wehub-resource-sync
757 lines
27 KiB
Python
757 lines
27 KiB
Python
# Copyright (c) OpenAI
|
|
#
|
|
# Licensed under the MIT License.
|
|
# See LICENSE file in the project root for full license information.
|
|
|
|
"""
|
|
Unit tests for the internal `Converter` class defined in
|
|
`agents.models.openai_chatcompletions`. The converter is responsible for
|
|
translating between internal "item" structures (e.g., `ResponseOutputMessage`
|
|
and related types from `openai.types.responses`) and the ChatCompletion message
|
|
structures defined by the OpenAI client library.
|
|
|
|
These tests exercise both conversion directions:
|
|
|
|
- `Converter.message_to_output_items` turns a `ChatCompletionMessage` (as
|
|
returned by the OpenAI API) into a list of `ResponseOutputItem` instances.
|
|
|
|
- `Converter.items_to_messages` takes in either a simple string prompt, or a
|
|
list of input/output items such as `ResponseOutputMessage` and
|
|
`ResponseFunctionToolCallParam` dicts, and constructs a list of
|
|
`ChatCompletionMessageParam` dicts suitable for sending back to the API.
|
|
"""
|
|
|
|
from __future__ import annotations
|
|
|
|
import logging
|
|
from typing import Any, Literal, cast
|
|
|
|
import pytest
|
|
from openai import omit
|
|
from openai.types.chat import ChatCompletionMessage, ChatCompletionMessageFunctionToolCall
|
|
from openai.types.chat.chat_completion_message_custom_tool_call import (
|
|
ChatCompletionMessageCustomToolCall,
|
|
Custom,
|
|
)
|
|
from openai.types.chat.chat_completion_message_tool_call import Function
|
|
from openai.types.responses import (
|
|
ResponseFunctionToolCall,
|
|
ResponseFunctionToolCallParam,
|
|
ResponseInputAudioParam,
|
|
ResponseInputTextParam,
|
|
ResponseOutputMessage,
|
|
ResponseOutputRefusal,
|
|
ResponseOutputText,
|
|
)
|
|
from openai.types.responses.response_input_item_param import FunctionCallOutput
|
|
|
|
from agents.agent_output import AgentOutputSchema
|
|
from agents.exceptions import UserError
|
|
from agents.items import TResponseInputItem
|
|
from agents.models.chatcmpl_converter import Converter
|
|
from agents.models.fake_id import FAKE_RESPONSES_ID
|
|
|
|
|
|
def test_message_to_output_items_with_text_only():
|
|
"""
|
|
Make sure a simple ChatCompletionMessage with string content is converted
|
|
into a single ResponseOutputMessage containing one ResponseOutputText.
|
|
"""
|
|
msg = ChatCompletionMessage(role="assistant", content="Hello")
|
|
items = Converter.message_to_output_items(msg)
|
|
# Expect exactly one output item (the message)
|
|
assert len(items) == 1
|
|
message_item = cast(ResponseOutputMessage, items[0])
|
|
assert message_item.id == FAKE_RESPONSES_ID
|
|
assert message_item.role == "assistant"
|
|
assert message_item.type == "message"
|
|
assert message_item.status == "completed"
|
|
# Message content should have exactly one text part with the same text.
|
|
assert len(message_item.content) == 1
|
|
text_part = cast(ResponseOutputText, message_item.content[0])
|
|
assert text_part.type == "output_text"
|
|
assert text_part.text == "Hello"
|
|
|
|
|
|
def test_message_to_output_items_with_refusal():
|
|
"""
|
|
Make sure a message with a refusal string produces a ResponseOutputMessage
|
|
with a ResponseOutputRefusal content part.
|
|
"""
|
|
msg = ChatCompletionMessage(role="assistant", refusal="I'm sorry")
|
|
items = Converter.message_to_output_items(msg)
|
|
assert len(items) == 1
|
|
message_item = cast(ResponseOutputMessage, items[0])
|
|
assert len(message_item.content) == 1
|
|
refusal_part = cast(ResponseOutputRefusal, message_item.content[0])
|
|
assert refusal_part.type == "refusal"
|
|
assert refusal_part.refusal == "I'm sorry"
|
|
|
|
|
|
def test_message_to_output_items_with_tool_call():
|
|
"""
|
|
If the ChatCompletionMessage contains one or more tool_calls, they should
|
|
be reflected as separate `ResponseFunctionToolCall` items appended after
|
|
the message item.
|
|
"""
|
|
tool_call = ChatCompletionMessageFunctionToolCall(
|
|
id="tool1",
|
|
type="function",
|
|
function=Function(name="myfn", arguments='{"x":1}'),
|
|
)
|
|
msg = ChatCompletionMessage(role="assistant", content="Hi", tool_calls=[tool_call])
|
|
items = Converter.message_to_output_items(msg)
|
|
# Should produce a message item followed by one function tool call item
|
|
assert len(items) == 2
|
|
message_item = cast(ResponseOutputMessage, items[0])
|
|
assert isinstance(message_item, ResponseOutputMessage)
|
|
fn_call_item = cast(ResponseFunctionToolCall, items[1])
|
|
assert fn_call_item.id == FAKE_RESPONSES_ID
|
|
assert fn_call_item.call_id == tool_call.id
|
|
assert fn_call_item.name == tool_call.function.name
|
|
assert fn_call_item.arguments == tool_call.function.arguments
|
|
assert fn_call_item.type == "function_call"
|
|
|
|
|
|
def test_message_to_output_items_with_custom_tool_call_keeps_default_compatibility():
|
|
"""Custom tool calls should keep the default Chat Completions behavior."""
|
|
tool_call = ChatCompletionMessageCustomToolCall(
|
|
id="tool1",
|
|
type="custom",
|
|
custom=Custom(name="raw_tool", input="payload"),
|
|
)
|
|
msg = ChatCompletionMessage(role="assistant", tool_calls=[tool_call])
|
|
|
|
assert Converter.message_to_output_items(msg) == []
|
|
|
|
|
|
def test_message_to_output_items_with_custom_tool_call_raises_in_strict_mode():
|
|
"""Strict validation should fail explicitly instead of dropping custom tool calls."""
|
|
tool_call = ChatCompletionMessageCustomToolCall(
|
|
id="tool1",
|
|
type="custom",
|
|
custom=Custom(name="raw_tool", input="payload"),
|
|
)
|
|
msg = ChatCompletionMessage(role="assistant", tool_calls=[tool_call])
|
|
|
|
with pytest.raises(UserError, match="Custom tool calls are not supported"):
|
|
Converter.message_to_output_items(msg, strict_feature_validation=True)
|
|
|
|
|
|
def test_message_to_output_items_with_mixed_custom_tool_call_raises_in_strict_mode():
|
|
"""Strict validation should not partially hide an unsupported custom tool call."""
|
|
function_tool_call = ChatCompletionMessageFunctionToolCall(
|
|
id="function-tool",
|
|
type="function",
|
|
function=Function(name="myfn", arguments='{"x":1}'),
|
|
)
|
|
custom_tool_call = ChatCompletionMessageCustomToolCall(
|
|
id="custom-tool",
|
|
type="custom",
|
|
custom=Custom(name="raw_tool", input="payload"),
|
|
)
|
|
msg = ChatCompletionMessage(
|
|
role="assistant",
|
|
tool_calls=[function_tool_call, custom_tool_call],
|
|
)
|
|
|
|
with pytest.raises(UserError, match="Custom tool calls are not supported"):
|
|
Converter.message_to_output_items(msg, strict_feature_validation=True)
|
|
|
|
|
|
def test_items_to_messages_with_string_user_content():
|
|
"""
|
|
A simple string as the items argument should be converted into a user
|
|
message param dict with the same content.
|
|
"""
|
|
result = Converter.items_to_messages("Ask me anything")
|
|
assert isinstance(result, list)
|
|
assert len(result) == 1
|
|
msg = result[0]
|
|
assert msg["role"] == "user"
|
|
assert msg["content"] == "Ask me anything"
|
|
|
|
|
|
def test_items_to_messages_with_easy_input_message():
|
|
"""
|
|
Given an easy input message dict (just role/content), the converter should
|
|
produce the appropriate ChatCompletionMessageParam with the same content.
|
|
"""
|
|
items: list[TResponseInputItem] = [
|
|
{
|
|
"role": "user",
|
|
"content": "How are you?",
|
|
}
|
|
]
|
|
messages = Converter.items_to_messages(items)
|
|
assert len(messages) == 1
|
|
out = messages[0]
|
|
assert out["role"] == "user"
|
|
# For simple string inputs, the converter returns the content as a bare string
|
|
assert out["content"] == "How are you?"
|
|
|
|
|
|
def test_items_to_messages_accepts_raw_chat_completions_user_content_parts():
|
|
"""
|
|
Raw Chat Completions content parts should be accepted as aliases for the SDK's
|
|
canonical input content shapes.
|
|
"""
|
|
items: list[TResponseInputItem] = [
|
|
# Cast the fixture because mypy cannot infer this raw chat-style dict as a specific
|
|
# member of the TResponseInputItem TypedDict union on its own.
|
|
cast(
|
|
TResponseInputItem,
|
|
{
|
|
"role": "user",
|
|
"content": [
|
|
{"type": "text", "text": "What is in this image?"},
|
|
{
|
|
"type": "image_url",
|
|
"image_url": {
|
|
"url": "https://example.com/image.png",
|
|
"detail": "high",
|
|
},
|
|
},
|
|
],
|
|
},
|
|
)
|
|
]
|
|
|
|
messages = Converter.items_to_messages(items)
|
|
|
|
assert len(messages) == 1
|
|
message = messages[0]
|
|
assert message["role"] == "user"
|
|
assert message["content"] == [
|
|
{"type": "text", "text": "What is in this image?"},
|
|
{
|
|
"type": "image_url",
|
|
"image_url": {
|
|
"url": "https://example.com/image.png",
|
|
"detail": "high",
|
|
},
|
|
},
|
|
]
|
|
|
|
|
|
def test_items_to_messages_with_output_message_and_function_call():
|
|
"""
|
|
Given a sequence of one ResponseOutputMessageParam followed by a
|
|
ResponseFunctionToolCallParam, the converter should produce a single
|
|
ChatCompletionAssistantMessageParam that includes both the assistant's
|
|
textual content and a populated `tool_calls` reflecting the function call.
|
|
"""
|
|
# Construct output message param dict with two content parts.
|
|
output_text: ResponseOutputText = ResponseOutputText(
|
|
text="Part 1",
|
|
type="output_text",
|
|
annotations=[],
|
|
logprobs=[],
|
|
)
|
|
refusal: ResponseOutputRefusal = ResponseOutputRefusal(
|
|
refusal="won't do that",
|
|
type="refusal",
|
|
)
|
|
resp_msg: ResponseOutputMessage = ResponseOutputMessage(
|
|
id="42",
|
|
type="message",
|
|
role="assistant",
|
|
status="completed",
|
|
content=[output_text, refusal],
|
|
)
|
|
# Construct a function call item dict (as if returned from model)
|
|
func_item: ResponseFunctionToolCallParam = {
|
|
"id": "99",
|
|
"call_id": "abc",
|
|
"name": "math",
|
|
"arguments": "{}",
|
|
"type": "function_call",
|
|
}
|
|
items: list[TResponseInputItem] = [
|
|
resp_msg.model_dump(), # type:ignore
|
|
func_item,
|
|
]
|
|
messages = Converter.items_to_messages(items)
|
|
# Should return a single assistant message
|
|
assert len(messages) == 1
|
|
assistant = messages[0]
|
|
assert assistant["role"] == "assistant"
|
|
# Content combines text portions of the output message
|
|
assert "content" in assistant
|
|
assert assistant["content"] == "Part 1"
|
|
# Refusal in output message should be represented in assistant message
|
|
assert "refusal" in assistant
|
|
assert assistant["refusal"] == refusal.refusal
|
|
# Tool calls list should contain one ChatCompletionMessageFunctionToolCall dict
|
|
tool_calls = assistant.get("tool_calls")
|
|
assert isinstance(tool_calls, list)
|
|
assert len(tool_calls) == 1
|
|
tool_call = tool_calls[0]
|
|
assert tool_call["type"] == "function"
|
|
assert tool_call["function"]["name"] == "math"
|
|
assert tool_call["function"]["arguments"] == "{}"
|
|
|
|
|
|
def test_convert_tool_choice_handles_standard_and_named_options() -> None:
|
|
"""
|
|
The `Converter.convert_tool_choice` method should return the omit sentinel
|
|
if no choice is provided, pass through values like "auto", "required",
|
|
or "none" unchanged, and translate any other string into a function
|
|
selection dict.
|
|
"""
|
|
assert Converter.convert_tool_choice(None) is omit
|
|
assert Converter.convert_tool_choice("auto") == "auto"
|
|
assert Converter.convert_tool_choice("required") == "required"
|
|
assert Converter.convert_tool_choice("none") == "none"
|
|
tool_choice_dict = Converter.convert_tool_choice("mytool")
|
|
assert isinstance(tool_choice_dict, dict)
|
|
assert tool_choice_dict["type"] == "function"
|
|
assert tool_choice_dict["function"]["name"] == "mytool"
|
|
|
|
|
|
def test_convert_tool_choice_allows_tool_search_as_named_function_for_chat_models() -> None:
|
|
tool_choice_dict = Converter.convert_tool_choice("tool_search")
|
|
assert isinstance(tool_choice_dict, dict)
|
|
assert tool_choice_dict["type"] == "function"
|
|
assert tool_choice_dict["function"]["name"] == "tool_search"
|
|
|
|
|
|
def test_convert_response_format_returns_not_given_for_plain_text_and_dict_for_schemas() -> None:
|
|
"""
|
|
The `Converter.convert_response_format` method should return the omit sentinel
|
|
when no output schema is provided or if the output schema indicates
|
|
plain text. For structured output schemas, it should return a dict
|
|
with type `json_schema` and include the generated JSON schema and
|
|
strict flag from the provided `AgentOutputSchema`.
|
|
"""
|
|
# when output is plain text (schema None or output_type str), do not include response_format
|
|
assert Converter.convert_response_format(None) is omit
|
|
assert Converter.convert_response_format(AgentOutputSchema(str)) is omit
|
|
# For e.g. integer output, we expect a response_format dict
|
|
schema = AgentOutputSchema(int)
|
|
resp_format = Converter.convert_response_format(schema)
|
|
assert isinstance(resp_format, dict)
|
|
assert resp_format["type"] == "json_schema"
|
|
assert resp_format["json_schema"]["name"] == "final_output"
|
|
assert "strict" in resp_format["json_schema"]
|
|
assert resp_format["json_schema"]["strict"] == schema.is_strict_json_schema()
|
|
assert "schema" in resp_format["json_schema"]
|
|
assert resp_format["json_schema"]["schema"] == schema.json_schema()
|
|
|
|
|
|
def test_items_to_messages_with_function_output_item():
|
|
"""
|
|
A function call output item should be converted into a tool role message
|
|
dict with the appropriate tool_call_id and content.
|
|
"""
|
|
func_output_item: FunctionCallOutput = {
|
|
"type": "function_call_output",
|
|
"call_id": "somecall",
|
|
"output": '{"foo": "bar"}',
|
|
}
|
|
messages = Converter.items_to_messages([func_output_item])
|
|
assert len(messages) == 1
|
|
tool_msg = messages[0]
|
|
assert tool_msg["role"] == "tool"
|
|
assert tool_msg["tool_call_id"] == func_output_item["call_id"]
|
|
assert tool_msg["content"] == func_output_item["output"]
|
|
|
|
|
|
def test_items_to_messages_with_non_text_only_function_output_uses_placeholder_by_default(
|
|
caplog: pytest.LogCaptureFixture,
|
|
):
|
|
"""Default conversion should keep running without sending an empty tool message."""
|
|
func_output_item: FunctionCallOutput = {
|
|
"type": "function_call_output",
|
|
"call_id": "somecall",
|
|
"output": [
|
|
{
|
|
"type": "input_image",
|
|
"image_url": "https://example.com/image.png",
|
|
}
|
|
],
|
|
}
|
|
|
|
with caplog.at_level(logging.WARNING, logger="openai.agents"):
|
|
messages = Converter.items_to_messages([func_output_item])
|
|
|
|
assert len(messages) == 1
|
|
tool_msg = messages[0]
|
|
assert tool_msg["role"] == "tool"
|
|
assert tool_msg["tool_call_id"] == func_output_item["call_id"]
|
|
assert tool_msg["content"] == "[tool output omitted]"
|
|
assert "Replacing the tool output with a placeholder" in caplog.text
|
|
|
|
|
|
def test_items_to_messages_with_non_text_only_function_output_raises_in_strict_mode():
|
|
"""Strict validation should fail explicitly instead of silently losing the output."""
|
|
func_output_item: FunctionCallOutput = {
|
|
"type": "function_call_output",
|
|
"call_id": "somecall",
|
|
"output": [
|
|
{
|
|
"type": "input_image",
|
|
"image_url": "https://example.com/image.png",
|
|
}
|
|
],
|
|
}
|
|
|
|
with pytest.raises(UserError, match="cannot be empty or contain only non-text content"):
|
|
Converter.items_to_messages([func_output_item], strict_feature_validation=True)
|
|
|
|
|
|
def test_items_to_messages_with_empty_function_output_uses_placeholder_by_default(
|
|
caplog: pytest.LogCaptureFixture,
|
|
):
|
|
"""Default conversion should not send an empty tool message."""
|
|
func_output_item: FunctionCallOutput = {
|
|
"type": "function_call_output",
|
|
"call_id": "somecall",
|
|
"output": [],
|
|
}
|
|
|
|
with caplog.at_level(logging.WARNING, logger="openai.agents"):
|
|
messages = Converter.items_to_messages([func_output_item])
|
|
|
|
assert len(messages) == 1
|
|
tool_msg = messages[0]
|
|
assert tool_msg["role"] == "tool"
|
|
assert tool_msg["tool_call_id"] == func_output_item["call_id"]
|
|
assert tool_msg["content"] == "[tool output omitted]"
|
|
assert "Replacing the tool output with a placeholder" in caplog.text
|
|
|
|
|
|
def test_items_to_messages_with_empty_function_output_raises_in_strict_mode():
|
|
"""Strict validation should fail explicitly instead of sending empty output."""
|
|
func_output_item: FunctionCallOutput = {
|
|
"type": "function_call_output",
|
|
"call_id": "somecall",
|
|
"output": [],
|
|
}
|
|
|
|
with pytest.raises(UserError, match="cannot be empty or contain only non-text content"):
|
|
Converter.items_to_messages([func_output_item], strict_feature_validation=True)
|
|
|
|
|
|
def test_items_to_messages_with_mixed_function_output_keeps_text_by_default(
|
|
caplog: pytest.LogCaptureFixture,
|
|
):
|
|
"""Default conversion should preserve text parts and omit unsupported non-text parts."""
|
|
func_output_item: FunctionCallOutput = {
|
|
"type": "function_call_output",
|
|
"call_id": "somecall",
|
|
"output": [
|
|
{"type": "input_text", "text": "visible text"},
|
|
{
|
|
"type": "input_image",
|
|
"image_url": "https://example.com/image.png",
|
|
},
|
|
],
|
|
}
|
|
|
|
with caplog.at_level(logging.WARNING, logger="openai.agents"):
|
|
messages = Converter.items_to_messages([func_output_item])
|
|
|
|
assert len(messages) == 1
|
|
tool_msg = messages[0]
|
|
assert tool_msg["role"] == "tool"
|
|
assert tool_msg["tool_call_id"] == func_output_item["call_id"]
|
|
assert tool_msg["content"] == [{"type": "text", "text": "visible text"}]
|
|
assert "tool output omitted" not in caplog.text
|
|
|
|
|
|
def test_items_to_messages_can_preserve_non_text_function_output() -> None:
|
|
"""Compatible providers can opt in to preserving non-text tool output."""
|
|
func_output_item: FunctionCallOutput = {
|
|
"type": "function_call_output",
|
|
"call_id": "somecall",
|
|
"output": [
|
|
{
|
|
"type": "input_image",
|
|
"image_url": "https://example.com/image.png",
|
|
}
|
|
],
|
|
}
|
|
|
|
messages = Converter.items_to_messages(
|
|
[func_output_item],
|
|
preserve_tool_output_all_content=True,
|
|
)
|
|
|
|
assert len(messages) == 1
|
|
tool_msg = messages[0]
|
|
assert tool_msg["role"] == "tool"
|
|
assert tool_msg["tool_call_id"] == func_output_item["call_id"]
|
|
assert tool_msg["content"] == [
|
|
{
|
|
"type": "image_url",
|
|
"image_url": {"url": "https://example.com/image.png", "detail": "auto"},
|
|
}
|
|
]
|
|
|
|
|
|
def test_extract_all_and_text_content_for_strings_and_lists():
|
|
"""
|
|
The converter provides helpers for extracting user-supplied message content
|
|
either as a simple string or as a list of `input_text` dictionaries.
|
|
When passed a bare string, both `extract_all_content` and
|
|
`extract_text_content` should return the string unchanged.
|
|
When passed a list of input dictionaries, `extract_all_content` should
|
|
produce a list of `ChatCompletionContentPart` dicts, and `extract_text_content`
|
|
should filter to only the textual parts.
|
|
"""
|
|
prompt = "just text"
|
|
assert Converter.extract_all_content(prompt) == prompt
|
|
assert Converter.extract_text_content(prompt) == prompt
|
|
text1: ResponseInputTextParam = {"type": "input_text", "text": "one"}
|
|
text2: ResponseInputTextParam = {"type": "input_text", "text": "two"}
|
|
all_parts = Converter.extract_all_content([text1, text2])
|
|
assert isinstance(all_parts, list)
|
|
assert len(all_parts) == 2
|
|
assert all_parts[0]["type"] == "text" and all_parts[0]["text"] == "one"
|
|
assert all_parts[1]["type"] == "text" and all_parts[1]["text"] == "two"
|
|
text_parts = Converter.extract_text_content([text1, text2])
|
|
assert isinstance(text_parts, list)
|
|
assert all(p["type"] == "text" for p in text_parts)
|
|
assert [p["text"] for p in text_parts] == ["one", "two"]
|
|
|
|
|
|
def test_extract_all_content_handles_input_audio():
|
|
"""
|
|
input_audio entries should translate into ChatCompletion input_audio parts.
|
|
"""
|
|
audio: ResponseInputAudioParam = {
|
|
"type": "input_audio",
|
|
"input_audio": {"data": "AAA=", "format": "wav"},
|
|
}
|
|
parts = Converter.extract_all_content([audio])
|
|
assert isinstance(parts, list)
|
|
assert parts == [
|
|
{
|
|
"type": "input_audio",
|
|
"input_audio": {"data": "AAA=", "format": "wav"},
|
|
}
|
|
]
|
|
|
|
|
|
def test_extract_all_content_preserves_prompt_cache_breakpoints() -> None:
|
|
breakpoint = {"mode": "explicit"}
|
|
content: list[dict[str, Any]] = [
|
|
{
|
|
"type": "input_text",
|
|
"text": "one",
|
|
"prompt_cache_breakpoint": breakpoint,
|
|
},
|
|
{
|
|
"type": "input_image",
|
|
"image_url": "https://example.com/image.png",
|
|
"prompt_cache_breakpoint": breakpoint,
|
|
},
|
|
{
|
|
"type": "input_audio",
|
|
"input_audio": {"data": "AAA=", "format": "wav"},
|
|
"prompt_cache_breakpoint": breakpoint,
|
|
},
|
|
{
|
|
"type": "input_file",
|
|
"file_data": "data:text/plain;base64,SGVsbG8=",
|
|
"filename": "hello.txt",
|
|
"prompt_cache_breakpoint": breakpoint,
|
|
},
|
|
]
|
|
|
|
parts = Converter.extract_all_content(content)
|
|
|
|
assert isinstance(parts, list)
|
|
assert [part["prompt_cache_breakpoint"] for part in parts] == [breakpoint] * 4
|
|
|
|
|
|
def test_raw_chat_content_aliases_preserve_prompt_cache_breakpoints() -> None:
|
|
breakpoint = {"mode": "explicit"}
|
|
|
|
parts = Converter.extract_all_content(
|
|
cast(
|
|
list[dict[str, Any]],
|
|
[
|
|
{"type": "text", "text": "one", "prompt_cache_breakpoint": breakpoint},
|
|
{
|
|
"type": "image_url",
|
|
"image_url": {"url": "https://example.com/image.png"},
|
|
"prompt_cache_breakpoint": breakpoint,
|
|
},
|
|
],
|
|
)
|
|
)
|
|
|
|
assert isinstance(parts, list)
|
|
assert [part["prompt_cache_breakpoint"] for part in parts] == [breakpoint, breakpoint]
|
|
|
|
|
|
def test_extract_all_content_rejects_invalid_input_audio():
|
|
"""
|
|
input_audio requires both data and format fields to be present.
|
|
"""
|
|
audio_missing_data = cast(
|
|
ResponseInputAudioParam,
|
|
{
|
|
"type": "input_audio",
|
|
"input_audio": {"format": "wav"},
|
|
},
|
|
)
|
|
with pytest.raises(UserError):
|
|
Converter.extract_all_content([audio_missing_data])
|
|
|
|
|
|
def test_items_to_messages_handles_system_and_developer_roles():
|
|
"""
|
|
Roles other than `user` (e.g. `system` and `developer`) need to be
|
|
converted appropriately whether provided as simple dicts or as full
|
|
`message` typed dicts.
|
|
"""
|
|
sys_items: list[TResponseInputItem] = [{"role": "system", "content": "setup"}]
|
|
sys_msgs = Converter.items_to_messages(sys_items)
|
|
assert len(sys_msgs) == 1
|
|
assert sys_msgs[0]["role"] == "system"
|
|
assert sys_msgs[0]["content"] == "setup"
|
|
dev_items: list[TResponseInputItem] = [{"role": "developer", "content": "debug"}]
|
|
dev_msgs = Converter.items_to_messages(dev_items)
|
|
assert len(dev_msgs) == 1
|
|
assert dev_msgs[0]["role"] == "developer"
|
|
assert dev_msgs[0]["content"] == "debug"
|
|
|
|
|
|
def test_maybe_input_message_allows_message_typed_dict():
|
|
"""
|
|
The `Converter.maybe_input_message` should recognize a dict with
|
|
"type": "message" and a supported role as an input message. Ensure
|
|
that such dicts are passed through by `items_to_messages`.
|
|
"""
|
|
# Construct a dict with the proper required keys for a ResponseInputParam.Message
|
|
message_dict: TResponseInputItem = {
|
|
"type": "message",
|
|
"role": "user",
|
|
"content": "hi",
|
|
}
|
|
assert Converter.maybe_input_message(message_dict) is not None
|
|
# items_to_messages should process this correctly
|
|
msgs = Converter.items_to_messages([message_dict])
|
|
assert len(msgs) == 1
|
|
assert msgs[0]["role"] == "user"
|
|
assert msgs[0]["content"] == "hi"
|
|
|
|
|
|
def test_tool_call_conversion():
|
|
"""
|
|
Test that tool calls are converted correctly.
|
|
"""
|
|
function_call = ResponseFunctionToolCallParam(
|
|
id="tool1",
|
|
call_id="abc",
|
|
name="math",
|
|
arguments="{}",
|
|
type="function_call",
|
|
)
|
|
|
|
messages = Converter.items_to_messages([function_call])
|
|
assert len(messages) == 1
|
|
tool_msg = messages[0]
|
|
assert tool_msg["role"] == "assistant"
|
|
assert tool_msg.get("content") is None
|
|
|
|
# Verify the content key exists in the message even when it is None.
|
|
# This is for Chat Completions API compatibility.
|
|
assert "content" in tool_msg, "content key should be present in assistant message"
|
|
|
|
tool_calls = list(tool_msg.get("tool_calls", []))
|
|
assert len(tool_calls) == 1
|
|
|
|
tool_call = tool_calls[0]
|
|
assert tool_call["id"] == function_call["call_id"]
|
|
assert tool_call["function"]["name"] == function_call["name"] # type: ignore
|
|
assert tool_call["function"]["arguments"] == function_call["arguments"] # type: ignore
|
|
|
|
|
|
@pytest.mark.parametrize("role", ["user", "system", "developer"])
|
|
def test_input_message_with_all_roles(role: str):
|
|
"""
|
|
The `Converter.maybe_input_message` should recognize a dict with
|
|
"type": "message" and a supported role as an input message. Ensure
|
|
that such dicts are passed through by `items_to_messages`.
|
|
"""
|
|
# Construct a dict with the proper required keys for a ResponseInputParam.Message
|
|
casted_role = cast(Literal["user", "system", "developer"], role)
|
|
message_dict: TResponseInputItem = {
|
|
"type": "message",
|
|
"role": casted_role,
|
|
"content": "hi",
|
|
}
|
|
assert Converter.maybe_input_message(message_dict) is not None
|
|
# items_to_messages should process this correctly
|
|
msgs = Converter.items_to_messages([message_dict])
|
|
assert len(msgs) == 1
|
|
assert msgs[0]["role"] == casted_role
|
|
assert msgs[0]["content"] == "hi"
|
|
|
|
|
|
def test_item_reference_errors():
|
|
"""
|
|
Test that item references are converted correctly.
|
|
"""
|
|
with pytest.raises(UserError):
|
|
Converter.items_to_messages(
|
|
[
|
|
{
|
|
"type": "item_reference",
|
|
"id": "item1",
|
|
}
|
|
]
|
|
)
|
|
|
|
|
|
class TestObject:
|
|
pass
|
|
|
|
|
|
def test_unknown_object_errors():
|
|
"""
|
|
Test that unknown objects are converted correctly.
|
|
"""
|
|
with pytest.raises(UserError, match="Unhandled item type or structure"):
|
|
# Purposely ignore the type error
|
|
Converter.items_to_messages([TestObject()]) # type: ignore
|
|
|
|
|
|
def test_assistant_messages_in_history():
|
|
"""
|
|
Test that assistant messages are added to the history.
|
|
"""
|
|
messages = Converter.items_to_messages(
|
|
[
|
|
{
|
|
"role": "user",
|
|
"content": "Hello",
|
|
},
|
|
{
|
|
"role": "assistant",
|
|
"content": "Hello?",
|
|
},
|
|
{
|
|
"role": "user",
|
|
"content": "What was my Name?",
|
|
},
|
|
]
|
|
)
|
|
|
|
assert messages == [
|
|
{"role": "user", "content": "Hello"},
|
|
{"role": "assistant", "content": "Hello?"},
|
|
{"role": "user", "content": "What was my Name?"},
|
|
]
|
|
assert len(messages) == 3
|
|
assert messages[0]["role"] == "user"
|
|
assert messages[0]["content"] == "Hello"
|
|
assert messages[1]["role"] == "assistant"
|
|
assert messages[1]["content"] == "Hello?"
|
|
assert messages[2]["role"] == "user"
|
|
assert messages[2]["content"] == "What was my Name?"
|