qiaoqichaoandClaude Code
d56c699ce6
按 docs/v2/05-implementation-plan.md §1 候选迁移表与 claude_use/T00 白名单， 从 55e2cdb 抽取 224 个文件（2.48 MB）到独立目录 F:/mini-OpenClaw-v2。 - 排除：凭据(.env/server-deploy.conf/旧DELIVERY.md)、运行数据(xingbang_data 等 4 个 SQLite)、依赖(venv/node_modules 符号链接)、归档(_backup*/test-evidence/ ori_file)、Office 临时文件 - 目标目录独立 git 仓库，初始恢复点；Gitea 远程与分支待 OQ-06 Q2 定案 - 未部署、未启动服务、未调用模型 Co-Authored-By: Claude Code <noreply@anthropic.com>
103 lines
3.6 KiB
Python
103 lines
3.6 KiB
Python
"""SearchKnowledgeBaseTool — LlamaIndex hybrid search (BM25 + Vector)."""
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import os
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from pathlib import Path
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from typing import Type, Optional
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from langchain_core.tools import BaseTool
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from pydantic import BaseModel, Field
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class SearchKnowledgeInput(BaseModel):
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query: str = Field(description="The search query to find relevant knowledge")
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class SearchKnowledgeBaseTool(BaseTool):
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name: str = "search_knowledge_base"
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description: str = (
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"Search the local knowledge base using hybrid retrieval (keyword + semantic). "
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"Use this when the user asks about specific knowledge or documents. "
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"Returns the most relevant passages from the knowledge base."
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)
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args_schema: Type[BaseModel] = SearchKnowledgeInput
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base_dir: str = ""
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_index: Optional[object] = None
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class Config:
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arbitrary_types_allowed = True
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def _build_index(self):
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"""Build or load LlamaIndex index from knowledge/ directory."""
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knowledge_dir = Path(self.base_dir) / "knowledge"
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storage_dir = Path(self.base_dir) / "storage"
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if not knowledge_dir.exists() or not any(knowledge_dir.iterdir()):
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return None
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try:
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from llama_index.core import (
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SimpleDirectoryReader,
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StorageContext,
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VectorStoreIndex,
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load_index_from_storage,
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)
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from llama_index.core.settings import Settings
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from llama_index.embeddings.openai import OpenAIEmbedding
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Settings.embed_model = OpenAIEmbedding(
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model=os.getenv("EMBEDDING_MODEL", "text-embedding-3-small"),
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api_key=os.getenv("OPENAI_API_KEY"),
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api_base=os.getenv("OPENAI_BASE_URL", "https://ai.devtool.tech/proxy/v1"),
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)
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# Try loading persisted index
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if storage_dir.exists() and any(storage_dir.iterdir()):
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try:
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storage_context = StorageContext.from_defaults(
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persist_dir=str(storage_dir)
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)
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return load_index_from_storage(storage_context)
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except Exception:
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pass
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# Build fresh index
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documents = SimpleDirectoryReader(
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str(knowledge_dir), recursive=True
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).load_data()
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if not documents:
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return None
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index = VectorStoreIndex.from_documents(documents)
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storage_dir.mkdir(parents=True, exist_ok=True)
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index.storage_context.persist(persist_dir=str(storage_dir))
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return index
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except ImportError as e:
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print(f"⚠️ LlamaIndex not fully installed: {e}")
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return None
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except Exception as e:
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print(f"⚠️ Index build error: {e}")
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return None
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def _run(self, query: str) -> str:
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if self._index is None:
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self._index = self._build_index()
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if self._index is None:
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return "📭 Knowledge base is empty. Add documents to backend/knowledge/ to enable search."
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try:
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query_engine = self._index.as_query_engine(similarity_top_k=3)
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response = query_engine.query(query)
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result = str(response)
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if len(result) > 5000:
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result = result[:5000] + "\n...[truncated]"
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return result
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except Exception as e:
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return f"❌ Search error: {str(e)}"
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def create_search_knowledge_tool(base_dir: Path) -> SearchKnowledgeBaseTool:
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return SearchKnowledgeBaseTool(base_dir=str(base_dir))
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