feat(rag): support RAGFlow in a new `RAGFlowKnowledgeBase` class rather than VDB

**Background**

Through recent investigation into RAGFlow, we found that it provides a complete, end-to-end RAG pipeline — including document parsing, chunking, indexing, and retrieval — as a fully managed solution. This makes it fundamentally different from a traditional vector database (VDB).

Forcing RAGFlow into our existing VDB abstraction would be a poor fit: it would either require bypassing RAGFlow's core strengths (e.g., its built-in document processing and indexing pipeline) or lead to an awkward, leaky abstraction. To fully leverage what RAGFlow offers, it should instead be integrated at the Knowledge layer, sitting alongside the existing Knowledge class rather than beneath it.

**Changes**

Add RAGFlowKnowledge class under the rag module
Implement the same interface as the existing Knowledge class, ensuring consistency and interoperability across different knowledge backends
Encapsulate RAGFlow's document parsing, indexing, and retrieval capabilities within the new class