feat(rag): support Redis vector search as a VDBStore backend

^{Originally posted by **izualzhy** July 23, 2026}
## Feature Request

It would be great if AgentScope could support Redis Vector Search as a `VDBStore` backend.

Many users already have Redis deployed in production. Supporting Redis would allow them to build small and medium-sized RAG applications without introducing an additional vector database such as Qdrant or Milvus.

Redis Vector Search provides native support for vector indexing (HNSW/FLAT), similarity search, and metadata filtering, making it a good fit for the existing `KnowledgeBase` + `VDBStore` abstraction.

From my understanding of the current design, this could fit naturally into the existing architecture as another `VDBStore` implementation:

```
KnowledgeBase
│
▼
VectorStoreBase
│
┌────────────────┴────────────────┐
│ │ │
QdrantStore ... RedisVectorStore (new)
│
├── create collection / index
├── insert documents
├── vector search
├── delete documents
├── list documents
└── (metadata_filter)
```

This would provide a lightweight deployment option while keeping the current architecture unchanged.

If this feature aligns with the project roadmap, I'd be happy to contribute an implementation.