Use VectorStoreContent vs InterleavedContent in vector store files

This extracts the existing logic to convert chunks to
VectorStoreContent objects into a reusable method and uses that when
returning our list of Vector Store File contents.

It also adds an xfail test for deleting vector store files, as that's
not implemented yet but parking the implementation of that for now.

Signed-off-by: Ben Browning <bbrownin@redhat.com>

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

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)