mistral-vibe/tests/test_reasoning_content.py

from __future__ import annotations

from unittest.mock import MagicMock

import httpx
import mistralai
import pytest
import respx

from tests.conftest import build_test_agent_loop, build_test_vibe_config
from tests.mock.utils import mock_llm_chunk
from tests.stubs.fake_backend import FakeBackend
from vibe.core.config import ModelConfig, ProviderConfig, VibeConfig
from vibe.core.llm.backend.generic import GenericBackend, OpenAIAdapter
from vibe.core.llm.backend.mistral import MistralBackend, MistralMapper, ParsedContent
from vibe.core.llm.format import APIToolFormatHandler
from vibe.core.types import AssistantEvent, LLMMessage, ReasoningEvent, Role


def make_config() -> VibeConfig:
    return build_test_vibe_config(
        auto_compact_threshold=0,
        system_prompt_id="tests",
        include_project_context=False,
        include_prompt_detail=False,
        include_model_info=False,
        include_commit_signature=False,
        enabled_tools=[],
        tools={},
    )


class TestMistralMapperParseContent:
    def test_parse_content_string_returns_content_only(self):
        mapper = MistralMapper()
        result = mapper.parse_content("Hello, world!")

        assert result == ParsedContent(content="Hello, world!", reasoning_content=None)

    def test_parse_content_text_chunk_returns_content_only(self):
        mapper = MistralMapper()
        content: list[mistralai.ContentChunk] = [
            mistralai.TextChunk(type="text", text="Hello from text chunk")
        ]

        result = mapper.parse_content(content)

        assert result == ParsedContent(
            content="Hello from text chunk", reasoning_content=None
        )

    def test_parse_content_thinking_chunk_extracts_reasoning(self):
        mapper = MistralMapper()
        content: list[mistralai.ContentChunk] = [
            mistralai.ThinkChunk(
                type="thinking",
                thinking=[mistralai.TextChunk(type="text", text="Let me think...")],
            ),
            mistralai.TextChunk(type="text", text="The answer is 42."),
        ]

        result = mapper.parse_content(content)

        assert result == ParsedContent(
            content="The answer is 42.", reasoning_content="Let me think..."
        )

    def test_parse_content_multiple_thinking_chunks_concatenates(self):
        mapper = MistralMapper()
        content: list[mistralai.ContentChunk] = [
            mistralai.ThinkChunk(
                type="thinking",
                thinking=[mistralai.TextChunk(type="text", text="First thought. ")],
            ),
            mistralai.ThinkChunk(
                type="thinking",
                thinking=[mistralai.TextChunk(type="text", text="Second thought.")],
            ),
            mistralai.TextChunk(type="text", text="Final answer."),
        ]

        result = mapper.parse_content(content)

        assert result == ParsedContent(
            content="Final answer.", reasoning_content="First thought. Second thought."
        )

    def test_parse_content_thinking_only_returns_empty_content(self):
        mapper = MistralMapper()
        content: list[mistralai.ContentChunk] = [
            mistralai.ThinkChunk(
                type="thinking",
                thinking=[mistralai.TextChunk(type="text", text="Just thinking...")],
            )
        ]

        result = mapper.parse_content(content)

        assert result == ParsedContent(content="", reasoning_content="Just thinking...")

    def test_parse_content_empty_list_returns_empty(self):
        mapper = MistralMapper()
        content: list[mistralai.ContentChunk] = []

        result = mapper.parse_content(content)

        assert result == ParsedContent(content="", reasoning_content=None)


class TestMistralMapperPrepareMessage:
    def test_prepare_assistant_message_without_reasoning(self):
        mapper = MistralMapper()
        msg = LLMMessage(role=Role.assistant, content="Hello!")

        result = mapper.prepare_message(msg)

        assert isinstance(result, mistralai.AssistantMessage)
        assert result.content == "Hello!"

    def test_prepare_assistant_message_with_reasoning_creates_chunks(self):
        mapper = MistralMapper()
        msg = LLMMessage(
            role=Role.assistant,
            content="The answer is 42.",
            reasoning_content="Let me calculate...",
        )

        result = mapper.prepare_message(msg)

        assert isinstance(result, mistralai.AssistantMessage)
        assert isinstance(result.content, list)
        assert len(result.content) == 2

        think_chunk = result.content[0]
        assert isinstance(think_chunk, mistralai.ThinkChunk)
        assert think_chunk.type == "thinking"
        assert len(think_chunk.thinking) == 1
        inner_chunk = think_chunk.thinking[0]
        assert isinstance(inner_chunk, mistralai.TextChunk)
        assert inner_chunk.text == "Let me calculate..."

        text_chunk = result.content[1]
        assert isinstance(text_chunk, mistralai.TextChunk)
        assert text_chunk.type == "text"
        assert text_chunk.text == "The answer is 42."

    def test_prepare_assistant_message_with_reasoning_and_none_content(self):
        mapper = MistralMapper()
        msg = LLMMessage(
            role=Role.assistant, content=None, reasoning_content="Just thinking..."
        )

        result = mapper.prepare_message(msg)

        assert isinstance(result, mistralai.AssistantMessage)
        assert isinstance(result.content, list)
        assert len(result.content) == 2

        think_chunk = result.content[0]
        assert isinstance(think_chunk, mistralai.ThinkChunk)
        assert think_chunk.type == "thinking"
        assert len(think_chunk.thinking) == 1
        inner_chunk = think_chunk.thinking[0]
        assert isinstance(inner_chunk, mistralai.TextChunk)
        assert inner_chunk.text == "Just thinking..."

        text_chunk = result.content[1]
        assert isinstance(text_chunk, mistralai.TextChunk)
        assert text_chunk.text == ""


class TestGenericBackendReasoningContent:
    @pytest.mark.asyncio
    async def test_complete_extracts_reasoning_content(self):
        base_url = "https://api.example.com"
        json_response = {
            "id": "fake_id",
            "created": 1234567890,
            "model": "test-model",
            "usage": {"prompt_tokens": 10, "completion_tokens": 20, "total_tokens": 30},
            "object": "chat.completion",
            "choices": [
                {
                    "index": 0,
                    "finish_reason": "stop",
                    "message": {
                        "role": "assistant",
                        "content": "The answer is 42.",
                        "reasoning_content": "Let me think step by step...",
                    },
                }
            ],
        }

        with respx.mock(base_url=base_url) as mock_api:
            mock_api.post("/v1/chat/completions").mock(
                return_value=httpx.Response(status_code=200, json=json_response)
            )
            provider = ProviderConfig(
                name="test", api_base=f"{base_url}/v1", api_key_env_var="API_KEY"
            )
            backend = GenericBackend(provider=provider)
            model = ModelConfig(name="test-model", provider="test", alias="test")
            messages = [LLMMessage(role=Role.user, content="What is the answer?")]

            result = await backend.complete(
                model=model,
                messages=messages,
                temperature=0.2,
                tools=None,
                max_tokens=None,
                tool_choice=None,
                extra_headers=None,
            )

            assert result.message.content == "The answer is 42."
            assert result.message.reasoning_content == "Let me think step by step..."

    @pytest.mark.asyncio
    async def test_complete_streaming_extracts_reasoning_content(self):
        base_url = "https://api.example.com"
        chunks = [
            b'data: {"id":"id1","object":"chat.completion.chunk","created":123,"model":"test","choices":[{"index":0,"delta":{"role":"assistant","reasoning_content":"Thinking..."},"finish_reason":null}]}',
            b'data: {"id":"id1","object":"chat.completion.chunk","created":123,"model":"test","choices":[{"index":0,"delta":{"content":"Answer"},"finish_reason":null}]}',
            b'data: {"id":"id1","object":"chat.completion.chunk","created":123,"model":"test","choices":[{"index":0,"delta":{},"finish_reason":"stop"}],"usage":{"prompt_tokens":10,"completion_tokens":5}}',
            b"data: [DONE]",
        ]

        with respx.mock(base_url=base_url) as mock_api:
            mock_api.post("/v1/chat/completions").mock(
                return_value=httpx.Response(
                    status_code=200,
                    stream=httpx.ByteStream(stream=b"\n\n".join(chunks)),
                    headers={"Content-Type": "text/event-stream"},
                )
            )
            provider = ProviderConfig(
                name="test", api_base=f"{base_url}/v1", api_key_env_var="API_KEY"
            )
            backend = GenericBackend(provider=provider)
            model = ModelConfig(name="test-model", provider="test", alias="test")
            messages = [LLMMessage(role=Role.user, content="Stream please")]

            results = []
            async for chunk in backend.complete_streaming(
                model=model,
                messages=messages,
                temperature=0.2,
                tools=None,
                max_tokens=None,
                tool_choice=None,
                extra_headers=None,
            ):
                results.append(chunk)

            assert results[0].message.reasoning_content == "Thinking..."
            assert results[0].message.content == ""
            assert results[1].message.content == "Answer"
            assert results[1].message.reasoning_content is None


class TestAPIToolFormatHandlerReasoningContent:
    def test_process_api_response_message_extracts_reasoning_content(self):
        handler = APIToolFormatHandler()

        mock_message = MagicMock()
        mock_message.role = "assistant"
        mock_message.content = "The answer is 42."
        mock_message.reasoning_content = "Let me think..."
        mock_message.reasoning_signature = None
        mock_message.tool_calls = None

        result = handler.process_api_response_message(mock_message)

        assert result.content == "The answer is 42."
        assert result.reasoning_content == "Let me think..."

    def test_process_api_response_message_handles_missing_reasoning_content(self):
        handler = APIToolFormatHandler()

        mock_message = MagicMock(spec=["role", "content", "tool_calls"])
        mock_message.role = "assistant"
        mock_message.content = "Hello"
        mock_message.tool_calls = None

        result = handler.process_api_response_message(mock_message)

        assert result.content == "Hello"
        assert result.reasoning_content is None


class TestAgentLoopStreamingReasoningEvents:
    @pytest.mark.asyncio
    async def test_streaming_accumulates_reasoning_in_message(self):
        backend = FakeBackend([
            mock_llm_chunk(content="", reasoning_content="First thought. "),
            mock_llm_chunk(content="", reasoning_content="Second thought."),
            mock_llm_chunk(content="Final answer."),
        ])
        agent = build_test_agent_loop(
            config=make_config(), backend=backend, enable_streaming=True
        )

        [_ async for _ in agent.act("Think and answer")]

        assistant_msg = next(m for m in agent.messages if m.role == Role.assistant)
        assert assistant_msg.reasoning_content == "First thought. Second thought."
        assert assistant_msg.content == "Final answer."

    @pytest.mark.asyncio
    async def test_streaming_content_only_no_reasoning(self):
        backend = FakeBackend([
            mock_llm_chunk(content="Hello "),
            mock_llm_chunk(content="world!"),
        ])
        agent = build_test_agent_loop(
            config=make_config(), backend=backend, enable_streaming=True
        )

        events = [event async for event in agent.act("Say hello")]

        reasoning_events = [e for e in events if isinstance(e, ReasoningEvent)]
        assert len(reasoning_events) == 0

        assistant_events = [e for e in events if isinstance(e, AssistantEvent)]
        assert len(assistant_events) == 1

        assistant_msg = next(m for m in agent.messages if m.role == Role.assistant)
        assert assistant_msg.reasoning_content is None
        assert assistant_msg.content == "Hello world!"


class TestLLMMessageReasoningContent:
    def test_llm_message_from_dict_with_reasoning_content(self):
        data = {
            "role": "assistant",
            "content": "Answer",
            "reasoning_content": "Thinking...",
        }

        msg = LLMMessage.model_validate(data)

        assert msg.reasoning_content == "Thinking..."

    def test_llm_message_model_dump_includes_reasoning_content(self):
        msg = LLMMessage(
            role=Role.assistant, content="Answer", reasoning_content="Thinking..."
        )

        dumped = msg.model_dump(exclude_none=True)

        assert dumped["reasoning_content"] == "Thinking..."

    def test_llm_message_model_dump_excludes_none_reasoning_content(self):
        msg = LLMMessage(role=Role.assistant, content="Answer")

        dumped = msg.model_dump(exclude_none=True)

        assert "reasoning_content" not in dumped


class TestReasoningFieldNameConversion:
    def test_reasoning_to_api_keeps_default_field(self):
        adapter = OpenAIAdapter()
        msg_dict = {
            "role": "assistant",
            "content": "Answer",
            "reasoning_content": "Thinking...",
        }

        result = adapter._reasoning_to_api(msg_dict, "reasoning_content")

        assert result["reasoning_content"] == "Thinking..."
        assert "reasoning" not in result

    def test_reasoning_to_api_renames_to_custom_field(self):
        adapter = OpenAIAdapter()
        msg_dict = {
            "role": "assistant",
            "content": "Answer",
            "reasoning_content": "Thinking...",
        }

        result = adapter._reasoning_to_api(msg_dict, "reasoning")

        assert result["reasoning"] == "Thinking..."
        assert "reasoning_content" not in result

    def test_reasoning_from_api_converts_custom_field(self):
        adapter = OpenAIAdapter()
        msg_dict = {
            "role": "assistant",
            "content": "Answer",
            "reasoning": "Thinking...",
        }

        result = adapter._reasoning_from_api(msg_dict, "reasoning")

        assert result["reasoning_content"] == "Thinking..."
        assert "reasoning" not in result

    def test_reasoning_from_api_keeps_default_field(self):
        adapter = OpenAIAdapter()
        msg_dict = {
            "role": "assistant",
            "content": "Answer",
            "reasoning_content": "Thinking...",
        }

        result = adapter._reasoning_from_api(msg_dict, "reasoning_content")

        assert result["reasoning_content"] == "Thinking..."

    @pytest.mark.asyncio
    async def test_complete_with_custom_reasoning_field_name(self):
        base_url = "https://api.example.com"
        json_response = {
            "id": "fake_id",
            "created": 1234567890,
            "model": "test-model",
            "usage": {"prompt_tokens": 10, "completion_tokens": 20, "total_tokens": 30},
            "object": "chat.completion",
            "choices": [
                {
                    "index": 0,
                    "finish_reason": "stop",
                    "message": {
                        "role": "assistant",
                        "content": "The answer is 42.",
                        "reasoning": "Let me think step by step...",
                    },
                }
            ],
        }

        with respx.mock(base_url=base_url) as mock_api:
            mock_api.post("/v1/chat/completions").mock(
                return_value=httpx.Response(status_code=200, json=json_response)
            )
            provider = ProviderConfig(
                name="test",
                api_base=f"{base_url}/v1",
                api_key_env_var="API_KEY",
                reasoning_field_name="reasoning",
            )
            backend = GenericBackend(provider=provider)
            model = ModelConfig(name="test-model", provider="test", alias="test")
            messages = [LLMMessage(role=Role.user, content="What is the answer?")]

            result = await backend.complete(
                model=model,
                messages=messages,
                temperature=0.2,
                tools=None,
                max_tokens=None,
                tool_choice=None,
                extra_headers=None,
            )

            assert result.message.content == "The answer is 42."
            assert result.message.reasoning_content == "Let me think step by step..."

    @pytest.mark.asyncio
    async def test_streaming_with_custom_reasoning_field_name(self):
        base_url = "https://api.example.com"
        chunks = [
            b'data: {"id":"id1","object":"chat.completion.chunk","created":123,"model":"test","choices":[{"index":0,"delta":{"role":"assistant","reasoning":"Thinking..."},"finish_reason":null}]}',
            b'data: {"id":"id1","object":"chat.completion.chunk","created":123,"model":"test","choices":[{"index":0,"delta":{"content":"Answer"},"finish_reason":null}]}',
            b'data: {"id":"id1","object":"chat.completion.chunk","created":123,"model":"test","choices":[{"index":0,"delta":{},"finish_reason":"stop"}],"usage":{"prompt_tokens":10,"completion_tokens":5}}',
            b"data: [DONE]",
        ]

        with respx.mock(base_url=base_url) as mock_api:
            mock_api.post("/v1/chat/completions").mock(
                return_value=httpx.Response(
                    status_code=200,
                    stream=httpx.ByteStream(stream=b"\n\n".join(chunks)),
                    headers={"Content-Type": "text/event-stream"},
                )
            )
            provider = ProviderConfig(
                name="test",
                api_base=f"{base_url}/v1",
                api_key_env_var="API_KEY",
                reasoning_field_name="reasoning",
            )
            backend = GenericBackend(provider=provider)
            model = ModelConfig(name="test-model", provider="test", alias="test")
            messages = [LLMMessage(role=Role.user, content="Stream please")]

            results = []
            async for chunk in backend.complete_streaming(
                model=model,
                messages=messages,
                temperature=0.2,
                tools=None,
                max_tokens=None,
                tool_choice=None,
                extra_headers=None,
            ):
                results.append(chunk)

            assert results[0].message.reasoning_content == "Thinking..."
            assert results[1].message.content == "Answer"


class TestMistralReasoningFieldNameValidation:
    def test_mistral_backend_rejects_custom_reasoning_field_name(self):
        provider = ProviderConfig(
            name="mistral",
            api_base="https://api.mistral.ai/v1",
            api_key_env_var="MISTRAL_API_KEY",
            reasoning_field_name="reasoning",
        )

        with pytest.raises(ValueError) as exc_info:
            MistralBackend(provider=provider)

        assert "does not support custom reasoning_field_name" in str(exc_info.value)
        assert "reasoning" in str(exc_info.value)

    def test_mistral_backend_accepts_default_reasoning_field_name(self):
        provider = ProviderConfig(
            name="mistral",
            api_base="https://api.mistral.ai/v1",
            api_key_env_var="MISTRAL_API_KEY",
            reasoning_field_name="reasoning_content",
        )

        backend = MistralBackend(provider=provider)
        assert backend is not None