diff --git a/docs/openapi.json b/docs/openapi.json
index 693ff1b9e..154e0b263 100644
--- a/docs/openapi.json
+++ b/docs/openapi.json
@@ -3664,7 +3664,7 @@
                     "rlsapi-v1"
                 ],
                 "summary": "Infer Endpoint",
-                "description": "Handle rlsapi v1 /infer requests for stateless inference.\n\nThis endpoint serves requests from the RHEL Lightspeed Command Line Assistant (CLA).\n\nAccepts a question with optional context (stdin, attachments, terminal output,\nsystem info) and returns an LLM-generated response.\n\nArgs:\n    infer_request: The inference request containing question and context.\n    auth: Authentication tuple from the configured auth provider.\n\nReturns:\n    RlsapiV1InferResponse containing the generated response text and request ID.\n\nRaises:\n    HTTPException: 503 if the LLM service is unavailable.",
+                "description": "Handle rlsapi v1 /infer requests for stateless inference.\n\nThis endpoint serves requests from the RHEL Lightspeed Command Line Assistant (CLA).\n\nAccepts a question with optional context (stdin, attachments, terminal output,\nsystem info) and returns an LLM-generated response.\n\nArgs:\n    infer_request: The inference request containing question and context.\n    request: The FastAPI request object for accessing headers and state.\n    background_tasks: FastAPI background tasks for async Splunk event sending.\n    auth: Authentication tuple from the configured auth provider.\n\nReturns:\n    RlsapiV1InferResponse containing the generated response text and request ID.\n\nRaises:\n    HTTPException: 503 if the LLM service is unavailable.",
                 "operationId": "infer_endpoint_v1_infer_post",
                 "requestBody": {
                     "content": {
@@ -4290,7 +4290,7 @@
                 ],
                 "summary": "Handle A2A Jsonrpc",
                 "description": "Handle A2A JSON-RPC requests following the A2A protocol specification.\n\nThis endpoint uses the DefaultRequestHandler from the A2A SDK to handle\nall JSON-RPC requests including message/send, message/stream, etc.\n\nThe A2A SDK application is created per-request to include authentication\ncontext while still leveraging FastAPI's authorization middleware.\n\nAutomatically detects streaming requests (message/stream JSON-RPC method)\nand returns a StreamingResponse to enable real-time chunk delivery.\n\nArgs:\n    request: FastAPI request object\n    auth: Authentication tuple\n    mcp_headers: MCP headers for context propagation\n\nReturns:\n    JSON-RPC response or streaming response",
-                "operationId": "handle_a2a_jsonrpc_a2a_get",
+                "operationId": "handle_a2a_jsonrpc_a2a_post",
                 "responses": {
                     "200": {
                         "description": "Successful Response",
@@ -4308,7 +4308,7 @@
                 ],
                 "summary": "Handle A2A Jsonrpc",
                 "description": "Handle A2A JSON-RPC requests following the A2A protocol specification.\n\nThis endpoint uses the DefaultRequestHandler from the A2A SDK to handle\nall JSON-RPC requests including message/send, message/stream, etc.\n\nThe A2A SDK application is created per-request to include authentication\ncontext while still leveraging FastAPI's authorization middleware.\n\nAutomatically detects streaming requests (message/stream JSON-RPC method)\nand returns a StreamingResponse to enable real-time chunk delivery.\n\nArgs:\n    request: FastAPI request object\n    auth: Authentication tuple\n    mcp_headers: MCP headers for context propagation\n\nReturns:\n    JSON-RPC response or streaming response",
-                "operationId": "handle_a2a_jsonrpc_a2a_get",
+                "operationId": "handle_a2a_jsonrpc_a2a_post",
                 "responses": {
                     "200": {
                         "description": "Successful Response",
@@ -5335,11 +5335,11 @@
                         "description": "Dimensionality of embedding vectors.",
                         "default": 768
                     },
-                    "vector_db_id": {
+                    "vector_store_id": {
                         "type": "string",
                         "minLength": 1,
-                        "title": "Vector DB ID",
-                        "description": "Vector DB identification."
+                        "title": "Vector Store ID",
+                        "description": "Vector store identification."
                     },
                     "db_path": {
                         "type": "string",
@@ -5352,7 +5352,7 @@
                 "type": "object",
                 "required": [
                     "rag_id",
-                    "vector_db_id",
+                    "vector_store_id",
                     "db_path"
                 ],
                 "title": "ByokRag",
@@ -8431,11 +8431,23 @@
                         ],
                         "title": "Doc Title",
                         "description": "Title of the referenced document"
+                    },
+                    "doc_id": {
+                        "anyOf": [
+                            {
+                                "type": "string"
+                            },
+                            {
+                                "type": "null"
+                            }
+                        ],
+                        "title": "Doc Id",
+                        "description": "ID of the referenced document"
                     }
                 },
                 "type": "object",
                 "title": "ReferencedDocument",
-                "description": "Model representing a document referenced in generating a response.\n\nAttributes:\n    doc_url: Url to the referenced doc.\n    doc_title: Title of the referenced doc."
+                "description": "Model representing a document referenced in generating a response.\n\nAttributes:\n    doc_url: Url to the referenced doc.\n    doc_title: Title of the referenced doc.\n    doc_id: ID of the referenced doc."
             },
             "RlsapiV1Attachment": {
                 "properties": {
diff --git a/docs/openapi.md b/docs/openapi.md
index f576011e5..b681967dc 100644
--- a/docs/openapi.md
+++ b/docs/openapi.md
@@ -3200,6 +3200,8 @@ system info) and returns an LLM-generated response.
 
 Args:
     infer_request: The inference request containing question and context.
+    request: The FastAPI request object for accessing headers and state.
+    background_tasks: FastAPI background tasks for async Splunk event sending.
     auth: Authentication tuple from the configured auth provider.
 
 Returns:
@@ -4184,7 +4186,7 @@ BYOK (Bring Your Own Knowledge) RAG configuration.
 | rag_type | string | Type of RAG database. |
 | embedding_model | string | Embedding model identification |
 | embedding_dimension | integer | Dimensionality of embedding vectors. |
-| vector_db_id | string | Vector DB identification. |
+| vector_store_id | string | Vector store identification. |
 | db_path | string | Path to RAG database. |
 
 
@@ -5316,12 +5318,14 @@ Model representing a document referenced in generating a response.
 Attributes:
     doc_url: Url to the referenced doc.
     doc_title: Title of the referenced doc.
+    doc_id: ID of the referenced doc.
 
 
 | Field | Type | Description |
 |-------|------|-------------|
 | doc_url |  | URL of the referenced document |
 | doc_title |  | Title of the referenced document |
+| doc_id |  | ID of the referenced document |
 
 
 ## RlsapiV1Attachment
diff --git a/src/app/endpoints/query.py b/src/app/endpoints/query.py
index ce0c87bed..eddf30f86 100644
--- a/src/app/endpoints/query.py
+++ b/src/app/endpoints/query.py
@@ -1,8 +1,6 @@
 """Handler for REST API call to provide answer to query."""
 
-import ast
 import logging
-import re
 from datetime import UTC, datetime
 from typing import Annotated, Any, Optional
 
@@ -14,7 +12,6 @@
     RateLimitError,  # type: ignore
 )
 from llama_stack_client.types.model_list_response import ModelListResponse
-from llama_stack_client.types.shared.interleaved_content_item import TextContentItem
 from sqlalchemy.exc import SQLAlchemyError
 
 import constants
@@ -36,7 +33,6 @@
     PromptTooLongResponse,
     QueryResponse,
     QuotaExceededResponse,
-    ReferencedDocument,
     ServiceUnavailableResponse,
     UnauthorizedResponse,
     UnprocessableEntityResponse,
@@ -553,39 +549,6 @@ def is_input_shield(shield: Shield) -> bool:
     return _is_inout_shield(shield) or not is_output_shield(shield)
 
 
-def parse_metadata_from_text_item(
-    text_item: TextContentItem,
-) -> Optional[ReferencedDocument]:
-    """
-    Parse a single TextContentItem to extract referenced documents.
-
-    Args:
-        text_item (TextContentItem): The TextContentItem containing metadata.
-
-    Returns:
-        ReferencedDocument: A ReferencedDocument object containing 'doc_url' and 'doc_title'
-        representing the referenced documents found in the metadata.
-    """
-    docs: list[ReferencedDocument] = []
-    if not isinstance(text_item, TextContentItem):
-        return docs
-
-    metadata_blocks = re.findall(
-        r"Metadata:\s*({.*?})(?:\n|$)", text_item.text, re.DOTALL
-    )
-    for block in metadata_blocks:
-        try:
-            data = ast.literal_eval(block)
-            url = data.get("docs_url")
-            title = data.get("title")
-            if url and title:
-                return ReferencedDocument(doc_url=url, doc_title=title)
-            logger.debug("Invalid metadata block (missing url or title): %s", block)
-        except (ValueError, SyntaxError) as e:
-            logger.debug("Failed to parse metadata block: %s | Error: %s", block, e)
-    return None
-
-
 def validate_attachments_metadata(attachments: list[Attachment]) -> None:
     """Validate the attachments metadata provided in the request.
 
diff --git a/src/app/endpoints/query_v2.py b/src/app/endpoints/query_v2.py
index 64f4c5341..359fe8c3d 100644
--- a/src/app/endpoints/query_v2.py
+++ b/src/app/endpoints/query_v2.py
@@ -541,11 +541,11 @@ def parse_referenced_documents_from_responses_api(
         response: The OpenAI Response API response object
 
     Returns:
-        list[ReferencedDocument]: List of referenced documents with doc_url and doc_title
+        list[ReferencedDocument]: List of referenced documents with doc_url, doc_title and doc_id
     """
     documents: list[ReferencedDocument] = []
-    # Use a set to track unique documents by (doc_url, doc_title) tuple
-    seen_docs: set[tuple[Optional[str], Optional[str]]] = set()
+    # Use a set to track unique documents by (doc_url, doc_title, doc_id) tuple
+    seen_docs: set[tuple[Optional[str], Optional[str], Optional[str]]] = set()
 
     # Handle None response (e.g., when agent fails)
     if response is None or not response.output:
@@ -560,74 +560,31 @@ def parse_referenced_documents_from_responses_api(
             for result in results:
                 # Handle both object and dict access
                 if isinstance(result, dict):
-                    filename = result.get("filename")
                     attributes = result.get("attributes", {})
                 else:
-                    filename = getattr(result, "filename", None)
                     attributes = getattr(result, "attributes", {})
 
                 # Try to get URL from attributes
                 # Look for common URL fields in attributes
                 doc_url = (
-                    attributes.get("link")
+                    attributes.get("doc_url")
+                    or attributes.get("docs_url")
                     or attributes.get("url")
-                    or attributes.get("doc_url")
+                    or attributes.get("link")
                 )
+                doc_title = attributes.get("title")
+                doc_id = attributes.get("document_id") or attributes.get("doc_id")
 
-                # If we have at least a filename or url
-                if filename or doc_url:
+                if doc_title or doc_url:
                     # Treat empty string as None for URL to satisfy Optional[AnyUrl]
                     final_url = doc_url if doc_url else None
-                    if (final_url, filename) not in seen_docs:
+                    if (final_url, doc_title, doc_id) not in seen_docs:
                         documents.append(
-                            ReferencedDocument(doc_url=final_url, doc_title=filename)
-                        )
-                        seen_docs.add((final_url, filename))
-
-        # 2. Parse from message content annotations
-        elif item_type == "message":
-            content = getattr(output_item, "content", None)
-            if isinstance(content, list):
-                for part in content:
-                    # Skip if part is a string or doesn't have annotations
-                    if isinstance(part, str):
-                        continue
-
-                    annotations = getattr(part, "annotations", []) or []
-                    for annotation in annotations:
-                        # Handle both object and dict access for annotations
-                        if isinstance(annotation, dict):
-                            anno_type = annotation.get("type")
-                            anno_url = annotation.get("url")
-                            anno_title = annotation.get("title") or annotation.get(
-                                "filename"
-                            )
-                        else:
-                            anno_type = getattr(annotation, "type", None)
-                            anno_url = getattr(annotation, "url", None)
-                            anno_title = getattr(annotation, "title", None) or getattr(
-                                annotation, "filename", None
+                            ReferencedDocument(
+                                doc_url=final_url, doc_title=doc_title, doc_id=doc_id
                             )
-
-                        if anno_type == "url_citation":
-                            # Treat empty string as None
-                            final_url = anno_url if anno_url else None
-                            if (final_url, anno_title) not in seen_docs:
-                                documents.append(
-                                    ReferencedDocument(
-                                        doc_url=final_url, doc_title=anno_title
-                                    )
-                                )
-                                seen_docs.add((final_url, anno_title))
-
-                        elif anno_type == "file_citation":
-                            if (None, anno_title) not in seen_docs:
-                                documents.append(
-                                    ReferencedDocument(
-                                        doc_url=None, doc_title=anno_title
-                                    )
-                                )
-                                seen_docs.add((None, anno_title))
+                        )
+                        seen_docs.add((final_url, doc_title, doc_id))
 
     return documents
 
diff --git a/src/llama_stack_configuration.py b/src/llama_stack_configuration.py
index 0e45e0614..d6af46a65 100644
--- a/src/llama_stack_configuration.py
+++ b/src/llama_stack_configuration.py
@@ -11,10 +11,9 @@
 from pathlib import Path
 from typing import Any
 
+import yaml
 from azure.core.exceptions import ClientAuthenticationError
 from azure.identity import ClientSecretCredential, CredentialUnavailableError
-
-import yaml
 from llama_stack.core.stack import replace_env_vars
 
 logger = logging.getLogger(__name__)
@@ -115,51 +114,145 @@ def setup_azure_entra_id_token(
 # =============================================================================
 
 
-def construct_vector_dbs_section(
+def construct_storage_backends_section(
+    ls_config: dict[str, Any], byok_rag: list[dict[str, Any]]
+) -> dict[str, Any]:
+    """Construct storage.backends section in Llama Stack configuration file.
+
+    Builds the storage.backends section for a Llama Stack configuration by
+    preserving existing backends and adding new ones for each BYOK RAG.
+
+    Parameters:
+        ls_config (dict[str, Any]): Existing Llama Stack configuration mapping.
+        byok_rag (list[dict[str, Any]]): List of BYOK RAG definitions.
+
+    Returns:
+        dict[str, Any]: The storage.backends dict with new backends added.
+    """
+    output: dict[str, Any] = {}
+
+    # preserve existing backends
+    if "storage" in ls_config and "backends" in ls_config["storage"]:
+        output = ls_config["storage"]["backends"].copy()
+
+    # add new backends for each BYOK RAG
+    for brag in byok_rag:
+        vector_store_id = brag.get("vector_store_id", "")
+        backend_name = f"byok_{vector_store_id}_storage"
+        output[backend_name] = {
+            "type": "kv_sqlite",
+            "db_path": brag.get("db_path", f".llama/{vector_store_id}.db"),
+        }
+    logger.info(
+        "Added %s backends into storage.backends section, total backends %s",
+        len(byok_rag),
+        len(output),
+    )
+    return output
+
+
+def construct_vector_stores_section(
     ls_config: dict[str, Any], byok_rag: list[dict[str, Any]]
 ) -> list[dict[str, Any]]:
-    """Construct vector_dbs section in Llama Stack configuration file.
+    """Construct registered_resources.vector_stores section in Llama Stack config.
 
-    Builds the vector_dbs section for a Llama Stack configuration.
+    Builds the vector_stores section for a Llama Stack configuration.
 
     Parameters:
         ls_config (dict[str, Any]): Existing Llama Stack configuration mapping
-        used as the base; existing `vector_dbs` entries are preserved if
-        present.
+        used as the base; existing `registered_resources.vector_stores` entries
+        are preserved if present.
         byok_rag (list[dict[str, Any]]): List of BYOK RAG definitions to be added to
-        the `vector_dbs` section.
+        the `vector_stores` section.
 
     Returns:
-        list[dict[str, Any]]: The `vector_dbs` list where each entry is a mapping with keys:
-            - `vector_db_id`: identifier of the vector database
+        list[dict[str, Any]]: The `vector_stores` list where each entry is a mapping with keys:
+            - `vector_store_id`: identifier of the vector store
             - `provider_id`: provider identifier prefixed with `"byok_"`
             - `embedding_model`: name of the embedding model
             - `embedding_dimension`: embedding vector dimensionality
     """
     output = []
 
-    # fill-in existing vector_dbs entries
-    if "vector_dbs" in ls_config:
-        output = ls_config["vector_dbs"]
+    # fill-in existing vector_stores entries from registered_resources
+    if "registered_resources" in ls_config:
+        if "vector_stores" in ls_config["registered_resources"]:
+            output = ls_config["registered_resources"]["vector_stores"].copy()
 
-    # append new vector_dbs entries
+    # append new vector_stores entries
     for brag in byok_rag:
+        vector_store_id = brag.get("vector_store_id", "")
         output.append(
             {
-                "vector_db_id": brag.get("vector_db_id", ""),
-                "provider_id": "byok_" + brag.get("vector_db_id", ""),
+                "vector_store_id": vector_store_id,
+                "provider_id": f"byok_{vector_store_id}",
                 "embedding_model": brag.get("embedding_model", ""),
                 "embedding_dimension": brag.get("embedding_dimension"),
             }
         )
     logger.info(
-        "Added %s items into vector_dbs section, total items %s",
+        "Added %s items into registered_resources.vector_stores, total items %s",
         len(byok_rag),
         len(output),
     )
     return output
 
 
+def construct_models_section(
+    ls_config: dict[str, Any], byok_rag: list[dict[str, Any]]
+) -> list[dict[str, Any]]:
+    """Construct registered_resources.models section with embedding models.
+
+    Adds embedding model entries for each BYOK RAG configuration.
+
+    Parameters:
+        ls_config (dict[str, Any]): Existing Llama Stack configuration mapping.
+        byok_rag (list[dict[str, Any]]): List of BYOK RAG definitions.
+
+    Returns:
+        list[dict[str, Any]]: The models list with embedding models added.
+    """
+    output: list[dict[str, Any]] = []
+
+    # preserve existing models
+    if "registered_resources" in ls_config:
+        if "models" in ls_config["registered_resources"]:
+            output = ls_config["registered_resources"]["models"].copy()
+
+    # add embedding models for each BYOK RAG
+    for brag in byok_rag:
+        embedding_model = brag.get("embedding_model", "")
+        vector_store_id = brag.get("vector_store_id", "")
+        embedding_dimension = brag.get("embedding_dimension")
+
+        # Strip sentence-transformers/ prefix if present
+        provider_model_id = embedding_model
+        if provider_model_id.startswith("sentence-transformers/"):
+            provider_model_id = provider_model_id[len("sentence-transformers/") :]
+
+        # Skip if embedding model already registered
+        existing_model_ids = [m.get("provider_model_id") for m in output]
+        if provider_model_id in existing_model_ids:
+            continue
+
+        output.append(
+            {
+                "model_id": f"byok_{vector_store_id}_embedding",
+                "model_type": "embedding",
+                "provider_id": "sentence-transformers",
+                "provider_model_id": provider_model_id,
+                "metadata": {
+                    "embedding_dimension": embedding_dimension,
+                },
+            }
+        )
+    logger.info(
+        "Added embedding models into registered_resources.models, total models %s",
+        len(output),
+    )
+    return output
+
+
 def construct_vector_io_providers_section(
     ls_config: dict[str, Any], byok_rag: list[dict[str, Any]]
 ) -> list[dict[str, Any]]:
@@ -179,28 +272,29 @@ def construct_vector_io_providers_section(
     Returns:
         list[dict[str, Any]]: The resulting providers/vector_io list containing
         the original entries (if any) plus one entry per item in `byok_rag`.
-        Each appended entry has `provider_id` set to "byok_<vector_db_id>",
-        `provider_type` set from the RAG item, and a `config` with a `kvstore`
-        pointing to ".llama/<vector_db_id>.db", `namespace` as None, and `type`
-        "sqlite".
+        Each appended entry has `provider_id` set to "byok_<vector_store_id>",
+        `provider_type` set from the RAG item, and a `config` with `persistence`
+        referencing the corresponding backend.
     """
     output = []
 
     # fill-in existing vector_io entries
     if "providers" in ls_config and "vector_io" in ls_config["providers"]:
-        output = ls_config["providers"]["vector_io"]
+        output = ls_config["providers"]["vector_io"].copy()
 
     # append new vector_io entries
     for brag in byok_rag:
+        vector_store_id = brag.get("vector_store_id", "")
+        backend_name = f"byok_{vector_store_id}_storage"
+        provider_id = f"byok_{vector_store_id}"
         output.append(
             {
-                "provider_id": "byok_" + brag.get("vector_db_id", ""),
+                "provider_id": provider_id,
                 "provider_type": brag.get("rag_type", "inline::faiss"),
                 "config": {
-                    "kvstore": {
-                        "db_path": ".llama/" + brag.get("vector_db_id", "") + ".db",
-                        "namespace": None,
-                        "type": "sqlite",
+                    "persistence": {
+                        "namespace": "vector_io::faiss",
+                        "backend": backend_name,
                     }
                 },
             }
@@ -225,14 +319,33 @@ def enrich_byok_rag(ls_config: dict[str, Any], byok_rag: list[dict[str, Any]]) -
         return
 
     logger.info("Enriching Llama Stack config with BYOK RAG")
-    ls_config["vector_dbs"] = construct_vector_dbs_section(ls_config, byok_rag)
 
+    # Add storage backends
+    if "storage" not in ls_config:
+        ls_config["storage"] = {}
+    ls_config["storage"]["backends"] = construct_storage_backends_section(
+        ls_config, byok_rag
+    )
+
+    # Add vector_io providers
     if "providers" not in ls_config:
         ls_config["providers"] = {}
     ls_config["providers"]["vector_io"] = construct_vector_io_providers_section(
         ls_config, byok_rag
     )
 
+    # Add registered vector stores
+    if "registered_resources" not in ls_config:
+        ls_config["registered_resources"] = {}
+    ls_config["registered_resources"]["vector_stores"] = (
+        construct_vector_stores_section(ls_config, byok_rag)
+    )
+
+    # Add embedding models
+    ls_config["registered_resources"]["models"] = construct_models_section(
+        ls_config, byok_rag
+    )
+
 
 # =============================================================================
 # Main Generation Function (service/container mode only)
diff --git a/src/models/config.py b/src/models/config.py
index 771ed4fdf..15181ba4a 100644
--- a/src/models/config.py
+++ b/src/models/config.py
@@ -1480,11 +1480,11 @@ class ByokRag(ConfigurationBase):
         description="Dimensionality of embedding vectors.",
     )
 
-    vector_db_id: str = Field(
+    vector_store_id: str = Field(
         ...,
         min_length=1,
-        title="Vector DB ID",
-        description="Vector DB identification.",
+        title="Vector Store ID",
+        description="Vector store identification.",
     )
 
     db_path: FilePath = Field(
diff --git a/src/models/responses.py b/src/models/responses.py
index 9749f95f9..1ce11d6d2 100644
--- a/src/models/responses.py
+++ b/src/models/responses.py
@@ -8,8 +8,8 @@
 from pydantic import AnyUrl, BaseModel, Field
 from pydantic_core import SchemaError
 
-from quota.quota_exceed_error import QuotaExceedError
 from models.config import Action, Configuration
+from quota.quota_exceed_error import QuotaExceedError
 from utils.types import RAGChunk, ToolCallSummary, ToolResultSummary
 
 SUCCESSFUL_RESPONSE_DESCRIPTION = "Successful response"
@@ -369,6 +369,7 @@ class ReferencedDocument(BaseModel):
     Attributes:
         doc_url: Url to the referenced doc.
         doc_title: Title of the referenced doc.
+        doc_id: ID of the referenced doc.
     """
 
     doc_url: Optional[AnyUrl] = Field(
@@ -379,6 +380,8 @@ class ReferencedDocument(BaseModel):
         None, description="Title of the referenced document"
     )
 
+    doc_id: Optional[str] = Field(None, description="ID of the referenced document")
+
 
 class QueryResponse(AbstractSuccessfulResponse):
     """Model representing LLM response to a query.
diff --git a/tests/configuration/run.yaml b/tests/configuration/run.yaml
index 6bb776005..7a1702bc8 100644
--- a/tests/configuration/run.yaml
+++ b/tests/configuration/run.yaml
@@ -16,54 +16,48 @@ benchmarks: []
 container_image: null
 datasets: []
 external_providers_dir: null
-inference_store:
-  db_path: .llama/distributions/ollama/inference_store.db
-  type: sqlite
 logging: null
-metadata_store:
-  db_path: .llama/distributions/ollama/registry.db
-  namespace: null
-  type: sqlite
 providers:
   agents:
-  - config:
-      persistence_store:
-        db_path: .llama/distributions/ollama/agents_store.db
-        namespace: null
-        type: sqlite
-      responses_store:
-        db_path: .llama/distributions/ollama/responses_store.db
-        type: sqlite
-    provider_id: meta-reference
+  - provider_id: meta-reference
     provider_type: inline::meta-reference
+    config:
+      persistence:
+        agent_state:
+          namespace: agents_state
+          backend: kv_default
+        responses:
+          table_name: agents_responses
+          backend: sql_default
   datasetio:
-  - config:
-      kvstore:
-        db_path: .llama/distributions/ollama/huggingface_datasetio.db
-        namespace: null
-        type: sqlite
-    provider_id: huggingface
+  - provider_id: huggingface
     provider_type: remote::huggingface
-  - config:
+    config:
       kvstore:
-        db_path: .llama/distributions/ollama/localfs_datasetio.db
-        namespace: null
-        type: sqlite
-    provider_id: localfs
+        namespace: huggingface_datasetio
+        backend: kv_default
+  - provider_id: localfs
     provider_type: inline::localfs
-  eval:
-  - config:
+    config:
       kvstore:
-        db_path: .llama/distributions/ollama/meta_reference_eval.db
-        namespace: null
-        type: sqlite
-    provider_id: meta-reference
+        namespace: localfs_datasetio
+        backend: kv_default
+  eval:
+  - provider_id: meta-reference
     provider_type: inline::meta-reference
+    config:
+      kvstore:
+        namespace: eval_store
+        backend: kv_default
   inference:
-    - provider_id: openai
-      provider_type: remote::openai
-      config:
-        api_key: ${env.OPENAI_API_KEY}
+  - provider_id: openai
+    provider_type: remote::openai
+    config:
+      api_key: ${env.OPENAI_API_KEY}
+      allowed_models: ["gpt-4-turbo"]
+  - provider_id: sentence-transformers
+    provider_type: inline::sentence-transformers
+    config: {}
   post_training:
   - config:
       checkpoint_format: huggingface
@@ -95,15 +89,17 @@ providers:
     provider_id: meta-reference
     provider_type: inline::meta-reference
   tool_runtime:
-    - provider_id: model-context-protocol
-      provider_type: remote::model-context-protocol
-      config: {}
+  - provider_id: rag-runtime
+    provider_type: inline::rag-runtime
+    config: {}
+  - provider_id: model-context-protocol
+    provider_type: remote::model-context-protocol
+    config: {}
   vector_io:
   - config:
-      kvstore:
-        db_path: .llama/distributions/ollama/faiss_store.db
-        namespace: null
-        type: sqlite
+      persistence:
+        namespace: vector_io::faiss
+        backend: kv_default
     provider_id: faiss
     provider_type: inline::faiss
 scoring_fns: []
@@ -115,11 +111,41 @@ server:
   tls_cafile: null
   tls_certfile: null
   tls_keyfile: null
-shields: []
-vector_dbs: []
-
-models:
-  - model_id: gpt-4-turbo
-    provider_id: openai
-    model_type: llm
-    provider_model_id: gpt-4-turbo
+storage:
+  backends:
+    kv_default:
+      type: kv_sqlite
+      db_path: ${env.KV_STORE_PATH:=~/.llama/storage/kv_store.db}
+    sql_default:
+      type: sql_sqlite
+      db_path: ${env.SQL_STORE_PATH:=~/.llama/storage/sql_store.db}
+  stores:
+    metadata:
+      namespace: registry
+      backend: kv_default
+    inference:
+      table_name: inference_store
+      backend: sql_default
+    conversations:
+      table_name: openai_conversations
+      backend: sql_default
+    prompts:
+      namespace: prompts
+      backend: kv_default
+registered_resources:
+  models: []
+  shields: []
+  vector_stores: []
+  datasets: []
+  scoring_fns: []
+  benchmarks: []
+  tool_groups:
+  - toolgroup_id: builtin::rag
+    provider_id: rag-runtime
+telemetry:
+  enabled: true
+vector_stores:
+  default_provider_id: faiss
+  default_embedding_model:
+    provider_id: sentence-transformers
+    model_id: nomic-ai/nomic-embed-text-v1.5
diff --git a/tests/unit/app/endpoints/test_query.py b/tests/unit/app/endpoints/test_query.py
index b900af9b5..459c991aa 100644
--- a/tests/unit/app/endpoints/test_query.py
+++ b/tests/unit/app/endpoints/test_query.py
@@ -8,14 +8,11 @@
 
 import pytest
 from fastapi import HTTPException, Request, status
-from llama_stack_client.types.shared.interleaved_content_item import TextContentItem
-from pydantic import AnyUrl
 from pytest_mock import MockerFixture
 
 from app.endpoints.query import (
     evaluate_model_hints,
     is_transcripts_enabled,
-    parse_metadata_from_text_item,
     select_model_and_provider_id,
     validate_attachments_metadata,
 )
@@ -23,7 +20,6 @@
 from models.config import Action
 from models.database.conversations import UserConversation
 from models.requests import Attachment, QueryRequest
-from models.responses import ReferencedDocument
 from utils.token_counter import TokenCounter
 
 # User ID must be proper UUID
@@ -392,48 +388,6 @@ def test_validate_attachments_metadata_invalid_content_type() -> None:
     )
 
 
-def test_parse_metadata_from_text_item_valid(mocker: MockerFixture) -> None:
-    """Test parsing metadata from a TextContentItem."""
-    text = """
-    Some text...
-    Metadata: {"docs_url": "https://redhat.com", "title": "Example Doc"}
-    """
-    mock_item = mocker.Mock(spec=TextContentItem)
-    mock_item.text = text
-
-    doc = parse_metadata_from_text_item(mock_item)
-
-    assert isinstance(doc, ReferencedDocument)
-    assert doc.doc_url == AnyUrl("https://redhat.com")
-    assert doc.doc_title == "Example Doc"
-
-
-def test_parse_metadata_from_text_item_missing_title(mocker: MockerFixture) -> None:
-    """Test parsing metadata from a TextContentItem with missing title."""
-    mock_item = mocker.Mock(spec=TextContentItem)
-    mock_item.text = """Metadata: {"docs_url": "https://redhat.com"}"""
-    doc = parse_metadata_from_text_item(mock_item)
-    assert doc is None
-
-
-def test_parse_metadata_from_text_item_missing_url(mocker: MockerFixture) -> None:
-    """Test parsing metadata from a TextContentItem with missing url."""
-    mock_item = mocker.Mock(spec=TextContentItem)
-    mock_item.text = """Metadata: {"title": "Example Doc"}"""
-    doc = parse_metadata_from_text_item(mock_item)
-    assert doc is None
-
-
-def test_parse_metadata_from_text_item_malformed_url(mocker: MockerFixture) -> None:
-    """Test parsing metadata from a TextContentItem with malformed url."""
-    mock_item = mocker.Mock(spec=TextContentItem)
-    mock_item.text = (
-        """Metadata: {"docs_url": "not a valid url", "title": "Example Doc"}"""
-    )
-    doc = parse_metadata_from_text_item(mock_item)
-    assert doc is None
-
-
 def test_no_tools_parameter_backward_compatibility() -> None:
     """Test that default behavior is unchanged when no_tools parameter is not specified."""
     # This test ensures that existing code that doesn't specify no_tools continues to work
diff --git a/tests/unit/app/endpoints/test_query_v2.py b/tests/unit/app/endpoints/test_query_v2.py
index b4b4ec5ee..db9601ff1 100644
--- a/tests/unit/app/endpoints/test_query_v2.py
+++ b/tests/unit/app/endpoints/test_query_v2.py
@@ -909,14 +909,22 @@ def _create_file_search_output(mocker: MockerFixture) -> Any:
     # Create mock result objects with proper attributes matching real llama-stack response
     result_1 = mocker.Mock()
     result_1.filename = "file2.pdf"
-    result_1.attributes = {"url": "http://example.com/doc2"}
+    result_1.attributes = {
+        "docs_url": "http://example.com/doc2",
+        "title": "Title 1",
+        "document_id": "doc-123",
+    }
     result_1.text = "Sample text from file2.pdf"
     result_1.score = 0.95
     result_1.file_id = "file-123"
     result_1.model_dump = mocker.Mock(
         return_value={
             "filename": "file2.pdf",
-            "attributes": {"url": "http://example.com/doc2"},
+            "attributes": {
+                "docs_url": "http://example.com/doc2",
+                "title": "Title 1",
+                "document_id": "doc-123",
+            },
             "text": "Sample text from file2.pdf",
             "score": 0.95,
             "file_id": "file-123",
@@ -925,14 +933,22 @@ def _create_file_search_output(mocker: MockerFixture) -> Any:
 
     result_2 = mocker.Mock()
     result_2.filename = "file3.docx"
-    result_2.attributes = {}
+    result_2.attributes = {
+        "docs_url": "http://example.com/doc3",
+        "title": "Title 2",
+        "document_id": "doc-456",
+    }
     result_2.text = "Sample text from file3.docx"
     result_2.score = 0.85
     result_2.file_id = "file-456"
     result_2.model_dump = mocker.Mock(
         return_value={
             "filename": "file3.docx",
-            "attributes": {},
+            "attributes": {
+                "docs_url": "http://example.com/doc3",
+                "title": "Title 2",
+                "document_id": "doc-456",
+            },
             "text": "Sample text from file3.docx",
             "score": 0.85,
             "file_id": "file-456",
@@ -975,27 +991,20 @@ async def test_retrieve_response_parses_referenced_documents(
         mock_client, "model-docs", qr, token="tkn", provider_id="test-provider"
     )
 
-    assert len(referenced_docs) == 4
+    # Referenced documents are now extracted only from file_search_call attributes
+    assert len(referenced_docs) == 2
 
-    # Verify Doc 1 (URL citation)
-    doc1 = next((d for d in referenced_docs if d.doc_title == "Doc 1"), None)
+    # Verify Title 1 (File search result with URL)
+    doc1 = next((d for d in referenced_docs if d.doc_title == "Title 1"), None)
     assert doc1
-    assert str(doc1.doc_url) == "http://example.com/doc1"
+    assert str(doc1.doc_url) == "http://example.com/doc2"
+    assert doc1.doc_id == "doc-123"
 
-    # Verify file1.txt (File citation)
-    doc2 = next((d for d in referenced_docs if d.doc_title == "file1.txt"), None)
+    # Verify Title 2 (File search result with URL)
+    doc2 = next((d for d in referenced_docs if d.doc_title == "Title 2"), None)
     assert doc2
-    assert doc2.doc_url is None
-
-    # Verify file2.pdf (File search result with URL)
-    doc3 = next((d for d in referenced_docs if d.doc_title == "file2.pdf"), None)
-    assert doc3
-    assert str(doc3.doc_url) == "http://example.com/doc2"
-
-    # Verify file3.docx (File search result without URL)
-    doc4 = next((d for d in referenced_docs if d.doc_title == "file3.docx"), None)
-    assert doc4
-    assert doc4.doc_url is None
+    assert str(doc2.doc_url) == "http://example.com/doc3"
+    assert doc2.doc_id == "doc-456"
 
     # Verify RAG chunks were extracted from file_search_call results
     assert len(_summary.rag_chunks) == 2
diff --git a/tests/unit/cache/test_postgres_cache.py b/tests/unit/cache/test_postgres_cache.py
index 2855c9daa..699c93102 100644
--- a/tests/unit/cache/test_postgres_cache.py
+++ b/tests/unit/cache/test_postgres_cache.py
@@ -601,7 +601,7 @@ def test_insert_and_get_with_referenced_documents(
     inserted_json_str = sql_params[-3]
 
     assert json.loads(inserted_json_str) == [
-        {"doc_url": "http://example.com/", "doc_title": "Test Doc"}
+        {"doc_url": "http://example.com/", "doc_title": "Test Doc", "doc_id": None}
     ]
 
     # Simulate the database returning that data
@@ -612,7 +612,7 @@ def test_insert_and_get_with_referenced_documents(
         "bar",
         "start_time",
         "end_time",
-        [{"doc_url": "http://example.com/", "doc_title": "Test Doc"}],
+        [{"doc_url": "http://example.com/", "doc_title": "Test Doc", "doc_id": None}],
         None,  # tool_calls
         None,  # tool_results
     )
diff --git a/tests/unit/models/config/test_byok_rag.py b/tests/unit/models/config/test_byok_rag.py
index d66373479..e8cf2f54f 100644
--- a/tests/unit/models/config/test_byok_rag.py
+++ b/tests/unit/models/config/test_byok_rag.py
@@ -20,7 +20,7 @@ def test_byok_rag_configuration_default_values() -> None:
 
     byok_rag = ByokRag(  # pyright: ignore[reportCallIssue]
         rag_id="rag_id",
-        vector_db_id="vector_db_id",
+        vector_store_id="vector_store_id",
         db_path="tests/configuration/rag.txt",
     )
     assert byok_rag is not None
@@ -28,7 +28,7 @@ def test_byok_rag_configuration_default_values() -> None:
     assert byok_rag.rag_type == DEFAULT_RAG_TYPE
     assert byok_rag.embedding_model == DEFAULT_EMBEDDING_MODEL
     assert byok_rag.embedding_dimension == DEFAULT_EMBEDDING_DIMENSION
-    assert byok_rag.vector_db_id == "vector_db_id"
+    assert byok_rag.vector_store_id == "vector_store_id"
     assert byok_rag.db_path == Path("tests/configuration/rag.txt")
 
 
@@ -38,7 +38,7 @@ def test_byok_rag_configuration_nondefault_values() -> None:
     Verify that ByokRag class accepts and stores non-default configuration values.
 
     Asserts that rag_id, rag_type, embedding_model, embedding_dimension, and
-    vector_db_id match the provided inputs and that db_path is converted to a
+    vector_store_id match the provided inputs and that db_path is converted to a
     Path.
     """
 
@@ -47,7 +47,7 @@ def test_byok_rag_configuration_nondefault_values() -> None:
         rag_type="rag_type",
         embedding_model="embedding_model",
         embedding_dimension=1024,
-        vector_db_id="vector_db_id",
+        vector_store_id="vector_store_id",
         db_path=Path("tests/configuration/rag.txt"),
     )
     assert byok_rag is not None
@@ -55,7 +55,7 @@ def test_byok_rag_configuration_nondefault_values() -> None:
     assert byok_rag.rag_type == "rag_type"
     assert byok_rag.embedding_model == "embedding_model"
     assert byok_rag.embedding_dimension == 1024
-    assert byok_rag.vector_db_id == "vector_db_id"
+    assert byok_rag.vector_store_id == "vector_store_id"
     assert byok_rag.db_path == Path("tests/configuration/rag.txt")
 
 
@@ -68,7 +68,7 @@ def test_byok_rag_configuration_wrong_dimension() -> None:
             rag_type="rag_type",
             embedding_model="embedding_model",
             embedding_dimension=-1024,
-            vector_db_id="vector_db_id",
+            vector_store_id="vector_store_id",
             db_path=Path("tests/configuration/rag.txt"),
         )
 
@@ -84,7 +84,7 @@ def test_byok_rag_configuration_empty_rag_id() -> None:
             rag_type="rag_type",
             embedding_model="embedding_model",
             embedding_dimension=1024,
-            vector_db_id="vector_db_id",
+            vector_store_id="vector_store_id",
             db_path=Path("tests/configuration/rag.txt"),
         )
 
@@ -107,7 +107,7 @@ def test_byok_rag_configuration_empty_rag_type() -> None:
             rag_type="",
             embedding_model="embedding_model",
             embedding_dimension=1024,
-            vector_db_id="vector_db_id",
+            vector_store_id="vector_store_id",
             db_path=Path("tests/configuration/rag.txt"),
         )
 
@@ -123,12 +123,12 @@ def test_byok_rag_configuration_empty_embedding_model() -> None:
             rag_type="rag_type",
             embedding_model="",
             embedding_dimension=1024,
-            vector_db_id="vector_db_id",
+            vector_store_id="vector_store_id",
             db_path=Path("tests/configuration/rag.txt"),
         )
 
 
-def test_byok_rag_configuration_empty_vector_db_id() -> None:
+def test_byok_rag_configuration_empty_vector_store_id() -> None:
     """Test the ByokRag constructor."""
 
     with pytest.raises(
@@ -139,6 +139,6 @@ def test_byok_rag_configuration_empty_vector_db_id() -> None:
             rag_type="rag_type",
             embedding_model="embedding_model",
             embedding_dimension=1024,
-            vector_db_id="",
+            vector_store_id="",
             db_path=Path("tests/configuration/rag.txt"),
         )
diff --git a/tests/unit/models/config/test_dump_configuration.py b/tests/unit/models/config/test_dump_configuration.py
index c7e1e2ec5..e96613ab8 100644
--- a/tests/unit/models/config/test_dump_configuration.py
+++ b/tests/unit/models/config/test_dump_configuration.py
@@ -815,7 +815,7 @@ def test_dump_configuration_byok(tmp_path: Path) -> None:
         byok_rag=[
             ByokRag(
                 rag_id="rag_id",
-                vector_db_id="vector_db_id",
+                vector_store_id="vector_store_id",
                 db_path="tests/configuration/rag.txt",
             ),
         ],
@@ -934,7 +934,7 @@ def test_dump_configuration_byok(tmp_path: Path) -> None:
                     "embedding_model": "sentence-transformers/all-mpnet-base-v2",
                     "rag_id": "rag_id",
                     "rag_type": "inline::faiss",
-                    "vector_db_id": "vector_db_id",
+                    "vector_store_id": "vector_store_id",
                 },
             ],
             "quota_handlers": {
diff --git a/tests/unit/test_llama_stack_configuration.py b/tests/unit/test_llama_stack_configuration.py
index f6cef3a98..4b058910f 100644
--- a/tests/unit/test_llama_stack_configuration.py
+++ b/tests/unit/test_llama_stack_configuration.py
@@ -8,8 +8,10 @@
 
 from llama_stack_configuration import (
     generate_configuration,
-    construct_vector_dbs_section,
+    construct_vector_stores_section,
     construct_vector_io_providers_section,
+    construct_storage_backends_section,
+    construct_models_section,
 )
 from models.config import (
     Configuration,
@@ -20,55 +22,59 @@
 )
 
 # =============================================================================
-# Test construct_vector_dbs_section
+# Test construct_vector_stores_section
 # =============================================================================
 
 
-def test_construct_vector_dbs_section_empty() -> None:
+def test_construct_vector_stores_section_empty() -> None:
     """Test with no BYOK RAG config."""
     ls_config: dict[str, Any] = {}
     byok_rag: list[dict[str, Any]] = []
-    output = construct_vector_dbs_section(ls_config, byok_rag)
+    output = construct_vector_stores_section(ls_config, byok_rag)
     assert len(output) == 0
 
 
-def test_construct_vector_dbs_section_preserves_existing() -> None:
-    """Test preserves existing vector_dbs entries."""
+def test_construct_vector_stores_section_preserves_existing() -> None:
+    """Test preserves existing vector_stores entries."""
     ls_config = {
-        "vector_dbs": [
-            {"vector_db_id": "existing", "provider_id": "existing_provider"},
-        ]
+        "registered_resources": {
+            "vector_stores": [
+                {"vector_store_id": "existing", "provider_id": "existing_provider"},
+            ]
+        }
     }
     byok_rag: list[dict[str, Any]] = []
-    output = construct_vector_dbs_section(ls_config, byok_rag)
+    output = construct_vector_stores_section(ls_config, byok_rag)
     assert len(output) == 1
-    assert output[0]["vector_db_id"] == "existing"
+    assert output[0]["vector_store_id"] == "existing"
 
 
-def test_construct_vector_dbs_section_adds_new() -> None:
+def test_construct_vector_stores_section_adds_new() -> None:
     """Test adds new BYOK RAG entries."""
     ls_config: dict[str, Any] = {}
     byok_rag = [
         {
             "rag_id": "rag1",
-            "vector_db_id": "db1",
+            "vector_store_id": "store1",
             "embedding_model": "test-model",
             "embedding_dimension": 512,
         },
     ]
-    output = construct_vector_dbs_section(ls_config, byok_rag)
+    output = construct_vector_stores_section(ls_config, byok_rag)
     assert len(output) == 1
-    assert output[0]["vector_db_id"] == "db1"
-    assert output[0]["provider_id"] == "byok_db1"
+    assert output[0]["vector_store_id"] == "store1"
+    assert output[0]["provider_id"] == "byok_store1"
     assert output[0]["embedding_model"] == "test-model"
     assert output[0]["embedding_dimension"] == 512
 
 
-def test_construct_vector_dbs_section_merge() -> None:
+def test_construct_vector_stores_section_merge() -> None:
     """Test merges existing and new entries."""
-    ls_config = {"vector_dbs": [{"vector_db_id": "existing"}]}
-    byok_rag = [{"vector_db_id": "new_db"}]
-    output = construct_vector_dbs_section(ls_config, byok_rag)
+    ls_config = {
+        "registered_resources": {"vector_stores": [{"vector_store_id": "existing"}]}
+    }
+    byok_rag = [{"vector_store_id": "new_store"}]
+    output = construct_vector_stores_section(ls_config, byok_rag)
     assert len(output) == 2
 
 
@@ -99,14 +105,120 @@ def test_construct_vector_io_providers_section_adds_new() -> None:
     ls_config: dict[str, Any] = {"providers": {}}
     byok_rag = [
         {
-            "vector_db_id": "db1",
+            "vector_store_id": "store1",
             "rag_type": "inline::faiss",
         },
     ]
     output = construct_vector_io_providers_section(ls_config, byok_rag)
     assert len(output) == 1
-    assert output[0]["provider_id"] == "byok_db1"
+    assert output[0]["provider_id"] == "byok_store1"
     assert output[0]["provider_type"] == "inline::faiss"
+    assert output[0]["config"]["persistence"]["backend"] == "byok_store1_storage"
+    assert output[0]["config"]["persistence"]["namespace"] == "vector_io::faiss"
+
+
+# =============================================================================
+# Test construct_storage_backends_section
+# =============================================================================
+
+
+def test_construct_storage_backends_section_empty() -> None:
+    """Test with no BYOK RAG config."""
+    ls_config: dict[str, Any] = {}
+    byok_rag: list[dict[str, Any]] = []
+    output = construct_storage_backends_section(ls_config, byok_rag)
+    assert len(output) == 0
+
+
+def test_construct_storage_backends_section_preserves_existing() -> None:
+    """Test preserves existing backends."""
+    ls_config = {
+        "storage": {
+            "backends": {
+                "kv_default": {"type": "kv_sqlite", "db_path": "~/.llama/kv.db"}
+            }
+        }
+    }
+    byok_rag: list[dict[str, Any]] = []
+    output = construct_storage_backends_section(ls_config, byok_rag)
+    assert len(output) == 1
+    assert "kv_default" in output
+
+
+def test_construct_storage_backends_section_adds_new() -> None:
+    """Test adds new BYOK RAG backend entries."""
+    ls_config: dict[str, Any] = {}
+    byok_rag = [
+        {
+            "vector_store_id": "store1",
+            "db_path": "/path/to/store1.db",
+        },
+    ]
+    output = construct_storage_backends_section(ls_config, byok_rag)
+    assert len(output) == 1
+    assert "byok_store1_storage" in output
+    assert output["byok_store1_storage"]["type"] == "kv_sqlite"
+    assert output["byok_store1_storage"]["db_path"] == "/path/to/store1.db"
+
+
+# =============================================================================
+# Test construct_models_section
+# =============================================================================
+
+
+def test_construct_models_section_empty() -> None:
+    """Test with no BYOK RAG config."""
+    ls_config: dict[str, Any] = {}
+    byok_rag: list[dict[str, Any]] = []
+    output = construct_models_section(ls_config, byok_rag)
+    assert len(output) == 0
+
+
+def test_construct_models_section_preserves_existing() -> None:
+    """Test preserves existing models."""
+    ls_config = {
+        "registered_resources": {
+            "models": [{"model_id": "existing", "model_type": "llm"}]
+        }
+    }
+    byok_rag: list[dict[str, Any]] = []
+    output = construct_models_section(ls_config, byok_rag)
+    assert len(output) == 1
+    assert output[0]["model_id"] == "existing"
+
+
+def test_construct_models_section_adds_embedding_model() -> None:
+    """Test adds embedding model from BYOK RAG."""
+    ls_config: dict[str, Any] = {}
+    byok_rag = [
+        {
+            "vector_store_id": "store1",
+            "embedding_model": "sentence-transformers/all-mpnet-base-v2",
+            "embedding_dimension": 768,
+        },
+    ]
+    output = construct_models_section(ls_config, byok_rag)
+    assert len(output) == 1
+    assert output[0]["model_id"] == "byok_store1_embedding"
+    assert output[0]["model_type"] == "embedding"
+    assert output[0]["provider_id"] == "sentence-transformers"
+    assert output[0]["provider_model_id"] == "all-mpnet-base-v2"
+    assert output[0]["metadata"]["embedding_dimension"] == 768
+
+
+def test_construct_models_section_strips_prefix() -> None:
+    """Test strips sentence-transformers/ prefix from embedding model."""
+    ls_config: dict[str, Any] = {}
+    byok_rag = [
+        {
+            "vector_store_id": "store1",
+            "embedding_model": "sentence-transformers//usr/path/model",
+            "embedding_dimension": 768,
+        },
+    ]
+    output = construct_models_section(ls_config, byok_rag)
+    assert len(output) == 1
+    assert output[0]["provider_model_id"] == "/usr/path/model"
 
 
 # =============================================================================
@@ -164,10 +276,11 @@ def test_generate_configuration_with_byok(tmp_path: Path) -> None:
         "byok_rag": [
             {
                 "rag_id": "rag1",
-                "vector_db_id": "db1",
+                "vector_store_id": "store1",
                 "embedding_model": "test-model",
                 "embedding_dimension": 256,
                 "rag_type": "inline::faiss",
+                "db_path": "/tmp/store1.db",
             },
         ],
     }
@@ -178,5 +291,19 @@ def test_generate_configuration_with_byok(tmp_path: Path) -> None:
     with open(outfile, encoding="utf-8") as f:
         result = yaml.safe_load(f)
 
-    db_ids = [db["vector_db_id"] for db in result["vector_dbs"]]
-    assert "db1" in db_ids
+    # Check registered_resources.vector_stores
+    store_ids = [
+        s["vector_store_id"] for s in result["registered_resources"]["vector_stores"]
+    ]
+    assert "store1" in store_ids
+
+    # Check storage.backends
+    assert "byok_store1_storage" in result["storage"]["backends"]
+
+    # Check providers.vector_io
+    provider_ids = [p["provider_id"] for p in result["providers"]["vector_io"]]
+    assert "byok_store1" in provider_ids
+
+    # Check registered_resources.models for embedding model
+    model_ids = [m["model_id"] for m in result["registered_resources"]["models"]]
+    assert "byok_store1_embedding" in model_ids