fix: 移除 chat_stream_service.py 中的知识库权限过滤逻辑，已在工具层添加

backend/package/yuxi/knowledge/indexing.py

@@ -1,100 +0,0 @@
-"""Knowledge text chunking helpers.
-
-Parser and markdown conversion logic has been moved to ``yuxi.plugins.parser``.
-This module only keeps chunking-related utilities.
-"""
-
-from pathlib import Path
-
-from langchain_community.document_loaders import (
-    CSVLoader,
-    JSONLoader,
-    TextLoader,
-    UnstructuredHTMLLoader,
-    UnstructuredMarkdownLoader,
-    UnstructuredWordDocumentLoader,
-)
-from langchain_text_splitters import RecursiveCharacterTextSplitter
-
-
-def chunk_with_parser(file_path, params=None):
-    """
-    使用文件解析器将文件切分成固定大小的块
-
-    Args:
-        file_path: 文件路径
-        params: 参数
-    """
-    params = params or {}
-    chunk_size = int(params.get("chunk_size", 500))
-    chunk_overlap = int(params.get("chunk_overlap", 100))
-
-    file_type = Path(file_path).suffix.lower()
-
-    # 选择合适的加载器
-    if file_type in [".txt"]:
-        loader = TextLoader(file_path)
-
-    elif file_type in [".md"]:
-        loader = UnstructuredMarkdownLoader(file_path)
-
-    elif file_type in [".docx", ".doc"]:
-        loader = UnstructuredWordDocumentLoader(file_path)
-
-    elif file_type in [".html", ".htm"]:
-        loader = UnstructuredHTMLLoader(file_path)
-
-    elif file_type in [".json"]:
-        loader = JSONLoader(file_path, jq_schema=".")
-
-    elif file_type in [".csv"]:
-        loader = CSVLoader(file_path)
-
-    else:
-        raise ValueError(f"不支持的文件类型: {file_type}")
-
-    # 加载文档
-    docs = loader.load()
-
-    # 创建文本分割器
-    text_splitter = RecursiveCharacterTextSplitter(
-        chunk_size=chunk_size,
-        chunk_overlap=chunk_overlap,
-        separators=["\n\n", "\n", ".", " ", ""],
-    )
-
-    # 分割文档
-    nodes = text_splitter.split_documents(docs)
-
-    # 添加序号信息到metadata
-    for i, node in enumerate(nodes):
-        if node.metadata is None:
-            node.metadata = {}
-        node.metadata["chunk_idx"] = i
-
-    return nodes
-
-
-def chunk_text(text, params=None):
-    """
-    将文本切分成固定大小的块
-    """
-    params = params or {}
-    chunk_size = int(params.get("chunk_size", 500))
-    chunk_overlap = int(params.get("chunk_overlap", 100))
-
-    # 创建文本分割器
-    text_splitter = RecursiveCharacterTextSplitter(
-        chunk_size=chunk_size, chunk_overlap=chunk_overlap, separators=["\n\n", "\n", ".", " ", ""]
-    )
-
-    # 分割文档
-    nodes = text_splitter.split_text(text)
-
-    # 添加序号信息到metadata
-    nodes = [{"text": node, "metadata": {"chunk_idx": i}} for i, node in enumerate(nodes)]
-    return nodes
-
-
-def chunk(text_or_path, params=None):
-    raise NotImplementedError("chunk is deprecated, use chunk_with_parser or chunk_text instead")

backend/package/yuxi/services/chat_stream_service.py

@@ -16,7 +16,6 @@ from yuxi.repositories.conversation_repository import ConversationRepository
 from yuxi.storage.postgres.manager import pg_manager
 from yuxi.storage.postgres.models_business import User
 from yuxi.utils.logging_config import logger
-from yuxi import knowledge_base
 from yuxi.utils.question_utils import (
     normalize_options as _normalize_interrupt_options,
 )
@@ -441,24 +440,6 @@ async def stream_agent_chat(
         # LangGraph 会自动从 checkpointer 恢复 state（包括 uploads）
         # 无需手动加载或传递
 
-        # 根据用户权限过滤知识库
-        requested_knowledge_names = input_context.get("knowledges")
-        logger.info(f"Requesting knowledges: {requested_knowledge_names}")
-        if requested_knowledge_names and isinstance(requested_knowledge_names, list) and requested_knowledge_names:
-            user_info = {"role": "user", "department_id": department_id}
-            accessible_databases = await knowledge_base.get_databases_by_user(user_info)
-            accessible_kb_names = {
-                db.get("name")
-                for db in accessible_databases.get("databases", [])
-                if isinstance(db, dict) and db.get("name")
-            }
-            logger.info(f"Accessible knowledges: {accessible_kb_names}")
-
-            filtered_knowledge_names = [kb for kb in requested_knowledge_names if kb in accessible_kb_names]
-            blocked_knowledge_names = [kb for kb in requested_knowledge_names if kb not in accessible_kb_names]
-            if blocked_knowledge_names:
-                logger.warning(f"用户 {user_id} 无权访问知识库: {blocked_knowledge_names}, 已自动过滤")
-            input_context["knowledges"] = filtered_knowledge_names
         full_msg = None
         accumulated_content = []
         async for msg, metadata in agent.stream_messages(messages, input_context=input_context):