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https://github.com/meta-llama/llama-stack.git
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ca47d90926
# What does this PR do?
When a model decides to use an MCP tool call that requires no arguments,
it sets the `arguments` field to `None`. This causes the user to see a
`400 bad requst error` due to validation errors down the stack because
this field gets removed when being parsed by an openai compatible
inference provider like vLLM
This PR ensures that, as soon as the tool call args are accumulated
while streaming, we check to ensure no tool call function arguments are
set to None - if they are we replace them with "{}"
<!-- If resolving an issue, uncomment and update the line below -->
Closes #3456
## Test Plan
Added new unit test to verify that any tool calls with function
arguments set to `None` get handled correctly
---------
Signed-off-by: Jaideep Rao <jrao@redhat.com>
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>
299 lines
11 KiB
Python
299 lines
11 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 pytest
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from openai import BadRequestError, OpenAI
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from llama_stack.core.library_client import LlamaStackAsLibraryClient
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@pytest.mark.parametrize(
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"stream",
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[
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True,
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False,
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],
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)
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@pytest.mark.parametrize(
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"tools",
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[
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[],
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[
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{
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"type": "function",
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"name": "get_weather",
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"description": "Get the weather in a given city",
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"parameters": {
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"type": "object",
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"properties": {
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"city": {"type": "string", "description": "The city to get the weather for"},
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},
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},
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}
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],
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],
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)
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def test_responses_store(compat_client, text_model_id, stream, tools):
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if not isinstance(compat_client, OpenAI):
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pytest.skip("OpenAI client is required until responses.delete() exists in llama-stack-client")
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message = "What's the weather in Tokyo?" + (
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" YOU MUST USE THE get_weather function to get the weather." if tools else ""
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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=[
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{
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"role": "user",
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"content": message,
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}
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],
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stream=stream,
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tools=tools,
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)
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if stream:
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# accumulate the streamed content
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content = ""
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response_id = None
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for chunk in response:
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if response_id is None:
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response_id = chunk.response.id
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if chunk.type == "response.completed":
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response_id = chunk.response.id
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output_type = chunk.response.output[0].type
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if output_type == "message":
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content = chunk.response.output[0].content[0].text
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else:
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response_id = response.id
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output_type = response.output[0].type
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if output_type == "message":
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content = response.output[0].content[0].text
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# test retrieve response
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retrieved_response = compat_client.responses.retrieve(response_id)
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assert retrieved_response.id == response_id
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assert retrieved_response.model == text_model_id
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assert retrieved_response.output[0].type == output_type, retrieved_response
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if output_type == "message":
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assert retrieved_response.output[0].content[0].text == content
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# Delete the response
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delete_response = compat_client.responses.delete(response_id)
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assert delete_response is None
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with pytest.raises(BadRequestError):
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compat_client.responses.retrieve(response_id)
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def test_list_response_input_items(compat_client, text_model_id):
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"""Test the new list_openai_response_input_items endpoint."""
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message = "What is the capital of France?"
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# Create a response first
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response = compat_client.responses.create(
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model=text_model_id,
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input=[
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{
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"role": "user",
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"content": message,
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}
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],
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stream=False,
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)
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response_id = response.id
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# Test the new list input items endpoint
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input_items_response = compat_client.responses.input_items.list(response_id=response_id)
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# Verify the structure follows OpenAI API spec
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assert input_items_response.object == "list"
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assert hasattr(input_items_response, "data")
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assert isinstance(input_items_response.data, list)
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assert len(input_items_response.data) > 0
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# Verify the input item contains our message
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input_item = input_items_response.data[0]
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assert input_item.type == "message"
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assert input_item.role == "user"
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assert message in str(input_item.content)
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def test_list_response_input_items_with_limit_and_order(openai_client, client_with_models, text_model_id):
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"""Test the list input items endpoint with limit and order parameters."""
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if isinstance(client_with_models, LlamaStackAsLibraryClient):
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pytest.skip("OpenAI responses are not supported when testing with library client yet.")
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client = openai_client
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# Create a response with multiple input messages to test limit and order
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# Use distinctive content to make order verification more reliable
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messages = [
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{"role": "user", "content": "Message A: What is the capital of France?"},
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{"role": "assistant", "content": "The capital of France is Paris."},
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{"role": "user", "content": "Message B: What about Spain?"},
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{"role": "assistant", "content": "The capital of Spain is Madrid."},
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{"role": "user", "content": "Message C: And Italy?"},
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]
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response = client.responses.create(
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model=text_model_id,
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input=messages,
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stream=False,
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)
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response_id = response.id
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# First get all items to establish baseline
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all_items_response = client.responses.input_items.list(response_id=response_id)
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assert all_items_response.object == "list"
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total_items = len(all_items_response.data)
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assert total_items == 5 # Should have all 5 input messages
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# Test 1: Limit parameter - request only 2 items
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limited_response = client.responses.input_items.list(response_id=response_id, limit=2)
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assert limited_response.object == "list"
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assert len(limited_response.data) == min(2, total_items) # Should be exactly 2 or total if less
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# Test 2: Edge case - limit larger than available items
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large_limit_response = client.responses.input_items.list(response_id=response_id, limit=10)
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assert large_limit_response.object == "list"
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assert len(large_limit_response.data) == total_items # Should return all available items
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# Test 3: Edge case - limit of 1
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single_item_response = client.responses.input_items.list(response_id=response_id, limit=1)
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assert single_item_response.object == "list"
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assert len(single_item_response.data) == 1
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# Test 4: Order parameter - ascending vs descending
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asc_response = client.responses.input_items.list(response_id=response_id, order="asc")
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desc_response = client.responses.input_items.list(response_id=response_id, order="desc")
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assert asc_response.object == "list"
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assert desc_response.object == "list"
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assert len(asc_response.data) == len(desc_response.data) == total_items
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# Verify order is actually different (if we have multiple items)
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if total_items > 1:
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# First item in asc should be last item in desc (reversed order)
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first_asc_content = str(asc_response.data[0].content)
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first_desc_content = str(desc_response.data[0].content)
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last_asc_content = str(asc_response.data[-1].content)
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last_desc_content = str(desc_response.data[-1].content)
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# The first item in asc should be the last item in desc (and vice versa)
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assert first_asc_content == last_desc_content, (
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f"Expected first asc ({first_asc_content}) to equal last desc ({last_desc_content})"
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)
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assert last_asc_content == first_desc_content, (
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f"Expected last asc ({last_asc_content}) to equal first desc ({first_desc_content})"
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)
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# Verify the distinctive content markers are in the right positions
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assert "Message A" in first_asc_content, "First item in ascending order should contain 'Message A'"
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assert "Message C" in first_desc_content, "First item in descending order should contain 'Message C'"
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# Test 5: Combined limit and order
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combined_response = client.responses.input_items.list(response_id=response_id, limit=3, order="desc")
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assert combined_response.object == "list"
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assert len(combined_response.data) == min(3, total_items)
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# Test 6: Verify combined response has correct order for first few items
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if total_items >= 3:
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# Should get the last 3 items in descending order (most recent first)
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assert "Message C" in str(combined_response.data[0].content), "First item should be most recent (Message C)"
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# The exact second and third items depend on the implementation, but let's verify structure
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for item in combined_response.data:
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assert hasattr(item, "content")
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assert hasattr(item, "role")
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assert hasattr(item, "type")
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assert item.type == "message"
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assert item.role in ["user", "assistant"]
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@pytest.mark.skip(reason="Tool calling is not reliable.")
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def test_function_call_output_response(openai_client, client_with_models, text_model_id):
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"""Test handling of function call outputs in responses."""
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if isinstance(client_with_models, LlamaStackAsLibraryClient):
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pytest.skip("OpenAI responses are not supported when testing with library client yet.")
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client = openai_client
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# First create a response that triggers a function call
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response = client.responses.create(
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model=text_model_id,
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input=[
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{
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"role": "user",
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"content": "what's the weather in tokyo? You MUST call the `get_weather` function to find out.",
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}
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],
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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 the weather in a given city",
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"parameters": {
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"type": "object",
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"properties": {
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"city": {"type": "string", "description": "The city to get the weather for"},
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},
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},
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}
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],
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stream=False,
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)
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# Verify we got a function call
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assert response.output[0].type == "function_call"
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call_id = response.output[0].call_id
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# Now send the function call output as a follow-up
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response2 = client.responses.create(
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model=text_model_id,
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input=[{"type": "function_call_output", "call_id": call_id, "output": "sunny and warm"}],
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previous_response_id=response.id,
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stream=False,
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)
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# Verify the second response processed successfully
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assert response2.id is not None
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assert response2.output[0].type == "message"
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assert (
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"sunny" in response2.output[0].content[0].text.lower() or "warm" in response2.output[0].content[0].text.lower()
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)
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def test_function_call_output_response_with_none_arguments(openai_client, client_with_models, text_model_id):
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"""Test handling of function call outputs in responses when function does not accept arguments."""
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if isinstance(client_with_models, LlamaStackAsLibraryClient):
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pytest.skip("OpenAI responses are not supported when testing with library client yet.")
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client = openai_client
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# First create a response that triggers a function call
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response = client.responses.create(
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model=text_model_id,
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input=[
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{
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"role": "user",
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"content": "what's the current time? You MUST call the `get_current_time` function to find out.",
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}
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],
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tools=[
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{
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"type": "function",
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"name": "get_current_time",
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"description": "Get the current time",
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"parameters": {},
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}
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],
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stream=False,
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)
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# Verify we got a function call
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assert response.output[0].type == "function_call"
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assert response.output[0].arguments == "{}"
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_ = response.output[0].call_id
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