diff --git a/docs/examples/sample_vector_io_config.yaml b/docs/examples/sample_vector_io_config.yaml
new file mode 100644
index 000000000..3f2ba1e17
--- /dev/null
+++ b/docs/examples/sample_vector_io_config.yaml
@@ -0,0 +1,127 @@
+# Sample Vector IO Configuration with Embedding Model Defaults
+# This example demonstrates the new provider-level embedding configuration features
+
+# Image and version info
+version: 3
+image_name: my-embedding-app
+
+# APIs to serve
+apis:
+  - inference
+  - vector_io
+
+# Provider configurations
+providers:
+  # Inference provider for embedding models
+  inference:
+    - provider_id: local_inference
+      provider_type: inline::ollama
+      config:
+        url: http://localhost:11434
+
+  # Vector IO providers with embedding model defaults
+  vector_io:
+    # FAISS for fast local search with lightweight embeddings
+    - provider_id: fast_local_search
+      provider_type: inline::faiss
+      config:
+        kvstore:
+          provider_type: sqlite
+          config:
+            db_path: ~/.llama/distributions/my-app/faiss_store.db
+        # NEW: Default embedding model for this provider
+        embedding_model: "all-MiniLM-L6-v2"
+        # Dimension auto-lookup: 384 (from model registry)
+
+    # SQLite Vec for lightweight vector storage with Matryoshka embeddings
+    - provider_id: compact_storage
+      provider_type: inline::sqlite_vec
+      config:
+        db_path: ~/.llama/distributions/my-app/sqlite_vec.db
+        kvstore:
+          provider_type: sqlite
+          config:
+            db_name: sqlite_vec_registry.db
+        # Matryoshka embedding with custom dimension
+        embedding_model: "nomic-embed-text"
+        embedding_dimension: 256  # Reduced from default 768 for efficiency
+
+    # Chroma for persistent local storage
+    - provider_id: persistent_search
+      provider_type: inline::chroma
+      config:
+        db_path: ~/.llama/distributions/my-app/chroma.db
+        # High-quality embeddings for semantic search
+        embedding_model: "sentence-transformers/all-mpnet-base-v2"
+        # Auto-lookup dimension from model registry
+
+    # Qdrant Cloud for production-scale search (when available)
+    - provider_id: cloud_search
+      provider_type: remote::qdrant
+      config:
+        api_key: "${env.QDRANT_API_KEY}"
+        url: "${env.QDRANT_URL}"
+        # Production-grade embedding model
+        embedding_model: "text-embedding-3-small"
+        embedding_dimension: 512  # Custom dimension for performance
+
+# Model registry - ensure embedding models are properly configured
+models:
+  # Lightweight embedding model (384 dimensions)
+  - model_id: all-MiniLM-L6-v2
+    provider_id: local_inference
+    provider_model_id: sentence-transformers/all-MiniLM-L6-v2
+    model_type: embedding
+    metadata:
+      embedding_dimension: 384
+      description: "Fast, lightweight embeddings for general use"
+
+  # Matryoshka embedding model (variable dimensions)
+  - model_id: nomic-embed-text
+    provider_id: local_inference
+    provider_model_id: nomic-embed-text
+    model_type: embedding
+    metadata:
+      embedding_dimension: 768  # Default, can be overridden
+      description: "Flexible Matryoshka embeddings supporting variable dimensions"
+
+  # High-quality embedding model (768 dimensions)
+  - model_id: sentence-transformers/all-mpnet-base-v2
+    provider_id: local_inference
+    provider_model_id: sentence-transformers/all-mpnet-base-v2
+    model_type: embedding
+    metadata:
+      embedding_dimension: 768
+      description: "High-quality embeddings for semantic search"
+
+  # OpenAI embedding model (for cloud usage)
+  - model_id: text-embedding-3-small
+    provider_id: openai_inference  # Would need OpenAI provider configured
+    provider_model_id: text-embedding-3-small
+    model_type: embedding
+    metadata:
+      embedding_dimension: 1536  # Default OpenAI dimension
+      description: "OpenAI's efficient embedding model"
+
+# Optional: Configure specific vector databases (will use provider defaults)
+vector_dbs:
+  # Uses fast_local_search provider defaults (all-MiniLM-L6-v2, 384 dims)
+  - vector_db_id: general_docs
+    provider_id: fast_local_search
+
+  # Uses compact_storage provider defaults (nomic-embed-text, 256 dims)
+  - vector_db_id: compressed_knowledge
+    provider_id: compact_storage
+
+  # Uses persistent_search provider defaults (all-mpnet-base-v2, 768 dims)
+  - vector_db_id: semantic_library
+    provider_id: persistent_search
+
+# Server configuration
+server:
+  host: 0.0.0.0
+  port: 5000
+
+# Logging configuration
+logging:
+  level: INFO
diff --git a/docs/examples/vector_io_embedding_configuration.md b/docs/examples/vector_io_embedding_configuration.md
new file mode 100644
index 000000000..f59006d7c
--- /dev/null
+++ b/docs/examples/vector_io_embedding_configuration.md
@@ -0,0 +1,302 @@
+# Vector IO Embedding Model Configuration
+
+## Overview
+
+Vector IO providers now support configuring default embedding models at the provider level. This allows you to:
+- Set a default embedding model for each vector store provider
+- Support Matryoshka embeddings with custom dimensions
+- Automatic dimension lookup from the model registry
+- Maintain backward compatibility with existing configurations
+
+## Configuration Options
+
+### Provider-Level Embedding Configuration
+
+Add `embedding_model` and `embedding_dimension` fields to your vector IO provider configuration:
+
+```yaml
+providers:
+  vector_io:
+    - provider_id: my_faiss_store
+      provider_type: inline::faiss
+      config:
+        kvstore:
+          provider_type: sqlite
+          config:
+            db_path: ~/.llama/distributions/my-app/faiss_store.db
+        # NEW: Configure default embedding model
+        embedding_model: "all-MiniLM-L6-v2"
+        # Optional: Only needed for variable-dimension models
+        # embedding_dimension: 384
+```
+
+### Embedding Model Selection Priority
+
+The system uses a 3-tier priority system for selecting embedding models:
+
+1. **Explicit API Parameters** (highest priority)
+   ```python
+   # API call explicitly specifies model - this takes precedence
+   await vector_io.openai_create_vector_store(
+       name="my-store",
+       embedding_model="nomic-embed-text",  # Explicit override
+       embedding_dimension=256,
+   )
+   ```
+
+2. **Provider Config Defaults** (middle priority)
+   ```yaml
+   # Provider config provides default when no explicit model specified
+   config:
+     embedding_model: "all-MiniLM-L6-v2"
+     embedding_dimension: 384
+   ```
+
+3. **System Default** (fallback)
+   ```
+   # Uses first available embedding model from model registry
+   # Maintains backward compatibility
+   ```
+
+## Provider Examples
+
+### FAISS with Default Embedding Model
+
+```yaml
+providers:
+  vector_io:
+    - provider_id: faiss_store
+      provider_type: inline::faiss
+      config:
+        kvstore:
+          provider_type: sqlite
+          config:
+            db_path: ~/.llama/distributions/my-app/faiss_store.db
+        embedding_model: "all-MiniLM-L6-v2"
+        # Dimension auto-lookup: 384 (from model registry)
+```
+
+### SQLite Vec with Matryoshka Embedding
+
+```yaml
+providers:
+  vector_io:
+    - provider_id: sqlite_vec_store
+      provider_type: inline::sqlite_vec
+      config:
+        db_path: ~/.llama/distributions/my-app/sqlite_vec.db
+        kvstore:
+          provider_type: sqlite
+          config:
+            db_name: sqlite_vec_registry.db
+        embedding_model: "nomic-embed-text"
+        embedding_dimension: 256  # Override default 768 to 256
+```
+
+### Chroma with Provider Default
+
+```yaml
+providers:
+  vector_io:
+    - provider_id: chroma_store
+      provider_type: inline::chroma
+      config:
+        db_path: ~/.llama/distributions/my-app/chroma.db
+        embedding_model: "sentence-transformers/all-mpnet-base-v2"
+        # Auto-lookup dimension from model registry
+```
+
+### Remote Qdrant Configuration
+
+```yaml
+providers:
+  vector_io:
+    - provider_id: qdrant_cloud
+      provider_type: remote::qdrant
+      config:
+        api_key: "${env.QDRANT_API_KEY}"
+        url: "https://my-cluster.qdrant.tech"
+        embedding_model: "text-embedding-3-small"
+        embedding_dimension: 512  # Custom dimension for Matryoshka model
+```
+
+### Multiple Providers with Different Models
+
+```yaml
+providers:
+  vector_io:
+    # Fast, lightweight embeddings for simple search
+    - provider_id: fast_search
+      provider_type: inline::faiss
+      config:
+        kvstore:
+          provider_type: sqlite
+          config:
+            db_path: ~/.llama/fast_search.db
+        embedding_model: "all-MiniLM-L6-v2"  # 384 dimensions
+
+    # High-quality embeddings for semantic search
+    - provider_id: semantic_search
+      provider_type: remote::qdrant
+      config:
+        api_key: "${env.QDRANT_API_KEY}"
+        embedding_model: "text-embedding-3-large"  # 3072 dimensions
+
+    # Flexible Matryoshka embeddings
+    - provider_id: flexible_search
+      provider_type: inline::chroma
+      config:
+        db_path: ~/.llama/flexible_search.db
+        embedding_model: "nomic-embed-text"
+        embedding_dimension: 256  # Reduced from default 768
+```
+
+## Model Registry Configuration
+
+Ensure your embedding models are registered in the model registry:
+
+```yaml
+models:
+  - model_id: all-MiniLM-L6-v2
+    provider_id: huggingface
+    provider_model_id: sentence-transformers/all-MiniLM-L6-v2
+    model_type: embedding
+    metadata:
+      embedding_dimension: 384
+
+  - model_id: nomic-embed-text
+    provider_id: ollama
+    provider_model_id: nomic-embed-text
+    model_type: embedding
+    metadata:
+      embedding_dimension: 768  # Default, can be overridden
+
+  - model_id: text-embedding-3-small
+    provider_id: openai
+    provider_model_id: text-embedding-3-small
+    model_type: embedding
+    metadata:
+      embedding_dimension: 1536  # Default for OpenAI model
+```
+
+## API Usage Examples
+
+### Using Provider Defaults
+
+```python
+# Uses the embedding model configured in the provider config
+vector_store = await vector_io.openai_create_vector_store(
+    name="documents", provider_id="faiss_store"  # Will use configured embedding_model
+)
+```
+
+### Explicit Override
+
+```python
+# Overrides provider defaults with explicit parameters
+vector_store = await vector_io.openai_create_vector_store(
+    name="documents",
+    embedding_model="text-embedding-3-large",  # Override provider default
+    embedding_dimension=1024,  # Custom dimension
+    provider_id="faiss_store",
+)
+```
+
+### Matryoshka Embedding Usage
+
+```python
+# Provider configured with nomic-embed-text and dimension 256
+vector_store = await vector_io.openai_create_vector_store(
+    name="compact_embeddings", provider_id="flexible_search"  # Uses Matryoshka config
+)
+
+# Or override with different dimension
+vector_store = await vector_io.openai_create_vector_store(
+    name="full_embeddings",
+    embedding_dimension=768,  # Use full dimension
+    provider_id="flexible_search",
+)
+```
+
+## Migration Guide
+
+### Updating Existing Configurations
+
+Your existing configurations will continue to work without changes. To add provider-level defaults:
+
+1. **Add embedding model fields** to your provider configs
+2. **Test the configuration** to ensure expected behavior
+3. **Remove explicit embedding_model parameters** from API calls if desired
+
+### Before (explicit parameters required):
+```python
+# Had to specify embedding model every time
+await vector_io.openai_create_vector_store(
+    name="store1", embedding_model="all-MiniLM-L6-v2"
+)
+```
+
+### After (provider defaults):
+```yaml
+# Configure once in provider config
+config:
+  embedding_model: "all-MiniLM-L6-v2"
+```
+
+```python
+# No need to specify repeatedly
+await vector_io.openai_create_vector_store(name="store1")
+await vector_io.openai_create_vector_store(name="store2")
+await vector_io.openai_create_vector_store(name="store3")
+```
+
+## Best Practices
+
+### 1. Model Selection
+- Use **lightweight models** (e.g., `all-MiniLM-L6-v2`) for simple semantic search
+- Use **high-quality models** (e.g., `text-embedding-3-large`) for complex retrieval
+- Consider **Matryoshka models** (e.g., `nomic-embed-text`) for flexible dimension requirements
+
+### 2. Provider Configuration
+- Configure embedding models at the **provider level** for consistency
+- Use **environment variables** for API keys and sensitive configuration
+- Set up **multiple providers** with different models for different use cases
+
+### 3. Dimension Management
+- Let the system **auto-lookup dimensions** when possible
+- Only specify `embedding_dimension` for **Matryoshka embeddings** or custom requirements
+- Ensure **model registry** has correct dimension metadata
+
+### 4. Performance Optimization
+- Use **smaller dimensions** for faster search (e.g., 256 instead of 768)
+- Consider **multiple vector stores** with different embedding models for different content types
+- Test **different embedding models** to find the best balance for your use case
+
+## Troubleshooting
+
+### Common Issues
+
+**Model not found error:**
+```
+ValueError: Embedding model 'my-model' not found in model registry
+```
+**Solution:** Ensure the model is registered in your model configuration.
+
+**Missing dimension metadata:**
+```
+ValueError: Embedding model 'my-model' has no embedding_dimension in metadata
+```
+**Solution:** Add `embedding_dimension` to the model's metadata in your model registry.
+
+**Invalid dimension override:**
+```
+ValueError: Override dimension must be positive, got -1
+```
+**Solution:** Use positive integers for `embedding_dimension` values.
+
+### Debugging Tips
+
+1. **Check model registry:** Verify embedding models are properly registered
+2. **Review provider config:** Ensure `embedding_model` matches registry IDs
+3. **Test explicit parameters:** Override provider defaults to isolate issues
+4. **Check logs:** Look for embedding model selection messages in router logs
\ No newline at end of file
diff --git a/docs/source/providers/vector_io/inline_chromadb.md b/docs/source/providers/vector_io/inline_chromadb.md
index 172215414..c4f6bb166 100644
--- a/docs/source/providers/vector_io/inline_chromadb.md
+++ b/docs/source/providers/vector_io/inline_chromadb.md
@@ -42,6 +42,8 @@ See [Chroma's documentation](https://docs.trychroma.com/docs/overview/introducti
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
 | `db_path` | `<class 'str'>` | No | PydanticUndefined |  |
+| `embedding_model` | `str \| None` | No |  | Optional default embedding model for this provider. If not specified, will use system default. |
+| `embedding_dimension` | `int \| None` | No |  | Optional embedding dimension override. Only needed for models with variable dimensions (e.g., Matryoshka embeddings). If not specified, will auto-lookup from model registry. |
 
 ## Sample Configuration
 
diff --git a/docs/source/providers/vector_io/inline_faiss.md b/docs/source/providers/vector_io/inline_faiss.md
index bcff66f3f..6f4a88561 100644
--- a/docs/source/providers/vector_io/inline_faiss.md
+++ b/docs/source/providers/vector_io/inline_faiss.md
@@ -38,6 +38,8 @@ more details about Faiss in general.
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
 | `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite |  |
+| `embedding_model` | `str \| None` | No |  | Optional default embedding model for this provider. If not specified, will use system default. |
+| `embedding_dimension` | `int \| None` | No |  | Optional embedding dimension override. Only needed for models with variable dimensions (e.g., Matryoshka embeddings). If not specified, will auto-lookup from model registry. |
 
 ## Sample Configuration
 
diff --git a/docs/source/providers/vector_io/inline_meta-reference.md b/docs/source/providers/vector_io/inline_meta-reference.md
index 0aac445bd..4d05869a6 100644
--- a/docs/source/providers/vector_io/inline_meta-reference.md
+++ b/docs/source/providers/vector_io/inline_meta-reference.md
@@ -9,6 +9,8 @@ Meta's reference implementation of a vector database.
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
 | `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite |  |
+| `embedding_model` | `str \| None` | No |  | Optional default embedding model for this provider. If not specified, will use system default. |
+| `embedding_dimension` | `int \| None` | No |  | Optional embedding dimension override. Only needed for models with variable dimensions (e.g., Matryoshka embeddings). If not specified, will auto-lookup from model registry. |
 
 ## Sample Configuration
 
diff --git a/docs/source/providers/vector_io/inline_milvus.md b/docs/source/providers/vector_io/inline_milvus.md
index 3b3aad3fc..bd6e033ce 100644
--- a/docs/source/providers/vector_io/inline_milvus.md
+++ b/docs/source/providers/vector_io/inline_milvus.md
@@ -13,6 +13,8 @@ Please refer to the remote provider documentation.
 | `db_path` | `<class 'str'>` | No | PydanticUndefined |  |
 | `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | Config for KV store backend (SQLite only for now) |
 | `consistency_level` | `<class 'str'>` | No | Strong | The consistency level of the Milvus server |
+| `embedding_model` | `str \| None` | No |  | Optional default embedding model for this provider. If not specified, will use system default. |
+| `embedding_dimension` | `int \| None` | No |  | Optional embedding dimension override. Only needed for models with variable dimensions (e.g., Matryoshka embeddings). If not specified, will auto-lookup from model registry. |
 
 ## Sample Configuration
 
diff --git a/docs/source/providers/vector_io/inline_qdrant.md b/docs/source/providers/vector_io/inline_qdrant.md
index 63e2d81d8..504914300 100644
--- a/docs/source/providers/vector_io/inline_qdrant.md
+++ b/docs/source/providers/vector_io/inline_qdrant.md
@@ -51,6 +51,8 @@ See the [Qdrant documentation](https://qdrant.tech/documentation/) for more deta
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
 | `path` | `<class 'str'>` | No | PydanticUndefined |  |
+| `embedding_model` | `str \| None` | No |  | Optional default embedding model for this provider. If not specified, will use system default. |
+| `embedding_dimension` | `int \| None` | No |  | Optional embedding dimension override. Only needed for models with variable dimensions (e.g., Matryoshka embeddings). If not specified, will auto-lookup from model registry. |
 
 ## Sample Configuration
 
diff --git a/docs/source/providers/vector_io/inline_sqlite-vec.md b/docs/source/providers/vector_io/inline_sqlite-vec.md
index ae7c45b21..71c9af876 100644
--- a/docs/source/providers/vector_io/inline_sqlite-vec.md
+++ b/docs/source/providers/vector_io/inline_sqlite-vec.md
@@ -207,6 +207,8 @@ See [sqlite-vec's GitHub repo](https://github.com/asg017/sqlite-vec/tree/main) f
 |-------|------|----------|---------|-------------|
 | `db_path` | `<class 'str'>` | No | PydanticUndefined | Path to the SQLite database file |
 | `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | Config for KV store backend (SQLite only for now) |
+| `embedding_model` | `str \| None` | No |  | Optional default embedding model for this provider. If not specified, will use system default. |
+| `embedding_dimension` | `int \| None` | No |  | Optional embedding dimension override. Only needed for models with variable dimensions (e.g., Matryoshka embeddings). If not specified, will auto-lookup from model registry. |
 
 ## Sample Configuration
 
diff --git a/docs/source/providers/vector_io/inline_sqlite_vec.md b/docs/source/providers/vector_io/inline_sqlite_vec.md
index 7e14bb8bd..c553b636f 100644
--- a/docs/source/providers/vector_io/inline_sqlite_vec.md
+++ b/docs/source/providers/vector_io/inline_sqlite_vec.md
@@ -12,6 +12,8 @@ Please refer to the sqlite-vec provider documentation.
 |-------|------|----------|---------|-------------|
 | `db_path` | `<class 'str'>` | No | PydanticUndefined | Path to the SQLite database file |
 | `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | Config for KV store backend (SQLite only for now) |
+| `embedding_model` | `str \| None` | No |  | Optional default embedding model for this provider. If not specified, will use system default. |
+| `embedding_dimension` | `int \| None` | No |  | Optional embedding dimension override. Only needed for models with variable dimensions (e.g., Matryoshka embeddings). If not specified, will auto-lookup from model registry. |
 
 ## Sample Configuration
 
diff --git a/docs/source/providers/vector_io/remote_chromadb.md b/docs/source/providers/vector_io/remote_chromadb.md
index cc1dcc4d1..1946cfaec 100644
--- a/docs/source/providers/vector_io/remote_chromadb.md
+++ b/docs/source/providers/vector_io/remote_chromadb.md
@@ -41,6 +41,8 @@ See [Chroma's documentation](https://docs.trychroma.com/docs/overview/introducti
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
 | `url` | `str \| None` | No | PydanticUndefined |  |
+| `embedding_model` | `str \| None` | No |  | Optional default embedding model for this provider. If not specified, will use system default. |
+| `embedding_dimension` | `int \| None` | No |  | Optional embedding dimension override. Only needed for models with variable dimensions (e.g., Matryoshka embeddings). If not specified, will auto-lookup from model registry. |
 
 ## Sample Configuration
 
diff --git a/docs/source/providers/vector_io/remote_milvus.md b/docs/source/providers/vector_io/remote_milvus.md
index f3089e615..a438b6019 100644
--- a/docs/source/providers/vector_io/remote_milvus.md
+++ b/docs/source/providers/vector_io/remote_milvus.md
@@ -115,6 +115,8 @@ For more details on TLS configuration, refer to the [TLS setup guide](https://mi
 | `token` | `str \| None` | No | PydanticUndefined | The token of the Milvus server |
 | `consistency_level` | `<class 'str'>` | No | Strong | The consistency level of the Milvus server |
 | `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig, annotation=NoneType, required=False, default='sqlite', discriminator='type'` | No |  | Config for KV store backend (SQLite only for now) |
+| `embedding_model` | `str \| None` | No |  | Optional default embedding model for this provider. If not specified, will use system default. |
+| `embedding_dimension` | `int \| None` | No |  | Optional embedding dimension override. Only needed for models with variable dimensions (e.g., Matryoshka embeddings). If not specified, will auto-lookup from model registry. |
 | `config` | `dict` | No | {} | This configuration allows additional fields to be passed through to the underlying Milvus client. See the [Milvus](https://milvus.io/docs/install-overview.md) documentation for more details about Milvus in general. |
 
 > **Note**: This configuration class accepts additional fields beyond those listed above. You can pass any additional configuration options that will be forwarded to the underlying provider.
diff --git a/docs/source/providers/vector_io/remote_pgvector.md b/docs/source/providers/vector_io/remote_pgvector.md
index 685b98f37..a6de9f699 100644
--- a/docs/source/providers/vector_io/remote_pgvector.md
+++ b/docs/source/providers/vector_io/remote_pgvector.md
@@ -40,6 +40,8 @@ See [PGVector's documentation](https://github.com/pgvector/pgvector) for more de
 | `db` | `str \| None` | No | postgres |  |
 | `user` | `str \| None` | No | postgres |  |
 | `password` | `str \| None` | No | mysecretpassword |  |
+| `embedding_model` | `str \| None` | No |  | Optional default embedding model for this provider. If not specified, will use system default. |
+| `embedding_dimension` | `int \| None` | No |  | Optional embedding dimension override. Only needed for models with variable dimensions (e.g., Matryoshka embeddings). If not specified, will auto-lookup from model registry. |
 
 ## Sample Configuration
 
diff --git a/docs/source/providers/vector_io/remote_qdrant.md b/docs/source/providers/vector_io/remote_qdrant.md
index 14c821f35..66818d382 100644
--- a/docs/source/providers/vector_io/remote_qdrant.md
+++ b/docs/source/providers/vector_io/remote_qdrant.md
@@ -20,6 +20,8 @@ Please refer to the inline provider documentation.
 | `prefix` | `str \| None` | No |  |  |
 | `timeout` | `int \| None` | No |  |  |
 | `host` | `str \| None` | No |  |  |
+| `embedding_model` | `str \| None` | No |  | Optional default embedding model for this provider. If not specified, will use system default. |
+| `embedding_dimension` | `int \| None` | No |  | Optional embedding dimension override. Only needed for models with variable dimensions (e.g., Matryoshka embeddings). If not specified, will auto-lookup from model registry. |
 
 ## Sample Configuration
 
diff --git a/docs/source/providers/vector_io/remote_weaviate.md b/docs/source/providers/vector_io/remote_weaviate.md
index b7f811c35..ef3b64f57 100644
--- a/docs/source/providers/vector_io/remote_weaviate.md
+++ b/docs/source/providers/vector_io/remote_weaviate.md
@@ -33,6 +33,13 @@ To install Weaviate see the [Weaviate quickstart documentation](https://weaviate
 See [Weaviate's documentation](https://weaviate.io/developers/weaviate) for more details about Weaviate in general.
 
 
+## Configuration
+
+| Field | Type | Required | Default | Description |
+|-------|------|----------|---------|-------------|
+| `embedding_model` | `str \| None` | No |  | Optional default embedding model for this provider. If not specified, will use system default. |
+| `embedding_dimension` | `int \| None` | No |  | Optional embedding dimension override. Only needed for models with variable dimensions (e.g., Matryoshka embeddings). If not specified, will auto-lookup from model registry. |
+
 ## Sample Configuration
 
 ```yaml
diff --git a/llama_stack/distribution/routers/vector_io.py b/llama_stack/distribution/routers/vector_io.py
index 4bd5952dc..6bb914877 100644
--- a/llama_stack/distribution/routers/vector_io.py
+++ b/llama_stack/distribution/routers/vector_io.py
@@ -7,9 +7,7 @@
 import asyncio
 from typing import Any
 
-from llama_stack.apis.common.content_types import (
-    InterleavedContent,
-)
+from llama_stack.apis.common.content_types import InterleavedContent
 from llama_stack.apis.models import ModelType
 from llama_stack.apis.vector_io import (
     Chunk,
@@ -28,6 +26,7 @@ from llama_stack.apis.vector_io import (
 )
 from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import HealthResponse, HealthStatus, RoutingTable
+from llama_stack.providers.utils.vector_io.embedding_utils import get_provider_embedding_model_info
 
 logger = get_logger(name=__name__, category="core")
 
@@ -51,10 +50,10 @@ class VectorIORouter(VectorIO):
         pass
 
     async def _get_first_embedding_model(self) -> tuple[str, int] | None:
-        """Get the first available embedding model identifier."""
+        """Get the first available embedding model identifier (DEPRECATED - use embedding_utils instead)."""
         try:
             # Get all models from the routing table
-            all_models = await self.routing_table.get_all_with_type("model")
+            all_models = await self.routing_table.get_all_with_type("model")  # type: ignore
 
             # Filter for embedding models
             embedding_models = [
@@ -75,6 +74,31 @@ class VectorIORouter(VectorIO):
             logger.error(f"Error getting embedding models: {e}")
             return None
 
+    async def _get_provider_config(self, provider_id: str | None = None) -> Any:
+        """Get the provider configuration object for embedding model defaults."""
+        try:
+            # If no provider_id specified, get the first available provider
+            if provider_id is None and hasattr(self.routing_table, "impls_by_provider_id"):
+                available_providers = list(self.routing_table.impls_by_provider_id.keys())  # type: ignore
+                if available_providers:
+                    provider_id = available_providers[0]
+                else:
+                    logger.warning("No vector IO providers available")
+                    return None
+
+            if provider_id and hasattr(self.routing_table, "impls_by_provider_id"):
+                provider_impl = self.routing_table.impls_by_provider_id.get(provider_id)  # type: ignore
+                if provider_impl and hasattr(provider_impl, "__provider_config__"):
+                    return provider_impl.__provider_config__
+                else:
+                    logger.debug(f"Provider {provider_id} has no config object attached")
+                    return None
+
+            return None
+        except Exception as e:
+            logger.error(f"Error getting provider config: {e}")
+            return None
+
     async def register_vector_db(
         self,
         vector_db_id: str,
@@ -84,7 +108,7 @@ class VectorIORouter(VectorIO):
         provider_vector_db_id: str | None = None,
     ) -> None:
         logger.debug(f"VectorIORouter.register_vector_db: {vector_db_id}, {embedding_model}")
-        await self.routing_table.register_vector_db(
+        await self.routing_table.register_vector_db(  # type: ignore
             vector_db_id,
             embedding_model,
             embedding_dimension,
@@ -127,13 +151,64 @@ class VectorIORouter(VectorIO):
     ) -> VectorStoreObject:
         logger.debug(f"VectorIORouter.openai_create_vector_store: name={name}, provider_id={provider_id}")
 
-        # If no embedding model is provided, use the first available one
-        if embedding_model is None:
-            embedding_model_info = await self._get_first_embedding_model()
+        # Use the new 3-tier priority system for embedding model selection
+        provider_config = await self._get_provider_config(provider_id)
+
+        # Log the resolution context for debugging
+        logger.debug(f"Resolving embedding model for vector store '{name}' with provider_id={provider_id}")
+        logger.debug(f"Explicit model: {embedding_model}, explicit dimension: {embedding_dimension}")
+        logger.debug(
+            f"Provider config embedding_model: {getattr(provider_config, 'embedding_model', None) if provider_config else None}"
+        )
+        logger.debug(
+            f"Provider config embedding_dimension: {getattr(provider_config, 'embedding_dimension', None) if provider_config else None}"
+        )
+
+        try:
+            embedding_model_info = await get_provider_embedding_model_info(
+                routing_table=self.routing_table,
+                provider_config=provider_config,
+                explicit_model_id=embedding_model,
+                explicit_dimension=embedding_dimension,
+            )
+
             if embedding_model_info is None:
                 raise ValueError("No embedding model provided and no embedding models available in the system")
-            embedding_model, embedding_dimension = embedding_model_info
-            logger.info(f"No embedding model specified, using first available: {embedding_model}")
+
+            resolved_model, resolved_dimension = embedding_model_info
+
+            # Enhanced logging to show resolution path
+            if embedding_model is not None:
+                logger.info(
+                    f"✅ Vector store '{name}': Using EXPLICIT embedding model '{resolved_model}' (dimension: {resolved_dimension})"
+                )
+            elif provider_config and getattr(provider_config, "embedding_model", None):
+                logger.info(
+                    f"✅ Vector store '{name}': Using PROVIDER DEFAULT embedding model '{resolved_model}' (dimension: {resolved_dimension}) from provider '{provider_id}'"
+                )
+                if getattr(provider_config, "embedding_dimension", None):
+                    logger.info(f"   └── Provider config dimension override: {resolved_dimension}")
+                else:
+                    logger.info(f"   └── Auto-lookup dimension from model registry: {resolved_dimension}")
+            else:
+                logger.info(
+                    f"✅ Vector store '{name}': Using SYSTEM DEFAULT embedding model '{resolved_model}' (dimension: {resolved_dimension})"
+                )
+                logger.warning(
+                    f"⚠️  Consider configuring a default embedding model for provider '{provider_id}' to avoid fallback behavior"
+                )
+
+            embedding_model, embedding_dimension = resolved_model, resolved_dimension
+
+        except Exception as e:
+            logger.error(
+                f"❌ Failed to resolve embedding model for vector store '{name}' with provider '{provider_id}': {e}"
+            )
+            logger.error(f"   Debug info - Explicit: model={embedding_model}, dim={embedding_dimension}")
+            logger.error(
+                f"   Debug info - Provider: model={getattr(provider_config, 'embedding_model', None) if provider_config else None}, dim={getattr(provider_config, 'embedding_dimension', None) if provider_config else None}"
+            )
+            raise ValueError(f"Unable to determine embedding model for vector store '{name}': {e}") from e
 
         vector_db_id = name
         registered_vector_db = await self.routing_table.register_vector_db(
diff --git a/llama_stack/providers/inline/vector_io/chroma/config.py b/llama_stack/providers/inline/vector_io/chroma/config.py
index 81e2f289e..d50740537 100644
--- a/llama_stack/providers/inline/vector_io/chroma/config.py
+++ b/llama_stack/providers/inline/vector_io/chroma/config.py
@@ -6,12 +6,25 @@
 
 from typing import Any
 
-from pydantic import BaseModel
+from pydantic import BaseModel, Field
 
 
 class ChromaVectorIOConfig(BaseModel):
     db_path: str
+    embedding_model: str | None = Field(
+        default=None,
+        description="Optional default embedding model for this provider. If not specified, will use system default.",
+    )
+    embedding_dimension: int | None = Field(
+        default=None,
+        description="Optional embedding dimension override. Only needed for models with variable dimensions (e.g., Matryoshka embeddings). If not specified, will auto-lookup from model registry.",
+    )
 
     @classmethod
     def sample_run_config(cls, db_path: str = "${env.CHROMADB_PATH}", **kwargs: Any) -> dict[str, Any]:
-        return {"db_path": db_path}
+        return {
+            "db_path": db_path,
+            # Optional: Configure default embedding model for this provider
+            # "embedding_model": "all-MiniLM-L6-v2",
+            # "embedding_dimension": 384,  # Only needed for variable-dimension models
+        }
diff --git a/llama_stack/providers/inline/vector_io/faiss/config.py b/llama_stack/providers/inline/vector_io/faiss/config.py
index cbcbb1762..67ad59694 100644
--- a/llama_stack/providers/inline/vector_io/faiss/config.py
+++ b/llama_stack/providers/inline/vector_io/faiss/config.py
@@ -6,7 +6,7 @@
 
 from typing import Any
 
-from pydantic import BaseModel
+from pydantic import BaseModel, Field
 
 from llama_stack.providers.utils.kvstore.config import (
     KVStoreConfig,
@@ -18,6 +18,14 @@ from llama_stack.schema_utils import json_schema_type
 @json_schema_type
 class FaissVectorIOConfig(BaseModel):
     kvstore: KVStoreConfig
+    embedding_model: str | None = Field(
+        default=None,
+        description="Optional default embedding model for this provider. If not specified, will use system default.",
+    )
+    embedding_dimension: int | None = Field(
+        default=None,
+        description="Optional embedding dimension override. Only needed for models with variable dimensions (e.g., Matryoshka embeddings). If not specified, will auto-lookup from model registry.",
+    )
 
     @classmethod
     def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> dict[str, Any]:
@@ -25,5 +33,8 @@ class FaissVectorIOConfig(BaseModel):
             "kvstore": SqliteKVStoreConfig.sample_run_config(
                 __distro_dir__=__distro_dir__,
                 db_name="faiss_store.db",
-            )
+            ),
+            # Optional: Configure default embedding model for this provider
+            # "embedding_model": "all-MiniLM-L6-v2",
+            # "embedding_dimension": 384,  # Only needed for variable-dimension models
         }
diff --git a/llama_stack/providers/inline/vector_io/milvus/config.py b/llama_stack/providers/inline/vector_io/milvus/config.py
index 8cbd056be..aceb7a6c1 100644
--- a/llama_stack/providers/inline/vector_io/milvus/config.py
+++ b/llama_stack/providers/inline/vector_io/milvus/config.py
@@ -20,6 +20,14 @@ class MilvusVectorIOConfig(BaseModel):
     db_path: str
     kvstore: KVStoreConfig = Field(description="Config for KV store backend (SQLite only for now)")
     consistency_level: str = Field(description="The consistency level of the Milvus server", default="Strong")
+    embedding_model: str | None = Field(
+        default=None,
+        description="Optional default embedding model for this provider. If not specified, will use system default.",
+    )
+    embedding_dimension: int | None = Field(
+        default=None,
+        description="Optional embedding dimension override. Only needed for models with variable dimensions (e.g., Matryoshka embeddings). If not specified, will auto-lookup from model registry.",
+    )
 
     @classmethod
     def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> dict[str, Any]:
@@ -29,4 +37,7 @@ class MilvusVectorIOConfig(BaseModel):
                 __distro_dir__=__distro_dir__,
                 db_name="milvus_registry.db",
             ),
+            # Optional: Configure default embedding model for this provider
+            # "embedding_model": "all-MiniLM-L6-v2",
+            # "embedding_dimension": 384,  # Only needed for variable-dimension models
         }
diff --git a/llama_stack/providers/inline/vector_io/qdrant/config.py b/llama_stack/providers/inline/vector_io/qdrant/config.py
index 7cc91d918..ffd5b3c65 100644
--- a/llama_stack/providers/inline/vector_io/qdrant/config.py
+++ b/llama_stack/providers/inline/vector_io/qdrant/config.py
@@ -7,7 +7,7 @@
 
 from typing import Any
 
-from pydantic import BaseModel
+from pydantic import BaseModel, Field
 
 from llama_stack.schema_utils import json_schema_type
 
@@ -15,9 +15,20 @@ from llama_stack.schema_utils import json_schema_type
 @json_schema_type
 class QdrantVectorIOConfig(BaseModel):
     path: str
+    embedding_model: str | None = Field(
+        default=None,
+        description="Optional default embedding model for this provider. If not specified, will use system default.",
+    )
+    embedding_dimension: int | None = Field(
+        default=None,
+        description="Optional embedding dimension override. Only needed for models with variable dimensions (e.g., Matryoshka embeddings). If not specified, will auto-lookup from model registry.",
+    )
 
     @classmethod
     def sample_run_config(cls, __distro_dir__: str) -> dict[str, Any]:
         return {
             "path": "${env.QDRANT_PATH:=~/.llama/" + __distro_dir__ + "}/" + "qdrant.db",
+            # Optional: Configure default embedding model for this provider
+            # "embedding_model": "all-MiniLM-L6-v2",
+            # "embedding_dimension": 384,  # Only needed for variable-dimension models
         }
diff --git a/llama_stack/providers/inline/vector_io/sqlite_vec/config.py b/llama_stack/providers/inline/vector_io/sqlite_vec/config.py
index 525ed4b1f..f0876c8ab 100644
--- a/llama_stack/providers/inline/vector_io/sqlite_vec/config.py
+++ b/llama_stack/providers/inline/vector_io/sqlite_vec/config.py
@@ -17,6 +17,14 @@ from llama_stack.providers.utils.kvstore.config import (
 class SQLiteVectorIOConfig(BaseModel):
     db_path: str = Field(description="Path to the SQLite database file")
     kvstore: KVStoreConfig = Field(description="Config for KV store backend (SQLite only for now)")
+    embedding_model: str | None = Field(
+        default=None,
+        description="Optional default embedding model for this provider. If not specified, will use system default.",
+    )
+    embedding_dimension: int | None = Field(
+        default=None,
+        description="Optional embedding dimension override. Only needed for models with variable dimensions (e.g., Matryoshka embeddings). If not specified, will auto-lookup from model registry.",
+    )
 
     @classmethod
     def sample_run_config(cls, __distro_dir__: str) -> dict[str, Any]:
@@ -26,4 +34,7 @@ class SQLiteVectorIOConfig(BaseModel):
                 __distro_dir__=__distro_dir__,
                 db_name="sqlite_vec_registry.db",
             ),
+            # Optional: Configure default embedding model for this provider
+            # "embedding_model": "all-MiniLM-L6-v2",
+            # "embedding_dimension": 384,  # Only needed for variable-dimension models
         }
diff --git a/llama_stack/providers/remote/vector_io/chroma/config.py b/llama_stack/providers/remote/vector_io/chroma/config.py
index bd11d5f8c..a6f1b9386 100644
--- a/llama_stack/providers/remote/vector_io/chroma/config.py
+++ b/llama_stack/providers/remote/vector_io/chroma/config.py
@@ -6,12 +6,25 @@
 
 from typing import Any
 
-from pydantic import BaseModel
+from pydantic import BaseModel, Field
 
 
 class ChromaVectorIOConfig(BaseModel):
     url: str | None
+    embedding_model: str | None = Field(
+        default=None,
+        description="Optional default embedding model for this provider. If not specified, will use system default.",
+    )
+    embedding_dimension: int | None = Field(
+        default=None,
+        description="Optional embedding dimension override. Only needed for models with variable dimensions (e.g., Matryoshka embeddings). If not specified, will auto-lookup from model registry.",
+    )
 
     @classmethod
     def sample_run_config(cls, url: str = "${env.CHROMADB_URL}", **kwargs: Any) -> dict[str, Any]:
-        return {"url": url}
+        return {
+            "url": url,
+            # Optional: Configure default embedding model for this provider
+            # "embedding_model": "all-MiniLM-L6-v2",
+            # "embedding_dimension": 384,  # Only needed for variable-dimension models
+        }
diff --git a/llama_stack/providers/remote/vector_io/milvus/config.py b/llama_stack/providers/remote/vector_io/milvus/config.py
index e3f51b4f4..0dde3ceb7 100644
--- a/llama_stack/providers/remote/vector_io/milvus/config.py
+++ b/llama_stack/providers/remote/vector_io/milvus/config.py
@@ -18,6 +18,14 @@ class MilvusVectorIOConfig(BaseModel):
     token: str | None = Field(description="The token of the Milvus server")
     consistency_level: str = Field(description="The consistency level of the Milvus server", default="Strong")
     kvstore: KVStoreConfig | None = Field(description="Config for KV store backend (SQLite only for now)", default=None)
+    embedding_model: str | None = Field(
+        default=None,
+        description="Optional default embedding model for this provider. If not specified, will use system default.",
+    )
+    embedding_dimension: int | None = Field(
+        default=None,
+        description="Optional embedding dimension override. Only needed for models with variable dimensions (e.g., Matryoshka embeddings). If not specified, will auto-lookup from model registry.",
+    )
 
     # This configuration allows additional fields to be passed through to the underlying Milvus client.
     # See the [Milvus](https://milvus.io/docs/install-overview.md) documentation for more details about Milvus in general.
@@ -25,4 +33,10 @@ class MilvusVectorIOConfig(BaseModel):
 
     @classmethod
     def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> dict[str, Any]:
-        return {"uri": "${env.MILVUS_ENDPOINT}", "token": "${env.MILVUS_TOKEN}"}
+        return {
+            "uri": "${env.MILVUS_ENDPOINT}",
+            "token": "${env.MILVUS_TOKEN}",
+            # Optional: Configure default embedding model for this provider
+            # "embedding_model": "all-MiniLM-L6-v2",
+            # "embedding_dimension": 384,  # Only needed for variable-dimension models
+        }
diff --git a/llama_stack/providers/remote/vector_io/pgvector/config.py b/llama_stack/providers/remote/vector_io/pgvector/config.py
index 92908aa8a..82a82cfbd 100644
--- a/llama_stack/providers/remote/vector_io/pgvector/config.py
+++ b/llama_stack/providers/remote/vector_io/pgvector/config.py
@@ -18,15 +18,32 @@ class PGVectorVectorIOConfig(BaseModel):
     db: str | None = Field(default="postgres")
     user: str | None = Field(default="postgres")
     password: str | None = Field(default="mysecretpassword")
+    embedding_model: str | None = Field(
+        default=None,
+        description="Optional default embedding model for this provider. If not specified, will use system default.",
+    )
+    embedding_dimension: int | None = Field(
+        default=None,
+        description="Optional embedding dimension override. Only needed for models with variable dimensions (e.g., Matryoshka embeddings). If not specified, will auto-lookup from model registry.",
+    )
 
     @classmethod
     def sample_run_config(
         cls,
         host: str = "${env.PGVECTOR_HOST:=localhost}",
-        port: int = "${env.PGVECTOR_PORT:=5432}",
+        port: int | str = "${env.PGVECTOR_PORT:=5432}",
         db: str = "${env.PGVECTOR_DB}",
         user: str = "${env.PGVECTOR_USER}",
         password: str = "${env.PGVECTOR_PASSWORD}",
         **kwargs: Any,
     ) -> dict[str, Any]:
-        return {"host": host, "port": port, "db": db, "user": user, "password": password}
+        return {
+            "host": host,
+            "port": port,
+            "db": db,
+            "user": user,
+            "password": password,
+            # Optional: Configure default embedding model for this provider
+            # "embedding_model": "all-MiniLM-L6-v2",
+            # "embedding_dimension": 384,  # Only needed for variable-dimension models
+        }
diff --git a/llama_stack/providers/remote/vector_io/qdrant/config.py b/llama_stack/providers/remote/vector_io/qdrant/config.py
index 314d3f5f1..ac80bbc49 100644
--- a/llama_stack/providers/remote/vector_io/qdrant/config.py
+++ b/llama_stack/providers/remote/vector_io/qdrant/config.py
@@ -6,7 +6,7 @@
 
 from typing import Any
 
-from pydantic import BaseModel
+from pydantic import BaseModel, Field
 
 from llama_stack.schema_utils import json_schema_type
 
@@ -23,9 +23,20 @@ class QdrantVectorIOConfig(BaseModel):
     prefix: str | None = None
     timeout: int | None = None
     host: str | None = None
+    embedding_model: str | None = Field(
+        default=None,
+        description="Optional default embedding model for this provider. If not specified, will use system default.",
+    )
+    embedding_dimension: int | None = Field(
+        default=None,
+        description="Optional embedding dimension override. Only needed for models with variable dimensions (e.g., Matryoshka embeddings). If not specified, will auto-lookup from model registry.",
+    )
 
     @classmethod
     def sample_run_config(cls, **kwargs: Any) -> dict[str, Any]:
         return {
             "api_key": "${env.QDRANT_API_KEY}",
+            # Optional: Configure default embedding model for this provider
+            # "embedding_model": "all-MiniLM-L6-v2",
+            # "embedding_dimension": 384,  # Only needed for variable-dimension models
         }
diff --git a/llama_stack/providers/remote/vector_io/weaviate/config.py b/llama_stack/providers/remote/vector_io/weaviate/config.py
index a8c6e3e2c..4354430c4 100644
--- a/llama_stack/providers/remote/vector_io/weaviate/config.py
+++ b/llama_stack/providers/remote/vector_io/weaviate/config.py
@@ -6,7 +6,7 @@
 
 from typing import Any
 
-from pydantic import BaseModel
+from pydantic import BaseModel, Field
 
 
 class WeaviateRequestProviderData(BaseModel):
@@ -15,6 +15,19 @@ class WeaviateRequestProviderData(BaseModel):
 
 
 class WeaviateVectorIOConfig(BaseModel):
+    embedding_model: str | None = Field(
+        default=None,
+        description="Optional default embedding model for this provider. If not specified, will use system default.",
+    )
+    embedding_dimension: int | None = Field(
+        default=None,
+        description="Optional embedding dimension override. Only needed for models with variable dimensions (e.g., Matryoshka embeddings). If not specified, will auto-lookup from model registry.",
+    )
+
     @classmethod
     def sample_run_config(cls, **kwargs: Any) -> dict[str, Any]:
-        return {}
+        return {
+            # Optional: Configure default embedding model for this provider
+            # "embedding_model": "all-MiniLM-L6-v2",
+            # "embedding_dimension": 384,  # Only needed for variable-dimension models
+        }
diff --git a/llama_stack/providers/utils/vector_io/embedding_utils.py b/llama_stack/providers/utils/vector_io/embedding_utils.py
new file mode 100644
index 000000000..7f13ade78
--- /dev/null
+++ b/llama_stack/providers/utils/vector_io/embedding_utils.py
@@ -0,0 +1,153 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from llama_stack.apis.models import ModelType
+from llama_stack.log import get_logger
+from llama_stack.providers.datatypes import RoutingTable
+
+logger = get_logger(name=__name__, category="core")
+
+
+async def get_embedding_model_info(
+    model_id: str, routing_table: RoutingTable, override_dimension: int | None = None
+) -> tuple[str, int]:
+    """
+    Get embedding model info with auto-dimension lookup.
+
+    This function validates that the specified model is an embedding model
+    and returns its embedding dimensions, with support for Matryoshka embeddings
+    through dimension overrides.
+
+    Args:
+        model_id: The embedding model identifier to look up
+        routing_table: Access to the model registry for validation and dimension lookup
+        override_dimension: Optional dimension override for Matryoshka models that
+                          support variable dimensions (e.g., nomic-embed-text)
+
+    Returns:
+        tuple: (model_id, embedding_dimension)
+
+    Raises:
+        ValueError: If model not found, not an embedding model, or missing dimension info
+    """
+    try:
+        # Look up the model in the routing table
+        model = await routing_table.get_object_by_identifier("model", model_id)  # type: ignore
+        if model is None:
+            raise ValueError(f"Embedding model '{model_id}' not found in model registry")
+
+        # Validate that this is an embedding model
+        if not hasattr(model, "model_type") or model.model_type != ModelType.embedding:
+            raise ValueError(
+                f"Model '{model_id}' is not an embedding model (type: {getattr(model, 'model_type', 'unknown')})"
+            )
+
+        # If override dimension is provided, use it (for Matryoshka embeddings)
+        if override_dimension is not None:
+            if override_dimension <= 0:
+                raise ValueError(f"Override dimension must be positive, got {override_dimension}")
+            logger.info(f"Using override dimension {override_dimension} for embedding model '{model_id}'")
+            return model_id, override_dimension
+
+        # Extract embedding dimension from model metadata
+        if not hasattr(model, "metadata") or not model.metadata:
+            raise ValueError(f"Embedding model '{model_id}' has no metadata")
+
+        embedding_dimension = model.metadata.get("embedding_dimension")
+        if embedding_dimension is None:
+            raise ValueError(f"Embedding model '{model_id}' has no embedding_dimension in metadata")
+
+        if not isinstance(embedding_dimension, int) or embedding_dimension <= 0:
+            raise ValueError(f"Invalid embedding_dimension for model '{model_id}': {embedding_dimension}")
+
+        logger.debug(f"Auto-lookup successful for embedding model '{model_id}': dimension {embedding_dimension}")
+        return model_id, embedding_dimension
+
+    except Exception as e:
+        logger.error(f"Error looking up embedding model info for '{model_id}': {e}")
+        raise
+
+
+async def get_provider_embedding_model_info(
+    routing_table: RoutingTable,
+    provider_config,
+    explicit_model_id: str | None = None,
+    explicit_dimension: int | None = None,
+) -> tuple[str, int] | None:
+    """
+    Get embedding model info with provider-level defaults and explicit overrides.
+
+    This function implements the priority order for embedding model selection:
+    1. Explicit parameters (from API calls)
+    2. Provider config defaults (NEW - from VectorIOConfig)
+    3. System default (current fallback behavior)
+
+    Args:
+        routing_table: Access to the model registry
+        provider_config: The VectorIOConfig object with potential embedding_model defaults
+        explicit_model_id: Explicit model ID from API call (highest priority)
+        explicit_dimension: Explicit dimension from API call (highest priority)
+
+    Returns:
+        tuple: (model_id, embedding_dimension) or None if no model available
+
+    Raises:
+        ValueError: If model validation fails
+    """
+    try:
+        # Priority 1: Explicit parameters (existing behavior)
+        if explicit_model_id is not None:
+            logger.debug(f"Using explicit embedding model: {explicit_model_id}")
+            return await get_embedding_model_info(explicit_model_id, routing_table, explicit_dimension)
+
+        # Priority 2: Provider config default (NEW)
+        if hasattr(provider_config, "embedding_model") and provider_config.embedding_model:
+            logger.info(f"Using provider config default embedding model: {provider_config.embedding_model}")
+            override_dim = None
+            if hasattr(provider_config, "embedding_dimension") and provider_config.embedding_dimension:
+                override_dim = provider_config.embedding_dimension
+                logger.info(f"Using provider config dimension override: {override_dim}")
+
+            return await get_embedding_model_info(provider_config.embedding_model, routing_table, override_dim)
+
+        # Priority 3: System default (existing fallback behavior)
+        logger.debug("No explicit model or provider default, falling back to system default")
+        return await _get_first_embedding_model_fallback(routing_table)
+
+    except Exception as e:
+        logger.error(f"Error getting provider embedding model info: {e}")
+        raise
+
+
+async def _get_first_embedding_model_fallback(routing_table: RoutingTable) -> tuple[str, int] | None:
+    """
+    Fallback to get the first available embedding model (existing behavior).
+
+    This maintains backward compatibility by preserving the original logic
+    from VectorIORouter._get_first_embedding_model().
+    """
+    try:
+        # Get all models from the routing table
+        all_models = await routing_table.get_all_with_type("model")  # type: ignore
+
+        # Filter for embedding models
+        embedding_models = [
+            model for model in all_models if hasattr(model, "model_type") and model.model_type == ModelType.embedding
+        ]
+
+        if embedding_models:
+            dimension = embedding_models[0].metadata.get("embedding_dimension", None)
+            if dimension is None:
+                raise ValueError(f"Embedding model {embedding_models[0].identifier} has no embedding dimension")
+
+            logger.info(f"System fallback: using first available embedding model {embedding_models[0].identifier}")
+            return embedding_models[0].identifier, dimension
+        else:
+            logger.warning("No embedding models found in the routing table")
+            return None
+    except Exception as e:
+        logger.error(f"Error getting fallback embedding model: {e}")
+        return None
diff --git a/tests/unit/providers/utils/vector_io/__init__.py b/tests/unit/providers/utils/vector_io/__init__.py
new file mode 100644
index 000000000..756f351d8
--- /dev/null
+++ b/tests/unit/providers/utils/vector_io/__init__.py
@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
diff --git a/tests/unit/providers/utils/vector_io/test_embedding_utils.py b/tests/unit/providers/utils/vector_io/test_embedding_utils.py
new file mode 100644
index 000000000..d1daebb90
--- /dev/null
+++ b/tests/unit/providers/utils/vector_io/test_embedding_utils.py
@@ -0,0 +1,320 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from unittest.mock import AsyncMock
+
+import pytest
+
+from llama_stack.apis.models import ModelType
+from llama_stack.providers.utils.vector_io.embedding_utils import (
+    _get_first_embedding_model_fallback,
+    get_embedding_model_info,
+    get_provider_embedding_model_info,
+)
+
+
+class MockModel:
+    """Mock model object for testing."""
+
+    def __init__(self, identifier: str, model_type: ModelType, metadata: dict | None = None):
+        self.identifier = identifier
+        self.model_type = model_type
+        self.metadata = metadata
+
+
+class MockConfig:
+    """Mock provider config for testing."""
+
+    def __init__(self, embedding_model: str | None = None, embedding_dimension: int | None = None):
+        self.embedding_model = embedding_model
+        self.embedding_dimension = embedding_dimension
+
+
+@pytest.fixture
+def mock_routing_table():
+    """Create a mock routing table for testing."""
+    routing_table = AsyncMock()
+
+    # Mock embedding models
+    embedding_models = [
+        MockModel(identifier="all-MiniLM-L6-v2", model_type=ModelType.embedding, metadata={"embedding_dimension": 384}),
+        MockModel(identifier="nomic-embed-text", model_type=ModelType.embedding, metadata={"embedding_dimension": 768}),
+    ]
+
+    # Mock LLM model (should be filtered out)
+    llm_model = MockModel(identifier="llama-3.1-8b", model_type=ModelType.llm, metadata={})
+
+    all_models = embedding_models + [llm_model]
+
+    async def mock_get_object_by_identifier(type_name: str, identifier: str):
+        if type_name == "model":
+            for model in all_models:
+                if model.identifier == identifier:
+                    return model
+        return None
+
+    async def mock_get_all_with_type(type_name: str):
+        if type_name == "model":
+            return all_models
+        return []
+
+    routing_table.get_object_by_identifier.side_effect = mock_get_object_by_identifier
+    routing_table.get_all_with_type.side_effect = mock_get_all_with_type
+
+    return routing_table
+
+
+class TestGetEmbeddingModelInfo:
+    """Test the core get_embedding_model_info function."""
+
+    @pytest.mark.asyncio
+    async def test_valid_embedding_model(self, mock_routing_table):
+        """Test successful lookup of a valid embedding model."""
+        model_id, dimension = await get_embedding_model_info("all-MiniLM-L6-v2", mock_routing_table)
+
+        assert model_id == "all-MiniLM-L6-v2"
+        assert dimension == 384
+
+    @pytest.mark.asyncio
+    async def test_embedding_model_with_override_dimension(self, mock_routing_table):
+        """Test Matryoshka embedding with dimension override."""
+        model_id, dimension = await get_embedding_model_info(
+            "nomic-embed-text", mock_routing_table, override_dimension=256
+        )
+
+        assert model_id == "nomic-embed-text"
+        assert dimension == 256  # Should use override, not default 768
+
+    @pytest.mark.asyncio
+    async def test_model_not_found(self, mock_routing_table):
+        """Test error when model doesn't exist."""
+        with pytest.raises(ValueError, match="not found in model registry"):
+            await get_embedding_model_info("non-existent-model", mock_routing_table)
+
+    @pytest.mark.asyncio
+    async def test_non_embedding_model(self, mock_routing_table):
+        """Test error when model is not an embedding model."""
+        with pytest.raises(ValueError, match="is not an embedding model"):
+            await get_embedding_model_info("llama-3.1-8b", mock_routing_table)
+
+    @pytest.mark.asyncio
+    async def test_model_missing_dimension_metadata(self, mock_routing_table):
+        """Test error when embedding model has no dimension metadata."""
+        # Create a model with non-empty metadata dict missing embedding_dimension
+        bad_model = MockModel(
+            identifier="bad-embedding-model",
+            model_type=ModelType.embedding,
+            metadata={"some_other_field": "value"},  # Non-empty but missing embedding_dimension
+        )
+
+        async def mock_get_bad_model(type_name: str, identifier: str):
+            if type_name == "model" and identifier == "bad-embedding-model":
+                return bad_model
+            return await mock_routing_table.get_object_by_identifier(type_name, identifier)
+
+        mock_routing_table.get_object_by_identifier.side_effect = mock_get_bad_model
+
+        with pytest.raises(ValueError, match="has no embedding_dimension in metadata"):
+            await get_embedding_model_info("bad-embedding-model", mock_routing_table)
+
+    @pytest.mark.asyncio
+    async def test_invalid_override_dimension(self, mock_routing_table):
+        """Test error with invalid override dimension."""
+        with pytest.raises(ValueError, match="Override dimension must be positive"):
+            await get_embedding_model_info("all-MiniLM-L6-v2", mock_routing_table, override_dimension=0)
+
+        with pytest.raises(ValueError, match="Override dimension must be positive"):
+            await get_embedding_model_info("all-MiniLM-L6-v2", mock_routing_table, override_dimension=-10)
+
+
+class TestGetProviderEmbeddingModelInfo:
+    """Test the provider-level embedding model selection with priority system."""
+
+    @pytest.mark.asyncio
+    async def test_priority_1_explicit_parameters(self, mock_routing_table):
+        """Test highest priority: explicit parameters."""
+        config = MockConfig(embedding_model="nomic-embed-text", embedding_dimension=512)
+
+        # Explicit parameters should override config
+        result = await get_provider_embedding_model_info(
+            routing_table=mock_routing_table,
+            provider_config=config,
+            explicit_model_id="all-MiniLM-L6-v2",  # Should use this
+            explicit_dimension=256,  # Should use this
+        )
+
+        assert result is not None
+        model_id, dimension = result
+        assert model_id == "all-MiniLM-L6-v2"
+        assert dimension == 256
+
+    @pytest.mark.asyncio
+    async def test_priority_2_provider_config_defaults(self, mock_routing_table):
+        """Test middle priority: provider config defaults."""
+        config = MockConfig(embedding_model="nomic-embed-text", embedding_dimension=512)
+
+        # No explicit parameters, should use config
+        model_id, dimension = await get_provider_embedding_model_info(
+            routing_table=mock_routing_table, provider_config=config, explicit_model_id=None, explicit_dimension=None
+        )
+
+        assert model_id == "nomic-embed-text"
+        assert dimension == 512  # Config override
+
+    @pytest.mark.asyncio
+    async def test_priority_2_provider_config_model_only(self, mock_routing_table):
+        """Test provider config with model but no dimension override."""
+        config = MockConfig(embedding_model="all-MiniLM-L6-v2")  # No dimension override
+
+        model_id, dimension = await get_provider_embedding_model_info(
+            routing_table=mock_routing_table, provider_config=config, explicit_model_id=None, explicit_dimension=None
+        )
+
+        assert model_id == "all-MiniLM-L6-v2"
+        assert dimension == 384  # Auto-lookup from model metadata
+
+    @pytest.mark.asyncio
+    async def test_priority_3_system_default(self, mock_routing_table):
+        """Test lowest priority: system default fallback."""
+        config = MockConfig()  # No defaults set
+
+        model_id, dimension = await get_provider_embedding_model_info(
+            routing_table=mock_routing_table, provider_config=config, explicit_model_id=None, explicit_dimension=None
+        )
+
+        # Should get first available embedding model
+        assert model_id == "all-MiniLM-L6-v2"
+        assert dimension == 384
+
+    @pytest.mark.asyncio
+    async def test_no_provider_config(self, mock_routing_table):
+        """Test with None provider config."""
+        model_id, dimension = await get_provider_embedding_model_info(
+            routing_table=mock_routing_table, provider_config=None, explicit_model_id=None, explicit_dimension=None
+        )
+
+        # Should fall back to system default
+        assert model_id == "all-MiniLM-L6-v2"
+        assert dimension == 384
+
+    @pytest.mark.asyncio
+    async def test_no_embedding_models_available(self, mock_routing_table):
+        """Test when no embedding models are available."""
+
+        # Mock routing table with no embedding models
+        async def mock_get_all_empty(type_name: str):
+            return []  # No models
+
+        mock_routing_table.get_all_with_type.side_effect = mock_get_all_empty
+
+        config = MockConfig()
+
+        result = await get_provider_embedding_model_info(
+            routing_table=mock_routing_table, provider_config=config, explicit_model_id=None, explicit_dimension=None
+        )
+
+        assert result is None
+
+
+class TestGetFirstEmbeddingModelFallback:
+    """Test the fallback function for system defaults."""
+
+    @pytest.mark.asyncio
+    async def test_successful_fallback(self, mock_routing_table):
+        """Test successful fallback to first embedding model."""
+        model_id, dimension = await _get_first_embedding_model_fallback(mock_routing_table)
+
+        assert model_id == "all-MiniLM-L6-v2"
+        assert dimension == 384
+
+    @pytest.mark.asyncio
+    async def test_no_embedding_models_fallback(self, mock_routing_table):
+        """Test fallback when no embedding models exist."""
+
+        # Mock empty model list
+        async def mock_get_all_empty(type_name: str):
+            return []
+
+        mock_routing_table.get_all_with_type.side_effect = mock_get_all_empty
+
+        result = await _get_first_embedding_model_fallback(mock_routing_table)
+        assert result is None
+
+    @pytest.mark.asyncio
+    async def test_embedding_model_missing_dimension_fallback(self, mock_routing_table):
+        """Test fallback when embedding model has no dimension - should return None."""
+        bad_model = MockModel(
+            identifier="bad-embedding",
+            model_type=ModelType.embedding,
+            metadata={},  # Missing dimension
+        )
+
+        async def mock_get_all_bad(type_name: str):
+            return [bad_model] if type_name == "model" else []
+
+        mock_routing_table.get_all_with_type.side_effect = mock_get_all_bad
+
+        # The function should return None (not raise) when model has no dimension
+        result = await _get_first_embedding_model_fallback(mock_routing_table)
+        assert result is None
+
+
+class TestBackwardCompatibility:
+    """Test that the new system maintains backward compatibility."""
+
+    @pytest.mark.asyncio
+    async def test_explicit_model_still_works(self, mock_routing_table):
+        """Test that explicitly specifying embedding model still works as before."""
+        model_id, dimension = await get_embedding_model_info("all-MiniLM-L6-v2", mock_routing_table)
+
+        assert model_id == "all-MiniLM-L6-v2"
+        assert dimension == 384
+
+    @pytest.mark.asyncio
+    async def test_system_fallback_unchanged(self, mock_routing_table):
+        """Test that system fallback behavior is unchanged."""
+        # This should behave exactly like the old _get_first_embedding_model
+        model_id, dimension = await _get_first_embedding_model_fallback(mock_routing_table)
+
+        assert model_id == "all-MiniLM-L6-v2"
+        assert dimension == 384
+
+
+class TestMatryoshkaEmbeddings:
+    """Test specific Matryoshka embedding scenarios."""
+
+    @pytest.mark.asyncio
+    async def test_nomic_embed_text_default(self, mock_routing_table):
+        """Test nomic-embed-text with default dimension."""
+        model_id, dimension = await get_embedding_model_info("nomic-embed-text", mock_routing_table)
+
+        assert model_id == "nomic-embed-text"
+        assert dimension == 768  # Default dimension
+
+    @pytest.mark.asyncio
+    async def test_nomic_embed_text_override(self, mock_routing_table):
+        """Test nomic-embed-text with dimension override."""
+        model_id, dimension = await get_embedding_model_info(
+            "nomic-embed-text", mock_routing_table, override_dimension=256
+        )
+
+        assert model_id == "nomic-embed-text"
+        assert dimension == 256  # Overridden dimension
+
+    @pytest.mark.asyncio
+    async def test_provider_config_matryoshka_override(self, mock_routing_table):
+        """Test provider config with Matryoshka dimension override."""
+        config = MockConfig(
+            embedding_model="nomic-embed-text",
+            embedding_dimension=128,  # Custom dimension
+        )
+
+        model_id, dimension = await get_provider_embedding_model_info(
+            routing_table=mock_routing_table, provider_config=config, explicit_model_id=None, explicit_dimension=None
+        )
+
+        assert model_id == "nomic-embed-text"
+        assert dimension == 128  # Should use provider config override