"""Memory evidence 组装与检索契约（纯函数 / 无 DB）。"""

import pytest

from app.features.memory import evidence as evidence_mod
from app.features.memory.evidence_format import format_evidence_chunks_for_chat_prompt
from app.features.memory.evidence import (
    EMPTY_EVIDENCE_BUNDLE,
    _facts_to_dicts,
    _stories_to_dicts,
    _timeline_to_dicts,
    retrieve_evidence_bundle_sync,
)
from app.features.memory.schemas import EvidenceBundle


class _FakeEmbedding:
    def is_available(self) -> bool:
        return True

    def embed_text_sync(self, text: str) -> list[float]:
        return [0.25, 0.5, 0.75]


def test_retrieve_evidence_bundle_sync_uses_vector_search(
    monkeypatch: pytest.MonkeyPatch,
) -> None:
    searched: list[tuple] = []

    def fake_search(session, user_id, emb, top_k):
        searched.append((user_id, emb, top_k))
        return [
            {
                "id": "c1",
                "content": "chunk body",
                "chunk_index": 0,
                "distance": 0.1,
            }
        ]

    def fake_meta(user_id, q, top_k):
        return {
            "relevant_facts": [],
            "timeline_hints": [],
            "relevant_summaries": [],
            "relevant_stories": [],
        }

    monkeypatch.setattr(evidence_mod, "search_chunks_vector_sync", fake_search)
    monkeypatch.setattr(
        evidence_mod, "fetch_evidence_metadata_parallel_sync", fake_meta
    )

    out = retrieve_evidence_bundle_sync(
        session=object(),
        user_id="u1",
        query="  hello  ",
        top_k=7,
        embedding_provider=_FakeEmbedding(),
    )
    assert len(searched) == 1
    assert searched[0][0] == "u1"
    assert searched[0][1] == [0.25, 0.5, 0.75]
    assert searched[0][2] == 7
    assert out["relevant_chunks"] == [
        {"id": "c1", "content": "chunk body", "chunk_index": 0},
    ]


def test_empty_evidence_bundle_keys() -> None:
    assert set(EMPTY_EVIDENCE_BUNDLE.keys()) == {
        "relevant_chunks",
        "relevant_summaries",
        "relevant_facts",
        "timeline_hints",
        "relevant_stories",
    }


def test_evidence_bundle_model_accepts_dict() -> None:
    b = EvidenceBundle.model_validate(EMPTY_EVIDENCE_BUNDLE)
    assert b.relevant_chunks == []


def test_format_helpers_empty() -> None:
    assert _facts_to_dicts([]) == []
    assert _timeline_to_dicts([]) == []
    assert _stories_to_dicts([]) == []


def test_format_evidence_chunks_for_chat_prompt_reframes_and_labels() -> None:
    evidence = {
        "relevant_chunks": [
            {"id": "chunk-1", "content": "我小时候在河边长大，夏天常去玩水。"},
        ],
        "relevant_summaries": [],
        "relevant_facts": [],
        "timeline_hints": [],
        "relevant_stories": [],
    }
    text = format_evidence_chunks_for_chat_prompt(evidence)
    assert "聊天专用" in text
    assert "归因" in text
    assert "[M1]" in text
    assert "用户曾说" in text
    assert "我小时候在河边长大" in text


def test_slice_interview_memory_empty_bundle():
    from app.features.memory.chat_memory_injection import slice_interview_memory

    s = slice_interview_memory(None, "你好")
    assert s.prompt_excerpt == ""
    assert s.anchor_source == ""
    assert s.planner_preview == ""
    assert s.had_retrieval is False


def test_slice_interview_memory_retrieval_not_equal_inject_dismissive():
    """有检索预览但 gating 后不进主 prompt / anchor。"""
    from app.features.memory.chat_memory_injection import slice_interview_memory

    evidence = {
        "relevant_chunks": [
            {"id": "c1", "content": "很久以前在校园礼堂排练到很晚。"},
        ],
        "relevant_summaries": [],
        "relevant_facts": [],
        "timeline_hints": [],
        "relevant_stories": [],
    }
    s = slice_interview_memory(evidence, "哈哈，早就不会了")
    assert s.prompt_excerpt == ""
    assert s.anchor_source == ""
    assert s.planner_preview.strip() != ""
    assert s.had_retrieval is True


def test_slice_interview_memory_minimal_inject_when_aligned():
    from app.features.memory.chat_memory_injection import slice_interview_memory

    evidence = {
        "relevant_chunks": [
            {"id": "c1", "content": "你在校园演出里饰演罗密欧。"},
        ],
        "relevant_summaries": [],
        "relevant_facts": [],
        "timeline_hints": [],
        "relevant_stories": [],
    }
    s = slice_interview_memory(evidence, "那次排练其实挺紧张的，灯光一打我就忘词。")
    assert "记忆线索" in s.prompt_excerpt
    assert "校园演出" in s.prompt_excerpt or "罗密欧" in s.prompt_excerpt
    assert s.anchor_source
    assert s.had_retrieval is True


def test_slice_interview_memory_keeps_first_person_but_marks_ownership():
    from app.features.memory.chat_memory_injection import slice_interview_memory

    evidence = {
        "relevant_chunks": [
            {"id": "c1", "content": "我小时候在河边长大，夏天常去玩水。"},
        ],
        "relevant_summaries": [],
        "relevant_facts": [],
        "timeline_hints": [],
        "relevant_stories": [],
    }
    s = slice_interview_memory(evidence, "那条河一到夏天就特别热闹，我现在都记得。")
    assert "用户曾说" in s.prompt_excerpt
    assert "我小时候在河边长大" in s.prompt_excerpt
    assert s.anchor_source.startswith("用户曾说")


def test_slice_interview_memory_suppresses_long_new_topic():
    from app.features.memory.chat_memory_injection import slice_interview_memory

    evidence = {
        "relevant_chunks": [
            {"id": "c1", "content": "旧记忆关于河边。"},
        ],
        "relevant_summaries": [],
        "relevant_facts": [],
        "timeline_hints": [],
        "relevant_stories": [],
    }
    long_msg = "我今天想随便聊聊工作里的事，项目压力很大。" * 6
    assert len(long_msg) > 72
    s = slice_interview_memory(evidence, long_msg)
    assert s.prompt_excerpt == ""
    assert s.anchor_source == ""