
||||
return await self._execute_javascript(code, inputs, context)
|
||||
|
||||
async def _execute_python(self, code: str, inputs: dict[str, Any], context: ExecutionContext, timeout: float) -> dict[str, Any]:
|
||||
# 安全检查
|
||||
warnings = _check_code_safety(code)
|
||||
if warnings:
|
||||
logger.warning('Code safety warnings: %s', warnings)
|
||||
return {'output': None, 'console': '', 'error': '; '.join(warnings)}
|
||||
|
||||
stdout_capture = io.StringIO()
|
||||
old_stdout = sys.stdout
|
||||
|
||||
def _exec_code():
|
||||
nonlocal stdout_capture
|
||||
sys.stdout = stdout_capture
|
||||
try:
|
||||
# 使用更安全的执行方式
|
||||
compiled = compile(code, '<workflow>', 'exec')
|
||||
safe_globals = {
|
||||
'__builtins__': _SAFE_BUILTINS,
|
||||
'__name__': '__workflow_sandbox__',
|
||||
}
|
||||
local_vars = {'inputs': inputs, 'output': None}
|
||||
exec(compiled, safe_globals, local_vars)
|
||||
return local_vars.get('output')
|
||||
finally:
|
||||
sys.stdout = old_stdout
|
||||
|
||||
try:
|
||||
output = _run_with_timeout(_exec_code, timeout)
|
||||
console_output = stdout_capture.getvalue()
|
||||
return {'output': output, 'console': console_output, 'error': None}
|
||||
except _ExecutionTimeoutError as e:
|
||||
logger.error('Code execution timeout: %s', e)
|
||||
return {'output': None, 'console': stdout_capture.getvalue(), 'error': str(e)}
|
||||
except Exception as e:
|
||||
logger.error('Code execution error: %s', e)
|
||||
return {'output': None, 'console': stdout_capture.getvalue(), 'error': f'{type(e).__name__}: {e}'}
|
||||
|
||||
async def _execute_javascript(self, code: str, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
|
||||
return {'output': None, 'console': '', 'error': 'JavaScript execution is not implemented'}
|
||||
@@ -1,125 +0,0 @@
|
||||
"""Condition Node - branch based on condition
|
||||
|
||||
Node metadata is loaded from: ../../templates/metadata/nodes/condition.yaml
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import re
|
||||
import signal
|
||||
from typing import Any
|
||||
|
||||
from langbot_plugin.api.entities.builtin.workflow.entities import ExecutionContext
|
||||
from ..node import WorkflowNode, workflow_node
|
||||
from ..safe_eval import safe_eval_with_vars
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# 正则表达式超时限制（秒）
|
||||
_REGEX_TIMEOUT = 2
|
||||
|
||||
|
||||
class _RegexTimeoutError(Exception):
|
||||
"""正则表达式超时错误"""
|
||||
pass
|
||||
|
||||
|
||||
def _handle_timeout(signum, frame):
|
||||
"""超时信号处理"""
|
||||
raise _RegexTimeoutError('Regex match timed out')
|
||||
|
||||
|
||||
def _safe_regex_match(pattern: str, text: str) -> tuple[bool, str]:
|
||||
"""安全地执行正则表达式匹配，带有超时限制"""
|
||||
# 设置超时信号
|
||||
old_handler = signal.signal(signal.SIGALRM, _handle_timeout)
|
||||
signal.setitimer(signal.ITIMER_REAL, _REGEX_TIMEOUT)
|
||||
|
||||
try:
|
||||
result = bool(re.match(pattern, str(text)))
|
||||
return result, ''
|
||||
except _RegexTimeoutError:
|
||||
logger.warning('Regex match timed out for pattern: %s', pattern[:50])
|
||||
return False, 'Regex match timed out'
|
||||
except re.error as e:
|
||||
logger.warning('Invalid regex pattern: %s', e)
|
||||
return False, f'Invalid regex: {e}'
|
||||
finally:
|
||||
signal.setitimer(signal.ITIMER_REAL, 0)
|
||||
signal.signal(signal.SIGALRM, old_handler)
|
||||
|
||||
|
||||
@workflow_node('condition')
|
||||
class ConditionNode(WorkflowNode):
|
||||
"""Condition node - branch based on condition"""
|
||||
|
||||
category = 'control'
|
||||
|
||||
async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
|
||||
condition_type = self.get_config('condition_type', 'expression')
|
||||
input_data = inputs.get('input')
|
||||
|
||||
result = False
|
||||
|
||||
if condition_type == 'expression':
|
||||
expression = self.get_config('expression', 'false')
|
||||
result = await self._evaluate_expression(expression, input_data, context)
|
||||
elif condition_type == 'comparison':
|
||||
result = await self._evaluate_comparison(input_data, context)
|
||||
elif condition_type == 'contains':
|
||||
left = self.get_config('left_value', '')
|
||||
right = self.get_config('right_value', '')
|
||||
result = right in left
|
||||
elif condition_type == 'empty':
|
||||
result = not bool(input_data)
|
||||
elif condition_type == 'regex':
|
||||
left = self.get_config('left_value', '')
|
||||
pattern = self.get_config('right_value', '')
|
||||
result, error = _safe_regex_match(pattern, left)
|
||||
if error:
|
||||
return {'true': None, 'false': input_data, 'error': error}
|
||||
|
||||
if result:
|
||||
return {'true': input_data, 'false': None}
|
||||
else:
|
||||
return {'true': None, 'false': input_data}
|
||||
|
||||
async def _evaluate_expression(self, expression: str, data: Any, context: ExecutionContext) -> bool:
|
||||
try:
|
||||
local_vars = {'input': data, 'data': data, 'variables': context.variables}
|
||||
return bool(safe_eval_with_vars(expression, local_vars))
|
||||
except Exception as e:
|
||||
logger.warning('Expression evaluation error: %s', e)
|
||||
return False
|
||||
|
||||
async def _evaluate_comparison(self, data: Any, context: ExecutionContext) -> bool:
|
||||
left = self.get_config('left_value', '')
|
||||
right = self.get_config('right_value', '')
|
||||
operator = self.get_config('operator', '==')
|
||||
|
||||
try:
|
||||
left_num = float(left)
|
||||
right_num = float(right)
|
||||
|
||||
if operator == '==':
|
||||
return left_num == right_num
|
||||
elif operator == '!=':
|
||||
return left_num != right_num
|
||||
elif operator == '>':
|
||||
return left_num > right_num
|
||||
elif operator == '<':
|
||||
return left_num < right_num
|
||||
elif operator == '>=':
|
||||
return left_num >= right_num
|
||||
elif operator == '<=':
|
||||
return left_num <= right_num
|
||||
except ValueError:
|
||||
if operator == '==':
|
||||
return left == right
|
||||
elif operator == '!=':
|
||||
return left != right
|
||||
elif operator in ('>', '<', '>=', '<='):
|
||||
return False
|
||||
|
||||
return False
|
||||
@@ -1,39 +0,0 @@
|
||||
"""Coze Bot Node - call Coze API bot
|
||||
|
||||
Node metadata is loaded from: ../../templates/metadata/nodes/coze_bot.yaml
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any
|
||||
|
||||
from langbot_plugin.api.entities.builtin.workflow.entities import ExecutionContext
|
||||
from ..node import WorkflowNode, workflow_node
|
||||
|
||||
@workflow_node('coze_bot')
|
||||
class CozeBotNode(WorkflowNode):
|
||||
"""Coze bot node - call Coze API bot"""
|
||||
|
||||
category = 'integration'
|
||||
|
||||
async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
|
||||
api_key = self.get_config('api_key', '')
|
||||
bot_id = self.get_config('bot_id', '')
|
||||
api_base = self.get_config('api_base', 'https://api.coze.cn')
|
||||
query = inputs.get('query', '')
|
||||
conversation_id = inputs.get('conversation_id')
|
||||
|
||||
# Safe API key truncation
|
||||
masked_key = f'{api_key[:4]}...{api_key[-4:]}' if len(api_key) > 8 else '***' if api_key else ''
|
||||
|
||||
return {
|
||||
'answer': '',
|
||||
'conversation_id': conversation_id,
|
||||
'success': False,
|
||||
'_debug': {
|
||||
'api_key': masked_key,
|
||||
'bot_id': bot_id,
|
||||
'api_base': api_base,
|
||||
'query': query,
|
||||
},
|
||||
}
|
||||
@@ -1,26 +0,0 @@
|
||||
"""Cron Trigger Node - triggers workflow on schedule
|
||||
|
||||
Node metadata is loaded from: ../../templates/metadata/nodes/cron_trigger.yaml
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any
|
||||
|
||||
from langbot_plugin.api.entities.builtin.workflow.entities import ExecutionContext
|
||||
from ..node import WorkflowNode, workflow_node
|
||||
|
||||
@workflow_node('cron_trigger')
|
||||
class CronTriggerNode(WorkflowNode):
|
||||
"""Cron trigger node - triggers workflow on schedule"""
|
||||
|
||||
category = 'trigger'
|
||||
|
||||
async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
|
||||
from datetime import datetime
|
||||
|
||||
return {
|
||||
'timestamp': datetime.now().isoformat(),
|
||||
'schedule': self.get_config('cron', ''),
|
||||
'context': context.trigger_data,
|
||||
}
|
||||
@@ -1,68 +0,0 @@
|
||||
"""Data Transform Node - transform data using templates or JSONPath
|
||||
|
||||
Node metadata is loaded from: ../../templates/metadata/nodes/data_transform.yaml
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any
|
||||
|
||||
from langbot_plugin.api.entities.builtin.workflow.entities import ExecutionContext
|
||||
from ..node import WorkflowNode, workflow_node
|
||||
from ..safe_eval import safe_eval_with_vars
|
||||
|
||||
@workflow_node('data_transform')
|
||||
class DataTransformNode(WorkflowNode):
|
||||
"""Data transform node - transform data using templates or JSONPath"""
|
||||
|
||||
category = 'process'
|
||||
|
||||
async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
|
||||
data = inputs.get('data')
|
||||
transform_type = self.get_config('transform_type', 'template')
|
||||
|
||||
if transform_type == 'template':
|
||||
template = self.get_config('template', '')
|
||||
result = self._apply_template(template, data, context)
|
||||
elif transform_type == 'jsonpath':
|
||||
expression = self.get_config('expression', '$')
|
||||
result = self._apply_jsonpath(expression, data)
|
||||
elif transform_type == 'expression':
|
||||
expression = self.get_config('expression', '')
|
||||
result = self._evaluate_expression(expression, data, context)
|
||||
else:
|
||||
result = data
|
||||
|
||||
return {'result': result}
|
||||
|
||||
def _apply_template(self, template: str, data: Any, context: ExecutionContext) -> str:
|
||||
result = template
|
||||
if isinstance(data, dict):
|
||||
for key, value in data.items():
|
||||
result = result.replace(f'{{{{data.{key}}}}}', str(value))
|
||||
for key, value in context.variables.items():
|
||||
result = result.replace(f'{{{{variables.{key}}}}}', str(value))
|
||||
return result
|
||||
|
||||
def _apply_jsonpath(self, expression: str, data: Any) -> Any:
|
||||
if expression == '$':
|
||||
return data
|
||||
if expression.startswith('$.'):
|
||||
parts = expression[2:].split('.')
|
||||
result = data
|
||||
for part in parts:
|
||||
if isinstance(result, dict):
|
||||
result = result.get(part)
|
||||
elif isinstance(result, list) and part.isdigit():
|
||||
result = result[int(part)]
|
||||
else:
|
||||
return None
|
||||
return result
|
||||
return data
|
||||
|
||||
def _evaluate_expression(self, expression: str, data: Any, context: ExecutionContext) -> Any:
|
||||
local_vars = {'data': data, 'variables': context.variables}
|
||||
try:
|
||||
return safe_eval_with_vars(expression, local_vars)
|
||||
except Exception:
|
||||
return None
|
||||
@@ -1,38 +0,0 @@
|
||||
"""Database Query Node - execute database queries
|
||||
|
||||
Node metadata is loaded from: ../../templates/metadata/nodes/database_query.yaml
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any
|
||||
|
||||
from langbot_plugin.api.entities.builtin.workflow.entities import ExecutionContext
|
||||
from ..node import WorkflowNode, workflow_node
|
||||
|
||||
@workflow_node('database_query')
|
||||
class DatabaseQueryNode(WorkflowNode):
|
||||
"""Database query node - execute database queries"""
|
||||
|
||||
category = 'integration'
|
||||
|
||||
async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
|
||||
connection_type = self.get_config('connection_type', 'postgresql')
|
||||
query = self.get_config('query', '')
|
||||
query_type = self.get_config('query_type', 'select')
|
||||
timeout = self.get_config('timeout', 30)
|
||||
|
||||
parameters = inputs.get('parameters', {})
|
||||
|
||||
return {
|
||||
'results': [],
|
||||
'row_count': 0,
|
||||
'success': False,
|
||||
'_debug': {
|
||||
'connection_type': connection_type,
|
||||
'query': query,
|
||||
'query_type': query_type,
|
||||
'timeout': timeout,
|
||||
'parameters': parameters,
|
||||
},
|
||||
}
|
||||
@@ -1,37 +0,0 @@
|
||||
"""Dify Knowledge Query Node - query Dify knowledge base
|
||||
|
||||
Node metadata is loaded from: ../../templates/metadata/nodes/dify_knowledge_query.yaml
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any
|
||||
|
||||
from langbot_plugin.api.entities.builtin.workflow.entities import ExecutionContext
|
||||
from ..node import WorkflowNode, workflow_node
|
||||
|
||||
@workflow_node('dify_knowledge_query')
|
||||
class DifyKnowledgeQueryNode(WorkflowNode):
|
||||
"""Dify knowledge base query node - query Dify knowledge base"""
|
||||
|
||||
category = 'integration'
|
||||
|
||||
async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
|
||||
base_url = self.get_config('base_url', 'https://api.dify.ai/v1')
|
||||
api_key = self.get_config('api_key', '')
|
||||
dataset_id = self.get_config('dataset_id', '')
|
||||
query = inputs.get('query', '')
|
||||
|
||||
# Safe API key truncation
|
||||
masked_key = f'{api_key[:4]}...{api_key[-4:]}' if len(api_key) > 8 else '***' if api_key else ''
|
||||
|
||||
return {
|
||||
'results': [],
|
||||
'success': False,
|
||||
'_debug': {
|
||||
'base_url': base_url,
|
||||
'api_key': masked_key,
|
||||
'dataset_id': dataset_id,
|
||||
'query': query,
|
||||
},
|
||||
}
|
||||
@@ -1,39 +0,0 @@
|
||||
"""Dify Workflow Node - call Dify service API
|
||||
|
||||
Node metadata is loaded from: ../../templates/metadata/nodes/dify_workflow.yaml
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any
|
||||
|
||||
from langbot_plugin.api.entities.builtin.workflow.entities import ExecutionContext
|
||||
from ..node import WorkflowNode, workflow_node
|
||||
|
||||
@workflow_node('dify_workflow')
|
||||
class DifyWorkflowNode(WorkflowNode):
|
||||
"""Dify workflow node - call Dify service API"""
|
||||
|
||||
category = 'integration'
|
||||
|
||||
async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
|
||||
base_url = self.get_config('base_url', 'https://api.dify.ai/v1')
|
||||
api_key = self.get_config('api_key', '')
|
||||
app_type = self.get_config('app_type', 'chat')
|
||||
query = inputs.get('query', '')
|
||||
conversation_id = inputs.get('conversation_id')
|
||||
|
||||
# Safe API key truncation
|
||||
masked_key = f'{api_key[:4]}...{api_key[-4:]}' if len(api_key) > 8 else '***' if api_key else ''
|
||||
|
||||
return {
|
||||
'answer': '',
|
||||
'conversation_id': conversation_id,
|
||||
'success': False,
|
||||
'_debug': {
|
||||
'base_url': base_url,
|
||||
'api_key': masked_key,
|
||||
'app_type': app_type,
|
||||
'query': query,
|
||||
},
|
||||
}
|
||||
@@ -1,33 +0,0 @@
|
||||
"""End Node - marks the end of workflow execution
|
||||
|
||||
Node metadata is loaded from: ../../templates/metadata/nodes/end.yaml
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any
|
||||
|
||||
from langbot_plugin.api.entities.builtin.workflow.entities import ExecutionContext
|
||||
from ..node import WorkflowNode, workflow_node
|
||||
|
||||
@workflow_node('end')
|
||||
class EndNode(WorkflowNode):
|
||||
"""End node - marks the end of workflow execution"""
|
||||
|
||||
category = 'control'
|
||||
|
||||
async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
|
||||
result = inputs.get('result')
|
||||
output_format = self.get_config('output_format', 'passthrough')
|
||||
|
||||
if output_format == 'text':
|
||||
return {'output': str(result)}
|
||||
elif output_format == 'json':
|
||||
import json
|
||||
|
||||
try:
|
||||
return {'output': json.dumps(result, ensure_ascii=False)}
|
||||
except Exception:
|
||||
return {'output': str(result)}
|
||||
else:
|
||||
return {'output': result}
|
||||
@@ -1,28 +0,0 @@
|
||||
"""Event Trigger Node - triggers workflow on system events
|
||||
|
||||
Node metadata is loaded from: ../../templates/metadata/nodes/event_trigger.yaml
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from datetime import datetime
|
||||
from typing import Any
|
||||
|
||||
from langbot_plugin.api.entities.builtin.workflow.entities import ExecutionContext
|
||||
from ..node import WorkflowNode, workflow_node
|
||||
|
||||
@workflow_node('event_trigger')
|
||||
class EventTriggerNode(WorkflowNode):
|
||||
"""Event trigger node - triggers workflow on system events"""
|
||||
|
||||
category = 'trigger'
|
||||
|
||||
async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
|
||||
# Safe access to trigger_data which may be None
|
||||
trigger_data = context.trigger_data or {}
|
||||
|
||||
return {
|
||||
'event_type': trigger_data.get('event_type', ''),
|
||||
'event_data': trigger_data.get('event_data', {}),
|
||||
'timestamp': trigger_data.get('timestamp', datetime.now().isoformat()),
|
||||
}
|
||||
@@ -1,152 +0,0 @@
|
||||
"""HTTP Request Node - make HTTP API calls
|
||||
|
||||
Node metadata is loaded from: ../../templates/metadata/nodes/http_request.yaml
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import ipaddress
|
||||
import logging
|
||||
from typing import Any
|
||||
from urllib.parse import urlparse
|
||||
|
||||
from langbot_plugin.api.entities.builtin.workflow.entities import ExecutionContext
|
||||
from ..node import WorkflowNode, workflow_node
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# 内网地址黑名单
|
||||
_PRIVATE_NETWORKS = [
|
||||
ipaddress.ip_network('10.0.0.0/8'),
|
||||
ipaddress.ip_network('172.16.0.0/12'),
|
||||
ipaddress.ip_network('192.168.0.0/16'),
|
||||
ipaddress.ip_network('127.0.0.0/8'),
|
||||
ipaddress.ip_network('169.254.0.0/16'),
|
||||
ipaddress.ip_network('0.0.0.0/8'),
|
||||
ipaddress.ip_network('::1/128'),
|
||||
ipaddress.ip_network('fc00::/7'),
|
||||
ipaddress.ip_network('fe80::/10'),
|
||||
]
|
||||
|
||||
# 危险协议
|
||||
_DANGEROUS_SCHEMES = {'file', 'gopher', 'dict', 'ftp', 'telnet'}
|
||||
|
||||
|
||||
def _is_safe_url(url: str) -> tuple[bool, str]:
|
||||
"""检查 URL 是否安全（非内网地址）"""
|
||||
try:
|
||||
parsed = urlparse(url)
|
||||
except Exception as e:
|
||||
return False, f'Invalid URL: {e}'
|
||||
|
||||
# 检查协议
|
||||
scheme = parsed.scheme.lower()
|
||||
if scheme in _DANGEROUS_SCHEMES:
|
||||
return False, f'Dangerous scheme: {scheme}'
|
||||
|
||||
if scheme not in ('http', 'https'):
|
||||
return False, f'Unsupported scheme: {scheme}'
|
||||
|
||||
# 检查主机名
|
||||
hostname = parsed.hostname
|
||||
if not hostname:
|
||||
return False, 'Missing hostname'
|
||||
|
||||
# 检查是否是危险主机名
|
||||
dangerous_hosts = {'localhost', '0.0.0.0', '127.0.0.1', '::1'}
|
||||
if hostname.lower() in dangerous_hosts:
|
||||
return False, f'Dangerous hostname: {hostname}'
|
||||
|
||||
# 解析 IP 地址并检查是否在私有网络
|
||||
try:
|
||||
ip = ipaddress.ip_address(hostname)
|
||||
for network in _PRIVATE_NETWORKS:
|
||||
if ip in network:
|
||||
return False, f'Private network address: {ip}'
|
||||
except ValueError:
|
||||
# 不是 IP 地址，尝试 DNS 解析检查
|
||||
# 这里可以添加 DNS 解析检查，但为了避免复杂性，暂时跳过
|
||||
pass
|
||||
|
||||
return True, ''
|
||||
|
||||
|
||||
@workflow_node('http_request')
|
||||
class HTTPRequestNode(WorkflowNode):
|
||||
"""HTTP request node - make HTTP API calls"""
|
||||
|
||||
category = 'action'
|
||||
|
||||
async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
|
||||
import aiohttp
|
||||
|
||||
url = self.get_config('url', '')
|
||||
method = self.get_config('method', 'GET').upper()
|
||||
timeout = self.get_config('timeout', 30)
|
||||
content_type = self.get_config('content_type', 'application/json')
|
||||
allow_redirects = self.get_config('allow_redirects', False) # 默认禁用重定向
|
||||
|
||||
# 限制超时时间
|
||||
timeout = min(max(timeout, 1), 120)
|
||||
|
||||
if not url:
|
||||
return {'response': None, 'status_code': 0, 'headers': {}, 'error': 'No URL provided'}
|
||||
|
||||
# 安全检查 URL
|
||||
is_safe, error_msg = _is_safe_url(url)
|
||||
if not is_safe:
|
||||
logger.warning('Unsafe URL blocked: %s - %s', url, error_msg)
|
||||
return {'response': None, 'status_code': 0, 'headers': {}, 'error': f'Unsafe URL: {error_msg}'}
|
||||
|
||||
# 验证 HTTP 方法
|
||||
allowed_methods = {'GET', 'POST', 'PUT', 'DELETE', 'PATCH', 'HEAD', 'OPTIONS'}
|
||||
if method not in allowed_methods:
|
||||
return {'response': None, 'status_code': 0, 'headers': {}, 'error': f'Invalid method: {method}'}
|
||||
|
||||
# 创建 headers 副本，避免修改输入
|
||||
headers = dict(inputs.get('headers', {}))
|
||||
headers['Content-Type'] = content_type
|
||||
|
||||
auth_type = self.get_config('auth_type', 'none')
|
||||
auth_config = self.get_config('auth_config', {})
|
||||
|
||||
if auth_type == 'bearer':
|
||||
headers['Authorization'] = f'Bearer {auth_config.get("token", "")}'
|
||||
elif auth_type == 'api_key':
|
||||
header_name = auth_config.get('header', 'X-API-Key')
|
||||
headers[header_name] = auth_config.get('key', '')
|
||||
|
||||
body = inputs.get('body')
|
||||
|
||||
logger.info('HTTP %s %s (timeout=%s)', method, url, timeout)
|
||||
|
||||
try:
|
||||
async with aiohttp.ClientSession() as session:
|
||||
async with session.request(
|
||||
method=method,
|
||||
url=url,
|
||||
json=body if content_type == 'application/json' else None,
|
||||
data=body if content_type != 'application/json' else None,
|
||||
headers=headers,
|
||||
timeout=aiohttp.ClientTimeout(total=timeout),
|
||||
allow_redirects=allow_redirects,
|
||||
) as response:
|
||||
try:
|
||||
response_data = await response.json()
|
||||
except Exception:
|
||||
response_data = await response.text()
|
||||
|
||||
logger.info('HTTP %s %s -> %d', method, url, response.status)
|
||||
|
||||
return {
|
||||
'response': response_data,
|
||||
'status_code': response.status,
|
||||
'headers': dict(response.headers),
|
||||
'error': None,
|
||||
}
|
||||
except aiohttp.ClientError as e:
|
||||
logger.error('HTTP request failed: %s', e)
|
||||
return {'response': None, 'status_code': 0, 'headers': {}, 'error': f'HTTP error: {e}'}
|
||||
except Exception as e:
|
||||
logger.error('HTTP request unexpected error: %s', e)
|
||||
return {'response': None, 'status_code': 0, 'headers': {}, 'error': f'Unexpected error: {e}'}
|
||||
@@ -1,32 +0,0 @@
|
||||
"""Iterator Node - Dify-style iterator for processing array items"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any
|
||||
|
||||
from langbot_plugin.api.entities.builtin.workflow.entities import ExecutionContext
|
||||
from ..node import WorkflowNode, workflow_node
|
||||
|
||||
@workflow_node('iterator')
|
||||
class IteratorNode(WorkflowNode):
|
||||
"""Iterator node - iterate over array items one by one"""
|
||||
|
||||
category = 'control'
|
||||
|
||||
async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
|
||||
items = inputs.get('items', [])
|
||||
if not isinstance(items, list):
|
||||
items = [items] if items else []
|
||||
|
||||
max_iterations = self.get_config('max_iterations', 1000)
|
||||
items = items[:max_iterations]
|
||||
|
||||
return {
|
||||
'item': items[0] if items else None,
|
||||
'index': 0,
|
||||
'is_first': True,
|
||||
'is_last': len(items) <= 1,
|
||||
'results': [],
|
||||
'completed': len(items) == 0,
|
||||
'_items': items,
|
||||
}
|
||||
@@ -1,21 +0,0 @@
|
||||
"""Knowledge Retrieval Node - search in knowledge base
|
||||
|
||||
Node metadata is loaded from: ../../templates/metadata/nodes/knowledge_retrieval.yaml
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any
|
||||
|
||||
from langbot_plugin.api.entities.builtin.workflow.entities import ExecutionContext
|
||||
from ..node import WorkflowNode, workflow_node
|
||||
|
||||
@workflow_node('knowledge_retrieval')
|
||||
class KnowledgeRetrievalNode(WorkflowNode):
|
||||
"""Knowledge retrieval node - search in knowledge base"""
|
||||
|
||||
category = 'process'
|
||||
|
||||
async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
|
||||
query = inputs.get('query', '')
|
||||
return {'documents': [], 'citations': [], 'context': f'[Knowledge base search for: {query}]'}
|
||||
@@ -1,37 +0,0 @@
|
||||
"""Langflow Flow Node - call Langflow API
|
||||
|
||||
Node metadata is loaded from: ../../templates/metadata/nodes/langflow_flow.yaml
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any
|
||||
|
||||
from langbot_plugin.api.entities.builtin.workflow.entities import ExecutionContext
|
||||
from ..node import WorkflowNode, workflow_node
|
||||
|
||||
@workflow_node('langflow_flow')
|
||||
class LangflowFlowNode(WorkflowNode):
|
||||
"""Langflow flow node - call Langflow API"""
|
||||
|
||||
category = 'integration'
|
||||
|
||||
async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
|
||||
base_url = self.get_config('base_url', 'http://localhost:7860')
|
||||
api_key = self.get_config('api_key', '')
|
||||
flow_id = self.get_config('flow_id', '')
|
||||
input_value = inputs.get('input_value', '')
|
||||
|
||||
# Safe API key truncation
|
||||
masked_key = f'{api_key[:4]}...{api_key[-4:]}' if len(api_key) > 8 else '***' if api_key else ''
|
||||
|
||||
return {
|
||||
'result': None,
|
||||
'success': False,
|
||||
'_debug': {
|
||||
'base_url': base_url,
|
||||
'api_key': masked_key,
|
||||
'flow_id': flow_id,
|
||||
'input_value': input_value,
|
||||
},
|
||||
}
|
||||
@@ -1,829 +0,0 @@
|
||||
"""LLM Call Node - invoke large language model with Agent capabilities.
|
||||
|
||||
Supports:
|
||||
- Primary model with fallback models
|
||||
- Knowledge base retrieval with reranking
|
||||
- Max round context control
|
||||
- Streaming output
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
import re
|
||||
import time
|
||||
from typing import Any, AsyncGenerator
|
||||
|
||||
import langbot_plugin.api.entities.builtin.provider.message as provider_message
|
||||
import langbot_plugin.api.entities.builtin.rag.context as rag_context
|
||||
|
||||
from langbot_plugin.api.entities.builtin.workflow.entities import ExecutionContext
|
||||
from ..node import WorkflowNode, workflow_node
|
||||
from .. import monitoring_helper
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Pre-compiled regex patterns for CoT content removal (performance optimization)
|
||||
_THINK_PATTERNS = [
|
||||
re.compile(r'<think>.*?</think>', re.DOTALL | re.IGNORECASE),
|
||||
re.compile(r'<thought>.*?</thought>', re.DOTALL | re.IGNORECASE),
|
||||
re.compile(r'<reasoning>.*?</reasoning>', re.DOTALL | re.IGNORECASE),
|
||||
re.compile(r'<\u601d\u8003>.*?</\u601d\u8003>', re.DOTALL | re.IGNORECASE),
|
||||
re.compile(r'<\u63a8\u7406>.*?</\u63a8\u7406>', re.DOTALL | re.IGNORECASE),
|
||||
]
|
||||
|
||||
# Template variable regex
|
||||
_TEMPLATE_VAR_RE = re.compile(r'\{\{([^}]+)\}\}')
|
||||
|
||||
|
||||
@workflow_node('llm_call')
|
||||
class LLMCallNode(WorkflowNode):
|
||||
"""LLM call node - invoke large language model"""
|
||||
|
||||
category = 'process'
|
||||
|
||||
def _resolve_template(self, template: str, inputs: dict[str, Any], context: ExecutionContext) -> str:
|
||||
"""Resolve {{variable}} placeholders in a template string."""
|
||||
if not template:
|
||||
return ''
|
||||
|
||||
unresolved_vars = []
|
||||
|
||||
def replacer(match: re.Match) -> str:
|
||||
expr = match.group(1).strip()
|
||||
# Try inputs first
|
||||
if expr in inputs:
|
||||
return str(inputs[expr])
|
||||
# Try context variables
|
||||
if expr.startswith('variables.'):
|
||||
var_name = expr[len('variables.'):]
|
||||
return str(context.variables.get(var_name, ''))
|
||||
# Try message context
|
||||
if expr.startswith('message.') and context.message_context:
|
||||
attr = expr[len('message.'):]
|
||||
return str(getattr(context.message_context, attr, ''))
|
||||
unresolved_vars.append(expr)
|
||||
return match.group(0) # leave unresolved
|
||||
|
||||
result = _TEMPLATE_VAR_RE.sub(replacer, template)
|
||||
|
||||
# Log warning for unresolved variables
|
||||
if unresolved_vars:
|
||||
logger.warning(
|
||||
f'LLM call node {self.node_id}: unresolved template variables: {unresolved_vars}'
|
||||
)
|
||||
|
||||
return result
|
||||
|
||||
def _remove_think_content(self, text: str) -> str:
|
||||
"""Remove CoT (Chain of Thought) thinking content from response."""
|
||||
if not text:
|
||||
return text
|
||||
|
||||
result = text
|
||||
for pattern in _THINK_PATTERNS:
|
||||
result = pattern.sub('', result)
|
||||
|
||||
return result.strip()
|
||||
|
||||
def _apply_content_filter(self, text: str) -> tuple[str, bool, str]:
|
||||
"""Apply content safety filter to text.
|
||||
|
||||
Returns:
|
||||
(filtered_text, is_blocked, user_notice)
|
||||
"""
|
||||
if not text or not self.ap:
|
||||
return text, False, ''
|
||||
|
||||
# Check if content filter is enabled
|
||||
safety_config = getattr(self.ap, 'pipeline_cfg', None)
|
||||
if not safety_config:
|
||||
return text, False, ''
|
||||
|
||||
# Check sensitive words
|
||||
sensitive_words = []
|
||||
try:
|
||||
if hasattr(self.ap, 'sensitive_meta') and hasattr(self.ap.sensitive_meta, 'data'):
|
||||
sensitive_words = self.ap.sensitive_meta.data.get('words', [])
|
||||
except Exception as e:
|
||||
logger.warning("Failed to load sensitive words from sensitive_meta: %s", e)
|
||||
sensitive_words = []
|
||||
|
||||
if not sensitive_words:
|
||||
return text, False, ''
|
||||
|
||||
found = False
|
||||
filtered_text = text
|
||||
for word in sensitive_words:
|
||||
try:
|
||||
matches = re.findall(word, filtered_text, re.IGNORECASE)
|
||||
if matches:
|
||||
found = True
|
||||
mask_word = ''
|
||||
mask = '*'
|
||||
try:
|
||||
if hasattr(self.ap, 'sensitive_meta') and hasattr(self.ap.sensitive_meta, 'data'):
|
||||
mask_word = self.ap.sensitive_meta.data.get('mask_word', '')
|
||||
mask = self.ap.sensitive_meta.data.get('mask', '*')
|
||||
except Exception as e:
|
||||
# Keep default mask settings when sensitive metadata is unavailable or malformed.
|
||||
logger.debug(
|
||||
f'LLM call node {self.node_id}: failed to read sensitive mask config, using defaults: {e}'
|
||||
)
|
||||
|
||||
for m in matches:
|
||||
if mask_word:
|
||||
filtered_text = filtered_text.replace(m, mask_word)
|
||||
else:
|
||||
filtered_text = filtered_text.replace(m, mask * len(m))
|
||||
except re.error:
|
||||
# Invalid regex pattern, skip
|
||||
continue
|
||||
|
||||
if found:
|
||||
return filtered_text, False, '消息中存在不合适的内容, 请修改'
|
||||
|
||||
return text, False, ''
|
||||
|
||||
# RAG combined prompt template (same as localagent.py)
|
||||
RAG_COMBINED_PROMPT_TEMPLATE = """
|
||||
The following are relevant context entries retrieved from the knowledge base.
|
||||
Please use them to answer the user's message.
|
||||
Respond in the same language as the user's input.
|
||||
|
||||
<context>
|
||||
{rag_context}
|
||||
</context>
|
||||
|
||||
<user_message>
|
||||
{user_message}
|
||||
</user_message>
|
||||
"""
|
||||
|
||||
def _build_system_prompt_with_format(self, base_prompt: str, output_format: str, json_schema: str) -> str:
|
||||
"""Build system prompt with output format instructions."""
|
||||
prompt = base_prompt
|
||||
|
||||
if output_format == 'json':
|
||||
prompt += '\n\nPlease respond in valid JSON format.'
|
||||
if json_schema:
|
||||
prompt += f'\nFollow this JSON schema:\n{json_schema}'
|
||||
elif output_format == 'markdown':
|
||||
prompt += '\n\nPlease respond in Markdown format.'
|
||||
|
||||
return prompt
|
||||
|
||||
def _build_messages_from_prompt_array(
|
||||
self,
|
||||
prompt_array: list[dict],
|
||||
inputs: dict[str, Any],
|
||||
context: ExecutionContext,
|
||||
output_format: str,
|
||||
json_schema: str,
|
||||
) -> list[provider_message.Message]:
|
||||
"""Build messages list from prompt array (same format as pipeline).
|
||||
|
||||
Each item in prompt_array is {role: str, content: str}.
|
||||
Resolves template variables in content.
|
||||
"""
|
||||
messages: list[provider_message.Message] = []
|
||||
|
||||
for item in prompt_array:
|
||||
role = item.get('role', 'user')
|
||||
content = item.get('content', '')
|
||||
|
||||
# Resolve template variables in content
|
||||
resolved_content = self._resolve_template(content, inputs, context)
|
||||
|
||||
# Apply format instructions to system prompt
|
||||
if role == 'system':
|
||||
resolved_content = self._build_system_prompt_with_format(
|
||||
resolved_content, output_format, json_schema
|
||||
)
|
||||
|
||||
messages.append(provider_message.Message(role=role, content=resolved_content))
|
||||
|
||||
return messages
|
||||
|
||||
async def _get_model_candidates(self, model_uuid: str, fallback_models: list) -> list:
|
||||
"""Build ordered list of models to try: primary model + fallback models."""
|
||||
candidates = []
|
||||
|
||||
# Primary model
|
||||
if model_uuid:
|
||||
try:
|
||||
primary = await self.ap.model_mgr.get_model_by_uuid(model_uuid)
|
||||
candidates.append(primary)
|
||||
except ValueError:
|
||||
logger.warning(f'[LLM:{self.node_id}] Primary model {model_uuid} not found')
|
||||
|
||||
# Fallback models
|
||||
for fb_uuid in fallback_models:
|
||||
try:
|
||||
fb_model = await self.ap.model_mgr.get_model_by_uuid(fb_uuid)
|
||||
candidates.append(fb_model)
|
||||
except ValueError:
|
||||
logger.warning(f'[LLM:{self.node_id}] Fallback model {fb_uuid} not found, skipping')
|
||||
|
||||
return candidates
|
||||
|
||||
async def _invoke_with_fallback(
|
||||
self,
|
||||
candidates: list,
|
||||
messages: list,
|
||||
funcs: list | None,
|
||||
extra_args: dict,
|
||||
) -> tuple[Any, Any]:
|
||||
"""Try non-streaming invocation with sequential fallback. Returns (message, model_used)."""
|
||||
last_error = None
|
||||
for model in candidates:
|
||||
try:
|
||||
msg = await model.provider.invoke_llm(
|
||||
query=None,
|
||||
model=model,
|
||||
messages=messages,
|
||||
funcs=funcs if model.model_entity.abilities.__contains__('func_call') else [],
|
||||
extra_args=extra_args,
|
||||
)
|
||||
return msg, model
|
||||
except Exception as e:
|
||||
last_error = e
|
||||
logger.warning(f'[LLM:{self.node_id}] Model {model.model_entity.name} failed: {e}, trying next...')
|
||||
raise last_error or RuntimeError('No model candidates available')
|
||||
|
||||
async def _retrieve_knowledge(
|
||||
self,
|
||||
user_message_text: str,
|
||||
knowledge_bases: list[str],
|
||||
rerank_model_uuid: str,
|
||||
rerank_top_k: int,
|
||||
) -> str:
|
||||
"""Retrieve from knowledge bases and optionally rerank results.
|
||||
|
||||
Returns the enhanced user message text with RAG context, or original text if no results.
|
||||
"""
|
||||
if not knowledge_bases or not user_message_text:
|
||||
return user_message_text
|
||||
|
||||
all_results: list[rag_context.RetrievalResultEntry] = []
|
||||
|
||||
# Retrieve from each knowledge base
|
||||
for kb_uuid in knowledge_bases:
|
||||
try:
|
||||
kb = await self.ap.rag_mgr.get_knowledge_base_by_uuid(kb_uuid)
|
||||
if not kb:
|
||||
logger.warning(f'[LLM:{self.node_id}] Knowledge base {kb_uuid} not found, skipping')
|
||||
continue
|
||||
|
||||
result = await kb.retrieve(user_message_text, settings={})
|
||||
if result:
|
||||
all_results.extend(result)
|
||||
except Exception as e:
|
||||
logger.warning(f'[LLM:{self.node_id}] Failed to retrieve from KB {kb_uuid}: {e}')
|
||||
|
||||
# Rerank step: re-score results using a rerank model if configured
|
||||
if all_results and rerank_model_uuid:
|
||||
try:
|
||||
rerank_model = await self.ap.model_mgr.get_rerank_model_by_uuid(rerank_model_uuid)
|
||||
|
||||
doc_texts = []
|
||||
for entry in all_results:
|
||||
text = ' '.join(c.text for c in entry.content if c.type == 'text' and c.text)
|
||||
doc_texts.append(text)
|
||||
|
||||
doc_texts_capped = doc_texts[:64] # Cap for reranker input
|
||||
scores = await rerank_model.provider.invoke_rerank(
|
||||
model=rerank_model,
|
||||
query=user_message_text,
|
||||
documents=doc_texts_capped,
|
||||
)
|
||||
|
||||
scored = sorted(scores, key=lambda x: x.get('relevance_score', 0), reverse=True)
|
||||
top_indices = [s['index'] for s in scored[:rerank_top_k] if s['index'] < len(all_results)]
|
||||
all_results = [all_results[i] for i in top_indices]
|
||||
|
||||
logger.info(
|
||||
f'[LLM:{self.node_id}] Rerank complete: {len(doc_texts)} docs -> top {len(all_results)} kept (top_k={rerank_top_k})'
|
||||
)
|
||||
except ValueError:
|
||||
logger.warning(f'[LLM:{self.node_id}] Rerank model {rerank_model_uuid} not found, skipping rerank')
|
||||
except Exception as e:
|
||||
logger.warning(f'[LLM:{self.node_id}] Rerank failed, using original order: {e}')
|
||||
|
||||
# Build RAG context text
|
||||
if all_results:
|
||||
texts = []
|
||||
idx = 1
|
||||
for entry in all_results:
|
||||
for content in entry.content:
|
||||
if content.type == 'text' and content.text is not None:
|
||||
texts.append(f'[{idx}] {content.text}')
|
||||
idx += 1
|
||||
rag_context_text = '\n\n'.join(texts)
|
||||
return self.RAG_COMBINED_PROMPT_TEMPLATE.format(
|
||||
rag_context=rag_context_text,
|
||||
user_message=user_message_text,
|
||||
)
|
||||
|
||||
return user_message_text
|
||||
|
||||
def _build_messages_with_history(
|
||||
self,
|
||||
system_prompt: str,
|
||||
user_message_text: str,
|
||||
context: ExecutionContext,
|
||||
max_round: int,
|
||||
) -> list[provider_message.Message]:
|
||||
"""Build messages list with conversation history up to max_round."""
|
||||
messages: list[provider_message.Message] = []
|
||||
|
||||
# Add system prompt
|
||||
if system_prompt:
|
||||
messages.append(provider_message.Message(role='system', content=system_prompt))
|
||||
|
||||
# Get conversation history from context
|
||||
conversation_history = context.variables.get('_conversation_history', [])
|
||||
|
||||
# Apply max_round limit (each round = 1 user + 1 assistant message)
|
||||
if max_round > 0 and conversation_history:
|
||||
# Keep only the last max_round * 2 messages (user + assistant pairs)
|
||||
max_messages = max_round * 2
|
||||
if len(conversation_history) > max_messages:
|
||||
conversation_history = conversation_history[-max_messages:]
|
||||
|
||||
# Add conversation history
|
||||
for msg in conversation_history:
|
||||
if isinstance(msg, dict):
|
||||
role = msg.get('role', 'user')
|
||||
content = msg.get('content', '')
|
||||
messages.append(provider_message.Message(role=role, content=content))
|
||||
elif hasattr(msg, 'role') and hasattr(msg, 'content'):
|
||||
messages.append(provider_message.Message(role=msg.role, content=msg.content))
|
||||
|
||||
# Add current user message
|
||||
messages.append(provider_message.Message(role='user', content=user_message_text))
|
||||
|
||||
return messages
|
||||
|
||||
def _save_to_conversation_history(
|
||||
self,
|
||||
context: ExecutionContext,
|
||||
user_message_text: str,
|
||||
response_text: str,
|
||||
max_round: int,
|
||||
) -> None:
|
||||
"""Save the exchange to conversation history."""
|
||||
if max_round <= 0:
|
||||
return
|
||||
|
||||
history = context.variables.get('_conversation_history', [])
|
||||
history.append({'role': 'user', 'content': user_message_text})
|
||||
history.append({'role': 'assistant', 'content': response_text})
|
||||
|
||||
# Enforce max_round limit
|
||||
max_messages = max_round * 2
|
||||
if len(history) > max_messages:
|
||||
history = history[-max_messages:]
|
||||
|
||||
context.variables['_conversation_history'] = history
|
||||
|
||||
async def execute(self, inputs: dict[str, Any], context: ExecutionContext) -> dict[str, Any]:
|
||||
# Support both new model_config format and legacy model + fallback_models format
|
||||
model_config = self.get_config('model_config', None)
|
||||
if model_config and isinstance(model_config, dict):
|
||||
# New format: {primary: uuid, fallbacks: [uuid1, uuid2, ...]}
|
||||
model_uuid = model_config.get('primary', '')
|
||||
fallback_models = model_config.get('fallbacks', [])
|
||||
else:
|
||||
# Legacy format: separate model and fallback_models
|
||||
model_uuid = self.get_config('model', '')
|
||||
fallback_models = self.get_config('fallback_models', [])
|
||||
|
||||
if not model_uuid:
|
||||
raise ValueError('No model configured for LLM call node')
|
||||
|
||||
if not self.ap:
|
||||
raise RuntimeError('Application instance not available - cannot call LLM')
|
||||
|
||||
# Get error handling config
|
||||
exception_handling = self.get_config('exception_handling', 'show-error')
|
||||
failure_hint = self.get_config('failure_hint', 'Request failed.')
|
||||
track_function_calls = self.get_config('track_function_calls', False)
|
||||
|
||||
# Get output format and json_schema config
|
||||
output_format = self.get_config('output_format', 'text')
|
||||
json_schema = self.get_config('json_schema', '')
|
||||
|
||||
# Agent config: knowledge bases, rerank, max_round
|
||||
# (fallback_models already resolved above from model_config or fallback_models)
|
||||
knowledge_bases = self.get_config('knowledge_bases', [])
|
||||
rerank_model = self.get_config('rerank_model', '')
|
||||
rerank_top_k = self.get_config('rerank_top_k', 5)
|
||||
max_round = self.get_config('max_round', 10)
|
||||
|
||||
# Resolve prompts - support both new prompt array format and legacy format
|
||||
prompt_array = self.get_config('prompt')
|
||||
user_prompt = '' # Initialize for later use in _save_to_conversation_history
|
||||
|
||||
if prompt_array and isinstance(prompt_array, list):
|
||||
# New format: prompt array like pipeline
|
||||
messages = self._build_messages_from_prompt_array(
|
||||
prompt_array, inputs, context, output_format, json_schema
|
||||
)
|
||||
|
||||
# Get user input text for knowledge retrieval
|
||||
user_input = inputs.get('input', '')
|
||||
|
||||
# Knowledge retrieval: enhance user input with RAG context
|
||||
user_input = await self._retrieve_knowledge(
|
||||
user_message_text=user_input,
|
||||
knowledge_bases=knowledge_bases,
|
||||
rerank_model_uuid=rerank_model,
|
||||
rerank_top_k=rerank_top_k,
|
||||
)
|
||||
|
||||
# Track user_prompt for conversation history
|
||||
user_prompt = user_input
|
||||
|
||||
# Add user input as last message
|
||||
if user_input:
|
||||
messages.append(provider_message.Message(role='user', content=user_input))
|
||||
|
||||
# Apply max_round to conversation history
|
||||
conversation_history = context.variables.get('_conversation_history', [])
|
||||
if max_round > 0 and conversation_history:
|
||||
max_messages = max_round * 2
|
||||
if len(conversation_history) > max_messages:
|
||||
conversation_history = conversation_history[-max_messages:]
|
||||
# Insert conversation history before user input
|
||||
history_messages = []
|
||||
for msg in conversation_history:
|
||||
if isinstance(msg, dict):
|
||||
role = msg.get('role', 'user')
|
||||
content = msg.get('content', '')
|
||||
history_messages.append(provider_message.Message(role=role, content=content))
|
||||
elif hasattr(msg, 'role') and hasattr(msg, 'content'):
|
||||
history_messages.append(provider_message.Message(role=msg.role, content=msg.content))
|
||||
# Insert history before user message
|
||||
if history_messages and len(messages) > 0:
|
||||
messages = messages[:-1] + history_messages + [messages[-1]]
|
||||
else:
|
||||
# Legacy format: separate system_prompt and user_prompt_template
|
||||
system_prompt = self._resolve_template(self.get_config('system_prompt') or '', inputs, context)
|
||||
user_prompt_template = self.get_config('user_prompt_template')
|
||||
if user_prompt_template is None:
|
||||
user_prompt_template = '{{input}}'
|
||||
user_prompt = self._resolve_template(user_prompt_template, inputs, context)
|
||||
|
||||
# Build system prompt with format instructions
|
||||
system_prompt = self._build_system_prompt_with_format(system_prompt, output_format, json_schema)
|
||||
|
||||
# Knowledge retrieval: enhance user prompt with RAG context
|
||||
user_prompt = await self._retrieve_knowledge(
|
||||
user_message_text=user_prompt,
|
||||
knowledge_bases=knowledge_bases,
|
||||
rerank_model_uuid=rerank_model,
|
||||
rerank_top_k=rerank_top_k,
|
||||
)
|
||||
|
||||
# Build messages with conversation history
|
||||
messages = self._build_messages_with_history(
|
||||
system_prompt=system_prompt,
|
||||
user_message_text=user_prompt,
|
||||
context=context,
|
||||
max_round=max_round,
|
||||
)
|
||||
|
||||
# Get model candidates (primary + fallbacks)
|
||||
candidates = await self._get_model_candidates(model_uuid, fallback_models)
|
||||
if not candidates:
|
||||
raise ValueError('No valid model candidates available')
|
||||
|
||||
# Build extra args from config
|
||||
extra_args: dict[str, Any] = {}
|
||||
temperature = self.get_config('temperature')
|
||||
if temperature is not None:
|
||||
extra_args['temperature'] = float(temperature)
|
||||
max_tokens = self.get_config('max_tokens', 0)
|
||||
if max_tokens and int(max_tokens) > 0:
|
||||
extra_args['max_tokens'] = int(max_tokens)
|
||||
|
||||
# Track start time for duration calculation
|
||||
self._llm_start_time = time.time()
|
||||
|
||||
# Invoke LLM with fallback
|
||||
try:
|
||||
result_message, used_model = await self._invoke_with_fallback(
|
||||
candidates=candidates,
|
||||
messages=messages,
|
||||
funcs=None,
|
||||
extra_args=extra_args,
|
||||
)
|
||||
except Exception as e:
|
||||
logger.warning(f'[LLM:{self.node_id}] LLM call failed: {e}')
|
||||
|
||||
# Handle based on exception handling strategy
|
||||
if exception_handling == 'show-error':
|
||||
raise
|
||||
elif exception_handling == 'show-hint':
|
||||
return {
|
||||
'response': failure_hint,
|
||||
'usage': {
|
||||
'prompt_tokens': 0,
|
||||
'completion_tokens': 0,
|
||||
'total_tokens': 0,
|
||||
},
|
||||
'error': str(e),
|
||||
'error_hint_shown': True,
|
||||
}
|
||||
else: # hide
|
||||
return {
|
||||
'response': '',
|
||||
'usage': {
|
||||
'prompt_tokens': 0,
|
||||
'completion_tokens': 0,
|
||||
'total_tokens': 0,
|
||||
},
|
||||
'error': str(e),
|
||||
}
|
||||
|
||||
# Extract response text
|
||||
response_text = ''
|
||||
if isinstance(result_message.content, str):
|
||||
response_text = result_message.content
|
||||
elif isinstance(result_message.content, list):
|
||||
for elem in result_message.content:
|
||||
if hasattr(elem, 'text') and elem.text:
|
||||
response_text += elem.text
|
||||
elif isinstance(elem, str):
|
||||
response_text += elem
|
||||
|
||||
# Remove CoT content (always remove to avoid leaking internal reasoning)
|
||||
response_text = self._remove_think_content(response_text)
|
||||
|
||||
# Initialize usage default
|
||||
usage = {
|
||||
'prompt_tokens': 0,
|
||||
'completion_tokens': 0,
|
||||
'total_tokens': 0,
|
||||
}
|
||||
|
||||
# Apply content safety filter
|
||||
response_text, is_blocked, filter_notice = self._apply_content_filter(response_text)
|
||||
if is_blocked:
|
||||
logger.warning(f'[LLM:{self.node_id}] Response blocked by content filter: {filter_notice}')
|
||||
return {
|
||||
'response': filter_notice,
|
||||
'usage': usage,
|
||||
'blocked_by_filter': True,
|
||||
}
|
||||
|
||||
# Extract usage info
|
||||
if hasattr(result_message, 'usage') and result_message.usage:
|
||||
u = result_message.usage
|
||||
# Handle both object and dict usage
|
||||
if isinstance(u, dict):
|
||||
usage = {
|
||||
'prompt_tokens': u.get('prompt_tokens', 0) or 0,
|
||||
'completion_tokens': u.get('completion_tokens', 0) or 0,
|
||||
'total_tokens': u.get('total_tokens', 0) or 0,
|
||||
}
|
||||
else:
|
||||
usage = {
|
||||
'prompt_tokens': getattr(u, 'prompt_tokens', 0) or 0,
|
||||
'completion_tokens': getattr(u, 'completion_tokens', 0) or 0,
|
||||
'total_tokens': getattr(u, 'total_tokens', 0) or 0,
|
||||
}
|
||||
elif hasattr(result_message, 'token_usage') and result_message.token_usage:
|
||||
u = result_message.token_usage
|
||||
# Handle both object and dict token_usage
|
||||
if isinstance(u, dict):
|
||||
usage = {
|
||||
'prompt_tokens': u.get('prompt_tokens', 0) or 0,
|
||||
'completion_tokens': u.get('completion_tokens', 0) or 0,
|
||||
'total_tokens': u.get('total_tokens', 0) or 0,
|
||||
}
|
||||
else:
|
||||
usage = {
|
||||
'prompt_tokens': getattr(u, 'prompt_tokens', 0) or 0,
|
||||
'completion_tokens': getattr(u, 'completion_tokens', 0) or 0,
|
||||
'total_tokens': getattr(u, 'total_tokens', 0) or 0,
|
||||
}
|
||||
|
||||
# Log successful response (matching Pipeline's cut_str behavior)
|
||||
def _cut_str(s: str) -> str:
|
||||
s0 = s.split('\n')[0]
|
||||
if len(s0) > 20 or '\n' in s:
|
||||
s0 = s0[:20] + '...'
|
||||
return s0
|
||||
logger.info(f'[LLM:{self.node_id}] Response: {_cut_str(response_text)}')
|
||||
|
||||
# Record LLM call log only (response log is redundant)
|
||||
try:
|
||||
if self.ap and context.query:
|
||||
workflow_id = context.workflow_id or ''
|
||||
workflow_name = context.variables.get('_workflow_name', 'Workflow')
|
||||
bot_name = context.variables.get('_bot_name', 'Workflow')
|
||||
node_name = self.get_config('name', self.node_id)
|
||||
model_name = used_model.model_entity.name if used_model else 'unknown'
|
||||
|
||||
# Calculate duration
|
||||
duration_ms = 0
|
||||
if hasattr(self, '_llm_start_time'):
|
||||
duration_ms = int((time.time() - self._llm_start_time) * 1000)
|
||||
|
||||
# Get message_id for LLM call association
|
||||
message_id = context.variables.get('_monitoring_message_id')
|
||||
|
||||
# Record LLM call log with message_id association
|
||||
await monitoring_helper.WorkflowMonitoringHelper.record_llm_call_log(
|
||||
ap=self.ap,
|
||||
query=context.query,
|
||||
workflow_id=workflow_id,
|
||||
workflow_name=workflow_name,
|
||||
node_name=node_name,
|
||||
model_name=model_name,
|
||||
input_tokens=usage.get('prompt_tokens', 0),
|
||||
output_tokens=usage.get('completion_tokens', 0),
|
||||
duration_ms=duration_ms,
|
||||
status='success',
|
||||
bot_name=bot_name,
|
||||
context_vars=context.variables,
|
||||
message_id=message_id,
|
||||
)
|
||||
except Exception as e:
|
||||
logger.warning(f'[LLM:{self.node_id}] Failed to record LLM logs: {e}')
|
||||
|
||||
# Save to conversation history
|
||||
self._save_to_conversation_history(
|
||||
context=context,
|
||||
user_message_text=user_prompt,
|
||||
response_text=response_text,
|
||||
max_round=max_round,
|
||||
)
|
||||
|
||||
# Build result
|
||||
result: dict[str, Any] = {
|
||||
'response': response_text,
|
||||
'usage': usage,
|
||||
'model_used': used_model.model_entity.name if used_model else None,
|
||||
'model_uuid': used_model.model_entity.uuid if used_model else None,
|
||||
}
|
||||
|
||||
# Parse JSON output if format is json
|
||||
if output_format == 'json' and response_text:
|
||||
try:
|
||||
result['parsed'] = json.loads(response_text)
|
||||
except json.JSONDecodeError as e:
|
||||
logger.warning(f'[LLM:{self.node_id}] Failed to parse JSON: {e}')
|
||||
result['parsed'] = None
|
||||
result['parse_error'] = str(e)
|
||||
|
||||
# Add function call tracking info if configured
|
||||
if track_function_calls:
|
||||
result['function_calls'] = []
|
||||
|
||||
return result
|
||||
|
||||
async def execute_stream(
|
||||
self, inputs: dict[str, Any], context: ExecutionContext
|
||||
) -> AsyncGenerator[str, None]:
|
||||
"""Execute the LLM call with streaming output.
|
||||
|
||||
Yields chunks of response text as they arrive.
|
||||
Falls back to non-streaming if streaming is not available.
|
||||
"""
|
||||
# Support both new model_config format and legacy model + fallback_models format
|
||||
model_config = self.get_config('model_config', None)
|
||||
if model_config and isinstance(model_config, dict):
|
||||
model_uuid = model_config.get('primary', '')
|
||||
else:
|
||||
model_uuid = self.get_config('model', '')
|
||||
|
||||
if not model_uuid:
|
||||
raise ValueError('No model configured for LLM call node')
|
||||
|
||||
if not self.ap:
|
||||
raise RuntimeError('Application instance not available - cannot call LLM')
|
||||
|
||||
exception_handling = self.get_config('exception_handling', 'show-error')
|
||||
failure_hint = self.get_config('failure_hint', 'Request failed.')
|
||||
|
||||
# Resolve prompts - support both new prompt array format and legacy format
|
||||
prompt_array = self.get_config('prompt')
|
||||
if prompt_array and isinstance(prompt_array, list):
|
||||
# New format: prompt array like pipeline
|
||||
messages = self._build_messages_from_prompt_array(
|
||||
prompt_array, inputs, context, 'text', '' # No format instructions for streaming
|
||||
)
|
||||
|
||||
# Add user input
|
||||
user_input = inputs.get('input', '')
|
||||
if user_input:
|
||||
messages.append(provider_message.Message(role='user', content=user_input))
|
||||
else:
|
||||
# Legacy format
|
||||
system_prompt = self._resolve_template(self.get_config('system_prompt') or '', inputs, context)
|
||||
user_prompt_template = self.get_config('user_prompt_template')
|
||||
if user_prompt_template is None:
|
||||
user_prompt_template = '{{input}}'
|
||||
user_prompt = self._resolve_template(user_prompt_template, inputs, context)
|
||||
|
||||
# Build messages
|
||||
messages = []
|
||||
if system_prompt:
|
||||
messages.append(provider_message.Message(role='system', content=system_prompt))
|
||||
messages.append(provider_message.Message(role='user', content=user_prompt))
|
||||
|
||||
# Get model
|
||||
runtime_model = await self.ap.model_mgr.get_model_by_uuid(model_uuid)
|
||||
|
||||
# Build extra args
|
||||
extra_args: dict[str, Any] = {}
|
||||
temperature = self.get_config('temperature')
|
||||
if temperature is not None:
|
||||
extra_args['temperature'] = float(temperature)
|
||||
max_tokens = self.get_config('max_tokens', 0)
|
||||
if max_tokens and int(max_tokens) > 0:
|
||||
extra_args['max_tokens'] = int(max_tokens)
|
||||
|
||||
logger.info(f'[LLM:{self.node_id}] Streaming model {model_uuid}')
|
||||
|
||||
try:
|
||||
# Try streaming first
|
||||
stream = runtime_model.provider.invoke_llm_stream(
|
||||
query=None,
|
||||
model=runtime_model,
|
||||
messages=messages,
|
||||
funcs=None,
|
||||
extra_args=extra_args,
|
||||
)
|
||||
|
||||
full_response = ''
|
||||
in_think_block = False
|
||||
async for chunk in stream:
|
||||
chunk_text = ''
|
||||
if hasattr(chunk, 'content'):
|
||||
if isinstance(chunk.content, str):
|
||||
chunk_text = chunk.content
|
||||
elif isinstance(chunk.content, list):
|
||||
for elem in chunk.content:
|
||||
if hasattr(elem, 'text') and elem.text:
|
||||
chunk_text += elem.text
|
||||
elif isinstance(elem, str):
|
||||
chunk_text += elem
|
||||
|
||||
if chunk_text:
|
||||
# Filter <think> blocks in streaming mode
|
||||
if '<think>' in chunk_text or '<thought>' in chunk_text:
|
||||
in_think_block = True
|
||||
if in_think_block:
|
||||
if '</think>' in chunk_text or '</thought>' in chunk_text:
|
||||
in_think_block = False
|
||||
chunk_text = chunk_text.split('</think>')[-1].split('</thought>')[-1]
|
||||
else:
|
||||
chunk_text = ''
|
||||
|
||||
if chunk_text:
|
||||
full_response += chunk_text
|
||||
yield chunk_text
|
||||
|
||||
# Store in context for downstream nodes
|
||||
context.variables['_last_llm_response'] = full_response
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f'[LLM:{self.node_id}] Streaming failed, falling back - {e}')
|
||||
# Fallback to non-streaming
|
||||
try:
|
||||
result_message = await runtime_model.provider.invoke_llm(
|
||||
query=None,
|
||||
model=runtime_model,
|
||||
messages=messages,
|
||||
funcs=None,
|
||||
extra_args=extra_args,
|
||||
)
|
||||
response_text = self._extract_response_text(result_message)
|
||||
# Always remove <think> content in fallback
|
||||
response_text = self._remove_think_content(response_text)
|
||||
yield response_text
|
||||
context.variables['_last_llm_response'] = response_text
|
||||
except Exception as e2:
|
||||
logger.error(f'[LLM:{self.node_id}] Fallback also failed - {e2}')
|
||||
if exception_handling == 'show-hint':
|
||||
yield failure_hint
|
||||
elif exception_handling != 'hide':
|
||||
raise
|
||||
|
||||
def _extract_response_text(self, result_message: provider_message.Message) -> str:
|
||||
"""Extract response text from LLM result message."""
|
||||
response_text = ''
|
||||
if isinstance(result_message.content, str):
|
||||
response_text = result_message.content
|
||||
elif isinstance(result_message.content, list):
|
||||
for elem in result_message.content:
|
||||
if hasattr(elem, 'text') and elem.text:
|
||||
response_text += elem.text
|
||||
elif isinstance(elem, str):
|
||||
response_text += elem
|
||||
return response_text
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user