diff --git a/docs/_static/llama-stack-spec.html b/docs/_static/llama-stack-spec.html
index 5751cca2d..2b576a1a9 100644
--- a/docs/_static/llama-stack-spec.html
+++ b/docs/_static/llama-stack-spec.html
@@ -13756,6 +13756,24 @@
                 "type": "object",
                 "title": "Response"
             },
+            "VectorStoreContent": {
+                "type": "object",
+                "properties": {
+                    "type": {
+                        "type": "string",
+                        "const": "text"
+                    },
+                    "text": {
+                        "type": "string"
+                    }
+                },
+                "additionalProperties": false,
+                "required": [
+                    "type",
+                    "text"
+                ],
+                "title": "VectorStoreContent"
+            },
             "VectorStoreFileContentsResponse": {
                 "type": "object",
                 "properties": {
@@ -13793,7 +13811,7 @@
                     "content": {
                         "type": "array",
                         "items": {
-                            "$ref": "#/components/schemas/InterleavedContentItem"
+                            "$ref": "#/components/schemas/VectorStoreContent"
                         }
                     }
                 },
@@ -13879,24 +13897,6 @@
                 ],
                 "title": "OpenaiSearchVectorStoreRequest"
             },
-            "VectorStoreContent": {
-                "type": "object",
-                "properties": {
-                    "type": {
-                        "type": "string",
-                        "const": "text"
-                    },
-                    "text": {
-                        "type": "string"
-                    }
-                },
-                "additionalProperties": false,
-                "required": [
-                    "type",
-                    "text"
-                ],
-                "title": "VectorStoreContent"
-            },
             "VectorStoreSearchResponse": {
                 "type": "object",
                 "properties": {
diff --git a/docs/_static/llama-stack-spec.yaml b/docs/_static/llama-stack-spec.yaml
index 79e9285b5..160193e6a 100644
--- a/docs/_static/llama-stack-spec.yaml
+++ b/docs/_static/llama-stack-spec.yaml
@@ -9616,6 +9616,19 @@ components:
     Response:
       type: object
       title: Response
+    VectorStoreContent:
+      type: object
+      properties:
+        type:
+          type: string
+          const: text
+        text:
+          type: string
+      additionalProperties: false
+      required:
+        - type
+        - text
+      title: VectorStoreContent
     VectorStoreFileContentsResponse:
       type: object
       properties:
@@ -9636,7 +9649,7 @@ components:
         content:
           type: array
           items:
-            $ref: '#/components/schemas/InterleavedContentItem'
+            $ref: '#/components/schemas/VectorStoreContent'
       additionalProperties: false
       required:
         - file_id
@@ -9693,19 +9706,6 @@ components:
       required:
         - query
       title: OpenaiSearchVectorStoreRequest
-    VectorStoreContent:
-      type: object
-      properties:
-        type:
-          type: string
-          const: text
-        text:
-          type: string
-      additionalProperties: false
-      required:
-        - type
-        - text
-      title: VectorStoreContent
     VectorStoreSearchResponse:
       type: object
       properties:
diff --git a/llama_stack/apis/vector_io/vector_io.py b/llama_stack/apis/vector_io/vector_io.py
index ab5b3e567..6a674356d 100644
--- a/llama_stack/apis/vector_io/vector_io.py
+++ b/llama_stack/apis/vector_io/vector_io.py
@@ -12,7 +12,6 @@ from typing import Annotated, Any, Literal, Protocol, runtime_checkable
 
 from pydantic import BaseModel, Field
 
-from llama_stack.apis.common.content_types import InterleavedContentItem
 from llama_stack.apis.inference import InterleavedContent
 from llama_stack.apis.vector_dbs import VectorDB
 from llama_stack.providers.utils.telemetry.trace_protocol import trace_protocol
@@ -208,7 +207,7 @@ class VectorStoreFileContentsResponse(BaseModel):
     file_id: str
     filename: str
     attributes: dict[str, Any]
-    content: list[InterleavedContentItem]
+    content: list[VectorStoreContent]
 
 
 @json_schema_type
diff --git a/llama_stack/providers/utils/memory/openai_vector_store_mixin.py b/llama_stack/providers/utils/memory/openai_vector_store_mixin.py
index 32bcccd97..2602acd4f 100644
--- a/llama_stack/providers/utils/memory/openai_vector_store_mixin.py
+++ b/llama_stack/providers/utils/memory/openai_vector_store_mixin.py
@@ -12,7 +12,6 @@ import uuid
 from abc import ABC, abstractmethod
 from typing import Any
 
-from llama_stack.apis.common.content_types import InterleavedContentItem, TextContentItem
 from llama_stack.apis.files import Files
 from llama_stack.apis.files.files import OpenAIFileObject
 from llama_stack.apis.vector_dbs import VectorDB
@@ -386,33 +385,7 @@ class OpenAIVectorStoreMixin(ABC):
                     if not self._matches_filters(chunk.metadata, filters):
                         continue
 
-                # content is InterleavedContent
-                if isinstance(chunk.content, str):
-                    content = [
-                        VectorStoreContent(
-                            type="text",
-                            text=chunk.content,
-                        )
-                    ]
-                elif isinstance(chunk.content, list):
-                    # TODO: Add support for other types of content
-                    content = [
-                        VectorStoreContent(
-                            type="text",
-                            text=item.text,
-                        )
-                        for item in chunk.content
-                        if item.type == "text"
-                    ]
-                else:
-                    if chunk.content.type != "text":
-                        raise ValueError(f"Unsupported content type: {chunk.content.type}")
-                    content = [
-                        VectorStoreContent(
-                            type="text",
-                            text=chunk.content.text,
-                        )
-                    ]
+                content = self._chunk_to_vector_store_content(chunk)
 
                 response_data_item = VectorStoreSearchResponse(
                     file_id=chunk.metadata.get("file_id", ""),
@@ -488,6 +461,36 @@ class OpenAIVectorStoreMixin(ABC):
             # Unknown filter type, default to no match
             raise ValueError(f"Unsupported filter type: {filter_type}")
 
+    def _chunk_to_vector_store_content(self, chunk: Chunk) -> list[VectorStoreContent]:
+        # content is InterleavedContent
+        if isinstance(chunk.content, str):
+            content = [
+                VectorStoreContent(
+                    type="text",
+                    text=chunk.content,
+                )
+            ]
+        elif isinstance(chunk.content, list):
+            # TODO: Add support for other types of content
+            content = [
+                VectorStoreContent(
+                    type="text",
+                    text=item.text,
+                )
+                for item in chunk.content
+                if item.type == "text"
+            ]
+        else:
+            if chunk.content.type != "text":
+                raise ValueError(f"Unsupported content type: {chunk.content.type}")
+            content = [
+                VectorStoreContent(
+                    type="text",
+                    text=chunk.content.text,
+                )
+            ]
+        return content
+
     async def openai_attach_file_to_vector_store(
         self,
         vector_store_id: str,
@@ -634,20 +637,14 @@ class OpenAIVectorStoreMixin(ABC):
         file_info = await self._load_openai_vector_store_file(vector_store_id, file_id)
         dict_chunks = await self._load_openai_vector_store_file_contents(vector_store_id, file_id)
         chunks = [Chunk.model_validate(c) for c in dict_chunks]
-        contents: list[InterleavedContentItem] = []
+        content = []
         for chunk in chunks:
-            content = chunk.content
-            if isinstance(content, str):
-                contents.append(TextContentItem(text=content))
-            elif isinstance(content, InterleavedContentItem):
-                contents.append(content)
-            else:
-                contents.extend(contents)
+            content.extend(self._chunk_to_vector_store_content(chunk))
         return VectorStoreFileContentsResponse(
             file_id=file_id,
             filename=file_info.get("filename", ""),
             attributes=file_info.get("attributes", {}),
-            content=contents,
+            content=content,
         )
 
     async def openai_update_vector_store_file(
@@ -684,6 +681,10 @@ class OpenAIVectorStoreMixin(ABC):
         await self._delete_openai_vector_store_file_from_storage(vector_store_id, file_id)
 
         # TODO: We need to actually delete the embeddings from the underlying vector store...
+        # Also uncomment the corresponding integration test marked as xfail
+        #
+        # test_openai_vector_store_delete_file_removes_from_vector_store in
+        # tests/integration/vector_io/test_openai_vector_stores.py
 
         # Update in-memory cache
         store_info["file_ids"].remove(file_id)
diff --git a/tests/integration/vector_io/test_openai_vector_stores.py b/tests/integration/vector_io/test_openai_vector_stores.py
index e7eccf46d..0440cd21c 100644
--- a/tests/integration/vector_io/test_openai_vector_stores.py
+++ b/tests/integration/vector_io/test_openai_vector_stores.py
@@ -440,7 +440,7 @@ def test_openai_vector_store_search_with_max_num_results(
     assert len(search_response.data) == 2
 
 
-def test_openai_vector_store_attach_file_response_attributes(compat_client_with_empty_stores, client_with_models):
+def test_openai_vector_store_attach_file(compat_client_with_empty_stores, client_with_models):
     """Test OpenAI vector store attach file."""
     skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
 
@@ -453,7 +453,7 @@ def test_openai_vector_store_attach_file_response_attributes(compat_client_with_
     vector_store = compat_client.vector_stores.create(name="test_store")
 
     # Create a file
-    test_content = b"This is a test file"
+    test_content = b"The secret string is foobazbar."
     with BytesIO(test_content) as file_buffer:
         file_buffer.name = "openai_test.txt"
         file = compat_client.files.create(file=file_buffer, purpose="assistants")
@@ -480,6 +480,16 @@ def test_openai_vector_store_attach_file_response_attributes(compat_client_with_
     assert updated_vector_store.file_counts.failed == 0
     assert updated_vector_store.file_counts.in_progress == 0
 
+    # Search using OpenAI API to confirm our file attached
+    search_response = compat_client.vector_stores.search(
+        vector_store_id=vector_store.id, query="What is the secret string?", max_num_results=1
+    )
+    assert search_response is not None
+    assert len(search_response.data) > 0
+    top_result = search_response.data[0]
+    top_content = top_result.content[0].text
+    assert "foobazbar" in top_content.lower()
+
 
 def test_openai_vector_store_attach_files_on_creation(compat_client_with_empty_stores, client_with_models):
     """Test OpenAI vector store attach files on creation."""
@@ -689,6 +699,49 @@ def test_openai_vector_store_delete_file(compat_client_with_empty_stores, client
     assert updated_vector_store.file_counts.in_progress == 0
 
 
+# TODO: Remove this xfail once we have a way to remove embeddings from vector store
+@pytest.mark.xfail(reason="Vector Store Files delete doesn't remove embeddings from vecntor store", strict=True)
+def test_openai_vector_store_delete_file_removes_from_vector_store(compat_client_with_empty_stores, client_with_models):
+    """Test OpenAI vector store delete file removes from vector store."""
+    skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)
+
+    if isinstance(compat_client_with_empty_stores, LlamaStackClient):
+        pytest.skip("Vector Store Files attach is not yet supported with LlamaStackClient")
+
+    compat_client = compat_client_with_empty_stores
+
+    # Create a vector store
+    vector_store = compat_client.vector_stores.create(name="test_store")
+
+    # Create a file
+    test_content = b"The secret string is foobazbar."
+    with BytesIO(test_content) as file_buffer:
+        file_buffer.name = "openai_test.txt"
+        file = compat_client.files.create(file=file_buffer, purpose="assistants")
+
+    # Attach the file to the vector store
+    file_attach_response = compat_client.vector_stores.files.create(
+        vector_store_id=vector_store.id,
+        file_id=file.id,
+    )
+    assert file_attach_response.status == "completed"
+
+    # Search using OpenAI API to confirm our file attached
+    search_response = compat_client.vector_stores.search(
+        vector_store_id=vector_store.id, query="What is the secret string?", max_num_results=1
+    )
+    assert "foobazbar" in search_response.data[0].content[0].text.lower()
+
+    # Delete the file
+    compat_client.vector_stores.files.delete(vector_store_id=vector_store.id, file_id=file.id)
+
+    # Search using OpenAI API to confirm our file deleted
+    search_response = compat_client.vector_stores.search(
+        vector_store_id=vector_store.id, query="What is the secret string?", max_num_results=1
+    )
+    assert not search_response.data
+
+
 def test_openai_vector_store_update_file(compat_client_with_empty_stores, client_with_models):
     """Test OpenAI vector store update file."""
     skip_if_provider_doesnt_support_openai_vector_stores(client_with_models)