mirror of
https://github.com/langbot-app/LangBot.git
synced 2026-06-02 20:14:36 +00:00
Compare commits
12 Commits
feat/agent
...
feat/multi
| Author | SHA1 | Date | |
|---|---|---|---|
|
d568bbedc2 | ||
|
|
d78a4fdea4 | ||
|
|
894709d577 | ||
|
|
6823069103 | ||
|
|
699545a196 | ||
|
|
f0061817ea | ||
|
|
688202e7d1 | ||
|
|
d46b762d03 | ||
|
|
0963fd5443 | ||
|
|
6471770737 | ||
|
|
314b7d15bb | ||
|
|
c758908745 |
@@ -47,6 +47,8 @@ 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
|
||||
|
||||
@@ -47,6 +47,8 @@ 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
|
||||
|
||||
@@ -46,6 +46,8 @@ 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
|
||||
|
||||
@@ -46,6 +46,8 @@ 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
|
||||
|
||||
@@ -46,6 +46,8 @@ 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
|
||||
|
||||
@@ -46,6 +46,8 @@ 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
|
||||
|
||||
@@ -46,6 +46,8 @@ 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
|
||||
|
||||
@@ -48,6 +48,8 @@ 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
|
||||
|
||||
@@ -46,6 +46,8 @@ 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
|
||||
|
||||
@@ -1,335 +0,0 @@
|
||||
# Agent-owned Context 协议设计
|
||||
|
||||
本文档描述插件化 AgentRunner 场景下的上下文边界。结论先行：LangBot 不应成为最终 agentic context manager；LangBot 应提供 context substrate，AgentRunner 或其背后的 agent runtime 自己决定如何管理历史、压缩、召回和 KV cache。
|
||||
|
||||
## 当前状态
|
||||
|
||||
**当前分支已落地**：
|
||||
|
||||
- ✅ `AgentRunContext` — event-first context 模型
|
||||
- ✅ `ContextAccess` — cursor、inline policy、available APIs
|
||||
- ✅ `AgentRunAPIProxy.history` — page/search API
|
||||
- ✅ `AgentRunAPIProxy.events` — get/page API
|
||||
- ✅ `AgentRunAPIProxy.artifacts` — metadata/read_range API
|
||||
- ✅ `AgentRunAPIProxy.state` — get/set/delete API
|
||||
- ✅ EventLog / Transcript / ArtifactStore — host 事实源
|
||||
- ✅ PersistentStateStore — 持久化状态存储
|
||||
- ✅ `max-round` / host-side history window 已从 LangBot Host/Pipeline 语义中移除；如某 runner 仍需要类似参数，应由该 runner 自己解释配置
|
||||
- ✅ 外部 harness context projection 已用 Claude Code runner 做 MVP 验证：context 文件、skill 投影、MCP 配置和 host-owned resume state
|
||||
|
||||
## 1. 设计原则
|
||||
|
||||
### 1.1 Agent 拥有上下文策略
|
||||
|
||||
不同 runner 背后的 runtime 差异很大：
|
||||
|
||||
- 官方 local-agent 可能依赖 LangBot 的模型、工具、知识库和存储。
|
||||
- Claude Code SDK / Codex 类 runtime 可能有自己的 session、transcript、tool loop 和上下文压缩。
|
||||
- Pi Agent SDK 或外部 agent 平台可能只需要当前事件和一个外部 conversation key。
|
||||
|
||||
因此 LangBot 不应强行决定最终传给模型的历史窗口。Host 只提供：
|
||||
|
||||
- 当前事件的完整结构化信息。
|
||||
- 稳定身份和会话引用。
|
||||
- 可授权读取的 history / event / artifact / state API。
|
||||
- 可投影给外部 harness 的 scoped context、MCP、skill 和 resource refs。
|
||||
- payload hard cap 和权限 guardrail。
|
||||
|
||||
### 1.2 不再把 `max-round` 作为目标设计
|
||||
|
||||
`max-round` 这类历史窗口参数不应继续作为 AgentRunner 协议或 Pipeline adapter 的核心概念。
|
||||
|
||||
如果某个 runner 仍需要“最多读取多少轮历史”这样的策略参数，应由该 runner 在自己的 manifest/config schema 中声明，并作为 binding config 存到 `ctx.config` / `runner_config`。Host 只提供 history pull API、cursor、hard cap 和权限边界；runner 自己决定是否读取、读取多少、如何截断和压缩。
|
||||
|
||||
当前 official local-agent 方向是通过 Host history API 拉取 transcript，并由 runner 自己管理模型上下文。它不依赖 Pipeline adapter 下发历史窗口。
|
||||
|
||||
新协议不应该问“LangBot 每轮裁几轮历史给 agent”，而应该问：
|
||||
|
||||
- 这类 runner 是否自管 context？
|
||||
- 事件到来时 host 应 inline 哪些最小信息？
|
||||
- agent 需要更多上下文时通过什么 API 拉取？
|
||||
- host 如何保证安全、可审计和可分页？
|
||||
|
||||
### 1.3 Host 保存事实源，Agent 管理 working context
|
||||
|
||||
三类数据要分开：
|
||||
|
||||
- `EventLog`: Host 保存原始事件、工具调用、投递结果、错误和系统事件。
|
||||
- `Transcript`: Host 从 EventLog 投影出的对话视图，用于 UI、审计和按需历史读取。
|
||||
- `Working context`: Agent 本轮实际送进模型或 runtime 的上下文，由 AgentRunner 决定。
|
||||
|
||||
LangBot 不再提供 host-side bootstrap window。简单 runner 如果需要历史窗口，应在 runner 内部通过 Host history API 拉取并裁剪。
|
||||
|
||||
## 2. Event 到来时传什么
|
||||
|
||||
默认 `AgentRunContext` 应尽量小且稳定：
|
||||
|
||||
```python
|
||||
class AgentRunContext(BaseModel):
|
||||
run_id: str
|
||||
trigger: AgentTrigger
|
||||
event: AgentEventContext
|
||||
conversation: ConversationContext | None
|
||||
actor: ActorContext | None
|
||||
subject: SubjectContext | None
|
||||
input: AgentInput
|
||||
delivery: DeliveryContext
|
||||
resources: AgentResources
|
||||
context: ContextAccess
|
||||
state: AgentRunState
|
||||
runtime: AgentRuntimeContext
|
||||
config: dict[str, Any]
|
||||
```
|
||||
|
||||
默认规则：
|
||||
|
||||
- Host MUST NOT inline full history by default.
|
||||
- Host SHOULD inline only current event / input and context handles.
|
||||
- Runner owns working-context assembly.
|
||||
- Runner MAY use Host history / event / artifact / state / storage APIs when authorized.
|
||||
- Official runners MUST consume Host infrastructure through the same public APIs as third-party runners.
|
||||
|
||||
### 2.1 必须 inline 的内容
|
||||
|
||||
每次 run 必须 inline：
|
||||
|
||||
- 当前 event 的稳定类型、id、时间、source。
|
||||
- 当前输入文本和结构化内容。
|
||||
- 附件 / 文件 / 图片的 metadata 和 artifact ref。
|
||||
- actor、subject、conversation、thread、bot、workspace。
|
||||
- delivery 能力，例如是否支持 streaming、reply target、平台限制。
|
||||
- 已授权资源列表。
|
||||
- context cursors 和可用 API 能力。
|
||||
- runner binding config。
|
||||
|
||||
这些是 agent 决定下一步需要的最低信息。
|
||||
|
||||
### 2.2 默认不 inline 的内容
|
||||
|
||||
默认不要 inline：
|
||||
|
||||
- 完整历史消息。
|
||||
- 大文件全文。
|
||||
- 大工具结果。
|
||||
- 全量知识库内容。
|
||||
- 平台原始 payload 大对象。
|
||||
- 每轮重新生成的大段 summary。
|
||||
|
||||
这些会破坏跨进程序列化成本、泄露范围、KV cache 稳定性，也会迫使 host 替 agent 做 context 策略。
|
||||
|
||||
### 2.3 不提供 Host Bootstrap Window
|
||||
|
||||
`AgentRunContext.bootstrap` 可以作为协议里的可选扩展字段保留，但 LangBot Host 默认不填历史窗口，也不通过 Pipeline 配置决定窗口大小。
|
||||
|
||||
如果 runner 需要类似 `recent_tail` 的策略，它应在自己的 manifest/config schema 中声明参数，并在 runner 内部通过 `history_page` / `history_search` 读取、裁剪和压缩历史。Host 只负责权限、分页、hard cap 和事实源。
|
||||
|
||||
## 3. ContextAccess
|
||||
|
||||
`ContextAccess` 是 host 交给 agent 的上下文读取入口描述：
|
||||
|
||||
```python
|
||||
class ContextAccess(BaseModel):
|
||||
conversation_id: str | None
|
||||
thread_id: str | None
|
||||
latest_cursor: str | None
|
||||
event_seq: int | None
|
||||
transcript_seq: int | None
|
||||
has_history_before: bool
|
||||
inline_policy: InlineContextPolicy
|
||||
available_apis: ContextAPICapabilities
|
||||
```
|
||||
|
||||
它告诉 agent：
|
||||
|
||||
- 当前事件位于哪条 conversation / thread。
|
||||
- 若需要更多历史，从哪个 cursor 开始拉。
|
||||
- host inline 了什么，没 inline 什么。
|
||||
- 当前 run 有哪些 context API 权限。
|
||||
|
||||
## 4. Agent 如何获取更多上下文
|
||||
|
||||
所有 API 都必须走 `AgentRunAPIProxy`，并由 host 用 `run_id` 校验。
|
||||
|
||||
### 4.1 History API
|
||||
|
||||
```python
|
||||
await api.history.page(
|
||||
conversation_id=ctx.context.conversation_id,
|
||||
before_cursor=ctx.context.latest_cursor,
|
||||
limit=50,
|
||||
direction="backward",
|
||||
include_artifacts=False,
|
||||
)
|
||||
```
|
||||
|
||||
返回：
|
||||
|
||||
```python
|
||||
class HistoryPage(BaseModel):
|
||||
items: list[TranscriptItem]
|
||||
next_cursor: str | None
|
||||
prev_cursor: str | None
|
||||
has_more: bool
|
||||
```
|
||||
|
||||
约束：
|
||||
|
||||
- `limit` 有 host hard cap。
|
||||
- 默认只能读当前 conversation / thread。
|
||||
- 跨会话读取必须有 manifest permission + binding policy。
|
||||
- 返回 artifact ref，不默认返回大文件内容。
|
||||
|
||||
### 4.2 Search API
|
||||
|
||||
```python
|
||||
await api.history.search(
|
||||
query="用户之前提到的数据库连接信息",
|
||||
filters={
|
||||
"conversation_id": ctx.context.conversation_id,
|
||||
"event_types": ["message.received"],
|
||||
},
|
||||
top_k=10,
|
||||
)
|
||||
```
|
||||
|
||||
Search 可以先用数据库全文索引，后续再接 embedding recall。它是 host 提供的检索能力，不等于 agent 的长期记忆策略。
|
||||
|
||||
### 4.3 Event API
|
||||
|
||||
```python
|
||||
await api.events.get(event_id)
|
||||
await api.events.page(before_cursor=..., limit=...)
|
||||
```
|
||||
|
||||
Event API 用于读取非消息事件、工具事件、系统事件。Agent 不应把所有事件都当成 user/assistant message。
|
||||
|
||||
### 4.4 Artifact API
|
||||
|
||||
```python
|
||||
await api.artifacts.metadata(artifact_id)
|
||||
await api.artifacts.read_range(artifact_id, offset=0, length=65536)
|
||||
await api.artifacts.open_stream(artifact_id)
|
||||
```
|
||||
|
||||
约束：
|
||||
|
||||
- 校验 artifact 所属 conversation / run / binding。
|
||||
- 校验 MIME、大小、过期时间和权限。
|
||||
- 大文件按 range/stream 读取。
|
||||
- 工具大结果也应 artifact 化。
|
||||
|
||||
### 4.5 State API
|
||||
|
||||
```python
|
||||
await api.state.get(scope="conversation", key="external.session_id")
|
||||
await api.state.set(scope="conversation", key="summary.checkpoint", value=...)
|
||||
```
|
||||
|
||||
State 是可选寄宿能力。自管 runtime 可以完全不用；依附 LangBot 的官方 runner 可以使用。
|
||||
|
||||
### 4.6 External harness context projection
|
||||
|
||||
Claude Code、Codex、Kimi Code 这类 runtime 通常已经有自己的 session、工具 loop、MCP 加载、上下文压缩和工作目录。LangBot 不应把这类 runner 强行改造成“host prompt assembler”，而应提供可审计的事件和资源投影。
|
||||
|
||||
推荐 projection 形态：
|
||||
|
||||
- `agent-context.json`：结构化 JSON，包含 `run_id`、`event`、`actor`、`subject`、`input`、`delivery`、`resources`、`context`、`state`、`runtime`。
|
||||
- `LANGBOT_CONTEXT.md`：人类可读摘要，用于 code-agent harness 快速理解当前 IM 事件。
|
||||
- `resources`：只包含本次 run 授权后的模型、工具、知识库、artifact、state/storage 句柄，不暴露 Host 内部私有对象。
|
||||
- `skills`：Host 或 binding 把已授权 skill 投影为目标 harness 可读目录，例如 Claude Code 的 `.claude/skills/<name>/SKILL.md`。
|
||||
- `MCP config`：Host 或 binding 提供 scoped MCP 配置，runner adapter 转成目标 harness 的配置文件或 CLI 参数。
|
||||
- `state pointers`：外部 session id、working directory、checkpoint 等小型 JSON 状态通过 Host state API 保存，例如 `external.session_id`、`external.working_directory`。
|
||||
|
||||
当前 Claude Code runner MVP 使用 schema `langbot.agent_runner.external_harness_context.v1`，并已通过 WebUI Debug Chat 验证 context 文件、skill 文件、MCP config 和 resume state 的基本链路。
|
||||
|
||||
这类 projection 是“把 LangBot 事实源和授权资源交给 harness”，不是“由 LangBot 决定最终模型上下文”。外部 harness 可以继续使用自己的 transcript、工具权限和压缩策略。
|
||||
|
||||
## 5. Runner manifest 中的上下文声明
|
||||
|
||||
建议增加：
|
||||
|
||||
```yaml
|
||||
context:
|
||||
ownership: self_managed | host_bootstrap | hybrid
|
||||
bootstrap: none | current_event | recent_tail | summary_tail
|
||||
max_inline_events: 0
|
||||
max_inline_bytes: 0
|
||||
supports_history_pull: true
|
||||
supports_history_search: true
|
||||
supports_artifact_pull: true
|
||||
owns_compaction: true
|
||||
wants_static_context_refs: true
|
||||
```
|
||||
|
||||
语义：
|
||||
|
||||
- `self_managed`: Host 不主动 inline 历史，只提供 event 和 handles。
|
||||
- `host_bootstrap`: Host 为简单 runner inline 一个小窗口。
|
||||
- `hybrid`: Host inline summary/tail，runner 仍可按需拉更多。
|
||||
- `owns_compaction`: runner 负责压缩，host 不做语义摘要。
|
||||
- `wants_static_context_refs`: host 用 ref/hash 描述静态内容，减少重复 payload。
|
||||
|
||||
## 6. KV cache 友好的上下文管理
|
||||
|
||||
如果目标是支持 Claude Code SDK、Codex、Pi Agent SDK 等 runtime，必须避免每轮由 LangBot 重组大块 prompt。
|
||||
|
||||
建议：
|
||||
|
||||
- 稳定 session key：`workspace/bot/binding/runner/conversation/thread`。
|
||||
- 静态内容使用 `ref + version/hash`：system prompt、resource manifest、tool schema、platform policy。
|
||||
- 每轮只传 delta：当前 event、artifact refs、少量 runtime metadata。
|
||||
- 历史 append-only：不要每轮改写同一段 history 文本。
|
||||
- Summary checkpoint 稳定：只有压缩发生时产生新 checkpoint，不要每轮微调。
|
||||
- 大文件和工具结果 artifact 化。
|
||||
- Tool/context API schema 稳定，数据通过 API 拉取，而不是塞入 prompt。
|
||||
- 对自管 runtime，优先让它复用自身 session/cache，而不是强制 LangBot 每轮重放 transcript。
|
||||
|
||||
## 7. Host guardrail
|
||||
|
||||
Agent 自管 context 不代表无限制访问。LangBot 仍必须控制：
|
||||
|
||||
- 每次 run 的 active `run_id`。
|
||||
- runner identity。
|
||||
- 当前 binding 的 resource policy。
|
||||
- conversation / actor / subject scope。
|
||||
- page size、artifact read size、API rate limit。
|
||||
- 跨会话读取权限。
|
||||
- 数据脱敏和敏感变量过滤。
|
||||
- 审计日志。
|
||||
|
||||
Host 不负责“最佳上下文策略”，但负责“不越权、不爆内存、不不可审计”。
|
||||
|
||||
## 8. 官方 runner 与业务编排边界
|
||||
|
||||
官方 runner 插件可以选择把状态寄宿在 LangBot，但它们必须和第三方 runner 一样通过公开 Host APIs 消费这些能力。
|
||||
|
||||
LangBot core 不应内置官方 agent 的业务流程：
|
||||
|
||||
- 不内置 prompt 组装策略。
|
||||
- 不内置 tool loop。
|
||||
- 不内置 RAG 编排策略。
|
||||
- 不内置 summary / compaction 策略。
|
||||
- 不内置“local-agent 专用”的状态字段。
|
||||
|
||||
官方 local-agent 应作为“依附 LangBot 基础设施的复杂 runner 参考实现”存在：
|
||||
|
||||
- transcript / history 通过 `api.history.page()` 或 `api.history.search()` 读取。
|
||||
- summary、checkpoint、外部 session id、用户偏好通过 `api.state` 或 `api.storage` 保存。
|
||||
- 图片、文件、工具大结果通过 `api.artifacts` 读取。
|
||||
- 模型、工具、知识库通过 `api.models`、`api.tools`、`api.knowledge` 调用。
|
||||
|
||||
这样 LangBot 保持为通用 agent host，不变成内置 agent 框架。
|
||||
|
||||
## 9. 当前实现需要调整
|
||||
|
||||
**已完成（当前分支）**：
|
||||
|
||||
- ✅ `max-round` 不再是协议字段，也不再是 Host / Pipeline 通用语义
|
||||
- ✅ 新 runner 默认不收到历史窗口
|
||||
- ✅ `AgentRunContext` 增加 `context` / cursor / access capabilities
|
||||
- ✅ `AgentRunAPIProxy` 增加 history / events / artifacts / state API
|
||||
- ✅ Host 增加持久 EventLog / Transcript / ArtifactStore / PersistentStateStore
|
||||
- ✅ `run_from_query()` 委托到 event-first `run(event, binding)`
|
||||
- ✅ Claude Code external harness smoke：context JSON / Markdown、skill、MCP config、`external.session_id` / `external.working_directory`
|
||||
|
||||
这样 LangBot 既能服务依附 host 基础设施的官方 runner，也能服务自带 memory/session/cache 的外部 agent runtime。
|
||||
@@ -1,237 +0,0 @@
|
||||
# Event Based Agent 预留设计
|
||||
|
||||
> **注意**：本文档是 future design note，不是当前分支实现范围。
|
||||
>
|
||||
> EventGateway、EventRouter、Event subscription/notification 由其他分支实现。
|
||||
> 本分支只预留 event-first 入口和 envelope/binding models。
|
||||
> 2026-05-29 的 local-agent / Claude Code runner smoke 只验证本分支的 `run(event, binding)` 调度边界，不表示 EBA 分支已经完成联调。
|
||||
|
||||
本文档描述未来 EBA 接入时，事件如何进入 LangBot、如何触发 AgentRunner，以及如何复用插件化 agent 基础设施。
|
||||
|
||||
本阶段不实现完整 EventBus / EventRouter / Platform API。本阶段要做的是把协议边界设计对，避免当前消息入口继续绑死 Pipeline 和用户文本消息。
|
||||
|
||||
## 1. 设计目标
|
||||
|
||||
- 消息、撤回、入群、好友申请、定时任务、API 调用都能抽象为 host event。
|
||||
- EventRouter 可以根据 event type、bot、workspace、conversation、actor、subject 解析 AgentBinding。
|
||||
- AgentRunner 通过同一套 orchestrator 被调用。
|
||||
- 非消息事件不伪造成用户文本消息。
|
||||
- 平台动作执行通过显式 capability / permission / result type 预留，不混入普通文本回复。
|
||||
|
||||
## 2. 事件不是消息
|
||||
|
||||
`message.received` 只是事件的一种。协议不应假设：
|
||||
|
||||
- 一定有用户文本。
|
||||
- 一定有 conversation history。
|
||||
- 一定要返回一条聊天消息。
|
||||
- actor 一定等于 sender。
|
||||
- subject 一定等于当前消息。
|
||||
|
||||
例如：
|
||||
|
||||
| event_type | actor | subject | input |
|
||||
| --- | --- | --- | --- |
|
||||
| `message.received` | 发消息的人 | 当前消息 | 文本、图片、文件等 |
|
||||
| `message.recalled` | 撤回操作者，未知时为系统 | 被撤回消息 | 通常为空 |
|
||||
| `group.member_joined` | 新成员或邀请人 | 群/成员关系 | 通常为空 |
|
||||
| `friend.request_received` | 申请人 | 好友申请 | 验证消息或申请理由 |
|
||||
| `schedule.triggered` | 系统 | 定时任务 | 任务 payload |
|
||||
| `api.invoked` | API caller | API request | request payload |
|
||||
|
||||
## 3. Event Envelope
|
||||
|
||||
建议事件 envelope：
|
||||
|
||||
```python
|
||||
class AgentEventEnvelope(BaseModel):
|
||||
event_id: str
|
||||
event_type: str
|
||||
event_time: int | None
|
||||
source: EventSource
|
||||
workspace_id: str | None
|
||||
bot_id: str | None
|
||||
conversation_id: str | None
|
||||
thread_id: str | None
|
||||
actor: ActorRef | None
|
||||
subject: SubjectRef | None
|
||||
input: AgentInput
|
||||
delivery: DeliveryContext
|
||||
raw_ref: RawEventRef | None
|
||||
metadata: dict[str, Any] = {}
|
||||
```
|
||||
|
||||
顶层字段使用 LangBot 稳定协议名。平台原始事件名和原始 payload 放到 `metadata` 或 `raw_ref`，不直接成为 runner 的稳定依赖。
|
||||
|
||||
## 4. Event Source
|
||||
|
||||
事件来源可以包括：
|
||||
|
||||
- `platform_adapter`: 飞书、QQ、微信、Telegram 等 IM 平台。
|
||||
- `webui`: Debug Chat、控制台操作。
|
||||
- `http_api`: 外部系统调用 LangBot。
|
||||
- `scheduler`: 定时任务。
|
||||
- `system`: runtime、plugin、maintenance 事件。
|
||||
|
||||
同一个 event source 可以产生多个 event type。EventRouter 不应该写死平台 adapter 的类名。
|
||||
|
||||
## 5. Event Binding
|
||||
|
||||
EBA 中，AgentBinding 取代 Pipeline runner 配置成为触发关系：
|
||||
|
||||
```python
|
||||
class AgentBinding(BaseModel):
|
||||
binding_id: str
|
||||
enabled: bool
|
||||
event_types: list[str]
|
||||
scope: BindingScope
|
||||
filters: list[EventFilter]
|
||||
runner_id: str
|
||||
runner_config: dict[str, Any]
|
||||
resource_policy: ResourcePolicy
|
||||
state_policy: StatePolicy
|
||||
delivery_policy: DeliveryPolicy
|
||||
```
|
||||
|
||||
Binding scope 示例：
|
||||
|
||||
- workspace 全局。
|
||||
- bot 级别。
|
||||
- platform channel 级别。
|
||||
- conversation / group / thread 级别。
|
||||
- user / actor 级别。
|
||||
|
||||
旧 Pipeline 可以迁移为 `message.received` 的 binding source，但不是唯一 binding source。
|
||||
|
||||
## 6. EventRouter 调用链
|
||||
|
||||
目标调用链：
|
||||
|
||||
```text
|
||||
Platform Adapter / WebUI / API
|
||||
-> Event Gateway normalize payload
|
||||
-> EventLog append raw event
|
||||
-> EventRouter resolve bindings
|
||||
-> AgentRunOrchestrator.run(event, binding)
|
||||
-> AgentRunContextBuilder.build(event, binding)
|
||||
-> PluginRuntimeConnector.run_agent()
|
||||
-> AgentRunResult stream
|
||||
-> DeliveryController render / platform action
|
||||
```
|
||||
|
||||
约束：
|
||||
|
||||
- `run_from_event()` 必须复用现有 orchestrator 能力。
|
||||
- 不能为 EBA 单独实现另一套 plugin runner 调用协议。
|
||||
- 不能让非消息事件绕过 resource authorization。
|
||||
- Delivery 和 platform action 要走统一权限模型。
|
||||
- 外部 harness runner 也应通过同一套 envelope/binding/context/result 协议接入；EBA 不应为 Claude Code / Codex / Kimi Code 单独发明队列协议。
|
||||
|
||||
## 7. Delivery Context
|
||||
|
||||
Event 不一定回复到当前聊天窗口。需要显式 delivery：
|
||||
|
||||
```python
|
||||
class DeliveryContext(BaseModel):
|
||||
surface: str
|
||||
reply_target: ReplyTarget | None
|
||||
supports_streaming: bool
|
||||
supports_edit: bool
|
||||
supports_reaction: bool
|
||||
max_message_size: int | None
|
||||
platform_capabilities: dict[str, Any] = {}
|
||||
```
|
||||
|
||||
消息事件通常带 reply target。系统事件可能没有默认 reply target，需要 runner 返回 `action.requested` 或由 binding 的 delivery policy 决定投递位置。
|
||||
|
||||
## 8. AgentRunResult 与平台动作
|
||||
|
||||
当前消息路径主要消费：
|
||||
|
||||
- `message.delta`
|
||||
- `message.completed`
|
||||
- `run.completed`
|
||||
- `run.failed`
|
||||
|
||||
EBA 后需要预留：
|
||||
|
||||
- `action.requested`: 请求 host 执行平台动作。
|
||||
- `artifact.created`: runner 生成文件或大结果。
|
||||
- `delivery.requested`: 请求投递到某个 surface。
|
||||
|
||||
示例：
|
||||
|
||||
```json
|
||||
{
|
||||
"type": "action.requested",
|
||||
"data": {
|
||||
"action": "friend.request.accept",
|
||||
"target": {"platform": "wechat", "request_id": "..."},
|
||||
"reason": "policy matched"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
Host 必须校验：
|
||||
|
||||
- runner manifest 是否声明 platform_api capability。
|
||||
- binding 是否授权该 action。
|
||||
- actor / bot / workspace 是否允许。
|
||||
- 是否需要人工审批。
|
||||
|
||||
本阶段如收到 `action.requested`，可以只记录 telemetry，不执行。
|
||||
|
||||
## 9. 与 Context 协议的关系
|
||||
|
||||
EBA 事件进入 AgentRunner 时仍使用 [AGENT_CONTEXT_PROTOCOL.md](./AGENT_CONTEXT_PROTOCOL.md) 的原则：
|
||||
|
||||
- inline 当前事件。
|
||||
- 大 payload 用 raw/artifact ref。
|
||||
- 不默认 inline 完整 history。
|
||||
- agent 按需通过 API 拉 history/event/artifact/state。
|
||||
- Host 保留 EventLog 和权限 guardrail。
|
||||
|
||||
非消息事件可以被投影进 Transcript，但不能强制伪装为 user message。AgentRunner 可以根据 event type 自己决定是否把它纳入模型上下文。
|
||||
|
||||
## 10. 当前实现与目标差距
|
||||
|
||||
**当前分支已落地（Event-first 基础设施）**：
|
||||
|
||||
- ✅ `AgentRunOrchestrator` — event-first `run(event, binding)` 入口
|
||||
- ✅ `AgentRunContextBuilder` — event-first context 构建
|
||||
- ✅ `AgentEventEnvelope` 模型
|
||||
- ✅ `AgentBinding` 模型
|
||||
- ✅ `AgentRunResult` 基础消息流
|
||||
- ✅ `ctx.event` 的最小消息事件封装
|
||||
- ✅ `PipelineAdapter` — Query → Event + Binding 转换
|
||||
- ✅ `run_from_query()` → `run(event, binding)` 委托
|
||||
- ✅ EventLog / Transcript / ArtifactStore
|
||||
- ✅ History / Event / Artifact / State pull APIs
|
||||
- ✅ 当前消息事件 path 已用 `local-agent` 与 Claude Code external harness runner 做本地 smoke
|
||||
|
||||
**其他分支负责（非本分支范围）**：
|
||||
|
||||
- EventGateway 实现
|
||||
- EventRouter 实现
|
||||
- Event subscription / notification
|
||||
- EventLog 持久化管理 UI
|
||||
- AgentBinding 持久化 UI
|
||||
- 平台动作执行 (`action.requested` 执行器)
|
||||
|
||||
**未来 EBA 完整落地需要**：
|
||||
|
||||
- EventGateway 完整实现
|
||||
- EventRouter 与 BindingResolver 集成
|
||||
- AgentBinding 持久模型和 UI
|
||||
- DeliveryContext 完整实现
|
||||
- platform action permission model 和执行器
|
||||
- 真实平台事件接入
|
||||
|
||||
## 11. 落地顺序
|
||||
|
||||
1. 先把当前 Pipeline 消息入口适配成 `message.received` event。
|
||||
2. 增加 `AgentBinding` 抽象，先由 Pipeline config 生成。
|
||||
3. `AgentRunContextBuilder` 改为从 event + binding 构造 context。
|
||||
4. 引入 EventLog / Transcript。
|
||||
5. 增加非消息事件的协议测试，不接真实平台。
|
||||
6. 再接入真实 EventRouter 和 platform action。
|
||||
@@ -1,427 +0,0 @@
|
||||
# LangBot Host 与 SDK 基础设施设计
|
||||
|
||||
本文档描述 LangBot 和 SDK 为插件化 AgentRunner 共同提供的基础设施。它不以 Pipeline 为中心，也不以官方 local-agent 的实现方式为前提。
|
||||
|
||||
## 1. 目标
|
||||
|
||||
LangBot 要转为 agent host，而不是内置 runner 容器：
|
||||
|
||||
- 接收 IM、WebUI、API 和未来 EventRouter 产生的事件。
|
||||
- 根据事件、bot、workspace、scope 解析应该调用的 agent binding。
|
||||
- 发现、校验和调用插件提供的 AgentRunner。
|
||||
- 为每次 run 提供受限资源、状态、存储、上下文引用和生命周期控制。
|
||||
- 接收 AgentRunner 返回的事件流，并投递到 IM、WebUI 或其他 output surface。
|
||||
|
||||
SDK 要提供稳定协议：
|
||||
|
||||
- `AgentRunner` 组件定义。
|
||||
- runner manifest / capabilities / permissions / config schema。
|
||||
- `AgentRunContext` 输入 envelope。
|
||||
- `AgentRunResult` 输出事件流。
|
||||
- `AgentRunAPIProxy` 运行期受限 API。
|
||||
|
||||
## 2. 非目标
|
||||
|
||||
- 不把 Pipeline 当作长期架构中心。
|
||||
- 不要求所有 AgentRunner 依赖 LangBot 的上下文管理。
|
||||
- 不要求官方 local-agent 的旧行为反向塑造 host 协议。
|
||||
- 不在 host 中实现通用 agentic prompt assembler。
|
||||
- 不强制 runner 使用 LangBot state / storage；LangBot 只提供可选、受控的寄宿能力。
|
||||
- **不实现 EventGateway**：EventGateway 是 future integration point，由外部 event branch 提供。本分支只定义 host-side envelope/binding models 和 `run(event, binding)` 入口。
|
||||
|
||||
## 3. 分层架构
|
||||
|
||||
目标结构：
|
||||
|
||||
```text
|
||||
IM / WebUI / API / EventRouter (future)
|
||||
|
|
||||
v
|
||||
Event Gateway (future - external event branch)
|
||||
|
|
||||
v
|
||||
AgentBindingResolver
|
||||
|
|
||||
v
|
||||
AgentRunOrchestrator
|
||||
|-- AgentRunnerRegistry
|
||||
|-- AgentResourceBuilder
|
||||
|-- AgentContextBuilder
|
||||
|-- AgentRunSessionRegistry
|
||||
|-- PersistentStateStore / EventLogStore / TranscriptStore / ArtifactStore
|
||||
v
|
||||
Plugin Runtime / AgentRunner
|
||||
|
|
||||
v
|
||||
AgentRunResult stream
|
||||
|
|
||||
v
|
||||
Delivery / Renderer / Platform API
|
||||
```
|
||||
|
||||
**当前状态**：
|
||||
- `PipelineAdapter` 作为当前入口 adapter，将 Pipeline Query 转换为 `AgentEventEnvelope` + `AgentBinding`
|
||||
- `run_from_query()` 内部委托到 `run(event, binding)`
|
||||
- EventLog / Transcript / ArtifactStore / PersistentStateStore 已落地
|
||||
- `local-agent` 与 Claude Code runner 已通过本地 WebUI smoke，验证同一条 `run(event, binding)` path 可服务 host-infra runner 与外部 harness runner
|
||||
- EventGateway 由外部 event branch 实现
|
||||
|
||||
当前 Pipeline 只应接入在 Pipeline adapter 位置。它可以继续产生 `message.received`，但不应继续拥有 runner 选择、上下文裁剪和业务 agent 执行的核心语义。
|
||||
|
||||
## 4. LangBot 侧能力
|
||||
|
||||
### 4.1 Event Gateway（Future Integration Point）
|
||||
|
||||
> **注意**：EventGateway 由外部 event branch 实现，不在本分支范围。本分支只预留 event-first 入口和 envelope/binding models。
|
||||
|
||||
Event Gateway 将负责把入口统一成 host event：
|
||||
|
||||
- IM 平台消息。
|
||||
- WebUI debug chat 消息。
|
||||
- API 触发。
|
||||
- 后续非消息事件，例如入群、撤回、好友申请。
|
||||
|
||||
输出应是稳定 envelope，而不是 Pipeline Query 私有结构：
|
||||
|
||||
```python
|
||||
class AgentEventEnvelope(BaseModel):
|
||||
event_id: str
|
||||
event_type: str
|
||||
event_time: int | None
|
||||
source: str
|
||||
bot_id: str | None
|
||||
workspace_id: str | None
|
||||
conversation_id: str | None
|
||||
thread_id: str | None
|
||||
actor: ActorRef | None
|
||||
subject: SubjectRef | None
|
||||
input: AgentInput
|
||||
delivery: DeliveryContext
|
||||
raw_ref: RawEventRef | None
|
||||
```
|
||||
|
||||
**当前 adapter source**：`PipelineAdapter.query_to_event(query)` 从 Pipeline Query 生成 `AgentEventEnvelope`。
|
||||
|
||||
原始平台 payload 可以存为 raw event 或 artifact ref；不要把平台私有字段直接扩散到 AgentRunner 顶层协议。
|
||||
|
||||
### 4.2 Agent Binding
|
||||
|
||||
Agent binding 是”什么事件调用哪个 runner、带什么绑定配置”的持久配置。它替代长期依赖 Pipeline runner config 的角色。
|
||||
|
||||
建议模型：
|
||||
|
||||
```python
|
||||
class AgentBinding(BaseModel):
|
||||
binding_id: str
|
||||
scope: BindingScope
|
||||
event_types: list[str]
|
||||
runner_id: str
|
||||
runner_config: dict[str, Any]
|
||||
resource_policy: ResourcePolicy
|
||||
state_policy: StatePolicy
|
||||
delivery_policy: DeliveryPolicy
|
||||
enabled: bool
|
||||
```
|
||||
|
||||
**当前 adapter source**：`PipelineAdapter.pipeline_config_to_binding(query, runner_id)` 从 Pipeline config 生成临时 `AgentBinding`。
|
||||
|
||||
Pipeline 当前可以被迁移为一种 binding source：
|
||||
|
||||
- Pipeline AI runner config -> `AgentBinding`
|
||||
- Pipeline extension preference -> `resource_policy`
|
||||
- Pipeline output settings -> `delivery_policy`
|
||||
|
||||
但新设计不应再把这些字段命名为 Pipeline 专属概念。
|
||||
|
||||
### 4.3 AgentRunnerRegistry
|
||||
|
||||
Registry 负责收集 runner descriptor：
|
||||
|
||||
- 插件 runtime 提供的 `AgentRunner`。
|
||||
- 可能存在的 host adapter runner。
|
||||
- 开发期本地插件 runner。
|
||||
|
||||
Descriptor 必须包含：
|
||||
|
||||
```python
|
||||
class AgentRunnerDescriptor(BaseModel):
|
||||
id: str
|
||||
source: Literal["plugin", "host_adapter"]
|
||||
label: I18nObject
|
||||
description: I18nObject | None = None
|
||||
capabilities: AgentRunnerCapabilities
|
||||
permissions: AgentRunnerPermissions
|
||||
config_schema: list[DynamicFormItemSchema]
|
||||
plugin: PluginRef | None = None
|
||||
```
|
||||
|
||||
`plugin:author/name/runner` 仍可作为稳定 id 格式。多个 binding 指向同一个 runner id 时，不创建多个插件实例。
|
||||
|
||||
### 4.4 AgentRunOrchestrator
|
||||
|
||||
Orchestrator 是唯一运行入口：
|
||||
|
||||
```text
|
||||
run(event, binding)
|
||||
-> resolve runner descriptor
|
||||
-> build resources
|
||||
-> build context
|
||||
-> register run session
|
||||
-> call plugin runtime
|
||||
-> normalize result stream
|
||||
-> update state
|
||||
-> unregister run session
|
||||
```
|
||||
|
||||
它负责：
|
||||
|
||||
- `run_id` 生成和生命周期。
|
||||
- timeout / deadline / cancellation。
|
||||
- 插件异常隔离。
|
||||
- result schema 校验和大小限制。
|
||||
- state.updated 处理。
|
||||
- delivery backpressure 和 telemetry。
|
||||
|
||||
`run_from_query()` 这类 API 可以保留为 Pipeline adapter 入口，但内部应转换成 event + binding 后走统一 `run()`。
|
||||
|
||||
### 4.5 Resource Authorization
|
||||
|
||||
LangBot 在每次 run 前生成 `ctx.resources`。资源来自三层约束：
|
||||
|
||||
- runner manifest 声明的 permissions。
|
||||
- binding/resource policy 允许的资源范围。
|
||||
- 当前 event / actor / bot / workspace 的实际权限。
|
||||
|
||||
资源类型包括：
|
||||
|
||||
- models
|
||||
- tools
|
||||
- knowledge bases
|
||||
- files / artifacts
|
||||
- storage
|
||||
- platform capabilities
|
||||
- history / transcript access
|
||||
|
||||
运行期 action 必须再次通过 `run_id` 校验。SDK 侧本地校验只用于开发体验，host 侧校验才是安全边界。
|
||||
|
||||
### 4.6 State 与 Storage
|
||||
|
||||
LangBot 可以提供 host-owned state，让 AgentRunner 把状态寄宿在 LangBot：
|
||||
|
||||
- conversation state
|
||||
- actor state
|
||||
- subject state
|
||||
- runner/binding state
|
||||
- workspace state
|
||||
|
||||
但这不是强制。外部 agent runtime 可以维护自己的 session 和 memory。LangBot 只需要提供：
|
||||
|
||||
- 授权开关。
|
||||
- scope key。
|
||||
- get/set/list/delete API。
|
||||
- 持久化 backend。
|
||||
- 审计和清理策略。
|
||||
|
||||
当前进程内 state store 只能作为过渡实现，不能作为正式生产语义。
|
||||
|
||||
### 4.7 EventLog / Transcript / Artifact
|
||||
|
||||
LangBot 应提供事实源能力：
|
||||
|
||||
- `EventLog`: 保存原始事件、系统事件、工具调用、投递结果、错误。
|
||||
- `Transcript`: 面向对话 UI / agent history 的消息投影。
|
||||
- `ArtifactStore`: 保存大文件、多模态输入、工具大结果、平台附件。
|
||||
|
||||
AgentRunner 可以读取这些能力，但不能被迫使用 LangBot 作为唯一记忆系统。
|
||||
|
||||
### 4.8 Prompt / Instruction Package（占位）
|
||||
|
||||
旧 Pipeline 入口目前可以把 preprocessing 后的有效 prompt 放进 adapter metadata，
|
||||
这是为了保持旧入口行为，不是长期协议。目标形态应是 Host 保存或生成一个
|
||||
run-scoped instruction package，runner 通过 Host API 拉取：
|
||||
|
||||
- Host 负责记录静态绑定 prompt、host hook / user plugin 产生的 instruction
|
||||
fragment、来源和审计信息。
|
||||
- `ctx.context.available_apis.prompt_get` 只表示拉取能力是否可用。
|
||||
- Runner 拉取 instruction package 后，仍由 runner 自己决定如何与 history、RAG、
|
||||
tool 结果、memory 和当前输入组装最终模型 prompt。
|
||||
- Host 不实现通用 agentic prompt assembler，也不把 Pipeline adapter prompt 作为
|
||||
长期业务输入契约。
|
||||
|
||||
### 4.9 External harness resource projection
|
||||
|
||||
Claude Code、Codex、Kimi Code 等外部 harness runner 可能不会直接调用 LangBot 的 model/tool loop，而是把 LangBot 事件和授权资源投影到自己的 harness 中执行。Host 侧仍要保持统一边界：
|
||||
|
||||
- Host 负责构造 event-first context、资源授权、state/storage、EventLog/Transcript/ArtifactStore 和审计。
|
||||
- Host 或 binding policy 负责决定哪些 MCP server、skill、artifact、history/state 句柄可以投影给 runner。
|
||||
- Runner plugin 负责把 scoped projection 转成目标 harness 可消费的形式，例如 context JSON/Markdown、MCP config、skill 目录、环境变量或 CLI 参数。
|
||||
- 外部 harness 负责自己的 native session、tool loop、压缩、权限模式和 resume 机制。
|
||||
|
||||
当前 Claude Code runner MVP 已验证：
|
||||
|
||||
- LangBot event-first context 可以写入 `agent-context.json` / `LANGBOT_CONTEXT.md`。
|
||||
- binding 中的 skill / MCP 配置可以投影到 Claude Code 原生目录和 CLI 参数。
|
||||
- `external.session_id` 与 `external.working_directory` 可以通过 Host state 保存并用于 resume。
|
||||
|
||||
发布级路径隔离、secret 过滤、MCP allowlist、工具白名单、资源配额和 workspace 清理不属于当前协议闭环，详见 [SECURITY_HARDENING.md](./SECURITY_HARDENING.md)。
|
||||
|
||||
## 5. SDK 侧协议
|
||||
|
||||
### 5.1 AgentRunner 组件
|
||||
|
||||
```python
|
||||
class AgentRunner(BaseComponent):
|
||||
__kind__ = "AgentRunner"
|
||||
|
||||
@classmethod
|
||||
def get_capabilities(cls) -> AgentRunnerCapabilities:
|
||||
...
|
||||
|
||||
@classmethod
|
||||
def get_config_schema(cls) -> list[dict]:
|
||||
...
|
||||
|
||||
async def run(self, ctx: AgentRunContext) -> AsyncGenerator[AgentRunResult, None]:
|
||||
...
|
||||
```
|
||||
|
||||
### 5.2 Capabilities
|
||||
|
||||
建议能力声明：
|
||||
|
||||
```yaml
|
||||
capabilities:
|
||||
streaming: true
|
||||
tool_calling: true
|
||||
knowledge_retrieval: true
|
||||
multimodal_input: true
|
||||
event_context: true
|
||||
platform_api: false
|
||||
interrupt: true
|
||||
stateful_session: true
|
||||
self_managed_context: true
|
||||
host_state: optional
|
||||
```
|
||||
|
||||
`self_managed_context` 表示 runner 或外部 runtime 自己管理上下文。Host 不应给它强塞历史窗口，只提供当前事件和 context handles。
|
||||
|
||||
### 5.3 Permissions
|
||||
|
||||
```yaml
|
||||
permissions:
|
||||
models: ["invoke", "stream", "rerank"]
|
||||
tools: ["detail", "call"]
|
||||
knowledge_bases: ["list", "retrieve"]
|
||||
history: ["page", "search"]
|
||||
events: ["get", "page"]
|
||||
artifacts: ["metadata", "read"]
|
||||
storage: ["plugin", "workspace", "binding"]
|
||||
files: ["config", "knowledge"]
|
||||
platform_api: []
|
||||
```
|
||||
|
||||
权限声明是 runner 需要的最大能力，实际可用资源仍由 binding 和当前运行上下文裁剪。
|
||||
|
||||
### 5.4 AgentRunContext
|
||||
|
||||
Context 顶层应是 event-first，而不是 Query-first：
|
||||
|
||||
```python
|
||||
class AgentRunContext(BaseModel):
|
||||
run_id: str
|
||||
trigger: AgentTrigger
|
||||
event: AgentEventContext
|
||||
conversation: ConversationContext | None = None
|
||||
actor: ActorContext | None = None
|
||||
subject: SubjectContext | None = None
|
||||
input: AgentInput
|
||||
resources: AgentResources
|
||||
context: ContextAccess
|
||||
state: AgentRunState
|
||||
runtime: AgentRuntimeContext
|
||||
config: dict[str, Any]
|
||||
```
|
||||
|
||||
`messages` 可以作为兼容字段或 bootstrap 字段，但不应继续是协议核心。
|
||||
|
||||
### 5.5 AgentRunResult
|
||||
|
||||
输出应是事件流：
|
||||
|
||||
```python
|
||||
class AgentRunResult(BaseModel):
|
||||
type: Literal[
|
||||
"message.delta",
|
||||
"message.completed",
|
||||
"tool.call.started",
|
||||
"tool.call.completed",
|
||||
"state.updated",
|
||||
"artifact.created",
|
||||
"action.requested",
|
||||
"run.completed",
|
||||
"run.failed",
|
||||
]
|
||||
data: dict[str, Any] = {}
|
||||
```
|
||||
|
||||
当前消息回复只消费 `message.delta` / `message.completed` / `run.failed`。平台动作执行等 EBA 和 platform API 权限落地后再启用。
|
||||
|
||||
### 5.6 AgentRunAPIProxy
|
||||
|
||||
Proxy 是 runner 访问 host 能力的唯一入口：
|
||||
|
||||
- model APIs
|
||||
- tool APIs
|
||||
- knowledge APIs
|
||||
- state / storage APIs
|
||||
- history / event APIs
|
||||
- artifact APIs
|
||||
- platform APIs
|
||||
|
||||
所有请求必须带 `run_id`，host 侧按 active run session 验证 runner identity 和 resource ACL。
|
||||
|
||||
## 6. 当前实现与目标差距
|
||||
|
||||
**已落地（当前分支）**：
|
||||
|
||||
- ✅ `AgentRunnerRegistry`
|
||||
- ✅ `AgentRunOrchestrator` — event-first `run(event, binding)`
|
||||
- ✅ `AgentRunContextBuilder` — event-first context
|
||||
- ✅ `AgentResourceBuilder`
|
||||
- ✅ `AgentRunSessionRegistry`
|
||||
- ✅ `AgentRunAPIProxy` — model / tool / knowledge / history / event / artifact / state APIs
|
||||
- ✅ `PipelineAdapter` — Query → Event + Binding
|
||||
- ✅ `AgentBinding` 抽象
|
||||
- ✅ `AgentEventEnvelope` 抽象
|
||||
- ✅ `max-round` 从目标协议中移除；类似历史窗口参数若仍需要，应由具体 runner 的 manifest/config schema 暴露为 binding config
|
||||
- ✅ `PersistentStateStore` — 持久化状态存储
|
||||
- ✅ `EventLogStore` / `TranscriptStore` / `ArtifactStore`
|
||||
- ✅ history / artifact / event 的受限拉取 API
|
||||
- ✅ Claude Code external harness MVP：context/resource projection 与 host-owned resume state smoke
|
||||
|
||||
**其他分支负责（非本分支范围）**：
|
||||
|
||||
- EventGateway 实现
|
||||
- EventRouter 实现
|
||||
- AgentBinding 持久化 UI
|
||||
- platform API 动作执行
|
||||
- 发布级 security hardening
|
||||
|
||||
## 7. 落地顺序
|
||||
|
||||
**已完成**：
|
||||
|
||||
1. ✅ 固化 README 路由和专题文档边界。
|
||||
2. ✅ 在 Host 中抽象 `AgentBinding`，由 Pipeline adapter 生成。
|
||||
3. ✅ 将 `AgentRunContextBuilder` 改为 event-first。
|
||||
4. ✅ 增加持久 transcript/event log/artifact/state 存储模型。
|
||||
5. ✅ 扩展 `AgentRunAPIProxy` 的 history / artifact / state API。
|
||||
6. ✅ 将 Pipeline-only 字段下沉到 Pipeline adapter。
|
||||
7. ✅ 官方 runner 插件迁移完成（7 个插件）。
|
||||
8. ✅ Claude Code runner MVP smoke：外部 harness context 投影和 state handoff。
|
||||
|
||||
**后续工作（其他分支）**：
|
||||
|
||||
- EventGateway 实现
|
||||
- EventRouter 与 BindingResolver 集成
|
||||
- 平台动作执行器
|
||||
@@ -1,552 +0,0 @@
|
||||
# Agent Runner 插件化当前实现与收尾计划
|
||||
|
||||
> 2026-05-29 状态说明：本文档是实现推进计划和历史上下文，不是最新验收结论的唯一来源。当前设计入口见 [README.md](./README.md)，协议边界见 [PROTOCOL_V1.md](./PROTOCOL_V1.md)，进度见 [PROGRESS.md](./PROGRESS.md)，下一轮测试入口见 [PHASE1_QA_ACCEPTANCE_MATRIX.md](./PHASE1_QA_ACCEPTANCE_MATRIX.md)。
|
||||
|
||||
本文档面向实现 agent，用来把当前 AgentRunner 插件化实现推进到可迁移状态。
|
||||
|
||||
当前代码已经不是从零开始的 PoC。LangBot 已经具备 registry、orchestrator、context/resource builder、result normalizer 和插件 runtime action。本计划重点描述剩余工作：补齐宿主通用能力、对齐旧内置 runner 行为、完成官方 runner 插件迁移验收。
|
||||
|
||||
## 1. 最终状态
|
||||
|
||||
LangBot 最终只保留 Agent Runner 的宿主能力：
|
||||
|
||||
- 发现 runner：`AgentRunnerRegistry`
|
||||
- 选择 runner：Pipeline 配置和未来事件绑定配置
|
||||
- 构造上下文：`AgentRunContext`
|
||||
- 裁剪资源：模型、工具、知识库、文件、存储、平台能力
|
||||
- 调度执行：`AgentRunOrchestrator`
|
||||
- 归一结果：`AgentRunResult` -> 当前 Pipeline 的 `Message` / `MessageChunk`
|
||||
- 隔离错误：插件异常、协议错误、超时、结果过大不能破坏主流程
|
||||
- 迁移旧配置：把旧内置 runner 配置迁到官方 AgentRunner 插件配置
|
||||
- 转发调用：插件 runtime 只维护已安装插件本身的运行实例，Pipeline 不创建插件实例或 runner 实例
|
||||
|
||||
LangBot 不再长期维护内置业务 runner 分支。`local-agent`、Dify、n8n、Coze、DashScope、Langflow、Tbox 等都迁到官方 AgentRunner 插件。
|
||||
|
||||
迁移期间允许旧 `RequestRunner` 文件继续存在，作为行为对齐基准和回退分析材料。它们不影响当前进度；真正的最终条件是主聊天执行路径不再依赖旧 runner。
|
||||
|
||||
## 1.1 当前状态快照
|
||||
|
||||
已完成或基本完成：
|
||||
|
||||
- `AgentRunnerDescriptor`、runner id 解析、registry。
|
||||
- `AgentRunOrchestrator` 替换 `ChatMessageHandler` 内部 runner 调度。
|
||||
- `AgentRunContextBuilder`、`AgentResourceBuilder`、`AgentResultNormalizer`。
|
||||
- `ai.runner.id` + `ai.runner_config[id]` 的读取与旧配置映射。
|
||||
- AgentRunner runtime action：`LIST_AGENT_RUNNERS`、`RUN_AGENT`。
|
||||
- run-scoped proxy authorization：模型、工具、知识库、存储、文件。
|
||||
- EventLog / Transcript / ArtifactStore / PersistentStateStore。
|
||||
- Pipeline adapter 已委托到 event-first `run(event, binding)`。
|
||||
- `local-agent` 与 Claude Code runner 已通过本地 WebUI smoke。
|
||||
|
||||
仍需收尾：
|
||||
|
||||
- Docs final QA 与安装/发布文档整理。
|
||||
- timeout/deadline、取消、插件无输出、协议错误的端到端保护。
|
||||
- 官方 runner 插件安装/预装/迁移缺失处理。
|
||||
- 安全发布级 hardening：路径隔离、权限边界、secret、MCP/skill 投影策略、资源配额、审计。此项不阻塞当前协议闭环，详见 [SECURITY_HARDENING.md](./SECURITY_HARDENING.md)。
|
||||
- Codex / Kimi runner 全量接入、issue-centric 队列、复杂 workflow engine 和 EBA 分支完整联调。
|
||||
|
||||
## 2. 高层架构
|
||||
|
||||
```text
|
||||
Pipeline MessageProcessor / future EventRouter
|
||||
|
|
||||
v
|
||||
AgentRunOrchestrator
|
||||
|
|
||||
+--> AgentRunnerRegistry
|
||||
| +--> plugin runtime LIST_AGENT_RUNNERS
|
||||
| +--> descriptor cache / validation
|
||||
|
|
||||
+--> AgentRunContextBuilder
|
||||
+--> AgentResourceBuilder
|
||||
+--> AgentResultNormalizer
|
||||
|
|
||||
v
|
||||
PluginRuntimeConnector.run_agent()
|
||||
|
|
||||
v
|
||||
SDK Runtime RUN_AGENT -> plugin AgentRunner.run()
|
||||
```
|
||||
|
||||
关键约束：
|
||||
|
||||
- `ChatMessageHandler` 不解析 `plugin:*`，不实例化 wrapper，不知道 runner 组件细节。
|
||||
- `PipelineService.get_pipeline_metadata()` 不直接访问插件 runtime，而是读取 registry。
|
||||
- 旧 `RequestRunner` 只作为迁移参考，不作为最终运行路径。
|
||||
- `AgentRunOrchestrator` 是 LangBot 侧运行编排层：负责 runner 绑定解析、资源授权、context envelope provisioning、run scope 注册、插件调用和结果归一化；不负责决定 Agent 的最终 prompt/window/压缩策略。
|
||||
- 插件是无状态执行单元：多个 Pipeline 可以绑定同一个 runner id，并分别保存自己的 `ai.runner_config[id]`；运行时 LangBot 只把当前绑定配置放入 `ctx.config` 转发给同一个插件 runner。
|
||||
- 禁止按 Pipeline 或 runner config 创建多个插件实例。需要跨请求持久化的状态必须走明确授权的 plugin storage / workspace storage / 外部服务，不能隐式保存在 per-pipeline 插件对象里。
|
||||
- EBA 只做字段预留，不在本轮实现 EventBus、EventRouter、平台动作执行。
|
||||
|
||||
## 3. 新增 LangBot 模块
|
||||
|
||||
建议新增：
|
||||
|
||||
```text
|
||||
src/langbot/pkg/agent/
|
||||
__init__.py
|
||||
runner/
|
||||
__init__.py
|
||||
descriptor.py
|
||||
errors.py
|
||||
id.py
|
||||
registry.py
|
||||
context_builder.py
|
||||
resource_builder.py
|
||||
orchestrator.py
|
||||
result_normalizer.py
|
||||
config_migration.py
|
||||
```
|
||||
|
||||
### 3.1 descriptor.py
|
||||
|
||||
定义 LangBot 内部使用的 descriptor：
|
||||
|
||||
```python
|
||||
class AgentRunnerDescriptor(BaseModel):
|
||||
id: str
|
||||
source: Literal["plugin"]
|
||||
label: dict[str, str]
|
||||
description: dict[str, str] | None = None
|
||||
plugin_author: str
|
||||
plugin_name: str
|
||||
runner_name: str
|
||||
plugin_version: str | None = None
|
||||
protocol_version: str = "1"
|
||||
config_schema: list[dict[str, Any]] = []
|
||||
capabilities: dict[str, bool] = {}
|
||||
permissions: dict[str, list[str]] = {}
|
||||
raw_manifest: dict[str, Any] = {}
|
||||
```
|
||||
|
||||
`source == "builtin"` 不作为最终目标。如果实现阶段需要临时 adapter，必须标记为测试过渡代码，并在官方插件跑通后删除。
|
||||
|
||||
### 3.2 id.py
|
||||
|
||||
统一 runner id 解析和生成：
|
||||
|
||||
- 插件 runner id：`plugin:{author}/{plugin_name}/{runner_name}`
|
||||
- `parse_runner_id(id)` 返回结构化对象
|
||||
- 禁止业务代码手写字符串 split
|
||||
- PoC 已存在的 `plugin:author/name/runner` 继续作为合法 id
|
||||
|
||||
### 3.3 registry.py
|
||||
|
||||
职责：
|
||||
|
||||
- 调用 `ap.plugin_connector.list_agent_runners(bound_plugins=None)` 拉取插件 runner
|
||||
- 校验 manifest：
|
||||
- `kind == AgentRunner`
|
||||
- `metadata.name` 存在
|
||||
- `metadata.label` 存在
|
||||
- `spec.protocol_version` 兼容，默认 `1`
|
||||
- `spec.config` 是 list，默认空
|
||||
- `spec.capabilities` 是 dict，默认空
|
||||
- `spec.permissions` 是 dict，默认空
|
||||
- 输出 `AgentRunnerDescriptor`
|
||||
- 缓存 discovery 结果，提供 `refresh()`
|
||||
- 单个插件 manifest 失败只记录 warning，不影响其它 runner
|
||||
|
||||
刷新触发点：
|
||||
|
||||
- 插件安装、卸载、升级、重启后
|
||||
- Pipeline metadata 请求时发现缓存为空
|
||||
- 可选 TTL，优先保证正确性
|
||||
|
||||
### 3.4 context_builder.py / pipeline_adapter.py
|
||||
|
||||
`context_builder.py` 只负责从 `AgentEventEnvelope + AgentBinding` 构造 SDK v1 `AgentRunContext`。Pipeline Query 的读取、参数过滤和 prompt 提取属于 `PipelineAdapter`，但 PipelineAdapter 不再做历史窗口裁剪或 bootstrap 打包。
|
||||
|
||||
当前消息 Pipeline 进入 agent runner 的路径：
|
||||
|
||||
```text
|
||||
Query
|
||||
-> PipelineAdapter.query_to_event(query)
|
||||
-> PipelineAdapter.pipeline_config_to_binding(query, runner_id)
|
||||
-> PipelineAdapter.build_adapter_context(query, binding)
|
||||
-> AgentRunOrchestrator.run(event, binding, adapter_context=...)
|
||||
-> AgentRunContextBuilder.build_context_from_event(...)
|
||||
```
|
||||
|
||||
Protocol v1 context 的稳定字段：
|
||||
|
||||
- `run_id`: 新 UUID，不使用 query id 作为全局 run id
|
||||
- `trigger.type`: 事件触发类型，例如 `message.received`
|
||||
- `conversation`: conversation/thread/launcher/sender/bot/pipeline 投影
|
||||
- `event`: 稳定事件上下文
|
||||
- `actor`: 触发者
|
||||
- `subject`: 当前消息、群、频道或其它事件主体
|
||||
- `input`: 当前事件输入，不是历史消息窗口
|
||||
- `delivery`: 输出 surface 和平台投递能力
|
||||
- `resources`: 由 `resource_builder` 基于 binding policy 注入
|
||||
- `state`: `PersistentStateStore` 读取的 host-managed scoped state snapshot
|
||||
- `runtime`: host/version/workspace/bot/query/trace/deadline
|
||||
- `config`: 当前 binding 对该 runner id 的配置，即 `runner_config`
|
||||
- `bootstrap`: 可选扩展字段；LangBot Host 默认不填历史窗口
|
||||
- `adapter`: Pipeline 或其它入口 adapter 的元数据
|
||||
|
||||
Pipeline adapter 的 `prompt` 和公开业务变量不进入顶层协议字段：
|
||||
|
||||
- filtered params -> `ctx.adapter.extra["params"]`
|
||||
- legacy/effective prompt 可以暂存到 `ctx.adapter.extra["prompt"]`，但 official
|
||||
runner 不应把它当作行为契约
|
||||
- LangBot Host 不生成 `bootstrap.messages`、`adapter_messages` 或 context packaging 元数据
|
||||
|
||||
现阶段不要把新的压缩或 token-budget 裁剪塞回 Pipeline stage。Pipeline 只负责入口适配；完整历史和长期上下文由 EventLog / Transcript / pull APIs / future ContextCompressor 支撑。
|
||||
|
||||
### 3.4.1 Agentic context plan
|
||||
|
||||
EventLog / Transcript / Host pull APIs 已落地，`ContextCompressor` 仍是设计预留。
|
||||
目标是让 Pipeline 逐步退化为入口 adapter，让 AgentRunner 层拥有上下文打包职责。
|
||||
|
||||
建议 Host 保持三类事实源和受限 API：
|
||||
|
||||
```text
|
||||
ConversationStore / EventLog
|
||||
-> durable append-only raw messages, events, tool results, artifact refs
|
||||
ConversationProjection
|
||||
-> converts events into agent-readable conversation history
|
||||
ContextCompressor
|
||||
-> future optional service for summaries/checkpoints, requested and consumed by runners
|
||||
```
|
||||
|
||||
关键原则：
|
||||
|
||||
- 完整历史属于 LangBot host，不属于插件实例。插件仍是 singleton/stateless。
|
||||
- `ctx.bootstrap.messages` 不是 Host 默认下发的 working context。
|
||||
- 每轮不能全量复制/序列化完整历史给插件 runtime；否则长会话会产生 O(n) 成本和跨进程 payload 膨胀。
|
||||
- `max-round` 或类似窗口规则不属于 LangBot Host / Pipeline 语义。
|
||||
- LiteLLM 接入后，模型窗口元信息应作为 resource/runtime metadata 暴露给 runner，由 runner 决定预算和压缩策略。
|
||||
- `ContextCompressor` 生成的是派生 summary/checkpoint，不能覆盖或删除 raw history。
|
||||
- 重启恢复依赖持久化 store 和 summary checkpoint，不依赖 `SessionManager` 里的进程内 conversation list。
|
||||
|
||||
未来需要的受限 API：
|
||||
|
||||
```python
|
||||
api.get_conversation_messages(cursor: str | None, limit: int) -> HistoryPage
|
||||
api.get_context_summary(scope: str = "conversation") -> ContextSummary | None
|
||||
api.request_context_compaction(policy: dict) -> CompactionResult
|
||||
```
|
||||
|
||||
这些 API 必须绑定 `run_id`、runner id、actor/subject scope 和资源权限；Host 需要限制
|
||||
page size、总字节数、deadline 和可访问 conversation。
|
||||
|
||||
### 3.4.2 Large artifacts and tool collaboration
|
||||
|
||||
大文件、多模态输入和工具产物不要内联进 prompt、bootstrap 或 tool result。后续统一用
|
||||
artifact/resource ref 协作：
|
||||
|
||||
- message/content 里只放小文本和必要摘要。
|
||||
- 大文件、图片、音频、长工具输出返回 `artifact_id`、`mime_type`、`size`、`digest`、
|
||||
`summary`、`expires_at`、`permissions`。
|
||||
- `/tmp` 只能作为单次 run 的临时 staging，用于插件或工具短时间读写；它不是 durable store，
|
||||
也不能作为重启恢复依据。
|
||||
- box/object storage 是长期 artifact 的目标位置。当前分支尚未合并 box 能力，因此本轮只写文档预留，不实现 API。
|
||||
- 工具之间传递大结果时应传 artifact ref，不传完整 blob。Agent 需要读取时走受限 proxy。
|
||||
|
||||
未来建议 API：
|
||||
|
||||
```python
|
||||
api.get_artifact_metadata(artifact_id: str) -> ArtifactMetadata
|
||||
api.open_artifact_stream(artifact_id: str) -> AsyncIterator[bytes]
|
||||
api.read_artifact_range(artifact_id: str, offset: int, length: int) -> bytes
|
||||
api.create_temp_artifact(name: str, content_type: str, ttl_seconds: int) -> ArtifactWriter
|
||||
```
|
||||
|
||||
安全约束：
|
||||
|
||||
- Host 校验 artifact 是否属于当前 run、conversation、actor/subject scope 或授权资源。
|
||||
- 默认不允许插件直接读任意本地路径，包括 `/tmp` 任意路径。
|
||||
- 临时文件应有 TTL 和清理机制；box artifact 应有 retention policy。
|
||||
- 多模态文件进入模型前，由 runner/context packager 决定传引用、摘要、缩略图还是实际 bytes。
|
||||
|
||||
### 3.5 resource_builder.py
|
||||
|
||||
执行前做三层裁剪：
|
||||
|
||||
1. runner manifest 声明的 `spec.permissions`
|
||||
2. Pipeline 的 `extensions_preferences`
|
||||
3. 当前 Pipeline runner 绑定配置中选择的资源范围
|
||||
|
||||
输出写入 `ctx.resources`，至少覆盖：
|
||||
|
||||
- models：可调用模型 UUID、类型、能力摘要。包括 LLM、fallback LLM、rerank 等 runner config schema 中选择的模型类资源。
|
||||
- tools：可见工具 manifest，使用当前 bound plugins / MCP server 范围
|
||||
- knowledge_bases：可检索知识库列表
|
||||
- storage：plugin storage / workspace storage 权限摘要
|
||||
- files：允许读取的配置文件、知识文件摘要
|
||||
- platform_capabilities：本阶段只声明，不执行平台动作
|
||||
|
||||
注意：旧的 unrestricted proxy action 必须二次校验，不能只靠 context 声明。AgentRunner 可用资源应来自 `ctx.resources`，不是插件 runtime 的全局能力。
|
||||
|
||||
本阶段不接入 sandbox/skills，也不预留 runner 可见字段。后续相关分支合并后，
|
||||
执行、文件、skill、MCP 等能力应先由 Host 侧封装成普通 tool，再通过
|
||||
`ctx.resources.tools` 进入 runner；runner 不应识别或硬编码执行环境 provider。
|
||||
|
||||
资源裁剪要尽量通用，不应只写死 local-agent：
|
||||
|
||||
- `model-fallback-selector` 授权 primary/fallback LLM。
|
||||
- `llm-model-selector` 授权 LLM。
|
||||
- `rerank-model-selector` 授权 rerank 模型。
|
||||
- `knowledge-base-multi-selector` 授权知识库。
|
||||
- 后续新增 selector 时应在 resource builder 中统一扩展。
|
||||
|
||||
### 3.5.1 future EventRouter 预留
|
||||
|
||||
当前分支不实现 EBA EventRouter，但 AgentRunner 协议必须从现在开始兼容非消息事件。未来不要为消息撤回、群成员加入、好友申请各写一套 runner wrapper；统一入口应是：
|
||||
|
||||
```text
|
||||
EventRouter -> AgentRunOrchestrator.run_from_event(event_request)
|
||||
```
|
||||
|
||||
EBA 落地后，`ConversationStore` 不应只保存聊天消息，而应从 `EventLog` 投影生成：
|
||||
|
||||
```text
|
||||
Platform Adapter
|
||||
-> EventLog append raw event
|
||||
-> ConversationProjection update message/history view when applicable
|
||||
-> EventRouter resolve binding
|
||||
-> AgentRunOrchestrator.run_from_event(event_request)
|
||||
-> Context packager builds working context from projection + state + artifacts
|
||||
```
|
||||
|
||||
这样消息事件、工具事件、群成员事件、好友申请事件可以共用同一套 run/session/state/resource
|
||||
边界；非消息事件也不需要伪造成一条用户文本消息。
|
||||
|
||||
`event_request` 至少需要包含：
|
||||
|
||||
- `event_type`: 稳定协议名，例如 `message.recalled`、`group.member_joined`、`friend.request_received`
|
||||
- `event_id` / `event_timestamp`
|
||||
- `event_data`: 平台原始 payload 摘要和 source event type
|
||||
- `actor`: 触发者，例如撤回操作者、新成员、好友申请人
|
||||
- `subject`: 事件作用对象，例如被撤回消息、群/成员关系、好友申请
|
||||
- `conversation`: 可选。群事件有 launcher 语义，好友申请可能还没有 conversation
|
||||
- `input`: 可选结构化输入。非消息事件允许 `text=None`、`contents=[]`
|
||||
- `binding`: 事件绑定解析出的 runner id、runner config、资源范围
|
||||
|
||||
先保留的稳定事件名：
|
||||
|
||||
- `message.received`
|
||||
- `message.recalled`
|
||||
- `group.member_joined`
|
||||
- `friend.request_received`
|
||||
|
||||
这些事件名应作为插件协议的一部分保持稳定。平台原始事件名只能进入 `event_data`，不能成为 `ctx.event.event_type` 的公共契约。
|
||||
|
||||
### 3.6 result_normalizer.py
|
||||
|
||||
只接受 SDK v1 result：
|
||||
|
||||
- `message.delta`
|
||||
- `message.completed`
|
||||
- `tool.call.started`
|
||||
- `tool.call.completed`
|
||||
- `state.updated`
|
||||
- `run.completed`
|
||||
- `run.failed`
|
||||
- `action.requested` 允许实验性返回，但本阶段只记录 telemetry，不执行
|
||||
|
||||
映射：
|
||||
|
||||
- `message.delta.data.chunk` -> `provider_message.MessageChunk`
|
||||
- `message.completed.data.message` -> `provider_message.Message`
|
||||
- `run.completed.data.message` -> `provider_message.Message`
|
||||
- `run.failed` -> 抛出受控异常，让 `ChatMessageHandler` 使用现有错误策略
|
||||
- 工具和状态事件默认不 yield 到 Pipeline，只记录 debug/telemetry
|
||||
|
||||
防护：
|
||||
|
||||
- 未知 type warning 后忽略
|
||||
- 单 result 序列化大小限制
|
||||
- provider message schema 校验失败转 `run.failed`
|
||||
- 插件没有输出任何消息时，按 runner failed 处理
|
||||
|
||||
### 3.7 orchestrator.py
|
||||
|
||||
核心入口：
|
||||
|
||||
```python
|
||||
async def run_from_query(query: pipeline_query.Query) -> AsyncGenerator[Message | MessageChunk, None]:
|
||||
runner_id = resolve_runner_id(query.pipeline_config)
|
||||
descriptor = await registry.get(runner_id, bound_plugins=query.variables.get("_pipeline_bound_plugins"))
|
||||
ctx = await context_builder.from_query(query, descriptor)
|
||||
async for raw in plugin_connector.run_agent(...):
|
||||
async for message in result_normalizer.normalize(raw):
|
||||
yield message
|
||||
```
|
||||
|
||||
必须覆盖：
|
||||
|
||||
- runner id 不存在
|
||||
- 插件系统关闭
|
||||
- runner 不在 bound plugins 范围内
|
||||
- 插件 runtime 断连
|
||||
- runner 协议版本不兼容
|
||||
- run 超时
|
||||
- task cancellation
|
||||
|
||||
## 4. 配置模型直接切换
|
||||
|
||||
配置模型表达的是 Pipeline 到 runner id 的绑定，不表达插件实例。插件安装后由 plugin runtime 管理单个插件运行实例；不同 Pipeline 选择同一个 runner id 时，只是保存不同的 `runner_config[id]`，调用时随 `AgentRunContext.config` 传入。
|
||||
|
||||
目标格式：
|
||||
|
||||
```json
|
||||
{
|
||||
"ai": {
|
||||
"runner": {
|
||||
"id": "plugin:langbot/local-agent/default",
|
||||
"expire-time": 0
|
||||
},
|
||||
"runner_config": {
|
||||
"plugin:langbot/local-agent/default": {}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
兼容读取：
|
||||
|
||||
- 优先读 `ai.runner.id`
|
||||
- 没有 `id` 时读旧 `ai.runner.runner`
|
||||
- 旧内置 runner 名通过迁移表映射：
|
||||
- `local-agent` -> `plugin:langbot/local-agent/default`
|
||||
- `dify-service-api` -> `plugin:langbot/dify-agent/default`
|
||||
- `n8n-service-api` -> `plugin:langbot/n8n-agent/default`
|
||||
- `coze-api` -> `plugin:langbot/coze-agent/default`
|
||||
- `dashscope-app-api` -> `plugin:langbot/dashscope-agent/default`
|
||||
- `langflow-api` -> `plugin:langbot/langflow-agent/default`
|
||||
- `tbox-app-api` -> `plugin:langbot/tbox-agent/default`
|
||||
|
||||
写入策略：
|
||||
|
||||
- 新 UI 只写 `ai.runner.id` 和 `ai.runner_config`
|
||||
- 后端 update 接口接受旧字段，但保存时归一成新格式
|
||||
- migration 最后统一落库
|
||||
|
||||
## 5. 需要修改的 LangBot 范围
|
||||
|
||||
必须修改：
|
||||
|
||||
- `src/langbot/pkg/core/app.py`
|
||||
- 增加 `agent_runner_registry` / `agent_run_orchestrator` 属性
|
||||
- `src/langbot/pkg/core/stages/build_app.py`
|
||||
- 初始化 Agent 子系统
|
||||
- `src/langbot/pkg/pipeline/process/handlers/chat.py`
|
||||
- 删除 `PluginAgentRunnerWrapper`
|
||||
- 删除内置 runner 查找逻辑
|
||||
- 调用 orchestrator
|
||||
- `src/langbot/pkg/api/http/service/pipeline.py`
|
||||
- metadata 从 registry 生成
|
||||
- `src/langbot/pkg/plugin/connector.py`
|
||||
- `list_agent_runners()` / `run_agent()` 增加协议校验和 bound plugin 参数
|
||||
- `src/langbot/pkg/plugin/handler.py`
|
||||
- proxy action 二次权限校验
|
||||
- `src/langbot/pkg/pipeline/preproc/preproc.py`
|
||||
- 不再只为 `local-agent` 构造工具、知识库、模型
|
||||
- 对所有 agent runner 保留 multimodal input
|
||||
- `src/langbot/pkg/pipeline/pipelinemgr.py`
|
||||
- runner name 监控改读 `runner.id`
|
||||
- `src/langbot/templates/metadata/pipeline/ai.yaml`
|
||||
- runner 字段从 `runner` 迁到 `id`
|
||||
- `src/langbot/templates/default-pipeline-config.json`
|
||||
- 默认 runner 改为官方 local-agent 插件 id
|
||||
- `web/src/app/home/pipelines/components/pipeline-form/PipelineFormComponent.tsx`
|
||||
- 当前 runner 改读 `ai.runner.id`
|
||||
- runner 配置区改写入 `ai.runner_config[id]`
|
||||
|
||||
最终删除或停用：
|
||||
|
||||
- `src/langbot/pkg/provider/runner.py` 的业务注册路径
|
||||
- `src/langbot/pkg/provider/runners/*` 的运行入口
|
||||
|
||||
可以暂时保留文件作为官方插件迁移参考，但不应被运行时引用。
|
||||
|
||||
## 6. 收尾实现顺序
|
||||
|
||||
### Step 1：补齐宿主上下文
|
||||
|
||||
- SDK `AgentRunContext` 保持 event-first：`event/input/delivery/resources/context/state/runtime/config/bootstrap/adapter`。
|
||||
- LangBot context builder 只从 `AgentEventEnvelope + AgentBinding` 写入稳定协议字段。
|
||||
- Pipeline adapter 可以把公开业务变量写入 `ctx.adapter.extra["params"]`；legacy/effective prompt 若保留在 `ctx.adapter.extra["prompt"]`，也只属于 adapter metadata。
|
||||
- 保持 `ctx.config` 只表达静态绑定配置。
|
||||
|
||||
### Step 2：增强宿主 AgentRun proxy action
|
||||
|
||||
- `invoke_llm` / `invoke_llm_stream` 通过 `run_id/query_id` 找回当前 Query。
|
||||
- 自动合并 model persisted `extra_args` 与 action-level override。
|
||||
- 自动应用 pipeline `remove-think`，并允许 action 显式 override。
|
||||
- `call_tool` 传回当前 Query，恢复旧工具调用上下文。
|
||||
- `retrieve_knowledge` 保持 `bot_uuid`、`sender_id`、`session_name` 等 settings。
|
||||
- `invoke_rerank` 使用 run-scoped model authorization。
|
||||
|
||||
### Step 3：泛化资源构建
|
||||
|
||||
- 按 manifest permissions + bound plugins/MCP + runner config schema 构造资源。
|
||||
- 支持 primary/fallback LLM、rerank model、KB selector。
|
||||
- 不把 local-agent 特例扩散到通用资源层。
|
||||
|
||||
### Step 4：local-agent parity
|
||||
|
||||
- 使用静态绑定配置 `ctx.config["prompt"]`，不读取 `ctx.adapter.extra["prompt"]`。
|
||||
- 通过 Host history API 拉取 transcript，不读取 `ctx.bootstrap.messages` 或 adapter window 字段。
|
||||
- 当前 user message 从 `ctx.input.contents` 构造，保留多模态内容。
|
||||
- RAG 只替换/插入文本部分，不丢图片/文件。
|
||||
- streaming/non-streaming 默认跟随 `runtime.metadata.streaming_supported`。
|
||||
- 首轮 fallback 成功后，tool loop 固定使用 committed model。
|
||||
- tool loop 继续传可用 tools，支持多步工具调用。
|
||||
- rerank 通过授权模型资源调用。
|
||||
|
||||
### Step 5：端到端保护和测试
|
||||
|
||||
- 插件无输出时按 runner failed 处理。
|
||||
- timeout/deadline 覆盖 plugin runtime、模型调用和外部 runner 调用。
|
||||
- runner 协议错误转受控错误。
|
||||
- 覆盖 local-agent 用户可见行为：普通回复、流式、工具、多步工具、KB、rerank、多模态、绑定 prompt、history API。
|
||||
|
||||
### Step 6：官方 runner 迁移
|
||||
|
||||
- 官方插件 ready 后移除内置 runner registry
|
||||
- 删除或隔离 provider runners 的运行引用
|
||||
- 测试旧 runner 名只能通过 migration 映射到插件 id
|
||||
|
||||
### Step 7：历史配置迁移
|
||||
|
||||
- 写 persistence migration
|
||||
- 更新 default pipeline config
|
||||
- 对已存在 Pipeline 执行旧字段到新字段迁移
|
||||
- 对监控/日志里的 runner 字段改用新 id
|
||||
|
||||
## 7. 测试要求
|
||||
|
||||
单测：
|
||||
|
||||
- runner id parse / format
|
||||
- registry manifest 校验、失败隔离、bound plugins 过滤
|
||||
- context builder 从 query 生成完整 v1 context
|
||||
- resource builder 三层裁剪
|
||||
- result normalizer 对每种 result type 的映射
|
||||
- 旧配置 resolve 和 migration
|
||||
|
||||
集成测试：
|
||||
|
||||
- fake AgentRunner 插件可被 Pipeline 选择
|
||||
- streaming 输出仍能更新 message card
|
||||
- 插件异常返回用户可理解错误，不中断 runtime
|
||||
- runner 不在 bound plugins 时不可执行
|
||||
- 未授权工具 / 知识库 / 模型 proxy 调用被拒绝
|
||||
- 旧 `local-agent` Pipeline 配置迁到官方插件 id
|
||||
|
||||
## 8. 验收标准
|
||||
|
||||
- LangBot Pipeline 可以选择插件 AgentRunner 并完成非流式和流式回复。
|
||||
- `ChatMessageHandler` 不包含插件 runner 解析和 wrapper。
|
||||
- `PipelineService` 不直接拼插件 runner metadata。
|
||||
- 所有 runner 配置使用 `ai.runner.id` + `ai.runner_config`。
|
||||
- 插件 runtime 不为每个 Pipeline 或 runner 配置创建插件实例；`runner_config` 只作为绑定配置随 `ctx.config` 传入。
|
||||
- 主聊天路径不再通过旧内置 runner 执行业务 runner。迁移期间旧文件可以保留。
|
||||
- 插件只能访问 `ctx.resources` 授权的模型、工具、知识库和文件。
|
||||
- 宿主 action 能为 AgentRunner 调用恢复必要 Query 语义，插件不需要拿裸 Query。
|
||||
- 官方 `local-agent` 插件对外行为与旧内置 local-agent 对齐。
|
||||
- EBA 相关字段只作为 context/result 预留，不执行平台动作。
|
||||
@@ -1,329 +0,0 @@
|
||||
# 官方 AgentRunner 插件迁移计划
|
||||
|
||||
本文档描述内置 `RequestRunner` 迁出 LangBot 后，官方 runner 插件如何组织、迁移和验收。
|
||||
它是 [HOST_SDK_INFRASTRUCTURE.md](./HOST_SDK_INFRASTRUCTURE.md) 和
|
||||
[AGENT_CONTEXT_PROTOCOL.md](./AGENT_CONTEXT_PROTOCOL.md) 的下游落地计划，不是 LangBot
|
||||
宿主协议的设计前提。
|
||||
|
||||
官方 `local-agent` 可以外移，也可以重写。设计重点不是保留旧内置 runner 的内部结构，
|
||||
而是验证一个依附 LangBot host 基础设施的官方 agent 能否完整工作。同时，LangBot 的
|
||||
host 协议必须服务 Claude Code SDK、Codex、Pi Agent SDK、外部 Agent 平台等自管
|
||||
context/runtime 的 runner，不能被官方插件的实现细节绑死。
|
||||
|
||||
当前实现已经进入过渡阶段：
|
||||
|
||||
- LangBot 主聊天路径通过 `AgentRunOrchestrator` 调用插件化 `AgentRunner`。
|
||||
- 旧 `src/langbot/pkg/provider/runners/*` 仍保留，作为迁移参考和回退分析材料；在官方插件迁移完成前不要求删除。
|
||||
- 官方 runner 当前以独立插件目录/仓库推进，例如 `langbot-local-agent/` 和 `langbot-agent-runner/*-agent/`。不再要求先落地单一 monorepo。
|
||||
- `claude-code-agent` 与 `codex-agent` 已作为外部 harness runner MVP 接入，用来验证 Claude Code / Codex / Kimi Code 这类自管 runtime 的边界。
|
||||
|
||||
## 1. 为什么新仓库
|
||||
|
||||
官方 runner 插件会和 LangBot 主仓库、SDK 仓库以不同节奏迭代：
|
||||
|
||||
- LangBot 主仓库只维护宿主协议和调度。
|
||||
- SDK 仓库维护 AgentRunner 组件和 runtime 协议。
|
||||
- 官方 runner 插件承载业务 runner 的具体实现和第三方平台适配。
|
||||
|
||||
不要把官方 runner 插件重新绑死在 LangBot 主仓库内。允许开发期使用本地路径插件，但运行边界必须保持为：
|
||||
|
||||
- LangBot 提供通用宿主能力：当前事件、context handles、资源授权、状态/存储、历史、artifact、模型/工具/知识库调用代理、结果归一。
|
||||
- 插件消费这些公开能力，实现具体 runner 行为。
|
||||
- LangBot 默认不把全量历史消息 inline 给 runner；runner 按需通过授权 API 拉取历史和 artifact。
|
||||
- 旧内置 runner 只作为行为对齐的基准，不作为长期运行路径。
|
||||
|
||||
## 2. 仓库结构
|
||||
|
||||
当前推荐策略是“官方插件可独立发布，必要时共享 SDK helper”。开发期可以采用本地多目录布局：
|
||||
|
||||
```text
|
||||
langbot-app/
|
||||
langbot-local-agent/
|
||||
manifest.yaml
|
||||
components/agent_runner/default.yaml
|
||||
components/agent_runner/default.py
|
||||
pkg/
|
||||
tests/
|
||||
langbot-agent-runner/
|
||||
claude-code-agent/
|
||||
codex-agent/
|
||||
n8n-agent/
|
||||
...
|
||||
```
|
||||
|
||||
后续可以把多个官方 runner 聚合进 monorepo，也可以继续独立发布。这个选择不影响协议设计；协议边界由 SDK 和 LangBot 宿主保证。
|
||||
|
||||
如果多个 runner 出现重复逻辑，优先沉淀到 SDK 或一个明确的共享 helper 包，不要把宿主私有结构泄漏给插件。
|
||||
|
||||
## 3. 插件命名和 runner id
|
||||
|
||||
固定映射：
|
||||
|
||||
| 旧 runner | 官方插件 | runner id |
|
||||
| --- | --- | --- |
|
||||
| `local-agent` | `langbot/local-agent` | `plugin:langbot/local-agent/default` |
|
||||
| `dify-service-api` | `langbot/dify-agent` | `plugin:langbot/dify-agent/default` |
|
||||
| `n8n-service-api` | `langbot/n8n-agent` | `plugin:langbot/n8n-agent/default` |
|
||||
| `coze-api` | `langbot/coze-agent` | `plugin:langbot/coze-agent/default` |
|
||||
| - | `langbot/claude-code-agent` | `plugin:langbot/claude-code-agent/default` |
|
||||
| - | `langbot/codex-agent` | `plugin:langbot/codex-agent/default` |
|
||||
| `dashscope-app-api` | `langbot/dashscope-agent` | `plugin:langbot/dashscope-agent/default` |
|
||||
| `langflow-api` | `langbot/langflow-agent` | `plugin:langbot/langflow-agent/default` |
|
||||
| `tbox-app-api` | `langbot/tbox-agent` | `plugin:langbot/tbox-agent/default` |
|
||||
|
||||
每个插件可以后续提供多个 runner，但迁移目标的默认 runner 统一叫 `default`。
|
||||
|
||||
## 4. 迁移优先级
|
||||
|
||||
### Batch 1：打通协议
|
||||
|
||||
1. `local-agent`
|
||||
2. `claude-code-agent`
|
||||
3. `codex-agent`
|
||||
4. `dify-agent`
|
||||
|
||||
原因：
|
||||
|
||||
- `local-agent` 覆盖模型、工具、知识库、流式、会话历史，是能力最完整的基准。
|
||||
- `claude-code-agent` / `codex-agent` 代表 Claude Code / Codex / Kimi Code 这类本地或外部 code-agent harness：它们通常自带 session、tool loop、上下文压缩和权限模型，LangBot 主要提供 IM 事件、资源投影、审计和状态指针。
|
||||
- `dify-agent` 代表外部 Agent 平台调用，配置和错误处理能验证传统 service API runner 的迁移方式。
|
||||
|
||||
### Batch 2：迁移外部 workflow runner
|
||||
|
||||
1. `n8n-agent`
|
||||
2. `langflow-agent`
|
||||
|
||||
这批主要验证 webhook/workflow 输入输出、timeout、外部 conversation id。
|
||||
|
||||
### Batch 3：迁移平台 Agent API
|
||||
|
||||
1. `coze-agent`
|
||||
2. `dashscope-agent`
|
||||
3. `tbox-agent`
|
||||
|
||||
这批主要验证平台特有响应格式、引用资料、文件/图片输入。
|
||||
|
||||
## 5. 每个官方插件的组件要求
|
||||
|
||||
每个插件至少包含：
|
||||
|
||||
```yaml
|
||||
apiVersion: langbot/v1
|
||||
kind: AgentRunner
|
||||
metadata:
|
||||
name: default
|
||||
label:
|
||||
en_US: Dify Agent
|
||||
zh_Hans: Dify Agent
|
||||
description:
|
||||
en_US: Run a Dify application as a LangBot AgentRunner.
|
||||
zh_Hans: 将 Dify 应用作为 LangBot AgentRunner 运行。
|
||||
spec:
|
||||
config: []
|
||||
capabilities:
|
||||
streaming: true
|
||||
tool_calling: false
|
||||
knowledge_retrieval: false
|
||||
multimodal_input: false
|
||||
event_context: true
|
||||
platform_api: false
|
||||
interrupt: false
|
||||
stateful_session: true
|
||||
permissions:
|
||||
models: []
|
||||
tools: []
|
||||
knowledge_bases: []
|
||||
storage: ["plugin"]
|
||||
files: []
|
||||
platform_api: []
|
||||
execution:
|
||||
python:
|
||||
path: ./main.py
|
||||
attr: DefaultAgentRunner
|
||||
```
|
||||
|
||||
## 6. local-agent 插件方向
|
||||
|
||||
`local-agent` 是官方插件中的重要消费者，但不是宿主协议的设计中心。它可以选择复用
|
||||
旧实现，也可以完全重写。它需要证明：一个主要依附 LangBot host 能力的 agent runner
|
||||
可以通过公开协议完成模型、工具、知识库、状态、history、artifact、上下文压缩和消息投递。
|
||||
|
||||
LangBot core 不应为了 local-agent 保留业务编排逻辑。local-agent 的 prompt 组装、history
|
||||
拉取、summary/checkpoint、tool loop、RAG 编排、fallback、多模态处理都应在插件内完成。
|
||||
|
||||
迁移或重写时需要覆盖旧内置 runner 的用户可见能力：
|
||||
|
||||
- model primary/fallback 选择
|
||||
- prompt
|
||||
- knowledge-bases
|
||||
- rerank-model
|
||||
- rerank-top-k
|
||||
- function calling
|
||||
- streaming
|
||||
- multimodal input
|
||||
- conversation history
|
||||
- monitoring metadata
|
||||
|
||||
与 LangBot 主仓库的责任边界：
|
||||
|
||||
- LangBot 构造当前事件、结构化输入、资源授权、context handles、state/storage 能力和 delivery 能力
|
||||
- LangBot 不默认 inline 全量历史，不替插件组装最终模型上下文
|
||||
- 插件负责选择模型、拼请求、调用 LLM、处理 tool call loop、输出 result stream
|
||||
- 插件不能绕过 `ctx.resources` 调用未授权模型、工具或知识库
|
||||
|
||||
为了保持旧内置 runner 的用户可见行为，`local-agent` 插件应消费宿主处理后的有效输入和
|
||||
受限 API，而不是读取宿主内部私有结构：
|
||||
|
||||
- `ctx.event` / `ctx.input`：当前结构化输入，必须保留图片、文件等多模态内容。
|
||||
- `ctx.context`：history cursor、inline policy、可用 context API。
|
||||
- `AgentRunAPIProxy.history`：按需读取 transcript，而不是依赖 host 每轮强塞历史窗口。
|
||||
- `AgentRunAPIProxy.artifacts`：按需读取图片、文件、工具大结果。
|
||||
- `AgentRunAPIProxy.state` / storage：保存 summary、外部 conversation id、用户偏好等可选状态。
|
||||
- `ctx.resources`：已授权模型、工具、知识库、文件和 storage。
|
||||
- `ctx.runtime.metadata.streaming_supported`：当前 adapter 是否能消费流式输出。
|
||||
- 宿主代理 action：模型、工具、知识库、rerank 调用必须通过 `run_id` 校验资源权限。
|
||||
|
||||
`local-agent` 不应消费 Pipeline adapter 生成的历史窗口，也不应读取
|
||||
`ctx.adapter.extra.prompt`。它应从绑定配置读取静态 `prompt`，并通过 Host
|
||||
history API 拉取 transcript。Pipeline adapter 不保留 Host-side window 兼容逻辑。
|
||||
|
||||
建议 local-agent manifest 使用 hybrid 或 self-managed context：
|
||||
|
||||
```yaml
|
||||
context:
|
||||
ownership: hybrid
|
||||
bootstrap: current_event
|
||||
max_inline_events: 0
|
||||
max_inline_bytes: 0
|
||||
supports_history_pull: true
|
||||
supports_history_search: true
|
||||
supports_artifact_pull: true
|
||||
owns_compaction: true
|
||||
wants_static_context_refs: true
|
||||
```
|
||||
|
||||
这表示：LangBot 只给当前事件和 context handles；local-agent 自己决定是否拉取历史、是否搜索、
|
||||
何时摘要、如何构造最终 prompt。
|
||||
|
||||
### 6.1 Native Execution / Skills 后续接入
|
||||
|
||||
本阶段不把 sandbox/skills 做成 AgentRunner 协议字段，也不预留 runner 可见字段。
|
||||
后续 sandbox/skills 分支合并后，命令执行、文件操作、skill、MCP managed process
|
||||
等能力应先由 LangBot Host 封装成 scoped tools，再通过 `ctx.resources.tools`
|
||||
暴露给 runner。
|
||||
|
||||
这让 local-agent 只消费授权后的 Host 基础设施，而不是直接持有宿主机执行能力。
|
||||
Claude Code / Codex 这类外部 harness runner 仍可先保留自己的执行模型，但要在文档和
|
||||
配置中明确它们是否使用 LangBot 提供的工具投影。
|
||||
|
||||
## 7. 外部 runner 插件要求
|
||||
|
||||
外部平台 runner 迁移时遵循：
|
||||
|
||||
- 旧配置字段尽量保持同名，便于 migration 复制
|
||||
- 输出统一转换为 `AgentRunResult`
|
||||
- 外部 API timeout 从 runner config 读取
|
||||
- 平台 conversation id 存 plugin storage 或 context runtime state，不能依赖 LangBot 内置 conversation uuid 私有结构
|
||||
- 流式支持按平台能力声明，没有流式就只发 `message.completed`
|
||||
|
||||
### 7.1 Code-agent harness runner 要求
|
||||
|
||||
Claude Code、Codex、Kimi Code 这类 runner 不一定通过 LangBot 的模型/工具 loop 执行。它们可以依赖自己的 harness，但仍必须遵守 LangBot 的宿主边界：
|
||||
|
||||
- 输入来自 `ctx.event` / `ctx.input`，不能直接依赖 Pipeline 私有 `Query`。
|
||||
- LangBot 授权后的资源应被投影为 harness 可读的 context 文件、MCP 配置、skill 目录、环境变量或 CLI 参数。
|
||||
- 外部 session id、workspace、checkpoint 等跨轮次指针应写入 Host state 或 plugin storage；插件实例本身保持无状态。
|
||||
- CLI / subprocess runner 必须处理 timeout、取消、空输出、非零退出和 stderr 映射。
|
||||
- 如果外部 harness 选择使用 LangBot 托管执行能力，它应通过 scoped MCP/tool
|
||||
投影消费 Host 授权资源；否则它属于 external harness mode，不能声称具备
|
||||
LangBot-managed 执行隔离。
|
||||
- 外部 harness 的 permission mode、allowed/disallowed tools、MCP 配置只是一层执行约束；LangBot 仍负责调用前的资源授权、路径策略、secret 过滤和审计。发布级要求见 [SECURITY_HARDENING.md](./SECURITY_HARDENING.md)。
|
||||
|
||||
### 7.2 SDK-owned LangBot MCP bridge
|
||||
|
||||
Claude Code / Codex 这类外部 harness 不能直接持有 Python 进程内的
|
||||
`plugin_runtime_handler`，因此不能像 `local-agent` 一样直接调用
|
||||
`AgentRunAPIProxy`。当前轻量方案是由 SDK 提供一层 per-run MCP bridge：
|
||||
|
||||
- `AgentRunner.create_external_mcp_bridge(ctx)` 是 runner 父类入口。
|
||||
- Bridge 由 `AgentRunAPIProxy` 和 `AgentRunContext` 构造，生命周期只覆盖当前 run。
|
||||
- Bridge 暴露 SDK 中显式注解的 `AgentRunExternalTools`，而不是扫描或导出全部 SDK action。
|
||||
- MCP tool schema 由注解和 Pydantic args model 生成；runner 插件不各自手写 LangBot tool schema。
|
||||
- stdio MCP proxy 只把外部 harness 的 MCP 调用转发回当前 run 的本地 bridge。
|
||||
- run 结束后 bridge 关闭；这不是 LangBot 主程序全局 MCP server。
|
||||
|
||||
第一批工具保持很小：当前事件快照、history page、knowledge retrieve、authorized tool call。后续新增工具必须先进入 SDK-owned annotated surface，再由 MCP adapter 自动投影。
|
||||
|
||||
## 8. Claude Code runner 当前形态
|
||||
|
||||
当前 `claude-code-agent` 是最小可运行 MVP，用来证明外部 harness runner 可以接入同一套 AgentRunner 协议。
|
||||
|
||||
### 8.1 基本行为
|
||||
|
||||
- Runner ID：`plugin:langbot/claude-code-agent/default`
|
||||
- 执行方式：本地 Claude Code CLI print mode，默认命令为 `claude -p`
|
||||
- 默认输出：`message.completed` + `run.completed`
|
||||
- 默认权限：`permission-mode=plan`、`max-turns=1`、`disallowedTools=AskUserQuestion`
|
||||
- 默认状态：如果 Claude Code 返回 `session_id`，runner 通过 `state.updated` 写回 `external.session_id`
|
||||
- 工作目录：优先使用 binding config 的 `working-directory`，其次使用 Host state 中的 `external.working_directory`
|
||||
|
||||
### 8.2 Context / skill / MCP 投影
|
||||
|
||||
Claude Code runner 当前把 LangBot event-first context 投影给外部 harness：
|
||||
|
||||
- 写入 `agent-context.json`，schema 为 `langbot.agent_runner.external_harness_context.v1`
|
||||
- 写入 `LANGBOT_CONTEXT.md`，作为人类可读摘要
|
||||
- 将 prompt prefix 指向 context 文件路径
|
||||
- 可把 binding 提供的 `skills-json` 写入 Claude Code 原生 `.claude/skills/<name>/SKILL.md`
|
||||
- 可把 binding 提供的 `mcp-config-json` 写成每次 run 的 MCP config，并通过 `--mcp-config` / `--strict-mcp-config` 传给 Claude Code
|
||||
- 可通过 `enable-langbot-mcp=true` 启用 SDK-owned per-run LangBot MCP bridge，使 Claude Code 通过 MCP 调用受限的 `AgentRunAPIProxy` 能力
|
||||
|
||||
这些投影目前由 runner adapter 完成；长期更理想的形态是 LangBot Host 负责生成 scoped resource projection，runner 只负责适配 Claude Code 的原生目录和 CLI 参数。
|
||||
|
||||
### 8.3 已验证能力
|
||||
|
||||
2026-05-29 本地验证：
|
||||
|
||||
- WebUI Debug Chat 能通过 Pipeline adapter 调用 `claude-code-agent`
|
||||
- Claude Code 能读取 LangBot context 文件并按指令输出 sentinel
|
||||
- Skill 文件可以投影到 `.claude/skills/`
|
||||
- MCP config 可以通过 binding config 投影为 Claude Code CLI 参数
|
||||
- SDK-owned per-run LangBot MCP bridge 可以被真实 Claude Code CLI 调用，并通过 `langbot_get_current_event` 读取当前 run_id
|
||||
- `external.session_id` 与 `external.working_directory` 可以写入 host-owned state，用于后续 resume
|
||||
- `codex-agent` 可通过 WebUI Debug Chat 调用本机 Codex CLI，读取 LangBot event context，并把 Codex `thread_id` 写入 host-owned state
|
||||
- SDK-owned per-run LangBot MCP bridge 可以被真实 Codex CLI 调用，并通过 `langbot_get_current_event` 读取当前 run_id
|
||||
- 对需要代理的本地运行环境，`codex-agent` 可通过 binding config 的 `environment-json` 显式传递非 secret 环境变量
|
||||
|
||||
下一轮测试入口见 [PHASE1_QA_ACCEPTANCE_MATRIX.md](./PHASE1_QA_ACCEPTANCE_MATRIX.md)。
|
||||
|
||||
### 8.4 当前限制
|
||||
|
||||
- 不是发布级安全边界实现。
|
||||
- 默认只做本地 CLI 调用，不实现完整执行隔离或 workspace 生命周期。
|
||||
- 不实现 issue-centric 队列、复杂 workflow engine 或长期任务调度。
|
||||
- 不代表 Codex 发布级能力或 Kimi runner 已完成；当前只验证外部 harness runner 的协议形态。
|
||||
|
||||
## 9. 发布和安装策略
|
||||
|
||||
最终 LangBot 安装或升级时需要保证官方 runner 插件可用。可选方案：
|
||||
|
||||
1. 首次启动检测缺失官方 runner 插件并提示安装。
|
||||
2. 打包发行版时预装官方 runner 插件。
|
||||
3. 在 migration 前检查对应插件是否存在，不存在则自动安装或阻止迁移。
|
||||
|
||||
建议实现顺序：
|
||||
|
||||
- 开发阶段使用本地路径插件。
|
||||
- 发布前支持 marketplace 安装。
|
||||
- 历史配置 migration 只在官方插件可用时执行。
|
||||
- 迁移期间保留旧内置 runner 文件，直到对应官方插件通过 parity 验收。
|
||||
|
||||
## 10. 验收标准
|
||||
|
||||
- 每个旧 runner 都有对应官方 AgentRunner 插件。
|
||||
- 旧 runner 配置能无损复制到新 `runner_config[id]`。
|
||||
- LangBot 主聊天路径不再通过 `RequestRunner` 执行业务 runner。
|
||||
- 官方插件测试覆盖非流式、流式、错误、timeout、配置缺失。
|
||||
- `local-agent` 插件能完成模型 fallback、tool calling、知识库检索、多模态输入、静态绑定 prompt 消费、history API 拉取、rerank。
|
||||
- `claude-code-agent` 或同类 code-agent harness runner 能消费 event-first context、投影 scoped resources、保存 external session state，并通过 WebUI Debug Chat smoke。
|
||||
- 对外行为与旧内置 local-agent runner 保持一致；代码结构不需要相同。
|
||||
@@ -1,245 +0,0 @@
|
||||
# Agent Runner QA 指南
|
||||
|
||||
本文档是 agent-runner 插件化下一轮测试的唯一 QA 入口。它合并并取代旧的 Phase 1 验收矩阵与 2026-05-18 / 2026-05-29 两份本地 QA 报告。
|
||||
|
||||
目标不是保留完整历史流水账，而是指导测试 agent 用最小但高价值的路径判断当前分支是否仍然健康。
|
||||
|
||||
## 1. 测试边界
|
||||
|
||||
当前主线验证的是 AgentRunner Protocol v1：
|
||||
|
||||
```text
|
||||
event -> binding -> runner.run(ctx) -> result stream
|
||||
```
|
||||
|
||||
本指南验证：
|
||||
|
||||
- Host 能通过当前 Pipeline adapter 进入 event-first `run(event, binding)` 主链路。
|
||||
- Runner 来自插件 registry，而不是旧内置 runner 分支。
|
||||
- `local-agent` 能消费 Host 模型、工具、知识库、history、state、artifact 等基础设施。
|
||||
- 外部 harness runner（Claude Code / Codex）能消费 event-first context，并把 session / working directory 等指针写回 host-owned state。
|
||||
- 错误、权限裁剪、无输出、timeout 等路径不会破坏主聊天流程。
|
||||
|
||||
本指南不验证：
|
||||
|
||||
- Runtime Control Plane v2。
|
||||
- EventGateway / EventRouter 完整落地。
|
||||
- 发布级 path isolation、secret filtering、MCP allowlist、资源配额和 workspace cleanup。
|
||||
- 所有外部服务 runner 的真实凭据联调。
|
||||
|
||||
这些属于后续能力或发布门槛，分别见 [RUNTIME_CONTROL_PLANE_V2.md](./RUNTIME_CONTROL_PLANE_V2.md) 与 [SECURITY_HARDENING.md](./SECURITY_HARDENING.md)。
|
||||
|
||||
## 2. 状态定义
|
||||
|
||||
测试报告只使用以下状态：
|
||||
|
||||
| 状态 | 含义 |
|
||||
| --- | --- |
|
||||
| PASS | 按步骤执行，用户可见行为和日志证据都满足通过条件。 |
|
||||
| FAIL | 环境可用，但行为不满足通过条件。 |
|
||||
| BLOCKED | 凭据、CLI、外部服务、测试数据或本地配置缺失导致无法执行。必须写清阻塞原因。 |
|
||||
| N/A | 当前 runner 或平台明确不支持该能力。必须引用 manifest、文档或配置说明。 |
|
||||
|
||||
不能使用“看起来正常”“大概通过”“基本没问题”等模糊状态。
|
||||
|
||||
## 3. 执行顺序
|
||||
|
||||
推荐按以下顺序执行，前一层失败时不要继续扩大测试面：
|
||||
|
||||
1. Host / SDK / runner 单测。
|
||||
2. WebUI 登录与 Pipeline Debug Chat 基础 smoke。
|
||||
3. `local-agent` 高价值场景。
|
||||
4. Claude Code / Codex 外部 harness smoke。
|
||||
5. 权限和错误路径补充检查。
|
||||
6. 汇总 PASS / FAIL / BLOCKED，并给出下一步建议。
|
||||
|
||||
用户可见流程必须通过 WebUI 或真实消息平台验证。API / curl 只能作为诊断证据，不能单独让 UI case PASS。
|
||||
|
||||
## 4. 必跑基线
|
||||
|
||||
### 4.1 单测基线
|
||||
|
||||
在 LangBot 仓库运行：
|
||||
|
||||
```bash
|
||||
uv run --frozen pytest tests/unit_tests/agent
|
||||
```
|
||||
|
||||
如果本次改动只触及默认配置或 API service，也至少补跑相关目标测试，例如：
|
||||
|
||||
```bash
|
||||
uv run pytest tests/unit_tests/api/test_pipeline_service_defaults.py
|
||||
```
|
||||
|
||||
通过条件：
|
||||
|
||||
- agent 单测全 PASS，或失败项已确认与本次 agent-runner 路径无关。
|
||||
- 若失败来自 `context_builder`、`orchestrator`、`session_registry`、`resource_builder`、`plugin/handler.py` 的 run action 权限路径，不应进入 UI smoke。
|
||||
|
||||
### 4.2 环境基线
|
||||
|
||||
用 `langbot-skills` 做环境检查：
|
||||
|
||||
```bash
|
||||
cd "$LANGBOT_SKILLS_REPO"
|
||||
bin/lbs env doctor
|
||||
bin/lbs case list
|
||||
```
|
||||
|
||||
`LANGBOT_SKILLS_REPO` 指向当前工作区里的 `langbot-skills` 仓库。优先使用已有 case，而不是临时发明测试路径。
|
||||
|
||||
推荐首批 case：
|
||||
|
||||
- `webui-login-state`
|
||||
- `pipeline-debug-chat`
|
||||
- `local-agent-basic-debug-chat`
|
||||
- `local-agent-rag-debug-chat`（改动涉及 RAG / knowledge）
|
||||
- `local-agent-plugin-tool-call-debug-chat`（改动涉及 tool / resource policy）
|
||||
|
||||
## 5. WebUI 主链路 Smoke
|
||||
|
||||
### 5.1 Runner registry
|
||||
|
||||
步骤：
|
||||
|
||||
1. 打开 WebUI Pipeline 配置页。
|
||||
2. 查看 AI runner 下拉列表。
|
||||
3. 选择 `plugin:langbot/local-agent/default`。
|
||||
4. 保存并刷新页面。
|
||||
|
||||
通过条件：
|
||||
|
||||
- runner 选项来自插件 registry。
|
||||
- 保存后配置仍为 `ai.runner.id` + `ai.runner_config[id]`。
|
||||
- `runner_config` 表示 binding config，不表示插件实例状态。
|
||||
- 插件没有循环重启或 metadata 加载失败。
|
||||
|
||||
### 5.2 主聊天路径
|
||||
|
||||
步骤：
|
||||
|
||||
1. 使用绑定 `plugin:langbot/local-agent/default` 的 Pipeline。
|
||||
2. 在 Debug Chat 发送确定性普通文本。
|
||||
3. 查看 WebUI 回复和后端日志。
|
||||
|
||||
通过条件：
|
||||
|
||||
- 用户可见回复正常。
|
||||
- 后端日志显示走 `AgentRunOrchestrator` / `RUN_AGENT`。
|
||||
- 不走旧内置 local-agent 主执行分支。
|
||||
- conversation transcript 写入用户消息和助手消息。
|
||||
|
||||
## 6. `local-agent` 高价值测试
|
||||
|
||||
只保留最能覆盖架构边界的场景。
|
||||
|
||||
| ID | 场景 | 操作 | 通过条件 |
|
||||
| --- | --- | --- | --- |
|
||||
| LA-01 | 绑定 prompt | 配置 system prompt 后发送文本。 | runner 使用 `ctx.config.prompt`，不读取 `ctx.adapter.extra["prompt"]`；回复体现绑定 prompt。 |
|
||||
| LA-02 | history API | 连续两轮对话，第二轮引用第一轮 marker。 | runner 通过 Host history API 或自管上下文读取历史，不依赖 bootstrap window。 |
|
||||
| LA-03 | 流式 / 非流式 | 分别用支持流式和关闭流式的路径发送文本。 | 流式 UI 不重复、不空白；非流式只输出最终消息。 |
|
||||
| LA-04 | 工具调用 | 绑定测试工具，发送会触发工具的 prompt。 | `ctx.resources.tools` 只包含授权工具；工具调用 started/completed；最终回复包含工具结果。 |
|
||||
| LA-05 | RAG | 绑定测试知识库，发送命中文档的 prompt。 | `ctx.resources.knowledge_bases` 包含所选知识库；runner 通过授权 API 检索；回复使用检索内容。 |
|
||||
| LA-06 | 多模态 | 发送图片输入。 | `ctx.input.contents` 保留图片；支持视觉模型时正常处理，不支持时受控失败。 |
|
||||
| LA-07 | fallback / 错误 | 模拟 primary 模型失败或 runner 抛错。 | fallback 或 `run.failed` 行为受控；后续请求不受影响。 |
|
||||
| LA-08 | 无输出保护 | 测试 runner 完成但不产出消息。 | 不产生空白成功回复；按受控失败或明确缺陷处理。 |
|
||||
|
||||
Rerank、remove-think、文件输入等场景只在本次改动直接涉及时补测，不作为每轮必跑项。
|
||||
|
||||
## 7. 外部 Harness Runner Smoke
|
||||
|
||||
这些测试用于验证 Claude Code / Codex 这类自管 runtime 能走同一条 Host 协议路径。若本机没有 CLI、登录态或代理配置，标记 BLOCKED，不要伪造 PASS。
|
||||
|
||||
### 7.1 Claude Code runner
|
||||
|
||||
步骤：
|
||||
|
||||
1. 确认 `claude` CLI 在 LangBot runtime host 上可执行。
|
||||
2. 绑定 `plugin:langbot/claude-code-agent/default`。
|
||||
3. 使用保守权限模式和确定性 prompt。
|
||||
4. 在 Debug Chat 执行一次真实 smoke。
|
||||
5. 检查 context / skill / MCP projection 和 host-owned state。
|
||||
|
||||
通过条件：
|
||||
|
||||
- WebUI 可见回复包含预期 sentinel。
|
||||
- context JSON schema 为 `langbot.agent_runner.external_harness_context.v1` 或当前文档声明的等价 schema。
|
||||
- context 包含 event、input、delivery、resources、context、state。
|
||||
- 如启用 skills / MCP，投影路径和配置可被 Claude Code 读取。
|
||||
- `external.session_id` / `external.working_directory` 写入 host-owned state。
|
||||
- CLI missing、nonzero exit、timeout、empty output 都转成受控 `run.failed`。
|
||||
|
||||
### 7.2 Codex runner
|
||||
|
||||
步骤：
|
||||
|
||||
1. 确认 `codex` CLI 在 LangBot runtime host 上可执行。
|
||||
2. 绑定 `plugin:langbot/codex-agent/default`。
|
||||
3. 如需要代理，使用 binding config 的 `environment-json` 显式传入。
|
||||
4. 在 Debug Chat 执行一次真实 smoke。
|
||||
5. 检查 JSONL 事件、last message、host-owned state。
|
||||
|
||||
通过条件：
|
||||
|
||||
- WebUI 可见回复包含预期 sentinel。
|
||||
- Codex JSONL 至少包含 thread/session 起始事件、agent message、turn completed。
|
||||
- `external.session_id` / `external.working_directory` 写入 host-owned state。
|
||||
- timeout/cancel 不遗留 orphan CLI 子进程。
|
||||
- CLI missing、nonzero exit、timeout、empty output 都转成受控 `run.failed`。
|
||||
|
||||
### 7.3 API 型外部 runner
|
||||
|
||||
Dify、n8n、Coze、DashScope、Langflow、Tbox 等外部服务 runner 不作为每轮必跑项。只有在本次改动触及对应 runner 或凭据已经可用时执行 smoke。
|
||||
|
||||
通过条件：
|
||||
|
||||
- runner 可选，配置可保存。
|
||||
- 请求成功，或外部服务错误被清晰返回。
|
||||
- 外部服务凭据缺失时标记 BLOCKED，并记录缺失项。
|
||||
|
||||
## 8. 权限与隔离补充
|
||||
|
||||
以下优先用单测 / targeted fixture 覆盖，不要求每次通过 UI 人工构造恶意 runner。
|
||||
|
||||
| 场景 | 推荐证据 |
|
||||
| --- | --- |
|
||||
| 未授权模型调用被拒绝 | `plugin/handler.py` run action 权限测试或目标单测。 |
|
||||
| 未授权工具调用被拒绝 | `ctx.resources.tools` 与 host action 拒绝日志。 |
|
||||
| 未授权知识库检索被拒绝 | `ctx.resources.knowledge_bases` 与 host action 拒绝日志。 |
|
||||
| run_id 结束后复用被拒绝 | session registry 注销测试。 |
|
||||
| 插件身份不匹配被拒绝 | `caller_plugin_identity` mismatch 测试。 |
|
||||
| storage/state scope 越权被拒绝 | state/storage proxy 单测。 |
|
||||
|
||||
如果这些单测失败，不能用 WebUI 正常回复替代。
|
||||
|
||||
## 9. 证据要求
|
||||
|
||||
每轮测试报告至少记录：
|
||||
|
||||
- LangBot commit、SDK commit、相关 runner 插件 commit。
|
||||
- Pipeline UUID/name、runner id、关键 runner config 摘要。
|
||||
- WebUI 截图或 Playwright 操作记录。
|
||||
- 后端日志中对应 query id / run id 的关键行。
|
||||
- `langbot-skills` case/report 路径。
|
||||
- 外部 harness runner 的 context 文件、session id、working directory、CLI 错误摘要。
|
||||
- FAIL/BLOCKED 的复现步骤和归属仓库建议。
|
||||
|
||||
报告结论必须回答：
|
||||
|
||||
- 是否建议继续进入下一阶段测试。
|
||||
- 是否存在主聊天路径阻塞。
|
||||
- 是否只是凭据 / 外部服务 / 本机 CLI 缺失导致 BLOCKED。
|
||||
- 是否需要进入 [SECURITY_HARDENING.md](./SECURITY_HARDENING.md) 的发布级验收。
|
||||
|
||||
## 10. 历史高价值记录
|
||||
|
||||
历史报告已合并为本指南，不再保留单独文档。后续若需要追溯，优先查看 `langbot-skills/reports/` 下的原始执行报告。
|
||||
|
||||
截至 2026-05-29，已有本地 smoke 证明：
|
||||
|
||||
- `local-agent` 可以通过 Pipeline Debug Chat 走插件化 `AgentRunOrchestrator` 主链路。
|
||||
- Claude Code runner 可以通过同一条 `run(event, binding)` 路径执行。
|
||||
- Claude Code runner 可以读取 LangBot event-first context / skill / MCP 投影，并写回 `external.session_id` / `external.working_directory`。
|
||||
- Codex runner 可以通过同一条路径执行，并把 Codex `thread_id` 写回 host-owned state。
|
||||
|
||||
这些记录只证明本地协议闭环可用，不代表发布级 security hardening 已完成。
|
||||
@@ -1,157 +0,0 @@
|
||||
# Agent Runner 插件化实现进度
|
||||
|
||||
本文档跟踪 Agent Runner 插件化的实现状态，便于快速了解当前进度。
|
||||
|
||||
## 总体进度
|
||||
|
||||
**当前阶段**: Phase 3.5 已完成，Event-first 基础设施已完成；2026-05-29 已通过本地 `local-agent` 与 Claude Code runner smoke。
|
||||
|
||||
| Phase | 描述 | 状态 |
|
||||
|-------|------|------|
|
||||
| Phase 0 | PoC 验证 | ✅ 完成 |
|
||||
| Phase 1 | 核心架构（Registry、Orchestrator、上下文模型） | ✅ 完成 |
|
||||
| Phase 2 | 权限、能力声明、资源注入 | ✅ 完成 |
|
||||
| Phase 3 | 内置 runner 迁移到插件 | ✅ 完成（7/7） |
|
||||
| Phase 3.5 | Event-first 基础设施 | ✅ 完成 |
|
||||
| Phase 3.6 | 外部 harness runner 协议 smoke | ✅ 完成（Claude Code MVP） |
|
||||
| Phase 4 | EBA 事件支持 | 🔲 未开始（已预留 event-first 入口，EventGateway 由其他分支实现） |
|
||||
|
||||
---
|
||||
|
||||
## 详细状态
|
||||
|
||||
### SDK 侧 (`langbot-plugin-sdk`)
|
||||
|
||||
| 组件 | 状态 | 备注 |
|
||||
|------|------|------|
|
||||
| `AgentRunner` 组件 | ✅ | `api/definition/components/agent_runner/runner.py` |
|
||||
| `AgentRunContext` | ✅ | `api/entities/builtin/agent_runner/context.py` |
|
||||
| `AgentRunResult` | ✅ | `api/entities/builtin/agent_runner/result.py` |
|
||||
| `AgentRunnerCapabilities` | ✅ | `api/entities/builtin/agent_runner/capabilities.py` |
|
||||
| `AgentRunnerPermissions` | ✅ | `api/entities/builtin/agent_runner/permissions.py` |
|
||||
| EBA 事件模型 (Event/Actor/Subject) | ✅ | `api/entities/builtin/agent_runner/event.py` |
|
||||
| `LIST_AGENT_RUNNERS` action | ✅ | `runtime/io/handlers/control.py` |
|
||||
| `RUN_AGENT` action | ✅ | `runtime/io/handlers/control.py` |
|
||||
| `AgentRunAPIProxy` | ✅ | `api/proxies/agent_run_api.py` |
|
||||
| Pull API handlers (State/History/Event/Artifact) | ✅ | `runtime/io/handlers/plugin.py` |
|
||||
| `caller_plugin_identity` injection | ✅ | Pull API handlers inject caller identity |
|
||||
|
||||
### LangBot 侧
|
||||
|
||||
| 组件 | 状态 | 备注 |
|
||||
|------|------|------|
|
||||
| `AgentRunnerRegistry` | ✅ | `pkg/agent/runner/registry.py` |
|
||||
| `AgentRunOrchestrator` | ✅ | `pkg/agent/runner/orchestrator.py` - event-first `run(event, binding)` |
|
||||
| `AgentRunnerDescriptor` | ✅ | `pkg/agent/runner/descriptor.py` |
|
||||
| `AgentResourceBuilder` | ✅ | `pkg/agent/runner/resource_builder.py` |
|
||||
| `AgentRunContextBuilder` | ✅ | `pkg/agent/runner/context_builder.py` - event-first context |
|
||||
| `AgentResultNormalizer` | ✅ | `pkg/agent/runner/result_normalizer.py` |
|
||||
| `ConfigMigration` | ✅ | `pkg/agent/runner/config_migration.py` |
|
||||
| `PipelineAdapter` | ✅ | `pkg/agent/runner/pipeline_adapter.py` - Query → Event + Binding |
|
||||
| `run_from_query()` → `run(event, binding)` | ✅ | Pipeline 路径委托到 event-first path |
|
||||
| `ChatMessageHandler` 集成 | ✅ | 使用 orchestrator 替代 wrapper |
|
||||
| `PipelineService` 集成 | ✅ | 从 registry 获取 runner metadata |
|
||||
| Plugin connector | ✅ | `list_agent_runners()` / `run_agent()` |
|
||||
| `EventLogStore` | ✅ | `pkg/agent/runner/event_log_store.py` |
|
||||
| `TranscriptStore` | ✅ | `pkg/agent/runner/transcript_store.py` |
|
||||
| `ArtifactStore` | ✅ | `pkg/agent/runner/artifact_store.py` |
|
||||
| `PersistentStateStore` | ✅ | `pkg/agent/runner/persistent_state_store.py` |
|
||||
| History / Event pull APIs | ✅ | Orchestrator + APIProxy |
|
||||
| Artifact pull APIs | ✅ | Orchestrator + APIProxy |
|
||||
| State pull APIs | ✅ | Orchestrator + APIProxy |
|
||||
| `artifact.created` / `state.updated` handling | ✅ | Event-first handlers in orchestrator |
|
||||
| Pipeline path host capability coverage | ✅ | EventLog/Transcript/ArtifactStore/PersistentStateStore |
|
||||
| External harness state handoff | ✅ | `external.session_id` / `external.working_directory` 写入 PersistentStateStore |
|
||||
|
||||
### 官方插件
|
||||
|
||||
> 外部服务插件仓库：`/home/glwuy/langbot-app/langbot-agent-runner/`
|
||||
> 本地 Local Agent 插件仓库：`/home/glwuy/langbot-app/langbot-local-agent/`
|
||||
|
||||
| 插件 | 状态 | 备注 |
|
||||
|------|------|------|
|
||||
| `local-agent` | ✅ 已完成 | 核心功能：模型、工具、知识库、流式、会话 |
|
||||
| `dify-agent` | ✅ 已完成 | 支持 chat/agent/workflow 三种应用类型 |
|
||||
| `n8n-agent` | ✅ 已完成 | Webhook 调用，支持 basic/jwt/header 认证 |
|
||||
| `coze-agent` | ✅ 已完成 | 多模态输入，思维链处理 |
|
||||
| `claude-code-agent` | ✅ MVP smoke 通过 | 本地 Claude Code CLI；context / skill / MCP 投影；host-owned resume state |
|
||||
| `dashscope-agent` | ✅ 已完成 | 阿里云百炼，支持 agent/workflow 两种模式 |
|
||||
| `langflow-agent` | ✅ 已完成 | SSE 流式，tweaks 配置支持 |
|
||||
| `tbox-agent` | ✅ 已完成 | 蚂蚁百宝箱，多模态输入 |
|
||||
|
||||
**注意**: LangBot 内置 runner（`pkg/provider/runners/`）已停用，文件顶部添加了 DEPRECATED 注释。
|
||||
|
||||
### 本地验收
|
||||
|
||||
| 日期 | 范围 | 状态 | 证据 |
|
||||
|------|------|------|------|
|
||||
| 2026-05-29 | `local-agent` Pipeline Debug Chat | ✅ PASS | `langbot-skills/reports/2026-05-29-17-59-00-462-08-00-pipeline-debug-chat.md` |
|
||||
| 2026-05-29 | `claude-code-agent` Pipeline Debug Chat | ✅ PASS | `langbot-skills/reports/2026-05-29-18-03-31-169-08-00-pipeline-debug-chat.md` |
|
||||
| 2026-05-29 | Claude Code context / skill / MCP projection | ✅ PASS | `langbot-skills/reports/claude-code-agent-resource-context-20260529.md` |
|
||||
| 2026-05-29 | Claude Code resume state | ✅ PASS | `langbot-skills/reports/claude-code-agent-real-workdir-20260529.md` |
|
||||
|
||||
---
|
||||
|
||||
## 未完成但仍属本分支收尾
|
||||
|
||||
以下项目属于本分支收尾工作：
|
||||
|
||||
- [x] Smoke / manual validation — `local-agent`、Claude Code MVP、Codex MVP 已通过本地 WebUI smoke
|
||||
- [ ] Docs final QA
|
||||
- [ ] Claude Code runner 文档、安装和 marketplace 发布准备
|
||||
|
||||
---
|
||||
|
||||
## 非本分支范围
|
||||
|
||||
以下能力由其他分支负责：
|
||||
|
||||
| 能力 | 负责分支 | 备注 |
|
||||
|------|----------|------|
|
||||
| EventGateway implementation | event branch | 完整事件网关、事件路由、持久化管理 |
|
||||
| Event subscription / notification | event branch | 事件订阅、推送通知 |
|
||||
| BindingResolver persistence UI | 其他模块 | 绑定配置的持久化 UI |
|
||||
| Event router integration | event branch | 与 BindingResolver 集成 |
|
||||
| Scheduler / background event source | 其他模块 | 定时任务、后台事件源 |
|
||||
| Security release hardening | 后续 release gate | 路径隔离、权限边界、secret、MCP/skill 投影策略、资源配额、审计 |
|
||||
| Codex / Kimi runner 全量接入 | 后续 runner 插件工作 | Codex MVP 已打通；Codex 发布级能力、Kimi runner 和全量 hardening 仍不扩大到当前协议闭环 |
|
||||
| Issue-centric 产品模型 / 异步队列 / workflow engine | 后续产品架构 | 不属于当前 agent-runner plugin 协议闭环 |
|
||||
|
||||
---
|
||||
|
||||
## 待办事项
|
||||
|
||||
### 高优先级
|
||||
|
||||
- [x] 工具详情 API — SDK `GET_TOOL_DETAIL` action、`AgentRunAPIProxy.get_tool_detail()` 与 Host 侧授权校验已接通
|
||||
- [x] Pipeline `run_from_query()` → `run(event, binding)` — 已完成
|
||||
- [x] EventLog / Transcript / ArtifactStore / PersistentStateStore — 已完成
|
||||
- [x] History / Event / Artifact / State pull APIs — 已完成
|
||||
- [x] `caller_plugin_identity` 验证路径 — 已完成
|
||||
|
||||
### 低优先级 / 未来
|
||||
|
||||
- [ ] EBA 完整集成 — EventGateway、event subscription、event notification 由其他分支实现
|
||||
- [ ] 平台 API 动作执行 — `action.requested` 结果类型存在但未执行
|
||||
- [ ] 安全发布级 hardening — 作为生产默认启用前的 release gate，不阻塞当前协议闭环
|
||||
|
||||
---
|
||||
|
||||
## 关键决策记录
|
||||
|
||||
| 日期 | 决策 |
|
||||
|------|------|
|
||||
| 2026-05-10 | Phase 0 集成测试通过，SDK v1 协议验证成功 |
|
||||
| 2026-05-13 | Phase 3 完成：所有 7 个官方 runner 插件迁移完成 |
|
||||
| 2026-05-23 | Phase 3.5 完成：`run_from_query()` 委托到 event-first `run(event, binding)`，Pipeline path 获得 host capabilities |
|
||||
| 2026-05-29 | 本地 `local-agent` 与 `claude-code-agent` 通过 WebUI smoke；Claude Code runner 验证 external harness context 投影和 host-owned resume state |
|
||||
|
||||
---
|
||||
|
||||
## 相关文档
|
||||
|
||||
- [README.md](./README.md) — 总体设计
|
||||
- [PHASE1_QA_ACCEPTANCE_MATRIX.md](./PHASE1_QA_ACCEPTANCE_MATRIX.md) — Agent Runner QA 指南和下一轮测试入口
|
||||
- [OFFICIAL_RUNNER_PLUGINS.md](./OFFICIAL_RUNNER_PLUGINS.md) — 官方插件仓库计划
|
||||
- [SECURITY_HARDENING.md](./SECURITY_HARDENING.md) — 安全发布级 hardening 后续门槛
|
||||
- [IMPLEMENTATION_PLAN.md](./IMPLEMENTATION_PLAN.md) — 具体实施细节
|
||||
@@ -1,702 +0,0 @@
|
||||
# LangBot AgentRunner Protocol v1
|
||||
|
||||
本文档定义 LangBot Host 与插件 SDK / Runtime / AgentRunner 之间的协议合同。它优先描述”稳定接口应是什么”，不描述具体落地任务。
|
||||
|
||||
## 当前状态
|
||||
|
||||
**Protocol v1 已在当前分支落地**：
|
||||
|
||||
- ✅ SDK 定义 `AgentRunnerManifest`、`AgentRunContext`、`AgentRunResult`、`AgentRunAPIProxy`
|
||||
- ✅ Runtime 支持 `LIST_AGENT_RUNNERS` 和 `RUN_AGENT`
|
||||
- ✅ Host 支持 `run_id` session authorization
|
||||
- ✅ Host 能从当前 Pipeline 入口生成 event-first context
|
||||
- ✅ `messages` 降级为 optional bootstrap
|
||||
- ✅ `max-round` 不出现在协议实体中，也不属于 Host / Pipeline 语义；类似参数若存在，由 runner 自己解释 `ctx.config`
|
||||
- ✅ Proxy 覆盖 model、tool、knowledge、state/storage
|
||||
- ✅ History / Event / Artifact / State API 已落地
|
||||
- ✅ EventLog / Transcript / ArtifactStore / PersistentStateStore 已落地
|
||||
- ✅ `local-agent` 与 Claude Code runner 已通过本地 WebUI smoke，验证 host-infra runner 与外部 harness runner 共享同一协议路径
|
||||
|
||||
## 1. 协议目标
|
||||
|
||||
Protocol v1 要解决四件事：
|
||||
|
||||
- LangBot 如何发现插件提供的 AgentRunner。
|
||||
- LangBot 如何把一次事件调用封装成 `AgentRunContext`。
|
||||
- AgentRunner 如何以事件流形式返回运行结果。
|
||||
- AgentRunner 如何通过受限 API 访问 LangBot host 能力。
|
||||
|
||||
Protocol v1 不定义：
|
||||
|
||||
- LangBot 内部如何持久化 AgentBinding。
|
||||
- AgentRunner 内部如何组装 prompt、压缩历史、管理 memory。
|
||||
- 官方 local-agent 的具体实现。
|
||||
- Pipeline 的长期配置模型。
|
||||
- 发布级安全 hardening 的完整实现；当前只定义 Host 侧资源、权限、状态和审计边界，release gate 见 [SECURITY_HARDENING.md](./SECURITY_HARDENING.md)。
|
||||
|
||||
## 2. 参与方
|
||||
|
||||
| 名称 | 职责 |
|
||||
| --- | --- |
|
||||
| LangBot Host | 事件入口、绑定解析、权限、资源、存储、生命周期、结果投递。 |
|
||||
| Plugin Runtime | 加载插件，响应 Host 的 runner discovery 和 run 调用。 |
|
||||
| AgentRunner | 插件提供的 agent 执行组件。 |
|
||||
| AgentRunAPIProxy | AgentRunner 访问 Host 能力的受限 API。 |
|
||||
| AgentBinding | Host 内部的事件到 runner 绑定配置，不直接暴露给 SDK。 |
|
||||
|
||||
`AgentBinding` 只影响 Host 构造出的 `ctx.config`、`ctx.resources`、`ctx.context` 和 `ctx.delivery`。SDK 不需要知道 binding 的持久化形态。
|
||||
|
||||
外部 harness runner（Claude Code、Codex、Kimi Code 等）仍然是 `AgentRunner`。Protocol v1 只要求它们消费 event-first `AgentRunContext`、返回 `AgentRunResult`，并通过 Host 授权的 state/storage/artifact APIs 保存跨轮次指针。它们内部可以继续使用自己的 session、tool loop、MCP、上下文压缩和权限模型。
|
||||
|
||||
## 3. Discovery 协议
|
||||
|
||||
### 3.1 LIST_AGENT_RUNNERS
|
||||
|
||||
Host 调用 Plugin Runtime 获取当前插件暴露的 runner 列表。该请求不需要额外 payload。
|
||||
|
||||
Runtime 返回：
|
||||
|
||||
```python
|
||||
class ListAgentRunnersResponse(BaseModel):
|
||||
runners: list[AgentRunnerManifest]
|
||||
```
|
||||
|
||||
### 3.2 AgentRunnerManifest
|
||||
|
||||
```python
|
||||
class AgentRunnerManifest(BaseModel):
|
||||
id: str
|
||||
name: str
|
||||
label: I18nObject
|
||||
description: I18nObject | None = None
|
||||
capabilities: AgentRunnerCapabilities
|
||||
permissions: AgentRunnerPermissions
|
||||
context: AgentRunnerContextPolicy
|
||||
config_schema: list[DynamicFormItemSchema] = []
|
||||
metadata: dict[str, Any] = {}
|
||||
```
|
||||
|
||||
字段要求：
|
||||
|
||||
- `id` 必须稳定，推荐 `plugin:author/name/runner`。
|
||||
- `name` 是插件内 runner 名称，例如 `default`。
|
||||
- `config_schema` 只描述绑定配置表单，不代表插件实例状态。
|
||||
- `metadata` 只能放展示、诊断、非稳定扩展信息。
|
||||
|
||||
### 3.3 Capabilities
|
||||
|
||||
```python
|
||||
class AgentRunnerCapabilities(BaseModel):
|
||||
streaming: bool = False
|
||||
tool_calling: bool = False
|
||||
knowledge_retrieval: bool = False
|
||||
multimodal_input: bool = False
|
||||
event_context: bool = True
|
||||
platform_api: bool = False
|
||||
interrupt: bool = False
|
||||
stateful_session: bool = False
|
||||
self_managed_context: bool = True
|
||||
```
|
||||
|
||||
语义：
|
||||
|
||||
- `streaming`: runner 可以返回 `message.delta`。
|
||||
- `tool_calling`: runner 可能调用 Host tool APIs。
|
||||
- `knowledge_retrieval`: runner 可能调用 Host knowledge APIs。
|
||||
- `multimodal_input`: runner 可以处理非纯文本 input / artifact。
|
||||
- `event_context`: runner 理解 event-first 输入。
|
||||
- `platform_api`: runner 可能请求平台动作。
|
||||
- `interrupt`: runner 支持取消或中断。
|
||||
- `stateful_session`: runner 可能维护跨 run 会话状态。
|
||||
- `self_managed_context`: runner 自己管理 working context，Host 不应默认 inline 历史。
|
||||
|
||||
### 3.4 Permissions
|
||||
|
||||
```python
|
||||
class AgentRunnerPermissions(BaseModel):
|
||||
models: list[Literal["invoke", "stream", "rerank"]] = []
|
||||
tools: list[Literal["detail", "call"]] = []
|
||||
knowledge_bases: list[Literal["list", "retrieve"]] = []
|
||||
history: list[Literal["page", "search"]] = []
|
||||
events: list[Literal["get", "page"]] = []
|
||||
artifacts: list[Literal["metadata", "read"]] = []
|
||||
storage: list[Literal["plugin", "workspace", "binding"]] = []
|
||||
files: list[Literal["config", "knowledge"]] = []
|
||||
platform_api: list[str] = []
|
||||
```
|
||||
|
||||
Manifest permissions 是 runner 需要的最大能力。实际可用资源还要经过 Host binding policy 和当前 run scope 裁剪。
|
||||
|
||||
### 3.5 Context Policy
|
||||
|
||||
```python
|
||||
class AgentRunnerContextPolicy(BaseModel):
|
||||
ownership: Literal["self_managed", "host_bootstrap", "hybrid"] = "self_managed"
|
||||
bootstrap: Literal["none", "current_event", "recent_tail", "summary_tail"] = "current_event"
|
||||
max_inline_events: int = 0
|
||||
max_inline_bytes: int = 0
|
||||
supports_history_pull: bool = True
|
||||
supports_history_search: bool = False
|
||||
supports_artifact_pull: bool = True
|
||||
owns_compaction: bool = True
|
||||
wants_static_context_refs: bool = True
|
||||
```
|
||||
|
||||
Host 不使用该声明给 runner inline 历史窗口。默认原则：
|
||||
|
||||
- Host 不得默认 inline 全量历史。
|
||||
- Host 只 inline 当前 event / input 和 context handles。
|
||||
- Runner 拥有 working context assembly。
|
||||
- Runner 可在授权后通过 Host history / event / artifact / state APIs 拉取更多上下文。
|
||||
- `max-round` 或类似窗口参数不属于 Protocol v1 字段，也不属于 Pipeline / Host 通用语义；如果某个 runner 需要，应由 runner 自己解释 `ctx.config`。
|
||||
|
||||
## 4. Run 协议
|
||||
|
||||
### 4.1 RUN_AGENT
|
||||
|
||||
Host 调用 Runtime：
|
||||
|
||||
```python
|
||||
class AgentRunRequest(BaseModel):
|
||||
runner_id: str
|
||||
runner_name: str
|
||||
context: AgentRunContext
|
||||
```
|
||||
|
||||
Runtime 返回 `AgentRunResult` 异步流。
|
||||
|
||||
插件运行时可以继续在底层 transport 中使用 `plugin_author`、`plugin_name`、`runner_name` 定位组件，但协议语义以 `runner_id` 和 `context` 为准。
|
||||
|
||||
### 4.2 AgentRunContext
|
||||
|
||||
```python
|
||||
class AgentRunContext(BaseModel):
|
||||
run_id: str
|
||||
trigger: AgentTrigger
|
||||
event: AgentEventContext
|
||||
conversation: ConversationContext | None = None
|
||||
actor: ActorContext | None = None
|
||||
subject: SubjectContext | None = None
|
||||
input: AgentInput
|
||||
delivery: DeliveryContext
|
||||
resources: AgentResources
|
||||
context: ContextAccess
|
||||
state: AgentRunState
|
||||
runtime: AgentRuntimeContext
|
||||
config: dict[str, Any] = {}
|
||||
bootstrap: BootstrapContext | None = None
|
||||
adapter: AdapterContext | None = None
|
||||
metadata: dict[str, Any] = {}
|
||||
```
|
||||
|
||||
核心约束：
|
||||
|
||||
- `event` 是必选字段，Protocol v1 是 event-first。
|
||||
- `input` 表示当前事件的主输入，不等于历史消息。
|
||||
- `bootstrap` 是可选字段；LangBot Host 默认不填历史窗口。
|
||||
- `adapter` 只放 Pipeline adapter 字段，runner 不应依赖它做长期能力。
|
||||
- `config` 是 Host binding config，不是插件实例状态。
|
||||
|
||||
### 4.3 AgentTrigger
|
||||
|
||||
```python
|
||||
class AgentTrigger(BaseModel):
|
||||
type: str
|
||||
source: Literal["platform", "webui", "api", "scheduler", "system", "pipeline_adapter"]
|
||||
timestamp: int | None = None
|
||||
```
|
||||
|
||||
`trigger.type` 应与 `event.event_type` 一致或更粗粒度。例如 Pipeline 兼容入口触发消息时：
|
||||
|
||||
```json
|
||||
{
|
||||
"type": "message.received",
|
||||
"source": "pipeline_adapter"
|
||||
}
|
||||
```
|
||||
|
||||
### 4.4 AgentEventContext
|
||||
|
||||
```python
|
||||
class AgentEventContext(BaseModel):
|
||||
event_id: str
|
||||
event_type: str
|
||||
event_time: int | None = None
|
||||
source: str
|
||||
source_event_type: str | None = None
|
||||
raw_ref: RawEventRef | None = None
|
||||
data: dict[str, Any] = {}
|
||||
```
|
||||
|
||||
要求：
|
||||
|
||||
- `event_type` 使用 LangBot 稳定协议名，例如 `message.received`。
|
||||
- 平台原始事件名放入 `source_event_type`。
|
||||
- 大型原始 payload 必须放入 `raw_ref` 或 artifact，不应直接塞入 `data`。
|
||||
|
||||
### 4.5 Actor / Subject / Conversation
|
||||
|
||||
```python
|
||||
class ConversationContext(BaseModel):
|
||||
conversation_id: str | None = None
|
||||
thread_id: str | None = None
|
||||
launcher_type: str | None = None
|
||||
launcher_id: str | None = None
|
||||
bot_id: str | None = None
|
||||
workspace_id: str | None = None
|
||||
|
||||
class ActorContext(BaseModel):
|
||||
actor_type: str
|
||||
actor_id: str | None = None
|
||||
actor_name: str | None = None
|
||||
metadata: dict[str, Any] = {}
|
||||
|
||||
class SubjectContext(BaseModel):
|
||||
subject_type: str
|
||||
subject_id: str | None = None
|
||||
data: dict[str, Any] = {}
|
||||
```
|
||||
|
||||
示例：
|
||||
|
||||
- 消息事件：actor 是发消息的人，subject 是当前消息。
|
||||
- 入群事件：actor 是新成员或邀请人，subject 是群/成员关系。
|
||||
- 定时事件：actor 可以是 system，subject 是 schedule。
|
||||
|
||||
### 4.6 AgentInput
|
||||
|
||||
```python
|
||||
class AgentInput(BaseModel):
|
||||
text: str | None = None
|
||||
contents: list[ContentElement] = []
|
||||
attachments: list[ArtifactRef] = []
|
||||
message_chain: dict[str, Any] | None = None
|
||||
```
|
||||
|
||||
要求：
|
||||
|
||||
- 文本、多模态、附件都属于当前 event input。
|
||||
- 大文件、图片、音频、工具大结果应以 artifact ref 传递。
|
||||
- `message_chain` 是平台兼容字段，不应成为长期稳定依赖。
|
||||
|
||||
### 4.7 DeliveryContext
|
||||
|
||||
```python
|
||||
class DeliveryContext(BaseModel):
|
||||
surface: str
|
||||
reply_target: dict[str, Any] | None = None
|
||||
supports_streaming: bool = False
|
||||
supports_edit: bool = False
|
||||
supports_reaction: bool = False
|
||||
max_message_size: int | None = None
|
||||
platform_capabilities: dict[str, Any] = {}
|
||||
```
|
||||
|
||||
Runner 可以参考 delivery 能力决定返回 `message.delta`、`message.completed` 或 `action.requested`。
|
||||
|
||||
### 4.8 ContextAccess
|
||||
|
||||
```python
|
||||
class ContextAccess(BaseModel):
|
||||
conversation_id: str | None = None
|
||||
thread_id: str | None = None
|
||||
latest_cursor: str | None = None
|
||||
event_seq: int | None = None
|
||||
transcript_seq: int | None = None
|
||||
has_history_before: bool = False
|
||||
inline_policy: InlineContextPolicy
|
||||
available_apis: ContextAPICapabilities
|
||||
```
|
||||
|
||||
`ContextAccess` 告诉 runner：Host inline 了什么、没有 inline 什么、如果需要更多上下文应该通过哪些 API 拉取。
|
||||
它不是 Host 的业务上下文编排策略，而是 runner 按需读取上下文的入口说明。
|
||||
|
||||
```python
|
||||
class InlineContextPolicy(BaseModel):
|
||||
mode: Literal["none", "current_event", "recent_tail", "summary_tail"]
|
||||
delivered_count: int = 0
|
||||
source_total_count: int | None = None
|
||||
messages_complete: bool = False
|
||||
reason: str | None = None
|
||||
|
||||
class ContextAPICapabilities(BaseModel):
|
||||
history_page: bool = False
|
||||
history_search: bool = False
|
||||
event_get: bool = False
|
||||
event_page: bool = False
|
||||
artifact_metadata: bool = False
|
||||
artifact_read: bool = False
|
||||
state: bool = False
|
||||
storage: bool = False
|
||||
```
|
||||
|
||||
### 4.9 BootstrapContext
|
||||
|
||||
```python
|
||||
class BootstrapContext(BaseModel):
|
||||
messages: list[Message] = []
|
||||
summary: str | None = None
|
||||
artifacts: list[ArtifactRef] = []
|
||||
metadata: dict[str, Any] = {}
|
||||
```
|
||||
|
||||
约束：
|
||||
|
||||
- `bootstrap.messages` 不是 LangBot Host 的默认行为。
|
||||
- 自管 context runner 默认应收到空 bootstrap。
|
||||
- Host 不应为了”帮 agent 更聪明”而自动拼接完整 transcript。
|
||||
- 类似历史窗口策略应由具体 runner 自己解释 binding config，并通过 Host history API 拉取历史；new/official runners 不应依赖 Pipeline adapter 下发历史窗口。
|
||||
|
||||
### 4.10 RuntimeContext
|
||||
|
||||
```python
|
||||
class AgentRuntimeContext(BaseModel):
|
||||
host: str = "langbot"
|
||||
langbot_version: str | None = None
|
||||
trace_id: str
|
||||
deadline_at: float | None = None
|
||||
locale: str | None = None
|
||||
timezone: str | None = None
|
||||
static_refs: dict[str, StaticContextRef] = {}
|
||||
metadata: dict[str, Any] = {}
|
||||
```
|
||||
|
||||
`static_refs` 用于 KV cache 友好的静态上下文引用，例如 system policy、tool schema、resource manifest 的 hash/version。
|
||||
|
||||
### 4.11 State
|
||||
|
||||
```python
|
||||
class AgentRunState(BaseModel):
|
||||
conversation: dict[str, Any] = {}
|
||||
actor: dict[str, Any] = {}
|
||||
subject: dict[str, Any] = {}
|
||||
runner: dict[str, Any] = {}
|
||||
```
|
||||
|
||||
State 是可选 host-owned snapshot。Runner 也可以完全自管状态。
|
||||
|
||||
## 5. Resources
|
||||
|
||||
```python
|
||||
class AgentResources(BaseModel):
|
||||
models: list[ModelResource] = []
|
||||
tools: list[ToolResource] = []
|
||||
knowledge_bases: list[KnowledgeBaseResource] = []
|
||||
files: list[FileResource] = []
|
||||
storage: StorageResource = StorageResource()
|
||||
platform_capabilities: dict[str, Any] = {}
|
||||
```
|
||||
|
||||
资源列表是本次 run 的授权结果。History / Event / Artifact 访问通过 permissions、`ctx.context.available_apis` 和 Host 侧 run session 校验控制，不作为可枚举 resource list 暴露。Runner 只能通过 `AgentRunAPIProxy` 访问这些能力。
|
||||
|
||||
## 6. Result Stream
|
||||
|
||||
### 6.1 AgentRunResult
|
||||
|
||||
```python
|
||||
class AgentRunResult(BaseModel):
|
||||
run_id: str
|
||||
type: str
|
||||
data: dict[str, Any] = {}
|
||||
sequence: int | None = None
|
||||
timestamp: int | None = None
|
||||
```
|
||||
|
||||
### 6.2 稳定 result types
|
||||
|
||||
| type | 说明 |
|
||||
| --- | --- |
|
||||
| `message.delta` | 流式消息片段。 |
|
||||
| `message.completed` | 完整消息。 |
|
||||
| `tool.call.started` | runner 开始工具调用的可观测事件。 |
|
||||
| `tool.call.completed` | runner 完成工具调用的可观测事件。 |
|
||||
| `artifact.created` | runner 生成 artifact。 |
|
||||
| `state.updated` | runner 请求更新 host-owned state。 |
|
||||
| `action.requested` | runner 请求 Host 执行平台动作。 |
|
||||
| `run.completed` | run 正常结束。 |
|
||||
| `run.failed` | run 失败。 |
|
||||
|
||||
Host 必须忽略未知 result type 并记录 warning，除非该 type 明确要求强校验。
|
||||
|
||||
### 6.3 message.delta
|
||||
|
||||
```json
|
||||
{
|
||||
"type": "message.delta",
|
||||
"data": {
|
||||
"chunk": {
|
||||
"role": "assistant",
|
||||
"content": "hel"
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### 6.4 message.completed
|
||||
|
||||
```json
|
||||
{
|
||||
"type": "message.completed",
|
||||
"data": {
|
||||
"message": {
|
||||
"role": "assistant",
|
||||
"content": "hello"
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### 6.5 state.updated
|
||||
|
||||
```json
|
||||
{
|
||||
"type": "state.updated",
|
||||
"data": {
|
||||
"scope": "conversation",
|
||||
"key": "external.session_id",
|
||||
"value": "abc"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
Host 必须校验 scope、key、value 大小和 JSON 可序列化性。
|
||||
|
||||
### 6.6 action.requested
|
||||
|
||||
```json
|
||||
{
|
||||
"type": "action.requested",
|
||||
"data": {
|
||||
"action": "message.edit",
|
||||
"target": {"message_id": "..."},
|
||||
"payload": {"text": "..."}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
Protocol v1 只定义表达方式。Host 是否执行 action 取决于 platform API 能力、binding policy、审批策略和实现阶段。
|
||||
|
||||
## 7. AgentRunAPIProxy
|
||||
|
||||
所有 proxy action 必须携带 `run_id`。Host 必须校验：
|
||||
|
||||
- active run session 存在。
|
||||
- caller plugin identity 匹配。
|
||||
- resource 在本次 `ctx.resources` 中授权。
|
||||
- scope 不越界。
|
||||
- payload size / rate limit / deadline 合法。
|
||||
|
||||
### 7.1 Model APIs
|
||||
|
||||
```python
|
||||
await api.models.invoke(model_id, messages, tools=None, extra_args=None)
|
||||
await api.models.stream(model_id, messages, tools=None, extra_args=None)
|
||||
await api.models.rerank(model_id, query, documents, top_k=None)
|
||||
```
|
||||
|
||||
### 7.2 Tool APIs
|
||||
|
||||
```python
|
||||
await api.tools.get_detail(tool_name)
|
||||
await api.tools.call(tool_name, parameters)
|
||||
```
|
||||
|
||||
### 7.3 Knowledge APIs
|
||||
|
||||
```python
|
||||
await api.knowledge.retrieve(kb_id, query_text, top_k=5, filters=None)
|
||||
```
|
||||
|
||||
### 7.4 History APIs
|
||||
|
||||
```python
|
||||
await api.history.page(
|
||||
conversation_id=None,
|
||||
before_cursor=None,
|
||||
after_cursor=None,
|
||||
limit=50,
|
||||
direction="backward",
|
||||
include_artifacts=False,
|
||||
)
|
||||
|
||||
await api.history.search(
|
||||
query,
|
||||
filters=None,
|
||||
top_k=10,
|
||||
)
|
||||
```
|
||||
|
||||
History API 返回 Transcript projection，不返回原始平台 payload。
|
||||
|
||||
### 7.5 Event APIs
|
||||
|
||||
```python
|
||||
await api.events.get(event_id)
|
||||
await api.events.page(before_cursor=None, limit=50)
|
||||
```
|
||||
|
||||
Event API 返回稳定 event envelope 或受限 raw ref，不默认返回大 payload。
|
||||
|
||||
### 7.6 Artifact APIs
|
||||
|
||||
```python
|
||||
await api.artifacts.metadata(artifact_id)
|
||||
await api.artifacts.read_range(artifact_id, offset=0, length=65536)
|
||||
await api.artifacts.open_stream(artifact_id)
|
||||
```
|
||||
|
||||
Artifact API 必须支持大小限制、MIME 校验、过期时间和授权范围。
|
||||
|
||||
### 7.7 State / Storage APIs
|
||||
|
||||
```python
|
||||
await api.state.get(scope, key)
|
||||
await api.state.set(scope, key, value)
|
||||
await api.state.delete(scope, key)
|
||||
|
||||
await api.storage.get(area, key)
|
||||
await api.storage.set(area, key, value)
|
||||
await api.storage.delete(area, key)
|
||||
await api.storage.list(area, prefix=None)
|
||||
```
|
||||
|
||||
建议区分：
|
||||
|
||||
- `state`: 小型 JSON 状态，适合 conversation / actor / runner / binding。
|
||||
- `storage`: blob 或较大数据，适合插件私有数据、workspace 数据、checkpoint。
|
||||
|
||||
### 7.8 Platform APIs
|
||||
|
||||
```python
|
||||
await api.platform.request_action(action, target, payload)
|
||||
```
|
||||
|
||||
平台 API 是受限能力。默认不开放。需要 runner manifest、binding policy、用户审批策略同时允许。
|
||||
|
||||
## 8. 错误模型
|
||||
|
||||
Host API 错误统一返回：
|
||||
|
||||
```python
|
||||
class AgentAPIError(BaseModel):
|
||||
code: str
|
||||
message: str
|
||||
retryable: bool = False
|
||||
details: dict[str, Any] = {}
|
||||
```
|
||||
|
||||
建议 code：
|
||||
|
||||
| code | 说明 |
|
||||
| --- | --- |
|
||||
| `unauthorized` | 未授权访问资源或 scope。 |
|
||||
| `not_found` | 资源不存在或对当前 runner 不可见。 |
|
||||
| `deadline_exceeded` | 超过 run deadline。 |
|
||||
| `payload_too_large` | 请求或响应过大。 |
|
||||
| `rate_limited` | Host 限流。 |
|
||||
| `invalid_argument` | 参数错误。 |
|
||||
| `runtime_error` | Host 或下游能力错误。 |
|
||||
|
||||
Runner 失败使用 `run.failed`：
|
||||
|
||||
```json
|
||||
{
|
||||
"type": "run.failed",
|
||||
"data": {
|
||||
"code": "runner.error",
|
||||
"message": "failed to call external agent",
|
||||
"retryable": false
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## 9. Timeout 与 Cancellation
|
||||
|
||||
Host 在 `ctx.runtime.deadline_at` 中下发总 deadline。SDK proxy 必须用该 deadline 限制单次 action timeout。
|
||||
|
||||
取消语义：
|
||||
|
||||
- Host 可以取消 active run。
|
||||
- Runtime 应尽力中断 runner。
|
||||
- Runner 支持中断时应返回或触发 `run.failed`，code 为 `cancelled`。
|
||||
- Host 必须 unregister active run session。
|
||||
|
||||
## 10. Security 与 Guardrail
|
||||
|
||||
Protocol v1 的安全边界在 Host：
|
||||
|
||||
- Runner 不能直接访问未授权 model/tool/kb/history/artifact/storage。
|
||||
- SDK 本地校验只提升开发体验，不能替代 Host 校验。
|
||||
- 所有 resource id 对 runner 来说都是 opaque。
|
||||
- 默认只能访问当前 conversation / thread 的 history。
|
||||
- 跨会话、workspace 级 history 或 storage 必须额外授权。
|
||||
- 大 payload 必须 artifact 化。
|
||||
- Host 必须记录 run_id、runner_id、action、resource、scope、result。
|
||||
|
||||
对外部 harness runner，边界进一步拆分为：
|
||||
|
||||
- Host 在调用前完成 binding/resource policy 裁剪、路径策略、secret 过滤和审计记录。
|
||||
- Runner plugin 把授权后的 context/resource projection 适配为目标 harness 的 context 文件、MCP 配置、skill 目录、环境变量或 CLI 参数。
|
||||
- Claude Code / Codex / Kimi Code 等外部 harness 的 native permission mode、allowed/disallowed tools 和执行隔离策略只是额外执行约束，不能替代 Host 侧授权。
|
||||
- 外部 session id、working directory、checkpoint 等跨轮次指针应作为小型 JSON state 保存，例如 `external.session_id`、`external.working_directory`。
|
||||
|
||||
完整路径隔离、MCP allowlist、secret redaction、配额、workspace 清理和发布级安全测试不属于当前 Protocol v1 smoke 闭环，详见 [SECURITY_HARDENING.md](./SECURITY_HARDENING.md)。
|
||||
|
||||
Host 不负责业务编排：
|
||||
|
||||
- 不拼接全量历史。
|
||||
- 不替 runner 做业务 prompt assembly。
|
||||
- 不内置 agent memory 策略。
|
||||
- 不内置 tool loop 业务流程。
|
||||
- 不内置上下文压缩策略。
|
||||
|
||||
这些能力可以由官方或第三方 AgentRunner 插件实现，并通过公开 Host APIs 消费 LangBot 的状态、历史、存储、artifact、模型、工具和知识库能力。
|
||||
|
||||
## 11. Pipeline Adapter
|
||||
|
||||
Pipeline 是当前入口 adapter，不是协议中心。
|
||||
|
||||
**当前分支已实现**：
|
||||
|
||||
- ✅ `PipelineAdapter.query_to_event(query)` — 从 `Query` 构造 `AgentEventEnvelope`
|
||||
- ✅ `PipelineAdapter.pipeline_config_to_binding(query, runner_id)` — 从 Pipeline config 构造临时 AgentBinding
|
||||
- ✅ `run_from_query()` 委托到 `run(event, binding)`
|
||||
- ✅ runner-specific config 从 Pipeline 当前绑定配置透传到 `AgentBinding.runner_config` / `ctx.config`
|
||||
- ✅ Query-only 字段放入 `adapter` context
|
||||
|
||||
Pipeline adapter 负责：
|
||||
|
||||
- 从 `Query` 构造 `AgentEventContext`。
|
||||
- 从 Pipeline config 构造临时 AgentBinding。
|
||||
- 从当前 runner binding config 构造 `ctx.config`。
|
||||
- 保留必要的 legacy adapter metadata，但不定义历史窗口、prompt 组装或 agentic context 策略。
|
||||
- 后续若需要传递 preprocessing / hook 后的有效指令，应通过 Host prompt/instruction
|
||||
package pull API 暴露能力位和引用，而不是继续把 prompt 推入 `ctx.adapter.extra`。
|
||||
- 将 Query-only 字段放入 `adapter`。
|
||||
|
||||
Runner 不应长期依赖 `adapter`。新 runner 应只依赖 event-first context 和 Host APIs。
|
||||
|
||||
## 12. 最小 v1 完成标准
|
||||
|
||||
Protocol v1 已在当前分支完成：
|
||||
|
||||
- ✅ SDK 定义 `AgentRunnerManifest`、`AgentRunContext`、`AgentRunResult`、`AgentRunAPIProxy`
|
||||
- ✅ Runtime 支持 `LIST_AGENT_RUNNERS` 和 `RUN_AGENT`
|
||||
- ✅ Host 支持 `run_id` session authorization
|
||||
- ✅ Host 能从当前 Pipeline 入口生成 event-first context
|
||||
- ✅ `messages` 降级为 optional bootstrap
|
||||
- ✅ `max-round` 不出现在协议实体中，也不属于 Host / Pipeline 语义
|
||||
- ✅ Proxy 至少覆盖 model、tool、knowledge、state/storage
|
||||
- ✅ History / event / artifact API 已落地
|
||||
- ✅ EventLog / Transcript / ArtifactStore / PersistentStateStore 已落地
|
||||
- ✅ 外部 harness runner 最小 smoke 已落地：Claude Code runner 能消费 event-first context、返回消息、写回 `external.session_id` / `external.working_directory`
|
||||
|
||||
## 13. 开放问题
|
||||
|
||||
- `AgentBinding` 是否需要进入 SDK 文档作为只读诊断信息，还是完全 Host 内部。
|
||||
- `TranscriptItem` 的最小字段集如何定义。
|
||||
- ArtifactStore 是否复用现有 BinaryStorage backend，还是引入独立实体。
|
||||
- State 与 Storage 的边界是否需要更强类型。
|
||||
- `platform_api` action 的审批模型如何表达。
|
||||
- 多 runner 并发处理同一 event 时，result delivery 的冲突策略如何定义。
|
||||
- Host 侧 scoped MCP / skill / workspace projection 是否需要从 runner config 上移为一等 resource projection API。
|
||||
@@ -1,125 +0,0 @@
|
||||
# Agent Runner 插件化文档入口
|
||||
|
||||
本文档是 agent-runner 插件化工作的路由页。具体设计拆到独立文档中维护，避免把 LangBot 宿主架构、SDK 协议、上下文管理、EBA 预留和官方 runner 迁移混在同一份 README 里。
|
||||
|
||||
## 本分支目标
|
||||
|
||||
**本分支目标：AgentRunner 外化 / 插件化基础设施**
|
||||
|
||||
本分支只做 LangBot 作为 Agent Host 的基础能力建设：
|
||||
|
||||
- LangBot 与 SDK 的稳定协议合同（Protocol v1）
|
||||
- Host-side `AgentEventEnvelope` / `AgentBinding` 模型
|
||||
- `run(event, binding)` event-first 入口
|
||||
- `PipelineAdapter`：Pipeline Query → AgentEventEnvelope + AgentBinding
|
||||
- EventLog / Transcript / ArtifactStore / PersistentStateStore
|
||||
- History / Event / Artifact / State pull APIs
|
||||
- SDK runtime forwarding pull APIs + `caller_plugin_identity` 验证路径
|
||||
|
||||
## 本分支不实现
|
||||
|
||||
以下能力由其他分支负责，本分支只预留 integration point：
|
||||
|
||||
- **EventGateway**：完整事件网关实现、事件路由、事件持久化管理
|
||||
- **Event subscription / Event notification**：事件订阅、推送通知
|
||||
- **BindingResolver persistence UI**：绑定配置的持久化 UI 和 event router 集成（如由其他模块负责）
|
||||
- **Scheduler / Background event source**：定时任务、后台事件源
|
||||
- **Runtime control plane v2**：runtime registry、heartbeat、task queue、daemon claim、progress/cancel 和 runtime audit
|
||||
|
||||
EventGateway 在本文档中描述为 **future integration point**，由外部 event branch 提供。本分支只定义 host-side envelope/binding models 和 `run(event, binding)` orchestrator 入口。
|
||||
|
||||
## 当前状态
|
||||
|
||||
**当前 Pipeline 是入口 adapter，不再是 agent runner 设计核心。**
|
||||
|
||||
当前主入口仍可由 Pipeline 触发，但内部已转换成 event-first path：
|
||||
|
||||
1. `run_from_query()` 使用 `PipelineAdapter.query_to_event(query)` 转换为 `AgentEventEnvelope`
|
||||
2. `run_from_query()` 使用 `PipelineAdapter.pipeline_config_to_binding(query, runner_id)` 转换为 `AgentBinding`
|
||||
3. `run_from_query()` 委托到 `run(event, binding, bound_plugins, adapter_context)`
|
||||
|
||||
Pipeline path 已获得 event-first host capabilities：
|
||||
- EventLog / Transcript 写入
|
||||
- ArtifactStore 注册
|
||||
- PersistentStateStore 状态持久化
|
||||
- History / Event / Artifact / State pull APIs 可用
|
||||
|
||||
## 设计文档
|
||||
|
||||
| 文档 | 关注点 |
|
||||
| --- | --- |
|
||||
| [PROTOCOL_V1.md](./PROTOCOL_V1.md) | LangBot Host 与 SDK / Runtime / AgentRunner 的协议合同：run context、result stream、proxy actions、错误和 adapter 边界。 |
|
||||
| [HOST_SDK_INFRASTRUCTURE.md](./HOST_SDK_INFRASTRUCTURE.md) | LangBot 宿主能力、SDK 协议、runner 发现、绑定、权限、状态、存储、生命周期和调用链。 |
|
||||
| [AGENT_CONTEXT_PROTOCOL.md](./AGENT_CONTEXT_PROTOCOL.md) | Agent-owned context 方向：事件到来时 LangBot 传什么，agent 如何按需拉取更多历史 / artifact / state，以及如何支持 KV cache 友好的上下文管理。 |
|
||||
| [EVENT_BASED_AGENT.md](./EVENT_BASED_AGENT.md) | EBA 预留：事件模型、事件来源、触发绑定、非消息事件如何复用 AgentRunner 调度。**标注为 future design note**。 |
|
||||
| [RUNTIME_CONTROL_PLANE_V2.md](./RUNTIME_CONTROL_PLANE_V2.md) | Agent Platform v2 / runtime 管控面预留：Host 新增 runtime registry、heartbeat、task queue、daemon 执行和 audit；管理插件构建在这些 Host 能力之上。**标注为 future design note**。 |
|
||||
| [OFFICIAL_RUNNER_PLUGINS.md](./OFFICIAL_RUNNER_PLUGINS.md) | 官方 runner 插件迁移，包括 local-agent 和外部 runner。它是下游落地计划，不是 LangBot 基础能力设计的前置约束。 |
|
||||
| [PHASE1_QA_ACCEPTANCE_MATRIX.md](./PHASE1_QA_ACCEPTANCE_MATRIX.md) | Agent Runner QA 指南：保留最高价值测试路径，指导 agent 开展下一轮 WebUI / runner smoke 验证。 |
|
||||
| [SECURITY_HARDENING.md](./SECURITY_HARDENING.md) | 安全发布级 hardening 的后续发布门槛：路径隔离、权限边界、secret、资源配额、MCP / skill 投影和审计。 |
|
||||
| [PROGRESS.md](./PROGRESS.md) | 当前实现进度、已验收能力、未完成收尾和非本分支范围。 |
|
||||
|
||||
## 工作拆分
|
||||
|
||||
### 1. LangBot + SDK 基础设施
|
||||
|
||||
目标是把 LangBot 从内置 runner 执行器变成 agent host：
|
||||
|
||||
- LangBot 与 SDK 的稳定协议合同
|
||||
- runner manifest / descriptor / registry
|
||||
- agent binding 与配置解析
|
||||
- run orchestration 和生命周期管理
|
||||
- resource authorization 与 `run_id` 级权限校验
|
||||
- host-owned state / storage / event log / transcript / artifact 能力
|
||||
- SDK `AgentRunner`、`AgentRunContext`、`AgentRunResult`、`AgentRunAPIProxy`
|
||||
|
||||
协议合同详见 [PROTOCOL_V1.md](./PROTOCOL_V1.md)。
|
||||
|
||||
详见 [HOST_SDK_INFRASTRUCTURE.md](./HOST_SDK_INFRASTRUCTURE.md)。
|
||||
|
||||
### 2. Agent-owned context
|
||||
|
||||
LangBot 不应成为最终 agentic context manager。它应提供事实源、默认上下文引用和按需读取 API；agent 或其背后的 runtime 负责历史剪裁、摘要、召回和 KV cache 策略。
|
||||
|
||||
`max-round` 这类历史窗口参数不应作为目标协议继续扩展；如果某个 runner 仍需要类似策略，应由该 runner 的 manifest/config schema 暴露为 binding config。
|
||||
|
||||
详见 [AGENT_CONTEXT_PROTOCOL.md](./AGENT_CONTEXT_PROTOCOL.md)。
|
||||
|
||||
### 3. Event Based Agent（Future）
|
||||
|
||||
消息只是事件的一种。后续 `message.received`、`message.recalled`、`group.member_joined`、`friend.request_received` 等事件都应能通过统一事件 envelope 触发 AgentRunner。
|
||||
|
||||
**本分支不实现 EBA 完整能力，只预留：**
|
||||
- event-first envelope (`AgentEventEnvelope`)
|
||||
- AgentBinding model
|
||||
- `run(event, binding)` 入口
|
||||
- PipelineAdapter（当前 AgentEventEnvelope / AgentBinding 的 Pipeline adapter source）
|
||||
|
||||
详见 [EVENT_BASED_AGENT.md](./EVENT_BASED_AGENT.md)。
|
||||
|
||||
### 4. 官方 runner 插件
|
||||
|
||||
官方 `local-agent` 和外部 runner 迁移是下游工作。它们需要依附 LangBot 提供的宿主能力，但不应反过来决定宿主协议。
|
||||
|
||||
`local-agent` 可以外移，也可以重写。验收重点是它能完整消费 LangBot 的模型、工具、知识库、存储、事件、history API 和 result stream，而不是保留旧内置 runner 的内部结构。
|
||||
|
||||
详见 [OFFICIAL_RUNNER_PLUGINS.md](./OFFICIAL_RUNNER_PLUGINS.md)。
|
||||
|
||||
### 5. Runtime Control Plane v2（Future）
|
||||
|
||||
当前 AgentRunner v1 主线只负责 `event -> binding -> runner.run(ctx) -> result stream`。
|
||||
后续 Agent Platform v2 可以在 Host 侧新增 runtime registry、heartbeat、task queue、daemon claim、progress/cancel 和 runtime audit。
|
||||
|
||||
在这些 Host 能力之上，可以构建独立 agent 管控面插件；插件负责 UI、策略和编排体验，runtime/task 的事实源仍由 Host 持有。
|
||||
|
||||
详见 [RUNTIME_CONTROL_PLANE_V2.md](./RUNTIME_CONTROL_PLANE_V2.md)。
|
||||
|
||||
## 已确认决策
|
||||
|
||||
- 一个插件可以声明多个 `AgentRunner` 组件，每个组件独立暴露 manifest、配置 schema、能力和权限。
|
||||
- 插件本身按单实例、无状态执行单元理解；不同绑定不创建多个插件实例。
|
||||
- 绑定只保存 runner id 和绑定配置，不代表插件实例状态。
|
||||
- LangBot 可以提供 host-owned state / storage 能力，让 runner 把状态寄宿在 LangBot；但这应该是授权能力，不是强制要求。
|
||||
- 官方 runner 插件是协议消费者，不是协议设计的优先约束。
|
||||
- Pipeline 是当前入口 adapter，不是未来架构中心。
|
||||
- EventGateway 是 future integration point，由外部 event branch 提供。
|
||||
- Runtime control plane 是 v2 Host capability layer，不阻塞当前 AgentRunner v1 主线；agent 管控面插件应构建在该 Host 能力层之上。
|
||||
@@ -1,225 +0,0 @@
|
||||
# Agent Runtime Control Plane V2
|
||||
|
||||
本文档记录后续 Agent Platform / runtime 管控面的设计方向。它是当前讨论中的 **v2 文档**，但这里的 v2 指 Host capability layer / runtime control plane，不是 `AgentRunner Protocol v2`，也不属于当前 AgentRunner Protocol v1 插件化主线的交付范围。
|
||||
|
||||
## 1. 结论
|
||||
|
||||
当前主线应继续收口 AgentRunner v1：
|
||||
|
||||
```text
|
||||
message/event -> binding -> runner.run(ctx) -> result stream
|
||||
```
|
||||
|
||||
Runtime Control Plane v2 在 Host 侧新增 runtime control plane：
|
||||
|
||||
```text
|
||||
event -> task -> runtime selection -> daemon claim -> execute -> progress/audit/result
|
||||
```
|
||||
|
||||
在 Runtime Control Plane v2 之上，可以构建独立的 agent 管控面插件。插件负责 UI、策略和编排体验；runtime、task、heartbeat、audit 的事实源必须属于 LangBot Host，而不是插件私有 storage。
|
||||
|
||||
## 2. 不影响 v1 主线
|
||||
|
||||
v2 不应改变 AgentRunner v1 的基本契约：
|
||||
|
||||
- 现有 `local-agent`、Dify、n8n、Coze 等 runner 仍可按 v1 直接执行。
|
||||
- 当前 Claude Code / Codex MVP runner 可以继续作为本机 subprocess 开发路径。
|
||||
- Host v1 已有的 event-first context、resource authorization、history / event / artifact / state / storage pull APIs 继续保留。
|
||||
- Pipeline 仍只是当前入口 adapter，不参与 v2 runtime 管控面的设计中心。
|
||||
|
||||
v2 只是在 Host 上新增一层可选能力。需要管控面的 runner 或管理插件可以声明使用它；不需要的 runner 不受影响。
|
||||
|
||||
## 3. 当前 Host 能力与缺口
|
||||
|
||||
当前 Host 已经具备 v2 的基础设施底座：
|
||||
|
||||
- `AgentEventEnvelope` / `AgentBinding`
|
||||
- run-scoped resource authorization
|
||||
- EventLog / Transcript / ArtifactStore / PersistentStateStore
|
||||
- History / Event / Artifact / State / Storage pull APIs
|
||||
- AgentRunner result stream 和受控错误回流
|
||||
- binding config 与 host-owned state
|
||||
|
||||
这些能力足够支持一次 `runner.run(ctx)` 内的安全执行，但不足以承担完整 runtime 管控面。
|
||||
|
||||
v2 还需要 Host 新增：
|
||||
|
||||
- runtime registry：runtime id、所属 workspace、所在机器、provider 能力、状态。
|
||||
- capability discovery：`claude` / `codex` / 其它 CLI 是否存在、版本、登录状态、执行隔离能力。
|
||||
- heartbeat / liveness：runtime 在线、忙闲、最后心跳、可用 slot。
|
||||
- task queue：enqueue、claim、start、progress、complete、fail、cancel。
|
||||
- workspace mapping：LangBot workspace / project 如何映射到 runtime 上的真实目录、仓库或挂载。
|
||||
- secret / env projection：按授权向 runtime 投影 token、代理、MCP 配置、技能和环境变量。
|
||||
- runtime audit：stdout、stderr、事件流、产物、失败原因、执行耗时、使用量。
|
||||
- control API / UI：选择 runtime、测试 runtime、查看状态、下线、取消任务、重试任务。
|
||||
|
||||
## 4. 角色边界
|
||||
|
||||
### 4.1 LangBot Host
|
||||
|
||||
Host 是事实源和控制面内核：
|
||||
|
||||
- 保存 runtime / task / heartbeat / audit 状态。
|
||||
- 做权限校验、资源裁剪、workspace 绑定和审计。
|
||||
- 决定任务是否可被某 runtime claim。
|
||||
- 将执行结果统一回写到 event / transcript / artifact / state。
|
||||
|
||||
Host 不应内置具体 agent CLI 的复杂业务逻辑，也不应把某个官方 runner 的特殊行为提升为通用协议。
|
||||
|
||||
### 4.2 Agent 管控面插件
|
||||
|
||||
管理插件是 v2 control plane 的产品化管理层：
|
||||
|
||||
- 展示 runtime、agent、task、进度、失败、审计。
|
||||
- 提供策略配置，例如默认 runtime、provider 偏好、并发限制、重试策略。
|
||||
- 触发 runtime 测试、任务取消、任务重试、手动分配。
|
||||
|
||||
管理插件不应把 runtime/task 的事实源放进自己的 plugin storage。它应该调用 Host v2 API。
|
||||
|
||||
### 4.3 Runtime daemon / worker
|
||||
|
||||
Runtime daemon 负责真实执行：
|
||||
|
||||
- 在所在机器上检测 CLI 和版本。
|
||||
- 管理工作目录、仓库、挂载、临时文件和进程。
|
||||
- 从 Host claim 任务，执行后上报 progress / complete / fail。
|
||||
- 将 stdout / stderr / artifacts / session id 回流 Host。
|
||||
|
||||
Claude Code、Codex、OpenCode、Gemini CLI 等 provider 适配逻辑应主要落在 daemon / worker 或 provider adapter 中。
|
||||
|
||||
## 5. 部署形态
|
||||
|
||||
### 5.1 uv / local embedded
|
||||
|
||||
用户用 `uv` 或源码直接启动 LangBot 时，LangBot 进程所在机器就是 runtime host。
|
||||
|
||||
这种模式下可以直接检测用户主机上的 `claude`、`codex` 等 CLI，也可以直接 subprocess 执行。它适合个人开发和本地 smoke，但不应作为团队级管控面的唯一形态。
|
||||
|
||||
### 5.2 Docker embedded
|
||||
|
||||
用户用 Docker 启动 LangBot 时，runtime host 是容器，不是宿主机。
|
||||
|
||||
因此：
|
||||
|
||||
- 只能检测容器内的 `claude`、`codex`。
|
||||
- 只能使用容器内的 HOME、PATH、凭据和挂载目录。
|
||||
- 如果镜像未安装 CLI，或未挂载认证文件 / workspace，CLI runner 会不可用。
|
||||
|
||||
Docker embedded 可以作为高级部署选项，但需要用户显式安装 CLI、挂载工作区和凭据。Host 不应假设 Docker 容器能自动访问宿主机 CLI。
|
||||
|
||||
### 5.3 Sidecar daemon
|
||||
|
||||
推荐的 v2 形态是 sidecar daemon：
|
||||
|
||||
```text
|
||||
LangBot Host (Docker or server)
|
||||
<-> Runtime daemon on user host / worker host
|
||||
-> claude / codex / other CLI
|
||||
```
|
||||
|
||||
这种模式下，LangBot 可以跑在 Docker 内，runtime daemon 跑在宿主机或独立 worker 机器上。daemon 负责检测本机 CLI、持有本机凭据和工作区访问能力。
|
||||
|
||||
### 5.4 Remote runtime
|
||||
|
||||
团队场景可以使用远端 runtime：
|
||||
|
||||
- 开发机、构建机、云主机或专用 worker。
|
||||
- 多个 workspace 可绑定不同 runtime。
|
||||
- Host 只通过 registry / task queue / heartbeat / audit 进行管理。
|
||||
|
||||
### 5.5 API-only agent
|
||||
|
||||
Dify、n8n、Coze、DashScope 等 API 型 runner 不依赖本地 CLI。它们可以继续按 v1 直接执行，也可以在未来按需要接入 v2 task/audit。
|
||||
|
||||
## 6. 与 Claude Code / Codex MVP runner 的关系
|
||||
|
||||
当前 Claude Code / Codex runner 是 v1 runner：
|
||||
|
||||
```text
|
||||
runner.run(ctx) -> subprocess("claude" / "codex")
|
||||
```
|
||||
|
||||
它们适合验证 Host context 投影、state resume、result stream 和基础 CLI 调用，但有明确限制：
|
||||
|
||||
- 命令只在 LangBot runtime host 上执行。
|
||||
- Docker 环境只能看到容器内 CLI。
|
||||
- 没有 runtime registry、heartbeat、task queue、cancel、workspace lifecycle。
|
||||
- 不提供发布级执行隔离、secret projection、团队级 audit。
|
||||
|
||||
v2 不需要删除这些 runner。它们可以继续作为 dev / MVP 路径存在。未来若接入管控面，可以增加 runtime-managed 执行模式：
|
||||
|
||||
```text
|
||||
runner binding -> Host task -> runtime daemon -> provider CLI -> Host result
|
||||
```
|
||||
|
||||
## 7. 最小 v2 API 草案
|
||||
|
||||
以下仅记录能力边界，不代表最终 API 命名。
|
||||
|
||||
Runtime：
|
||||
|
||||
- `runtime.register`
|
||||
- `runtime.heartbeat`
|
||||
- `runtime.list`
|
||||
- `runtime.get`
|
||||
- `runtime.disable`
|
||||
- `runtime.capabilities.report`
|
||||
- `runtime.capabilities.probe`
|
||||
|
||||
Task：
|
||||
|
||||
- `task.enqueue`
|
||||
- `task.claim`
|
||||
- `task.start`
|
||||
- `task.progress`
|
||||
- `task.complete`
|
||||
- `task.fail`
|
||||
- `task.cancel`
|
||||
- `task.retry`
|
||||
|
||||
Workspace：
|
||||
|
||||
- `runtime.workspace.bind`
|
||||
- `runtime.workspace.unbind`
|
||||
- `runtime.workspace.resolve`
|
||||
|
||||
Audit / artifacts：
|
||||
|
||||
- `task.log.append`
|
||||
- `task.artifact.create`
|
||||
- `task.events.page`
|
||||
|
||||
这些 API 应由 Host 提供，并受 workspace、runtime、binding、actor 和 plugin identity 约束。
|
||||
|
||||
## 8. 管控面插件可以构建的能力
|
||||
|
||||
基于 v2 Host 能力，可以实现一个类似 Multica 的 agent 管控面插件：
|
||||
|
||||
- runtime 列表、在线状态、CLI 能力、版本、认证状态。
|
||||
- agent profile 与 runtime/provider 绑定。
|
||||
- 任务看板、任务详情、进度流、失败原因、重试和取消。
|
||||
- workspace 到 runtime 目录 / 仓库的映射管理。
|
||||
- provider capability 测试，例如 Claude Code / Codex 是否可执行。
|
||||
- 审计视图：输入、输出、工具、artifact、stdout/stderr、session id。
|
||||
- 策略配置：并发、队列、默认 runtime、fallback runtime、权限模式。
|
||||
|
||||
该插件应该是 Host v2 的消费者，而不是 Host v2 的替代品。
|
||||
|
||||
## 9. 设计原则
|
||||
|
||||
- v1 先稳定，v2 可选叠加。
|
||||
- Host 保存事实源，插件提供管理体验。
|
||||
- Runtime daemon 执行具体 CLI 和本机资源访问。
|
||||
- Docker 不假设拥有宿主机 CLI；需要 sidecar 或显式挂载。
|
||||
- Pipeline 不进入 v2 控制面中心。
|
||||
- 直接 subprocess runner 可保留，但只作为 local/dev/MVP 路径。
|
||||
- 发布级能力必须经过 Host 权限、审计和资源边界。
|
||||
|
||||
## 10. 待定问题
|
||||
|
||||
- runtime daemon 与 Host 的认证模型：workspace token、device token、还是 scoped PAT。
|
||||
- task 与 AgentRunner binding 的映射关系：由 binding 直接 enqueue，还是由独立 task policy 决定。
|
||||
- runtime capability schema 的稳定字段：provider、version、login status、execution isolation、workspace access、slot。
|
||||
- secret projection 的边界：Host 存储、用户本机存储、或外部 secret manager。
|
||||
- Docker compose 是否提供官方 sidecar daemon 示例。
|
||||
- v2 UI 是核心前端的一部分，还是完全由管理插件提供。
|
||||
@@ -1,73 +0,0 @@
|
||||
# Agent Runner Security Hardening
|
||||
|
||||
本文档记录 agent-runner 插件化进入生产发布前需要补齐的安全与稳定加固项。
|
||||
|
||||
## 状态
|
||||
|
||||
**当前结论：暂不塞进本阶段 agent-runner plugin 协议闭环。**
|
||||
|
||||
本阶段目标是验证 LangBot 可以通过统一的 `run(event, binding)` 协议接入 `local-agent` 与外部 harness runner（如 Claude Code runner），并能传递事件、上下文、资源句柄、状态和结果流。
|
||||
|
||||
安全发布级 hardening 是后续 release gate，不应阻塞当前协议闭环，但必须作为进入生产默认启用前的验收条件。
|
||||
|
||||
## 责任边界
|
||||
|
||||
### LangBot Host 负责
|
||||
|
||||
- 资源授权：决定某个 `run_id` / binding 可以访问哪些模型、RAG、MCP、skill、artifact、history、state。
|
||||
- 资源投影：只把授权后的资源句柄、配置片段或上下文文件传给 runner。
|
||||
- 路径策略：限制 workspace / context file / artifact 的允许路径和清理策略。
|
||||
- Secret 策略：过滤环境变量、配置、日志和 transcript 中的 secret。
|
||||
- 运行约束：配置超时、轮次、并发、配额、输出大小和取消路径。
|
||||
- 审计记录：记录事件、绑定、资源授权、runner 调用、外部 harness session id、关键错误和结果摘要。
|
||||
|
||||
### Runner Plugin 负责
|
||||
|
||||
- 遵守 LangBot 下发的 binding config、授权资源和运行约束。
|
||||
- 将 LangBot 资源投影成目标 runner 可消费的形式，例如 context 文件、MCP 配置、环境变量或 CLI 参数。
|
||||
- 不把长期状态保存在插件实例内；需要跨轮次保存的外部 session id / working directory 等状态应写入 host-owned state。
|
||||
- 对外部进程做最小必要封装，包括命令参数构造、超时、取消、输出解析和错误映射。
|
||||
|
||||
### 外部 Harness 负责
|
||||
|
||||
Claude Code、Codex、Kimi Code 等外部 harness 可以继续使用自身的权限模型、工具 allow / deny 规则、MCP 加载策略、session/resume 机制和沙箱能力。
|
||||
|
||||
但外部 harness 不是 LangBot 的唯一安全边界。LangBot 仍必须在调用前完成资源授权、路径限制、secret 过滤和审计记录。
|
||||
|
||||
## 当前 MVP 可接受边界
|
||||
|
||||
当前阶段可以接受以下前提：
|
||||
|
||||
- 由可信管理员配置 runner binding。
|
||||
- 工作目录和 context 输出目录为显式配置或 host 生成路径。
|
||||
- 外部 runner 默认使用保守权限，例如 plan / no-write 模式或禁用高风险工具。
|
||||
- 通过 timeout、max turns、输出长度和进程取消降低失控风险。
|
||||
- 通过 host-owned state 保存 `external.session_id`、`external.working_directory` 等 resume 所需指针。
|
||||
|
||||
这些前提足够做本地 E2E 与协议验收，不等同于生产发布完成。
|
||||
|
||||
## Release Gate Checklist
|
||||
|
||||
进入生产默认启用前，需要补齐：
|
||||
|
||||
- Path isolation：workspace allowlist、路径规范化、防止 `..` 逃逸、context / artifact 清理。
|
||||
- Permission boundary：runner 能力声明、binding 级资源授权、run 级权限校验。
|
||||
- Secret handling：环境变量白名单、配置脱敏、日志和 transcript redaction。
|
||||
- MCP policy：MCP server allowlist、scoped token、tool allow / deny、危险工具审计。
|
||||
- Skill projection policy：skill 来源验证、只读投影、版本和摘要记录。
|
||||
- Process isolation：进程组管理、取消、超时、CPU / 内存 / 输出配额。
|
||||
- State lifecycle：session id、workspace、artifact 的过期、清理、迁移和审计。
|
||||
- Audit first-class：事件、资源授权、外部命令、session id、结果摘要可追踪。
|
||||
- UI / Admin control：管理员能看到 runner 权限、风险提示、资源绑定和禁用入口。
|
||||
- Test matrix：路径逃逸、secret 泄漏、权限拒绝、timeout、取消、MCP deny、resume、cleanup、audit 完整性。
|
||||
|
||||
## 非当前范围
|
||||
|
||||
以下内容不属于本阶段协议闭环：
|
||||
|
||||
- 完整异步队列与 issue-centric 产品模型。
|
||||
- 复杂 workflow engine。
|
||||
- Codex / Kimi runner 全量接入。
|
||||
- EBA 分支完整迁移和联调。
|
||||
- 发布级安全 hardening 的完整实现。
|
||||
|
||||
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。
|
||||
@@ -105,9 +105,6 @@ classifiers = [
|
||||
"Topic :: Communications :: Chat",
|
||||
]
|
||||
|
||||
[tool.uv.sources]
|
||||
langbot-plugin = { path = "../langbot-plugin-sdk", editable = true }
|
||||
|
||||
[project.urls]
|
||||
Homepage = "https://langbot.app"
|
||||
Documentation = "https://docs.langbot.app"
|
||||
@@ -226,3 +223,4 @@ skip-magic-trailing-comma = false
|
||||
|
||||
# Like Black, automatically detect the appropriate line ending.
|
||||
line-ending = "auto"
|
||||
|
||||
|
||||
@@ -1,37 +0,0 @@
|
||||
"""Agent runner subsystem for LangBot."""
|
||||
from __future__ import annotations
|
||||
|
||||
from .runner.descriptor import AgentRunnerDescriptor
|
||||
from .runner.id import parse_runner_id, format_runner_id, RunnerIdParts, is_plugin_runner_id
|
||||
from .runner.errors import (
|
||||
AgentRunnerError,
|
||||
RunnerNotFoundError,
|
||||
RunnerNotAuthorizedError,
|
||||
RunnerProtocolError,
|
||||
RunnerExecutionError,
|
||||
)
|
||||
from .runner.registry import AgentRunnerRegistry
|
||||
from .runner.context_builder import AgentRunContextBuilder
|
||||
from .runner.resource_builder import AgentResourceBuilder
|
||||
from .runner.result_normalizer import AgentResultNormalizer
|
||||
from .runner.orchestrator import AgentRunOrchestrator
|
||||
from .runner.config_migration import ConfigMigration
|
||||
|
||||
__all__ = [
|
||||
'AgentRunnerDescriptor',
|
||||
'parse_runner_id',
|
||||
'format_runner_id',
|
||||
'is_plugin_runner_id',
|
||||
'RunnerIdParts',
|
||||
'AgentRunnerError',
|
||||
'RunnerNotFoundError',
|
||||
'RunnerNotAuthorizedError',
|
||||
'RunnerProtocolError',
|
||||
'RunnerExecutionError',
|
||||
'AgentRunnerRegistry',
|
||||
'AgentRunContextBuilder',
|
||||
'AgentResourceBuilder',
|
||||
'AgentResultNormalizer',
|
||||
'AgentRunOrchestrator',
|
||||
'ConfigMigration',
|
||||
]
|
||||
@@ -1,52 +0,0 @@
|
||||
"""Agent runner modules."""
|
||||
from __future__ import annotations
|
||||
|
||||
from .descriptor import AgentRunnerDescriptor
|
||||
from .id import parse_runner_id, format_runner_id, RunnerIdParts
|
||||
from .errors import (
|
||||
AgentRunnerError,
|
||||
RunnerNotFoundError,
|
||||
RunnerNotAuthorizedError,
|
||||
RunnerProtocolError,
|
||||
RunnerExecutionError,
|
||||
)
|
||||
from .registry import AgentRunnerRegistry
|
||||
from .context_builder import AgentRunContextBuilder
|
||||
from .resource_builder import AgentResourceBuilder
|
||||
from .result_normalizer import AgentResultNormalizer
|
||||
from .orchestrator import AgentRunOrchestrator
|
||||
from .config_migration import ConfigMigration
|
||||
from .session_registry import AgentRunSessionRegistry, AgentRunSession, get_session_registry
|
||||
from .events import (
|
||||
MESSAGE_RECEIVED,
|
||||
MESSAGE_RECALLED,
|
||||
GROUP_MEMBER_JOINED,
|
||||
FRIEND_REQUEST_RECEIVED,
|
||||
RESERVED_EVENT_TYPES,
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
'AgentRunnerDescriptor',
|
||||
'parse_runner_id',
|
||||
'format_runner_id',
|
||||
'RunnerIdParts',
|
||||
'AgentRunnerError',
|
||||
'RunnerNotFoundError',
|
||||
'RunnerNotAuthorizedError',
|
||||
'RunnerProtocolError',
|
||||
'RunnerExecutionError',
|
||||
'AgentRunnerRegistry',
|
||||
'AgentRunContextBuilder',
|
||||
'AgentResourceBuilder',
|
||||
'AgentResultNormalizer',
|
||||
'AgentRunOrchestrator',
|
||||
'ConfigMigration',
|
||||
'AgentRunSessionRegistry',
|
||||
'AgentRunSession',
|
||||
'get_session_registry',
|
||||
'MESSAGE_RECEIVED',
|
||||
'MESSAGE_RECALLED',
|
||||
'GROUP_MEMBER_JOINED',
|
||||
'FRIEND_REQUEST_RECEIVED',
|
||||
'RESERVED_EVENT_TYPES',
|
||||
]
|
||||
@@ -1,300 +0,0 @@
|
||||
"""Artifact store for managing Host-owned artifacts."""
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import datetime
|
||||
import typing
|
||||
import uuid
|
||||
import base64
|
||||
|
||||
import sqlalchemy
|
||||
from sqlalchemy.ext.asyncio import AsyncEngine, AsyncSession
|
||||
from sqlalchemy.orm import sessionmaker
|
||||
|
||||
from ...entity.persistence.artifact import AgentArtifact
|
||||
from ...entity.persistence.bstorage import BinaryStorage
|
||||
|
||||
|
||||
class ArtifactStore:
|
||||
"""Store for AgentArtifact records.
|
||||
|
||||
Handles artifact metadata registration and content retrieval.
|
||||
Actual blob storage is delegated to BinaryStorage or external storage.
|
||||
|
||||
All methods are async and use the provided database engine.
|
||||
"""
|
||||
|
||||
engine: AsyncEngine
|
||||
|
||||
# Hard limits
|
||||
MAX_INLINE_READ_BYTES = 1024 * 1024 # 1MB max for inline base64
|
||||
MAX_RANGE_READ_BYTES = 10 * 1024 * 1024 # 10MB max for range reads
|
||||
|
||||
def __init__(self, engine: AsyncEngine):
|
||||
self.engine = engine
|
||||
self._session_factory = sessionmaker(
|
||||
engine, class_=AsyncSession, expire_on_commit=False
|
||||
)
|
||||
|
||||
async def register_artifact(
|
||||
self,
|
||||
artifact_id: str | None,
|
||||
artifact_type: str,
|
||||
source: str,
|
||||
storage_key: str | None = None,
|
||||
storage_type: str = 'binary_storage',
|
||||
mime_type: str | None = None,
|
||||
name: str | None = None,
|
||||
size_bytes: int | None = None,
|
||||
sha256: str | None = None,
|
||||
conversation_id: str | None = None,
|
||||
run_id: str | None = None,
|
||||
runner_id: str | None = None,
|
||||
bot_id: str | None = None,
|
||||
workspace_id: str | None = None,
|
||||
expires_at: datetime.datetime | None = None,
|
||||
metadata: dict[str, typing.Any] | None = None,
|
||||
content: bytes | None = None,
|
||||
) -> str:
|
||||
"""Register a new artifact.
|
||||
|
||||
If content is provided and storage_key is None, stores content
|
||||
in BinaryStorage automatically.
|
||||
|
||||
Args:
|
||||
artifact_id: Unique artifact ID (generated if None)
|
||||
artifact_type: Type of artifact (image, file, voice, tool_result, etc.)
|
||||
source: Source of artifact (platform, runner, tool, system)
|
||||
storage_key: Key in BinaryStorage or external reference
|
||||
storage_type: Storage type (binary_storage, file, url)
|
||||
mime_type: MIME type
|
||||
name: Original file name
|
||||
size_bytes: Size in bytes
|
||||
sha256: SHA256 hash
|
||||
conversation_id: Conversation ID
|
||||
run_id: Run ID that created this
|
||||
runner_id: Runner ID that created this
|
||||
bot_id: Bot UUID
|
||||
workspace_id: Workspace ID
|
||||
expires_at: Expiration time
|
||||
metadata: Additional metadata
|
||||
content: Optional content to store in BinaryStorage
|
||||
|
||||
Returns:
|
||||
The artifact_id
|
||||
"""
|
||||
if artifact_id is None:
|
||||
artifact_id = str(uuid.uuid4())
|
||||
|
||||
# If content provided, store in BinaryStorage
|
||||
if content is not None and storage_key is None:
|
||||
storage_key = f"artifact:{artifact_id}"
|
||||
storage_type = 'binary_storage'
|
||||
if size_bytes is None:
|
||||
size_bytes = len(content)
|
||||
|
||||
async with self._session_factory() as session:
|
||||
# Store content in BinaryStorage if provided
|
||||
if content is not None:
|
||||
binary_storage = BinaryStorage(
|
||||
unique_key=f'artifact:{artifact_id}',
|
||||
key=storage_key,
|
||||
owner_type='artifact',
|
||||
owner='host',
|
||||
value=content,
|
||||
)
|
||||
session.add(binary_storage)
|
||||
|
||||
# Store artifact metadata
|
||||
artifact = AgentArtifact(
|
||||
artifact_id=artifact_id,
|
||||
artifact_type=artifact_type,
|
||||
mime_type=mime_type,
|
||||
name=name,
|
||||
size_bytes=size_bytes,
|
||||
sha256=sha256,
|
||||
source=source,
|
||||
storage_key=storage_key,
|
||||
storage_type=storage_type,
|
||||
conversation_id=conversation_id,
|
||||
run_id=run_id,
|
||||
runner_id=runner_id,
|
||||
bot_id=bot_id,
|
||||
workspace_id=workspace_id,
|
||||
created_at=datetime.datetime.utcnow(),
|
||||
expires_at=expires_at,
|
||||
metadata_json=json.dumps(metadata) if metadata else None,
|
||||
)
|
||||
session.add(artifact)
|
||||
await session.commit()
|
||||
|
||||
return artifact_id
|
||||
|
||||
async def get_metadata(
|
||||
self,
|
||||
artifact_id: str,
|
||||
) -> dict[str, typing.Any] | None:
|
||||
"""Get artifact metadata (public fields only, no internal storage info).
|
||||
|
||||
Args:
|
||||
artifact_id: Artifact ID
|
||||
|
||||
Returns:
|
||||
Artifact metadata dict compatible with SDK ArtifactMetadata, or None if not found
|
||||
"""
|
||||
async with self._session_factory() as session:
|
||||
result = await session.execute(
|
||||
sqlalchemy.select(AgentArtifact).where(
|
||||
AgentArtifact.artifact_id == artifact_id
|
||||
)
|
||||
)
|
||||
row = result.scalars().first()
|
||||
if row is None:
|
||||
return None
|
||||
return self._row_to_public_dict(row)
|
||||
|
||||
async def _get_internal_record(
|
||||
self,
|
||||
artifact_id: str,
|
||||
) -> AgentArtifact | None:
|
||||
"""Get full artifact record including internal fields.
|
||||
|
||||
Used internally by read_artifact to access storage_key/storage_type.
|
||||
|
||||
Args:
|
||||
artifact_id: Artifact ID
|
||||
|
||||
Returns:
|
||||
AgentArtifact ORM instance, or None if not found
|
||||
"""
|
||||
async with self._session_factory() as session:
|
||||
result = await session.execute(
|
||||
sqlalchemy.select(AgentArtifact).where(
|
||||
AgentArtifact.artifact_id == artifact_id
|
||||
)
|
||||
)
|
||||
return result.scalars().first()
|
||||
|
||||
async def read_artifact(
|
||||
self,
|
||||
artifact_id: str,
|
||||
offset: int = 0,
|
||||
limit: int | None = None,
|
||||
) -> dict[str, typing.Any] | None:
|
||||
"""Read artifact content.
|
||||
|
||||
For small artifacts, returns content_base64 directly.
|
||||
For large artifacts, returns file_key for chunked transfer.
|
||||
|
||||
Args:
|
||||
artifact_id: Artifact ID
|
||||
offset: Byte offset to start reading from (must be >= 0)
|
||||
limit: Maximum bytes to read (must be > 0 if provided)
|
||||
|
||||
Returns:
|
||||
ArtifactReadResult dict, or None if not found
|
||||
|
||||
Raises:
|
||||
ValueError: If offset < 0 or limit <= 0
|
||||
"""
|
||||
# Validate offset and limit
|
||||
if offset < 0:
|
||||
raise ValueError("offset must be >= 0")
|
||||
|
||||
if limit is not None and limit <= 0:
|
||||
raise ValueError("limit must be > 0")
|
||||
|
||||
# Get internal record (includes storage_key/storage_type)
|
||||
record = await self._get_internal_record(artifact_id)
|
||||
if record is None:
|
||||
return None
|
||||
|
||||
storage_type = record.storage_type or 'binary_storage'
|
||||
storage_key = record.storage_key
|
||||
size_bytes = record.size_bytes or 0
|
||||
|
||||
# Cap limit at hard limit
|
||||
if limit is None:
|
||||
limit = self.MAX_INLINE_READ_BYTES
|
||||
limit = min(limit, self.MAX_RANGE_READ_BYTES)
|
||||
|
||||
# For binary_storage, read content
|
||||
if storage_type == 'binary_storage' and storage_key:
|
||||
content = await self._read_binary_storage(storage_key)
|
||||
if content is None:
|
||||
return None
|
||||
|
||||
# Apply offset and limit
|
||||
if offset > 0:
|
||||
content = content[offset:]
|
||||
if limit and len(content) > limit:
|
||||
content = content[:limit]
|
||||
has_more = True
|
||||
else:
|
||||
has_more = False
|
||||
|
||||
return {
|
||||
'artifact_id': artifact_id,
|
||||
'mime_type': record.mime_type,
|
||||
'size_bytes': size_bytes,
|
||||
'offset': offset,
|
||||
'length': len(content),
|
||||
'content_base64': base64.b64encode(content).decode('utf-8'),
|
||||
'file_key': None,
|
||||
'has_more': has_more,
|
||||
}
|
||||
|
||||
# For other storage types, return storage reference
|
||||
# (caller can use file_key for chunked transfer)
|
||||
return {
|
||||
'artifact_id': artifact_id,
|
||||
'mime_type': record.mime_type,
|
||||
'size_bytes': size_bytes,
|
||||
'offset': offset,
|
||||
'length': None,
|
||||
'content_base64': None,
|
||||
'file_key': storage_key,
|
||||
'has_more': False,
|
||||
}
|
||||
|
||||
async def _read_binary_storage(self, key: str) -> bytes | None:
|
||||
"""Read content from BinaryStorage.
|
||||
|
||||
Uses unique_key for isolation to prevent cross-artifact access.
|
||||
|
||||
Args:
|
||||
key: The unique_key used when storing the artifact
|
||||
|
||||
Returns:
|
||||
Content bytes, or None if not found
|
||||
"""
|
||||
async with self._session_factory() as session:
|
||||
result = await session.execute(
|
||||
sqlalchemy.select(BinaryStorage).where(BinaryStorage.unique_key == key)
|
||||
)
|
||||
row = result.scalars().first()
|
||||
if row is None:
|
||||
return None
|
||||
return row.value
|
||||
|
||||
def _row_to_public_dict(self, row: AgentArtifact) -> dict[str, typing.Any]:
|
||||
"""Convert an AgentArtifact row to public dict.
|
||||
|
||||
Returns only fields that match SDK ArtifactMetadata entity.
|
||||
Host-only fields (bot_id, workspace_id, storage_key, storage_type) are excluded.
|
||||
"""
|
||||
return {
|
||||
'artifact_id': row.artifact_id,
|
||||
'artifact_type': row.artifact_type,
|
||||
'mime_type': row.mime_type,
|
||||
'name': row.name,
|
||||
'size_bytes': row.size_bytes,
|
||||
'sha256': row.sha256,
|
||||
'source': row.source,
|
||||
'conversation_id': row.conversation_id,
|
||||
'run_id': row.run_id,
|
||||
'runner_id': row.runner_id,
|
||||
'created_at': int(row.created_at.timestamp()) if row.created_at else None,
|
||||
'expires_at': int(row.expires_at.timestamp()) if row.expires_at else None,
|
||||
'metadata': json.loads(row.metadata_json) if row.metadata_json else {},
|
||||
}
|
||||
@@ -1,230 +0,0 @@
|
||||
"""Configuration migration for agent runner IDs."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import typing
|
||||
|
||||
from .id import is_plugin_runner_id
|
||||
|
||||
|
||||
# Mapping from old built-in runner names to official plugin runner IDs
|
||||
OLD_RUNNER_TO_PLUGIN_RUNNER_ID = {
|
||||
'local-agent': 'plugin:langbot/local-agent/default',
|
||||
'dify-service-api': 'plugin:langbot/dify-agent/default',
|
||||
'n8n-service-api': 'plugin:langbot/n8n-agent/default',
|
||||
'coze-api': 'plugin:langbot/coze-agent/default',
|
||||
'dashscope-app-api': 'plugin:langbot/dashscope-agent/default',
|
||||
'langflow-api': 'plugin:langbot/langflow-agent/default',
|
||||
'tbox-app-api': 'plugin:langbot/tbox-agent/default',
|
||||
}
|
||||
|
||||
|
||||
class ConfigMigration:
|
||||
"""Configuration migration helper for agent runner IDs.
|
||||
|
||||
Responsibilities:
|
||||
- Resolve runner ID from new ai.runner.id or old ai.runner.runner
|
||||
- Map old built-in runner names to official plugin runner IDs
|
||||
- Extract runtime runner config from ai.runner_config
|
||||
- Migrate old ai.<runner-name> blocks into ai.runner_config
|
||||
"""
|
||||
|
||||
@staticmethod
|
||||
def resolve_runner_id(pipeline_config: dict[str, typing.Any]) -> str | None:
|
||||
"""Resolve runner ID from pipeline configuration.
|
||||
|
||||
Priority:
|
||||
1. New format: ai.runner.id (must be plugin:* format)
|
||||
2. Old format: ai.runner.runner (mapped to plugin:* if built-in)
|
||||
|
||||
Args:
|
||||
pipeline_config: Pipeline configuration dict
|
||||
|
||||
Returns:
|
||||
Runner ID string, or None if not configured
|
||||
"""
|
||||
ai_config = pipeline_config.get('ai', {})
|
||||
runner_config = ai_config.get('runner', {})
|
||||
|
||||
# Check new format first
|
||||
runner_id = runner_config.get('id')
|
||||
if runner_id:
|
||||
if is_plugin_runner_id(runner_id):
|
||||
return runner_id
|
||||
# If it's not a plugin ID, try to map it as old runner name
|
||||
return OLD_RUNNER_TO_PLUGIN_RUNNER_ID.get(runner_id, runner_id)
|
||||
|
||||
# Check old format
|
||||
old_runner_name = runner_config.get('runner')
|
||||
if old_runner_name:
|
||||
# If already plugin:* format, return directly
|
||||
if is_plugin_runner_id(old_runner_name):
|
||||
return old_runner_name
|
||||
# Map old built-in runner to official plugin ID
|
||||
mapped_id = OLD_RUNNER_TO_PLUGIN_RUNNER_ID.get(old_runner_name)
|
||||
if mapped_id:
|
||||
return mapped_id
|
||||
# Return old name if no mapping exists (will error in registry)
|
||||
return old_runner_name
|
||||
|
||||
return None
|
||||
|
||||
@staticmethod
|
||||
def resolve_runner_config(
|
||||
pipeline_config: dict[str, typing.Any],
|
||||
runner_id: str,
|
||||
) -> dict[str, typing.Any]:
|
||||
"""Resolve runner binding configuration from pipeline configuration.
|
||||
|
||||
Runtime code should only read the migrated format. Legacy
|
||||
ai.<runner-name> blocks are handled by migration helpers, not by the
|
||||
hot path.
|
||||
|
||||
Args:
|
||||
pipeline_config: Pipeline configuration dict
|
||||
runner_id: Resolved runner ID
|
||||
|
||||
Returns:
|
||||
Runner configuration dict (empty if not found)
|
||||
"""
|
||||
ai_config = pipeline_config.get('ai', {})
|
||||
|
||||
# Check new format
|
||||
runner_configs = ai_config.get('runner_config', {})
|
||||
if runner_id in runner_configs:
|
||||
return runner_configs[runner_id]
|
||||
|
||||
return {}
|
||||
|
||||
@staticmethod
|
||||
def resolve_legacy_runner_config(
|
||||
pipeline_config: dict[str, typing.Any],
|
||||
runner_id: str,
|
||||
) -> dict[str, typing.Any]:
|
||||
"""Resolve old ai.<runner-name> config for migration only."""
|
||||
ai_config = pipeline_config.get('ai', {})
|
||||
|
||||
# Try to find old runner name from runner_id
|
||||
old_runner_name = None
|
||||
for old_name, mapped_id in OLD_RUNNER_TO_PLUGIN_RUNNER_ID.items():
|
||||
if mapped_id == runner_id:
|
||||
old_runner_name = old_name
|
||||
break
|
||||
|
||||
if old_runner_name:
|
||||
old_config = ai_config.get(old_runner_name, {})
|
||||
if old_config:
|
||||
old_config = dict(old_config)
|
||||
if runner_id == OLD_RUNNER_TO_PLUGIN_RUNNER_ID['local-agent']:
|
||||
old_config.pop('max-round', None)
|
||||
return ConfigMigration.normalize_runner_config_for_migration(runner_id, old_config)
|
||||
|
||||
return {}
|
||||
|
||||
@staticmethod
|
||||
def normalize_runner_config_for_migration(
|
||||
runner_id: str,
|
||||
runner_config: dict[str, typing.Any],
|
||||
) -> dict[str, typing.Any]:
|
||||
"""Normalize released legacy runner config before storing binding config.
|
||||
|
||||
Runtime code should not carry aliases. This helper is intentionally used
|
||||
only by config migration so AgentRunner implementations can consume the
|
||||
current manifest-defined field names.
|
||||
"""
|
||||
normalized = dict(runner_config)
|
||||
|
||||
if runner_id == OLD_RUNNER_TO_PLUGIN_RUNNER_ID['local-agent']:
|
||||
legacy_kb = normalized.pop('knowledge-base', None)
|
||||
if 'knowledge-bases' not in normalized:
|
||||
if isinstance(legacy_kb, str) and legacy_kb and legacy_kb not in {'__none__', '__none'}:
|
||||
normalized['knowledge-bases'] = [legacy_kb]
|
||||
elif legacy_kb is not None:
|
||||
normalized['knowledge-bases'] = []
|
||||
|
||||
return normalized
|
||||
|
||||
@staticmethod
|
||||
def get_old_runner_name(runner_id: str) -> str | None:
|
||||
"""Get old runner name from mapped runner ID.
|
||||
|
||||
Args:
|
||||
runner_id: Plugin runner ID
|
||||
|
||||
Returns:
|
||||
Old runner name if mapped, None otherwise
|
||||
"""
|
||||
for old_name, mapped_id in OLD_RUNNER_TO_PLUGIN_RUNNER_ID.items():
|
||||
if mapped_id == runner_id:
|
||||
return old_name
|
||||
return None
|
||||
|
||||
@staticmethod
|
||||
def get_expire_time(pipeline_config: dict[str, typing.Any]) -> int:
|
||||
"""Get conversation expire time from configuration.
|
||||
|
||||
Args:
|
||||
pipeline_config: Pipeline configuration dict
|
||||
|
||||
Returns:
|
||||
Expire time in seconds (0 means no expiry)
|
||||
"""
|
||||
ai_config = pipeline_config.get('ai', {})
|
||||
runner_config = ai_config.get('runner', {})
|
||||
return runner_config.get('expire-time', 0)
|
||||
|
||||
@staticmethod
|
||||
def migrate_pipeline_config(pipeline_config: dict[str, typing.Any]) -> dict[str, typing.Any]:
|
||||
"""Migrate pipeline config to new format.
|
||||
|
||||
This converts old ai.runner.runner and ai.<runner-name> to
|
||||
new ai.runner.id and ai.runner_config format.
|
||||
|
||||
Args:
|
||||
pipeline_config: Original pipeline configuration
|
||||
|
||||
Returns:
|
||||
Migrated pipeline configuration
|
||||
"""
|
||||
# Create copy
|
||||
new_config = dict(pipeline_config)
|
||||
ai_config = new_config.get('ai', {})
|
||||
if not ai_config:
|
||||
return new_config
|
||||
|
||||
runner_config = ai_config.get('runner', {})
|
||||
runner_configs = ai_config.get('runner_config', {})
|
||||
|
||||
# Resolve runner ID
|
||||
runner_id = ConfigMigration.resolve_runner_id(pipeline_config)
|
||||
if runner_id:
|
||||
# Set new format
|
||||
runner_config['id'] = runner_id
|
||||
# Remove old runner field if present
|
||||
if 'runner' in runner_config and is_plugin_runner_id(runner_config['runner']):
|
||||
# Already migrated plugin:* format, keep as id
|
||||
pass
|
||||
elif 'runner' in runner_config:
|
||||
# Old built-in runner name, remove after migration
|
||||
old_name = runner_config['runner']
|
||||
if old_name in OLD_RUNNER_TO_PLUGIN_RUNNER_ID:
|
||||
del runner_config['runner']
|
||||
|
||||
# Migrate runner config
|
||||
resolved_config = ConfigMigration.resolve_runner_config(pipeline_config, runner_id)
|
||||
if not resolved_config:
|
||||
resolved_config = ConfigMigration.resolve_legacy_runner_config(pipeline_config, runner_id)
|
||||
if resolved_config:
|
||||
resolved_config = ConfigMigration.normalize_runner_config_for_migration(runner_id, resolved_config)
|
||||
runner_configs[runner_id] = resolved_config
|
||||
# Remove old runner config block
|
||||
for old_name, mapped_id in OLD_RUNNER_TO_PLUGIN_RUNNER_ID.items():
|
||||
if mapped_id == runner_id and old_name in ai_config:
|
||||
del ai_config[old_name]
|
||||
|
||||
# Update configs
|
||||
ai_config['runner'] = runner_config
|
||||
ai_config['runner_config'] = runner_configs
|
||||
new_config['ai'] = ai_config
|
||||
|
||||
return new_config
|
||||
@@ -1,208 +0,0 @@
|
||||
"""Helpers for interpreting AgentRunner DynamicForm configuration."""
|
||||
from __future__ import annotations
|
||||
|
||||
import typing
|
||||
|
||||
from .descriptor import AgentRunnerDescriptor
|
||||
|
||||
|
||||
LLM_MODEL_SELECTOR_TYPES = {'model-fallback-selector', 'llm-model-selector'}
|
||||
KB_SELECTOR_TYPES = {'knowledge-base-multi-selector'}
|
||||
PROMPT_EDITOR_TYPES = {'prompt-editor'}
|
||||
NONE_SENTINELS = {'', '__none__', '__none'}
|
||||
|
||||
|
||||
def iter_schema_items(
|
||||
descriptor: AgentRunnerDescriptor | None,
|
||||
field_types: set[str],
|
||||
) -> typing.Iterator[dict[str, typing.Any]]:
|
||||
"""Yield descriptor config schema items whose type is in field_types."""
|
||||
if descriptor is None:
|
||||
return
|
||||
for item in descriptor.config_schema or []:
|
||||
if not isinstance(item, dict):
|
||||
continue
|
||||
if item.get('type') in field_types:
|
||||
yield item
|
||||
|
||||
|
||||
def has_permission(
|
||||
descriptor: AgentRunnerDescriptor | None,
|
||||
name: str,
|
||||
actions: set[str],
|
||||
) -> bool:
|
||||
"""Return whether a runner descriptor requests one of the given actions."""
|
||||
if descriptor is None:
|
||||
return False
|
||||
configured_actions = descriptor.permissions.get(name, [])
|
||||
return any(action in configured_actions for action in actions)
|
||||
|
||||
|
||||
def uses_host_models(descriptor: AgentRunnerDescriptor | None) -> bool:
|
||||
"""Return whether LangBot should resolve model resources for this runner."""
|
||||
return (
|
||||
has_permission(descriptor, 'models', {'invoke', 'stream', 'list'})
|
||||
and any(True for _ in iter_schema_items(descriptor, LLM_MODEL_SELECTOR_TYPES))
|
||||
)
|
||||
|
||||
|
||||
def uses_host_tools(descriptor: AgentRunnerDescriptor | None) -> bool:
|
||||
"""Return whether LangBot should expose tool resources to this runner."""
|
||||
return (
|
||||
descriptor is not None
|
||||
and descriptor.supports_tool_calling()
|
||||
and has_permission(descriptor, 'tools', {'list', 'detail', 'call'})
|
||||
)
|
||||
|
||||
|
||||
def uses_host_knowledge_bases(descriptor: AgentRunnerDescriptor | None) -> bool:
|
||||
"""Return whether LangBot should expose knowledge-base resources to this runner."""
|
||||
return (
|
||||
descriptor is not None
|
||||
and descriptor.supports_knowledge_retrieval()
|
||||
and has_permission(descriptor, 'knowledge_bases', {'list', 'retrieve'})
|
||||
)
|
||||
|
||||
|
||||
def extract_prompt_config(
|
||||
descriptor: AgentRunnerDescriptor | None,
|
||||
runner_config: dict[str, typing.Any],
|
||||
default_prompt: list[dict[str, typing.Any]],
|
||||
) -> list[dict[str, typing.Any]]:
|
||||
"""Extract the prompt-editor value selected by the runner schema."""
|
||||
for item in iter_schema_items(descriptor, PROMPT_EDITOR_TYPES):
|
||||
field_name = item.get('name')
|
||||
if field_name and field_name in runner_config:
|
||||
configured_prompt = runner_config[field_name]
|
||||
if isinstance(configured_prompt, list):
|
||||
return configured_prompt
|
||||
default_value = item.get('default')
|
||||
if isinstance(default_value, list):
|
||||
return default_value
|
||||
return default_prompt
|
||||
|
||||
|
||||
def extract_model_selection(
|
||||
descriptor: AgentRunnerDescriptor | None,
|
||||
runner_config: dict[str, typing.Any],
|
||||
) -> tuple[str, list[str]]:
|
||||
"""Extract primary/fallback LLM selections from schema-defined fields."""
|
||||
primary_uuid = ''
|
||||
fallback_uuids: list[str] = []
|
||||
|
||||
for item in iter_schema_items(descriptor, LLM_MODEL_SELECTOR_TYPES):
|
||||
field_name = item.get('name')
|
||||
if not field_name:
|
||||
continue
|
||||
|
||||
value = runner_config.get(field_name, item.get('default'))
|
||||
if item.get('type') == 'model-fallback-selector':
|
||||
if isinstance(value, str):
|
||||
primary_uuid = value
|
||||
elif isinstance(value, dict):
|
||||
primary_uuid = value.get('primary') or ''
|
||||
fallbacks = value.get('fallbacks', [])
|
||||
if isinstance(fallbacks, list):
|
||||
fallback_uuids = [fallback for fallback in fallbacks if isinstance(fallback, str)]
|
||||
break
|
||||
|
||||
if item.get('type') == 'llm-model-selector' and isinstance(value, str):
|
||||
primary_uuid = value
|
||||
break
|
||||
|
||||
return primary_uuid, fallback_uuids
|
||||
|
||||
|
||||
def extract_knowledge_base_uuids(
|
||||
descriptor: AgentRunnerDescriptor | None,
|
||||
runner_config: dict[str, typing.Any],
|
||||
) -> list[str]:
|
||||
"""Extract configured knowledge-base UUIDs from schema-defined fields."""
|
||||
if not uses_host_knowledge_bases(descriptor):
|
||||
return []
|
||||
|
||||
kb_uuids: list[str] = []
|
||||
for item in iter_schema_items(descriptor, KB_SELECTOR_TYPES):
|
||||
field_name = item.get('name')
|
||||
if not field_name:
|
||||
continue
|
||||
value = runner_config.get(field_name, item.get('default', []))
|
||||
if isinstance(value, list):
|
||||
kb_uuids.extend(
|
||||
kb_uuid for kb_uuid in value if isinstance(kb_uuid, str) and kb_uuid not in NONE_SENTINELS
|
||||
)
|
||||
|
||||
return list(dict.fromkeys(kb_uuids))
|
||||
|
||||
|
||||
def iter_config_model_refs(
|
||||
descriptor: AgentRunnerDescriptor,
|
||||
runner_config: dict[str, typing.Any],
|
||||
) -> typing.Iterator[tuple[str, str]]:
|
||||
"""Yield model references declared by schema-defined model selector fields."""
|
||||
for item in descriptor.config_schema or []:
|
||||
if not isinstance(item, dict):
|
||||
continue
|
||||
|
||||
field_name = item.get('name')
|
||||
field_type = item.get('type')
|
||||
if not field_name or field_name not in runner_config:
|
||||
continue
|
||||
|
||||
value = runner_config.get(field_name)
|
||||
if field_type == 'model-fallback-selector':
|
||||
if isinstance(value, str) and value not in NONE_SENTINELS:
|
||||
yield 'llm', value
|
||||
elif isinstance(value, dict):
|
||||
primary = value.get('primary')
|
||||
if isinstance(primary, str) and primary not in NONE_SENTINELS:
|
||||
yield 'llm', primary
|
||||
fallbacks = value.get('fallbacks', [])
|
||||
if isinstance(fallbacks, list):
|
||||
for fallback_uuid in fallbacks:
|
||||
if isinstance(fallback_uuid, str) and fallback_uuid not in NONE_SENTINELS:
|
||||
yield 'llm', fallback_uuid
|
||||
elif field_type == 'llm-model-selector':
|
||||
if isinstance(value, str) and value not in NONE_SENTINELS:
|
||||
yield 'llm', value
|
||||
elif field_type == 'rerank-model-selector':
|
||||
if isinstance(value, str) and value not in NONE_SENTINELS:
|
||||
yield 'rerank', value
|
||||
|
||||
|
||||
def set_empty_llm_model_selection(
|
||||
descriptor: AgentRunnerDescriptor,
|
||||
runner_config: dict[str, typing.Any],
|
||||
model_uuid: str,
|
||||
) -> bool:
|
||||
"""Set the first empty schema-defined LLM selector to model_uuid."""
|
||||
for item in iter_schema_items(descriptor, LLM_MODEL_SELECTOR_TYPES):
|
||||
field_name = item.get('name')
|
||||
field_type = item.get('type')
|
||||
if not field_name:
|
||||
continue
|
||||
|
||||
value = runner_config.get(field_name, item.get('default'))
|
||||
if field_type == 'model-fallback-selector':
|
||||
if isinstance(value, dict):
|
||||
primary = value.get('primary') or ''
|
||||
if primary not in NONE_SENTINELS:
|
||||
return False
|
||||
fallbacks = value.get('fallbacks', [])
|
||||
runner_config[field_name] = {
|
||||
'primary': model_uuid,
|
||||
'fallbacks': fallbacks if isinstance(fallbacks, list) else [],
|
||||
}
|
||||
return True
|
||||
if isinstance(value, str) and value not in NONE_SENTINELS:
|
||||
return False
|
||||
runner_config[field_name] = {'primary': model_uuid, 'fallbacks': []}
|
||||
return True
|
||||
|
||||
if field_type == 'llm-model-selector':
|
||||
if isinstance(value, str) and value not in NONE_SENTINELS:
|
||||
return False
|
||||
runner_config[field_name] = model_uuid
|
||||
return True
|
||||
|
||||
return False
|
||||
@@ -1,427 +0,0 @@
|
||||
"""Agent run context builder for provisioning AgentRunContext envelopes."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import uuid
|
||||
import time
|
||||
import typing
|
||||
|
||||
from ...core import app
|
||||
from .descriptor import AgentRunnerDescriptor
|
||||
from .persistent_state_store import get_persistent_state_store
|
||||
from .host_models import AgentEventEnvelope, AgentBinding
|
||||
|
||||
|
||||
DEFAULT_RUNNER_TIMEOUT_SECONDS = 300
|
||||
|
||||
|
||||
# Internal models for the agent runner context protocol.
|
||||
|
||||
|
||||
class AgentTrigger(typing.TypedDict):
|
||||
"""Agent trigger information."""
|
||||
|
||||
type: str
|
||||
source: str # 'pipeline' or 'event_router'
|
||||
timestamp: int | None
|
||||
|
||||
|
||||
class ConversationContext(typing.TypedDict):
|
||||
"""Conversation context."""
|
||||
|
||||
conversation_id: str | None
|
||||
thread_id: str | None
|
||||
launcher_type: str | None
|
||||
launcher_id: str | None
|
||||
sender_id: str | None
|
||||
bot_id: str | None
|
||||
workspace_id: str | None
|
||||
session_id: str | None
|
||||
pipeline_uuid: str | None
|
||||
|
||||
|
||||
class AgentInput(typing.TypedDict):
|
||||
"""Agent input."""
|
||||
|
||||
text: str | None
|
||||
contents: list[dict[str, typing.Any]]
|
||||
message_chain: dict[str, typing.Any] | None
|
||||
attachments: list[dict[str, typing.Any]]
|
||||
|
||||
|
||||
class AgentRunState(typing.TypedDict):
|
||||
"""Agent run state with 4 scopes."""
|
||||
|
||||
conversation: dict[str, typing.Any]
|
||||
actor: dict[str, typing.Any]
|
||||
subject: dict[str, typing.Any]
|
||||
runner: dict[str, typing.Any]
|
||||
|
||||
|
||||
# Resource payload models matching langbot-plugin-sdk/resources.py.
|
||||
|
||||
|
||||
class ModelResource(typing.TypedDict):
|
||||
"""Model resource payload."""
|
||||
|
||||
model_id: str
|
||||
model_type: str | None
|
||||
provider: str | None
|
||||
|
||||
|
||||
class ToolResource(typing.TypedDict):
|
||||
"""Tool resource payload."""
|
||||
|
||||
tool_name: str
|
||||
tool_type: str | None
|
||||
description: str | None
|
||||
|
||||
|
||||
class KnowledgeBaseResource(typing.TypedDict):
|
||||
"""Knowledge base resource payload."""
|
||||
|
||||
kb_id: str
|
||||
kb_name: str | None
|
||||
kb_type: str | None
|
||||
|
||||
|
||||
class FileResource(typing.TypedDict):
|
||||
"""File resource payload."""
|
||||
|
||||
file_id: str
|
||||
file_name: str | None
|
||||
mime_type: str | None
|
||||
source: str | None
|
||||
|
||||
|
||||
class StorageResource(typing.TypedDict):
|
||||
"""Storage resource payload."""
|
||||
|
||||
plugin_storage: bool
|
||||
workspace_storage: bool
|
||||
|
||||
|
||||
class AgentResources(typing.TypedDict):
|
||||
"""Agent resources payload."""
|
||||
|
||||
models: list[ModelResource]
|
||||
tools: list[ToolResource]
|
||||
knowledge_bases: list[KnowledgeBaseResource]
|
||||
files: list[FileResource]
|
||||
storage: StorageResource
|
||||
platform_capabilities: dict[str, typing.Any]
|
||||
|
||||
|
||||
class AgentRuntimeContext(typing.TypedDict):
|
||||
"""Agent runtime context."""
|
||||
|
||||
langbot_version: str | None
|
||||
sdk_protocol_version: str
|
||||
query_id: int | None
|
||||
trace_id: str | None
|
||||
deadline_at: float | None
|
||||
metadata: dict[str, typing.Any]
|
||||
|
||||
|
||||
class AgentRunContextPayload(typing.TypedDict):
|
||||
"""AgentRunContext payload passed to an agent runner.
|
||||
|
||||
Protocol v1 structure - matches SDK AgentRunContext.
|
||||
|
||||
Note: The 'config' field contains the binding config from ai.runner_config[runner_id],
|
||||
which is Pipeline's configuration for this specific runner binding (not plugin instance config).
|
||||
"""
|
||||
|
||||
run_id: str
|
||||
trigger: AgentTrigger
|
||||
conversation: ConversationContext | None
|
||||
event: dict[str, typing.Any] # REQUIRED for Protocol v1
|
||||
actor: dict[str, typing.Any] | None
|
||||
subject: dict[str, typing.Any] | None
|
||||
input: AgentInput
|
||||
delivery: dict[str, typing.Any] # REQUIRED for Protocol v1
|
||||
resources: AgentResources
|
||||
context: dict[str, typing.Any] # ContextAccess - REQUIRED for Protocol v1
|
||||
state: AgentRunState
|
||||
runtime: AgentRuntimeContext
|
||||
config: dict[str, typing.Any] # Binding config from ai.runner_config[runner_id]
|
||||
bootstrap: dict[str, typing.Any] | None # Optional bootstrap context
|
||||
adapter: dict[str, typing.Any] | None # Pipeline adapter context
|
||||
metadata: dict[str, typing.Any] # Additional metadata
|
||||
|
||||
|
||||
class AgentRunContextBuilder:
|
||||
"""Builder for provisioning AgentRunContext.
|
||||
|
||||
Responsibilities:
|
||||
- Generate new run_id (UUID, not query id)
|
||||
- Set trigger type based on event source
|
||||
- Build conversation context from event
|
||||
- Build input from event
|
||||
- Build state snapshot from PersistentStateStore
|
||||
- Build runtime context with host info, trace_id, deadline
|
||||
- Set config from runner binding configuration.
|
||||
|
||||
Pipeline Query adaptation belongs to PipelineAdapter, not this builder.
|
||||
"""
|
||||
|
||||
ap: app.Application
|
||||
|
||||
def __init__(self, ap: app.Application):
|
||||
self.ap = ap
|
||||
|
||||
async def build_context_from_event(
|
||||
self,
|
||||
event: AgentEventEnvelope,
|
||||
binding: AgentBinding,
|
||||
descriptor: AgentRunnerDescriptor,
|
||||
resources: AgentResources,
|
||||
) -> AgentRunContextPayload:
|
||||
"""Build AgentRunContext from event-first envelope.
|
||||
|
||||
This is the main entry point for Protocol v1.
|
||||
Does NOT inline full history by default.
|
||||
|
||||
Args:
|
||||
event: Event envelope
|
||||
binding: Agent binding configuration
|
||||
descriptor: Runner descriptor
|
||||
resources: Built resources
|
||||
|
||||
Returns:
|
||||
AgentRunContextPayload for the runner
|
||||
"""
|
||||
# Generate new run_id
|
||||
run_id = str(uuid.uuid4())
|
||||
|
||||
# Build trigger from event
|
||||
trigger: AgentTrigger = {
|
||||
'type': event.event_type,
|
||||
'source': event.source,
|
||||
'timestamp': event.event_time or int(time.time()),
|
||||
}
|
||||
|
||||
# Build conversation context from event
|
||||
conversation: ConversationContext | None = None
|
||||
if event.conversation_id:
|
||||
conversation = {
|
||||
'session_id': None, # Pipeline adapter field
|
||||
'conversation_id': event.conversation_id,
|
||||
'thread_id': event.thread_id,
|
||||
'launcher_type': None, # Will be filled from actor/subject if needed
|
||||
'launcher_id': None,
|
||||
'sender_id': event.actor.actor_id if event.actor else None,
|
||||
'bot_id': event.bot_id,
|
||||
'workspace_id': event.workspace_id,
|
||||
'pipeline_uuid': binding.pipeline_uuid, # Pipeline adapter field
|
||||
}
|
||||
|
||||
# Build event context (Protocol v1 event-first)
|
||||
event_context = {
|
||||
'event_id': event.event_id,
|
||||
'event_type': event.event_type,
|
||||
'event_time': event.event_time,
|
||||
'source': event.source,
|
||||
'source_event_type': event.source_event_type,
|
||||
'raw_ref': event.raw_ref.model_dump(mode='json') if event.raw_ref else None,
|
||||
'data': event.data,
|
||||
}
|
||||
|
||||
# Build actor context
|
||||
actor_context = None
|
||||
if event.actor:
|
||||
actor_context = {
|
||||
'actor_type': event.actor.actor_type,
|
||||
'actor_id': event.actor.actor_id,
|
||||
'actor_name': event.actor.actor_name,
|
||||
}
|
||||
|
||||
# Build subject context
|
||||
subject_context = None
|
||||
if event.subject:
|
||||
subject_context = {
|
||||
'subject_type': event.subject.subject_type,
|
||||
'subject_id': event.subject.subject_id,
|
||||
'data': event.subject.data,
|
||||
}
|
||||
|
||||
# Build input from event
|
||||
input: AgentInput = {
|
||||
'text': event.input.text,
|
||||
'contents': [c.model_dump(mode='json') if hasattr(c, 'model_dump') else c for c in event.input.contents],
|
||||
'message_chain': event.input.message_chain,
|
||||
'attachments': [
|
||||
a.model_dump(mode='json') if hasattr(a, 'model_dump') else a for a in event.input.attachments
|
||||
],
|
||||
}
|
||||
|
||||
# Build context access (no history inlined by default for Protocol v1)
|
||||
# Populate with actual values from stores
|
||||
context_access = await self._build_context_access(event, descriptor, binding)
|
||||
|
||||
# Build state snapshot from persistent state store (event-first Protocol v1)
|
||||
persistent_state_store = get_persistent_state_store(self.ap.persistence_mgr.get_db_engine())
|
||||
state: AgentRunState = await persistent_state_store.build_snapshot_from_event(event, binding, descriptor)
|
||||
|
||||
# Build runtime context
|
||||
runtime: AgentRuntimeContext = {
|
||||
'langbot_version': self.ap.ver_mgr.get_current_version(),
|
||||
'sdk_protocol_version': descriptor.protocol_version,
|
||||
'query_id': None, # No query_id in event-first mode
|
||||
'trace_id': run_id,
|
||||
'deadline_at': self._build_deadline_from_binding(binding),
|
||||
'metadata': {
|
||||
'bot_id': event.bot_id,
|
||||
'workspace_id': event.workspace_id,
|
||||
'streaming_supported': event.delivery.supports_streaming,
|
||||
'model_context_window_tokens': None,
|
||||
# TODO(model-info): populate model_context_window_tokens after
|
||||
# LiteLLM/model metadata lands. Runners fall back to their
|
||||
# binding config until Host can provide the real window.
|
||||
},
|
||||
}
|
||||
|
||||
# Build delivery context
|
||||
delivery_context = {
|
||||
'surface': event.delivery.surface,
|
||||
'reply_target': event.delivery.reply_target,
|
||||
'supports_streaming': event.delivery.supports_streaming,
|
||||
'supports_edit': event.delivery.supports_edit,
|
||||
'supports_reaction': event.delivery.supports_reaction,
|
||||
'max_message_size': event.delivery.max_message_size,
|
||||
'platform_capabilities': event.delivery.platform_capabilities,
|
||||
}
|
||||
|
||||
# Build adapter context (empty for event-first)
|
||||
adapter_context = {
|
||||
'query_id': None,
|
||||
'pipeline_uuid': binding.pipeline_uuid,
|
||||
'extra': {},
|
||||
}
|
||||
|
||||
# Build full context - Protocol v1 structure
|
||||
context: AgentRunContextPayload = {
|
||||
'run_id': run_id,
|
||||
'trigger': trigger,
|
||||
'conversation': conversation,
|
||||
'event': event_context, # REQUIRED
|
||||
'actor': actor_context,
|
||||
'subject': subject_context,
|
||||
'input': input,
|
||||
'delivery': delivery_context, # REQUIRED
|
||||
'resources': resources,
|
||||
'context': context_access, # ContextAccess - REQUIRED
|
||||
'state': state,
|
||||
'runtime': runtime,
|
||||
'config': binding.runner_config,
|
||||
'bootstrap': None,
|
||||
'adapter': adapter_context,
|
||||
'metadata': {}, # Additional metadata
|
||||
}
|
||||
|
||||
return context
|
||||
|
||||
def _build_deadline_from_binding(self, binding: AgentBinding) -> float | None:
|
||||
"""Build deadline timestamp from binding timeout config.
|
||||
|
||||
Args:
|
||||
binding: Agent binding with runner_config
|
||||
|
||||
Returns:
|
||||
Deadline timestamp or None
|
||||
"""
|
||||
timeout = binding.runner_config.get('timeout', DEFAULT_RUNNER_TIMEOUT_SECONDS)
|
||||
if timeout is None:
|
||||
return None
|
||||
|
||||
try:
|
||||
timeout_seconds = float(timeout)
|
||||
except (TypeError, ValueError):
|
||||
return None
|
||||
|
||||
if timeout_seconds <= 0:
|
||||
return None
|
||||
|
||||
return time.time() + timeout_seconds
|
||||
|
||||
async def _build_context_access(
|
||||
self,
|
||||
event: AgentEventEnvelope,
|
||||
descriptor: AgentRunnerDescriptor,
|
||||
binding: AgentBinding | None = None,
|
||||
) -> dict[str, typing.Any]:
|
||||
"""Build ContextAccess with actual values from stores.
|
||||
|
||||
Args:
|
||||
event: Event envelope
|
||||
descriptor: Runner descriptor
|
||||
binding: Agent binding (required for state_policy in event-first mode)
|
||||
|
||||
Returns:
|
||||
ContextAccess dict
|
||||
"""
|
||||
conversation_id = event.conversation_id
|
||||
|
||||
# Check if history APIs are available for this runner
|
||||
# Based on runner permissions
|
||||
permissions = descriptor.permissions or {}
|
||||
history_permissions = permissions.get('history', [])
|
||||
event_permissions = permissions.get('events', [])
|
||||
artifact_permissions = permissions.get('artifacts', [])
|
||||
|
||||
history_page_enabled = 'page' in history_permissions and conversation_id is not None
|
||||
history_search_enabled = 'search' in history_permissions and conversation_id is not None
|
||||
event_get_enabled = 'get' in event_permissions
|
||||
event_page_enabled = 'page' in event_permissions and conversation_id is not None
|
||||
artifact_metadata_enabled = 'metadata' in artifact_permissions
|
||||
artifact_read_enabled = 'read' in artifact_permissions
|
||||
|
||||
# Determine state API availability based on binding state_policy.
|
||||
state_enabled = False
|
||||
if binding is not None:
|
||||
state_policy = binding.state_policy
|
||||
if state_policy.enable_state and state_policy.state_scopes:
|
||||
state_enabled = True
|
||||
|
||||
# Get latest cursor and has_history_before if conversation exists
|
||||
latest_cursor = None
|
||||
has_history_before = False
|
||||
|
||||
if conversation_id:
|
||||
try:
|
||||
from .transcript_store import TranscriptStore
|
||||
|
||||
store = TranscriptStore(self.ap.persistence_mgr.get_db_engine())
|
||||
|
||||
latest_cursor = await store.get_latest_cursor(conversation_id)
|
||||
if latest_cursor:
|
||||
has_history_before = True
|
||||
except Exception as e:
|
||||
self.ap.logger.warning(f'Failed to get transcript cursor: {e}')
|
||||
|
||||
return {
|
||||
'conversation_id': conversation_id,
|
||||
'thread_id': event.thread_id,
|
||||
'latest_cursor': latest_cursor,
|
||||
'event_seq': None, # Will be populated when EventLog is written
|
||||
'transcript_seq': int(latest_cursor) if latest_cursor else None,
|
||||
'has_history_before': has_history_before,
|
||||
'inline_policy': {
|
||||
'mode': 'current_event',
|
||||
'delivered_count': 0,
|
||||
'source_total_count': None,
|
||||
'messages_complete': False,
|
||||
'reason': 'self_managed_context',
|
||||
},
|
||||
'available_apis': {
|
||||
'history_page': history_page_enabled,
|
||||
'history_search': history_search_enabled,
|
||||
'event_get': event_get_enabled,
|
||||
'event_page': event_page_enabled,
|
||||
'artifact_metadata': artifact_metadata_enabled,
|
||||
'artifact_read': artifact_read_enabled,
|
||||
'state': state_enabled,
|
||||
'storage': True,
|
||||
'prompt_get': False,
|
||||
},
|
||||
}
|
||||
@@ -1,72 +0,0 @@
|
||||
"""Agent runner descriptor."""
|
||||
from __future__ import annotations
|
||||
|
||||
import typing
|
||||
import pydantic
|
||||
|
||||
|
||||
class AgentRunnerDescriptor(pydantic.BaseModel):
|
||||
"""Descriptor for an agent runner.
|
||||
|
||||
Represents the discovered metadata for a runner, including
|
||||
its identity, capabilities, permissions, and configuration schema.
|
||||
"""
|
||||
|
||||
id: str
|
||||
"""Unique runner ID: plugin:author/plugin_name/runner_name"""
|
||||
|
||||
source: typing.Literal['plugin']
|
||||
"""Runner source type"""
|
||||
|
||||
label: dict[str, str]
|
||||
"""Display labels keyed by locale (e.g., en_US, zh_Hans)"""
|
||||
|
||||
description: dict[str, str] | None = None
|
||||
"""Optional description keyed by locale"""
|
||||
|
||||
plugin_author: str
|
||||
"""Plugin author from manifest"""
|
||||
|
||||
plugin_name: str
|
||||
"""Plugin name from manifest"""
|
||||
|
||||
runner_name: str
|
||||
"""AgentRunner component name from manifest"""
|
||||
|
||||
plugin_version: str | None = None
|
||||
"""Optional plugin version"""
|
||||
|
||||
protocol_version: str = '1'
|
||||
"""SDK protocol version, default '1'"""
|
||||
|
||||
config_schema: list[dict[str, typing.Any]] = []
|
||||
"""Configuration schema using DynamicForm format"""
|
||||
|
||||
capabilities: dict[str, bool] = {}
|
||||
"""Runner capabilities: streaming, tool_calling, knowledge_retrieval, etc."""
|
||||
|
||||
permissions: dict[str, list[str]] = {}
|
||||
"""Requested permissions: models, tools, knowledge_bases, storage, files, platform_api"""
|
||||
|
||||
raw_manifest: dict[str, typing.Any] = {}
|
||||
"""Original manifest for reference"""
|
||||
|
||||
model_config = pydantic.ConfigDict(
|
||||
extra='allow',
|
||||
)
|
||||
|
||||
def get_plugin_id(self) -> str:
|
||||
"""Return plugin identifier as author/name."""
|
||||
return f'{self.plugin_author}/{self.plugin_name}'
|
||||
|
||||
def supports_streaming(self) -> bool:
|
||||
"""Check if runner supports streaming output."""
|
||||
return self.capabilities.get('streaming', False)
|
||||
|
||||
def supports_tool_calling(self) -> bool:
|
||||
"""Check if runner supports tool calling."""
|
||||
return self.capabilities.get('tool_calling', False)
|
||||
|
||||
def supports_knowledge_retrieval(self) -> bool:
|
||||
"""Check if runner supports knowledge retrieval."""
|
||||
return self.capabilities.get('knowledge_retrieval', False)
|
||||
@@ -1,37 +0,0 @@
|
||||
"""Agent runner errors."""
|
||||
from __future__ import annotations
|
||||
|
||||
|
||||
class AgentRunnerError(Exception):
|
||||
"""Base error for agent runner operations."""
|
||||
pass
|
||||
|
||||
|
||||
class RunnerNotFoundError(AgentRunnerError):
|
||||
"""Runner not found in registry."""
|
||||
def __init__(self, runner_id: str):
|
||||
self.runner_id = runner_id
|
||||
super().__init__(f'Agent runner not found: {runner_id}')
|
||||
|
||||
|
||||
class RunnerNotAuthorizedError(AgentRunnerError):
|
||||
"""Runner not authorized for this pipeline."""
|
||||
def __init__(self, runner_id: str, bound_plugins: list[str] | None):
|
||||
self.runner_id = runner_id
|
||||
self.bound_plugins = bound_plugins
|
||||
super().__init__(f'Agent runner {runner_id} not authorized for bound_plugins={bound_plugins}')
|
||||
|
||||
|
||||
class RunnerProtocolError(AgentRunnerError):
|
||||
"""Runner protocol version mismatch or invalid manifest."""
|
||||
def __init__(self, runner_id: str, message: str):
|
||||
self.runner_id = runner_id
|
||||
super().__init__(f'Agent runner protocol error for {runner_id}: {message}')
|
||||
|
||||
|
||||
class RunnerExecutionError(AgentRunnerError):
|
||||
"""Runner execution failed."""
|
||||
def __init__(self, runner_id: str, message: str, retryable: bool = False):
|
||||
self.runner_id = runner_id
|
||||
self.retryable = retryable
|
||||
super().__init__(f'Agent runner {runner_id} execution failed: {message}')
|
||||
@@ -1,255 +0,0 @@
|
||||
"""EventLog store for writing and querying event records."""
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import datetime
|
||||
import typing
|
||||
import uuid
|
||||
|
||||
import sqlalchemy
|
||||
from sqlalchemy.ext.asyncio import AsyncEngine, AsyncSession
|
||||
from sqlalchemy.orm import sessionmaker
|
||||
|
||||
from ...entity.persistence.event_log import EventLog
|
||||
|
||||
|
||||
class EventLogStore:
|
||||
"""Store for EventLog records.
|
||||
|
||||
Handles writing events to the event log and querying them.
|
||||
All methods are async and use the provided database engine.
|
||||
"""
|
||||
|
||||
engine: AsyncEngine
|
||||
|
||||
# Hard limits
|
||||
MAX_INPUT_SUMMARY_LENGTH = 1000
|
||||
|
||||
def __init__(self, engine: AsyncEngine):
|
||||
self.engine = engine
|
||||
self._session_factory = sessionmaker(
|
||||
engine, class_=AsyncSession, expire_on_commit=False
|
||||
)
|
||||
|
||||
async def append_event(
|
||||
self,
|
||||
event_id: str | None,
|
||||
event_type: str,
|
||||
source: str,
|
||||
bot_id: str | None = None,
|
||||
workspace_id: str | None = None,
|
||||
conversation_id: str | None = None,
|
||||
thread_id: str | None = None,
|
||||
actor_type: str | None = None,
|
||||
actor_id: str | None = None,
|
||||
actor_name: str | None = None,
|
||||
subject_type: str | None = None,
|
||||
subject_id: str | None = None,
|
||||
input_summary: str | None = None,
|
||||
input_json: dict[str, typing.Any] | None = None,
|
||||
raw_ref: str | None = None,
|
||||
run_id: str | None = None,
|
||||
runner_id: str | None = None,
|
||||
event_time: datetime.datetime | None = None,
|
||||
metadata: dict[str, typing.Any] | None = None,
|
||||
) -> str:
|
||||
"""Append an event to the event log.
|
||||
|
||||
Args:
|
||||
event_id: Unique event ID (generated if None)
|
||||
event_type: Event type
|
||||
source: Event source
|
||||
bot_id: Bot UUID
|
||||
workspace_id: Workspace ID
|
||||
conversation_id: Conversation ID
|
||||
thread_id: Thread ID
|
||||
actor_type: Actor type
|
||||
actor_id: Actor ID
|
||||
actor_name: Actor display name
|
||||
subject_type: Subject type
|
||||
subject_id: Subject ID
|
||||
input_summary: Brief input summary
|
||||
input_json: Full input JSON
|
||||
raw_ref: Reference to raw event payload
|
||||
run_id: Run ID processing this event
|
||||
runner_id: Runner ID processing this event
|
||||
event_time: When the event occurred
|
||||
metadata: Additional metadata
|
||||
|
||||
Returns:
|
||||
The event_id
|
||||
"""
|
||||
if event_id is None:
|
||||
event_id = str(uuid.uuid4())
|
||||
|
||||
# Truncate input summary if too long
|
||||
if input_summary and len(input_summary) > self.MAX_INPUT_SUMMARY_LENGTH:
|
||||
input_summary = input_summary[:self.MAX_INPUT_SUMMARY_LENGTH - 3] + "..."
|
||||
|
||||
async with self._session_factory() as session:
|
||||
event = EventLog(
|
||||
event_id=event_id,
|
||||
event_type=event_type,
|
||||
event_time=event_time,
|
||||
source=source,
|
||||
bot_id=bot_id,
|
||||
workspace_id=workspace_id,
|
||||
conversation_id=conversation_id,
|
||||
thread_id=thread_id,
|
||||
actor_type=actor_type,
|
||||
actor_id=actor_id,
|
||||
actor_name=actor_name,
|
||||
subject_type=subject_type,
|
||||
subject_id=subject_id,
|
||||
input_summary=input_summary,
|
||||
input_json=json.dumps(input_json) if input_json else None,
|
||||
raw_ref=raw_ref,
|
||||
run_id=run_id,
|
||||
runner_id=runner_id,
|
||||
metadata_json=json.dumps(metadata) if metadata else None,
|
||||
created_at=datetime.datetime.utcnow(),
|
||||
)
|
||||
session.add(event)
|
||||
await session.commit()
|
||||
|
||||
return event_id
|
||||
|
||||
async def get_event(
|
||||
self,
|
||||
event_id: str,
|
||||
) -> dict[str, typing.Any] | None:
|
||||
"""Get a single event by ID.
|
||||
|
||||
Args:
|
||||
event_id: Event ID
|
||||
|
||||
Returns:
|
||||
Event record as dict, or None if not found
|
||||
"""
|
||||
async with self._session_factory() as session:
|
||||
result = await session.execute(
|
||||
sqlalchemy.select(EventLog).where(EventLog.event_id == event_id)
|
||||
)
|
||||
row = result.scalars().first()
|
||||
if row is None:
|
||||
return None
|
||||
return self._row_to_dict(row)
|
||||
|
||||
async def page_events(
|
||||
self,
|
||||
conversation_id: str | None = None,
|
||||
event_types: list[str] | None = None,
|
||||
before_seq: int | None = None,
|
||||
limit: int = 50,
|
||||
) -> tuple[list[dict[str, typing.Any]], int | None, bool]:
|
||||
"""Page through event records.
|
||||
|
||||
Args:
|
||||
conversation_id: Filter by conversation ID
|
||||
event_types: Filter by event types
|
||||
before_seq: Get events before this sequence number
|
||||
limit: Maximum items to return (capped at 100)
|
||||
|
||||
Returns:
|
||||
Tuple of (items, next_seq, has_more)
|
||||
"""
|
||||
limit = min(limit, 100) # Hard cap
|
||||
|
||||
async with self._session_factory() as session:
|
||||
query = sqlalchemy.select(EventLog)
|
||||
|
||||
if conversation_id is not None:
|
||||
query = query.where(EventLog.conversation_id == conversation_id)
|
||||
|
||||
if event_types:
|
||||
query = query.where(EventLog.event_type.in_(event_types))
|
||||
|
||||
if before_seq is not None:
|
||||
query = query.where(EventLog.id < before_seq)
|
||||
|
||||
query = query.order_by(EventLog.id.desc()).limit(limit + 1)
|
||||
|
||||
result = await session.execute(query)
|
||||
rows = result.scalars().all()
|
||||
|
||||
items = [self._row_to_dict(row) for row in rows[:limit]]
|
||||
has_more = len(rows) > limit
|
||||
next_seq = items[-1]['id'] if items and has_more else None
|
||||
|
||||
return items, next_seq, has_more
|
||||
|
||||
async def get_latest_cursor(
|
||||
self,
|
||||
conversation_id: str,
|
||||
) -> str | None:
|
||||
"""Get the latest cursor for a conversation.
|
||||
|
||||
Args:
|
||||
conversation_id: Conversation ID
|
||||
|
||||
Returns:
|
||||
Cursor string (seq number), or None if no events
|
||||
"""
|
||||
async with self._session_factory() as session:
|
||||
result = await session.execute(
|
||||
sqlalchemy.select(EventLog.id)
|
||||
.where(EventLog.conversation_id == conversation_id)
|
||||
.order_by(EventLog.id.desc())
|
||||
.limit(1)
|
||||
)
|
||||
row = result.scalars().first()
|
||||
if row is None:
|
||||
return None
|
||||
return str(row)
|
||||
|
||||
async def has_events_before(
|
||||
self,
|
||||
conversation_id: str,
|
||||
seq: int,
|
||||
) -> bool:
|
||||
"""Check if there are events before a sequence number.
|
||||
|
||||
Args:
|
||||
conversation_id: Conversation ID
|
||||
seq: Sequence number
|
||||
|
||||
Returns:
|
||||
True if there are events before
|
||||
"""
|
||||
async with self._session_factory() as session:
|
||||
result = await session.execute(
|
||||
sqlalchemy.select(sqlalchemy.func.count())
|
||||
.select_from(EventLog)
|
||||
.where(
|
||||
EventLog.conversation_id == conversation_id,
|
||||
EventLog.id < seq,
|
||||
)
|
||||
)
|
||||
count = result.scalar()
|
||||
return count > 0
|
||||
|
||||
def _row_to_dict(self, row: EventLog) -> dict[str, typing.Any]:
|
||||
"""Convert an EventLog row to dict."""
|
||||
return {
|
||||
'id': row.id,
|
||||
'event_id': row.event_id,
|
||||
'event_type': row.event_type,
|
||||
'event_time': int(row.event_time.timestamp()) if row.event_time else None,
|
||||
'source': row.source,
|
||||
'bot_id': row.bot_id,
|
||||
'workspace_id': row.workspace_id,
|
||||
'conversation_id': row.conversation_id,
|
||||
'thread_id': row.thread_id,
|
||||
'actor_type': row.actor_type,
|
||||
'actor_id': row.actor_id,
|
||||
'actor_name': row.actor_name,
|
||||
'subject_type': row.subject_type,
|
||||
'subject_id': row.subject_id,
|
||||
'input_summary': row.input_summary,
|
||||
'input_json': json.loads(row.input_json) if row.input_json else None,
|
||||
'raw_ref': row.raw_ref,
|
||||
'run_id': row.run_id,
|
||||
'runner_id': row.runner_id,
|
||||
'created_at': int(row.created_at.timestamp()) if row.created_at else None,
|
||||
'metadata': json.loads(row.metadata_json) if row.metadata_json else {},
|
||||
}
|
||||
@@ -1,25 +0,0 @@
|
||||
"""Canonical AgentRunner event names reserved for future EBA integration."""
|
||||
from __future__ import annotations
|
||||
|
||||
|
||||
MESSAGE_RECEIVED = 'message.received'
|
||||
"""A normal message entered the current Pipeline."""
|
||||
|
||||
MESSAGE_RECALLED = 'message.recalled'
|
||||
"""A platform message was recalled or deleted."""
|
||||
|
||||
GROUP_MEMBER_JOINED = 'group.member_joined'
|
||||
"""A new member joined a group/channel conversation."""
|
||||
|
||||
FRIEND_REQUEST_RECEIVED = 'friend.request_received'
|
||||
"""A new friend/contact request was received."""
|
||||
|
||||
|
||||
RESERVED_EVENT_TYPES = frozenset(
|
||||
{
|
||||
MESSAGE_RECEIVED,
|
||||
MESSAGE_RECALLED,
|
||||
GROUP_MEMBER_JOINED,
|
||||
FRIEND_REQUEST_RECEIVED,
|
||||
}
|
||||
)
|
||||
@@ -1,172 +0,0 @@
|
||||
"""Agent event envelope and binding models for LangBot Host.
|
||||
|
||||
These are Host-internal models, not exposed to SDK.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import typing
|
||||
import pydantic
|
||||
|
||||
from langbot_plugin.api.entities.builtin.agent_runner.event import (
|
||||
ActorContext,
|
||||
SubjectContext,
|
||||
RawEventRef,
|
||||
)
|
||||
from langbot_plugin.api.entities.builtin.agent_runner.input import AgentInput
|
||||
from langbot_plugin.api.entities.builtin.agent_runner.delivery import DeliveryContext
|
||||
|
||||
|
||||
class AgentEventEnvelope(pydantic.BaseModel):
|
||||
"""Event envelope for LangBot Host event gateway.
|
||||
|
||||
This is the unified input model that replaces Query-first approach.
|
||||
IM / WebUI / API / EventRouter all produce this envelope.
|
||||
"""
|
||||
|
||||
event_id: str
|
||||
"""Unique event identifier."""
|
||||
|
||||
event_type: str
|
||||
"""Event type (message.received, message.recalled, etc.)."""
|
||||
|
||||
event_time: int | None = None
|
||||
"""Event timestamp (epoch seconds)."""
|
||||
|
||||
source: str
|
||||
"""Event source (platform, webui, api, scheduler, system)."""
|
||||
|
||||
source_event_type: str | None = None
|
||||
"""Original source event type, when available."""
|
||||
|
||||
bot_id: str | None = None
|
||||
"""Bot UUID handling this event."""
|
||||
|
||||
workspace_id: str | None = None
|
||||
"""Workspace ID (for multi-tenant)."""
|
||||
|
||||
conversation_id: str | None = None
|
||||
"""Conversation ID."""
|
||||
|
||||
thread_id: str | None = None
|
||||
"""Thread ID (for platforms supporting threads)."""
|
||||
|
||||
actor: ActorContext | None = None
|
||||
"""Actor (who triggered the event)."""
|
||||
|
||||
subject: SubjectContext | None = None
|
||||
"""Subject (what the event is about)."""
|
||||
|
||||
input: AgentInput
|
||||
"""Event input."""
|
||||
|
||||
delivery: DeliveryContext
|
||||
"""Delivery context."""
|
||||
|
||||
raw_ref: RawEventRef | None = None
|
||||
"""Reference to raw event payload."""
|
||||
|
||||
data: dict[str, typing.Any] = pydantic.Field(default_factory=dict)
|
||||
"""Small structured event payload. Large payloads should be referenced via raw_ref/artifacts."""
|
||||
|
||||
|
||||
# Binding scope types
|
||||
class BindingScope(pydantic.BaseModel):
|
||||
"""Scope for agent binding."""
|
||||
|
||||
scope_type: typing.Literal["bot", "pipeline", "workspace", "global"] = "pipeline"
|
||||
"""Scope type."""
|
||||
|
||||
scope_id: str | None = None
|
||||
"""Scope identifier (bot_uuid, pipeline_uuid, etc.)."""
|
||||
|
||||
|
||||
class ResourcePolicy(pydantic.BaseModel):
|
||||
"""Resource policy for agent binding.
|
||||
|
||||
Controls what resources the runner can access.
|
||||
"""
|
||||
|
||||
allowed_model_uuids: list[str] | None = None
|
||||
"""Additional model UUID grants. None means no additional model grants."""
|
||||
|
||||
allowed_tool_names: list[str] | None = None
|
||||
"""Additional tool name grants. None means no additional tool grants."""
|
||||
|
||||
allowed_kb_uuids: list[str] | None = None
|
||||
"""Additional knowledge base UUID grants. None means no additional KB grants."""
|
||||
|
||||
allow_plugin_storage: bool = True
|
||||
"""Whether plugin storage is allowed."""
|
||||
|
||||
allow_workspace_storage: bool = False
|
||||
"""Whether workspace storage is allowed."""
|
||||
|
||||
|
||||
class StatePolicy(pydantic.BaseModel):
|
||||
"""State policy for agent binding.
|
||||
|
||||
Controls state management behavior.
|
||||
"""
|
||||
|
||||
enable_state: bool = True
|
||||
"""Whether host-owned state is enabled."""
|
||||
|
||||
state_scopes: list[typing.Literal["conversation", "actor", "subject", "runner"]] = (
|
||||
pydantic.Field(default_factory=lambda: ["conversation", "actor"])
|
||||
)
|
||||
"""Enabled state scopes."""
|
||||
|
||||
|
||||
class DeliveryPolicy(pydantic.BaseModel):
|
||||
"""Delivery policy for agent binding.
|
||||
|
||||
Controls how results are delivered.
|
||||
"""
|
||||
|
||||
enable_streaming: bool = True
|
||||
"""Whether streaming output is enabled."""
|
||||
|
||||
enable_reply: bool = True
|
||||
"""Whether reply is enabled."""
|
||||
|
||||
max_message_size: int | None = None
|
||||
"""Maximum message size."""
|
||||
|
||||
|
||||
class AgentBinding(pydantic.BaseModel):
|
||||
"""Binding configuration for mapping events to runners.
|
||||
|
||||
This is Host-internal model for event-to-runner binding.
|
||||
It replaces the old Pipeline runner config role.
|
||||
"""
|
||||
|
||||
binding_id: str
|
||||
"""Unique binding identifier."""
|
||||
|
||||
scope: BindingScope = pydantic.Field(default_factory=BindingScope)
|
||||
"""Binding scope."""
|
||||
|
||||
event_types: list[str] = pydantic.Field(default_factory=lambda: ["message.received"])
|
||||
"""Event types this binding handles."""
|
||||
|
||||
runner_id: str
|
||||
"""Runner ID to invoke."""
|
||||
|
||||
runner_config: dict[str, typing.Any] = pydantic.Field(default_factory=dict)
|
||||
"""Runner binding configuration."""
|
||||
|
||||
resource_policy: ResourcePolicy = pydantic.Field(default_factory=ResourcePolicy)
|
||||
"""Resource policy."""
|
||||
|
||||
state_policy: StatePolicy = pydantic.Field(default_factory=StatePolicy)
|
||||
"""State policy."""
|
||||
|
||||
delivery_policy: DeliveryPolicy = pydantic.Field(default_factory=DeliveryPolicy)
|
||||
"""Delivery policy."""
|
||||
|
||||
enabled: bool = True
|
||||
"""Whether binding is enabled."""
|
||||
|
||||
# Fields for Pipeline adapter
|
||||
pipeline_uuid: str | None = None
|
||||
"""Pipeline UUID (for Pipeline adapter)."""
|
||||
@@ -1,91 +0,0 @@
|
||||
"""Agent runner ID parsing and formatting."""
|
||||
from __future__ import annotations
|
||||
|
||||
import dataclasses
|
||||
|
||||
|
||||
@dataclasses.dataclass(frozen=True)
|
||||
class RunnerIdParts:
|
||||
"""Parsed runner ID components."""
|
||||
source: str # 'plugin' (future: 'builtin')
|
||||
plugin_author: str
|
||||
plugin_name: str
|
||||
runner_name: str
|
||||
|
||||
def to_plugin_id(self) -> str:
|
||||
"""Return plugin identifier as author/name."""
|
||||
return f'{self.plugin_author}/{self.plugin_name}'
|
||||
|
||||
|
||||
def parse_runner_id(runner_id: str) -> RunnerIdParts:
|
||||
"""Parse runner ID string into components.
|
||||
|
||||
Args:
|
||||
runner_id: Runner ID in format 'plugin:author/plugin_name/runner_name'
|
||||
|
||||
Returns:
|
||||
RunnerIdParts with parsed components
|
||||
|
||||
Raises:
|
||||
ValueError: If runner_id format is invalid
|
||||
"""
|
||||
if runner_id.startswith('plugin:'):
|
||||
parts = runner_id[7:].split('/')
|
||||
if len(parts) != 3:
|
||||
raise ValueError(
|
||||
f'Invalid plugin runner ID format: {runner_id}. '
|
||||
f'Expected: plugin:author/plugin_name/runner_name'
|
||||
)
|
||||
plugin_author, plugin_name, runner_name = parts
|
||||
if not plugin_author or not plugin_name or not runner_name:
|
||||
raise ValueError(
|
||||
f'Invalid plugin runner ID: {runner_id}. '
|
||||
f'author, plugin_name, and runner_name must be non-empty'
|
||||
)
|
||||
return RunnerIdParts(
|
||||
source='plugin',
|
||||
plugin_author=plugin_author,
|
||||
plugin_name=plugin_name,
|
||||
runner_name=runner_name,
|
||||
)
|
||||
else:
|
||||
# Only plugin runner IDs are valid at the protocol boundary.
|
||||
raise ValueError(
|
||||
f'Invalid runner ID format: {runner_id}. '
|
||||
f'Expected: plugin:author/plugin_name/runner_name'
|
||||
)
|
||||
|
||||
|
||||
def format_runner_id(
|
||||
source: str,
|
||||
plugin_author: str,
|
||||
plugin_name: str,
|
||||
runner_name: str,
|
||||
) -> str:
|
||||
"""Format runner ID from components.
|
||||
|
||||
Args:
|
||||
source: Runner source ('plugin')
|
||||
plugin_author: Plugin author
|
||||
plugin_name: Plugin name
|
||||
runner_name: Runner component name
|
||||
|
||||
Returns:
|
||||
Runner ID string
|
||||
"""
|
||||
if source == 'plugin':
|
||||
return f'plugin:{plugin_author}/{plugin_name}/{runner_name}'
|
||||
else:
|
||||
raise ValueError(f'Invalid runner source: {source}')
|
||||
|
||||
|
||||
def is_plugin_runner_id(runner_id: str) -> bool:
|
||||
"""Check if runner ID is a plugin runner.
|
||||
|
||||
Args:
|
||||
runner_id: Runner ID string
|
||||
|
||||
Returns:
|
||||
True if runner ID starts with 'plugin:'
|
||||
"""
|
||||
return runner_id.startswith('plugin:')
|
||||
@@ -1,884 +0,0 @@
|
||||
"""Agent run orchestrator for coordinating runner execution."""
|
||||
from __future__ import annotations
|
||||
|
||||
import typing
|
||||
import traceback
|
||||
import asyncio
|
||||
import time
|
||||
|
||||
from langbot_plugin.api.entities.builtin.provider import message as provider_message
|
||||
from langbot_plugin.api.entities.builtin.pipeline import query as pipeline_query
|
||||
from langbot_plugin.entities.io.errors import ActionCallTimeoutError
|
||||
|
||||
from ...core import app
|
||||
from .descriptor import AgentRunnerDescriptor
|
||||
from .registry import AgentRunnerRegistry
|
||||
from .context_builder import AgentRunContextBuilder, AgentRunContextPayload
|
||||
from .resource_builder import AgentResourceBuilder
|
||||
from .result_normalizer import AgentResultNormalizer
|
||||
from .persistent_state_store import get_persistent_state_store, PersistentStateStore
|
||||
from .session_registry import get_session_registry, AgentRunSessionRegistry
|
||||
from .config_migration import ConfigMigration
|
||||
from .host_models import AgentEventEnvelope, AgentBinding
|
||||
from .pipeline_adapter import PipelineAdapter
|
||||
from .state_scope import build_state_context
|
||||
from .errors import (
|
||||
RunnerNotFoundError,
|
||||
RunnerExecutionError,
|
||||
RunnerProtocolError,
|
||||
)
|
||||
|
||||
|
||||
# Maximum inline artifact content size (1MB)
|
||||
MAX_ARTIFACT_INLINE_BYTES = 1 * 1024 * 1024
|
||||
|
||||
|
||||
class AgentRunOrchestrator:
|
||||
"""Orchestrator for agent runner execution.
|
||||
|
||||
Responsibilities:
|
||||
- Resolve runner ID from pipeline config (new or old format)
|
||||
- Get runner descriptor from registry
|
||||
- Provision AgentRunContext envelope from Query
|
||||
- Build AgentResources with permission filtering
|
||||
- Invoke plugin runtime RUN_AGENT action
|
||||
- Normalize AgentRunResult to Pipeline messages
|
||||
- Handle errors, timeouts, protocol errors
|
||||
- Maintain streaming card behavior
|
||||
|
||||
Entry points:
|
||||
- run(event, binding): Main entry for event-first Protocol v1
|
||||
- run_from_query(query): Pipeline adapter wrapper
|
||||
"""
|
||||
|
||||
ap: app.Application
|
||||
|
||||
registry: AgentRunnerRegistry
|
||||
|
||||
context_builder: AgentRunContextBuilder
|
||||
|
||||
resource_builder: AgentResourceBuilder
|
||||
|
||||
result_normalizer: AgentResultNormalizer
|
||||
|
||||
# Cached singleton references (set in __init__)
|
||||
_session_registry: AgentRunSessionRegistry
|
||||
_persistent_state_store: PersistentStateStore | None
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
ap: app.Application,
|
||||
registry: AgentRunnerRegistry,
|
||||
):
|
||||
self.ap = ap
|
||||
self.registry = registry
|
||||
self.context_builder = AgentRunContextBuilder(ap)
|
||||
self.resource_builder = AgentResourceBuilder(ap)
|
||||
self.result_normalizer = AgentResultNormalizer(ap)
|
||||
# Cache singleton references to avoid per-request getter calls
|
||||
self._session_registry = get_session_registry()
|
||||
self._persistent_state_store = None # Lazy init on first use
|
||||
|
||||
async def run(
|
||||
self,
|
||||
event: AgentEventEnvelope,
|
||||
binding: AgentBinding,
|
||||
bound_plugins: list[str] | None = None,
|
||||
adapter_context: dict[str, typing.Any] | None = None,
|
||||
) -> typing.AsyncGenerator[provider_message.Message | provider_message.MessageChunk, None]:
|
||||
"""Run agent runner from event-first envelope.
|
||||
|
||||
This is the main entry point for Protocol v1.
|
||||
Event Gateway -> AgentBindingResolver -> run(event, binding).
|
||||
|
||||
Args:
|
||||
event: Event envelope from event gateway
|
||||
binding: Agent binding configuration
|
||||
bound_plugins: Optional list of bound plugin identities for authorization
|
||||
adapter_context: Optional adapter context from Pipeline adapter
|
||||
|
||||
Yields:
|
||||
Message or MessageChunk for pipeline response
|
||||
|
||||
Raises:
|
||||
RunnerNotFoundError: If runner not found
|
||||
RunnerNotAuthorizedError: If runner not authorized
|
||||
RunnerExecutionError: If runner execution failed
|
||||
"""
|
||||
runner_id = binding.runner_id
|
||||
|
||||
# Get runner descriptor
|
||||
descriptor = await self.registry.get(runner_id, bound_plugins)
|
||||
|
||||
# Build resources from binding
|
||||
resources = await self.resource_builder.build_resources_from_binding(
|
||||
event=event,
|
||||
binding=binding,
|
||||
descriptor=descriptor,
|
||||
)
|
||||
|
||||
# Build context from event + binding
|
||||
context = await self.context_builder.build_context_from_event(
|
||||
event=event,
|
||||
binding=binding,
|
||||
descriptor=descriptor,
|
||||
resources=resources,
|
||||
)
|
||||
|
||||
# Merge adapter context if provided (for Pipeline adapter)
|
||||
if adapter_context:
|
||||
# Merge params into adapter.extra
|
||||
if 'params' in adapter_context:
|
||||
context['adapter']['extra']['params'] = adapter_context['params']
|
||||
# Merge prompt into adapter.extra for Pipeline adapter consumers.
|
||||
if 'prompt' in adapter_context:
|
||||
context['adapter']['extra']['prompt'] = adapter_context['prompt']
|
||||
# Set query_id if provided
|
||||
if adapter_context.get('query_id'):
|
||||
context['runtime']['query_id'] = adapter_context['query_id']
|
||||
|
||||
# Build state context for State API handlers
|
||||
state_context = build_state_context(event, binding, descriptor)
|
||||
|
||||
# Register session for proxy action permission validation
|
||||
run_id = context['run_id']
|
||||
query_id = context['runtime'].get('query_id') # May be None for pure event-first mode
|
||||
await self._session_registry.register(
|
||||
run_id=run_id,
|
||||
runner_id=descriptor.id,
|
||||
query_id=query_id,
|
||||
plugin_identity=descriptor.get_plugin_id(),
|
||||
resources=resources,
|
||||
permissions=descriptor.permissions or {},
|
||||
conversation_id=event.conversation_id,
|
||||
state_policy={
|
||||
'enable_state': binding.state_policy.enable_state,
|
||||
'state_scopes': list(binding.state_policy.state_scopes),
|
||||
},
|
||||
state_context=state_context,
|
||||
)
|
||||
|
||||
# Write incoming event to EventLog
|
||||
event_log_id = await self._write_event_log(
|
||||
event=event,
|
||||
binding=binding,
|
||||
run_id=run_id,
|
||||
runner_id=descriptor.id,
|
||||
)
|
||||
|
||||
# Register incoming attachments so input/transcript artifact_refs are resolvable.
|
||||
await self._register_input_artifacts(
|
||||
event=event,
|
||||
run_id=run_id,
|
||||
runner_id=descriptor.id,
|
||||
)
|
||||
|
||||
# Write user message to Transcript if message.received
|
||||
if event.event_type == 'message.received' and event.conversation_id:
|
||||
await self._write_user_transcript(
|
||||
event=event,
|
||||
event_log_id=event_log_id,
|
||||
)
|
||||
|
||||
# Track artifact refs for assistant transcript (cleared after each message.completed)
|
||||
pending_artifact_refs: list[dict[str, typing.Any]] = []
|
||||
|
||||
try:
|
||||
# Run via plugin connector
|
||||
async for result_dict in self._invoke_runner(descriptor, context):
|
||||
# Handle artifact.created first - consume before normalizer
|
||||
if result_dict.get('type') == 'artifact.created':
|
||||
artifact_ref = await self._handle_artifact_created(
|
||||
result_dict=result_dict,
|
||||
event=event,
|
||||
run_id=run_id,
|
||||
runner_id=descriptor.id,
|
||||
)
|
||||
pending_artifact_refs.append(artifact_ref)
|
||||
# Pass to normalizer for logging, but don't yield to pipeline
|
||||
await self.result_normalizer.normalize(result_dict, descriptor)
|
||||
continue
|
||||
|
||||
# Handle state.updated first - consume before normalizer
|
||||
if result_dict.get('type') == 'state.updated':
|
||||
await self._handle_state_updated_event(result_dict, event, binding, descriptor)
|
||||
# Pass to normalizer for logging, but don't yield to pipeline
|
||||
await self.result_normalizer.normalize(result_dict, descriptor)
|
||||
continue
|
||||
|
||||
# Handle message.completed - write to Transcript
|
||||
if result_dict.get('type') == 'message.completed' and event.conversation_id:
|
||||
# Merge pending artifact refs with message's own refs
|
||||
merged_refs = self._merge_artifact_refs(
|
||||
pending_artifact_refs,
|
||||
result_dict,
|
||||
)
|
||||
# Clear pending refs after attaching to this message
|
||||
pending_artifact_refs.clear()
|
||||
|
||||
await self._write_assistant_transcript(
|
||||
result_dict=result_dict,
|
||||
event=event,
|
||||
run_id=run_id,
|
||||
runner_id=descriptor.id,
|
||||
artifact_refs=merged_refs if merged_refs else None,
|
||||
)
|
||||
|
||||
# Normalize result for other types
|
||||
result = await self.result_normalizer.normalize(result_dict, descriptor)
|
||||
if result is not None:
|
||||
yield result
|
||||
finally:
|
||||
# Unregister session after run completes (success or error)
|
||||
await self._session_registry.unregister(run_id)
|
||||
|
||||
async def run_from_query(
|
||||
self,
|
||||
query: pipeline_query.Query,
|
||||
) -> typing.AsyncGenerator[provider_message.Message | provider_message.MessageChunk, None]:
|
||||
"""Run agent runner from pipeline query.
|
||||
|
||||
This is the Pipeline adapter wrapper for the Query-based flow.
|
||||
It delegates to the event-first run(event, binding) method.
|
||||
|
||||
For the new event-first Protocol v1, use run(event, binding) instead.
|
||||
|
||||
Args:
|
||||
query: Pipeline query with pipeline_config, session, messages, etc.
|
||||
|
||||
Yields:
|
||||
Message or MessageChunk for pipeline response
|
||||
|
||||
Raises:
|
||||
RunnerNotFoundError: If runner not found
|
||||
RunnerNotAuthorizedError: If runner not authorized
|
||||
RunnerExecutionError: If runner execution failed
|
||||
"""
|
||||
# Resolve runner ID using ConfigMigration
|
||||
runner_id = ConfigMigration.resolve_runner_id(query.pipeline_config)
|
||||
if not runner_id:
|
||||
raise RunnerNotFoundError('no runner configured')
|
||||
|
||||
# Convert Query to event-first envelope
|
||||
event = PipelineAdapter.query_to_event(query)
|
||||
|
||||
# Convert Pipeline config to binding
|
||||
binding = PipelineAdapter.pipeline_config_to_binding(query, runner_id)
|
||||
|
||||
# Extract bound plugins for authorization
|
||||
bound_plugins = query.variables.get('_pipeline_bound_plugins')
|
||||
|
||||
# Build adapter context for Pipeline-specific fields
|
||||
adapter_context = PipelineAdapter.build_adapter_context(query, binding)
|
||||
|
||||
# Delegate to event-first run()
|
||||
async for result in self.run(
|
||||
event,
|
||||
binding,
|
||||
bound_plugins=bound_plugins,
|
||||
adapter_context=adapter_context,
|
||||
):
|
||||
yield result
|
||||
|
||||
async def _invoke_runner(
|
||||
self,
|
||||
descriptor: AgentRunnerDescriptor,
|
||||
context: AgentRunContextPayload,
|
||||
) -> typing.AsyncGenerator[dict[str, typing.Any], None]:
|
||||
"""Invoke runner via plugin connector.
|
||||
|
||||
Args:
|
||||
descriptor: Runner descriptor
|
||||
context: AgentRunContext dict
|
||||
|
||||
Yields:
|
||||
Raw result dicts from plugin runtime
|
||||
|
||||
Raises:
|
||||
RunnerExecutionError: If plugin system disabled or runtime error
|
||||
"""
|
||||
if not self.ap.plugin_connector.is_enable_plugin:
|
||||
raise RunnerExecutionError(
|
||||
descriptor.id,
|
||||
'Plugin system is disabled',
|
||||
retryable=False,
|
||||
)
|
||||
|
||||
try:
|
||||
gen = self.ap.plugin_connector.run_agent(
|
||||
plugin_author=descriptor.plugin_author,
|
||||
plugin_name=descriptor.plugin_name,
|
||||
runner_name=descriptor.runner_name,
|
||||
context=context,
|
||||
)
|
||||
|
||||
while True:
|
||||
try:
|
||||
result_dict = await self._next_with_deadline(gen, descriptor, context)
|
||||
except StopAsyncIteration:
|
||||
break
|
||||
yield result_dict
|
||||
|
||||
except asyncio.TimeoutError as e:
|
||||
raise RunnerExecutionError(
|
||||
descriptor.id,
|
||||
'Runner timed out (code: runner.timeout)',
|
||||
retryable=True,
|
||||
) from e
|
||||
except ActionCallTimeoutError as e:
|
||||
raise RunnerExecutionError(
|
||||
descriptor.id,
|
||||
f'{e} (code: runner.timeout)',
|
||||
retryable=True,
|
||||
) from e
|
||||
except RunnerExecutionError:
|
||||
raise
|
||||
except Exception as e:
|
||||
# Wrap unexpected errors
|
||||
self.ap.logger.error(
|
||||
f'Runner {descriptor.id} unexpected error: {traceback.format_exc()}'
|
||||
)
|
||||
raise RunnerExecutionError(
|
||||
descriptor.id,
|
||||
str(e),
|
||||
retryable=False,
|
||||
)
|
||||
|
||||
async def _next_with_deadline(
|
||||
self,
|
||||
gen: typing.AsyncGenerator[dict[str, typing.Any], None],
|
||||
descriptor: AgentRunnerDescriptor,
|
||||
context: AgentRunContextPayload,
|
||||
) -> dict[str, typing.Any]:
|
||||
"""Read the next runner result while enforcing the run deadline."""
|
||||
remaining = self._remaining_deadline_seconds(context)
|
||||
if remaining is not None and remaining <= 0:
|
||||
await self._close_generator(gen, descriptor)
|
||||
raise asyncio.TimeoutError
|
||||
|
||||
try:
|
||||
if remaining is None:
|
||||
return await anext(gen)
|
||||
return await asyncio.wait_for(anext(gen), timeout=remaining)
|
||||
except StopAsyncIteration:
|
||||
if self._is_deadline_exhausted(context):
|
||||
raise asyncio.TimeoutError
|
||||
raise
|
||||
except asyncio.TimeoutError:
|
||||
await self._close_generator(gen, descriptor)
|
||||
raise
|
||||
|
||||
def _remaining_deadline_seconds(
|
||||
self,
|
||||
context: AgentRunContextPayload,
|
||||
) -> float | None:
|
||||
runtime = context.get('runtime') or {}
|
||||
deadline_at = runtime.get('deadline_at')
|
||||
if deadline_at is None:
|
||||
return None
|
||||
try:
|
||||
return float(deadline_at) - time.time()
|
||||
except (TypeError, ValueError):
|
||||
return None
|
||||
|
||||
def _is_deadline_exhausted(self, context: AgentRunContextPayload) -> bool:
|
||||
remaining = self._remaining_deadline_seconds(context)
|
||||
return remaining is not None and remaining <= 0
|
||||
|
||||
async def _close_generator(
|
||||
self,
|
||||
gen: typing.AsyncGenerator[dict[str, typing.Any], None],
|
||||
descriptor: AgentRunnerDescriptor,
|
||||
) -> None:
|
||||
try:
|
||||
await gen.aclose()
|
||||
except Exception as e:
|
||||
self.ap.logger.warning(f'Failed to close timed-out runner {descriptor.id}: {e}')
|
||||
|
||||
def resolve_runner_id_for_telemetry(self, query: pipeline_query.Query) -> str | None:
|
||||
"""Resolve runner ID for telemetry/logging without full execution.
|
||||
|
||||
Args:
|
||||
query: Pipeline query
|
||||
|
||||
Returns:
|
||||
Runner ID string, or None
|
||||
"""
|
||||
return ConfigMigration.resolve_runner_id(query.pipeline_config)
|
||||
|
||||
async def _handle_state_updated_event(
|
||||
self,
|
||||
result_dict: dict[str, typing.Any],
|
||||
event: AgentEventEnvelope,
|
||||
binding: AgentBinding,
|
||||
descriptor: AgentRunnerDescriptor,
|
||||
) -> None:
|
||||
"""Handle state.updated result in event-first mode.
|
||||
|
||||
Persists state to database via PersistentStateStore.
|
||||
|
||||
Args:
|
||||
result_dict: Raw result dict with type='state.updated'
|
||||
event: Event envelope
|
||||
binding: Agent binding configuration
|
||||
descriptor: Runner descriptor
|
||||
"""
|
||||
data = result_dict.get('data', {})
|
||||
|
||||
scope = data.get('scope')
|
||||
if not scope:
|
||||
raise RunnerProtocolError(
|
||||
descriptor.id,
|
||||
'state.updated missing required field: scope',
|
||||
)
|
||||
|
||||
# Extract key and value
|
||||
key = data.get('key')
|
||||
value = data.get('value')
|
||||
|
||||
if not key:
|
||||
raise RunnerProtocolError(
|
||||
descriptor.id,
|
||||
'state.updated missing required field: key',
|
||||
)
|
||||
|
||||
# Lazy init persistent state store
|
||||
if self._persistent_state_store is None:
|
||||
self._persistent_state_store = get_persistent_state_store(
|
||||
self.ap.persistence_mgr.get_db_engine()
|
||||
)
|
||||
|
||||
# Apply update to persistent state store
|
||||
success, error = await self._persistent_state_store.apply_update_from_event(
|
||||
event=event,
|
||||
binding=binding,
|
||||
descriptor=descriptor,
|
||||
scope=scope,
|
||||
key=key,
|
||||
value=value,
|
||||
logger=self.ap.logger,
|
||||
)
|
||||
|
||||
if success:
|
||||
self.ap.logger.debug(
|
||||
f'Runner {descriptor.id} state.updated (event mode): scope={scope}, key={key}'
|
||||
)
|
||||
elif error:
|
||||
self.ap.logger.warning(
|
||||
f'Runner {descriptor.id} state.updated rejected: {error}'
|
||||
)
|
||||
|
||||
async def _write_event_log(
|
||||
self,
|
||||
event: AgentEventEnvelope,
|
||||
binding: AgentBinding,
|
||||
run_id: str,
|
||||
runner_id: str,
|
||||
) -> str:
|
||||
"""Write incoming event to EventLog.
|
||||
|
||||
Args:
|
||||
event: Event envelope
|
||||
binding: Agent binding
|
||||
run_id: Run ID
|
||||
runner_id: Runner ID
|
||||
|
||||
Returns:
|
||||
Event log ID
|
||||
"""
|
||||
import datetime
|
||||
|
||||
from .event_log_store import EventLogStore
|
||||
store = EventLogStore(self.ap.persistence_mgr.get_db_engine())
|
||||
|
||||
# Build input summary
|
||||
input_summary = None
|
||||
input_json = None
|
||||
if event.input:
|
||||
if event.input.text:
|
||||
input_summary = event.input.text[:1000]
|
||||
input_json = {
|
||||
'text': event.input.text,
|
||||
'contents': [c.model_dump(mode='json') if hasattr(c, 'model_dump') else c for c in event.input.contents],
|
||||
'attachments': [a.model_dump(mode='json') if hasattr(a, 'model_dump') else a for a in event.input.attachments],
|
||||
}
|
||||
|
||||
return await store.append_event(
|
||||
event_id=event.event_id,
|
||||
event_type=event.event_type,
|
||||
source=event.source,
|
||||
bot_id=event.bot_id,
|
||||
workspace_id=event.workspace_id,
|
||||
conversation_id=event.conversation_id,
|
||||
thread_id=event.thread_id,
|
||||
actor_type=event.actor.actor_type if event.actor else None,
|
||||
actor_id=event.actor.actor_id if event.actor else None,
|
||||
actor_name=event.actor.actor_name if event.actor else None,
|
||||
subject_type=event.subject.subject_type if event.subject else None,
|
||||
subject_id=event.subject.subject_id if event.subject else None,
|
||||
input_summary=input_summary,
|
||||
input_json=input_json,
|
||||
run_id=run_id,
|
||||
runner_id=runner_id,
|
||||
event_time=datetime.datetime.fromtimestamp(event.event_time) if event.event_time else None,
|
||||
)
|
||||
|
||||
async def _register_input_artifacts(
|
||||
self,
|
||||
event: AgentEventEnvelope,
|
||||
run_id: str,
|
||||
runner_id: str,
|
||||
) -> None:
|
||||
"""Register current-event attachments referenced by AgentInput."""
|
||||
if not event.input or not event.input.attachments:
|
||||
return
|
||||
|
||||
from .artifact_store import ArtifactStore
|
||||
store = ArtifactStore(self.ap.persistence_mgr.get_db_engine())
|
||||
|
||||
for attachment in event.input.attachments:
|
||||
data = attachment.model_dump(mode='json') if hasattr(attachment, 'model_dump') else attachment
|
||||
if not isinstance(data, dict):
|
||||
continue
|
||||
|
||||
artifact_id = data.get('artifact_id')
|
||||
artifact_type = data.get('artifact_type') or 'file'
|
||||
if not artifact_id:
|
||||
continue
|
||||
|
||||
content, parsed_mime_type = self._decode_attachment_content(data.get('content'))
|
||||
url = data.get('url')
|
||||
platform_ref_id = data.get('id')
|
||||
storage_key = None
|
||||
storage_type = 'metadata_only'
|
||||
if content is None:
|
||||
if url:
|
||||
storage_key = url
|
||||
storage_type = 'url'
|
||||
elif platform_ref_id:
|
||||
storage_key = platform_ref_id
|
||||
storage_type = 'platform_ref'
|
||||
|
||||
metadata = {
|
||||
'input_attachment': True,
|
||||
'input_source': data.get('source') or 'platform',
|
||||
}
|
||||
if url:
|
||||
metadata['url'] = url
|
||||
if platform_ref_id:
|
||||
metadata['platform_ref_id'] = platform_ref_id
|
||||
|
||||
try:
|
||||
await store.register_artifact(
|
||||
artifact_id=artifact_id,
|
||||
artifact_type=artifact_type,
|
||||
source='platform',
|
||||
storage_key=storage_key,
|
||||
storage_type=storage_type,
|
||||
mime_type=data.get('mime_type') or parsed_mime_type,
|
||||
name=data.get('name'),
|
||||
size_bytes=data.get('size') or (len(content) if content is not None else None),
|
||||
conversation_id=event.conversation_id,
|
||||
run_id=run_id,
|
||||
runner_id=runner_id,
|
||||
bot_id=event.bot_id,
|
||||
workspace_id=event.workspace_id,
|
||||
metadata=metadata,
|
||||
content=content,
|
||||
)
|
||||
except Exception as e:
|
||||
self.ap.logger.warning(
|
||||
f'Failed to register input artifact {artifact_id}: {e}'
|
||||
)
|
||||
|
||||
def _decode_attachment_content(
|
||||
self,
|
||||
content: typing.Any,
|
||||
) -> tuple[bytes | None, str | None]:
|
||||
"""Decode base64 attachment content, including data URLs."""
|
||||
if not isinstance(content, str) or not content:
|
||||
return None, None
|
||||
|
||||
import base64
|
||||
import binascii
|
||||
|
||||
mime_type = None
|
||||
payload = content
|
||||
if content.startswith('data:') and ',' in content:
|
||||
header, payload = content.split(',', 1)
|
||||
if ';base64' in header:
|
||||
mime_type = header[5:].split(';', 1)[0] or None
|
||||
|
||||
try:
|
||||
return base64.b64decode(payload, validate=False), mime_type
|
||||
except (binascii.Error, ValueError):
|
||||
return None, mime_type
|
||||
|
||||
async def _write_user_transcript(
|
||||
self,
|
||||
event: AgentEventEnvelope,
|
||||
event_log_id: str,
|
||||
) -> None:
|
||||
"""Write user message to Transcript.
|
||||
|
||||
Args:
|
||||
event: Event envelope
|
||||
event_log_id: Event log ID
|
||||
"""
|
||||
from .transcript_store import TranscriptStore
|
||||
store = TranscriptStore(self.ap.persistence_mgr.get_db_engine())
|
||||
|
||||
# Build content
|
||||
content = event.input.text if event.input else None
|
||||
content_json = None
|
||||
if event.input:
|
||||
content_json = {
|
||||
'role': 'user',
|
||||
'content': [c.model_dump(mode='json') if hasattr(c, 'model_dump') else c for c in event.input.contents] if event.input.contents else [],
|
||||
}
|
||||
|
||||
# Build artifact refs
|
||||
artifact_refs = []
|
||||
if event.input and event.input.attachments:
|
||||
for a in event.input.attachments:
|
||||
artifact_refs.append(a.model_dump(mode='json') if hasattr(a, 'model_dump') else a)
|
||||
|
||||
await store.append_transcript(
|
||||
transcript_id=None, # Auto-generate
|
||||
event_id=event_log_id,
|
||||
conversation_id=event.conversation_id,
|
||||
role='user',
|
||||
content=content,
|
||||
content_json=content_json,
|
||||
artifact_refs=artifact_refs if artifact_refs else None,
|
||||
thread_id=event.thread_id,
|
||||
item_type='message',
|
||||
metadata={
|
||||
'actor_type': event.actor.actor_type if event.actor else None,
|
||||
'actor_id': event.actor.actor_id if event.actor else None,
|
||||
},
|
||||
)
|
||||
|
||||
async def _handle_artifact_created(
|
||||
self,
|
||||
result_dict: dict[str, typing.Any],
|
||||
event: AgentEventEnvelope,
|
||||
run_id: str,
|
||||
runner_id: str,
|
||||
) -> dict[str, typing.Any]:
|
||||
"""Handle artifact.created result - register artifact and write EventLog.
|
||||
|
||||
Args:
|
||||
result_dict: Raw result dict with type='artifact.created'
|
||||
event: Event envelope
|
||||
run_id: Current run ID
|
||||
runner_id: Runner ID
|
||||
|
||||
Returns:
|
||||
Artifact reference dict for Transcript
|
||||
|
||||
Raises:
|
||||
RunnerProtocolError: On validation failures or registration errors
|
||||
"""
|
||||
import base64
|
||||
import uuid
|
||||
|
||||
from .artifact_store import ArtifactStore
|
||||
from .event_log_store import EventLogStore
|
||||
|
||||
data = result_dict.get('data', {})
|
||||
|
||||
# Validate run_id matches current context
|
||||
result_run_id = result_dict.get('run_id')
|
||||
if result_run_id and result_run_id != run_id:
|
||||
raise RunnerProtocolError(
|
||||
runner_id,
|
||||
f'artifact.created run_id mismatch: expected {run_id}, got {result_run_id}',
|
||||
)
|
||||
|
||||
# Extract artifact fields
|
||||
artifact_id = data.get('artifact_id') or str(uuid.uuid4())
|
||||
artifact_type = data.get('artifact_type')
|
||||
if not artifact_type:
|
||||
raise RunnerProtocolError(
|
||||
runner_id,
|
||||
'artifact.created missing required field: artifact_type',
|
||||
)
|
||||
|
||||
mime_type = data.get('mime_type')
|
||||
name = data.get('name')
|
||||
size_bytes = data.get('size_bytes')
|
||||
sha256 = data.get('sha256')
|
||||
metadata = data.get('metadata')
|
||||
content_base64 = data.get('content_base64')
|
||||
|
||||
# Decode and validate content if provided
|
||||
content: bytes | None = None
|
||||
if content_base64:
|
||||
try:
|
||||
content = base64.b64decode(content_base64, validate=True)
|
||||
except Exception as e:
|
||||
raise RunnerProtocolError(
|
||||
runner_id,
|
||||
f'artifact.created invalid base64 content: {e}',
|
||||
)
|
||||
|
||||
# Validate content size
|
||||
if len(content) > MAX_ARTIFACT_INLINE_BYTES:
|
||||
raise RunnerProtocolError(
|
||||
runner_id,
|
||||
f'artifact.created content size {len(content)} bytes exceeds limit {MAX_ARTIFACT_INLINE_BYTES} bytes',
|
||||
)
|
||||
|
||||
# Register artifact via ArtifactStore
|
||||
artifact_store = ArtifactStore(self.ap.persistence_mgr.get_db_engine())
|
||||
try:
|
||||
registered_id = await artifact_store.register_artifact(
|
||||
artifact_id=artifact_id,
|
||||
artifact_type=artifact_type,
|
||||
source='runner',
|
||||
mime_type=mime_type,
|
||||
name=name,
|
||||
size_bytes=size_bytes,
|
||||
sha256=sha256,
|
||||
conversation_id=event.conversation_id,
|
||||
run_id=run_id,
|
||||
runner_id=runner_id,
|
||||
bot_id=event.bot_id,
|
||||
workspace_id=event.workspace_id,
|
||||
metadata=metadata,
|
||||
content=content,
|
||||
)
|
||||
except Exception as e:
|
||||
raise RunnerProtocolError(
|
||||
runner_id,
|
||||
f'artifact.created failed to register artifact: {e}',
|
||||
)
|
||||
|
||||
# Write to EventLog
|
||||
event_log_store = EventLogStore(self.ap.persistence_mgr.get_db_engine())
|
||||
await event_log_store.append_event(
|
||||
event_id=str(uuid.uuid4()),
|
||||
event_type='artifact.created',
|
||||
source='runner',
|
||||
bot_id=event.bot_id,
|
||||
workspace_id=event.workspace_id,
|
||||
conversation_id=event.conversation_id,
|
||||
thread_id=event.thread_id,
|
||||
actor_type=event.actor.actor_type if event.actor else None,
|
||||
actor_id=event.actor.actor_id if event.actor else None,
|
||||
actor_name=event.actor.actor_name if event.actor else None,
|
||||
input_summary=f'Artifact created: {artifact_type}',
|
||||
input_json={
|
||||
'artifact_id': registered_id,
|
||||
'artifact_type': artifact_type,
|
||||
'mime_type': mime_type,
|
||||
'name': name,
|
||||
'size_bytes': size_bytes,
|
||||
},
|
||||
run_id=run_id,
|
||||
runner_id=runner_id,
|
||||
)
|
||||
|
||||
# Return artifact ref for Transcript
|
||||
return {
|
||||
'artifact_id': registered_id,
|
||||
'artifact_type': artifact_type,
|
||||
'mime_type': mime_type,
|
||||
'name': name,
|
||||
}
|
||||
|
||||
def _merge_artifact_refs(
|
||||
self,
|
||||
pending_refs: list[dict[str, typing.Any]],
|
||||
result_dict: dict[str, typing.Any],
|
||||
) -> list[dict[str, typing.Any]]:
|
||||
"""Merge pending artifact refs with message's own refs, deduplicating by artifact_id.
|
||||
|
||||
Args:
|
||||
pending_refs: Artifact refs accumulated from artifact.created events
|
||||
result_dict: Result dict that may contain message with artifact_refs
|
||||
|
||||
Returns:
|
||||
Merged and deduplicated list of artifact refs
|
||||
"""
|
||||
# Start with pending refs
|
||||
merged = list(pending_refs)
|
||||
seen_ids = {ref.get('artifact_id') for ref in pending_refs if ref.get('artifact_id')}
|
||||
|
||||
# Extract refs from message data if present
|
||||
data = result_dict.get('data', {})
|
||||
message = data.get('message', {})
|
||||
message_refs = message.get('artifact_refs', [])
|
||||
|
||||
if isinstance(message_refs, list):
|
||||
for ref in message_refs:
|
||||
if isinstance(ref, dict):
|
||||
artifact_id = ref.get('artifact_id')
|
||||
if artifact_id and artifact_id not in seen_ids:
|
||||
merged.append(ref)
|
||||
seen_ids.add(artifact_id)
|
||||
|
||||
return merged
|
||||
|
||||
async def _write_assistant_transcript(
|
||||
self,
|
||||
result_dict: dict[str, typing.Any],
|
||||
event: AgentEventEnvelope,
|
||||
run_id: str,
|
||||
runner_id: str,
|
||||
artifact_refs: list[dict[str, typing.Any]] | None = None,
|
||||
) -> None:
|
||||
"""Write assistant message to Transcript.
|
||||
|
||||
Args:
|
||||
result_dict: Result dict from runner
|
||||
event: Original event envelope
|
||||
run_id: Run ID
|
||||
runner_id: Runner ID
|
||||
artifact_refs: Optional artifact references to include
|
||||
"""
|
||||
import uuid
|
||||
|
||||
from .transcript_store import TranscriptStore
|
||||
store = TranscriptStore(self.ap.persistence_mgr.get_db_engine())
|
||||
|
||||
data = result_dict.get('data', {})
|
||||
message = data.get('message', {})
|
||||
|
||||
# Build content
|
||||
content = None
|
||||
content_json = None
|
||||
|
||||
if isinstance(message.get('content'), str):
|
||||
content = message['content']
|
||||
content_json = message
|
||||
elif isinstance(message.get('content'), list):
|
||||
# Extract text from content list
|
||||
text_parts = []
|
||||
for c in message['content']:
|
||||
if isinstance(c, dict) and c.get('type') == 'text':
|
||||
text_parts.append(c.get('text', ''))
|
||||
content = ' '.join(text_parts) if text_parts else None
|
||||
content_json = message
|
||||
|
||||
# Generate a unique event ID for assistant message
|
||||
assistant_event_id = str(uuid.uuid4())
|
||||
|
||||
await store.append_transcript(
|
||||
transcript_id=str(uuid.uuid4()),
|
||||
event_id=assistant_event_id,
|
||||
conversation_id=event.conversation_id,
|
||||
role='assistant',
|
||||
content=content,
|
||||
content_json=content_json,
|
||||
artifact_refs=artifact_refs,
|
||||
thread_id=event.thread_id,
|
||||
item_type='message',
|
||||
run_id=run_id,
|
||||
runner_id=runner_id,
|
||||
metadata={
|
||||
'run_id': run_id,
|
||||
'runner_id': runner_id,
|
||||
},
|
||||
)
|
||||
@@ -1,431 +0,0 @@
|
||||
"""Persistent state store for AgentRunner protocol state.
|
||||
|
||||
This module provides a database-backed state store for event-first Protocol v1.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import typing
|
||||
import json
|
||||
import threading
|
||||
from datetime import datetime
|
||||
|
||||
import sqlalchemy
|
||||
from sqlalchemy.ext.asyncio import AsyncEngine
|
||||
from sqlalchemy import select, delete, update
|
||||
|
||||
from .descriptor import AgentRunnerDescriptor
|
||||
from .host_models import AgentEventEnvelope, AgentBinding
|
||||
from .state_scope import (
|
||||
VALID_STATE_SCOPES,
|
||||
build_state_scope_key,
|
||||
get_binding_identity,
|
||||
normalize_state_key,
|
||||
)
|
||||
from ...entity.persistence.agent_runner_state import AgentRunnerState
|
||||
|
||||
|
||||
# Maximum value_json size (256KB)
|
||||
MAX_VALUE_JSON_BYTES = 256 * 1024
|
||||
|
||||
|
||||
class PersistentStateStore:
|
||||
"""Database-backed state store for AgentRunner protocol state.
|
||||
|
||||
IMPORTANT: This is HOST-OWNED protocol state, NOT plugin instance state.
|
||||
|
||||
This store provides:
|
||||
1. Persistent storage across runs via database
|
||||
2. Scope isolation by runner_id + binding_identity + scope
|
||||
3. Policy enforcement (enable_state, state_scopes)
|
||||
4. JSON value validation and size limits
|
||||
|
||||
Used by:
|
||||
- Event-first Protocol v1 (async methods)
|
||||
- State API handlers (get/set/delete/list)
|
||||
"""
|
||||
|
||||
def __init__(self, db_engine: AsyncEngine):
|
||||
self._db_engine = db_engine
|
||||
|
||||
def _get_scope_key(
|
||||
self,
|
||||
scope: str,
|
||||
event: AgentEventEnvelope,
|
||||
binding: AgentBinding,
|
||||
descriptor: AgentRunnerDescriptor,
|
||||
) -> str | None:
|
||||
"""Get scope key for given scope."""
|
||||
return build_state_scope_key(scope, event, binding, descriptor)
|
||||
|
||||
def _check_scope_enabled(self, scope: str, binding: AgentBinding) -> bool:
|
||||
"""Check if scope is enabled by binding's state_policy."""
|
||||
state_policy = binding.state_policy
|
||||
if not state_policy.enable_state:
|
||||
return False
|
||||
return scope in state_policy.state_scopes
|
||||
|
||||
def _validate_json_value(
|
||||
self,
|
||||
value: typing.Any,
|
||||
logger: typing.Any = None,
|
||||
) -> tuple[str | None, str | None]:
|
||||
"""Validate and serialize value to JSON.
|
||||
|
||||
Returns:
|
||||
Tuple of (json_string, error_message). If error_message is not None,
|
||||
json_string will be None.
|
||||
"""
|
||||
try:
|
||||
json_str = json.dumps(value, ensure_ascii=False)
|
||||
except (TypeError, ValueError) as e:
|
||||
return None, f'Value is not JSON-serializable: {e}'
|
||||
|
||||
# Check size limit
|
||||
json_bytes = len(json_str.encode('utf-8'))
|
||||
if json_bytes > MAX_VALUE_JSON_BYTES:
|
||||
return None, f'Value size {json_bytes} bytes exceeds limit {MAX_VALUE_JSON_BYTES} bytes'
|
||||
|
||||
return json_str, None
|
||||
|
||||
# ========== Async DB Operations ==========
|
||||
|
||||
async def build_snapshot_from_event(
|
||||
self,
|
||||
event: AgentEventEnvelope,
|
||||
binding: AgentBinding,
|
||||
descriptor: AgentRunnerDescriptor,
|
||||
) -> dict[str, dict[str, typing.Any]]:
|
||||
"""Build state snapshot for all scopes from event and binding.
|
||||
|
||||
Reads from database, respects state_policy.
|
||||
"""
|
||||
state_policy = binding.state_policy
|
||||
|
||||
# If state is disabled, return all empty scopes
|
||||
if not state_policy.enable_state:
|
||||
return {
|
||||
'conversation': {},
|
||||
'actor': {},
|
||||
'subject': {},
|
||||
'runner': {},
|
||||
}
|
||||
|
||||
snapshot: dict[str, dict[str, typing.Any]] = {
|
||||
'conversation': {},
|
||||
'actor': {},
|
||||
'subject': {},
|
||||
'runner': {},
|
||||
}
|
||||
|
||||
async with self._db_engine.connect() as conn:
|
||||
for scope in VALID_STATE_SCOPES:
|
||||
if not self._check_scope_enabled(scope, binding):
|
||||
continue
|
||||
|
||||
scope_key = self._get_scope_key(scope, event, binding, descriptor)
|
||||
if not scope_key:
|
||||
continue
|
||||
|
||||
# Query all state entries for this scope_key
|
||||
result = await conn.execute(
|
||||
select(AgentRunnerState.state_key, AgentRunnerState.value_json)
|
||||
.where(AgentRunnerState.scope_key == scope_key)
|
||||
)
|
||||
rows = result.fetchall()
|
||||
|
||||
for row in rows:
|
||||
key = row.state_key
|
||||
value_json = row.value_json
|
||||
if value_json:
|
||||
try:
|
||||
snapshot[scope][key] = json.loads(value_json)
|
||||
except json.JSONDecodeError:
|
||||
pass # Skip invalid JSON
|
||||
|
||||
# Seed external.conversation_id from event.conversation_id if not set
|
||||
if self._check_scope_enabled('conversation', binding) and event.conversation_id:
|
||||
if 'external.conversation_id' not in snapshot['conversation']:
|
||||
snapshot['conversation']['external.conversation_id'] = event.conversation_id
|
||||
|
||||
return snapshot
|
||||
|
||||
async def apply_update_from_event(
|
||||
self,
|
||||
event: AgentEventEnvelope,
|
||||
binding: AgentBinding,
|
||||
descriptor: AgentRunnerDescriptor,
|
||||
scope: str,
|
||||
key: str,
|
||||
value: typing.Any,
|
||||
logger: typing.Any = None,
|
||||
) -> tuple[bool, str | None]:
|
||||
"""Apply a state update from event context.
|
||||
|
||||
Returns:
|
||||
Tuple of (success, error_message). If success is False, error_message
|
||||
contains the reason.
|
||||
"""
|
||||
state_policy = binding.state_policy
|
||||
|
||||
# Check if state is disabled
|
||||
if not state_policy.enable_state:
|
||||
return False, 'State is disabled by binding policy'
|
||||
|
||||
# Validate scope
|
||||
if scope not in VALID_STATE_SCOPES:
|
||||
return False, f'Invalid scope: {scope}'
|
||||
|
||||
# Check if scope is enabled
|
||||
if not self._check_scope_enabled(scope, binding):
|
||||
return False, f'Scope "{scope}" not enabled by binding policy'
|
||||
|
||||
# Map accepted key aliases
|
||||
key = normalize_state_key(key)
|
||||
|
||||
# Get scope key
|
||||
scope_key = self._get_scope_key(scope, event, binding, descriptor)
|
||||
if not scope_key:
|
||||
return False, f'Missing identity for scope "{scope}"'
|
||||
|
||||
# Validate and serialize value
|
||||
value_json, error = self._validate_json_value(value, logger)
|
||||
if error:
|
||||
return False, error
|
||||
|
||||
# Build context fields
|
||||
binding_identity = get_binding_identity(binding)
|
||||
|
||||
async with self._db_engine.begin() as conn:
|
||||
# Check if entry exists
|
||||
result = await conn.execute(
|
||||
select(AgentRunnerState.id)
|
||||
.where(AgentRunnerState.scope_key == scope_key)
|
||||
.where(AgentRunnerState.state_key == key)
|
||||
)
|
||||
existing = result.first()
|
||||
|
||||
now = datetime.utcnow()
|
||||
|
||||
if existing:
|
||||
# Update existing entry
|
||||
await conn.execute(
|
||||
update(AgentRunnerState)
|
||||
.where(AgentRunnerState.id == existing.id)
|
||||
.values(
|
||||
value_json=value_json,
|
||||
updated_at=now,
|
||||
)
|
||||
)
|
||||
else:
|
||||
# Insert new entry
|
||||
await conn.execute(
|
||||
sqlalchemy.insert(AgentRunnerState).values(
|
||||
runner_id=descriptor.id,
|
||||
binding_identity=binding_identity,
|
||||
scope=scope,
|
||||
scope_key=scope_key,
|
||||
state_key=key,
|
||||
value_json=value_json,
|
||||
bot_id=event.bot_id,
|
||||
workspace_id=event.workspace_id,
|
||||
conversation_id=event.conversation_id,
|
||||
thread_id=event.thread_id,
|
||||
actor_type=event.actor.actor_type if event.actor else None,
|
||||
actor_id=event.actor.actor_id if event.actor else None,
|
||||
subject_type=event.subject.subject_type if event.subject else None,
|
||||
subject_id=event.subject.subject_id if event.subject else None,
|
||||
created_at=now,
|
||||
updated_at=now,
|
||||
)
|
||||
)
|
||||
|
||||
return True, None
|
||||
|
||||
async def state_get(
|
||||
self,
|
||||
scope_key: str,
|
||||
state_key: str,
|
||||
) -> typing.Any:
|
||||
"""Get a single state value by scope_key and state_key.
|
||||
|
||||
Used by State API handlers.
|
||||
"""
|
||||
state_key = normalize_state_key(state_key)
|
||||
|
||||
async with self._db_engine.connect() as conn:
|
||||
result = await conn.execute(
|
||||
select(AgentRunnerState.value_json)
|
||||
.where(AgentRunnerState.scope_key == scope_key)
|
||||
.where(AgentRunnerState.state_key == state_key)
|
||||
)
|
||||
row = result.first()
|
||||
|
||||
if not row or not row.value_json:
|
||||
return None
|
||||
|
||||
try:
|
||||
return json.loads(row.value_json)
|
||||
except json.JSONDecodeError:
|
||||
return None
|
||||
|
||||
async def state_set(
|
||||
self,
|
||||
scope_key: str,
|
||||
state_key: str,
|
||||
value: typing.Any,
|
||||
runner_id: str,
|
||||
binding_identity: str,
|
||||
scope: str,
|
||||
context: dict[str, typing.Any] | None = None,
|
||||
logger: typing.Any = None,
|
||||
) -> tuple[bool, str | None]:
|
||||
"""Set a state value.
|
||||
|
||||
Used by State API handlers.
|
||||
Context contains optional fields like bot_id, conversation_id, etc.
|
||||
"""
|
||||
state_key = normalize_state_key(state_key)
|
||||
|
||||
# Validate and serialize value
|
||||
value_json, error = self._validate_json_value(value, logger)
|
||||
if error:
|
||||
return False, error
|
||||
|
||||
context = context or {}
|
||||
|
||||
async with self._db_engine.begin() as conn:
|
||||
# Check if entry exists
|
||||
result = await conn.execute(
|
||||
select(AgentRunnerState.id)
|
||||
.where(AgentRunnerState.scope_key == scope_key)
|
||||
.where(AgentRunnerState.state_key == state_key)
|
||||
)
|
||||
existing = result.first()
|
||||
|
||||
now = datetime.utcnow()
|
||||
|
||||
if existing:
|
||||
# Update existing entry
|
||||
await conn.execute(
|
||||
update(AgentRunnerState)
|
||||
.where(AgentRunnerState.id == existing.id)
|
||||
.values(
|
||||
value_json=value_json,
|
||||
updated_at=now,
|
||||
)
|
||||
)
|
||||
else:
|
||||
# Insert new entry
|
||||
await conn.execute(
|
||||
sqlalchemy.insert(AgentRunnerState).values(
|
||||
runner_id=runner_id,
|
||||
binding_identity=binding_identity,
|
||||
scope=scope,
|
||||
scope_key=scope_key,
|
||||
state_key=state_key,
|
||||
value_json=value_json,
|
||||
bot_id=context.get('bot_id'),
|
||||
workspace_id=context.get('workspace_id'),
|
||||
conversation_id=context.get('conversation_id'),
|
||||
thread_id=context.get('thread_id'),
|
||||
actor_type=context.get('actor_type'),
|
||||
actor_id=context.get('actor_id'),
|
||||
subject_type=context.get('subject_type'),
|
||||
subject_id=context.get('subject_id'),
|
||||
created_at=now,
|
||||
updated_at=now,
|
||||
)
|
||||
)
|
||||
|
||||
return True, None
|
||||
|
||||
async def state_delete(
|
||||
self,
|
||||
scope_key: str,
|
||||
state_key: str,
|
||||
) -> bool:
|
||||
"""Delete a state value.
|
||||
|
||||
Returns True if deleted, False if not found.
|
||||
"""
|
||||
state_key = normalize_state_key(state_key)
|
||||
|
||||
async with self._db_engine.begin() as conn:
|
||||
result = await conn.execute(
|
||||
delete(AgentRunnerState)
|
||||
.where(AgentRunnerState.scope_key == scope_key)
|
||||
.where(AgentRunnerState.state_key == state_key)
|
||||
.returning(AgentRunnerState.id)
|
||||
)
|
||||
deleted = result.first()
|
||||
return deleted is not None
|
||||
|
||||
async def state_list(
|
||||
self,
|
||||
scope_key: str,
|
||||
prefix: str | None = None,
|
||||
limit: int = 100,
|
||||
) -> tuple[list[str], bool]:
|
||||
"""List state keys in a scope.
|
||||
|
||||
Returns tuple of (keys, has_more).
|
||||
"""
|
||||
# Enforce limit cap
|
||||
limit = min(limit, 100)
|
||||
|
||||
async with self._db_engine.connect() as conn:
|
||||
query = (
|
||||
select(AgentRunnerState.state_key)
|
||||
.where(AgentRunnerState.scope_key == scope_key)
|
||||
.order_by(AgentRunnerState.state_key)
|
||||
.limit(limit + 1) # Fetch one extra to check has_more
|
||||
)
|
||||
|
||||
if prefix:
|
||||
prefix = normalize_state_key(prefix)
|
||||
query = query.where(
|
||||
AgentRunnerState.state_key.like(f'{prefix}%')
|
||||
)
|
||||
|
||||
result = await conn.execute(query)
|
||||
rows = result.fetchall()
|
||||
|
||||
keys = [row.state_key for row in rows[:limit]]
|
||||
has_more = len(rows) > limit
|
||||
|
||||
return keys, has_more
|
||||
|
||||
async def clear_all(self) -> None:
|
||||
"""Clear all state entries (for testing)."""
|
||||
async with self._db_engine.begin() as conn:
|
||||
await conn.execute(delete(AgentRunnerState))
|
||||
|
||||
|
||||
# Global singleton persistent state store
|
||||
_persistent_state_store: PersistentStateStore | None = None
|
||||
_persistent_state_store_lock = threading.Lock()
|
||||
|
||||
|
||||
def get_persistent_state_store(db_engine: AsyncEngine | None = None) -> PersistentStateStore:
|
||||
"""Get the global persistent state store singleton.
|
||||
|
||||
Args:
|
||||
db_engine: Database engine (required on first call)
|
||||
|
||||
Returns:
|
||||
PersistentStateStore singleton
|
||||
"""
|
||||
global _persistent_state_store
|
||||
with _persistent_state_store_lock:
|
||||
if _persistent_state_store is None:
|
||||
if db_engine is None:
|
||||
raise RuntimeError("db_engine required for first call to get_persistent_state_store")
|
||||
_persistent_state_store = PersistentStateStore(db_engine)
|
||||
return _persistent_state_store
|
||||
|
||||
|
||||
def reset_persistent_state_store() -> None:
|
||||
"""Reset the global persistent state store (for testing)."""
|
||||
global _persistent_state_store
|
||||
with _persistent_state_store_lock:
|
||||
_persistent_state_store = None
|
||||
@@ -1,626 +0,0 @@
|
||||
"""Pipeline adapter for converting Query to event-first envelope.
|
||||
|
||||
This adapter bridges the Query/Pipeline entry point with the event-first
|
||||
Protocol v1 architecture.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import hashlib
|
||||
import typing
|
||||
|
||||
from langbot_plugin.api.entities.builtin.pipeline import query as pipeline_query
|
||||
from langbot_plugin.api.entities.builtin.platform import message as platform_message
|
||||
from langbot_plugin.api.entities.builtin.agent_runner.event import (
|
||||
AgentEventContext,
|
||||
ConversationContext,
|
||||
ActorContext,
|
||||
SubjectContext,
|
||||
RawEventRef,
|
||||
)
|
||||
from langbot_plugin.api.entities.builtin.agent_runner.input import AgentInput
|
||||
from langbot_plugin.api.entities.builtin.agent_runner.delivery import DeliveryContext
|
||||
|
||||
from .host_models import (
|
||||
AgentEventEnvelope,
|
||||
AgentBinding,
|
||||
BindingScope,
|
||||
ResourcePolicy,
|
||||
StatePolicy,
|
||||
DeliveryPolicy,
|
||||
)
|
||||
from . import events as runner_events
|
||||
|
||||
|
||||
class PipelineAdapter:
|
||||
"""Adapter for converting Pipeline Query to event-first envelope.
|
||||
|
||||
This adapter is responsible for:
|
||||
- Converting Query to AgentEventEnvelope
|
||||
- Converting Pipeline config to temporary AgentBinding
|
||||
- Putting Query-only fields into adapter context
|
||||
"""
|
||||
|
||||
INTERNAL_PREFIX = '_'
|
||||
SENSITIVE_PATTERNS = ('secret', 'token', 'key', 'password', 'credential', 'api_key', 'apikey')
|
||||
PERMISSION_VARS = ('_pipeline_bound_plugins', '_authorized', '_permission')
|
||||
|
||||
@classmethod
|
||||
def query_to_event(
|
||||
cls,
|
||||
query: pipeline_query.Query,
|
||||
) -> AgentEventEnvelope:
|
||||
"""Convert Pipeline Query to AgentEventEnvelope.
|
||||
|
||||
Args:
|
||||
query: Pipeline query
|
||||
|
||||
Returns:
|
||||
AgentEventEnvelope for event-first processing
|
||||
"""
|
||||
# Build event context
|
||||
event = cls._build_event_context(query)
|
||||
|
||||
# Build conversation context
|
||||
conversation = cls._build_conversation_context(query)
|
||||
|
||||
# Build actor context
|
||||
actor = cls._build_actor_context(query)
|
||||
|
||||
# Build subject context
|
||||
subject = cls._build_subject_context(query)
|
||||
|
||||
# Build input
|
||||
input = cls._build_input(query)
|
||||
|
||||
# Build delivery context
|
||||
delivery = cls._build_delivery_context(query)
|
||||
|
||||
# Build raw ref
|
||||
raw_ref = cls._build_raw_ref(query)
|
||||
|
||||
return AgentEventEnvelope(
|
||||
event_id=event.event_id or str(query.query_id),
|
||||
event_type=event.event_type or runner_events.MESSAGE_RECEIVED,
|
||||
event_time=event.event_time,
|
||||
source="pipeline_adapter",
|
||||
source_event_type=event.source_event_type,
|
||||
bot_id=query.bot_uuid,
|
||||
workspace_id=None, # Not available in Query
|
||||
conversation_id=conversation.conversation_id,
|
||||
thread_id=conversation.thread_id,
|
||||
actor=actor,
|
||||
subject=subject,
|
||||
input=input,
|
||||
delivery=delivery,
|
||||
raw_ref=raw_ref,
|
||||
data=event.data,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def pipeline_config_to_binding(
|
||||
cls,
|
||||
query: pipeline_query.Query,
|
||||
runner_id: str,
|
||||
) -> AgentBinding:
|
||||
"""Convert Pipeline config to temporary AgentBinding.
|
||||
|
||||
Args:
|
||||
query: Pipeline query
|
||||
runner_id: Resolved runner ID
|
||||
|
||||
Returns:
|
||||
AgentBinding for this run
|
||||
"""
|
||||
pipeline_config = query.pipeline_config or {}
|
||||
ai_config = pipeline_config.get('ai', {})
|
||||
runner_config = ai_config.get('runner_config', {}).get(runner_id, {})
|
||||
pipeline_uuid = getattr(query, 'pipeline_uuid', None)
|
||||
|
||||
# Build scope
|
||||
scope = BindingScope(
|
||||
scope_type="pipeline",
|
||||
scope_id=pipeline_uuid,
|
||||
)
|
||||
|
||||
# Build resource policy from pipeline config
|
||||
resource_policy = ResourcePolicy(
|
||||
allowed_model_uuids=cls._extract_allowed_models(query),
|
||||
allowed_tool_names=cls._extract_allowed_tools(query),
|
||||
allowed_kb_uuids=cls._extract_allowed_kbs(query),
|
||||
)
|
||||
|
||||
# Build state policy
|
||||
state_policy = StatePolicy(
|
||||
enable_state=True,
|
||||
state_scopes=["conversation", "actor", "subject", "runner"],
|
||||
)
|
||||
|
||||
# Build delivery policy
|
||||
delivery_policy = DeliveryPolicy(
|
||||
enable_streaming=True,
|
||||
enable_reply=True,
|
||||
)
|
||||
|
||||
return AgentBinding(
|
||||
binding_id=f"pipeline_{pipeline_uuid or 'default'}_{runner_id}",
|
||||
scope=scope,
|
||||
event_types=[runner_events.MESSAGE_RECEIVED],
|
||||
runner_id=runner_id,
|
||||
runner_config=runner_config,
|
||||
resource_policy=resource_policy,
|
||||
state_policy=state_policy,
|
||||
delivery_policy=delivery_policy,
|
||||
enabled=True,
|
||||
pipeline_uuid=pipeline_uuid,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def build_adapter_context(
|
||||
cls,
|
||||
query: pipeline_query.Query,
|
||||
binding: AgentBinding,
|
||||
) -> dict[str, typing.Any]:
|
||||
"""Build Query-derived fields for the Pipeline adapter entry."""
|
||||
return {
|
||||
'params': cls.build_params(query),
|
||||
'prompt': cls.build_prompt(query),
|
||||
'query_id': getattr(query, 'query_id', None),
|
||||
}
|
||||
|
||||
@classmethod
|
||||
def build_params(cls, query: pipeline_query.Query) -> dict[str, typing.Any]:
|
||||
"""Build adapter params from Pipeline variables with host filtering."""
|
||||
params: dict[str, typing.Any] = {}
|
||||
variables = getattr(query, 'variables', None)
|
||||
if not variables:
|
||||
return params
|
||||
|
||||
for key, value in variables.items():
|
||||
if key.startswith(cls.INTERNAL_PREFIX):
|
||||
continue
|
||||
key_lower = key.lower()
|
||||
if any(pattern in key_lower for pattern in cls.SENSITIVE_PATTERNS):
|
||||
continue
|
||||
if any(key == perm_var or key.startswith(perm_var) for perm_var in cls.PERMISSION_VARS):
|
||||
continue
|
||||
if cls.is_json_serializable(value):
|
||||
params[key] = value
|
||||
|
||||
return params
|
||||
|
||||
@classmethod
|
||||
def build_prompt(cls, query: pipeline_query.Query) -> list[dict[str, typing.Any]]:
|
||||
"""Build effective prompt messages from Pipeline preprocessing output."""
|
||||
prompt = getattr(query, 'prompt', None)
|
||||
messages = getattr(prompt, 'messages', None)
|
||||
if not messages:
|
||||
return []
|
||||
return [cls._dump_message(msg) for msg in messages]
|
||||
|
||||
@classmethod
|
||||
def is_json_serializable(cls, value: typing.Any) -> bool:
|
||||
"""Return whether a value can safely cross the adapter boundary as JSON."""
|
||||
if value is None or isinstance(value, (str, int, float, bool)):
|
||||
return True
|
||||
if isinstance(value, (list, tuple)):
|
||||
return all(cls.is_json_serializable(item) for item in value)
|
||||
if isinstance(value, dict):
|
||||
return all(
|
||||
isinstance(k, str) and cls.is_json_serializable(v)
|
||||
for k, v in value.items()
|
||||
)
|
||||
return False
|
||||
|
||||
@staticmethod
|
||||
def _dump_message(message: typing.Any) -> dict[str, typing.Any]:
|
||||
"""Serialize a provider message-like object."""
|
||||
if hasattr(message, 'model_dump'):
|
||||
return message.model_dump(mode='json')
|
||||
if isinstance(message, dict):
|
||||
return message
|
||||
return {
|
||||
'role': getattr(message, 'role', None),
|
||||
'content': getattr(message, 'content', None),
|
||||
}
|
||||
|
||||
# Private helper methods
|
||||
|
||||
@classmethod
|
||||
def _build_event_context(
|
||||
cls,
|
||||
query: pipeline_query.Query,
|
||||
) -> AgentEventContext:
|
||||
"""Build AgentEventContext from Query."""
|
||||
message_event = getattr(query, 'message_event', None)
|
||||
|
||||
event_data: dict[str, typing.Any] = {}
|
||||
if message_event and hasattr(message_event, 'model_dump'):
|
||||
try:
|
||||
event_data = message_event.model_dump(mode='json')
|
||||
except TypeError:
|
||||
event_data = message_event.model_dump()
|
||||
except Exception:
|
||||
event_data = {}
|
||||
event_data.pop('source_platform_object', None)
|
||||
|
||||
source_event_type = None
|
||||
if message_event:
|
||||
source_event_type = getattr(message_event, 'type', None)
|
||||
|
||||
message_chain = getattr(query, 'message_chain', None)
|
||||
message_id = getattr(message_chain, 'message_id', None)
|
||||
if message_id == -1:
|
||||
message_id = None
|
||||
|
||||
event_time = None
|
||||
if message_event:
|
||||
event_time = getattr(message_event, 'time', None)
|
||||
if isinstance(event_time, (int, float)):
|
||||
event_time = int(event_time)
|
||||
|
||||
source_event_id = str(message_id or query.query_id)
|
||||
return AgentEventContext(
|
||||
event_id=cls._build_scoped_event_id(query, source_event_id, event_time),
|
||||
event_type=runner_events.MESSAGE_RECEIVED,
|
||||
event_time=event_time,
|
||||
source="pipeline_adapter",
|
||||
source_event_type=source_event_type,
|
||||
data=event_data,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def _build_scoped_event_id(
|
||||
cls,
|
||||
query: pipeline_query.Query,
|
||||
source_event_id: str,
|
||||
event_time: int | None,
|
||||
) -> str:
|
||||
"""Build a globally unique host event id from pipeline-local ids."""
|
||||
launcher_type = getattr(query, 'launcher_type', None)
|
||||
launcher_type_value = getattr(launcher_type, 'value', launcher_type) if launcher_type is not None else None
|
||||
scope_parts = [
|
||||
'pipeline_adapter',
|
||||
getattr(query, 'pipeline_uuid', None),
|
||||
getattr(query, 'bot_uuid', None),
|
||||
launcher_type_value,
|
||||
getattr(query, 'launcher_id', None),
|
||||
getattr(query, 'sender_id', None),
|
||||
source_event_id,
|
||||
event_time,
|
||||
]
|
||||
scoped = '|'.join('' if part is None else str(part) for part in scope_parts)
|
||||
digest = hashlib.sha256(scoped.encode('utf-8')).hexdigest()[:32]
|
||||
return f'pipeline:{digest}'
|
||||
|
||||
@classmethod
|
||||
def _build_conversation_context(
|
||||
cls,
|
||||
query: pipeline_query.Query,
|
||||
) -> ConversationContext:
|
||||
"""Build ConversationContext from Query."""
|
||||
# Handle launcher_type safely
|
||||
launcher_type = getattr(query, 'launcher_type', None)
|
||||
launcher_type_value = None
|
||||
if launcher_type is not None:
|
||||
launcher_type_value = getattr(launcher_type, 'value', launcher_type)
|
||||
|
||||
# Handle launcher_id
|
||||
launcher_id = getattr(query, 'launcher_id', None)
|
||||
|
||||
# Build session_id from launcher info if available
|
||||
session_id = None
|
||||
if launcher_type_value and launcher_id:
|
||||
session_id = f'{launcher_type_value}_{launcher_id}'
|
||||
|
||||
# Handle session and conversation_id
|
||||
conversation_id = None
|
||||
session = getattr(query, 'session', None)
|
||||
if session:
|
||||
conversation = getattr(session, 'using_conversation', None)
|
||||
if conversation:
|
||||
conversation_id = getattr(conversation, 'uuid', None)
|
||||
|
||||
if not conversation_id:
|
||||
variables = getattr(query, 'variables', None) or {}
|
||||
conversation_id = variables.get('conversation_id') or None
|
||||
|
||||
if not conversation_id:
|
||||
conversation_id = session_id
|
||||
|
||||
# Handle sender_id
|
||||
sender_id = getattr(query, 'sender_id', None)
|
||||
if sender_id is not None:
|
||||
sender_id = str(sender_id)
|
||||
|
||||
# Handle bot_uuid
|
||||
bot_uuid = getattr(query, 'bot_uuid', None)
|
||||
|
||||
# Handle pipeline_uuid
|
||||
pipeline_uuid = getattr(query, 'pipeline_uuid', None)
|
||||
|
||||
return ConversationContext(
|
||||
conversation_id=str(conversation_id) if conversation_id is not None else None,
|
||||
thread_id=None,
|
||||
launcher_type=launcher_type_value,
|
||||
launcher_id=launcher_id,
|
||||
sender_id=sender_id,
|
||||
bot_id=bot_uuid,
|
||||
workspace_id=None,
|
||||
session_id=session_id,
|
||||
pipeline_uuid=pipeline_uuid,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def _build_actor_context(
|
||||
cls,
|
||||
query: pipeline_query.Query,
|
||||
) -> ActorContext:
|
||||
"""Build ActorContext from Query."""
|
||||
message_event = getattr(query, 'message_event', None)
|
||||
sender = getattr(message_event, 'sender', None) if message_event else None
|
||||
sender_id = getattr(query, 'sender_id', None)
|
||||
actor_id = getattr(sender, 'id', None) if sender else None
|
||||
if actor_id is None:
|
||||
actor_id = sender_id
|
||||
actor_name = sender.get_name() if sender and hasattr(sender, 'get_name') else None
|
||||
|
||||
return ActorContext(
|
||||
actor_type="user",
|
||||
actor_id=str(actor_id) if actor_id is not None else None,
|
||||
actor_name=actor_name,
|
||||
metadata={},
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def _build_subject_context(
|
||||
cls,
|
||||
query: pipeline_query.Query,
|
||||
) -> SubjectContext:
|
||||
"""Build SubjectContext from Query."""
|
||||
message_chain = getattr(query, 'message_chain', None)
|
||||
message_id = getattr(message_chain, 'message_id', None) if message_chain else None
|
||||
if message_id == -1:
|
||||
message_id = None
|
||||
|
||||
query_id = getattr(query, 'query_id', None)
|
||||
|
||||
# Safely get launcher_type
|
||||
launcher_type = getattr(query, 'launcher_type', None)
|
||||
launcher_type_value = None
|
||||
if launcher_type is not None:
|
||||
launcher_type_value = getattr(launcher_type, 'value', launcher_type)
|
||||
|
||||
return SubjectContext(
|
||||
subject_type="message",
|
||||
subject_id=str(message_id or query_id or ''),
|
||||
data={
|
||||
"launcher_type": launcher_type_value,
|
||||
"launcher_id": getattr(query, 'launcher_id', None),
|
||||
"sender_id": str(getattr(query, 'sender_id', '')) if getattr(query, 'sender_id', None) else None,
|
||||
"bot_uuid": getattr(query, 'bot_uuid', None),
|
||||
"pipeline_uuid": getattr(query, 'pipeline_uuid', None),
|
||||
},
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def _build_input(
|
||||
cls,
|
||||
query: pipeline_query.Query,
|
||||
) -> AgentInput:
|
||||
"""Build AgentInput from Query."""
|
||||
text = None
|
||||
text_parts: list[str] = []
|
||||
contents: list[dict[str, typing.Any]] = []
|
||||
|
||||
user_message = getattr(query, 'user_message', None)
|
||||
if user_message:
|
||||
content = getattr(user_message, 'content', None)
|
||||
if isinstance(content, list):
|
||||
for elem in content:
|
||||
# Handle both real objects and mocks
|
||||
if hasattr(elem, 'model_dump'):
|
||||
contents.append(elem.model_dump(mode='json'))
|
||||
elif isinstance(elem, dict):
|
||||
contents.append(elem)
|
||||
else:
|
||||
# For mocks, extract type and text attributes
|
||||
elem_type = getattr(elem, 'type', None)
|
||||
if elem_type == 'text':
|
||||
elem_text = getattr(elem, 'text', None)
|
||||
contents.append({'type': 'text', 'text': elem_text})
|
||||
if elem_text:
|
||||
text_parts.append(elem_text)
|
||||
continue
|
||||
|
||||
# Extract text for the text field
|
||||
if hasattr(elem, 'type') and getattr(elem, 'type', None) == 'text':
|
||||
elem_text = getattr(elem, 'text', None)
|
||||
if elem_text:
|
||||
text_parts.append(elem_text)
|
||||
elif content is not None:
|
||||
text = str(content)
|
||||
contents.append({'type': 'text', 'text': text})
|
||||
|
||||
if text_parts:
|
||||
text = ''.join(text_parts)
|
||||
|
||||
message_chain_dict = None
|
||||
message_chain = getattr(query, 'message_chain', None)
|
||||
if message_chain:
|
||||
if hasattr(message_chain, 'model_dump'):
|
||||
message_chain_dict = message_chain.model_dump(mode='json')
|
||||
|
||||
attachments = cls._build_attachments(query, contents)
|
||||
|
||||
return AgentInput(
|
||||
text=text,
|
||||
contents=contents,
|
||||
message_chain=message_chain_dict,
|
||||
attachments=attachments,
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def _build_attachments(
|
||||
cls,
|
||||
query: pipeline_query.Query,
|
||||
contents: list[dict[str, typing.Any]],
|
||||
) -> list[dict[str, typing.Any]]:
|
||||
"""Extract attachments from query."""
|
||||
import uuid
|
||||
|
||||
attachments: list[dict[str, typing.Any]] = []
|
||||
|
||||
for elem in contents:
|
||||
elem_type = elem.get('type')
|
||||
artifact_id = str(uuid.uuid4()) # Generate unique ID
|
||||
|
||||
if elem_type == 'image_url':
|
||||
image_url = elem.get('image_url') or {}
|
||||
attachments.append({
|
||||
'artifact_id': artifact_id,
|
||||
'artifact_type': 'image',
|
||||
'source': 'url',
|
||||
'url': image_url.get('url') if isinstance(image_url, dict) else str(image_url),
|
||||
})
|
||||
elif elem_type == 'image_base64':
|
||||
attachments.append({
|
||||
'artifact_id': artifact_id,
|
||||
'artifact_type': 'image',
|
||||
'source': 'base64',
|
||||
'content': elem.get('image_base64'),
|
||||
})
|
||||
elif elem_type == 'file_url':
|
||||
attachments.append({
|
||||
'artifact_id': artifact_id,
|
||||
'artifact_type': 'file',
|
||||
'source': 'url',
|
||||
'url': elem.get('file_url'),
|
||||
'name': elem.get('file_name'),
|
||||
})
|
||||
elif elem_type == 'file_base64':
|
||||
attachments.append({
|
||||
'artifact_id': artifact_id,
|
||||
'artifact_type': 'file',
|
||||
'source': 'base64',
|
||||
'content': elem.get('file_base64'),
|
||||
'name': elem.get('file_name'),
|
||||
})
|
||||
|
||||
message_chain = getattr(query, 'message_chain', None)
|
||||
if message_chain:
|
||||
try:
|
||||
for component in message_chain:
|
||||
artifact_id = str(uuid.uuid4()) # Generate unique ID
|
||||
|
||||
if isinstance(component, platform_message.Image):
|
||||
attachments.append({
|
||||
'artifact_id': artifact_id,
|
||||
'artifact_type': 'image',
|
||||
'source': 'message_chain',
|
||||
'id': component.image_id or None,
|
||||
'url': component.url or None,
|
||||
})
|
||||
elif isinstance(component, platform_message.File):
|
||||
attachments.append({
|
||||
'artifact_id': artifact_id,
|
||||
'artifact_type': 'file',
|
||||
'source': 'message_chain',
|
||||
'id': component.id or None,
|
||||
'name': component.name or None,
|
||||
})
|
||||
elif isinstance(component, platform_message.Voice):
|
||||
attachments.append({
|
||||
'artifact_id': artifact_id,
|
||||
'artifact_type': 'voice',
|
||||
'source': 'message_chain',
|
||||
'id': component.voice_id or None,
|
||||
'url': component.url or None,
|
||||
})
|
||||
except TypeError:
|
||||
# message_chain is not iterable (e.g., a Mock object)
|
||||
pass
|
||||
|
||||
return attachments
|
||||
|
||||
@classmethod
|
||||
def _build_delivery_context(
|
||||
cls,
|
||||
query: pipeline_query.Query,
|
||||
) -> DeliveryContext:
|
||||
"""Build DeliveryContext from Query."""
|
||||
message_chain = getattr(query, 'message_chain', None)
|
||||
return DeliveryContext(
|
||||
surface="platform",
|
||||
reply_target={
|
||||
"message_id": getattr(message_chain, 'message_id', None),
|
||||
},
|
||||
supports_streaming=True,
|
||||
supports_edit=False,
|
||||
supports_reaction=False,
|
||||
platform_capabilities={},
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def _build_raw_ref(
|
||||
cls,
|
||||
query: pipeline_query.Query,
|
||||
) -> RawEventRef | None:
|
||||
"""Build RawEventRef from Query."""
|
||||
# For now, we don't store raw event payload
|
||||
return None
|
||||
|
||||
@classmethod
|
||||
def _extract_allowed_models(
|
||||
cls,
|
||||
query: pipeline_query.Query,
|
||||
) -> list[str] | None:
|
||||
"""Extract allowed model UUIDs from query."""
|
||||
model_uuids: list[str] = []
|
||||
model_uuid = getattr(query, 'use_llm_model_uuid', None)
|
||||
if model_uuid:
|
||||
model_uuids.append(model_uuid)
|
||||
|
||||
variables = getattr(query, 'variables', None) or {}
|
||||
for fallback_uuid in variables.get('_fallback_model_uuids', []) or []:
|
||||
if fallback_uuid and fallback_uuid not in model_uuids:
|
||||
model_uuids.append(fallback_uuid)
|
||||
|
||||
return model_uuids or None
|
||||
|
||||
@classmethod
|
||||
def _extract_allowed_tools(
|
||||
cls,
|
||||
query: pipeline_query.Query,
|
||||
) -> list[str] | None:
|
||||
"""Extract allowed tool names from query."""
|
||||
use_funcs = getattr(query, 'use_funcs', None)
|
||||
if not use_funcs:
|
||||
return None
|
||||
try:
|
||||
tool_names = []
|
||||
for func in use_funcs:
|
||||
if isinstance(func, dict):
|
||||
name = func.get('name')
|
||||
elif hasattr(func, 'name'):
|
||||
name = func.name
|
||||
else:
|
||||
continue
|
||||
if name:
|
||||
tool_names.append(name)
|
||||
return tool_names if tool_names else None
|
||||
except (TypeError, AttributeError):
|
||||
return None
|
||||
|
||||
@classmethod
|
||||
def _extract_allowed_kbs(
|
||||
cls,
|
||||
query: pipeline_query.Query,
|
||||
) -> list[str] | None:
|
||||
"""Extract allowed knowledge base UUIDs from query."""
|
||||
variables = getattr(query, 'variables', None)
|
||||
if not variables:
|
||||
return None
|
||||
kb_uuids = variables.get('_knowledge_base_uuids')
|
||||
if kb_uuids:
|
||||
return kb_uuids
|
||||
return None
|
||||
@@ -1,293 +0,0 @@
|
||||
"""Agent runner registry for discovering and caching runner descriptors."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import typing
|
||||
import asyncio
|
||||
|
||||
from ...core import app
|
||||
from .descriptor import AgentRunnerDescriptor
|
||||
from .id import parse_runner_id, format_runner_id
|
||||
from .errors import RunnerNotFoundError, RunnerNotAuthorizedError
|
||||
|
||||
|
||||
class AgentRunnerRegistry:
|
||||
"""Registry for discovering and managing agent runners.
|
||||
|
||||
Responsibilities:
|
||||
- Discover runners from plugin runtime via LIST_AGENT_RUNNERS
|
||||
- Validate runner manifests (kind, metadata, spec)
|
||||
- Cache discovered runners for performance
|
||||
- Filter runners by bound plugins
|
||||
- Handle manifest errors gracefully (log warning, skip runner)
|
||||
"""
|
||||
|
||||
ap: app.Application
|
||||
|
||||
_cache: dict[str, AgentRunnerDescriptor] | None
|
||||
"""Cached runner descriptors keyed by runner ID"""
|
||||
|
||||
_cache_lock: asyncio.Lock
|
||||
"""Lock for cache refresh operations"""
|
||||
|
||||
def __init__(self, ap: app.Application):
|
||||
self.ap = ap
|
||||
self._cache = None
|
||||
self._cache_lock = asyncio.Lock()
|
||||
|
||||
async def _discover_runners(self) -> dict[str, AgentRunnerDescriptor]:
|
||||
"""Discover runners from plugin runtime.
|
||||
|
||||
Always discovers ALL runners (no bound_plugins filter).
|
||||
The cache should contain unfiltered discovery results.
|
||||
|
||||
Returns:
|
||||
Dict of runner descriptors keyed by runner ID
|
||||
"""
|
||||
if not self.ap.plugin_connector.is_enable_plugin:
|
||||
return {}
|
||||
|
||||
runners: dict[str, AgentRunnerDescriptor] = {}
|
||||
|
||||
try:
|
||||
# Always list all runners (bound_plugins=None)
|
||||
plugin_runners = await self.ap.plugin_connector.list_agent_runners(None)
|
||||
|
||||
for runner_data in plugin_runners:
|
||||
try:
|
||||
descriptor = self._validate_and_build_descriptor(runner_data)
|
||||
if descriptor is not None:
|
||||
runners[descriptor.id] = descriptor
|
||||
except Exception as e:
|
||||
plugin_author = runner_data.get('plugin_author', 'unknown')
|
||||
plugin_name = runner_data.get('plugin_name', 'unknown')
|
||||
runner_name = runner_data.get('runner_name', 'unknown')
|
||||
self.ap.logger.warning(
|
||||
f'Invalid runner manifest for plugin:{plugin_author}/{plugin_name}/{runner_name}: {e}'
|
||||
)
|
||||
continue
|
||||
|
||||
except Exception as e:
|
||||
self.ap.logger.warning(f'Failed to list agent runners from plugin runtime: {e}')
|
||||
return {}
|
||||
|
||||
return runners
|
||||
|
||||
def _validate_and_build_descriptor(self, runner_data: dict[str, typing.Any]) -> AgentRunnerDescriptor | None:
|
||||
"""Validate runner manifest and build descriptor.
|
||||
|
||||
Args:
|
||||
runner_data: Raw runner data from plugin runtime with fields:
|
||||
- plugin_author, plugin_name, runner_name
|
||||
- manifest (full component manifest dict)
|
||||
- protocol_version, capabilities, permissions, config (extracted from spec)
|
||||
|
||||
Returns:
|
||||
AgentRunnerDescriptor if valid, None if invalid
|
||||
"""
|
||||
plugin_author = runner_data.get('plugin_author', '')
|
||||
plugin_name = runner_data.get('plugin_name', '')
|
||||
runner_name = runner_data.get('runner_name', '')
|
||||
|
||||
if not plugin_author or not plugin_name or not runner_name:
|
||||
return None
|
||||
|
||||
manifest = runner_data.get('manifest', {})
|
||||
|
||||
# Validate kind
|
||||
kind = manifest.get('kind', '')
|
||||
if kind != 'AgentRunner':
|
||||
return None
|
||||
|
||||
# Validate metadata
|
||||
metadata = manifest.get('metadata', {})
|
||||
name = metadata.get('name', '')
|
||||
if not name:
|
||||
return None
|
||||
|
||||
# metadata.label must exist
|
||||
label = metadata.get('label', {})
|
||||
if not label:
|
||||
label = {name: name} # fallback
|
||||
|
||||
spec = manifest.get('spec', {})
|
||||
|
||||
# SDK now provides these directly extracted from spec. Fall back to
|
||||
# manifest.spec for older runtimes/tests that return the raw manifest.
|
||||
protocol_version = runner_data.get('protocol_version') or spec.get('protocol_version', '1')
|
||||
config_schema = runner_data.get('config') or spec.get('config', [])
|
||||
capabilities = runner_data.get('capabilities') or spec.get('capabilities', {})
|
||||
permissions = runner_data.get('permissions') or spec.get('permissions', {})
|
||||
|
||||
# Build descriptor
|
||||
runner_id = format_runner_id(
|
||||
source='plugin',
|
||||
plugin_author=plugin_author,
|
||||
plugin_name=plugin_name,
|
||||
runner_name=runner_name,
|
||||
)
|
||||
|
||||
return AgentRunnerDescriptor(
|
||||
id=runner_id,
|
||||
source='plugin',
|
||||
label=label,
|
||||
description=metadata.get('description') or runner_data.get('runner_description'),
|
||||
plugin_author=plugin_author,
|
||||
plugin_name=plugin_name,
|
||||
runner_name=runner_name,
|
||||
plugin_version=runner_data.get('plugin_version'),
|
||||
protocol_version=protocol_version,
|
||||
config_schema=config_schema,
|
||||
capabilities=capabilities,
|
||||
permissions=permissions,
|
||||
raw_manifest=manifest,
|
||||
)
|
||||
|
||||
async def refresh(self) -> None:
|
||||
"""Refresh runner cache.
|
||||
|
||||
Always discovers ALL runners (no bound_plugins filter).
|
||||
The cache contains unfiltered discovery results.
|
||||
"""
|
||||
async with self._cache_lock:
|
||||
self._cache = await self._discover_runners()
|
||||
|
||||
async def list_runners(
|
||||
self,
|
||||
bound_plugins: list[str] | None = None,
|
||||
use_cache: bool = True,
|
||||
) -> list[AgentRunnerDescriptor]:
|
||||
"""List available runners.
|
||||
|
||||
Args:
|
||||
bound_plugins: Optional filter for bound plugins (applied locally)
|
||||
use_cache: Use cached data if available
|
||||
|
||||
Returns:
|
||||
List of runner descriptors
|
||||
"""
|
||||
if use_cache and self._cache is not None:
|
||||
# Filter from cache
|
||||
return self._filter_runners_by_bound_plugins(self._cache, bound_plugins)
|
||||
|
||||
# Discover fresh (always full list)
|
||||
runners = await self._discover_runners()
|
||||
|
||||
# Update cache (full list, unfiltered)
|
||||
async with self._cache_lock:
|
||||
self._cache = runners
|
||||
|
||||
# Filter locally
|
||||
return self._filter_runners_by_bound_plugins(runners, bound_plugins)
|
||||
|
||||
def _filter_runners_by_bound_plugins(
|
||||
self,
|
||||
runners: dict[str, AgentRunnerDescriptor],
|
||||
bound_plugins: list[str] | None,
|
||||
) -> list[AgentRunnerDescriptor]:
|
||||
"""Filter runners by bound plugins.
|
||||
|
||||
Args:
|
||||
runners: Dict of runner descriptors
|
||||
bound_plugins: Optional filter (None means all plugins allowed)
|
||||
|
||||
Returns:
|
||||
Filtered list of runner descriptors
|
||||
"""
|
||||
if bound_plugins is None:
|
||||
# All plugins allowed
|
||||
return list(runners.values())
|
||||
|
||||
allowed_plugin_ids = set(bound_plugins)
|
||||
filtered = []
|
||||
for descriptor in runners.values():
|
||||
plugin_id = descriptor.get_plugin_id()
|
||||
if plugin_id in allowed_plugin_ids:
|
||||
filtered.append(descriptor)
|
||||
|
||||
return filtered
|
||||
|
||||
async def get(
|
||||
self,
|
||||
runner_id: str,
|
||||
bound_plugins: list[str] | None = None,
|
||||
) -> AgentRunnerDescriptor:
|
||||
"""Get a specific runner descriptor.
|
||||
|
||||
Args:
|
||||
runner_id: Runner ID to lookup
|
||||
bound_plugins: Optional bound plugins filter
|
||||
|
||||
Returns:
|
||||
AgentRunnerDescriptor
|
||||
|
||||
Raises:
|
||||
RunnerNotFoundError: If runner not found
|
||||
RunnerNotAuthorizedError: If runner not in bound plugins
|
||||
"""
|
||||
# Parse and validate runner ID format
|
||||
try:
|
||||
parse_runner_id(runner_id)
|
||||
except ValueError as e:
|
||||
raise RunnerNotFoundError(runner_id) from e
|
||||
|
||||
# Get from cache or discover (always full list)
|
||||
if self._cache is None:
|
||||
await self.refresh()
|
||||
|
||||
if self._cache is None:
|
||||
raise RunnerNotFoundError(runner_id)
|
||||
|
||||
descriptor = self._cache.get(runner_id)
|
||||
if descriptor is None:
|
||||
raise RunnerNotFoundError(runner_id)
|
||||
|
||||
# Check authorization
|
||||
if bound_plugins is not None:
|
||||
plugin_id = descriptor.get_plugin_id()
|
||||
if plugin_id not in bound_plugins:
|
||||
raise RunnerNotAuthorizedError(runner_id, bound_plugins)
|
||||
|
||||
return descriptor
|
||||
|
||||
async def get_runner_metadata_for_pipeline(self) -> list[dict[str, typing.Any]]:
|
||||
"""Get runner metadata for pipeline configuration UI.
|
||||
|
||||
Returns runner options and their config schemas for the DynamicForm.
|
||||
"""
|
||||
# Get all runners (no bound plugin filter for metadata listing)
|
||||
runners = await self.list_runners(bound_plugins=None)
|
||||
|
||||
options = []
|
||||
stages = []
|
||||
|
||||
for descriptor in runners:
|
||||
config_schema = []
|
||||
for index, config_item in enumerate(descriptor.config_schema):
|
||||
item = dict(config_item)
|
||||
if not item.get('id'):
|
||||
item_name = item.get('name') or str(index)
|
||||
item['id'] = f'{descriptor.id}.{item_name}'
|
||||
config_schema.append(item)
|
||||
|
||||
# Add runner option
|
||||
options.append(
|
||||
{
|
||||
'name': descriptor.id,
|
||||
'label': descriptor.label,
|
||||
'description': descriptor.description,
|
||||
}
|
||||
)
|
||||
|
||||
# Add config schema as stage if not empty
|
||||
if descriptor.config_schema:
|
||||
stages.append(
|
||||
{
|
||||
'name': descriptor.id,
|
||||
'label': descriptor.label,
|
||||
'description': descriptor.description,
|
||||
'config': config_schema,
|
||||
}
|
||||
)
|
||||
|
||||
return options, stages
|
||||
@@ -1,268 +0,0 @@
|
||||
"""Agent resource builder for constructing authorized resources."""
|
||||
from __future__ import annotations
|
||||
|
||||
import typing
|
||||
|
||||
from ...core import app
|
||||
from .descriptor import AgentRunnerDescriptor
|
||||
from .context_builder import (
|
||||
AgentResources,
|
||||
ModelResource,
|
||||
ToolResource,
|
||||
KnowledgeBaseResource,
|
||||
StorageResource,
|
||||
)
|
||||
from . import config_schema
|
||||
from .host_models import AgentEventEnvelope, AgentBinding
|
||||
|
||||
|
||||
class AgentResourceBuilder:
|
||||
"""Builder for constructing AgentResources with permission filtering.
|
||||
|
||||
Responsibilities:
|
||||
- Apply 3-layer permission filtering:
|
||||
1. Runner manifest declared permissions
|
||||
2. Pipeline extensions_preference (bound plugins/MCP servers)
|
||||
3. Runner binding config selected resources
|
||||
- Build models list from authorized models
|
||||
- Build tools list from bound plugins/MCP servers
|
||||
- Build knowledge_bases list from config
|
||||
- Build storage and files permissions summary
|
||||
|
||||
Note: This only builds the resource declaration. The actual proxy actions
|
||||
in handler.py must still validate against ctx.resources at runtime.
|
||||
|
||||
Resource field names match the plugin SDK payload:
|
||||
- ModelResource: model_id, model_type, provider
|
||||
- ToolResource: tool_name, tool_type, description
|
||||
- KnowledgeBaseResource: kb_id, kb_name, kb_type
|
||||
- StorageResource: plugin_storage, workspace_storage
|
||||
"""
|
||||
|
||||
ap: app.Application
|
||||
|
||||
def __init__(self, ap: app.Application):
|
||||
self.ap = ap
|
||||
|
||||
async def build_resources_from_binding(
|
||||
self,
|
||||
event: AgentEventEnvelope,
|
||||
binding: AgentBinding,
|
||||
descriptor: AgentRunnerDescriptor,
|
||||
) -> AgentResources:
|
||||
"""Build AgentResources from event and binding.
|
||||
|
||||
This is the main entry point for Protocol v1.
|
||||
|
||||
Args:
|
||||
event: Event envelope
|
||||
binding: Agent binding with resource policy
|
||||
descriptor: Runner descriptor with permissions and capabilities
|
||||
|
||||
Returns:
|
||||
AgentResources dict with filtered resource lists
|
||||
"""
|
||||
# Layer 1: Runner manifest permissions
|
||||
manifest_perms = descriptor.permissions
|
||||
|
||||
# Layer 2: Binding resource policy
|
||||
resource_policy = binding.resource_policy
|
||||
|
||||
# Layer 3: Runner binding config
|
||||
runner_config = binding.runner_config
|
||||
|
||||
# Build each resource category
|
||||
models = await self._build_models_from_binding(
|
||||
manifest_perms, resource_policy, descriptor, runner_config
|
||||
)
|
||||
tools = await self._build_tools_from_binding(
|
||||
manifest_perms, resource_policy, binding
|
||||
)
|
||||
knowledge_bases = await self._build_knowledge_bases_from_binding(
|
||||
manifest_perms, resource_policy, descriptor, runner_config
|
||||
)
|
||||
storage = self._build_storage_from_binding(manifest_perms, binding)
|
||||
|
||||
return {
|
||||
'models': models,
|
||||
'tools': tools,
|
||||
'knowledge_bases': knowledge_bases,
|
||||
'files': [], # Files are populated at runtime
|
||||
'storage': storage,
|
||||
'platform_capabilities': {}, # Reserved for EBA
|
||||
}
|
||||
|
||||
async def _build_models_from_binding(
|
||||
self,
|
||||
manifest_perms: dict[str, list[str]],
|
||||
resource_policy: typing.Any,
|
||||
descriptor: AgentRunnerDescriptor,
|
||||
runner_config: dict[str, typing.Any],
|
||||
) -> list[ModelResource]:
|
||||
"""Build models list from binding."""
|
||||
models: list[ModelResource] = []
|
||||
seen_model_ids: set[str] = set()
|
||||
|
||||
model_perms = manifest_perms.get('models', [])
|
||||
allow_llm = 'invoke' in model_perms or 'stream' in model_perms
|
||||
allow_rerank = 'rerank' in model_perms
|
||||
if not allow_llm and not allow_rerank:
|
||||
return models
|
||||
|
||||
# Get additional model UUID grants from resource policy.
|
||||
allowed_uuids = resource_policy.allowed_model_uuids
|
||||
|
||||
# Add model resources from binding config schema
|
||||
await self._append_config_declared_model_resources(
|
||||
models=models,
|
||||
seen_model_ids=seen_model_ids,
|
||||
descriptor=descriptor,
|
||||
runner_config=runner_config,
|
||||
include_llm=allow_llm,
|
||||
include_rerank=allow_rerank,
|
||||
)
|
||||
|
||||
# Add explicitly allowed models
|
||||
if allowed_uuids and allow_llm:
|
||||
for model_uuid in allowed_uuids:
|
||||
await self._append_llm_model_resource(models, seen_model_ids, model_uuid)
|
||||
|
||||
return models
|
||||
|
||||
async def _build_tools_from_binding(
|
||||
self,
|
||||
manifest_perms: dict[str, list[str]],
|
||||
resource_policy: typing.Any,
|
||||
binding: AgentBinding,
|
||||
) -> list[ToolResource]:
|
||||
"""Build tools list from binding."""
|
||||
tools: list[ToolResource] = []
|
||||
|
||||
# Check manifest permission
|
||||
tool_perms = manifest_perms.get('tools', [])
|
||||
if 'detail' not in tool_perms and 'call' not in tool_perms:
|
||||
return tools
|
||||
|
||||
# Get tool names from resource policy
|
||||
allowed_names = resource_policy.allowed_tool_names
|
||||
|
||||
if allowed_names:
|
||||
for tool_name in allowed_names:
|
||||
tools.append({
|
||||
'tool_name': tool_name,
|
||||
'tool_type': None,
|
||||
'description': None,
|
||||
})
|
||||
|
||||
return tools
|
||||
|
||||
async def _build_knowledge_bases_from_binding(
|
||||
self,
|
||||
manifest_perms: dict[str, list[str]],
|
||||
resource_policy: typing.Any,
|
||||
descriptor: AgentRunnerDescriptor,
|
||||
runner_config: dict[str, typing.Any],
|
||||
) -> list[KnowledgeBaseResource]:
|
||||
"""Build knowledge bases list from binding."""
|
||||
kb_resources: list[KnowledgeBaseResource] = []
|
||||
|
||||
# Check manifest permission
|
||||
kb_perms = manifest_perms.get('knowledge_bases', [])
|
||||
if 'list' not in kb_perms and 'retrieve' not in kb_perms:
|
||||
return kb_resources
|
||||
|
||||
# Get KB UUID grants from schema-defined config fields.
|
||||
kb_uuids = config_schema.extract_knowledge_base_uuids(descriptor, runner_config)
|
||||
|
||||
# Also include resource policy grants.
|
||||
allowed_uuids = resource_policy.allowed_kb_uuids
|
||||
if allowed_uuids:
|
||||
kb_uuids = list(dict.fromkeys([*kb_uuids, *allowed_uuids]))
|
||||
|
||||
for kb_uuid in kb_uuids:
|
||||
try:
|
||||
kb = await self.ap.rag_mgr.get_knowledge_base_by_uuid(kb_uuid)
|
||||
if kb:
|
||||
kb_resources.append({
|
||||
'kb_id': kb_uuid,
|
||||
'kb_name': kb.get_name(),
|
||||
'kb_type': kb.knowledge_base_entity.kb_type if hasattr(kb.knowledge_base_entity, 'kb_type') else None,
|
||||
})
|
||||
except Exception as e:
|
||||
self.ap.logger.warning(f'Failed to build knowledge base resource {kb_uuid}: {e}')
|
||||
|
||||
return kb_resources
|
||||
|
||||
def _build_storage_from_binding(
|
||||
self,
|
||||
manifest_perms: dict[str, list[str]],
|
||||
binding: AgentBinding,
|
||||
) -> StorageResource:
|
||||
"""Build storage permissions from binding."""
|
||||
storage_perms = manifest_perms.get('storage', [])
|
||||
resource_policy = binding.resource_policy
|
||||
|
||||
return {
|
||||
'plugin_storage': 'plugin' in storage_perms and resource_policy.allow_plugin_storage,
|
||||
'workspace_storage': 'workspace' in storage_perms and resource_policy.allow_workspace_storage,
|
||||
}
|
||||
|
||||
async def _append_config_declared_model_resources(
|
||||
self,
|
||||
models: list[ModelResource],
|
||||
seen_model_ids: set[str],
|
||||
descriptor: AgentRunnerDescriptor,
|
||||
runner_config: dict[str, typing.Any],
|
||||
include_llm: bool,
|
||||
include_rerank: bool,
|
||||
) -> None:
|
||||
"""Authorize model-like values selected through DynamicForm fields."""
|
||||
for model_type, model_uuid in config_schema.iter_config_model_refs(descriptor, runner_config):
|
||||
if model_type == 'llm' and include_llm:
|
||||
await self._append_llm_model_resource(models, seen_model_ids, model_uuid)
|
||||
elif model_type == 'rerank' and include_rerank:
|
||||
await self._append_rerank_model_resource(models, seen_model_ids, model_uuid)
|
||||
|
||||
async def _append_llm_model_resource(
|
||||
self,
|
||||
models: list[ModelResource],
|
||||
seen_model_ids: set[str],
|
||||
model_uuid: str | None,
|
||||
) -> None:
|
||||
"""Append an LLM model resource if it exists and has not been added."""
|
||||
if not model_uuid or model_uuid == '__none__' or model_uuid in seen_model_ids:
|
||||
return
|
||||
|
||||
try:
|
||||
model = await self.ap.model_mgr.get_model_by_uuid(model_uuid)
|
||||
if model and model.model_entity:
|
||||
models.append({
|
||||
'model_id': model_uuid,
|
||||
'model_type': getattr(model.model_entity, 'model_type', None),
|
||||
'provider': getattr(model.provider_entity, 'name', None) if hasattr(model, 'provider_entity') else None,
|
||||
})
|
||||
seen_model_ids.add(model_uuid)
|
||||
except Exception as e:
|
||||
self.ap.logger.warning(f'Failed to build LLM model resource {model_uuid}: {e}')
|
||||
|
||||
async def _append_rerank_model_resource(
|
||||
self,
|
||||
models: list[ModelResource],
|
||||
seen_model_ids: set[str],
|
||||
model_uuid: str | None,
|
||||
) -> None:
|
||||
"""Append a rerank model resource if it exists and has not been added."""
|
||||
if not model_uuid or model_uuid == '__none__' or model_uuid in seen_model_ids:
|
||||
return
|
||||
|
||||
try:
|
||||
model = await self.ap.model_mgr.get_rerank_model_by_uuid(model_uuid)
|
||||
if model and model.model_entity:
|
||||
models.append({
|
||||
'model_id': model_uuid,
|
||||
'model_type': getattr(model.model_entity, 'model_type', 'rerank') or 'rerank',
|
||||
'provider': getattr(model.provider_entity, 'name', None) if hasattr(model, 'provider_entity') else None,
|
||||
})
|
||||
seen_model_ids.add(model_uuid)
|
||||
except Exception as e:
|
||||
self.ap.logger.warning(f'Failed to build rerank model resource {model_uuid}: {e}')
|
||||
@@ -1,193 +0,0 @@
|
||||
"""Agent result normalizer for converting AgentRunResult to Pipeline messages."""
|
||||
from __future__ import annotations
|
||||
|
||||
import typing
|
||||
|
||||
from langbot_plugin.api.entities.builtin.provider import message as provider_message
|
||||
|
||||
from ...core import app
|
||||
from .descriptor import AgentRunnerDescriptor
|
||||
from .errors import RunnerExecutionError, RunnerProtocolError
|
||||
|
||||
|
||||
# Maximum size for a single result payload (prevent memory exhaustion)
|
||||
MAX_RESULT_SIZE_BYTES = 1024 * 1024 # 1 MB
|
||||
|
||||
|
||||
class AgentResultNormalizer:
|
||||
"""Normalizer for converting AgentRunResult to Pipeline messages.
|
||||
|
||||
Responsibilities:
|
||||
- Accept only supported result types (message.delta, message.completed, etc.)
|
||||
- Map message.delta -> MessageChunk
|
||||
- Map message.completed -> Message
|
||||
- Map run.completed (with message) -> Message
|
||||
- Handle run.failed as controlled error
|
||||
- Ignore unknown types with warning
|
||||
- Validate result size
|
||||
- Validate message schema
|
||||
|
||||
Accepted result types:
|
||||
- message.delta
|
||||
- message.completed
|
||||
- tool.call.started
|
||||
- tool.call.completed
|
||||
- state.updated
|
||||
- run.completed
|
||||
- run.failed
|
||||
- action.requested (log only, don't execute)
|
||||
"""
|
||||
|
||||
ap: app.Application
|
||||
|
||||
def __init__(self, ap: app.Application):
|
||||
self.ap = ap
|
||||
|
||||
async def normalize(
|
||||
self,
|
||||
result_dict: dict[str, typing.Any],
|
||||
descriptor: AgentRunnerDescriptor,
|
||||
) -> provider_message.Message | provider_message.MessageChunk | None:
|
||||
"""Normalize AgentRunResult to Message or MessageChunk.
|
||||
|
||||
Args:
|
||||
result_dict: Raw result dict from plugin runtime
|
||||
descriptor: Runner descriptor for error context
|
||||
|
||||
Returns:
|
||||
Message, MessageChunk, or None (for non-message events)
|
||||
|
||||
Raises:
|
||||
RunnerExecutionError: On run.failed
|
||||
RunnerProtocolError: On invalid result format
|
||||
"""
|
||||
# Validate result type
|
||||
result_type = result_dict.get('type')
|
||||
if not result_type:
|
||||
raise RunnerProtocolError(descriptor.id, 'Missing result type')
|
||||
|
||||
# Validate result size
|
||||
try:
|
||||
import json
|
||||
result_json = json.dumps(result_dict)
|
||||
if len(result_json) > MAX_RESULT_SIZE_BYTES:
|
||||
self.ap.logger.warning(
|
||||
f'Runner {descriptor.id} result too large ({len(result_json)} bytes), truncating'
|
||||
)
|
||||
# Truncate content if possible
|
||||
data = result_dict.get('data', {})
|
||||
if 'chunk' in data or 'message' in data:
|
||||
content = data.get('chunk', {}).get('content', '') or data.get('message', {}).get('content', '')
|
||||
if isinstance(content, str) and len(content) > 10000:
|
||||
# Keep reasonable length
|
||||
data['chunk'] = {'role': 'assistant', 'content': content[:10000] + '...[truncated]'}
|
||||
except Exception as e:
|
||||
self.ap.logger.warning(f'Failed to validate runner {descriptor.id} result size: {e}')
|
||||
|
||||
# Handle each result type
|
||||
data = result_dict.get('data', {})
|
||||
|
||||
if result_type == 'message.delta':
|
||||
return self._normalize_message_delta(data, descriptor)
|
||||
|
||||
elif result_type == 'message.completed':
|
||||
return self._normalize_message_completed(data, descriptor)
|
||||
|
||||
elif result_type == 'tool.call.started':
|
||||
# Log only, don't yield to pipeline
|
||||
self.ap.logger.debug(
|
||||
f'Runner {descriptor.id} tool call started: {data.get("tool_name", "unknown")}'
|
||||
)
|
||||
return None
|
||||
|
||||
elif result_type == 'tool.call.completed':
|
||||
# Log only, don't yield to pipeline
|
||||
self.ap.logger.debug(
|
||||
f'Runner {descriptor.id} tool call completed: {data.get("tool_name", "unknown")}'
|
||||
)
|
||||
return None
|
||||
|
||||
elif result_type == 'state.updated':
|
||||
# Log for telemetry, don't yield to pipeline
|
||||
# Orchestrator already handles the actual PersistentStateStore update.
|
||||
scope = data.get('scope', 'unknown')
|
||||
key = data.get('key', 'unknown')
|
||||
value_repr = repr(data.get('value', '...'))[:100] # Truncate for log
|
||||
self.ap.logger.debug(
|
||||
f'Runner {descriptor.id} state.updated logged: scope={scope}, key={key}, value={value_repr}'
|
||||
)
|
||||
return None
|
||||
|
||||
elif result_type == 'run.completed':
|
||||
# May include final message
|
||||
if 'message' in data:
|
||||
return self._normalize_message_completed(data, descriptor)
|
||||
# If no message, it's just completion signal
|
||||
return None
|
||||
|
||||
elif result_type == 'run.failed':
|
||||
error_msg = data.get('error', 'Unknown error')
|
||||
error_code = data.get('code', 'unknown')
|
||||
retryable = data.get('retryable', False)
|
||||
raise RunnerExecutionError(
|
||||
descriptor.id,
|
||||
f'{error_msg} (code: {error_code})',
|
||||
retryable=retryable,
|
||||
)
|
||||
|
||||
elif result_type == 'action.requested':
|
||||
# Reserved for EBA - log only, don't execute
|
||||
self.ap.logger.info(
|
||||
f'Runner {descriptor.id} requested action (not executed in current phase): '
|
||||
f'{data.get("action", "unknown")}'
|
||||
)
|
||||
return None
|
||||
|
||||
elif result_type == 'artifact.created':
|
||||
# Log for telemetry, consumed by orchestrator
|
||||
artifact_id = data.get('artifact_id', 'unknown')
|
||||
artifact_type = data.get('artifact_type', 'unknown')
|
||||
self.ap.logger.debug(
|
||||
f'Runner {descriptor.id} artifact.created logged: artifact_id={artifact_id}, type={artifact_type}'
|
||||
)
|
||||
return None
|
||||
|
||||
else:
|
||||
# Unknown type - warn and ignore.
|
||||
self.ap.logger.warning(
|
||||
f'Runner {descriptor.id} returned unknown result type: {result_type}. '
|
||||
f'Expected supported types (message.delta, message.completed, run.completed, run.failed, etc.)'
|
||||
)
|
||||
return None
|
||||
|
||||
def _normalize_message_delta(
|
||||
self,
|
||||
data: dict[str, typing.Any],
|
||||
descriptor: AgentRunnerDescriptor,
|
||||
) -> provider_message.MessageChunk:
|
||||
"""Normalize message.delta to MessageChunk."""
|
||||
chunk_data = data.get('chunk', {})
|
||||
if not chunk_data:
|
||||
raise RunnerProtocolError(descriptor.id, 'message.delta missing chunk data')
|
||||
|
||||
try:
|
||||
chunk = provider_message.MessageChunk.model_validate(chunk_data)
|
||||
return chunk
|
||||
except Exception as e:
|
||||
raise RunnerProtocolError(descriptor.id, f'Invalid chunk schema: {e}')
|
||||
|
||||
def _normalize_message_completed(
|
||||
self,
|
||||
data: dict[str, typing.Any],
|
||||
descriptor: AgentRunnerDescriptor,
|
||||
) -> provider_message.Message:
|
||||
"""Normalize message.completed to Message."""
|
||||
message_data = data.get('message', {})
|
||||
if not message_data:
|
||||
raise RunnerProtocolError(descriptor.id, 'message.completed missing message data')
|
||||
|
||||
try:
|
||||
msg = provider_message.Message.model_validate(message_data)
|
||||
return msg
|
||||
except Exception as e:
|
||||
raise RunnerProtocolError(descriptor.id, f'Invalid message schema: {e}')
|
||||
@@ -1,250 +0,0 @@
|
||||
"""Agent run session registry for proxy action permission validation."""
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import typing
|
||||
import time
|
||||
import threading
|
||||
|
||||
from .context_builder import AgentResources
|
||||
|
||||
|
||||
class AgentRunSessionStatus(typing.TypedDict):
|
||||
"""Status tracking for agent run session."""
|
||||
started_at: int
|
||||
last_activity_at: int
|
||||
|
||||
|
||||
class AgentRunSession(typing.TypedDict):
|
||||
"""Session for an active agent runner execution.
|
||||
|
||||
Stored in AgentRunSessionRegistry for proxy action permission validation.
|
||||
|
||||
Fields:
|
||||
run_id: Unique run identifier (UUID from AgentRunContext)
|
||||
runner_id: Runner descriptor ID (plugin:author/name/runner)
|
||||
query_id: Pipeline query ID
|
||||
plugin_identity: Plugin identifier (author/name) of the runner
|
||||
conversation_id: Conversation ID for history/event access
|
||||
resources: Authorized resources for this run (from AgentResources)
|
||||
permissions: Runner permissions from descriptor (artifacts, history, events, etc.)
|
||||
state_policy: State policy from binding (enable_state, state_scopes)
|
||||
state_context: Context for state API (scope_keys, binding_identity, etc.)
|
||||
status: Session status tracking
|
||||
_authorized_ids: Pre-computed authorized resource IDs for O(1) lookup
|
||||
"""
|
||||
run_id: str
|
||||
runner_id: str
|
||||
query_id: int | None
|
||||
plugin_identity: str # author/name
|
||||
conversation_id: str | None
|
||||
resources: AgentResources
|
||||
permissions: dict[str, list[str]]
|
||||
state_policy: dict[str, typing.Any] # {enable_state: bool, state_scopes: list}
|
||||
state_context: dict[str, typing.Any] # {scope_keys: dict, binding_identity: str, ...}
|
||||
status: AgentRunSessionStatus
|
||||
_authorized_ids: dict[str, set[str]] # Pre-computed sets for O(1) lookup
|
||||
|
||||
|
||||
class AgentRunSessionRegistry:
|
||||
"""Registry for active agent run sessions.
|
||||
|
||||
Host-owned registry for tracking active AgentRunner executions.
|
||||
Used by proxy actions in handler.py to validate resource access.
|
||||
|
||||
Key: run_id (UUID from AgentRunContext)
|
||||
Value: AgentRunSession with authorized resources
|
||||
|
||||
Thread-safe via asyncio.Lock.
|
||||
"""
|
||||
|
||||
_sessions: dict[str, AgentRunSession]
|
||||
_lock: asyncio.Lock
|
||||
|
||||
def __init__(self):
|
||||
self._sessions = {}
|
||||
self._lock = asyncio.Lock()
|
||||
|
||||
async def register(
|
||||
self,
|
||||
run_id: str,
|
||||
runner_id: str,
|
||||
query_id: int | None,
|
||||
plugin_identity: str,
|
||||
resources: AgentResources,
|
||||
conversation_id: str | None = None,
|
||||
permissions: dict[str, list[str]] | None = None,
|
||||
state_policy: dict[str, typing.Any] | None = None,
|
||||
state_context: dict[str, typing.Any] | None = None,
|
||||
) -> None:
|
||||
"""Register a new agent run session.
|
||||
|
||||
Args:
|
||||
run_id: Unique run identifier
|
||||
runner_id: Runner descriptor ID
|
||||
query_id: Pipeline query ID
|
||||
plugin_identity: Plugin identifier (author/name)
|
||||
resources: Authorized resources for this run
|
||||
conversation_id: Conversation ID for history/event access
|
||||
permissions: Runner permissions from descriptor (artifacts, history, events, etc.)
|
||||
state_policy: State policy from binding (enable_state, state_scopes)
|
||||
state_context: Context for state API (scope_keys, binding_identity, etc.)
|
||||
"""
|
||||
now = int(time.time())
|
||||
|
||||
# Normalize permissions to empty dict if None
|
||||
permissions = permissions or {}
|
||||
|
||||
# Normalize state_policy to defaults if None
|
||||
if state_policy is None:
|
||||
state_policy = {'enable_state': True, 'state_scopes': ['conversation', 'actor']}
|
||||
|
||||
# Normalize state_context to empty dict if None
|
||||
state_context = state_context or {}
|
||||
|
||||
# Pre-compute authorized resource IDs for O(1) lookup
|
||||
authorized_ids: dict[str, set[str]] = {
|
||||
'model': {m.get('model_id') for m in resources.get('models', [])},
|
||||
'tool': {t.get('tool_name') for t in resources.get('tools', [])},
|
||||
'knowledge_base': {kb.get('kb_id') for kb in resources.get('knowledge_bases', [])},
|
||||
'file': {f.get('file_id') for f in resources.get('files', [])},
|
||||
}
|
||||
|
||||
# NOTE: state_policy and state_context are stored at session top-level,
|
||||
# NOT in resources. Resources should only contain resource authorization info.
|
||||
session: AgentRunSession = {
|
||||
'run_id': run_id,
|
||||
'runner_id': runner_id,
|
||||
'query_id': query_id,
|
||||
'plugin_identity': plugin_identity,
|
||||
'conversation_id': conversation_id,
|
||||
'resources': resources, # Original AgentResources, no state metadata mixed in
|
||||
'permissions': permissions,
|
||||
'state_policy': state_policy,
|
||||
'state_context': state_context,
|
||||
'status': {
|
||||
'started_at': now,
|
||||
'last_activity_at': now,
|
||||
},
|
||||
'_authorized_ids': authorized_ids,
|
||||
}
|
||||
|
||||
async with self._lock:
|
||||
self._sessions[run_id] = session
|
||||
|
||||
async def unregister(self, run_id: str) -> None:
|
||||
"""Unregister an agent run session.
|
||||
|
||||
Args:
|
||||
run_id: Unique run identifier
|
||||
"""
|
||||
async with self._lock:
|
||||
if run_id in self._sessions:
|
||||
del self._sessions[run_id]
|
||||
|
||||
async def get(self, run_id: str) -> AgentRunSession | None:
|
||||
"""Get session by run_id.
|
||||
|
||||
Args:
|
||||
run_id: Unique run identifier
|
||||
|
||||
Returns:
|
||||
AgentRunSession if found, None otherwise
|
||||
"""
|
||||
async with self._lock:
|
||||
return self._sessions.get(run_id)
|
||||
|
||||
async def update_activity(self, run_id: str) -> None:
|
||||
"""Update last activity timestamp for session.
|
||||
|
||||
Args:
|
||||
run_id: Unique run identifier
|
||||
"""
|
||||
async with self._lock:
|
||||
if run_id in self._sessions:
|
||||
self._sessions[run_id]['status']['last_activity_at'] = int(time.time())
|
||||
|
||||
def is_resource_allowed(
|
||||
self,
|
||||
session: AgentRunSession,
|
||||
resource_type: str,
|
||||
resource_id: str,
|
||||
) -> bool:
|
||||
"""Check if resource access is allowed for this session.
|
||||
|
||||
Uses pre-computed authorized IDs for O(1) lookup.
|
||||
|
||||
Args:
|
||||
session: AgentRunSession to check
|
||||
resource_type: Resource type ('model', 'tool', 'knowledge_base', 'storage', 'file')
|
||||
resource_id: Resource identifier (model_id, tool_name, kb_id, 'plugin'/'workspace', file_key)
|
||||
|
||||
Returns:
|
||||
True if resource is authorized, False otherwise
|
||||
"""
|
||||
authorized_ids = session.get('_authorized_ids', {})
|
||||
|
||||
if resource_type in ('model', 'tool', 'knowledge_base', 'file'):
|
||||
return resource_id in authorized_ids.get(resource_type, set())
|
||||
|
||||
if resource_type == 'storage':
|
||||
storage = session['resources'].get('storage', {})
|
||||
if resource_id == 'plugin':
|
||||
return storage.get('plugin_storage', False)
|
||||
elif resource_id == 'workspace':
|
||||
return storage.get('workspace_storage', False)
|
||||
return False
|
||||
|
||||
return False
|
||||
|
||||
async def list_active_runs(self) -> list[AgentRunSession]:
|
||||
"""List all active run sessions.
|
||||
|
||||
Returns:
|
||||
List of active AgentRunSession dicts
|
||||
"""
|
||||
async with self._lock:
|
||||
return list(self._sessions.values())
|
||||
|
||||
async def cleanup_stale_sessions(self, max_age_seconds: int = 3600) -> int:
|
||||
"""Cleanup sessions that have been inactive for too long.
|
||||
|
||||
Args:
|
||||
max_age_seconds: Maximum inactivity time in seconds (default 1 hour)
|
||||
|
||||
Returns:
|
||||
Number of sessions cleaned up
|
||||
"""
|
||||
now = int(time.time())
|
||||
cleaned = 0
|
||||
|
||||
async with self._lock:
|
||||
stale_run_ids = []
|
||||
for run_id, session in self._sessions.items():
|
||||
last_activity = session['status'].get('last_activity_at', 0)
|
||||
if now - last_activity > max_age_seconds:
|
||||
stale_run_ids.append(run_id)
|
||||
|
||||
for run_id in stale_run_ids:
|
||||
del self._sessions[run_id]
|
||||
cleaned += 1
|
||||
|
||||
return cleaned
|
||||
|
||||
|
||||
# Global registry instance (singleton)
|
||||
_global_registry: AgentRunSessionRegistry | None = None
|
||||
_global_registry_lock = threading.Lock()
|
||||
|
||||
|
||||
def get_session_registry() -> AgentRunSessionRegistry:
|
||||
"""Get global session registry instance (thread-safe singleton).
|
||||
|
||||
Returns:
|
||||
AgentRunSessionRegistry singleton
|
||||
"""
|
||||
global _global_registry
|
||||
with _global_registry_lock:
|
||||
if _global_registry is None:
|
||||
_global_registry = AgentRunSessionRegistry()
|
||||
return _global_registry
|
||||
@@ -1,113 +0,0 @@
|
||||
"""State scope key helpers for AgentRunner host-owned state."""
|
||||
from __future__ import annotations
|
||||
|
||||
import typing
|
||||
|
||||
from .descriptor import AgentRunnerDescriptor
|
||||
from .host_models import AgentBinding, AgentEventEnvelope
|
||||
|
||||
|
||||
VALID_STATE_SCOPES = ('conversation', 'actor', 'subject', 'runner')
|
||||
|
||||
STATE_KEY_ALIASES = {
|
||||
'conversation_id': 'external.conversation_id',
|
||||
}
|
||||
|
||||
|
||||
def normalize_state_key(key: str) -> str:
|
||||
"""Map accepted public aliases to protocol state keys."""
|
||||
return STATE_KEY_ALIASES.get(key, key)
|
||||
|
||||
|
||||
def get_binding_identity(binding: AgentBinding) -> str:
|
||||
"""Return the stable binding identity used for state isolation."""
|
||||
if binding.binding_id:
|
||||
return binding.binding_id
|
||||
|
||||
scope = binding.scope
|
||||
if scope.scope_type and scope.scope_id:
|
||||
return f'{scope.scope_type}:{scope.scope_id}'
|
||||
|
||||
return 'unknown_binding'
|
||||
|
||||
|
||||
def build_state_scope_key(
|
||||
scope: str,
|
||||
event: AgentEventEnvelope,
|
||||
binding: AgentBinding,
|
||||
descriptor: AgentRunnerDescriptor,
|
||||
) -> str | None:
|
||||
"""Build the storage key for one state scope.
|
||||
|
||||
Returns None when the event lacks the identity required by that scope.
|
||||
"""
|
||||
binding_identity = get_binding_identity(binding)
|
||||
|
||||
if scope == 'conversation':
|
||||
if not event.conversation_id:
|
||||
return None
|
||||
parts = [descriptor.id, binding_identity, event.conversation_id]
|
||||
if event.thread_id:
|
||||
parts.append(event.thread_id)
|
||||
return f'conversation:{":".join(parts)}'
|
||||
|
||||
if scope == 'actor':
|
||||
if not event.actor or not event.actor.actor_id:
|
||||
return None
|
||||
parts = [
|
||||
descriptor.id,
|
||||
binding_identity,
|
||||
event.actor.actor_type or 'user',
|
||||
event.actor.actor_id,
|
||||
]
|
||||
return f'actor:{":".join(parts)}'
|
||||
|
||||
if scope == 'subject':
|
||||
if not event.subject or not event.subject.subject_id:
|
||||
return None
|
||||
parts = [
|
||||
descriptor.id,
|
||||
binding_identity,
|
||||
event.subject.subject_type or 'unknown',
|
||||
event.subject.subject_id,
|
||||
]
|
||||
return f'subject:{":".join(parts)}'
|
||||
|
||||
if scope == 'runner':
|
||||
return f'runner:{descriptor.id}:{binding_identity}'
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def build_state_scope_keys(
|
||||
event: AgentEventEnvelope,
|
||||
binding: AgentBinding,
|
||||
descriptor: AgentRunnerDescriptor,
|
||||
) -> dict[str, str]:
|
||||
"""Build all available scope keys for an event/binding pair."""
|
||||
scope_keys: dict[str, str] = {}
|
||||
for scope in VALID_STATE_SCOPES:
|
||||
scope_key = build_state_scope_key(scope, event, binding, descriptor)
|
||||
if scope_key:
|
||||
scope_keys[scope] = scope_key
|
||||
return scope_keys
|
||||
|
||||
|
||||
def build_state_context(
|
||||
event: AgentEventEnvelope,
|
||||
binding: AgentBinding,
|
||||
descriptor: AgentRunnerDescriptor,
|
||||
) -> dict[str, typing.Any]:
|
||||
"""Build the State API context stored in the run session."""
|
||||
return {
|
||||
'scope_keys': build_state_scope_keys(event, binding, descriptor),
|
||||
'binding_identity': get_binding_identity(binding),
|
||||
'bot_id': event.bot_id,
|
||||
'workspace_id': event.workspace_id,
|
||||
'conversation_id': event.conversation_id,
|
||||
'thread_id': event.thread_id,
|
||||
'actor_type': event.actor.actor_type if event.actor else None,
|
||||
'actor_id': event.actor.actor_id if event.actor else None,
|
||||
'subject_type': event.subject.subject_type if event.subject else None,
|
||||
'subject_id': event.subject.subject_id if event.subject else None,
|
||||
}
|
||||
@@ -1,290 +0,0 @@
|
||||
"""Transcript store for writing and querying conversation history."""
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import datetime
|
||||
import typing
|
||||
import uuid
|
||||
|
||||
import sqlalchemy
|
||||
from sqlalchemy.ext.asyncio import AsyncEngine, AsyncSession
|
||||
from sqlalchemy.orm import sessionmaker
|
||||
|
||||
from ...entity.persistence.transcript import Transcript
|
||||
|
||||
|
||||
class TranscriptStore:
|
||||
"""Store for Transcript records.
|
||||
|
||||
Handles writing transcript items and querying them for history API.
|
||||
All methods are async and use the provided database engine.
|
||||
"""
|
||||
|
||||
engine: AsyncEngine
|
||||
|
||||
# Hard limits
|
||||
MAX_CONTENT_LENGTH = 4000
|
||||
HARD_LIMIT = 100
|
||||
|
||||
def __init__(self, engine: AsyncEngine):
|
||||
self.engine = engine
|
||||
self._session_factory = sessionmaker(
|
||||
engine, class_=AsyncSession, expire_on_commit=False
|
||||
)
|
||||
|
||||
async def append_transcript(
|
||||
self,
|
||||
transcript_id: str | None,
|
||||
event_id: str,
|
||||
conversation_id: str,
|
||||
role: str,
|
||||
content: str | None = None,
|
||||
content_json: dict[str, typing.Any] | None = None,
|
||||
artifact_refs: list[dict[str, typing.Any]] | None = None,
|
||||
thread_id: str | None = None,
|
||||
item_type: str = "message",
|
||||
run_id: str | None = None,
|
||||
runner_id: str | None = None,
|
||||
metadata: dict[str, typing.Any] | None = None,
|
||||
) -> str:
|
||||
"""Append a transcript item.
|
||||
|
||||
Args:
|
||||
transcript_id: Unique transcript ID (generated if None)
|
||||
event_id: Source event ID
|
||||
conversation_id: Conversation ID
|
||||
role: Message role (user, assistant, system, tool)
|
||||
content: Text content
|
||||
content_json: Full structured content
|
||||
artifact_refs: Artifact references
|
||||
thread_id: Thread ID
|
||||
item_type: Item type
|
||||
run_id: Run ID that generated this
|
||||
runner_id: Runner ID that generated this
|
||||
metadata: Additional metadata
|
||||
|
||||
Returns:
|
||||
The transcript_id
|
||||
"""
|
||||
if transcript_id is None:
|
||||
transcript_id = str(uuid.uuid4())
|
||||
|
||||
# Truncate content if too long
|
||||
if content and len(content) > self.MAX_CONTENT_LENGTH:
|
||||
content = content[:self.MAX_CONTENT_LENGTH - 3] + "..."
|
||||
|
||||
async with self._session_factory() as session:
|
||||
item = Transcript(
|
||||
transcript_id=transcript_id,
|
||||
event_id=event_id,
|
||||
conversation_id=conversation_id,
|
||||
thread_id=thread_id,
|
||||
role=role,
|
||||
item_type=item_type,
|
||||
content=content,
|
||||
content_json=json.dumps(content_json) if content_json else None,
|
||||
artifact_refs_json=json.dumps(artifact_refs) if artifact_refs else None,
|
||||
seq=0,
|
||||
run_id=run_id,
|
||||
runner_id=runner_id,
|
||||
created_at=datetime.datetime.utcnow(),
|
||||
metadata_json=json.dumps(metadata) if metadata else None,
|
||||
)
|
||||
session.add(item)
|
||||
await session.flush()
|
||||
item.seq = item.id or await self._get_next_seq(conversation_id)
|
||||
await session.commit()
|
||||
|
||||
return transcript_id
|
||||
|
||||
async def page_transcript(
|
||||
self,
|
||||
conversation_id: str,
|
||||
before_seq: int | None = None,
|
||||
after_seq: int | None = None,
|
||||
limit: int = 50,
|
||||
direction: str = "backward",
|
||||
include_artifacts: bool = False,
|
||||
) -> tuple[list[dict[str, typing.Any]], int | None, int | None, bool]:
|
||||
"""Page through transcript items.
|
||||
|
||||
Args:
|
||||
conversation_id: Conversation ID
|
||||
before_seq: Get items before this sequence (backward)
|
||||
after_seq: Get items after this sequence (forward)
|
||||
limit: Maximum items to return (capped at 100)
|
||||
direction: 'backward' (older) or 'forward' (newer)
|
||||
include_artifacts: Include artifact refs
|
||||
|
||||
Returns:
|
||||
Tuple of (items, next_seq, prev_seq, has_more)
|
||||
"""
|
||||
limit = min(limit, self.HARD_LIMIT)
|
||||
|
||||
async with self._session_factory() as session:
|
||||
query = sqlalchemy.select(Transcript).where(
|
||||
Transcript.conversation_id == conversation_id
|
||||
)
|
||||
|
||||
if direction == "backward" and before_seq is not None:
|
||||
query = query.where(Transcript.seq < before_seq)
|
||||
query = query.order_by(Transcript.seq.desc())
|
||||
elif direction == "forward" and after_seq is not None:
|
||||
query = query.where(Transcript.seq > after_seq)
|
||||
query = query.order_by(Transcript.seq.asc())
|
||||
else:
|
||||
# Default: most recent items first (backward from latest)
|
||||
query = query.order_by(Transcript.seq.desc())
|
||||
|
||||
query = query.limit(limit + 1)
|
||||
|
||||
result = await session.execute(query)
|
||||
rows = result.scalars().all()
|
||||
|
||||
items = [self._row_to_dict(row, include_artifacts) for row in rows[:limit]]
|
||||
has_more = len(rows) > limit
|
||||
|
||||
# Calculate cursors
|
||||
next_seq = None
|
||||
prev_seq = None
|
||||
|
||||
if direction == "backward":
|
||||
# Items are in descending order
|
||||
if items:
|
||||
next_seq = items[-1].get('seq') if has_more else None
|
||||
prev_seq = items[0].get('seq')
|
||||
else:
|
||||
# Items are in ascending order
|
||||
if items:
|
||||
next_seq = items[-1].get('seq') if has_more else None
|
||||
prev_seq = items[0].get('seq')
|
||||
|
||||
return items, next_seq, prev_seq, has_more
|
||||
|
||||
async def search_transcript(
|
||||
self,
|
||||
conversation_id: str,
|
||||
query_text: str,
|
||||
filters: dict[str, typing.Any] | None = None,
|
||||
top_k: int = 10,
|
||||
) -> list[dict[str, typing.Any]]:
|
||||
"""Search transcript items.
|
||||
|
||||
Basic implementation using LIKE filtering.
|
||||
|
||||
Args:
|
||||
conversation_id: Conversation ID
|
||||
query_text: Search query
|
||||
filters: Optional filters
|
||||
top_k: Maximum results
|
||||
|
||||
Returns:
|
||||
List of matching items
|
||||
"""
|
||||
async with self._session_factory() as session:
|
||||
query = sqlalchemy.select(Transcript).where(
|
||||
Transcript.conversation_id == conversation_id,
|
||||
Transcript.content.ilike(f"%{query_text}%"),
|
||||
)
|
||||
|
||||
# Apply additional filters
|
||||
if filters:
|
||||
if 'roles' in filters:
|
||||
query = query.where(Transcript.role.in_(filters['roles']))
|
||||
if 'item_types' in filters:
|
||||
query = query.where(Transcript.item_type.in_(filters['item_types']))
|
||||
|
||||
query = query.order_by(Transcript.seq.desc()).limit(top_k)
|
||||
|
||||
result = await session.execute(query)
|
||||
rows = result.scalars().all()
|
||||
|
||||
return [self._row_to_dict(row, include_artifacts=True) for row in rows]
|
||||
|
||||
async def get_latest_cursor(
|
||||
self,
|
||||
conversation_id: str,
|
||||
) -> str | None:
|
||||
"""Get the latest cursor for a conversation.
|
||||
|
||||
Args:
|
||||
conversation_id: Conversation ID
|
||||
|
||||
Returns:
|
||||
Cursor string (seq number), or None if no items
|
||||
"""
|
||||
async with self._session_factory() as session:
|
||||
result = await session.execute(
|
||||
sqlalchemy.select(Transcript.seq)
|
||||
.where(Transcript.conversation_id == conversation_id)
|
||||
.order_by(Transcript.seq.desc())
|
||||
.limit(1)
|
||||
)
|
||||
row = result.scalars().first()
|
||||
if row is None:
|
||||
return None
|
||||
return str(row)
|
||||
|
||||
async def has_history_before(
|
||||
self,
|
||||
conversation_id: str,
|
||||
seq: int,
|
||||
) -> bool:
|
||||
"""Check if there is history before a sequence number.
|
||||
|
||||
Args:
|
||||
conversation_id: Conversation ID
|
||||
seq: Sequence number
|
||||
|
||||
Returns:
|
||||
True if there are items before
|
||||
"""
|
||||
async with self._session_factory() as session:
|
||||
result = await session.execute(
|
||||
sqlalchemy.select(sqlalchemy.func.count())
|
||||
.select_from(Transcript)
|
||||
.where(
|
||||
Transcript.conversation_id == conversation_id,
|
||||
Transcript.seq < seq,
|
||||
)
|
||||
)
|
||||
count = result.scalar()
|
||||
return count > 0
|
||||
|
||||
async def _get_next_seq(self, conversation_id: str) -> int:
|
||||
"""Fallback next sequence number for stores that cannot expose autoincrement IDs."""
|
||||
async with self._session_factory() as session:
|
||||
result = await session.execute(
|
||||
sqlalchemy.select(sqlalchemy.func.max(Transcript.seq))
|
||||
.where(Transcript.conversation_id == conversation_id)
|
||||
)
|
||||
max_seq = result.scalar()
|
||||
return (max_seq or 0) + 1
|
||||
|
||||
def _row_to_dict(
|
||||
self,
|
||||
row: Transcript,
|
||||
include_artifacts: bool = False,
|
||||
) -> dict[str, typing.Any]:
|
||||
"""Convert a Transcript row to dict."""
|
||||
result = {
|
||||
'transcript_id': row.transcript_id,
|
||||
'event_id': row.event_id,
|
||||
'conversation_id': row.conversation_id,
|
||||
'thread_id': row.thread_id,
|
||||
'role': row.role,
|
||||
'item_type': row.item_type,
|
||||
'content': row.content,
|
||||
'content_json': json.loads(row.content_json) if row.content_json else None,
|
||||
'seq': row.seq,
|
||||
'cursor': str(row.seq),
|
||||
'created_at': int(row.created_at.timestamp()) if row.created_at else None,
|
||||
'metadata': json.loads(row.metadata_json) if row.metadata_json else {},
|
||||
}
|
||||
|
||||
if include_artifacts and row.artifact_refs_json:
|
||||
result['artifact_refs'] = json.loads(row.artifact_refs_json)
|
||||
else:
|
||||
result['artifact_refs'] = []
|
||||
|
||||
return result
|
||||
@@ -179,8 +179,6 @@ class AdaptersRouterGroup(group.RouterGroup):
|
||||
"""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
|
||||
|
||||
@@ -208,60 +206,32 @@ class AdaptersRouterGroup(group.RouterGroup):
|
||||
|
||||
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
|
||||
def on_qrcode(qr_data_url: str, _qr_url: str):
|
||||
def _update():
|
||||
session['qr_data_url'] = qr_data_url
|
||||
session['expire_at'] = time.time() + 180
|
||||
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'
|
||||
loop.call_soon_threadsafe(_update)
|
||||
|
||||
result = await client.login(
|
||||
max_retries=1,
|
||||
poll_timeout_ms=180_000,
|
||||
on_qrcode=on_qrcode,
|
||||
)
|
||||
session['status'] = 'success'
|
||||
session['token'] = result.token
|
||||
session['base_url'] = result.base_url
|
||||
session['account_id'] = result.account_id
|
||||
except Exception as e:
|
||||
session['status'] = 'error'
|
||||
session['error'] = str(e)
|
||||
error_message = str(e)
|
||||
if 'expired' in error_message.lower() or 'max retries exceeded' in error_message.lower():
|
||||
session['status'] = 'expired'
|
||||
session['error'] = 'QR code expired'
|
||||
else:
|
||||
session['status'] = 'error'
|
||||
session['error'] = error_message
|
||||
finally:
|
||||
await client.close()
|
||||
|
||||
@@ -295,7 +265,11 @@ class AdaptersRouterGroup(group.RouterGroup):
|
||||
if not session:
|
||||
return self.http_status(404, -1, 'Session not found')
|
||||
|
||||
data = {'status': session['status']}
|
||||
data = {
|
||||
'status': session['status'],
|
||||
'qr_data_url': session['qr_data_url'],
|
||||
'expire_at': session['expire_at'],
|
||||
}
|
||||
|
||||
if session['status'] == 'success':
|
||||
data['token'] = session['token']
|
||||
@@ -305,6 +279,9 @@ class AdaptersRouterGroup(group.RouterGroup):
|
||||
elif session['status'] == 'error':
|
||||
data['error'] = session['error']
|
||||
_weixin_login_sessions.pop(session_id, None)
|
||||
elif session['status'] == 'expired':
|
||||
data['error'] = session['error']
|
||||
_weixin_login_sessions.pop(session_id, None)
|
||||
|
||||
return self.success(data=data)
|
||||
|
||||
|
||||
@@ -140,17 +140,6 @@ 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'],
|
||||
|
||||
@@ -9,8 +9,6 @@ from ....core import app
|
||||
from ....entity.persistence import model as persistence_model
|
||||
from ....entity.persistence import pipeline as persistence_pipeline
|
||||
from ....provider.modelmgr import requester as model_requester
|
||||
from ....agent.runner.config_migration import ConfigMigration
|
||||
from ....agent.runner import config_schema
|
||||
|
||||
|
||||
def _parse_provider_api_keys(provider_dict: dict) -> dict:
|
||||
@@ -42,40 +40,6 @@ class LLMModelsService:
|
||||
def __init__(self, ap: app.Application) -> None:
|
||||
self.ap = ap
|
||||
|
||||
async def _get_runner_descriptor(self, runner_id: str):
|
||||
registry = getattr(self.ap, 'agent_runner_registry', None)
|
||||
if registry is None:
|
||||
return None
|
||||
try:
|
||||
return await registry.get(runner_id, bound_plugins=None)
|
||||
except Exception as e:
|
||||
logger = getattr(self.ap, 'logger', None)
|
||||
if logger:
|
||||
logger.warning(f'Failed to load AgentRunner descriptor while setting default model: {e}')
|
||||
return None
|
||||
|
||||
async def _auto_set_default_pipeline_llm_model(self, pipeline: persistence_pipeline.LegacyPipeline, model_uuid: str):
|
||||
pipeline_config = pipeline.config
|
||||
if not isinstance(pipeline_config, dict):
|
||||
return
|
||||
|
||||
runner_id = ConfigMigration.resolve_runner_id(pipeline_config)
|
||||
if not runner_id:
|
||||
return
|
||||
|
||||
descriptor = await self._get_runner_descriptor(runner_id)
|
||||
if descriptor is None:
|
||||
return
|
||||
|
||||
ai_config = pipeline_config.setdefault('ai', {})
|
||||
runner_configs = ai_config.setdefault('runner_config', {})
|
||||
runner_config = runner_configs.setdefault(runner_id, {})
|
||||
|
||||
if not config_schema.set_empty_llm_model_selection(descriptor, runner_config, model_uuid):
|
||||
return
|
||||
|
||||
await self.ap.pipeline_service.update_pipeline(pipeline.uuid, {'config': pipeline_config})
|
||||
|
||||
async def get_llm_models(self, include_secret: bool = True) -> list[dict]:
|
||||
"""Get all LLM models with provider info"""
|
||||
result = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_model.LLMModel))
|
||||
@@ -145,6 +109,7 @@ class LLMModelsService:
|
||||
self.ap.model_mgr.llm_models.append(runtime_llm_model)
|
||||
|
||||
if auto_set_to_default_pipeline:
|
||||
# set the default pipeline model to this model
|
||||
result = await self.ap.persistence_mgr.execute_async(
|
||||
sqlalchemy.select(persistence_pipeline.LegacyPipeline).where(
|
||||
persistence_pipeline.LegacyPipeline.is_default == True
|
||||
@@ -152,7 +117,15 @@ class LLMModelsService:
|
||||
)
|
||||
pipeline = result.first()
|
||||
if pipeline is not None:
|
||||
await self._auto_set_default_pipeline_llm_model(pipeline, model_data['uuid'])
|
||||
model_config = pipeline.config.get('ai', {}).get('local-agent', {}).get('model', {})
|
||||
if not model_config.get('primary', ''):
|
||||
pipeline_config = pipeline.config
|
||||
pipeline_config['ai']['local-agent']['model'] = {
|
||||
'primary': model_data['uuid'],
|
||||
'fallbacks': [],
|
||||
}
|
||||
pipeline_data = {'config': pipeline_config}
|
||||
await self.ap.pipeline_service.update_pipeline(pipeline.uuid, pipeline_data)
|
||||
|
||||
return model_data['uuid']
|
||||
|
||||
|
||||
@@ -3,22 +3,17 @@ from __future__ import annotations
|
||||
import uuid
|
||||
import json
|
||||
import sqlalchemy
|
||||
import typing
|
||||
|
||||
from ....core import app
|
||||
from ....entity.persistence import pipeline as persistence_pipeline
|
||||
|
||||
# Prefer the official local-agent plugin when it is installed. This is not a
|
||||
# built-in fallback: when no AgentRunner plugin is available, the default
|
||||
# pipeline stays unbound so the UI can guide users to install a runner.
|
||||
PREFERRED_DEFAULT_RUNNER_ID = 'plugin:langbot/local-agent/default'
|
||||
|
||||
|
||||
default_stage_order = [
|
||||
'GroupRespondRuleCheckStage', # 群响应规则检查
|
||||
'BanSessionCheckStage', # 封禁会话检查
|
||||
'PreContentFilterStage', # 内容过滤前置阶段
|
||||
'PreProcessor', # 预处理器
|
||||
'ConversationMessageTruncator', # 会话消息截断器
|
||||
'RequireRateLimitOccupancy', # 请求速率限制占用
|
||||
'MessageProcessor', # 处理器
|
||||
'ReleaseRateLimitOccupancy', # 释放速率限制占用
|
||||
@@ -35,108 +30,11 @@ class PipelineService:
|
||||
def __init__(self, ap: app.Application) -> None:
|
||||
self.ap = ap
|
||||
|
||||
def _get_default_values_from_schema(self, config_schema: list[dict[str, typing.Any]]) -> dict[str, typing.Any]:
|
||||
"""Build runner config defaults from a DynamicForm schema."""
|
||||
defaults: dict[str, typing.Any] = {}
|
||||
for item in config_schema:
|
||||
name = item.get('name')
|
||||
if not name:
|
||||
continue
|
||||
if 'default' in item:
|
||||
defaults[name] = item['default']
|
||||
return defaults
|
||||
|
||||
async def get_default_pipeline_config(self) -> dict[str, typing.Any]:
|
||||
"""Get the default pipeline config, rendering runner defaults from installed plugins."""
|
||||
from ....utils import paths as path_utils
|
||||
|
||||
template_path = path_utils.get_resource_path('templates/default-pipeline-config.json')
|
||||
with open(template_path, 'r', encoding='utf-8') as f:
|
||||
config = json.load(f)
|
||||
|
||||
agent_runner_registry = getattr(self.ap, 'agent_runner_registry', None)
|
||||
if agent_runner_registry is None:
|
||||
return config
|
||||
|
||||
try:
|
||||
runners = await agent_runner_registry.list_runners(bound_plugins=None)
|
||||
except Exception as e:
|
||||
logger = getattr(self.ap, 'logger', None)
|
||||
if logger:
|
||||
logger.warning(f'Failed to load plugin agent runners for default pipeline config: {e}')
|
||||
return config
|
||||
|
||||
if not runners:
|
||||
return config
|
||||
|
||||
selected_runner = next(
|
||||
(runner for runner in runners if runner.id == PREFERRED_DEFAULT_RUNNER_ID),
|
||||
runners[0],
|
||||
)
|
||||
ai_config = config.setdefault('ai', {})
|
||||
runner_config = ai_config.setdefault('runner', {})
|
||||
runner_config['id'] = selected_runner.id
|
||||
runner_config.setdefault('expire-time', 0)
|
||||
|
||||
ai_config['runner_config'] = {
|
||||
selected_runner.id: self._get_default_values_from_schema(selected_runner.config_schema),
|
||||
}
|
||||
|
||||
return config
|
||||
|
||||
async def get_pipeline_metadata(self) -> list[dict]:
|
||||
"""Get pipeline metadata with dynamically loaded plugin runners from registry"""
|
||||
import copy
|
||||
|
||||
# Deep copy AI metadata to avoid modifying the original
|
||||
ai_metadata = copy.deepcopy(self.ap.pipeline_config_meta_ai)
|
||||
|
||||
# Find the runner stage
|
||||
runner_stage = None
|
||||
for stage in ai_metadata.get('stages', []):
|
||||
if stage.get('name') == 'runner':
|
||||
runner_stage = stage
|
||||
break
|
||||
|
||||
if runner_stage:
|
||||
# Find the runner select config (now uses 'id' field)
|
||||
for config_item in runner_stage.get('config', []):
|
||||
if config_item.get('name') == 'id':
|
||||
# Get plugin agent runners from registry
|
||||
try:
|
||||
(
|
||||
runner_options,
|
||||
runner_stages,
|
||||
) = await self.ap.agent_runner_registry.get_runner_metadata_for_pipeline()
|
||||
|
||||
# Replace options entirely with registry options
|
||||
# Only installed/available runners should be shown
|
||||
config_item['options'] = runner_options
|
||||
|
||||
# Prefer the official local-agent plugin when installed; otherwise use the first
|
||||
# discoverable runner. If no runner is available, leave the default unset so the
|
||||
# UI can recommend installing an AgentRunner plugin, similar to the RAG flow.
|
||||
if runner_options and 'default' not in config_item:
|
||||
default_option = next(
|
||||
(option for option in runner_options if option['name'] == PREFERRED_DEFAULT_RUNNER_ID),
|
||||
runner_options[0],
|
||||
)
|
||||
config_item['default'] = default_option['name']
|
||||
|
||||
# Add corresponding stage configuration for each runner
|
||||
for stage_config in runner_stages:
|
||||
# Avoid duplicate stages
|
||||
existing_stage_names = {s.get('name') for s in ai_metadata.get('stages', [])}
|
||||
if stage_config['name'] not in existing_stage_names:
|
||||
ai_metadata['stages'].append(stage_config)
|
||||
|
||||
except Exception as e:
|
||||
self.ap.logger.warning(f'Failed to load plugin agent runners from registry: {e}')
|
||||
|
||||
return [
|
||||
self.ap.pipeline_config_meta_trigger,
|
||||
self.ap.pipeline_config_meta_safety,
|
||||
ai_metadata,
|
||||
self.ap.pipeline_config_meta_ai,
|
||||
self.ap.pipeline_config_meta_output,
|
||||
]
|
||||
|
||||
@@ -176,6 +74,8 @@ class PipelineService:
|
||||
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
|
||||
|
||||
# Check limitation
|
||||
limitation = self.ap.instance_config.data.get('system', {}).get('limitation', {})
|
||||
max_pipelines = limitation.get('max_pipelines', -1)
|
||||
@@ -189,7 +89,9 @@ class PipelineService:
|
||||
pipeline_data['stages'] = default_stage_order.copy()
|
||||
pipeline_data['is_default'] = default
|
||||
|
||||
pipeline_data['config'] = await self.get_default_pipeline_config()
|
||||
template_path = path_utils.get_resource_path('templates/default-pipeline-config.json')
|
||||
with open(template_path, 'r', encoding='utf-8') as f:
|
||||
pipeline_data['config'] = json.load(f)
|
||||
|
||||
# Ensure extensions_preferences is set with enable_all_plugins and enable_all_mcp_servers=True by default
|
||||
if 'extensions_preferences' not in pipeline_data:
|
||||
@@ -211,16 +113,10 @@ class PipelineService:
|
||||
return pipeline_data['uuid']
|
||||
|
||||
async def update_pipeline(self, pipeline_uuid: str, pipeline_data: dict) -> None:
|
||||
from ....agent.runner.config_migration import ConfigMigration
|
||||
|
||||
pipeline_data = pipeline_data.copy()
|
||||
for protected_field in ('uuid', 'for_version', 'stages', 'is_default'):
|
||||
pipeline_data.pop(protected_field, None)
|
||||
|
||||
# Migrate config to new format before saving
|
||||
if 'config' in pipeline_data:
|
||||
pipeline_data['config'] = ConfigMigration.migrate_pipeline_config(pipeline_data['config'])
|
||||
|
||||
await self.ap.persistence_mgr.execute_async(
|
||||
sqlalchemy.update(persistence_pipeline.LegacyPipeline)
|
||||
.where(persistence_pipeline.LegacyPipeline.uuid == pipeline_uuid)
|
||||
|
||||
@@ -4,7 +4,6 @@ import logging
|
||||
import asyncio
|
||||
import traceback
|
||||
import os
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
from ..platform import botmgr as im_mgr
|
||||
from ..platform.webhook_pusher import WebhookPusher
|
||||
@@ -45,9 +44,6 @@ from ..vector import mgr as vectordb_mgr
|
||||
from ..telemetry import telemetry as telemetry_module
|
||||
from ..survey import manager as survey_module
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from ..agent.runner import AgentRunnerRegistry, AgentRunOrchestrator
|
||||
|
||||
|
||||
class Application:
|
||||
"""Runtime application object and context"""
|
||||
@@ -162,11 +158,6 @@ class Application:
|
||||
|
||||
maintenance_service: maintenance_service.MaintenanceService = None
|
||||
|
||||
# Agent runner subsystem
|
||||
agent_runner_registry: AgentRunnerRegistry = None
|
||||
|
||||
agent_run_orchestrator: AgentRunOrchestrator = None
|
||||
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
|
||||
22
src/langbot/pkg/core/migrations/m009_msg_truncator_cfg.py
Normal file
22
src/langbot/pkg/core/migrations/m009_msg_truncator_cfg.py
Normal file
@@ -0,0 +1,22 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from .. import migration
|
||||
|
||||
|
||||
@migration.migration_class('msg-truncator-cfg-migration', 9)
|
||||
class MsgTruncatorConfigMigration(migration.Migration):
|
||||
"""迁移"""
|
||||
|
||||
async def need_migrate(self) -> bool:
|
||||
"""判断当前环境是否需要运行此迁移"""
|
||||
return 'msg-truncate' not in self.ap.pipeline_cfg.data
|
||||
|
||||
async def run(self):
|
||||
"""执行迁移"""
|
||||
|
||||
self.ap.pipeline_cfg.data['msg-truncate'] = {
|
||||
'method': 'round',
|
||||
'round': {'max-round': 10},
|
||||
}
|
||||
|
||||
await self.ap.pipeline_cfg.dump_config()
|
||||
@@ -36,7 +36,6 @@ from ...vector import mgr as vectordb_mgr
|
||||
from .. import taskmgr
|
||||
from ...telemetry import telemetry as telemetry_module
|
||||
from ...survey import manager as survey_module
|
||||
from ...agent.runner import AgentRunnerRegistry, AgentRunOrchestrator
|
||||
|
||||
|
||||
@stage.stage_class('BuildAppStage')
|
||||
@@ -180,12 +179,5 @@ class BuildAppStage(stage.BootingStage):
|
||||
await plugin_connector_inst.initialize()
|
||||
ap.plugin_connector = plugin_connector_inst
|
||||
|
||||
# Initialize agent runner subsystem
|
||||
agent_runner_registry_inst = AgentRunnerRegistry(ap)
|
||||
ap.agent_runner_registry = agent_runner_registry_inst
|
||||
|
||||
agent_run_orchestrator_inst = AgentRunOrchestrator(ap, agent_runner_registry_inst)
|
||||
ap.agent_run_orchestrator = agent_run_orchestrator_inst
|
||||
|
||||
ctrl = controller.Controller(ap)
|
||||
ap.ctrl = ctrl
|
||||
|
||||
@@ -1,88 +0,0 @@
|
||||
"""Agent runner state persistence entity for host-owned state."""
|
||||
from __future__ import annotations
|
||||
|
||||
import sqlalchemy
|
||||
import datetime
|
||||
|
||||
from .base import Base
|
||||
|
||||
|
||||
class AgentRunnerState(Base):
|
||||
"""AgentRunnerState stores host-owned state for AgentRunner protocol.
|
||||
|
||||
State is:
|
||||
- Host-owned: Managed by LangBot, not by plugin instances
|
||||
- Scope-isolated: Separated by runner_id + binding_identity + scope
|
||||
- Policy-enforced: Controlled by StatePolicy (enable_state, state_scopes)
|
||||
|
||||
Scope key design:
|
||||
- conversation: runner_id + binding_id + conversation_id [+ thread_id]
|
||||
- actor: runner_id + binding_id + actor_type + actor_id
|
||||
- subject: runner_id + binding_id + subject_type + subject_id
|
||||
- runner: runner_id + binding_id
|
||||
|
||||
This table is the production store for AgentRunner state.
|
||||
"""
|
||||
|
||||
__tablename__ = 'agent_runner_state'
|
||||
|
||||
id = sqlalchemy.Column(sqlalchemy.Integer, primary_key=True, autoincrement=True)
|
||||
"""Auto-increment ID for sequencing."""
|
||||
|
||||
# Identity
|
||||
runner_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True)
|
||||
"""Runner descriptor ID (plugin:author/name/runner)."""
|
||||
|
||||
binding_identity = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True)
|
||||
"""Binding identity for isolation (binding_id or scope_type:scope_id)."""
|
||||
|
||||
scope = sqlalchemy.Column(sqlalchemy.String(50), nullable=False, index=True)
|
||||
"""State scope: 'conversation', 'actor', 'subject', or 'runner'."""
|
||||
|
||||
scope_key = sqlalchemy.Column(sqlalchemy.String(512), nullable=False, index=True)
|
||||
"""Full scope key for unique lookup (includes all identity parts)."""
|
||||
|
||||
state_key = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
|
||||
"""State key within scope (should use namespace prefix like external.*)."""
|
||||
|
||||
value_json = sqlalchemy.Column(sqlalchemy.Text, nullable=True)
|
||||
"""State value as JSON string (size-limited by host)."""
|
||||
|
||||
# Context fields for querying/filtering
|
||||
bot_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True)
|
||||
"""Bot UUID if applicable."""
|
||||
|
||||
workspace_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
|
||||
"""Workspace ID for multi-tenant."""
|
||||
|
||||
conversation_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True)
|
||||
"""Conversation ID for conversation scope."""
|
||||
|
||||
thread_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
|
||||
"""Thread ID for thread-scoped conversation state."""
|
||||
|
||||
actor_type = sqlalchemy.Column(sqlalchemy.String(50), nullable=True)
|
||||
"""Actor type for actor scope."""
|
||||
|
||||
actor_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True)
|
||||
"""Actor ID for actor scope."""
|
||||
|
||||
subject_type = sqlalchemy.Column(sqlalchemy.String(50), nullable=True)
|
||||
"""Subject type for subject scope."""
|
||||
|
||||
subject_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
|
||||
"""Subject ID for subject scope."""
|
||||
|
||||
# Lifecycle
|
||||
created_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, default=datetime.datetime.utcnow)
|
||||
"""When this state entry was created."""
|
||||
|
||||
updated_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, default=datetime.datetime.utcnow, onupdate=datetime.datetime.utcnow)
|
||||
"""When this state entry was last updated."""
|
||||
|
||||
# Unique constraint: scope_key + state_key
|
||||
__table_args__ = (
|
||||
sqlalchemy.UniqueConstraint('scope_key', 'state_key', name='uq_agent_runner_state_scope_key_state_key'),
|
||||
sqlalchemy.Index('ix_agent_runner_state_runner_binding', 'runner_id', 'binding_identity'),
|
||||
sqlalchemy.Index('ix_agent_runner_state_scope_key_lookup', 'scope_key'),
|
||||
)
|
||||
@@ -1,77 +0,0 @@
|
||||
"""Artifact persistence entity for Host-owned artifact store."""
|
||||
from __future__ import annotations
|
||||
|
||||
import sqlalchemy
|
||||
import datetime
|
||||
|
||||
from .base import Base
|
||||
|
||||
|
||||
class AgentArtifact(Base):
|
||||
"""AgentArtifact stores metadata for large files, images, tool results, etc.
|
||||
|
||||
This table only stores metadata. The actual blob content is stored in
|
||||
BinaryStorage or external storage, referenced by storage_key.
|
||||
|
||||
Artifacts are accessed via artifact_metadata and artifact_read APIs
|
||||
with run_id authorization.
|
||||
"""
|
||||
|
||||
__tablename__ = 'agent_artifact'
|
||||
|
||||
id = sqlalchemy.Column(sqlalchemy.Integer, primary_key=True, autoincrement=True)
|
||||
"""Auto-increment ID for sequencing."""
|
||||
|
||||
artifact_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, unique=True, index=True)
|
||||
"""Unique artifact identifier."""
|
||||
|
||||
artifact_type = sqlalchemy.Column(sqlalchemy.String(50), nullable=False)
|
||||
"""Artifact type: 'image', 'file', 'voice', 'tool_result', 'platform_attachment', etc."""
|
||||
|
||||
mime_type = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
|
||||
"""MIME type of the content."""
|
||||
|
||||
name = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
|
||||
"""Original file name (if applicable)."""
|
||||
|
||||
size_bytes = sqlalchemy.Column(sqlalchemy.BigInteger, nullable=True)
|
||||
"""Size in bytes."""
|
||||
|
||||
sha256 = sqlalchemy.Column(sqlalchemy.String(64), nullable=True)
|
||||
"""SHA256 hash of content (for integrity verification)."""
|
||||
|
||||
source = sqlalchemy.Column(sqlalchemy.String(50), nullable=False)
|
||||
"""Source of artifact: 'platform', 'runner', 'tool', 'system'."""
|
||||
|
||||
# Storage reference (points to BinaryStorage or external storage)
|
||||
storage_key = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
|
||||
"""Key in BinaryStorage or external storage reference."""
|
||||
|
||||
storage_type = sqlalchemy.Column(sqlalchemy.String(50), nullable=False, default='binary_storage')
|
||||
"""Storage type: 'binary_storage', 'file', 'url', etc."""
|
||||
|
||||
# Context
|
||||
conversation_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True)
|
||||
"""Conversation this artifact belongs to."""
|
||||
|
||||
run_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True)
|
||||
"""Run ID that created this artifact."""
|
||||
|
||||
runner_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
|
||||
"""Runner ID that created this artifact."""
|
||||
|
||||
bot_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
|
||||
"""Bot UUID that handled this artifact."""
|
||||
|
||||
workspace_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
|
||||
"""Workspace ID for multi-tenant deployments."""
|
||||
|
||||
# Lifecycle
|
||||
created_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, default=datetime.datetime.utcnow)
|
||||
"""When this artifact was created."""
|
||||
|
||||
expires_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=True)
|
||||
"""When this artifact expires (optional)."""
|
||||
|
||||
metadata_json = sqlalchemy.Column(sqlalchemy.Text, nullable=True)
|
||||
"""Additional metadata as JSON string."""
|
||||
@@ -1,85 +0,0 @@
|
||||
"""EventLog persistence entity for storing auditable event facts."""
|
||||
from __future__ import annotations
|
||||
|
||||
import sqlalchemy
|
||||
import datetime
|
||||
|
||||
from .base import Base
|
||||
|
||||
|
||||
class EventLog(Base):
|
||||
"""EventLog stores auditable event records for AgentRunner.
|
||||
|
||||
This is the fact source for events - messages, tool calls, system events, etc.
|
||||
Large payloads are stored separately as artifacts; this table stores
|
||||
references and summaries.
|
||||
"""
|
||||
|
||||
__tablename__ = 'event_log'
|
||||
|
||||
id = sqlalchemy.Column(sqlalchemy.Integer, primary_key=True, autoincrement=True)
|
||||
"""Auto-increment ID for sequencing."""
|
||||
|
||||
event_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, unique=True, index=True)
|
||||
"""Unique event identifier."""
|
||||
|
||||
event_type = sqlalchemy.Column(sqlalchemy.String(100), nullable=False, index=True)
|
||||
"""Event type (message.received, tool.call.started, etc.)."""
|
||||
|
||||
event_time = sqlalchemy.Column(sqlalchemy.DateTime, nullable=True)
|
||||
"""When the event occurred."""
|
||||
|
||||
source = sqlalchemy.Column(sqlalchemy.String(50), nullable=False)
|
||||
"""Event source (platform, webui, api, scheduler, system, pipeline_adapter)."""
|
||||
|
||||
bot_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True)
|
||||
"""Bot UUID that handled this event."""
|
||||
|
||||
workspace_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
|
||||
"""Workspace ID for multi-tenant deployments."""
|
||||
|
||||
conversation_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True)
|
||||
"""Conversation ID this event belongs to."""
|
||||
|
||||
thread_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
|
||||
"""Thread ID if platform supports threads."""
|
||||
|
||||
# Actor information
|
||||
actor_type = sqlalchemy.Column(sqlalchemy.String(50), nullable=True)
|
||||
"""Actor type (user, system, runner)."""
|
||||
|
||||
actor_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
|
||||
"""Actor identifier."""
|
||||
|
||||
actor_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
|
||||
"""Actor display name."""
|
||||
|
||||
# Subject information
|
||||
subject_type = sqlalchemy.Column(sqlalchemy.String(50), nullable=True)
|
||||
"""Subject type (message, tool_call, artifact)."""
|
||||
|
||||
subject_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
|
||||
"""Subject identifier."""
|
||||
|
||||
# Input information
|
||||
input_summary = sqlalchemy.Column(sqlalchemy.Text, nullable=True)
|
||||
"""Brief summary of input (truncated text, max 1000 chars)."""
|
||||
|
||||
input_json = sqlalchemy.Column(sqlalchemy.Text, nullable=True)
|
||||
"""Full input JSON if reasonably sized (AgentInput as JSON string)."""
|
||||
|
||||
# Raw event reference
|
||||
raw_ref = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
|
||||
"""Reference to raw event payload in ArtifactStore."""
|
||||
|
||||
run_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True)
|
||||
"""Run ID that processed this event."""
|
||||
|
||||
runner_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
|
||||
"""Runner ID that processed this event."""
|
||||
|
||||
created_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, default=datetime.datetime.utcnow)
|
||||
"""When this record was created."""
|
||||
|
||||
metadata_json = sqlalchemy.Column(sqlalchemy.Text, nullable=True)
|
||||
"""Additional metadata as JSON string."""
|
||||
@@ -1,72 +0,0 @@
|
||||
"""Transcript persistence entity for conversation history projection."""
|
||||
from __future__ import annotations
|
||||
|
||||
import sqlalchemy
|
||||
import datetime
|
||||
|
||||
from .base import Base
|
||||
|
||||
|
||||
class Transcript(Base):
|
||||
"""Transcript stores conversation-oriented message projection for history API.
|
||||
|
||||
This is a projection of EventLog, optimized for agent history retrieval.
|
||||
It includes message content and artifact refs, but not raw platform payloads.
|
||||
"""
|
||||
|
||||
__tablename__ = 'transcript'
|
||||
|
||||
id = sqlalchemy.Column(sqlalchemy.Integer, primary_key=True, autoincrement=True)
|
||||
"""Auto-increment ID for sequencing."""
|
||||
|
||||
transcript_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, unique=True, index=True)
|
||||
"""Unique transcript item identifier."""
|
||||
|
||||
event_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True)
|
||||
"""Reference to the source event in EventLog."""
|
||||
|
||||
conversation_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True)
|
||||
"""Conversation this item belongs to."""
|
||||
|
||||
thread_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
|
||||
"""Thread ID if platform supports threads."""
|
||||
|
||||
role = sqlalchemy.Column(sqlalchemy.String(50), nullable=False)
|
||||
"""Message role: 'user', 'assistant', 'system', or 'tool'."""
|
||||
|
||||
item_type = sqlalchemy.Column(sqlalchemy.String(50), nullable=False, default='message')
|
||||
"""Item type: 'message', 'tool_call', 'tool_result', 'system'."""
|
||||
|
||||
# Content
|
||||
content = sqlalchemy.Column(sqlalchemy.Text, nullable=True)
|
||||
"""Text content summary (may be truncated for large messages, max 4000 chars)."""
|
||||
|
||||
content_json = sqlalchemy.Column(sqlalchemy.Text, nullable=True)
|
||||
"""Full structured content as JSON string (Message model dump)."""
|
||||
|
||||
# Artifact references
|
||||
artifact_refs_json = sqlalchemy.Column(sqlalchemy.Text, nullable=True)
|
||||
"""Artifact references as JSON string (list of ArtifactRef)."""
|
||||
|
||||
# Sequence for cursor-based pagination
|
||||
seq = sqlalchemy.Column(sqlalchemy.Integer, nullable=False, index=True)
|
||||
"""Monotonic cursor sequence for pagination."""
|
||||
|
||||
# Context
|
||||
run_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True)
|
||||
"""Run ID that generated this item (for assistant messages)."""
|
||||
|
||||
runner_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
|
||||
"""Runner ID that generated this item."""
|
||||
|
||||
created_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, default=datetime.datetime.utcnow)
|
||||
"""When this item was created."""
|
||||
|
||||
metadata_json = sqlalchemy.Column(sqlalchemy.Text, nullable=True)
|
||||
"""Additional metadata as JSON string (sender_id, platform, etc.)."""
|
||||
|
||||
# Indexes
|
||||
__table_args__ = (
|
||||
sqlalchemy.Index('ix_transcript_conversation_seq', 'conversation_id', 'seq'),
|
||||
sqlalchemy.Index('ix_transcript_conversation_created', 'conversation_id', 'created_at'),
|
||||
)
|
||||
@@ -13,28 +13,6 @@ from sqlalchemy.engine import Connection
|
||||
|
||||
from langbot.pkg.entity.persistence.base import Base
|
||||
|
||||
# Import all ORM models so they are registered with Base.metadata
|
||||
# This is required for autogenerate to detect model changes
|
||||
from langbot.pkg.entity.persistence import (
|
||||
agent_runner_state,
|
||||
apikey,
|
||||
artifact,
|
||||
bot,
|
||||
bstorage,
|
||||
event_log,
|
||||
mcp,
|
||||
metadata,
|
||||
model,
|
||||
monitoring,
|
||||
pipeline,
|
||||
plugin,
|
||||
rag,
|
||||
transcript,
|
||||
user,
|
||||
vector,
|
||||
webhook,
|
||||
)
|
||||
|
||||
target_metadata = Base.metadata
|
||||
|
||||
|
||||
|
||||
@@ -1,145 +0,0 @@
|
||||
"""Migrate pipeline config to new runner format
|
||||
|
||||
Revision ID: 0004_migrate_runner_config
|
||||
Revises: 0003_add_rerank_models
|
||||
Create Date: 2026-05-10
|
||||
"""
|
||||
|
||||
import json
|
||||
import sqlalchemy as sa
|
||||
from alembic import op
|
||||
|
||||
revision = '0004_migrate_runner_config'
|
||||
down_revision = '0003_add_rerank_models'
|
||||
branch_labels = None
|
||||
depends_on = None
|
||||
|
||||
# Mapping from old built-in runner names to official plugin runner IDs
|
||||
OLD_RUNNER_TO_PLUGIN_RUNNER_ID = {
|
||||
'local-agent': 'plugin:langbot/local-agent/default',
|
||||
'dify-service-api': 'plugin:langbot/dify-agent/default',
|
||||
'n8n-service-api': 'plugin:langbot/n8n-agent/default',
|
||||
'coze-api': 'plugin:langbot/coze-agent/default',
|
||||
'dashscope-app-api': 'plugin:langbot/dashscope-agent/default',
|
||||
'langflow-api': 'plugin:langbot/langflow-agent/default',
|
||||
'tbox-app-api': 'plugin:langbot/tbox-agent/default',
|
||||
}
|
||||
|
||||
|
||||
def is_plugin_runner_id(runner_id: str) -> bool:
|
||||
"""Check if runner ID is in plugin:* format."""
|
||||
return runner_id.startswith('plugin:')
|
||||
|
||||
|
||||
def normalize_runner_config_for_migration(runner_id: str, runner_config: dict) -> dict:
|
||||
"""Normalize released legacy runner fields before storing binding config."""
|
||||
normalized = dict(runner_config)
|
||||
|
||||
if runner_id == OLD_RUNNER_TO_PLUGIN_RUNNER_ID['local-agent']:
|
||||
legacy_kb = normalized.pop('knowledge-base', None)
|
||||
if 'knowledge-bases' not in normalized:
|
||||
if isinstance(legacy_kb, str) and legacy_kb and legacy_kb not in {'__none__', '__none'}:
|
||||
normalized['knowledge-bases'] = [legacy_kb]
|
||||
elif legacy_kb is not None:
|
||||
normalized['knowledge-bases'] = []
|
||||
|
||||
return normalized
|
||||
|
||||
|
||||
def migrate_pipeline_config(config: dict) -> dict:
|
||||
"""Migrate pipeline config to new format."""
|
||||
new_config = dict(config)
|
||||
ai_config = new_config.get('ai', {})
|
||||
if not ai_config:
|
||||
return new_config
|
||||
|
||||
runner_config = ai_config.get('runner', {})
|
||||
runner_configs = ai_config.get('runner_config', {})
|
||||
|
||||
# Check for new format first
|
||||
runner_id = runner_config.get('id')
|
||||
if runner_id and is_plugin_runner_id(runner_id):
|
||||
if runner_id in runner_configs:
|
||||
runner_configs[runner_id] = normalize_runner_config_for_migration(
|
||||
runner_id,
|
||||
runner_configs[runner_id],
|
||||
)
|
||||
ai_config['runner_config'] = runner_configs
|
||||
new_config['ai'] = ai_config
|
||||
return new_config
|
||||
|
||||
# Check for old format
|
||||
old_runner_name = runner_config.get('runner')
|
||||
if old_runner_name:
|
||||
# Map to new runner ID
|
||||
if is_plugin_runner_id(old_runner_name):
|
||||
runner_id = old_runner_name
|
||||
else:
|
||||
runner_id = OLD_RUNNER_TO_PLUGIN_RUNNER_ID.get(old_runner_name, old_runner_name)
|
||||
|
||||
# Set new format
|
||||
runner_config['id'] = runner_id
|
||||
|
||||
# Remove old runner field if it's a mapped built-in runner
|
||||
if old_runner_name in OLD_RUNNER_TO_PLUGIN_RUNNER_ID:
|
||||
del runner_config['runner']
|
||||
|
||||
# Migrate runner-specific config and remove old config blocks
|
||||
if old_runner_name in ai_config:
|
||||
old_runner_config = ai_config[old_runner_name]
|
||||
if old_runner_config:
|
||||
runner_configs[runner_id] = normalize_runner_config_for_migration(runner_id, old_runner_config)
|
||||
# Remove old config block after migration
|
||||
del ai_config[old_runner_name]
|
||||
|
||||
# Also check if runner_id has config under other old name formats
|
||||
for old_name, mapped_id in OLD_RUNNER_TO_PLUGIN_RUNNER_ID.items():
|
||||
if mapped_id == runner_id and old_name in ai_config:
|
||||
runner_configs[runner_id] = normalize_runner_config_for_migration(runner_id, ai_config[old_name])
|
||||
# Remove old config block after migration
|
||||
del ai_config[old_name]
|
||||
|
||||
# Update configs
|
||||
ai_config['runner'] = runner_config
|
||||
ai_config['runner_config'] = runner_configs
|
||||
new_config['ai'] = ai_config
|
||||
|
||||
return new_config
|
||||
|
||||
|
||||
def upgrade() -> None:
|
||||
"""Migrate existing pipeline configs to new runner format."""
|
||||
conn = op.get_bind()
|
||||
inspector = sa.inspect(conn)
|
||||
|
||||
# Check if pipelines table exists (may not exist in fresh install)
|
||||
if 'pipelines' not in inspector.get_table_names():
|
||||
return
|
||||
|
||||
# Get all pipelines
|
||||
result = conn.execute(sa.text('SELECT uuid, config FROM pipelines'))
|
||||
pipelines = result.fetchall()
|
||||
|
||||
for pipeline_uuid, config_json in pipelines:
|
||||
if not config_json:
|
||||
continue
|
||||
|
||||
try:
|
||||
config = json.loads(config_json)
|
||||
migrated_config = migrate_pipeline_config(config)
|
||||
|
||||
# Only update if config changed
|
||||
if json.dumps(config, sort_keys=True) != json.dumps(migrated_config, sort_keys=True):
|
||||
conn.execute(
|
||||
sa.text('UPDATE pipelines SET config = :config WHERE uuid = :uuid'),
|
||||
{'config': json.dumps(migrated_config), 'uuid': pipeline_uuid},
|
||||
)
|
||||
except Exception:
|
||||
# Skip invalid configs
|
||||
continue
|
||||
|
||||
|
||||
def downgrade() -> None:
|
||||
"""Downgrade is not supported for data migration."""
|
||||
# No downgrade - keep configs in new format
|
||||
pass
|
||||
@@ -1,102 +0,0 @@
|
||||
"""add_event_log_and_transcript_tables
|
||||
|
||||
Revision ID: 58846a8d7a81
|
||||
Revises: 0004_migrate_runner_config
|
||||
Create Date: 2026-05-23 15:41:47.030841
|
||||
"""
|
||||
from alembic import op
|
||||
import sqlalchemy as sa
|
||||
|
||||
# revision identifiers
|
||||
revision = '58846a8d7a81'
|
||||
down_revision = '0004_migrate_runner_config'
|
||||
branch_labels = None
|
||||
depends_on = None
|
||||
|
||||
|
||||
def upgrade() -> None:
|
||||
# Create event_log table
|
||||
op.create_table(
|
||||
'event_log',
|
||||
sa.Column('id', sa.Integer(), primary_key=True, autoincrement=True),
|
||||
sa.Column('event_id', sa.String(255), nullable=False, unique=True),
|
||||
sa.Column('event_type', sa.String(100), nullable=False),
|
||||
sa.Column('event_time', sa.DateTime(), nullable=True),
|
||||
sa.Column('source', sa.String(50), nullable=False),
|
||||
sa.Column('bot_id', sa.String(255), nullable=True),
|
||||
sa.Column('workspace_id', sa.String(255), nullable=True),
|
||||
sa.Column('conversation_id', sa.String(255), nullable=True),
|
||||
sa.Column('thread_id', sa.String(255), nullable=True),
|
||||
sa.Column('actor_type', sa.String(50), nullable=True),
|
||||
sa.Column('actor_id', sa.String(255), nullable=True),
|
||||
sa.Column('actor_name', sa.String(255), nullable=True),
|
||||
sa.Column('subject_type', sa.String(50), nullable=True),
|
||||
sa.Column('subject_id', sa.String(255), nullable=True),
|
||||
sa.Column('input_summary', sa.Text(), nullable=True),
|
||||
sa.Column('input_json', sa.Text(), nullable=True),
|
||||
sa.Column('raw_ref', sa.String(255), nullable=True),
|
||||
sa.Column('run_id', sa.String(255), nullable=True),
|
||||
sa.Column('runner_id', sa.String(255), nullable=True),
|
||||
sa.Column('created_at', sa.DateTime(), nullable=False, server_default=sa.text('(CURRENT_TIMESTAMP)')),
|
||||
sa.Column('metadata_json', sa.Text(), nullable=True),
|
||||
)
|
||||
|
||||
# Create indexes for event_log
|
||||
with op.batch_alter_table('event_log', schema=None) as batch_op:
|
||||
batch_op.create_index('ix_event_log_event_id', ['event_id'], unique=True)
|
||||
batch_op.create_index('ix_event_log_event_type', ['event_type'], unique=False)
|
||||
batch_op.create_index('ix_event_log_bot_id', ['bot_id'], unique=False)
|
||||
batch_op.create_index('ix_event_log_conversation_id', ['conversation_id'], unique=False)
|
||||
batch_op.create_index('ix_event_log_run_id', ['run_id'], unique=False)
|
||||
|
||||
# Create transcript table
|
||||
op.create_table(
|
||||
'transcript',
|
||||
sa.Column('id', sa.Integer(), primary_key=True, autoincrement=True),
|
||||
sa.Column('transcript_id', sa.String(255), nullable=False, unique=True),
|
||||
sa.Column('event_id', sa.String(255), nullable=False),
|
||||
sa.Column('conversation_id', sa.String(255), nullable=False),
|
||||
sa.Column('thread_id', sa.String(255), nullable=True),
|
||||
sa.Column('role', sa.String(50), nullable=False),
|
||||
sa.Column('item_type', sa.String(50), nullable=False, server_default='message'),
|
||||
sa.Column('content', sa.Text(), nullable=True),
|
||||
sa.Column('content_json', sa.Text(), nullable=True),
|
||||
sa.Column('artifact_refs_json', sa.Text(), nullable=True),
|
||||
sa.Column('seq', sa.Integer(), nullable=False),
|
||||
sa.Column('run_id', sa.String(255), nullable=True),
|
||||
sa.Column('runner_id', sa.String(255), nullable=True),
|
||||
sa.Column('created_at', sa.DateTime(), nullable=False, server_default=sa.text('(CURRENT_TIMESTAMP)')),
|
||||
sa.Column('metadata_json', sa.Text(), nullable=True),
|
||||
)
|
||||
|
||||
# Create indexes for transcript
|
||||
with op.batch_alter_table('transcript', schema=None) as batch_op:
|
||||
batch_op.create_index('ix_transcript_transcript_id', ['transcript_id'], unique=True)
|
||||
batch_op.create_index('ix_transcript_event_id', ['event_id'], unique=False)
|
||||
batch_op.create_index('ix_transcript_conversation_id', ['conversation_id'], unique=False)
|
||||
batch_op.create_index('ix_transcript_conversation_seq', ['conversation_id', 'seq'], unique=False)
|
||||
batch_op.create_index('ix_transcript_conversation_created', ['conversation_id', 'created_at'], unique=False)
|
||||
batch_op.create_index('ix_transcript_run_id', ['run_id'], unique=False)
|
||||
|
||||
|
||||
def downgrade() -> None:
|
||||
# Drop transcript table
|
||||
with op.batch_alter_table('transcript', schema=None) as batch_op:
|
||||
batch_op.drop_index('ix_transcript_run_id')
|
||||
batch_op.drop_index('ix_transcript_conversation_created')
|
||||
batch_op.drop_index('ix_transcript_conversation_seq')
|
||||
batch_op.drop_index('ix_transcript_conversation_id')
|
||||
batch_op.drop_index('ix_transcript_event_id')
|
||||
batch_op.drop_index('ix_transcript_transcript_id')
|
||||
|
||||
op.drop_table('transcript')
|
||||
|
||||
# Drop event_log table
|
||||
with op.batch_alter_table('event_log', schema=None) as batch_op:
|
||||
batch_op.drop_index('ix_event_log_run_id')
|
||||
batch_op.drop_index('ix_event_log_conversation_id')
|
||||
batch_op.drop_index('ix_event_log_bot_id')
|
||||
batch_op.drop_index('ix_event_log_event_type')
|
||||
batch_op.drop_index('ix_event_log_event_id')
|
||||
|
||||
op.drop_table('event_log')
|
||||
@@ -1,68 +0,0 @@
|
||||
# Alembic script.py.mako — template for auto-generated revisions
|
||||
"""add agent_runner_state table for host-owned persistent state
|
||||
|
||||
Revision ID: 6dfd3dd7f0c7
|
||||
Revises: a1b2c3d4e5f6
|
||||
Create Date: 2026-05-23 19:49:08.529110
|
||||
"""
|
||||
from alembic import op
|
||||
import sqlalchemy as sa
|
||||
|
||||
|
||||
# revision identifiers
|
||||
revision = '6dfd3dd7f0c7'
|
||||
down_revision = 'a1b2c3d4e5f6'
|
||||
branch_labels = None
|
||||
depends_on = None
|
||||
|
||||
|
||||
def upgrade() -> None:
|
||||
# ### commands auto generated by Alembic - please adjust! ###
|
||||
op.create_table('agent_runner_state',
|
||||
sa.Column('id', sa.Integer(), autoincrement=True, nullable=False),
|
||||
sa.Column('runner_id', sa.String(length=255), nullable=False),
|
||||
sa.Column('binding_identity', sa.String(length=255), nullable=False),
|
||||
sa.Column('scope', sa.String(length=50), nullable=False),
|
||||
sa.Column('scope_key', sa.String(length=512), nullable=False),
|
||||
sa.Column('state_key', sa.String(length=255), nullable=False),
|
||||
sa.Column('value_json', sa.Text(), nullable=True),
|
||||
sa.Column('bot_id', sa.String(length=255), nullable=True),
|
||||
sa.Column('workspace_id', sa.String(length=255), nullable=True),
|
||||
sa.Column('conversation_id', sa.String(length=255), nullable=True),
|
||||
sa.Column('thread_id', sa.String(length=255), nullable=True),
|
||||
sa.Column('actor_type', sa.String(length=50), nullable=True),
|
||||
sa.Column('actor_id', sa.String(length=255), nullable=True),
|
||||
sa.Column('subject_type', sa.String(length=50), nullable=True),
|
||||
sa.Column('subject_id', sa.String(length=255), nullable=True),
|
||||
sa.Column('created_at', sa.DateTime(), nullable=False),
|
||||
sa.Column('updated_at', sa.DateTime(), nullable=False),
|
||||
sa.PrimaryKeyConstraint('id'),
|
||||
sa.UniqueConstraint('scope_key', 'state_key', name='uq_agent_runner_state_scope_key_state_key')
|
||||
)
|
||||
with op.batch_alter_table('agent_runner_state', schema=None) as batch_op:
|
||||
batch_op.create_index(batch_op.f('ix_agent_runner_state_actor_id'), ['actor_id'], unique=False)
|
||||
batch_op.create_index(batch_op.f('ix_agent_runner_state_binding_identity'), ['binding_identity'], unique=False)
|
||||
batch_op.create_index(batch_op.f('ix_agent_runner_state_bot_id'), ['bot_id'], unique=False)
|
||||
batch_op.create_index(batch_op.f('ix_agent_runner_state_conversation_id'), ['conversation_id'], unique=False)
|
||||
batch_op.create_index('ix_agent_runner_state_runner_binding', ['runner_id', 'binding_identity'], unique=False)
|
||||
batch_op.create_index(batch_op.f('ix_agent_runner_state_runner_id'), ['runner_id'], unique=False)
|
||||
batch_op.create_index(batch_op.f('ix_agent_runner_state_scope'), ['scope'], unique=False)
|
||||
batch_op.create_index(batch_op.f('ix_agent_runner_state_scope_key'), ['scope_key'], unique=False)
|
||||
|
||||
# ### end Alembic commands ###
|
||||
|
||||
|
||||
def downgrade() -> None:
|
||||
# ### commands auto generated by Alembic - please adjust! ###
|
||||
with op.batch_alter_table('agent_runner_state', schema=None) as batch_op:
|
||||
batch_op.drop_index(batch_op.f('ix_agent_runner_state_scope_key'))
|
||||
batch_op.drop_index(batch_op.f('ix_agent_runner_state_scope'))
|
||||
batch_op.drop_index(batch_op.f('ix_agent_runner_state_runner_id'))
|
||||
batch_op.drop_index('ix_agent_runner_state_runner_binding')
|
||||
batch_op.drop_index(batch_op.f('ix_agent_runner_state_conversation_id'))
|
||||
batch_op.drop_index(batch_op.f('ix_agent_runner_state_bot_id'))
|
||||
batch_op.drop_index(batch_op.f('ix_agent_runner_state_binding_identity'))
|
||||
batch_op.drop_index(batch_op.f('ix_agent_runner_state_actor_id'))
|
||||
|
||||
op.drop_table('agent_runner_state')
|
||||
# ### end Alembic commands ###
|
||||
@@ -1,55 +0,0 @@
|
||||
"""add_agent_artifact_table
|
||||
|
||||
Revision ID: a1b2c3d4e5f6
|
||||
Revises: 58846a8d7a81
|
||||
Create Date: 2026-05-23 20:00:00.000000
|
||||
"""
|
||||
from alembic import op
|
||||
import sqlalchemy as sa
|
||||
|
||||
# revision identifiers
|
||||
revision = 'a1b2c3d4e5f6'
|
||||
down_revision = '58846a8d7a81'
|
||||
branch_labels = None
|
||||
depends_on = None
|
||||
|
||||
|
||||
def upgrade() -> None:
|
||||
# Create agent_artifact table
|
||||
op.create_table(
|
||||
'agent_artifact',
|
||||
sa.Column('id', sa.Integer(), primary_key=True, autoincrement=True),
|
||||
sa.Column('artifact_id', sa.String(255), nullable=False, unique=True),
|
||||
sa.Column('artifact_type', sa.String(50), nullable=False),
|
||||
sa.Column('mime_type', sa.String(255), nullable=True),
|
||||
sa.Column('name', sa.String(255), nullable=True),
|
||||
sa.Column('size_bytes', sa.BigInteger(), nullable=True),
|
||||
sa.Column('sha256', sa.String(64), nullable=True),
|
||||
sa.Column('source', sa.String(50), nullable=False),
|
||||
sa.Column('storage_key', sa.String(255), nullable=True),
|
||||
sa.Column('storage_type', sa.String(50), nullable=False, server_default='binary_storage'),
|
||||
sa.Column('conversation_id', sa.String(255), nullable=True),
|
||||
sa.Column('run_id', sa.String(255), nullable=True),
|
||||
sa.Column('runner_id', sa.String(255), nullable=True),
|
||||
sa.Column('bot_id', sa.String(255), nullable=True),
|
||||
sa.Column('workspace_id', sa.String(255), nullable=True),
|
||||
sa.Column('created_at', sa.DateTime(), nullable=False, server_default=sa.text('(CURRENT_TIMESTAMP)')),
|
||||
sa.Column('expires_at', sa.DateTime(), nullable=True),
|
||||
sa.Column('metadata_json', sa.Text(), nullable=True),
|
||||
)
|
||||
|
||||
# Create indexes for agent_artifact
|
||||
with op.batch_alter_table('agent_artifact', schema=None) as batch_op:
|
||||
batch_op.create_index('ix_agent_artifact_artifact_id', ['artifact_id'], unique=True)
|
||||
batch_op.create_index('ix_agent_artifact_conversation_id', ['conversation_id'], unique=False)
|
||||
batch_op.create_index('ix_agent_artifact_run_id', ['run_id'], unique=False)
|
||||
|
||||
|
||||
def downgrade() -> None:
|
||||
# Drop agent_artifact table
|
||||
with op.batch_alter_table('agent_artifact', schema=None) as batch_op:
|
||||
batch_op.drop_index('ix_agent_artifact_run_id')
|
||||
batch_op.drop_index('ix_agent_artifact_conversation_id')
|
||||
batch_op.drop_index('ix_agent_artifact_artifact_id')
|
||||
|
||||
op.drop_table('agent_artifact')
|
||||
@@ -118,6 +118,9 @@ class DBMigrateV3Config(migration.DBMigration):
|
||||
'runner': self.ap.provider_cfg.data['runner'],
|
||||
}
|
||||
pipeline_config['ai']['local-agent']['model'] = model_uuid
|
||||
pipeline_config['ai']['local-agent']['max-round'] = self.ap.pipeline_cfg.data['msg-truncate']['round'][
|
||||
'max-round'
|
||||
]
|
||||
|
||||
pipeline_config['ai']['local-agent']['prompt'] = [
|
||||
{
|
||||
|
||||
0
src/langbot/pkg/pipeline/msgtrun/__init__.py
Normal file
0
src/langbot/pkg/pipeline/msgtrun/__init__.py
Normal file
35
src/langbot/pkg/pipeline/msgtrun/msgtrun.py
Normal file
35
src/langbot/pkg/pipeline/msgtrun/msgtrun.py
Normal file
@@ -0,0 +1,35 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from .. import stage, entities
|
||||
from . import truncator
|
||||
from ...utils import importutil
|
||||
import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
|
||||
from . import truncators
|
||||
|
||||
importutil.import_modules_in_pkg(truncators)
|
||||
|
||||
|
||||
@stage.stage_class('ConversationMessageTruncator')
|
||||
class ConversationMessageTruncator(stage.PipelineStage):
|
||||
"""Conversation message truncator
|
||||
|
||||
Used to truncate the conversation message chain to adapt to the LLM message length limit.
|
||||
"""
|
||||
|
||||
trun: truncator.Truncator
|
||||
|
||||
async def initialize(self, pipeline_config: dict):
|
||||
use_method = 'round'
|
||||
|
||||
for trun in truncator.preregistered_truncators:
|
||||
if trun.name == use_method:
|
||||
self.trun = trun(self.ap)
|
||||
break
|
||||
else:
|
||||
raise ValueError(f'Unknown truncator: {use_method}')
|
||||
|
||||
async def process(self, query: pipeline_query.Query, stage_inst_name: str) -> entities.StageProcessResult:
|
||||
"""处理"""
|
||||
query = await self.trun.truncate(query)
|
||||
|
||||
return entities.StageProcessResult(result_type=entities.ResultType.CONTINUE, new_query=query)
|
||||
56
src/langbot/pkg/pipeline/msgtrun/truncator.py
Normal file
56
src/langbot/pkg/pipeline/msgtrun/truncator.py
Normal file
@@ -0,0 +1,56 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import typing
|
||||
import abc
|
||||
|
||||
from ...core import app
|
||||
import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
|
||||
|
||||
preregistered_truncators: list[typing.Type[Truncator]] = []
|
||||
|
||||
|
||||
def truncator_class(
|
||||
name: str,
|
||||
) -> typing.Callable[[typing.Type[Truncator]], typing.Type[Truncator]]:
|
||||
"""截断器类装饰器
|
||||
|
||||
Args:
|
||||
name (str): 截断器名称
|
||||
|
||||
Returns:
|
||||
typing.Callable[[typing.Type[Truncator]], typing.Type[Truncator]]: 装饰器
|
||||
"""
|
||||
|
||||
def decorator(cls: typing.Type[Truncator]) -> typing.Type[Truncator]:
|
||||
assert issubclass(cls, Truncator)
|
||||
|
||||
cls.name = name
|
||||
|
||||
preregistered_truncators.append(cls)
|
||||
|
||||
return cls
|
||||
|
||||
return decorator
|
||||
|
||||
|
||||
class Truncator(abc.ABC):
|
||||
"""消息截断器基类"""
|
||||
|
||||
name: str
|
||||
|
||||
ap: app.Application
|
||||
|
||||
def __init__(self, ap: app.Application):
|
||||
self.ap = ap
|
||||
|
||||
async def initialize(self):
|
||||
pass
|
||||
|
||||
@abc.abstractmethod
|
||||
async def truncate(self, query: pipeline_query.Query) -> pipeline_query.Query:
|
||||
"""截断
|
||||
|
||||
一般只需要操作query.messages，也可以扩展操作query.prompt, query.user_message。
|
||||
请勿操作其他字段。
|
||||
"""
|
||||
pass
|
||||
30
src/langbot/pkg/pipeline/msgtrun/truncators/round.py
Normal file
30
src/langbot/pkg/pipeline/msgtrun/truncators/round.py
Normal file
@@ -0,0 +1,30 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from .. import truncator
|
||||
import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
|
||||
|
||||
|
||||
@truncator.truncator_class('round')
|
||||
class RoundTruncator(truncator.Truncator):
|
||||
"""Truncate the conversation message chain to adapt to the LLM message length limit."""
|
||||
|
||||
async def truncate(self, query: pipeline_query.Query) -> pipeline_query.Query:
|
||||
"""截断"""
|
||||
max_round = query.pipeline_config['ai']['local-agent']['max-round']
|
||||
|
||||
temp_messages = []
|
||||
|
||||
current_round = 0
|
||||
|
||||
# Traverse from back to front
|
||||
for msg in query.messages[::-1]:
|
||||
if current_round < max_round:
|
||||
temp_messages.append(msg)
|
||||
if msg.role == 'user':
|
||||
current_round += 1
|
||||
else:
|
||||
break
|
||||
|
||||
query.messages = temp_messages[::-1]
|
||||
|
||||
return query
|
||||
@@ -28,6 +28,7 @@ from . import (
|
||||
wrapper,
|
||||
preproc,
|
||||
ratelimit,
|
||||
msgtrun,
|
||||
)
|
||||
|
||||
importutil.import_modules_in_pkgs(
|
||||
@@ -41,6 +42,7 @@ importutil.import_modules_in_pkgs(
|
||||
wrapper,
|
||||
preproc,
|
||||
ratelimit,
|
||||
msgtrun,
|
||||
]
|
||||
)
|
||||
|
||||
@@ -436,9 +438,6 @@ class PipelineManager:
|
||||
# initialize stage containers according to pipeline_entity.stages
|
||||
stage_containers: list[StageInstContainer] = []
|
||||
for stage_name in pipeline_entity.stages:
|
||||
if stage_name not in self.stage_dict:
|
||||
self.ap.logger.warning(f'Pipeline stage {stage_name} is not registered; skipping')
|
||||
continue
|
||||
stage_containers.append(StageInstContainer(inst_name=stage_name, inst=self.stage_dict[stage_name](self.ap)))
|
||||
|
||||
for stage_container in stage_containers:
|
||||
|
||||
@@ -1,7 +1,6 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import datetime
|
||||
import typing
|
||||
|
||||
from .. import stage, entities
|
||||
from langbot_plugin.api.entities.builtin.provider import message as provider_message
|
||||
@@ -10,14 +9,6 @@ import langbot_plugin.api.entities.builtin.platform.message as platform_message
|
||||
import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
|
||||
import langbot_plugin.api.entities.builtin.platform.events as platform_events
|
||||
|
||||
from ...agent.runner.descriptor import AgentRunnerDescriptor
|
||||
from ...agent.runner.config_migration import ConfigMigration
|
||||
from ...agent.runner import config_schema
|
||||
|
||||
|
||||
DEFAULT_PROMPT_CONFIG = [
|
||||
{'role': 'system', 'content': 'You are a helpful assistant.'},
|
||||
]
|
||||
|
||||
@stage.stage_class('PreProcessor')
|
||||
class PreProcessor(stage.PipelineStage):
|
||||
@@ -34,109 +25,52 @@ class PreProcessor(stage.PipelineStage):
|
||||
- use_funcs
|
||||
"""
|
||||
|
||||
async def _get_runner_descriptor(
|
||||
self,
|
||||
runner_id: str | None,
|
||||
bound_plugins: list[str] | None,
|
||||
) -> AgentRunnerDescriptor | None:
|
||||
if not runner_id:
|
||||
return None
|
||||
|
||||
registry = getattr(self.ap, 'agent_runner_registry', None)
|
||||
if registry is None:
|
||||
return None
|
||||
|
||||
try:
|
||||
return await registry.get(runner_id, bound_plugins)
|
||||
except Exception as e:
|
||||
self.ap.logger.debug(f'Unable to load AgentRunner descriptor for {runner_id}: {e}')
|
||||
return None
|
||||
|
||||
async def _resolve_llm_model(
|
||||
self,
|
||||
primary_uuid: str,
|
||||
) -> typing.Any | None:
|
||||
if primary_uuid in config_schema.NONE_SENTINELS:
|
||||
return None
|
||||
try:
|
||||
return await self.ap.model_mgr.get_model_by_uuid(primary_uuid)
|
||||
except ValueError:
|
||||
self.ap.logger.warning(f'LLM model {primary_uuid} not found or not configured')
|
||||
return None
|
||||
|
||||
async def _resolve_fallback_models(self, fallback_uuids: list[str]) -> list[str]:
|
||||
valid_fallbacks = []
|
||||
for fallback_uuid in fallback_uuids:
|
||||
if fallback_uuid in config_schema.NONE_SENTINELS:
|
||||
continue
|
||||
try:
|
||||
await self.ap.model_mgr.get_model_by_uuid(fallback_uuid)
|
||||
valid_fallbacks.append(fallback_uuid)
|
||||
except ValueError:
|
||||
self.ap.logger.warning(f'Fallback model {fallback_uuid} not found, skipping')
|
||||
return valid_fallbacks
|
||||
|
||||
def _runner_accepts_multimodal_input(self, descriptor: AgentRunnerDescriptor | None) -> bool:
|
||||
if descriptor is None:
|
||||
return True
|
||||
return descriptor.capabilities.get('multimodal_input', False)
|
||||
|
||||
def _model_supports_vision(self, llm_model: typing.Any | None) -> bool:
|
||||
if not llm_model:
|
||||
return False
|
||||
abilities = getattr(getattr(llm_model, 'model_entity', None), 'abilities', [])
|
||||
return 'vision' in abilities
|
||||
|
||||
def _should_keep_image_inputs(
|
||||
self,
|
||||
descriptor: AgentRunnerDescriptor | None,
|
||||
uses_host_models: bool,
|
||||
llm_model: typing.Any | None,
|
||||
) -> bool:
|
||||
if not self._runner_accepts_multimodal_input(descriptor):
|
||||
return False
|
||||
if uses_host_models:
|
||||
return self._model_supports_vision(llm_model)
|
||||
return True
|
||||
|
||||
def _strip_images_from_history(self, query: pipeline_query.Query) -> None:
|
||||
for msg in query.messages:
|
||||
if isinstance(msg.content, list):
|
||||
msg.content = [elem for elem in msg.content if elem.type != 'image_url']
|
||||
|
||||
async def process(
|
||||
self,
|
||||
query: pipeline_query.Query,
|
||||
stage_inst_name: str,
|
||||
) -> entities.StageProcessResult:
|
||||
"""Process"""
|
||||
# Resolve runner ID using ConfigMigration (supports both new and old formats)
|
||||
runner_id = ConfigMigration.resolve_runner_id(query.pipeline_config)
|
||||
|
||||
# Get runner config from ai.runner_config[runner_id].
|
||||
runner_config = ConfigMigration.resolve_runner_config(query.pipeline_config, runner_id) if runner_id else {}
|
||||
query.variables = query.variables or {}
|
||||
bound_plugins = query.variables.get('_pipeline_bound_plugins', None)
|
||||
bound_mcp_servers = query.variables.get('_pipeline_bound_mcp_servers', None)
|
||||
descriptor = await self._get_runner_descriptor(runner_id, bound_plugins)
|
||||
selected_runner = query.pipeline_config['ai']['runner']['runner']
|
||||
|
||||
session = await self.ap.sess_mgr.get_session(query)
|
||||
|
||||
uses_host_models = config_schema.uses_host_models(descriptor)
|
||||
# When not local-agent, llm_model is None
|
||||
llm_model = None
|
||||
if uses_host_models:
|
||||
primary_uuid, fallback_uuids = config_schema.extract_model_selection(descriptor, runner_config)
|
||||
llm_model = await self._resolve_llm_model(primary_uuid)
|
||||
valid_fallbacks = await self._resolve_fallback_models(fallback_uuids)
|
||||
if valid_fallbacks:
|
||||
query.variables['_fallback_model_uuids'] = valid_fallbacks
|
||||
if selected_runner == 'local-agent':
|
||||
# Read model config — new format is { primary: str, fallbacks: [str] },
|
||||
# but handle legacy plain string for backward compatibility
|
||||
model_config = query.pipeline_config['ai']['local-agent'].get('model', {})
|
||||
if isinstance(model_config, str):
|
||||
# Legacy format: plain UUID string
|
||||
primary_uuid = model_config
|
||||
fallback_uuids = []
|
||||
else:
|
||||
primary_uuid = model_config.get('primary', '')
|
||||
fallback_uuids = model_config.get('fallbacks', [])
|
||||
|
||||
prompt_config = config_schema.extract_prompt_config(descriptor, runner_config, DEFAULT_PROMPT_CONFIG)
|
||||
if primary_uuid:
|
||||
try:
|
||||
llm_model = await self.ap.model_mgr.get_model_by_uuid(primary_uuid)
|
||||
except ValueError:
|
||||
self.ap.logger.warning(f'LLM model {primary_uuid} not found or not configured')
|
||||
|
||||
# Resolve fallback model UUIDs
|
||||
if fallback_uuids:
|
||||
valid_fallbacks = []
|
||||
for fb_uuid in fallback_uuids:
|
||||
try:
|
||||
await self.ap.model_mgr.get_model_by_uuid(fb_uuid)
|
||||
valid_fallbacks.append(fb_uuid)
|
||||
except ValueError:
|
||||
self.ap.logger.warning(f'Fallback model {fb_uuid} not found, skipping')
|
||||
if valid_fallbacks:
|
||||
query.variables['_fallback_model_uuids'] = valid_fallbacks
|
||||
|
||||
conversation = await self.ap.sess_mgr.get_conversation(
|
||||
query,
|
||||
session,
|
||||
prompt_config,
|
||||
query.pipeline_config['ai']['local-agent']['prompt'],
|
||||
query.pipeline_uuid,
|
||||
query.bot_uuid,
|
||||
)
|
||||
@@ -145,7 +79,7 @@ class PreProcessor(stage.PipelineStage):
|
||||
# been idle for longer than the configured conversation expire time.
|
||||
# The idle window is measured from the last preprocess/update time, not
|
||||
# from the conversation creation time.
|
||||
conversation_expire_time = ConfigMigration.get_expire_time(query.pipeline_config)
|
||||
conversation_expire_time = query.pipeline_config.get('ai', {}).get('runner', {}).get('expire-time', None)
|
||||
now = datetime.datetime.now()
|
||||
if conversation_expire_time is not None and conversation_expire_time > 0:
|
||||
last_update_time = getattr(conversation, 'update_time', None) or getattr(conversation, 'create_time', None)
|
||||
@@ -167,14 +101,15 @@ class PreProcessor(stage.PipelineStage):
|
||||
query.prompt = conversation.prompt.copy()
|
||||
query.messages = conversation.messages.copy()
|
||||
|
||||
if uses_host_models:
|
||||
if selected_runner == 'local-agent':
|
||||
query.use_funcs = []
|
||||
if llm_model:
|
||||
query.use_llm_model_uuid = llm_model.model_entity.uuid
|
||||
|
||||
if config_schema.uses_host_tools(descriptor) and llm_model.model_entity.abilities.__contains__(
|
||||
'func_call'
|
||||
):
|
||||
if llm_model.model_entity.abilities.__contains__('func_call'):
|
||||
# Get bound plugins and MCP servers for filtering tools
|
||||
bound_plugins = query.variables.get('_pipeline_bound_plugins', None)
|
||||
bound_mcp_servers = query.variables.get('_pipeline_bound_mcp_servers', None)
|
||||
query.use_funcs = await self.ap.tool_mgr.get_all_tools(bound_plugins, bound_mcp_servers)
|
||||
|
||||
self.ap.logger.debug(f'Bound plugins: {bound_plugins}')
|
||||
@@ -183,18 +118,10 @@ class PreProcessor(stage.PipelineStage):
|
||||
|
||||
# If primary model doesn't support func_call but fallback models exist,
|
||||
# load tools anyway since fallback models may support them
|
||||
if (
|
||||
config_schema.uses_host_tools(descriptor)
|
||||
and not query.use_funcs
|
||||
and query.variables.get('_fallback_model_uuids')
|
||||
):
|
||||
if not query.use_funcs and query.variables.get('_fallback_model_uuids'):
|
||||
bound_plugins = query.variables.get('_pipeline_bound_plugins', None)
|
||||
bound_mcp_servers = query.variables.get('_pipeline_bound_mcp_servers', None)
|
||||
query.use_funcs = await self.ap.tool_mgr.get_all_tools(bound_plugins, bound_mcp_servers)
|
||||
elif config_schema.uses_host_tools(descriptor):
|
||||
query.use_funcs = await self.ap.tool_mgr.get_all_tools(bound_plugins, bound_mcp_servers)
|
||||
|
||||
self.ap.logger.debug(f'Bound plugins: {bound_plugins}')
|
||||
self.ap.logger.debug(f'Bound MCP servers: {bound_mcp_servers}')
|
||||
self.ap.logger.debug(f'Use funcs: {query.use_funcs}')
|
||||
|
||||
sender_name = ''
|
||||
|
||||
@@ -219,21 +146,32 @@ class PreProcessor(stage.PipelineStage):
|
||||
}
|
||||
query.variables.update(variables)
|
||||
|
||||
keep_image_inputs = self._should_keep_image_inputs(descriptor, uses_host_models, llm_model)
|
||||
if not keep_image_inputs:
|
||||
self._strip_images_from_history(query)
|
||||
# Check if this model supports vision, if not, remove all images
|
||||
# TODO this checking should be performed in runner, and in this stage, the image should be reserved
|
||||
if (
|
||||
selected_runner == 'local-agent'
|
||||
and llm_model
|
||||
and not llm_model.model_entity.abilities.__contains__('vision')
|
||||
):
|
||||
for msg in query.messages:
|
||||
if isinstance(msg.content, list):
|
||||
for me in msg.content:
|
||||
if me.type == 'image_url':
|
||||
msg.content.remove(me)
|
||||
|
||||
content_list: list[provider_message.ContentElement] = []
|
||||
|
||||
plain_text = ''
|
||||
quote_msg = query.pipeline_config['trigger'].get('misc', {}).get('combine-quote-message', False)
|
||||
quote_msg = query.pipeline_config['trigger'].get('misc', '').get('combine-quote-message')
|
||||
|
||||
for me in query.message_chain:
|
||||
if isinstance(me, platform_message.Plain):
|
||||
content_list.append(provider_message.ContentElement.from_text(me.text))
|
||||
plain_text += me.text
|
||||
elif isinstance(me, platform_message.Image):
|
||||
if keep_image_inputs:
|
||||
if selected_runner != 'local-agent' or (
|
||||
llm_model and llm_model.model_entity.abilities.__contains__('vision')
|
||||
):
|
||||
if me.base64 is not None:
|
||||
content_list.append(provider_message.ContentElement.from_image_base64(me.base64))
|
||||
elif isinstance(me, platform_message.Voice):
|
||||
@@ -252,7 +190,9 @@ class PreProcessor(stage.PipelineStage):
|
||||
if isinstance(msg, platform_message.Plain):
|
||||
content_list.append(provider_message.ContentElement.from_text(msg.text))
|
||||
elif isinstance(msg, platform_message.Image):
|
||||
if keep_image_inputs:
|
||||
if selected_runner != 'local-agent' or (
|
||||
llm_model and llm_model.model_entity.abilities.__contains__('vision')
|
||||
):
|
||||
if msg.base64 is not None:
|
||||
content_list.append(provider_message.ContentElement.from_image_base64(msg.base64))
|
||||
elif isinstance(msg, platform_message.File):
|
||||
@@ -272,12 +212,14 @@ class PreProcessor(stage.PipelineStage):
|
||||
|
||||
query.user_message = provider_message.Message(role='user', content=content_list)
|
||||
|
||||
# Extract configured KB UUIDs into query variables so PromptPreProcessing
|
||||
# plugins can still adjust the authorized retrieval set before run_agent.
|
||||
query.variables['_knowledge_base_uuids'] = config_schema.extract_knowledge_base_uuids(
|
||||
descriptor,
|
||||
runner_config,
|
||||
)
|
||||
# Extract knowledge base UUIDs into query variables so plugins can modify them
|
||||
# during PromptPreProcessing before the runner performs retrieval.
|
||||
kb_uuids = query.pipeline_config['ai']['local-agent'].get('knowledge-bases', [])
|
||||
if not kb_uuids:
|
||||
old_kb_uuid = query.pipeline_config['ai']['local-agent'].get('knowledge-base', '')
|
||||
if old_kb_uuid and old_kb_uuid != '__none__':
|
||||
kb_uuids = [old_kb_uuid]
|
||||
query.variables['_knowledge_base_uuids'] = list(kb_uuids)
|
||||
|
||||
# =========== 触发事件 PromptPreProcessing
|
||||
|
||||
|
||||
@@ -9,28 +9,29 @@ from datetime import datetime
|
||||
|
||||
from .. import handler
|
||||
from ... import entities
|
||||
from ....provider import runner as runner_module
|
||||
|
||||
import langbot_plugin.api.entities.events as events
|
||||
from ....utils import constants, runner as runner_utils
|
||||
from ....utils import importutil, constants, runner as runner_utils
|
||||
from ....provider import runners
|
||||
import langbot_plugin.api.entities.builtin.provider.session as provider_session
|
||||
import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
|
||||
import langbot_plugin.api.entities.builtin.provider.message as provider_message
|
||||
|
||||
|
||||
importutil.import_modules_in_pkg(runners)
|
||||
|
||||
|
||||
class ChatMessageHandler(handler.MessageHandler):
|
||||
"""Chat message handler using AgentRunOrchestrator.
|
||||
|
||||
This handler delegates all runner execution to the agent_run_orchestrator,
|
||||
which resolves runner ID, builds context, invokes plugin runtime,
|
||||
and normalizes results.
|
||||
"""
|
||||
|
||||
async def handle(
|
||||
self,
|
||||
query: pipeline_query.Query,
|
||||
) -> typing.AsyncGenerator[entities.StageProcessResult, None]:
|
||||
"""Handle chat message by delegating to AgentRunOrchestrator."""
|
||||
# Trigger plugin event
|
||||
"""处理"""
|
||||
# 调API
|
||||
# 生成器
|
||||
|
||||
# 触发插件事件
|
||||
event_class = (
|
||||
events.PersonNormalMessageReceived
|
||||
if query.launcher_type == provider_session.LauncherTypes.PERSON
|
||||
@@ -51,7 +52,7 @@ class ChatMessageHandler(handler.MessageHandler):
|
||||
bound_plugins = query.variables.get('_pipeline_bound_plugins', None)
|
||||
event_ctx = await self.ap.plugin_connector.emit_event(event, bound_plugins)
|
||||
|
||||
is_create_card = False # Track if streaming card was created
|
||||
is_create_card = False # 判断下是否需要创建流式卡片
|
||||
|
||||
if event_ctx.is_prevented_default():
|
||||
if event_ctx.event.reply_message_chain is not None:
|
||||
@@ -82,85 +83,83 @@ class ChatMessageHandler(handler.MessageHandler):
|
||||
is_stream = False
|
||||
|
||||
try:
|
||||
for r in runner_module.preregistered_runners:
|
||||
if r.name == query.pipeline_config['ai']['runner']['runner']:
|
||||
runner = r(self.ap, query.pipeline_config)
|
||||
break
|
||||
else:
|
||||
raise ValueError(f'Request Runner not found: {query.pipeline_config["ai"]["runner"]["runner"]}')
|
||||
# Mark start time for telemetry
|
||||
start_ts = time.time()
|
||||
|
||||
# Create a single resp_message_id for the entire streaming response
|
||||
resp_message_id = uuid.uuid4()
|
||||
if is_stream:
|
||||
resp_message_id = uuid.uuid4()
|
||||
chunk_count = 0 # Track streaming chunks to reduce excessive logging
|
||||
|
||||
# Use AgentRunOrchestrator to run the agent
|
||||
# This replaces direct runner lookup and PluginAgentRunnerWrapper
|
||||
async for result in self.ap.agent_run_orchestrator.run_from_query(query):
|
||||
result.resp_message_id = str(resp_message_id)
|
||||
|
||||
# For streaming mode, pop previous response before adding new chunk
|
||||
# This allows incremental card updates
|
||||
if is_stream:
|
||||
async for result in runner.run(query):
|
||||
result.resp_message_id = str(resp_message_id)
|
||||
if query.resp_messages:
|
||||
query.resp_messages.pop()
|
||||
if query.resp_message_chain:
|
||||
query.resp_message_chain.pop()
|
||||
# 此时连接外部 AI 服务正常,创建卡片
|
||||
if not is_create_card: # 只有不是第一次才创建卡片
|
||||
await query.adapter.create_message_card(str(resp_message_id), query.message_event)
|
||||
is_create_card = True
|
||||
query.resp_messages.append(result)
|
||||
|
||||
# Create streaming card on first result (connection established)
|
||||
if is_stream and not is_create_card:
|
||||
await query.adapter.create_message_card(str(resp_message_id), query.message_event)
|
||||
is_create_card = True
|
||||
chunk_count += 1
|
||||
# Only log every 10th chunk to reduce excessive logging during streaming
|
||||
# This prevents memory overflow from thousands of log entries per conversation
|
||||
# First chunk uses INFO level to confirm connection establishment
|
||||
if chunk_count == 1:
|
||||
self.ap.logger.info(
|
||||
f'Conversation({query.query_id}) Streaming started: {self.cut_str(result.readable_str())}'
|
||||
)
|
||||
elif chunk_count % 10 == 0:
|
||||
self.ap.logger.debug(
|
||||
f'Conversation({query.query_id}) Streaming chunk {chunk_count}: {self.cut_str(result.readable_str())}'
|
||||
)
|
||||
|
||||
query.resp_messages.append(result)
|
||||
if result.content is not None:
|
||||
text_length += len(result.content)
|
||||
|
||||
# Logging (reduce verbosity for streaming chunks)
|
||||
if not is_stream:
|
||||
self.ap.logger.info(
|
||||
f'Conversation({query.query_id}) Response: {self.cut_str(result.readable_str())}'
|
||||
)
|
||||
yield entities.StageProcessResult(result_type=entities.ResultType.CONTINUE, new_query=query)
|
||||
|
||||
if result.content is not None:
|
||||
text_length += len(result.content)
|
||||
|
||||
yield entities.StageProcessResult(result_type=entities.ResultType.CONTINUE, new_query=query)
|
||||
|
||||
# Log final summary after streaming completes
|
||||
if is_stream:
|
||||
chunk_count = len(query.resp_messages)
|
||||
# Log final summary after streaming completes
|
||||
self.ap.logger.info(
|
||||
f'Conversation({query.query_id}) Streaming completed: {chunk_count} chunks, {text_length} chars'
|
||||
)
|
||||
|
||||
# Update conversation history
|
||||
else:
|
||||
async for result in runner.run(query):
|
||||
query.resp_messages.append(result)
|
||||
|
||||
self.ap.logger.info(
|
||||
f'Conversation({query.query_id}) Response: {self.cut_str(result.readable_str())}'
|
||||
)
|
||||
|
||||
if result.content is not None:
|
||||
text_length += len(result.content)
|
||||
|
||||
yield entities.StageProcessResult(result_type=entities.ResultType.CONTINUE, new_query=query)
|
||||
|
||||
query.session.using_conversation.messages.append(query.user_message)
|
||||
|
||||
query.session.using_conversation.messages.extend(query.resp_messages)
|
||||
|
||||
except Exception as e:
|
||||
# Import orchestrator errors for specific handling
|
||||
from ....agent.runner.errors import (
|
||||
RunnerNotFoundError,
|
||||
RunnerNotAuthorizedError,
|
||||
RunnerExecutionError,
|
||||
)
|
||||
|
||||
error_info = f'{traceback.format_exc()}'
|
||||
self.ap.logger.error(f'Conversation({query.query_id}) Request Failed: {error_info}')
|
||||
traceback.print_exc()
|
||||
|
||||
# Handle specific runner errors with appropriate messages
|
||||
if isinstance(e, RunnerNotFoundError):
|
||||
user_notice = f'Agent runner not found: {e.runner_id}'
|
||||
elif isinstance(e, RunnerNotAuthorizedError):
|
||||
user_notice = 'Agent runner not authorized for this pipeline'
|
||||
elif isinstance(e, RunnerExecutionError):
|
||||
if e.retryable:
|
||||
user_notice = 'Agent runner temporarily unavailable. Please try again.'
|
||||
else:
|
||||
user_notice = 'Agent runner execution failed.'
|
||||
else:
|
||||
# Use existing exception handling
|
||||
exception_handling = query.pipeline_config['output']['misc'].get('exception-handling', 'show-hint')
|
||||
exception_handling = query.pipeline_config['output']['misc'].get('exception-handling', 'show-hint')
|
||||
|
||||
if exception_handling == 'show-error':
|
||||
user_notice = f'{e}'
|
||||
elif exception_handling == 'show-hint':
|
||||
user_notice = query.pipeline_config['output']['misc'].get('failure-hint', 'Request failed.')
|
||||
else: # hide
|
||||
user_notice = None
|
||||
if exception_handling == 'show-error':
|
||||
user_notice = f'{e}'
|
||||
elif exception_handling == 'show-hint':
|
||||
user_notice = query.pipeline_config['output']['misc'].get('failure-hint', 'Request failed.')
|
||||
else: # hide
|
||||
user_notice = None
|
||||
|
||||
yield entities.StageProcessResult(
|
||||
result_type=entities.ResultType.INTERRUPT,
|
||||
@@ -170,7 +169,7 @@ class ChatMessageHandler(handler.MessageHandler):
|
||||
debug_notice=traceback.format_exc(),
|
||||
)
|
||||
finally:
|
||||
# Telemetry reporting
|
||||
# Telemetry reporting: collect minimal per-query execution info and send asynchronously
|
||||
try:
|
||||
end_ts = time.time()
|
||||
duration_ms = None
|
||||
@@ -178,14 +177,16 @@ class ChatMessageHandler(handler.MessageHandler):
|
||||
duration_ms = int((end_ts - start_ts) * 1000)
|
||||
|
||||
adapter_name = query.adapter.__class__.__name__ if hasattr(query, 'adapter') else None
|
||||
runner_name = (
|
||||
query.pipeline_config.get('ai', {}).get('runner', {}).get('runner')
|
||||
if query.pipeline_config
|
||||
else None
|
||||
)
|
||||
|
||||
# Use orchestrator to resolve runner ID for telemetry
|
||||
runner_name = self.ap.agent_run_orchestrator.resolve_runner_id_for_telemetry(query)
|
||||
|
||||
# Model name if available
|
||||
# Model name if using localagent
|
||||
model_name = None
|
||||
try:
|
||||
if getattr(query, 'use_llm_model_uuid', None):
|
||||
if runner_name == 'local-agent' and getattr(query, 'use_llm_model_uuid', None):
|
||||
m = await self.ap.model_mgr.get_model_by_uuid(query.use_llm_model_uuid)
|
||||
if m and getattr(m, 'model_entity', None):
|
||||
model_name = getattr(m.model_entity, 'name', None)
|
||||
@@ -195,7 +196,7 @@ class ChatMessageHandler(handler.MessageHandler):
|
||||
pipeline_plugins = query.variables.get('_pipeline_bound_plugins', None)
|
||||
|
||||
runner_category = runner_utils.get_runner_category_from_runner(
|
||||
runner_name, None, query.pipeline_config
|
||||
runner_name, runner, query.pipeline_config
|
||||
)
|
||||
|
||||
payload = {
|
||||
@@ -213,6 +214,7 @@ class ChatMessageHandler(handler.MessageHandler):
|
||||
'timestamp': datetime.utcnow().isoformat(),
|
||||
}
|
||||
|
||||
# Send telemetry asynchronously and do not block pipeline via app's telemetry manager
|
||||
await self.ap.telemetry.start_send_task(payload)
|
||||
|
||||
# Trigger survey event on first successful non-WebSocket response
|
||||
@@ -220,4 +222,5 @@ class ChatMessageHandler(handler.MessageHandler):
|
||||
if self.ap.survey:
|
||||
await self.ap.survey.trigger_event('first_bot_response_success')
|
||||
except Exception as ex:
|
||||
self.ap.logger.warning(f'Failed to send telemetry: {ex}')
|
||||
# Ensure telemetry issues do not affect normal flow
|
||||
self.ap.logger.warning(f'Failed to send telemetry: {ex}')
|
||||
|
||||
@@ -3,6 +3,7 @@ import typing
|
||||
import asyncio
|
||||
import traceback
|
||||
import datetime
|
||||
import json
|
||||
|
||||
import aiocqhttp
|
||||
import pydantic
|
||||
@@ -293,6 +294,29 @@ 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)
|
||||
|
||||
|
||||
@@ -84,20 +84,6 @@ class WebPageBotAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter
|
||||
):
|
||||
self.listeners.pop(event_type, None)
|
||||
|
||||
async def is_stream_output_supported(self) -> bool:
|
||||
"""Delegate stream output check to ws_adapter."""
|
||||
if self._ws_adapter is not None:
|
||||
return await self._ws_adapter.is_stream_output_supported()
|
||||
return False
|
||||
|
||||
async def create_message_card(
|
||||
self, message_id: str | int, event: platform_events.MessageEvent
|
||||
) -> bool:
|
||||
"""Delegate create_message_card to ws_adapter."""
|
||||
if self._ws_adapter is not None:
|
||||
return await self._ws_adapter.create_message_card(message_id, event)
|
||||
return False
|
||||
|
||||
async def is_muted(self, group_id: int) -> bool:
|
||||
return False
|
||||
|
||||
|
||||
@@ -194,15 +194,6 @@ class PluginRuntimeConnector:
|
||||
async def initialize_plugins(self):
|
||||
pass
|
||||
|
||||
async def _refresh_agent_runner_registry(self) -> None:
|
||||
registry = getattr(self.ap, 'agent_runner_registry', None)
|
||||
if registry is None:
|
||||
return
|
||||
try:
|
||||
await registry.refresh()
|
||||
except Exception as e:
|
||||
self.ap.logger.warning(f'Failed to refresh agent runner registry: {e}')
|
||||
|
||||
async def ping_plugin_runtime(self):
|
||||
if not hasattr(self, 'handler'):
|
||||
raise PluginRuntimeNotConnectedError('Plugin runtime is not connected')
|
||||
@@ -376,7 +367,6 @@ class PluginRuntimeConnector:
|
||||
task_context.metadata.update(metadata)
|
||||
|
||||
await self._wait_for_installed_plugin_ready(plugin_author, plugin_name, task_context)
|
||||
await self._refresh_agent_runner_registry()
|
||||
|
||||
async def upgrade_plugin(
|
||||
self,
|
||||
@@ -395,8 +385,6 @@ class PluginRuntimeConnector:
|
||||
if task_context is not None:
|
||||
task_context.trace(trace)
|
||||
|
||||
await self._refresh_agent_runner_registry()
|
||||
|
||||
async def delete_plugin(
|
||||
self,
|
||||
plugin_author: str,
|
||||
@@ -421,8 +409,6 @@ class PluginRuntimeConnector:
|
||||
task_context.trace('Cleaning up plugin configuration and storage...')
|
||||
await self.handler.cleanup_plugin_data(plugin_author, plugin_name)
|
||||
|
||||
await self._refresh_agent_runner_registry()
|
||||
|
||||
async def list_plugins(self, component_kinds: list[str] | None = None) -> list[dict[str, Any]]:
|
||||
"""List plugins, optionally filtered by component kinds.
|
||||
|
||||
@@ -613,53 +599,6 @@ class PluginRuntimeConnector:
|
||||
|
||||
yield cmd_ret
|
||||
|
||||
# AgentRunner methods
|
||||
async def list_agent_runners(self, bound_plugins: list[str] | None = None) -> list[dict[str, Any]]:
|
||||
"""List all available AgentRunner components.
|
||||
|
||||
Returns list of dicts with plugin_author, plugin_name, runner_name, manifest, etc.
|
||||
"""
|
||||
if not self.is_enable_plugin:
|
||||
return []
|
||||
|
||||
runners_data = await self.handler.list_agent_runners(include_plugins=bound_plugins)
|
||||
return runners_data
|
||||
|
||||
async def run_agent(
|
||||
self,
|
||||
plugin_author: str,
|
||||
plugin_name: str,
|
||||
runner_name: str,
|
||||
context: dict[str, Any],
|
||||
) -> typing.AsyncGenerator[dict[str, Any], None]:
|
||||
"""Run an AgentRunner from a plugin.
|
||||
|
||||
Args:
|
||||
plugin_author: Plugin author
|
||||
plugin_name: Plugin name
|
||||
runner_name: AgentRunner component name
|
||||
context: AgentRunContext as dict
|
||||
|
||||
Yields:
|
||||
AgentRunResult dicts
|
||||
"""
|
||||
if not self.is_enable_plugin:
|
||||
# Return a protocol-level failure result.
|
||||
yield {
|
||||
'type': 'run.failed',
|
||||
'data': {
|
||||
'error': 'Plugin system is disabled',
|
||||
'code': 'plugin.disabled',
|
||||
'retryable': False,
|
||||
},
|
||||
}
|
||||
return
|
||||
|
||||
gen = self.handler.run_agent(plugin_author, plugin_name, runner_name, context)
|
||||
|
||||
async for ret in gen:
|
||||
yield ret
|
||||
|
||||
async def retrieve_knowledge(
|
||||
self,
|
||||
plugin_author: str,
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -171,8 +171,7 @@ class BailianChatCompletions(modelscopechatcmpl.ModelScopeChatCompletions):
|
||||
# 解析 chunk 数据
|
||||
if hasattr(chunk, 'choices') and chunk.choices:
|
||||
choice = chunk.choices[0]
|
||||
delta_obj = getattr(choice, 'delta', None)
|
||||
delta = delta_obj.model_dump() if delta_obj is not None else {}
|
||||
delta = choice.delta.model_dump() if hasattr(choice, 'delta') else {}
|
||||
finish_reason = getattr(choice, 'finish_reason', None)
|
||||
else:
|
||||
delta = {}
|
||||
|
||||
@@ -359,8 +359,7 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
|
||||
|
||||
if hasattr(chunk, 'choices') and chunk.choices:
|
||||
choice = chunk.choices[0]
|
||||
delta_obj = getattr(choice, 'delta', None)
|
||||
delta = delta_obj.model_dump() if delta_obj is not None else {}
|
||||
delta = choice.delta.model_dump() if hasattr(choice, 'delta') else {}
|
||||
|
||||
finish_reason = getattr(choice, 'finish_reason', None)
|
||||
else:
|
||||
|
||||
@@ -132,8 +132,7 @@ class GeminiChatCompletions(chatcmpl.OpenAIChatCompletions):
|
||||
|
||||
if hasattr(chunk, 'choices') and chunk.choices:
|
||||
choice = chunk.choices[0]
|
||||
delta_obj = getattr(choice, 'delta', None)
|
||||
delta = delta_obj.model_dump() if delta_obj is not None else {}
|
||||
delta = choice.delta.model_dump() if hasattr(choice, 'delta') else {}
|
||||
|
||||
finish_reason = getattr(choice, 'finish_reason', None)
|
||||
else:
|
||||
|
||||
@@ -144,8 +144,7 @@ class JieKouAIChatCompletions(chatcmpl.OpenAIChatCompletions):
|
||||
# 解析 chunk 数据
|
||||
if hasattr(chunk, 'choices') and chunk.choices:
|
||||
choice = chunk.choices[0]
|
||||
delta_obj = getattr(choice, 'delta', None)
|
||||
delta = delta_obj.model_dump() if delta_obj is not None else {}
|
||||
delta = choice.delta.model_dump() if hasattr(choice, 'delta') else {}
|
||||
finish_reason = getattr(choice, 'finish_reason', None)
|
||||
else:
|
||||
delta = {}
|
||||
@@ -160,7 +159,7 @@ class JieKouAIChatCompletions(chatcmpl.OpenAIChatCompletions):
|
||||
# reasoning_content = delta.get('reasoning_content', '')
|
||||
|
||||
if remove_think:
|
||||
if delta.get('content') is not None:
|
||||
if delta['content'] is not None:
|
||||
if '<think>' in delta['content'] and not thinking_started and not thinking_ended:
|
||||
thinking_started = True
|
||||
continue
|
||||
|
||||
@@ -391,8 +391,7 @@ class ModelScopeChatCompletions(requester.ProviderAPIRequester):
|
||||
# 解析 chunk 数据
|
||||
if hasattr(chunk, 'choices') and chunk.choices:
|
||||
choice = chunk.choices[0]
|
||||
delta_obj = getattr(choice, 'delta', None)
|
||||
delta = delta_obj.model_dump() if delta_obj is not None else {}
|
||||
delta = choice.delta.model_dump() if hasattr(choice, 'delta') else {}
|
||||
finish_reason = getattr(choice, 'finish_reason', None)
|
||||
else:
|
||||
delta = {}
|
||||
|
||||
@@ -144,8 +144,7 @@ class PPIOChatCompletions(chatcmpl.OpenAIChatCompletions):
|
||||
# 解析 chunk 数据
|
||||
if hasattr(chunk, 'choices') and chunk.choices:
|
||||
choice = chunk.choices[0]
|
||||
delta_obj = getattr(choice, 'delta', None)
|
||||
delta = delta_obj.model_dump() if delta_obj is not None else {}
|
||||
delta = choice.delta.model_dump() if hasattr(choice, 'delta') else {}
|
||||
finish_reason = getattr(choice, 'finish_reason', None)
|
||||
else:
|
||||
delta = {}
|
||||
@@ -160,7 +159,7 @@ class PPIOChatCompletions(chatcmpl.OpenAIChatCompletions):
|
||||
# reasoning_content = delta.get('reasoning_content', '')
|
||||
|
||||
if remove_think:
|
||||
if delta.get('content') is not None:
|
||||
if delta['content'] is not None:
|
||||
if '<think>' in delta['content'] and not thinking_started and not thinking_ended:
|
||||
thinking_started = True
|
||||
continue
|
||||
|
||||
@@ -1,12 +1,3 @@
|
||||
"""
|
||||
Legacy Coze API Runner.
|
||||
|
||||
DEPRECATED: This runner has been migrated to the AgentRunner plugin format.
|
||||
Use the official `langbot/coze-agent` plugin instead.
|
||||
|
||||
Migration target: /home/glwuy/langbot-app/langbot-agent-runner/coze-agent/
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import typing
|
||||
|
||||
@@ -1,12 +1,3 @@
|
||||
"""
|
||||
Legacy DashScope (阿里云百炼) API Runner.
|
||||
|
||||
DEPRECATED: This runner has been migrated to the AgentRunner plugin format.
|
||||
Use the official `langbot/dashscope-agent` plugin instead.
|
||||
|
||||
Migration target: /home/glwuy/langbot-app/langbot-agent-runner/dashscope-agent/
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import typing
|
||||
|
||||
@@ -1,12 +1,3 @@
|
||||
"""
|
||||
Legacy Dify Service API Runner.
|
||||
|
||||
DEPRECATED: This runner has been migrated to the AgentRunner plugin format.
|
||||
Use the official `langbot/dify-agent` plugin instead.
|
||||
|
||||
Migration target: /home/glwuy/langbot-app/langbot-agent-runner/dify-agent/
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import typing
|
||||
|
||||
@@ -1,12 +1,3 @@
|
||||
"""
|
||||
Legacy Langflow API Runner.
|
||||
|
||||
DEPRECATED: This runner has been migrated to the AgentRunner plugin format.
|
||||
Use the official `langbot/langflow-agent` plugin instead.
|
||||
|
||||
Migration target: /home/glwuy/langbot-app/langbot-agent-runner/langflow-agent/
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import typing
|
||||
|
||||
@@ -1,12 +1,3 @@
|
||||
"""
|
||||
Legacy Local Agent Runner.
|
||||
|
||||
DEPRECATED: This runner has been migrated to the AgentRunner plugin format.
|
||||
Use the official `langbot/local-agent` plugin instead.
|
||||
|
||||
Migration target: /home/glwuy/langbot-app/langbot-local-agent/
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
@@ -20,8 +11,8 @@ import langbot_plugin.api.entities.builtin.rag.context as rag_context
|
||||
|
||||
|
||||
rag_combined_prompt_template = """
|
||||
The following are relevant context entries retrieved from the knowledge base.
|
||||
Please use them to answer the user's message.
|
||||
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>
|
||||
|
||||
@@ -1,12 +1,3 @@
|
||||
"""
|
||||
Legacy n8n Service API Runner.
|
||||
|
||||
DEPRECATED: This runner has been migrated to the AgentRunner plugin format.
|
||||
Use the official `langbot/n8n-agent` plugin instead.
|
||||
|
||||
Migration target: /home/glwuy/langbot-app/langbot-agent-runner/n8n-agent/
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import typing
|
||||
|
||||
@@ -1,12 +1,3 @@
|
||||
"""
|
||||
Legacy Tbox (蚂蚁百宝箱) API Runner.
|
||||
|
||||
DEPRECATED: This runner has been migrated to the AgentRunner plugin format.
|
||||
Use the official `langbot/tbox-agent` plugin instead.
|
||||
|
||||
Migration target: /home/glwuy/langbot-app/langbot-agent-runner/tbox-agent/
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import typing
|
||||
|
||||
@@ -384,21 +384,6 @@ class MCPLoader(loader.ToolLoader):
|
||||
return True
|
||||
return False
|
||||
|
||||
async def _get_tool(self, name: str) -> resource_tool.LLMTool | None:
|
||||
"""Get tool by name.
|
||||
|
||||
Args:
|
||||
name: Tool name to find
|
||||
|
||||
Returns:
|
||||
LLMTool if found, None otherwise
|
||||
"""
|
||||
for session in self.sessions.values():
|
||||
for function in session.get_tools():
|
||||
if function.name == name:
|
||||
return function
|
||||
return None
|
||||
|
||||
async def invoke_tool(self, name: str, parameters: dict, query: pipeline_query.Query) -> typing.Any:
|
||||
"""执行工具调用"""
|
||||
for session in self.sessions.values():
|
||||
|
||||
@@ -40,27 +40,6 @@ class ToolManager:
|
||||
|
||||
return all_functions
|
||||
|
||||
async def get_tool_by_name(self, name: str) -> resource_tool.LLMTool | None:
|
||||
"""Get tool by name from plugin or MCP loaders.
|
||||
|
||||
Args:
|
||||
name: Tool name (format: plugin_author/plugin_name/tool_name or mcp_server/tool_name)
|
||||
|
||||
Returns:
|
||||
LLMTool if found, None otherwise
|
||||
"""
|
||||
# Try plugin loader first
|
||||
tool = await self.plugin_tool_loader._get_tool(name)
|
||||
if tool:
|
||||
return tool
|
||||
|
||||
# Try MCP loader
|
||||
tool = await self.mcp_tool_loader._get_tool(name)
|
||||
if tool:
|
||||
return tool
|
||||
|
||||
return None
|
||||
|
||||
async def generate_tools_for_openai(self, use_funcs: list[resource_tool.LLMTool]) -> list:
|
||||
"""生成函数列表"""
|
||||
tools = []
|
||||
|
||||
@@ -107,7 +107,7 @@ class RAGRuntimeService:
|
||||
)
|
||||
|
||||
async def get_file_stream(self, storage_path: str) -> bytes:
|
||||
"""Handle GET_KNOWLEDGE_FILE_STREAM action.
|
||||
"""Handle GET_KNOWLEDEGE_FILE_STREAM action.
|
||||
|
||||
Uses the storage manager abstraction to load file content,
|
||||
regardless of the underlying storage provider.
|
||||
|
||||
@@ -38,10 +38,57 @@
|
||||
},
|
||||
"ai": {
|
||||
"runner": {
|
||||
"id": "",
|
||||
"runner": "local-agent",
|
||||
"expire-time": 0
|
||||
},
|
||||
"runner_config": {}
|
||||
"local-agent": {
|
||||
"model": {
|
||||
"primary": "",
|
||||
"fallbacks": []
|
||||
},
|
||||
"max-round": 10,
|
||||
"prompt": [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are a helpful assistant."
|
||||
}
|
||||
],
|
||||
"knowledge-bases": [],
|
||||
"rerank-model": "",
|
||||
"rerank-top-k": 5
|
||||
},
|
||||
"dify-service-api": {
|
||||
"base-url": "https://api.dify.ai/v1",
|
||||
"app-type": "chat",
|
||||
"api-key": "your-api-key",
|
||||
"timeout": 30
|
||||
},
|
||||
"dashscope-app-api": {
|
||||
"app-type": "agent",
|
||||
"api-key": "your-api-key",
|
||||
"app-id": "your-app-id",
|
||||
"references-quote": "参考资料来自:"
|
||||
},
|
||||
"n8n-service-api": {
|
||||
"webhook-url": "http://your-n8n-webhook-url",
|
||||
"auth-type": "none",
|
||||
"basic-username": "",
|
||||
"basic-password": "",
|
||||
"jwt-secret": "",
|
||||
"jwt-algorithm": "HS256",
|
||||
"header-name": "",
|
||||
"header-value": "",
|
||||
"timeout": 120,
|
||||
"output-key": "response"
|
||||
},
|
||||
"langflow-api": {
|
||||
"base-url": "http://localhost:7860",
|
||||
"api-key": "your-api-key",
|
||||
"flow-id": "your-flow-id",
|
||||
"input-type": "chat",
|
||||
"output-type": "chat",
|
||||
"tweaks": "{}"
|
||||
}
|
||||
},
|
||||
"output": {
|
||||
"long-text-processing": {
|
||||
|
||||
@@ -34,5 +34,11 @@
|
||||
"limit": 60
|
||||
}
|
||||
}
|
||||
},
|
||||
"msg-truncate": {
|
||||
"method": "round",
|
||||
"round": {
|
||||
"max-round": 10
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -11,13 +11,42 @@ stages:
|
||||
en_US: Strategy to call AI to process messages
|
||||
zh_Hans: 调用 AI 处理消息的方式
|
||||
config:
|
||||
- name: id
|
||||
- name: runner
|
||||
label:
|
||||
en_US: Runner
|
||||
zh_Hans: 运行器
|
||||
type: select
|
||||
required: true
|
||||
# Options and default are dynamically populated from AgentRunnerRegistry
|
||||
default: local-agent
|
||||
options:
|
||||
- name: local-agent
|
||||
label:
|
||||
en_US: Local Agent
|
||||
zh_Hans: 内置 Agent
|
||||
- name: dify-service-api
|
||||
label:
|
||||
en_US: Dify Service API
|
||||
zh_Hans: Dify 服务 API
|
||||
- name: n8n-service-api
|
||||
label:
|
||||
en_US: n8n Workflow API
|
||||
zh_Hans: n8n 工作流 API
|
||||
- name: coze-api
|
||||
label:
|
||||
en_US: Coze API
|
||||
zh_Hans: 扣子 API
|
||||
- name: tbox-app-api
|
||||
label:
|
||||
en_US: Tbox App API
|
||||
zh_Hans: 蚂蚁百宝箱平台 API
|
||||
- name: dashscope-app-api
|
||||
label:
|
||||
en_US: Aliyun Dashscope App API
|
||||
zh_Hans: 阿里云百炼平台 API
|
||||
- name: langflow-api
|
||||
label:
|
||||
en_US: Langflow API
|
||||
zh_Hans: Langflow API
|
||||
- name: expire-time
|
||||
label:
|
||||
en_US: Conversation expire time (seconds)
|
||||
@@ -38,6 +67,496 @@ stages:
|
||||
type: integer
|
||||
required: true
|
||||
default: 0
|
||||
# Runner config stages are dynamically added from AgentRunnerRegistry
|
||||
# Each plugin runner's config schema is added as a separate stage
|
||||
# The stage name matches the runner id for frontend matching
|
||||
- name: local-agent
|
||||
label:
|
||||
en_US: Local Agent
|
||||
zh_Hans: 内置 Agent
|
||||
description:
|
||||
en_US: Configure the embedded agent of the pipeline
|
||||
zh_Hans: 配置内置 Agent
|
||||
config:
|
||||
- name: model
|
||||
label:
|
||||
en_US: Model
|
||||
zh_Hans: 模型
|
||||
type: model-fallback-selector
|
||||
required: true
|
||||
default:
|
||||
primary: ''
|
||||
fallbacks: []
|
||||
- name: max-round
|
||||
label:
|
||||
en_US: Max Round
|
||||
zh_Hans: 最大回合数
|
||||
description:
|
||||
en_US: The maximum number of previous messages that the agent can remember
|
||||
zh_Hans: 最大前文消息回合数
|
||||
type: integer
|
||||
required: true
|
||||
default: 10
|
||||
show_if:
|
||||
field: __system.is_wizard
|
||||
operator: neq
|
||||
value: true
|
||||
- name: prompt
|
||||
label:
|
||||
en_US: Prompt
|
||||
zh_Hans: 提示词
|
||||
description:
|
||||
en_US: The prompt of the agent
|
||||
zh_Hans: 除非您了解消息结构，否则请只使用 system 单提示词
|
||||
type: prompt-editor
|
||||
required: true
|
||||
default:
|
||||
- role: system
|
||||
content: "You are a helpful assistant."
|
||||
- name: knowledge-bases
|
||||
label:
|
||||
en_US: Knowledge Bases
|
||||
zh_Hans: 知识库
|
||||
description:
|
||||
en_US: Configure the knowledge bases to use for the agent, if not selected, the agent will directly use the LLM to reply
|
||||
zh_Hans: 配置用于提升回复质量的知识库，若不选择，则直接使用大模型回复
|
||||
type: knowledge-base-multi-selector
|
||||
required: false
|
||||
default: []
|
||||
show_if:
|
||||
field: __system.is_wizard
|
||||
operator: neq
|
||||
value: true
|
||||
- name: rerank-model
|
||||
label:
|
||||
en_US: Rerank Model
|
||||
zh_Hans: 重排序模型
|
||||
description:
|
||||
en_US: Optional rerank model to improve retrieval quality by re-scoring retrieved chunks
|
||||
zh_Hans: 可选的重排序模型，通过重新评分检索结果来提升检索质量
|
||||
type: rerank-model-selector
|
||||
required: false
|
||||
default: ''
|
||||
show_if:
|
||||
field: knowledge-bases
|
||||
operator: neq
|
||||
value: []
|
||||
- name: rerank-top-k
|
||||
label:
|
||||
en_US: Rerank Top K
|
||||
zh_Hans: 重排序保留数量
|
||||
description:
|
||||
en_US: Number of top results to keep after reranking
|
||||
zh_Hans: 重排序后保留的最相关结果数量
|
||||
type: integer
|
||||
required: false
|
||||
default: 5
|
||||
show_if:
|
||||
field: rerank-model
|
||||
operator: neq
|
||||
value: ''
|
||||
- name: dify-service-api
|
||||
label:
|
||||
en_US: Dify Service API
|
||||
zh_Hans: Dify 服务 API
|
||||
description:
|
||||
en_US: Configure the Dify service API of the pipeline
|
||||
zh_Hans: 配置 Dify 服务 API
|
||||
config:
|
||||
- name: base-url
|
||||
label:
|
||||
en_US: Base URL
|
||||
zh_Hans: 基础 URL
|
||||
type: string
|
||||
required: true
|
||||
options:
|
||||
- name: 'https://api.dify.ai/v1'
|
||||
label:
|
||||
en_US: Dify Cloud
|
||||
zh_Hans: Dify 云服务
|
||||
default: 'https://api.dify.ai/v1'
|
||||
- name: base-prompt
|
||||
label:
|
||||
en_US: Base PROMPT
|
||||
zh_Hans: 基础提示词
|
||||
description:
|
||||
en_US: When Dify receives a message with empty input (only images), it will pass this default prompt into it.
|
||||
zh_Hans: 当 Dify 接收到输入文字为空（仅图片）的消息时，传入该默认提示词
|
||||
type: string
|
||||
required: true
|
||||
default: "When the file content is readable, please read the content of this file. When the file is an image, describe the content of this image."
|
||||
- name: app-type
|
||||
label:
|
||||
en_US: App Type
|
||||
zh_Hans: 应用类型
|
||||
type: select
|
||||
required: true
|
||||
default: chat
|
||||
options:
|
||||
- name: chat
|
||||
label:
|
||||
en_US: Chat
|
||||
zh_Hans: 聊天（包括Chatflow）
|
||||
- name: agent
|
||||
label:
|
||||
en_US: Agent
|
||||
zh_Hans: Agent
|
||||
- name: workflow
|
||||
label:
|
||||
en_US: Workflow
|
||||
zh_Hans: 工作流
|
||||
- name: api-key
|
||||
label:
|
||||
en_US: API Key
|
||||
zh_Hans: API 密钥
|
||||
type: string
|
||||
required: true
|
||||
default: 'your-api-key'
|
||||
- name: n8n-service-api
|
||||
label:
|
||||
en_US: n8n Workflow API
|
||||
zh_Hans: n8n 工作流 API
|
||||
description:
|
||||
en_US: Configure the n8n workflow API of the pipeline
|
||||
zh_Hans: 配置 n8n 工作流 API
|
||||
config:
|
||||
- name: webhook-url
|
||||
label:
|
||||
en_US: Webhook URL
|
||||
zh_Hans: Webhook URL
|
||||
description:
|
||||
en_US: The webhook URL of the n8n workflow
|
||||
zh_Hans: n8n 工作流的 webhook URL
|
||||
type: string
|
||||
required: true
|
||||
default: 'http://your-n8n-webhook-url'
|
||||
- name: auth-type
|
||||
label:
|
||||
en_US: Authentication Type
|
||||
zh_Hans: 认证类型
|
||||
description:
|
||||
en_US: The authentication type for the webhook call
|
||||
zh_Hans: webhook 调用的认证类型
|
||||
type: select
|
||||
required: true
|
||||
default: 'none'
|
||||
options:
|
||||
- name: 'none'
|
||||
label:
|
||||
en_US: None
|
||||
zh_Hans: 无认证
|
||||
- name: 'basic'
|
||||
label:
|
||||
en_US: Basic Auth
|
||||
zh_Hans: 基本认证
|
||||
- name: 'jwt'
|
||||
label:
|
||||
en_US: JWT
|
||||
zh_Hans: JWT认证
|
||||
- name: 'header'
|
||||
label:
|
||||
en_US: Header Auth
|
||||
zh_Hans: 请求头认证
|
||||
- name: basic-username
|
||||
label:
|
||||
en_US: Username
|
||||
zh_Hans: 用户名
|
||||
description:
|
||||
en_US: The username for Basic Auth
|
||||
zh_Hans: 基本认证的用户名
|
||||
type: string
|
||||
required: false
|
||||
default: ''
|
||||
show_if:
|
||||
field: auth-type
|
||||
operator: eq
|
||||
value: 'basic'
|
||||
- name: basic-password
|
||||
label:
|
||||
en_US: Password
|
||||
zh_Hans: 密码
|
||||
description:
|
||||
en_US: The password for Basic Auth
|
||||
zh_Hans: 基本认证的密码
|
||||
type: string
|
||||
required: false
|
||||
default: ''
|
||||
show_if:
|
||||
field: auth-type
|
||||
operator: eq
|
||||
value: 'basic'
|
||||
- name: jwt-secret
|
||||
label:
|
||||
en_US: Secret
|
||||
zh_Hans: 密钥
|
||||
description:
|
||||
en_US: The secret for JWT authentication
|
||||
zh_Hans: JWT认证的密钥
|
||||
type: string
|
||||
required: false
|
||||
default: ''
|
||||
show_if:
|
||||
field: auth-type
|
||||
operator: eq
|
||||
value: 'jwt'
|
||||
- name: jwt-algorithm
|
||||
label:
|
||||
en_US: Algorithm
|
||||
zh_Hans: 算法
|
||||
description:
|
||||
en_US: The algorithm for JWT authentication
|
||||
zh_Hans: JWT认证的算法
|
||||
type: string
|
||||
required: false
|
||||
default: 'HS256'
|
||||
show_if:
|
||||
field: auth-type
|
||||
operator: eq
|
||||
value: 'jwt'
|
||||
- name: header-name
|
||||
label:
|
||||
en_US: Header Name
|
||||
zh_Hans: 请求头名称
|
||||
description:
|
||||
en_US: The header name for Header Auth
|
||||
zh_Hans: 请求头认证的名称
|
||||
type: string
|
||||
required: false
|
||||
default: ''
|
||||
show_if:
|
||||
field: auth-type
|
||||
operator: eq
|
||||
value: 'header'
|
||||
- name: header-value
|
||||
label:
|
||||
en_US: Header Value
|
||||
zh_Hans: 请求头值
|
||||
description:
|
||||
en_US: The header value for Header Auth
|
||||
zh_Hans: 请求头认证的值
|
||||
type: string
|
||||
required: false
|
||||
default: ''
|
||||
show_if:
|
||||
field: auth-type
|
||||
operator: eq
|
||||
value: 'header'
|
||||
- name: timeout
|
||||
label:
|
||||
en_US: Timeout
|
||||
zh_Hans: 超时时间
|
||||
description:
|
||||
en_US: The timeout in seconds for the webhook call
|
||||
zh_Hans: webhook 调用的超时时间（秒）
|
||||
type: integer
|
||||
required: false
|
||||
default: 120
|
||||
- name: output-key
|
||||
label:
|
||||
en_US: Output Key
|
||||
zh_Hans: 输出键名
|
||||
description:
|
||||
en_US: The key name of the output in the webhook response
|
||||
zh_Hans: webhook 响应中输出内容的键名
|
||||
type: string
|
||||
required: false
|
||||
default: 'response'
|
||||
- name: coze-api
|
||||
label:
|
||||
en_US: coze API
|
||||
zh_Hans: 扣子 API
|
||||
description:
|
||||
en_US: Configure the Coze API of the pipeline
|
||||
zh_Hans: 配置Coze API
|
||||
config:
|
||||
- name: api-key
|
||||
label:
|
||||
en_US: API Key
|
||||
zh_Hans: API 密钥
|
||||
description:
|
||||
en_US: The API key for the Coze server
|
||||
zh_Hans: Coze服务器的 API 密钥
|
||||
type: string
|
||||
required: true
|
||||
default: ''
|
||||
- name: bot-id
|
||||
label:
|
||||
en_US: Bot ID
|
||||
zh_Hans: 机器人 ID
|
||||
description:
|
||||
en_US: The ID of the bot to run
|
||||
zh_Hans: 要运行的机器人 ID
|
||||
type: string
|
||||
required: true
|
||||
default: ''
|
||||
- name: api-base
|
||||
label:
|
||||
en_US: API Base URL
|
||||
zh_Hans: API 基础 URL
|
||||
description:
|
||||
en_US: The base URL for the Coze API, please use https://api.coze.com for global Coze edition(coze.com).
|
||||
zh_Hans: Coze API 的基础 URL，请使用 https://api.coze.com 用于全球 Coze 版（coze.com）
|
||||
type: string
|
||||
options:
|
||||
- name: 'https://api.coze.cn'
|
||||
label:
|
||||
en_US: Coze China
|
||||
zh_Hans: Coze 中国版
|
||||
- name: 'https://api.coze.com'
|
||||
label:
|
||||
en_US: Coze Global
|
||||
zh_Hans: Coze 全球版
|
||||
default: "https://api.coze.cn"
|
||||
- name: auto-save-history
|
||||
label:
|
||||
en_US: Auto Save History
|
||||
zh_Hans: 自动保存历史
|
||||
description:
|
||||
en_US: Whether to automatically save conversation history
|
||||
zh_Hans: 是否自动保存对话历史
|
||||
type: boolean
|
||||
default: true
|
||||
- name: timeout
|
||||
label:
|
||||
en_US: Request Timeout
|
||||
zh_Hans: 请求超时
|
||||
description:
|
||||
en_US: Timeout in seconds for API requests
|
||||
zh_Hans: API 请求超时时间（秒）
|
||||
type: number
|
||||
default: 120
|
||||
- name: tbox-app-api
|
||||
label:
|
||||
en_US: Tbox App API
|
||||
zh_Hans: 蚂蚁百宝箱平台 API
|
||||
description:
|
||||
en_US: Configure the Tbox App API of the pipeline
|
||||
zh_Hans: 配置蚂蚁百宝箱平台 API
|
||||
config:
|
||||
- name: api-key
|
||||
label:
|
||||
en_US: API Key
|
||||
zh_Hans: API 密钥
|
||||
type: string
|
||||
required: true
|
||||
default: ''
|
||||
- name: app-id
|
||||
label:
|
||||
en_US: App ID
|
||||
zh_Hans: 应用 ID
|
||||
type: string
|
||||
required: true
|
||||
default: ''
|
||||
- name: dashscope-app-api
|
||||
label:
|
||||
en_US: Aliyun Dashscope App API
|
||||
zh_Hans: 阿里云百炼平台 API
|
||||
description:
|
||||
en_US: Configure the Aliyun Dashscope App API of the pipeline
|
||||
zh_Hans: 配置阿里云百炼平台 API
|
||||
config:
|
||||
- name: app-type
|
||||
label:
|
||||
en_US: App Type
|
||||
zh_Hans: 应用类型
|
||||
type: select
|
||||
required: true
|
||||
default: agent
|
||||
options:
|
||||
- name: agent
|
||||
label:
|
||||
en_US: Agent
|
||||
zh_Hans: Agent
|
||||
- name: workflow
|
||||
label:
|
||||
en_US: Workflow
|
||||
zh_Hans: 工作流
|
||||
- name: api-key
|
||||
label:
|
||||
en_US: API Key
|
||||
zh_Hans: API 密钥
|
||||
type: string
|
||||
required: true
|
||||
default: 'your-api-key'
|
||||
- name: app-id
|
||||
label:
|
||||
en_US: App ID
|
||||
zh_Hans: 应用 ID
|
||||
type: string
|
||||
required: true
|
||||
default: 'your-app-id'
|
||||
- name: references_quote
|
||||
label:
|
||||
en_US: References Quote
|
||||
zh_Hans: 引用文本
|
||||
description:
|
||||
en_US: The text prompt when the references are included
|
||||
zh_Hans: 包含引用资料时的文本提示
|
||||
type: string
|
||||
required: false
|
||||
default: '参考资料来自:'
|
||||
- name: langflow-api
|
||||
label:
|
||||
en_US: Langflow API
|
||||
zh_Hans: Langflow API
|
||||
description:
|
||||
en_US: Configure the Langflow API of the pipeline, call the Langflow flow through the `Simplified Run Flow` interface
|
||||
zh_Hans: 配置 Langflow API，通过 `Simplified Run Flow` 接口调用 Langflow 的流程
|
||||
config:
|
||||
- name: base-url
|
||||
label:
|
||||
en_US: Base URL
|
||||
zh_Hans: 基础 URL
|
||||
description:
|
||||
en_US: The base URL of the Langflow server
|
||||
zh_Hans: Langflow 服务器的基础 URL
|
||||
type: string
|
||||
required: true
|
||||
default: 'http://localhost:7860'
|
||||
- name: api-key
|
||||
label:
|
||||
en_US: API Key
|
||||
zh_Hans: API 密钥
|
||||
description:
|
||||
en_US: The API key for the Langflow server
|
||||
zh_Hans: Langflow 服务器的 API 密钥
|
||||
type: string
|
||||
required: true
|
||||
default: 'your-api-key'
|
||||
- name: flow-id
|
||||
label:
|
||||
en_US: Flow ID
|
||||
zh_Hans: 流程 ID
|
||||
description:
|
||||
en_US: The ID of the flow to run
|
||||
zh_Hans: 要运行的流程 ID
|
||||
type: string
|
||||
required: true
|
||||
default: 'your-flow-id'
|
||||
- name: input-type
|
||||
label:
|
||||
en_US: Input Type
|
||||
zh_Hans: 输入类型
|
||||
description:
|
||||
en_US: The input type for the flow
|
||||
zh_Hans: 流程的输入类型
|
||||
type: string
|
||||
required: false
|
||||
default: 'chat'
|
||||
- name: output-type
|
||||
label:
|
||||
en_US: Output Type
|
||||
zh_Hans: 输出类型
|
||||
description:
|
||||
en_US: The output type for the flow
|
||||
zh_Hans: 流程的输出类型
|
||||
type: string
|
||||
required: false
|
||||
default: 'chat'
|
||||
- name: tweaks
|
||||
label:
|
||||
en_US: Tweaks
|
||||
zh_Hans: 调整参数
|
||||
description:
|
||||
en_US: Optional tweaks to apply to the flow
|
||||
zh_Hans: 可选的流程调整参数
|
||||
type: json
|
||||
required: false
|
||||
default: '{}'
|
||||
|
||||
@@ -18,7 +18,6 @@ import langbot_plugin.api.entities.builtin.provider.session as provider_session
|
||||
|
||||
# Counter for generating unique IDs
|
||||
_query_counter = 0
|
||||
DEFAULT_RUNNER_ID = "plugin:langbot/local-agent/default"
|
||||
|
||||
|
||||
def _next_query_id() -> int:
|
||||
@@ -164,12 +163,10 @@ def _base_query(
|
||||
"bot_uuid": "test-bot-uuid",
|
||||
"pipeline_config": {
|
||||
"ai": {
|
||||
"runner": {"id": DEFAULT_RUNNER_ID},
|
||||
"runner_config": {
|
||||
DEFAULT_RUNNER_ID: {
|
||||
"model": {"primary": "test-model-uuid", "fallbacks": []},
|
||||
"prompt": [{"role": "system", "content": "test-prompt"}],
|
||||
},
|
||||
"runner": {"runner": "local-agent"},
|
||||
"local-agent": {
|
||||
"model": {"primary": "test-model-uuid", "fallbacks": []},
|
||||
"prompt": "test-prompt",
|
||||
},
|
||||
},
|
||||
"output": {"misc": {"at-sender": False, "quote-origin": False}},
|
||||
@@ -472,4 +469,4 @@ def at_all_query(
|
||||
sender_id=sender_id,
|
||||
adapter=adapter,
|
||||
**overrides,
|
||||
)
|
||||
)
|
||||
66
tests/test_cwe94_debug_exec.py
Normal file
66
tests/test_cwe94_debug_exec.py
Normal file
@@ -0,0 +1,66 @@
|
||||
"""
|
||||
PoC test for CWE-94: Authenticated RCE via exec() on user-supplied Python code.
|
||||
|
||||
The /api/v1/system/debug/exec endpoint passes raw HTTP body to exec(),
|
||||
allowing arbitrary code execution when debug_mode is True.
|
||||
|
||||
This test verifies that:
|
||||
1. The exec() endpoint is removed from the codebase entirely.
|
||||
2. No route matches /api/v1/system/debug/exec.
|
||||
"""
|
||||
|
||||
import ast
|
||||
import pathlib
|
||||
|
||||
# Resolve project root (one level up from tests/)
|
||||
_PROJECT_ROOT = pathlib.Path(__file__).resolve().parent.parent
|
||||
|
||||
VULN_FILE = (
|
||||
_PROJECT_ROOT
|
||||
/ "src"
|
||||
/ "langbot"
|
||||
/ "pkg"
|
||||
/ "api"
|
||||
/ "http"
|
||||
/ "controller"
|
||||
/ "groups"
|
||||
/ "system.py"
|
||||
)
|
||||
|
||||
|
||||
def test_no_exec_call_in_system_controller():
|
||||
"""Verify there is no exec() call in system.py that takes user input."""
|
||||
with open(VULN_FILE, "r") as f:
|
||||
source = f.read()
|
||||
|
||||
tree = ast.parse(source)
|
||||
|
||||
exec_calls = []
|
||||
for node in ast.walk(tree):
|
||||
if isinstance(node, ast.Call):
|
||||
func = node.func
|
||||
# Match bare exec() call
|
||||
if isinstance(func, ast.Name) and func.id == "exec":
|
||||
exec_calls.append(node.lineno)
|
||||
|
||||
assert len(exec_calls) == 0, (
|
||||
f"Found exec() call(s) at line(s) {exec_calls} in system.py. "
|
||||
"User-supplied code must never be passed to exec()."
|
||||
)
|
||||
|
||||
|
||||
def test_no_debug_exec_route():
|
||||
"""Verify the /debug/exec route is not registered."""
|
||||
with open(VULN_FILE, "r") as f:
|
||||
source = f.read()
|
||||
|
||||
assert "debug/exec" not in source, (
|
||||
"The /debug/exec route still exists in system.py. "
|
||||
"This endpoint allows arbitrary code execution and must be removed."
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_no_exec_call_in_system_controller()
|
||||
test_no_debug_exec_route()
|
||||
print("All tests passed!")
|
||||
@@ -1,2 +0,0 @@
|
||||
"""Tests for agent runner subsystem."""
|
||||
from __future__ import annotations
|
||||
@@ -1,78 +0,0 @@
|
||||
"""Shared test fixtures for agent runner tests."""
|
||||
from __future__ import annotations
|
||||
|
||||
import typing
|
||||
|
||||
|
||||
def make_resources(
|
||||
models: list[dict] | None = None,
|
||||
tools: list[dict] | None = None,
|
||||
knowledge_bases: list[dict] | None = None,
|
||||
storage: dict | None = None,
|
||||
files: list[dict] | None = None,
|
||||
) -> dict[str, typing.Any]:
|
||||
"""Create a minimal AgentResources dict for testing.
|
||||
|
||||
Args:
|
||||
models: List of model dicts with 'model_id' key
|
||||
tools: List of tool dicts with 'tool_name' key
|
||||
knowledge_bases: List of KB dicts with 'kb_id' key
|
||||
storage: Storage permissions dict
|
||||
files: List of file dicts with 'file_id' key
|
||||
|
||||
Returns:
|
||||
AgentResources dict with all required fields
|
||||
"""
|
||||
return {
|
||||
'models': models or [],
|
||||
'tools': tools or [],
|
||||
'knowledge_bases': knowledge_bases or [],
|
||||
'files': files or [],
|
||||
'storage': storage or {'plugin_storage': False, 'workspace_storage': False},
|
||||
'platform_capabilities': {},
|
||||
}
|
||||
|
||||
|
||||
def make_session(
|
||||
run_id: str = 'test-run-id',
|
||||
runner_id: str = 'plugin:test/test-runner/default',
|
||||
query_id: int | None = 1,
|
||||
plugin_identity: str = 'test/test-runner',
|
||||
resources: dict | None = None,
|
||||
) -> dict[str, typing.Any]:
|
||||
"""Create a minimal AgentRunSession dict for testing.
|
||||
|
||||
Args:
|
||||
run_id: Unique run identifier
|
||||
runner_id: Runner descriptor ID
|
||||
query_id: Pipeline query ID
|
||||
plugin_identity: Plugin identifier (author/name)
|
||||
resources: AgentResources dict (uses make_resources() default if None)
|
||||
|
||||
Returns:
|
||||
AgentRunSession dict with all required fields including pre-computed _authorized_ids
|
||||
"""
|
||||
import time
|
||||
now = int(time.time())
|
||||
res = resources or make_resources()
|
||||
|
||||
# Pre-compute authorized IDs for O(1) lookup (matching production behavior)
|
||||
authorized_ids: dict[str, set[str]] = {
|
||||
'model': {m.get('model_id') for m in res.get('models', [])},
|
||||
'tool': {t.get('tool_name') for t in res.get('tools', [])},
|
||||
'knowledge_base': {kb.get('kb_id') for kb in res.get('knowledge_bases', [])},
|
||||
'file': {f.get('file_id') for f in res.get('files', [])},
|
||||
}
|
||||
|
||||
return {
|
||||
'run_id': run_id,
|
||||
'runner_id': runner_id,
|
||||
'query_id': query_id,
|
||||
'plugin_identity': plugin_identity,
|
||||
'resources': res,
|
||||
'status': {
|
||||
'started_at': now,
|
||||
'last_activity_at': now,
|
||||
},
|
||||
'_authorized_ids': authorized_ids,
|
||||
}
|
||||
@@ -1,625 +0,0 @@
|
||||
"""Tests for ArtifactStore and artifact action handlers."""
|
||||
from __future__ import annotations
|
||||
|
||||
import pytest
|
||||
from unittest.mock import MagicMock, AsyncMock, patch
|
||||
import base64
|
||||
import datetime
|
||||
import asyncio
|
||||
|
||||
from langbot.pkg.agent.runner.artifact_store import ArtifactStore
|
||||
from langbot.pkg.agent.runner.session_registry import (
|
||||
AgentRunSessionRegistry,
|
||||
get_session_registry,
|
||||
)
|
||||
|
||||
|
||||
class TestArtifactStore:
|
||||
"""Test ArtifactStore operations."""
|
||||
|
||||
def _make_mock_engine(self):
|
||||
"""Create a mock database engine for AsyncSession-based store.
|
||||
|
||||
Note: The new store uses AsyncSession, so we need to mock
|
||||
the session factory behavior.
|
||||
"""
|
||||
from unittest.mock import MagicMock, AsyncMock, patch
|
||||
from sqlalchemy.ext.asyncio import AsyncEngine
|
||||
|
||||
engine = MagicMock(spec=AsyncEngine)
|
||||
return engine
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_register_artifact_generates_id(self):
|
||||
"""Test register_artifact generates ID if not provided."""
|
||||
engine = self._make_mock_engine()
|
||||
store = ArtifactStore(engine)
|
||||
|
||||
# Mock the session factory
|
||||
mock_session = AsyncMock()
|
||||
mock_session.add = MagicMock()
|
||||
mock_session.commit = AsyncMock()
|
||||
|
||||
with patch.object(store, '_session_factory') as mock_factory:
|
||||
mock_factory.return_value.__aenter__.return_value = mock_session
|
||||
|
||||
artifact_id = await store.register_artifact(
|
||||
artifact_id=None,
|
||||
artifact_type="image",
|
||||
source="platform",
|
||||
)
|
||||
|
||||
assert artifact_id is not None
|
||||
assert len(artifact_id) == 36 # UUID format
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_register_artifact_with_content(self):
|
||||
"""Test register_artifact stores content in BinaryStorage."""
|
||||
engine = self._make_mock_engine()
|
||||
store = ArtifactStore(engine)
|
||||
|
||||
mock_session = AsyncMock()
|
||||
mock_session.add = MagicMock()
|
||||
mock_session.commit = AsyncMock()
|
||||
|
||||
with patch.object(store, '_session_factory') as mock_factory:
|
||||
mock_factory.return_value.__aenter__.return_value = mock_session
|
||||
|
||||
content = b"test image content"
|
||||
artifact_id = await store.register_artifact(
|
||||
artifact_id="art_001",
|
||||
artifact_type="image",
|
||||
source="platform",
|
||||
content=content,
|
||||
)
|
||||
|
||||
assert artifact_id == "art_001"
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_register_artifact_with_storage_key(self):
|
||||
"""Test register_artifact with pre-existing storage_key."""
|
||||
engine = self._make_mock_engine()
|
||||
store = ArtifactStore(engine)
|
||||
|
||||
mock_session = AsyncMock()
|
||||
mock_session.add = MagicMock()
|
||||
mock_session.commit = AsyncMock()
|
||||
|
||||
with patch.object(store, '_session_factory') as mock_factory:
|
||||
mock_factory.return_value.__aenter__.return_value = mock_session
|
||||
|
||||
artifact_id = await store.register_artifact(
|
||||
artifact_id="art_002",
|
||||
artifact_type="file",
|
||||
source="runner",
|
||||
storage_key="existing_key",
|
||||
storage_type="binary_storage",
|
||||
size_bytes=1024,
|
||||
)
|
||||
|
||||
assert artifact_id == "art_002"
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_get_metadata_not_found(self):
|
||||
"""Test get_metadata returns None if not found."""
|
||||
engine = self._make_mock_engine()
|
||||
store = ArtifactStore(engine)
|
||||
|
||||
mock_result = MagicMock()
|
||||
mock_result.scalars.return_value.first.return_value = None
|
||||
|
||||
mock_session = AsyncMock()
|
||||
mock_session.execute = AsyncMock(return_value=mock_result)
|
||||
|
||||
with patch.object(store, '_session_factory') as mock_factory:
|
||||
mock_factory.return_value.__aenter__.return_value = mock_session
|
||||
|
||||
metadata = await store.get_metadata("nonexistent")
|
||||
|
||||
assert metadata is None
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_read_artifact_validates_offset(self):
|
||||
"""Test read_artifact rejects negative offset."""
|
||||
engine = self._make_mock_engine()
|
||||
store = ArtifactStore(engine)
|
||||
|
||||
with pytest.raises(ValueError, match="offset must be >= 0"):
|
||||
await store.read_artifact("art_001", offset=-1)
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_read_artifact_validates_limit(self):
|
||||
"""Test read_artifact rejects zero or negative limit."""
|
||||
engine = self._make_mock_engine()
|
||||
store = ArtifactStore(engine)
|
||||
|
||||
with pytest.raises(ValueError, match="limit must be > 0"):
|
||||
await store.read_artifact("art_001", limit=0)
|
||||
|
||||
with pytest.raises(ValueError, match="limit must be > 0"):
|
||||
await store.read_artifact("art_001", limit=-5)
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_read_artifact_not_found(self):
|
||||
"""Test read_artifact returns None if not found."""
|
||||
engine = self._make_mock_engine()
|
||||
store = ArtifactStore(engine)
|
||||
|
||||
mock_result = MagicMock()
|
||||
mock_result.scalars.return_value.first.return_value = None
|
||||
|
||||
mock_session = AsyncMock()
|
||||
mock_session.execute = AsyncMock(return_value=mock_result)
|
||||
|
||||
with patch.object(store, '_session_factory') as mock_factory:
|
||||
mock_factory.return_value.__aenter__.return_value = mock_session
|
||||
|
||||
result = await store.read_artifact("nonexistent")
|
||||
assert result is None
|
||||
|
||||
|
||||
class TestArtifactAuthorization:
|
||||
"""Test artifact action handler authorization."""
|
||||
|
||||
@pytest.fixture
|
||||
def mock_session_registry(self):
|
||||
"""Create a fresh session registry for testing."""
|
||||
# Reset global registry
|
||||
import langbot.pkg.agent.runner.session_registry as reg
|
||||
reg._global_registry = None
|
||||
return get_session_registry()
|
||||
|
||||
@pytest.fixture
|
||||
def mock_handler(self):
|
||||
"""Create a mock handler for testing actions."""
|
||||
from langbot_plugin.runtime.io.handler import Handler
|
||||
|
||||
class MockHandler(Handler):
|
||||
def __init__(self):
|
||||
self._responses = {}
|
||||
|
||||
async def call_action(self, action, data, timeout=30):
|
||||
# Simulate error response for missing run_id
|
||||
if not data.get("run_id"):
|
||||
return {"ok": False, "message": "run_id is required"}
|
||||
return {"ok": True, "data": {}}
|
||||
|
||||
return MockHandler()
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_artifact_metadata_requires_run_id(self, mock_handler):
|
||||
"""Test artifact_metadata requires run_id."""
|
||||
result = await mock_handler.call_action(
|
||||
"artifact_metadata",
|
||||
{"run_id": None, "artifact_id": "art_001"},
|
||||
)
|
||||
|
||||
assert result.get("ok") is False or "error" in str(result).lower()
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_artifact_read_requires_run_id(self, mock_handler):
|
||||
"""Test artifact_read requires run_id."""
|
||||
result = await mock_handler.call_action(
|
||||
"artifact_read",
|
||||
{"run_id": None, "artifact_id": "art_001"},
|
||||
)
|
||||
|
||||
assert result.get("ok") is False or "error" in str(result).lower()
|
||||
|
||||
|
||||
class TestArtifactAccessValidation:
|
||||
"""Test _validate_artifact_access authorization rules."""
|
||||
|
||||
def _call_validate(self, session, metadata, operation="metadata"):
|
||||
"""Helper to call the validation function."""
|
||||
from langbot.pkg.plugin.handler import _validate_artifact_access
|
||||
return _validate_artifact_access(session, metadata, operation)
|
||||
|
||||
def test_global_artifact_denied_by_default(self):
|
||||
"""Artifacts without conversation_id are denied by default (no global access)."""
|
||||
session = {
|
||||
"run_id": "run_001",
|
||||
"conversation_id": "conv_001",
|
||||
"permissions": {"artifacts": ["metadata", "read"]},
|
||||
}
|
||||
metadata = {
|
||||
"artifact_id": "art_global",
|
||||
"conversation_id": None, # No conversation scope
|
||||
"run_id": None, # Not created by any run
|
||||
}
|
||||
|
||||
is_allowed, error = self._call_validate(session, metadata)
|
||||
assert is_allowed is False
|
||||
assert "denied" in error.lower()
|
||||
|
||||
def test_own_run_artifact_allowed(self):
|
||||
"""Artifacts created by same run are allowed (even cross-conversation)."""
|
||||
session = {
|
||||
"run_id": "run_001",
|
||||
"conversation_id": "conv_001",
|
||||
"permissions": {"artifacts": ["metadata", "read"]},
|
||||
}
|
||||
metadata = {
|
||||
"artifact_id": "art_001",
|
||||
"conversation_id": "conv_other", # Different conversation
|
||||
"run_id": "run_001", # Same run
|
||||
}
|
||||
|
||||
is_allowed, error = self._call_validate(session, metadata)
|
||||
assert is_allowed is True
|
||||
assert error is None
|
||||
|
||||
def test_same_conversation_allowed(self):
|
||||
"""Artifacts in same conversation are allowed."""
|
||||
session = {
|
||||
"run_id": "run_001",
|
||||
"conversation_id": "conv_001",
|
||||
"permissions": {"artifacts": ["metadata", "read"]},
|
||||
}
|
||||
metadata = {
|
||||
"artifact_id": "art_001",
|
||||
"conversation_id": "conv_001", # Same as session
|
||||
"run_id": "run_other", # Different run
|
||||
}
|
||||
|
||||
is_allowed, error = self._call_validate(session, metadata)
|
||||
assert is_allowed is True
|
||||
assert error is None
|
||||
|
||||
def test_different_conversation_and_run_denied(self):
|
||||
"""Artifacts in different conversation and different run are denied."""
|
||||
session = {
|
||||
"run_id": "run_001",
|
||||
"conversation_id": "conv_001",
|
||||
"permissions": {"artifacts": ["metadata", "read"]},
|
||||
}
|
||||
metadata = {
|
||||
"artifact_id": "art_001",
|
||||
"conversation_id": "conv_other", # Different conversation
|
||||
"run_id": "run_other", # Different run
|
||||
}
|
||||
|
||||
is_allowed, error = self._call_validate(session, metadata)
|
||||
assert is_allowed is False
|
||||
assert "denied" in error.lower()
|
||||
|
||||
def test_session_without_conversation_denied_for_conversation_artifact(self):
|
||||
"""Session without conversation_id cannot access conversation-scoped artifacts."""
|
||||
session = {
|
||||
"run_id": "run_001",
|
||||
"conversation_id": None, # No conversation
|
||||
"permissions": {"artifacts": ["metadata", "read"]},
|
||||
}
|
||||
metadata = {
|
||||
"artifact_id": "art_001",
|
||||
"conversation_id": "conv_001", # Has conversation
|
||||
"run_id": "run_other", # Different run
|
||||
}
|
||||
|
||||
is_allowed, error = self._call_validate(session, metadata)
|
||||
assert is_allowed is False
|
||||
|
||||
def test_session_without_conversation_allowed_for_own_artifact(self):
|
||||
"""Session without conversation can access artifacts it created."""
|
||||
session = {
|
||||
"run_id": "run_001",
|
||||
"conversation_id": None, # No conversation
|
||||
"permissions": {"artifacts": ["metadata", "read"]},
|
||||
}
|
||||
metadata = {
|
||||
"artifact_id": "art_001",
|
||||
"conversation_id": "conv_001", # Has conversation
|
||||
"run_id": "run_001", # Same run (created by this run)
|
||||
}
|
||||
|
||||
is_allowed, error = self._call_validate(session, metadata)
|
||||
assert is_allowed is True
|
||||
|
||||
|
||||
class TestContextAccessArtifactAPIs:
|
||||
"""Test ContextAccess reflects artifact API permissions."""
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_context_access_has_artifact_apis_when_permitted(self):
|
||||
"""Test ContextAccess shows artifact APIs when permissions allow."""
|
||||
# This tests the context builder logic
|
||||
# When artifact permissions include 'metadata' and 'read',
|
||||
# available_apis should reflect that
|
||||
permissions = {"artifacts": ["metadata", "read"]}
|
||||
|
||||
# Check that permissions are properly interpreted
|
||||
artifact_metadata_enabled = "metadata" in permissions.get("artifacts", [])
|
||||
artifact_read_enabled = "read" in permissions.get("artifacts", [])
|
||||
|
||||
assert artifact_metadata_enabled is True
|
||||
assert artifact_read_enabled is True
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_context_access_no_artifact_apis_without_permission(self):
|
||||
"""Test ContextAccess hides artifact APIs when permissions denied."""
|
||||
permissions = {"artifacts": []}
|
||||
|
||||
artifact_metadata_enabled = "metadata" in permissions.get("artifacts", [])
|
||||
artifact_read_enabled = "read" in permissions.get("artifacts", [])
|
||||
|
||||
assert artifact_metadata_enabled is False
|
||||
assert artifact_read_enabled is False
|
||||
|
||||
|
||||
class TestArtifactMetadataFieldAlignment:
|
||||
"""Test that Host returns metadata compatible with SDK ArtifactMetadata."""
|
||||
|
||||
def test_row_to_public_dict_excludes_host_only_fields(self):
|
||||
"""_row_to_public_dict should not return Host-only fields."""
|
||||
from langbot.pkg.agent.runner.artifact_store import ArtifactStore
|
||||
from langbot.pkg.entity.persistence.artifact import AgentArtifact
|
||||
from unittest.mock import MagicMock
|
||||
|
||||
# Create a mock row
|
||||
mock_row = MagicMock(spec=AgentArtifact)
|
||||
mock_row.artifact_id = "art_001"
|
||||
mock_row.artifact_type = "image"
|
||||
mock_row.mime_type = "image/png"
|
||||
mock_row.name = "test.png"
|
||||
mock_row.size_bytes = 1024
|
||||
mock_row.sha256 = "abc123"
|
||||
mock_row.source = "platform"
|
||||
mock_row.conversation_id = "conv_001"
|
||||
mock_row.run_id = "run_001"
|
||||
mock_row.runner_id = "plugin:test/plugin/runner"
|
||||
mock_row.created_at = datetime.datetime(2024, 1, 1, 0, 0, 0)
|
||||
mock_row.expires_at = None
|
||||
mock_row.metadata_json = None
|
||||
|
||||
# These are Host-only fields that should NOT be in output
|
||||
# (they don't exist in SDK ArtifactMetadata)
|
||||
mock_row.bot_id = "bot_001"
|
||||
mock_row.workspace_id = "ws_001"
|
||||
mock_row.storage_key = "artifact:art_001"
|
||||
mock_row.storage_type = "binary_storage"
|
||||
|
||||
store = ArtifactStore(MagicMock())
|
||||
result = store._row_to_public_dict(mock_row)
|
||||
|
||||
# SDK-compatible fields should be present
|
||||
assert result["artifact_id"] == "art_001"
|
||||
assert result["artifact_type"] == "image"
|
||||
assert result["source"] == "platform"
|
||||
assert result["conversation_id"] == "conv_001"
|
||||
assert result["run_id"] == "run_001"
|
||||
|
||||
# Host-only fields should NOT be present
|
||||
assert "bot_id" not in result
|
||||
assert "workspace_id" not in result
|
||||
assert "storage_key" not in result
|
||||
assert "storage_type" not in result
|
||||
|
||||
|
||||
class TestSessionRegistryPermissions:
|
||||
"""Test that session registry stores and retrieves permissions correctly."""
|
||||
|
||||
@pytest.fixture
|
||||
def session_registry(self):
|
||||
"""Create a fresh session registry for testing."""
|
||||
import langbot.pkg.agent.runner.session_registry as reg
|
||||
reg._global_registry = None
|
||||
return get_session_registry()
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_register_stores_permissions(self, session_registry):
|
||||
"""Test that register() stores permissions from descriptor."""
|
||||
await session_registry.register(
|
||||
run_id="run_001",
|
||||
runner_id="plugin:author/plugin/runner",
|
||||
query_id=None,
|
||||
plugin_identity="author/plugin",
|
||||
resources={
|
||||
"models": [],
|
||||
"tools": [],
|
||||
"knowledge_bases": [],
|
||||
"files": [],
|
||||
"storage": {"plugin_storage": True, "workspace_storage": False},
|
||||
"platform_capabilities": {},
|
||||
},
|
||||
permissions={
|
||||
"artifacts": ["metadata", "read"],
|
||||
"history": ["page"],
|
||||
"events": ["get"],
|
||||
},
|
||||
conversation_id="conv_001",
|
||||
)
|
||||
|
||||
session = await session_registry.get("run_001")
|
||||
assert session is not None
|
||||
assert session["permissions"]["artifacts"] == ["metadata", "read"]
|
||||
assert session["permissions"]["history"] == ["page"]
|
||||
assert session["permissions"]["events"] == ["get"]
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_register_with_empty_permissions(self, session_registry):
|
||||
"""Test that register() handles empty permissions."""
|
||||
await session_registry.register(
|
||||
run_id="run_002",
|
||||
runner_id="plugin:author/plugin/runner",
|
||||
query_id=None,
|
||||
plugin_identity="author/plugin",
|
||||
resources={
|
||||
"models": [],
|
||||
"tools": [],
|
||||
"knowledge_bases": [],
|
||||
"files": [],
|
||||
"storage": {"plugin_storage": True, "workspace_storage": False},
|
||||
"platform_capabilities": {},
|
||||
},
|
||||
permissions={},
|
||||
conversation_id="conv_001",
|
||||
)
|
||||
|
||||
session = await session_registry.get("run_002")
|
||||
assert session is not None
|
||||
assert session["permissions"] == {}
|
||||
|
||||
|
||||
class TestArtifactStoreRealSQLite:
|
||||
"""Test ArtifactStore with real SQLite database."""
|
||||
|
||||
@pytest.fixture
|
||||
async def db_engine(self):
|
||||
"""Create an in-memory SQLite database for testing."""
|
||||
from sqlalchemy.ext.asyncio import create_async_engine
|
||||
from sqlalchemy import text
|
||||
from langbot.pkg.entity.persistence.base import Base
|
||||
from langbot.pkg.entity.persistence.artifact import AgentArtifact
|
||||
from langbot.pkg.entity.persistence.bstorage import BinaryStorage
|
||||
|
||||
engine = create_async_engine("sqlite+aiosqlite:///:memory:")
|
||||
|
||||
# Create tables
|
||||
async with engine.begin() as conn:
|
||||
# Create tables manually for in-memory DB
|
||||
await conn.run_sync(Base.metadata.create_all)
|
||||
|
||||
yield engine
|
||||
|
||||
await engine.dispose()
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_register_get_metadata_round_trip(self, db_engine):
|
||||
"""Test register_artifact -> get_metadata round trip with real DB."""
|
||||
store = ArtifactStore(db_engine)
|
||||
|
||||
# Register artifact with content
|
||||
content = b"test image content for round trip"
|
||||
artifact_id = await store.register_artifact(
|
||||
artifact_id="art_real_001",
|
||||
artifact_type="image",
|
||||
source="platform",
|
||||
mime_type="image/png",
|
||||
name="test.png",
|
||||
content=content,
|
||||
conversation_id="conv_001",
|
||||
run_id="run_001",
|
||||
)
|
||||
|
||||
assert artifact_id == "art_real_001"
|
||||
|
||||
# Get metadata
|
||||
metadata = await store.get_metadata(artifact_id)
|
||||
assert metadata is not None
|
||||
assert metadata["artifact_id"] == "art_real_001"
|
||||
assert metadata["artifact_type"] == "image"
|
||||
assert metadata["mime_type"] == "image/png"
|
||||
assert metadata["source"] == "platform"
|
||||
assert metadata["conversation_id"] == "conv_001"
|
||||
assert metadata["run_id"] == "run_001"
|
||||
|
||||
# Verify Host-only fields are NOT in public metadata
|
||||
assert "storage_key" not in metadata
|
||||
assert "storage_type" not in metadata
|
||||
assert "bot_id" not in metadata
|
||||
assert "workspace_id" not in metadata
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_read_artifact_round_trip(self, db_engine):
|
||||
"""Test register_artifact -> read_artifact round trip with real DB."""
|
||||
store = ArtifactStore(db_engine)
|
||||
|
||||
# Register artifact with content
|
||||
content = b"test file content for read test"
|
||||
artifact_id = await store.register_artifact(
|
||||
artifact_id="art_real_002",
|
||||
artifact_type="file",
|
||||
source="runner",
|
||||
mime_type="text/plain",
|
||||
name="test.txt",
|
||||
content=content,
|
||||
conversation_id="conv_001",
|
||||
run_id="run_001",
|
||||
)
|
||||
|
||||
# Read artifact
|
||||
result = await store.read_artifact(artifact_id)
|
||||
assert result is not None
|
||||
assert result["artifact_id"] == "art_real_002"
|
||||
assert result["mime_type"] == "text/plain"
|
||||
assert result["offset"] == 0
|
||||
assert result["length"] == len(content)
|
||||
assert result["has_more"] is False
|
||||
|
||||
# Verify content
|
||||
decoded_content = base64.b64decode(result["content_base64"])
|
||||
assert decoded_content == content
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_read_artifact_with_offset_limit(self, db_engine):
|
||||
"""Test read_artifact with offset and limit."""
|
||||
store = ArtifactStore(db_engine)
|
||||
|
||||
# Register artifact with content
|
||||
content = b"0123456789" * 100 # 1000 bytes
|
||||
artifact_id = await store.register_artifact(
|
||||
artifact_id="art_real_003",
|
||||
artifact_type="file",
|
||||
source="runner",
|
||||
mime_type="application/octet-stream",
|
||||
content=content,
|
||||
)
|
||||
|
||||
# Read with offset
|
||||
result = await store.read_artifact(artifact_id, offset=100, limit=100)
|
||||
assert result is not None
|
||||
assert result["offset"] == 100
|
||||
assert result["length"] == 100
|
||||
|
||||
# Verify content
|
||||
decoded_content = base64.b64decode(result["content_base64"])
|
||||
assert decoded_content == content[100:200]
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_read_artifact_has_more(self, db_engine):
|
||||
"""Test read_artifact sets has_more correctly."""
|
||||
store = ArtifactStore(db_engine)
|
||||
|
||||
# Register artifact with content
|
||||
content = b"0123456789" * 100 # 1000 bytes
|
||||
artifact_id = await store.register_artifact(
|
||||
artifact_id="art_real_004",
|
||||
artifact_type="file",
|
||||
source="runner",
|
||||
content=content,
|
||||
)
|
||||
|
||||
# Read with limit smaller than content
|
||||
result = await store.read_artifact(artifact_id, offset=0, limit=100)
|
||||
assert result is not None
|
||||
assert result["has_more"] is True
|
||||
assert result["length"] == 100
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_metadata_sdk_validation(self, db_engine):
|
||||
"""Test that metadata can be validated by SDK ArtifactMetadata."""
|
||||
from langbot_plugin.api.entities.builtin.agent_runner.artifact import ArtifactMetadata
|
||||
|
||||
store = ArtifactStore(db_engine)
|
||||
|
||||
# Register artifact
|
||||
artifact_id = await store.register_artifact(
|
||||
artifact_id="art_real_005",
|
||||
artifact_type="file",
|
||||
source="runner",
|
||||
mime_type="application/pdf",
|
||||
name="document.pdf",
|
||||
size_bytes=1024,
|
||||
conversation_id="conv_001",
|
||||
run_id="run_001",
|
||||
runner_id="plugin:test/plugin/runner",
|
||||
)
|
||||
|
||||
# Get metadata
|
||||
metadata = await store.get_metadata(artifact_id)
|
||||
assert metadata is not None
|
||||
|
||||
# Should not raise ValidationError
|
||||
validated = ArtifactMetadata.model_validate(metadata)
|
||||
assert validated.artifact_id == "art_real_005"
|
||||
assert validated.artifact_type == "file"
|
||||
@@ -1,553 +0,0 @@
|
||||
"""Tests for ChatMessageHandler behavior with AgentRunOrchestrator.
|
||||
|
||||
Tests focus on:
|
||||
- Streaming mode behavior (single resp_message_id, pop/append pattern)
|
||||
- Non-streaming mode behavior (no pop)
|
||||
- Orchestrator invocation
|
||||
- Error handling for RunnerNotFoundError, RunnerExecutionError
|
||||
|
||||
Avoids circular imports by using proper import structure.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import uuid
|
||||
import pytest
|
||||
from unittest.mock import AsyncMock, MagicMock, patch
|
||||
|
||||
from langbot.pkg.agent.runner.errors import (
|
||||
RunnerNotFoundError,
|
||||
RunnerExecutionError,
|
||||
RunnerNotAuthorizedError,
|
||||
)
|
||||
from langbot.pkg.agent.runner.config_migration import ConfigMigration
|
||||
|
||||
|
||||
# Define mock classes in dependency order (no forward references needed)
|
||||
|
||||
class MockLauncherType:
|
||||
value = 'person'
|
||||
|
||||
|
||||
class MockConversation:
|
||||
uuid = 'conv-uuid'
|
||||
messages = []
|
||||
|
||||
|
||||
class MockMessage:
|
||||
role = 'user'
|
||||
content = 'Hello'
|
||||
|
||||
|
||||
class MockAdapter:
|
||||
is_stream = False
|
||||
|
||||
async def is_stream_output_supported(self):
|
||||
return self.is_stream
|
||||
|
||||
async def create_message_card(self, resp_message_id, message_event):
|
||||
pass
|
||||
|
||||
|
||||
class MockSession:
|
||||
launcher_type = MockLauncherType()
|
||||
launcher_id = 'user123'
|
||||
using_conversation = MockConversation()
|
||||
|
||||
|
||||
class MockQuery:
|
||||
"""Mock Query for testing."""
|
||||
def __init__(self):
|
||||
self.query_id = 1
|
||||
self.launcher_type = MockLauncherType()
|
||||
self.launcher_id = 'user123'
|
||||
self.sender_id = 'user123'
|
||||
self.bot_uuid = 'bot-uuid'
|
||||
self.pipeline_uuid = 'pipeline-uuid'
|
||||
self.pipeline_config = {
|
||||
'ai': {
|
||||
'runner': {
|
||||
'id': 'plugin:langbot/local-agent/default',
|
||||
},
|
||||
'runner_config': {},
|
||||
},
|
||||
'output': {
|
||||
'misc': {
|
||||
'exception-handling': 'show-hint',
|
||||
'failure-hint': 'Request failed.',
|
||||
},
|
||||
},
|
||||
}
|
||||
self.variables = {}
|
||||
self.session = MockSession()
|
||||
self.user_message = MockMessage()
|
||||
self.messages = []
|
||||
self.resp_messages = []
|
||||
self.resp_message_chain = None
|
||||
self.adapter = MockAdapter()
|
||||
self.message_event = MagicMock()
|
||||
self.message_chain = MagicMock()
|
||||
|
||||
|
||||
class MockMessageChunk:
|
||||
"""Mock MessageChunk for testing."""
|
||||
def __init__(self, content, resp_message_id=None):
|
||||
self.role = 'assistant'
|
||||
self.content = content
|
||||
self.resp_message_id = resp_message_id
|
||||
self.is_final = False
|
||||
|
||||
def readable_str(self):
|
||||
return self.content
|
||||
|
||||
|
||||
class MockEventContext:
|
||||
"""Mock event context for testing."""
|
||||
def __init__(self, prevented=False, reply_message_chain=None, user_message_alter=None):
|
||||
self._prevented = prevented
|
||||
self.event = MagicMock()
|
||||
self.event.reply_message_chain = reply_message_chain
|
||||
self.event.user_message_alter = user_message_alter
|
||||
|
||||
def is_prevented_default(self):
|
||||
return self._prevented
|
||||
|
||||
|
||||
class MockAgentRunOrchestrator:
|
||||
"""Mock AgentRunOrchestrator for testing."""
|
||||
def __init__(self, chunks=None, error=None):
|
||||
self._chunks = chunks or []
|
||||
self._error = error
|
||||
|
||||
async def run_from_query(self, query):
|
||||
"""Async generator that yields chunks or raises error."""
|
||||
if self._error:
|
||||
raise self._error
|
||||
for chunk in self._chunks:
|
||||
yield chunk
|
||||
|
||||
def resolve_runner_id_for_telemetry(self, query):
|
||||
return 'plugin:langbot/local-agent/default'
|
||||
|
||||
|
||||
class MockApplication:
|
||||
"""Mock Application for testing."""
|
||||
def __init__(self, orchestrator=None):
|
||||
self.agent_run_orchestrator = orchestrator or MockAgentRunOrchestrator()
|
||||
self.logger = MagicMock()
|
||||
self.logger.info = MagicMock()
|
||||
self.logger.debug = MagicMock()
|
||||
self.logger.warning = MagicMock()
|
||||
self.logger.error = MagicMock()
|
||||
|
||||
# Mock plugin_connector
|
||||
self.plugin_connector = MagicMock()
|
||||
self.plugin_connector.emit_event = AsyncMock(return_value=MockEventContext())
|
||||
|
||||
# Mock telemetry
|
||||
self.telemetry = MagicMock()
|
||||
self.telemetry.start_send_task = AsyncMock()
|
||||
|
||||
# Mock survey
|
||||
self.survey = MagicMock()
|
||||
self.survey.trigger_event = AsyncMock()
|
||||
|
||||
# Mock model_mgr
|
||||
self.model_mgr = MagicMock()
|
||||
self.model_mgr.get_model_by_uuid = AsyncMock(return_value=None)
|
||||
|
||||
|
||||
class TestStreamingBehavior:
|
||||
"""Tests for streaming mode behavior."""
|
||||
|
||||
def test_single_resp_message_id_for_streaming(self):
|
||||
"""Streaming mode should use single resp_message_id for entire response."""
|
||||
# Simulate the streaming logic: resp_message_id created outside loop
|
||||
resp_message_id = uuid.uuid4()
|
||||
|
||||
chunks = ['Hello', ' World', '!']
|
||||
resp_messages = []
|
||||
|
||||
for chunk in chunks:
|
||||
result = MockMessageChunk(chunk)
|
||||
result.resp_message_id = str(resp_message_id)
|
||||
|
||||
# Pop old chunk (streaming behavior)
|
||||
if resp_messages:
|
||||
resp_messages.pop()
|
||||
resp_messages.append(result)
|
||||
|
||||
# All chunks should have same resp_message_id
|
||||
assert len(resp_messages) == 1 # Only last chunk remains after pop/append
|
||||
assert resp_messages[0].resp_message_id == str(resp_message_id)
|
||||
|
||||
def test_pop_before_append_in_streaming(self):
|
||||
"""Streaming mode should pop old chunk before appending new."""
|
||||
resp_message_id = uuid.uuid4()
|
||||
resp_messages = []
|
||||
|
||||
# First chunk - no pop
|
||||
chunk1 = MockMessageChunk('Hello')
|
||||
chunk1.resp_message_id = str(resp_message_id)
|
||||
resp_messages.append(chunk1)
|
||||
assert len(resp_messages) == 1
|
||||
|
||||
# Second chunk - pop first, then append
|
||||
if resp_messages:
|
||||
resp_messages.pop()
|
||||
chunk2 = MockMessageChunk('Hello World')
|
||||
chunk2.resp_message_id = str(resp_message_id)
|
||||
resp_messages.append(chunk2)
|
||||
assert len(resp_messages) == 1
|
||||
assert resp_messages[0].content == 'Hello World'
|
||||
|
||||
def test_non_streaming_no_pop(self):
|
||||
"""Non-streaming mode should NOT pop previous responses."""
|
||||
resp_messages = []
|
||||
|
||||
# First message
|
||||
msg1 = MockMessageChunk('Response 1')
|
||||
resp_messages.append(msg1)
|
||||
assert len(resp_messages) == 1
|
||||
|
||||
# Second message - should NOT pop in non-streaming
|
||||
msg2 = MockMessageChunk('Response 2')
|
||||
resp_messages.append(msg2)
|
||||
assert len(resp_messages) == 2
|
||||
|
||||
|
||||
class TestConfigMigrationInChatHandler:
|
||||
"""Tests for ConfigMigration usage in chat handler context."""
|
||||
|
||||
def test_resolve_runner_id_from_pipeline_config(self):
|
||||
"""Chat handler should use ConfigMigration to resolve runner ID."""
|
||||
pipeline_config = {
|
||||
'ai': {
|
||||
'runner': {
|
||||
'id': 'plugin:langbot/local-agent/default',
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
runner_id = ConfigMigration.resolve_runner_id(pipeline_config)
|
||||
assert runner_id == 'plugin:langbot/local-agent/default'
|
||||
|
||||
def test_resolve_runner_id_from_old_format(self):
|
||||
"""ConfigMigration should handle old runner format."""
|
||||
pipeline_config = {
|
||||
'ai': {
|
||||
'runner': {
|
||||
'runner': 'local-agent',
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
runner_id = ConfigMigration.resolve_runner_id(pipeline_config)
|
||||
assert runner_id == 'plugin:langbot/local-agent/default'
|
||||
|
||||
|
||||
class TestErrorHandling:
|
||||
"""Tests for orchestrator error handling."""
|
||||
|
||||
def test_runner_not_found_error_properties(self):
|
||||
"""RunnerNotFoundError should have runner_id property."""
|
||||
error = RunnerNotFoundError('plugin:notexist/unknown/default')
|
||||
assert error.runner_id == 'plugin:notexist/unknown/default'
|
||||
assert 'not found' in str(error)
|
||||
|
||||
def test_runner_execution_error_retryable(self):
|
||||
"""RunnerExecutionError should have retryable property."""
|
||||
error = RunnerExecutionError(
|
||||
'plugin:langbot/local-agent/default',
|
||||
'Upstream timeout',
|
||||
retryable=True,
|
||||
)
|
||||
assert error.runner_id == 'plugin:langbot/local-agent/default'
|
||||
assert error.retryable is True
|
||||
assert 'timeout' in str(error)
|
||||
|
||||
def test_runner_execution_error_not_retryable(self):
|
||||
"""RunnerExecutionError can be non-retryable."""
|
||||
error = RunnerExecutionError(
|
||||
'plugin:langbot/local-agent/default',
|
||||
'Configuration error',
|
||||
retryable=False,
|
||||
)
|
||||
assert error.retryable is False
|
||||
|
||||
def test_runner_not_authorized_error_properties(self):
|
||||
"""RunnerNotAuthorizedError should have bound_plugins property."""
|
||||
error = RunnerNotAuthorizedError(
|
||||
'plugin:langbot/local-agent/default',
|
||||
['langbot/dify-agent'],
|
||||
)
|
||||
assert error.runner_id == 'plugin:langbot/local-agent/default'
|
||||
assert error.bound_plugins == ['langbot/dify-agent']
|
||||
|
||||
|
||||
class TestChatHandlerImports:
|
||||
"""Test that chat handler can be imported without circular import."""
|
||||
|
||||
def test_import_chat_handler_module(self):
|
||||
"""Import chat handler module should work."""
|
||||
# This test verifies the import works without circular dependency
|
||||
from langbot.pkg.pipeline.process.handlers import chat
|
||||
assert chat.ChatMessageHandler is not None
|
||||
|
||||
def test_chat_handler_class_exists(self):
|
||||
"""ChatMessageHandler class should be defined."""
|
||||
from langbot.pkg.pipeline.process.handlers.chat import ChatMessageHandler
|
||||
assert ChatMessageHandler.__name__ == 'ChatMessageHandler'
|
||||
|
||||
def test_chat_handler_has_handle_method(self):
|
||||
"""ChatMessageHandler should have async generator handle method."""
|
||||
from langbot.pkg.pipeline.process.handlers.chat import ChatMessageHandler
|
||||
assert hasattr(ChatMessageHandler, 'handle')
|
||||
# handle returns AsyncGenerator, so check for async generator function
|
||||
import inspect
|
||||
assert inspect.isasyncgenfunction(ChatMessageHandler.handle)
|
||||
|
||||
|
||||
class TestChatHandlerAsyncBehavior:
|
||||
"""Real async tests for ChatMessageHandler.handle() with mocked orchestrator."""
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_streaming_single_resp_message_id(self):
|
||||
"""Streaming mode: all chunks should have same resp_message_id."""
|
||||
from langbot.pkg.pipeline.process.handlers.chat import ChatMessageHandler
|
||||
from langbot.pkg.pipeline import entities
|
||||
|
||||
# Create chunks for streaming
|
||||
chunks = [
|
||||
MockMessageChunk('Hello'),
|
||||
MockMessageChunk('Hello World'),
|
||||
MockMessageChunk('Hello World!'),
|
||||
]
|
||||
|
||||
orchestrator = MockAgentRunOrchestrator(chunks=chunks)
|
||||
mock_ap = MockApplication(orchestrator=orchestrator)
|
||||
|
||||
# Mock event context to not prevent default
|
||||
event_ctx = MockEventContext(prevented=False)
|
||||
mock_ap.plugin_connector.emit_event = AsyncMock(return_value=event_ctx)
|
||||
|
||||
query = MockQuery()
|
||||
query.adapter.is_stream = True # Enable streaming mode
|
||||
|
||||
handler = ChatMessageHandler(mock_ap)
|
||||
|
||||
# Mock event creation and StageProcessResult to bypass pydantic validation
|
||||
mock_event = MagicMock()
|
||||
mock_event.return_value = MagicMock()
|
||||
|
||||
def make_result(*args, **kwargs):
|
||||
return MagicMock(result_type=kwargs.get('result_type', entities.ResultType.CONTINUE))
|
||||
|
||||
with patch('langbot.pkg.pipeline.process.handlers.chat.events') as mock_events_module, \
|
||||
patch('langbot.pkg.pipeline.entities.StageProcessResult', side_effect=make_result):
|
||||
mock_events_module.PersonNormalMessageReceived = mock_event
|
||||
mock_events_module.GroupNormalMessageReceived = mock_event
|
||||
|
||||
results = []
|
||||
async for result in handler.handle(query):
|
||||
results.append(result)
|
||||
|
||||
# Verify single resp_message_id
|
||||
resp_ids = [msg.resp_message_id for msg in query.resp_messages if hasattr(msg, 'resp_message_id')]
|
||||
assert len(set(resp_ids)) == 1 # All same ID
|
||||
|
||||
# Verify pop/append pattern: only last chunk remains
|
||||
assert len(query.resp_messages) == 1
|
||||
assert query.resp_messages[0].content == 'Hello World!'
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_non_streaming_no_pop(self):
|
||||
"""Non-streaming mode: all chunks should remain."""
|
||||
from langbot.pkg.pipeline.process.handlers.chat import ChatMessageHandler
|
||||
from langbot.pkg.pipeline import entities
|
||||
|
||||
chunks = [
|
||||
MockMessageChunk('Response 1'),
|
||||
MockMessageChunk('Response 2'),
|
||||
]
|
||||
|
||||
orchestrator = MockAgentRunOrchestrator(chunks=chunks)
|
||||
mock_ap = MockApplication(orchestrator=orchestrator)
|
||||
mock_ap.plugin_connector.emit_event = AsyncMock(return_value=MockEventContext(prevented=False))
|
||||
|
||||
query = MockQuery()
|
||||
query.adapter.is_stream = False # Disable streaming mode
|
||||
|
||||
handler = ChatMessageHandler(mock_ap)
|
||||
|
||||
mock_event = MagicMock()
|
||||
mock_event.return_value = MagicMock()
|
||||
|
||||
def make_result(*args, **kwargs):
|
||||
return MagicMock(result_type=kwargs.get('result_type', entities.ResultType.CONTINUE))
|
||||
|
||||
with patch('langbot.pkg.pipeline.process.handlers.chat.events') as mock_events_module, \
|
||||
patch('langbot.pkg.pipeline.entities.StageProcessResult', side_effect=make_result):
|
||||
mock_events_module.PersonNormalMessageReceived = mock_event
|
||||
mock_events_module.GroupNormalMessageReceived = mock_event
|
||||
|
||||
results = []
|
||||
async for result in handler.handle(query):
|
||||
results.append(result)
|
||||
|
||||
# No pop: all chunks should remain
|
||||
assert len(query.resp_messages) == 2
|
||||
assert query.resp_messages[0].content == 'Response 1'
|
||||
assert query.resp_messages[1].content == 'Response 2'
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_runner_not_found_error(self):
|
||||
"""Handler should catch RunnerNotFoundError and return INTERRUPT."""
|
||||
from langbot.pkg.pipeline.process.handlers.chat import ChatMessageHandler
|
||||
from langbot.pkg.pipeline import entities
|
||||
|
||||
orchestrator = MockAgentRunOrchestrator(
|
||||
error=RunnerNotFoundError('plugin:notexist/unknown/default')
|
||||
)
|
||||
mock_ap = MockApplication(orchestrator=orchestrator)
|
||||
mock_ap.plugin_connector.emit_event = AsyncMock(return_value=MockEventContext(prevented=False))
|
||||
|
||||
query = MockQuery()
|
||||
|
||||
handler = ChatMessageHandler(mock_ap)
|
||||
|
||||
mock_event = MagicMock()
|
||||
mock_event.return_value = MagicMock()
|
||||
|
||||
def make_result(*args, **kwargs):
|
||||
return MagicMock(
|
||||
result_type=kwargs.get('result_type'),
|
||||
user_notice=kwargs.get('user_notice'),
|
||||
)
|
||||
|
||||
with patch('langbot.pkg.pipeline.process.handlers.chat.events') as mock_events_module, \
|
||||
patch('langbot.pkg.pipeline.entities.StageProcessResult', side_effect=make_result):
|
||||
mock_events_module.PersonNormalMessageReceived = mock_event
|
||||
mock_events_module.GroupNormalMessageReceived = mock_event
|
||||
|
||||
results = []
|
||||
async for result in handler.handle(query):
|
||||
results.append(result)
|
||||
|
||||
# Should return INTERRUPT with user_notice
|
||||
assert len(results) == 1
|
||||
assert results[0].result_type == entities.ResultType.INTERRUPT
|
||||
assert 'not found' in results[0].user_notice
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_runner_not_authorized_error(self):
|
||||
"""Handler should catch RunnerNotAuthorizedError and return INTERRUPT."""
|
||||
from langbot.pkg.pipeline.process.handlers.chat import ChatMessageHandler
|
||||
from langbot.pkg.pipeline import entities
|
||||
|
||||
orchestrator = MockAgentRunOrchestrator(
|
||||
error=RunnerNotAuthorizedError('plugin:langbot/local-agent/default', ['other/plugin'])
|
||||
)
|
||||
mock_ap = MockApplication(orchestrator=orchestrator)
|
||||
mock_ap.plugin_connector.emit_event = AsyncMock(return_value=MockEventContext(prevented=False))
|
||||
|
||||
query = MockQuery()
|
||||
|
||||
handler = ChatMessageHandler(mock_ap)
|
||||
|
||||
mock_event = MagicMock()
|
||||
mock_event.return_value = MagicMock()
|
||||
|
||||
def make_result(*args, **kwargs):
|
||||
return MagicMock(
|
||||
result_type=kwargs.get('result_type'),
|
||||
user_notice=kwargs.get('user_notice'),
|
||||
)
|
||||
|
||||
with patch('langbot.pkg.pipeline.process.handlers.chat.events') as mock_events_module, \
|
||||
patch('langbot.pkg.pipeline.entities.StageProcessResult', side_effect=make_result):
|
||||
mock_events_module.PersonNormalMessageReceived = mock_event
|
||||
mock_events_module.GroupNormalMessageReceived = mock_event
|
||||
|
||||
results = []
|
||||
async for result in handler.handle(query):
|
||||
results.append(result)
|
||||
|
||||
assert len(results) == 1
|
||||
assert results[0].result_type == entities.ResultType.INTERRUPT
|
||||
assert 'not authorized' in results[0].user_notice
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_runner_execution_error_retryable(self):
|
||||
"""Handler should catch retryable RunnerExecutionError."""
|
||||
from langbot.pkg.pipeline.process.handlers.chat import ChatMessageHandler
|
||||
from langbot.pkg.pipeline import entities
|
||||
|
||||
orchestrator = MockAgentRunOrchestrator(
|
||||
error=RunnerExecutionError('plugin:langbot/local-agent/default', 'timeout', retryable=True)
|
||||
)
|
||||
mock_ap = MockApplication(orchestrator=orchestrator)
|
||||
mock_ap.plugin_connector.emit_event = AsyncMock(return_value=MockEventContext(prevented=False))
|
||||
|
||||
query = MockQuery()
|
||||
|
||||
handler = ChatMessageHandler(mock_ap)
|
||||
|
||||
mock_event = MagicMock()
|
||||
mock_event.return_value = MagicMock()
|
||||
|
||||
def make_result(*args, **kwargs):
|
||||
return MagicMock(
|
||||
result_type=kwargs.get('result_type'),
|
||||
user_notice=kwargs.get('user_notice'),
|
||||
)
|
||||
|
||||
with patch('langbot.pkg.pipeline.process.handlers.chat.events') as mock_events_module, \
|
||||
patch('langbot.pkg.pipeline.entities.StageProcessResult', side_effect=make_result):
|
||||
mock_events_module.PersonNormalMessageReceived = mock_event
|
||||
mock_events_module.GroupNormalMessageReceived = mock_event
|
||||
|
||||
results = []
|
||||
async for result in handler.handle(query):
|
||||
results.append(result)
|
||||
|
||||
assert len(results) == 1
|
||||
assert results[0].result_type == entities.ResultType.INTERRUPT
|
||||
assert 'temporarily unavailable' in results[0].user_notice
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_prevented_default_with_reply(self):
|
||||
"""When event prevented default with reply, use reply message."""
|
||||
from langbot.pkg.pipeline.process.handlers.chat import ChatMessageHandler
|
||||
from langbot.pkg.pipeline import entities
|
||||
|
||||
# Mock reply message chain
|
||||
reply_chain = MockMessageChunk('Reply from plugin')
|
||||
|
||||
mock_ap = MockApplication()
|
||||
mock_ap.plugin_connector.emit_event = AsyncMock(
|
||||
return_value=MockEventContext(prevented=True, reply_message_chain=reply_chain)
|
||||
)
|
||||
|
||||
query = MockQuery()
|
||||
|
||||
handler = ChatMessageHandler(mock_ap)
|
||||
|
||||
mock_event = MagicMock()
|
||||
mock_event.return_value = MagicMock()
|
||||
|
||||
def make_result(*args, **kwargs):
|
||||
return MagicMock(result_type=kwargs.get('result_type', entities.ResultType.CONTINUE))
|
||||
|
||||
with patch('langbot.pkg.pipeline.process.handlers.chat.events') as mock_events_module, \
|
||||
patch('langbot.pkg.pipeline.entities.StageProcessResult', side_effect=make_result):
|
||||
mock_events_module.PersonNormalMessageReceived = mock_event
|
||||
mock_events_module.GroupNormalMessageReceived = mock_event
|
||||
|
||||
results = []
|
||||
async for result in handler.handle(query):
|
||||
results.append(result)
|
||||
|
||||
# Should return CONTINUE with reply message
|
||||
assert len(results) == 1
|
||||
assert results[0].result_type == entities.ResultType.CONTINUE
|
||||
assert len(query.resp_messages) == 1
|
||||
@@ -1,251 +0,0 @@
|
||||
"""Tests for agent runner config migration."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
|
||||
from langbot.pkg.agent.runner.config_migration import (
|
||||
ConfigMigration,
|
||||
OLD_RUNNER_TO_PLUGIN_RUNNER_ID,
|
||||
)
|
||||
|
||||
|
||||
class TestOldRunnerMapping:
|
||||
"""Tests for OLD_RUNNER_TO_PLUGIN_RUNNER_ID mapping."""
|
||||
|
||||
def test_local_agent_mapping(self):
|
||||
"""Local-agent should map to official plugin."""
|
||||
assert OLD_RUNNER_TO_PLUGIN_RUNNER_ID['local-agent'] == 'plugin:langbot/local-agent/default'
|
||||
|
||||
def test_dify_mapping(self):
|
||||
"""Dify should map to official plugin."""
|
||||
assert OLD_RUNNER_TO_PLUGIN_RUNNER_ID['dify-service-api'] == 'plugin:langbot/dify-agent/default'
|
||||
|
||||
def test_n8n_mapping(self):
|
||||
"""n8n should map to official plugin."""
|
||||
assert OLD_RUNNER_TO_PLUGIN_RUNNER_ID['n8n-service-api'] == 'plugin:langbot/n8n-agent/default'
|
||||
|
||||
def test_coze_mapping(self):
|
||||
"""Coze should map to official plugin."""
|
||||
assert OLD_RUNNER_TO_PLUGIN_RUNNER_ID['coze-api'] == 'plugin:langbot/coze-agent/default'
|
||||
|
||||
def test_all_runners_mapped(self):
|
||||
"""All old runners should have mapping."""
|
||||
expected_runners = [
|
||||
'local-agent',
|
||||
'dify-service-api',
|
||||
'n8n-service-api',
|
||||
'coze-api',
|
||||
'dashscope-app-api',
|
||||
'langflow-api',
|
||||
'tbox-app-api',
|
||||
]
|
||||
for runner in expected_runners:
|
||||
assert runner in OLD_RUNNER_TO_PLUGIN_RUNNER_ID
|
||||
mapped = OLD_RUNNER_TO_PLUGIN_RUNNER_ID[runner]
|
||||
assert mapped.startswith('plugin:langbot/')
|
||||
assert mapped.endswith('/default')
|
||||
|
||||
|
||||
class TestResolveRunnerId:
|
||||
"""Tests for ConfigMigration.resolve_runner_id."""
|
||||
|
||||
def test_resolve_new_format_runner_id(self):
|
||||
"""Resolve runner ID from new format."""
|
||||
pipeline_config = {
|
||||
'ai': {
|
||||
'runner': {
|
||||
'id': 'plugin:langbot/local-agent/default',
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
runner_id = ConfigMigration.resolve_runner_id(pipeline_config)
|
||||
assert runner_id == 'plugin:langbot/local-agent/default'
|
||||
|
||||
def test_resolve_old_format_runner_name(self):
|
||||
"""Resolve runner ID from old format."""
|
||||
pipeline_config = {
|
||||
'ai': {
|
||||
'runner': {
|
||||
'runner': 'local-agent',
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
runner_id = ConfigMigration.resolve_runner_id(pipeline_config)
|
||||
assert runner_id == 'plugin:langbot/local-agent/default'
|
||||
|
||||
def test_resolve_old_format_plugin_runner(self):
|
||||
"""Resolve already migrated plugin:* runner."""
|
||||
pipeline_config = {
|
||||
'ai': {
|
||||
'runner': {
|
||||
'runner': 'plugin:alice/my-agent/custom',
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
runner_id = ConfigMigration.resolve_runner_id(pipeline_config)
|
||||
assert runner_id == 'plugin:alice/my-agent/custom'
|
||||
|
||||
def test_resolve_no_runner_config(self):
|
||||
"""Resolve runner ID when not configured."""
|
||||
pipeline_config = {}
|
||||
|
||||
runner_id = ConfigMigration.resolve_runner_id(pipeline_config)
|
||||
assert runner_id is None
|
||||
|
||||
def test_resolve_priority_new_over_old(self):
|
||||
"""New format takes priority over old format."""
|
||||
pipeline_config = {
|
||||
'ai': {
|
||||
'runner': {
|
||||
'id': 'plugin:langbot/local-agent/default',
|
||||
'runner': 'dify-service-api', # This should be ignored
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
runner_id = ConfigMigration.resolve_runner_id(pipeline_config)
|
||||
assert runner_id == 'plugin:langbot/local-agent/default'
|
||||
|
||||
|
||||
class TestResolveRunnerConfig:
|
||||
"""Tests for ConfigMigration.resolve_runner_config."""
|
||||
|
||||
def test_resolve_new_format_config(self):
|
||||
"""Resolve runner config from new format."""
|
||||
pipeline_config = {
|
||||
'ai': {
|
||||
'runner_config': {
|
||||
'plugin:langbot/local-agent/default': {
|
||||
'model': 'uuid-123',
|
||||
'custom_option': 10,
|
||||
},
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
config = ConfigMigration.resolve_runner_config(
|
||||
pipeline_config,
|
||||
'plugin:langbot/local-agent/default',
|
||||
)
|
||||
assert config == {'model': 'uuid-123', 'custom_option': 10}
|
||||
|
||||
def test_resolve_old_format_config(self):
|
||||
"""Runtime config resolver should not read old format."""
|
||||
pipeline_config = {
|
||||
'ai': {
|
||||
'local-agent': {
|
||||
'model': 'uuid-123',
|
||||
'custom_option': 10,
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
config = ConfigMigration.resolve_runner_config(
|
||||
pipeline_config,
|
||||
'plugin:langbot/local-agent/default',
|
||||
)
|
||||
assert config == {}
|
||||
|
||||
def test_resolve_legacy_config_for_migration(self):
|
||||
"""Migration helper should read old format."""
|
||||
pipeline_config = {
|
||||
'ai': {
|
||||
'local-agent': {
|
||||
'model': 'uuid-123',
|
||||
'custom_option': 10,
|
||||
'knowledge-base': 'kb-123',
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
config = ConfigMigration.resolve_legacy_runner_config(
|
||||
pipeline_config,
|
||||
'plugin:langbot/local-agent/default',
|
||||
)
|
||||
assert config == {'model': 'uuid-123', 'custom_option': 10, 'knowledge-bases': ['kb-123']}
|
||||
assert 'knowledge-base' not in config
|
||||
|
||||
def test_resolve_no_config(self):
|
||||
"""Resolve runner config when not found."""
|
||||
pipeline_config = {}
|
||||
|
||||
config = ConfigMigration.resolve_runner_config(
|
||||
pipeline_config,
|
||||
'plugin:langbot/local-agent/default',
|
||||
)
|
||||
assert config == {}
|
||||
|
||||
def test_resolve_priority_new_over_old(self):
|
||||
"""New format config takes priority."""
|
||||
pipeline_config = {
|
||||
'ai': {
|
||||
'runner_config': {
|
||||
'plugin:langbot/local-agent/default': {
|
||||
'model': 'new-uuid',
|
||||
},
|
||||
},
|
||||
'local-agent': {
|
||||
'model': 'old-uuid',
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
config = ConfigMigration.resolve_runner_config(
|
||||
pipeline_config,
|
||||
'plugin:langbot/local-agent/default',
|
||||
)
|
||||
assert config == {'model': 'new-uuid'}
|
||||
|
||||
|
||||
class TestGetExpireTime:
|
||||
"""Tests for ConfigMigration.get_expire_time."""
|
||||
|
||||
def test_get_expire_time_zero(self):
|
||||
"""Get expire time when zero."""
|
||||
pipeline_config = {
|
||||
'ai': {
|
||||
'runner': {
|
||||
'expire-time': 0,
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
expire_time = ConfigMigration.get_expire_time(pipeline_config)
|
||||
assert expire_time == 0
|
||||
|
||||
def test_get_expire_time_positive(self):
|
||||
"""Get expire time when positive."""
|
||||
pipeline_config = {
|
||||
'ai': {
|
||||
'runner': {
|
||||
'expire-time': 3600,
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
expire_time = ConfigMigration.get_expire_time(pipeline_config)
|
||||
assert expire_time == 3600
|
||||
|
||||
def test_get_expire_time_default(self):
|
||||
"""Get expire time when not configured."""
|
||||
pipeline_config = {}
|
||||
|
||||
expire_time = ConfigMigration.get_expire_time(pipeline_config)
|
||||
assert expire_time == 0
|
||||
|
||||
|
||||
class TestGetOldRunnerName:
|
||||
"""Tests for ConfigMigration.get_old_runner_name."""
|
||||
|
||||
def test_get_old_runner_name_mapped(self):
|
||||
"""Get old runner name for mapped runner ID."""
|
||||
old_name = ConfigMigration.get_old_runner_name('plugin:langbot/local-agent/default')
|
||||
assert old_name == 'local-agent'
|
||||
|
||||
def test_get_old_runner_name_not_mapped(self):
|
||||
"""Get old runner name for unmapped runner ID."""
|
||||
old_name = ConfigMigration.get_old_runner_name('plugin:alice/my-agent/custom')
|
||||
assert old_name is None
|
||||
@@ -1,300 +0,0 @@
|
||||
"""Tests for pipeline config migration to new runner format."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
|
||||
from langbot.pkg.agent.runner.config_migration import ConfigMigration
|
||||
|
||||
|
||||
class TestMigratePipelineConfig:
|
||||
"""Tests for ConfigMigration.migrate_pipeline_config."""
|
||||
|
||||
def test_migrate_old_local_agent_config(self):
|
||||
"""Old local-agent config should migrate to plugin format."""
|
||||
old_config = {
|
||||
'ai': {
|
||||
'runner': {
|
||||
'runner': 'local-agent',
|
||||
'expire-time': 0,
|
||||
},
|
||||
'local-agent': {
|
||||
'model': {'primary': 'model-uuid', 'fallbacks': []},
|
||||
'knowledge-base': 'kb-uuid',
|
||||
'prompt': [{'role': 'system', 'content': 'Hello'}],
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
migrated = ConfigMigration.migrate_pipeline_config(old_config)
|
||||
|
||||
# Should have new format
|
||||
assert migrated['ai']['runner']['id'] == 'plugin:langbot/local-agent/default'
|
||||
assert 'runner' not in migrated['ai']['runner'] or migrated['ai']['runner'].get('runner') != 'local-agent'
|
||||
|
||||
# Config should be in runner_config
|
||||
assert 'plugin:langbot/local-agent/default' in migrated['ai']['runner_config']
|
||||
assert migrated['ai']['runner_config']['plugin:langbot/local-agent/default']['knowledge-bases'] == ['kb-uuid']
|
||||
assert 'knowledge-base' not in migrated['ai']['runner_config']['plugin:langbot/local-agent/default']
|
||||
assert 'max-round' not in migrated['ai']['runner_config']['plugin:langbot/local-agent/default']
|
||||
|
||||
# Expire-time preserved
|
||||
assert migrated['ai']['runner']['expire-time'] == 0
|
||||
|
||||
def test_migrate_old_dify_service_api_config(self):
|
||||
"""Old dify-service-api config should migrate to dify-agent plugin."""
|
||||
old_config = {
|
||||
'ai': {
|
||||
'runner': {
|
||||
'runner': 'dify-service-api',
|
||||
'expire-time': 300,
|
||||
},
|
||||
'dify-service-api': {
|
||||
'base-url': 'https://api.dify.ai/v1',
|
||||
'api-key': 'test-key',
|
||||
'app-type': 'chat',
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
migrated = ConfigMigration.migrate_pipeline_config(old_config)
|
||||
|
||||
assert migrated['ai']['runner']['id'] == 'plugin:langbot/dify-agent/default'
|
||||
assert 'plugin:langbot/dify-agent/default' in migrated['ai']['runner_config']
|
||||
assert migrated['ai']['runner_config']['plugin:langbot/dify-agent/default']['api-key'] == 'test-key'
|
||||
assert migrated['ai']['runner']['expire-time'] == 300
|
||||
|
||||
def test_new_format_config_stays_unchanged(self):
|
||||
"""New format config should not change."""
|
||||
new_config = {
|
||||
'ai': {
|
||||
'runner': {
|
||||
'id': 'plugin:langbot/local-agent/default',
|
||||
'expire-time': 0,
|
||||
},
|
||||
'runner_config': {
|
||||
'plugin:langbot/local-agent/default': {
|
||||
'model': {'primary': '', 'fallbacks': []},
|
||||
'custom-option': 10,
|
||||
},
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
migrated = ConfigMigration.migrate_pipeline_config(new_config)
|
||||
|
||||
# Should remain unchanged
|
||||
assert migrated['ai']['runner']['id'] == 'plugin:langbot/local-agent/default'
|
||||
assert migrated['ai']['runner_config']['plugin:langbot/local-agent/default']['custom-option'] == 10
|
||||
|
||||
def test_new_format_local_agent_config_normalizes_legacy_kb_key(self):
|
||||
"""Migration should normalize legacy KB aliases before runtime."""
|
||||
config = {
|
||||
'ai': {
|
||||
'runner': {
|
||||
'id': 'plugin:langbot/local-agent/default',
|
||||
},
|
||||
'runner_config': {
|
||||
'plugin:langbot/local-agent/default': {
|
||||
'knowledge-base': 'kb-legacy',
|
||||
},
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
migrated = ConfigMigration.migrate_pipeline_config(config)
|
||||
runner_config = migrated['ai']['runner_config']['plugin:langbot/local-agent/default']
|
||||
|
||||
assert runner_config == {'knowledge-bases': ['kb-legacy']}
|
||||
|
||||
def test_migrate_all_old_runners(self):
|
||||
"""All old runner names should be migrated."""
|
||||
old_runners = [
|
||||
'local-agent',
|
||||
'dify-service-api',
|
||||
'n8n-service-api',
|
||||
'coze-api',
|
||||
'dashscope-app-api',
|
||||
'langflow-api',
|
||||
'tbox-app-api',
|
||||
]
|
||||
|
||||
expected_ids = [
|
||||
'plugin:langbot/local-agent/default',
|
||||
'plugin:langbot/dify-agent/default',
|
||||
'plugin:langbot/n8n-agent/default',
|
||||
'plugin:langbot/coze-agent/default',
|
||||
'plugin:langbot/dashscope-agent/default',
|
||||
'plugin:langbot/langflow-agent/default',
|
||||
'plugin:langbot/tbox-agent/default',
|
||||
]
|
||||
|
||||
for old_runner, expected_id in zip(old_runners, expected_ids):
|
||||
config = {
|
||||
'ai': {
|
||||
'runner': {'runner': old_runner, 'expire-time': 0},
|
||||
old_runner: {'test-key': 'test-value'},
|
||||
},
|
||||
}
|
||||
migrated = ConfigMigration.migrate_pipeline_config(config)
|
||||
assert migrated['ai']['runner']['id'] == expected_id
|
||||
assert expected_id in migrated['ai']['runner_config']
|
||||
|
||||
def test_migrate_empty_config(self):
|
||||
"""Empty config should not break."""
|
||||
config = {}
|
||||
migrated = ConfigMigration.migrate_pipeline_config(config)
|
||||
assert migrated == {}
|
||||
|
||||
def test_migrate_config_without_ai_section(self):
|
||||
"""Config without ai section should not break."""
|
||||
config = {'trigger': {}}
|
||||
migrated = ConfigMigration.migrate_pipeline_config(config)
|
||||
assert 'trigger' in migrated
|
||||
|
||||
def test_expire_time_preserved(self):
|
||||
"""expire-time should be preserved during migration."""
|
||||
old_config = {
|
||||
'ai': {
|
||||
'runner': {
|
||||
'runner': 'local-agent',
|
||||
'expire-time': 3600,
|
||||
},
|
||||
'local-agent': {},
|
||||
},
|
||||
}
|
||||
|
||||
migrated = ConfigMigration.migrate_pipeline_config(old_config)
|
||||
assert migrated['ai']['runner']['expire-time'] == 3600
|
||||
|
||||
|
||||
class TestDefaultPipelineConfig:
|
||||
"""Tests for default-pipeline-config.json format."""
|
||||
|
||||
def test_default_config_is_new_format(self):
|
||||
"""Default pipeline template should use the new runner config shape."""
|
||||
from langbot.pkg.utils import paths as path_utils
|
||||
|
||||
template_path = path_utils.get_resource_path('templates/default-pipeline-config.json')
|
||||
with open(template_path, 'r', encoding='utf-8') as f:
|
||||
config = json.load(f)
|
||||
|
||||
# Should have new format
|
||||
assert 'ai' in config
|
||||
assert 'runner' in config['ai']
|
||||
assert 'id' in config['ai']['runner']
|
||||
assert config['ai']['runner']['id'] == ''
|
||||
|
||||
# Plugin runner selection and config defaults are rendered at creation
|
||||
# time from installed AgentRunner metadata.
|
||||
assert 'runner_config' in config['ai']
|
||||
assert config['ai']['runner_config'] == {}
|
||||
|
||||
# Should NOT have old local-agent key
|
||||
assert 'local-agent' not in config['ai']
|
||||
|
||||
def test_default_config_does_not_hardcode_plugin_schema(self):
|
||||
"""Default template should not duplicate plugin-provided config schema."""
|
||||
from langbot.pkg.utils import paths as path_utils
|
||||
|
||||
template_path = path_utils.get_resource_path('templates/default-pipeline-config.json')
|
||||
with open(template_path, 'r', encoding='utf-8') as f:
|
||||
config = json.load(f)
|
||||
|
||||
assert config['ai']['runner_config'] == {}
|
||||
|
||||
|
||||
class TestResolveRunnerIdAliases:
|
||||
"""Tests for runner id alias resolution."""
|
||||
|
||||
def test_resolve_new_format_id(self):
|
||||
"""resolve_runner_id should work with new format."""
|
||||
config = {
|
||||
'ai': {
|
||||
'runner': {'id': 'plugin:test/my-runner/default'},
|
||||
},
|
||||
}
|
||||
runner_id = ConfigMigration.resolve_runner_id(config)
|
||||
assert runner_id == 'plugin:test/my-runner/default'
|
||||
|
||||
def test_resolve_old_format_runner(self):
|
||||
"""resolve_runner_id should map old format to plugin ID."""
|
||||
config = {
|
||||
'ai': {
|
||||
'runner': {'runner': 'local-agent'},
|
||||
},
|
||||
}
|
||||
runner_id = ConfigMigration.resolve_runner_id(config)
|
||||
assert runner_id == 'plugin:langbot/local-agent/default'
|
||||
|
||||
def test_resolve_plugin_format_in_runner_field(self):
|
||||
"""resolve_runner_id should handle plugin:* in runner field."""
|
||||
config = {
|
||||
'ai': {
|
||||
'runner': {'runner': 'plugin:langbot/local-agent/default'},
|
||||
},
|
||||
}
|
||||
runner_id = ConfigMigration.resolve_runner_id(config)
|
||||
assert runner_id == 'plugin:langbot/local-agent/default'
|
||||
|
||||
def test_resolve_new_format_priority(self):
|
||||
"""New format id should take priority over old runner field."""
|
||||
config = {
|
||||
'ai': {
|
||||
'runner': {
|
||||
'id': 'plugin:new-runner/default',
|
||||
'runner': 'local-agent', # Old field, should be ignored
|
||||
},
|
||||
},
|
||||
}
|
||||
runner_id = ConfigMigration.resolve_runner_id(config)
|
||||
assert runner_id == 'plugin:new-runner/default'
|
||||
|
||||
|
||||
class TestResolveRunnerConfig:
|
||||
"""Tests for runtime runner config resolution."""
|
||||
|
||||
def test_resolve_new_format_config(self):
|
||||
"""resolve_runner_config should read from runner_config."""
|
||||
config = {
|
||||
'ai': {
|
||||
'runner_config': {
|
||||
'plugin:langbot/local-agent/default': {'custom-option': 20},
|
||||
},
|
||||
},
|
||||
}
|
||||
runner_config = ConfigMigration.resolve_runner_config(config, 'plugin:langbot/local-agent/default')
|
||||
assert runner_config['custom-option'] == 20
|
||||
|
||||
def test_resolve_old_format_config(self):
|
||||
"""resolve_runner_config should not read old ai.local-agent at runtime."""
|
||||
config = {
|
||||
'ai': {
|
||||
'local-agent': {'max-round': 15, 'custom-option': 20},
|
||||
},
|
||||
}
|
||||
runner_config = ConfigMigration.resolve_runner_config(config, 'plugin:langbot/local-agent/default')
|
||||
assert runner_config == {}
|
||||
|
||||
def test_resolve_legacy_runner_config_for_migration(self):
|
||||
"""resolve_legacy_runner_config should read old ai.local-agent for migration."""
|
||||
config = {
|
||||
'ai': {
|
||||
'local-agent': {'max-round': 15, 'custom-option': 20},
|
||||
},
|
||||
}
|
||||
runner_config = ConfigMigration.resolve_legacy_runner_config(config, 'plugin:langbot/local-agent/default')
|
||||
assert runner_config == {'custom-option': 20}
|
||||
|
||||
def test_resolve_new_format_priority(self):
|
||||
"""New format runner_config should take priority."""
|
||||
config = {
|
||||
'ai': {
|
||||
'runner_config': {
|
||||
'plugin:langbot/local-agent/default': {'custom-option': 25},
|
||||
},
|
||||
'local-agent': {'max-round': 10, 'custom-option': 10}, # Old, should be ignored
|
||||
},
|
||||
}
|
||||
runner_config = ConfigMigration.resolve_runner_config(config, 'plugin:langbot/local-agent/default')
|
||||
assert runner_config['custom-option'] == 25
|
||||
@@ -1,179 +0,0 @@
|
||||
"""Tests for Pipeline adapter params and prompt packaging."""
|
||||
from __future__ import annotations
|
||||
|
||||
from langbot.pkg.agent.runner.pipeline_adapter import PipelineAdapter
|
||||
|
||||
|
||||
class FakeMessage:
|
||||
"""Fake prompt/history message."""
|
||||
def __init__(self, content='Hello'):
|
||||
self.content = content
|
||||
self.role = 'user'
|
||||
|
||||
def model_dump(self, mode='json'):
|
||||
return {'role': self.role, 'content': self.content}
|
||||
|
||||
|
||||
class FakePrompt:
|
||||
"""Fake prompt container."""
|
||||
def __init__(self, messages=None):
|
||||
self.messages = messages or []
|
||||
|
||||
|
||||
class TestBuildParams:
|
||||
"""Tests for PipelineAdapter.build_params filtering."""
|
||||
|
||||
def test_params_empty_when_no_variables(self):
|
||||
query = type('Query', (), {'variables': None})()
|
||||
assert PipelineAdapter.build_params(query) == {}
|
||||
|
||||
def test_params_filters_underscore_prefix(self):
|
||||
query = type('Query', (), {
|
||||
'variables': {
|
||||
'_internal_var': 'should_be_excluded',
|
||||
'_pipeline_bound_plugins': ['a/b'],
|
||||
'_monitoring_bot_name': 'Bot',
|
||||
'public_var': 'should_be_included',
|
||||
},
|
||||
})()
|
||||
|
||||
params = PipelineAdapter.build_params(query)
|
||||
assert '_internal_var' not in params
|
||||
assert '_pipeline_bound_plugins' not in params
|
||||
assert '_monitoring_bot_name' not in params
|
||||
assert params['public_var'] == 'should_be_included'
|
||||
|
||||
def test_params_filters_sensitive_naming(self):
|
||||
query = type('Query', (), {
|
||||
'variables': {
|
||||
'api_key': 'secret123',
|
||||
'API_KEY': 'secret456',
|
||||
'token': 'tok123',
|
||||
'secret': 'sec123',
|
||||
'password': 'pass123',
|
||||
'credential': 'cred123',
|
||||
'user_api_key': 'should_be_excluded',
|
||||
'user_secret_key': 'should_be_excluded',
|
||||
'my_token_value': 'should_be_excluded',
|
||||
'user_password_hash': 'should_be_excluded',
|
||||
'public_name': 'should_be_included',
|
||||
'safe_value': 'should_be_included',
|
||||
},
|
||||
})()
|
||||
|
||||
params = PipelineAdapter.build_params(query)
|
||||
assert 'api_key' not in params
|
||||
assert 'API_KEY' not in params
|
||||
assert 'token' not in params
|
||||
assert 'secret' not in params
|
||||
assert 'password' not in params
|
||||
assert 'credential' not in params
|
||||
assert 'user_api_key' not in params
|
||||
assert 'user_secret_key' not in params
|
||||
assert 'my_token_value' not in params
|
||||
assert 'user_password_hash' not in params
|
||||
assert 'public_name' in params
|
||||
assert 'safe_value' in params
|
||||
|
||||
def test_params_keeps_common_public_vars(self):
|
||||
query = type('Query', (), {
|
||||
'variables': {
|
||||
'launcher_type': 'telegram',
|
||||
'launcher_id': 'group_123',
|
||||
'sender_id': 'user_001',
|
||||
'session_id': 'sess_abc',
|
||||
'msg_create_time': 1234567890,
|
||||
'group_name': 'Tech Group',
|
||||
'sender_name': 'John',
|
||||
'user_message_text': 'Hello world',
|
||||
},
|
||||
})()
|
||||
|
||||
params = PipelineAdapter.build_params(query)
|
||||
assert params['launcher_type'] == 'telegram'
|
||||
assert params['launcher_id'] == 'group_123'
|
||||
assert params['sender_id'] == 'user_001'
|
||||
assert params['session_id'] == 'sess_abc'
|
||||
assert params['msg_create_time'] == 1234567890
|
||||
assert params['group_name'] == 'Tech Group'
|
||||
assert params['sender_name'] == 'John'
|
||||
assert params['user_message_text'] == 'Hello world'
|
||||
|
||||
def test_params_filters_non_json_serializable(self):
|
||||
class CustomObject:
|
||||
pass
|
||||
|
||||
query = type('Query', (), {
|
||||
'variables': {
|
||||
'string_value': 'hello',
|
||||
'int_value': 42,
|
||||
'float_value': 3.14,
|
||||
'bool_value': True,
|
||||
'null_value': None,
|
||||
'list_value': ['a', 'b', 'c'],
|
||||
'dict_value': {'nested': 'value'},
|
||||
'custom_object': CustomObject(),
|
||||
},
|
||||
})()
|
||||
|
||||
params = PipelineAdapter.build_params(query)
|
||||
assert 'string_value' in params
|
||||
assert 'int_value' in params
|
||||
assert 'float_value' in params
|
||||
assert 'bool_value' in params
|
||||
assert 'null_value' in params
|
||||
assert 'list_value' in params
|
||||
assert 'dict_value' in params
|
||||
assert 'custom_object' not in params
|
||||
|
||||
def test_params_filters_nested_non_serializable(self):
|
||||
class CustomObject:
|
||||
pass
|
||||
|
||||
query = type('Query', (), {
|
||||
'variables': {
|
||||
'nested_list_with_bad': ['a', CustomObject(), 'c'],
|
||||
'nested_dict_with_bad': {'good': 'value', 'bad': CustomObject()},
|
||||
'good_nested_list': ['a', ['b', 'c']],
|
||||
'good_nested_dict': {'outer': {'inner': 'value'}},
|
||||
},
|
||||
})()
|
||||
|
||||
params = PipelineAdapter.build_params(query)
|
||||
assert 'nested_list_with_bad' not in params
|
||||
assert 'nested_dict_with_bad' not in params
|
||||
assert 'good_nested_list' in params
|
||||
assert 'good_nested_dict' in params
|
||||
|
||||
def test_is_json_serializable_primitives_and_collections(self):
|
||||
assert PipelineAdapter.is_json_serializable(None) is True
|
||||
assert PipelineAdapter.is_json_serializable('string') is True
|
||||
assert PipelineAdapter.is_json_serializable(42) is True
|
||||
assert PipelineAdapter.is_json_serializable(['a', 'b']) is True
|
||||
assert PipelineAdapter.is_json_serializable({'key': 'value'}) is True
|
||||
assert PipelineAdapter.is_json_serializable((1, 2, 3)) is True
|
||||
|
||||
def test_is_json_serializable_rejects_sets_and_objects(self):
|
||||
class CustomObject:
|
||||
pass
|
||||
|
||||
assert PipelineAdapter.is_json_serializable(CustomObject()) is False
|
||||
assert PipelineAdapter.is_json_serializable({1, 2, 3}) is False
|
||||
assert PipelineAdapter.is_json_serializable([1, {2, 3}]) is False
|
||||
assert PipelineAdapter.is_json_serializable({'key': {1, 2}}) is False
|
||||
|
||||
|
||||
class TestBuildPrompt:
|
||||
"""Tests for PipelineAdapter.build_prompt."""
|
||||
|
||||
def test_prompt_empty_when_missing(self):
|
||||
query = type('Query', (), {})()
|
||||
assert PipelineAdapter.build_prompt(query) == []
|
||||
|
||||
def test_prompt_serializes_messages(self):
|
||||
query = type('Query', (), {
|
||||
'prompt': FakePrompt([FakeMessage('Effective prompt')]),
|
||||
})()
|
||||
|
||||
prompt = PipelineAdapter.build_prompt(query)
|
||||
assert prompt == [{'role': 'user', 'content': 'Effective prompt'}]
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user