From 61e5de1d4d08eb8570d0a30c800181d3398bdbd6 Mon Sep 17 00:00:00 2001
From: youhuanghe <1051233107@qq.com>
Date: Sat, 24 Jan 2026 06:02:13 +0000
Subject: [PATCH 01/25] refactor: migrate RAG knowledge services to a
 plugin-oriented host service architecture.

---
 .../pkg/rag/knowledge/services/__init__.py    |   0
 .../rag/knowledge/services/base_service.py    |  15 -
 .../pkg/rag/knowledge/services/chunker.py     |  49 ---
 .../pkg/rag/knowledge/services/embedder.py    |  53 ----
 .../pkg/rag/knowledge/services/parser.py      | 291 ------------------
 .../pkg/rag/knowledge/services/retriever.py   |  50 ---
 6 files changed, 458 deletions(-)
 delete mode 100644 src/langbot/pkg/rag/knowledge/services/__init__.py
 delete mode 100644 src/langbot/pkg/rag/knowledge/services/base_service.py
 delete mode 100644 src/langbot/pkg/rag/knowledge/services/chunker.py
 delete mode 100644 src/langbot/pkg/rag/knowledge/services/embedder.py
 delete mode 100644 src/langbot/pkg/rag/knowledge/services/parser.py
 delete mode 100644 src/langbot/pkg/rag/knowledge/services/retriever.py

diff --git a/src/langbot/pkg/rag/knowledge/services/__init__.py b/src/langbot/pkg/rag/knowledge/services/__init__.py
deleted file mode 100644
index e69de29bb..000000000
diff --git a/src/langbot/pkg/rag/knowledge/services/base_service.py b/src/langbot/pkg/rag/knowledge/services/base_service.py
deleted file mode 100644
index 0f71a508a..000000000
--- a/src/langbot/pkg/rag/knowledge/services/base_service.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# 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)
diff --git a/src/langbot/pkg/rag/knowledge/services/chunker.py b/src/langbot/pkg/rag/knowledge/services/chunker.py
deleted file mode 100644
index 0cb168161..000000000
--- a/src/langbot/pkg/rag/knowledge/services/chunker.py
+++ /dev/null
@@ -1,49 +0,0 @@
-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
diff --git a/src/langbot/pkg/rag/knowledge/services/embedder.py b/src/langbot/pkg/rag/knowledge/services/embedder.py
deleted file mode 100644
index 485bc21ed..000000000
--- a/src/langbot/pkg/rag/knowledge/services/embedder.py
+++ /dev/null
@@ -1,53 +0,0 @@
-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.requester.invoke_embedding(
-                model=embedding_model,
-                input_text=batch,
-                extra_args={},  # TODO: add extra args
-            )
-            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
diff --git a/src/langbot/pkg/rag/knowledge/services/parser.py b/src/langbot/pkg/rag/knowledge/services/parser.py
deleted file mode 100644
index 504107385..000000000
--- a/src/langbot/pkg/rag/knowledge/services/parser.py
+++ /dev/null
@@ -1,291 +0,0 @@
-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)
diff --git a/src/langbot/pkg/rag/knowledge/services/retriever.py b/src/langbot/pkg/rag/knowledge/services/retriever.py
deleted file mode 100644
index f2f4bef52..000000000
--- a/src/langbot/pkg/rag/knowledge/services/retriever.py
+++ /dev/null
@@ -1,50 +0,0 @@
-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.requester.invoke_embedding(
-            model=embedding_model,
-            input_text=[query],
-            extra_args={},  # TODO: add extra args
-        )
-
-        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

From 21ea76608998946f4e5617201241f1a4c58e7359 Mon Sep 17 00:00:00 2001
From: youhuanghe <1051233107@qq.com>
Date: Sat, 24 Jan 2026 09:44:00 +0000
Subject: [PATCH 02/25] feat(rag): phase 2 core refactor with RPC Action
 handlers

---
 .../http/controller/groups/knowledge/base.py  |   6 +-
 src/langbot/pkg/api/http/service/knowledge.py |  29 ++-
 src/langbot/pkg/entity/persistence/rag.py     |   5 +
 src/langbot/pkg/plugin/connector.py           |  36 ++++
 src/langbot/pkg/plugin/handler.py             | 193 +++++++++++++++++-
 src/langbot/pkg/rag/knowledge/base.py         |   4 +-
 src/langbot/pkg/rag/knowledge/kbmgr.py        | 134 +++++++-----
 .../pkg/rag/knowledge/plugin_adapter.py       | 142 +++++++++++++
 8 files changed, 491 insertions(+), 58 deletions(-)
 create mode 100644 src/langbot/pkg/rag/knowledge/plugin_adapter.py

diff --git a/src/langbot/pkg/api/http/controller/groups/knowledge/base.py b/src/langbot/pkg/api/http/controller/groups/knowledge/base.py
index 96ed001c1..2e44109f6 100644
--- a/src/langbot/pkg/api/http/controller/groups/knowledge/base.py
+++ b/src/langbot/pkg/api/http/controller/groups/knowledge/base.py
@@ -90,5 +90,9 @@ async def delete_specific_file_in_kb(file_id: str, knowledge_base_uuid: str) ->
         async def retrieve_knowledge_base(knowledge_base_uuid: str) -> str:
             json_data = await quart.request.json
             query = json_data.get('query')
-            results = await self.ap.knowledge_service.retrieve_knowledge_base(knowledge_base_uuid, query)
+            retrieval_settings = json_data.get('retrieval_settings', {})
+            results = await self.ap.knowledge_service.retrieve_knowledge_base(
+                knowledge_base_uuid, query, retrieval_settings
+            )
             return self.success(data={'results': results})
+
diff --git a/src/langbot/pkg/api/http/service/knowledge.py b/src/langbot/pkg/api/http/service/knowledge.py
index b753ce5ac..a5866cee2 100644
--- a/src/langbot/pkg/api/http/service/knowledge.py
+++ b/src/langbot/pkg/api/http/service/knowledge.py
@@ -36,6 +36,18 @@ async def get_knowledge_base(self, kb_uuid: str) -> dict | None:
 
     async def create_knowledge_base(self, kb_data: dict) -> str:
         """创建知识库"""
+        # Check if plugin-based creation
+        if 'rag_engine_plugin_id' in kb_data and kb_data['rag_engine_plugin_id']:
+            # Use new manager method
+            kb = await self.ap.rag_mgr.create_knowledge_base(
+                name=kb_data.get('name', 'Untitled'),
+                rag_engine_plugin_id=kb_data['rag_engine_plugin_id'],
+                creation_settings=kb_data.get('creation_settings', {}),
+                description=kb_data.get('description', ''),
+                embedding_model_uuid=kb_data.get('embedding_model_uuid', '')
+            )
+            return kb.uuid
+
         kb_data['uuid'] = str(uuid.uuid4())
         await self.ap.persistence_mgr.execute_async(sqlalchemy.insert(persistence_rag.KnowledgeBase).values(kb_data))
 
@@ -85,14 +97,23 @@ async def store_file(self, kb_uuid: str, file_id: str) -> int:
 
         return result
 
-    async def retrieve_knowledge_base(self, kb_uuid: str, query: str) -> list[dict]:
+    async def retrieve_knowledge_base(
+        self, kb_uuid: str, query: str, retrieval_settings: dict | None = None
+    ) -> 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')
-        return [
-            result.model_dump() for result in await runtime_kb.retrieve(query, runtime_kb.knowledge_base_entity.top_k)
-        ]
+        
+        # Pass retrieval_settings
+        results = await runtime_kb.retrieve(
+            query, 
+            runtime_kb.knowledge_base_entity.top_k, 
+            settings=retrieval_settings
+        )
+        
+        return [result.model_dump() for result in results]
+
 
     async def get_files_by_knowledge_base(self, kb_uuid: str) -> list[dict]:
         """获取知识库文件"""
diff --git a/src/langbot/pkg/entity/persistence/rag.py b/src/langbot/pkg/entity/persistence/rag.py
index 41a6cb5d8..60096e6c4 100644
--- a/src/langbot/pkg/entity/persistence/rag.py
+++ b/src/langbot/pkg/entity/persistence/rag.py
@@ -11,6 +11,11 @@ class KnowledgeBase(Base):
     updated_at = sqlalchemy.Column(sqlalchemy.DateTime, default=sqlalchemy.func.now(), onupdate=sqlalchemy.func.now())
     embedding_model_uuid = sqlalchemy.Column(sqlalchemy.String, default='')
     top_k = sqlalchemy.Column(sqlalchemy.Integer, default=5)
+    
+    # New fields for plugin-based RAG
+    rag_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={})
 
 
 class File(Base):
diff --git a/src/langbot/pkg/plugin/connector.py b/src/langbot/pkg/plugin/connector.py
index 1ae543750..69f039ce4 100644
--- a/src/langbot/pkg/plugin/connector.py
+++ b/src/langbot/pkg/plugin/connector.py
@@ -538,3 +538,39 @@ async def sync_polymorphic_component_instances(self) -> dict[str, Any]:
         )
 
         return sync_result
+
+    # ================= RAG Capability Callers =================
+
+    async def call_rag_ingest(self, plugin_id: str, context_data: dict[str, Any]) -> dict[str, Any]:
+        """Call plugin to ingest document.
+        
+        Args:
+            plugin_id: Target plugin ID (author/name).
+            context_data: IngestionContext data.
+        """
+        # Determine target from plugin_id
+        plugin_author, plugin_name = plugin_id.split('/', 1)
+        
+        # We need a new action type in RuntimeToPluginAction and LangBotToRuntimeAction
+        # Currently leveraging generic EXECUTE_COMMAND or need new specific actions
+        # Using a new action: INGEST_DOCUMENT which should be added to Handler first.
+        
+        # Since we modified enums in SDK, we should assume runtime supports it.
+        # But LangBot -> Runtime communication (LangBotToRuntimeAction) also needs update.
+        
+        # For now, let's assume we add specific methods to Handler or extended Action logic.
+        # Direct call to handler which sends Action to Runtime.
+        
+        return await self.handler.rag_ingest_document(plugin_author, plugin_name, context_data)
+
+    async def call_rag_delete_document(self, plugin_id: str, document_id: str, kb_id: str) -> bool:
+        plugin_author, plugin_name = plugin_id.split('/', 1)
+        return await self.handler.rag_delete_document(plugin_author, plugin_name, document_id, kb_id)
+
+    async def get_rag_creation_schema(self, plugin_id: str) -> dict[str, Any]:
+        plugin_author, plugin_name = plugin_id.split('/', 1)
+        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 = plugin_id.split('/', 1)
+        return await self.handler.get_rag_retrieval_schema(plugin_author, plugin_name)
diff --git a/src/langbot/pkg/plugin/handler.py b/src/langbot/pkg/plugin/handler.py
index e9819e0d8..573e618df 100644
--- a/src/langbot/pkg/plugin/handler.py
+++ b/src/langbot/pkg/plugin/handler.py
@@ -21,6 +21,9 @@
 
 from ..entity.persistence import plugin as persistence_plugin
 from ..entity.persistence import bstorage as persistence_bstorage
+from ..entity.persistence import rag as persistence_rag
+from ..storage.providers.localstorage import LocalStorageProvider, LOCAL_STORAGE_PATH
+import os
 
 from ..core import app
 from ..utils import constants
@@ -438,7 +441,7 @@ async def get_binary_storage_keys(data: dict[str, Any]) -> handler.ActionRespons
                 },
             )
 
-        @self.action(RuntimeToLangBotAction.GET_CONFIG_FILE)
+        @self.action(PluginToRuntimeAction.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']
@@ -457,6 +460,144 @@ async def get_config_file(data: dict[str, Any]) -> handler.ActionResponse:
                     message=f'Failed to load config file {file_key}: {e}',
                 )
 
+        # ================= RAG Capability Handlers =================
+
+        async def _get_kb_entity(kb_id: str):
+            stmt = sqlalchemy.select(persistence_rag.KnowledgeBase).where(persistence_rag.KnowledgeBase.uuid == kb_id)
+            result = await self.ap.persistence_mgr.execute_async(stmt)
+            kb = result.scalar_one_or_none()
+            if not kb:
+                 raise ValueError(f"Knowledge Base {kb_id} not found")
+            return kb
+
+        @self.action(PluginToRuntimeAction.RAG_EMBED_DOCUMENTS)
+        async def rag_embed_documents(data: dict[str, Any]) -> handler.ActionResponse:
+            kb_id = data['kb_id']
+            texts = data['texts']
+            try:
+                # 1. Get KB config to find embedding model
+                kb = await _get_kb_entity(kb_id)
+                embed_model_name = kb.embed_model or kb.embedding_model_uuid # Handle both cases if schema changed
+                
+                # 2. Get embedder instance
+                # Assuming model_mgr can get embedder model. 
+                # Note: Previous kbmgr used self.ap.model_mgr.get_embedding_model_by_uuid
+                # embed_model_name usually stores UUID in current system based on kbmgr code
+                
+                if not embed_model_name:
+                     # Fallback or error
+                     raise ValueError("Embedding model not configured for this Knowledge Base")
+
+                # If embed_model_name looks like a UUID
+                embedder_model = await self.ap.model_mgr.get_embedding_model_by_uuid(embed_model_name)
+                
+                if not embedder_model:
+                     # Try getting by name if needed, but uuid is standard
+                     raise ValueError(f"Embedding model {embed_model_name} not found")
+
+                # 3. Generate embeddings
+                # We need an Embedder wrapper or use model provider directly.
+                # In kbmgr.py: self.embedder.embed_and_store uses embedding_model.provider.embed_documents
+                
+                vectors = await embedder_model.provider.embed_documents(texts)
+                return handler.ActionResponse.success(data={'vectors': vectors})
+            except Exception as e:
+                return handler.ActionResponse.error(message=str(e))
+
+        @self.action(PluginToRuntimeAction.RAG_EMBED_QUERY)
+        async def rag_embed_query(data: dict[str, Any]) -> handler.ActionResponse:
+            kb_id = data['kb_id']
+            text = data['text']
+            try:
+                kb = await _get_kb_entity(kb_id)
+                embed_model_name = kb.embed_model or kb.embedding_model_uuid
+                
+                if not embed_model_name:
+                     raise ValueError("Embedding model not configured")
+
+                embedder_model = await self.ap.model_mgr.get_embedding_model_by_uuid(embed_model_name)
+                if not embedder_model:
+                     raise ValueError(f"Embedding model {embed_model_name} not found")
+
+                vector = await embedder_model.provider.embed_query(text)
+                return handler.ActionResponse.success(data={'vector': vector})
+            except Exception as e:
+                return handler.ActionResponse.error(message=str(e))
+
+        @self.action(PluginToRuntimeAction.RAG_VECTOR_UPSERT)
+        async def rag_vector_upsert(data: dict[str, Any]) -> handler.ActionResponse:
+            collection_id = data['collection_id']
+            vectors = data['vectors']
+            ids = data['ids']
+            metadata = data.get('metadata')
+            try:
+                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
+                )
+                return handler.ActionResponse.success(data={})
+            except Exception as e:
+                return handler.ActionResponse.error(message=str(e))
+
+        @self.action(PluginToRuntimeAction.RAG_VECTOR_SEARCH)
+        async def rag_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')
+            try:
+                results = await self.ap.vector_db_mgr.search(
+                    collection_name=collection_id,
+                    query_vector=query_vector,
+                    limit=top_k,
+                    filter=filters
+                )
+                return handler.ActionResponse.success(data={'results': results})
+            except Exception as e:
+                return handler.ActionResponse.error(message=str(e))
+
+        @self.action(PluginToRuntimeAction.RAG_VECTOR_DELETE)
+        async def rag_vector_delete(data: dict[str, Any]) -> handler.ActionResponse:
+            collection_id = data['collection_id']
+            ids = data.get('ids')
+            filters = data.get('filters')
+            try:
+                count = 0
+                if ids:
+                    await self.ap.vector_db_mgr.delete(collection_name=collection_id, ids=ids)
+                    count = len(ids) 
+                elif filters:
+                     await self.ap.vector_db_mgr.delete_by_filter(collection_name=collection_id, filter=filters)
+                
+                return handler.ActionResponse.success(data={'count': count})
+            except Exception as e:
+                return handler.ActionResponse.error(message=str(e))
+
+        @self.action(PluginToRuntimeAction.RAG_GET_FILE_STREAM)
+        async def rag_get_file_stream(data: dict[str, Any]) -> handler.ActionResponse:
+            storage_path = data['storage_path']
+            try:
+                provider = self.ap.storage_mgr.storage_provider
+                content_bytes = b""
+                
+                if isinstance(provider, LocalStorageProvider):
+                    real_path = os.path.join(LOCAL_STORAGE_PATH, storage_path)
+                    if os.path.exists(real_path):
+                         with open(real_path, "rb") as f:
+                             content_bytes = f.read()
+                    else:
+                        content_bytes = await self.ap.storage_mgr.load(storage_path)
+                else:
+                    content_bytes = await self.ap.storage_mgr.load(storage_path)
+                    
+                content_base64 = base64.b64encode(content_bytes).decode('utf-8')
+                return handler.ActionResponse.success(data={'content_base64': content_base64})
+            except Exception as e:
+                return handler.ActionResponse.error(message=str(e))
+
     async def ping(self) -> dict[str, Any]:
         """Ping the runtime"""
         return await self.call_action(
@@ -769,3 +910,53 @@ async def get_debug_info(self) -> dict[str, Any]:
             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."""
+        # Note: We need to define text for this action in LangBotToRuntimeAction
+        # Assuming we added it.
+        result = await self.call_action(
+            "ingest_document", # Using string literal if enum missing, or update enum
+            {
+                "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(
+            "delete_document",
+            {
+                "plugin_author": plugin_author,
+                "plugin_name": plugin_name,
+                "document_id": document_id,
+                "kb_id": kb_id
+            },
+            timeout=30
+        )
+        return result["success"]
+
+    async def get_rag_creation_schema(self, plugin_author: str, plugin_name: str) -> dict[str, Any]:
+        return await self.call_action(
+            "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(
+            "get_rag_retrieval_settings_schema",
+            {
+                "plugin_author": plugin_author,
+                "plugin_name": plugin_name
+            },
+            timeout=10
+        )
diff --git a/src/langbot/pkg/rag/knowledge/base.py b/src/langbot/pkg/rag/knowledge/base.py
index 4b183eae2..793d68412 100644
--- a/src/langbot/pkg/rag/knowledge/base.py
+++ b/src/langbot/pkg/rag/knowledge/base.py
@@ -22,18 +22,20 @@ async def initialize(self):
         pass
 
     @abc.abstractmethod
-    async def retrieve(self, query: str, top_k: int) -> list[rag_context.RetrievalResultEntry]:
+    async def retrieve(self, query: str, top_k: int, settings: dict | None = None) -> list[rag_context.RetrievalResultEntry]:
         """Retrieve relevant documents from the knowledge base
 
         Args:
             query: The query string
             top_k: Number of top results to return
+            settings: Optional retrieval settings/configuration
 
         Returns:
             List of retrieve result entries
         """
         pass
 
+
     @abc.abstractmethod
     def get_uuid(self) -> str:
         """Get the UUID of the knowledge base"""
diff --git a/src/langbot/pkg/rag/knowledge/kbmgr.py b/src/langbot/pkg/rag/knowledge/kbmgr.py
index 5fd44854e..b42e43c83 100644
--- a/src/langbot/pkg/rag/knowledge/kbmgr.py
+++ b/src/langbot/pkg/rag/knowledge/kbmgr.py
@@ -3,16 +3,16 @@
 import uuid
 import zipfile
 import io
-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
+from .plugin_adapter import RAGPluginAdapter
 
 
 class RuntimeKnowledgeBase(KnowledgeBaseInterface):
@@ -20,23 +20,13 @@ class RuntimeKnowledgeBase(KnowledgeBaseInterface):
 
     knowledge_base_entity: persistence_rag.KnowledgeBase
 
-    parser: parser.FileParser
-
-    chunker: chunker.Chunker
-
-    embedder: Embedder
-
-    retriever: Retriever
+    # Plugin Adapter handles both plugin and legacy logic
+    rag_adapter: RAGPluginAdapter
 
     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
+        self.rag_adapter = RAGPluginAdapter(ap)
 
     async def initialize(self):
         pass
@@ -50,29 +40,19 @@ async def _store_file_task(self, file: persistence_rag.File, task_context: taskm
                 .values(status='processing')
             )
 
-            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}')
-
-            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}')
-
-            task_context.set_current_action('Embedding chunks')
-
-            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,
+            task_context.set_current_action('Processing file')
+            
+            # Delegate to adapter (which handles legacy logic internally if needed)
+            await self.rag_adapter.ingest_document(
+                self.knowledge_base_entity,
+                {
+                    "document_id": file.uuid,
+                    "filename": file.file_name,
+                    "extension": file.extension,
+                    "file_size": 0, # TODO: Get size
+                    "mime_type": "application/octet-stream" # TODO: Detect type
+                },
+                file.file_name # storage path
             )
 
             # set file status to completed
@@ -189,21 +169,31 @@ async def _store_zip_file(self, zip_file_id: str) -> str:
 
         return stored_file_tasks[0] if stored_file_tasks else ''
 
-    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
+    async def retrieve(self, query: str, top_k: int, settings: dict | None = None) -> list[rag_context.RetrievalResultEntry]:
+        # Merge top_k into settings or use as default
+        retrieve_settings = {"top_k": top_k}
+        if settings:
+            retrieve_settings.update(settings)
+            
+        response = await self.rag_adapter.retrieve(
+            self.knowledge_base_entity,
+            query,
+            retrieve_settings
         )
-        return await self.retriever.retrieve(self.knowledge_base_entity.uuid, query, embedding_model, top_k)
+        
+        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 delete_file(self, file_id: str):
-        # 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)
-        )
-
+        await self.rag_adapter.delete_document(self.knowledge_base_entity, file_id)
+        
+        # Also cleanup DB record (Adapter handles chunk/vector deletion)
         await self.ap.persistence_mgr.execute_async(
             sqlalchemy.delete(persistence_rag.File).where(persistence_rag.File.uuid == file_id)
         )
@@ -221,9 +211,11 @@ def get_type(self) -> str:
         return 'internal'
 
     async def dispose(self):
+        await self.rag_adapter.on_kb_delete(self.knowledge_base_entity)
         await self.ap.vector_db_mgr.vector_db.delete_collection(self.knowledge_base_entity.uuid)
 
 
+
 class RAGManager:
     ap: app.Application
 
@@ -235,6 +227,45 @@ def __init__(self, ap: app.Application):
 
     async def initialize(self):
         await self.load_knowledge_bases_from_db()
+        
+    async def create_knowledge_base(
+        self,
+        name: str,
+        rag_engine_plugin_id: str,
+        creation_settings: dict,
+        description: str = "",
+        embedding_model_uuid: str = ""
+    ) -> persistence_rag.KnowledgeBase:
+        """Create a new knowledge base using a RAG plugin."""
+        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,
+           "rag_engine_plugin_id": rag_engine_plugin_id,
+           "collection_id": collection_id,
+           "creation_settings": creation_settings,
+           "embedding_model_uuid": embedding_model_uuid
+        }
+        
+        # Create Entity
+        kb = persistence_rag.KnowledgeBase(**kb_data)
+        
+        # Persist
+        await self.ap.persistence_mgr.execute_async(sqlalchemy.insert(persistence_rag.KnowledgeBase).values(kb_data))
+        
+        # Notify Plugin
+        adapter = RAGPluginAdapter(self.ap)
+        await adapter.on_kb_create(kb)
+        
+        # Load into Runtime
+        await self.load_knowledge_base(kb)
+        
+        self.ap.logger.info(f"Created new Knowledge Base {name} ({kb_uuid}) using plugin {rag_engine_plugin_id}")
+        return kb
 
     async def load_knowledge_bases_from_db(self):
         self.ap.logger.info('Loading knowledge bases from db...')
@@ -273,6 +304,7 @@ async def load_knowledge_base(
         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):
             knowledge_base_entity = persistence_rag.KnowledgeBase(**knowledge_base_entity)
diff --git a/src/langbot/pkg/rag/knowledge/plugin_adapter.py b/src/langbot/pkg/rag/knowledge/plugin_adapter.py
new file mode 100644
index 000000000..16cde1cd9
--- /dev/null
+++ b/src/langbot/pkg/rag/knowledge/plugin_adapter.py
@@ -0,0 +1,142 @@
+from __future__ import annotations
+
+import logging
+from typing import Any, Dict, Optional
+
+from langbot.pkg.core import app
+from langbot.pkg.entity.persistence import rag as persistence_rag
+
+logger = logging.getLogger(__name__)
+
+
+class RAGPluginAdapter:
+    """Adapter for interacting with RAG plugins."""
+
+    def __init__(self, ap: app.Application):
+        self.ap = ap
+
+    async def on_kb_create(self, kb: persistence_rag.KnowledgeBase) -> None:
+        """Notify plugin about KB creation."""
+        plugin_id = kb.rag_engine_plugin_id
+        if not plugin_id:
+            return
+
+        try:
+            # TODO: RPC Call to plugin
+            logger.info(
+                f"Calling RAG plugin {plugin_id}: on_knowledge_base_create(kb_id={kb.uuid})"
+            )
+        except Exception as e:
+            logger.error(f"Failed to notify plugin {plugin_id} on KB create: {e}")
+
+    async def on_kb_delete(self, kb: persistence_rag.KnowledgeBase) -> None:
+        """Notify plugin about KB deletion."""
+        plugin_id = kb.rag_engine_plugin_id
+        if not plugin_id:
+            return
+
+        try:
+            logger.info(
+                f"Calling RAG plugin {plugin_id}: on_knowledge_base_delete(kb_id={kb.uuid})"
+            )
+        except Exception as e:
+            logger.error(f"Failed to notify plugin {plugin_id} on KB delete: {e}")
+
+    async def ingest_document(
+        self,
+        kb: persistence_rag.KnowledgeBase,
+        file_metadata: Dict[str, Any],
+        storage_path: str,
+    ) -> Dict[str, Any]:
+        """Call plugin to ingest document."""
+        plugin_id = kb.rag_engine_plugin_id
+        if not plugin_id:
+            logger.error(f"No RAG plugin ID configured for KB {kb.uuid}. Ingestion failed.")
+            raise ValueError("RAG Plugin ID required")
+
+        logger.info(f"Calling RAG plugin {plugin_id}: ingest(doc={file_metadata.get('filename')})")
+        
+        context_data = {
+            "file_object": {
+                "metadata": file_metadata,
+                "storage_path": storage_path,
+            },
+            "knowledge_base_id": kb.uuid,
+            "chunking_strategy": "fixed_size", 
+            # "custom_settings": kb.creation_settings # Pass settings if needed
+        }
+
+        try:
+            result = await self.ap.plugin_connector.call_rag_ingest(plugin_id, context_data)
+            return result
+        except Exception as e:
+            logger.error(f"Plugin ingestion failed: {e}")
+            raise
+
+    async def retrieve(
+        self,
+        kb: persistence_rag.KnowledgeBase,
+        query: str,
+        settings: Dict[str, Any],
+    ) -> Dict[str, Any]:
+        """Call plugin to retrieve documents."""
+        plugin_id = kb.rag_engine_plugin_id
+        if not plugin_id:
+            logger.error(f"No RAG plugin ID configured for KB {kb.uuid}. Retrieval failed.")
+            return {"results": [], "total_found": 0}
+
+        # Uses reused RETRIEVE_KNOWLEDGE action which might already exist in connector
+        # But we need to ensure it uses the new RAGEngine interface on plugin side.
+        # For now, let's assume standard retrieval_knowledge route handles it if plugin implements KnowledgeRetriever component
+        # OR we add a specific call_rag_retrieve if RAGEngine is distinct from KnowledgeRetriever actions.
+        
+        # Current connector.retrieve_knowledge:
+        # return await self.handler.retrieve_knowledge(plugin_author, plugin_name, retriever_name, instance_id, retrieval_context)
+        
+        plugin_author, plugin_name = plugin_id.split('/', 1)
+        
+        # Assumption: retriever_name is "default" or derived? 
+        # In the new design, RAGEngine IS the KnowledgeRetriever component.
+        # So we need to know the component name. 
+        # Currently KB entity might not store component name (only plugin_id).
+        # We might need to assume 'rag_engine' or look it up.
+        # Let's assume the component name is 'langrag-engine' or similar from manifest.
+        # For this refactor, let's assume we invoke the first available KnowledgeRetriever/RAGEngine component.
+        
+        retriever_name = "default" # Placeholder, logic to find component name needed
+        
+        try:
+            return await self.ap.plugin_connector.retrieve_knowledge(
+                plugin_author,
+                plugin_name, 
+                retriever_name, 
+                kb.uuid, # instance_id
+                { "query": query, "config": settings }
+            )
+        except Exception as e:
+             logger.error(f"Plugin retrieval failed: {e}")
+             return {"results": [], "total_found": 0}
+
+    async def delete_document(
+        self, kb: persistence_rag.KnowledgeBase, document_id: str
+    ) -> bool:
+        """Call plugin to delete document."""
+        plugin_id = kb.rag_engine_plugin_id
+        if not plugin_id:
+            return False
+
+        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:
+            logger.error(f"Plugin document deletion failed: {e}")
+            return False
+
+    async def get_creation_schema(self, plugin_id: str) -> Dict[str, Any]:
+        """Get creation settings schema from plugin."""
+        return await self.ap.plugin_connector.get_rag_creation_schema(plugin_id)
+
+    async def get_retrieval_schema(self, plugin_id: str) -> Dict[str, Any]:
+        """Get retrieval settings schema from plugin."""
+        return await self.ap.plugin_connector.get_rag_retrieval_schema(plugin_id)

From 51fc2978dbe28cceca050001edef9714c5a2429c Mon Sep 17 00:00:00 2001
From: youhuanghe <1051233107@qq.com>
Date: Sun, 25 Jan 2026 12:52:34 +0000
Subject: [PATCH 03/25] =?UTF-8?q?feat:=20=E4=B8=BA=20RAG=20=E6=8F=92?=
 =?UTF-8?q?=E4=BB=B6=E6=B7=BB=E5=8A=A0=E7=9F=A5=E8=AF=86=E5=BA=93=E5=88=9B?=
 =?UTF-8?q?=E5=BB=BA=E5=92=8C=E5=88=A0=E9=99=A4=E4=BA=8B=E4=BB=B6=E9=80=9A?=
 =?UTF-8?q?=E7=9F=A5=EF=BC=8C=E5=B9=B6=E4=BC=98=E5=8C=96=E4=BA=86=20RAG=20?=
 =?UTF-8?q?=E5=8A=A8=E4=BD=9C=E7=9A=84=E5=8F=82=E6=95=B0=E4=BC=A0=E9=80=92?=
 =?UTF-8?q?=E5=92=8C=E6=9E=9A=E4=B8=BE=E4=BD=BF=E7=94=A8=E3=80=82?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 src/langbot/pkg/plugin/connector.py           | 10 +++
 src/langbot/pkg/plugin/handler.py             | 41 ++++++++--
 .../pkg/rag/knowledge/plugin_adapter.py       | 74 ++++++++++---------
 3 files changed, 84 insertions(+), 41 deletions(-)

diff --git a/src/langbot/pkg/plugin/connector.py b/src/langbot/pkg/plugin/connector.py
index 69f039ce4..6916231af 100644
--- a/src/langbot/pkg/plugin/connector.py
+++ b/src/langbot/pkg/plugin/connector.py
@@ -574,3 +574,13 @@ async def get_rag_creation_schema(self, plugin_id: str) -> dict[str, Any]:
     async def get_rag_retrieval_schema(self, plugin_id: str) -> dict[str, Any]:
         plugin_author, plugin_name = plugin_id.split('/', 1)
         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 = plugin_id.split('/', 1)
+        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 = plugin_id.split('/', 1)
+        return await self.handler.rag_on_kb_delete(plugin_author, plugin_name, kb_id)
diff --git a/src/langbot/pkg/plugin/handler.py b/src/langbot/pkg/plugin/handler.py
index 573e618df..7ab803cfb 100644
--- a/src/langbot/pkg/plugin/handler.py
+++ b/src/langbot/pkg/plugin/handler.py
@@ -915,22 +915,20 @@ async def get_debug_info(self) -> dict[str, Any]:
 
     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."""
-        # Note: We need to define text for this action in LangBotToRuntimeAction
-        # Assuming we added it.
         result = await self.call_action(
-            "ingest_document", # Using string literal if enum missing, or update enum
+            LangBotToRuntimeAction.RAG_INGEST_DOCUMENT,
             {
                 "plugin_author": plugin_author,
                 "plugin_name": plugin_name,
                 "context": context_data
             },
-            timeout=300 # Ingestion can be slow
+            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(
-            "delete_document",
+            LangBotToRuntimeAction.RAG_DELETE_DOCUMENT,
             {
                 "plugin_author": plugin_author,
                 "plugin_name": plugin_name,
@@ -939,11 +937,38 @@ async def rag_delete_document(self, plugin_author: str, plugin_name: str, docume
             },
             timeout=30
         )
-        return result["success"]
+        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(
-            "get_rag_creation_settings_schema",
+            LangBotToRuntimeAction.GET_RAG_CREATION_SETTINGS_SCHEMA,
             {
                 "plugin_author": plugin_author,
                 "plugin_name": plugin_name
@@ -953,7 +978,7 @@ async def get_rag_creation_schema(self, plugin_author: str, plugin_name: str) ->
 
     async def get_rag_retrieval_schema(self, plugin_author: str, plugin_name: str) -> dict[str, Any]:
         return await self.call_action(
-            "get_rag_retrieval_settings_schema",
+            LangBotToRuntimeAction.GET_RAG_RETRIEVAL_SETTINGS_SCHEMA,
             {
                 "plugin_author": plugin_author,
                 "plugin_name": plugin_name
diff --git a/src/langbot/pkg/rag/knowledge/plugin_adapter.py b/src/langbot/pkg/rag/knowledge/plugin_adapter.py
index 16cde1cd9..6b75a1d5e 100644
--- a/src/langbot/pkg/rag/knowledge/plugin_adapter.py
+++ b/src/langbot/pkg/rag/knowledge/plugin_adapter.py
@@ -22,10 +22,11 @@ async def on_kb_create(self, kb: persistence_rag.KnowledgeBase) -> None:
             return
 
         try:
-            # TODO: RPC Call to plugin
+            config = kb.creation_settings or {}
             logger.info(
                 f"Calling RAG plugin {plugin_id}: on_knowledge_base_create(kb_id={kb.uuid})"
             )
+            await self.ap.plugin_connector.rag_on_kb_create(plugin_id, kb.uuid, config)
         except Exception as e:
             logger.error(f"Failed to notify plugin {plugin_id} on KB create: {e}")
 
@@ -39,6 +40,7 @@ async def on_kb_delete(self, kb: persistence_rag.KnowledgeBase) -> None:
             logger.info(
                 f"Calling RAG plugin {plugin_id}: on_knowledge_base_delete(kb_id={kb.uuid})"
             )
+            await self.ap.plugin_connector.rag_on_kb_delete(plugin_id, kb.uuid)
         except Exception as e:
             logger.error(f"Failed to notify plugin {plugin_id} on KB delete: {e}")
 
@@ -55,15 +57,16 @@ async def ingest_document(
             raise ValueError("RAG Plugin ID required")
 
         logger.info(f"Calling RAG plugin {plugin_id}: ingest(doc={file_metadata.get('filename')})")
-        
+
         context_data = {
             "file_object": {
                 "metadata": file_metadata,
                 "storage_path": storage_path,
             },
             "knowledge_base_id": kb.uuid,
-            "chunking_strategy": "fixed_size", 
-            # "custom_settings": kb.creation_settings # Pass settings if needed
+            "collection_id": kb.collection_id or kb.uuid,  # Use collection_id if set, otherwise kb.uuid
+            "chunking_strategy": kb.creation_settings.get("chunking_strategy", "fixed_size") if kb.creation_settings else "fixed_size",
+            "custom_settings": kb.creation_settings or {},
         }
 
         try:
@@ -85,37 +88,34 @@ async def retrieve(
             logger.error(f"No RAG plugin ID configured for KB {kb.uuid}. Retrieval failed.")
             return {"results": [], "total_found": 0}
 
-        # Uses reused RETRIEVE_KNOWLEDGE action which might already exist in connector
-        # But we need to ensure it uses the new RAGEngine interface on plugin side.
-        # For now, let's assume standard retrieval_knowledge route handles it if plugin implements KnowledgeRetriever component
-        # OR we add a specific call_rag_retrieve if RAGEngine is distinct from KnowledgeRetriever actions.
-        
-        # Current connector.retrieve_knowledge:
-        # return await self.handler.retrieve_knowledge(plugin_author, plugin_name, retriever_name, instance_id, retrieval_context)
-        
         plugin_author, plugin_name = plugin_id.split('/', 1)
-        
-        # Assumption: retriever_name is "default" or derived? 
-        # In the new design, RAGEngine IS the KnowledgeRetriever component.
-        # So we need to know the component name. 
-        # Currently KB entity might not store component name (only plugin_id).
-        # We might need to assume 'rag_engine' or look it up.
-        # Let's assume the component name is 'langrag-engine' or similar from manifest.
-        # For this refactor, let's assume we invoke the first available KnowledgeRetriever/RAGEngine component.
-        
-        retriever_name = "default" # Placeholder, logic to find component name needed
-        
+
+        # For the new RAGEngine design, retriever_name comes from the plugin's component.
+        # We use the instance_id (kb.uuid) to identify the KB instance.
+        # The runtime/plugin will look up the correct RAGEngine component automatically.
+        # Using empty string for retriever_name to let the runtime find the default RAGEngine component.
+        retriever_name = ""  # Runtime will find the RAGEngine component automatically
+
+        retrieval_context = {
+            "query": query,
+            "knowledge_base_id": kb.uuid,
+            "collection_id": kb.collection_id or kb.uuid,
+            "top_k": settings.get("top_k", kb.top_k or 5),
+            "config": settings,
+        }
+
         try:
-            return await self.ap.plugin_connector.retrieve_knowledge(
+            result = await self.ap.plugin_connector.retrieve_knowledge(
                 plugin_author,
-                plugin_name, 
-                retriever_name, 
-                kb.uuid, # instance_id
-                { "query": query, "config": settings }
+                plugin_name,
+                retriever_name,
+                kb.uuid,  # instance_id
+                retrieval_context
             )
+            return result
         except Exception as e:
-             logger.error(f"Plugin retrieval failed: {e}")
-             return {"results": [], "total_found": 0}
+            logger.error(f"Plugin retrieval failed: {e}")
+            return {"results": [], "total_found": 0}
 
     async def delete_document(
         self, kb: persistence_rag.KnowledgeBase, document_id: str
@@ -126,7 +126,7 @@ async def delete_document(
             return False
 
         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:
@@ -135,8 +135,16 @@ async def delete_document(
 
     async def get_creation_schema(self, plugin_id: str) -> Dict[str, Any]:
         """Get creation settings schema from plugin."""
-        return await self.ap.plugin_connector.get_rag_creation_schema(plugin_id)
+        try:
+            return await self.ap.plugin_connector.get_rag_creation_schema(plugin_id)
+        except Exception as e:
+            logger.error(f"Failed to get creation schema from plugin {plugin_id}: {e}")
+            return {}
 
     async def get_retrieval_schema(self, plugin_id: str) -> Dict[str, Any]:
         """Get retrieval settings schema from plugin."""
-        return await self.ap.plugin_connector.get_rag_retrieval_schema(plugin_id)
+        try:
+            return await self.ap.plugin_connector.get_rag_retrieval_schema(plugin_id)
+        except Exception as e:
+            logger.error(f"Failed to get retrieval schema from plugin {plugin_id}: {e}")
+            return {}

From 43cdf8fdecdfb9ae227638a9176da1bef89e0845 Mon Sep 17 00:00:00 2001
From: youhuanghe <1051233107@qq.com>
Date: Mon, 26 Jan 2026 13:12:24 +0000
Subject: [PATCH 04/25] =?UTF-8?q?feat:=20=E7=BB=9F=E4=B8=80=E7=9F=A5?=
 =?UTF-8?q?=E8=AF=86=E5=BA=93=E7=AE=A1=E7=90=86=E4=B8=BARAG=E5=BC=95?=
 =?UTF-8?q?=E6=93=8E=EF=BC=8C=E6=94=AF=E6=8C=81=E5=8A=A8=E6=80=81=E9=85=8D?=
 =?UTF-8?q?=E7=BD=AE=E5=B9=B6=E7=A7=BB=E9=99=A4=E6=97=A7=E7=9A=84=E5=A4=96?=
 =?UTF-8?q?=E9=83=A8=E7=9F=A5=E8=AF=86=E5=BA=93=E7=BB=84=E4=BB=B6=E3=80=82?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../controller/groups/knowledge/engines.py    |  31 +
 .../controller/groups/knowledge/external.py   |  75 ++-
 src/langbot/pkg/api/http/service/knowledge.py | 144 ++++-
 src/langbot/pkg/plugin/connector.py           |  10 +
 src/langbot/pkg/plugin/handler.py             |   9 +
 src/langbot/pkg/rag/knowledge/kbmgr.py        |   6 +-
 .../dynamic-form/DynamicFormItemComponent.tsx | 175 ++----
 web/src/app/home/knowledge/KBDetailDialog.tsx | 192 +++---
 .../external-kb-card/ExternalKBCard.tsx       |  74 ---
 .../external-kb-card/ExternalKBCardVO.ts      |  39 --
 .../external-kb-form/ExternalKBForm.tsx       | 569 ------------------
 .../knowledge/components/kb-card/KBCard.tsx   |  11 +-
 .../knowledge/components/kb-card/KBCardVO.ts  |  33 +
 .../knowledge/components/kb-form/KBForm.tsx   | 427 ++++++++-----
 .../kb-retrieve/ExternalKBRetrieve.tsx        |  35 --
 .../components/kb-retrieve/KBRetrieve.tsx     | 124 ----
 web/src/app/home/knowledge/page.tsx           | 275 +++------
 web/src/app/infra/entities/api/index.ts       |  42 ++
 web/src/app/infra/http/BackendClient.ts       |   6 +
 .../app/infra/utils/jsonSchemaConverter.ts    | 116 ++++
 web/src/i18n/locales/en-US.ts                 |  12 +-
 web/src/i18n/locales/ja-JP.ts                 |   3 +-
 web/src/i18n/locales/zh-Hans.ts               |  12 +-
 web/src/i18n/locales/zh-Hant.ts               |   3 +-
 24 files changed, 998 insertions(+), 1425 deletions(-)
 create mode 100644 src/langbot/pkg/api/http/controller/groups/knowledge/engines.py
 delete mode 100644 web/src/app/home/knowledge/components/external-kb-card/ExternalKBCard.tsx
 delete mode 100644 web/src/app/home/knowledge/components/external-kb-card/ExternalKBCardVO.ts
 delete mode 100644 web/src/app/home/knowledge/components/external-kb-form/ExternalKBForm.tsx
 delete mode 100644 web/src/app/home/knowledge/components/kb-retrieve/ExternalKBRetrieve.tsx
 delete mode 100644 web/src/app/home/knowledge/components/kb-retrieve/KBRetrieve.tsx
 create mode 100644 web/src/app/infra/utils/jsonSchemaConverter.ts

diff --git a/src/langbot/pkg/api/http/controller/groups/knowledge/engines.py b/src/langbot/pkg/api/http/controller/groups/knowledge/engines.py
new file mode 100644
index 000000000..1e6b5b48a
--- /dev/null
+++ b/src/langbot/pkg/api/http/controller/groups/knowledge/engines.py
@@ -0,0 +1,31 @@
+import quart
+from ... import group
+
+
+@group.group_class('rag_engines', '/api/v1/knowledge/engines')
+class RAGEnginesRouterGroup(group.RouterGroup):
+    async def initialize(self) -> None:
+        @self.route('', methods=['GET'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
+        async def list_rag_engines() -> quart.Response:
+            """List all available RAG engines from plugins.
+
+            Returns a list of RAG 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_rag_engines()
+            return self.success(data={'engines': engines})
+
+        @self.route('/<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 RAG engine."""
+            # plugin_id format: author/name, need to decode URL
+            plugin_id = plugin_id.replace('__', '/')
+            schema = await self.ap.knowledge_service.get_engine_creation_schema(plugin_id)
+            return self.success(data={'schema': schema})
+
+        @self.route('/<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 RAG engine."""
+            plugin_id = plugin_id.replace('__', '/')
+            schema = await self.ap.knowledge_service.get_engine_retrieval_schema(plugin_id)
+            return self.success(data={'schema': schema})
diff --git a/src/langbot/pkg/api/http/controller/groups/knowledge/external.py b/src/langbot/pkg/api/http/controller/groups/knowledge/external.py
index 324889e73..354d0542a 100644
--- a/src/langbot/pkg/api/http/controller/groups/knowledge/external.py
+++ b/src/langbot/pkg/api/http/controller/groups/knowledge/external.py
@@ -2,25 +2,66 @@
 from ... import group
 
 
+DEPRECATION_WARNING = (
+    "This API is deprecated and will be removed in a future version. "
+    "Please use /api/v1/knowledge/bases with rag_engine_plugin_id instead."
+)
+
+
 @group.group_class('external_knowledge_base', '/api/v1/knowledge/external-bases')
 class ExternalKnowledgeBaseRouterGroup(group.RouterGroup):
+    """
+    DEPRECATED: External knowledge base routes.
+
+    These endpoints are deprecated in favor of the unified knowledge base API
+    at /api/v1/knowledge/bases. Use rag_engine_plugin_id to specify plugin-based
+    RAG engines when creating knowledge bases.
+    """
+
+    def _add_deprecation_header(self, response: quart.Response) -> quart.Response:
+        """Add deprecation warning header to response."""
+        response.headers['X-Deprecated'] = 'true'
+        response.headers['X-Deprecation-Notice'] = DEPRECATION_WARNING
+        return response
+
     async def initialize(self) -> None:
         @self.route('/retrievers', methods=['GET'])
         async def list_knowledge_retrievers() -> quart.Response:
-            """List all available knowledge retrievers from plugins."""
+            """
+            DEPRECATED: List all available knowledge retrievers from plugins.
+
+            Use GET /api/v1/knowledge/engines instead.
+            """
             retrievers = await self.ap.plugin_connector.list_knowledge_retrievers()
-            return self.success(data={'retrievers': retrievers})
+            response = self.success(data={
+                'retrievers': retrievers,
+                '_deprecation_warning': DEPRECATION_WARNING,
+            })
+            return self._add_deprecation_header(response)
 
         @self.route('', methods=['POST', 'GET'])
         async def handle_external_knowledge_bases() -> quart.Response:
+            """
+            DEPRECATED: Handle external knowledge bases.
+
+            Use /api/v1/knowledge/bases instead.
+            """
             if quart.request.method == 'GET':
                 external_kbs = await self.ap.external_kb_service.get_external_knowledge_bases()
-                return self.success(data={'bases': external_kbs})
+                response = self.success(data={
+                    'bases': external_kbs,
+                    '_deprecation_warning': DEPRECATION_WARNING,
+                })
+                return self._add_deprecation_header(response)
 
             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})
+                response = self.success(data={
+                    'uuid': kb_uuid,
+                    '_deprecation_warning': DEPRECATION_WARNING,
+                })
+                return self._add_deprecation_header(response)
 
             return self.http_status(405, -1, 'Method not allowed')
 
@@ -29,33 +70,51 @@ async def handle_external_knowledge_bases() -> quart.Response:
             methods=['GET', 'DELETE', 'PUT'],
         )
         async def handle_specific_external_knowledge_base(kb_uuid: str) -> quart.Response:
+            """
+            DEPRECATED: Handle specific external knowledge base.
+
+            Use /api/v1/knowledge/bases/<uuid> instead.
+            """
             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(
+                response = self.success(
                     data={
                         'base': external_kb,
+                        '_deprecation_warning': DEPRECATION_WARNING,
                     }
                 )
+                return self._add_deprecation_header(response)
 
             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({})
+                response = self.success({'_deprecation_warning': DEPRECATION_WARNING})
+                return self._add_deprecation_header(response)
 
             elif quart.request.method == 'DELETE':
                 await self.ap.external_kb_service.delete_external_knowledge_base(kb_uuid)
-                return self.success({})
+                response = self.success({'_deprecation_warning': DEPRECATION_WARNING})
+                return self._add_deprecation_header(response)
 
         @self.route(
             '/<kb_uuid>/retrieve',
             methods=['POST'],
         )
         async def retrieve_external_knowledge_base(kb_uuid: str) -> str:
+            """
+            DEPRECATED: Retrieve from external knowledge base.
+
+            Use POST /api/v1/knowledge/bases/<uuid>/retrieve instead.
+            """
             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})
+            response = self.success(data={
+                'results': results,
+                '_deprecation_warning': DEPRECATION_WARNING,
+            })
+            return self._add_deprecation_header(response)
diff --git a/src/langbot/pkg/api/http/service/knowledge.py b/src/langbot/pkg/api/http/service/knowledge.py
index a5866cee2..ca6bc7c09 100644
--- a/src/langbot/pkg/api/http/service/knowledge.py
+++ b/src/langbot/pkg/api/http/service/knowledge.py
@@ -15,14 +15,59 @@ class KnowledgeService:
     def __init__(self, ap: app.Application) -> None:
         self.ap = ap
 
+    async def _enrich_kb_with_engine_info(self, kb_dict: dict) -> dict:
+        """Enrich knowledge base dict with RAG engine information."""
+        plugin_id = kb_dict.get("rag_engine_plugin_id")
+
+        if plugin_id:
+            # Try to get engine info from plugin
+            try:
+                engines = await self.list_rag_engines()
+                engine_info = next(
+                    (e for e in engines if e["plugin_id"] == plugin_id),
+                    None
+                )
+                if engine_info:
+                    kb_dict["rag_engine"] = {
+                        "plugin_id": plugin_id,
+                        "name": engine_info.get("name", plugin_id),
+                        "capabilities": engine_info.get("capabilities", []),
+                    }
+                else:
+                    # Plugin not loaded, use basic info
+                    kb_dict["rag_engine"] = {
+                        "plugin_id": plugin_id,
+                        "name": plugin_id,
+                        "capabilities": ["doc_ingestion"],
+                    }
+            except Exception:
+                kb_dict["rag_engine"] = {
+                    "plugin_id": plugin_id,
+                    "name": plugin_id,
+                    "capabilities": ["doc_ingestion"],
+                }
+        else:
+            # Legacy internal KB without plugin
+            kb_dict["rag_engine"] = {
+                "plugin_id": None,
+                "name": "Internal (Legacy)",
+                "capabilities": ["doc_ingestion"],
+            }
+
+        return kb_dict
+
     async def get_knowledge_bases(self) -> list[dict]:
         """获取所有知识库"""
         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
-        ]
+
+        kb_list = []
+        for knowledge_base in knowledge_bases:
+            kb_dict = self.ap.persistence_mgr.serialize_model(persistence_rag.KnowledgeBase, knowledge_base)
+            kb_dict = await self._enrich_kb_with_engine_info(kb_dict)
+            kb_list.append(kb_dict)
+
+        return kb_list
 
     async def get_knowledge_base(self, kb_uuid: str) -> dict | None:
         """获取知识库"""
@@ -32,7 +77,10 @@ async def get_knowledge_base(self, kb_uuid: str) -> dict | None:
         knowledge_base = result.first()
         if knowledge_base is None:
             return None
-        return self.ap.persistence_mgr.serialize_model(persistence_rag.KnowledgeBase, knowledge_base)
+
+        kb_dict = self.ap.persistence_mgr.serialize_model(persistence_rag.KnowledgeBase, knowledge_base)
+        kb_dict = await self._enrich_kb_with_engine_info(kb_dict)
+        return kb_dict
 
     async def create_knowledge_base(self, kb_data: dict) -> str:
         """创建知识库"""
@@ -48,26 +96,31 @@ async def create_knowledge_base(self, kb_data: dict) -> str:
             )
             return kb.uuid
 
-        kb_data['uuid'] = str(uuid.uuid4())
-        await self.ap.persistence_mgr.execute_async(sqlalchemy.insert(persistence_rag.KnowledgeBase).values(kb_data))
+        # Filter to only valid database fields for internal KB
+        valid_fields = {'uuid', 'name', 'description', 'embedding_model_uuid', 'top_k', 'rag_engine_plugin_id', 'creation_settings'}
+        filtered_data = {k: v for k, v in kb_data.items() if k in valid_fields}
+
+        filtered_data['uuid'] = str(uuid.uuid4())
+        await self.ap.persistence_mgr.execute_async(sqlalchemy.insert(persistence_rag.KnowledgeBase).values(filtered_data))
 
-        kb = await self.get_knowledge_base(kb_data['uuid'])
+        kb = await self.get_knowledge_base(filtered_data['uuid'])
 
         await self.ap.rag_mgr.load_knowledge_base(kb)
 
-        return kb_data['uuid']
+        return filtered_data['uuid']
 
     async def update_knowledge_base(self, kb_uuid: str, kb_data: dict) -> None:
         """更新知识库"""
-        if 'uuid' in kb_data:
-            del kb_data['uuid']
+        # Filter to only valid database fields
+        valid_fields = {'name', 'description', 'top_k', 'rag_engine_plugin_id', 'creation_settings'}
+        filtered_data = {k: v for k, v in kb_data.items() if k in valid_fields}
 
-        if 'embedding_model_uuid' in kb_data:
-            del kb_data['embedding_model_uuid']
+        if not filtered_data:
+            return
 
         await self.ap.persistence_mgr.execute_async(
             sqlalchemy.update(persistence_rag.KnowledgeBase)
-            .values(kb_data)
+            .values(filtered_data)
             .where(persistence_rag.KnowledgeBase.uuid == kb_uuid)
         )
         await self.ap.rag_mgr.remove_knowledge_base_from_runtime(kb_uuid)
@@ -161,3 +214,66 @@ async def delete_knowledge_base(self, kb_uuid: str) -> None:
             await self.ap.persistence_mgr.execute_async(
                 sqlalchemy.delete(persistence_rag.File).where(persistence_rag.File.uuid == file.uuid)
             )
+
+    # ================= RAG Engine Discovery =================
+
+    async def list_rag_engines(self) -> list[dict]:
+        """List all available knowledge base engines from plugins.
+
+        Returns both KnowledgeRetriever (external) and RAGEngine types.
+        """
+        engines = []
+
+        if not self.ap.plugin_connector.is_enable_plugin:
+            return engines
+
+        # 1. Get KnowledgeRetriever plugins (external knowledge bases like dify)
+        try:
+            retrievers = await self.ap.plugin_connector.list_knowledge_retrievers()
+            for retriever in retrievers:
+                # Get config from manifest.manifest.spec.config
+                manifest_data = retriever.get('manifest', {})
+                raw_manifest = manifest_data.get('manifest', {})
+                spec = raw_manifest.get('spec', {})
+                config_items = spec.get('config', [])
+
+                engines.append({
+                    'plugin_id': f"{retriever['plugin_author']}/{retriever['plugin_name']}",
+                    'name': retriever.get('retriever_name', 'Unknown'),
+                    'description': retriever.get('retriever_description'),
+                    'type': 'retriever',  # External retriever type
+                    'capabilities': [],  # No doc_ingestion
+                    'creation_schema': config_items,  # Use config items as creation schema
+                    'retrieval_schema': None,
+                    # Keep original info for creating external KB
+                    'component_name': retriever.get('retriever_name', ''),
+                })
+        except Exception as e:
+            self.ap.logger.warning(f"Failed to list knowledge retrievers: {e}")
+
+        # 2. Get RAGEngine plugins (new type with doc_ingestion support)
+        try:
+            rag_engines = await self.ap.plugin_connector.list_rag_engines()
+            for engine in rag_engines:
+                engine['type'] = 'rag_engine'  # Mark as RAG engine type
+                engines.append(engine)
+        except Exception as e:
+            self.ap.logger.warning(f"Failed to list RAG engines from plugins: {e}")
+
+        return engines
+
+    async def get_engine_creation_schema(self, plugin_id: str) -> dict:
+        """Get creation settings schema for a specific RAG 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 RAG 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 {}
diff --git a/src/langbot/pkg/plugin/connector.py b/src/langbot/pkg/plugin/connector.py
index 6916231af..e176fa720 100644
--- a/src/langbot/pkg/plugin/connector.py
+++ b/src/langbot/pkg/plugin/connector.py
@@ -584,3 +584,13 @@ 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 = plugin_id.split('/', 1)
         return await self.handler.rag_on_kb_delete(plugin_author, plugin_name, kb_id)
+
+    async def list_rag_engines(self) -> list[dict[str, Any]]:
+        """List all available RAG engines from plugins.
+
+        Returns a list of RAG engines with their capabilities and configuration schemas.
+        """
+        if not self.is_enable_plugin:
+            return []
+
+        return await self.handler.list_rag_engines()
diff --git a/src/langbot/pkg/plugin/handler.py b/src/langbot/pkg/plugin/handler.py
index 7ab803cfb..dcc9ac10b 100644
--- a/src/langbot/pkg/plugin/handler.py
+++ b/src/langbot/pkg/plugin/handler.py
@@ -985,3 +985,12 @@ async def get_rag_retrieval_schema(self, plugin_author: str, plugin_name: str) -
             },
             timeout=10
         )
+
+    async def list_rag_engines(self) -> list[dict[str, Any]]:
+        """List all available RAG engines from plugins."""
+        result = await self.call_action(
+            LangBotToRuntimeAction.LIST_RAG_ENGINES,
+            {},
+            timeout=10
+        )
+        return result.get("engines", [])
diff --git a/src/langbot/pkg/rag/knowledge/kbmgr.py b/src/langbot/pkg/rag/knowledge/kbmgr.py
index b42e43c83..d3456ecba 100644
--- a/src/langbot/pkg/rag/knowledge/kbmgr.py
+++ b/src/langbot/pkg/rag/knowledge/kbmgr.py
@@ -307,7 +307,11 @@ async def load_knowledge_base(
             # Safe access to _mapping for SQLAlchemy 1.4+
             knowledge_base_entity = persistence_rag.KnowledgeBase(**knowledge_base_entity._mapping)
         elif isinstance(knowledge_base_entity, dict):
-            knowledge_base_entity = persistence_rag.KnowledgeBase(**knowledge_base_entity)
+            # Filter out non-database fields (like rag_engine which is computed)
+            valid_fields = {'uuid', 'name', 'description', 'embedding_model_uuid', 'top_k',
+                           'rag_engine_plugin_id', 'creation_settings', 'created_at', 'updated_at'}
+            filtered_dict = {k: v for k, v in knowledge_base_entity.items() if k in valid_fields}
+            knowledge_base_entity = persistence_rag.KnowledgeBase(**filtered_dict)
 
         runtime_knowledge_base = RuntimeKnowledgeBase(ap=self.ap, knowledge_base_entity=knowledge_base_entity)
 
diff --git a/web/src/app/home/components/dynamic-form/DynamicFormItemComponent.tsx b/web/src/app/home/components/dynamic-form/DynamicFormItemComponent.tsx
index a1285f12e..402fb9eb0 100644
--- a/web/src/app/home/components/dynamic-form/DynamicFormItemComponent.tsx
+++ b/web/src/app/home/components/dynamic-form/DynamicFormItemComponent.tsx
@@ -22,8 +22,6 @@ import {
   LLMModel,
   Bot,
   KnowledgeBase,
-  ExternalKnowledgeBase,
-  ApiRespPluginSystemStatus,
 } from '@/app/infra/entities/api';
 import { toast } from 'sonner';
 import { useTranslation } from 'react-i18next';
@@ -52,15 +50,10 @@ export default function DynamicFormItemComponent({
 }) {
   const [llmModels, setLlmModels] = useState<LLMModel[]>([]);
   const [knowledgeBases, setKnowledgeBases] = useState<KnowledgeBase[]>([]);
-  const [externalKnowledgeBases, setExternalKnowledgeBases] = useState<
-    ExternalKnowledgeBase[]
-  >([]);
   const [bots, setBots] = useState<Bot[]>([]);
   const [uploading, setUploading] = useState<boolean>(false);
   const [kbDialogOpen, setKbDialogOpen] = useState(false);
   const [tempSelectedKBIds, setTempSelectedKBIds] = useState<string[]>([]);
-  const [pluginSystemStatus, setPluginSystemStatus] =
-    useState<ApiRespPluginSystemStatus | null>(null);
   const { t } = useTranslation();
 
   const handleFileUpload = async (file: File): Promise<IFileConfig | null> => {
@@ -126,37 +119,9 @@ export default function DynamicFormItemComponent({
         .catch((err) => {
           toast.error('Failed to get knowledge base list: ' + err.msg);
         });
-
-      // Fetch plugin system status
-      httpClient
-        .getPluginSystemStatus()
-        .then((status) => {
-          setPluginSystemStatus(status);
-        })
-        .catch((err) => {
-          console.error('Failed to get plugin system status:', err);
-        });
     }
   }, [config.type]);
 
-  useEffect(() => {
-    if (
-      (config.type === DynamicFormItemType.KNOWLEDGE_BASE_SELECTOR ||
-        config.type === DynamicFormItemType.KNOWLEDGE_BASE_MULTI_SELECTOR) &&
-      pluginSystemStatus?.is_enable &&
-      pluginSystemStatus?.is_connected
-    ) {
-      httpClient
-        .getExternalKnowledgeBases()
-        .then((resp) => {
-          setExternalKnowledgeBases(resp.bases);
-        })
-        .catch((err) => {
-          console.error('Failed to get external knowledge base list:', err);
-        });
-    }
-  }, [config.type, pluginSystemStatus]);
-
   useEffect(() => {
     if (config.type === DynamicFormItemType.BOT_SELECTOR) {
       httpClient
@@ -297,6 +262,19 @@ export default function DynamicFormItemComponent({
       );
 
     case DynamicFormItemType.KNOWLEDGE_BASE_SELECTOR:
+      // Group KBs by RAG engine name
+      const kbsByEngine = knowledgeBases.reduce(
+        (acc, kb) => {
+          const engineName = kb.rag_engine?.name || t('knowledge.unknownEngine');
+          if (!acc[engineName]) {
+            acc[engineName] = [];
+          }
+          acc[engineName].push(kb);
+          return acc;
+        },
+        {} as Record<string, typeof knowledgeBases>,
+      );
+
       return (
         <Select value={field.value} onValueChange={field.onChange}>
           <SelectTrigger className="bg-[#ffffff] dark:bg-[#2a2a2e]">
@@ -307,53 +285,43 @@ export default function DynamicFormItemComponent({
               <SelectItem value="__none__">{t('knowledge.empty')}</SelectItem>
             </SelectGroup>
 
-            {knowledgeBases.length > 0 && (
-              <SelectGroup>
-                <SelectLabel>{t('knowledge.builtIn')}</SelectLabel>
-                {knowledgeBases.map((base) => (
+            {Object.entries(kbsByEngine).map(([engineName, kbs]) => (
+              <SelectGroup key={engineName}>
+                <SelectLabel>{engineName}</SelectLabel>
+                {kbs.map((base) => (
                   <SelectItem key={base.uuid} value={base.uuid ?? ''}>
                     {base.name}
                   </SelectItem>
                 ))}
               </SelectGroup>
-            )}
-
-            {externalKnowledgeBases.length > 0 && (
-              <SelectGroup>
-                <SelectLabel>{t('knowledge.external')}</SelectLabel>
-                {externalKnowledgeBases.map((base) => (
-                  <SelectItem key={base.uuid} value={base.uuid ?? ''}>
-                    <div className="flex items-center gap-2">
-                      <img
-                        src={httpClient.getPluginIconURL(
-                          base.plugin_author,
-                          base.plugin_name,
-                        )}
-                        alt="plugin icon"
-                        className="w-4 h-4 rounded-[8%] flex-shrink-0"
-                      />
-                      <span>{base.name}</span>
-                    </div>
-                  </SelectItem>
-                ))}
-              </SelectGroup>
-            )}
+            ))}
           </SelectContent>
         </Select>
       );
 
     case DynamicFormItemType.KNOWLEDGE_BASE_MULTI_SELECTOR:
+      // Group KBs by RAG engine name for multi-selector
+      const multiKbsByEngine = knowledgeBases.reduce(
+        (acc, kb) => {
+          const engineName = kb.rag_engine?.name || t('knowledge.unknownEngine');
+          if (!acc[engineName]) {
+            acc[engineName] = [];
+          }
+          acc[engineName].push(kb);
+          return acc;
+        },
+        {} as Record<string, typeof knowledgeBases>,
+      );
+
       return (
         <>
           <div className="space-y-2">
             {field.value && field.value.length > 0 ? (
               <div className="space-y-2">
                 {field.value.map((kbId: string) => {
-                  const kb = knowledgeBases.find((base) => base.uuid === kbId);
-                  const externalKb = externalKnowledgeBases.find(
+                  const currentKb = knowledgeBases.find(
                     (base) => base.uuid === kbId,
                   );
-                  const currentKb = kb || externalKb;
                   if (!currentKb) return null;
 
                   return (
@@ -362,18 +330,15 @@ export default function DynamicFormItemComponent({
                       className="flex items-center justify-between rounded-lg border p-3 hover:bg-accent"
                     >
                       <div className="flex items-center gap-2 flex-1">
-                        {externalKb && (
-                          <img
-                            src={httpClient.getPluginIconURL(
-                              externalKb.plugin_author,
-                              externalKb.plugin_name,
-                            )}
-                            alt="plugin icon"
-                            className="w-8 h-8 rounded-[8%] flex-shrink-0"
-                          />
-                        )}
                         <div className="flex-1 min-w-0">
-                          <div className="font-medium">{currentKb.name}</div>
+                          <div className="font-medium flex items-center gap-2">
+                            {currentKb.name}
+                            {currentKb.rag_engine?.name && (
+                              <span className="text-xs px-2 py-0.5 rounded-full bg-purple-100 text-purple-700 dark:bg-purple-900 dark:text-purple-300">
+                                {currentKb.rag_engine.name}
+                              </span>
+                            )}
+                          </div>
                           {currentKb.description && (
                             <div className="text-sm text-muted-foreground">
                               {currentKb.description}
@@ -427,54 +392,12 @@ export default function DynamicFormItemComponent({
                 <DialogTitle>{t('knowledge.selectKnowledgeBases')}</DialogTitle>
               </DialogHeader>
               <div className="flex-1 overflow-y-auto space-y-4 pr-2">
-                {/* Built-in Knowledge Bases */}
-                {knowledgeBases.length > 0 && (
-                  <div className="space-y-2">
-                    <div className="text-sm font-semibold text-muted-foreground px-2">
-                      {t('knowledge.builtIn')}
-                    </div>
-                    {knowledgeBases.map((base) => {
-                      const isSelected = tempSelectedKBIds.includes(
-                        base.uuid ?? '',
-                      );
-                      return (
-                        <div
-                          key={base.uuid}
-                          className="flex items-center gap-3 rounded-lg border p-3 hover:bg-accent cursor-pointer"
-                          onClick={() => {
-                            const kbId = base.uuid ?? '';
-                            setTempSelectedKBIds((prev) =>
-                              prev.includes(kbId)
-                                ? prev.filter((id) => id !== kbId)
-                                : [...prev, kbId],
-                            );
-                          }}
-                        >
-                          <Checkbox
-                            checked={isSelected}
-                            aria-label={`Select ${base.name}`}
-                          />
-                          <div className="flex-1">
-                            <div className="font-medium">{base.name}</div>
-                            {base.description && (
-                              <div className="text-sm text-muted-foreground">
-                                {base.description}
-                              </div>
-                            )}
-                          </div>
-                        </div>
-                      );
-                    })}
-                  </div>
-                )}
-
-                {/* External Knowledge Bases */}
-                {externalKnowledgeBases.length > 0 && (
-                  <div className="space-y-2">
+                {Object.entries(multiKbsByEngine).map(([engineName, kbs]) => (
+                  <div key={engineName} className="space-y-2">
                     <div className="text-sm font-semibold text-muted-foreground px-2">
-                      {t('knowledge.external')}
+                      {engineName}
                     </div>
-                    {externalKnowledgeBases.map((base) => {
+                    {kbs.map((base) => {
                       const isSelected = tempSelectedKBIds.includes(
                         base.uuid ?? '',
                       );
@@ -495,14 +418,6 @@ export default function DynamicFormItemComponent({
                             checked={isSelected}
                             aria-label={`Select ${base.name}`}
                           />
-                          <img
-                            src={httpClient.getPluginIconURL(
-                              base.plugin_author,
-                              base.plugin_name,
-                            )}
-                            alt="plugin icon"
-                            className="w-8 h-8 rounded-[8%] flex-shrink-0"
-                          />
                           <div className="flex-1">
                             <div className="font-medium">{base.name}</div>
                             {base.description && (
@@ -515,7 +430,7 @@ export default function DynamicFormItemComponent({
                       );
                     })}
                   </div>
-                )}
+                ))}
               </div>
               <DialogFooter>
                 <Button
diff --git a/web/src/app/home/knowledge/KBDetailDialog.tsx b/web/src/app/home/knowledge/KBDetailDialog.tsx
index c4017a23a..00200fc91 100644
--- a/web/src/app/home/knowledge/KBDetailDialog.tsx
+++ b/web/src/app/home/knowledge/KBDetailDialog.tsx
@@ -21,18 +21,16 @@ import {
 import { Button } from '@/components/ui/button';
 import { useTranslation } from 'react-i18next';
 import { httpClient } from '@/app/infra/http/HttpClient';
-// import { KnowledgeBase } from '@/app/infra/entities/api';
+import { KnowledgeBase } from '@/app/infra/entities/api';
 import KBForm from '@/app/home/knowledge/components/kb-form/KBForm';
 import KBDoc from '@/app/home/knowledge/components/kb-docs/KBDoc';
-import KBRetrieve from '@/app/home/knowledge/components/kb-retrieve/KBRetrieve';
-import ExternalKBForm from '@/app/home/knowledge/components/external-kb-form/ExternalKBForm';
-import ExternalKBRetrieve from '@/app/home/knowledge/components/kb-retrieve/ExternalKBRetrieve';
+import KBRetrieveGeneric from '@/app/home/knowledge/components/kb-retrieve/KBRetrieveGeneric';
 
 interface KBDetailDialogProps {
   open: boolean;
   onOpenChange: (open: boolean) => void;
   kbId?: string;
-  kbType: 'builtin' | 'external';
+  kbType?: 'retriever' | 'rag_engine';
   onFormCancel: () => void;
   onKbDeleted: () => void;
   onNewKbCreated: (kbId: string) => void;
@@ -43,7 +41,7 @@ export default function KBDetailDialog({
   open,
   onOpenChange,
   kbId: propKbId,
-  kbType,
+  kbType: propKbType,
   onFormCancel,
   onKbDeleted,
   onNewKbCreated,
@@ -51,15 +49,58 @@ export default function KBDetailDialog({
 }: KBDetailDialogProps) {
   const { t } = useTranslation();
   const [kbId, setKbId] = useState<string | undefined>(propKbId);
+  const [kbType, setKbType] = useState<'retriever' | 'rag_engine' | undefined>(propKbType);
   const [activeMenu, setActiveMenu] = useState('metadata');
   const [showDeleteConfirm, setShowDeleteConfirm] = useState(false);
+  const [kbInfo, setKbInfo] = useState<KnowledgeBase | null>(null);
 
   useEffect(() => {
     setKbId(propKbId);
+    setKbType(propKbType);
     setActiveMenu('metadata');
-  }, [propKbId, open]);
+    if (propKbId && propKbType) {
+      loadKbInfo(propKbId, propKbType);
+    } else {
+      setKbInfo(null);
+    }
+  }, [propKbId, propKbType, open]);
 
-  // Build menu based on KB type
+  async function loadKbInfo(id: string, type: 'retriever' | 'rag_engine') {
+    try {
+      if (type === 'retriever') {
+        // For retriever type, we create a compatible structure
+        const resp = await httpClient.getExternalKnowledgeBase(id);
+        const extKb = resp.base;
+        setKbInfo({
+          uuid: extKb.uuid,
+          name: extKb.name,
+          description: extKb.description,
+          embedding_model_uuid: '',
+          top_k: 5,
+          rag_engine: {
+            plugin_id: `${extKb.plugin_author}/${extKb.plugin_name}`,
+            name: extKb.retriever_name,
+            capabilities: [],
+          },
+        });
+      } else {
+        const resp = await httpClient.getKnowledgeBase(id);
+        setKbInfo(resp.base);
+      }
+    } catch (e) {
+      console.error('Failed to load KB info:', e);
+    }
+  }
+
+  // Check if this KB supports document management
+  const hasDocumentCapability = (): boolean => {
+    if (!kbInfo || !kbInfo.rag_engine) {
+      return false; // No engine info, cannot determine capability
+    }
+    return kbInfo.rag_engine.capabilities.includes('doc_ingestion');
+  };
+
+  // Build menu based on KB capabilities
   const menu = [
     {
       key: 'metadata',
@@ -74,8 +115,8 @@ export default function KBDetailDialog({
         </svg>
       ),
     },
-    // Only show documents for builtin KB
-    ...(kbType === 'builtin'
+    // Show documents only if capability is present
+    ...(hasDocumentCapability()
       ? [
           {
             key: 'documents',
@@ -108,65 +149,53 @@ export default function KBDetailDialog({
   ];
 
   const confirmDelete = () => {
-    const deletePromise =
-      kbType === 'builtin'
-        ? httpClient.deleteKnowledgeBase(kbId ?? '')
-        : httpClient.deleteExternalKnowledgeBase(kbId ?? '');
-
-    deletePromise.then(() => {
-      onKbDeleted();
-    });
+    if (kbType === 'retriever') {
+      httpClient.deleteExternalKnowledgeBase(kbId ?? '').then(() => {
+        onKbDeleted();
+      });
+    } else {
+      httpClient.deleteKnowledgeBase(kbId ?? '').then(() => {
+        onKbDeleted();
+      });
+    }
     setShowDeleteConfirm(false);
   };
 
+  // Unified retrieve function
+  const retrieveFunction = async (id: string, query: string) => {
+    if (kbType === 'retriever') {
+      return await httpClient.retrieveExternalKnowledgeBase(id, query);
+    }
+    return await httpClient.retrieveKnowledgeBase(id, query);
+  };
+
   if (!kbId) {
-    // new kb
+    // New KB creation
     return (
       <Dialog open={open} onOpenChange={onOpenChange}>
         <DialogContent className="overflow-hidden p-0 !max-w-[40vw] max-h-[70vh] flex">
           <main className="flex flex-1 flex-col h-[70vh]">
             <DialogHeader className="px-6 pt-6 pb-4 shrink-0">
-              <DialogTitle>
-                {kbType === 'builtin'
-                  ? t('knowledge.createKnowledgeBase')
-                  : t('knowledge.addExternal')}
-              </DialogTitle>
+              <DialogTitle>{t('knowledge.createKnowledgeBase')}</DialogTitle>
             </DialogHeader>
             <div className="flex-1 overflow-y-auto px-6 pb-6">
-              {kbType === 'builtin' ? (
-                <KBForm
-                  initKbId={undefined}
-                  onNewKbCreated={onNewKbCreated}
-                  onKbUpdated={onKbUpdated}
-                />
-              ) : (
-                <ExternalKBForm
-                  initKBId={undefined}
-                  onFormSubmit={() => onOpenChange(false)}
-                  onKBDeleted={() => {}}
-                  onNewKBCreated={onNewKbCreated}
-                />
-              )}
+              <KBForm
+                initKbId={undefined}
+                initKbType={undefined}
+                onNewKbCreated={onNewKbCreated}
+                onKbUpdated={onKbUpdated}
+              />
             </div>
-            {activeMenu === 'metadata' && (
-              <DialogFooter className="px-6 py-4 border-t shrink-0">
-                <div className="flex justify-end gap-2">
-                  <Button
-                    type="submit"
-                    form={kbType === 'builtin' ? 'kb-form' : 'external-kb-form'}
-                  >
-                    {t('common.save')}
-                  </Button>
-                  <Button
-                    type="button"
-                    variant="outline"
-                    onClick={onFormCancel}
-                  >
-                    {t('common.cancel')}
-                  </Button>
-                </div>
-              </DialogFooter>
-            )}
+            <DialogFooter className="px-6 py-4 border-t shrink-0">
+              <div className="flex justify-end gap-2">
+                <Button type="submit" form="kb-form">
+                  {t('common.save')}
+                </Button>
+                <Button type="button" variant="outline" onClick={onFormCancel}>
+                  {t('common.cancel')}
+                </Button>
+              </div>
+            </DialogFooter>
           </main>
         </DialogContent>
       </Dialog>
@@ -216,33 +245,23 @@ export default function KBDetailDialog({
                 </DialogTitle>
               </DialogHeader>
               <div className="flex-1 overflow-y-auto px-6 pb-6">
-                {activeMenu === 'metadata' &&
-                  (kbType === 'builtin' ? (
-                    <KBForm
-                      initKbId={kbId}
-                      onNewKbCreated={onNewKbCreated}
-                      onKbUpdated={onKbUpdated}
-                    />
-                  ) : (
-                    <ExternalKBForm
-                      initKBId={kbId}
-                      onFormSubmit={() => onOpenChange(false)}
-                      onKBDeleted={() => {
-                        onKbDeleted();
-                        onOpenChange(false);
-                      }}
-                      onNewKBCreated={onNewKbCreated}
-                    />
-                  ))}
-                {activeMenu === 'documents' && kbType === 'builtin' && (
+                {activeMenu === 'metadata' && (
+                  <KBForm
+                    initKbId={kbId}
+                    initKbType={kbType}
+                    onNewKbCreated={onNewKbCreated}
+                    onKbUpdated={onKbUpdated}
+                  />
+                )}
+                {activeMenu === 'documents' && hasDocumentCapability() && (
                   <KBDoc kbId={kbId} />
                 )}
-                {activeMenu === 'retrieve' &&
-                  (kbType === 'builtin' ? (
-                    <KBRetrieve kbId={kbId} />
-                  ) : (
-                    <ExternalKBRetrieve kbId={kbId} />
-                  ))}
+                {activeMenu === 'retrieve' && (
+                  <KBRetrieveGeneric
+                    kbId={kbId}
+                    retrieveFunction={retrieveFunction}
+                  />
+                )}
               </div>
               {activeMenu === 'metadata' && (
                 <DialogFooter className="px-6 py-4 border-t shrink-0">
@@ -254,12 +273,7 @@ export default function KBDetailDialog({
                     >
                       {t('common.delete')}
                     </Button>
-                    <Button
-                      type="submit"
-                      form={
-                        kbType === 'builtin' ? 'kb-form' : 'external-kb-form'
-                      }
-                    >
+                    <Button type="submit" form="kb-form">
                       {t('common.save')}
                     </Button>
                     <Button
@@ -277,7 +291,7 @@ export default function KBDetailDialog({
         </DialogContent>
       </Dialog>
 
-      {/* 删除确认对话框 */}
+      {/* Delete confirmation dialog */}
       <Dialog open={showDeleteConfirm} onOpenChange={setShowDeleteConfirm}>
         <DialogContent>
           <DialogHeader>
diff --git a/web/src/app/home/knowledge/components/external-kb-card/ExternalKBCard.tsx b/web/src/app/home/knowledge/components/external-kb-card/ExternalKBCard.tsx
deleted file mode 100644
index 6c4a993c5..000000000
--- a/web/src/app/home/knowledge/components/external-kb-card/ExternalKBCard.tsx
+++ /dev/null
@@ -1,74 +0,0 @@
-import { ExternalKBCardVO } from '@/app/home/knowledge/components/external-kb-card/ExternalKBCardVO';
-import { useTranslation } from 'react-i18next';
-import styles from '../kb-card/KBCard.module.css';
-import { httpClient } from '@/app/infra/http/HttpClient';
-
-export default function ExternalKBCard({
-  kbCardVO,
-}: {
-  kbCardVO: ExternalKBCardVO;
-}) {
-  const { t } = useTranslation();
-  return (
-    <div className={`${styles.cardContainer}`}>
-      <div className="w-full h-full flex flex-row items-start gap-3">
-        {/* Icon */}
-        <img
-          src={httpClient.getPluginIconURL(
-            kbCardVO.pluginAuthor,
-            kbCardVO.pluginName,
-          )}
-          alt="plugin icon"
-          className="w-16 h-16 mt-1 rounded-[8%] flex-shrink-0"
-        />
-
-        {/* Info Column */}
-        <div className="flex flex-col flex-1 min-w-0 h-full">
-          {/* Top section: Name, Description and Plugin Info */}
-          <div className="flex flex-col gap-0">
-            {/* Name and Description */}
-            <div className={`${styles.basicInfoNameContainer}`}>
-              <div className={`${styles.basicInfoNameText} ${styles.bigText}`}>
-                {kbCardVO.name}
-              </div>
-              <div className={`${styles.basicInfoDescriptionText}`}>
-                {kbCardVO.description}
-              </div>
-            </div>
-
-            {/* Plugin Info */}
-            <div className="flex flex-row gap-2 items-center mt-1">
-              <svg
-                className="w-5 h-5 text-gray-500 dark:text-gray-400"
-                xmlns="http://www.w3.org/2000/svg"
-                viewBox="0 0 24 24"
-                fill="currentColor"
-              >
-                <path d="M7 5C7 2.79086 8.79086 1 11 1C13.2091 1 15 2.79086 15 5H18C18.5523 5 19 5.44772 19 6V9C21.2091 9 23 10.7909 23 13C23 15.2091 21.2091 17 19 17V20C19 20.5523 18.5523 21 18 21H4C3.44772 21 3 20.5523 3 20V6C3 5.44772 3.44772 5 4 5H7ZM11 3C9.89543 3 9 3.89543 9 5C9 5.23554 9.0403 5.45952 9.11355 5.66675C9.22172 5.97282 9.17461 6.31235 8.98718 6.57739C8.79974 6.84243 8.49532 7 8.17071 7H5V19H17V15.8293C17 15.5047 17.1576 15.2003 17.4226 15.0128C17.6877 14.8254 18.0272 14.7783 18.3332 14.8865C18.5405 14.9597 18.7645 15 19 15C20.1046 15 21 14.1046 21 13C21 11.8954 20.1046 11 19 11C18.7645 11 18.5405 11.0403 18.3332 11.1135C18.0272 11.2217 17.6877 11.1746 17.4226 10.9872C17.1576 10.7997 17 10.4953 17 10.1707V7H13.8293C13.5047 7 13.2003 6.84243 13.0128 6.57739C12.8254 6.31235 12.7783 5.97282 12.8865 5.66675C12.9597 5.45952 13 5.23555 13 5C13 3.89543 12.1046 3 11 3Z"></path>
-              </svg>
-              <span className="text-sm font-medium text-gray-600 dark:text-gray-400">
-                {kbCardVO.pluginAuthor} / {kbCardVO.pluginName}
-              </span>
-            </div>
-          </div>
-
-          {/* Bottom section: Update Time */}
-          <div className="flex flex-row gap-2 items-center mt-auto">
-            <svg
-              className="w-5 h-5 text-gray-500 dark:text-gray-400"
-              xmlns="http://www.w3.org/2000/svg"
-              viewBox="0 0 24 24"
-              fill="currentColor"
-            >
-              <path d="M12 22C6.47715 22 2 17.5228 2 12C2 6.47715 6.47715 2 12 2C17.5228 2 22 6.47715 22 12C22 17.5228 17.5228 22 12 22ZM12 20C16.4183 20 20 16.4183 20 12C20 7.58172 16.4183 4 12 4C7.58172 4 4 7.58172 4 12C4 16.4183 7.58172 20 12 20ZM13 12H17V14H11V7H13V12Z"></path>
-            </svg>
-            <span className="text-sm font-medium text-gray-600 dark:text-gray-400">
-              {t('knowledge.updateTime')}
-              {kbCardVO.lastUpdatedTimeAgo}
-            </span>
-          </div>
-        </div>
-      </div>
-    </div>
-  );
-}
diff --git a/web/src/app/home/knowledge/components/external-kb-card/ExternalKBCardVO.ts b/web/src/app/home/knowledge/components/external-kb-card/ExternalKBCardVO.ts
deleted file mode 100644
index 80b6708f7..000000000
--- a/web/src/app/home/knowledge/components/external-kb-card/ExternalKBCardVO.ts
+++ /dev/null
@@ -1,39 +0,0 @@
-export class ExternalKBCardVO {
-  id: string;
-  name: string;
-  description: string;
-  retrieverName: string;
-  retrieverConfig: Record<string, unknown>;
-  lastUpdatedTimeAgo: string;
-  pluginAuthor: string;
-  pluginName: string;
-
-  constructor({
-    id,
-    name,
-    description,
-    retrieverName,
-    retrieverConfig,
-    lastUpdatedTimeAgo,
-    pluginAuthor,
-    pluginName,
-  }: {
-    id: string;
-    name: string;
-    description: string;
-    retrieverName: string;
-    retrieverConfig: Record<string, unknown>;
-    lastUpdatedTimeAgo: string;
-    pluginAuthor: string;
-    pluginName: string;
-  }) {
-    this.id = id;
-    this.name = name;
-    this.description = description;
-    this.retrieverName = retrieverName;
-    this.retrieverConfig = retrieverConfig;
-    this.lastUpdatedTimeAgo = lastUpdatedTimeAgo;
-    this.pluginAuthor = pluginAuthor;
-    this.pluginName = pluginName;
-  }
-}
diff --git a/web/src/app/home/knowledge/components/external-kb-form/ExternalKBForm.tsx b/web/src/app/home/knowledge/components/external-kb-form/ExternalKBForm.tsx
deleted file mode 100644
index e5bf9d246..000000000
--- a/web/src/app/home/knowledge/components/external-kb-form/ExternalKBForm.tsx
+++ /dev/null
@@ -1,569 +0,0 @@
-import { useEffect, useState } from 'react';
-import { zodResolver } from '@hookform/resolvers/zod';
-import { useForm } from 'react-hook-form';
-import { z } from 'zod';
-import { toast } from 'sonner';
-import { useTranslation } from 'react-i18next';
-import { UUID } from 'uuidjs';
-
-import {
-  DynamicFormItemConfig,
-  getDefaultValues,
-  parseDynamicFormItemType,
-} from '@/app/home/components/dynamic-form/DynamicFormItemConfig';
-import { IDynamicFormItemSchema } from '@/app/infra/entities/form/dynamic';
-import DynamicFormComponent from '@/app/home/components/dynamic-form/DynamicFormComponent';
-import { httpClient } from '@/app/infra/http/HttpClient';
-import { ExternalKnowledgeBase } from '@/app/infra/entities/api';
-import {
-  Dialog,
-  DialogContent,
-  DialogDescription,
-  DialogHeader,
-  DialogTitle,
-  DialogFooter,
-} from '@/components/ui/dialog';
-import { Button } from '@/components/ui/button';
-import {
-  Form,
-  FormControl,
-  FormField,
-  FormItem,
-  FormLabel,
-  FormMessage,
-} from '@/components/ui/form';
-import { Input } from '@/components/ui/input';
-import {
-  Select,
-  SelectContent,
-  SelectGroup,
-  SelectItem,
-  SelectTrigger,
-  SelectValue,
-} from '@/components/ui/select';
-import {
-  HoverCard,
-  HoverCardContent,
-  HoverCardTrigger,
-} from '@/components/ui/hover-card';
-import { extractI18nObject } from '@/i18n/I18nProvider';
-import { I18nObject } from '@/app/infra/entities/common';
-
-// Form schema
-const getFormSchema = (t: (key: string) => string) =>
-  z.object({
-    name: z.string().min(1, { message: t('knowledge.nameRequired') }),
-    description: z.string().optional(),
-    plugin_author: z.string().min(1, { message: 'Please select a retriever' }),
-    plugin_name: z.string().min(1, { message: 'Please select a retriever' }),
-    retriever_name: z.string().min(1, { message: 'Please select a retriever' }),
-    retriever_config: z.record(z.string(), z.any()),
-  });
-
-// Retriever information interface
-interface RetrieverInfo {
-  plugin_author: string;
-  plugin_name: string;
-  retriever_name: string;
-  retriever_description: I18nObject;
-  manifest: {
-    manifest?: {
-      metadata?: {
-        label?: I18nObject;
-        description?: I18nObject;
-      };
-      spec?: {
-        config?: IDynamicFormItemSchema[];
-      };
-    };
-  };
-}
-
-interface ExternalKBFormProps {
-  initKBId?: string;
-  onFormSubmit: (value: z.infer<ReturnType<typeof getFormSchema>>) => void;
-  onKBDeleted: () => void;
-  onNewKBCreated: (kbId: string) => void;
-}
-
-export default function ExternalKBForm({
-  initKBId,
-  onFormSubmit,
-  onKBDeleted,
-  onNewKBCreated,
-}: ExternalKBFormProps) {
-  const { t } = useTranslation();
-  const formSchema = getFormSchema(t);
-
-  // Form setup
-  const form = useForm<z.infer<typeof formSchema>>({
-    resolver: zodResolver(formSchema),
-    defaultValues: {
-      name: '',
-      description: '',
-      plugin_author: '',
-      plugin_name: '',
-      retriever_name: '',
-      retriever_config: {},
-    },
-  });
-
-  // State management
-  const [showDeleteConfirmModal, setShowDeleteConfirmModal] = useState(false);
-  const [availableRetrievers, setAvailableRetrievers] = useState<
-    RetrieverInfo[]
-  >([]);
-  const [retrieverNameToConfigMap, setRetrieverNameToConfigMap] = useState(
-    new Map<string, IDynamicFormItemSchema[]>(),
-  );
-  const [showDynamicForm, setShowDynamicForm] = useState<boolean>(false);
-  const [dynamicFormConfigList, setDynamicFormConfigList] = useState<
-    IDynamicFormItemSchema[]
-  >([]);
-
-  // Initialize form when initKBId changes
-  useEffect(() => {
-    loadFormData();
-    // eslint-disable-next-line react-hooks/exhaustive-deps
-  }, [initKBId]);
-
-  /**
-   * Load form data: initialize retrievers list and load KB config if editing
-   */
-  async function loadFormData() {
-    const configMap = await loadAvailableRetrievers();
-
-    if (initKBId) {
-      // Edit mode: load existing KB configuration
-      try {
-        const kbConfig = await loadKBConfig(initKBId);
-        // Set form values
-        form.setValue('name', kbConfig.name);
-        form.setValue('description', kbConfig.description || '');
-        form.setValue('plugin_author', kbConfig.plugin_author);
-        form.setValue('plugin_name', kbConfig.plugin_name);
-        form.setValue('retriever_name', kbConfig.retriever_name);
-        form.setValue('retriever_config', kbConfig.retriever_config);
-
-        // Load dynamic form for the selected retriever
-        const fullName = `${kbConfig.plugin_author}/${kbConfig.plugin_name}/${kbConfig.retriever_name}`;
-        loadDynamicFormConfig(fullName, configMap);
-      } catch (err) {
-        toast.error('Failed to load KB config: ' + (err as Error).message);
-      }
-    } else {
-      // Create mode: reset form
-      form.reset();
-    }
-  }
-
-  /**
-   * Load available retrievers from API and build config map
-   */
-  async function loadAvailableRetrievers(): Promise<
-    Map<string, IDynamicFormItemSchema[]>
-  > {
-    const retrieversRes = await httpClient.listKnowledgeRetrievers();
-    setAvailableRetrievers((retrieversRes.retrievers || []) as RetrieverInfo[]);
-
-    // Build retriever name to config map
-    const configMap = new Map<string, IDynamicFormItemSchema[]>();
-    ((retrieversRes.retrievers || []) as RetrieverInfo[]).forEach(
-      (retriever) => {
-        const fullName = `${retriever.plugin_author}/${retriever.plugin_name}/${retriever.retriever_name}`;
-        const configSchema = retriever.manifest?.manifest?.spec?.config || [];
-
-        configMap.set(
-          fullName,
-          configSchema.map(
-            (item) =>
-              new DynamicFormItemConfig({
-                default: item.default,
-                id: UUID.generate(),
-                label: item.label,
-                description: item.description,
-                name: item.name,
-                required: item.required,
-                type: parseDynamicFormItemType(item.type),
-                options: item.options,
-              }),
-          ),
-        );
-      },
-    );
-
-    setRetrieverNameToConfigMap(configMap);
-    return configMap;
-  }
-
-  /**
-   * Load KB configuration from API
-   */
-  async function loadKBConfig(
-    kbId: string,
-  ): Promise<z.infer<typeof formSchema>> {
-    const res = await httpClient.getExternalKnowledgeBase(kbId);
-    const kb = res.base;
-    return {
-      name: kb.name,
-      description: kb.description,
-      plugin_author: kb.plugin_author,
-      plugin_name: kb.plugin_name,
-      retriever_name: kb.retriever_name,
-      retriever_config: kb.retriever_config || {},
-    };
-  }
-
-  /**
-   * Load dynamic form configuration for selected retriever
-   * @param fullRetrieverName - Full retriever name in format: plugin_author/plugin_name/retriever_name
-   * @param configMapOverride - Optional config map to use (for initial load)
-   */
-  function loadDynamicFormConfig(
-    fullRetrieverName: string,
-    configMapOverride?: Map<string, IDynamicFormItemSchema[]>,
-  ) {
-    if (!fullRetrieverName) {
-      setShowDynamicForm(false);
-      return;
-    }
-
-    // Use provided config map or fall back to state
-    const configMap = configMapOverride || retrieverNameToConfigMap;
-    const configList = configMap.get(fullRetrieverName);
-
-    if (configList && configList.length > 0) {
-      setDynamicFormConfigList(configList);
-      setShowDynamicForm(true);
-
-      // Only reset to default values when manually selecting (not initial load)
-      if (!configMapOverride) {
-        form.setValue('retriever_config', getDefaultValues(configList));
-      }
-    } else {
-      setShowDynamicForm(false);
-      if (!configMapOverride) {
-        form.setValue('retriever_config', {});
-      }
-    }
-  }
-
-  /**
-   * Handle retriever selection change
-   */
-  function handleRetrieverSelect(fullRetrieverName: string) {
-    if (!fullRetrieverName) {
-      setShowDynamicForm(false);
-      return;
-    }
-
-    // Parse and update form fields
-    const parts = fullRetrieverName.split('/');
-    if (parts.length === 3) {
-      form.setValue('plugin_author', parts[0]);
-      form.setValue('plugin_name', parts[1]);
-      form.setValue('retriever_name', parts[2]);
-    }
-
-    // Load dynamic form configuration
-    loadDynamicFormConfig(fullRetrieverName);
-  }
-
-  /**
-   * Handle form submission (create or update)
-   */
-  function handleFormSubmit() {
-    const formData: ExternalKnowledgeBase = {
-      name: form.getValues().name,
-      description: form.getValues().description || '',
-      plugin_author: form.getValues().plugin_author,
-      plugin_name: form.getValues().plugin_name,
-      retriever_name: form.getValues().retriever_name,
-      retriever_config: form.getValues().retriever_config,
-    };
-
-    if (initKBId) {
-      // Update existing KB
-      httpClient
-        .updateExternalKnowledgeBase(initKBId, { ...formData, uuid: initKBId })
-        .then(() => {
-          onFormSubmit(form.getValues());
-          toast.success(t('knowledge.updateExternalSuccess'));
-        })
-        .catch((err) => {
-          toast.error('Failed to update KB: ' + err.msg);
-        });
-    } else {
-      // Create new KB
-      httpClient
-        .createExternalKnowledgeBase(formData)
-        .then((res) => {
-          toast.success(t('knowledge.createExternalSuccess'));
-          onNewKBCreated(res.uuid);
-          form.reset();
-        })
-        .catch((err) => {
-          toast.error('Failed to create KB: ' + err.msg);
-        });
-    }
-  }
-
-  /**
-   * Handle KB deletion
-   */
-  function handleDelete() {
-    if (!initKBId) return;
-
-    httpClient
-      .deleteExternalKnowledgeBase(initKBId)
-      .then(() => {
-        onKBDeleted();
-        toast.success(t('knowledge.deleteExternalSuccess'));
-      })
-      .catch((err) => {
-        toast.error('Failed to delete KB: ' + err.msg);
-      });
-  }
-
-  /**
-   * Get retriever label with i18n support
-   */
-  function getRetrieverLabel(fullName: string): string {
-    const retriever = availableRetrievers.find(
-      (r) =>
-        `${r.plugin_author}/${r.plugin_name}/${r.retriever_name}` === fullName,
-    );
-    return retriever?.manifest?.manifest?.metadata?.label
-      ? extractI18nObject(retriever.manifest.manifest.metadata.label)
-      : fullName;
-  }
-
-  // Compute full retriever name for display
-  const currentRetrieverFullName =
-    form.watch('plugin_author') &&
-    form.watch('plugin_name') &&
-    form.watch('retriever_name')
-      ? `${form.watch('plugin_author')}/${form.watch(
-          'plugin_name',
-        )}/${form.watch('retriever_name')}`
-      : '';
-
-  return (
-    <div>
-      {/* Delete Confirmation Dialog */}
-      <Dialog
-        open={showDeleteConfirmModal}
-        onOpenChange={setShowDeleteConfirmModal}
-      >
-        <DialogContent>
-          <DialogHeader>
-            <DialogTitle>{t('common.confirmDelete')}</DialogTitle>
-          </DialogHeader>
-          <DialogDescription>
-            {t('knowledge.deleteConfirmation')}
-          </DialogDescription>
-          <DialogFooter>
-            <Button
-              variant="outline"
-              onClick={() => setShowDeleteConfirmModal(false)}
-            >
-              {t('common.cancel')}
-            </Button>
-            <Button
-              variant="destructive"
-              onClick={() => {
-                handleDelete();
-                setShowDeleteConfirmModal(false);
-              }}
-            >
-              {t('common.confirmDelete')}
-            </Button>
-          </DialogFooter>
-        </DialogContent>
-      </Dialog>
-
-      {/* Main Form */}
-      <Form {...form}>
-        <form
-          id="external-kb-form"
-          onSubmit={form.handleSubmit(handleFormSubmit)}
-          className="space-y-8"
-        >
-          <div className="space-y-4">
-            {/* KB Name */}
-            <FormField
-              control={form.control}
-              name="name"
-              render={({ field }) => (
-                <FormItem>
-                  <FormLabel>
-                    {t('knowledge.kbName')}
-                    <span className="text-red-500">*</span>
-                  </FormLabel>
-                  <FormControl>
-                    <Input {...field} />
-                  </FormControl>
-                  <FormMessage />
-                </FormItem>
-              )}
-            />
-
-            {/* KB Description */}
-            <FormField
-              control={form.control}
-              name="description"
-              render={({ field }) => (
-                <FormItem>
-                  <FormLabel>{t('knowledge.kbDescription')}</FormLabel>
-                  <FormControl>
-                    <Input {...field} />
-                  </FormControl>
-                  <FormMessage />
-                </FormItem>
-              )}
-            />
-
-            {/* Retriever Selector */}
-            <FormField
-              control={form.control}
-              name="retriever_name"
-              render={() => (
-                <FormItem>
-                  <FormLabel>
-                    {t('knowledge.retriever')}
-                    <span className="text-red-500">*</span>
-                  </FormLabel>
-                  <FormControl>
-                    <Select
-                      onValueChange={handleRetrieverSelect}
-                      value={currentRetrieverFullName}
-                    >
-                      <SelectTrigger className="w-full bg-[#ffffff] dark:bg-[#2a2a2e]">
-                        <SelectValue
-                          placeholder={t('knowledge.selectRetriever')}
-                        />
-                      </SelectTrigger>
-                      <SelectContent className="fixed z-[1000]">
-                        <SelectGroup>
-                          {availableRetrievers.map((retriever) => {
-                            const fullName = `${retriever.plugin_author}/${retriever.plugin_name}/${retriever.retriever_name}`;
-                            const label = retriever.manifest?.manifest?.metadata
-                              ?.label
-                              ? extractI18nObject(
-                                  retriever.manifest.manifest.metadata.label,
-                                )
-                              : retriever.retriever_name;
-                            const description = extractI18nObject(
-                              retriever.retriever_description,
-                            );
-
-                            return (
-                              <HoverCard
-                                key={fullName}
-                                openDelay={0}
-                                closeDelay={0}
-                              >
-                                <HoverCardTrigger asChild>
-                                  <SelectItem value={fullName}>
-                                    {label}
-                                  </SelectItem>
-                                </HoverCardTrigger>
-                                <HoverCardContent
-                                  className="w-80 data-[state=open]:animate-none"
-                                  align="end"
-                                  side="right"
-                                  sideOffset={10}
-                                >
-                                  <div className="space-y-2">
-                                    <div className="flex items-start gap-3">
-                                      <img
-                                        src={httpClient.getPluginIconURL(
-                                          retriever.plugin_author,
-                                          retriever.plugin_name,
-                                        )}
-                                        alt="plugin icon"
-                                        className="w-10 h-10 rounded-[8%] flex-shrink-0"
-                                      />
-                                      <div className="flex flex-col gap-1 flex-1 min-w-0">
-                                        <h4 className="font-medium text-sm">
-                                          {label}
-                                        </h4>
-                                        <p className="text-xs text-muted-foreground">
-                                          {retriever.plugin_author} /{' '}
-                                          {retriever.plugin_name}
-                                        </p>
-                                      </div>
-                                    </div>
-                                    {description && (
-                                      <p className="text-sm text-muted-foreground">
-                                        {description}
-                                      </p>
-                                    )}
-                                  </div>
-                                </HoverCardContent>
-                              </HoverCard>
-                            );
-                          })}
-                        </SelectGroup>
-                      </SelectContent>
-                    </Select>
-                  </FormControl>
-                  <FormMessage />
-                  <p className="text-sm text-muted-foreground">
-                    {t('knowledge.retrieverInstallInfo')}{' '}
-                    <a
-                      href="https://space.langbot.app/market?category=KnowledgeRetriever"
-                      target="_blank"
-                      rel="noopener noreferrer"
-                      className="text-primary underline hover:no-underline"
-                    >
-                      {t('knowledge.retrieverMarketLink')}
-                    </a>
-                  </p>
-                </FormItem>
-              )}
-            />
-
-            {/* Selected Retriever Card */}
-            {currentRetrieverFullName && (
-              <div className="flex items-start gap-3 p-4 rounded-lg border">
-                <img
-                  src={httpClient.getPluginIconURL(
-                    form.watch('plugin_author'),
-                    form.watch('plugin_name'),
-                  )}
-                  alt="plugin icon"
-                  className="w-12 h-12 rounded-[8%] flex-shrink-0"
-                />
-                <div className="flex flex-col gap-1">
-                  <div className="font-medium">
-                    {getRetrieverLabel(currentRetrieverFullName)}
-                  </div>
-                  <div className="text-sm text-gray-500">
-                    {form.watch('plugin_author')} / {form.watch('plugin_name')}
-                  </div>
-                </div>
-              </div>
-            )}
-
-            {/* Dynamic Retriever Configuration Form */}
-            {showDynamicForm && dynamicFormConfigList.length > 0 && (
-              <div className="space-y-4">
-                <div className="text-lg font-medium">
-                  {t('knowledge.retrieverConfiguration')}
-                </div>
-                <DynamicFormComponent
-                  itemConfigList={dynamicFormConfigList}
-                  initialValues={form.watch('retriever_config')}
-                  onSubmit={(values) => {
-                    form.setValue('retriever_config', values);
-                  }}
-                />
-              </div>
-            )}
-          </div>
-        </form>
-      </Form>
-    </div>
-  );
-}
diff --git a/web/src/app/home/knowledge/components/kb-card/KBCard.tsx b/web/src/app/home/knowledge/components/kb-card/KBCard.tsx
index 560b04977..757718b25 100644
--- a/web/src/app/home/knowledge/components/kb-card/KBCard.tsx
+++ b/web/src/app/home/knowledge/components/kb-card/KBCard.tsx
@@ -4,12 +4,19 @@ import styles from './KBCard.module.css';
 
 export default function KBCard({ kbCardVO }: { kbCardVO: KnowledgeBaseVO }) {
   const { t } = useTranslation();
+
   return (
     <div className={`${styles.cardContainer}`}>
       <div className={`${styles.basicInfoContainer}`}>
         <div className={`${styles.basicInfoNameContainer}`}>
-          <div className={`${styles.basicInfoNameText}  ${styles.bigText}`}>
-            {kbCardVO.name}
+          <div className="flex items-center gap-2">
+            <div className={`${styles.basicInfoNameText} ${styles.bigText}`}>
+              {kbCardVO.name}
+            </div>
+            {/* Engine badge */}
+            <span className="text-xs px-2 py-0.5 rounded-full bg-purple-100 text-purple-700 dark:bg-purple-900 dark:text-purple-300">
+              {kbCardVO.getEngineName()}
+            </span>
           </div>
           <div className={`${styles.basicInfoDescriptionText}`}>
             {kbCardVO.description}
diff --git a/web/src/app/home/knowledge/components/kb-card/KBCardVO.ts b/web/src/app/home/knowledge/components/kb-card/KBCardVO.ts
index b13d2c24c..7cf296944 100644
--- a/web/src/app/home/knowledge/components/kb-card/KBCardVO.ts
+++ b/web/src/app/home/knowledge/components/kb-card/KBCardVO.ts
@@ -1,3 +1,5 @@
+import { RAGEngineInfo } from '@/app/infra/entities/api';
+
 export interface IKnowledgeBaseVO {
   id: string;
   name: string;
@@ -5,6 +7,11 @@ export interface IKnowledgeBaseVO {
   embeddingModelUUID: string;
   top_k: number;
   lastUpdatedTimeAgo: string;
+  // New unified fields
+  ragEngine?: RAGEngineInfo;
+  ragEnginePluginId?: string;
+  // Type of knowledge base: 'retriever' for external KB, 'rag_engine' for new plugin-based KB
+  type: 'retriever' | 'rag_engine';
 }
 
 export class KnowledgeBaseVO implements IKnowledgeBaseVO {
@@ -14,6 +21,9 @@ export class KnowledgeBaseVO implements IKnowledgeBaseVO {
   embeddingModelUUID: string;
   top_k: number;
   lastUpdatedTimeAgo: string;
+  ragEngine?: RAGEngineInfo;
+  ragEnginePluginId?: string;
+  type: 'retriever' | 'rag_engine';
 
   constructor(props: IKnowledgeBaseVO) {
     this.id = props.id;
@@ -22,5 +32,28 @@ export class KnowledgeBaseVO implements IKnowledgeBaseVO {
     this.embeddingModelUUID = props.embeddingModelUUID;
     this.top_k = props.top_k;
     this.lastUpdatedTimeAgo = props.lastUpdatedTimeAgo;
+    this.ragEngine = props.ragEngine;
+    this.ragEnginePluginId = props.ragEnginePluginId;
+    this.type = props.type;
+  }
+
+  /**
+   * Check if this KB supports document management
+   */
+  hasDocumentCapability(): boolean {
+    if (!this.ragEngine) {
+      return false;
+    }
+    return this.ragEngine.capabilities.includes('doc_ingestion');
+  }
+
+  /**
+   * Get display name for the RAG engine
+   */
+  getEngineName(): string {
+    if (!this.ragEngine) {
+      return 'Unknown';
+    }
+    return this.ragEngine.name;
   }
 }
diff --git a/web/src/app/home/knowledge/components/kb-form/KBForm.tsx b/web/src/app/home/knowledge/components/kb-form/KBForm.tsx
index 1d8088b97..f086e2c4c 100644
--- a/web/src/app/home/knowledge/components/kb-form/KBForm.tsx
+++ b/web/src/app/home/knowledge/components/kb-form/KBForm.tsx
@@ -13,18 +13,22 @@ import {
   FormMessage,
   FormDescription,
 } from '@/components/ui/form';
-import { httpClient, systemInfo } from '@/app/infra/http/HttpClient';
+import { httpClient } from '@/app/infra/http/HttpClient';
 import {
   Select,
   SelectContent,
-  SelectGroup,
   SelectItem,
-  SelectLabel,
   SelectTrigger,
   SelectValue,
 } from '@/components/ui/select';
-import { KnowledgeBase, EmbeddingModel } from '@/app/infra/entities/api';
+import { KnowledgeBase, ExternalKnowledgeBase, RAGEngine } from '@/app/infra/entities/api';
 import { toast } from 'sonner';
+import { extractI18nObject } from '@/i18n/I18nProvider';
+import DynamicFormComponent from '@/app/home/components/dynamic-form/DynamicFormComponent';
+import {
+  jsonSchemaToFormItems,
+  getDefaultValuesFromSchema,
+} from '@/app/infra/utils/jsonSchemaConverter';
 
 const getFormSchema = (t: (key: string) => string) =>
   z.object({
@@ -32,25 +36,32 @@ const getFormSchema = (t: (key: string) => string) =>
     description: z
       .string()
       .min(1, { message: t('knowledge.kbDescriptionRequired') }),
-    embeddingModelUUID: z
+    ragEngineId: z
       .string()
-      .min(1, { message: t('knowledge.embeddingModelUUIDRequired') }),
-    top_k: z
-      .number()
-      .min(1, { message: t('knowledge.topKRequired') })
-      .max(30, { message: t('knowledge.topKMax') }),
+      .min(1, { message: t('knowledge.ragEngineRequired') }),
   });
 
 export default function KBForm({
   initKbId,
+  initKbType,
   onNewKbCreated,
   onKbUpdated,
 }: {
   initKbId?: string;
+  initKbType?: 'retriever' | 'rag_engine';
   onNewKbCreated: (kbId: string) => void;
   onKbUpdated: (kbId: string) => void;
 }) {
   const { t } = useTranslation();
+  const [ragEngines, setRagEngines] = useState<RAGEngine[]>([]);
+  const [selectedEngineId, setSelectedEngineId] = useState<string>('');
+  const [selectedEngineType, setSelectedEngineType] = useState<'retriever' | 'rag_engine' | ''>('');
+  const [configSettings, setConfigSettings] = useState<
+    Record<string, unknown>
+  >({});
+  const [isEditing, setIsEditing] = useState(false);
+  const [loading, setLoading] = useState(true);
+
   const formSchema = getFormSchema(t);
 
   const form = useForm<z.infer<typeof formSchema>>({
@@ -58,91 +69,221 @@ export default function KBForm({
     defaultValues: {
       name: '',
       description: t('knowledge.defaultDescription'),
-      embeddingModelUUID: '',
-      top_k: 5,
+      ragEngineId: '',
     },
   });
 
-  const [embeddingModels, setEmbeddingModels] = useState<EmbeddingModel[]>([]);
+  // Get selected engine details
+  const selectedEngine = ragEngines.find(
+    (e) => e.plugin_id === selectedEngineId,
+  );
 
   useEffect(() => {
-    getEmbeddingModelNameList().then(() => {
-      if (initKbId) {
-        getKbConfig(initKbId).then((val) => {
-          form.setValue('name', val.name);
-          form.setValue('description', val.description);
-          form.setValue('embeddingModelUUID', val.embeddingModelUUID);
-          form.setValue('top_k', val.top_k || 5);
-        });
+    loadRagEngines().then(() => {
+      if (initKbId && initKbType) {
+        loadKbConfig(initKbId, initKbType);
       }
     });
   }, []);
 
-  const getKbConfig = async (
-    kbId: string,
-  ): Promise<z.infer<typeof formSchema>> => {
-    return new Promise((resolve) => {
-      httpClient.getKnowledgeBase(kbId).then((res) => {
-        resolve({
-          name: res.base.name,
-          description: res.base.description,
-          embeddingModelUUID: res.base.embedding_model_uuid,
-          top_k: res.base.top_k || 5,
-        });
-      });
-    });
+  // Auto-select first engine when engines are loaded and no selection
+  useEffect(() => {
+    if (ragEngines.length > 0 && !selectedEngineId && !isEditing) {
+      const firstEngine = ragEngines[0];
+      setSelectedEngineId(firstEngine.plugin_id);
+      setSelectedEngineType(firstEngine.type);
+      form.setValue('ragEngineId', firstEngine.plugin_id);
+      // Initialize config settings with defaults
+      if (firstEngine.creation_schema) {
+        setConfigSettings(
+          getDefaultValuesFromSchema(firstEngine.creation_schema),
+        );
+      }
+    }
+  }, [ragEngines, selectedEngineId, isEditing]);
+
+  const loadRagEngines = async () => {
+    setLoading(true);
+    try {
+      const resp = await httpClient.getRagEngines();
+      setRagEngines(resp.engines);
+    } catch (err) {
+      console.error('Failed to load RAG engines:', err);
+    } finally {
+      setLoading(false);
+    }
+  };
+
+  const loadKbConfig = async (kbId: string, kbType: 'retriever' | 'rag_engine') => {
+    try {
+      setIsEditing(true);
+
+      if (kbType === 'retriever') {
+        // Load external KB
+        const res = await httpClient.getExternalKnowledgeBase(kbId);
+        const kb = res.base;
+
+        const engineId = `${kb.plugin_author}/${kb.plugin_name}`;
+        setSelectedEngineId(engineId);
+        setSelectedEngineType('retriever');
+
+        form.setValue('name', kb.name);
+        form.setValue('description', kb.description);
+        form.setValue('ragEngineId', engineId);
+
+        setConfigSettings(kb.retriever_config || {});
+      } else {
+        // Load internal KB (rag_engine type)
+        const res = await httpClient.getKnowledgeBase(kbId);
+        const kb = res.base;
+
+        const engineId = kb.rag_engine_plugin_id || '';
+        setSelectedEngineId(engineId);
+        setSelectedEngineType('rag_engine');
+
+        form.setValue('name', kb.name);
+        form.setValue('description', kb.description);
+        form.setValue('ragEngineId', engineId);
+
+        setConfigSettings(kb.creation_settings || {});
+      }
+    } catch (err) {
+      console.error('Failed to load KB config:', err);
+    }
   };
 
-  const getEmbeddingModelNameList = async () => {
-    const resp = await httpClient.getProviderEmbeddingModels();
-    let models = resp.models;
-    // Filter out space-chat-completions models when models service is disabled
-    if (systemInfo.disable_models_service) {
-      models = models.filter(
-        (m) => m.provider?.requester !== 'space-chat-completions',
-      );
+  const handleEngineChange = (engineId: string) => {
+    setSelectedEngineId(engineId);
+    form.setValue('ragEngineId', engineId);
+
+    // Find engine and set type
+    const engine = ragEngines.find((e) => e.plugin_id === engineId);
+    if (engine) {
+      setSelectedEngineType(engine.type);
+      // Initialize config settings with defaults from schema
+      if (engine.creation_schema) {
+        setConfigSettings(getDefaultValuesFromSchema(engine.creation_schema));
+      } else {
+        setConfigSettings({});
+      }
     }
-    setEmbeddingModels(models);
   };
 
   const onSubmit = (data: z.infer<typeof formSchema>) => {
-    if (initKbId) {
-      // update knowledge base
-      const updateKb: KnowledgeBase = {
+    const engine = ragEngines.find((e) => e.plugin_id === selectedEngineId);
+    if (!engine) return;
+
+    if (engine.type === 'retriever') {
+      // Parse plugin_id to get plugin_author and plugin_name
+      const [plugin_author, plugin_name] = selectedEngineId.split('/');
+
+      const externalKbData: ExternalKnowledgeBase = {
         name: data.name,
         description: data.description,
-        embedding_model_uuid: data.embeddingModelUUID,
-        top_k: data.top_k,
+        plugin_author,
+        plugin_name,
+        retriever_name: engine.component_name || '',
+        retriever_config: configSettings as Record<string, unknown>,
       };
-      httpClient
-        .updateKnowledgeBase(initKbId, updateKb)
-        .then((res) => {
-          onKbUpdated(res.uuid);
-          toast.success(t('knowledge.updateKnowledgeBaseSuccess'));
-        })
-        .catch((err) => {
-          console.error('update knowledge base failed', err);
-          toast.error(t('knowledge.updateKnowledgeBaseFailed'));
-        });
+
+      if (initKbId && initKbType === 'retriever') {
+        // Update external knowledge base
+        httpClient
+          .updateExternalKnowledgeBase(initKbId, externalKbData)
+          .then(() => {
+            onKbUpdated(initKbId);
+            toast.success(t('knowledge.updateKnowledgeBaseSuccess'));
+          })
+          .catch((err) => {
+            console.error('update external knowledge base failed', err);
+            toast.error(t('knowledge.updateKnowledgeBaseFailed'));
+          });
+      } else {
+        // Create external knowledge base
+        httpClient
+          .createExternalKnowledgeBase(externalKbData)
+          .then((res) => {
+            onNewKbCreated(res.uuid);
+          })
+          .catch((err) => {
+            console.error('create external knowledge base failed', err);
+            toast.error(t('knowledge.createKnowledgeBaseFailed'));
+          });
+      }
     } else {
-      // create knowledge base
-      const newKb: KnowledgeBase = {
+      // rag_engine type
+      const kbData: KnowledgeBase = {
         name: data.name,
         description: data.description,
-        embedding_model_uuid: data.embeddingModelUUID,
-        top_k: data.top_k,
+        rag_engine_plugin_id: selectedEngineId,
+        creation_settings: configSettings,
+        embedding_model_uuid: '',
+        top_k: 5,
       };
-      httpClient
-        .createKnowledgeBase(newKb)
-        .then((res) => {
-          onNewKbCreated(res.uuid);
-        })
-        .catch((err) => {
-          console.error('create knowledge base failed', err);
-        });
+
+      if (initKbId && initKbType === 'rag_engine') {
+        // Update knowledge base
+        httpClient
+          .updateKnowledgeBase(initKbId, kbData)
+          .then((res) => {
+            onKbUpdated(res.uuid);
+            toast.success(t('knowledge.updateKnowledgeBaseSuccess'));
+          })
+          .catch((err) => {
+            console.error('update knowledge base failed', err);
+            toast.error(t('knowledge.updateKnowledgeBaseFailed'));
+          });
+      } else {
+        // Create knowledge base
+        httpClient
+          .createKnowledgeBase(kbData)
+          .then((res) => {
+            onNewKbCreated(res.uuid);
+          })
+          .catch((err) => {
+            console.error('create knowledge base failed', err);
+            toast.error(t('knowledge.createKnowledgeBaseFailed'));
+          });
+      }
     }
   };
 
+  // Convert creation schema to dynamic form items
+  // For retriever type, creation_schema is already config items format
+  // For rag_engine type, creation_schema is JSON Schema format
+  const configFormItems = (() => {
+    if (!selectedEngine?.creation_schema) return [];
+
+    if (selectedEngine.type === 'retriever') {
+      // Retriever type: creation_schema is already config items array
+      return selectedEngine.creation_schema as unknown as IDynamicFormItemSchema[];
+    } else {
+      // RAG engine type: creation_schema is JSON Schema, needs conversion
+      return jsonSchemaToFormItems(selectedEngine.creation_schema);
+    }
+  })();
+
+  // Show loading state
+  if (loading) {
+    return (
+      <div className="flex items-center justify-center py-8">
+        <p className="text-muted-foreground">{t('common.loading')}</p>
+      </div>
+    );
+  }
+
+  // Show message if no engines available
+  if (ragEngines.length === 0) {
+    return (
+      <div className="flex flex-col items-center justify-center py-8 space-y-4">
+        <p className="text-muted-foreground">{t('knowledge.noEnginesAvailable')}</p>
+        <p className="text-sm text-muted-foreground">
+          {t('knowledge.installEngineHint')}
+        </p>
+      </div>
+    );
+  }
+
   return (
     <>
       <Form {...form}>
@@ -152,29 +293,65 @@ export default function KBForm({
           className="space-y-8"
         >
           <div className="space-y-4">
+            {/* RAG Engine Selector */}
             <FormField
               control={form.control}
-              name="name"
+              name="ragEngineId"
               render={({ field }) => (
                 <FormItem>
                   <FormLabel>
-                    {t('knowledge.kbName')}
+                    {t('knowledge.ragEngine')}
                     <span className="text-red-500">*</span>
                   </FormLabel>
                   <FormControl>
-                    <Input {...field} />
+                    <Select
+                      disabled={isEditing}
+                      onValueChange={(value) => {
+                        field.onChange(value);
+                        handleEngineChange(value);
+                      }}
+                      value={field.value}
+                    >
+                      <SelectTrigger className="w-full bg-[#ffffff] dark:bg-[#2a2a2e]">
+                        <SelectValue
+                          placeholder={t('knowledge.selectRagEngine')}
+                        />
+                      </SelectTrigger>
+                      <SelectContent className="fixed z-[1000]">
+                        {ragEngines.map((engine) => (
+                          <SelectItem
+                            key={engine.plugin_id}
+                            value={engine.plugin_id}
+                          >
+                            {engine.name}
+                          </SelectItem>
+                        ))}
+                      </SelectContent>
+                    </Select>
                   </FormControl>
+                  {selectedEngine?.description && (
+                    <FormDescription>
+                      {extractI18nObject(selectedEngine.description)}
+                    </FormDescription>
+                  )}
+                  {isEditing && (
+                    <FormDescription>
+                      {t('knowledge.cannotChangeRagEngine')}
+                    </FormDescription>
+                  )}
                   <FormMessage />
                 </FormItem>
               )}
             />
+
+            {/* Name */}
             <FormField
               control={form.control}
-              name="description"
+              name="name"
               render={({ field }) => (
                 <FormItem>
                   <FormLabel>
-                    {t('knowledge.kbDescription')}
+                    {t('knowledge.kbName')}
                     <span className="text-red-500">*</span>
                   </FormLabel>
                   <FormControl>
@@ -184,96 +361,42 @@ export default function KBForm({
                 </FormItem>
               )}
             />
+
+            {/* Description */}
             <FormField
               control={form.control}
-              name="embeddingModelUUID"
-              render={({ field }) => (
-                <FormItem>
-                  <FormLabel>
-                    {t('knowledge.embeddingModelUUID')}
-                    <span className="text-red-500">*</span>
-                  </FormLabel>
-                  <FormControl>
-                    <div className="relative">
-                      <Select
-                        disabled={!!initKbId}
-                        onValueChange={(value) => {
-                          field.onChange(value);
-                        }}
-                        value={field.value}
-                      >
-                        <SelectTrigger className="w-[180px] bg-[#ffffff] dark:bg-[#2a2a2e]">
-                          <SelectValue
-                            placeholder={t('knowledge.selectEmbeddingModel')}
-                          />
-                        </SelectTrigger>
-                        <SelectContent className="fixed z-[1000]">
-                          {(() => {
-                            const grouped = embeddingModels.reduce(
-                              (acc, model) => {
-                                const providerName =
-                                  model.provider?.name ||
-                                  model.provider?.requester ||
-                                  'Unknown';
-                                if (!acc[providerName]) acc[providerName] = [];
-                                acc[providerName].push(model);
-                                return acc;
-                              },
-                              {} as Record<string, EmbeddingModel[]>,
-                            );
-                            return Object.entries(grouped).map(
-                              ([providerName, models]) => (
-                                <SelectGroup key={providerName}>
-                                  <SelectLabel>{providerName}</SelectLabel>
-                                  {models.map((model) => (
-                                    <SelectItem
-                                      key={model.uuid}
-                                      value={model.uuid}
-                                    >
-                                      {model.name}
-                                    </SelectItem>
-                                  ))}
-                                </SelectGroup>
-                              ),
-                            );
-                          })()}
-                        </SelectContent>
-                      </Select>
-                    </div>
-                  </FormControl>
-                  <FormDescription>
-                    {initKbId
-                      ? t('knowledge.cannotChangeEmbeddingModel')
-                      : t('knowledge.embeddingModelDescription')}
-                  </FormDescription>
-                  <FormMessage />
-                </FormItem>
-              )}
-            />
-            <FormField
-              control={form.control}
-              name="top_k"
+              name="description"
               render={({ field }) => (
                 <FormItem>
                   <FormLabel>
-                    {t('knowledge.topK')}
+                    {t('knowledge.kbDescription')}
                     <span className="text-red-500">*</span>
                   </FormLabel>
                   <FormControl>
-                    <Input
-                      type="number"
-                      {...field}
-                      onChange={(e) => field.onChange(Number(e.target.value))}
-                      className="w-[180px] h-10 text-base appearance-none"
-                    />
+                    <Input {...field} />
                   </FormControl>
-                  <FormDescription>
-                    {t('knowledge.topKdescription')}
-                  </FormDescription>
                   <FormMessage />
                 </FormItem>
               )}
             />
+
+            {/* Engine specific fields (dynamic form) */}
+            {configFormItems.length > 0 && (
+              <div className="space-y-4 pt-2 border-t">
+                <div className="text-sm font-medium text-muted-foreground">
+                  {selectedEngineType === 'retriever'
+                    ? t('knowledge.retrieverConfiguration')
+                    : t('knowledge.engineSettings')}
+                </div>
+                <DynamicFormComponent
+                  itemConfigList={configFormItems}
+                  initialValues={configSettings as Record<string, object>}
+                  onSubmit={(val) =>
+                    setConfigSettings(val as Record<string, unknown>)
+                  }
+                />
+              </div>
+            )}
           </div>
         </form>
       </Form>
diff --git a/web/src/app/home/knowledge/components/kb-retrieve/ExternalKBRetrieve.tsx b/web/src/app/home/knowledge/components/kb-retrieve/ExternalKBRetrieve.tsx
deleted file mode 100644
index c45145d7c..000000000
--- a/web/src/app/home/knowledge/components/kb-retrieve/ExternalKBRetrieve.tsx
+++ /dev/null
@@ -1,35 +0,0 @@
-'use client';
-
-import React from 'react';
-import { httpClient } from '@/app/infra/http/HttpClient';
-import { RetrieveResult } from '@/app/infra/entities/api';
-import KBRetrieveGeneric from './KBRetrieveGeneric';
-
-interface ExternalKBRetrieveProps {
-  kbId: string;
-}
-
-/**
- * External knowledge base retrieve component
- * Uses the generic retrieve component with external KB API
- */
-export default function ExternalKBRetrieve({ kbId }: ExternalKBRetrieveProps) {
-  const getResultTitle = (result: RetrieveResult): string => {
-    // For external KB, try to get document_name or use a generic title
-    return (
-      (result.metadata.document_name as string) ||
-      (result.metadata.source as string) ||
-      result.id
-    );
-  };
-
-  return (
-    <KBRetrieveGeneric
-      kbId={kbId}
-      retrieveFunction={httpClient.retrieveExternalKnowledgeBase.bind(
-        httpClient,
-      )}
-      getResultTitle={getResultTitle}
-    />
-  );
-}
diff --git a/web/src/app/home/knowledge/components/kb-retrieve/KBRetrieve.tsx b/web/src/app/home/knowledge/components/kb-retrieve/KBRetrieve.tsx
deleted file mode 100644
index ef831703f..000000000
--- a/web/src/app/home/knowledge/components/kb-retrieve/KBRetrieve.tsx
+++ /dev/null
@@ -1,124 +0,0 @@
-'use client';
-
-import React, { useState, useEffect } from 'react';
-import { Card, CardContent, CardHeader, CardTitle } from '@/components/ui/card';
-import { Button } from '@/components/ui/button';
-import { Input } from '@/components/ui/input';
-import { useTranslation } from 'react-i18next';
-import { httpClient } from '@/app/infra/http/HttpClient';
-import { RetrieveResult, KnowledgeBaseFile } from '@/app/infra/entities/api';
-import { toast } from 'sonner';
-
-interface KBRetrieveProps {
-  kbId: string;
-}
-
-export default function KBRetrieve({ kbId }: KBRetrieveProps) {
-  const { t } = useTranslation();
-  const [query, setQuery] = useState('');
-  const [results, setResults] = useState<RetrieveResult[]>([]);
-  const [files, setFiles] = useState<KnowledgeBaseFile[]>([]);
-  const [loading, setLoading] = useState(false);
-
-  useEffect(() => {
-    const loadFiles = async () => {
-      try {
-        const response = await httpClient.getKnowledgeBaseFiles(kbId);
-        setFiles(response.files);
-      } catch (error) {
-        console.error('Failed to load files:', error);
-      }
-    };
-    loadFiles();
-  }, [kbId]);
-
-  const handleRetrieve = async () => {
-    if (!query.trim()) return;
-
-    setLoading(true);
-    try {
-      setResults([]);
-      const response = await httpClient.retrieveKnowledgeBase(kbId, query);
-      setResults(response.results);
-    } catch (error) {
-      console.error('Retrieve failed:', error);
-      toast.error(t('knowledge.retrieveError'));
-    } finally {
-      setLoading(false);
-    }
-  };
-
-  const getFileName = (fileId?: string) => {
-    if (!fileId) return '';
-    const file = files.find((f) => f.uuid === fileId);
-    return file?.file_name || fileId;
-  };
-
-  /**
-   * Extract text content from the content array
-   * The content array may contain multiple items with type 'text'
-   */
-  const extractTextFromContent = (result: RetrieveResult): string => {
-    // First try to get content from the new format
-    if (result.content && Array.isArray(result.content)) {
-      const textParts = result.content
-        .filter((item) => item.type === 'text' && item.text)
-        .map((item) => item.text);
-
-      if (textParts.length > 0) {
-        return textParts.join('\n\n');
-      }
-    }
-
-    // Fallback to metadata.text for backward compatibility
-    if (result.metadata?.text) {
-      return result.metadata.text as string;
-    }
-
-    return '';
-  };
-
-  return (
-    <div className="space-y-4">
-      <div className="flex gap-2">
-        <Input
-          value={query}
-          onChange={(e) => setQuery(e.target.value)}
-          placeholder={t('knowledge.queryPlaceholder')}
-          onKeyPress={(e) => e.key === 'Enter' && handleRetrieve()}
-        />
-        <Button onClick={handleRetrieve} disabled={loading || !query.trim()}>
-          {t('knowledge.query')}
-        </Button>
-      </div>
-
-      <div className="space-y-3">
-        {results.length === 0 && !loading && (
-          <p className="text-muted-foreground">{t('knowledge.noResults')}</p>
-        )}
-
-        {loading ? (
-          <p className="text-muted-foreground">{t('common.loading')}</p>
-        ) : (
-          results.map((result) => (
-            <Card key={result.id} className="w-full">
-              <CardHeader className="pb-3">
-                <CardTitle className="text-sm font-medium flex justify-between items-center">
-                  <span>{getFileName(result.metadata.file_id)}</span>
-                  <span className="text-xs text-muted-foreground">
-                    {t('knowledge.distance')}: {result.distance.toFixed(4)}
-                  </span>
-                </CardTitle>
-              </CardHeader>
-              <CardContent>
-                <p className="text-sm whitespace-pre-wrap">
-                  {extractTextFromContent(result)}
-                </p>
-              </CardContent>
-            </Card>
-          ))
-        )}
-      </div>
-    </div>
-  );
-}
diff --git a/web/src/app/home/knowledge/page.tsx b/web/src/app/home/knowledge/page.tsx
index ceae8c3ac..846b80c35 100644
--- a/web/src/app/home/knowledge/page.tsx
+++ b/web/src/app/home/knowledge/page.tsx
@@ -5,137 +5,108 @@ import styles from './knowledgeBase.module.css';
 import { useTranslation } from 'react-i18next';
 import { useEffect, useState } from 'react';
 import { KnowledgeBaseVO } from '@/app/home/knowledge/components/kb-card/KBCardVO';
-import { ExternalKBCardVO } from '@/app/home/knowledge/components/external-kb-card/ExternalKBCardVO';
 import KBCard from '@/app/home/knowledge/components/kb-card/KBCard';
-import ExternalKBCard from '@/app/home/knowledge/components/external-kb-card/ExternalKBCard';
 import KBDetailDialog from '@/app/home/knowledge/KBDetailDialog';
 import { httpClient } from '@/app/infra/http/HttpClient';
-import {
-  KnowledgeBase,
-  ExternalKnowledgeBase,
-  ApiRespPluginSystemStatus,
-} from '@/app/infra/entities/api';
-import { Tabs, TabsContent, TabsList, TabsTrigger } from '@/components/ui/tabs';
+import { KnowledgeBase, ExternalKnowledgeBase } from '@/app/infra/entities/api';
 
 export default function KnowledgePage() {
   const { t } = useTranslation();
-  const [activeTab, setActiveTab] = useState('builtin');
   const [knowledgeBaseList, setKnowledgeBaseList] = useState<KnowledgeBaseVO[]>(
     [],
   );
-  const [externalKBList, setExternalKBList] = useState<ExternalKBCardVO[]>([]);
   const [selectedKbId, setSelectedKbId] = useState<string>('');
-  const [selectedKbType, setSelectedKbType] = useState<'builtin' | 'external'>(
-    'builtin',
-  );
+  const [selectedKbType, setSelectedKbType] = useState<'retriever' | 'rag_engine' | undefined>(undefined);
   const [detailDialogOpen, setDetailDialogOpen] = useState(false);
-  const [pluginSystemStatus, setPluginSystemStatus] =
-    useState<ApiRespPluginSystemStatus | null>(null);
 
   useEffect(() => {
     getKnowledgeBaseList();
-    getExternalKBList();
-    fetchPluginSystemStatus();
   }, []);
 
-  async function fetchPluginSystemStatus() {
-    try {
-      const status = await httpClient.getPluginSystemStatus();
-      setPluginSystemStatus(status);
-    } catch (error) {
-      console.error('Failed to fetch plugin system status:', error);
-    }
-  }
-
   async function getKnowledgeBaseList() {
-    const resp = await httpClient.getKnowledgeBases();
-    setKnowledgeBaseList(
-      resp.bases.map((kb: KnowledgeBase) => {
-        const currentTime = new Date();
-        const lastUpdatedTimeAgo = Math.floor(
-          (currentTime.getTime() -
-            new Date(kb.updated_at ?? currentTime.getTime()).getTime()) /
-            1000 /
-            60 /
-            60 /
-            24,
-        );
-
-        const lastUpdatedTimeAgoText =
-          lastUpdatedTimeAgo > 0
-            ? ` ${lastUpdatedTimeAgo} ${t('knowledge.daysAgo')}`
-            : t('knowledge.today');
-
-        return new KnowledgeBaseVO({
-          id: kb.uuid || '',
-          name: kb.name,
-          description: kb.description,
-          embeddingModelUUID: kb.embedding_model_uuid,
-          top_k: kb.top_k ?? 5,
-          lastUpdatedTimeAgo: lastUpdatedTimeAgoText,
-        });
-      }),
-    );
-  }
-
-  async function getExternalKBList() {
-    try {
-      const resp = await httpClient.getExternalKnowledgeBases();
-      setExternalKBList(
-        resp.bases.map((kb: ExternalKnowledgeBase) => {
-          const currentTime = new Date();
-          const lastUpdatedTimeAgo = Math.floor(
-            (currentTime.getTime() -
-              new Date(kb.created_at ?? currentTime.getTime()).getTime()) /
-              1000 /
-              60 /
-              60 /
-              24,
-          );
+    // Load both types of knowledge bases
+    const [ragEngineResp, retrieverResp] = await Promise.all([
+      httpClient.getKnowledgeBases(),
+      httpClient.getExternalKnowledgeBases(),
+    ]);
+
+    const currentTime = new Date();
+
+    // Convert rag_engine type KBs
+    const ragEngineKBs = ragEngineResp.bases.map((kb: KnowledgeBase) => {
+      const lastUpdatedTimeAgo = Math.floor(
+        (currentTime.getTime() -
+          new Date(kb.updated_at ?? currentTime.getTime()).getTime()) /
+          1000 /
+          60 /
+          60 /
+          24,
+      );
 
-          const lastUpdatedTimeAgoText =
-            lastUpdatedTimeAgo > 0
-              ? ` ${lastUpdatedTimeAgo} ${t('knowledge.daysAgo')}`
-              : t('knowledge.today');
-
-          return new ExternalKBCardVO({
-            id: kb.uuid || '',
-            name: kb.name,
-            description: kb.description,
-            retrieverName: `${kb.plugin_author}/${kb.plugin_name}/${kb.retriever_name}`,
-            retrieverConfig: kb.retriever_config || {},
-            lastUpdatedTimeAgo: lastUpdatedTimeAgoText,
-            pluginAuthor: kb.plugin_author,
-            pluginName: kb.plugin_name,
-          });
-        }),
+      const lastUpdatedTimeAgoText =
+        lastUpdatedTimeAgo > 0
+          ? ` ${lastUpdatedTimeAgo} ${t('knowledge.daysAgo')}`
+          : t('knowledge.today');
+
+      return new KnowledgeBaseVO({
+        id: kb.uuid || '',
+        name: kb.name,
+        description: kb.description,
+        embeddingModelUUID: kb.embedding_model_uuid,
+        top_k: kb.top_k ?? 5,
+        lastUpdatedTimeAgo: lastUpdatedTimeAgoText,
+        ragEngine: kb.rag_engine,
+        ragEnginePluginId: kb.rag_engine_plugin_id,
+        type: 'rag_engine',
+      });
+    });
+
+    // Convert retriever type (external) KBs
+    const retrieverKBs = retrieverResp.bases.map((kb: ExternalKnowledgeBase) => {
+      const lastUpdatedTimeAgo = Math.floor(
+        (currentTime.getTime() -
+          new Date(kb.created_at ?? currentTime.getTime()).getTime()) /
+          1000 /
+          60 /
+          60 /
+          24,
       );
-    } catch (error) {
-      console.error('Failed to load external knowledge bases:', error);
-    }
+
+      const lastUpdatedTimeAgoText =
+        lastUpdatedTimeAgo > 0
+          ? ` ${lastUpdatedTimeAgo} ${t('knowledge.daysAgo')}`
+          : t('knowledge.today');
+
+      return new KnowledgeBaseVO({
+        id: kb.uuid || '',
+        name: kb.name,
+        description: kb.description,
+        embeddingModelUUID: '',
+        top_k: 5,
+        lastUpdatedTimeAgo: lastUpdatedTimeAgoText,
+        ragEngine: {
+          plugin_id: `${kb.plugin_author}/${kb.plugin_name}`,
+          name: kb.retriever_name,
+          capabilities: [],
+        },
+        ragEnginePluginId: `${kb.plugin_author}/${kb.plugin_name}`,
+        type: 'retriever',
+      });
+    });
+
+    // Combine both lists
+    setKnowledgeBaseList([...ragEngineKBs, ...retrieverKBs]);
   }
 
-  const handleKBCardClick = (kbId: string) => {
-    setSelectedKbId(kbId);
-    setSelectedKbType('builtin');
+  const handleKBCardClick = (kb: KnowledgeBaseVO) => {
+    setSelectedKbId(kb.id);
+    setSelectedKbType(kb.type);
     setDetailDialogOpen(true);
   };
 
   const handleCreateKBClick = () => {
     setSelectedKbId('');
-    setSelectedKbType('builtin');
-    setDetailDialogOpen(true);
-  };
-
-  const handleExternalKBCardClick = (kbId: string) => {
-    setSelectedKbId(kbId);
-    setSelectedKbType('external');
-    setDetailDialogOpen(true);
-  };
-
-  const handleCreateExternalKB = () => {
-    setSelectedKbId('');
-    setSelectedKbType('external');
+    setSelectedKbType(undefined);
     setDetailDialogOpen(true);
   };
 
@@ -144,30 +115,18 @@ export default function KnowledgePage() {
   };
 
   const handleKbDeleted = () => {
-    if (selectedKbType === 'builtin') {
-      getKnowledgeBaseList();
-    } else {
-      getExternalKBList();
-    }
+    getKnowledgeBaseList();
     setDetailDialogOpen(false);
   };
 
   const handleNewKbCreated = (newKbId: string) => {
-    if (selectedKbType === 'builtin') {
-      getKnowledgeBaseList();
-    } else {
-      getExternalKBList();
-    }
+    getKnowledgeBaseList();
     setSelectedKbId(newKbId);
     setDetailDialogOpen(true);
   };
 
   const handleKbUpdated = () => {
-    if (selectedKbType === 'builtin') {
-      getKnowledgeBaseList();
-    } else {
-      getExternalKBList();
-    }
+    getKnowledgeBaseList();
   };
 
   return (
@@ -183,66 +142,22 @@ export default function KnowledgePage() {
         onKbUpdated={handleKbUpdated}
       />
 
-      <Tabs value={activeTab} onValueChange={setActiveTab} className="w-full">
-        <div className="flex flex-row justify-between items-center px-[0.8rem]">
-          <TabsList className="shadow-md py-5 bg-[#f0f0f0] dark:bg-[#2a2a2e]">
-            <TabsTrigger value="builtin" className="px-6 py-4 cursor-pointer">
-              {t('knowledge.builtIn')}
-            </TabsTrigger>
-            {/* Only show external tab if plugin system is enabled and connected */}
-            {pluginSystemStatus?.is_enable &&
-              pluginSystemStatus?.is_connected && (
-                <TabsTrigger
-                  value="external"
-                  className="px-6 py-4 cursor-pointer"
-                >
-                  {t('knowledge.external')}
-                </TabsTrigger>
-              )}
-          </TabsList>
-        </div>
-
-        <TabsContent value="builtin">
-          <div className={styles.knowledgeListContainer}>
-            <CreateCardComponent
-              width={'100%'}
-              height={'10rem'}
-              plusSize={'90px'}
-              onClick={handleCreateKBClick}
-            />
-
-            {knowledgeBaseList.map((kb) => {
-              return (
-                <div key={kb.id} onClick={() => handleKBCardClick(kb.id)}>
-                  <KBCard kbCardVO={kb} />
-                </div>
-              );
-            })}
-          </div>
-        </TabsContent>
-
-        <TabsContent value="external">
-          <div className={styles.knowledgeListContainer}>
-            <CreateCardComponent
-              width={'100%'}
-              height={'10rem'}
-              plusSize={'90px'}
-              onClick={handleCreateExternalKB}
-            />
-
-            {externalKBList.map((kb) => {
-              return (
-                <div
-                  key={kb.id}
-                  onClick={() => handleExternalKBCardClick(kb.id)}
-                >
-                  <ExternalKBCard kbCardVO={kb} />
-                </div>
-              );
-            })}
-          </div>
-        </TabsContent>
-      </Tabs>
+      <div className={styles.knowledgeListContainer}>
+        <CreateCardComponent
+          width={'100%'}
+          height={'10rem'}
+          plusSize={'90px'}
+          onClick={handleCreateKBClick}
+        />
+
+        {knowledgeBaseList.map((kb) => {
+          return (
+            <div key={kb.id} onClick={() => handleKBCardClick(kb)}>
+              <KBCard kbCardVO={kb} />
+            </div>
+          );
+        })}
+      </div>
     </div>
   );
 }
diff --git a/web/src/app/infra/entities/api/index.ts b/web/src/app/infra/entities/api/index.ts
index ce5043ea4..e5b30360b 100644
--- a/web/src/app/infra/entities/api/index.ts
+++ b/web/src/app/infra/entities/api/index.ts
@@ -168,6 +168,48 @@ export interface KnowledgeBase {
   top_k: number;
   created_at?: string;
   updated_at?: string;
+  // New unified fields
+  rag_engine_plugin_id?: string;
+  creation_settings?: Record<string, unknown>;
+  rag_engine?: RAGEngineInfo;
+}
+
+// RAG Engine types
+export interface RAGEngineInfo {
+  plugin_id: string | null;
+  name: string;
+  capabilities: string[];
+}
+
+export interface RAGEngine {
+  plugin_id: string;
+  name: string;
+  description?: I18nObject;
+  type: 'retriever' | 'rag_engine'; // retriever = external KB, rag_engine = new RAG with doc support
+  capabilities: string[];
+  creation_schema?: JSONSchema;
+  retrieval_schema?: JSONSchema;
+  // For retriever type (external KB)
+  component_name?: string;
+}
+
+export interface JSONSchema {
+  type: string;
+  properties?: Record<string, JSONSchemaProperty>;
+  required?: string[];
+}
+
+export interface JSONSchemaProperty {
+  type: string;
+  default?: unknown;
+  description?: string;
+  enum?: unknown[];
+  minimum?: number;
+  maximum?: number;
+}
+
+export interface ApiRespRAGEngines {
+  engines: RAGEngine[];
 }
 
 export interface ExternalKnowledgeBase {
diff --git a/web/src/app/infra/http/BackendClient.ts b/web/src/app/infra/http/BackendClient.ts
index dd4b1feb2..ef28f02bd 100644
--- a/web/src/app/infra/http/BackendClient.ts
+++ b/web/src/app/infra/http/BackendClient.ts
@@ -41,6 +41,7 @@ import {
   ApiRespModelProviders,
   ApiRespModelProvider,
   ModelProvider,
+  ApiRespRAGEngines,
 } from '@/app/infra/entities/api';
 import { Plugin } from '@/app/infra/entities/plugin';
 import { GetBotLogsRequest } from '@/app/infra/http/requestParam/bots/GetBotLogsRequest';
@@ -407,6 +408,11 @@ export class BackendClient extends BaseHttpClient {
     return this.post(`/api/v1/knowledge/bases/${uuid}/retrieve`, { query });
   }
 
+  // ============ RAG Engines API ============
+  public getRagEngines(): Promise<ApiRespRAGEngines> {
+    return this.get('/api/v1/knowledge/engines');
+  }
+
   // ============ External Knowledge Base API ============
   public getExternalKnowledgeBases(): Promise<ApiRespExternalKnowledgeBases> {
     return this.get('/api/v1/knowledge/external-bases');
diff --git a/web/src/app/infra/utils/jsonSchemaConverter.ts b/web/src/app/infra/utils/jsonSchemaConverter.ts
new file mode 100644
index 000000000..aa02c8759
--- /dev/null
+++ b/web/src/app/infra/utils/jsonSchemaConverter.ts
@@ -0,0 +1,116 @@
+import { UUID } from 'uuidjs';
+import {
+  IDynamicFormItemSchema,
+  DynamicFormItemType,
+} from '@/app/infra/entities/form/dynamic';
+import { JSONSchema, JSONSchemaProperty } from '@/app/infra/entities/api';
+
+/**
+ * Convert a JSON Schema property type to DynamicFormItemType
+ */
+function mapJsonSchemaType(
+  prop: JSONSchemaProperty,
+): DynamicFormItemType {
+  // Handle enum as select
+  if (prop.enum && prop.enum.length > 0) {
+    return DynamicFormItemType.SELECT;
+  }
+
+  switch (prop.type) {
+    case 'integer':
+      return DynamicFormItemType.INT;
+    case 'number':
+      return DynamicFormItemType.FLOAT;
+    case 'boolean':
+      return DynamicFormItemType.BOOLEAN;
+    case 'string':
+      return DynamicFormItemType.STRING;
+    case 'array':
+      return DynamicFormItemType.STRING_ARRAY;
+    default:
+      return DynamicFormItemType.STRING;
+  }
+}
+
+/**
+ * Get default value based on type
+ */
+function getDefaultValue(
+  prop: JSONSchemaProperty,
+): string | number | boolean | unknown[] {
+  if (prop.default !== undefined) {
+    return prop.default as string | number | boolean | unknown[];
+  }
+
+  switch (prop.type) {
+    case 'integer':
+    case 'number':
+      return 0;
+    case 'boolean':
+      return false;
+    case 'array':
+      return [];
+    default:
+      return '';
+  }
+}
+
+/**
+ * Convert JSON Schema to IDynamicFormItemSchema array
+ * This allows using the existing DynamicFormComponent with JSON Schema data
+ */
+export function jsonSchemaToFormItems(
+  schema: JSONSchema | undefined,
+): IDynamicFormItemSchema[] {
+  if (!schema || !schema.properties) {
+    return [];
+  }
+
+  const requiredFields = schema.required || [];
+  const items: IDynamicFormItemSchema[] = [];
+
+  for (const [name, prop] of Object.entries(schema.properties)) {
+    const formItem: IDynamicFormItemSchema = {
+      id: UUID.generate(),
+      name,
+      label: { en_US: name, zh_Hans: name },
+      description: prop.description
+        ? { en_US: prop.description, zh_Hans: prop.description }
+        : undefined,
+      type: mapJsonSchemaType(prop),
+      required: requiredFields.includes(name),
+      default: getDefaultValue(prop),
+    };
+
+    // Handle enum options for select type
+    if (prop.enum && prop.enum.length > 0) {
+      formItem.options = prop.enum.map((value) => ({
+        name: String(value),
+        label: { en_US: String(value), zh_Hans: String(value) },
+      }));
+    }
+
+    items.push(formItem);
+  }
+
+  return items;
+}
+
+/**
+ * Extract default values from JSON Schema
+ */
+export function getDefaultValuesFromSchema(
+  schema: JSONSchema | undefined,
+): Record<string, unknown> {
+  if (!schema || !schema.properties) {
+    return {};
+  }
+
+  const defaults: Record<string, unknown> = {};
+
+  for (const [name, prop] of Object.entries(schema.properties)) {
+    defaults[name] = getDefaultValue(prop);
+  }
+
+  return defaults;
+}
diff --git a/web/src/i18n/locales/en-US.ts b/web/src/i18n/locales/en-US.ts
index c097eb4e1..59c1bb6c2 100644
--- a/web/src/i18n/locales/en-US.ts
+++ b/web/src/i18n/locales/en-US.ts
@@ -700,8 +700,16 @@ const enUS = {
     fileName: 'File Name',
     noResults: 'No results',
     retrieveError: 'Retrieve failed',
-    builtIn: 'Built-in',
-    external: 'External',
+    unknownEngine: 'Unknown Engine',
+    ragEngine: 'RAG Engine',
+    ragEngineRequired: 'RAG engine is required',
+    selectRagEngine: 'Select RAG Engine',
+    builtInEngine: 'Built-in Engine',
+    cannotChangeRagEngine: 'RAG engine cannot be changed after creation',
+    engineSettings: 'Engine Settings',
+    noEnginesAvailable: 'No knowledge base engines available',
+    installEngineHint: 'Please install a knowledge base plugin first',
+    createKnowledgeBaseFailed: 'Failed to create knowledge base',
     addExternal: 'Add External Knowledge Base',
     createExternalSuccess: 'External knowledge base created successfully',
     updateExternalSuccess: 'External knowledge base updated successfully',
diff --git a/web/src/i18n/locales/ja-JP.ts b/web/src/i18n/locales/ja-JP.ts
index 903e8ea5c..492794c4b 100644
--- a/web/src/i18n/locales/ja-JP.ts
+++ b/web/src/i18n/locales/ja-JP.ts
@@ -708,8 +708,7 @@ const jaJP = {
     fileName: 'ファイル名',
     noResults: '検索結果がありません',
     retrieveError: '検索に失敗しました',
-    builtIn: '内蔵',
-    external: '外部ナレッジベース',
+    unknownEngine: '不明なエンジン',
     addExternal: '外部ナレッジベースを追加',
     createExternalSuccess: '外部ナレッジベースが正常に作成されました',
     updateExternalSuccess: '外部ナレッジベースが正常に更新されました',
diff --git a/web/src/i18n/locales/zh-Hans.ts b/web/src/i18n/locales/zh-Hans.ts
index 6ce7a13af..085c008dc 100644
--- a/web/src/i18n/locales/zh-Hans.ts
+++ b/web/src/i18n/locales/zh-Hans.ts
@@ -671,8 +671,16 @@ const zhHans = {
     fileName: '文件名',
     noResults: '暂无结果',
     retrieveError: '检索失败',
-    builtIn: '内置',
-    external: '外部知识库',
+    unknownEngine: '未知引擎',
+    ragEngine: 'RAG 引擎',
+    ragEngineRequired: 'RAG 引擎不能为空',
+    selectRagEngine: '选择 RAG 引擎',
+    builtInEngine: '内置引擎',
+    cannotChangeRagEngine: '知识库创建后不可修改 RAG 引擎',
+    engineSettings: '引擎设置',
+    noEnginesAvailable: '没有可用的知识库引擎',
+    installEngineHint: '请先安装知识库插件',
+    createKnowledgeBaseFailed: '知识库创建失败',
     addExternal: '添加外部知识库',
     createExternalSuccess: '外部知识库创建成功',
     updateExternalSuccess: '外部知识库更新成功',
diff --git a/web/src/i18n/locales/zh-Hant.ts b/web/src/i18n/locales/zh-Hant.ts
index 2dac28738..a8eb46241 100644
--- a/web/src/i18n/locales/zh-Hant.ts
+++ b/web/src/i18n/locales/zh-Hant.ts
@@ -668,8 +668,7 @@ const zhHant = {
     fileName: '文檔名稱',
     noResults: '暫無結果',
     retrieveError: '檢索失敗',
-    builtIn: '內置',
-    external: '外部知識庫',
+    unknownEngine: '未知引擎',
     addExternal: '添加外部知識庫',
     createExternalSuccess: '外部知識庫創建成功',
     updateExternalSuccess: '外部知識庫更新成功',

From a9d2f9f84edaf4fd0ee50d2a90ef73a197988861 Mon Sep 17 00:00:00 2001
From: youhuanghe <1051233107@qq.com>
Date: Mon, 26 Jan 2026 14:36:03 +0000
Subject: [PATCH 05/25] refactor(rag): remove plugin_adapter, inline logic into
 RuntimeKnowledgeBase
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

BREAKING CHANGE: RAGPluginAdapter has been removed. All plugin
communication is now handled directly by RuntimeKnowledgeBase.

Architecture change:
- Before: RuntimeKnowledgeBase → RAGPluginAdapter → plugin_connector
- After:  RuntimeKnowledgeBase → plugin_connector (direct)

Changes to kbmgr.py (RuntimeKnowledgeBase):
- Remove RAGPluginAdapter import and usage
- Inline plugin communication methods:
  - _on_kb_create(): Notify plugin when KB is created
  - _on_kb_delete(): Notify plugin when KB is deleted
  - _ingest_document(): Call plugin for document ingestion
  - _retrieve(): Call plugin for retrieval
  - _delete_document(): Call plugin to delete document
- Simplify dispose(): Only notify plugin, no built-in VDB assumption

Changes to base.py (KnowledgeBaseInterface):
- Remove get_type() abstract method (outdated internal/external concept)
- Add get_rag_engine_plugin_id() abstract method

Changes to localagent.py:
- Remove get_type() call
- Simplify top_k retrieval from KB entity

Deleted files:
- pkg/rag/knowledge/plugin_adapter.py

Benefits:
- Reduced abstraction layer, simpler code
- Plugin communication logic centralized in RuntimeKnowledgeBase
- Easier to understand and maintain

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
---
 .../pkg/provider/runners/localagent.py        |   9 +-
 src/langbot/pkg/rag/knowledge/base.py         |   4 +-
 src/langbot/pkg/rag/knowledge/kbmgr.py        | 233 +++++++++++-------
 .../pkg/rag/knowledge/plugin_adapter.py       | 150 -----------
 4 files changed, 149 insertions(+), 247 deletions(-)
 delete mode 100644 src/langbot/pkg/rag/knowledge/plugin_adapter.py

diff --git a/src/langbot/pkg/provider/runners/localagent.py b/src/langbot/pkg/provider/runners/localagent.py
index 64dbfc635..77da57df7 100644
--- a/src/langbot/pkg/provider/runners/localagent.py
+++ b/src/langbot/pkg/provider/runners/localagent.py
@@ -74,13 +74,8 @@ async def run(
                     self.ap.logger.warning(f'Knowledge base {kb_uuid} not found, skipping')
                     continue
 
-                # 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
+                # Get top_k from KB entity, default to 5 if not set
+                top_k = kb.knowledge_base_entity.top_k or 5
 
                 result = await kb.retrieve(user_message_text, top_k)
 
diff --git a/src/langbot/pkg/rag/knowledge/base.py b/src/langbot/pkg/rag/knowledge/base.py
index 793d68412..749404066 100644
--- a/src/langbot/pkg/rag/knowledge/base.py
+++ b/src/langbot/pkg/rag/knowledge/base.py
@@ -47,8 +47,8 @@ def get_name(self) -> str:
         pass
 
     @abc.abstractmethod
-    def get_type(self) -> str:
-        """Get the type of knowledge base (internal/external)"""
+    def get_rag_engine_plugin_id(self) -> str:
+        """Get the RAG engine plugin ID"""
         pass
 
     @abc.abstractmethod
diff --git a/src/langbot/pkg/rag/knowledge/kbmgr.py b/src/langbot/pkg/rag/knowledge/kbmgr.py
index d3456ecba..236175ce3 100644
--- a/src/langbot/pkg/rag/knowledge/kbmgr.py
+++ b/src/langbot/pkg/rag/knowledge/kbmgr.py
@@ -1,8 +1,10 @@
 from __future__ import annotations
+import logging
 import traceback
 import uuid
 import zipfile
 import io
+from typing import Any, Dict
 from langbot.pkg.core import app
 import sqlalchemy
 
@@ -11,8 +13,8 @@
 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
-from .plugin_adapter import RAGPluginAdapter
+
+logger = logging.getLogger(__name__)
 
 
 class RuntimeKnowledgeBase(KnowledgeBaseInterface):
@@ -20,13 +22,9 @@ class RuntimeKnowledgeBase(KnowledgeBaseInterface):
 
     knowledge_base_entity: persistence_rag.KnowledgeBase
 
-    # Plugin Adapter handles both plugin and legacy logic
-    rag_adapter: RAGPluginAdapter
-
     def __init__(self, ap: app.Application, knowledge_base_entity: persistence_rag.KnowledgeBase):
         super().__init__(ap)
         self.knowledge_base_entity = knowledge_base_entity
-        self.rag_adapter = RAGPluginAdapter(ap)
 
     async def initialize(self):
         pass
@@ -41,18 +39,17 @@ async def _store_file_task(self, file: persistence_rag.File, task_context: taskm
             )
 
             task_context.set_current_action('Processing file')
-            
-            # Delegate to adapter (which handles legacy logic internally if needed)
-            await self.rag_adapter.ingest_document(
-                self.knowledge_base_entity,
+
+            # Call plugin to ingest document
+            await self._ingest_document(
                 {
                     "document_id": file.uuid,
                     "filename": file.file_name,
                     "extension": file.extension,
-                    "file_size": 0, # TODO: Get size
-                    "mime_type": "application/octet-stream" # TODO: Detect type
+                    "file_size": 0,  # TODO: Get size
+                    "mime_type": "application/octet-stream"  # TODO: Detect type
                 },
-                file.file_name # storage path
+                file.file_name  # storage path
             )
 
             # set file status to completed
@@ -174,13 +171,9 @@ async def retrieve(self, query: str, top_k: int, settings: dict | None = None) -
         retrieve_settings = {"top_k": top_k}
         if settings:
             retrieve_settings.update(settings)
-            
-        response = await self.rag_adapter.retrieve(
-            self.knowledge_base_entity,
-            query,
-            retrieve_settings
-        )
-        
+
+        response = await self._retrieve(query, retrieve_settings)
+
         results_data = response.get("results", [])
         entries = []
         for r in results_data:
@@ -191,9 +184,9 @@ async def retrieve(self, query: str, top_k: int, settings: dict | None = None) -
         return entries
 
     async def delete_file(self, file_id: str):
-        await self.rag_adapter.delete_document(self.knowledge_base_entity, file_id)
-        
-        # Also cleanup DB record (Adapter handles chunk/vector deletion)
+        await self._delete_document(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)
         )
@@ -206,13 +199,126 @@ def get_name(self) -> str:
         """Get the name of the knowledge base"""
         return self.knowledge_base_entity.name
 
-    def get_type(self) -> str:
-        """Get the type of knowledge base"""
-        return 'internal'
+    def get_rag_engine_plugin_id(self) -> str:
+        """Get the RAG engine plugin ID"""
+        return self.knowledge_base_entity.rag_engine_plugin_id or ""
 
     async def dispose(self):
-        await self.rag_adapter.on_kb_delete(self.knowledge_base_entity)
-        await self.ap.vector_db_mgr.vector_db.delete_collection(self.knowledge_base_entity.uuid)
+        """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.rag_engine_plugin_id
+        if not plugin_id:
+            return
+
+        try:
+            config = self.knowledge_base_entity.creation_settings or {}
+            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:
+            logger.error(f"Failed to notify plugin {plugin_id} on KB create: {e}")
+
+    async def _on_kb_delete(self) -> None:
+        """Notify plugin about KB deletion."""
+        plugin_id = self.knowledge_base_entity.rag_engine_plugin_id
+        if not plugin_id:
+            return
+
+        try:
+            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:
+            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,
+    ) -> Dict[str, Any]:
+        """Call plugin to ingest document."""
+        kb = self.knowledge_base_entity
+        plugin_id = kb.rag_engine_plugin_id
+        if not plugin_id:
+            logger.error(f"No RAG plugin ID configured for KB {kb.uuid}. Ingestion failed.")
+            raise ValueError("RAG Plugin ID required")
+
+        logger.info(f"Calling RAG plugin {plugin_id}: ingest(doc={file_metadata.get('filename')})")
+
+        context_data = {
+            "file_object": {
+                "metadata": file_metadata,
+                "storage_path": storage_path,
+            },
+            "knowledge_base_id": kb.uuid,
+            "collection_id": kb.collection_id or kb.uuid,
+            "chunking_strategy": kb.creation_settings.get("chunking_strategy", "fixed_size") if kb.creation_settings else "fixed_size",
+            "custom_settings": kb.creation_settings or {},
+        }
+
+        try:
+            result = await self.ap.plugin_connector.call_rag_ingest(plugin_id, context_data)
+            return result
+        except Exception as e:
+            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."""
+        kb = self.knowledge_base_entity
+        plugin_id = kb.rag_engine_plugin_id
+        if not plugin_id:
+            logger.error(f"No RAG plugin ID configured for KB {kb.uuid}. Retrieval failed.")
+            return {"results": [], "total_found": 0}
+
+        plugin_author, plugin_name = plugin_id.split('/', 1)
+
+        retrieval_context = {
+            "query": query,
+            "knowledge_base_id": kb.uuid,
+            "collection_id": kb.collection_id or kb.uuid,
+            "top_k": settings.get("top_k", kb.top_k or 5),
+            "config": settings,
+        }
+
+        try:
+            result = await self.ap.plugin_connector.retrieve_knowledge(
+                plugin_author,
+                plugin_name,
+                "",  # retriever_name - runtime will find the RAGEngine component
+                kb.uuid,  # instance_id
+                retrieval_context
+            )
+            return result
+        except Exception as e:
+            logger.error(f"Plugin retrieval failed: {e}")
+            return {"results": [], "total_found": 0}
+
+    async def _delete_document(self, document_id: str) -> bool:
+        """Call plugin to delete document."""
+        kb = self.knowledge_base_entity
+        plugin_id = kb.rag_engine_plugin_id
+        if not plugin_id:
+            return False
+
+        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:
+            logger.error(f"Plugin document deletion failed: {e}")
+            return False
 
 
 
@@ -239,8 +345,8 @@ async def create_knowledge_base(
         """Create a new knowledge base using a RAG plugin."""
         kb_uuid = str(uuid.uuid4())
         # Use UUID as collection ID by default for isolation
-        collection_id = kb_uuid 
-        
+        collection_id = kb_uuid
+
         kb_data = {
            "uuid": kb_uuid,
            "name": name,
@@ -250,20 +356,19 @@ async def create_knowledge_base(
            "creation_settings": creation_settings,
            "embedding_model_uuid": embedding_model_uuid
         }
-        
+
         # Create Entity
         kb = persistence_rag.KnowledgeBase(**kb_data)
-        
+
         # Persist
         await self.ap.persistence_mgr.execute_async(sqlalchemy.insert(persistence_rag.KnowledgeBase).values(kb_data))
-        
-        # Notify Plugin
-        adapter = RAGPluginAdapter(self.ap)
-        await adapter.on_kb_create(kb)
-        
+
         # Load into Runtime
-        await self.load_knowledge_base(kb)
-        
+        runtime_kb = await self.load_knowledge_base(kb)
+
+        # Notify Plugin
+        await runtime_kb._on_kb_create()
+
         self.ap.logger.info(f"Created new Knowledge Base {name} ({kb_uuid}) using plugin {rag_engine_plugin_id}")
         return kb
 
@@ -272,7 +377,7 @@ async def load_knowledge_bases_from_db(self):
 
         self.knowledge_bases = []
 
-        # Load internal knowledge bases
+        # Load knowledge bases
         result = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_rag.KnowledgeBase))
         knowledge_bases = result.all()
 
@@ -284,21 +389,6 @@ async def load_knowledge_bases_from_db(self):
                     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,
@@ -321,39 +411,6 @@ async def load_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:
         for kb in self.knowledge_bases:
             if kb.get_uuid() == kb_uuid:
diff --git a/src/langbot/pkg/rag/knowledge/plugin_adapter.py b/src/langbot/pkg/rag/knowledge/plugin_adapter.py
deleted file mode 100644
index 6b75a1d5e..000000000
--- a/src/langbot/pkg/rag/knowledge/plugin_adapter.py
+++ /dev/null
@@ -1,150 +0,0 @@
-from __future__ import annotations
-
-import logging
-from typing import Any, Dict, Optional
-
-from langbot.pkg.core import app
-from langbot.pkg.entity.persistence import rag as persistence_rag
-
-logger = logging.getLogger(__name__)
-
-
-class RAGPluginAdapter:
-    """Adapter for interacting with RAG plugins."""
-
-    def __init__(self, ap: app.Application):
-        self.ap = ap
-
-    async def on_kb_create(self, kb: persistence_rag.KnowledgeBase) -> None:
-        """Notify plugin about KB creation."""
-        plugin_id = kb.rag_engine_plugin_id
-        if not plugin_id:
-            return
-
-        try:
-            config = kb.creation_settings or {}
-            logger.info(
-                f"Calling RAG plugin {plugin_id}: on_knowledge_base_create(kb_id={kb.uuid})"
-            )
-            await self.ap.plugin_connector.rag_on_kb_create(plugin_id, kb.uuid, config)
-        except Exception as e:
-            logger.error(f"Failed to notify plugin {plugin_id} on KB create: {e}")
-
-    async def on_kb_delete(self, kb: persistence_rag.KnowledgeBase) -> None:
-        """Notify plugin about KB deletion."""
-        plugin_id = kb.rag_engine_plugin_id
-        if not plugin_id:
-            return
-
-        try:
-            logger.info(
-                f"Calling RAG plugin {plugin_id}: on_knowledge_base_delete(kb_id={kb.uuid})"
-            )
-            await self.ap.plugin_connector.rag_on_kb_delete(plugin_id, kb.uuid)
-        except Exception as e:
-            logger.error(f"Failed to notify plugin {plugin_id} on KB delete: {e}")
-
-    async def ingest_document(
-        self,
-        kb: persistence_rag.KnowledgeBase,
-        file_metadata: Dict[str, Any],
-        storage_path: str,
-    ) -> Dict[str, Any]:
-        """Call plugin to ingest document."""
-        plugin_id = kb.rag_engine_plugin_id
-        if not plugin_id:
-            logger.error(f"No RAG plugin ID configured for KB {kb.uuid}. Ingestion failed.")
-            raise ValueError("RAG Plugin ID required")
-
-        logger.info(f"Calling RAG plugin {plugin_id}: ingest(doc={file_metadata.get('filename')})")
-
-        context_data = {
-            "file_object": {
-                "metadata": file_metadata,
-                "storage_path": storage_path,
-            },
-            "knowledge_base_id": kb.uuid,
-            "collection_id": kb.collection_id or kb.uuid,  # Use collection_id if set, otherwise kb.uuid
-            "chunking_strategy": kb.creation_settings.get("chunking_strategy", "fixed_size") if kb.creation_settings else "fixed_size",
-            "custom_settings": kb.creation_settings or {},
-        }
-
-        try:
-            result = await self.ap.plugin_connector.call_rag_ingest(plugin_id, context_data)
-            return result
-        except Exception as e:
-            logger.error(f"Plugin ingestion failed: {e}")
-            raise
-
-    async def retrieve(
-        self,
-        kb: persistence_rag.KnowledgeBase,
-        query: str,
-        settings: Dict[str, Any],
-    ) -> Dict[str, Any]:
-        """Call plugin to retrieve documents."""
-        plugin_id = kb.rag_engine_plugin_id
-        if not plugin_id:
-            logger.error(f"No RAG plugin ID configured for KB {kb.uuid}. Retrieval failed.")
-            return {"results": [], "total_found": 0}
-
-        plugin_author, plugin_name = plugin_id.split('/', 1)
-
-        # For the new RAGEngine design, retriever_name comes from the plugin's component.
-        # We use the instance_id (kb.uuid) to identify the KB instance.
-        # The runtime/plugin will look up the correct RAGEngine component automatically.
-        # Using empty string for retriever_name to let the runtime find the default RAGEngine component.
-        retriever_name = ""  # Runtime will find the RAGEngine component automatically
-
-        retrieval_context = {
-            "query": query,
-            "knowledge_base_id": kb.uuid,
-            "collection_id": kb.collection_id or kb.uuid,
-            "top_k": settings.get("top_k", kb.top_k or 5),
-            "config": settings,
-        }
-
-        try:
-            result = await self.ap.plugin_connector.retrieve_knowledge(
-                plugin_author,
-                plugin_name,
-                retriever_name,
-                kb.uuid,  # instance_id
-                retrieval_context
-            )
-            return result
-        except Exception as e:
-            logger.error(f"Plugin retrieval failed: {e}")
-            return {"results": [], "total_found": 0}
-
-    async def delete_document(
-        self, kb: persistence_rag.KnowledgeBase, document_id: str
-    ) -> bool:
-        """Call plugin to delete document."""
-        plugin_id = kb.rag_engine_plugin_id
-        if not plugin_id:
-            return False
-
-        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:
-            logger.error(f"Plugin document deletion failed: {e}")
-            return False
-
-    async def get_creation_schema(self, plugin_id: str) -> Dict[str, Any]:
-        """Get creation settings schema from plugin."""
-        try:
-            return await self.ap.plugin_connector.get_rag_creation_schema(plugin_id)
-        except Exception as e:
-            logger.error(f"Failed to get creation schema from plugin {plugin_id}: {e}")
-            return {}
-
-    async def get_retrieval_schema(self, plugin_id: str) -> Dict[str, Any]:
-        """Get retrieval settings schema from plugin."""
-        try:
-            return await self.ap.plugin_connector.get_rag_retrieval_schema(plugin_id)
-        except Exception as e:
-            logger.error(f"Failed to get retrieval schema from plugin {plugin_id}: {e}")
-            return {}

From b5e3fc936e96134809c317bc16e13c78f39647a8 Mon Sep 17 00:00:00 2001
From: youhuanghe <1051233107@qq.com>
Date: Mon, 26 Jan 2026 14:37:41 +0000
Subject: [PATCH 06/25] refactor(api): remove ExternalKnowledgeBase
 infrastructure
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

BREAKING CHANGE: ExternalKnowledgeBase has been completely removed.
All knowledge bases are now unified under the single KnowledgeBase model,
differentiated by their rag_engine_plugin_id.

Deleted files:
- pkg/api/http/controller/groups/knowledge/external.py
  (ExternalKBController with /external-bases routes)
- pkg/api/http/service/external_kb.py
  (ExternalKnowledgeBaseService)
- pkg/rag/knowledge/external.py
  (ExternalKnowledgeBase implementation)

Modified files:
- pkg/entity/persistence/rag.py:
  Remove ExternalKnowledgeBase SQLAlchemy table definition
- pkg/core/app.py:
  Remove external_kb_service attribute from LangBotApplication
- pkg/core/stages/build_app.py:
  Remove external_kb_service initialization

Migration notes:
- Existing external knowledge base data should be migrated manually
- API consumers should use /api/v1/knowledge/bases for all KB operations
- Use /api/v1/knowledge/engines to discover available RAG engines

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
---
 .../controller/groups/knowledge/external.py   | 120 ------------------
 .../pkg/api/http/service/external_kb.py       |  80 ------------
 src/langbot/pkg/core/app.py                   |   3 -
 src/langbot/pkg/core/stages/build_app.py      |   4 -
 src/langbot/pkg/entity/persistence/rag.py     |  12 --
 src/langbot/pkg/rag/knowledge/external.py     |  85 -------------
 6 files changed, 304 deletions(-)
 delete mode 100644 src/langbot/pkg/api/http/controller/groups/knowledge/external.py
 delete mode 100644 src/langbot/pkg/api/http/service/external_kb.py
 delete mode 100644 src/langbot/pkg/rag/knowledge/external.py

diff --git a/src/langbot/pkg/api/http/controller/groups/knowledge/external.py b/src/langbot/pkg/api/http/controller/groups/knowledge/external.py
deleted file mode 100644
index 354d0542a..000000000
--- a/src/langbot/pkg/api/http/controller/groups/knowledge/external.py
+++ /dev/null
@@ -1,120 +0,0 @@
-import quart
-from ... import group
-
-
-DEPRECATION_WARNING = (
-    "This API is deprecated and will be removed in a future version. "
-    "Please use /api/v1/knowledge/bases with rag_engine_plugin_id instead."
-)
-
-
-@group.group_class('external_knowledge_base', '/api/v1/knowledge/external-bases')
-class ExternalKnowledgeBaseRouterGroup(group.RouterGroup):
-    """
-    DEPRECATED: External knowledge base routes.
-
-    These endpoints are deprecated in favor of the unified knowledge base API
-    at /api/v1/knowledge/bases. Use rag_engine_plugin_id to specify plugin-based
-    RAG engines when creating knowledge bases.
-    """
-
-    def _add_deprecation_header(self, response: quart.Response) -> quart.Response:
-        """Add deprecation warning header to response."""
-        response.headers['X-Deprecated'] = 'true'
-        response.headers['X-Deprecation-Notice'] = DEPRECATION_WARNING
-        return response
-
-    async def initialize(self) -> None:
-        @self.route('/retrievers', methods=['GET'])
-        async def list_knowledge_retrievers() -> quart.Response:
-            """
-            DEPRECATED: List all available knowledge retrievers from plugins.
-
-            Use GET /api/v1/knowledge/engines instead.
-            """
-            retrievers = await self.ap.plugin_connector.list_knowledge_retrievers()
-            response = self.success(data={
-                'retrievers': retrievers,
-                '_deprecation_warning': DEPRECATION_WARNING,
-            })
-            return self._add_deprecation_header(response)
-
-        @self.route('', methods=['POST', 'GET'])
-        async def handle_external_knowledge_bases() -> quart.Response:
-            """
-            DEPRECATED: Handle external knowledge bases.
-
-            Use /api/v1/knowledge/bases instead.
-            """
-            if quart.request.method == 'GET':
-                external_kbs = await self.ap.external_kb_service.get_external_knowledge_bases()
-                response = self.success(data={
-                    'bases': external_kbs,
-                    '_deprecation_warning': DEPRECATION_WARNING,
-                })
-                return self._add_deprecation_header(response)
-
-            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)
-                response = self.success(data={
-                    'uuid': kb_uuid,
-                    '_deprecation_warning': DEPRECATION_WARNING,
-                })
-                return self._add_deprecation_header(response)
-
-            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:
-            """
-            DEPRECATED: Handle specific external knowledge base.
-
-            Use /api/v1/knowledge/bases/<uuid> instead.
-            """
-            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')
-
-                response = self.success(
-                    data={
-                        'base': external_kb,
-                        '_deprecation_warning': DEPRECATION_WARNING,
-                    }
-                )
-                return self._add_deprecation_header(response)
-
-            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)
-                response = self.success({'_deprecation_warning': DEPRECATION_WARNING})
-                return self._add_deprecation_header(response)
-
-            elif quart.request.method == 'DELETE':
-                await self.ap.external_kb_service.delete_external_knowledge_base(kb_uuid)
-                response = self.success({'_deprecation_warning': DEPRECATION_WARNING})
-                return self._add_deprecation_header(response)
-
-        @self.route(
-            '/<kb_uuid>/retrieve',
-            methods=['POST'],
-        )
-        async def retrieve_external_knowledge_base(kb_uuid: str) -> str:
-            """
-            DEPRECATED: Retrieve from external knowledge base.
-
-            Use POST /api/v1/knowledge/bases/<uuid>/retrieve instead.
-            """
-            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)
-            response = self.success(data={
-                'results': results,
-                '_deprecation_warning': DEPRECATION_WARNING,
-            })
-            return self._add_deprecation_header(response)
diff --git a/src/langbot/pkg/api/http/service/external_kb.py b/src/langbot/pkg/api/http/service/external_kb.py
deleted file mode 100644
index 4ac5d0fcc..000000000
--- a/src/langbot/pkg/api/http/service/external_kb.py
+++ /dev/null
@@ -1,80 +0,0 @@
-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)
diff --git a/src/langbot/pkg/core/app.py b/src/langbot/pkg/core/app.py
index c23d114fb..e7b0ea97c 100644
--- a/src/langbot/pkg/core/app.py
+++ b/src/langbot/pkg/core/app.py
@@ -28,7 +28,6 @@
 from ..api.http.service import mcp as mcp_service
 from ..api.http.service import apikey as apikey_service
 from ..api.http.service import webhook as webhook_service
-from ..api.http.service import external_kb as external_kb_service
 from ..discover import engine as discover_engine
 from ..storage import mgr as storagemgr
 from ..utils import logcache
@@ -133,8 +132,6 @@ 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
diff --git a/src/langbot/pkg/core/stages/build_app.py b/src/langbot/pkg/core/stages/build_app.py
index 791b5a9ee..17e258053 100644
--- a/src/langbot/pkg/core/stages/build_app.py
+++ b/src/langbot/pkg/core/stages/build_app.py
@@ -25,7 +25,6 @@
 from ...api.http.service import mcp as mcp_service
 from ...api.http.service import apikey as apikey_service
 from ...api.http.service import webhook as webhook_service
-from ...api.http.service import external_kb as external_kb_service
 from ...discover import engine as discover_engine
 from ...storage import mgr as storagemgr
 from ...utils import logcache
@@ -70,9 +69,6 @@ async def run(self, ap: app.Application):
         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
 
diff --git a/src/langbot/pkg/entity/persistence/rag.py b/src/langbot/pkg/entity/persistence/rag.py
index 60096e6c4..f7c41ba38 100644
--- a/src/langbot/pkg/entity/persistence/rag.py
+++ b/src/langbot/pkg/entity/persistence/rag.py
@@ -33,15 +33,3 @@ 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)
-    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())
diff --git a/src/langbot/pkg/rag/knowledge/external.py b/src/langbot/pkg/rag/knowledge/external.py
deleted file mode 100644
index f1a5fed3b..000000000
--- a/src/langbot/pkg/rag/knowledge/external.py
+++ /dev/null
@@ -1,85 +0,0 @@
-"""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}')

From 60135b4e5e22c23a380536c138eb7ef3fdc03adb Mon Sep 17 00:00:00 2001
From: youhuanghe <1051233107@qq.com>
Date: Mon, 26 Jan 2026 14:38:19 +0000
Subject: [PATCH 07/25] refactor(plugin): remove list_knowledge_retrievers from
 connector
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Remove deprecated list_knowledge_retrievers functionality from the
plugin communication layer. This aligns with the SDK change that
removed the LIST_KNOWLEDGE_RETRIEVERS action.

Changes:
- connector.py: Remove list_knowledge_retrievers() method
- handler.py: Remove list_knowledge_retrievers() handler

The functionality is replaced by the new /api/v1/knowledge/engines
endpoint which lists available RAGEngine components with their
capabilities and configuration schemas.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
---
 src/langbot/pkg/plugin/connector.py | 41 ++++-------------------------
 src/langbot/pkg/plugin/handler.py   | 12 ---------
 2 files changed, 5 insertions(+), 48 deletions(-)

diff --git a/src/langbot/pkg/plugin/connector.py b/src/langbot/pkg/plugin/connector.py
index e176fa720..079cea758 100644
--- a/src/langbot/pkg/plugin/connector.py
+++ b/src/langbot/pkg/plugin/connector.py
@@ -463,15 +463,6 @@ async def execute_command(
 
             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,
@@ -480,7 +471,7 @@ async def retrieve_knowledge(
         instance_id: str,
         retrieval_context: dict[str, Any],
     ) -> list[dict[str, Any]]:
-        """Retrieve knowledge using a KnowledgeRetriever instance."""
+        """Retrieve knowledge using a RAGEngine instance."""
         if not self.is_enable_plugin:
             return []
 
@@ -503,40 +494,18 @@ def dispose(self):
     async def sync_polymorphic_component_instances(self) -> dict[str, Any]:
         """Sync polymorphic component instances with runtime.
 
-        This collects all external knowledge bases from database and sends to runtime
-        to ensure instance integrity across restarts.
+        Currently no polymorphic components need syncing.
+        This method is kept for future extensibility.
         """
         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
+        # No polymorphic components to sync
         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'],
-                }
-            )
-
-        self.ap.logger.info(f'Syncing {len(required_instances)} polymorphic component instances to runtime')
 
-        # Send to runtime
+        # Send to runtime (empty list)
         sync_result = await self.handler.sync_polymorphic_component_instances(required_instances)
 
-        self.ap.logger.info(
-            f'Sync complete: {len(sync_result.get("success_instances", []))} succeeded, '
-            f'{len(sync_result.get("failed_instances", []))} failed'
-        )
-
         return sync_result
 
     # ================= RAG Capability Callers =================
diff --git a/src/langbot/pkg/plugin/handler.py b/src/langbot/pkg/plugin/handler.py
index dcc9ac10b..8671b21da 100644
--- a/src/langbot/pkg/plugin/handler.py
+++ b/src/langbot/pkg/plugin/handler.py
@@ -857,18 +857,6 @@ async def execute_command(
         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,

From a934a61aaea876d03d4c2003d54cf5119768d7e0 Mon Sep 17 00:00:00 2001
From: youhuanghe <1051233107@qq.com>
Date: Mon, 26 Jan 2026 14:39:17 +0000
Subject: [PATCH 08/25] refactor(service): update knowledge service with
 capability-based checks
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Replace type-based checks with capability-based checks for file
operations, aligning with the unified knowledge base architecture.

Changes to knowledge.py:
- store_file(): Replace get_type() check with doc_ingestion capability check
- delete_file(): Replace get_type() check with doc_ingestion capability check
- list_rag_engines(): Remove list_knowledge_retrievers call, simplify to
  only list RAGEngine components (KnowledgeRetriever type removed)

Changes to pipelines.py:
- Minor cleanup related to knowledge base references

The capability-based approach allows RAG engines to declare their
supported features (doc_ingestion, chunking_config, rerank, hybrid_search)
and the system responds accordingly, rather than hardcoding behavior
based on internal/external type distinction.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
---
 pyproject.toml                                |  2 +-
 .../controller/groups/pipelines/pipelines.py  |  2 +-
 src/langbot/pkg/api/http/service/knowledge.py | 57 ++++++-------------
 3 files changed, 19 insertions(+), 42 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index a5aee60cb..b91ec68af 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -64,7 +64,7 @@ dependencies = [
     "chromadb>=0.4.24",
     "qdrant-client (>=1.15.1,<2.0.0)",
     "pyseekdb>=0.1.0",
-    "langbot-plugin==0.2.4",
+    "langbot-plugin @ file:///home/yhh/workspace/langbot-plugin-sdk",
     "asyncpg>=0.30.0",
     "line-bot-sdk>=3.19.0",
     "tboxsdk>=0.0.10",
diff --git a/src/langbot/pkg/api/http/controller/groups/pipelines/pipelines.py b/src/langbot/pkg/api/http/controller/groups/pipelines/pipelines.py
index 1828fb2b9..4493d2e72 100644
--- a/src/langbot/pkg/api/http/controller/groups/pipelines/pipelines.py
+++ b/src/langbot/pkg/api/http/controller/groups/pipelines/pipelines.py
@@ -68,7 +68,7 @@ async def _(pipeline_uuid: str) -> str:
                     return self.http_status(404, -1, 'pipeline not found')
 
                 # Only include plugins with pipeline-related components (Command, EventListener, Tool)
-                # Plugins that only have KnowledgeRetriever components are not suitable for pipeline extensions
+                # Plugins that only have RAGEngine 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)
diff --git a/src/langbot/pkg/api/http/service/knowledge.py b/src/langbot/pkg/api/http/service/knowledge.py
index ca6bc7c09..d5228d3c9 100644
--- a/src/langbot/pkg/api/http/service/knowledge.py
+++ b/src/langbot/pkg/api/http/service/knowledge.py
@@ -131,14 +131,16 @@ async def update_knowledge_base(self, kb_uuid: str, kb_data: dict) -> None:
 
     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')
-        # Only internal KBs support file storage
-        if runtime_kb.get_type() != 'internal':
-            raise Exception('Only internal knowledge bases support file storage')
+
+        # Check if the RAG engine supports document ingestion
+        kb_info = await self.get_knowledge_base(kb_uuid)
+        capabilities = kb_info.get('rag_engine', {}).get('capabilities', [])
+        if 'doc_ingestion' not in capabilities:
+            raise Exception('This knowledge base does not support document upload')
+
         result = await runtime_kb.store_file(file_id)
 
         # Update the KB's updated_at timestamp
@@ -181,9 +183,13 @@ async def delete_file(self, kb_uuid: str, file_id: str) -> None:
         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')
-        # Only internal KBs support file deletion
-        if runtime_kb.get_type() != 'internal':
-            raise Exception('Only internal knowledge bases support file deletion')
+
+        # Check if the RAG engine supports document ingestion
+        kb_info = await self.get_knowledge_base(kb_uuid)
+        capabilities = kb_info.get('rag_engine', {}).get('capabilities', [])
+        if 'doc_ingestion' not in capabilities:
+            raise Exception('This knowledge base does not support document deletion')
+
         await runtime_kb.delete_file(file_id)
 
         # Update the KB's updated_at timestamp
@@ -218,45 +224,16 @@ async def delete_knowledge_base(self, kb_uuid: str) -> None:
     # ================= RAG Engine Discovery =================
 
     async def list_rag_engines(self) -> list[dict]:
-        """List all available knowledge base engines from plugins.
-
-        Returns both KnowledgeRetriever (external) and RAGEngine types.
-        """
+        """List all available RAG engines from plugins."""
         engines = []
 
         if not self.ap.plugin_connector.is_enable_plugin:
             return engines
 
-        # 1. Get KnowledgeRetriever plugins (external knowledge bases like dify)
-        try:
-            retrievers = await self.ap.plugin_connector.list_knowledge_retrievers()
-            for retriever in retrievers:
-                # Get config from manifest.manifest.spec.config
-                manifest_data = retriever.get('manifest', {})
-                raw_manifest = manifest_data.get('manifest', {})
-                spec = raw_manifest.get('spec', {})
-                config_items = spec.get('config', [])
-
-                engines.append({
-                    'plugin_id': f"{retriever['plugin_author']}/{retriever['plugin_name']}",
-                    'name': retriever.get('retriever_name', 'Unknown'),
-                    'description': retriever.get('retriever_description'),
-                    'type': 'retriever',  # External retriever type
-                    'capabilities': [],  # No doc_ingestion
-                    'creation_schema': config_items,  # Use config items as creation schema
-                    'retrieval_schema': None,
-                    # Keep original info for creating external KB
-                    'component_name': retriever.get('retriever_name', ''),
-                })
-        except Exception as e:
-            self.ap.logger.warning(f"Failed to list knowledge retrievers: {e}")
-
-        # 2. Get RAGEngine plugins (new type with doc_ingestion support)
+        # Get RAGEngine plugins
         try:
             rag_engines = await self.ap.plugin_connector.list_rag_engines()
-            for engine in rag_engines:
-                engine['type'] = 'rag_engine'  # Mark as RAG engine type
-                engines.append(engine)
+            engines.extend(rag_engines)
         except Exception as e:
             self.ap.logger.warning(f"Failed to list RAG engines from plugins: {e}")
 

From 45c15df5f63adb0c7e27c951121ff93840f6aafc Mon Sep 17 00:00:00 2001
From: youhuanghe <1051233107@qq.com>
Date: Mon, 26 Jan 2026 14:40:22 +0000
Subject: [PATCH 09/25] feat(web): unify knowledge base UI, remove external KB
 components
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

BREAKING CHANGE: The internal/external knowledge base distinction
has been removed from the frontend. All knowledge bases are now
displayed in a unified list, differentiated by their RAG engine.

Changes to page.tsx:
- Remove Tab component (内置/外置 tabs)
- Remove selectedKbType state
- Unified knowledge base list display
- Single "Create Knowledge Base" button for all types

Changes to KBDetailDialog.tsx:
- Remove kbType prop
- Simplify dialog logic for unified KB handling
- Documents menu item conditionally shown based on doc_ingestion capability

Changes to KBForm.tsx:
- Remove retriever type handling code
- Simplify form for unified KB creation
- Dynamic form rendering based on RAG engine's creation_schema

Changes to KBCardVO.ts:
- Remove 'type' field from KBCardVO interface

Changes to BackendClient.ts:
- Remove all external KB related methods:
  - getExternalKnowledgeBases()
  - getExternalKnowledgeBase()
  - createExternalKnowledgeBase()
  - updateExternalKnowledgeBase()
  - deleteExternalKnowledgeBase()
  - retrieveFromExternalKnowledgeBase()

Changes to api/index.ts:
- Remove ExternalKnowledgeBase interface definition

UI/UX improvements:
- Users no longer need to understand internal vs external distinction
- RAG engine selection is now the primary differentiator
- Documents panel visibility is capability-driven (doc_ingestion)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
---
 web/src/app/home/knowledge/KBDetailDialog.tsx |  55 ++----
 .../knowledge/components/kb-card/KBCardVO.ts  |   5 -
 .../knowledge/components/kb-form/KBForm.tsx   | 168 +++++-------------
 web/src/app/home/knowledge/page.tsx           |  52 +-----
 web/src/app/infra/entities/api/index.ts       |  22 ---
 web/src/app/infra/http/BackendClient.ts       |  44 -----
 6 files changed, 60 insertions(+), 286 deletions(-)

diff --git a/web/src/app/home/knowledge/KBDetailDialog.tsx b/web/src/app/home/knowledge/KBDetailDialog.tsx
index 00200fc91..d6c4ab2f9 100644
--- a/web/src/app/home/knowledge/KBDetailDialog.tsx
+++ b/web/src/app/home/knowledge/KBDetailDialog.tsx
@@ -30,7 +30,6 @@ interface KBDetailDialogProps {
   open: boolean;
   onOpenChange: (open: boolean) => void;
   kbId?: string;
-  kbType?: 'retriever' | 'rag_engine';
   onFormCancel: () => void;
   onKbDeleted: () => void;
   onNewKbCreated: (kbId: string) => void;
@@ -41,7 +40,6 @@ export default function KBDetailDialog({
   open,
   onOpenChange,
   kbId: propKbId,
-  kbType: propKbType,
   onFormCancel,
   onKbDeleted,
   onNewKbCreated,
@@ -49,44 +47,24 @@ export default function KBDetailDialog({
 }: KBDetailDialogProps) {
   const { t } = useTranslation();
   const [kbId, setKbId] = useState<string | undefined>(propKbId);
-  const [kbType, setKbType] = useState<'retriever' | 'rag_engine' | undefined>(propKbType);
   const [activeMenu, setActiveMenu] = useState('metadata');
   const [showDeleteConfirm, setShowDeleteConfirm] = useState(false);
   const [kbInfo, setKbInfo] = useState<KnowledgeBase | null>(null);
 
   useEffect(() => {
     setKbId(propKbId);
-    setKbType(propKbType);
     setActiveMenu('metadata');
-    if (propKbId && propKbType) {
-      loadKbInfo(propKbId, propKbType);
+    if (propKbId) {
+      loadKbInfo(propKbId);
     } else {
       setKbInfo(null);
     }
-  }, [propKbId, propKbType, open]);
+  }, [propKbId, open]);
 
-  async function loadKbInfo(id: string, type: 'retriever' | 'rag_engine') {
+  async function loadKbInfo(id: string) {
     try {
-      if (type === 'retriever') {
-        // For retriever type, we create a compatible structure
-        const resp = await httpClient.getExternalKnowledgeBase(id);
-        const extKb = resp.base;
-        setKbInfo({
-          uuid: extKb.uuid,
-          name: extKb.name,
-          description: extKb.description,
-          embedding_model_uuid: '',
-          top_k: 5,
-          rag_engine: {
-            plugin_id: `${extKb.plugin_author}/${extKb.plugin_name}`,
-            name: extKb.retriever_name,
-            capabilities: [],
-          },
-        });
-      } else {
-        const resp = await httpClient.getKnowledgeBase(id);
-        setKbInfo(resp.base);
-      }
+      const resp = await httpClient.getKnowledgeBase(id);
+      setKbInfo(resp.base);
     } catch (e) {
       console.error('Failed to load KB info:', e);
     }
@@ -95,7 +73,7 @@ export default function KBDetailDialog({
   // Check if this KB supports document management
   const hasDocumentCapability = (): boolean => {
     if (!kbInfo || !kbInfo.rag_engine) {
-      return false; // No engine info, cannot determine capability
+      return false;
     }
     return kbInfo.rag_engine.capabilities.includes('doc_ingestion');
   };
@@ -149,23 +127,14 @@ export default function KBDetailDialog({
   ];
 
   const confirmDelete = () => {
-    if (kbType === 'retriever') {
-      httpClient.deleteExternalKnowledgeBase(kbId ?? '').then(() => {
-        onKbDeleted();
-      });
-    } else {
-      httpClient.deleteKnowledgeBase(kbId ?? '').then(() => {
-        onKbDeleted();
-      });
-    }
+    httpClient.deleteKnowledgeBase(kbId ?? '').then(() => {
+      onKbDeleted();
+    });
     setShowDeleteConfirm(false);
   };
 
-  // Unified retrieve function
+  // Retrieve function
   const retrieveFunction = async (id: string, query: string) => {
-    if (kbType === 'retriever') {
-      return await httpClient.retrieveExternalKnowledgeBase(id, query);
-    }
     return await httpClient.retrieveKnowledgeBase(id, query);
   };
 
@@ -181,7 +150,6 @@ export default function KBDetailDialog({
             <div className="flex-1 overflow-y-auto px-6 pb-6">
               <KBForm
                 initKbId={undefined}
-                initKbType={undefined}
                 onNewKbCreated={onNewKbCreated}
                 onKbUpdated={onKbUpdated}
               />
@@ -248,7 +216,6 @@ export default function KBDetailDialog({
                 {activeMenu === 'metadata' && (
                   <KBForm
                     initKbId={kbId}
-                    initKbType={kbType}
                     onNewKbCreated={onNewKbCreated}
                     onKbUpdated={onKbUpdated}
                   />
diff --git a/web/src/app/home/knowledge/components/kb-card/KBCardVO.ts b/web/src/app/home/knowledge/components/kb-card/KBCardVO.ts
index 7cf296944..88e9b7b31 100644
--- a/web/src/app/home/knowledge/components/kb-card/KBCardVO.ts
+++ b/web/src/app/home/knowledge/components/kb-card/KBCardVO.ts
@@ -7,11 +7,8 @@ export interface IKnowledgeBaseVO {
   embeddingModelUUID: string;
   top_k: number;
   lastUpdatedTimeAgo: string;
-  // New unified fields
   ragEngine?: RAGEngineInfo;
   ragEnginePluginId?: string;
-  // Type of knowledge base: 'retriever' for external KB, 'rag_engine' for new plugin-based KB
-  type: 'retriever' | 'rag_engine';
 }
 
 export class KnowledgeBaseVO implements IKnowledgeBaseVO {
@@ -23,7 +20,6 @@ export class KnowledgeBaseVO implements IKnowledgeBaseVO {
   lastUpdatedTimeAgo: string;
   ragEngine?: RAGEngineInfo;
   ragEnginePluginId?: string;
-  type: 'retriever' | 'rag_engine';
 
   constructor(props: IKnowledgeBaseVO) {
     this.id = props.id;
@@ -34,7 +30,6 @@ export class KnowledgeBaseVO implements IKnowledgeBaseVO {
     this.lastUpdatedTimeAgo = props.lastUpdatedTimeAgo;
     this.ragEngine = props.ragEngine;
     this.ragEnginePluginId = props.ragEnginePluginId;
-    this.type = props.type;
   }
 
   /**
diff --git a/web/src/app/home/knowledge/components/kb-form/KBForm.tsx b/web/src/app/home/knowledge/components/kb-form/KBForm.tsx
index f086e2c4c..8811db566 100644
--- a/web/src/app/home/knowledge/components/kb-form/KBForm.tsx
+++ b/web/src/app/home/knowledge/components/kb-form/KBForm.tsx
@@ -21,7 +21,7 @@ import {
   SelectTrigger,
   SelectValue,
 } from '@/components/ui/select';
-import { KnowledgeBase, ExternalKnowledgeBase, RAGEngine } from '@/app/infra/entities/api';
+import { KnowledgeBase, RAGEngine } from '@/app/infra/entities/api';
 import { toast } from 'sonner';
 import { extractI18nObject } from '@/i18n/I18nProvider';
 import DynamicFormComponent from '@/app/home/components/dynamic-form/DynamicFormComponent';
@@ -43,19 +43,16 @@ const getFormSchema = (t: (key: string) => string) =>
 
 export default function KBForm({
   initKbId,
-  initKbType,
   onNewKbCreated,
   onKbUpdated,
 }: {
   initKbId?: string;
-  initKbType?: 'retriever' | 'rag_engine';
   onNewKbCreated: (kbId: string) => void;
   onKbUpdated: (kbId: string) => void;
 }) {
   const { t } = useTranslation();
   const [ragEngines, setRagEngines] = useState<RAGEngine[]>([]);
   const [selectedEngineId, setSelectedEngineId] = useState<string>('');
-  const [selectedEngineType, setSelectedEngineType] = useState<'retriever' | 'rag_engine' | ''>('');
   const [configSettings, setConfigSettings] = useState<
     Record<string, unknown>
   >({});
@@ -80,8 +77,8 @@ export default function KBForm({
 
   useEffect(() => {
     loadRagEngines().then(() => {
-      if (initKbId && initKbType) {
-        loadKbConfig(initKbId, initKbType);
+      if (initKbId) {
+        loadKbConfig(initKbId);
       }
     });
   }, []);
@@ -91,7 +88,6 @@ export default function KBForm({
     if (ragEngines.length > 0 && !selectedEngineId && !isEditing) {
       const firstEngine = ragEngines[0];
       setSelectedEngineId(firstEngine.plugin_id);
-      setSelectedEngineType(firstEngine.type);
       form.setValue('ragEngineId', firstEngine.plugin_id);
       // Initialize config settings with defaults
       if (firstEngine.creation_schema) {
@@ -114,39 +110,21 @@ export default function KBForm({
     }
   };
 
-  const loadKbConfig = async (kbId: string, kbType: 'retriever' | 'rag_engine') => {
+  const loadKbConfig = async (kbId: string) => {
     try {
       setIsEditing(true);
 
-      if (kbType === 'retriever') {
-        // Load external KB
-        const res = await httpClient.getExternalKnowledgeBase(kbId);
-        const kb = res.base;
+      const res = await httpClient.getKnowledgeBase(kbId);
+      const kb = res.base;
 
-        const engineId = `${kb.plugin_author}/${kb.plugin_name}`;
-        setSelectedEngineId(engineId);
-        setSelectedEngineType('retriever');
+      const engineId = kb.rag_engine_plugin_id || '';
+      setSelectedEngineId(engineId);
 
-        form.setValue('name', kb.name);
-        form.setValue('description', kb.description);
-        form.setValue('ragEngineId', engineId);
+      form.setValue('name', kb.name);
+      form.setValue('description', kb.description);
+      form.setValue('ragEngineId', engineId);
 
-        setConfigSettings(kb.retriever_config || {});
-      } else {
-        // Load internal KB (rag_engine type)
-        const res = await httpClient.getKnowledgeBase(kbId);
-        const kb = res.base;
-
-        const engineId = kb.rag_engine_plugin_id || '';
-        setSelectedEngineId(engineId);
-        setSelectedEngineType('rag_engine');
-
-        form.setValue('name', kb.name);
-        form.setValue('description', kb.description);
-        form.setValue('ragEngineId', engineId);
-
-        setConfigSettings(kb.creation_settings || {});
-      }
+      setConfigSettings(kb.creation_settings || {});
     } catch (err) {
       console.error('Failed to load KB config:', err);
     }
@@ -156,11 +134,9 @@ export default function KBForm({
     setSelectedEngineId(engineId);
     form.setValue('ragEngineId', engineId);
 
-    // Find engine and set type
+    // Find engine and initialize config settings with defaults from schema
     const engine = ragEngines.find((e) => e.plugin_id === engineId);
     if (engine) {
-      setSelectedEngineType(engine.type);
-      // Initialize config settings with defaults from schema
       if (engine.creation_schema) {
         setConfigSettings(getDefaultValuesFromSchema(engine.creation_schema));
       } else {
@@ -170,97 +146,45 @@ export default function KBForm({
   };
 
   const onSubmit = (data: z.infer<typeof formSchema>) => {
-    const engine = ragEngines.find((e) => e.plugin_id === selectedEngineId);
-    if (!engine) return;
-
-    if (engine.type === 'retriever') {
-      // Parse plugin_id to get plugin_author and plugin_name
-      const [plugin_author, plugin_name] = selectedEngineId.split('/');
+    const kbData: KnowledgeBase = {
+      name: data.name,
+      description: data.description,
+      rag_engine_plugin_id: selectedEngineId,
+      creation_settings: configSettings,
+      embedding_model_uuid: '',
+      top_k: 5,
+    };
 
-      const externalKbData: ExternalKnowledgeBase = {
-        name: data.name,
-        description: data.description,
-        plugin_author,
-        plugin_name,
-        retriever_name: engine.component_name || '',
-        retriever_config: configSettings as Record<string, unknown>,
-      };
-
-      if (initKbId && initKbType === 'retriever') {
-        // Update external knowledge base
-        httpClient
-          .updateExternalKnowledgeBase(initKbId, externalKbData)
-          .then(() => {
-            onKbUpdated(initKbId);
-            toast.success(t('knowledge.updateKnowledgeBaseSuccess'));
-          })
-          .catch((err) => {
-            console.error('update external knowledge base failed', err);
-            toast.error(t('knowledge.updateKnowledgeBaseFailed'));
-          });
-      } else {
-        // Create external knowledge base
-        httpClient
-          .createExternalKnowledgeBase(externalKbData)
-          .then((res) => {
-            onNewKbCreated(res.uuid);
-          })
-          .catch((err) => {
-            console.error('create external knowledge base failed', err);
-            toast.error(t('knowledge.createKnowledgeBaseFailed'));
-          });
-      }
+    if (initKbId) {
+      // Update knowledge base
+      httpClient
+        .updateKnowledgeBase(initKbId, kbData)
+        .then((res) => {
+          onKbUpdated(res.uuid);
+          toast.success(t('knowledge.updateKnowledgeBaseSuccess'));
+        })
+        .catch((err) => {
+          console.error('update knowledge base failed', err);
+          toast.error(t('knowledge.updateKnowledgeBaseFailed'));
+        });
     } else {
-      // rag_engine type
-      const kbData: KnowledgeBase = {
-        name: data.name,
-        description: data.description,
-        rag_engine_plugin_id: selectedEngineId,
-        creation_settings: configSettings,
-        embedding_model_uuid: '',
-        top_k: 5,
-      };
-
-      if (initKbId && initKbType === 'rag_engine') {
-        // Update knowledge base
-        httpClient
-          .updateKnowledgeBase(initKbId, kbData)
-          .then((res) => {
-            onKbUpdated(res.uuid);
-            toast.success(t('knowledge.updateKnowledgeBaseSuccess'));
-          })
-          .catch((err) => {
-            console.error('update knowledge base failed', err);
-            toast.error(t('knowledge.updateKnowledgeBaseFailed'));
-          });
-      } else {
-        // Create knowledge base
-        httpClient
-          .createKnowledgeBase(kbData)
-          .then((res) => {
-            onNewKbCreated(res.uuid);
-          })
-          .catch((err) => {
-            console.error('create knowledge base failed', err);
-            toast.error(t('knowledge.createKnowledgeBaseFailed'));
-          });
-      }
+      // Create knowledge base
+      httpClient
+        .createKnowledgeBase(kbData)
+        .then((res) => {
+          onNewKbCreated(res.uuid);
+        })
+        .catch((err) => {
+          console.error('create knowledge base failed', err);
+          toast.error(t('knowledge.createKnowledgeBaseFailed'));
+        });
     }
   };
 
   // Convert creation schema to dynamic form items
-  // For retriever type, creation_schema is already config items format
-  // For rag_engine type, creation_schema is JSON Schema format
   const configFormItems = (() => {
     if (!selectedEngine?.creation_schema) return [];
-
-    if (selectedEngine.type === 'retriever') {
-      // Retriever type: creation_schema is already config items array
-      return selectedEngine.creation_schema as unknown as IDynamicFormItemSchema[];
-    } else {
-      // RAG engine type: creation_schema is JSON Schema, needs conversion
-      return jsonSchemaToFormItems(selectedEngine.creation_schema);
-    }
+    return jsonSchemaToFormItems(selectedEngine.creation_schema);
   })();
 
   // Show loading state
@@ -384,9 +308,7 @@ export default function KBForm({
             {configFormItems.length > 0 && (
               <div className="space-y-4 pt-2 border-t">
                 <div className="text-sm font-medium text-muted-foreground">
-                  {selectedEngineType === 'retriever'
-                    ? t('knowledge.retrieverConfiguration')
-                    : t('knowledge.engineSettings')}
+                  {t('knowledge.engineSettings')}
                 </div>
                 <DynamicFormComponent
                   itemConfigList={configFormItems}
diff --git a/web/src/app/home/knowledge/page.tsx b/web/src/app/home/knowledge/page.tsx
index 846b80c35..3c1c0f91f 100644
--- a/web/src/app/home/knowledge/page.tsx
+++ b/web/src/app/home/knowledge/page.tsx
@@ -8,7 +8,7 @@ import { KnowledgeBaseVO } from '@/app/home/knowledge/components/kb-card/KBCardV
 import KBCard from '@/app/home/knowledge/components/kb-card/KBCard';
 import KBDetailDialog from '@/app/home/knowledge/KBDetailDialog';
 import { httpClient } from '@/app/infra/http/HttpClient';
-import { KnowledgeBase, ExternalKnowledgeBase } from '@/app/infra/entities/api';
+import { KnowledgeBase } from '@/app/infra/entities/api';
 
 export default function KnowledgePage() {
   const { t } = useTranslation();
@@ -16,7 +16,6 @@ export default function KnowledgePage() {
     [],
   );
   const [selectedKbId, setSelectedKbId] = useState<string>('');
-  const [selectedKbType, setSelectedKbType] = useState<'retriever' | 'rag_engine' | undefined>(undefined);
   const [detailDialogOpen, setDetailDialogOpen] = useState(false);
 
   useEffect(() => {
@@ -24,16 +23,11 @@ export default function KnowledgePage() {
   }, []);
 
   async function getKnowledgeBaseList() {
-    // Load both types of knowledge bases
-    const [ragEngineResp, retrieverResp] = await Promise.all([
-      httpClient.getKnowledgeBases(),
-      httpClient.getExternalKnowledgeBases(),
-    ]);
+    const resp = await httpClient.getKnowledgeBases();
 
     const currentTime = new Date();
 
-    // Convert rag_engine type KBs
-    const ragEngineKBs = ragEngineResp.bases.map((kb: KnowledgeBase) => {
+    const kbs = resp.bases.map((kb: KnowledgeBase) => {
       const lastUpdatedTimeAgo = Math.floor(
         (currentTime.getTime() -
           new Date(kb.updated_at ?? currentTime.getTime()).getTime()) /
@@ -57,56 +51,19 @@ export default function KnowledgePage() {
         lastUpdatedTimeAgo: lastUpdatedTimeAgoText,
         ragEngine: kb.rag_engine,
         ragEnginePluginId: kb.rag_engine_plugin_id,
-        type: 'rag_engine',
       });
     });
 
-    // Convert retriever type (external) KBs
-    const retrieverKBs = retrieverResp.bases.map((kb: ExternalKnowledgeBase) => {
-      const lastUpdatedTimeAgo = Math.floor(
-        (currentTime.getTime() -
-          new Date(kb.created_at ?? currentTime.getTime()).getTime()) /
-          1000 /
-          60 /
-          60 /
-          24,
-      );
-
-      const lastUpdatedTimeAgoText =
-        lastUpdatedTimeAgo > 0
-          ? ` ${lastUpdatedTimeAgo} ${t('knowledge.daysAgo')}`
-          : t('knowledge.today');
-
-      return new KnowledgeBaseVO({
-        id: kb.uuid || '',
-        name: kb.name,
-        description: kb.description,
-        embeddingModelUUID: '',
-        top_k: 5,
-        lastUpdatedTimeAgo: lastUpdatedTimeAgoText,
-        ragEngine: {
-          plugin_id: `${kb.plugin_author}/${kb.plugin_name}`,
-          name: kb.retriever_name,
-          capabilities: [],
-        },
-        ragEnginePluginId: `${kb.plugin_author}/${kb.plugin_name}`,
-        type: 'retriever',
-      });
-    });
-
-    // Combine both lists
-    setKnowledgeBaseList([...ragEngineKBs, ...retrieverKBs]);
+    setKnowledgeBaseList(kbs);
   }
 
   const handleKBCardClick = (kb: KnowledgeBaseVO) => {
     setSelectedKbId(kb.id);
-    setSelectedKbType(kb.type);
     setDetailDialogOpen(true);
   };
 
   const handleCreateKBClick = () => {
     setSelectedKbId('');
-    setSelectedKbType(undefined);
     setDetailDialogOpen(true);
   };
 
@@ -135,7 +92,6 @@ export default function KnowledgePage() {
         open={detailDialogOpen}
         onOpenChange={setDetailDialogOpen}
         kbId={selectedKbId || undefined}
-        kbType={selectedKbType}
         onFormCancel={handleFormCancel}
         onKbDeleted={handleKbDeleted}
         onNewKbCreated={handleNewKbCreated}
diff --git a/web/src/app/infra/entities/api/index.ts b/web/src/app/infra/entities/api/index.ts
index e5b30360b..591401fd6 100644
--- a/web/src/app/infra/entities/api/index.ts
+++ b/web/src/app/infra/entities/api/index.ts
@@ -185,12 +185,9 @@ export interface RAGEngine {
   plugin_id: string;
   name: string;
   description?: I18nObject;
-  type: 'retriever' | 'rag_engine'; // retriever = external KB, rag_engine = new RAG with doc support
   capabilities: string[];
   creation_schema?: JSONSchema;
   retrieval_schema?: JSONSchema;
-  // For retriever type (external KB)
-  component_name?: string;
 }
 
 export interface JSONSchema {
@@ -212,25 +209,6 @@ export interface ApiRespRAGEngines {
   engines: RAGEngine[];
 }
 
-export interface ExternalKnowledgeBase {
-  uuid?: string;
-  name: string;
-  description: string;
-  created_at?: string;
-  plugin_author: string;
-  plugin_name: string;
-  retriever_name: string;
-  retriever_config?: Record<string, unknown>;
-}
-
-export interface ApiRespExternalKnowledgeBases {
-  bases: ExternalKnowledgeBase[];
-}
-
-export interface ApiRespExternalKnowledgeBase {
-  base: ExternalKnowledgeBase;
-}
-
 export interface ApiRespKnowledgeBaseFiles {
   files: KnowledgeBaseFile[];
 }
diff --git a/web/src/app/infra/http/BackendClient.ts b/web/src/app/infra/http/BackendClient.ts
index ef28f02bd..3bbd6f3d9 100644
--- a/web/src/app/infra/http/BackendClient.ts
+++ b/web/src/app/infra/http/BackendClient.ts
@@ -35,9 +35,6 @@ import {
   ApiRespMCPServers,
   ApiRespMCPServer,
   MCPServer,
-  ExternalKnowledgeBase,
-  ApiRespExternalKnowledgeBases,
-  ApiRespExternalKnowledgeBase,
   ApiRespModelProviders,
   ApiRespModelProvider,
   ModelProvider,
@@ -413,47 +410,6 @@ export class BackendClient extends BaseHttpClient {
     return this.get('/api/v1/knowledge/engines');
   }
 
-  // ============ External Knowledge Base API ============
-  public getExternalKnowledgeBases(): Promise<ApiRespExternalKnowledgeBases> {
-    return this.get('/api/v1/knowledge/external-bases');
-  }
-
-  public getExternalKnowledgeBase(
-    uuid: string,
-  ): Promise<ApiRespExternalKnowledgeBase> {
-    return this.get(`/api/v1/knowledge/external-bases/${uuid}`);
-  }
-
-  public createExternalKnowledgeBase(
-    base: ExternalKnowledgeBase,
-  ): Promise<{ uuid: string }> {
-    return this.post('/api/v1/knowledge/external-bases', base);
-  }
-
-  public updateExternalKnowledgeBase(
-    uuid: string,
-    base: ExternalKnowledgeBase,
-  ): Promise<{ uuid: string }> {
-    return this.put(`/api/v1/knowledge/external-bases/${uuid}`, base);
-  }
-
-  public deleteExternalKnowledgeBase(uuid: string): Promise<object> {
-    return this.delete(`/api/v1/knowledge/external-bases/${uuid}`);
-  }
-
-  public retrieveExternalKnowledgeBase(
-    uuid: string,
-    query: string,
-  ): Promise<ApiRespKnowledgeBaseRetrieve> {
-    return this.post(`/api/v1/knowledge/external-bases/${uuid}/retrieve`, {
-      query,
-    });
-  }
-
-  public listKnowledgeRetrievers(): Promise<{ retrievers: unknown[] }> {
-    return this.get('/api/v1/knowledge/external-bases/retrievers');
-  }
-
   // ============ Plugins API ============
   public getPlugins(): Promise<ApiRespPlugins> {
     return this.get('/api/v1/plugins');

From f24d360b97d3933968057c382d41649591696125 Mon Sep 17 00:00:00 2001
From: youhuanghe <1051233107@qq.com>
Date: Tue, 27 Jan 2026 04:47:56 +0000
Subject: [PATCH 10/25] refactor(plugin): code review improvements for RAG
 handlers
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

- Unify embed_model field naming to embedding_model_uuid only
- Add structured error responses with error_type for RAG actions
- Fix file_size and mime_type detection in _store_file_task
- Improve error handling with detailed error context (error_type, original_error)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
---
 src/langbot/pkg/plugin/handler.py      | 88 ++++++++++++++++----------
 src/langbot/pkg/rag/knowledge/kbmgr.py | 14 +++-
 2 files changed, 66 insertions(+), 36 deletions(-)

diff --git a/src/langbot/pkg/plugin/handler.py b/src/langbot/pkg/plugin/handler.py
index 8671b21da..12ac950b5 100644
--- a/src/langbot/pkg/plugin/handler.py
+++ b/src/langbot/pkg/plugin/handler.py
@@ -477,32 +477,31 @@ async def rag_embed_documents(data: dict[str, Any]) -> handler.ActionResponse:
             try:
                 # 1. Get KB config to find embedding model
                 kb = await _get_kb_entity(kb_id)
-                embed_model_name = kb.embed_model or kb.embedding_model_uuid # Handle both cases if schema changed
-                
+                embed_model_uuid = kb.embedding_model_uuid
+
                 # 2. Get embedder instance
-                # Assuming model_mgr can get embedder model. 
-                # Note: Previous kbmgr used self.ap.model_mgr.get_embedding_model_by_uuid
-                # embed_model_name usually stores UUID in current system based on kbmgr code
-                
-                if not embed_model_name:
-                     # Fallback or error
-                     raise ValueError("Embedding model not configured for this Knowledge Base")
-
-                # If embed_model_name looks like a UUID
-                embedder_model = await self.ap.model_mgr.get_embedding_model_by_uuid(embed_model_name)
-                
+                if not embed_model_uuid:
+                     return handler.ActionResponse.error(
+                         message="Embedding model not configured for this Knowledge Base",
+                         data={"error_type": "EmbeddingError", "kb_id": kb_id}
+                     )
+
+                embedder_model = await self.ap.model_mgr.get_embedding_model_by_uuid(embed_model_uuid)
+
                 if not embedder_model:
-                     # Try getting by name if needed, but uuid is standard
-                     raise ValueError(f"Embedding model {embed_model_name} not found")
+                     return handler.ActionResponse.error(
+                         message=f"Embedding model {embed_model_uuid} not found",
+                         data={"error_type": "EmbeddingError", "model_uuid": embed_model_uuid}
+                     )
 
                 # 3. Generate embeddings
-                # We need an Embedder wrapper or use model provider directly.
-                # In kbmgr.py: self.embedder.embed_and_store uses embedding_model.provider.embed_documents
-                
                 vectors = await embedder_model.provider.embed_documents(texts)
                 return handler.ActionResponse.success(data={'vectors': vectors})
             except Exception as e:
-                return handler.ActionResponse.error(message=str(e))
+                return handler.ActionResponse.error(
+                    message=str(e),
+                    data={"error_type": "EmbeddingError", "original_error": type(e).__name__}
+                )
 
         @self.action(PluginToRuntimeAction.RAG_EMBED_QUERY)
         async def rag_embed_query(data: dict[str, Any]) -> handler.ActionResponse:
@@ -510,19 +509,28 @@ async def rag_embed_query(data: dict[str, Any]) -> handler.ActionResponse:
             text = data['text']
             try:
                 kb = await _get_kb_entity(kb_id)
-                embed_model_name = kb.embed_model or kb.embedding_model_uuid
-                
-                if not embed_model_name:
-                     raise ValueError("Embedding model not configured")
+                embed_model_uuid = kb.embedding_model_uuid
 
-                embedder_model = await self.ap.model_mgr.get_embedding_model_by_uuid(embed_model_name)
+                if not embed_model_uuid:
+                     return handler.ActionResponse.error(
+                         message="Embedding model not configured",
+                         data={"error_type": "EmbeddingError", "kb_id": kb_id}
+                     )
+
+                embedder_model = await self.ap.model_mgr.get_embedding_model_by_uuid(embed_model_uuid)
                 if not embedder_model:
-                     raise ValueError(f"Embedding model {embed_model_name} not found")
+                     return handler.ActionResponse.error(
+                         message=f"Embedding model {embed_model_uuid} not found",
+                         data={"error_type": "EmbeddingError", "model_uuid": embed_model_uuid}
+                     )
 
                 vector = await embedder_model.provider.embed_query(text)
                 return handler.ActionResponse.success(data={'vector': vector})
             except Exception as e:
-                return handler.ActionResponse.error(message=str(e))
+                return handler.ActionResponse.error(
+                    message=str(e),
+                    data={"error_type": "EmbeddingError", "original_error": type(e).__name__}
+                )
 
         @self.action(PluginToRuntimeAction.RAG_VECTOR_UPSERT)
         async def rag_vector_upsert(data: dict[str, Any]) -> handler.ActionResponse:
@@ -540,7 +548,10 @@ async def rag_vector_upsert(data: dict[str, Any]) -> handler.ActionResponse:
                 )
                 return handler.ActionResponse.success(data={})
             except Exception as e:
-                return handler.ActionResponse.error(message=str(e))
+                return handler.ActionResponse.error(
+                    message=str(e),
+                    data={"error_type": "VectorStoreError", "collection_id": collection_id, "original_error": type(e).__name__}
+                )
 
         @self.action(PluginToRuntimeAction.RAG_VECTOR_SEARCH)
         async def rag_vector_search(data: dict[str, Any]) -> handler.ActionResponse:
@@ -557,7 +568,10 @@ async def rag_vector_search(data: dict[str, Any]) -> handler.ActionResponse:
                 )
                 return handler.ActionResponse.success(data={'results': results})
             except Exception as e:
-                return handler.ActionResponse.error(message=str(e))
+                return handler.ActionResponse.error(
+                    message=str(e),
+                    data={"error_type": "VectorStoreError", "collection_id": collection_id, "original_error": type(e).__name__}
+                )
 
         @self.action(PluginToRuntimeAction.RAG_VECTOR_DELETE)
         async def rag_vector_delete(data: dict[str, Any]) -> handler.ActionResponse:
@@ -568,13 +582,16 @@ async def rag_vector_delete(data: dict[str, Any]) -> handler.ActionResponse:
                 count = 0
                 if ids:
                     await self.ap.vector_db_mgr.delete(collection_name=collection_id, ids=ids)
-                    count = len(ids) 
+                    count = len(ids)
                 elif filters:
                      await self.ap.vector_db_mgr.delete_by_filter(collection_name=collection_id, filter=filters)
-                
+
                 return handler.ActionResponse.success(data={'count': count})
             except Exception as e:
-                return handler.ActionResponse.error(message=str(e))
+                return handler.ActionResponse.error(
+                    message=str(e),
+                    data={"error_type": "VectorStoreError", "collection_id": collection_id, "original_error": type(e).__name__}
+                )
 
         @self.action(PluginToRuntimeAction.RAG_GET_FILE_STREAM)
         async def rag_get_file_stream(data: dict[str, Any]) -> handler.ActionResponse:
@@ -582,7 +599,7 @@ async def rag_get_file_stream(data: dict[str, Any]) -> handler.ActionResponse:
             try:
                 provider = self.ap.storage_mgr.storage_provider
                 content_bytes = b""
-                
+
                 if isinstance(provider, LocalStorageProvider):
                     real_path = os.path.join(LOCAL_STORAGE_PATH, storage_path)
                     if os.path.exists(real_path):
@@ -592,11 +609,14 @@ async def rag_get_file_stream(data: dict[str, Any]) -> handler.ActionResponse:
                         content_bytes = await self.ap.storage_mgr.load(storage_path)
                 else:
                     content_bytes = await self.ap.storage_mgr.load(storage_path)
-                    
+
                 content_base64 = base64.b64encode(content_bytes).decode('utf-8')
                 return handler.ActionResponse.success(data={'content_base64': content_base64})
             except Exception as e:
-                return handler.ActionResponse.error(message=str(e))
+                return handler.ActionResponse.error(
+                    message=str(e),
+                    data={"error_type": "FileServiceError", "storage_path": storage_path, "original_error": type(e).__name__}
+                )
 
     async def ping(self) -> dict[str, Any]:
         """Ping the runtime"""
diff --git a/src/langbot/pkg/rag/knowledge/kbmgr.py b/src/langbot/pkg/rag/knowledge/kbmgr.py
index 236175ce3..f524bb8d7 100644
--- a/src/langbot/pkg/rag/knowledge/kbmgr.py
+++ b/src/langbot/pkg/rag/knowledge/kbmgr.py
@@ -1,5 +1,6 @@
 from __future__ import annotations
 import logging
+import mimetypes
 import traceback
 import uuid
 import zipfile
@@ -40,14 +41,23 @@ async def _store_file_task(self, file: persistence_rag.File, task_context: taskm
 
             task_context.set_current_action('Processing file')
 
+            # Get file size from storage
+            file_bytes = await self.ap.storage_mgr.storage_provider.load(file.file_name)
+            file_size = len(file_bytes) if file_bytes else 0
+
+            # Detect MIME type from extension
+            mime_type, _ = mimetypes.guess_type(file.file_name)
+            if mime_type is None:
+                mime_type = "application/octet-stream"
+
             # Call plugin to ingest document
             await self._ingest_document(
                 {
                     "document_id": file.uuid,
                     "filename": file.file_name,
                     "extension": file.extension,
-                    "file_size": 0,  # TODO: Get size
-                    "mime_type": "application/octet-stream"  # TODO: Detect type
+                    "file_size": file_size,
+                    "mime_type": mime_type
                 },
                 file.file_name  # storage path
             )

From caceb11c77f6e7584158194ed1ced55c0e83a1ad Mon Sep 17 00:00:00 2001
From: youhuanghe <1051233107@qq.com>
Date: Wed, 4 Feb 2026 15:06:05 +0000
Subject: [PATCH 11/25] refactor(rag): refactor KB dynamic form and vector
 manager

- Frontend: Refactor Knowledge Base form using DynamicForm components.
- Frontend: Remove obsolete jsonSchemaConverter utility.
- Backend: Update VectorManager and PluginHandler to support new RAG architecture.
- Chore: Update dependencies in pyproject.toml.
---
 pyproject.toml                                |   6 +-
 src/langbot/pkg/api/http/service/knowledge.py |   6 +-
 src/langbot/pkg/plugin/handler.py             |  80 +++++++-----
 .../pkg/provider/modelmgr/requester.py        |   9 ++
 src/langbot/pkg/rag/knowledge/kbmgr.py        |   3 +
 src/langbot/pkg/vector/mgr.py                 |  77 ++++++++++++
 uv.lock                                       |   4 +-
 .../dynamic-form/DynamicFormComponent.tsx     |   3 +
 .../dynamic-form/DynamicFormItemComponent.tsx |  47 +++++++
 .../knowledge/components/kb-card/KBCardVO.ts  |   4 +-
 .../knowledge/components/kb-form/KBForm.tsx   |  61 ++++++---
 web/src/app/infra/entities/api/index.ts       |  23 +---
 web/src/app/infra/entities/form/dynamic.ts    |   1 +
 .../app/infra/utils/jsonSchemaConverter.ts    | 116 ------------------
 web/src/i18n/locales/en-US.ts                 |   2 +
 web/src/i18n/locales/zh-Hans.ts               |   2 +
 16 files changed, 259 insertions(+), 185 deletions(-)
 delete mode 100644 web/src/app/infra/utils/jsonSchemaConverter.ts

diff --git a/pyproject.toml b/pyproject.toml
index f396b5a15..7fc8d82f8 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -64,7 +64,7 @@ dependencies = [
     "chromadb>=0.4.24",
     "qdrant-client (>=1.15.1,<2.0.0)",
     "pyseekdb==1.0.0b7",
-    "langbot-plugin @ file:///home/yhh/workspace/langbot-plugin-sdk",
+    "langbot-plugin",
     "asyncpg>=0.30.0",
     "line-bot-sdk>=3.19.0",
     "tboxsdk>=0.0.10",
@@ -215,3 +215,7 @@ skip-magic-trailing-comma = false
 # Like Black, automatically detect the appropriate line ending.
 line-ending = "auto"
 
+
+[tool.uv.sources]
+langbot-plugin = { path = "/home/yhh/workspace/langbot-plugin-sdk", editable = true }
+
diff --git a/src/langbot/pkg/api/http/service/knowledge.py b/src/langbot/pkg/api/http/service/knowledge.py
index d5228d3c9..aa2882dc4 100644
--- a/src/langbot/pkg/api/http/service/knowledge.py
+++ b/src/langbot/pkg/api/http/service/knowledge.py
@@ -112,7 +112,7 @@ async def create_knowledge_base(self, kb_data: dict) -> str:
     async def update_knowledge_base(self, kb_uuid: str, kb_data: dict) -> None:
         """更新知识库"""
         # Filter to only valid database fields
-        valid_fields = {'name', 'description', 'top_k', 'rag_engine_plugin_id', 'creation_settings'}
+        valid_fields = {'name', 'description', 'top_k', 'rag_engine_plugin_id', 'creation_settings', 'embedding_model_uuid'}
         filtered_data = {k: v for k, v in kb_data.items() if k in valid_fields}
 
         if not filtered_data:
@@ -235,7 +235,9 @@ async def list_rag_engines(self) -> list[dict]:
             rag_engines = await self.ap.plugin_connector.list_rag_engines()
             engines.extend(rag_engines)
         except Exception as e:
-            self.ap.logger.warning(f"Failed to list RAG engines from plugins: {e}")
+            self.ap.logger.warning(f"Failed to list RAG engines from plugins: {type(e)} {e}")
+            import traceback
+            traceback.print_exc()
 
         return engines
 
diff --git a/src/langbot/pkg/plugin/handler.py b/src/langbot/pkg/plugin/handler.py
index d17b603f1..e6893b298 100644
--- a/src/langbot/pkg/plugin/handler.py
+++ b/src/langbot/pkg/plugin/handler.py
@@ -42,6 +42,9 @@ def __init__(
     ):
         super().__init__(connection, disconnect_callback)
         self.ap = ap
+        import sys
+        sys.stderr.write(f"DEBUG_HANDLER: Initializing RuntimeConnectionHandler. Actions: {list(self.actions.keys())}\n")
+        sys.stderr.flush()
 
         @self.action(RuntimeToLangBotAction.INITIALIZE_PLUGIN_SETTINGS)
         async def initialize_plugin_settings(data: dict[str, Any]) -> handler.ActionResponse:
@@ -465,11 +468,23 @@ async def get_config_file(data: dict[str, Any]) -> handler.ActionResponse:
         async def _get_kb_entity(kb_id: str):
             stmt = sqlalchemy.select(persistence_rag.KnowledgeBase).where(persistence_rag.KnowledgeBase.uuid == kb_id)
             result = await self.ap.persistence_mgr.execute_async(stmt)
-            kb = result.scalar_one_or_none()
-            if not kb:
+            row = result.first()
+            if not row:
                  raise ValueError(f"Knowledge Base {kb_id} not found")
+            # Convert Row to KnowledgeBase entity
+            kb = persistence_rag.KnowledgeBase(**row._mapping)
             return kb
 
+        def _get_embedding_model_uuid(kb: persistence_rag.KnowledgeBase) -> str | None:
+            """Get embedding model UUID from creation_settings (preferred) or KB field (fallback)."""
+            # First check creation_settings (new approach - plugin defines via creation_schema)
+            if kb.creation_settings and isinstance(kb.creation_settings, dict):
+                embed_uuid = kb.creation_settings.get('embedding_model_uuid')
+                if embed_uuid:
+                    return embed_uuid
+            # Fallback to KB field (backward compatibility)
+            return kb.embedding_model_uuid
+
         @self.action(PluginToRuntimeAction.RAG_EMBED_DOCUMENTS)
         async def rag_embed_documents(data: dict[str, Any]) -> handler.ActionResponse:
             kb_id = data['kb_id']
@@ -477,30 +492,28 @@ async def rag_embed_documents(data: dict[str, Any]) -> handler.ActionResponse:
             try:
                 # 1. Get KB config to find embedding model
                 kb = await _get_kb_entity(kb_id)
-                embed_model_uuid = kb.embedding_model_uuid
+                embed_model_uuid = _get_embedding_model_uuid(kb)
 
                 # 2. Get embedder instance
                 if not embed_model_uuid:
                      return handler.ActionResponse.error(
-                         message="Embedding model not configured for this Knowledge Base",
-                         data={"error_type": "EmbeddingError", "kb_id": kb_id}
+                         message=f"Embedding model not configured for this Knowledge Base (kb_id: {kb_id})"
                      )
 
                 embedder_model = await self.ap.model_mgr.get_embedding_model_by_uuid(embed_model_uuid)
 
                 if not embedder_model:
                      return handler.ActionResponse.error(
-                         message=f"Embedding model {embed_model_uuid} not found",
-                         data={"error_type": "EmbeddingError", "model_uuid": embed_model_uuid}
+                         message=f"Embedding model {embed_model_uuid} not found"
                      )
 
                 # 3. Generate embeddings
-                vectors = await embedder_model.provider.embed_documents(texts)
+                vectors = await embedder_model.embed_documents(texts)
                 return handler.ActionResponse.success(data={'vectors': vectors})
             except Exception as e:
+                import json
                 return handler.ActionResponse.error(
-                    message=str(e),
-                    data={"error_type": "EmbeddingError", "original_error": type(e).__name__}
+                    message=f"{str(e)} (Original Error: {type(e).__name__}, Data: {json.dumps({'error_type': 'EmbeddingError', 'original_error': type(e).__name__})})"
                 )
 
         @self.action(PluginToRuntimeAction.RAG_EMBED_QUERY)
@@ -509,27 +522,25 @@ async def rag_embed_query(data: dict[str, Any]) -> handler.ActionResponse:
             text = data['text']
             try:
                 kb = await _get_kb_entity(kb_id)
-                embed_model_uuid = kb.embedding_model_uuid
+                embed_model_uuid = _get_embedding_model_uuid(kb)
 
                 if not embed_model_uuid:
                      return handler.ActionResponse.error(
-                         message="Embedding model not configured",
-                         data={"error_type": "EmbeddingError", "kb_id": kb_id}
+                         message=f"Embedding model not configured (kb_id: {kb_id})"
                      )
 
                 embedder_model = await self.ap.model_mgr.get_embedding_model_by_uuid(embed_model_uuid)
                 if not embedder_model:
                      return handler.ActionResponse.error(
-                         message=f"Embedding model {embed_model_uuid} not found",
-                         data={"error_type": "EmbeddingError", "model_uuid": embed_model_uuid}
+                         message=f"Embedding model {embed_model_uuid} not found"
                      )
 
-                vector = await embedder_model.provider.embed_query(text)
+                vector = await embedder_model.embed_query(text)
                 return handler.ActionResponse.success(data={'vector': vector})
             except Exception as e:
+                import json
                 return handler.ActionResponse.error(
-                    message=str(e),
-                    data={"error_type": "EmbeddingError", "original_error": type(e).__name__}
+                    message=f"{str(e)} (Original Error: {type(e).__name__}, Data: {json.dumps({'error_type': 'EmbeddingError', 'original_error': type(e).__name__})})"
                 )
 
         @self.action(PluginToRuntimeAction.RAG_VECTOR_UPSERT)
@@ -548,9 +559,9 @@ async def rag_vector_upsert(data: dict[str, Any]) -> handler.ActionResponse:
                 )
                 return handler.ActionResponse.success(data={})
             except Exception as e:
+                import json
                 return handler.ActionResponse.error(
-                    message=str(e),
-                    data={"error_type": "VectorStoreError", "collection_id": collection_id, "original_error": type(e).__name__}
+                    message=f"{str(e)} (Original Error: {type(e).__name__}, Data: {json.dumps({'error_type': 'VectorStoreError', 'collection_id': collection_id, 'original_error': type(e).__name__})})"
                 )
 
         @self.action(PluginToRuntimeAction.RAG_VECTOR_SEARCH)
@@ -568,9 +579,9 @@ async def rag_vector_search(data: dict[str, Any]) -> handler.ActionResponse:
                 )
                 return handler.ActionResponse.success(data={'results': results})
             except Exception as e:
+                import json
                 return handler.ActionResponse.error(
-                    message=str(e),
-                    data={"error_type": "VectorStoreError", "collection_id": collection_id, "original_error": type(e).__name__}
+                    message=f"{str(e)} (Original Error: {type(e).__name__}, Data: {json.dumps({'error_type': 'VectorStoreError', 'collection_id': collection_id, 'original_error': type(e).__name__})})"
                 )
 
         @self.action(PluginToRuntimeAction.RAG_VECTOR_DELETE)
@@ -588,9 +599,9 @@ async def rag_vector_delete(data: dict[str, Any]) -> handler.ActionResponse:
 
                 return handler.ActionResponse.success(data={'count': count})
             except Exception as e:
+                import json
                 return handler.ActionResponse.error(
-                    message=str(e),
-                    data={"error_type": "VectorStoreError", "collection_id": collection_id, "original_error": type(e).__name__}
+                    message=f"{str(e)} (Original Error: {type(e).__name__}, Data: {json.dumps({'error_type': 'VectorStoreError', 'collection_id': collection_id, 'original_error': type(e).__name__})})"
                 )
 
         @self.action(PluginToRuntimeAction.RAG_GET_FILE_STREAM)
@@ -613,11 +624,24 @@ async def rag_get_file_stream(data: dict[str, Any]) -> handler.ActionResponse:
                 content_base64 = base64.b64encode(content_bytes).decode('utf-8')
                 return handler.ActionResponse.success(data={'content_base64': content_base64})
             except Exception as e:
+                import json
                 return handler.ActionResponse.error(
-                    message=str(e),
-                    data={"error_type": "FileServiceError", "storage_path": storage_path, "original_error": type(e).__name__}
+                    message=f"{str(e)} (Original Error: {type(e).__name__}, Data: {json.dumps({'error_type': 'FileServiceError', 'storage_path': storage_path, 'original_error': type(e).__name__})})"
                 )
 
+        @self.action(CommonAction.PING)
+        async def ping(data: dict[str, Any]) -> handler.ActionResponse:
+            """Ping"""
+            return handler.ActionResponse.success(
+                data={
+                     "pong": "pong",
+                },
+            )
+
+        import sys
+        sys.stderr.write(f"DEBUG_HANDLER: RuntimeConnectionHandler initialized. Actions: {list(self.actions.keys())}\n")
+        sys.stderr.flush()
+
     async def ping(self) -> dict[str, Any]:
         """Ping the runtime"""
         return await self.call_action(
@@ -897,7 +921,7 @@ async def retrieve_knowledge(
             },
             timeout=30,
         )
-        return result['retrieval_results']
+        return result
 
     async def sync_polymorphic_component_instances(self, required_instances: list[dict[str, Any]]) -> dict[str, Any]:
         """Sync polymorphic component instances with runtime"""
@@ -999,6 +1023,6 @@ async def list_rag_engines(self) -> list[dict[str, Any]]:
         result = await self.call_action(
             LangBotToRuntimeAction.LIST_RAG_ENGINES,
             {},
-            timeout=10
+            timeout=60
         )
         return result.get("engines", [])
diff --git a/src/langbot/pkg/provider/modelmgr/requester.py b/src/langbot/pkg/provider/modelmgr/requester.py
index c281d8aec..3a55cef4b 100644
--- a/src/langbot/pkg/provider/modelmgr/requester.py
+++ b/src/langbot/pkg/provider/modelmgr/requester.py
@@ -283,6 +283,15 @@ def __init__(
         self.model_entity = model_entity
         self.provider = provider
 
+    async def embed_documents(self, texts: list[str]) -> list[list[float]]:
+        """Embed a list of documents"""
+        return await self.provider.invoke_embedding(self, texts)
+
+    async def embed_query(self, text: str) -> list[float]:
+        """Embed a query string"""
+        vectors = await self.provider.invoke_embedding(self, [text])
+        return vectors[0] if vectors else []
+
 
 class ProviderAPIRequester(metaclass=abc.ABCMeta):
     """Provider API请求器"""
diff --git a/src/langbot/pkg/rag/knowledge/kbmgr.py b/src/langbot/pkg/rag/knowledge/kbmgr.py
index f524bb8d7..fedcae5d6 100644
--- a/src/langbot/pkg/rag/knowledge/kbmgr.py
+++ b/src/langbot/pkg/rag/knowledge/kbmgr.py
@@ -262,6 +262,9 @@ async def _ingest_document(
 
         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,
diff --git a/src/langbot/pkg/vector/mgr.py b/src/langbot/pkg/vector/mgr.py
index f0cb742cf..d20d21ffa 100644
--- a/src/langbot/pkg/vector/mgr.py
+++ b/src/langbot/pkg/vector/mgr.py
@@ -65,3 +65,80 @@ async def initialize(self):
         else:
             self.vector_db = ChromaVectorDatabase(self.ap)
             self.ap.logger.warning('No vector database backend configured, defaulting to Chroma.')
+
+    async def upsert(
+        self,
+        collection_name: str,
+        vectors: list[list[float]],
+        ids: list[str],
+        metadata: list[dict] | None = None,
+    ):
+        """Proxy: Upsert vectors"""
+        await self.vector_db.get_or_create_collection(collection_name)
+        await self.vector_db.add_embeddings(
+            collection=collection_name,
+            ids=ids,
+            embeddings_list=vectors,
+            metadatas=metadata or [{} for _ in vectors],
+        )
+
+    async def search(
+        self,
+        collection_name: str,
+        query_vector: list[float],
+        limit: int,
+        filter: dict | None = None,
+    ) -> list[dict]:
+        """Proxy: Search vectors"""
+        import numpy as np
+        # Ensure collection exists before searching? Or let it fail/return empty?
+        # Usually implementations handle collection not found.
+        results = await self.vector_db.search(
+            collection=collection_name,
+            query_embedding=np.array(query_vector),
+            k=limit,
+            # Note: filters are not yet unified in VectorDatabase abstract method signature properly in all impls,
+            # but we pass what we can. The ABI above shows search(collection, query_embedding, k).
+            # We might need to handle filters if implementations support it.
+        )
+        
+        # Convert results to standard format
+        # Abstract return type is Dict[str, Any] which usually contains 'ids', 'distances', 'metadatas', 'documents'
+        # We want to return list of results
+        
+        # Assuming typical Chroma/Standard format:
+        # { 'ids': [['id1']], 'distances': [[0.1]], 'metadatas': [[{}]], 'documents': [['text']] }
+        # Note: VectorDatabase.search implementation details vary.
+        # Let's inspect ChromaVectorDatabase.search to match return format logic.
+        
+        # For now, let's implement a basic return structure assuming the Dict matches standard.
+        parsed_results = []
+        if not results or 'ids' not in results or not results['ids']:
+             return []
+             
+        # Flatten if list of lists (batch search)
+        r_ids = results['ids'][0] if isinstance(results['ids'], list) and results['ids'] and isinstance(results['ids'][0], list) else results['ids']
+        r_dists = results['distances'][0] if isinstance(results['distances'], list) and results['distances'] and isinstance(results['distances'][0], list) else results['distances']
+        r_metas = results['metadatas'][0] if isinstance(results['metadatas'], list) and results['metadatas'] and isinstance(results['metadatas'][0], list) else results['metadatas']
+        
+        for i, id_val in enumerate(r_ids):
+            parsed_results.append({
+                'id': id_val,
+                'score': r_dists[i] if r_dists is not None else 0.0,
+                'metadata': r_metas[i] if r_metas else {},
+            })
+            
+        return parsed_results
+
+    async def delete(self, collection_name: str, ids: list[str]):
+        """Proxy: Delete vectors by ID"""
+        for doc_id in ids:
+             await self.vector_db.delete_by_file_id(collection_name, doc_id)
+
+    async def delete_by_filter(self, collection_name: str, filter: dict):
+        """Proxy: Delete vectors by filter"""
+        # Base VectorDatabase doesn't support delete_by_filter explicitly in the interface shown (only delete_by_file_id and delete_collection)
+        # We might need to extend VectorDatabase or implemented logic here.
+        # For now, log warning or implement if VDB supports it.
+        # Given the error was 'AttributeError: ... has no attribute upsert', implementing aliases is step 1.
+        pass
diff --git a/uv.lock b/uv.lock
index a5e59131c..967008b8c 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1902,7 +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", directory = "../langbot-plugin-sdk" },
+    { name = "langbot-plugin", editable = "../langbot-plugin-sdk" },
     { name = "langchain", specifier = ">=0.2.0" },
     { name = "langchain-text-splitters", specifier = ">=0.0.1" },
     { name = "lark-oapi", specifier = ">=1.4.15" },
@@ -1959,7 +1959,7 @@ dev = [
 [[package]]
 name = "langbot-plugin"
 version = "0.2.4"
-source = { directory = "../langbot-plugin-sdk" }
+source = { editable = "../langbot-plugin-sdk" }
 dependencies = [
     { name = "aiofiles" },
     { name = "dotenv" },
diff --git a/web/src/app/home/components/dynamic-form/DynamicFormComponent.tsx b/web/src/app/home/components/dynamic-form/DynamicFormComponent.tsx
index f46d42b50..87700e317 100644
--- a/web/src/app/home/components/dynamic-form/DynamicFormComponent.tsx
+++ b/web/src/app/home/components/dynamic-form/DynamicFormComponent.tsx
@@ -55,6 +55,9 @@ export default function DynamicFormComponent({
           case 'llm-model-selector':
             fieldSchema = z.string();
             break;
+          case 'embedding-model-selector':
+            fieldSchema = z.string();
+            break;
           case 'knowledge-base-selector':
             fieldSchema = z.string();
             break;
diff --git a/web/src/app/home/components/dynamic-form/DynamicFormItemComponent.tsx b/web/src/app/home/components/dynamic-form/DynamicFormItemComponent.tsx
index 0f1b72df9..d94a7ace2 100644
--- a/web/src/app/home/components/dynamic-form/DynamicFormItemComponent.tsx
+++ b/web/src/app/home/components/dynamic-form/DynamicFormItemComponent.tsx
@@ -22,6 +22,7 @@ import {
   LLMModel,
   Bot,
   KnowledgeBase,
+  EmbeddingModel,
 } from '@/app/infra/entities/api';
 import { toast } from 'sonner';
 import { useTranslation } from 'react-i18next';
@@ -49,6 +50,7 @@ export default function DynamicFormItemComponent({
   onFileUploaded?: (fileKey: string) => void;
 }) {
   const [llmModels, setLlmModels] = useState<LLMModel[]>([]);
+  const [embeddingModels, setEmbeddingModels] = useState<EmbeddingModel[]>([]);
   const [knowledgeBases, setKnowledgeBases] = useState<KnowledgeBase[]>([]);
   const [bots, setBots] = useState<Bot[]>([]);
   const [uploading, setUploading] = useState<boolean>(false);
@@ -109,6 +111,19 @@ export default function DynamicFormItemComponent({
     }
   }, [config.type]);
 
+  useEffect(() => {
+    if (config.type === DynamicFormItemType.EMBEDDING_MODEL_SELECTOR) {
+      httpClient
+        .getProviderEmbeddingModels()
+        .then((resp) => {
+          setEmbeddingModels(resp.models);
+        })
+        .catch((err) => {
+          toast.error('Failed to get embedding model list: ' + err.msg);
+        });
+    }
+  }, [config.type]);
+
   useEffect(() => {
     if (
       config.type === DynamicFormItemType.KNOWLEDGE_BASE_SELECTOR ||
@@ -264,6 +279,38 @@ export default function DynamicFormItemComponent({
         </Select>
       );
 
+    case DynamicFormItemType.EMBEDDING_MODEL_SELECTOR:
+      // Group embedding models by provider
+      const groupedEmbeddingModels = embeddingModels.reduce(
+        (acc, model) => {
+          const providerName = model.provider?.name || 'Unknown';
+          if (!acc[providerName]) acc[providerName] = [];
+          acc[providerName].push(model);
+          return acc;
+        },
+        {} as Record<string, EmbeddingModel[]>,
+      );
+
+      return (
+        <Select value={field.value} onValueChange={field.onChange}>
+          <SelectTrigger className="bg-[#ffffff] dark:bg-[#2a2a2e]">
+            <SelectValue placeholder={t('knowledge.selectEmbeddingModel')} />
+          </SelectTrigger>
+          <SelectContent>
+            {Object.entries(groupedEmbeddingModels).map(([providerName, models]) => (
+              <SelectGroup key={providerName}>
+                <SelectLabel>{providerName}</SelectLabel>
+                {models.map((model) => (
+                  <SelectItem key={model.uuid} value={model.uuid}>
+                    {model.name}
+                  </SelectItem>
+                ))}
+              </SelectGroup>
+            ))}
+          </SelectContent>
+        </Select>
+      );
+
     case DynamicFormItemType.KNOWLEDGE_BASE_SELECTOR:
       // Group KBs by RAG engine name
       const kbsByEngine = knowledgeBases.reduce(
diff --git a/web/src/app/home/knowledge/components/kb-card/KBCardVO.ts b/web/src/app/home/knowledge/components/kb-card/KBCardVO.ts
index 81ae4b78c..9bb6b00df 100644
--- a/web/src/app/home/knowledge/components/kb-card/KBCardVO.ts
+++ b/web/src/app/home/knowledge/components/kb-card/KBCardVO.ts
@@ -4,7 +4,7 @@ export interface IKnowledgeBaseVO {
   id: string;
   name: string;
   description: string;
-  embeddingModelUUID: string;
+  embeddingModelUUID?: string;
   top_k: number;
   lastUpdatedTimeAgo: string;
   emoji?: string;
@@ -16,7 +16,7 @@ export class KnowledgeBaseVO implements IKnowledgeBaseVO {
   id: string;
   name: string;
   description: string;
-  embeddingModelUUID: string;
+  embeddingModelUUID?: string;
   top_k: number;
   lastUpdatedTimeAgo: string;
   emoji?: string;
diff --git a/web/src/app/home/knowledge/components/kb-form/KBForm.tsx b/web/src/app/home/knowledge/components/kb-form/KBForm.tsx
index 21179f5c6..a49e4736d 100644
--- a/web/src/app/home/knowledge/components/kb-form/KBForm.tsx
+++ b/web/src/app/home/knowledge/components/kb-form/KBForm.tsx
@@ -26,10 +26,13 @@ import { KnowledgeBase, RAGEngine } from '@/app/infra/entities/api';
 import { toast } from 'sonner';
 import { extractI18nObject } from '@/i18n/I18nProvider';
 import DynamicFormComponent from '@/app/home/components/dynamic-form/DynamicFormComponent';
+import { IDynamicFormItemSchema } from '@/app/infra/entities/form/dynamic';
 import {
-  jsonSchemaToFormItems,
-  getDefaultValuesFromSchema,
-} from '@/app/infra/utils/jsonSchemaConverter';
+  DynamicFormItemConfig,
+  getDefaultValues,
+  parseDynamicFormItemType,
+} from '@/app/home/components/dynamic-form/DynamicFormItemConfig';
+import { UUID } from 'uuidjs';
 
 const getFormSchema = (t: (key: string) => string) =>
   z.object({
@@ -43,6 +46,36 @@ const getFormSchema = (t: (key: string) => string) =>
       .min(1, { message: t('knowledge.ragEngineRequired') }),
   });
 
+/**
+ * Parse creation schema from RAG engine to IDynamicFormItemSchema[]
+ * Same pattern as ExternalKBForm uses for retriever config
+ */
+function parseCreationSchema(
+  // eslint-disable-next-line @typescript-eslint/no-explicit-any
+  schemaItems: any | any[] | undefined,
+): IDynamicFormItemSchema[] {
+  if (!schemaItems) return [];
+
+  // Handle wrapped schema (e.g. { schema: [...] }) which might be returned by the API
+  const items = Array.isArray(schemaItems) ? schemaItems : schemaItems.schema;
+
+  if (!items || !Array.isArray(items)) return [];
+
+  return items.map(
+    (item) =>
+      new DynamicFormItemConfig({
+        default: item.default,
+        id: UUID.generate(),
+        label: item.label,
+        description: item.description,
+        name: item.name,
+        required: item.required,
+        type: parseDynamicFormItemType(item.type),
+        options: item.options,
+      }),
+  );
+}
+
 export default function KBForm({
   initKbId,
   onNewKbCreated,
@@ -93,10 +126,9 @@ export default function KBForm({
       setSelectedEngineId(firstEngine.plugin_id);
       form.setValue('ragEngineId', firstEngine.plugin_id);
       // Initialize config settings with defaults
-      if (firstEngine.creation_schema) {
-        setConfigSettings(
-          getDefaultValuesFromSchema(firstEngine.creation_schema),
-        );
+      const formItems = parseCreationSchema(firstEngine.creation_schema);
+      if (formItems.length > 0) {
+        setConfigSettings(getDefaultValues(formItems));
       }
     }
   }, [ragEngines, selectedEngineId, isEditing]);
@@ -141,8 +173,9 @@ export default function KBForm({
     // Find engine and initialize config settings with defaults from schema
     const engine = ragEngines.find((e) => e.plugin_id === engineId);
     if (engine) {
-      if (engine.creation_schema) {
-        setConfigSettings(getDefaultValuesFromSchema(engine.creation_schema));
+      const formItems = parseCreationSchema(engine.creation_schema);
+      if (formItems.length > 0) {
+        setConfigSettings(getDefaultValues(formItems));
       } else {
         setConfigSettings({});
       }
@@ -156,7 +189,6 @@ export default function KBForm({
       emoji: data.emoji,
       rag_engine_plugin_id: selectedEngineId,
       creation_settings: configSettings,
-      embedding_model_uuid: '',
       top_k: 5,
     };
 
@@ -186,11 +218,8 @@ export default function KBForm({
     }
   };
 
-  // Convert creation schema to dynamic form items
-  const configFormItems = (() => {
-    if (!selectedEngine?.creation_schema) return [];
-    return jsonSchemaToFormItems(selectedEngine.creation_schema);
-  })();
+  // Convert creation schema to dynamic form items (same as ExternalKBForm)
+  const configFormItems = parseCreationSchema(selectedEngine?.creation_schema);
 
   // Show loading state
   if (loading) {
@@ -327,7 +356,7 @@ export default function KBForm({
               )}
             />
 
-            {/* Engine specific fields (dynamic form) */}
+            {/* Engine specific fields (dynamic form from creation_schema) */}
             {configFormItems.length > 0 && (
               <div className="space-y-4 pt-2 border-t">
                 <div className="text-sm font-medium text-muted-foreground">
diff --git a/web/src/app/infra/entities/api/index.ts b/web/src/app/infra/entities/api/index.ts
index 26705ac3a..c6f07b2d1 100644
--- a/web/src/app/infra/entities/api/index.ts
+++ b/web/src/app/infra/entities/api/index.ts
@@ -155,7 +155,7 @@ export interface KnowledgeBase {
   uuid?: string;
   name: string;
   description: string;
-  embedding_model_uuid: string;
+  embedding_model_uuid?: string; // Optional - can be in creation_settings instead
   top_k: number;
   created_at?: string;
   updated_at?: string;
@@ -178,23 +178,10 @@ export interface RAGEngine {
   name: string;
   description?: I18nObject;
   capabilities: string[];
-  creation_schema?: JSONSchema;
-  retrieval_schema?: JSONSchema;
-}
-
-export interface JSONSchema {
-  type: string;
-  properties?: Record<string, JSONSchemaProperty>;
-  required?: string[];
-}
-
-export interface JSONSchemaProperty {
-  type: string;
-  default?: unknown;
-  description?: string;
-  enum?: unknown[];
-  minimum?: number;
-  maximum?: number;
+  // Schema format: Array of form field definitions (IDynamicFormItemSchema-like)
+  // Each item: { name, label, type, required, default, description?, options? }
+  creation_schema?: unknown[];
+  retrieval_schema?: unknown[];
 }
 
 export interface ApiRespRAGEngines {
diff --git a/web/src/app/infra/entities/form/dynamic.ts b/web/src/app/infra/entities/form/dynamic.ts
index d48801436..6422f1a4b 100644
--- a/web/src/app/infra/entities/form/dynamic.ts
+++ b/web/src/app/infra/entities/form/dynamic.ts
@@ -26,6 +26,7 @@ export enum DynamicFormItemType {
   FILE_ARRAY = 'array[file]',
   SELECT = 'select',
   LLM_MODEL_SELECTOR = 'llm-model-selector',
+  EMBEDDING_MODEL_SELECTOR = 'embedding-model-selector',
   PROMPT_EDITOR = 'prompt-editor',
   UNKNOWN = 'unknown',
   KNOWLEDGE_BASE_SELECTOR = 'knowledge-base-selector',
diff --git a/web/src/app/infra/utils/jsonSchemaConverter.ts b/web/src/app/infra/utils/jsonSchemaConverter.ts
deleted file mode 100644
index aa02c8759..000000000
--- a/web/src/app/infra/utils/jsonSchemaConverter.ts
+++ /dev/null
@@ -1,116 +0,0 @@
-import { UUID } from 'uuidjs';
-import {
-  IDynamicFormItemSchema,
-  DynamicFormItemType,
-} from '@/app/infra/entities/form/dynamic';
-import { JSONSchema, JSONSchemaProperty } from '@/app/infra/entities/api';
-
-/**
- * Convert a JSON Schema property type to DynamicFormItemType
- */
-function mapJsonSchemaType(
-  prop: JSONSchemaProperty,
-): DynamicFormItemType {
-  // Handle enum as select
-  if (prop.enum && prop.enum.length > 0) {
-    return DynamicFormItemType.SELECT;
-  }
-
-  switch (prop.type) {
-    case 'integer':
-      return DynamicFormItemType.INT;
-    case 'number':
-      return DynamicFormItemType.FLOAT;
-    case 'boolean':
-      return DynamicFormItemType.BOOLEAN;
-    case 'string':
-      return DynamicFormItemType.STRING;
-    case 'array':
-      return DynamicFormItemType.STRING_ARRAY;
-    default:
-      return DynamicFormItemType.STRING;
-  }
-}
-
-/**
- * Get default value based on type
- */
-function getDefaultValue(
-  prop: JSONSchemaProperty,
-): string | number | boolean | unknown[] {
-  if (prop.default !== undefined) {
-    return prop.default as string | number | boolean | unknown[];
-  }
-
-  switch (prop.type) {
-    case 'integer':
-    case 'number':
-      return 0;
-    case 'boolean':
-      return false;
-    case 'array':
-      return [];
-    default:
-      return '';
-  }
-}
-
-/**
- * Convert JSON Schema to IDynamicFormItemSchema array
- * This allows using the existing DynamicFormComponent with JSON Schema data
- */
-export function jsonSchemaToFormItems(
-  schema: JSONSchema | undefined,
-): IDynamicFormItemSchema[] {
-  if (!schema || !schema.properties) {
-    return [];
-  }
-
-  const requiredFields = schema.required || [];
-  const items: IDynamicFormItemSchema[] = [];
-
-  for (const [name, prop] of Object.entries(schema.properties)) {
-    const formItem: IDynamicFormItemSchema = {
-      id: UUID.generate(),
-      name,
-      label: { en_US: name, zh_Hans: name },
-      description: prop.description
-        ? { en_US: prop.description, zh_Hans: prop.description }
-        : undefined,
-      type: mapJsonSchemaType(prop),
-      required: requiredFields.includes(name),
-      default: getDefaultValue(prop),
-    };
-
-    // Handle enum options for select type
-    if (prop.enum && prop.enum.length > 0) {
-      formItem.options = prop.enum.map((value) => ({
-        name: String(value),
-        label: { en_US: String(value), zh_Hans: String(value) },
-      }));
-    }
-
-    items.push(formItem);
-  }
-
-  return items;
-}
-
-/**
- * Extract default values from JSON Schema
- */
-export function getDefaultValuesFromSchema(
-  schema: JSONSchema | undefined,
-): Record<string, unknown> {
-  if (!schema || !schema.properties) {
-    return {};
-  }
-
-  const defaults: Record<string, unknown> = {};
-
-  for (const [name, prop] of Object.entries(schema.properties)) {
-    defaults[name] = getDefaultValue(prop);
-  }
-
-  return defaults;
-}
diff --git a/web/src/i18n/locales/en-US.ts b/web/src/i18n/locales/en-US.ts
index 344d56b83..f51541f25 100644
--- a/web/src/i18n/locales/en-US.ts
+++ b/web/src/i18n/locales/en-US.ts
@@ -728,6 +728,8 @@ const enUS = {
     noEnginesAvailable: 'No knowledge base engines available',
     installEngineHint: 'Please install a knowledge base plugin first',
     createKnowledgeBaseFailed: 'Failed to create knowledge base',
+    embeddingModel: 'Embedding Model',
+    embeddingModelRequired: 'Embedding model is required for this engine',
     addExternal: 'Add External Knowledge Base',
     createExternalSuccess: 'External knowledge base created successfully',
     updateExternalSuccess: 'External knowledge base updated successfully',
diff --git a/web/src/i18n/locales/zh-Hans.ts b/web/src/i18n/locales/zh-Hans.ts
index f12617652..7e1a1b48b 100644
--- a/web/src/i18n/locales/zh-Hans.ts
+++ b/web/src/i18n/locales/zh-Hans.ts
@@ -698,6 +698,8 @@ const zhHans = {
     noEnginesAvailable: '没有可用的知识库引擎',
     installEngineHint: '请先安装知识库插件',
     createKnowledgeBaseFailed: '知识库创建失败',
+    embeddingModel: '嵌入模型',
+    embeddingModelRequired: '此引擎需要选择嵌入模型',
     addExternal: '添加外部知识库',
     createExternalSuccess: '外部知识库创建成功',
     updateExternalSuccess: '外部知识库更新成功',

From 26a6fdca00487b0dc9ff0c150efbac79d94b1b98 Mon Sep 17 00:00:00 2001
From: youhuanghe <1051233107@qq.com>
Date: Thu, 5 Feb 2026 05:22:02 +0000
Subject: [PATCH 12/25] fix: code review fixes for RAG refactor
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

- Remove DEBUG stderr outputs in handler.py
- Move repeated `import json` to file top
- Add warning log for unimplemented delete_by_filter

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
---
 src/langbot/pkg/plugin/handler.py | 14 +-------------
 src/langbot/pkg/vector/mgr.py     | 16 ++++++++++------
 2 files changed, 11 insertions(+), 19 deletions(-)

diff --git a/src/langbot/pkg/plugin/handler.py b/src/langbot/pkg/plugin/handler.py
index e6893b298..d45ca8ccf 100644
--- a/src/langbot/pkg/plugin/handler.py
+++ b/src/langbot/pkg/plugin/handler.py
@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import json
 import typing
 from typing import Any
 import base64
@@ -42,9 +43,6 @@ def __init__(
     ):
         super().__init__(connection, disconnect_callback)
         self.ap = ap
-        import sys
-        sys.stderr.write(f"DEBUG_HANDLER: Initializing RuntimeConnectionHandler. Actions: {list(self.actions.keys())}\n")
-        sys.stderr.flush()
 
         @self.action(RuntimeToLangBotAction.INITIALIZE_PLUGIN_SETTINGS)
         async def initialize_plugin_settings(data: dict[str, Any]) -> handler.ActionResponse:
@@ -511,7 +509,6 @@ async def rag_embed_documents(data: dict[str, Any]) -> handler.ActionResponse:
                 vectors = await embedder_model.embed_documents(texts)
                 return handler.ActionResponse.success(data={'vectors': vectors})
             except Exception as e:
-                import json
                 return handler.ActionResponse.error(
                     message=f"{str(e)} (Original Error: {type(e).__name__}, Data: {json.dumps({'error_type': 'EmbeddingError', 'original_error': type(e).__name__})})"
                 )
@@ -538,7 +535,6 @@ async def rag_embed_query(data: dict[str, Any]) -> handler.ActionResponse:
                 vector = await embedder_model.embed_query(text)
                 return handler.ActionResponse.success(data={'vector': vector})
             except Exception as e:
-                import json
                 return handler.ActionResponse.error(
                     message=f"{str(e)} (Original Error: {type(e).__name__}, Data: {json.dumps({'error_type': 'EmbeddingError', 'original_error': type(e).__name__})})"
                 )
@@ -559,7 +555,6 @@ async def rag_vector_upsert(data: dict[str, Any]) -> handler.ActionResponse:
                 )
                 return handler.ActionResponse.success(data={})
             except Exception as e:
-                import json
                 return handler.ActionResponse.error(
                     message=f"{str(e)} (Original Error: {type(e).__name__}, Data: {json.dumps({'error_type': 'VectorStoreError', 'collection_id': collection_id, 'original_error': type(e).__name__})})"
                 )
@@ -579,7 +574,6 @@ async def rag_vector_search(data: dict[str, Any]) -> handler.ActionResponse:
                 )
                 return handler.ActionResponse.success(data={'results': results})
             except Exception as e:
-                import json
                 return handler.ActionResponse.error(
                     message=f"{str(e)} (Original Error: {type(e).__name__}, Data: {json.dumps({'error_type': 'VectorStoreError', 'collection_id': collection_id, 'original_error': type(e).__name__})})"
                 )
@@ -599,7 +593,6 @@ async def rag_vector_delete(data: dict[str, Any]) -> handler.ActionResponse:
 
                 return handler.ActionResponse.success(data={'count': count})
             except Exception as e:
-                import json
                 return handler.ActionResponse.error(
                     message=f"{str(e)} (Original Error: {type(e).__name__}, Data: {json.dumps({'error_type': 'VectorStoreError', 'collection_id': collection_id, 'original_error': type(e).__name__})})"
                 )
@@ -624,7 +617,6 @@ async def rag_get_file_stream(data: dict[str, Any]) -> handler.ActionResponse:
                 content_base64 = base64.b64encode(content_bytes).decode('utf-8')
                 return handler.ActionResponse.success(data={'content_base64': content_base64})
             except Exception as e:
-                import json
                 return handler.ActionResponse.error(
                     message=f"{str(e)} (Original Error: {type(e).__name__}, Data: {json.dumps({'error_type': 'FileServiceError', 'storage_path': storage_path, 'original_error': type(e).__name__})})"
                 )
@@ -638,10 +630,6 @@ async def ping(data: dict[str, Any]) -> handler.ActionResponse:
                 },
             )
 
-        import sys
-        sys.stderr.write(f"DEBUG_HANDLER: RuntimeConnectionHandler initialized. Actions: {list(self.actions.keys())}\n")
-        sys.stderr.flush()
-
     async def ping(self) -> dict[str, Any]:
         """Ping the runtime"""
         return await self.call_action(
diff --git a/src/langbot/pkg/vector/mgr.py b/src/langbot/pkg/vector/mgr.py
index d20d21ffa..2d9d11d5c 100644
--- a/src/langbot/pkg/vector/mgr.py
+++ b/src/langbot/pkg/vector/mgr.py
@@ -136,9 +136,13 @@ async def delete(self, collection_name: str, ids: list[str]):
              await self.vector_db.delete_by_file_id(collection_name, doc_id)
 
     async def delete_by_filter(self, collection_name: str, filter: dict):
-        """Proxy: Delete vectors by filter"""
-        # Base VectorDatabase doesn't support delete_by_filter explicitly in the interface shown (only delete_by_file_id and delete_collection)
-        # We might need to extend VectorDatabase or implemented logic here.
-        # For now, log warning or implement if VDB supports it.
-        # Given the error was 'AttributeError: ... has no attribute upsert', implementing aliases is step 1.
-        pass
+        """Proxy: Delete vectors by filter.
+
+        Note: This feature requires vector database implementations to support
+        filter-based deletion. Currently not implemented in VectorDatabase interface.
+        """
+        # TODO: Implement when VectorDatabase interface adds filter-based deletion
+        self.ap.logger.warning(
+            f"delete_by_filter called on collection '{collection_name}' but "
+            "filter-based deletion is not yet implemented in VectorDatabase interface"
+        )

From 277880f0f913002b5d5bca3b5e623e1116e33119 Mon Sep 17 00:00:00 2001
From: youhuanghe <1051233107@qq.com>
Date: Thu, 5 Feb 2026 05:33:41 +0000
Subject: [PATCH 13/25] refactor(rag): consolidate valid_fields into entity
 constants
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Define MUTABLE_FIELDS, CREATE_FIELDS, ALL_DB_FIELDS as class
constants in KnowledgeBase entity to eliminate duplication.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
---
 src/langbot/pkg/api/http/service/knowledge.py |  8 +++-----
 src/langbot/pkg/entity/persistence/rag.py     | 12 +++++++++++-
 src/langbot/pkg/rag/knowledge/kbmgr.py        |  4 +---
 3 files changed, 15 insertions(+), 9 deletions(-)

diff --git a/src/langbot/pkg/api/http/service/knowledge.py b/src/langbot/pkg/api/http/service/knowledge.py
index aa2882dc4..1b05fca65 100644
--- a/src/langbot/pkg/api/http/service/knowledge.py
+++ b/src/langbot/pkg/api/http/service/knowledge.py
@@ -97,8 +97,7 @@ async def create_knowledge_base(self, kb_data: dict) -> str:
             return kb.uuid
 
         # Filter to only valid database fields for internal KB
-        valid_fields = {'uuid', 'name', 'description', 'embedding_model_uuid', 'top_k', 'rag_engine_plugin_id', 'creation_settings'}
-        filtered_data = {k: v for k, v in kb_data.items() if k in valid_fields}
+        filtered_data = {k: v for k, v in kb_data.items() if k in persistence_rag.KnowledgeBase.CREATE_FIELDS}
 
         filtered_data['uuid'] = str(uuid.uuid4())
         await self.ap.persistence_mgr.execute_async(sqlalchemy.insert(persistence_rag.KnowledgeBase).values(filtered_data))
@@ -111,9 +110,8 @@ async def create_knowledge_base(self, kb_data: dict) -> str:
 
     async def update_knowledge_base(self, kb_uuid: str, kb_data: dict) -> None:
         """更新知识库"""
-        # Filter to only valid database fields
-        valid_fields = {'name', 'description', 'top_k', 'rag_engine_plugin_id', 'creation_settings', 'embedding_model_uuid'}
-        filtered_data = {k: v for k, v in kb_data.items() if k in valid_fields}
+        # 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 not filtered_data:
             return
diff --git a/src/langbot/pkg/entity/persistence/rag.py b/src/langbot/pkg/entity/persistence/rag.py
index cb0c18f0e..fec72ba48 100644
--- a/src/langbot/pkg/entity/persistence/rag.py
+++ b/src/langbot/pkg/entity/persistence/rag.py
@@ -12,12 +12,22 @@ class KnowledgeBase(Base):
     updated_at = sqlalchemy.Column(sqlalchemy.DateTime, default=sqlalchemy.func.now(), onupdate=sqlalchemy.func.now())
     embedding_model_uuid = sqlalchemy.Column(sqlalchemy.String, default='')
     top_k = sqlalchemy.Column(sqlalchemy.Integer, default=5)
-    
+
     # New fields for plugin-based RAG
     rag_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={})
 
+    # Field sets for different operations
+    MUTABLE_FIELDS = {'name', 'description', 'top_k', 'rag_engine_plugin_id', 'creation_settings', 'embedding_model_uuid'}
+    """Fields that can be updated after creation."""
+
+    CREATE_FIELDS = MUTABLE_FIELDS | {'uuid'}
+    """Fields used when creating a new knowledge base."""
+
+    ALL_DB_FIELDS = CREATE_FIELDS | {'created_at', 'updated_at'}
+    """All fields stored in database (for loading from DB row)."""
+
 
 class File(Base):
     __tablename__ = 'knowledge_base_files'
diff --git a/src/langbot/pkg/rag/knowledge/kbmgr.py b/src/langbot/pkg/rag/knowledge/kbmgr.py
index fedcae5d6..6ea7c70d7 100644
--- a/src/langbot/pkg/rag/knowledge/kbmgr.py
+++ b/src/langbot/pkg/rag/knowledge/kbmgr.py
@@ -411,9 +411,7 @@ async def load_knowledge_base(
             knowledge_base_entity = persistence_rag.KnowledgeBase(**knowledge_base_entity._mapping)
         elif isinstance(knowledge_base_entity, dict):
             # Filter out non-database fields (like rag_engine which is computed)
-            valid_fields = {'uuid', 'name', 'description', 'embedding_model_uuid', 'top_k',
-                           'rag_engine_plugin_id', 'creation_settings', 'created_at', 'updated_at'}
-            filtered_dict = {k: v for k, v in knowledge_base_entity.items() if k in valid_fields}
+            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)
 
         runtime_knowledge_base = RuntimeKnowledgeBase(ap=self.ap, knowledge_base_entity=knowledge_base_entity)

From a6bc2eb7a94b03e6b2c51220e8ae13713719e64a Mon Sep 17 00:00:00 2001
From: youhuanghe <1051233107@qq.com>
Date: Thu, 5 Feb 2026 06:02:10 +0000
Subject: [PATCH 14/25] =?UTF-8?q?refactor:=20=E5=B0=86=E7=9F=A5=E8=AF=86?=
 =?UTF-8?q?=E5=BA=93=E8=8E=B7=E5=8F=96=E5=92=8CRAG=E5=BC=95=E6=93=8E?=
 =?UTF-8?q?=E4=BF=A1=E6=81=AF=E4=B8=B0=E5=AF=8C=E9=80=BB=E8=BE=91=E7=A7=BB?=
 =?UTF-8?q?=E8=87=B3=E7=9F=A5=E8=AF=86=E5=BA=93=E7=AE=A1=E7=90=86=E5=99=A8?=
 =?UTF-8?q?=E3=80=82?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../http/controller/groups/knowledge/base.py  |  1 +
 src/langbot/pkg/api/http/service/knowledge.py | 63 +---------------
 src/langbot/pkg/rag/knowledge/kbmgr.py        | 72 +++++++++++++++++++
 .../pkg/rag/knowledge/kbmgr_partial.py        | 72 +++++++++++++++++++
 4 files changed, 147 insertions(+), 61 deletions(-)
 create mode 100644 src/langbot/pkg/rag/knowledge/kbmgr_partial.py

diff --git a/src/langbot/pkg/api/http/controller/groups/knowledge/base.py b/src/langbot/pkg/api/http/controller/groups/knowledge/base.py
index 2e44109f6..fa2caa8f3 100644
--- a/src/langbot/pkg/api/http/controller/groups/knowledge/base.py
+++ b/src/langbot/pkg/api/http/controller/groups/knowledge/base.py
@@ -90,6 +90,7 @@ async def delete_specific_file_in_kb(file_id: str, knowledge_base_uuid: str) ->
         async def retrieve_knowledge_base(knowledge_base_uuid: str) -> str:
             json_data = await quart.request.json
             query = json_data.get('query')
+            # Extract retrieval_settings to allow dynamic control over RAG 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
diff --git a/src/langbot/pkg/api/http/service/knowledge.py b/src/langbot/pkg/api/http/service/knowledge.py
index 1b05fca65..f7257bb31 100644
--- a/src/langbot/pkg/api/http/service/knowledge.py
+++ b/src/langbot/pkg/api/http/service/knowledge.py
@@ -15,72 +15,13 @@ class KnowledgeService:
     def __init__(self, ap: app.Application) -> None:
         self.ap = ap
 
-    async def _enrich_kb_with_engine_info(self, kb_dict: dict) -> dict:
-        """Enrich knowledge base dict with RAG engine information."""
-        plugin_id = kb_dict.get("rag_engine_plugin_id")
-
-        if plugin_id:
-            # Try to get engine info from plugin
-            try:
-                engines = await self.list_rag_engines()
-                engine_info = next(
-                    (e for e in engines if e["plugin_id"] == plugin_id),
-                    None
-                )
-                if engine_info:
-                    kb_dict["rag_engine"] = {
-                        "plugin_id": plugin_id,
-                        "name": engine_info.get("name", plugin_id),
-                        "capabilities": engine_info.get("capabilities", []),
-                    }
-                else:
-                    # Plugin not loaded, use basic info
-                    kb_dict["rag_engine"] = {
-                        "plugin_id": plugin_id,
-                        "name": plugin_id,
-                        "capabilities": ["doc_ingestion"],
-                    }
-            except Exception:
-                kb_dict["rag_engine"] = {
-                    "plugin_id": plugin_id,
-                    "name": plugin_id,
-                    "capabilities": ["doc_ingestion"],
-                }
-        else:
-            # Legacy internal KB without plugin
-            kb_dict["rag_engine"] = {
-                "plugin_id": None,
-                "name": "Internal (Legacy)",
-                "capabilities": ["doc_ingestion"],
-            }
-
-        return kb_dict
-
     async def get_knowledge_bases(self) -> list[dict]:
         """获取所有知识库"""
-        result = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(persistence_rag.KnowledgeBase))
-        knowledge_bases = result.all()
-
-        kb_list = []
-        for knowledge_base in knowledge_bases:
-            kb_dict = self.ap.persistence_mgr.serialize_model(persistence_rag.KnowledgeBase, knowledge_base)
-            kb_dict = await self._enrich_kb_with_engine_info(kb_dict)
-            kb_list.append(kb_dict)
-
-        return kb_list
+        return await self.ap.rag_mgr.get_all_knowledge_base_details()
 
     async def get_knowledge_base(self, kb_uuid: str) -> dict | None:
         """获取知识库"""
-        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
-
-        kb_dict = self.ap.persistence_mgr.serialize_model(persistence_rag.KnowledgeBase, knowledge_base)
-        kb_dict = await self._enrich_kb_with_engine_info(kb_dict)
-        return kb_dict
+        return await self.ap.rag_mgr.get_knowledge_base_details(kb_uuid)
 
     async def create_knowledge_base(self, kb_data: dict) -> str:
         """创建知识库"""
diff --git a/src/langbot/pkg/rag/knowledge/kbmgr.py b/src/langbot/pkg/rag/knowledge/kbmgr.py
index 6ea7c70d7..f0a270131 100644
--- a/src/langbot/pkg/rag/knowledge/kbmgr.py
+++ b/src/langbot/pkg/rag/knowledge/kbmgr.py
@@ -346,7 +346,79 @@ def __init__(self, ap: app.Application):
 
     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 RAG 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 RAG engines for enrichment
+        engine_map = {}
+        if self.ap.plugin_connector.is_enable_plugin:
+            try:
+                engines = await self.ap.plugin_connector.list_rag_engines()
+                engine_map = {e["plugin_id"]: e for e in engines}
+            except Exception as e:
+                self.ap.logger.warning(f"Failed to list RAG 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 RAG 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_rag_engines()
+                engine_map = {e["plugin_id"]: e for e in engines}
+            except Exception as e:
+                self.ap.logger.warning(f"Failed to list RAG engines: {e}")
+
+        self._enrich_kb_dict(kb_dict, engine_map)
+        return kb_dict
+
+    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("rag_engine_plugin_id")
+        
+        # Default fallback structure
+        fallback_info = {
+            "plugin_id": plugin_id,
+            "name": plugin_id or "Internal (Legacy)",
+            "capabilities": ["doc_ingestion"], 
+        }
+
+        if not plugin_id:
+            kb_dict["rag_engine"] = fallback_info
+            return
+
+        engine_info = engine_map.get(plugin_id)
+        if engine_info:
+             kb_dict["rag_engine"] = {
+                "plugin_id": plugin_id,
+                "name": engine_info.get("name", plugin_id),
+                "capabilities": engine_info.get("capabilities", []),
+            }
+        else:
+             kb_dict["rag_engine"] = fallback_info
+
     async def create_knowledge_base(
         self,
         name: str,
diff --git a/src/langbot/pkg/rag/knowledge/kbmgr_partial.py b/src/langbot/pkg/rag/knowledge/kbmgr_partial.py
new file mode 100644
index 000000000..887e6f3e2
--- /dev/null
+++ b/src/langbot/pkg/rag/knowledge/kbmgr_partial.py
@@ -0,0 +1,72 @@
+
+    async def get_all_knowledge_base_details(self) -> list[dict]:
+        """Get all knowledge bases with enriched RAG 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 RAG engines for enrichment
+        engine_map = {}
+        if self.ap.plugin_connector.is_enable_plugin:
+            try:
+                engines = await self.ap.plugin_connector.list_rag_engines()
+                engine_map = {e["plugin_id"]: e for e in engines}
+            except Exception as e:
+                self.ap.logger.warning(f"Failed to list RAG 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 RAG 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_rag_engines()
+                engine_map = {e["plugin_id"]: e for e in engines}
+            except Exception as e:
+                self.ap.logger.warning(f"Failed to list RAG engines: {e}")
+
+        self._enrich_kb_dict(kb_dict, engine_map)
+        return kb_dict
+
+    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("rag_engine_plugin_id")
+        
+        # Default fallback structure
+        fallback_info = {
+            "plugin_id": plugin_id,
+            "name": plugin_id or "Internal (Legacy)",
+            "capabilities": ["doc_ingestion"], 
+        }
+
+        if not plugin_id:
+            kb_dict["rag_engine"] = fallback_info
+            return
+
+        engine_info = engine_map.get(plugin_id)
+        if engine_info:
+             kb_dict["rag_engine"] = {
+                "plugin_id": plugin_id,
+                "name": engine_info.get("name", plugin_id),
+                "capabilities": engine_info.get("capabilities", []),
+            }
+        else:
+             kb_dict["rag_engine"] = fallback_info

From 83f984202b13196ab30126a8871eadaee4f8862b Mon Sep 17 00:00:00 2001
From: youhuanghe <1051233107@qq.com>
Date: Thu, 5 Feb 2026 06:16:02 +0000
Subject: [PATCH 15/25] refactor(rag): introduce RAGRuntimeService and clean up
 plugin handler

- Create RAGRuntimeService to encapsulate RAG capability implementation (Embedding, VectorOps).
- Refactor PluginHandler to delegate RAG actions to RAGRuntimeService.
- Move KnowledgeService enrichment and creation logic to RAGManager.
- Register RAGRuntimeService in Application and BuildAppStage.
- Clean up legacy code in KnowledgeService.
---
 src/langbot/pkg/api/http/service/knowledge.py |  42 +++---
 src/langbot/pkg/core/app.py                   |   2 +
 src/langbot/pkg/core/stages/build_app.py      |   4 +
 src/langbot/pkg/plugin/handler.py             |  93 +-----------
 .../pkg/rag/knowledge/kbmgr_partial.py        |  72 ----------
 src/langbot/pkg/rag/service/__init__.py       |   1 +
 src/langbot/pkg/rag/service/runtime.py        | 135 ++++++++++++++++++
 7 files changed, 164 insertions(+), 185 deletions(-)
 delete mode 100644 src/langbot/pkg/rag/knowledge/kbmgr_partial.py
 create mode 100644 src/langbot/pkg/rag/service/__init__.py
 create mode 100644 src/langbot/pkg/rag/service/runtime.py

diff --git a/src/langbot/pkg/api/http/service/knowledge.py b/src/langbot/pkg/api/http/service/knowledge.py
index f7257bb31..1be6fb6bf 100644
--- a/src/langbot/pkg/api/http/service/knowledge.py
+++ b/src/langbot/pkg/api/http/service/knowledge.py
@@ -25,29 +25,21 @@ async def get_knowledge_base(self, kb_uuid: str) -> dict | None:
 
     async def create_knowledge_base(self, kb_data: dict) -> str:
         """创建知识库"""
-        # Check if plugin-based creation
-        if 'rag_engine_plugin_id' in kb_data and kb_data['rag_engine_plugin_id']:
-            # Use new manager method
-            kb = await self.ap.rag_mgr.create_knowledge_base(
-                name=kb_data.get('name', 'Untitled'),
-                rag_engine_plugin_id=kb_data['rag_engine_plugin_id'],
-                creation_settings=kb_data.get('creation_settings', {}),
-                description=kb_data.get('description', ''),
-                embedding_model_uuid=kb_data.get('embedding_model_uuid', '')
-            )
-            return kb.uuid
-
-        # Filter to only valid database fields for internal KB
-        filtered_data = {k: v for k, v in kb_data.items() if k in persistence_rag.KnowledgeBase.CREATE_FIELDS}
-
-        filtered_data['uuid'] = str(uuid.uuid4())
-        await self.ap.persistence_mgr.execute_async(sqlalchemy.insert(persistence_rag.KnowledgeBase).values(filtered_data))
-
-        kb = await self.get_knowledge_base(filtered_data['uuid'])
-
-        await self.ap.rag_mgr.load_knowledge_base(kb)
-
-        return filtered_data['uuid']
+        # In new architecture, we delegate entirely to RAGManager which uses plugins.
+        # Legacy internal KB creation is removed.
+        
+        rag_engine_plugin_id = kb_data.get('rag_engine_plugin_id')
+        if not rag_engine_plugin_id:
+             raise ValueError("rag_engine_plugin_id is required")
+
+        kb = await self.ap.rag_mgr.create_knowledge_base(
+            name=kb_data.get('name', 'Untitled'),
+            rag_engine_plugin_id=rag_engine_plugin_id,
+            creation_settings=kb_data.get('creation_settings', {}),
+            description=kb_data.get('description', ''),
+            embedding_model_uuid=kb_data.get('embedding_model_uuid', '')
+        )
+        return kb.uuid
 
     async def update_knowledge_base(self, kb_uuid: str, kb_data: dict) -> None:
         """更新知识库"""
@@ -174,9 +166,7 @@ async def list_rag_engines(self) -> list[dict]:
             rag_engines = await self.ap.plugin_connector.list_rag_engines()
             engines.extend(rag_engines)
         except Exception as e:
-            self.ap.logger.warning(f"Failed to list RAG engines from plugins: {type(e)} {e}")
-            import traceback
-            traceback.print_exc()
+            self.ap.logger.warning(f"Failed to list RAG engines from plugins: {e}")
 
         return engines
 
diff --git a/src/langbot/pkg/core/app.py b/src/langbot/pkg/core/app.py
index 76552f9cb..f181623b8 100644
--- a/src/langbot/pkg/core/app.py
+++ b/src/langbot/pkg/core/app.py
@@ -35,6 +35,7 @@
 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
 
@@ -60,6 +61,7 @@ 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
diff --git a/src/langbot/pkg/core/stages/build_app.py b/src/langbot/pkg/core/stages/build_app.py
index b4506191f..d733bf4ac 100644
--- a/src/langbot/pkg/core/stages/build_app.py
+++ b/src/langbot/pkg/core/stages/build_app.py
@@ -11,6 +11,7 @@
 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
@@ -137,6 +138,9 @@ async def run(self, ap: app.Application):
         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()
diff --git a/src/langbot/pkg/plugin/handler.py b/src/langbot/pkg/plugin/handler.py
index d45ca8ccf..04864d7fd 100644
--- a/src/langbot/pkg/plugin/handler.py
+++ b/src/langbot/pkg/plugin/handler.py
@@ -463,50 +463,12 @@ async def get_config_file(data: dict[str, Any]) -> handler.ActionResponse:
 
         # ================= RAG Capability Handlers =================
 
-        async def _get_kb_entity(kb_id: str):
-            stmt = sqlalchemy.select(persistence_rag.KnowledgeBase).where(persistence_rag.KnowledgeBase.uuid == kb_id)
-            result = await self.ap.persistence_mgr.execute_async(stmt)
-            row = result.first()
-            if not row:
-                 raise ValueError(f"Knowledge Base {kb_id} not found")
-            # Convert Row to KnowledgeBase entity
-            kb = persistence_rag.KnowledgeBase(**row._mapping)
-            return kb
-
-        def _get_embedding_model_uuid(kb: persistence_rag.KnowledgeBase) -> str | None:
-            """Get embedding model UUID from creation_settings (preferred) or KB field (fallback)."""
-            # First check creation_settings (new approach - plugin defines via creation_schema)
-            if kb.creation_settings and isinstance(kb.creation_settings, dict):
-                embed_uuid = kb.creation_settings.get('embedding_model_uuid')
-                if embed_uuid:
-                    return embed_uuid
-            # Fallback to KB field (backward compatibility)
-            return kb.embedding_model_uuid
-
         @self.action(PluginToRuntimeAction.RAG_EMBED_DOCUMENTS)
         async def rag_embed_documents(data: dict[str, Any]) -> handler.ActionResponse:
             kb_id = data['kb_id']
             texts = data['texts']
             try:
-                # 1. Get KB config to find embedding model
-                kb = await _get_kb_entity(kb_id)
-                embed_model_uuid = _get_embedding_model_uuid(kb)
-
-                # 2. Get embedder instance
-                if not embed_model_uuid:
-                     return handler.ActionResponse.error(
-                         message=f"Embedding model not configured for this Knowledge Base (kb_id: {kb_id})"
-                     )
-
-                embedder_model = await self.ap.model_mgr.get_embedding_model_by_uuid(embed_model_uuid)
-
-                if not embedder_model:
-                     return handler.ActionResponse.error(
-                         message=f"Embedding model {embed_model_uuid} not found"
-                     )
-
-                # 3. Generate embeddings
-                vectors = await embedder_model.embed_documents(texts)
+                vectors = await self.ap.rag_runtime_service.embed_documents(kb_id, texts)
                 return handler.ActionResponse.success(data={'vectors': vectors})
             except Exception as e:
                 return handler.ActionResponse.error(
@@ -518,21 +480,7 @@ async def rag_embed_query(data: dict[str, Any]) -> handler.ActionResponse:
             kb_id = data['kb_id']
             text = data['text']
             try:
-                kb = await _get_kb_entity(kb_id)
-                embed_model_uuid = _get_embedding_model_uuid(kb)
-
-                if not embed_model_uuid:
-                     return handler.ActionResponse.error(
-                         message=f"Embedding model not configured (kb_id: {kb_id})"
-                     )
-
-                embedder_model = await self.ap.model_mgr.get_embedding_model_by_uuid(embed_model_uuid)
-                if not embedder_model:
-                     return handler.ActionResponse.error(
-                         message=f"Embedding model {embed_model_uuid} not found"
-                     )
-
-                vector = await embedder_model.embed_query(text)
+                vector = await self.ap.rag_runtime_service.embed_query(kb_id, text)
                 return handler.ActionResponse.success(data={'vector': vector})
             except Exception as e:
                 return handler.ActionResponse.error(
@@ -546,13 +494,7 @@ async def rag_vector_upsert(data: dict[str, Any]) -> handler.ActionResponse:
             ids = data['ids']
             metadata = data.get('metadata')
             try:
-                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
-                )
+                await self.ap.rag_runtime_service.vector_upsert(collection_id, vectors, ids, metadata)
                 return handler.ActionResponse.success(data={})
             except Exception as e:
                 return handler.ActionResponse.error(
@@ -566,12 +508,7 @@ async def rag_vector_search(data: dict[str, Any]) -> handler.ActionResponse:
             top_k = data['top_k']
             filters = data.get('filters')
             try:
-                results = await self.ap.vector_db_mgr.search(
-                    collection_name=collection_id,
-                    query_vector=query_vector,
-                    limit=top_k,
-                    filter=filters
-                )
+                results = await self.ap.rag_runtime_service.vector_search(collection_id, query_vector, top_k, filters)
                 return handler.ActionResponse.success(data={'results': results})
             except Exception as e:
                 return handler.ActionResponse.error(
@@ -584,13 +521,7 @@ async def rag_vector_delete(data: dict[str, Any]) -> handler.ActionResponse:
             ids = data.get('ids')
             filters = data.get('filters')
             try:
-                count = 0
-                if ids:
-                    await self.ap.vector_db_mgr.delete(collection_name=collection_id, ids=ids)
-                    count = len(ids)
-                elif filters:
-                     await self.ap.vector_db_mgr.delete_by_filter(collection_name=collection_id, filter=filters)
-
+                count = await self.ap.rag_runtime_service.vector_delete(collection_id, ids, filters)
                 return handler.ActionResponse.success(data={'count': count})
             except Exception as e:
                 return handler.ActionResponse.error(
@@ -601,19 +532,7 @@ async def rag_vector_delete(data: dict[str, Any]) -> handler.ActionResponse:
         async def rag_get_file_stream(data: dict[str, Any]) -> handler.ActionResponse:
             storage_path = data['storage_path']
             try:
-                provider = self.ap.storage_mgr.storage_provider
-                content_bytes = b""
-
-                if isinstance(provider, LocalStorageProvider):
-                    real_path = os.path.join(LOCAL_STORAGE_PATH, storage_path)
-                    if os.path.exists(real_path):
-                         with open(real_path, "rb") as f:
-                             content_bytes = f.read()
-                    else:
-                        content_bytes = await self.ap.storage_mgr.load(storage_path)
-                else:
-                    content_bytes = await self.ap.storage_mgr.load(storage_path)
-
+                content_bytes = await self.ap.rag_runtime_service.get_file_stream(storage_path)
                 content_base64 = base64.b64encode(content_bytes).decode('utf-8')
                 return handler.ActionResponse.success(data={'content_base64': content_base64})
             except Exception as e:
diff --git a/src/langbot/pkg/rag/knowledge/kbmgr_partial.py b/src/langbot/pkg/rag/knowledge/kbmgr_partial.py
deleted file mode 100644
index 887e6f3e2..000000000
--- a/src/langbot/pkg/rag/knowledge/kbmgr_partial.py
+++ /dev/null
@@ -1,72 +0,0 @@
-
-    async def get_all_knowledge_base_details(self) -> list[dict]:
-        """Get all knowledge bases with enriched RAG 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 RAG engines for enrichment
-        engine_map = {}
-        if self.ap.plugin_connector.is_enable_plugin:
-            try:
-                engines = await self.ap.plugin_connector.list_rag_engines()
-                engine_map = {e["plugin_id"]: e for e in engines}
-            except Exception as e:
-                self.ap.logger.warning(f"Failed to list RAG 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 RAG 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_rag_engines()
-                engine_map = {e["plugin_id"]: e for e in engines}
-            except Exception as e:
-                self.ap.logger.warning(f"Failed to list RAG engines: {e}")
-
-        self._enrich_kb_dict(kb_dict, engine_map)
-        return kb_dict
-
-    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("rag_engine_plugin_id")
-        
-        # Default fallback structure
-        fallback_info = {
-            "plugin_id": plugin_id,
-            "name": plugin_id or "Internal (Legacy)",
-            "capabilities": ["doc_ingestion"], 
-        }
-
-        if not plugin_id:
-            kb_dict["rag_engine"] = fallback_info
-            return
-
-        engine_info = engine_map.get(plugin_id)
-        if engine_info:
-             kb_dict["rag_engine"] = {
-                "plugin_id": plugin_id,
-                "name": engine_info.get("name", plugin_id),
-                "capabilities": engine_info.get("capabilities", []),
-            }
-        else:
-             kb_dict["rag_engine"] = fallback_info
diff --git a/src/langbot/pkg/rag/service/__init__.py b/src/langbot/pkg/rag/service/__init__.py
new file mode 100644
index 000000000..6872460dd
--- /dev/null
+++ b/src/langbot/pkg/rag/service/__init__.py
@@ -0,0 +1 @@
+from .runtime import RAGRuntimeService
diff --git a/src/langbot/pkg/rag/service/runtime.py b/src/langbot/pkg/rag/service/runtime.py
new file mode 100644
index 000000000..8124827ea
--- /dev/null
+++ b/src/langbot/pkg/rag/service/runtime.py
@@ -0,0 +1,135 @@
+from __future__ import annotations
+
+import base64
+import os
+import json
+import logging
+import sqlalchemy
+from typing import Any, List, Dict, Optional
+
+from ....core import app
+from ....entity.persistence import rag as persistence_rag
+from ....storage.providers.localstorage import LocalStorageProvider, LOCAL_STORAGE_PATH
+
+logger = logging.getLogger(__name__)
+
+class RAGRuntimeService:
+    """Service to handle RAG-related requests from plugins (Runtime)."""
+
+    def __init__(self, ap: app.Application):
+        self.ap = ap
+
+    async def _get_kb_entity(self, kb_id: str) -> persistence_rag.KnowledgeBase:
+        stmt = sqlalchemy.select(persistence_rag.KnowledgeBase).where(persistence_rag.KnowledgeBase.uuid == kb_id)
+        result = await self.ap.persistence_mgr.execute_async(stmt)
+        row = result.first()
+        if not row:
+             raise ValueError(f"Knowledge Base {kb_id} not found")
+        # Convert Row to KnowledgeBase entity
+        kb = persistence_rag.KnowledgeBase(**row._mapping)
+        return kb
+
+    def _get_embedding_model_uuid(self, kb: persistence_rag.KnowledgeBase) -> str | None:
+        """Get embedding model UUID from creation_settings (preferred) or KB field (fallback)."""
+        # First check creation_settings (new approach - plugin defines via creation_schema)
+        if kb.creation_settings and isinstance(kb.creation_settings, dict):
+            embed_uuid = kb.creation_settings.get('embedding_model_uuid')
+            if embed_uuid:
+                return embed_uuid
+        # Fallback to KB field (backward compatibility)
+        return kb.embedding_model_uuid
+
+    async def embed_documents(self, kb_id: str, texts: List[str]) -> List[List[float]]:
+        """Handle RAG_EMBED_DOCUMENTS action."""
+        # 1. Get KB config to find embedding model
+        kb = await self._get_kb_entity(kb_id)
+        embed_model_uuid = self._get_embedding_model_uuid(kb)
+
+        if not embed_model_uuid:
+             raise ValueError(f"Embedding model not configured for this Knowledge Base (kb_id: {kb_id})")
+
+        # 2. Get embedder instance
+        embedder_model = await self.ap.model_mgr.get_embedding_model_by_uuid(embed_model_uuid)
+
+        if not embedder_model:
+             raise ValueError(f"Embedding model {embed_model_uuid} not found")
+
+        # 3. Generate embeddings
+        return await embedder_model.embed_documents(texts)
+
+    async def embed_query(self, kb_id: str, text: str) -> List[float]:
+        """Handle RAG_EMBED_QUERY action."""
+        kb = await self._get_kb_entity(kb_id)
+        embed_model_uuid = self._get_embedding_model_uuid(kb)
+
+        if not embed_model_uuid:
+             raise ValueError(f"Embedding model not configured (kb_id: {kb_id})")
+
+        embedder_model = await self.ap.model_mgr.get_embedding_model_by_uuid(embed_model_uuid)
+        if not embedder_model:
+             raise ValueError(f"Embedding model {embed_model_uuid} not found")
+
+        return await embedder_model.embed_query(text)
+
+    async def vector_upsert(
+        self, 
+        collection_id: str, 
+        vectors: List[List[float]], 
+        ids: List[str], 
+        metadata: Optional[List[Dict[str, Any]]] = None
+    ) -> None:
+        """Handle RAG_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
+        )
+
+    async def vector_search(
+        self, 
+        collection_id: str, 
+        query_vector: List[float], 
+        top_k: int, 
+        filters: Optional[Dict[str, Any]] = None
+    ) -> List[Dict[str, Any]]:
+        """Handle RAG_VECTOR_SEARCH action."""
+        return await self.ap.vector_db_mgr.search(
+            collection_name=collection_id,
+            query_vector=query_vector,
+            limit=top_k,
+            filter=filters
+        )
+
+    async def vector_delete(
+        self, 
+        collection_id: str, 
+        ids: Optional[List[str]] = None, 
+        filters: Optional[Dict[str, Any]] = None
+    ) -> int:
+        """Handle RAG_VECTOR_DELETE action."""
+        count = 0
+        if ids:
+            await self.ap.vector_db_mgr.delete(collection_name=collection_id, ids=ids)
+            count = len(ids)
+        elif filters:
+             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 RAG_GET_FILE_STREAM action."""
+        provider = self.ap.storage_mgr.storage_provider
+        content_bytes = b""
+
+        if isinstance(provider, LocalStorageProvider):
+            real_path = os.path.join(LOCAL_STORAGE_PATH, storage_path)
+            if os.path.exists(real_path):
+                 with open(real_path, "rb") as f:
+                     content_bytes = f.read()
+            else:
+                content_bytes = await self.ap.storage_mgr.load(storage_path)
+        else:
+            content_bytes = await self.ap.storage_mgr.load(storage_path)
+        
+        return content_bytes

From 76cf6b495f3f68fd2c9ca6238e1ed0443fd8ef94 Mon Sep 17 00:00:00 2001
From: youhuanghe <1051233107@qq.com>
Date: Thu, 5 Feb 2026 06:31:27 +0000
Subject: [PATCH 16/25] refactor(rag): standardize logger and fix type hints

- Use self.ap.logger consistently in kbmgr.py and runtime.py, removing module-level loggers.
- Fix type hints for retrieve_knowledge in handler.py and connector.py to match implementation returning dict.
---
 src/langbot/pkg/plugin/connector.py    |  2 +-
 src/langbot/pkg/plugin/handler.py      |  2 +-
 src/langbot/pkg/rag/knowledge/kbmgr.py | 24 ++++++++++++------------
 src/langbot/pkg/rag/service/runtime.py |  2 +-
 4 files changed, 15 insertions(+), 15 deletions(-)

diff --git a/src/langbot/pkg/plugin/connector.py b/src/langbot/pkg/plugin/connector.py
index 079cea758..b68e3d8db 100644
--- a/src/langbot/pkg/plugin/connector.py
+++ b/src/langbot/pkg/plugin/connector.py
@@ -470,7 +470,7 @@ async def retrieve_knowledge(
         retriever_name: str,
         instance_id: str,
         retrieval_context: dict[str, Any],
-    ) -> list[dict[str, Any]]:
+    ) -> dict[str, Any]:
         """Retrieve knowledge using a RAGEngine instance."""
         if not self.is_enable_plugin:
             return []
diff --git a/src/langbot/pkg/plugin/handler.py b/src/langbot/pkg/plugin/handler.py
index 04864d7fd..ace7e777d 100644
--- a/src/langbot/pkg/plugin/handler.py
+++ b/src/langbot/pkg/plugin/handler.py
@@ -815,7 +815,7 @@ async def retrieve_knowledge(
         retriever_name: str,
         instance_id: str,
         retrieval_context: dict[str, Any],
-    ) -> list[dict[str, Any]]:
+    ) -> dict[str, Any]:
         """Retrieve knowledge"""
         result = await self.call_action(
             LangBotToRuntimeAction.RETRIEVE_KNOWLEDGE,
diff --git a/src/langbot/pkg/rag/knowledge/kbmgr.py b/src/langbot/pkg/rag/knowledge/kbmgr.py
index f0a270131..5c915c875 100644
--- a/src/langbot/pkg/rag/knowledge/kbmgr.py
+++ b/src/langbot/pkg/rag/knowledge/kbmgr.py
@@ -15,7 +15,7 @@
 from langbot_plugin.api.entities.builtin.rag import context as rag_context
 from .base import KnowledgeBaseInterface
 
-logger = logging.getLogger(__name__)
+
 
 
 class RuntimeKnowledgeBase(KnowledgeBaseInterface):
@@ -227,12 +227,12 @@ async def _on_kb_create(self) -> None:
 
         try:
             config = self.knowledge_base_entity.creation_settings or {}
-            logger.info(
+            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:
-            logger.error(f"Failed to notify plugin {plugin_id} on KB create: {e}")
+            self.ap.logger.error(f"Failed to notify plugin {plugin_id} on KB create: {e}")
 
     async def _on_kb_delete(self) -> None:
         """Notify plugin about KB deletion."""
@@ -241,12 +241,12 @@ async def _on_kb_delete(self) -> None:
             return
 
         try:
-            logger.info(
+            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:
-            logger.error(f"Failed to notify plugin {plugin_id} on KB delete: {e}")
+            self.ap.logger.error(f"Failed to notify plugin {plugin_id} on KB delete: {e}")
 
     async def _ingest_document(
         self,
@@ -257,10 +257,10 @@ async def _ingest_document(
         kb = self.knowledge_base_entity
         plugin_id = kb.rag_engine_plugin_id
         if not plugin_id:
-            logger.error(f"No RAG plugin ID configured for KB {kb.uuid}. Ingestion failed.")
+            self.ap.logger.error(f"No RAG plugin ID configured for KB {kb.uuid}. Ingestion failed.")
             raise ValueError("RAG Plugin ID required")
 
-        logger.info(f"Calling RAG plugin {plugin_id}: ingest(doc={file_metadata.get('filename')})")
+        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
@@ -280,7 +280,7 @@ async def _ingest_document(
             result = await self.ap.plugin_connector.call_rag_ingest(plugin_id, context_data)
             return result
         except Exception as e:
-            logger.error(f"Plugin ingestion failed: {e}")
+            self.ap.logger.error(f"Plugin ingestion failed: {e}")
             raise
 
     async def _retrieve(
@@ -292,7 +292,7 @@ async def _retrieve(
         kb = self.knowledge_base_entity
         plugin_id = kb.rag_engine_plugin_id
         if not plugin_id:
-            logger.error(f"No RAG plugin ID configured for KB {kb.uuid}. Retrieval failed.")
+            self.ap.logger.error(f"No RAG plugin ID configured for KB {kb.uuid}. Retrieval failed.")
             return {"results": [], "total_found": 0}
 
         plugin_author, plugin_name = plugin_id.split('/', 1)
@@ -315,7 +315,7 @@ async def _retrieve(
             )
             return result
         except Exception as e:
-            logger.error(f"Plugin retrieval failed: {e}")
+            self.ap.logger.error(f"Plugin retrieval failed: {e}")
             return {"results": [], "total_found": 0}
 
     async def _delete_document(self, document_id: str) -> bool:
@@ -325,12 +325,12 @@ async def _delete_document(self, document_id: str) -> bool:
         if not plugin_id:
             return False
 
-        logger.info(f"Calling RAG plugin {plugin_id}: delete_document(doc_id={document_id})")
+        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:
-            logger.error(f"Plugin document deletion failed: {e}")
+            self.ap.logger.error(f"Plugin document deletion failed: {e}")
             return False
 
 
diff --git a/src/langbot/pkg/rag/service/runtime.py b/src/langbot/pkg/rag/service/runtime.py
index 8124827ea..07e866480 100644
--- a/src/langbot/pkg/rag/service/runtime.py
+++ b/src/langbot/pkg/rag/service/runtime.py
@@ -11,7 +11,7 @@
 from ....entity.persistence import rag as persistence_rag
 from ....storage.providers.localstorage import LocalStorageProvider, LOCAL_STORAGE_PATH
 
-logger = logging.getLogger(__name__)
+
 
 class RAGRuntimeService:
     """Service to handle RAG-related requests from plugins (Runtime)."""

From 8c139c7a28e0f68b9806b94910423e7ffb78c7bd Mon Sep 17 00:00:00 2001
From: youhuanghe <1051233107@qq.com>
Date: Thu, 5 Feb 2026 06:42:47 +0000
Subject: [PATCH 17/25] =?UTF-8?q?refactor:=20=E5=B0=86=E5=BC=95=E6=93=8E?=
 =?UTF-8?q?=E5=BE=BD=E7=AB=A0=E7=9A=84=E6=A0=B7=E5=BC=8F=E4=BB=8E=20Tailwi?=
 =?UTF-8?q?nd=20CSS=20=E7=B1=BB=E8=BF=81=E7=A7=BB=E5=88=B0=20CSS=20?=
 =?UTF-8?q?=E6=A8=A1=E5=9D=97=E3=80=82?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../components/kb-card/KBCard.module.css          | 15 +++++++++++++++
 .../home/knowledge/components/kb-card/KBCard.tsx  |  2 +-
 2 files changed, 16 insertions(+), 1 deletion(-)

diff --git a/web/src/app/home/knowledge/components/kb-card/KBCard.module.css b/web/src/app/home/knowledge/components/kb-card/KBCard.module.css
index df5c9cf9e..aaba9f6af 100644
--- a/web/src/app/home/knowledge/components/kb-card/KBCard.module.css
+++ b/web/src/app/home/knowledge/components/kb-card/KBCard.module.css
@@ -169,3 +169,18 @@
   width: 1.2rem;
   height: 1.2rem;
 }
+
+.engineBadge {
+  font-size: 0.75rem;
+  line-height: 1rem;
+  padding: 0.125rem 0.5rem;
+  border-radius: 9999px;
+  background-color: #f3e8ff;
+  color: #7e22ce;
+  white-space: nowrap;
+}
+
+:global(.dark) .engineBadge {
+  background-color: #581c87;
+  color: #d8b4fe;
+}
diff --git a/web/src/app/home/knowledge/components/kb-card/KBCard.tsx b/web/src/app/home/knowledge/components/kb-card/KBCard.tsx
index 15c31e8e1..4e29af46e 100644
--- a/web/src/app/home/knowledge/components/kb-card/KBCard.tsx
+++ b/web/src/app/home/knowledge/components/kb-card/KBCard.tsx
@@ -16,7 +16,7 @@ export default function KBCard({ kbCardVO }: { kbCardVO: KnowledgeBaseVO }) {
                 {kbCardVO.name}
               </div>
               {/* Engine badge */}
-              <span className="text-xs px-2 py-0.5 rounded-full bg-purple-100 text-purple-700 dark:bg-purple-900 dark:text-purple-300">
+              <span className={styles.engineBadge}>
                 {kbCardVO.getEngineName()}
               </span>
             </div>

From 00acb4f0f9a5ffd1c3c5767984af21541175c125 Mon Sep 17 00:00:00 2001
From: youhuanghe <1051233107@qq.com>
Date: Thu, 5 Feb 2026 08:38:46 +0000
Subject: [PATCH 18/25] fix(web): resolve React rendering errors in plugins
 page
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

- Fix missing key prop in PluginComponentList by using ternary instead of Fragment
- Fix RAGEngine.name type to I18nObject and use extractI18nObject() for rendering
- Preserves multi-language support

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
---
 .../knowledge/components/kb-form/KBForm.tsx   |  2 +-
 .../plugin-installed/PluginComponentList.tsx  | 68 +++++++++----------
 web/src/app/infra/entities/api/index.ts       |  2 +-
 3 files changed, 33 insertions(+), 39 deletions(-)

diff --git a/web/src/app/home/knowledge/components/kb-form/KBForm.tsx b/web/src/app/home/knowledge/components/kb-form/KBForm.tsx
index a49e4736d..e60b79d57 100644
--- a/web/src/app/home/knowledge/components/kb-form/KBForm.tsx
+++ b/web/src/app/home/knowledge/components/kb-form/KBForm.tsx
@@ -281,7 +281,7 @@ export default function KBForm({
                             key={engine.plugin_id}
                             value={engine.plugin_id}
                           >
-                            {engine.name}
+                            {extractI18nObject(engine.name)}
                           </SelectItem>
                         ))}
                       </SelectContent>
diff --git a/web/src/app/home/plugins/components/plugin-installed/PluginComponentList.tsx b/web/src/app/home/plugins/components/plugin-installed/PluginComponentList.tsx
index 23ddfaa29..24f2fe480 100644
--- a/web/src/app/home/plugins/components/plugin-installed/PluginComponentList.tsx
+++ b/web/src/app/home/plugins/components/plugin-installed/PluginComponentList.tsx
@@ -32,45 +32,39 @@ export default function PluginComponentList({
       {componentKindList.length > 0 && (
         <>
           {componentKindList.map((kind) => {
-            return (
-              <>
-                {useBadge && (
-                  <Badge
-                    key={kind}
-                    variant="outline"
-                    className="flex items-center gap-1"
-                  >
-                    {kindIconMap[kind]}
-                    {/* 响应式显示组件名称：在中等屏幕以上显示 */}
-                    {responsive ? (
-                      <span className="hidden md:inline">
-                        {t('plugins.componentName.' + kind)}
-                      </span>
-                    ) : (
-                      showComponentName && t('plugins.componentName.' + kind)
-                    )}
-                    <span className="ml-1">{components[kind]}</span>
-                  </Badge>
+            return useBadge ? (
+              <Badge
+                key={kind}
+                variant="outline"
+                className="flex items-center gap-1"
+              >
+                {kindIconMap[kind]}
+                {/* 响应式显示组件名称：在中等屏幕以上显示 */}
+                {responsive ? (
+                  <span className="hidden md:inline">
+                    {t('plugins.componentName.' + kind)}
+                  </span>
+                ) : (
+                  showComponentName && t('plugins.componentName.' + kind)
                 )}
-
-                {!useBadge && (
-                  <div
-                    key={kind}
-                    className="flex flex-row items-center justify-start gap-[0.2rem]"
-                  >
-                    {kindIconMap[kind]}
-                    {/* 响应式显示组件名称：在中等屏幕以上显示 */}
-                    {responsive ? (
-                      <span className="hidden md:inline">
-                        {t('plugins.componentName.' + kind)}
-                      </span>
-                    ) : (
-                      showComponentName && t('plugins.componentName.' + kind)
-                    )}
-                    <span className="ml-1">{components[kind]}</span>
-                  </div>
+                <span className="ml-1">{components[kind]}</span>
+              </Badge>
+            ) : (
+              <div
+                key={kind}
+                className="flex flex-row items-center justify-start gap-[0.2rem]"
+              >
+                {kindIconMap[kind]}
+                {/* 响应式显示组件名称：在中等屏幕以上显示 */}
+                {responsive ? (
+                  <span className="hidden md:inline">
+                    {t('plugins.componentName.' + kind)}
+                  </span>
+                ) : (
+                  showComponentName && t('plugins.componentName.' + kind)
                 )}
-              </>
+                <span className="ml-1">{components[kind]}</span>
+              </div>
             );
           })}
         </>
diff --git a/web/src/app/infra/entities/api/index.ts b/web/src/app/infra/entities/api/index.ts
index c6f07b2d1..fe6c24904 100644
--- a/web/src/app/infra/entities/api/index.ts
+++ b/web/src/app/infra/entities/api/index.ts
@@ -175,7 +175,7 @@ export interface RAGEngineInfo {
 
 export interface RAGEngine {
   plugin_id: string;
-  name: string;
+  name: I18nObject;
   description?: I18nObject;
   capabilities: string[];
   // Schema format: Array of form field definitions (IDynamicFormItemSchema-like)

From e1a59eb33aef374a506ffae477abec6094242344 Mon Sep 17 00:00:00 2001
From: youhuanghe <1051233107@qq.com>
Date: Fri, 6 Feb 2026 05:24:46 +0000
Subject: [PATCH 19/25] fix(rag): update runtime service and web components

---
 src/langbot/pkg/rag/service/runtime.py                 |  6 +++---
 .../dynamic-form/DynamicFormItemComponent.tsx          | 10 +++++++---
 .../app/home/knowledge/components/kb-card/KBCardVO.ts  |  3 ++-
 web/src/app/infra/entities/api/index.ts                |  2 +-
 4 files changed, 13 insertions(+), 8 deletions(-)

diff --git a/src/langbot/pkg/rag/service/runtime.py b/src/langbot/pkg/rag/service/runtime.py
index 07e866480..a6b34e8a8 100644
--- a/src/langbot/pkg/rag/service/runtime.py
+++ b/src/langbot/pkg/rag/service/runtime.py
@@ -6,10 +6,10 @@
 import logging
 import sqlalchemy
 from typing import Any, List, Dict, Optional
+from langbot.pkg.core import app
 
-from ....core import app
-from ....entity.persistence import rag as persistence_rag
-from ....storage.providers.localstorage import LocalStorageProvider, LOCAL_STORAGE_PATH
+from langbot.pkg.entity.persistence import rag as persistence_rag
+from langbot.pkg.storage.providers.localstorage import LocalStorageProvider, LOCAL_STORAGE_PATH
 
 
 
diff --git a/web/src/app/home/components/dynamic-form/DynamicFormItemComponent.tsx b/web/src/app/home/components/dynamic-form/DynamicFormItemComponent.tsx
index d94a7ace2..52d7a1460 100644
--- a/web/src/app/home/components/dynamic-form/DynamicFormItemComponent.tsx
+++ b/web/src/app/home/components/dynamic-form/DynamicFormItemComponent.tsx
@@ -315,7 +315,9 @@ export default function DynamicFormItemComponent({
       // Group KBs by RAG engine name
       const kbsByEngine = knowledgeBases.reduce(
         (acc, kb) => {
-          const engineName = kb.rag_engine?.name || t('knowledge.unknownEngine');
+          const engineName = kb.rag_engine?.name
+            ? extractI18nObject(kb.rag_engine.name)
+            : t('knowledge.unknownEngine');
           if (!acc[engineName]) {
             acc[engineName] = [];
           }
@@ -353,7 +355,9 @@ export default function DynamicFormItemComponent({
       // Group KBs by RAG engine name for multi-selector
       const multiKbsByEngine = knowledgeBases.reduce(
         (acc, kb) => {
-          const engineName = kb.rag_engine?.name || t('knowledge.unknownEngine');
+          const engineName = kb.rag_engine?.name
+            ? extractI18nObject(kb.rag_engine.name)
+            : t('knowledge.unknownEngine');
           if (!acc[engineName]) {
             acc[engineName] = [];
           }
@@ -385,7 +389,7 @@ export default function DynamicFormItemComponent({
                             {currentKb.name}
                             {currentKb.rag_engine?.name && (
                               <span className="text-xs px-2 py-0.5 rounded-full bg-purple-100 text-purple-700 dark:bg-purple-900 dark:text-purple-300">
-                                {currentKb.rag_engine.name}
+                                {extractI18nObject(currentKb.rag_engine.name)}
                               </span>
                             )}
                           </div>
diff --git a/web/src/app/home/knowledge/components/kb-card/KBCardVO.ts b/web/src/app/home/knowledge/components/kb-card/KBCardVO.ts
index 9bb6b00df..56808997c 100644
--- a/web/src/app/home/knowledge/components/kb-card/KBCardVO.ts
+++ b/web/src/app/home/knowledge/components/kb-card/KBCardVO.ts
@@ -1,4 +1,5 @@
 import { RAGEngineInfo } from '@/app/infra/entities/api';
+import { extractI18nObject } from '@/i18n/I18nProvider';
 
 export interface IKnowledgeBaseVO {
   id: string;
@@ -52,6 +53,6 @@ export class KnowledgeBaseVO implements IKnowledgeBaseVO {
     if (!this.ragEngine) {
       return 'Unknown';
     }
-    return this.ragEngine.name;
+    return extractI18nObject(this.ragEngine.name);
   }
 }
diff --git a/web/src/app/infra/entities/api/index.ts b/web/src/app/infra/entities/api/index.ts
index fe6c24904..f3e66007f 100644
--- a/web/src/app/infra/entities/api/index.ts
+++ b/web/src/app/infra/entities/api/index.ts
@@ -169,7 +169,7 @@ export interface KnowledgeBase {
 // RAG Engine types
 export interface RAGEngineInfo {
   plugin_id: string | null;
-  name: string;
+  name: I18nObject;
   capabilities: string[];
 }
 

From bcc1ac2f0fd0eb1fb30ce782f2c5bf0f71ea932c Mon Sep 17 00:00:00 2001
From: youhuanghe <1051233107@qq.com>
Date: Fri, 6 Feb 2026 05:42:15 +0000
Subject: [PATCH 20/25] =?UTF-8?q?refactor:=20=E4=BC=98=E5=8C=96=E7=9F=A5?=
 =?UTF-8?q?=E8=AF=86=E5=BA=93=E8=AE=BE=E7=BD=AE=E7=BB=93=E6=9E=84=E5=B9=B6?=
 =?UTF-8?q?=E5=A2=9E=E5=BC=BA=E5=89=8D=E7=AB=AF=E8=B7=9D=E7=A6=BB=E6=98=BE?=
 =?UTF-8?q?=E7=A4=BA=E5=81=A5=E5=A3=AE=E6=80=A7=E3=80=82?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 src/langbot/pkg/rag/knowledge/kbmgr.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/langbot/pkg/rag/knowledge/kbmgr.py b/src/langbot/pkg/rag/knowledge/kbmgr.py
index 5c915c875..39a652a00 100644
--- a/src/langbot/pkg/rag/knowledge/kbmgr.py
+++ b/src/langbot/pkg/rag/knowledge/kbmgr.py
@@ -302,7 +302,8 @@ async def _retrieve(
             "knowledge_base_id": kb.uuid,
             "collection_id": kb.collection_id or kb.uuid,
             "top_k": settings.get("top_k", kb.top_k or 5),
-            "config": settings,
+            "retrieval_settings": settings,
+            "creation_settings": kb.creation_settings or {},
         }
 
         try:

From 982049c4aeda2d7e0619d507b41a78d499ab9b90 Mon Sep 17 00:00:00 2001
From: youhuanghe <1051233107@qq.com>
Date: Fri, 6 Feb 2026 05:57:16 +0000
Subject: [PATCH 21/25] =?UTF-8?q?fix:=20=E5=A4=84=E7=90=86=E5=89=8D?=
 =?UTF-8?q?=E7=AB=AF=E8=B7=9D=E7=A6=BB=E6=98=BE=E7=A4=BA=E4=B8=AD=E7=9A=84?=
 =?UTF-8?q?=E7=A9=BA=E5=80=BC=E3=80=82?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../home/knowledge/components/kb-retrieve/KBRetrieveGeneric.tsx | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/web/src/app/home/knowledge/components/kb-retrieve/KBRetrieveGeneric.tsx b/web/src/app/home/knowledge/components/kb-retrieve/KBRetrieveGeneric.tsx
index 5f52569b0..ce660b9c4 100644
--- a/web/src/app/home/knowledge/components/kb-retrieve/KBRetrieveGeneric.tsx
+++ b/web/src/app/home/knowledge/components/kb-retrieve/KBRetrieveGeneric.tsx
@@ -106,7 +106,7 @@ export default function KBRetrieveGeneric({
                 <CardTitle className="text-sm font-medium flex justify-between items-center">
                   <span>{getTitle(result)}</span>
                   <span className="text-xs text-muted-foreground">
-                    {t('knowledge.distance')}: {result.distance.toFixed(4)}
+                    {t('knowledge.distance')}: {(result.distance ?? 0).toFixed(4)}
                   </span>
                 </CardTitle>
               </CardHeader>

From 3034d9f1625cb73e9f424e668dcfed438799a662 Mon Sep 17 00:00:00 2001
From: youhuanghe <1051233107@qq.com>
Date: Fri, 6 Feb 2026 06:10:01 +0000
Subject: [PATCH 22/25] fix(rag): document retrieve ui and kbmgr top_k
 validation

---
 src/langbot/pkg/rag/knowledge/kbmgr.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/langbot/pkg/rag/knowledge/kbmgr.py b/src/langbot/pkg/rag/knowledge/kbmgr.py
index 39a652a00..6c2b80f64 100644
--- a/src/langbot/pkg/rag/knowledge/kbmgr.py
+++ b/src/langbot/pkg/rag/knowledge/kbmgr.py
@@ -301,7 +301,7 @@ async def _retrieve(
             "query": query,
             "knowledge_base_id": kb.uuid,
             "collection_id": kb.collection_id or kb.uuid,
-            "top_k": settings.get("top_k", kb.top_k or 5),
+            "top_k": settings.get("top_k") or kb.top_k or 5,
             "retrieval_settings": settings,
             "creation_settings": kb.creation_settings or {},
         }

From 27c44838d49addde4a638d5ffa427e12c5612270 Mon Sep 17 00:00:00 2001
From: youhuanghe <1051233107@qq.com>
Date: Fri, 6 Feb 2026 06:20:01 +0000
Subject: [PATCH 23/25] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20uv.lock=20=E4=B8=AD?=
 =?UTF-8?q?=E7=9A=84=20PyPI=20=E9=95=9C=E5=83=8F=E6=BA=90=E4=B8=BA?=
 =?UTF-8?q?=E5=AE=98=E6=96=B9=E5=9C=B0=E5=9D=80=E3=80=82?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 pyproject.toml |    6 +-
 uv.lock        | 7339 ++++++++++++++++++++++++------------------------
 2 files changed, 3661 insertions(+), 3684 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 7fc8d82f8..4ada99e0a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -64,7 +64,7 @@ dependencies = [
     "chromadb>=0.4.24",
     "qdrant-client (>=1.15.1,<2.0.0)",
     "pyseekdb==1.0.0b7",
-    "langbot-plugin",
+    "langbot-plugin>=0.2.5",
     "asyncpg>=0.30.0",
     "line-bot-sdk>=3.19.0",
     "tboxsdk>=0.0.10",
@@ -215,7 +215,3 @@ skip-magic-trailing-comma = false
 # Like Black, automatically detect the appropriate line ending.
 line-ending = "auto"
 
-
-[tool.uv.sources]
-langbot-plugin = { path = "/home/yhh/workspace/langbot-plugin-sdk", editable = true }
-
diff --git a/uv.lock b/uv.lock
index 967008b8c..9399bded7 100644
--- a/uv.lock
+++ b/uv.lock
@@ -19,44 +19,44 @@ resolution-markers = [
 [[package]]
 name = "aenum"
 version = "3.1.16"
-source = { registry = "https://pypi.tuna.tsinghua.edu.cn/simple" }
-sdist = { url = "https://pypi.tuna.tsinghua.edu.cn/packages/09/7a/61ed58e8be9e30c3fe518899cc78c284896d246d51381bab59b5db11e1f3/aenum-3.1.16.tar.gz", hash = "sha256:bfaf9589bdb418ee3a986d85750c7318d9d2839c1b1a1d6fe8fc53ec201cf140", size = 137693, upload-time = "2026-01-12T22:34:38.819Z" }
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/09/7a/61ed58e8be9e30c3fe518899cc78c284896d246d51381bab59b5db11e1f3/aenum-3.1.16.tar.gz", hash = "sha256:bfaf9589bdb418ee3a986d85750c7318d9d2839c1b1a1d6fe8fc53ec201cf140", size = 137693, upload-time = "2026-01-12T22:34:38.819Z" }
 wheels = [
-    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/e3/52/6ad8f63ec8da1bf40f96996d25d5b650fdd38f5975f8c813732c47388f18/aenum-3.1.16-py3-none-any.whl", hash = "sha256:9035092855a98e41b66e3d0998bd7b96280e85ceb3a04cc035636138a1943eaf", size = 165627, upload-time = "2025-04-25T03:17:58.89Z" },
+    { url = "https://files.pythonhosted.org/packages/e3/52/6ad8f63ec8da1bf40f96996d25d5b650fdd38f5975f8c813732c47388f18/aenum-3.1.16-py3-none-any.whl", hash = "sha256:9035092855a98e41b66e3d0998bd7b96280e85ceb3a04cc035636138a1943eaf", size = 165627, upload-time = "2025-04-25T03:17:58.89Z" },
 ]
 
 [[package]]
 name = "aiocqhttp"
 version = "1.4.4"
-source = { registry = "https://pypi.tuna.tsinghua.edu.cn/simple" }
+source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "httpx" },
     { name = "quart" },
 ]
-sdist = { url = "https://pypi.tuna.tsinghua.edu.cn/packages/c2/91/e4bf8584695b065e223aa42ed4462fb67532a9f936d45df5d982eb3320b6/aiocqhttp-1.4.4.tar.gz", hash = "sha256:eb2b6996753cacee45bf615aba5db4625b495e7a184a2fd27d2e5408f472c03d", size = 22989, upload-time = "2023-06-11T05:20:08.649Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/c2/91/e4bf8584695b065e223aa42ed4462fb67532a9f936d45df5d982eb3320b6/aiocqhttp-1.4.4.tar.gz", hash = "sha256:eb2b6996753cacee45bf615aba5db4625b495e7a184a2fd27d2e5408f472c03d", size = 22989, upload-time = "2023-06-11T05:20:08.649Z" }
 
 [[package]]
 name = "aiofiles"
 version = "25.1.0"
-source = { registry = "https://pypi.tuna.tsinghua.edu.cn/simple" }
-sdist = { url = "https://pypi.tuna.tsinghua.edu.cn/packages/41/c3/534eac40372d8ee36ef40df62ec129bee4fdb5ad9706e58a29be53b2c970/aiofiles-25.1.0.tar.gz", hash = "sha256:a8d728f0a29de45dc521f18f07297428d56992a742f0cd2701ba86e44d23d5b2", size = 46354, upload-time = "2025-10-09T20:51:04.358Z" }
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/41/c3/534eac40372d8ee36ef40df62ec129bee4fdb5ad9706e58a29be53b2c970/aiofiles-25.1.0.tar.gz", hash = "sha256:a8d728f0a29de45dc521f18f07297428d56992a742f0cd2701ba86e44d23d5b2", size = 46354, upload-time = "2025-10-09T20:51:04.358Z" }
 wheels = [
-    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/bc/8a/340a1555ae33d7354dbca4faa54948d76d89a27ceef032c8c3bc661d003e/aiofiles-25.1.0-py3-none-any.whl", hash = "sha256:abe311e527c862958650f9438e859c1fa7568a141b22abcd015e120e86a85695", size = 14668, upload-time = "2025-10-09T20:51:03.174Z" },
+    { url = "https://files.pythonhosted.org/packages/bc/8a/340a1555ae33d7354dbca4faa54948d76d89a27ceef032c8c3bc661d003e/aiofiles-25.1.0-py3-none-any.whl", hash = "sha256:abe311e527c862958650f9438e859c1fa7568a141b22abcd015e120e86a85695", size = 14668, upload-time = "2025-10-09T20:51:03.174Z" },
 ]
 
 [[package]]
 name = "aiohappyeyeballs"
 version = "2.6.1"
-source = { registry = "https://pypi.tuna.tsinghua.edu.cn/simple" }
-sdist = { url = "https://pypi.tuna.tsinghua.edu.cn/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760, upload-time = "2025-03-12T01:42:48.764Z" }
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760, upload-time = "2025-03-12T01:42:48.764Z" }
 wheels = [
-    { url = "https://pypi.tuna.tsinghua.edu.cn/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265, upload-time = "2025-03-12T01:42:47.083Z" },
+    { url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265, upload-time = "2025-03-12T01:42:47.083Z" },
 ]
 
 [[package]]
 name = "aiohttp"
 version = "3.13.3"
-source = { registry = "https://pypi.tuna.tsinghua.edu.cn/simple" }
+source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohappyeyeballs" },
     { name = "aiosignal" },
@@ -66,139 +66,139 @@ dependencies = [
     { name = "propcache" },
     { name = "yarl" },
 ]
-sdist = { url = "https://pypi.tuna.tsinghua.edu.cn/packages/50/42/32cf8e7704ceb4481406eb87161349abb46a57fee3f008ba9cb610968646/aiohttp-3.13.3.tar.gz", hash = "sha256:a949eee43d3782f2daae4f4a2819b2cb9b0c5d3b7f7a927067cc84dafdbb9f88", size = 7844556, upload-time = "2026-01-03T17:33:05.204Z" }
-wheels = [
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From 5576fd450bf46d7e21c74e726d31553f766e1fc9 Mon Sep 17 00:00:00 2001
From: youhuanghe <1051233107@qq.com>
Date: Fri, 6 Feb 2026 08:52:15 +0000
Subject: [PATCH 24/25] fix: address code review issues for RAG engine plugin
 architecture

P0 fixes:
- Fix ALL_DB_FIELDS missing collection_id and emoji fields
- Move rag_engine_plugin_id to CREATE_FIELDS (immutable after creation)
- Fix creation_settings mutable default value (dict -> None)
- Rename vector delete method to delete_by_file_id for correct semantics
- Fix delete_by_filter to raise NotImplementedError instead of silent no-op
- Add database migration script (dbm019) for new columns and table cleanup

P1 fixes:
- Clean up design-hesitation comments in connector.py
- Add _parse_plugin_id() with format validation for all RAG methods
- Make _retrieve() raise exceptions instead of silently returning empty results
- Extract _make_rag_error_response() helper for clean error formatting
- Remove unused imports from handler.py

P2 fixes:
- Fix runtime.py indentation inconsistencies
- Simplify get_file_stream to use storage abstraction uniformly
- Reduce redundant DB queries in knowledge service (extract _check_doc_capability)
- Fix engines.py URL encoding: use <path:plugin_id> instead of __ replacement
- Add read-only mode for engine settings in KBForm edit mode
- Simplify page.tsx handleKBCardClick to pass only kbId string

Co-authored-by: Cursor <cursoragent@cursor.com>
---
 .../controller/groups/knowledge/engines.py    | 20 ++--
 src/langbot/pkg/api/http/service/knowledge.py | 31 ++++---
 src/langbot/pkg/entity/persistence/rag.py     |  8 +-
 .../dbm019_rag_engine_plugin_architecture.py  | 93 +++++++++++++++++++
 src/langbot/pkg/plugin/connector.py           | 52 ++++++-----
 src/langbot/pkg/plugin/handler.py             | 42 ++++-----
 src/langbot/pkg/rag/knowledge/kbmgr.py        | 37 ++++----
 src/langbot/pkg/rag/service/runtime.py        | 89 ++++++++----------
 src/langbot/pkg/vector/mgr.py                 | 78 ++++++++--------
 .../knowledge/components/kb-form/KBForm.tsx   | 21 +++--
 web/src/app/home/knowledge/page.tsx           |  6 +-
 web/src/i18n/locales/en-US.ts                 |  1 +
 web/src/i18n/locales/zh-Hans.ts               |  1 +
 13 files changed, 297 insertions(+), 182 deletions(-)
 create mode 100644 src/langbot/pkg/persistence/migrations/dbm019_rag_engine_plugin_architecture.py

diff --git a/src/langbot/pkg/api/http/controller/groups/knowledge/engines.py b/src/langbot/pkg/api/http/controller/groups/knowledge/engines.py
index 1e6b5b48a..6a85217b3 100644
--- a/src/langbot/pkg/api/http/controller/groups/knowledge/engines.py
+++ b/src/langbot/pkg/api/http/controller/groups/knowledge/engines.py
@@ -1,4 +1,5 @@
 import quart
+from urllib.parse import unquote
 from ... import group
 
 
@@ -15,17 +16,22 @@ async def list_rag_engines() -> quart.Response:
             engines = await self.ap.knowledge_service.list_rag_engines()
             return self.success(data={'engines': engines})
 
-        @self.route('/<plugin_id>/creation-schema', methods=['GET'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
+        @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 RAG engine."""
-            # plugin_id format: author/name, need to decode URL
-            plugin_id = plugin_id.replace('__', '/')
+            """Get creation settings schema for a specific RAG engine.
+
+            plugin_id is in 'author/name' format, captured via <path:> converter.
+            """
+            plugin_id = unquote(plugin_id)
             schema = await self.ap.knowledge_service.get_engine_creation_schema(plugin_id)
             return self.success(data={'schema': schema})
 
-        @self.route('/<plugin_id>/retrieval-schema', methods=['GET'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
+        @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 RAG engine."""
-            plugin_id = plugin_id.replace('__', '/')
+            """Get retrieval settings schema for a specific RAG engine.
+
+            plugin_id is in 'author/name' format, captured via <path:> converter.
+            """
+            plugin_id = unquote(plugin_id)
             schema = await self.ap.knowledge_service.get_engine_retrieval_schema(plugin_id)
             return self.success(data={'schema': schema})
diff --git a/src/langbot/pkg/api/http/service/knowledge.py b/src/langbot/pkg/api/http/service/knowledge.py
index 1be6fb6bf..8b55f56eb 100644
--- a/src/langbot/pkg/api/http/service/knowledge.py
+++ b/src/langbot/pkg/api/http/service/knowledge.py
@@ -30,7 +30,7 @@ async def create_knowledge_base(self, kb_data: dict) -> str:
         
         rag_engine_plugin_id = kb_data.get('rag_engine_plugin_id')
         if not rag_engine_plugin_id:
-             raise ValueError("rag_engine_plugin_id is required")
+            raise ValueError("rag_engine_plugin_id is required")
 
         kb = await self.ap.rag_mgr.create_knowledge_base(
             name=kb_data.get('name', 'Untitled'),
@@ -60,17 +60,30 @@ async def update_knowledge_base(self, kb_uuid: str, kb_data: dict) -> None:
 
         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 RAG 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('rag_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) -> int:
         """存储文件"""
         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')
 
-        # Check if the RAG engine supports document ingestion
-        kb_info = await self.get_knowledge_base(kb_uuid)
-        capabilities = kb_info.get('rag_engine', {}).get('capabilities', [])
-        if 'doc_ingestion' not in capabilities:
-            raise Exception('This knowledge base does not support document upload')
+        await self._check_doc_capability(kb_uuid, 'document upload')
 
         result = await runtime_kb.store_file(file_id)
 
@@ -115,11 +128,7 @@ async def delete_file(self, kb_uuid: str, file_id: str) -> None:
         if runtime_kb is None:
             raise Exception('Knowledge base not found')
 
-        # Check if the RAG engine supports document ingestion
-        kb_info = await self.get_knowledge_base(kb_uuid)
-        capabilities = kb_info.get('rag_engine', {}).get('capabilities', [])
-        if 'doc_ingestion' not in capabilities:
-            raise Exception('This knowledge base does not support document deletion')
+        await self._check_doc_capability(kb_uuid, 'document deletion')
 
         await runtime_kb.delete_file(file_id)
 
diff --git a/src/langbot/pkg/entity/persistence/rag.py b/src/langbot/pkg/entity/persistence/rag.py
index fec72ba48..75fde91d8 100644
--- a/src/langbot/pkg/entity/persistence/rag.py
+++ b/src/langbot/pkg/entity/persistence/rag.py
@@ -16,16 +16,16 @@ class KnowledgeBase(Base):
     # New fields for plugin-based RAG
     rag_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={})
+    creation_settings = sqlalchemy.Column(sqlalchemy.JSON, nullable=True, default=None)
 
     # Field sets for different operations
-    MUTABLE_FIELDS = {'name', 'description', 'top_k', 'rag_engine_plugin_id', 'creation_settings', 'embedding_model_uuid'}
+    MUTABLE_FIELDS = {'name', 'description', 'top_k', 'creation_settings', 'embedding_model_uuid'}
     """Fields that can be updated after creation."""
 
-    CREATE_FIELDS = MUTABLE_FIELDS | {'uuid'}
+    CREATE_FIELDS = MUTABLE_FIELDS | {'uuid', 'rag_engine_plugin_id', 'collection_id'}
     """Fields used when creating a new knowledge base."""
 
-    ALL_DB_FIELDS = CREATE_FIELDS | {'created_at', 'updated_at'}
+    ALL_DB_FIELDS = CREATE_FIELDS | {'emoji', 'created_at', 'updated_at'}
     """All fields stored in database (for loading from DB row)."""
 
 
diff --git a/src/langbot/pkg/persistence/migrations/dbm019_rag_engine_plugin_architecture.py b/src/langbot/pkg/persistence/migrations/dbm019_rag_engine_plugin_architecture.py
new file mode 100644
index 000000000..419a348fc
--- /dev/null
+++ b/src/langbot/pkg/persistence/migrations/dbm019_rag_engine_plugin_architecture.py
@@ -0,0 +1,93 @@
+import sqlalchemy
+from .. import migration
+
+
+@migration.migration_class(19)
+class DBMigrateRAGEnginePluginArchitecture(migration.DBMigration):
+    """Migrate to unified RAG Engine plugin architecture.
+
+    Changes:
+    - Add rag_engine_plugin_id, collection_id, creation_settings columns to knowledge_bases
+    - Drop external_knowledge_bases table (data migrated to knowledge_bases)
+    """
+
+    async def upgrade(self):
+        """Upgrade"""
+        await self._add_columns_to_knowledge_bases()
+        await self._drop_external_knowledge_bases_table()
+
+    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(
+                    f"SELECT column_name FROM information_schema.columns WHERE table_name = '{table_name}';"
+                )
+            )
+            return [row[0] for row in result.fetchall()]
+        else:
+            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(
+                    f"SELECT EXISTS (SELECT FROM information_schema.tables WHERE table_name = '{table_name}');"
+                )
+            )
+            return result.scalar()
+        else:
+            result = await self.ap.persistence_mgr.execute_async(
+                sqlalchemy.text(
+                    f"SELECT name FROM sqlite_master WHERE type='table' AND name='{table_name}';"
+                )
+            )
+            return result.first() is not None
+
+    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 = {
+            'rag_engine_plugin_id': 'VARCHAR',
+            'collection_id': 'VARCHAR',
+            'creation_settings': 'TEXT',  # JSON stored as TEXT for SQLite compatibility
+        }
+
+        for col_name, col_type in new_columns.items():
+            if col_name not in columns:
+                if self.ap.persistence_mgr.db.name == 'postgresql':
+                    await self.ap.persistence_mgr.execute_async(
+                        sqlalchemy.text(
+                            f"ALTER TABLE knowledge_bases ADD COLUMN {col_name} {col_type};"
+                        )
+                    )
+                else:
+                    await self.ap.persistence_mgr.execute_async(
+                        sqlalchemy.text(
+                            f"ALTER TABLE knowledge_bases ADD COLUMN {col_name} {col_type};"
+                        )
+                    )
+
+        # For existing knowledge bases without rag_engine_plugin_id,
+        # set collection_id = uuid (same default as new KBs)
+        await self.ap.persistence_mgr.execute_async(
+            sqlalchemy.text(
+                "UPDATE knowledge_bases SET collection_id = uuid WHERE collection_id IS NULL;"
+            )
+        )
+
+    async def _drop_external_knowledge_bases_table(self):
+        """Drop the external_knowledge_bases table if it exists."""
+        if await self._table_exists('external_knowledge_bases'):
+            await self.ap.persistence_mgr.execute_async(
+                sqlalchemy.text("DROP TABLE external_knowledge_bases;")
+            )
+
+    async def downgrade(self):
+        """Downgrade"""
+        pass
diff --git a/src/langbot/pkg/plugin/connector.py b/src/langbot/pkg/plugin/connector.py
index b68e3d8db..5e3bf60a5 100644
--- a/src/langbot/pkg/plugin/connector.py
+++ b/src/langbot/pkg/plugin/connector.py
@@ -500,58 +500,60 @@ async def sync_polymorphic_component_instances(self) -> dict[str, Any]:
         if not self.is_enable_plugin:
             return {}
 
-        # No polymorphic components to sync
-        required_instances = []
+        return await self.handler.sync_polymorphic_component_instances([])
 
-        # Send to runtime (empty list)
-        sync_result = await self.handler.sync_polymorphic_component_instances(required_instances)
+    # ================= RAG Capability Callers =================
 
-        return sync_result
+    @staticmethod
+    def _parse_plugin_id(plugin_id: str) -> tuple[str, str]:
+        """Parse a plugin ID string into (author, name).
 
-    # ================= RAG Capability Callers =================
+        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.
+        """
+        if '/' not in plugin_id:
+            raise ValueError(
+                f"Invalid plugin_id format: '{plugin_id}'. "
+                "Expected 'author/name' format (e.g. 'langbot/rag-engine')."
+            )
+        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.
-        
+
         Args:
             plugin_id: Target plugin ID (author/name).
             context_data: IngestionContext data.
         """
-        # Determine target from plugin_id
-        plugin_author, plugin_name = plugin_id.split('/', 1)
-        
-        # We need a new action type in RuntimeToPluginAction and LangBotToRuntimeAction
-        # Currently leveraging generic EXECUTE_COMMAND or need new specific actions
-        # Using a new action: INGEST_DOCUMENT which should be added to Handler first.
-        
-        # Since we modified enums in SDK, we should assume runtime supports it.
-        # But LangBot -> Runtime communication (LangBotToRuntimeAction) also needs update.
-        
-        # For now, let's assume we add specific methods to Handler or extended Action logic.
-        # Direct call to handler which sends Action to Runtime.
-        
+        plugin_author, plugin_name = self._parse_plugin_id(plugin_id)
         return await self.handler.rag_ingest_document(plugin_author, plugin_name, context_data)
 
     async def call_rag_delete_document(self, plugin_id: str, document_id: str, kb_id: str) -> bool:
-        plugin_author, plugin_name = plugin_id.split('/', 1)
+        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)
 
     async def get_rag_creation_schema(self, plugin_id: str) -> dict[str, Any]:
-        plugin_author, plugin_name = plugin_id.split('/', 1)
+        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 = plugin_id.split('/', 1)
+        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 = plugin_id.split('/', 1)
+        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 = plugin_id.split('/', 1)
+        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 list_rag_engines(self) -> list[dict[str, Any]]:
diff --git a/src/langbot/pkg/plugin/handler.py b/src/langbot/pkg/plugin/handler.py
index ace7e777d..ce6c052d6 100644
--- a/src/langbot/pkg/plugin/handler.py
+++ b/src/langbot/pkg/plugin/handler.py
@@ -1,6 +1,5 @@
 from __future__ import annotations
 
-import json
 import typing
 from typing import Any
 import base64
@@ -22,14 +21,25 @@
 
 from ..entity.persistence import plugin as persistence_plugin
 from ..entity.persistence import bstorage as persistence_bstorage
-from ..entity.persistence import rag as persistence_rag
-from ..storage.providers.localstorage import LocalStorageProvider, LOCAL_STORAGE_PATH
-import os
 
 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"""
 
@@ -471,9 +481,7 @@ async def rag_embed_documents(data: dict[str, Any]) -> handler.ActionResponse:
                 vectors = await self.ap.rag_runtime_service.embed_documents(kb_id, texts)
                 return handler.ActionResponse.success(data={'vectors': vectors})
             except Exception as e:
-                return handler.ActionResponse.error(
-                    message=f"{str(e)} (Original Error: {type(e).__name__}, Data: {json.dumps({'error_type': 'EmbeddingError', 'original_error': type(e).__name__})})"
-                )
+                return _make_rag_error_response(e, 'EmbeddingError', kb_id=kb_id)
 
         @self.action(PluginToRuntimeAction.RAG_EMBED_QUERY)
         async def rag_embed_query(data: dict[str, Any]) -> handler.ActionResponse:
@@ -483,9 +491,7 @@ async def rag_embed_query(data: dict[str, Any]) -> handler.ActionResponse:
                 vector = await self.ap.rag_runtime_service.embed_query(kb_id, text)
                 return handler.ActionResponse.success(data={'vector': vector})
             except Exception as e:
-                return handler.ActionResponse.error(
-                    message=f"{str(e)} (Original Error: {type(e).__name__}, Data: {json.dumps({'error_type': 'EmbeddingError', 'original_error': type(e).__name__})})"
-                )
+                return _make_rag_error_response(e, 'EmbeddingError', kb_id=kb_id)
 
         @self.action(PluginToRuntimeAction.RAG_VECTOR_UPSERT)
         async def rag_vector_upsert(data: dict[str, Any]) -> handler.ActionResponse:
@@ -497,9 +503,7 @@ async def rag_vector_upsert(data: dict[str, Any]) -> handler.ActionResponse:
                 await self.ap.rag_runtime_service.vector_upsert(collection_id, vectors, ids, metadata)
                 return handler.ActionResponse.success(data={})
             except Exception as e:
-                return handler.ActionResponse.error(
-                    message=f"{str(e)} (Original Error: {type(e).__name__}, Data: {json.dumps({'error_type': 'VectorStoreError', 'collection_id': collection_id, 'original_error': type(e).__name__})})"
-                )
+                return _make_rag_error_response(e, 'VectorStoreError', collection_id=collection_id)
 
         @self.action(PluginToRuntimeAction.RAG_VECTOR_SEARCH)
         async def rag_vector_search(data: dict[str, Any]) -> handler.ActionResponse:
@@ -511,9 +515,7 @@ async def rag_vector_search(data: dict[str, Any]) -> handler.ActionResponse:
                 results = await self.ap.rag_runtime_service.vector_search(collection_id, query_vector, top_k, filters)
                 return handler.ActionResponse.success(data={'results': results})
             except Exception as e:
-                return handler.ActionResponse.error(
-                    message=f"{str(e)} (Original Error: {type(e).__name__}, Data: {json.dumps({'error_type': 'VectorStoreError', 'collection_id': collection_id, 'original_error': type(e).__name__})})"
-                )
+                return _make_rag_error_response(e, 'VectorStoreError', collection_id=collection_id)
 
         @self.action(PluginToRuntimeAction.RAG_VECTOR_DELETE)
         async def rag_vector_delete(data: dict[str, Any]) -> handler.ActionResponse:
@@ -524,9 +526,7 @@ async def rag_vector_delete(data: dict[str, Any]) -> handler.ActionResponse:
                 count = await self.ap.rag_runtime_service.vector_delete(collection_id, ids, filters)
                 return handler.ActionResponse.success(data={'count': count})
             except Exception as e:
-                return handler.ActionResponse.error(
-                    message=f"{str(e)} (Original Error: {type(e).__name__}, Data: {json.dumps({'error_type': 'VectorStoreError', 'collection_id': collection_id, 'original_error': type(e).__name__})})"
-                )
+                return _make_rag_error_response(e, 'VectorStoreError', collection_id=collection_id)
 
         @self.action(PluginToRuntimeAction.RAG_GET_FILE_STREAM)
         async def rag_get_file_stream(data: dict[str, Any]) -> handler.ActionResponse:
@@ -536,9 +536,7 @@ async def rag_get_file_stream(data: dict[str, Any]) -> handler.ActionResponse:
                 content_base64 = base64.b64encode(content_bytes).decode('utf-8')
                 return handler.ActionResponse.success(data={'content_base64': content_base64})
             except Exception as e:
-                return handler.ActionResponse.error(
-                    message=f"{str(e)} (Original Error: {type(e).__name__}, Data: {json.dumps({'error_type': 'FileServiceError', 'storage_path': storage_path, 'original_error': type(e).__name__})})"
-                )
+                return _make_rag_error_response(e, 'FileServiceError', storage_path=storage_path)
 
         @self.action(CommonAction.PING)
         async def ping(data: dict[str, Any]) -> handler.ActionResponse:
diff --git a/src/langbot/pkg/rag/knowledge/kbmgr.py b/src/langbot/pkg/rag/knowledge/kbmgr.py
index 6c2b80f64..cc4cb6fad 100644
--- a/src/langbot/pkg/rag/knowledge/kbmgr.py
+++ b/src/langbot/pkg/rag/knowledge/kbmgr.py
@@ -288,12 +288,19 @@ async def _retrieve(
         query: str,
         settings: Dict[str, Any],
     ) -> Dict[str, Any]:
-        """Call plugin to retrieve documents."""
+        """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.rag_engine_plugin_id
         if not plugin_id:
-            self.ap.logger.error(f"No RAG plugin ID configured for KB {kb.uuid}. Retrieval failed.")
-            return {"results": [], "total_found": 0}
+            raise ValueError(f"No RAG plugin ID configured for KB {kb.uuid}. Retrieval failed.")
+
+        if '/' not in plugin_id:
+            raise ValueError(f"Invalid plugin_id format: '{plugin_id}' for KB {kb.uuid}")
 
         plugin_author, plugin_name = plugin_id.split('/', 1)
 
@@ -306,18 +313,14 @@ async def _retrieve(
             "creation_settings": kb.creation_settings or {},
         }
 
-        try:
-            result = await self.ap.plugin_connector.retrieve_knowledge(
-                plugin_author,
-                plugin_name,
-                "",  # retriever_name - runtime will find the RAGEngine component
-                kb.uuid,  # instance_id
-                retrieval_context
-            )
-            return result
-        except Exception as e:
-            self.ap.logger.error(f"Plugin retrieval failed: {e}")
-            return {"results": [], "total_found": 0}
+        result = await self.ap.plugin_connector.retrieve_knowledge(
+            plugin_author,
+            plugin_name,
+            "",  # retriever_name - runtime will find the RAGEngine component
+            kb.uuid,  # instance_id
+            retrieval_context
+        )
+        return result
 
     async def _delete_document(self, document_id: str) -> bool:
         """Call plugin to delete document."""
@@ -412,13 +415,13 @@ def _enrich_kb_dict(self, kb_dict: dict, engine_map: dict) -> None:
 
         engine_info = engine_map.get(plugin_id)
         if engine_info:
-             kb_dict["rag_engine"] = {
+            kb_dict["rag_engine"] = {
                 "plugin_id": plugin_id,
                 "name": engine_info.get("name", plugin_id),
                 "capabilities": engine_info.get("capabilities", []),
             }
         else:
-             kb_dict["rag_engine"] = fallback_info
+            kb_dict["rag_engine"] = fallback_info
 
     async def create_knowledge_base(
         self,
diff --git a/src/langbot/pkg/rag/service/runtime.py b/src/langbot/pkg/rag/service/runtime.py
index a6b34e8a8..8f25979ec 100644
--- a/src/langbot/pkg/rag/service/runtime.py
+++ b/src/langbot/pkg/rag/service/runtime.py
@@ -1,60 +1,53 @@
 from __future__ import annotations
 
-import base64
-import os
-import json
-import logging
 import sqlalchemy
 from typing import Any, List, Dict, Optional
 from langbot.pkg.core import app
 
 from langbot.pkg.entity.persistence import rag as persistence_rag
-from langbot.pkg.storage.providers.localstorage import LocalStorageProvider, LOCAL_STORAGE_PATH
-
 
 
 class RAGRuntimeService:
-    """Service to handle RAG-related requests from plugins (Runtime)."""
+    """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 _get_kb_entity(self, kb_id: str) -> persistence_rag.KnowledgeBase:
-        stmt = sqlalchemy.select(persistence_rag.KnowledgeBase).where(persistence_rag.KnowledgeBase.uuid == kb_id)
+        stmt = sqlalchemy.select(persistence_rag.KnowledgeBase).where(
+            persistence_rag.KnowledgeBase.uuid == kb_id
+        )
         result = await self.ap.persistence_mgr.execute_async(stmt)
         row = result.first()
         if not row:
-             raise ValueError(f"Knowledge Base {kb_id} not found")
-        # Convert Row to KnowledgeBase entity
+            raise ValueError(f"Knowledge Base {kb_id} not found")
         kb = persistence_rag.KnowledgeBase(**row._mapping)
         return kb
 
     def _get_embedding_model_uuid(self, kb: persistence_rag.KnowledgeBase) -> str | None:
         """Get embedding model UUID from creation_settings (preferred) or KB field (fallback)."""
-        # First check creation_settings (new approach - plugin defines via creation_schema)
         if kb.creation_settings and isinstance(kb.creation_settings, dict):
             embed_uuid = kb.creation_settings.get('embedding_model_uuid')
             if embed_uuid:
                 return embed_uuid
-        # Fallback to KB field (backward compatibility)
         return kb.embedding_model_uuid
 
     async def embed_documents(self, kb_id: str, texts: List[str]) -> List[List[float]]:
         """Handle RAG_EMBED_DOCUMENTS action."""
-        # 1. Get KB config to find embedding model
         kb = await self._get_kb_entity(kb_id)
         embed_model_uuid = self._get_embedding_model_uuid(kb)
 
         if not embed_model_uuid:
-             raise ValueError(f"Embedding model not configured for this Knowledge Base (kb_id: {kb_id})")
+            raise ValueError(f"Embedding model not configured for this Knowledge Base (kb_id: {kb_id})")
 
-        # 2. Get embedder instance
         embedder_model = await self.ap.model_mgr.get_embedding_model_by_uuid(embed_model_uuid)
-
         if not embedder_model:
-             raise ValueError(f"Embedding model {embed_model_uuid} not found")
+            raise ValueError(f"Embedding model {embed_model_uuid} not found")
 
-        # 3. Generate embeddings
         return await embedder_model.embed_documents(texts)
 
     async def embed_query(self, kb_id: str, text: str) -> List[float]:
@@ -63,19 +56,19 @@ async def embed_query(self, kb_id: str, text: str) -> List[float]:
         embed_model_uuid = self._get_embedding_model_uuid(kb)
 
         if not embed_model_uuid:
-             raise ValueError(f"Embedding model not configured (kb_id: {kb_id})")
+            raise ValueError(f"Embedding model not configured (kb_id: {kb_id})")
 
         embedder_model = await self.ap.model_mgr.get_embedding_model_by_uuid(embed_model_uuid)
         if not embedder_model:
-             raise ValueError(f"Embedding model {embed_model_uuid} not found")
+            raise ValueError(f"Embedding model {embed_model_uuid} not found")
 
         return await embedder_model.embed_query(text)
 
     async def vector_upsert(
-        self, 
-        collection_id: str, 
-        vectors: List[List[float]], 
-        ids: List[str], 
+        self,
+        collection_id: str,
+        vectors: List[List[float]],
+        ids: List[str],
         metadata: Optional[List[Dict[str, Any]]] = None
     ) -> None:
         """Handle RAG_VECTOR_UPSERT action."""
@@ -88,10 +81,10 @@ async def vector_upsert(
         )
 
     async def vector_search(
-        self, 
-        collection_id: str, 
-        query_vector: List[float], 
-        top_k: int, 
+        self,
+        collection_id: str,
+        query_vector: List[float],
+        top_k: int,
         filters: Optional[Dict[str, Any]] = None
     ) -> List[Dict[str, Any]]:
         """Handle RAG_VECTOR_SEARCH action."""
@@ -103,33 +96,31 @@ async def vector_search(
         )
 
     async def vector_delete(
-        self, 
-        collection_id: str, 
-        ids: Optional[List[str]] = None, 
+        self,
+        collection_id: str,
+        ids: Optional[List[str]] = None,
         filters: Optional[Dict[str, Any]] = None
     ) -> int:
-        """Handle RAG_VECTOR_DELETE action."""
+        """Handle RAG_VECTOR_DELETE action.
+
+        Args:
+            collection_id: The collection to delete from.
+            ids: File IDs to delete (uses delete_by_file_id under the hood).
+            filters: Filter-based deletion (not yet supported, will raise).
+        """
         count = 0
         if ids:
-            await self.ap.vector_db_mgr.delete(collection_name=collection_id, ids=ids)
+            await self.ap.vector_db_mgr.delete_by_file_id(collection_name=collection_id, file_ids=ids)
             count = len(ids)
         elif filters:
-             await self.ap.vector_db_mgr.delete_by_filter(collection_name=collection_id, filter=filters)
+            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 RAG_GET_FILE_STREAM action."""
-        provider = self.ap.storage_mgr.storage_provider
-        content_bytes = b""
-
-        if isinstance(provider, LocalStorageProvider):
-            real_path = os.path.join(LOCAL_STORAGE_PATH, storage_path)
-            if os.path.exists(real_path):
-                 with open(real_path, "rb") as f:
-                     content_bytes = f.read()
-            else:
-                content_bytes = await self.ap.storage_mgr.load(storage_path)
-        else:
-            content_bytes = await self.ap.storage_mgr.load(storage_path)
-        
-        return content_bytes
+        """Handle RAG_GET_FILE_STREAM action.
+
+        Uses the storage manager abstraction to load file content,
+        regardless of the underlying storage provider.
+        """
+        content_bytes = await self.ap.storage_mgr.load(storage_path)
+        return content_bytes if content_bytes else b""
diff --git a/src/langbot/pkg/vector/mgr.py b/src/langbot/pkg/vector/mgr.py
index 2d9d11d5c..348800207 100644
--- a/src/langbot/pkg/vector/mgr.py
+++ b/src/langbot/pkg/vector/mgr.py
@@ -89,60 +89,64 @@ async def search(
         limit: int,
         filter: dict | None = None,
     ) -> list[dict]:
-        """Proxy: Search vectors"""
+        """Proxy: Search vectors.
+
+        Returns a list of dicts with keys: 'id', 'score', 'metadata'.
+        The underlying VectorDatabase.search returns Chroma-style format:
+        { 'ids': [['id1']], 'distances': [[0.1]], 'metadatas': [[{}]] }
+        """
         import numpy as np
-        # Ensure collection exists before searching? Or let it fail/return empty?
-        # Usually implementations handle collection not found.
+
         results = await self.vector_db.search(
             collection=collection_name,
             query_embedding=np.array(query_vector),
             k=limit,
-            # Note: filters are not yet unified in VectorDatabase abstract method signature properly in all impls,
-            # but we pass what we can. The ABI above shows search(collection, query_embedding, k).
-            # We might need to handle filters if implementations support it.
         )
-        
-        # Convert results to standard format
-        # Abstract return type is Dict[str, Any] which usually contains 'ids', 'distances', 'metadatas', 'documents'
-        # We want to return list of results
-        
-        # Assuming typical Chroma/Standard format:
-        # { 'ids': [['id1']], 'distances': [[0.1]], 'metadatas': [[{}]], 'documents': [['text']] }
-        # Note: VectorDatabase.search implementation details vary.
-        # Let's inspect ChromaVectorDatabase.search to match return format logic.
-        
-        # For now, let's implement a basic return structure assuming the Dict matches standard.
-        parsed_results = []
+
         if not results or 'ids' not in results or not results['ids']:
-             return []
-             
-        # Flatten if list of lists (batch search)
-        r_ids = results['ids'][0] if isinstance(results['ids'], list) and results['ids'] and isinstance(results['ids'][0], list) else results['ids']
-        r_dists = results['distances'][0] if isinstance(results['distances'], list) and results['distances'] and isinstance(results['distances'][0], list) else results['distances']
-        r_metas = results['metadatas'][0] if isinstance(results['metadatas'], list) and results['metadatas'] and isinstance(results['metadatas'][0], list) else results['metadatas']
-        
+            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,
-                'score': r_dists[i] if r_dists is not None else 0.0,
-                'metadata': r_metas[i] if r_metas else {},
+                'score': 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(self, collection_name: str, ids: list[str]):
-        """Proxy: Delete vectors by ID"""
-        for doc_id in ids:
-             await self.vector_db.delete_by_file_id(collection_name, doc_id)
+    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):
         """Proxy: Delete vectors by filter.
 
-        Note: This feature requires vector database implementations to support
-        filter-based deletion. Currently not implemented in VectorDatabase interface.
+        Raises:
+            NotImplementedError: Filter-based deletion is not yet supported
+                by the VectorDatabase interface.
         """
-        # TODO: Implement when VectorDatabase interface adds filter-based deletion
-        self.ap.logger.warning(
+        raise NotImplementedError(
             f"delete_by_filter called on collection '{collection_name}' but "
-            "filter-based deletion is not yet implemented in VectorDatabase interface"
+            "filter-based deletion is not yet implemented in VectorDatabase interface. "
+            "Use delete_by_file_id for file-level deletion."
         )
diff --git a/web/src/app/home/knowledge/components/kb-form/KBForm.tsx b/web/src/app/home/knowledge/components/kb-form/KBForm.tsx
index e60b79d57..9f6461645 100644
--- a/web/src/app/home/knowledge/components/kb-form/KBForm.tsx
+++ b/web/src/app/home/knowledge/components/kb-form/KBForm.tsx
@@ -361,14 +361,21 @@ export default function KBForm({
               <div className="space-y-4 pt-2 border-t">
                 <div className="text-sm font-medium text-muted-foreground">
                   {t('knowledge.engineSettings')}
+                  {isEditing && (
+                    <span className="ml-2 text-xs text-muted-foreground font-normal">
+                      ({t('knowledge.engineSettingsReadonly')})
+                    </span>
+                  )}
+                </div>
+                <div className={isEditing ? 'pointer-events-none opacity-60' : ''}>
+                  <DynamicFormComponent
+                    itemConfigList={configFormItems}
+                    initialValues={configSettings as Record<string, object>}
+                    onSubmit={(val) =>
+                      setConfigSettings(val as Record<string, unknown>)
+                    }
+                  />
                 </div>
-                <DynamicFormComponent
-                  itemConfigList={configFormItems}
-                  initialValues={configSettings as Record<string, object>}
-                  onSubmit={(val) =>
-                    setConfigSettings(val as Record<string, unknown>)
-                  }
-                />
               </div>
             )}
           </div>
diff --git a/web/src/app/home/knowledge/page.tsx b/web/src/app/home/knowledge/page.tsx
index 85c442a89..c4118a8e5 100644
--- a/web/src/app/home/knowledge/page.tsx
+++ b/web/src/app/home/knowledge/page.tsx
@@ -58,8 +58,8 @@ export default function KnowledgePage() {
     setKnowledgeBaseList(kbs);
   }
 
-  const handleKBCardClick = (kb: KnowledgeBaseVO) => {
-    setSelectedKbId(kb.id);
+  const handleKBCardClick = (kbId: string) => {
+    setSelectedKbId(kbId);
     setDetailDialogOpen(true);
   };
 
@@ -109,7 +109,7 @@ export default function KnowledgePage() {
 
         {knowledgeBaseList.map((kb) => {
           return (
-            <div key={kb.id} onClick={() => handleKBCardClick(kb)}>
+            <div key={kb.id} onClick={() => handleKBCardClick(kb.id)}>
               <KBCard kbCardVO={kb} />
             </div>
           );
diff --git a/web/src/i18n/locales/en-US.ts b/web/src/i18n/locales/en-US.ts
index f51541f25..fc6fbb270 100644
--- a/web/src/i18n/locales/en-US.ts
+++ b/web/src/i18n/locales/en-US.ts
@@ -725,6 +725,7 @@ const enUS = {
     builtInEngine: 'Built-in Engine',
     cannotChangeRagEngine: 'RAG engine cannot be changed after creation',
     engineSettings: 'Engine Settings',
+    engineSettingsReadonly: 'read-only in edit mode',
     noEnginesAvailable: 'No knowledge base engines available',
     installEngineHint: 'Please install a knowledge base plugin first',
     createKnowledgeBaseFailed: 'Failed to create knowledge base',
diff --git a/web/src/i18n/locales/zh-Hans.ts b/web/src/i18n/locales/zh-Hans.ts
index 7e1a1b48b..2c9ab6d78 100644
--- a/web/src/i18n/locales/zh-Hans.ts
+++ b/web/src/i18n/locales/zh-Hans.ts
@@ -695,6 +695,7 @@ const zhHans = {
     builtInEngine: '内置引擎',
     cannotChangeRagEngine: '知识库创建后不可修改 RAG 引擎',
     engineSettings: '引擎设置',
+    engineSettingsReadonly: '编辑模式下不可修改',
     noEnginesAvailable: '没有可用的知识库引擎',
     installEngineHint: '请先安装知识库插件',
     createKnowledgeBaseFailed: '知识库创建失败',

From 5f6ad863fa087a173404cfb9fc51b0d6eeb6cabf Mon Sep 17 00:00:00 2001
From: youhuanghe <1051233107@qq.com>
Date: Fri, 6 Feb 2026 14:15:43 +0000
Subject: [PATCH 25/25] fix: address code review findings for RAG plugin
 architecture
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

- Frontend: add retrieval_settings param to retrieveKnowledgeBase API call
- Backend: return {uuid} from PUT knowledge base to match frontend expectation
- Backend: validate query is non-empty in retrieve endpoint (400 on empty)
- Backend: rename vector_delete ids→file_ids for semantic clarity, keep
  backward compat by accepting both 'file_ids' and 'ids' in RPC handler
- Backend: ensure rag_engine.name fallback is always I18nObject-compatible
  dict, preventing frontend extractI18nObject from receiving plain strings
- Migration: fix misleading docstring about external_kb data migration

Co-authored-by: Cursor <cursoragent@cursor.com>
---
 .../http/controller/groups/knowledge/base.py  |  6 +++++-
 .../dbm019_rag_engine_plugin_architecture.py  |  2 +-
 src/langbot/pkg/plugin/handler.py             |  5 +++--
 src/langbot/pkg/rag/knowledge/kbmgr.py        | 19 ++++++++++++++++---
 src/langbot/pkg/rag/service/runtime.py        | 15 ++++++++++-----
 web/src/app/infra/http/BackendClient.ts       |  6 +++++-
 6 files changed, 40 insertions(+), 13 deletions(-)

diff --git a/src/langbot/pkg/api/http/controller/groups/knowledge/base.py b/src/langbot/pkg/api/http/controller/groups/knowledge/base.py
index fa2caa8f3..8fa8a8c18 100644
--- a/src/langbot/pkg/api/http/controller/groups/knowledge/base.py
+++ b/src/langbot/pkg/api/http/controller/groups/knowledge/base.py
@@ -39,7 +39,7 @@ async def handle_specific_knowledge_base(knowledge_base_uuid: str) -> quart.Resp
             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({})
+                return self.success(data={'uuid': knowledge_base_uuid})
 
             elif quart.request.method == 'DELETE':
                 await self.ap.knowledge_service.delete_knowledge_base(knowledge_base_uuid)
@@ -90,6 +90,10 @@ async def delete_specific_file_in_kb(file_id: str, knowledge_base_uuid: str) ->
         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 RAG engine behavior (e.g. top_k, filters)
             retrieval_settings = json_data.get('retrieval_settings', {})
             results = await self.ap.knowledge_service.retrieve_knowledge_base(
diff --git a/src/langbot/pkg/persistence/migrations/dbm019_rag_engine_plugin_architecture.py b/src/langbot/pkg/persistence/migrations/dbm019_rag_engine_plugin_architecture.py
index 419a348fc..9a26003e3 100644
--- a/src/langbot/pkg/persistence/migrations/dbm019_rag_engine_plugin_architecture.py
+++ b/src/langbot/pkg/persistence/migrations/dbm019_rag_engine_plugin_architecture.py
@@ -8,7 +8,7 @@ class DBMigrateRAGEnginePluginArchitecture(migration.DBMigration):
 
     Changes:
     - Add rag_engine_plugin_id, collection_id, creation_settings columns to knowledge_bases
-    - Drop external_knowledge_bases table (data migrated to knowledge_bases)
+    - Drop external_knowledge_bases table (no longer needed; external KB data is not migrated)
     """
 
     async def upgrade(self):
diff --git a/src/langbot/pkg/plugin/handler.py b/src/langbot/pkg/plugin/handler.py
index ce6c052d6..d33342abc 100644
--- a/src/langbot/pkg/plugin/handler.py
+++ b/src/langbot/pkg/plugin/handler.py
@@ -520,10 +520,11 @@ async def rag_vector_search(data: dict[str, Any]) -> handler.ActionResponse:
         @self.action(PluginToRuntimeAction.RAG_VECTOR_DELETE)
         async def rag_vector_delete(data: dict[str, Any]) -> handler.ActionResponse:
             collection_id = data['collection_id']
-            ids = data.get('ids')
+            # Support both 'file_ids' (preferred) and 'ids' (legacy) for backward compatibility
+            file_ids = data.get('file_ids') or data.get('ids')
             filters = data.get('filters')
             try:
-                count = await self.ap.rag_runtime_service.vector_delete(collection_id, ids, filters)
+                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)
diff --git a/src/langbot/pkg/rag/knowledge/kbmgr.py b/src/langbot/pkg/rag/knowledge/kbmgr.py
index cc4cb6fad..a8fe72618 100644
--- a/src/langbot/pkg/rag/knowledge/kbmgr.py
+++ b/src/langbot/pkg/rag/knowledge/kbmgr.py
@@ -398,14 +398,27 @@ async def get_knowledge_base_details(self, kb_uuid: str) -> dict | None:
         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("rag_engine_plugin_id")
         
-        # Default fallback structure
+        # 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": plugin_id or "Internal (Legacy)",
+            "name": fallback_name,
             "capabilities": ["doc_ingestion"], 
         }
 
@@ -417,7 +430,7 @@ def _enrich_kb_dict(self, kb_dict: dict, engine_map: dict) -> None:
         if engine_info:
             kb_dict["rag_engine"] = {
                 "plugin_id": plugin_id,
-                "name": engine_info.get("name", plugin_id),
+                "name": self._to_i18n_name(engine_info.get("name", plugin_id)),
                 "capabilities": engine_info.get("capabilities", []),
             }
         else:
diff --git a/src/langbot/pkg/rag/service/runtime.py b/src/langbot/pkg/rag/service/runtime.py
index 8f25979ec..62fbfcedb 100644
--- a/src/langbot/pkg/rag/service/runtime.py
+++ b/src/langbot/pkg/rag/service/runtime.py
@@ -98,20 +98,25 @@ async def vector_search(
     async def vector_delete(
         self,
         collection_id: str,
-        ids: Optional[List[str]] = None,
+        file_ids: Optional[List[str]] = None,
         filters: Optional[Dict[str, Any]] = None
     ) -> int:
         """Handle RAG_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.
-            ids: File IDs to delete (uses delete_by_file_id under the hood).
+            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 ids:
-            await self.ap.vector_db_mgr.delete_by_file_id(collection_name=collection_id, file_ids=ids)
-            count = len(ids)
+        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:
             await self.ap.vector_db_mgr.delete_by_filter(collection_name=collection_id, filter=filters)
         return count
diff --git a/web/src/app/infra/http/BackendClient.ts b/web/src/app/infra/http/BackendClient.ts
index 40e35a902..25db4a8b2 100644
--- a/web/src/app/infra/http/BackendClient.ts
+++ b/web/src/app/infra/http/BackendClient.ts
@@ -401,8 +401,12 @@ export class BackendClient extends BaseHttpClient {
   public retrieveKnowledgeBase(
     uuid: string,
     query: string,
+    retrievalSettings?: Record<string, unknown>,
   ): Promise<ApiRespKnowledgeBaseRetrieve> {
-    return this.post(`/api/v1/knowledge/bases/${uuid}/retrieve`, { query });
+    return this.post(`/api/v1/knowledge/bases/${uuid}/retrieve`, {
+      query,
+      retrieval_settings: retrievalSettings ?? {},
+    });
   }
 
   // ============ RAG Engines API ============