mirror of
https://github.com/bytedance/deer-flow.git
synced 2026-04-25 11:18:22 +00:00
9d0a42c1fb
Major refactoring of deerflow/runtime/: - runs/callbacks/ - new callback system (builder, events, title, tokens) - runs/internal/ - execution internals (executor, supervisor, stream_logic, registry) - runs/internal/execution/ - execution artifacts and events handling - runs/facade.py - high-level run facade - runs/observer.py - run observation protocol - runs/types.py - type definitions - runs/store/ - simplified store interfaces (create, delete, query, event) Refactor stream_bridge/: - Replace old providers with contract.py and exceptions.py - Remove async_provider.py, base.py, memory.py Add documentation: - README.md and README_zh.md for runtime module Remove deprecated: - manager.py moved to internal/ - worker.py, schemas.py - user_context.py Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
79 lines
2.6 KiB
Python
79 lines
2.6 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: runs execution internals (SSE publishing) and
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gateway thread state/history 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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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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result[key] = serialize_lc_object(value)
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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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return [serialize_lc_object(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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