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255 lines
8.7 KiB
Python
255 lines
8.7 KiB
Python
from __future__ import annotations
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from typing import Any
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import msgspec
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from plyngent.agent.responses_bridge import (
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chat_messages_to_responses_input,
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chat_param_to_responses_kwargs,
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reasoning_summary_text,
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response_to_assistant_message,
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response_to_chat_completion,
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responses_status_to_finish_reason,
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tool_items_to_response_tools,
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)
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from plyngent.lmproto.openai.model import Response, ResponseFunctionToolCallOutput
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from plyngent.lmproto.openai_compatible.model import (
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AssistantChatMessage,
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AssistantFunctionTool,
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AssistantFunctionToolCall,
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ChatCompletionsParam,
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SystemChatMessage,
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ToolChatMessage,
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ToolFunction,
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ToolFunctionItem,
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UserChatMessage,
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)
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def test_tool_items_to_response_tools() -> None:
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items = [
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ToolFunctionItem(
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function=ToolFunction(
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name="read_file",
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description="Read a file",
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parameters={"type": "object", "properties": {"path": {"type": "string"}}},
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)
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)
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]
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tools = tool_items_to_response_tools(items)
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assert len(tools) == 1
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assert tools[0].name == "read_file"
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def test_chat_messages_to_input_and_instructions() -> None:
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messages = [
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SystemChatMessage(content="You are helpful."),
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UserChatMessage(content="hi"),
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AssistantChatMessage(
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content=None,
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tool_calls=[
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AssistantFunctionToolCall(
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id="call_1",
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function=AssistantFunctionTool(name="read_file", arguments='{"path":"a"}'),
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)
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],
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),
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ToolChatMessage(content="file body", tool_call_id="call_1"),
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]
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instructions, items = chat_messages_to_responses_input(messages)
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assert instructions == "You are helpful."
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assert len(items) == 3 # user, function_call, function_call_output
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def test_parallel_tool_outputs_interleaved_after_calls() -> None:
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"""Regression: DeepSeek Responses rejects call, call, output, output batches.
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Each ``function_call_output`` must directly follow its ``function_call`` so
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strict/adjacency-matching servers (DeepSeek) accept parallel tool rounds.
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"""
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messages = [
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UserChatMessage(content="run both"),
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AssistantChatMessage(
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content=None,
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tool_calls=[
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AssistantFunctionToolCall(
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id="call_01_LPBuUG3KTaMDIwUoLwTE9127",
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function=AssistantFunctionTool(name="read_file", arguments='{"path":"a"}'),
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),
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AssistantFunctionToolCall(
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id="call_02_ABCD",
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function=AssistantFunctionTool(name="regex_files", arguments='{"pattern":"todo"}'),
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),
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],
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),
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ToolChatMessage(content="file body", tool_call_id="call_01_LPBuUG3KTaMDIwUoLwTE9127"),
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ToolChatMessage(content="grep hit", tool_call_id="call_02_ABCD"),
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]
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_, items = chat_messages_to_responses_input(messages)
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types = [item.get("type") if isinstance(item, dict) else item.__struct_config__.tag for item in items]
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assert types == [
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"message",
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"function_call",
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"function_call_output",
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"function_call",
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"function_call_output",
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]
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# Each output is paired with its own call, in stream order.
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call_a = items[1]
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out_a = items[2]
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call_b = items[3]
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out_b = items[4]
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assert isinstance(call_a, dict) and isinstance(call_b, dict)
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assert isinstance(out_a, ResponseFunctionToolCallOutput)
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assert isinstance(out_b, ResponseFunctionToolCallOutput)
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assert out_a.call_id == call_a["call_id"]
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assert out_b.call_id == call_b["call_id"]
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def test_response_to_assistant_with_tools() -> None:
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raw = {
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"id": "resp_1",
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"object": "response",
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"created_at": 1,
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"model": "gpt-test",
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"status": "completed",
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"output": [
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{
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"id": "msg_1",
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"type": "message",
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"role": "assistant",
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"status": "completed",
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"content": [{"type": "output_text", "text": "done", "annotations": []}],
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},
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{
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"type": "function_call",
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"call_id": "call_9",
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"name": "add",
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"arguments": '{"a":1}',
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"status": "completed",
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},
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],
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"usage": {"input_tokens": 10, "output_tokens": 5, "total_tokens": 15},
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}
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response = msgspec.convert(raw, Response)
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from msgspec import UNSET
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assistant = response_to_assistant_message(response)
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assert assistant.content == "done"
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assert assistant.tool_calls is not UNSET
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assert isinstance(assistant.tool_calls, list)
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call0 = assistant.tool_calls[0]
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assert isinstance(call0, AssistantFunctionToolCall)
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assert call0.id == "call_9"
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assert call0.function.name == "add"
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completion = response_to_chat_completion(response)
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assert completion.choices[0].message.content == "done"
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assert isinstance(completion.usage, dict)
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assert completion.usage["input_tokens"] == 10
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assert completion.choices[0].finish_reason == "tool_calls"
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def _reasoning_response(
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*,
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output_reasoning: bool = False,
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top_level: dict[str, Any] | None = None,
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) -> Response:
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output: list[dict[str, Any]] = []
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if output_reasoning:
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output.append(
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{
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"id": "reason_1",
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"type": "reasoning",
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"summary": [{"type": "summary_text", "text": "openai-style reasoning"}],
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}
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)
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return Response(
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id="resp_r",
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created_at=1,
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model="deepseek-v4-flash",
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output=output,
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reasoning=top_level,
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)
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def test_reasoning_summary_text_deepseek_top_level() -> None:
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"""DeepSeek Responses returns reasoning in the top-level ``reasoning`` field."""
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response = _reasoning_response(top_level={"content": [{"type": "reasoning_content", "text": "think step by step"}]})
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assert reasoning_summary_text(response) == "think step by step"
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assistant = response_to_assistant_message(response)
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assert assistant.reasoning_content == "think step by step"
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def test_reasoning_summary_text_prefers_top_level() -> None:
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response = _reasoning_response(
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output_reasoning=True,
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top_level={"summary": [{"type": "summary_text", "text": "top-level reasoning"}]},
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)
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assert reasoning_summary_text(response) == "top-level reasoning"
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def test_reasoning_summary_text_falls_back_to_output_items() -> None:
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response = _reasoning_response(output_reasoning=True)
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assert reasoning_summary_text(response) == "openai-style reasoning"
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def test_reasoning_summary_text_empty_when_absent() -> None:
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assert reasoning_summary_text(_reasoning_response()) == ""
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def test_responses_status_to_finish_reason_incomplete() -> None:
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body = {
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"id": "resp_i",
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"object": "response",
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"created_at": 1,
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"model": "gpt-test",
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"status": "incomplete",
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"incomplete_details": {"reason": "max_output_tokens"},
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"output": [
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{
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"id": "msg_1",
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"type": "message",
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"role": "assistant",
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"status": "incomplete",
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"content": [{"type": "output_text", "text": "partial", "annotations": []}],
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}
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],
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}
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resp = msgspec.convert(body, Response)
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assert responses_status_to_finish_reason(resp, has_tool_calls=False) == "length"
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completion = response_to_chat_completion(resp)
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assert completion.choices[0].finish_reason == "length"
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def test_chat_param_to_responses_kwargs() -> None:
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param = ChatCompletionsParam(
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model="gpt-test",
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messages=[SystemChatMessage(content="sys"), UserChatMessage(content="hi")],
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tools=[ToolFunctionItem(function=ToolFunction(name="t", parameters={"type": "object"}))],
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temperature=0.2,
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)
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kwargs = chat_param_to_responses_kwargs(param)
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assert kwargs["model"] == "gpt-test"
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assert kwargs["instructions"] == "sys"
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assert kwargs["store"] is False
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assert kwargs["temperature"] == 0.2
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assert len(kwargs["tools"]) == 1
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def test_provider_tools_merged_after_local_functions() -> None:
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param = ChatCompletionsParam(
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model="gpt-test",
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messages=[UserChatMessage(content="hi")],
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tools=[ToolFunctionItem(function=ToolFunction(name="local_tool", parameters={"type": "object"}))],
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)
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kwargs = chat_param_to_responses_kwargs(
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param,
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provider_tools=[{"type": "web_search"}, {"type": "file_search", "vector_store_ids": ["vs_1"]}],
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)
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tools = kwargs["tools"]
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assert len(tools) == 3
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assert tools[0].name == "local_tool"
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assert tools[1]["type"] == "web_search"
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assert tools[2]["vector_store_ids"] == ["vs_1"]
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