docs(vectordbs): correct vector store config defaults and imports

Each fix verified against mem0/vector_stores/ source and configs:

- baidu: table_name default mem0_table → mem0 (BaiduDBConfig)
- langchain: TS import mem0ai → mem0ai/oss; fix undefined LangchainVectorStore
  variable to use MemoryVectorStore directly
- mongodb: collection_name default mem0_collection → mem0; mongo_uri default
  corrected to mongodb://localhost:27017 (MongoDBConfig)
- neon: hnsw default False → True (PGVectorConfig.hnsw = True)
- neptune_analytics: extra name vector_stores → vector-stores (pyproject.toml)
- pgvector: diskann default True → False; hnsw default False → True
  (PGVectorConfig)
- upstash-vector: move enable_embeddings inside config: {} block
  (UpstashVectorConfig field, not top-level VectorStoreConfig)
- valkey: remove non-existent distance_metric row (not in ValkeyConfig);
  fix extra name vector_stores → vector-stores
- vertex_ai: region is required (no default in GoogleMatchingEngineConfig)
- weaviate: install pip install weaviate weaviate-client → weaviate-client
  (weaviate is not a separate package)
- overview: update TS supported vector DBs list to reflect actual
  mem0-ts/src/oss/src/vector_stores/ files

docs/components/vectordbs/dbs/valkey.mdx

@@ -9,7 +9,7 @@ description: "Use Valkey as an open-source vector store in Mem0 for high-perform
 ## Installation
 
 ```bash
-pip install mem0ai[vector_stores]
+pip install mem0ai[vector-stores]
 ```
 
 ## Usage
@@ -51,7 +51,6 @@ Here are the parameters available for configuring Valkey:
 | `hnsw_ef_construction` | Size of dynamic candidate list for HNSW | `200` |
 | `hnsw_ef_runtime` | Size of dynamic candidate list for search | `10` |
 | `cluster_mode` | Enable cluster mode for Valkey cluster (CME) deployments | `false` |
-| `distance_metric` | Distance metric for vector similarity | `cosine` |
 
 ## Cluster Mode