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# What does this PR do? closes #3268 closes #3498 When resuming from previous response ID, currently we attempt to convert from the stored responses input to chat completion messages, which is not always possible, e.g. for tool calls where some data is lost once converted from chat completion message to repsonses input format. This PR stores the chat completion messages that correspond to the _last_ call to chat completion, which is sufficient to be resumed from in the next responses API call, where we load these saved messages and skip conversion entirely. Separate issue to optimize storage: https://github.com/llamastack/llama-stack/issues/3646 ## Test Plan existing CI tests
614 lines
24 KiB
Python
614 lines
24 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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import json
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import os
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import httpx
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import openai
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import pytest
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from llama_stack import LlamaStackAsLibraryClient
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from llama_stack.core.datatypes import AuthenticationRequiredError
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from tests.common.mcp import dependency_tools, make_mcp_server
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from .fixtures.test_cases import (
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custom_tool_test_cases,
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file_search_test_cases,
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mcp_tool_test_cases,
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multi_turn_tool_execution_streaming_test_cases,
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multi_turn_tool_execution_test_cases,
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web_search_test_cases,
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)
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from .helpers import new_vector_store, setup_mcp_tools, upload_file, wait_for_file_attachment
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from .streaming_assertions import StreamingValidator
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@pytest.mark.parametrize("case", web_search_test_cases)
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def test_response_non_streaming_web_search(compat_client, text_model_id, case):
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response = compat_client.responses.create(
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model=text_model_id,
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input=case.input,
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tools=case.tools,
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stream=False,
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)
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assert len(response.output) > 1
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assert response.output[0].type == "web_search_call"
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assert response.output[0].status == "completed"
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assert response.output[1].type == "message"
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assert response.output[1].status == "completed"
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assert response.output[1].role == "assistant"
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assert len(response.output[1].content) > 0
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assert case.expected.lower() in response.output_text.lower().strip()
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@pytest.mark.parametrize("case", file_search_test_cases)
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def test_response_non_streaming_file_search(compat_client, text_model_id, tmp_path, case):
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if isinstance(compat_client, LlamaStackAsLibraryClient):
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pytest.skip("Responses API file search is not yet supported in library client.")
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vector_store = new_vector_store(compat_client, "test_vector_store")
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if case.file_content:
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file_name = "test_response_non_streaming_file_search.txt"
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file_path = tmp_path / file_name
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file_path.write_text(case.file_content)
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elif case.file_path:
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file_path = os.path.join(os.path.dirname(__file__), "fixtures", case.file_path)
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file_name = os.path.basename(file_path)
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else:
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raise ValueError("No file content or path provided for case")
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file_response = upload_file(compat_client, file_name, file_path)
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# Attach our file to the vector store
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compat_client.vector_stores.files.create(
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vector_store_id=vector_store.id,
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file_id=file_response.id,
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)
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# Wait for the file to be attached
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wait_for_file_attachment(compat_client, vector_store.id, file_response.id)
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# Update our tools with the right vector store id
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tools = case.tools
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for tool in tools:
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if tool["type"] == "file_search":
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tool["vector_store_ids"] = [vector_store.id]
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# Create the response request, which should query our vector store
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response = compat_client.responses.create(
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model=text_model_id,
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input=case.input,
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tools=tools,
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stream=False,
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include=["file_search_call.results"],
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)
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# Verify the file_search_tool was called
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assert len(response.output) > 1
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assert response.output[0].type == "file_search_call"
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assert response.output[0].status == "completed"
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assert response.output[0].queries # ensure it's some non-empty list
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assert response.output[0].results
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assert case.expected.lower() in response.output[0].results[0].text.lower()
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assert response.output[0].results[0].score > 0
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# Verify the output_text generated by the response
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assert case.expected.lower() in response.output_text.lower().strip()
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def test_response_non_streaming_file_search_empty_vector_store(compat_client, text_model_id):
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if isinstance(compat_client, LlamaStackAsLibraryClient):
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pytest.skip("Responses API file search is not yet supported in library client.")
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vector_store = new_vector_store(compat_client, "test_vector_store")
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# Create the response request, which should query our vector store
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response = compat_client.responses.create(
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model=text_model_id,
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input="How many experts does the Llama 4 Maverick model have?",
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tools=[{"type": "file_search", "vector_store_ids": [vector_store.id]}],
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stream=False,
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include=["file_search_call.results"],
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)
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# Verify the file_search_tool was called
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assert len(response.output) > 1
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assert response.output[0].type == "file_search_call"
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assert response.output[0].status == "completed"
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assert response.output[0].queries # ensure it's some non-empty list
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assert not response.output[0].results # ensure we don't get any results
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# Verify some output_text was generated by the response
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assert response.output_text
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def test_response_sequential_file_search(compat_client, text_model_id, tmp_path):
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"""Test file search with sequential responses using previous_response_id."""
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if isinstance(compat_client, LlamaStackAsLibraryClient):
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pytest.skip("Responses API file search is not yet supported in library client.")
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vector_store = new_vector_store(compat_client, "test_vector_store")
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# Create a test file with content
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file_content = "The Llama 4 Maverick model has 128 experts in its mixture of experts architecture."
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file_name = "test_sequential_file_search.txt"
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file_path = tmp_path / file_name
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file_path.write_text(file_content)
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file_response = upload_file(compat_client, file_name, file_path)
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# Attach the file to the vector store
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compat_client.vector_stores.files.create(
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vector_store_id=vector_store.id,
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file_id=file_response.id,
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)
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# Wait for the file to be attached
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wait_for_file_attachment(compat_client, vector_store.id, file_response.id)
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tools = [{"type": "file_search", "vector_store_ids": [vector_store.id]}]
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# First response request with file search
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response = compat_client.responses.create(
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model=text_model_id,
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input="How many experts does the Llama 4 Maverick model have?",
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tools=tools,
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stream=False,
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include=["file_search_call.results"],
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)
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# Verify the file_search_tool was called
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assert len(response.output) > 1
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assert response.output[0].type == "file_search_call"
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assert response.output[0].status == "completed"
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assert response.output[0].queries
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assert response.output[0].results
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assert "128" in response.output_text or "experts" in response.output_text.lower()
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# Second response request using previous_response_id
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response2 = compat_client.responses.create(
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model=text_model_id,
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input="Can you tell me more about the architecture?",
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tools=tools,
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stream=False,
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previous_response_id=response.id,
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include=["file_search_call.results"],
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)
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# Verify the second response has output
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assert len(response2.output) >= 1
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assert response2.output_text
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# The second response should maintain context from the first
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final_message = [output for output in response2.output if output.type == "message"]
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assert len(final_message) >= 1
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assert final_message[-1].role == "assistant"
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assert final_message[-1].status == "completed"
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@pytest.mark.parametrize("case", mcp_tool_test_cases)
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def test_response_non_streaming_mcp_tool(compat_client, text_model_id, case):
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if not isinstance(compat_client, LlamaStackAsLibraryClient):
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pytest.skip("in-process MCP server is only supported in library client")
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with make_mcp_server() as mcp_server_info:
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tools = setup_mcp_tools(case.tools, mcp_server_info)
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response = compat_client.responses.create(
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model=text_model_id,
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input=case.input,
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tools=tools,
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stream=False,
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)
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assert len(response.output) >= 3
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list_tools = response.output[0]
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assert list_tools.type == "mcp_list_tools"
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assert list_tools.server_label == "localmcp"
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assert len(list_tools.tools) == 2
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assert {t.name for t in list_tools.tools} == {
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"get_boiling_point",
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"greet_everyone",
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}
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call = response.output[1]
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assert call.type == "mcp_call"
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assert call.name == "get_boiling_point"
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assert json.loads(call.arguments) == {
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"liquid_name": "myawesomeliquid",
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"celsius": True,
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}
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assert call.error is None
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assert "-100" in call.output
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# sometimes the model will call the tool again, so we need to get the last message
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message = response.output[-1]
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text_content = message.content[0].text
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assert "boiling point" in text_content.lower()
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with make_mcp_server(required_auth_token="test-token") as mcp_server_info:
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tools = setup_mcp_tools(case.tools, mcp_server_info)
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exc_type = (
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AuthenticationRequiredError
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if isinstance(compat_client, LlamaStackAsLibraryClient)
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else (httpx.HTTPStatusError, openai.AuthenticationError)
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)
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with pytest.raises(exc_type):
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compat_client.responses.create(
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model=text_model_id,
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input=case.input,
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tools=tools,
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stream=False,
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)
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for tool in tools:
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if tool["type"] == "mcp":
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tool["headers"] = {"Authorization": "Bearer test-token"}
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response = compat_client.responses.create(
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model=text_model_id,
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input=case.input,
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tools=tools,
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stream=False,
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)
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assert len(response.output) >= 3
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@pytest.mark.parametrize("case", mcp_tool_test_cases)
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def test_response_sequential_mcp_tool(compat_client, text_model_id, case):
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if not isinstance(compat_client, LlamaStackAsLibraryClient):
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pytest.skip("in-process MCP server is only supported in library client")
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with make_mcp_server() as mcp_server_info:
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tools = setup_mcp_tools(case.tools, mcp_server_info)
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response = compat_client.responses.create(
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model=text_model_id,
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input=case.input,
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tools=tools,
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stream=False,
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)
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assert len(response.output) >= 3
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list_tools = response.output[0]
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assert list_tools.type == "mcp_list_tools"
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assert list_tools.server_label == "localmcp"
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assert len(list_tools.tools) == 2
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assert {t.name for t in list_tools.tools} == {
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"get_boiling_point",
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"greet_everyone",
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}
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call = response.output[1]
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assert call.type == "mcp_call"
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assert call.name == "get_boiling_point"
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assert json.loads(call.arguments) == {
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"liquid_name": "myawesomeliquid",
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"celsius": True,
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}
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assert call.error is None
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assert "-100" in call.output
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# sometimes the model will call the tool again, so we need to get the last message
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message = response.output[-1]
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text_content = message.content[0].text
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assert "boiling point" in text_content.lower()
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response2 = compat_client.responses.create(
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model=text_model_id, input=case.input, tools=tools, stream=False, previous_response_id=response.id
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)
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assert len(response2.output) >= 1
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message = response2.output[-1]
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text_content = message.content[0].text
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assert "boiling point" in text_content.lower()
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@pytest.mark.parametrize("case", mcp_tool_test_cases)
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@pytest.mark.parametrize("approve", [True, False])
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def test_response_mcp_tool_approval(compat_client, text_model_id, case, approve):
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if not isinstance(compat_client, LlamaStackAsLibraryClient):
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pytest.skip("in-process MCP server is only supported in library client")
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with make_mcp_server() as mcp_server_info:
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tools = setup_mcp_tools(case.tools, mcp_server_info)
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for tool in tools:
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tool["require_approval"] = "always"
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response = compat_client.responses.create(
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model=text_model_id,
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input=case.input,
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tools=tools,
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stream=False,
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)
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assert len(response.output) >= 2
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list_tools = response.output[0]
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assert list_tools.type == "mcp_list_tools"
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assert list_tools.server_label == "localmcp"
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assert len(list_tools.tools) == 2
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assert {t.name for t in list_tools.tools} == {
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"get_boiling_point",
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"greet_everyone",
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}
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approval_request = response.output[1]
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assert approval_request.type == "mcp_approval_request"
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assert approval_request.name == "get_boiling_point"
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assert json.loads(approval_request.arguments) == {
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"liquid_name": "myawesomeliquid",
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"celsius": True,
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}
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# send approval response
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response = compat_client.responses.create(
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previous_response_id=response.id,
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model=text_model_id,
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input=[{"type": "mcp_approval_response", "approval_request_id": approval_request.id, "approve": approve}],
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tools=tools,
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stream=False,
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)
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if approve:
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assert len(response.output) >= 3
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list_tools = response.output[0]
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assert list_tools.type == "mcp_list_tools"
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assert list_tools.server_label == "localmcp"
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assert len(list_tools.tools) == 2
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assert {t.name for t in list_tools.tools} == {
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"get_boiling_point",
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"greet_everyone",
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}
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call = response.output[1]
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assert call.type == "mcp_call"
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assert call.name == "get_boiling_point"
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assert json.loads(call.arguments) == {
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"liquid_name": "myawesomeliquid",
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"celsius": True,
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}
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assert call.error is None
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assert "-100" in call.output
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# sometimes the model will call the tool again, so we need to get the last message
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message = response.output[-1]
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text_content = message.content[0].text
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assert "boiling point" in text_content.lower()
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else:
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assert len(response.output) >= 1
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for output in response.output:
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assert output.type != "mcp_call"
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@pytest.mark.parametrize("case", custom_tool_test_cases)
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def test_response_non_streaming_custom_tool(compat_client, text_model_id, case):
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response = compat_client.responses.create(
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model=text_model_id,
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input=case.input,
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tools=case.tools,
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stream=False,
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)
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assert len(response.output) == 1
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assert response.output[0].type == "function_call"
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assert response.output[0].status == "completed"
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assert response.output[0].name == "get_weather"
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@pytest.mark.parametrize("case", custom_tool_test_cases)
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def test_response_function_call_ordering_1(compat_client, text_model_id, case):
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response = compat_client.responses.create(
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model=text_model_id,
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input=case.input,
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tools=case.tools,
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stream=False,
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)
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assert len(response.output) == 1
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assert response.output[0].type == "function_call"
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assert response.output[0].status == "completed"
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assert response.output[0].name == "get_weather"
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inputs = []
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inputs.append(
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{
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"role": "user",
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"content": case.input,
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}
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)
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inputs.append(
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{
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"type": "function_call_output",
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"output": "It is raining.",
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"call_id": response.output[0].call_id,
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}
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)
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response = compat_client.responses.create(
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model=text_model_id, input=inputs, tools=case.tools, stream=False, previous_response_id=response.id
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)
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assert len(response.output) == 1
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def test_response_function_call_ordering_2(compat_client, text_model_id):
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tools = [
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{
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"type": "function",
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"name": "get_weather",
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"description": "Get current temperature for a given location.",
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"parameters": {
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"additionalProperties": False,
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"properties": {
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"location": {
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"description": "City and country e.g. Bogotá, Colombia",
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"type": "string",
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}
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},
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"required": ["location"],
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"type": "object",
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},
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}
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]
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inputs = [
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{
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"role": "user",
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"content": "Is the weather better in San Francisco or Los Angeles?",
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}
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]
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response = compat_client.responses.create(
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model=text_model_id,
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input=inputs,
|
|
tools=tools,
|
|
stream=False,
|
|
)
|
|
for output in response.output:
|
|
if output.type == "function_call" and output.status == "completed" and output.name == "get_weather":
|
|
inputs.append(output)
|
|
for output in response.output:
|
|
if output.type == "function_call" and output.status == "completed" and output.name == "get_weather":
|
|
weather = "It is raining."
|
|
if "Los Angeles" in output.arguments:
|
|
weather = "It is cloudy."
|
|
inputs.append(
|
|
{
|
|
"type": "function_call_output",
|
|
"output": weather,
|
|
"call_id": output.call_id,
|
|
}
|
|
)
|
|
response = compat_client.responses.create(
|
|
model=text_model_id,
|
|
input=inputs,
|
|
tools=tools,
|
|
stream=False,
|
|
)
|
|
assert len(response.output) == 1
|
|
assert "Los Angeles" in response.output_text
|
|
|
|
|
|
@pytest.mark.parametrize("case", multi_turn_tool_execution_test_cases)
|
|
def test_response_non_streaming_multi_turn_tool_execution(compat_client, text_model_id, case):
|
|
"""Test multi-turn tool execution where multiple MCP tool calls are performed in sequence."""
|
|
if not isinstance(compat_client, LlamaStackAsLibraryClient):
|
|
pytest.skip("in-process MCP server is only supported in library client")
|
|
|
|
with make_mcp_server(tools=dependency_tools()) as mcp_server_info:
|
|
tools = setup_mcp_tools(case.tools, mcp_server_info)
|
|
|
|
response = compat_client.responses.create(
|
|
input=case.input,
|
|
model=text_model_id,
|
|
tools=tools,
|
|
)
|
|
|
|
# Verify we have MCP tool calls in the output
|
|
mcp_list_tools = [output for output in response.output if output.type == "mcp_list_tools"]
|
|
mcp_calls = [output for output in response.output if output.type == "mcp_call"]
|
|
message_outputs = [output for output in response.output if output.type == "message"]
|
|
|
|
# Should have exactly 1 MCP list tools message (at the beginning)
|
|
assert len(mcp_list_tools) == 1, f"Expected exactly 1 mcp_list_tools, got {len(mcp_list_tools)}"
|
|
assert mcp_list_tools[0].server_label == "localmcp"
|
|
assert len(mcp_list_tools[0].tools) == 5 # Updated for dependency tools
|
|
expected_tool_names = {
|
|
"get_user_id",
|
|
"get_user_permissions",
|
|
"check_file_access",
|
|
"get_experiment_id",
|
|
"get_experiment_results",
|
|
}
|
|
assert {t.name for t in mcp_list_tools[0].tools} == expected_tool_names
|
|
|
|
assert len(mcp_calls) >= 1, f"Expected at least 1 mcp_call, got {len(mcp_calls)}"
|
|
for mcp_call in mcp_calls:
|
|
assert mcp_call.error is None, f"MCP call should not have errors, got: {mcp_call.error}"
|
|
|
|
assert len(message_outputs) >= 1, f"Expected at least 1 message output, got {len(message_outputs)}"
|
|
|
|
final_message = message_outputs[-1]
|
|
assert final_message.role == "assistant", f"Final message should be from assistant, got {final_message.role}"
|
|
assert final_message.status == "completed", f"Final message should be completed, got {final_message.status}"
|
|
assert len(final_message.content) > 0, "Final message should have content"
|
|
|
|
expected_output = case.expected
|
|
assert expected_output.lower() in response.output_text.lower(), (
|
|
f"Expected '{expected_output}' to appear in response: {response.output_text}"
|
|
)
|
|
|
|
|
|
@pytest.mark.parametrize("case", multi_turn_tool_execution_streaming_test_cases)
|
|
def test_response_streaming_multi_turn_tool_execution(compat_client, text_model_id, case):
|
|
"""Test streaming multi-turn tool execution where multiple MCP tool calls are performed in sequence."""
|
|
if not isinstance(compat_client, LlamaStackAsLibraryClient):
|
|
pytest.skip("in-process MCP server is only supported in library client")
|
|
|
|
with make_mcp_server(tools=dependency_tools()) as mcp_server_info:
|
|
tools = setup_mcp_tools(case.tools, mcp_server_info)
|
|
|
|
stream = compat_client.responses.create(
|
|
input=case.input,
|
|
model=text_model_id,
|
|
tools=tools,
|
|
stream=True,
|
|
)
|
|
|
|
chunks = []
|
|
for chunk in stream:
|
|
chunks.append(chunk)
|
|
|
|
# Use validator for common streaming checks
|
|
validator = StreamingValidator(chunks)
|
|
validator.assert_basic_event_sequence()
|
|
validator.assert_response_consistency()
|
|
validator.assert_has_tool_calls()
|
|
validator.assert_has_mcp_events()
|
|
validator.assert_rich_streaming()
|
|
|
|
# Get the final response from the last chunk
|
|
final_chunk = chunks[-1]
|
|
if hasattr(final_chunk, "response"):
|
|
final_response = final_chunk.response
|
|
|
|
# Verify multi-turn MCP tool execution results
|
|
mcp_list_tools = [output for output in final_response.output if output.type == "mcp_list_tools"]
|
|
mcp_calls = [output for output in final_response.output if output.type == "mcp_call"]
|
|
message_outputs = [output for output in final_response.output if output.type == "message"]
|
|
|
|
# Should have exactly 1 MCP list tools message (at the beginning)
|
|
assert len(mcp_list_tools) == 1, f"Expected exactly 1 mcp_list_tools, got {len(mcp_list_tools)}"
|
|
assert mcp_list_tools[0].server_label == "localmcp"
|
|
assert len(mcp_list_tools[0].tools) == 5 # Updated for dependency tools
|
|
expected_tool_names = {
|
|
"get_user_id",
|
|
"get_user_permissions",
|
|
"check_file_access",
|
|
"get_experiment_id",
|
|
"get_experiment_results",
|
|
}
|
|
assert {t.name for t in mcp_list_tools[0].tools} == expected_tool_names
|
|
|
|
# Should have at least 1 MCP call (the model should call at least one tool)
|
|
assert len(mcp_calls) >= 1, f"Expected at least 1 mcp_call, got {len(mcp_calls)}"
|
|
|
|
# All MCP calls should be completed (verifies our tool execution works)
|
|
for mcp_call in mcp_calls:
|
|
assert mcp_call.error is None, f"MCP call should not have errors, got: {mcp_call.error}"
|
|
|
|
# Should have at least one final message response
|
|
assert len(message_outputs) >= 1, f"Expected at least 1 message output, got {len(message_outputs)}"
|
|
|
|
# Final message should be from assistant and completed
|
|
final_message = message_outputs[-1]
|
|
assert final_message.role == "assistant", (
|
|
f"Final message should be from assistant, got {final_message.role}"
|
|
)
|
|
assert final_message.status == "completed", f"Final message should be completed, got {final_message.status}"
|
|
assert len(final_message.content) > 0, "Final message should have content"
|
|
|
|
# Check that the expected output appears in the response
|
|
expected_output = case.expected
|
|
assert expected_output.lower() in final_response.output_text.lower(), (
|
|
f"Expected '{expected_output}' to appear in response: {final_response.output_text}"
|
|
)
|