"""Base RAG 使用示例""" from base_rag import BaseRAG class SimpleRAG(BaseRAG): def ingest(self, file_path: str): documents = self.load_and_split_documents(file_path) self.add_documents_to_vector_store(documents) print(f"导入 {len(documents)} 个文档") def query(self, question: str) -> str: docs = self.similarity_search(question) return f"找到 {len(docs)} 个相关文档" if __name__ == "__main__": # 本地模型配置 config = {"type": "local", "model_name": "sentence-transformers/all-MiniLM-L6-v2"} rag = SimpleRAG(embedding_config=config) print("RAG初始化完成!") # rag.ingest("your_document.txt") # result = rag.query("你的问题") # print(result)