More work on file_search verification test

This gets the file_search verification test working against ollama,
fireworks, and api.openai.com. We don't have the entirety of the
vector store API implemented in Llama Stack yet, so this still has a
bit of a hack to swap between using only OpenAI-compatible APIs versus
using the LlamaStackClient to insert content into our vector stores.

Outside of actually inserting file contents, the rest of the test
works the same and uses only the OpenAI client for all of these providers.

How to run the tests:

Ollama (sometimes flakes with small model):

```
ollama run llama3.2:3b

INFERENCE_MODEL="meta-llama/Llama-3.2-3B-Instruct" \
llama stack run ./llama_stack/templates/ollama/run.yaml \
  --image-type venv \
  --env OLLAMA_URL="http://0.0.0.0:11434"

pytest -sv \
  'tests/verifications/openai_api/test_responses.py::test_response_non_streaming_file_search' \
  --base-url=http://localhost:8321/v1/openai/v1 \
  --model meta-llama/Llama-3.2-3B-Instruct
```

Fireworks via Llama Stack:

```
llama stack run llama_stack/templates/fireworks/run.yaml

pytest -sv \
  'tests/verifications/openai_api/test_responses.py::test_response_non_streaming_file_search' \
  --base-url=http://localhost:8321/v1/openai/v1 \
  --model meta-llama/Llama-3.3-70B-Instruct
```

OpenAI directly:

```
pytest -sv \
  'tests/verifications/openai_api/test_responses.py::test_response_non_streaming_file_search' \
  --base-url=https://api.openai.com/v1 \
  --model gpt-4o
```

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

tests/verifications/openai_api/test_responses.py

@@ -5,6 +5,7 @@
 # the root directory of this source tree.
 
 import json
+import time
 
 import httpx
 import openai
@@ -265,25 +266,27 @@ def test_response_non_streaming_web_search(request, openai_client, model, provid
     ids=case_id_generator,
 )
 def test_response_non_streaming_file_search(
-    base_url, request, openai_client, model, provider, verification_config, case
+    base_url, request, openai_client, model, provider, verification_config, tmp_path, case
 ):
+    if isinstance(openai_client, LlamaStackAsLibraryClient):
+        pytest.skip("Responses API file search is not yet supported in library client.")
+
     test_name_base = get_base_test_name(request)
     if should_skip_test(verification_config, provider, model, test_name_base):
         pytest.skip(f"Skipping {test_name_base} for model {model} on provider {provider} based on config.")
 
-    lls_client = LlamaStackClient(base_url=base_url.replace("/v1/openai/v1", ""))
-    vector_db_id = "test_vector_store"
-
-    # Ensure the test starts from a clean vector store
-    try:
-        lls_client.vector_dbs.unregister(vector_db_id=vector_db_id)
-    except Exception:
-        pass
-
-    lls_client.vector_dbs.register(
-        vector_db_id=vector_db_id,
-        embedding_model="all-MiniLM-L6-v2",
+    vector_stores = openai_client.vector_stores.list()
+    for vector_store in vector_stores:
+        if vector_store.name == "test_vector_store":
+            openai_client.vector_stores.delete(vector_store_id=vector_store.id)
+    vector_store = openai_client.vector_stores.create(
+        name="test_vector_store",
+        # extra_body={
+        #     "embedding_model": "all-MiniLM-L6-v2",
+        #     "embedding_dimension": 384,
+        # },
     )
+
     doc_content = "Llama 4 Maverick has 128 experts"
     chunks = [
         {
@@ -294,18 +297,49 @@ def test_response_non_streaming_file_search(
             },
         },
     ]
-    lls_client.vector_io.insert(vector_db_id=vector_db_id, chunks=chunks)
+
+    file_name = "test_response_non_streaming_file_search.txt"
+    files = openai_client.files.list()
+    for file in files:
+        if file.filename == file_name:
+            openai_client.files.delete(file_id=file.id)
+    file_path = tmp_path / file_name
+    file_path.write_text(doc_content)
+    file_response = openai_client.files.create(file=open(file_path, "rb"), purpose="assistants")
+
+    if "api.openai.com" in base_url:
+        file_attach_response = openai_client.vector_stores.files.create(
+            vector_store_id=vector_store.id,
+            file_id=file_response.id,
+        )
+        while file_attach_response.status == "in_progress":
+            time.sleep(0.1)
+            file_attach_response = openai_client.vector_stores.files.retrieve(
+                vector_store_id=vector_store.id,
+                file_id=file_response.id,
+            )
+    else:
+        # TODO: only until we have a way to insert content into OpenAI vector stores
+        lls_client = LlamaStackClient(base_url=base_url.replace("/v1/openai/v1", ""))
+        lls_client.vector_io.insert(vector_db_id=vector_store.id, chunks=chunks)
+
+    tools = case["tools"]
+    for tool in tools:
+        if tool["type"] == "file_search":
+            tool["vector_store_ids"] = [vector_store.id]
 
     response = openai_client.responses.create(
         model=model,
         input=case["input"],
         tools=case["tools"],
         stream=False,
+        include=["file_search_call.results"],
     )
     assert len(response.output) > 1
     assert response.output[0].type == "file_search_call"
     assert response.output[0].status == "completed"
     assert response.output[0].queries  # ensure it's some non-empty list
+    assert response.output[0].results
     assert response.output[0].results[0].text == doc_content
     assert response.output[0].results[0].score > 0
     assert response.output[1].type == "message"