mirror of
https://github.com/meta-llama/llama-stack.git
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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,
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tools=tools,
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stream=False,
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)
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for output in response.output:
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if output.type == "function_call" and output.status == "completed" and output.name == "get_weather":
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inputs.append(output)
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for output in response.output:
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if output.type == "function_call" and output.status == "completed" and output.name == "get_weather":
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weather = "It is raining."
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if "Los Angeles" in output.arguments:
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weather = "It is cloudy."
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inputs.append(
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{
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"type": "function_call_output",
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"output": weather,
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"call_id": output.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,
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input=inputs,
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tools=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 "Los Angeles" in response.output_text
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@pytest.mark.parametrize("case", multi_turn_tool_execution_test_cases)
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def test_response_non_streaming_multi_turn_tool_execution(compat_client, text_model_id, case):
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"""Test multi-turn tool execution where multiple MCP tool calls are performed in sequence."""
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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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|
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with make_mcp_server(tools=dependency_tools()) as mcp_server_info:
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tools = setup_mcp_tools(case.tools, mcp_server_info)
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|
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response = compat_client.responses.create(
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input=case.input,
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model=text_model_id,
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tools=tools,
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)
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|
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# Verify we have MCP tool calls in the output
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mcp_list_tools = [output for output in response.output if output.type == "mcp_list_tools"]
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mcp_calls = [output for output in response.output if output.type == "mcp_call"]
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message_outputs = [output for output in response.output if output.type == "message"]
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|
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# Should have exactly 1 MCP list tools message (at the beginning)
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assert len(mcp_list_tools) == 1, f"Expected exactly 1 mcp_list_tools, got {len(mcp_list_tools)}"
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assert mcp_list_tools[0].server_label == "localmcp"
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assert len(mcp_list_tools[0].tools) == 5 # Updated for dependency tools
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|
expected_tool_names = {
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"get_user_id",
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"get_user_permissions",
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"check_file_access",
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"get_experiment_id",
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"get_experiment_results",
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}
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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)}"
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for mcp_call in mcp_calls:
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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]
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|
assert final_message.role == "assistant", f"Final message should be from assistant, got {final_message.role}"
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|
assert final_message.status == "completed", f"Final message should be completed, got {final_message.status}"
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assert len(final_message.content) > 0, "Final message should have content"
|
|
|
|
expected_output = case.expected
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|
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)
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|
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:
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|
tools = setup_mcp_tools(case.tools, mcp_server_info)
|
|
|
|
stream = compat_client.responses.create(
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|
input=case.input,
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|
model=text_model_id,
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|
tools=tools,
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|
stream=True,
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|
)
|
|
|
|
chunks = []
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|
for chunk in stream:
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|
chunks.append(chunk)
|
|
|
|
# Use validator for common streaming checks
|
|
validator = StreamingValidator(chunks)
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|
validator.assert_basic_event_sequence()
|
|
validator.assert_response_consistency()
|
|
validator.assert_has_tool_calls()
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|
validator.assert_has_mcp_events()
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|
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}"
|
|
)
|