feat: Add Valkey as a vector database option (using Valkey GLIDE)

Summary

Add Valkey as a supported vector database in llmware, using the official Valkey GLIDE sync client (valkey-glide-sync).

Related discussion: #1296

Motivation

• Valkey is the open-source (BSD-3-Clause) successor to Redis, backed by the Linux Foundation
• The valkey-search module provides vector similarity search with HNSW/FLAT indexing, plus full-text, tag, and numeric range queries
• Cloud-native: AWS ElastiCache 9.0+ and Google Cloud Memorystore both support Valkey with vector search
• The existing EmbeddingRedis class uses redis-py which targets the SSPL-licensed Redis server

Implementation

Following the existing pattern (EmbeddingRedis, EmbeddingMilvus, etc.):

• ValkeyConfig in configs.py — host, port, credentials, TLS (all env-var backed)
• EmbeddingValkey in embeddings.py — standalone class implementing create_new_embedding, search_index, delete_index
• Registry — "valkey" added to VectorDBRegistry and _supported["vector_db"]
• Dependency — valkey-glide-sync (lazy-imported, optional)
• Tests — unit tests for config/registry + integration tests for create/search/delete

Requirements

• Valkey server >= 9.1 with valkey-search module >= 1.2.0
• Python: pip install valkey-glide-sync (v2.3.1+, supports Python 3.9-3.13)
• Docker for testing: docker run -d -p 6379:6379 valkey/valkey-bundle:9.1.0-rc2

Design Decisions

1. Standalone class — API differences with GLIDE vs redis-py make a shared base impractical
2. Sync client only — matches the synchronous pattern used by all other embedding classes
3. No cluster mode — standalone only for initial implementation
4. Individual hset writes — HNSW indexing processes writes synchronously, causing Batch/pipeline timeouts; individual calls are reliable and match EmbeddingRedis pattern