feat: File search tool for Responses API

This is an initial working prototype of wiring up the `file_search` builtin
tool for the Responses API to our existing rag knowledge search tool.

I stubbed in a new test (that uses a hardcoded url hybrid of the OpenAI
and Llama Stack clients for now, only until we finish landing the vector
store APIs and insertion support).

Note that this is currently under tests/verification only because it
sometimes flakes with tool calling of the small Llama-3.2-3B model we run
in CI (and that I use as an example below). We'd want to make the test a
bit more robust in some way if we moved this over to tests/integration and
ran it in CI.

```
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
```

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

tests/verifications/openai_api/test_responses.py

@@ -9,6 +9,7 @@ import json
 import httpx
 import openai
 import pytest
+from llama_stack_client import LlamaStackClient
 
 from llama_stack import LlamaStackAsLibraryClient
 from llama_stack.distribution.datatypes import AuthenticationRequiredError
@@ -258,6 +259,62 @@ def test_response_non_streaming_web_search(request, openai_client, model, provid
     assert case["output"].lower() in response.output_text.lower().strip()
 
 
+@pytest.mark.parametrize(
+    "case",
+    responses_test_cases["test_response_file_search"]["test_params"]["case"],
+    ids=case_id_generator,
+)
+def test_response_non_streaming_file_search(
+    base_url, request, openai_client, model, provider, verification_config, case
+):
+    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",
+    )
+    doc_content = "Llama 4 Maverick has 128 experts"
+    chunks = [
+        {
+            "content": doc_content,
+            "mime_type": "text/plain",
+            "metadata": {
+                "document_id": "doc1",
+            },
+        },
+    ]
+    lls_client.vector_io.insert(vector_db_id=vector_db_id, chunks=chunks)
+
+    response = openai_client.responses.create(
+        model=model,
+        input=case["input"],
+        tools=case["tools"],
+        stream=False,
+    )
+    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[0].text == doc_content
+    assert response.output[0].results[0].score > 0
+    assert response.output[1].type == "message"
+    assert response.output[1].status == "completed"
+    assert response.output[1].role == "assistant"
+    assert len(response.output[1].content) > 0
+    assert case["output"].lower() in response.output_text.lower().strip()
+
+
 @pytest.mark.parametrize(
     "case",
     responses_test_cases["test_response_mcp_tool"]["test_params"]["case"],