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chore!: remove the agents (sessions and turns) API (#4055)
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- Removes the deprecated agents (sessions and turns) API that was marked alpha in 0.3.0 - Cleans up unused imports and orphaned types after the API removal - Removes `SessionNotFoundError` and `AgentTurnInputType` which are no longer needed The agents API is completely superseded by the Responses + Conversations APIs, and the client SDK Agent class already uses those implementations. Corresponding client-side PR: https://github.com/llamastack/llama-stack-client-python/pull/295
This commit is contained in:
Ashwin Bharambe
GitHub
parent
a6ddbae0ed
commit
a8a8aa56c0
1037 changed files with 393 additions and 309806 deletions
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# 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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|
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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"
|
||||
message = response_to_check.output[0]
|
||||
|
||||
assert len(message.content) > 0, "Message should have content"
|
||||
content_item = message.content[0]
|
||||
assert content_item.type == "output_text", (
|
||||
f"Content type should be 'output_text' for safe output, got {content_item.type}"
|
||||
)
|
||||
assert len(content_item.text.strip()) > 0, "Text content should not be empty"
|
||||
|
||||
|
||||
def test_guardrails_with_tools(compat_client, text_model_id):
|
||||
"""Test that guardrails work correctly when tools are present."""
|
||||
if not isinstance(compat_client, OpenAI):
|
||||
pytest.skip("OpenAI client is required until responses API exists in llama-stack-client")
|
||||
|
||||
response = compat_client.responses.create(
|
||||
model=text_model_id,
|
||||
input=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": "What's the weather like? Please help me in a safe and appropriate way.",
|
||||
}
|
||||
],
|
||||
tools=[
|
||||
{
|
||||
"type": "function",
|
||||
"name": "get_weather",
|
||||
"description": "Get the weather in a given city",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"city": {"type": "string", "description": "The city to get the weather for"},
|
||||
},
|
||||
},
|
||||
}
|
||||
],
|
||||
extra_body={"guardrails": ["llama-guard"]},
|
||||
stream=False,
|
||||
)
|
||||
|
||||
# Verify response completes successfully with tools and guardrails
|
||||
assert response.id is not None
|
||||
assert len(response.output) > 0
|
||||
|
||||
# Response should be either a function call or a message
|
||||
output_type = response.output[0].type
|
||||
assert output_type in ["function_call", "message"]
|
||||
|
||||
|
||||
def test_response_with_instructions(openai_client, client_with_models, text_model_id):
|
||||
"""Test instructions parameter in the responses object."""
|
||||
if isinstance(client_with_models, LlamaStackAsLibraryClient):
|
||||
pytest.skip("OpenAI responses are not supported when testing with library client yet.")
|
||||
|
||||
client = openai_client
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "user",
|
||||
"content": "What is the capital of France?",
|
||||
}
|
||||
]
|
||||
|
||||
# First create a response without instructions parameter
|
||||
response_w_o_instructions = client.responses.create(
|
||||
model=text_model_id,
|
||||
input=messages,
|
||||
stream=False,
|
||||
)
|
||||
|
||||
# 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
|
||||
Loading…
Add table
Add a link
Reference in a new issue