"""新架构下的检索与 Milvus dense-only 约定测试。"""

from __future__ import annotations

from app.application.agent.services import AgentConversationService
from app.application.knowledge.services import KnowledgeRetrievalService
from app.domain.conversation.models import AnswerResult, AnswerSource, ConversationSession
from app.domain.retrieval import RetrievalQuery, RetrievedChunk


class FakeRetriever:
    def __init__(self) -> None:
        self.queries: list[RetrievalQuery] = []

    def retrieve(self, query: RetrievalQuery) -> list[RetrievedChunk]:
        self.queries.append(query)
        return [
            RetrievedChunk(
                chunk_id="chunk-1",
                doc_id="doc-1",
                doc_name="测试法规",
                content="法规正文",
                score=0.91,
                section_title="第一章",
                page_number=1,
                metadata={"section_title": "第一章"},
            )
        ]

    def search(self, query: str, top_k: int, filters: str | None = None) -> list[RetrievedChunk]:
        return self.retrieve(RetrievalQuery(query=query, top_k=top_k, filters=filters))


class FakeAnswerGenerator:
    def generate(
        self,
        *,
        query: str,
        retrieved_chunks: list[RetrievedChunk],
        history: list[dict[str, str]] | None = None,
        provider: str | None = None,
        model: str | None = None,
        prompt_template: str | None = None,
    ) -> AnswerResult:
        return AnswerResult(
            answer=f"回答: {query}",
            sources=[
                AnswerSource(
                    doc_id=item.doc_id,
                    doc_name=item.doc_name,
                    chunk_id=item.chunk_id,
                    section_title=item.section_title,
                    page_number=item.page_number,
                    score=item.score,
                    content=item.content,
                    metadata=item.metadata,
                )
                for item in retrieved_chunks
            ],
            model=model or "deepseek-v4-flash",
            latency_ms=12,
            retrieved_count=len(retrieved_chunks),
            context_tokens=128,
        )

    def stream_generate(self, **kwargs):
        sources = [source.__dict__ for source in self.generate(**kwargs).sources]
        yield {"event": "sources", "data": sources}
        yield {"event": "content", "data": "流式回答"}
        yield {"event": "done", "data": {"retrieved_count": 1}}


class FakeConversationStore:
    def __init__(self) -> None:
        self.sessions: dict[str, ConversationSession] = {}

    def create_session(self, metadata: dict | None = None) -> ConversationSession:
        session = ConversationSession(session_id="sess-1", created_at=1, updated_at=1, metadata=metadata or {})
        self.sessions[session.session_id] = session
        return session

    def get_session(self, session_id: str) -> ConversationSession | None:
        return self.sessions.get(session_id)

    def save_message(self, session_id: str, *, role: str, content: str, sources: list[dict] | None = None):
        session = self.sessions.get(session_id)
        if session is None:
            return None
        session.messages.append(type("Msg", (), {"role": role, "content": content})())
        return session

    def delete_session(self, session_id: str) -> bool:
        return self.sessions.pop(session_id, None) is not None

    def list_sessions(self) -> list[dict]:
        return [{"session_id": key, "message_count": len(value.messages), "created_at": value.created_at, "updated_at": value.updated_at} for key, value in self.sessions.items()]


def test_knowledge_retrieval_service_builds_retrieval_query():
    retriever = FakeRetriever()
    service = KnowledgeRetrievalService(retriever=retriever)

    results = service.retrieve(query="机动车安全", top_k=3, filters='doc_name == "测试法规"')

    assert len(results) == 1
    assert retriever.queries[0].query == "机动车安全"
    assert retriever.queries[0].top_k == 3
    assert retriever.queries[0].filters == 'doc_name == "测试法规"'


def test_agent_conversation_service_reuses_shared_retrieval_service():
    retriever = FakeRetriever()
    retrieval_service = KnowledgeRetrievalService(retriever=retriever)
    conversation_store = FakeConversationStore()
    service = AgentConversationService(
        retrieval_service=retrieval_service,
        answer_generator=FakeAnswerGenerator(),
        conversation_store=conversation_store,
    )

    session_id, result = service.chat(query="问一个问题", top_k=2, model="qwen3.5-flash")

    assert session_id == "sess-1"
    assert result.answer == "回答: 问一个问题"
    assert result.retrieved_count == 1
    assert retriever.queries[0].top_k == 2
    assert len(conversation_store.sessions["sess-1"].messages) == 2