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Add pdf support to file_search for Responses API

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This adds basic PDF support (using our existing `parse_pdf` function)
to the file_search tool and corresponding Vector Files API.

When a PDF file is uploaded and attached to a vector store, we parse
the pdf and then chunk its content as normal. This is not the best
solution long-term, but it does match what we've been doing so far for
PDF files in the memory tool.

Signed-off-by: Ben Browning <bbrownin@redhat.com>
This commit is contained in:
Ben Browning 2025-06-11 16:45:28 -04:00
parent 57eccf023d
commit 055885bd5a
4 changed files with 41 additions and 33 deletions
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39
tests/verifications/openai_api/test_responses.py
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@ -5,6 +5,7 @@
# the root directory of this source tree.
import json
import os
import time
import httpx
@ -38,7 +39,7 @@ def _new_vector_store(openai_client, name):
return vector_store
def _new_file(openai_client, name, content, tmp_path):
def _upload_file(openai_client, name, file_path):
# Ensure we don't reuse an existing file
files = openai_client.files.list()
for file in files:
@ -46,8 +47,6 @@ def _new_file(openai_client, name, content, tmp_path):
openai_client.files.delete(file_id=file.id)
# Upload a text file with our document content
file_path = tmp_path / name
file_path.write_text(content)
return openai_client.files.create(file=open(file_path, "rb"), purpose="assistants")
@ -291,7 +290,7 @@ def test_response_non_streaming_web_search(request, openai_client, model, provid
responses_test_cases["test_response_file_search"]["test_params"]["case"],
ids=case_id_generator,
)
def test_response_non_streaming_file_search_simple_text(
def test_response_non_streaming_file_search(
request, openai_client, model, provider, verification_config, tmp_path, case
):
if isinstance(openai_client, LlamaStackAsLibraryClient):
@ -303,8 +302,17 @@ def test_response_non_streaming_file_search_simple_text(
vector_store = _new_vector_store(openai_client, "test_vector_store")
file_content = "Llama 4 Maverick has 128 experts"
file_response = _new_file(openai_client, "test_response_non_streaming_file_search.txt", file_content, tmp_path)
if "file_content" in case:
file_name = "test_response_non_streaming_file_search.txt"
file_path = tmp_path / file_name
file_path.write_text(case["file_content"])
elif "file_path" in case:
file_path = os.path.join(os.path.dirname(__file__), "fixtures", case["file_path"])
file_name = os.path.basename(file_path)
else:
raise ValueError(f"No file content or path provided for case {case['case_id']}")
file_response = _upload_file(openai_client, file_name, file_path)
# Attach our file to the vector store
file_attach_response = openai_client.vector_stores.files.create(
@ -343,7 +351,7 @@ def test_response_non_streaming_file_search_simple_text(
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 == file_content
assert case["output"].lower() in response.output[0].results[0].text.lower()
assert response.output[0].results[0].score > 0
# Verify the assistant response that summarizes the results
@ -354,13 +362,8 @@ def test_response_non_streaming_file_search_simple_text(
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_empty_vector_store(
request, openai_client, model, provider, verification_config, tmp_path, case
request, openai_client, model, provider, verification_config
):
if isinstance(openai_client, LlamaStackAsLibraryClient):
pytest.skip("Responses API file search is not yet supported in library client.")
@ -371,17 +374,11 @@ def test_response_non_streaming_file_search_empty_vector_store(
vector_store = _new_vector_store(openai_client, "test_vector_store")
# Update our tools with the right vector store id
tools = case["tools"]
for tool in tools:
if tool["type"] == "file_search":
tool["vector_store_ids"] = [vector_store.id]
# Create the response request, which should query our vector store
response = openai_client.responses.create(
model=model,
input=case["input"],
tools=case["tools"],
input="How many experts does the Llama 4 Maverick model have?",
tools=[{"type": "file_search", "vector_store_ids": [vector_store.id]}],
stream=False,
include=["file_search_call.results"],
)