chore: mini-openclaw 初始版本

Next.js 前端 + FastAPI/LangChain 后端透明 agent平台 - 含 .gitignore, 敏感文件(sessions数据/.env/venv等)已排除 - 待后续添加 trajectory 自进化机制
2026-09-01 16:40:10 +08:00
parent 73796a6abd
commit 42ce992a0f
76 changed files with 14207 additions and 0 deletions
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+"""SearchKnowledgeBaseTool — LlamaIndex hybrid search (BM25 + Vector)."""
+
+import os
+from pathlib import Path
+from typing import Type, Optional
+
+from langchain_core.tools import BaseTool
+from pydantic import BaseModel, Field
+
+
+class SearchKnowledgeInput(BaseModel):
+    query: str = Field(description="The search query to find relevant knowledge")
+
+
+class SearchKnowledgeBaseTool(BaseTool):
+    name: str = "search_knowledge_base"
+    description: str = (
+        "Search the local knowledge base using hybrid retrieval (keyword + semantic). "
+        "Use this when the user asks about specific knowledge or documents. "
+        "Returns the most relevant passages from the knowledge base."
+    )
+    args_schema: Type[BaseModel] = SearchKnowledgeInput
+    base_dir: str = ""
+    _index: Optional[object] = None
+
+    class Config:
+        arbitrary_types_allowed = True
+
+    def _build_index(self):
+        """Build or load LlamaIndex index from knowledge/ directory."""
+        knowledge_dir = Path(self.base_dir) / "knowledge"
+        storage_dir = Path(self.base_dir) / "storage"
+
+        if not knowledge_dir.exists() or not any(knowledge_dir.iterdir()):
+            return None
+
+        try:
+            from llama_index.core import (
+                SimpleDirectoryReader,
+                StorageContext,
+                VectorStoreIndex,
+                load_index_from_storage,
+            )
+            from llama_index.core.settings import Settings
+            from llama_index.embeddings.openai import OpenAIEmbedding
+
+            Settings.embed_model = OpenAIEmbedding(
+                model=os.getenv("EMBEDDING_MODEL", "text-embedding-3-small"),
+                api_key=os.getenv("OPENAI_API_KEY"),
+                api_base=os.getenv("OPENAI_BASE_URL", "https://ai.devtool.tech/proxy/v1"),
+            )
+
+            # Try loading persisted index
+            if storage_dir.exists() and any(storage_dir.iterdir()):
+                try:
+                    storage_context = StorageContext.from_defaults(
+                        persist_dir=str(storage_dir)
+                    )
+                    return load_index_from_storage(storage_context)
+                except Exception:
+                    pass
+
+            # Build fresh index
+            documents = SimpleDirectoryReader(
+                str(knowledge_dir), recursive=True
+            ).load_data()
+
+            if not documents:
+                return None
+
+            index = VectorStoreIndex.from_documents(documents)
+            storage_dir.mkdir(parents=True, exist_ok=True)
+            index.storage_context.persist(persist_dir=str(storage_dir))
+            return index
+
+        except ImportError as e:
+            print(f"⚠️ LlamaIndex not fully installed: {e}")
+            return None
+        except Exception as e:
+            print(f"⚠️ Index build error: {e}")
+            return None
+
+    def _run(self, query: str) -> str:
+        if self._index is None:
+            self._index = self._build_index()
+
+        if self._index is None:
+            return "📭 Knowledge base is empty. Add documents to backend/knowledge/ to enable search."
+
+        try:
+            query_engine = self._index.as_query_engine(similarity_top_k=3)
+            response = query_engine.query(query)
+            result = str(response)
+            if len(result) > 5000:
+                result = result[:5000] + "\n...[truncated]"
+            return result
+        except Exception as e:
+            return f"❌ Search error: {str(e)}"
+
+
+def create_search_knowledge_tool(base_dir: Path) -> SearchKnowledgeBaseTool:
+    return SearchKnowledgeBaseTool(base_dir=str(base_dir))