"""Canonical serialization for LangChain / LangGraph objects.

Provides a single source of truth for converting LangChain message
objects, Pydantic models, and LangGraph state dicts into plain
JSON-serialisable Python structures.

Consumers: ``deerflow.runtime.runs.worker`` (SSE publishing) and
``app.gateway.routers.threads`` (REST responses).
"""

from __future__ import annotations

import dataclasses
from typing import Any


def serialize_lc_object(obj: Any) -> Any:
    """Recursively serialize a LangChain object to a JSON-serialisable dict."""
    if obj is None:
        return None
    if isinstance(obj, (str, int, float, bool)):
        return obj
    if isinstance(obj, dict):
        return {k: serialize_lc_object(v) for k, v in obj.items()}
    if isinstance(obj, (list, tuple)):
        return [serialize_lc_object(item) for item in obj]
    # Pydantic v2
    if hasattr(obj, "model_dump"):
        try:
            return obj.model_dump()
        except Exception:
            pass
    # Pydantic v1 / older objects
    if hasattr(obj, "dict"):
        try:
            return obj.dict()
        except Exception:
            pass
    # Dataclass instances —— LangGraph 的 ``Interrupt`` 就是 dataclass 没有
    # model_dump 也没有 dict 方法；之前 fallback 到 ``str(obj)`` 会把
    # ``Interrupt(value=..., id='...')`` 序列化成字符串，下游 SDK
    # (``@langchain/langgraph-sdk``) 期待 dict 结构 → 直接丢弃 → AI 写作
    # 的 4 个干预卡永远不弹（参 ai-writing-优化.md §阶段 3 排查记录）。
    if dataclasses.is_dataclass(obj) and not isinstance(obj, type):
        try:
            return {k: serialize_lc_object(v) for k, v in dataclasses.asdict(obj).items()}
        except Exception:
            pass
    # Last resort
    try:
        return str(obj)
    except Exception:
        return repr(obj)


def serialize_channel_values(channel_values: dict[str, Any]) -> dict[str, Any]:
    """Serialize channel values, stripping internal LangGraph keys.

    ``__pregel_*`` 键始终剥离。``__interrupt__`` 仅在**为空时**剥离 —— 因为
    ``@langchain/langgraph-sdk`` 的 ``useStream`` 只在 ``event === "values"``
    通道里读 ``data.__interrupt__`` 并通过 ``stream.interrupt`` 暴露给上层。
    AI 写作的 4 个干预卡（material/outline/draft/review）就是靠它触发的；
    继续过滤会让前端永远拿不到 interrupt。

    主聊天不消费 ``stream.interrupt``，但若我们让一个空数组溜过去，SDK 会
    fallback 成 ``{when: "breakpoint"}`` 的伪 interrupt —— 所以仅在数组非空
    时保留，两侧都不破坏。
    """
    result: dict[str, Any] = {}
    for key, value in channel_values.items():
        if key.startswith("__pregel_"):
            continue
        if key == "__interrupt__" and not value:
            continue
        result[key] = serialize_lc_object(value)
    return result


def serialize_messages_tuple(obj: Any) -> Any:
    """Serialize a messages-mode tuple ``(chunk, metadata)``."""
    if isinstance(obj, tuple) and len(obj) == 2:
        chunk, metadata = obj
        return [serialize_lc_object(chunk), metadata if isinstance(metadata, dict) else {}]
    return serialize_lc_object(obj)


def serialize(obj: Any, *, mode: str = "") -> Any:
    """Serialize LangChain objects with mode-specific handling.

    * ``messages`` — obj is ``(message_chunk, metadata_dict)``
    * ``values`` — obj is the full state dict; ``__pregel_*`` keys stripped
    * everything else — recursive ``model_dump()`` / ``dict()`` fallback
    """
    if mode == "messages":
        return serialize_messages_tuple(obj)
    if mode == "values":
        return serialize_channel_values(obj) if isinstance(obj, dict) else serialize_lc_object(obj)
    return serialize_lc_object(obj)