feat: Add ValkeyMemoryService with vector similarity search (Valkey Search module)

## Summary

Add a `ValkeyMemoryService` implementation for the ADK community memory module, backed by Valkey using the Valkey Search module for vector similarity search (HNSW). This would use the `valkey-glide` client library and provide functionality analogous to `VertexAiRagMemoryService` for developers with Valkey infrastructure.

## Motivation

Currently, the community memory module has limited backend options. Valkey (the open-source fork of Redis) with the Search module provides a high-performance, self-hosted vector database option that many teams already run in production. Adding a Valkey-backed memory service would expand the ecosystem for developers who prefer or already use Valkey infrastructure.

## Proposed Implementation

- `ValkeyMemoryService` class implementing `BaseMemoryService` interface
- `ValkeyMemoryServiceConfig` (Pydantic) with configurable: `similarity_top_k`, `vector_distance_threshold`, `embedding_dimensions`, `key_prefix`, `index_name`, `distance_metric` (COSINE/L2/IP), `ttl_seconds`
- Accepts a configurable async embedding function (users bring their own embedder — OpenAI, Gemini, sentence-transformers, etc.)
- Stores memories as Valkey Hash keys with FLOAT32 vector embeddings
- Uses `FT.CREATE` with VECTOR field (HNSW algorithm) + TAG fields for `app_name`/`user_id` scoped queries
- Uses `FT.SEARCH` with KNN for vector similarity retrieval, pre-filtered by TAG
- Implements both `add_session_to_memory` and `add_events_to_memory`
- Uses `Batch` pipelining for efficient ingestion (1 round trip regardless of event count)
- `asyncio.Lock` with double-check locking for thread-safe index creation
- Configurable distance threshold, distance metric, TTL

## Dependencies

- `valkey-glide >= 2.4.0` (as optional dependency under `[valkey]` extra)
- Valkey server with the Search module loaded (e.g., `valkey/valkey-bundle` image)

## Testing Plan

- Unit tests (mocked client): cover all public methods, edge cases, error conditions
- Integration tests: run against live Valkey 9.1 (`valkey/valkey-bundle` image) covering vector similarity search, user/app isolation, top-k limits, distance threshold filtering, metadata preservation, index idempotency

Start by reading the existing BaseMemoryService interface and the analogous VertexAiRagMemoryService implementation. Review the proposed ValkeyMemoryServiceConfig, public methods, and testing plan, then verify the mocked-client and live Valkey integration cases described in the issue. Done means the service supports scoped vector retrieval, ingestion, configuration, and the listed edge cases.

Written by the indexing model from the issue text.