144 lines
5.6 KiB
Python
144 lines
5.6 KiB
Python
"""Canonical serialization for LangChain / LangGraph objects.
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Provides a single source of truth for converting LangChain message
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objects, Pydantic models, and LangGraph state dicts into plain
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JSON-serialisable Python structures.
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Consumers: ``deerflow.runtime.runs.worker`` (SSE publishing) and
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``app.gateway.routers.threads`` (REST responses).
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"""
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from __future__ import annotations
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import dataclasses
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from typing import Any
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from deerflow.runtime.display_payload import slim_display_messages
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from deerflow.utils.tool_visibility import internal_tool_call_ids, project_outward_messages
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def serialize_lc_object(obj: Any) -> Any:
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"""Recursively serialize a LangChain object to a JSON-serialisable dict."""
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if obj is None:
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return None
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if isinstance(obj, (str, int, float, bool)):
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return obj
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if isinstance(obj, dict):
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return {k: serialize_lc_object(v) for k, v in obj.items()}
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if isinstance(obj, (list, tuple)):
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return [serialize_lc_object(item) for item in obj]
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# Pydantic v2
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if hasattr(obj, "model_dump"):
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try:
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return obj.model_dump()
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except Exception:
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pass
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# Pydantic v1 / older objects
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if hasattr(obj, "dict"):
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try:
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return obj.dict()
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except Exception:
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pass
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# Dataclass instances —— LangGraph 的 ``Interrupt`` 就是 dataclass 没有
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# model_dump 也没有 dict 方法；之前 fallback 到 ``str(obj)`` 会把
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# ``Interrupt(value=..., id='...')`` 序列化成字符串，下游 SDK
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# (``@langchain/langgraph-sdk``) 期待 dict 结构 → 直接丢弃 → AI 写作
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# 的 4 个干预卡永远不弹（参 ai-writing-优化.md §阶段 3 排查记录）。
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if dataclasses.is_dataclass(obj) and not isinstance(obj, type):
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try:
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return {k: serialize_lc_object(v) for k, v in dataclasses.asdict(obj).items()}
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except Exception:
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pass
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# Last resort
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try:
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return str(obj)
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except Exception:
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return repr(obj)
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def serialize_channel_values(
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channel_values: dict[str, Any],
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*,
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slim_display: bool = False,
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) -> dict[str, Any]:
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"""Serialize channel values, stripping internal LangGraph keys.
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``__pregel_*`` 键始终剥离。``__interrupt__`` 仅在**为空时**剥离 —— 因为
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``@langchain/langgraph-sdk`` 的 ``useStream`` 只在 ``event === "values"``
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通道里读 ``data.__interrupt__`` 并通过 ``stream.interrupt`` 暴露给上层。
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AI 写作的 4 个干预卡（material/outline/draft/review）就是靠它触发的；
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继续过滤会让前端永远拿不到 interrupt。
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主聊天不消费 ``stream.interrupt``，但若我们让一个空数组溜过去，SDK 会
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fallback 成 ``{when: "breakpoint"}`` 的伪 interrupt —— 所以仅在数组非空
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时保留，两侧都不破坏。
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"""
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result: dict[str, Any] = {}
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for key, value in channel_values.items():
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if key.startswith("__pregel_"):
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continue
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if key == "__interrupt__" and not value:
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continue
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serialized = serialize_lc_object(value)
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if key == "messages" and isinstance(serialized, list):
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serialized = project_outward_messages(serialized)
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if slim_display:
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serialized = slim_display_messages(serialized)
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elif slim_display and key == "viewed_images" and isinstance(serialized, dict):
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serialized = {
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path: ({"mime_type": data.get("mime_type")} if isinstance(data, dict) else data)
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for path, data in serialized.items()
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}
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result[key] = serialized
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return result
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def serialize_messages_tuple(
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obj: Any,
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*,
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known_internal_tool_call_ids: set[str] | None = None,
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) -> Any:
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"""Serialize a messages-mode tuple ``(chunk, metadata)``."""
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if isinstance(obj, tuple) and len(obj) == 2:
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chunk, metadata = obj
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serialized_chunk = serialize_lc_object(chunk)
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if known_internal_tool_call_ids is not None:
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known_internal_tool_call_ids.update(
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internal_tool_call_ids([serialized_chunk])
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)
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projected = project_outward_messages(
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[serialized_chunk],
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known_internal_tool_call_ids=known_internal_tool_call_ids or (),
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)
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if not projected:
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if isinstance(serialized_chunk, dict):
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known_id = str(serialized_chunk.get("tool_call_id") or "")
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if known_internal_tool_call_ids is not None:
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known_internal_tool_call_ids.discard(known_id)
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return None
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return [projected[0], metadata if isinstance(metadata, dict) else {}]
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projected = project_outward_messages([serialize_lc_object(obj)])
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return projected[0] if projected else None
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def serialize(
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obj: Any,
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*,
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mode: str = "",
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known_internal_tool_call_ids: set[str] | None = None,
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) -> Any:
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"""Serialize LangChain objects with mode-specific handling.
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* ``messages`` — obj is ``(message_chunk, metadata_dict)``
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* ``values`` — obj is the full state dict; ``__pregel_*`` keys stripped
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* everything else — recursive ``model_dump()`` / ``dict()`` fallback
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"""
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if mode == "messages":
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return serialize_messages_tuple(
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obj,
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known_internal_tool_call_ids=known_internal_tool_call_ids,
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)
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if mode == "values":
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return serialize_channel_values(obj) if isinstance(obj, dict) else serialize_lc_object(obj)
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return serialize_lc_object(obj)
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