feat: Add support for notification and request events, and enhance the event handling capabilities.

README.md

@@ -19,10 +19,9 @@ English / [简体中文](README_CN.md) / [繁體中文](README_TW.md) / [日本
 [![GitHub stars](https://img.shields.io/github/stars/langbot-app/LangBot?style=social)](https://github.com/langbot-app/LangBot/stargazers)
 
 <a href="https://langbot.app">Website</a> ｜
-<a href="https://docs.langbot.app/en/insight/features">Features</a> ｜
-<a href="https://docs.langbot.app/en/insight/guide">Docs</a> ｜
-<a href="https://docs.langbot.app/en/tags/readme">API</a> ｜
-<a href="https://space.langbot.app/cloud">Cloud</a> ｜
+<a href="https://docs.langbot.app/en/insight/features.html">Features</a> ｜
+<a href="https://docs.langbot.app/en/insight/guide.html">Docs</a> ｜
+<a href="https://docs.langbot.app/en/tags/readme.html">API</a> ｜
 <a href="https://space.langbot.app">Plugin Market</a> ｜
 <a href="https://langbot.featurebase.app/roadmap">Roadmap</a>
 
@@ -45,16 +44,12 @@ LangBot is an **open-source, production-grade platform** for building AI-powered
 - **Web Management Panel** — Configure, manage, and monitor your bots through an intuitive browser interface. No YAML editing required.
 - **Multi-Pipeline Architecture** — Different bots for different scenarios, with comprehensive monitoring and exception handling.
 
-[→ Learn more about all features](https://docs.langbot.app/en/insight/features)
+[→ Learn more about all features](https://docs.langbot.app/en/insight/features.html)
 
 ---
 
 ## Quick Start
 
-### ☁️ LangBot Cloud (Recommended)
-
-**[LangBot Cloud](https://space.langbot.app/cloud)** — Zero deployment, ready to use.
-
 ### One-Line Launch
 
 ```bash
@@ -76,7 +71,7 @@ docker compose up -d
 [![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/en-US/templates/ZKTBDH)
 [![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)
 
-**More options:** [Docker](https://docs.langbot.app/en/deploy/langbot/docker) · [Manual](https://docs.langbot.app/en/deploy/langbot/manual) · [BTPanel](https://docs.langbot.app/en/deploy/langbot/one-click/bt) · [Kubernetes](./docker/README_K8S.md)
+**More options:** [Docker](https://docs.langbot.app/en/deploy/langbot/docker.html) · [Manual](https://docs.langbot.app/en/deploy/langbot/manual.html) · [BTPanel](https://docs.langbot.app/en/deploy/langbot/one-click/bt.html) · [Kubernetes](./docker/README_K8S.md)
 
 ---
 
@@ -124,7 +119,7 @@ docker compose up -d
 | [接口 AI](https://jiekou.ai/) | Gateway | ✅ |
 | [302.AI](https://share.302.ai/SuTG99) | Gateway | ✅ |
 
-[→ View all integrations](https://docs.langbot.app/en/insight/features)
+[→ View all integrations](https://docs.langbot.app/en/insight/features.html)
 
 ---
 

README_CN.md

@@ -24,7 +24,6 @@
 <a href="https://docs.langbot.app/zh/insight/features.html">特性</a> ｜
 <a href="https://docs.langbot.app/zh/insight/guide.html">文档</a> ｜
 <a href="https://docs.langbot.app/zh/tags/readme.html">API</a> ｜
-<a href="https://space.langbot.app/cloud">Cloud</a> ｜
 <a href="https://space.langbot.app">插件市场</a> ｜
 <a href="https://langbot.featurebase.app/roadmap">路线图</a>
 
@@ -53,10 +52,6 @@ LangBot 是一个**开源的生产级平台**，用于构建 AI 驱动的即时
 
 ## 快速开始
 
-### ☁️ LangBot Cloud（推荐）
-
-**[LangBot Cloud](https://space.langbot.app/cloud)** — 免部署，开箱即用。
-
 ### 一键启动
 
 ```bash

README_ES.md

@@ -50,10 +50,6 @@ LangBot es una **plataforma de código abierto y grado de producción** para con
 
 ## Inicio Rápido
 
-### ☁️ LangBot Cloud (Recomendado)
-
-**[LangBot Cloud](https://space.langbot.app/cloud)** — Sin despliegue, listo para usar.
-
 ### Lanzamiento en una línea
 
 ```bash

README_FR.md

@@ -50,10 +50,6 @@ LangBot est une **plateforme open-source de niveau production** pour créer des
 
 ## Démarrage Rapide
 
-### ☁️ LangBot Cloud (Recommandé)
-
-**[LangBot Cloud](https://space.langbot.app/cloud)** — Sans déploiement, prêt à utiliser.
-
 ### Lancement en une ligne
 
 ```bash

README_JP.md

@@ -50,10 +50,6 @@ LangBot は、AI搭載のインスタントメッセージングボットを構
 
 ## クイックスタート
 
-### ☁️ LangBot Cloud（推奨）
-
-**[LangBot Cloud](https://space.langbot.app/cloud)** — デプロイ不要、すぐに使えます。
-
 ### ワンライン起動
 
 ```bash

README_KO.md

@@ -50,10 +50,6 @@ LangBot은 AI 기반 인스턴트 메시징 봇을 구축하기 위한 **오픈
 
 ## 빠른 시작
 
-### ☁️ LangBot Cloud (추천)
-
-**[LangBot Cloud](https://space.langbot.app/cloud)** — 배포 없이 바로 사용.
-
 ### 원라인 실행
 
 ```bash

README_RU.md

@@ -50,10 +50,6 @@ LangBot — это **платформа с открытым исходным к
 
 ## Быстрый старт
 
-### ☁️ LangBot Cloud (Рекомендуется)
-
-**[LangBot Cloud](https://space.langbot.app/cloud)** — Без развёртывания, готово к использованию.
-
 ### Запуск одной командой
 
 ```bash

README_TW.md

@@ -52,10 +52,6 @@ LangBot 是一個**開源的生產級平台**，用於建構 AI 驅動的即時
 
 ## 快速開始
 
-### ☁️ LangBot Cloud（推薦）
-
-**[LangBot Cloud](https://space.langbot.app/cloud)** — 免部署，開箱即用。
-
 ### 一鍵啟動
 
 ```bash

README_VI.md

@@ -50,10 +50,6 @@ LangBot là một **nền tảng mã nguồn mở, cấp sản xuất** để x
 
 ## Bắt đầu nhanh
 
-### ☁️ LangBot Cloud (Khuyên dùng)
-
-**[LangBot Cloud](https://space.langbot.app/cloud)** — Không cần triển khai, sẵn sàng sử dụng.
-
 ### Khởi chạy một dòng
 
 ```bash

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "langbot"
-version = "4.9.1"
+version = "4.8.4"
 description = "Production-grade platform for building agentic IM bots"
 readme = "README.md"
 license-files = ["LICENSE"]
@@ -61,17 +61,16 @@ dependencies = [
     "html2text>=2024.2.26",
     "langchain>=0.2.0",
     "langchain-text-splitters>=0.0.1",
-    "chromadb>=1.0.0,<2.0.0",
+    "chromadb>=0.4.24",
     "qdrant-client (>=1.15.1,<2.0.0)",
-    "pyseekdb==1.1.0.post3",
-    "langbot-plugin==0.3.1",
+    "pyseekdb==1.0.0b7",
+    "langbot-plugin==0.2.6",
     "asyncpg>=0.30.0",
     "line-bot-sdk>=3.19.0",
     "tboxsdk>=0.0.10",
     "boto3>=1.35.0",
     "pymilvus>=2.6.4",
     "pgvector>=0.4.1",
-    "botocore>=1.42.39",
 ]
 keywords = [
     "bot",

src/langbot/__init__.py

@@ -1,3 +1,3 @@
 """LangBot - Production-grade platform for building agentic IM bots"""
 
-__version__ = '4.9.1'
+__version__ = '4.8.4'

src/langbot/libs/wechatpad_api/util/http_util.py

@@ -1,5 +1,5 @@
 import requests
-from langbot.pkg.utils import httpclient
+import aiohttp
 
 
 def post_json(base_url, token, data=None):
@@ -63,16 +63,16 @@ async def async_request(
     """
     headers = {'Content-Type': 'application/json'}
     url = f'{base_url}?key={token_key}'
-    session = httpclient.get_session()
-    async with session.request(
-        method=method, url=url, params=params, headers=headers, data=data, json=json
-    ) as response:
-        response.raise_for_status()  # 如果状态码不是200，抛出异常
-        result = await response.json()
-        # print(result)
-        return result
-        # if result.get('Code') == 200:
-        #
-        #     return await result
-        # else:
-        #     raise RuntimeError("请求失败",response.text)
+    async with aiohttp.ClientSession() as session:
+        async with session.request(
+            method=method, url=url, params=params, headers=headers, data=data, json=json
+        ) as response:
+            response.raise_for_status()  # 如果状态码不是200，抛出异常
+            result = await response.json()
+            # print(result)
+            return result
+            # if result.get('Code') == 200:
+            #
+            #     return await result
+            # else:
+            #     raise RuntimeError("请求失败",response.text)

src/langbot/libs/wecom_ai_bot_api/api.py

@@ -199,253 +199,6 @@ class StreamSessionManager:
                 self._msg_index.pop(msg_id, None)
 
 
-async def download_encrypted_file(download_url: str, encoding_aes_key: str, logger: EventLogger) -> Optional[str]:
-    """Download an AES-encrypted file from WeChat Work and return as data URI.
-
-    Args:
-        download_url: The encrypted file download URL.
-        encoding_aes_key: The AES key used for decryption (base64-encoded, without trailing '=').
-        logger: Logger instance.
-
-    Returns:
-        A data URI string (e.g. 'data:image/jpeg;base64,...') or None on failure.
-    """
-    if not download_url:
-        return None
-    async with httpx.AsyncClient() as client:
-        response = await client.get(download_url)
-        if response.status_code != 200:
-            await logger.error(f'failed to get file: {response.text}')
-            return None
-        encrypted_bytes = response.content
-
-    aes_key = base64.b64decode(encoding_aes_key + '=')
-    iv = aes_key[:16]
-
-    cipher = AES.new(aes_key, AES.MODE_CBC, iv)
-    decrypted = cipher.decrypt(encrypted_bytes)
-
-    pad_len = decrypted[-1]
-    decrypted = decrypted[:-pad_len]
-
-    if decrypted.startswith(b'\xff\xd8'):
-        mime_type = 'image/jpeg'
-    elif decrypted.startswith(b'\x89PNG'):
-        mime_type = 'image/png'
-    elif decrypted.startswith((b'GIF87a', b'GIF89a')):
-        mime_type = 'image/gif'
-    elif decrypted.startswith(b'BM'):
-        mime_type = 'image/bmp'
-    elif decrypted.startswith(b'II*\x00') or decrypted.startswith(b'MM\x00*'):
-        mime_type = 'image/tiff'
-    else:
-        mime_type = 'application/octet-stream'
-
-    base64_str = base64.b64encode(decrypted).decode('utf-8')
-    return f'data:{mime_type};base64,{base64_str}'
-
-
-async def parse_wecom_bot_message(
-    msg_json: dict[str, Any], encoding_aes_key: str, logger: EventLogger
-) -> dict[str, Any]:
-    """Parse a decrypted WeChat Work AI Bot message JSON into a unified message dict.
-
-    This is the shared message parsing logic used by both webhook and WebSocket modes.
-
-    Args:
-        msg_json: The decrypted message JSON from WeChat Work.
-        encoding_aes_key: AES key for file decryption.
-        logger: Logger instance.
-
-    Returns:
-        A dict suitable for constructing a WecomBotEvent.
-    """
-    message_data: dict[str, Any] = {}
-
-    msg_type = msg_json.get('msgtype', '')
-    if msg_type:
-        message_data['msgtype'] = msg_type
-
-    if msg_json.get('chattype', '') == 'single':
-        message_data['type'] = 'single'
-    elif msg_json.get('chattype', '') == 'group':
-        message_data['type'] = 'group'
-
-    max_inline_file_size = 5 * 1024 * 1024
-
-    async def _safe_download(url: str):
-        if not url:
-            return None
-        return await download_encrypted_file(url, encoding_aes_key, logger)
-
-    if msg_type == 'text':
-        message_data['content'] = msg_json.get('text', {}).get('content')
-    elif msg_type == 'markdown':
-        message_data['content'] = msg_json.get('markdown', {}).get('content') or msg_json.get('text', {}).get(
-            'content', ''
-        )
-    elif msg_type == 'image':
-        picurl = msg_json.get('image', {}).get('url', '')
-        base64_data = await _safe_download(picurl)
-        if base64_data:
-            message_data['picurl'] = base64_data
-            message_data['images'] = [base64_data]
-    elif msg_type == 'voice':
-        voice_info = msg_json.get('voice', {}) or {}
-        download_url = voice_info.get('url')
-        message_data['voice'] = {
-            'url': download_url,
-            'md5sum': voice_info.get('md5sum') or voice_info.get('md5'),
-            'filesize': voice_info.get('filesize') or voice_info.get('size'),
-            'sdkfileid': voice_info.get('sdkfileid') or voice_info.get('fileid'),
-        }
-        if voice_info.get('content'):
-            message_data['content'] = voice_info.get('content')
-        if (message_data['voice'].get('filesize') or 0) <= max_inline_file_size:
-            voice_base64 = await _safe_download(download_url)
-            if voice_base64:
-                message_data['voice']['base64'] = voice_base64
-    elif msg_type == 'video':
-        video_info = msg_json.get('video', {}) or {}
-        download_url = video_info.get('url')
-        video_data = {
-            'url': download_url,
-            'filesize': video_info.get('filesize') or video_info.get('size'),
-            'sdkfileid': video_info.get('sdkfileid') or video_info.get('fileid'),
-            'md5sum': video_info.get('md5sum') or video_info.get('md5'),
-            'filename': video_info.get('filename') or video_info.get('name'),
-        }
-        if (video_data.get('filesize') or 0) <= max_inline_file_size:
-            video_base64 = await _safe_download(download_url)
-            if video_base64:
-                video_data['base64'] = video_base64
-        message_data['video'] = video_data
-    elif msg_type == 'file':
-        file_info = msg_json.get('file', {}) or {}
-        download_url = file_info.get('url') or file_info.get('fileurl')
-        file_data = {
-            'filename': file_info.get('filename') or file_info.get('name'),
-            'filesize': file_info.get('filesize') or file_info.get('size'),
-            'md5sum': file_info.get('md5sum') or file_info.get('md5'),
-            'sdkfileid': file_info.get('sdkfileid') or file_info.get('fileid'),
-            'download_url': download_url,
-            'extra': file_info,
-        }
-        if (file_data.get('filesize') or 0) <= max_inline_file_size:
-            file_base64 = await _safe_download(download_url)
-            if file_base64:
-                file_data['base64'] = file_base64
-        message_data['file'] = file_data
-    elif msg_type == 'link':
-        message_data['link'] = msg_json.get('link', {})
-        if not message_data.get('content'):
-            title = message_data['link'].get('title', '')
-            desc = message_data['link'].get('description') or message_data['link'].get('digest', '')
-            message_data['content'] = '\n'.join(filter(None, [title, desc]))
-    elif msg_type == 'mixed':
-        items = msg_json.get('mixed', {}).get('msg_item', [])
-        texts = []
-        images = []
-        files = []
-        voices = []
-        videos = []
-        links = []
-        for item in items:
-            item_type = item.get('msgtype')
-            if item_type == 'text':
-                texts.append(item.get('text', {}).get('content', ''))
-            elif item_type == 'image':
-                img_url = item.get('image', {}).get('url')
-                base64_data = await _safe_download(img_url)
-                if base64_data:
-                    images.append(base64_data)
-            elif item_type == 'file':
-                file_info = item.get('file', {}) or {}
-                download_url = file_info.get('url') or file_info.get('fileurl')
-                file_data = {
-                    'filename': file_info.get('filename') or file_info.get('name'),
-                    'filesize': file_info.get('filesize') or file_info.get('size'),
-                    'md5sum': file_info.get('md5sum') or file_info.get('md5'),
-                    'sdkfileid': file_info.get('sdkfileid') or file_info.get('fileid'),
-                    'download_url': download_url,
-                    'extra': file_info,
-                }
-                if (file_data.get('filesize') or 0) <= max_inline_file_size:
-                    file_base64 = await _safe_download(download_url)
-                    if file_base64:
-                        file_data['base64'] = file_base64
-                files.append(file_data)
-            elif item_type == 'voice':
-                voice_info = item.get('voice', {}) or {}
-                download_url = voice_info.get('url')
-                voice_data = {
-                    'url': download_url,
-                    'md5sum': voice_info.get('md5sum') or voice_info.get('md5'),
-                    'filesize': voice_info.get('filesize') or voice_info.get('size'),
-                    'sdkfileid': voice_info.get('sdkfileid') or voice_info.get('fileid'),
-                }
-                if voice_info.get('content'):
-                    texts.append(voice_info.get('content'))
-                if (voice_data.get('filesize') or 0) <= max_inline_file_size:
-                    voice_base64 = await _safe_download(download_url)
-                    if voice_base64:
-                        voice_data['base64'] = voice_base64
-                voices.append(voice_data)
-            elif item_type == 'video':
-                video_info = item.get('video', {}) or {}
-                download_url = video_info.get('url')
-                video_data = {
-                    'url': download_url,
-                    'filesize': video_info.get('filesize') or video_info.get('size'),
-                    'sdkfileid': video_info.get('sdkfileid') or video_info.get('fileid'),
-                    'md5sum': video_info.get('md5sum') or video_info.get('md5'),
-                    'filename': video_info.get('filename') or video_info.get('name'),
-                }
-                if (video_data.get('filesize') or 0) <= max_inline_file_size:
-                    video_base64 = await _safe_download(download_url)
-                    if video_base64:
-                        video_data['base64'] = video_base64
-                videos.append(video_data)
-            elif item_type == 'link':
-                links.append(item.get('link', {}))
-
-        if texts:
-            message_data['content'] = ' '.join(texts)
-        if images:
-            message_data['images'] = images
-            message_data['picurl'] = images[0]
-        if files:
-            message_data['files'] = files
-            message_data['file'] = files[0]
-        if voices:
-            message_data['voices'] = voices
-            message_data['voice'] = voices[0]
-        if videos:
-            message_data['videos'] = videos
-            message_data['video'] = videos[0]
-        if links:
-            message_data['link'] = links[0]
-        if items:
-            message_data['attachments'] = items
-    else:
-        message_data['raw_msg'] = msg_json
-
-    from_info = msg_json.get('from', {})
-    message_data['userid'] = from_info.get('userid', '')
-    message_data['username'] = from_info.get('alias', '') or from_info.get('name', '') or from_info.get('userid', '')
-
-    if msg_json.get('chattype', '') == 'group':
-        message_data['chatid'] = msg_json.get('chatid', '')
-        message_data['chatname'] = msg_json.get('chatname', '') or msg_json.get('chatid', '')
-
-    message_data['msgid'] = msg_json.get('msgid', '')
-
-    if msg_json.get('aibotid'):
-        message_data['aibotid'] = msg_json.get('aibotid', '')
-
-    return message_data
-
-
 class WecomBotClient:
     def __init__(self, Token: str, EnCodingAESKey: str, Corpid: str, logger: EventLogger, unified_mode: bool = False):
         """企业微信智能机器人客户端。
@@ -702,7 +455,196 @@ class WecomBotClient:
         return await self._handle_post_initial_response(msg_json, nonce)
 
     async def get_message(self, msg_json):
-        return await parse_wecom_bot_message(msg_json, self.EnCodingAESKey, self.logger)
+        message_data = {}
+
+        msg_type = msg_json.get('msgtype', '')
+        if msg_type:
+            message_data['msgtype'] = msg_type
+
+        if msg_json.get('chattype', '') == 'single':
+            message_data['type'] = 'single'
+        elif msg_json.get('chattype', '') == 'group':
+            message_data['type'] = 'group'
+
+        max_inline_file_size = 5 * 1024 * 1024  # avoid decoding very large payloads by default
+
+        async def _safe_download(url: str):
+            if not url:
+                return None
+            return await self.download_url_to_base64(url, self.EnCodingAESKey)
+
+        if msg_type == 'text':
+            message_data['content'] = msg_json.get('text', {}).get('content')
+        elif msg_type == 'markdown':
+            message_data['content'] = msg_json.get('markdown', {}).get('content') or msg_json.get('text', {}).get(
+                'content', ''
+            )
+        elif msg_type == 'image':
+            picurl = msg_json.get('image', {}).get('url', '')
+            base64_data = await _safe_download(picurl)
+            if base64_data:
+                message_data['picurl'] = base64_data
+                message_data['images'] = [base64_data]
+        elif msg_type == 'voice':
+            voice_info = msg_json.get('voice', {}) or {}
+            download_url = voice_info.get('url')
+            message_data['voice'] = {
+                'url': download_url,
+                'md5sum': voice_info.get('md5sum') or voice_info.get('md5'),
+                'filesize': voice_info.get('filesize') or voice_info.get('size'),
+                'sdkfileid': voice_info.get('sdkfileid') or voice_info.get('fileid'),
+            }
+            # 企业微信智能转写文本（如果已有）直接复用，避免重复转写
+            if voice_info.get('content'):
+                message_data['content'] = voice_info.get('content')
+            if (message_data['voice'].get('filesize') or 0) <= max_inline_file_size:
+                voice_base64 = await _safe_download(download_url)
+                if voice_base64:
+                    message_data['voice']['base64'] = voice_base64
+        elif msg_type == 'video':
+            video_info = msg_json.get('video', {}) or {}
+            download_url = video_info.get('url')
+            video_data = {
+                'url': download_url,
+                'filesize': video_info.get('filesize') or video_info.get('size'),
+                'sdkfileid': video_info.get('sdkfileid') or video_info.get('fileid'),
+                'md5sum': video_info.get('md5sum') or video_info.get('md5'),
+                'filename': video_info.get('filename') or video_info.get('name'),
+            }
+            if (video_data.get('filesize') or 0) <= max_inline_file_size:
+                video_base64 = await _safe_download(download_url)
+                if video_base64:
+                    video_data['base64'] = video_base64
+            message_data['video'] = video_data
+        elif msg_type == 'file':
+            file_info = msg_json.get('file', {}) or {}
+            download_url = file_info.get('url') or file_info.get('fileurl')
+            file_data = {
+                'filename': file_info.get('filename') or file_info.get('name'),
+                'filesize': file_info.get('filesize') or file_info.get('size'),
+                'md5sum': file_info.get('md5sum') or file_info.get('md5'),
+                'sdkfileid': file_info.get('sdkfileid') or file_info.get('fileid'),
+                'download_url': download_url,
+                'extra': file_info,
+            }
+            if (file_data.get('filesize') or 0) <= max_inline_file_size:
+                file_base64 = await _safe_download(download_url)
+                if file_base64:
+                    file_data['base64'] = file_base64
+            message_data['file'] = file_data
+        elif msg_type == 'link':
+            message_data['link'] = msg_json.get('link', {})
+            if not message_data.get('content'):
+                title = message_data['link'].get('title', '')
+                desc = message_data['link'].get('description') or message_data['link'].get('digest', '')
+                message_data['content'] = '\n'.join(filter(None, [title, desc]))
+        elif msg_type == 'mixed':
+            items = msg_json.get('mixed', {}).get('msg_item', [])
+            texts = []
+            images = []
+            files = []
+            voices = []
+            videos = []
+            links = []
+            for item in items:
+                item_type = item.get('msgtype')
+                if item_type == 'text':
+                    texts.append(item.get('text', {}).get('content', ''))
+                elif item_type == 'image':
+                    img_url = item.get('image', {}).get('url')
+                    base64_data = await _safe_download(img_url)
+                    if base64_data:
+                        images.append(base64_data)
+                elif item_type == 'file':
+                    file_info = item.get('file', {}) or {}
+                    download_url = file_info.get('url') or file_info.get('fileurl')
+                    file_data = {
+                        'filename': file_info.get('filename') or file_info.get('name'),
+                        'filesize': file_info.get('filesize') or file_info.get('size'),
+                        'md5sum': file_info.get('md5sum') or file_info.get('md5'),
+                        'sdkfileid': file_info.get('sdkfileid') or file_info.get('fileid'),
+                        'download_url': download_url,
+                        'extra': file_info,
+                    }
+                    if (file_data.get('filesize') or 0) <= max_inline_file_size:
+                        file_base64 = await _safe_download(download_url)
+                        if file_base64:
+                            file_data['base64'] = file_base64
+                    files.append(file_data)
+                elif item_type == 'voice':
+                    voice_info = item.get('voice', {}) or {}
+                    download_url = voice_info.get('url')
+                    voice_data = {
+                        'url': download_url,
+                        'md5sum': voice_info.get('md5sum') or voice_info.get('md5'),
+                        'filesize': voice_info.get('filesize') or voice_info.get('size'),
+                        'sdkfileid': voice_info.get('sdkfileid') or voice_info.get('fileid'),
+                    }
+                    if voice_info.get('content'):
+                        texts.append(voice_info.get('content'))
+                    if (voice_data.get('filesize') or 0) <= max_inline_file_size:
+                        voice_base64 = await _safe_download(download_url)
+                        if voice_base64:
+                            voice_data['base64'] = voice_base64
+                    voices.append(voice_data)
+                elif item_type == 'video':
+                    video_info = item.get('video', {}) or {}
+                    download_url = video_info.get('url')
+                    video_data = {
+                        'url': download_url,
+                        'filesize': video_info.get('filesize') or video_info.get('size'),
+                        'sdkfileid': video_info.get('sdkfileid') or video_info.get('fileid'),
+                        'md5sum': video_info.get('md5sum') or video_info.get('md5'),
+                        'filename': video_info.get('filename') or video_info.get('name'),
+                    }
+                    if (video_data.get('filesize') or 0) <= max_inline_file_size:
+                        video_base64 = await _safe_download(download_url)
+                        if video_base64:
+                            video_data['base64'] = video_base64
+                    videos.append(video_data)
+                elif item_type == 'link':
+                    links.append(item.get('link', {}))
+
+            if texts:
+                message_data['content'] = ' '.join(texts)  # 拼接所有 text
+            if images:
+                message_data['images'] = images
+                message_data['picurl'] = images[0]  # 只保留第一个 image
+            if files:
+                message_data['files'] = files
+                message_data['file'] = files[0]
+            if voices:
+                message_data['voices'] = voices
+                message_data['voice'] = voices[0]
+            if videos:
+                message_data['videos'] = videos
+                message_data['video'] = videos[0]
+            if links:
+                message_data['link'] = links[0]
+            if items:
+                message_data['attachments'] = items
+        else:
+            message_data['raw_msg'] = msg_json
+
+        # Extract user information
+        from_info = msg_json.get('from', {})
+        message_data['userid'] = from_info.get('userid', '')
+        message_data['username'] = (
+            from_info.get('alias', '') or from_info.get('name', '') or from_info.get('userid', '')
+        )
+
+        # Extract chat/group information
+        if msg_json.get('chattype', '') == 'group':
+            message_data['chatid'] = msg_json.get('chatid', '')
+            # Try to get group name if available
+            message_data['chatname'] = msg_json.get('chatname', '') or msg_json.get('chatid', '')
+
+        message_data['msgid'] = msg_json.get('msgid', '')
+
+        if msg_json.get('aibotid'):
+            message_data['aibotid'] = msg_json.get('aibotid', '')
+
+        return message_data
 
     async def _handle_message(self, event: wecombotevent.WecomBotEvent):
         """
@@ -770,7 +712,39 @@ class WecomBotClient:
         return decorator
 
     async def download_url_to_base64(self, download_url, encoding_aes_key):
-        return await download_encrypted_file(download_url, encoding_aes_key, self.logger)
+        async with httpx.AsyncClient() as client:
+            response = await client.get(download_url)
+            if response.status_code != 200:
+                await self.logger.error(f'failed to get file: {response.text}')
+                return None
+
+            encrypted_bytes = response.content
+
+        aes_key = base64.b64decode(encoding_aes_key + '=')  # base64 补齐
+        iv = aes_key[:16]
+
+        cipher = AES.new(aes_key, AES.MODE_CBC, iv)
+        decrypted = cipher.decrypt(encrypted_bytes)
+
+        pad_len = decrypted[-1]
+        decrypted = decrypted[:-pad_len]
+
+        if decrypted.startswith(b'\xff\xd8'):  # JPEG
+            mime_type = 'image/jpeg'
+        elif decrypted.startswith(b'\x89PNG'):  # PNG
+            mime_type = 'image/png'
+        elif decrypted.startswith((b'GIF87a', b'GIF89a')):  # GIF
+            mime_type = 'image/gif'
+        elif decrypted.startswith(b'BM'):  # BMP
+            mime_type = 'image/bmp'
+        elif decrypted.startswith(b'II*\x00') or decrypted.startswith(b'MM\x00*'):  # TIFF
+            mime_type = 'image/tiff'
+        else:
+            mime_type = 'application/octet-stream'
+
+        # 转 base64
+        base64_str = base64.b64encode(decrypted).decode('utf-8')
+        return f'data:{mime_type};base64,{base64_str}'
 
     async def run_task(self, host: str, port: int, *args, **kwargs):
         """

src/langbot/libs/wecom_ai_bot_api/ws_client.py

@@ -1,596 +0,0 @@
-"""WeChat Work AI Bot WebSocket long connection client.
-
-Implements the WebSocket protocol for receiving messages and sending replies
-via a persistent connection to wss://openws.work.weixin.qq.com, as an
-alternative to the HTTP callback (webhook) mode.
-
-Protocol reference: https://developer.work.weixin.qq.com/document/path/101463
-Official Node.js SDK: https://github.com/WecomTeam/aibot-node-sdk
-"""
-
-from __future__ import annotations
-
-import asyncio
-import json
-import secrets
-import time
-import traceback
-from typing import Any, Callable, Optional
-
-import aiohttp
-
-from langbot.libs.wecom_ai_bot_api import wecombotevent
-from langbot.libs.wecom_ai_bot_api.api import parse_wecom_bot_message
-from langbot.pkg.platform.logger import EventLogger
-
-DEFAULT_WS_URL = 'wss://openws.work.weixin.qq.com'
-
-# WebSocket frame command constants
-CMD_SUBSCRIBE = 'aibot_subscribe'
-CMD_HEARTBEAT = 'ping'
-CMD_MSG_CALLBACK = 'aibot_msg_callback'
-CMD_EVENT_CALLBACK = 'aibot_event_callback'
-CMD_RESPOND_MSG = 'aibot_respond_msg'
-CMD_RESPOND_WELCOME = 'aibot_respond_welcome_msg'
-CMD_RESPOND_UPDATE = 'aibot_respond_update_msg'
-CMD_SEND_MSG = 'aibot_send_msg'
-
-
-def _generate_req_id(prefix: str) -> str:
-    """Generate a unique request ID in the format: {prefix}_{timestamp}_{random}."""
-    ts = int(time.time() * 1000)
-    rand = secrets.token_hex(4)
-    return f'{prefix}_{ts}_{rand}'
-
-
-class WecomBotWsClient:
-    """WeChat Work AI Bot WebSocket long connection client.
-
-    Provides message receiving, streaming reply, proactive message sending,
-    and event callback handling over a persistent WebSocket connection.
-    """
-
-    def __init__(
-        self,
-        bot_id: str,
-        secret: str,
-        logger: EventLogger,
-        encoding_aes_key: str = '',
-        ws_url: str = DEFAULT_WS_URL,
-        heartbeat_interval: float = 30.0,
-        max_reconnect_attempts: int = -1,
-        reconnect_base_delay: float = 1.0,
-        reconnect_max_delay: float = 30.0,
-    ):
-        self.bot_id = bot_id
-        self.secret = secret
-        self.logger = logger
-        self.encoding_aes_key = encoding_aes_key
-        self.ws_url = ws_url
-        self.heartbeat_interval = heartbeat_interval
-        self.max_reconnect_attempts = max_reconnect_attempts
-        self.reconnect_base_delay = reconnect_base_delay
-        self.reconnect_max_delay = reconnect_max_delay
-
-        self._ws: Optional[aiohttp.ClientWebSocketResponse] = None
-        self._session: Optional[aiohttp.ClientSession] = None
-        self._running = False
-        self._heartbeat_task: Optional[asyncio.Task] = None
-        self._missed_pong_count = 0
-        self._max_missed_pong = 2
-        self._reconnect_attempts = 0
-
-        # Message handler registry (same pattern as WecomBotClient)
-        self._message_handlers: dict[str, list[Callable]] = {}
-        # Message deduplication
-        self._msg_id_map: dict[str, int] = {}
-
-        # Pending ACK futures: req_id -> Future[dict]
-        self._pending_acks: dict[str, asyncio.Future] = {}
-        # Per-req_id serial reply queues
-        self._reply_queues: dict[str, asyncio.Queue] = {}
-        self._reply_workers: dict[str, asyncio.Task] = {}
-        self._reply_ack_timeout = 5.0
-
-        # Stream ID tracking for WebSocket mode
-        self._stream_ids: dict[str, str] = {}  # msg_id -> req_id|stream_id
-        # Dedup: skip sending when content hasn't changed
-        self._stream_last_content: dict[str, str] = {}  # msg_id -> last content sent
-
-    # ── Public API ──────────────────────────────────────────────────
-
-    async def connect(self):
-        """Connect to WebSocket server with automatic reconnection.
-
-        This method blocks until disconnect() is called or max reconnect
-        attempts are exhausted.
-        """
-        self._running = True
-        self._reconnect_attempts = 0
-
-        while self._running:
-            try:
-                await self._connect_once()
-            except Exception:
-                if not self._running:
-                    break
-                await self.logger.error(f'WebSocket connection error: {traceback.format_exc()}')
-
-            if not self._running:
-                break
-
-            # Reconnect with exponential backoff
-            if self.max_reconnect_attempts != -1 and self._reconnect_attempts >= self.max_reconnect_attempts:
-                await self.logger.error(f'Max reconnect attempts reached ({self.max_reconnect_attempts}), giving up')
-                break
-
-            self._reconnect_attempts += 1
-            delay = min(
-                self.reconnect_base_delay * (2 ** (self._reconnect_attempts - 1)),
-                self.reconnect_max_delay,
-            )
-            await self.logger.info(f'Reconnecting in {delay:.1f}s (attempt {self._reconnect_attempts})...')
-            await asyncio.sleep(delay)
-
-    async def disconnect(self):
-        """Gracefully disconnect from the WebSocket server."""
-        self._running = False
-        if self._heartbeat_task and not self._heartbeat_task.done():
-            self._heartbeat_task.cancel()
-        for task in self._reply_workers.values():
-            if not task.done():
-                task.cancel()
-        if self._ws and not self._ws.closed:
-            await self._ws.close()
-        self._ws = None
-        if self._session and not self._session.closed:
-            await self._session.close()
-        self._session = None
-
-    def on_message(self, msg_type: str) -> Callable:
-        """Decorator to register a message handler.
-
-        Same interface as WecomBotClient.on_message for compatibility.
-
-        Args:
-            msg_type: 'single', 'group', or specific message type.
-        """
-
-        def decorator(func: Callable[[wecombotevent.WecomBotEvent], Any]):
-            if msg_type not in self._message_handlers:
-                self._message_handlers[msg_type] = []
-            self._message_handlers[msg_type].append(func)
-            return func
-
-        return decorator
-
-    async def reply_stream(
-        self,
-        req_id: str,
-        stream_id: str,
-        content: str,
-        finish: bool = False,
-    ) -> Optional[dict]:
-        """Send a streaming reply frame.
-
-        Args:
-            req_id: The req_id from the original message frame (must be passed through).
-            stream_id: The stream ID for this streaming session.
-            content: The content to send (supports Markdown).
-            finish: Whether this is the final chunk.
-
-        Returns:
-            The ACK frame dict, or None on failure.
-        """
-        body = {
-            'msgtype': 'stream',
-            'stream': {
-                'id': stream_id,
-                'finish': finish,
-                'content': content,
-            },
-        }
-        return await self._send_reply(req_id, body)
-
-    async def reply_text(self, req_id: str, content: str) -> Optional[dict]:
-        """Send a non-streaming text reply.
-
-        Args:
-            req_id: The req_id from the original message frame.
-            content: The text content to reply.
-
-        Returns:
-            The ACK frame dict, or None on failure.
-        """
-        body = {
-            'msgtype': 'markdown',
-            'markdown': {
-                'content': content,
-            },
-        }
-        return await self._send_reply(req_id, body)
-
-    async def send_message(self, chat_id: str, content: str, msgtype: str = 'markdown') -> Optional[dict]:
-        """Proactively send a message to a specified chat.
-
-        Args:
-            chat_id: The chat ID (userid for single chat, chatid for group chat).
-            content: The message content.
-            msgtype: Message type, 'markdown' by default.
-
-        Returns:
-            The ACK frame dict, or None on failure.
-        """
-        req_id = _generate_req_id(CMD_SEND_MSG)
-        body: dict[str, Any] = {
-            'chatid': chat_id,
-            'msgtype': msgtype,
-        }
-        if msgtype == 'markdown':
-            body['markdown'] = {'content': content}
-        elif msgtype == 'text':
-            body['text'] = {'content': content}
-        return await self._send_reply(req_id, body, cmd=CMD_SEND_MSG)
-
-    async def push_stream_chunk(self, msg_id: str, content: str, is_final: bool = False) -> bool:
-        """Push a streaming chunk for a given message ID.
-
-        Compatible interface with WecomBotClient.push_stream_chunk.
-
-        Args:
-            msg_id: The original message ID.
-            content: The cumulative content from the pipeline.
-            is_final: Whether this is the final chunk.
-
-        Returns:
-            True if the stream session exists and chunk was sent.
-        """
-        key = self._stream_ids.get(msg_id)
-        if not key:
-            return False
-        req_id, stream_id = key.split('|', 1)
-        try:
-            # Skip sending if content hasn't changed (e.g. during tool call argument streaming)
-            if not is_final and content == self._stream_last_content.get(msg_id):
-                return True
-            await self.reply_stream(req_id, stream_id, content, finish=is_final)
-            self._stream_last_content[msg_id] = content
-            if is_final:
-                self._stream_ids.pop(msg_id, None)
-                self._stream_last_content.pop(msg_id, None)
-            return True
-        except Exception:
-            await self.logger.error(f'Failed to push stream chunk: {traceback.format_exc()}')
-            return False
-
-    async def set_message(self, msg_id: str, content: str):
-        """Fallback: send content as a final stream chunk or direct reply.
-
-        Compatible interface with WecomBotClient.set_message.
-        """
-        handled = await self.push_stream_chunk(msg_id, content, is_final=True)
-        if not handled:
-            await self.logger.warning(f'No active stream for msg_id={msg_id}, message dropped')
-
-    # ── Connection lifecycle ────────────────────────────────────────
-
-    async def _connect_once(self):
-        """Establish a single WebSocket connection, authenticate, and listen."""
-        await self.logger.info(f'Connecting to {self.ws_url}...')
-
-        self._session = aiohttp.ClientSession()
-        try:
-            self._ws = await self._session.ws_connect(self.ws_url)
-            self._missed_pong_count = 0
-            self._reconnect_attempts = 0
-            await self.logger.info('WebSocket connected, sending auth...')
-
-            await self._send_auth()
-
-            # Wait for auth response
-            auth_ok = await self._wait_for_auth()
-            if not auth_ok:
-                await self.logger.error('Authentication failed')
-                return
-
-            await self.logger.info('Authenticated successfully')
-
-            # Start heartbeat
-            self._heartbeat_task = asyncio.create_task(self._heartbeat_loop())
-
-            try:
-                await self._listen_loop()
-            finally:
-                if self._heartbeat_task and not self._heartbeat_task.done():
-                    self._heartbeat_task.cancel()
-                self._clear_pending_acks('Connection closed')
-        finally:
-            if self._ws and not self._ws.closed:
-                await self._ws.close()
-            self._ws = None
-            if self._session and not self._session.closed:
-                await self._session.close()
-            self._session = None
-
-    async def _send_auth(self):
-        """Send the authentication frame."""
-        frame = {
-            'cmd': CMD_SUBSCRIBE,
-            'headers': {'req_id': _generate_req_id(CMD_SUBSCRIBE)},
-            'body': {
-                'bot_id': self.bot_id,
-                'secret': self.secret,
-            },
-        }
-        await self._send_frame(frame)
-
-    async def _wait_for_auth(self) -> bool:
-        """Wait for and validate the authentication response."""
-        try:
-            msg = await asyncio.wait_for(self._ws.receive(), timeout=10.0)
-            if msg.type in (aiohttp.WSMsgType.TEXT,):
-                frame = json.loads(msg.data)
-                req_id = frame.get('headers', {}).get('req_id', '')
-                if req_id.startswith(CMD_SUBSCRIBE) and frame.get('errcode') == 0:
-                    return True
-                await self.logger.error(f'Auth response: errcode={frame.get("errcode")}, errmsg={frame.get("errmsg")}')
-                return False
-            elif msg.type in (aiohttp.WSMsgType.ERROR, aiohttp.WSMsgType.CLOSED, aiohttp.WSMsgType.CLOSING):
-                await self.logger.error(f'WebSocket closed during auth: {msg.type}')
-                return False
-            await self.logger.error(f'Unexpected message type during auth: {msg.type}')
-            return False
-        except asyncio.TimeoutError:
-            await self.logger.error('Auth response timeout')
-            return False
-
-    async def _heartbeat_loop(self):
-        """Periodically send heartbeat pings."""
-        try:
-            while self._running and self._ws and not self._ws.closed:
-                await asyncio.sleep(self.heartbeat_interval)
-                if not self._running or not self._ws or self._ws.closed:
-                    break
-
-                if self._missed_pong_count >= self._max_missed_pong:
-                    await self.logger.warning(
-                        f'No heartbeat ack for {self._missed_pong_count} consecutive pings, connection considered dead'
-                    )
-                    await self._ws.close()
-                    break
-
-                self._missed_pong_count += 1
-                frame = {
-                    'cmd': CMD_HEARTBEAT,
-                    'headers': {'req_id': _generate_req_id(CMD_HEARTBEAT)},
-                }
-                try:
-                    await self._send_frame(frame)
-                except Exception:
-                    break
-        except asyncio.CancelledError:
-            pass
-
-    async def _listen_loop(self):
-        """Listen for incoming WebSocket frames and dispatch them."""
-        async for msg in self._ws:
-            if not self._running:
-                break
-            if msg.type == aiohttp.WSMsgType.TEXT:
-                try:
-                    frame = json.loads(msg.data)
-                    await self._handle_frame(frame)
-                except json.JSONDecodeError:
-                    await self.logger.error(f'Failed to parse WebSocket message: {str(msg.data)[:200]}')
-                except Exception:
-                    await self.logger.error(f'Error handling frame: {traceback.format_exc()}')
-            elif msg.type == aiohttp.WSMsgType.BINARY:
-                try:
-                    frame = json.loads(msg.data)
-                    await self._handle_frame(frame)
-                except Exception:
-                    await self.logger.error(f'Error handling binary frame: {traceback.format_exc()}')
-            elif msg.type in (aiohttp.WSMsgType.ERROR, aiohttp.WSMsgType.CLOSED, aiohttp.WSMsgType.CLOSING):
-                await self.logger.warning(f'WebSocket connection closed: {msg.type}')
-                break
-
-    # ── Frame handling ──────────────────────────────────────────────
-
-    async def _handle_frame(self, frame: dict):
-        """Route an incoming frame to the appropriate handler."""
-        cmd = frame.get('cmd', '')
-
-        # Message push
-        if cmd == CMD_MSG_CALLBACK:
-            asyncio.create_task(self._handle_message_callback(frame))
-            return
-
-        # Event push
-        if cmd == CMD_EVENT_CALLBACK:
-            asyncio.create_task(self._handle_event_callback(frame))
-            return
-
-        # No cmd → response/ACK frame, dispatch by req_id prefix
-        req_id = frame.get('headers', {}).get('req_id', '')
-
-        # Check pending ACKs first
-        if req_id in self._pending_acks:
-            future = self._pending_acks.pop(req_id)
-            if not future.done():
-                future.set_result(frame)
-            return
-
-        # Heartbeat response
-        if req_id.startswith(CMD_HEARTBEAT):
-            if frame.get('errcode') == 0:
-                self._missed_pong_count = 0
-            return
-
-        # Unknown frame
-        await self.logger.warning(f'Unknown frame: {json.dumps(frame, ensure_ascii=False)[:200]}')
-
-    async def _handle_message_callback(self, frame: dict):
-        """Handle an incoming message callback frame."""
-        try:
-            body = frame.get('body', {})
-            req_id = frame.get('headers', {}).get('req_id', '')
-
-            # Parse message using shared logic
-            message_data = await parse_wecom_bot_message(body, self.encoding_aes_key, self.logger)
-            if not message_data:
-                return
-
-            # Generate stream_id for this message and store the mapping
-            stream_id = _generate_req_id('stream')
-            msg_id = message_data.get('msgid', '')
-            if msg_id:
-                self._stream_ids[msg_id] = f'{req_id}|{stream_id}'
-            message_data['stream_id'] = stream_id
-            message_data['req_id'] = req_id
-
-            event = wecombotevent.WecomBotEvent(message_data)
-            await self._dispatch_event(event)
-        except Exception:
-            await self.logger.error(f'Error in message callback: {traceback.format_exc()}')
-
-    async def _handle_event_callback(self, frame: dict):
-        """Handle an incoming event callback frame (enter_chat, template_card_event, etc.)."""
-        try:
-            body = frame.get('body', {})
-            req_id = frame.get('headers', {}).get('req_id', '')
-
-            event_info = body.get('event', {})
-            event_type = event_info.get('eventtype', '')
-
-            message_data = {
-                'msgtype': 'event',
-                'type': body.get('chattype', 'single'),
-                'event': event_info,
-                'eventtype': event_type,
-                'msgid': body.get('msgid', ''),
-                'aibotid': body.get('aibotid', ''),
-                'req_id': req_id,
-            }
-
-            from_info = body.get('from', {})
-            message_data['userid'] = from_info.get('userid', '')
-            message_data['username'] = from_info.get('alias', '') or from_info.get('userid', '')
-
-            if body.get('chatid'):
-                message_data['chatid'] = body.get('chatid', '')
-
-            event = wecombotevent.WecomBotEvent(message_data)
-
-            # Dispatch to event-specific handlers
-            if event_type in self._message_handlers:
-                for handler in self._message_handlers[event_type]:
-                    await handler(event)
-
-            # Also dispatch to generic 'event' handlers
-            if 'event' in self._message_handlers:
-                for handler in self._message_handlers['event']:
-                    await handler(event)
-
-        except Exception:
-            await self.logger.error(f'Error in event callback: {traceback.format_exc()}')
-
-    async def _dispatch_event(self, event: wecombotevent.WecomBotEvent):
-        """Dispatch a message event to registered handlers with deduplication."""
-        try:
-            message_id = event.message_id
-            if message_id in self._msg_id_map:
-                self._msg_id_map[message_id] += 1
-                return
-            self._msg_id_map[message_id] = 1
-
-            msg_type = event.type
-            if msg_type in self._message_handlers:
-                for handler in self._message_handlers[msg_type]:
-                    await handler(event)
-        except Exception:
-            await self.logger.error(f'Error dispatching event: {traceback.format_exc()}')
-
-    # ── Reply sending with serial queue ─────────────────────────────
-
-    async def _send_reply(
-        self,
-        req_id: str,
-        body: dict,
-        cmd: str = CMD_RESPOND_MSG,
-    ) -> Optional[dict]:
-        """Send a reply frame and wait for ACK.
-
-        Replies with the same req_id are serialized to maintain ordering.
-        """
-        if not self._ws or self._ws.closed:
-            return None
-
-        frame = {
-            'cmd': cmd,
-            'headers': {'req_id': req_id},
-            'body': body,
-        }
-
-        # Ensure serial delivery per req_id
-        if req_id not in self._reply_queues:
-            self._reply_queues[req_id] = asyncio.Queue()
-            self._reply_workers[req_id] = asyncio.create_task(self._reply_queue_worker(req_id))
-
-        future: asyncio.Future = asyncio.get_event_loop().create_future()
-        await self._reply_queues[req_id].put((frame, future))
-        return await future
-
-    async def _reply_queue_worker(self, req_id: str):
-        """Process reply queue items serially for a given req_id."""
-        queue = self._reply_queues[req_id]
-        try:
-            while self._running:
-                try:
-                    frame, future = await asyncio.wait_for(queue.get(), timeout=60.0)
-                except asyncio.TimeoutError:
-                    # Queue idle, clean up worker
-                    break
-
-                try:
-                    ack = await self._send_and_wait_ack(frame)
-                    if not future.done():
-                        future.set_result(ack)
-                except Exception as e:
-                    if not future.done():
-                        future.set_exception(e)
-        except asyncio.CancelledError:
-            pass
-        finally:
-            self._reply_queues.pop(req_id, None)
-            self._reply_workers.pop(req_id, None)
-
-    async def _send_and_wait_ack(self, frame: dict) -> Optional[dict]:
-        """Send a frame and wait for the corresponding ACK."""
-        req_id = frame['headers']['req_id']
-        ack_future: asyncio.Future = asyncio.get_event_loop().create_future()
-        self._pending_acks[req_id] = ack_future
-
-        try:
-            await self._send_frame(frame)
-            result = await asyncio.wait_for(ack_future, timeout=self._reply_ack_timeout)
-            if result.get('errcode', 0) != 0:
-                await self.logger.warning(
-                    f'Reply ACK error: errcode={result.get("errcode")}, errmsg={result.get("errmsg")}'
-                )
-            return result
-        except asyncio.TimeoutError:
-            self._pending_acks.pop(req_id, None)
-            await self.logger.warning(f'Reply ACK timeout ({self._reply_ack_timeout}s) for req_id={req_id}')
-            return None
-
-    async def _send_frame(self, frame: dict):
-        """Send a JSON frame over the WebSocket connection."""
-        if self._ws and not self._ws.closed:
-            await self._ws.send_str(json.dumps(frame, ensure_ascii=False))
-
-    def _clear_pending_acks(self, reason: str):
-        """Reject all pending ACK futures on disconnection."""
-        for req_id, future in self._pending_acks.items():
-            if not future.done():
-                future.set_exception(ConnectionError(reason))
-        self._pending_acks.clear()

src/langbot/libs/wecom_customer_service_api/api.py

@@ -10,7 +10,6 @@ from typing import Callable
 from .wecomcsevent import WecomCSEvent
 import langbot_plugin.api.entities.builtin.platform.message as platform_message
 import aiofiles
-import time
 
 
 class WecomCSClient:
@@ -35,10 +34,6 @@ class WecomCSClient:
         self.unified_mode = unified_mode
         self.app = Quart(__name__)
 
-        # Customer info cache: {external_userid: (info_dict, timestamp)}
-        self._customer_cache: dict[str, tuple[dict, float]] = {}
-        self._cache_ttl = 60  # Cache TTL in seconds (1 minute)
-
         # 只有在非统一模式下才注册独立路由
         if not self.unified_mode:
             self.app.add_url_rule(
@@ -383,53 +378,3 @@ class WecomCSClient:
     async def get_media_id(self, image: platform_message.Image):
         media_id = await self.upload_to_work(image=image)
         return media_id
-
-    async def get_customer_info(self, external_userid: str) -> dict | None:
-        """
-        Get customer information by external_userid with caching.
-
-        Uses a 1-minute cache to avoid repeated API calls for the same user.
-
-        Args:
-            external_userid: The external user ID of the customer.
-
-        Returns:
-            Customer info dict with 'nickname', 'avatar', etc., or None if not found.
-        """
-        # Check cache first
-        current_time = time.time()
-        if external_userid in self._customer_cache:
-            cached_info, cached_time = self._customer_cache[external_userid]
-            if current_time - cached_time < self._cache_ttl:
-                return cached_info
-
-        # Cache miss or expired, fetch from API
-        if not await self.check_access_token():
-            self.access_token = await self.get_access_token(self.secret)
-
-        url = f'{self.base_url}/kf/customer/batchget?access_token={self.access_token}'
-
-        payload = {
-            'external_userid_list': [external_userid],
-        }
-
-        async with httpx.AsyncClient() as client:
-            response = await client.post(url, json=payload)
-            data = response.json()
-
-            if data.get('errcode') in [40014, 42001]:
-                self.access_token = await self.get_access_token(self.secret)
-                return await self.get_customer_info(external_userid)
-
-            if data.get('errcode', 0) != 0:
-                if self.logger:
-                    await self.logger.warning(f'Failed to get customer info: {data}')
-                return None
-
-            customer_list = data.get('customer_list', [])
-            if customer_list:
-                customer_info = customer_list[0]
-                # Store in cache
-                self._customer_cache[external_userid] = (customer_info, current_time)
-                return customer_info
-            return None

src/langbot/pkg/api/http/controller/groups/knowledge/base.py

@@ -13,10 +13,7 @@ class KnowledgeBaseRouterGroup(group.RouterGroup):
 
             elif quart.request.method == 'POST':
                 json_data = await quart.request.json
-                try:
-                    knowledge_base_uuid = await self.ap.knowledge_service.create_knowledge_base(json_data)
-                except ValueError as e:
-                    return self.http_status(400, -1, str(e))
+                knowledge_base_uuid = await self.ap.knowledge_service.create_knowledge_base(json_data)
                 return self.success(data={'uuid': knowledge_base_uuid})
 
             return self.http_status(405, -1, 'Method not allowed')
@@ -42,7 +39,7 @@ class KnowledgeBaseRouterGroup(group.RouterGroup):
             elif quart.request.method == 'PUT':
                 json_data = await quart.request.json
                 await self.ap.knowledge_service.update_knowledge_base(knowledge_base_uuid, json_data)
-                return self.success(data={'uuid': knowledge_base_uuid})
+                return self.success({})
 
             elif quart.request.method == 'DELETE':
                 await self.ap.knowledge_service.delete_knowledge_base(knowledge_base_uuid)
@@ -68,12 +65,8 @@ class KnowledgeBaseRouterGroup(group.RouterGroup):
                 if not file_id:
                     return self.http_status(400, -1, 'File ID is required')
 
-                parser_plugin_id = json_data.get('parser_plugin_id')
-
                 # 调用服务层方法将文件与知识库关联
-                task_id = await self.ap.knowledge_service.store_file(
-                    knowledge_base_uuid, file_id, parser_plugin_id=parser_plugin_id
-                )
+                task_id = await self.ap.knowledge_service.store_file(knowledge_base_uuid, file_id)
                 return self.success(
                     {
                         'task_id': task_id,
@@ -97,13 +90,5 @@ class KnowledgeBaseRouterGroup(group.RouterGroup):
         async def retrieve_knowledge_base(knowledge_base_uuid: str) -> str:
             json_data = await quart.request.json
             query = json_data.get('query')
-
-            if not query or not query.strip():
-                return self.http_status(400, -1, 'Query is required and cannot be empty')
-
-            # Extract retrieval_settings to allow dynamic control over Knowledge Engine behavior (e.g. top_k, filters)
-            retrieval_settings = json_data.get('retrieval_settings', {})
-            results = await self.ap.knowledge_service.retrieve_knowledge_base(
-                knowledge_base_uuid, query, retrieval_settings
-            )
+            results = await self.ap.knowledge_service.retrieve_knowledge_base(knowledge_base_uuid, query)
             return self.success(data={'results': results})

src/langbot/pkg/api/http/controller/groups/knowledge/engines.py

@@ -1,45 +0,0 @@
-import quart
-from urllib.parse import unquote
-from ... import group
-
-
-@group.group_class('knowledge_engines', '/api/v1/knowledge/engines')
-class KnowledgeEnginesRouterGroup(group.RouterGroup):
-    async def initialize(self) -> None:
-        @self.route('', methods=['GET'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
-        async def list_knowledge_engines() -> quart.Response:
-            """List all available Knowledge Engines from plugins.
-
-            Returns a list of Knowledge Engines with their capabilities and configuration schemas.
-            This is used by the frontend to render the knowledge base creation wizard.
-            """
-            engines = await self.ap.knowledge_service.list_knowledge_engines()
-            return self.success(data={'engines': engines})
-
-        @self.route(
-            '/<path:plugin_id>/creation-schema', methods=['GET'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY
-        )
-        async def get_engine_creation_schema(plugin_id: str) -> quart.Response:
-            """Get creation settings schema for a specific Knowledge Engine.
-
-            plugin_id is in 'author/name' format, captured via <path:> converter.
-            """
-            plugin_id = unquote(plugin_id)
-            if '/' not in plugin_id:
-                return self.http_status(400, -1, 'Invalid plugin_id format. Expected author/name.')
-            schema = await self.ap.knowledge_service.get_engine_creation_schema(plugin_id)
-            return self.success(data={'schema': schema})
-
-        @self.route(
-            '/<path:plugin_id>/retrieval-schema', methods=['GET'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY
-        )
-        async def get_engine_retrieval_schema(plugin_id: str) -> quart.Response:
-            """Get retrieval settings schema for a specific Knowledge Engine.
-
-            plugin_id is in 'author/name' format, captured via <path:> converter.
-            """
-            plugin_id = unquote(plugin_id)
-            if '/' not in plugin_id:
-                return self.http_status(400, -1, 'Invalid plugin_id format. Expected author/name.')
-            schema = await self.ap.knowledge_service.get_engine_retrieval_schema(plugin_id)
-            return self.success(data={'schema': schema})

src/langbot/pkg/api/http/controller/groups/knowledge/external.py

@@ -0,0 +1,61 @@
+import quart
+from ... import group
+
+
+@group.group_class('external_knowledge_base', '/api/v1/knowledge/external-bases')
+class ExternalKnowledgeBaseRouterGroup(group.RouterGroup):
+    async def initialize(self) -> None:
+        @self.route('/retrievers', methods=['GET'])
+        async def list_knowledge_retrievers() -> quart.Response:
+            """List all available knowledge retrievers from plugins."""
+            retrievers = await self.ap.plugin_connector.list_knowledge_retrievers()
+            return self.success(data={'retrievers': retrievers})
+
+        @self.route('', methods=['POST', 'GET'])
+        async def handle_external_knowledge_bases() -> quart.Response:
+            if quart.request.method == 'GET':
+                external_kbs = await self.ap.external_kb_service.get_external_knowledge_bases()
+                return self.success(data={'bases': external_kbs})
+
+            elif quart.request.method == 'POST':
+                json_data = await quart.request.json
+                kb_uuid = await self.ap.external_kb_service.create_external_knowledge_base(json_data)
+                return self.success(data={'uuid': kb_uuid})
+
+            return self.http_status(405, -1, 'Method not allowed')
+
+        @self.route(
+            '/<kb_uuid>',
+            methods=['GET', 'DELETE', 'PUT'],
+        )
+        async def handle_specific_external_knowledge_base(kb_uuid: str) -> quart.Response:
+            if quart.request.method == 'GET':
+                external_kb = await self.ap.external_kb_service.get_external_knowledge_base(kb_uuid)
+
+                if external_kb is None:
+                    return self.http_status(404, -1, 'external knowledge base not found')
+
+                return self.success(
+                    data={
+                        'base': external_kb,
+                    }
+                )
+
+            elif quart.request.method == 'PUT':
+                json_data = await quart.request.json
+                await self.ap.external_kb_service.update_external_knowledge_base(kb_uuid, json_data)
+                return self.success({})
+
+            elif quart.request.method == 'DELETE':
+                await self.ap.external_kb_service.delete_external_knowledge_base(kb_uuid)
+                return self.success({})
+
+        @self.route(
+            '/<kb_uuid>/retrieve',
+            methods=['POST'],
+        )
+        async def retrieve_external_knowledge_base(kb_uuid: str) -> str:
+            json_data = await quart.request.json
+            query = json_data.get('query')
+            results = await self.ap.external_kb_service.retrieve_external_knowledge_base(kb_uuid, query)
+            return self.success(data={'results': results})

src/langbot/pkg/api/http/controller/groups/knowledge/migration.py

@@ -1,372 +0,0 @@
-import asyncio
-import json
-
-import httpx
-import quart
-import sqlalchemy
-
-from ... import group
-from ......core import taskmgr
-from ......entity.persistence import metadata as persistence_metadata
-from langbot_plugin.runtime.plugin.mgr import PluginInstallSource
-
-LANGRAG_PLUGIN_AUTHOR = 'langbot-team'
-LANGRAG_PLUGIN_NAME = 'LangRAG'
-LANGRAG_PLUGIN_ID = f'{LANGRAG_PLUGIN_AUTHOR}/{LANGRAG_PLUGIN_NAME}'
-DEFAULT_SPACE_URL = 'https://space.langbot.app'
-
-# Old Retriever plugin_name -> New Connector plugin_name
-EXTERNAL_PLUGIN_NAME_MAPPING = {
-    'DifyDatasetsRetriever': 'DifyDatasetsConnector',
-    'RAGFlowRetriever': 'RAGFlowConnector',
-    'FastGPTRetriever': 'FastGPTConnector',
-}
-
-# Per-plugin: which old retriever_config fields belong to creation_settings.
-# Remaining fields go to retrieval_settings.
-# None means ALL fields go to creation_settings (no retrieval_schema).
-EXTERNAL_PLUGIN_CREATION_FIELDS: dict[str, set[str] | None] = {
-    'langbot-team/DifyDatasetsConnector': {'api_base_url', 'dify_apikey', 'dataset_id'},
-    'langbot-team/RAGFlowConnector': {'api_base_url', 'api_key', 'dataset_ids'},
-    'langbot-team/FastGPTConnector': None,  # all fields -> creation_settings
-}
-
-
-@group.group_class('knowledge/migration', '/api/v1/knowledge/migration')
-class KnowledgeMigrationRouterGroup(group.RouterGroup):
-    async def _get_migration_flag(self) -> bool:
-        """Check if rag_plugin_migration_needed flag is set."""
-        result = await self.ap.persistence_mgr.execute_async(
-            sqlalchemy.select(persistence_metadata.Metadata).where(
-                persistence_metadata.Metadata.key == 'rag_plugin_migration_needed'
-            )
-        )
-        row = result.first()
-        return row is not None and row.value == 'true'
-
-    async def _set_migration_flag(self, value: str):
-        """Set rag_plugin_migration_needed flag."""
-        await self.ap.persistence_mgr.execute_async(
-            sqlalchemy.update(persistence_metadata.Metadata)
-            .where(persistence_metadata.Metadata.key == 'rag_plugin_migration_needed')
-            .values(value=value)
-        )
-
-    async def _table_exists(self, table_name: str) -> bool:
-        """Check if a table exists."""
-        if self.ap.persistence_mgr.db.name == 'postgresql':
-            result = await self.ap.persistence_mgr.execute_async(
-                sqlalchemy.text(
-                    'SELECT EXISTS (SELECT FROM information_schema.tables WHERE table_name = :table_name);'
-                ).bindparams(table_name=table_name)
-            )
-            return result.scalar()
-        else:
-            result = await self.ap.persistence_mgr.execute_async(
-                sqlalchemy.text("SELECT name FROM sqlite_master WHERE type='table' AND name=:table_name;").bindparams(
-                    table_name=table_name
-                )
-            )
-            return result.first() is not None
-
-    async def _install_plugin_from_marketplace(
-        self, plugin_id: str, task_context: taskmgr.TaskContext, space_url: str
-    ) -> None:
-        """Install a single plugin from the marketplace."""
-        p_author, p_name = plugin_id.split('/', 1)
-        self.ap.logger.info(f'RAG migration: installing plugin {plugin_id} from marketplace...')
-        task_context.trace(f'Installing plugin {plugin_id} from marketplace...')
-
-        async with httpx.AsyncClient(trust_env=True, timeout=15) as client:
-            resp = await client.get(f'{space_url}/api/v1/marketplace/plugins/{p_author}/{p_name}')
-            resp.raise_for_status()
-            p_data = resp.json().get('data', {}).get('plugin', {})
-            p_version = p_data.get('latest_version')
-            if not p_version:
-                raise Exception(f'Could not determine latest version for {plugin_id}')
-
-        await self.ap.plugin_connector.install_plugin(
-            PluginInstallSource.MARKETPLACE,
-            {
-                'plugin_author': p_author,
-                'plugin_name': p_name,
-                'plugin_version': p_version,
-            },
-            task_context=task_context,
-        )
-        self.ap.logger.info(f'RAG migration: plugin {plugin_id} install request sent.')
-
-    async def _execute_rag_migration(self, task_context: taskmgr.TaskContext, install_plugin: bool = True):
-        """Execute RAG migration: install required plugins and restore backup data."""
-        warnings = []
-
-        # Collect all plugins we need: LangRAG (always) + connector plugins (from external KBs)
-        needed_plugins: dict[str, str] = {
-            LANGRAG_PLUGIN_ID: LANGRAG_PLUGIN_NAME,
-        }
-
-        has_external = await self._table_exists('external_knowledge_bases')
-        if has_external:
-            result = await self.ap.persistence_mgr.execute_async(
-                sqlalchemy.text('SELECT DISTINCT plugin_author, plugin_name FROM external_knowledge_bases;')
-            )
-            for row in result.fetchall():
-                plugin_author = row[0] or ''
-                plugin_name = row[1] or ''
-                mapped_name = EXTERNAL_PLUGIN_NAME_MAPPING.get(plugin_name, plugin_name)
-                plugin_id = f'{plugin_author}/{mapped_name}'
-                if plugin_id not in needed_plugins:
-                    needed_plugins[plugin_id] = mapped_name
-
-        self.ap.logger.info(f'RAG migration: plugins needed: {list(needed_plugins.keys())}')
-
-        if install_plugin:
-            # Step 1: Install all required plugins from marketplace
-            task_context.trace('Installing required plugins...', action='install-plugin')
-            space_url = self.ap.instance_config.data.get('space', {}).get('url', DEFAULT_SPACE_URL).rstrip('/')
-
-            for plugin_id in needed_plugins:
-                try:
-                    await self._install_plugin_from_marketplace(plugin_id, task_context, space_url)
-                except Exception as e:
-                    self.ap.logger.warning(f'RAG migration: plugin {plugin_id} install returned: {e}')
-                    task_context.trace(f'Plugin install note ({plugin_id}): {e}')
-
-            # Step 2: Wait for all plugins to become available as knowledge engines
-            task_context.trace(
-                f'Waiting for plugins to become available: {list(needed_plugins.keys())}...',
-                action='wait-plugin',
-            )
-            max_retries = 30
-            engine_id_set: set[str] = set()
-            for i in range(max_retries):
-                try:
-                    engines = await self.ap.plugin_connector.list_knowledge_engines()
-                    engine_id_set = {e.get('plugin_id') for e in engines}
-                except Exception:
-                    pass
-                if all(pid in engine_id_set for pid in needed_plugins):
-                    self.ap.logger.info(f'RAG migration: all plugins ready: {engine_id_set}')
-                    task_context.trace('All required plugins are ready.')
-                    break
-                if i == max_retries - 1:
-                    still_missing = [pid for pid in needed_plugins if pid not in engine_id_set]
-                    warning = f'Plugin(s) {still_missing} did not become available after {max_retries} retries'
-                    self.ap.logger.warning(f'RAG migration: {warning}')
-                    warnings.append(warning)
-                    task_context.trace(warning)
-                await asyncio.sleep(2)
-        else:
-            try:
-                engines = await self.ap.plugin_connector.list_knowledge_engines()
-                engine_id_set = {e.get('plugin_id') for e in engines}
-            except Exception:
-                engine_id_set = set()
-
-        # Step 3: Restore internal knowledge bases from backup
-        task_context.trace('Restoring internal knowledge bases...', action='restore-internal')
-        if await self._table_exists('knowledge_bases_backup'):
-            result = await self.ap.persistence_mgr.execute_async(
-                sqlalchemy.text('SELECT * FROM knowledge_bases_backup;')
-            )
-            rows = result.fetchall()
-            columns = result.keys()
-
-            for row in rows:
-                row_dict = dict(zip(columns, row))
-                kb_uuid = row_dict.get('uuid')
-                name = row_dict.get('name', 'Untitled')
-                description = row_dict.get('description', '')
-                emoji = row_dict.get('emoji', '\U0001f4da')
-                embedding_model_uuid = row_dict.get('embedding_model_uuid', '')
-                top_k = row_dict.get('top_k', 5)
-                created_at = row_dict.get('created_at')
-                updated_at = row_dict.get('updated_at')
-
-                creation_settings = json.dumps({'embedding_model_uuid': embedding_model_uuid})
-                retrieval_settings = json.dumps({'top_k': top_k})
-
-                await self.ap.persistence_mgr.execute_async(
-                    sqlalchemy.text(
-                        'INSERT INTO knowledge_bases '
-                        '(uuid, name, description, emoji, created_at, updated_at, '
-                        'knowledge_engine_plugin_id, collection_id, creation_settings, retrieval_settings) '
-                        'VALUES (:uuid, :name, :description, :emoji, :created_at, :updated_at, '
-                        ':plugin_id, :collection_id, :creation_settings, :retrieval_settings);'
-                    ).bindparams(
-                        uuid=kb_uuid,
-                        name=name,
-                        description=description,
-                        emoji=emoji,
-                        created_at=created_at,
-                        updated_at=updated_at,
-                        plugin_id=LANGRAG_PLUGIN_ID,
-                        collection_id=kb_uuid,
-                        creation_settings=creation_settings,
-                        retrieval_settings=retrieval_settings,
-                    )
-                )
-
-                try:
-                    config = {'embedding_model_uuid': embedding_model_uuid}
-                    await self.ap.plugin_connector.rag_on_kb_create(LANGRAG_PLUGIN_ID, kb_uuid, config)
-                    task_context.trace(f'Restored internal KB: {name} ({kb_uuid})')
-                except Exception as e:
-                    warning = f'Failed to notify plugin for KB {name} ({kb_uuid}): {e}'
-                    warnings.append(warning)
-                    task_context.trace(warning)
-
-            await self.ap.rag_mgr.load_knowledge_bases_from_db()
-
-        # Step 4: Restore external knowledge bases
-        task_context.trace('Restoring external knowledge bases...', action='restore-external')
-        if has_external:
-            result = await self.ap.persistence_mgr.execute_async(
-                sqlalchemy.text('SELECT * FROM external_knowledge_bases;')
-            )
-            rows = result.fetchall()
-            columns = result.keys()
-
-            self.ap.logger.info(
-                f'RAG migration: {len(rows)} external KB(s) to restore. Available engines: {engine_id_set}'
-            )
-            task_context.trace(f'Found {len(rows)} external KB(s). Available engines: {engine_id_set}')
-
-            for row in rows:
-                row_dict = dict(zip(columns, row))
-                kb_uuid = row_dict.get('uuid')
-                name = row_dict.get('name', 'Untitled')
-                description = row_dict.get('description', '')
-                emoji = row_dict.get('emoji', '\U0001f517')
-                plugin_author = row_dict.get('plugin_author', '')
-                plugin_name = row_dict.get('plugin_name', '')
-                retriever_config = row_dict.get('retriever_config', {})
-                created_at = row_dict.get('created_at')
-
-                mapped_plugin_name = EXTERNAL_PLUGIN_NAME_MAPPING.get(plugin_name, plugin_name)
-                external_plugin_id = f'{plugin_author}/{mapped_plugin_name}'
-
-                self.ap.logger.info(
-                    f'RAG migration: processing external KB "{name}" ({kb_uuid}), '
-                    f'plugin: {plugin_author}/{plugin_name} -> {external_plugin_id}'
-                )
-
-                if isinstance(retriever_config, str):
-                    try:
-                        retriever_config = json.loads(retriever_config)
-                    except (json.JSONDecodeError, TypeError):
-                        retriever_config = {}
-
-                creation_fields = EXTERNAL_PLUGIN_CREATION_FIELDS.get(external_plugin_id)
-                if creation_fields is None:
-                    creation_settings_dict = retriever_config
-                    retrieval_settings_dict = {}
-                else:
-                    creation_settings_dict = {k: v for k, v in retriever_config.items() if k in creation_fields}
-                    retrieval_settings_dict = {k: v for k, v in retriever_config.items() if k not in creation_fields}
-
-                await self.ap.persistence_mgr.execute_async(
-                    sqlalchemy.text(
-                        'INSERT INTO knowledge_bases '
-                        '(uuid, name, description, emoji, created_at, updated_at, '
-                        'knowledge_engine_plugin_id, collection_id, creation_settings, retrieval_settings) '
-                        'VALUES (:uuid, :name, :description, :emoji, :created_at, :updated_at, '
-                        ':plugin_id, :collection_id, :creation_settings, :retrieval_settings);'
-                    ).bindparams(
-                        uuid=kb_uuid,
-                        name=name,
-                        description=description,
-                        emoji=emoji,
-                        created_at=created_at,
-                        updated_at=created_at,
-                        plugin_id=external_plugin_id,
-                        collection_id=kb_uuid,
-                        creation_settings=json.dumps(creation_settings_dict),
-                        retrieval_settings=json.dumps(retrieval_settings_dict),
-                    )
-                )
-
-                if external_plugin_id not in engine_id_set:
-                    warning = (
-                        f'External KB "{name}" ({kb_uuid}) record saved, but plugin {external_plugin_id} '
-                        f'is not installed yet. Install the connector plugin to use it.'
-                    )
-                    warnings.append(warning)
-                    task_context.trace(warning)
-                else:
-                    try:
-                        await self.ap.plugin_connector.rag_on_kb_create(
-                            external_plugin_id, kb_uuid, creation_settings_dict
-                        )
-                        task_context.trace(f'Restored external KB: {name} ({kb_uuid})')
-                    except Exception as e:
-                        warning = f'Failed to notify plugin for external KB {name} ({kb_uuid}): {e}'
-                        warnings.append(warning)
-                        task_context.trace(warning)
-
-            await self.ap.rag_mgr.load_knowledge_bases_from_db()
-
-        # Step 5: Clear migration flag
-        await self._set_migration_flag('false')
-        task_context.trace('RAG migration completed.', action='done')
-
-        if warnings:
-            task_context.trace(f'Completed with {len(warnings)} warning(s).')
-
-    async def initialize(self) -> None:
-        @self.route('/status', methods=['GET'], auth_type=group.AuthType.USER_TOKEN)
-        async def _() -> str:
-            needed = await self._get_migration_flag()
-
-            internal_kb_count = 0
-            external_kb_count = 0
-
-            if needed:
-                if await self._table_exists('knowledge_bases_backup'):
-                    result = await self.ap.persistence_mgr.execute_async(
-                        sqlalchemy.text('SELECT COUNT(*) FROM knowledge_bases_backup;')
-                    )
-                    internal_kb_count = result.scalar() or 0
-
-                if await self._table_exists('external_knowledge_bases'):
-                    result = await self.ap.persistence_mgr.execute_async(
-                        sqlalchemy.text('SELECT COUNT(*) FROM external_knowledge_bases;')
-                    )
-                    external_kb_count = result.scalar() or 0
-
-            return self.success(
-                data={
-                    'needed': needed,
-                    'internal_kb_count': internal_kb_count,
-                    'external_kb_count': external_kb_count,
-                }
-            )
-
-        @self.route('/execute', methods=['POST'], auth_type=group.AuthType.USER_TOKEN)
-        async def _() -> str:
-            needed = await self._get_migration_flag()
-            if not needed:
-                return self.http_status(400, -1, 'RAG migration is not needed')
-
-            data = await quart.request.get_json(silent=True) or {}
-            install_plugin = data.get('install_plugin', True)
-
-            ctx = taskmgr.TaskContext.new()
-            wrapper = self.ap.task_mgr.create_user_task(
-                self._execute_rag_migration(task_context=ctx, install_plugin=install_plugin),
-                kind='rag-migration',
-                name='rag-migration-execute',
-                label='Migrating knowledge bases to plugin architecture',
-                context=ctx,
-            )
-
-            return self.success(data={'task_id': wrapper.id})
-
-        @self.route('/dismiss', methods=['POST'], auth_type=group.AuthType.USER_TOKEN)
-        async def _() -> str:
-            needed = await self._get_migration_flag()
-            if not needed:
-                return self.http_status(400, -1, 'RAG migration is not needed')
-
-            await self._set_migration_flag('false')
-            return self.success()

src/langbot/pkg/api/http/controller/groups/knowledge/parsers.py

@@ -1,16 +0,0 @@
-import quart
-from ... import group
-
-
-@group.group_class('parsers', '/api/v1/knowledge/parsers')
-class ParsersRouterGroup(group.RouterGroup):
-    async def initialize(self) -> None:
-        @self.route('', methods=['GET'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
-        async def list_parsers() -> quart.Response:
-            """List all available parsers from plugins.
-
-            Optional query parameter `mime_type` to filter parsers by supported MIME type.
-            """
-            mime_type = quart.request.args.get('mime_type')
-            parsers = await self.ap.knowledge_service.list_parsers(mime_type)
-            return self.success(data={'parsers': parsers})

src/langbot/pkg/api/http/controller/groups/monitoring.py

@@ -52,7 +52,6 @@ class MonitoringRouterGroup(group.RouterGroup):
             # Parse query parameters
             bot_ids = quart.request.args.getlist('botId')
             pipeline_ids = quart.request.args.getlist('pipelineId')
-            session_ids = quart.request.args.getlist('sessionId')
             start_time_str = quart.request.args.get('startTime')
             end_time_str = quart.request.args.get('endTime')
             limit = int(quart.request.args.get('limit', 100))
@@ -65,7 +64,6 @@ class MonitoringRouterGroup(group.RouterGroup):
             messages, total = await self.ap.monitoring_service.get_messages(
                 bot_ids=bot_ids if bot_ids else None,
                 pipeline_ids=pipeline_ids if pipeline_ids else None,
-                session_ids=session_ids if session_ids else None,
                 start_time=start_time,
                 end_time=end_time,
                 limit=limit,

src/langbot/pkg/api/http/controller/groups/pipelines/pipelines.py

@@ -68,7 +68,7 @@ class PipelinesRouterGroup(group.RouterGroup):
                     return self.http_status(404, -1, 'pipeline not found')
 
                 # Only include plugins with pipeline-related components (Command, EventListener, Tool)
-                # Plugins that only have KnowledgeEngine components are not suitable for pipeline extensions
+                # Plugins that only have KnowledgeRetriever components are not suitable for pipeline extensions
                 pipeline_component_kinds = ['Command', 'EventListener', 'Tool']
                 plugins = await self.ap.plugin_connector.list_plugins(component_kinds=pipeline_component_kinds)
                 mcp_servers = await self.ap.mcp_service.get_mcp_servers(contain_runtime_info=True)

src/langbot/pkg/api/http/controller/groups/survey.py

@@ -1,47 +0,0 @@
-import quart
-
-from .. import group
-
-
-@group.group_class('survey', '/api/v1/survey')
-class SurveyRouterGroup(group.RouterGroup):
-    async def initialize(self) -> None:
-        @self.route('/pending', methods=['GET'], auth_type=group.AuthType.USER_TOKEN)
-        async def _get_pending() -> str:
-            """Get pending survey for the frontend to display."""
-            survey = self.ap.survey.get_pending_survey() if self.ap.survey else None
-            return self.success(data={'survey': survey})
-
-        @self.route('/respond', methods=['POST'], auth_type=group.AuthType.USER_TOKEN)
-        async def _respond() -> str:
-            """Submit survey response."""
-            json_data = await quart.request.json
-            survey_id = json_data.get('survey_id')
-            answers = json_data.get('answers', {})
-            completed = json_data.get('completed', True)
-
-            if not survey_id:
-                return self.fail(1, 'survey_id required')
-
-            if self.ap.survey:
-                ok = await self.ap.survey.submit_response(survey_id, answers, completed)
-                if ok:
-                    return self.success()
-                return self.fail(2, 'Failed to submit response')
-            return self.fail(3, 'Survey not available')
-
-        @self.route('/dismiss', methods=['POST'], auth_type=group.AuthType.USER_TOKEN)
-        async def _dismiss() -> str:
-            """Dismiss survey."""
-            json_data = await quart.request.json
-            survey_id = json_data.get('survey_id')
-
-            if not survey_id:
-                return self.fail(1, 'survey_id required')
-
-            if self.ap.survey:
-                ok = await self.ap.survey.dismiss_survey(survey_id)
-                if ok:
-                    return self.success()
-                return self.fail(2, 'Failed to dismiss')
-            return self.fail(3, 'Survey not available')

src/langbot/pkg/api/http/service/external_kb.py

@@ -0,0 +1,80 @@
+from __future__ import annotations
+
+from ....core import app
+import sqlalchemy
+from langbot.pkg.entity.persistence import rag as persistence_rag
+import uuid
+
+
+class ExternalKBService:
+    """External KB service"""
+
+    ap: app.Application
+
+    def __init__(self, ap: app.Application) -> None:
+        self.ap = ap
+
+    # External Knowledge Base methods
+    async def get_external_knowledge_bases(self) -> list[dict]:
+        result = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_rag.ExternalKnowledgeBase))
+        external_kbs = result.all()
+        return [
+            self.ap.persistence_mgr.serialize_model(persistence_rag.ExternalKnowledgeBase, external_kb)
+            for external_kb in external_kbs
+        ]
+
+    async def get_external_knowledge_base(self, kb_uuid: str) -> dict | None:
+        result = await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.select(persistence_rag.ExternalKnowledgeBase).where(
+                persistence_rag.ExternalKnowledgeBase.uuid == kb_uuid
+            )
+        )
+        external_kb = result.first()
+        if external_kb is None:
+            return None
+        return self.ap.persistence_mgr.serialize_model(persistence_rag.ExternalKnowledgeBase, external_kb)
+
+    async def create_external_knowledge_base(self, kb_data: dict) -> str:
+        kb_data['uuid'] = str(uuid.uuid4())
+        await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.insert(persistence_rag.ExternalKnowledgeBase).values(kb_data)
+        )
+
+        kb = await self.get_external_knowledge_base(kb_data['uuid'])
+
+        await self.ap.rag_mgr.load_external_knowledge_base(kb)
+
+        return kb_data['uuid']
+
+    async def retrieve_external_knowledge_base(self, kb_uuid: str, query: str) -> list[dict]:
+        """Retrieve external knowledge base"""
+        runtime_kb = await self.ap.rag_mgr.get_knowledge_base_by_uuid(kb_uuid)
+        if runtime_kb is None:
+            raise Exception('Knowledge base not found')
+        return [
+            result.model_dump() for result in await runtime_kb.retrieve(query, 5)
+        ]  # top_k is just a placeholder for external knowledge base
+
+    async def update_external_knowledge_base(self, kb_uuid: str, kb_data: dict) -> None:
+        if 'uuid' in kb_data:
+            del kb_data['uuid']
+
+        await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.update(persistence_rag.ExternalKnowledgeBase)
+            .values(kb_data)
+            .where(persistence_rag.ExternalKnowledgeBase.uuid == kb_uuid)
+        )
+        await self.ap.rag_mgr.remove_knowledge_base_from_runtime(kb_uuid)
+
+        kb = await self.get_external_knowledge_base(kb_uuid)
+
+        await self.ap.rag_mgr.load_external_knowledge_base(kb)
+
+    async def delete_external_knowledge_base(self, kb_uuid: str) -> None:
+        await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.delete(persistence_rag.ExternalKnowledgeBase).where(
+                persistence_rag.ExternalKnowledgeBase.uuid == kb_uuid
+            )
+        )
+
+        await self.ap.rag_mgr.delete_knowledge_base(kb_uuid)

src/langbot/pkg/api/http/service/knowledge.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import uuid
 import sqlalchemy
 
 from ....core import app
@@ -16,77 +17,64 @@ class KnowledgeService:
 
     async def get_knowledge_bases(self) -> list[dict]:
         """获取所有知识库"""
-        return await self.ap.rag_mgr.get_all_knowledge_base_details()
+        result = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_rag.KnowledgeBase))
+        knowledge_bases = result.all()
+        return [
+            self.ap.persistence_mgr.serialize_model(persistence_rag.KnowledgeBase, knowledge_base)
+            for knowledge_base in knowledge_bases
+        ]
 
     async def get_knowledge_base(self, kb_uuid: str) -> dict | None:
         """获取知识库"""
-        return await self.ap.rag_mgr.get_knowledge_base_details(kb_uuid)
+        result = await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.select(persistence_rag.KnowledgeBase).where(persistence_rag.KnowledgeBase.uuid == kb_uuid)
+        )
+        knowledge_base = result.first()
+        if knowledge_base is None:
+            return None
+        return self.ap.persistence_mgr.serialize_model(persistence_rag.KnowledgeBase, knowledge_base)
 
     async def create_knowledge_base(self, kb_data: dict) -> str:
         """创建知识库"""
-        # In new architecture, we delegate entirely to RAGManager which uses plugins.
-        # Legacy internal KB creation is removed.
+        kb_data['uuid'] = str(uuid.uuid4())
+        await self.ap.persistence_mgr.execute_async(sqlalchemy.insert(persistence_rag.KnowledgeBase).values(kb_data))
 
-        knowledge_engine_plugin_id = kb_data.get('knowledge_engine_plugin_id')
-        if not knowledge_engine_plugin_id:
-            raise ValueError('knowledge_engine_plugin_id is required')
+        kb = await self.get_knowledge_base(kb_data['uuid'])
 
-        kb = await self.ap.rag_mgr.create_knowledge_base(
-            name=kb_data.get('name', 'Untitled'),
-            knowledge_engine_plugin_id=knowledge_engine_plugin_id,
-            creation_settings=kb_data.get('creation_settings', {}),
-            retrieval_settings=kb_data.get('retrieval_settings', {}),
-            description=kb_data.get('description', ''),
-        )
-        return kb.uuid
+        await self.ap.rag_mgr.load_knowledge_base(kb)
+
+        return kb_data['uuid']
 
     async def update_knowledge_base(self, kb_uuid: str, kb_data: dict) -> None:
         """更新知识库"""
-        # Filter to only mutable fields
-        filtered_data = {k: v for k, v in kb_data.items() if k in persistence_rag.KnowledgeBase.MUTABLE_FIELDS}
+        if 'uuid' in kb_data:
+            del kb_data['uuid']
 
-        if not filtered_data:
-            return
+        if 'embedding_model_uuid' in kb_data:
+            del kb_data['embedding_model_uuid']
 
         await self.ap.persistence_mgr.execute_async(
             sqlalchemy.update(persistence_rag.KnowledgeBase)
-            .values(filtered_data)
+            .values(kb_data)
             .where(persistence_rag.KnowledgeBase.uuid == kb_uuid)
         )
         await self.ap.rag_mgr.remove_knowledge_base_from_runtime(kb_uuid)
 
         kb = await self.get_knowledge_base(kb_uuid)
-        if kb is None:
-            raise Exception('Knowledge base not found after update')
 
         await self.ap.rag_mgr.load_knowledge_base(kb)
 
-    async def _check_doc_capability(self, kb_uuid: str, operation: str) -> None:
-        """Check if the KB's Knowledge Engine supports document operations.
-
-        Args:
-            kb_uuid: Knowledge base UUID.
-            operation: Human-readable operation name for error messages.
-
-        Raises:
-            Exception: If the KB does not support doc_ingestion.
-        """
-        kb_info = await self.ap.rag_mgr.get_knowledge_base_details(kb_uuid)
-        if not kb_info:
-            raise Exception('Knowledge base not found')
-        capabilities = kb_info.get('knowledge_engine', {}).get('capabilities', [])
-        if 'doc_ingestion' not in capabilities:
-            raise Exception(f'This knowledge base does not support {operation}')
-
-    async def store_file(self, kb_uuid: str, file_id: str, parser_plugin_id: str | None = None) -> str:
+    async def store_file(self, kb_uuid: str, file_id: str) -> int:
         """存储文件"""
+        # await self.ap.persistence_mgr.execute_async(sqlalchemy.insert(persistence_rag.File).values(kb_id=kb_uuid, file_id=file_id))
+        # await self.ap.rag_mgr.store_file(file_id)
         runtime_kb = await self.ap.rag_mgr.get_knowledge_base_by_uuid(kb_uuid)
         if runtime_kb is None:
             raise Exception('Knowledge base not found')
-
-        await self._check_doc_capability(kb_uuid, 'document upload')
-
-        result = await runtime_kb.store_file(file_id, parser_plugin_id=parser_plugin_id)
+        # Only internal KBs support file storage
+        if runtime_kb.get_type() != 'internal':
+            raise Exception('Only internal knowledge bases support file storage')
+        result = await runtime_kb.store_file(file_id)
 
         # Update the KB's updated_at timestamp
         await self.ap.persistence_mgr.execute_async(
@@ -97,18 +85,14 @@ class KnowledgeService:
 
         return result
 
-    async def retrieve_knowledge_base(
-        self, kb_uuid: str, query: str, retrieval_settings: dict | None = None
-    ) -> list[dict]:
+    async def retrieve_knowledge_base(self, kb_uuid: str, query: str) -> list[dict]:
         """检索知识库"""
         runtime_kb = await self.ap.rag_mgr.get_knowledge_base_by_uuid(kb_uuid)
         if runtime_kb is None:
             raise Exception('Knowledge base not found')
-
-        # Pass retrieval_settings
-        results = await runtime_kb.retrieve(query, settings=retrieval_settings)
-
-        return [result.model_dump() for result in results]
+        return [
+            result.model_dump() for result in await runtime_kb.retrieve(query, runtime_kb.knowledge_base_entity.top_k)
+        ]
 
     async def get_files_by_knowledge_base(self, kb_uuid: str) -> list[dict]:
         """获取知识库文件"""
@@ -123,9 +107,9 @@ class KnowledgeService:
         runtime_kb = await self.ap.rag_mgr.get_knowledge_base_by_uuid(kb_uuid)
         if runtime_kb is None:
             raise Exception('Knowledge base not found')
-
-        await self._check_doc_capability(kb_uuid, 'document deletion')
-
+        # Only internal KBs support file deletion
+        if runtime_kb.get_type() != 'internal':
+            raise Exception('Only internal knowledge bases support file deletion')
         await runtime_kb.delete_file(file_id)
 
         # Update the KB's updated_at timestamp
@@ -137,14 +121,13 @@ class KnowledgeService:
 
     async def delete_knowledge_base(self, kb_uuid: str) -> None:
         """删除知识库"""
-        # Delete from DB first to commit the deletion, then clean up runtime/plugin (best-effort)
+        await self.ap.rag_mgr.delete_knowledge_base(kb_uuid)
+
         await self.ap.persistence_mgr.execute_async(
             sqlalchemy.delete(persistence_rag.KnowledgeBase).where(persistence_rag.KnowledgeBase.uuid == kb_uuid)
         )
 
         # delete files
-        # NOTE: Chunk cleanup is for legacy (pre-plugin) KBs that stored chunks locally.
-        # For plugin-based Knowledge Engines, the Chunk table is not populated, so this is a no-op.
         files = await self.ap.persistence_mgr.execute_async(
             sqlalchemy.select(persistence_rag.File).where(persistence_rag.File.kb_id == kb_uuid)
         )
@@ -157,53 +140,3 @@ class KnowledgeService:
             await self.ap.persistence_mgr.execute_async(
                 sqlalchemy.delete(persistence_rag.File).where(persistence_rag.File.uuid == file.uuid)
             )
-
-        # Remove from runtime and notify plugin (best-effort, DB is already cleaned up)
-        await self.ap.rag_mgr.delete_knowledge_base(kb_uuid)
-
-    # ================= Knowledge Engine Discovery =================
-
-    async def list_knowledge_engines(self) -> list[dict]:
-        """List all available Knowledge Engines from plugins."""
-        engines = []
-
-        if not self.ap.plugin_connector.is_enable_plugin:
-            return engines
-
-        # Get KnowledgeEngine plugins
-        try:
-            knowledge_engines = await self.ap.plugin_connector.list_knowledge_engines()
-            engines.extend(knowledge_engines)
-        except Exception as e:
-            self.ap.logger.warning(f'Failed to list Knowledge Engines from plugins: {e}')
-
-        return engines
-
-    async def list_parsers(self, mime_type: str | None = None) -> list[dict]:
-        """List available parsers, optionally filtered by MIME type."""
-        if not self.ap.plugin_connector.is_enable_plugin:
-            return []
-        try:
-            parsers = await self.ap.plugin_connector.list_parsers()
-            if mime_type:
-                parsers = [p for p in parsers if mime_type in p.get('supported_mime_types', [])]
-            return parsers
-        except Exception as e:
-            self.ap.logger.warning(f'Failed to list parsers: {e}')
-            return []
-
-    async def get_engine_creation_schema(self, plugin_id: str) -> dict:
-        """Get creation settings schema for a specific Knowledge Engine."""
-        try:
-            return await self.ap.plugin_connector.get_rag_creation_schema(plugin_id)
-        except Exception as e:
-            self.ap.logger.warning(f'Failed to get creation schema for {plugin_id}: {e}')
-            return {}
-
-    async def get_engine_retrieval_schema(self, plugin_id: str) -> dict:
-        """Get retrieval settings schema for a specific Knowledge Engine."""
-        try:
-            return await self.ap.plugin_connector.get_rag_retrieval_schema(plugin_id)
-        except Exception as e:
-            self.ap.logger.warning(f'Failed to get retrieval schema for {plugin_id}: {e}')
-            return {}

src/langbot/pkg/api/http/service/monitoring.py

@@ -30,10 +30,8 @@ class MonitoringService:
         level: str = 'info',
         platform: str | None = None,
         user_id: str | None = None,
-        user_name: str | None = None,
         runner_name: str | None = None,
         variables: str | None = None,
-        role: str = 'user',
     ) -> str:
         """Record a message"""
         message_id = str(uuid.uuid4())
@@ -50,10 +48,8 @@ class MonitoringService:
             'level': level,
             'platform': platform,
             'user_id': user_id,
-            'user_name': user_name,
             'runner_name': runner_name,
             'variables': variables,
-            'role': role,
         }
 
         await self.ap.persistence_mgr.execute_async(
@@ -154,7 +150,6 @@ class MonitoringService:
         pipeline_name: str,
         platform: str | None = None,
         user_id: str | None = None,
-        user_name: str | None = None,
     ) -> None:
         """Record a new session"""
         session_data = {
@@ -169,7 +164,6 @@ class MonitoringService:
             'is_active': True,
             'platform': platform,
             'user_id': user_id,
-            'user_name': user_name,
         }
 
         await self.ap.persistence_mgr.execute_async(
@@ -361,7 +355,6 @@ class MonitoringService:
         self,
         bot_ids: list[str] | None = None,
         pipeline_ids: list[str] | None = None,
-        session_ids: list[str] | None = None,
         start_time: datetime.datetime | None = None,
         end_time: datetime.datetime | None = None,
         limit: int = 100,
@@ -374,8 +367,6 @@ class MonitoringService:
             conditions.append(persistence_monitoring.MonitoringMessage.bot_id.in_(bot_ids))
         if pipeline_ids:
             conditions.append(persistence_monitoring.MonitoringMessage.pipeline_id.in_(pipeline_ids))
-        if session_ids:
-            conditions.append(persistence_monitoring.MonitoringMessage.session_id.in_(session_ids))
         if start_time:
             conditions.append(persistence_monitoring.MonitoringMessage.timestamp >= start_time)
         if end_time:

src/langbot/pkg/api/http/service/space.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from langbot.pkg.utils import httpclient
+import aiohttp
 import typing
 import datetime
 import time
@@ -99,49 +99,49 @@ class SpaceService:
         space_config = self._get_space_config()
         space_url = space_config['url']
 
-        session = httpclient.get_session()
-        async with session.post(
-            f'{space_url}/api/v1/accounts/oauth/token',
-            json={'code': code, 'instance_id': constants.instance_id},
-        ) as response:
-            if response.status != 200:
-                raise ValueError(f'Failed to exchange OAuth code: {await response.text()}')
-            data = await response.json()
-            if data.get('code') != 0:
-                raise ValueError(f'Failed to exchange OAuth code: {data.get("msg")}')
-            return data.get('data', {})
+        async with aiohttp.ClientSession() as session:
+            async with session.post(
+                f'{space_url}/api/v1/accounts/oauth/token',
+                json={'code': code, 'instance_id': constants.instance_id},
+            ) as response:
+                if response.status != 200:
+                    raise ValueError(f'Failed to exchange OAuth code: {await response.text()}')
+                data = await response.json()
+                if data.get('code') != 0:
+                    raise ValueError(f'Failed to exchange OAuth code: {data.get("msg")}')
+                return data.get('data', {})
 
     async def refresh_token(self, refresh_token: str) -> typing.Dict:
         """Refresh Space access token"""
         space_config = self._get_space_config()
         space_url = space_config['url']
 
-        session = httpclient.get_session()
-        async with session.post(
-            f'{space_url}/api/v1/accounts/token/refresh', json={'refresh_token': refresh_token}
-        ) as response:
-            if response.status != 200:
-                raise ValueError(f'Failed to refresh token: {await response.text()}')
-            data = await response.json()
-            if data.get('code') != 0:
-                raise ValueError(f'Failed to refresh token: {data.get("msg")}')
-            return data.get('data', {})
+        async with aiohttp.ClientSession() as session:
+            async with session.post(
+                f'{space_url}/api/v1/accounts/token/refresh', json={'refresh_token': refresh_token}
+            ) as response:
+                if response.status != 200:
+                    raise ValueError(f'Failed to refresh token: {await response.text()}')
+                data = await response.json()
+                if data.get('code') != 0:
+                    raise ValueError(f'Failed to refresh token: {data.get("msg")}')
+                return data.get('data', {})
 
     async def get_user_info_raw(self, access_token: str) -> typing.Dict:
         """Get user info from Space using access token (no validation)"""
         space_config = self._get_space_config()
         space_url = space_config['url']
 
-        session = httpclient.get_session()
-        async with session.get(
-            f'{space_url}/api/v1/accounts/me', headers={'Authorization': f'Bearer {access_token}'}
-        ) as response:
-            if response.status != 200:
-                raise ValueError(f'Failed to get user info: {await response.text()}')
-            data = await response.json()
-            if data.get('code') != 0:
-                raise ValueError(f'Failed to get user info: {data.get("msg")}')
-            return data.get('data', {})
+        async with aiohttp.ClientSession() as session:
+            async with session.get(
+                f'{space_url}/api/v1/accounts/me', headers={'Authorization': f'Bearer {access_token}'}
+            ) as response:
+                if response.status != 200:
+                    raise ValueError(f'Failed to get user info: {await response.text()}')
+                data = await response.json()
+                if data.get('code') != 0:
+                    raise ValueError(f'Failed to get user info: {data.get("msg")}')
+                return data.get('data', {})
 
     # === API calls with token validation ===
 
@@ -178,12 +178,12 @@ class SpaceService:
         space_config = self._get_space_config()
         space_url = space_config['url']
 
-        session = httpclient.get_session()
-        async with session.get(f'{space_url}/api/v1/models') as response:
-            if response.status != 200:
-                raise ValueError(f'Failed to get models: {await response.text()}')
-            data = await response.json()
-            if data.get('code') != 0:
-                raise ValueError(f'Failed to get models: {data.get("msg")}')
-            models_data = data.get('data', {}).get('models', [])
-            return [SpaceModel.model_validate(model_dict) for model_dict in models_data]
+        async with aiohttp.ClientSession() as session:
+            async with session.get(f'{space_url}/api/v1/models') as response:
+                if response.status != 200:
+                    raise ValueError(f'Failed to get models: {await response.text()}')
+                data = await response.json()
+                if data.get('code') != 0:
+                    raise ValueError(f'Failed to get models: {data.get("msg")}')
+                models_data = data.get('data', {}).get('models', [])
+                return [SpaceModel.model_validate(model_dict) for model_dict in models_data]

src/langbot/pkg/core/app.py

@@ -9,14 +9,12 @@ from ..platform import botmgr as im_mgr
 from ..platform.webhook_pusher import WebhookPusher
 from ..provider.session import sessionmgr as llm_session_mgr
 from ..provider.modelmgr import modelmgr as llm_model_mgr
-
 from langbot.pkg.provider.tools import toolmgr as llm_tool_mgr
 from ..config import manager as config_mgr
 from ..command import cmdmgr
 from ..plugin import connector as plugin_connector
 from ..pipeline import pool
 from ..pipeline import controller, pipelinemgr
-from ..pipeline import aggregator as message_aggregator
 from ..utils import version as version_mgr, proxy as proxy_mgr
 from ..persistence import mgr as persistencemgr
 from ..api.http.controller import main as http_controller
@@ -30,18 +28,16 @@ from ..api.http.service import knowledge as knowledge_service
 from ..api.http.service import mcp as mcp_service
 from ..api.http.service import apikey as apikey_service
 from ..api.http.service import webhook as webhook_service
+from ..api.http.service import external_kb as external_kb_service
 from ..api.http.service import monitoring as monitoring_service
-
 from ..discover import engine as discover_engine
 from ..storage import mgr as storagemgr
 from ..utils import logcache
 from . import taskmgr
 from . import entities as core_entities
 from ..rag.knowledge import kbmgr as rag_mgr
-from ..rag.service import RAGRuntimeService
 from ..vector import mgr as vectordb_mgr
 from ..telemetry import telemetry as telemetry_module
-from ..survey import manager as survey_module
 
 
 class Application:
@@ -65,7 +61,6 @@ class Application:
     model_mgr: llm_model_mgr.ModelManager = None
 
     rag_mgr: rag_mgr.RAGManager = None
-    rag_runtime_service: RAGRuntimeService = None
 
     # TODO move to pipeline
     tool_mgr: llm_tool_mgr.ToolManager = None
@@ -101,8 +96,6 @@ class Application:
 
     query_pool: pool.QueryPool = None
 
-    msg_aggregator: message_aggregator.MessageAggregator = None
-
     ctrl: controller.Controller = None
 
     pipeline_mgr: pipelinemgr.PipelineManager = None
@@ -141,6 +134,8 @@ class Application:
 
     knowledge_service: knowledge_service.KnowledgeService = None
 
+    external_kb_service: external_kb_service.ExternalKBService = None
+
     mcp_service: mcp_service.MCPService = None
 
     apikey_service: apikey_service.ApiKeyService = None
@@ -149,8 +144,6 @@ class Application:
 
     telemetry: telemetry_module.TelemetryManager = None
 
-    survey: survey_module.SurveyManager = None
-
     monitoring_service: monitoring_service.MonitoringService = None
 
     def __init__(self):

src/langbot/pkg/core/bootutils/deps.py

@@ -1,4 +1,3 @@
-import importlib.util
 import pip
 import os
 from ...utils import pkgmgr
@@ -50,10 +49,9 @@ async def check_deps() -> list[str]:
 
     missing_deps = []
     for dep in required_deps:
-        # Use find_spec instead of __import__ to avoid actually loading
-        # all modules into memory. find_spec only checks if the module
-        # can be found, without executing module-level code.
-        if importlib.util.find_spec(dep) is None:
+        try:
+            __import__(dep)
+        except ImportError:
             missing_deps.append(dep)
     return missing_deps
 

src/langbot/pkg/core/stages/build_app.py

@@ -5,14 +5,12 @@ import asyncio
 from .. import stage, app
 from ...utils import version, proxy
 from ...pipeline import pool, controller, pipelinemgr
-from ...pipeline import aggregator as message_aggregator
 from ...plugin import connector as plugin_connector
 from ...command import cmdmgr
 from ...provider.session import sessionmgr as llm_session_mgr
 from ...provider.modelmgr import modelmgr as llm_model_mgr
 from ...provider.tools import toolmgr as llm_tool_mgr
 from ...rag.knowledge import kbmgr as rag_mgr
-from ...rag.service import RAGRuntimeService
 from ...platform import botmgr as im_mgr
 from ...platform.webhook_pusher import WebhookPusher
 from ...persistence import mgr as persistencemgr
@@ -27,6 +25,7 @@ from ...api.http.service import knowledge as knowledge_service
 from ...api.http.service import mcp as mcp_service
 from ...api.http.service import apikey as apikey_service
 from ...api.http.service import webhook as webhook_service
+from ...api.http.service import external_kb as external_kb_service
 from ...api.http.service import monitoring as monitoring_service
 from ...discover import engine as discover_engine
 from ...storage import mgr as storagemgr
@@ -34,7 +33,6 @@ from ...utils import logcache
 from ...vector import mgr as vectordb_mgr
 from .. import taskmgr
 from ...telemetry import telemetry as telemetry_module
-from ...survey import manager as survey_module
 
 
 @stage.stage_class('BuildAppStage')
@@ -73,6 +71,9 @@ class BuildAppStage(stage.BootingStage):
         knowledge_service_inst = knowledge_service.KnowledgeService(ap)
         ap.knowledge_service = knowledge_service_inst
 
+        external_kb_service_inst = external_kb_service.ExternalKBService(ap)
+        ap.external_kb_service = external_kb_service_inst
+
         mcp_service_inst = mcp_service.MCPService(ap)
         ap.mcp_service = mcp_service_inst
 
@@ -108,11 +109,6 @@ class BuildAppStage(stage.BootingStage):
         await telemetry_inst.initialize()
         ap.telemetry = telemetry_inst
 
-        # Survey manager
-        survey_inst = survey_module.SurveyManager(ap)
-        await survey_inst.initialize()
-        ap.survey = survey_inst
-
         cmd_mgr_inst = cmdmgr.CommandManager(ap)
         await cmd_mgr_inst.initialize()
         ap.cmd_mgr = cmd_mgr_inst
@@ -141,17 +137,10 @@ class BuildAppStage(stage.BootingStage):
         await pipeline_mgr.initialize()
         ap.pipeline_mgr = pipeline_mgr
 
-        # Initialize message aggregator (after pipeline_mgr, as it needs pipeline config)
-        msg_aggregator_inst = message_aggregator.MessageAggregator(ap)
-        ap.msg_aggregator = msg_aggregator_inst
-
         rag_mgr_inst = rag_mgr.RAGManager(ap)
         await rag_mgr_inst.initialize()
         ap.rag_mgr = rag_mgr_inst
 
-        # Initialize RAG Runtime Service for plugins
-        ap.rag_runtime_service = RAGRuntimeService(ap)
-
         # 初始化向量数据库管理器
         vectordb_mgr_inst = vectordb_mgr.VectorDBManager(ap)
         await vectordb_mgr_inst.initialize()

src/langbot/pkg/entity/persistence/monitoring.py

@@ -20,10 +20,8 @@ class MonitoringMessage(Base):
     level = sqlalchemy.Column(sqlalchemy.String(50), nullable=False)  # info, warning, error, debug
     platform = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
     user_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
-    user_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)  # User display name
     runner_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)  # Runner name for this query
     variables = sqlalchemy.Column(sqlalchemy.Text, nullable=True)  # Query variables as JSON string
-    role = sqlalchemy.Column(sqlalchemy.String(50), nullable=True, default='user')  # user, assistant
 
 
 class MonitoringLLMCall(Base):
@@ -65,7 +63,6 @@ class MonitoringSession(Base):
     is_active = sqlalchemy.Column(sqlalchemy.Boolean, nullable=False, default=True, index=True)
     platform = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
     user_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
-    user_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)  # User display name
 
 
 class MonitoringError(Base):

src/langbot/pkg/entity/persistence/rag.py

@@ -10,21 +10,8 @@ class KnowledgeBase(Base):
     emoji = sqlalchemy.Column(sqlalchemy.String(10), nullable=True, default='📚')
     created_at = sqlalchemy.Column(sqlalchemy.DateTime, default=sqlalchemy.func.now())
     updated_at = sqlalchemy.Column(sqlalchemy.DateTime, default=sqlalchemy.func.now(), onupdate=sqlalchemy.func.now())
-    # New fields for plugin-based RAG
-    knowledge_engine_plugin_id = sqlalchemy.Column(sqlalchemy.String, nullable=True)
-    collection_id = sqlalchemy.Column(sqlalchemy.String, nullable=True)
-    creation_settings = sqlalchemy.Column(sqlalchemy.JSON, nullable=True, default=None)
-    retrieval_settings = sqlalchemy.Column(sqlalchemy.JSON, nullable=True, default=None)
-
-    # Field sets for different operations
-    MUTABLE_FIELDS = {'name', 'description', 'retrieval_settings'}
-    """Fields that can be updated after creation."""
-
-    CREATE_FIELDS = MUTABLE_FIELDS | {'uuid', 'knowledge_engine_plugin_id', 'collection_id', 'creation_settings'}
-    """Fields used when creating a new knowledge base."""
-
-    ALL_DB_FIELDS = CREATE_FIELDS | {'emoji', 'created_at', 'updated_at'}
-    """All fields stored in database (for loading from DB row)."""
+    embedding_model_uuid = sqlalchemy.Column(sqlalchemy.String, default='')
+    top_k = sqlalchemy.Column(sqlalchemy.Integer, default=5)
 
 
 class File(Base):
@@ -42,3 +29,16 @@ class Chunk(Base):
     uuid = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True, unique=True)
     file_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
     text = sqlalchemy.Column(sqlalchemy.Text)
+
+
+class ExternalKnowledgeBase(Base):
+    __tablename__ = 'external_knowledge_bases'
+    uuid = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True, unique=True)
+    name = sqlalchemy.Column(sqlalchemy.String, index=True)
+    description = sqlalchemy.Column(sqlalchemy.Text)
+    emoji = sqlalchemy.Column(sqlalchemy.String(10), nullable=True, default='🔗')
+    plugin_author = sqlalchemy.Column(sqlalchemy.String, nullable=False)
+    plugin_name = sqlalchemy.Column(sqlalchemy.String, nullable=False)
+    retriever_name = sqlalchemy.Column(sqlalchemy.String, nullable=False)
+    retriever_config = sqlalchemy.Column(sqlalchemy.JSON, nullable=False, default={})
+    created_at = sqlalchemy.Column(sqlalchemy.DateTime, default=sqlalchemy.func.now())

src/langbot/pkg/persistence/migrations/dbm019_monitoring_message_role.py

@@ -1,24 +0,0 @@
-import sqlalchemy
-from .. import migration
-
-
-@migration.migration_class(19)
-class DBMigrateMonitoringMessageRole(migration.DBMigration):
-    """Add role column to monitoring_messages table"""
-
-    async def upgrade(self):
-        """Upgrade"""
-        try:
-            sql_text = sqlalchemy.text("ALTER TABLE monitoring_messages ADD COLUMN role VARCHAR(50) DEFAULT 'user'")
-            await self.ap.persistence_mgr.execute_async(sql_text)
-        except Exception:
-            # Column may already exist
-            pass
-
-    async def downgrade(self):
-        """Downgrade"""
-        try:
-            sql_text = sqlalchemy.text('ALTER TABLE monitoring_messages DROP COLUMN role')
-            await self.ap.persistence_mgr.execute_async(sql_text)
-        except Exception:
-            pass

src/langbot/pkg/persistence/migrations/dbm020_knowledge_engine_plugin_architecture.py

@@ -1,161 +0,0 @@
-import sqlalchemy
-from .. import migration
-
-
-@migration.migration_class(20)
-class DBMigrateKnowledgeEnginePluginArchitecture(migration.DBMigration):
-    """Migrate to unified Knowledge Engine plugin architecture.
-
-    Changes:
-    - Backup existing knowledge_bases data to knowledge_bases_backup
-    - Clear knowledge_bases table and add new plugin architecture columns
-    - Drop old columns (PostgreSQL only; SQLite leaves them unmapped)
-    - Preserve external_knowledge_bases table as-is for future migration
-    - Set rag_plugin_migration_needed flag in metadata if old data exists
-    """
-
-    async def upgrade(self):
-        """Upgrade"""
-        has_internal_data = await self._backup_knowledge_bases()
-        has_external_data = await self._check_external_knowledge_bases()
-        await self._clear_knowledge_bases()
-        await self._add_columns_to_knowledge_bases()
-        await self._drop_old_columns()
-        if has_internal_data or has_external_data:
-            await self._set_migration_flag()
-
-    async def _get_table_columns(self, table_name: str) -> list[str]:
-        """Get column names from a table (works for both SQLite and PostgreSQL)."""
-        if self.ap.persistence_mgr.db.name == 'postgresql':
-            result = await self.ap.persistence_mgr.execute_async(
-                sqlalchemy.text(
-                    'SELECT column_name FROM information_schema.columns WHERE table_name = :table_name;'
-                ).bindparams(table_name=table_name)
-            )
-            return [row[0] for row in result.fetchall()]
-        else:
-            # SQLite PRAGMA does not support bind parameters; validate identifier.
-            if not table_name.isidentifier():
-                raise ValueError(f'Invalid table name: {table_name}')
-            result = await self.ap.persistence_mgr.execute_async(sqlalchemy.text(f'PRAGMA table_info({table_name});'))
-            return [row[1] for row in result.fetchall()]
-
-    async def _table_exists(self, table_name: str) -> bool:
-        """Check if a table exists."""
-        if self.ap.persistence_mgr.db.name == 'postgresql':
-            result = await self.ap.persistence_mgr.execute_async(
-                sqlalchemy.text(
-                    'SELECT EXISTS (SELECT FROM information_schema.tables WHERE table_name = :table_name);'
-                ).bindparams(table_name=table_name)
-            )
-            return result.scalar()
-        else:
-            result = await self.ap.persistence_mgr.execute_async(
-                sqlalchemy.text("SELECT name FROM sqlite_master WHERE type='table' AND name=:table_name;").bindparams(
-                    table_name=table_name
-                )
-            )
-            return result.first() is not None
-
-    async def _backup_knowledge_bases(self) -> bool:
-        """Backup knowledge_bases data. Returns True if data was backed up."""
-        result = await self.ap.persistence_mgr.execute_async(sqlalchemy.text('SELECT COUNT(*) FROM knowledge_bases;'))
-        count = result.scalar()
-        if count == 0:
-            return False
-
-        # Drop backup table if it already exists (from a previous failed migration)
-        if await self._table_exists('knowledge_bases_backup'):
-            await self.ap.persistence_mgr.execute_async(sqlalchemy.text('DROP TABLE knowledge_bases_backup;'))
-
-        await self.ap.persistence_mgr.execute_async(
-            sqlalchemy.text('CREATE TABLE knowledge_bases_backup AS SELECT * FROM knowledge_bases;')
-        )
-        self.ap.logger.info(
-            'Backed up %d knowledge base(s) to knowledge_bases_backup table.',
-            count,
-        )
-        return True
-
-    async def _check_external_knowledge_bases(self) -> bool:
-        """Check if external_knowledge_bases table exists and has data.
-
-        The table is preserved as-is (not dropped) for future migration.
-        """
-        if not await self._table_exists('external_knowledge_bases'):
-            return False
-
-        result = await self.ap.persistence_mgr.execute_async(
-            sqlalchemy.text('SELECT COUNT(*) FROM external_knowledge_bases;')
-        )
-        count = result.scalar()
-        if count > 0:
-            self.ap.logger.info(
-                'Found %d external knowledge base(s) in external_knowledge_bases table. '
-                'Table preserved for future migration.',
-                count,
-            )
-        return count > 0
-
-    async def _clear_knowledge_bases(self):
-        """Clear all rows from knowledge_bases table (preserve table structure)."""
-        await self.ap.persistence_mgr.execute_async(sqlalchemy.text('DELETE FROM knowledge_bases;'))
-
-    async def _add_columns_to_knowledge_bases(self):
-        """Add new RAG plugin architecture columns to knowledge_bases table."""
-        columns = await self._get_table_columns('knowledge_bases')
-
-        new_columns = {
-            'knowledge_engine_plugin_id': 'VARCHAR',
-            'collection_id': 'VARCHAR',
-            'creation_settings': 'TEXT',  # JSON stored as TEXT for SQLite compatibility
-            'retrieval_settings': 'TEXT',
-        }
-
-        for col_name, col_type in new_columns.items():
-            if col_name not in columns:
-                await self.ap.persistence_mgr.execute_async(
-                    sqlalchemy.text(f'ALTER TABLE knowledge_bases ADD COLUMN {col_name} {col_type};')
-                )
-
-    async def _drop_old_columns(self):
-        """Drop embedding_model_uuid and top_k columns (PostgreSQL only).
-
-        SQLite does not support DROP COLUMN in older versions, so we leave the
-        columns in place — the SQLAlchemy entity simply won't map them.
-        """
-        if self.ap.persistence_mgr.db.name != 'postgresql':
-            return
-
-        columns = await self._get_table_columns('knowledge_bases')
-
-        if 'embedding_model_uuid' in columns:
-            await self.ap.persistence_mgr.execute_async(
-                sqlalchemy.text('ALTER TABLE knowledge_bases DROP COLUMN embedding_model_uuid;')
-            )
-
-        if 'top_k' in columns:
-            await self.ap.persistence_mgr.execute_async(
-                sqlalchemy.text('ALTER TABLE knowledge_bases DROP COLUMN top_k;')
-            )
-
-    async def _set_migration_flag(self):
-        """Set rag_plugin_migration_needed flag in metadata table."""
-        # Check if the key already exists
-        result = await self.ap.persistence_mgr.execute_async(
-            sqlalchemy.text("SELECT value FROM metadata WHERE key = 'rag_plugin_migration_needed';")
-        )
-        row = result.first()
-        if row is not None:
-            await self.ap.persistence_mgr.execute_async(
-                sqlalchemy.text("UPDATE metadata SET value = 'true' WHERE key = 'rag_plugin_migration_needed';")
-            )
-        else:
-            await self.ap.persistence_mgr.execute_async(
-                sqlalchemy.text("INSERT INTO metadata (key, value) VALUES ('rag_plugin_migration_needed', 'true');")
-            )
-        self.ap.logger.info('Set rag_plugin_migration_needed=true in metadata.')
-
-    async def downgrade(self):
-        """Downgrade"""
-        pass

src/langbot/pkg/persistence/migrations/dbm021_merge_exception_handling.py

@@ -1,74 +0,0 @@
-from .. import migration
-
-import sqlalchemy
-import json
-
-
-@migration.migration_class(21)
-class DBMigrateMergeExceptionHandling(migration.DBMigration):
-    """Merge hide-exception and block-failed-request-output into a single exception-handling select option,
-    and add failure-hint field.
-
-    Conversion logic:
-    - block-failed-request-output=true  ->  exception-handling: hide
-    - hide-exception=true               ->  exception-handling: show-hint
-    - hide-exception=false              ->  exception-handling: show-error
-    """
-
-    async def upgrade(self):
-        """Upgrade"""
-        result = await self.ap.persistence_mgr.execute_async(
-            sqlalchemy.text('SELECT uuid, config FROM legacy_pipelines')
-        )
-        pipelines = result.fetchall()
-
-        current_version = self.ap.ver_mgr.get_current_version()
-
-        for pipeline_row in pipelines:
-            uuid = pipeline_row[0]
-            config = json.loads(pipeline_row[1]) if isinstance(pipeline_row[1], str) else pipeline_row[1]
-
-            if 'output' not in config:
-                config['output'] = {}
-            if 'misc' not in config['output']:
-                config['output']['misc'] = {}
-
-            misc = config['output']['misc']
-
-            # Determine new exception-handling value from legacy fields
-            hide_exception = misc.get('hide-exception', True)
-            block_failed = misc.get('block-failed-request-output', False)
-
-            if block_failed:
-                exception_handling = 'hide'
-            elif hide_exception:
-                exception_handling = 'show-hint'
-            else:
-                exception_handling = 'show-error'
-
-            misc['exception-handling'] = exception_handling
-
-            # Add failure-hint with default value
-            misc['failure-hint'] = 'Request failed.'
-
-            # Remove legacy fields
-            misc.pop('hide-exception', None)
-
-            if self.ap.persistence_mgr.db.name == 'postgresql':
-                await self.ap.persistence_mgr.execute_async(
-                    sqlalchemy.text(
-                        'UPDATE legacy_pipelines SET config = :config::jsonb, for_version = :for_version WHERE uuid = :uuid'
-                    ),
-                    {'config': json.dumps(config), 'for_version': current_version, 'uuid': uuid},
-                )
-            else:
-                await self.ap.persistence_mgr.execute_async(
-                    sqlalchemy.text(
-                        'UPDATE legacy_pipelines SET config = :config, for_version = :for_version WHERE uuid = :uuid'
-                    ),
-                    {'config': json.dumps(config), 'for_version': current_version, 'uuid': uuid},
-                )
-
-    async def downgrade(self):
-        """Downgrade"""
-        pass

src/langbot/pkg/persistence/migrations/dbm022_monitoring_user_name.py

@@ -1,73 +0,0 @@
-import sqlalchemy
-from .. import migration
-
-
-@migration.migration_class(22)
-class DBMigrateMonitoringUserId(migration.DBMigration):
-    """Add user_id and user_name columns to monitoring_sessions table
-
-    This migration adds the missing user_id column and also ensures user_name
-    column exists (in case migration 21 failed or was skipped).
-    """
-
-    async def _table_exists(self, table_name: str) -> bool:
-        """Check if a table exists (works for both SQLite and PostgreSQL)."""
-        if self.ap.persistence_mgr.db.name == 'postgresql':
-            result = await self.ap.persistence_mgr.execute_async(
-                sqlalchemy.text(
-                    'SELECT EXISTS (SELECT FROM information_schema.tables WHERE table_name = :table_name);'
-                ).bindparams(table_name=table_name)
-            )
-            return bool(result.scalar())
-        else:
-            result = await self.ap.persistence_mgr.execute_async(
-                sqlalchemy.text("SELECT name FROM sqlite_master WHERE type='table' AND name=:table_name;").bindparams(
-                    table_name=table_name
-                )
-            )
-            return result.first() is not None
-
-    async def _get_table_columns(self, table_name: str) -> list[str]:
-        """Get column names from a table (works for both SQLite and PostgreSQL)."""
-        if self.ap.persistence_mgr.db.name == 'postgresql':
-            result = await self.ap.persistence_mgr.execute_async(
-                sqlalchemy.text(
-                    'SELECT column_name FROM information_schema.columns WHERE table_name = :table_name;'
-                ).bindparams(table_name=table_name)
-            )
-            return [row[0] for row in result.fetchall()]
-        else:
-            if not table_name.isidentifier():
-                raise ValueError(f'Invalid table name: {table_name}')
-            result = await self.ap.persistence_mgr.execute_async(sqlalchemy.text(f'PRAGMA table_info({table_name});'))
-            return [row[1] for row in result.fetchall()]
-
-    async def _add_column_if_not_exists(self, table_name: str, column_name: str, column_type: str):
-        """Add a column to a table if it does not already exist."""
-        columns = await self._get_table_columns(table_name)
-        if column_name in columns:
-            self.ap.logger.debug('%s column already exists in %s.', column_name, table_name)
-            return
-        await self.ap.persistence_mgr.execute_async(
-            sqlalchemy.text(f'ALTER TABLE {table_name} ADD COLUMN {column_name} {column_type};')
-        )
-        self.ap.logger.info('Added %s column to %s table.', column_name, table_name)
-
-    async def upgrade(self):
-        # Check if monitoring_sessions table exists
-        if not await self._table_exists('monitoring_sessions'):
-            self.ap.logger.warning('monitoring_sessions table does not exist, skipping migration.')
-            return
-
-        # Add user_id column to monitoring_sessions table
-        await self._add_column_if_not_exists('monitoring_sessions', 'user_id', 'VARCHAR(255)')
-
-        # Add user_name column to monitoring_sessions table (in case migration 21 failed)
-        await self._add_column_if_not_exists('monitoring_sessions', 'user_name', 'VARCHAR(255)')
-
-        # Add user_name column to monitoring_messages table (in case migration 21 failed)
-        if await self._table_exists('monitoring_messages'):
-            await self._add_column_if_not_exists('monitoring_messages', 'user_name', 'VARCHAR(255)')
-
-    async def downgrade(self):
-        pass

src/langbot/pkg/persistence/migrations/dbm023_model_fallback_config.py

@@ -1,102 +0,0 @@
-from .. import migration
-
-import sqlalchemy
-import json
-
-
-@migration.migration_class(23)
-class DBMigrateModelFallbackConfig(migration.DBMigration):
-    """Convert model field from plain UUID string to object with primary/fallbacks"""
-
-    async def upgrade(self):
-        """Upgrade"""
-        result = await self.ap.persistence_mgr.execute_async(
-            sqlalchemy.text('SELECT uuid, config FROM legacy_pipelines')
-        )
-        pipelines = result.fetchall()
-
-        current_version = self.ap.ver_mgr.get_current_version()
-
-        for pipeline_row in pipelines:
-            uuid = pipeline_row[0]
-            config = json.loads(pipeline_row[1]) if isinstance(pipeline_row[1], str) else pipeline_row[1]
-
-            if 'ai' not in config or 'local-agent' not in config['ai']:
-                continue
-
-            local_agent = config['ai']['local-agent']
-            changed = False
-
-            # Convert model from string to object
-            model_value = local_agent.get('model', '')
-            if isinstance(model_value, str):
-                local_agent['model'] = {
-                    'primary': model_value,
-                    'fallbacks': [],
-                }
-                changed = True
-
-            # Remove leftover fallback-models field if present
-            if 'fallback-models' in local_agent:
-                del local_agent['fallback-models']
-                changed = True
-
-            if not changed:
-                continue
-
-            # Update using raw SQL with compatibility for both SQLite and PostgreSQL
-            if self.ap.persistence_mgr.db.name == 'postgresql':
-                await self.ap.persistence_mgr.execute_async(
-                    sqlalchemy.text(
-                        'UPDATE legacy_pipelines SET config = :config::jsonb, for_version = :for_version WHERE uuid = :uuid'
-                    ),
-                    {'config': json.dumps(config), 'for_version': current_version, 'uuid': uuid},
-                )
-            else:
-                await self.ap.persistence_mgr.execute_async(
-                    sqlalchemy.text(
-                        'UPDATE legacy_pipelines SET config = :config, for_version = :for_version WHERE uuid = :uuid'
-                    ),
-                    {'config': json.dumps(config), 'for_version': current_version, 'uuid': uuid},
-                )
-
-    async def downgrade(self):
-        """Downgrade"""
-        result = await self.ap.persistence_mgr.execute_async(
-            sqlalchemy.text('SELECT uuid, config FROM legacy_pipelines')
-        )
-        pipelines = result.fetchall()
-
-        current_version = self.ap.ver_mgr.get_current_version()
-
-        for pipeline_row in pipelines:
-            uuid = pipeline_row[0]
-            config = json.loads(pipeline_row[1]) if isinstance(pipeline_row[1], str) else pipeline_row[1]
-
-            if 'ai' not in config or 'local-agent' not in config['ai']:
-                continue
-
-            local_agent = config['ai']['local-agent']
-
-            # Convert model from object back to string
-            model_value = local_agent.get('model', '')
-            if isinstance(model_value, dict):
-                local_agent['model'] = model_value.get('primary', '')
-            else:
-                continue
-
-            # Update using raw SQL with compatibility for both SQLite and PostgreSQL
-            if self.ap.persistence_mgr.db.name == 'postgresql':
-                await self.ap.persistence_mgr.execute_async(
-                    sqlalchemy.text(
-                        'UPDATE legacy_pipelines SET config = :config::jsonb, for_version = :for_version WHERE uuid = :uuid'
-                    ),
-                    {'config': json.dumps(config), 'for_version': current_version, 'uuid': uuid},
-                )
-            else:
-                await self.ap.persistence_mgr.execute_async(
-                    sqlalchemy.text(
-                        'UPDATE legacy_pipelines SET config = :config, for_version = :for_version WHERE uuid = :uuid'
-                    ),
-                    {'config': json.dumps(config), 'for_version': current_version, 'uuid': uuid},
-                )

src/langbot/pkg/persistence/migrations/dbm024_wecombot_websocket_mode.py

@@ -1,49 +0,0 @@
-from .. import migration
-
-import sqlalchemy
-import json
-
-
-@migration.migration_class(24)
-class DBMigrateWecomBotWebSocketMode(migration.DBMigration):
-    """Add enable-webhook field to existing wecombot adapter configs.
-
-    Existing wecombot bots were all using webhook mode, so we set
-    enable-webhook=true to preserve their behavior after the new
-    WebSocket long connection mode is introduced as default.
-    """
-
-    async def upgrade(self):
-        """Upgrade"""
-        result = await self.ap.persistence_mgr.execute_async(
-            sqlalchemy.text("SELECT uuid, adapter_config FROM bots WHERE adapter = 'wecombot'")
-        )
-        bots = result.fetchall()
-
-        for bot_row in bots:
-            bot_uuid = bot_row[0]
-            adapter_config = json.loads(bot_row[1]) if isinstance(bot_row[1], str) else bot_row[1]
-
-            if 'enable-webhook' in adapter_config:
-                continue
-
-            # Determine mode based on existing config: if webhook fields are present, keep webhook mode
-            has_webhook_config = bool(
-                adapter_config.get('Token') and adapter_config.get('EncodingAESKey') and adapter_config.get('Corpid')
-            )
-            adapter_config['enable-webhook'] = has_webhook_config
-
-            if self.ap.persistence_mgr.db.name == 'postgresql':
-                await self.ap.persistence_mgr.execute_async(
-                    sqlalchemy.text('UPDATE bots SET adapter_config = :config::jsonb WHERE uuid = :uuid'),
-                    {'config': json.dumps(adapter_config), 'uuid': bot_uuid},
-                )
-            else:
-                await self.ap.persistence_mgr.execute_async(
-                    sqlalchemy.text('UPDATE bots SET adapter_config = :config WHERE uuid = :uuid'),
-                    {'config': json.dumps(adapter_config), 'uuid': bot_uuid},
-                )
-
-    async def downgrade(self):
-        """Downgrade"""
-        pass

src/langbot/pkg/pipeline/aggregator.py

@@ -1,289 +0,0 @@
-"""Message Aggregator Module
-
-This module provides message aggregation/debounce functionality.
-When users send multiple messages consecutively, the aggregator will wait
-for a configurable delay period and merge them into a single message
-before processing.
-"""
-
-from __future__ import annotations
-
-import asyncio
-import time
-import typing
-from dataclasses import dataclass, field
-
-import langbot_plugin.api.entities.builtin.platform.message as platform_message
-import langbot_plugin.api.entities.builtin.platform.events as platform_events
-import langbot_plugin.api.entities.builtin.provider.session as provider_session
-import langbot_plugin.api.definition.abstract.platform.adapter as abstract_platform_adapter
-
-if typing.TYPE_CHECKING:
-    from ..core import app
-
-# Maximum number of messages to buffer before forcing a flush
-MAX_BUFFER_MESSAGES = 10
-
-
-@dataclass
-class PendingMessage:
-    """A pending message waiting to be aggregated"""
-
-    bot_uuid: str
-    launcher_type: provider_session.LauncherTypes
-    launcher_id: typing.Union[int, str]
-    sender_id: typing.Union[int, str]
-    message_event: platform_events.MessageEvent
-    message_chain: platform_message.MessageChain
-    adapter: abstract_platform_adapter.AbstractMessagePlatformAdapter
-    pipeline_uuid: typing.Optional[str]
-    timestamp: float = field(default_factory=time.time)
-
-
-@dataclass
-class SessionBuffer:
-    """Buffer for a single session's pending messages"""
-
-    session_id: str
-    messages: list[PendingMessage] = field(default_factory=list)
-    timer_task: typing.Optional[asyncio.Task] = None
-    last_message_time: float = field(default_factory=time.time)
-
-
-class MessageAggregator:
-    """Message aggregator that buffers and merges consecutive messages
-
-    This class implements a debounce mechanism for incoming messages.
-    When a message arrives, it starts a timer. If more messages arrive
-    before the timer expires, they are buffered. When the timer expires,
-    all buffered messages are merged and sent to the query pool.
-    """
-
-    ap: app.Application
-
-    buffers: dict[str, SessionBuffer]
-    """Session ID -> SessionBuffer mapping"""
-
-    lock: asyncio.Lock
-    """Lock for thread-safe buffer operations"""
-
-    def __init__(self, ap: app.Application):
-        self.ap = ap
-        self.buffers = {}
-        self.lock = asyncio.Lock()
-
-    def _get_session_id(
-        self,
-        bot_uuid: str,
-        launcher_type: provider_session.LauncherTypes,
-        launcher_id: typing.Union[int, str],
-    ) -> str:
-        """Generate a unique session ID"""
-        return f'{bot_uuid}:{launcher_type.value}:{launcher_id}'
-
-    async def _get_aggregation_config(self, pipeline_uuid: typing.Optional[str]) -> tuple[bool, float]:
-        """Get aggregation configuration for a pipeline
-
-        Returns:
-            tuple: (enabled, delay_seconds)
-        """
-        default_enabled = False
-        default_delay = 1.5
-
-        if pipeline_uuid is None:
-            return default_enabled, default_delay
-
-        # Get pipeline from pipeline manager
-        pipeline = await self.ap.pipeline_mgr.get_pipeline_by_uuid(pipeline_uuid)
-        if pipeline is None:
-            return default_enabled, default_delay
-
-        config = pipeline.pipeline_entity.config or {}
-        trigger_config = config.get('trigger', {})
-        aggregation_config = trigger_config.get('message-aggregation', {})
-
-        enabled = aggregation_config.get('enabled', default_enabled)
-
-        delay_raw = aggregation_config.get('delay', default_delay)
-        try:
-            delay = float(delay_raw)
-        except (TypeError, ValueError):
-            delay = default_delay
-
-        # Clamp delay to valid range
-        delay = max(1.0, min(10.0, delay))
-
-        return enabled, delay
-
-    async def add_message(
-        self,
-        bot_uuid: str,
-        launcher_type: provider_session.LauncherTypes,
-        launcher_id: typing.Union[int, str],
-        sender_id: typing.Union[int, str],
-        message_event: platform_events.MessageEvent,
-        message_chain: platform_message.MessageChain,
-        adapter: abstract_platform_adapter.AbstractMessagePlatformAdapter,
-        pipeline_uuid: typing.Optional[str] = None,
-    ) -> None:
-        """Add a message to the aggregation buffer
-
-        If aggregation is disabled for the pipeline, the message is sent
-        directly to the query pool. Otherwise, it's buffered and will be
-        merged with other messages from the same session.
-        """
-        enabled, delay = await self._get_aggregation_config(pipeline_uuid)
-
-        if not enabled:
-            # Aggregation disabled, send directly to query pool
-            await self.ap.query_pool.add_query(
-                bot_uuid=bot_uuid,
-                launcher_type=launcher_type,
-                launcher_id=launcher_id,
-                sender_id=sender_id,
-                message_event=message_event,
-                message_chain=message_chain,
-                adapter=adapter,
-                pipeline_uuid=pipeline_uuid,
-            )
-            return
-
-        session_id = self._get_session_id(bot_uuid, launcher_type, launcher_id)
-
-        pending_msg = PendingMessage(
-            bot_uuid=bot_uuid,
-            launcher_type=launcher_type,
-            launcher_id=launcher_id,
-            sender_id=sender_id,
-            message_event=message_event,
-            message_chain=message_chain,
-            adapter=adapter,
-            pipeline_uuid=pipeline_uuid,
-        )
-
-        force_flush = False
-        async with self.lock:
-            if session_id in self.buffers:
-                buffer = self.buffers[session_id]
-                # Cancel existing timer (just cancel, don't await inside lock)
-                if buffer.timer_task and not buffer.timer_task.done():
-                    buffer.timer_task.cancel()
-                buffer.messages.append(pending_msg)
-            else:
-                buffer = SessionBuffer(
-                    session_id=session_id,
-                    messages=[pending_msg],
-                )
-                self.buffers[session_id] = buffer
-
-            buffer.last_message_time = time.time()
-
-            # Check if buffer reached max capacity
-            if len(buffer.messages) >= MAX_BUFFER_MESSAGES:
-                force_flush = True
-            else:
-                # Start new timer
-                buffer.timer_task = asyncio.create_task(self._delayed_flush(session_id, delay))
-
-        if force_flush:
-            await self._flush_buffer(session_id)
-
-    async def _delayed_flush(self, session_id: str, delay: float) -> None:
-        """Wait for delay then flush the buffer"""
-        try:
-            await asyncio.sleep(delay)
-            await self._flush_buffer(session_id)
-        except asyncio.CancelledError:
-            # Timer was cancelled, new message arrived
-            pass
-
-    async def _flush_buffer(self, session_id: str) -> None:
-        """Flush the buffer for a session, merging all messages"""
-        async with self.lock:
-            buffer = self.buffers.pop(session_id, None)
-
-        if buffer is None or not buffer.messages:
-            return
-
-        if len(buffer.messages) == 1:
-            # Only one message, no need to merge
-            msg = buffer.messages[0]
-            await self.ap.query_pool.add_query(
-                bot_uuid=msg.bot_uuid,
-                launcher_type=msg.launcher_type,
-                launcher_id=msg.launcher_id,
-                sender_id=msg.sender_id,
-                message_event=msg.message_event,
-                message_chain=msg.message_chain,
-                adapter=msg.adapter,
-                pipeline_uuid=msg.pipeline_uuid,
-            )
-            return
-
-        # Merge multiple messages
-        merged_msg = self._merge_messages(buffer.messages)
-        await self.ap.query_pool.add_query(
-            bot_uuid=merged_msg.bot_uuid,
-            launcher_type=merged_msg.launcher_type,
-            launcher_id=merged_msg.launcher_id,
-            sender_id=merged_msg.sender_id,
-            message_event=merged_msg.message_event,
-            message_chain=merged_msg.message_chain,
-            adapter=merged_msg.adapter,
-            pipeline_uuid=merged_msg.pipeline_uuid,
-        )
-
-    def _merge_messages(self, messages: list[PendingMessage]) -> PendingMessage:
-        """Merge multiple messages into one
-
-        The merged message uses the first message as base and combines
-        all message chains with newline separators.
-        The original message_event is kept unmodified to preserve
-        message metadata (message_id, etc.) for reply/quote.
-        """
-        if len(messages) == 1:
-            return messages[0]
-
-        base_msg = messages[0]
-
-        # Build merged message chain
-        merged_chain = platform_message.MessageChain([])
-
-        for i, msg in enumerate(messages):
-            if i > 0:
-                # Add newline separator between messages
-                merged_chain.append(platform_message.Plain(text='\n'))
-
-            # Copy all components from this message
-            for component in msg.message_chain:
-                merged_chain.append(component)
-
-        # Keep message_event unmodified (preserves original message_id and
-        # metadata for reply/quote), only pass merged chain separately
-        return PendingMessage(
-            bot_uuid=base_msg.bot_uuid,
-            launcher_type=base_msg.launcher_type,
-            launcher_id=base_msg.launcher_id,
-            sender_id=base_msg.sender_id,
-            message_event=base_msg.message_event,
-            message_chain=merged_chain,
-            adapter=base_msg.adapter,
-            pipeline_uuid=base_msg.pipeline_uuid,
-        )
-
-    async def flush_all(self) -> None:
-        """Flush all pending buffers immediately
-
-        This is useful during shutdown to ensure no messages are lost.
-        """
-        # Snapshot session IDs and cancel all timers under lock
-        async with self.lock:
-            session_ids = list(self.buffers.keys())
-            for sid in session_ids:
-                buffer = self.buffers.get(sid)
-                if buffer and buffer.timer_task and not buffer.timer_task.done():
-                    buffer.timer_task.cancel()
-
-        # Flush each buffer outside the lock
-        for session_id in session_ids:
-            await self._flush_buffer(session_id)

src/langbot/pkg/pipeline/cntfilter/filters/baiduexamine.py

@@ -1,9 +1,10 @@
 from __future__ import annotations
 
+import aiohttp
+
 from .. import entities
 from .. import filter as filter_model
 import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
-from langbot.pkg.utils import httpclient
 
 BAIDU_EXAMINE_URL = 'https://aip.baidubce.com/rest/2.0/solution/v1/text_censor/v2/user_defined?access_token={}'
 BAIDU_EXAMINE_TOKEN_URL = 'https://aip.baidubce.com/oauth/2.0/token'
@@ -14,50 +15,50 @@ class BaiduCloudExamine(filter_model.ContentFilter):
     """百度云内容审核"""
 
     async def _get_token(self) -> str:
-        session = httpclient.get_session()
-        async with session.post(
-            BAIDU_EXAMINE_TOKEN_URL,
-            params={
-                'grant_type': 'client_credentials',
-                'client_id': self.ap.pipeline_cfg.data['baidu-cloud-examine']['api-key'],
-                'client_secret': self.ap.pipeline_cfg.data['baidu-cloud-examine']['api-secret'],
-            },
-        ) as resp:
-            return (await resp.json())['access_token']
+        async with aiohttp.ClientSession() as session:
+            async with session.post(
+                BAIDU_EXAMINE_TOKEN_URL,
+                params={
+                    'grant_type': 'client_credentials',
+                    'client_id': self.ap.pipeline_cfg.data['baidu-cloud-examine']['api-key'],
+                    'client_secret': self.ap.pipeline_cfg.data['baidu-cloud-examine']['api-secret'],
+                },
+            ) as resp:
+                return (await resp.json())['access_token']
 
     async def process(self, query: pipeline_query.Query, message: str) -> entities.FilterResult:
-        session = httpclient.get_session()
-        async with session.post(
-            BAIDU_EXAMINE_URL.format(await self._get_token()),
-            headers={
-                'Content-Type': 'application/x-www-form-urlencoded',
-                'Accept': 'application/json',
-            },
-            data=f'text={message}'.encode('utf-8'),
-        ) as resp:
-            result = await resp.json()
+        async with aiohttp.ClientSession() as session:
+            async with session.post(
+                BAIDU_EXAMINE_URL.format(await self._get_token()),
+                headers={
+                    'Content-Type': 'application/x-www-form-urlencoded',
+                    'Accept': 'application/json',
+                },
+                data=f'text={message}'.encode('utf-8'),
+            ) as resp:
+                result = await resp.json()
 
-            if 'error_code' in result:
-                return entities.FilterResult(
-                    level=entities.ResultLevel.BLOCK,
-                    replacement=message,
-                    user_notice='',
-                    console_notice=f'百度云判定出错，错误信息：{result["error_msg"]}',
-                )
-            else:
-                conclusion = result['conclusion']
-
-                if conclusion in ('合规'):
-                    return entities.FilterResult(
-                        level=entities.ResultLevel.PASS,
-                        replacement=message,
-                        user_notice='',
-                        console_notice=f'百度云判定结果：{conclusion}',
-                    )
-                else:
+                if 'error_code' in result:
                     return entities.FilterResult(
                         level=entities.ResultLevel.BLOCK,
                         replacement=message,
-                        user_notice='消息中存在不合适的内容, 请修改',
-                        console_notice=f'百度云判定结果：{conclusion}',
+                        user_notice='',
+                        console_notice=f'百度云判定出错，错误信息：{result["error_msg"]}',
                     )
+                else:
+                    conclusion = result['conclusion']
+
+                    if conclusion in ('合规'):
+                        return entities.FilterResult(
+                            level=entities.ResultLevel.PASS,
+                            replacement=message,
+                            user_notice='',
+                            console_notice=f'百度云判定结果：{conclusion}',
+                        )
+                    else:
+                        return entities.FilterResult(
+                            level=entities.ResultLevel.BLOCK,
+                            replacement=message,
+                            user_notice='消息中存在不合适的内容, 请修改',
+                            console_notice=f'百度云判定结果：{conclusion}',
+                        )

src/langbot/pkg/pipeline/config_coercion.py

@@ -1,105 +0,0 @@
-from __future__ import annotations
-
-import logging
-
-logger = logging.getLogger(__name__)
-
-# metadata type -> coercion function
-_COERCE_MAP = {
-    'integer': lambda v: int(v),
-    'number': lambda v: float(v),
-    'float': lambda v: float(v),
-}
-
-
-def _coerce_bool(v):
-    if isinstance(v, bool):
-        return v
-    if isinstance(v, str):
-        if v.lower() == 'true':
-            return True
-        if v.lower() == 'false':
-            return False
-        raise ValueError(f'Cannot convert string {v!r} to bool')
-    return bool(v)
-
-
-def _coerce_value(value, expected_type: str):
-    """Convert a single value to the expected type.
-
-    Returns the converted value, or the original value if no conversion needed.
-    """
-    if value is None:
-        return value
-
-    if expected_type == 'boolean':
-        if isinstance(value, bool):
-            return value
-        return _coerce_bool(value)
-
-    coerce_fn = _COERCE_MAP.get(expected_type)
-    if coerce_fn is None:
-        return value
-
-    # Already the correct type
-    if expected_type == 'integer' and isinstance(value, int) and not isinstance(value, bool):
-        return value
-    if expected_type in ('number', 'float') and isinstance(value, (int, float)) and not isinstance(value, bool):
-        return float(value)
-
-    return coerce_fn(value)
-
-
-def coerce_pipeline_config(
-    config: dict,
-    *metadata_list: dict,
-) -> None:
-    """Coerce pipeline config values according to metadata type definitions.
-
-    Walks each metadata dict (trigger, safety, ai, output) and converts
-    config values in-place so that strings coming from the JSON column are
-    cast to their declared types (integer, number/float, boolean).
-
-    Args:
-        config: The pipeline config dict to modify in-place.
-        *metadata_list: Metadata dicts loaded from the YAML templates.
-    """
-    for meta in metadata_list:
-        section_name = meta.get('name')
-        if not section_name or section_name not in config:
-            continue
-
-        section = config[section_name]
-        if not isinstance(section, dict):
-            continue
-
-        for stage_def in meta.get('stages', []):
-            stage_name = stage_def.get('name')
-            if not stage_name or stage_name not in section:
-                continue
-
-            stage_config = section[stage_name]
-            if not isinstance(stage_config, dict):
-                continue
-
-            for field_def in stage_def.get('config', []):
-                field_name = field_def.get('name')
-                field_type = field_def.get('type')
-                if not field_name or not field_type or field_name not in stage_config:
-                    continue
-
-                old_value = stage_config[field_name]
-                try:
-                    new_value = _coerce_value(old_value, field_type)
-                    if new_value is not old_value:
-                        stage_config[field_name] = new_value
-                except (ValueError, TypeError) as e:
-                    logger.warning(
-                        'Failed to coerce config %s.%s.%s (%r) to %s: %s',
-                        section_name,
-                        stage_name,
-                        field_name,
-                        old_value,
-                        field_type,
-                        e,
-                    )

src/langbot/pkg/pipeline/monitoring_helper.py

@@ -34,15 +34,6 @@ class MonitoringHelper:
             # Check if session exists, if not, record session start
             session_id = f'{query.launcher_type}_{query.launcher_id}'
 
-            # Get sender name from message event
-            sender_name = None
-            if hasattr(query, 'message_event'):
-                if hasattr(query.message_event, 'sender'):
-                    if hasattr(query.message_event.sender, 'nickname'):
-                        sender_name = query.message_event.sender.nickname
-                    elif hasattr(query.message_event.sender, 'member_name'):
-                        sender_name = query.message_event.sender.member_name
-
             # Try to record message
             # Use JSON serialization to preserve message chain structure (including image URLs, etc.)
             if hasattr(query, 'message_chain') and hasattr(query.message_chain, 'model_dump'):
@@ -66,7 +57,6 @@ class MonitoringHelper:
                 if hasattr(query.launcher_type, 'value')
                 else str(query.launcher_type),
                 user_id=query.sender_id,
-                user_name=sender_name,
                 runner_name=runner_name,
                 variables=None,  # Will be updated in record_query_success
             )
@@ -90,7 +80,6 @@ class MonitoringHelper:
                     if hasattr(query.launcher_type, 'value')
                     else str(query.launcher_type),
                     user_id=query.sender_id,
-                    user_name=sender_name,
                 )
 
             return message_id
@@ -125,70 +114,6 @@ class MonitoringHelper:
         except Exception as e:
             ap.logger.error(f'Failed to record query success: {e}')
 
-    @staticmethod
-    async def record_query_response(
-        ap: app.Application,
-        query: pipeline_query.Query,
-        bot_id: str,
-        bot_name: str,
-        pipeline_id: str,
-        pipeline_name: str,
-        runner_name: str | None = None,
-    ):
-        """Record bot response message to monitoring"""
-        try:
-            session_id = f'{query.launcher_type}_{query.launcher_id}'
-
-            # Get sender name from message event
-            sender_name = None
-            if hasattr(query, 'message_event'):
-                if hasattr(query.message_event, 'sender'):
-                    if hasattr(query.message_event.sender, 'nickname'):
-                        sender_name = query.message_event.sender.nickname
-                    elif hasattr(query.message_event.sender, 'member_name'):
-                        sender_name = query.message_event.sender.member_name
-
-            # Extract response content from resp_message_chain
-            if hasattr(query, 'resp_message_chain') and query.resp_message_chain:
-                # Serialize the last response message chain
-                last_resp = query.resp_message_chain[-1]
-                if hasattr(last_resp, 'model_dump'):
-                    message_content = json.dumps(last_resp.model_dump(), ensure_ascii=False)
-                else:
-                    message_content = str(last_resp)
-            elif hasattr(query, 'resp_messages') and query.resp_messages:
-                last_resp = query.resp_messages[-1]
-                if hasattr(last_resp, 'get_content_platform_message_chain'):
-                    chain = last_resp.get_content_platform_message_chain()
-                    if hasattr(chain, 'model_dump'):
-                        message_content = json.dumps(chain.model_dump(), ensure_ascii=False)
-                    else:
-                        message_content = str(chain)
-                else:
-                    message_content = str(last_resp)
-            else:
-                return  # No response to record
-
-            await ap.monitoring_service.record_message(
-                bot_id=bot_id,
-                bot_name=bot_name,
-                pipeline_id=pipeline_id,
-                pipeline_name=pipeline_name,
-                message_content=message_content,
-                session_id=session_id,
-                status='success',
-                level='info',
-                platform=query.launcher_type.value
-                if hasattr(query.launcher_type, 'value')
-                else str(query.launcher_type),
-                user_id=query.sender_id,
-                user_name=sender_name,
-                runner_name=runner_name,
-                role='assistant',
-            )
-        except Exception as e:
-            ap.logger.error(f'Failed to record query response: {e}')
-
     @staticmethod
     async def record_query_error(
         ap: app.Application,
@@ -204,15 +129,6 @@ class MonitoringHelper:
         try:
             session_id = f'{query.launcher_type}_{query.launcher_id}'
 
-            # Get sender name from message event
-            sender_name = None
-            if hasattr(query, 'message_event'):
-                if hasattr(query.message_event, 'sender'):
-                    if hasattr(query.message_event.sender, 'nickname'):
-                        sender_name = query.message_event.sender.nickname
-                    elif hasattr(query.message_event.sender, 'member_name'):
-                        sender_name = query.message_event.sender.member_name
-
             # Record error message
             message_id = await ap.monitoring_service.record_message(
                 bot_id=bot_id,
@@ -227,7 +143,6 @@ class MonitoringHelper:
                 if hasattr(query.launcher_type, 'value')
                 else str(query.launcher_type),
                 user_id=query.sender_id,
-                user_name=sender_name,
                 runner_name=runner_name,
             )
 

src/langbot/pkg/pipeline/pipelinemgr.py

@@ -13,7 +13,6 @@ import langbot_plugin.api.entities.builtin.platform.message as platform_message
 import langbot_plugin.api.entities.builtin.platform.events as platform_events
 import langbot_plugin.api.entities.events as events
 from ..utils import importutil
-from .config_coercion import coerce_pipeline_config
 
 import langbot_plugin.api.entities.builtin.provider.session as provider_session
 import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
@@ -340,20 +339,6 @@ class RuntimePipeline:
                 except Exception as e:
                     self.ap.logger.error(f'Failed to record query success: {e}')
 
-                # Record bot response message
-                try:
-                    await monitoring_helper.MonitoringHelper.record_query_response(
-                        ap=self.ap,
-                        query=query,
-                        bot_id=query.bot_uuid or 'unknown',
-                        bot_name=bot_name,
-                        pipeline_id=self.pipeline_entity.uuid,
-                        pipeline_name=pipeline_name,
-                        runner_name=runner_name,
-                    )
-                except Exception as e:
-                    self.ap.logger.error(f'Failed to record query response: {e}')
-
         except Exception as e:
             inst_name = query.current_stage_name if query.current_stage_name else 'unknown'
             self.ap.logger.error(f'Error processing query {query.query_id} stage={inst_name} : {e}')
@@ -384,6 +369,8 @@ class RuntimePipeline:
 class PipelineManager:
     """流水线管理器"""
 
+    # ====== 4.0 ======
+
     ap: app.Application
 
     pipelines: list[RuntimePipeline]
@@ -421,14 +408,6 @@ class PipelineManager:
         elif isinstance(pipeline_entity, dict):
             pipeline_entity = persistence_pipeline.LegacyPipeline(**pipeline_entity)
 
-        coerce_pipeline_config(
-            pipeline_entity.config,
-            getattr(self.ap, 'pipeline_config_meta_trigger', {'name': 'trigger', 'stages': []}),
-            getattr(self.ap, 'pipeline_config_meta_safety', {'name': 'safety', 'stages': []}),
-            getattr(self.ap, 'pipeline_config_meta_ai', {'name': 'ai', 'stages': []}),
-            getattr(self.ap, 'pipeline_config_meta_output', {'name': 'output', 'stages': []}),
-        )
-
         # initialize stage containers according to pipeline_entity.stages
         stage_containers: list[StageInstContainer] = []
         for stage_name in pipeline_entity.stages:

src/langbot/pkg/pipeline/preproc/preproc.py

@@ -36,36 +36,17 @@ class PreProcessor(stage.PipelineStage):
         session = await self.ap.sess_mgr.get_session(query)
 
         # When not local-agent, llm_model is None
-        llm_model = None
-        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', [])
-
-            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
+        try:
+            llm_model = (
+                await self.ap.model_mgr.get_model_by_uuid(query.pipeline_config['ai']['local-agent']['model'])
+                if selected_runner == 'local-agent'
+                else None
+            )
+        except ValueError:
+            self.ap.logger.warning(
+                f'LLM model {query.pipeline_config["ai"]["local-agent"]["model"] + " "}not found or not configured'
+            )
+            llm_model = None
 
         conversation = await self.ap.sess_mgr.get_conversation(
             query,
@@ -80,28 +61,20 @@ class PreProcessor(stage.PipelineStage):
         query.prompt = conversation.prompt.copy()
         query.messages = conversation.messages.copy()
 
-        if selected_runner == 'local-agent':
+        if selected_runner == 'local-agent' and llm_model:
             query.use_funcs = []
-            if llm_model:
-                query.use_llm_model_uuid = llm_model.model_entity.uuid
+            query.use_llm_model_uuid = llm_model.model_entity.uuid
 
-                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}')
-                    self.ap.logger.debug(f'Bound MCP servers: {bound_mcp_servers}')
-                    self.ap.logger.debug(f'Use funcs: {query.use_funcs}')
-
-            # If primary model doesn't support func_call but fallback models exist,
-            # load tools anyway since fallback models may support them
-            if not query.use_funcs and query.variables.get('_fallback_model_uuids'):
+            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}')
+                self.ap.logger.debug(f'Bound MCP servers: {bound_mcp_servers}')
+                self.ap.logger.debug(f'Use funcs: {query.use_funcs}')
+
         sender_name = ''
 
         if isinstance(query.message_event, platform_events.GroupMessage):

src/langbot/pkg/pipeline/process/handlers/chat.py

@@ -12,7 +12,7 @@ from ... import entities
 from ....provider import runner as runner_module
 
 import langbot_plugin.api.entities.events as events
-from ....utils import importutil, constants, runner as runner_utils
+from ....utils import importutil, constants
 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
@@ -149,19 +149,12 @@ class ChatMessageHandler(handler.MessageHandler):
                 self.ap.logger.error(f'Conversation({query.query_id}) Request Failed: {error_info}')
                 traceback.print_exc()
 
-                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
+                hide_exception_info = query.pipeline_config['output']['misc']['hide-exception']
 
                 yield entities.StageProcessResult(
                     result_type=entities.ResultType.INTERRUPT,
                     new_query=query,
-                    user_notice=user_notice,
+                    user_notice='请求失败' if hide_exception_info else f'{e}',
                     error_notice=f'{e}',
                     debug_notice=traceback.format_exc(),
                 )
@@ -192,15 +185,10 @@ class ChatMessageHandler(handler.MessageHandler):
 
                     pipeline_plugins = query.variables.get('_pipeline_bound_plugins', None)
 
-                    runner_category = runner_utils.get_runner_category_from_runner(
-                        runner_name, runner, query.pipeline_config
-                    )
-
                     payload = {
                         'query_id': query.query_id,
                         'adapter': adapter_name,
                         'runner': runner_name,
-                        'runner_category': runner_category,
                         'duration_ms': duration_ms,
                         'model_name': model_name,
                         'version': constants.semantic_version,
@@ -212,11 +200,6 @@ class ChatMessageHandler(handler.MessageHandler):
 
                     # 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
-                    if not locals().get('error_info') and adapter_name and 'WebSocket' not in adapter_name:
-                        if self.ap.survey:
-                            await self.ap.survey.trigger_event('first_bot_response_success')
                 except Exception as ex:
                     # Ensure telemetry issues do not affect normal flow
                     self.ap.logger.warning(f'Failed to send telemetry: {ex}')

src/langbot/pkg/platform/botmgr.py

@@ -18,6 +18,7 @@ import langbot_plugin.api.entities.builtin.provider.session as provider_session
 import langbot_plugin.api.entities.builtin.platform.events as platform_events
 import langbot_plugin.api.entities.builtin.platform.message as platform_message
 import langbot_plugin.api.definition.abstract.platform.adapter as abstract_platform_adapter
+import langbot_plugin.api.entities.events as events
 
 
 class RuntimeBot:
@@ -82,7 +83,7 @@ class RuntimeBot:
                     if custom_launcher_id:
                         launcher_id = custom_launcher_id
 
-                await self.ap.msg_aggregator.add_message(
+                await self.ap.query_pool.add_query(
                     bot_uuid=self.bot_entity.uuid,
                     launcher_type=provider_session.LauncherTypes.PERSON,
                     launcher_id=launcher_id,
@@ -125,7 +126,7 @@ class RuntimeBot:
                     if custom_launcher_id:
                         launcher_id = custom_launcher_id
 
-                await self.ap.msg_aggregator.add_message(
+                await self.ap.query_pool.add_query(
                     bot_uuid=self.bot_entity.uuid,
                     launcher_type=provider_session.LauncherTypes.GROUP,
                     launcher_id=launcher_id,
@@ -141,6 +142,56 @@ class RuntimeBot:
         self.adapter.register_listener(platform_events.FriendMessage, on_friend_message)
         self.adapter.register_listener(platform_events.GroupMessage, on_group_message)
 
+        async def on_notice(
+            event: platform_events.NoticeEvent,
+            adapter: abstract_platform_adapter.AbstractMessagePlatformAdapter,
+        ):
+            await self.logger.info(f'Notice event: {event.notice_type} {event.sub_type}')
+
+            try:
+                event_obj = events.NoticeReceived(
+                    notice_type=event.notice_type,
+                    sub_type=event.sub_type,
+                    group_id=event.group_id,
+                    user_id=event.user_id,
+                    operator_id=event.operator_id,
+                    target_id=event.target_id,
+                    message_id=event.message_id,
+                    duration=event.duration,
+                    file=event.file,
+                    honor_type=event.honor_type,
+                )
+
+                if hasattr(self.ap, 'plugin_connector') and self.ap.plugin_connector:
+                    await self.ap.plugin_connector.emit_event(event_obj)
+            except Exception:
+                await self.logger.error(f'Error emitting notice event: {traceback.format_exc()}')
+
+        self.adapter.register_listener(platform_events.NoticeEvent, on_notice)
+
+        async def on_request(
+            event: platform_events.RequestEvent,
+            adapter: abstract_platform_adapter.AbstractMessagePlatformAdapter,
+        ):
+            await self.logger.info(f'Request event: {event.request_type} {event.sub_type}')
+
+            try:
+                event_obj = events.RequestReceived(
+                    request_type=event.request_type,
+                    sub_type=event.sub_type,
+                    user_id=event.user_id,
+                    group_id=event.group_id,
+                    comment=event.comment,
+                    flag=event.flag,
+                )
+
+                if hasattr(self.ap, 'plugin_connector') and self.ap.plugin_connector:
+                    await self.ap.plugin_connector.emit_event(event_obj)
+            except Exception:
+                await self.logger.error(f'Error emitting request event: {traceback.format_exc()}')
+
+        self.adapter.register_listener(platform_events.RequestEvent, on_request)
+
     async def run(self):
         async def exception_wrapper():
             try:
@@ -282,8 +333,6 @@ class PlatformManager:
         return runtime_bot
 
     async def get_bot_by_uuid(self, bot_uuid: str) -> RuntimeBot | None:
-        if self.websocket_proxy_bot and self.websocket_proxy_bot.bot_entity.uuid == bot_uuid:
-            return self.websocket_proxy_bot
         for bot in self.bots:
             if bot.bot_entity.uuid == bot_uuid:
                 return bot

src/langbot/pkg/platform/sources/aiocqhttp.py

@@ -306,9 +306,8 @@ class AiocqhttpEventConverter(abstract_platform_adapter.AbstractEventConverter):
 
     @staticmethod
     async def target2yiri(event: aiocqhttp.Event, bot=None):
-        yiri_chain = await AiocqhttpMessageConverter.target2yiri(event.message, event.message_id, bot)
-
         if event.message_type == 'group':
+            yiri_chain = await AiocqhttpMessageConverter.target2yiri(event.message, event.message_id, bot)
             permission = 'MEMBER'
 
             if 'role' in event.sender:
@@ -334,6 +333,7 @@ class AiocqhttpEventConverter(abstract_platform_adapter.AbstractEventConverter):
             )
             return converted_event
         elif event.message_type == 'private':
+            yiri_chain = await AiocqhttpMessageConverter.target2yiri(event.message, event.message_id, bot)
             return platform_events.FriendMessage(
                 sender=platform_entities.Friend(
                     id=event.sender['user_id'],
@@ -344,6 +344,57 @@ class AiocqhttpEventConverter(abstract_platform_adapter.AbstractEventConverter):
                 time=event.time,
                 source_platform_object=event,
             )
+        elif event.post_type == 'notice':
+            yiri_chain = platform_message.MessageChain(
+                [
+                    platform_message.Source(id=-1, time=datetime.datetime.now()),
+                    platform_message.Notice(
+                        notice_type=event.get('notice_type', ''),
+                        sub_type=event.get('sub_type', ''),
+                        user_id=event.get('user_id', None),
+                        target_id=event.get('target_id', None),
+                        group_id=event.get('group_id', None),
+                        operator_id=event.get('operator_id', None),
+                        message_id=event.get('message_id', None),
+                        duration=event.get('duration', None),
+                        file=event.get('file', None),
+                        honor_type=event.get('honor_type', None),
+                    ),
+                ]
+            )
+            return platform_events.NoticeEvent(
+                notice_type=event.get('notice_type', ''),
+                sub_type=event.get('sub_type', ''),
+                user_id=event.get('user_id', None),
+                target_id=event.get('target_id', None),
+                group_id=event.get('group_id', None),
+                time=event.time,
+                source_platform_object=event,
+            )
+        elif event.post_type == 'request':
+            yiri_chain = platform_message.MessageChain(
+                [
+                    platform_message.Source(id=-1, time=datetime.datetime.now()),
+                    platform_message.Request(
+                        request_type=event.get('request_type', ''),
+                        sub_type=event.get('sub_type', ''),
+                        user_id=event.get('user_id', None),
+                        group_id=event.get('group_id', None),
+                        comment=event.get('comment', ''),
+                        flag=event.get('flag', ''),
+                    ),
+                ]
+            )
+            return platform_events.RequestEvent(
+                request_type=event.get('request_type', ''),
+                sub_type=event.get('sub_type', ''),
+                user_id=event.get('user_id', None),
+                group_id=event.get('group_id', None),
+                comment=event.get('comment', ''),
+                flag=event.get('flag', ''),
+                time=event.time,
+                source_platform_object=event,
+            )
 
 
 class AiocqhttpAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
@@ -375,18 +426,6 @@ class AiocqhttpAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter)
             self.bot = aiocqhttp.CQHttp()
 
     async def send_message(self, target_type: str, target_id: str, message: platform_message.MessageChain):
-        # Check if message contains a Forward component
-        forward_msg = message.get_first(platform_message.Forward)
-        if forward_msg:
-            if target_type == 'group':
-                # Send as merged forward message via OneBot API
-                await self._send_forward_message(int(target_id), forward_msg)
-                return
-            else:
-                await self.logger.warning(
-                    f'Forward message is only supported for group targets, got target_type={target_type}. Falling through to normal send.'
-                )
-
         aiocq_msg = (await AiocqhttpMessageConverter.yiri2target(message))[0]
 
         if target_type == 'group':
@@ -394,90 +433,6 @@ class AiocqhttpAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter)
         elif target_type == 'person':
             await self.bot.send_private_msg(user_id=int(target_id), message=aiocq_msg)
 
-    async def _send_forward_message(self, group_id: int, forward: platform_message.Forward):
-        """Send a merged forward message to a group using NapCat extended API."""
-        messages = []
-
-        for node in forward.node_list:
-            # Build content for each node
-            content = []
-            if node.message_chain:
-                for component in node.message_chain:
-                    if isinstance(component, platform_message.Plain):
-                        if component.text:
-                            content.append({'type': 'text', 'data': {'text': component.text}})
-                    elif isinstance(component, platform_message.Image):
-                        img_data = {}
-                        if component.base64:
-                            b64 = component.base64
-                            if b64.startswith('data:'):
-                                b64 = b64.split(',', 1)[-1] if ',' in b64 else b64
-                            img_data['file'] = f'base64://{b64}'
-                        elif component.url:
-                            img_data['file'] = component.url
-                        elif component.path:
-                            img_data['file'] = str(component.path)
-
-                        if img_data:
-                            content.append({'type': 'image', 'data': img_data})
-
-            if not content:
-                continue
-
-            # Build node data - use user_id and nickname format for NapCat
-            user_id = str(node.sender_id) if node.sender_id else str(self.bot_account_id or '10000')
-            node_data = {
-                'type': 'node',
-                'data': {
-                    'user_id': user_id,
-                    'nickname': node.sender_name or '未知',
-                    'content': content,
-                },
-            }
-
-            messages.append(node_data)
-
-        if not messages:
-            return
-
-        # Build the full message payload for NapCat's send_forward_msg API
-        # This matches the format used by GiveMeSetuPlugin
-        bot_id = str(self.bot_account_id) if self.bot_account_id else '10000'
-        payload = {
-            'group_id': group_id,
-            'user_id': bot_id,  # Required by NapCat for display
-            'messages': messages,
-        }
-
-        # Add display settings if available
-        if forward.display:
-            if forward.display.title:
-                payload['news'] = [{'text': forward.display.title}]
-            if forward.display.brief:
-                payload['prompt'] = forward.display.brief
-            if forward.display.summary:
-                payload['summary'] = forward.display.summary
-            if forward.display.source:
-                payload['source'] = forward.display.source
-
-        try:
-            # Use send_forward_msg (NapCat extended API) instead of send_group_forward_msg
-            await self.logger.info(
-                f'Sending forward message to group {group_id} with {len(messages)} nodes, payload keys: {list(payload.keys())}'
-            )
-            result = await self.bot.call_action('send_forward_msg', **payload)
-            await self.logger.info(f'Forward message sent to group {group_id}, result: {result}')
-        except Exception as e:
-            await self.logger.error(f'Failed to send forward message to group {group_id}: {e}')
-            # Fallback: try standard OneBot API with integer group_id
-            try:
-                await self.logger.info('Trying fallback API send_group_forward_msg')
-                await self.bot.call_action('send_group_forward_msg', group_id=group_id, messages=messages)
-                await self.logger.info(f'Forward message sent via fallback API to group {group_id}')
-            except Exception as e2:
-                await self.logger.error(f'Fallback also failed: {e2}')
-                raise
-
     async def reply_message(
         self,
         message_source: platform_events.MessageEvent,
@@ -509,12 +464,31 @@ class AiocqhttpAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter)
                 await self.logger.error(f'Error in on_message: {traceback.format_exc()}')
                 traceback.print_exc()
 
+        async def on_notice(event: aiocqhttp.Event):
+            self.bot_account_id = event.self_id
+            try:
+                return await callback(await self.event_converter.target2yiri(event, self.bot), self)
+            except Exception:
+                await self.logger.error(f'Error in on_notice: {traceback.format_exc()}')
+                traceback.print_exc()
+
+        async def on_request(event: aiocqhttp.Event):
+            self.bot_account_id = event.self_id
+            try:
+                return await callback(await self.event_converter.target2yiri(event, self.bot), self)
+            except Exception:
+                await self.logger.error(f'Error in on_request: {traceback.format_exc()}')
+                traceback.print_exc()
+
         if event_type == platform_events.GroupMessage:
             self.bot.on_message('group')(on_message)
             # self.bot.on_notice()(on_message)
         elif event_type == platform_events.FriendMessage:
             self.bot.on_message('private')(on_message)
-            # self.bot.on_notice()(on_message)
+        elif event_type == platform_events.NoticeEvent:
+            self.bot.on_notice()(on_notice)
+        elif event_type == platform_events.RequestEvent:
+            self.bot.on_request()(on_request)
         # print(event_type)
 
         async def on_websocket_connection(event: aiocqhttp.Event):

src/langbot/pkg/platform/sources/discord.py

@@ -14,7 +14,7 @@ import io
 import asyncio
 from enum import Enum
 
-from langbot.pkg.utils import httpclient
+import aiohttp
 import pydantic
 
 import langbot_plugin.api.definition.abstract.platform.adapter as abstract_platform_adapter
@@ -622,23 +622,23 @@ class DiscordMessageConverter(abstract_platform_adapter.AbstractMessageConverter
                     image_bytes = base64.b64decode(base64_data)
                 elif ele.url:
                     # 从URL下载图片
-                    session = httpclient.get_session()
-                    async with session.get(ele.url) as response:
-                        image_bytes = await response.read()
-                        # 从URL或Content-Type推断文件类型
-                        content_type = response.headers.get('Content-Type', '')
-                        if 'jpeg' in content_type or 'jpg' in content_type:
-                            filename = f'{uuid.uuid4()}.jpg'
-                        elif 'gif' in content_type:
-                            filename = f'{uuid.uuid4()}.gif'
-                        elif 'webp' in content_type:
-                            filename = f'{uuid.uuid4()}.webp'
-                        elif ele.url.lower().endswith(('.jpg', '.jpeg')):
-                            filename = f'{uuid.uuid4()}.jpg'
-                        elif ele.url.lower().endswith('.gif'):
-                            filename = f'{uuid.uuid4()}.gif'
-                        elif ele.url.lower().endswith('.webp'):
-                            filename = f'{uuid.uuid4()}.webp'
+                    async with aiohttp.ClientSession() as session:
+                        async with session.get(ele.url) as response:
+                            image_bytes = await response.read()
+                            # 从URL或Content-Type推断文件类型
+                            content_type = response.headers.get('Content-Type', '')
+                            if 'jpeg' in content_type or 'jpg' in content_type:
+                                filename = f'{uuid.uuid4()}.jpg'
+                            elif 'gif' in content_type:
+                                filename = f'{uuid.uuid4()}.gif'
+                            elif 'webp' in content_type:
+                                filename = f'{uuid.uuid4()}.webp'
+                            elif ele.url.lower().endswith(('.jpg', '.jpeg')):
+                                filename = f'{uuid.uuid4()}.jpg'
+                            elif ele.url.lower().endswith('.gif'):
+                                filename = f'{uuid.uuid4()}.gif'
+                            elif ele.url.lower().endswith('.webp'):
+                                filename = f'{uuid.uuid4()}.webp'
                 elif ele.path:
                     # 从文件路径读取图片
                     # 确保路径没有空字节
@@ -702,9 +702,9 @@ class DiscordMessageConverter(abstract_platform_adapter.AbstractMessageConverter
                     file_base64 = ele.base64.split(',')[-1]
                     file_bytes = base64.b64decode(file_base64)
                 elif ele.url:
-                    session = httpclient.get_session()
-                    async with session.get(ele.url) as response:
-                        file_bytes = await response.read()
+                    async with aiohttp.ClientSession() as session:
+                        async with session.get(ele.url) as response:
+                            file_bytes = await response.read()
                 if file_bytes:
                     files.append(discord.File(fp=io.BytesIO(file_bytes), filename=filename))
             elif isinstance(ele, platform_message.File):
@@ -717,9 +717,9 @@ class DiscordMessageConverter(abstract_platform_adapter.AbstractMessageConverter
                     else:
                         file_bytes = base64.b64decode(ele.base64)
                 elif ele.url:
-                    session = httpclient.get_session()
-                    async with session.get(ele.url) as response:
-                        file_bytes = await response.read()
+                    async with aiohttp.ClientSession() as session:
+                        async with session.get(ele.url) as response:
+                            file_bytes = await response.read()
                 if file_bytes:
                     files.append(discord.File(fp=io.BytesIO(file_bytes), filename=filename))
             elif isinstance(ele, platform_message.Forward):
@@ -775,12 +775,12 @@ class DiscordMessageConverter(abstract_platform_adapter.AbstractMessageConverter
 
         # attachments
         for attachment in message.attachments:
-            session = httpclient.get_session(trust_env=True)
-            async with session.get(attachment.url) as response:
-                image_data = await response.read()
-                image_base64 = base64.b64encode(image_data).decode('utf-8')
-                image_format = response.headers['Content-Type']
-                element_list.append(platform_message.Image(base64=f'data:{image_format};base64,{image_base64}'))
+            async with aiohttp.ClientSession(trust_env=True) as session:
+                async with session.get(attachment.url) as response:
+                    image_data = await response.read()
+                    image_base64 = base64.b64encode(image_data).decode('utf-8')
+                    image_format = response.headers['Content-Type']
+                    element_list.append(platform_message.Image(base64=f'data:{image_format};base64,{image_base64}'))
 
         return platform_message.MessageChain(element_list)
 

src/langbot/pkg/platform/sources/kook.py

@@ -9,8 +9,6 @@ import traceback
 import time
 
 import aiohttp
-
-from langbot.pkg.utils import httpclient
 import websockets
 import pydantic
 
@@ -122,16 +120,16 @@ class KookMessageConverter(abstract_platform_adapter.AbstractMessageConverter):
             if content:
                 # Download image and convert to base64
                 try:
-                    session = httpclient.get_session()
-                    async with session.get(content) as response:
-                        if response.status == 200:
-                            image_bytes = await response.read()
-                            image_base64 = base64.b64encode(image_bytes).decode('utf-8')
-                            # Detect image format
-                            content_type = response.headers.get('Content-Type', 'image/png')
-                            components.append(
-                                platform_message.Image(base64=f'data:{content_type};base64,{image_base64}')
-                            )
+                    async with aiohttp.ClientSession() as session:
+                        async with session.get(content) as response:
+                            if response.status == 200:
+                                image_bytes = await response.read()
+                                image_base64 = base64.b64encode(image_bytes).decode('utf-8')
+                                # Detect image format
+                                content_type = response.headers.get('Content-Type', 'image/png')
+                                components.append(
+                                    platform_message.Image(base64=f'data:{content_type};base64,{image_base64}')
+                                )
                 except Exception:
                     # If download fails, just add as plain text
                     components.append(platform_message.Plain(text=f'[Image: {content}]'))
@@ -297,17 +295,17 @@ class KookAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
             'Authorization': f'Bot {self.config["token"]}',
         }
 
-        session = httpclient.get_session()
-        async with session.get(base_url, params=params, headers=headers) as response:
-            if response.status == 200:
-                data = await response.json()
-                if data.get('code') == 0:
-                    gateway_url = data['data']['url']
-                    return gateway_url
+        async with aiohttp.ClientSession() as session:
+            async with session.get(base_url, params=params, headers=headers) as response:
+                if response.status == 200:
+                    data = await response.json()
+                    if data.get('code') == 0:
+                        gateway_url = data['data']['url']
+                        return gateway_url
+                    else:
+                        raise Exception(f'Failed to get gateway URL: {data.get("message")}')
                 else:
-                    raise Exception(f'Failed to get gateway URL: {data.get("message")}')
-            else:
-                raise Exception(f'Failed to get gateway URL: HTTP {response.status}')
+                    raise Exception(f'Failed to get gateway URL: HTTP {response.status}')
 
     async def _get_bot_user_info(self) -> dict:
         """Get bot's own user information from KOOK API"""
@@ -317,17 +315,17 @@ class KookAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
             'Authorization': f'Bot {self.config["token"]}',
         }
 
-        session = httpclient.get_session()
-        async with session.get(base_url, headers=headers) as response:
-            if response.status == 200:
-                data = await response.json()
-                if data.get('code') == 0:
-                    user_info = data['data']
-                    return user_info
+        async with aiohttp.ClientSession() as session:
+            async with session.get(base_url, headers=headers) as response:
+                if response.status == 200:
+                    data = await response.json()
+                    if data.get('code') == 0:
+                        user_info = data['data']
+                        return user_info
+                    else:
+                        raise Exception(f'Failed to get bot user info: {data.get("message")}')
                 else:
-                    raise Exception(f'Failed to get bot user info: {data.get("message")}')
-            else:
-                raise Exception(f'Failed to get bot user info: HTTP {response.status}')
+                    raise Exception(f'Failed to get bot user info: HTTP {response.status}')
 
     async def _handle_hello(self, data: dict):
         """Handle HELLO signal (signal 1)"""
@@ -512,7 +510,7 @@ class KookAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
 
         try:
             if not self.http_session:
-                self.http_session = httpclient.get_session()
+                self.http_session = aiohttp.ClientSession()
 
             async with self.http_session.post(url, json=payload, headers=headers) as response:
                 if response.status == 200:
@@ -578,7 +576,7 @@ class KookAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
 
         try:
             if not self.http_session:
-                self.http_session = httpclient.get_session()
+                self.http_session = aiohttp.ClientSession()
 
             async with self.http_session.post(url, json=payload, headers=headers) as response:
                 if response.status == 200:
@@ -626,7 +624,7 @@ class KookAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
 
         try:
             # Create HTTP session
-            self.http_session = httpclient.get_session()
+            self.http_session = aiohttp.ClientSession()
 
             await self.logger.info('Starting KOOK adapter')
 

src/langbot/pkg/platform/sources/lark.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 import lark_oapi
-from lark_oapi.api.im.v1 import CreateImageRequest, CreateImageRequestBody, CreateFileRequest, CreateFileRequestBody
+from lark_oapi.api.im.v1 import CreateImageRequest, CreateImageRequestBody
 import traceback
 import typing
 import asyncio
@@ -17,7 +17,7 @@ import tempfile
 import os
 import mimetypes
 
-from langbot.pkg.utils import httpclient
+import aiohttp
 import lark_oapi.ws.exception
 import quart
 from lark_oapi.api.im.v1 import *
@@ -78,13 +78,13 @@ class LarkMessageConverter(abstract_platform_adapter.AbstractMessageConverter):
                 return None
         elif msg.url:
             try:
-                session = httpclient.get_session()
-                async with session.get(msg.url) as response:
-                    if response.status == 200:
-                        image_bytes = await response.read()
-                    else:
-                        print(f'Failed to download image from {msg.url}: HTTP {response.status}')
-                        return None
+                async with aiohttp.ClientSession() as session:
+                    async with session.get(msg.url) as response:
+                        if response.status == 200:
+                            image_bytes = await response.read()
+                        else:
+                            print(f'Failed to download image from {msg.url}: HTTP {response.status}')
+                            return None
             except Exception as e:
                 print(f'Failed to download image from {msg.url}: {e}')
                 traceback.print_exc()
@@ -141,88 +141,6 @@ class LarkMessageConverter(abstract_platform_adapter.AbstractMessageConverter):
             traceback.print_exc()
             return None
 
-    @staticmethod
-    async def upload_file_to_lark(
-        file_bytes: bytes,
-        api_client: lark_oapi.Client,
-        file_type: str,
-        file_name: str = 'file',
-        duration: typing.Optional[int] = None,
-    ) -> typing.Optional[str]:
-        """Upload a file to Lark and return the file_key, or None if upload fails.
-
-        Args:
-            file_bytes: Raw file bytes.
-            api_client: Lark API client.
-            file_type: Lark file type, e.g. 'opus', 'mp4', 'pdf', 'doc', etc.
-            file_name: Display name for the file.
-            duration: Duration in milliseconds (for audio files).
-        """
-        try:
-            with tempfile.NamedTemporaryFile(delete=False) as temp_file:
-                temp_file.write(file_bytes)
-                temp_file_path = temp_file.name
-
-            try:
-                body_builder = (
-                    CreateFileRequestBody.builder()
-                    .file_type(file_type)
-                    .file_name(file_name)
-                    .file(open(temp_file_path, 'rb'))
-                )
-                if duration is not None:
-                    body_builder = body_builder.duration(duration)
-
-                request = CreateFileRequest.builder().request_body(body_builder.build()).build()
-
-                response = await api_client.im.v1.file.acreate(request)
-
-                if not response.success():
-                    print(
-                        f'client.im.v1.file.create failed, code: {response.code}, msg: {response.msg}, log_id: {response.get_log_id()}'
-                    )
-                    return None
-
-                return response.data.file_key
-            finally:
-                os.unlink(temp_file_path)
-        except Exception as e:
-            print(f'Failed to upload file to Lark: {e}')
-            traceback.print_exc()
-            return None
-
-    @staticmethod
-    async def _get_media_bytes(
-        msg: typing.Union[platform_message.Voice, platform_message.File],
-    ) -> typing.Optional[bytes]:
-        """Get bytes from a Voice or File message (base64, url, or path)."""
-        data = None
-
-        if msg.base64:
-            try:
-                base64_str = msg.base64
-                if ',' in base64_str:
-                    base64_str = base64_str.split(',', 1)[1]
-                data = base64.b64decode(base64_str)
-            except Exception:
-                pass
-        elif msg.url:
-            try:
-                session = httpclient.get_session()
-                async with session.get(msg.url) as resp:
-                    if resp.status == 200:
-                        data = await resp.read()
-            except Exception:
-                pass
-        elif msg.path:
-            try:
-                with open(msg.path, 'rb') as f:
-                    data = f.read()
-            except Exception:
-                pass
-
-        return data
-
     @staticmethod
     async def yiri2target(
         message_chain: platform_message.MessageChain, api_client: lark_oapi.Client
@@ -232,10 +150,10 @@ class LarkMessageConverter(abstract_platform_adapter.AbstractMessageConverter):
         Returns:
             Tuple of (text_elements, image_keys):
             - text_elements: List of paragraphs for post message format
-            - media_items: List of dicts with 'msg_type' and 'content' for separate media messages
+            - image_keys: List of image_key strings for separate image messages
         """
         message_elements = []
-        media_items = []
+        image_keys = []
         pending_paragraph = []
 
         # Regex pattern to match Markdown image syntax: ![alt](url)
@@ -278,77 +196,40 @@ class LarkMessageConverter(abstract_platform_adapter.AbstractMessageConverter):
                 # Check for and extract Markdown images from text
                 cleaned_text, extracted_urls = await process_text_with_images(text)
 
-                # Split by blank lines to create separate paragraphs for Lark post format.
-                # Lark truncates md elements at the first \n\n, so we must use the
-                # post format's native paragraph structure instead.
+                # Add cleaned text if not empty
                 if cleaned_text:
-                    segments = re.split(r'\n\s*\n', cleaned_text)
-                    for i, segment in enumerate(segments):
-                        segment = segment.strip()
-                        if not segment:
-                            continue
-                        if i > 0 and pending_paragraph:
-                            message_elements.append(pending_paragraph)
-                            pending_paragraph = []
-                        pending_paragraph.append({'tag': 'md', 'text': segment})
+                    pending_paragraph.append({'tag': 'md', 'text': cleaned_text})
 
                 # Process extracted image URLs
                 for url in extracted_urls:
+                    # Create a temporary Image message to upload
                     temp_image = platform_message.Image(url=url)
                     image_key = await LarkMessageConverter.upload_image_to_lark(temp_image, api_client)
                     if image_key:
-                        media_items.append({'msg_type': 'image', 'content': {'image_key': image_key}})
+                        image_keys.append(image_key)
 
             elif isinstance(msg, platform_message.At):
                 pending_paragraph.append({'tag': 'at', 'user_id': msg.target, 'style': []})
             elif isinstance(msg, platform_message.AtAll):
                 pending_paragraph.append({'tag': 'at', 'user_id': 'all', 'style': []})
             elif isinstance(msg, platform_message.Image):
+                # Upload image and get image_key
                 image_key = await LarkMessageConverter.upload_image_to_lark(msg, api_client)
                 if image_key:
-                    media_items.append({'msg_type': 'image', 'content': {'image_key': image_key}})
-            elif isinstance(msg, platform_message.Voice):
-                data = await LarkMessageConverter._get_media_bytes(msg)
-                if data:
-                    duration = int(msg.length * 1000) if msg.length else None
-                    file_key = await LarkMessageConverter.upload_file_to_lark(
-                        data, api_client, file_type='opus', file_name='voice.opus', duration=duration
-                    )
-                    if file_key:
-                        media_items.append({'msg_type': 'audio', 'content': {'file_key': file_key}})
-            elif isinstance(msg, platform_message.File):
-                data = await LarkMessageConverter._get_media_bytes(msg)
-                if data:
-                    file_name = msg.name or 'file'
-                    # Guess file_type from extension
-                    ext = os.path.splitext(file_name)[1].lstrip('.').lower() if file_name else ''
-                    file_type_map = {
-                        'opus': 'opus',
-                        'mp4': 'mp4',
-                        'pdf': 'pdf',
-                        'doc': 'doc',
-                        'docx': 'doc',
-                        'xls': 'xls',
-                        'xlsx': 'xls',
-                        'ppt': 'ppt',
-                        'pptx': 'ppt',
-                    }
-                    file_type = file_type_map.get(ext, 'stream')
-                    file_key = await LarkMessageConverter.upload_file_to_lark(
-                        data, api_client, file_type=file_type, file_name=file_name
-                    )
-                    if file_key:
-                        media_items.append({'msg_type': 'file', 'content': {'file_key': file_key}})
+                    # Store image_key for separate image message
+                    image_keys.append(image_key)
             elif isinstance(msg, platform_message.Forward):
                 for node in msg.node_list:
-                    sub_elements, sub_media = await LarkMessageConverter.yiri2target(node.message_chain, api_client)
+                    sub_elements, sub_image_keys = await LarkMessageConverter.yiri2target(
+                        node.message_chain, api_client
+                    )
                     message_elements.extend(sub_elements)
-                    media_items.extend(sub_media)
+                    image_keys.extend(sub_image_keys)
 
         if pending_paragraph:
             message_elements.append(pending_paragraph)
 
-        return message_elements, media_items
+        return message_elements, image_keys
 
     @staticmethod
     async def target2yiri(
@@ -575,127 +456,6 @@ class LarkMessageConverter(abstract_platform_adapter.AbstractMessageConverter):
 
 
 class LarkEventConverter(abstract_platform_adapter.AbstractEventConverter):
-    _processed_thread_quote_cache: typing.ClassVar[dict[str, float]] = {}
-    _processed_thread_quote_cache_max_size: typing.ClassVar[int] = 4096
-    _processed_thread_quote_cache_ttl_seconds: typing.ClassVar[int] = 86400
-
-    @classmethod
-    def _prune_processed_thread_quote_cache(cls, now: typing.Optional[float] = None) -> None:
-        if now is None:
-            now = time.time()
-
-        expire_before = now - cls._processed_thread_quote_cache_ttl_seconds
-        while cls._processed_thread_quote_cache:
-            oldest_key, oldest_ts = next(iter(cls._processed_thread_quote_cache.items()))
-            if oldest_ts >= expire_before:
-                break
-            cls._processed_thread_quote_cache.pop(oldest_key, None)
-
-        while len(cls._processed_thread_quote_cache) > cls._processed_thread_quote_cache_max_size:
-            oldest_key = next(iter(cls._processed_thread_quote_cache))
-            cls._processed_thread_quote_cache.pop(oldest_key, None)
-
-    @classmethod
-    def _mark_thread_quote_processed(cls, thread_id: str) -> None:
-        now = time.time()
-        cls._prune_processed_thread_quote_cache(now)
-        cls._processed_thread_quote_cache[thread_id] = now
-
-    @classmethod
-    def _extract_quote_message_id(cls, message: EventMessage) -> typing.Optional[str]:
-        """
-        Extract the message ID to quote from the given message.
-
-        Rules:
-        - First thread reply in a topic: return parent_id and mark topic as processed
-        - Follow-up thread replies in the same topic: return None
-        - Non-thread message: return parent_id if valid (non-empty, different from message_id)
-
-        Thread reply state is kept in a bounded TTL cache to avoid unbounded memory growth.
-        """
-        parent_id = getattr(message, 'parent_id', None)
-        if not parent_id:
-            return None
-
-        message_id = getattr(message, 'message_id', None)
-        if parent_id == message_id:
-            return None
-
-        thread_id = getattr(message, 'thread_id', None)
-        if thread_id:
-            cls._prune_processed_thread_quote_cache()
-            if thread_id in cls._processed_thread_quote_cache:
-                return None
-            cls._mark_thread_quote_processed(thread_id)
-
-        return parent_id
-
-    @staticmethod
-    def _build_event_message_from_message_item(message_item: Message) -> typing.Optional[EventMessage]:
-        """
-        Build EventMessage from SDK typed Message item.
-
-        Returns None if body or content is missing.
-        """
-        body = getattr(message_item, 'body', None)
-        if not body:
-            return None
-
-        content = getattr(body, 'content', None)
-        if not content:
-            return None
-
-        event_data = {
-            'message_id': message_item.message_id,
-            'message_type': message_item.msg_type,
-            'content': content,
-            'create_time': message_item.create_time,
-            'mentions': getattr(message_item, 'mentions', []) or [],
-        }
-
-        # Preserve thread-related fields
-        if hasattr(message_item, 'parent_id') and message_item.parent_id:
-            event_data['parent_id'] = message_item.parent_id
-        if hasattr(message_item, 'root_id') and message_item.root_id:
-            event_data['root_id'] = message_item.root_id
-        if hasattr(message_item, 'thread_id') and message_item.thread_id:
-            event_data['thread_id'] = message_item.thread_id
-        if hasattr(message_item, 'chat_id') and message_item.chat_id:
-            event_data['chat_id'] = message_item.chat_id
-
-        return EventMessage(event_data)
-
-    @staticmethod
-    async def _fetch_quoted_message(
-        quote_message_id: str,
-        api_client: lark_oapi.Client,
-    ) -> typing.Optional[platform_message.MessageChain]:
-        """
-        Fetch the quoted message and convert to MessageChain.
-
-        Returns None if:
-        - API call fails
-        - Response items is empty
-        - Message item normalization fails
-        """
-        request = GetMessageRequest.builder().message_id(quote_message_id).build()
-        response = await api_client.im.v1.message.aget(request)
-
-        if not response.success():
-            return None
-
-        items = getattr(response.data, 'items', None)
-        if not items:
-            return None
-
-        message_item = items[0]
-        event_message = LarkEventConverter._build_event_message_from_message_item(message_item)
-        if event_message is None:
-            return None
-
-        quote_chain = await LarkMessageConverter.target2yiri(event_message, api_client)
-        return quote_chain
-
     @staticmethod
     async def yiri2target(
         event: platform_events.MessageEvent,
@@ -708,23 +468,6 @@ class LarkEventConverter(abstract_platform_adapter.AbstractEventConverter):
     ) -> platform_events.Event:
         message_chain = await LarkMessageConverter.target2yiri(event.event.message, api_client)
 
-        # Check for quote/reply message
-        quote_message_id = LarkEventConverter._extract_quote_message_id(event.event.message)
-        if quote_message_id:
-            quote_chain = await LarkEventConverter._fetch_quoted_message(quote_message_id, api_client)
-            if quote_chain:
-                # Filter out Source component from quoted chain, keep only content
-                quote_origin = platform_message.MessageChain(
-                    [comp for comp in quote_chain if not isinstance(comp, platform_message.Source)]
-                )
-                if quote_origin:
-                    message_chain.append(
-                        platform_message.Quote(
-                            message_id=quote_message_id,
-                            origin=quote_origin,
-                        )
-                    )
-
         if event.event.message.chat_type == 'p2p':
             return platform_events.FriendMessage(
                 sender=platform_entities.Friend(
@@ -908,32 +651,6 @@ class LarkAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
             self.request_tenant_access_token(tenant_key)
         return self.tenant_access_tokens.get(tenant_key)['token'] if self.tenant_access_tokens.get(tenant_key) else None
 
-    def get_launcher_id(self, event: platform_events.MessageEvent) -> str | None:
-        """
-        Get topic-scoped launcher_id for thread-aware session isolation.
-
-        For group thread messages, returns "{group_id}_{thread_id}"
-        to ensure conversation context stays stable per topic.
-
-        Returns None for non-thread messages or P2P messages.
-        """
-        source_event = getattr(event.source_platform_object, 'event', None)
-        if not source_event:
-            return None
-
-        message = getattr(source_event, 'message', None)
-        if not message:
-            return None
-
-        thread_id = getattr(message, 'thread_id', None)
-        if not thread_id:
-            return None
-
-        if isinstance(event, platform_events.GroupMessage):
-            return f'{event.group.id}_{thread_id}'
-
-        return None
-
     def build_api_client(self, config):
         app_id = config['app_id']
         app_secret = config['app_secret']
@@ -1200,40 +917,23 @@ class LarkAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
     ):
         # 不再需要了，因为message_id已经被包含到message_chain中
         # lark_event = await self.event_converter.yiri2target(message_source)
-        text_elements, media_items = await self.message_converter.yiri2target(message, self.api_client)
+        text_elements, image_keys = await self.message_converter.yiri2target(message, self.api_client)
 
         # Send text message if there are text elements
         if text_elements:
-            # Determine msg_type based on content: use 'post' if at mentions
-            # are present (requires post paragraph structure), otherwise 'text'
-            needs_post = any(ele['tag'] == 'at' for paragraph in text_elements for ele in paragraph)
-
-            if needs_post:
-                msg_type = 'post'
-                final_content = json.dumps(
-                    {
-                        'zh_Hans': {
-                            'title': '',
-                            'content': text_elements,
-                        },
-                    }
-                )
-            else:
-                msg_type = 'text'
-                parts = []
-                for paragraph in text_elements:
-                    para_text = ''.join(ele.get('text', '') for ele in paragraph)
-                    if para_text:
-                        parts.append(para_text)
-                final_content = json.dumps({'text': '\n\n'.join(parts)})
-
+            final_content = {
+                'zh_Hans': {
+                    'title': '',
+                    'content': text_elements,
+                },
+            }
             request: ReplyMessageRequest = (
                 ReplyMessageRequest.builder()
                 .message_id(message_source.message_chain.message_id)
                 .request_body(
                     ReplyMessageRequestBody.builder()
-                    .content(final_content)
-                    .msg_type(msg_type)
+                    .content(json.dumps(final_content))
+                    .msg_type('post')
                     .reply_in_thread(False)
                     .uuid(str(uuid.uuid4()))
                     .build()
@@ -1263,15 +963,17 @@ class LarkAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
                     f'client.im.v1.message.reply failed, code: {response.code}, msg: {response.msg}, log_id: {response.get_log_id()}, resp: \n{json.dumps(json.loads(response.raw.content), indent=4, ensure_ascii=False)}'
                 )
 
-        # Send media messages separately (image, audio, file, etc.)
-        for media in media_items:
+        # Send image messages separately using msg_type='image'
+        for image_key in image_keys:
+            image_content = json.dumps({'image_key': image_key})
+
             request: ReplyMessageRequest = (
                 ReplyMessageRequest.builder()
                 .message_id(message_source.message_chain.message_id)
                 .request_body(
                     ReplyMessageRequestBody.builder()
-                    .content(json.dumps(media['content']))
-                    .msg_type(media['msg_type'])
+                    .content(image_content)
+                    .msg_type('image')
                     .reply_in_thread(False)
                     .uuid(str(uuid.uuid4()))
                     .build()
@@ -1298,7 +1000,7 @@ class LarkAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
 
             if not response.success():
                 raise Exception(
-                    f'client.im.v1.message.reply ({media["msg_type"]}) failed, code: {response.code}, msg: {response.msg}, log_id: {response.get_log_id()}, resp: \n{json.dumps(json.loads(response.raw.content), indent=4, ensure_ascii=False)}'
+                    f'client.im.v1.message.reply (image) failed, code: {response.code}, msg: {response.msg}, log_id: {response.get_log_id()}, resp: \n{json.dumps(json.loads(response.raw.content), indent=4, ensure_ascii=False)}'
                 )
 
     async def reply_message_chunk(
@@ -1316,16 +1018,15 @@ class LarkAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
         message_id = bot_message.resp_message_id
         msg_seq = bot_message.msg_sequence
         if msg_seq % 8 == 0 or is_final:
-            text_elements, media_items = await self.message_converter.yiri2target(message, self.api_client)
+            text_elements, image_keys = await self.message_converter.yiri2target(message, self.api_client)
 
             text_message = ''
             if text_elements:
-                parts = []
-                for paragraph in text_elements:
-                    para_text = ''.join(ele['text'] for ele in paragraph if ele['tag'] in ('text', 'md'))
-                    if para_text:
-                        parts.append(para_text)
-                text_message = '\n\n'.join(parts)
+                for ele in text_elements[0]:
+                    if ele['tag'] == 'text':
+                        text_message += ele['text']
+                    elif ele['tag'] == 'md':
+                        text_message += ele['text']
 
             # content = {
             #     'type': 'card_json',
@@ -1375,30 +1076,6 @@ class LarkAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
                 )
                 return
 
-            # Send media messages when streaming is done
-            if is_final and media_items:
-                for media in media_items:
-                    media_request: ReplyMessageRequest = (
-                        ReplyMessageRequest.builder()
-                        .message_id(message_source.message_chain.message_id)
-                        .request_body(
-                            ReplyMessageRequestBody.builder()
-                            .content(json.dumps(media['content']))
-                            .msg_type(media['msg_type'])
-                            .reply_in_thread(False)
-                            .uuid(str(uuid.uuid4()))
-                            .build()
-                        )
-                        .build()
-                    )
-                    media_response: ReplyMessageResponse = await self.api_client.im.v1.message.areply(
-                        media_request, req_opt
-                    )
-                    if not media_response.success():
-                        raise Exception(
-                            f'client.im.v1.message.reply ({media["msg_type"]}) failed, code: {media_response.code}, msg: {media_response.msg}, log_id: {media_response.get_log_id()}'
-                        )
-
     async def is_muted(self, group_id: int) -> bool:
         return False
 

src/langbot/pkg/platform/sources/legacy/gewechat.py

@@ -9,7 +9,7 @@ import copy
 import threading
 
 import quart
-from langbot.pkg.utils import httpclient
+import aiohttp
 
 import langbot_plugin.api.definition.abstract.platform.adapter as abstract_platform_adapter
 from ....core import app
@@ -639,14 +639,14 @@ class GeWeChatAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
 
     async def run_async(self):
         if not self.config['token']:
-            session = httpclient.get_session()
-            async with session.post(
-                f'{self.config["gewechat_url"]}/v2/api/tools/getTokenId',
-                json={'app_id': self.config['app_id']},
-            ) as response:
-                if response.status != 200:
-                    raise Exception(f'获取gewechat token失败: {await response.text()}')
-                self.config['token'] = (await response.json())['data']
+            async with aiohttp.ClientSession() as session:
+                async with session.post(
+                    f'{self.config["gewechat_url"]}/v2/api/tools/getTokenId',
+                    json={'app_id': self.config['app_id']},
+                ) as response:
+                    if response.status != 200:
+                        raise Exception(f'获取gewechat token失败: {await response.text()}')
+                    self.config['token'] = (await response.json())['data']
 
         self.bot = gewechat_client.GewechatClient(f'{self.config["gewechat_url"]}/v2/api', self.config['token'])
 

src/langbot/pkg/platform/sources/telegram.py

@@ -1,5 +1,4 @@
 from __future__ import annotations
-import time
 
 
 import telegram
@@ -10,9 +9,9 @@ import telegramify_markdown
 import typing
 import traceback
 import base64
+import aiohttp
 import pydantic
 
-from langbot.pkg.utils import httpclient
 import langbot_plugin.api.definition.abstract.platform.adapter as abstract_platform_adapter
 import langbot_plugin.api.entities.builtin.platform.message as platform_message
 import langbot_plugin.api.entities.builtin.platform.events as platform_events
@@ -34,9 +33,9 @@ class TelegramMessageConverter(abstract_platform_adapter.AbstractMessageConverte
                 if component.base64:
                     photo_bytes = base64.b64decode(component.base64)
                 elif component.url:
-                    session = httpclient.get_session()
-                    async with session.get(component.url) as response:
-                        photo_bytes = await response.read()
+                    async with aiohttp.ClientSession() as session:
+                        async with session.get(component.url) as response:
+                            photo_bytes = await response.read()
                 elif component.path:
                     with open(component.path, 'rb') as f:
                         photo_bytes = f.read()
@@ -75,9 +74,10 @@ class TelegramMessageConverter(abstract_platform_adapter.AbstractMessageConverte
             file_bytes = None
             file_format = ''
 
-            async with httpclient.get_session(trust_env=True).get(file.file_path) as response:
-                file_bytes = await response.read()
-                file_format = 'image/jpeg'
+            async with aiohttp.ClientSession(trust_env=True) as session:
+                async with session.get(file.file_path) as response:
+                    file_bytes = await response.read()
+                    file_format = 'image/jpeg'
 
             message_components.append(
                 platform_message.Image(
@@ -94,8 +94,9 @@ class TelegramMessageConverter(abstract_platform_adapter.AbstractMessageConverte
             file_bytes = None
             file_format = message.voice.mime_type or 'audio/ogg'
 
-            async with httpclient.get_session(trust_env=True).get(file.file_path) as response:
-                file_bytes = await response.read()
+            async with aiohttp.ClientSession(trust_env=True) as session:
+                async with session.get(file.file_path) as response:
+                    file_bytes = await response.read()
 
             message_components.append(
                 platform_message.Voice(
@@ -193,31 +194,7 @@ class TelegramAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
         )
 
     async def send_message(self, target_type: str, target_id: str, message: platform_message.MessageChain):
-        components = await TelegramMessageConverter.yiri2target(message, self.bot)
-
-        chat_id_str, _, thread_id_str = str(target_id).partition('#')
-        chat_id: int | str = int(chat_id_str) if chat_id_str.lstrip('-').isdigit() else chat_id_str
-        message_thread_id = int(thread_id_str) if thread_id_str and thread_id_str.isdigit() else None
-
-        for component in components:
-            component_type = component.get('type')
-            args = {'chat_id': chat_id}
-            if message_thread_id is not None:
-                args['message_thread_id'] = message_thread_id
-
-            if component_type == 'text':
-                text = component.get('text', '')
-                if self.config['markdown_card'] is True:
-                    text = telegramify_markdown.markdownify(content=text)
-                    args['parse_mode'] = 'MarkdownV2'
-                args['text'] = text
-                await self.bot.send_message(**args)
-            elif component_type == 'photo':
-                photo = component.get('photo')
-                if photo is None:
-                    continue
-                args['photo'] = telegram.InputFile(photo)
-                await self.bot.send_photo(**args)
+        pass
 
     async def reply_message(
         self,
@@ -251,39 +228,6 @@ class TelegramAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
 
         await self.bot.send_message(**args)
 
-    def _process_markdown(self, text: str) -> str:
-        if self.config.get('markdown_card', False):
-            return telegramify_markdown.markdownify(content=text)
-        return text
-
-    def _build_message_args(self, chat_id: int, text: str, message_thread_id: int = None, **extra_args) -> dict:
-        args = {'chat_id': chat_id, 'text': self._process_markdown(text), **extra_args}
-        if message_thread_id:
-            args['message_thread_id'] = message_thread_id
-        if self.config.get('markdown_card', False):
-            args['parse_mode'] = 'MarkdownV2'
-        return args
-
-    async def create_message_card(self, message_id, event):
-        assert isinstance(event.source_platform_object, Update)
-        update = event.source_platform_object
-        chat_id = update.effective_chat.id
-        chat_type = update.effective_chat.type
-        message_thread_id = update.message.message_thread_id
-
-        if chat_type == 'private':
-            draft_id = int(time.time() * 1000)
-            self.msg_stream_id[message_id] = ('private', draft_id)
-
-            args = self._build_message_args(chat_id, 'Thinking...', message_thread_id, draft_id=draft_id)
-            await self.bot.send_message_draft(**args)
-        else:
-            args = self._build_message_args(chat_id, 'Thinking...', message_thread_id)
-            send_msg = await self.bot.send_message(**args)
-            self.msg_stream_id[message_id] = ('group', send_msg.message_id)
-
-        return True
-
     async def reply_message_chunk(
         self,
         message_source: platform_events.MessageEvent,
@@ -292,47 +236,59 @@ class TelegramAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
         quote_origin: bool = False,
         is_final: bool = False,
     ):
-        message_id = bot_message.resp_message_id
         msg_seq = bot_message.msg_sequence
-        assert isinstance(message_source.source_platform_object, Update)
-        update = message_source.source_platform_object
-        chat_id = update.effective_chat.id
-        message_thread_id = update.message.message_thread_id
+        if (msg_seq - 1) % 8 == 0 or is_final:
+            assert isinstance(message_source.source_platform_object, Update)
+            components = await TelegramMessageConverter.yiri2target(message, self.bot)
+            args = {}
+            message_id = message_source.source_platform_object.message.id
 
-        if message_id not in self.msg_stream_id:
-            return
+            component = components[0]
+            if message_id not in self.msg_stream_id:  # 当消息回复第一次时，发送新消息
+                # time.sleep(0.6)
+                if component['type'] == 'text':
+                    if self.config['markdown_card'] is True:
+                        content = telegramify_markdown.markdownify(
+                            content=component['text'],
+                        )
+                    else:
+                        content = component['text']
+                    args = {
+                        'chat_id': message_source.source_platform_object.effective_chat.id,
+                        'text': content,
+                    }
+                    if message_source.source_platform_object.message.message_thread_id:
+                        args['message_thread_id'] = message_source.source_platform_object.message.message_thread_id
 
-        chat_mode, draft_id = self.msg_stream_id[message_id]
-        components = await TelegramMessageConverter.yiri2target(message, self.bot)
+                    if quote_origin:
+                        args['reply_to_message_id'] = message_source.source_platform_object.message.id
 
-        if not components or components[0]['type'] != 'text':
-            if is_final and bot_message.tool_calls is None:
-                self.msg_stream_id.pop(message_id)
-            return
+                    if self.config['markdown_card'] is True:
+                        args['parse_mode'] = 'MarkdownV2'
 
-        content = components[0]['text']
+                send_msg = await self.bot.send_message(**args)
+                send_msg_id = send_msg.message_id
+                self.msg_stream_id[message_id] = send_msg_id
+            else:  # 存在消息的时候直接编辑消息1
+                if component['type'] == 'text':
+                    if self.config['markdown_card'] is True:
+                        content = telegramify_markdown.markdownify(
+                            content=component['text'],
+                        )
+                    else:
+                        content = component['text']
+                    args = {
+                        'message_id': self.msg_stream_id[message_id],
+                        'chat_id': message_source.source_platform_object.effective_chat.id,
+                        'text': content,
+                    }
+                    if self.config['markdown_card'] is True:
+                        args['parse_mode'] = 'MarkdownV2'
 
-        if chat_mode == 'private':
-            args = self._build_message_args(chat_id, content, message_thread_id, draft_id=draft_id)
-            await self.bot.send_message_draft(**args)
-            if is_final and bot_message.tool_calls is None:
-                del args['draft_id']
-                await self.bot.send_message(**args)
-                self.msg_stream_id.pop(message_id)
-        else:
-            stream_id = draft_id
-            if (msg_seq - 1) % 8 == 0 or is_final:
-                args = {
-                    'message_id': stream_id,
-                    'chat_id': chat_id,
-                    'text': self._process_markdown(content),
-                }
-                if self.config.get('markdown_card', False):
-                    args['parse_mode'] = 'MarkdownV2'
                 await self.bot.edit_message_text(**args)
-
             if is_final and bot_message.tool_calls is None:
-                self.msg_stream_id.pop(message_id)
+                # self.seq = 1  # 消息回复结束之后重置seq
+                self.msg_stream_id.pop(message_id)  # 消息回复结束之后删除流式消息id
 
     def get_launcher_id(self, event: platform_events.MessageEvent) -> str | None:
         if not isinstance(event.source_platform_object, Update):

src/langbot/pkg/platform/sources/websocket_adapter.py

@@ -37,24 +37,16 @@ class WebSocketSession:
     id: str
     message_lists: dict[str, list[WebSocketMessage]] = {}
     """消息列表 {pipeline_uuid: [messages]}"""
-    stream_message_indexes: dict[str, dict[str, int]] = {}
-    """流式消息索引 {pipeline_uuid: {resp_message_id: message_index}}"""
 
     def __init__(self, id: str):
         self.id = id
         self.message_lists = {}
-        self.stream_message_indexes = {}
 
     def get_message_list(self, pipeline_uuid: str) -> list[WebSocketMessage]:
         if pipeline_uuid not in self.message_lists:
             self.message_lists[pipeline_uuid] = []
         return self.message_lists[pipeline_uuid]
 
-    def get_stream_message_indexes(self, pipeline_uuid: str) -> dict[str, int]:
-        if pipeline_uuid not in self.stream_message_indexes:
-            self.stream_message_indexes[pipeline_uuid] = {}
-        return self.stream_message_indexes[pipeline_uuid]
-
 
 class WebSocketAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
     """WebSocket适配器 - 支持双向实时通信"""
@@ -97,46 +89,20 @@ class WebSocketAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter)
         target_id: str,
         message: platform_message.MessageChain,
     ) -> dict:
-        """发送消息 - 这里用于主动推送消息到前端
+        """发送消息 - 这里用于主动推送消息到前端"""
+        message_data = {
+            'type': 'bot_message',
+            'target_type': target_type,
+            'target_id': target_id,
+            'content': str(message),
+            'message_chain': [component.__dict__ for component in message],
+            'timestamp': datetime.now().isoformat(),
+        }
 
-        对于 WebSocket 适配器，我们需要将消息广播到正确的 pipeline 连接。
-        target_id 可能是 launcher_id（如 websocket_xxx）或 pipeline_uuid。
-        我们需要尝试两种方式来确保消息能够送达。
-        """
-        # 获取当前的 pipeline_uuid
-        pipeline_uuid = self.ap.platform_mgr.websocket_proxy_bot.bot_entity.use_pipeline_uuid
-        session_type = 'group' if target_type == 'group' else 'person'
+        # 推送到所有相关连接
+        await self.outbound_message_queue.put(message_data)
 
-        # 选择会话
-        session = self.websocket_group_session if session_type == 'group' else self.websocket_person_session
-
-        # 生成唯一消息ID
-        msg_id = len(session.get_message_list(pipeline_uuid)) + 1
-
-        message_data = WebSocketMessage(
-            id=msg_id,
-            role='assistant',
-            content=str(message),
-            message_chain=[component.__dict__ for component in message],
-            timestamp=datetime.now().isoformat(),
-            is_final=True,
-        )
-
-        # 保存到历史记录
-        session.get_message_list(pipeline_uuid).append(message_data)
-
-        # 直接广播到当前pipeline的连接
-        await ws_connection_manager.broadcast_to_pipeline(
-            pipeline_uuid,
-            {
-                'type': 'response',
-                'session_type': session_type,
-                'data': message_data.model_dump(),
-            },
-            session_type=session_type,
-        )
-
-        return message_data.model_dump()
+        return message_data
 
     async def reply_message(
         self,
@@ -203,16 +169,10 @@ class WebSocketAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter)
         pipeline_uuid = self.ap.platform_mgr.websocket_proxy_bot.bot_entity.use_pipeline_uuid
         session_type = 'group' if isinstance(message_source, platform_events.GroupMessage) else 'person'
         message_list = session.get_message_list(pipeline_uuid)
-        stream_message_indexes = session.get_stream_message_indexes(pipeline_uuid)
 
-        # Streaming messages in LangBot have a stable resp_message_id during the same assistant reply.
-        # Use it as the primary key to avoid overwriting an old card from a previous reply.
-        resp_message_id = str(getattr(bot_message, 'resp_message_id', '') or '')
-        existing_index = stream_message_indexes.get(resp_message_id) if resp_message_id else None
-
-        message_is_final = is_final and bot_message.tool_calls is None
-
-        if existing_index is None or existing_index >= len(message_list):
+        # 检查是否是新的流式消息（通过bot_message对象判断）
+        # 如果列表为空，或者最后一条消息已经is_final=True，则创建新消息
+        if not message_list or message_list[-1].is_final:
             # 创建新消息
             msg_id = len(message_list) + 1
             message_data = WebSocketMessage(
@@ -221,31 +181,27 @@ class WebSocketAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter)
                 content=str(message),
                 message_chain=[component.__dict__ for component in message],
                 timestamp=datetime.now().isoformat(),
-                is_final=message_is_final,
+                is_final=is_final and bot_message.tool_calls is None,
             )
 
-            # 立即添加到历史记录（即使is_final=False），以便后续块可以更新它
-            message_list.append(message_data)
-            if resp_message_id:
-                stream_message_indexes[resp_message_id] = len(message_list) - 1
+            # 只有在is_final时才保存到历史记录
+            if is_final and bot_message.tool_calls is None:
+                message_list.append(message_data)
         else:
-            # 更新同一条流式消息
-            old_message = message_list[existing_index]
-            msg_id = old_message.id
+            # 更新最后一条消息
+            msg_id = message_list[-1].id
             message_data = WebSocketMessage(
                 id=msg_id,
                 role='assistant',
                 content=str(message),
                 message_chain=[component.__dict__ for component in message],
-                timestamp=old_message.timestamp,  # 保持原始时间戳
-                is_final=message_is_final,
+                timestamp=message_list[-1].timestamp,  # 保持原始时间戳
+                is_final=is_final and bot_message.tool_calls is None,
             )
 
-            # 更新历史记录中的对应消息
-            message_list[existing_index] = message_data
-
-        if message_is_final and resp_message_id:
-            stream_message_indexes.pop(resp_message_id, None)
+            # 如果是final，更新历史记录中的最后一条
+            if is_final and bot_message.tool_calls is None:
+                message_list[-1] = message_data
 
         # 直接广播到所有该pipeline的连接，包含session_type信息
         await ws_connection_manager.broadcast_to_pipeline(
@@ -454,10 +410,6 @@ class WebSocketAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter)
         if session_type == 'person':
             if pipeline_uuid in self.websocket_person_session.message_lists:
                 self.websocket_person_session.message_lists[pipeline_uuid] = []
-            if pipeline_uuid in self.websocket_person_session.stream_message_indexes:
-                self.websocket_person_session.stream_message_indexes[pipeline_uuid] = {}
         else:
             if pipeline_uuid in self.websocket_group_session.message_lists:
                 self.websocket_group_session.message_lists[pipeline_uuid] = []
-            if pipeline_uuid in self.websocket_group_session.stream_message_indexes:
-                self.websocket_group_session.stream_message_indexes[pipeline_uuid] = {}

src/langbot/pkg/platform/sources/wecombot.py

@@ -11,7 +11,6 @@ import langbot_plugin.api.entities.builtin.platform.entities as platform_entitie
 from ..logger import EventLogger
 from langbot.libs.wecom_ai_bot_api.wecombotevent import WecomBotEvent
 from langbot.libs.wecom_ai_bot_api.api import WecomBotClient
-from langbot.libs.wecom_ai_bot_api.ws_client import WecomBotWsClient
 
 
 class WecomBotMessageConverter(abstract_platform_adapter.AbstractMessageConverter):
@@ -177,42 +176,27 @@ class WecomBotEventConverter(abstract_platform_adapter.AbstractEventConverter):
 
 
 class WecomBotAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
-    bot: typing.Union[WecomBotClient, WecomBotWsClient]
+    bot: WecomBotClient
     bot_account_id: str
     message_converter: WecomBotMessageConverter = WecomBotMessageConverter()
     event_converter: WecomBotEventConverter = WecomBotEventConverter()
     config: dict
     bot_uuid: str = None
-    _ws_mode: bool = False
 
     def __init__(self, config: dict, logger: EventLogger):
-        enable_webhook = config.get('enable-webhook', False)
+        required_keys = ['Token', 'EncodingAESKey', 'Corpid', 'BotId']
+        missing_keys = [key for key in required_keys if key not in config]
+        if missing_keys:
+            raise Exception(f'WecomBot 缺少配置项: {missing_keys}')
 
-        if not enable_webhook:
-            bot = WecomBotWsClient(
-                bot_id=config['BotId'],
-                secret=config['Secret'],
-                logger=logger,
-                encoding_aes_key=config.get('EncodingAESKey', ''),
-            )
-            ws_mode = True
-        else:
-            # Webhook callback mode
-            required_keys = ['Token', 'EncodingAESKey', 'Corpid']
-            missing_keys = [key for key in required_keys if key not in config or not config[key]]
-            if missing_keys:
-                raise Exception(f'WecomBot webhook mode missing config: {missing_keys}')
-
-            bot = WecomBotClient(
-                Token=config['Token'],
-                EnCodingAESKey=config['EncodingAESKey'],
-                Corpid=config['Corpid'],
-                logger=logger,
-                unified_mode=True,
-            )
-            ws_mode = False
-
-        bot_account_id = config.get('BotId', '')
+        bot = WecomBotClient(
+            Token=config['Token'],
+            EnCodingAESKey=config['EncodingAESKey'],
+            Corpid=config['Corpid'],
+            logger=logger,
+            unified_mode=True,
+        )
+        bot_account_id = config['BotId']
 
         super().__init__(
             config=config,
@@ -220,7 +204,6 @@ class WecomBotAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
             bot=bot,
             bot_account_id=bot_account_id,
         )
-        self._ws_mode = ws_mode
 
     async def reply_message(
         self,
@@ -229,15 +212,7 @@ class WecomBotAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
         quote_origin: bool = False,
     ):
         content = await self.message_converter.yiri2target(message)
-        if self._ws_mode:
-            event = message_source.source_platform_object
-            req_id = event.get('req_id', '')
-            if req_id:
-                await self.bot.reply_text(req_id, content)
-            else:
-                await self.bot.set_message(event.message_id, content)
-        else:
-            await self.bot.set_message(message_source.source_platform_object.message_id, content)
+        await self.bot.set_message(message_source.source_platform_object.message_id, content)
 
     async def reply_message_chunk(
         self,
@@ -247,22 +222,31 @@ class WecomBotAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
         quote_origin: bool = False,
         is_final: bool = False,
     ):
+        """将流水线增量输出写入企业微信 stream 会话。
+
+        Args:
+            message_source: 流水线提供的原始消息事件。
+            bot_message: 当前片段对应的模型元信息（未使用）。
+            message: 需要回复的消息链。
+            quote_origin: 是否引用原消息（企业微信暂不支持）。
+            is_final: 标记当前片段是否为最终回复。
+
+        Returns:
+            dict: 包含 `stream` 键，标识写入是否成功。
+
+        Example:
+            在流水线 `reply_message_chunk` 调用中自动触发，无需手动调用。
+        """
+        # 转换为纯文本（智能机器人当前协议仅支持文本流）
         content = await self.message_converter.yiri2target(message)
         msg_id = message_source.source_platform_object.message_id
 
-        if self._ws_mode:
-            success = await self.bot.push_stream_chunk(msg_id, content, is_final=is_final)
-            if not success and is_final:
-                event = message_source.source_platform_object
-                req_id = event.get('req_id', '')
-                if req_id:
-                    await self.bot.reply_text(req_id, content)
-            return {'stream': success}
-        else:
-            success = await self.bot.push_stream_chunk(msg_id, content, is_final=is_final)
-            if not success and is_final:
-                await self.bot.set_message(msg_id, content)
-            return {'stream': success}
+        # 将片段推送到 WecomBotClient 中的队列，返回值用于判断是否走降级逻辑
+        success = await self.bot.push_stream_chunk(msg_id, content, is_final=is_final)
+        if not success and is_final:
+            # 未命中流式队列时使用旧有 set_message 兜底
+            await self.bot.set_message(msg_id, content)
+        return {'stream': success}
 
     async def is_stream_output_supported(self) -> bool:
         """智能机器人侧默认开启流式能力。
@@ -275,11 +259,7 @@ class WecomBotAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
         return True
 
     async def send_message(self, target_type, target_id, message):
-        if self._ws_mode:
-            content = await self.message_converter.yiri2target(message)
-            await self.bot.send_message(target_id, content)
-        else:
-            pass
+        pass
 
     def register_listener(
         self,
@@ -308,25 +288,29 @@ class WecomBotAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
         self.bot_uuid = bot_uuid
 
     async def handle_unified_webhook(self, bot_uuid: str, path: str, request):
-        if self._ws_mode:
-            return None
+        """处理统一 webhook 请求。
+
+        Args:
+            bot_uuid: Bot 的 UUID
+            path: 子路径（如果有的话）
+            request: Quart Request 对象
+
+        Returns:
+            响应数据
+        """
         return await self.bot.handle_unified_webhook(request)
 
     async def run_async(self):
-        if self._ws_mode:
-            await self.bot.connect()
-        else:
+        # 统一 webhook 模式下，不启动独立的 Quart 应用
+        # 保持运行但不启动独立端口
 
-            async def keep_alive():
-                while True:
-                    await asyncio.sleep(1)
+        async def keep_alive():
+            while True:
+                await asyncio.sleep(1)
 
-            await keep_alive()
+        await keep_alive()
 
     async def kill(self) -> bool:
-        if self._ws_mode:
-            await self.bot.disconnect()
-            return True
         return False
 
     async def unregister_listener(

src/langbot/pkg/platform/sources/wecombot.yaml

@@ -11,64 +11,35 @@ metadata:
   icon: wecombot.png
 spec:
   config:
-    - name: BotId
-      label:
-        en_US: BotId
-        zh_Hans: 机器人ID (BotId)
-      type: string
-      required: true
-      default: ""
-    - name: enable-webhook
-      label:
-        en_US: Enable Webhook Mode
-        zh_Hans: 启用Webhook模式
-      description:
-        en_US: If enabled, the bot will use webhook mode to receive messages. Otherwise, it will use WS long connection mode
-        zh_Hans: 如果启用，机器人将使用 Webhook 模式接收消息。否则，将使用 WS 长连接模式
-      type: boolean
-      required: true
-      default: false
-    - name: Secret
-      label:
-        en_US: Secret
-        zh_Hans: 机器人密钥 (Secret)
-      description:
-        en_US: Required for WebSocket long connection mode
-        zh_Hans: 使用 WS 长连接模式时必填
-      type: string
-      required: false
-      default: ""
     - name: Corpid
       label:
         en_US: Corpid
         zh_Hans: 企业ID
-      description:
-        en_US: Required for Webhook mode
-        zh_Hans: 使用 Webhook 模式时必填
       type: string
-      required: false
+      required: true
       default: ""
     - name: Token
       label:
         en_US: Token
         zh_Hans: 令牌 (Token)
-      description:
-        en_US: Required for Webhook mode
-        zh_Hans: 使用 Webhook 模式时必填
       type: string
-      required: false
+      required: true
       default: ""
     - name: EncodingAESKey
       label:
         en_US: EncodingAESKey
         zh_Hans: 消息加解密密钥 (EncodingAESKey)
-      description:
-        en_US: Required for Webhook mode. Optional for WebSocket mode (used for file decryption)
-        zh_Hans: 使用 Webhook 模式时必填。WebSocket 模式下可选（用于文件解密）
+      type: string
+      required: true
+      default: ""
+    - name: BotId
+      label:
+        en_US: BotId
+        zh_Hans: 机器人ID
       type: string
       required: false
       default: ""
 execution:
   python:
     path: ./wecombot.py
-    attr: WecomBotAdapter
+    attr: WecomBotAdapter

src/langbot/pkg/platform/sources/wecomcs.py

@@ -81,33 +81,22 @@ class WecomEventConverter(abstract_platform_adapter.AbstractEventConverter):
             return event.source_platform_object
 
     @staticmethod
-    async def target2yiri(event: WecomCSEvent, bot: WecomCSClient = None):
+    async def target2yiri(event: WecomCSEvent):
         """
         将 WecomEvent 转换为平台的 FriendMessage 对象。
 
         Args:
             event (WecomEvent): 企业微信客服事件。
-            bot (WecomCSClient): 企业微信客服客户端，用于获取用户信息。
 
         Returns:
             platform_events.FriendMessage: 转换后的 FriendMessage 对象。
         """
-        # Try to get customer nickname from WeChat API
-        nickname = str(event.user_id)
-        if bot and event.user_id:
-            try:
-                customer_info = await bot.get_customer_info(event.user_id)
-                if customer_info and customer_info.get('nickname'):
-                    nickname = customer_info.get('nickname')
-            except Exception:
-                pass  # Fall back to user_id as nickname
-
         # 转换消息链
         if event.type == 'text':
             yiri_chain = await WecomMessageConverter.target2yiri(event.message, event.message_id)
             friend = platform_entities.Friend(
                 id=f'u{event.user_id}',
-                nickname=nickname,
+                nickname=str(event.user_id),
                 remark='',
             )
 
@@ -117,7 +106,7 @@ class WecomEventConverter(abstract_platform_adapter.AbstractEventConverter):
         elif event.type == 'image':
             friend = platform_entities.Friend(
                 id=f'u{event.user_id}',
-                nickname=nickname,
+                nickname=str(event.user_id),
                 remark='',
             )
 
@@ -198,7 +187,7 @@ class WecomCSAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter):
         async def on_message(event: WecomCSEvent):
             self.bot_account_id = event.receiver_id
             try:
-                return await callback(await self.event_converter.target2yiri(event, self.bot), self)
+                return await callback(await self.event_converter.target2yiri(event), self)
             except Exception:
                 await self.logger.error(f'Error in wecomcs callback: {traceback.format_exc()}')
 

src/langbot/pkg/platform/webhook_pusher.py

@@ -3,8 +3,6 @@ from __future__ import annotations
 import asyncio
 import logging
 import aiohttp
-
-from langbot.pkg.utils import httpclient
 import uuid
 from typing import TYPE_CHECKING
 
@@ -121,23 +119,23 @@ class WebhookPusher:
             dict | None: The response JSON if successful, None otherwise
         """
         try:
-            session = httpclient.get_session()
-            async with session.post(
-                url,
-                json=payload,
-                headers={'Content-Type': 'application/json'},
-                timeout=aiohttp.ClientTimeout(total=15),
-            ) as response:
-                if response.status >= 400:
-                    self.logger.warning(f'Webhook {url} returned status {response.status}')
-                    return None
-                else:
-                    self.logger.debug(f'Successfully pushed to webhook {url}')
-                    try:
-                        return await response.json()
-                    except Exception as json_error:
-                        self.logger.debug(f'Failed to parse JSON response from webhook {url}: {json_error}')
+            async with aiohttp.ClientSession() as session:
+                async with session.post(
+                    url,
+                    json=payload,
+                    headers={'Content-Type': 'application/json'},
+                    timeout=aiohttp.ClientTimeout(total=15),
+                ) as response:
+                    if response.status >= 400:
+                        self.logger.warning(f'Webhook {url} returned status {response.status}')
                         return None
+                    else:
+                        self.logger.debug(f'Successfully pushed to webhook {url}')
+                        try:
+                            return await response.json()
+                        except Exception as json_error:
+                            self.logger.debug(f'Failed to parse JSON response from webhook {url}: {json_error}')
+                            return None
         except asyncio.TimeoutError:
             self.logger.warning(f'Timeout pushing to webhook {url}')
             return None

src/langbot/pkg/plugin/connector.py

@@ -7,6 +7,7 @@ import typing
 import os
 import sys
 import httpx
+import traceback
 import sqlalchemy
 from async_lru import alru_cache
 from langbot_plugin.api.entities.builtin.pipeline.query import provider_session
@@ -101,6 +102,12 @@ class PluginRuntimeConnector:
             self.handler_task = asyncio.create_task(self.handler.run())
             _ = await self.handler.ping()
             self.ap.logger.info('Connected to plugin runtime.')
+            # Sync polymorphic component instances after connection
+            try:
+                await self.sync_polymorphic_component_instances()
+            except Exception as e:
+                traceback.print_exc()
+                self.ap.logger.error(f'Failed to sync polymorphic component instances: {e}')
             await self.handler_task
 
         task: asyncio.Task | None = None
@@ -456,18 +463,30 @@ class PluginRuntimeConnector:
 
             yield cmd_ret
 
+    # KnowledgeRetriever methods
+    async def list_knowledge_retrievers(self, bound_plugins: list[str] | None = None) -> list[dict[str, Any]]:
+        """List all available KnowledgeRetriever components."""
+        if not self.is_enable_plugin:
+            return []
+
+        retrievers_data = await self.handler.list_knowledge_retrievers(include_plugins=bound_plugins)
+        return retrievers_data
+
     async def retrieve_knowledge(
         self,
         plugin_author: str,
         plugin_name: str,
         retriever_name: str,
+        instance_id: str,
         retrieval_context: dict[str, Any],
-    ) -> dict[str, Any]:
-        """Retrieve knowledge using a KnowledgeEngine instance."""
+    ) -> list[dict[str, Any]]:
+        """Retrieve knowledge using a KnowledgeRetriever instance."""
         if not self.is_enable_plugin:
-            return {'results': []}
+            return []
 
-        return await self.handler.retrieve_knowledge(plugin_author, plugin_name, retriever_name, retrieval_context)
+        return await self.handler.retrieve_knowledge(
+            plugin_author, plugin_name, retriever_name, instance_id, retrieval_context
+        )
 
     def dispose(self):
         # No need to consider the shutdown on Windows
@@ -481,84 +500,41 @@ class PluginRuntimeConnector:
             self.heartbeat_task.cancel()
             self.heartbeat_task = None
 
-    @staticmethod
-    def _parse_plugin_id(plugin_id: str) -> tuple[str, str]:
-        """Parse a plugin ID string into (author, name).
+    async def sync_polymorphic_component_instances(self) -> dict[str, Any]:
+        """Sync polymorphic component instances with runtime.
 
-        Args:
-            plugin_id: Plugin ID in 'author/name' format.
-
-        Returns:
-            Tuple of (plugin_author, plugin_name).
-
-        Raises:
-            ValueError: If plugin_id is not in the expected 'author/name' format.
+        This collects all external knowledge bases from database and sends to runtime
+        to ensure instance integrity across restarts.
         """
-        if '/' not in plugin_id:
-            raise ValueError(
-                f"Invalid plugin_id format: '{plugin_id}'. Expected 'author/name' format (e.g. 'langbot/rag-engine')."
+        if not self.is_enable_plugin:
+            return {}
+
+        # ===== external knowledge bases =====
+
+        external_kbs = await self.ap.external_kb_service.get_external_knowledge_bases()
+
+        # Build required_instances list
+        required_instances = []
+        for kb in external_kbs:
+            required_instances.append(
+                {
+                    'instance_id': kb['uuid'],
+                    'plugin_author': kb['plugin_author'],
+                    'plugin_name': kb['plugin_name'],
+                    'component_kind': 'KnowledgeRetriever',
+                    'component_name': kb['retriever_name'],
+                    'config': kb['retriever_config'],
+                }
             )
-        return plugin_id.split('/', 1)
 
-    async def call_rag_ingest(self, plugin_id: str, context_data: dict[str, Any]) -> dict[str, Any]:
-        """Call plugin to ingest document.
+        self.ap.logger.info(f'Syncing {len(required_instances)} polymorphic component instances to runtime')
 
-        Args:
-            plugin_id: Target plugin ID (author/name).
-            context_data: IngestionContext data.
-        """
-        plugin_author, plugin_name = self._parse_plugin_id(plugin_id)
-        return await self.handler.rag_ingest_document(plugin_author, plugin_name, context_data)
+        # Send to runtime
+        sync_result = await self.handler.sync_polymorphic_component_instances(required_instances)
 
-    async def call_rag_delete_document(self, plugin_id: str, document_id: str, kb_id: str) -> bool:
-        plugin_author, plugin_name = self._parse_plugin_id(plugin_id)
-        return await self.handler.rag_delete_document(plugin_author, plugin_name, document_id, kb_id)
+        self.ap.logger.info(
+            f'Sync complete: {len(sync_result.get("success_instances", []))} succeeded, '
+            f'{len(sync_result.get("failed_instances", []))} failed'
+        )
 
-    async def get_rag_creation_schema(self, plugin_id: str) -> dict[str, Any]:
-        plugin_author, plugin_name = self._parse_plugin_id(plugin_id)
-        return await self.handler.get_rag_creation_schema(plugin_author, plugin_name)
-
-    async def get_rag_retrieval_schema(self, plugin_id: str) -> dict[str, Any]:
-        plugin_author, plugin_name = self._parse_plugin_id(plugin_id)
-        return await self.handler.get_rag_retrieval_schema(plugin_author, plugin_name)
-
-    async def rag_on_kb_create(self, plugin_id: str, kb_id: str, config: dict[str, Any]) -> dict[str, Any]:
-        """Notify plugin about KB creation."""
-        plugin_author, plugin_name = self._parse_plugin_id(plugin_id)
-        return await self.handler.rag_on_kb_create(plugin_author, plugin_name, kb_id, config)
-
-    async def rag_on_kb_delete(self, plugin_id: str, kb_id: str) -> dict[str, Any]:
-        """Notify plugin about KB deletion."""
-        plugin_author, plugin_name = self._parse_plugin_id(plugin_id)
-        return await self.handler.rag_on_kb_delete(plugin_author, plugin_name, kb_id)
-
-    async def call_rag_retrieve(self, plugin_id: str, retrieval_context: dict[str, Any]) -> dict[str, Any]:
-        """Call plugin to retrieve knowledge.
-
-        Args:
-            plugin_id: Target plugin ID (author/name).
-            retrieval_context: RetrievalContext data.
-        """
-        plugin_author, plugin_name = self._parse_plugin_id(plugin_id)
-        return await self.handler.retrieve_knowledge(plugin_author, plugin_name, '', retrieval_context)
-
-    async def list_knowledge_engines(self) -> list[dict[str, Any]]:
-        """List all available Knowledge Engines from plugins.
-
-        Returns a list of Knowledge Engines with their capabilities and configuration schemas.
-        """
-        if not self.is_enable_plugin:
-            return []
-
-        return await self.handler.list_knowledge_engines()
-
-    async def list_parsers(self) -> list[dict[str, Any]]:
-        """List all available parsers from plugins."""
-        if not self.is_enable_plugin:
-            return []
-        return await self.handler.list_parsers()
-
-    async def call_parser(self, plugin_id: str, context_data: dict[str, Any], file_bytes: bytes) -> dict[str, Any]:
-        """Call plugin to parse a document."""
-        plugin_author, plugin_name = self._parse_plugin_id(plugin_id)
-        return await self.handler.parse_document(plugin_author, plugin_name, context_data, file_bytes)
+        return sync_result

src/langbot/pkg/plugin/handler.py

@@ -26,20 +26,6 @@ from ..core import app
 from ..utils import constants
 
 
-def _make_rag_error_response(error: Exception, error_type: str, **extra_context) -> handler.ActionResponse:
-    """Create a clean error response for RAG operations.
-
-    Args:
-        error: The caught exception.
-        error_type: A category string like 'EmbeddingError', 'VectorStoreError'.
-        **extra_context: Additional context fields for the error message.
-    """
-    context_parts = [f'{k}={v}' for k, v in extra_context.items()]
-    context_str = f' [{", ".join(context_parts)}]' if context_parts else ''
-    message = f'[{error_type}/{type(error).__name__}]{context_str} {str(error)}'
-    return handler.ActionResponse.error(message=message)
-
-
 class RuntimeConnectionHandler(handler.Handler):
     """Runtime connection handler"""
 
@@ -293,7 +279,6 @@ class RuntimeConnectionHandler(handler.Handler):
             target_id = data['target_id']
             message_chain = data['message_chain']
 
-            # Use custom deserializer that properly handles Forward messages
             message_chain_obj = platform_message.MessageChain.model_validate(message_chain)
 
             bot = await self.ap.platform_mgr.get_bot_by_uuid(bot_uuid)
@@ -337,14 +322,7 @@ class RuntimeConnectionHandler(handler.Handler):
                 )
 
             messages_obj = [provider_message.Message.model_validate(message) for message in messages]
-
-            # The func field is excluded during model_dump() in plugin side (marked as exclude=True),
-            # but it's a required field for LLMTool validation. We need to provide a placeholder
-            # function when reconstructing the LLMTool objects from serialized data.
-            async def _placeholder_func(**kwargs):
-                pass
-
-            funcs_obj = [resource_tool.LLMTool.model_validate({**func, 'func': _placeholder_func}) for func in funcs]
+            funcs_obj = [resource_tool.LLMTool.model_validate(func) for func in funcs]
 
             result = await llm_model.provider.invoke_llm(
                 query=None,
@@ -460,7 +438,7 @@ class RuntimeConnectionHandler(handler.Handler):
                 },
             )
 
-        @self.action(PluginToRuntimeAction.GET_CONFIG_FILE)
+        @self.action(RuntimeToLangBotAction.GET_CONFIG_FILE)
         async def get_config_file(data: dict[str, Any]) -> handler.ActionResponse:
             """Get a config file by file key"""
             file_key = data['file_key']
@@ -479,223 +457,6 @@ class RuntimeConnectionHandler(handler.Handler):
                     message=f'Failed to load config file {file_key}: {e}',
                 )
 
-        # ================= RAG Capability Handlers =================
-
-        @self.action(PluginToRuntimeAction.INVOKE_EMBEDDING)
-        async def invoke_embedding(data: dict[str, Any]) -> handler.ActionResponse:
-            embedding_model_uuid = data['embedding_model_uuid']
-            texts = data['texts']
-
-            embedding_model = await self.ap.model_mgr.get_embedding_model_by_uuid(embedding_model_uuid)
-            if embedding_model is None:
-                return handler.ActionResponse.error(
-                    message=f'Embedding model with embedding_model_uuid {embedding_model_uuid} not found',
-                )
-
-            try:
-                vectors = await embedding_model.provider.invoke_embedding(embedding_model, texts)
-                return handler.ActionResponse.success(data={'vectors': vectors})
-            except Exception as e:
-                return _make_rag_error_response(e, 'EmbeddingError', embedding_model_uuid=embedding_model_uuid)
-
-        @self.action(PluginToRuntimeAction.VECTOR_UPSERT)
-        async def vector_upsert(data: dict[str, Any]) -> handler.ActionResponse:
-            collection_id = data['collection_id']
-            vectors = data['vectors']
-            ids = data['ids']
-            metadata = data.get('metadata')
-            documents = data.get('documents')
-            if len(vectors) != len(ids):
-                return handler.ActionResponse.error(message='vectors and ids must have same length')
-            if metadata and len(metadata) != len(vectors):
-                return handler.ActionResponse.error(message='metadata must match vectors length')
-            if documents and len(documents) != len(vectors):
-                return handler.ActionResponse.error(message='documents must match vectors length')
-            try:
-                await self.ap.rag_runtime_service.vector_upsert(
-                    collection_id,
-                    vectors,
-                    ids,
-                    metadata,
-                    documents,
-                )
-                return handler.ActionResponse.success(data={})
-            except Exception as e:
-                return _make_rag_error_response(e, 'VectorStoreError', collection_id=collection_id)
-
-        @self.action(PluginToRuntimeAction.VECTOR_SEARCH)
-        async def vector_search(data: dict[str, Any]) -> handler.ActionResponse:
-            collection_id = data['collection_id']
-            query_vector = data['query_vector']
-            top_k = data['top_k']
-            filters = data.get('filters')
-            search_type = data.get('search_type', 'vector')
-            query_text = data.get('query_text', '')
-            try:
-                results = await self.ap.rag_runtime_service.vector_search(
-                    collection_id,
-                    query_vector,
-                    top_k,
-                    filters,
-                    search_type,
-                    query_text,
-                )
-                return handler.ActionResponse.success(data={'results': results})
-            except Exception as e:
-                return _make_rag_error_response(e, 'VectorStoreError', collection_id=collection_id)
-
-        @self.action(PluginToRuntimeAction.VECTOR_DELETE)
-        async def vector_delete(data: dict[str, Any]) -> handler.ActionResponse:
-            collection_id = data['collection_id']
-            file_ids = data.get('file_ids')
-            filters = data.get('filters')
-            try:
-                count = await self.ap.rag_runtime_service.vector_delete(collection_id, file_ids, filters)
-                return handler.ActionResponse.success(data={'count': count})
-            except Exception as e:
-                return _make_rag_error_response(e, 'VectorStoreError', collection_id=collection_id)
-
-        @self.action(PluginToRuntimeAction.GET_KNOWLEDEGE_FILE_STREAM)
-        async def get_knowledge_file_stream(data: dict[str, Any]) -> handler.ActionResponse:
-            storage_path = data['storage_path']
-            try:
-                content_bytes = await self.ap.rag_runtime_service.get_file_stream(storage_path)
-                file_key = await self.send_file(content_bytes, '')
-                return handler.ActionResponse.success(data={'file_key': file_key})
-            except Exception as e:
-                return _make_rag_error_response(e, 'FileServiceError', storage_path=storage_path)
-
-        @self.action(PluginToRuntimeAction.LIST_PARSERS)
-        async def list_parsers(data: dict[str, Any]) -> handler.ActionResponse:
-            """Plugin requests host to list available parser plugins."""
-            mime_type = data.get('mime_type')
-            try:
-                parsers = await self.ap.knowledge_service.list_parsers(mime_type)
-                return handler.ActionResponse.success(data={'parsers': parsers})
-            except Exception as e:
-                return _make_rag_error_response(e, 'ParserDiscoveryError', mime_type=mime_type)
-
-        @self.action(PluginToRuntimeAction.INVOKE_PARSER)
-        async def invoke_parser(data: dict[str, Any]) -> handler.ActionResponse:
-            """Plugin requests host to invoke a parser plugin."""
-            plugin_author = data['plugin_author']
-            plugin_name = data['plugin_name']
-            storage_path = data['storage_path']
-            mime_type = data.get('mime_type', 'application/octet-stream')
-            filename = data.get('filename', '')
-            metadata = data.get('metadata', {})
-            try:
-                # Read file from storage
-                file_bytes = await self.ap.rag_runtime_service.get_file_stream(storage_path)
-                context_data = {
-                    'mime_type': mime_type,
-                    'filename': filename,
-                    'metadata': metadata,
-                }
-                result = await self.ap.plugin_connector.call_parser(
-                    f'{plugin_author}/{plugin_name}', context_data, file_bytes
-                )
-                return handler.ActionResponse.success(data=result)
-            except Exception as e:
-                return _make_rag_error_response(e, 'ParserError')
-
-        # ================= Knowledge Base Query APIs =================
-
-        @self.action(PluginToRuntimeAction.LIST_PIPELINE_KNOWLEDGE_BASES)
-        async def list_pipeline_knowledge_bases(data: dict[str, Any]) -> handler.ActionResponse:
-            """List knowledge bases configured for the current query's pipeline."""
-            query_id = data['query_id']
-
-            if query_id not in self.ap.query_pool.cached_queries:
-                return handler.ActionResponse.error(
-                    message=f'Query with query_id {query_id} not found',
-                )
-
-            query = self.ap.query_pool.cached_queries[query_id]
-
-            kb_uuids = []
-            if query.pipeline_config:
-                local_agent_config = query.pipeline_config.get('ai', {}).get('local-agent', {})
-                kb_uuids = local_agent_config.get('knowledge-bases', [])
-                # Backward compatibility
-                if not kb_uuids:
-                    old_kb_uuid = local_agent_config.get('knowledge-base', '')
-                    if old_kb_uuid and old_kb_uuid != '__none__':
-                        kb_uuids = [old_kb_uuid]
-
-            knowledge_bases = []
-            for kb_uuid in kb_uuids:
-                kb = await self.ap.rag_mgr.get_knowledge_base_by_uuid(kb_uuid)
-                if kb:
-                    knowledge_bases.append(
-                        {
-                            'uuid': kb.get_uuid(),
-                            'name': kb.get_name(),
-                            'description': kb.knowledge_base_entity.description or '',
-                        }
-                    )
-
-            return handler.ActionResponse.success(data={'knowledge_bases': knowledge_bases})
-
-        @self.action(PluginToRuntimeAction.RETRIEVE_KNOWLEDGE_BASE)
-        async def retrieve_knowledge_base(data: dict[str, Any]) -> handler.ActionResponse:
-            """Retrieve documents from a knowledge base within the pipeline's scope."""
-            query_id = data['query_id']
-            kb_id = data['kb_id']
-            query_text = data['query_text']
-            top_k = data.get('top_k', 5)
-            filters = data.get('filters', {})
-
-            if query_id not in self.ap.query_pool.cached_queries:
-                return handler.ActionResponse.error(
-                    message=f'Query with query_id {query_id} not found',
-                )
-
-            query = self.ap.query_pool.cached_queries[query_id]
-
-            # Validate kb_id is in pipeline's allowed list
-            allowed_kb_uuids = []
-            if query.pipeline_config:
-                local_agent_config = query.pipeline_config.get('ai', {}).get('local-agent', {})
-                allowed_kb_uuids = local_agent_config.get('knowledge-bases', [])
-                if not allowed_kb_uuids:
-                    old_kb_uuid = local_agent_config.get('knowledge-base', '')
-                    if old_kb_uuid and old_kb_uuid != '__none__':
-                        allowed_kb_uuids = [old_kb_uuid]
-
-            if kb_id not in allowed_kb_uuids:
-                return handler.ActionResponse.error(
-                    message=f'Knowledge base {kb_id} is not configured for this pipeline',
-                )
-
-            kb = await self.ap.rag_mgr.get_knowledge_base_by_uuid(kb_id)
-            if not kb:
-                return handler.ActionResponse.error(
-                    message=f'Knowledge base {kb_id} not found',
-                )
-
-            try:
-                entries = await kb.retrieve(
-                    query_text,
-                    settings={
-                        'top_k': top_k,
-                        'filters': filters,
-                    },
-                )
-                results = [entry.model_dump(mode='json') for entry in entries]
-                return handler.ActionResponse.success(data={'results': results})
-            except Exception as e:
-                return _make_rag_error_response(e, 'RetrievalError', kb_id=kb_id)
-
-        @self.action(CommonAction.PING)
-        async def ping(data: dict[str, Any]) -> handler.ActionResponse:
-            """Ping"""
-            return handler.ActionResponse.success(
-                data={
-                    'pong': 'pong',
-                },
-            )
-
     async def ping(self) -> dict[str, Any]:
         """Ping the runtime"""
         return await self.call_action(
@@ -955,13 +716,26 @@ class RuntimeConnectionHandler(handler.Handler):
         async for ret in gen:
             yield ret
 
+    # KnowledgeRetriever methods
+    async def list_knowledge_retrievers(self, include_plugins: list[str] | None = None) -> list[dict[str, Any]]:
+        """List knowledge retrievers"""
+        result = await self.call_action(
+            LangBotToRuntimeAction.LIST_KNOWLEDGE_RETRIEVERS,
+            {
+                'include_plugins': include_plugins,
+            },
+            timeout=10,
+        )
+        return result['retrievers']
+
     async def retrieve_knowledge(
         self,
         plugin_author: str,
         plugin_name: str,
         retriever_name: str,
+        instance_id: str,
         retrieval_context: dict[str, Any],
-    ) -> dict[str, Any]:
+    ) -> list[dict[str, Any]]:
         """Retrieve knowledge"""
         result = await self.call_action(
             LangBotToRuntimeAction.RETRIEVE_KNOWLEDGE,
@@ -969,10 +743,22 @@ class RuntimeConnectionHandler(handler.Handler):
                 'plugin_author': plugin_author,
                 'plugin_name': plugin_name,
                 'retriever_name': retriever_name,
+                'instance_id': instance_id,
                 'retrieval_context': retrieval_context,
             },
             timeout=30,
         )
+        return result['retrieval_results']
+
+    async def sync_polymorphic_component_instances(self, required_instances: list[dict[str, Any]]) -> dict[str, Any]:
+        """Sync polymorphic component instances with runtime"""
+        result = await self.call_action(
+            LangBotToRuntimeAction.SYNC_POLYMORPHIC_COMPONENT_INSTANCES,
+            {
+                'required_instances': required_instances,
+            },
+            timeout=30,
+        )
         return result
 
     async def get_debug_info(self) -> dict[str, Any]:
@@ -983,91 +769,3 @@ class RuntimeConnectionHandler(handler.Handler):
             timeout=10,
         )
         return result
-
-    # ================= RAG Capability Callers (LangBot -> Runtime) =================
-
-    async def rag_ingest_document(
-        self, plugin_author: str, plugin_name: str, context_data: dict[str, Any]
-    ) -> dict[str, Any]:
-        """Send INGEST_DOCUMENT action to runtime."""
-        result = await self.call_action(
-            LangBotToRuntimeAction.RAG_INGEST_DOCUMENT,
-            {'plugin_author': plugin_author, 'plugin_name': plugin_name, 'context': context_data},
-            timeout=300,  # Ingestion can be slow
-        )
-        return result
-
-    async def rag_delete_document(self, plugin_author: str, plugin_name: str, document_id: str, kb_id: str) -> bool:
-        result = await self.call_action(
-            LangBotToRuntimeAction.RAG_DELETE_DOCUMENT,
-            {'plugin_author': plugin_author, 'plugin_name': plugin_name, 'document_id': document_id, 'kb_id': kb_id},
-            timeout=30,
-        )
-        return result.get('success', False)
-
-    async def rag_on_kb_create(
-        self, plugin_author: str, plugin_name: str, kb_id: str, config: dict[str, Any]
-    ) -> dict[str, Any]:
-        """Notify plugin about KB creation."""
-        result = await self.call_action(
-            LangBotToRuntimeAction.RAG_ON_KB_CREATE,
-            {'plugin_author': plugin_author, 'plugin_name': plugin_name, 'kb_id': kb_id, 'config': config},
-            timeout=30,
-        )
-        return result
-
-    async def rag_on_kb_delete(self, plugin_author: str, plugin_name: str, kb_id: str) -> dict[str, Any]:
-        """Notify plugin about KB deletion."""
-        result = await self.call_action(
-            LangBotToRuntimeAction.RAG_ON_KB_DELETE,
-            {'plugin_author': plugin_author, 'plugin_name': plugin_name, 'kb_id': kb_id},
-            timeout=30,
-        )
-        return result
-
-    async def get_rag_creation_schema(self, plugin_author: str, plugin_name: str) -> dict[str, Any]:
-        return await self.call_action(
-            LangBotToRuntimeAction.GET_RAG_CREATION_SETTINGS_SCHEMA,
-            {'plugin_author': plugin_author, 'plugin_name': plugin_name},
-            timeout=10,
-        )
-
-    async def get_rag_retrieval_schema(self, plugin_author: str, plugin_name: str) -> dict[str, Any]:
-        return await self.call_action(
-            LangBotToRuntimeAction.GET_RAG_RETRIEVAL_SETTINGS_SCHEMA,
-            {'plugin_author': plugin_author, 'plugin_name': plugin_name},
-            timeout=10,
-        )
-
-    async def list_knowledge_engines(self) -> list[dict[str, Any]]:
-        """List all available Knowledge Engines from plugins."""
-        result = await self.call_action(LangBotToRuntimeAction.LIST_KNOWLEDGE_ENGINES, {}, timeout=60)
-        return result.get('engines', [])
-
-    # ================= Parser Capability Callers (LangBot -> Runtime) =================
-
-    async def list_parsers(self) -> list[dict[str, Any]]:
-        """List all available parsers from plugins."""
-        result = await self.call_action(LangBotToRuntimeAction.LIST_PARSERS, {}, timeout=60)
-        return result.get('parsers', [])
-
-    async def parse_document(
-        self, plugin_author: str, plugin_name: str, context_data: dict[str, Any], file_bytes: bytes
-    ) -> dict[str, Any]:
-        """Send PARSE_DOCUMENT action to runtime.
-
-        Sends file content via chunked FILE_CHUNK transfer, then invokes
-        the PARSE_DOCUMENT action with a file_key reference.
-        """
-        # Send file to runtime via chunked transfer
-        file_key = await self.send_file(file_bytes, '')
-
-        # Include file_key in context_data for the runtime to read
-        context_data['file_key'] = file_key
-
-        result = await self.call_action(
-            LangBotToRuntimeAction.PARSE_DOCUMENT,
-            {'plugin_author': plugin_author, 'plugin_name': plugin_name, 'context': context_data},
-            timeout=300,
-        )
-        return result

src/langbot/pkg/provider/runners/difysvapi.py

@@ -72,28 +72,6 @@ class DifyServiceAPIRunner(runner.RequestRunner):
                 content = f'<think>\n{thinking_content}\n</think>\n{content}'.strip()
             return content, thinking_content
 
-    def _extract_dify_text_output(self, value: typing.Any) -> str:
-        """Extract text content from Dify output payload."""
-        if value is None:
-            return ''
-        if isinstance(value, dict):
-            content = value.get('content')
-            if isinstance(content, str):
-                return content
-            return json.dumps(value, ensure_ascii=False)
-        if isinstance(value, str):
-            text = value.strip()
-            if not text:
-                return ''
-            try:
-                parsed = json.loads(text)
-            except json.JSONDecodeError:
-                return value
-            if isinstance(parsed, dict) and isinstance(parsed.get('content'), str):
-                return parsed['content']
-            return value
-        return str(value)
-
     async def _preprocess_user_message(self, query: pipeline_query.Query) -> tuple[str, list[dict]]:
         """预处理用户消息，提取纯文本，并将图片/文件上传到 Dify 服务
 
@@ -214,8 +192,7 @@ class DifyServiceAPIRunner(runner.RequestRunner):
             if mode == 'workflow':
                 if chunk['event'] == 'node_finished':
                     if chunk['data']['node_type'] == 'answer':
-                        answer = self._extract_dify_text_output(chunk['data']['outputs'].get('answer'))
-                        content, _ = self._process_thinking_content(answer)
+                        content, _ = self._process_thinking_content(chunk['data']['outputs']['answer'])
 
                         yield provider_message.Message(
                             role='assistant',
@@ -428,7 +405,6 @@ class DifyServiceAPIRunner(runner.RequestRunner):
             for f in upload_files
         ]
 
-        mode = 'basic'
         basic_mode_pending_chunk = ''
 
         inputs = {}
@@ -441,7 +417,6 @@ class DifyServiceAPIRunner(runner.RequestRunner):
         is_final = False
         think_start = False
         think_end = False
-        yielded_final = False
 
         remove_think = self.pipeline_config['output'].get('misc', '').get('remove-think')
 
@@ -455,12 +430,11 @@ class DifyServiceAPIRunner(runner.RequestRunner):
         ):
             self.ap.logger.debug('dify-chat-chunk: ' + str(chunk))
 
-            if chunk['event'] == 'workflow_started':
-                mode = 'workflow'
-            elif chunk['event'] in ('node_started', 'node_finished', 'workflow_finished'):
-                # Some Dify deployments may omit workflow_started in streamed chunks.
-                mode = 'workflow'
-
+            # if chunk['event'] == 'workflow_started':
+            #     mode = 'workflow'
+            # if mode == 'workflow':
+            # elif mode == 'basic':
+            # 因为都只是返回的 message也没有工具调用什么的，暂时不分类
             if chunk['event'] == 'message':
                 message_idx += 1
                 if remove_think:
@@ -483,30 +457,14 @@ class DifyServiceAPIRunner(runner.RequestRunner):
 
             if chunk['event'] == 'message_end':
                 is_final = True
-            elif chunk['event'] == 'workflow_finished':
-                is_final = True
-                if chunk['data'].get('error'):
-                    raise errors.DifyAPIError(chunk['data']['error'])
 
-            if mode == 'workflow' and chunk['event'] == 'node_finished':
-                if chunk['data'].get('node_type') == 'answer':
-                    answer = self._extract_dify_text_output(chunk['data'].get('outputs', {}).get('answer'))
-                    if answer:
-                        basic_mode_pending_chunk = answer
-
-            if (
-                not yielded_final
-                and (is_final or message_idx % 8 == 0)
-                and (basic_mode_pending_chunk != '' or is_final)
-            ):
+            if is_final or message_idx % 8 == 0:
                 # content, _ = self._process_thinking_content(basic_mode_pending_chunk)
                 yield provider_message.MessageChunk(
                     role='assistant',
                     content=basic_mode_pending_chunk,
                     is_final=is_final,
                 )
-                if is_final:
-                    yielded_final = True
 
         if chunk is None:
             raise errors.DifyAPIError('Dify API 没有返回任何响应，请检查网络连接和API配置')

src/langbot/pkg/provider/runners/localagent.py

@@ -4,7 +4,6 @@ import json
 import copy
 import typing
 from .. import runner
-from ..modelmgr import requester as modelmgr_requester
 import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
 import langbot_plugin.api.entities.builtin.provider.message as provider_message
 import langbot_plugin.api.entities.builtin.rag.context as rag_context
@@ -27,109 +26,19 @@ Respond in the same language as the user's input.
 
 @runner.runner_class('local-agent')
 class LocalAgentRunner(runner.RequestRunner):
-    """Local agent request runner"""
+    """本地Agent请求运行器"""
 
-    async def _get_model_candidates(
-        self,
-        query: pipeline_query.Query,
-    ) -> list[modelmgr_requester.RuntimeLLMModel]:
-        """Build ordered list of models to try: primary model + fallback models."""
-        candidates = []
+    class ToolCallTracker:
+        """工具调用追踪器"""
 
-        # Primary model
-        if query.use_llm_model_uuid:
-            try:
-                primary = await self.ap.model_mgr.get_model_by_uuid(query.use_llm_model_uuid)
-                candidates.append(primary)
-            except ValueError:
-                self.ap.logger.warning(f'Primary model {query.use_llm_model_uuid} not found')
-
-        # Fallback models
-        fallback_uuids = (query.variables or {}).get('_fallback_model_uuids', [])
-        for fb_uuid in fallback_uuids:
-            try:
-                fb_model = await self.ap.model_mgr.get_model_by_uuid(fb_uuid)
-                candidates.append(fb_model)
-            except ValueError:
-                self.ap.logger.warning(f'Fallback model {fb_uuid} not found, skipping')
-
-        return candidates
-
-    async def _invoke_with_fallback(
-        self,
-        query: pipeline_query.Query,
-        candidates: list[modelmgr_requester.RuntimeLLMModel],
-        messages: list,
-        funcs: list,
-        remove_think: bool,
-    ) -> tuple[provider_message.Message, modelmgr_requester.RuntimeLLMModel]:
-        """Try non-streaming invocation with sequential fallback. Returns (message, model_used)."""
-        last_error = None
-        for model in candidates:
-            try:
-                msg = await model.provider.invoke_llm(
-                    query,
-                    model,
-                    messages,
-                    funcs if model.model_entity.abilities.__contains__('func_call') else [],
-                    extra_args=model.model_entity.extra_args,
-                    remove_think=remove_think,
-                )
-                return msg, model
-            except Exception as e:
-                last_error = e
-                self.ap.logger.warning(f'Model {model.model_entity.name} failed: {e}, trying next fallback...')
-        raise last_error or RuntimeError('No model candidates available')
-
-    async def _invoke_stream_with_fallback(
-        self,
-        query: pipeline_query.Query,
-        candidates: list[modelmgr_requester.RuntimeLLMModel],
-        messages: list,
-        funcs: list,
-        remove_think: bool,
-    ) -> tuple[typing.AsyncGenerator, modelmgr_requester.RuntimeLLMModel]:
-        """Try streaming invocation with sequential fallback. Returns (stream_generator, model_used).
-
-        Fallback is only possible before any chunks have been yielded to the client.
-        Once streaming starts, the model is committed.
-        """
-        last_error = None
-        for model in candidates:
-            try:
-                stream = model.provider.invoke_llm_stream(
-                    query,
-                    model,
-                    messages,
-                    funcs if model.model_entity.abilities.__contains__('func_call') else [],
-                    extra_args=model.model_entity.extra_args,
-                    remove_think=remove_think,
-                )
-                # Attempt to get the first chunk to verify the stream works
-                first_chunk = await stream.__anext__()
-
-                async def _chain_stream(first, rest):
-                    yield first
-                    async for chunk in rest:
-                        yield chunk
-
-                return _chain_stream(first_chunk, stream), model
-            except StopAsyncIteration:
-                # Empty stream — treat as success (model returned nothing)
-                async def _empty_stream():
-                    return
-                    yield  # make it a generator
-
-                return _empty_stream(), model
-            except Exception as e:
-                last_error = e
-                self.ap.logger.warning(f'Model {model.model_entity.name} stream failed: {e}, trying next fallback...')
-        raise last_error or RuntimeError('No model candidates available')
+        def __init__(self):
+            self.active_calls: dict[str, dict] = {}
+            self.completed_calls: list[provider_message.ToolCall] = []
 
     async def run(
         self, query: pipeline_query.Query
     ) -> typing.AsyncGenerator[provider_message.Message | provider_message.MessageChunk, None]:
-        """Run request"""
+        """运行请求"""
         pending_tool_calls = []
 
         # Get knowledge bases list (new field)
@@ -165,14 +74,15 @@ class LocalAgentRunner(runner.RequestRunner):
                     self.ap.logger.warning(f'Knowledge base {kb_uuid} not found, skipping')
                     continue
 
-                result = await kb.retrieve(
-                    user_message_text,
-                    settings={
-                        'bot_uuid': query.bot_uuid or '',
-                        'sender_id': str(query.sender_id),
-                        'session_name': f'{query.session.launcher_type.value}_{query.session.launcher_id}',
-                    },
-                )
+                # Get top_k based on KB type
+                if kb.get_type() == 'internal':
+                    top_k = kb.knowledge_base_entity.top_k
+                elif kb.get_type() == 'external':
+                    top_k = 5  # external kb's top_k is managed by plugin config
+                else:
+                    top_k = 5  # default fallback
+
+                result = await kb.retrieve(user_message_text, top_k)
 
                 if result:
                     all_results.extend(result)
@@ -187,9 +97,9 @@ class LocalAgentRunner(runner.RequestRunner):
                         if content.type == 'text' and content.text is not None:
                             texts.append(f'[{idx}] {content.text}')
                             idx += 1
-                rag_context_text = '\n\n'.join(texts)
+                rag_context = '\n\n'.join(texts)
                 final_user_message_text = rag_combined_prompt_template.format(
-                    rag_context=rag_context_text, user_message=user_message_text
+                    rag_context=rag_context, user_message=user_message_text
                 )
 
             else:
@@ -211,51 +121,51 @@ class LocalAgentRunner(runner.RequestRunner):
 
         remove_think = query.pipeline_config['output'].get('misc', '').get('remove-think')
 
-        # Build ordered candidate list (primary + fallbacks)
-        candidates = await self._get_model_candidates(query)
-        if not candidates:
-            raise RuntimeError('No LLM model configured for local-agent runner')
+        use_llm_model = await self.ap.model_mgr.get_model_by_uuid(query.use_llm_model_uuid)
 
         self.ap.logger.debug(
-            f'localagent req: query={query.query_id} req_messages={req_messages} '
-            f'candidates={[m.model_entity.name for m in candidates]}'
+            f'localagent req: query={query.query_id} req_messages={req_messages} use_llm_model={query.use_llm_model_uuid}'
         )
 
         if not is_stream:
-            # Non-streaming: invoke with fallback
-            msg, use_llm_model = await self._invoke_with_fallback(
+            # 非流式输出，直接请求
+
+            msg = await use_llm_model.provider.invoke_llm(
                 query,
-                candidates,
+                use_llm_model,
                 req_messages,
                 query.use_funcs,
-                remove_think,
+                extra_args=use_llm_model.model_entity.extra_args,
+                remove_think=remove_think,
             )
             yield msg
             final_msg = msg
         else:
-            # Streaming: invoke with fallback
+            # 流式输出，需要处理工具调用
             tool_calls_map: dict[str, provider_message.ToolCall] = {}
             msg_idx = 0
-            accumulated_content = ''
+            accumulated_content = ''  # 从开始累积的所有内容
             last_role = 'assistant'
             msg_sequence = 1
-
-            stream_src, use_llm_model = await self._invoke_stream_with_fallback(
+            async for msg in use_llm_model.provider.invoke_llm_stream(
                 query,
-                candidates,
+                use_llm_model,
                 req_messages,
                 query.use_funcs,
-                remove_think,
-            )
-            async for msg in stream_src:
+                extra_args=use_llm_model.model_entity.extra_args,
+                remove_think=remove_think,
+            ):
                 msg_idx = msg_idx + 1
 
+                # 记录角色
                 if msg.role:
                     last_role = msg.role
 
+                # 累积内容
                 if msg.content:
                     accumulated_content += msg.content
 
+                # 处理工具调用
                 if msg.tool_calls:
                     for tool_call in msg.tool_calls:
                         if tool_call.id not in tool_calls_map:
@@ -267,18 +177,21 @@ class LocalAgentRunner(runner.RequestRunner):
                                 ),
                             )
                         if tool_call.function and tool_call.function.arguments:
+                            # 流式处理中，工具调用参数可能分多个chunk返回，需要追加而不是覆盖
                             tool_calls_map[tool_call.id].function.arguments += tool_call.function.arguments
-
+                # continue
+                # 每8个chunk或最后一个chunk时，输出所有累积的内容
                 if msg_idx % 8 == 0 or msg.is_final:
                     msg_sequence += 1
                     yield provider_message.MessageChunk(
                         role=last_role,
-                        content=accumulated_content,
+                        content=accumulated_content,  # 输出所有累积内容
                         tool_calls=list(tool_calls_map.values()) if (tool_calls_map and msg.is_final) else None,
                         is_final=msg.is_final,
                         msg_sequence=msg_sequence,
                     )
 
+            # 创建最终消息用于后续处理
             final_msg = provider_message.MessageChunk(
                 role=last_role,
                 content=accumulated_content,
@@ -293,8 +206,7 @@ class LocalAgentRunner(runner.RequestRunner):
 
         req_messages.append(final_msg)
 
-        # Once a model succeeds, commit to it for the tool call loop
-        # (no fallback mid-conversation — different models may interpret tool results differently)
+        # 持续请求，只要还有待处理的工具调用就继续处理调用
         while pending_tool_calls:
             for tool_call in pending_tool_calls:
                 try:
@@ -335,6 +247,7 @@ class LocalAgentRunner(runner.RequestRunner):
 
                     req_messages.append(msg)
                 except Exception as e:
+                    # 工具调用出错，添加一个报错信息到 req_messages
                     err_msg = provider_message.Message(role='tool', content=f'err: {e}', tool_call_id=tool_call.id)
 
                     yield err_msg
@@ -342,38 +255,39 @@ class LocalAgentRunner(runner.RequestRunner):
                     req_messages.append(err_msg)
 
             self.ap.logger.debug(
-                f'localagent req: query={query.query_id} req_messages={req_messages} '
-                f'use_llm_model={use_llm_model.model_entity.name}'
+                f'localagent req: query={query.query_id} req_messages={req_messages} use_llm_model={query.use_llm_model_uuid}'
             )
 
             if is_stream:
                 tool_calls_map = {}
                 msg_idx = 0
-                accumulated_content = ''
+                accumulated_content = ''  # 从开始累积的所有内容
                 last_role = 'assistant'
                 msg_sequence = first_end_sequence
 
-                tool_stream_src = use_llm_model.provider.invoke_llm_stream(
+                async for msg in use_llm_model.provider.invoke_llm_stream(
                     query,
                     use_llm_model,
                     req_messages,
-                    query.use_funcs if use_llm_model.model_entity.abilities.__contains__('func_call') else [],
+                    query.use_funcs,
                     extra_args=use_llm_model.model_entity.extra_args,
                     remove_think=remove_think,
-                )
-                async for msg in tool_stream_src:
+                ):
                     msg_idx += 1
 
+                    # 记录角色
                     if msg.role:
                         last_role = msg.role
 
-                    # Prepend first-round content on first chunk of tool-call round
+                    # 第一次请求工具调用时的内容
                     if msg_idx == 1:
                         accumulated_content = first_content if first_content is not None else accumulated_content
 
+                    # 累积内容
                     if msg.content:
                         accumulated_content += msg.content
 
+                    # 处理工具调用
                     if msg.tool_calls:
                         for tool_call in msg.tool_calls:
                             if tool_call.id not in tool_calls_map:
@@ -385,13 +299,15 @@ class LocalAgentRunner(runner.RequestRunner):
                                     ),
                                 )
                             if tool_call.function and tool_call.function.arguments:
+                                # 流式处理中，工具调用参数可能分多个chunk返回，需要追加而不是覆盖
                                 tool_calls_map[tool_call.id].function.arguments += tool_call.function.arguments
 
+                    # 每8个chunk或最后一个chunk时，输出所有累积的内容
                     if msg_idx % 8 == 0 or msg.is_final:
                         msg_sequence += 1
                         yield provider_message.MessageChunk(
                             role=last_role,
-                            content=accumulated_content,
+                            content=accumulated_content,  # 输出所有累积内容
                             tool_calls=list(tool_calls_map.values()) if (tool_calls_map and msg.is_final) else None,
                             is_final=msg.is_final,
                             msg_sequence=msg_sequence,
@@ -404,12 +320,12 @@ class LocalAgentRunner(runner.RequestRunner):
                     msg_sequence=msg_sequence,
                 )
             else:
-                # Non-streaming: use committed model directly (no fallback in tool loop)
+                # 处理完所有调用，再次请求
                 msg = await use_llm_model.provider.invoke_llm(
                     query,
                     use_llm_model,
                     req_messages,
-                    query.use_funcs if use_llm_model.model_entity.abilities.__contains__('func_call') else [],
+                    query.use_funcs,
                     extra_args=use_llm_model.model_entity.extra_args,
                     remove_think=remove_think,
                 )

src/langbot/pkg/provider/runners/n8nsvapi.py

@@ -5,8 +5,6 @@ import json
 import uuid
 import aiohttp
 
-from langbot.pkg.utils import httpclient
-
 from .. import runner
 from ...core import app
 import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
@@ -219,50 +217,50 @@ class N8nServiceAPIRunner(runner.RequestRunner):
                 self.ap.logger.debug('no auth')
 
             # 调用webhook
-            session = httpclient.get_session()
-            if is_stream:
-                # 流式请求
-                async with session.post(
-                    self.webhook_url, json=payload, headers=headers, auth=auth, timeout=self.timeout
-                ) as response:
-                    if response.status != 200:
-                        error_text = await response.text()
-                        self.ap.logger.error(f'n8n webhook call failed: {response.status}, {error_text}')
-                        raise Exception(f'n8n webhook call failed: {response.status}, {error_text}')
-
-                    # 处理流式响应
-                    async for chunk in self._process_stream_response(response):
-                        yield chunk
-            else:
-                async with session.post(
-                    self.webhook_url, json=payload, headers=headers, auth=auth, timeout=self.timeout
-                ) as response:
-                    try:
-                        async for chunk in self._process_stream_response(response):
-                            output_content = chunk.content if chunk.is_final else ''
-                    except:
-                        # 非流式请求（保持原有逻辑）
+            async with aiohttp.ClientSession() as session:
+                if is_stream:
+                    # 流式请求
+                    async with session.post(
+                        self.webhook_url, json=payload, headers=headers, auth=auth, timeout=self.timeout
+                    ) as response:
                         if response.status != 200:
                             error_text = await response.text()
                             self.ap.logger.error(f'n8n webhook call failed: {response.status}, {error_text}')
                             raise Exception(f'n8n webhook call failed: {response.status}, {error_text}')
 
-                        # 解析响应
-                        response_data = await response.json()
-                        self.ap.logger.debug(f'n8n webhook response: {response_data}')
+                        # 处理流式响应
+                        async for chunk in self._process_stream_response(response):
+                            yield chunk
+                else:
+                    async with session.post(
+                        self.webhook_url, json=payload, headers=headers, auth=auth, timeout=self.timeout
+                    ) as response:
+                        try:
+                            async for chunk in self._process_stream_response(response):
+                                output_content = chunk.content if chunk.is_final else ''
+                        except:
+                            # 非流式请求（保持原有逻辑）
+                            if response.status != 200:
+                                error_text = await response.text()
+                                self.ap.logger.error(f'n8n webhook call failed: {response.status}, {error_text}')
+                                raise Exception(f'n8n webhook call failed: {response.status}, {error_text}')
 
-                        # 从响应中提取输出
-                        if self.output_key in response_data:
-                            output_content = response_data[self.output_key]
-                        else:
-                            # 如果没有指定的输出键，则使用整个响应
-                            output_content = json.dumps(response_data, ensure_ascii=False)
+                            # 解析响应
+                            response_data = await response.json()
+                            self.ap.logger.debug(f'n8n webhook response: {response_data}')
 
-                    # 返回消息
-                    yield provider_message.Message(
-                        role='assistant',
-                        content=output_content,
-                    )
+                            # 从响应中提取输出
+                            if self.output_key in response_data:
+                                output_content = response_data[self.output_key]
+                            else:
+                                # 如果没有指定的输出键，则使用整个响应
+                                output_content = json.dumps(response_data, ensure_ascii=False)
+
+                        # 返回消息
+                        yield provider_message.Message(
+                            role='assistant',
+                            content=output_content,
+                        )
         except Exception as e:
             self.ap.logger.error(f'n8n webhook call exception: {str(e)}')
             raise N8nAPIError(f'n8n webhook call exception: {str(e)}')

src/langbot/pkg/rag/knowledge/base.py

@@ -22,12 +22,12 @@ class KnowledgeBaseInterface(metaclass=abc.ABCMeta):
         pass
 
     @abc.abstractmethod
-    async def retrieve(self, query: str, settings: dict | None = None) -> list[rag_context.RetrievalResultEntry]:
+    async def retrieve(self, query: str, top_k: int) -> list[rag_context.RetrievalResultEntry]:
         """Retrieve relevant documents from the knowledge base
 
         Args:
             query: The query string
-            settings: Optional per-request retrieval settings overrides
+            top_k: Number of top results to return
 
         Returns:
             List of retrieve result entries
@@ -45,8 +45,8 @@ class KnowledgeBaseInterface(metaclass=abc.ABCMeta):
         pass
 
     @abc.abstractmethod
-    def get_knowledge_engine_plugin_id(self) -> str:
-        """Get the Knowledge Engine plugin ID"""
+    def get_type(self) -> str:
+        """Get the type of knowledge base (internal/external)"""
         pass
 
     @abc.abstractmethod

src/langbot/pkg/rag/knowledge/external.py

@@ -0,0 +1,85 @@
+"""External knowledge base implementation"""
+
+from __future__ import annotations
+
+from langbot.pkg.core import app
+from langbot.pkg.entity.persistence import rag as persistence_rag
+from langbot_plugin.api.entities.builtin.rag import context as rag_context
+from .base import KnowledgeBaseInterface
+
+
+class ExternalKnowledgeBase(KnowledgeBaseInterface):
+    """External knowledge base that queries via HTTP API or plugin retriever"""
+
+    external_kb_entity: persistence_rag.ExternalKnowledgeBase
+
+    # Plugin retriever instance ID
+    retriever_instance_id: str | None
+
+    def __init__(self, ap: app.Application, external_kb_entity: persistence_rag.ExternalKnowledgeBase):
+        super().__init__(ap)
+        self.external_kb_entity = external_kb_entity
+        self.retriever_instance_id = None
+
+    async def initialize(self):
+        """Initialize the external knowledge base"""
+        # Use KB UUID as instance ID
+        # Instance creation is now handled by the unified sync mechanism
+        # when LangBot connects to runtime
+        self.retriever_instance_id = self.external_kb_entity.uuid
+
+        self.ap.logger.info(
+            f'Initialized external KB {self.external_kb_entity.uuid}, instance will be created by sync mechanism'
+        )
+
+    async def retrieve(self, query: str, top_k: int = 5) -> list[rag_context.RetrievalResultEntry]:
+        """Retrieve documents from external knowledge base via plugin retriever"""
+        if not self.retriever_instance_id:
+            self.ap.logger.error(f'No retriever instance for KB {self.external_kb_entity.uuid}')
+            return []
+
+        try:
+            results = await self.ap.plugin_connector.retrieve_knowledge(
+                self.external_kb_entity.plugin_author,
+                self.external_kb_entity.plugin_name,
+                self.external_kb_entity.retriever_name,
+                self.retriever_instance_id,
+                {'query': query},
+            )
+
+            # Convert plugin results to RetrievalResultEntry
+            retrieval_entries = []
+            for result in results:
+                retrieval_entries.append(rag_context.RetrievalResultEntry(**result))
+
+            return retrieval_entries
+        except Exception as e:
+            self.ap.logger.error(f'Plugin retriever error: {e}')
+            import traceback
+
+            traceback.print_exc()
+            return []
+
+    def get_uuid(self) -> str:
+        """Get the UUID of the external knowledge base"""
+        return self.external_kb_entity.uuid
+
+    def get_name(self) -> str:
+        """Get the name of the external knowledge base"""
+        return self.external_kb_entity.name
+
+    def get_type(self) -> str:
+        """Get the type of knowledge base"""
+        return 'external'
+
+    async def dispose(self):
+        """Clean up resources"""
+        # Trigger sync to immediately delete the instance from plugin process
+        # This ensures instance is cleaned up without waiting for next LangBot restart
+        try:
+            await self.ap.plugin_connector.sync_polymorphic_component_instances()
+            self.ap.logger.info(
+                f'Disposed external KB {self.external_kb_entity.uuid}, triggered sync to delete instance'
+            )
+        except Exception as e:
+            self.ap.logger.error(f'Failed to sync after disposing KB: {e}')

src/langbot/pkg/rag/knowledge/kbmgr.py

@@ -1,19 +1,18 @@
 from __future__ import annotations
-import mimetypes
-import os.path
 import traceback
 import uuid
 import zipfile
 import io
-from typing import Any
+from .services import parser, chunker
 from langbot.pkg.core import app
+from langbot.pkg.rag.knowledge.services.embedder import Embedder
+from langbot.pkg.rag.knowledge.services.retriever import Retriever
 import sqlalchemy
-
-
 from langbot.pkg.entity.persistence import rag as persistence_rag
 from langbot.pkg.core import taskmgr
 from langbot_plugin.api.entities.builtin.rag import context as rag_context
 from .base import KnowledgeBaseInterface
+from .external import ExternalKnowledgeBase
 
 
 class RuntimeKnowledgeBase(KnowledgeBaseInterface):
@@ -21,16 +20,28 @@ class RuntimeKnowledgeBase(KnowledgeBaseInterface):
 
     knowledge_base_entity: persistence_rag.KnowledgeBase
 
+    parser: parser.FileParser
+
+    chunker: chunker.Chunker
+
+    embedder: Embedder
+
+    retriever: Retriever
+
     def __init__(self, ap: app.Application, knowledge_base_entity: persistence_rag.KnowledgeBase):
         super().__init__(ap)
         self.knowledge_base_entity = knowledge_base_entity
+        self.parser = parser.FileParser(ap=self.ap)
+        self.chunker = chunker.Chunker(ap=self.ap)
+        self.embedder = Embedder(ap=self.ap)
+        self.retriever = Retriever(ap=self.ap)
+        # 传递kb_id给retriever
+        self.retriever.kb_id = knowledge_base_entity.uuid
 
     async def initialize(self):
         pass
 
-    async def _store_file_task(
-        self, file: persistence_rag.File, task_context: taskmgr.TaskContext, parser_plugin_id: str | None = None
-    ):
+    async def _store_file_task(self, file: persistence_rag.File, task_context: taskmgr.TaskContext):
         try:
             # set file status to processing
             await self.ap.persistence_mgr.execute_async(
@@ -39,45 +50,30 @@ class RuntimeKnowledgeBase(KnowledgeBaseInterface):
                 .values(status='processing')
             )
 
-            task_context.set_current_action('Processing file')
+            task_context.set_current_action('Parsing file')
+            # parse file
+            text = await self.parser.parse(file.file_name, file.extension)
+            if not text:
+                raise Exception(f'No text extracted from file {file.file_name}')
 
-            # Get file size from storage
-            file_size = await self.ap.storage_mgr.storage_provider.size(file.file_name)
+            task_context.set_current_action('Chunking file')
+            # chunk file
+            chunks_texts = await self.chunker.chunk(text)
+            if not chunks_texts:
+                raise Exception(f'No chunks extracted from file {file.file_name}')
 
-            # Detect MIME type from extension
-            mime_type, _ = mimetypes.guess_type(file.file_name)
-            if mime_type is None:
-                mime_type = 'application/octet-stream'
+            task_context.set_current_action('Embedding chunks')
 
-            # If a parser plugin is specified, call it before ingestion
-            parsed_content = None
-            if parser_plugin_id:
-                task_context.set_current_action('Parsing file')
-                file_bytes = await self.ap.storage_mgr.storage_provider.load(file.file_name)
-                parse_context = {
-                    'mime_type': mime_type,
-                    'filename': file.file_name,
-                    'metadata': {},
-                }
-                parsed_content = await self.ap.plugin_connector.call_parser(parser_plugin_id, parse_context, file_bytes)
-
-            # Call plugin to ingest document
-            result = await self._ingest_document(
-                {
-                    'document_id': file.uuid,
-                    'filename': file.file_name,
-                    'extension': file.extension,
-                    'file_size': file_size,
-                    'mime_type': mime_type,
-                },
-                file.file_name,  # storage path
-                parsed_content=parsed_content,
+            embedding_model = await self.ap.model_mgr.get_embedding_model_by_uuid(
+                self.knowledge_base_entity.embedding_model_uuid
+            )
+            # embed chunks
+            await self.embedder.embed_and_store(
+                kb_id=self.knowledge_base_entity.uuid,
+                file_id=file.uuid,
+                chunks=chunks_texts,
+                embedding_model=embedding_model,
             )
-
-            # Check plugin result status
-            if result.get('status') == 'failed':
-                error_msg = result.get('error_message', 'Plugin ingestion returned failed status')
-                raise Exception(error_msg)
 
             # set file status to completed
             await self.ap.persistence_mgr.execute_async(
@@ -101,17 +97,16 @@ class RuntimeKnowledgeBase(KnowledgeBaseInterface):
             # delete file from storage
             await self.ap.storage_mgr.storage_provider.delete(file.file_name)
 
-    async def store_file(self, file_id: str, parser_plugin_id: str | None = None) -> str:
+    async def store_file(self, file_id: str) -> str:
         # pre checking
         if not await self.ap.storage_mgr.storage_provider.exists(file_id):
             raise Exception(f'File {file_id} not found')
 
         file_name = file_id
-        _, ext = os.path.splitext(file_name)
-        extension = ext.lstrip('.').lower() if ext else ''
+        extension = file_name.split('.')[-1].lower()
 
         if extension == 'zip':
-            return await self._store_zip_file(file_id, parser_plugin_id=parser_plugin_id)
+            return await self._store_zip_file(file_id)
 
         file_uuid = str(uuid.uuid4())
         kb_id = self.knowledge_base_entity.uuid
@@ -131,7 +126,7 @@ class RuntimeKnowledgeBase(KnowledgeBaseInterface):
         # run background task asynchronously
         ctx = taskmgr.TaskContext.new()
         wrapper = self.ap.task_mgr.create_user_task(
-            self._store_file_task(file_obj, task_context=ctx, parser_plugin_id=parser_plugin_id),
+            self._store_file_task(file_obj, task_context=ctx),
             kind='knowledge-operation',
             name=f'knowledge-store-file-{file_id}',
             label=f'Store file {file_id}',
@@ -139,7 +134,7 @@ class RuntimeKnowledgeBase(KnowledgeBaseInterface):
         )
         return wrapper.id
 
-    async def _store_zip_file(self, zip_file_id: str, parser_plugin_id: str | None = None) -> str:
+    async def _store_zip_file(self, zip_file_id: str) -> str:
         """Handle ZIP file by extracting each document and storing them separately."""
         self.ap.logger.info(f'Processing ZIP file: {zip_file_id}')
 
@@ -155,8 +150,7 @@ class RuntimeKnowledgeBase(KnowledgeBaseInterface):
                 if file_info.is_dir() or file_info.filename.startswith('.'):
                     continue
 
-                _, file_ext = os.path.splitext(file_info.filename)
-                file_extension = file_ext.lstrip('.').lower()
+                file_extension = file_info.filename.split('.')[-1].lower()
                 if file_extension not in supported_extensions:
                     self.ap.logger.debug(f'Skipping unsupported file in ZIP: {file_info.filename}')
                     continue
@@ -165,18 +159,18 @@ class RuntimeKnowledgeBase(KnowledgeBaseInterface):
                     file_content = zip_ref.read(file_info.filename)
 
                     base_name = file_info.filename.replace('/', '_').replace('\\', '_')
-                    file_stem, file_ext = os.path.splitext(base_name)
-                    extension = file_ext.lstrip('.')
+                    extension = base_name.split('.')[-1]
+                    file_name = base_name.split('.')[0]
 
-                    if file_stem.startswith('__MACOSX'):
+                    if file_name.startswith('__MACOSX'):
                         continue
 
-                    extracted_file_id = file_stem + '_' + str(uuid.uuid4())[:8] + '.' + extension
+                    extracted_file_id = file_name + '_' + str(uuid.uuid4())[:8] + '.' + extension
                     # save file to storage
 
                     await self.ap.storage_mgr.storage_provider.save(extracted_file_id, file_content)
 
-                    task_id = await self.store_file(extracted_file_id, parser_plugin_id=parser_plugin_id)
+                    task_id = await self.store_file(extracted_file_id)
                     stored_file_tasks.append(task_id)
 
                     self.ap.logger.info(
@@ -195,28 +189,21 @@ class RuntimeKnowledgeBase(KnowledgeBaseInterface):
 
         return stored_file_tasks[0] if stored_file_tasks else ''
 
-    async def retrieve(self, query: str, settings: dict | None = None) -> list[rag_context.RetrievalResultEntry]:
-        # Merge stored retrieval_settings with per-request overrides
-        stored = self.knowledge_base_entity.retrieval_settings or {}
-        merged = {**stored, **(settings or {})}
-        if 'top_k' not in merged:
-            merged['top_k'] = 5  # fallback default
-
-        response = await self._retrieve(query, merged)
-
-        results_data = response.get('results', [])
-        entries = []
-        for r in results_data:
-            if isinstance(r, dict):
-                entries.append(rag_context.RetrievalResultEntry(**r))
-            elif isinstance(r, rag_context.RetrievalResultEntry):
-                entries.append(r)
-        return entries
+    async def retrieve(self, query: str, top_k: int) -> list[rag_context.RetrievalResultEntry]:
+        embedding_model = await self.ap.model_mgr.get_embedding_model_by_uuid(
+            self.knowledge_base_entity.embedding_model_uuid
+        )
+        return await self.retriever.retrieve(self.knowledge_base_entity.uuid, query, embedding_model, top_k)
 
     async def delete_file(self, file_id: str):
-        await self._delete_document(file_id)
+        # delete vector
+        await self.ap.vector_db_mgr.vector_db.delete_by_file_id(self.knowledge_base_entity.uuid, file_id)
+
+        # delete chunk
+        await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.delete(persistence_rag.Chunk).where(persistence_rag.Chunk.file_id == file_id)
+        )
 
-        # Also cleanup DB record
         await self.ap.persistence_mgr.execute_async(
             sqlalchemy.delete(persistence_rag.File).where(persistence_rag.File.uuid == file_id)
         )
@@ -229,295 +216,32 @@ class RuntimeKnowledgeBase(KnowledgeBaseInterface):
         """Get the name of the knowledge base"""
         return self.knowledge_base_entity.name
 
-    def get_knowledge_engine_plugin_id(self) -> str:
-        """Get the Knowledge Engine plugin ID"""
-        return self.knowledge_base_entity.knowledge_engine_plugin_id or ''
+    def get_type(self) -> str:
+        """Get the type of knowledge base"""
+        return 'internal'
 
     async def dispose(self):
-        """Dispose the knowledge base, notifying the plugin to cleanup."""
-        await self._on_kb_delete()
-
-    # ========== Plugin Communication Methods ==========
-
-    async def _on_kb_create(self) -> None:
-        """Notify plugin about KB creation."""
-        plugin_id = self.knowledge_base_entity.knowledge_engine_plugin_id
-        if not plugin_id:
-            return
-
-        try:
-            config = self.knowledge_base_entity.creation_settings or {}
-            self.ap.logger.info(
-                f'Calling RAG plugin {plugin_id}: on_knowledge_base_create(kb_id={self.knowledge_base_entity.uuid})'
-            )
-            await self.ap.plugin_connector.rag_on_kb_create(plugin_id, self.knowledge_base_entity.uuid, config)
-        except Exception as e:
-            self.ap.logger.error(f'Failed to notify plugin {plugin_id} on KB create: {e}')
-            raise
-
-    async def _on_kb_delete(self) -> None:
-        """Notify plugin about KB deletion."""
-        plugin_id = self.knowledge_base_entity.knowledge_engine_plugin_id
-        if not plugin_id:
-            return
-
-        try:
-            self.ap.logger.info(
-                f'Calling RAG plugin {plugin_id}: on_knowledge_base_delete(kb_id={self.knowledge_base_entity.uuid})'
-            )
-            await self.ap.plugin_connector.rag_on_kb_delete(plugin_id, self.knowledge_base_entity.uuid)
-        except Exception as e:
-            self.ap.logger.error(f'Failed to notify plugin {plugin_id} on KB delete: {e}')
-
-    async def _ingest_document(
-        self,
-        file_metadata: dict[str, Any],
-        storage_path: str,
-        parsed_content: dict[str, Any] | None = None,
-    ) -> dict[str, Any]:
-        """Call plugin to ingest document."""
-        kb = self.knowledge_base_entity
-        plugin_id = kb.knowledge_engine_plugin_id
-        if not plugin_id:
-            self.ap.logger.error(f'No RAG plugin ID configured for KB {kb.uuid}. Ingestion failed.')
-            raise ValueError('RAG Plugin ID required')
-
-        self.ap.logger.info(f'Calling RAG plugin {plugin_id}: ingest(doc={file_metadata.get("filename")})')
-
-        # Inject knowledge_base_id into file metadata as required by SDK schema
-        file_metadata['knowledge_base_id'] = kb.uuid
-
-        context_data = {
-            'file_object': {
-                'metadata': file_metadata,
-                'storage_path': storage_path,
-            },
-            'knowledge_base_id': kb.uuid,
-            'collection_id': kb.collection_id or kb.uuid,
-            'creation_settings': kb.creation_settings or {},
-            'parsed_content': parsed_content,
-        }
-
-        try:
-            result = await self.ap.plugin_connector.call_rag_ingest(plugin_id, context_data)
-            return result
-        except Exception as e:
-            self.ap.logger.error(f'Plugin ingestion failed: {e}')
-            raise
-
-    async def _retrieve(
-        self,
-        query: str,
-        settings: dict[str, Any],
-    ) -> dict[str, Any]:
-        """Call plugin to retrieve documents.
-
-        Raises:
-            ValueError: If no RAG plugin is configured for this KB.
-            Exception: If the plugin retrieval call fails.
-        """
-        kb = self.knowledge_base_entity
-        plugin_id = kb.knowledge_engine_plugin_id
-        if not plugin_id:
-            raise ValueError(f'No RAG plugin ID configured for KB {kb.uuid}. Retrieval failed.')
-
-        # Session context (e.g. session_name) stays in retrieval_settings
-        # for plugins that need it. Do NOT move them into filters, as filters
-        # are passed directly to vector_search by some plugins (e.g. LangRAG)
-        # and would cause empty results when the metadata field doesn't exist.
-        filters = settings.pop('filters', {})
-
-        retrieval_context = {
-            'query': query,
-            'knowledge_base_id': kb.uuid,
-            'collection_id': kb.collection_id or kb.uuid,
-            'retrieval_settings': settings,
-            'creation_settings': kb.creation_settings or {},
-            'filters': filters,
-        }
-
-        result = await self.ap.plugin_connector.call_rag_retrieve(
-            plugin_id,
-            retrieval_context,
-        )
-        return result
-
-    async def _delete_document(self, document_id: str) -> bool:
-        """Call plugin to delete document."""
-        kb = self.knowledge_base_entity
-        plugin_id = kb.knowledge_engine_plugin_id
-        if not plugin_id:
-            return False
-
-        self.ap.logger.info(f'Calling RAG plugin {plugin_id}: delete_document(doc_id={document_id})')
-
-        try:
-            return await self.ap.plugin_connector.call_rag_delete_document(plugin_id, document_id, kb.uuid)
-        except Exception as e:
-            self.ap.logger.error(f'Plugin document deletion failed: {e}')
-            return False
+        await self.ap.vector_db_mgr.vector_db.delete_collection(self.knowledge_base_entity.uuid)
 
 
 class RAGManager:
     ap: app.Application
 
-    knowledge_bases: dict[str, KnowledgeBaseInterface]
+    knowledge_bases: list[KnowledgeBaseInterface]
 
     def __init__(self, ap: app.Application):
         self.ap = ap
-        self.knowledge_bases = {}
+        self.knowledge_bases = []
 
     async def initialize(self):
         await self.load_knowledge_bases_from_db()
 
-    async def get_all_knowledge_base_details(self) -> list[dict]:
-        """Get all knowledge bases with enriched Knowledge Engine details."""
-        # 1. Get raw KBs from DB
-        result = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_rag.KnowledgeBase))
-        knowledge_bases = result.all()
-
-        # 2. Get all available Knowledge Engines for enrichment
-        engine_map = {}
-        if self.ap.plugin_connector.is_enable_plugin:
-            try:
-                engines = await self.ap.plugin_connector.list_knowledge_engines()
-                engine_map = {e['plugin_id']: e for e in engines}
-            except Exception as e:
-                self.ap.logger.warning(f'Failed to list Knowledge Engines: {e}')
-
-        # 3. Serialize and enrich
-        kb_list = []
-        for kb in knowledge_bases:
-            kb_dict = self.ap.persistence_mgr.serialize_model(persistence_rag.KnowledgeBase, kb)
-            self._enrich_kb_dict(kb_dict, engine_map)
-            kb_list.append(kb_dict)
-
-        return kb_list
-
-    async def get_knowledge_base_details(self, kb_uuid: str) -> dict | None:
-        """Get specific knowledge base with enriched Knowledge Engine details."""
-        result = await self.ap.persistence_mgr.execute_async(
-            sqlalchemy.select(persistence_rag.KnowledgeBase).where(persistence_rag.KnowledgeBase.uuid == kb_uuid)
-        )
-        kb = result.first()
-        if not kb:
-            return None
-
-        kb_dict = self.ap.persistence_mgr.serialize_model(persistence_rag.KnowledgeBase, kb)
-
-        # Fetch engines
-        engine_map = {}
-        if self.ap.plugin_connector.is_enable_plugin:
-            try:
-                engines = await self.ap.plugin_connector.list_knowledge_engines()
-                engine_map = {e['plugin_id']: e for e in engines}
-            except Exception as e:
-                self.ap.logger.warning(f'Failed to list Knowledge Engines: {e}')
-
-        self._enrich_kb_dict(kb_dict, engine_map)
-        return kb_dict
-
-    @staticmethod
-    def _to_i18n_name(name) -> dict:
-        """Ensure name is always an I18nObject-compatible dict.
-
-        If *name* is already a dict (with ``en_US`` / ``zh_Hans`` keys) it is
-        returned as-is.  A plain string is wrapped into an I18nObject so the
-        frontend ``extractI18nObject`` helper never receives an unexpected type.
-        """
-        if isinstance(name, dict):
-            return name
-        return {'en_US': str(name), 'zh_Hans': str(name)}
-
-    def _enrich_kb_dict(self, kb_dict: dict, engine_map: dict) -> None:
-        """Helper to inject engine info into KB dict."""
-        plugin_id = kb_dict.get('knowledge_engine_plugin_id')
-
-        # Default fallback structure — name must be I18nObject for frontend compatibility
-        fallback_name = self._to_i18n_name(plugin_id or 'Internal (Legacy)')
-        fallback_info = {
-            'plugin_id': plugin_id,
-            'name': fallback_name,
-            'capabilities': [],
-        }
-
-        if not plugin_id:
-            kb_dict['knowledge_engine'] = fallback_info
-            return
-
-        engine_info = engine_map.get(plugin_id)
-        if engine_info:
-            kb_dict['knowledge_engine'] = {
-                'plugin_id': plugin_id,
-                'name': self._to_i18n_name(engine_info.get('name', plugin_id)),
-                'capabilities': engine_info.get('capabilities', []),
-            }
-        else:
-            kb_dict['knowledge_engine'] = fallback_info
-
-    async def create_knowledge_base(
-        self,
-        name: str,
-        knowledge_engine_plugin_id: str,
-        creation_settings: dict,
-        retrieval_settings: dict | None = None,
-        description: str = '',
-    ) -> persistence_rag.KnowledgeBase:
-        """Create a new knowledge base using a RAG plugin."""
-        # Validate that the Knowledge Engine plugin exists
-        if self.ap.plugin_connector.is_enable_plugin:
-            try:
-                engines = await self.ap.plugin_connector.list_knowledge_engines()
-                engine_ids = [e.get('plugin_id') for e in engines]
-                if knowledge_engine_plugin_id not in engine_ids:
-                    raise ValueError(f'Knowledge Engine plugin {knowledge_engine_plugin_id} not found')
-            except ValueError:
-                raise
-            except Exception as e:
-                self.ap.logger.warning(f'Failed to validate Knowledge Engine plugin existence: {e}')
-
-        kb_uuid = str(uuid.uuid4())
-        # Use UUID as collection ID by default for isolation
-        collection_id = kb_uuid
-
-        kb_data = {
-            'uuid': kb_uuid,
-            'name': name,
-            'description': description,
-            'knowledge_engine_plugin_id': knowledge_engine_plugin_id,
-            'collection_id': collection_id,
-            'creation_settings': creation_settings,
-            'retrieval_settings': retrieval_settings or {},
-        }
-
-        # Create Entity
-        kb = persistence_rag.KnowledgeBase(**kb_data)
-
-        # Persist
-        await self.ap.persistence_mgr.execute_async(sqlalchemy.insert(persistence_rag.KnowledgeBase).values(kb_data))
-
-        # Load into Runtime
-        runtime_kb = await self.load_knowledge_base(kb)
-
-        # Notify Plugin — rollback DB record and runtime entry on failure
-        try:
-            await runtime_kb._on_kb_create()
-        except Exception:
-            self.knowledge_bases.pop(kb_uuid, None)
-            await self.ap.persistence_mgr.execute_async(
-                sqlalchemy.delete(persistence_rag.KnowledgeBase).where(persistence_rag.KnowledgeBase.uuid == kb_uuid)
-            )
-            raise
-
-        self.ap.logger.info(f'Created new Knowledge Base {name} ({kb_uuid}) using plugin {knowledge_engine_plugin_id}')
-        return kb
-
     async def load_knowledge_bases_from_db(self):
         self.ap.logger.info('Loading knowledge bases from db...')
 
-        self.knowledge_bases = {}
+        self.knowledge_bases = []
 
-        # Load knowledge bases
+        # Load internal knowledge bases
         result = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_rag.KnowledgeBase))
         knowledge_bases = result.all()
 
@@ -529,37 +253,86 @@ class RAGManager:
                     f'Error loading knowledge base {knowledge_base.uuid}: {e}\n{traceback.format_exc()}'
                 )
 
+        # Load external knowledge bases
+        external_result = await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.select(persistence_rag.ExternalKnowledgeBase)
+        )
+        external_kbs = external_result.all()
+
+        for external_kb in external_kbs:
+            try:
+                # Don't trigger sync during batch loading - will sync once after LangBot connects to runtime
+                await self.load_external_knowledge_base(external_kb, trigger_sync=False)
+            except Exception as e:
+                self.ap.logger.error(
+                    f'Error loading external knowledge base {external_kb.uuid}: {e}\n{traceback.format_exc()}'
+                )
+
     async def load_knowledge_base(
         self,
         knowledge_base_entity: persistence_rag.KnowledgeBase | sqlalchemy.Row | dict,
     ) -> RuntimeKnowledgeBase:
         if isinstance(knowledge_base_entity, sqlalchemy.Row):
-            # Safe access to _mapping for SQLAlchemy 1.4+
             knowledge_base_entity = persistence_rag.KnowledgeBase(**knowledge_base_entity._mapping)
         elif isinstance(knowledge_base_entity, dict):
-            # Filter out non-database fields (like knowledge_engine which is computed)
-            filtered_dict = {
-                k: v for k, v in knowledge_base_entity.items() if k in persistence_rag.KnowledgeBase.ALL_DB_FIELDS
-            }
-            knowledge_base_entity = persistence_rag.KnowledgeBase(**filtered_dict)
+            knowledge_base_entity = persistence_rag.KnowledgeBase(**knowledge_base_entity)
 
         runtime_knowledge_base = RuntimeKnowledgeBase(ap=self.ap, knowledge_base_entity=knowledge_base_entity)
 
         await runtime_knowledge_base.initialize()
 
-        self.knowledge_bases[runtime_knowledge_base.get_uuid()] = runtime_knowledge_base
+        self.knowledge_bases.append(runtime_knowledge_base)
 
         return runtime_knowledge_base
 
+    async def load_external_knowledge_base(
+        self,
+        external_kb_entity: persistence_rag.ExternalKnowledgeBase | sqlalchemy.Row | dict,
+        trigger_sync: bool = True,
+    ) -> ExternalKnowledgeBase:
+        """Load external knowledge base into runtime
+
+        Args:
+            external_kb_entity: External KB entity to load
+            trigger_sync: Whether to trigger sync after loading (default True for manual creation, False for batch loading)
+        """
+        if isinstance(external_kb_entity, sqlalchemy.Row):
+            external_kb_entity = persistence_rag.ExternalKnowledgeBase(**external_kb_entity._mapping)
+        elif isinstance(external_kb_entity, dict):
+            external_kb_entity = persistence_rag.ExternalKnowledgeBase(**external_kb_entity)
+
+        external_kb = ExternalKnowledgeBase(ap=self.ap, external_kb_entity=external_kb_entity)
+
+        await external_kb.initialize()
+
+        self.knowledge_bases.append(external_kb)
+
+        # Trigger sync to create the instance immediately (for manual creation)
+        # Skip sync during batch loading from DB to avoid multiple sync calls
+        if trigger_sync:
+            try:
+                await self.ap.plugin_connector.sync_polymorphic_component_instances()
+                self.ap.logger.info(f'Triggered sync after loading external KB {external_kb_entity.uuid}')
+            except Exception as e:
+                self.ap.logger.error(f'Failed to sync after loading external KB: {e}')
+
+        return external_kb
+
     async def get_knowledge_base_by_uuid(self, kb_uuid: str) -> KnowledgeBaseInterface | None:
-        return self.knowledge_bases.get(kb_uuid)
+        for kb in self.knowledge_bases:
+            if kb.get_uuid() == kb_uuid:
+                return kb
+        return None
 
     async def remove_knowledge_base_from_runtime(self, kb_uuid: str):
-        self.knowledge_bases.pop(kb_uuid, None)
+        for kb in self.knowledge_bases:
+            if kb.get_uuid() == kb_uuid:
+                self.knowledge_bases.remove(kb)
+                return
 
     async def delete_knowledge_base(self, kb_uuid: str):
-        kb = self.knowledge_bases.pop(kb_uuid, None)
-        if kb is not None:
-            await kb.dispose()
-        else:
-            self.ap.logger.warning(f'Knowledge base {kb_uuid} not found in runtime, skipping plugin notification')
+        for kb in self.knowledge_bases:
+            if kb.get_uuid() == kb_uuid:
+                await kb.dispose()
+                self.knowledge_bases.remove(kb)
+                return

src/langbot/pkg/rag/knowledge/services/base_service.py

@@ -0,0 +1,15 @@
+# 封装异步操作
+import asyncio
+
+
+class BaseService:
+    def __init__(self):
+        pass
+
+    async def _run_sync(self, func, *args, **kwargs):
+        """
+        在单独的线程中运行同步函数。
+        如果第一个参数是 session，则在 to_thread 中获取新的 session。
+        """
+
+        return await asyncio.to_thread(func, *args, **kwargs)

src/langbot/pkg/rag/knowledge/services/chunker.py

@@ -0,0 +1,49 @@
+from __future__ import annotations
+
+import json
+from typing import List
+from langbot.pkg.rag.knowledge.services import base_service
+from langbot.pkg.core import app
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+
+
+class Chunker(base_service.BaseService):
+    """
+    A class for splitting long texts into smaller, overlapping chunks.
+    """
+
+    def __init__(self, ap: app.Application, chunk_size: int = 500, chunk_overlap: int = 50):
+        self.ap = ap
+        self.chunk_size = chunk_size
+        self.chunk_overlap = chunk_overlap
+        if self.chunk_overlap >= self.chunk_size:
+            self.ap.logger.warning(
+                'Chunk overlap is greater than or equal to chunk size. This may lead to empty or malformed chunks.'
+            )
+
+    def _split_text_sync(self, text: str) -> List[str]:
+        """
+        Synchronously splits a long text into chunks with specified overlap.
+        This is a CPU-bound operation, intended to be run in a separate thread.
+        """
+        if not text:
+            return []
+
+        text_splitter = RecursiveCharacterTextSplitter(
+            chunk_size=self.chunk_size,
+            chunk_overlap=self.chunk_overlap,
+            length_function=len,
+            is_separator_regex=False,
+        )
+        return text_splitter.split_text(text)
+
+    async def chunk(self, text: str) -> List[str]:
+        """
+        Asynchronously chunks a given text into smaller pieces.
+        """
+        self.ap.logger.info(f'Chunking text (length: {len(text)})...')
+        # Run the synchronous splitting logic in a separate thread
+        chunks = await self._run_sync(self._split_text_sync, text)
+        self.ap.logger.info(f'Text chunked into {len(chunks)} pieces.')
+        self.ap.logger.debug(f'Chunks: {json.dumps(chunks, indent=4, ensure_ascii=False)}')
+        return chunks

src/langbot/pkg/rag/knowledge/services/embedder.py

@@ -0,0 +1,55 @@
+from __future__ import annotations
+import uuid
+from typing import List
+from langbot.pkg.rag.knowledge.services.base_service import BaseService
+from langbot.pkg.entity.persistence import rag as persistence_rag
+from langbot.pkg.core import app
+from langbot.pkg.provider.modelmgr.requester import RuntimeEmbeddingModel
+import sqlalchemy
+
+
+class Embedder(BaseService):
+    def __init__(self, ap: app.Application) -> None:
+        super().__init__()
+        self.ap = ap
+
+    async def embed_and_store(
+        self, kb_id: str, file_id: str, chunks: List[str], embedding_model: RuntimeEmbeddingModel
+    ) -> list[persistence_rag.Chunk]:
+        # save chunk to db
+        chunk_entities: list[persistence_rag.Chunk] = []
+        chunk_ids: list[str] = []
+
+        for chunk_text in chunks:
+            chunk_uuid = str(uuid.uuid4())
+            chunk_ids.append(chunk_uuid)
+            chunk_entity = persistence_rag.Chunk(uuid=chunk_uuid, file_id=file_id, text=chunk_text)
+            chunk_entities.append(chunk_entity)
+
+        chunk_dicts = [
+            self.ap.persistence_mgr.serialize_model(persistence_rag.Chunk, chunk) for chunk in chunk_entities
+        ]
+
+        await self.ap.persistence_mgr.execute_async(sqlalchemy.insert(persistence_rag.Chunk).values(chunk_dicts))
+
+        # get embeddings (batch size limit: 64 for OpenAI)
+        MAX_BATCH_SIZE = 64
+        embeddings_list: list[list[float]] = []
+
+        for i in range(0, len(chunks), MAX_BATCH_SIZE):
+            batch = chunks[i : i + MAX_BATCH_SIZE]
+            batch_embeddings = await embedding_model.provider.invoke_embedding(
+                model=embedding_model,
+                input_text=batch,
+                extra_args={},  # TODO: add extra args
+                knowledge_base_id=kb_id,
+                call_type='embedding',
+            )
+            embeddings_list.extend(batch_embeddings)
+
+        # save embeddings to vdb
+        await self.ap.vector_db_mgr.vector_db.add_embeddings(kb_id, chunk_ids, embeddings_list, chunk_dicts)
+
+        self.ap.logger.info(f'Successfully saved {len(chunk_entities)} embeddings to Knowledge Base.')
+
+        return chunk_entities

src/langbot/pkg/rag/knowledge/services/parser.py

@@ -0,0 +1,291 @@
+from __future__ import annotations
+
+import PyPDF2
+import io
+from docx import Document
+import chardet
+from typing import Union, Callable, Any
+import markdown
+from bs4 import BeautifulSoup
+import re
+import asyncio  # Import asyncio for async operations
+from langbot.pkg.core import app
+
+
+class FileParser:
+    """
+    A robust file parser class to extract text content from various document formats.
+    It supports TXT, PDF, DOCX, XLSX, CSV, Markdown, HTML, and EPUB files.
+    All core file reading operations are designed to be run synchronously in a thread pool
+    to avoid blocking the asyncio event loop.
+    """
+
+    def __init__(self, ap: app.Application):
+        self.ap = ap
+
+    async def _run_sync(self, sync_func: Callable, *args: Any, **kwargs: Any) -> Any:
+        """
+        Runs a synchronous function in a separate thread to prevent blocking the event loop.
+        This is a general utility method for wrapping blocking I/O operations.
+        """
+        try:
+            return await asyncio.to_thread(sync_func, *args, **kwargs)
+        except Exception as e:
+            self.ap.logger.error(f'Error running synchronous function {sync_func.__name__}: {e}')
+            raise
+
+    async def parse(self, file_name: str, extension: str) -> Union[str, None]:
+        """
+        Parses the file based on its extension and returns the extracted text content.
+        This is the main asynchronous entry point for parsing.
+
+        Args:
+            file_name (str): The name of the file to be parsed, get from ap.storage_mgr
+
+        Returns:
+            Union[str, None]: The extracted text content as a single string, or None if parsing fails.
+        """
+
+        file_extension = extension.lower()
+        parser_method = getattr(self, f'_parse_{file_extension}', None)
+
+        if parser_method is None:
+            self.ap.logger.error(f'Unsupported file format: {file_extension} for file {file_name}')
+            return None
+
+        try:
+            # Pass file_path to the specific parser methods
+            return await parser_method(file_name)
+        except Exception as e:
+            self.ap.logger.error(f'Failed to parse {file_extension} file {file_name}: {e}')
+            return None
+
+    # --- Helper for reading files with encoding detection ---
+    async def _read_file_content(self, file_name: str) -> Union[str, bytes]:
+        """
+        Reads a file with automatic encoding detection, ensuring the synchronous
+        file read operation runs in a separate thread.
+        """
+
+        # def _read_sync():
+        #     with open(file_path, 'rb') as file:
+        #         raw_data = file.read()
+        #         detected = chardet.detect(raw_data)
+        #         encoding = detected['encoding'] or 'utf-8'
+
+        #     if mode == 'r':
+        #         return raw_data.decode(encoding, errors='ignore')
+        #     return raw_data  # For binary mode
+
+        # return await self._run_sync(_read_sync)
+        file_bytes = await self.ap.storage_mgr.storage_provider.load(file_name)
+
+        detected = chardet.detect(file_bytes)
+        encoding = detected['encoding'] or 'utf-8'
+
+        return file_bytes.decode(encoding, errors='ignore')
+
+    # --- Specific Parser Methods ---
+
+    async def _parse_txt(self, file_name: str) -> str:
+        """Parses a TXT file and returns its content."""
+        self.ap.logger.info(f'Parsing TXT file: {file_name}')
+        return await self._read_file_content(file_name)
+
+    async def _parse_pdf(self, file_name: str) -> str:
+        """Parses a PDF file and returns its text content."""
+        self.ap.logger.info(f'Parsing PDF file: {file_name}')
+
+        # def _parse_pdf_sync():
+        #     text_content = []
+        #     with open(file_name, 'rb') as file:
+        #         pdf_reader = PyPDF2.PdfReader(file)
+        #         for page in pdf_reader.pages:
+        #             text = page.extract_text()
+        #             if text:
+        #                 text_content.append(text)
+        #     return '\n'.join(text_content)
+
+        # return await self._run_sync(_parse_pdf_sync)
+
+        pdf_bytes = await self.ap.storage_mgr.storage_provider.load(file_name)
+
+        def _parse_pdf_sync():
+            pdf_reader = PyPDF2.PdfReader(io.BytesIO(pdf_bytes))
+            text_content = []
+            for page in pdf_reader.pages:
+                text = page.extract_text()
+                if text:
+                    text_content.append(text)
+            return '\n'.join(text_content)
+
+        return await self._run_sync(_parse_pdf_sync)
+
+    async def _parse_docx(self, file_name: str) -> str:
+        """Parses a DOCX file and returns its text content."""
+        self.ap.logger.info(f'Parsing DOCX file: {file_name}')
+
+        docx_bytes = await self.ap.storage_mgr.storage_provider.load(file_name)
+
+        def _parse_docx_sync():
+            doc = Document(io.BytesIO(docx_bytes))
+            text_content = [paragraph.text for paragraph in doc.paragraphs if paragraph.text.strip()]
+            return '\n'.join(text_content)
+
+        return await self._run_sync(_parse_docx_sync)
+
+    async def _parse_doc(self, file_name: str) -> str:
+        """Handles .doc files, explicitly stating lack of direct support."""
+        self.ap.logger.warning(f'Direct .doc parsing is not supported for {file_name}. Please convert to .docx first.')
+        raise NotImplementedError('Direct .doc parsing not supported. Please convert to .docx first.')
+
+    # async def _parse_xlsx(self, file_name: str) -> str:
+    #     """Parses an XLSX file, returning text from all sheets."""
+    #     self.ap.logger.info(f'Parsing XLSX file: {file_name}')
+
+    #     xlsx_bytes = await self.ap.storage_mgr.storage_provider.load(file_name)
+
+    #     def _parse_xlsx_sync():
+    #         excel_file = pd.ExcelFile(io.BytesIO(xlsx_bytes))
+    #         all_sheet_content = []
+    #         for sheet_name in excel_file.sheet_names:
+    #             df = pd.read_excel(io.BytesIO(xlsx_bytes), sheet_name=sheet_name)
+    #             sheet_text = f'--- Sheet: {sheet_name} ---\n{df.to_string(index=False)}\n'
+    #             all_sheet_content.append(sheet_text)
+    #         return '\n'.join(all_sheet_content)
+
+    #     return await self._run_sync(_parse_xlsx_sync)
+
+    # async def _parse_csv(self, file_name: str) -> str:
+    #     """Parses a CSV file and returns its content as a string."""
+    #     self.ap.logger.info(f'Parsing CSV file: {file_name}')
+
+    #     csv_bytes = await self.ap.storage_mgr.storage_provider.load(file_name)
+
+    #     def _parse_csv_sync():
+    #         # pd.read_csv can often detect encoding, but explicit detection is safer
+    #         # raw_data = self._read_file_content(
+    #         #     file_name, mode='rb'
+    #         # )  # Note: this will need to be await outside this sync function
+    #         # _ = raw_data
+    #         # For simplicity, we'll let pandas handle encoding internally after a raw read.
+    #         # A more robust solution might pass encoding directly to pd.read_csv after detection.
+    #         detected = chardet.detect(io.BytesIO(csv_bytes))
+    #         encoding = detected['encoding'] or 'utf-8'
+    #         df = pd.read_csv(io.BytesIO(csv_bytes), encoding=encoding)
+    #         return df.to_string(index=False)
+
+    #     return await self._run_sync(_parse_csv_sync)
+
+    async def _parse_md(self, file_name: str) -> str:
+        """Parses a Markdown file, converting it to structured plain text."""
+        self.ap.logger.info(f'Parsing Markdown file: {file_name}')
+
+        md_bytes = await self.ap.storage_mgr.storage_provider.load(file_name)
+
+        def _parse_markdown_sync():
+            md_content = io.BytesIO(md_bytes).read().decode('utf-8', errors='ignore')
+            html_content = markdown.markdown(
+                md_content, extensions=['extra', 'codehilite', 'tables', 'toc', 'fenced_code']
+            )
+            soup = BeautifulSoup(html_content, 'html.parser')
+            text_parts = []
+            for element in soup.children:
+                if element.name in ['h1', 'h2', 'h3', 'h4', 'h5', 'h6']:
+                    level = int(element.name[1])
+                    text_parts.append('#' * level + ' ' + element.get_text().strip())
+                elif element.name == 'p':
+                    text = element.get_text().strip()
+                    if text:
+                        text_parts.append(text)
+                elif element.name in ['ul', 'ol']:
+                    for li in element.find_all('li'):
+                        text_parts.append(f'* {li.get_text().strip()}')
+                elif element.name == 'pre':
+                    code_block = element.get_text().strip()
+                    if code_block:
+                        text_parts.append(f'```\n{code_block}\n```')
+                elif element.name == 'table':
+                    table_str = self._extract_table_to_markdown_sync(element)  # Call sync helper
+                    if table_str:
+                        text_parts.append(table_str)
+                elif element.name:
+                    text = element.get_text(separator=' ', strip=True)
+                    if text:
+                        text_parts.append(text)
+            cleaned_text = re.sub(r'\n\s*\n', '\n\n', '\n'.join(text_parts))
+            return cleaned_text.strip()
+
+        return await self._run_sync(_parse_markdown_sync)
+
+    async def _parse_html(self, file_name: str) -> str:
+        """Parses an HTML file, extracting structured plain text."""
+        self.ap.logger.info(f'Parsing HTML file: {file_name}')
+
+        html_bytes = await self.ap.storage_mgr.storage_provider.load(file_name)
+
+        def _parse_html_sync():
+            html_content = io.BytesIO(html_bytes).read().decode('utf-8', errors='ignore')
+            soup = BeautifulSoup(html_content, 'html.parser')
+            for script_or_style in soup(['script', 'style']):
+                script_or_style.decompose()
+            text_parts = []
+            for element in soup.body.children if soup.body else soup.children:
+                if element.name in ['h1', 'h2', 'h3', 'h4', 'h5', 'h6']:
+                    level = int(element.name[1])
+                    text_parts.append('#' * level + ' ' + element.get_text().strip())
+                elif element.name == 'p':
+                    text = element.get_text().strip()
+                    if text:
+                        text_parts.append(text)
+                elif element.name in ['ul', 'ol']:
+                    for li in element.find_all('li'):
+                        text = li.get_text().strip()
+                        if text:
+                            text_parts.append(f'* {text}')
+                elif element.name == 'table':
+                    table_str = self._extract_table_to_markdown_sync(element)  # Call sync helper
+                    if table_str:
+                        text_parts.append(table_str)
+                elif element.name:
+                    text = element.get_text(separator=' ', strip=True)
+                    if text:
+                        text_parts.append(text)
+            cleaned_text = re.sub(r'\n\s*\n', '\n\n', '\n'.join(text_parts))
+            return cleaned_text.strip()
+
+        return await self._run_sync(_parse_html_sync)
+
+    def _add_toc_items_sync(self, toc_list: list, text_content: list, level: int):
+        """Recursively adds TOC items to text_content (synchronous helper)."""
+        indent = '  ' * level
+        for item in toc_list:
+            if isinstance(item, tuple):
+                chapter, subchapters = item
+                text_content.append(f'{indent}- {chapter.title}')
+                self._add_toc_items_sync(subchapters, text_content, level + 1)
+            else:
+                text_content.append(f'{indent}- {item.title}')
+
+    def _extract_table_to_markdown_sync(self, table_element: BeautifulSoup) -> str:
+        """Helper to convert a BeautifulSoup table element into a Markdown table string (synchronous)."""
+        headers = [th.get_text().strip() for th in table_element.find_all('th')]
+        rows = []
+        for tr in table_element.find_all('tr'):
+            cells = [td.get_text().strip() for td in tr.find_all('td')]
+            if cells:
+                rows.append(cells)
+
+        if not headers and not rows:
+            return ''
+
+        table_lines = []
+        if headers:
+            table_lines.append(' | '.join(headers))
+            table_lines.append(' | '.join(['---'] * len(headers)))
+
+        for row_cells in rows:
+            padded_cells = row_cells + [''] * (len(headers) - len(row_cells)) if headers else row_cells
+            table_lines.append(' | '.join(padded_cells))
+
+        return '\n'.join(table_lines)

src/langbot/pkg/rag/knowledge/services/retriever.py

@@ -0,0 +1,53 @@
+from __future__ import annotations
+
+from . import base_service
+from ....core import app
+from ....provider.modelmgr.requester import RuntimeEmbeddingModel
+from langbot_plugin.api.entities.builtin.rag import context as rag_context
+from langbot_plugin.api.entities.builtin.provider.message import ContentElement
+
+
+class Retriever(base_service.BaseService):
+    def __init__(self, ap: app.Application):
+        super().__init__()
+        self.ap = ap
+
+    async def retrieve(
+        self, kb_id: str, query: str, embedding_model: RuntimeEmbeddingModel, k: int = 5
+    ) -> list[rag_context.RetrievalResultEntry]:
+        self.ap.logger.info(
+            f"Retrieving for query: '{query[:10]}' with k={k} using {embedding_model.model_entity.uuid}"
+        )
+
+        query_embedding: list[float] = await embedding_model.provider.invoke_embedding(
+            model=embedding_model,
+            input_text=[query],
+            extra_args={},  # TODO: add extra args
+            knowledge_base_id=kb_id,
+            query_text=query,
+            call_type='retrieve',
+        )
+
+        vector_results = await self.ap.vector_db_mgr.vector_db.search(kb_id, query_embedding[0], k)
+
+        # 'ids' shape mirrors the Chroma-style response contract for compatibility
+        matched_vector_ids = vector_results.get('ids', [[]])[0]
+        distances = vector_results.get('distances', [[]])[0]
+        vector_metadatas = vector_results.get('metadatas', [[]])[0]
+
+        if not matched_vector_ids:
+            self.ap.logger.info('No relevant chunks found in vector database.')
+            return []
+
+        result: list[rag_context.RetrievalResultEntry] = []
+
+        for i, id in enumerate(matched_vector_ids):
+            entry = rag_context.RetrievalResultEntry(
+                id=id,
+                content=[ContentElement.from_text(vector_metadatas[i].get('text', ''))],
+                metadata=vector_metadatas[i],
+                distance=distances[i],
+            )
+            result.append(entry)
+
+        return result

src/langbot/pkg/rag/service/__init__.py

@@ -1 +0,0 @@
-from .runtime import RAGRuntimeService as RAGRuntimeService

src/langbot/pkg/rag/service/runtime.py

@@ -1,89 +0,0 @@
-from __future__ import annotations
-
-import posixpath
-from typing import Any
-from langbot.pkg.core import app
-
-
-class RAGRuntimeService:
-    """Service to handle RAG-related requests from plugins (Runtime).
-
-    This service acts as the bridge between plugin RPC requests and
-    LangBot's infrastructure (embedding models, vector databases, file storage).
-    """
-
-    def __init__(self, ap: app.Application):
-        self.ap = ap
-
-    async def vector_upsert(
-        self,
-        collection_id: str,
-        vectors: list[list[float]],
-        ids: list[str],
-        metadata: list[dict[str, Any]] | None = None,
-        documents: list[str] | None = None,
-    ) -> None:
-        """Handle VECTOR_UPSERT action."""
-        metadatas = metadata if metadata else [{} for _ in vectors]
-        await self.ap.vector_db_mgr.upsert(
-            collection_name=collection_id,
-            vectors=vectors,
-            ids=ids,
-            metadata=metadatas,
-            documents=documents,
-        )
-
-    async def vector_search(
-        self,
-        collection_id: str,
-        query_vector: list[float],
-        top_k: int,
-        filters: dict[str, Any] | None = None,
-        search_type: str = 'vector',
-        query_text: str = '',
-    ) -> list[dict[str, Any]]:
-        """Handle VECTOR_SEARCH action."""
-        return await self.ap.vector_db_mgr.search(
-            collection_name=collection_id,
-            query_vector=query_vector,
-            limit=top_k,
-            filter=filters,
-            search_type=search_type,
-            query_text=query_text,
-        )
-
-    async def vector_delete(
-        self, collection_id: str, file_ids: list[str] | None = None, filters: dict[str, Any] | None = None
-    ) -> int:
-        """Handle VECTOR_DELETE action.
-
-        Deletes vectors associated with the given file IDs from the collection.
-        Each file_id corresponds to a document whose vectors will be removed.
-
-        Args:
-            collection_id: The collection to delete from.
-            file_ids: File IDs whose associated vectors should be deleted.
-                Each file_id maps to a set of vectors stored with that file_id
-                in their metadata.
-            filters: Filter-based deletion (not yet supported, will raise).
-        """
-        count = 0
-        if file_ids:
-            await self.ap.vector_db_mgr.delete_by_file_id(collection_name=collection_id, file_ids=file_ids)
-            count = len(file_ids)
-        elif filters:
-            count = await self.ap.vector_db_mgr.delete_by_filter(collection_name=collection_id, filter=filters)
-        return count
-
-    async def get_file_stream(self, storage_path: str) -> bytes:
-        """Handle GET_KNOWLEDEGE_FILE_STREAM action.
-
-        Uses the storage manager abstraction to load file content,
-        regardless of the underlying storage provider.
-        """
-        # Validate storage_path to prevent path traversal
-        normalized = posixpath.normpath(storage_path)
-        if normalized.startswith('/') or '..' in normalized.split('/'):
-            raise ValueError('Invalid storage path')
-        content_bytes = await self.ap.storage_mgr.storage_provider.load(normalized)
-        return content_bytes if content_bytes else b''

src/langbot/pkg/storage/mgr.py

@@ -3,7 +3,7 @@ from __future__ import annotations
 
 from ..core import app
 from . import provider
-from .providers import localstorage
+from .providers import localstorage, s3storage
 
 
 class StorageMgr:
@@ -21,8 +21,6 @@ class StorageMgr:
         storage_type = storage_config.get('use', 'local')
 
         if storage_type == 's3':
-            from .providers import s3storage
-
             self.storage_provider = s3storage.S3StorageProvider(self.ap)
             self.ap.logger.info('Initialized S3 storage backend.')
         else:

src/langbot/pkg/storage/provider.py

@@ -43,13 +43,6 @@ class StorageProvider(abc.ABC):
     ):
         pass
 
-    @abc.abstractmethod
-    async def size(
-        self,
-        key: str,
-    ) -> int:
-        pass
-
     @abc.abstractmethod
     async def delete_dir_recursive(
         self,

src/langbot/pkg/storage/providers/localstorage.py

@@ -47,12 +47,6 @@ class LocalStorageProvider(provider.StorageProvider):
     ):
         os.remove(os.path.join(LOCAL_STORAGE_PATH, f'{key}'))
 
-    async def size(
-        self,
-        key: str,
-    ) -> int:
-        return os.path.getsize(os.path.join(LOCAL_STORAGE_PATH, f'{key}'))
-
     async def delete_dir_recursive(
         self,
         dir_path: str,

src/langbot/pkg/storage/providers/s3storage.py

@@ -117,21 +117,6 @@ class S3StorageProvider(provider.StorageProvider):
             self.ap.logger.error(f'Failed to delete from S3: {e}')
             raise
 
-    async def size(
-        self,
-        key: str,
-    ) -> int:
-        """Get object size from S3 without downloading it"""
-        try:
-            response = self.s3_client.head_object(
-                Bucket=self.bucket_name,
-                Key=key,
-            )
-            return response['ContentLength']
-        except Exception as e:
-            self.ap.logger.error(f'Failed to get size from S3: {e}')
-            raise
-
     async def delete_dir_recursive(
         self,
         dir_path: str,

src/langbot/pkg/survey/__init__.py

@@ -1 +0,0 @@
-"""Survey module for in-product surveys triggered by events."""

src/langbot/pkg/survey/manager.py

@@ -1,148 +0,0 @@
-"""Survey manager: tracks events, communicates with Space to fetch/submit surveys."""
-
-from __future__ import annotations
-
-import asyncio
-import json
-import typing
-import httpx
-import sqlalchemy
-
-from ..core import app as core_app
-from ..entity.persistence.metadata import Metadata
-from ..utils import constants
-
-SURVEY_TRIGGERED_KEY = 'survey_triggered_events'
-
-
-class SurveyManager:
-    """Manages survey lifecycle: event tracking, pending survey fetch, submission."""
-
-    def __init__(self, ap: core_app.Application):
-        self.ap = ap
-        self._triggered_events: set[str] = set()
-        self._pending_survey: typing.Optional[dict] = None
-        self._space_url: str = ''
-
-    async def initialize(self):
-        space_config = self.ap.instance_config.data.get('space', {})
-        self._space_url = space_config.get('url', '').rstrip('/')
-        await self._load_triggered_events()
-
-    async def _load_triggered_events(self):
-        """Load previously triggered events from metadata table."""
-        try:
-            result = await self.ap.persistence_mgr.execute_async(
-                sqlalchemy.select(Metadata).where(Metadata.key == SURVEY_TRIGGERED_KEY)
-            )
-            row = result.first()
-            if row:
-                self._triggered_events = set(json.loads(row[0].value))
-        except Exception:
-            self._triggered_events = set()
-
-    async def _save_triggered_events(self):
-        """Persist triggered events to metadata table."""
-        try:
-            value = json.dumps(list(self._triggered_events))
-            result = await self.ap.persistence_mgr.execute_async(
-                sqlalchemy.select(Metadata).where(Metadata.key == SURVEY_TRIGGERED_KEY)
-            )
-            if result.first():
-                await self.ap.persistence_mgr.execute_async(
-                    sqlalchemy.update(Metadata).where(Metadata.key == SURVEY_TRIGGERED_KEY).values(value=value)
-                )
-            else:
-                await self.ap.persistence_mgr.execute_async(
-                    sqlalchemy.insert(Metadata).values(key=SURVEY_TRIGGERED_KEY, value=value)
-                )
-        except Exception as e:
-            self.ap.logger.debug(f'Failed to save survey triggered events: {e}')
-
-    def _is_space_configured(self) -> bool:
-        space_config = self.ap.instance_config.data.get('space', {})
-        if space_config.get('disable_telemetry', False):
-            return False
-        return bool(self._space_url)
-
-    async def trigger_event(self, event: str):
-        """Called when an event occurs. Checks Space for a pending survey."""
-        if event in self._triggered_events:
-            return
-        if not self._is_space_configured():
-            return
-
-        self._triggered_events.add(event)
-        await self._save_triggered_events()
-
-        # Check for pending survey asynchronously
-        asyncio.create_task(self._fetch_pending_survey(event))
-
-    async def _fetch_pending_survey(self, event: str):
-        """Fetch pending survey from Space for this event."""
-        try:
-            url = f'{self._space_url}/api/v1/survey/pending'
-            payload = {
-                'instance_id': constants.instance_id,
-                'event': event,
-            }
-            async with httpx.AsyncClient(timeout=httpx.Timeout(10)) as client:
-                resp = await client.post(url, json=payload)
-                if resp.status_code == 200:
-                    data = resp.json()
-                    if data.get('code') == 0 and data.get('data', {}).get('survey'):
-                        self._pending_survey = data['data']['survey']
-                        self.ap.logger.info(f'Survey pending: {self._pending_survey.get("survey_id")}')
-        except Exception as e:
-            self.ap.logger.debug(f'Failed to fetch pending survey: {e}')
-
-    def get_pending_survey(self) -> typing.Optional[dict]:
-        """Return the current pending survey (if any) for the frontend to display."""
-        return self._pending_survey
-
-    def clear_pending_survey(self):
-        """Clear the pending survey (after user responds or dismisses)."""
-        self._pending_survey = None
-
-    async def submit_response(self, survey_id: str, answers: dict, completed: bool = True) -> bool:
-        """Submit a survey response to Space."""
-        if not self._is_space_configured():
-            return False
-        try:
-            url = f'{self._space_url}/api/v1/survey/respond'
-            payload = {
-                'survey_id': survey_id,
-                'instance_id': constants.instance_id,
-                'answers': answers,
-                'metadata': {
-                    'version': constants.semantic_version,
-                },
-                'completed': completed,
-            }
-            async with httpx.AsyncClient(timeout=httpx.Timeout(10)) as client:
-                resp = await client.post(url, json=payload)
-                if resp.status_code == 200:
-                    self.clear_pending_survey()
-                    return True
-        except Exception as e:
-            self.ap.logger.warning(f'Failed to submit survey response: {e}')
-        return False
-
-    async def dismiss_survey(self, survey_id: str) -> bool:
-        """Dismiss a survey."""
-        if not self._is_space_configured():
-            return False
-        try:
-            url = f'{self._space_url}/api/v1/survey/dismiss'
-            payload = {
-                'survey_id': survey_id,
-                'instance_id': constants.instance_id,
-            }
-            async with httpx.AsyncClient(timeout=httpx.Timeout(10)) as client:
-                resp = await client.post(url, json=payload)
-                if resp.status_code == 200:
-                    self.clear_pending_survey()
-                    return True
-        except Exception as e:
-            self.ap.logger.warning(f'Failed to dismiss survey: {e}')
-        return False

src/langbot/pkg/telemetry/telemetry.py

@@ -60,7 +60,7 @@ class TelemetryManager:
                     except Exception:
                         sanitized['query_id'] = str(sanitized.get('query_id', ''))
 
-                for sfield in ('adapter', 'runner', 'runner_category', 'model_name', 'version', 'error', 'timestamp'):
+                for sfield in ('adapter', 'runner', 'model_name', 'version', 'error', 'timestamp'):
                     v = sanitized.get(sfield)
                     sanitized[sfield] = '' if v is None else str(v)
 

src/langbot/pkg/utils/constants.py

@@ -2,7 +2,7 @@ import langbot
 
 semantic_version = f'v{langbot.__version__}'
 
-required_database_version = 24
+required_database_version = 18
 """Tag the version of the database schema, used to check if the database needs to be migrated"""
 
 debug_mode = False

src/langbot/pkg/utils/httpclient.py

@@ -1,43 +0,0 @@
-"""Shared aiohttp.ClientSession to avoid repeated SSL context creation.
-
-Each call to `aiohttp.ClientSession()` creates a new `TCPConnector` which in turn
-creates a new `ssl.SSLContext` and loads all system root certificates. This is
-extremely expensive in both CPU and memory (~270MB total allocations observed via
-memray profiling).
-
-This module provides a shared session pool so that all HTTP client code in LangBot
-reuses the same underlying SSL context and connection pool.
-"""
-
-from __future__ import annotations
-
-import aiohttp
-
-_sessions: dict[str, aiohttp.ClientSession] = {}
-
-
-def get_session(*, trust_env: bool = False) -> aiohttp.ClientSession:
-    """Get or create a shared aiohttp.ClientSession.
-
-    Args:
-        trust_env: Whether to trust environment variables for proxy settings.
-
-    Returns:
-        A shared aiohttp.ClientSession instance.
-    """
-    key = f'trust_env={trust_env}'
-
-    session = _sessions.get(key)
-    if session is None or session.closed:
-        session = aiohttp.ClientSession(trust_env=trust_env)
-        _sessions[key] = session
-
-    return session
-
-
-async def close_all():
-    """Close all shared sessions. Call on application shutdown."""
-    for session in _sessions.values():
-        if not session.closed:
-            await session.close()
-    _sessions.clear()

src/langbot/pkg/utils/image.py

@@ -5,8 +5,6 @@ from urllib.parse import urlparse, parse_qs
 import ssl
 
 import aiohttp
-
-from langbot.pkg.utils import httpclient
 import PIL.Image
 import httpx
 
@@ -49,54 +47,53 @@ async def get_gewechat_image_base64(
     )
 
     try:
-        session = httpclient.get_session()
-        # 获取图片下载链接
-        try:
-            async with session.post(
-                f'{gewechat_url}/v2/api/message/downloadImage',
-                headers=headers,
-                json={'appId': app_id, 'type': image_type, 'xml': xml_content},
-                timeout=timeout,
-            ) as response:
-                if response.status != 200:
-                    # print(response)
-                    raise Exception(f'获取gewechat图片下载失败: {await response.text()}')
+        async with aiohttp.ClientSession(timeout=timeout) as session:
+            # 获取图片下载链接
+            try:
+                async with session.post(
+                    f'{gewechat_url}/v2/api/message/downloadImage',
+                    headers=headers,
+                    json={'appId': app_id, 'type': image_type, 'xml': xml_content},
+                ) as response:
+                    if response.status != 200:
+                        # print(response)
+                        raise Exception(f'获取gewechat图片下载失败: {await response.text()}')
 
-                resp_data = await response.json()
-                if resp_data.get('ret') != 200:
-                    raise Exception(f'获取gewechat图片下载链接失败: {resp_data}')
+                    resp_data = await response.json()
+                    if resp_data.get('ret') != 200:
+                        raise Exception(f'获取gewechat图片下载链接失败: {resp_data}')
 
-                file_url = resp_data['data']['fileUrl']
-        except asyncio.TimeoutError:
-            raise Exception('获取图片下载链接超时')
-        except aiohttp.ClientError as e:
-            raise Exception(f'获取图片下载链接网络错误: {str(e)}')
+                    file_url = resp_data['data']['fileUrl']
+            except asyncio.TimeoutError:
+                raise Exception('获取图片下载链接超时')
+            except aiohttp.ClientError as e:
+                raise Exception(f'获取图片下载链接网络错误: {str(e)}')
 
-        # 解析原始URL并替换端口
-        base_url = gewechat_file_url
-        download_url = f'{base_url}/download/{file_url}'
+            # 解析原始URL并替换端口
+            base_url = gewechat_file_url
+            download_url = f'{base_url}/download/{file_url}'
 
-        # 下载图片
-        try:
-            async with session.get(download_url) as img_response:
-                if img_response.status != 200:
-                    raise Exception(f'下载图片失败: {await img_response.text()}, URL: {download_url}')
+            # 下载图片
+            try:
+                async with session.get(download_url) as img_response:
+                    if img_response.status != 200:
+                        raise Exception(f'下载图片失败: {await img_response.text()}, URL: {download_url}')
 
-                image_data = await img_response.read()
+                    image_data = await img_response.read()
 
-                content_type = img_response.headers.get('Content-Type', '')
-                if content_type:
-                    image_format = content_type.split('/')[-1]
-                else:
-                    image_format = file_url.split('.')[-1]
+                    content_type = img_response.headers.get('Content-Type', '')
+                    if content_type:
+                        image_format = content_type.split('/')[-1]
+                    else:
+                        image_format = file_url.split('.')[-1]
 
-                base64_str = base64.b64encode(image_data).decode('utf-8')
+                    base64_str = base64.b64encode(image_data).decode('utf-8')
 
-                return base64_str, image_format
-        except asyncio.TimeoutError:
-            raise Exception(f'下载图片超时, URL: {download_url}')
-        except aiohttp.ClientError as e:
-            raise Exception(f'下载图片网络错误: {str(e)}, URL: {download_url}')
+                    return base64_str, image_format
+            except asyncio.TimeoutError:
+                raise Exception(f'下载图片超时, URL: {download_url}')
+            except aiohttp.ClientError as e:
+                raise Exception(f'下载图片网络错误: {str(e)}, URL: {download_url}')
     except Exception as e:
         raise Exception(f'获取图片失败: {str(e)}') from e
 
@@ -107,24 +104,24 @@ async def get_wecom_image_base64(pic_url: str) -> tuple[str, str]:
     :param pic_url: 企业微信图片URL
     :return: (base64_str, image_format)
     """
-    session = httpclient.get_session()
-    async with session.get(pic_url) as response:
-        if response.status != 200:
-            raise Exception(f'Failed to download image: {response.status}')
+    async with aiohttp.ClientSession() as session:
+        async with session.get(pic_url) as response:
+            if response.status != 200:
+                raise Exception(f'Failed to download image: {response.status}')
 
-        # 读取图片数据
-        image_data = await response.read()
+            # 读取图片数据
+            image_data = await response.read()
 
-        # 获取图片格式
-        content_type = response.headers.get('Content-Type', '')
-        image_format = content_type.split('/')[-1]  # 例如 'image/jpeg' -> 'jpeg'
+            # 获取图片格式
+            content_type = response.headers.get('Content-Type', '')
+            image_format = content_type.split('/')[-1]  # 例如 'image/jpeg' -> 'jpeg'
 
-        # 转换为 base64
-        import base64
+            # 转换为 base64
+            import base64
 
-        image_base64 = base64.b64encode(image_data).decode('utf-8')
+            image_base64 = base64.b64encode(image_data).decode('utf-8')
 
-        return image_base64, image_format
+            return image_base64, image_format
 
 
 async def get_qq_official_image_base64(pic_url: str, content_type: str) -> tuple[str, str]:
@@ -155,19 +152,21 @@ async def get_qq_image_bytes(image_url: str, query: dict = {}) -> tuple[bytes, s
     ssl_context = ssl.create_default_context()
     ssl_context.check_hostname = False
     ssl_context.verify_mode = ssl.CERT_NONE
-    session = httpclient.get_session()
-    async with session.get(image_url, params=query, ssl=ssl_context, timeout=aiohttp.ClientTimeout(total=30.0)) as resp:
-        resp.raise_for_status()
-        file_bytes = await resp.read()
-        content_type = resp.headers.get('Content-Type')
-        if not content_type:
-            image_format = 'jpeg'
-        elif not content_type.startswith('image/'):
-            pil_img = PIL.Image.open(io.BytesIO(file_bytes))
-            image_format = pil_img.format.lower()
-        else:
-            image_format = content_type.split('/')[-1]
-        return file_bytes, image_format
+    async with aiohttp.ClientSession(trust_env=False) as session:
+        async with session.get(
+            image_url, params=query, ssl=ssl_context, timeout=aiohttp.ClientTimeout(total=30.0)
+        ) as resp:
+            resp.raise_for_status()
+            file_bytes = await resp.read()
+            content_type = resp.headers.get('Content-Type')
+            if not content_type:
+                image_format = 'jpeg'
+            elif not content_type.startswith('image/'):
+                pil_img = PIL.Image.open(io.BytesIO(file_bytes))
+                image_format = pil_img.format.lower()
+            else:
+                image_format = content_type.split('/')[-1]
+            return file_bytes, image_format
 
 
 async def qq_image_url_to_base64(image_url: str) -> typing.Tuple[str, str]:
@@ -205,11 +204,11 @@ async def extract_b64_and_format(image_base64_data: str) -> typing.Tuple[str, st
 async def get_slack_image_to_base64(pic_url: str, bot_token: str):
     headers = {'Authorization': f'Bearer {bot_token}'}
     try:
-        session = httpclient.get_session()
-        async with session.get(pic_url, headers=headers) as resp:
-            mime_type = resp.headers.get('Content-Type', 'application/octet-stream')
-            file_bytes = await resp.read()
-            base64_str = base64.b64encode(file_bytes).decode('utf-8')
-        return f'data:{mime_type};base64,{base64_str}'
+        async with aiohttp.ClientSession() as session:
+            async with session.get(pic_url, headers=headers) as resp:
+                mime_type = resp.headers.get('Content-Type', 'application/octet-stream')
+                file_bytes = await resp.read()
+                base64_str = base64.b64encode(file_bytes).decode('utf-8')
+            return f'data:{mime_type};base64,{base64_str}'
     except Exception as e:
         raise (e)

src/langbot/pkg/utils/runner.py

@@ -1,105 +0,0 @@
-from __future__ import annotations
-
-from urllib.parse import urlparse
-
-
-class RunnerCategory:
-    LOCAL = 'local'
-    CLOUD = 'cloud'
-    UNKNOWN = 'unknown'
-
-
-CLOUD_DOMAINS = [
-    '.n8n.cloud',
-    '.n8n.io',
-    'api.dify.ai',
-    'cloud.dify.ai',
-    '.coze.com',
-    '.coze.cn',
-    'cloud.langflow.ai',
-    '.langflow.org',
-]
-
-LOCAL_PATTERNS = [
-    'localhost',
-    '127.0.0.1',
-    '0.0.0.0',
-    '192.168.',
-    '10.',
-    '172.16.',
-    '172.17.',
-    '172.18.',
-    '172.19.',
-    '172.20.',
-    '172.21.',
-    '172.22.',
-    '172.23.',
-    '172.24.',
-    '172.25.',
-    '172.26.',
-    '172.27.',
-    '172.28.',
-    '172.29.',
-    '172.30.',
-    '172.31.',
-]
-
-
-def get_runner_category(runner_name: str, runner_url: str) -> str:
-    if not runner_url:
-        return RunnerCategory.UNKNOWN
-
-    try:
-        parsed_url = urlparse(runner_url)
-        host = parsed_url.hostname.lower() if parsed_url.hostname else ''
-    except Exception:
-        return RunnerCategory.UNKNOWN
-
-    for pattern in LOCAL_PATTERNS:
-        if host.startswith(pattern):
-            return RunnerCategory.LOCAL
-
-    for domain in CLOUD_DOMAINS:
-        if host.endswith(domain):
-            return RunnerCategory.CLOUD
-
-    return RunnerCategory.CLOUD
-
-
-def get_runner_info(runner_name: str, runner_url: str) -> dict:
-    return {
-        'name': runner_name,
-        'url': runner_url,
-        'category': get_runner_category(runner_name, runner_url),
-    }
-
-
-def is_cloud_runner(runner_name: str, runner_url: str) -> bool:
-    return get_runner_category(runner_name, runner_url) == RunnerCategory.CLOUD
-
-
-def is_local_runner(runner_name: str, runner_url: str) -> bool:
-    return get_runner_category(runner_name, runner_url) == RunnerCategory.LOCAL
-
-
-def extract_runner_url(runner_name: str, runner, pipeline_config: dict | None) -> str | None:
-    if not runner or not hasattr(runner, 'pipeline_config'):
-        return None
-
-    ai_config = pipeline_config.get('ai', {}) if pipeline_config else {}
-
-    if runner_name == 'dify-service-api':
-        return ai_config.get('dify-service-api', {}).get('base-url')
-    elif runner_name == 'n8n-service-api':
-        return ai_config.get('n8n-service-api', {}).get('webhook-url')
-    elif runner_name == 'coze-api':
-        return ai_config.get('coze-api', {}).get('api-base')
-    elif runner_name == 'langflow-api':
-        return ai_config.get('langflow-api', {}).get('base-url')
-
-    return None
-
-
-def get_runner_category_from_runner(runner_name: str, runner, pipeline_config: dict | None) -> str:
-    runner_url = extract_runner_url(runner_name, runner, pipeline_config)
-    return get_runner_category(runner_name, runner_url)

src/langbot/pkg/vector/filter_utils.py

@@ -1,69 +0,0 @@
-"""Shared utilities for metadata filter handling across VDB backends.
-
-Canonical filter format (Chroma-style ``where`` syntax):
-
-    {"file_id": "abc"}                      # implicit $eq
-    {"file_id": {"$eq": "abc"}}             # explicit $eq
-    {"created_at": {"$gte": 1700000000}}    # comparison
-    {"file_type": {"$in": ["pdf", "docx"]}} # in-list
-
-Multiple top-level keys are AND-ed.  Supported operators:
-``$eq``, ``$ne``, ``$gt``, ``$gte``, ``$lt``, ``$lte``, ``$in``, ``$nin``.
-"""
-
-from __future__ import annotations
-
-import logging
-from typing import Any
-
-SUPPORTED_OPS = frozenset({'$eq', '$ne', '$gt', '$gte', '$lt', '$lte', '$in', '$nin'})
-
-logger = logging.getLogger(__name__)
-
-
-def normalize_filter(
-    raw: dict[str, Any] | None,
-) -> list[tuple[str, str, Any]]:
-    """Parse a canonical filter dict into ``[(field, op, value)]`` triples.
-
-    Returns an empty list when *raw* is ``None`` or empty.
-
-    Raises ``ValueError`` on unsupported operators or malformed entries.
-    """
-    if not raw:
-        return []
-
-    triples: list[tuple[str, str, Any]] = []
-    for field, condition in raw.items():
-        if isinstance(condition, dict):
-            for op, value in condition.items():
-                if op not in SUPPORTED_OPS:
-                    raise ValueError(f'Unsupported filter operator: {op}')
-                triples.append((field, op, value))
-        else:
-            # Bare value -> implicit $eq
-            triples.append((field, '$eq', condition))
-    return triples
-
-
-def strip_unsupported_fields(
-    triples: list[tuple[str, str, Any]],
-    supported_fields: set[str],
-) -> list[tuple[str, str, Any]]:
-    """Return only triples whose field is in *supported_fields*.
-
-    Dropped fields are logged at WARNING level so the caller knows they were
-    silently ignored (useful for Milvus / pgvector which only store a fixed
-    schema).
-    """
-    kept: list[tuple[str, str, Any]] = []
-    for field, op, value in triples:
-        if field in supported_fields:
-            kept.append((field, op, value))
-        else:
-            logger.warning(
-                'Filter field %r is not supported by this backend and will be ignored (supported: %s)',
-                field,
-                ', '.join(sorted(supported_fields)),
-            )
-    return kept

src/langbot/pkg/vector/mgr.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 from ..core import app
-from .vdb import VectorDatabase, SearchType
+from .vdb import VectorDatabase
 from .vdbs.chroma import ChromaVectorDatabase
 from .vdbs.qdrant import QdrantVectorDatabase
 from .vdbs.seekdb import SeekDBVectorDatabase
@@ -65,95 +65,3 @@ class VectorDBManager:
         else:
             self.vector_db = ChromaVectorDatabase(self.ap)
             self.ap.logger.warning('No vector database backend configured, defaulting to Chroma.')
-
-    def get_supported_search_types(self) -> list[str]:
-        """Return the search types supported by the current VDB backend."""
-        if self.vector_db is None:
-            return [SearchType.VECTOR.value]
-        return [st.value for st in self.vector_db.supported_search_types()]
-
-    async def upsert(
-        self,
-        collection_name: str,
-        vectors: list[list[float]],
-        ids: list[str],
-        metadata: list[dict] | None = None,
-        documents: list[str] | None = None,
-    ):
-        """Proxy: Upsert vectors"""
-        await self.vector_db.add_embeddings(
-            collection=collection_name,
-            ids=ids,
-            embeddings_list=vectors,
-            metadatas=metadata or [{} for _ in vectors],
-            documents=documents,
-        )
-
-    async def search(
-        self,
-        collection_name: str,
-        query_vector: list[float],
-        limit: int,
-        filter: dict | None = None,
-        search_type: str = 'vector',
-        query_text: str = '',
-    ) -> list[dict]:
-        """Proxy: Search vectors.
-
-        Returns a list of dicts with keys: 'id', 'distance', 'metadata'.
-        The underlying VectorDatabase.search returns Chroma-style format:
-        { 'ids': [['id1']], 'distances': [[0.1]], 'metadatas': [[{}]] }
-        """
-        results = await self.vector_db.search(
-            collection=collection_name,
-            query_embedding=query_vector,
-            k=limit,
-            search_type=search_type,
-            query_text=query_text,
-            filter=filter,
-        )
-
-        if not results or 'ids' not in results or not results['ids']:
-            return []
-
-        # Flatten nested lists (Chroma returns batch-style: list of lists)
-        raw_ids = results['ids']
-        raw_dists = results.get('distances', [])
-        raw_metas = results.get('metadatas', [])
-
-        r_ids = raw_ids[0] if raw_ids and isinstance(raw_ids[0], list) else raw_ids
-        r_dists = raw_dists[0] if raw_dists and isinstance(raw_dists[0], list) else raw_dists
-        r_metas = raw_metas[0] if raw_metas and isinstance(raw_metas[0], list) else raw_metas
-
-        parsed_results = []
-        for i, id_val in enumerate(r_ids):
-            parsed_results.append(
-                {
-                    'id': id_val,
-                    'distance': r_dists[i] if r_dists and i < len(r_dists) else 0.0,
-                    'metadata': r_metas[i] if r_metas and i < len(r_metas) else {},
-                }
-            )
-
-        return parsed_results
-
-    async def delete_by_file_id(self, collection_name: str, file_ids: list[str]):
-        """Proxy: Delete vectors by file_id (metadata-level identifier).
-
-        This delegates to VectorDatabase.delete_by_file_id which removes
-        all vectors associated with the given file IDs.
-        """
-        for file_id in file_ids:
-            await self.vector_db.delete_by_file_id(collection_name, file_id)
-
-    async def delete_collection(self, collection_name: str):
-        """Proxy: Delete an entire collection."""
-        await self.vector_db.delete_collection(collection_name)
-
-    async def delete_by_filter(self, collection_name: str, filter: dict) -> int:
-        """Proxy: Delete vectors by metadata filter.
-
-        Returns:
-            Number of deleted vectors (best-effort; some backends return 0).
-        """
-        return await self.vector_db.delete_by_filter(collection_name, filter)

src/langbot/pkg/vector/vdb.py

@@ -1,28 +1,10 @@
 from __future__ import annotations
 import abc
-import enum
 from typing import Any, Dict
 import numpy as np
 
 
-class SearchType(str, enum.Enum):
-    """Supported search types for vector databases."""
-
-    VECTOR = 'vector'
-    FULL_TEXT = 'full_text'
-    HYBRID = 'hybrid'
-
-
 class VectorDatabase(abc.ABC):
-    @classmethod
-    def supported_search_types(cls) -> list[SearchType]:
-        """Return the search types supported by this VDB backend.
-
-        Default: vector search only. Override in subclasses that support
-        full-text or hybrid search.
-        """
-        return [SearchType.VECTOR]
-
     @abc.abstractmethod
     async def add_embeddings(
         self,
@@ -30,47 +12,14 @@ class VectorDatabase(abc.ABC):
         ids: list[str],
         embeddings_list: list[list[float]],
         metadatas: list[dict[str, Any]],
-        documents: list[str] | None = None,
+        documents: list[str],
     ) -> None:
-        """Add vector data to the specified collection.
-
-        Args:
-            collection: Collection name.
-            ids: Unique IDs for each vector.
-            embeddings_list: List of embedding vectors.
-            metadatas: List of metadata dicts.
-            documents: Optional raw text documents. Required for full-text
-                and hybrid search in backends that support them.
-        """
+        """Add vector data to the specified collection."""
         pass
 
     @abc.abstractmethod
-    async def search(
-        self,
-        collection: str,
-        query_embedding: np.ndarray,
-        k: int = 5,
-        search_type: str = 'vector',
-        query_text: str = '',
-        filter: dict[str, Any] | None = None,
-    ) -> Dict[str, Any]:
-        """Search for the most similar vectors in the specified collection.
-
-        Args:
-            collection: Collection name.
-            query_embedding: Query vector for similarity search.
-            k: Number of results to return.
-            search_type: One of 'vector', 'full_text', 'hybrid'.
-            query_text: Raw query text, used for full_text and hybrid search.
-            filter: Optional metadata filters using Chroma-style ``where``
-                syntax.  Multiple top-level keys are AND-ed.  Supported
-                operators: ``$eq``, ``$ne``, ``$gt``, ``$gte``, ``$lt``,
-                ``$lte``, ``$in``, ``$nin``.  Example::
-
-                    {"file_id": "abc"}
-                    {"created_at": {"$gte": 1700000000}}
-                    {"file_type": {"$in": ["pdf", "docx"]}}
-        """
+    async def search(self, collection: str, query_embedding: np.ndarray, k: int = 5) -> Dict[str, Any]:
+        """Search for the most similar vectors in the specified collection."""
         pass
 
     @abc.abstractmethod
@@ -78,20 +27,6 @@ class VectorDatabase(abc.ABC):
         """Delete vectors from the specified collection by file_id."""
         pass
 
-    @abc.abstractmethod
-    async def delete_by_filter(self, collection: str, filter: dict[str, Any]) -> int:
-        """Delete vectors matching the given metadata filter.
-
-        Args:
-            collection: Collection name.
-            filter: Metadata filter dict in canonical format (see ``search``).
-
-        Returns:
-            Number of deleted vectors (best-effort; backends that cannot
-            report an exact count may return 0).
-        """
-        pass
-
     @abc.abstractmethod
     async def get_or_create_collection(self, collection: str):
         """Get or create collection."""

src/langbot/pkg/vector/vdbs/chroma.py

@@ -2,14 +2,11 @@ from __future__ import annotations
 import asyncio
 from typing import Any
 from chromadb import PersistentClient
-from langbot.pkg.vector.vdb import VectorDatabase, SearchType
+from langbot.pkg.vector.vdb import VectorDatabase
 from langbot.pkg.core import app
 import chromadb
 import chromadb.errors
 
-# RRF smoothing constant (standard value from the literature)
-_RRF_K = 60
-
 
 class ChromaVectorDatabase(VectorDatabase):
     def __init__(self, ap: app.Application, base_path: str = './data/chroma'):
@@ -17,10 +14,6 @@ class ChromaVectorDatabase(VectorDatabase):
         self.client = PersistentClient(path=base_path)
         self._collections = {}
 
-    @classmethod
-    def supported_search_types(cls) -> list[SearchType]:
-        return [SearchType.VECTOR, SearchType.FULL_TEXT, SearchType.HYBRID]
-
     async def get_or_create_collection(self, collection: str) -> chromadb.Collection:
         if collection not in self._collections:
             self._collections[collection] = await asyncio.to_thread(
@@ -35,192 +28,27 @@ class ChromaVectorDatabase(VectorDatabase):
         ids: list[str],
         embeddings_list: list[list[float]],
         metadatas: list[dict[str, Any]],
-        documents: list[str] | None = None,
     ) -> None:
         col = await self.get_or_create_collection(collection)
-        kwargs: dict[str, Any] = dict(embeddings=embeddings_list, ids=ids, metadatas=metadatas)
-        if documents is not None:
-            kwargs['documents'] = documents
-        await asyncio.to_thread(col.upsert, **kwargs)
-        self.ap.logger.info(f"Upserted {len(ids)} embeddings to Chroma collection '{collection}'.")
+        await asyncio.to_thread(col.add, embeddings=embeddings_list, ids=ids, metadatas=metadatas)
+        self.ap.logger.info(f"Added {len(ids)} embeddings to Chroma collection '{collection}'.")
 
-    async def search(
-        self,
-        collection: str,
-        query_embedding: list[float],
-        k: int = 5,
-        search_type: str = 'vector',
-        query_text: str = '',
-        filter: dict[str, Any] | None = None,
-    ) -> dict[str, Any]:
+    async def search(self, collection: str, query_embedding: list[float], k: int = 5) -> dict[str, Any]:
         col = await self.get_or_create_collection(collection)
-
-        if search_type == SearchType.FULL_TEXT:
-            return await self._full_text_search(col, collection, k, query_text, filter)
-        elif search_type == SearchType.HYBRID:
-            return await self._hybrid_search(col, collection, query_embedding, k, query_text, filter)
-
-        # Default: vector search
-        return await self._vector_search(col, collection, query_embedding, k, filter)
-
-    async def _vector_search(
-        self,
-        col: chromadb.Collection,
-        collection: str,
-        query_embedding: list[float],
-        k: int,
-        filter: dict[str, Any] | None,
-    ) -> dict[str, Any]:
-        query_kwargs: dict[str, Any] = dict(
+        results = await asyncio.to_thread(
+            col.query,
             query_embeddings=query_embedding,
             n_results=k,
             include=['metadatas', 'distances', 'documents'],
         )
-        if filter:
-            query_kwargs['where'] = filter
-        results = await asyncio.to_thread(col.query, **query_kwargs)
-        self.ap.logger.info(
-            f"Chroma vector search in '{collection}' returned {len(results.get('ids', [[]])[0])} results."
-        )
+        self.ap.logger.info(f"Chroma search in '{collection}' returned {len(results.get('ids', [[]])[0])} results.")
         return results
 
-    async def _full_text_search(
-        self,
-        col: chromadb.Collection,
-        collection: str,
-        k: int,
-        query_text: str,
-        filter: dict[str, Any] | None,
-    ) -> dict[str, Any]:
-        if not query_text:
-            return {'ids': [[]], 'metadatas': [[]], 'distances': [[]], 'documents': [[]]}
-
-        get_kwargs: dict[str, Any] = dict(
-            where_document={'$contains': query_text},
-            include=['metadatas', 'documents'],
-            limit=k,
-        )
-        if filter:
-            get_kwargs['where'] = filter
-        results = await asyncio.to_thread(col.get, **get_kwargs)
-
-        # col.get returns flat lists; wrap into column-major format.
-        # Distances are all 0.0 because Chroma's local $contains is a boolean
-        # filter with no relevance scoring.  Chroma's BM25 sparse embedding
-        # function (ChromaBm25EmbeddingFunction) can generate scored sparse
-        # vectors, but sparse vector *indexing* is only available on Chroma
-        # Cloud, not locally.  For ranked results, use hybrid mode or apply a
-        # reranker in a downstream stage.
-        ids = results.get('ids', [])
-        metadatas = results.get('metadatas', []) or [None] * len(ids)
-        documents = results.get('documents', []) or [None] * len(ids)
-        distances = [0.0] * len(ids)
-
-        self.ap.logger.info(f"Chroma full-text search in '{collection}' returned {len(ids)} results.")
-        return {'ids': [ids], 'metadatas': [metadatas], 'distances': [distances], 'documents': [documents]}
-
-    async def _hybrid_search(
-        self,
-        col: chromadb.Collection,
-        collection: str,
-        query_embedding: list[float],
-        k: int,
-        query_text: str,
-        filter: dict[str, Any] | None,
-    ) -> dict[str, Any]:
-        # Fall back to pure vector search when no text is provided
-        if not query_text:
-            return await self._vector_search(col, collection, query_embedding, k, filter)
-
-        # Run vector search and full-text search in parallel
-        vector_task = self._vector_search(col, collection, query_embedding, k, filter)
-        text_task = self._full_text_search(col, collection, k, query_text, filter)
-        vector_results, text_results = await asyncio.gather(vector_task, text_task)
-
-        vector_ids = vector_results.get('ids', [[]])[0]
-        text_ids = text_results.get('ids', [[]])[0]
-
-        if not vector_ids and not text_ids:
-            return {'ids': [[]], 'metadatas': [[]], 'distances': [[]], 'documents': [[]]}
-
-        # RRF fusion
-        fused = self._rrf_fuse([vector_ids, text_ids], k)
-        if not fused:
-            return {'ids': [[]], 'metadatas': [[]], 'distances': [[]], 'documents': [[]]}
-
-        fused_ids = [doc_id for doc_id, _ in fused]
-
-        # Fetch full metadata and documents for fused results
-        fetched = await asyncio.to_thread(col.get, ids=fused_ids, include=['metadatas', 'documents'])
-
-        # col.get returns results in arbitrary order; re-order to match fused ranking
-        fetched_map: dict[str, tuple] = {}
-        for i, fid in enumerate(fetched.get('ids', [])):
-            meta = (fetched.get('metadatas') or [None] * len(fetched['ids']))[i]
-            doc = (fetched.get('documents') or [None] * len(fetched['ids']))[i]
-            fetched_map[fid] = (meta, doc)
-
-        ordered_ids = []
-        ordered_metas = []
-        ordered_docs = []
-        ordered_dists = []
-
-        # Normalize RRF scores to 0~1 distances via min-max scaling.
-        # Raw RRF scores are tiny (e.g. 0.016~0.033 with k=60) so a naive
-        # ``1 - score`` would compress all distances into a narrow 0.96~0.98
-        # band with almost no discriminative power.  Min-max normalization
-        # spreads them across the full 0~1 range (0.0 = best match).
-        max_score = fused[0][1]
-        min_score = fused[-1][1]
-        score_range = max_score - min_score
-
-        for doc_id, score in fused:
-            if doc_id in fetched_map:
-                meta, doc = fetched_map[doc_id]
-                ordered_ids.append(doc_id)
-                ordered_metas.append(meta)
-                ordered_docs.append(doc)
-                if score_range > 0:
-                    ordered_dists.append(1.0 - (score - min_score) / score_range)
-                else:
-                    ordered_dists.append(0.0)
-
-        self.ap.logger.info(
-            f"Chroma hybrid search in '{collection}' returned {len(ordered_ids)} results "
-            f'(vector={len(vector_ids)}, text={len(text_ids)}).'
-        )
-        return {
-            'ids': [ordered_ids],
-            'metadatas': [ordered_metas],
-            'distances': [ordered_dists],
-            'documents': [ordered_docs],
-        }
-
-    @staticmethod
-    def _rrf_fuse(result_lists: list[list[str]], k: int) -> list[tuple[str, float]]:
-        """Reciprocal Rank Fusion over multiple ranked ID lists.
-
-        Returns a list of (doc_id, rrf_score) sorted by descending score,
-        truncated to *k* entries.
-        """
-        scores: dict[str, float] = {}
-        for ranked_ids in result_lists:
-            for rank, doc_id in enumerate(ranked_ids):
-                scores[doc_id] = scores.get(doc_id, 0.0) + 1.0 / (_RRF_K + rank + 1)
-        sorted_results = sorted(scores.items(), key=lambda x: x[1], reverse=True)
-        return sorted_results[:k]
-
     async def delete_by_file_id(self, collection: str, file_id: str) -> None:
         col = await self.get_or_create_collection(collection)
         await asyncio.to_thread(col.delete, where={'file_id': file_id})
         self.ap.logger.info(f"Deleted embeddings from Chroma collection '{collection}' with file_id: {file_id}")
 
-    async def delete_by_filter(self, collection: str, filter: dict[str, Any]) -> int:
-        col = await self.get_or_create_collection(collection)
-        await asyncio.to_thread(col.delete, where=filter)
-        self.ap.logger.info(f"Deleted embeddings from Chroma collection '{collection}' by filter")
-        return 0  # Chroma delete does not return a count
-
     async def delete_collection(self, collection: str):
         if collection in self._collections:
             del self._collections[collection]

src/langbot/pkg/vector/vdbs/milvus.py

@@ -4,51 +4,8 @@ from typing import Any, Dict
 from pymilvus import MilvusClient, DataType, CollectionSchema, FieldSchema
 from pymilvus.milvus_client.index import IndexParams
 from langbot.pkg.vector.vdb import VectorDatabase
-from langbot.pkg.vector.filter_utils import normalize_filter, strip_unsupported_fields
 from langbot.pkg.core import app
 
-# Milvus schema only stores these metadata fields; filter on other fields is
-# silently dropped with a warning.
-_MILVUS_SUPPORTED_FIELDS = {'text', 'file_id', 'chunk_uuid'}
-
-
-def _build_milvus_expr(filter_dict: dict[str, Any]) -> str:
-    """Translate canonical filter dict into a Milvus boolean expression string."""
-    triples = normalize_filter(filter_dict)
-    triples = strip_unsupported_fields(triples, _MILVUS_SUPPORTED_FIELDS)
-    if not triples:
-        return ''
-
-    parts: list[str] = []
-    for field, op, value in triples:
-        if op == '$eq':
-            parts.append(f'{field} == {_milvus_literal(value)}')
-        elif op == '$ne':
-            parts.append(f'{field} != {_milvus_literal(value)}')
-        elif op == '$gt':
-            parts.append(f'{field} > {_milvus_literal(value)}')
-        elif op == '$gte':
-            parts.append(f'{field} >= {_milvus_literal(value)}')
-        elif op == '$lt':
-            parts.append(f'{field} < {_milvus_literal(value)}')
-        elif op == '$lte':
-            parts.append(f'{field} <= {_milvus_literal(value)}')
-        elif op == '$in':
-            items = ', '.join(_milvus_literal(v) for v in value)
-            parts.append(f'{field} in [{items}]')
-        elif op == '$nin':
-            items = ', '.join(_milvus_literal(v) for v in value)
-            parts.append(f'{field} not in [{items}]')
-    return ' and '.join(parts)
-
-
-def _milvus_literal(value: Any) -> str:
-    """Format a Python value as a Milvus expression literal."""
-    if isinstance(value, str):
-        escaped = value.replace('\\', '\\\\').replace('"', '\\"')
-        return f'"{escaped}"'
-    return str(value)
-
 
 class MilvusVectorDatabase(VectorDatabase):
     """Milvus vector database implementation"""
@@ -198,7 +155,6 @@ class MilvusVectorDatabase(VectorDatabase):
         ids: list[str],
         embeddings_list: list[list[float]],
         metadatas: list[dict[str, Any]],
-        documents: list[str] | None = None,
     ) -> None:
         """Add vector embeddings to Milvus collection
 
@@ -244,15 +200,7 @@ class MilvusVectorDatabase(VectorDatabase):
 
         self.ap.logger.info(f"Added {len(ids)} embeddings to Milvus collection '{collection}'")
 
-    async def search(
-        self,
-        collection: str,
-        query_embedding: list[float],
-        k: int = 5,
-        search_type: str = 'vector',
-        query_text: str = '',
-        filter: dict[str, Any] | None = None,
-    ) -> Dict[str, Any]:
+    async def search(self, collection: str, query_embedding: list[float], k: int = 5) -> Dict[str, Any]:
         """Search for similar vectors in Milvus collection
 
         Args:
@@ -269,19 +217,14 @@ class MilvusVectorDatabase(VectorDatabase):
         # Perform search
         search_params = {'metric_type': 'COSINE', 'params': {}}
 
-        search_kwargs: dict[str, Any] = dict(
+        results = await asyncio.to_thread(
+            self.client.search,
             collection_name=collection,
             data=[query_embedding],
             limit=k,
             search_params=search_params,
             output_fields=['text', 'file_id', 'chunk_uuid'],
         )
-        if filter:
-            expr = _build_milvus_expr(filter)
-            if expr:
-                search_kwargs['filter'] = expr
-
-        results = await asyncio.to_thread(self.client.search, **search_kwargs)
 
         # Convert results to Chroma-compatible format
         # Milvus returns: [[ {id, distance, entity: {...}} ]]
@@ -325,21 +268,6 @@ class MilvusVectorDatabase(VectorDatabase):
         await asyncio.to_thread(self.client.delete, collection_name=collection, filter=f'file_id == "{file_id}"')
         self.ap.logger.info(f"Deleted embeddings from Milvus collection '{collection}' with file_id: {file_id}")
 
-    async def delete_by_filter(self, collection: str, filter: dict[str, Any]) -> int:
-        collection = self._normalize_collection_name(collection)
-        await self.get_or_create_collection(collection)
-
-        expr = _build_milvus_expr(filter)
-        if not expr:
-            self.ap.logger.warning(
-                f"Milvus delete_by_filter on '{collection}': filter produced empty expression, skipping"
-            )
-            return 0
-
-        await asyncio.to_thread(self.client.delete, collection_name=collection, filter=expr)
-        self.ap.logger.info(f"Deleted embeddings from Milvus collection '{collection}' by filter")
-        return 0  # Milvus delete does not return a count
-
     async def delete_collection(self, collection: str):
         """Delete a Milvus collection
 

src/langbot/pkg/vector/vdbs/pgvector_db.py

@@ -5,21 +5,10 @@ from sqlalchemy.orm import declarative_base
 from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession, async_sessionmaker
 from pgvector.sqlalchemy import Vector
 from langbot.pkg.vector.vdb import VectorDatabase
-from langbot.pkg.vector.filter_utils import normalize_filter, strip_unsupported_fields
 from langbot.pkg.core import app
 
 Base = declarative_base()
 
-# pgvector schema only stores these metadata fields.
-_PG_SUPPORTED_FIELDS = {'text', 'file_id', 'chunk_uuid'}
-
-# Map schema field names to SQLAlchemy columns (resolved lazily from PgVectorEntry).
-_PG_COLUMN_MAP = {
-    'text': 'text',
-    'file_id': 'file_id',
-    'chunk_uuid': 'chunk_uuid',
-}
-
 
 class PgVectorEntry(Base):
     """SQLAlchemy model for pgvector entries"""
@@ -34,33 +23,6 @@ class PgVectorEntry(Base):
     chunk_uuid = Column(String)
 
 
-def _build_pg_conditions(filter_dict: dict[str, Any]) -> list:
-    """Translate canonical filter dict into a list of SQLAlchemy conditions."""
-    triples = normalize_filter(filter_dict)
-    triples = strip_unsupported_fields(triples, _PG_SUPPORTED_FIELDS)
-
-    conditions = []
-    for field, op, value in triples:
-        col = getattr(PgVectorEntry, _PG_COLUMN_MAP[field])
-        if op == '$eq':
-            conditions.append(col == value)
-        elif op == '$ne':
-            conditions.append(col != value)
-        elif op == '$gt':
-            conditions.append(col > value)
-        elif op == '$gte':
-            conditions.append(col >= value)
-        elif op == '$lt':
-            conditions.append(col < value)
-        elif op == '$lte':
-            conditions.append(col <= value)
-        elif op == '$in':
-            conditions.append(col.in_(value))
-        elif op == '$nin':
-            conditions.append(col.notin_(value))
-    return conditions
-
-
 class PgVectorDatabase(VectorDatabase):
     """PostgreSQL with pgvector extension database implementation"""
 
@@ -147,7 +109,6 @@ class PgVectorDatabase(VectorDatabase):
         ids: list[str],
         embeddings_list: list[list[float]],
         metadatas: list[dict[str, Any]],
-        documents: list[str] | None = None,
     ) -> None:
         """Add vector embeddings to pgvector
 
@@ -181,15 +142,7 @@ class PgVectorDatabase(VectorDatabase):
                 self.ap.logger.error(f'Error adding embeddings to pgvector: {e}')
                 raise
 
-    async def search(
-        self,
-        collection: str,
-        query_embedding: list[float],
-        k: int = 5,
-        search_type: str = 'vector',
-        query_text: str = '',
-        filter: dict[str, Any] | None = None,
-    ) -> Dict[str, Any]:
+    async def search(self, collection: str, query_embedding: list[float], k: int = 5) -> Dict[str, Any]:
         """Search for similar vectors using cosine distance
 
         Args:
@@ -221,10 +174,6 @@ class PgVectorDatabase(VectorDatabase):
                     .limit(k)
                 )
 
-                if filter:
-                    for cond in _build_pg_conditions(filter):
-                        stmt = stmt.filter(cond)
-
                 result = await session.execute(stmt)
                 rows = result.fetchall()
 
@@ -276,39 +225,6 @@ class PgVectorDatabase(VectorDatabase):
                 self.ap.logger.error(f'Error deleting from pgvector: {e}')
                 raise
 
-    async def delete_by_filter(self, collection: str, filter: dict[str, Any]) -> int:
-        """Delete vectors matching a metadata filter.
-
-        Args:
-            collection: Collection name
-            filter: Canonical metadata filter dict
-        """
-        conditions = _build_pg_conditions(filter)
-        if not conditions:
-            self.ap.logger.warning(
-                f"pgvector delete_by_filter on '{collection}': filter produced no conditions, skipping"
-            )
-            return 0
-
-        await self.get_or_create_collection(collection)
-
-        async with self.AsyncSessionLocal() as session:
-            try:
-                from sqlalchemy import delete
-
-                stmt = delete(PgVectorEntry).where(PgVectorEntry.collection == collection)
-                for cond in conditions:
-                    stmt = stmt.where(cond)
-                result = await session.execute(stmt)
-                await session.commit()
-                deleted = result.rowcount
-                self.ap.logger.info(f"Deleted {deleted} embeddings from pgvector collection '{collection}' by filter")
-                return deleted
-            except Exception as e:
-                await session.rollback()
-                self.ap.logger.error(f'Error deleting from pgvector by filter: {e}')
-                raise
-
     async def delete_collection(self, collection: str):
         """Delete all vectors in a collection
 

src/langbot/pkg/vector/vdbs/qdrant.py

@@ -5,37 +5,6 @@ from typing import Any, Dict, List
 from qdrant_client import AsyncQdrantClient, models
 from langbot.pkg.core import app
 from langbot.pkg.vector.vdb import VectorDatabase
-from langbot.pkg.vector.filter_utils import normalize_filter
-
-
-def _build_qdrant_filter(filter_dict: dict[str, Any]) -> models.Filter:
-    """Translate canonical filter dict into a Qdrant ``models.Filter``."""
-    triples = normalize_filter(filter_dict)
-    must: list[models.Condition] = []
-    must_not: list[models.Condition] = []
-
-    for field, op, value in triples:
-        if op == '$eq':
-            must.append(models.FieldCondition(key=field, match=models.MatchValue(value=value)))
-        elif op == '$ne':
-            must_not.append(models.FieldCondition(key=field, match=models.MatchValue(value=value)))
-        elif op == '$in':
-            must.append(models.FieldCondition(key=field, match=models.MatchAny(any=value)))
-        elif op == '$nin':
-            must_not.append(models.FieldCondition(key=field, match=models.MatchAny(any=value)))
-        elif op in ('$gt', '$gte', '$lt', '$lte'):
-            range_kwargs: dict[str, Any] = {}
-            if op == '$gt':
-                range_kwargs['gt'] = value
-            elif op == '$gte':
-                range_kwargs['gte'] = value
-            elif op == '$lt':
-                range_kwargs['lt'] = value
-            elif op == '$lte':
-                range_kwargs['lte'] = value
-            must.append(models.FieldCondition(key=field, range=models.Range(**range_kwargs)))
-
-    return models.Filter(must=must or None, must_not=must_not or None)
 
 
 class QdrantVectorDatabase(VectorDatabase):
@@ -79,7 +48,6 @@ class QdrantVectorDatabase(VectorDatabase):
         ids: List[str],
         embeddings_list: List[List[float]],
         metadatas: List[Dict[str, Any]],
-        documents: List[str] | None = None,
     ) -> None:
         if not embeddings_list:
             return
@@ -92,29 +60,19 @@ class QdrantVectorDatabase(VectorDatabase):
         await self.client.upsert(collection_name=collection, points=points)
         self.ap.logger.info(f"Added {len(ids)} embeddings to Qdrant collection '{collection}'.")
 
-    async def search(
-        self,
-        collection: str,
-        query_embedding: list[float],
-        k: int = 5,
-        search_type: str = 'vector',
-        query_text: str = '',
-        filter: dict[str, Any] | None = None,
-    ) -> dict[str, Any]:
+    async def search(self, collection: str, query_embedding: list[float], k: int = 5) -> dict[str, Any]:
         exists = await self.client.collection_exists(collection)
         if not exists:
             return {'ids': [[]], 'metadatas': [[]], 'distances': [[]]}
 
-        query_kwargs: dict[str, Any] = dict(
-            collection_name=collection,
-            query=query_embedding,
-            limit=k,
-            with_payload=True,
-        )
-        if filter:
-            query_kwargs['query_filter'] = _build_qdrant_filter(filter)
-
-        hits = (await self.client.query_points(**query_kwargs)).points
+        hits = (
+            await self.client.query_points(
+                collection_name=collection,
+                query=query_embedding,
+                limit=k,
+                with_payload=True,
+            )
+        ).points
         ids = [str(hit.id) for hit in hits]
         metadatas = [hit.payload or {} for hit in hits]
         # Qdrant's score is similarity; convert to a pseudo-distance for consistency
@@ -137,19 +95,6 @@ class QdrantVectorDatabase(VectorDatabase):
         )
         self.ap.logger.info(f"Deleted embeddings from Qdrant collection '{collection}' with file_id: {file_id}")
 
-    async def delete_by_filter(self, collection: str, filter: dict[str, Any]) -> int:
-        exists = await self.client.collection_exists(collection)
-        if not exists:
-            return 0
-
-        qdrant_filter = _build_qdrant_filter(filter)
-        await self.client.delete(
-            collection_name=collection,
-            points_selector=qdrant_filter,
-        )
-        self.ap.logger.info(f"Deleted embeddings from Qdrant collection '{collection}' by filter")
-        return 0  # Qdrant delete does not return a count
-
     async def delete_collection(self, collection: str):
         try:
             await self.client.delete_collection(collection)

src/langbot/pkg/vector/vdbs/seekdb.py

@@ -5,7 +5,7 @@ from typing import Any, Dict, List
 
 
 from langbot.pkg.core import app
-from langbot.pkg.vector.vdb import VectorDatabase, SearchType
+from langbot.pkg.vector.vdb import VectorDatabase
 
 try:
     import pyseekdb
@@ -25,13 +25,9 @@ class SeekDBVectorDatabase(VectorDatabase):
     SeekDB is an AI-native search database by OceanBase that unifies
     relational, vector, text, JSON and GIS in a single engine.
 
-    Supports embedded mode, remote server mode, and full-text/hybrid search.
+    Supports both embedded mode and remote server mode.
     """
 
-    @classmethod
-    def supported_search_types(cls) -> list[SearchType]:
-        return [SearchType.VECTOR, SearchType.FULL_TEXT, SearchType.HYBRID]
-
     def __init__(self, ap: app.Application):
         if not SEEKDB_AVAILABLE:
             raise ImportError('pyseekdb is not installed. Install it with: pip install pyseekdb')
@@ -93,7 +89,6 @@ class SeekDBVectorDatabase(VectorDatabase):
             {
                 '\x00': '',
                 '\\': '\\\\',
-                "'": "''",  # Standard SQL escaping (OceanBase NO_BACKSLASH_ESCAPES)
                 '"': '\\"',
                 '\n': '\\n',
                 '\r': '\\r',
@@ -116,10 +111,8 @@ class SeekDBVectorDatabase(VectorDatabase):
 
         # Collection doesn't exist, create it
         if vector_size is None:
-            raise ValueError(
-                f"Cannot create SeekDB collection '{collection}' without knowing the vector dimension. "
-                'Ensure add_embeddings is called before any standalone get_or_create_collection.'
-            )
+            # Default dimension if not specified
+            vector_size = 384
 
         # Create HNSW configuration
         config = HNSWConfiguration(dimension=vector_size, distance='cosine')
@@ -154,12 +147,7 @@ class SeekDBVectorDatabase(VectorDatabase):
         return await self._get_or_create_collection_internal(collection)
 
     async def add_embeddings(
-        self,
-        collection: str,
-        ids: List[str],
-        embeddings_list: List[List[float]],
-        metadatas: List[Dict[str, Any]],
-        documents: List[str] | None = None,
+        self, collection: str, ids: List[str], embeddings_list: List[List[float]], metadatas: List[Dict[str, Any]]
     ) -> None:
         """Add vector embeddings to the specified collection.
 
@@ -168,7 +156,6 @@ class SeekDBVectorDatabase(VectorDatabase):
             ids: List of document IDs
             embeddings_list: List of embedding vectors
             metadatas: List of metadata dictionaries
-            documents: Optional raw text documents for full-text search support
         """
         if not embeddings_list:
             return
@@ -179,33 +166,17 @@ class SeekDBVectorDatabase(VectorDatabase):
 
         cleaned_metadatas = [self._clean_metadata(meta) for meta in metadatas]
 
-        kwargs: Dict[str, Any] = dict(ids=ids, embeddings=embeddings_list, metadatas=cleaned_metadatas)
-        if documents is not None:
-            kwargs['documents'] = [doc.translate(self._escape_table) for doc in documents]
-        await asyncio.to_thread(coll.add, **kwargs)
+        await asyncio.to_thread(coll.add, ids=ids, embeddings=embeddings_list, metadatas=cleaned_metadatas)
 
         self.ap.logger.info(f"Added {len(ids)} embeddings to SeekDB collection '{collection}'")
 
-    async def search(
-        self,
-        collection: str,
-        query_embedding: List[float],
-        k: int = 5,
-        search_type: str = 'vector',
-        query_text: str = '',
-        filter: Dict[str, Any] | None = None,
-    ) -> Dict[str, Any]:
+    async def search(self, collection: str, query_embedding: List[float], k: int = 5) -> Dict[str, Any]:
         """Search for the most similar vectors in the specified collection.
 
-        SeekDB supports vector, full-text, and hybrid search modes.
-
         Args:
             collection: Collection name
-            query_embedding: Query vector (used for vector and hybrid modes)
+            query_embedding: Query vector
             k: Number of results to return
-            search_type: One of 'vector', 'full_text', 'hybrid'
-            query_text: Raw query text (used for full_text and hybrid modes)
-            filter: Optional metadata filters (Chroma-style ``where`` syntax).
 
         Returns:
             Dictionary with 'ids', 'metadatas', 'distances' keys
@@ -222,73 +193,11 @@ class SeekDBVectorDatabase(VectorDatabase):
         else:
             coll = self._collections[collection]
 
-        # Route by search type.
-        # pyseekdb's query() always requires embeddings, so full-text and
-        # hybrid modes use hybrid_search() which supports text-only queries
-        # and returns the same nested-list format with distances.
-        if search_type == SearchType.FULL_TEXT:
-            if not query_text:
-                return {'ids': [[]], 'metadatas': [[]], 'distances': [[]]}
+        # Perform query
+        # SeekDB's query() returns: {'ids': [[...]], 'metadatas': [[...]], 'distances': [[...]]}
+        results = await asyncio.to_thread(coll.query, query_embeddings=query_embedding, n_results=k)
 
-            query_cfg: Dict[str, Any] = {
-                'where_document': {'$contains': query_text},
-                'n_results': k,
-            }
-            if filter:
-                query_cfg['where'] = filter
-
-            # TODO: pyseekdb hybrid_search with query-only (no knn) returns None
-            # for IDs due to column name mismatch (*/_id vs _id).
-            # See: https://github.com/oceanbase/pyseekdb/issues/171
-            results = await asyncio.to_thread(
-                coll.hybrid_search,
-                query=query_cfg,
-                knn=None,
-                n_results=k,
-                include=['documents', 'metadatas'],
-            )
-
-        elif search_type == SearchType.HYBRID:
-            if not query_text:
-                # Fall back to pure vector search when no text is provided
-                query_kwargs: Dict[str, Any] = {
-                    'n_results': k,
-                    'query_embeddings': query_embedding,
-                }
-                if filter:
-                    query_kwargs['where'] = filter
-                results = await asyncio.to_thread(coll.query, **query_kwargs)
-            else:
-                query_cfg = {
-                    'where_document': {'$contains': query_text},
-                    'n_results': k,
-                }
-                knn_cfg: Dict[str, Any] = {
-                    'query_embeddings': query_embedding,
-                    'n_results': k,
-                }
-                if filter:
-                    query_cfg['where'] = filter
-                    knn_cfg['where'] = filter
-
-                results = await asyncio.to_thread(
-                    coll.hybrid_search,
-                    query=query_cfg,
-                    knn=knn_cfg,
-                    rank={'rrf': {}},
-                    n_results=k,
-                    include=['documents', 'metadatas'],
-                )
-        else:
-            # Default: vector search via query()
-            query_kwargs = {'n_results': k, 'query_embeddings': query_embedding}
-            if filter:
-                query_kwargs['where'] = filter
-            results = await asyncio.to_thread(coll.query, **query_kwargs)
-
-        self.ap.logger.info(
-            f"SeekDB {search_type} search in '{collection}' returned {len(results.get('ids', [[]])[0])} results"
-        )
+        self.ap.logger.info(f"SeekDB search in '{collection}' returned {len(results.get('ids', [[]])[0])} results")
 
         return results
 
@@ -318,28 +227,6 @@ class SeekDBVectorDatabase(VectorDatabase):
 
         self.ap.logger.info(f"Deleted embeddings from SeekDB collection '{collection}' with file_id: {file_id}")
 
-    async def delete_by_filter(self, collection: str, filter: Dict[str, Any]) -> int:
-        """Delete vectors from the collection by metadata filter.
-
-        Args:
-            collection: Collection name
-            filter: Chroma-style ``where`` filter dict
-        """
-        exists = await asyncio.to_thread(self.client.has_collection, collection)
-        if not exists:
-            self.ap.logger.warning(f"SeekDB collection '{collection}' not found for deletion")
-            return 0
-
-        if collection not in self._collections:
-            coll = await asyncio.to_thread(self.client.get_collection, collection, embedding_function=None)
-            self._collections[collection] = coll
-        else:
-            coll = self._collections[collection]
-
-        await asyncio.to_thread(coll.delete, where=filter)
-        self.ap.logger.info(f"Deleted embeddings from SeekDB collection '{collection}' by filter")
-        return 0  # SeekDB delete does not return a count
-
     async def delete_collection(self, collection: str):
         """Delete the entire collection.
 

src/langbot/templates/default-pipeline-config.json

@@ -17,10 +17,6 @@
             "prefix": [],
             "regexp": []
         },
-        "message-aggregation": {
-            "enabled": false,
-            "delay": 1.5
-        },
         "misc": {
             "combine-quote-message": true
         }
@@ -95,12 +91,11 @@
             "max": 0
         },
         "misc": {
-            "exception-handling": "show-hint",
-            "failure-hint": "Request failed.",
+            "hide-exception": true,
             "at-sender": true,
             "quote-origin": true,
             "track-function-calls": false,
             "remove-think": false
         }
     }
-}
+}

src/langbot/templates/metadata/pipeline/ai.yaml

@@ -59,11 +59,8 @@ stages:
         label:
           en_US: Model
           zh_Hans: 模型
-        type: model-fallback-selector
+        type: llm-model-selector
         required: true
-        default:
-          primary: ''
-          fallbacks: []
       - name: max-round
         label:
           en_US: Max Round

src/langbot/templates/metadata/pipeline/output.yaml

@@ -78,39 +78,13 @@ stages:
       en_US: Misc
       zh_Hans: 杂项
     config:
-      - name: exception-handling
+      - name: hide-exception
         label:
-          en_US: Exception Handling Strategy
-          zh_Hans: 异常处理策略
-        description:
-          en_US: Controls how error messages are displayed to the user when an AI request fails
-          zh_Hans: 控制 AI 请求失败时向用户展示错误信息的方式
-        type: select
+          en_US: Hide Exception
+          zh_Hans: 不输出异常信息给用户
+        type: boolean
         required: true
-        default: show-hint
-        options:
-          - name: show-error
-            label:
-              en_US: Show Full Error
-              zh_Hans: 显示完整报错信息
-          - name: show-hint
-            label:
-              en_US: Show Failure Hint
-              zh_Hans: 仅文字提示
-          - name: hide
-            label:
-              en_US: Hide All
-              zh_Hans: 不显示任何异常信息
-      - name: failure-hint
-        label:
-          en_US: Failure Hint Text
-          zh_Hans: 失败提示文本
-        description:
-          en_US: The text to display when a request fails. Only effective when Exception Handling Strategy is set to "Show Failure Hint"
-          zh_Hans: 请求失败时显示的提示文本，仅在异常处理策略设置为"仅文字提示"时生效
-        type: string
-        required: false
-        default: 'Request failed.'
+        default: true
       - name: at-sender
         label:
           en_US: At Sender
@@ -145,4 +119,3 @@ stages:
         type: boolean
         required: true
         default: false
-

src/langbot/templates/metadata/pipeline/trigger.yaml

@@ -123,34 +123,6 @@ stages:
         type: array[string]
         required: true
         default: []
-  - name: message-aggregation
-    label:
-      en_US: Message Aggregation
-      zh_Hans: 消息聚合
-    description:
-      en_US: When a user sends multiple messages consecutively, wait for a period and merge them into one before processing
-      zh_Hans: 当用户连续发送多条消息时，等待一段时间后合并为一条消息再处理（防抖）
-    config:
-      - name: enabled
-        label:
-          en_US: Enable Message Aggregation
-          zh_Hans: 启用消息聚合
-        description:
-          en_US: If enabled, consecutive messages from the same user will be merged after a delay
-          zh_Hans: 如果启用，同一用户连续发送的消息将在延迟后合并处理
-        type: boolean
-        required: true
-        default: false
-      - name: delay
-        label:
-          en_US: Aggregation Delay (seconds)
-          zh_Hans: 聚合延迟（秒）
-        description:
-          en_US: 'Wait time before merging messages. Range: 1.0-10.0 seconds.'
-          zh_Hans: '合并消息前的等待时间。范围：1.0-10.0 秒。'
-        type: float
-        required: true
-        default: 1.5
   - name: misc
     label:
       en_US: Misc

tests/unit_tests/pipeline/test_config_coercion.py

@@ -1,113 +0,0 @@
-"""Unit tests for config_coercion module"""
-
-from __future__ import annotations
-
-import pytest
-
-from langbot.pkg.pipeline.config_coercion import _coerce_value, coerce_pipeline_config
-
-
-class TestCoerceValue:
-    """Tests for _coerce_value function"""
-
-    def test_none_passthrough(self):
-        assert _coerce_value(None, 'integer') is None
-        assert _coerce_value(None, 'boolean') is None
-
-    def test_string_to_integer(self):
-        assert _coerce_value('120', 'integer') == 120
-        assert _coerce_value('0', 'integer') == 0
-        assert _coerce_value('-5', 'integer') == -5
-
-    def test_integer_passthrough(self):
-        assert _coerce_value(42, 'integer') == 42
-
-    def test_string_to_float(self):
-        assert _coerce_value('3.14', 'number') == 3.14
-        assert _coerce_value('3.14', 'float') == 3.14
-
-    def test_int_to_float(self):
-        assert _coerce_value(3, 'number') == 3.0
-        assert isinstance(_coerce_value(3, 'number'), float)
-
-    def test_float_passthrough(self):
-        assert _coerce_value(3.14, 'float') == 3.14
-
-    def test_string_to_bool(self):
-        assert _coerce_value('true', 'boolean') is True
-        assert _coerce_value('True', 'boolean') is True
-        assert _coerce_value('false', 'boolean') is False
-        assert _coerce_value('False', 'boolean') is False
-
-    def test_bool_passthrough(self):
-        assert _coerce_value(True, 'boolean') is True
-        assert _coerce_value(False, 'boolean') is False
-
-    def test_invalid_bool_string_raises(self):
-        with pytest.raises(ValueError):
-            _coerce_value('notabool', 'boolean')
-
-    def test_unknown_type_passthrough(self):
-        assert _coerce_value('hello', 'string') == 'hello'
-        assert _coerce_value('hello', 'unknown') == 'hello'
-
-    def test_invalid_integer_raises(self):
-        with pytest.raises(ValueError):
-            _coerce_value('abc', 'integer')
-
-
-class TestCoercePipelineConfig:
-    """Tests for coerce_pipeline_config function"""
-
-    def _make_meta(self, section_name: str, stage_name: str, fields: list[dict]) -> dict:
-        return {
-            'name': section_name,
-            'stages': [{'name': stage_name, 'config': fields}],
-        }
-
-    def test_coerce_integer_in_config(self):
-        config = {'trigger': {'misc': {'timeout': '120'}}}
-        meta = self._make_meta('trigger', 'misc', [{'name': 'timeout', 'type': 'integer'}])
-        coerce_pipeline_config(config, meta)
-        assert config['trigger']['misc']['timeout'] == 120
-
-    def test_coerce_boolean_in_config(self):
-        config = {'output': {'misc': {'at-sender': 'true'}}}
-        meta = self._make_meta('output', 'misc', [{'name': 'at-sender', 'type': 'boolean'}])
-        coerce_pipeline_config(config, meta)
-        assert config['output']['misc']['at-sender'] is True
-
-    def test_missing_section_skipped(self):
-        config = {'ai': {}}
-        meta = self._make_meta('trigger', 'misc', [{'name': 'x', 'type': 'integer'}])
-        coerce_pipeline_config(config, meta)  # should not raise
-
-    def test_missing_field_skipped(self):
-        config = {'trigger': {'misc': {}}}
-        meta = self._make_meta('trigger', 'misc', [{'name': 'nonexistent', 'type': 'integer'}])
-        coerce_pipeline_config(config, meta)  # should not raise
-
-    def test_invalid_value_logs_warning(self, caplog):
-        config = {'trigger': {'misc': {'timeout': 'abc'}}}
-        meta = self._make_meta('trigger', 'misc', [{'name': 'timeout', 'type': 'integer'}])
-        import logging
-
-        with caplog.at_level(logging.WARNING):
-            coerce_pipeline_config(config, meta)
-        assert config['trigger']['misc']['timeout'] == 'abc'  # unchanged
-        assert 'Failed to coerce' in caplog.text
-
-    def test_empty_metadata(self):
-        config = {'trigger': {'misc': {'timeout': '120'}}}
-        coerce_pipeline_config(config)  # no metadata args, should not raise
-
-    def test_multiple_metadata(self):
-        config = {
-            'trigger': {'misc': {'timeout': '120'}},
-            'output': {'misc': {'at-sender': 'false'}},
-        }
-        meta_trigger = self._make_meta('trigger', 'misc', [{'name': 'timeout', 'type': 'integer'}])
-        meta_output = self._make_meta('output', 'misc', [{'name': 'at-sender', 'type': 'boolean'}])
-        coerce_pipeline_config(config, meta_trigger, meta_output)
-        assert config['trigger']['misc']['timeout'] == 120
-        assert config['output']['misc']['at-sender'] is False

tests/unit_tests/plugin/test_plugin_component_filtering.py

@@ -38,11 +38,13 @@ async def test_plugin_list_filter_by_component_kinds():
                 'manifest': {
                     'metadata': {
                         'author': 'author2',
-                        'name': 'plugin_with_knowledge_engine_only',
+                        'name': 'plugin_with_knowledge_retriever_only',
                     }
                 }
             },
-            'components': [{'manifest': {'manifest': {'kind': 'KnowledgeEngine', 'metadata': {'name': 'retriever1'}}}}],
+            'components': [
+                {'manifest': {'manifest': {'kind': 'KnowledgeRetriever', 'metadata': {'name': 'retriever1'}}}}
+            ],
         },
         {
             'debug': False,
@@ -79,7 +81,7 @@ async def test_plugin_list_filter_by_component_kinds():
                 }
             },
             'components': [
-                {'manifest': {'manifest': {'kind': 'KnowledgeEngine', 'metadata': {'name': 'retriever2'}}}},
+                {'manifest': {'manifest': {'kind': 'KnowledgeRetriever', 'metadata': {'name': 'retriever2'}}}},
                 {'manifest': {'manifest': {'kind': 'Tool', 'metadata': {'name': 'tool2'}}}},
             ],
         },
@@ -106,8 +108,8 @@ async def test_plugin_list_filter_by_component_kinds():
     assert 'plugin_with_command' in plugin_names
     assert 'plugin_with_event_listener' in plugin_names
     assert 'plugin_with_mixed_components' in plugin_names
-    # Plugin with only KnowledgeEngine should NOT be included
-    assert 'plugin_with_knowledge_engine_only' not in plugin_names
+    # Plugin with only KnowledgeRetriever should NOT be included
+    assert 'plugin_with_knowledge_retriever_only' not in plugin_names
 
 
 @pytest.mark.asyncio
@@ -148,7 +150,9 @@ async def test_plugin_list_filter_no_filter():
                     }
                 }
             },
-            'components': [{'manifest': {'manifest': {'kind': 'KnowledgeEngine', 'metadata': {'name': 'retriever1'}}}}],
+            'components': [
+                {'manifest': {'manifest': {'kind': 'KnowledgeRetriever', 'metadata': {'name': 'retriever1'}}}}
+            ],
         },
     ]
 
@@ -185,7 +189,7 @@ async def test_plugin_list_filter_empty_result():
     connector = PluginRuntimeConnector(mock_app, AsyncMock())
     connector.handler = MagicMock()
 
-    # Mock plugin data - only KnowledgeEngine plugins
+    # Mock plugin data - only KnowledgeRetriever plugins
     mock_plugins = [
         {
             'debug': False,
@@ -197,7 +201,9 @@ async def test_plugin_list_filter_empty_result():
                     }
                 }
             },
-            'components': [{'manifest': {'manifest': {'kind': 'KnowledgeEngine', 'metadata': {'name': 'retriever1'}}}}],
+            'components': [
+                {'manifest': {'manifest': {'kind': 'KnowledgeRetriever', 'metadata': {'name': 'retriever1'}}}}
+            ],
         },
     ]
 

uv.lock

@@ -964,30 +964,6 @@ wheels = [
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-version = "12.9.4"
-source = { registry = "https://pypi.org/simple" }
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-]
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-    { url = "https://files.pythonhosted.org/packages/6c/19/90ac264acc00f6df8a49378eedec9fd2db3061bf9263bf9f39fd3d8377c3/cuda_bindings-12.9.4-cp314-cp314t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d80bffc357df9988dca279734bc9674c3934a654cab10cadeed27ce17d8635ee", size = 11924658, upload-time = "2025-10-21T14:52:10.411Z" },
-]
-
-[[package]]
-name = "cuda-pathfinder"
-version = "1.4.1"
-source = { registry = "https://pypi.org/simple" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/07/02/59a5bc738a09def0b49aea0e460bdf97f65206d0d041246147cf6207e69c/cuda_pathfinder-1.4.1-py3-none-any.whl", hash = "sha256:40793006082de88e0950753655e55558a446bed9a7d9d0bcb48b2506d50ed82a", size = 43903, upload-time = "2026-03-06T21:05:24.372Z" },
-]
-
 [[package]]
 name = "dashscope"
 version = "1.25.10"
@@ -1112,7 +1088,7 @@ wheels = [
 
 [[package]]
 name = "flask"
-version = "3.1.3"
+version = "3.1.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "blinker" },
@@ -1122,9 +1098,9 @@ dependencies = [
     { name = "markupsafe" },
     { name = "werkzeug" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/26/00/35d85dcce6c57fdc871f3867d465d780f302a175ea360f62533f12b27e2b/flask-3.1.3.tar.gz", hash = "sha256:0ef0e52b8a9cd932855379197dd8f94047b359ca0a78695144304cb45f87c9eb", size = 759004, upload-time = "2026-02-19T05:00:57.678Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/dc/6d/cfe3c0fcc5e477df242b98bfe186a4c34357b4847e87ecaef04507332dab/flask-3.1.2.tar.gz", hash = "sha256:bf656c15c80190ed628ad08cdfd3aaa35beb087855e2f494910aa3774cc4fd87", size = 720160, upload-time = "2025-08-19T21:03:21.205Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/ec/f9/7f9263c5695f4bd0023734af91bedb2ff8209e8de6ead162f35d8dc762fd/flask-3.1.2-py3-none-any.whl", hash = "sha256:ca1d8112ec8a6158cc29ea4858963350011b5c846a414cdb7a954aa9e967d03c", size = 103308, upload-time = "2025-08-19T21:03:19.499Z" },
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 [[package]]
@@ -1753,15 +1729,6 @@ wheels = [
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 ]
 
-[[package]]
-name = "joblib"
-version = "1.5.3"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/41/f2/d34e8b3a08a9cc79a50b2208a93dce981fe615b64d5a4d4abee421d898df/joblib-1.5.3.tar.gz", hash = "sha256:8561a3269e6801106863fd0d6d84bb737be9e7631e33aaed3fb9ce5953688da3", size = 331603, upload-time = "2025-12-15T08:41:46.427Z" }
-wheels = [
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-]
-
 [[package]]
 name = "jsonpatch"
 version = "1.33"
@@ -1832,7 +1799,7 @@ wheels = [
 
 [[package]]
 name = "langbot"
-version = "4.9.1"
+version = "4.8.3"
 source = { editable = "." }
 dependencies = [
     { name = "aiocqhttp" },
@@ -1846,7 +1813,6 @@ dependencies = [
     { name = "asyncpg" },
     { name = "beautifulsoup4" },
     { name = "boto3" },
-    { name = "botocore" },
     { name = "certifi" },
     { name = "chardet" },
     { name = "chromadb" },
@@ -1925,10 +1891,9 @@ requires-dist = [
     { name = "asyncpg", specifier = ">=0.30.0" },
     { name = "beautifulsoup4", specifier = ">=4.12.3" },
     { name = "boto3", specifier = ">=1.35.0" },
-    { name = "botocore", specifier = ">=1.42.39" },
     { name = "certifi", specifier = ">=2025.4.26" },
     { name = "chardet", specifier = ">=5.2.0" },
-    { name = "chromadb", specifier = ">=1.0.0,<2.0.0" },
+    { name = "chromadb", specifier = ">=0.4.24" },
     { name = "colorlog", specifier = "~=6.6.0" },
     { name = "cryptography", specifier = ">=44.0.3" },
     { name = "dashscope", specifier = ">=1.25.10" },
@@ -1937,7 +1902,7 @@ requires-dist = [
     { name = "ebooklib", specifier = ">=0.18" },
     { name = "gewechat-client", specifier = ">=0.1.5" },
     { name = "html2text", specifier = ">=2024.2.26" },
-    { name = "langbot-plugin", specifier = "==0.3.1" },
+    { name = "langbot-plugin", specifier = "==0.2.5" },
     { name = "langchain", specifier = ">=0.2.0" },
     { name = "langchain-text-splitters", specifier = ">=0.0.1" },
     { name = "lark-oapi", specifier = ">=1.4.15" },
@@ -1960,7 +1925,7 @@ requires-dist = [
     { name = "pymilvus", specifier = ">=2.6.4" },
     { name = "pynacl", specifier = ">=1.5.0" },
     { name = "pypdf2", specifier = ">=3.0.1" },
-    { name = "pyseekdb", specifier = "==1.1.0.post3" },
+    { name = "pyseekdb", specifier = "==1.0.0b7" },
     { name = "python-docx", specifier = ">=1.1.0" },
     { name = "python-socks", specifier = ">=2.7.1" },
     { name = "python-telegram-bot", specifier = ">=22.0" },
@@ -1993,7 +1958,7 @@ dev = [
 
 [[package]]
 name = "langbot-plugin"
-version = "0.3.1"
+version = "0.2.5"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiofiles" },
@@ -2011,28 +1976,28 @@ dependencies = [
     { name = "watchdog" },
     { name = "websockets" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/4e/ed/b440e26ebc40983abf00dd343338101ada3381065fb3347401ba75f873fe/langbot_plugin-0.3.1.tar.gz", hash = "sha256:0839dcb4cfe689fc670d0ded29b57e6a3f683d8f7326eaa771a5b753675459ac", size = 170285, upload-time = "2026-03-12T15:07:01.918Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/28/0e/117dfc00f36323cce2369be5176d5cd5247ff52edb34791413af9623f290/langbot_plugin-0.2.5.tar.gz", hash = "sha256:a1bf04c1c07b30c72fb9b28e1330372bb4a43ae2db309394435fc088c513cfd5", size = 103910, upload-time = "2026-01-29T13:55:34.328Z" }
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 [[package]]
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-version = "1.2.12"
+version = "1.2.7"
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     { name = "langgraph" },
     { name = "pydantic" },
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+    { url = "https://files.pythonhosted.org/packages/dd/c8/9ce37ae34870834c7d00bb14ff4876b700db31b928635e3307804dc41d74/langchain-1.2.7-py3-none-any.whl", hash = "sha256:1d643c8ca569bcde2470b853807f74f0768b3982d25d66d57db21a166aabda72", size = 108827, upload-time = "2026-01-23T15:22:09.771Z" },
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 [[package]]
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-version = "1.2.18"
+version = "1.2.7"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
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@@ -2044,9 +2009,9 @@ dependencies = [
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 ]
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+    { url = "https://files.pythonhosted.org/packages/6e/6f/34a9fba14d191a67f7e2ee3dbce3e9b86d2fa7310e2c7f2c713583481bd2/langchain_core-1.2.7-py3-none-any.whl", hash = "sha256:452f4fef7a3d883357b22600788d37e3d8854ef29da345b7ac7099f33c31828b", size = 490232, upload-time = "2026-01-09T17:44:24.236Z" },
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 [[package]]
@@ -2063,7 +2028,7 @@ wheels = [
 
 [[package]]
 name = "langgraph"
-version = "1.1.1"
+version = "1.0.7"
 source = { registry = "https://pypi.org/simple" }
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@@ -2073,9 +2038,9 @@ dependencies = [
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+    { url = "https://files.pythonhosted.org/packages/ad/e4/8d97cca767bcc1be76d16fb76951608305561c6e056811587f36cb1316a8/werkzeug-3.1.5-py3-none-any.whl", hash = "sha256:5111e36e91086ece91f93268bb39b4a35c1e6f1feac762c9c822ded0a4e322dc", size = 225025, upload-time = "2026-01-08T17:49:21.859Z" },
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web/package-lock.json

@@ -32,7 +32,7 @@
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-        "axios": "^1.13.5",
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@@ -56,7 +56,6 @@
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         "rehype-highlight": "^7.0.2",
         "rehype-raw": "^7.0.0",
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         "remark-gfm": "^4.0.1",
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@@ -3799,13 +3798,13 @@
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+      "resolved": "https://registry.npmjs.org/axios/-/axios-1.13.4.tgz",
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       "license": "MIT",
       "dependencies": {
-        "follow-redirects": "^1.15.11",
-        "form-data": "^4.0.5",
+        "follow-redirects": "^1.15.6",
+        "form-data": "^4.0.4",
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@@ -5971,21 +5970,6 @@
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@@ -9408,20 +9392,6 @@
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