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add64e8e2a
# Problem The current inline provider appends the user provided instructions to messages as a system prompt, but the returned response object does not contain the instructions field (as specified in the OpenAI responses spec). # What does this PR do? This pull request adds the instruction field to the response object definition and updates the inline provider. It also ensures that instructions from previous response is not carried over to the next response (as specified in the openAI spec). Closes #[3566](https://github.com/llamastack/llama-stack/issues/3566) ## Test Plan - Tested manually for change in model response w.r.t supplied instructions field. - Added unit test to check that the instructions from previous response is not carried over to the next response. - Added integration tests to check instructions parameter in the returned response object. - Added new recordings for the integration tests. --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
518 lines
19 KiB
Python
518 lines
19 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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@pytest.mark.parametrize("stream", [True, False])
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def test_input_safety_guardrails(compat_client, text_model_id, stream):
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"""Test that input safety guardrails block inappropriate content."""
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if not isinstance(compat_client, OpenAI):
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pytest.skip("OpenAI client is required until responses API exists in llama-stack-client")
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# Test with guardrail that should block inappropriate content
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# Note: This test assumes guardrails are configured in the test environment
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inappropriate_message = "How can I hurt someone?"
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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": inappropriate_message,
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}
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],
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stream=stream,
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extra_body={"guardrails": ["llama-guard"]}, # Guardrails passed via extra_body
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)
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if stream:
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# Input violations cause immediate refusal - only one completion chunk
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final_response = None
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for chunk in response:
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if chunk.type == "response.completed":
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final_response = chunk.response
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break
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assert final_response is not None
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response_to_check = final_response
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else:
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response_to_check = response
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# Check if the response contains a refusal (indicating guardrail blocked content)
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assert response_to_check.output[0].type == "message"
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message = response_to_check.output[0]
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# Guardrail should ALWAYS block inappropriate input - expect refusal response
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assert len(message.content) > 0, "Content should not be empty"
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content_item = message.content[0]
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assert content_item.type == "refusal", f"Expected refusal content, but got: {content_item.type}"
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assert len(content_item.refusal.strip()) > 0, "Refusal message should not be empty"
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@pytest.mark.parametrize("stream", [True, False])
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def test_output_safety_guardrails_unsafe_content(compat_client, text_model_id, stream):
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"""Test that output safety guardrails block unsafe content."""
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if not isinstance(compat_client, OpenAI):
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pytest.skip("OpenAI client is required until responses API exists in llama-stack-client")
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# Use an unsafe prompt that should be blocked by guardrail evaluation
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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": "Describe violent content",
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}
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],
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stream=stream,
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extra_body={"guardrails": ["llama-guard"]}, # Output guardrail validation
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)
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if stream:
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# For streaming, collect the final response
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last_chunk = None
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for chunk in response:
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last_chunk = chunk
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assert last_chunk is not None
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assert last_chunk.type == "response.completed", f"Expected final chunk to be completion, got {last_chunk.type}"
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response_to_check = last_chunk.response
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else:
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response_to_check = response
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assert response_to_check.output[0].type == "message"
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message = response_to_check.output[0]
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assert len(message.content) > 0, "Message should have content"
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content_item = message.content[0]
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assert content_item.type == "refusal", (
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f"Content type should be 'refusal' for unsafe output, got {content_item.type}"
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)
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assert len(content_item.refusal.strip()) > 0, "Refusal message should not be empty"
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@pytest.mark.parametrize("stream", [True, False])
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def test_output_safety_guardrails_safe_content(compat_client, text_model_id, stream):
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"""Test that output safety guardrails allow safe content."""
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if not isinstance(compat_client, OpenAI):
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pytest.skip("OpenAI client is required until responses API exists in llama-stack-client")
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# Use a safe prompt that should pass guardrail evaluation
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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": "What's your name?",
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}
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],
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stream=stream,
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extra_body={"guardrails": ["llama-guard"]}, # Output guardrail validation
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)
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if stream:
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# For streaming, collect the final response
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last_chunk = None
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for chunk in response:
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last_chunk = chunk
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assert last_chunk is not None
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assert last_chunk.type == "response.completed", f"Expected final chunk to be completion, got {last_chunk.type}"
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response_to_check = last_chunk.response
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else:
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response_to_check = response
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assert response_to_check.output[0].type == "message"
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message = response_to_check.output[0]
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assert len(message.content) > 0, "Message should have content"
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content_item = message.content[0]
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assert content_item.type == "output_text", (
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f"Content type should be 'output_text' for safe output, got {content_item.type}"
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)
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assert len(content_item.text.strip()) > 0, "Text content should not be empty"
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def test_guardrails_with_tools(compat_client, text_model_id):
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"""Test that guardrails work correctly when tools are present."""
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if not isinstance(compat_client, OpenAI):
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pytest.skip("OpenAI client is required until responses API exists in llama-stack-client")
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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": "What's the weather like? Please help me in a safe and appropriate way.",
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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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extra_body={"guardrails": ["llama-guard"]},
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stream=False,
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)
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# Verify response completes successfully with tools and guardrails
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assert response.id is not None
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assert len(response.output) > 0
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# Response should be either a function call or a message
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output_type = response.output[0].type
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assert output_type in ["function_call", "message"]
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def test_response_with_instructions(openai_client, client_with_models, text_model_id):
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"""Test instructions parameter in the responses object."""
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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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messages = [
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{
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"role": "user",
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"content": "What is the capital of France?",
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}
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]
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# First create a response without instructions parameter
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response_w_o_instructions = 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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|
# Verify we have None in the instructions field
|
|
assert response_w_o_instructions.instructions is None
|
|
|
|
# Next create a response and pass instructions parameter
|
|
instructions = "You are a helpful assistant."
|
|
response_with_instructions = client.responses.create(
|
|
model=text_model_id,
|
|
instructions=instructions,
|
|
input=messages,
|
|
stream=False,
|
|
)
|
|
|
|
# Verify we have a valid instructions field
|
|
assert response_with_instructions.instructions == instructions
|
|
|
|
# Finally test instructions parameter with a previous response id
|
|
instructions2 = "You are a helpful assistant and speak in pirate language."
|
|
response_with_instructions2 = client.responses.create(
|
|
model=text_model_id,
|
|
instructions=instructions2,
|
|
input=messages,
|
|
previous_response_id=response_with_instructions.id,
|
|
stream=False,
|
|
)
|
|
|
|
# Verify instructions from previous response was not carried over to the next response
|
|
assert response_with_instructions2.instructions == instructions2
|