126 lines
4.1 KiB
Python
126 lines
4.1 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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from typing import Any
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_TIMESTAMP_KEYS: tuple[str, ...] = ("deerflow_created_at", "created_at", "timestamp", "sent_at")
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_MESSAGE_TYPES: set[str] = {"human", "ai", "tool", "system", "function", "chat"}
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def _read_message_timestamp(message: dict[str, Any]) -> str | None:
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top = message.get("created_at")
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if isinstance(top, str) and top:
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return top
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additional_kwargs = message.get("additional_kwargs")
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if isinstance(additional_kwargs, dict):
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for key in _TIMESTAMP_KEYS:
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value = additional_kwargs.get(key)
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if isinstance(value, str) and value:
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return value
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response_metadata = message.get("response_metadata")
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if isinstance(response_metadata, dict):
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for key in _TIMESTAMP_KEYS:
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value = response_metadata.get(key)
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if isinstance(value, str) and value:
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return value
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return None
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def _attach_created_at(message: Any) -> Any:
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if not isinstance(message, dict):
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return message
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if message.get("type") not in _MESSAGE_TYPES:
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return message
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timestamp = _read_message_timestamp(message)
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if timestamp:
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message["created_at"] = timestamp
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return message
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def _normalize_message_timestamps(payload: Any) -> Any:
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if isinstance(payload, list):
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return [_attach_created_at(item) for item in payload]
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return _attach_created_at(payload)
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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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# 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(channel_values: dict[str, Any]) -> dict[str, Any]:
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"""Serialize channel values, stripping internal LangGraph keys.
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Internal keys like ``__pregel_*`` and ``__interrupt__`` are removed
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to match what the LangGraph Platform API returns.
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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_") or key == "__interrupt__":
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continue
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serialized = serialize_lc_object(value)
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if key == "messages":
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serialized = _normalize_message_timestamps(serialized)
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result[key] = serialized
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return result
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def serialize_messages_tuple(obj: Any) -> 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 = _normalize_message_timestamps(serialize_lc_object(chunk))
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return [serialized_chunk, metadata if isinstance(metadata, dict) else {}]
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return serialize_lc_object(obj)
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def serialize(obj: Any, *, mode: str = "") -> 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(obj)
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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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