284 lines
9.5 KiB
Python
284 lines
9.5 KiB
Python
"""知识库工具模块"""
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import inspect
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from typing import Any
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from langchain_core.tools import tool
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from langgraph.prebuilt.tool_node import ToolRuntime
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from pydantic import BaseModel, Field
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from yuxi import knowledge_base
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from yuxi.utils import logger
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# ========== 通用知识库工具函数 ==========
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class ListKBsInput(BaseModel):
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"""列出用户可访问的知识库输入模型"""
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# Langchain 的 runtime 注入机制要求必须有参数
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dummy: str = Field(default="", description="Dummy parameter - ignore") # Add this
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@tool(args_schema=ListKBsInput)
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async def list_kbs(dummy: str, runtime: ToolRuntime) -> str: # Now has 2 params
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"""列出当前用户可访问的知识库列表
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返回用户基于权限可访问的知识库名称列表。这个列表是根据用户的角色和部门信息过滤后的结果，
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但不包括用户在当前对话中未启用的知识库。
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Returns:
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用户可访问的知识库名称列表（字符串格式）
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"""
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# 从 runtime.context 获取用户信息
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runtime_context = runtime.context
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user_id = getattr(runtime_context, "user_id", None)
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if not user_id:
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return "无法获取用户信息"
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# 打印 runtime—context 中的所有信息以进行调试
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logger.debug(f"Runtime context: {runtime_context.__dict__}")
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# 获取用户在当前对话中启用的知识库列表
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enabled_kb_names = getattr(runtime_context, "knowledges", []) or []
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# 获取用户可访问的知识库列表（包含名称和描述）
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try:
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result = await knowledge_base.get_databases_by_raw_id(user_id)
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all_kbs = result.get("databases", [])
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except Exception as e:
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logger.error(f"获取用户知识库列表失败: {e}")
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return f"获取知识库列表失败: {str(e)}"
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all_kb_names = [kb["name"] for kb in all_kbs]
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logger.debug(f"用户 {user_id} 可访问的知识库列表: {all_kb_names}")
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logger.debug(f"用户 {user_id} 当前对话启用的知识库列表: {enabled_kb_names}")
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# 与启用的知识库取交集
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available_kbs = [kb for kb in all_kbs if kb["name"] in enabled_kb_names]
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if not available_kbs:
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return "当前没有可访问的知识库"
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# 格式化输出（包含名称和描述）
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kb_list = []
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for kb in available_kbs:
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name = kb.get("name", "")
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desc = kb.get("description") or "无描述"
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kb_list.append({"name": name, "description": desc})
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return kb_list
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class GetMindmapInput(BaseModel):
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"""获取思维导图输入模型"""
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kb_name: str = Field(description="知识库名称，用于指定要获取思维导图的知识库")
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@tool(args_schema=GetMindmapInput)
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async def get_mindmap(kb_name: str, runtime: ToolRuntime) -> str:
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"""获取指定知识库的思维导图结构
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当用户想要了解知识库的整体结构、文件分类、知识架构时使用此工具。
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返回知识库的思维导图层级结构。
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Args:
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kb_name: 知识库名称
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Returns:
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知识库的思维导图结构（文本格式）
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"""
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if not kb_name:
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return "请提供知识库名称"
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# 获取所有检索器
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retrievers = knowledge_base.get_retrievers()
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# 查找对应的知识库
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target_db_id = None
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target_info = None
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for db_id, info in retrievers.items():
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if info["name"] == kb_name:
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target_db_id = db_id
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target_info = info
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break
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if not target_db_id:
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return f"知识库 '{kb_name}' 不存在"
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try:
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from yuxi.repositories.knowledge_base_repository import KnowledgeBaseRepository
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kb_repo = KnowledgeBaseRepository()
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kb = await kb_repo.get_by_id(target_db_id)
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if kb is None:
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return f"知识库 {target_info['name']} 不存在"
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mindmap_data = kb.mindmap
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if not mindmap_data:
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return f"知识库 {target_info['name']} 还没有生成思维导图。"
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# 将思维导图数据转换为文本格式
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def mindmap_to_text(node, level=0):
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"""递归将思维导图JSON转换为层级文本"""
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indent = " " * level
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text = f"{indent}- {node.get('content', '')}\n"
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for child in node.get("children", []):
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text += mindmap_to_text(child, level + 1)
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return text
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mindmap_text = f"知识库 {target_info['name']} 的思维导图结构：\n\n"
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mindmap_text += mindmap_to_text(mindmap_data)
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return mindmap_text
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except Exception as e:
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logger.error(f"获取思维导图失败: {e}")
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return f"获取思维导图失败: {str(e)}"
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class QueryKBInput(BaseModel):
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"""知识库检索输入模型"""
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kb_name: str = Field(description="知识库名称，用于指定要在哪个知识库中检索")
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query_text: str = Field(
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description=(
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"查询的关键词，查询的时候，应该尽量以可能帮助回答这个问题的关键词进行查询，"
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"不要直接使用用户的原始输入去查询。"
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)
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)
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file_name: str | None = Field(
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default=None,
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description=(
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"（非必要不启用此参数，留空即可）当已经读取思维导图之后，可以指定文件关键词，支持模糊匹配。\n"
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"仅当检索结果过多且不相关，需要进一步缩小范围时使用。"
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),
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)
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async def _resolve_visible_knowledge_bases_for_query(runtime: ToolRuntime | None) -> list[dict[str, Any]]:
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if runtime is None:
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return []
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context = getattr(runtime, "context", None)
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if context is None:
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return []
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visible_kbs = getattr(context, "_visible_knowledge_bases", None)
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if isinstance(visible_kbs, list):
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return visible_kbs
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try:
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from yuxi.agents.backends.knowledge_base_backend import resolve_visible_knowledge_bases_for_context
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return await resolve_visible_knowledge_bases_for_context(context)
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except Exception as exc: # noqa: BLE001
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logger.warning(f"解析会话可见知识库失败，跳过 filepath 注入: {exc}")
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return []
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def _find_query_target(
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*,
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kb_name: str,
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retrievers: dict[str, Any],
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visible_kbs: list[dict[str, Any]],
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) -> tuple[str | None, dict[str, Any] | None, str | None]:
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if visible_kbs:
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matched_kbs = [db for db in visible_kbs if str(db.get("name") or "").strip() == kb_name]
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if not matched_kbs:
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return None, None, f"知识库 '{kb_name}' 不存在或当前会话未启用"
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if len(matched_kbs) > 1:
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return None, None, f"知识库 '{kb_name}' 存在重名，请先调整名称后重试"
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target_db_id = str(matched_kbs[0].get("db_id") or "")
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target_info = retrievers.get(target_db_id)
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if target_info is None:
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return None, None, f"知识库 '{kb_name}' 不存在"
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return target_db_id, target_info, None
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for db_id, info in retrievers.items():
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if info["name"] == kb_name:
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return str(db_id), info, None
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return None, None, f"知识库 '{kb_name}' 不存在"
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@tool(args_schema=QueryKBInput)
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async def query_kb(kb_name: str, query_text: str, file_name: str | None = None, runtime: ToolRuntime = None) -> Any:
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"""在指定知识库中检索内容
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当用户需要查询具体内容时使用此工具。根据关键词在知识库中检索相关文档片段。
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Args:
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kb_name: 知识库名称
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query_text: 查询的关键词
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file_name: （可选）文件名称过滤
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Returns:
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检索结果
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"""
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if not kb_name:
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return "请提供知识库名称"
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if not query_text:
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return "请提供查询内容"
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# 获取所有检索器
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retrievers = knowledge_base.get_retrievers()
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visible_kbs = await _resolve_visible_knowledge_bases_for_query(runtime)
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target_db_id, target_info, target_error = _find_query_target(
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kb_name=kb_name,
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retrievers=retrievers,
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visible_kbs=visible_kbs,
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)
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if target_error:
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return target_error
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metadata = target_info.get("metadata") if isinstance(target_info, dict) else None
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kb_type = str((metadata or {}).get("kb_type") or "").strip().lower()
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if kb_type != "milvus":
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return f"知识库 '{kb_name}' 不是 Milvus 类型，当前 query_kb 仅支持 Milvus"
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try:
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retriever = target_info["retriever"]
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kwargs = {}
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if file_name:
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kwargs["file_name"] = file_name
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if inspect.iscoroutinefunction(retriever):
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result = await retriever(query_text, **kwargs)
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else:
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result = retriever(query_text, **kwargs)
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if not isinstance(result, list):
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return f"知识库 '{kb_name}' 返回结果不是 Milvus chunks 列表，当前 query_kb 仅支持 Milvus"
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from yuxi.agents.backends.knowledge_base_backend import inject_filepaths_into_retrieval_result
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return await inject_filepaths_into_retrieval_result(
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retrieval_chunks=result,
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visible_kbs=visible_kbs,
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target_db_id=target_db_id,
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target_kb_name=kb_name,
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)
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except Exception as e:
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logger.error(f"检索失败: {e}")
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return f"检索失败: {str(e)}"
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def get_common_kb_tools() -> list:
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"""获取通用知识库工具列表
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返回 3 个通用工具：
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- list_kbs: 列出用户可访问的知识库
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- get_mindmap: 获取指定知识库的思维导图
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- query_kb: 在指定知识库中检索
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"""
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return [list_kbs, get_mindmap, query_kb]
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